[
{
"docid": "c72b82fd27377184306b8188a338c97e",
"score": "0.6239534",
"text": "def classify_image(self):",
"title": ""
},
{
"docid": "109a085d249590784c5f23a9833de41f",
"score": "0.6049097",
"text": "def convert_to_mask(x_coord, y_coord, sample_name, class_name, ann_img):\n \n # coordinates must be joined to x-y pairs and into a numpy array \n # because thats how cv2's fillConvexPoly() wants them\n all_poly_coords = []\n for xy in zip(x_coord, y_coord):\n all_poly_coords.append(xy)\n all_poly_coords = np.array(all_poly_coords)\n \n # depending of the class of that region, paint it a different shade\n if class_name == \"branch\":\n cv2.fillConvexPoly(ann_img, all_poly_coords, 1)\n if class_name == \"box\":\n cv2.fillConvexPoly(ann_img, all_poly_coords, 2)\n if class_name == \"camera-bag\":\n cv2.fillConvexPoly(ann_img, all_poly_coords, 3)\n if class_name == \"tree\":\n cv2.fillConvexPoly(ann_img, all_poly_coords, 4)\n\n # return mask with added region\n return ann_img",
"title": ""
},
{
"docid": "d87208e726028cb48b952a8a64d6eba3",
"score": "0.5976198",
"text": "def gray_image(self):\r\n self.image = self.image.convert(\"L\")",
"title": ""
},
{
"docid": "cbcbcd463289789b28d9f72322fbe07f",
"score": "0.5922888",
"text": "def gray2m11range(image):\n return 2.0 * image / 255.0 - 1.0",
"title": ""
},
{
"docid": "49349aab7f3e2983dd9379d18c92ec32",
"score": "0.58568263",
"text": "def preprocess(self, image, annotations, image_min_side, image_max_side):\n\t\traise NotImplementedError()",
"title": ""
},
{
"docid": "1749a729d5c3f9b93027e7c2aee5ec90",
"score": "0.5824374",
"text": "def segment_border_to_interior_intensity(vol, segment, label_map):\n\n\n\tbox_bounds = segment.bounding_box\n\n\n\t\n\tcropped_vol = vol[box_bounds.xmin:box_bounds.xmax, box_bounds.ymin:box_bounds.ymax, box_bounds.zmin:box_bounds.zmax]\n\t\n\t\n\t#cropped_mask = np.zeros((box_bounds.xmax - box_bounds.xmin, box_bounds.ymax - box_bounds.ymin, box_bounds.zmax - box_bounds.zmin))\n\t\n\t\n\tcropped_mask = label_map[box_bounds.xmin:box_bounds.xmax, box_bounds.ymin:box_bounds.ymax, box_bounds.zmin:box_bounds.zmax] == segment.label\n\t\n\t#for voxel in segment.list_of_voxel_tuples:\n\t#\tcropped_mask[voxel[0] - box_bounds.xmin-1, voxel[1] - box_bounds.ymin-1, voxel[2] - box_bounds.zmin-1] = 1\n\n\tcropped_mask_dilated = ndimage.morphology.binary_dilation(cropped_mask )\n\tcropped_mask_eroded = ndimage.morphology.binary_erosion(cropped_mask )\n\t\n\tif cropped_mask_eroded.sum() < 10:\n\t\tcropped_mask_eroded = cropped_mask\n\t\n\tcropped_mask_border = cropped_mask_dilated - cropped_mask_eroded\n\n\tinterior_intensity = avg_intensity(cropped_vol,cropped_mask_eroded)\t\n\tborder_intensity = avg_intensity(cropped_vol,cropped_mask_border)\n\t\n\tif interior_intensity < 0.001:\n\t\tinterior_intensity = 0.001\n\n\n\treturn border_intensity / interior_intensity",
"title": ""
},
{
"docid": "3300d589bc20105596b3577079b4e977",
"score": "0.574878",
"text": "def grayscale(img):\n return #TODO call openCV function grayscale",
"title": ""
},
{
"docid": "7a92cd74d52ea01f82c9c6c8b1bf400f",
"score": "0.56862825",
"text": "def image_preprocessing(image):\n\timage = np.reshape(image, (-1, 16))\n\timage = image.astype(np.float32)\n\t#image = image/255.0 - 0.5\n\treturn image",
"title": ""
},
{
"docid": "4e83bedb26979b1b921f6d7cc9acdfb9",
"score": "0.56780964",
"text": "def apply_ocr(image):\n\n image = array(image)\n image[image<=125]=0\n image[image>126] = 255\n image = blur(image, (2,2))\n \n return i2d(image, output_type=Output.DICT)",
"title": ""
},
{
"docid": "0635307a575f67ddfa62ed0aaf67eb1c",
"score": "0.5663966",
"text": "def preprocess_image(img):\n\n # Converting image color BGR to GRAY\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n plt.imshow(gray) # display the gray image\n plt.show()\n\n # Using binary threshold\n _, binary = cv2.threshold(gray, 200, 255, cv2.THRESH_BINARY_INV)\n plt.imshow(binary, cmap='gray') # display the threshold image\n plt.show()\n\n return gray, binary",
"title": ""
},
{
"docid": "52c0e7fe525106a7348ab62060a78db4",
"score": "0.5643106",
"text": "def image_preprocessing(image):\n\treturn cv2.GaussianBlur(cv2.cvtColor(image, cv2.COLOR_BGR2GRAY), (5,5), 0)",
"title": ""
},
{
"docid": "3b31c79ea8fd601c43e780950c3df42d",
"score": "0.56367666",
"text": "def detectandlabel(mymodel,img,labels,thresh = 0.0):\r\n img = img.copy()\r\n for bbox,subimg in segment_characters(binarize(img)):\r\n c,prob = predict_class(mymodel,subimg)\r\n if c >= len(labels) or prob < thresh:\r\n continue\r\n label = labels[c]\r\n img = cv2.rectangle(img, bbox,(255,0,0), 5)\r\n cv2.putText(img,label,(bbox[0],bbox[1]-50),cv2.FONT_HERSHEY_SIMPLEX,3,(255,0,0),7)\r\n return img",
"title": ""
},
{
"docid": "201af15aa75984990d99621ba666dee3",
"score": "0.5633936",
"text": "def get_gray_and_ab(image: dict) -> Tuple[dict, tf.Tensor]:\n img = image['input_1']\n gray = rgb_to_gray(img)\n lab = rgb_to_lab(img)\n ab = lab[:, :, 1:]\n image['input_1'] = gray\n image['input_2'] = gray\n return image, ab",
"title": ""
},
{
"docid": "a8dbf05e1a064d8dbd39aa167b218c39",
"score": "0.5612047",
"text": "def _process_image(directory, name):\n # Read the image file.\n filename = os.path.join(directory, DIRECTORY_IMAGES, name + '.jpg')\n print(filename)\n image_data = cv2.imread(filename)\n # Read the XML annotation file.\n filename = os.path.join(directory, DIRECTORY_ANNOTATIONS, name + '.xml')\n tree = ET.parse(filename)\n root = tree.getroot()\n\n # Image shape.\n size = root.find('size')\n shape = [int(size.find('height').text),\n int(size.find('width').text),\n int(size.find('depth').text)]\n # Find annotations.\n bboxes = []\n labels = []\n labels_text = []\n difficult = []\n truncated = []\n for obj in root.findall('object'):\n label = obj.find('name').text\n labels_text.append(label.encode('ascii'))\n\n if obj.find('difficult'):\n difficult.append(int(obj.find('difficult').text))\n else:\n difficult.append(0)\n if obj.find('truncated'):\n truncated.append(int(obj.find('truncated').text))\n else:\n truncated.append(0)\n\n bbox = obj.find('bndbox')\n bboxes.append((float(bbox.find('ymin').text) / shape[0],\n float(bbox.find('xmin').text) / shape[1],\n float(bbox.find('ymax').text) / shape[0],\n float(bbox.find('xmax').text) / shape[1]\n ))\n x0 = int(bbox.find('xmin').text)\n y0 = int(bbox.find('ymin').text)\n x1 = int(bbox.find('xmax').text)\n y1 = int(bbox.find('ymax').text)\n cv2.rectangle(image_data, (x0, y0), (x1, y1), (255, 0, 0), 2)\n\n # cv2.imshow(\"disp\", image_data)\n # cv2.waitKey(0)\n return image_data, shape, bboxes, labels_text, difficult, truncated",
"title": ""
},
{
"docid": "4b98460d02c4a2bcc3d45276f6725d3f",
"score": "0.56032085",
"text": "def image_preprocess(dataframe):",
"title": ""
},
{
"docid": "4aad45d16b1b6a69d355ab3879fbc77d",
"score": "0.55752164",
"text": "def get_ann(ann):\n box = ann[\"bbox\"]\n return bbox(ann[\"image_id\"], ann[\"category_id\"], box[0], box[1], box[2], box[3])",
"title": ""
},
{
"docid": "38b2daa35fce9be1f7f04fd02a458c31",
"score": "0.55323774",
"text": "def FindGreyScale( self ):\n\t\treturn None",
"title": ""
},
{
"docid": "04132b1dd76e24b380b8015463c12e43",
"score": "0.5518384",
"text": "def get_gt_dots(ann_path, img_height, img_width, mode=\"train\"):\n txt_list = open(ann_path, 'r').readlines()\n gt = format_label(mode, txt_list)\n assert gt.shape[1] == 3\n gt[:, 0] = gt[:, 0].clip(0, img_width - 1)\n gt[:, 1] = gt[:, 1].clip(0, img_height - 1)\n return gt",
"title": ""
},
{
"docid": "3816cdd60cb5da6a46770260564a75bd",
"score": "0.5515101",
"text": "def generate_from_image_diff(array_foreground, array_background, threshold=10):\n if len(array_foreground.shape) == 3:\n array_foreground = np.mean(array_foreground, 2)\n if len(array_background.shape) == 3:\n array_background = np.mean(array_background, 2)\n diffarray = abs(array_foreground[:, :] - array_background[:, :])\n masked_array = np.ma.masked_where(diffarray < threshold, diffarray, copy=True)\n binary_array = np.invert(masked_array.mask)\n labeled_map, n = ndimage.label(binary_array)\n return labeled_map",
"title": ""
},
{
"docid": "1be163585e57e4cb1aad73077643359c",
"score": "0.5513965",
"text": "def visual2bbox(face_image_np_rec,face_locations_rec,face_image_np,face_locations):\n print('bbox of rec ', face_locations_rec)\n print('bbox of alinment ', face_locations)\n \n plt.figure(figsize = (8,8)) \n x ,y , width, height = rect_to_bbox(face_locations_rec[0])\n x2 ,y2 = x + width, y + height \n cv2.rectangle(face_image_np_rec, (x,y), (x2,y2), (0,0,255), 1)\n plt.imshow(face_image_np_rec) \n \n plt.figure(figsize = (8,8)) \n x ,y , width, height = face_locations[0]\n x2 ,y2 = x + width, y + height \n cv2.rectangle(face_image_np, (x,y), (x2,y2), (0,0,255), 1)\n plt.imshow(face_image_np) \n plt.show()\n \n plt.figure(figsize = (8,8)) \n fl = [{'top_lip':face_landmarks[0]['top_lip'] ,\n 'bottom_lip':face_landmarks_rec[0]['bottom_lip']\n \n }]\n \n fll = draw_landmarks(self.face_path ,fl)\n \n image = draw_landmarks_withcv2(face_image_np, fll, color=(255, 0, 0), thickness=2)\n plt.imshow(image)\n plt.axis('off')\n plt.show()",
"title": ""
},
{
"docid": "c00ff6057e414d3d27967385ba53d30e",
"score": "0.55122477",
"text": "def to_grey(self):\n\n self.feed = cv2.cvtColor(self.feed, cv2.COLOR_BGR2GRAY)",
"title": ""
},
{
"docid": "3b4eef60ccf658e0d447b79e91ba78d3",
"score": "0.55050904",
"text": "def bgr_to_lab(image: torch.Tensor) -> torch.Tensor:\n \n # Convert from Linear RGB to sRGB\n b: torch.Tensor = image[..., 0, :, :]\n g: torch.Tensor = image[..., 1, :, :]\n r: torch.Tensor = image[..., 2, :, :]\n\n rs: torch.Tensor = torch.where(r > 0.04045, torch.pow(((r + 0.055) / 1.055), 2.4), r / 12.92)\n gs: torch.Tensor = torch.where(g > 0.04045, torch.pow(((g + 0.055) / 1.055), 2.4), g / 12.92)\n bs: torch.Tensor = torch.where(b > 0.04045, torch.pow(((b + 0.055) / 1.055), 2.4), b / 12.92)\n\n image_s = torch.stack([rs, gs, bs], dim=-3)\n\n xyz_im: torch.Tensor = rgb_to_xyz(image_s)\n\n # normalize for D65 white point\n xyz_ref_white = torch.tensor([0.95047, 1., 1.08883], device=xyz_im.device, dtype=xyz_im.dtype)[..., :, None, None]\n xyz_normalized = torch.div(xyz_im, xyz_ref_white)\n\n power = torch.pow(xyz_normalized, 1 / 3)\n scale = 7.787 * xyz_normalized + 4. / 29.\n xyz_int = torch.where(xyz_normalized > 0.008856, power, scale)\n\n x: torch.Tensor = xyz_int[..., 0, :, :]\n y: torch.Tensor = xyz_int[..., 1, :, :]\n z: torch.Tensor = xyz_int[..., 2, :, :]\n\n L: torch.Tensor = (116. * y) - 16.\n a: torch.Tensor = 500. * (x - y)\n _b: torch.Tensor = 200. * (y - z)\n\n out: torch.Tensor = torch.stack([L, a, _b], dim=-3)\n\n return out",
"title": ""
},
{
"docid": "2e1231319778a8d782ef80912e563f1d",
"score": "0.549889",
"text": "def annotate(self, img):\n arr = np.asarray(img)\n face_locations = self.detector.detect(arr)\n\n for (x1, y1, x2, y2) in face_locations:\n arr = cv2.rectangle(arr,\n (int(x1), int(y1)),\n (int(x2), int(y2)),\n self.BOX_LINE_COLOR,\n self.BOX_LINE_WIDTH)\n ret = Image.fromarray(arr)\n\n return ret",
"title": ""
},
{
"docid": "ae3043ab57c5ba1f51821e1693ebe638",
"score": "0.5487968",
"text": "def bbox_to_image(img, bbox, value, line_thickness=2):\n cv2.rectangle(img, (int(bbox[0]), int(bbox[1])),\n (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3])), value,\n line_thickness)",
"title": ""
},
{
"docid": "8ef17d1d8e72f50e74779ad2a99972b8",
"score": "0.5486478",
"text": "def grayscale(img):\n\n grayscale_img = img.convert(\"L\")\n return grayscale_img",
"title": ""
},
{
"docid": "7d1b29d2c325c235d94572921aa57ac9",
"score": "0.5479673",
"text": "def image_converter(image_sr_array, image_mid_array, image_high_array, flag, boundarypixels, MAX_RGB):\n psnr_sr = 10 * np.log10(MAX_RGB ** 2 / (np.mean(np.square(image_high_array - image_sr_array))))\n psnr_itp = 10 * np.log10(MAX_RGB ** 2 / (np.mean(np.square(image_high_array - image_mid_array))))\n # from IPython import embed; embed(); exit()\n ssim_sr = ssim(np.uint8(image_sr_array*255/MAX_RGB), np.uint8(image_high_array*255/MAX_RGB), gaussian_weights=True, use_sample_covariance=False)\n ssim_itp = ssim(np.uint8(image_mid_array*255/MAX_RGB), np.uint8(image_high_array*255/MAX_RGB), gaussian_weights=True, use_sample_covariance=False)\n score = [psnr_itp, psnr_sr, ssim_itp, ssim_sr]\n return score",
"title": ""
},
{
"docid": "27286a2b27524eb9966a4349a927bff9",
"score": "0.54617",
"text": "def get_grayscale(c):\n min = min(c)\n max = max(c)\n\n greyscale_values = np.interp(c, [min, max], [0, 255]).astype(int)",
"title": ""
},
{
"docid": "adcd6d33352bec2c55f1017f692f5400",
"score": "0.54579765",
"text": "def get_ann_info(self, idx):\n\n img_id = self.data_infos[idx]['id']\n xml_path = osp.join(self.img_prefix, 'Annotations', f'{img_id}.xml')\n tree = ET.parse(xml_path)\n root = tree.getroot()\n bboxes = []\n labels = []\n bboxes_ignore = []\n labels_ignore = []\n for obj in root.findall('object'):\n name = obj.find('label').text\n if name not in self.CLASSES:\n continue\n label = self.cat2label[name]\n difficult = int(obj.find('difficult').text)\n bnd_box = obj.find('bndbox')\n # Coordinates may be float type\n bbox = [\n int(float(bnd_box.find('x0').text)),\n int(float(bnd_box.find('y0').text)),\n int(float(bnd_box.find('x1').text)),\n int(float(bnd_box.find('y1').text)),\n int(float(bnd_box.find('x2').text)),\n int(float(bnd_box.find('y2').text)),\n int(float(bnd_box.find('x3').text)),\n int(float(bnd_box.find('y3').text)), \n ]\n # drop ignore and difficult\n # ignore = False\n # if self.min_size:\n # assert not self.test_mode\n # w = bbox[2] - bbox[0]\n # h = bbox[3] - bbox[1]\n # if w < self.min_size or h < self.min_size:\n # ignore = True\n # if difficult or ignore:\n # bboxes_ignore.append(bbox)\n # labels_ignore.append(label)\n # else:\n bboxes.append(bbox)\n labels.append(label)\n if not bboxes:\n bboxes = np.zeros((0, 5))\n labels = np.zeros((0, ))\n else:\n bboxes = np.array(bboxes, ndmin=2) - 1\n labels = np.array(labels)\n if not bboxes_ignore:\n bboxes_ignore = np.zeros((0, 5))\n labels_ignore = np.zeros((0, ))\n else:\n bboxes_ignore = np.array(bboxes_ignore, ndmin=2) - 1\n labels_ignore = np.array(labels_ignore)\n \n n_bboxes = []\n for i in range(bboxes.shape[0]):\n\n bbox = bboxes[i, :]\n\n cx = (bbox[0] + bbox[2] + bbox[4] + bbox[6]) / 4\n cy = (bbox[1] + bbox[3] + bbox[5] + bbox[7]) / 4\n w = math.sqrt(math.pow((bbox[0] - bbox[2]), 2) + math.pow((bbox[1] - bbox[3]), 2))\n h = math.sqrt(math.pow((bbox[2] - bbox[4]), 2) + math.pow((bbox[3] - bbox[5]), 2))\n\n if w < h:\n w, h = h, w\n theta = math.atan((bbox[5] - bbox[3]) / (bbox[4] - bbox[2] + 1e-3))\n else:\n theta = math.atan((bbox[3] - bbox[1]) / (bbox[2] - bbox[0] + 1e-3))\n n_bboxes.append([cx, cy, w, h, theta])\n\n ann = dict(\n bboxes=np.array(n_bboxes).astype(np.float32),\n labels=np.array(labels).astype(np.int64),\n bboxes_ignore=bboxes_ignore.astype(np.float32),\n labels_ignore=labels_ignore.astype(np.int64))\n return ann",
"title": ""
},
{
"docid": "c15616ccd27939352089a6374d599824",
"score": "0.54550046",
"text": "def detect(original_image, min_score, max_overlap, top_k, suppress=None):\n image = np.array(original_image).astype('float32')\n H, W, C = image.shape\n image = transform(image)\n image = jt.array(image[np.newaxis,:]).float32()\n predicted_locs, predicted_scores = model(image)\n det_boxes, det_labels, det_scores = model.detect_objects(predicted_locs, predicted_scores, min_score=min_score, max_overlap=max_overlap, top_k=top_k)\n det_boxes = det_boxes[0]\n original_dims = np.array([[W, H, W, H]])\n det_boxes = det_boxes * original_dims\n det_labels = [rev_label_map[l] for l in det_labels[0]]\n if det_labels == ['background']:\n return original_image\n annotated_image = original_image\n draw = ImageDraw.Draw(annotated_image)\n font = ImageFont.truetype(\"ahronbd.ttf\", 15)\n for i in range(det_boxes.shape[0]):\n if suppress is not None:\n if det_labels[i] in suppress:\n continue\n box_location = det_boxes[i].tolist()\n draw.rectangle(xy=box_location, outline=label_color_map[det_labels[i]])\n draw.rectangle(xy=[l + 1. for l in box_location], outline=label_color_map[det_labels[i]]) \n text_size = font.getsize(det_labels[i].upper())\n text_location = [box_location[0] + 2., box_location[1] - text_size[1]]\n textbox_location = [box_location[0], box_location[1] - text_size[1], box_location[0] + text_size[0] + 4., box_location[1]]\n draw.rectangle(xy=textbox_location, fill=label_color_map[det_labels[i]])\n draw.text(xy=text_location, text=det_labels[i].upper(), fill='white', font=font)\n del draw\n return annotated_image",
"title": ""
},
{
"docid": "378ccca69540fbe4a52206825536682a",
"score": "0.5453775",
"text": "def get_unet_border_weight_map(\n annotation,\n w0=5.0,\n sigma=13.54591536778324,\n eps=1e-32):\n # https://github.com/czbiohub/microDL/blob/master/micro_dl/utils/masks.py\n # if there is only one label, zero return the array as is\n if np.sum(annotation) == 0:\n return annotation\n\n # Masks could be saved as .npy bools, if so convert to uint8 and generate\n # labels from binary\n if annotation.dtype == bool:\n annotation = annotation.astype(np.uint8)\n assert annotation.dtype in [\n np.uint8,\n np.uint16,\n ], \"Expected data type uint, it is {}\".format(annotation.dtype)\n labeled_array = annotation.copy()\n inner = distance_transform_edt(annotation)\n inner = (inner.max() - inner) / inner.max()\n inner[annotation == 0] = 0\n # if there is only one label or only background\n if len(np.unique(labeled_array)) == 1:\n return inner\n # if there is only one label and background\n if len(np.unique(labeled_array)) == 2:\n if 0 in np.unique(labeled_array):\n return inner\n # cells instances for distance computation\n # 4 connected i.e default (cross-shaped)\n # structuring element to measure connectivy\n # If cells are 8 connected/touching they are labeled as one single object\n # Loss metric on such borders is not useful\n # class balance weights w_c(x)\n unique_values = np.unique(labeled_array).tolist()\n weight_map = [0] * len(unique_values)\n for index, unique_value in enumerate(unique_values):\n mask = np.zeros(\n (annotation.shape[0],\n annotation.shape[1]),\n dtype=np.float64)\n mask[annotation == unique_value] = 1\n weight_map[index] = 1 / mask.sum()\n\n # this normalization is important - foreground pixels must have weight 1\n weight_map = [i / max(weight_map) for i in weight_map]\n\n wc = np.zeros((annotation.shape[0], annotation.shape[1]), dtype=np.float64)\n for index, unique_value in enumerate(unique_values):\n wc[annotation == unique_value] = weight_map[index]\n # cells instances for distance computation\n # 4 connected i.e default (cross-shaped)\n # structuring element to measure connectivy\n # If cells are 8 connected/touching they are labeled as one single object\n # Loss metric on such borders is not useful\n # Not NEED to find labels\n # labeled_array, _ = scipy.ndimage.measurements.label(annotation)\n # cells distance map\n border_loss_map = np.zeros(\n (annotation.shape[0], annotation.shape[1]), dtype=np.float64\n )\n distance_maps = np.zeros(\n (annotation.shape[0], annotation.shape[1], np.max(labeled_array)),\n dtype=np.float64,\n )\n\n if np.max(labeled_array) >= 2:\n for index in range(np.max(labeled_array)):\n mask = np.ones_like(labeled_array)\n mask[labeled_array == index + 1] = 0\n distance_maps[:, :,\n index] = distance_transform_edt(mask)\n distance_maps = np.sort(distance_maps, 2)\n d1 = distance_maps[:, :, 0]\n d2 = distance_maps[:, :, 1]\n border_loss_map = w0 * np.exp((-1 * (d1 + d2) ** 2) / (2 * (sigma ** 2)))\n\n zero_label = np.zeros(\n (annotation.shape[0],\n annotation.shape[1]),\n dtype=np.float64)\n zero_label[labeled_array == 0] = 1\n border_loss_map = np.multiply(border_loss_map, zero_label)\n return border_loss_map + inner + wc",
"title": ""
},
{
"docid": "646ae4de8bb929bdccba1fe2ddf1dec7",
"score": "0.5451614",
"text": "def preprocess(self, resized_inputs):\n return (2.0 / 255.0) * resized_inputs - 1.0",
"title": ""
},
{
"docid": "646ae4de8bb929bdccba1fe2ddf1dec7",
"score": "0.5451614",
"text": "def preprocess(self, resized_inputs):\n return (2.0 / 255.0) * resized_inputs - 1.0",
"title": ""
},
{
"docid": "7823fc57ce949d79608ec31b1e09d519",
"score": "0.544763",
"text": "def annotate(input_file, output_dir):\n logger.info(\"---\")\n logger.info('Input image: \"{}\"'.format(os.path.abspath(input_file)))\n image = Image.from_file(input_file)\n intensity = mean_intensity_projection(image)\n norm_intensity = normalise(intensity)\n norm_rgb = np.dstack([norm_intensity, norm_intensity, norm_intensity])\n\n name = fpath2name(input_file)\n png_name = name + \".png\"\n csv_name = name + \".csv\"\n png_path = os.path.join(output_dir, png_name)\n csv_path = os.path.join(output_dir, csv_name)\n\n tubes = find_tubes(input_file, output_dir)\n grains, difficult = find_grains(input_file, output_dir)\n tubes = remove_tubes_not_touching_grains(tubes, grains)\n tubes = remove_tubes_that_are_grains(tubes, grains)\n\n ann = AnnotatedImage.from_grayscale(intensity)\n\n num_grains = 0\n for n, i in enumerate(grains.identifiers):\n n = n + 1\n region = grains.region_by_identifier(i)\n ann.mask_region(region.inner.inner.inner.border.dilate(),\n color=(0, 255, 0))\n num_grains = n\n\n num_tubes = 0\n for n, i in enumerate(tubes.identifiers):\n n = n + 1\n region = tubes.region_by_identifier(i)\n highlight = norm_rgb * pretty_color(i)\n ann[region] = highlight[region]\n ann.mask_region(region.dilate(3).border.dilate(3),\n color=pretty_color(i))\n num_tubes = n\n\n ann.text_at(\"Num grains: {:3d}\".format(num_grains), (10, 10),\n antialias=True, color=(0, 255, 0), size=48)\n logger.info(\"Num grains: {:3d}\".format(num_grains))\n\n ann.text_at(\"Num tubes : {:3d}\".format(num_tubes), (60, 10),\n antialias=True, color=(255, 0, 255), size=48)\n logger.info(\"Num tubes : {:3d}\".format(num_tubes))\n\n logger.info('Output image: \"{}\"'.format(os.path.abspath(png_path)))\n with open(png_path, \"wb\") as fh:\n fh.write(ann.png())\n\n logger.info('Output csv: \"{}\"'.format(os.path.abspath(csv_path)))\n with open(csv_path, \"w\") as fh:\n fh.write(\"{},{},{}\\n\".format(png_name, num_grains, num_tubes))\n\n return png_name, num_grains, num_tubes",
"title": ""
},
{
"docid": "22d0c6c4c5596266802bd98c3672e8c3",
"score": "0.5437551",
"text": "def decode_segmap(label_mask, n_classes, plot=False):\n label_colours = get_pascal_labels()\n r = label_mask.copy()\n g = label_mask.copy()\n b = label_mask.copy()\n for ll in range(0, n_classes):\n r[label_mask == ll] = label_colours[ll, 0]\n g[label_mask == ll] = label_colours[ll, 1]\n b[label_mask == ll] = label_colours[ll, 2]\n rgb = np.zeros((label_mask.shape[0], label_mask.shape[1], 3))\n rgb[:, :, 0] = r / 255.0\n rgb[:, :, 1] = g / 255.0\n rgb[:, :, 2] = b / 255.0\n if plot:\n plt.imshow(rgb)\n plt.show()\n else:\n return rgb",
"title": ""
},
{
"docid": "14bd3e62a67824897eee545d17d32a3b",
"score": "0.54360145",
"text": "def _parse_ann_info(self, img_info, ann_info):\n gt_bboxes = []\n gt_labels = []\n gt_bboxes_ignore = []\n gt_masks_ann = []\n\n for i, ann in enumerate(ann_info):\n if ann.get('ignore', False):\n continue\n x1, y1, w, h = ann['bbox']\n if ann['area'] <= 0 or w < 1 or h < 1:\n continue\n bbox = [x1, y1, x1 + w - 1, y1 + h - 1]\n if ann.get('iscrowd', False):\n gt_bboxes_ignore.append(bbox)\n else:\n gt_bboxes.append(bbox)\n gt_labels.append(self.cat2label[ann['category_id']])\n gt_masks_ann.append(ann['segmentation'])\n\n if gt_bboxes:\n gt_bboxes = np.array(gt_bboxes, dtype=np.float32)\n gt_labels = np.array(gt_labels, dtype=np.int64)\n else:\n gt_bboxes = np.zeros((0, 4), dtype=np.float32)\n gt_labels = np.array([], dtype=np.int64)\n\n if gt_bboxes_ignore:\n gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)\n else:\n gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)\n\n seg_map = img_info['filename'].replace('jpg', 'png')\n\n ann = dict(\n bboxes=gt_bboxes,\n labels=gt_labels,\n bboxes_ignore=gt_bboxes_ignore,\n masks=gt_masks_ann,\n seg_map=seg_map)\n\n return ann",
"title": ""
},
{
"docid": "bdc5bedc4a5685d850b5b4e7b7c1f296",
"score": "0.5432011",
"text": "def gray2innerouterbound(gray, width):\n h, w = gray.shape[:2]\n gray_cp = gray.copy()\n gray_cp[gray == 76] = 255\n gray_cp[gray == 151] = 255\n bound = np.zeros_like(gray, dtype=np.uint8)\n label = gray2mask(gray_cp)\n\n label_binary = label_binarize(label.flatten(), classes=range(0, 3))\n label_binary = np.reshape(label_binary, (h, w, -1))\n bound_binary = np.zeros_like(label_binary)\n\n for i in range(3): # number of classes before edge detection\n tmp = ndimage.distance_transform_cdt(label_binary[:, :, i], 'taxicab')\n cdt = np.logical_and(tmp >= 1, tmp <= width)\n bound_binary[:, :, i] = cdt\n\n bound[bound_binary[:, :, 0] != 0] = 2 # outer bound marked as 2\n bound[bound_binary[:, :, 1] != 0] = 1 # inner bound marked as 1\n\n return bound",
"title": ""
},
{
"docid": "ce8edc317aa172caa555242d2014d56c",
"score": "0.5431405",
"text": "def transform_label(label_orig, sz):\n label = copy.deepcopy(label_orig)\n label = Image.fromarray(label.squeeze().astype(np.uint8))\n label = label.resize( (sz[0],sz[1]),Image.NEAREST)\n label = np.array(label, dtype=np.int32)\n return label",
"title": ""
},
{
"docid": "9f58f2d34528419447fb4a73a7a8bcea",
"score": "0.5426623",
"text": "def preprocess(img):\n # Convert to HSV and get value (luminance) channel, in order to make more robust\n hsv = rgb_to_hsv(img)\n v = hsv[:,:,2]\n \n # Apply Gaussian Blur to reduce the noise in edge detection\n kernel_size = 5\n out = gaussian_blur(v, kernel_size) \n return out",
"title": ""
},
{
"docid": "45f80b58a8c1f5a1061741d189034192",
"score": "0.5425294",
"text": "def label2mask(label_array):\n img = Image.new('RGB', (label_array.shape[1], label_array.shape[0]))\n pixels = img.load()\n for j_, j in enumerate(label_array):\n for k_, k in enumerate(j):\n if k <= len(label_colours):\n pixels[k_, j_] = label_colours[k]\n return img",
"title": ""
},
{
"docid": "7a39aad3f519921e53b932b570b6014c",
"score": "0.54225767",
"text": "def make_label(xml_file, rgb_img):\n\n row_max, col_max = rgb_img.shape[0:2]\n res = np.zeros((row_max, col_max))\n\n mydoc = minidom.parse(xml_file)\n polygons = mydoc.getElementsByTagName('polygon')\n for poly in polygons:\n rows = []\n cols = []\n for point in poly.getElementsByTagName('pt'):\n x = int(point.getElementsByTagName('x')[0].firstChild.data)\n y = int(point.getElementsByTagName('y')[0].firstChild.data)\n rows.append(y)\n cols.append(x)\n rr, cc = polygon(rows, cols)\n res[rr, cc] = 1\n\n return res",
"title": ""
},
{
"docid": "796506f92d742ca133d23d9190dee3db",
"score": "0.5421",
"text": "def PrepareGreyScale( self ):\n\t\treturn None",
"title": ""
},
{
"docid": "ca7ac06e7929e0b943eb3bcac0604d9c",
"score": "0.5418515",
"text": "def __preprocessImage(img):\n\n imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n imgThresh = cv2.adaptiveThreshold(imgGray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2) \n return imgThresh",
"title": ""
},
{
"docid": "731ae5ec16cfa2d7c143197b660408da",
"score": "0.54180425",
"text": "def original_image(x):\n\n x = x.copy()\n x = x + VGG16_OFFSET\n x = x / 255.0\n # To RGB\n x = x[::-1,:,:]\n return x",
"title": ""
},
{
"docid": "846f5b78427f89caca12bbb068f0a175",
"score": "0.5415394",
"text": "def decode_labels(image, label, input_img_shapes, sess_tmp, threshold=0.3):\n image = cv2.resize(image, (input_img_shapes[1], input_img_shapes[0]))\n box_priors = np.array([[0.57273, 0.677385], [1.87446, 2.06253], [3.33843, 5.47434], [7.88282, 3.52778], [9.77052, 9.16828]])\n obj_prob = label[..., 4]\n # print(obj_prob[:2, 0, :])\n # print('the max of conf:{}'.format(np.max(obj_prob)))\n class_prob = label[..., 5:]\n # class_prob = np.max(class_prob, -1)\n # print('the max of class prob:{}'.format(np.max(class_prob)))\n\n mask = label[..., 4] > threshold\n boxes = label[mask]\n\n box_number = np.sum(np.int64(mask))\n\n if box_number >= 0:\n biases = np.array([[[[i, j, k] for k in range(5)] for j in range(13)] for i in range(13)])\n # biases: [box_number, 3], for each box, the coordinate information is [cell_x, cell_y, in_which_cell]\n biases = biases[mask]\n\n # convert the box coordinate from the feature map (1x1) to be the real coordinate (height, width),\n # define the x-axis direction is down and y-axis direction is right\n rate_x = input_img_shapes[0] / 13.0\n rate_y = input_img_shapes[1] / 13.0\n\n original_coordinates = np.zeros((box_number, 5), dtype=np.int64)\n for box_i in range(box_number):\n x = (boxes[box_i][0] + biases[box_i, 0]) * rate_x\n y = (boxes[box_i][1] + biases[box_i, 1]) * rate_y\n higth = boxes[box_i][2] * box_priors[biases[box_i, 2], 0] * rate_x\n width = boxes[box_i][3] * box_priors[biases[box_i, 2], 1] * rate_y\n\n x1 = np.int64(x - higth / 2.0)\n y1 = np.int64(y - width / 2.0)\n x2 = np.int64(x + higth / 2.0)\n y2 = np.int64(y + width / 2.0)\n # here, the last dim should be the class id, start from 1\n original_coordinates[box_i] = [x1, y1, x2, y2, np.argmax(boxes[box_i][5:])]\n\n box_coor = original_coordinates[..., :4]\n box_score = boxes[..., 4]\n\n selected_indeces = sess_tmp.run(tf.image.non_max_suppression(box_coor, box_score, max_output_size=3))\n selected_boxes = sess_tmp.run(tf.gather(original_coordinates, selected_indeces))\n image = draw_boxes_on_image(image, selected_boxes)\n image = cv2.cvtColor(np.uint8(image), cv2.COLOR_RGB2BGR)\n return image",
"title": ""
},
{
"docid": "a97d2eabc47bf8d07da8a2d8e44d42da",
"score": "0.54126084",
"text": "def calc_brightRange(img, ROI):\n pass",
"title": ""
},
{
"docid": "3c9b3cbb1b9e44041839f13c6e0dc334",
"score": "0.5412483",
"text": "def _parse_ann_info(self, img_info, ann_info):\n gt_bboxes = []\n gt_labels = []\n gt_bboxes_ignore = []\n gt_masks_ann = []\n for i, ann in enumerate(ann_info):\n # {'id': 46226, 'image_id': 3637, 'category_id': 8, 'a_segm':, 'i_segm': , 'a_bbox': [1141, 224, 82, 123], 'i_bbox': [1141, 224, 82, 123], 'a_area': 10086.0, 'i_area': 10086.0, 'oco_id': 3, 'ico_id': 0}\n # if ann.get('ignore', False):\n # continue\n x1, y1, w, h = ann['a_bbox']\n inter_w = max(0, min(x1 + w, img_info['width']) - max(x1, 0))\n inter_h = max(0, min(y1 + h, img_info['height']) - max(y1, 0))\n if inter_w * inter_h == 0:\n continue\n if ann['a_area'] <= 0 or w < 1 or h < 1:\n continue\n if ann['category_id'] not in self.cat_ids:\n continue\n bbox = [x1, y1, x1 + w, y1 + h]\n # if ann.get('iscrowd', False):\n # gt_bboxes_ignore.append(bbox)\n # else:\n gt_bboxes.append(bbox)\n gt_labels.append(self.cat2label[ann['category_id']])\n # gt_masks_ann.append(ann.get('i_segm', None))\n\n if gt_bboxes:\n gt_bboxes = np.array(gt_bboxes, dtype=np.float32)\n gt_labels = np.array(gt_labels, dtype=np.int64)\n else:\n gt_bboxes = np.zeros((0, 4), dtype=np.float32)\n gt_labels = np.array([], dtype=np.int64)\n\n if gt_bboxes_ignore:\n gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)\n else:\n gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)\n\n # seg_map = img_info['filename'].replace('jpg', 'png')\n\n ann = dict(\n bboxes=gt_bboxes,\n labels=gt_labels,\n bboxes_ignore=gt_bboxes_ignore,\n masks=gt_masks_ann)\n\n return ann",
"title": ""
},
{
"docid": "ee736008db31a2a4a5723b1d37606d14",
"score": "0.5394742",
"text": "def load_cocottributes_annotation(idx, im_size, label_ids):\n patch= Patch.query.get(idx)\n im = patch.crop(savename = None, make_square = True, resize = im_size)\n ann_vec = patch.consensus_vec(label_ids)\n if ann_vec == []:\n ann_vec,_ = patch.annotation_vector(label_ids, consensus=True)\n #multilabel = np.array([1 if x >= 0.5 else -1 if x == -1 or (x > 0 and x < 0.5) else 0 for x in ann_vec])\n multilabel = np.array([1 if x >= 0.5 else 0 for x in ann_vec])\n return im, multilabel",
"title": ""
},
{
"docid": "0a7cf7ba928dc000a30a11fd13a0a27e",
"score": "0.53932697",
"text": "def visualize(img, mask):\n out = np.float32(img)/255\n msk = CMAP[mask % CMAP.shape[0], :]\n msk[mask == 0, :] = 0.\n out = out*0.5 + msk*0.5\n return (out*255).astype(np.uint8)",
"title": ""
},
{
"docid": "019ac208b36b3bd66c4eeb2e5d097c39",
"score": "0.5383998",
"text": "def _get_imganno(self, idx):\n return self.annos[idx]",
"title": ""
},
{
"docid": "bcc29fadca6fe8d2febccc388558038b",
"score": "0.53713936",
"text": "def draw_labels(image, np_mask, label):\n if np.sum(np_mask) > 0:\n x, y = np.argwhere(np_mask == 1)[0]\n image = imgaug.imgaug.draw_text(image, x, y, label, color=(255, 255, 255), size=50)\n return image",
"title": ""
},
{
"docid": "e3b94f7c4b433b33b55de7dd4d8909b6",
"score": "0.5356755",
"text": "def gray2bound(gray, n_classes=3, width=2):\n\n h, w = gray.shape[:2]\n if n_classes <= 3: # if n_classes less than 3, cal and noncal are not considered\n gray[gray == 76] = 255\n gray[gray == 151] = 255\n\n label = gray2mask(gray)\n label_binary = label_binarize(label.flatten(), classes=range(0, n_classes))\n label_binary = np.reshape(label_binary, (h, w, -1))\n bound_binary = np.zeros_like(label_binary)\n\n for i in range(n_classes): # number of classes before edge detection\n tmp = ndimage.distance_transform_cdt(label_binary[:, :, i], 'taxicab')\n cdt = np.logical_and(tmp >= 1, tmp <= width)\n bound_binary[:, :, i] = cdt\n\n bound = np.any(bound_binary, axis=2).astype(np.uint8)\n\n return bound",
"title": ""
},
{
"docid": "86ff3013cd81d98298f403d925bd655e",
"score": "0.5355123",
"text": "def colorMotionSuperpixels(self):\n if not hasattr(self, 'motion_superpixel_labels'):\n return None\n\n boundary_img = self.getSegmentedImage()\n\n motion_superpix_img = np.copy(boundary_img)\n\n for label in self.motion_superpixel_labels:\n label_pix_x, label_pix_y = np.where(self.label_frame == label) \n \n motion_superpix_img[label_pix_x,label_pix_y,:] = np.array([0,255,0])\n\n return motion_superpix_img",
"title": ""
},
{
"docid": "b5d778d234bc603e4996ba820d95a8b6",
"score": "0.5343431",
"text": "def decode_segmap(label_mask, n_classes=6, label_colours=get_seismic_labels()):\n r = label_mask.copy()\n g = label_mask.copy()\n b = label_mask.copy()\n for ll in range(0, n_classes):\n r[label_mask == ll] = label_colours[ll, 0]\n g[label_mask == ll] = label_colours[ll, 1]\n b[label_mask == ll] = label_colours[ll, 2]\n rgb = np.zeros((label_mask.shape[0], label_mask.shape[1], label_mask.shape[2], 3))\n rgb[:, :, :, 0] = r / 255.0\n rgb[:, :, :, 1] = g / 255.0\n rgb[:, :, :, 2] = b / 255.0\n return np.transpose(rgb, (0, 3, 1, 2))",
"title": ""
},
{
"docid": "b26e5b5685c4b50e7f1a05902eedc5ff",
"score": "0.5342992",
"text": "def img2map(self, ob):\n ob_tranformed = []\n t = np.array([[1, 0, 0, -300],\n [0, -1, 0, 300],\n [0, 0, -1, 0],\n [0, 0, 0, 1]])\n for p in ob:\n p = np.array(p) # in case it is not already numpy array\n p = np.hstack((p, [0, 1]))\n p = t.dot(p).astype(int)\n ob_tranformed.append(p[:2])\n return np.array(ob_tranformed)",
"title": ""
},
{
"docid": "93d06564a6dba1e7a95485b1118b8307",
"score": "0.5341198",
"text": "def segment_image(self, img):\n # YOUR CODE HERE\n # author @Siddarth A53299801\n # normalize image\n if img.dtype == np.uint8:\n img = img.astype(np.float32)\n img = img / 255.0\n\n sh = img.shape\n X = np.reshape(img, (sh[0] * sh[1], 3))\n o = np.ones((X.shape[0], 1))\n X = np.concatenate((o, X), 1)\n\n # Load the weights\n w = self.w\n\n print(\"\\nBounding Box - B.B - Statistics are as follows: \")\n print(X.shape, w.shape)\n print(X.dtype, np.min(X), np.max(X))\n\n # predicting the mask\n y_pred = np.matmul(X, w) >= 0\n y_pred = y_pred.astype(np.uint8)\n print(\"YPred Stats: \", y_pred.shape, y_pred.dtype, np.min(y_pred), np.max(y_pred))\n mask_img = np.reshape(y_pred, (sh[0], sh[1]))\n print(\"Mask Image Stats: \", mask_img.shape, mask_img.dtype, np.min(mask_img), np.max(mask_img))\n return mask_img",
"title": ""
},
{
"docid": "1e96f6bb70adf046aa5f7df6eebd264b",
"score": "0.5340764",
"text": "def preProcessLabel(label, size):\n shape = label.shape\n\n label = imgResize(label, size)\n label = binarize(label, shape)\n return label",
"title": ""
},
{
"docid": "7cc40b1c1dc79367e0c0f62791edc007",
"score": "0.53315467",
"text": "def extract_features(self, im, **kwargs):",
"title": ""
},
{
"docid": "a0216c7478f27dec0f79a821ff9d5433",
"score": "0.5327406",
"text": "def visual_result(image, label, alpha=0.7):\n image = (image * rgb_std + rgb_mean) * 255\n image, label = image.astype(np.int), label.astype(np.int)\n H, W, C = image.shape\n masks_color = np.zeros(shape=[H, W, C])\n inv_masks_color = np.zeros(shape=[H, W, C])\n cls = []\n for i in range(H):\n for j in range(W):\n cls_idx = label[i, j]\n masks_color[i, j] = np.array(colormap[cls_idx])\n cls.append(cls_idx)\n if classes[cls_idx] == \"background\":\n inv_masks_color[i, j] = alpha * image[i, j]\n\n show_image = np.zeros(shape=[224, 672, 3])\n cls = set(cls)\n for x in cls:\n print(\"=> \", classes[x])\n show_image[:, :224, :] = image\n show_image[:, 224:448, :] = masks_color\n show_image[:, 448:, :] = (1-alpha)*image + alpha*masks_color + inv_masks_color\n show_image = Image.fromarray(np.uint8(show_image))\n return show_image",
"title": ""
},
{
"docid": "92b74ec0a599d1835d3e7cacf30f1478",
"score": "0.53197855",
"text": "def rgb2gray_genome(rgb):\r\n return np.dot(rgb[..., :3], [0.2989, 0.5870, 0.1140])",
"title": ""
},
{
"docid": "18b48c6cdb18fc4eede8db3b7f4f661b",
"score": "0.5309658",
"text": "def visual_result(no, target_features, image, prediction,label,alpha=0.5):\n image, label = np.asarray(image).astype(np.uint8), np.asarray(label).astype(np.uint8)\n prediction = np.asarray(prediction)\n image = image[0]\n\n image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n prediction = prediction[0]\n numpy_prediction = np.zeros_like(prediction)\n label = label[0]\n\n H, W, C = image.shape\n H, W, nC = label.shape\n masks_color = np.zeros(shape=[H, W, C])\n lables_color = np.zeros(shape=[H, W, C])\n inv_masks_color = np.zeros(shape=[H, W, C])\n result_image = np.zeros_like(image)\n no2name_dict = dict()\n no2name_dict['1'] = 'Third_eyelid_protrude'\n no2name_dict['2'] = 'blepharitis_inflammation'\n no2name_dict['3'] = 'blepharitis_inner_inflammation'\n no2name_dict['4'] = 'corneal_pus'\n no2name_dict['5'] = 'corneal_scratch'\n no2name_dict['6'] = 'corneal'\n no2name_dict['7'] = 'conjunctivitis_flare'\n no2name_dict['8'] = 'conjunctivitis_swll'\n no2name_dict['9'] = 'conjunctivitis_white_inflammation'\n no2name_dict['10'] = 'gataract'\n no2name_dict['11'] = 'gataract_initial'\n\n result_dict = dict()\n result_dict['Third_eyelid_protrude'] = 0\n result_dict['blepharitis_inflammation'] = 0\n result_dict['blepharitis_inner_inflammation'] = 0\n result_dict['corneal_pus'] = 0\n result_dict['corneal_scratch'] = 0\n result_dict['corneal'] = 0\n result_dict['conjunctivitis_flare'] = 0\n result_dict['conjunctivitis_swll'] = 0\n result_dict['conjunctivitis_white_inflammation'] = 0\n result_dict['gataract'] = 0\n result_dict['gataract_initial'] = 0\n\n result_prob_dict = dict()\n result_prob_dict['Third_eyelid_protrude'] = 0\n result_prob_dict['blepharitis_inflammation'] = 0\n result_prob_dict['blepharitis_inner_inflammation'] = 0\n result_prob_dict['corneal_pus'] = 0\n result_prob_dict['corneal_scratch'] = 0\n result_prob_dict['corneal'] = 0\n result_prob_dict['conjunctivitis_flare'] = 0\n result_prob_dict['conjunctivitis_swll'] = 0\n result_prob_dict['conjunctivitis_white_inflammation'] = 0\n result_prob_dict['gataract'] = 0\n result_prob_dict['gataract_initial'] = 0\n\n cls = []\n c_i=1\n for k in range(nC):\n tag_label=0\n if k in target_features:\n NofValidPixels = 0\n maxRatio = 0\n # if np.max(label[...,k])> 0 and k> 0:\n print(\"no\",no, np.max(prediction[...,k]))\n for i in range(H):\n for j in range(W):\n if prediction[i, j,k] > 0.3:\n numpy_prediction[i, j, k] = 1\n NofValidPixels += 1\n if maxRatio < prediction[i, j, k]:\n maxRatio = prediction[i, j, k]\n else:\n numpy_prediction[i, j, k] = 0\n cls_idx = label[i, j, k]\n\n if cls_idx >0 and k>0:\n tag_label=1\n lables_color[i, j] = np.array(colormap[2])\n # cls.append(cls_idx)\n else:\n lables_color[i, j] = np.array(colormap[0])\n\n if numpy_prediction[i, j, k] >0.5 and k>0:\n masks_color[i, j] = np.array(colormap[1])\n # cls.append(cls_idx)\n else:\n masks_color[i, j] = np.array(colormap[0])\n\n\n\n c_i += 1\n masks_color = masks_color.astype(np.uint8)\n show_image = np.zeros(shape=[512, 1024, 3])\n cls = set(cls)\n # /\n NofPixels = masks_color.shape[0] * masks_color.shape[1]\n NofValidPixels = NofValidPixels\n print(\"ratio valid pixels : \", NofValidPixels / NofPixels)\n if NofValidPixels/NofPixels<0.9:\n if NofValidPixels/NofPixels>0.01:\n result_image += masks_color + lables_color.astype(np.uint8)\n disease_name=no2name_dict[str(c_i-1)]\n result_dict[disease_name]+=1\n result_prob_dict[disease_name]=maxRatio\n else:\n masks_color = np.zeros(shape=[H, W, C]).astype(np.uint8)\n\n\n\n # show_image = np.floor((1-alpha)*image) + np.floor((alpha*2/3)*masks_color) + np.floor((alpha/3)*lables_color)\n # show_image = (1-alpha)*image + alpha*lables_color\n show_image[:,:512,:] = image\n show_image[:,512:,:] = masks_color+lables_color.astype(np.uint8)\n # print(\"no\", k, np.max(masks_color), len(np.where(masks_color > 0)[0]))\n # show_image = Image.fromarray(np.uint8(show_image))\n show_image = show_image.astype(np.uint8)\n base_save_folder = '/home/projects/src/refineData/outputs_2nd/snapshot/'\n if not os.path.isdir(base_save_folder):\n os.makedirs(base_save_folder)\n os.chmod(base_save_folder,0o777)\n if tag_label==1:\n cv2.imwrite(base_save_folder + '/sol_no_{}_{}.png'.format(no, k), show_image)\n else:\n cv2.imwrite(base_save_folder + '/snapshot_no_{}_{}.png'.format(no, k), show_image)\n # cv2.imwrite(base_save_folder + '/result_no_{}_{}.png'.format(no, k), result_image)\n # cv2.imwrite(base_save_folder+'/original_no_{}_{}.png'.format(no,k),image)\n # cv2.WaitKey(0)\n print(result_dict)",
"title": ""
},
{
"docid": "4bb50e11607dc01b25c0d91814389ced",
"score": "0.5309595",
"text": "def grayscale(image):\n newImage = image.copy()\n pixels = newImage.load()\n minX, minY, width, height = image.getbbox()\n for y in range(height):\n for x in range(width):\n rgb = pixels[x,y]\n avg = int((rgb[0] + rgb[1] + rgb[1])/3)\n pixels[x,y] = (avg, avg, avg)\n return newImage",
"title": ""
},
{
"docid": "4a71ebb668e3d2a1be7cbbee68f22730",
"score": "0.53089267",
"text": "def transform_annotations(self, annotation, transforms, image_size, orig_image_size):\n bbox = BoxMode.convert(annotation[\"bbox\"], annotation[\"bbox_mode\"], BoxMode.XYXY_ABS)\n # Note that bbox is 1d (per-instance bounding box)\n annotation[\"bbox\"] = transforms.apply_box([bbox])[0]\n annotation[\"bbox_mode\"] = BoxMode.XYXY_ABS\n\n # each instance contains 1 mask\n annotation[\"segmentation\"] = self._process_mask(annotation[\"segmentation\"], transforms, orig_image_size)\n\n # camera\n h, w = image_size\n #annotation[\"K\"] = [annotation[\"K\"][0], w / 2.0, h / 2.0]\n if \"pose\" in annotation.keys():\n annotation[\"pose\"] = torch.tensor(annotation[\"pose\"])\n annotation[\"parameters\"] = torch.tensor(annotation[\"parameters\"])\n\n return annotation",
"title": ""
},
{
"docid": "021fd470d3045beca0b325109670bdd4",
"score": "0.5301394",
"text": "def decode_segmap(self, label_mask, plot=False):\n label_colours = self.get_pascal_labels()\n r = label_mask.copy()\n g = label_mask.copy()\n b = label_mask.copy()\n for ll in range(0, self.n_classes):\n r[label_mask == ll] = label_colours[ll, 0]\n g[label_mask == ll] = label_colours[ll, 1]\n b[label_mask == ll] = label_colours[ll, 2]\n rgb = np.zeros((label_mask.shape[0], label_mask.shape[1], 3))\n rgb[:, :, 0] = r / 255.0\n rgb[:, :, 1] = g / 255.0\n rgb[:, :, 2] = b / 255.0\n if plot:\n plt.imshow(rgb)\n plt.show()\n else:\n return rgb",
"title": ""
},
{
"docid": "e22be9521638dc9501dfa586e3cc7a2b",
"score": "0.5300996",
"text": "def grayify(image):\n grayscale_image = image.convert(\"L\")\n return(grayscale_image)",
"title": ""
},
{
"docid": "2139d2a9efea5bb4c3b316ce24032d3c",
"score": "0.5294917",
"text": "def image2island(impath,g,nsuper=4,size=10,color=\"black\"):\n from PIL import Image\n im = Image.open(impath)\n im = im.convert('RGBA')\n data = np.array(im) # convert to array\n red, green, blue, alpha = data.T # store data for readbility\n if color==\"black\": #retain the black color\n retain = (red < 20) & (blue < 20) & (green < 20)\n elif color==\"red\": #retain the black color\n retain = (red > 200) & (blue < 20) & (green < 20)\n elif color==\"blue\": #retain the black color\n retain = (red < 20) & (blue > 200) & (green < 20)\n elif color==\"green\": #retain the black color\n retain = (red < 20) & (blue < 20) & (green > 200)\n else: raise # unrecognized\n data[..., :-1][retain.T] = (0, 0, 0) # set as black\n data[..., :-1][np.logical_not(retain.T)] = (255, 255, 255) # set as white\n# data[..., :-1][not retain.T] = (255, 255, 255) # set as black\n im2 = Image.fromarray(data) # convert to image\n im2 = im2.convert(\"L\") # to black and white\n bw = np.asarray(im2).copy() # convert to array\n bw[bw < 128] = 0 # Black\n bw[bw >= 128] = 1 # White\n bw = bw.transpose() # transpose image\n # now create a supercell\n nx,ny = bw.shape # size of the image\n go = g.supercell(nsuper*size) # build supercell\n go.center()\n minx = -size\n maxx = size\n miny = -size*bw.shape[1]/bw.shape[0]\n maxy = size*bw.shape[1]/bw.shape[0]\n def finter(rtmp):\n x = rtmp[0]\n y = rtmp[1]\n x = (x - minx)/(maxx-minx)\n y = (y - miny)/(maxy-miny)\n xi = (nx-1)*x # normalized\n yi = (ny-1)*y # normalized\n xi,yi = int(round(xi)),int(round(yi)) # integer\n if not 0<xi<bw.shape[0]: return False\n if not 0<yi<bw.shape[1]: return False\n if bw[xi,yi]==0: return True\n else: return False\n go = intersec(go,finter)\n go.dimensionality = 0 # zero dimensional\n go.celldis = None\n return go",
"title": ""
},
{
"docid": "c4d1c7d20c118b360561e5fefec95f13",
"score": "0.5294035",
"text": "def change(image_array, map_draw_flag, binary_flag, output):\n for i in range(0, image_array.shape[0]):\n for j in range(0, image_array.shape[1]):\n if map_draw_flag:\n if mapinner(image_array[i][j]):\n output[i][j][0] = 1 if binary_flag == 1 else 255\n else:\n if drawinner(image_array[i][j]):\n output[i][j][1] = 1 if binary_flag == 1 else 255",
"title": ""
},
{
"docid": "136d4361576498210ac03ada4ec077ba",
"score": "0.52925843",
"text": "def _normalize_data(image, label):\r\n image = tf.cast(image, tf.float32)\r\n image = image / 255.0\r\n image = image - 0.5\r\n image = image * 2.0\r\n\r\n #label = tf.cast(label, tf.float32)\r\n #label = label / 255.0\r\n\r\n return image, label",
"title": ""
},
{
"docid": "283e8d7b527430c8954ff786dc21e51b",
"score": "0.5285756",
"text": "def extractFeature(img):\n\treturn 0",
"title": ""
},
{
"docid": "be081b500036afb7c234bea39d83d9c6",
"score": "0.5283656",
"text": "def convert_label_to_input_image(in_label):\n\n # Convert to binary [0, 1] mask\n # Weird scaling issue when the labels were generated (are loaded) ?\n # For individual contours the second contour has a different value so it has three unique\n # values. Even for full labels this scaling is needed.\n in_label[in_label >= 0.25] = 1\n in_label[in_label < 0.25] = 0\n\n # increase dimensionality to match input\n # assumes first channel (0) is for batch size\n in_label = torch.cat((in_label, in_label, in_label), 1)\n\n return in_label",
"title": ""
},
{
"docid": "5ae86974f229eaf8652023159e9b5017",
"score": "0.5281471",
"text": "def draw_img_preds(img, depth_pred, seg_pred, uncertainty_threshold=0.0, apply_depth_mask=False):\n plt.figure(0, figsize=(8, 6))\n\n # plot input img\n plt.subplot(2, 3, 1)\n plt.title('RGB')\n plt.imshow(img)\n plt.gca().axes.get_yaxis().set_ticks([])\n plt.gca().axes.get_xaxis().set_ticks([])\n\n # plot depth image\n plt.subplot(2, 3, 2)\n plt.title('depth estimation')\n depth_pred = depth_pred[0, :, :]\n plt.imshow(depth_pred)\n plt.gca().axes.get_yaxis().set_ticks([])\n plt.gca().axes.get_xaxis().set_ticks([])\n\n # plot segmentation\n plt.subplot(2, 3, 3)\n plt.title('segmentation')\n seg_labels = np.argmax(seg_pred, 0) + 1\n mask = np.zeros(shape=(seg_labels.shape[0], seg_labels.shape[1], 3))\n for key in CLASSES:\n class_mask = np.isin(seg_labels, np.asarray(key))\n mask[:, :, 0] += class_mask * CLASS_COLORS[key][0]\n mask[:, :, 1] += class_mask * CLASS_COLORS[key][1]\n mask[:, :, 2] += class_mask * CLASS_COLORS[key][2]\n mask = np.clip(mask, 0, 1)\n plt.imshow(img)\n plt.imshow(mask, alpha=0.3)\n plt.gca().axes.get_yaxis().set_ticks([])\n plt.gca().axes.get_xaxis().set_ticks([])\n\n # plot masked depth image\n plt.subplot(2, 3, 5)\n plt.title('masked de')\n if apply_depth_mask:\n # mask high gradient regions ~ these are usually not as accurate\n grad = np.asarray(np.gradient(depth_pred))\n grad = np.abs(grad[0, :, :]) + np.abs(grad[1, :, :])\n grad_mask = grad < 0.9\n\n depth_mask = depth_pred < 50.0 # mask everything that is farther than 50m\n depth_pred = depth_pred * depth_mask * grad_mask\n\n plt.imshow(depth_pred)\n plt.gca().axes.get_yaxis().set_ticks([])\n plt.gca().axes.get_xaxis().set_ticks([])\n\n # plot masked seg\n plt.subplot(2, 3, 6)\n plt.title('masked seg')\n # mask out pixels where the certainty of the class prediction is lower than the uncertainty threshold\n uc = np.max(seg_pred, 0)\n uc_mask = uc > uncertainty_threshold\n seg_labels = np.argmax(seg_pred, 0) + 1\n seg_labels *= uc_mask\n mask = np.zeros(shape=(seg_labels.shape[0], seg_labels.shape[1], 3))\n for key in CLASSES:\n class_mask = np.isin(seg_labels, np.asarray(key))\n mask[:, :, 0] += class_mask * CLASS_COLORS[key][0]\n mask[:, :, 1] += class_mask * CLASS_COLORS[key][1]\n mask[:, :, 2] += class_mask * CLASS_COLORS[key][2]\n mask = np.clip(mask, 0, 1)\n plt.imshow(img)\n plt.imshow(mask, alpha=0.3)\n plt.gca().axes.get_yaxis().set_ticks([])\n plt.gca().axes.get_xaxis().set_ticks([])\n\n plt.draw()",
"title": ""
},
{
"docid": "8f7cde410292617b7892b4851d894942",
"score": "0.5281428",
"text": "def mark_regions_image(self, image, hist):\n\n\n temp=image\n\n h = temp.shape\n size2 = h[0], h[1], 3\n temp2 = np.zeros(size2, dtype=np.uint8)\n\n for px in range(1, h[0]-10):\n for py in range(1, h[1]-10):\n\n if (temp[px][py]!=0) and [hist[int(temp[px,py])]>15]:\n\n if (temp[px,py]%4==0):\n\n temp2[px,py]=(255,0,0)\n\n elif (temp[px, py] % 6 == 0):\n temp2[px, py] = (0, 255, 0)\n\n elif (temp[px, py] % 2 == 0):\n temp2[px, py] = (0, 0, 255)\n\n elif (temp[px, py] % 5 == 0):\n temp2[px, py] = (255, 255, 0)\n elif (temp[px, py] % 3 == 0):\n temp2[px, py] = (155, 0, 155)\n elif (temp[px, py] % 7 == 0):\n temp2[px, py] = (255, 155, 0)\n else:\n temp2[px, py] = (0, 255, 255)\n\n font = cv2.FONT_HERSHEY_SIMPLEX\n #cv2.putText(temp2, 'OpenCV', (50, 250), font, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n cv2.imshow('Colored Regions', temp2)\n cv2.waitKey(0)\n return temp2",
"title": ""
},
{
"docid": "a440041fec16cb496b5698526a3ae84b",
"score": "0.5281239",
"text": "def labeled_mask_from_experiment(exp):\n with PIL.Image.open(\n exp['ophys_average_intensity_projection_image']) as fim:\n im = np.array(fim)\n # layered mask with uint32 ids as intensities\n mask = layered_mask_from_rois(exp['cell_rois'], im.shape)\n\n # relabel as ordered uint16 and a translating map\n x = np.unique(mask)\n relabeled = np.zeros_like(mask).astype('uint16')\n rdict = {}\n for i, ix in enumerate(x):\n ind = np.nonzero(mask == ix)\n relabeled[ind] = i\n rdict[str(i)] = int(ix)\n\n # do not list the background\n rdict.pop(\"0\")\n\n return relabeled, rdict",
"title": ""
},
{
"docid": "e04a8fd744df8111d152281a46d5bbcf",
"score": "0.5276558",
"text": "def generate_from_single_image(decoder, threshold=50, preprocess=None, decoder_callback=None):\n img_array = decoder.decode(callback=decoder_callback)\n if len(img_array.shape) == 3:\n img_array = np.mean(img_array, 2)\n if preprocess is not None:\n array = preprocess(img_array)\n else:\n array = img_array\n masked_array = np.ma.masked_where(array > threshold, array, copy=True)\n labeled_map, n = ndimage.label(masked_array.mask)\n return (img_array, labeled_map)",
"title": ""
},
{
"docid": "cbb53b6bdc4c7b5121f218575e927e37",
"score": "0.5275968",
"text": "def drawThreshold(img):\n font = cv2.FONT_HERSHEY_SIMPLEX\n gray = img[:,:,1]\n blur = cv2.GaussianBlur(gray,(5,5),0)\n img1 = adaptiveThreshold(blur,detectThreshold)\n img1Not = cv2.bitwise_not(img1)\n \n zeros = np.zeros_like(img)\n fg = cv2.bitwise_or(zeros,(0,0,255),mask = img1)\n bg = cv2.bitwise_and(img,(255,255,255),mask = img1Not)\n \n newImg = cv2.add(fg,bg)\n s = \"detectThreshold = \"+ \"%5.2f\" % detectThreshold\n cv2.putText(newImg,s,(10,50), font, 1,(0,0,255),1,cv2.LINE_AA)\n\n return newImg",
"title": ""
},
{
"docid": "a7661958c8c0ee395130ac12d72182f9",
"score": "0.5270205",
"text": "def binarize(img,filter_size = 21):\r\n img = cv2.cvtColor(img.copy(), cv2.COLOR_RGB2GRAY)\r\n img = cv2.GaussianBlur(img,(21,21),0)\r\n _,img = cv2.threshold(img,0,255,cv2.THRESH_OTSU | cv2.THRESH_BINARY )\r\n\r\n # if the image is predominately white, flip the color\r\n whitefrac = np.sum(img)/(255*img.shape[0]*img.shape[0])\r\n if whitefrac > .5:\r\n img = 255 - img\r\n return img",
"title": ""
},
{
"docid": "b12e17e31a75fc4dfd9f8f938d11a7a4",
"score": "0.52680737",
"text": "def add_eges_grayscale(image):\n greyscale = rgb2gray(image)\n laplacian = np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]])\n edges = scipy.ndimage.filters.correlate(greyscale, laplacian)\n for index,value in np.ndenumerate(edges):\n edges[index] = 255-greyscale[index] if value == 0 else 0\n return edges",
"title": ""
},
{
"docid": "9367db02a22a294ced74ce20d6e20566",
"score": "0.5266831",
"text": "def draw_above_threshold(self,threshold):\n \n print \"threshold luminance: {}\".format(threshold)\n size = self.image.width * self.image.height\n px = []\n py = []\n \n #store a 2d array of pixels\n array_2 = []\n \n #append pixels that are likely part of an event to array_2\n first_px = [self.image.width-1,self.image.height-1]\n last_px = [0,0]\n for y in range(0,self.image.height-1):\n row = []\n for x in range(0,self.image.width-1):\n r,g,b = self.pixelObj[x,y]\n brightness = (r + g + b)/3\n if brightness >= threshold:\n \n if x < first_px[0]:\n first_px[0] = x\n if y < first_px[1]:\n first_px[1] = y \n \n if x > last_px[0]:\n last_px[0] = x\n if y > last_px[1]:\n last_px[1] = y\n \n self.pixelObj[x,y] = (100,100,100)\n row.append([brightness,brightness,brightness])\n px.append(x)\n py.append(y)\n else:\n row.append([0,0,0])\n array_2.append(row)\n \n #crop the image so only the selected pixels are visible\n np_array = np.array(array_2)\n crop = (first_px[0],first_px[1],last_px[0],last_px[1])\n \n width, height = (abs(last_px[0]-first_px[0]),abs(last_px[1]-first_px[1]))\n if width <= 3 or height <= 3:\n print \"Insufficient area: {} px ({}x{})\".format(width * height,width,height)\n return\n\n \n straight = Image.new('RGB', (len(array_2[0]), len(array_2)))\n straight.putdata([tuple(p) for row in array_2 for p in row])\n straight = straight.crop(crop)\n \n #save image and replace if specified\n straight.save('{}_edited.png'.format(self.name))\n if self.replaces:\n os.system('rm -f {}'.format(self.name))\n new_arr = np.array(straight)",
"title": ""
},
{
"docid": "1fe08a8f0eb731026e221ce025880fca",
"score": "0.5264102",
"text": "def np_image(self):\n pass",
"title": ""
},
{
"docid": "efd3db6c4181aa5f7a8e87f05fd0e96e",
"score": "0.5261619",
"text": "def grayscale(img):\n return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)",
"title": ""
},
{
"docid": "efd3db6c4181aa5f7a8e87f05fd0e96e",
"score": "0.5261619",
"text": "def grayscale(img):\n return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)",
"title": ""
},
{
"docid": "efd3db6c4181aa5f7a8e87f05fd0e96e",
"score": "0.5261619",
"text": "def grayscale(img):\n return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)",
"title": ""
},
{
"docid": "2eae5919cf87a63381b2a6be4b28f03c",
"score": "0.52526844",
"text": "def shapefile_to_annotations(shapefile, rgb, savedir=\".\"):\n #Read shapefile\n gdf = gp.read_file(shapefile)\n \n #get coordinates\n df = gdf.geometry.bounds\n \n #raster bounds\n with rasterio.open(rgb) as src:\n left, bottom, right, top = src.bounds\n \n #Transform project coordinates to image coordinates\n df[\"tile_xmin\"] = df.minx - left\n df[\"tile_xmin\"] = df[\"tile_xmin\"].astype(int)\n \n df[\"tile_xmax\"] = df.maxx - left\n df[\"tile_xmax\"] = df[\"tile_xmax\"].astype(int)\n \n #UTM is given from the top, but origin of an image is top left\n \n df[\"tile_ymax\"] = top - df.miny \n df[\"tile_ymax\"] = df[\"tile_ymax\"].astype(int)\n \n df[\"tile_ymin\"] = top - df.maxy\n df[\"tile_ymin\"] = df[\"tile_ymin\"].astype(int) \n \n #Add labels is they exist\n if \"label\" in gdf.columns:\n df[\"label\"] = gdf[\"label\"]\n else:\n df[\"label\"] = \"Tree\"\n \n #add filename\n df[\"image_path\"] = os.path.basename(rgb)\n \n #select columns\n result = df[[\"image_path\",\"tile_xmin\",\"tile_ymin\",\"tile_xmax\",\"tile_ymax\",\"label\"]]\n result = result.rename(columns={\"tile_xmin\":\"xmin\",\"tile_ymin\":\"ymin\",\"tile_xmax\":\"xmax\",\"tile_ymax\":\"ymax\"})\n image_name = os.path.splitext(os.path.basename(rgb))[0]\n csv_filename = os.path.join(savedir, \"{}.csv\".format(image_name))\n \n #ensure no zero area polygons due to rounding to pixel size\n result = result[~(result.xmin == result.xmax)]\n result = result[~(result.ymin == result.ymax)]\n \n #write file\n result.to_csv(csv_filename,index=False)",
"title": ""
},
{
"docid": "45c56c247babfe47cc3fdeaee381930e",
"score": "0.5250915",
"text": "def darknetify(bbox, imshape):\n xmin, ymin, xmax, ymax = bbox\n imheight = imshape[0]\n imwidth = imshape[1]\n x = (xmin + xmax)/2 / imwidth\n y = (ymin + ymax)/2 / imheight\n w = (xmax - xmin) / imwidth\n h = (ymax - ymin) / imheight\n clsid = 0 # We're only using 1 class for targets;\n #a separate nn will classify their type\n return ' '.join(str(x) for x in (clsid, x, y, w, h))",
"title": ""
},
{
"docid": "8d8baf23037545d3b79a0c4550e1e2b5",
"score": "0.5247526",
"text": "def grayscale_img(image):\n return np.dot(image[..., :3], [0.299, 0.587, 0.114])",
"title": ""
},
{
"docid": "f879a660703c474fb558676f8d2cc93d",
"score": "0.5245138",
"text": "def assemble_labels (viewer,raw_label,mod_label): \r\n tmp = binary_fill_holes(mod_label)\r\n thresh = 1\r\n points = viewer.layers['Points'].data \r\n im = raw_label\r\n seeds = []\r\n for j in range(points.shape[0]):\r\n seeds.append(Point(round(points[j,0]),round(points[j,1])))\r\n binaryImg = regionGrow(im,seeds,thresh)\r\n inverse = 1-binaryImg.astype(int)\r\n multiplied = cv2.multiply(inverse, im,dtype=cv2.CV_8U)\r\n merged = join_segmentations(multiplied.astype(int),tmp.astype(int))\r\n viewer.add_labels(merged>0, name='merged_mask')\r\n viewer.add_labels(label(merged.astype(int)), name='merged_labels')",
"title": ""
},
{
"docid": "76c3eca0853e916f7b31b73eff121783",
"score": "0.5244108",
"text": "def enhance_img(img):\r\n kernel = morp.disk(32)\r\n img_local = rank.equalize(img.astype(np.uint8), selem=kernel)\r\n\r\n enhanced = cv2.GaussianBlur(img_local, (5, 5), 0)\r\n return enhanced",
"title": ""
},
{
"docid": "1c304133994ed3bc35b575c937bf8f3e",
"score": "0.5244088",
"text": "def format_img_channels(img, C):",
"title": ""
},
{
"docid": "e713bc09f26011af2c03a14a75ebe33a",
"score": "0.52440834",
"text": "def __blur_edges__(self, imaged):\n image = self.np.array(imaged)\n image = self.cv2.cvtColor(image, self.cv2.COLOR_RGBA2BGRA)\n # extract alpha channel\n a = image[:, :, 3]\n # blur alpha channel\n ab = self.cv2.GaussianBlur(a, (0, 0), sigmaX=2, sigmaY=2, borderType=self.cv2.BORDER_DEFAULT)\n # stretch so that 255 -> 255 and 127.5 -> 0\n aa = self.skimage.exposure.rescale_intensity(ab, in_range=(140, 255), out_range=(0, 255))\n # replace alpha channel in input with new alpha channel\n out = image.copy()\n out[:, :, 3] = aa\n image = self.cv2.cvtColor(out, self.cv2.COLOR_BGRA2RGBA)\n return Image.fromarray(image)",
"title": ""
},
{
"docid": "5290b605828d908ddfb73ccd42f3c4b3",
"score": "0.52413917",
"text": "def gray2outerbound(gray, width):\n h, w = gray.shape[:2]\n gray[gray == 76] = 255\n gray[gray == 151] = 255\n\n label = gray2mask(gray)\n label_binary = label_binarize(label.flatten(), classes=range(0, 3))\n label_binary = np.reshape(label_binary, (h, w, -1))\n\n tmp = ndimage.distance_transform_cdt(label_binary[:, :, 0], 'taxicab')\n outer_bound = np.logical_and(tmp >= 1, tmp <= width).astype(np.uint8)\n\n return outer_bound",
"title": ""
},
{
"docid": "fbcbe70221e47f73663d47e71e89a270",
"score": "0.52410036",
"text": "def resize_annotation_bbox(df, target_size):\n target_size = int(target_size)\n y_new = []\n for i, bbox in enumerate(df['bbox']):\n path = df['fn_orig'][i]\n # the original microfiber images has different sizes\n img = cv2.imread(str(path))\n r, c, _ = img.shape # original img size\n y = np.array([int(b) for b in bbox.split()])\n if len(y) == 4:\n new_bb = bb_hw_numpy(resize_bbox((r, c), y,\n (target_size, target_size)))\n y_new.append(' '.join([str(int(n)) for n in new_bb]))\n elif len(y) > 4:\n temp_bbs = []\n for j in range(int(len(y) / 4)):\n resized_bb = bb_hw_numpy(resize_bbox((r, c),\n y[j * 4:(j * 4 + 4)],\n (target_size, target_size)))\n resized_bb = ' '.join([str(int(n)) for n in resized_bb])\n temp_bbs.append(resized_bb)\n y_new.append(' '.join(temp_bbs))\n\n df['bbox_resized'] = y_new",
"title": ""
},
{
"docid": "130e7e47c4b2faf71fa0291e9e8c8113",
"score": "0.5237399",
"text": "def toimage(arr, high=..., low=..., cmin=..., cmax=..., pal=..., mode=..., channel_axis=...):\n ...",
"title": ""
},
{
"docid": "4b568252713111d0df71b19e997abd00",
"score": "0.5234785",
"text": "def denormalise(self,img):\r\n img *= (0.229, 0.224, 0.225)\r\n img += (0.485, 0.456, 0.406)\r\n img *= 255.000\r\n img=img.astype(np.uint8).clip(0,255)\r\n return img",
"title": ""
},
{
"docid": "7117476a9b5f5dfcacfd75c5e1ef3142",
"score": "0.5228705",
"text": "def blob_coloring(self, image):\n\n\n h = image.shape\n size = h[0], h[1], 1\n\n temp = np.zeros(size, dtype=np.uint8)\n\n\n\n image=255-image\n\n\n\n\n\n k=1\n\n\n b1=h[0]-10\n b2= h[1]-10\n for py in range(1, h[1]-1):\n for px in range(1, h[0]-1):\n\n if (image[px, py] == 0):\n continue\n else:\n\n if (image[px,py]==255) and (image[px,py-1]==0) and (image[px-1,py]==0):\n temp[px,py]=k\n\n k=k+1\n if (image[px, py] == 255) and (image[px, py - 1] == 0) and (image[px - 1, py] == 255):\n temp[px, py]= temp[px-1, py]\n\n\n if (image[px, py] == 255) and (image[px, py - 1] == 255) and (image[px - 1, py] == 0):\n temp[px, py] = temp[px , py-1]\n if (image[px, py] == 255) and (image[px, py - 1] == 255) and (image[px - 1, py] == 255):\n temp[px, py] = temp[px, py-1]\n temp[px-1 , py]=temp[px , py-1]\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n regions = temp\n\n return regions,k,image",
"title": ""
},
{
"docid": "efb6711c682a95a9202190d76e73c933",
"score": "0.5222325",
"text": "def prepro(image):\n image = image[::4, ::4, :] # downsample by factor of 4\n image = color.rgb2gray(image) # turn to grayscale\n return image - 0.5 # 0-center",
"title": ""
},
{
"docid": "88cd23e56c02c6b9c2256cd0cf5f62ae",
"score": "0.5219541",
"text": "def preprocess_image(image):\n image = image / 255.\n\n return image",
"title": ""
},
{
"docid": "f7ba29dc81517a7394c962522f93da86",
"score": "0.52192533",
"text": "def convert_to_grey_scale(image):\n out = color.rgb2gray(image)\n\n ### YOUR CODE HERE\n pass\n ### END YOUR CODE\n\n return out",
"title": ""
},
{
"docid": "b3b69cf13de3911f53a159920d6f7ed4",
"score": "0.5218723",
"text": "def _parse_ann_info(self, img_info, ann_info):\n # shared annotations for visible and full objects\n gt_labels = []\n gt_layer_orders = []\n gt_pair_orders = []\n gt_bboxes_ignore = []\n # visible annotations\n gt_v_bboxes = []\n gt_v_masks = []\n # full annotations\n gt_f_bboxes = []\n gt_f_masks = []\n w, h = img_info['width'], img_info['height']\n\n for i, ann in enumerate(ann_info):\n if ann.get('ignore', False):\n continue\n if 'inmodal_bbox' in ann.keys(): # processing the old kins dataset\n ann['a_bbox'] = ann['bbox']\n ann['i_bbox'] = ann['inmodal_bbox']\n x1, y1, w1, h1 = ann['a_bbox']\n x2, y2, w2, h2 = ann['i_bbox']\n if w1 < 1 or h1 < 1 or w2 < 1 or h2 < 1:\n continue\n f_bbox = [x1, y1, x1 + w1 - 1, y1 + h1 - 1]\n gt_f_bboxes.append(f_bbox)\n v_bbox = [x2, y2, x2 + w2 - 1, y2 + h2 - 1]\n gt_v_bboxes.append(v_bbox)\n\n gt_labels.append(self.cat2label[ann['category_id']])\n if 'layer_order' in ann.keys():\n ann['ico_id'] = ann['layer_order']\n gt_layer_orders.append(ann['ico_id'])\n if 'inmodal_seg' in ann.keys():\n ann['i_segm'] = ann['inmodal_seg']\n gt_v_masks.append(ann['i_segm'])\n if 'segmentation' in ann.keys():\n gt_f_masks.append(ann['segmentation'])\n else:\n # decide the amodal mask first to get pairwise order\n rles = maskUtils.frPyObjects(ann['a_segm'], h, w)\n rle = maskUtils.merge(rles)\n f_mask = maskUtils.decode(rle).squeeze()\n gt_f_masks.append(f_mask)\n if 'pair_order' in ann.keys():\n gt_pair_orders.append(list(ann['pair_order'].values()))\n\n if 'pair_order' not in ann.keys():\n gt_pair_orders = pairwise_ranking(gt_f_masks, gt_layer_orders)\n\n if gt_f_bboxes:\n gt_labels = np.array(gt_labels, dtype=np.int64)\n gt_layer_orders = np.array(gt_layer_orders, dtype=np.int64)\n gt_pair_orders = np.array(gt_pair_orders, dtype=np.int64)\n gt_v_bboxes = np.array(gt_v_bboxes, dtype=np.float32)\n gt_f_bboxes = np.array(gt_f_bboxes, dtype=np.float32)\n else:\n gt_labels = np.array([], dtype=np.int64)\n gt_layer_orders = np.array([], dtype=np.int64)\n gt_pair_orders = np.array([], dtype=np.int64)\n gt_v_bboxes = np.zeros((0, 4), dtype=np.float32)\n gt_f_bboxes = np.zeros((0, 4), dtype=np.float32)\n\n if gt_bboxes_ignore:\n gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)\n else:\n gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)\n\n ann = dict(\n bboxes=gt_v_bboxes,\n labels=gt_labels,\n bboxes_ignore=gt_bboxes_ignore,\n masks=gt_v_masks,\n f_bboxes=gt_f_bboxes,\n f_masks=gt_f_masks,\n l_orders=gt_layer_orders,\n p_orders=gt_pair_orders,\n cat2label=self.cat2label\n )\n\n return ann",
"title": ""
},
{
"docid": "f643836c0b6f1fde4be8361515a326ec",
"score": "0.5217195",
"text": "def visusalize_detections(img_path, xmin, ymin, xmax, ymax, score, plt_name='output', ext='.png', visualization_folder=None, thresh=0.5):\n \n im = cv2.imread(img_path)\n xmin = np.array(xmin)\n ymin = np.array(ymin)\n xmax = np.array(xmax)\n ymax = np.array(ymax)\n score = np.array(score)\n \n inds = np.where(score[:] >= thresh)[0]\n #print('inds:',inds)\n xmin = xmin[inds]\n xmax = xmax[inds]\n ymin = ymin[inds]\n ymax = ymax[inds]\n score = score[inds]\n \n #print(xmin)\n #print(ymin)\n #print(xmax)\n #print(ymax)\n fig, ax = plt.subplots(figsize=(12, 12))\n\n\n if im.shape[0] == 3:\n im_cp = im.copy()\n im_cp = im_cp.transpose((1, 2, 0))\n #if im.min() < 0:\n # pixel_means = cfg.PIXEL_MEANS\n # im_cp = im_cp + pixel_means\n\n im = im_cp.astype(dtype=np.uint8)\n\n im = im[:, :, (2, 1, 0)]\n\n ax.imshow(im, aspect='equal')\n\t\n if score.shape[0] == 0: \n return \n\t\t\n for i in range(score.shape[0]): \n ax.add_patch(\n plt.Rectangle((xmin[i], ymin[i]),\n xmax[i]-xmin[i],\n ymax[i]-ymin[i], fill=False,\n edgecolor=(0, score[i], 0), linewidth=3)\n )\n\t \n\n plt.axis('off')\n plt.tight_layout()\n plt.draw()\n if visualization_folder is not None:\n if not os.path.exists(visualization_folder):\n os.makedirs(visualization_folder)\n plt_name += ext\n plt.savefig(os.path.join(visualization_folder,plt_name),bbox_inches='tight')\n print('Saved {}'.format(os.path.join(visualization_folder, plt_name)))\n else:\n print('Visualizing {}!'.format(plt_name))\n plt.show()\n plt.clf()\n plt.cla()",
"title": ""
},
{
"docid": "c6b22161c67a3b00e9649b9cda767fc8",
"score": "0.52135235",
"text": "def __call__(self, img):\n return ImageOps.equalize(img.convert('RGB'))",
"title": ""
},
{
"docid": "3fcfd045d13a4db639c68b66fca7e9b1",
"score": "0.5210693",
"text": "def __init__(self, image, skin_mask, label_number,rectangle_slices):\n \n self.label_number = label_number\n self.y0 = rectangle_slices[0].start\n self.y1 = rectangle_slices[0].stop\n self.x0 = rectangle_slices[1].start \n self.x1 = rectangle_slices[1].stop\n\n self.bounding_rectangle_size = (self.x1-self.x0)*(self.y1-self.y0)\n self.bounding_rectangle_skin_pixels = np.count_nonzero(skin_mask[rectangle_slices])\n self.bounding_ratio_skin_pixels = self.bounding_rectangle_skin_pixels / self.bounding_rectangle_size\n self.image_ratio_skin_pixels = self.bounding_rectangle_skin_pixels / (image.shape[0]*image.shape[1])\n self.bounding_rectangle_average_pixel_intensity = np.average(image[rectangle_slices].take([0], axis=2))",
"title": ""
}
]
032798927d4a503e48066d0471f63a83
Label connected domains, calculate area fractions of dominant domain Also marks domains that are present on both sides with BLUE v0.2
[
{
"docid": "3462743ba1f2afaed0f77efcbc9ed176",
"score": "0.5651883",
"text": "def Label(im, Opt):\n BCount=(im==0).sum()\n WCount=(im.size-BCount)\n BFrac=BCount/(im.size)\n WFrac=WCount/(im.size)\n LabArray, LNumFeat = scipy.ndimage.measurements.label(im)\n WDomFrac=(LabArray==1).sum()/(WCount)\n WDomI=1\n for i in range(2,LNumFeat):\n TestFrac=(LabArray==i).sum()/(WCount)\n if TestFrac > WDomFrac:\n WDomFrac=TestFrac\n WDomI=i\n \n #print(\"Dominant index %d is %f of total\" % (LDomI, LDomFrac))\n WDomMask= ( LabArray==WDomI )*255;\n WDomMaskI=Image.fromarray(WDomMask).convert(mode=\"L\")\n \"\"\"\n Part 2 doing domains top.bottom\n \"\"\"\n #Size=int(Opt.NmPP*10) # what is the top zone height? here 10 nm\n Size = 2 # cus lower res IDE \n TLab=np.unique(LabArray[:Size]);BLab=np.unique(LabArray[-Size:]); # What areas are at top? Which are at bottom? \n ThroughLab=np.intersect1d(TLab,BLab, assume_unique=True);\n ThroughLab=ThroughLab[ThroughLab!=0]; # remove zero as it is background\n ThroughMask=np.in1d(LabArray, ThroughLab).reshape(LabArray.shape)\n ThroughMaskI=Image.fromarray(ThroughMask*255).convert(mode=\"L\") \n \n \"\"\"\n Part 3 making images\n \"\"\"\n (CIMH,CIMW)=im.shape\n \n RImage=Image.new('RGB',(CIMW,CIMH),'Red')\n BImage=Image.new('RGB',(CIMW,CIMH),'Blue')\n WLabI=scipy.misc.toimage(LabArray).convert(mode=\"RGB\") # Labeled image\n WDomCI=Image.composite(RImage,Image.fromarray(100*np.uint8(im)).convert(mode=\"RGB\"),WDomMaskI) # Red Dom on Original\n WLabDomCI=Image.composite(RImage,WLabI,WDomMaskI) # red dom on mask image\n WThroughCI=Image.composite(BImage,Image.fromarray(100*np.uint8(im)).convert(mode=\"RGB\"),ThroughMaskI)\n if Opt.LabelSh == 1: \n WLabI.show()\n WDomCI.show()\n WLabDomCI.show()\n WThroughCI.show()\n if Opt.LabelSa == 1:\n WLabI.save(os.path.join(Opt.FPath,\"output\",Opt.FName+\"Lab.tif\"))\n WDomCI.save(os.path.join(Opt.FPath,\"output\",Opt.FName+\"DomC.tif\"))\n WDomCI.save(os.path.join(Opt.FPath,\"output\",Opt.FName+\"LabDomC.tif\"))\n WThroughCI.save(os.path.join(Opt.FPath,\"output\",Opt.FName+\"ThroughDomC.tif\"))\n return(WFrac, BFrac, WDomI, WDomFrac);",
"title": ""
}
]
[
{
"docid": "581cae9893a0ea09573afb9484c4db18",
"score": "0.613723",
"text": "def areaDisjFraction(hemlabels, dablabels):\n botharea = np.count_nonzero(np.logical_or(hemlabels, dablabels))\n if botharea == 0:\n return 0.\n else:\n return float(np.count_nonzero(dablabels)) / botharea",
"title": ""
},
{
"docid": "0ca3fc0cb691f970a3590f3c082c4e8c",
"score": "0.55446583",
"text": "def _stat_domain(self):\n dump_log(\"Start calculating domain stats\")\n for domain, value in self.d_count.items():\n count_1 = value[\"1st_count\"]\n count_2 = value[\"2nd_count\"]\n count_no_1 = self.g_count_1 - count_1\n count_no_2 = self.g_count_2 - count_2\n data = \\\n numpy.array([[count_1, count_2], [count_no_1, count_no_2]])\n x2, p, dof, exp = stats.chi2_contingency(data)\n self.d_count[domain][\"p_value\"] = p\n if p < self.d_config[\"CONFIG\"][\"THRESHOLD_P_VALUE\"]:\n l_exp = list(exp.ravel())\n b_check = False\n for num_exp in l_exp:\n if num_exp <= self.d_config[\"CONFIG\"][\"COCHRAN_RULE\"]:\n b_check = True\n break\n if b_check:\n continue\n res = data - exp\n res_var = numpy.zeros(res.shape)\n it = numpy.nditer(data, flags=[\"multi_index\"])\n d_sum = data.sum()\n while not it.finished:\n var = (1 - (data[:, it.multi_index[1]].sum() / d_sum)) * \\\n (1 - (data[it.multi_index[0], :].sum() / d_sum))\n res_var[it.multi_index[0], it.multi_index[1]] = var\n it.iternext()\n stdres = res / numpy.sqrt(exp * res_var)\n if stdres[0][0] >= 1.96:\n self.d_count_1st[domain] = dict()\n self.d_count_1st[domain][\"1st_count\"] = count_1\n self.d_count_1st[domain][\"2nd_count\"] = count_2\n self.d_count_1st[domain][\"p_value\"] = p\n if stdres[0][1] >= 1.96:\n self.d_count_2nd[domain] = dict()\n self.d_count_2nd[domain][\"1st_count\"] = count_1\n self.d_count_2nd[domain][\"2nd_count\"] = count_2\n self.d_count_2nd[domain][\"p_value\"] = p\n\n return True",
"title": ""
},
{
"docid": "ced0ca938f410dd45560da34d448adc7",
"score": "0.5361558",
"text": "def area_covered(self):\r\n\r\n a = 0\r\n c = self.get_leaf_list()\r\n\r\n for r in c:\r\n a += get_rectangle_area(r.value)\r\n\r\n return a",
"title": ""
},
{
"docid": "a48d0a382d7956facbf2bd423fcecb7f",
"score": "0.5319947",
"text": "def calculate_population_category_diversity(y_predicted, content_array):\n ave_coverage = content_array.map(lambda (id, array): sum(array)).mean()\n rating_array_raw = y_predicted.keyBy(lambda row: row[1]).join(content_array)\\\n .map(lambda (id, (rating, array)): array).collect()\n rating_array = map(sum,zip(*np.array(rating_array_raw)))\n cat_diversity = sum([r/float(len(rating_array_raw)) for r in rating_array])*ave_coverage/float(len(rating_array))\n\n return cat_diversity",
"title": ""
},
{
"docid": "d5299cc38a281f95812a32ee21986749",
"score": "0.5289708",
"text": "def calculate_gene_dstrarea(genes_df, temp_df, normalize=1):\n result = (temp_df.query('AUC_UseFLAG == 1')\n .groupby('GeneID')['PrecursorArea_dstrAdj']\n .sum()\n .divide(normalize)\n .to_frame(name='AreaSum_dstrAdj')\n )\n genes_df = genes_df.merge(result, how='left',\n left_on='GeneID', right_index=True)\n genes_df.loc[genes_df['IDSet'] == 3, 'AreaSum_dstrAdj'] = 0\n return genes_df",
"title": ""
},
{
"docid": "ca924ccf5bfe43cb0ac9efbbe2580f35",
"score": "0.5283437",
"text": "def getDivisions():",
"title": ""
},
{
"docid": "d45f875431ccd4a13005e9f13b9fb5e3",
"score": "0.52229595",
"text": "def labels(c1, c2):\n if (c2[1] - c1[0]) >= 0:\n return 1 # green bar\n else:\n return 0",
"title": ""
},
{
"docid": "2a8a3b3099cac27ed7a3d4ddba7a99a4",
"score": "0.52054596",
"text": "def label_region_nd(label_img):\n\n group = regionprops(label_img)\n cluster_area = np.zeros(len(group))\n for k in range(len(group)):\n cluster_area[k] = group[k].area\n max_cluster_area = np.max(cluster_area)\n\n return max_cluster_area",
"title": ""
},
{
"docid": "f53034c5e55ed6a8b40f1f969b28e5e2",
"score": "0.5112679",
"text": "def graph_connectivity(adata, label_key):\n if 'neighbors' not in adata.uns:\n raise KeyError(\n 'Please compute the neighborhood graph before running this function!'\n )\n\n clust_res = []\n\n for label in adata.obs[label_key].cat.categories:\n adata_sub = adata[adata.obs[label_key].isin([label])]\n _, labels = connected_components(\n adata_sub.obsp['connectivities'],\n connection='strong'\n )\n tab = pd.value_counts(labels)\n clust_res.append(tab.max() / sum(tab))\n\n return np.mean(clust_res)",
"title": ""
},
{
"docid": "8d1979f08df5c1d6a066ec115f369a79",
"score": "0.5094451",
"text": "def _nsd_base(a_to_b, b_to_a, threshold):\n if isinstance(a_to_b, int):\n return 0\n if isinstance(b_to_a, int):\n return 0\n numel_a = len(a_to_b)\n numel_b = len(b_to_a)\n tp_a = np.sum(a_to_b <= threshold) / numel_a\n tp_b = np.sum(b_to_a <= threshold) / numel_b\n fp = np.sum(a_to_b > threshold) / numel_a\n fn = np.sum(b_to_a > threshold) / numel_b\n dc = (tp_a + tp_b) / (tp_a + tp_b + fp + fn + sys.float_info.min)\n return dc",
"title": ""
},
{
"docid": "ec9ea10c89593d3dbc82ee25534f25e2",
"score": "0.50753975",
"text": "def dissimilarity(self):\n totDist = 0\n for c in self.clusters:\n totDist += c.variability()\n return totDist",
"title": ""
},
{
"docid": "25a1a0de91c555830b70497318475de2",
"score": "0.5062052",
"text": "def ComputeArea(self):\r\n pass",
"title": ""
},
{
"docid": "7aa08d2cd19a94cc7b5ee290946f94b8",
"score": "0.50553507",
"text": "def areas_matched(self, class_labels):\n # set up storage\n areas = np.zeros(len(self.shapes.names()))\n \n # each UnitCell shape knows its area, so use it\n if self.shapes.shape_type() is 'UnitCell':\n for ishape, n_matched in enumerate(self.count_matched(class_labels)):\n uc_area = self.shapes[self.shapes.names()[ishape]].area()\n logging.debug('%s area %0.2f' % (self.shapes.names()[ishape],\n uc_area))\n areas[ishape] = float(n_matched) * uc_area\n\n # or use Delaunay triangulation to estimate enclosed area\n else: \n for ishape, sn in enumerate(self.shapes.names()):\n ids = self.particles_matched(class_labels, sn)\n if len(ids) > 0:\n area, edges = self.delaunay_neighbors.area_of_point_set(ids,\n self.config.x[ids],\n self.config.y[ids])\n areas[ishape] = area\n \n # return\n return areas",
"title": ""
},
{
"docid": "adcb8c1f59cde5cb4b97ceec01585327",
"score": "0.5050305",
"text": "def discrimAnalysis(x, y):\r\n num_males = np.count_nonzero(y == 1)\r\n num_females = np.count_nonzero(y == 2)\r\n N = x.shape[0]\r\n\r\n indicator_male = y.copy()\r\n indicator_male[indicator_male == 2] = 0\r\n indicator_male = np.expand_dims(indicator_male, axis=1)\r\n indicator_male_double = np.concatenate([indicator_male, indicator_male], axis=1)\r\n\r\n indicator_female = y.copy()\r\n indicator_female[indicator_female == 1] = 0\r\n indicator_female[indicator_female == 2] = 1 \r\n indicator_female = np.expand_dims(indicator_female, axis=1)\r\n indicator_female_double = np.concatenate([indicator_female, indicator_female], axis=1)\r\n\r\n mu_male = np.sum(np.multiply(indicator_male_double, x), axis=0) / num_males\r\n mu_female = np.sum(np.multiply(indicator_female_double, x), axis=0) / num_females\r\n print(\"Male means\")\r\n print(mu_male)\r\n print('Female means')\r\n print(mu_female)\r\n\r\n cov = np.zeros((x.shape[1], x.shape[1]))\r\n for n in range(0, N):\r\n if y[n] == 1:\r\n diff = np.expand_dims(x[n], axis=1)-np.expand_dims(mu_male, axis=1)\r\n cov = cov + np.dot(diff, diff.T) \r\n else:\r\n diff = np.expand_dims(x[n], axis=1)-np.expand_dims(mu_female, axis=1)\r\n cov = cov + np.dot(diff, diff.T)\r\n cov = cov / (N)\r\n print('cov')\r\n print(cov)\r\n\r\n cov_male = np.zeros(cov.shape)\r\n cov_female = np.zeros(cov.shape)\r\n for n in range(0, N):\r\n if y[n] == 1:\r\n diff = np.expand_dims(x[n], axis=1)-np.expand_dims(mu_male, axis=1)\r\n cov_male = cov_male + np.dot(diff, diff.T)\r\n else:\r\n diff = np.expand_dims(x[n], axis=1)-np.expand_dims(mu_female, axis=1)\r\n cov_female = cov_female + np.dot(diff, diff.T)\r\n cov_male = cov_male / (num_males)\r\n cov_female = cov_female / (num_females)\r\n print('cov_male')\r\n print(cov_male)\r\n print('cov_female')\r\n print(cov_female)\r\n\r\n # plot N data points\r\n male_height = []\r\n male_weight = []\r\n female_height = []\r\n female_weight = []\r\n for n in range(0, N):\r\n if y[n] == 1:\r\n male_height.append(x[n][0])\r\n male_weight.append(x[n][1])\r\n else:\r\n female_height.append(x[n][0])\r\n female_weight.append(x[n][1])\r\n\r\n # there are N height and N weight values \r\n x_limits = np.linspace(50, 80, N) \r\n y_limits = np.linspace(80, 280, N) \r\n x_mesh, y_mesh = np.meshgrid(x_limits, y_limits)\r\n\r\n male_lda_criteria = []\r\n male_qda_criteria = []\r\n female_lda_criteria = []\r\n female_qda_criteria = []\r\n x_coordinates = x_mesh[0].reshape(100, 1)\r\n for n in range(0, N):\r\n y_coordinates = y_mesh[n].reshape(100, 1)\r\n x_set = np.concatenate((x_coordinates, y_coordinates), axis=1)\r\n male_lda_criteria.append(util.density_Gaussian(mu_male,cov,x_set))\r\n female_lda_criteria.append(util.density_Gaussian(mu_female,cov,x_set))\r\n male_qda_criteria.append(util.density_Gaussian(mu_male,cov_male,x_set))\r\n female_qda_criteria.append(util.density_Gaussian(mu_female,cov_female,x_set))\r\n\r\n plt.scatter(male_height, male_weight, color = 'b', label='Male')\r\n plt.scatter(female_height, female_weight, color = 'r', label='Female')\r\n plt.legend(loc=2)\r\n male_CS = plt.contour(x_mesh, y_mesh, male_lda_criteria, colors='b')\r\n female_CS = plt.contour(x_mesh, y_mesh, female_lda_criteria, colors='r')\r\n lda_decision_boundary = np.asarray(male_lda_criteria) - np.asarray(female_lda_criteria)\r\n plt.contour(x_mesh, y_mesh, lda_decision_boundary, colors='k', levels=[0])\r\n plt.xlabel('Height')\r\n plt.ylabel('Weight')\r\n plt.title('LDA Contours and Decision Boundary')\r\n plt.savefig('lda.pdf')\r\n plt.show()\r\n\r\n plt.scatter(male_height, male_weight, color = 'b', label='Male')\r\n plt.scatter(female_height, female_weight, color = 'r', label='Female')\r\n plt.legend(loc=2)\r\n plt.contour(x_mesh, y_mesh, male_qda_criteria, colors='b')\r\n plt.contour(x_mesh, y_mesh, female_qda_criteria, colors='r')\r\n qda_decision_boundary = np.asarray(male_qda_criteria) - np.asarray(female_qda_criteria)\r\n plt.contour(x_mesh, y_mesh, qda_decision_boundary, colors='k', levels=[0])\r\n plt.xlabel('Height')\r\n plt.ylabel('Weight')\r\n plt.title('QDA Contours and Decision Boundary')\r\n plt.savefig('qda.pdf')\r\n plt.show()\r\n return (mu_male,mu_female,cov,cov_male,cov_female)",
"title": ""
},
{
"docid": "c1203927d5e9e82e5dc9a32a4057e97e",
"score": "0.5047957",
"text": "def score_bridges(self):\n\n scored_bridges = []\n bridge_nodes = set()\n for node1, node2 in self.bridges:\n size1 = self.cluster_size[node1]\n size2 = self.cluster_size[node2]\n # using harmonic average\n score = (size1*size2)/(size1+size2)\n if size1 >= size2:\n if node1 not in bridge_nodes:\n scored_bridges.append((node1, score))\n bridge_nodes.add(node1)\n else: \n if node2 not in bridge_nodes:\n scored_bridges.append((node2, score))\n bridge_nodes.add(node2)\n\n # can optimize a tiny bit applying set to scored_bridges instead. \n self.scored_bridges = sorted(list(scored_bridges), key=lambda x:-x[1])",
"title": ""
},
{
"docid": "487d592b5ec2504c6dab0dd2b6d80c31",
"score": "0.50285745",
"text": "def test_discretize_domain():\n test_class = FlowFieldTest()\n x, y, z = test_class.instance._discretize_turbine_domain()\n assert np.shape(x) == (2, 5, 5) and type(x) is np.ndarray \\\n and np.shape(y) == (2, 5, 5) and type(y) is np.ndarray \\\n and np.shape(z) == (2, 5, 5) and type(z) is np.ndarray",
"title": ""
},
{
"docid": "10f6a06a9b8eb43358b89d14f50b89ca",
"score": "0.5025853",
"text": "def remove_disconnects(rho):\n # connected components analysis\n label_img, nr_labels = measure.label(rho,background=0,return_num=True)\n # only keep the two largest labels (background + largest component)\n max_labels = np.argsort([np.sum(label_img==i) for i in range(nr_labels+1)])[-2:]\n # mask on all labels not part of the largest components\n small_label_mask = np.logical_and(label_img!=max_labels[0],label_img!=max_labels[1])\n # set all small labels to background\n label_img[small_label_mask] = 0\n # convert to zero-one\n label_img[label_img>0] = 1\n return label_img",
"title": ""
},
{
"docid": "5736ebb877496517d45243bef5062842",
"score": "0.4992796",
"text": "def addtotalareas(cls): #class parameter is traditionally cls\n total = 0\n for eachareacircle in cls.allcircleslist:\n total = total + eachareacircle.area()\n return total",
"title": ""
},
{
"docid": "540bb6bf2d926d296d36588f0ef86e47",
"score": "0.4992326",
"text": "def LF_DaG_NO_CONCLUSION(c):\n positive_num = np.sum(\n [\n LF_DaG_ASSOCIATION(c) == POSITIVE,\n LF_DG_IS_BIOMARKER(c) == POSITIVE,\n LF_DG_DIAGNOSIS(c) == POSITIVE,\n LF_DaG_CELLULAR_ACTIVITY(c) == POSITIVE,\n LF_DaG_WEAK_ASSOCIATION(c) == NEGATIVE,\n LF_DaG_NO_ASSOCIATION(c) == NEGATIVE,\n ]\n )\n negative_num = np.abs(\n np.sum(\n [\n LF_DG_METHOD_DESC(c) == NEGATIVE,\n LF_DG_TITLE(c) == NEGATIVE,\n LF_DG_NO_VERB(c) == NEGATIVE,\n ]\n )\n )\n if positive_num - negative_num >= 1:\n return ABSTAIN\n return NEGATIVE",
"title": ""
},
{
"docid": "e48fac7c0af2bafba0c813fc14a997c2",
"score": "0.4971619",
"text": "def discourse_diversity(self):\n # entropy\n nodes = []\n bcs = []\n coms = []\n \n for n, data in self.finalGraph.nodes(data=True):\n nodes.append(n)\n bcs.append(data[\"bc\"])\n coms.append(data[\"com\"])\n\n df = pd.DataFrame({\"node\": nodes, \"bc\": bcs, \"com\": coms}) # Because Pandas DataFrames are cool\n nodes_df = df.sort_values(\"bc\", ascending=False)\n self.nodes_df = nodes_df # store the original graph\n\n self.stats[\"communities\"] = len(nodes_df.com.value_counts())\n\n top_nodes = nodes_df # If topn_nodes is preferred .ìloc[[:self.topn_nodes]\n\n top_comms = nodes_df.com.value_counts() # If topn_nodes is preferred .ìloc[:self.topn_nodescomms]\n\n # Count topn nodes in topn communities\n cnt = Counter() # keeps values sorted, as opposed to dictionary\n for com_number in list(top_comms.index):\n cnt[com_number] = 0\n for word in top_nodes.node.values:\n if word in df[df[\"com\"] == com_number][\"node\"].values:\n cnt[com_number] += 1\n\n ord_dict = OrderedDict()\n for community_no, count in cnt.most_common(): # self.topn_comms if preferred\n ord_dict[community_no] = count\n\n coms = []\n counts = []\n for com, count in cnt.most_common(): # defaults to all, if topn is preferred self.topn_comms\n coms.append(com)\n counts.append(count)\n\n topn_dist_df = pd.DataFrame({\"count\": counts}, index=coms)\n \n # print(topn_dist_df) # how many nodes in every community\n\n # print(self.bc_top_split['community'])\n\n TSN = len(self.bc_top_split['community']) # Total Split Nodes (top influential nodes Jenks=2)\n E_split = eta(list(self.bc_top_split['community']))\n E_split_ideal = eta(list(range(1, len(self.bc_top_split['community']) + 1)))\n self.stats[\"TopBCNodesInComm\"] = TSN\n \n # entropy ration for Split Nodes\n if E_split_ideal > 0:\n ES = E_split/E_split_ideal\n else:\n ES = 0\n \n # proportion of the top community in BC nodes\n rs_mode = self.bc_top_split['community'].mode()\n if len(rs_mode) > 0:\n most_freq_com_SN = self.bc_top_split['community'].mode()[0]\n RSN = len(self.bc_top_split[self.bc_top_split['community']==most_freq_com_SN])/len(self.bc_top_split['community'])\n else:\n RSN = 0\n self.stats[\"TopBCNodesProp\"] = RSN\n\n\n\n TTN = len(self.bc_top['community']) # Total Top Nodes\n E_top = eta(list(self.bc_top['community']))\n E_top_ideal = eta(list(range(1, len(self.bc_top['community']) + 1)))\n self.stats[\"BCNodesInComm\"] = TTN\n \n # entropy for Top Nodes\n if E_top_ideal > 0:\n ET = E_top/E_top_ideal\n else:\n ET = 0\n\n # proportion of the top community in BC nodes\n rt_mode = self.bc_top['community'].mode()\n if len(rt_mode) > 0:\n most_freq_com_TN = self.bc_top['community'].mode()[0]\n RTN = len(self.bc_top[self.bc_top['community']==most_freq_com_TN])/len(self.bc_top['community'])\n else:\n RTN = 0\n self.stats[\"BCNodesProp\"] = RTN\n\n\n TLN = len(self.bc_top_lax['community'])\n E_top_lax = eta(list(self.bc_top_lax['community']))\n E_top_lax_ideal = eta(list(range(1, len(self.bc_top_lax['community']) + 1)))\n self.stats[\"BCNodesLaxInComm\"] = TLN\n\n\n if E_top_lax_ideal > 0:\n EL = E_top_lax/E_top_lax_ideal\n else:\n EL = 0\n\n # proportion of the top community in BC nodes\n rl_mode = self.bc_top_lax['community'].mode()\n if len(rl_mode) > 0:\n most_freq_com_LN = self.bc_top_lax['community'].mode()[0]\n RLN = len(self.bc_top_lax[self.bc_top_lax['community']==most_freq_com_LN])/len(self.bc_top_lax['community'])\n else:\n RLN = 0\n self.stats[\"BCNodesLaxProp\"] = RLN\n\n\n self.stats[\"entropyTopFirst\"] = ES\n self.stats[\"entropyTop\"] = ET\n self.stats[\"entropyTopLax\"] = EL\n\n E = EL\n\n # percentage of nodes in largest community\n nr_of_nodes = 0\n for part in self.partitions:\n for node in part:\n nr_of_nodes += 1\n\n if (len(self.partitions) > 0):\n C = len(self.partitions[0]) / nr_of_nodes\n else: \n C = 0\n self.C = C\n self.stats[\"nodesInTopCom\"] = C\n\n # how many of the top BC nodes in the top component\n sn_in_top_c = 0\n tn_in_top_c = 0\n ln_in_top_c = 0\n \n if (len(self.partitions) > 0):\n for node in self.partitions[0]:\n if ((self.bc_top_split).index == node).any():\n sn_in_top_c += 1\n if ((self.bc_top).index == node).any():\n tn_in_top_c += 1\n if ((self.bc_top_lax).index == node).any():\n ln_in_top_c += 1\n\n \n # number of nodes from the top comm in BC to total length of inf nodes\n if len(self.bc_top_split) > 0:\n BCST = sn_in_top_c/len(self.bc_top_split)\n else: \n BCST = 0\n\n if len(self.bc_top) > 0: \n BCTT = tn_in_top_c/len(self.bc_top)\n else:\n BCTT = 0\n\n if len(self.bc_top_lax) > 0:\n BCLT = ln_in_top_c/len(self.bc_top_lax)\n else: \n BCLT = 0\n\n # percentage of nodes in the giant component\n if (nr_of_nodes > 0):\n G = len(self.igraph.components().giant().vs) / nr_of_nodes\n else:\n G = 0\n self.G = G\n self.stats[\"nodesInGiantCom\"] = G\n\n M = self.M\n\n if nr_of_nodes == 0:\n self.stats[\"biasIndex\"] = \"Dispersed\"\n\n elif TSN <= 4: # very few top BC nodes, so influence distribution is biased – alert!\n \n if ES == 0 and G > 0.5 and TSN != 1: # they all belong to the same community and > 50% of the graph is connected\n\n # as ES == 0, all the top BC nodes belong to only 1 community, that's why BCST is either 0 or 1\n\n if TSN == 0:\n if (self.cutoffGraph.number_of_nodes() > 0):\n if G < 0.5:\n self.stats[\"biasIndex\"] = \"Dispersed\"\n else:\n if M > 0.65: # pronounced comm, several center of influence, one is focused but the rest is dispersed\n self.stats[\"biasIndex\"] = \"Dispersed\" \n elif 0.4 <= M <= 0.65: # while community structure may be high, all he most inf nodes are in the biggest comm\n self.stats[\"biasIndex\"] = \"Polarized\" #TODO may need modification\n elif 0.2 <= M < 0.4: # low comm, influence center with all top BC nodes in it\n self.stats[\"biasIndex\"] = \"Focused\"\n elif M < 0.2: # low comm, influence center with all top BC nodes in it\n self.stats[\"biasIndex\"] = \"Biased\" \n else:\n self.stats[\"biasIndex\"] = \"Dispersed\" \n \n elif C > 0.5 and BCST == 1: # > 50% of nodes in the top comm and all top BC belong to it\n \n self.stats[\"biasIndex\"] = \"Biased\" # everything is towards the same narrative\n \n elif C > 0.5 and BCST == 0: # > 50% of nodes in top comm, but all the influential words in another comm\n \n if M > 0.4: # pronounced comm, BC nodes oppose the top C\n self.stats[\"biasIndex\"] = \"Polarized\" # TODO interesting idea here \n elif 0.2 > M < 0.4: # not pronounced comm, BC nodes support the top C\n self.stats[\"biasIndex\"] = \"Focused\"\n elif M < 0.2: #low comm, 2 centers of influence, super linked\n self.stats[\"biasIndex\"] = \"Biased\"\n \n elif C <= 0.5 and BCST == 1: # less than half (but signif num) are in top C as well as all the top BC\n\n if M > 0.65: # pronounced comm, several center of influence, one is focused but the rest is dispersed\n self.stats[\"biasIndex\"] = \"Dispersed\" \n elif 0.4 < M <= 0.65: # while community structure may be high, all he most inf nodes are in the biggest comm\n self.stats[\"biasIndex\"] = \"Polarized\" #TODO may need modification\n elif M < 0.4: # low comm, influence center with all top BC nodes in it\n self.stats[\"biasIndex\"] = \"Biased\"\n\n elif C <= 0.5 and BCST == 0: # less than half in top (signif) comm but ALL top BC nodes are in another\n \n if M > 0.65: # very pronounced community structure, 2 centers of influence\n self.stats[\"biasIndex\"] = \"Diversified\" \n elif 0.65 > M >= 0.4: # pronounced comm, 2 centers of influence, connected\n self.stats[\"biasIndex\"] = \"Polarized\" #TODO interesting idea here \n elif 0.2 < M < 0.4: # not pronounced comm, 2 centers of influence, linked\n self.stats[\"biasIndex\"] = \"Focused\"\n elif M < 0.2: #low comm, 2 centers of influence, super linked\n self.stats[\"biasIndex\"] = \"Biased\" \n\n elif ES == 0 and G < 0.5 and TSN != 1: # all the nodes are in the same community but less than half nodes are in G\n\n if M > 0.65:\n self.stats[\"biasIndex\"] = \"Dispersed\" \n elif M > 0.4:\n self.stats[\"biasIndex\"] = \"Focused\" \n elif M <= 0.4:\n self.stats[\"biasIndex\"] = \"Biased\" \n \n elif ES >= 0.75 and TSN > 2:\n\n if M > 0.65:\n self.stats[\"biasIndex\"] = \"Dispersed\"\n elif 0.4 <= M <= 0.65:\n self.stats[\"biasIndex\"] = \"Diversified\"\n elif 0.2 < M < 0.4:\n self.stats[\"biasIndex\"] = \"Focused\"\n else:\n self.stats[\"biasIndex\"] = \"Biased\"\n \n elif (ES < 0.75 and ES > 0) or (ES == 0 and TSN == 1) or (TSN <= 2 and ES >= 0.75): # either it's ab aab, aabb or aaab or a\n \n # here we analyze like for TSN > 4 but with a bigger number of nodes\n if (TTN > 4) or (2 < TTN <= 4 and TSN == 1): #means influence is relatively spread\n\n if ET == 0 and G < 0.5: # all the nodes are in the same community but less than half nodes are in G\n \n if M > 0.65:\n self.stats[\"biasIndex\"] = \"Dispersed\" \n if M > 0.4:\n self.stats[\"biasIndex\"] = \"Focused\" \n elif M <= 0.4:\n self.stats[\"biasIndex\"] = \"Biased\" \n\n elif ET == 0 and G > 0.5:\n\n if BCTT == 1: # all the top nodes (and quite a few of them!) are in the tom comm\n \n if M >= 0.4: # while community structure may be high, all he most inf nodes are in the biggest comm\n self.stats[\"biasIndex\"] = \"Focused\"\n elif M < 0.4: # low comm, influence center with all top BC nodes in it\n self.stats[\"biasIndex\"] = \"Biased\"\n\n elif BCTT == 0: # less than half in top (signif) comm but ALL top BC nodes are in ANOTHER\n \n if M > 0.65: # very pronounced community structure, 2 centers of influence\n self.stats[\"biasIndex\"] = \"Diversified\" \n elif 0.65 > M >= 0.4: # pronounced comm, 2 centers of influence, connected\n self.stats[\"biasIndex\"] = \"Polarized\" #TODO interesting idea here \n elif 0.2 < M < 0.4: # not pronounced comm, 2 centers of influence, linked\n self.stats[\"biasIndex\"] = \"Focused\"\n elif M < 0.2: #low comm, 2 centers of influence, super linked\n self.stats[\"biasIndex\"] = \"Biased\" \n \n \n \n if ET > 0.42 and RTN <= 0.5: # a few nodes but most of them are not in top comm\n if M > 0.65:\n self.stats[\"biasIndex\"] = \"Dispersed\"\n elif 0.4 <= M <= 0.65:\n self.stats[\"biasIndex\"] = \"Diversified\"\n elif 0.2 < M < 0.4:\n self.stats[\"biasIndex\"] = \"Focused\"\n else:\n self.stats[\"biasIndex\"] = \"Biased\"\n\n elif ET > 0.42 and RTN > 0.5: # there's quite a few nodes, most of them are in the same comm, but the rest is quite dispersed\n\n if M > 0.65 and BCTT < 0.5: # most nodes are not in top comm\n self.stats[\"biasIndex\"] = \"Dispersed\"\n elif M >= 0.4 and BCTT <= 0.8:\n self.stats[\"biasIndex\"] = \"Diversified\"\n elif M > 0.2 and BCTT <= 0.8:\n self.stats[\"biasIndex\"] = \"Focused\"\n else:\n self.stats[\"biasIndex\"] = \"Biased\"\n\n elif (ET <= 0.42 and RTN > 0.5) or (RTN > 0.75): # there's a few nodes , most of them are in the same com, the rest is also homogeneous\n \n if M > 0.65:\n self.stats[\"biasIndex\"] = \"Diversified\"\n elif 0.65 >= M > 0.4:\n self.stats[\"biasIndex\"] = \"Polarized\" #TODO interesting idea here\n elif M < 0.4:\n self.stats[\"biasIndex\"] = \"Biased\"\n \n elif (ET <= 0.42 and RTN <= 0.5): # there's a few nodes, most of them not in the top comm, the rest is homogeneous\n\n if M > 0.65:\n self.stats[\"biasIndex\"] = \"Dispersed\"\n elif 0.65 >= M > 0.4:\n self.stats[\"biasIndex\"] = \"Diversified\"\n elif 0.2 < M < 0.4:\n self.stats[\"biasIndex\"] = \"Focused\"\n else:\n self.stats[\"biasIndex\"] = \"Biased\"\n \n # we had too few top nodes, so we expanded the search. means influence is very well spread\n elif (TLN > 4) or (TLN < 4 and TLN >= TSN):\n\n if EL > 0.42 and RLN <= 0.5: # a few nodes but most of them are not in top comm\n \n if M > 0.65:\n self.stats[\"biasIndex\"] = \"Dispersed\"\n elif 0.4 <= M <= 0.65:\n self.stats[\"biasIndex\"] = \"Diversified\"\n elif 0.2 < M < 0.4:\n self.stats[\"biasIndex\"] = \"Focused\"\n else:\n self.stats[\"biasIndex\"] = \"Biased\"\n\n elif EL > 0.42 and RLN > 0.5: # there's quite a few nodes, most of them are in the same comm, but the rest is quite dispersed\n\n if M > 0.65 and BCLT < 0.5: # most nodes are not in top comm\n self.stats[\"biasIndex\"] = \"Dispersed\"\n elif M >= 0.4 and BCLT <= 0.8:\n self.stats[\"biasIndex\"] = \"Diversified\"\n elif M > 0.2 and BCLT <= 0.8:\n self.stats[\"biasIndex\"] = \"Focused\"\n else:\n self.stats[\"biasIndex\"] = \"Biased\"\n\n elif (EL <= 0.42 and RLN > 0.5) or (RLN > 0.75): # there's a few nodes , most of them are in the same com, the rest is also homogeneous\n \n if M > 0.65:\n self.stats[\"biasIndex\"] = \"Diversified\"\n elif 0.65 >= M > 0.4:\n self.stats[\"biasIndex\"] = \"Polarized\" #TODO interesting idea here\n elif M < 0.4:\n self.stats[\"biasIndex\"] = \"Biased\"\n \n elif (EL <= 0.42 and RLN <= 0.5): # there's a few nodes, most of them not in the top comm, the rest is homogeneous\n\n if M > 0.65:\n self.stats[\"biasIndex\"] = \"Dispersed\"\n elif 0.65 >= M > 0.4:\n self.stats[\"biasIndex\"] = \"Diversified\"\n elif 0.2 < M < 0.4:\n self.stats[\"biasIndex\"] = \"Focused\"\n else:\n self.stats[\"biasIndex\"] = \"Biased\" \n\n else: # didn't find \n if RSN > 0.5: # aaab or aab\n # we didn't find any other separation, so probably these N nodes are outliers\n if M > 0.65:\n if BCST > 0.5: #dispersed community but most BC nodes are in top C \n self.stats[\"biasIndex\"] = \"Diversified\"\n elif BCST <= 0.5:\n self.stats[\"biasIndex\"] = \"Dispersed\"\n elif 0.4 <= M <= 0.65:\n if BCST > 0.5:\n self.stats[\"biasIndex\"] = \"Focused\"\n elif BCST <= 0.5:\n self.stats[\"biasIndex\"] = \"Diversified\"\n elif 0.2 < M < 0.4:\n if BCST > 0.5:\n self.stats[\"biasIndex\"] = \"Biased\"\n elif BCST <= 0.5:\n self.stats[\"biasIndex\"] = \"Focused\"\n else:\n self.stats[\"biasIndex\"] = \"Biased\"\n\n elif RSN <= 0.5: #ab #aabb\n #TODO interesting idea here\n if M > 0.65:\n self.stats[\"biasIndex\"] = \"Dispersed\"\n elif 0.4 <= M <= 0.65:\n self.stats[\"biasIndex\"] = \"Diversified\"\n elif 0.2 < M < 0.4:\n self.stats[\"biasIndex\"] = \"Focused\"\n elif M < 0.2:\n self.stats[\"biasIndex\"] = \"Biased\"\n \n\n\n\n \n elif TSN > 4:\n\n if ES == 0 and G < 0.5: # all the nodes are in the same community but less than half nodes are in G\n \n if M > 0.65:\n self.stats[\"biasIndex\"] = \"Dispersed\" \n if M > 0.4:\n self.stats[\"biasIndex\"] = \"Focused\" \n elif M <= 0.4:\n self.stats[\"biasIndex\"] = \"Biased\" \n\n elif ES == 0 and G > 0.5:\n\n if BCST == 1: # all the top nodes (and quite a few of them!) are in the tom comm\n \n if M >= 0.4: # while community structure may be high, all he most inf nodes are in the biggest comm\n self.stats[\"biasIndex\"] = \"Focused\"\n elif M < 0.4: # low comm, influence center with all top BC nodes in it\n self.stats[\"biasIndex\"] = \"Biased\"\n\n if BCST == 0: # less than half in top (signif) comm but ALL top BC nodes are in ANOTHER\n \n if M > 0.65: # very pronounced community structure, 2 centers of influence\n self.stats[\"biasIndex\"] = \"Diversified\" \n elif 0.65 > M >= 0.4: # pronounced comm, 2 centers of influence, connected\n self.stats[\"biasIndex\"] = \"Polarized\" #TODO interesting idea here \n elif 0.2 < M < 0.4: # not pronounced comm, 2 centers of influence, linked\n self.stats[\"biasIndex\"] = \"Focused\"\n elif M < 0.2: #low comm, 2 centers of influence, super linked\n self.stats[\"biasIndex\"] = \"Biased\" \n \n elif ES > 0.42 and RSN <= 0.5:\n \n if M > 0.65:\n self.stats[\"biasIndex\"] = \"Dispersed\"\n elif 0.4 <= M <= 0.65:\n self.stats[\"biasIndex\"] = \"Diversified\" #TODO interesting thing here\n elif 0.2 < M < 0.4:\n self.stats[\"biasIndex\"] = \"Focused\"\n else:\n self.stats[\"biasIndex\"] = \"Biased\"\n\n elif ES > 0.42 and RSN > 0.5:\n\n if M > 0.65 and BCST < 0.5:\n self.stats[\"biasIndex\"] = \"Dispersed\"\n elif M >= 0.4 and BCST <= 0.8:\n self.stats[\"biasIndex\"] = \"Diversified\"\n elif M > 0.2 and BCST <= 0.8:\n self.stats[\"biasIndex\"] = \"Focused\"\n else:\n self.stats[\"biasIndex\"] = \"Biased\"\n\n elif (ES <= 0.42 and RSN > 0.5) or (RSN > 0.75):\n \n if M > 0.65:\n self.stats[\"biasIndex\"] = \"Diversified\"\n elif 0.65 >= M > 0.4:\n self.stats[\"biasIndex\"] = \"Focused\"\n elif M < 0.4:\n self.stats[\"biasIndex\"] = \"Biased\"\n \n elif (ES <= 0.42 and RSN <= 0.5):\n\n if M > 0.65:\n self.stats[\"biasIndex\"] = \"Dispersed\"\n elif 0.65 >= M > 0.4:\n self.stats[\"biasIndex\"] = \"Diversified\"\n elif 0.2 < M < 0.4:\n self.stats[\"biasIndex\"] = \"Focused\"\n else:\n self.stats[\"biasIndex\"] = \"Biased\"\n \n # END OF BIAS INDEX CALCULATION\n\n self.biasIndex = self.stats[\"biasIndex\"]\n\n if self.biasIndex == 'Biased':\n print(\"{} bias index: BIASED\".format(self.textname))\n elif self.biasIndex == 'Focused':\n print(\"{} bias index: FOCUSED\".format(self.textname))\n elif self.biasIndex == 'Diversified':\n print(\"{} bias index: DIVERSIFIED\".format(self.textname))\n elif self.biasIndex == 'Polarized':\n print(\"{} bias index: POLARIZED\".format(self.textname))\n elif self.biasIndex == 'Dispersed':\n print(\"{} bias index: DISPERSED\".format(self.textname))\n\n\n # Possible plotting, in notebook add ``%matplotlib inline`` and ``import matplotlib.pyplot as plt``\n if (nr_of_nodes > 0):\n if self.plot:\n print('plotting ...')\n xticks = [str(i) for i in list(topn_dist_df.index)]\n ax = topn_dist_df.plot(\n title=\"Node-distribution in communities.\\n{} entropy:{:.3f}\".format(\n self.cutoffGraph.graph['name'],\n E), use_index=False)\n ax.set_xlabel(\"Community number\\n\")\n ax.set_ylabel(\"Words (nodes) per community.\")\n ax.set_xticks(np.arange(len(xticks)))\n ax.set_xticklabels(xticks)\n plt.show()\n\n #TODO - this will change, only testing\n self.plottable=Discourse(self)",
"title": ""
},
{
"docid": "b3f765654e120d4c3bd9f01bd53d30a3",
"score": "0.4969649",
"text": "def compute_label_confs(self):\n label_confs = [label + ', id=' + str(i) + ', ' + str(format(\n self.confs[i] * 100, '.2f')) + '%' for i, label in enumerate(self.labels)]\n return label_confs",
"title": ""
},
{
"docid": "584c2717aaf6c458129a2e1ba0253daa",
"score": "0.4967278",
"text": "def cal_AA(self):\n\n df_edge_list_reverse = pd.DataFrame()\n df_edge_list_reverse['source'] = self.df_edge_list['target']\n df_edge_list_reverse['target'] = self.df_edge_list['source']\n\n df_all_nodes_pair = pd.concat([df_edge_list_reverse, self.df_edge_list])\n df_neighbor_count = df_all_nodes_pair.groupby(['source']).count()\n\n df_neighbor_count = df_neighbor_count.reset_index()\n df_neighbor_count.rename(columns={'target': 'count'}, inplace=True)\n \"\"\"\n get common neighbours\n \"\"\"\n\n df_common_neighbor = pd.merge(df_all_nodes_pair, df_all_nodes_pair, on=['target'], how='left').dropna()\n df_common_neighbor = df_common_neighbor[df_common_neighbor['source_x'] != df_common_neighbor['source_y']]\n\n df_common_neighbor = pd.merge(df_common_neighbor, df_neighbor_count, left_on=['target'], right_on=['source'],\n how='left').dropna()\n\n df_common_neighbor = df_common_neighbor[['source_x', 'source_y', 'count']]\n df_common_neighbor['count'] = df_common_neighbor['count'].map(lambda x: 1.0 / math.log(x))\n\n df_AA_list = df_common_neighbor.groupby(['source_x', 'source_y']).sum()\n df_AA_list = df_AA_list.reset_index()\n\n df_AA_list.rename(columns={'count': 'similarity', 'source_x': 'source', 'source_y': 'target'}, inplace=True)\n return df_AA_list",
"title": ""
},
{
"docid": "4fdb21730026d454270f3730ab56fe65",
"score": "0.4967269",
"text": "def __call__(self, labels, attr):\n regions_set = set(labels)\n obj_val = sum(\n self.metric(attr[i].reshape(1, -1), attr[j].reshape(1, -1))\n for r in regions_set\n for i, j in itertools.combinations(np.where(labels == r)[0], 2))\n \n return float(obj_val)",
"title": ""
},
{
"docid": "6ad523bcf55494d1b0aa631668c55d8d",
"score": "0.4965129",
"text": "def apply_domain(self, df):\n super().apply_domain(df, col=self.label_col)",
"title": ""
},
{
"docid": "4a318ad05709973f3d5cb357691d53c1",
"score": "0.49583808",
"text": "def calc_objective(labels, label_ce):\n objv = 0.0\n for label in labels:\n idx = np.isfinite(label)\n objv += normalized_mutual_info_score(\n label_ce[idx], label[idx], average_method='geometric')\n objv /= labels.shape[0]\n return objv",
"title": ""
},
{
"docid": "8b5721bc31f05d6a65f6ec93c2ca8800",
"score": "0.49500594",
"text": "def lcv(domains, domain):\n # Initialize array for count of each value\n count = [0 for i in range(int(math.sqrt(len(domains))))]\n\n # Iterate through domains, counting each time the values are used\n for dom in domains.items():\n for val in dom[1]:\n count[val-1] += 1\n\n # Now sort by the count of each value\n domain_count = [(count[val-1], val) for val in domain]\n domain_count.sort()\n new_domain = [val[1] for val in domain_count]\n return new_domain",
"title": ""
},
{
"docid": "75b9d487abe6bce35a603ca0d0fd96ef",
"score": "0.49497637",
"text": "def get_can_label(self):\n if not len(self.graph):\n self.create_mol_graph()\n syms = self.ase_atoms.get_chemical_symbols()\n weights = [io_ptable.masses[io_ptable.elements.index(x)] for x in syms]\n inds = np.nonzero(self.graph)\n tmpgraph = self.graph.copy()\n for j in range(len(syms)): # Set diagonal to weights\n tmpgraph[j, j] = weights[j]\n for i, x in enumerate(inds[0]):\n y = inds[1][i]\n # Add factor of 100\n tmpgraph[x, y] = weights[x]*weights[y]*tmpgraph[x, y] / 100.0\n with np.errstate(over='raise'):\n try:\n det = np.linalg.det(tmpgraph)\n except:\n det = np.linalg.det(tmpgraph/100.0)\n if 'e+' in str(det):\n safedet = str(det).split(\n 'e+')[0][0:10]+'e+'+str(det).split('e+')[1]\n else:\n safedet = str(det)[0:10]\n return safedet",
"title": ""
},
{
"docid": "28934d7eec711fede248b71151342aac",
"score": "0.4945146",
"text": "def averageClusteringUndirected(self):\r\n graph = self.getGraph()\r\n dirGraph = {}\r\n \r\n for i in graph.keys():\r\n dirGraph[i] = set()\r\n for i in graph.keys():\r\n for j in graph[i]:\r\n dirGraph[i].add(j)\r\n dirGraph[j].add(i)\r\n \r\n total = 0 \r\n for i in dirGraph:\r\n ''' calculate the number of the pairs of neighbors of node i \r\n which are adiacents\r\n ''' \r\n neighs = len(dirGraph[i])\r\n pairsNeigh = ( neighs * (neighs-1) )/2 # all pairs\r\n ''' check how many nehgbors of node i have a connection '''\r\n triangles = 0\r\n for j in dirGraph[i]:\r\n for k in dirGraph[i]:\r\n if k in dirGraph[j]:\r\n triangles += 1\r\n if pairsNeigh > 0:\r\n total += float(triangles)/(2*pairsNeigh)\r\n return float(total)/len(dirGraph)",
"title": ""
},
{
"docid": "f1b3a728064b60588ca076a157b2dc10",
"score": "0.49318907",
"text": "def my_use_percentage(segments,before,after=None):\n return len(before.vetoed(segments.active))/len(before)*100",
"title": ""
},
{
"docid": "6185c2ee6b1510c7649112dde2462765",
"score": "0.49272957",
"text": "def normalized_cut(graph):\n labels = cut_normalized(range(10), graph.to_undirected())\n print(labels)",
"title": ""
},
{
"docid": "568e8c88887ebbefb170fcfc73a59ebc",
"score": "0.49272773",
"text": "def ticks(self, domain_min, domain_max):\n def coverage(dmin, dmax, lmin, lmax):\n range = dmax - dmin\n return 1 - 0.5 * (numpy.power(dmax - lmax,\n 2) + numpy.power(dmin - lmin,\n 2)) / numpy.power(0.1 * range,\n 2)\n\n def coverage_max(dmin, dmax, span):\n range = dmax - dmin\n if span > range:\n half = (span - range) / 2.0\n return 1 - numpy.power(half, 2) / numpy.power(0.1 * range, 2)\n else:\n return 1\n\n def density(k, m, dmin, dmax, lmin, lmax):\n r = (k - 1.0) / (lmax - lmin)\n rt = (m - 1.0) / (max(lmax, dmax) - min(lmin, dmin))\n return 2 - max(r / rt, rt / r)\n\n def density_max(k, m):\n if k >= m:\n return 2 - (k - 1.0) / (m - 1.0)\n else:\n return 1\n\n def simplicity(q, Q, j, lmin, lmax, lstep):\n eps = 1e-10\n n = len(Q)\n i = Q.index(q) + 1\n v = 1 if ((lmin % lstep < eps or (lstep - lmin % lstep)\n < eps) and lmin <= 0 and lmax >= 0) else 0\n return (n - i) / (n - 1.0) + v - j\n\n def simplicity_max(q, Q, j):\n n = len(Q)\n i = Q.index(q) + 1\n v = 1\n return (n - i) / (n - 1.0) + v - j\n\n def legibility(lmin, lmax, lstep):\n return 1\n\n def legibility_max(lmin, lmax, lstep):\n return 1 # pragma: no cover\n\n def extended(dmin, dmax, m, Q, only_inside, w):\n n = len(Q)\n best_score = -2.0\n\n j = 1.0\n while j < float('infinity'):\n for q in Q:\n sm = simplicity_max(q, Q, j)\n\n if w[0] * sm + w[1] + w[2] + w[3] < best_score:\n j = float('infinity')\n break\n\n k = 2.0\n while k < float('infinity'):\n dm = density_max(k, m)\n\n if w[0] * sm + w[1] + w[2] * dm + w[3] < best_score:\n break\n\n delta = (dmax - dmin) / (k + 1.0) / j / q\n z = numpy.ceil(numpy.log10(delta))\n\n while z < float('infinity'):\n step = j * q * numpy.power(10, z)\n cm = coverage_max(dmin, dmax, step * (k - 1.0))\n\n if w[0] * sm + w[1] * cm + \\\n w[2] * dm + w[3] < best_score:\n break\n\n min_start = numpy.floor(\n dmax / step) * j - (k - 1.0) * j\n max_start = numpy.ceil(dmin / step) * j\n\n if min_start > max_start:\n z = z + 1\n break\n\n for start in range(\n int(min_start),\n int(max_start) + 1):\n lmin = start * (step / j)\n lmax = lmin + step * (k - 1.0)\n lstep = step\n\n s = simplicity(q, Q, j, lmin, lmax, lstep)\n c = coverage(dmin, dmax, lmin, lmax)\n d = density(k, m, dmin, dmax, lmin, lmax)\n l = legibility(lmin, lmax, lstep)\n\n score = w[0] * s + w[1] * \\\n c + w[2] * d + w[3] * l\n\n if score > best_score and (not only_inside or (lmin >= dmin and lmax <= dmax)):\n best_score = score\n best = (lmin, lmax, lstep, q, k)\n z = z + 1\n k = k + 1\n j = j + 1\n return best\n\n lmin, lmax, lstep, q, k = extended(\n domain_min, domain_max, self._count - 1, self._steps, self._only_inside, self._weights)\n locations = numpy.arange(k) * lstep + lmin\n digits = max(0, int(numpy.max(-numpy.floor(numpy.log10(lstep)), 0)))\n labels = [self._format.format(location, digits=digits) for location in locations]\n titles = numpy.repeat(None, len(labels))\n return locations, labels, titles",
"title": ""
},
{
"docid": "dac7f50deb782c2f9e2c6c991dc509fc",
"score": "0.49205077",
"text": "def _calculate_adj_area(self, coords):\n\n pol = Polygon(coords)\n cmp_index = GenUtil.calculate_compactness_index(pol.area, pol.length)\n adj_area = GenUtil.calculate_adjusted_area(pol.area, cmp_index)\n\n return adj_area",
"title": ""
},
{
"docid": "7c1a6a0caa28490951b02cdabecd69a7",
"score": "0.49166757",
"text": "def visualize_balance_of_dataset(y, name):\n u, counts = np.unique(y, return_counts=True)\n sum_counts = np.sum(counts)\n distro_list = []\n for i in counts:\n distro =(i / sum_counts) * 100\n distro_list.append(distro)\n # print('distribution = ', distro_list) \n plt.figure(figsize=(10, 5))\n if name == \"Train\":\n col = \"blue\"\n else:\n col = \"red\"\n plt.bar(u, counts, color=col)\n plt.title(name + \" Dataset Distribution\")\n plt.xticks(np.arange(min(u), max(u)+1, 1.0))\n plt.xlabel(\"Label Categories - Numerical Characters\")\n plt.ylabel(\"Number of Label Category Occurrences\")\n plt.savefig(name + \"_barChart.png\")",
"title": ""
},
{
"docid": "5debbc7f0327ebf6cb02b78f1f74eb3f",
"score": "0.49151975",
"text": "def __call__(self, labels, attr):\n regions_set = set(labels)\n region_totals =[sum(attr[i] for i in np.where(labels == r)[0])\n for r in regions_set]\n obj_val = float(max(region_totals)-min(region_totals))\n return obj_val",
"title": ""
},
{
"docid": "eb150baf5d7326470ad041c893bd27db",
"score": "0.49095762",
"text": "def markovsScore (models, data, labels):\n all = labels.size\n corrects = 0\n for i in range (len (data)):\n if markovsCouncil (models, data[i]) == labels[i]:\n corrects += 1\n return corrects / all * 100",
"title": ""
},
{
"docid": "b609b34284a5206423902169462bf53a",
"score": "0.4907743",
"text": "def get_area_under_graph(graph):\n # bla bla bla.\n pass",
"title": ""
},
{
"docid": "9a219022c3a2886d13c6474b9fe6b2a8",
"score": "0.49073094",
"text": "def REGULAR(dseglist):\r\n ordered = order_segments(dseglist)\r\n ordered = ordered[0]\r\n \r\n # for seg in dseglist:\r\n # output( \"s,e=\",get_ds_ends(seg))\r\n # output('len ordered=',len(ordered))\r\n # for seg in ordered:\r\n # output( \"ordered s,e=\",get_ds_ends(seg))\r\n\r\n numsides = len(dseglist)\r\n \r\n # this makes the sides equal to largest of existing sides:\r\n sidelength = max(map(lambda seg: wxPointUtil.distance(*get_ds_ends(seg)), dseglist))\r\n \r\n # this makes the sides equal to average of existing sides:\r\n # sidelength = 0\r\n # for seg in dseglist:\r\n # sidelength += wxPointUtil.distance(*get_ds_ends(seg))\r\n #sidelength = sidelength / numsides\r\n\r\n # positive polarity is this end is common to the next seg.\r\n polarity = []\r\n s,e = get_ds_ends(ordered[0]) # only need e here\r\n for i in range(len(ordered)-1):\r\n sn,en = get_ds_ends(ordered[(i+1)%len(ordered)])\r\n polarity.append((close_enough(e[0],sn[0]) and close_enough(e[1],sn[1])) or \r\n (close_enough(e[0],en[0]) and close_enough(e[1],en[1])))\r\n e = en\r\n polarity.append(polarity[0])\r\n angle = 2*math.pi / numsides\r\n \r\n # get angle of first segment, use this as the starting angle\r\n # if positive polarity, end is the anchor\r\n \r\n\r\n \r\n angleincrement = 2*math.pi/numsides\r\n for i in range(len(ordered)-1):\r\n if polarity[i]:\r\n anchor,free = get_ds_ends(ordered[i])\r\n else:\r\n free,anchor = get_ds_ends(ordered[i])\r\n \r\n if i == 0:\r\n firstanchor = anchor\r\n vector = free - anchor\r\n startangle = -math.atan2(vector[1],vector[0]) # - is kicad angle polarity CCW\r\n #wxPointUtil.scale(vector*sidelength/mag(vector))\r\n\r\n angle = startangle+2*math.pi*i/numsides\r\n # output( 'anchor,free,angle=',anchor,free,angle*180/math.pi)\r\n vector = wxPointUtil.towxPoint(sidelength,angle)\r\n\r\n endpoint = anchor+vector\r\n # TODO: update to work with S_ARC\r\n if polarity[i]:\r\n ordered[i].SetEnd(endpoint) \r\n # if i==0:\r\n # output( 'setend')\r\n else:\r\n ordered[i].SetStart(endpoint)\r\n # if i==0:\r\n # output( 'setstart')\r\n if polarity[i+1]:\r\n ordered[i+1].SetStart(endpoint) \r\n # output( 'setstart')\r\n else:\r\n ordered[i+1].SetEnd(endpoint)\r\n # output( 'setend')\r\n\r\n # ordered[i].SetEnd(endpoint) if polarity[i] else ordered[i].SetStart(endpoint)\r\n # ordered[i+1].SetStart(endpoint) if polarity[i+1] else ordered[i+1].SetEnd(endpoint)\r",
"title": ""
},
{
"docid": "cb178b272f22c83cc765ef6711a46b96",
"score": "0.49059832",
"text": "def aa(y_true, y_pred):\n acc_cl = []\n for label in np.unique(y_pred):\n acc_cl.append(np.sum(y_true[y_pred == label] == y_pred[y_pred == label]) / len(y_pred[y_pred == label]))\n return np.nanmean(acc_cl), acc_cl",
"title": ""
},
{
"docid": "18e2674a3cf59a7e00f285c36044e1cc",
"score": "0.4888339",
"text": "def __call__(self, labels, attr):\n g = gpd.GeoDataFrame(attr, columns = ['geometry'])\n g['templab'] = labels\n lengths = g.dissolve(by ='templab').length\n \n return sum(lengths)",
"title": ""
},
{
"docid": "d50324420c94de8e0776a431a6e79143",
"score": "0.48878697",
"text": "def calc_conservation_per_domain(alignmentfiles, repr_accs, normalize = True,gapcutoff=0.8):\n\n from Bio import AlignIO\n regexFull = re.compile(\"^(\\S+_\\S+)\\|(\\S+)\\/(\\d+-\\d+): (.+)\\|(\\w+)(\\/.+)?\")\n\n entropyDictFull = defaultdict(lambda: np.NaN)\n entropyH10DictFull = defaultdict(lambda: np.NaN)\n if type(alignmentfiles) == type(''):\n alignmentfiles = [alignmentfiles]\n\n for alignmentfile in alignmentfiles:\n alignment = AlignIO.read(alignmentfile, \"fasta\")\n alignment_filtered = filter_alignment(alignment, repr_accs)\n\n # Calculate Shannon entropy\n msa = [str(x.seq).replace('.', '-').upper() for x in alignment_filtered]\n msaH10 = convert_H10_as_array(alignment_filtered)\n aliSize = len(msa[0])\n entropy = []\n entropyH10 = []\n\n\n\n for record in alignment:\n header = record.description\n match = regexFull.match(header)\n if match:\n prositeName = match.group(4)\n prositeID = match.group(5)\n else:\n print(header)\n\n shannon = []\n shannonH10 = []\n for i in range(aliSize):\n shannon.append(calculate_shannon_entropy([x[i] for x in msa], gapcutoff))\n shannonH10.append(calculate_shannon_entropy([x[i] for x in msaH10],gapcutoff))\n\n #We will use a dataframe to store our data because it's faster to merge with our original dataset later on\n if normalize:\n shannon = norm_0_1(shannon, maxvalue=4.34)\n shannonH10 = norm_0_1(shannonH10, maxvalue=3.15)\n\n df = pd.DataFrame(list(zip(shannon, shannonH10)),\n columns=[\"shannon\", 'shannonH10'])\n df[\"prositeName\"] = prositeName\n df[\"prositeID\"] = prositeID\n df = df.reset_index()\n #transform the index (0-based) into \"alignment_position\" which is also 0-based\n df = df.rename(columns={'index':'alignment_position'})\n\n return df",
"title": ""
},
{
"docid": "8459a8ccfd6f80bfd900fdcc5702d995",
"score": "0.48792765",
"text": "def plot_domain_frequencies(self):\n return functools.reduce(\n operator.add,\n (\n self.plot_cpu_frequencies(domain[0]).relabel(\n f'Frequencies of domain CPUS {\", \".join(map(str, domain))}'\n )\n for domain in self.trace.plat_info['freq-domains']\n )\n ).cols(1)",
"title": ""
},
{
"docid": "ccee7076dbba737460b251506a85b027",
"score": "0.48785737",
"text": "def plot_prob_contours(Gaus_dist_a, Gaus_dist_b, with_unbalance=False):\n\n assert(isinstance(Gaus_dist_a, model.GausDS) and isinstance(Gaus_dist_b, model.GausDS))\n\n X_a, X_b = Gaus_dist_a.data, Gaus_dist_b.data\n\n n_a = len(X_a)\n n_b = len(X_b)\n\n l_s_scalar_min = -9\n l_s_scalar_max = 9\n ls_x1 = np.linspace(l_s_scalar_min, l_s_scalar_max, 100)\n ls_x2 = np.linspace(l_s_scalar_min, l_s_scalar_max, 100)\n mg_x1, mg_x2 = np.meshgrid(ls_x1, ls_x2)\n\n pdf_a = Gaus_dist_a.Gaussian_pdf(mg_x1, mg_x2, 100)\n pdf_b = Gaus_dist_b.Gaussian_pdf(mg_x1, mg_x2, 100)\n\n pdf_a = pdf_a * n_a/(n_a+n_b)\n pdf_b = pdf_b * n_b/(n_a+n_b)\n\n fig = plt.figure()\n ax = fig.add_subplot(111)\n ax.axis('equal') # !!! axis equals so that make circle a circle\n\n # ax.set_title(\"Sa != Sb\")\n ax.set_title(\"2 Class Classification\")\n\n ax.scatter(X_a[:, 0], X_a[:, 1], marker='.', c='r', label='class a')\n ax.scatter(X_b[:, 0], X_b[:, 1], marker='+', c='b', label='class b')\n\n ax.contour(mg_x1, mg_x2, pdf_a, 10)\n ax.contour(mg_x1, mg_x2, pdf_b, 10)\n\n # get the decision border\n log_odds = np.log(pdf_a) - np.log(pdf_b)\n list_border = []\n for i in range(99):\n for j in range(99):\n if (log_odds[i][j]*log_odds[i][j+1] < 0) or (log_odds[i][j]*log_odds[i+1][j] < 0) \\\n or log_odds[i][j] == 0:\n list_border.append([i, j])\n\n bd = np.array(list_border)\n X1 = np.linspace(l_s_scalar_min, l_s_scalar_max, 100)\n Y1 = np.linspace(l_s_scalar_min, l_s_scalar_max, 100)\n ax.scatter(X1[bd[:, 0]], Y1[bd[:, 1]], marker='.', s=15, color='brown', label='decision border')\n\n # optimal choice of w\n init_w = np.array([1, -2]).reshape(-1, 1)\n\n # plot the line of w with balanced fisher score\n # equal num of points between class a and class b\n fs_clf = model.FisherScoreClassifier(X_a, X_b, init_w)\n w_star = fs_clf.classify(plot=False)\n w_star = np.array(w_star)\n xielv = w_star[1]/w_star[0]\n x_point = np.linspace(-5, 3, 100)\n y_point = x_point * xielv - 4\n plt.plot(x_point, y_point, c='g', label='optimal w')\n\n # plot the line of w with unbalanced fisher score\n # different num of points between class a and class b\n if with_unbalance:\n w_star = fs_clf.classify(balanced=False, plot=False)\n w_star = np.array(w_star)\n xielv = w_star[1]/w_star[0]\n x_point = np.linspace(-5, 3, 100)\n y_point = x_point * xielv - 4\n plt.plot(x_point, y_point, c='purple', label='unbalanced F(w)')\n\n leg = ax.legend(loc='upper right', fancybox=True, fontsize=8)\n leg.get_frame().set_alpha(0.5)\n\n plt.show()",
"title": ""
},
{
"docid": "6485832d8d4709f98440cd4ff8b11697",
"score": "0.48777118",
"text": "def area_metric_robust(D1, D2):\n\n if np.size(D1) > np.size(D2):\n d1 = D2\n d2 = D1\n else:\n d1 = D1\n d2 = D2 # D1 will always be the larger data set\n\n Pxs, xs = ecdf(d1) # Compute the ecdf of the data sets\n Pys, ys = ecdf(d2)\n\n Pys_eqx = Pxs\n Pys_pure = Pys[0:-1] # this does not work with a single datum\n Pall = np.sort(np.append(Pys_eqx, Pys_pure))\n\n ys_eq_all = np.zeros(len(Pall))\n ys_eq_all[0] = ys[0]\n ys_eq_all[-1] = ys[-1]\n for k in range(1, len(Pall)-1):\n ys_eq_all[k] = interpCDF_2(ys, Pys, Pall[k])\n\n xs_eq_all = np.zeros(len(Pall))\n xs_eq_all[0] = xs[0]\n xs_eq_all[-1] = xs[-1]\n for k in range(1, len(Pall)-1):\n xs_eq_all[k] = interpCDF_2(xs, Pxs, Pall[k])\n\n diff_all_s = abs(ys_eq_all-xs_eq_all)\n diff_all_s = diff_all_s[range(1, len(diff_all_s))]\n diff_all_p = np.diff(Pall)\n area = np.matrix(diff_all_p) * np.matrix(diff_all_s).T\n\n return np.array(area)[0]",
"title": ""
},
{
"docid": "a9ed6f39d3ad032d6e5969166dbd6719",
"score": "0.487672",
"text": "def cal_dcd_scores(self, n_samples=1000, lognorm=True, n_components=2):\n # smaller is better\n train, test = self.create_divergence_matrix(n_samples=n_samples,\n lognorm=lognorm,\n n_components=n_components,\n normalize_per_code=True,\n decode=False)\n # diag = np.diagflat(np.diag(density_mat))\n # higher is better\n train = 1. - train\n test = 1 - test\n d = (metrics.disentanglement_score(train) +\n metrics.disentanglement_score(test)) / 2.\n c = (metrics.completeness_score(train) +\n metrics.completeness_score(test)) / 2.\n return d, c",
"title": ""
},
{
"docid": "f634eda25444928e474adbf1490df080",
"score": "0.48718885",
"text": "def plot_boundaries():\n\n attributes = [\n \"acousticness\", \"danceability\", \"energy\", \"instrumentalness\",\n \"liveness\", \"loudness\", \"speechiness\", \"valence\"\n ]\n\n count = 0\n\n track_a = {\"id\": \"6j7ShkX2wTd7pXSmRQQtrK\"}\n track_b = {\"id\": \"2AVkArcfALVk2X8sfPRzya\"}\n\n example_track_a = TrackData.get_track(track_a).get_metadata()\n example_track_b = TrackData.get_track(track_b).get_metadata()\n\n for user, session in Session.get_users_with_surveys():\n\n if not (count == 10 or count == 11):\n count += 1\n continue\n count += 1\n\n binary_boundary = BinaryBoundary(user)\n features_boundary = BinaryBoundaryWithFeatures(user)\n kde_boundary = KDEBoundary(user)\n histogram_boundary = HistogramBoundary(user)\n print(f\"User: {count}\")\n\n fig, axs = plt.subplots(nrows=4, ncols=2, figsize=(13, 13))\n x_index = 0\n y_index = 0\n\n handles = None\n labels = None\n\n for feature in attributes:\n\n histogram_data = histogram_boundary.boundaries[feature]\n kde_data = kde_boundary.boundaries[feature]\n\n hist_values = np.array(list(histogram_data.values()))\n max_y_value = max(hist_values)\n\n bins = np.arange(0.1, 1.2, 0.1)\n\n min_boundary = binary_boundary.boundaries[feature][\"min\"]\n max_boundary = binary_boundary.boundaries[feature][\"max\"]\n\n if max_boundary < min_boundary + 0.02:\n max_boundary = max_boundary + 0.01\n\n widths = np.array([-0.1 for _ in bins])\n\n kde_bins = np.linspace(0.0, 1.0, 1000)[: np.newaxis]\n log_score = kde_data.score_samples(kde_bins.reshape(-1, 1))\n\n ax1 = axs[y_index][x_index]\n ax1.bar(bins, height=hist_values, width=widths, alpha=0.5, label=\"Histogram\", align=\"edge\", color=\"blue\")\n\n ax1.vlines(\n [min_boundary, max_boundary], ymin=0, ymax=max_y_value,\n linewidth=2, color=\"#d95f02\", label=\"Boundary\"\n )\n\n ax1.set_xlim(right=1.0)\n\n ax2 = ax1.twinx()\n ax2.plot(kde_bins, np.exp(log_score), color=\"#1b9e77\", label=\"KDE\", linewidth=2)\n ax2.set_ylim(bottom=0)\n ax2.set_xlim(right=1.0)\n\n ax1.vlines(\n example_track_a[feature], ymin=0, ymax=max_y_value,\n linewidth=4, color=\"black\", label=\"Example Track 1\", linestyle=\"dotted\"\n )\n ax1.vlines(\n example_track_b[feature], ymin=0, ymax=max_y_value,\n linewidth=4, color=\"black\", label=\"Example Track 2\", linestyle=\"dashed\"\n )\n\n h1, l1 = ax1.get_legend_handles_labels()\n h2, l2 = ax2.get_legend_handles_labels()\n\n handles = h1 + h2\n labels = l1 + l2\n\n ax1.set_xticks(np.arange(0, 1.1, 0.1))\n ax1.tick_params('x', rotation=45)\n ax1.set_title(f\"{feature.capitalize()}\", fontsize=14)\n\n x_index += 1\n if x_index >= 2:\n y_index += 1\n x_index = 0\n\n # 0: Boundary\n # 1: Example Track 1\n # 2: Example Track 2\n # 3: Histogram\n # 4: KDE\n order = [0, 3, 4, 1, 2]\n\n fig.suptitle(f\"Example boundaries, user {'A' if count == 11 else 'B'}\", fontsize=20)\n fig.tight_layout()\n fig.legend(\n [handles[idx] for idx in order], [labels[idx] for idx in order],\n loc=9, bbox_to_anchor=(0.5, 0.95), prop={'size': 15}, framealpha=1\n )\n\n plt.show()",
"title": ""
},
{
"docid": "841240b5394554198d896ea778f8ba3e",
"score": "0.48688507",
"text": "def check_convergence(labels, folder='./'):\n from emcee import autocorr \n x = np.load(folder+'samples.npy')\n inter_var = []\n fig, axarr = plt.subplots(2, figsize=(10,10))\n ax,bx = axarr.flatten()\n for j in range(len(x[0,0])):\n chain = x[:, :, j].T\n # Compute the estimators for a few different chain lengths\n N = np.exp(np.linspace(np.log(100), np.log(chain.shape[1]), 10)).astype(int)\n gw2010 = np.empty(len(N))\n new = np.empty(len(N))\n test = np.empty(len(N))\n for i, n in enumerate(N):\n test[i] = autocorr.integrated_time(chain[:,:n],c=5)\n ax.loglog(N, test, \"o-\", label=labels[j])\n\n #compute the interchain variance\n inter_var.append(np.std(chain))\n\n # Plot the comparisons\n ax.set_xlabel(\"number of samples, $N$\")\n ax.set_ylabel(r\"$\\tau$ estimates\")\n ax.legend(fontsize=14)\n\n intra_var = []\n for i in range(x.shape[1]):\n param_var = []\n for j in range(x.shape[2]):\n chain = x[:,i,j].T \n #compute intra-chain variance\n param_var.append(np.std(chain))\n intra_var.append(param_var)\n\n #compute the ratios of the intra and interchain variances\n ylim = 10\n for j in range(len(inter_var)):\n all_vals = []\n for var in intra_var[j]:\n ratio = inter_var[j] / var\n all_vals.append(ratio)\n if ratio > ylim:\n bx.scatter(j,ylim-0.5,marker='^', alpha=0.75, c='dodgerblue')\n else:\n bx.scatter(j, ratio,c='dodgerblue')\n bx.scatter(j, np.mean(all_vals), marker='s', s=100, c='k')\n bx.plot([0,j],[1,1], 'r--', label='Ideal Ratio')\n bx.legend(fontsize=14)\n bx.set_xlabel('parameter')\n bx.set_ylabel(r'${\\rm Var}_{\\rm inter} / {\\rm Var}_{\\rm Intra}$ for each chain')\n bx.set_ylim([0,ylim])\n plt.show()\n plt.close()",
"title": ""
},
{
"docid": "e7a9f4a4e4e412128ed9a7cbbab56702",
"score": "0.4860509",
"text": "def similarity_cal_module_5(dgassos, graph, gncutoff=1):\n gvs = set(graph.vs['name'])\n\n dgassos_new = {}\n for d in dgassos.keys():\n dgleft = gvs.intersection(dgassos[d])\n if len(dgleft) >= gncutoff:\n dgassos_new[d] = dgleft\n diseases = list(dgassos_new.keys())\n print(\"there are {} diseases can be calculated.\".format(len(diseases)))\n\n sims = {}\n for i in range(0, len(diseases)-1):\n sims[diseases[i]] = {}\n for j in range(i+1, len(diseases)):\n gsi = dgassos_new[diseases[i]]\n gsj = dgassos_new[diseases[j]]\n gsintersect = gsi.intersection(gsj)\n gsid = gsi.difference(gsj)\n gsjd = gsj.difference(gsi)\n conncount = 0\n if len(gsintersect) != 0:\n for g in gsid:\n conncount += connected_count(graph, g, gsintersect)\n for g in gsjd:\n conncount += connected_count(graph, g, gsintersect)\n for g in gsid:\n conncount += connected_count(graph, g, gsjd)\n sim = (len(gsintersect)**2+conncount)/(len(gsi)*len(gsj))\n sims[diseases[i]][diseases[j]] = sim\n if diseases[j] not in sims.keys():\n sims[diseases[j]] = {}\n sims[diseases[j]][diseases[i]] = sim\n print(i, \"done..\")\n normsims = {}\n maxsims = {}\n for d in diseases:\n maxsims[d] = max([i for i in sims[d].values()])\n for i in range(0, len(diseases)-1):\n normsims[diseases[i]] = {}\n for j in range(i+1, len(diseases)):\n avgmaxsim = (maxsims[diseases[i]] + maxsims[diseases[j]])/2\n if avgmaxsim == 0:\n normsims[diseases[i]][diseases[j]] = 0\n else:\n normsims[diseases[i]][diseases[j]] = sims[diseases[i]][diseases[j]]/avgmaxsim\n print(i, \"done...\")\n return normsims",
"title": ""
},
{
"docid": "9411031f46df9daa9353a8655f8e4b22",
"score": "0.48600683",
"text": "def district_area(self):\n if self.district:\n try:\n return (self.district.transform(102009, clone=True).area / 1000000) * 0.38610\n except Exception:\n return",
"title": ""
},
{
"docid": "47925b3ffc37ade2d0a85b17595a7676",
"score": "0.48596826",
"text": "def _marginalize_over_angerr(self, c_e, c_d, c_e_r):\n\n presel_data = self.dataset[np.intersect1d(np.nonzero(np.isclose(self.dataset[:, 0], self.true_energy_bins[c_e])),\n np.nonzero(np.isclose(self.dataset[:, 2], np.rad2deg(self.declination_bins[c_d]))))]\n\n reduced_data = presel_data[np.nonzero(np.isclose(presel_data[:, 4], self.reco_energy_bins[c_e, c_d][c_e_r]))]\n\n\n bins = np.array(sorted(list(set(reduced_data[:, 6]).union(\n set(reduced_data[:, 7])))))\n if bins.shape[0] != 0:\n frac_counts = np.zeros(bins.shape[0]-1)\n\n #marginalise over uninteresting quantities\n for c_b, b in enumerate(bins[:-1]):\n indices = np.nonzero(np.isclose(b, reduced_data[:, 6]))\n frac_counts[c_b] = np.sum(reduced_data[indices, -1])\n return frac_counts, bins\n\n else:\n return None, None",
"title": ""
},
{
"docid": "7ab043cd3334b0fb8296cbc693cea23e",
"score": "0.48583674",
"text": "def addtotalareas():\n total = 0\n for eachareacircle in CircleTotalAreas.allcircleslist:\n total = total + eachareacircle.area()\n return total",
"title": ""
},
{
"docid": "925e61b04df0c54a46e1922deaa77eff",
"score": "0.48567036",
"text": "def io_ratio(observations, group_labels):\n group_labels = np.array(group_labels)\n unique_groups = np.unique(group_labels)\n in_group = 0.0\n out_group = 0.0\n\n for g in unique_groups:\n if np.sum(group_labels == g) == 0:\n continue\n\n in_obs = np.array(observations[group_labels == g, :], ndmin=2)\n out_obs = np.array(observations[group_labels != g, :], ndmin=2)\n\n in_group += np.mean(pdist(in_obs))\n out_group += np.mean(cdist(in_obs, out_obs))\n\n return in_group / out_group",
"title": ""
},
{
"docid": "7980d7f499cf4f565b1bdf2d21261775",
"score": "0.48430055",
"text": "def get_kl_divergence(domain, targets, scores):\n\n nbin = 50 # Number of histogram bins\n a_b_pos, b_a_pos = smoothed_hist_kl_distance(scores[np.logical_and(domain == 0, targets == 1)],\n scores[np.logical_and(\n domain == 1, targets == 1)],\n nbins=nbin)\n a_b_neg, b_a_neg = smoothed_hist_kl_distance(scores[np.logical_and(domain == 0, targets == 0)],\n scores[np.logical_and(\n domain == 1, targets == 0)],\n nbins=nbin)\n\n return np.median([a_b_pos] + [a_b_neg] + [b_a_pos] + [b_a_neg])",
"title": ""
},
{
"docid": "efeeb3fa5d706540ad6f12a0d5916a16",
"score": "0.48415655",
"text": "def _get_complete_domain(self):\n domain = dict().fromkeys(self.variables)\n for key in domain:\n domain[key] = range(2) if is_aux(key) else range(10)\n domain[self.op1[-1]] = range(1, 10)\n domain[self.op2[-1]] = range(1, 10)\n domain[self.result[-1]] = range(1, 10)\n if len(self.result) > max(len(self.op1), len(self.op2)):\n domain[self.result[-1]] = [1]\n if len(self.op1) == len(self.op2):\n domain[self.extra_variables[-1]] = [1]\n return domain",
"title": ""
},
{
"docid": "64793ed830f1c26af715630e8bbdf578",
"score": "0.48310858",
"text": "def create_domain(self):\n print \"creating domain ...\"\n a1 = Orange.feature.Continuous(\"Optimal\")\n a2 = Orange.feature.Continuous(\"Manhattan\")\n a3 = Orange.feature.Continuous(\"NCorrect\")\n a4 = Orange.feature.Continuous(\"NCorrectOrdered\")\n a5 = Orange.feature.Continuous(\"ManhattanFirst\")\n a6 = Orange.feature.Continuous(\"ManhattanFirstZero\")\n a7 = Orange.feature.Continuous(\"ManhattanProd\")\n a8 = Orange.feature.Continuous(\"DistanceZeroMid\")\n a9 = Orange.feature.Continuous(\"DistanceZeroFirst\")\n a10 = Orange.feature.Discrete(\"Finished\", values=[\"yes\",\"no\"])\n a11 = Orange.feature.Continuous(\"MaxMDistance\")\n a12 = Orange.feature.Continuous(\"DistPairs\")\n a13 = Orange.feature.Continuous(\"SequenceScore\")\n a14 = Orange.feature.Discrete(\"123on401\", values=[\"yes\",\"no\"])\n a15 = Orange.feature.Discrete(\"741on401\", values=[\"yes\",\"no\"])\n #positions = [Orange.feature.Continuous(\"Dist(%d on place%d)\"%(i,v)) for i in range(9) for v in range(9)]\n positions = [Orange.feature.Discrete(\"Dist(%d on place%d)\"%(i,v), values = [\"yes\", \"no\"]) \n for i in range(9) for v in range(9)]\n \n #self.attributes = [a2, a3, a4, a5, a11] + positions\n self.attributes = positions + [a10]\n \n \n\n self.funcs = {a1.name: optimal, a2.name: manhattan, a3.name: ncorrect,\n a4.name: ncorrect_ordered, a5.name: manhattan_first,\n a6.name: manhattan_first_zero, a7.name: manhattan_prod,\n a8.name: distance_zero_mid, a9.name: distance_zero_first,\n a10.name: finished, a11.name: max_manh, a12.name: distance_pairs,\n a13.name: sequence_score, a14.name: pos123, a15.name: pos741}\n for pi in range(9):\n for vi in range(9):\n #self.funcs[\"Dist(%d on place%d)\"%(pi,vi)] = Position(pi,vi)\n self.funcs[\"Dist(%d on place%d)\"%(pi,vi)] = PositionDiscrete(pi,vi)\n \n# for ai, a in enumerate(self.attributes):\n# print \"Index: %d, name = %s\"%(ai, a.name)\n \n self.att_ids = {a.name:i for i,a in enumerate(self.attributes)}\n self.domain = Orange.data.Domain(self.attributes, a10)\n id = Orange.feature.String(\"id\")\n mid = Orange.feature.Descriptor.new_meta_id()\n self.domain.add_meta(mid, id)\n\n eval = Orange.feature.Continuous(\"eval\") \n mid = Orange.feature.Descriptor.new_meta_id()\n self.domain.add_meta(mid, eval)\n \n trace = Orange.feature.String(\"trace\") \n mid = Orange.feature.Descriptor.new_meta_id()\n self.domain.add_meta(mid, trace)",
"title": ""
},
{
"docid": "79a12382d34cd70e7500a21be4d6e895",
"score": "0.48259488",
"text": "def norm_change_annual_flow(sites: list, before_bc: pd.DataFrame, after_bc: pd.DataFrame, colors = list,\n fontsize_title=60, fontsize_labels=40, fontsize_tick= 30):\n\n mpl.rcParams['figure.figsize']=(30,20)\n\n WY_grouper = calc_water_year(before_bc)\n after_bc_annual = after_bc.groupby(WY_grouper).sum()\n before_bc_annual = before_bc.groupby(WY_grouper).sum()\n\n after_bc_annual, before_bc_annual = dst.normalize_pair(data=after_bc_annual, norming_data=before_bc_annual)\n\n diff_annual = after_bc_annual - before_bc_annual\n\n max_mag = np.abs(np.max(diff_annual.max()))\n min_mag = np.abs(np.min(diff_annual.min()))\n extreme_mag = np.max([max_mag, min_mag])\n\n n_rows, n_cols = determine_row_col(len(sites))\n\n fig, axs = plt.subplots(n_rows, n_cols)\n\n plt.suptitle(\"Change in Annual Flow Volume from Bias Correction\",\n fontsize=fontsize_title, y=1.05)\n\n axs_list = axs.ravel().tolist()\n\n i=0\n for site in sites:\n ax = axs_list[i]\n ax.bar(diff_annual.index, diff_annual[site].values, color=color_list[i])\n ax.set_title(site, fontsize=fontsize_tick)\n ax.set_ylim(top = extreme_mag, bottom=-extreme_mag)\n ax.tick_params(axis='both', labelsize=fontsize_tick)\n i += 1\n\n while i < len(axs_list):\n axs_list[i].axis('off')\n i += 1\n\n fig.text(0.5, -0.04, 'Hydrologic Year',\n ha='center', va = 'bottom', fontsize=fontsize_labels);\n fig.text(-0.04, 0.5, \"Normalized Change in Annual Flow Volume\",\n va='center', rotation = 'vertical', fontsize=fontsize_labels);\n\n plt.tight_layout()",
"title": ""
},
{
"docid": "740bd89375e563a95897769b31b787bb",
"score": "0.4825399",
"text": "def calcul_acc(labels, preds):\n return sum(1 for x, y in zip(labels, preds) if x == y) / len(labels)",
"title": ""
},
{
"docid": "357d60301aea4661c916d5ba6c2dd11f",
"score": "0.48163047",
"text": "def calc_segmentation_consistency(output, reference, divergence_types=['kl', 'contour'],\n divergence_weights=[1.0, 0.5], class_weights=None, scales=[0],\n mask=None, is_gt=False):\n dist = 0.\n num_classes = reference.size(1)\n if mask is None:\n # apply masks so that only gradients on certain regions will be backpropagated.\n mask = torch.ones_like(output).float().to(reference.device)\n mask .requires_grad = False\n for scale in scales:\n if scale > 0:\n output_reference = torch.nn.AvgPool2d(2 ** scale)(reference)\n output_new = torch.nn.AvgPool2d(2 ** scale)(output)\n else:\n output_reference = reference\n output_new = output\n for divergence_type, d_weight in zip(divergence_types, divergence_weights):\n loss = 0.\n if divergence_type == 'kl':\n '''\n standard kl loss\n '''\n loss = kl_divergence(\n pred=output_new, reference=output_reference, mask=mask, is_gt=is_gt)\n elif divergence_type == 'ce':\n loss = cross_entropy_2D(\n input=output_new, target=output_reference, mask=mask, is_gt=is_gt)\n elif divergence_type == 'weighted ce':\n assert class_weights is not None, 'must assign class weights'\n loss = cross_entropy_2D(\n input=output_new, target=output_reference, mask=mask, is_gt=is_gt, weight=class_weights)\n elif divergence_type == 'Dice':\n use_gpu = False if output_reference.device == torch.device(\n 'cpu') else True\n loss = SoftDiceLoss(n_classes=num_classes, use_gpu=use_gpu)(\n input=output_new, target=output_reference, mask=mask, is_gt=is_gt)\n elif divergence_type == 'mse':\n n, h, w = output_new.size(\n 0), output_new.size(2), output_new.size(3)\n if not is_gt:\n target_pred = torch.softmax(output_reference, dim=1)\n else:\n target_pred = output_reference\n input_pred = torch.softmax(output_new, dim=1)\n loss = torch.nn.MSELoss(reduction='sum')(\n target=target_pred * mask, input=input_pred * mask)\n loss = loss / (n * h * w)\n elif divergence_type == 'contour': # contour-based loss\n if not is_gt:\n target_pred = torch.softmax(output_reference, dim=1)\n else:\n target_pred = output_reference\n input_pred = torch.softmax(output_new, dim=1)\n cnt = 0\n for i in range(1, num_classes):\n cnt += 1\n loss += contour_loss(input=input_pred[:, [i], ], target=(target_pred[:, [i]]), ignore_background=False, mask=mask,\n one_hot_target=False)\n if cnt > 0:\n loss /= cnt\n\n else:\n raise NotImplementedError\n\n print('{}:{}'.format(divergence_type, loss.item()))\n\n dist += 2 ** scale * (d_weight * loss)\n return dist / (1.0 * len(scales))",
"title": ""
},
{
"docid": "2504d3257a30e684a088cf488fe17f28",
"score": "0.48009068",
"text": "def discrimAnalysis(x, y):\r\n ### TODO: Write your code here\r\n male_x, female_x = splitData(x, y)\r\n \r\n ## mean\r\n mu_male = meanEstimator(male_x)\r\n mu_female = meanEstimator(female_x)\r\n mu = meanEstimator(x)\r\n \r\n ## mean values print\r\n #print(\"male mu: \", mu_male)\r\n #print(\"female mu: \", mu_female)\r\n \r\n ## covariance values\r\n cov = covarianceMatrix(x, mu)\r\n cov_male = covarianceMatrix(male_x, mu_male)\r\n cov_female = covarianceMatrix(female_x, mu_female)\r\n\r\n #print(\"Overall cov. matrix:\\n\", cov) \r\n #print(\"Male cov. matrix:\\n\", cov_male)\r\n #print(\"Female cov. matrix:\\n\", cov_female)\r\n \r\n # LDA calc.\r\n height, weight, result = LDA([0.5, 0.5], cov, mu_male, mu_female)\r\n \r\n #QDA calc.\r\n height_Q, weight_Q, result_Q = QDA([0.5, 0.5], \r\n cov_male, \r\n cov_female, \r\n mu_male, \r\n mu_female)\r\n \r\n ## gaussian calculations\r\n x_set = np.dstack((height, weight))\r\n x_set = np.reshape(x_set, (len(height)*len(weight), 2))\r\n density_male = util.density_Gaussian(mu_male, cov_male, x_set).reshape(height.shape[0], height.shape[1])\r\n density_female = util.density_Gaussian(mu_female, cov_female, x_set).reshape(height.shape[0], height.shape[1])\r\n \r\n ## figure 1 (just scatter)\r\n dataScatterPlot(male_x, female_x)\r\n plotDecisionBoundary(height, weight, result)\r\n gaussianContour(height, weight, density_male, density_female)\r\n plt.savefig(\"lda.pdf\")\r\n \r\n #figure 2 (QDA)\r\n dataScatterPlot(male_x, female_x)\r\n plotDecisionBoundary(height, weight, result_Q)\r\n gaussianContour(height, weight, density_male, density_female)\r\n plt.savefig(\"qda.pdf\")\r\n \r\n return (mu_male,mu_female,cov,cov_male,cov_female)",
"title": ""
},
{
"docid": "16bf88605a8c374121f344a4eb6dcfdc",
"score": "0.4800308",
"text": "def accuracy(label_trues, label_preds, n_class=7):\n hist = np.zeros((n_class, n_class))\n for lt, lp in zip(label_trues, label_preds):\n hist += _fast_hist(lt.flatten(), lp.flatten(), n_class) # n_class, n_class\n\n acc = np.diag(hist).sum() / hist.sum()\n acc_cls = np.diag(hist) / hist.sum(axis=1)\n acc_cls = np.nanmean(acc_cls)\n\n return acc_cls",
"title": ""
},
{
"docid": "3dc23adb60cb374f713afabcd5bac400",
"score": "0.47988722",
"text": "def labeledge(dag,p):\n \"\"\" Chickering, D. M., Learning equivalence classes of Bayesian-network structures, The Journal of Machine Learning Research,2002. \"\"\"\n\n ansdag=[[] for i in range(p)]\n edgenum=[[] for i in range(p)]\n edgeord=edgeorder(dag,p)\n NEdge=len(dag)\n for i in range(NEdge):\n ansdag[edgeord[i][1]].append(edgeord[i][0])\n edgenum[edgeord[i][1]].append(i)\n unlabled=len(dag)\n edgelabel=[0 for i in range(unlabled)]\n while unlabled>0:\n v=edgelabel.index(0) #step 4 , v is index of x to y\n x=edgeord[v][0]\n y=edgeord[v][1]\n for wi in [i for i in range(len(ansdag[x])) if edgelabel[edgenum[x][i]]==1]:\n if ansdag[x][wi] not in ansdag[y]:\n for j in edgenum[y]:\n if edgelabel[j]==0:\n unlabled-=1\n edgelabel[j]=1\n continue\n else:\n wyi=ansdag[y].index(ansdag[x][wi])\n if edgelabel[edgenum[y][wyi]]==0:\n unlabled-=1\n edgelabel[edgenum[y][wyi]]=1\n #begin step 8 \n if len([z for z in ansdag[y] if z!=x and z not in ansdag[x]])>0:\n for yi in edgenum[y]:\n if edgelabel[yi]==0:\n unlabled-=1\n edgelabel[yi]=1\n else:\n for yi in edgenum[y]:\n if edgelabel[yi]==0:\n unlabled-=1\n edgelabel[yi]=-1\n for i in range(len(dag)):\n edgeord[i].append(edgelabel[i]) \n return(edgeord)",
"title": ""
},
{
"docid": "d978b3cba7bd6f28ca8f5a2fe2ea044e",
"score": "0.47960457",
"text": "def segregation(self):\n _, _, _, frac_same = self.count_neighbors()\n return np.nanmean(frac_same)",
"title": ""
},
{
"docid": "b6b22a60df3908037d22ada56869a61c",
"score": "0.4795801",
"text": "def area_of_interest(domain='WY'):\n if domain == 'CONUS':\n # Restrict cells to the coterminous US\n bbox = {\n 'latmax' : 50,\n 'latmin' : 25,\n 'lonmax': -65,\n 'lonmin': -125,\n }\n elif domain == 'NAmer':\n # Restrict cells to North America (US and Canada, including Alaska)\n # For now it's not including much of Alaska\n bbox = {\n 'latmax' : 71,\n 'latmin' : 25,\n 'lonmax': -65,\n 'lonmin': -169,\n }\n else:\n # Load CSO domain JSON\n cso_domains_url = \"https://raw.githubusercontent.com/snowmodel-tools/preprocess_python/master/CSO_domains.json\"\n domains_resp = requests.get(cso_domains_url)\n domains = domains_resp.json()\n # Domain bounding box and projection\n bbox = domains[domain]['Bbox']\n # bbox_delta = 0.1\n\n bbox_proj = 'epsg:4326'\n bbox_latlon_gs = gpd.GeoSeries(\n box(bbox['lonmin'], bbox['latmin'], bbox['lonmax'], bbox['latmax']),\n crs=bbox_proj\n )\n bbox_ease2_gs = bbox_latlon_gs.to_crs(EASE2G_epsg_str)\n\n return bbox_latlon_gs, bbox_ease2_gs",
"title": ""
},
{
"docid": "b19a910b81f257df07d549092362498d",
"score": "0.47759676",
"text": "def calculate_full_areas(genes_df, temp_df, area_col, normalize):\n qstring = 'AUC_UseFLAG == 1'\n full = temp_df.query(qstring).groupby('GeneID')[area_col].sum()/normalize\n full.name = 'AreaSum_max'\n\n # full_adj = (temp_df.query(qstring)\n # .assign(gpAdj = lambda x: x[area_col] / x['GeneIDCount_All'])\n # .groupby('GeneID')['gpAdj'] # temp column\n # .sum()/normalize\n # )\n # full_adj.name = 'AreaSum_gpcAdj'\n\n # qstring_s = qstring + ' & IDG < 4'\n # strict = temp_df.query(qstring_s).groupby('GeneID')[area_col].sum()\n # strict.name = ''\n\n qstring_u = qstring + ' & GeneIDCount_All == 1'\n uniq = temp_df.query(qstring_u).groupby('GeneID')[area_col].sum()/normalize\n uniq.name = 'AreaSum_u2g_all'\n\n qstring_u0 = qstring_u + ' & MissedCleavages == 0'\n uniq_0 = temp_df.query(qstring_u0).groupby('GeneID')[area_col].sum()/normalize\n uniq_0.name = 'AreaSum_u2g_0'\n result = pd.concat( (full, uniq, uniq_0), copy=False, axis=1) .fillna(0)\n genes_df = genes_df.merge(result, how='left',\n left_on='GeneID', right_index=True)\n return genes_df",
"title": ""
},
{
"docid": "5b28feb969388d7b4dcf1d1e4decb1b1",
"score": "0.4775404",
"text": "def labels():",
"title": ""
},
{
"docid": "89d4fb23ddc860e94cff93a8d809f3de",
"score": "0.47730404",
"text": "def sensitivity(edges, f_dists):\n\n nodes = np.arange(max(edges[0]) + 1)\n # It is unlikely, that a node has no edges.\n # For data with many geographic outliers use\n # nodes = list(set(edges[0]))\n # to get unique nodes (and adjust code below)\n denominator = np.zeros(nodes.shape)\n for i, d in enumerate(f_dists):\n denominator[edges[0][i]] = denominator[edges[0][i]] + d\n\n return 1 - f_dists / denominator[edges[0]]",
"title": ""
},
{
"docid": "3472ebdce72306781febcf52735c4e6b",
"score": "0.47722837",
"text": "def plot_SpecSubsetConc_comparisons():\n\n # --- Datasets to use?\n RunDict = {}\n RunDict['Iso.Unlimited'] = '/mnt/lustre/users/ts551/GC/rundirs/P_ARNA/geosfp_4x5_aciduptake.v12.9.0.ARNA.Isotherm.Diags.v9.Iso.UnlimitedAll/OutputDir/'\n\n RunDict['Base'] = '/mnt/lustre/users/ts551/GC/rundirs/P_ARNA/geosfp_4x5_aciduptake.v12.9.0.ARNA.Isotherm.Diags.v9.Base/OutputDir/'\n\n # Get IGAC runs\n res = '4x5'\n GC_version ='v13.4'\n RunSet = 'IGAC.ARNAv12.72L'\n folder4netCDF = True\n RunDict = ar.get_dict_of_GEOSChem_model_output(RunSet=RunSet,\n GC_version=GC_version,\n res=res,\n folder4netCDF=folder4netCDF)\n\n # --- ARNA\n campaign = 'ARNA'\n dsD = {}\n for key in RunDict.keys():\n folder = RunDict[ key ]\n print(key, folder)\n # Get the data of species to plot\n # HNO2, NOx, NO, NO2, CO, ozone,\n data = get_model_noon_vertical_conc(folder=folder, campaign=campaign)\n\n dsD[key] = data\n\n\n # Setup a PDF to store values\n# savetitle = 'ARNA_SpecConcSub_Noon_mean'\n savetitle = 'ARNA_SpecConcSub_daytime_mean'\n if campaign == 'FIREX-AQ' :\n savetitle = 'ARNA_FIREXAQ_SpecConcSub_daytime_mean'\n# savetitle = 'ARNA_FIREXAQ_SpecConcSub_noon_mean'\n\n pdff = AC.plot2pdfmulti(title=savetitle, open=True, dpi=dpi)\n\n\n # Units to use?\n units_d = {\n 'CO': 'ppbv', 'O3': 'ppbv', 'NO2': 'pptv', 'NO': 'pptv', 'NOx': 'pptv',\n 'HNO2': 'pptv', 'HONO': 'pptv',\n 'NIT': 'pptv', 'NITs': 'pptv', 'SO4s': 'pptv', 'SO4': 'pptv',\n 'NH4': 'pptv',\n 'SO4-all': 'pptv', 'NIT-all': 'pptv', 'SAL-all': 'pptv',\n }\n\n # Plot up\n vars2plot = 'O3', 'HNO2', 'NOx', 'NO', 'NO2', 'CO', 'SAL-all', 'NIT-all'\n for var2plot in vars2plot:\n\n # Loop model data\n for key in dsD.keys():\n # Get units and scaling\n units = units_d[var2plot]\n scaleby = AC.get_unit_scaling(units)\n\n # Select data to plot\n X = dsD[key][var2plot] * scaleby\n\n# Y = dsD[key].lev\n PressVar = 'Met_PMID'\n Y = dsD[key][PressVar].values\n Y = AC.hPa_to_Km(Y)\n\n # plot up\n plt.plot(X, Y, label=key)\n\n\n # invert y axis\n ax = plt.gca()\n# ax.invert_yaxis()\n\n # Set x scale as log\n ax.set_xscale('log')\n\n # Add a second axis in kilometres?\n# AC.hPa2Km\n\n\n # plot\n plt.legend()\n\n # Beautify plot\n plt.title( var2plot)\n\n # Save to PDF\n AC.plot2pdfmulti(pdff, savetitle, dpi=dpi, tight=True)\n plt.close()\n\n # Save entire pdf\n AC.plot2pdfmulti(pdff, savetitle, close=True, dpi=dpi)\n plt.close('all')",
"title": ""
},
{
"docid": "a8a9d968e9a3cf1a41399dd125e2704a",
"score": "0.47696343",
"text": "def denormalize_ged(g1, g2, nged):\n return round(nged * (g1.num_nodes + g2.num_nodes) / 2)",
"title": ""
},
{
"docid": "972d25dc5cf8e97b18e15f3048f95c09",
"score": "0.4769071",
"text": "def cal_CN(self):\n\n df_edge_list_reverse = pd.DataFrame()\n df_edge_list_reverse['source'] = self.df_edge_list['target']\n df_edge_list_reverse['target'] = self.df_edge_list['source']\n\n df_all_nodes_pair = pd.concat([df_edge_list_reverse, self.df_edge_list])\n\n \"\"\"\n get common neighbours\n \"\"\"\n\n df_common_neighbor = pd.merge(df_all_nodes_pair, df_all_nodes_pair, on=['target'], how='left').dropna()\n df_common_neighbor = df_common_neighbor[df_common_neighbor['source_x'] != df_common_neighbor['source_y']]\n df_common_neighbor_count = df_common_neighbor.groupby(['source_x', 'source_y']).count()\n df_common_neighbor_count = df_common_neighbor_count.reset_index()\n\n df_common_neighbor_count.rename(columns={'target': 'similarity'}, inplace=True)\n df_common_neighbor_count.rename(columns={'source_x': 'source', 'source_y': 'target'}, inplace=True)\n return df_common_neighbor_count",
"title": ""
},
{
"docid": "f20e82bed113b663a573b26a9b4847c9",
"score": "0.47685957",
"text": "def calculate_fraction(self, labels):\n return sum(labels) / len(labels)",
"title": ""
},
{
"docid": "dcc409643cf18370c9b43b7b19c17b3e",
"score": "0.4765537",
"text": "def get_alternative_domains(self):\n domain1 = []\n domain2 = []\n for i, p in enumerate(self.coords):\n # the two adjacent vertices and the middle points with them\n q1 = self.coords[(i + 1) % len(self.coords)]\n q2 = self.coords[i - 1]\n m1 = helpers.normalize((p + q1) / 2)\n m2 = helpers.normalize((p + q2) / 2)\n\n if self.type:\n if (len(self.word) + i) % 2 == 0:\n domain1.append((m1, p, m2, self.center))\n else:\n domain2.append((m1, p, m2, self.center))\n\n else:\n if len(self.word) % 2 == 0:\n domain1.append((m1, p, self.center))\n domain2.append((m2, p, self.center))\n else:\n domain1.append((m2, p, self.center))\n domain2.append((m1, p, self.center))\n\n return domain1, domain2",
"title": ""
},
{
"docid": "a6d4c3acd1e400b1d5e3ced958cf4292",
"score": "0.47535765",
"text": "def apply_LDA(Nchoose, features, labels, train_set, test_set, test_subsets, Nbp, \n acc_thresh=None, topX=5, plot=True):\n Nsubsets = test_subsets.shape[-1]\n colour_inds = np.array(get_colour_inds(Nbp, Nchoose=Nchoose))\n accuracy = np.zeros((len(colour_inds), Nsubsets))\n for ii in range(len(colour_inds)):\n X_train = features[train_set,:][:, colour_inds[ii]]\n Y_train = labels[train_set]\n X_train = StandardScaler().fit_transform(X_train)\n# print(colour_inds[ii],X_train.shape, Y_train.shape)\n\n lda = LinearDiscriminantAnalysis(store_covariance=True)\n lda.fit(X_train, Y_train).transform(X_train)\n for jj in range(Nsubsets):\n X_test = features[test_subsets[:,jj],:][:, colour_inds[ii]]\n X_test = StandardScaler().fit_transform(X_test)\n Y_test = labels[test_subsets[:,jj]]\n lda_pred = lda.predict(X_test)\n lda_wrong = (abs(lda_pred-Y_test)>0)\n# print(\"test{}: Nwrong={}, {}, {}\".format(jj, lda_wrong.sum(), lda_pred[lda_wrong], Y_test[lda_wrong]))\n accuracy[ii, jj] = 1.-(lda_wrong.sum()/float(X_test.shape[0]))\n# print(\"Nchoose={}, avg false positive:{}\".format(Nchoose, \n# np.nanmean(accuracy, axis=1)))\n if plot:\n plt.figure(figsize=(6,4))\n plt.hist(np.nanmean(accuracy, axis=1))\n plt.axvline(acc_thresh, ls='--', color='C1', label='Threshold')\n plt.legend()\n plt.xlabel('Avg Accuracy for {} filters'.format(Nchoose))\n# good = (np.nanmean(accuracy, axis=1)>=acc_thresh) \n# good = good | (np.nanmean(accuracy, axis=1) == np.nanmax(np.nanmean(accuracy, axis=1)))\n if acc_thresh is not None:\n good = (np.nanmean(accuracy, axis=1)>=acc_thresh) \n else:\n good = np.argsort(np.nanmean(accuracy, axis=1))[::-1][:topX]\n# print(\"Best filters={}; Avg Accuracy={}; Std={}\".format(colour_inds[good], np.nanmean(accuracy, axis=1)[good], \n# np.nanstd(accuracy, axis=1)[good]))\n return colour_inds[good], np.nanmean(accuracy, axis=1)[good], \\\n np.nanstd(accuracy, axis=1)[good]",
"title": ""
},
{
"docid": "ef52c89c73626b4e5db05a3c6b5b62a5",
"score": "0.4749354",
"text": "def plot_rejection_classification_diagram(in_probs,\n in_labels,\n out_probs,\n num_thresholds=100,\n plot_theoretical_max=True,\n ax=None,\n **plot_kwargs):\n in_probs = np.array(in_probs)\n out_probs = np.array(out_probs)\n in_probs, _ = verify_probability_shapes(in_probs)\n out_probs, _ = verify_probability_shapes(out_probs)\n\n in_labels = np.array(in_labels)\n percents_rejected = np.linspace(0.0, 0.99, num=num_thresholds)\n # Iterate over fine-grained thresholds to find closest corresponding threshold\n thresholds = np.linspace(0.0, 1.0, num=100 * num_thresholds)\n in_confidences = np.max(in_probs, axis=1)\n out_confidences = np.max(out_probs, axis=1)\n confidences = np.hstack([in_confidences, out_confidences])\n in_predictions = np.argmax(in_probs, axis=1)\n in_predictions_correct = (in_predictions == in_labels)\n accuracies = []\n theoretical_max_accuracies = []\n\n num_in = in_confidences.shape[0]\n num_out = out_confidences.shape[0]\n percent_out = num_out / (num_in + num_out)\n\n # Initialize variables to run through a subset of thresholds for each p\n # instead of running through the entire range of thresholds.\n init_threshold = 0\n final_threshold = len(thresholds)\n\n for p in percents_rejected:\n if p < percent_out:\n theoretical_max_accuracies.append(\n num_in / ((1. - p) * (num_in + num_out)))\n else:\n theoretical_max_accuracies.append(1.0)\n for i in range(init_threshold, final_threshold):\n threshold = thresholds[i]\n p_at_threshold = np.sum(confidences < threshold) / (num_in + num_out)\n if p_at_threshold >= p:\n num_in_above_p = np.sum(in_confidences >= threshold)\n num_out_above_p = np.sum(out_confidences >= threshold)\n num_in_above_p_correct = np.sum(\n in_predictions_correct[in_confidences >= threshold])\n\n accuracies.append((num_in_above_p_correct + 1e-6) /\n (num_in_above_p + num_out_above_p + 1e-6))\n # For future percents_rejected, only look at thresholds greater than i\n init_threshold = i\n break\n\n if ax is None:\n ax = plt.gca()\n ax.plot(percents_rejected, accuracies, **plot_kwargs)\n if plot_theoretical_max:\n ax.plot(percents_rejected, theoretical_max_accuracies,\n label='Theoretical maximum', color='g')\n ax.set_xlabel(r'Percent of data rejected by uncertainty')\n ax.set_ylabel(r'Accuracy')\n ax.set_title('Rejection Classification Diagram', fontsize=20)\n ax.set_xlim([0.0, 1.0])\n return ax",
"title": ""
},
{
"docid": "5096a0bf90e34a0af6b2bd27d8d0eb14",
"score": "0.47418585",
"text": "def cal_dcmi_scores(self, mean=True, n_neighbors=3):\n train, test = self.create_mutualinfo_matrix(mean=mean,\n n_neighbors=n_neighbors)\n d = (metrics.disentanglement_score(train) +\n metrics.disentanglement_score(test)) / 2.\n c = (metrics.completeness_score(train) +\n metrics.completeness_score(test)) / 2.\n return d, c",
"title": ""
},
{
"docid": "e30876c485fd9b58413bf79253b880cc",
"score": "0.4741051",
"text": "def domain_shape(self):\n return self._domain_shape()",
"title": ""
},
{
"docid": "ccce7b64bb380d490e3d77bf33d8997f",
"score": "0.47359517",
"text": "def test_edge_confidence(self):\r\n\r\n def extraction(Xs, Ys, mask):\r\n return mask[Ys, Xs].mean()\r\n\r\n # Create a regular dummy mask\r\n mask = np.arange(25).reshape([5, 5]).repeat(5, axis=0).repeat(5, axis=1)\r\n mask[12, 12] = 25 # add an iregular center\r\n\r\n # make the object with this mask\r\n obj = segment_group(mask)\r\n\r\n # get the mask with only edges\r\n edge_mask = obj._outline()\r\n\r\n # assign the edge confidences\r\n obj.edge_confidence(extraction, [edge_mask])\r\n\r\n # test they are all one\r\n for seg in obj.seg_dict.values():\r\n for edge_val in seg.conf_dict.values():\r\n assert (edge_val == 1.), 'every edge pixel averaged should just be 1'",
"title": ""
},
{
"docid": "b9816a32499f54c478fb9ea890115b26",
"score": "0.47324613",
"text": "def kl_divergence_annual_compare(flow_dataset: xr.Dataset, sites: list,\n raw_var: str, raw_name: str,\n ref_var: str, ref_name: str,\n bc_vars: list, bc_names: list,\n plot_colors: list, title = \"Annual KL Diveregence Before/After Bias Correction\",\n fontsize_title = 40, fontsize_tick = 30, fontsize_labels = 40,\n fontsize_legend = 30,\n showfliers = False, sharex = True, sharey = 'row', TINY_VAL = 1e-6,\n figsize = (30,20), show_y_grid = True):\n\n raw_flows = flow_dataset[raw_var].to_pandas()\n ref_flows = flow_dataset[ref_var].to_pandas()\n bc_flows = list()\n for bc_var in bc_vars:\n bc_flows.append(flow_dataset[bc_var].to_pandas())\n\n WY_grouper = calc_water_year(raw_flows)\n WY_array = np.arange(WY_grouper[0], WY_grouper[-1], 1)\n\n n_rows, n_cols = determine_row_col(len(sites))\n fig, axs = plt.subplots(n_rows, n_cols, figsize = figsize, sharex = sharex, sharey = sharey)\n axs_list = axs.ravel().tolist()\n\n kldiv_refraw_annual = pd.DataFrame(index = WY_array, columns = sites)\n kldiv_refbc_annuals = list()\n for bc_var in bc_vars:\n kldiv_refbc_annuals.append(pd.DataFrame(index = WY_array, columns = sites))\n\n plt.suptitle(title, fontsize = fontsize_title, y = 1.05)\n\n for WY in WY_array:\n raw_flow_WY = raw_flows[f\"{WY}-10-01\":f\"{WY+1}-09-30\"]\n ref_flow_WY = ref_flows[f\"{WY}-10-01\":f\"{WY+1}-09-30\"]\n bc_flow_WYs = list()\n for bc_flow in bc_flows:\n bc_flow_WYs.append(bc_flow[f\"{WY}-10-01\":f\"{WY+1}-09-30\"])\n total_bins = int(np.sqrt(len(raw_flow_WY.index)))\n\n for site in sites:\n ref_WY_site_vals = ref_flow_WY[site].values\n raw_WY_site_vals = raw_flow_WY[site].values\n bc_WY_site_vals = list()\n for bc_flow_WY in bc_flow_WYs:\n bc_WY_site_vals.append(bc_flow_WY[site].values)\n\n ref_WY_site_pdf, ref_WY_site_edges = np.histogram(ref_WY_site_vals, bins = total_bins,\n density = True)\n raw_WY_site_pdf = np.histogram(raw_WY_site_vals, bins = ref_WY_site_edges, density = True)[0]\n bc_WY_site_pdfs = list()\n for bc_WY_site_val in bc_WY_site_vals:\n bc_WY_site_pdf = np.histogram(bc_WY_site_val, bins = ref_WY_site_edges, density = True)[0]\n bc_WY_site_pdf[bc_WY_site_pdf == 0] = TINY_VAL\n bc_WY_site_pdfs.append(bc_WY_site_pdf)\n\n ref_WY_site_pdf[ref_WY_site_pdf == 0] = TINY_VAL\n raw_WY_site_pdf[raw_WY_site_pdf == 0] = TINY_VAL\n\n kldiv_refraw_annual.loc[WY][site] = scipy.stats.entropy(pk = raw_WY_site_pdf, qk = ref_WY_site_pdf)\n for i, (kldiv_refbc_annual, bc_WY_site_pdf) in enumerate(zip(kldiv_refbc_annuals, bc_WY_site_pdfs)):\n kldiv_refbc_annual.loc[WY][site] = scipy.stats.entropy(pk = bc_WY_site_pdf, qk = ref_WY_site_pdf)\n kldiv_refbc_annuals[i] = kldiv_refbc_annual\n\n plot_labels = [raw_name]\n plot_labels.extend(bc_names)\n\n for i, site in enumerate(sites):\n ax=axs_list[i]\n plot_vals = [kldiv_refraw_annual[site].values]\n plot_vals.extend([kldiv_refbc_annual[site].values for kldiv_refbc_annual in kldiv_refbc_annuals])\n box_dict = ax.boxplot(plot_vals, patch_artist = True, showfliers = showfliers, widths = 0.8, notch = True)\n for item in ['boxes', 'fliers', 'medians', 'means']:\n for sub_item, color in zip(box_dict[item], plot_colors):\n plt.setp(sub_item, color = color)\n ax.set_title(site, fontsize = fontsize_labels)\n ax.set_xticks(np.arange(1, len(plot_labels)+1))\n ax.set_xticklabels(plot_labels, fontsize = fontsize_tick, rotation = 45)\n ax.tick_params(axis = 'both', labelsize = fontsize_tick)\n if show_y_grid:\n ax.grid(which = 'major', axis = 'y', alpha = 0.5)\n\n # gets rid of any spare axes\n i += 1\n while i < len(axs_list):\n axs_list[i].axis('off')\n i += 1\n # ensures last axes is off to make room for the legend\n axs_list[-1].axis('off')\n\n fig.text(-0.04, 0.5, \"Annual KL Divergence\",\n va = 'center', rotation = 'vertical', fontsize = fontsize_labels);\n\n fig.text(0.5, -0.04, r'$KL(P_{' + f'{ref_name}' + r'} || P_{scenario})$',\n va = 'bottom', ha = 'center', fontsize = fontsize_labels);\n\n axs_list[-1].legend(handles=custom_legend(names=plot_labels, colors = plot_colors),\n fontsize = fontsize_legend, loc = 'center')\n\n plt.tight_layout()\n\n return fig, axs",
"title": ""
},
{
"docid": "f9467ee4949403a5c45ea5eda99ec413",
"score": "0.47255844",
"text": "def computing_active_inactive_ratio(df,prot_id):\n counting = df.loc[:,[\"DeepAffinity Protein ID\", \"label\"]].groupby([\"DeepAffinity Protein ID\", \"label\"]).size()\n count_df = counting.to_frame(name = 'size').reset_index()\n count_prot = count_df.loc[count_df[\"DeepAffinity Protein ID\"]==prot_id, :]\n if count_prot.shape[0] == 0:\n return np.nan\n n_labels = count_prot.shape[0]\n if n_labels == 1:\n label_label = count_prot[\"label\"].values[0]\n if label_label == 1.0:\n ratio = 1.0\n else:\n ratio = 0.0\n else:\n n_actives = count_prot.loc[count_prot[\"label\"]==1.0, \"size\"].values[0]\n n_total = n_actives + count_prot.loc[count_prot[\"label\"]==0.0, \"size\"].values[0]\n ratio = n_actives/n_total\n return ratio",
"title": ""
},
{
"docid": "539afbca79c6f97b876e8a062a2ef684",
"score": "0.47239718",
"text": "def deltaCon(A1, A2, g):\n vcount = A1.shape[0] # the number of nodes\n\n # random partition\n partitions = dict()\n for i in range(g):\n partitions[i] = np.zeros(vcount)\n for idx, membership in enumerate(np.random.choice(range(g), vcount)):\n partitions[membership][idx] += 1\n \n for i in range(g):\n s0 = partitions[i]\n s1 = fastBeliefPropagation(A1, s0)\n s2 = fastBeliefPropagation(A2, s0)\n if i == 0:\n S1 = s1.reshape((-1, 1))\n S2 = s1.reshape((-1, 1))\n else:\n S1 = np.concatenate((S1, s1.reshape((-1, 1))), axis=1)\n S2 = np.concatenate((S2, s2.reshape((-1, 1))), axis=1)\n\n\n similarity = 1 / (1 + __rootED(S1, S2))\n return similarity",
"title": ""
},
{
"docid": "deb588ef60c8f89d4e5405e1280691e6",
"score": "0.47206712",
"text": "def _compute_conservative_assignment_and_score_lists(\n confidence_df,\n assignment_df,\n label_graph,\n label_to_name,\n og,\n blacklist_labels=None\n ):\n print('Adjusting results to compute conservative metrics.')\n # Instantiate list of blacklisted labels\n if blacklist_labels is None:\n blacklist_labels = set()\n\n # Map each item to its set of annotated labels\n item_to_labels = {\n item: [\n label\n for label in assignment_df.columns\n if assignment_df.loc[item][label]\n ]\n for item in assignment_df.index\n }\n\n # Map each item to its most-specific labels\n print(\"Mapping items to their most-specific labels...\")\n all_labels = set(label_graph.get_all_nodes())\n item_to_ms_labels = {}\n for item, labels in item_to_labels.items():\n ms_item_labels = label_graph.most_specific_nodes(\n set(labels) & all_labels\n )\n ms_item_labels = ms_item_labels - blacklist_labels\n item_to_ms_labels[item] = ms_item_labels\n print(\"done.\")\n\n # Create new nodes in the label graph corresponding to \n # joint-labels -- that is, sets of labels for which there\n # exists a sample labelled with both labels. For example,\n # if an experiment is labelled with both 'PBMC' and\n # 'T cell', then we create a new label 'PBMC & T cell'\n mod_label_graph = label_graph.copy()\n mod_label_to_name = {\n label: name\n for label, name in label_to_name.items()\n }\n\n # Create all joint-nodes\n item_to_new_ms_labels = defaultdict(lambda: set())\n for item, ms_labels in item_to_ms_labels.items():\n # Create a joint label\n if len(ms_labels) > 1:\n joint_label = frozenset(ms_labels)\n mod_label_to_name[joint_label] = \" & \".join([\n mod_label_to_name[ms_label]\n for ms_label in ms_labels\n ])\n item_to_new_ms_labels[item].add(joint_label)\n # Create from the joint label to the labels that \n # it includes.\n for ms_label in ms_labels:\n mod_label_graph.add_edge(ms_label, joint_label)\n print(\"Created joint label '%s' (%s)\" % (\n mod_label_to_name[joint_label],\n joint_label\n ))\n\n # Make a 'deep' copy of the mappings from experiments to most-specific \n # labels. Then recompute the most-specific labels and predictions now \n # that we have added these new join-labels\n mod_item_to_ms_labels = {\n item: set(ms_labels)\n for item, ms_labels in item_to_ms_labels.items()\n }\n for item, new_ms_labels in item_to_new_ms_labels.items():\n mod_item_to_ms_labels[item].update(new_ms_labels)\n mod_item_to_ms_labels = {\n item: mod_label_graph.most_specific_nodes(labels)\n for item, labels in mod_item_to_ms_labels.items()\n }\n\n # If the sample is most-specifically labeled as PBMC, then\n # for our purposes, we treat mononuclear cell as its most\n # specific label \n item_to_ms_labels = mod_item_to_ms_labels\n for item, ms_labels in item_to_ms_labels.items():\n if PBMC_TERM_ID in ms_labels:\n ms_labels.add(\n MONONUCLEAR_CELL_TERM_ID\n ) \n\n # For each item, get all of the ancestors of all descendants\n # of it's most-specific labels\n item_to_anc_desc_ms_labels = {}\n for item, ms_labels in item_to_ms_labels.items():\n desc_ms_labels = set()\n for ms_label in ms_labels:\n desc_ms_labels.update(\n mod_label_graph.descendent_nodes(ms_label)\n )\n anc_desc_ms_labels = set()\n for desc_ms_label in desc_ms_labels:\n anc_desc_ms_labels.update(\n mod_label_graph.ancestor_nodes(desc_ms_label)\n )\n # Make sure that the item's labels are not included in\n # this set\n anc_desc_ms_labels = anc_desc_ms_labels - set(item_to_labels[item])\n item_to_anc_desc_ms_labels[item] = anc_desc_ms_labels\n\n # Iterate over all labels and construct the list of assignment-values \n # (True or False) and classifier-produced confidence-scores for only the \n # set of items that are relevant for computing the conservative-metrics\n # for the label\n skipped_pairs = set()\n pair_to_items = defaultdict(lambda: set())\n label_to_cons_assigneds = {}\n label_to_cons_scores = {}\n for curr_label in set(all_labels) & set(assignment_df.columns):\n print(\"Examining label %s\" % og.id_to_term[curr_label].name)\n\n # Assignment-values for this label\n filtered_assignments = []\n\n # Classifier-scores for this label\n filtered_scores = []\n\n # The set of items not considered for this label\n skipped_items = set()\n\n # Ancestors of the current label\n anc_labels = set(mod_label_graph.ancestor_nodes(curr_label)) - set([curr_label])\n\n # Iterate over each item and determine whether it should be included\n # in the computation of curr_label's metrics\n for item in assignment_df.index:\n assigned = assignment_df.loc[item][curr_label]\n score = confidence_df.loc[item][curr_label] \n ms_labels = item_to_ms_labels[item]\n anc_desc_ms_labels = item_to_anc_desc_ms_labels[item]\n # NOTE this is the crucial step in which we skip\n # samples that have a most-specific label that is\n # an ancestor of the current label or an ancestor\n # of a descendent of the current label\n if len(set(ms_labels) & anc_labels) > 0:\n for ms_label in set(ms_labels) & set(anc_labels):\n pair = (ms_label, curr_label)\n skipped_pairs.add(pair)\n pair_to_items[pair].add(item)\n skipped_items.add(item)\n continue\n if curr_label in anc_desc_ms_labels:\n skipped_items.add(item)\n continue\n filtered_assignments.append(assigned)\n filtered_scores.append(score)\n label_to_cons_assigneds[curr_label] = filtered_assignments\n label_to_cons_scores[curr_label] = filtered_scores\n print(\"Label %s\" % label_to_name[curr_label])\n print(\"N samples in ranking: %d\" % len(filtered_assignments))\n print(\"N skipped: %d\" % len(skipped_items))\n print(\"Sample of skipped %s\" % list(skipped_items)[0:20])\n print()\n label_to_assigneds = dict(label_to_cons_assigneds)\n label_to_scores = dict(label_to_cons_scores) \n\n # Print some data on which samples were filtered from this analysis\n filtering_da = [\n (\n og.id_to_term[pair[0]].name, \n og.id_to_term[pair[1]].name, \n len(pair_to_items[pair])\n )\n for pair in skipped_pairs\n ]\n filtering_df = pd.DataFrame(\n data = filtering_da,\n columns = ['', '', 'Number of samples removed'] # TODO\n )\n return label_to_assigneds, label_to_scores",
"title": ""
},
{
"docid": "074445f34e39f85a2fe49772123013b9",
"score": "0.47205225",
"text": "def _discr_rdf(dissimilarities, labels):\n # calculates test statistics distribution\n rdfs = []\n\n for i, label in enumerate(labels):\n di = dissimilarities[i]\n\n # All other samples except its own label\n idx = labels == label\n Dij = di[~idx]\n\n # All samples except itself\n idx[i] = False\n Dii = di[idx]\n\n rdf = [1 - ((Dij < d).sum() + 0.5 * (Dij == d).sum()) / Dij.size for d in Dii]\n rdfs.append(rdf)\n\n out = np.full((len(rdfs), max(map(len, rdfs))), np.nan)\n for i, rdf in enumerate(rdfs):\n out[i, : len(rdf)] = rdf\n\n return out",
"title": ""
},
{
"docid": "eb4fed8132c6844801b3aeba8b7e1e03",
"score": "0.47031784",
"text": "def iou_binary(preds, labels, EMPTY=1., ignore=None, per_image=True):\n if not per_image:\n preds, labels = (preds,), (labels,)\n ious = []\n for pred, label in zip(preds, labels):\n intersection = ((label == 1) & (pred == 1)).sum()\n union = ((label == 1) | ((pred == 1) & (label != ignore))).sum()\n if not union:\n iou = EMPTY\n else:\n iou = float(intersection) / union\n ious.append(iou)\n iou = mean(ious) # mean accross images if per_image\n return 100 * iou",
"title": ""
},
{
"docid": "4b7ddb2f81aee58175dd6397516b12ec",
"score": "0.47011453",
"text": "def calc_area(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "b1e43d2554fb301836572f27cfae4eb5",
"score": "0.4694676",
"text": "def area(self):\n if self.point_1[1] == self.point_2[1]:\n A = self.point_1 \n D = self.point_2\n B = self.point_3\n C = self.point_4\n elif self.point_1[1] == self.point3[1] :\n A = self.point_1\n D = self.point_3\n B = self.point_2\n C = self.point_4\n else:\n A = self.point_1\n D = self.point_4\n B = self.point_2\n C = self.point_3\n \n base_1 = A[0] + D[0]\n base_2 = B[0] + C[0]\n if C[1] > D[1]:\n height = C[1] - D[1]\n else:\n height = D[1] - C[1]\n return height * ((base_1 + base_2)/2)",
"title": ""
},
{
"docid": "89c20d1a0df39f477d826685f9c3b292",
"score": "0.46939436",
"text": "def ki_67_percentage(mask_positive, mask_negative):\n total_area = mask_positive.shape[0] * mask_positive.shape[1]\n area_positive = np.sum(mask_positive)\n visible_area_negative = np.sum(mask_negative)\n visible_area_negative_percentage = visible_area_negative / (total_area - area_positive)\n hidden_area_negative = visible_area_negative_percentage * area_positive\n area_negative = visible_area_negative + hidden_area_negative\n return area_positive / (area_positive + area_negative)",
"title": ""
},
{
"docid": "a93c6da28e50217b0827a055452d84d6",
"score": "0.46878663",
"text": "def test_DomainArchitecture(self):\r\n #Because we short circult interation, must close handle explicitly\r\n handle = open(EX_APAF)\r\n tree = next(PhyloXMLIO.parse(handle))\r\n handle.close()\r\n clade = tree.clade[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\r\n darch = clade.sequences[0].domain_architecture\r\n self.assertTrue(isinstance(darch, PX.DomainArchitecture))\r\n self.assertEqual(darch.length, 1249)\r\n for domain, start, end, conf, value in zip(darch.domains,\r\n (6, 109, 605, 647, 689, 733, 872, 993, 1075, 1117, 1168),\r\n (90, 414, 643, 685, 729, 771, 910, 1031, 1113, 1155, 1204),\r\n (7.0e-26, 7.2e-117, 2.4e-6, 1.1e-12, 2.4e-7, 4.7e-14, 2.5e-8,\r\n 4.6e-6, 6.3e-7, 1.4e-7, 0.3),\r\n ('CARD', 'NB-ARC', 'WD40', 'WD40', 'WD40', 'WD40', 'WD40',\r\n 'WD40', 'WD40', 'WD40', 'WD40')):\r\n self.assertTrue(isinstance(domain, PX.ProteinDomain))\r\n self.assertEqual(domain.start + 1, start)\r\n self.assertEqual(domain.end, end)\r\n self.assertAlmostEqual(domain.confidence, conf)\r\n self.assertEqual(domain.value, value)",
"title": ""
},
{
"docid": "014acbd2d3e65672d0cda2f2b360b8b4",
"score": "0.46777096",
"text": "def survey_overlap(xs, ys, xs1, ys1, c_graph = True, radius = -0.1, nbins = 1000):\n\n\timport numpy as np\n\timport matplotlib.pyplot as plt\n\n\txwalls = np.linspace( min(xs) - 5.0, max(xs) + 5.0, nbins + 1 )\n\tywalls = np.linspace( min(ys) - 5.0, max(ys) + 5.0, nbins + 1 )\n\n\tim, xs_bin, ys_bin, ax = plt.hist2d(xs, ys, bins = (xwalls, ywalls) )\n\txs_mids = 0.5*(xs_bin[:-1] + xs_bin[1:])\n\tys_mids = 0.5*(ys_bin[:-1] + ys_bin[1:])\n\tplt.close()\n\tim[im>0] = 1\n\tconts = plt.contour(xs_mids, ys_mids, im.T, 1)\n\t\n\txwalls1 = np.linspace( min(xs1) - 5.0, max(xs1) + 5.0, nbins + 1)\n\tywalls1 = np.linspace( min(ys1) - 5.0, max(ys1) + 5.0, nbins + 1)\n\tim1, xs_bin1, ys_bin1, ax1 = plt.hist2d(xs1, ys1, bins = (xwalls1, ywalls1))\n\txs_mids1 = 0.5*(xs_bin1[:-1] + xs_bin1[1:])\n\tys_mids1 = 0.5*(ys_bin1[:-1] + ys_bin1[1:])\n\tplt.close()\n\tim1[im1>0] = 1\n\n\tconts = plt.contour(xs_mids, ys_mids, im.T, 1)\n\tconts1 = plt.contour(xs_mids1, ys_mids1, im1.T, 1)\n\tplt.show()",
"title": ""
},
{
"docid": "2b399fb557d69e3b663db4421b17dc2f",
"score": "0.46743822",
"text": "def dstat(genotypes, groups, gind):\n\n num = 0\n den = 0\n snpnum = -1\n\n baba = []\n abba = []\n\n def af(gt, group, gind):\n \"\"\"\n Frequency of the major allele.\n \"\"\"\n gts = [int(gt[i]) for i in gind[group] if gt[i] != \"9\"]\n n_inds = len(gts)\n if n_inds == 0:\n return None\n return float(sum(gts)) / n_inds\n\n for row in genotypes:\n snpnum += 1\n\n w = af(row, groups[0], gind)\n x = af(row, groups[1], gind)\n y = af(row, groups[2], gind)\n z = af(row, groups[3], gind)\n\n if None in (w, x, y, z):\n continue\n\n # Follow the BABA-ABBA convention, as in AdmixTools.\n num += (w - x)*(y - z)\n den += (w + x - 2*w*x)*(y + z - 2*y*z)\n\n if (abs(w-y) < abs(w-x) and abs(w-y) < abs(y-x) and\n abs(w-y) < abs(w-z) and abs(w-y) < abs(y-z) and\n abs(x-z) < abs(w-x) and abs(x-z) < abs(y-x) and\n abs(x-z) < abs(w-z) and abs(x-z) < abs(y-z)):\n baba.append(snpnum)\n elif (abs(x-y) < abs(x-w) and abs(x-y) < abs(y-w) and\n abs(x-y) < abs(x-z) and abs(x-y) < abs(y-z) and\n abs(w-z) < abs(x-w) and abs(w-z) < abs(y-w) and\n abs(w-z) < abs(x-z) and abs(w-z) < abs(y-z)):\n abba.append(snpnum)\n\n try:\n d = num / den\n except ZeroDivisionError:\n d = 0\n\n return d, frozenset(baba), frozenset(abba)",
"title": ""
},
{
"docid": "1ced48af9c8ab780e119bbcb612dcfd7",
"score": "0.4673081",
"text": "def plotDensityMap(scores):\r\n\r\n\r\n TRIANGLE=np.array([[math.cos(math.pi*0.5), math.sin(math.pi*0.5)],\r\n [math.cos(math.pi*1.166), math.sin(math.pi*1.166)],\r\n [math.cos(math.pi*1.833), math.sin(math.pi*1.833)]])\r\n\r\n\r\n pointsX=[score.dot(TRIANGLE)[0] for score in scores]\r\n pointsY=[score.dot(TRIANGLE)[1] for score in scores]\r\n\r\n vertices=[]\r\n vertices.append(np.array([1,0,0]).dot(TRIANGLE))\r\n vertices.append(np.array([0,1,0]).dot(TRIANGLE))\r\n vertices.append(np.array([0,0,1]).dot(TRIANGLE))\r\n for i in range(3):\r\n p1=vertices[i]\r\n if i==2:\r\n p2=vertices[0]\r\n else:\r\n p2=vertices[i+1]\r\n c=0.5*(p1+p2)\r\n plt.plot([p1[0], p2[0]], [p1[1], p2[1]], color='k', linestyle='-', linewidth=2)\r\n plt.plot([0, c[0]], [0, c[1]], color='k', linestyle='-', linewidth=1)\r\n\r\n\r\n\r\n ax=plt.gca()\r\n ax.set_xlim([-1.2, 1.32])\r\n ax.set_ylim([-0.7,1.3])\r\n\r\n ax.text(0.8, -0.6, 'Bipolar')\r\n ax.text(-1.1, -0.6, 'Healthy')\r\n ax.text(-0.15, 1.05, 'Borderline')\r\n\r\n\r\n data=[[pointsX[i], pointsY[i]] for i in range(len(pointsX))]\r\n\r\n H, xedges, yedges=np.histogram2d(pointsX,pointsY,bins=40,normed=True)\r\n norm=H.sum()\r\n contour1=0.75\r\n target1=norm*contour1\r\n def objective(limit, target):\r\n w=np.where(H>limit)\r\n count=H[w]\r\n return count.sum()-target\r\n\r\n level1=scipy.optimize.bisect(objective, H.min(), H.max(), args=(target1,))\r\n levels=[level1]\r\n\r\n data=np.array(data)\r\n #plt.scatter(np.array(pointsX), np.array(pointsY))\r\n sns.kdeplot(np.array(pointsX), np.array(pointsY), shade=True, ax=ax)\r\n sns.kdeplot(np.array(pointsX), np.array(pointsY), n_levels=3, ax=ax, cmap=\"Reds\")\r\n plt.show()",
"title": ""
},
{
"docid": "9ed95984ffb2dba87d4beae82512ced6",
"score": "0.4669756",
"text": "def det_anomaly_score(actual, predicted, dimension):\n actual = grouper(dimension, actual[0])\n predicted = grouper(dimension, predicted[0])\n n_seq = len(actual)\n the_sum = 0\n for item in list(zip(actual, predicted)):\n diff = [a_i - b_i for a_i, b_i in zip(item[0], item[1])]\n the_sum += np.linalg.norm(diff, 2)\n return((1 / n_seq) * the_sum)",
"title": ""
},
{
"docid": "85df212172129682af7cf4a0c6090630",
"score": "0.46675202",
"text": "def py_cpu_nms(dets, probs, thresh):\n # [cx, cy, w, h]\n x1 = dets[:, 0] - dets[:, 2]/2.0\n y1 = dets[:, 1] - dets[:, 3]/2.0\n x2 = dets[:, 0] + dets[:, 2]/2.0\n y2 = dets[:, 1] + dets[:, 3]/2.0\n \n areas = dets[:, 2] * dets[:, 3]\n order = probs.argsort()[::-1]\n keep = []\n keep_ = [False]*len(order)\n while order.size > 0:\n i = order[0]\n # keep.append(i)\n keep_[i] = True\n xx1 = np.maximum(x1[i], x1[order[1:]])\n yy1 = np.maximum(y1[i], y1[order[1:]])\n xx2 = np.minimum(x2[i], x2[order[1:]])\n yy2 = np.minimum(y2[i], y2[order[1:]])\n \n w = np.maximum(0.0, xx2 - xx1 + 1)\n h = np.maximum(0.0, yy2 - yy1 + 1)\n inter = w * h\n ovr = inter / (areas[i] + areas[order[1:]] - inter)\n inds = np.where(ovr < thresh)[0]\n order = order[inds + 1]\n return keep_",
"title": ""
},
{
"docid": "0e54e859951e87e95c8ca58597d2002a",
"score": "0.46670318",
"text": "def cal_HDI(self):\n\n df_edge_list_reverse = pd.DataFrame()\n df_edge_list_reverse['source'] = self.df_edge_list['target']\n df_edge_list_reverse['target'] = self.df_edge_list['source']\n # print df_edge_list_reverse\n\n df_all_nodes_pair = pd.concat([df_edge_list_reverse, self.df_edge_list])\n df_neighbor_count = df_all_nodes_pair.groupby(['source']).count()\n df_neighbor_count = df_neighbor_count.reset_index()\n df_neighbor_count.rename(columns={'target': 'nei_count'}, inplace=True)\n\n \"\"\"\n get common neighbours\n \"\"\"\n\n df_common_neighbor = pd.merge(df_all_nodes_pair, df_all_nodes_pair, on=['target'], how='left').dropna()\n df_common_neighbor = df_common_neighbor[df_common_neighbor['source_x'] != df_common_neighbor['source_y']]\n\n df_common_neighbor_count = df_common_neighbor.groupby(['source_x', 'source_y']).count()\n df_common_neighbor_count = df_common_neighbor_count.reset_index()\n # print df_common_neighbor_count\n\n df_common_neighbor_count.rename(columns={'target': 'CN'}, inplace=True)\n\n df_common_neighbor_with_total_neighbor = pd.merge(df_common_neighbor_count, df_neighbor_count,\n left_on=['source_x'], right_on=['source'], how='left')\n\n df_common_neighbor_with_total_neighbor = df_common_neighbor_with_total_neighbor[\n ['source_x', 'source_y', 'CN', 'nei_count']]\n df_common_neighbor_with_total_neighbor = pd.merge(df_common_neighbor_with_total_neighbor, df_neighbor_count,\n left_on=['source_y'], right_on=['source'], how='left')\n\n df_common_neighbor_with_total_neighbor['nei_min'] = df_common_neighbor_with_total_neighbor[\n ['nei_count_x', 'nei_count_y']].max(axis=1)\n # print df_common_neighbor_with_total_neighbor.head()\n\n df_common_neighbor_with_total_neighbor['similarity'] = df_common_neighbor_with_total_neighbor['CN'] / \\\n df_common_neighbor_with_total_neighbor['nei_min']\n\n df_HDI_list = df_common_neighbor_with_total_neighbor[['source_x', 'source_y', 'similarity']].copy()\n\n df_HDI_list.rename(columns={'source_x': 'source', 'source_y': 'target'}, inplace=True)\n\n # print df_HDI_list.head()\n return df_HDI_list",
"title": ""
},
{
"docid": "a6d0febca310972f4d694ee6e14aaeea",
"score": "0.46629694",
"text": "def label_region(img_size, img_idx, l_stat, c_alpha):\n\n img_sub = np.unravel_index(img_idx, img_size)\n img_tp = np.zeros(shape=img_size)\n img_tp[img_sub] = l_stat\n img_tp[img_tp <= c_alpha] = 0\n img_tp[img_tp > c_alpha] = 1\n\n def label_region_1d(label_img):\n \"\"\"\n Label connected regions and return the corresponding maximum area.\n\n Args:\n label_img (vector): vector or matrix after thresholding\n \"\"\"\n\n idx_roi = np.where(label_img == 1)\n run = []\n group = [run]\n expect = None\n for v in idx_roi:\n if (v == expect) or (expect is None):\n run.append(v)\n else:\n run = [v]\n group.append(run)\n expect = v + 1\n\n cluster_len = np.zeros(len(group))\n for k in range(len(group)):\n cluster_len[k] = len(group[k])\n max_cluster_len = np.max(cluster_len)\n\n return max_cluster_len\n\n def label_region_nd(label_img):\n \"\"\"\n Label connected regions and return the corresponding maximum area.\n\n Args:\n label_img (matrix): vector or matrix after thresholding\n \"\"\"\n\n group = regionprops(label_img)\n cluster_area = np.zeros(len(group))\n for k in range(len(group)):\n cluster_area[k] = group[k].area\n max_cluster_area = np.max(cluster_area)\n\n return max_cluster_area\n\n if img_size[0] == 1:\n max_area = label_region_1d(img_tp)\n else:\n max_area = label_region_nd(img_tp)\n\n return max_area",
"title": ""
},
{
"docid": "818d0f2658adda9cb543fd52fe030b14",
"score": "0.46628585",
"text": "def cal_RA(self):\n df_edge_list_reverse = pd.DataFrame()\n df_edge_list_reverse['source'] = self.df_edge_list['target']\n df_edge_list_reverse['target'] = self.df_edge_list['source']\n\n df_all_nodes_pair = pd.concat([df_edge_list_reverse, self.df_edge_list])\n df_neighbor_count = df_all_nodes_pair.groupby(['source']).count()\n\n df_neighbor_count = df_neighbor_count.reset_index()\n df_neighbor_count.rename(columns={'target': 'count'}, inplace=True)\n \"\"\"\n get common neighbours\n \"\"\"\n\n df_common_neighbor = pd.merge(df_all_nodes_pair, df_all_nodes_pair, on=['target'], how='left').dropna()\n df_common_neighbor = df_common_neighbor[df_common_neighbor['source_x'] != df_common_neighbor['source_y']]\n\n df_common_neighbor = pd.merge(df_common_neighbor, df_neighbor_count, left_on=['target'], right_on=['source'],\n how='left').dropna()\n\n df_common_neighbor = df_common_neighbor[['source_x', 'source_y', 'count']]\n df_common_neighbor['count'] = df_common_neighbor['count'].map(lambda x: 1.0 / x)\n\n df_RA_list = df_common_neighbor.groupby(['source_x', 'source_y']).sum()\n df_RA_list = df_RA_list.reset_index()\n\n df_RA_list.rename(columns={'source_x': 'source', 'source_y': 'target', 'count': 'similarity'}, inplace=True)\n print(df_RA_list.head(10))\n return df_RA_list",
"title": ""
},
{
"docid": "3c33d484f8cbffbd6104a31eef9c5107",
"score": "0.46618295",
"text": "def lengthPerArea(self, _x1, _x2 = 0.5):\r\n if _x2 < _x1:\r\n _temp = _x2\r\n _x2 = _x1\r\n _x1 = _temp\r\n \r\n if self.semiMajor0 == self.semiMajor1:\r\n semiMajor = self.semiMajor0\r\n if self.semiMinor0 == self.semiMinor1:\r\n # cylinder\r\n semiMinor = self.semiMinor0\r\n coneFact = _x2 - _x1\r\n else:\r\n # semi-minor axis is changing, semi-major is constant\r\n semiMinor = 0.5 * (self.semiMinor0 + self.semiMinor0)\r\n minorRatio = semiMinor / (self.semiMinor1 - self.semiMinor0)\r\n coneFact = minorRatio * \\\r\n scipy.log( (minorRatio + _x2 - 0.5) / (minorRatio + _x1 - 0.5) )\r\n if coneFact < 0:\r\n coneFact = -coneFact\r\n else:\r\n semiMajor = 0.5 * (self.semiMajor0 + self.semiMajor0)\r\n majorRatio = semiMajor / (self.semiMajor1 - self.semiMajor0)\r\n if self.semiMinor0 == self.semiMinor1:\r\n # semi-major axis is changing, semi-minor is constant\r\n semiMinor = self.semiMinor0\r\n coneFact = majorRatio * \\\r\n scipy.log( (majorRatio + _x2 - 0.5) / (majorRatio + _x1 - 0.5) )\r\n if coneFact < 0:\r\n coneFact = -coneFact\r\n else:\r\n semiMinor = 0.5 * (self.semiMinor0 + self.semiMinor0)\r\n minorRatio = semiMinor / (self.semiMinor1 - self.semiMinor0)\r\n ratioProd = majorRatio * minorRatio\r\n avgRatio = 0.5 * (minorRatio + majorRatio)\r\n scale1 = (_x1 - 0.5) / ratioProd + avgRatio\r\n scale2 = (_x2 - 0.5) / ratioProd + avgRatio\r\n \r\n if avgRatio > 1.0:\r\n # answer in terms of logs\r\n ratioRoot = scipy.sqrt(avgRatio**2 - 1.0)\r\n scale1 /= ratioRoot\r\n scale2 /= ratioRoot\r\n coneFact = 0.5 * ratioProd / ratioRoot * \\\r\n scipy.log((scale2 - 1) * (scale1 + 1) / ((scale2 + 1) * scale1 - 1))\r\n elif avgRatio < 1.0:\r\n # answer in terms of arctan\r\n ratioRoot = scipy.sqrt(1.0 - avgRatio**2)\r\n scale1 /= ratioRoot\r\n scale2 /= ratioRoot\r\n coneFact = ratioProd / ratioRoot * \\\r\n (scipy.arctan(scale2) - scipy.arctan(scale1))\r\n else:\r\n # answer in terms of 1/x\r\n coneFact = ratioProd / scale1 - ratioProd / scale2\r\n \r\n return coneFact * self.length / (scipy.pi * semiMajor * semiMinor)",
"title": ""
},
{
"docid": "4ee87041b063f88158fba157dcd42505",
"score": "0.46573743",
"text": "def plot_domain_frequency_transitions(self, pct: bool=False):\n return functools.reduce(\n operator.add,\n (\n self.plot_cpu_frequency_transitions(\n cpu=domain[0],\n domain_label=True,\n pct=pct,\n )\n for domain in self.trace.plat_info['freq-domains']\n )\n ).cols(1)",
"title": ""
},
{
"docid": "ae2bd2891bc00720ae57fdb126dc92c0",
"score": "0.46563694",
"text": "def averageClustering(self):\r\n total = 0\r\n for i in self.graphDict:\r\n ''' calculate the number of the pairs of neighbors of node i which are adiacents '''\r\n neighs = len(self.graphDict[i])\r\n pairsNeigh = ( neighs * (neighs-1) )/2 # all pairs\r\n ''' check how many nehgbors of node i have a connection '''\r\n triangles = 0\r\n for j in self.graphDict[i]:\r\n for k in self.graphDict[i]:\r\n if k in self.graphDict[j]:\r\n triangles += 1\r\n if pairsNeigh > 0:\r\n total += float(triangles)/pairsNeigh\r\n \r\n return float(total)/self.numOfVertices()",
"title": ""
},
{
"docid": "c70236f3df7318741c52edc077a09588",
"score": "0.46499154",
"text": "def nms(self, dets, scores):\r\n x1 = dets[:, 0] #xmin\r\n y1 = dets[:, 1] #ymin\r\n x2 = dets[:, 2] #xmax\r\n y2 = dets[:, 3] #ymax\r\n\r\n areas = (x2 - x1) * (y2 - y1) # the size of bbox\r\n order = scores.argsort()[::-1] # sort bounding boxes by decreasing order\r\n\r\n keep = [] # store the final bounding boxes\r\n while order.size > 0:\r\n i = order[0] #the index of the bbox with highest confidence\r\n keep.append(i) #save it to keep\r\n xx1 = np.maximum(x1[i], x1[order[1:]])\r\n yy1 = np.maximum(y1[i], y1[order[1:]])\r\n xx2 = np.minimum(x2[i], x2[order[1:]])\r\n yy2 = np.minimum(y2[i], y2[order[1:]])\r\n\r\n w = np.maximum(1e-28, xx2 - xx1)\r\n h = np.maximum(1e-28, yy2 - yy1)\r\n inter = w * h\r\n\r\n # Cross Area / (bbox + particular area - Cross Area)\r\n ovr = inter / (areas[i] + areas[order[1:]] - inter)\r\n #reserve all the boundingbox whose ovr less than thresh\r\n inds = np.where(ovr <= self.nms_thresh)[0]\r\n order = order[inds + 1]\r\n\r\n return keep",
"title": ""
},
{
"docid": "30b9327dbd2f94d5fb4364901a9416e5",
"score": "0.46436828",
"text": "def calculate_lb_score_per_taxa(\n self,\n avg_PDis:list,\n avg_dist:float\n ) -> list:\n LBis = []\n for PDi in avg_PDis:\n try:\n LBis.append((((PDi/avg_dist)-1)*100))\n except ZeroDivisionError:\n try:\n print(\"Invalid tree. Tree should contain branch lengths\")\n sys.exit()\n except BrokenPipeError:\n pass\n \n return LBis",
"title": ""
},
{
"docid": "7061c4647e3718b65ce6ee85b1c43a97",
"score": "0.4640851",
"text": "def select_node_counts(self):\n self.discretize_perimeter()\n\n # Precalculate density fields along perimeter:\n\n (left_i,left_i_rigid),(right_i,right_i_rigid)=self.calculate_coord_count(self.angle_to_segments[0],\n self.angle_to_segments[180],\n self.perimeter_scales[:,0])\n (left_j,left_j_rigid),(right_j,right_j_rigid)=self.calculate_coord_count(self.angle_to_segments[90],\n self.angle_to_segments[270],\n self.perimeter_scales[:,1])\n\n # Necessary to have order match grid order below\n left_i=left_i[::-1]\n left_i_rigid=left_i_rigid[::-1]\n right_i=right_i[::-1]\n right_i_rigid=right_i_rigid[::-1]\n\n self.left_i=left_i\n self.left_i_rigid=left_i_rigid\n self.left_j=left_j\n self.left_j_rigid=left_j_rigid\n \n self.right_i=right_i\n self.right_i_rigid=right_i_rigid\n self.right_j=right_j\n self.right_j_rigid=right_j_rigid",
"title": ""
},
{
"docid": "31dbe07890d971688a66715d505c2865",
"score": "0.46408102",
"text": "def precision(database_code, database_labels, validation_code, validation_labels, Rs, dist_type='hamming'):\n assert set(np.unique(database_code).tolist()) == set([-1, 1])\n assert set(np.unique(validation_code).tolist()) == set([-1, 1])\n assert len(database_labels.shape) == 2\n assert len(validation_labels.shape) == 2\n \n query_num = validation_code.shape[0]\n \n if dist_type == 'hamming':\n dist = calc_hammingDist(database_code, validation_code)\n ids = np.argsort(dist, axis=0)\n elif dist_type == 'cosine':\n sim = np.dot(database_code, validation_code.T)\n ids = np.argsort(-sim, axis=0)\n else:\n raise Exception('Unsupported distance type: {}'.format(dist_type))\n \n \n APx = {R: [] for R in Rs}\n\n for i in tqdm(range(query_num)):\n label = validation_labels[i]\n idx = ids[:, i]\n imatch = (np.dot(database_labels[idx, :], label) > 0).astype(np.int)\n for R in Rs:\n relevant_num = np.sum(imatch[:R])\n if relevant_num != 0:\n APx[R].append(float(relevant_num) / R)\n \n #Compute 2 types of precisions: one ignores 0-relevant and one includes 0-relevant\n return {R: (np.mean(np.array(APxR)), np.sum(np.array(APxR)) / query_num) for (R, APxR) in APx.items()}",
"title": ""
}
]
d47023e0d7e2286a238bfd7f0b51b634
Computes the mean area of all labeled objects and takes the square root. Gives a length in number of pixels. To get a length in physical units, multiply the output of this function by the physical pixel size.
Get the name of the environment either from current conda environment, from remote directory, or inferred from git repo.
[
{
"docid": "f789b3b2d6563fa62e592b2215c5cb76",
"score": "0.7944121",
"text": "def get_env_name(infer: bool = False) -> str:\n # pylint: disable=redefined-outer-name\n if not infer:\n return get_active_conda_env_name()\n try:\n remote_dir = infer_remote_dir()\n history = EnvIO(env_directory=remote_dir).get_history()\n return history.name\n except CondaEnvTrackerHistoryNotFoundError as err:\n raise CondaEnvTrackerHistoryNotFoundError(\n f\"Cannot infer name from history, often resolved by passing the name argument. Full error: {str(err)}\"\n )",
"title": ""
}
]
[
{
"docid": "09e74824f0c88194610f3ef561ad4b14",
"score": "0.7596804",
"text": "def get_conda_env_path(conn: fabric.Connection, envname: str = 'base') -> pathlib.Path:\n stream = io.StringIO()\n conn.run('conda info --envs --json', replace_env=False, out_stream=stream)\n info = json.loads(stream.getvalue())\n return next(iter(p for p in (pathlib.Path(s) for s in info['envs']) if p.name == envname))",
"title": ""
},
{
"docid": "36476d535564412f8f7170704174b69c",
"score": "0.68478495",
"text": "def environment(self) -> str:\n return pulumi.get(self, \"environment\")",
"title": ""
},
{
"docid": "00dd702087c0b52f8a7481a2ffc3aac8",
"score": "0.683109",
"text": "def get_env():\n if 'test' in basedir:\n return 'test'\n if 'revmic' in basedir:\n return 'prod'\n if 'michael' in basedir:\n return 'dev'\n return 'default'",
"title": ""
},
{
"docid": "ff18d946107a03e6c7160f7a820b1ab2",
"score": "0.6823848",
"text": "def environment_name(self) -> Optional[str]:\n return pulumi.get(self, \"environment_name\")",
"title": ""
},
{
"docid": "7a2da517ccb0e81974290f004bb160e0",
"score": "0.67405933",
"text": "def get_env(env_variable: str) -> str:\n return os.environ.get(env_variable, 'local').upper()",
"title": ""
},
{
"docid": "1e205fa470f17331c63fff4ea738e6de",
"score": "0.6703609",
"text": "def environment_name(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"environment_name\")",
"title": ""
},
{
"docid": "d0bbdbcd0736cd7984536c705f7fecea",
"score": "0.66822726",
"text": "def get_environment():\n os_env = os.environ.get('ENVIRONMENT', '')\n\n if os_env.lower() == 'production':\n env = 'production'\n elif os_env.lower() == 'development':\n env = 'development'\n else:\n env = 'local'\n\n return env",
"title": ""
},
{
"docid": "4de4e2441ebcdab4ebef0d395cee66df",
"score": "0.66553897",
"text": "def get_env():\n return envs[environ.get('FLASK_ENV', default='config')]",
"title": ""
},
{
"docid": "aded0abe03989e1642fa5994323b3659",
"score": "0.6638746",
"text": "def get_environment_name(self):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "0e0de3cb625ac5e59cf57739a67f568f",
"score": "0.65355885",
"text": "def env_name(self):\n return self.env.name",
"title": ""
},
{
"docid": "7c63460c0b0236489496afa97c4eaff9",
"score": "0.6528589",
"text": "def environment(self) -> str:\n return self.__environment",
"title": ""
},
{
"docid": "db99aee86b5dcc6ca4088783d944c591",
"score": "0.64826673",
"text": "def env_info():\n if 'ipykernel' in sys.modules:\n return 'jupyter_notebook'\n elif 'IPython' in sys.modules:\n return 'ipython_terminal'\n else:\n return 'terminal'",
"title": ""
},
{
"docid": "e0b4fd65fbdbcad88fb28dd8bd86d300",
"score": "0.6470066",
"text": "def get_env():\n name = request.args.get('name')\n try:\n lib = __import__(name)\n except ImportError:\n return 'Cannot import {}'.format(name)\n return lib.__name__",
"title": ""
},
{
"docid": "58fc9dc82d14faca97c7ccdd70b82b31",
"score": "0.6432391",
"text": "def get_envs():\n json_str = check_output([\"conda\", \"info\", \"--json\"]).decode()\n info = json.loads(json_str)\n return info['envs']",
"title": ""
},
{
"docid": "919279f384287cf0bcbc3ac9ff7e547f",
"score": "0.6383709",
"text": "def environment(self) -> Optional[pulumi.Input['EnvironmentArgs']]:\n return pulumi.get(self, \"environment\")",
"title": ""
},
{
"docid": "919279f384287cf0bcbc3ac9ff7e547f",
"score": "0.6383709",
"text": "def environment(self) -> Optional[pulumi.Input['EnvironmentArgs']]:\n return pulumi.get(self, \"environment\")",
"title": ""
},
{
"docid": "307fe351cbbc5e39b0a81c631686b62d",
"score": "0.6374366",
"text": "def environment_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"environment_name\")",
"title": ""
},
{
"docid": "307fe351cbbc5e39b0a81c631686b62d",
"score": "0.6374366",
"text": "def environment_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"environment_name\")",
"title": ""
},
{
"docid": "104020a9ae1d2eb674cee222ccd21658",
"score": "0.6367172",
"text": "def get_environment(name):\n return _environments_cache[name]",
"title": ""
},
{
"docid": "d5280cbc06c9ab86382e1ce34a7cfe79",
"score": "0.6324078",
"text": "def environment(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"environment\")",
"title": ""
},
{
"docid": "b5d7e72a1fb24d6376253d1f52777e42",
"score": "0.6287024",
"text": "def get_environment(self, env_name: str) -> Optional[Environment]:\n env_list = self._env_api.environments_get()\n for env in env_list:\n if env.name == env_name:\n return env\n return None",
"title": ""
},
{
"docid": "e78bb8e02987eda34fd901099a8d58b1",
"score": "0.627856",
"text": "def get_env(self):\n return self._env",
"title": ""
},
{
"docid": "155bd3d7ed3f0e394dd67e68c169206e",
"score": "0.6236693",
"text": "def get_virtualenv_path():\n if hasattr(sys, 'real_prefix'):\n return sys.prefix\n\n if hasattr(sys, 'base_prefix') and sys.base_prefix != sys.prefix:\n return sys.prefix\n\n if 'conda' in sys.prefix:\n return sys.prefix\n\n return None",
"title": ""
},
{
"docid": "9155438a6a6755a7394a585138a28175",
"score": "0.6227984",
"text": "def env(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['EnvVarArgs']]]]:\n return pulumi.get(self, \"env\")",
"title": ""
},
{
"docid": "9155438a6a6755a7394a585138a28175",
"score": "0.6227984",
"text": "def env(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['EnvVarArgs']]]]:\n return pulumi.get(self, \"env\")",
"title": ""
},
{
"docid": "1a96f6e673fa2427c1e1a99a5c87e7c1",
"score": "0.6190845",
"text": "def env(self) -> Dict[str, str]:\n return ORIG_ENV",
"title": ""
},
{
"docid": "8b8978366a32caae13415c49ba5744d5",
"score": "0.617872",
"text": "def _GetEnviron(self, name):\n try:\n return os.environ[name]\n except KeyError:\n raise ConfigurationError('%s is not set in environment.' % name)",
"title": ""
},
{
"docid": "86c36db4d8344f8b00c16818391aadbb",
"score": "0.61784595",
"text": "def env(self) -> Optional[Sequence['outputs.StorageClusterSpecAutopilotEnv']]:\n return pulumi.get(self, \"env\")",
"title": ""
},
{
"docid": "968bd71c8f5a0918129a01252a7f237d",
"score": "0.61698496",
"text": "def get_environment_config():\n from ..connect import main as _glconnect\n unity = _glconnect.get_unity()\n return unity.list_globals(False)",
"title": ""
},
{
"docid": "9323201948eb865b124c095efe501b70",
"score": "0.6144131",
"text": "def default_environment():\n\n # NOTE(dittrich): I know this code has multiple return points\n # but it is simpler and easier to understand this way.\n #\n # Highest priority is inhereted environment variable.\n environment = os.getenv('D2_ENVIRONMENT', None)\n if environment is not None:\n return environment\n #\n # Next is saved file in current working directory.\n local_default = get_saved_default_environment()\n if local_default not in ['', None]:\n return local_default\n #\n # Lowest priority is the directory path basename.\n return os.path.basename(os.getcwd())",
"title": ""
},
{
"docid": "9323201948eb865b124c095efe501b70",
"score": "0.6144131",
"text": "def default_environment():\n\n # NOTE(dittrich): I know this code has multiple return points\n # but it is simpler and easier to understand this way.\n #\n # Highest priority is inhereted environment variable.\n environment = os.getenv('D2_ENVIRONMENT', None)\n if environment is not None:\n return environment\n #\n # Next is saved file in current working directory.\n local_default = get_saved_default_environment()\n if local_default not in ['', None]:\n return local_default\n #\n # Lowest priority is the directory path basename.\n return os.path.basename(os.getcwd())",
"title": ""
},
{
"docid": "cdc4ad6d3ade3954be11f15f81ad893f",
"score": "0.6135233",
"text": "def env(self) -> Optional[Sequence['outputs.StorageClusterSpecEnv']]:\n return pulumi.get(self, \"env\")",
"title": ""
},
{
"docid": "b9725402aec524c393e8fc0544f4cd50",
"score": "0.610127",
"text": "def get_default_environment():\n virtual_env = _get_virtual_env_from_var()\n if virtual_env is not None:\n return virtual_env\n\n for environment in find_python_environments():\n return environment",
"title": ""
},
{
"docid": "f31626c66eb31d6d81eb88ad9720e9e2",
"score": "0.6081558",
"text": "def fromEnv(self):\n\t\treturn env.getEnv(self)",
"title": ""
},
{
"docid": "b96d51e4226144f89b3e4aec7d75da01",
"score": "0.6080151",
"text": "def _get_environment(self):\n return self.__environment",
"title": ""
},
{
"docid": "c0b8627ea0176c8cb54f84ccf727ef5c",
"score": "0.60656536",
"text": "def env(self) -> Optional[Sequence['outputs.StorageClusterSpecUserInterfaceEnv']]:\n return pulumi.get(self, \"env\")",
"title": ""
},
{
"docid": "ee3ef732755920c61bc5a3ce95debd4e",
"score": "0.6060195",
"text": "def _get_conda_python(self):\n try:\n m = self._get_conda_manager()\n return m.get_conda_python()\n except ImportError: # modules directory is not available\n\n # -----------------------------------------------------------------------\n # duplicate logic from get_conda_python function in install_conda.py\n # since install_conda.py may not be available\n def m_get_conda_python(self):\n m_conda_python = os.path.join('bin', 'python')\n if self.isPlatformWindows():\n m_conda_python = self.op.join('python.exe')\n elif self.isPlatformMacOSX():\n m_conda_python = os.path.join('python.app', 'Contents',\n 'MacOS', 'python')\n return m_conda_python\n # -----------------------------------------------------------------------\n\n conda_python = None\n\n # (case 1)\n # use default location or file provided to --use-conda\n if self.use_conda == '' or os.path.isfile(self.use_conda):\n conda_python = self.op.join('..', 'conda_base',\n m_get_conda_python(self))\n if self.isPlatformWindows():\n conda_python = self.op.join(os.getcwd(), 'conda_base', m_get_conda_python(self))\n # (case 2)\n # use path provided to --use-conda\n elif os.path.isdir(self.use_conda):\n self.use_conda = os.path.abspath(self.use_conda)\n conda_python = os.path.join(self.use_conda, m_get_conda_python(self))\n else:\n raise RuntimeError(\"\"\"\nThe --use-conda flag can accept a directory to a conda environment or a\nfile that defines a conda environment. Please make sure a valid conda\nenvironment exists in or is defined by {conda_env}.\n\"\"\".format(conda_env=self.use_conda))\n\n if conda_python is None:\n raise RuntimeError('A conda version of python could not be found.')\n\n return conda_python",
"title": ""
},
{
"docid": "e2fc92f03bfb6f7df5d5aa1cb99b784a",
"score": "0.60249835",
"text": "def get_env(name, default=None):\n for var in api.current_actor().configuration.leapp_env_vars:\n if var.name == name:\n return var.value\n return default",
"title": ""
},
{
"docid": "0801a57f74a50729b81423c01540efad",
"score": "0.6014137",
"text": "def get_environment(self):\n return self._environment",
"title": ""
},
{
"docid": "1dfdb4cb3dfe421b77b6d4cd4930b1c6",
"score": "0.6008921",
"text": "def get_env_name(env_var_name) -> str:\r\n virtual_env_path = os.environ.get(env_var_name)\r\n if not virtual_env_path:\r\n return 'default'\r\n else:\r\n return os.path.basename(virtual_env_path)",
"title": ""
},
{
"docid": "4e01be076e55705668fae0d3fd34dcda",
"score": "0.5983337",
"text": "def get_environment_config():\n unity = _glconnect.get_unity()\n return unity.list_globals(False)",
"title": ""
},
{
"docid": "18770a1668c4c20c6933f0b65db36aba",
"score": "0.5965647",
"text": "def _get_env_var(self, name):\n return os.environ[name] if name in os.environ else \"\"",
"title": ""
},
{
"docid": "8b3ebe5104bea2a987453cb999d2107f",
"score": "0.595991",
"text": "def env(self) -> Optional[Sequence['outputs.StorageClusterSpecNodesEnv']]:\n return pulumi.get(self, \"env\")",
"title": ""
},
{
"docid": "1b7243287848f891a6830cc994217b93",
"score": "0.5956502",
"text": "def test_get_conda_env():\n pytest_enable_socket()\n\n ## Test that the base environemnet is returned\n croot = sp.check_output(['conda', 'info', '--root'])\n conda_env, conda_path = utils.get_conda_env()\n assert conda_path.strip() == croot.decode(\"utf8\").strip()\n assert conda_env.strip() == os.path.basename(croot).decode(\"utf8\").strip()\n\n\n ## Test with conda_root() set as prefix\n conda_env, conda_path = utils.get_conda_env(prefix=utils.conda_root())\n assert conda_path.strip() == croot.decode(\"utf8\").strip()\n assert conda_env.strip() == os.path.basename(croot).decode(\"utf8\").strip()\n\n ### Test with environment name\n conda_env, conda_path = utils.get_conda_env(prefix=conda_env)\n assert conda_path.strip() == croot.decode(\"utf8\").strip()\n assert conda_env.strip() == os.path.basename(croot).decode(\"utf8\").strip()\n\n\n ## Test new environment set as prefix\n env_name = \"test_croot\"\n temp_env = os.path.join(utils.conda_root(), \"envs\", env_name)\n ### Remove temp env if it already exists\n sp.check_output([\"conda\", \"env\", \"remove\", \"--name\", env_name])\n try: \n shutil.rmtree(temp_env)\n except Exception:\n pass \n\n ### Create the temp environment\n sp.check_output([\"conda\", \"create\", \"--name\", env_name])\n\n ### Test with environment name\n conda_env, conda_path = utils.get_conda_env(prefix=env_name)\n assert conda_path.strip() == str(temp_env)\n assert conda_env.strip() == env_name\n\n ### Test with environment path\n conda_env, conda_path = utils.get_conda_env(prefix=temp_env)\n assert conda_path.strip() == str(temp_env)\n assert conda_env.strip() == env_name\n\n ### Remove temp env\n sp.check_output([\"conda\", \"env\", \"remove\", \"--name\", env_name])\n try:\n shutil.rmtree(temp_env)\n except Exception:\n pass\n assert os.path.exists(temp_env) == False",
"title": ""
},
{
"docid": "55f545cd9a6b5f94ce5f93d314d6cdbc",
"score": "0.5948327",
"text": "def env(scope, varname):\n return [os.environ.get(varname[0], '')]",
"title": ""
},
{
"docid": "a62a2252d7cd4f11aabbb8438d7f9e1f",
"score": "0.59405476",
"text": "def env(self) -> Optional[gdb.Value]:\n return self._variant['env'] if not self.is_dynamic else None",
"title": ""
},
{
"docid": "01766cadfe33f566244fd2ca2e776554",
"score": "0.5925549",
"text": "def environment(self) -> 'outputs.GetFunctionEnvironmentResult':\n return pulumi.get(self, \"environment\")",
"title": ""
},
{
"docid": "132ec85857758086c85cd1805a7fe564",
"score": "0.5911311",
"text": "def conda_url(self):\n api_info = self.api.download_get_api_info()\n conda_url = api_info.get('conda_url', 'https://conda.anaconda.org')\n conda_url = conda_url[:-1] if conda_url[-1] == '/' else conda_url\n return conda_url",
"title": ""
},
{
"docid": "05a56794898f4d8c8593b4758b7787de",
"score": "0.5903882",
"text": "def env(self) -> Optional[Sequence['outputs.StorageClusterSpecStorkEnv']]:\n return pulumi.get(self, \"env\")",
"title": ""
},
{
"docid": "ba11115ff0b295cdba56d40acb4246b6",
"score": "0.5857058",
"text": "def get_remote_env_var(self, env_var):\n env_vars = list(map(lambda x: x.result(), self.rex().exec_broad(fr'bash -lc \"echo \\${env_var}\"')))\n\n return env_vars[0]",
"title": ""
},
{
"docid": "b0a01cf91786029c22a9805b5ea82c0b",
"score": "0.58518374",
"text": "def env(self):\n return self._env_cfg",
"title": ""
},
{
"docid": "279882fa12205b4d438fc351d4d1ed4e",
"score": "0.5848244",
"text": "def check_env_conda(cls, name: str) -> bool:\n args = [\"conda\", \"env\", \"list\"]\n ret = utils.cmdline(args)\n env_found = False\n for line in ret.stdout.splitlines():\n if line.startswith(name):\n env_found = True\n break\n return env_found",
"title": ""
},
{
"docid": "56519ec76328f20517923c910ef46ffd",
"score": "0.5846986",
"text": "def env(self):\n git_dir = self.m.path['start_dir'].join('git')\n git_bin = git_dir.join('bin')\n return self.m.env({'PATH': self.m.path.pathsep.join(\n [str(git_dir), str(git_bin), '%(PATH)s'])})",
"title": ""
},
{
"docid": "d20e718139a9073e3bdd7634cfdfc24a",
"score": "0.5836369",
"text": "def test_get_base_env():\n\n ## Get the base environmnet\n from conda.core.envs_manager import list_all_known_prefixes\n base = min(list_all_known_prefixes())\n\n ## Test that the base conda root is returned correctly \n base_prefix = utils.get_base_env(cur_prefix = utils.conda_root())\n assert base_prefix == base\n\n ## Test that the prefix is or is not in the environmnets \n ### List of enviroments\n environments = [os.path.join(x+\"/\") for x in utils.check_output([\"conda\", \"info\", \"--env\"]).strip().replace(\"*\",\"\").replace(\"\\n\",\" \").split(\" \") if os.path.isdir(x)]\n base_env = min(environments)\n env_name = \"temp_env_not_base\"\n temp_env = os.path.join(utils.conda_root(), \"envs\", env_name)\n\n try:\n utils.prefix_in_conda(temp_env)\n except utils.CondaEnvironmentNotFound as e:\n assert \"The prefix supplied is not a conda environment: {}\".format(temp_env) in str(e) \n except Exception as e:\n assert False\n\n ## Test that a non-conda prefix is handled \n try:\n utils.get_base_env(cur_prefix = env_name)\n assert False\n except utils.CondaEnvironmentNotFound as e: \n pass\n\n ## Create the env\n sp.check_output([\"conda\", \"create\", \"--name\", env_name])\n\n ## Test get_base_env on the new env\n base_prefix = utils.get_base_env(cur_prefix = env_name)\n assert base_prefix == base\n\n ## Test get_base_env on the new env\n base_prefix = utils.get_base_env(cur_prefix = temp_env)\n assert base_prefix == base\n \n ### Remove temp env\n sp.check_output([\"conda\", \"env\", \"remove\", \"--name\", env_name])\n try:\n shutil.rmtree(temp_env)\n except Exception:\n pass\n assert os.path.exists(temp_env) == False",
"title": ""
},
{
"docid": "8a8d06669148154fd70e25a3693b1a54",
"score": "0.5833431",
"text": "def environment_id(self) -> Optional[str]:\n return pulumi.get(self, \"environment_id\")",
"title": ""
},
{
"docid": "3ea81fb3aff630e3c2b90ac6d89bf0e7",
"score": "0.5820396",
"text": "def env_from(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['EnvFromSourceArgs']]]]:\n return pulumi.get(self, \"env_from\")",
"title": ""
},
{
"docid": "3ea81fb3aff630e3c2b90ac6d89bf0e7",
"score": "0.5820396",
"text": "def env_from(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['EnvFromSourceArgs']]]]:\n return pulumi.get(self, \"env_from\")",
"title": ""
},
{
"docid": "1260cb5fef173cc53f6ccfdb9aeae255",
"score": "0.5820233",
"text": "def get_url():\n return git('config', '--get', 'remote.origin.url', _show=False)[0]",
"title": ""
},
{
"docid": "7c3732ab2e3372b78c4c0cd12d0da11e",
"score": "0.5809245",
"text": "def environ():\n from projex.envmanager import EnvManager\n\n return EnvManager.current()",
"title": ""
},
{
"docid": "1f0bf98df77da9027536334566866064",
"score": "0.57849365",
"text": "def get_environment_from_request(self):\n return Environment.objects.get(api_key=self.kwargs['environment_api_key'])",
"title": ""
},
{
"docid": "1f0bf98df77da9027536334566866064",
"score": "0.57849365",
"text": "def get_environment_from_request(self):\n return Environment.objects.get(api_key=self.kwargs['environment_api_key'])",
"title": ""
},
{
"docid": "a33eb32537e15238866aa9d3d391e98a",
"score": "0.57811165",
"text": "def get_k8s_env() -> K8sEnv:\n out = subprocess.check_output(['kubectl', 'config', 'current-context'])\n\n outstr = out.decode('utf-8').strip()\n if outstr == 'docker-for-desktop':\n return K8sEnv.D4M\n elif 'gke' in outstr:\n return K8sEnv.GKE\n elif outstr == 'minikube':\n return K8sEnv.MINIKUBE\n else:\n raise Exception('Unable to find a matching k8s env for output \"{}\"'. format(outstr))",
"title": ""
},
{
"docid": "c2411758ca427c5401164a4f0c3b7022",
"score": "0.57805943",
"text": "def list_envs() -> List[str]:\n logger.info(f\"Listing conda environments...\")\n command = copy(BASE_CONDA_ENV_COMMAND)\n command.extend([\"list\", \"--json\"])\n proc_output = run_subprocess(command)\n proc_json = loads(proc_output)\n env_locations = proc_json[\"envs\"]\n environments = []\n for env_location in env_locations:\n name = Path(env_location).stem\n if name.startswith(\"miniconda\") or name.startswith(\"anaconda\"):\n name = \"base\"\n environments.append(name)\n return environments",
"title": ""
},
{
"docid": "a549590235840f53669517b9f8cc7ad5",
"score": "0.5755601",
"text": "def get_environment(self, params):\n return os.environ.copy()",
"title": ""
},
{
"docid": "cd240d4efd4601f4731a6e6ee66a4c34",
"score": "0.574717",
"text": "def current():\n with _lock:\n ident = identifier()\n\n envs = _current_envs.get(ident)\n if envs:\n return envs[-1]\n return None",
"title": ""
},
{
"docid": "56fc2a2004d0f83b93c00b8e59685000",
"score": "0.5735212",
"text": "def get_env(self, env):\n for env_data in self.client.execute(ENVS_QUERY)[\"envs\"]:\n if env_data[\"name\"] == env:\n env_data[\"namespaces\"] = set(n[\"name\"] for n in env_data[\"namespaces\"])\n break\n else:\n raise ValueError(f\"cannot find env '{env}'\")\n\n return env_data",
"title": ""
},
{
"docid": "fbbbb63d79234bc9ba06464165e4b1a5",
"score": "0.57295305",
"text": "def default_env(self):\n return self._default_env",
"title": ""
},
{
"docid": "f98a8edb7b271a5bb2858f35a28e7f15",
"score": "0.5718124",
"text": "def remote(self) -> str:\n if len(self.repo.remotes) > 1:\n self._remote = self.repo.remote(self._remote_name)\n if not self._remote:\n raise EnvironmentError(f\"Remote {self._remote_name} not found\")\n elif len(self.repo.remotes) == 1:\n self._remote = self.repo.remotes[0]\n else:\n raise EnvironmentError(\"No remote associated into the repository\")\n return self._remote",
"title": ""
},
{
"docid": "0d326d622040696776007cbdc305c116",
"score": "0.569893",
"text": "def get_env_config(self):\n return self._rpc({\n \"Type\": \"Client\",\n \"Request\": \"EnvironmentGet\"})",
"title": ""
},
{
"docid": "12207feb9deb0dde6c494af76ef8bf4d",
"score": "0.56910676",
"text": "def get_env(self) -> Optional[VecEnv]:\n return self.env",
"title": ""
},
{
"docid": "74ce47d40149bc50a33380a99b0a457b",
"score": "0.5674827",
"text": "def _get_build_env(env):\n env_override = \"\"\n if env is None:\n return env_override\n if not isinstance(env, dict):\n raise SaltInvocationError(\"'env' must be a Python dictionary\")\n for key, value in env.items():\n env_override += \"{}={}\\n\".format(key, value)\n env_override += \"export {}\\n\".format(key)\n return env_override",
"title": ""
},
{
"docid": "10178e41f6e62593dfd20c9183a71b9f",
"score": "0.5664573",
"text": "def get_remote_name() -> Optional[bytes]:\n remote_name = b''\n remote_num = 0\n get_remotes_name_cmd = 'git remote'.split()\n task = subprocess.Popen(\n get_remotes_name_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n out, err = task.communicate()\n remotes = out[:-1].split(b'\\n')\n if not err:\n for remote in remotes:\n get_remotes_url_cmd = (\n b'git config --get remote.%s.url' % remote).split()\n task = subprocess.Popen(\n get_remotes_url_cmd, stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n remote_url, err = task.communicate()\n if not err:\n if remote_url.endswith(b'oppia/oppia.git\\n'):\n remote_num += 1\n remote_name = remote\n else:\n raise ValueError(err)\n else:\n raise ValueError(err)\n\n if not remote_num:\n raise Exception(\n 'Error: Please set upstream for the lint checks to run '\n 'efficiently. To do that follow these steps:\\n'\n '1. Run the command \\'git remote -v\\'\\n'\n '2a. If upstream is listed in the command output, then run the '\n 'command \\'git remote set-url upstream '\n 'https://github.com/oppia/oppia.git\\'\\n'\n '2b. If upstream is not listed in the command output, then run the '\n 'command \\'git remote add upstream '\n 'https://github.com/oppia/oppia.git\\'\\n'\n )\n\n if remote_num > 1:\n print(\n 'Warning: Please keep only one remote branch for oppia:develop '\n 'to run the lint checks efficiently.\\n')\n return None\n return remote_name",
"title": ""
},
{
"docid": "b6a5cf2b15f3564efc3f7fec853ab827",
"score": "0.563216",
"text": "def get_environment(flow_rp):\n environments_val = flow_rp.get_environments()\n environments_list = environments_val[_VALUE]\n environments = {\n env[_PROPERTIES][_DISPLAY_NAME]: env[_NAME]\n for env in environments_list\n }\n\n environment_keys = list(environments.keys())\n\n sid = prompt_choice_list('Please select an environment:', environment_keys)\n environment = environments[environment_keys[sid]]\n\n print('Environment selected: {}'.format(environment_keys[sid]))\n\n return environment",
"title": ""
},
{
"docid": "000c00355a5af58689118e9c886d58ab",
"score": "0.5629444",
"text": "def get_config_from_env(config_name, default=None):\n LOG.debug(\"Getting environmental variable '%s'\", config_name)\n return os.environ.get(config_name, default)",
"title": ""
},
{
"docid": "3b08afef138b41555fece1b167337399",
"score": "0.56168485",
"text": "def repo_name(self) -> str:\n return pulumi.get(self, \"repo_name\")",
"title": ""
},
{
"docid": "db3d27120b472cdb3dcbad705a20fd67",
"score": "0.5613533",
"text": "def get_environment(name):\r\n if name == \"_default\":\r\n return env_from_template(name)\r\n filename = os.path.join(\"environments\", name + \".json\")\r\n try:\r\n with open(filename) as f:\r\n try:\r\n return json.loads(f.read())\r\n except ValueError as e:\r\n msg = 'LittleChef found the following error in'\r\n msg += ' \"{0}\":\\n {1}'.format(filename, str(e))\r\n abort(msg)\r\n except IOError:\r\n raise FileNotFoundError('File {0} not found'.format(filename))",
"title": ""
},
{
"docid": "c5a6ef716a3e2dda1897421db80ee643",
"score": "0.5600405",
"text": "def getTopEnvName():\n return 'DAR_INST_TOP'",
"title": ""
},
{
"docid": "753be3bfba5a1fddc06f529d2d615dee",
"score": "0.5598987",
"text": "def get_default_env(cls) -> \"DESEnv\":\n return DESEnv.__DEFAULT_ENV",
"title": ""
},
{
"docid": "04de04c43b1ac7f726c8cd120efa783a",
"score": "0.55968314",
"text": "def environment(self):\n return ''",
"title": ""
},
{
"docid": "76e2c2883a6c86736d685b73b0b25a50",
"score": "0.55878526",
"text": "def get_env(self):\n env = {}\n for line in self.execute('env'):\n key, val = line.split('=', 1)\n env[key] = val\n return env",
"title": ""
},
{
"docid": "cb9a9f40e1f6edc9624bb81c46f219aa",
"score": "0.5575313",
"text": "def get_command_environment(self) -> Dict[str, str]:\n env = bases.buildd.default_command_environment()\n env[\"CHARMCRAFT_MANAGED_MODE\"] = \"1\"\n\n # Pass-through host environment that target may need.\n for env_key in [\"http_proxy\", \"https_proxy\", \"no_proxy\"]:\n if env_key in os.environ:\n env[env_key] = os.environ[env_key]\n\n return env",
"title": ""
},
{
"docid": "91182c69007ab2de9722e98f8ba48444",
"score": "0.55488896",
"text": "def _repo_name(self):\n\n if self._git_repo_name is not None:\n return self._git_repo_name\n else:\n reponame = self.repo.git_dir.split(os.sep)[-2]\n if reponame.strip() == '':\n return 'unknown_repo'\n return reponame",
"title": ""
},
{
"docid": "5c2812a4bc1b8bba361230dd9a4f5d47",
"score": "0.55379623",
"text": "def get_environ_variable(name):\n try:\n return os.environ[name]\n except KeyError:\n error_msg = 'Environment variable {} must be set'.format(name)\n raise ImproperlyConfigured(error_msg)",
"title": ""
},
{
"docid": "8f3843e9f8a7996240b4be6d7f9843f8",
"score": "0.55323195",
"text": "def env_prefix(self):\n return self._env_prefix",
"title": ""
},
{
"docid": "61c31783e391d8b3099a3e423fb8c4a5",
"score": "0.5531388",
"text": "def cf_env(self):\n args = self.workflow.args\n\n return {\n 'CF_ENV': args.environment or '',\n 'CF_PROJECT': args.project_name,\n # deprecate this env var\n 'CF_ENV_NAME': args.project_name,\n }",
"title": ""
},
{
"docid": "b0f80d9eb528c2fbac62b659aa190e5a",
"score": "0.55307907",
"text": "def _locate_pip_inside_conda(env_name):\n pip = _path_to_pip_in_env_with_name(shutil.which(\"conda\"), env_name)\n\n # this might happen if the environment does not contain python/pip\n if not Path(pip).exists():\n err = (\n f\"Could not locate pip in environment {env_name!r}, make sure \"\n \"it is included in your environment.yml and try again\"\n )\n telemetry.log_api(\n \"install-error\", metadata={\"type\": \"no_pip_env\", \"exception\": err}\n )\n raise BaseException(err)\n\n return pip",
"title": ""
},
{
"docid": "373a5bb4d5cfef8a0d84ccf5cadb645d",
"score": "0.552034",
"text": "def github_org(self):\n return self._config['repo']['github_org']",
"title": ""
},
{
"docid": "601e60b5d216a2bddb616b07539ceea8",
"score": "0.54993343",
"text": "def get_env_requirement(var_name):\n try:\n return os.environ[var_name]\n except KeyError:\n error_msg = \"Could not find the environment variable %s\" % var_name\n raise ImproperlyConfigured(error_msg)",
"title": ""
},
{
"docid": "cb8a24078dcc5d1af3cad7dbaaa1fa89",
"score": "0.54971105",
"text": "def get_from_env(self, key):\n return os.environ[key]",
"title": ""
},
{
"docid": "7ece072070d31fcd9e742cffe70f082d",
"score": "0.5493721",
"text": "def environment(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['TaskDefinitionKeyValuePairArgs']]]]:\n return pulumi.get(self, \"environment\")",
"title": ""
},
{
"docid": "f08a224ada1c01009dba8fadd8ac6ee3",
"score": "0.5492601",
"text": "def env_from(self) -> Optional[List[\"EnvFromSource\"]]:\n return self.__env_from",
"title": ""
},
{
"docid": "f08a224ada1c01009dba8fadd8ac6ee3",
"score": "0.5492601",
"text": "def env_from(self) -> Optional[List[\"EnvFromSource\"]]:\n return self.__env_from",
"title": ""
},
{
"docid": "f3e4bd7940f2bd6eba3d7f553ec952b7",
"score": "0.5491858",
"text": "def get_current_branch_name():\n\tbranch_name = subprocess.check_output(['git', 'rev-parse', '--abbrev-ref', 'HEAD'])\n\treturn branch_name.decode(\"utf-8\").rstrip()",
"title": ""
},
{
"docid": "0b48b980294c0333b73cabe7cc8a6a98",
"score": "0.5481975",
"text": "def env(self) -> Optional[List[\"EnvVar\"]]:\n return self.__env",
"title": ""
},
{
"docid": "0b48b980294c0333b73cabe7cc8a6a98",
"score": "0.5481975",
"text": "def env(self) -> Optional[List[\"EnvVar\"]]:\n return self.__env",
"title": ""
},
{
"docid": "8c38a08746e222cc9d60d44f7b91c618",
"score": "0.5474452",
"text": "def repo(self) -> str:\n return self._metadata.repo",
"title": ""
},
{
"docid": "b5f361a48d6604edb5349d71d81006c5",
"score": "0.5474218",
"text": "def identify_current_repo(repos):\n for h in git.hashes():\n if h in repos:\n return repos[h]\n return None",
"title": ""
},
{
"docid": "08731ead5b2ecb1d5336ad059eaf5cb6",
"score": "0.54723644",
"text": "def determine_env() -> int:\n cd = Path(\"../..\").resolve()\n if \"C:\" in str(cd):\n return 0\n elif \"lustre\" in str(cd):\n return 1\n else:\n return 0",
"title": ""
},
{
"docid": "086d2669da24598dffe97c367909ad1a",
"score": "0.5464385",
"text": "def env_dir(self):\n return os.path.join(self._dir, 'env')",
"title": ""
},
{
"docid": "b227d842a9de5db3338e8cff12b64c97",
"score": "0.5461256",
"text": "def get_environments():\n try:\n with open(environment_file) as f:\n paths = f.readlines()\n except IOError:\n paths = []\n environments = set(\n os.path.normpath(env.strip()) for env in paths if os.path.isdir(env.strip())\n )\n env_dirs = (\n os.path.join(conda_base, \"envs\"),\n os.path.join(os.path.expanduser(\"~\"), \".conda\", \"envs\"),\n )\n for env_dir in env_dirs:\n if os.path.isdir(env_dir):\n for d in os.listdir(env_dir):\n d = os.path.join(env_dir, d)\n if os.path.isdir(d):\n environments.add(d)\n\n return environments",
"title": ""
}
]
274993236c68c93987a2bd3d55fd0eb4
Returns the number of unread messages in the user's mbox of a given type.
[
{
"docid": "940b33f472fb0674c8ac24b43ad96f72",
"score": "0.82448024",
"text": "def get_unread(self, site, profile_name, mbox_type='inbox'):\n total = 0\n \n if _get_mailbox(site, profile_name, mbox_type) is None:\n return total\n mbox_queues = self.get_queues(site, profile_name, mbox_type)\n for mbox_q in mbox_queues:\n for msg_no in mbox_q._messages:\n raw_msg = mbox_q._messages[msg_no]\n if not STATUS_READ in raw_msg.flags:\n total += 1\n \n return total",
"title": ""
}
]
[
{
"docid": "98429e9cb42c0c5b03da2b2fcc1326e9",
"score": "0.694137",
"text": "def unread_messages_count(self) -> int:\n return self._message_storage.unread_count",
"title": ""
},
{
"docid": "d5e526e27ef01e5174d787af7c57ef22",
"score": "0.69291246",
"text": "def get_unread(self):\n total = 0\n \n for msg_no in self._messages:\n raw_msg = self._messages[msg_no]\n if not STATUS_READ in raw_msg.flags:\n total += 1\n \n return total",
"title": ""
},
{
"docid": "3673a6d5479849d40278cad95f1179fe",
"score": "0.6878066",
"text": "def get_count_unread(user):\n conn = connect()\n cur = conn.cursor()\n\n cur.execute(\"\"\"SELECT COUNT(messages) FROM messages WHERE messages.receiver = '%s' AND messages.status = 'Unread'\"\"\" % (user))\n results = cur.fetchall()\n all_messages_for_user = [item[0] for item in results]\n\n conn.commit()\n conn.close()\n\n return all_messages_for_user",
"title": ""
},
{
"docid": "343465074af5a00260e0b037fd769511",
"score": "0.68464667",
"text": "def inbox_count_for(user):\n return Message.objects.filter(\n recipient=user,\n read__isnull=True,\n recipient_deleted__isnull=True,\n ).count()",
"title": ""
},
{
"docid": "535805ee99d258b2368c515a47209e27",
"score": "0.6800597",
"text": "def count_unread_messages(self):\n\n\t\treturn self.user.received_messages.filter(date_received=None).count()",
"title": ""
},
{
"docid": "4d91b1af85a201d19c4854c496898763",
"score": "0.67822254",
"text": "def get_unread_count(user):\n return UserNotification.objects.filter(recipient=user, unread=True).count()",
"title": ""
},
{
"docid": "884634c2079360bae5fbe7924059f7bb",
"score": "0.66291726",
"text": "def get_unread_count(user):\n return Conversation.query.filter(\n Conversation.unread, Conversation.user_id == user.id\n ).count()",
"title": ""
},
{
"docid": "43c84c5163dff64cd25aa934e7ff2cb8",
"score": "0.6610905",
"text": "def count_msg_of_type(msg_type):\n result = Message.query \\\n .filter(Message.type == msg_type) \\\n .count()\n return result",
"title": ""
},
{
"docid": "04e0ea5fa8794520d88d8813cc1cca90",
"score": "0.65492016",
"text": "def count(self, message_type: type) -> int:\n self._check_bus()\n return len(self._messages_by_type[message_type])",
"title": ""
},
{
"docid": "5031e742e4a2f5af49bdb471461eceeb",
"score": "0.6225393",
"text": "def unread(self):\n return self.feeditem_set.filter(is_read=False).count()",
"title": ""
},
{
"docid": "ffdce908325e2614f24250db9b0a72ce",
"score": "0.6216302",
"text": "def get_unread_messages(self):\n service = self.service\n results = service.users().messages().list(userId='me', labelIds=['INBOX', 'UNREAD']).execute()\n messages = results.get('messages', [])\n\n if not messages:\n print('No unread threads found.')\n return None\n else:\n print('Found unread messages:', messages, '\\n')\n return messages",
"title": ""
},
{
"docid": "5209c75855d399b02524775e86187946",
"score": "0.58973277",
"text": "def gmail_unread_count(username, password):\n # Build the authentication string\n b64auth = base64.encodestring(\"%s:%s\" % (username, password))\n auth = \"Basic \" + b64auth\n\n # Build the request\n req = urllib2.Request(\"https://mail.google.com/mail/feed/atom/\")\n req.add_header(\"Authorization\", auth)\n handle = urllib2.urlopen(req)\n\n # Build an XML dom tree of the feed\n dom = parse(handle)\n handle.close()\n\n # Get the \"fullcount\" xml object\n count_obj = dom.getElementsByTagName(\"fullcount\")[0]\n # get its text and convert it to an integer\n return int(count_obj.firstChild.wholeText)",
"title": ""
},
{
"docid": "6b495fbd3ac2f2dec2e6fe239fa83010",
"score": "0.5784117",
"text": "def get_unread():",
"title": ""
},
{
"docid": "8b77574c56de0a253a1bf82f0c2448d1",
"score": "0.5777752",
"text": "def set_unread_count():",
"title": ""
},
{
"docid": "7bf8949540a29e4e3499cf574d0a3158",
"score": "0.5753119",
"text": "def test_count_unread_messages_for(self):\n jane = User.objects.get(pk=2)\n\n # Jane has one unread message from john\n unread_messages = MessageRecipient.objects.count_unread_messages_for(jane)\n\n self.failUnlessEqual(unread_messages, 1)",
"title": ""
},
{
"docid": "c1a3b607669b5ad4a8f5f551e397eb4c",
"score": "0.5734465",
"text": "def mboxstat(self, mbox):\n try:\n sele_ret, msgs_cnt = self.imap.select(mbox, readonly = True)\n\n except imaplib.IMAP4_SSL.error:\n return False\n\n if sele_ret == \"OK\":\n return True\n else: \n return False",
"title": ""
},
{
"docid": "274b05a2041fb86dae1f91a125cbaab6",
"score": "0.5670949",
"text": "def get_unread_message_count_for(parser, token):\n try:\n tag_name, arg = token.contents.split(None, 1)\n except ValueError as e:\n raise template.TemplateSyntaxError(\n \"%s tag requires arguments\" % token.contents.split()[0]\n ) from e\n m = re.search(r\"(.*?) as (\\w+)\", arg)\n if not m:\n raise template.TemplateSyntaxError(\n \"%s tag had invalid arguments\" % tag_name\n )\n user, var_name = m.groups()\n return MessageCount(user, var_name)",
"title": ""
},
{
"docid": "29c32e8a10957b02b39416648777e15f",
"score": "0.5625927",
"text": "def readInbox():\n if len(inbox) == 0:\n return 0\n else:\n return inbox.pop(0)",
"title": ""
},
{
"docid": "cd66976cf23ab2b9556d1bc012836464",
"score": "0.5558872",
"text": "def test_push_unread_count_message_count(self) -> None:\n # Carry out common push count tests and setup\n self._test_push_unread_count()\n\n # Carry out our option-value specific test\n #\n # We're counting every unread message, so there should now be 3 since the\n # last read receipt\n self._check_push_attempt(7, 3)",
"title": ""
},
{
"docid": "7017be4d8e1e2f1a36b7ce4dc945813e",
"score": "0.5549587",
"text": "def has_unread_messages(self):\n\n\t\treturn self.user.received_messages.filter(date_received=None).count() + self.user.system_notifications.filter(resolved=False).count() > 0",
"title": ""
},
{
"docid": "094974fb7b63e9fc099cd29e2d9b92fb",
"score": "0.5525923",
"text": "def get_unread(self):\n return requests.get(self.baseurl + \"unread/\", headers=self.headers).json()",
"title": ""
},
{
"docid": "02bb48f7ffdd88460d7fca073aef1e7f",
"score": "0.54457486",
"text": "def read_messages_count(self) -> int:\n return self._message_storage.read_count",
"title": ""
},
{
"docid": "a487d02b06abafcbfa192f86ebf2904f",
"score": "0.5328862",
"text": "def get_unread_requests(cls):\n return cls.unread_requests_counter",
"title": ""
},
{
"docid": "a487d02b06abafcbfa192f86ebf2904f",
"score": "0.5328862",
"text": "def get_unread_requests(cls):\n return cls.unread_requests_counter",
"title": ""
},
{
"docid": "3705f16db7886ababd6a2174318a7740",
"score": "0.53002006",
"text": "def message_count(self):\n return self._messages.qsize()",
"title": ""
},
{
"docid": "6aeb40edd1c947efb0c920f7401e7b1c",
"score": "0.5279209",
"text": "def fetch_mail(self):\n\t\ts = \"\"\n\t\tself.M.select(\"Inbox\")\n\n\t\trv, data1 = self.M.search(None, \"UnSeen\") \n\t\tif rv != \"OK\":\n\t\t\treturn \"An issue was encountered retreiving unread email\"\n\n\t\tfor x in data1[0].split():\n\t\t\t# get the x-th unread message that was returned from the server\n\t\t\trv, data = self.M.fetch(x, \"(RFC822)\")\n\n\t\t\t# get subject and sender \n\t\t\tmessage = email.message_from_string(data[0][1])\n\t\t\tsender = message[\"from\"]\n\t\t\tsubject = message[\"subject\"]\n\n\t\t\t# get the body\n\t\t\tbody = \"\"\n\t\t\tif message.get_content_maintype() == \"multipart\":\n\t\t\t\tfor part in message.walk():\n\t\t\t\t\tif part.get_content_type() == \"text/plain\":\n\t\t\t\t\t\tbody = part.get_payload(decode=True)\n\t\t\t\t\telse:\n\t\t\t\t\t\tcontinue\n\n\t\t\t# append this to all things that will be returned\n\t\t\ts += \"%s: %s\\n%s\\n\\n\" % (sender, subject, body)\n\n\t\t\t# mark as read\n\t\t\tself.M.store(x,'+FLAGS','\\Seen')\n\n\t\treturn s",
"title": ""
},
{
"docid": "530496a057ec4868646b27a5cc60ea12",
"score": "0.5259501",
"text": "def is_unread(self):\n return self._unread",
"title": ""
},
{
"docid": "60695c1f40f073dfb478269b1ce19a15",
"score": "0.5239401",
"text": "def getNumMessages(self):\n return self.numMessages",
"title": ""
},
{
"docid": "665cb74bf2f275334158d76c965446b1",
"score": "0.5233794",
"text": "def inbox(request, template_name='messages/inbox.html'):\n message_list = None\n type='received'\n category= {}\n filter = request.GET.get(\"filter\", None)\n\n if filter is not None:\n if cat_map[filter] is not None:\n category = { 'category': cat_map[filter] }\n\n message_list = InternalMessage.objects.inbox_for(request.user, **category)\n\n pd_message_list = []\n for message in message_list:\n pd_message_list.append(ReceivedMessage(message))\n\n return render_to_response(template_name, {\n 'message_list': pd_message_list,\n 'type': type,\n 'cat_messages': True,\n 'filter': filter,\n\n }, context_instance=RequestContext(request))",
"title": ""
},
{
"docid": "4ea4c33e0bf6ef063cb76584fb39f00b",
"score": "0.52305746",
"text": "async def get_read_count(self, ctx):\r\n\r\n try:\r\n msg_count = await self.bot.db.field(\"SELECT read_message_count FROM probot WHERE guild_id = $1\", ctx.guild.id)\r\n except Exception as exc:\r\n return HARD_MESSAGE_COUNT\r\n return msg_count or HARD_MESSAGE_COUNT",
"title": ""
},
{
"docid": "aa59ea35a185a89e9bb32eb944d929cd",
"score": "0.5215564",
"text": "def check_messages(self, mark_read=False):\n if len(self.message_callbacks) == 0:\n return\n messages = self.reddit.get_unread(unset_has_mail=mark_read)\n total_read = 0\n for message in messages:\n with self.database_context() as db:\n editable = EditableContainer(message)\n if db.query(Editable).filter(Editable.id == editable.id).first():\n if mark_read:\n message.mark_as_read()\n continue\n\n # if a callback was made, mark as read.\n if self.check_callbacks(editable, self.message_callbacks) or mark_read:\n message.mark_as_read()\n total_read += 1\n db.add(Editable(id=editable.id))\n return total_read",
"title": ""
},
{
"docid": "4c11af8c9a552c122e3ea926001406bb",
"score": "0.5209998",
"text": "def count_msgs(self) -> int:\n assert self.env is not None\n with self.env.begin(write=False) as txn:\n meta_db = self.env.open_db(\n key=persipubsub.database.META_DB, txn=txn, create=False)\n meta_stat = txn.stat(db=meta_db) # type: Dict[str, int]\n\n return meta_stat['entries']",
"title": ""
},
{
"docid": "818ecbb69c46eb1d753d7bef4a6d722b",
"score": "0.5175912",
"text": "def get_message_count(user):\n return Conversation.query.filter(Conversation.user_id == user.id).count()",
"title": ""
},
{
"docid": "adc660fd3f4add9538cf4712336f045d",
"score": "0.51670116",
"text": "def count(self, type_uri):\r\n return len(self.get(type_uri))",
"title": ""
},
{
"docid": "9c6de766b1557d5f1b030ba305147bcd",
"score": "0.51633817",
"text": "def get_unread_message_count_between(parser, token):\n try:\n tag_name, arg = token.contents.split(None, 1)\n except ValueError as e:\n raise template.TemplateSyntaxError(\n \"%s tag requires arguments\" % token.contents.split()[0]\n ) from e\n m = re.search(r\"(.*?) and (.*?) as (\\w+)\", arg)\n if not m:\n raise template.TemplateSyntaxError(\n \"%s tag had invalid arguments\" % tag_name\n )\n um_from_user, um_to_user, var_name = m.groups()\n return MessageCount(um_from_user, var_name, um_to_user)",
"title": ""
},
{
"docid": "3d8dc904f6d080b3a86f14fee8ee8742",
"score": "0.51625633",
"text": "def size(self):\n return sum(1 for m in self._list_messages())",
"title": ""
},
{
"docid": "dcde136a8eda70db9117f262b1ad8ef4",
"score": "0.5145728",
"text": "def reset_unread_count():",
"title": ""
},
{
"docid": "ba257c064da21925a0f88bbfad6178da",
"score": "0.5097651",
"text": "def test_count_unread_messages_between(self):\n john = User.objects.get(pk=1)\n jane = User.objects.get(pk=2)\n\n # Jane should have one unread message from john\n unread_messages = MessageRecipient.objects.count_unread_messages_between(jane, john)\n\n self.failUnlessEqual(unread_messages, 1)",
"title": ""
},
{
"docid": "69be8e942fff354d3f76074d0296f7b9",
"score": "0.49994367",
"text": "def get_email_count(self, obj):\n return obj.email_count",
"title": ""
},
{
"docid": "d5573adf301fabf091108edabcf0356c",
"score": "0.49765676",
"text": "def get_count(self, unit_type: UnitTypeId) -> int:\n count = 0\n\n count += self.cache.own(unit_type).amount\n\n return count",
"title": ""
},
{
"docid": "51ecd76100af0a90c181f6e333be6ae2",
"score": "0.49554616",
"text": "def notifications_count(self, receiver):\r\n return Notification.objects.filter(receiver=receiver, \r\n is_seen=False, is_active=True).exclude(sender=receiver).count()",
"title": ""
},
{
"docid": "8d9a017cb54a5dddc2f06f9dbeb00ff2",
"score": "0.49499083",
"text": "def _check_topic(self, topic_name, msg_type):\n types, topic_info = self.bag.get_type_and_topic_info(topic_name)\n assert topic_info[topic_name].msg_type == msg_type\n message_count = topic_info[topic_name].message_count\n assert message_count > 0\n return message_count",
"title": ""
},
{
"docid": "9f738a74e2aa80b8177a41d33744c2b5",
"score": "0.49303246",
"text": "def get_count_unread_community(action, user, community='NULL', channel='NULL'):\n conn = connect()\n cur = conn.cursor()\n\n if(action == 'Community'):\n cur.execute(\"\"\"SELECT community.community_channels FROM community WHERE community_name = '%s'\"\"\" % (community))\n results = cur.fetchall()\n st = [item[0] for item in results]\n curr_com = st[0].split(',')\n #print(curr_com)\n total_count = 0\n\n for row in curr_com:\n cur.execute(\"\"\"SELECT COUNT(chan_message) FROM community_channels WHERE status = 'Unread' AND community_name = '%s' AND channel_name = '%s'\"\"\" % (community, row))\n results = cur.fetchall()\n xt = [item[0] for item in results]\n total_count = total_count + xt[0]\n \n conn.close()\n\n return total_count\n\n\n elif(action == 'Channel'):\n cur.execute(\"\"\"SELECT COUNT(chan_message) FROM community_channels WHERE status = 'Unread' AND community_name = '%s' AND channel_name = '%s'\"\"\" % (community, channel))\n results = cur.fetchall()\n st = [item[0] for item in results]\n\n conn.close()\n\n return st[0]\n\n else:\n conn.close()\n raise Exception(\"Incorrect use of function\")",
"title": ""
},
{
"docid": "588cafb65e08c684d361b952c1c22a25",
"score": "0.4914318",
"text": "def message_count(self) -> pulumi.Output[float]:\n return pulumi.get(self, \"message_count\")",
"title": ""
},
{
"docid": "084d88b77f5e8a4ed67b2b5eabee98b3",
"score": "0.4907612",
"text": "def count_messages(file):\n count = 0\n while 1:\n line = file.readline()\n if not line: break\n if line[:5] == \"From \":\n count = count + 1\n file.seek(0)\n return count",
"title": ""
},
{
"docid": "b31e4445716f71c65e98ade2eaa663d3",
"score": "0.49002123",
"text": "def all_messages_count(self) -> int:\n return self._message_storage.all_count",
"title": ""
},
{
"docid": "471519bf1553fc5af4c19d06b61992a8",
"score": "0.4881824",
"text": "def count_failed(self, issuetype=ALL):\n return len(self.failed(issuetype))",
"title": ""
},
{
"docid": "2721ad0f907291c04ddf14bdb66f7cee",
"score": "0.4860154",
"text": "def get_email_length( msg ):\n return log(len(msg.get_payload()))",
"title": ""
},
{
"docid": "b58b3f4a1ecebaf665cdfa13ffdef3d7",
"score": "0.48599574",
"text": "def inboxList(self):\n function = \"messages\"",
"title": ""
},
{
"docid": "38690b59896ab2749083930c0d1c32b8",
"score": "0.48397484",
"text": "def get_unread_notifications(user, limit=None):\n\n if not user:\n return None\n\n notifications = cache_mgr.get_cache(\"notification-%s\" % user.username)\n if notifications is None:\n\n notifications = {\"has_more\": False,\n \"use_facebook\": settings.MAKAHIKI_USE_FACEBOOK}\n\n # Find undisplayed alert notifications.\n notifications.update({\"alerts\": get_user_alert_notifications(user)})\n\n # Get unread notifications\n unread_notifications = user.usernotification_set.filter(\n unread=True,).order_by(\"-level\", \"-created_at\")\n if limit:\n if unread_notifications.count() > limit:\n notifications.update({\"has_more\": True})\n unread_notifications = unread_notifications[:limit]\n\n for item in unread_notifications:\n item.fb_contents = _strip_html_tag(item.contents)\n notifications.update({\"unread\": unread_notifications})\n\n cache_mgr.set_cache(\"notification-%s\" % user.username, notifications, 1800)\n return notifications",
"title": ""
},
{
"docid": "66333e13d8fbf0246302b60aa123eb75",
"score": "0.4837109",
"text": "def archived_messages_count(self) -> int:\n return self._message_storage.archived_count",
"title": ""
},
{
"docid": "0fcd3ee46eb25623da2a48e74c13a207",
"score": "0.48290542",
"text": "def test_get_unread_status_string(self):\n request = self.factory.get(\"\")\n request.user = self.local_user\n\n with patch(\n \"bookwyrm.activitystreams.ActivityStream.get_unread_count\"\n ) as mock_count, patch(\n \"bookwyrm.activitystreams.ActivityStream.get_unread_count_by_status_type\"\n ) as mock_count_by_status:\n mock_count.return_value = 3\n mock_count_by_status.return_value = {\"review\": 5}\n result = views.get_unread_status_string(request, \"home\")\n\n self.assertIsInstance(result, JsonResponse)\n data = json.loads(result.getvalue())\n self.assertEqual(data[\"count\"], \"Load 5 unread statuses\")",
"title": ""
},
{
"docid": "137e21d60c5f4779aaefd2e8c6e1e1ca",
"score": "0.48266402",
"text": "def get_subscriber_count(self, obj):\n return obj.dispatchedemail_set.count()",
"title": ""
},
{
"docid": "6323c72fd63447e2fd0df01e0db66546",
"score": "0.48163298",
"text": "def count_applied(self, issuetype=ALL):\n return len(self.applied(issuetype))",
"title": ""
},
{
"docid": "36901768cff546948a3feb1a3101bf6b",
"score": "0.48080146",
"text": "def info(self):\n info = {'total': None,\n 'recent': None,\n 'unseen': None,\n 'uidnext': None,\n 'uidvalidity': None}\n status, result = self.imap.status(\n utils.b('\"') + self.selected_folder_utf7 + utils.b('\"'),\n '(MESSAGES RECENT UIDNEXT UIDVALIDITY UNSEEN)'\n )\n if result:\n \"\"\"Sample response:\n '\"INBOX\" (MESSAGES 7527 RECENT 0 UIDNEXT 21264 UIDVALIDITY 2\n UNSEEN 1)'\n \"\"\"\n where = utils.b_to_str(result[0])\n messages = re.search('MESSAGES ([0-9]+)', where)\n\n if messages:\n info['total'] = int(messages.group(1))\n recent = re.search('RECENT ([0-9]+)', where)\n if recent:\n info['recent'] = int(recent.group(1))\n unseen = re.search('UNSEEN ([0-9]+)', where)\n if unseen:\n info['unseen'] = int(unseen.group(1))\n uidnext = re.search('UIDNEXT ([0-9]+)', where)\n if uidnext:\n info['uidnext'] = int(uidnext.group(1))\n uidvalidity = re.search('UIDVALIDITY ([0-9]+)', where)\n if uidvalidity:\n info['uidvalidity'] = int(uidvalidity.group(1))\n\n return info",
"title": ""
},
{
"docid": "55a734d7e12b4212981260c17e016b77",
"score": "0.47946528",
"text": "def count(self):\n return len(self.emails)",
"title": ""
},
{
"docid": "b3dbb4b5affc72fd54af21212c474bc7",
"score": "0.47647718",
"text": "def count_of(self, item_type): # pragma: no cover",
"title": ""
},
{
"docid": "30a8957cb4d964399efb2bc7cbca3abe",
"score": "0.47612786",
"text": "def inbox_for(self, user):\n return self.filter(\n recipient=user,\n recipient_deleted__isnull=True,\n )",
"title": ""
},
{
"docid": "bf0c6bb1d2a7a0dfbb3df54769436a58",
"score": "0.4758008",
"text": "def fetch_pms(self):\n\n valid = r\"[A-Za-z0-9_-]+\"\n\n for msg in self.reddit.inbox.unread():\n author = str(msg.author)\n valid_user = re.match(valid, author)\n if msg.subject == configuration.message_subject and valid_user:\n self.process_pm(msg.body, author, msg)",
"title": ""
},
{
"docid": "9ac47f12fa1d6c103808dec0373df111",
"score": "0.47378373",
"text": "def active_message_count(self) -> float:\n return pulumi.get(self, \"active_message_count\")",
"title": ""
},
{
"docid": "a3791da60794f16b6ac549da9d6cd079",
"score": "0.47312135",
"text": "def unread(self, _name: str, _char: int) -> int:\n return 0",
"title": ""
},
{
"docid": "129f7aa85a3a1be00d8364fc53457ad4",
"score": "0.47259942",
"text": "def mbox(self):\r\n return self._mbox",
"title": ""
},
{
"docid": "cffe78a4ab15610cd6d8cfe684bc1d1d",
"score": "0.47194615",
"text": "def get_email_count_by_name(df, type=None):\n counts_df = df \\\n .groupby(type)[type] \\\n .count() \\\n .to_frame() \\\n .rename(columns={type: \"count\"}) \\\n .reset_index() \\\n .sort_values(\"count\", ascending=False) \\\n .reset_index(drop=True)\n return counts_df",
"title": ""
},
{
"docid": "ad99e8f1520827c0cd61e2c0d17ce452",
"score": "0.4713228",
"text": "def count(self, event_type=None):\n\n sets = self.csets\n if event_type in sets:\n return len(sets[event_type])\n else:\n return 0",
"title": ""
},
{
"docid": "eb6119dfbe6070b712c8edbed150fdd4",
"score": "0.4709117",
"text": "def get(self, type_: EventType, user: discord.Member, channel: discord.Channel) -> int:\n key = self._make_tuple(type_, user, channel)\n try:\n return self.data[key]\n except KeyError:\n return 0",
"title": ""
},
{
"docid": "7dfcdbc12d26f9c2725cc565c2e14bbf",
"score": "0.46976158",
"text": "def count_actions_in(evts, msg_type=None):\n cpt = 0\n try:\n for e in evts:\n if (msg_type == e['event_type']):\n cpt += 1\n elif msg_type is None:\n cpt += 1\n except Exception as ex:\n print('%s__ e=%s' % (ex.__repr__(), e))\n return cpt",
"title": ""
},
{
"docid": "baf48302092ed509f648feb09b52d454",
"score": "0.46896192",
"text": "def mailbox_list(self, account_type=\"ex\", enabled=None, **kwargs):\n if enabled:\n kwargs[\"enabled\"] = True\n elif enabled == False: #explicitly test for false in case no param passed\n kwargs[\"enabled\"] = False\n path = \"customers/me/domains/%s/%s/mailboxes\" % (self.domain, account_type)\n data = self._call(self.host, path, kwargs)\n return data",
"title": ""
},
{
"docid": "0a69844029167abe06ccfe74f1e769f8",
"score": "0.46810246",
"text": "def total_active_invites(self, module):\n total = 0\n for db_user in User.objects():\n inviter = db_user.inviter\n if inviter is not None:\n if inviter.user_id == self.user_id:\n if module.guild.get_member(db_user.user_id) is not None:\n total += 1\n return total",
"title": ""
},
{
"docid": "11c1df28b0a7e663a33dfc44427467ec",
"score": "0.46707955",
"text": "def getmailIDs(subject):\n imapObj = imapclient.IMAPClient('imap.gmail.com', ssl=True)\n imapObj.login(address, pw)\n imapObj.select_folder('INBOX', readonly=False)\n UIds = imapObj.search(['SINCE', today, 'SUBJECT', subject, 'UNSEEN'])\n return imapObj, UIds",
"title": ""
},
{
"docid": "6fb9683184faca3e511fb22b8ed91422",
"score": "0.46661448",
"text": "def do_count(self, type):\n return self.collection.count_documents()",
"title": ""
},
{
"docid": "03d486efb1094c332a162ab7ee6c3513",
"score": "0.46449926",
"text": "def _checkInbox(self):\n inbox_labels = [u'INBOX', u'UNREAD']\n details = None\n\n messages = self._getNewMessages()\n for message in messages:\n details = self._getMessageDetails(msg_id=message['id'])\n\n if set(inbox_labels) - set(details['labelIds']):\n # Not a unread message which is in Inbox.\n # Just ignore it for now.\n continue\n\n headers = {}\n for raw_header in details['payload']['headers']:\n headers[raw_header['name'].lower()] = raw_header['value']\n\n self._checkMessage(headers)\n # Any message is removed so that we will not process it later.\n self._trash(msg_id=message['id'])\n\n if details:\n self._history_id = int(details['historyId'])\n self.setRegistryValue('historyID', value=self._history_id)",
"title": ""
},
{
"docid": "98e0f7011ee31889cca4ab2955c579f2",
"score": "0.4632578",
"text": "def get_mailbox_usage_mailbox_counts(\n self,\n period, # type: str\n **kwargs # type: Any\n ):\n # type: (...) -> List[\"models.MicrosoftGraphMailboxUsageMailboxCounts\"]\n cls = kwargs.pop('cls', None) # type: ClsType[List[\"models.MicrosoftGraphMailboxUsageMailboxCounts\"]]\n error_map = {\n 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError\n }\n error_map.update(kwargs.pop('error_map', {}))\n accept = \"application/json\"\n\n # Construct URL\n url = self.get_mailbox_usage_mailbox_counts.metadata['url'] # type: ignore\n path_format_arguments = {\n 'period': self._serialize.url(\"period\", period, 'str'),\n }\n url = self._client.format_url(url, **path_format_arguments)\n\n # Construct parameters\n query_parameters = {} # type: Dict[str, Any]\n\n # Construct headers\n header_parameters = {} # type: Dict[str, Any]\n header_parameters['Accept'] = self._serialize.header(\"accept\", accept, 'str')\n\n request = self._client.get(url, query_parameters, header_parameters)\n pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)\n response = pipeline_response.http_response\n\n if response.status_code not in [200]:\n map_error(status_code=response.status_code, response=response, error_map=error_map)\n error = self._deserialize(models.OdataError, response)\n raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)\n\n deserialized = self._deserialize('[MicrosoftGraphMailboxUsageMailboxCounts]', pipeline_response)\n\n if cls:\n return cls(pipeline_response, deserialized, {})\n\n return deserialized",
"title": ""
},
{
"docid": "65de810738c049a6114a292fa709afa2",
"score": "0.46321726",
"text": "async def get_moderation_count(self, guild_id, user_id):\n sql = \"\"\"\n SELECT COUNT(userid) FROM {}.moderation\n WHERE serverid = $1 AND userid = $2;\n \"\"\".format(self.schema)\n return await self.pool.fetchval(sql, guild_id, user_id)",
"title": ""
},
{
"docid": "570ece8117c345ff8e59c5b1cca0fb67",
"score": "0.46282303",
"text": "def check_messages(hero):\n\n hero.account.inbox_alert = False",
"title": ""
},
{
"docid": "60685d7924f0130d68d77f041abd41c2",
"score": "0.462268",
"text": "def count_types(self) -> int:\n return self._count_model(Type)",
"title": ""
},
{
"docid": "595b9b3d723decc2d5110149e0c5c997",
"score": "0.46218836",
"text": "async def get_count(self, guild, message):\n count = 0\n message = await self.database.get_one_starboard_message(guild.id, message.id)\n if message is not None:\n count = message.count\n return count",
"title": ""
},
{
"docid": "a3ca9bda47d543a0b2b528e7711a98bb",
"score": "0.46188736",
"text": "def get_folder_message_list(self, folder, unread_only = True):\n path = '/folder/' + folder\n\n if unread_only:\n path += '/unread'\n\n response, messages = self.send_request(path)\n\n return messages",
"title": ""
},
{
"docid": "ba7e71ada2a15f23960cec9c159f2714",
"score": "0.45996648",
"text": "def test_get_inbox_messages(self):\n pass",
"title": ""
},
{
"docid": "f2506c2bc1f59243ee51cf269ce18c8f",
"score": "0.459716",
"text": "def checkEmail(q):\n username = TPasswords.tickerbell_email\n password = TPasswords.tickerbell_email_pw\n imap = imaplib.IMAP4_SSL(\"imap.gmail.com\", '993')\n imap.login(username, password)\n while (1):\n status, messageCount = imap.select(\"INBOX\")\n if status != 'OK':\n print(\"Could not connect to gmail inbox\")\n return\n\n #get IDs of unread emails and put into a list named IDs\n #IDs SHOULD be in order oldest to newest\n status, IDs = imap.search(None, 'UnSeen')\n IDs = IDs[0].split()\n if len(IDs) > 0:\n for i, ID in enumerate(IDs):\n #IDs is now a list of ID strings\n IDs[i] = ID.decode('utf-8')\n for ID in IDs:\n status, msg = imap.fetch(ID, \"(RFC822)\")\n if status != 'OK':\n print(\"Email {0} could not be retrieved.\".format(ID))\n else:\n #### https://www.thepythoncode.com/article/reading-emails-in-python ####\n for response in msg:\n if isinstance(response, tuple):\n # parse a bytes email into a message object\n msg = email.message_from_bytes(response[1])\n # decode the email subject\n subject = decode_header(msg[\"Subject\"])[0][0]\n if isinstance(subject, bytes):\n # if it's a bytes, decode to str\n subject = subject.decode()\n # email sender\n from_ = msg.get(\"From\")\n from_ = from_.split()\n from_ = from_[len(from_)-1].strip('<>')\n return_username = from_.split('@')[0]\n address = from_.split('@')[1]\n #AUTHORIZING RECIPIENT\n if from_ not in TAlert.emails and from_ not in TAlert.phoneNumbers:\n print(\"Email sender {0} invalid personnel\".format(from_))\n break\n commands = []\n if msg.is_multipart():\n # iterate over email parts\n for part in msg.walk():\n # extract content type of email\n content_type = part.get_content_type()\n content_disposition = str(part.get(\"Content-Disposition\"))\n try:\n # get the email body\n body = part.get_payload(decode=True).decode()\n except:\n pass\n if content_type == \"text/plain\" and \"attachment\" not in content_disposition:\n # print text/plain emails and skip attachments\n for line in body.splitlines():\n commands.append(line)\n elif \"attachment\" in content_disposition:\n # download attachment\n filename = part.get_filename()\n if filename:\n filepath = os.path.join(os.getcwd(), filename)\n # download attachment and save it\n open(filepath, \"wb\").write(part.get_payload(decode=True))\n commands = handleEmailAttachment(filename)\n os.remove(filepath)\n else:\n # extract content type of email\n content_type = msg.get_content_type()\n # get the email body\n body = msg.get_payload(decode=True).decode()\n if content_type == \"text/plain\":\n # print only text email parts\n for line in body.splitlines():\n commands.append(line.lower().strip())\n #redirect stdout temporarily\n old_stdout = sys.stdout\n sys.stdout = mystdout = StringIO()\n for command in commands:\n if command == \"alert start\":\n q.put(('alert start'))\n elif command not in [\"alert start\", \"remote on\", \"quit\"]:\n TickerBell.handleInput(command)\n sys.stdout = old_stdout\n message = mystdout.getvalue()\n if from_ in TAlert.emails:\n TAlert.sendEmail(message, receiver)\n else:\n for i in range(0, int(len(message) / 160)):\n start = i * 160\n end = start + 159\n TAlert.sendEmail(message[start:end], from_)\n sleep(2)\n imap.close()\n imap.logout()",
"title": ""
},
{
"docid": "bf41d3fdb0ecab5bbff0f376750afef6",
"score": "0.4594744",
"text": "def msg_queue_size(self):\n\n rtn = 0\n try:\n cursor = self.connection.cursor()\n qry = \"SELECT COUNT(ALL) FROM msg_queue\"\n cursor.execute(qry)\n val = cursor.fetchall()\n if len(val) < 1:\n rtn = 0\n rtn = str(val[0][0])\n except Exception as e:\n logging.error(\"msg_queue_size:\" + qry + \":\" + str(e))\n cursor.close()\n return str(rtn)",
"title": ""
},
{
"docid": "4d042c1393aadc0ac7ade9df1673cf39",
"score": "0.45942438",
"text": "async def unread(client, args):\n\n def callback():\n \"\"\"Print unread count(s)\"\"\"\n\n # For each service\n counts = {}\n for service in client.unread_services:\n name, direct, indirect = service\n safe_name = html.escape(name)\n\n # If it's exactly the service we're looking for, just return the count\n if safe_name == args.services:\n count = direct\n if not args.direct:\n count += indirect\n print(count)\n return\n\n # If the service in included in the services we're looking for\n if args.services in (\"total\", \"all\") or safe_name in args.services:\n counts[safe_name] = direct\n if not args.direct:\n counts[safe_name] += indirect\n\n # Get total notifications\n if args.services == \"total\":\n print(sum(counts.values()))\n return\n\n # Finally, print each service notifications on a different line\n print(\n \"\\n\".join(\n f\"{name}: {count}\"\n for name, count in counts.items()\n )\n )\n\n # Do print counts and keep running if tail mode enabled\n callback()\n if args.tail:\n client.on_change(callback)\n await asyncio.get_running_loop().create_future()",
"title": ""
},
{
"docid": "4b381f89c6e88954411bff9c596590d7",
"score": "0.45915714",
"text": "def getNumberLasers(self):\r\n self.message = lumencor_httpcommand(command ='GET CHMAP', ip=self.ip)\r\n if self.message['message'][0]=='A':\r\n return len(self.message['message'].split(' '))-2\r\n return 0",
"title": ""
},
{
"docid": "eb5c778074f66998cd119da4ce236201",
"score": "0.458805",
"text": "def mboxlist(self):\n mbox_ret, mbox_raw = self.imap.list()\n subm_ret, subm_raw = self.imap.lsub()\n\n list_raw = (mbox_raw + subm_raw)\n\n if (mbox_ret, subm_ret) == (\"OK\", \"OK\"):\n mbox_list = self.parsemboxlist(list_raw)\n\n else:\n raise Exception(\"Server returned invalid response to list command\")\n\n return [x for x in mbox_list if self.mboxstat(x)]",
"title": ""
},
{
"docid": "a076a55fbc5f7c395bfaef274d654f86",
"score": "0.4568376",
"text": "def nb_events_by_activity_type(self) -> dict:\n return self.count_events_by_activity_type()",
"title": ""
},
{
"docid": "26afb981945bcb465956c2eddeebeec5",
"score": "0.4562087",
"text": "def get_entry_count(self):\n return len(self.mime_lookup)",
"title": ""
},
{
"docid": "26afb981945bcb465956c2eddeebeec5",
"score": "0.4562087",
"text": "def get_entry_count(self):\n return len(self.mime_lookup)",
"title": ""
},
{
"docid": "c6d6d0bf82d437efe5615332e841337f",
"score": "0.45607117",
"text": "def _getMessagesFromInbox(self):\n label_ids = ['INBOX']\n messages = []\n\n try:\n response = self._google.users().messages().list(\n userId='me',\n labelIds=label_ids,\n ).execute()\n\n if 'messages' in response:\n messages.extend(response['messages'])\n\n while 'nextPageToken' in response:\n page_token = response['nextPageToken']\n response = self._google.users().messages().list(\n userId='me',\n labelIds=label_ids,\n pageToken=page_token,\n ).execute()\n messages.extend(response['messages'])\n\n return messages\n\n except errors.HttpError, error:\n self.log.error(\n '_getMessagesFromInbox: An error occurred: %s' % error)\n return []",
"title": ""
},
{
"docid": "f2593b55f1e210cb832f9009ef2fcfb2",
"score": "0.45593047",
"text": "def get_inbox(self, inbox, auth):\n\n if inbox is None:\n inbox = self.__number\n\n return auth.messages.list(to=inbox)",
"title": ""
},
{
"docid": "049ee2faf2e6ece5e5b59a2d3b807dc2",
"score": "0.45558015",
"text": "def test_get_inbox(self):\n user = User.objects.get(pk=1)\n inbox_messages = Message.objects.get_inbox_for(user)\n\n self.failUnlessEqual(len(inbox_messages), 1)\n self.failUnlessEqual(inbox_messages[0].body,\n 'Hello from your mother')",
"title": ""
},
{
"docid": "52549237af009095a69f3bcf98fc55af",
"score": "0.45504746",
"text": "def count_actions(fname=None, msg_type=None):\n k = 0\n for evts in load_conv_events(fname):\n for e in evts:\n if e['event_type'] == msg_type or msg_type is None:\n k += 1\n return k",
"title": ""
},
{
"docid": "b751d3c34db5866eceb5bd73f1aa54c9",
"score": "0.45353124",
"text": "def number_following(self, cr, uid, model_name=\"mail.thread\", context=None):\n user = self.pool.get('res.users').browse(cr, uid, uid, context=context)\n return self.pool.get('mail.followers').search(cr, uid, [('res_model', '=', model_name), ('partner_id', '=', user.partner_id.id)], count=True, context=context)",
"title": ""
},
{
"docid": "0d4a3964d8c6e00c2748e922bdba6645",
"score": "0.4528173",
"text": "def increment_unread_requests(cls):\n cls.unread_requests_counter += 1\n return",
"title": ""
},
{
"docid": "0d4a3964d8c6e00c2748e922bdba6645",
"score": "0.4528173",
"text": "def increment_unread_requests(cls):\n cls.unread_requests_counter += 1\n return",
"title": ""
},
{
"docid": "c175e990570a55827198cf579b6bb12d",
"score": "0.45063373",
"text": "def count(self, key: str) -> int:\n if key == 'all':\n return len(self.data)\n elif key.title().replace('To', 'to') in ('Watching', 'Reading', 'Completed', 'On-Hold', 'Dropped', 'Plan to Watch', 'Plan to Read'):\n return len([i for i in self.data if i.my_status == key.title().replace('To', 'to')])\n return 0",
"title": ""
},
{
"docid": "fb79963ae494c2b93bc349a7bf218250",
"score": "0.4505438",
"text": "def __len__(self):\n declared = self.channel.queue_declare(self.queue_name, passive=True)\n return declared.method.message_count",
"title": ""
},
{
"docid": "4a762fb5ca3dce3480a2fa0019ea1093",
"score": "0.44920695",
"text": "def total_invites(self):\n total = 0\n for db_user in User.objects():\n inviter = db_user.inviter\n if inviter is not None:\n if inviter.user_id == self.user_id:\n total += 1\n return total",
"title": ""
},
{
"docid": "c0c8fd5b9eda9543cf9180790382dbc8",
"score": "0.44913486",
"text": "def find_user_notifications(request):\n notifications = request.user.twitteruser.notification_set\n new_notification = 0\n for notice in notifications.get_queryset().all():\n if not notice.has_seen:\n new_notification += 1\n return {\"notification\": new_notification}",
"title": ""
},
{
"docid": "1e86419874d7cf0b8b5356be7c6aae5d",
"score": "0.44900954",
"text": "def test_push_unread_count_group_by_room(self) -> None:\n # Carry out common push count tests and setup\n self._test_push_unread_count()\n\n # Carry out our option-value specific test\n #\n # This push should still only contain an unread count of 1 (for 1 unread room)\n self._check_push_attempt(7, 1)",
"title": ""
},
{
"docid": "ba974c9b4142ba578b33d0d3f4ac83e3",
"score": "0.4477009",
"text": "def rooms_table_size(self):\n\n rtn = 0\n try:\n cursor = self.connection.cursor()\n qry = \"SELECT COUNT(ALL) FROM rooms\"\n cursor.execute(qry)\n val = cursor.fetchall()\n if len(val) < 1:\n rtn = 0\n rtn = int(val[0][0])\n except Exception as e:\n logging.error(\"ERROR: msg_queue_size(): \" + qry + \":\" + str(e))\n cursor.close()\n return int(rtn)",
"title": ""
},
{
"docid": "07007717334ad495104321581ddb2f9f",
"score": "0.44648585",
"text": "def slacking_count(self, message_json):\n if message_json.get('type') != 'message':\n return\n if 'subtype' in message_json.keys():\n return\n if 'bot_id' in message_json.keys():\n return\n if message_json.get('user') in self.ignore_user_list:\n return\n\n self.slacking_dict[message_json.get('channel', '')][message_json.get('user', '')] += 1",
"title": ""
}
]
[
{
"docid": "1fc64426902988012c1a7778bfd6ceca",
"score": "0.0",
"text": "def img_pre_process(img):\n # Chop off 1/3 from the top and cut bottom 150px(which contains the head of car)\n shape = img.shape\n img = img[int(shape[0] / 3):shape[0] - 150, 0:shape[1]]\n img = img / 255.\n print(img.shape)\n\n # Resize the image\n resize_img = resize(img, (128, 384), mode='reflect')\n # Return the image sized as a 4D array\n return resize_img # np.resize(img, (w, h, c)",
"title": ""
}
]
[
{
"docid": "4b27d0b9a56e8d4d8524a2066c858a1d",
"score": "0.79944336",
"text": "def process(self, image):\n pass",
"title": ""
},
{
"docid": "b92044a4dd259f62f19f82eaa207ff3e",
"score": "0.74050933",
"text": "def process(self, grabbed_image):\n raise NotImplementedError(\"\")",
"title": ""
},
{
"docid": "5e80d30643edad4a6e718fe8e7b338c0",
"score": "0.7040692",
"text": "def takeAndProcessImage (processType):\n\n # Creat a client to perform the processing\n client = createClient()\n\n # Request a new image to be processed\n newImage = getImage()\n\n # What form of processing do we want?\n if (\"logo\" == processType):\n # Check for logos\n result = detectLogo(client, newImage)\n elif (\"text\" == processType):\n # Check for text\n result = detectText(client, newImage)\n else:\n # Default to labels\n result = detectLabels(client, newImage)\n \n return result",
"title": ""
},
{
"docid": "7212399b3f2460c399ab3d12049ce670",
"score": "0.69169545",
"text": "def post_process(self, *args, **kwargs):\n return self.image_processor.post_process(*args, **kwargs)",
"title": ""
},
{
"docid": "bbaa991a8cdd4f891dfae5d9e82ce71b",
"score": "0.66806924",
"text": "def proc_img(self, img): \n raise NotImplementedError('Must be implemented by subclasses')",
"title": ""
},
{
"docid": "c58dbf53782c9dd17e3c689413d10ba6",
"score": "0.6671303",
"text": "def process_image():\n # Histogram Equalization [default]\n # Contrast Stretching\n # Log Compression\n # Reverse Video\n # Gamma Correction\n r = request.get_json()\n check = validateInputs(r)\n if check == 1:\n logging.error(\"Insufficient information\")\n return Response(json.dumps({'Error': 'Key Missing'}), status=500)\n username = r['username']\n method = r['processing']\n usertoprocess = User.objects.raw({\"_id\": username}).first()\n for k in usertoprocess.imgslist:\n if k == \"\":\n continue\n if k['filename'] == r['filename']:\n whichim = k\n imstr = k['imgstring']\n image = decode_image_fromb64(imstr, k['filetype'], k['dimensions'])\n break\n start = time.time()\n if method.lower() == \"histogram equalization\":\n processed = histogram_equalize(image)\n elif method.lower() == \"contrast stretching\":\n processed = contrast_stretch(image)\n elif method.lower() == \"log compression\":\n processed = log_compress(image)\n elif method.lower() == \"reverse video\":\n processed = reverse_video(image)\n elif method.lower() == \"gamma correction\":\n processed = gamma_correct(image)\n else:\n return \"no method found\"\n if processed == \"TypeError\":\n logging.error(\"Image Invalid\")\n return Response(json.dumps({'Error': 'Image Type Invalid'}),\n status=500)\n end = time.time()\n elapsed_time = end - start\n if method.lower() in whichim[\"processeddict\"]:\n data = whichim[\"processeddict\"][method.lower()]\n times_run = data[3] + 1\n else:\n times_run = 1\n whichim[\"processeddict\"][method.lower()] = [processed,\n datetime.datetime.now(),\n elapsed_time,\n times_run]\n usertoprocess.save()\n logging.info(\"Image Processing successful\")\n return \"Success\"",
"title": ""
},
{
"docid": "ec7c55c795fdce3cb81a988d732b7566",
"score": "0.6640429",
"text": "def process_image(self):\n\n args = ['python3', '/home/ubuntu/Desktop/CAPSTONE_R/chess-irs/frame.py',\n 'detect', '--input', self.new_state_raw_img, '--output',\n self.new_state_proc_img]\n\n p = subprocess.run(args, stdout=subprocess.DEVNULL, stderr=subprocess.STDOUT)",
"title": ""
},
{
"docid": "7648e8af58093a35fb24c3055eae6940",
"score": "0.65882206",
"text": "def image():",
"title": ""
},
{
"docid": "ee3b9ba97a1105ccd20911d660d15d48",
"score": "0.6578494",
"text": "def _process_image(directory, name):\n # Read the image file.\n filename = directory + DIRECTORY_IMAGES + name + '.png'\n image_data = tf.gfile.FastGFile(filename, 'r').read()\n\n # Image caracteristics\n size = Image.open(filename).size\n shape = [size[0], size[1], 3]\n\n # Read the TXT annotation file.\n filename = os.path.join(directory, DIRECTORY_ANNOTATIONS, name + '.txt')\n\n labels, bboxes = _parse_rec(filename, shape)\n\n return image_data, shape, bboxes, labels",
"title": ""
},
{
"docid": "566df00dac6b6580ea7b83cb50792312",
"score": "0.6570799",
"text": "def getImage():\n\n # Get the PID of the raspistil process that was launched from crontab using a\n # command similar to\n # @reboot raspistill -o /tmp/aiyimage.jpg -s -t 0 -w 640 -h 480 &\n raspistillPID = subprocess.check_output([\"pidof\", \"raspistill\"])\n\n # Request that a picture is taken by sending a USR1 signal to the Raspistill process\n os.kill(int(raspistillPID), signal.SIGUSR1)\n\n # Wait for the photo to be taken\n time.sleep(0.5)\n\n # The location of the file is hardcoded.\n file_name = \"/tmp/aiyimage.jpg\"\n\n # Loads the image into memory, ready for processing, and return it\n with io.open(file_name, 'rb') as image_file:\n content = image_file.read()\n return types.Image(content=content)\n\n # We failed to load in the image, or process it for some reason\n return None",
"title": ""
},
{
"docid": "80eb10b773ad09197754e0cc154be5cf",
"score": "0.6564306",
"text": "def _process_image(filename):\n\t # Create a generic TensorFlow-based utility for converting all image codings.\n\tcoder = ImageCoder()\n\t# Read the image file.\n\twith tf.gfile.FastGFile(filename, 'rb') as f:\n\t\timage_data = f.read()\n\n\timage_data = coder.png_to_jpeg(image_data)\n\n\t# Decode the RGB JPEG.\n\timage = coder.decode_jpeg(image_data)\n\timage = tf.image.convert_image_dtype(image, dtype=tf.float32)\n\timage=tf.reshape(1-tf.image.rgb_to_grayscale(image),[height*width])\n\treturn image",
"title": ""
},
{
"docid": "36f8739071343541b40896d9726cbf52",
"score": "0.6560644",
"text": "def get_image(self, bp):\n pass",
"title": ""
},
{
"docid": "f01c563c11b13f39303ad4497270a3ac",
"score": "0.651668",
"text": "def _process_image(filename, coder):\n # Read the image file.\n image_data = tf.gfile.FastGFile(filename, 'rb').read()\n\n # Decode the RGB JPEG.\n image = coder.decode_jpeg(image_data)\n\n # Check that image converted to RGB\n assert len(image.shape) == 3\n height = image.shape[0]\n width = image.shape[1]\n assert image.shape[2] == 3\n\n return image_data, height, width",
"title": ""
},
{
"docid": "28eda599f633cbd2b7bc71288b3fb0fc",
"score": "0.64870495",
"text": "def _process_image(filename, coder):\n # Read the image file.\n with tf.gfile.FastGFile(filename, 'r') as f:\n image_data = f.read()\n\n # Convert any PNG to JPEG's for consistency.\n if _is_png(filename):\n print('Converting PNG to JPEG for %s' % filename)\n image_data = coder.png_to_jpeg(image_data)\n\n # Decode the RGB JPEG.\n image = coder.decode_jpeg(image_data)\n\n # Check that image converted to RGB\n assert len(image.shape) == 3\n height = image.shape[0]\n width = image.shape[1]\n assert image.shape[2] == 3\n\n return image_data, height, width",
"title": ""
},
{
"docid": "598ac1048497fbc36bfe27476dcce783",
"score": "0.6486401",
"text": "def __img_processing(cls, img):\n\n # Pega a imagem\n new_image = cv.imread(img)\n\n # Normalizar a imagem (largura=64, altura=64, canais=3)\n new_image = cv.cvtColor(new_image, cv.COLOR_BGR2RGB)\n new_image = cv.resize(new_image, (128, 64), interpolation=cv.INTER_AREA)\n new_image = new_image.astype('float32')\n new_image /= 255\n\n # Formatar para o formato do tensorFlow (qtd, altura, largura, dimensoes)\n # Verificar se isso não vai dar problema.\n new_image = np.expand_dims(new_image, axis = 0)\n\n return new_image",
"title": ""
},
{
"docid": "ad15455f8d0490899279e0611d53ec16",
"score": "0.6455995",
"text": "def process_image(image):\n print('*** Start Processing Image ***')\n \n process = transforms.Compose([transforms.Resize(256),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n transforms.Normalize(means, stdevs)])\n \n img_pil = Image.open(image)\n img_tensor = process(img_pil)\n \n print('*** Processing Image Complete ***')\n \n return img_tensor",
"title": ""
},
{
"docid": "728a580873cf95c87e7c276f376b7b25",
"score": "0.6435586",
"text": "def get_and_process_image(self):\n\t\terr,res,img = vrep.simxGetVisionSensorImage(self.clientID,self.visionSensorHandle,0,vrep.simx_opmode_oneshot_wait)\n\n\t\tcolval = numpy.zeros((res[0]*res[1],3))\n\t\ti = 0\n\t\tfor pix in range(res[0]*res[1]):\n\t\t\tfor col in range(3):\n\t\t\t\tif img[i] >= 0:\n\t\t\t\t\tcolval[pix][col] = img[i]\n\t\t\t\telse:\n\t\t\t\t\tcolval[pix][col] = img[i] + 256\n\t\t\t\ti += 1\n\n\t\tred = 0.0\n\t\tred_position = 0.0\n\t\tgreen = 0.0\n\t\tgreen_position = 0.0\n\t\tblue = 0.0\n\t\tblue_position = 0.0\n\n\t\ti = 0\n\t\tfor pix in colval:\n\t\t\ti += 1\n\t\t\tposition = (i % 32)\n\t\t\tif (pix[0] > 200 and pix[1] < 100 and pix[2] < 100):\n\t\t\t\tred += 1\n\t\t\t\tred_position += -1 + position * 0.0625 \n\t\t\telif (pix[0] < 100 and pix[1] > 200 and pix[2] < 100):\n\t\t\t\tgreen += 1\n\t\t\t\tgreen_position += -1 + position * 0.0625\n\t\t\telif (pix[0] < 100 and pix[1] < 100 and pix[2] > 200):\n\t\t\t\tblue += 1\n\t\t\t\tblue_position += -1 + position * 0.0625\n\t\t\n\t\tif (red > 0):\n\t\t\tred_position = red_position / red\n\t\tif (green > 0):\n\t\t\tgreen_position = green_position / green\n\t\tif (blue > 0):\n\t\t\tblue_position = blue_position / blue\n\n\t\tinput_vars = [red / 256, red_position, green / 256, green_position, blue / 256, blue_position]\n\t\treturn input_vars",
"title": ""
},
{
"docid": "0695e42b6ec0efda87de7bc16efa256c",
"score": "0.6422601",
"text": "def process_image(path, data_group_folder, file_path):\n filename = file_path.split('/')[-1]\n current_path = os.path.join(path, os.path.join(r'{0}/IMG/'.format(data_group_folder), filename))\n image = cv2.imread(current_path)\n # Convert to RGB\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n return image",
"title": ""
},
{
"docid": "e61bc830729431d3770dc1738f557f59",
"score": "0.6407284",
"text": "def process_image(filepath):\n img = pp.load_image_as_grayscale(filepath)\n img = pp.autocrop_edges(img)\n img = pp.apply_blur(img)\n img = pp.circle_darkest(img)\n pp.save_image(filepath, img)\n return img",
"title": ""
},
{
"docid": "4c6733cfecdbe8fdd607a2c0f4f22346",
"score": "0.63953424",
"text": "def process_image(self,img):\n print(\"starting image processing of file with path:\",img)\n new_image = ImageData(\n jname=self.job_name,\n frame = self.name,\n path = img,\n const = self.constants, \n db_ref = self.db_ref,\n )\n self.image_data_ls.append(new_image)\n self.image_ref_ls.append(new_image.im_id)",
"title": ""
},
{
"docid": "749461c93e36457a91da8ee7ff9d9786",
"score": "0.63952726",
"text": "def image (self):\n\n\t\ti = yield threads.deferToThread(self.camera.getImage)\n\n\t\tif i is None:\n\t\t\tprint \"No image\"\n\n\t\tdefer.returnValue(i)",
"title": ""
},
{
"docid": "df24500edf2ba2e80de71131bcc4943c",
"score": "0.63754076",
"text": "def process_image(self, data, preprocessing=None, postprocessing=None):\n if isinstance(data, str):\n logger.debug(\"Load image: {}\".format(data))\n image, org_image = self.__load_image__(data) # Load image\n if image is False or org_image is False:\n return False\n if preprocessing: # If an algorithm that preprocesses is specified,\n # then this algorithm should immediately remove the background\n image = preprocessing.run(self, image, org_image)\n else:\n image = self.__get_output__(image, org_image) # If this is not, then just remove the background\n if postprocessing: # If a postprocessing algorithm is specified, we send it an image without a background\n image = postprocessing.run(self, image, org_image)\n return image",
"title": ""
},
{
"docid": "df24500edf2ba2e80de71131bcc4943c",
"score": "0.63754076",
"text": "def process_image(self, data, preprocessing=None, postprocessing=None):\n if isinstance(data, str):\n logger.debug(\"Load image: {}\".format(data))\n image, org_image = self.__load_image__(data) # Load image\n if image is False or org_image is False:\n return False\n if preprocessing: # If an algorithm that preprocesses is specified,\n # then this algorithm should immediately remove the background\n image = preprocessing.run(self, image, org_image)\n else:\n image = self.__get_output__(image, org_image) # If this is not, then just remove the background\n if postprocessing: # If a postprocessing algorithm is specified, we send it an image without a background\n image = postprocessing.run(self, image, org_image)\n return image",
"title": ""
},
{
"docid": "a64d2ad4bdaac3e0eae8b8a360069261",
"score": "0.63645416",
"text": "def process_image(img_path, thickness=3):\n\n img = mpimg.imread(img_path)\n\n return _process_image(img, thickness=thickness)",
"title": ""
},
{
"docid": "fc5faeb78447639adcbfd9c5a7b0d57c",
"score": "0.6342809",
"text": "def _get_image(self):\n return self.__image",
"title": ""
},
{
"docid": "fc5faeb78447639adcbfd9c5a7b0d57c",
"score": "0.6342809",
"text": "def _get_image(self):\n return self.__image",
"title": ""
},
{
"docid": "206681a888a715c0307709be43e19aae",
"score": "0.63032174",
"text": "def image(self):\n print(\"Calculating image for parameters: {}\".format(self.p))\n return transform_image(self._image, self.p[0], self.p[1], self.p[2],\n self.p[3], self.p[4])",
"title": ""
},
{
"docid": "2d60c79ad960bffedea25b5aaa329f4d",
"score": "0.6301444",
"text": "def process_image(self, data, preprocessing=None, postprocessing=None):\n if isinstance(data, str):\n logger.debug(\"Load image: {}\".format(data))\n image, orig_image = self.__load_image__(data) # Load image\n if image is False or orig_image is False:\n return False\n if preprocessing: # If an algorithm that preprocesses is specified,\n # then this algorithm should immediately remove the background\n image = preprocessing.run(self, image, orig_image)\n else:\n image = self.__get_output__(image, orig_image) # If this is not, then just remove the background\n if postprocessing: # If a postprocessing algorithm is specified, we send it an image without a background\n image = postprocessing.run(self, image, orig_image)\n return image",
"title": ""
},
{
"docid": "6ca02a80711efef822908ea778e44a3f",
"score": "0.62759316",
"text": "def post(self):\n data = request.files[\"image_file\"]\n if data.filename != '':\n results = main.process_image(data)\n headers = {'Content-Type': 'text/html', 'Cache-Control': 'no-store'}\n return make_response(render_template('results.html', images = results),200,headers)",
"title": ""
},
{
"docid": "c9d38a69e40e2c9791e20f59ff678461",
"score": "0.62692183",
"text": "def _process_image(filename, coder):\n # Read the image file.\n with tf.gfile.FastGFile(filename, 'r') as f:\n image_data = f.read()\n\n if _is2convert(filename):\n print('Reencoding to JPEG for %s' % filename)\n image_data = coder.re_encode_jpeg(image_data)\n\n return image_data",
"title": ""
},
{
"docid": "1e9f118aa806ba10ffbe5c090f7e2afe",
"score": "0.62566334",
"text": "def processImage(self):\n if self.difCent is None:\n return\n QApplication.setOverrideCursor(Qt.WaitCursor)\n QApplication.processEvents()\n flags = self.getFlags()\n try:\n self.difCent.process(flags)\n except:\n QApplication.restoreOverrideCursor()\n errMsg = QMessageBox()\n errMsg.setText('Unexpected error')\n msg = 'Please report the problem with error message below and the input image\\n\\n'\n msg += \"Error : \"+str(sys.exc_info()[0]) +'\\n\\n'+str(traceback.format_exc())\n errMsg.setInformativeText(msg)\n errMsg.setStandardButtons(QMessageBox.Ok)\n errMsg.setIcon(QMessageBox.Warning)\n errMsg.setFixedWidth(300)\n errMsg.exec_()\n raise\n\n self.updateParams()\n self.writeData()\n self.refreshUI()\n QApplication.restoreOverrideCursor()\n QApplication.processEvents()",
"title": ""
},
{
"docid": "69816c12c902033e1cf3d1e1cf157fae",
"score": "0.62435424",
"text": "def process_image(image_path):\n # Call global varibales\n global means, std\n # Open image with PIL\n image = Image.open(image_path)\n\n # Resize the image\n if image.size[0] > image.size[1]:\n image.thumbnail((10000, 256))\n else:\n image.thumbnail((256, 10000))\n\n # Crop the image\n left_margin = (image.width-224)/2\n bottom_margin = (image.height-224)/2\n right_margin = left_margin + 224\n top_margin = bottom_margin + 224\n image = image.crop((left_margin, bottom_margin, right_margin,\n top_margin))\n # Normalize\n image = np.array(image)/255\n image = (image - means)/std\n\n # Move color channels to first dimension as expected by PyTorch\n image = image.transpose((2, 0, 1))\n\n return image",
"title": ""
},
{
"docid": "1860e63b6defdd82f934bb4b8615a5e3",
"score": "0.6216545",
"text": "def get_image(data):\n image = Image.open(BytesIO(base64.b64decode(data['image'])))\n image = np.asarray(image)\n image = process_image(image)\n image = np.array([image])\n return image",
"title": ""
},
{
"docid": "05cf1fd95322bcc46727cee1fb814d2b",
"score": "0.6152597",
"text": "def process_image(image):\n width, height = image.size\n ratio = width / height\n if width < height:\n width = 256\n height = int(256 / ratio)\n else:\n height = 256\n width = int(256 * ratio)\n image = image.resize((width, height))\n image = image.crop((width / 2 - 112, height / 2 - 112, width / 2 + 112, height / 2 + 112))\n np_image = np.array(image)\n np_image = np_image.astype(float)\n np_image = np_image / 255\n np_image = (np_image - np.array([0.485, 0.456, 0.406])) / np.array([0.229, 0.224, 0.225])\n\n return np.transpose(np_image, (2, 0, 1))",
"title": ""
},
{
"docid": "02be9f99315f4f6b5f1b35be2c3d6074",
"score": "0.6125608",
"text": "def _parse_and_processed(self, example_serialized):\n image_buffer, label, bbox, _ = self._parse_fn(example_serialized)\n image = self._image_preprocessing(image_buffer, bbox)\n return image, label",
"title": ""
},
{
"docid": "5160a5232e579ba83082bb57b12e1bb4",
"score": "0.61234045",
"text": "def __call__(self, results):\n\n if self.file_client is None:\n self.file_client = mmcv.FileClient(**self.file_client_args)\n\n if results['img_prefix'] is not None:\n filename = osp.join(results['img_prefix'],\n results['img_info']['filename'])\n else:\n filename = results['img_info']['filename']\n\n img_bytes = self.file_client.get(filename)\n img = mmcv.imfrombytes(img_bytes, flag=self.color_type)\n if self.to_float32:\n img = img.astype(np.float32)\n\n results['filename'] = filename\n results['ori_filename'] = results['img_info']['filename']\n results['img'] = img\n results['img_shape'] = img.shape\n results['ori_shape'] = img.shape\n results['img_fields'] = ['img']\n return results",
"title": ""
},
{
"docid": "a23a430eabec5a5ffc0bae79b9bb049c",
"score": "0.6122378",
"text": "def process_image(self, base64_string: str) -> str:\n self.convert_base64_to_image(base64_string)\n self.corp_image()\n self.change_image_pixels()\n return self.image_to_string()",
"title": ""
},
{
"docid": "8ba02a3226ed59234a99acd2ae29e487",
"score": "0.6118086",
"text": "def _process_image(self, data: np.ndarray,\n outfile: Optional[pathlib.Path] = None) -> np.ndarray:\n\n if data.ndim != 2:\n raise ValueError('Expected 2D image, got {}: {}'.format(data.shape, outfile))\n\n # if self._frame_count == 0:\n # print('Dtype: {}'.format(data.dtype))\n # print('Rows, Cols: {},{}'.format(*data.shape))\n\n # Crop the image\n data = data[self._crop_y_st:self._crop_y_ed, self._crop_x_st:self._crop_x_ed]\n if data.shape[0] == 0 or data.shape[1] == 0:\n err = 'Invalid image shape after bbox x({},{}) y({},{}): {}x{}: {}'\n err = err.format(self._crop_x_st,\n self._crop_x_ed,\n self._crop_y_st,\n self._crop_y_ed,\n data.shape[0],\n data.shape[1],\n outfile)\n raise ValueError(err)\n\n # Fix the contrast\n if self._fix_contrast not in (None, 'raw'):\n data = image_utils.fix_contrast(data, mode=self._fix_contrast)\n # Force the final image to be 8-bit\n data = np.round(data*255)\n data[data < 0] = 0\n data[data > 255] = 255\n data = data.astype(np.uint8)\n\n # Convert to RGB\n data = np.stack([data, data, data], axis=2)\n\n if data.ndim not in (2, 3):\n raise ValueError('Invalid segment after processing {}: {}'.format(data.shape, outfile))\n return data",
"title": ""
},
{
"docid": "633a8b73b62ea802eda6deff54172aa3",
"score": "0.61167383",
"text": "def processImage(self):\n if self.bioImg is None:\n return\n QApplication.setOverrideCursor(Qt.WaitCursor)\n QApplication.processEvents()\n settings = self.getSettings()\n try:\n self.bioImg.process(settings)\n except Exception as e:\n QApplication.restoreOverrideCursor()\n errMsg = QMessageBox()\n errMsg.setText('Unexpected error')\n msg = 'Please report the problem with error message below and the input image\\n\\n'\n msg += \"Error : \" + str(sys.exc_info()[0]) + '\\n\\n' + str(traceback.format_exc())\n errMsg.setInformativeText(msg)\n errMsg.setStandardButtons(QMessageBox.Ok)\n errMsg.setIcon(QMessageBox.Warning)\n errMsg.setFixedWidth(300)\n errMsg.exec_()\n raise\n\n self.updateParams()\n self.csvManager.writeNewData(self.bioImg)\n self.csvManager2.writeNewData(self.bioImg)\n self.resetUI()\n self.refreshStatusbar()\n QApplication.restoreOverrideCursor()",
"title": ""
},
{
"docid": "c07c062e43fcd7dce0c4346fd6876897",
"score": "0.60999334",
"text": "def __call__(self, results):\n\n if self.file_client is None:\n self.file_client = mmcv.FileClient(**self.file_client_args)\n\n if results['img_prefix'] is not None:\n filename = osp.join(results['img_prefix'],\n results['img_info']['filename'])\n else:\n filename = results['img_info']['filename']\n\n img_bytes = self.file_client.get(filename)\n img = mmcv.imfrombytes(\n img_bytes, flag=self.color_type, channel_order=self.channel_order)\n if self.to_float32:\n img = img.astype(np.float32)\n\n results['filename'] = filename\n results['ori_filename'] = results['img_info']['filename']\n results['img'] = img\n results['img_shape'] = img.shape\n results['ori_shape'] = img.shape\n results['img_fields'] = ['img']\n results['ori_img'] = results['img']\n return results",
"title": ""
},
{
"docid": "4f6e024f61ccf3109576c7c12b2f4ddc",
"score": "0.60888064",
"text": "def take_image(self):\n return_value, image = self.camera.read()\n\n if self.color and self.write_to_file:\n self.write_image(image)\n if not self.color:\n image = self.monochrome_image(image)\n if self.write_to_file:\n self.write_image(image)\n\n # Changing the image to a 1D array.\n flat = image.ravel()\n return flat",
"title": ""
},
{
"docid": "1133a1c9fc4a9421b320571046a80b11",
"score": "0.6075215",
"text": "def process_image(fname, output_dir, poller):\n result = []\n try:\n preprocessed_image, original_image = preprocess_image(fname, (poller.params.input_height, poller.params.input_width))\n results = classify_image(poller.params.model, preprocessed_image,\n threshold=poller.params.threshold, labels=poller.params.labels,\n mean=poller.params.mean, stdev=poller.params.stdev)\n out_file = os.path.join(output_dir, os.path.splitext(os.path.basename(fname))[0] + \".json\")\n with open(out_file, \"w\") as f:\n f.write(results.to_json_string())\n result.append(out_file)\n except KeyboardInterrupt:\n poller.keyboard_interrupt()\n except:\n poller.error(\"Failed to process image: %s\\n%s\" % (fname, traceback.format_exc()))\n return result",
"title": ""
},
{
"docid": "3113916df7dc83ec3c876fc473ecf1db",
"score": "0.6072556",
"text": "def load_proc_image(self,image_path):\n load_path = self.image_path_prefix+'proc_img'+image_path+'.jpg'\n image = imread(load_path,plugin='pil')\n return image",
"title": ""
},
{
"docid": "095f119d3843642e048f3d02d60e42a1",
"score": "0.60693157",
"text": "def get_image(self,imagefile): \n \n # Load an image and convert it to an array\n img = nibabel.load(os.path.join(self.sourcedir,imagefile))\n \n return img.get_data()",
"title": ""
},
{
"docid": "40e45e05f0553276e2205255e0cc3efc",
"score": "0.6064062",
"text": "def process_img_pred_celery(user_id, frame_processed, frame_orig, frame_dict, output_dict):\n \n from .wsgi_aux import app\n with app.app_context():\n output_dict = process_img_pred(user_id, frame_processed, frame_orig, frame_dict, output_dict)\n socketio.emit('response_image', output_dict, room=output_dict['room'])",
"title": ""
},
{
"docid": "9b112aec8b86c37a5ea859687775c5a9",
"score": "0.605764",
"text": "def process_image(img_path):\n\n img = image.load_img(img_path, target_size=(224, 224))\n img_array = image.img_to_array(img)\n img_array = np.expand_dims(img_array, axis=0)\n pImg = mobilenet.preprocess_input(img_array)\n return pImg",
"title": ""
},
{
"docid": "bf217935dcaa6e661d1d4d2791e4e1b3",
"score": "0.60563534",
"text": "def process_image(path):\n # Read Image\n image = ndimage.imread(path)\n # Crop Image from top and bottom\n image = image[60:140,:,:]\n # Resize the Image to match the input size of the model\n image = cv2.resize(image,(64,64),cv2.INTER_AREA)\n return image",
"title": ""
},
{
"docid": "aaeb5535fb9f5808623943d2194db077",
"score": "0.6048713",
"text": "def calculate(self):\n \n\n opts, args = self.op.parse_args(self.args) \n\n (addr_space, symtab, types) = load_and_identify_image(self.op, self.opts)\n\n filename = self.opts.filename\n temp = filename.replace(\"\\\\\", \"/\").lower().split(\"/\")\n imgname = temp[-1]\n\n if not opts.offset is None:\n \n try:\n offset = int(opts.offset, 16)\n except:\n op.error(\"EPROCESS offset must be a hexidecimal number.\")\n \n try:\n flat_address_space = FileAddressSpace(filename)\n except:\n op.error(\"Unable to open image file %s\" %(filename))\n\n directory_table_base = process_dtb(flat_address_space, types, offset)\n\n process_address_space = create_addr_space(addr_space, directory_table_base)\n\n if process_address_space is None:\n print \"Error obtaining address space for process [%d]\" % (process_id)\n return\n\n\n image_file_name = process_imagename(flat_address_space, types, offset)\n\n process_id = process_pid(flat_address_space, types, offset)\n \n peb = process_peb(flat_address_space, types, offset)\n\n if not process_address_space.is_valid_address(peb):\n #print \"Unable to read PEB for task.\"\n command_line = \"UNKNOWN\"\n return\n\n command_line = process_command_line(process_address_space, types, peb)\n\n if command_line is None:\n command_line = \"UNKNOWN\"\n\n \n modules = process_ldrs(process_address_space, types, peb)\n\n #if len(modules) > 0:\n #print \"%-12s %-12s %s\"%('Base','Size','Path')\n \n for module in modules:\n if not process_address_space.is_valid_address(module):\n return\n path = module_path(process_address_space, types, module)\n if path is None:\n path = \"UNKNOWN\"\n \n base = module_base(process_address_space, types, module)\n if base is None:\n base = \"UNKNOWN\"\n else:\n base = \"0x\" % (base)\n \n size = module_size(process_address_space, types, module)\n if size is None:\n size = \"UNKNOWN\"\n else:\n size = \"0x\" % (size)\n \n yield (image_file_name, process_id,command_line,base,size,path, imgname) \n \n else:\n \n # get list of windows processes\n all_tasks = process_list(addr_space, types, symtab) \n\n if not opts.pid == None:\n all_tasks = process_find_pid(addr_space,types, symtab, all_tasks, opts.pid)\n if len(all_tasks) == 0:\n print \"Error process [%d] not found\"%opts.pid\n\n \n for task in all_tasks:\n\n if not addr_space.is_valid_address(task):\n continue\n \n #if len(all_tasks) > 1:\n # print \"%s\"%star_line\n \n image_file_name = process_imagename(addr_space, types, task)\n\n process_id = process_pid(addr_space, types, task)\n\n process_address_space = process_addr_space(addr_space, types, task, opts.filename)\n\n if process_address_space is None:\n print \"Error obtaining address space for process [%d]\" % (process_id)\n continue\n \n peb = process_peb(addr_space, types, task)\n\n if not process_address_space.is_valid_address(peb):\n #print \"Unable to read PEB for task.\"\n command_line = \"UNKNOWN\"\n continue\n else:\n command_line = process_command_line(process_address_space, types, peb)\n\n if command_line is None:\n command_line = \"UNKNOWN\"\n\n #print \"Command line : %s\" % (command_line)\n \n #print \"something below\"\n #print read_unicode_string(process_address_space, types,\n # ['_PEB', 'CSDVersion'], peb)\n \n modules = process_ldrs(process_address_space, types, peb)\n\n #if len(modules) > 0:\n # print \"%-12s %-12s %s\"%('Base','Size','Path')\n \n for module in modules:\n if not process_address_space.is_valid_address(module):\n continue\n path = module_path(process_address_space, types, module)\n if path is None:\n path = \"UNKNOWN\"\n \n base = module_base(process_address_space, types, module)\n if base is None:\n base = \"UNKNOWN\"\n else:\n base = \"0x%1x\" % (base)\n \n size = module_size(process_address_space, types, module)\n if size is None:\n size = \"UNKNOWN\"\n else:\n size = \"0x%1x\" % (size)\n \n yield (image_file_name, process_id,command_line,base,size,path, imgname)",
"title": ""
},
{
"docid": "891e9dd94d1f002f07ffc0972b1eb2e9",
"score": "0.6027528",
"text": "def process_image(input, **kwargs):\n from interface import load_pars\n from image import Image\n import os\n\n # Try to load input assuming it's a parameter save file or a dictionary.\n # load_pars returns None if this doesn't work.\n img, err = load_pars(input)\n\n # If load_pars fails (returns None), assume that input is an image file. If it's not a\n # valid image file (but is an existing file), an error will be raised\n # by img.process() during reading of the file.\n if img == None:\n if os.path.exists(input):\n img = Image({'filename': input})\n else:\n raise RuntimeError(\"File '\" + input + \"' not found.\")\n\n # Now process it. Any kwargs specified by the user will\n # override those read in from the parameter save file or dictionary.\n img.process(**kwargs)\n return img",
"title": ""
},
{
"docid": "70b4565cd61c48d9ff3b3d53acc8081c",
"score": "0.6027032",
"text": "def _handle_result(self, result):\n image_num = 0\n for img_response in result:\n self._image_data[image_num] = img_response.shot.image.data\n image_num += 1",
"title": ""
},
{
"docid": "52d8cf11cdaa8cc76007af61756982f5",
"score": "0.60248125",
"text": "def _parse_and_processed(self, example_serialized):\n image, label = self._parse_fn(example_serialized)\n image = self._image_preprocessing(image)\n return image, label",
"title": ""
},
{
"docid": "03e8723e63a10344dd75501fb7f47ed2",
"score": "0.6014056",
"text": "def process_image(image):\n # Process a PIL image for use in a PyTorch model\n im = Image.open(image)\n\n # pytorch transforms can be used on and PIL.Image object\n normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n transform = transforms.Compose([transforms.Resize(256),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n normalize])\n im = transform(im)\n return im",
"title": ""
},
{
"docid": "2a8a9e2df1a7c6f8b8effa9395bf08cd",
"score": "0.6009029",
"text": "def _process_image(self, image):\n expects_means = [0.485, 0.456, 0.406]\n expects_std = [0.229, 0.224, 0.225]\n\n image = Image.open(image).convert(\"RGB\")\n\n # Any reason not to let transforms do all the work here?\n transform = transforms.Compose([\n transforms.Resize(256),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n transforms.Normalize(expects_means, expects_std)])\n\n return transform(image)",
"title": ""
},
{
"docid": "e386c238f0df318f75b896ab26d82152",
"score": "0.6004115",
"text": "def output(self):\r\n return (self.image_viewer.image, None)",
"title": ""
},
{
"docid": "71d5b5c62c52eaaf22db63f5f08d19ac",
"score": "0.5995343",
"text": "def load_and_process_image(image_path):\n image = load_img(image_path)\n image = img_to_array(image)\n image = preprocess_input(image)\n image = np.expand_dims(image, axis=0)\n return image",
"title": ""
},
{
"docid": "98a3098f5d83bff469a8edb9778b5609",
"score": "0.5980046",
"text": "def image_processing(root, img_name):\n\t# Parse image\n\timage = misc.imread(os.path.join(root,img_name), flatten= 0)\n\timage_array = misc.bytescale(image)\n\t#image_array = to_grey(image_array)\n\n\t# Crop image\n\ttop = find_top(image_array)\n\tbottom = find_bottom(image_array)\n\tleft = find_left(image_array)\n\tright = find_right(image_array)\n\n\t# Find some orientation with center search\n\tcenters = find_approx_centers(image_array, top, bottom, left, right)\n\n\t# Classify centers\n\tclassification = classify(image_array, centers, os.path.join(root,img_name))\n\n\t# Debug\n\t#print_center(image_array, centers, classification)\n\treturn classification",
"title": ""
},
{
"docid": "374fec0210cdb8dd8d68a91137d98c68",
"score": "0.5974606",
"text": "def get_binary_result(self):\n return self.binary_image",
"title": ""
},
{
"docid": "70d5a19a17e07e3937196b0a5be38632",
"score": "0.5973736",
"text": "def get_raw_image(self):\n\t\treturn self._raw_image",
"title": ""
},
{
"docid": "5ea471eac3bd587c3de4d07f7f354a42",
"score": "0.59690523",
"text": "def process_image(self, image, input_shape) -> np.ndarray:\r\n width, height = image.size\r\n # ensure image type is compatible with model and convert if not\r\n if image.mode != \"RGB\":\r\n image = image.convert(\"RGB\")\r\n # center crop image (you can substitute any other method to make a square image, such as just resizing or padding edges with 0)\r\n if width != height:\r\n square_size = min(width, height)\r\n left = (width - square_size) / 2\r\n top = (height - square_size) / 2\r\n right = (width + square_size) / 2\r\n bottom = (height + square_size) / 2\r\n # Crop the center of the image\r\n image = image.crop((left, top, right, bottom))\r\n # now the image is square, resize it to be the right shape for the model input\r\n input_width, input_height = input_shape[1:3]\r\n if image.width != input_width or image.height != input_height:\r\n image = image.resize((input_width, input_height))\r\n\r\n # make 0-1 float instead of 0-255 int (that PIL Image loads by default)\r\n image = np.asarray(image) / 255.0\r\n # format input as model expects\r\n return image.reshape(input_shape).astype(np.float32)",
"title": ""
},
{
"docid": "f20e37ef98474e029eb65dd246e6ad6f",
"score": "0.5962878",
"text": "def __call__(self, results):\n # Image is bgr\n img = results['img'][..., ::-1]\n img = Image.fromarray(img)\n img = self.transform(img)\n img = np.asarray(img)\n img = img[..., ::-1]\n results['img'] = img\n return results",
"title": ""
},
{
"docid": "7f08ee2ea54bbf608cd8696a6fc4fd40",
"score": "0.59553367",
"text": "def proc_img(self, img, w=700):\n img = img[:,:w]\n return img",
"title": ""
},
{
"docid": "a2f60a6de4d615b3ed4b1aeaa553aecb",
"score": "0.5952116",
"text": "def get_image(self):\n self.drawer.flush()\n return self.img",
"title": ""
},
{
"docid": "ce0485f06240d3c92b80431f3c6cb0ab",
"score": "0.59459233",
"text": "def image (self):\n\n\t\ttry:\n\t\t\tflag, img_array = yield threads.deferToThread(self.camera.read)\n\t\texcept SystemError:\n\t\t\treturn\n\n\t\tif flag is False:\n\t\t\tprint \"No image\"\n\t\t\treturn\n\n\t\tdefer.returnValue(SimpleCV.Image(\n\t\t\tsource = cv.fromarray(img_array),\n\t\t\tcv2image = True)\n\t\t)",
"title": ""
},
{
"docid": "29ea012497e3a71ed5875e7e5f847cea",
"score": "0.5933279",
"text": "def read_image_response_body():\n image_file = open(os.path.join(os.path.dirname(__file__), 'image.gif'), 'rb')\n return image_file.read()",
"title": ""
},
{
"docid": "fd0c18f3f93958b45ee8ab19ce5973e5",
"score": "0.59317374",
"text": "def process_image(self, image, dims=False):\n image = self._load_image(image)\n return ImageCheck.check_and_crop(image, dims)",
"title": ""
},
{
"docid": "48c7bea4b6c79e6ab9ad5fa1fb959378",
"score": "0.59283745",
"text": "def get_processed_image(username):\n try:\n user = User.objects.raw({'_id': username}).first()\n return user.processed_image\n except DoesNotExist:\n return None",
"title": ""
},
{
"docid": "cf063ae9a6b268cdf1f03159abc84d99",
"score": "0.59189093",
"text": "def process(self, image):\n try:\n if self.processing_job.isAlive():\n self.display(\"Processing to slow\")\n return\n except AttributeError:\n pass\n self.processing_job = ProcessingJob()\n self.processing_job.image = image\n self.processing_job.results = self.results\n self.processing_job.start()",
"title": ""
},
{
"docid": "315236e700ecbed35751a702546ad7f7",
"score": "0.5918512",
"text": "def process_data(self,img_path): \n img = tf.io.read_file(img_path)\n img = tf.io.decode_jpeg(img, channels=1)\n img = tf.image.resize(img, (196,196))\n img = tf.cast(img / 255., tf.float32)\n img = tf.reshape(img, (1,196,196,1))\n img = np.array(img)\n \n return img",
"title": ""
},
{
"docid": "756a69c1e63f5e8bbf440e4e12c88708",
"score": "0.5916828",
"text": "def get_raw_image(self,imagefile): \n \n # Load an image and convert it to an array\n img = nibabel.load(os.path.join(self.sourcedir,imagefile))\n \n return img",
"title": ""
},
{
"docid": "2cbe90c7643430c46bce855da8e20d14",
"score": "0.59126264",
"text": "def getImage(self):\n\t\tglobal root, image\n\n\t\tpostscript = self.canvas.postscript(colormode=\"gray\")\n\t\timage = Image.open(io.BytesIO(postscript.encode('utf-8')))\n\n\n\t\troot.destroy()",
"title": ""
},
{
"docid": "40420202161809fb202893ab29622c96",
"score": "0.59038264",
"text": "def get_image(self):\n return Image.open(self.get_image_path())",
"title": ""
},
{
"docid": "7c64ea4058547424faad6497c63fec9b",
"score": "0.5901178",
"text": "async def __call__(self, image: Image) -> Image:\n ...",
"title": ""
},
{
"docid": "0d2f42ffa5e27dcf9991326f7da5e6de",
"score": "0.58970296",
"text": "def load_image(self):",
"title": ""
},
{
"docid": "fc53cb0723f3bd6c81bffd5c9b1673d7",
"score": "0.5895032",
"text": "def process_image(img):\n\n img = Image.open(img)\n img.thumbnail((256, 256))\n\n width, height = img.size # Get dimensions\n\n new_width = 224\n new_height = 224\n left = (width - new_width) / 2\n top = (height - new_height) / 2\n right = (width + new_width) / 2\n bottom = (height + new_height) / 2\n\n img = img.crop((left, top, right, bottom))\n\n np_image = np.array(img)\n img = np_image / 255\n\n mean = np.array([0.485, 0.456, 0.406])\n stdv = np.array([0.229, 0.224, 0.225])\n img = (img - mean) / stdv\n\n img = img.transpose((2, 0, 1))\n return torch.from_numpy(img)",
"title": ""
},
{
"docid": "bc68fee4b35c5d6ceaee80cb746428d8",
"score": "0.58904344",
"text": "def process(self, statePair):\n #_, img = statePair\n img = statePair[:,:,1]\n assert img.shape == (self.im_width, self.im_height)\n return np.squeeze(np.reshape(img, [self.im_width * self.im_height, -1]) - self.ref)",
"title": ""
},
{
"docid": "cd2f5905749c8bdf07718d505fa441f7",
"score": "0.5888443",
"text": "def process_image(*args, keycache, config, patientcache):\n # check file type\n task, obj, _ = args\n if task != \"process\" or Path(obj.key).suffix.lower() != \".dcm\":\n # not an image, don't do anything with it\n return bonobo.constants.NOT_MODIFIED\n\n # check if work is already done\n image_in_cache = keycache.exists(obj.key)\n image_uuid = Path(obj.key).stem\n metadata_in_cache = keycache.exists(f\"{image_uuid}.json\")\n if metadata_in_cache and image_in_cache:\n # files exist, nothing to do here\n return\n\n # download the image\n image_data = PartialDicom(obj.Object()).download()\n if image_data is None:\n # we couldn't read the image data correctly\n logger.warning(\n f\"Object '{obj.key}' couldn't be loaded as a DICOM file, skipping!\"\n )\n return\n\n # extract the required data from the image\n patient_id = image_data.PatientID\n study_id = image_data.StudyInstanceUID\n series_id = image_data.SeriesInstanceUID\n group = patientcache.get_group(patient_id)\n if group is not None:\n training_set = group == \"training\"\n else:\n logger.error(\n f\"Image without patient data: {obj.key}; \"\n + f\"included patient ID: {patient_id}; \"\n + \"skipping!\"\n )\n return\n prefix = (\n constants.TRAINING_PREFIX\n if training_set\n else constants.VALIDATION_PREFIX\n )\n image_type = constants.MODALITY.get(\n image_data[\"Modality\"].value, \"unknown\"\n )\n\n date = get_date_from_key(obj.key)\n if date:\n # the location of the new files\n new_key = posixpath.join(\n prefix,\n image_type,\n patient_id,\n study_id,\n series_id,\n Path(obj.key).name,\n )\n metadata_key = posixpath.join(\n prefix,\n f\"{image_type}-metadata\",\n patient_id,\n study_id,\n series_id,\n f\"{image_uuid}.json\",\n )\n # send off to copy or upload steps\n if not object_exists(new_key):\n yield \"copy\", obj, new_key\n if not object_exists(metadata_key):\n yield \"metadata\", metadata_key, image_data",
"title": ""
},
{
"docid": "ae1018b175ed67c5b50527733f846ceb",
"score": "0.5886974",
"text": "def _process_image(\n self, image: JpegImageFile, width: int, body: str,\n author: str) -> Image:\n resized = self._resize(image, width)\n draw = ImageDraw.Draw(resized)\n header_font = fit_header_font(body, resized.size, self.font_file)\n body_font = fit_footer_font(author, resized.size, self.font_file)\n img_W, img_H = resized.size\n header_w, _ = draw.textsize(body, font=header_font)\n body_w, body_h = draw.textsize(author, font=body_font)\n draw.text(((img_W-header_w)/2, 20),\n body, fill=\"white\", font=header_font)\n draw.text(((img_W-body_w)/2, img_H-body_h*2),\n author, fill=\"white\", font=body_font)\n return resized",
"title": ""
},
{
"docid": "3775182cc2511abcc298ddfc1aee53cf",
"score": "0.58860236",
"text": "def get_input(self):\n\t\treturn self.img",
"title": ""
},
{
"docid": "89cdc69cb02fa8fa9da781e663d5ff37",
"score": "0.58795017",
"text": "def image (self):\n\n\t\ti = self.camera.getImage()\n\n\t\tif i is None:\n\t\t\tprint \"No image\"\n\n\t\treturn i",
"title": ""
},
{
"docid": "96a70f2017105d586a6fa87bf63d6717",
"score": "0.58792675",
"text": "def get_image(self):\n return self.image",
"title": ""
},
{
"docid": "ea63f56df667f880734b4e86665802a4",
"score": "0.58791053",
"text": "def transform(self, image):\n pass",
"title": ""
},
{
"docid": "70cc03838fee0d79136d37db6f17aa4f",
"score": "0.5877867",
"text": "def processImage(arg1, image=False):\n\timg = arg1\n\tif(not image):\n\t\timg = cv2.imread(arg1,0)\n\timg = cv2.GaussianBlur(img, (5,5), 0)\n\timg = cv2.resize(img,(SIDE_SIZE,SIDE_SIZE))\n\timg = cv2.adaptiveThreshold(img,1,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)\n\t#img = list(map((lambda x: x/float(1)-0.1), img))\n\treturn np.reshape(img, (TOTAL_SIZE, 1))",
"title": ""
},
{
"docid": "16d499eb7197e74ca8fea66cf9cda2fb",
"score": "0.5876384",
"text": "def _processor(self):\n\n # Get latest image from Mailbox\n image = self._mailbox.get()\n\n # Call Every Registered Callback\n if self._running:\n for callback in self.callbacks:\n callback(image)\n\n # Update Statistics\n self._update_dt()",
"title": ""
},
{
"docid": "74745997b4cd616fddf62fb8763b6f44",
"score": "0.5866133",
"text": "def processImg(img, camvals):\n img = imutils.resize(img, width = camvals[\"width\"], height = camvals[\"height\"])\n img = cv2.cvtColor(img, camvals[\"color\"])\n img = Image.fromarray(img)\n imgbytes = io.BytesIO()\n img.save(imgbytes, format = \"JPEG\") #, quality=camvals[\"quality\"])\n imgbytes = imgbytes.getvalue()\n imgbytes = zlib.compress(imgbytes, camvals[\"compression\"])\n return imgbytes",
"title": ""
},
{
"docid": "bc03e82c25ec1360114ad27120ec8e2b",
"score": "0.58655936",
"text": "def process_image_for_saving(image: np.ndarray) -> np.ndarray:\n # Copy image\n img = image.copy()\n\n # Remove batch dimension\n img = np.squeeze(img, 0)\n\n # Add mean (from VGG19 model)\n img[:, :, 0] += 103.939\n img[:, :, 1] += 116.779\n img[:, :, 2] += 123.68\n\n # BGR to RGB\n img = img[:, :, ::-1]\n\n # Clip values\n img = np.clip(img, 0, 255).astype(\"uint8\")\n return img",
"title": ""
},
{
"docid": "853c3593c0d51a2887a4c802d4e30dec",
"score": "0.58615106",
"text": "def read_image(file: IO) -> Image:",
"title": ""
},
{
"docid": "0cdb02d8aecf3fc39deed1acd65fffa9",
"score": "0.5858906",
"text": "def process_one(self, filename, process_steps, bias, pixel_flat=None, illum_flat=None, bpm=None):\n # Load raw image\n rawImage = rawimage.RawImage(filename, self.spectrograph, self.det)\n # Process\n processrawImage = processrawimage.ProcessRawImage(rawImage, self.par, bpm=bpm)\n processedImage = processrawImage.process(process_steps, bias=bias, pixel_flat=pixel_flat,\n illum_flat=illum_flat)\n # Return\n return processedImage",
"title": ""
},
{
"docid": "c2147e3da796d3defcfd5b551a89f60f",
"score": "0.58515847",
"text": "def get_file_image(self, *args, **kwargs): # real signature unknown\n pass",
"title": ""
},
{
"docid": "5f5074d1886be337ae3fe6365cebae7e",
"score": "0.5848452",
"text": "def process_image(image):\n\n image = Image.fromarray(image)\n # Resize\n img = image.resize((256, 256))\n\n # Center crop\n width = 256\n height = 256\n new_width = 224\n new_height = 224\n\n left = (width - new_width) / 2\n top = (height - new_height) / 2\n right = (width + new_width) / 2\n bottom = (height + new_height) / 2\n img = img.crop((left, top, right, bottom))\n\n # Convert to numpy, transpose color dimension and normalize\n rgbimg = Image.new(\"RGB\", img.size)\n rgbimg.paste(img)\n img = rgbimg\n img = np.array(img).transpose((2, 0, 1)) / 256\n\n # Standardization\n means = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))\n stds = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))\n\n img = img - means\n img = img / stds\n\n return img",
"title": ""
},
{
"docid": "d53e63357d6dde3a817ad0bced299ae6",
"score": "0.58356947",
"text": "def post(self):\n model_name = self.get_argument(\"model-name\", default=\"mosaic\")\n model = self.get_model(model_name)\n content_image = self.request.files.get(\"content-image\", None)\n if content_image is None or len(content_image) == 0:\n return self.write_error(500)\n content_image = content_image[0]\n input_im = Image.open(BytesIO(content_image.body))\n output_image = yield stylizer_pool.submit(\n handle_input_image,\n model,\n input_im,\n self.use_cuda,\n self.gpu_idx,\n False\n )\n self.set_header(\"Content-Type\", \"image/jpeg\")\n self.set_header(\"Refresh\", \"1\")\n self.set_header(\"content-transfer-encoding\", \"binary\")\n self.set_header(\"Access-Control-Allow-Origin\", \"*\")\n self.set_header(\"Access-Control-Allow-Headers\", \"x-requested-with\")\n self.set_header(\"Access-Control-Allow-Methods\", \"POST, GET\")\n output_data = BytesIO()\n output_image.save(output_data, format=\"JPEG\")\n self.write(base64.b64encode(output_data.getvalue()))",
"title": ""
},
{
"docid": "3651dfa7f1939a96702854c26241db8f",
"score": "0.58274",
"text": "def getAssembledImg(self):\n img = self.det.image(self.evt)\n return img",
"title": ""
},
{
"docid": "68367f035b4c5c5fdd9c7fdc29803a8e",
"score": "0.58238506",
"text": "def PNG2Data(image_folder_location): \r\n #Make a vector of PNG files in the image directory so it can repeat \r\n #process on all images in directory\r\n images = []\r\n os.chdir(image_folder_location)\r\n for image in os.listdir(image_folder_location):\r\n if image.endswith(\".png\"):\r\n images.append(image)\r\n \r\n #create empty lists to compile information\r\n filenames = []\r\n \r\n #make a subdirectory to put the image exports if the directory\r\n #does not already exist\r\n if not os.path.exists('CroppedImages'):\r\n os.makedirs('CroppedImages')\r\n \r\n #create variable for name of folder with cropped images\r\n cropped_images_folder_location = image_folder_location + '/CroppedImages'\r\n\r\n #make a subdirectory to put the form number image exports if the directory\r\n #does not already exist\r\n if not os.path.exists('FormNumber'):\r\n os.makedirs('FormNumber')\r\n \r\n #loop through files in folder and find their form number\r\n for image in images:\r\n #find the filename\r\n filename = findFileName(image)\r\n filenames.append(filename)\r\n \r\n #find the form number and page number\r\n form_number = findFormNumber(image)\r\n \r\n #check if form_number found. If not, continue to next image. \r\n if form_number is None:\r\n continue\r\n \r\n page_number = findPageNumber(image)\r\n \r\n #set image coordinates\r\n setImageCoords(image)\r\n \r\n #find coords needed for form number\r\n coords = switchCoords2(form_number, page_number)\r\n \r\n #determine if this file contains data based on page_info lookup table and then\r\n #crop the image if it does\r\n if isinstance(page_info[form_number], int):\r\n if page_info[form_number] == page_number:\r\n #loop through the coordinates for each attribute and create a new image\r\n for key, value in coords.items():\r\n #only do the crop if there is the correct number of dimensions\r\n if len(value) != 4:\r\n if len(value) == 0:\r\n print(key, \" has no coords yet.\")\r\n continue\r\n else:\r\n print(key, \"has an incorrect number of dimensions.\")\r\n continue\r\n else:\r\n crop(image, value, 'CroppedImages/' + filename + '_' + key +'.png')\r\n else:\r\n print(\"Not a data form\")\r\n else:\r\n if (page_info[form_number][0] == page_number) or (page_info[form_number][1] == page_number):\r\n #loop through the coordinates for each attribute and create a new image\r\n for key, value in coords.items():\r\n #only do the crop if there is the correct number of dimensions\r\n if len(value) != 4:\r\n if len(value) == 0:\r\n print(key, \" has no coords yet.\")\r\n continue\r\n else:\r\n print(key, \"has an incorrect number of dimensions.\")\r\n continue\r\n else:\r\n crop(image, value, 'CroppedImages/' + filename + '_' + key +'.png')\r\n else:\r\n print(\"Not a data form\")\r\n \r\n #remove the split PDF images so process not repeated on them\r\n os.remove(image)\r\n \r\n #run tesseract on folder with cropped images\r\n tesseract(cropped_images_folder_location)\r\n \r\n ###have a process complete message\r\n easygui.msgbox(\"Completed!\", title=\"i9 Processing\")",
"title": ""
},
{
"docid": "ea74190f1991994a34ecf44d43026d51",
"score": "0.582367",
"text": "def __call__(self, results):\n\n img = results['img']\n x_start, y_start, x_stop, y_stop = results['win']\n width = x_stop - x_start\n height = y_stop - y_start\n\n patch = img[y_start:y_stop, x_start:x_stop]\n if height > patch.shape[0] or width > patch.shape[1]:\n patch = mmcv.impad(patch, shape=(height, width))\n\n if self.to_float32:\n patch = patch.astype(np.float32)\n\n results['filename'] = None\n results['ori_filename'] = None\n results['img'] = patch\n results['img_shape'] = patch.shape\n results['ori_shape'] = patch.shape\n results['img_fields'] = ['img']\n return results",
"title": ""
},
{
"docid": "fbf963ccb476c15741f0bd20c0b01baf",
"score": "0.58138454",
"text": "def get_image_data(self, use_original=False):\n\n if self._extracted_merged_image and use_original:\n return self._extracted_merged_image\n\n r = Renderer(self)\n return r.render()",
"title": ""
},
{
"docid": "ace96a4d72bb91e2ff3ea500ef99cca7",
"score": "0.58082485",
"text": "def export_img(self):\n self._update()\n return self._img",
"title": ""
},
{
"docid": "65c9cad040a4596928984814894e2a51",
"score": "0.5791",
"text": "def image_callback(self, msg):\n\n\t\tself.image = self.bridge.imgmsg_to_cv2(msg, desired_encoding=\"bgr8\")",
"title": ""
},
{
"docid": "69d2fb81871b0922d445c4938163de78",
"score": "0.57897717",
"text": "def image(x):\n return x.image()",
"title": ""
},
{
"docid": "2f11084b5e73f303b869a571d85959d8",
"score": "0.5788476",
"text": "def __call__(self, results):\n\n if self.file_client is None:\n self.file_client = mmcv.FileClient(**self.file_client_args)\n\n if results['img_prefix'] is not None:\n filename = [\n osp.join(results['img_prefix'], fname)\n for fname in results['img_info']['filename']\n ]\n else:\n filename = results['img_info']['filename']\n\n img = []\n for name in filename:\n img_bytes = self.file_client.get(name)\n img.append(mmcv.imfrombytes(img_bytes, flag=self.color_type))\n img = np.stack(img, axis=-1)\n if self.to_float32:\n img = img.astype(np.float32)\n\n results['filename'] = filename\n results['ori_filename'] = results['img_info']['filename']\n results['img'] = img\n results['img_shape'] = img.shape\n results['ori_shape'] = img.shape\n # Set initial values for default meta_keys\n results['pad_shape'] = img.shape\n results['scale_factor'] = 1.0\n num_channels = 1 if len(img.shape) < 3 else img.shape[2]\n results['img_norm_cfg'] = dict(\n mean=np.zeros(num_channels, dtype=np.float32),\n std=np.ones(num_channels, dtype=np.float32),\n to_rgb=False)\n return results",
"title": ""
},
{
"docid": "966c5d0c7f11cca0db6491f413328811",
"score": "0.5788234",
"text": "def get_image (self, *args, **kwargs):\r\n # Just an alias for semantic clarity...\r\n return self.build_image(*args,**kwargs)",
"title": ""
},
{
"docid": "4c1b6b875af885cee88417b67633a71e",
"score": "0.578538",
"text": "def run(self, image: iter, **kwargs) -> None:",
"title": ""
},
{
"docid": "8c45b6aa73c0f122a4710f629206b8bf",
"score": "0.57712483",
"text": "def image_transform(instance):\n byte_array = base64.b64decode(instance[\"data\"])\n image = Image.open(io.BytesIO(byte_array))\n instance[\"data\"] = image_processing(image).tolist()\n logging.info(instance)\n return instance",
"title": ""
}
]
6f79505ce9f122994b5991bf039a6436
polbasis(self) > string Summary Return the polarization basis in the calibration table ('L' for linear or 'C' for circular). Description This member function returns the polarization basis in the calibration table ('L' for linear or 'C' for circular).
[
{
"docid": "9ddff4da2e30336611fc206b7183ff3b",
"score": "0.61899257",
"text": "def Lri_basis(self):\n return self.Lri_basis_set",
"title": ""
},
{
"docid": "8a5e35c53d30934fa38fb888e9874ef7",
"score": "0.6093768",
"text": "def getBasis(self):\n return self.__basis",
"title": ""
},
{
"docid": "0e2631b044b7bf878e5badba9c43f304",
"score": "0.60009676",
"text": "def get_polynomial_basis(self) -> typing.List[AnyFunction]:\n return self._basis",
"title": ""
},
{
"docid": "1f608217eb66b50c81dbbc09e2f153bf",
"score": "0.58080673",
"text": "def getBasisFunctionsWithLVal(self):\n\t\traise NotImplementedError(\"\")",
"title": ""
},
{
"docid": "01feac688224ddcfeefcec0adc7a9b86",
"score": "0.56200033",
"text": "def __init__(self):\n super().__init__()\n self._name = \"Single-Qubit Pauli Overcomplete Preparation Basis\"\n self._basis = {\"0\": np.array([[1, 0], [0, 0]]).astype(complex),\n \"1\": np.array([[0, 0], [0, 1]]).astype(complex),\n \"A\": np.array([[1, 1], [1, 1]]).astype(complex)/2,\n \"D\": np.array([[1, -1], [-1, 1]]).astype(complex)/2,\n \"L\": np.array([[1, -1j], [1j, 1]]).astype(complex)/2,\n \"R\": np.array([[1, 1j], [-1j, 1]]).astype(complex)/2}",
"title": ""
},
{
"docid": "ce31de3387e5a439b4ad7f6712233aed",
"score": "0.5590029",
"text": "def basis(self) -> float:\n return self.__basis",
"title": ""
},
{
"docid": "3267d7d1b0e051f05eab3a396830a30f",
"score": "0.55111456",
"text": "def one_basis(self):\n basis = self.basis_name()\n if basis == 'serre-cartan' or basis == 'arnonc':\n return (0,)\n if not self._generic:\n return ()\n return ((), ())",
"title": ""
},
{
"docid": "5eaeddbcb774fcb8981ee10a2659a814",
"score": "0.5510506",
"text": "def __init__(self):\n super().__init__()\n self._name = \"Single-Qubit Pauli Preparation Basis\"\n self._basis = {\"0\": np.array([[1, 0], [0, 0]]).astype(complex),\n \"1\": np.array([[0, 0], [0, 1]]).astype(complex),\n \"A\": np.array([[1, 1], [1, 1]]).astype(complex)/2,\n \"L\": np.array([[1, -1j], [1j, 1]]).astype(complex)/2}",
"title": ""
},
{
"docid": "4e1c8df58d2a20ca15ee615359527e19",
"score": "0.550433",
"text": "def get_polynomial_basis(self) -> typing.List[AnyFunction]:\n pass",
"title": ""
},
{
"docid": "f8fa50364774bc80b66ae82e0580316e",
"score": "0.5483415",
"text": "def get_polynomial_basis(self) -> typing.List[AnyFunction]:\n raise NotImplementedError()",
"title": ""
},
{
"docid": "f8fa50364774bc80b66ae82e0580316e",
"score": "0.5483415",
"text": "def get_polynomial_basis(self) -> typing.List[AnyFunction]:\n raise NotImplementedError()",
"title": ""
},
{
"docid": "d1bf4f16313fbac13082a4cd6f0d070f",
"score": "0.53959364",
"text": "def Ri_rpa_basis_set(self):\n return self.Ri_aux_basis_set",
"title": ""
},
{
"docid": "f6d5abd0822b83997a93e7fba822cba5",
"score": "0.53462464",
"text": "def poly(self) -> str:\n return self.alert_data[\"poly\"]",
"title": ""
},
{
"docid": "9e031b2ace25d0f24f58da416d132971",
"score": "0.5339238",
"text": "def getPyQ(self):\n symorder = {'S': 0, 'P': 1, 'D': 2, 'F': 3}\n \"\"\"order of orbitals, first s then p, d, f\"\"\"\n for atno,contr in self.basis_data.iteritems():\n contr.sort(cmp=lambda x,y: symorder[x[0]]-symorder[y[0]])\n return(self.basis_data)",
"title": ""
},
{
"docid": "1bf1614737176549e133d9afbda5f4c0",
"score": "0.53381765",
"text": "def get_basis(self):\n return self.jonesbasis",
"title": ""
},
{
"docid": "0d70d667a0f8079245771a9bf966d5ee",
"score": "0.529723",
"text": "def poincare_polynomial(self):\n charpoly = self.characteristic_polynomial()\n R = charpoly.parent()\n x = R.gen(0)\n poincare = (-x)**self.dimension() * charpoly(-QQ(1)/x)\n return R(poincare)",
"title": ""
},
{
"docid": "f4e1d1e783dd029294dd4931b9809905",
"score": "0.5226901",
"text": "def derivation_module_basis(self, algorithm=\"singular\"):\n alg = algorithm # prevent possible changes to a global variable\n if alg == \"singular\":\n #import sage.libs.singular.function_factory\n #syz = sage.libs.singular.function_factory.ff.syz\n f = self.defining_polynomial()\n I = f + f.jacobian_ideal()\n IS = I._singular_()\n ISS = IS.syz()\n MSTD = ISS.mstd()\n basis = MSTD[2]._sage_().transpose().submatrix(0,1)\n try:\n det = basis.det()\n # Check using Saito's criterion\n if det / f in f.parent().base_ring() and not det.is_zero():\n return basis.rows()\n except ValueError: # Non-square matrix or det = 0\n pass\n # Check if it is free\n if not self.is_free(algorithm=alg):\n return None\n # The syzygy module did not give a basis, but since it is free,\n # fallback to the Barakat-Cuntz method\n alg = \"BC\"\n if alg == \"BC\":\n C = self.derivation_module_free_chain()\n if C is not None:\n if not C: # C is an empty list\n S = self.parent().ambient_space().symmetric_space()\n return matrix.identity(S, self.dimension()).rows()\n from sage.misc.misc_c import prod\n return prod(reversed(C)).rows()\n return None\n else:\n raise ValueError(\"invalid algorithm\")",
"title": ""
},
{
"docid": "ab5d059b80f97931dd5c8d6d2ce0725f",
"score": "0.5215356",
"text": "def getRestultPolynomialLagrage(self, points):\n x, y = points\n final_pol = np.polynomial.Polynomial([0.])\n n = len(x) # banyak point\n for i in range(n):\n p = np.polynomial.Polynomial([1.]) # pembilang\n q = 1 # penyebut\n for j in range(n):\n if i == j:\n continue\n p_temp = np.polynomial.Polynomial([-x[j], 1.]) # x - x[j]\n p = np.polymul(p, p_temp)\n q_temp = x[i] - x[j] # x[i] - x[j]\n q *= q_temp\n p *= y[i]/q\n final_pol = np.polyadd(final_pol, p)\n p = np.flip(final_pol[0].coef, axis=0)\n return p",
"title": ""
},
{
"docid": "b0bfc7171a2b646cc634666c99cc09f3",
"score": "0.52094346",
"text": "def basis(self):\n try:\n return self.__basis\n except AttributeError:\n M = self._matrix_space()\n B = M.basis()\n self.__basis = tuple([self(x) for x in B])\n return self.__basis",
"title": ""
},
{
"docid": "641b60e591650fd1defcac4db8c62a1f",
"score": "0.519928",
"text": "def defining_polynomial(self):\n return self.defining_polynomials()[0]",
"title": ""
},
{
"docid": "1250d9c62808155a67d5b24077c96804",
"score": "0.5197438",
"text": "def basisOppervlakte(self):\n return self._basisOppervlakte.get_waarde()",
"title": ""
},
{
"docid": "682b05748678621f188126d5b886244d",
"score": "0.51462626",
"text": "def basis(self, basis):\n from sage.misc.superseded import deprecation\n deprecation(10052, 'The .basis() method is deprecated. Use .change_basis() instead.')\n return self.change_basis(basis)",
"title": ""
},
{
"docid": "dcf18be4dffd24c32e81db6097038faa",
"score": "0.5131381",
"text": "def basis_fields(self, points):\n B_coupling = self.mesh_conductor.B_coupling(points)\n\n return B_coupling @ self.basis",
"title": ""
},
{
"docid": "cf0077e74456362da573b0a3969176d9",
"score": "0.50254124",
"text": "def primitive_basis(symbols: List[str]) -> LatticeBasis:\n if len(symbols) != 1:\n raise ValueError(_basis_err.format(\"primitive\"))\n return [(symbols[0], _pt0)]",
"title": ""
},
{
"docid": "bc2f7fcef2362741a2aafd0896735a06",
"score": "0.5024902",
"text": "def final_basis(self, text):\n atomic_position_last_index = text.rfind(\"ATOMIC_POSITIONS (crystal)\")\n if atomic_position_last_index < 0:\n return self.initial_basis(text)\n number_of_atoms = self._number_of_atoms(text)\n basis = self._extract_basis(text[atomic_position_last_index:], number_of_atoms)\n\n # final basis is in crystal units, hence it needs to be converted into angstrom.\n final_lattice_vectors = self.final_lattice_vectors(text)\n lattice_matrix = np.array([final_lattice_vectors[\"vectors\"][key] for key in [\"a\", \"b\", \"c\"]]).reshape((3, 3))\n for coordinate in basis[\"coordinates\"]:\n coordinate[\"value\"] = np.dot(coordinate[\"value\"], lattice_matrix).tolist()\n\n return basis",
"title": ""
},
{
"docid": "78cf7315b8fde1afc1b3493f5eb7036d",
"score": "0.5006696",
"text": "def Ri_aux_basis(self):\n return self.Ri_aux_basis_set",
"title": ""
},
{
"docid": "7fa04ac104f9327afd2024ef96755d2a",
"score": "0.50039446",
"text": "def character_polynomial(self):\n \n #Create the polynomial ring we will use\n k = self.size()\n P = PolynomialRing(QQ, k, 'x')\n x = P.gens()\n\n #Expand s_mu in the power sum basis\n import sf.sfa\n s = sf.sfa.SFASchur(QQ)\n p = sf.sfa.SFAPower(QQ)\n ps_mu = p(s(self))\n\n #Replace each p_i by i*x_i-1\n items = ps_mu.monomial_coefficients().items() #items contains a list of (partition, coeff) pairs\n partition_to_monomial = lambda part: prod([ (i*x[i-1]-1) for i in part ])\n res = [ [partition_to_monomial(mc[0]), mc[1]] for mc in items ]\n\n #Write things in the monomial basis\n res = [ prod(pair) for pair in res ]\n res = sum( res )\n\n #Apply the umbral operator and return the result\n return misc.umbral_operation(res)",
"title": ""
},
{
"docid": "9874e97c24ee861a12e14b3712282920",
"score": "0.49805024",
"text": "def load_basis(basis_name):\n basis_set = dict()\n reading = False\n current_function = None\n basis_folder = os.path.join(os.path.dirname(os.path.realpath(__file__)),'..','basis')\n for line in open(os.path.join(basis_folder,basis_name.lower()+'.basis')):\n if line.startswith(\"#\"):\n continue\n if line.startswith(\"BASIS\"):\n reading = True\n if reading is True:\n if line.startswith(\"END\"):\n reading = False\n continue\n ls = line.split()\n if len(ls) == 2:\n if ls[1].isalpha(): # if title line like \"H S\"\n elem, spdf = ls\n if elem not in basis_set:\n basis_set[elem] = []\n current_function = ContractedBasisFunction(ftype=spdf, coefs=[])\n basis_set[elem].append(current_function)\n else: # if value line like \" 5.4471780 0.1562850\"\n a, c = float(ls[0]), float(ls[1])\n # normalize Gaussian (2a/pi)^3/4 * exp(-a*r^2)\n norm = (2.0 * a / np.pi)**0.75\n current_function.coefs.append((a, c*norm))\n return basis_set",
"title": ""
},
{
"docid": "f312fee5594a8a716fbe5c741586a40d",
"score": "0.49579558",
"text": "def _basis_dictionary(self,basis):\n a = self.change_basis(basis)\n return a.monomial_coefficients()",
"title": ""
},
{
"docid": "cd296e131d033074335b3d2a14c61687",
"score": "0.49547455",
"text": "def basis_column(self):\n if self.__basis is None:\n self.__basis = Column(\n self.__data.get_column_attributes(self.BASIS_NAME),\n self.__data,\n self.BASIS_NAME)\n return self.__basis",
"title": ""
},
{
"docid": "fb64553b5e86644292283e820d33961c",
"score": "0.49311262",
"text": "def derived_b_lc(self):\n\n if not np.all([p in self.columns for p in ['P0', 'P1']]):\n return\n\n # get period and period derivative\n P0 = self.catalogue['P0']\n P1 = self.catalogue['P1']\n\n BLC = np.full(self.catalogue_len, np.nan)\n idx = (P1 > 0.) & np.isfinite(P1) & np.isfinite(P0)\n BLC[idx] = 3.0e8*np.sqrt(P1[idx])*np.abs(P0[idx])**(-5./2.)\n self.update(BLC, name='B_LC')",
"title": ""
},
{
"docid": "e0508a54693db4a6b21221b8257f0c97",
"score": "0.4928441",
"text": "def change_basis(self, basis='milnor'):\n A = self.parent()\n return A._change_basis(self, basis)",
"title": ""
},
{
"docid": "dabf52d8dce22e2423ed00411243bb03",
"score": "0.49275562",
"text": "def rational_function_string(self):\n if self._rational_function == None:\n def stringify_cone(cone,fundamental_parallelepiped):\n d = cone.ambient_dimension\n V = cone.generators\n def stringify_monomial(z):\n return \"*\".join((\"z%d**%d\" % (i,z[i]) for i in xrange(d)))\n num = \"+\".join((stringify_monomial(t) for t in fundamental_parallelepiped))\n den = \"*\".join(( \"(1-%s)\" % stringify_monomial(c) for c in V))\n return (\"(%s)/(%s)\" % (num,den))\n rational_functions = []\n for cone, multiplicity in self.symbolic_cones().iteritems():\n pi = self.fundamental_parallelepipeds()[cone]\n rational_functions.append( str(multiplicity) + \"*\" + stringify_cone(cone,pi) )\n if len(rational_functions) > 0:\n self._rational_function = \"+\".join(rational_functions)\n else:\n self._rational_function = \"0\"\n return self._rational_function",
"title": ""
},
{
"docid": "68ca9c1f8c53d16f8971631d9a73b0f4",
"score": "0.49100542",
"text": "def initial_basis(self, text):\n alat = self._get_alat(text)\n number_of_atoms = self._number_of_atoms(text)\n basis_in_alat_units = self._extract_basis(text[text.find(\"positions (alat units)\") :], number_of_atoms)\n for coordinate in basis_in_alat_units[\"coordinates\"]:\n coordinate[\"value\"] = [x * alat * Constant.BOHR for x in coordinate[\"value\"]]\n return basis_in_alat_units",
"title": ""
},
{
"docid": "f6af659d43e57e6e96425e4b64d081ae",
"score": "0.49017513",
"text": "def pr_as_poly(self, spamTuple, circuit, comm=None, memLimit=None):\n return self.prs_as_polys(spamTuple[0], [spamTuple[1]], circuit,\n comm, memLimit)[0]",
"title": ""
},
{
"docid": "2ee0001b48926fee72f5e5a245c77b46",
"score": "0.48807964",
"text": "def wh_pol(n_0, n_m, n_c, pol, lmda):",
"title": ""
},
{
"docid": "5d8f1eaf0bb507b71f727234ecbf1be3",
"score": "0.4878286",
"text": "def corrprodsforpol(self, pol=-1):\n return _msmetadata.msmetadata_corrprodsforpol(self, pol)",
"title": ""
},
{
"docid": "ed9afe944f279c04e54ead8dd4e5847a",
"score": "0.4870391",
"text": "def lc(self):\n try:\n return self.__lc\n except AttributeError:\n if self.is_zero():\n return self.base_ring()._zero_element\n R = self.parent()\n f = self._MPolynomial_element__element.dict()\n self.__lc = f[self._MPolynomial_element__element.lcmt( R.term_order().greater_tuple )]\n return self.__lc",
"title": ""
},
{
"docid": "65cf15501d551b31c0626475d4a56c8b",
"score": "0.4870248",
"text": "def _pauli_string_expectation(self, basis, err_param, add_param = None):\n bas_ind = {'I':0, 'X':1, 'Y':2, 'Z':3}\n for i in range(self._number_of_qubits):\n self._densitymatrix = np.reshape(self._densitymatrix,(4**(i),4,4**(self._number_of_qubits-i-1)))\n if basis[i] == 'X':\n self._densitymatrix[:,1,:] *= err_param\n self._densitymatrix[:,2,:] = 0\n self._densitymatrix[:,3,:] = 0\n elif basis[i] == 'Y':\n self._densitymatrix[:,1,:] = 0\n self._densitymatrix[:,2,:] *= err_param\n self._densitymatrix[:,3,:] = 0\n elif basis[i] == 'Z':\n self._densitymatrix[:,1,:] = 0\n self._densitymatrix[:,2,:] = 0\n self._densitymatrix[:,3,:] *= err_param\n\n self._densitymatrix = np.reshape(self._densitymatrix, self._number_of_qubits * [4])\n index = tuple([bas_ind[x] for x in basis])\n expectation = self._densitymatrix[index] * 2**self._number_of_qubits\n\n return expectation",
"title": ""
},
{
"docid": "08c773213e20b63fb030cc682541d9e4",
"score": "0.48663837",
"text": "def test_get_current_lp_with_1(self):\n origin_number = 3\n origin_cons = [[1, 1, 1, '<=', 5],\n [1, 0, 0, '>=', 0],\n [0, 1, 0, '>=', 0],\n [0, 0, 1, '>=', 0]]\n origin_obj = ['max', 3, 4, 6]\n fixed = [-1, -1, 1]\n s = bandb.BandB(origin_number, origin_cons, origin_obj)\n number, cons, obj, constant = s.get_current_lp(fixed)\n self.assertEqual(number, 2)\n self.assertEqual(cons, [[1, 1, '<=', 4],\n [1, 0, '>=', 0],\n [0, 1, '>=', 0],\n [0, 0, '>=', -1]])\n self.assertEqual(obj, ['max', 3, 4])\n self.assertEqual(constant, 6)",
"title": ""
},
{
"docid": "d5baf27482df59dfa76f154a4bd752b7",
"score": "0.48517194",
"text": "def orbitalBasisFunctions(self):\n\t\traise NotImplementedError(\"\")",
"title": ""
},
{
"docid": "1c56c06ffdde885226869c0445cbc2ed",
"score": "0.48399302",
"text": "def _get_poly_formula(\n self,\n geometry: dict[str, Any],\n nn_sites: list[dict[str, Any]],\n nnn_sites: list[dict[str, Any]],\n ) -> str | None:\n\n def order_elements(el):\n if self.use_iupac_formula:\n return [get_el_sp(el).X, el]\n return [get_el_sp(el).iupac_ordering, el]\n\n nnn_geometries = [nnn_site[\"geometry\"] for nnn_site in nnn_sites]\n\n poly_formula = None\n if geometry[\"type\"] in connected_geometries and any(\n nnn_geometry[\"type\"] in connected_geometries\n for nnn_geometry in nnn_geometries\n ):\n nn_els = [get_el(nn_site[\"element\"]) for nn_site in nn_sites]\n comp = Composition(\"\".join(nn_els))\n el_amt_dict = comp.get_el_amt_dict()\n\n poly_formula = \"\"\n for e in sorted(el_amt_dict.keys(), key=order_elements):\n poly_formula += e\n poly_formula += str(formula_double_format(el_amt_dict[e]))\n\n return poly_formula",
"title": ""
},
{
"docid": "a90700b1af0bc3000aa82f56d18dc7cc",
"score": "0.48396713",
"text": "def _get_poly_coeff(args):\n p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11 = args\n\n p10, p11 = np.deg2rad([p10, p11])\n\n coeff = [0.5, 1.5, 2.5, 3.5, 4.5, 5.5]\n\n # Upper surface \n Cup = np.ones((6,6), dtype=np.float64)\n\n for i in range(6):\n Cup[1,i] = p2 ** coeff[i]\n Cup[2,i] = coeff[i]\n Cup[3,i] = coeff[i] * p2 **(coeff[i] - 1)\n \n Cup[4,0] = -0.25 * p2 **(-1.5)\n Cup[4,1] = 0.75 * p2 **(-0.5)\n Cup[4,2] = 3.75 * p2 **( 0.5)\n Cup[4,3] = 8.75 * p2 **( 1.5)\n Cup[4,4] = 15.75 * p2 **( 2.5)\n Cup[4,5] = 24.75 * p2 **( 3.5)\n \n Cup[5,1:] = 0\n\n Bup = np.zeros(6)\n\n Bup[0] = p8 + p9/2\n Bup[1] = p3\n Bup[2] = np.tan(p10 - p11/2)\n Bup[4] = p4\n Bup[5] = np.sqrt(2*p1)\n\n Aup = np.linalg.solve(Cup, Bup)\n \n # Lower surface\n Clo = np.ones((6,6), dtype=np.float64)\n\n for i in range(6):\n Clo[1,i] = p5 ** coeff[i]\n Clo[2,i] = coeff[i]\n Clo[3,i] = coeff[i] * p5 **(coeff[i] - 1)\n\n Clo[4,0] = -0.25 * p5 **(-1.5)\n Clo[4,1] = 0.75 * p5 **(-0.5)\n Clo[4,2] = 3.75 * p5 **( 0.5)\n Clo[4,3] = 8.75 * p5 **( 1.5)\n Clo[4,4] = 15.75 * p5 **( 2.5)\n Clo[4,5] = 24.75 * p5 **( 3.5)\n\n Clo[5,1:] = 0\n\n Blo = np.zeros(6)\n \n Blo[0] = p8 - p9/2\n Blo[1] = p6\n Blo[2] = np.tan(p10 + p11/2)\n Blo[4] = p7\n Blo[5] = -np.sqrt(2*p1)\n\n Alo = np.linalg.solve(Clo, Blo)\n\n return Aup, Alo",
"title": ""
},
{
"docid": "1cccd5d91701211f1e81c2c27956516c",
"score": "0.48070532",
"text": "def defining_polynomial(self):\n S = self.parent().ambient_space().symmetric_space()\n return S.prod(H.to_symmetric_space() for H in self)",
"title": ""
},
{
"docid": "089347f9b1432ada8ae0f4d607dd0b6f",
"score": "0.480602",
"text": "def get_degree_polynomial(settings):\n assert(isinstance(settings, RbfSettings))\n if (settings.rbf == 'cubic' or settings.rbf == 'thin_plate_spline'):\n return 1\n elif (settings.rbf == 'linear' or settings.rbf == 'multiquadric'):\n return 0\n else:\n return -1",
"title": ""
},
{
"docid": "1b9d788628e0922bb648b75fc91790be",
"score": "0.47768706",
"text": "def p1(self):\n return _min3p.f90wrap_biol__get__p1()",
"title": ""
},
{
"docid": "bda15edb1cd271a200f478b0fd74df20",
"score": "0.4775564",
"text": "def L_to_basis(L):\n\n if L==3: return basis_L3\n if L==4: return basis_L4\n if L==5: return basis_L5\n else: print('nope')",
"title": ""
},
{
"docid": "1ab5070c64a0e5297c74eccf75d5fb7e",
"score": "0.47732982",
"text": "def init_preparation_basis(val: Union[str, PreparationBasis] = None) -> PreparationBasis:\n pb: PreparationBasis = None\n if val is None: # if the basis is not set, use the default PauliPrepBasis\n pb = PauliPrepBasis()\n elif isinstance(val, PreparationBasis):\n pb = val\n elif isinstance(val, str): # Construct the measurement basis from its name\n if val not in SUPPORTED_PREPARATION_BASIS:\n raise ArgumentError(\"in init_preparation_basis(): '{}' is not supported preparation basis!\".format(val))\n else:\n pb = getattr(sys.modules[__name__], val + 'PrepBasis')()\n else:\n raise ArgumentError(\"in init_preparation_basis(): unsupported input value type {}!\".format(type(val)))\n\n return pb",
"title": ""
},
{
"docid": "77b079447f588cfffd922b02bbdf5210",
"score": "0.47634554",
"text": "def __init__(self):\n super().__init__()\n self._name = \"Single-Qubit Pauli Measurement Basis\"\n self._basis = QUBIT_PAULI_BASIS",
"title": ""
},
{
"docid": "d2156139ecc9ee07c0299ae2443e465a",
"score": "0.47402325",
"text": "def test_get_basis_polynomial_family():\n assert_true(type(c.basis_polynomial_family) is str)",
"title": ""
},
{
"docid": "50718ec15db25c509519cf98b73f4832",
"score": "0.4739271",
"text": "def Ri_mp2_basis_set(self):\n return self.Ri_aux_basis_set",
"title": ""
},
{
"docid": "eec3ddc0a9a8e5b41970e8d6cec461e0",
"score": "0.47308514",
"text": "def test_get_current_lp_with_0_and_1(self):\n origin_number = 3\n origin_cons = [[1, 1, 1, '<=', 5],\n [1, 0, 0, '>=', 0],\n [0, 1, 0, '>=', 0],\n [0, 0, 1, '>=', 0]]\n origin_obj = ['max', 3, 4, 6]\n fixed = [-1, 0, 1]\n s = bandb.BandB(origin_number, origin_cons, origin_obj)\n number, cons, obj, constant = s.get_current_lp(fixed)\n self.assertEqual(number, 1)\n self.assertEqual(cons, [[1, '<=', 4],\n [1, '>=', 0],\n [0, '>=', 0],\n [0, '>=', -1]])\n self.assertEqual(obj, ['max', 3])\n self.assertEqual(constant, 6)",
"title": ""
},
{
"docid": "343f9ef30d455fa480cb910ca35e4683",
"score": "0.47148764",
"text": "def get_poly(self):\n # check if there is any poly?\n if self._poly is not None:\n return tuple(self._poly)",
"title": ""
},
{
"docid": "e1da2d94f6cbd4fd2e3364b3f5b8bc49",
"score": "0.47092453",
"text": "def srelfac_lc(self):\n return _min3p.f90wrap_chem__get__srelfac_lc()",
"title": ""
},
{
"docid": "94a0597aa174639111fc9643276c9302",
"score": "0.47086746",
"text": "def heisenberg_1d(param_dict):\n\n L = param_dict['L']\n J = param_dict['J']\n bc = param_dict.get('bc', 'closed')\n\n coupmax = L-1 if bc=='open' else L\n pauli_x = [ embed([qt.sigmax(), qt.sigmax()], L, [i, (i+1)%2]) for i in range(coupmax)]\n pauli_y = [ embed([qt.sigmay(), qt.sigmay()], L, [i, (i+1)%2]) for i in range(coupmax)]\n pauli_z = [ embed([qt.sigmaz(), qt.sigmaz()], L, [i, (i+1)%2]) for i in range(coupmax)]\n\n if bc=='closed' and L==2:\n J = J/2\n\n return -J * (1/4) * (sum(pauli_x) + sum(pauli_y) + sum(pauli_z))",
"title": ""
},
{
"docid": "622c1a5e529f2557f80bd106e57f80a3",
"score": "0.47048718",
"text": "def get_ell_pol(self):\n return self.ell_pol",
"title": ""
},
{
"docid": "4f743b673d762bc5d99a2970ed44a5af",
"score": "0.46922323",
"text": "def test_get_current_lp_with_0(self):\n origin_number = 3\n origin_cons = [[1, 1, 1, '<=', 5],\n [1, 0, 0, '>=', 0],\n [0, 1, 0, '>=', 0],\n [0, 0, 1, '>=', 0]]\n origin_obj = ['max', 3, 4, 6]\n fixed = [-1, -1, 0]\n s = bandb.BandB(origin_number, origin_cons, origin_obj)\n number, cons, obj, constant = s.get_current_lp(fixed)\n self.assertEqual(number, 2)\n self.assertEqual(cons, [[1, 1, '<=', 5],\n [1, 0, '>=', 0],\n [0, 1, '>=', 0],\n [0, 0, '>=', 0]])\n self.assertEqual(obj, ['max', 3, 4])\n self.assertEqual(constant, 0)",
"title": ""
},
{
"docid": "b20143003f05d88ad9eb288c749b608a",
"score": "0.4678735",
"text": "def solve_initial_basis(self):\r\n # new_basis = [i + 1 for i, val in enumerate(self.c) if val > 0]\r\n # other = [i + 1 for i, val in enumerate(self.c) if val <= 0]\r\n # if len(new_basis) >= self.eqn:\r\n # self.basis = new_basis[:self.eqn]\r\n # else:\r\n # self.basis = sorted(new_basis + other[:self.eqn - len(new_basis)])\r\n c = np.array([0 if i < self.vars else -1 for i in range(self.vars + self.eqn)])\r\n A = np.concatenate(([self.A[row] if self.b[row] > 0\r\n else -self.A[row] for row in range(self.eqn)],\r\n np.identity(self.eqn)), axis=1)\r\n b = np.abs(self.b)\r\n\r\n p = Simplex(c, 0, A, b)\r\n p.show_steps(self.display)\r\n\r\n # detect for negative b value\r\n p.set_basis([x for x in range(self.vars + 1, self.vars + self.eqn + 1)])\r\n p.solve()\r\n print('-' * 100)\r\n x = p.get_BFS()\r\n\r\n assert p.k == 0, \"Infeasible Primal Program\"\r\n\r\n self.basis = p.get_basis()",
"title": ""
},
{
"docid": "cc3bcfe3baa8355627f75474c3479631",
"score": "0.46769893",
"text": "def test_with_pauli_basis(self, num_wires):\n # generators of PauliRot\n paulirot_gens = -0.5j * pauli_basis_matrices(num_wires)\n # With many entries, each containing one basis element\n paulirot_coeffs = _one_parameter_paulirot_coeffs(paulirot_gens, num_wires)\n assert qml.math.allclose(paulirot_coeffs, np.eye(4**num_wires - 1))\n # With a \"single entry\" containing all basis elements\n paulirot_coeffs = _one_parameter_paulirot_coeffs([paulirot_gens], num_wires)\n assert qml.math.allclose(paulirot_coeffs, np.eye(4**num_wires - 1))",
"title": ""
},
{
"docid": "4f8b03d516eea33a3b23e75eaa2cc84a",
"score": "0.46695307",
"text": "def calc_pol(self, fil, path, sys_err=sys_err):\n\n # load in the table and its data\n table = fits.open(path)\n table_data = table[1].data\n\n # get the filter-wavelength weight\n weights = np.interp(table_data[0]['wavelength'], fil['wavelength'],\n fil['weight'], left=0, right=0)\n # define the flux\n flux = table[0].data\n\n # get the flux weight for q\n qweight = table_data[0]['q'] * flux * weights\n # sum/integrate it\n qsum = np.trapz(qweight, table_data[0]['wavelength'])\n\n # get the weight for just the flux\n fweight = flux * weights\n # sum it\n fsum = np.trapz(fweight, table_data[0]['wavelength'])\n\n # find q\n qval = qsum / fsum\n\n # get the flux weight for u\n uweight = table_data[0]['u'] * flux * weights\n # sum it\n usum = np.trapz(uweight, table_data[0]['wavelength'])\n\n # find u\n uval = usum / fsum\n\n # find the total polarized light\n pol_tot = np.sqrt(qval**2 + uval**2)\n\n # find the position angle of the light, convert to deg\n pos_ang = 0.5 * np.arctan2(uval, qval) * u.rad\n pos_ang = pos_ang.to(u.deg).value\n\n # grab the date to sort by later\n date = table[0].header[self.datekey]\n\n # define the error from the og table\n err = table_data[0]['error']\n\n # get weighted flux errors\n ferr = pd.DataFrame(weights * flux * err, columns=['pol_err'])\n ferr = ferr.loc[(ferr!=0).any(axis=1)]\n\n # get poisson error\n fish = np.sum(ferr) / np.sum(flux * weights) / np.sqrt(ferr.size)\n\n # turn the start and end times for sys errs into Time objs\n start = Time(sys_err['Start_Date'].values.astype(str), scale='utc')\n end = Time(sys_err['End_Date'].values.astype(str), scale='utc')\n\n # turn the date of obs into a time object and find which err it falls into\n time = Time(date, scale='utc')\n errmatch = (time >= start) & (time <= end)\n\n if all(errmatch) is False:\n t = pd.Series(start-time).append(pd.Series(end-time))\n errmatch = np.abs(t).values.argmin()\n if errmatch > 19:\n errmatch -= 19\n # get the err for the polization\n errpol = np.sqrt(fish**2 + sys_err[fil.index.name][errmatch]**2)\n\n # get the error for the position angle\n errang = 90 / np.pi * errpol / pol_tot\n\n table.close()\n\n return pol_tot, pos_ang, date, errpol.values[0], errang.values[0]",
"title": ""
},
{
"docid": "a33863e5225ce039578c85e22fd18035",
"score": "0.46663117",
"text": "def get_fwhm_pol(self):\n return self.fwhm_ell_pol",
"title": ""
},
{
"docid": "7f6050165b3968b7d3abbeb13bb52ec3",
"score": "0.46379414",
"text": "def get_poland():\r\n poland = Country('Poland')\r\n poland.get_gdp('https://www.quandl.com/api/v3'\r\n '/datasets/WWDI/POL_NY_GDP_PCAP_CD.'\r\n 'json?api_key=2jhCWecEKmuxzVY9ifwp')\r\n poland.get_investment_inflows('https://www.quandl.com/'\r\n 'api/v3/datasets/WWDI/'\r\n 'POL_BX_KLT_DINV_WD_GD_ZS.'\r\n 'json?api_key=2jhCWecEKmuxzVY9ifwp')\r\n poland.get_investment_outflows('https://www.quandl.com/'\r\n 'api/v3/datasets/WWDI/'\r\n 'POL_BM_KLT_DINV_GD_ZS.'\r\n 'json?api_key=2jhCWecEKmuxzVY9ifwp')\r\n poland.get_manufacturing('https://www.quandl.com/api'\r\n '/v3/datasets/WWDI/POL_NV_'\r\n 'IND_MANF_ZS.json?api_key'\r\n '=2jhCWecEKmuxzVY9ifwp')\r\n return poland",
"title": ""
},
{
"docid": "adc44e64472e813683fe196624ffafdd",
"score": "0.4611685",
"text": "def GetRDFPolarizability(mol):\n\n filename='temp'\n lcoordinates=_ReadCoordinates(filename) \n result=CalculatePolarizabilityRDF(lcoordinates)\n \n return result",
"title": ""
},
{
"docid": "9573dadede6f84b2c69c3c6f1acc20a2",
"score": "0.46069956",
"text": "def plot_basis_function(self, basis_i, basis_type):\n\n\t\tn_plot_pts = 100\n\n\t\txi_vals = numpy.linspace(-1., 1., n_plot_pts)\n\t\teta_vals = numpy.linspace(-1., 1., n_plot_pts)\n\n\t\txi_vals, eta_vals = numpy.meshgrid(xi_vals, eta_vals)\n\n\t\tif basis_type == \"basis\":\n\n\t\t\tphi_vals = numpy.zeros((n_plot_pts, n_plot_pts))\n\t\t\t\n\t\t\tfor i in range(n_plot_pts):\n\t\t\t\tfor j in range(n_plot_pts):\n\t\t\t\t\txi_ij, eta_ij = xi_vals[i,j], eta_vals[i,j]\n\t\t\t\t\t\n\t\t\t\t\tif self.point_in_reference_triangle(xi_ij, eta_ij):\n\t\t\t\t\t\tphi_vals[i,j] = self.psi_hat_basis[basis_i](xi_ij, eta_ij)\n\t\t\t\t\telse:\n\t\t\t\t\t\tphi_vals[i,j] = 0\n\n\t\t\tplt.figure()\n\t\t\t#ax = plt.gcf().gca(projection='3d')\n\t\t\t#surf = ax.plot_surface(xi_vals, eta_vals, phi_vals, cmap=cm.coolwarm, linewidth=0, antialiased=False)\n\t\t\tplt.contourf(xi_vals, eta_vals, phi_vals, 100, cmap=plt.get_cmap(\"jet\"))\n\t\t\tplt.colorbar()\n\t\t\tplt.xlabel(r\"$\\xi$\")\n\t\t\tplt.ylabel(r\"$\\eta$\")\n\n\t\t\tplt.xlim([-0.1,1.1])\n\t\t\tplt.ylim([-0.1,1.1])\n\n\t\telif basis_type == \"grad_basis\":\n\t\t\t\n\t\t\tphi_vals_xi = numpy.zeros((n_plot_pts, n_plot_pts))\n\t\t\tphi_vals_eta = numpy.zeros((n_plot_pts, n_plot_pts))\n\t\t\t\n\t\t\tfor i in range(n_plot_pts):\n\t\t\t\tfor j in range(n_plot_pts):\n\n\t\t\t\t\txi_ij, eta_ij = xi_vals[i,j], eta_vals[i,j]\n\t\t\t\t\t\n\t\t\t\t\tif self.point_in_reference_triangle(xi_ij, eta_ij):\n\t\t\t\t\t\tphi_vals_xi[i,j] = self.grad_psi_hat_basis[basis_i][0](xi_ij, eta_ij)\n\t\t\t\t\t\tphi_vals_eta[i,j] = self.grad_psi_hat_basis[basis_i][1](xi_ij, eta_ij)\n\t\t\t\t\telse:\n\t\t\t\t\t\tphi_vals_xi[i,j] = 0\n\t\t\t\t\t\tphi_vals_eta[i,j] = 0\n\n\n\t\t\tplt.figure()\n\t\t\t#ax = plt.gcf().gca(projection='3d')\n\t\t\t#surf = ax.plot_surface(xi_vals, eta_vals, phi_vals_xi, cmap=cm.coolwarm, linewidth=0, antialiased=False)\n\t\t\tplt.contourf(xi_vals, eta_vals, phi_vals_xi, 100, cmap=plt.get_cmap(\"jet\"))\n\t\t\tplt.colorbar()\n\t\t\tplt.xlabel(r\"$\\xi$\")\n\t\t\tplt.ylabel(r\"$\\eta$\")\n\t\t\tplt.title(\"xi Derivative\")\n\n\t\t\tplt.xlim([-0.1,1.1])\n\t\t\tplt.ylim([-0.1,1.1])\n\n\t\t\tplt.figure()\n\t\t\t#ax = plt.gcf().gca(projection='3d')\n\t\t\t#surf = ax.plot_surface(xi_vals, eta_vals, phi_vals_eta, cmap=cm.coolwarm, linewidth=0, antialiased=False)\n\t\t\tplt.contourf(xi_vals, eta_vals, phi_vals_eta, 100, cmap=plt.get_cmap(\"jet\"))\n\t\t\tplt.colorbar()\n\t\t\tplt.xlabel(r\"$\\xi$\")\n\t\t\tplt.ylabel(r\"$\\eta$\")\n\t\t\tplt.title(\"eta Derivative\")\n\n\t\t\tplt.xlim([-0.1,1.1])\n\t\t\tplt.ylim([-0.1,1.1])\n\n\t\telse:\n\t\t\traise ValueError(\"Unsupported\")",
"title": ""
},
{
"docid": "81a3cef63686004cef93ab0ad1adfd0e",
"score": "0.46033826",
"text": "def get_QP_bandstructure(self):\n if self._qpbs is None:\n self._qpbs = self._get_bandstructure(\"qp\")\n return self._qpbs",
"title": ""
},
{
"docid": "9b44b850126a02b8f3a10836e8fb9416",
"score": "0.45987618",
"text": "def change_basis(twoD_pts, l_bpb_po):\n mat1 = (l_bpb_po).inv();\n return np.array((mat1*(Matrix(twoD_pts.transpose()))).transpose(), dtype='double');",
"title": ""
},
{
"docid": "9c52898c887165ed593ee84696243c24",
"score": "0.45884678",
"text": "def make_LV_crl_basis(mesh, foc):\n\n VV = dolfin.VectorFunctionSpace(mesh, \"CG\", 1)\n V = dolfin.FunctionSpace(mesh, \"CG\", 1)\n\n if dolfin.DOLFIN_VERSION_MAJOR > 1.6:\n dofs_x = V.tabulate_dof_coordinates().reshape((-1, mesh.geometry().dim()))\n else:\n dm = V.dofmap()\n dofs_x = dm.tabulate_all_coordinates(mesh).reshape((-1, mesh.geometry().dim()))\n\n e_c = make_unit_vector(V, VV, dofs_x, fill_coordinates_ec)\n e_l = make_unit_vector(V, VV, dofs_x, fill_coordinates_el, foc)\n e_r = calc_cross_products(e_c, e_l, VV)\n\n e_c.rename(\"c0\", \"local_basis_function\")\n e_r.rename(\"r0\", \"local_basis_function\")\n e_l.rename(\"l0\", \"local_basis_function\")\n\n return e_c, e_r, e_l",
"title": ""
},
{
"docid": "9cf1f064bf72c1f421f21a1816b54841",
"score": "0.4587992",
"text": "def basisName(self):\n\t\traise NotImplementedError(\"\")",
"title": ""
},
{
"docid": "a5927beb8eb74058920e61878025a846",
"score": "0.45871404",
"text": "def rgascal(self):\n return _min3p.f90wrap_chem__get__rgascal()",
"title": ""
},
{
"docid": "2960f6b58dfa82669691fd1dd96e1721",
"score": "0.45870912",
"text": "def degree_on_basis(self, t):\n def p_degree(m, mult=1, prime=2):\n \"\"\"\n For m=(n_1, n_2, n_3, ...), Sum_i (mult) * n_i * (p^i - 1)\n \"\"\"\n i = 0\n deg = 0\n for n in m:\n i += 1\n deg += n*mult*(prime**i - 1)\n return deg\n\n def q_degree(m, prime=3):\n \"\"\"\n For m=(n_0, n_1, n_2, ...), Sum_i 2*p^(n_i) - 1\n \"\"\"\n deg = 0\n for n in m:\n deg += 2*prime**n - 1\n return deg\n\n p = self.prime()\n basis = self.basis_name()\n # milnor\n if basis == 'milnor':\n if not self._generic:\n return p_degree(t)\n else:\n return q_degree(t[0], prime=p) + p_degree(t[1], prime=p, mult=2)\n # serre-cartan, arnonc\n if not self._generic and (basis == 'serre-cartan' or basis == 'arnonc'):\n return sum(t)\n if self._generic and basis == 'serre-cartan':\n bockstein = True\n n = 0\n for j in t:\n if bockstein:\n if j != 0:\n n += 1\n bockstein = False\n else:\n n += 2 * j * (p - 1)\n bockstein = True\n return n\n\n # wood_y:\n if basis == 'woody' or basis == 'woodz':\n # each entry in t is a pair (m,k), corresponding to w(m,k), defined by\n # `w(m,k) = \\text{Sq}^{2^m (2^{k+1}-1)}`.\n return sum(2**m * (2**(k+1)-1) for (m,k) in t)\n\n # wall, arnon_a\n if basis.find('wall') >= 0 or basis.find('arnona') >= 0:\n # Wall: each entry in t is a pair (m,k), corresponding to\n # Q^m_k, defined by `Q^m_k = Sq(2^k) Sq(2^{k+1})\n # ... Sq(2^m)`.\n #\n # Arnon A: each entry in t is a pair (m,k), corresponding\n # to X^m_k, defined by `X^m_k = Sq(2^m) ... Sq(2^{k+1})\n # Sq(2^k)`\n return sum(2**k * (2**(m-k+1)-1) for (m,k) in t)\n\n # pst, comm\n if basis.find('pst') >= 0 or basis.find('comm') >= 0:\n if not self._generic:\n # Pst: each entry in t is a pair (i,j), corresponding to P^i_j\n #\n # Comm: each entry in t is a pair (i,j), corresponding\n # to c_{i,j}, the iterated commutator defined by\n # c_{i,1} = Sq(2^i) and c_{i,j} = [c_{i,j-1},\n # Sq(2^{i+j-1})].\n return sum(2**m * (2**k - 1) for (m,k) in t)\n # p odd:\n #\n # Pst: have pair (Q, P) where Q is a tuple of Q's, as in\n # the Milnor basis, and P is a tuple of terms of the form\n # ((i,j), n), corresponding to (P^i_j)^n.\n #\n # Comm: similarly (Q, C) with Q as above and C a tuple\n # with each entry in t is of the form ((s,t), n),\n # corresponding to c_{s,t}^n. here c_{s,t} is the the\n # iterated commutator defined by c_{s,1} = P(p^s) and\n # c_{s,t} = [P(p^{s+t-1}), c_{s,t-1}].\n q_deg = q_degree(t[0], prime=p)\n p_deg = sum(2 * n * p**s * (p**t - 1) for ((s,t), n) in t[1])\n return q_deg + p_deg",
"title": ""
},
{
"docid": "143a2e5a808619a55b3c68d9f8a2ab8d",
"score": "0.45868084",
"text": "def linresp(self):\n\n self.polar1 = 0\n self.polar2 = 0\n # <0|Y1(B) * A_bar|0>\n self.polar1 += ndot(\"ai,ia->\", self.ccpert_A.build_Avo(), self.y1_B)\n # <0|Y2(B) * A_bar|0>\n self.polar1 += ndot(\"abij,ijab->\", self.ccpert_A.build_Avvoo(), self.y2_B, prefactor=0.5)\n self.polar1 += ndot(\"baji,ijab->\", self.ccpert_A.build_Avvoo(), self.y2_B, prefactor=0.5)\n # <0|[A_bar, X(B)]|0>\n self.polar2 += ndot(\"ia,ia->\", self.ccpert_A.build_Aov(), self.x1_B, prefactor=2.0)\n # <0|L1(0)[A_bar, X1(B)]|0>\n tmp = ndot('ia,ic->ac', self.l1, self.x1_B)\n self.polar2 += ndot('ac,ac->', tmp, self.ccpert_A.build_Avv())\n tmp = ndot('ia,ka->ik', self.l1, self.x1_B)\n self.polar2 -= ndot('ik,ki->', tmp, self.ccpert_A.build_Aoo())\n # <0|L1(0)[A_bar, X2(B)]|0>\n tmp = ndot('ia,jb->ijab', self.l1, self.ccpert_A.build_Aov())\n self.polar2 += ndot('ijab,ijab->', tmp, self.x2_B, prefactor=2.0)\n self.polar2 += ndot('ijab,ijba->', tmp, self.x2_B, prefactor=-1.0)\n # <0|L2(0)[A_bar, X1(B)]|0>\n tmp = ndot('ijbc,bcaj->ia', self.l2, self.ccpert_A.build_Avvvo())\n self.polar2 += ndot('ia,ia->', tmp, self.x1_B)\n tmp = ndot('ijab,kbij->ak', self.l2, self.ccpert_A.build_Aovoo())\n self.polar2 -= ndot('ak,ka->', tmp, self.x1_B, prefactor=0.5)\n tmp = ndot('ijab,kaji->bk', self.l2, self.ccpert_A.build_Aovoo())\n self.polar2 -= ndot('bk,kb->', tmp, self.x1_B, prefactor=0.5)\n # <0|L2(0)[A_bar, X1(B)]|0>\n tmp = ndot('ijab,kjab->ik', self.l2, self.x2_B)\n self.polar2 -= ndot('ik,ki->', tmp, self.ccpert_A.build_Aoo(), prefactor=0.5)\n tmp = ndot('ijab,kiba->jk', self.l2, self.x2_B,)\n self.polar2 -= ndot('jk,kj->', tmp, self.ccpert_A.build_Aoo(), prefactor=0.5)\n tmp = ndot('ijab,ijac->bc', self.l2, self.x2_B,)\n self.polar2 += ndot('bc,bc->', tmp, self.ccpert_A.build_Avv(), prefactor=0.5)\n tmp = ndot('ijab,ijcb->ac', self.l2, self.x2_B,)\n self.polar2 += ndot('ac,ac->', tmp, self.ccpert_A.build_Avv(), prefactor=0.5)\n\n self.polar = -1.0*(self.polar1 + self.polar2)\n\n return self.polar",
"title": ""
},
{
"docid": "668305c0e92f97b51fd1023cf63dc9f6",
"score": "0.45781037",
"text": "def get_rbf_function(settings):\n assert(isinstance(settings, RbfSettings))\n if (settings.rbf == 'cubic'):\n return _cubic\n elif (settings.rbf == 'thin_plate_spline'):\n return _thin_plate_spline\n elif (settings.rbf == 'linear'):\n return _linear\n elif (settings.rbf == 'multiquadric'):\n return _multiquadric",
"title": ""
},
{
"docid": "0ffd436e087f8b199a128188aca8b9d6",
"score": "0.45772862",
"text": "def linear_sorption(self):\n return _min3p.f90wrap_chem__get__linear_sorption()",
"title": ""
},
{
"docid": "a3c9900ec96130092925ec5c81f1dd12",
"score": "0.4575087",
"text": "def policy(self):\r\n if len(self.resx) > 0:\r\n plan = np.array(self.resx)\r\n planmat = np.array([int(i) for i in plan[:,1]]).reshape((self.h,len(self.trans)))\r\n return(planmat)\r\n else:\r\n return(print(\"Nothing to show\"))",
"title": ""
},
{
"docid": "f5bd1f5498ac6ef0d10e7de3c1c13376",
"score": "0.45664927",
"text": "def to_pbw_basis(self):\n return self.parent().pbw_element(self)",
"title": ""
},
{
"docid": "6bdfe4a7fceff80212ed8bea3508f468",
"score": "0.45644403",
"text": "def basis(P1,P2):\n P1s = pol_dict[P1]\n P2s = pol_dict[P2]\n return np.matrix([[P1s[0]*P2s[0]],\n [P1s[1]*P2s[1]],\n [P1s[0]*P2s[1]],\n [P1s[1]*P2s[0]]])",
"title": ""
},
{
"docid": "d8d21db9e880f1f3dfa2e98612030316",
"score": "0.45634764",
"text": "def eval_poly(self):\n\n poly_int = np.polyint(np.append(self.c_t, [0])) # indefinite integral of polynomial\n poly_eval = np.polyval(poly_int, [0, 1]) # evaluate polynomial in [0, 1]\n poly_eval = np.diff(poly_eval) # difference of evaluated polynomial\n\n return poly_eval",
"title": ""
},
{
"docid": "4c100312714c45b6ade71c6df72cc2c4",
"score": "0.45497555",
"text": "def crystal_system(self):\n #FIXME(tdaff): must be aligned with x to work\n if self.alpha == self.beta == self.gamma == 90:\n if self.a == self.b == self.c:\n return 'cubic'\n elif self.a == self.b or self.a == self.c or self.b == self.c:\n return 'tetragonal'\n else:\n return 'orthorhombic'\n elif self.alpha == self.beta == 90:\n if self.a == self.b and self.gamma == 120:\n return 'hexagonal'\n else:\n return 'monoclinic'\n elif self.alpha == self.gamma == 90:\n if self.a == self.c and self.beta == 120:\n return 'hexagonal'\n else:\n return 'monoclinic'\n elif self.beta == self.gamma == 90:\n if self.b == self.c and self.alpha == 120:\n return 'hexagonal'\n else:\n return 'monoclinic'\n elif self.a == self.b == self.c and self.alpha == self.beta == self.gamma:\n return 'trigonal'\n else:\n return 'triclinic'",
"title": ""
},
{
"docid": "1ed122eccbd8f64a0ec58d593ef4d8bc",
"score": "0.4540945",
"text": "def detect_basis(self):\r\n raise NotImplementedError",
"title": ""
},
{
"docid": "93892c362e4191adce32ab548a06f97b",
"score": "0.45072585",
"text": "def l_prfx(self):\n return _min3p.f90wrap_gen__get__l_prfx()",
"title": ""
},
{
"docid": "49c77801ca9c16e8fa54d920f3352174",
"score": "0.4504084",
"text": "def ramification_module_decomposition_hurwitz_curve(self):\n if self.matrix_degree()!=2:\n raise ValueError(\"Degree must be 2.\")\n F = self.base_ring()\n q = F.order() \n from sage.misc.misc import SAGE_EXTCODE\n gapcode = SAGE_EXTCODE + '/gap/joyner/hurwitz_crv_rr_sp.gap'\n gap.eval('Read(\"'+gapcode+'\")')\n mults = gap.eval(\"ram_module_hurwitz(\"+str(q)+\")\")\n return eval(mults)",
"title": ""
},
{
"docid": "52074f331ac2dd1581f5116a3661e4e0",
"score": "0.4498074",
"text": "def algebra_generators(self):\n return self.basis()",
"title": ""
},
{
"docid": "334bb15012f6445cfe874f37ccee60aa",
"score": "0.4496081",
"text": "def __init__(self):\n super().__init__()\n self._name = \"2-Qubit Gell-Mann Measurement Basis\"\n self._basis = GELL_MANN_BASIS",
"title": ""
},
{
"docid": "3c1f316ecc1bf81f53be873d766cd1fc",
"score": "0.44939253",
"text": "def rlb(self):\n rlb = np.zeros_like(self.pos)\n xyz = self.xyzmw\n rlb[:, 0] = np.sqrt(xyz[:, 0] ** 2 + xyz[:, 1] ** 2 + xyz[:, 2] ** 2)\n rlb[:, 1] = np.arctan2(xyz[:, 1], xyz[:, 0]) * 180 / np.pi\n rlb[:, 2] = np.arcsin(xyz[:, 2] / rlb[:, 0]) * 180 / np.pi\n return rlb",
"title": ""
},
{
"docid": "096a98d3b17cfbfa5a192d9add4130cc",
"score": "0.449322",
"text": "def get_material_flow_basis(self):\n return MaterialFlowBasis.molar",
"title": ""
},
{
"docid": "b5c961acd0e286d1816060d0631383c4",
"score": "0.44894618",
"text": "def passive_matrix_from_angle(basis, angle):\n c = np.cos(angle)\n s = np.sin(angle)\n\n if basis == 0:\n R = np.array([[1.0, 0.0, 0.0],\n [0.0, c, s],\n [0.0, -s, c]])\n elif basis == 1:\n R = np.array([[c, 0.0, -s],\n [0.0, 1.0, 0.0],\n [s, 0.0, c]])\n elif basis == 2:\n R = np.array([[c, s, 0.0],\n [-s, c, 0.0],\n [0.0, 0.0, 1.0]])\n else:\n raise ValueError(\"Basis must be in [0, 1, 2]\")\n\n return R",
"title": ""
},
{
"docid": "feccff3965a9318bb8f8d8255d1c3e0c",
"score": "0.44866404",
"text": "def basis(self, X_b, Y_b, basis_type=\"simple\"):\n XY = X_b.T @ Y_b / len(Y_b)\n XX = np.mean(X_b, 0)\n if basis_type == \"simple\":\n return np.concatenate([XY, XX])\n elif basis_type == \"quad\":\n tmp = (X_b.T @ X_b).reshape(-1)\n return np.concatenate([XY, XX, tmp])\n elif basis_type == \"original\":\n return np.concatenate([X_b.reshape(-1), Y_b])\n else:\n raise AssertionError(\"invalid basis type\")",
"title": ""
},
{
"docid": "4194fabac7bd09b2451fba4d7c8f4ca5",
"score": "0.44833133",
"text": "def init_measurement_basis(val: Union[str, MeasurementBasis] = None) -> MeasurementBasis:\n mb: MeasurementBasis = None\n if val is None: # if the basis is not set, use the default PauliMeasBasis\n mb = PauliMeasBasis()\n elif isinstance(val, MeasurementBasis):\n mb = val\n elif isinstance(val, str): # Construct the measurement basis from its name\n if val not in SUPPORTED_MEASUREMENT_BASIS:\n raise ArgumentError(\"in init_measurement_basis(): '{}' is not supported measurement basis!\".format(val))\n else:\n mb = getattr(sys.modules[__name__], val + 'MeasBasis')()\n else:\n raise ArgumentError(\"in init_measurement_basis(): unsupported input value type {}!\".format(type(val)))\n\n return mb",
"title": ""
},
{
"docid": "685f11a3506e392a2c61ed3eaff714ab",
"score": "0.44678122",
"text": "def get_basis_vectors(self):\r\n \r\n # Return standard bases if the orbit is for the solar system root\r\n if self.a is None:\r\n X = np.array([1, 0, 0])\r\n Y = np.array([0, 1, 0])\r\n Z = np.array([0, 0, 1])\r\n return X, Y, Z\r\n elif not self.X is None:\r\n return self.X, self.Y, self.Z\r\n else:\r\n # Get state vector at periapsis\r\n rp, vp = self.get_state_vector(self.get_time(0))\r\n \r\n # Get vector normal to the orbital plane\r\n Z = np.cross(rp,vp)\r\n Z = Z / norm(Z)\r\n \r\n # Set first basis vector based on celestial longitude\r\n X = np.array([1, 0, 0]) # assumed celestial longitude\r\n X = X - np.dot(X,Z) * Z/norm(Z)**2\r\n if norm(X) < 1E-15:\r\n X = np.array([0, math.copysign(1,Z[0]), 0])\r\n Z = np.array([math.copysign(1,Z[0]), 0, 0])\r\n else:\r\n X = X / norm(X)\r\n \r\n # Determine second basis through cross product of the others\r\n Y = np.cross(Z,X)\r\n Y = Y / norm(Y)\r\n \r\n self.X = X\r\n self.Y = Y\r\n self.Z = Z\r\n \r\n return X, Y, Z",
"title": ""
},
{
"docid": "b73e6137f7c127b64e4001ad73777b3f",
"score": "0.44606206",
"text": "def characteristic_polynomial(self):\n from sage.rings.polynomial.polynomial_ring import polygen\n x = polygen(QQ, 'x')\n if self.rank() == 1:\n return x**(self.dimension() - 1) * (x - len(self))\n\n H = self[0]\n R = self.restriction(H)\n charpoly_R = R.characteristic_polynomial()\n D = self.deletion(H)\n charpoly_D = D.characteristic_polynomial()\n return charpoly_D - charpoly_R",
"title": ""
},
{
"docid": "75669629a44c3afcceb5b95240f54d41",
"score": "0.44574896",
"text": "def tfinal_lc(self):\n return _min3p.f90wrap_chem__get__tfinal_lc()",
"title": ""
},
{
"docid": "6f050c36b713e775ade65b93c7995574",
"score": "0.44503808",
"text": "def getCoefficientRing(self):\n # short-cut self._coefficient_ring is None case\n return self._coefficient_ring",
"title": ""
},
{
"docid": "4d45b7e86e45bbca22aef4cd6e7de5c0",
"score": "0.4448082",
"text": "def GetRDFPolarizability(mol):\n\n filename = 'temp'\n ChargeCoordinates = _ReadCoordinates(filename)\n result = CalculatePolarizabilityRDF(ChargeCoordinates)\n\n return result",
"title": ""
},
{
"docid": "b5d36672a672bdfa650de9692d7f7a35",
"score": "0.44449207",
"text": "def _change_basis_on_basis(self, t, basis='milnor'):\n from sage.matrix.constructor import matrix\n from sage.rings.all import GF\n from .steenrod_algebra_bases import steenrod_algebra_basis,\\\n convert_from_milnor_matrix\n from .steenrod_algebra_misc import get_basis_name\n basis = get_basis_name(basis, self.prime(), generic=self._generic)\n if basis == self.basis_name():\n return self({t: 1})\n a = self._milnor_on_basis(t)\n if basis == 'milnor':\n return a\n d = a.monomial_coefficients()\n p = self.prime()\n deg = a.degree()\n A = SteenrodAlgebra(basis=basis, p=p, generic=self._generic)\n if deg == 0:\n return A(a.leading_coefficient())\n Bnew = steenrod_algebra_basis(deg, basis, p, generic=self._generic)\n Bmil = steenrod_algebra_basis(deg, 'milnor', p, generic=self._generic)\n v = []\n for a in Bmil:\n v.append(d.get(a, 0))\n out = (matrix(GF(p), 1, len(v), v) *\n convert_from_milnor_matrix(deg, basis, p, generic=self._generic))\n new_d = dict(zip(Bnew, out[0]))\n return A(new_d)",
"title": ""
},
{
"docid": "0ccfe58818874f4d953c1e69cf838a71",
"score": "0.44433168",
"text": "def derived_bsurf_i(self):\n\n from .utils import B_field\n\n if 'P1_I' not in self.columns:\n self.derived_p1_i()\n\n if not np.all([p in self.columns for p in ['P0', 'P1_I']]):\n return\n\n # get period and period derivative\n P0 = self.catalogue['P0']\n P1_I = self.catalogue['P1_I']\n BSURFI = B_field(P0, P1_I)\n self.update(BSURFI, name='BSURF_I')",
"title": ""
},
{
"docid": "28a3a1b1eec1e9afd302584793c6e708",
"score": "0.44400072",
"text": "def Aux_basis(self):\n return self.Aux_basis_set",
"title": ""
},
{
"docid": "76f17a6f06f711917681a008d946a27b",
"score": "0.4438974",
"text": "def polarCoord_Bfield(self):\r\n try:\r\n self.dRu_coord\r\n except:\r\n self.polarCoord()\r\n \r\n pol, tor = np.meshgrid(self.u_dom, self.v_dom)\r\n \r\n pol_xm = np.dot(self.xm_nyq.reshape(self.md_nyq, 1), pol.reshape(1, self.v_num * self.u_num))\r\n tor_xn = np.dot(self.xn_nyq.reshape(self.md_nyq, 1), tor.reshape(1, self.v_num * self.u_num))\r\n \r\n cos_pol = np.cos(pol_xm)\r\n cos_tor = np.cos(tor_xn)\r\n\r\n sin_pol = np.sin(pol_xm)\r\n sin_tor = np.sin(tor_xn)\r\n \r\n cos_mu_nv = cos_pol*cos_tor + sin_pol*sin_tor\r\n sin_mu_nv = sin_pol*cos_tor - sin_tor*cos_pol\r\n\r\n Bs_coord = np.dot(self.bsmns, sin_mu_nv).reshape(self.ns, self.v_num, self.u_num)\r\n Bu_coord = np.dot(self.bumnc, cos_mu_nv).reshape(self.ns, self.v_num, self.u_num)\r\n Bv_coord = np.dot(self.bvmnc, cos_mu_nv).reshape(self.ns, self.v_num, self.u_num)\r\n \r\n B_norm = 1. / (self.dRs_coord * self.dZu_coord - self.dRu_coord * self.dZs_coord)\r\n Br_coord = (self.dZu_coord * Bs_coord - self.dZs_coord * Bu_coord) * B_norm \r\n Bp_coord = ( ( (Bs_coord * (self.dRu_coord*self.dZv_coord - self.dRv_coord*self.dZu_coord) + Bu_coord * (self.dRv_coord*self.dZs_coord - self.dRs_coord*self.dZv_coord)) * B_norm ) + Bv_coord) / self.R_coord\r\n Bz_coord = (self.dRs_coord * Bu_coord - self.dRu_coord * Bs_coord) * B_norm\r\n \r\n self.B_field = np.stack((Br_coord, Bp_coord, Bz_coord), axis=3)",
"title": ""
},
{
"docid": "51afc9ceab256ba3aa5ac032623da438",
"score": "0.44374007",
"text": "def piola_kirchhoff2(self, u):\n a0, b0 = self.parameters['a0'], self.parameters['b0']\n a1, b1 = self.parameters['a1'], self.parameters['b1']\n a2, b2 = self.parameters['a2'], self.parameters['b2']\n a3, b3 = self.parameters['a3'], self.parameters['b3']\n\n # xyz -> fsn\n R = as_tensor(self.fiber_vectors)\n\n # Right Cauchy-Green deformation in xyz and fsn basis.\n C = right_cauchy_green_deformation(u)\n C_ = R*C*R.T\n\n # Invariants of B for the shape term.\n I4_ff = C_[0, 0]\n I4_ss = C_[1, 1]\n I4_nn = C_[2, 2]\n I8_fs = C_[0, 1]\n I1 = I4_ff + I4_ss + I4_nn\n\n # Derivatives of the invariants of C.\n # TODO Double check this formulation.\n ef, es, _ = self.fiber_vectors\n dW_I1 = a0*exp(b0*(I1 - 3))*Identity(u.geometric_dimension())\n dW_I4_ff = 2*a1*exp(b1*(I4_ff - 1)**2)*(I4_ff - 1)\n dW_I4_ss = 2*a2*exp(b2*(I4_ss - 1)**2)*(I4_ss - 1)\n dW_I8_fs = a3*exp(b3*I8_fs**2)*I8_fs*dot(ef, es)\n\n # Shape term component of S in fsn basis.\n Ss_ = 2*as_tensor(((dW_I1 + dW_I4_ff, dW_I8_fs, 0),\n (dW_I8_fs, dW_I1 + dW_I4_ss, 0),\n (0, 0, dW_I1)))\n\n # Combine the terms, rotate to Cartesian basis, and return.\n S = R.T*Ss_*R\n return S",
"title": ""
},
{
"docid": "05de3d3cd7d4a12df7dac4fd077f1a8b",
"score": "0.443614",
"text": "def poly_to_str(p):\n polystr = ''\n\n for i in range(len(p)):\n coeff = abs(p[i])\n if coeff == 0:\n continue\n xterm = get_xterm(p, i)\n opp = get_opp(p, i)\n polystr += '{0}{1}{2}'.format(opp, coeff, xterm)\n\n return polystr",
"title": ""
},
{
"docid": "fbbb3700e3d01f71eb12eb6327e089b2",
"score": "0.44317186",
"text": "def poly_constructor(n,c,x,basis):\r\n p=0\r\n for j in range(n):\r\n p += (basis[0][j]*c[j])*(x**(basis[1][j]))\r\n \r\n \r\n return p",
"title": ""
}
]
f73a218e6f4e61b5fc497d4418151388
Randomly select one of the variables in the test defined.
[
{
"docid": "7af41151fcc2e2c972c0fbf47c98c16b",
"score": "0.67626154",
"text": "def _select_random_variable_for_call(\n test_case: tc.TestCase, position: int\n ) -> vr.VariableReference | None:\n candidates: list[vr.VariableReference] = [\n var\n for var in test_case.get_all_objects(position)\n if not var.is_primitive()\n and not isinstance(\n test_case.get_statement(var.get_statement_position()),\n stmt.NoneStatement,\n )\n ]\n\n if len(candidates) == 0:\n return None\n # TODO(fk) sort based on distance and use rank selection.\n return randomness.choice(candidates)",
"title": ""
}
]
Apply Wordnet lemmatizer to text (go to root word)
[
{
"docid": "de04606a08eb8f89ecf33e73eaccd774",
"score": "0.76061803",
"text": "def lemmatizer(text):\n wnl = WordNetLemmatizer()\n text = [wnl.lemmatize(word) for word in text.split()]\n return \" \".join(text)",
"title": ""
}
]
[
{
"docid": "04bc11774c8564671a1c272290fcbd55",
"score": "0.7877054",
"text": "def lemmatizer(text):\n lemmatizer = WordNetLemmatizer()\n\n #Iterate through words and lemmatize\n i = 0\n for word in text:\n text[i] = lemmatizer.lemmatize(word)\n i = i + 1\n return text",
"title": ""
},
{
"docid": "aadf5769dec81b75317abb5b3ded3850",
"score": "0.75761193",
"text": "def lematize(words):\n lemmatizer = WordNetLemmatizer()\n doc = [lemmatizer.lemmatize(x[0], wordnet_tags(x[1])) for x in pos_tag(words)]\n return ' '.join(doc)",
"title": ""
},
{
"docid": "76c377a03d2fd47bd9134bde43e3ce69",
"score": "0.74065566",
"text": "def lemmatizeText(parsedData):\n wnl = WordNetLemmatizer()\n for d in parsedData:\n pos = pos_tag(word_tokenize(d[\"text\"].lower()))\n lems = lemmatize_with_pos(wnl, pos)\n # d[\"tokens\"] = word_tokenize(d[\"text\"])\n d[\"lemmas\"] = lems",
"title": ""
},
{
"docid": "93443e160d863becd4400dcd20f2a7a4",
"score": "0.73142326",
"text": "def tokenize2(text):\n \n text= [char if char not in string.punctuation else \" \" for char in text.lower()]\n text=\"\".join(text)\n #split text into individual words\n text = text.split()\n # Remove stop words \n sw= stopwords.words('english')\n text = [word for word in text if word not in sw]\n lemmed_text = [WordNetLemmatizer().lemmatize(word) for word in text]\n \n return lemmed_text",
"title": ""
},
{
"docid": "7fb4c7249ba3f3d0a53560a003148cf8",
"score": "0.7205104",
"text": "def normalise(self,word):\n word = word.lower()\n #word = stemmer.stem_word(word)\n stemmer = nltk.stem.porter.PorterStemmer()\n lemmatizer = nltk.WordNetLemmatizer()\n word = lemmatizer.lemmatize(word)\n return word",
"title": ""
},
{
"docid": "357f628e903bac1044673ea08361bb94",
"score": "0.7177658",
"text": "def lematizing_text(data):\n \n lemmatizer = WordNetLemmatizer() \n lemmatized_output = []\n\n for tweet in data:\n lemmed = ' '.join([lemmatizer.lemmatize(w) for w in tweet])\n lemmatized_output.append(lemmed)\n \n return lemmatized_output",
"title": ""
},
{
"docid": "21e895462dc9ad96e1d829f0cba8a04f",
"score": "0.70998114",
"text": "def lemmatize(data):\n lemmatizer = nltk.stem.WordNetLemmatizer()\n return [lemmatizer.lemmatize(word) for word in data]",
"title": ""
},
{
"docid": "e1863e5e33ebed06bde1a31f63b75e53",
"score": "0.7046299",
"text": "def tokenize(text):\n \n stop_words = stopwords.words(\"english\")\n lemmatizer = WordNetLemmatizer()\n \n # Normalization : \n # Replace punctuations with \" \" and make string lowercase\n # Replace all punctuations except apostrophes\n text = re.sub(\"[^a-zA-Z0-9']\", \" \", text.lower())\n \n # Tokenize: Split to words\n tokenized = text.split(\" \")\n tokenized = [word for word in tokenized if word != \"\" ]\n \n # Remove stop_words\n cleaned = [word for word in tokenized if word not in stop_words]\n \n # Part of speech tagging\n tagged = nltk.pos_tag(cleaned)\n \n # Lemmatize\n lemmatized = []\n\n tag_dict = {\"J\": wordnet.ADJ,\n \"N\": wordnet.NOUN,\n \"V\": wordnet.VERB,\n \"R\": wordnet.ADV}\n\n for word, raw_tag in tagged:\n tag = tag_dict.get(raw_tag[0].upper(), wordnet.ADV)\n lemmatized.append(lemmatizer.lemmatize(word, pos = tag))\n\n return lemmatized",
"title": ""
},
{
"docid": "edcdbeb323796b0e81729543909bbac6",
"score": "0.70317405",
"text": "def tokenize(text):\n \n \n # Normalize by replacing all values other than alphabets and numbers with space and convert all the words to lower case\n text = re.sub(r\"[^a-zA-Z0-9]\", \" \", text.lower()) \n \n #tokenize the text\n words = word_tokenize(text)\n \n #drop all teh stopwords and lemmatize\n words = [w for w in words if w not in stopwords.words('english')]\n lemmed_words = [WordNetLemmatizer().lemmatize(w) for w in words]\n \n return lemmed_words",
"title": ""
},
{
"docid": "8a7bf798a8dbb713e59b33e3225ce84d",
"score": "0.70284337",
"text": "def normalise(word):\n\n lowercase_word = word.lower()\n\n nltk_tagged = nltk.pos_tag([lowercase_word])\n wordnet_tagged = map(lambda x: (x[0], nltk_to_wordnet_tag(x[1])), nltk_tagged)\n lemma = str()\n for word, tag in wordnet_tagged:\n if tag is None:\n lemma = word\n else:\n lemma = lemmatizer.lemmatize(word, tag)\n return lemma",
"title": ""
},
{
"docid": "13476bf4e68eaf5aa121a7334c041674",
"score": "0.6986198",
"text": "def lemmatize_text(text):\n\n tokens = nltk.regexp_tokenize(text, sentence_re)\n for word in tokens:\n if word in punctuations:\n tokens.remove(word)\n tagged_tokens = nltk.tag.pos_tag(tokens)\n lemmatized_text = [normalise(word) for word, tag in tagged_tokens if acceptable_word(word)]\n return lemmatized_text",
"title": ""
},
{
"docid": "b5a7648097119653b1cbd73a0e39fe17",
"score": "0.6964392",
"text": "def tokenize(text):\n # Normalize text\n text = re.sub(r\"[^a-zA-Z0-9]\", \" \", text.lower())\n # Tokenize text\n words = word_tokenize(text)\n # Remove stop words\n words = [w for w in words if w not in stopwords.words(\"english\")]\n # Reduce words to their root form\n lemmed = [WordNetLemmatizer().lemmatize(w) for w in words]\n\n return lemmed",
"title": ""
},
{
"docid": "c4d86e3add3544c0a7bfc124b3e72075",
"score": "0.69499004",
"text": "def normalise(word):\n word = word.lower()\n # word = stemmer.stem_word(word) #if we consider stemmer then results comes with stemmed word, but in this case word will not match with comment\n word = lemmatizer.lemmatize(word)\n return word",
"title": ""
},
{
"docid": "7c524c23f8d38096f202b04cdf67c060",
"score": "0.68733096",
"text": "def tokenize(text):\n \n #initialize word lemmatizer\n lemmatizer = WordNetLemmatizer() \n #remove punctuation and uwanted characters\n text=re.sub(\"r[^a-zA-Z0-9]\",\" \",text)\n #tokenzie the words\n words= nltk.word_tokenize(text)\n #lower case the words and lemmantize them\n words = [lemmatizer.lemmatize(word.lower()) for word in words]\n #remove stop words\n words= [word for word in words if word not in stopwords.words('english')]\n \n return words",
"title": ""
},
{
"docid": "b5317344fd5d425667af9c27d693b7b8",
"score": "0.6853423",
"text": "def mp_nltk_lematize(input_t):\n\n if input_t[2]:\n tokens = []\n for t in input_t[2].split():\n if len(t) < 4:\n tokens.append(t)\n else:\n tokens.append(lemmatizer.lemmatize(t))\n input_t[2] = \" \".join(tokens)\n input_t[2] = re.sub(\n r'\\w+:\\/{2}[\\d\\w-]+(\\.[\\d\\w-]+)*(?:(?:\\/[^\\s/]*))*',\n '',\n input_t[2])\n input_t[2] = re.sub(r'\\W+', ' ', input_t[2]).strip().lower()\n return input_t[2]",
"title": ""
},
{
"docid": "15189ed87ed57882b0b510f30e0d3b7a",
"score": "0.684229",
"text": "def normalise(word):\n word = word.lower()\n print word\n word = stemmer.stem_word(word)\n word = lemmatizer.lemmatize(word)\n return word",
"title": ""
},
{
"docid": "016ca64e57bd84f7c89b7e55273150fd",
"score": "0.68036675",
"text": "def tree_words(self, wn_lemmatize=False):\n lemmas = self.tree_lemmas(wn_lemmatize=wn_lemmatize)\n return [x[0] for x in lemmas]",
"title": ""
},
{
"docid": "d590957fc39e55404a313ac1638ea932",
"score": "0.67995805",
"text": "def lemmatize(self, words):\r\n lemmed = [WordNetLemmatizer().lemmatize(w) for w in words]\r\n return lemmed",
"title": ""
},
{
"docid": "f05fbe1d6d6d5d37c5db7c18a64c49be",
"score": "0.67739016",
"text": "def normalise(word):\n\tword = word.lower()\n\t# word = stemmer.stem_word(word) #if we consider stemmer then results comes with stemmed word, but in this case word will not match with comment\n\tword = lemmatizer.lemmatize(word)\n\treturn word",
"title": ""
},
{
"docid": "8cbdb0fcc966add2ccc1c7c33ae7be20",
"score": "0.6773597",
"text": "def word_tokenizer(text):\n\n\t# start tokenizing\n\ttry:\n\t\t# # create spacey object\n\t\t# spacy_doc = nlp(text)\n\t\t# Lemmatize tokens, remove punctuation and remove stopwords.\n\t\treturn [token.lemma_ for token in text if token.is_alpha and not token.is_stop and len(token) > 1]\n\texcept Exception, e:\n\t\tlogging.error('[{}] : {}'.format(sys._getframe().f_code.co_name,e))\n\t\texit(1)",
"title": ""
},
{
"docid": "7473831fd2dff00e897b34bab9e443fd",
"score": "0.6758672",
"text": "def pre_process_text(text):\n # Cconvert to lowercase, remove punctuations and unneeded characters, then strip\n text = re.sub(r'[^\\w\\s]', '', str(text).lower().strip())\n\n # Tokenize\n lst_text = text.split()\n # Remove Stopwords\n lst_text = [word for word in lst_text if word not in lst_stopwords]\n\n # Lemmatisation (convert the word into root word)\n lem = nltk.stem.wordnet.WordNetLemmatizer()\n lst_text = [lem.lemmatize(word) for word in lst_text]\n\n # Rejoin tokenized string\n text = \" \".join(lst_text)\n return text",
"title": ""
},
{
"docid": "b753018ae017e12ba916a92217ce7a97",
"score": "0.674717",
"text": "def preprocess_pipeline(text):\n words = tokenize(text[0])\n lemmed_words = lematize(words)\n return lemmed_words",
"title": ""
},
{
"docid": "dc40d594d40c6264757adc42b76313c6",
"score": "0.67336124",
"text": "def tokenize(text):\n #tokenize text\n tokens = word_tokenize(text)\n \n # initiate lemmatizer\n lemmatizer = WordNetLemmatizer()\n\n # iterate through each token\n clean_tokens = []\n for tok in tokens:\n \n # lemmatize, normalize case, and remove leading/trailing white space\n clean_tok = lemmatizer.lemmatize(tok).lower().strip() \n clean_tokens.append(clean_tok)\n return clean_tokens",
"title": ""
},
{
"docid": "7dd15845f26d4f381e7fb077df818202",
"score": "0.67247325",
"text": "def lemmatize(segments):\r\n for segment in segments:\r\n pos_list = nltk.pos_tag(segment)\r\n for i in range(len(segment)):\r\n pos = get_wordnet_pos(pos_list[i][1])\r\n segment[i] = LEMMATIZER.lemmatize(segment[i].lower(), pos) # make segment lowercase before lemmatizing\r\n return segments",
"title": ""
},
{
"docid": "d3ee0e3c08d119c3100b4d03ed2f9fab",
"score": "0.6687493",
"text": "def tree_lemmas(self, wn_format=False, wn_lemmatize=False):\n word_tag = []\n for tree in self.trees:\n word_tag += tree.pos()\n return self.wn_lemmatizer(\n word_tag, wn_format=wn_format, wn_lemmatize=wn_lemmatize)",
"title": ""
},
{
"docid": "4b75a640612d0b1edf28046f0d798913",
"score": "0.6669173",
"text": "def lemmatize(word_list):\n\n wordnet_lemmatizer = WordNetLemmatizer()\n word_list_lemmatized = []\n\n for word in word_list:\n if word[0].isalpha():\n try:\n word_list_lemmatized.append(wordnet_lemmatizer.lemmatize(word[0], pos=penn_to_wn(word[1])))\n except:\n try:\n word_list_lemmatized.append(wordnet_lemmatizer.lemmatize(word[0]))\n except:\n pass\n\n return word_list_lemmatized",
"title": ""
},
{
"docid": "480c2c9ee2798e33bb3c287c9354f5b7",
"score": "0.6662673",
"text": "def tokenize(text):\n raw_toks = word_tokenize(text)\n lem = WordNetLemmatizer()\n tokens = [lem.lemmatize(t).strip().lower() for t in raw_toks if t not in stopwords.words(\"english\")] \n return tokens",
"title": ""
},
{
"docid": "2b07c1eb5271e08fe53f5522bd0275a7",
"score": "0.6641279",
"text": "def __wn_lemmatize(self, lemma):\n string, tag = lemma\n wnl = WordNetLemmatizer()\n if tag in ('a', 'n', 'r', 'v'):\n string = wnl.lemmatize(string, tag)\n else:\n string = wnl.lemmatize(string)\n return (string, tag)",
"title": ""
},
{
"docid": "46ac4dee280d0eff559b78d73b776023",
"score": "0.6637313",
"text": "def lemmatize_words(tokens):\n lemmatizer = WordNetLemmatizer()\n lemmas = []\n for word in tokens:\n lemma = lemmatizer.lemmatize(word)\n lemmas.append(lemma)\n return lemmas",
"title": ""
},
{
"docid": "b8e1240fa575c1fed4563a889937dd9a",
"score": "0.6608097",
"text": "def lemmatization(data_frame): \n from textblob import Word\n data_frame['review'] = data_frame['review'].apply(lambda x: \" \".join([Word(word).lemmatize() for word in x.split()]))\n return data_frame",
"title": ""
},
{
"docid": "2eef355d52f903560f50fac4bdfbee51",
"score": "0.6558888",
"text": "def tokenize(text):\n detected_urls = re.findall(url_regex, text)\n for url in detected_urls:\n text = text.replace(url, \"urlplaceholder\")\n\n tokens = word_tokenize(text)\n lemmatizer = WordNetLemmatizer()\n\n clean_tokens = []\n for tok in tokens:\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n\n return clean_tokens\n #pass",
"title": ""
},
{
"docid": "223303b6c7f31c25c1a95533c9246f5e",
"score": "0.65576214",
"text": "def lemmatize_tokens(tweet_tokens):\n lemmatizer = WordNetLemmatizer()\n tags = nltk.pos_tag(tweet_tokens)\n tags_word_net = [get_wordnet_pos(w[1]) for w in tags]\n lem_result = [] \n for i in range(len(tags_word_net)):\n if tags_word_net[i]: \n lem_result.append(lemmatizer.lemmatize(tags[i][0],tags_word_net[i]))\n else:\n lem_result.append(tags[i][0])\n return lem_result",
"title": ""
},
{
"docid": "fd1b33992da6183d994dd8864d2e4b68",
"score": "0.6552774",
"text": "def tokenize(text):\n\n tokens = nltk.word_tokenize(text)\n lemmatizer = nltk.WordNetLemmatizer()\n clean_tokens = [lemmatizer.lemmatize(token).lower().strip() for token in tokens]\n return clean_tokens",
"title": ""
},
{
"docid": "50430d3127f89a07c04aeca3124e69de",
"score": "0.6552474",
"text": "def process(self, text):\n \n return self.my_lemmatize(self.remove_stopword(self.clean_text(text)))",
"title": ""
},
{
"docid": "62999b2b8f5e675620980001a58770d6",
"score": "0.65255034",
"text": "def __lemmatize(self, text):\n return [self.__lemmatizer.lemmatize(w) for w in text]",
"title": ""
},
{
"docid": "b444273200178788bc0359908a0bb8ef",
"score": "0.6523032",
"text": "def tokenize(text):\n\n #### Normalize (convert to lower case and remove punctuation) text\n text = re.sub(\"[^a-z,A-Z,0-9]\", \" \", text.lower().strip())\n\n #### Tokenize text to words\n text = word_tokenize(text)\n\n #### Remove stop words\n text = [i for i in text if i not in stopwords.words('english') ]\n\n #### Lemmatize\n text = [WordNetLemmatizer().lemmatize(x, pos = 'n') for x in text]\n text = [WordNetLemmatizer().lemmatize(x, pos = 'v') for x in text]\n\n return text",
"title": ""
},
{
"docid": "c02227e3fc4dc0f1bec32c432ac97b1f",
"score": "0.65064806",
"text": "def tokenize(text): \n \n stop_words = stopwords.words(\"english\")\n lemmatizer = WordNetLemmatizer()\n \n\n # normalize case and remove punctuation\n text = re.sub(r\"[^a-zA-Z0-9]\", \" \", text.lower())\n \n # tokenize text\n tokens = word_tokenize(text)\n \n # lemmatize andremove stop words\n tokens = [lemmatizer.lemmatize(word) for word in tokens if word not in stop_words]\n\n return tokens",
"title": ""
},
{
"docid": "2f351bf2dffa7fe7d1b6319c1a3cf985",
"score": "0.6475991",
"text": "def tokenize(text):\n # remove punctuation\n text = re.sub(r'[^a-zA-Z0-9]', ' ',text)\n \n # tokenize text\n tokens = word_tokenize(text)\n \n # initiate lemmatizer\n lemmatizer = WordNetLemmatizer()\n \n # iterate through each token\n clean_tokens = []\n for tok in tokens:\n \n # lemmatize, normalize case, and remove leading/trailing white space\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n \n return clean_tokens",
"title": ""
},
{
"docid": "99c9517edaeabbbe322d9e4115c27845",
"score": "0.64716494",
"text": "def tokenize(text):\n stop_words = stopwords.words(\"english\")\n lemmatizer = WordNetLemmatizer()\n # normalize case and remove punctuation\n text = re.sub(r\"[^a-zA-Z0-9]\", \" \", text.lower())\n \n # tokenize text\n tokens = word_tokenize(text)\n \n # lemmatize and remove the stop words\n tokens = [lemmatizer.lemmatize(word) for word in tokens if word not in stop_words] \n \n return tokens",
"title": ""
},
{
"docid": "3c23b40ee44a76342771ae3d58a8cb66",
"score": "0.6456129",
"text": "def tokenize(text):\n\n lemm = WordNetLemmatizer()\n \n text = word_tokenize(text)\n text = [lemm.lemmatize(x).lower().strip() for x in text]\n text = filter(lambda x: x not in skip_tokens, text)\n \n return \" \".join(text)",
"title": ""
},
{
"docid": "d80792104cd594925497e1effa3c2e0c",
"score": "0.645452",
"text": "def tokenize(text):\n tokens = word_tokenize(text)\n lemmatizer = WordNetLemmatizer()\n\n clean_tokens = []\n for tok in tokens:\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n \n return clean_tokens",
"title": ""
},
{
"docid": "4b4b465a5e03b9e0762e76ab4ffe6963",
"score": "0.64461315",
"text": "def normalise(self,word):\n word = word.lower()\n # word = stemmer.stem_word(word) #if we consider stemmer then results comes with stemmed word, but in this case word will not match with comment\n word = self.lemmatizer.lemmatize(word)\n return str(word).strip()",
"title": ""
},
{
"docid": "79358ab0f2032f042034cc197e48f383",
"score": "0.6440574",
"text": "def mp_spacy_lemmatize(input_t):\n if input_t[2]:\n\n doc = NLP(input_t[2])\n input_t[2] = \" \".join([token.lemma_ for token in doc])\n input_t[2] = re.sub(\n r'\\w+:\\/{2}[\\d\\w-]+(\\.[\\d\\w-]+)*(?:(?:\\/[^\\s/]*))*',\n '',\n input_t[2])\n input_t[2] = re.sub(r'\\W+', ' ', input_t[2]).strip().lower()\n return input_t[2]",
"title": ""
},
{
"docid": "db83f33dcb6ac948cc1dee4c0f710101",
"score": "0.6433062",
"text": "def tokenize(text):\n\n # use nltk library \n tokens = word_tokenize(text)\n\n # use nltk lemmatizer \n lemma = WordNetLemmatizer()\n \n # lemmatize, make it lower case, and remove spaces\n tokens = [lemma.lemmatize(t).lower().strip() for t in tokens]\n\n return tokens",
"title": ""
},
{
"docid": "95b8bf3dcee81bb965f2d31adafa76b7",
"score": "0.641855",
"text": "def tokenize(text):\n tokens = word_tokenize(text)\n lemmatizer = WordNetLemmatizer()\n clean_tokens = []\n for tok in tokens:\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n return clean_tokens",
"title": ""
},
{
"docid": "5c3d6cf5fb48c5bcafcfb1a54fcd9648",
"score": "0.64020586",
"text": "def tokenize(text): \n # remove punctuation\n text = re.sub(r\"[^\\w\\s]\", \"\", text)\n \n # tokenize text\n tokens = word_tokenize(text)\n \n # initiate lemmatizer\n lemmatizer = WordNetLemmatizer()\n \n # stopping word\n stop_words = set(stopwords.words('english')) \n \n # iterate through each token\n clean_tokens = []\n for tok in tokens:\n # lemmatize, normalize case, and remove leading/trailing white space\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n if clean_tok not in stop_words:\n clean_tokens.append(clean_tok)\n\n return clean_tokens",
"title": ""
},
{
"docid": "1884b433a05208f82b874c67a9a0c06e",
"score": "0.63928205",
"text": "def normalize(document, stopwords=STOPWORDS):\n\n for token, tag in document:\n token = token.lower().strip()\n\n if is_punct(token) or (token in stopwords):\n continue\n\n yield lemmatizer.lemmatize(token, wnpos(tag))",
"title": ""
},
{
"docid": "a9740b4931568d9e999a55ec41b3ca7c",
"score": "0.6383255",
"text": "def lemmatize_tokens(tokens):\n lemmatizer = nltk.WordNetLemmatizer()\n lemmatized_tokens = [lemmatizer.lemmatize(token) for token in tokens]\n return lemmatized_tokens",
"title": ""
},
{
"docid": "5904031b83c0e1e91afe66868e833d20",
"score": "0.637792",
"text": "def apply(cls,\n input,\n stemmer) -> str:\n \"\"\"\n Choose the available algorithm, which user has entered\n \"\"\"\n algorithm = cls.__choose_stemmer(\n stemmer=stemmer\n )()\n \"\"\"\n Lemmatize received tokens from function self.__tokenize()\n \"\"\"\n stems = [\n algorithm.stem(\n word=token\n )\n for token in cls.__tokenize(input)\n ]\n \"\"\"\n Extract the text from the lemmatized tokens\n \"\"\"\n return ' '.join(stems)",
"title": ""
},
{
"docid": "09840688be52cf820bfc07433727ef61",
"score": "0.6369183",
"text": "def lemmatizing(self, text):\n if self.lang not in self.languages:\n raise LangDependencyError(\"Lemmatizing - language not defined\")\n \n if self.lang == \"portuguese\":\n text = self.portuguese_lemmatizing(text)\n elif self.lang == \"spanish\":\n raise LangDependencyError(\"Lemmatizing - language not implemented for lemmatizing\")\n elif self.lang == \"english\":\n raise LangDependencyError(\"Lemmatizing - language not implemented for lemmatizing\")\n elif self.lang == \"italian\":\n raise LangDependencyError(\"Lemmatizing - language not implemented for lemmatizing\")\n\n return text",
"title": ""
},
{
"docid": "95cfe9e375d8c094a40025d495016c33",
"score": "0.6334536",
"text": "def tokenize(text):\n # splitting the sentence(s) into tokens/words\n tokens = word_tokenize(text)\n\n # initializing the lemmatizer\n lemmatizer = WordNetLemmatizer()\n\n # iterate through each token\n clean_tokens = []\n for token in tokens:\n clean_token = lemmatizer.lemmatize(token.lower().strip())\n clean_tokens.append(clean_token)\n\n return clean_tokens",
"title": ""
},
{
"docid": "8e7c27012d4b1d79dfc6b32a785ea975",
"score": "0.63303983",
"text": "def lemmatizer(tagger, document, stopwords):\n tags = tagger.tag_text(document)\n lemmatized_doc = []\n\n for elt in tags:\n clean = elt.split('\\t')\n try:\n if clean[2] not in stopwords:\n lemmatized_doc.append(str(clean[2]))\n except:\n pass\n return \" \".join(lemmatized_doc)",
"title": ""
},
{
"docid": "41e8a23b9394caeafbc316943807f42a",
"score": "0.632665",
"text": "def lemmatize_as_string(document):\n try:\n doc = nlp(text=document)\n lemmatized_words = []\n for token in doc:\n lemmatized_words.append(token.lemma_)\n lemmatized_document = \" \".join(lemmatized_words)\n return lemmatized_document\n except:\n return \"\"",
"title": ""
},
{
"docid": "8898306da707337b3ef5fe876fc34498",
"score": "0.63158315",
"text": "def tokenize(text):\n\n stop_words = stopwords.words(\"english\")\n lemmatizer = WordNetLemmatizer()\n\n # normalize case and remove punctuation\n text = re.sub(r\"[^a-zA-Z0-9]\", \" \", text.lower())\n\n # tokenize text\n tokens = word_tokenize(text)\n\n # lemmatize and remove stop words\n tokens = [lemmatizer.lemmatize(word) for word in tokens if word not in stop_words]\n\n return tokens",
"title": ""
},
{
"docid": "aa8ce10d81be0418462dace5e4176e3b",
"score": "0.6315783",
"text": "def _lemmatization(self, texts):\n lemmatized = [[token.lemma_ for token in self._nlp(' '.join(sent)) if token.pos_ in self._allowed_pos]\n for sent in texts]\n return lemmatized",
"title": ""
},
{
"docid": "d9929c075b3658f8a99815dc0f6ea652",
"score": "0.63129747",
"text": "def token_lemma(data):\n tokenizer = nltk.tokenize.TreebankWordTokenizer()\n lemmatizer = nltk.stem.WordNetLemmatizer()\n return [lemmatizer.lemmatize(word) for word in tokenizer.tokenize(data)]",
"title": ""
},
{
"docid": "43f2d5eaf2b6e141a29cb5ec154b7d67",
"score": "0.6306946",
"text": "def tokenize(text):\n tokens = word_tokenize(text)\n lemmatizer = WordNetLemmatizer()\n\n clean_tokens = []\n for tok in tokens:\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n\n return clean_tokens",
"title": ""
},
{
"docid": "2a510b0e31d98f063c7ef5ea228afd7e",
"score": "0.6301819",
"text": "def lem(words: list, lemmatiser_cls=WordNetLemmatizer) -> list:\n\n lemmatizer = lemmatiser_cls()\n return seq(words).map(partial(lemmatizer.lemmatize, pos='v')).list()",
"title": ""
},
{
"docid": "1530f130e8d10729a5cd5eea1c9b312f",
"score": "0.6286428",
"text": "def lemmatise_text(token_list: list):\n wordnet_lemmatizer = WordNetLemmatizer()\n lemma_list = []\n # Convert each word to each lemma and append to a new list\n for word in token_list:\n lemma_list.append(wordnet_lemmatizer.lemmatize(word))\n return lemma_list",
"title": ""
},
{
"docid": "eae2ca22367053b4c48674552dc797e4",
"score": "0.62861496",
"text": "def lemmatize(self, _func=None):\n\n def lemmatize_decorator(func):\n self._check_id(func)\n self.chain[self.id] += func.__name__ + \"-\"\n\n @nldmethod\n def lemmatize_wrapper(_input=None):\n lemmatizer = WordNetLemmatizer()\n result = func(_input) if _input else func()\n if isinstance(result, list):\n if len(result) > 0 and isinstance(result[0], tuple):\n if self.logger:\n self.logger.info('Lemmatize : input is tuple')\n for i in range(len(result)):\n result[i] = list(result[i])\n result[i][0] = lemmatizer.lemmatize(result[i][0])\n result[i] = tuple(result[i])\n return result\n return [lemmatizer.lemmatize(word) for word in result]\n return lemmatize_wrapper\n if not _func:\n return lemmatize_decorator\n else:\n return lemmatize_decorator(_func)",
"title": ""
},
{
"docid": "4f7e01171444000e3c1f659e35c3bb9c",
"score": "0.6277009",
"text": "def tokenize(text, lemmatizer=WordNetLemmatizer()):\n url_regex = 'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+'\n\n # Detect URLs\n detected_urls = re.findall(url_regex, text)\n for url in detected_urls:\n text = text.replace(url, 'urlplaceholder')\n\n # Normalize and tokenize\n tokens = nltk.word_tokenize(re.sub(r\"[^a-zA-Z0-9]\", \" \", text.lower()))\n\n tokens = [t for t in tokens if t not in stopwords.words('english')]\n\n tokens = [lemmatizer.lemmatize(t) for t in tokens]\n\n return tokens",
"title": ""
},
{
"docid": "06be923fc8dcaff86cb8a7bc15150f8f",
"score": "0.6270527",
"text": "def tokenize(text):\n \n # remove url place holder\n \n url_regex= r'(https?://\\S+)'\n text = re.sub(url_regex, 'urlplaceholder',text)\n \n #tokenize message into words \n \n tokens=word_tokenize(text)\n \n #remove the stop words \n \n filtered_tokens=[w for w in tokens if not w in stopwords.words('english')]\n \n #remove punctuation and tokens containing non alphabetic symbols\n \n alpha_tokens=[token.lower() for token in filtered_tokens if token.isalpha()]\n \n # make a default dictionary for the pos tagging \n tag_map = defaultdict(lambda : wordnet.NOUN)\n tag_map['J'] = wordnet.ADJ\n tag_map['V'] = wordnet.VERB\n tag_map['R'] = wordnet.ADV\n\n #lemmatize tokens using pos tags from defaulct dict\n \n clean_tokens=[]\n lmtzr = WordNetLemmatizer()\n for token, tag in pos_tag(alpha_tokens):\n clean_tokens.append(lmtzr.lemmatize(token, tag_map[tag[0]]))\n \n \n return clean_tokens",
"title": ""
},
{
"docid": "512c0963c8b7289b2ba4ec60e49bd6ff",
"score": "0.6242194",
"text": "def lemmatize(sentences):\n\tlemmatizer = WordNetLemmatizer()\n\tlemmatized_sentences = []\n\tfor s in sentences:\n\t\tlemmatized_s = []\n\t\tfor word in s:\n\t\t\tlemmatized_s.append(lemmatizer.lemmatize(word))\n\t\tlemmatized_sentences.append(lemmatized_s)\n\n\treturn lemmatized_sentences",
"title": ""
},
{
"docid": "861d17b891715ae6fe02dab8fc9e258c",
"score": "0.62417716",
"text": "def pos_lemmas(self, wn_format=False, wn_lemmatize=False):\n pos = self.pos\n pos = pos.strip()\n word_tag = list(map((lambda x : tuple(x.split(\"/\"))), re.split(r\"\\s+\", pos)))\n word_tag = [x for x in word_tag if len(x) == 2]\n word_tag = self.wn_lemmatizer(\n word_tag, wn_format=wn_format, wn_lemmatize=wn_lemmatize)\n return word_tag",
"title": ""
},
{
"docid": "a5f4a528fc2125c50be4855043400c1b",
"score": "0.62313986",
"text": "def clean_up(text):\n # lemma = WordNetLemmatizer()\n lemmatizer = nltk.WordNetLemmatizer().lemmatize\n text = re.sub('\\W+', ' ', str(text))\n # print(\"step1:\", text)\n text = re.sub(r'[0-9]+', '', text.lower())\n # correcting spellings of words - user complaints are bound to have spelling mistakes\n # text = TextBlob(text).correct()\n # print(\"step2:\", text)\n word_pos = nltk.pos_tag(nltk.word_tokenize(text))\n normalized_text_lst = [lemmatizer(x[0], get_wordnet_pos(x[1])).lower() for x in word_pos]\n # print(\"step3:\", normalized_text_lst)\n stop_words_free = [i for i in normalized_text_lst if i not in english_stopwords and len(i) > 3]\n # print(\"step4:\", stop_words_free)\n stop_words_free = list(set(stop_words_free))\n return (stop_words_free)",
"title": ""
},
{
"docid": "331da77597ad1df38b7959126d3643dc",
"score": "0.62291944",
"text": "def clean_lemmatize_token(tweet):\n stop_words = set(stopwords.words('english'))\n cleaned = tweet.translate(str.maketrans('', '', string.punctuation)).lower()\n tokenized = word_tokenize(cleaned)\n filtered = [w for w in tokenized if not w in stop_words]\n lemmatizer = WordNetLemmatizer()\n lemmatized = []\n for word in filtered:\n lemmatized.append(lemmatizer.lemmatize(word))\n to_remove = ['rt','mention','sxsw','link']\n lemmatized = [w for w in lemmatized if w not in to_remove]\n lemmatized = ' '.join(lemmatized)\n return lemmatized",
"title": ""
},
{
"docid": "6b6576f4bfaf18cbedca083ed82728c2",
"score": "0.6224641",
"text": "def lemmatize_strings(body_text, language = 'da', remove_stopwords_ = True):\n \n if isinstance(body_text, str):\n body_text = [body_text] #Convert whatever passed to a list to support passing of single string\n \n if not hasattr(body_text, '__iter__'):\n raise TypeError('Passed argument should be a sequence.')\n \n lemmatizer = lemmy.load(language) #load lemmatizing dictionary\n \n lemma_list = [] #list to store each lemmatized string \n\n word_regex = re.compile('[a-zA-Z0-9æøåÆØÅ]+') #All charachters and digits i.e. all possible words\n\n for string in body_text:\n #remove punctuation and split words\n matches = word_regex.findall(string)\n\n #split words and lowercase them unless they are all caps\n lemmatized_string = [word.lower() if not word.isupper() else word for word in matches]\n \n #remove words that are in the stopwords file\n if remove_stopwords_:\n lemmatized_string = remove_stopwords(lemmatized_string)\n \n #lemmatize each word and choose the shortest word of suggested lemmatizations\n lemmatized_string = [min(lemmatizer.lemmatize('', word), key=len) for word in lemmatized_string]\n\n #remove words that are in the stopwords file\n if remove_stopwords_:\n lemmatized_string = remove_stopwords(lemmatized_string)\n\n lemma_list.append(' '.join(lemmatized_string))\n\n return lemma_list if len(lemma_list) > 1 else lemma_list[0] #return list if list was passed, else return string",
"title": ""
},
{
"docid": "8d4f29574c92adad5260091cf9db7d28",
"score": "0.6223193",
"text": "def tokenize(text):\n \n # text transformation\n text = text.lower()\n text = re.sub(r\"[^a-zA-Z0-9]\",\" \",text)\n text = text.strip()\n \n #stop ords\n stop_words = stopwords.words(\"english\")\n \n #tokenize and lematize\n tokens = word_tokenize(text)\n lemmatizer = WordNetLemmatizer()\n \n #detele stop words\n clean_tokens = [lemmatizer.lemmatize(word) for word in tokens if word not in stop_words]\n\n return clean_tokens",
"title": ""
},
{
"docid": "e078b420af0e13701fc1a543d5957fdc",
"score": "0.61953324",
"text": "def tokenize(text):\n # remove non-numerical non-character types and tokenize text\n tokens = word_tokenize(re.sub(r\"[^a-zA-Z0-9]\", \" \", text.lower()))\n\n # Instantiate lemmatizer and stemmer\n wnl = WordNetLemmatizer()\n ps = PorterStemmer()\n\n # lemmatize tokens of nouns and verbs and stemming\n clean_tokens = []\n for tok in tokens:\n clean_tok = wnl.lemmatize(tok)\n clean_tok = wnl.lemmatize(clean_tok, 'v')\n clean_tok = ps.stem(clean_tok)\n clean_tokens.append(clean_tok.strip())\n\n return clean_tokens",
"title": ""
},
{
"docid": "64db3e1fd6cb01bae3f3e936a4a91d1c",
"score": "0.61664844",
"text": "def normalize_en(tokens, type, analyzer):\n process = True\n normalized_tokens = []\n for t in tokens:\n if t in en_proc_stop_words:\n process = False\n if process:\n normalized_tokens.append(analyzer.lemmatize(t))\n else:\n normalized_tokens.append(t)\n return normalized_tokens\n\n # return [analyzer.lemmatize(t) for t in tokens]",
"title": ""
},
{
"docid": "e10b4622ad5e743a2b38db6cdcbfddf1",
"score": "0.6159294",
"text": "def lemmatize(self, sentence):\n lang = self.lang.lower()\n if lang == 'es' or lang == 'spanish':\n return [lemma_es(word) for word in word_tokenize(sentence)]\n elif lang == 'fr' or lang == 'french':\n return [stem_fr.stem(word) for word in word_tokenize(sentence)]\n elif lang == 'de' or lang == 'german':\n result = list()\n if use_compound_split_german:\n sentence, _ = LanguageDetection.split_compound(sentence, 'de')\n for word in word_tokenize(sentence):\n lemma_word = stem_de.stem(word)\n if word and word[0].isupper():\n first_char = lemma_word[0]\n remaining_str = lemma_word[1:] if len(lemma_word) > 1 else ''\n result.append(first_char.upper() + remaining_str)\n else:\n result.append(lemma_word)\n return result\n elif lang in ['nl','dutch']:\n return self.dutch_lemmatizer(sentence)\n elif lang == 'ar' or lang == 'arabic':\n return [stem_ar.stem(word) for word in word_tokenize(sentence)]\n elif lang == 'ru' or lang == 'russian':\n return [stem_ru.stem(word) for word in word_tokenize(sentence)]\n elif lang == 'sv' or lang == 'swedish':\n return [stem_sv.stem(word) for word in word_tokenize(sentence)]\n elif lang == 'zh' or lang == 'chinese':\n return self.chinese_tokenize(sentence)\n elif lang in [\"id\",\"bhasa\",\"ms\",\"malay\",\"indonesian\"]:\n return sastrawi_stemmer.stem(str(sentence)).split(\" \")\n elif lang in [\"ja\",\"japanese\"]:\n return stem_ja.tokenize(sentence)\n elif lang == 'ko' or lang == 'korean':\n return self.getKoLemmaTokens(sentence)\n elif lang == 'fi' or lang == 'finnish':\n return word_tokenize(sentence)\n elif lang == 'pl' or lang == 'polish':\n return word_tokenize(sentence)\n elif lang == 'uk' or lang == 'ukranian':\n return word_tokenize(sentence)\n elif lang == 'kk' or lang == 'kazakh':\n return kazakh_lemma_tokenizer(sentence)\n else:\n return [lemma_en(word) for word in word_tokenize(sentence)]",
"title": ""
},
{
"docid": "5240d78445521278906ef327fe838c5c",
"score": "0.6154293",
"text": "def _tokenize(text):\n tokens = word_tokenize(text)\n lemmatizer = WordNetLemmatizer()\n\n clean_tokens = []\n for tok in tokens:\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n\n return clean_tokens",
"title": ""
},
{
"docid": "b675273dc8887edd469a05e656a318bc",
"score": "0.6141228",
"text": "def tokenize(text):\n # regex pattern for detecting a url\n url_regex = 'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+'\n\n # get list of all urls using regex\n detected_urls = re.findall(url_regex,text)\n\n # replace each url in text string with placeholder\n for url in detected_urls:\n text = text.replace(url,'urlplaceholder')\n\n # tokenize text\n tokens = word_tokenize(text)\n\n # initiate lemmatizer\n lemmatizer = WordNetLemmatizer()\n\n # iterate through each token\n clean_tokens = []\n for tok in tokens:\n # lemmatize, normalize case, and remove leading/trailing white space\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n\n return clean_tokens",
"title": ""
},
{
"docid": "cbb5b2907e63f76ef78d1d20d4de2492",
"score": "0.6138159",
"text": "def pos_words(self, wn_lemmatize=False):\n lemmas = self.pos_lemmas(wn_lemmatize=wn_lemmatize)\n return [x[0] for x in lemmas]",
"title": ""
},
{
"docid": "265a7c0c4d5418119a0922c510b1d94e",
"score": "0.61237025",
"text": "def tokenize(text):\n # get tokens from text\n tokens= WhitespaceTokenizer().tokenize(text)\n lemmatizer= WordNetLemmatizer()\n \n # clean tokens\n processed_tokens=[]\n for token in tokens:\n token=lemmatizer.lemmatize(token).lower().strip('!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~')\n token=re.sub(r'\\[[^.,;:]]*\\]','', token)\n \n # add token to compiled list if not empty\n if token !='':\n processed_tokens.append(token)\n return processed_tokens",
"title": ""
},
{
"docid": "b35c610690947c2d0896e05a3740aa69",
"score": "0.6120525",
"text": "def lemmatize_with_pos(lemmatizer, words):\n pos_tagged_words = nltk.pos_tag(words)\n return [lemmatizer.lemmatize(w, pos=Fine2CoarsePosTags.get(pos, 'n')) for w, pos in pos_tagged_words]",
"title": ""
},
{
"docid": "0621a3c36d1714145f0436824cb1cd1f",
"score": "0.61138994",
"text": "def softmax_words(self, output):",
"title": ""
},
{
"docid": "df779b915cdd7032d10f027a8e9e996f",
"score": "0.6106239",
"text": "def lemmatize(tokens):\n\treturn [lmtzr.lemmatize(w) for w in tokens]",
"title": ""
},
{
"docid": "e8cff7cf6adad200eb1e919a0bad7040",
"score": "0.610595",
"text": "def lemmatize_document_list(documents):\n # Lemmatize\n lemmatized_berichte = [lemmatize_as_string(x) for x in documents]\n # Make everything lowercase\n lemmatized_berichte = [x.lower() for x in lemmatized_berichte]\n # Concatenate as a long string\n lemmatized_berichte = [' '.join(x.split(\" \")) for x in lemmatized_berichte]\n return lemmatized_berichte",
"title": ""
},
{
"docid": "bcdaff3c3cfd1a4c1149d26b0ab4ce5f",
"score": "0.6105473",
"text": "def pre_process(character_text):\n character_text = expand_contractions(character_text) #xall the function to expand contractions\n lemmatizer = WordNetLemmatizer()\n character_text = re.sub('\\W', ' ', character_text) #remove all special character\n character_text = re.sub(r'\\s+[a-zA-Z]\\s+', ' ', character_text) #remove single character like 's\n character_text = re.sub(r'\\s+[a-zA-Z]\\s+', ' ', character_text) #remove single character from the start\n character_text.lower()\n character_text = re.sub('[\\s]+', ' ', character_text, flags=re.I)\n character_text = re.sub('[\\n]+', ' ', character_text)\n character_text = ' '.join(character_text.split()) \n #character_text.dropna(inplace=True)\n tokens=[]\n lemmatised_tokens = []\n stop_words = set(stopwords.words('english'))\n text = character_text.translate(table)\n for w in text.split(\" \"):\n if w not in stop_words:\n lemmatised_tokens.append(lemmatizer.lemmatize(w.lower()))\n# tokens = [' '.join(l) for l in nltk.bigrams(lemmatised_tokens)] + lemmatised_tokens #usebigrams \n \n \"\"\"\n posttagger = CRFTagger()\n posttagger.set_model_file(\"crf_pos.tagger\")\n tokens = posttagger.tag(tokens)\n tokens = list('@'.join(w) for w in tokens)\n tokens = [' '.join(l) for l in nltk.bigrams(tokens)] + tokens #usebigrams \n \"\"\"\n return lemmatised_tokens",
"title": ""
},
{
"docid": "65009f45c8631ee90d2cf7a21f64b083",
"score": "0.61020064",
"text": "def preprocess_df(df):\n df['sk'] = df.text.str.split(' ').apply(\\\n lambda x:' '.join([w for w in x if w not in text.ENGLISH_STOP_WORDS])) \n df['nltk'] = df.text.str.split(' ').apply(\\\n lambda x:' '.join([w for w in x if w not in stopwords.words('english')])) \n # initialize wordnet lemmatizer and pre-create lemmatized words\n wnl = WordNetLemmatizer()\n df['text_lem'] = df.text.str.split(' ').apply(lambda x:\\\n ' '.join([wnl.lemmatize(w) for w in x]))\n df['sk_lem'] = df.sk.str.split(' ').apply(lambda x:\\\n ' '.join([wnl.lemmatize(w) for w in x]))\n df['nltk_lem'] = df.nltk.str.split(' ').apply(lambda x:\\\n ' '.join([wnl.lemmatize(w) for w in x]))\n return df",
"title": ""
},
{
"docid": "0e0b6294f7c509099d02e414d1a17b1f",
"score": "0.6089804",
"text": "def get_processed_text(text=\"\"):\n clean_text = re.sub('[^a-zA-Z0-9 \\n\\.]', ' ', text)\n tokens = tokenizer.tokenize(clean_text)\n tokens = [lemmatizer.lemmatize(token.lower().strip()) for token in tokens\n if token not in stopwords and len(token) >= 2]\n return tokens",
"title": ""
},
{
"docid": "9152570938b6c764b97b0f5fd4eb086e",
"score": "0.60859346",
"text": "def make_wordle_from_mallet(word_weights_file,topics,words,outfolder, \n font_path, dpi):\n print(\"\\nLaunched make_wordle_from_mallet.\")\n\n from wordcloud import WordCloud\n import random\n\n if not os.path.exists(outfolder):\n os.makedirs(outfolder)\n \n def read_mallet_output(word_weights_file):\n \"\"\"Reads Mallet output (topics with words and word weights) into dataframe.\"\"\" \n word_scores = pd.read_table(word_weights_file, header=None, sep=\"\\t\")\n word_scores = word_scores.sort(columns=[0,2], axis=0, ascending=[True, False])\n word_scores_grouped = word_scores.groupby(0)\n #print(word_scores.head())\n return word_scores_grouped\n\n def get_wordlewords(words,topic):\n \"\"\"Transform Mallet output for wordle generation.\"\"\"\n topic_word_scores = read_mallet_output(word_weights_file).get_group(topic)\n top_topic_word_scores = topic_word_scores.iloc[0:words]\n topic_words = top_topic_word_scores.loc[:,1].tolist()\n word_scores = top_topic_word_scores.loc[:,2].tolist()\n wordlewords = \"\"\n j = 0\n for word in topic_words:\n word = word\n score = word_scores[j]\n j += 1\n wordlewords = wordlewords + ((word + \" \") * score)\n return wordlewords",
"title": ""
},
{
"docid": "5c60441b2dce7d5be2fef65d916f822d",
"score": "0.6073282",
"text": "def clean_up_sentence(sentence):\r\n\r\n lemmatizer = WordNetLemmatizer()\r\n sentence_words = nltk.word_tokenize(sentence)\r\n return [lemmatizer.lemmatize(word.lower()) for word in sentence_words]",
"title": ""
},
{
"docid": "51da43b0b7189f7824053e4f3ba250df",
"score": "0.60597473",
"text": "def tokenize(text):\n \n #Detect url \n url_regex = 'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+'\n \n # get list of all urls using regex\n detected_urls = re.findall(url_regex, text)\n \n # replace each url in text string with placeholder\n for url in detected_urls:\n text = text.replace(url, \"urlplaceholder\")\n\n # tokenize text and initiate lemmatizer\n tokens = word_tokenize(text)\n lemmatizer = WordNetLemmatizer()\n \n # Remove stopwords\n tokens = [w for w in tokens if w not in stopwords.words(\"english\")]\n\n # iterate through each token\n clean_tokens = []\n for tok in tokens:\n \n # lemmatize, normalize case, and remove leading/trailing white space\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n\n return clean_tokens",
"title": ""
},
{
"docid": "63d48fbe5662732ac6f6208a5b85ca72",
"score": "0.6031939",
"text": "def tokenize(text):\n \n tokens = word_tokenize(text)\n lemmatizer = WordNetLemmatizer()\n stop_words = stopwords.words(\"english\")\n\n clean_tokens = []\n for tok in tokens:\n if tok not in stop_words:\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n\n return clean_tokens",
"title": ""
},
{
"docid": "725444c9cdbbad0344e87558fac6b9ac",
"score": "0.60298574",
"text": "def process(text, max_length=None):\n nlp = spacy.load('en', max_length=max_length)\n doc = nlp(text)\n\n sentences = []\n for sent in doc.sents:\n sentences.append({\n \"words\": [token.text for token in sent],\n \"lemmas\": [token.lemma_ for token in sent],\n \"pos\": [token.pos_ for token in sent],\n })\n\n return Document(sentences=[Sentence(words=s['words'], pos=s['pos'], lemmas=s['lemmas'], length=len(s['words']))\n for s in sentences])",
"title": ""
},
{
"docid": "1d63054c2d2e9661c5c3ae6674a46f7f",
"score": "0.6028903",
"text": "def lemmatise_text(txt: str, lib_l: str = None) -> str:\n if lib_l is None:\n pass\n elif lib_l == 'nltk':\n txt_list = word_tokenize(txt)\n return \" \".join([lemmatiser_nltk.lemmatize(word=w) for w in txt_list])\n elif lib_l == 'spacy':\n txt = nlp(txt)\n return \" \".join(token.lemma_ for token in txt)\n else:\n raise Exception(f\"Sorry, entered library, {lib_l}, is not recognised.\\n\"\n + \"Please enter one from [None, 'nltk', 'spacy']\")",
"title": ""
},
{
"docid": "585b1321cb97b8d9282ba9536537af3a",
"score": "0.60182667",
"text": "def tokenize(text):\n # Remove punctuation characters \n punctuation_characters = 'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+'\n detected_characters = re.findall(punctuation_characters, text)\n for ch in detected_characters :\n text = text.replace(ch, \"\") \n #convert to lower\n text = text.lower()\n # Extract the word tokens from the provided text\n tokens = word_tokenize(text)\n #Lemmanitizer to remove inflectional and derivationally related forms of a word\n lemmatizer = WordNetLemmatizer()\n # List of clean tokens\n clean_tokens =[]\n for tok in tokens:\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n return clean_tokens",
"title": ""
},
{
"docid": "f50ff1715c9162ffb9e37f1a056d7480",
"score": "0.6016047",
"text": "def lemmatizer(wordform, pos_tag):\n\n if pos_tag[0] == \"j\": #i.e. the Penn tag for adjectives\n pos_tag=wn.ADJ #i.e. \"a\"\n\n lemma = wnl.lemmatize(wordform, pos_tag[0])\n return lemma",
"title": ""
},
{
"docid": "63dabdd2253e53716b97df8a7ecaa960",
"score": "0.5996638",
"text": "def word_tokenizer(self):\n return",
"title": ""
},
{
"docid": "671fa51e6c71b238090ca54256ebcb68",
"score": "0.59899294",
"text": "def tokenize_text_en(sample):\n # get the tokens using spaCy\n tokens = nlp_en(sample) # parser(sample)\n\n # lemmatize\n lemmas = []\n for tok in tokens:\n lemmas.append(tok.lemma_.lower().strip() if tok.lemma_ != \"-PRON-\" else tok.lower_)\n tokens = lemmas\n\n # stoplist the tokens\n tokens = [tok for tok in tokens if tok not in STOPLIST]\n\n # stoplist symbols\n tokens = [tok for tok in tokens if tok not in SYMBOLS]\n\n # remove large strings of whitespace\n while \"\" in tokens:\n tokens.remove(\"\")\n while \" \" in tokens:\n tokens.remove(\" \")\n while \"\\n\" in tokens:\n tokens.remove(\"\\n\")\n while \"\\n\\n\" in tokens:\n tokens.remove(\"\\n\\n\")\n while \"nan\" in tokens:\n tokens.remove(\"nan\")\n while \"..\" in tokens:\n tokens.remove(\"..\")\n # print(tokens)\n return tokens",
"title": ""
},
{
"docid": "04e3e7e3b961a33ba962b6be1d580a46",
"score": "0.5989709",
"text": "def process_words(texts, stop_words=stop_words, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']):\n texts = [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts]\n texts = [bigram_mod[doc] for doc in texts]\n texts = [trigram_mod[bigram_mod[doc]] for doc in texts]\n texts_out = []\n nlp = spacy.load('en', disable=['parser', 'ner'])\n for sent in texts:\n doc = nlp(\" \".join(sent)) \n texts_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags])\n # remove stopwords once more after lemmatization\n texts_out = [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts_out] \n return texts_out",
"title": ""
},
{
"docid": "8f5ddcf7aa2c2e1b60b8d96281969442",
"score": "0.59877634",
"text": "def preprocess_string(text):\n\treturn lemmatize(stopword_removal(tokenize(text)))",
"title": ""
},
{
"docid": "1c89783ac02c49722e4675560bb44afc",
"score": "0.5982832",
"text": "def extract_tokens(self, text, target):\n stoplist = stopwords.words('english')\n lemmatizer = nltk.WordNetLemmatizer()\n corpus=[]\n for txt, label in zip(text, target):\n tokenized_text = [lemmatizer.lemmatize(word.lower()) for word in word_tokenize(txt[0]) if (not word in stoplist) and (word.isalpha()) and (len(word)>=3)]\n corpus.append((tokenized_text, label))\n return corpus",
"title": ""
},
{
"docid": "1969f34c4a0139078fb6365a4a738191",
"score": "0.5979593",
"text": "def lemmatize(word):\n forms = [word for pos_form in get_word_forms(word).values() for word in pos_form]\n forms.sort()\n forms.sort(key=len)\n try:\n return forms[0]\n except IndexError:\n raise ValueError(\"{} is not a real word\".format(word))",
"title": ""
},
{
"docid": "23a7c23d62fded042ccfe71ca5b71058",
"score": "0.5977578",
"text": "def lemmatize_verbs(word_list):\n lemmatizer = WordNetLemmatizer()\n return [lemmatizer.lemmatize(word, pos='v') for word in word_list]",
"title": ""
},
{
"docid": "8f201b9e342f23648e9059def6eeffc4",
"score": "0.5977086",
"text": "def lemmatizeSentence(inputSentence):\n \n outputSentence = []\n inputSentence = word_tokenize(inputSentence)\n\n for token, tag in pos_tag(inputSentence):\n lemma = lemmatizer.lemmatize(token, tag_map[tag[0]])\n outputSentence.append(lemma)\n\n return \" \".join(outputSentence)",
"title": ""
},
{
"docid": "0dd178023e8c66e9145cba604151d018",
"score": "0.5963174",
"text": "def tokenize(text):\n\n # we convert the text to lower case\n text = text.lower()\n\n # we remove any url contained in the text\n\n url_regex = 'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+'\n url_in_msg = re.findall(url_regex, text)\n for url in url_in_msg:\n text = text.replace(url, \"urlplaceholder\")\n\n # we remove the punctuation\n text = re.sub(r\"[^a-z0-9\\s]\", \" \", text)\n\n # we tokenize the text\n words = word_tokenize(text)\n\n # we lemmatize and remove the stop words\n words = [lemmatizer.lemmatize(word) for word in words if word not in stopwords.words('english')]\n\n return words",
"title": ""
}
]
1fb062405dd05b13bd0419651eb3cbe4
Function that makes and save the figure with the local power spectrum
[
{
"docid": "4a5d367a67fff5a98d7a0e2bc5885895",
"score": "0.58250314",
"text": "def makeAveragePSLocalFigure(averagePSLocal,figureFileName,gridSize): \r\n pylab.figure()\r\n for i in range(gridSize[0]):\r\n for j in range(gridSize[1]):\r\n pylab.subplot(gridSize[0],gridSize[1],i*gridSize[1]+j+1)\r\n pylab.imshow(np.log(averagePSLocal[i,j]),cmap = \"gray\")\r\n pylab.contour(np.log(averagePSLocal[i,j]))\r\n pylab.axis(\"off\")\r\n pylab.savefig(figureFileName)",
"title": ""
}
]
[
{
"docid": "5c6401c84a8eda78f36dc21df5ceb618",
"score": "0.6694815",
"text": "def plotter_saver():\n xdat = get_xdat()\n FFCache.xdat = xdat\n\n p0 = [\n 2.14928399e+00, 6.30221940e-01, 2.75166059e+00, -1.96259549e+00,\n 7.03808465e-01, 5.26047000e-03, 1.49757697e-02, 5.81073410e-01,\n 1.59915284e-01, 5.60375907e+04, 9.44655438e-01, 5.89994829e-03\n ]\n # low plx solution\n p0 = [\n 1.96702792e+00, 7.26357060e-01, 2.51915752e+00, -8.37697825e-01,\n 6.87868987e-01, 1.18811095e-02, 1.46097125e-02, 6.77618428e-01,\n 2.33218856e-01, 5.60220478e+04, 1.46077603e-04, 1.94347621e-05\n ]\n if True:\n M = like(p0, xdat, -0.5, getModel=True)\n print(wrap_post(p0))\n tab = atpy.Table()\n tab['times'] = xdat['mjd']\n tab['flux'] = xdat['flux']\n tab['eflux'] = xdat['eflux']\n tab['xplx'] = xdat['plxs'][0]\n tab['yplx'] = xdat['plxs'][1]\n tab['mod'] = M\n tab.write('xx.fits', overwrite=True)\n # write the data",
"title": ""
},
{
"docid": "ba78965c2580d796b0bc69c29a0ee14c",
"score": "0.6406317",
"text": "def save_spectrum(freqs, waveform_freq_space, output_path, figsize=(10, 4), xlabel='Frequency (Hz)', ylabel='Magnitude', title=None, color='b'):\n\n fig = plot_spectrum(freqs, waveform_freq_space, show=False, figsize=figsize, xlabel=xlabel, ylabel=ylabel, title=title, color=color)\n fig.savefig(output_path, bbox_inches='tight')",
"title": ""
},
{
"docid": "fba762fb3f6b7530cd174a54f969ae30",
"score": "0.6302121",
"text": "def save_waveform(timesteps, waveform, output_path, figsize=(10, 4), xlabel='Time (s)', ylabel='y', title=None, color='b'):\n\n fig = plot_waveform(timesteps, waveform, show=False, figsize=figsize, xlabel=xlabel, ylabel=ylabel, title=title, color=color)\n fig.savefig(output_path, bbox_inches='tight')",
"title": ""
},
{
"docid": "b128560d986e616a70e3322b82dbc3a7",
"score": "0.6191265",
"text": "def savePlotHysteresis(self, Amp, delay):\n plt.figure(figsize=(10, 6))\n plt.suptitle('Normvalue: ' + str(Amp * 1000) + \" mV\")\n plt.title(str(self.Power) + ' mW ' + str(int(delay)) + \" ps\")\n plt.plot(self.resultList[:, 0], self.resultList[:, 1] * 1000, 'o-b',\n lw=2, ms=2,\n label='Not pumped')\n plt.plot(self.resultList[:, 0], self.resultList[:, 2] * 1000, 's-r',\n lw=2, ms=2,\n label='Pumped')\n plt.xlabel(\"Current (A)\")\n plt.ylabel(\"Balanced Diode Signal (mV)\")\n plt.grid()\n plt.legend()\n plt.savefig(\n str(SaveImageFile) + 'SampleHysteresisCurrent' + str(int(delay)) +\n '.png', bbox_inches=\"tight\", dpi=300)",
"title": ""
},
{
"docid": "0e59fe9967fbc0e899909d5d47ece0bb",
"score": "0.61460745",
"text": "def figure_exp_2(fig_type = '.png', save_traces = False, show_traces = False):\n temp, save_folder, specific_folder, temp, file_save = get_figure_data_settings(fig_no = 2)\n folder_save = save_folder + specific_folder + '_results/'\n file2save_npz = 'fig2_' + '.npz'\n file2save_filtnpz = 'fig2_filt_' + '.npz'\n file2save = 'fig2_' + fig_type\n \n filt_for_fig = 'high_pass2.npz'\n dh.filter_data(folder_save, file_save, folder_save, filt_for_fig, 2.0, \n electrodes = [2],N = 100, filter_type = 'high_pass')\n display.plot_data(folder_save,file2save,folder_save,filt_for_fig,x_scale = 'ms', time_range = [4050,4800],electrodes=[2])\n \n if save_traces:\n \"\"\" it must be done only if the recording was gap free but \n it was not saved in the trace format yet\"\"\"\n\n #display.plot_data(folder_save,file2save,folder_save,file_save,x_scale = 'ms')#,time_range=time_range)\n new_file = 'filtered_data_above500.npz'\n #dh.filter_data(folder_save, file_save, folder_save, new_file, 500.0, \n # electrodes = [2],N = 100, filter_type = 'high_pass')\n \n #dh.trigger_on_spike(folder_save, new_file, folder_save, file2save_filtnpz, thresh = -30, el=0,\n # time_range=[-15,50])\n \n #display.plot_data(folder_save,file2save,folder_save,file2save_filtnpz,\n # x_scale = 'ms', y_range = [-20,20],electrodes = [0,2])\n # #time_range = [15,23])\n \n all_times1, all_width1, all_depth1 = dh.calculate_spike_width_in_traces(folder_save,\n file2save_filtnpz, traces = [2, 5, 9, 13, 16, 17, 18, 21], time_range = [16.9,18.7])\n all_times2, all_width2, all_depth2 = dh.calculate_spike_width_in_traces(folder_save,\n file2save_filtnpz, traces = [6, 19, 48, 73, 88, 103, 110, 119, 126], time_range = [19.0,22.0])\n \n print 'half width of spike 1 (calculated on some traces): ' + str(np.mean(all_width1)) + ' ms'\n print 'depth of spike 1 (calculated on some traces): ' + str(np.mean(all_depth1)) + ' ms'\n print 'half width of spike 2 (calculated on some traces): ' + str(np.mean(all_width2)) + ' ms'\n print 'depth of spike 2 (calculated on some traces): ' + str(np.mean(all_depth2)) + ' ms' \n\n file_save = 'IPSP_traces2_' + fig_type\n time_range = [5,40] # in ms\n y_range = [-20, 35]\n data_details = dh.read_npzdata(folder_save, file2save_npz, \"data\", \"scale\", \"fs\")\n display.plot_data_one_mean(data_details, folder_save, file_save, x_scale = 'ms',\n title = 'normal ringer', sweeps = [60, 66, 72, 86, 91], #, 51, 57, 66, 67, 72, 77, 86, 91, 113, 122, 128], #[8,25, 29,32,33],\n electrodes = [0,2], y_range = y_range, time_range = time_range,\n remove_avg = True) \n \n file_save = 'IPSP_spike2_' + fig_type\n display.plot_data_one_mean(data_details, folder_save, file_save, x_scale = 'ms',\n title = 'normal ringer', sweeps = [23, 26, 62, 89, 100], #, 17, 18, 26, 39, 45, 49, 53, 62, 71, 84, 89, 100, 103, 108, 124],#[4,10, 11, 14, 18],\n electrodes = [0,2], y_range = y_range, time_range = time_range,\n remove_avg = True) \n if show_traces:\n plt.show()",
"title": ""
},
{
"docid": "bf2a7cac6aac110470d561a6d3a47727",
"score": "0.6104321",
"text": "def createSpectrumPlot(self):\n\n fig = plt.figure(figsize=(8,6),dpi=80)\n fig.set_facecolor('#ededed')\n \n # Format plot\n ax = plt.subplot(111)\n\n ax.set_xlabel(\"Frequency [MHz]\")\n ax.set_ylabel(\"Power [-]\")\n ax.set_xlim(1000,1500)\n ax.set_ylim(0,1000)\n \n fig.canvas.draw()\n \n return fig, ax",
"title": ""
},
{
"docid": "bcdc9a79e7aaceaebc54caf226176b8a",
"score": "0.6049133",
"text": "def saveSpectrum(self):\n filename, __ = _getSaveFileName(\n self,\n u\"Save Spectrum\",\n self._settings.value(\"imagePath\"),\n \"plain text (*.txt)\")\n if not filename:\n return\n if \".\" not in filename:\n filename += \".txt\"\n self.spectrumPlotter.save_spectrum(filename)",
"title": ""
},
{
"docid": "abfe7633c5b625ad0f94a5fc74a1cb54",
"score": "0.6027024",
"text": "def save_spectrum(fname,tobs,params,pnames,stdp,l,f,res):\n\ttbhdu = fits.BinTableHDU.from_columns([fits.Column(name='wavelength', format='E', array=l), fits.Column(name='flux', format='E', array=f), fits.Column(name='residuals', format='E', array=res)])\n\tprihdr = fits.Header()\n\t\n\tfor i in range(len(pnames)):\n\t\tprihdr[pnames[i]] = params[i] # best fit params\n\t\tprihdr[pnames[i]+'_std'] = stdp[i] # \n\tprihdr['obs_time'] = tobs\n\tprihdu = fits.PrimaryHDU(header=prihdr)\n\tthdulist = fits.HDUList([prihdu, tbhdu])\n\tif os.path.isfile('../DATA/FTS_processed/%s' %fname):\n\t\tos.system('rm ../DATA/FTS_processed/%s' %fname)\n\t\t\n\tthdulist.writeto('../DATA/FTS_processed/%s' %fname)",
"title": ""
},
{
"docid": "71bec9ca523cd03d2799e245463b4878",
"score": "0.60255283",
"text": "def main05_gain_scheduling_modified_steep_ramp():\n x10, x20 = 0, 0\n eta0 = 0\n xhat10, xhat20 = 0, 0\n x0 = np.array([x10, x20, eta0, xhat10, xhat20])\n r = lambda t : (t<10)*1/10*t + (t>=10)*1\n alpha = lambda t : r(t)\n t_start, t_end = 0, 120\n\n t,x1,x2,eta,xhat1,xhat2,r,y = simulate_modified(r, alpha, x0, t_start, t_end)\n\n fig, ax = fc.new_figure()\n ax.plot(t, r, 'r--', label = '$r$')\n ax.plot(t, y, label = '$y$')\n ax.set_xlabel('$t$')\n ax.grid()\n ax.legend()\n\n Path('./figures').mkdir(parents=True, exist_ok=True)\n plt.savefig('figures/gain_scheduling_modified_steep_ramp.pdf', pad_inches=0.0)",
"title": ""
},
{
"docid": "1bf84ac760d1d73d16f9b1103f5eb871",
"score": "0.6023966",
"text": "def figure_exp_0(fig_type = '.png',save_traces=False, show_traces = True):\n temp, save_folder, specific_folder, temp, file_save = get_figure_data_settings(fig_no = 0)\n file2save_npz = 'fig0_' + '.npz'\n \n file2save = 'DC_seizures' + fig_type\n file2save_example = 'egDC_seizures' + fig_type\n folder_save = save_folder + specific_folder + '_results/'\n y_range = [-200,200]\n #display.plot_data(folder_save, file2save_example,folder_save,file_save,x_scale = 'sec', \n # y_range=y_range, time_range = [90000,160000], electrodes=[],\n # y_range_intra = [-2,0]) \n\n if save_traces:\n #it must be done only if the recording was gap free but \n #it was not saved in the trace format yet\n\n #display.plot_data(folder_save,file2save,folder_save,file_save,x_scale = 'ms')#,time_range=time_range)\n \n #dh.trigger_on_spike(folder_save, file_save, folder_save, file2save_npz, thresh = -1.1, el=0,\n # time_range=[-25000,60000], up = False, center_on_peak = False)\n \n display.plot_data(folder_save,file2save,folder_save,file2save_npz,x_scale = 'ms', \n electrodes = [0,1,2,3],y_range =y_range)\n #import pdb; pdb.set_trace() \n\n time_range = [] # in ms\n data_details = dh.read_npzdata(folder_save, file2save_npz, \"data\", \"scale\", \"fs\")\n file_save = 'DC_shift_all' + fig_type\n #import pdb; pdb.set_trace()\n display.plot_data_one_mean(data_details, folder_save, file_save, x_scale = 'ms',\n title = 'interictal events', sweeps = [0,1,2],\n electrodes = [0,1,2,3], y_range = y_range, time_range = time_range,\n remove_avg = True) \n if show_traces:\n plt.show()",
"title": ""
},
{
"docid": "001d495683f96441317f801353054b10",
"score": "0.60154146",
"text": "def figure_exp_26(fig_type = '.eps',save_traces = False, show_traces = False):\n temp, save_folder, specific_folder, temp, file_save = get_figure_data_settings(fig_no = 26)\n #file2save_npz = 'fig25_' + '.npz'\n \n \n folder_save = save_folder + specific_folder + '_results/'\n file2save = 'inh_synch_1' + fig_type\n \n #display.plot_data(folder_save, file2save,folder_save,file_save,x_scale = 'sec', \n # y_range=[], time_range = [], electrodes=[0,1,2,3],\n # y_range_intra = []) \n\n\n y_range = [-120,100]\n #time_range = [] # in ms\n data_details = dh.read_npzdata(folder_save, file_save, \"data\", \"scale\", \"fs\")\n file_save = 'gaba_a_synch_1' + fig_type\n #import pdb; pdb.set_trace()\n display.plot_data_one_mean(data_details, folder_save, file_save, x_scale = 'ms',\n title = 'inhibitory synchrony', sweeps = [27,40,43,21,16], #2,8,16,21,23], #,27,40,43,48],\n electrodes = [0,2,3], y_range = y_range, time_range = [],\n remove_avg = True) \n plt.show()",
"title": ""
},
{
"docid": "05666e9c3b49aa1ae7c7bd651aea1efe",
"score": "0.6001972",
"text": "def main04_gain_scheduling_modified_ramp():\n x10, x20 = 0, 0\n eta0 = 0\n xhat10, xhat20 = 0, 0\n x0 = np.array([x10, x20, eta0, xhat10, xhat20])\n r = lambda t : (t<100)*1/100*t + (t>=100)*1\n alpha = lambda t : r(t)\n t_start, t_end = 0, 120\n\n t,x1,x2,eta,xhat1,xhat2,r,y = simulate_modified(r, alpha, x0, t_start, t_end)\n\n fig, ax = fc.new_figure()\n ax.plot(t, r, 'r--', label = '$r$')\n ax.plot(t, y, label = '$y$')\n ax.set_xlabel('$t$')\n ax.grid()\n ax.legend()\n\n Path('./figures').mkdir(parents=True, exist_ok=True)\n plt.savefig('figures/gain_scheduling_modified_ramp.pdf', pad_inches=0.0)",
"title": ""
},
{
"docid": "1013ebc2a97b308f1fe1c2c0a077e73f",
"score": "0.59988344",
"text": "def figure_exp_15_1(fig_type = '.png', show_traces = False):\n temp, save_folder, specific_folder, temp, file_save = get_figure_data_settings(fig_no = 15.1)\n folder_save = save_folder + specific_folder + '_results/' \n \n data_details = dh.read_npzdata(folder_save, file_save, \"data\", \"scale\", \"fs\")\n file_save = '_noEPSP' + fig_type\n \n display.plot_data_one_mean(data_details, folder_save, file_save, x_scale = 'ms',\n title = 'No EPSP induced', sweeps = [1,3,5,7,9,10],\n electrodes = [0,1,2, 3], y_range = [-10, 10], remove_avg = True,\n time_range = [23, 48]) \n if show_traces:\n plt.show() \n \n #folder_save,file_save,folder, file, x_scale = 'sec',\n # title = 'Data', time_range = [], y_range =[-30, 70],\n # electrodes = [],y_range_intra = []\n #display.plot_data(folder_save, file_save + fig_type, folder_save,file_save, x_scale = 'ms',\n # y_range =[-10, 10])\n \n \n \n #import pdb; pdb.set_trace() ",
"title": ""
},
{
"docid": "7d43da31d57742040d50e4608f9c1dcf",
"score": "0.5987321",
"text": "def figure_exp_1(fig_type = '.png'):\n temp, save_folder, specific_folder, temp, file_save = get_figure_data_settings(fig_no = 1)\n folder_save = save_folder + specific_folder + '_results/'\n \n # plot trace of few waves\n file2save = 'seizure_like' + fig_type\n tit = 'seizure like activity in human subiculum'\n time_range = [140000,180000]# in ms\n y_range = [-100,100]\n display.plot_data(folder_save,file2save,folder_save,file_save,x_scale = 'ms',\n title=tit,time_range=time_range,y_range = y_range)\n \n # plot only one wave\n file2save = 'one_wave' + fig_type\n tit = 'seizure like activity in human subiculum'\n time_range = [149900,151500]# in ms\n y_range = [-100,100]\n display.plot_data(folder_save,file2save,folder_save,file_save,x_scale = 'ms',\n title=tit,time_range=time_range,y_range = y_range)",
"title": ""
},
{
"docid": "b8bda6d882aa8b2cf053676d6beb0b29",
"score": "0.5970684",
"text": "def figure_exp_27(fig_type = '.png', save_traces = False, show_traces = False):\n temp, save_folder, specific_folder, temp, file_save = get_figure_data_settings(fig_no = 27)\n file2save_npz = 'fig27_' + '.npz'\n \n folder_save = save_folder + specific_folder + '_results/'\n file2save = 'gaba_a_synch' + fig_type\n #y_range = [-500,700] #[-15,15]\n #time_range =[3480,3510] \n #display.plot_data(folder_save, file2save,folder_save,file_save,x_scale = 'sec', \n # y_range=[], time_range = [], electrodes=[0,1,2,3],\n # y_range_intra = [-50,0]) \n #import pdb; pdb.set_trace()\n\n if save_traces:\n #it must be done only if the recording was gap free but \n #it was not saved in the trace format yet\n\n #display.plot_data(folder_save,file2save,folder_save,file_save,x_scale = 'ms')#,time_range=time_range)\n dh.trigger_on_spike(folder_save, file_save, folder_save, file2save_npz, thresh = -25, el=3,\n time_range=[-200,1000], up = False, center_on_peak = False)\n display.plot_data(folder_save,file2save,folder_save,file2save_npz,x_scale = 'ms', \n electrodes = [0,2,3],y_range =[])\n\n #time_range = [] # in ms\n data_details = dh.read_npzdata(folder_save, file2save_npz, \"data\", \"scale\", \"fs\")\n file_save = 'gabaA_synch_fig27_group1' + fig_type\n #import pdb; pdb.set_trace()\n display.plot_data_one_mean(data_details, folder_save, file_save, x_scale = 'ms',\n title = 'GABAa synch, cell2', sweeps = [0,4,10,13],\n electrodes = [0,2,3], y_range = [], time_range = [],\n remove_avg = True) \n file_save = 'gabaA_synch_fig27_group2' + fig_type \n display.plot_data_one_mean(data_details, folder_save, file_save, x_scale = 'ms',\n title = 'GABAa synch, cell2', sweeps = [2,8,15,18,21],\n electrodes = [0,2,3], y_range = [], time_range = [],\n \n remove_avg = True)",
"title": ""
},
{
"docid": "53d21d1b575e1a14797c46f00c88259b",
"score": "0.59470975",
"text": "def plot_stave(self):\n plt.figure(figsize=(self.modules*(ALPIDE_COLS//ALPIDE_ROWS)*STAVE_SIZE_MODIFIER,QUAD_STAVES*STAVE_SIZE_MODIFIER))\n stave=self.remap_to_stave()\n plt.imshow(stave, interpolation='bilinear')\n plt.clim(0, 10*(self.max_charge+1))\n plt.xticks(list(range(0,ALPIDE_COLS*CHIP_PER_LANE*self.modules,ALPIDE_COLS*CHIP_PER_LANE))+[ALPIDE_COLS*self.modules*CHIP_PER_LANE-1])\n plt.xlabel('Pixel')\n plt.yticks(list(range(0,ALPIDE_ROWS*QUAD_STAVES +1,ALPIDE_ROWS)))\n plt.ylabel('Pixel')\n cbar = plt.colorbar()\n cbar.set_label(\"Threshold (electrons)\")\n filename = f\"{self.basename}stave{self.feeid}.{self._plot_extension}\"\n print(f\"Image stored in {filename}\")\n plt.savefig(filename, bbox_inches='tight', dpi=1200)\n plt.close()",
"title": ""
},
{
"docid": "f4465088112562ca02dd580ef25efeb3",
"score": "0.59387684",
"text": "def dataPlot_savebutton_cmd(self):\n plotname = self.dir+str(\"%.5d\" % self.currentobj)+'_MiG1D_specplot.pdf'\n self.dataPlot_fig.savefig(plotname)\n print(' - Saved plot window to \\n '+plotname)",
"title": ""
},
{
"docid": "442f6a85fb8e757652a9af84b928412e",
"score": "0.5914415",
"text": "def spectrum(spectrum_data, save=None, show=True, xlim=None, ylim=(0, 100)):\n if save is not None or show:\n (x, to_plot) = (spectrum_data[0], spectrum_data[1:])\n pyplot.clf()\n pyplot.gca().set_autoscale_on(False)\n pyplot.xlim(x[1], x[-1])\n if xlim is not None: pyplot.xlim(*xlim)\n pyplot.ylim(ylim[0], ylim[1])\n pyplot.xscale('log')\n pyplot.xlabel('frequency')\n pyplot.ylabel('power (dB)')\n for y in to_plot: pyplot.plot(x, y)\n if save is not None: pyplot.savefig(save + \"spectrum.png\")\n if show: pyplot.show()",
"title": ""
},
{
"docid": "bbefaadf53e7029959e0bb015441b03d",
"score": "0.58985144",
"text": "def compute(A, b, w, T, resolution=500):\n t = linspace(0, T, resolution+1)\n u = damped_vibrations(t, A, b, w)\n plt.figure() # needed to avoid adding curves in plot\n plt.plot(t, u)\n plt.title('A=%g, b=%g, w=%g' % (A, b, w))\n '''\n if not os.path.isdir('static'):\n os.mkdir('static')\n else:\n # Remove old plot files\n for filename in glob.glob(os.path.join('static', '*.png')):\n os.remove(filename)\n # Use time since Jan 1, 1970 in filename in order make\n # a unique filename that the browser has not chached\n plotfile = os.path.join('static', str(time.time()) + '.png')\n plt.savefig(plotfile)\n return plotfile\n '''\n\n # Make Matplotlib write to BytesIO file object and grab\n # return the object's string\n from io import BytesIO\n figfile = BytesIO()\n plt.savefig(figfile, format='png')\n figfile.seek(0) # rewind to beginning of file\n import base64\n figdata_png = base64.b64encode(figfile.getvalue())\n print figdata_png\n return figdata_png",
"title": ""
},
{
"docid": "8d10da21a2c7214236028be1452ee9bc",
"score": "0.58963424",
"text": "def mkExtinctionDemoFigSmall( z=2.0 ):\n import numpy as np\n import sncosmo\n # from sncosmost import hstbandpasses, ccsnmodels\n from matplotlib import rc\n rc('text',usetex=True)\n rc('text.latex', preamble='\\usepackage[usenames]{xcolor}')\n from matplotlib import pyplot as pl\n from matplotlib import ticker\n from pytools import plotsetup\n from scipy import interpolate as scint\n\n\n fig = plotsetup.fullpaperfig( 1, [8,3] )\n\n\n # load the O'Donnell 1994 dust model\n dust = sncosmo.OD94Dust()\n snIa = sncosmo.Model( source='hsiao', effects=[dust],\n effect_names=['host'], effect_frames=['rest'])\n\n ax1 = pl.gca()\n\n\n f127m = sncosmo.get_bandpass( 'f127m' )\n f139m = sncosmo.get_bandpass( 'f139m' )\n f153m = sncosmo.get_bandpass( 'f153m' )\n\n f125w = sncosmo.get_bandpass( 'f125w' )\n f140w = sncosmo.get_bandpass( 'f140w' )\n f160w = sncosmo.get_bandpass( 'f160w' )\n\n wf127m = f127m.wave / 10000.\n wf139m = f139m.wave / 10000.\n wf153m = f153m.wave / 10000.\n\n wf125w = f125w.wave / 10000.\n wf140w = f140w.wave / 10000.\n wf160w = f160w.wave / 10000.\n\n # ax2 = ax1.twinx()\n ax2 = ax1\n ax2.plot( wf127m, f127m.trans, color='darkmagenta', ls='-', lw=2)\n ax2.plot( wf153m, f153m.trans, color='darkorange', ls='-', lw=2)\n\n ax2.plot( wf125w, f125w.trans, color='darkmagenta', ls='--', lw=2)\n ax2.plot( wf160w, f160w.trans, color='darkorange', ls='--', lw=2)\n\n intf127m = scint.interp1d( wf127m, f127m.trans, bounds_error=False, fill_value=0 )\n intf153m = scint.interp1d( wf153m, f153m.trans, bounds_error=False, fill_value=0 )\n\n colorlist1, colorlist2 = [], []\n for Av,ls, alpha in zip([2,1,0],[':','--','-'],[0.1,0.3,0.5]):\n snIa.set( z=z, t0=0, hostr_v=3.1, hostebv=Av/3.1 )\n colorlist1.append( snIa.bandmag( 'f127m','ab', 0) - snIa.bandmag( 'f125w','ab', 0) )\n colorlist2.append( snIa.bandmag( 'f153m','ab', 0) - snIa.bandmag( 'f160w','ab', 0) )\n\n snwave = np.arange( 6000., 20000., 10. )\n snflux = snIa.flux( 0, snwave )\n snwave = snwave / 10000.\n snflux = 0.12 * snflux / snflux[400]\n ax1.plot( snwave, snflux, color='k', ls=ls, lw=1, label='%.1f'%Av )\n overlap127 = np.min( [snflux, intf127m(snwave)], axis=0 )\n ax2.fill_between( snwave, np.zeros(len(snwave)), overlap127, color='darkmagenta', alpha=alpha )\n overlap153 = np.min( [snflux, intf153m(snwave)], axis=0 )\n pl.fill_between( snwave, np.zeros(len(snwave)), overlap153, color='darkorange', alpha=alpha )\n\n\n ax1.legend(loc='upper left', bbox_to_anchor=(0.0,0.9),frameon=False,fontsize=11 )\n ax1.text( 0.08, 0.88, 'A$_V$', transform=ax1.transAxes, ha='left',va='bottom',fontsize=11 )\n ax1.text( 0.13, 0.88, '$\\Delta$m$_{127}$', color='darkmagenta',transform=ax1.transAxes, ha='left',va='bottom',fontsize=11 )\n ax1.text( 0.23, 0.88, '$\\Delta$m$_{153}$', color='darkorange',transform=ax1.transAxes, ha='left',va='bottom',fontsize=11 )\n\n ax1.text( 0.14, 0.78, '%.3f'%colorlist1[0], color='darkmagenta',transform=ax1.transAxes, ha='left',va='bottom',fontsize=11 )\n ax1.text( 0.23, 0.78, '%.3f'%colorlist2[0], color='darkorange',transform=ax1.transAxes, ha='left',va='bottom',fontsize=11 )\n\n ax1.text( 0.14, 0.68, '%.3f'%colorlist1[1], color='darkmagenta',transform=ax1.transAxes, ha='left',va='bottom',fontsize=11 )\n ax1.text( 0.23, 0.68, '%.3f'%colorlist2[1], color='darkorange',transform=ax1.transAxes, ha='left',va='bottom',fontsize=11 )\n\n ax1.text( 0.14, 0.58, '%.3f'%colorlist1[2], color='darkmagenta',transform=ax1.transAxes, ha='left',va='bottom',fontsize=11 )\n ax1.text( 0.23, 0.58, '%.3f'%colorlist2[2], color='darkorange',transform=ax1.transAxes, ha='left',va='bottom',fontsize=11 )\n\n # title=+\n # '\\\\textcolor{DarlMagenta}{W}' +\n # '\\\\textcolor{F153M-F160W')#, handlelength=0.5, numpoints=3)\n # ax1.text( 0.15,0.95,,ha='left',va='bottom')\n\n ax1.yaxis.set_major_locator( ticker.MultipleLocator( 0.1 ) )\n ax1.yaxis.set_minor_locator( ticker.MultipleLocator( 0.05 ) )\n ax1.xaxis.set_major_locator( ticker.MultipleLocator( 0.2 ) )\n ax1.xaxis.set_minor_locator( ticker.MultipleLocator( 0.1 ) )\n ax1.set_xlabel('wavelength ($\\mu$m)')\n ax1.set_ylabel('SN Flux or Filter Transmission\\n (arbitrary units)')\n\n ax1.set_xlim(0.6,2.0)\n ax1.set_ylim( 0.0, 0.7 )\n ax1.set_yticklabels([])\n\n ax1.text(1.27,0.6,'F127M,F125W',color='darkmagenta',fontsize=9, ha='center',va='center')\n ax1.text(1.53,0.6,'F153M,F160W',color='darkorange',fontsize=9, ha='center',va='center')\n\n fig.subplots_adjust( left=0.12, right=0.95, bottom=0.18, top=0.92 )",
"title": ""
},
{
"docid": "ae5d60f574e3a55a887e434c7f27ecd2",
"score": "0.58927673",
"text": "def save_show(full_name, save, show, ext):\n plt.tight_layout()\n if save:\n plt.savefig(full_name+ext, bbox_inches='tight', transparent=True,\n pad_inches=0.1)\n if show:\n plt.show()\n else:\n plt.close()",
"title": ""
},
{
"docid": "c010dbeda7604c2137b02fc1663b63f0",
"score": "0.5888778",
"text": "def glt_figure():\n r = r_input\n # first Gamma function\n gamma = 0.4\n s = transform(gamma)\n label = '$\\\\gamma$ = ' + str(gamma)\n data_plot(r, s, 'red', label)\n # second Gamma function\n gamma = 2.5\n s = transform(gamma)\n label = '$\\\\gamma$ = ' + str(gamma)\n data_plot(r, s, 'blue', label)\n matplotlib.pyplot.savefig(output + 'Power_Law_GLTs.jpg')\n matplotlib.pyplot.close()\n return",
"title": ""
},
{
"docid": "514bdf8ac99f5611882a7b4d97bcc34e",
"score": "0.588317",
"text": "def save_plot(self, filename, a, title=\"\", xlabel=\"\", ylabel=\"\", color=plt.cm.hot, vmin=None, vmax=None):\n interactive = plt.isinteractive()\n fig = self.make_plot(a, title, xlabel, ylabel, color, vmin, vmax)\n plt.savefig(filename)\n if interactive:\n plt.ion()\n return fig\n else:\n plt.close()",
"title": ""
},
{
"docid": "5cb8cae77d42a6683920367dc3ac8407",
"score": "0.5866776",
"text": "def dataPlot_plot(self,verbose=False,refresh=False,newobj=False,fullzoom=False):\n self.dataPlot_fig.canvas.set_window_title('1D spectrum of object '+str(self.currentobj))\n xlow, xhigh, ylow, yhigh = self.dataPlot_getwindowinfo()\n if fullzoom:\n xlow, xhigh, ylow, yhigh = self.DPxlow_full, self.DPxhigh_full, self.DPylow_full, self.DPyhigh_full\n #----------------- Define emission line list -----------------\n llist = MiGs.linelistdic(listversion='full')\n linelist = np.asarray([llist[key][1] for key in llist.keys()])\n linename = [llist[key][0] for key in llist.keys()]\n #----------------- Refreshing plot window-----------------\n if refresh:\n self.dataPlot_fig.clf() # clearing figure\n self.dataPlot_ax = self.dataPlot_fig.add_subplot(111)\n #----------------- Grab info from sliders -----------------\n smoothlevel = float(self.varslidersmooth.get())\n if verbose: print(' - Grabbed the Gauss smooth level ',smoothlevel,' from the slider')\n redshift = float(self.varsliderz.get())\n if verbose: print(' - Grabbed the redshift '+str(\"%.3f\" % redshift)+' from the slider')\n\n try:\n zbyhand = float(self.byhandz.get())\n if type(zbyhand) == float:\n redshift = zbyhand\n if verbose: print(' But the redshift',zbyhand,'was found in \"by-hand\" field so using that instead ')\n self.varsliderz.set(zbyhand)\n except:\n pass\n\n #----------------- Flambda spec -----------------\n xrangeflam = self.DPxrange\n contamplotted = False\n ymax = []\n ymin = []\n\n for ii in range(len(self.fits1Dfound)):\n color = self.DPcolor[ii]\n wave1D = self.DP_wave_all[ii]/self.DPxscale # wavelengths converted from A to micron\n flux1D = self.DP_flux_all[ii]\n flux1Derr = self.DP_fluxerr_all[ii]\n contam = self.DP_contam_all[ii]\n\n if (len(flux1D) >= 1):\n ymin.append(np.min(flux1D))\n ymax.append(np.max(flux1D))\n labstr = self.fits1Dfound[ii].split('/')[-1]\n if self.latex:\n labstr = labstr.replace('_','\\_')\n # - - - - - - - - - - Spectrum itself - - - - - - - - - -\n self.dataPlot_ax.plot(wave1D, flux1D, color=color,linestyle='-',\n linewidth=self.DPlwidth*1.5, alpha=0.40)\n\n # - - - - - - - - - - Smoothed spectrum - - - - - - - - - -\n filtersigma = smoothlevel\n flux1D_smooth = scipy.ndimage.filters.gaussian_filter1d(flux1D, filtersigma,cval=0.0)\n self.dataPlot_ax.plot(wave1D, flux1D_smooth, color=color,linestyle='-',\n linewidth=self.DPlwidth*1.5, alpha=1.0,label=labstr)\n\n # - - - - - Shaded error region around curve if requested - - - - -\n if (self.err1Dboxvar.get() != '0'):\n xwinmin, xwinmax, ywinmin, ywinmax = self.dataPlot_getwindowinfo()\n serr = flux1Derr\n serr[flux1Derr > 1e3] = 1e3 # fix errors to prevent \"OverflowError: Allocated too many blocks\"\n filllow = np.clip(flux1D,ywinmin,ywinmax)-serr\n fillhigh = np.clip(flux1D,ywinmin,ywinmax)+serr\n plt.fill_between(wave1D,filllow,fillhigh,alpha=0.20,color=color)\n\n # - - - - - - - - - - Contam curve is present - - - - - - - - - -\n if (contam != -99).any():\n self.dataPlot_ax.plot(wave1D, contam, color=color,linestyle='--',\n linewidth=self.DPlwidth, alpha=0.40)\n contam_smooth = scipy.ndimage.filters.gaussian_filter1d(contam, filtersigma,cval=0.0)\n self.dataPlot_ax.plot(wave1D, contam_smooth, color=color,linestyle='--',\n linewidth=self.DPlwidth, alpha=1.0)\n contamplotted = True\n\n # - - - - - - - - - - Sky spectrum - - - - - - - - - -\n if (self.skyboxvar.get() != '0'):\n objinfo = MiGs.get_objinfo(self.infofile,self.currentobj,self.col_infoid)\n if (np.max(wave1D) < 1.0) & (len(objinfo) == 1):\n fieldno = objinfo['FIELD_ID']\n skyMUSEfilename = glob.glob(self.MUSEskydatdir+'SKY*cdfs*-'+str(\"%.2d\" % fieldno)+'*av.fits')\n skyMUSE = afits.open(skyMUSEfilename[0])[1].data\n skywave = skyMUSE['lambda']/self.DPxscale\n skyent = np.where((skywave > np.min(wave1D)) & (skywave < np.max(wave1D)))[0]\n skywave = skywave[skyent]\n skylow = np.zeros(len(skywave))\n skyflux = skyMUSE['data'][skyent]\n skyhigh = skyflux/1.0\n elif self.skyspectrum:\n skywave = self.skydat['lam']\n skyent = np.where((skywave > np.min(wave1D)) & (skywave < np.max(wave1D)))[0]\n skywave = skywave[skyent]\n skylow = np.zeros(len(skywave))\n skyflux = self.skydat['flux'][skyent]\n skymax = np.sort(flux1D)[np.round(len(flux1D)*0.95)]\n skyhigh = skyflux/np.max(skyflux)*skymax\n else:\n skywave = wave1D\n skylow = np.zeros(len(skywave))-100.\n skyhigh = np.zeros(len(skywave))+100.\n\n plt.fill_between(skywave,skylow,skyhigh,alpha=0.3,color='black')\n skyhigh_smooth = scipy.ndimage.filters.gaussian_filter1d(skyhigh, filtersigma,cval=0.0)\n plt.fill_between(skywave,skylow,skyhigh_smooth,alpha=0.8,color='black')\n\n # set ranges based on spectra\n if (len(ymin) != 0) & (len(ymax) != 0):\n yrangeflam = [0.95*min(ymin), 1.05*max(ymax)]\n # if yrangeflam[0] < -0.01: yrangeflam[0] = -0.01\n # if yrangeflam[1] > 10.0: yrangeflam[1] = 10.0\n else:\n yrangeflam = 0.0, 1000.0\n\n if not newobj: # only check window if not plotting new object\n if (ylow != yrangeflam[0]) or (yhigh != yrangeflam[1]):\n yrangeflam = [ylow,yhigh]\n Dyrange = yrangeflam[1]-yrangeflam[0]\n self.dataPlot_ax.set_ylim(yrangeflam)\n\n if not newobj: # only check window if not plotting new object\n if (xlow != xrangeflam[0]) or (xhigh != xrangeflam[1]):\n xrangeflam = [xlow,xhigh]\n self.dataPlot_ax.set_xlim(xrangeflam)\n\n if self.latex:\n xlab = '$\\lambda / [\\mu\\mathrm{m}]$'\n ylab = '$f_\\\\lambda$ / ['+self.fluxunit+']'\n else:\n xlab = 'lambda / [micron]'\n ylab = 'f_lambda / ['+self.fluxunit+']'\n\n self.dataPlot_ax.set_xlabel(xlab)\n self.dataPlot_ax.set_ylabel(ylab)\n\n self.dataPlotManager.canvas.draw()\n\n # === plot emission lines for scale ===\n for ii in range(len(linelist)):\n lineposition = linelist[ii]/self.DPxscale*(redshift+1.0)\n self.dataPlot_ax.plot(np.zeros(2)+lineposition,yrangeflam,color='#006600',alpha=0.7,\n linestyle='-',linewidth=self.DPlwidth*2)\n\n if self.lineuncertainty:\n if (self.lineuncertainty <= 1.0) & (self.lineuncertainty > 0.0): # treat input as Delta z uncertainty\n zoffset = self.lineuncertainty\n elif self.lineuncertainty > 1.0: # treat input as Delta v uncertainty\n zoffset = self.lineuncertainty*(redshift+1.0) / 299792.458\n else:\n if self.vb: print(' WARNING: Invalid value of \"lineuncertainty\" using dz=0.1')\n zoffset = 0.1\n\n linexmin = ( (redshift-zoffset) +1) * linelist[ii]/self.DPxscale\n linexmax = ( (redshift+zoffset) +1) * linelist[ii]/self.DPxscale\n lineymin = yrangeflam[0]\n lineymax = yrangeflam[1]\n\n plt.fill_between(np.asarray([linexmin,linexmax]),np.zeros(2)+lineymin,np.zeros(2)+lineymax,\n alpha=0.2,color='#006600')\n\n voffset = zoffset * 299792.458 / (redshift+1.0)\n\n textpos = linelist[ii]/self.DPxscale*(redshift+1.0)\n if (textpos > xrangeflam[0]) & (textpos < xrangeflam[1]):\n self.dataPlot_ax.text(textpos,yrangeflam[0]+Dyrange*0.05,\n linename[ii],color='#006600',size=self.DPFsize-3.,rotation='vertical',\n horizontalalignment='right',verticalalignment='bottom')\n\n # === position legend ===\n box = self.dataPlot_ax.get_position()\n self.dataPlot_ax.set_position([box.x0, box.y0, box.width, box.height * 0.83])\n\n\n\n if (self.skyboxvar.get() != '0'):\n self.dataPlot_ax.plot(0,0,'black',alpha=0.8,label='Sky spectrum',linewidth=self.DPlwidth*2)\n if contamplotted:\n self.dataPlot_ax.plot(0,0,'black',alpha=0.8,label='Contamination',linewidth=self.DPlwidth,ls='--')\n self.dataPlot_ax.plot(0,0,'green',label='Lines at $z$ = '+str(\"%.3f\" % redshift),linewidth=self.DPlwidth*2)\n if self.lineuncertainty:\n linelab = 'Line uncertainty $z$ +/- '+str(\"%.4f\" % zoffset)+' (+/- '+str(\"%.f\" % voffset)+' km/s)'\n self.dataPlot_ax.plot(0,0,'#006600',alpha=0.4,label=linelab,linewidth=self.DPlwidth*5)\n\n leg = self.dataPlot_ax.legend(fancybox=True, loc='upper center',numpoints=1,prop={'size':self.DPFsize-3.},\n ncol=2,bbox_to_anchor=(0.5, 1.27))\n #leg.get_frame().set_alpha(0.7)\n\n self.dataPlotManager.canvas.draw()",
"title": ""
},
{
"docid": "90d8c06e4b52fcc68f06707d44542f12",
"score": "0.5853634",
"text": "def plot_device_power_energy(sim_result, optimisation_model: pyo.Model, dev, filename=None, energy_fill_opacity=None):\n res = sim_result\n optimiser = optimisation_model\n dev_data = optimiser.all_devices[dev].dev_data\n device_name = \"{}:{}\".format(dev, dev_data.name)\n\n if dev_data.model == \"storagehydrogen\": # Fixme: replace with isinstance class type\n # isinstance(dev_data.model,oogeso.dto.DeviceStorageHydrogenData)\n carrier = \"hydrogen\"\n flow_title = \"Flow (Sm3/s)\"\n energy_storage_title = \"Energy storage( Sm3)\"\n else:\n carrier = \"el\"\n flow_title = \"Power (MW)\"\n energy_storage_title = \"Energy storage (MWh)\"\n # Power flow in/out\n df_flow = res.device_flow[dev, carrier].unstack(\"terminal\")\n if res.device_storage_energy is None:\n df_storage_energy = pd.DataFrame()\n else:\n df_storage_energy = res.device_storage_energy.unstack(\"device\")\n if dev in df_storage_energy:\n df_storage_energy = df_storage_energy[dev]\n df_storage_energy.index = df_storage_energy.index + 1\n\n if plotter == \"plotly\":\n fig = plotly.subplots.make_subplots(specs=[[{\"secondary_y\": True}]])\n for col in df_flow.columns:\n fig.add_scatter(\n x=df_flow.index,\n y=df_flow[col],\n line_shape=\"hv\",\n name=col,\n secondary_y=True,\n fill=\"tozeroy\",\n )\n if not df_storage_energy.empty:\n fig.add_scatter(\n x=df_storage_energy.index,\n y=df_storage_energy,\n name=\"storage\",\n secondary_y=False,\n fill=\"tozeroy\",\n ) # ,line=dict(dash='dot'))\n if energy_fill_opacity is not None:\n k = len(fig[\"data\"]) - 1\n linecol = plotly.colors.DEFAULT_PLOTLY_COLORS[k]\n opacity = energy_fill_opacity\n fillcol = \"rgba({}, {})\".format(linecol[4:][:-1], opacity)\n fig[\"data\"][k][\"fillcolor\"] = fillcol\n fig[\"data\"][k][\"fill\"] = \"tozeroy\"\n fig.update_yaxes(title_text=energy_storage_title, secondary_y=False, side=\"right\")\n fig.update_xaxes(title_text=\"Timestep\")\n fig.update_yaxes(title_text=flow_title, secondary_y=True, side=\"left\")\n\n elif plotter == \"matplotlib\":\n fig = plt.figure(figsize=(12, 4))\n plt.title(device_name)\n ax = plt.gca()\n df_flow.plot(ax=ax, drawstyle=\"steps-post\", marker=\".\")\n ax.set_xlabel(\"Timestep\")\n ax.set_ylabel(flow_title)\n tmin = df_flow.index.get_level_values(\"time\").min()\n tmax = df_flow.index.get_level_values(\"time\").max() + 1\n ax.set_ylim(0, dev_data.flow_max)\n ax.legend(loc=\"upper left\") # , bbox_to_anchor =(1.01,0),frameon=False)\n\n if not df_storage_energy.empty:\n ax2 = ax.twinx()\n ax2.grid(None)\n df_storage_energy.plot(ax=ax2, linestyle=\":\", color=\"black\")\n ax2.set_ylabel(\"Energy (MWh)\") # ,color=\"red\")\n if dev_data.model in [\"storage_el\"]:\n ax2.set_ylim(0, dev_data.max_E)\n elif dev_data.model in [\"well_injection\"]:\n ax2.set_ylim(-dev_data.max_E / 2, dev_data.max_E / 2)\n # ax2.tick_params(axis='y', labelcolor=\"red\")\n ax2.legend(loc=\"upper right\")\n ax.set_xlim(tmin, tmax)\n if filename is not None:\n plt.savefig(filename, bbox_inches=\"tight\")\n else:\n raise ValueError(f\"Plotter: {plotter} has not been implemented for plot device power energy.\")\n return fig",
"title": ""
},
{
"docid": "2060cd57d78865d9d9ce45c8021331d8",
"score": "0.5845508",
"text": "def snapshot_figure(self,**kwargs):\n fig=plt.figure(1)\n fig.clf()\n ax=fig.add_subplot(1,1,1)\n\n self.plot_cell_scalar(ax, self.zi_agg['min'],'b-')\n self.plot_cell_scalar(ax, self.ei, 'g-')\n ax.plot(self.grd.cells_center()[:,0],\n np.maximum( self.eta_fn(self.grd.cells_center(),self.t),\n self.zi_agg['min']),\n color='orange')\n return fig",
"title": ""
},
{
"docid": "45d6878eb5626ac55f0ec53e37eb0ff6",
"score": "0.5827475",
"text": "def save_and_show(\n fig, ax, save, show, close, filename, file_format, dpi, axis_off, extent\n):\n # save the figure if specified\n\n if save:\n start_time = time.time()\n\n # create the save folder if it doesn't already exist\n if not os.path.exists(settings.imgs_folder):\n os.makedirs(settings.imgs_folder)\n path_filename = os.path.join(\n settings.imgs_folder, os.extsep.join([filename, file_format])\n )\n\n if not isinstance(ax, (np.ndarray, list)):\n ax = [ax]\n if file_format == \"svg\":\n for ax in ax:\n # if the file_format is svg, prep the fig/ax a bit for saving\n ax.axis(\"off\")\n ax.set_position([0, 0, 1, 1])\n ax.patch.set_alpha(0.0)\n fig.patch.set_alpha(0.0)\n fig.savefig(\n path_filename,\n bbox_inches=0,\n format=file_format,\n facecolor=fig.get_facecolor(),\n transparent=True,\n )\n else:\n if extent is None:\n if len(ax) == 1:\n if axis_off:\n for ax in ax:\n # if axis is turned off, constrain the saved\n # figure's extent to the interior of the axis\n extent = ax.get_window_extent().transformed(\n fig.dpi_scale_trans.inverted()\n )\n else:\n extent = \"tight\"\n fig.savefig(\n path_filename,\n dpi=dpi,\n bbox_inches=extent,\n format=file_format,\n facecolor=fig.get_facecolor(),\n transparent=True,\n )\n log(\n \"Saved the figure to disk in {:,.2f} seconds\".format(\n time.time() - start_time\n )\n )\n\n # show the figure if specified\n if show:\n start_time = time.time()\n plt.show()\n # fig.show()\n log(\"Showed the plot in {:,.2f} seconds\".format(time.time() - start_time))\n # if show=False, close the figure if close=True to prevent display\n elif close:\n plt.close()\n\n return fig, ax",
"title": ""
},
{
"docid": "ec78324954dfdb075c20d1a2561756bf",
"score": "0.5824394",
"text": "def _visualize_data(self, save_figure, show_figure):\n plt.figure()\n for i in range(1, len(self.output_ports) + 1):\n if i < len(self.OSCOPE_CHANNEL_COLORS) + 1:\n color = self.OSCOPE_CHANNEL_COLORS[i - 1]\n else:\n color = _generate_random_color_hexcode()\n plt.plot(self.result_data[0, :], self.result_data[i, :], color)\n\n legends = []\n for i in range(len(self.output_ports)):\n legends.append(self.output_ports[i])\n\n plt.legend(legends)\n plt.xlabel('Wavelength (nm)')\n plt.ylabel('Voltage (au)')\n plt.grid(True)\n plt.tight_layout()\n\n if save_figure:\n plt.savefig(self.path + \"graph\")\n if show_figure:\n plt.show()",
"title": ""
},
{
"docid": "d616eec5545b9a7763b70eb0c448eb50",
"score": "0.5822541",
"text": "def save_plot(self, filename) -> None: \n #Save the results as an image\n filename = str(self.dimension) + 'd_model_results_'+str(self.layermax)+'_layers'\n self.plot().savefig(filename+'.png', dpi=300)\n pass",
"title": ""
},
{
"docid": "3cba2a8dbdafe34fedc4e2ed915928c3",
"score": "0.58088595",
"text": "def plotPowerSpectra(self, iids, maxl=500., removeDipole=True,\n title=None, legendloc='upper left', color=None, labels=None,\n savefig=False, outfileRoot=None, plotkwargs=None):\n iids = self._checkPlottable(iids)\n # Check if the slicer has a power spectrum visualization.\n for iid in iids:\n slicer = self.slicers[iid]\n if (not hasattr(slicer, 'plotPowerSpectrum')):\n iids.remove(iid)\n if len(iids) == 0:\n warnings.warn('Removed all iids')\n return\n # Build a plot title.\n if title is None:\n title = self._buildPlotTitle(iids)\n if labels is None:\n labels = self._buildLegendLabels(iids)\n # Plot the data.\n fignum = None\n addLegend = False\n for i, iid in enumerate(iids):\n # If we're at the end of the list, add the legend.\n if i == len(iids) - 1:\n addLegend = True\n label = labels[i]\n # Set up plotDict.\n plotDict = {'title':title, 'label':label, 'addLegend':addLegend,\n 'legendloc':legendloc, 'color':color, 'maxl':maxl,\n 'removeDipole':removeDipole}\n if plotkwargs is not None:\n plotDict.update(plotkwargs[i])\n # Plot data.\n fignum = self.slicers[iid].plotPowerSpectrum(self.metricValues[iid],\\\n fignum=fignum, **plotDict)\n if savefig:\n if outfileRoot is not None:\n outroot = outfileRoot + title\n else:\n outroot = title\n outfile = self._buildOutfileName(self.iid_next, outfileRoot=outroot + title, plotType='ps')\n plt.savefig(os.path.join(self.outDir, outfile), figformat=self.figformat, dpi=self.dpi)\n if self.thumbnail:\n thumbname = self._getThumbName(outfile)\n thumbfile = os.path.join(self.outDir, thumbname)\n plt.savefig(thumbfile, dpi=72)\n if self.resultsDb:\n # Don't have a metricID corresonding to this combo of metrics, add to metric table.\n metricNames = self.joinMetricNames(iids)\n slicerNames = ' '.join(list(self.uniqueSlicerNames(iids)))\n simDataNames = ' '.join(list(self.uniqueSimDataNames(iids)))\n metadata = self.combineMetadata(iids)\n metricId = self.resultsDb.updateMetric(metricNames, slicerNames, simDataNames, None, metadata,\n None)\n displayDict = {}\n displayDict.update(self.displayDicts[iids[-1]])\n displayDict['order'] += 1\n displayDict['caption'] = self.captionFigure(iids, 'Combined power spectrum')\n if displayDict['subgroup'] is None:\n displayDict['subgroup'] = 'Combo PS'\n self.resultsDb.updateDisplay(metricId, displayDict)\n self.resultsDb.updatePlot(metricId, 'ComboPowerSpectrum', outfile)\n else:\n outfile = None\n return fignum, title, outfile",
"title": ""
},
{
"docid": "3f3ea28abe867d72a4d959ced7040043",
"score": "0.57929623",
"text": "def onSave(self,event):\n x,y,e = self.calcSpec()\n dialog=wx.FileDialog(self,\"Name the spectm file\",\n wildcard=\"ASCII (dat)|*.dat\",\n style=wx.FD_SAVE|wx.FD_OVERWRITE_PROMPT)\n if dialog.ShowModal()==wx.ID_OK:\n with open(dialog.GetPath(),\"w\") as stream:\n for i in range(len(x)):\n stream.write(\"%f\\t%f\\t%f\\n\"%(x[i],y[i],e[i]))\n self.Show(False)",
"title": ""
},
{
"docid": "f9f4fac216d2939119cbcd3f7359513c",
"score": "0.5758722",
"text": "def figure_exp_21(fig_type = '.png', save_traces = False, show_traces = False):\n temp, save_folder, specific_folder, temp, file_save = get_figure_data_settings(fig_no = 21)\n file2save_npz = 'fig21_' + '.npz'\n \n file2save = 'PTX_block' + fig_type\n folder_save = save_folder + specific_folder + '_results/'\n y_range = [-15,90]\n display.plot_data(folder_save, file2save,folder_save,file_save,x_scale = 'sec', \n y_range=y_range, time_range = [68000,76000], electrodes=[1,2,3],\n y_range_intra = y_range) \n if show_traces:\n plt.show()",
"title": ""
},
{
"docid": "7f5c1ff0a56c56c29c8a567a018a547b",
"score": "0.57454187",
"text": "def plotSpectra(self, opening_path=None, save_fig=True, verbose=True, x=None, y=None):\n\n import matplotlib.pyplot as plt\n from astropy.visualization import astropy_mpl_style\n plt.style.use(astropy_mpl_style)\n from astropy.io import fits\n\n def wavelength(pixel, w1, dw):\n wave = w1 + (pixel - 1) * dw\n return wave\n\n def pixel(wave, w1, dw):\n pixel = old_div((wave - w1), dw) + 1\n return pixel\n\n fig, ax = plt.subplots(self.n_apertures + 1, 1)\n fig.set_size_inches(15, 5 * (self.n_apertures + 1))\n\n axall = ax[self.n_apertures].twiny()\n\n# defined self.spectra\n\n # reading in spectra files\n self.spectra = {}\n for i in range(1, self.n_apertures + 1):\n open_image = fits.open(\n self.path + self.image + '.{:04}'.format(i) + '.fits')\n if verbose:\n print(open_image.info())\n\n image_data = open_image[0].data\n\n self.spectra[i] = image_data\n if len(image_data) == 2:\n self.spectra[i] = image_data[0]\n\n open_image.close()\n\n image_data = self.spectra[i]\n\n if self.direction == 0:\n n_pix = len(image_data[1][0])\n else:\n n_pix = len(image_data)\n\n if opening_path != None:\n n_pix = len(image_data[opening_path])\n\n x_pix = np.linspace(1, n_pix, n_pix)\n # self.x_pix = x_pix\n x_wav = wavelength(x_pix, self.w1, self.dw)\n\n if self.direction == 0:\n y_val = image_data[1][0]\n else:\n y_val = image_data\n\n if verbose:\n print('x pixels: ', n_pix)\n print('x wavelengths:', x_wav[0], x_wav[-1])\n\n axi = ax[i - 1].twiny()\n ax[i - 1].plot(x_wav, y_val, linewidth=.5)\n axi.plot(x_pix, y_val, linewidth=.5)\n\n if x == None:\n # could be done much better; also fix i vs. i-1\n x = [x_wav.min(), x_wav.max()]\n\n ax[i - 1].set_xlim(x[0], x[1])\n axi.set_xlim(pixel(x[0], self.w1, self.dw),\n pixel(x[1], self.w1, self.dw))\n\n axi.set_title(self.name[i - 1], y=1.18)\n\n ax[i - 1].set_ylabel('Raw Flux Value')\n ax[i - 1].set_xlabel('Wavelength')\n axi.set_xlabel('X pixel value')\n\n # all plot\n ax[self.n_apertures].plot(x_wav, y_val, linewidth=.5)\n axall.plot(x_pix, y_val, linewidth=.5, label=self.name[i - 1])\n\n # all plot\n ax[self.n_apertures].set_xlim(x[0], x[1])\n axall.set_xlim(pixel(x[0], self.w1, self.dw),\n pixel(x[1], self.w1, self.dw))\n axall.set_title('all', y=1.18)\n ax[self.n_apertures].set_ylabel('Raw Flux Value')\n ax[self.n_apertures].set_xlabel('Wavelength')\n axall.set_xlabel('X pixel value')\n axall.legend(shadow=True, title='Spectra', fancybox=True,\n loc=\"upper left\", bbox_to_anchor=(1, 1))\n\n fig.show()\n\n del plt\n del astropy_mpl_style\n del fits\n\n if save_fig:\n import os\n directory = self.path + '/plots/'\n if not os.path.exists(directory):\n os.makedirs(directory)\n filenameend = '_x' + str(x[0]) + ':' + str(x[1]) + '.jpg'\n fig.savefig(directory + self.image + '_spectraplot' +\n filenameend, bbox_inches='tight')\n del os",
"title": ""
},
{
"docid": "d367f865a4787ca58639ed0ec83655f7",
"score": "0.5740698",
"text": "def example():\n #Create test image\n xg,yg = np.meshgrid(np.linspace(-1,1,100),np.linspace(-1,1,100))\n img = np.polynomial.legendre.legval2d(xg,yg,[[0,1,0],[0,.5,0],[1,0,0]])\n #Take gradients\n gx,gy = np.gradient(img)\n #Create a boundary region\n rad = np.sqrt(xg**2+yg**2)\n img[rad>1] = np.nan\n gx[rad>1] = np.nan\n gy[rad>1] = np.nan\n #Reconstruct wavefront\n recon = southwell(gx,gy,1e-12,1.)\n\n #Plot results\n fig = plt.figure()\n fig.add_subplot(1,3,1)\n plt.imshow(img)\n plt.title('Original')\n plt.colorbar()\n fig.add_subplot(1,3,2)\n plt.imshow(recon)\n plt.title('Reconstructed')\n plt.colorbar()\n fig.add_subplot(1,3,3)\n plt.imshow(img-recon)\n plt.title('Residual')\n plt.colorbar()\n return recon",
"title": ""
},
{
"docid": "a9b9e2597857c21f6d8ff3cfaeb1da26",
"score": "0.57378095",
"text": "def powerspectrum(self, eigenmodes=False):\n\n spacing = (np.max(self.times) - np.min(self.times))/len(self.times)\n frequencies = rfftfreq(len(self.times), spacing)\n\n\n markerfreqs = [sp.sqrt(self.D[i,i])/(np.pi*2) for i in range(self.n)]\n\n fig, ax = plt.subplots()\n fig.set_figwidth(12)\n fig.set_figheight(6)\n\n if eigenmodes:\n pseries = self.eigts\n labels = [r\"\\phi_{}\".format(i) for i in range(self.n)]\n title = \"Frequency Power Spectrum, eigenmode basis\"\n else:\n pseries = self.timeseries\n labels = [sp.latex(label) for label in self.q]\n title = \"Frequency Power Spectrum, coordinate basis\"\n\n for i in range(self.n):\n amplitude = rfft(pseries[:,i])\n power = np.abs(amplitude)**2\n ax.plot(frequencies, power, label=\"${}$\".format(labels[i]))\n\n ax.set_ylabel(\"Power\")\n ax.set_xlabel(\"Frequency ($s^{-1}$)\")\n ax.loglog()\n ax.set_title(title)\n ylim = ax.get_ylim()\n for freq in markerfreqs:\n line, = ax.plot([freq, freq], ylim, \":r\")\n ax.text(freq, ylim[0], \"{:.2f}\".format(freq), color=\"red\", va=\"bottom\", ha=\"center\",\n bbox=dict(facecolor='white', edgecolor='white'))\n line.set_label(\"Linear eigenmode frequencies\")\n ax.set_ylim(ylim)\n ax.legend()\n return fig",
"title": ""
},
{
"docid": "1ea29a0e2565b3bfbdc396b8eed89850",
"score": "0.57320136",
"text": "def figure_exp_25(fig_type = '.png', show_traces = False):\n temp, save_folder, specific_folder, temp, file_save = get_figure_data_settings(fig_no = 25)\n #file2save_npz = 'fig25_' + '.npz'\n \n \n folder_save = save_folder + specific_folder + '_results/'\n file2save = 'exc_synch_zoom' + fig_type\n y_range = [-500,700] #[-15,15]\n time_range =[3480,3510] \n display.plot_data(folder_save, file2save,folder_save,file_save,x_scale = 'sec', \n y_range=[], time_range = time_range, electrodes=[0,1,2,3],\n y_range_intra = [-50,0]) \n \n file2save = 'exc_synch' + fig_type\n y_range = [-1000,1300] #[-15,15]\n time_range =[2800,3900]\n display.plot_data(folder_save, file2save,folder_save,file_save,x_scale = 'sec', \n y_range=y_range, time_range = time_range, electrodes=[0,1,2,3],\n y_range_intra = []) \n\n\n #time_range = [] # in ms\n data_details = dh.read_npzdata(folder_save, file_save, \"data\", \"scale\", \"fs\")\n file_save = 'exc_synch_' + fig_type\n #import pdb; pdb.set_trace()\n display.plot_data_one_mean(data_details, folder_save, file_save, x_scale = 'sec',\n title = 'excitatory synchrony', sweeps = [],\n electrodes = [0,1,2,3], y_range = y_range, time_range = time_range,\n remove_avg = True) \n if show_traces:\n plt.show()",
"title": ""
},
{
"docid": "49f58631ed9d9c2e8be5b20cc2601f8b",
"score": "0.5731807",
"text": "def make_power_figure(\n resource_display_name: str,\n data: pd.DataFrame,\n forecast_data: Optional[pd.DataFrame],\n schedule_data: Optional[pd.DataFrame],\n show_consumption_as_positive: bool,\n shared_x_range: Range1d,\n tools: List[str] = None,\n sizing_mode=\"scale_width\",\n) -> Figure:\n if show_consumption_as_positive:\n title = \"Electricity consumption of %s\" % resource_display_name\n else:\n title = \"Electricity production from %s\" % resource_display_name\n if data.empty:\n title = title.replace(\"Electricity\", \"Prognosed\")\n\n return create_graph(\n data,\n unit=\"MW\",\n legend_location=\"top_right\",\n legend_labels=(\"Actual\", \"Forecast\", None)\n if schedule_data is None or schedule_data[\"event_value\"].isnull().all()\n else (\"Actual\", \"Forecast\", \"Schedule\"),\n forecasts=forecast_data,\n schedules=schedule_data,\n title=title,\n x_range=shared_x_range,\n x_label=\"Time (resolution of %s)\"\n % determine_resolution(data, forecast_data, schedule_data),\n y_label=\"Power (in MW)\",\n show_y_floats=True,\n tools=tools,\n sizing_mode=sizing_mode,\n )",
"title": ""
},
{
"docid": "37a20063aa23c811d51ecf216ce035e7",
"score": "0.5729813",
"text": "def plot_datamap(self):\n plt.imshow(self.data)\n plt.clim(0, self.max_charge+1)\n cbar = plt.colorbar()\n cbar.set_label(\"Threshold (electrons)\")\n filename = f\"{self.basename}datamap{self.feeid}.{self._plot_extension}\"\n print(f\"Image stored in {filename}\")\n plt.savefig(filename, bbox_inches='tight')\n plt.close()",
"title": ""
},
{
"docid": "884f95b900d4ddeeeea5460e77083bb1",
"score": "0.5714797",
"text": "def save_fig(self, filename, ext='png', close=True, verbose=False):\n import os.path\n \n if self.minor == 'minorxy': \n path=('/Users/vprzybylo/Desktop/icefiles/agg_model/agg_notes/graphics/python/'+\n str(len(self.clusters))+'crystals/'+str(len(self.clusters))+'xtals_hist/minorxy/')\n\n else:\n path=('/Users/vprzybylo/Desktop/icefiles/agg_model/agg_notes/graphics/python/'+\n str(len(self.clusters))+'crystals/'+str(len(self.clusters))+'xtals_hist/depth/')\n\n filename = \"%s.%s\" % (filename, ext)\n if path == '':\n path = '.'\n\n # If the directory does not exist, create it\n if not os.path.exists(path):\n os.makedirs(path)\n\n # The final path to save to\n savepath = os.path.join(path, filename)\n\n if verbose:\n print(\"Saving figure to '%s'...\" % savepath),\n\n # Actually save the figure\n plt.savefig(savepath)\n\n # Close it\n if close:\n plt.close() \n \n \n \"\"\"Utilities for running ice particle simulations.\"\"\"",
"title": ""
},
{
"docid": "f4cbd6649fcb1b72581321721ae60d7e",
"score": "0.569858",
"text": "def plot_spectrum(self, output_dir='output/', units='si', verbose=True):\n if units == 'si':\n flux = self.flux_si\n fluxerr = self.fluxerr_si\n elif units == 'jy':\n flux = self.flux_jy\n fluxerr = self.fluxerr_jy\n else:\n raise ValueError('Units must be one of (\"si\", \"jy\")')\n\n \"\"\"\n Smooth spectrum? Well, a window length of 5 is basically no smoothing\n for this resolution of spectrum. Might as well skip it.\n\n # ax.plot(self.wave, flux, '-', lw=2, label=self.basename)\n # smoothed_flux = helpers.smooth(flux, window_len=5)\n # ax.plot(self.wave, smoothed_flux, '--', lw=1, label='smoothed')\n \"\"\"\n\n # Plot full cont subtracted spectrum\n fig, ax = plt.subplots(figsize=(9, 6))\n ax.errorbar(self.wave, flux, fluxerr, color='r', ecolor='0.45',\n lw=2, elinewidth=1)\n\n # Set plot parameters.\n ax.axhline(y=0, color='k', ls='-', zorder=-10, lw=1)\n ax.set_xlabel('Wavelength (micron)', fontsize=12)\n ax.set_ylabel(r'Flux density (${\\rm W}/{\\rm m}^2$)', fontsize=12)\n ax.set_title(self.basename + ' -- Full continuum-subtracted spectrum')\n ax.grid(ls=':')\n ax.minorticks_on()\n ax.tick_params(direction='in', which='both', right=True, top=True)\n\n # Save and close.\n pdf_filename = output_dir + self.basename + '.pdf'\n fig.savefig(pdf_filename, format='pdf', bbox_inches='tight')\n plt.close()\n fig.clear()\n\n if verbose:\n print('Saved: ', pdf_filename)",
"title": ""
},
{
"docid": "8d8478fc1d363bf179485463d7056083",
"score": "0.5696789",
"text": "def __call__(self) -> None:\n self.save_figure()\n print(self.export_string)\n return",
"title": ""
},
{
"docid": "bd8f7fbac57b8f8bd4731b5f4d60eb8d",
"score": "0.5694435",
"text": "def figure_exp_20(fig_type = '.png', save_traces = False, show_traces = False):\n temp, save_folder, specific_folder, temp, file_save = get_figure_data_settings(fig_no = 20)\n file2save_npz = 'fig20_' + '.npz'\n \n file2save = 'interictal' + fig_type\n folder_save = save_folder + specific_folder + '_results/'\n y_range = [-15,90]\n display.plot_data(folder_save, file2save,folder_save,file_save,x_scale = 'sec', \n y_range=y_range, time_range = [51000,59000], electrodes=[1,2,3],\n y_range_intra = y_range) \n\n if save_traces:\n #it must be done only if the recording was gap free but \n #it was not saved in the trace format yet\n\n #display.plot_data(folder_save,file2save,folder_save,file_save,x_scale = 'ms')#,time_range=time_range)\n dh.trigger_on_spike(folder_save, file_save, folder_save, file2save_npz, thresh = 25, el=3,\n time_range=[-200,200], up = True, center_on_peak = True)\n display.plot_data(folder_save,file2save,folder_save,file2save_npz,x_scale = 'ms', \n electrodes = [1,2,3],y_range =y_range)\n #import pdb; pdb.set_trace() \n\n time_range = [100,300] # in ms\n data_details = dh.read_npzdata(folder_save, file2save_npz, \"data\", \"scale\", \"fs\")\n file_save = 'interictal' + fig_type\n #import pdb; pdb.set_trace()\n display.plot_data_one_mean(data_details, folder_save, file_save, x_scale = 'ms',\n title = 'interictal events', sweeps = [5,6,4,7,8,9,10,11,12,13],\n electrodes = [1,2,3], y_range = [], time_range = time_range,\n remove_avg = True) \n if show_traces:\n plt.show()",
"title": ""
},
{
"docid": "d30dfb5d718e491f833b47b8ce2c709a",
"score": "0.5688892",
"text": "def power_map(ice_power_grid_file, floorplan_file, output_format, pd_min, pd_max):\n ptrace = load_3DICE_grid_file(ice_power_grid_file, convert_K_to_C=False)\n \n flp = Floorplan.from_file(floorplan_file)\n px_height_mm = _get_px_size_mm_from_flp_ttrace(flp, ptrace)\n px_height_cm = 10 * px_height_mm\n px_area_cm_sq = px_height_cm * px_height_cm\n \n axes = plot_power_density(ptrace / px_area_cm_sq, pd_min=pd_min, pd_max=pd_max)\n \n for step, ax in enumerate(axes):\n fig = ax.get_figure()\n figname = output_format.format(step=step)\n fig.savefig(figname)",
"title": ""
},
{
"docid": "380d0c5d22d53347394598f13d81b505",
"score": "0.56859505",
"text": "def saveplot_energy_resolution(SimuE, RecoE, Outfile=\"EnergyResolution.png\", cta_site=None):\n\n ax = plot_energy_resolution(SimuE, RecoE)\n\n if cta_site != None:\n ax = plot_energy_resolution_cta_requirements(cta_site, ax=ax)\n\n plt.savefig(Outfile, bbox_inches=\"tight\", format='png', dpi=200)\n plt.close()\n return ax",
"title": ""
},
{
"docid": "43486701220f331f3dc8067926c43edb",
"score": "0.56852216",
"text": "def _save_raster(self, dpi=300):\n self.component.do_layout(force=True)\n # NOTE saving only works properly when dpi is a multiple of 72\n gc = PlotGraphicsContext((int(self.component.outer_width),\n int(self.component.outer_height)),\n dpi=np.ceil(dpi / 72.0)*72)\n gc.render_component(self.component)\n gc.save(self.filename)",
"title": ""
},
{
"docid": "0f0be408f56b19f07783a47300234c34",
"score": "0.5676913",
"text": "def figure_exp_8(fig_type = '.png', save_traces = False):\n temp, save_folder, specific_folder, temp, file_save = get_figure_data_settings(fig_no = 8)\n file2save_npz = 'fig8_' + '.npz'\n file2save = 'part_of_trace' + fig_type\n \n folder_save = save_folder + specific_folder + '_results/'\n #display.plot_data(folder_save, file2save,folder_save,file_save,x_scale = 'sec', \n # electrodes = [1,2,3], y_range=[-100,100])\n\n if save_traces:\n #it must be done only if the recording was gap free but \n #it was not saved in the trace format yet\n\n #display.plot_data(folder_save,file2save,folder_save,file_save,x_scale = 'ms')#,time_range=time_range)\n dh.trigger_on_spike(folder_save, file_save, folder_save, file2save_npz, thresh = -30, el=2,\n time_range=[-200,200], up = False, center_on_peak = True)\n #display.plot_data(folder_save,file2save,folder_save,file2save_npz,x_scale = 'ms', \n # electrodes = [1,2,3],y_range =[-100, 100])\n\n\n time_range = [100,400] # in ms\n data_details = dh.read_npzdata(folder_save, file2save_npz, \"data\", \"scale\", \"fs\")\n file_save = 'interictal' + fig_type\n #import pdb; pdb.set_trace()\n display.plot_data_one_mean(data_details, folder_save, file_save, x_scale = 'ms',\n title = 'interictal events', sweeps = [0,1,2,3,4,5,6],\n electrodes = [1,2,3], y_range = [-100,100], time_range = time_range,\n remove_avg = True) \n plt.show()",
"title": ""
},
{
"docid": "c5790bdfab8dc8d330667ffc9f4afad6",
"score": "0.566134",
"text": "def savefigure(title, xlabel, ylabel, Y, labels, x, path_save_dir, type = \"linear\"):\n plt.figure(figsize = (10,8))\n plt.title(title)\n plt.xlabel(xlabel)\n plt.ylabel(ylabel)\n plt.grid(True)\n plt.ylim((0,16))\n if len(Y) != len(labels):\n print(\"Number of labels and plots don't match!\")\n return\n\n for i in range(len(Y)):\n y = Y[i]\n if type == \"linear\":\n plt.plot(x, y, label = labels[i])\n elif type == \"semilogx\":\n plt.semilogx(x, y, label = labels[i])\n plt.legend()\n try:\n os.mkdir(path_save_dir)\n print(\"folder created at \" + path_save_dir)\n except FileExistsError:\n print(\"folder already exists at \" + path_save_dir)\n path_save_fig = join(path_save_dir, title + \".png\")\n plt.savefig(path_save_fig)\n print(title + \".png saved at \" + path_save_fig)\n plt.close()\n\n return",
"title": ""
},
{
"docid": "bdffa974dfa1303823d5845e18baa613",
"score": "0.56549466",
"text": "def makeAveragePSRadialFigure(radialFreq,averagePSRadial,figureFileName): \r\n pylab.figure()\r\n pylab.loglog(radialFreq,averagePSRadial,'.')\r\n pylab.xlabel(\"Frequecy\")\r\n pylab.ylabel(\"Radial Power Spectrum\")\r\n pylab.savefig(figureFileName)",
"title": ""
},
{
"docid": "4d8fcd69d7a9718b8a50bf7dc91282ef",
"score": "0.5653953",
"text": "def save_and_show(fig, ax, save, show, filename, file_format, dpi):\n # save the figure if specified\n if save:\n start_time = time.time()\n \n # create the save folder if it doesn't already exist\n if not os.path.exists(_imgs_folder):\n os.makedirs(_imgs_folder)\n path_filename = '{}/{}.{}'.format(_imgs_folder, filename, file_format)\n if file_format == 'svg':\n # if the file_format is svg, prep the fig/ax a bit for saving\n ax.axis('off')\n ax.set_position([0, 0, 1, 1])\n ax.patch.set_alpha(0.)\n fig.patch.set_alpha(0.)\n fig.savefig(path_filename, bbox_inches=0, transparent=True, format=file_format)\n else:\n fig.savefig(path_filename, dpi=dpi, bbox_inches='tight', format=file_format)\n log('Saved the figure to disk in {:,.2f} seconds'.format(time.time()-start_time))\n \n # show the figure if specified\n if show:\n start_time = time.time()\n plt.show()\n log('Showed the plot in {:,.2f} seconds'.format(time.time()-start_time))\n \n return fig, ax",
"title": ""
},
{
"docid": "7f7689a095a06ff6fd609436f134c978",
"score": "0.5650501",
"text": "def figure_exp_7(fig_type = '.png', save_traces = False,show_traces = False):\n temp, save_folder, specific_folder, temp, file_save = get_figure_data_settings(fig_no = 7)\n file2save_npz = 'fig6_' + '.npz'\n file2save = 'part_of_trace' + fig_type\n \n folder_save = save_folder + specific_folder + '_results/'\n #display.plot_data(folder_save, file2save,folder_save,file_save,x_scale = 'sec', \n # electrodes = [1,2,3], y_range=[-1000,1000])\n \n if save_traces:\n #it must be done only if the recording was gap free but \n #it was not saved in the trace format yet\n\n #display.plot_data(folder_save,file2save,folder_save,file_save,x_scale = 'ms')#,time_range=time_range)\n # if aligned on the second electrode:\n #dh.trigger_on_spike(folder_save, file_save, folder_save, file2save_npz, thresh = -100, el=2,\n # time_range=[-600,600], up = False, center_on_peak = True)\n \n display.plot_data(folder_save,file2save,folder_save,file2save_npz,x_scale = 'ms', \n electrodes = [0,1,2,3],y_range =[-1000, 1000])\n \n \n different_events = [[1,4,5,7,16], [9,10],[2,6,8]]\n files_save = [\"synch_I\", \"synch_II\", \"synch_III\"] \n titles = [\"synch I, 6 events\", \"synch II, 2 events\", \"synch III, 3 events\"]\n #y_ranges = [[-500,1200],[-300,800],[-100,100]]\n \n time_range = [0,1500] # in ms\n data_details = dh.read_npzdata(folder_save, file2save_npz, \"data\", \"scale\", \"fs\")\n \n y_range = [] #[-500,500]\n \n for next_id in range(len(different_events)):\n\n file_save = files_save[next_id] + fig_type\n\n #import pdb; pdb.set_trace()\n display.plot_data_one_mean(data_details, folder_save, file_save, x_scale = 'ms',\n title = titles[next_id], sweeps = different_events[next_id],\n electrodes = [0,1,2,3], y_range = y_range, time_range = time_range,\n remove_avg = True) \n if show_traces:\n plt.show()",
"title": ""
},
{
"docid": "498a15bb122e6b40944eacf433c105c0",
"score": "0.56395435",
"text": "def figure_exp_6_1(fig_type = '.png', save_traces = False,show_traces = False):\n temp, save_folder, specific_folder, temp, file_save = get_figure_data_settings(fig_no = 6.1)\n file2save_npz = 'fig6_1_' + '.npz'\n file2save = 'part_of_trace' + fig_type\n \n folder_save = save_folder + specific_folder + '_results/'\n #display.plot_data(folder_save, file2save,folder_save,file_save,x_scale = 'sec', \n # electrodes = [0,2,3], y_range=[-1000,1000])\n \n if save_traces:\n #it must be done only if the recording was gap free but \n #it was not saved in the trace format yet\n\n #display.plot_data(folder_save,file2save,folder_save,file_save,x_scale = 'ms')#,time_range=time_range)\n dh.trigger_on_spike(folder_save, file_save, folder_save, file2save_npz, thresh = -500, el=2,\n time_range=[-300,1500], up = False, center_on_peak = False)\n display.plot_data(folder_save,file2save,folder_save,file2save_npz,x_scale = 'ms', \n electrodes = [0,2,3],y_range =[-300, 1500])\n \n \n fig_save = 'large_synch_2' + fig_type\n time_range = [0,800] # in ms\n\n data_details = dh.read_npzdata(folder_save, file2save_npz, \"data\", \"scale\", \"fs\")\n display.plot_data_one_mean(data_details, folder_save, fig_save, x_scale = 'ms',\n title = 'large Gabaa synchronised events', sweeps = [8, 6, 7,9,10],\n y_range_intra = [-10, 20], electrodes = [0,2], y_range = [-1000,1000],\n remove_avg = True, time_range = time_range) \n if show_traces:\n plt.show()",
"title": ""
},
{
"docid": "cb719770867a670256fccb681d4a7e18",
"score": "0.5635164",
"text": "def simple_plot():\n # https://matplotlib.org/3.1.0/gallery/lines_bars_and_markers/simple_plot.html#sphx-glr-gallery-lines-bars-and-markers-simple-plot-py\n # Data for plotting\n # Modified to write only to a file and close the figure.\n t = np.arange(0.0, 2.0, 0.01)\n s = 1 + np.sin(2 * np.pi * t)\n\n\n fig, ax = plt.subplots()\n ax.plot(t, s, color= \"green\")\n\n ax.set(xlabel='time (s)', ylabel='voltage (mV)',\n title='About as simple as it gets, folks')\n ax.grid()\n print('Writing out figure to simple_plot.png')\n fig.savefig(\"simple_plot.png\")\n plt.close()",
"title": ""
},
{
"docid": "379b205a96acc6e7f192e64206e9851e",
"score": "0.56266963",
"text": "def savePNG(self):\n pylab.savefig(self.outPathEntry.get())",
"title": ""
},
{
"docid": "b630d43f26dcab246076ce0c1e541d36",
"score": "0.5624538",
"text": "def save_fig(location=\"./\", name=\"plot\"):\n plt.savefig(location + name + \".pdf\")\n plt.savefig(location + name + \".png\")",
"title": ""
},
{
"docid": "bf29bade97a9d33473b47f07ada86cef",
"score": "0.5624398",
"text": "def _save_power_outputs(self, model):\n\n for i in model.SCENARIOS:\n # get the power output\n power_output_name = self.bidding_model_object.power_output\n model.fs[i].power_output_ref = pyo.Reference(\n getattr(model.fs[i], power_output_name)\n )\n\n return",
"title": ""
},
{
"docid": "a5bb0e7294d2693cfeb2ddc3a7e0ca2c",
"score": "0.5618493",
"text": "def make_fig(self):\n\n self.fig, ax = plt.subplots()\n self.fig.show()\n\n self.im = ax.imshow(self.numpy_rast)\n\n if self.title:\n self.fig.suptitle(self.title, fontsize = 20)\n\n self.im.set_data(self.numpy_rast)\n self.fig.canvas.draw()\n return",
"title": ""
},
{
"docid": "3f52d65408467a8a45a578c46574dbea",
"score": "0.56108814",
"text": "def plot_stave(self, force_binary):\n plt.figure(figsize=(self.modules*(ALPIDE_COLS//ALPIDE_ROWS)*STAVE_SIZE_MODIFIER,QUAD_STAVES*STAVE_SIZE_MODIFIER))\n stave = self.remap_to_stave()\n assert np.count_nonzero(stave<0) == 0\n stave = stave/float(self.n_events)\n stave = self.interpolate(stave,scale=20)\n plt.imshow(stave, cmap=\"tab20c\", norm=mpl.colors.LogNorm(vmin=1./float(self.n_events), vmax=1.))#plt.cm.gray)#\"binary\")\n plt.xticks(list(range(0,ALPIDE_COLS*CHIP_PER_LANE*self.modules,ALPIDE_COLS*CHIP_PER_LANE))+[ALPIDE_COLS*self.modules*CHIP_PER_LANE-1])\n plt.xlabel('Pixel')\n plt.yticks(list(range(0,ALPIDE_ROWS*QUAD_STAVES +1,ALPIDE_ROWS)))\n plt.ylabel('Pixel')\n cbar = plt.colorbar()\n cbar.set_label(\" Hits / Event\")\n filename = f\"{self.basename}stave{self.feeid}.{self._plot_extension}\"\n print(f\"Image stored in {filename}\")\n plt.savefig(filename, bbox_inches='tight', dpi=1200)\n plt.close()",
"title": ""
},
{
"docid": "e42c6f100d80eaa3e313d36f526015a7",
"score": "0.56107545",
"text": "def construct_powerspectrum(self, recalculate: bool = False) -> None:\n print('Constructing the power spectrum...')\n start_time = time.time()\n self.powerspectrum.construct_data(recalculate=recalculate)\n end_time = time.time()\n print(f' Done in {end_time - start_time:0.2f}s')",
"title": ""
},
{
"docid": "55b7e5a239aa2a1f6b7b4cdab910544b",
"score": "0.5601454",
"text": "def plot_spec(spec,transition_name, \n\t\t\t\tdwave = 10., dflux_window_up = 0.0, dflux_window_down=0.0):\n\twave,flux,error,dfp,dfm = spec\n\tline_region = np.median(wave)\n\n\tfig1 = pl.figure(figsize=(16,8))\n\tax1 = fig1.add_subplot(111)\n\tax1.clear()\n\tax1.set_xlabel(r'$\\lambda$ ($\\AA$)')\n\tax1.set_ylabel(r'$\\rm Flux$')\n\n\t# Rest central wavelength vertical line\n\trest_central_wave = transition_dict[transition_name].wave\n\tpl.axvline(rest_central_wave,lw=2,ls='--',color = 'b')\n\n\t# Original data. \n\tpl.step(wave,flux,color = 'k')\n\tpl.step(wave,error,color = 'r')\n\tpl.xlim(Utilities.zoom_region(line_region,dwave,dflux_window_up,dflux_window_down)[0])\n\n\t# Two points setting boundary of data for calculation \n\tx_record = []; y_record = []\n\tpoints_to_fit, = pl.plot(x_record,y_record,'bo',ms = 8)\n\n\t# Gaussian Lines\n\tgauss_wave = []; gauss_flux = []\n\tgauss_fit, = pl.plot(gauss_wave,gauss_flux,'b',lw = 1.5)\n\n\t# define dummy variables\n\tdwave = 10;\n\tbig_shift = 0.5; \t small_shift = 0.1\n\tbig_zoom = 0.5; \t zoom = 0.1\n\tdflux_window_up = 0.0; dflux_window_down = 0.0\n\tflux_zoom = 2e-15; \t big_flux_zoom = 5e-15\n\n\t########################################################################################\n\t\n\tdef shift_spec(event):\n\t\t\"\"\"\n\t\tInteractive click/plotting Event \n\n\t\tParameters\n\t\t---------------------------------------------------------------------------\n\t\tevent: obj\n\t\t\tMouse clicks or button press when focus on the plotting window\n\n\t\tReturns\n\t\t---------------------------------------------------------------------------\n\t\tcentroid_wave: float\n\t\t\tFitted gaussian centroid of observed wavelength for the input transition; it's \n\t\t\tused for calculating offset against the rest wavelength\t\t\n\t\t\"\"\"\n\t\tglobal transition_name; global centroid_wave\n\t\tglobal line_region; global dwave\n\t\tglobal dflux_window_up; global dflux_window_down\n\t\t\n\t\tix, iy = event.xdata, event.ydata\n\t\t#######################################################################\n\t\t#\t\t\t\t\t\t\t\t\t\t\t\t\t\t \t\t\t #\n\t\t#\tWINDOW Control \t\t\t\t\t \t\t\t\t\t \t\t\t #\n\t\t#\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t #\n\t\t#######################################################################\n\t\tif event.key == '}':\n\t\t\t# Move plotting and spec to right by amount 'big_shift'\n\t\t\tline_region += big_shift\n\t\t\tpl.xlim(Utilities.zoom_region(line_region,dwave,dflux_window_up,dflux_window_down)[0])\n\t\t\n\t\telif event.key == '{':\n\t\t\t# Move plotting and spec to left by amount 'big_shift'\n\t\t\tline_region -= big_shift\n\t\t\tpl.xlim(Utilities.zoom_region(line_region,dwave,dflux_window_up,dflux_window_down)[0])\n\t\t\n\t\telif event.key == ']':\n\t\t\t# Move plotting and spec to right by amount 'small_shift'\n\t\t\tline_region += small_shift\n\t\t\tpl.xlim(Utilities.zoom_region(line_region,dwave,dflux_window_up,dflux_window_down)[0])\n\t\t\n\t\telif event.key == '[':\n\t\t\t# Move plotting and spec to left by amount 'small_shift'\n\t\t\tline_region -= small_shift\n\t\t\tpl.xlim(Utilities.zoom_region(line_region,dwave,dflux_window_up,dflux_window_down)[0])\n\t\t\n\t\telif event.key == '-':\n\t\t\t# Zoom in horizontally in wavelength by amount 'zoom'\n\t\t\tdwave += zoom\n\t\t\tpl.xlim(Utilities.zoom_region(line_region,dwave,dflux_window_up,dflux_window_down)[0])\n\t\t\n\t\telif event.key == '=':\n\t\t\t# Zoom out horizontally in wavelength by amount 'zoom'\n\t\t\tdwave -= zoom\n\t\t\tpl.xlim(Utilities.zoom_region(line_region,dwave,dflux_window_up,dflux_window_down)[0])\n\t\t\n\t\telif event.key == '_':\n\t\t\t# Zoom in horizontally in wavelength by amount 'big_zoom'\n\t\t\tdwave += big_zoom\n\t\t\tpl.xlim(Utilities.zoom_region(line_region,dwave,dflux_window_up,dflux_window_down)[0])\n\t\t\n\t\telif event.key == '+':\n\t\t\t# Zoom out horizontally in wavelength by amount 'big_zoom'\n\t\t\tdwave -= big_zoom\n\t\t\tpl.xlim(Utilities.zoom_region(line_region,dwave,dflux_window_up,dflux_window_down)[0])\n\t\t\n\t\telif event.key =='b':\n\t\t\t# Zoom (b)ottom in flux by amount 'flux_zoom'\n\t\t\tdflux_window_up += flux_zoom\n\t\t\tpl.ylim(Utilities.zoom_region(line_region,dwave,dflux_window_up,dflux_window_down)[1])\n\t\t\n\t\telif event.key =='B':\n\t\t\t# UN-Zoom (b)ottom in flux by amount 'flux_zoom'\n\t\t\tdflux_window_up -= flux_zoom\n\t\t\tpl.ylim(Utilities.zoom_region(line_region,dwave,dflux_window_up,dflux_window_down)[1])\n\t\t\n\t\telif event.key =='t':\n\t\t\t# Zoom in the (t)op in flux by amount 'flux_zoom'\n\t\t\tdflux_window_down -= flux_zoom\n\t\t\tpl.ylim(Utilities.zoom_region(line_region,dwave,dflux_window_up,dflux_window_down)[1])\t\t\t\n\t\t\n\t\telif event.key =='T':\n\t\t\t# Zoom out the (t)op in flux by amount 'flux_zoom'\n\t\t\tdflux_window_down += flux_zoom\n\t\t\tpl.ylim(Utilities.zoom_region(line_region,dwave,dflux_window_up,dflux_window_down)[1])\n\t\t\n\t\telif event.key =='m':\n\t\t\t# Zoom in the bottom in flux by amount 'big_flux_zoom'\n\t\t\tdflux_window_up += big_flux_zoom\n\t\t\tpl.ylim(Utilities.zoom_region(line_region,dwave,dflux_window_up,dflux_window_down)[1])\n\t\t\n\t\telif event.key =='M':\n\t\t\t# Zoom out the bottom in flux by amount 'big_flux_zoom'\n\t\t\tdflux_window_up -= big_flux_zoom\n\t\t\tpl.ylim(Utilities.zoom_region(line_region,dwave,dflux_window_up,dflux_window_down)[1])\n\t\t\n\t\telif event.key =='u':\n\t\t\t# Zoom in the top flux by amount 'big_flux_zoom'\n\t\t\tdflux_window_down -= big_flux_zoom\n\t\t\tpl.ylim(Utilities.zoom_region(line_region,dwave,dflux_window_up,dflux_window_down)[1])\t\t\t\n\t\t\n\t\telif event.key =='U':\n\t\t\t# Zoom out the top in flux by amount 'big_flux_zoom'\n\t\t\tdflux_window_down += big_flux_zoom\n\t\t\tpl.ylim(Utilities.zoom_region(line_region,dwave,dflux_window_up,dflux_window_down)[1])\n\t\t\n\t\telif event.key =='r':\n\t\t\t# (r)eplot the spectral window from the original starting point\n\t\t\tdwave = 10; dflux_window_up = 0.0; dflux_window_down = 0.0\n\t\t\tline_region = np.median(wave)\n\t\t\tpl.xlim(Utilities.zoom_region(line_region,dwave,dflux_window_up,dflux_window_down)[0])\n\t\t\tpl.ylim(Utilities.zoom_region(line_region,dwave,dflux_window_up,dflux_window_down)[1])\n\t\telif event.key == '?':\n\t\t\tprint '\\n'\n\t\t\tUtilities.printLine()\n\t\t\tprint '?\tShow keys map (What is shown here)'\n\n\t\t\tUtilities.printLine()\n\t\t\tprint 'WINDOW CONTROL KEYS:'\n\t\t\tprint '}\t\tshift to right with 0.5 Angstrom'\n\t\t\tprint '{\t\tshift to left with 0.5 Angstrom'\n\t\t\tprint ']\t\tshift to right with 0.1 Angstrom'\n\t\t\tprint '[\t\tshift to left with 0.1 Angstrom'\n\t\t\tprint 'shift +/-\tZoom in/out by 0.5 Angstrom'\n\t\t\tprint '+/-\t\tZoom in/out by 0.1 Angstrom'\n\t\t\tprint 'T/t\t\tZoom top by 1e-15'\n\t\t\tprint 'B/b\t\tZoom bottom by 1e-15'\n\t\t\tprint 'U/u\t\tZoom top by 5e-15'\n\t\t\tprint 'M/m\t\tZoom bottom by 5e-15'\n\t\t\tprint 'r\t\treplot'\n\n\t\t\tUtilities.printLine()\n\t\t\tprint 'FITTING SPEC KEYS:'\n\t\t\tprint 'a\tAdd points'\n\t\t\tprint 'shift+g\tFit Gaussian'\n\t\t\tUtilities.printLine()\n\n\t\t#######################################################################\n\t\t#\t\t\t\t\t\t\t\t\t\t\t\t\t\t \t\t\t #\n\t\t#\tFitting Control \t\t\t\t\t \t\t\t\t\t \t\t #\n\t\t#\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t #\n\t\t#######################################################################\n\t\telif event.key == 'a':\n\t\t\t# Add 2 Points setting boundary for fitting data\n\n\t\t\tif len(x_record) < 2:\n\t\t\t\tx_record.append(ix); y_record.append(iy)\n\t\t\t\t\n\t\t\telse:\n\t\t\t\tdel x_record[:]; del y_record[:]\n\t\t\t\tx_record.append(ix); y_record.append(iy)\n\t\t\t\t\n\t\telif event.key == 'G':\n\t\t\t# Fitt a Gaussian based on the data selected by the two points \n\t\t\t# in spectrum\n\n\t\t\tif not x_record: \n\t\t\t\tprint 'No data selected to fit.'\n\t\t\t\tpass\n\t\t\telse:\n\t\t\t\tp1,p2 = np.transpose(np.array([x_record,y_record]))\n\n\t\t\t\tx1,y1 = p1; x2,y2 = p2\n\t\t\t\ttemp_spec = Utilities.Select_Data(spec,[x1,x2])\n\t\t\t\testimated_cont_level = np.mean((y1,y2))\n\t\t\t\tgauss_params = Utilities.Fit_Gaussian(temp_spec,estimated_cont_level)\n\n\t\t\t\tif gauss_params:\n\t\t\t\t\tamplitude,centroid_wave,sigma_width = gauss_params\n\n\t\t\t\t\t# Apparent column density \n\t\t\t\t\tlogN = Utilities.ComputeAppColumn(temp_spec,transition_name)\n\n\t\t\t\t\t# Print out results of gaussian fit \n\t\t\t\t\tUtilities.Print_LineInfo(gauss_params,logN,transition_name)\n\n\t\t\t\t\t# Make the plot to show goodness of fit\n\t\t\t\t\tgauss_flux = Utilities.Gaussian_function(temp_spec[0],amplitude,centroid_wave,sigma_width)\n\t\t\t\t\tgauss_wave = temp_spec[0];\n\t\t\t\t\tgauss_fit.set_xdata(gauss_wave)\n\t\t\t\t\tgauss_fit.set_ydata(gauss_flux + estimated_cont_level)\n\t\t\t\telse:\n\t\t\t\t\tpass\n\n\t\tpoints_to_fit.set_xdata(x_record)\n\t\tpoints_to_fit.set_ydata(y_record)\n\n\t\tpl.draw() # needed for instant response. \n\n\tciv = fig1.canvas.mpl_connect('key_press_event', shift_spec)\n\tpl.show()\n\n\t# Exit function properly\n\ttry:\n\t\t# Test if centroid_wave exits\n\t\tcentroid_wave\n\texcept NameError:\n\t\tprint 'No Gaussian fitted to any lines; returning...'\n\t\treturn np.nan\n\telse:\n\t\tif abs(centroid_wave - transition_dict[transition_name].wave) > 2: \n\t\t\tprint 'Note: %f has a large deviation > 2 angstrom' % centroid_wave\n\t\t\tprint 'Results not recorded.'\n\t\t\treturn np.nan\n\t\telse:\n\t\t\treturn centroid_wave",
"title": ""
},
{
"docid": "6d74a791d2b7fabcf77ab0a069a7ce50",
"score": "0.5599773",
"text": "def plot_save(plt, country: str, city: str, output_folder: str) -> None:\n plot_path = Path(f\"{output_folder}/{country}/{city}\")\n plot_path.mkdir(exist_ok=True, parents=True)\n plt.savefig(fname=plot_path / f\"min_max_temperature dependence in {city}.png\", dpi=150)\n print(f\"File 'Plot of min_max_temperature dependence in {city}.png' was created.\")\n plt.close()",
"title": ""
},
{
"docid": "5e9b0dc55be6b1cfb15c772eeacfc559",
"score": "0.55877715",
"text": "def plotImageWavelength(self, save_fig=False, verbose=True, x=None, y=None, vmin=0, vmax=250, blackout=False):\n\n import matplotlib.pyplot as plt\n from astropy.visualization import astropy_mpl_style\n plt.style.use(astropy_mpl_style)\n from astropy.io import fits\n\n open_image = fits.open(self.path + self.image + '.fits')\n if verbose:\n print(open_image.info())\n image_data = open_image[0].data\n open_image.close()\n\n # determining where end of markings will be\n center_end = 50\n back_end = 40\n\n if x != None:\n center_end = int(x[0] + (x[1] - x[0]) * .1)\n back_end = int(x[0] + (x[1] - x[0]) * .05)\n\n # marking trace_center\n trace = self.trace_center\n image_data[:, int(trace - 1):int(trace + 1)] *= .8\n\n if blackout:\n image_data[:, int(trace - 1):int(trace + 1)] = 0\n# HERE\n # marking image with centers\n for i in range(self.n_apertures):\n cen = self.center[i]\n wid = self.width[i]\n image_data[0:center_end + 15 * (self.n_apertures - i), int(\n cen - old_div(wid, 2)):int(cen + old_div(wid, 2))] *= .7\n\n b1, b2, b3, b4 = [b + trace for b in self.background[i]]\n image_data[0:back_end, int(b1):int(b2)] *= .6\n image_data[0:back_end, int(b3):int(b4)] *= .6\n\n if blackout:\n image_data[0:center_end + 15 * (self.n_apertures - i), int(\n cen - old_div(wid, 2)):int(cen + old_div(wid, 2))] = 50\n image_data[0:back_end, int(b1):int(b2)] = 100\n image_data[0:back_end, int(b3):int(b4)] = 100\n\n fig, ax = plt.subplots(1, 1)\n fig.set_size_inches(10, 10)\n ax.imshow(image_data, cmap='gray', vmin=vmin, vmax=vmax)\n if x != None:\n ax.set_xlim(x[0], x[1])\n if y != None:\n ax.set_ylim(y[0], y[1])\n ax.grid() # takes away grid\n ax.set_title(self.image)\n fig.show()\n\n del plt\n del astropy_mpl_style\n del fits\n\n if save_fig:\n import os\n directory = self.path + '/plots/'\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n filenameend = '.jpg'\n if x != None:\n filenameend = '_x' + str(x[0]) + ':' + str(x[1]) + '.jpg'\n\n fig.savefig(directory + self.image + '_apertureplot' +\n filenameend, bbox_inches='tight')\n del os",
"title": ""
},
{
"docid": "e42dab4d86dc6631b18974195603348e",
"score": "0.55863166",
"text": "def _save_raster(self):\r\n from chaco.api import PlotGraphicsContext\r\n gc = PlotGraphicsContext((int(self.component.outer_width), int(self.component.outer_height)))\r\n self.component.draw(gc, mode=\"normal\")\r\n gc.save(self.filename)\r\n return",
"title": ""
},
{
"docid": "79c4f60dd68ef2a9bdb6627df83cca06",
"score": "0.5585067",
"text": "def saveRadon2DImage(self):\n self.ax1.set_title(\"Radon transform\\n(Sinogram)\")\n self.ax1.set_xlabel(\"Projection angle (deg)\")\n self.ax1.set_ylabel(\"Projection position (pixels)\")\n self.ax1.imshow(self.radonOutput, cmap=plt.cm.Greys_r, extent=(0, 180, 0, self.radonOutput.shape[0]), aspect='auto')\n self.fig.savefig('radon2D_Image.png')\n self.ax1.cla()",
"title": ""
},
{
"docid": "a5ea8fd82b7e1a70a849e5efa0a4cae3",
"score": "0.5559849",
"text": "def figure_exp_6(fig_type = '.png', save_traces = False,show_traces = False):\n temp, save_folder, specific_folder, temp, file_save = get_figure_data_settings(fig_no = 6)\n file2save_npz = 'fig6_' + '.npz'\n file2save = 'part_of_trace' + fig_type\n \n folder_save = save_folder + specific_folder + '_results/'\n #display.plot_data(folder_save, file2save,folder_save,file_save,x_scale = 'sec', \n # electrodes = [0,2,3], y_range=[-1000,1000])\n \n if save_traces:\n #it must be done only if the recording was gap free but \n #it was not saved in the trace format yet\n\n #display.plot_data(folder_save,file2save,folder_save,file_save,x_scale = 'ms')#,time_range=time_range)\n dh.trigger_on_spike(folder_save, file_save, folder_save, file2save_npz, thresh = -500, el=2,\n time_range=[-300,1500], up = False, center_on_peak = False)\n display.plot_data(folder_save,file2save,folder_save,file2save_npz,x_scale = 'ms', \n electrodes = [0,2,3],y_range =[-300, 1500])\n \n \n fig_save = 'large_synch_20' + fig_type\n #time_range = [50,400] # in ms\n data_details = dh.read_npzdata(folder_save, file2save_npz, \"data\", \"scale\", \"fs\")\n display.plot_data_one_mean(data_details, folder_save, fig_save, x_scale = 'ms',\n title = 'large Gabaa synchronised events', sweeps = [0, 1,2,4,5,6],\n y_range_intra = [-10, 20], electrodes = [0,2,3], y_range = [-1000,1000],\n remove_avg = True) \n if show_traces:\n plt.show()",
"title": ""
},
{
"docid": "cc2f3512db936a12ee167c663775a91d",
"score": "0.55588657",
"text": "def outdoor_windspeed_plot(self, save = False):\n global df1\n df1 = pd.read_sql_query(\"SELECT * FROM {}.{} WHERE datetime BETWEEN '{}' AND \\\n '{}'\".format(\"weather\", \"weather_all\", self.t0,\\\n self.tn), con = self.engine)\n df1 = df1.loc[:,['datetime', 'Wind Speed, m/s', 'Gust Speed, m/s', 'Wind Direction']]\n u = df1['Wind Direction'].to_numpy()\n \n U = np.sin(np.radians(u))\n V = np.cos(np.radians(u))\n wdf_plot = df1.set_index(\"datetime\")\n wdf_plot['u'] = U\n wdf_plot['v'] = V\n wdf_plot['y'] = 0\n \n \n converter = mdates.ConciseDateConverter()\n munits.registry[np.datetime64] = converter\n munits.registry[datetime.date] = converter\n munits.registry[datetime.datetime] = converter\n \n fig, ax1 = plt.subplots()\n ax1.plot(wdf_plot['Gust Speed, m/s'],color = 'silver', label = 'Gust Speed', zorder=1)\n \n \n \n ax1.set_ylabel('Gust speed (m/sec)')\n ax1.set_xlabel('Time')\n # ax2 = ax1.twinx()\n ax1.plot(wdf_plot['Wind Speed, m/s'], label = 'Wind Speed', zorder=2)\n ax1.quiver(wdf_plot.index, wdf_plot['Wind Speed, m/s'], U, V , width = 0.001, zorder=3)\n ax1.set_ylabel('wind speed (m/sec) and direction (up is north)')\n \n plt.ylim(bottom=-0.1)\n title = \"Wind and Gust speed during {}\".format(self.experiment)\n\n plt.legend( loc='upper right')\n plt.title(title)\n if save:\n plt.savefig(title + '.png', bbox_inches='tight', dpi=400)\n plt.show() \n \n return fig",
"title": ""
},
{
"docid": "6b4ff6ca95efd2c295eb4e3c5d485a5b",
"score": "0.55561817",
"text": "def main03_gain_scheduling_modified():\n x10, x20 = 0, 0\n eta0 = 0\n xhat10, xhat20 = 0, 0\n x0 = np.array([x10, x20, eta0, xhat10, xhat20])\n r = lambda t : 0.2 + (t>30)*0.2 + (t>60)*0.2 + (t>90)*0.2\n alpha = lambda t : r(t)\n t_start, t_end = 0, 140\n\n t,x1,x2,eta,xhat1,xhat2,r,y = simulate_modified(r, alpha, x0, t_start, t_end)\n\n fig, ax = fc.new_figure()\n ax.plot(t, r, 'r--', label = '$r$')\n ax.plot(t, y, label = '$y$')\n ax.set_xlabel('$t$')\n ax.grid()\n ax.legend()\n\n Path('./figures').mkdir(parents=True, exist_ok=True)\n plt.savefig('figures/gain_scheduling_modified.pdf', pad_inches=0.0)",
"title": ""
},
{
"docid": "4bacc60a5ea1e22df0754b455b00bf74",
"score": "0.5533225",
"text": "def figure_statespace(name, image, ext=ext, N_blur=N_blur, N_noise=N_noise, N_trials=N_trials, D_x=D_x, D_V=D_V, sigma_noise=sigma_noise, range_blur=range_blur, range_noise=range_noise,X_0_statespace=X_0_statespace, Y_0_statespace=Y_0_statespace, V_X_statespace=V_X_statespace, V_Y_statespace=V_Y_statespace, N_step=N_step, N_show=N_show_statespace, N_show_step=N_show_step, width=width, progress=PROGRESS, mode='noise', figures=True):\n\n # HINT : D_V =-1 means we do condensation without prediction\n v_D_V = np.logspace(-range_blur, range_blur, num=N_blur, base=10)*D_V\n v_D_x = np.logspace(-range_blur, range_blur, num=N_blur, base=10)*D_x\n v_noise = np.logspace(-range_noise, range_noise, num=N_noise, base=10)*noise\n# v_D_V[-1] = -1\n\n\n figname_statespace_precision = os.path.join(figpath, name) + '_statespace_precision.pdf'\n figname_statespace_bias = os.path.join(figpath, name) + '_statespace_bias.pdf'\n figname_statespace_precision_init = os.path.join(figpath, name) + '_statespace_precision_init.pdf'\n figname_statespace_bias_init = os.path.join(figpath, name) + '_statespace_bias_init.pdf'\n figname_statespace_phases = os.path.join(figpath, name) + '_statespace_phases.pdf'\n figname_statespace = os.path.join(figpath, name) + '_statespace.pdf'\n# figures = [figname_statespace_precision, figname_statespace_bias, figname_statespace_phases, figname_statespace]\n figures_statespace = 'statespace_figures/'\n mats_statespace = 'statespace_mat/'\n matname_statespace = mats_statespace + name + '_statespace.npy'\n\n def fname(i_blur, i_noise, i_trial):\n for folder in (mats_statespace, figures_statespace):\n if not(os.path.isdir(folder)):os.mkdir(folder)\n if mode=='noise':\n figname = figures_statespace + name + '_D_x-' + str(i_blur) + '_noise-' + str(i_noise)\n else:\n figname = figures_statespace + name + '_D_V-' + str(i_blur) + '_D_x-' + str(i_noise)\n\n if i_trial > 0: figname += '_trial-' + str(i_trial)\n matname = figname.replace(figures_statespace, mats_statespace) + '.npy'\n return matname, figname\n#\n# for i_noise, noise_ in enumerate(v_noise):\n# for i_trial in range(N_trials):\n# for i_blur, D_x_ in enumerate(v_D_x):\n# matname, figname = fname(i_blur, i_noise, i_trial)\n# try:\n# if i_trial > 0: os.rename(figname.replace('D_x', 'D_V'), figname)\n# os.rename(matname.replace('D_x', 'D_V'), matname)\n# except:\n# print matname.replace('D_x', 'D_V')\n figname = name +'-'+ str(parameter)+'='+ str(value).replace('.', '_')\n\n def generate_latex_table(N_blur, N_noise, N_step, name, show='all', empty_line=False):\n fig_width = .7/N_blur\n table = '\\\\begin{tabular}{' + N_blur*'c' + '}%\\n'\n # v_D_V, v_noise = [4, 2, 0], [8, 6, 4, 2, 0] #range(N_blur-1, 0, -3), range(N_blur-1, 0, -2)\n # v_D_V, v_noise = range(N_blur-1, -1, -1), range(N_blur-1, -1, -1)\n v_D_V, v_noise = range(N_blur-1, -1, -N_step), range(0, N_noise, N_step)\n\n for i_blur in v_D_V:\n for i_noise in v_noise:\n if (show == 'col' and i_noise == v_noise[len(v_noise)/2]) or (show == 'row' and i_blur == v_D_V[len(v_D_V)/2]) or (show == 'all'):\n if mode=='noise':\n table += '\\\\includegraphics[width=' + str(fig_width) + '\\\\textheight]{' + name + '_D_x-' + str(i_blur) + '_noise-' + str(i_noise) + '.png}'\n else:\n table += '\\\\includegraphics[width=' + str(fig_width) + '\\\\textheight]{' + name + '_D_V-' + str(i_blur) + '_D_x-' + str(i_noise) + '.png}'\n\n if not(i_noise == v_noise[-1]):\n table += '&%\\n' # to the next cell\n\n if not(i_blur == v_D_V[-1]):\n table += '\\\\\\\\%\\n' # to the next row\n if empty_line: table += (N_blur-1)*'&' + '\\\\\\\\%\\n' # an empty line\n\n table += '\\n\\\\end{tabular}%\\n'\n fic = open(figures_statespace + name + 'table_' + show + '.tex', 'w')\n fic.write(table)\n fic.close()\n\n\n # First, do all individual simulations statespace analysis\n switch_break = False\n if not(os.path.isfile(matname_statespace + LOCK)):# and not(os.path.isfile(matname_statespace)):\n # study over 2-3 orders of magnitude\n # main loop\n N_X, N_Y, N_frame = image.shape\n if progress:\n pbar = pyprind.ProgBar(N_blur*N_noise*N_trials, title=\"State-space\")\n shuffle_D_V = np.random.permutation(np.arange(N_blur))\n shuffle_D_x = np.random.permutation(np.arange(N_blur))\n shuffle_noise = np.random.permutation(np.arange(N_noise))\n if mode=='noise':\n for i_noise in range(N_noise):\n image_ = image.copy()\n image_ += v_noise[shuffle_noise[i_noise]] * np.random.randn(N_X, N_Y, N_frame)\n for i_blur in range(N_blur):#enumerate(v_D_x[shuffle_D_x]):\n for i_trial in range(N_trials):\n# for i_blur, D_V_ in enumerate(v_D_V[shuffle_D_V]):\n matname, figname = fname(shuffle_D_x[i_blur], shuffle_noise[i_noise], i_trial)\n if not(os.path.isfile(matname + LOCK)) and not(os.path.isfile(matname)):\n# mat_condensation(matname, image_, D_V=D_V_, D_x=v_D_x[shuffle_D_V[i_blur]], progress=False)\n mat_condensation(matname, image_, D_x=v_D_x[shuffle_D_x[i_blur]], D_V=v_D_V[shuffle_D_x[i_blur]], progress=False)\n# mat_condensation(matname, image_, D_x=D_x_, progress=False)\n# else:\n# # TODO : no prediction = condensation with D_V=0, D_x = inf, resampling = 0\n# mat_condensation(matname, image_, D_V=0, progress=False)\n # if we perform a novel individual run, we should remoe forward dependencies, that is global evaluation of the tracking\n if os.path.isfile(matname_statespace): os.remove(matname_statespace)\n if i_trial == 0:\n if os.path.isfile(matname) and not(os.path.isfile(figname + '.png')):\n particles = np.load(matname)\n show_particles(particles[:, :, ::N_show_step], image=image_[:, :, 0]+image_[:, :, -1])\n pylab.savefig(figname + '.png')\n pylab.close('all')\n\n if progress: pbar.update(i_noise*N_trials*N_blur+i_blur*N_trials+i_trial)\n else:\n for i_D_x in range(N_blur):\n image_ = image.copy()\n image_ += noise * np.random.randn(N_X, N_Y, N_frame)\n for i_trial in range(N_trials):\n for i_blur in range(N_blur):\n matname, figname = fname(shuffle_D_x[i_D_x], shuffle_D_V[i_blur], i_trial)\n if not(os.path.isfile(matname + LOCK)) and not(os.path.isfile(matname)):\n mat_condensation(matname, image_, D_x=v_D_x[shuffle_D_x[i_blur]], D_V=v_D_V[shuffle_D_V[i_blur]], progress=False)\n if os.path.isfile(matname_statespace): os.remove(matname_statespace)\n if i_trial == 0:\n if os.path.isfile(matname) and not(os.path.isfile(figname + '.png')):\n particles = np.load(matname)\n show_particles(particles[:, :, ::N_show_step], image=image_[:, :, 0]+image_[:, :, -1])\n pylab.savefig(figname + '.png')\n pylab.close('all')\n\n if progress: pbar.update()#i_D_x*N_trials*N_blur+i_trial*N_blur+i_blur)\n\n generate_latex_table(N_blur, N_noise, N_step, name)\n generate_latex_table(N_blur, N_noise, N_step, name, show='row')\n generate_latex_table(N_blur, N_noise, N_step, name, show='col')\n\n\n # routine to evaluate tracking\n def tracker(particles, frame):\n # TODO: computing variability for poisition should use a circular gaussian = von mises\n # TODO: use circular variance of P_i defined over angles \\theta_i = 1 - |R| with R = \\sum_i P_i e^{i 2 \\theta_i} / \\sum_i P_i\n# print particles_.shape, N_frame, frame\n w = particles[4, :, frame]\n w /= w.sum()\n # TODO check the formula for the trajectory... @ frame=-1 we are not yet to the same point as in frame 0!\n particle_phys = [torus((X_0_statespace+frame/float(N_frame)*V_X_statespace*width), width),\n torus((Y_0_statespace+frame/float(N_frame)*V_Y_statespace*width), width),\n V_X_statespace, V_Y_statespace]\n diff = particles_[:4, :, frame]-np.array([particle_phys]).T\n tracking = np.sqrt((diff*w).sum(axis=1)**2) # bias = squared of the mean difference\n sigma = np.sqrt((diff**2*w).sum(axis=1)) # precision = mean squared error\n return np.array([tracking, sigma])\n\n\n # Then, evaluate tracking\n\n if os.path.isfile(matname_statespace):\n tracking = np.load(matname_statespace)\n else:\n if not(os.path.isfile(matname_statespace + LOCK)):\n # check that everything was computed in the first step\n # this is already checked above: we do it again because the individual simulations may have been launched in different runs\n if mode=='noise':\n for i_noise, noise_ in enumerate(v_noise):\n for i_trial in range(N_trials):\n for i_blur, D_x_ in enumerate(v_D_x):\n matname, figname = fname(i_blur, i_noise, i_trial)\n # print matname, os.path.isfile(matname + LOCK), not(os.path.isfile(matname))\n if os.path.isfile(matname + LOCK) or not(os.path.isfile(matname)):\n switch_break = True\n else:\n for i_D_V, D_V_ in enumerate(v_D_V):\n for i_trial in range(N_trials):\n for i_D_x, D_x_ in enumerate(v_D_x):\n matname, figname = fname(i_D_x, i_D_V, i_trial)\n # print matname, os.path.isfile(matname + LOCK), not(os.path.isfile(matname))\n if os.path.isfile(matname + LOCK) or not(os.path.isfile(matname)):\n switch_break = True\n\n # Now, do it\n if not(switch_break) and not(os.path.isfile(matname_statespace + '_lock')):\n # locking\n touch(matname_statespace + LOCK)\n touch(matname_statespace + '_lock')\n\n\n # remove forward dependencies, that is figures showing tracking\n for figname in [figname_statespace_precision, figname_statespace_bias, figname_statespace_phases, figname_statespace,\n figname_statespace_precision_init, figname_statespace_bias_init]:\n if os.path.isfile(figname): os.remove(figname)\n\n # tracking : [i_blur, i_noise, bias - precision, x-y-u-v , init or last]\n tracking = np.zeros((N_blur, N_noise, 2, 4, 2))#\n if progress:\n pbar = pyprind.ProgBar(N_blur*N_noise*N_trials, title=\"State-space\")\n if mode=='noise':\n for i_noise in range(N_noise):\n for i_trial in range(N_trials):\n for i_blur in range(N_blur):\n matname, figname = fname(i_blur, i_noise, i_trial)\n particles_ = np.load(matname)\n # doing everything on the last set of particles = particles_[:, :, -1]\n tracking[i_blur, i_noise, : , :, 0] += tracker(particles_, frame=0)/N_trials\n tracking[i_blur, i_noise, : , :, 1] += tracker(particles_, frame=N_frame-1)/N_trials\n\n if progress: pbar.update()#i_noise*N_trials*N_blur+i_trial*N_blur+i_blur)\n else:\n for i_D_V, D_V_ in enumerate(v_D_V):\n\n for i_trial in range(N_trials):\n for i_blur in range(N_blur):\n matname, figname = fname(i_blur, i_D_V, i_trial)\n particles_ = np.load(matname)\n # doing everything on the last set of particles = particles_[:, :, -1]\n tracking[i_blur, i_D_V, : , :, 0] += tracker(particles_, frame=0)/N_trials\n tracking[i_blur, i_D_V, : , :, 1] += tracker(particles_, frame=N_frame-1)/N_trials\n\n if progress: pbar.update()#i_noise*N_trials*N_blur+i_trial*N_blur+i_blur)\n\n np.save(matname_statespace, tracking)\n os.remove(matname_statespace + LOCK)\n os.remove(matname_statespace + '_lock')\n print('Evaluated tracking')# just did it\n\n else:\n switch_break = True\n print(matname_statespace, ' locked')# not finished in another process\n\n v_D_V_text = ['%.3f' % D_V for D_V in v_D_V]\n v_D_x_text = ['%.3f' % D_x for D_x in v_D_x]\n v_noise_text = ['%0.2f' % noise for noise in v_noise]\n\n def figure_4panels(mat, titles):\n vmax = 0#log10(tracking[: , :, 0, 0].T).max()\n vmin = mat[: , :, 0].min()\n fig = pylab.figure(figsize=(12, 8))\n a1 = fig.add_subplot(221)\n mapable = a1.pcolormesh(mat[: , :, 0], vmin=vmin, vmax=vmax, shading='auto')#, edgecolors='k')\n pylab.axis('tight')\n a2 = fig.add_subplot(222)\n a2.pcolormesh(mat[: , :, 1], vmin=vmin, vmax=vmax, shading='auto')#, edgecolors='k')\n pylab.axis('tight')\n a3 = fig.add_subplot(223)\n a3.pcolormesh(mat[: , :, 2], vmin=vmin, vmax=vmax, shading='auto')#, edgecolors='k')\n pylab.axis('tight')\n a4 = fig.add_subplot(224)\n a4.pcolormesh(mat[: , :, 3], vmin=vmin, vmax=vmax, shading='auto')#, edgecolors='k')\n pylab.axis('tight')\n\n for i_a, ax in enumerate([a1, a2, a3, a4]):\n ax.set_yticks(np.arange(0, N_noise, 2))\n ax.set_xticks(np.arange(0, N_blur, 2))\n ax.set_title(titles[i_a])\n ax.set_xticklabels([])\n ax.set_yticklabels([])\n if ax in [a3, a4]:\n ax.set_xticklabels(v_noise_text[::2])\n ax.set_xlabel('external noise')\n if ax in [a1, a3]:\n ax.set_ylabel('internal noise')\n ax.set_yticklabels(v_D_V_text[::2])\n ax.axis('tight')\n\n# for ax in [a1, a2]:\n# ax.set_xticklabels([])\n# ax.set_xlabel('')\n# for ax in [a2, a4]:\n# ax.set_yticklabels([])\n# ax.set_ylabel('')\n\n height = pylab.rcParams['figure.subplot.top']-pylab.rcParams['figure.subplot.bottom']\n# print [0.91, pylab.rcParams['figure.subplot.bottom'] + height/4., .025, height/2. ]\n a5 = pylab.axes([0.91, pylab.rcParams['figure.subplot.bottom'] + height/4., .025, height/2. ], facecolor='w') # [l, b, w, h]\n pylab.colorbar(mapable, cax=a5, ax=a1, format='%.2f')\n return fig\n# print tracking.min(), tracking.max(), tracking.mean()\n# print tracking[: , :, 0, :, 0].min(), tracking[: , :, 0, :, 0].max(), tracking[: , :, 0, :, 0].mean()\n# print tracking[: , :, 0, :, 1].min(), tracking[: , :, 0, :, 1].max(), tracking[: , :, 0, :, 1].mean()\n#\n # Finally, make figures\n if not(switch_break) and figures:\n # tracking : [i_blur, i_noise, bias - precision, x-y-u-v , init or last]\n if not(os.path.isfile(figname_statespace_bias_init)):\n fig = figure_4panels(np.log10(tracking[: , :, 0, :, 0]), ['tracking_X', 'tracking_Y', 'tracking_U', 'tracking_V'])\n fig.savefig(figname_statespace_bias_init)\n\n if not(os.path.isfile(figname_statespace_precision_init)):\n fig = figure_4panels(np.log10(tracking[: , :, 1, :, 0]), ['sigma_X', 'sigma_Y', 'sigma_U', 'sigma_V'])\n pylab.savefig(figname_statespace_precision_init)\n\n if not(os.path.isfile(figname_statespace_bias)):\n fig = figure_4panels(np.log10(tracking[: , :, 0, :, 1]), ['tracking_X', 'tracking_Y', 'tracking_U', 'tracking_V'])\n fig.savefig(figname_statespace_bias)\n\n if not(os.path.isfile(figname_statespace_precision)):\n fig = figure_4panels(np.log10(tracking[: , :, 1, :, 1]), ['sigma_X', 'sigma_Y', 'sigma_U', 'sigma_V'])\n pylab.savefig(figname_statespace_precision)\n\n#\n# if not(os.path.isfile(figname_statespace_phases)):\n# fig = figure_4panels(np.log10(tracking_pos_vel), ['tracking_pos', 'tracking_vel', 'sigma_pos','sigma_vel'])\n# pylab.savefig(figname_statespace_phases)\n#\n if not(os.path.isfile(figname_statespace_phases)):\n # tracking : [i_blur, i_noise, bias - precision, x-y-u-v , init or last]\n# tracking_pos_vel = np.zeros(tracking[: , :, 0, :, 1].shape) # precision at end\n# tracking_pos_vel[: , :, 0:2] = np.sqrt(.5*(tracking[: , :, :, 0, 1]**2+tracking[: , :, :, 1, 1]**2))\n# tracking_pos_vel[: , :, 2:4] = np.sqrt(.5*(tracking[: , :, :, 2, 1]**2+tracking[: , :, :, 3, 1]**2))\n# # TODO this is a HACK just showing precision\n# mat = np.log10(np.sqrt(tracking[: , :, 1, 0, 1]**2+tracking[: , :, 1, 1, 1]**2))\n# # U,V mat = np.log10(np.sqrt(tracking[: , :, 1, 2, 1]**2+tracking[: , :, 1, 3, 1]**2))\n# vmax = 0#log10(tracking[: , :, 0, 0].T).max()\n# vmin = mat.min()\n# fig = pylab.figure(figsize=(12, 8))\n# ax = fig.add_subplot(111)\n# mapable = ax.pcolormesh(mat, vmin=vmin, vmax=vmax, edgecolors='k')#\n## pylab.contour(v_D_V, v_noise, tracking_pos_vel[: , :, 0].T**2/2.+tracking_pos_vel[: , :, 1].T**2, N=1)\n## pylab.contourf(v_D_V, v_noise, tracking_pos_vel[: , :, 2].T**2/2.+tracking_pos_vel[: , :, 3].T**2, N=1)\n## # pylab.contour(v_D_V, v_noise, tracking_X[:-1, 1:].T**2/2.+tracking_V[:-1, 1:].T**2, N=1)\n## # pylab.contourf(v_D_V, v_noise, np.sqrt(sigma_X[:-1, 1:].T**2/4.+sigma_V[:-1, 1:].T**2), N=1)\n## pylab.axis('tight')\n## pylab.text(0.01, 0.012, 'Tracking', fontsize=12, weight='bold')\n## pylab.text(0.01, 0.027, 'No tracking', fontsize=12, weight='bold')\n## pylab.text(0.05, 0.012, 'False\\n tracking', fontsize=12, weight='bold')\n## ax.set_xticklabels([])\n## ax.set_yticklabels([])\n# ax.axis('tight')\n## pylab.colorbar(mapable, ax=ax, format='%.2f')\n\n tracking_vel = np.zeros(tracking[: , :, 0, 0, 1].shape) # precision at end\n tracking_vel = np.sqrt(.5*(tracking[: , :, 0, 2, 1]**2+tracking[: , :, 0, 3, 1]**2))\n bias_vel = np.zeros(tracking[: , :, 0, 0, 1].shape) # precision at end\n bias_vel = np.sqrt(.5*(tracking[: , :, 1, 2, 1]**2+tracking[: , :, 1, 3, 1]**2))\n\n fig = pylab.figure(figsize=(5, 10))\n a1 = fig.add_subplot(211)\n mapable = a1.pcolormesh(tracking_vel, shading='auto')#, edgecolors='k')\n pylab.axis('tight')\n a2 = fig.add_subplot(212)\n a2.pcolormesh(bias_vel, shading='auto')#, edgecolors='k')\n pylab.axis('tight')\n\n for i_a, ax in enumerate([a1, a2]):\n# ax.set_yticks(.5 + np.arange(0, N_noise, 2))\n# ax.set_xticks(.5 + np.arange(0, N_blur, 2))\n# ax.set_title(titles[i_a])\n# ax.set_xticklabels([])\n# ax.set_yticklabels([])\n if ax in [a1]:\n ax.set_xticklabels(v_noise_text[::2])\n ax.set_xlabel('external noise')\n if ax in [a1, a2]:\n ax.set_ylabel('internal noise')\n ax.set_yticklabels(v_D_V_text[::2])\n ax.axis('tight')\n\n # for ax in [a1, a2]:\n # ax.set_xticklabels([])\n # ax.set_xlabel('')\n # for ax in [a2, a4]:\n # ax.set_yticklabels([])\n # ax.set_ylabel('')\n\n height = pylab.rcParams['figure.subplot.top']-pylab.rcParams['figure.subplot.bottom']\n # print [0.91, pylab.rcParams['figure.subplot.bottom'] + height/4., .025, height/2. ]\n a5 = pylab.axes([0.91, pylab.rcParams['figure.subplot.bottom'] + height/4., .025, height/2. ], facecolor='w') # [l, b, w, h]\n pylab.colorbar(mapable, cax=a5, ax=a1, format='%.2f')\n pylab.savefig(figname_statespace_phases)",
"title": ""
},
{
"docid": "899d52177b06c2e9c67d3df7b79b44b8",
"score": "0.55327755",
"text": "def plot_spectra(wl, spec, colspec=\"k.-\", label=None, title=\"Spectrum\"):\n plt.plot(wl, spec, colspec, label=label)\n plt.title(title)\n plt.legend()\n plt.show(block=False)\n return None",
"title": ""
},
{
"docid": "bff59197a24d1ae4be79c179a33abdfc",
"score": "0.55288994",
"text": "def save_map_fig(map_trg,title,output_path,radius_cut=850,calib_type=\"point\"):\n xx = np.linspace(0,radius_cut,50)\n yy = np.linspace(0,180,50)\n xx,yy = np.meshgrid(xx,yy)\n zz = map_trg.func(xx,yy)\n fig = plt.figure(figsize=(10,6))\n plt.pcolormesh(xx,yy,zz)\n plt.colorbar()\n if calib_type == \"point\":\n plt.scatter(np.array(map_trg.radius),np.array(map_trg.theta),color=\"red\")\n else:\n plt.plot(np.array(map_trg.radius),np.array(map_trg.theta),color=\"red\")\n plt.xlabel(\"$R [mm]$\",fontsize=14)\n plt.ylabel(\"$\\\\theta [\\degree]$\",fontsize=14)\n plt.title(title,fontsize=16)\n plt.savefig(output_path)",
"title": ""
},
{
"docid": "e2ebf7236061d1e44d7028e1cf16aa24",
"score": "0.5527501",
"text": "def PlotSpectrum():\n\tplt.title(r\"Solar NA I D lines \")\n\tplt.plot(wave_vac,S_spec, color = \"royalblue\")\n\tplt.grid(linestyle = \"--\")\n\tplt.xlabel(r\"Wavelength $\\lambda$ [$\\mathrm{\\AA}$]\")\n\t# plt.subplots_adjust(bottom = 0.12, left = 0.15)\n\t# plt.savefig(savepath + \"solarspectran.pdf\")\n\tplt.show()",
"title": ""
},
{
"docid": "bd9667a048ecff2cfff49a7c2b3f8676",
"score": "0.5508835",
"text": "def plotOutput(X,Y,spectrumLabel,ydata,PBool=True,ydataBool=False,residuals=False,path=False):\n import matplotlib\n if PBool == False:\n matplotlib.use('Agg') #non-interactive backend\n import pylab as P\n P.ioff() #Ensure interactivity mode is off so that graph does not dissapear immediately\n fig = P.figure()\n maxYval = amax(Y)\n minYval = amin(Y)\n DynamicRange = maxYval - minYval\n if not ydataBool:\n P.plot(X,Y,'g', linewidth = 2.0)\n P.xlabel(r'Detuning (GHz)')\n P.ylabel(spectrumLabel)\n P.xlim(X[0],X[-1])\n P.ylim(minYval-0.02*DynamicRange,maxYval+0.02*DynamicRange)\n else:\n ax1 = fig.add_axes([0.15,0.30,0.75,0.65])\n ax1.plot(X,ydata,'k')\n ax1.plot(X,Y,'r--', linewidth=1.8)\n ax1.set_xlim(X[0],X[-1])\n ax1.set_ylim(minYval-0.02*DynamicRange,maxYval+0.02*DynamicRange)\n ax1.set_xticklabels([])\n P.ylabel(spectrumLabel)\n ax2 = fig.add_axes([0.15,0.10,0.75,0.15])\n ax2.plot(X,residuals*100.0,'k')\n ax2.set_xlim(X[0],X[-1])\n ax2.axhline(color='r', linestyle = '--', linewidth=1.8)\n P.xlabel(r'Detuning (GHz)')\n P.ylabel(r'Residuals $(\\times 100)$')\n if path:\n P.savefig(path)\n if PBool:\n P.show()",
"title": ""
},
{
"docid": "59de18a6d4347fb40cba96f7adcf158b",
"score": "0.549675",
"text": "def plotter(dataframe,filename):\n #Create a folder to save the files separetely\n folder_name = filename + \"_Plots\"\n if not os.path.exists(folder_name):\n os.makedirs(folder_name)\n \n print \"\\nSmoothing and Plotting\"\n for i in range(10):\n print \"\\n Channel \"+ str(i+1)\n df_temp = dataframe[[i]].dropna()\n #Exponential Moving Average\n df_temp['EMV'] = df_temp.ewm(span=100,min_periods=0,adjust=True).mean()\n #print \"Smoothened\"\n \n #Figures!\n #Fig 1 - initialization\n #plot = plt.figure()\n #plt.plot()\n plot = df_temp.plot(figsize=(25,10))\n fig = plot.get_figure()\n #print \"Plotted\"\n \n #Saving the plots in a subfolder\n \n fig.savefig(folder_name +'/'+ filename +\"_Channel_\"+str(i)+\".tif\",orientation='portrait',papertype='letter')\n print\n print \"Plotting Successfull!\"\n print \n print \"==================================================================================\"\n return df_temp",
"title": ""
},
{
"docid": "41e7b817c61b6cc3fd0451617e37fd79",
"score": "0.54948515",
"text": "def save_spectrogram(spec, hop_length, output_path, x_axis='time', y_axis='log', figsize=(10, 4), title=None):\n\n fig = plt.figure()\n plt.style.use('dark_background')\n plt.suptitle(title)\n librosa.display.specshow(spec, hop_length=hop_length, x_axis=x_axis, y_axis=y_axis)\n plt.gca().set_axis_off()\n plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n fig.savefig(output_path)\n fig.clear()\n plt.close('all')",
"title": ""
},
{
"docid": "79455ca82d0a2871f6a9250b4a366589",
"score": "0.5493766",
"text": "def plot_spectrum(wavelength, flux, error, rootname):\n flux = convolve(flux,Box1DKernel(5))\n plt.figure(rootname)\n plt.plot(wavelength, flux, '0.5', label='spectrum')\n plt.plot(wavelength, error, label='error') \n plt.legend()\n plt.show()\n plt.close()",
"title": ""
},
{
"docid": "29bdc2581b9628094cdaa541f20dd911",
"score": "0.54908895",
"text": "def generate_stopping_power_and_particle_energy_curve(file, save_figure=False, energy_unit='keV'):\n f = open(file, \"r\")\n lines = f.readlines()\n\n ## Delete header lines\n del lines[:4]\n\n list_eletronic_stopping_power = []\n list_nuclear_stopping_power = []\n list_particle_energy = []\n\n for line in lines:\n ## Split energy columns\n energy = re.split(r'\\s', line)\n\n particle_energy = replace_scientific_notation(energy[0])\n if energy_unit.lower() =='kev':\n particle_energy = particle_energy * 10 ** -3\n eletronic_stopping_power = replace_scientific_notation(energy[3])\n nuclear_stopping_power = replace_scientific_notation(energy[5])\n\n list_eletronic_stopping_power.append(eletronic_stopping_power)\n list_nuclear_stopping_power.append(nuclear_stopping_power)\n list_particle_energy.append(particle_energy)\n\n x = np.array(list_particle_energy)\n\n y1 = np.array(list_eletronic_stopping_power)\n y2 = np.array(list_nuclear_stopping_power)\n\n legend = re.search(r'\\.\\/SRIM_files\\/(.*)', file)\n if legend:\n legend = f'Stopping power of {legend.group(1)}'\n plt.plot(x, y2, color='r', label='eletronic')\n #plt.xscale('log')\n plt.yscale('log')\n # plt.plot(x, y2, color='g', label='nuclear')\n\n plt.xlabel(\"Particle energy [MeV]\")\n plt.ylabel(\"Stopping Power [MeV/mm]\")\n plt.title(f\"{legend}\")\n\n plt.yscale('log')\n plt.tight_layout(pad=2)\n\n if save_figure:\n plt.savefig(f'./images/{legend.replace(\" \", \"_\")}.png')\n\n else:\n plt.show()",
"title": ""
},
{
"docid": "94f64bec45cac96b2cac3c11b86f1180",
"score": "0.54878795",
"text": "def get_power_spectrum(p, f1, f2, jk_region=None, save_windows=True):\n try:\n fname = p.get_fname_cls(f1.name, f2.name, jk_region=jk_region)\n Cls = Spectrum.from_file(fname, f1.name, f2.name)\n except FileNotFoundError:\n bpw = p.get_bandpowers()\n wsp = get_mcm(p, f1, f2, jk_region=jk_region)\n Cls = Spectrum.from_fields(f1, f2, bpw, wsp, save_windows=save_windows)\n Cls.to_file(fname)\n return Cls",
"title": ""
},
{
"docid": "cb040ef454519939885f659e63524920",
"score": "0.5481449",
"text": "def visualize_spectrum(y):\n global _prev_spectrum, colorThisTime, count0\n #y = np.copy(interpolate(y, local_N_PIXELS))\n _prev_spectrum = np.copy(y)\n y = y*noHighStuff\n # Color channel mappings\n count0+=1\n keyObj.update(y)\n chordObj.update(y, keyObj.getKeyNum())\n beatObj.update(y)\n temp1 = rawFilt.update(y)\n temp2 = ledFilt.update(y)\n countEff = count0%nFramesCycle\n if count0%10==0:\n chordObj.printChord()\n\n if 0.0*nFramesCycle <= countEff <= 0.1*nFramesCycle:\n a1 = [1, 0, 0]\n a2 = [0, 0, 1]\n elif 0.1*nFramesCycle < countEff <= 0.4*nFramesCycle:\n temp = (1/0.3)*(countEff-0.1*nFramesCycle)/nFramesCycle\n a1 = [1-temp, temp, 0] \n a2 = [temp, 0, 1-temp]\n elif 0.4*nFramesCycle < countEff <= 0.5*nFramesCycle:\n a1 = [0, 1, 0]\n a2 = [1, 0, 0]\n elif 0.5*nFramesCycle < countEff <= 0.7*nFramesCycle:\n temp = (1/0.2)*(countEff-0.5*nFramesCycle)/nFramesCycle\n a1 = [0, 1-temp, temp] \n a2 = [1-temp, temp, 0]\n elif 0.7*nFramesCycle < countEff <= 0.8*nFramesCycle:\n a1 = [0, 0, 1]\n a2 = [0, 1, 0]\n elif 0.8*nFramesCycle < countEff <= 1.0*nFramesCycle:\n temp = (1/0.2)*(countEff-0.8*nFramesCycle)/nFramesCycle\n a1 = [temp, 0, 1-temp] \n a2 = [0, 1-temp, temp]\n \n #r = temp2 * 0.0\n #g = temp2 * 0.0\n #b = temp2 * 1.0\n \n r=a1[0]*temp2\n g=a1[1]*temp2;\n b=a1[2]*temp2;\n \n '''\n # tonic is blue\n if chordObj.getChordNum()==0:\n r = temp2 * 0.0\n g = temp2 * 0.0\n b = temp2 * 1.0\n # ii is yellow\n elif chordObj.getChordNum()==1:\n r = temp2 * 0.5\n g = temp2 * 0.5\n b = temp2 * 0.0\n # iii is orange\n elif chordObj.getChordNum()==2:\n r = temp2 * 0.66\n g = temp2 * 0.33\n b = temp2 * 0.0\n # IV is green\n elif chordObj.getChordNum()==3:\n r = temp2 * 0.0\n g = temp2 * 1.0\n b = temp2 * 0.0\n # V is red\n elif chordObj.getChordNum()==4:\n r = temp2 * 1.0\n g = temp2 * 0.0\n b = temp2 * 0.0\n # vi is purple\n elif chordObj.getChordNum()==5:\n r = temp2 * 0.5\n g = temp2 * 0.0\n b = temp2 * 0.5\n elif chordObj.getChordNum()==6:\n r = temp2 * 0.5\n g = temp2 * 0.5\n b = temp2 * 0.5\n '''\n \n \n '''\n if beatObj.beatRightNow():\n colorThisTime = (colorThisTime + 1)%3\n print(\"BEAT!!!!\")\n if colorThisTime == 0:\n r = temp2 * 1.0\n g = temp2 * 0.0\n b = temp2 * 0.0\n elif colorThisTime == 1:\n r = temp2 * 0.0\n g = temp2 * 1.0\n b = temp2 * 0.0\n if colorThisTime == 2:\n r = temp2 * 0.0\n g = temp2 * 0.0\n b = temp2 * 1.0\n '''\n output = np.array([r,g,b]) * 255\n #output = np.array([np.ones(local_N_PIXELS), np.ones(local_N_PIXELS), np.ones(local_N_PIXELS)]) * 255\n output2 = np.zeros([3, 2*local_N_PIXELS])\n output2[..., 0:local_N_PIXELS] = output[...,::-1]\n output2[..., local_N_PIXELS:2*local_N_PIXELS] = output\n return output2",
"title": ""
},
{
"docid": "292b6fc354491cf2c43a7dfe74c825b7",
"score": "0.54811364",
"text": "def imwrite_wrapper(show_func):\n def imwrite_func(model, dpath=None, dpi=180, asdiagnostic=True,\n ascheckpoint=None, verbose=1, **kwargs):\n import plottool as pt\n # Resolve path to save the image\n if dpath is None:\n if ascheckpoint is True:\n history_hashid = model.get_model_history_hashid()\n dpath = model._get_model_dpath(checkpoint_tag=history_hashid)\n elif asdiagnostic is True:\n dpath = model.get_epoch_diagnostic_dpath()\n else:\n dpath = model.training_dpath\n # Make the image\n fig = show_func(model, **kwargs)\n # Save the image\n output_fpath = pt.save_figure(fig=fig, dpath=dpath, dpi=dpi,\n verbose=verbose)\n return output_fpath\n return imwrite_func",
"title": ""
},
{
"docid": "ffe6ae48625f697a6cb0280d2cfc0800",
"score": "0.5479929",
"text": "def plot_spectra(eigval_col, savename=\"spectrum_onetrial.jpg\", figdir=savedir, fig=None, label=\"BP\"):\n eigmean = eigval_col.mean(axis=0)\n eiglim = np.percentile(eigval_col, [5, 95], axis=0)\n sortidx = np.argsort(-np.abs(eigmean))\n eigmean = np.abs(eigmean[sortidx])\n eiglim = eiglim[:, sortidx]\n eigN = len(eigmean)\n if fig is None:\n fig, axs = plt.subplots(1, 2, figsize=[10, 5])\n else:\n # plt.figure(fig.number)\n plt.figure(num=fig.number)\n axs = fig.axes\n plt.sca(axs[0])\n plt.plot(range(eigN), eigmean, alpha=0.6)\n plt.fill_between(range(eigN), eiglim[0, :], eiglim[1, :], alpha=0.3, label=label)\n plt.ylabel(\"eigenvalue\")\n plt.xlabel(\"eig id\")\n plt.legend()\n plt.sca(axs[1])\n plt.plot(range(eigN), np.log10(eigmean), alpha=0.6)\n plt.fill_between(range(eigN), np.log10(eiglim[0, :]), np.log10(eiglim[1, :]), alpha=0.3, label=label)\n plt.ylabel(\"eigenvalue(log)\")\n plt.xlabel(\"eig id\")\n plt.legend()\n st = plt.suptitle(\"Hessian Spectrum of StyleGAN\\n (error bar for [5,95] percentile among all samples)\")\n plt.savefig(join(figdir, savename), bbox_extra_artists=[st]) # this is working.\n # fig.show()\n return fig",
"title": ""
},
{
"docid": "1363eea180fea57c84355f68d832001b",
"score": "0.54797584",
"text": "def makeAveragePSFigure(averagePS, figureFileName): \r\n pylab.imshow(np.log(averagePS),cmap = \"gray\")\r\n pylab.contour(np.log(averagePS))\r\n pylab.axis(\"off\")\r\n pylab.savefig(figureFileName)",
"title": ""
},
{
"docid": "2b4ee1d0978acda14ddf77c00949f73e",
"score": "0.5479389",
"text": "def save_plot(base_path, rel_path, filename):\n dir_path = os.path.join(base_path, rel_path)\n os.makedirs(dir_path, exist_ok=True)\n plot_path = os.path.join(dir_path, filename)\n pl.savefig(plot_path)\n pl.close()\n\n return os.path.join(rel_path, filename)",
"title": ""
},
{
"docid": "a07e0976a889e28e70d21ca074bcb955",
"score": "0.54790896",
"text": "def save(self, filename):\n\n self.update_title()\n self.update_legend()\n self.figure.savefig(filename + '.' + self.outformat)",
"title": ""
},
{
"docid": "063098ef98257050ca5cb45636dfb358",
"score": "0.5477221",
"text": "def save_structure(mo, title: str = None, output: str = \"\", azi=None, ele=None):\n matplotlib.use('agg')\n\n fig, ax = generate_figure(mo)\n\n ax.view_init(elev=ele, azim=azi)\n\n directory = f\"{make_output_folder(output)}/{title if title else mo.name}.png\"\n\n plt.title(title if title else mo.name, fontsize=5)\n plt.savefig(directory)\n plt.close('all')",
"title": ""
},
{
"docid": "8dae707ae94cc3369b5c43340d66d534",
"score": "0.54718155",
"text": "def splat_assign_spectrum(self, auto=False):\n self.process_splatalogue(auto=auto)",
"title": ""
},
{
"docid": "0be51b10a2ca5bc70d97b212de39e2df",
"score": "0.54596823",
"text": "def save_champs_res(dom, x, y, champs, time, cmax, cmin, j):\n plt.figure()\n # define grid.\n xi = np.linspace(-5.1, 5.1, 800)\n yi = np.linspace(-.1, 10.1, 10000)\n norm = mplc.Normalize(cmin, cmax)\n #~ v = np.linspace(cmin, cmax, 100, endpoint=True)\n bounds=np.linspace(cmin,cmax,100)\n # grid the data.\n zi = griddata(x,y, champs, xi,yi, interp='linear')\n # contour the gridded data, plotting dots at the nonuniform data points.\n CS = plt.contourf(xi, yi, zi,100, vmax=cmax, vmin=cmin, norm=norm, levels=bounds, extend='both')\n #~ plt.autumn()\n cbar = plt.colorbar(CS)\n plt.clim(vmin=cmin, vmax=cmax)\n cbar.ax.set_ylabel('Temperature')\n #~ plt.plot(xi,dom.height*np.ones(len(xi)), '-k')\n # ~ plt.plot(xi,dom.height*np.ones(len(xi)) + dom.dz, '-g')\n # ~ plt.plot(xi,dom.height*np.ones(len(xi)) - dom.dz, '-g')\n\t\n # plot data points.\n #~ plt.scatter(x, y, marker='o', s=5, zorder=10)\n #~ plt.xlim(-0.1, 0.6)\n #~ plt.ylim(-0.1, 1.1)\n\n plt.xlabel('x')\n plt.ylabel('y')\n plt.title('Temperature au bout de %(g)s secondes'%{'g' : time})\n plt.savefig('res_%(g)s.png'%{'g' : '%06d' % j})\n plt.close()",
"title": ""
},
{
"docid": "d6e752852aefae96a28f9a30a2f7a75a",
"score": "0.5449565",
"text": "def save_spectrum(velocity, opacity, flux, em_mean, em_std, filename, \n sigma_tau, smoothed_od, sigma_smoothed_od, metadata=None):\n table = Table(meta={'name': filename, 'id': 'optical_depth'})\n table.add_column(Column(name='velocity', data=velocity, unit='m/s', description='LSRK velocity'))\n table.add_column(Column(name='optical_depth', data=opacity, description='Absorption as exp(-tau)'))\n table.add_column(Column(name='flux', data=flux, unit='Jy', description='Flux per beam'))\n table.add_column(Column(name='sigma_od', data=sigma_tau, description='Noise in the absorption profile, relative to exp(-tau)'))\n table.add_column(Column(name='em_mean', data=em_mean, unit='K', description='Mean brightness temperature around the source'))\n table.add_column(Column(name='em_std', data=em_std, unit='K', description='Noise level in the brightness temperature'))\n table.add_column(Column(name='smoothed_od', data=smoothed_od, description='Absorption as exp(-tau) smoothed using a 5 point Hanning window'))\n table.add_column(Column(name='sigma_smoothed_od', data=sigma_smoothed_od, description='1-sigma noise level in the absorption profile, relative to exp(-tau)'))\n\n votable = from_table(table)\n if metadata is not None:\n for key in metadata:\n votable.infos.append(Info(name=key, value=metadata[key]))\n writeto(votable, filename)",
"title": ""
},
{
"docid": "0542ce4df52cffe1dd24dab23599ea87",
"score": "0.54450804",
"text": "def figure_exp_24(fig_type = '.png'):\n temp, save_folder, specific_folder, temp, file_save = get_figure_data_settings(fig_no = 24)\n file2save_npz = 'fig24_' + '.npz'\n \n file2save = 'NBQX_block' + fig_type\n folder_save = save_folder + specific_folder + '_results/'\n y_range = [-30,30]\n display.plot_data(folder_save, file2save,folder_save,file_save,x_scale = 'sec', \n y_range=y_range, time_range = [11000,19000], electrodes=[1,2,3],\n y_range_intra = y_range)",
"title": ""
},
{
"docid": "cea6488197bf5ef2f51449faf3a016cc",
"score": "0.54408354",
"text": "def powerspectrum(self, max_cycles=50, sampling=5, vmax=100, iterate=False):\n\n # import SFT\n\n def tvcircle(radius=1, xcen=0, ycen=0, center=None, **kwargs):\n \"\"\"\n draw a circle on an image.\n\n radius\n xcen\n ycen\n center= tuple in (Y,X) order.\n \"\"\"\n if center is not None:\n xcen = center[1]\n ycen = center[0]\n t = np.arange(0, np.pi * 2.0, 0.01)\n t = t.reshape((len(t), 1))\n x = radius * np.cos(t) + xcen\n y = radius * np.sin(t) + ycen\n plt.plot(x, y, **kwargs)\n\n cmap = copy.copy(matplotlib.cm.get_cmap(poppy.conf.cmap_diverging))\n cmap.set_bad('0.3')\n\n plt.clf()\n plt.subplot(231)\n self.display(title='full wavefront', clear=False, colorbar=False, vmax=vmax)\n\n ps_pixel_size = 1. / sampling # how many cycles per pixel\n trans = SFT.SFT3(self.data, max_cycles * 2, max_cycles * 2 * sampling)\n\n abstrans = np.abs(trans)\n\n extent = [-max_cycles, max_cycles, -max_cycles, max_cycles]\n\n plt.subplot(233)\n plt.imshow(abstrans, extent=extent)\n plt.title(\"Power Spectrum of the phase\")\n plt.ylabel(\"cycles/aperture\")\n tvcircle(radius=5, color='k', linewidth=1) # , ls='--')\n tvcircle(radius=30, color='k', linewidth=1) # 2, ls='--')\n plt.gca().set_xbound(-max_cycles, max_cycles)\n plt.gca().set_ybound(-max_cycles, max_cycles)\n\n y, x = np.indices(abstrans.shape)\n y -= abstrans.shape[0] / 2.\n x -= abstrans.shape[1] / 2.\n r = np.sqrt(x ** 2 + y ** 2) * ps_pixel_size\n\n mask = np.ones_like(self.data)\n mask[np.where(self.amplitude == 0)] = np.nan\n wgood = np.where(self.amplitude != 0)\n\n components = []\n for i, label in enumerate(['low', 'mid', 'high']):\n plt.subplot(2, 3, i + 4)\n if label == 'low':\n condition = r <= 5\n elif label == 'mid':\n condition = (r > 5) & (r <= 30)\n else:\n condition = (r > 30)\n filtered = trans * condition\n\n inverse = SFT.SFT3(filtered, max_cycles * 2, self.opd.shape[0], inverse=True)\n inverse = inverse[::-1, ::-1] # I thought SFT did this but apparently this is necessary to get the high freqs right...\n\n plt.imshow(inverse.real * mask, vmin=(-vmax) / 1000., vmax=vmax / 1000,\n cmap=cmap) # vmax is in nm, but WFE is in microns, so convert\n plt.title(label + \" spatial frequencies\")\n rms = (np.sqrt((inverse.real[wgood] ** 2).mean()) * 1000)\n\n components.append(rms)\n plt.xlabel(\"%.3f nm RMS WFE\" % rms)\n\n return np.asarray(components)",
"title": ""
},
{
"docid": "aa031d6616902bbb71cafffe271e0834",
"score": "0.5439782",
"text": "def saveAllPosPlots():\n plt.clf(); makeRadPlot(); plt.savefig('plots/posDepRad.pdf');\n plt.clf(); makeRadPlotZoom(); plt.savefig('plots/posDepRadZoom.pdf');\n plt.clf(); makePolPlot(); plt.savefig('plots/posDepPol.pdf');\n plt.clf(); makePolPlotZoom(); plt.savefig('plots/posDepPolZoom.pdf');",
"title": ""
},
{
"docid": "60a6165722227a8d22b10b8be82d98bd",
"score": "0.5436176",
"text": "def save_waveform(self,filename):\n self.scope.save_waveform(self.n,filename)",
"title": ""
},
{
"docid": "f46c5bee83c8328198cb553ab111a1d3",
"score": "0.5429792",
"text": "def plot(self,output_folder= os.getcwd):\n fig1 = plt.figure()\n plt.plot(self.x,self.y,'bo')\n plt.plot(self.x , lorentzian(self.x,self.popt[0],self.popt[1],self.popt[2],self.popt[3]))\n plt.xlabel('Wave number $cm^{-1}$')\n plt.ylabel('Counts per second')\n plt.show()",
"title": ""
},
{
"docid": "a69d83f40a878a4471fd824561fb7d24",
"score": "0.5416961",
"text": "def oned_spectrum(self, verbose=True):\n\n #### Paremters from the full model\n self.grism_sci = np.cast[np.float64](self.im['SCI'].data)\n self.full_model = np.cast[np.float64](self.im['CONTAM'].data + self.im['MODEL'].data)\n self.the_object = np.cast[np.float64](self.im['MODEL'].data)\n self.grism_wht = np.cast[np.float64](self.im['WHT'].data)\n self.wave = np.cast[np.float64](self.im['WAVE'].data)\n self.ytrace = np.cast[np.float64](self.im['YTRACE'].data)\n self.sens = np.cast[np.float64](self.im['SENS'].data)\n self.root = self.file.split('_%05d' %(self.id))[0]\n \n t0 = time.time()\n \n obj_cleaned = self.grism_sci - (self.full_model - self.the_object)\n contam_cleaned = self.grism_sci - self.the_object\n variance = self.grism_wht**2\n \n #### Get extraction window where profile is > 10% of maximum \n osh = self.the_object.shape\n xarr = np.arange(osh[1])\n # xint14 = int(np.interp(1.3e4, self.wave, xarr))\n # if self.grism_element == 'G102':\n # xint14 = int(np.interp(0.98e4, self.wave, xarr))\n # #\n # if self.grism_element == 'G800L':\n # xint14 = int(np.interp(0.75e4, self.wave, xarr))\n #wlim = grism_wlimit[self.grism_element]\n wlim = self.wlim\n xint14 = int(np.interp(wlim[3], self.wave, xarr))\n \n yprof = np.arange(osh[0])\n profile = np.sum(self.the_object[:,xint14-10:xint14+10], axis=1)\n profile = profile/profile.sum()\n \n #profile2D = np.dot(profile.reshape((-1,1)), np.ones((1,osh[1])))\n window = profile >= 0.1*profile.max()\n \n #### Use object as the profile since it contains the y shift along the trace\n prof_x = np.sum(self.the_object, axis=0)\n profile2D = self.the_object/np.dot(np.ones((osh[0],1)), prof_x.reshape((1,-1)))\n \n if (1 == 0):\n plt.plot(yprof, profile, color='blue')\n plt.plot(yprof[window], profile[window], color='red', linewidth=3)\n print np.trapz(profile[window], xprof[window]) / np.trapz(profile, xprof)\n \n obj_cleaned[variance == 0] = 0\n contam_cleaned[variance == 0] = 0\n \n #### Simple sums, also weighted\n weights = 1./variance\n weights[variance == 0] = 0\n \n var_sum = np.sum(weights[window,:], axis=0)\n \n weighted_sum = np.sum((obj_cleaned*weights)[window,:], axis=0) / var_sum\n weighted_var = 1./np.sum(weights[window,:], axis=0)\n weighted_sig = np.sqrt(weighted_var)\n \n simple_sum = np.sum(obj_cleaned[window,:], axis=0)\n simple_var = np.sum(variance[window,:], axis=0)\n simple_sig = np.sqrt(simple_var)\n \n #### Optimal extraction\n opt_variance = variance.copy()\n opt_variance[opt_variance == 0] = 1.e6\n \n num = np.sum(profile2D*obj_cleaned/opt_variance, axis=0)\n #num_contam = np.sum(profile2D*contam_cleaned/opt_variance, axis=0)\n num_contam = np.sum(profile2D*(self.full_model - self.the_object)/opt_variance, axis=0)\n num_full = np.sum(profile2D*self.grism_sci/opt_variance, axis=0)\n \n denom = np.sum(profile2D**2 / opt_variance, axis=0)\n \n optimal_sum = num / denom\n optimal_sum_contam = num_contam / denom\n optimal_sum_full = num_full / denom\n \n optimal_var = 1./np.sum(profile2D**2/opt_variance, axis=0)\n optimal_sig = np.sqrt(optimal_var)\n \n trace_pix = np.cast[int](np.round(self.ytrace))\n #trace_spec = self.grism_sci[trace_pix,:]\n trace_spec = optimal_sum*0.\n trace_sig = optimal_sum*0.\n for i in range(osh[1]):\n trace_spec[i] = self.grism_sci[trace_pix[i],i]\n trace_sig[i] = self.grism_wht[trace_pix[i],i]\n \n scale_to_total = 1./np.max(profile) \n if not np.isfinite(scale_to_total):\n scale_to_total=-1\n \n c1 = pyfits.Column(name='wave', format='D', unit='ANGSTROMS', array=self.wave)\n c2 = pyfits.Column(name='flux', format='D', unit='ELECTRONS/S', array=optimal_sum_full)\n c3 = pyfits.Column(name='error', format='D', unit='ELECTRONS/S', array=optimal_sig)\n c4 = pyfits.Column(name='contam', format='D', unit='ELECTRONS/S', array=optimal_sum_contam)\n c5 = pyfits.Column(name='trace', format='D', unit='ELECTRONS/S', array=trace_spec)\n c6 = pyfits.Column(name='etrace', format='D', unit='ELECTRONS/S', array=trace_sig)\n c7 = pyfits.Column(name='sensitivity', format='D', unit='E/S / 1E-17 CGS', array=self.sens*(self.growx*self.growy))\n #print 'MAX SENS: %.f' %(self.sens.max())\n \n coldefs = pyfits.ColDefs([c1,c2,c3,c4,c5,c6,c7])\n head = pyfits.Header()\n head.update('ATRACE', scale_to_total, comment='Factor to scale trace to total flux')\n \n #ii = np.where(np.cast[int](self.cat.id) == self.id)[0][0]\n try:\n head.update('RA', self.im[0].header['RA'], comment='Target R.A.')\n head.update('DEC', self.im[0].header['DEC'], comment='Target Dec.')\n except:\n head.update('RA', 0., comment='Target R.A.')\n head.update('DEC', 0., comment='Target Dec.')\n #\n head.update('X_PIX', self.x_pix, comment='X pixel in interlaced image')\n head.update('Y_PIX', self.y_pix, comment='Y pixel in interlaced image')\n head.update('MAG', self.im[0].header['MAG'], comment='MAG_AUTO from interlaced catalog')\n \n tbHDU = pyfits.new_table(coldefs, header=head)\n tbHDU.writeto(self.root+'_%05d.1D.fits' %(self.id), clobber=True)\n \n if verbose:\n t1 = time.time(); dt = t1-t0; t0=t1\n print '1D spectrum (%.3f)' %(dt)",
"title": ""
},
{
"docid": "9da54d485cfbebc39986c765199afa50",
"score": "0.5416946",
"text": "def save_and_show_figs(self, save_figs=False, show_figs=True, fig_path=None):\n\n if self.reward:\n self.ax1.set_xlabel('Steps', fontsize=22)\n self.ax1.set_ylabel('Average Reward', fontsize=22)\n self.ax1.set_title('Bandit Testbed of Strategies', fontsize=22)\n self.ax1.legend(bbox_to_anchor=(1, 1), loc='upper left', fancybox=True, fontsize=20)\n self.ax1.set_xlim([0, len(self.average_rewards)])\n self.ax1.tick_params(axis='both', which='major', labelsize=20)\n self.ax1.tick_params(axis='both', which='minor', labelsize=20)\n\n if save_figs:\n if fig_path is None:\n fig_path = os.getcwd() + '/../figs'\n self.fig1.savefig(os.path.join(fig_path, 'bandit_rewards.png'), bbox_inches='tight')\n\n if self.regret:\n self.ax2.set_xlabel('Steps', fontsize=22)\n self.ax2.set_ylabel('Cumulative Regret', fontsize=22)\n self.ax2.set_title('Bandit Testbed of Strategies', fontsize=22)\n self.ax2.legend(bbox_to_anchor=(1, 1), loc='upper left', fancybox=True, fontsize=20)\n self.ax2.set_xlim([0, len(self.average_regret)])\n self.ax2.tick_params(axis='both', which='major', labelsize=20)\n self.ax2.tick_params(axis='both', which='minor', labelsize=20)\n\n if save_figs:\n if fig_path is None:\n fig_path = os.getcwd() + '/../figs'\n self.fig2.savefig(os.path.join(fig_path, 'bandit_regret.png'), bbox_inches='tight')\n\n if self.optimal:\n self.ax3.set_xlabel('Steps', fontsize=22)\n self.ax3.set_ylabel(r'$\\%$ Optimal Action', fontsize=22)\n self.ax3.set_title('Bandit Testbed of Strategies', fontsize=22)\n self.ax3.legend(bbox_to_anchor=(1, 1), loc='upper left', fancybox=True, fontsize=20)\n self.ax3.set_xlim([0, len(self.average_optimal)])\n self.ax3.tick_params(axis='both', which='major', labelsize=20)\n self.ax3.tick_params(axis='both', which='minor', labelsize=20)\n\n if save_figs:\n if fig_path is None:\n fig_path = os.getcwd() + '/../figs'\n self.fig3.savefig(os.path.join(fig_path, 'bandit_optimal_action.png'), bbox_inches='tight')\n\n if show_figs:\n plt.show()\n\n sns.reset_orig()",
"title": ""
},
{
"docid": "13a0d6bfbd0337c6befe6499234a16b3",
"score": "0.54161906",
"text": "def model_save_to_fits(data, metadata, savepath, version):\n if os.path.exists(savepath) == False:\n os.mkdir(savepath)\n file_name = make_component_filename(metadata, version)\n hdr = fits.Header(metadata)\n primary_hdu = fits.PrimaryHDU(header=hdr)\n hdu = fits.table_to_hdu(Table(data))\n descriptions =['midpoint of the wavelength bin', 'left/blue edge of the wavelength bin','right/red edge of the wavelength bin','average flux over the bin']\n hdu.header.insert(8, ('EXTNAME','SPECTRUM'))\n hdu.header.insert(9, ('EXTNO',2))\n [hdu.header.insert(i[0]+10, ('TDESC%s' %(i[0]), i[1])) for i in enumerate(descriptions)]\n hdul = fits.HDUList([primary_hdu, hdu])\n hdul.writeto(savepath+file_name+'.fits', overwrite=True)\n print('Spectrum saved as '+file_name+'.fits')",
"title": ""
},
{
"docid": "aaff2a6a9e8a1403ea28312c3030d7f9",
"score": "0.54116267",
"text": "def plot_topo_power(epochs, power, freq, layout=None, baseline=None,\r\n mode='mean', decim=1, colorbar=True, vmin=None, vmax=None,\r\n cmap=None, layout_scale=0.945, dB=True, title=None):\r\n times = epochs.times[::decim].copy()\r\n if mode is not None:\r\n if baseline is None:\r\n baseline = epochs.baseline\r\n power = rescale(power.copy(), times, baseline, mode)\r\n times *= 1e3\r\n if dB:\r\n power = 20 * np.log10(power)\r\n if vmin is None:\r\n vmin = power.min()\r\n if vmax is None:\r\n vmax = power.max()\r\n if layout is None:\r\n from .layouts.layout import find_layout\r\n layout = find_layout(epochs.info)\r\n\r\n power_imshow = partial(_imshow_tfr, tfr=power.copy(), freq=freq)\r\n\r\n fig = _plot_topo(info=epochs.info, times=times,\r\n show_func=power_imshow, layout=layout, decim=decim,\r\n colorbar=colorbar, vmin=vmin, vmax=vmax, cmap=cmap,\r\n layout_scale=layout_scale, title=title, border='w',\r\n x_label='Time (s)', y_label='Frequency (Hz)')\r\n\r\n return fig",
"title": ""
},
{
"docid": "fcf920cdfb8fd0932c195df145d3bec6",
"score": "0.53973335",
"text": "def save_hard_drive(self):\n self.dialog_box.clear()\n self.dialog_box.textCursor().insertText(\n \"Salvando nuvem de pontos...\\n\"\n )\n self.repaint()\n # path_pcd = (\n # \"/var/tmp/trms/crops\"\n # + self.mission_id\n # + \"/\"\n # + self.mission_id\n # + \"_\"\n # + self.current_stock\n # + \".pcd\"\n # )\n path_txt = (\n \"/var/tmp/trms/crops\"\n + self.mission_id\n + \"/\"\n + self.mission_id\n + \"_\"\n + self.current_stock\n + \".txt\"\n )\n path_png = (\n \"/var/tmp/trms/crops\"\n + self.mission_id\n + \"/\"\n + self.mission_id\n + \"_\"\n + self.current_stock\n + \".png\"\n )\n with open(self.path_to_cached_pc, \"r\", encoding=\"utf-8\") as cache_file:\n text = cache_file.read()\n with open(path_txt, \"w\", encoding=\"utf-8\") as txt_file:\n txt_file.write(text)\n\n os.system(\n \"./extconverter \"\n + path_txt\n + \" -D /var/tmp/trms/crops\"\n + self.mission_id\n + \"/\"\n )\n\n xyz_points = np.loadtxt(path_txt, delimiter=\" \")\n xyz_points = xyz_points[:, :3]\n z_points = xyz_points[:, 2]\n viewer = pptk.viewer(xyz_points, z_points)\n viewer.set(phi=-(np.pi / 2 - 0.1933), theta=np.pi / 2)\n if self.current_stock in [\"1A\", \"3A\"]:\n viewer.w_Resize()\n viewer.set(r=75)\n elif self.current_stock in [\"1B\", \"3B\"]:\n viewer.w_Resize()\n viewer.set(r=90)\n else:\n viewer.w_resize()\n viewer.set(r=150)\n box_plot = plt.boxplot(z_points)\n min_value = box_plot[\"whiskers\"][0]._y[\n 0\n ] # Minimum value of the minimum range\n max_value = box_plot[\"whiskers\"][1]._y[\n 1\n ] # Maximum value of the maximum range\n viewer.color_map(\"jet\", scale=[min_value, max_value])\n viewer.set(show_info=False)\n sleep(1.5)\n viewer.capture(path_png)\n sleep(0.5)\n viewer.close()\n volume = float(self.calc_click())\n modification_file_path = os.path.join(\n \"/var/tmp/trms/crops\" + self.mission_id, \"changes\"\n )\n with open(modification_file_path, \"w\", encoding=\"utf-8\") as changes:\n changes.write(self.current_stock + \" \" + str(volume))\n\n if self.review_mode:\n self.dialog_box.clear()\n self.dialog_box.textCursor().insertText(\n \"Nuvem de pontos revisada!\\n\"\n )\n else:\n self.dialog_box.clear()\n self.dialog_box.textCursor().insertText(\"Nuvem de pontos salva!\\n\")\n self.flag_modification = False\n self.repaint()",
"title": ""
},
{
"docid": "105b1504378d7becbec4eb135eb76c05",
"score": "0.5395983",
"text": "def plotCombinedFigures(x, y, w, s, title=\"\", left=400, right=800):\n fig, (axes, axesFFT) = plt.subplots(2, 1, figsize=(10, 10))\n plt.subplots_adjust(hspace=0.31)\n axes.plot(x, y, '-')\n axes.set_title(\"Interferogramme\")\n axes.set_xlabel(\"Déplacement du miroir 1 [µm]\")\n axes.set_ylabel(\"Voltage [mV]\")\n axesFFT.plot(w * 1000, abs(s), '.-')\n axesFFT.set_xlim(left=left, right=right)\n axesFFT.set_xlabel(\"Longueur d'onde [nm]\")\n axesFFT.set_ylabel(\"Intensité [-]\")\n axesFFT.set_title(title)\n plt.savefig(f\"FiguresIM/{title.replace(' ', '').replace(',', '').replace('.', '').replace('è', 'e')}.png\", dpi=300)",
"title": ""
},
{
"docid": "a9763d78a45fae090fadccac2eb0c516",
"score": "0.5393554",
"text": "def plot_power_spectrum(ax, modes, spectrum,\n label=\"\",\n dimensionless=True, mode=\"Pofk\", n_dim=3,\n x_units=None, y_units=None, h_factors=True,\n x_axis_label=True, y_axis_label=True,\n x_scale=\"log\", y_scale=\"log\",\n **kwargs):\n\n if mode.lower() == \"cl\":\n x_label = r\"$\\ell$\"\n if dimensionless:\n y_label = r\"$\\ell(\\ell+1)/2\\pi\\ C_\\ell$\"\n u = modes*(modes+1)/(2*pi)\n else:\n y_label = r\"$C_\\ell$\"\n u = 1\n elif mode.lower() == \"pofk\":\n x_label = r\"$k$\"\n if x_units is None:\n h_term = \"$h$\" if h_factors else \"\"\n x_units = \"[\" + h_term + \" \" + \"Mpc$^{-1}$\" + \"]\"\n x_label += \" \" + x_units\n if y_units is None:\n if h_factors:\n h_term = f\"$h^{{-{n_dim}}}$\"\n else:\n h_term = \"\"\n mpc_term = \"Mpc\" if n_dim == 1 else f\"Mpc$^{{{n_dim}}}$\"\n y_units = \"[\" + h_term + \" \" + mpc_term + \"]\"\n\n if dimensionless:\n y_label = r\"$\\Delta^2(k)$\"\n u = modes**n_dim / (2*pi)**n_dim * 4*pi\n else:\n y_label = r\"$P(k)$\"\n y_label += \" \" + y_units\n u = 1\n else:\n raise ValueError(f\"Mode {mode} not supported.\")\n\n ax.plot(modes, u*spectrum, label=label, **kwargs)\n\n if x_axis_label:\n ax.set_xlabel(x_label)\n if y_axis_label:\n ax.set_ylabel(y_label)\n\n ax.set_xscale(x_scale)\n ax.set_yscale(y_scale)",
"title": ""
},
{
"docid": "74d980d0458a894990ca5134c49aa464",
"score": "0.5389359",
"text": "def inoutplot(d,zparams={}):\n\n if 'fignr' in zparams.keys():\n plt.figure(zparams['fignr'])\n else:\n plt.figure(99)\n ax1=plt.subplot(221)\n plt.plot( d['tvec'], np.abs(d['tfield2'])**2)\n plt.plot( d['tvec'], np.abs(d['tfield1'])**2,linewidth=1)\n plt.legend([\"out\",\"in\"],loc=0)\n\n\n ax2=plt.subplot(222)\n plt.plot( d['omvec']/2.0/np.pi, db_abs2( d['ffield2']))\n plt.plot( d['omvec']/2.0/np.pi, db_abs2( d['ffield1']),linewidth=1)\n if 'fylim' in zparams.keys():\n plt.ylim(zparams['fylim'])\n plt.legend([\"out\",\"in\"],loc=0)\n ax3=plt.subplot(223)\n plt.imshow( np.abs(d['timefield'])**2,\n aspect='auto',\n origin='lower',\n extent=( np.min(d['tvec']), np.max(d['tvec']),\n np.min(d['zvec']), np.max(d['zvec'])))\n plt.colorbar()\n\n ax4=plt.subplot(224)\n ax=plt.imshow( db_abs2(d['freqfield']),\n aspect='auto',\n origin='lower',\n extent=( np.min(d['omvec'])/2.0/np.pi, np.max(d['omvec'])/2.0/np.pi,\n np.min(d['zvec']), np.max(d['zvec'])))\n plt.colorbar()\n if 'clim' in zparams.keys():\n ax.set_clim(zparams['clim'])\n\n ax1.set_xlabel(\"time / s\")\n ax1.set_ylabel(\"power / W\")\n ax2.set_xlabel(\"frequency / Hz\")\n ax2.set_ylabel(\"spectral energy density / dB\")\n ax3.set_xlabel(\"time / s\")\n ax3.set_ylabel(\"z / m\")\n ax4.set_xlabel(\"frequency / Hz\")\n ax4.set_ylabel(\"z / m\") \n\n return [ax1,ax2,ax3,ax4]",
"title": ""
},
{
"docid": "a0a76154d50560ec46e7fcc06d6b7585",
"score": "0.53862613",
"text": "def compute_powerspectrum(self):\n self.bk_directsample(psonly=True)",
"title": ""
}
]
Returns if there is any word in the trie that starts with the given prefix.
[
{
"docid": "c41d511488f18931b5996f998378ce6c",
"score": "0.7661376",
"text": "def startsWith(self, prefix: str) -> bool:\n node = self.root\n for char in prefix:\n if char not in node.children:\n return False\n node = node.children[char]\n\n return True",
"title": ""
}
]
[
{
"docid": "5d1a552ce96f28a17ff02733ba0212b7",
"score": "0.84394646",
"text": "def startsWith(self, prefix: str) -> bool: \n return(self.searchTrie(self.root, prefix, True))",
"title": ""
},
{
"docid": "0a82b07344eb20900f183e4a08b3d6a7",
"score": "0.843329",
"text": "def startsWith(self, prefix: str) -> bool:\n t = self.trie\n i = 0\n failed = False\n while i < len(prefix) and not failed:\n failed = prefix[i] not in t\n if not failed:\n t = t[prefix[i]]\n i += 1 \n return not failed",
"title": ""
},
{
"docid": "dadcffb3bf482974c68594b81af4c0b6",
"score": "0.8428586",
"text": "def startsWith(self, prefix):\n temp = self.trie\n for w in prefix:\n if w in temp:\n temp = temp[w]\n else:\n return False\n return True",
"title": ""
},
{
"docid": "c90848e750e488f323036b7590bb83e9",
"score": "0.84256893",
"text": "def startsWith(self, prefix: str) -> bool:\n node = self.trie\n for w in prefix:\n if w not in node:\n return False\n node = node[w]\n \n return True",
"title": ""
},
{
"docid": "30e50a38562c29bfa379745288d5cb9a",
"score": "0.84208626",
"text": "def startsWith(self, prefix: str) -> bool:\n root = self.trie\n for ch in prefix:\n if ch not in root:\n return False\n root = root[ch]\n return True",
"title": ""
},
{
"docid": "ed23fc2f0a62b646333995cb7b15cef6",
"score": "0.8417957",
"text": "def startsWith(self, prefix):\n node = self.search_trie(prefix, 0)\n return bool(node)",
"title": ""
},
{
"docid": "ff7f6a4d1565386de512cd8d4b67eb07",
"score": "0.8405622",
"text": "def startsWith(self, prefix):\n\n node = self.trie\n for c in prefix:\n node = node.get(c, None)\n if node is None:\n return False\n return True",
"title": ""
},
{
"docid": "1510a574d511019d15e307f7e9429bff",
"score": "0.8395297",
"text": "def startsWith(self, prefix: str) -> bool:\n trie = self.trie\n for char in prefix:\n if char not in trie:\n return False\n trie = trie[char]\n return True",
"title": ""
},
{
"docid": "9c2ffd8488ee039e0428ce464d494ff4",
"score": "0.82884425",
"text": "def startsWith(self, prefix):\n tmp = self.trie\n for charr in prefix:\n if charr not in tmp:\n return False\n tmp = tmp[charr]\n\n return True",
"title": ""
},
{
"docid": "f221b048f2af9c2c1522a42b21ac0a5a",
"score": "0.82635236",
"text": "def startsWith(self, prefix: str) -> bool:\n curr = self.trie\n for c in prefix: \n if c not in curr.keys():\n return False\n curr = curr[c]\n return True",
"title": ""
},
{
"docid": "fc622229ffd3dae60b19336b63686e4a",
"score": "0.82159823",
"text": "def startsWith(self, prefix):\n try:\n root = self\n for ch in prefix:\n root = root.trie[ch]\n except KeyError as e:\n return False\n\n return True",
"title": ""
},
{
"docid": "7332aa7edfd326e7fb3fa98b2dfdf5a3",
"score": "0.8149632",
"text": "def startsWith(self, prefix):\n node = self.tries.get(prefix[0])\n if not node: return False\n for s in prefix[1:]:\n if s not in node.children:\n return False\n node = node.children[s]\n return True\n\n\n\n\n # Your Trie object will be instantiated and called as such:\n # obj = Trie()\n # obj.insert(word)\n # param_2 = obj.search(word)\n # param_3 = obj.startsWith(prefix)",
"title": ""
},
{
"docid": "718570ddb9cc8c23ca8fa37cc243680b",
"score": "0.81220734",
"text": "def startsWith(self, prefix: str) -> bool:\n return True in [word.startswith(prefix) for word in self.__words]",
"title": ""
},
{
"docid": "fd545dbab21fa068abc9e50b31298770",
"score": "0.80960995",
"text": "def starts_with(self, prefix: str) -> bool:\n current_trie = self.head\n for index in range(1, len(prefix) + 1):\n node_index = ord(prefix[:index][-1]) - ord('a')\n if current_trie.child_nodes[node_index]:\n current_trie = current_trie.child_nodes[node_index]\n else:\n return False\n return True",
"title": ""
},
{
"docid": "5867ef803dc06a6f5ef5af4cc6b1b588",
"score": "0.8024399",
"text": "def startsWith(self, prefix: str) -> bool:\n curr = self.root\n\n for char in prefix:\n loc = Trie.child_loc(char)\n\n if not curr.children[loc]:\n return False\n\n curr = curr.children[loc]\n\n return True",
"title": ""
},
{
"docid": "b03c9bdc827b93a64515a669b111e77a",
"score": "0.79254913",
"text": "def startsWith(self, prefix):\n node = self.root\n for i in xrange(0, len(prefix)):\n node = node.search(i, prefix)\n if not node:\n return False\n return True",
"title": ""
},
{
"docid": "43415b0f787c71ba77188c1c34aee099",
"score": "0.79068255",
"text": "def startsWith(self, prefix):\n if len(prefix) == 0: return True\n found = self.__search__(prefix)\n return found and len(found) > 0",
"title": ""
},
{
"docid": "e64ff64b8765bfc2cca48ecdb32db920",
"score": "0.7892173",
"text": "def startsWith(self, prefix):\n node = self.root\n for char in prefix:\n if char not in node:\n return False\n node = node[char]\n return True",
"title": ""
},
{
"docid": "d269c1f2f1580f50501267553b4ef0a9",
"score": "0.7889207",
"text": "def startsWith(self, prefix):\n curr = self._root\n for idx, char in enumerate(prefix):\n if char not in curr['children']:\n return False\n curr = curr['children'][char]\n if idx == len(prefix) - 1:\n return True",
"title": ""
},
{
"docid": "95f36c358954b75cb3640ab12b19fc51",
"score": "0.78871316",
"text": "def startsWith(self, prefix: str) -> bool:\n p = self.root\n for i in prefix:\n if p.next[ord(i) - ord('a')] == None:\n return False\n p = p.next[ord(i) - ord('a')]\n \n return True",
"title": ""
},
{
"docid": "69530257be2eb7369d7a2a8e639db9b9",
"score": "0.78862256",
"text": "def startsWith(self, prefix: str) -> bool:\n s = self.root\n for char in prefix:\n if char not in s:\n return False\n else:\n s = s[char]\n return True",
"title": ""
},
{
"docid": "fa82b83f55c0f99c248cb350d8290641",
"score": "0.78860176",
"text": "def startsWith(self, prefix: str) -> bool:\n node = self.root\n for char in prefix:\n if char not in node:\n return False\n node = node[char]\n return True",
"title": ""
},
{
"docid": "e335f47ece9bd378ca40acd60b0a7453",
"score": "0.7869209",
"text": "def startsWith(self, prefix: str) -> bool:\r\n node = self.root\r\n for char in prefix:\r\n if char not in node:\r\n return False\r\n node = node[char]\r\n return True",
"title": ""
},
{
"docid": "6737c0f6384bf7ef8ccf0234f9a0272f",
"score": "0.7864041",
"text": "def trie_prefix(prefix, root):\n\n if not isinstance(root, TrieNode):\n raise TypeError(\"Argument `root` is not a TrieNode object\")\n\n walk = root\n\n for c in prefix:\n if c in walk.children:\n walk = walk.children[c]\n else:\n return False\n\n return walk",
"title": ""
},
{
"docid": "28b1b9668580b16e4137aa79a422c627",
"score": "0.7863121",
"text": "def startswith(self, prefix: str) -> bool:\n x = self._search(self.root, prefix, 0)\n if x is None:\n return False\n return True",
"title": ""
},
{
"docid": "48d23bc40f23bb0f525212d4ac86bb23",
"score": "0.78552794",
"text": "def startsWith(self, prefix: str) -> bool:\n node = self.tree\n for a in prefix:\n if a in node.keys():\n node = node[a]\n else:\n return False\n return True",
"title": ""
},
{
"docid": "c97c3a7f8a56bfd8f349e0ed65246620",
"score": "0.7827465",
"text": "def startsWith(self, prefix: str) -> bool:\n node = self.root\n for ch in prefix:\n if ch not in node:\n return False\n node = node[ch]\n return True",
"title": ""
},
{
"docid": "34809accce03ba726af77abc8ec1e1ea",
"score": "0.78244233",
"text": "def startsWith(self, prefix):\n cur = self.root\n for x in prefix:\n x_index = ord(x) - ord('a')\n if cur.next[x_index] == None:\n return False\n cur = cur.next[x_index]\n return True",
"title": ""
},
{
"docid": "a1e1abd580aa469604bc22b2ede2b31b",
"score": "0.7808699",
"text": "def startsWith(self, prefix: str) -> bool:\n pcrawl = self.root\n length = len(prefix)\n for level in range(length):\n index = self.chartoindex(prefix[level])\n if pcrawl.children[index]",
"title": ""
},
{
"docid": "ac15351bc6ae1b50f5771b2e83869251",
"score": "0.77895415",
"text": "def startsWith(self, prefix):\n now = self.root\n for c in prefix:\n idx = ord(c) - ord('a')\n if now.next[idx]:\n now = now.next[idx]\n else:\n return False\n return True",
"title": ""
},
{
"docid": "99156a0e7a2879f2c0d427cd0dd57bae",
"score": "0.7788128",
"text": "def startsWith(self, prefix: str) -> bool:\n current = self.root\n for w in prefix:\n current = current.children.get(w)\n if current == None:\n return False\n return True",
"title": ""
},
{
"docid": "23222ea917f93e3a814f4a08f54273c3",
"score": "0.7783289",
"text": "def startsWith(self, prefix):\r\n temp = self.root\r\n for c in prefix:\r\n index = ord(c) - ord('a')\r\n if not temp.next[index]:\r\n return False\r\n temp = temp.next[index]\r\n return True",
"title": ""
},
{
"docid": "f744e7f66bf2fc642ad80fe9fde16481",
"score": "0.7776757",
"text": "def startsWith(self, prefix: str) -> bool:\n return self.root.search(prefix, prefix=True)",
"title": ""
},
{
"docid": "1f9d1094ed034936ea328403737d8f30",
"score": "0.77750313",
"text": "def startsWith(self, prefix: str) -> bool:\n node = self.root\n\n for i in prefix:\n\n if node.children[ord(i)-ord('a')] == None:\n return False\n node = node.children[ord(i)-ord('a')]\n\n return True",
"title": ""
},
{
"docid": "07f1f2eb7cadea8ee03928fbd336b630",
"score": "0.7770272",
"text": "def startsWith(self, prefix: str) -> bool:\n node = self.searchPrefix(prefix)\n return node is not None",
"title": ""
},
{
"docid": "98a89cd0ad35dde1440281cd4455f0ad",
"score": "0.77297807",
"text": "def startsWith(self, prefix: str) -> bool:\n curr = self.root\n \n for ch in prefix:\n \n if ch not in curr:\n return False\n \n curr = curr[ch]\n \n return True",
"title": ""
},
{
"docid": "17359f1b661e9b481dae9e8a16e847a4",
"score": "0.7722284",
"text": "def startsWith(self, prefix):\n node = self.root\n for char in prefix:\n if char not in node.children:\n return False\n node = node.children[char]\n return True",
"title": ""
},
{
"docid": "38f0625d42d949f8d9bfcf56d633aa23",
"score": "0.77136356",
"text": "def startsWith(self, prefix: str) -> bool:\n node = self.root\n for w in prefix:\n if not node.children[w]:\n return False\n node = node.children[w]\n return True",
"title": ""
},
{
"docid": "2c1145793bc3afb816ebd2a8c3caaf4b",
"score": "0.77062017",
"text": "def startsWith(self, prefix):\r\n currNode = self.root\r\n for i in range(len(prefix)):\r\n char = prefix[i]\r\n if char not in currNode.children:\r\n return False\r\n currNode = currNode.children[char]\r\n return True",
"title": ""
},
{
"docid": "c8f7e6016a7d7473fef0a9703a64e295",
"score": "0.7701523",
"text": "def startsWith(self, prefix: str) -> bool:\n curr = self.root\n for c in prefix:\n if c not in curr.next:\n return False\n curr = curr.next[c]\n return True",
"title": ""
},
{
"docid": "39bdbe12a566613013c78aa916114ac1",
"score": "0.767962",
"text": "def startsWith(self, prefix):\n ptr = self.root\n for ch in prefix:\n if ch not in ptr['neighbors']:\n return False\n ptr = ptr['neighbors'][ch]\n \n return True",
"title": ""
},
{
"docid": "59cdab4afe4794e58986b9366cab42c3",
"score": "0.7674477",
"text": "def contains(self, word, prefix=False):\n cur_node = self\n for letter in word:\n if letter in cur_node.children:\n cur_node = cur_node.children[letter]\n else:\n return False\n\n # If the current node doesn't end a word, return False unless prefix==True\n return cur_node.word_end or prefix",
"title": ""
},
{
"docid": "39eda3b44bfea4df3832cf0a2083be7c",
"score": "0.7665837",
"text": "def starts_with(self, prefix):\n node = self.root\n for c in prefix:\n if c not in node.sons:\n return False\n node = node.sons[c]\n return True",
"title": ""
},
{
"docid": "5478dede2a5a35c33279787b84e5541c",
"score": "0.7652525",
"text": "def startsWith(self, prefix: str) -> bool:\n i = 0\n node = self.root\n while(i < len(prefix)):\n if prefix[i] in node.children:\n node = node.children[prefix[i]]\n else:\n return False\n i += 1\n return True",
"title": ""
},
{
"docid": "499a0956ea552268fd80805f3f8d353c",
"score": "0.76447237",
"text": "def starts_with(self, prefix):\n current = self.root\n for character in prefix:\n index = self.char_to_index(character)\n if not current.children[index]:\n return False\n current = current.children[index]\n return True",
"title": ""
},
{
"docid": "83286bc712ccdde74258048d9038bb74",
"score": "0.76354855",
"text": "def startsWith(self, prefix):\r\n poz = self.root\r\n length = len(prefix)\r\n for level in range(length):\r\n i = self._charToIndex(prefix[level])\r\n if not poz.children[i]:\r\n return False\r\n poz = poz.children[i]\r\n \r\n return True",
"title": ""
},
{
"docid": "72779097e49c00c54e86641eb34222a8",
"score": "0.762668",
"text": "def startsWith(self, prefix):\n tmp = self._dict\n for c in prefix:\n if not c in tmp:\n return False\n tmp = tmp[c]\n return True",
"title": ""
},
{
"docid": "4bc9104565954347fa5033ddcf2ee33d",
"score": "0.7621423",
"text": "def prefix_trie_matching(text: str, trie: dict) -> bool:\n node = trie[0]\n for i in range(len(text)):\n symbol = text[i]\n if symbol in node:\n node = node[symbol]\n else:\n break\n if len(node) == 0:\n return True\n else:\n return False",
"title": ""
},
{
"docid": "269d99d4a8ed987e47df281e428235c4",
"score": "0.7617441",
"text": "def startsWith(self, prefix: str) -> bool:\n node = self\n charList = [char for char in prefix]\n for char in charList:\n if char in node.childList:\n index = node.childList.index(char)\n node = node.children[index]\n else:\n return False\n return True",
"title": ""
},
{
"docid": "416945cc1bd317e495153f847d001c23",
"score": "0.7612122",
"text": "def startsWith(self, prefix: str) -> bool:\n\n p = self.root\n for i in prefix:\n p = p.children.get(i)\n if p == None:\n return False\n return True",
"title": ""
},
{
"docid": "24edd699647427ff35a36c4e8a4e2736",
"score": "0.759234",
"text": "def startsWith(self, prefix: str) -> bool:\n node = self.root\n for w in prefix:\n if w in node.children:\n node = node.children[w]\n else:\n return False\n \n return True",
"title": ""
},
{
"docid": "6ba1e8374f6ed04383ca8f477f23df24",
"score": "0.7589363",
"text": "def starts_with(self, prefix):\n curr = self.root\n for letter in prefix:\n if letter not in curr.children:\n return False\n curr = curr.children[letter]\n return True",
"title": ""
},
{
"docid": "10ab6a612250d7200dd523f7b135e70f",
"score": "0.7584371",
"text": "def startsWith(self, prefix: str) -> bool:\n if not prefix:\n return True\n root = self\n for each_char in prefix:\n idx = int(ord(each_char)) - 97\n if root.next[idx] is not None:\n root = root.next[idx]\n else:\n return False\n return True",
"title": ""
},
{
"docid": "c0d56b7b06d1b7b44dad302775ae7b40",
"score": "0.75830305",
"text": "def startsWith(self, prefix: str) -> bool:\n node = self.root\n for i in prefix:\n if i not in node.next:\n return False\n node = node.next[i]\n return True",
"title": ""
},
{
"docid": "4d0fdfb1629882b0e0022d06b01d0017",
"score": "0.7575522",
"text": "def startsWith(self, prefix):\n root = self.root\n for c in prefix:\n if c in root.children:\n root = root.children[c]\n else:\n return False\n return True",
"title": ""
},
{
"docid": "0d9bd9c263ee30451438c551bda78574",
"score": "0.7559532",
"text": "def startsWith(self, prefix: str) -> bool:\n if prefix == \"\":\n return True\n if prefix[0] not in self.stack.keys():\n return False\n else:\n return self.stack[prefix[0]].startsWith(prefix[1:])",
"title": ""
},
{
"docid": "a89ed3a9b7937a3a778e407ec40547fd",
"score": "0.75348955",
"text": "def startsWith(self, prefix: str) -> bool:\n cur = self.root\n for c in prefix:\n if c in cur.children:\n cur = cur.children[c]\n else:\n return False\n return True",
"title": ""
},
{
"docid": "a175851807344a121e3ef232460edbf3",
"score": "0.7517827",
"text": "def startsWith(self, prefix):\n children = self.root.children\n for c in prefix:\n if c not in children:\n return False\n treenode = children[c]\n children = treenode.children\n return True",
"title": ""
},
{
"docid": "35f817fbc94254f6d4f8a266e07deda3",
"score": "0.75149447",
"text": "def startsWith(self, prefix: str) -> bool:\n tmp = self.node\n for i in prefix:\n if i not in tmp.next:\n return False\n tmp = tmp.next[i]\n return True",
"title": ""
},
{
"docid": "35f817fbc94254f6d4f8a266e07deda3",
"score": "0.75149447",
"text": "def startsWith(self, prefix: str) -> bool:\n tmp = self.node\n for i in prefix:\n if i not in tmp.next:\n return False\n tmp = tmp.next[i]\n return True",
"title": ""
},
{
"docid": "2fa8597451d7632ee7e7d04ca8a11096",
"score": "0.7505829",
"text": "def startsWith(self, prefix: str) -> bool:\n return self._search(prefix) != {}",
"title": ""
},
{
"docid": "c8cbd81a8e8df3a52e87e9b854e6242a",
"score": "0.7498065",
"text": "def prefixExists(self, prefix):\n node = self.root\n for c in prefix:\n if not node.containsChild(c):\n return False\n node = node.getChildNode(c)\n return True",
"title": ""
},
{
"docid": "a185c140db17c98ef94e45bc59722249",
"score": "0.74885845",
"text": "def _has_tree_prefix(self, prefix: str) -> bool:\n return any(\n prefix in tree.name\n for tree in self.trees\n )",
"title": ""
},
{
"docid": "990d7b7f1557302d030b4bfb2f296d99",
"score": "0.7480631",
"text": "def startsWith(self, prefix: str) -> bool:\n curr = self.root\n for key in prefix:\n if key in curr.children:\n curr = curr.children[key]\n else:\n # mismatch of prefix, early return\n return False\n\n # reach the desired node that with the given prefix\n return True",
"title": ""
},
{
"docid": "a7719d910ebff037703b0492754a2204",
"score": "0.74685484",
"text": "def search(self, word: str) -> bool:\n node = self.trie\n for w in word:\n if w not in node:\n return False\n node = node[w]\n return '#' in node # check if word is inserted or just a prefix",
"title": ""
},
{
"docid": "408b662856bf843fadf7517547ae21b5",
"score": "0.7459677",
"text": "def match_any_prefix(text, prefixes):\n return any([text.startswith(pre) for pre in prefixes])",
"title": ""
},
{
"docid": "989ca54117ab751f1fe2106b4bc9f2d5",
"score": "0.7451152",
"text": "def startsWith(self, prefix):\n if len(prefix) == 0:\n return True\n\n if prefix[0] in self.son:\n return self.son[prefix[0]].startsWith(prefix[1:])\n else:\n return False",
"title": ""
},
{
"docid": "3e3c1c5d0ebe7996de5a70eea7e8fbaf",
"score": "0.7419602",
"text": "def startsWith(self, prefix):\n node = self.root\n for i in prefix:\n if not i in node.children:\n return False\n node = node.children[i]\n return True",
"title": ""
},
{
"docid": "e76e305535222a8672380810821dac9a",
"score": "0.739338",
"text": "def startsWith(self, prefix: str) -> bool:\n dic = self.dic\n for i in prefix:\n if i in dic:\n dic = dic[i]\n else:\n return False\n return True",
"title": ""
},
{
"docid": "4f04739f9a51ae0e0d4f6f7e46a71c0d",
"score": "0.73907334",
"text": "def startsWith(self, prefix):\n current = self.__root\n for c in prefix:\n if current.has_child(c):\n current = current.get_child(c)\n else:\n return False\n return True",
"title": ""
},
{
"docid": "2f2e440306e1739ab23872abbba4e4f3",
"score": "0.737301",
"text": "def starts_with_prefix(self, prefix):\n prefixed_words = list()\n\n if prefix is None:\n raise ValueError(\"Prefix must not be null/None.\")\n\n top_node = self.head\n for letter in prefix:\n if letter in top_node.children:\n top_node = top_node.children[letter]\n else:\n return prefixed_words\n \n if top_node == self.head:\n prefixes_queue = [node for key, node in top_node.children.items()]\n else:\n prefixes_queue = [top_node]\n\n while prefixes_queue:\n current_node = prefixes_queue.pop()\n if current_node.data is not None:\n prefixed_words.append(current_node.data)\n prefixes_queue = [node for key, node in current_node.children.items()] + prefixes_queue\n\n return prefixed_words",
"title": ""
},
{
"docid": "f60e020f9e12235652e39da5a71bb5d7",
"score": "0.73699576",
"text": "def is_prefix(self, word):\n word = word.replace('.', '').upper()\n return word in self.prefixes",
"title": ""
},
{
"docid": "7d91ea2d37d50a13b280ec66a60c96a5",
"score": "0.73532164",
"text": "def startsWith(self, prefix: str) -> bool:\n _, flag = self._end_node(prefix)\n return flag",
"title": ""
},
{
"docid": "339af29c8a943ee87f2b88fd98b2b497",
"score": "0.7338736",
"text": "def start_with_prefix(self, prefix):\n words = list()\n if prefix == None:\n raise ValueError('Requires not-Null prefix')\n \n # Determine end-of-prefix node\n top_node = self.head\n for letter in prefix:\n if letter in top_node.children:\n top_node = top_node.children[letter]\n else:\n # Prefix not in tree, go no further\n return words\n \n # Get words under prefix\n if top_node == self.head:\n queue = [node for key, node in top_node.children.iteritems()]\n else:\n queue = [top_node]\n \n # Perform a breadth first search under the prefix\n # A cool effect of using BFS as opposed to DFS is that BFS will return\n # a list of words ordered by increasing length\n while queue:\n current_node = queue.pop()\n if current_node.data != None:\n # Isn't it nice to not have to go back up the tree?\n words.append(current_node.data)\n \n queue = [node for key,node in current_node.children.iteritems()] + queue\n \n return words",
"title": ""
},
{
"docid": "73b71f0999d23c1c40d471bdbbf4ea90",
"score": "0.7332664",
"text": "def startswith(self, prefix):\n\n if not self.parts:\n return False\n else:\n return self.parts[0].startswith(prefix)",
"title": ""
},
{
"docid": "6a981798ff2e572ffd79ae9e778c1553",
"score": "0.7321072",
"text": "def matches_prefix(self, prefix: list) -> bool:\n # if there aren't enough tokens to match the whole prefix, no\n if len(self.tokens) - self.index < len(prefix):\n return False\n\n for i in range(len(prefix)):\n if self.tokens[self.index + i].name != prefix[i]:\n return False\n return True",
"title": ""
},
{
"docid": "6647ad3fcd687a5230643915cfa01c54",
"score": "0.7253987",
"text": "def find_prefix(root, prefix: str) -> Tuple[bool, int]:\n node = root\n # If the root node has no children, then return False.\n # Because it means we are trying to search in an empty trie\n if not root.children:\n return False, 0\n for char in prefix:\n char_not_found = True\n # Search through all the children of the present `node`\n for child in node.children:\n if child.char == char:\n # We found the char existing in the child.\n char_not_found = False\n # Assign node as the child containing the char and break\n node = child\n break\n # Return False anyway when we did not find a char.\n if char_not_found:\n return False, 0\n # Well, we are here means we have found the prefix. Return true to indicate that\n # And also the counter of the last node. This indicates how many words have this\n # prefix\n return True, node.counter",
"title": ""
},
{
"docid": "91f86d11bc037e81ff616f858ce49c64",
"score": "0.72529864",
"text": "def startsWith(self, prefix: str) -> bool:\n tmp, idx = self.head, 0\n while idx < len(prefix) and prefix[idx] in tmp: # 找到插入的节点\n tmp = tmp[prefix[idx]][1]\n idx += 1\n return idx == len(prefix)",
"title": ""
},
{
"docid": "b66d06fa64723300c2f056f8711cb2f1",
"score": "0.72032166",
"text": "def startswith(self, prefix):\n return self.value.startswith(prefix)",
"title": ""
},
{
"docid": "efb6804c871fb9c4fca5ce4dbf5e8ed8",
"score": "0.71434295",
"text": "def is_match(prefix: str, value: str) -> bool:\n assert prefix != '', 'Cannot match with empty prefix'\n return value.lower().startswith(prefix.lower())",
"title": ""
},
{
"docid": "89cf174a06d0286479f26754a52b2942",
"score": "0.7101888",
"text": "def search(self, word: str) -> bool:\n node = self.searchPrefix(word)\n return node is not None and node.isEnd()",
"title": ""
},
{
"docid": "a57920d7b639eb09bf0e6629c1912a4c",
"score": "0.7095519",
"text": "def __some_prefix(s, st):\n if s == '':\n return False\n for e in st:\n if e.startswith(s):\n return True\n return False",
"title": ""
},
{
"docid": "c883a3ce4b8257e39f049ec3096652b2",
"score": "0.709494",
"text": "def has_tree_prefix(mesh_id, tree_prefix):\n tree_numbers = get_mesh_tree_numbers(mesh_id)\n return any(tn.startswith(tree_prefix) for tn in tree_numbers)",
"title": ""
},
{
"docid": "19c59c4a0db4e86c26c2b62e5912b41e",
"score": "0.6931367",
"text": "def search(self, word: str) -> bool:\n curr = self.root\n for key in word:\n if key in curr.children:\n curr = curr.children[key]\n else:\n # mismatch of prefix, early return\n return False\n \n # check if the node contains a value\n return curr.hasValue",
"title": ""
},
{
"docid": "a5978eccf7f5642ae8699421d7e37bd2",
"score": "0.69043934",
"text": "def find_prefix_words(self, prefix):\n\n # Find prefix\n\n # Get list of suffixes from this point downward. This doesn't\n # include the prefix itself -- so four our sample trie, above,\n # 'ac' -> [e', 't']\n\n # Return list of words, joining the prefix to each suffix",
"title": ""
},
{
"docid": "a3beedd83ab299ba159455d27cc20e49",
"score": "0.6893603",
"text": "def is_path(self, prefix: str):\n if not prefix:\n return False\n\n # start exploration from the root node\n current = self.__root\n upper_case_prefix = prefix.upper()\n\n # check that for each character, there exists a child node link from our trie root\n for char in upper_case_prefix:\n child = current.get_child(char)\n if not child:\n # at this point, there's no link from the root to the current character\n # therefore, the provided prefix does not exist in our dictionary, and we can return early\n return False\n # otherwise: if a link (child) is found, continue exploring\n current = child\n\n # when we reach this point, all characters in the prefix have been found in the trie\n return True",
"title": ""
},
{
"docid": "7459587b15f745a9e2b4c764ad574940",
"score": "0.6868195",
"text": "def find_prefix(root, prefix: str, pos: str) -> Tuple[bool, int, str, list]:\n node = root\n matched = ''\n possibilities = []\n # If the root node has no children, then return False.\n # Because it means we are trying to search in an empty trie\n if not root.children:\n return False, 0, matched, possibilities\n for char in prefix:\n char_not_found = True\n # Search through all the children of the present `node`\n for child in node.children:\n if child.char == char:\n # We found the char existing in the child.\n char_not_found = False\n # Assign node as the child containing the char and break\n node = child\n matched += char\n if node.word_finished:\n possibilities.append(matched)\n break\n # Return False anyway when we did not find a char.\n if char_not_found:\n new_possibilities = []\n if node.word_finished:\n new_possibilities.append((matched, node.pos))\n for n in node.children:\n if n.word_finished:\n new_possibilities.append((matched + n.char, n.pos))\n return False, 0, matched, get_most_matched(new_possibilities, prefix, pos)\n # Well, we are here means we have found the prefix. Return true to indicate that\n # And also the counter of the last node. This indicates how many words have this\n # prefix\n return True, node.counter, matched, possibilities",
"title": ""
},
{
"docid": "9baae5de173f1fbdf9a59641437cb468",
"score": "0.6792369",
"text": "def __contains__(self, prefix):\n\n return self.fetch(prefix) is not None",
"title": ""
},
{
"docid": "13f96fbc883ad1694eaebccc0c15c02d",
"score": "0.6789298",
"text": "def search(self, word: str) -> bool:\n root = self.trie\n for ch in word:\n if ch not in root:\n return False\n root = root[ch]\n return 'word' in root",
"title": ""
},
{
"docid": "f476345a9f6a18334bbcab2f1f7e21be",
"score": "0.67870563",
"text": "def has_prefix(sub_s):\n\tglobal dict\n\n\tif sub_s[0] in dict:\n\t\tif len(sub_s) == 1:\n\t\t\treturn True\n\t\tif sub_s[0:2] in dict[sub_s[0]]:\n\t\t\tfor string in dict[sub_s[0]][sub_s[0:2]]:\n\t\t\t\tif string.startswith(sub_s):\n\t\t\t\t\treturn True\n\treturn False",
"title": ""
},
{
"docid": "47194bae76c5320e4e0b854f26f8428b",
"score": "0.67664164",
"text": "def is_prefix_code(words: List[str]) -> bool:\n for i, w1 in enumerate(words):\n for j, w2 in enumerate(words):\n if i != j and w1.startswith(w2):\n return False\n return True",
"title": ""
},
{
"docid": "7c391d1fa4aea12edf81c0c398d9c0c9",
"score": "0.67024386",
"text": "def find_count(root, prefix: str):\n node = root\n # If the root node has no children, then return False.\n # Because it means we are trying to search in an empty trie\n if not root.children:\n return False, 0\n for char in prefix:\n char_not_found = True\n # Search through all the children of the present `node`\n for child in node.children:\n if child.char == char:\n # We found the char existing in the child.\n char_not_found = False\n # Assign node as the child containing the char and break\n node = child\n break\n # Return False anyway when we did not find a char.\n if char_not_found:\n return 0\n # Well, we are here means we have found the prefix. Return true to indicate that\n # And also the counter of the last node. This indicates how many words have this\n # prefix\n return node.counter",
"title": ""
},
{
"docid": "c16ac616b4c1cdbe042041b0ca76fb02",
"score": "0.66946626",
"text": "def contains_word(self, word):\n if len(word) < 1:\n return False\n if word is None:\n raise ValueError(\"Trie.contains_word() requires a non-empty string.\")\n\n current_node, does_exist = self.head, True\n\n for letter in word:\n if letter in current_node.children:\n current_node = current_node.children[letter]\n else:\n does_exist = False\n break\n\n if does_exist:\n if current_node.data == None:\n does_exist = False\n\n return does_exist",
"title": ""
},
{
"docid": "20dbe843f09110a16eef746c0695e3a1",
"score": "0.66937584",
"text": "def is_complete_prefix_code(words: List[str]) -> bool:\n return is_prefix_code(words) and mcmillan_sum(words) == 1",
"title": ""
},
{
"docid": "d760d8344a924d43f86e2934f8d53785",
"score": "0.66620356",
"text": "def search(self, word: str) -> bool:\n current_trie = self.head\n for index in range(1, len(word) + 1):\n node_index = ord(word[:index][-1]) - ord('a')\n if current_trie.child_nodes[node_index]:\n current_trie = current_trie.child_nodes[node_index]\n else:\n return False\n return current_trie.child_nodes == [None] * 26",
"title": ""
},
{
"docid": "3e1cc68b7ea67dd792926a20cc5b670f",
"score": "0.6660197",
"text": "def search(self, word):\n try:\n root = self\n for ch in word:\n root = root.trie[ch]\n except KeyError as e:\n return False\n\n return root.word is not None",
"title": ""
},
{
"docid": "51b3c1b11249c833172fe27b90e424e6",
"score": "0.6658243",
"text": "def __contains__(self, word):\n\t\tcurrent_dict = self.keyword_trie_dict\n\t\tlen_covered = 0\n\t\tfor char in word:\n\t\t\tif char in current_dict:\n\t\t\t\tcurrent_dict = current_dict[char]\n\t\t\t\tlen_covered += 1\n\t\t\telse:\n\t\t\t\tbreak\n\t\treturn self._keyword in current_dict and len_covered == len(word)",
"title": ""
},
{
"docid": "a558d758f50c036ad466cc0bc6c799ab",
"score": "0.6637899",
"text": "def _has_tree_prefixes(self, prefixes) -> bool:\n return any(\n prefix in tree.name\n for prefix, tree in itt.product(prefixes, self.trees)\n )",
"title": ""
},
{
"docid": "8343044b390120efb637dec502505e13",
"score": "0.66144466",
"text": "def exists(self, prefix, node = None):\n if not node:\n node = self.root\n\n if not prefix.startswith(node.prefix):\n return False\n elif node.prefix == prefix:\n return True\n elif not node.children:\n return False\n else:\n for child in node.children:\n if prefix.startswith(child.prefix):\n return self.exists(prefix, child)",
"title": ""
},
{
"docid": "f8b4ea925ba7f27cf78b794e0e4d32c6",
"score": "0.65974736",
"text": "def find_words(self, prefix):\n cur_node = self.root\n for char in prefix:\n if char not in cur_node.next:\n return []\n cur_node = cur_node.next[char]\n ans = []\n if cur_node.is_word:\n ans.append(prefix)\n for key in cur_node.next.keys():\n self.dfs(prefix + key, cur_node.next[key], ans)\n return ans",
"title": ""
}
]
Decode given bytes object `data` as given type `name` and return the decoded data as a dictionary. >>> foo.decode('Question', b'0\\x0e\\x02\\x01\\x01\\x16\\x09Is 1+1=3?')
[
{
"docid": "be7ee2215b02a613745df021d80db130",
"score": "0.65237695",
"text": "def lft(self):\n return self._lft",
"title": ""
},
{
"docid": "d4e35465f4e9efa5863436ea473cfab4",
"score": "0.6152094",
"text": "def bfs(self):\n\n queue = Queue()\n queue.put(self)\n\n while not queue.empty():\n\n current_node = queue.get()\n print(current_node.data, end=' ')\n\n if current_node.left:\n queue.put(current_node.left)\n\n if current_node.right:\n queue.put(current_node.right)",
"title": ""
},
{
"docid": "0dc173b4edc9c3381884530f7b3e3085",
"score": "0.61099946",
"text": "def get_current_node(self):\n openSet = list(self.openSet)\n temp = openSet[0]\n for i in openSet:\n self.calculate_fscore(i)\n if self.fScore[i] < self.fScore[temp]:\n temp = i\n return temp",
"title": ""
},
{
"docid": "4309686b9b74b2a48f4e47bd3e776502",
"score": "0.60792744",
"text": "def bfs(self, start):\n if start in self.nodes:\n path = []\n\n # using a list for ease; not as efficient as dll\n q = []\n seen = set([])\n\n q.append(self.nodes[start])\n seen.add(start)\n\n while q:\n current = q.pop(0)\n path.append(current.key)\n for neighbor in current.neighbors:\n if neighbor.key not in seen:\n q.append(neighbor)\n seen.add(neighbor.key)\n return path\n else:\n print(\"This starting node is not in the graph.\")",
"title": ""
},
{
"docid": "005928c2f688e8c2ddcf2e4cc2321a2a",
"score": "0.5900905",
"text": "def bfs_search(self):\n\n queue = [(0,0)] # level and the position of the sink\n aug_path = {(0,0):[0]} # level,last_index store to path storage\n min_path = [] # one storing the min path\n visited = set()\n while len(queue) > 0:\n prev_level, queue_node = queue[0]\n queue = queue[1:]\n visited.add(queue_node)\n neigh = self.get_neighbors(queue_node)\n neigh = [i for i in neigh if i not in visited]\n level = prev_level + 1 # current level of the bfs\n path = aug_path[(prev_level,queue_node)]\n for node in neigh:\n edge = self.edge_flow[queue_node][node]\n if edge.capacity > edge.flow:\n aug_path[(level,node)] = path + [node]\n \n if node+1 == self.total_nodes:\n queue = []\n min_path = path +[node]\n else:\n \n queue.append((level, node))\n \n return self.path_edges(min_path)",
"title": ""
},
{
"docid": "16f242fa9d7041749e142380ff7166f3",
"score": "0.58604187",
"text": "def _get_bfs_tree(self) -> nx.DiGraph:\n tree = nx.minimum_spanning_tree(self.graph)\n return nx.bfs_tree(tree, self.home)",
"title": ""
},
{
"docid": "040c65d17ecd9e5aec108e27d516b6f4",
"score": "0.5856313",
"text": "def llf(self):\n return self._llf",
"title": ""
},
{
"docid": "13cf7617c9dac8f7eef91769cf6a5611",
"score": "0.582439",
"text": "def GetFS(self):",
"title": ""
},
{
"docid": "e861e40f91446bfba8c066aff5c37c98",
"score": "0.5658262",
"text": "def getLFO(self):\n\t\treturn self.lfo",
"title": ""
},
{
"docid": "043c3a3d6ee7ab65bee046c20f026234",
"score": "0.5586315",
"text": "def bfs(initial, goal_test, successors):\n # frontier representa os lugares que ainda nao visitamos\n frontier = Queue()\n frontier.push(Node(initial, None))\n # explored representa os lugares que ja foram visitados\n explored = {initial}\n\n # continua enquanto houver lugares para explorar\n while not frontier.empty:\n current_node = frontier.pop()\n current_state = current_node.state\n\n # se encontrar o objetivo retorna o no atual\n if goal_test(current_state):\n return current_node\n\n # verifica para onde podemos ir em seguida\n for child in successors(current_state):\n # ignora os nos filhos que ja foram visitados\n if child in explored:\n continue\n explored.add(child)\n frontier.push(Node(child, current_node))\n # passamos por todos os lugares e nao atingimos o objetivo\n return None",
"title": ""
},
{
"docid": "0469bf8e8fbf23ecd4c76f3e3ec2fe50",
"score": "0.55849457",
"text": "def bfs(root):\n\n to_visit = []\n if root:\n to_visit.append(root)\n while to_visit:\n current = to_visit.pop()\n if current.left:\n to_visit.append(current.left)\n if current.right:\n to_visit.append(current.right)\n if not to_visit or not current.right:\n return current",
"title": ""
},
{
"docid": "333e214c999d868735a7192b8da121bc",
"score": "0.55775005",
"text": "def bfs(self):\n queue = []\n queue.append(self.root)\n while queue:\n node = queue[0]\n print node.value\n if node.left_child:\n queue.append(node.left_child)\n if node.right_child:\n queue.append(node.right_child)\n del queue[0]",
"title": ""
},
{
"docid": "ed0a01edd130ec74113696b0fd09cc40",
"score": "0.5571185",
"text": "def Bfs(self,start):\n self.visited = dict()\n q = Queue()\n q.append(start)\n self.visited[start]=1\n if self.repr==1: \n while(len(q)):\n cur = q.pop()\n print(cur)\n if self.G.get(cur):\n for c in self.G[cur]:\n if self.visited.get(c):\n continue\n self.visited[c]=1\n q.append(c)\n\n if self.repr==2:\n while(len(q)):\n cur = q.pop()\n print(cur)\n u = cur-1\n for i in range(self.n):\n if self.visited.get(i+1):\n continue\n if self.G[u][i]:\n self.visited[i+1]=1\n q.append(i+1)",
"title": ""
},
{
"docid": "c93f1151ee0af17a1f54c5d7a90d3e21",
"score": "0.5523278",
"text": "def bfs(node):\n queue = []\n while node:\n print(node.value)\n if node.left:\n queue.insert(0, node.left)\n if node.right:\n queue.insert(0, node.right)\n if queue:\n node = queue.pop()\n else:\n break",
"title": ""
},
{
"docid": "e9dc98eef43f91c0b24b0dbfec7cbf22",
"score": "0.5491846",
"text": "def dfs(self, start):\n if start in self.nodes:\n path = []\n\n # using a list as a stack for ease\n s = []\n seen = set([])\n\n s.append(self.nodes[start])\n seen.add(start)\n\n while s:\n current = s.pop()\n path.append(current.key)\n for neighbor in current.neighbors:\n if neighbor.key not in seen:\n s.append(neighbor)\n seen.add(neighbor.key)\n return path\n else:\n print(\"This starting node is not in the graph.\")",
"title": ""
},
{
"docid": "f907e0a54fef1646edacc6897c511a73",
"score": "0.54915065",
"text": "def dfs(self, node):\n if not node:\n return\n\n self.rtn_list.append(node.data)\n print(node.data)\n\n self.dfs(node.left)\n self.dfs(node.right)",
"title": ""
},
{
"docid": "24088e22557e69f4ee9373fcf9cd2848",
"score": "0.54864484",
"text": "def next_node(self):\n path = self.run_bfs()\n return path[1]",
"title": ""
},
{
"docid": "33eeb7a807cef0fa00245725c908786f",
"score": "0.54852945",
"text": "def bfs(self, start_node_num):\n node = self.find_node(start_node_num)\n self._clear_visited()\n ret_list = []\n # Your code here\n queue = [node]\n node.visited = True\n def enqueue(n, q=queue):\n n.visited = True\n q.append(n)\n def unvisited_outgoing_edge(n, e):\n return ((e.node_from.value == n.value)\n and (not e.node_to.visited))\n while queue:\n node = queue.pop(0)\n ret_list.append(node.value)\n for e in node.edges:\n if unvisited_outgoing_edge(node, e):\n enqueue(e.node_to)\n return ret_list",
"title": ""
},
{
"docid": "8a7a69f58f85b2b8959c32318011eb13",
"score": "0.54811156",
"text": "def bfs(root):\n q = queue.Queue()\n q.put(root)\n\n while not q.empty():\n current_vertex = q.get()\n print(current_vertex)\n\n for vertex in current_vertex.neighbour_list:\n if not vertex.visited:\n vertex.visited = True\n q.put(vertex)",
"title": ""
},
{
"docid": "c40ff0b3453cb6812de6fe7f353f7144",
"score": "0.5419131",
"text": "def bfs(graph, start_node):\n node_queue = Queue()\n dist = {}\n parent = {}\n for node in graph.nodes():\n dist[node] = float(\"inf\")\n parent[node] = None\n dist[start_node] = 0\n node_queue.push(start_node)\n while len(node_queue) != 0:\n node = node_queue.pop()\n for neighbor in graph.get_neighbors(node):\n if dist[neighbor] == float(\"inf\"):\n dist[neighbor] = dist[node] + 1\n parent[neighbor] = node\n node_queue.push(neighbor)\n return dist, parent",
"title": ""
},
{
"docid": "94263564ee39b917a14039f58e25c88c",
"score": "0.5404512",
"text": "def dfs(graph, start_node):\n nonlocal current_label\n start_vertex = graph.get_vertex(start_node)\n start_vertex.mark_explored()\n for edge in start_vertex.get_edges():\n linked_vertex = graph.get_vertex(edge)\n if not linked_vertex.get_nodes()[0][1]:\n dfs(graph, edge)\n start_vertex.add_label(current_label)\n current_label -= 1",
"title": ""
},
{
"docid": "cd03e2ed62fb05ff80482718908701b6",
"score": "0.53835267",
"text": "def dfs(node):\n if node is None:\n return [-1,-1,-1]\n left,right = dfs(node.left), dfs(node.right)\n return [left[1]+1, right[0]+1, max(left[1]+1, right[0]+1, left[2], right[2])]\n #left[1]+1 for node.left.right\n #left[1]+1 for node.right.left",
"title": ""
},
{
"docid": "6477e9197ea527c783c4174da800932a",
"score": "0.5376386",
"text": "def dfs(self, start_node):\n\t\tself.visited = [False]*self.nodes\n\t\tprint \"dfs_recursive\"\n\t\tself.dfs_recursive(start_node)\n\t\tprint \"====\"\n\t\tprint \"dfs_iterative\"\n\t\tself.visited = [False]*self.nodes\n\t\tself.dfs_iterative(start_node)",
"title": ""
},
{
"docid": "62432411e86a69882541641151e1926e",
"score": "0.53730875",
"text": "def dfs(initial, goal_test, successors):\n # frontier representa os lugares que ainda nao visitamos\n frontier = Stack()\n frontier.push(Node(initial, None))\n # explored representa os lugares que ja foram visitados\n explored = {initial}\n\n # continua enquanto houver lugares para explorar\n while not frontier.empty:\n current_node = frontier.pop()\n current_state = current_node.state\n\n # se encontrar o objetivo retorna o no atual\n if goal_test(current_state):\n return current_node\n\n # verifica para onde podemos ir em seguida\n for child in successors(current_state):\n # ignora os nos filhos que ja foram visitados\n if child in explored:\n continue\n explored.add(child)\n frontier.push(Node(child, current_node))\n # passamos por todos os lugares e nao atingimos o objetivo\n return None",
"title": ""
},
{
"docid": "04e13520b02f722dfbf49f115c59e3df",
"score": "0.5349521",
"text": "def depthFirstSearch(problem):\n \n # Initialize Stack() instances for LIFO datastructure (Source: College 2, Slide 38)\n fringe = util.Stack()\n visited = util.Stack()\n \n \"\"\"\n Initialize the fringe with following indeces:\n [0] = state (or node) to be expanded\n [1] = list of actions up to this node\n \"\"\"\n \n fringe.push([problem.getStartState(), []])\n\n while not fringe.isEmpty():\n \n # Extract deepest node from the fringe\n state, actions = fringe.pop()\n \n # Goal test\n if problem.isGoalState(state):\n return actions\n \n # Get successors of current state\n successors = problem.getSuccessors(state)\n\n # Loop over successors\n for index in reversed(range(0, len(successors))):\n\n # Successors can't be visited, so check\n if successors[index][0] not in visited.list:\n \n # Push successors' state and actions to the fringe if not visited\n fringe.push([successors[index][0], actions + [successors[index][1]]])\n \n # Current node is visited\n visited.push(state)\n\n # Return empty list of actions in case no goal is present\n return []",
"title": ""
},
{
"docid": "c3192fe553f5bceec13920978c6fb623",
"score": "0.53470683",
"text": "def bfs(graph, initial_node, dest_node):\n # Queue to store the list of node which will be going to be visited\n Q = []\n # list to store the list of nodes visited\n visited_nodes = []\n # Dictionar key variable to store the list of parents of node visited\n parent_list = {}\n # dictionary key variable to store the distance between the two nodes\n distance_node = {}\n # Adding the intial node into the list\n distance_node[initial_node] = 0\n parent_list[initial_node]= None\n Q.append(initial_node)\n while(bool(Q)):\n cur_node = Q.pop(0)\n for neighbor_nodes in graph.neighbors(cur_node):\n if neighbor_nodes not in visited_nodes:\n visited_nodes.append(neighbor_nodes)\n parent_list[neighbor_nodes] = cur_node\n distance_node[neighbor_nodes] = distance_node[cur_node] + graph.distance_nodes(cur_node,neighbor_nodes)\n Q.append(neighbor_nodes)\n\n if dest_node in visited_nodes:\n break\n\n list = []\n start_node = dest_node\n #print(\"parent list =\",parent_list)\n\n #for cur_node,par_node in parent_list.items():\n # if par_node is not None:\n # edge = graph.get_edge(par_node,cur_node)\n # print(\"edge\",edge)\n # list.append(edge)\n while(parent_list[start_node] is not None):\n par_node = parent_list[start_node]\n #print(\"parent node\",par_node)\n edge = graph.get_edge(par_node,start_node)\n #print(\"edge\",edge)\n list.append(edge)\n start_node = par_node\n\n\n list.reverse()\n return list",
"title": ""
},
{
"docid": "ce60ddc0005c5442de501c331763cda2",
"score": "0.5345536",
"text": "def bfs(self, verbose: bool = True) -> list:\r\n all_states = []\r\n if verbose:\r\n print(\"**************Solving(BFS)*****************\")\r\n depth_count = 0\r\n states = 1\r\n queue = [self.state]\r\n while len(queue) != 0:\r\n if verbose:\r\n print(f\"\\rDepth: {depth_count} | States: {states}\", end='')\r\n new_open = []\r\n for state in queue:\r\n if self.quit:\r\n quit()\r\n if self.goal_test(state):\r\n if verbose:\r\n print()\r\n return self.tree.get_path(state)\r\n new_open += self.non_visited_states(state)\r\n queue = new_open\r\n depth_count += 1\r\n states += len(queue)\r\n all_states.extend(queue)\r\n raise StopIteration(\"Can't find Solution.\")",
"title": ""
},
{
"docid": "f26423ab2e76bb111ff831722366270a",
"score": "0.53446543",
"text": "def dfs_helper(self, start_node):\n ret_list = [start_node.value]\n start_node.visited = True\n edges_out = [e for e in start_node.edges\n if e.node_to.value != start_node.value]\n for edge in edges_out:\n if not edge.node_to.visited: ret_list.extend(self.dfs_helper(edge.node_to))\n return ret_list",
"title": ""
},
{
"docid": "8065d090a66ccbfa019e6896011e2915",
"score": "0.5343275",
"text": "def BFS(self, nd):\n #TODO: design your data structure here for your algorithm\n u = 0\n roads = dict()# key: length, value: path[]\n roadslength = []# all length of each path are in here\n prevNode = 0 \n start = 0\n \n for deadend in range(len(self.nodes)):\n if deadend != nd-1 and self.nd_dict[deadend].isEnd() and deadend+1 not in self.explored:\n path = self.BFS_2(nd,deadend+1)# a tople,([path],length)\n roads[path[1]] = path[0]\n roadslength.append(path[1])\n #TODO: update the information of your data structure\n if len(roadslength):\n shortestlength = min(roadslength)\n return roads[shortestlength]\n else:\n return []",
"title": ""
},
{
"docid": "e3f612d39ecc9154b00a9e27895c1528",
"score": "0.5342101",
"text": "def dfs_helper(self, start_node):\n ret_list = [start_node.value]\n start_node.visited = True\n edges_out = [e for e in start_node.edges\n if e.node_to.value != start_node.value]\n for edge in edges_out:\n if not edge.node_to.visited:\n ret_list.extend(self.dfs_helper(edge.node_to))\n return ret_list",
"title": ""
},
{
"docid": "3adb2cf1772c115dce4eb556f8021302",
"score": "0.5330284",
"text": "def get_lchild(self, node):\n return self.heap[2 * node + 1] if self.has_lchild(node) else None",
"title": ""
},
{
"docid": "a751f553fe569db4ef3afe8adec97cfb",
"score": "0.53252494",
"text": "def dfs(self, node):\r\n if not node:\r\n return None\r\n \r\n if (node.left):\r\n self.dfs(node.left)\r\n\r\n print(node.val, end=' -> ')\r\n\r\n if (node.right):\r\n self.dfs(node.right)",
"title": ""
},
{
"docid": "3c6021209e951d92f0c645706c985624",
"score": "0.53199184",
"text": "def dft(self, starting_vertex):\n q = Stack()\n visited = set() # (1a)\n q.push(starting_vertex) # (1)\n while q.size() > 0: # (2)\n v = q.pop()\n if v not in visited: # (2a)\n print(v)\n visited.add(v)\n for neighbor in self.get_neighbors(v): # (2b)\n q.push(neighbor)\n\n # ---------> [dft_NOTES] <---------\n # Depth First Traversal\n # (1) Init the stack [q] with the [starting_vertex] passed in\n # --> push [starting_vertex] onto our stack [q]\n # --> we are using a pre-built Stack()\n # --> SEE: projects>>graph>>[util.py]\n # --> WHY?\n # --> allows us to back up and try other paths\n # --> more details ⬇️\n # --> guided>>2201-graphs>>README>>recorded lecture\n # (1a) we will need a [visited] set\n # --> keep track of where we have been\n\t\t# (2) While our queue isn't empty ...\n # --> look at front of the stack : [v = q.pop()]\n # (2a) if [v] has not been visited ...\n # --> visit the node : we will just [print(v)]\n # --> add v to the visited set : [visited.add(v)]\n # (2b) add all neighbors of this node to the stack\n # --> get all the neighbors : [self.get_neighbors(v)]\n # --> loop thru each and push onto stack",
"title": ""
},
{
"docid": "2ac70e2b11dc129026d884f5bebdb9ca",
"score": "0.53139883",
"text": "def get_leftmost(self):\n if self.tree is None:\n return None\n else:\n ltree = self.left\n if ltree.is_empty:\n return self\n else:\n return ltree.get_leftmost()",
"title": ""
},
{
"docid": "af71abcc53bafc82496bdeb991022c7e",
"score": "0.5312154",
"text": "def get_current_node():\n return get_current_node.current[-1]",
"title": ""
},
{
"docid": "df28794914ebff0ccc6d2813e11ff1cf",
"score": "0.5301736",
"text": "def best_fs(self, heuristic=None, verbose: bool = True) -> list:\r\n heuristic = self.heuristic if heuristic is None else heuristic\r\n if heuristic is None:\r\n raise Exception(\"No heuristic function is provided.\")\r\n if verbose:\r\n print(\"**************Solving(BEST_FS)*****************\")\r\n depth_count = 0\r\n states = 1\r\n queue = [self.state]\r\n while len(queue) != 0:\r\n current_state = queue.pop()\r\n if verbose:\r\n print(f\"\\rDepth: {depth_count} | States: {states}\", end='')\r\n if self.quit:\r\n quit()\r\n if self.goal_test(current_state):\r\n if verbose:\r\n print()\r\n return self.tree.get_path(current_state)\r\n next_states = self.non_visited_states(current_state)\r\n heuristics = [heuristic(state) for state in next_states]\r\n queue.extend([state for _, state in sorted(zip(heuristics, next_states))])\r\n depth_count += 1\r\n states += len(next_states)\r\n raise Exception(\"Can't find Solution.\")",
"title": ""
},
{
"docid": "bccf72b67826d29976b8a59c0a6f2f19",
"score": "0.53009474",
"text": "def DFS(self, visitor, incoming):\n visitor.VisitLeaf(self, incoming)",
"title": ""
},
{
"docid": "0df8624dbbe4b905d3bb170756fba383",
"score": "0.5273659",
"text": "def dft(self, starting_vertex):\n # TODO\n visited = set()\n stack = deque()\n stack.append(starting_vertex)\n while len(stack) > 0:\n currNode = stack.pop()\n if currNode not in visited:\n visited.add(currNode)\n print(currNode)\n for neighbor in self.get_neighbors(currNode):\n stack.append(neighbor)",
"title": ""
},
{
"docid": "7fef8c43fbea55cb406f5db53fbe622d",
"score": "0.52731425",
"text": "def bfs(self, starting_vertex, destination_vertex):\n q = Queue()\n visited = set()\n q.enqueue([starting_vertex])\n \n \n while q.size() > 0:\n path = q.dequeue() \n last = path[-1]\n if last not in visited:\n \n visited.add(last)\n if last == destination_vertex:\n return path\n else:\n for each in self.get_neighbors(last):\n new_path = path + [each]\n q.enqueue(new_path)",
"title": ""
},
{
"docid": "a4f520a34f719f1be44ed196c1d47f60",
"score": "0.52715015",
"text": "def depthFirstSearch(problem):\n\n #Nodos a expandir\n OPEN = util.Stack()\n\n #Nodos expandidos\n CLOSE = []\n\n #initial position\n #(coordenada,direccion,coste,padre)\n startNode = (problem.getStartState(), \"\", 0,None)\n\n #Nodo iniicial --> pila\n OPEN.push(startNode )\n\n #Indices\n COORD = 0 #State\n DIR = 1 #Action\n COST = 2 #Cost\n FATHER = 3 #Father node\n\n while not OPEN.isEmpty():\n\n #Obtenemos elemento de la lista\n vertex = OPEN.pop()\n\n #Comprobamos si es un goal\n if problem.isGoalState(vertex[COORD]):\n path_to_Goal = []\n #Construimos camino al path\n while vertex[FATHER]!= None:\n path_to_Goal.append(vertex[DIR])\n vertex = vertex[FATHER]\n\n #Necesario para path= Raiz ----> Goal\n path_to_Goal.reverse()\n return path_to_Goal\n\n #Comprobar si no se expandio antes\n if not vertex[COORD] in CLOSE:\n\n #Expandemos el nodo\n sucesors = problem.getSuccessors(vertex[COORD])\n #recorremos los hijos del nodo\n for tempNode in sucesors:\n if not tempNode[COORD] in CLOSE:\n\n\n state=tempNode[COORD]\n action = tempNode[DIR]\n cost = tempNode[COST]\n fath = vertex\n #creamos un nuevo nodo\n newNode= (state,action,cost,fath)\n #Agregamos a nodos a expandir\n OPEN.push(newNode)\n\n #Cerramos el nodo expandido\n CLOSE.append(vertex[COORD])\n return []",
"title": ""
},
{
"docid": "ce9cc891c32b1cc04d22d53fb504d036",
"score": "0.5268616",
"text": "def bfs(self, start_node = None, visitor = lambda x: x):\n distances = {} # track visited nodes\n while len(distances) < len(self._nodes):\n break_first = bool(start_node)\n if not break_first:\n start_node = list(set(self._nodes).difference(set(distances.keys())))[0]\n distances[start_node] = 0\n que = deque([start_node])\n while len(que) > 0:\n current_id = que.popleft()\n for child_id in self.children(current_id):\n if child_id not in distances:\n que.append(child_id)\n distances[child_id] = distances[current_id] + 1\n visitor(child_id)\n if break_first: break\n return distances",
"title": ""
},
{
"docid": "20c26b22698ff62d282997c294f410e5",
"score": "0.52572304",
"text": "def dfs_recursive(self, start_node):\n\t\tif not self.visited[start_node]:\n\t\t\tself.visited[start_node] = True\n\t\t\tprint \"visited : \", start_node\n\t\t\tfor each_node in self.graph[start_node]:\n\t\t\t\tif not self.visited[each_node]:\n\t\t\t\t\tself.dfs_recursive(each_node)",
"title": ""
},
{
"docid": "92395f1ef938c6e8f364a09c65165ba2",
"score": "0.5253412",
"text": "def bfs_tree(adj_list, start):\n n = len(adj_list)\n parent_array = [None] * n\n state_array = [\"Undiscovered\"] * n\n queue = []\n state_array[start] = \"Discovered\"\n queue.append(start) #Enqueue\n return bfs_loop(adj_list, queue, state_array, parent_array)",
"title": ""
},
{
"docid": "30c41b019acc6bdf3848a33677480e75",
"score": "0.5247685",
"text": "def dfs(self, start_node_num):\n self._clear_visited()\n start_node = self.find_node(start_node_num)\n return self.dfs_helper(start_node)",
"title": ""
},
{
"docid": "5ad9a6d8f92f71a8d7da611079bdf221",
"score": "0.52263486",
"text": "def __best_first_graph_search(self, problem, f):\n f = self.__memoize(f, 'f')\n node = Node(problem.initial)\n\n assert node != None and node.state != None\n\n if problem.goal_test(node.state):\n return node\n frontier = PriorityQueue(min, f)\n\n\n frontier.append(node)\n explored = set()\n step = 0\n while frontier:\n step+=1\n \n node = frontier.pop()\n assert node != None and node.state != None, \"Estratto un nodo None\"\n \n #print '---- CURRENT NODE ----'\n #print node.state\n \n if problem.goal_test(node.state):\n return node, len(explored)+1\n explored.add(node.state)\n \n for child in node.expand(problem):\n assert child != None and child.state != None\n if child.state not in explored and child not in frontier:\n frontier.append(child)\n elif child in frontier:\n incumbent = frontier[child]\n if f(child) < f(incumbent):\n del frontier[incumbent]\n frontier.append(child)\n return None",
"title": ""
},
{
"docid": "e1b29e928c69f8d15e5c2ca87a4b8061",
"score": "0.52216387",
"text": "def Node_fspath(self: Node) -> LEGACY_PATH:\n return legacy_path(self.path)",
"title": ""
},
{
"docid": "ab81469afb9be272757e50d6f69c6064",
"score": "0.5218042",
"text": "def _get_logical_node(self):\n return self.__logical_node",
"title": ""
},
{
"docid": "09abf87935b1f62e282ba88171239374",
"score": "0.5217506",
"text": "def bfs(self, node=None):\n yield self\n last = self\n for node in self.bfs(last):\n for child in node.children:\n yield child\n last = child\n if last == node:\n return",
"title": ""
},
{
"docid": "62bc6914d35f5779d5d76b83abddf876",
"score": "0.52159667",
"text": "def bfs(self, starting_vertex, destination_vertex):\n # make a queue\n q = Queue()\n\n # enqueue our starting a node\n path = [starting_vertex]\n q.enqueue(path)\n\n # make a set to track if we've been at that node before \n visited = set() \n\n # while q is not empty\n while q.size() > 0:\n\n ## dequeue path at the front of our line\n current_path = q.dequeue()\n current_node = current_path[-1] # last item in list of nodes in our path.\n\n ### it this is our destination_vertex (or search node), return our current path\n if current_node == destination_vertex:\n return current_path\n\n ### if we haven't visited this node yet.\n if current_node not in visited:\n ### mark node as visited\n visited.add(current_node)\n ### get the node's neighbors\n neighbors = self.friendships[current_node]\n ### for each of the neighbors\n for neighbor in neighbors: \n #### add it to queue\n q.enqueue(current_path + [neighbor])",
"title": ""
},
{
"docid": "34e1a8060fd393f0059b799e77824dbb",
"score": "0.5210689",
"text": "def solve(self, l0, l2, m, gap_tol=1e-2, warm_start=None, mu=0.95,\n branching='maxfrac', l1solver='l1cd', number_of_dfs_levels=0,\n verbose=False, time_limit=3600, cd_max_itr=1000,\n kkt_max_itr=100):\n st = time.time()\n upper_bound, upper_beta, support = self. \\\n _warm_start(warm_start, verbose, l0, l2, m)\n if verbose:\n print(f\"initializing took {time.time() - st} seconds\")\n\n # root node\n self.root = Node(None, [], [], x=self.x, y=self.y,\n xi_norm=self.xi_norm)\n self.bfs_queue = queue.Queue()\n self.dfs_queue = queue.LifoQueue()\n self.bfs_queue.put(self.root)\n\n # lower and upper bounds initialization\n lower_bound, dual_bound = {}, {}\n self.levels = {0: 1}\n min_open_level = 0\n\n max_lower_bound_value = -sys.maxsize\n best_gap = gap_tol + 1\n\n if verbose:\n print(f'{number_of_dfs_levels} levels of depth used')\n\n while (self.bfs_queue.qsize() > 0 or self.dfs_queue.qsize() > 0) and \\\n (time.time() - st < time_limit):\n\n # get current node\n if self.dfs_queue.qsize() > 0:\n curr_node = self.dfs_queue.get()\n else:\n curr_node = self.bfs_queue.get()\n\n # prune?\n if curr_node.parent_dual and upper_bound <= curr_node.parent_dual:\n self.levels[curr_node.level] -= 1\n # self.leaves.append(current_node)\n continue\n \n rel_gap_tol = -1\n if best_gap <= 20 * gap_tol or \\\n time.time() - st > time_limit / 4:\n rel_gap_tol = 0\n if best_gap <= 10 * gap_tol or \\\n time.time() - st > 3 * time_limit / 4:\n rel_gap_tol = 1\n # calculate primal and dual values\n curr_primal, curr_dual = self. \\\n _solve_node(curr_node, l0, l2, m, l1solver, lower_bound,\n dual_bound, upper_bound, rel_gap_tol, cd_max_itr,\n kkt_max_itr)\n\n curr_upper_bound = curr_node.upper_solve(l0, l2, m)\n if curr_upper_bound < upper_bound:\n upper_bound = curr_upper_bound\n upper_beta = curr_node.upper_beta\n support = curr_node.support\n best_gap = \\\n (upper_bound - max_lower_bound_value) / abs(upper_bound)\n\n # update gap?\n if self.levels[min_open_level] == 0:\n del self.levels[min_open_level]\n max_lower_bound_value = max([j for i, j in dual_bound.items()\n if i <= min_open_level])\n best_gap = \\\n (upper_bound - max_lower_bound_value) / abs(upper_bound)\n if verbose:\n print(f'l: {min_open_level}, (d: {max_lower_bound_value}, '\n f'p: {lower_bound[min_open_level]}), '\n f'u: {upper_bound}, g: {best_gap}, '\n f't: {time.time() - st} s')\n min_open_level += 1\n\n # arrived at a solution?\n if best_gap <= gap_tol:\n return self._package_solution(upper_beta, upper_bound,\n lower_bound, best_gap, support,\n self.p, time.time() - st)\n\n # integral solution?\n if is_integral(curr_node.z, self.int_tol):\n curr_upper_bound = curr_primal\n if curr_upper_bound < upper_bound:\n upper_bound = curr_upper_bound\n upper_beta = curr_node.upper_beta\n support = curr_node.support\n if verbose:\n print('integral:', curr_node)\n best_gap = \\\n (upper_bound - max_lower_bound_value) / abs(upper_bound)\n # branch?\n elif curr_dual < upper_bound:\n left_node, right_node = branch(curr_node, self.x, l0, l2, m,\n self.xi_norm, self.int_tol,\n branching, mu)\n self.levels[curr_node.level + 1] = \\\n self.levels.get(curr_node.level + 1, 0) + 2\n if curr_node.level < min_open_level + number_of_dfs_levels:\n self.dfs_queue.put(right_node)\n self.dfs_queue.put(left_node)\n else:\n self.bfs_queue.put(right_node)\n self.bfs_queue.put(left_node)\n else:\n pass\n\n return self._package_solution(upper_beta, upper_bound, lower_bound,\n best_gap, support, self.p,\n time.time() - st)",
"title": ""
},
{
"docid": "36aeb4b0f3997370d2d545349a7af83c",
"score": "0.5209212",
"text": "def bfs_tree(self, src_vertex = 0):\n parents = self.breadth_first_search(src_vertex)[1]\n return parents",
"title": ""
},
{
"docid": "f69c0ddacf92fad4b9d102b75cd2357e",
"score": "0.52060425",
"text": "def dfs(node):\n if not node:\n return float('inf'), None\n if not node.left and not node.right:\n return 1, node.val\n ld, lv = dfs(node.left)\n rd, rv = dfs(node.right)\n if ld < rd:\n return ld+1, lv\n else:\n return rd+1, rv",
"title": ""
},
{
"docid": "61754d354f26bda446220f6749fe3183",
"score": "0.5196221",
"text": "def getNodeFringeFieldIN(self):\n\t\treturn self.__fringeFieldIN",
"title": ""
},
{
"docid": "be5a892615b9ce89cda82c38b60c18a5",
"score": "0.5184284",
"text": "def bfs(self, starting_vertex, destination_vertex):\n # TODO\n q = deque()\n # Each element in the queue\n q.append([starting_vertex])\n visited = set()\n while len(q) > 0:\n currPath = q.popleft() \n currNode = currPath[-1] \n if currNode == destination_vertex:\n return currPath\n if currNode not in visited:\n visited.add(currNode)\n for neighbor in self.get_neighbors(currNode):\n newPath = list(currPath)\n newPath.append(neighbor)\n q.append(newPath)",
"title": ""
},
{
"docid": "c65cc536f708838a3961342b68f895d8",
"score": "0.5182056",
"text": "def depthFirstSearch(problem):\n \"*** YOUR CODE HERE ***\"\n #util.raiseNotDefined()\n return __generalSearch(\"dfs\",problem,nullHeuristic)",
"title": ""
},
{
"docid": "2dcd0b038c1aef7ef45e80368d234aae",
"score": "0.51798636",
"text": "def dfs(node,direction):\n if node is None:\n return 0\n elif direction == -1:\n return 1+dfs(node.right,1)\n elif direction == 1:\n return 1+dfs(node.left,-1)",
"title": ""
},
{
"docid": "b630216a44a40a74bdec4aaec80021aa",
"score": "0.517526",
"text": "def lft(self, lft):\n\n self._lft = lft",
"title": ""
},
{
"docid": "7e44587174432732d1e3ba1e2bdb62e0",
"score": "0.51747566",
"text": "def bfs_visited(ugraph, start_node):\n queue = deque()\n visited = set()\n if start_node not in ugraph.keys():\n return None\n visited.add(start_node)\n queue.append(start_node)\n while len(queue) > 0:\n node = queue.popleft()\n for neighbor in list(ugraph[node]):\n if not neighbor in visited:\n visited.add(neighbor)\n queue.append(neighbor)\n return visited",
"title": ""
},
{
"docid": "b323297ddfcf1a6da098589a2560b41e",
"score": "0.51747423",
"text": "def dfs(self, start_node = None, visitor = lambda x: x):\n history = set()\n def visit(node):\n if node not in history:\n history.add(node)\n for child in self.children(node):\n visit(child)\n visitor(node) # callback comes here\n if not start_node:\n while len(history) < len(self._nodes):\n visit(list(set(self._nodes).difference(history))[0])\n else:\n visit(start_node)",
"title": ""
},
{
"docid": "ef6f168e80bbd395c67723c72153496c",
"score": "0.51731706",
"text": "def bfs(maze):\n # TODO: Write your code here\n (stRow, stCol) = maze.getStart()\n # print(maze.getObjectives())\n checked = set()\n # checked.add((stRow, stCol))\n queue = []\n queue.append((stRow, stCol))\n prev = dict()\n while len(queue) > 0:\n (row, col) = queue.pop(0)\n if (row, col) in checked:\n continue\n checked.add((row, col))\n # print(path)\n if maze.isObjective(row, col):\n path = []\n cur = (row, col)\n while cur in prev:\n path.append(cur)\n cur = prev[cur]\n path.append(cur)\n path.reverse()\n return path\n neighbors = maze.getNeighbors(row, col)\n for n in neighbors:\n if n not in checked:\n prev[n] = (row,col)\n # newPath = list(path)\n# newPath.append(n)\n queue.append(n)\n #else: \n # neighbors.remove(n)\n \n \n \n return []",
"title": ""
},
{
"docid": "3f48a4b1c6f2a288aaf29bbd332a3353",
"score": "0.5172477",
"text": "def LM(f):\n return smp_ground_LM(f.rep, f.lev, f.ord, f.dom)",
"title": ""
},
{
"docid": "64f67a48663ad3e1591293e54a6b3e98",
"score": "0.51596355",
"text": "def bfs(graph, start):\n\n # list to keep track of all visited nodes\n explored = []\n\n # the FIFO queue\n queue = []\n\n # add the start node to the queue\n queue.append(start)\n\n # keep looping until there are no nodes still to be checked\n while len(queue) > 0:\n\n # pop first item from queue (FIFO)\n node = queue.pop(0)\n\n # check if the node has already been explored\n if node not in explored:\n\n # add node to list of checked nodes\n explored.append(node)\n\n # get neighbours if node is present, otherwise default to empty list\n neighbours = graph.get(node, [])\n\n # add neighbours of node to queue\n for neighbour in neighbours:\n queue.append(neighbour)\n\n # return the explored nodes\n return explored",
"title": ""
},
{
"docid": "eae62765e043dbdc0c96bea52007d369",
"score": "0.51557684",
"text": "def fs(self):\n return self._fs",
"title": ""
},
{
"docid": "c9bbb0ad0794a4a753742ca2f9ac47df",
"score": "0.51550764",
"text": "def depthFirstSearch(problem):\n \"*** YOUR CODE HERE ***\"\n\n myXsb= pyxf.xsb(\"/Users/muthukumarsuresh/Downloads/XSB/bin/xsb\")\n myXsb.load('mazeeee.P')\n myXsb.load('dfs2.P')\n time.sleep(5)\n result = myXsb.query('planPath(X)')\n\n while(result == False):\n result = myXsb.query('planPath(X)')\n returnstr = result[0]['X']\n returnstr = returnstr[1:len(returnstr)-1]\n returnList = returnstr.split(',')\n s =Directions.SOUTH\n w = Directions.WEST\n e = Directions.EAST\n n = Directions.NORTH\n retList = []\n for element in returnList:\n if element == 's':\n retList.append(s)\n if element == 'e':\n retList.append(e)\n if element == 'w':\n retList.append(w)\n if element == 'n':\n retList.append(n)\n # returnList = returnList.reverse()\n return retList\n # util.raiseNotDefined()",
"title": ""
},
{
"docid": "61e3ddf19556c58f52aa956a33e9e136",
"score": "0.5154698",
"text": "def DLT(attributesList, matrix, heuristic, currentNode):\n #print(\"am i here\", currentNode)\n classColumn = matrix[:, (matrix.shape[1] - 1)]\n # If attributes list is empty\n if not attributesList:\n # Change node value most common value of class column\n currentNode.value = round(classColumn.sum(axis = 0) / matrix.shape[0])\n # If nodes are pure\n elif classColumn.sum(axis = 0) / matrix.shape[0] == 1:\n currentNode.value = 1\n elif classColumn.sum(axis = 0) == 0:\n currentNode.value = 0\n else:\n # Find max gain\n gainList=[]\n for a in attributesList:\n gainList.append(h.gain(a, matrix, heuristic))\n nextAtt = attributesList[gainList.index(max(gainList))]\n \n if currentNode is None:\n #print(\"where am i\")\n currentNode = Node(nextAtt, -1)\n currentNode.label = nextAtt\n for i in [0,1]:\n # Break into submatrices where attribute is 0 or 1\n subAtt = numpy.where(matrix[:, nextAtt] == i)\n submatrix = matrix[subAtt] \n \n if i == 0:\n attributesList.remove(nextAtt)\n currentNode.left = Node(-10)\n nextNode = currentNode.left\n DLT(attributesList, submatrix, heuristic, nextNode)\n attributesList.append(nextAtt)\n else:\n attributesList.remove(nextAtt)\n currentNode.right = Node(-10)\n nextNode = currentNode.right\n DLT(attributesList, submatrix, heuristic, nextNode)\n attributesList.append(nextAtt)\n return currentNode",
"title": ""
},
{
"docid": "724fd8ce4383eee102487edeffc64263",
"score": "0.5151105",
"text": "def search(self, problem):\n\n # return RECURSIVE-DLS(MAKE-NODE(INITIAL-STATE[problem]), problem, limit)\n return self._recursive_dls(Node(problem.get_initial_state()), problem, self._limit)",
"title": ""
},
{
"docid": "13a18a50084db93318f5317d05c1639a",
"score": "0.5150288",
"text": "def depthFirstSearch(problem):\n \n expanded = set() # used to not expand previously expanded nodes\n moves = [] # keeps track of actions for return\n if (problem.isGoalState(problem.getStartState()) == False):\n expanded.add(problem.getStartState())\n path = _dfs(problem, problem.getStartState(), expanded, moves)\n final = [] # used to return actions to take\n for move in path: # goes through every state in path\n for direc in moves:\n if direc[0] == move: # gets action associated to that state\n final += [direc[1]] # adds action to return list\n return final",
"title": ""
},
{
"docid": "2a2a46dbae1ddadbccbdafbdb66221cd",
"score": "0.51487803",
"text": "def set_l(self, node):\n self.left = node",
"title": ""
},
{
"docid": "59b77c50ed9f9b435e46244825054299",
"score": "0.5138312",
"text": "def dfs(self, l):\n if not self.real:\n l.append((self.p, self.word, self.wid))\n else:\n for k,v in self.child.items():\n v.dfs(l)",
"title": ""
},
{
"docid": "f7f84a6d570dbdb47a4398648cfa197c",
"score": "0.51378864",
"text": "def bfs(self, start, finish, graph):\n # create empty queue holding paths this time\n q = Queue()\n # empty set for visited vertexes\n visited = set()\n\n # enqueue a list representing path to starting_vertex which is just itself\n q.enqueue([start])\n\n # while the queue isn't empty\n while q.size() > 0:\n # dequeue path\n v = q.dequeue()\n # node is the last vertex in path\n node = v[-1]\n\n if node == finish:\n return [node]\n\n # if we haven't visited node yet\n if node not in visited:\n # loop through neighbors\n for neighbor in graph[node]:\n # add neighbor to path\n path = list(v)\n path.append(neighbor)\n # enqueue the path for more searching\n q.enqueue(path)\n # if neighbor is goal, return the path\n if neighbor == finish:\n return path\n \n # add vertex to visited after we've searched it\n visited.add(node)\n\n return None",
"title": ""
},
{
"docid": "ae4e6b6718667b15a2dc3caf3e30450c",
"score": "0.5130743",
"text": "def depthFirstSearch(problem):\n \"*** YOUR CODE HERE ***\"\n # 初始化搜索状态\n fringe = util.Stack()\n node = {\"state\":problem.getStartState(), \"path\":[], \"cost\":0}\n fringe.push(node)\n explored = set()\n # 构造循环展开搜索树\n while (not fringe.isEmpty()):\n # 获得待判定的叶子节点\n node = fringe.pop()\n # 判断节点是否满足目标要求，如果是一个可行解，就返回行动方案\n if problem.isGoalState(node[\"state\"]):\n return node[\"path\"]\n # 否则，就继续从这个叶子节点往下展开\n else:\n # 先判断一下这个节点是不是已经展开过了，避免重复展开\n if node[\"state\"] not in explored:\n for nextnode in problem.getSuccessors(node[\"state\"]):\n # 为了适应可能的数据结构为图，必须判定叶子节点是否已经访问过\n if nextnode[0] not in explored:\n nextnode = {\"state\":nextnode[0],\n \"path\":node[\"path\"]+[nextnode[1]],\n \"cost\":node[\"cost\"]+nextnode[2]}\n # 如果没有访问过，就将叶子节点添加到待搜索的节点集合中\n fringe.push(nextnode)\n # 最后不要忘记把搜索过的节点添加到访问过的节点集合中\n explored.add(node[\"state\"])",
"title": ""
},
{
"docid": "7fd8c6d5cab0e719327139a229fb2d73",
"score": "0.51249397",
"text": "def left(self):\n return self.edges[0]",
"title": ""
},
{
"docid": "c4f2553bff75c0df35c8cdea22fa002a",
"score": "0.51225144",
"text": "def bfs_dfs(graph, rac_class, start_node, end_node):\n rac_object = rac_class()\n dist = {}\n parent = {}\n # Initialise all the information for nodes\n for node in graph.nodes():\n dist[node] = float(\"inf\")\n parent[node] = None\n dist[start_node] = 0\n rac_object.push(start_node)\n # Below is the core algorithm for Bread First Search/Depth First Search\n while len(rac_object) != 0:\n node1 = rac_object.pop()\n # Check if we already reached the end node. If so, break out of the loop\n if node1 == end_node:\n break\n for neighbor in graph.get_neighbors(node1):\n if dist[neighbor] == float(\"inf\"):\n dist[neighbor] = dist[node1] + 1\n parent[neighbor] = node1\n rac_object.push(neighbor)\n return parent",
"title": ""
},
{
"docid": "ec3beca85225ca185da4c4d40d853860",
"score": "0.5109261",
"text": "def bfs(graph, start):\n\n\tvertex_q = Queue()\n\tvertex_q.put(start)\n\n\twhile vertex_q.qsize() > 0:\n\t\tcurrent = vertex_q.get()\n\t\tfor",
"title": ""
},
{
"docid": "7c85b591f3ff7e2863a52e6d1821849b",
"score": "0.5106741",
"text": "def get_rightmost_path(G):\n v_root = G.get_min_vertex()\n v_target = G.get_max_vertex()\n T_G = DFS(G=G, v=v_root)\n v_target = G.get_max_dfs_id_vertex()\n R = rightmost_path_BFS(T_G, v_root, v_target)\n #for v in R.vertices:\n # v.id = v.dfs_id\n R.reverse_graph()\n return R",
"title": ""
},
{
"docid": "fd1ed09a5dc81f81811d55cce48600b0",
"score": "0.51034385",
"text": "def _lg_undirected(G, selfloops=False, create_using=None):\n L = nx.empty_graph(0, create_using, default=G.__class__)\n\n # Graph specific functions for edges and sorted nodes.\n get_edges = _edge_func(G)\n sorted_node = _node_func(G)\n\n # Determine if we include self-loops or not.\n shift = 0 if selfloops else 1\n\n edges = set([])\n for u in G:\n # Label nodes as a sorted tuple of nodes in original graph.\n nodes = [sorted_node(*x) for x in get_edges(u)]\n\n if len(nodes) == 1:\n # Then the edge will be an isolated node in L.\n L.add_node(nodes[0])\n\n # Add a clique of `nodes` to graph. To prevent double adding edges,\n # especially important for multigraphs, we store the edges in\n # canonical form in a set.\n for i, a in enumerate(nodes):\n edges.update([_sorted_edge(a, b) for b in nodes[i + shift:]])\n\n L.add_edges_from(edges)\n return L",
"title": ""
},
{
"docid": "49859bc25eb54851e0ae3aebb78fc0f9",
"score": "0.5096115",
"text": "def dft_recursive(self, starting_vertex):\n def traverse(vertex, visited):\n neighbors = self.get_neighbors(vertex).values()\n if vertex not in visited:\n print(vertex)\n visited.append(vertex)\n \n for next_vert in neighbors:\n traverse(next_vert, visited)\n\n return traverse(starting_vertex, [])",
"title": ""
},
{
"docid": "990e8ad1eca5390eeeff863dba6d4753",
"score": "0.509476",
"text": "def DFS(self, start):\n pass",
"title": ""
},
{
"docid": "b9fb15f29ce64398a5be8d95adffe2f0",
"score": "0.50871783",
"text": "def RSSI_FSPL(self, node):\r\n map_name = \"RSSI_\" + str(node.get_Id())\r\n map_out = self.copy(map_name)\r\n Loc = node.get_Loc()\r\n x_point = float(Loc[0])\r\n y_point = float(Loc[1])\r\n z_point = float(Loc[2])\r\n map_out.dist(x_point, y_point, z_point)\r\n dist = map_out.get_Values()\r\n dist[dist < 0.001] = 0.001\r\n # d[km], freq[GHz] +92.45; d[m], freq[kHz] -87.55;\r\n # d[m], freq[MHz] -27.55; d[km], freq[MHz] +32.45;\r\n FSPL = 20.0 * np.log10(dist) + 20.0 * np.log10(float(node.get_FcMHz())) - 27.55\r\n FSPL = float(node.get_PowdBm()) - FSPL\r\n map_out.set_Values(FSPL)\r\n return map_out",
"title": ""
},
{
"docid": "65534407a48b072bc2d247e8e4e3f111",
"score": "0.50847125",
"text": "def _bfs(self,\n start: Vertex,\n destination: Vertex) -> Optional[list[Vertex]]:\n # queue to enforce a visiting order based on hop distance\n q = [start]\n\n # dictionary to keep track of visited nodes\n # along with their predecessors\n visited = {start: None}\n\n while len(q) > 0:\n current_vertex = q.pop(0)\n\n # destination vertex found\n if current_vertex == destination:\n break\n\n # add neighboring vertices to the queue\n for nbr in current_vertex.getConnections():\n if nbr not in visited:\n visited[nbr] = current_vertex\n q.append(nbr)\n\n if destination in visited:\n shortest_path = []\n temp = destination\n while temp != start:\n shortest_path.insert(0, temp)\n temp = visited[temp]\n shortest_path.insert(0, start)\n return shortest_path\n else:\n return None",
"title": ""
},
{
"docid": "b8343a1aa2cc7ded8c66dd206164ba62",
"score": "0.507099",
"text": "def dfs(self):\n def inner_dfs(node):\n # preorder visit\n if node.left_child:\n inner_dfs(node.left_child)\n # inorder visit\n print node.value\n if node.right_child:\n inner_dfs(node.right_child)\n # postorder visit\n return inner_dfs(self.root)",
"title": ""
},
{
"docid": "b8d3c79dba70f47d6f3025caf9b6b0d9",
"score": "0.5068718",
"text": "def depthFirstSearch(problem):\n # frontier will contain those node which needs to be explored.\n # Order will be Last In First Out\n frontier = util.Stack() \n \n explored = list() # explored List will contain list of nodes which are already explored.\n startState = problem.getStartState()\n node_with_direction_list = [startState]\n frontier.push(node_with_direction_list) # frontier is list of list (Direction to reach a Node and a Node)\n # Example frontier = list(['North','South','B'],['East','West','C'])\n # Here frontier contains the list of list of (Direction to reach B along with node B) and\n # (Direction to reach C along with node C).\n \n while frontier.isEmpty() != True:\n \n node_with_direction_list = frontier.pop()\n currentNode = node_with_direction_list.pop()\n # After pop operation, nodeWithDirectionList will contain only path to reach currentNode\n explored.append(currentNode)\n\n if problem.isGoalState(currentNode) == True:\n return node_with_direction_list\n \n for successor in problem.getSuccessors(currentNode):\n if explored.count(successor[0]) == 0:\n # Creating a list which consist of a direction from currentNode to Successor and a Successor\n child_node_and_direction = []\n child_node_and_direction.append(successor[1])\n child_node_and_direction.append(successor[0])\n # Will adding successor to frontier, the full path from starting node to successor is require.\n # By merging path of currentNode and path from currentNode to Successor will give full path of\n # Successor from starting Node\n frontier.push(node_with_direction_list + child_node_and_direction)\n\n return []",
"title": ""
},
{
"docid": "c2fd1701f5971e440c48fe8248d043c4",
"score": "0.5058268",
"text": "def bfs(self, starting_vertex, destination_vertex):\n # similar to bft, but must use paths\n #Create a queue\n q = Queue()\n visited = set()\n q.enqueue([starting_vertex])\n \n while q.size() > 0:\n #Dequeue the first path, and then grab the vertext from the end of the path in order to check for it in the set\n path = q.dequeue()\n last_vertex = path[-1]\n #check if it's been visited\n if last_vertex not in visited:\n #add to it\n visited.add(last_vertex)\n\n if last_vertex == destination_vertex:\n return path\n\n # add a path to vertex neighbors\n for neighbor in self.get_neighbors(last_vertex):\n #make a copy of the path\n path_copy = path.copy()\n #append neighbors to new path + enqueue \n path_copy.append(neighbor)\n q.enqueue(path_copy)",
"title": ""
},
{
"docid": "c6f6a3e908e2c6c0d01dbd7593df4465",
"score": "0.50527245",
"text": "def bfs(start,goal):\n\n path = []\n depth = []\n\n # initialize variables\n max_depth = 0 \n\n # initialize a Queue() object and add the start location to it:\n queue = Queue()\n queue.put(start)\n # initialize a set() object for visited list and add the start location to it\n visited = set()\n visited.add(start)\n\n # initialize Queue() object for depth calculation\n depth = Queue()\n depth.put(0)\n\n\n # define an empty dictionary, where you'll record how you moved through the grid and a goal location,\n branch = {}\n found = False\n\n max_fringe_size = 0 \n \n \n while not queue.empty():\n # deque and store the explored node\n current_node = queue.get()\n visited.add(current_node)\n dep = depth.get()\n \n \n # goal check\n if current_node == goal:\n print('Found the Solution')\n found = True\n break\n else:\n for action in valid_actions(current_node):\n # get movement indicator from actions list\n da = action.delta\n \n # tuple -> grid transformation\n grid = np.array(current_node).reshape(3,-1)\n \n # find grid index of 0\n index = np.where(grid == 0)\n x,y = int(index[0]),int(index[1])\n \n #grid manipulation to exchange 0 and neighbor elements. \n grid[x+da[0],y+da[1]],grid[x,y] = grid[x,y],grid[x+da[0],y+da[1]]\n \n # grid -> tuple transformation\n next_node = tuple(grid.flatten().tolist())\n \n\n # Check if the new node has been visited before.\n # If the node has not been visited:\n # 1. Mark it as visited\n # 2. Add it to the queue\n # 3. Add how I got there to branch\n if next_node not in visited:\n visited.add(next_node)\n queue.put(next_node)\n depth.put(dep+1)\n branch[next_node] = (current_node, action)\n\n fringe_size = queue.qsize()\n if fringe_size > max_fringe_size:\n max_fringe_size = fringe_size\n\n if dep + 1 > max_depth:\n max_depth = dep + 1\n\n nodes = 0\n\n if found:\n\n nodes = len(branch)\n \n # traceback to find the depth by using of the branch dictionary.\n n = goal\n #print(branch[n][0])\n while branch[n][0] != start:\n \n path.append(branch[n][1])\n n = branch[n][0]\n \n path.append(branch[n][1])\n\n return path[::-1],nodes,max_depth,max_fringe_size",
"title": ""
},
{
"docid": "3816c34840ae40724cfe00d115799544",
"score": "0.50462496",
"text": "def bfs_visited(ugraph, start_node):\n queue = deque([])\n visited = [start_node]\n queue.append(start_node)\n while (len(queue) != 0):\n next_node = queue.popleft()\n for item in ugraph[next_node]:\n if item not in visited:\n visited.append(item)\n queue.append(item)\n visited = set(visited)\n return visited",
"title": ""
},
{
"docid": "94d6c1f3bb41dabe450e8cc9e3b9f60e",
"score": "0.5038765",
"text": "def choose_next_node(self):\n\n for node in self.open_list:\n heuristic = Heuristic(current_node=node, final_node=self.final_node,\n heuristic=self.heuristic)\n h_score = heuristic.calculate()\n node.h = h_score\n node.f = self.get_f_score(h_score, node)\n\n # sort list of node by f-score, from higher to lower.\n self.open_list.sort(key=lambda x: x.f)\n\n if len(self.open_list) > 1 and self.open_list[0].f == self.open_list[1].f:\n return [node for node in self.open_list if node.f == self.open_list[0].f]\n else:\n self.solution_history.append(deepcopy(self.open_list[0]))\n return self.open_list[0]",
"title": ""
},
{
"docid": "39dd0e5cee85e2a048084d5bf9e2d457",
"score": "0.5038192",
"text": "def leaf_filesystem_layer(self):\n return self.filesystem_layers[-1]",
"title": ""
},
{
"docid": "48bbd29bfd91acb215ca7ee0c427aa68",
"score": "0.5036113",
"text": "def lnk(self):\n try:\n return self.__lnk\n except:\n self.__lnk = self.lnkh + np.log(self.cosmo_params['H0'] / 100.0)\n return self.__lnk",
"title": ""
},
{
"docid": "1345ac8fc14bd5f63799d69a105d70dc",
"score": "0.50332123",
"text": "def forw_bfs_subgraph(self, start_id):\r\n return self._bfs_subgraph(start_id, forward=True)",
"title": ""
},
{
"docid": "9025c08d3121725c5a40d2f6afa70543",
"score": "0.5008504",
"text": "def bfs_tree(self, graph, reverse=False):\n return nx.bfs_tree(self, graph, reverse=reverse)",
"title": ""
},
{
"docid": "ea9bdd94ebd8300763608978390837bb",
"score": "0.5000737",
"text": "def LT(f):\n return smp_ground_LT(f.rep, f.lev, f.ord, f.dom)",
"title": ""
},
{
"docid": "1a2d7f4e09113236ca9c2892bf09d435",
"score": "0.49923807",
"text": "def bfs(self):\n queue = []\n values = []\n dequeued = None\n\n queue.append(self.root)\n\n while len(queue):\n dequeued = queue.pop(0)\n values.append(dequeued.value)\n dequeued.left and queue.append(dequeued.left)\n dequeued.right and queue.append(dequeued.right)\n\n return values",
"title": ""
},
{
"docid": "7bba57d74feee14f3d9ce267afb9d7a1",
"score": "0.49863958",
"text": "def dofs_node(self):\n local_dof = np.arange( self.degree_freedom, dtype=int )\n global_dof = self.degree_freedom * self.index + local_dof\n\n return global_dof",
"title": ""
},
{
"docid": "5ce7df98d7bf12dd9caa10aaf0f78e57",
"score": "0.49855685",
"text": "def depthFirstSearch(problem):\n \"*** YOUR CODE HERE ***\"\n startState = problem.getStartState()\n expandedStatesInfo = {} # state:(fromState,fromDir)\n stack = util.Stack()\n \"stack entry : (state,fromState,fromDir)\" \n stack.push((startState, \"\", \"\"))\n \n while True:\n try: expState,fromState,fromDir = stack.pop()\n except IndexError:\n print \"No goal state found!!\"\n raise\n print \"expState : \", expState, fromState, fromDir\n\n if expState in expandedStatesInfo:\n print \"already expanded : \", expState\n continue\n\n print \"adding to expandedStatesInfo : \", expState\n expandedStatesInfo[expState] = (fromState,fromDir)\n\n if problem.isGoalState(expState):\n print \"reached goal state : \", expState\n break\n\n # handle the fringes\n fringesInfo = problem.getSuccessors(expState)\n print \"fringesInfo : \", fringesInfo\n for fringeState, fringeDir, fringeCostLocal in fringesInfo:\n print \"fringeState : \", fringeState, fringeDir, fringeCostLocal\n stack.push((fringeState,expState,fringeDir))\n # print \"stack : \", stack.list\n\n assert problem.isGoalState(expState)\n\n # backtrack the path\n path = []\n state = expState\n while state != startState:\n fromState, fromDir = expandedStatesInfo[state]\n print \"adding to path : \", state, fromState, fromDir\n path.append(fromDir)\n state = fromState\n\n path.reverse()\n print \"path : \", path\n return path",
"title": ""
},
{
"docid": "b60a5ddbededaa309bf99467bf7f29cb",
"score": "0.49790117",
"text": "def bfs(graph: Graph, root: str) -> List[str]:\n\n queue = deque([root])\n visited = []\n\n while queue:\n current = queue.pop()\n if current not in visited:\n neighbors = graph[current]\n queue.extendleft(neighbors)\n visited.append(current)\n\n return visited",
"title": ""
},
{
"docid": "3fd281944310936ccfa63a596dcc7808",
"score": "0.49755606",
"text": "def dft(self, starting_vertex):\n s = Stack()\n visited = set()\n s.push(starting_vertex)\n while s.size() > 0:\n poped = s.pop()\n if poped not in visited:\n visited.add(poped)\n for each in self.get_neighbors(poped):\n s.push(each)\n print(poped)",
"title": ""
},
{
"docid": "bb9798b86e40ee006ba1408a87dada8b",
"score": "0.4971379",
"text": "def bfs(maze):\n # TODO: Write your code here\n visited = []\n #path dictionary to store prev nodes\n path = {}\n path2 = []\n start = maze.getStart()\n queue = [start]\n path[start] = None\n #print(\"test1, \", type(path2))\n #queue_size check\n while(queue):\n curr = queue.pop(0)\n if (curr not in visited):\n visited.append(curr)\n r = curr[0]\n c = curr[1]\n #found goal\n if (maze.isObjective(r, c)):\n path2 = create_path(path, curr, start).copy()\n break\n\n neighbors = maze.getNeighbors(r, c)\n for i in neighbors:\n if (i not in visited):\n path[i] = curr\n queue.append(i)\n return path2",
"title": ""
},
{
"docid": "b28084e433244aaa3c48d69e265ce582",
"score": "0.49709606",
"text": "def depthFirstSearch(problem):\n \"*** YOUR CODE HERE ***\"\n\n # visited is a dictionary with location:([path], cost) pairs\n # i realize now that cost is kinda unnecessary lmao. whoops\n visited = {}\n stack = util.Stack()\n\n startState = problem.getStartState()\n if problem.isGoalState(startState):\n return []\n stack.push((startState, '', 0))\n # response are dictionaries for finding prev\n response = {\"North\": (0, -1), \"South\": (0, 1), \"East\": (-1, 0), \"West\": (1, 0)}\n\n # Main loop\n while not stack.isEmpty():\n # Pop from stack - node is (location, direction, cost)\n node = stack.pop()\n visited[node[0]] = (node[1], node[2])\n\n # Managing successors\n for s in problem.getSuccessors(node[0]):\n # Check goal state on creation\n if problem.isGoalState(s[0]):\n get = response[s[1]]\n back = (s[0][0] + get[0], s[0][1] + get[1])\n path = [s[1]]\n cost = s[2]\n while back != startState:\n path.append(visited[back][0])\n cost += visited[back][1]\n get = response[visited[back][0]]\n back = (back[0] + get[0], back[1] + get[1])\n path.reverse()\n return path\n\n # Check if s in frontier (stack)\n inFrontier = False\n for f in stack.list:\n if s[0] == f[0]:\n inFrontier = True\n # Put in stack if not visited or in frontier\n if (not s[0] in visited) and (not inFrontier):\n stack.push(s)\n return None",
"title": ""
},
{
"docid": "2cf5037d562209d5722f2798e534a53f",
"score": "0.49696815",
"text": "def depthFirstSearch(problem):\n DEBUG = False;\n if DEBUG == True: print \"Start:\", problem.getStartState()\n if DEBUG == True: print \"Is the start a goal?\", problem.isGoalState(problem.getStartState())\n if DEBUG == True: print \"Start's successors:\", problem.getSuccessors(problem.getStartState())\n \"*** YOUR CODE HERE ***\"\n \"\"\"Create the frontier\"\"\"\n frontier = util.Stack()\n \"\"\"Keep track of how you got somewhere\"\"\"\n frontierHash = {}\n \"\"\"Keep track of explored nodes with True/False hashes\"\"\"\n exploredHash = {}\n \"\"\"The current node being evaluated\"\"\"\n current = problem.getStartState()\n\n if problem.isGoalState(current):\n \"\"\"Start is the goal\"\"\"\n return []\n \"\"\"Otherwise, do some exploring\"\"\"\n frontier.push(current)\n exploredHash[current] = True\n frontierHash[current] = []\n \"\"\"Do the search on the frontier\"\"\"\n while frontier.isEmpty() == False:\n current = frontier.pop()\n if DEBUG == True: print \"Popping \",current,\"off of frontier.\"\n if problem.isGoalState(current):\n \"\"\"Return the path to the goal\"\"\"\n if DEBUG == True: print \"The goal is: \",current\n if DEBUG == True: print \"The path is: \",frontierHash[current]\n return frontierHash[current]\n \n successors = problem.getSuccessors(current)\n for successor in successors:\n \"\"\"If the node hasn't been explored, put it on the frontier\"\"\"\n if successor[0] not in exploredHash:\n\t\"\"\"Add node to the frontier\"\"\"\n frontier.push(successor[0])\n \"\"\"Add current to explored hashtable\"\"\"\n exploredHash[successor[0]] = True\n\t\"\"\"Add path to the node to the frontierHash\"\"\"\n\tpath = list(frontierHash[current])\n\tpath.append(successor[1])\n\tfrontierHash[successor[0]] = path\n\tif DEBUG == True: print \"Pushing \",successor[0],\" at \",path\n\n util.raiseNotDefined()",
"title": ""
},
{
"docid": "fcf23a86a658e6f2570f81d2a5b21d9f",
"score": "0.4969494",
"text": "def get_lhood(*args):\n args = validated_params(*args)\n T, edge_to_P, root, root_prior_distn1d, node_to_data_fvec1d = args\n\n root_lhoods = _get_root_lhoods(*args)\n if root_lhoods.any():\n return root_lhoods.sum()\n else:\n return None",
"title": ""
}
]
a0f7b02c15b120e78b8ea99e523bd88a
Test that the report is correct.
[
{
"docid": "2cee7de2673fae20e61b01ceaced3c26",
"score": "0.7139832",
"text": "def test_report(self):\n self.__birt.nr_manual_ltcs.return_value = 10, []\n self.__birt.nr_manual_ltcs_too_old.return_value = 5\n self.__birt.date_of_last_manual_test.return_value = datetime.datetime.now() - timedelta(days=5)\n self.assertTrue('5 van de 10 handmatige logische testgevallen zijn te lang geleden '\n '(meest recente 5 dag(en))' in self.__metric.report())",
"title": ""
}
]
[
{
"docid": "33413fe45069fc706964ce3f54eb8e12",
"score": "0.77926624",
"text": "def test_report(self):\n self.assertEqual('Van 4 van de 12 handmatige logische testgevallen is de uitvoeringstijd niet ingevuld.',\n self.__metric.report())",
"title": ""
},
{
"docid": "2a912c8a46e86e960f2b57a08bed772c",
"score": "0.7606778",
"text": "def test_report(self):\n self.assertEqual('De uitvoering van 8 van de 12 handmatige logische testgevallen kost 120 minuten.',\n self.__metric.report())",
"title": ""
},
{
"docid": "a855b7d0271388063f42115c085a2905",
"score": "0.75368786",
"text": "def test_for_report(self):\n pass",
"title": ""
},
{
"docid": "b09b5cb417f6b4b8428dcbd84424403d",
"score": "0.7462171",
"text": "def test_report(self):\n self.assertEqual('5 van de 10 unittesten falen.', self.__metric.report())",
"title": ""
},
{
"docid": "58ffe5a29e3da6c6bf14631385a37f45",
"score": "0.7457009",
"text": "def test_get_report(self):\n pass",
"title": ""
},
{
"docid": "bbc34740557a1e3882a906f17e0dc037",
"score": "0.7455599",
"text": "def test_report(self):\n self.__birt.nr_ltcs.return_value = 120, []\n self.__birt.reviewed_ltcs.return_value = 110, []\n self.assertEqual('Er zijn 10 niet gereviewde logische testgevallen, van in totaal 120 '\n 'logische testgevallen.', self.__metric.report())",
"title": ""
},
{
"docid": "0781a818e0fb96c075afe49a84f832af",
"score": "0.74532145",
"text": "def test_create_report1(self):\n pass",
"title": ""
},
{
"docid": "22c98af2be0c11d65e2062d79178742a",
"score": "0.74511546",
"text": "def test_report(self):\n self.__birt.approved_ltcs.return_value = 100, []\n self.__birt.reviewed_ltcs.return_value = 110, []\n self.assertEqual('Er zijn 10 niet goedgekeurde logische testgevallen, van in totaal 110 gereviewde '\n 'logische testgevallen.', self.__metric.report())",
"title": ""
},
{
"docid": "ee91ec45d972bd981bc15e9060ea1d71",
"score": "0.7442183",
"text": "def test_get_report1(self):\n pass",
"title": ""
},
{
"docid": "dec9eacb285a7ee1c3676624f21be4c7",
"score": "0.7419393",
"text": "def test_create_report(self):\n pass",
"title": ""
},
{
"docid": "e4d3d5e42a623237fea046dbbf2b4c3a",
"score": "0.73501694",
"text": "def test_process_report_request_correct(self) -> None:\n pass",
"title": ""
},
{
"docid": "9e05c857909d812e8e63ec06b04a4522",
"score": "0.71790683",
"text": "def test_with_no_data(self):\n actual_report = self.analysis.get_report_as_text()\n expected_report = \"\"\"\n (reported in boxes)\nmaximum weekly quay side throughput: 0\naverage weekly quay side throughput: 0.0\nstandard deviation: -1.0\nmaximum daily quay side throughput: 0.0\naverage daily quay side throughput: 0.0\nmaximum hourly quay side throughput: 0.0\naverage hourly quay side throughput: 0.0\n(daily and hourly values are simply scaled weekly values, rounding errors might exist)\n\"\"\"\n self.assertEqual(actual_report, expected_report)",
"title": ""
},
{
"docid": "3aa7dd87fb249e11e645ceec66e0027e",
"score": "0.71108896",
"text": "def test_report(self):\n self.__birt.nr_manual_ltcs.return_value = 10, []\n self.__birt.nr_ltcs.return_value = 120, []\n self.assertEqual('Er zijn 10 handmatige logische testgevallen, van in totaal 120 logische testgevallen.',\n self.__metric.report())",
"title": ""
},
{
"docid": "26e00f418c10789f6dc588bed8f592d6",
"score": "0.70995986",
"text": "def test_report(self):\n basket = FruitBasket(Path(__file__).parent.joinpath(\"data\", \"given.csv\"))\n result = report(basket)\n print(result)\n assert result.count(\"\\n\") == 28",
"title": ""
},
{
"docid": "3b4f45074fdadd74c24630c98c1d77be",
"score": "0.70927775",
"text": "def test_get_report_information_correct(self) -> None:\n self.mock_report_information_processor.process.return_value = Report(\n created_at=datetime(2020,10,12,13,0,0),\n patient_id=\"1000000\",\n document_text=\"stuff\"\n )\n\n report = self.get_report_information_use_case.get(self.correct_text_file)\n self.assertEqual(report, self.correct_output)\n self.mock_report_repository.add.assert_called_once()",
"title": ""
},
{
"docid": "a547a885813e9eb24b88dc3b9c3c5101",
"score": "0.7005789",
"text": "def test_test_report(self):\n self.__opener.contents = '''<result><successfulTestCount>1973</successfulTestCount>\n <failedTestCount>17</failedTestCount>\n <quarantinedTestCount>0</quarantinedTestCount>\n <skippedTestCount>2</skippedTestCount></result>'''\n self.assertEqual(17, self.__report.failed_tests('url'))\n self.assertEqual(1973, self.__report.passed_tests('url'))\n self.assertEqual(2, self.__report.skipped_tests('url'))",
"title": ""
},
{
"docid": "9e664b0311f6627f3de1d3ec1656ec2d",
"score": "0.6998808",
"text": "def test_report_when_untested(self):\n self.__birt.date_of_last_manual_test.return_value = datetime.datetime.min\n self.__birt.nr_manual_ltcs.return_value = 10, []\n self.assertEqual('De 10 handmatige logische testgevallen zijn nog niet allemaal uitgevoerd.',\n self.__metric.report())",
"title": ""
},
{
"docid": "1aa02f764a6e574f7147a32f0db39d52",
"score": "0.6933463",
"text": "def test_valid_report(self):\n self.assertEqual(\n first=\"\",\n second=_validate_report([None, \"Sneasel\", \"Momos\", \"Home\", '{:%H:%M}'.format(datetime.now() + timedelta(minutes=2))])\n )",
"title": ""
},
{
"docid": "60c867e2f1e7d0347404813d02203229",
"score": "0.69127405",
"text": "def test_report(self):\n self.__birt.nr_ltcs_to_be_automated.return_value = 25, []\n self.__birt.nr_automated_ltcs.return_value = 20, []\n self.assertEqual('Er zijn 5 nog te automatiseren logische testgevallen, van in totaal 25 '\n 'geautomatiseerde logische testgevallen.', self.__metric.report())",
"title": ""
},
{
"docid": "ffee03f1341246b6c89fc6daaeb1a85a",
"score": "0.6866708",
"text": "def test_reporting_policy(self):\n\n reporting_text1 = driver.find_element_by_xpath(\"//*[@id='footer-content']/footer/div/p[3]\").text\n reporting1 = \"Personal information provided above will not be used for any other purpose beyond this peer-to-peer program and required federal and state-level reporting.\"\n\n if assertEqual(reporting_text1, unicode(reporting1, \"utf-8\")):\n print('\\n') # adds line break\n print \"text not found\"\n else:\n print('\\n') # adds line break\n print \"'\", reporting_text1, \"'\", \" text is present\"\n\n reporting_text2 = driver.find_element_by_xpath(\"//*[@id='footer-content']/footer/div/p[4]\").text\n reporting2 = \"Consistent with federal and state reporting obligations including, but not limited to, the Federal Physician Payment Sunshine Act, CSL Behring will disclose any transfers of value given to healthcare providers attending our programs.\"\n\n if assertEqual(reporting_text2, unicode(reporting2, \"utf-8\")):\n print('\\n') # adds line break\n print \"text not found\"\n else:\n print('\\n') # adds line break\n print \"'\", reporting_text2, \"'\", \" text is present\"",
"title": ""
},
{
"docid": "8aae783fb5f6a26624df3f00bb6c1483",
"score": "0.67734313",
"text": "def test_report_with_untested(self):\n self.__birt.date_of_last_manual_test.return_value = datetime.datetime.now() - datetime.timedelta(days=60)\n self.__birt.nr_manual_ltcs.return_value = 10, []\n self.__birt.nr_manual_ltcs_too_old.return_value = 5\n self.assertTrue(\n self.__metric.report().startswith('5 van de 10 handmatige logische testgevallen zijn '\n 'te lang geleden (meest recente 60 dag(en))'))",
"title": ""
},
{
"docid": "e80330aa862f7dca50e431b811bc6951",
"score": "0.67699206",
"text": "def test_str(self) -> None:\n self.assertEqual(str(self.report), 'Beaver Creek TEST: 2019-01-09')",
"title": ""
},
{
"docid": "dcbf3b2cb07d4ed558f81f7eafbd0287",
"score": "0.66823614",
"text": "def test_report():\n report_command = next(readme_blocks)\n want = next(readme_blocks)\n simulator_status = phmdoctest.simulator.run_and_pytest(\n report_command, pytest_options=None)\n got = simulator_status.runner_status.stdout\n verify.a_and_b_are_the_same(want, got)",
"title": ""
},
{
"docid": "21106421aa10aee7fcff3292bc27547d",
"score": "0.66454893",
"text": "def test_all(self):\n my_report = report.ReportOptions()\n name = my_report.SECTION_NAME\n assert name == \"[REPORT]\"\n test_text = \"[REPORT]\\n\" + \\\n \"Status NO\\n\" + \\\n \"Energy NO\\n\" + \\\n \"Summary NO\\n\" + \\\n \"NODES N1 N2 N3 N17\\n\" + \\\n \"LINKS ALL\\n\" \\\n \"FLOW YES\\n\" + \\\n \"VELOCITY PRECISION 4\\n\" \\\n \"F-FACTOR PRECISION 4\\n \" \\\n \"VELOCITY ABOVE 3.0\"\n my_report = ReportOptionsReader.read(test_text)\n actual_text = ReportOptionsWriter.as_text(my_report)\n msg = '\\nSet:'+test_text+'\\nGet:'+actual_text\n self.assertTrue(match(actual_text, test_text), msg)",
"title": ""
},
{
"docid": "e37b1527841309dded70df82604de5d5",
"score": "0.66447735",
"text": "def test_simple(self):\n my_report = report.ReportOptions()\n my_report.pagesize = 64\n name = my_report.SECTION_NAME\n assert name == \"[REPORT]\"\n expected_text = \"[REPORT]\\n\" + \\\n \" Status \tNO\\n\" + \\\n \" Energy \tNO\\n\" + \\\n \" Page \\t64\\n\" + \\\n \" Summary \tYES\"\n actual_text = ReportOptionsWriter.as_text(my_report)\n msg = '\\nSet:'+expected_text+'\\nGet:'+actual_text\n self.assertTrue(match(actual_text, expected_text), msg)",
"title": ""
},
{
"docid": "12b8e007d80a7e431cb5e1d0ecd0aa70",
"score": "0.66099477",
"text": "def test(self):\n # -- Test --\n resp = self.request(\n self.client.get,\n '/admin/report/facility',\n {},\n self.admin_access_token\n )\n\n # (1)\n self.assertEqual(resp.status_code, 200)\n\n # (2)\n data = self.get_response_data(resp)\n self.assertIsInstance(data, list)\n\n # (3)\n self.assertEqual(len(data), 1)\n\n # (4)\n report = data[0]\n\n self.assertIn('id', report)\n id = report['id']\n self.assertIsInstance(id, str)\n self.assertEqual(len(id), 24)\n\n del report['id']\n\n self.report.update({'author': 'fake_student'})\n self.assertDictEqual(report, self.report)\n # -- Test --",
"title": ""
},
{
"docid": "f428b4b71b9af524da3dd8cfb4fcdb36",
"score": "0.65758437",
"text": "def test_write_report(capsys, donors):\n donors.create_report()\n out, _ = capsys.readouterr()\n assert \"Bill Murray\" in out\n assert \"Woody Harrelson\" in out\n assert out.index(\"Bill Murray\") < out.index(\"Woody Harrelson\")",
"title": ""
},
{
"docid": "7c56cb95ecd3129bc1453423b1cc2d4c",
"score": "0.6568402",
"text": "def test_report_without_jira(self):\n self.assertEqual('De uitvoeringstijd van handmatige logische testgevallen van <no name> kon niet gemeten '\n 'worden omdat de bron ManualLogicalTestCaseTracker niet is geconfigureerd.',\n metric.DurationOfManualLogicalTestCases(domain.Project(), domain.Project()).report())",
"title": ""
},
{
"docid": "cfc50fe67eccff9b2a8b527e3a3c3951",
"score": "0.65586543",
"text": "def test_write_report(report: ViyaDeploymentReport) -> None:\n # write the report and data file\n data_file, html_file = report.write_report()\n\n # check for expected files\n assert os.path.exists(data_file)\n assert os.path.isfile(data_file)\n assert os.path.exists(html_file)\n assert os.path.exists(html_file)\n\n # clean up files\n os.remove(data_file)\n os.remove(html_file)",
"title": ""
},
{
"docid": "9cdc84495caec38202647cc962a07cca",
"score": "0.65537614",
"text": "def test_report_line(self):\n line = ('218501217863',\n 1,\n '%s' % self._now,\n 'pod_name 218501217863',\n 'License',\n '1234',\n 'priority_item_nbr_001')\n msg = 'Default exporter line entry not as expected'\n received = self._e.get_report_line(line)\n expected = \"\"\"%s|%s|%s|%s|%s|%s|%s\"\"\" % ('218501217863',\n '1',\n self._now,\n 'pod_name 218501217863',\n 'License',\n '1234',\n 'priority_item_nbr_001')\n self.assertEqual(received, expected, msg)",
"title": ""
},
{
"docid": "e8111e36bdf7011c808d344e772c22ce",
"score": "0.6516754",
"text": "def test_duc_report(self):\n url = reverse('recoup_report')\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)",
"title": ""
},
{
"docid": "478263e2e1544607d844ec6c3eb6ebf9",
"score": "0.6491565",
"text": "def test_str(self):\n self.assertEqual(str(self.bmreport), 'Beaver Creek TEST: 2019-01-09')",
"title": ""
},
{
"docid": "a021d887ee61cbb4c77bbe89a4594418",
"score": "0.6476277",
"text": "def test_page(self):\n test_text = \"[REPORT]\\n\" + \\\n \" Status \tNO\\n\" + \\\n \" Summary \tYES\\n\" \\\n \" Energy \tNO\\n\" + \\\n \" Nodes \t Node1 Node2 Node3\\n\" \\\n \" Links \tLink1 Link2\\n \" \\\n \" Page 64\"\n my_report = ReportOptionsReader.read(test_text)\n actual_text = ReportOptionsWriter.as_text(my_report)\n msg = '\\nSet:'+test_text+'\\nGet:'+actual_text\n self.assertTrue(match(actual_text, test_text), msg)",
"title": ""
},
{
"docid": "c868fc521fb6a9bfd3e0485d2eefb4fb",
"score": "0.6460325",
"text": "def test_summary(self):\r\n assert '3 tests, 1 failure, 1 error in ' in self.output",
"title": ""
},
{
"docid": "3ac051fc9cab71dac4e4cc79820ae0e6",
"score": "0.6457043",
"text": "def TestArapahoe(self):\r\n result = arapahoe_basin_snow_report()\r\n print \"Output from method shown below:\\n\\n%s\" % result\r\n self.assertTrue(True)",
"title": ""
},
{
"docid": "a7b2c6811c76bec80d170fb78a08dcdf",
"score": "0.64400166",
"text": "def test_report(self):\n user = yaklient.User(CLEAR_LOC_1, USER_IDS[0])\n yak = user.post_yak(\"Test Message\", \"Hello\")\n comment = user.post_comment(\"Test Comment\", yak)\n user.report(yak, \"Report\")\n user.report(comment, \"Report\")\n delete_success = user.delete(yak)\n self.assertEqual(delete_success, True)\n self.assertEqual(yak.update(), False)",
"title": ""
},
{
"docid": "36a2c57a672841a7371f5ace4dbfe14b",
"score": "0.6433924",
"text": "def test_domainreport_view(self):\n self.import_reports()\n user = core_models.User.objects.get(username=\"admin\")\n self.client.force_login(user)\n url = reverse(\"modoboa_dmarc:domain_report\", args=[self.domain.pk])\n response = self.client.get(\"{}?period=2015-26\".format(url))\n self.assertContains(response, \"'Failed', 100.0\")",
"title": ""
},
{
"docid": "5e6b9c0d09e2fdb4838be7d546aeb731",
"score": "0.6393931",
"text": "def test_process_report_request_invalid_input_incorrect(self) -> None:\n pass",
"title": ""
},
{
"docid": "4630be84cc3f59af4b68719c5e654033",
"score": "0.639187",
"text": "def test_correct_output(self):\n # Change input/output files to testing files specified above\n purchase_analytics.parse_filenames = self.parse_filenames\n purchase_analytics.run_analysis()\n \n # Compare generated output file with gold output file\n with open(self.testing_output_file_name,\"r\") as f1:\n with open(self.testing_gold_output_file_name,\"r\") as f2:\n self.assertEqual(f1.read().strip(),f2.read().strip())",
"title": ""
},
{
"docid": "4630be84cc3f59af4b68719c5e654033",
"score": "0.639187",
"text": "def test_correct_output(self):\n # Change input/output files to testing files specified above\n purchase_analytics.parse_filenames = self.parse_filenames\n purchase_analytics.run_analysis()\n \n # Compare generated output file with gold output file\n with open(self.testing_output_file_name,\"r\") as f1:\n with open(self.testing_gold_output_file_name,\"r\") as f2:\n self.assertEqual(f1.read().strip(),f2.read().strip())",
"title": ""
},
{
"docid": "fb677cff84d3b9e3e589114adf5d3ee7",
"score": "0.63726556",
"text": "def test_write_report_unpopulated() -> None:\n # create unpopulated report instance\n report = ViyaDeploymentReport()\n\n # write report\n data_file, html_file = report.write_report()\n\n # make sure None is returned\n assert data_file is None\n assert html_file is None",
"title": ""
},
{
"docid": "aa088408c231a60861a78f31b0fa3125",
"score": "0.6366452",
"text": "def TestLoveland(self):\r\n result = loveland_snow_report()\r\n print \"Output from method shown below:\\n\\n%s\" % result\r\n self.assertTrue(True)",
"title": ""
},
{
"docid": "ac32d7454b9335c8993bc83c791e1268",
"score": "0.6337851",
"text": "def test_success(database):\n det_award_1 = DetachedAwardFinancialAssistanceFactory(period_of_performance_curr=\"20120725\")\n det_award_2 = DetachedAwardFinancialAssistanceFactory(period_of_performance_curr=None)\n det_award_3 = DetachedAwardFinancialAssistanceFactory(period_of_performance_curr=\"5\")\n det_award_4 = DetachedAwardFinancialAssistanceFactory(period_of_performance_curr=\"\")\n\n errors = number_of_errors(_FILE, database, models=[det_award_1, det_award_2, det_award_3, det_award_4])\n assert errors == 0",
"title": ""
},
{
"docid": "77d1f5dcc02faff3dbb9ea4714ba2920",
"score": "0.63351446",
"text": "def test_invoice_analysis(self):\n pass",
"title": ""
},
{
"docid": "8d20fc39b50783d66d62deb8e2e8d01e",
"score": "0.633226",
"text": "def test_success(database):\n\n state_code = States(state_code=\"NY\")\n # Required for these values\n det_award_1 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code=\"00*****\", record_type=1)\n det_award_2 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code=\"00FORGN\", record_type=1)\n det_award_3 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code=\"00FORgN\", record_type=2)\n det_award_4 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code=\"NY*****\", record_type=2)\n det_award_5 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code=\"Ny*****\", record_type=1)\n det_award_6 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code=\"NY**123\", record_type=1)\n det_award_7 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code=\"NY12345\", record_type=2)\n det_award_8 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code=\"NY1234R\", record_type=2)\n # Ignored for record type 3\n det_award_9 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code=\"AB12345\", record_type=3)\n errors = number_of_errors(_FILE, database, models=[det_award_1, det_award_2, det_award_3, det_award_4, det_award_5,\n det_award_6, det_award_7, det_award_8, det_award_9, state_code])\n assert errors == 0",
"title": ""
},
{
"docid": "ff92d31a2bc2a4e47318e0902f301070",
"score": "0.6331776",
"text": "def test_set(self):\n report = self.analytics.suites[test_report_suite].report\\\n .set('anomalyDetection',True)\\\n .set({\"test\":\"abc\",\"currentData\":True})\n \n self.assertEqual(report.raw['anomalyDetection'], True)\n self.assertEqual(report.raw['test'], \"abc\")\n self.assertEqual(report.raw['currentData'], True)\n \n with self.assertRaises(ValueError):\n report.set()",
"title": ""
},
{
"docid": "085bb33762cdcf1e423001389921f03d",
"score": "0.63256544",
"text": "def test_result(self):\n data = {'raw': get_content('coverage.out')}\n result = coverage_violation(data)\n result['status'].should.be.equal(STATUS_SUCCESS)\n result['plot']['cover'].should.be.equal(86)\n result['success_percent'].should.be.equal(86)",
"title": ""
},
{
"docid": "1b374f6456fa8ac56cc60501d51206c1",
"score": "0.631895",
"text": "def _report(self):",
"title": ""
},
{
"docid": "8c9eb93ad6f4fb71070faa485c6358dc",
"score": "0.63149506",
"text": "def test_report_view(self):\n year = factories.YearFactory.create()\n user = factories.UserFactory.create(\n _full_name=\"Fritz\", working_time_model=year.working_time_model\n )\n inactive = factories.UserFactory.create(\n working_time_model=year.working_time_model\n )\n Employment.objects.create(user=user, percentage=50, vacation_weeks=5)\n\n # New user has a different working time model...\n self.client.force_login(factories.UserFactory.create(_full_name=\"Hans\"))\n\n url = \"/report/annual-working-time/\"\n response = self.client.get(url)\n self.assertNotContains(response, str(user))\n self.assertNotContains(response, str(inactive))\n\n response = self.client.get(url + \"?user=active\")\n self.assertContains(response, str(user))\n self.assertNotContains(response, str(inactive))\n\n response = self.client.get(url + \"?user=\" + str(inactive.pk))\n self.assertContains(response, str(inactive))\n\n response = self.client.get(url + \"?year=2018\")\n self.assertEqual(\n messages(response),\n [\n \"No annual working time defined for user Hans\"\n \" with working time model Test.\"\n ],\n )\n\n other = factories.UserFactory.create(working_time_model=year.working_time_model)\n Employment.objects.create(user=other, percentage=50, vacation_weeks=5)\n factories.AbsenceFactory.create(user=other)\n\n response = self.client.get(url + \"?export=pdf&user=active\")\n self.assertEqual(response.status_code, 200)\n self.assertEqual(response[\"content-type\"], \"application/zip\")\n\n response = self.client.get(url + \"?export=pdf&user={}\".format(user.pk))\n self.assertEqual(response.status_code, 200)\n self.assertEqual(response[\"content-type\"], \"application/pdf\")",
"title": ""
},
{
"docid": "ac9c3c56b27bd0b8d6a0469ae9f77fe3",
"score": "0.631163",
"text": "def test_update_report_by_id1(self):\n pass",
"title": ""
},
{
"docid": "a8b57d43bc427821965b029e2e0e6a15",
"score": "0.6304681",
"text": "def TestEldora(self):\r\n result = eldora_snow_report()\r\n print \"Output from method shown below:\\n\\n%s\" % result\r\n self.assertTrue(True)",
"title": ""
},
{
"docid": "c7faf8bd8b90bd77ff872fc175b92687",
"score": "0.62872577",
"text": "def test_jsonReport(self):\n report = self.analytics.suites[test_report_suite].report.range(dateFrom,dateTo,granularity='day')\\\n .set(\"source\",\"standard\")\\\n .metric(\"pageviews\")\\\n .metric(\"visits\")\\\n .element(\"page\")\\\n .element(\"sitesection\", top=100, startingWith=1)\\\n .set(\"locale\",\"en_US\")\\\n .sortBy(\"visits\")\\\n .set(\"anomalyDetection\",True)\\\n .set(\"currentData\", True)\\\n .set(\"elementDataEncoding\",\"utf8\")\n\n testreport = self.analytics.suites[test_report_suite].jsonReport(report.json())\n self.assertEqual(report.json(),testreport.json(), \"The reportings aren't deserializing from JSON the same old:{} new:{}\".format(report.json(),testreport.json()))\n self.assertEqual(report.json(),testreport.__str__(), \"The reportings aren't deserializing to string __str__ the same old:{} new:{}\".format(report.json(),testreport.__str__()))",
"title": ""
},
{
"docid": "ea38a43d19bc55c370d0fd7a819cafe0",
"score": "0.62821436",
"text": "def test_report_details_views_with_no_monsterreports(self):\r\n response = self.client.get('/report/{}/'.format(random.randrange(0,1000,1)))\r\n\r\n self.assertEqual(response.status_code, 302)",
"title": ""
},
{
"docid": "da6db312d36db70b1e5f5cbe767ba5fd",
"score": "0.62778455",
"text": "def test_report_generator():\r\n donor_Db = Donor_Collection(get_sampleDB())\r\n\r\n report = donor_Db.report_generator()\r\n\r\n assert report.startswith(\"Donor Name | Total Given | Num Gifts| Average Gift |\")\r\n assert \"Christina Levermore $ 10000.00 1 $ 10000.00\" in report",
"title": ""
},
{
"docid": "7b5e71ccf8e32b6d7a0a16638451ef9f",
"score": "0.6255225",
"text": "def test_cash_analysis(self):\n pass",
"title": ""
},
{
"docid": "73fda46848a3970facda9dbb9cf7da53",
"score": "0.6248184",
"text": "def test_success(database):\n\n det_award = DetachedAwardFinancialAssistanceFactory(assistance_type=\"07\", face_value_loan_guarantee=0)\n det_award_2 = DetachedAwardFinancialAssistanceFactory(assistance_type=\"08\", face_value_loan_guarantee=20)\n\n errors = number_of_errors(_FILE, database, models=[det_award, det_award_2])\n assert errors == 0",
"title": ""
},
{
"docid": "4a3ec39dc5fa6dbf8900f90b2a4e3d90",
"score": "0.62437344",
"text": "def test_success(database):\n det_award = DetachedAwardFinancialAssistanceFactory(legal_entity_country_code=\"USA\", legal_entity_zip_last4=\"12345\")\n det_award_2 = DetachedAwardFinancialAssistanceFactory(legal_entity_country_code=\"USA\", legal_entity_zip_last4=None)\n det_award_null = DetachedAwardFinancialAssistanceFactory(legal_entity_country_code=\"UK\",\n legal_entity_zip_last4=None)\n det_award_null_2 = DetachedAwardFinancialAssistanceFactory(legal_entity_country_code=\"UK\",\n legal_entity_zip_last4='')\n\n errors = number_of_errors(_FILE, database, models=[det_award, det_award_2, det_award_null, det_award_null_2])\n assert errors == 0",
"title": ""
},
{
"docid": "d20f845668d118c39848965279494dec",
"score": "0.62130356",
"text": "def test_report_without_jira(self):\n self.assertEqual('De hoeveelheid logische testgevallen zonder ingevulde uitvoeringstijd van <no name> kon niet '\n 'gemeten worden omdat de bron ManualLogicalTestCaseTracker niet is geconfigureerd.',\n metric.ManualLogicalTestCasesWithoutDuration(domain.Project(), domain.Project()).report())",
"title": ""
},
{
"docid": "72ffce8c42041c48483cb87ebc68493c",
"score": "0.6203891",
"text": "def test(self):\n self.compare_outputs()",
"title": ""
},
{
"docid": "5925c273deea4ff2844cb37e2128c208",
"score": "0.62026334",
"text": "def test_get_all_reports1(self):\n pass",
"title": ""
},
{
"docid": "f0a7e0a0c6954a0fd80b0665a894c8e4",
"score": "0.6199768",
"text": "def test_CrearReporte(self):\n rp = Reporte()\n rp.descripcion = 'prueba'\n rp.porcentaje_alcanzado = '50'\n rp.horas_faltantes = '50'\n rp.fecha_reporte = '2016-01-01'\n rp.save()\n \n rp.save()\n \n self.assertTrue(Reporte.objects.filter(descripcion = 'prueba').exists(), \"El reporte no se a creado\")",
"title": ""
},
{
"docid": "6012cba79bb403df1774d648fab3af61",
"score": "0.61636615",
"text": "def test_reportar(self):\n examen = Exam.objects.get(nombre_materia='Matematicas')\n pregunta = Question.objects.get(text_preg='2 + 2 ?')\n response = self.client.get(reverse('reportar', args=[examen.id, pregunta.id]))\n self.assertEqual(response.resolver_match.func, reportar)",
"title": ""
},
{
"docid": "2c4b41caaba2e628beebfc049cb11549",
"score": "0.6159754",
"text": "def test_parse_report_file(self):\n test_file_dir = os.path.join('top', 'tests', 'files')\n file = 'VIC_VANA_REI_20131108145146.txt'\n\n received = self._e.parse_report_file(os.path.join(test_file_dir,\n file))\n expected = [{'identity_type_id': 9,\n 'identity_type_data': 'a1234',\n 'item_nbr': '6827668473420000130001',\n 'pod_name': 'Test Line1',\n 'pickup_ts': '2013-11-08 14:50:11',\n 'connote_nbr': '8473420000130'},\n {'identity_type_id': 9,\n 'identity_type_data': 'b1234',\n 'item_nbr': '6827668473420000131001',\n 'pod_name': 'Test Line2',\n 'pickup_ts': '2013-11-08 14:50:42',\n 'connote_nbr': '8473420000131'},\n {'identity_type_id': 9,\n 'identity_type_data': 'c1234',\n 'item_nbr': '6827668473420000131002',\n 'pod_name': 'Test Line3',\n 'pickup_ts': '2013-11-08 14:50:42',\n 'connote_nbr': '8473420000131'},\n {'identity_type_id': 9,\n 'identity_type_data': 'd1234',\n 'item_nbr': '6827668473420000131003',\n 'pod_name': 'Test Line4',\n 'pickup_ts': '2013-11-08 14:50:42',\n 'connote_nbr': '8473420000131'}]\n msg = 'Exporter report files parsed values error'\n self.assertListEqual(received, expected, msg)",
"title": ""
},
{
"docid": "6c2a95341c7fcfcb9478fa2976596c76",
"score": "0.6157855",
"text": "def testBasic(self, rc):\n dirname = mktemp('mvpa', 'test_report')\n report = rc('UnitTest report',\n title=\"Sample report for testing\",\n path=dirname)\n isdummy = isinstance(report, DummyReport)\n\n ohandlers = verbose.handlers\n verbose.handlers = [report]\n verbose.level = 3\n verbose(1, \"Starting\")\n verbose(2, \"Level 2\")\n\n if not isdummy:\n self.failUnless(len(report._story) == 2,\n msg=\"We should have got some lines from verbose\")\n\n if __debug__:\n odhandlers = debug.handlers\n debug.handlers = [report]\n oactive = debug.active\n debug.active = ['TEST'] + debug.active\n debug('TEST', \"Testing report as handler for debug\")\n if not isdummy:\n self.failUnless(len(report._story) == 4,\n msg=\"We should have got some lines from debug\")\n debug.active = oactive\n debug.handlers = odhandlers\n\n os.makedirs(dirname)\n\n if externals.exists('pylab plottable'):\n if not isdummy:\n clen = len(report._story)\n import pylab as P\n P.ioff()\n P.close('all')\n P.figure()\n P.plot([1, 2], [3, 2])\n\n P.figure()\n P.plot([2, 10], [3, 2])\n P.title(\"Figure 2 must be it\")\n report.figures()\n\n if not isdummy:\n self.failUnless(\n len(report._story) == clen+2,\n msg=\"We should have got some lines from figures\")\n\n report.text(\"Dugi bugi\")\n # make sure we don't puke on xml like text with crap\n report.text(\"<kaj>$lkj&*()^$%#%</kaj>\")\n report.text(\"locals:\\n%s globals:\\n%s\" % (`locals()`, `globals()`))\n # bloody XML - just to check that there is no puke\n report.xml(\"<b>Dugi bugi</b>\")\n report.save()\n\n if externals.exists('pylab'):\n import pylab as P\n P.close('all')\n P.ion()\n\n # cleanup\n if os.path.exists(dirname):\n # poor man recursive remove\n for f in os.listdir(dirname):\n try:\n os.remove(os.path.join(dirname, f))\n except:\n # could be a directory... but no deeper ones expected\n for f2 in os.listdir(os.path.join(dirname, f)):\n os.remove(os.path.join(dirname, f, f2))\n os.rmdir(os.path.join(dirname, f))\n os.rmdir(dirname)\n verbose.handlers = ohandlers",
"title": ""
},
{
"docid": "320e17e49ab3a5a92b65a4b86fb4890d",
"score": "0.61563045",
"text": "def test_financial_statement_analysis(self):\n pass",
"title": ""
},
{
"docid": "2a83ad9dd9666bba342be0391883f5f0",
"score": "0.61519486",
"text": "def verifyEquity2(self, record):\n \tself.assertEqual('2018-04-30', record['valuation date'])\n \tself.assertEqual('00899.HK', record['ticker'])\n \tself.assertEqual('HKD', record['currency'])\n \tself.assertEqual(267580, record['total market value'])\n \tself.assertEqual(-105228546.41, record['market value gain loss'], 4)\n \tself.assertEqual(0.01, record['percentage of fund'], 6)",
"title": ""
},
{
"docid": "b84f5f2cc4cc50faa98634a1e33a6933",
"score": "0.6131317",
"text": "def test_choose_unit_400():\n\n assert False",
"title": ""
},
{
"docid": "ad9c83db0db02eb745298e4208987045",
"score": "0.61167717",
"text": "def test_report_run(self,m):\n path = os.path.dirname(__file__)\n\n with open(path+'/mock_objects/basic_report.json') as data_file:\n json_response = data_file.read()\n\n with open(path+'/mock_objects/Report.Queue.json') as queue_file:\n report_queue = queue_file.read()\n\n #setup mock object\n m.post('https://api.omniture.com/admin/1.4/rest/?method=Report.Get', text=json_response)\n m.post('https://api.omniture.com/admin/1.4/rest/?method=Report.Queue', text=report_queue)\n\n\n self.assertIsInstance(self.analytics.suites[test_report_suite].report.run(), omniture.Report, \"The run method doesn't work to create a report\")",
"title": ""
},
{
"docid": "017fc1987aac77c29bed228c255b71ad",
"score": "0.6113997",
"text": "def test_create_report_file_entry(api_client, ofa_admin):\n user = ofa_admin\n api_client.login(username=user.username, password=\"test_password\")\n data = {\n \"original_filename\": \"report.txt\",\n \"quarter\": \"Q1\",\n \"slug\": uuid.uuid4(),\n \"user\": user.id,\n \"stt\": user.stt.id,\n \"year\": 2020,\n \"section\": \"Active Case Data\",\n }\n response = api_client.post(\"/v1/reports/\", data)\n assert response.status_code == status.HTTP_201_CREATED\n assert response.data[\"slug\"] == str(data[\"slug\"])\n\n assert ReportFile.objects.filter(\n slug=data[\"slug\"],\n year=data[\"year\"],\n section=data[\"section\"],\n version=1,\n user=user,\n ).exists()",
"title": ""
},
{
"docid": "3cea96da25854bae2d8e26d6f4f72fd6",
"score": "0.6112115",
"text": "def report():\n pass",
"title": ""
},
{
"docid": "ba777c8b4bb4d8b054a38bd4fddaedb9",
"score": "0.61117953",
"text": "def test_analyze(self):\n\n pass",
"title": ""
},
{
"docid": "30b0f4f24d70fd88b870cc39f5016e5e",
"score": "0.6108431",
"text": "def test_report_without_manual_testcases(self):\n self.__birt.date_of_last_manual_test.return_value = datetime.datetime.min\n self.__birt.nr_manual_ltcs.return_value = 0, []\n self.assertEqual(self.__metric.no_manual_tests_template.format(name='FakeSubject', unit=self.__metric.unit),\n self.__metric.report())",
"title": ""
},
{
"docid": "e41407d2da3e38efc5565a55a5484557",
"score": "0.61025345",
"text": "def AssertTest(test):\r\n\r\n assertionText = 'ControlSum test - more then 1 `RF`.'\r\n assert test['RF'].nunique() == 1, assertionText\r\n\r\n assertionText = 'ControlSum test - more then 1 `controlSum`.'\r\n controlSum = test.loc[test['Value'] == 'on', 'Field'].values[0]\r\n assert type(controlSum) == str, assertionText",
"title": ""
},
{
"docid": "6e1b414370a55c8177047859834d6c57",
"score": "0.6088687",
"text": "def test_can_access_reports_page_and_see_the_form(self):\n self.login()\n response = self.client.get(reverse(\"reports\"))\n self.assertTrue(response.status_code, 200)\n self.assertContains(response, \"Generate a report\")",
"title": ""
},
{
"docid": "51fa0f7465890887b84dbe1b9032ea9d",
"score": "0.6084817",
"text": "def testPerformanceReport(self):\n step = self.processingReport.retrieveStep('cmsRun1')\n perfInfo = self.reporter.getPerformanceInformation(step)\n self.assertEqual(len(self.trimNoneValues(perfInfo)), 21,\n 'Found less information than expected')\n self.assertEqual(perfInfo['PeakValueRss'], '891.617',\n 'Values do not match')\n self.assertEqual(perfInfo['readCachePercentageOps'], 0.995779157341,\n 'Values do not match')\n self.assertEqual(perfInfo['MaxEventTime'], '3.32538',\n 'Values do not match')\n\n step = self.processingReport.retrieveStep('logArch1')\n perfInfo = self.reporter.getPerformanceInformation(step)\n self.assertEqual(self.trimNoneValues(perfInfo), {},\n 'logArch1 performance info is not empty')\n\n step = self.processingReport.retrieveStep('stageOut1')\n perfInfo = self.reporter.getPerformanceInformation(step)\n self.assertEqual(self.trimNoneValues(perfInfo), {},\n 'stageOut1 performance info is not empty')\n\n step = self.errorReport.retrieveStep('cmsRun1')\n perfInfo = self.reporter.getPerformanceInformation(step)\n self.assertEqual(self.trimNoneValues(perfInfo), {},\n 'cmsRun1 performance info is not empty')\n\n step = self.errorReport.retrieveStep('logArch1')\n perfInfo = self.reporter.getPerformanceInformation(step)\n self.assertEqual(self.trimNoneValues(perfInfo), {},\n 'logArch1 performance info is not empty')\n\n step = self.errorReport.retrieveStep('stageOut1')\n perfInfo = self.reporter.getPerformanceInformation(step)\n self.assertEqual(self.trimNoneValues(perfInfo), {},\n 'stageOut1 performance info is not empty')",
"title": ""
},
{
"docid": "a1cd6811569b6f73df74e14a976dcc41",
"score": "0.60783243",
"text": "def test_stats_compare(self):\n result_expected = \"\" \\\n \"+--------------------------------------------------------------+---------------------+---------------------+-----------------+-----------------+-------+-------+-------+-------+-------+\\n\" \\\n \"| refstr | service_bibcode | classic_bibcode | service_conf | classic_score | match | miss | new | newu | diff |\\n\" \\\n \"+==============================================================+=====================+=====================+=================+=================+=======+=======+=======+=======+=======+\\n\" \\\n \"| J.-P. Uzan, Varying constants, gravitation and cosmology, | 2011LRR....14....2U | 2010arXiv1009.5514U | 1.0 | 1 | | | | | DIFF |\\n\" \\\n \"| Living Rev. Rel. 14 (2011) 2, [1009.5514]. | | | | | | | | | |\\n\" \\\n \"+--------------------------------------------------------------+---------------------+---------------------+-----------------+-----------------+-------+-------+-------+-------+-------+\\n\" \\\n \"| C. J. A. P. Martins, The status of varying constants: A | 2017RPPh...80l6902M | 2017arXiv170902923M | 1.0 | 1 | | | | | DIFF |\\n\" \\\n \"| review of the physics, searches and implications, | | | | | | | | | |\\n\" \\\n \"| 1709.02923. | | | | | | | | | |\\n\" \\\n \"+--------------------------------------------------------------+---------------------+---------------------+-----------------+-----------------+-------+-------+-------+-------+-------+\"\n result_got, num_references, num_resolved = self.app.get_service_classic_compare_stats_grid(source_bibcode='0001arXiv.........Z',\n source_filename=os.path.join(self.arXiv_stubdata_dir,'00001.raw'))\n self.assertEqual(result_got, result_expected)\n self.assertEqual(num_references, 2)\n self.assertEqual(num_resolved, 2)",
"title": ""
},
{
"docid": "fa4b257c3e385890ec8a34270f580037",
"score": "0.6076862",
"text": "def test_monster_report_creation(self):\r\n report = creates_monster_report(sighting=creates_sighting(researcher=creates_reseacher(),monster=creates_monster()))\r\n self.assertEqual(report.id, 1)\r\n monster_report = MonsterReport.objects.get(id=1)\r\n \r\n self.assertEqual(report.title, monster_report.title)\r\n self.assertEqual(report.filename, monster_report.filename)\r\n self.assertEqual(report.path, monster_report.path)\r\n #self.assertQuerysetEqual(report.sighting.values(), monster_report.sighting.values())\r",
"title": ""
},
{
"docid": "5d8fcb2c0e2239c299e927d682553700",
"score": "0.60713834",
"text": "def test_4_3_1_13(self):\n pass",
"title": ""
},
{
"docid": "7f6a02381368fcb7de63d92523de84e8",
"score": "0.6065926",
"text": "def test_update_report_by_id(self):\n pass",
"title": ""
},
{
"docid": "77241cd948f173c426c528f67d1f8623",
"score": "0.6065002",
"text": "def run_report(self):",
"title": ""
},
{
"docid": "15b6c964fef5b75137b1cd4c443c9e44",
"score": "0.6048618",
"text": "def test_failure(database):\n\n state_code = States(state_code=\"NY\")\n det_award_1 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code=\"001****\", record_type=1)\n det_award_2 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code=\"NA*****\", record_type=2)\n det_award_3 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code=\"NA1234R\", record_type=2)\n det_award_4 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code=\"\", record_type=1)\n det_award_5 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code=None, record_type=2)\n # Invalid ppop format\n det_award_6 = DetachedAwardFinancialAssistanceFactory(place_of_performance_code=\"NA1234X\", record_type=2)\n errors = number_of_errors(_FILE, database, models=[det_award_1, det_award_2, det_award_3, det_award_4, det_award_5,\n det_award_6, state_code])\n assert errors == 6",
"title": ""
},
{
"docid": "113bc3f461eca6a6cd618e8cefb00ca5",
"score": "0.6047885",
"text": "def test_summary(self):\r\n assert '1 test, 0 failures, 0 errors, 1 skip in ' in self.output",
"title": ""
},
{
"docid": "0ab9ddbc7671417f147da5da881a35a0",
"score": "0.604077",
"text": "def test_basic(self):\n self.assertEqual(solution(\"\"\"Valve AA has flow rate=0; tunnels lead to valves DD, II, BB\nValve BB has flow rate=13; tunnels lead to valves CC, AA\nValve CC has flow rate=2; tunnels lead to valves DD, BB\nValve DD has flow rate=20; tunnels lead to valves CC, AA, EE\nValve EE has flow rate=3; tunnels lead to valves FF, DD\nValve FF has flow rate=0; tunnels lead to valves EE, GG\nValve GG has flow rate=0; tunnels lead to valves FF, HH\nValve HH has flow rate=22; tunnel leads to valve GG\nValve II has flow rate=0; tunnels lead to valves AA, JJ\nValve JJ has flow rate=21; tunnel leads to valve II\"\"\"), 1707)",
"title": ""
},
{
"docid": "f7dbcd8ebb1212099aa048923afe817e",
"score": "0.6039031",
"text": "def scrab(self, report):\n assert False, \"You have to implement this function\"",
"title": ""
},
{
"docid": "db3ebbf37ba4c18071e3dcc9d3ac599a",
"score": "0.6032928",
"text": "def test_report_with_zero_unittests(self):\n report = FakeUnitTestReport(unittests=0)\n project = domain.Project(metric_sources={metric_source.UnitTestReport: report})\n failing_unittests = metric.FailingUnittests(subject=FakeSubject(), project=project)\n self.assertEqual('Er zijn geen unittesten.', failing_unittests.report())",
"title": ""
},
{
"docid": "fce7e4c48997c5121a3f620827776d44",
"score": "0.60319555",
"text": "def testReport(self):\n self.scorer.report(\n [self.url1, self.url2], [self.url3, self.url4])\n self.assertEquals(1, memcache.get('scoring:test:success:' + self.domain1))\n self.assertEquals(0, memcache.get('scoring:test:failure:' + self.domain1))\n self.assertEquals(1, memcache.get('scoring:test:success:' + self.domain2))\n self.assertEquals(1, memcache.get('scoring:test:failure:' + self.domain2))\n self.assertEquals(0, memcache.get('scoring:test:success:' + self.domain3))\n self.assertEquals(1, memcache.get('scoring:test:failure:' + self.domain3))\n\n self.scorer.report(\n [self.url1, self.url2, self.url3, self.url4], [])\n self.assertEquals(2, memcache.get('scoring:test:success:' + self.domain1))\n self.assertEquals(0, memcache.get('scoring:test:failure:' + self.domain1))\n self.assertEquals(3, memcache.get('scoring:test:success:' + self.domain2))\n self.assertEquals(1, memcache.get('scoring:test:failure:' + self.domain2))\n self.assertEquals(1, memcache.get('scoring:test:success:' + self.domain3))\n self.assertEquals(1, memcache.get('scoring:test:failure:' + self.domain3))\n\n self.scorer.report(\n [], [self.url1, self.url2, self.url3, self.url4])\n self.assertEquals(2, memcache.get('scoring:test:success:' + self.domain1))\n self.assertEquals(1, memcache.get('scoring:test:failure:' + self.domain1))\n self.assertEquals(3, memcache.get('scoring:test:success:' + self.domain2))\n self.assertEquals(3, memcache.get('scoring:test:failure:' + self.domain2))\n self.assertEquals(1, memcache.get('scoring:test:success:' + self.domain3))\n self.assertEquals(2, memcache.get('scoring:test:failure:' + self.domain3))",
"title": ""
},
{
"docid": "5d8d84fe9ab1ba200988c6c2255ff20a",
"score": "0.60315496",
"text": "def test_error_report(self):\n postJson = {\"submission_id\": self.error_report_submission_id}\n response = self.app.post_json(\n \"/v1/submission_error_reports/\", postJson, headers={\"x-session-id\":self.session_id})\n self.assertEqual(response.status_code, 200)\n self.assertEqual(\n response.headers.get(\"Content-Type\"), \"application/json\")\n self.assertEqual(len(response.json), 5)\n self.assertIn(\"cross_file_error_url\", response.json)",
"title": ""
},
{
"docid": "833618b712d9dc8f2303c2124b9276c5",
"score": "0.6012028",
"text": "def test_success(database):\n award_fin = AwardFinancialFactory(availability_type_code='X')\n award_fin_lower = AwardFinancialFactory(availability_type_code='x')\n award_fin_null = AwardFinancialFactory(availability_type_code=None)\n\n errors = number_of_errors(_FILE, database, models=[award_fin, award_fin_null, award_fin_lower])\n assert errors == 0",
"title": ""
},
{
"docid": "7d5f6f9833d9d06c892a2be2595208c7",
"score": "0.60055333",
"text": "def testEventInformationReport(self):\n eventInfo = self.reporter.getEventInformation('cmsRun1',\n self.processingReport)\n self.assertEqual(eventInfo['inputEvents'], 18192,\n 'Input events do not match')\n self.assertEqual(eventInfo['OutputEventInfo'].count('Run2012B-WElectron-PromptSkim-v1:USER:1603'), 1)\n self.assertEqual(eventInfo['OutputEventInfo'].count('Run2012B-LogErrorMonitor-PromptSkim-v1:USER:137'), 1)\n self.assertEqual(eventInfo['OutputEventInfo'].count('Run2012B-LogError-PromptSkim-v1:RAW-RECO:66'), 1)\n self.assertEqual(eventInfo['OutputEventInfo'].count('Run2012B-TOPElePlusJets-PromptSkim-v1:AOD:2320'), 1)\n self.assertEqual(eventInfo['OutputEventInfo'].count('Run2012B-HighMET-PromptSkim-v1:RAW-RECO:8'), 1)\n self.assertEqual(eventInfo['OutputEventInfo'].count('Run2012B-DiTau-PromptSkim-v1:RAW-RECO:192'), 1)\n\n eventInfo = self.reporter.getEventInformation('stageOut1',\n self.processingReport)\n self.assertEqual(eventInfo, {},\n 'stageOut1 event info is not empty')\n\n eventInfo = self.reporter.getEventInformation('logArch1',\n self.processingReport)\n self.assertEqual(eventInfo, {},\n 'logArch1 event info is not empty')\n\n eventInfo = self.reporter.getEventInformation('cmsRun1',\n self.mergeReport)\n self.assertEqual(eventInfo['inputEvents'], 0,\n 'Input events do not match')\n self.assertEqual(eventInfo['OutputEventInfo'].count('Run2012B-LogError-PromptSkim-v1:RAW-RECO:0'), 1)\n\n eventInfo = self.reporter.getEventInformation('cmsRun1',\n self.errorReport)\n self.assertEqual(eventInfo, {},\n 'Error report event info is not empty')",
"title": ""
},
{
"docid": "bfdc5fabc80aee2ecba1afae9f857904",
"score": "0.60054886",
"text": "def test_success(database):\n cc_1 = CountryCode(country_code='USA', country_name='United States', territory_free_state=False)\n cc_2 = CountryCode(country_code='UKR', country_name='Ukraine', territory_free_state=False)\n fabs = FABSFactory(place_of_perform_country_c='USA', record_type=1, correction_delete_indicatr='')\n fabs_2 = FABSFactory(place_of_perform_country_c='uKr', record_type=2, correction_delete_indicatr='C')\n fabs_3 = FABSFactory(place_of_perform_country_c='abc', record_type=3, correction_delete_indicatr=None)\n # Ignore correction delete indicator of D\n fabs_4 = FABSFactory(place_of_perform_country_c='xyz', record_type=1, correction_delete_indicatr='d')\n\n errors = number_of_errors(_FILE, database, models=[cc_1, cc_2, fabs, fabs_2, fabs_3, fabs_4])\n assert errors == 0",
"title": ""
},
{
"docid": "1d91d67fab2c431f9c15ee6872956e84",
"score": "0.5998276",
"text": "def test_get_all_reports(self):\n pass",
"title": ""
},
{
"docid": "7239cfe2bb6a3b1d87748fcf787681c7",
"score": "0.5986639",
"text": "def report(self):\n assert False, \"You have to implement this function\"",
"title": ""
},
{
"docid": "24d2be284efe60c818384249605548f9",
"score": "0.5985607",
"text": "def report(self):\n self.header()\n\n tests_results = self.checker.results()\n for tr in tests_results:\n self.summary(tr)\n\n self.footer()",
"title": ""
},
{
"docid": "fba95fc9d2b22b7717be2ac2baca40d5",
"score": "0.5983299",
"text": "def test_new_report_complete_as_assistant(self):\n user_promoter = User.objects.create_user('promoter', '[email protected]', 'testpassword')\n base_user_promoter = BaseUser.objects.create(user=user_promoter, name=\"PromotoraTest\",\n last_name_paternal=\"last_name_paternal\",\n last_name_maternal=\"last_name_maternal\",\n phone_number=\"phone_number\",\n email=\"[email protected]\",\n address=\"address\")\n base_user_promoter.save()\n\n community = Community.objects.create(name = 'Name',\n municipality = 'Municipality',\n state = 'State')\n\n promoter = Promoter.objects.create(base_user=base_user_promoter,\n contact_name = \"Contacto\",\n contact_phone_number = \"1234512312\"\n )\n create_user_for_group('Assistant')\n self.client.login(username=\"Assistant\", password=\"testpassword\")\n\n beneficiary = Beneficiary.objects.create(name=\"Rodolfo\",\n last_name_paternal=\"Rodriguez\",\n last_name_maternal=\"Rocha\",\n community=community,\n promoter=promoter,\n num_of_family_beneficiaries=16,\n contact_name=\"Juan\",\n contact_phone=\"4424325671\",\n account_number=123456,\n bank_name=\"Banamets\")\n\n self.client.login(username=\"user\", password=\"testpassword\")\n response = self.client.get('/administrative/production_report/')\n self.assertEqual(response.status_code, 302)",
"title": ""
},
{
"docid": "5a24b1d592455e11ac8f25a526deed70",
"score": "0.5981717",
"text": "def test_passing(self):\n pass",
"title": ""
},
{
"docid": "6c4ca71f9a9c391a37ae1b98830f785c",
"score": "0.5980563",
"text": "def test_4_3_1_14(self):\n pass",
"title": ""
},
{
"docid": "49087f00e36b822aed0a1ba01f52811c",
"score": "0.5973367",
"text": "def test_get_report(self, app, report, size, column, value):\n request = app.get(f'/ms-ophelie/customers/{report}', status=200)\n\n assert request.status_code == 200\n headers = request.headers\n assert headers['Content-Disposition'] == 'attachment; filename=customers.csv'\n\n body = request.body\n lines = body.split(b'\\n')\n assert len(lines) == size\n assert column in lines[0]\n assert value in lines[1]",
"title": ""
},
{
"docid": "77e157ccd55944e60ff82982def2138e",
"score": "0.59659743",
"text": "def test_invalid_report_missing_args(self):\n self.assertEqual(\n first=\"Missing information, please provide name and gym. Type *?help raid* for help\",\n second=_validate_report([\"Sneasel\"])\n )",
"title": ""
},
{
"docid": "7268abb184e3b5daf787c598f511ccfc",
"score": "0.5951311",
"text": "def test_monthly_employer_report_has_all_book_keeping_where_vat_is_required_fields(self):\n #arrange\n book_keeping_fields = [\n 'list_of_names_and_income_tax_where_vat_is_required',\n 'sum_of_income_tax_where_vat_is_required',\n 'list_of_names_and_social_security_employee_where_vat_is_required',\n 'sum_of_social_security_employer_where_vat_is_required',\n 'sum_of_social_security_where_vat_is_required'\n ]\n #act\n monthly_employer_report = self.reports_maker.monthly_employer_report(for_year=2015, for_month=1)\n #assert\n for field in book_keeping_fields:\n self.assertIn(field , monthly_employer_report['book_keeping_where_vat_is_required'])\n # print(monthly_employer_report['book_keeping_where_vat_is_required']['list_of_names_and_income_tax_where_vat_is_required'])",
"title": ""
},
{
"docid": "99983d844d9cbaa0856d5b2943e0615e",
"score": "0.5937383",
"text": "def test_repeat_information(self):\n self.fail('Condition not met.')",
"title": ""
}
]
d04a316f8aae73b9a1bd180338544829
Forward method for NetVladOrthoReg.
[
{
"docid": "cf964e07b576847f8055ac4ab4df9c93",
"score": "0.0",
"text": "def forward(self, inputs, **unused_params):\n\n reshaped_input = tf.reshape(inputs, [-1, self.max_frames, self.feature_size])\n\n #\n # New: Compute attention-based cluster similarity weights:\n #\n with tf.variable_scope(\"cluster_attention\"):\n encoder_block = transformer_utils.TransformerEncoderMod(feature_size=self.feature_size,\n hidden_size=self.encoder_hidden_size,\n num_heads=self.num_heads,\n attention_dropout=self.dropout_ratio,\n ff_filter_size=self.filter_size,\n ff_relu_dropout=0.1,\n is_train=self.is_training,\n scope_id=\"encode\",\n final_size=self.cluster_size)\n cluster_similarities = encoder_block.forward(reshaped_input)\n\n\n cluster_centres = tf.get_variable(\"cluster_centers\", [self.feature_size, self.cluster_size],\n initializer=tf.random_normal_initializer(\n stddev=1 / math.sqrt(self.feature_size)))\n\n # (xi - ck)\n reshaped_input = tf.expand_dims(reshaped_input, axis=3) # B x N x F x 1\n residuals = tf.subtract(reshaped_input, cluster_centres) # B x N x F x C\n\n # Sum of ak(xi) * (xi - ck)\n cluster_similarities = tf.expand_dims(cluster_similarities, axis=3) # B x N x C x 1\n weighted_residuals = tf.multiply(residuals, cluster_similarities)\n residual_sum = tf.reduce_sum(weighted_residuals, axis=1) # B x F x C\n\n # Normalization of flattened global descriptor:\n vlad = tf.nn.l2_normalize(residual_sum, 1) # Normalize per cluster\n vlad = tf.reshape(vlad, [-1, self.cluster_size * self.feature_size]) # Flatten\n vlad = tf.nn.l2_normalize(vlad, 1,\n name=self.scope_id + \"vlad_final\") # Normalize global descriptor\n\n #vlad = tf.reshape(vlad, [-1, self.cluster_size, self.feature_size]) # Optional: output shape\n\n # batch_size x (cluster_size * feature_size)\n return vlad",
"title": ""
}
]
[
{
"docid": "71873500bedcb65572a0302e356690c9",
"score": "0.6630026",
"text": "def forward(self):\n self.HR_G = self.netG(self.LR)",
"title": ""
},
{
"docid": "c5b726b3aa05d9e7850ccdbcc9745924",
"score": "0.63454056",
"text": "def forward(self):\n self.fodpred = self.net(self.fodlr)",
"title": ""
},
{
"docid": "ae22194d7828f224675efbaffef5ea8c",
"score": "0.60776716",
"text": "def forward(self):\n\t\tself.h = self.sigmoid(self.W @ self.patterns)\n\n\t\tones = np.ones(self.Npts)\n\t\tself.h = np.vstack((self.h, ones))\n\n\t\tself.o = self.sigmoid(self.V @ self.h)",
"title": ""
},
{
"docid": "3b865dd6f6fff494a36b7239e965a666",
"score": "0.6040339",
"text": "def _forward_pass(self, X):",
"title": ""
},
{
"docid": "f4be156efa75831e447a4c74200f57e6",
"score": "0.6039019",
"text": "def relu_forward_hook_function(module, ten_in, ten_out):\n self.forward_relu_outputs.append(ten_out)",
"title": ""
},
{
"docid": "f4be156efa75831e447a4c74200f57e6",
"score": "0.6039019",
"text": "def relu_forward_hook_function(module, ten_in, ten_out):\n self.forward_relu_outputs.append(ten_out)",
"title": ""
},
{
"docid": "9624eb632869b8978080bc4c20ae0a1d",
"score": "0.601469",
"text": "def forward(self, X: Tensor) -> Tensor:\n pass # pragma: no cover",
"title": ""
},
{
"docid": "9624eb632869b8978080bc4c20ae0a1d",
"score": "0.601469",
"text": "def forward(self, X: Tensor) -> Tensor:\n pass # pragma: no cover",
"title": ""
},
{
"docid": "79224f4788dd8dc90068d30a8bbd12d7",
"score": "0.6013028",
"text": "def forward(self):\n\n for hl in self.hidden_layers:\n hl.forward()\n self.output_layer.forward()",
"title": ""
},
{
"docid": "a49c4cc47f60f7764bf01b3292a079ff",
"score": "0.6004738",
"text": "def forward(self, x):\n\n ########################\n # PUT YOUR CODE HERE #\n #######################\n out = self.model(x) \n ########################\n # END OF YOUR CODE #\n #######################\n\n return out",
"title": ""
},
{
"docid": "29168d5432a7db1722bbc57fe62be284",
"score": "0.5969051",
"text": "def forward(self,z):\r\n o = z\r\n for l in self.layers:\r\n o = l.forward(o)\r\n return o",
"title": ""
},
{
"docid": "2ff4a19f9092b2a24ffe67f6ca8c410d",
"score": "0.59354204",
"text": "def forward(self, x):",
"title": ""
},
{
"docid": "afcc468744161c27617bba086e62d6c0",
"score": "0.5916494",
"text": "def forward(self, src):\n dim = src.shape[1]\n # dist = torch.norm(src, dim=1) #dist from center or add it before?\n\n # FIRST LAYER\n x11 = self.linear11(src)\n x12 = self.linear12(src)\n # ACTIVATIONS POST LAYER 1\n x11 = self.tanh(x11)\n x12 = self.gaussian(x12) # or sigmoid?\n # SECOND LAYER\n x21 = self.linear21(torch.cat([x11, x12], dim=1))\n x22 = self.linear22(torch.cat([x11, x12], dim=1))\n x23 = self.linear23(torch.cat([x11, x12], dim=1))\n # ACTIVATIONS POST LAYER 2\n x21 = self.sin(x21)\n x22 = self.cos(x22)\n x23 = self.gaussian(x23)\n # THIRD LAYER\n x3 = self.linear3(torch.cat([x21, x22, x23], dim=1))\n\n # ACTIVATIONS POST LAYER 3\n out = self.gaussian(x3) # or remove?\n\n # OUT\n # out=x3[-1] #Last dimension, ie size one_hot_dim\n out[:, 0] = self.sigmoid(out[:, 0])\n out[:, 1:] = self.softmax(out[:, 1:])\n return out[-1]",
"title": ""
},
{
"docid": "5e30cea036f8424609762682b80e694e",
"score": "0.59137875",
"text": "def forward(self, x):\n x = self.layer1(x)\n # x = self.layer2(x)\n x = self.output(x)\n return x.view(-1)",
"title": ""
},
{
"docid": "17caa37304daf2b1b7a7569e41676225",
"score": "0.5908465",
"text": "def _forward(self, features, *args):\n pass",
"title": ""
},
{
"docid": "8d406d3ad198ac2412f3c3059088a6e0",
"score": "0.5885031",
"text": "def forward(self, x):\n\n ########################\n # PUT YOUR CODE HERE #\n #######################\n x = x.reshape(-1, self.sz_input)\n\n for idx, layer in enumerate(self.layers):\n x = layer(x)\n\n out = x\n ########################\n # END OF YOUR CODE #\n #######################\n\n return out",
"title": ""
},
{
"docid": "d19f4ed6db35a61bf6cd3630fa703fc1",
"score": "0.5881967",
"text": "def forward(self, x: Tensor) -> Tensor:\n pass",
"title": ""
},
{
"docid": "7f85851c460215ae8fadb25b4b7ca67b",
"score": "0.58683854",
"text": "def forward(self, *args):\n with torch.no_grad():\n return self.net.forward(*args)",
"title": ""
},
{
"docid": "85cd1feb218ae1f41b27f62066e02f7e",
"score": "0.58388567",
"text": "def forward(self, x):\n\n ########################\n # PUT YOUR CODE HERE #\n #######################\n #raise NotImplementedError\n\n for pre_layer,activation in self.layers:\n x_tilde = pre_layer.forward(x)\n x = activation.forward(x_tilde)\n\n out = x\n ########################\n # END OF YOUR CODE #\n #######################\n\n return out",
"title": ""
},
{
"docid": "f7aa248dd90ac1cb0f8fd9c10154c7a1",
"score": "0.5833892",
"text": "def forward(self):\n self.out = self.network(self.batch_x)",
"title": ""
},
{
"docid": "56327e157301def0ffc8f8d3d16b3cb0",
"score": "0.5831324",
"text": "def forward(self, x):\n\n x = self.act(self.layers[0].forward(x))\n\n for i in range(1,self.nTh):\n x = x + self.h * self.act(self.layers[i](x))\n\n return x",
"title": ""
},
{
"docid": "ba53e46a3f74ec16c471340b6baed3d2",
"score": "0.58250225",
"text": "def forward(self, x):\n pass",
"title": ""
},
{
"docid": "37fe21fd5ed86ed152085c9b22b9ea5d",
"score": "0.5798656",
"text": "def forward(self, x):\n out = self.model.forward(x)\n return out",
"title": ""
},
{
"docid": "28b8bda0e724c030d1de252d668ef1c5",
"score": "0.57758516",
"text": "def forward(self, x): \n return self.model(x)",
"title": ""
},
{
"docid": "a2b3d66f8128144fee56cb9fcf306dfa",
"score": "0.5766445",
"text": "def forward( self, inputs ) :\n pass",
"title": ""
},
{
"docid": "5aabada339df6cd94a24bbbc076590d0",
"score": "0.57533294",
"text": "def forward(self, X_conv_out):\n \n X_proj = F.relu(self.projection(X_conv_out))\n X_gate = torch.sigmoid(self.gate(X_conv_out))\n X_highway = torch.mul(X_gate, X_proj) + torch.mul((1 - X_gate),X_conv_out)\n\n return X_highway",
"title": ""
},
{
"docid": "4e2bff5fa51961ae18374fdb7c3e7692",
"score": "0.5744095",
"text": "def forward(self, input):\n \n ########################\n # PUT YOUR CODE HERE #\n #######################\n shape = input.size()\n \n if len(shape) == 1:\n input.view(1, -1)\n shape = input.size()\n if len(shape) > 2:\n raise ValueError('Expected 1-D or 2-D tensor (got {})'.format(str(shape)))\n elif input.shape[1] != self.n_neurons:\n raise ValueError(\n 'Expected _ x {} tensor (got {} x {})'.format(str(self.n_neurons), str(shape[0]), str(shape[1])))\n\n fct = CustomBatchNormManualFunction()\n out = fct.apply(input, self.gammas, self.betas, self.eps)\n ########################\n # END OF YOUR CODE #\n #######################\n \n return out",
"title": ""
},
{
"docid": "b0ea9b98bf6ff2811767350aeafbac18",
"score": "0.5736423",
"text": "def forward(self, input: tensor) -> tensor:\n input, l0 = self.L0(input)\n input, l1 = self.L1(input)\n input, l2 = self.L2(input)\n input, l3 = self.L3(input)\n _, l4 = self.L4(input)\n\n input = self.R3(l4, l3)\n input = self.R2(input, l2)\n input = self.R1(input, l1)\n input = self.R0(input, l0)\n\n return self.last_layer(input)",
"title": ""
},
{
"docid": "29d6e055178d48f2d3f3e81e01447ec2",
"score": "0.57324886",
"text": "def forward(self, features):\n pass",
"title": ""
},
{
"docid": "8109fd839ede0481a335999357eda073",
"score": "0.5706891",
"text": "def forward(self, x):\n x =F.relu(self.l1(x))\n x =F.relu(self.l2(x))\n x =F.relu(self.l3(x))\n x =F.relu(self.l4(x))\n x =F.relu(self.l5(x))\n return x",
"title": ""
},
{
"docid": "1d38306cc3817d686794ff7f02fd581c",
"score": "0.570267",
"text": "def forward(self,x):\n x = F.relu(self.fc1(x))\n x = F.relu(self.fc2(x))\n return self.fc3(x)",
"title": ""
},
{
"docid": "78489c06434059700a9a6fbea726fc7f",
"score": "0.5702236",
"text": "def forward(self, x):\n return self.model_forward(x)",
"title": ""
},
{
"docid": "276a2a93faeefa2dce8330d968a46678",
"score": "0.56946826",
"text": "def forward(self):\n\n raise NotImplementedError",
"title": ""
},
{
"docid": "87c66da211cc0abeb34286506f89c9cb",
"score": "0.5691196",
"text": "def forward(self, x):\n\n x = self.fc1(x)\n x = self.relu(x)\n\n# x = self.fc2(x)\n# x = self.relu(x)\n\n x = self.fc3(x)\n \n x = x.view(-1, 979, 3)\n x = self.softmax(x)\n \n return x",
"title": ""
},
{
"docid": "6961c88745c0ac2d201e35993757de66",
"score": "0.5675258",
"text": "def forward(self, x) -> Tensor:\n # hack to enable backbone to work properly.\n self.model.device = self.device\n return self.model(x)",
"title": ""
},
{
"docid": "02282b08d4747e8781784235a7682ea3",
"score": "0.56741774",
"text": "def forward(self, x_or_z: Iterable[Tensor], c: Iterable[Tensor] = None,\n rev: bool = False, jac: bool = True) \\\n -> Tuple[Tuple[Tensor], Tensor]:\n raise NotImplementedError(\n f\"{self.__class__.__name__} does not provide forward(...) method\")",
"title": ""
},
{
"docid": "cd51289e6e2dd2d50c155cf8e8c5f429",
"score": "0.56569755",
"text": "def forward(self, X, use_parameter_copy=False):\r\n self.outputs_cache = []\r\n \r\n \"\"\"\r\n Sometimes the vector input will be a matrix [[3,2,1]]\r\n othertimes it will be just an array [3, 2, 1]\r\n So the program needs to convert 1d arrays into matrix arrays\r\n \"\"\"\r\n self.outputs_cache.append(np.matrix(X))\r\n \r\n previous_layer_input = self.outputs_cache[0]\r\n for i in range(len(self.layers)):\r\n layer = self.layers[i]\r\n if layer[0] == 'input':\r\n continue \r\n \r\n weight_matrix = self.weights[i-1]\r\n bias = self.biases[i-1]\r\n \r\n if(use_parameter_copy):\r\n weight_matrix = self.weights_copy[i-1]\r\n bias = self.biases_copy[i-1]\r\n \r\n if layer[0] == 'conv':\r\n opts = layer[1]\r\n height, width = self.layers[i-1][1][\"size\"]\r\n \r\n previous_layer_input = previous_layer_input.reshape(height, width)\r\n summed_inputs = self.convolution(previous_layer_input, self.weights[i-1], opts) + bias\r\n summed_inputs = summed_inputs.reshape(height, width)\r\n activation_func = opts[\"activation_func\"]\r\n else:\r\n \"\"\"\r\n Weights are off by one, but not layers.\r\n \"\"\"\r\n previous_height, previous_width = self.layers[i-1][1][\"size\"]\r\n previous_layer_input = previous_layer_input.reshape(1, previous_height * previous_width)\r\n summed_inputs = np.dot(previous_layer_input, weight_matrix) + bias\r\n activation_func = layer[0]\r\n \r\n previous_layer_input = self.activation_func[activation_func](summed_inputs)\r\n self.outputs_cache.append(previous_layer_input)\r\n \r\n return previous_layer_input",
"title": ""
},
{
"docid": "9e6dc8ce9c13b42548c958f9f381b0b1",
"score": "0.5641033",
"text": "def forward(self, x):\n\n return self.model.forward(x)",
"title": ""
},
{
"docid": "7f4095bfa578a59d446b375ad0df8ee8",
"score": "0.56231856",
"text": "def forward(self, inputs):\n if self.requires_dr:\n inputs[Properties.R].requires_grad_()\n if self.requires_stress:\n # Generate Cartesian displacement tensor\n displacement = torch.zeros_like(inputs[Properties.cell]).to(\n inputs[Properties.R].device\n )\n displacement.requires_grad = True\n inputs[\"displacement\"] = displacement\n\n # Apply to coordinates and cell\n inputs[Properties.R] = inputs[Properties.R] + torch.matmul(\n inputs[Properties.R], displacement\n )\n inputs[Properties.cell] = inputs[Properties.cell] + torch.matmul(\n inputs[Properties.cell], displacement\n )\n\n inputs[\"representation\"] = self.representation(inputs)\n outs = {}\n for output_model in self.output_modules:\n outs.update(output_model(inputs))\n return outs",
"title": ""
},
{
"docid": "e522388258a0322a19fee96b478139ee",
"score": "0.5610246",
"text": "def relu_forward(x):\n out = None\n #############################################################################\n # TODO: Implement the ReLU forward pass. #\n #############################################################################\n # 1 line of code expected\n\n pass\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n cache = x\n return out, cache",
"title": ""
},
{
"docid": "9c32dae2a350674e3ddde8751dd14f41",
"score": "0.560829",
"text": "def forward(self, input):\r\n x = input\r\n for l in self.linear_layers[:-1]:\r\n x = l(x)\r\n x = self.act(x)\r\n\r\n output_layer = self.linear_layers[-1]\r\n return output_layer(x)",
"title": ""
},
{
"docid": "b89073d391c5a001bbc024203cdcd526",
"score": "0.5607582",
"text": "def forward(self,x):\n\t\tout = self.linear1(x)\n\t\tout = self.act(out)\n\n\t\tout = self.linear2(out)\n\t\t\n\t\treturn out",
"title": ""
},
{
"docid": "c2663ed52055bdc9e2b7dbd9164e2b33",
"score": "0.56043416",
"text": "def forward(self, inputs):\n return gradient_reversal(inputs)",
"title": ""
},
{
"docid": "ee90ae088d42f53c0cf6b89d6e2ffb97",
"score": "0.559809",
"text": "def forward(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "ee90ae088d42f53c0cf6b89d6e2ffb97",
"score": "0.559809",
"text": "def forward(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "ee90ae088d42f53c0cf6b89d6e2ffb97",
"score": "0.559809",
"text": "def forward(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "cad7b342d7ea2bddf0e5bc39bbb39845",
"score": "0.55956966",
"text": "def forward(self, src):\n\n x1 = self.linear1(src)\n x1 = self.relu(x1) # or sigmoid\n if self.n_layers == 2:\n out = self.linear2(x1)\n # out=x3[-1] #Last dimension, ie size one_hot_dim\n out[:, 0] = self.sigmoid(out[:, 0])\n out[:, 1:] = self.softmax(out[:, 1:])\n else:\n x2 = self.linear2(x1)\n x2 = self.sigmoid(x2)\n out = self.linear3(x2)\n # out=x3[-1] #Last dimension, ie size one_hot_dim\n out[:, 0] = self.sigmoid(out[:, 0])\n out[:, 1:] = self.softmax(out[:, 1:])\n return out[-1]",
"title": ""
},
{
"docid": "ca2255abc12e6ea77bbf7af5996fa539",
"score": "0.5590943",
"text": "def forward(self,data):\n # def forward(self,gt,idx, img,tracker,bbox,se):\n \n template = data['template'].cuda().unsqueeze(0)\n search =data['search'].cuda().unsqueeze(0)\n bbox=data['bbox']\n\n \n zf,zf1 = self.backbone(template)\n xf,xf1 = self.backbone(search)\n xff,ress=self.grader(xf1,zf1)\n \n anchors=self.getcenter(xff,gt) #gt != bbox\n size=xff.size()[3] \n data2=self.generatedata(anchors,bbox,size)\n \n label_cls = t.Tensor(data2['label_cls']).cuda().unsqueeze(0)\n label_loc = t.Tensor(data2['label_loc']).cuda().unsqueeze(0)\n label_loc_weight = t.Tensor(data2['label_loc_weight']).cuda().unsqueeze(0)\n labelcls2=data2['label_cls2']\n \n \n \n #new3\n cls1,cls2,cls3,loc=self.new(xf,zf,ress)\n shape=cls1.size()[3]\n label_cls=label_cls[:,:,size//2-shape//2:size//2+shape//2+1,size//2-shape//2:size//2+shape//2+1].contiguous()\n label_loc=label_loc[:,:,:,size//2-shape//2:size//2+shape//2+1,size//2-shape//2:size//2+shape//2+1].contiguous()\n label_loc_weight=label_loc_weight[:,:,size//2-shape//2:size//2+shape//2+1,size//2-shape//2:size//2+shape//2+1].contiguous()\n \n pre=(8*(np.linspace(0,size-1,size))+63).reshape(-1,1)-287//2\n pr=t.zeros(size**2,4).cuda()\n pr[:,0]=t.Tensor(np.maximum(0,np.tile(pre,(size)).T.reshape(-1)+287//2))\n pr[:,1]=t.Tensor(np.maximum(0,np.tile(pre,(size)).reshape(-1)+287//2))\n \n labelxff=t.zeros_like(xff).cuda()\n labelxff[0,0,:,:]=(pr[:,0]-bbox[0]).reshape(21,21)\n labelxff[0,1,:,:]=(bbox[2]-pr[:,0]).reshape(21,21)\n labelxff[0,2,:,:]=(pr[:,1]-bbox[1]).reshape(21,21)\n labelxff[0,3,:,:]=(bbox[3]-pr[:,1]).reshape(21,21)\n labelxff=labelxff/143\n\n pr[:,2]=self.con(xff[0,0,:,:]).view(-1)+self.con(xff[0,1,:,:]).view(-1)\n pr[:,3]=self.con(xff[0,2,:,:]).view(-1)+self.con(xff[0,3,:,:]).view(-1)\n pr[:,0]=pr[:,0]-self.con(xff[0,0,:,:]).view(-1)+pr[:,2]/2\n pr[:,1]=pr[:,1]-self.con(xff[0,2,:,:]).view(-1)+pr[:,3]/2\n \n \n\n \n def transform(center):\n x, y, w, h = center[:,0], center[:,1], center[:,2], center[:,3]\n x1 = x - w * 0.5\n y1 = y - h * 0.5\n x2 = x + w * 0.5\n y2 = y + h * 0.5\n return t.cat((x1.view(-1,1),y1.view(-1,1),x2.view(-1,1),y2.view(-1,1)),1)\n pr=transform(pr)\n \n \n index=np.minimum(shape-1,np.maximum(0,np.int32((bbox-63-(size-shape)*4)/8)))\n w=int(index[2]-index[0])\n h=int(index[3]-index[1])\n\n weightcls3=t.zeros(1,1,shape,shape).cuda()\n weightcls3[0,0,index[1]:index[3]+1,index[0]:index[2]+1]=1\n weightcls33=t.zeros(1,1,shape,shape).cuda()\n for ii in np.arange(index[1],index[3]+1):\n for jj in np.arange(index[0],index[2]+1):\n l1=t.min(t.Tensor([ii-index[1]]),t.Tensor([(index[3]-ii)]))/(t.max(t.Tensor([ii-index[1]]),t.Tensor([(index[3]-ii)]))+1e-4)\n l2=t.min(t.Tensor([jj-index[0]]),t.Tensor([(index[2]-jj)]))/(t.max(t.Tensor([jj-index[0]]),t.Tensor([(index[2]-jj)]))+1e-4)\n weightcls33[0,0,ii,jj]=weightcls3[0,0,ii,jj]*t.sqrt(l1*l2)\n \n\n \n cls1 = self.log_softmax(cls1) \n cls2 = self.log_softmax(cls2) \n\n \n cls_loss1 = select_cross_entropy_loss(cls1, label_cls,0.5)\n cls_loss2 = select_cross_entropy_loss(cls2, labelcls2,0.5)\n cls_loss3 = l1loss(cls3, weightcls33,weightcls3) \n\n cls_loss= cls_loss3 + cls_loss1 + cls_loss2\n loc_loss = weight_l1_loss(loc, label_loc, label_loc_weight) \n \n weightxff=t.zeros(1,1,size,size).cuda()\n index2=np.int32((bbox-63)/8) #特征图上的位置\n w=int(index2[2]-index2[0])\n h=int(index2[3]-index2[1])\n weightxff[0,0,np.maximum(0,index2[1]-h//2):np.minimum(size,index2[3]+1+h//2),np.maximum(0,index2[0]-w//2):np.minimum(size,index2[2]+1+w//2)]=1\n\n \n shapeloss=l1loss(xff,labelxff,weightxff) \n \n \n\n \n outputs=loc_loss+cls_loss+shapeloss #2 4 1 都用loss2\n\n return outputs",
"title": ""
},
{
"docid": "0329eb71dcb93e2a2ed7e2efd1ed7153",
"score": "0.5589707",
"text": "def forward(self, target, source): \r\n #* Concatenate source and target\r\n x = torch.cat([target, source],1)\r\n x_1l = self.module1l(x)\r\n x_2l = self.module2l(x_1l)\r\n x_3l = self.module3l(x_2l)\r\n x_4l = self.module4l(x_3l)\r\n x_5l = self.module5l(x_4l)\r\n x_6l = self.module6l(x_5l)\r\n # embed = embed.view(-1, 1, 4, 4)\r\n # x = torch.cat([x_6l, embed], 1)\r\n x=x_6l\r\n\r\n r6 = self.module6r(x)\r\n x = torch.cat([r6, x_5l], 1)\r\n r5 = self.module5r(x)\r\n x = torch.cat([r5, x_4l], 1)\r\n r4 = self.module4r(x)\r\n x = torch.cat([r4, x_3l], 1)\r\n r3 = self.module3r(x)\r\n x = torch.cat([r3, x_2l], 1)\r\n r2 = self.module2r(x)\r\n x = torch.cat([r2, x_1l], 1)\r\n x = self.module1r(x)\r\n return x\r\n\r\n # x = self.leftmodule(x,embed)\r\n # x = self.rightmodule(x)\r\n # return x\r",
"title": ""
},
{
"docid": "78382338c45908367d6211e5e69ba844",
"score": "0.5586166",
"text": "def forward(self, x):\n ### START YOUR CODE HERE ### (You can change anything inside this block)\n x = self.layer1(x)\n x = self.layer2(x)\n x = self.layer3(x)\n x = self.flattening(x)\n x = self.layer4(x)\n x = self.layer5(x)\n return x\n ### END YOUR CODE HERE ### ",
"title": ""
},
{
"docid": "247124953ec614c39c027c2e71c44e72",
"score": "0.558313",
"text": "def forward(self, x):\n z = self.model(z)\n return z",
"title": ""
},
{
"docid": "b4ed1a4ce75679cc6f54fc809f89e993",
"score": "0.5575662",
"text": "def relu_forward(x):\n out, cache = None, x\n ########################################################################\n # TODO: Calculate {out} #\n ########################################################################\n\n ########################################################################\n # End of the code #\n ########################################################################\n return out, cache",
"title": ""
},
{
"docid": "985cbc1183bc26f98059ade0be88b7c5",
"score": "0.55717224",
"text": "def forward(self,):\n raise NotImplementedError",
"title": ""
},
{
"docid": "a8438774483e860bc23e3f7d39c3603b",
"score": "0.5570057",
"text": "def forward(self, x):\n arm_sources = list()\n odm_sources = list()\n for i in range(23):\n x = self.vgg[i](x)\n c2 = x\n c2 = self.L2Norm_4_3(c2)\n arm_sources.append(c2)\n for k in range(23, len(self.vgg)):\n x = self.vgg[k](x)\n c3 = x\n c3 = self.L2Norm_5_3(c3)\n arm_sources.append(c3)\n x = F.relu(self.extras[0](x), inplace=True)\n x = F.relu(self.extras[1](x), inplace=True)\n c4 = x\n arm_sources.append(c4)\n x = F.relu(self.extras[2](x), inplace=True)\n x = F.relu(self.extras[3](x), inplace=True)\n c5 = x\n arm_sources.append(c5)\n if len(self.extras) > 4:\n x = F.relu(self.extras[4](x), inplace=True)\n x = F.relu(self.extras[5](x), inplace=True)\n c6 = x\n arm_sources.append(c6)\n odm_sources = self.weave(arm_sources)\n return arm_sources, odm_sources",
"title": ""
},
{
"docid": "516daa46e26621a3a98aa41a1ed9ba00",
"score": "0.5569684",
"text": "def _forward(self, x):\n raise NotImplementedError(\"forward not implemented.\")",
"title": ""
},
{
"docid": "c205a780d50e749fde6e6c9398dcc404",
"score": "0.5567876",
"text": "def forward(self):\n pass",
"title": ""
},
{
"docid": "c3d10844ce77bccbe18c655ad7ed8165",
"score": "0.5562897",
"text": "def forward(self, x: Tensor) -> Tensor:\n direction1_conv = self.direction1_conv(x)\n direction2_conv = self.direction2_conv(x)\n direction1_feat = self.direction1_pool(direction1_conv)\n direction2_feat = self.direction2_pool(direction2_conv)\n aftpool_conv = self.aftpool_conv(direction1_feat + direction2_feat)\n conv1 = self.conv1(x)\n relu = self.relu(aftpool_conv + conv1)\n conv2 = self.conv2(relu)\n return conv2",
"title": ""
},
{
"docid": "531037f4aed7228617544351e3ad708d",
"score": "0.5551876",
"text": "def forward(self, x_conv_out): \n x_proj = torch.relu(self.conv_out_proj(x_conv_out))\n x_gate = torch.sigmoid(self.gate(x_conv_out))\n\n x_highway = x_gate * x_proj + (1 - x_gate) * x_conv_out\n\n return x_highway",
"title": ""
},
{
"docid": "7138b9e9d37c879ce8c047ef07ca30bd",
"score": "0.5547672",
"text": "def forward(self, x):\n\n sources = list()\n loc = list()\n conf = list()\n if self.modeltype == 'SSD300KL':\n loc_std = list()\n # apply vgg up to conv4_3 relu\n for k in range(23):\n # print('debug: apply vgg')\n x = self.vgg[k](x)\n\n if self.forward_vgg_base_only:\n return x\n # TODO: Why apply L2norm already? => because conv4_3 has larger scale than the rest\n s = self.L2Norm(x)\n sources.append(s)\n\n # apply vgg up to fc7 TODO: Why FC layers? => Doesn't use FC layers, UP TO FC layers..\n for k in range(23, len(self.vgg)):\n # print('debug2: apply vgg')\n x = self.vgg[k](x)\n sources.append(x)\n\n # apply extra layers and cache source layer outputs\n for k, v in enumerate(self.extras):\n # print('debug3: apply extra layers')\n x = F.relu(v(x), inplace=True)\n if k % 2 == 1: #TODO: Why only every second layer of the extra layers? => because thats how the paper states it. It has conv blocks of 2 conv layers\n sources.append(x)\n\n if self.modeltype != 'SSD300KL':\n # apply multibox head to source layers\n for (x, l, c) in zip(sources, self.loc, self.conf):\n # print('debug4: apply multibox head')\n loc.append(l(x).permute(0, 2, 3, 1).contiguous())\n conf.append(c(x).permute(0, 2, 3, 1).contiguous())\n\n loc = torch.cat([o.view(o.size(0), -1) for o in loc], 1)\n conf = torch.cat([o.view(o.size(0), -1) for o in conf], 1)\n # print('debug foward 1')\n if self.phase == \"test\":\n # if self.sampling_strategy != 'p-max_localization-stability' :\n output = self.detect(loc.view(loc.size(0), -1, 4), # loc preds\n self.softmax(conf.view(conf.size(0), -1,self.num_classes)), # conf preds\n self.priors.type(type(x.data)), # default boxes\n )\n # else:\n # output = self.detect()\n\n # training phase => no merging or other forwards used\n else:\n output = (\n loc.view(loc.size(0), -1, 4),\n conf.view(conf.size(0), -1, self.num_classes),\n self.priors\n )\n else:\n # apply multibox head to source layers\n for (x, l, c, std) in zip(sources, self.loc, self.conf, self.loc_std):\n # print('debug4: apply multibox head')\n loc.append(l(x).permute(0, 2, 3, 1).contiguous())\n conf.append(c(x).permute(0, 2, 3, 1).contiguous())\n loc_std.append(std(x).permute(0, 2, 3, 1).contiguous())\n\n loc = torch.cat([o.view(o.size(0), -1) for o in loc], 1)\n conf = torch.cat([o.view(o.size(0), -1) for o in conf], 1)\n loc_std = torch.cat([o.view(o.size(0), -1) for o in loc_std], 1)\n\n if self.phase == \"test\":\n # during training alpha = log(sigma^2), during testing, this needs to be converted back\n loc_std = torch.exp(loc_std)\n\n output = self.detect(loc.view(loc.size(0), -1, 4), # loc preds\n self.softmax(conf.view(conf.size(0), -1,self.num_classes)), # conf preds\n self.priors.type(type(x.data)), # default boxes\n torch.abs(loc_std.view(loc_std.size(0), -1, 4)) # alphas (predicted log of std deviations of loc preds)\n )\n else:\n # during training, alpha = log(sigma^2) is predicted\n output = (\n loc.view(loc.size(0), -1, 4),\n conf.view(conf.size(0), -1, self.num_classes),\n self.priors,\n torch.abs(loc_std.view(loc_std.size(0), -1, 4)) #alphas\n )\n\n return output",
"title": ""
},
{
"docid": "56661dc1490f81ce40e174ff628c76c1",
"score": "0.5547487",
"text": "def forward(self):\n self.fake_B = self.netG_A(self.real_A, self.prior_z_B) \n self.mu_z_realA = self.netE_A(torch.cat((self.real_A, self.fake_B),1))\n self.rec_A = self.netG_B(self.fake_B, self.mu_z_realA)\n self.rec_prior_z_B = self.netE_B(torch.cat((self.real_A, self.fake_B),1))\n \n self.fake_A = self.netG_B(self.real_B, self.prior_z_A) \n self.mu_z_realB = self.netE_B(torch.cat((self.fake_A, self.real_B),1))\n self.rec_B = self.netG_B(self.fake_A, self.mu_z_realB)\n self.rec_prior_z_A = self.netE_B(torch.cat((self.fake_A, self.real_B),1))",
"title": ""
},
{
"docid": "349080759e22f4c00e27fa2217537476",
"score": "0.5536985",
"text": "def forward(self, obs):\n return self.module(obs)",
"title": ""
},
{
"docid": "da7cef8509a5b765aa0ad6d7be6962b6",
"score": "0.55360246",
"text": "def forward(self, x):\n out = self.layers(x)\n return out",
"title": ""
},
{
"docid": "b17e5aa9fc23258b98d3d1fe505b770c",
"score": "0.55275697",
"text": "def forward(self, x:Tensor) -> Tensor:\n for layer in self.layers: \n x = layer(x)\n return x",
"title": ""
},
{
"docid": "768406794a38089e3b30c4511ebb8601",
"score": "0.5525852",
"text": "def forward(self, state):\n # Pass the input tensor through each of our operations\n x, h = self.input(state)\n #x = x[:, self.step_window - 1, :] ##batch, seq, inp_size\n # Reshaping the outputs such that it can be fit into the fully connected layer\n #x = x.contiguous().view(-1, self.hidden_dim)\n x = x.contiguous().view(state.shape[0], -1)\n \n x = self.relu(x)\n x = self.hidden(x)\n x = self.relu(x)\n x = self.hidden2(x)\n x = self.relu(x)\n \n x = self.output(x)\n \n return x",
"title": ""
},
{
"docid": "0bf2f17daa6068c949504a011b29cd85",
"score": "0.55236065",
"text": "def forward(self, x, debug=False):\n\n c = torch.addmv(self.bA, self.A, x) #apical input (plastic synapses)\n d = torch.addmv(self.b1, self.J1, x) #basal input (fixed weight synapses)\n r = torch.sigmoid( torch.addmv(torch.addmv(self.bW, self.Wx, x), self.Wh, self.h) ) #read gate (recurrent!)\n a1 = r*d + (1-r)*c #hidden layer activation. r=1 read from fixed syn, r=0 read from plastic \n self.h = self.f( a1 ) #hidden layer output \n \n u = torch.sigmoid( torch.addmv(torch.addmv(self.bU, self.Ux, x), self.Uh, self.h) ) #update gate \n self.A = u.unsqueeze(1)*self.A + torch.ger(1-u, x) #plastic weight update. u=1 retain, u=0 overwrite\n \n a2 = torch.addmv(self.b2, self.J2, self.h) #output layer activation\n y = self.fOut( a2 ) #output layer output\n \n if debug:\n execvars = locals()\n execvars = {var:execvars[var].clone().detach() for var in ('c','d','r','a1','u','a2','y')}\n execvars['h'] = self.h.clone().detach()\n execvars['A'] = self.A.clone().detach()\n execvars['WA'] = self.J1.clone().detach() + self.A.clone().detach()\n return execvars\n return y",
"title": ""
},
{
"docid": "ecbc7431c520c391565a384ac62149b0",
"score": "0.5522756",
"text": "def forward(self, *args):\n\n # If torch.is_grad_enabled() and self.module.training, update u and v.\n if self.module.training:\n self._update_u_v()\n # Otherwise don't update.\n else:\n self._noupdate_u_v()\n # In both above cases, this will still calculate the spectral normalization and set the normalized chosen attribute to the inner module.\n\n # Finally do a forward pass of the inner module.\n return self.module.forward(*args)",
"title": ""
},
{
"docid": "a9995ea64a8981285a3d826562aee53c",
"score": "0.55224055",
"text": "def forward(self, inputx):\n raise NotImplementedError",
"title": ""
},
{
"docid": "f5438f8330fad1418368bdcd5f07cb64",
"score": "0.55115527",
"text": "def forward(self, x):\n self.cache[\"input\"] = x\n for i in range(len(self.layers)):\n if i==0:\n self.layers[i].forward(x)\n else:\n previous = self.layers[i-1].output # (x, y) * (y, 1)\n self.layers[i].forward(previous)",
"title": ""
},
{
"docid": "6c6477566f2a99c431907bc6bc8f45a7",
"score": "0.5508786",
"text": "def forward(self, state: torch.Tensor) -> torch.Tensor:\n x = state\n x = self.fc1(x)\n x = F.relu(x)\n x = self.fc2(x)\n x = F.relu(x)\n # x = self.fc3(x)\n # x = F.relu(x)\n x = self.fc4(x)\n x = torch.tanh(x) # TODO: remove this layer\n\n return x",
"title": ""
},
{
"docid": "82113daec5e727e60bd3e95d6c9a8eb1",
"score": "0.5508522",
"text": "def forward_process(self):\n x = self.prev_layer.output\n self.z = np.add(np.dot(x, self.W), self.b)\n\n # if self.optimizer.name == \"SGD\":\n # if self.optimizer.nesterov:\n # nesterov_W = np.subtract(self.W, self.optimizer.gamma * self.cache_W)\n # nesterov_b = np.subtract(self.b, self.optimizer.gamma * self.cache_b)\n # self.z_nesterov = np.add(np.dot(x, nesterov_W), nesterov_b)\n\n self.output = self.act(self.z)\n\n assert self.output.shape == self.output_size\n\n for layer in self.next_layer:\n layer.forward_process()",
"title": ""
},
{
"docid": "91ec14ff33f80dce4673d276498a9571",
"score": "0.55082315",
"text": "def forward(self):\n self.fake_A_BC = self.netG_BC(self.real_A) # G_BC(A)\n self.fake_B_BC = self.netG_BC(self.real_B) # G_BC(B)\n self.fake_C_BC = self.netG_BC(self.real_C) # G_BC(C)\n\n #self.rev_A = self.netG_BC_rev(self.real_A) # G_BC(A)\n #self.rev_B = self.netG_BC_rev(self.real_B) # G_BC(B)\n #self.rev_C = self.netG_BC_rev(self.real_C) # G_BC(C)",
"title": ""
},
{
"docid": "75dcfbab03753e89ab11874fbc06169f",
"score": "0.5503262",
"text": "def forward(self, inputs: Tensor) -> Tensor:\r\n raise NotImplementedError",
"title": ""
},
{
"docid": "011d19e782a562e1586471caf680121f",
"score": "0.5502175",
"text": "def forward(self, input):\n\n ########################\n # PUT YOUR CODE HERE #\n\n # check correctness of shape\n if not (input.shape[1] == self.n_neurons): raise Exception(\n \"Input size is not correct. Input is {}, while it was initalized with {}\".format(input.shape[1],\n self.n_neurons))\n # Instantiate a CustomBatchNormManualFunction.\n custom_batch_norm_manual_function = CustomBatchNormManualFunction()\n\n # Call it via its .apply() method.\n out = custom_batch_norm_manual_function.apply(input, self.gamma, self.beta, self.eps)\n\n # END OF YOUR CODE #\n #######################\n\n return out",
"title": ""
},
{
"docid": "be4562a9ad281c4afcde3b47da5596ba",
"score": "0.5498866",
"text": "def forward(self, x:Tensor) -> Tensor:\n return x.relu()",
"title": ""
},
{
"docid": "9c6d8eec80bd43410ef547c77f6eb75b",
"score": "0.5489186",
"text": "def forward(self, x, x_tild):\n x_all = torch.cat([x, x_tild], -1)\n p = self.model(x_all)\n return p.view((-1,))",
"title": ""
},
{
"docid": "c9c646211abb2ca29fc63949793a4bbc",
"score": "0.5488537",
"text": "def forward(self, inputs, *args, **kwargs):\n\n x = inputs\n y = self.layer_norm(x)\n\n # Get layer output\n y = self.layer(y, *args, **kwargs)\n\n # Postprocessing: apply dropout and residual connection\n if self.is_train:\n y = tf.nn.dropout(y, rate=self.postprocess_dropout)\n return x + y",
"title": ""
},
{
"docid": "e2206fbbf75f13f47c5cb5e761969c56",
"score": "0.5486745",
"text": "def forward(self):\n\t\tpass",
"title": ""
},
{
"docid": "f495986d55c644da61da5c203c5349fa",
"score": "0.54825795",
"text": "def forward(ctx, run_ctx: RuntimeStates, *input_tensor_list: Tuple[torch.Tensor, ...]) -> Tuple[torch.Tensor, ...]:\n ctx.current_step = run_ctx.global_states.execution_step\n ctx.run_ctx = run_ctx\n\n if ctx.current_step >= 0:\n print(f\"{'='*6} Completed forward pass for STEP {ctx.current_step} {'='*6}\")\n\n if ORT_NO_INCREASE_GLOBAL_STEP[0] is False:\n ctx.run_ctx.global_states.execution_step += 1\n\n return tuple(t.detach().requires_grad_(t.requires_grad) for t in input_tensor_list)",
"title": ""
},
{
"docid": "207cecc02e7d760adfb3d8a0dff39672",
"score": "0.5481281",
"text": "def forward(self, x):\n return self.model(x)",
"title": ""
},
{
"docid": "207cecc02e7d760adfb3d8a0dff39672",
"score": "0.5481281",
"text": "def forward(self, x):\n return self.model(x)",
"title": ""
},
{
"docid": "207cecc02e7d760adfb3d8a0dff39672",
"score": "0.5481281",
"text": "def forward(self, x):\n return self.model(x)",
"title": ""
},
{
"docid": "149569d940b9a2dccfa17447de445736",
"score": "0.5479809",
"text": "def forward(self, state):\n x = state.unsqueeze(1) # add dimension for the convolutions\n x = F.relu(self.layers[0](x)) # first layer\n for layer in self.layers[1:-1]:\n x = F.relu(layer(x)) # middle layers\n return torch.tanh(self.layers[-1](x)) # last layer",
"title": ""
},
{
"docid": "549f23325347e218cadf325467e59ad5",
"score": "0.5479493",
"text": "def forward(self, x):\n\n ########################\n # PUT YOUR CODE HERE #\n\n # get all the features\n out = self.feats(x)\n\n # map all features to n_classes dimensional space\n out = self.linearLayer(out.view(x.shape[0], -1))\n\n # END OF YOUR CODE #\n #######################\n\n return out",
"title": ""
},
{
"docid": "30cde4e2ed4b830b912557c509ff2fd1",
"score": "0.5476395",
"text": "def forward(self, x):\n out = self.model(x)\n return self.last_layer(squeeze(out))",
"title": ""
},
{
"docid": "8ffa528e9b950535f3985a3684cd641a",
"score": "0.54756325",
"text": "def forward(self, obs):\n # Don't concat last two layers\n # Remeber each layer has an activation function\n # So count = 4\n\n in_put = obs.clone()\n\n for fc_layer, activation_f in group_layers(self.pi[:-4]):\n x = fc_layer(obs)\n x = activation_f(x)\n x = torch.cat([x, in_put], dim=-1)\n\n obs = x\n\n for layer in self.pi[-4:]:\n x = layer(x)\n\n act = x\n\n return act * self.act_limit",
"title": ""
},
{
"docid": "caf090e16074efbdeb2df731ec50e992",
"score": "0.5472815",
"text": "def rnn_forward(x, h0, Wx, Wh, b):\n h, cache = None, []\n ##############################################################################\n # TODO: Implement forward pass for a vanilla RNN running on a sequence of #\n # input data. You should use the rnn_step_forward function that you defined #\n # above. You can use a for loop to help compute the forward pass. #\n ##############################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n N, T, _ = x.shape\n _, H = h0.shape\n \n next_h = h0\n h = np.zeros((N, T, H))\n \n for t in np.arange(T):\n next_h, step_cache = rnn_step_forward(x[:, t, :], next_h, Wx, Wh, b)\n h[:, t, :] = next_h\n cache.append(step_cache)\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n ##############################################################################\n # END OF YOUR CODE #\n ##############################################################################\n return h, cache",
"title": ""
},
{
"docid": "ba5980f99929ee99f566dc2286371c7e",
"score": "0.5471552",
"text": "def forward(self, x: Union[Tensor, AbsEle]) -> Union[Tensor, AbsEle]:\n for lin in self.all_linears[:-1]:\n x = lin(x)\n x = self.acti(x)\n\n x = self.all_linears[-1](x)\n return x",
"title": ""
},
{
"docid": "1a5b2e78a87193ddba439f9fc58d1a22",
"score": "0.54670876",
"text": "def forward_pass(self, x, w, b, gamma, beta, mode='train'):\n affine_out = self.affine_layer.forward_pass(x, w, b)\n batch_norm_out = self.batch_norm_layer.forward_pass(affine_out, gamma, beta, mode)\n relu_out = self.relu_layer.forward_pass(batch_norm_out)\n\n return relu_out",
"title": ""
},
{
"docid": "1b2c053ecee0086f088378a215ceacc8",
"score": "0.5458805",
"text": "def forward(self, x):\n\n \"\"\" YOUR CODE HERE!\n Complete this function, based on the network architecture\n that you have chosen in Net.__init__\n \"\"\"\n # Hidden layer\n x = F.relu(self.fc1(x))\n\n # Output layer\n actions_value = self.out(x)\n return actions_value",
"title": ""
},
{
"docid": "70e42bc301299e488f13f0fb2c3b744a",
"score": "0.54522395",
"text": "def forward(self, x):\n out = self.net(x)\n return out",
"title": ""
},
{
"docid": "7f0dbff1fc023699cc6c78c9a692e296",
"score": "0.54430234",
"text": "def forward(self, graph_holders, init_feature, degree_norm=None):\n # pad a zeros to prevent empty graph happen\n zeros_tensor1 = paddle.zeros([1, init_feature.shape[-1]])\n zeros_tensor2 = paddle.zeros([1, 1], dtype=\"int64\")\n init_feature = paddle.concat([zeros_tensor1, init_feature])\n feature = init_feature\n self.hcl_buffer.append(feature)\n\n if degree_norm is not None:\n degree_norm = degree_norm.reshape([self.etype_len, -1]).T\n degree_norm = paddle.sum(degree_norm, -1)\n degree_norm = model_util.get_degree_norm(degree_norm)\n degree_norm = paddle.concat(\n [paddle.ones(\n [1, 1], dtype=\"float32\"), degree_norm], axis=0)\n\n for i in range(self.num_layers):\n graph_holder = graph_holders[self.num_layers - i - 1]\n num_nodes = graph_holder[0] + 1\n next_num_nodes = graph_holder[1] + 1\n edges_src = graph_holder[2] + 1\n edges_dst = graph_holder[3] + 1\n split_edges = graph_holder[4]\n\n # if self.return_weight:\n # edges_weight = graph_holder[5]\n\n nxt_fs = []\n feature = self.shared_sub_pre_layer[i](feature, degree_norm)\n self.hcl_buffer.append(feature)\n\n for j in range(self.etype_len):\n start = paddle.zeros(\n [1], dtype=\"int64\") if j == 0 else split_edges[j - 1]\n new_edges_src = paddle.concat(\n [zeros_tensor2, edges_src[start:split_edges[j]]])\n new_edges_dst = paddle.concat(\n [zeros_tensor2, edges_dst[start:split_edges[j]]])\n graph = pgl.Graph(\n num_nodes=num_nodes,\n edges=paddle.concat(\n [new_edges_src, new_edges_dst], axis=1))\n\n # generate feature of single relation\n nxt_f = self.rgnn_dict[(i, j)](graph, feature, next_num_nodes,\n degree_norm)\n nxt_fs.append(nxt_f)\n # feature intergation\n feature = self.rgnn_dict[(i, self.etype_len)](nxt_fs)\n\n # heter graph residual\n feature = init_feature[:\n next_num_nodes] * self.alpha_residual + feature * (\n 1 - self.alpha_residual)\n if degree_norm is not None:\n degree_norm = degree_norm[:next_num_nodes]\n # remove first zeros to prevent empty graph happen\n return feature[1:]",
"title": ""
},
{
"docid": "a2c0e63b2a040167a7702987e316c24e",
"score": "0.54401815",
"text": "def fc_relu_forward(x, w, b):\n out, cache = None, None\n \n ###########################################################################\n # TODO: Implement fc-relu forward pass. #\n ###########################################################################\n pass\n outs, fc = fc_forward(x, w, b)\n out, rl = relu_forward(outs)\n cache = (fc, rl) ############################################################################\n # END OF YOUR CODE #\n ############################################################################\n return out, cache",
"title": ""
},
{
"docid": "2b0a98e6fbd29c889234f684738e9c0a",
"score": "0.5437155",
"text": "def forward(self, inp):\n x = inp[0]\n seq_length, batch_size, _ = x.shape\n x_pe = self.pe[:seq_length, :]\n x_pe = torch.repeat_interleave(x_pe.unsqueeze(1), batch_size, dim=1)\n x = torch.cat([x, x_pe], dim=-1)\n x = self.dropout(torch.relu(self.normalize(self.linear(x))))\n inp[0] = x\n return inp",
"title": ""
},
{
"docid": "e856015093cfa58862e79f6b8a894129",
"score": "0.54359686",
"text": "def forward(self, x):\n\n x = F.relu(self.fc1(x))\n x = self.fc2(x)\n return x",
"title": ""
},
{
"docid": "e856015093cfa58862e79f6b8a894129",
"score": "0.54359686",
"text": "def forward(self, x):\n\n x = F.relu(self.fc1(x))\n x = self.fc2(x)\n return x",
"title": ""
},
{
"docid": "e856015093cfa58862e79f6b8a894129",
"score": "0.54359686",
"text": "def forward(self, x):\n\n x = F.relu(self.fc1(x))\n x = self.fc2(x)\n return x",
"title": ""
},
{
"docid": "76884cf7d2ff5f0a20bcc19d4809fdd2",
"score": "0.54320407",
"text": "def hybrid_forward(self, F, x, *args):\n x = self.feature(x)\n x = self.yolo_block(x)\n all_box_centers, all_box_scales, all_objectness, all_class_pred, all_anchors, \\\n all_offsets, all_feat_maps, all_detections = [], [], [], [], [], [], []\n if autograd.is_training():\n dets, box_centers, box_scales, objness, class_pred, anchors, offsets = self.yolo_output(x)\n all_box_centers.append(box_centers.reshape((0, -3, -1)))\n all_box_scales.append(box_scales.reshape((0, -3, -1)))\n all_objectness.append(objness.reshape((0, -3, -1)))\n all_class_pred.append(class_pred.reshape((0, -3, -1)))\n all_anchors.append(anchors)\n all_offsets.append(offsets)\n # here we use fake featmap to reduce memory consuption, only shape[2, 3] is used\n fake_featmap = F.zeros_like(x.slice_axis(axis=0, begin=0, end=1).slice_axis(axis=1, begin=0, end=1))\n all_feat_maps.append(fake_featmap)\n else:\n dets = self.yolo_output(x)\n all_detections.append(dets)\n\n if autograd.is_training():\n # during training, the network behaves differently since we don't need detection results\n if autograd.is_recording():\n # generate losses and return them directly\n box_preds = F.concat(*all_detections, dim=1)\n all_preds = [F.concat(*p, dim=1) for p in [\n all_objectness, all_box_centers, all_box_scales, all_class_pred]]\n all_targets = self._target_generator(box_preds, *args)\n return self._loss(*(all_preds + all_targets))\n\n # return raw predictions, this is only used in DataLoader transform function.\n return (F.concat(*all_detections, dim=1), all_anchors, all_offsets, all_feat_maps,\n F.concat(*all_box_centers, dim=1), F.concat(*all_box_scales, dim=1),\n F.concat(*all_objectness, dim=1), F.concat(*all_class_pred, dim=1))\n\n # concat all detection results from different stages\n result = F.concat(*all_detections, dim=1)\n # apply nms per class\n if self.nms_thresh > 0 and self.nms_thresh < 1:\n result = F.contrib.box_nms(\n result, overlap_thresh=self.nms_thresh, valid_thresh=0.01,\n topk=self.nms_topk, id_index=0, score_index=1, coord_start=2, force_suppress=False)\n if self.post_nms > 0:\n result = result.slice_axis(axis=1, begin=0, end=self.post_nms)\n ids = result.slice_axis(axis=-1, begin=0, end=1)\n scores = result.slice_axis(axis=-1, begin=1, end=2)\n bboxes = result.slice_axis(axis=-1, begin=2, end=None)\n return ids, scores, bboxes",
"title": ""
},
{
"docid": "825b62c9b8e727e3626cf1340fefaaa4",
"score": "0.54302377",
"text": "def forward(self):\n self.views[self.figure].forward()\n self.positions[self.figure].forward()",
"title": ""
},
{
"docid": "a0c69c942b7d1567c859b5f355fbc023",
"score": "0.54290086",
"text": "def forward(self, input, target):\n return self.updateOutput(input, target)",
"title": ""
},
{
"docid": "4fa42c073082abff8e6558e11a37d857",
"score": "0.5423739",
"text": "def forward(self, *args, **kwargs):\n x = super(PETRTransformerEncoder, self).forward(*args, **kwargs)\n if self.post_norm is not None:\n x = self.post_norm(x)\n return x",
"title": ""
},
{
"docid": "ac26ac9d7bf3014015bf66628671df6d",
"score": "0.542298",
"text": "def forward(self, state):\n x = F.relu(self.fc1(state))\n x = F.relu(self.fc2(x))\n x = F.relu(self.fc3(x))\n x = self.fc4(x)\n# x = self.fc3(x)\n \n return x",
"title": ""
}
]
[
{
"docid": "f8af1f1f9c72bf95108b654dee7cefb3",
"score": "0.6374559",
"text": "def setUp(self):\n super(TestConstants, self).setUp()",
"title": ""
},
{
"docid": "fbc5782d868b60b19dec637d5154487a",
"score": "0.5857609",
"text": "def _patch_main_src(project_dir, placeholder, value):\n path = os.path.join(project_dir, \"src\", \"integration_test.cc\")\n _patch_file(path, placeholder, value)",
"title": ""
},
{
"docid": "926edde0d15ffc946f6bdbbd561fe550",
"score": "0.5544705",
"text": "def setUp(self):\n self.test_path = \\\n '/mnt/S/Some/Path/to/a/file/with/numbers/file.0010.exr'",
"title": ""
},
{
"docid": "006d0cf4858745bd8dcd1408857a40ff",
"score": "0.55214983",
"text": "def setUp(self):\n self.test_path =\\\n os.path.abspath(os.path.join(os.sep,'mnt', 'S', 'Some','Path','to','a','file','with','numbers','file.0010.exr'))",
"title": ""
},
{
"docid": "117fc984b24cffd1560d4ec3ea0ff4be",
"score": "0.5487263",
"text": "def test_environ():\n testDir = os.path.dirname(__file__)\n mainDir = os.path.dirname(os.path.dirname(os.path.dirname(testDir)))\n rootDir = os.path.join(mainDir, \"src\", \"python\")\n assert rootDir is not None\n assert os.path.isdir(rootDir)\n sys.path.insert(0,rootDir)",
"title": ""
},
{
"docid": "198b979dbd3cb1c72f3bb83619469940",
"score": "0.54659396",
"text": "def test_update_custom_theme_settings(self):\n pass",
"title": ""
},
{
"docid": "676b561646794aa552f2802193cc58d1",
"score": "0.5430694",
"text": "def test_config(self):\n self.do_test_config()",
"title": ""
},
{
"docid": "870d15e487640d93b866baa658af9f5e",
"score": "0.5370025",
"text": "def rebase_paths(cls, config, base_path):\r\n for name in ('gettext_dir', 'resource_dir'):\r\n value = getattr(config, name, None)\r\n if value is not None:\r\n setattr(config, name, path.normpath(path.join(base_path, value)))",
"title": ""
},
{
"docid": "45f5b11b2b16a7c2ed3baf0f09a8ecde",
"score": "0.53533",
"text": "def check_constants():\n global BASE_URL, BASE_PATH\n if BASE_URL.endswith('/'):\n BASE_URL = BASE_URL[:-1]\n if BASE_PATH.endswith('/'):\n BASE_PATH = BASE_PATH[:-1]",
"title": ""
},
{
"docid": "54b0babb2a940ed2e33650aa2801f36e",
"score": "0.53252125",
"text": "def config_test(): # type: ignore",
"title": ""
},
{
"docid": "0d4700ab1c91cfab4301c14aed2eb379",
"score": "0.5315792",
"text": "def setUp(self):\n main.app.config['DATA_CSV'] = TEST_DATA_CSV\n main.app.config['USERS_XML'] = TEST_USERS_XML",
"title": ""
},
{
"docid": "996087468f01d8a2b8098d3d61fc2151",
"score": "0.531258",
"text": "def edit_test_name_conf(self):\n\n for line in fileinput.input('conf.py', inplace=True):\n # inside this loop the STDOUT will be redirected to the file\n print(line.replace('{Test}', '{' + self.test_name + '}'), end='')",
"title": ""
},
{
"docid": "adb400472c428fe5321870eb67c5eddf",
"score": "0.53124243",
"text": "def source_test_environ():\n log.debug('source_test_environ()')\n root_dir = path.dirname(path.realpath(oc.__file__))\n config_path = path.abspath(path.join(root_dir, oc.ENVIRONMENT_PATH))\n parser = configparser.ConfigParser()\n parser.read(config_path)\n for k, v in parser['dev'].items():\n os.environ[k] = v",
"title": ""
},
{
"docid": "211df47405048b742c839d2175170e5d",
"score": "0.5311669",
"text": "def setUp(self):\n self.workspace_dir = tempfile.mkdtemp()\n self.base_required_keys = [\n 'workspace_dir',\n 'lulc_lookup_uri',\n 'lulc_transition_matrix_uri',\n 'carbon_pool_initial_uri',\n 'carbon_pool_transient_uri',\n 'lulc_baseline_map_uri',\n 'lulc_baseline_year',\n ]",
"title": ""
},
{
"docid": "5e3bc62e9880c2e1f3113489d5285e69",
"score": "0.5298334",
"text": "def setUp(self):\n\n # Save the existing STYLEPATH (if there is one)\n self.stylepath = os.environ.get(\"STYLEPATH\", None)",
"title": ""
},
{
"docid": "f09064ea350bdfb82957fd405e0f2856",
"score": "0.52904224",
"text": "def constants_use_custom(v):\n global _constants_use_custom\n _constants_use_custom = v",
"title": ""
},
{
"docid": "7771e7fc2f090163db6e1b7692079760",
"score": "0.5284119",
"text": "def setup_testenv():\n get_tshark_status()\n # If testing from ./tests, change to root directory (useful in PyCharm)\n if os.getcwd().endswith('tests'):\n os.chdir('..')",
"title": ""
},
{
"docid": "57bc7c53b45bfbec78e06c06230c12cf",
"score": "0.5269316",
"text": "def setUp(self):\n testutil.HandlerTestBase.setUp(self)\n self.old_environ = os.environ.copy()\n os.environ['PATH_INFO'] = '/foobar_path'",
"title": ""
},
{
"docid": "e488823a8d84ca0a5f7c433e1806b388",
"score": "0.5269227",
"text": "def tearDown(self):\n super(TestConstants, self).tearDown()",
"title": ""
},
{
"docid": "97c990f4405373523fe2d3ac1b3e5306",
"score": "0.5260858",
"text": "def test_path_rebase():\r\n file = StringIO('''--gettext ../gettext\\n--android ../res''')\r\n file.name = '/opt/proj/android/shared/.config'\r\n c = read_config(file)\r\n print c.gettext_dir\r\n assert c.gettext_dir == '/opt/proj/android/gettext'\r\n assert c.resource_dir == '/opt/proj/android/res'",
"title": ""
},
{
"docid": "a10714653a7ceb06a271353099be8dd1",
"score": "0.52514654",
"text": "def reconfigure_environment(robot_file: Path) -> None:\n\n # Remove ourselves from the path and insert the competitor code\n sys.path.pop()\n sys.path.insert(0, str(ROOT / \"modules\"))\n sys.path.insert(0, str(robot_file.parent))\n\n os.chdir(str(robot_file.parent))",
"title": ""
},
{
"docid": "b7fd3aef4493d245d8d09dd1a84ab716",
"score": "0.52435184",
"text": "def test_expand_paths_vars(test_path, expected, monkeypatch):\n monkeypatch.setenv('TEST_PATH', '/test/path')\n assert utils.expand_paths_vars([test_path]) == [expected]",
"title": ""
},
{
"docid": "06e4cb0ac0d39a8afa4f82fcac11f91d",
"score": "0.51998633",
"text": "def setUp(self):\n super(BaseTestCase, self).setUp()\n self._original_flag_settings = dict((key, getattr(flag_settings, key))\n for key in flag_settings.__all__)\n for key in flag_settings.__all__:\n setattr(flag_settings, key, flag_settings._DEFAULTS[key])",
"title": ""
},
{
"docid": "8bef31afffe60cf6b3f8fab90eaa8b00",
"score": "0.5198444",
"text": "def test_only_set_on_env(self):\n # DEV: We have to set the environment variable first, since they get loaded into the class on definition\n os.environ['NEW_SETTING'] = 'set_by_env'\n\n TestConfiguration = self._get_test_configuration()\n self.assertEqual(TestConfiguration.NEW_SETTING, 'set_by_env')",
"title": ""
},
{
"docid": "f4934260b876a20a25a7bd9f3f9e4b7d",
"score": "0.518901",
"text": "def setUp(self):\n self.config_file = os.path.join(os.path.dirname(__file__),\n \"sample_cfg.txt\")\n super(TestOVFInjectConfig, self).setUp()",
"title": ""
},
{
"docid": "167eceff07b7277b0b902f92a14a27e5",
"score": "0.51639694",
"text": "def test_set_constant(self):\n self._test_constant_common(set)",
"title": ""
},
{
"docid": "3b0b47fa24160e187d4feab7721dca50",
"score": "0.5156388",
"text": "def temp_testing_env(test_client, temp_cache_env, monkeypatch):\n for k, v in test_client.items():\n monkeypatch.setenv(k, v)",
"title": ""
},
{
"docid": "d5d035a0627d2083c4ff7c0b57d0d0b8",
"score": "0.5148852",
"text": "def test_otoroshi_controllers_adminapi_global_config_controller_update_global_config(self):\n pass",
"title": ""
},
{
"docid": "456e0387636d1018f16001a5fc11d728",
"score": "0.51474917",
"text": "def test_dir(cls):\n return join(cls.root_dir, MATURITY_TEST)",
"title": ""
},
{
"docid": "77d490628e1cd921097b5cd41982989c",
"score": "0.51220614",
"text": "def pytest_modify_app_settings(settings):\n pass",
"title": ""
},
{
"docid": "bed48f8deacaacd35b7c30d390c3a878",
"score": "0.51192766",
"text": "def config_files(test_folder):\n base_dir = test_folder.parent\n return [base_dir.joinpath(\"config.toml\"), base_dir.joinpath(\"env\", \"config.toml\")]",
"title": ""
},
{
"docid": "f278fb5b5e52923e6edf138120055c9e",
"score": "0.5115623",
"text": "def test_env_var_2():",
"title": ""
},
{
"docid": "05f80efbd5861a0be1377220fef4c15d",
"score": "0.51103663",
"text": "def test_update_folder(self):\n pass",
"title": ""
},
{
"docid": "94286d3ab8d7e0eedac08b4576127c08",
"score": "0.5080099",
"text": "def test_env_var_1():",
"title": ""
},
{
"docid": "42a394f1588cca8647bb8789f3f52db8",
"score": "0.50796044",
"text": "def test_update_dedupe_settings(self):\n pass",
"title": ""
},
{
"docid": "4576de8de93bf9cf1daee3b86d636aba",
"score": "0.5079286",
"text": "def test_put_settings(self):\n pass",
"title": ""
},
{
"docid": "beaa5e6100938d01863af2b52646f06e",
"score": "0.5076165",
"text": "def test_env_prefix(self):\n # DEV: We have to set the environment variable first, since they get loaded into the class on definition\n os.environ['TEST_DEFAULT_SETTING'] = 'set_by_env'\n\n TestConfiguration = self._get_test_configuration(env_prefix='TEST_')\n self.assertEqual(TestConfiguration.DEFAULT_SETTING, 'set_by_env')",
"title": ""
},
{
"docid": "ef568ad1d110b907957ac840d8212acb",
"score": "0.5074793",
"text": "def edit(ctx):\n edit_files(ctx, None, \"config\")",
"title": ""
},
{
"docid": "e42b1a71e97c1f49e1af9c2b27d899a3",
"score": "0.50611037",
"text": "def tearDown(self):\r\n shutil.rmtree(self.tmp_dir, ignore_errors=True)\r\n pynag.Model.ObjectDefinition.objects.get_all()\r\n pynag.Model.config._edit_static_file(attribute='cfg_dir',old_value=self.tmp_dir,new_value=None)",
"title": ""
},
{
"docid": "61e631a37cad0920699c9d5bc7292fcb",
"score": "0.5060482",
"text": "def test_write_all_relative_flags_with_content(self):\n hfile = StringIO()\n flag = 'flag'\n path = 'path_to_rel'\n tag = '_CMAKE_C_FLAGS'\n clion_project_file_gen._write_all_relative_file_path_flags(\n hfile, {flag: path}, tag)\n hfile.seek(0)\n content = hfile.read()\n expected = clion_project_file_gen._SET_RELATIVE_PATH.format(\n tag, clion_project_file_gen._add_dollar_sign(tag), flag,\n clion_project_file_gen._build_cmake_path(path))\n self.assertEqual(content, expected)",
"title": ""
},
{
"docid": "2c398f6029f650f403f01fc5314a7eb7",
"score": "0.5056233",
"text": "def update_pathvals(config):\n if config[\"pathvals\"] is None:\n config[\"pathvals\"] = build_pathvals_fname(config)\n config[\"pathvals_zenodo\"] = True\n\n return config",
"title": ""
},
{
"docid": "c97d4e21b3d3a4a66c473a0d4624d6c3",
"score": "0.5056064",
"text": "def setUp(self):\r\n self.tmp_dir = tempfile.mkdtemp() # Will be deleted after test runs\r\n\r\n os.chdir(tests_dir)\r\n os.chdir('dataset01')\r\n pynag.Model.cfg_file = \"./nagios/nagios.cfg\"\r\n pynag.Model.config = None\r\n pynag.Model.pynag_directory = self.tmp_dir\r\n pynag.Model.ObjectDefinition.objects.get_all()\r\n pynag.Model.config._edit_static_file(attribute='cfg_dir', new_value=self.tmp_dir)",
"title": ""
},
{
"docid": "13a362b414fcb2cbb51b092374b042fb",
"score": "0.505048",
"text": "def update_resources(self, test):\n pass",
"title": ""
},
{
"docid": "01a0c5626fb8f407fdcaaedb9a129a75",
"score": "0.50455314",
"text": "def setUp(self):\n self.val_a = 1\n self.val_b = 1",
"title": ""
},
{
"docid": "5758b978af6ead72bec3677a531f946f",
"score": "0.5040186",
"text": "def test_prefix(self):\n return join(self.build_folder, '_t_env')",
"title": ""
},
{
"docid": "c427b7fd1aad3d37202c36e07f1b2172",
"score": "0.504013",
"text": "def setUp(self):\n\n for var in ENV:\n setattr(ENV, var, None)",
"title": ""
},
{
"docid": "f30bc678c4da6b5424574c664e2eab9c",
"score": "0.5037183",
"text": "def setUp(self):\r\n self.workspace_dir = tempfile.mkdtemp()",
"title": ""
},
{
"docid": "f30bc678c4da6b5424574c664e2eab9c",
"score": "0.5037183",
"text": "def setUp(self):\r\n self.workspace_dir = tempfile.mkdtemp()",
"title": ""
},
{
"docid": "f30bc678c4da6b5424574c664e2eab9c",
"score": "0.5037183",
"text": "def setUp(self):\r\n self.workspace_dir = tempfile.mkdtemp()",
"title": ""
},
{
"docid": "e4c9353d22d2e696cf76371716e5fcaa",
"score": "0.50354147",
"text": "def test_setting_directory_using_application_env_variable(\n self,\n monkeypatch,\n ):\n monkeypatch.setattr(\n os,\n 'environ',\n {\n 'ASTRALITY_CONFIG_HOME': '/test/dir',\n 'XDG_CONFIG_HOME': '/xdg/dir',\n },\n )\n assert resolve_config_directory() == Path('/test/dir')",
"title": ""
},
{
"docid": "8a42ef79ba7d402a5a0a98fe174b1756",
"score": "0.5034331",
"text": "def test_environment_variables(self):\n directory = tempfile.mkdtemp(\n prefix=\"rez_pip_boy_\", suffix=\"_test_environment_variables\"\n )\n\n if platform.system() == \"Windows\":\n os.environ[\"STUFF\"] = \"C:\"\n directory = (os.sep).join(directory.split(os.sep)[1:])\n directory = os.path.join(\"%STUFF%\", directory)\n else:\n os.environ[\"STUFF\"] = \"~\"\n directory = \"$STUFF\" + directory\n\n _run_command(\n \"rez_pip_boy --install six==1.14.0 --python-version=2.7 -- {directory}\".format(\n directory=directory\n )\n )\n\n atexit.register(\n functools.partial(\n shutil.rmtree, os.path.expanduser(os.path.expandvars(directory))\n )\n )\n\n source_directory = os.path.join(directory, \"six\", \"1.14.0\")\n self._verify_source_package(source_directory, [[\"python-2.7\"]])",
"title": ""
},
{
"docid": "7d414ba869600f2314dc0ca293a7aafb",
"score": "0.50328016",
"text": "def setUp(self):\n self.test_points = {}\n for test_file in glob(TEST_FILES_PATH + \"tests/*.py\"):\n env = {}\n with open(test_file) as f:\n exec(f.read(), env)\n self.test_points[env['test']['name']] = env['test']['points']\n return super().setUp()",
"title": ""
},
{
"docid": "52a0239c2e8041248269e6264ac3dfe7",
"score": "0.5015121",
"text": "def setUp(self):\n # add var/function/class/.. to globals:\n # self.scope[\"add\"] = lambda x: x+1\n # exec setup test script:\n # self.exec_other_script(\"setup.air\")\n # set custom parameter in Settings:\n # ST.THRESHOLD = 0.75\n super(AndroidCase, self).setUp()",
"title": ""
},
{
"docid": "480a8fed499240fad74a9ce85ecbe0fb",
"score": "0.5014918",
"text": "def setUp(self):\n # Setup of outdated tests:",
"title": ""
},
{
"docid": "60d48bc929f62dbc90231b8581dbc970",
"score": "0.5004683",
"text": "def setUp(self):\n assertRaisesRegexp = getattr(self, 'assertRaisesRegexp', None)\n self.assertRaisesRegexp = getattr(self, 'assertRaisesRegex', assertRaisesRegexp)\n self.lib_path = '/'",
"title": ""
},
{
"docid": "e6798b02a652b3dcf9b4e79f0ac88bfb",
"score": "0.50026256",
"text": "def setUp(self):\n self.workspace_dir = tempfile.mkdtemp()",
"title": ""
},
{
"docid": "172a1f96b69f76b179fa10f6325b6fee",
"score": "0.5001571",
"text": "def _update_env_vars(base_env: Dict, test_env: Dict) -> Dict:\n conflict = base_env.keys() & test_env.keys()\n if conflict:\n raise Exception(\n f\"The following environment variables cannot be overwritten for this test: {', '.join(conflict)}\"\n )\n return {**base_env, **test_env}",
"title": ""
},
{
"docid": "85bfc234e44dcdeb9e43aa90d360263f",
"score": "0.49924907",
"text": "def fix_paths(self, maindir):\n # We override any modification that could have been made in\n # .advenerc. Rationale: if the .advenerc was really correct, it\n # would have set the correct paths in the first place.\n print \"Overriding 'resources', 'locale', 'advene' and 'web' config paths\"\n self.path['resources']=os.path.sep.join((maindir, 'share'))\n self.path['locale']=os.path.sep.join( (maindir, 'locale') )\n self.path['web']=os.path.sep.join((maindir, 'share', 'web'))\n self.path['advene']=maindir\n\n if not os.path.exists(self.path['shotdetect']):\n if self.os == 'win32':\n sdname='shotdetect.exe'\n else:\n sdname='shotdetect'\n sd=find_in_path(sdname)\n if sd is not None:\n self.path['shotdetect']=sd\n else:\n sd=self.advenefile(sdname, 'resources')\n if os.path.exists(sd):\n self.path['shotdetect']=sd\n\n #config.data.path['plugins']=os.path.sep.join( (maindir, 'vlcplugins') )",
"title": ""
},
{
"docid": "468237c4b77cfcc249d818e9025af019",
"score": "0.4989056",
"text": "def test_build_tests (self):\n pass",
"title": ""
},
{
"docid": "bc7371e960d34e2e4d800deb28fb4775",
"score": "0.49861464",
"text": "def test_directory_custom(self):\n self.env.directory = '/tmp'\n assert Bundle('foo').get_files(self.env) == ['/tmp/foo']\n # We do not recognize references to modules.\n assert Bundle('module/bar').get_files(self.env) == ['/tmp/module/bar']",
"title": ""
},
{
"docid": "27f0aa582a68b5a17fceaac1bc60b3d9",
"score": "0.4974166",
"text": "def test_settings(self):\n folder = join(SCAFFOLDINGS_DIR, \"settings\")\n commands = (\n # Odoo should install\n (\"--stop-after-init\",),\n # Odoo settings work\n (\"./custom/scripts/test_settings.py\",),\n )\n if \"11.0\" in ODOO_VERSIONS:\n commands += (\n # Check Odoo settings using python-odoo-shell, which is available\n # only for Odoo 9-11 (for 8 too, but it had no built-in shell)\n (\"./custom/scripts/test_settings_python_odoo_shell.py\",),\n )\n commands += (\n # DB was created with the correct language\n (\n \"bash\",\n \"-xc\",\n \"\"\"test \"$(psql -Atqc \"SELECT code FROM res_lang\n WHERE active = TRUE\")\" == es_ES\"\"\",\n ),\n # If `preparedb` is executed, we should have `report.url` set\n (\"preparedb\",),\n (\"./custom/scripts/test_ir_config_parameters.py\",),\n )\n for sub_env in matrix():\n self.compose_test(folder, sub_env, *commands)",
"title": ""
},
{
"docid": "d00aafbd0e5b5c3c1ba7ad3a31c753bc",
"score": "0.49588907",
"text": "def update_resources(self, test):",
"title": ""
},
{
"docid": "ad1fd1a007a4bc1ceb1e3101ba3726de",
"score": "0.49565864",
"text": "def test_override_from_env(self):\n # DEV: We have to set the environment variable first, since they get loaded into the class on definition\n os.environ['DEFAULT_SETTING'] = 'set_by_env'\n\n TestConfiguration = self._get_test_configuration()\n self.assertEqual(TestConfiguration.DEFAULT_SETTING, 'set_by_env')",
"title": ""
},
{
"docid": "611e6e9a581329d28df5c1c384eaa74d",
"score": "0.49512306",
"text": "def testincludemenull(config):\n pass",
"title": ""
},
{
"docid": "dae2c5fee481cbac009d31f9bad83baa",
"score": "0.4944041",
"text": "def runTest(self):\r\n\r\n # Confirm default value.\r\n self._tester.go_to_admin(\"Basic Settings\")\r\n tc.find(r'<option value=\"WikiModule\" selected=\"selected\">'\r\n r'WikiModule</option>')\r\n tc.go(self._tester.url)\r\n tc.find(\"Welcome to Trac\")\r\n\r\n # Set to another valid default handler.\r\n self._tester.go_to_admin(\"Basic Settings\")\r\n tc.formvalue('modbasic', 'default_handler', 'TimelineModule')\r\n tc.submit()\r\n tc.find(\"Your changes have been saved.\")\r\n tc.find(r'<option value=\"TimelineModule\" selected=\"selected\">'\r\n r'TimelineModule</option>')\r\n tc.go(self._tester.url)\r\n tc.find(r'<h1>Timeline</h1>')\r\n\r\n # Set to valid disabled default handler.\r\n try:\r\n self._testenv.set_config('components',\r\n 'trac.timeline.web_ui.TimelineModule',\r\n 'disabled')\r\n self._tester.go_to_admin(\"Basic Settings\")\r\n tc.find(r'<option value=\"TimelineModule\">TimelineModule</option>')\r\n tc.find(r'<span class=\"hint\">TimelineModule is not a valid '\r\n r'IRequestHandler or is not enabled.</span>')\r\n tc.go(self._tester.url)\r\n tc.find(r'<h1>Configuration Error</h1>')\r\n tc.find(r'Cannot find an implementation of the '\r\n r'<code>IRequestHandler</code> interface named '\r\n r'<code>TimelineModule</code>')\r\n finally:\r\n self._testenv.remove_config('components',\r\n 'trac.timeline.web_ui.timelinemodule')\r\n\r\n # Set to invalid default handler.\r\n try:\r\n self._testenv.set_config('trac', 'default_handler',\r\n 'BatchModifyModule')\r\n self._tester.go_to_admin(\"Basic Settings\")\r\n tc.find(r'<option value=\"BatchModifyModule\">BatchModifyModule'\r\n r'</option>')\r\n tc.find(r'<span class=\"hint\">BatchModifyModule is not a valid '\r\n r'IRequestHandler or is not enabled.</span>')\r\n tc.go(self._tester.url)\r\n tc.find(r'<h1>Configuration Error</h1>')\r\n tc.find(r'<code>BatchModifyModule</code> is not a valid default '\r\n r'handler.')\r\n finally:\r\n self._testenv.set_config('trac', 'default_handler', 'WikiModule')",
"title": ""
},
{
"docid": "b19a1777b089dc5b1f187b28bb8a9b77",
"score": "0.49398404",
"text": "def test_otoroshi_controllers_adminapi_global_config_controller_patch_global_config(self):\n pass",
"title": ""
},
{
"docid": "67cc02fb2979674457d76455e9e77125",
"score": "0.49250656",
"text": "def test_expand_path_vars(monkeypatch):\n test_path = '/test/path'\n monkeypatch.setenv('TEST_PATH', test_path)\n assert utils.expand_path_vars('~') == os.path.expanduser('~')\n assert utils.expand_path_vars('$TEST_PATH') == test_path",
"title": ""
},
{
"docid": "1aee4e13a296e868457c0e3dab5ddd2e",
"score": "0.4922943",
"text": "def setUp(self):\n\n self.path = 'media/style.css'\n self.html_files = set(['html.py'])\n for template in glob.glob('templates/*.html'):\n self.html_files.add(template)",
"title": ""
},
{
"docid": "5d0a3043cd61ec1d1747f3e37dcc5e23",
"score": "0.49214402",
"text": "def setUp(self):\n self._out_dir = os.getcwd()",
"title": ""
},
{
"docid": "497bd6c2aaf5bce99f133cd37f66adc8",
"score": "0.4908257",
"text": "def test_work_directory_config(self):\n\n os.chdir(get_helper_directory())\n cli.add_config('config.py')\n self.assertEqual(general_config.hosts, config.general_config.hosts)\n os.chdir(work_dir)",
"title": ""
},
{
"docid": "5d0fcc034f80d98726e5c188b066fcc7",
"score": "0.49069148",
"text": "def pre_test_commands():\n # Set an indicator that a test is running, so we can set paths differently.\n env.HOUDINI_TOOLBOX_TESTING = True\n\n if test.name == \"unit\":\n env.HOUDINI_DSO_PATH.prepend(\"{root}/houdini/dso\")\n env.HOUDINI_OTLSCAN_PATH.prepend(\"{root}/houdini/otls\")\n\n # When doing unit tests we need to set the TOOLBAR_PATH variable to point to the folder\n # containing shelf files so that we can access and run tests against them.\n env.TOOLBAR_PATH = f\"{this.root}/houdini/toolbar\"",
"title": ""
},
{
"docid": "d5886bcdb47f794bb9201d1bb96c69fd",
"score": "0.4903894",
"text": "def setUp(self):\n # pylint: disable=no-member\n self.setUpPyfakefs()\n\n # pyhcl automatically writes \"parsetab.dat\" in its site-package path.\n for path in sys.path:\n if path.endswith('site-packages'):\n self.fs.makedirs(os.path.join(path, 'hcl'))\n\n # Create variables.tf file (and terraform/ directory).\n self.fs.create_file(\n VARIABLES_FILE,\n contents='\\n'.join([\n 'variable \"aws_account_id\" {}',\n 'variable \"aws_region\" {}',\n 'variable \"name_prefix\" {}',\n 'variable \"enable_carbon_black_downloader\" {}',\n 'variable \"carbon_black_url\" {}',\n 'variable \"encrypted_carbon_black_api_token\" {}'\n ])\n )\n\n # Create terraform.tfvars file.\n self._write_config()",
"title": ""
},
{
"docid": "6539f28f657dc3ee42e896de00e59862",
"score": "0.4900867",
"text": "def test_write_my_settings(monkeypatch, tmp_path, my_settings):\n monkeypatch.setattr(my_code, \"MY_SETTINGS_PATH\", tmp_path / \".my_fake_settings\")\n my_code.write_my_settings(my_settings)\n retrieved_settings = eval(my_code.MY_SETTINGS_PATH.read_text())\n assert retrieved_settings == my_settings",
"title": ""
},
{
"docid": "2b21ac15bbec94ba8b68943be02c75a9",
"score": "0.49005377",
"text": "def test_add_personal_alias_file_name_to_settings(self):\n print('--------------->', my_import('BB_HOME') )",
"title": ""
},
{
"docid": "248f7492f849e06551a5a65227c33493",
"score": "0.48945534",
"text": "def setUpClass(cls):\n # Change working directory to test location\n os.chdir(os.path.normpath(os.path.join(os.path.abspath(__file__), os.pardir)))\n # Use internal Aquaveo data directory to test protected models.\n config['pre_existing_data_dir'] = WINDOWS_CI_TEST_DATA_DIR",
"title": ""
},
{
"docid": "5f1f904c32297f44ee6b3782b88eceb4",
"score": "0.48921716",
"text": "def test_ci():\n test_type = os.environ.get('TEST_TYPE', '')\n if test_type == \"os-independent\":\n call_task('lint')\n else:\n call_task('test')",
"title": ""
},
{
"docid": "d47a831fa36a95827f9a859fbca98fea",
"score": "0.4891144",
"text": "def _patch_xcschemes(project_dir, placeholder, value):\n schemes = [\"integration_test.xcscheme\", \"integration_test_tvos.xcscheme\"]\n for scheme in schemes:\n path = os.path.join(\n project_dir,\n \"integration_test.xcodeproj\",\n \"xcshareddata\",\n \"xcschemes\",\n scheme)\n _patch_file(path, placeholder, value)",
"title": ""
},
{
"docid": "5752224e8328c6671c346a2540c7859c",
"score": "0.4886923",
"text": "def setUp(self):\n test_env_setup()",
"title": ""
},
{
"docid": "175a9ded8defbddcaa9f760fc97cda01",
"score": "0.48753557",
"text": "def tearDown(self):\n for key, value in self._original_flag_settings.items():\n setattr(flag_settings, key, value)\n super(BaseTestCase, self).tearDown()",
"title": ""
},
{
"docid": "bc8a499f0c6d9946d87b622715f2c2b1",
"score": "0.48705027",
"text": "def constant_test(self, *args, **kwargs):\n import constant\n print(constant.PI)\n print(constant.GRAVITY)",
"title": ""
},
{
"docid": "0974f3676777bbf703e043a5111871e6",
"score": "0.48668554",
"text": "def setUp(self):\n import os\n self.d7dname = os.path.join(os.path.dirname(__file__),\n \"Example_Drive01.d7d\")",
"title": ""
},
{
"docid": "1b93b169676be1a60a06abf5af95f78d",
"score": "0.48617452",
"text": "def test_config_file():\n return os.path.join(os.path.dirname(__file__), \"test.conf\")",
"title": ""
},
{
"docid": "51b64791198c7d63d26c7e10e947217e",
"score": "0.4859707",
"text": "def reformat_test_classes(config: Dict[str, Any], session_directory: Path) -> None:\n test_directory: Path = session_directory / \"solutions/correct/test\"\n test_classes = config.get(\"junit\").get(\"assessableTestClasses\")\n java_paths = []\n\n for test_class in test_classes:\n matches = list(test_directory.glob(f\"**/{test_class}\"))\n if not matches:\n raise FileNotFoundError\n match = matches[0]\n text = f\"{'.'.join(match.parts[5:])}\".replace(\".java\", \"\")\n java_paths.append(text)\n\n config[\"junit\"][\"assessableTestClasses\"] = java_paths",
"title": ""
},
{
"docid": "fce663f17527b09533b6fa4bdb516b4d",
"score": "0.48594043",
"text": "def before_all(context):\n context.test_dir = tempfile.mkdtemp()",
"title": ""
},
{
"docid": "1df57eb7f6c6f2712debcc484e3347d8",
"score": "0.48582196",
"text": "def test_update_project(self):\n pass",
"title": ""
},
{
"docid": "730d75cb618d6488ff4fc8973b98024c",
"score": "0.48519835",
"text": "def setUpModule():\n setUpAll()",
"title": ""
},
{
"docid": "f71ce2a8333144df29b6896092990239",
"score": "0.4848907",
"text": "def set_test_dir(directory):\n global TEST_FOLDER, TEST_IMAGES_FOLDERS, TEST_LABELS, MULTI_SCALES\n\n # check directory\n assert os.path.isdir(directory), '{} is not a directory!'.format(directory)\n\n # check whether there is a Label.csv file in this directory or not\n new_test_labels = os.path.join(directory, 'Label.csv')\n assert os.path.exists(new_test_labels), 'There is not label file {}!'.format(new_test_labels)\n\n # check whether there is scale folders in this directory or not\n new_scale_folders = []\n for scale in MULTI_SCALES:\n scale_folder = 'Image_' + str(scale) + 'x' + str(scale)\n new_scale_folder = os.path.join(directory, scale_folder)\n assert os.path.exists(new_scale_folder), '{} is not a directory!'.format(new_scale_folder)\n new_scale_folders.append(new_scale_folder)\n\n TEST_FOLDER = directory\n TEST_IMAGES_FOLDERS = new_scale_folders\n TEST_LABELS = new_test_labels",
"title": ""
},
{
"docid": "009866694bb5e27345d47355d040de14",
"score": "0.48472142",
"text": "def test_change_attribute(self):\n pass",
"title": ""
},
{
"docid": "5dc38e69888fd2d44881897b7d7a5d29",
"score": "0.4841835",
"text": "def test_multiple_conf_py(\n self, checkout_path, get_conf_py_path, _, get_config_params, docs_dir\n ):\n\n tmp_docs_dir = py.path.local(tempfile.mkdtemp())\n tmp_docs_dir.join('conf.py').write('')\n tmp_docs_dir.join('test').mkdir().join('conf.py').write('')\n docs_dir.return_value = str(tmp_docs_dir)\n checkout_path.return_value = str(tmp_docs_dir)\n get_config_params.return_value = {}\n get_conf_py_path.side_effect = ProjectConfigurationError\n python_env = Virtualenv(\n version=self.version,\n build_env=self.build_env,\n config=None,\n )\n base_sphinx = BaseSphinx(\n build_env=self.build_env,\n python_env=python_env,\n )\n with pytest.raises(ProjectConfigurationError):\n with override_settings(DOCROOT=tmp_docs_dir):\n base_sphinx.append_conf()",
"title": ""
},
{
"docid": "1df45c533ca18b311aee5c77f8254519",
"score": "0.48413974",
"text": "def _patch_reztoolsconfig():\n for member in dir(reztoolsconfig):\n if member.startswith(\"__\"):\n continue\n\n setattr(reztoolsconfig, \"_%s\" % member,\n getattr(reztoolsconfig, member))",
"title": ""
},
{
"docid": "904716a6ac2d72f262ee570ad98ba618",
"score": "0.48351654",
"text": "def test_update_project_brief(self):\n pass",
"title": ""
},
{
"docid": "df4f3875eb9c5b8b94fce4c1cbfb3ddd",
"score": "0.4833237",
"text": "def local(monkeypatch: MonkeyPatch):\n monkeypatch.setattr('fastgenomics.common.DEFAULT_APP_DIR', str(APP_DIR))\n monkeypatch.setattr('fastgenomics.common.DEFAULT_DATA_ROOT', str(DATA_ROOT))\n monkeypatch.setattr('fastgenomics.common._PATHS', local_paths)\n monkeypatch.setattr('fastgenomics.common._PARAMETERS', None)",
"title": ""
},
{
"docid": "268209a3fc5d0028b889ac5c9f234d8c",
"score": "0.4823046",
"text": "def constants(self):\n pass",
"title": ""
},
{
"docid": "ac0c996266f51e955e70903b3a67921d",
"score": "0.4822324",
"text": "def test_otoroshi_controllers_adminapi_global_config_controller_global_config(self):\n pass",
"title": ""
},
{
"docid": "ed09ad849e82343d1032cfbd139da764",
"score": "0.482176",
"text": "def set_up(self):\n self.dut.to_base_dir()\n self.tester.to_base_dir()",
"title": ""
},
{
"docid": "4722b1a96677c8701c200f932d7c13c8",
"score": "0.48212403",
"text": "def test_base_modules_regex(pyi_builder):\n pyi_builder.test_source(\n \"\"\"\n import resources_testmod\n print('OK')\n \"\"\")",
"title": ""
},
{
"docid": "6355504487d46b842c41e8a49849bbbd",
"score": "0.48186448",
"text": "def revert_edit(self):\n for line in fileinput.input('conf.py', inplace=True):\n # inside this loop the STDOUT will be redirected to the file\n print(line.replace('{' + self.test_name + '}', '{Test}'), end='')",
"title": ""
},
{
"docid": "53486a1085b45ccc9196a25dddb239c1",
"score": "0.4812623",
"text": "def configure_env_for_dev_build(session: nox.session) -> None:\n session.env[\"CFLAGS\"] = \"-Werror -Wno-deprecated-declarations -g --coverage\"\n session.env[\"COCOTB_LIBRARY_COVERAGE\"] = \"1\"\n session.env[\"CXXFLAGS\"] = \"-Werror\"\n session.env[\"LDFLAGS\"] = \"--coverage\"",
"title": ""
},
{
"docid": "1c72eb40bc801ac340ddd9ecd62007a9",
"score": "0.48070794",
"text": "def setUp(self):\n super(BackendTestCase, self).setUp()\n self.project_root = tempfile.mkdtemp(prefix=\"elpy-test\")\n self.addCleanup(shutil.rmtree, self.project_root, True)",
"title": ""
},
{
"docid": "b99fed4310acbbe7cd2f4be30d62b5f8",
"score": "0.4794812",
"text": "def set_vars(suite, procday, dummycase, testcase, svn_version, toacase):\n # suite.add_variable('TURTLES', 'I like turtles')\n suite.add_variable(\"ECF_MICRO\", \"%\")\n\n # Specify the python interpreter to be used:\n suite.add_variable(\"PYTHON\",\n \"PYTHONPATH=$PYTHONPATH:{0} {1}\".format(perm,\n python_path))\n\n # Directory on the remote machine, where all generated \n # files from \"ECF_HOME\" will be copied before execution\n # suite.add_variable(\"REMOTE_HOME\", remote_home_dir)\n\n # Directory on the remote machine, \n # where all jobs write their output\n suite.add_variable(\"REMOTE_LOGDIR\", remote_log_dir)\n\n # Remote user and host names\n suite.add_variable(\"REMOTE_USER\", remote_user_name)\n suite.add_variable(\"REMOTE_HOST\", remote_host_name)\n\n # Standard ecflow variables:\n suite.add_variable(\"ECF_HOME\", ecf_home_dir)\n suite.add_variable(\"ECF_FILES\", ecf_files_dir)\n suite.add_variable(\"ECF_INCLUDE\", ecf_include_dir)\n suite.add_variable(\"ECF_OUT\", ecf_out_dir)\n # default value\n suite.add_variable(\"EC_FILTER_TASKFILE\", \"n/a\")\n suite.add_variable(\"EC_TOTAL_SLAVES\", 1)\n suite.add_variable(\"EC_GET_ERA_SLAVES\", 1)\n suite.add_variable(\"NDAYS_SATDATA\", 1)\n\n # Miscellaneous:\n suite.add_variable(\"SVN_VERSION\", svn_version)\n suite.add_variable(\"ECF_TRIES\", '1')\n suite.add_variable(\"ECF_SUBMIT\", ecflow_submit)\n suite.add_variable(\"GACDB_CLIENT\", gacdb_client)\n suite.add_variable(\"MAKE_CFG_FILE\", make_cfg_files)\n suite.add_variable(\"COUNT_ORBIT_FILES\", count_orbit_files)\n suite.add_variable(\"CLEANUP_SCRATCH\", cleanup_scratch)\n suite.add_variable(\"ARCHIVE_DATA\", archive_data)\n suite.add_variable(\"ECFS_L3_DIR\", ecfs_l3_dir)\n suite.add_variable(\"ECFS_L2_DIR\", ecfs_l2_dir)\n suite.add_variable(\"LD_LIB_PATH\", ld_lib_path)\n suite.add_variable(\"PROCDAY\", procday)\n suite.add_variable(\"TESTRUN\", testcase)\n suite.add_variable(\"PROC_TOA\", toacase)\n suite.add_variable(\"DUMMYRUN\", dummycase)\n suite.add_variable(\"WRITE_MPMD_TASKFILE\", write_mpmd_taskfile)\n suite.add_variable(\"WRITE_MPMD_CFGFILES\", write_mpmd_cfgfiles)\n suite.add_variable(\"WRITE_MPMD_REMAP_TASKFILE\", write_mpmd_remap_taskfile)\n suite.add_variable(\"COUNT_AVHRR_ORBITS\", count_avhrr_orbits)\n suite.add_variable(\"MPMD_SUBMITTER\", mpmd_submitter)\n\n # some processing directories\n suite.add_variable(\"ESA_ROUTINE\", esa_routine)\n suite.add_variable(\"ESA_OUTPUTDIR\", esa_outputdir)\n suite.add_variable(\"ESA_LEVEL3DIR\", esa_level3dir)\n suite.add_variable(\"ESA_INPUTDIR\", esa_inputdir)\n suite.add_variable(\"ESA_LOGDIR\", esa_logdir)\n suite.add_variable(\"ESA_CONFIGDIR\", esa_configdir)\n suite.add_variable(\"ESA_LIST_L2FILES\", esa_listl2files)\n suite.add_variable(\"ESA_ECF_LOG_DIR\", esa_ecflogdir)\n\n # Config files\n suite.add_variable(\"CFG_PATHS_FILE\", cfg_paths_file)\n suite.add_variable(\"CFG_ATTRI_FILE\", cfg_attri_file)\n suite.add_variable(\"CFG_PREFIX\", cfg_prefix)\n suite.add_variable(\"CFG_SUFFIX\", cfg_suffix)\n suite.add_variable(\"SQL_AVHRR_GAC\", sql_avhrr_gac)\n\n # ksh scripts\n suite.add_variable(\"GET_AVHRR_KSH\", get_avhrr_ksh)\n suite.add_variable(\"GET_MODIS_KSH\", get_modis_ksh)\n suite.add_variable(\"GET_MARS_KSH\", get_mars_ksh)\n #suite.add_variable(\"GET_MARS_SEQUENTIAL_KSH\", get_mars_sequential_ksh)\n suite.add_variable(\"GET_AUX_KSH\", get_aux_ksh)\n suite.add_variable(\"PROC2_ORAC_KSH\", proc2_orac_ksh)\n suite.add_variable(\"SINGLE_DAY_KSH\", single_day_ksh)\n suite.add_variable(\"RUN_L2TOL3_KSH\", run_l2tol3_ksh)\n suite.add_variable(\"REMAP_ERA_PARALLEL_KSH\", remap_era_parallel_ksh)\n suite.add_variable(\"REMAP_ERA_SEQUENTIAL_KSH\", remap_era_sequential_ksh)\n suite.add_variable(\"WRAPPER_EXE\", wrapper_exe)\n suite.add_variable(\"BIG_FAM\", big_fam)\n suite.add_variable(\"MAINPROC_FAM\", mainproc_fam)",
"title": ""
},
{
"docid": "7d1cc4080a076e8937b73fcfca5da8a9",
"score": "0.4791834",
"text": "def setUp(self):\n self.config = open_config(CONF_FILE)\n self.train_file = os.path.join(TEMP_DIR, 'train_data.tsv')\n self.ann_file = os.path.join(TEMP_DIR, 'Cypripedium_section.ann')\n self.test_result = os.path.join(TEMP_DIR, 'test_result.tsv')\n self.train_dir = os.path.join(TEMP_DIR, 'train_data')\n self.ann_dir = os.path.join(TEMP_DIR, 'ann_dir')\n self.test_result_batch = os.path.join(TEMP_DIR, 'test_result_batch.tsv')\n\n for path in (self.train_dir, self.ann_dir):\n if not os.path.isdir(path):\n os.mkdir(path)",
"title": ""
}
]
6de1468ded4e201bad09caa6bd19724c
update parameters theta to make move probabilities (p, v) = f_theta(S) more closely match the improved search probabilities and self play closer to self play winner (pi, z). parameters f_theta^t+1 will theoretically be stronger
[
{
"docid": "bd8456f3f24ed1c045f7ed3f6bda792e",
"score": "0.65337104",
"text": "def load_images(self) -> Generator[np.ndarray, None, None]:\n for path_ in self._paths:\n if not os.path.exists(path=path_):\n raise ImageNotFoundError(path_)\n\n try:\n tiff = TIFF.open(filename=path_, mode='r')\n except IOError as e:\n raise TiffLoadError() from e\n\n for im in tiff.iter_images():\n arr = np.array(im, np.uint8)\n if len(arr.shape) > 2:\n logging.warning(\n 'Image {} has not two dimensional greyscale data, '\n 'actually has shape of {} - filtering first dimension.'.format(\n path_,\n arr.shape\n ))\n yield arr[:, :, 0]\n\n yield arr",
"title": ""
}
]
[
{
"docid": "37bc7bb047c51d9c59c99605658da6b9",
"score": "0.7312007",
"text": "def read_tiff_sequence (path):\n\ttomo_files = sorted(glob(path))\n\tnum_files = len(tomo_files)\n\n\t# Read first to understand sizes:\n\ttomo = imread(tomo_files[0])\n\n\t# Prepare dataset:\n\tdata = zeros((tomo.shape[0], tomo.shape[1], num_files), tomo.dtype)\n\n\t# Read all files:\n\tfor i in range(0, num_files): \n\t\t\n\t\tim = imread(tomo_files[i])\n\t\tdata[:,:,i] = im\n\n\treturn data",
"title": ""
},
{
"docid": "d8fa87586287cda2b8584f72aafd71fc",
"score": "0.7176566",
"text": "def numpy_from_tiff(path):\n\n return imageio.volread(path)",
"title": ""
},
{
"docid": "d00d838e1cf9bcdc38b99cf29a44fd66",
"score": "0.7149819",
"text": "def load_tiff(tiffpath):\r\n data = xr.open_rasterio(tiffpath)\r\n return data.values",
"title": ""
},
{
"docid": "b2018cf9e2be50c10a49cd500bbbc520",
"score": "0.70712024",
"text": "def _load_multipage_tiff(self, path):\n return np.array([np.array(p) for p in ImageSequence.Iterator(Image.open(path))])",
"title": ""
},
{
"docid": "923db230a355ead48188f99be93d84e1",
"score": "0.6913694",
"text": "def load_tiff_data(filePath, dtype='float32'):\n X = [];\n for dataFile in filePath:\n if not os.path.isfile(dataFile):\n raise RuntimeError('could not find file \"%s\"' % dataFile)\n \n # load the data from TIF files\n dataImg = Image.open(dataFile)\n \n Xi = np.array(dataImg, dtype=dtype)\n if len(Xi.shape)==3:\n Xi = Xi[...,0]\n Xi = np.reshape(Xi, (1, Xi.shape[0], Xi.shape[1])) # add a slice dimension\n X.append(Xi)\n if len(X)==0:\n raise RuntimeError('No file was found in {}!'.format(filePath))\n X = np.concatenate(X, axis=0) # list -> tensor\n \n return X",
"title": ""
},
{
"docid": "4bd4d63ee7aad4bb246d359787465661",
"score": "0.6861453",
"text": "def load_images(path):\n with gzip.open(path) as f:\n # First 16 bytes are magic_number, n_imgs, n_rows, n_cols\n pixels = np.frombuffer(f.read(), 'B', offset=16)\n return pixels.reshape(-1, 784)",
"title": ""
},
{
"docid": "158ba7107ed8eda522667fa101644fab",
"score": "0.67458874",
"text": "def get_images(self, subpath=\"\"):\n directory = self.path + \"Radiographs/\" + subpath\n filenames = fnmatch.filter(os.listdir(directory), '*.tif')\n images = []\n for fname in filenames:\n img = cv2.imread(directory + \"/\" + fname, 0)\n images.append(img)\n\n return np.array(images)",
"title": ""
},
{
"docid": "e955841079f75cc9bf1f1cf2698fda1a",
"score": "0.6691674",
"text": "def load_images(path, size=(64, 64)):\n data_list = list()\n for filename in listdir(path):\n try:\n pixels = load_img(path + filename, target_size=size) # load and resize the image\n pixels = img_to_array(pixels) # convert to numpy array\n data_list.append(pixels)\n except:\n pass\n return asarray(data_list)",
"title": ""
},
{
"docid": "8a1529ce2dad00e0cbb0e4c3ecb07518",
"score": "0.6569826",
"text": "def load_nii(paths: List[bytes]) -> np.ndarray:\n\n # creating the array to store the data\n data: Union[np.ndarray, None] = None\n\n # for every path in the list of given paths\n for i in range(len(paths)):\n\n # read the file\n d = sitk.GetArrayFromImage(sitk.ReadImage(paths[i].decode('utf-8')))\n\n # allocating the variable to store the data if it has not been allocated\n # already\n if data is None:\n data = np.zeros((len(paths), *d.shape), dtype=np.float32)\n\n # assigning the file contents to the data array\n data[i] = d\n\n # returning the data\n return data",
"title": ""
},
{
"docid": "19be8d371925e1fdbaf342863fce4aea",
"score": "0.64484924",
"text": "def load_images(directory, bits, mask=None):\r\n elements = []\r\n pixels = []\r\n shape = None\r\n for path in directory.glob(f\"*_{bits}bt_*.tif\"):\r\n #import pdb; pdb.set_trace()\r\n #print(path.basename)\r\n elements.append(path.stem.split(\"_\")[-1])\r\n if shape is None:\r\n shape = imread(path).shape\r\n pixels.append(imread(path).flatten())\r\n\r\n df = pd.DataFrame(np.dstack(pixels)[0], columns=elements)\r\n df = df.reset_index().rename(columns={\"index\": \"order\"})\r\n\r\n if mask:\r\n df['mask'] = (imread(mask).flatten() > 0).astype(int)\r\n else:\r\n df['mask'] = 1\r\n\r\n return df, shape",
"title": ""
},
{
"docid": "0c0af77398f7fa4b4db2daed672f9d98",
"score": "0.6446353",
"text": "def load_images(images_path, as_array=True):\n file_list = os.listdir('./HBTN')\n images = []\n file_names = []\n for file in sorted(file_list):\n path = images_path + '/' + file\n image = cv2.imread(path)\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n images.append(image)\n file_names.append(file)\n if as_array:\n images = np.asarray(images)\n return images, file_names",
"title": ""
},
{
"docid": "fc3a787ecb75eb398cd2bae4dc1c3e6b",
"score": "0.64295894",
"text": "def image_file_to_image_array(path):\n \n return io.imread(path)",
"title": ""
},
{
"docid": "52e48f890b4456bbd8bf53306754e41a",
"score": "0.6428036",
"text": "def imloader(fname):\n return np.asarray(imageio.imread(fname))",
"title": ""
},
{
"docid": "d47b487c567bb2c18cbe121c37796047",
"score": "0.6417844",
"text": "def numpy_parse_image(image_path):\n image_path = Path(bytes.decode(image_path))\n with TiffFile(image_path) as tifi:\n image = tifi.asarray()\n return image",
"title": ""
},
{
"docid": "99e6315f5f73a2700f28beb79ccfc8b1",
"score": "0.6310351",
"text": "def _images(path):\n with gzip.open(path) as f:\n # First 16 bytes are magic_number, n_imgs, n_rows, n_cols\n pixels = np.frombuffer(f.read(), 'B', offset=16)\n return pixels.reshape(-1, 784).astype('float32') / 255",
"title": ""
},
{
"docid": "80881c93347b15392b1061d77d95e846",
"score": "0.6308395",
"text": "def _prep(self, paths):\n assert isinstance(paths, str)\n ext = os.path.splitext(paths)[1]\n\n tif_path = None\n if ext == '.zip': # Need to unpack\n\n tif_path = self._unpack(paths)\n\n if ext == '.vrt': # Already unpacked\n\n unpack_folder = os.path.dirname(paths)\n tif_path = get_merged_path(unpack_folder)\n\n assert tif_path is not None, f'Error: Unsupported extension {ext}'\n\n return [tif_path]",
"title": ""
},
{
"docid": "a5dd56300408ecb6a582b257b9f7e95e",
"score": "0.6273469",
"text": "def load_images():\n url = \"https://clockdrawingimages1.s3.us-west-1.amazonaws.com/10000003.tif\"\n\n response = requests.get(url) # , stream = True)\n f = io.BytesIO(response.content)\n im = Image.open(f)\n imarray = np.logical_not(np.array(im)).astype(int)\n return imarray",
"title": ""
},
{
"docid": "9fdecf4e3741d38384dc4dbbf75c41ac",
"score": "0.6185078",
"text": "def read_images(path):\n id_num = 0\n images, id_nums = [], []\n\n # Process individual files from subdirectories in given path\n for dirname, dirnames, filenames in os.walk(path):\n\n for subdirname in dirnames:\n subject_path = os.path.join(dirname, subdirname) # path/subject_path/\n\n for imagename in os.listdir(subject_path): # path/subject_path/imagename\n img = read_single_image(os.path.join(subject_path, imagename))\n \n if img is not None:\n images.append(np.asarray(img, dtype='uint8'))\n id_nums.append(id_num)\n\n id_num += 1\n\n assert len(images) == len(id_nums)\n return [images, id_nums]",
"title": ""
},
{
"docid": "bed98cff4d02acf85007bfca69838506",
"score": "0.617069",
"text": "def path2arr(path):\n try:\n img = Image.open(path)\n img = img.resize((47, 55))\n # return np.array(img, dtype=\"int\")\n return img\n except:\n return None",
"title": ""
},
{
"docid": "9b1bd30407baaf5af38cdac2ded282f8",
"score": "0.6160901",
"text": "def load_dataset(path):\n data = load_files(path)\n dog_files = np.array(data['filenames'])\n dog_targets = np_utils.to_categorical(np.array(data['target']), 133)\n return dog_files, dog_targets",
"title": ""
},
{
"docid": "ce86a37fce1c56197358883e008be6b5",
"score": "0.6132814",
"text": "def extract_to_numpy(root_path):\n\n def process(filepath, N, type='images'):\n with gzip.open(filepath) as bs:\n if type == 'images':\n bs.read(16)\n temp_buf = bs.read(28 * 28 * N)\n temp_data = np.frombuffer(temp_buf, dtype=np.uint8).astype(np.float32)\n temp_data = temp_data.reshape(N, 28*28)\n return temp_data\n\n elif type == 'labels':\n bs.read(8)\n temp_buf = bs.read(1 * N)\n temp_labels = np.frombuffer(temp_buf, dtype=np.uint8).astype(np.int64)\n return temp_labels\n\n else:\n pass\n\n\n train_images = process(os.path.join(root_path, 'train-images-idx3-ubyte.gz'), N=60000)\n train_labels = process(os.path.join(root_path, 'train-labels-idx1-ubyte.gz'), N=60000, type='labels')\n test_images = process(os.path.join(root_path, 't10k-images-idx3-ubyte.gz'), N=10000)\n test_labels = process(os.path.join(root_path, 't10k-labels-idx1-ubyte.gz'), N=10000, type='labels')\n\n return train_images, train_labels, test_images, test_labels",
"title": ""
},
{
"docid": "d07790848fdbe7a3681f562e99512beb",
"score": "0.61297756",
"text": "def load_images_to_array(folder_path):\n\n # get all images\n images_paths = get_images_in_folder(folder_path)\n\n # convert images to numpy arrays\n return [np.array(Image.open(path)) for path in images_paths]",
"title": ""
},
{
"docid": "2b0b130a6635192f35ff1b5b06bf8722",
"score": "0.60836786",
"text": "def load_image_into_numpy_array(path):\n return np.array(Image.open(path))",
"title": ""
},
{
"docid": "cda15bbb1e60d5bbcb6d7ba21d551753",
"score": "0.6083134",
"text": "def load_image_into_numpy_array(path):\n return np.array(Image.open(path))",
"title": ""
},
{
"docid": "63465c0063074f278984225b322577b4",
"score": "0.60663617",
"text": "def tifffile_arr(fname, verbose=False):\r\n with TiffFile(fname) as tif:\r\n if verbose:\r\n print(\"\\nTiff file: {}\\nflags: {}\".format(fname, tif.flags))\r\n if tif.is_shaped and verbose:\r\n print(\"Shape info: {}\\n\".format(tif.shaped_metadata))\r\n if tif.is_geotiff and verbose:\r\n print(\"Geotiff info:\")\r\n d = tif.geotiff_metadata\r\n for key in d.keys():\r\n print(\"- {}: {}\".format(key, d[key]))\r\n #\r\n a = tif.asarray()\r\n #\r\n if tif.is_tiled:\r\n a = np.rollaxis(a, axis=2, start=0)\r\n return a, tif # uncomment and return tif for testing\r",
"title": ""
},
{
"docid": "7983e897cefc7efe269a8969cbaef4c1",
"score": "0.60507065",
"text": "def imread(files, **kwargs):\n kwargs_file = {}\n if 'multifile' in kwargs:\n kwargs_file['multifile'] = kwargs['multifile']\n del kwargs['multifile']\n else:\n kwargs_file['multifile'] = True\n kwargs_seq = {}\n if 'pattern' in kwargs:\n kwargs_seq['pattern'] = kwargs['pattern']\n del kwargs['pattern']\n\n if isinstance(files, basestring) and any(i in files for i in '?*'):\n files = glob.glob(files)\n if not files:\n raise ValueError('no files found')\n if len(files) == 1:\n files = files[0]\n\n if isinstance(files, basestring):\n with TiffFile(files, **kwargs_file) as tif:\n return tif.asarray(**kwargs)\n else:\n with TiffSequence(files, **kwargs_seq) as imseq:\n return imseq.asarray(**kwargs)",
"title": ""
},
{
"docid": "c6439a851ca5f4ffc773bac5a261b8cf",
"score": "0.6050581",
"text": "def open_tiffs(self):\n\n # Open tiffs\n filenames = self.filenames\n section_sets = dict()\n section_meta = dict()\n for fn in filenames:\n # Break up filename into components\n comp_ = path.basename(fn)[:-5].split(\"_\")\n if len(comp_) >= 6:\n section = comp_[2]\n # Add new section\n if section_sets.setdefault(section, dict()) == {}:\n im = imageio.imread(fn)\n section_meta[section] = {'shape':im.shape,'dtype':im.dtype,'filenames':[]}\n\n for i, comp in enumerate(comp_):\n # Add components\n section_sets[section].setdefault(i, set())\n section_sets[section][i].add(comp)\n section_meta[section]['filenames'].append(fn)\n\n im_names = []\n for s in section_sets.keys():\n # Lazy open images\n filenames = section_meta[s]['filenames']\n lazy_arrays = [dask.delayed(imageio.imread)(fn) for fn in filenames]\n shape = section_meta[s]['shape']\n dtype = section_meta[s]['dtype']\n lazy_arrays = [da.from_delayed(x, shape=shape, dtype=dtype) for x in lazy_arrays]\n\n # Organize images\n fn_comp_sets = list(section_sets[s].values())\n if len(comp_) == 6:\n comp_order = {'ch':0, 'AorB':1, 's':2, 'r':3, 'x':4, 'o':5}\n elif len(comp_) == 7:\n comp_order = {'ch':0, 'AorB':1, 's':2, 'r':3, 'i':4, 'x':5, 'o':6}\n int_comps = ['ch', 'r', 'x', 'o']\n for i in [comp_order[c] for c in comp_order.keys() if c in int_comps]:\n fn_comp_sets[i] = [int(x[1:]) for x in fn_comp_sets[i]]\n fn_comp_sets[i] = sorted(fn_comp_sets[i])\n if 'i' in comp_order.keys():\n i = comp_order['i']\n fn_comp_sets[i] = [int(x) for x in fn_comp_sets[i]]\n fn_comp_sets[i] = sorted(fn_comp_sets[i])\n remap_comps = [fn_comp_sets[0], fn_comp_sets[3], fn_comp_sets[4], fn_comp_sets[6], [1], fn_comp_sets[5]]\n # List of sorted x steps for calculating overlap\n #x_steps = sorted(list(fn_comp_sets[5]), reverse=True)\n x_steps = fn_comp_sets[5]\n else:\n remap_comps = [fn_comp_sets[0], fn_comp_sets[3], fn_comp_sets[5], [1], fn_comp_sets[4]]\n # List of sorted x steps for calculating overlap\n #x_steps = sorted(list(fn_comp_sets[4]), reverse=True)\n x_steps = fn_comp_sets[4]\n\n a = np.empty(tuple(map(len, remap_comps)), dtype=object)\n for fn, x in zip(filenames, lazy_arrays):\n comp_ = path.basename(fn)[:-5].split(\"_\")\n channel = fn_comp_sets[0].index(int(comp_[0][1:]))\n cycle = fn_comp_sets[3].index(int(comp_[3][1:]))\n co = comp_order['o']\n obj_step = fn_comp_sets[co].index(int(comp_[co][1:]))\n co = comp_order['x']\n x_step = fn_comp_sets[co].index(int(comp_[co][1:]))\n if 'i' in comp_order.keys():\n co = comp_order['i']\n image_i = fn_comp_sets[co].index(int(comp_[co]))\n a[channel, cycle, image_i, obj_step, 0, x_step] = x\n else:\n a[channel, cycle, obj_step, 0, x_step] = x\n\n # Label array\n if 'i' in comp_order.keys():\n dim_names = ['channel', 'cycle', 'image', 'obj_step', 'row', 'col']\n coord_values = {'channel':fn_comp_sets[0], 'cycle':fn_comp_sets[3], 'image':fn_comp_sets[4], 'obj_step':fn_comp_sets[6]}\n else:\n dim_names = ['channel', 'cycle', 'obj_step', 'row', 'col']\n coord_values = {'channel':fn_comp_sets[0], 'cycle':fn_comp_sets[3], 'obj_step':fn_comp_sets[5]}\n try:\n im = xr.DataArray(da.block(a.tolist()),\n dims = dim_names,\n coords = coord_values,\n name = s[1:])\n\n\n im = self.register_channels(im.squeeze())\n im = im.assign_attrs(first_group = 0, machine = '', scale=1,\n overlap=0, fixed_bg = 0)\n im_names.append(s[1:])\n except:\n im = None\n self.im.append(im)\n\n return im_names",
"title": ""
},
{
"docid": "e95d1be505e36b95f652b9c4ee0c3ce9",
"score": "0.60438997",
"text": "def load_data():\n \n # Creates a list of file names in the data directory\n filelist = glob.glob(\"../data/Images/*.tif\")\n \n # Loads all data images in a list\n data = [Image.open(fname) for fname in filelist]\n \n # Creates a list of file names in the labels directory\n filelist = glob.glob(\"../data/Labels/*.tif\")\n \n # Loads all labels images in a list\n labels = [Image.open(fname) for fname in filelist]\n\n return data, labels",
"title": ""
},
{
"docid": "0b8187cb38dce913b2d4d6bc00c62512",
"score": "0.60433304",
"text": "def load_np_files(data, target):\n\n # Get data\n x_data = np.load(data)\n y_data = np.load(target)\n\n # Need to add that extra dimension for grayscale depth of 1 channel\n x_data = np.expand_dims(x_data, 1)\n print(x_data.shape)\n\n # Zip image data and labels together\n data = [(x, y) for x, y in zip(x_data, y_data)]\n\n # turn targets into tensors\n y_tensor = torch.from_numpy(y_data)\n\n return data, y_tensor",
"title": ""
},
{
"docid": "1846dc9b9db69eef3b41372fad13f3a2",
"score": "0.60404104",
"text": "def load_data(mdir, n_h, n_w):\n f_images = mdir + 'images/'\n f_targets = mdir + 'targets/'\n f_i = os.listdir(f_images)\n f_i.sort()\n f_t = os.listdir(f_targets)\n f_t.sort() \n images, images_o = img_transform(f_images, f_i, n_h, n_w)\n targets = np.zeros((len(f_t), 1))\n for j in range(len(f_t)):\n data = json.load(open(f_targets + f_t[j]))\n targets[j] = data['relative_reading']\n return images, targets, images_o",
"title": ""
},
{
"docid": "26ec558835401c5bb0694e584adbc0d2",
"score": "0.6033038",
"text": "def load_and_resize_images(paths, resize):\n\n images = np.zeros((len(paths), *resize, 3), dtype=np.uint8)\n for i, path in enumerate(paths):\n img = imread(path)\n img = cv2.resize(img, resize[::-1])\n images[i] = img\n\n return images",
"title": ""
},
{
"docid": "9f60df7f5c97a7465860fd675f4839b2",
"score": "0.60119665",
"text": "def get_images():\n filename = 'images.npy'\n\n if os.path.exists(filename):\n return np.load(filename)\n else:\n X = []\n\n for path in glob.glob(vehicle_path):\n X.append(io.imread(path))\n for path in glob.glob(non_vehicle_path):\n X.append(io.imread(path))\n\n X = np.array(X)\n np.save(filename, X)\n\n return X",
"title": ""
},
{
"docid": "63efe72095efa8f3dde3d18ff9afede1",
"score": "0.5965849",
"text": "def load_image(path):\n return misc.imread(path)",
"title": ""
},
{
"docid": "46749ba41acda629e66efcf380e1271f",
"score": "0.59632874",
"text": "def import_imagesText(folder):\n images = []\n a =os.listdir(folder)\n for image in a: \n img=numpy.loadtxt(os.path.join(folder,image))\n images.append(img)\n return images",
"title": ""
},
{
"docid": "016a7e411db71bec8b2a8c64304a236d",
"score": "0.5959633",
"text": "def load_mnist(path, kind='train'):\r\n labels_path = os.path.join(path,\r\n '%s-labels-idx1-ubyte.gz'\r\n % kind)\r\n images_path = os.path.join(path,\r\n '%s-images-idx3-ubyte.gz'\r\n % kind)\r\n\r\n with gzip.open(labels_path, 'rb') as lbpath:\r\n labels = np.frombuffer(lbpath.read(), dtype=np.uint8, offset=8)\r\n\r\n with gzip.open(images_path, 'rb') as imgpath:\r\n images = np.frombuffer(imgpath.read(), dtype=np.uint8, offset=16).reshape(len(labels), 784)\r\n\r\n return images, labels",
"title": ""
},
{
"docid": "4440e23857a5bbf0bc4af50150450a66",
"score": "0.5948486",
"text": "def images_from_disk(im_path, names):\n images, nf = [], []\n for n in names:\n p = path.join(im_path, n)\n im = cv2.imread(p)\n if im is None:\n nf.append(p)\n else:\n images.append(im)\n return images, nf",
"title": ""
},
{
"docid": "6ba1119aad8bba40ba0dc83f47c0787b",
"score": "0.593786",
"text": "def read_tiff(img_path, uint8=True, cm=None):\n # Read the image\n read, imgs = cv2.imreadmulti(img_path, flags=cv2.IMREAD_ANYDEPTH) \n assert read, \"The image could not be read. Make sure the file is accessible and try again.\"\n \n if uint8:\n # Scale down to uint8\n imgs = [cv2.convertScaleAbs(img, alpha=(255/img.max())) for img in imgs] \n if cm is not None: \n # Apply colormap (for visualization only)\n imgs_cm = [cv2.applyColorMap(img, cm) for img in imgs] \n return imgs, imgs_cm\n\n return imgs",
"title": ""
},
{
"docid": "04f659de7f48596658429f8721d31d18",
"score": "0.5913557",
"text": "def read(self, path, gray=True):\n path_list = self._readAllFilesPath(path)\n images = defaultdict(lambda: defaultdict(list))\n for p in path_list:\n name_list = str.split(p.path, '\\\\')\n idnum, side = name_list[-3], name_list[-2]\n img = self._readImageFile(p.path, gray)\n if not img is None:\n images[idnum][side].append(img)\n return images",
"title": ""
},
{
"docid": "7b9adc5333ae1492b967abc04a7f765b",
"score": "0.5907543",
"text": "def read_images(rootpath, data_part):\n h, w = 32, 32\n n_folders = 43 if data_part == 'train' else 1\n images = [] # images\n labels = [] # corresponding labels\n # loop over all 42 classes\n for cls in range(0, n_folders):\n if data_part == 'train':\n prefix = rootpath + format(cls, '05d') + '/' # subdirectory for class\n f_annotation = prefix + 'GT-' + format(cls, '05d') + '.csv'\n else:\n prefix = rootpath\n f_annotation = prefix + 'GT-final_test.csv'\n gtFile = open(f_annotation) # annotations file\n gtReader = csv.reader(gtFile, delimiter=';') # csv parser for annotations\n # loop over all images in current annotations file\n for row in list(gtReader)[1:]:\n img = Image.open(prefix + row[0])\n img = img.resize((h, w), Image.ANTIALIAS)\n images.append(np.array(img)) # the 1th column is the filename\n labels.append(int(row[7])) # the 8th column is the label\n gtFile.close()\n return np.array(images, dtype=np.uint8), np.array(labels)",
"title": ""
},
{
"docid": "0d4a91f67e0b185cafb9c60a7a3d6ba0",
"score": "0.58999825",
"text": "def test_issue_pathlib():\n data = random_data(numpy.uint16, (219, 301))\n with TempFileName('pathlib') as fname:\n fname = pathlib.Path(fname)\n assert isinstance(fname, os.PathLike)\n # imwrite\n imwrite(fname, data)\n # imread\n im = imread(fname)\n assert_array_equal(im, data)\n # memmap\n im = memmap(fname)\n try:\n assert_array_equal(im, data)\n finally:\n del im\n # TiffFile\n with TiffFile(fname) as tif:\n with TempFileName('pathlib_out') as outfname:\n outfname = pathlib.Path(outfname)\n # out=file\n im = tif.asarray(out=outfname)\n try:\n assert isinstance(im, numpy.core.memmap)\n assert_array_equal(im, data)\n assert os.path.samefile(im.filename, str(outfname))\n finally:\n del im\n # TiffSequence\n with TiffSequence(fname) as tifs:\n im = tifs.asarray()\n assert_array_equal(im[0], data)\n with TiffSequence([fname]) as tifs:\n im = tifs.asarray()\n assert_array_equal(im[0], data)\n\n # TiffSequence container\n if SKIP_PRIVATE or SKIP_CODECS:\n pytest.skip(REASON)\n fname = pathlib.Path(private_file('TiffSequence.zip'))\n with TiffSequence('*.tif', container=fname, pattern=None) as tifs:\n im = tifs.asarray()\n assert im[9, 256, 256] == 135",
"title": ""
},
{
"docid": "8945b38c5c8a8588b33a18d9b113074b",
"score": "0.588466",
"text": "def read_tif_image(fname):\n img = Image.open(fname)\n # positive values correspond to counterclockwise rotation\n img_r = img.rotate(0)\n width = img_r.size[0]\n height = img_r.size[1]\n return np.array(img_r.getdata()).reshape(height,width)",
"title": ""
},
{
"docid": "2b45cb19fe449ed4b1ffb7883ef4ad57",
"score": "0.5884306",
"text": "def read_multiimg_PIL(tiffile):\n\n from PIL import Image\n import numpy as np\n\n img = Image.open(tiffile)\n\n imgs = []\n read = True\n\n frame = 0\n\n while read:\n try:\n img.seek(frame) # select this as the image\n imgs.append(np.array(img)[None,:,:])\n frame += 1\n except EOFError:\n # Not enough frames in img\n break\n\n return np.concatenate(imgs, axis=0)",
"title": ""
},
{
"docid": "8b52cc8efa3cfaba8390a84b13030035",
"score": "0.58840173",
"text": "def read_images(path, sz=None):\n c = 0\n X,y = [], []\n for dirname, dirnames, filenames in os.walk(path):\n for subdirname in dirnames:\n subject_path = os.path.join(dirname, subdirname)\n for filename in os.listdir(subject_path):\n try:\n im = Image.open(os.path.join(subject_path, filename))\n im = im.convert(\"L\")\n # resize to given size (if given)\n if (sz is not None):\n im = im.resize(sz, Image.ANTIALIAS)\n im=np.asarray(im, dtype=np.uint8)\n X.append(extractItem(im,'face'))\n y.append(c)\n except IOError, (errno, strerror):\n print \"I/O error({0}): {1}\".format(errno, strerror)\n except:\n print \"Unexpected error:\", sys.exc_info()[0]\n raise\n c = c+1\n return [X,y]",
"title": ""
},
{
"docid": "cfc0b37952806f942e19380949863e6e",
"score": "0.5879211",
"text": "def _load_mnist(path, kind='train'):\n labels_path = join(path, '{}-labels-idx1-ubyte.gz'.format(kind))\n with gzip.open(labels_path, 'rb') as fp:\n labels = np.frombuffer(fp.read(), dtype=np.uint8, offset=8)\n\n images_path = join(path, '{}-images-idx3-ubyte.gz'.format(kind))\n with gzip.open(images_path, 'rb') as fp:\n images = np.frombuffer(fp.read(), dtype=np.uint8, offset=16)\n\n return images.reshape(len(labels), 28, 28), labels",
"title": ""
},
{
"docid": "d4760328d22f9f4ee1a8a44be3ac723c",
"score": "0.58755875",
"text": "def load_path(path):\n path_tr = np.load(path)['paths']\n y_tr = np.load(path)['y']\n y_tr = y_tr.reshape((-1))\n y_tr = torch.Tensor(y_tr)\n return path_tr, y_tr",
"title": ""
},
{
"docid": "a7f6f897b4584f2b97beb60dc2575dd4",
"score": "0.58669764",
"text": "def download(self, path):\n\t\tfailed_files = []\n\t\tself.path = os.path.join(path)\n\t\tfilenames = self.__createfilenames()\n\t\tfilenames = self.__paths(filenames)\n\t\tprint filenames\n\t\ttiffiles = [] \n\t\tfor f in filenames:\n\t\t\tbinfile = self.__extract(f)\n\t\t\ttif = self.__process(binfile)\n\t\t\ttiffiles.append(tif)\n\t\t\tprint tif\n\t\treturn tiffiles",
"title": ""
},
{
"docid": "8ee0658046bcc957bb50ccf86678068c",
"score": "0.5858958",
"text": "def load_mnist(path, kind='train'):\n labels_path = os.path.join(path,\n '%s-labels-idx1-ubyte.gz'\n % kind)\n images_path = os.path.join(path,\n '%s-images-idx3-ubyte.gz'\n % kind)\n\n with gzip.open(labels_path, 'rb') as lbpath:\n labels = np.frombuffer(lbpath.read(), dtype=np.uint8,\n offset=8)\n\n with gzip.open(images_path, 'rb') as imgpath:\n images = np.frombuffer(imgpath.read(), dtype=np.uint8,\n offset=16).reshape(len(labels), 784)\n\n return images, labels",
"title": ""
},
{
"docid": "8ee0658046bcc957bb50ccf86678068c",
"score": "0.5858958",
"text": "def load_mnist(path, kind='train'):\n labels_path = os.path.join(path,\n '%s-labels-idx1-ubyte.gz'\n % kind)\n images_path = os.path.join(path,\n '%s-images-idx3-ubyte.gz'\n % kind)\n\n with gzip.open(labels_path, 'rb') as lbpath:\n labels = np.frombuffer(lbpath.read(), dtype=np.uint8,\n offset=8)\n\n with gzip.open(images_path, 'rb') as imgpath:\n images = np.frombuffer(imgpath.read(), dtype=np.uint8,\n offset=16).reshape(len(labels), 784)\n\n return images, labels",
"title": ""
},
{
"docid": "7545cda3d93fa58db4e6618202cbc969",
"score": "0.5847173",
"text": "def imread(fname, dtype=None, img_num=None, **kwargs):\n if isinstance(fname, string_types):\n with open(fname, 'rb') as f:\n im = Image.open(f)\n return pil_to_ndarray(im, dtype=dtype, img_num=img_num)\n else:\n im = Image.open(fname)\n return pil_to_ndarray(im, dtype=dtype, img_num=img_num)",
"title": ""
},
{
"docid": "0d3432cdccc36c6135892098db38c293",
"score": "0.58448523",
"text": "def load_images(self, img_path):\n \n # -- convert to absolute path and verify the path\n img_path = os.path.abspath(img_path)\n print \"Image source:\", img_path\n if not os.path.isdir(img_path):\n raise ValueError, \"%s is not a directory\" % (img_path)\n \n # -- extract the file names\n tree = os.walk(img_path)\n filelist = []\n #categories = tree.next()[1] \n for path, dirs, files in tree:\n if dirs != []:\n msgs = [\"invalid image tree structure:\"]\n for d in dirs:\n msgs += [\" \"+\"/\".join([root, d])]\n msg = \"\\n\".join(msgs)\n raise Exception, msg\n filelist += [ path+'/'+f for f in files if os.path.splitext(f)[-1] in self.extentions ]\n filelist.sort() \n \n # -- load and preprocess images\n for img_fname in filelist: #[0:1]:\n img = self.load_process_image(img_fname)\n self.images.append(img)\n #utils.visualize_array(img)\n \n #print len(categories), \"categories found:\"\n #print categories",
"title": ""
},
{
"docid": "a0e90d56df52d790eebb21c0590a1966",
"score": "0.58376414",
"text": "def load_images(self):\n\n images = []\n\n for image_path in self.images_path:\n images.append(img_to_array(load_img(image_path)))\n\n images = np.asarray(images)\n print('The images were successfully loaded.')\n \n return images",
"title": ""
},
{
"docid": "199c2b0c0f65a4c95a7a6f73e7f4ee3c",
"score": "0.5829741",
"text": "def load_rasters(path, subUL, band_ind): # Subset from original raster with extent and upperleft coord\n file_list = path # List image name\n assert len(file_list) == 2\n\n # Ensure the order of the list: base image first !!\n for file in file_list: # Organize file list\n img_name = str(file)\n if 'image' in img_name:\n base = file\n elif 'label' in img_name:\n label = file\n file_list = [base, label]\n \n stack = [] # Stack base and label together into a 3D array\n for file in file_list:\n if 'image' in str(file):\n data = gdal_array.LoadFile(str(file), xoff=subUL[0], yoff=subUL[1]) #.astype(np.int),ysize=extent[1],xsize=extent[0]\n data = data[tuple(band_ind),:,:] # Worldview image with 3rd dimension at first\n data = np.transpose(data,(1,2,0)) # Transpose 3rd to last \n print(data.shape)\n stack.append(data)\n else:\n data = gdal_array.LoadFile(str(file), xoff=subUL[0], yoff=subUL[1]) #.astype(np.int),xsize=extent[0],ysize=extent[1]\n if len(data.shape)==3: # For 3-band TIFF\n data = data[0,:,:]/255.0\n data = data[:,:,np.newaxis]\n print(data.shape)\n stack.append(data)\n# image = Image.fromarray(data)\n# data = nan_remover(data)\n# setattr(image, 'filename', file)\n # Ensure the size of base and label is are consistent\n assert stack[0].shape[0] == stack[-1].shape[0]\n assert stack[0].shape[1] == stack[-1].shape[1]\n return stack[:-1], stack[-1]",
"title": ""
},
{
"docid": "c62782552f5df688a9e931b1c3ec209b",
"score": "0.58200365",
"text": "def GetImgPaths(path):\n types = '(jpg|jpeg|png|bmp|tif|tiff|ppm|pgm|pbm)'\n return GetFilePaths(path, types)",
"title": ""
},
{
"docid": "48345630e55d293bfa95435ce1fed81e",
"score": "0.58179593",
"text": "def load_sample(path):\n _, extension = os.path.splitext(path)\n\n if extension == '.mat': \n output_data = scipy.io.loadmat(path)\n output_values = list(output_data.values())\n output_image = output_values[3]\n elif extension == '.npy':\n output_image = np.load(path)\n else:\n raise ValueError('Input file with extension %s is not a valid file type' %extension)\n return output_image",
"title": ""
},
{
"docid": "46214f8660f60a7647c55470b5214d6e",
"score": "0.5814822",
"text": "def load_multi(self, filenames):\n self.filenames = filenames\n self.pixels = None\n if filenames:\n self.loaded = True\n\n for f in filenames:\n ext = f.split(os.path.extsep)[-1]\n if ext == 'raw':\n with open(f) as fh:\n d = fh.read()\n\n # XXX this assumes pilatus 100K...\n p = np.fromstring(d, '>f').astype('int32').reshape((195,-1))\n else:\n im = Image.open(f)\n p = np.asarray(im)\n\n if self.pixels is None:\n self.pixels = p.copy()\n else:\n self.pixels += p",
"title": ""
},
{
"docid": "338541c47af5ae297561f5c4d91dedfa",
"score": "0.5780349",
"text": "def read_geotiff(image_path):\n\n ##print('Reading GeoTIFF data ...')\n #if type(image_path) is not list:\n # input = [image_path]\n\n ## read all arrays\n bs = []\n #for layer in input:\n with rasterio.open(image_path) as src:\n layer = src.read()#[0,:,:]\n try:\n crs = src.get_crs()\n except:\n crs = src.read_crs()\t \n w, h = (src.width, src.height)\n xmin, ymin, xmax, ymax = src.bounds\n\n del src\n bs.append({'bs':layer, 'w':w, 'h':h, 'xmin':xmin, 'xmax':xmax, 'ymin':ymin, 'ymax':ymax, 'crs':crs})\n\n ## resize arrays so common grid\n ##get bounds\n xmax = max([x['xmax'] for x in bs])\n xmin = min([x['xmin'] for x in bs])\n ymax = max([x['ymax'] for x in bs])\n ymin = min([x['ymin'] for x in bs])\n nz, nx, ny = np.shape(bs[0]['bs'])\n for k in range(len(bs)):\n bs[k]['h'] = nx\n bs[k]['w'] = ny\n bs[k]['xmin'] = xmin\n bs[k]['xmax'] = xmax\n bs[k]['ymin'] = ymin\n bs[k]['ymax'] = ymax\n\n img = np.dstack(bs[0]['bs']).astype('uint8').reshape((nx,ny,nz))\n\n return np.squeeze(img), bs",
"title": ""
},
{
"docid": "dbec83e6d9f66cbfaa75cd7b60066da2",
"score": "0.5774529",
"text": "def load_mnist(path, kind):\r\n\tlabels_path = os.path.join(path, '%s-labels-idx1-ubyte.gz' % kind)\r\n\timages_path = os.path.join(path, '%s-images-idx3-ubyte.gz' % kind)\r\n\r\n\twith gzip.open(labels_path, 'rb') as lbpath:\r\n\t\tlbpath.read(8)\r\n\t\tbuffer = lbpath.read()\r\n\t\tlabels = np.frombuffer(buffer, dtype = np.uint8)\r\n\twith gzip.open(images_path, 'rb') as imgpath:\r\n\t\timgpath.read(16)\r\n\t\tbuffer = imgpath.read()\r\n\t\timages = np.frombuffer(buffer, dtype = np.uint8).reshape(len(labels), 28, 28).astype(np.float64)\r\n\t\r\n\treturn images, labels",
"title": ""
},
{
"docid": "74e4e94852e262b01f35f5cb16d20b67",
"score": "0.57532895",
"text": "def loadResizeNormalizeImages (basepath, path_array, img_shape):\n images = np.empty ((len(path_array), img_shape[0], img_shape[1], img_shape[2]), dtype=np.float32)\n for i in range (len(path_array)):\n images[i] = normalizedArrayFromImageInPath (os.path.join(basepath,path_array[i]), img_shape)\n return images",
"title": ""
},
{
"docid": "46da15a81b0bc324ab242e82f4f4b066",
"score": "0.57515824",
"text": "def convert_to_numpy(dir_name, img_list):\n image_arr = list()\n for im in img_list:\n im_name = os.path.join(dir_name, im)\n im = np.array(Image.open(im_name))\n image_arr.append(im)\n \n return np.array(image_arr)",
"title": ""
},
{
"docid": "f9176aac4d7aa96a228a840d203a6ede",
"score": "0.5750715",
"text": "def load_array(path):\n path = str(path)\n if path.endswith('.npy'):\n try:\n import numpy as np\n mmap_mode = 'r' if op.getsize(path) > 1e8 else None\n return np.load(path, mmap_mode=mmap_mode)\n except ImportError:\n logger.warning(\"NumPy is not available.\")\n return\n except ValueError as e:\n logger.error(\"Impossible to read %s.\", path)\n raise e\n elif path.endswith('.tsv'):\n try:\n import pandas as pd\n return pd.read_csv(path, sep='\\t')\n except ImportError:\n logger.warning(\"Pandas is not available.\")\n except ValueError as e:\n logger.error(\"Impossible to read %s.\", path)\n raise e\n raise NotImplementedError(path)",
"title": ""
},
{
"docid": "15d9f5d0e5517ef2988d7fa1b48a39d9",
"score": "0.5746286",
"text": "def _load_images(self, track_ids):\n images = []\n for track_id in track_ids:\n fpath = spectr_template.format(track_id[:3] + '/' + track_id + '.png')\n print('Loading spectrogram: {} ({})'.format(fpath, self.dataset_label))\n images.append(np.asarray(Image.open(fpath).getdata()).reshape(img_width, img_height))\n return np.array(images)",
"title": ""
},
{
"docid": "a919d6e918e0e310226e40301e3d9702",
"score": "0.5743716",
"text": "def paths_to_tensor(img_paths: list) -> np.array:\n list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm.tqdm(img_paths)]\n return np.vstack(list_of_tensors)",
"title": ""
},
{
"docid": "da165fe3e3b337856e4bf3083271ece7",
"score": "0.57404053",
"text": "def read_img(path):\n img = Image.open(path)\n img_arr = np.array(img, dtype='int32')\n# img_arr = np.array(img.getdata())\n\n img.close()\n return img_arr",
"title": ""
},
{
"docid": "7fec3ff3e5dce1cc1678a485a676914a",
"score": "0.5727729",
"text": "def get_img_from_file(filepath):\n img_array = io.imread(filepath)\n return img_array",
"title": ""
},
{
"docid": "9d32d4013b73c767b4a6f21dab26615c",
"score": "0.5725686",
"text": "def im_open(pth):\n\n try:\n #Double check to make sure pth is a directory.\n assert pth.is_dir()\n #Get list of tif files in pth and sort based on 'stem' which is the index of the B-scan (i.e. y position)\n #files = sorted(pth.glob('*.tif'), key=sort_key) \n #load the collection\n #raw_stack = io.imread_collection(files)\n raw_stack = io.imread_collection(str(pth/'*.tif'))\n #turn them into a stack that can be manipulated as a multdimensional array.\n stack = io.collection.concatenate_images(raw_stack)\n \n return stack\n\n except AssertionError:\n #if the assert fails the program stops. This is a bit heavy handed...\n sys.exit(\"A non-directory object was given to the __open__ function\")",
"title": ""
},
{
"docid": "1b0bb6bc7272d49401acf26ec3da9301",
"score": "0.5725431",
"text": "def load_labeled_data():\n\n images = []\n labels = []\n\n for i in range(1, 10):\n path = (\"Data\", str(i), \"*.jpg\")\n filenames = glob.glob(\"/\".join(path))\n images_one_type = [cv2.imread(img) for img in filenames]\n labels_one_type = [i] * len(images_one_type)\n images += images_one_type\n labels += labels_one_type\n\n return images, labels",
"title": ""
},
{
"docid": "3d9c594f17e5bc468963cc31ebe1efdc",
"score": "0.57143843",
"text": "def load_mnist_test(path, kind):\n\n labels_path = os.path.join(path,\n '%s-labels-idx1-ubyte'\n % kind)\n images_path = os.path.join(path,\n '%s-images-idx3-ubyte'\n % kind)\n with open(labels_path, 'rb') as lbpath:\n magic, n = struct.unpack('>II',\n lbpath.read(8))\n labels = np.fromfile(lbpath,\n dtype=np.uint8)\n\n with open(images_path, 'rb') as imgpath:\n magic, num, rows, cols = struct.unpack('>IIII',\n imgpath.read(16))\n images = np.fromfile(imgpath,\n dtype=np.uint8).reshape(len(labels), 784)\n\n return images, labels",
"title": ""
},
{
"docid": "3145eeed5a30b6ea9db7a4bd6360c954",
"score": "0.5706746",
"text": "def load_images(path, size_of_image=SIZE_OF_IMAGE):\n\n X = [] # Stores images\n file_names = [] # Stores file names\n if os.path.isfile(path): # Single image\n try:\n # Read in image\n image = cv.imread(path)\n # Resize image so that all images are of the same size\n image = cv.resize(image, (size_of_image, size_of_image))\n\n X.append(image)\n file_names.append(path)\n except:\n print(\"Failed to read and resize image: \", path)\n\n else: # Folder containing images\n for path_image in os.listdir(path):\n if path_image[0] != '.': # To avoid .DS_Store files\n full_path = os.path.join(path, path_image)\n try:\n # Read in image\n image = cv.imread(full_path)\n # Resize image so that all images are of the same size\n image = cv.resize(image, (size_of_image, size_of_image))\n\n X.append(image)\n file_names.append(path_image)\n except:\n print(\"Failed to read and resize image: \", full_path)\n \n # Convert X into a numpy array\n X = np.array(X)\n\n return X, file_names",
"title": ""
},
{
"docid": "ffeea1653875e608bdb8ef0afff1b28e",
"score": "0.57016224",
"text": "def get_numpy_from_path(\n path: pathlib.Path, internal_path: str = \"/data\"\n) -> Tuple[np.array, Union[Tuple[int, int], bool]]:\n if path.suffix in cfg.TIFF_SUFFIXES:\n return numpy_from_tiff(path), True\n elif path.suffix in cfg.HDF5_SUFFIXES:\n nexus = path.suffix == \".nxs\"\n return numpy_from_hdf5(path, hdf5_path=internal_path, nexus=nexus)",
"title": ""
},
{
"docid": "b16f224d61b688da79dacd3a28e4b37f",
"score": "0.5686152",
"text": "def load_images_real(images_path, noise_path, reference_path, as_array=True, samples=None, scale=None):\n backgnd_paths = glob.glob(images_path + \"/*.jpeg\")\n noisy_img_paths = glob.glob(noise_path + \"/*.jpeg\")\n references = glob.glob(reference_path + \"/*.jpeg\")\n\n references = [path.split('/')[-1].split('.')[0] for path in references]\n backgnd_paths = [im for im in backgnd_paths if im.split('/')[-1].split('.')[0] not in references]\n backgnd_paths.sort(reverse=True)\n noisy_img_paths.sort(reverse=True)\n\n if samples != None:\n noisy_img_paths = noisy_img_paths[:samples]\n backgnd_paths = backgnd_paths[:samples]\n print(\"backgnd: \", backgnd_paths[0], \" - \", backgnd_paths[-1])\n print(\"loading\", end='')\n x_data = []\n x_noisy_data = []\n for n, img in enumerate(zip(backgnd_paths, noisy_img_paths)):\n if n % 100 == 0:\n print(\".\", end='')\n for i in range(2):\n img_data = cv2.imread(img[i])\n img_data = cv2.cvtColor(img_data, cv2.COLOR_BGR2RGB)\n if scale != None:\n img_data = rescale(img_data, scale)\n if i == 0:\n x_data.append(img_data)\n else:\n x_noisy_data.append(img_data)\n print(\"\")\n\n if as_array:\n x_data = np.stack(x_data, axis=0)\n x_noisy_data = np.stack(x_noisy_data, axis=0)\n\n return x_data, x_noisy_data",
"title": ""
},
{
"docid": "f15c1a0a6402d200a45a47f23b9cb6b2",
"score": "0.5683242",
"text": "def load_input(imgpath=\"\"):\n img_list = []\n for file in os.listdir(imgpath):\n filepath = imgpath + \"/\" + file\n print(\"filepath=\", filepath)\n img = load(filepath)\n img_list.append(img)\n return img_list",
"title": ""
},
{
"docid": "bcb67a27c4516dc7cc822724af0c2c20",
"score": "0.568194",
"text": "def load_images(directory):\n images = []\n jpgs = jpgs_in_dir(directory)\n for filename in jpgs:\n print(\"Loading\", filename)\n image = SimpleImage(filename)\n images.append(image)\n return images",
"title": ""
},
{
"docid": "1837a8cf3b8ff11daa069258bab454d0",
"score": "0.5679936",
"text": "def load_data(self, paths, targets, Path, use_left_right=True):\n\n dataset = []\n labels = []\n\n for i, t in enumerate(targets):\n\n if use_left_right:\n i_lrc = np.random.randint(3)\n if (i_lrc == 0):\n p = paths['left'][i].strip()\n shift_ang = .18\n if (i_lrc == 1):\n p = paths['center'][i].strip()\n shift_ang = 0\n if (i_lrc == 2):\n p = paths['right'][i].strip()\n shift_ang = -.18\n else:\n p = paths['center'][i].strip()\n shift_ang = 0\n\n path = '/'.join(Path.split('/')[0:-1]) + '/IMG/' + p.split('/')[-1]\n if i == 0:\n print('looking for images at {}'.format(path))\n\n im_side = cv2.imread(path)\n im_side = cv2.cvtColor(im_side, cv2.COLOR_BGR2RGB)\n\n dataset.append(im_side)\n labels.append(t + shift_ang)\n\n return np.array(dataset), np.array(labels)",
"title": ""
},
{
"docid": "030381cfcab71bed34cd5107fa77d509",
"score": "0.56769687",
"text": "def tif_to_numpy(tif_folder, band_list, n_bands, n_rows=None, n_cols=None): \n \n for i, im_band in enumerate(band_list):\n ds = gdal.Open(os.path.join(tif_folder, im_band), gdal.GA_ReadOnly)\n \n # initialize numpy array\n if i == 0:\n imageidx = 0\n if n_cols == None:\n n_cols = ds.RasterXSize\n if n_rows == None:\n n_rows = ds.RasterYSize \n n_cols = 688\n n_rows = 639\n image = np.zeros((n_rows, n_cols, n_bands)) \n \n # check if object has correct shape\n if not ds.RasterXSize == n_cols or not ds.RasterYSize == n_rows:\n print(\"resample... {}\".format(im_band))\n ds = gdal.Warp(\"\", ds, format='mem', width=n_cols, height=n_rows, resampleAlg=0)\n \n # read values in tiff image\n for b in range(ds.RasterCount):\n band = b+1\n srcband = ds.GetRasterBand(band)\n if srcband is None:\n continue\n \n # save values in numpy array\n image[:,:,imageidx] = srcband.ReadAsArray()\n imageidx += 1\n \n # reset\n ds = None\n \n return image",
"title": ""
},
{
"docid": "5e1363238a2fb56051578e3c2b0d5d22",
"score": "0.5676234",
"text": "def _build_test_dataset(filepaths, input_shape=(256,256,3), norm=255,\n single_channel=False):\n img_arr = np.stack([_load_img(f, norm=norm, num_channels=input_shape[2],\n resize=input_shape[:2]) \n for f in filepaths])\n\n mask = _make_test_mask(*input_shape)\n mask = np.stack([mask for _ in range(img_arr.shape[0])])\n\n return img_arr, mask",
"title": ""
},
{
"docid": "379716558b415d2c3ee085cad7b49dab",
"score": "0.5667801",
"text": "def load_imagesets(self):\n \n imageset_list = []\n for filename in os.listdir(loading_directory):\n if filename.endswith('.npy'):\n load_file = os.path.join(loading_directory, filename)\n imageset = np.load(load_file)\n imageset_list.append(imageset)\n print('Loading', load_file)\n \n return imageset_list",
"title": ""
},
{
"docid": "740ae593ddd05dd1df17645ea4b06102",
"score": "0.56672513",
"text": "def load_mnist(path, kind='train'):\n labels_path = os.path.join(path, '%s-labels-idx1-ubyte' % kind)\n images_path = os.path.join(path, '%s-images-idx3-ubyte' % kind)\n with open(labels_path, 'rb') as lbpath:\n magic, n = struct.unpack('>II', lbpath.read(8))\n labels = np.fromfile(lbpath, dtype=np.uint8)\n\n with open(images_path, 'rb') as imgpath:\n magic, num, rows, cols = struct.unpack('>IIII', imgpath.read(16))\n images = np.fromfile(imgpath, dtype=np.uint8).reshape(len(labels), 784)\n\n return images, labels",
"title": ""
},
{
"docid": "dc30f1371c1eafde13bfb4397c621e5e",
"score": "0.56643677",
"text": "def get_array(filename):\n return pyplot.imread(f'../bmp/{filename}', 'bmp')",
"title": ""
},
{
"docid": "414586e19fa95eddbf6365c913efa376",
"score": "0.56602234",
"text": "def load_image_file(file_path: str) -> np.ndarray:\n image_data = Image.open(file_path)\n return np.array(image_data).astype('uint8')",
"title": ""
},
{
"docid": "a1a9f09f948d3dc8189d80f061095c31",
"score": "0.565655",
"text": "def load_mnist(path, kind='train'):\n labels_path = os.path.join(path,\n '%s-labels.idx1-ubyte'\n % kind)\n images_path = os.path.join(path,\n '%s-images.idx3-ubyte'\n % kind)\n print(os.path.abspath(labels_path))\n with open(labels_path, 'rb') as lbpath:\n magic, n = struct.unpack('>II',\n lbpath.read(8))\n labels = np.fromfile(lbpath,\n dtype=np.uint8)\n\n with open(images_path, 'rb') as imgpath:\n magic, num, rows, cols = struct.unpack('>IIII',\n imgpath.read(16))\n images = np.fromfile(imgpath,\n dtype=np.uint8).reshape(len(labels), 784)\n\n return images, labels",
"title": ""
},
{
"docid": "8a2ad9c855f390002a6118a900c6707c",
"score": "0.5650054",
"text": "def load_mnist(path, kind):\n\n labels_path = os.path.join(path,\n '%s-labels-idx1-ubyte'\n % kind)\n images_path = os.path.join(path,\n '%s-images-idx3-ubyte'\n % kind)\n with open(labels_path, 'rb') as lbpath:\n magic, n = struct.unpack('>II',\n lbpath.read(8))\n labels = np.fromfile(lbpath,\n dtype=np.uint8)\n\n with open(images_path, 'rb') as imgpath:\n magic, num, rows, cols = struct.unpack('>IIII',\n imgpath.read(16))\n images = np.fromfile(imgpath,\n dtype=np.uint8).reshape(len(labels), 784)\n\n return images, labels",
"title": ""
},
{
"docid": "8d12b6190841852c361a3d85e1cdb299",
"score": "0.56458443",
"text": "def img_loader(data_path):\n # get format of data, using the extension\n ext = os.path.basename(data_path).split(os.path.extsep)[1]\n\n if not os.path.exists(data_path):\n raise IOError(\"No such file: %s\" % data_path)\n\n # load using numpy\n if ext == '.npy':\n img = np.load(data_path)\n\n # else default to PIL.Image supported extensions.\n # Loads most basic image formats.\n else:\n try:\n img = np.array(Image.open(data_path))\n except IOError:\n raise IOError(\"img_loader does not recognize file ext: %s\" % ext)\n return img",
"title": ""
},
{
"docid": "5206587847c263e30364f8a09097a1ce",
"score": "0.56386465",
"text": "def read(dataset = \"training\", path = \".\"):\n\n if dataset is \"training\":\n fname_img = os.path.join(path, 'train-images-idx3-ubyte')\n fname_lbl = os.path.join(path, 'train-labels-idx1-ubyte')\n elif dataset is \"testing\":\n fname_img = os.path.join(path, 't10k-images-idx3-ubyte')\n fname_lbl = os.path.join(path, 't10k-labels-idx1-ubyte')\n else:\n raise ValueError, \"dataset must be 'testing' or 'training'\"\n\n # Load everything in some numpy arrays\n with open(fname_lbl, 'rb') as flbl:\n magic, num = struct.unpack(\">II\", flbl.read(8))\n lbl = np.fromfile(flbl, dtype=np.int8)\n\n with open(fname_img, 'rb') as fimg:\n magic, num, rows, cols = struct.unpack(\">IIII\", fimg.read(16))\n print ('Done')\n img = np.fromfile(fimg, dtype=np.uint8).reshape(len(lbl), rows, cols)\n\n get_img = lambda idx: (lbl[idx], img[idx])\n\n # Create an iterator which returns each image in turn\n for i in xrange(len(lbl)):\n yield get_img(i)",
"title": ""
},
{
"docid": "fa3b8c555648d8f88a62f0cebb9a46a2",
"score": "0.5634524",
"text": "def read_tiff(self):\n image = Image.open(self._filepath)\n image_array = np.asarray(image)\n # Reshape to reflect dimensions of the map\n raster = image_array.reshape(self._shape)\n return raster",
"title": ""
},
{
"docid": "2aa7c554bf7f102e6ae50cb8588bc8be",
"score": "0.5624485",
"text": "def ReadImage(path):\n return sitk.GetArrayFromImage(sitk.ReadImage(path)).astype(np.float32)",
"title": ""
},
{
"docid": "47dc933b74965738403c5c9b6f30c9c7",
"score": "0.5623461",
"text": "def read(dataset = \"training\", path = \".\"):\n \n if dataset is \"training\":\n fname_img = os.path.join(path, 'train-images-idx3-ubyte')\n fname_lbl = os.path.join(path, 'train-labels-idx1-ubyte')\n elif dataset is \"testing\":\n fname_img = os.path.join(path, 't10k-images-idx3-ubyte')\n fname_lbl = os.path.join(path, 't10k-labels-idx1-ubyte')\n else:\n raise ValueError, \"dataset must be 'testing' or 'training'\"\n \n # Load everything in some numpy arrays\n with open(fname_lbl, 'rb') as flbl:\n magic, num = struct.unpack(\">II\", flbl.read(8))\n lbl = np.fromfile(flbl, dtype=np.int8)\n \n with open(fname_img, 'rb') as fimg:\n magic, num, rows, cols = struct.unpack(\">IIII\", fimg.read(16))\n img = np.fromfile(fimg, dtype=np.uint8).reshape(len(lbl), rows, cols)\n \n get_img = lambda idx: (lbl[idx], img[idx])\n \n # Create an iterator which returns each image in turn\n for i in xrange(len(lbl)):\n yield get_img(i)",
"title": ""
},
{
"docid": "a1bd2d67c7bc8e209d59628f26a6a5d2",
"score": "0.56223583",
"text": "def load_im(im_path: Path or str) -> np.array:\n out = np.asarray(Image.open(im_path))\n if out.ndim == 2:\n # Add dim to greyscales\n out = np.tile(out[:, :, None], 3)\n return out",
"title": ""
},
{
"docid": "7843c1a9418d1036269d19e223000083",
"score": "0.5619957",
"text": "def _demo_tif():\r\n# a = np.arange(5*3*4, dtype=np.int8).reshape(5, 3, 4) # int8\r\n# a = np.arange(5*3*4, dtype=np.int16).reshape(5, 3, 4) # int16\r\n# a = np.arange(5*3*4, dtype=np.int32).reshape(5, 3, 4) # int32\r\n# a = np.arange(5*3*4, dtype=np.int64).reshape(5, 3, 4) # int64\r\n# a = np.arange(5*3*4, dtype=np.float16).reshape(5, 3, 4) # float16\r\n# a = np.arange(5*3*4, dtype=np.float32).reshape(5, 3, 4) # float32\r\n a = np.arange(5*3*4, dtype=np.float64).reshape(5, 3, 4) # float64\r\n return a",
"title": ""
},
{
"docid": "73e35e5d8f576a9aca6b1fa1379524eb",
"score": "0.5619342",
"text": "def get_images_from_filename_array(coords,chips,classes,folder_names,res=(250,250)):\n\n images =[]\n boxes = []\n clses = []\n\n k = 0\n bi = 0\n\n for folder in folder_names:\n fnames = glob.glob(folder + \"*.tif\")\n fnames.sort()\n for fname in tqdm(fnames):\n #Needs to be \"X.tif\" ie (\"5.tif\")\n name = fname.split(\"\\\\\")[-1]\n arr = wv.get_image(fname)\n\n img,box,cls = wv.chip_image(arr,coords[chips==name],classes[chips==name],res)\n\n for im in img:\n images.append(im)\n for b in box:\n boxes.append(b)\n for c in cls:\n clses.append(cls)\n k = k + 1\n\n return images, boxes, clses",
"title": ""
},
{
"docid": "a8f12cba9850f66dc812fe8ddbfff9a5",
"score": "0.5618064",
"text": "def test_read_tigers(fname):\n # ftp://ftp.graphicsmagick.org/pub/tiff-samples\n with TiffFile(fname) as tif:\n byteorder = {'le': '<', 'be': '>'}[os.path.split(fname)[0][-2:]]\n databits = int(fname.rsplit('.tif')[0][-2:])\n\n # assert file properties\n assert_file_flags(tif)\n assert tif.byteorder == byteorder\n assert tif.is_bigtiff == ('bigtiff' in fname)\n assert len(tif.pages) == 1\n\n # assert page properties\n page = tif.pages.first\n assert_page_flags(page)\n assert page.tags['DocumentName'].value == os.path.basename(fname)\n assert page.imagewidth == 73\n assert page.imagelength == 76\n assert page.bitspersample == databits\n assert (page.photometric == RGB) == ('rgb' in fname)\n assert (page.photometric == PALETTE) == ('palette' in fname)\n assert page.is_tiled == ('tile' in fname)\n assert (page.planarconfig == CONTIG) == ('planar' not in fname)\n if 'minisblack' in fname:\n assert page.photometric == MINISBLACK\n\n # float24 not supported\n # if 'float' in fname and databits == 24:\n # with pytest.raises(ValueError):\n # data = tif.asarray()\n # return\n\n # assert data shapes\n data = tif.asarray()\n assert isinstance(data, numpy.ndarray)\n assert data.flags['C_CONTIGUOUS']\n # if 'palette' in fname:\n # shape = (76, 73, 3)\n if 'rgb' in fname:\n if 'planar' in fname:\n shape = (3, 76, 73)\n else:\n shape = (76, 73, 3)\n elif 'separated' in fname:\n if 'planar' in fname:\n shape = (4, 76, 73)\n else:\n shape = (76, 73, 4)\n else:\n shape = (76, 73)\n assert data.shape == shape\n\n # assert data types\n if 'float' in fname:\n if databits == 24:\n dtype = numpy.float32\n else:\n dtype = f'float{databits}'\n # elif 'palette' in fname:\n # dtype = numpy.uint16\n elif databits == 1:\n dtype = numpy.bool_\n elif databits <= 8:\n dtype = numpy.uint8\n elif databits <= 16:\n dtype = numpy.uint16\n elif databits <= 32:\n dtype = numpy.uint32\n elif databits <= 64:\n dtype = numpy.uint64\n assert data.dtype == dtype\n\n assert_decode_method(page, data)\n assert_aszarr_method(page, data)\n assert__str__(tif)",
"title": ""
},
{
"docid": "06af18d86f9fc20513d46237b57dd221",
"score": "0.5612518",
"text": "def _read_txt(self, path: str) -> Tuple[np.ndarray, np.ndarray]:\n image_root_path = os.path.join(self.data_path, \"core50_128x128\")\n\n paths, targets = [], []\n with open(path, \"r\") as f:\n for line in f:\n p, t = line.strip().split(\" \")\n paths.append(os.path.join(image_root_path, p))\n targets.append(int(t))\n\n return np.array(paths), np.array(targets)",
"title": ""
},
{
"docid": "cccf1a89413dda0f70bd77ee77ab2f10",
"score": "0.5608811",
"text": "def load_from_file(path, bands):\n dataset = gdal.Open(path, gdal.GA_ReadOnly)\n array = dataset.ReadAsArray()\n\n if len(array.shape) == 3:\n # The bands column is in the first position, but we want it last\n array = np.rollaxis(array, 0, 3)\n elif len(array.shape) == 2:\n # This image seems to have one band, so we add an axis for ease\n # of use in the rest of the library\n array = array[:, :, np.newaxis]\n\n image = array.astype('float32')\n\n return dataset, image, bands",
"title": ""
},
{
"docid": "23a5eb9e22d154b286afc07275b9baf2",
"score": "0.5608254",
"text": "def read(digits, dataset = \"training\", path = \".\"):\n\n if dataset is \"training\":\n # fname_img = os.path.join(path, 'train-images-idx3-ubyte')\n # fname_lbl = os.path.join(path, 'train-labels-idx1-ubyte')\n\n fname_img = path+\"train-images.idx3-ubyte\"\n fname_lbl = path+\"train-labels.idx1-ubyte\"\n elif dataset is \"testing\":\n # fname_img = os.path.join(path, 't10k-images-idx3-ubyte')\n # fname_lbl = os.path.join(path, 't10k-labels-idx1-ubyte')\n fname_img = path+\"t10k-images.idx3-ubyte\"\n fname_lbl = path+\"t10k-labels.idx1-ubyte\"\n else:\n raise ValueError, \"dataset must be 'testing' or 'training'\"\n\n flbl = open(fname_lbl, 'rb')\n magic_nr, size = struct.unpack(\">II\", flbl.read(8))\n lbl = array(\"b\", flbl.read())\n flbl.close()\n\n fimg = open(fname_img, 'rb')\n magic_nr, size, rows, cols = struct.unpack(\">IIII\", fimg.read(16))\n img = array(\"B\", fimg.read())\n fimg.close()\n\n ind = [ k for k in xrange(size) if lbl[k] in digits ]\n images = matrix(0, (len(ind), rows*cols))\n labels = matrix(0, (len(ind), 1))\n for i in xrange(len(ind)):\n images[i, :] = img[ ind[i]*rows*cols : (ind[i]+1)*rows*cols ]\n labels[i] = lbl[ind[i]]\n\n return images, labels",
"title": ""
},
{
"docid": "5c873227ff66c6bf7bd48b3cdb2b5baa",
"score": "0.55986685",
"text": "def get_image_arr(img):\r\n return np.asarray(Image.open(os.path.join(images_folder, img)))",
"title": ""
},
{
"docid": "3ccf6918d97c571b7c3f50a6345bb7fe",
"score": "0.5596762",
"text": "def load_mnist(path, kind='train'):\n labels_path = os.path.join(path,\n '%s-labels-idx1-ubyte'\n % kind)\n images_path = os.path.join(path,\n '%s-images-idx3-ubyte'\n % kind)\n with open(labels_path, 'rb') as lbpath:\n magic, n = struct.unpack('>II',\n lbpath.read(8))\n labels = np.fromfile(lbpath,\n dtype=np.uint8)\n\n with open(images_path, 'rb') as imgpath:\n magic, num, rows, cols = struct.unpack('>IIII',\n imgpath.read(16))\n images = np.fromfile(imgpath,\n dtype=np.uint8).reshape(len(labels), 784)\n\n return images, labels",
"title": ""
},
{
"docid": "3ccf6918d97c571b7c3f50a6345bb7fe",
"score": "0.5596762",
"text": "def load_mnist(path, kind='train'):\n labels_path = os.path.join(path,\n '%s-labels-idx1-ubyte'\n % kind)\n images_path = os.path.join(path,\n '%s-images-idx3-ubyte'\n % kind)\n with open(labels_path, 'rb') as lbpath:\n magic, n = struct.unpack('>II',\n lbpath.read(8))\n labels = np.fromfile(lbpath,\n dtype=np.uint8)\n\n with open(images_path, 'rb') as imgpath:\n magic, num, rows, cols = struct.unpack('>IIII',\n imgpath.read(16))\n images = np.fromfile(imgpath,\n dtype=np.uint8).reshape(len(labels), 784)\n\n return images, labels",
"title": ""
},
{
"docid": "0df3a32c5cdd27e865d88dbf14993a9a",
"score": "0.5592266",
"text": "def images_to_library(t_path, images_path): \r\n images_list = glob.iglob(os.path.join(images_path, \"*.jpg\")) #contains the files\r\n num_of_images = len(os.listdir(images_path)) #the num of files from the images library \r\n os.chdir(t_path)\r\n # cheaks if t_path is for test or train\r\n if r\"\\test\" in t_path: \r\n for jpgfile in images_list: #copy all of the images to test\r\n shutil.copy(jpgfile, t_path)\r\n else:\r\n counter = 0 #counts the number of times the loop repeat \r\n for jpgfile in images_list: #copy 70% of the images to train\r\n if (counter < 0.7*num_of_images):\r\n shutil.copy(jpgfile, t_path)\r\n counter = counter + 1",
"title": ""
},
{
"docid": "13efd2e6951812d6179edad5be822c6a",
"score": "0.5591965",
"text": "def load_data(path, size=(32, 32), sub_type=False):\n x = []\n y = []\n categories = os.listdir(path)\n\n for category in categories:\n print(\"loading category: \" + category, \"class: \", categories.index(category))\n category_path = os.path.join(path, category)\n if sub_type:\n for sub_type in os.listdir(category_path):\n sub_type_path = os.path.join(category_path, sub_type)\n for filename in os.listdir(sub_type_path):\n image_path = os.path.join(sub_type_path, filename)\n image = cv2.resize(cv2.imread(image_path), size, cv2.INTER_CUBIC)\n x.append(image.astype(np.float32) / 255.)\n y.append(categories.index(category))\n else:\n for filename in os.listdir(category_path):\n image_path = os.path.join(category_path, filename)\n image = cv2.resize(cv2.imread(image_path), size, cv2.INTER_CUBIC)\n x.append(image.astype(np.float32) / 255.)\n y.append(categories.index(category))\n\n return np.array(x), np.array(y), categories",
"title": ""
},
{
"docid": "996112fae50ca04f9a9f9a13d083e0b9",
"score": "0.5591404",
"text": "def load_data(self, path='data/patches_48/data.npz'):\n path = PROJECT + path\n\n imgs, gt_imgs, labels = pc.load_from_file(path)\n\n self.X = imgs\n self.Y = labels",
"title": ""
},
{
"docid": "4d904ff93e7ed68f16df706c95a16fd4",
"score": "0.5591299",
"text": "def load_image_into_numpy_array(path):\n img_data = tf.io.gfile.GFile(path, 'rb').read()\n image = Image.open(BytesIO(img_data))\n (im_width, im_height) = image.size\n return np.array(image.getdata()).reshape(\n (im_height, im_width, 3)).astype(np.uint8)",
"title": ""
}
]
f68ccc29c713b415872ba580eb64f23a
Compute the cosine similarity of all pairs in ``X``. To be added.
[
{
"docid": "2e9c56a0d4e0780642877e9c4cf20843",
"score": "0.7383222",
"text": "def load_user():\n g.user = User.objects(email=session.get('logged_in')).first()",
"title": ""
},
{
"docid": "9921d39e5804f4838eab20edbbce2cad",
"score": "0.7299501",
"text": "def authentication(app, user_model):\n login_manager.login_view = 'user.login'\n\n @login_manager.user_loader\n def load_user(uid):\n return user_model.query.get(uid)\n\n # @login_manager.token_loader\n def load_token(token):\n duration = app.config['REMEMBER_COOKIE_DURATION'].total_seconds()\n max = 999999999999\n serializer = URLSafeTimedSerializer(app.secret_key)\n\n data = serializer.loads(token, max_age=max)\n user_uid = data[0]\n\n return user_model.query.get(user_uid)",
"title": ""
},
{
"docid": "705936db05148316c7e76365f23784bd",
"score": "0.7239008",
"text": "def authentication(app, user_model):\n login_manager.login_view = 'user.login'\n\n @login_manager.user_loader\n def load_user(uid):\n return user_model.query.get(uid)\n\n @login_manager.token_loader\n def load_token(token):\n duration = app.config['REMEMBER_COOKIE_DURATION'].total_seconds()\n serializer = URLSafeTimedSerializer(app.secret_key)\n\n data = serializer.loads(token, max_age=duration)\n user_uid = data[0]\n\n return user_model.query.get(user_uid)",
"title": ""
},
{
"docid": "9cabbe8a75b820d558a765a8c5e3f68e",
"score": "0.70479035",
"text": "def load_logged_in_user():\n # import pdb; pdb.set_trace()\n # user_id = session.get('user_id')\n account_id = session.get('account_id')\n\n if account_id is None:\n g.user = None\n else:\n g.user = Account.query.get(account_id)",
"title": ""
},
{
"docid": "dd4d5ee56102cb61910c8ac00893eced",
"score": "0.67827636",
"text": "def try_login(self, request, userdict):\n\n # try to log in the user\n (user, errordict) = self.sitecomp_usermanager().login_user(userdict)\n if (user != None):\n # ok it's a success, user was found and loaded.\n # tell the session about the user's identity (i.e. the client browser BECOMES this new user and is logged in immediately)\n request.set_user(user)\n # and now a message to show the user on the next page they load\n request.add_sessionmessage_simple(\"You have successfully logged in.\",'success')\n # return success or error\n return errordict",
"title": ""
},
{
"docid": "9efaa0c2761deae0302b77c9b1cd642c",
"score": "0.6777426",
"text": "def load_logged_in_user():\n user_id = session.get(\"user_id\")\n\n if user_id is None:\n g.user = None\n else:\n g.user = (\n get_db().execute(\"SELECT * FROM user WHERE id = ?\", (user_id,)).fetchone()\n )",
"title": ""
},
{
"docid": "c4b9b87d193e420ad6d4ee5c47786a87",
"score": "0.6771475",
"text": "def init_login_manager(db):\n login_manager = flask_login.LoginManager()\n principals = flask_principal.Principal()\n login_manager.anonymous_user = Anonymous\n\n @login_manager.unauthorized_handler\n def unauthorized():\n flask.abort(403)\n\n @login_manager.user_loader\n def load_user(user_id):\n return db.session.query(UserAccount).get(int(user_id))\n\n @principals.identity_loader\n def identity_loader():\n return flask_principal.AnonymousIdentity()\n\n return login_manager, principals",
"title": ""
},
{
"docid": "e808da98ae19de3431e2ba51fc20a0b0",
"score": "0.66220355",
"text": "def authenticate(self):\n user = self.load_user()\n self.user_adapter.authenticate(user.login, user.password)\n logger.info('Successfully authenticated!')",
"title": ""
},
{
"docid": "5da8be12c006a5f5d25df300d15169ca",
"score": "0.65349",
"text": "def login(self):\r\n self.verify_user_exists()\r\n if len(self.users) == 1:\r\n self.load_user(self.users[0])\r\n return\r\n\r\n print('~ Users: ', str(self.users)[1:-1])\r\n username = ''\r\n while username not in self.users:\r\n username = input('\\n~ Username: ')\r\n if username not in self.users:\r\n print('\\n~ Please enter a valid username')\r\n continue\r\n self.load_user(username)",
"title": ""
},
{
"docid": "de6fa07d5567b0585ef23190555f6c3b",
"score": "0.6505009",
"text": "def user_access(self):\n self.verify_user(new_user)\n response = self.login_user(user_login)\n self.add_credentials(response.data['token'])",
"title": ""
},
{
"docid": "68f439963d591894a678278034ee131e",
"score": "0.64659286",
"text": "def do_login(user):\n\n session[CURR_USER_KEY] = user.id",
"title": ""
},
{
"docid": "68f439963d591894a678278034ee131e",
"score": "0.64659286",
"text": "def do_login(user):\n\n session[CURR_USER_KEY] = user.id",
"title": ""
},
{
"docid": "1aeb3bba33e0eb5870e2056ba497391b",
"score": "0.64284664",
"text": "def user_loader_callback(identity):\n\n if not identity:\n return None\n\n return User.query.filter_by(id=identity).first()",
"title": ""
},
{
"docid": "ec401362c3f27ec4974e24a9e4a1f04f",
"score": "0.64179",
"text": "def setup_user():\n if 'auth_user' in flask.session:\n user = dataBaser.User.query.get(flask.session['auth_user'])\n if user is None:\n # old bad cookie, no good\n del flask.session['auth_user']\n # save the user in `flask.g`, which is a set of globals for this request\n flask.g.user = user",
"title": ""
},
{
"docid": "53c23a6302db35fb2ba9df0892a21a6c",
"score": "0.64165235",
"text": "def login(self, user_id):\n pass",
"title": ""
},
{
"docid": "43ba4cf4d547be693a777a332ff95cf7",
"score": "0.6394678",
"text": "def getUserByLogin(self):",
"title": ""
},
{
"docid": "220813ba256b52fe6b60e3067c576226",
"score": "0.63622826",
"text": "def at_pre_login(self):\n pass",
"title": ""
},
{
"docid": "18d525be9fb2bd155cae3baaa6e28289",
"score": "0.6353752",
"text": "def session_login():\r\n response = user_obj.login_user()\r\n return response",
"title": ""
},
{
"docid": "d9e62b157a9a4bb7aeeb475c0809205b",
"score": "0.6322814",
"text": "def authenticate(self, *args, **kwargs):\r\n return User.objects.get(username='root')",
"title": ""
},
{
"docid": "1ee6e2b6b3c730dc7b57cbb7b2b5be5e",
"score": "0.63151944",
"text": "def _login(self):\n user_profile = self.query_user_profile()\n user_setting = self.query_user_setting()\n transfer_bid = self.get_transfer_bid()\n user_profile.update(user_setting)\n user_profile.update(transfer_bid)\n self._update_user_info(user_profile)",
"title": ""
},
{
"docid": "2b8f8a438ea7d6eb14cdd69e51454efe",
"score": "0.62990123",
"text": "def get_user_to_login(**kwargs):\n\n try:\n user = UserModel._default_manager.get(**kwargs)\n return user\n except ObjectDoesNotExist:\n raise ObjectDoesNotExist",
"title": ""
},
{
"docid": "9e4bc32284a141dd84a998cf61cb6ef2",
"score": "0.6269496",
"text": "def login_user(user):\n session[CURR_USER_KEY] = user.id",
"title": ""
},
{
"docid": "3c6d425126347f5da3ed5a1b7bc6450a",
"score": "0.6253112",
"text": "def login_user(request, user):\n\tfrom django.contrib.auth import load_backend, login\n\timport settings\n\tif not hasattr(user, 'backend'):\n\t\tfor backend in settings.AUTHENTICATION_BACKENDS:\n\t\t\tif user == load_backend(backend).get_user(user.pk):\n\t\t\t\tuser.backend = backend\n\t\t\t\tbreak\n\tif hasattr(user, 'backend'):\n\t\treturn login(request, user)",
"title": ""
},
{
"docid": "a3ae34843b2adf57aee6d6de4d32b7e2",
"score": "0.6237531",
"text": "def form_valid(self, form):\n user = form.save()\n login(self.request, user)\n return super().form_valid(form)",
"title": ""
},
{
"docid": "2b973c9495b348c137b30cc415aef0a9",
"score": "0.6185735",
"text": "async def load_user(self, request: Request) -> Any:\n if USER_KEY not in request:\n session = self.load_from_request(request)\n if \"id\" not in session:\n return None\n\n user = self._user_loader(session[\"id\"])\n if isawaitable(user):\n user = await user\n request[USER_KEY] = user\n\n return request[USER_KEY]",
"title": ""
},
{
"docid": "acad0c7f84c8fe6187311043997dbcb1",
"score": "0.617693",
"text": "def login():\n\tstore = get_default_store()\n\tuser = User.authenticate(store, request.form['email'],request.form['password'])\n\tif user:\n\t\tsession['user'] = user.id\n\t\treturn user.json()\n\tabort(403)",
"title": ""
},
{
"docid": "7c07f9c050d8e8f2b18983c8d2be6945",
"score": "0.6174583",
"text": "def loginAsManager(self, user='root', pwd='secret'):\n self.browser.open('http://nohost/plone/')\n self.browser.getLink('Log in').click()\n self.browser.getControl('Login Name').value = user\n self.browser.getControl('Password').value = pwd\n self.browser.getControl('Log in').click()",
"title": ""
},
{
"docid": "f173da31964a995aff940c35f09349f5",
"score": "0.61741865",
"text": "def login_user(service, user):\n session['logged_in'] = True\n session['user'] = user\n session['organization'] = dict(service.getRelatingOrganization(employeeEmail=user['email']))",
"title": ""
},
{
"docid": "96f20e1682be57c86ac098a987bcc7de",
"score": "0.61732084",
"text": "async def initialize(self) -> None:\n self.user = await self._load_data()\n if not self.user:\n LOGGER.debug('Session initialized without a user')\n return\n\n self.authenticated = await self.user.authenticate()\n if not self.authenticated:\n self.user = None\n return\n\n if self.user.should_refresh:\n await self.user.refresh()\n await self.save()",
"title": ""
},
{
"docid": "90ed83bdd0310f08a7f611bf359b0a23",
"score": "0.6167959",
"text": "def login_as(request, user):\n\tfrom django.contrib.auth import load_backend, login\n\tif not hasattr(user, 'backend'):\n\t\tfor backend in settings.AUTHENTICATION_BACKENDS:\n\t\t\tif user == load_backend(backend).get_user(user.pk):\n\t\t\t\tuser.backend = backend\n\t\t\t\tbreak\n\tif hasattr(user, 'backend'):\n\t\treturn login(request, user)",
"title": ""
},
{
"docid": "2e5ec4e5faac158d60739b9d77ed16fe",
"score": "0.6140863",
"text": "def configure_extensions(app):\n\n login_manager.init_app(app)\n login_manager.login_view = '.login'\n login_manager.login_message = u'Ops! Você ainda está deslogado.'\n login_manager.login_message_category = 'info'\n\n from bolao.models import User\n\n @login_manager.user_loader\n def load_user(userid):\n return User.query.get(userid)",
"title": ""
},
{
"docid": "aa9a264eba13989fde7cf7f779e6619f",
"score": "0.6134488",
"text": "def user_loader(user_id):\n return User.query.get(int(user_id))",
"title": ""
},
{
"docid": "d71bcfa30d3f91143fac59072aad5dc3",
"score": "0.61334944",
"text": "def user_loader(user_id):\n return User.query.get(user_id)",
"title": ""
},
{
"docid": "d71bcfa30d3f91143fac59072aad5dc3",
"score": "0.61334944",
"text": "def user_loader(user_id):\n return User.query.get(user_id)",
"title": ""
},
{
"docid": "641963c6e8e88ab25948bea0384a7f0d",
"score": "0.6106284",
"text": "def secondarylogin(self, request, *args, **kwargs):\n\n # Get the parameters from the request\n username = request.data['username']\n password = request.data['password']\n remember = request.data.get('remember', False)\n\n # I don't imagine I need to pass the username and password for this to work. :D\n\n # Attempt authentication\n user = authenticate(username=username, password=password)\n if user is not None:\n if user.is_active:\n login(request, user)\n # set the expiration to 0 if remember wasn't requested\n if not remember:\n request.session.set_expiry(0)\n return HttpResponse(self.serializer_class(user).data)\n else:\n return HttpResponse(status=status.HTTP_403_FORBIDDEN)\n else:\n return HttpResponse(status=status.HTTP_401_UNAUTHORIZED)",
"title": ""
},
{
"docid": "16e9cdbc3e4b07b19fb2e546361e9edc",
"score": "0.61047673",
"text": "def get_login_data(self, user):\r\n token, created = Token.objects.get_or_create(user=user)\r\n result = self.get_token_data(user, token)\r\n return result",
"title": ""
},
{
"docid": "8784c96b111d362dd6a07dd6f39a4a47",
"score": "0.6094695",
"text": "def configure_user(self, user):\r\n return user",
"title": ""
},
{
"docid": "cd961fd63fcbc085c765d2c8a7061fd7",
"score": "0.6094294",
"text": "def login():",
"title": ""
},
{
"docid": "cd961fd63fcbc085c765d2c8a7061fd7",
"score": "0.6094294",
"text": "def login():",
"title": ""
},
{
"docid": "48f2dd0d9a3a7449efccb53defe1c770",
"score": "0.60679466",
"text": "def set_user(self):\r\n if not self.user:\r\n response = flickr.auth.oauth.checkToken(format='parsed-json')\r\n if response['stat'] == \"ok\":\r\n self.user = response['oauth']['user']",
"title": ""
},
{
"docid": "ef04e9cdf71f20785f70820446776f11",
"score": "0.60487455",
"text": "def user_loader(username):\n user = User()\n user.id = username\n return user",
"title": ""
},
{
"docid": "329d0aa653e64a03640e8b6dc5aeeedd",
"score": "0.6043262",
"text": "def load_current_user():\n\n cookie_name = flask.current_app.config['COOKIE_NAME']\n flask.g.user = None\n if cookie_name in request.cookies:\n cookie_value = utils.decode_value(cookie_name, request.cookies[cookie_name])\n try:\n flask.g.user = _build_session_user(**json.loads(cookie_value))\n except Exception as e:\n # Ignore invalid user cookie and overwrite on the next login.\n _log.error('Can not load the user session: %s\\ncookie_value: %s' % (str(e), cookie_value))",
"title": ""
},
{
"docid": "41f1eff9f17107f3c942fe6b7151cd18",
"score": "0.60412294",
"text": "def post(self):\n return Auth.login_user()",
"title": ""
},
{
"docid": "f1fca10a405a216ec30571c103f187f1",
"score": "0.60168934",
"text": "def setup_auth(app, user_lookup):\n #ToDo: This has to be made more general to use it with other \"modules\" of the api.\n\n @app.before_request\n def load_user():\n ctx = _request_ctx_stack.top\n ctx.user = ApiUser()\n user_id = session.get(\"login_user_id\")\n if user_id is not None:\n user = user_lookup(user_id)\n if user is not None and not user.is_banned():\n ctx.user = user\n\n #todo: better session handling (permanent session, expiring, bla)",
"title": ""
},
{
"docid": "b04f4b261cef8104cf28d4390f14f80d",
"score": "0.60137856",
"text": "def load(self, user):\n\t\traise NotImplementedError('Subclass SessionStore and override load()')",
"title": ""
},
{
"docid": "8d44a273479f5f4412cefaf8ee7bde8e",
"score": "0.6007149",
"text": "def login(app):\n with allure.step('Login as a user'):\n app.signin.enter_actor(\n CREDENTIALS['User_name'],\n CREDENTIALS['User_password'])\n # loger('Login', 'info', 'Login as User')",
"title": ""
},
{
"docid": "48d87b1247a4ba21712deb46978ca2a0",
"score": "0.5978155",
"text": "def userinit():\n session.userid = auth.user\n session.continent = auth.user.continent\n session.country = auth.user.country\n session.subdivision = auth.user.subdivision\n session.level = auth.user.level\n return",
"title": ""
},
{
"docid": "f58072c2bdb82aeea34d19a6053b4ef8",
"score": "0.59691894",
"text": "def load(self, req_handler):\n user_id = req_handler.get_secure_cookie(self.SESSION_KEY)\n if not user_id:\n return None\n author = self.author_thing.find_by_id(int(user_id))\n req_handler.current_user = author",
"title": ""
},
{
"docid": "3dd3b49a13f471e289d5d43115ccad02",
"score": "0.5957962",
"text": "def load_user(self, username):\r\n with open(os.path.join(settings.USERS_DIR, username+'.yml'), 'r') as f:\r\n self.user = yaml.load(f)\r\n log.debug('Logged in as: '+self.user['user_api']['username'])",
"title": ""
},
{
"docid": "634e1f49a59fbb89a31f929b204da2df",
"score": "0.59478927",
"text": "def test_login_existing_user_success(self, mock_validate_request):\n mock_validate_request.return_value = True\n self.client.post(\n \"/lti/lti_initializer/\",\n {\n \"lis_person_contact_email_primary\": \"[email protected]\",\n \"custom_canvas_course_id\": \"327\",\n \"context_title\": \"test title\"\n }\n )\n user = User.objects.get(username=\"user1\")\n self.assertTrue(user.is_authenticated)",
"title": ""
},
{
"docid": "1e2b5517c56379db18076f5eba97e2cf",
"score": "0.59470356",
"text": "def load_user(user_id):\n db_session = db.get_session()\n return UserStore.get(db_session, user_id)",
"title": ""
},
{
"docid": "4208740198c9f35c85a9cf6377a72985",
"score": "0.59369904",
"text": "def test_login(self):\n models.User('THE_USER', '[email protected]', 'default')\n session.commit()\n response = self.client.get('/en/')\n self.assert_('THE_USER' not in response.data)\n self.login('THE_USER', 'default')\n response = self.client.get('/en/')\n self.assert_('THE_USER' in response.data)",
"title": ""
},
{
"docid": "382bf51bc51cdda17e8126052c0cdd96",
"score": "0.5931378",
"text": "def loginUser (self, un, pw):\n uid,h,spw = self.getSession (un, pw)\n flmo.req_privs (flmo.Handle (h), spw)\n u = flmwu.User (uid=uid)\n u.readTags ()\n return u",
"title": ""
},
{
"docid": "a62cabfa3f4aeb807e53374c712a1974",
"score": "0.5924504",
"text": "def login_user(username):\n save_user_session(username)\n show_home_screen()",
"title": ""
},
{
"docid": "ce986f838ce11d4fe339ae7a0e49b9b7",
"score": "0.59210575",
"text": "def get_user():\n pass",
"title": ""
},
{
"docid": "e0b3c2a893818e24e5d423d107c15aed",
"score": "0.59201837",
"text": "def on_login(self, username):",
"title": ""
},
{
"docid": "be5d05c7733a6973fa59186dc5cbf240",
"score": "0.5917878",
"text": "def user_loader(user_id):\n user_entry = User.getById(user_id)\n if user_entry is not None:\n user = User(user_entry[0], user_entry[1])\n return user\n else:\n return None",
"title": ""
},
{
"docid": "3bd7a75746e9ad0ca72d3fedbc9b2cd3",
"score": "0.59140617",
"text": "def user_login():\n data = request.get_json(force=True)\n email = data['email']\n password = data['password']\n\n try:\n logged_in_user = User.get_by_login_credentials(email=email, password=password)\n except ValueError as e:\n return jsonify(success=False, error=str(e)), 400\n\n auth_token = logged_in_user.generate_auth_token()\n return jsonify(success=True, auth_token=auth_token)",
"title": ""
},
{
"docid": "ed2aa0108eaf60a7602b423c3ead2e53",
"score": "0.5911971",
"text": "def test_force_login_with_backend_missing_get_user(self):\n self.client.force_login(self.u1)\n self.assertEqual(self.u1.backend, \"django.contrib.auth.backends.ModelBackend\")",
"title": ""
},
{
"docid": "c60f29b09f647949cb48ff5f8c426dd0",
"score": "0.5910997",
"text": "def login_user():\n\tuser_data = request.get_json()\n\n\tif not user_data:\n\t\traise AuthException(\"Missing user data\")\n\n\tuser = User(user_data['username'], password=user_data['password'])\n\n\tif (user.role != Role.admin and\n\t\t\tuser.role != Role.user and\n\t\t\tuser.role != Role.guest):\n\t\traise AuthException(\"Unsupported role\")\n\n\tuser = auth.login_user(user)\n\n\tsession_id = auth.store_session(user)\n\n\treturn(json_util.dumps({\"session_id\" : session_id, \"user\" : user.to_dict()}))",
"title": ""
},
{
"docid": "86af10186c500af05b92fc8dc36e5886",
"score": "0.59012175",
"text": "def _login(self, fsm):\n\n loginname = self.param(\"loginname\")\n passwd = self.param(\"password\")\n cr_id = self.param(\"cr_id\")\n cr_key = self.param(\"cr_key\")\n cr_auth = self.param(\"cr_auth\")\n\n if (loginname == None) or (loginname == \"\"):\n self._login_error()\n return\n if passwd == None:\n passwd = \"\"\n\n pwman = PasswordMan.PasswordMan(self.get_config(\"path_to_users_db\"), self.get_config(\"temp_dir\"))\n\n if cr_auth == \"on\":\n userId = pwman.get_userId_with_cr(loginname, passwd, cr_id)\n else:\n userId = pwman.get_userId(loginname, passwd)\n\n if userId < 0:\n self._login_error()\n #print \"login:\" + loginname + \" passwd:\" + passwd\n return\n else:\n self._login_succeed()",
"title": ""
},
{
"docid": "591e8eecb14e644d9b86a17f4e9055a0",
"score": "0.58985186",
"text": "def user_required(handler):\n def check_login(self, *args, **kwargs):\n auth = self.auth\n if not auth.get_user_by_session():\n self.redirect(self.uri_for('login'), abort=True)\n else:\n return handler(self, *args, **kwargs)\n\n return check_login",
"title": ""
},
{
"docid": "6a109d217064268b545e572f3ae364c3",
"score": "0.5898421",
"text": "def authenticate_human_user(self, user_login, user_password, auth_token=None):\n if not user_login:\n raise ValueError(\"Please supply a username to authenticate.\")\n\n if not user_password:\n raise ValueError(\"Please supply a password for the user.\")\n\n # Override permissions on Config obj\n original_login = self.config.user_login\n original_password = self.config.user_password\n original_auth_token = self.config.auth_token\n\n self.config.user_login = user_login\n self.config.user_password = user_password\n self.config.auth_token = auth_token\n\n try:\n data = self.find_one(\"HumanUser\", [[\"sg_status_list\", \"is\", \"act\"],\n [\"login\", \"is\", user_login]],\n [\"id\", \"login\"], \"\", \"all\")\n # Set back to default - There finally and except cannot be used together in python2.4\n self.config.user_login = original_login\n self.config.user_password = original_password\n self.config.auth_token = original_auth_token\n return data\n except Fault:\n # Set back to default - There finally and except cannot be used together in python2.4\n self.config.user_login = original_login\n self.config.user_password = original_password\n self.config.auth_token = original_auth_token\n except Exception:\n # Set back to default - There finally and except cannot be used together in python2.4\n self.config.user_login = original_login\n self.config.user_password = original_password\n self.config.auth_token = original_auth_token\n raise",
"title": ""
},
{
"docid": "39aa08ccde12c897798e24d5b38449cc",
"score": "0.58976084",
"text": "def before_request():\n g.user = None\n if 'openid' in session:\n g.user = User.query.filter_by(openid=session['openid']).first()",
"title": ""
},
{
"docid": "7eb2d9eee9b8088e9cd6fb0c37721c9b",
"score": "0.58855635",
"text": "def before_request():\n\n # Check whether or not the server-side cookie (session created on log in) exists and is valid\n\n g.user = okta_client.get_user(oidc.user_getfield(\n \"sub\")) if oidc.user_loggedin else None",
"title": ""
},
{
"docid": "367bbc31719e45440e4e78edbef8bf16",
"score": "0.58822334",
"text": "def as_user(self, user, restore='admin'):\n @contextlib.contextmanager\n def user_ctx():\n try:\n login_ok = False\n try:\n self.logout()\n except:\n pass\n if user:\n self.login(user)\n login_ok = True\n yield self\n finally:\n if login_ok:\n try:\n self.logout()\n except:\n pass\n if restore:\n self.login(restore)\n\n return user_ctx()",
"title": ""
},
{
"docid": "527f904931dec21e988b097378928711",
"score": "0.5875898",
"text": "def set_login(self, user, passwd):\n self.session.auth = (user, passwd)\n\n return",
"title": ""
},
{
"docid": "78df3fc52f2ea4e79def8a94cafd97ae",
"score": "0.5870215",
"text": "def login(self):\n r = self.get(self.home_page)\n self.user = self.extract_user_data(r.content)\n if self.user:\n self.is_logged_in = True\n else:\n time.sleep(2)\n login_page = self.home_page + 'login/?referrer=home_page'\n self.get(login_page)\n time.sleep(3)\n data = url_encode({\n 'source_url': '/login/?referrer=home_page',\n 'data': json.dumps({\n 'options': {'username_or_email': self.username_or_email,\n 'password': self.password},\n \"context\": {}\n }).replace(' ', '')\n })\n url = self.home_page + 'resource/UserSessionResource/create/'\n result = self.post(url=url, data=data, ajax=True).json()\n # By using get avoid KeyError if \"error\" key is not present\n error = result['resource_response'].get('error')\n if error is None:\n self.user = self.extract_user_data(self.get(self.home_page).content)\n self.is_logged_in = True\n else:\n raise PinterestLoginFailedException('[%s Login failed] %s' %\n (error['http_status'], error['message']))\n return self.is_logged_in",
"title": ""
},
{
"docid": "1696860695ab7dd50478f09eea7ffdfe",
"score": "0.5868915",
"text": "def login_user(self, token):\n if \"Project Service\" in self.rmt._config.sections():\n host = self.rmt._config.get(\"Project Service\", \"host\")\n protocol = self.rmt._config.get(\"Project Service\", \"protocol\")\n else:\n host = self.rmt._config.get(\"Default\", \"host\")\n protocol = self.rmt._config.get(\"Default\", \"protocol\")\n\n headers = {\n 'Content-Type': 'application/x-www-form-urlencoded',\n 'Authorization': 'Bearer ' + token\n }\n\n # Hit one of the API endpoints to effectively, login.\n url = protocol + '://' + host + '/' + API_VER + '/collection/'\n session = Session()\n request = Request('GET', url, headers=headers)\n prep = session.prepare_request(request)\n response = session.send(prep, verify=False)",
"title": ""
},
{
"docid": "d7181bb6645fd73e350500470a67316b",
"score": "0.5867491",
"text": "def login(self):\n return True",
"title": ""
},
{
"docid": "382087a9b4c7721eecb4cedcfedc8731",
"score": "0.5863386",
"text": "def on_start(self):\n self.login()",
"title": ""
},
{
"docid": "705800e8263d711048aa9e0817b41871",
"score": "0.58533853",
"text": "def _auth(self, uid=None):\n if uid is not None:\n self.uid = uid\n else:\n self.uid = self.load_user().uid\n\n self.user_adapter.uid = self.uid",
"title": ""
},
{
"docid": "ea70d08b398a187d1a0254622a1eafe9",
"score": "0.5852568",
"text": "def login_user():\n print('Login')\n auth = request.authorization\n # print(request.authorization)\n if not auth or not auth.username or not auth.password:\n return make_response('could not verify', 401, {'WWW.Authentication': 'Basic realm: \"login required\"'})\n # print(auth.username)\n session = Session()\n repository = UserRepository(session)\n users_db = repository.get_all()\n user = next((s for s in users_db if s.email == auth.username), None)\n if user is not None and check_password_hash(user.password, auth.password):\n token = jwt.encode(\n {'public_id': user.id, 'exp': datetime.datetime.utcnow() + datetime.timedelta(minutes=60)},\n app.config['SECRET_KEY'])\n return jsonify({'token': token, 'user': user.to_json()})\n return make_response('could not verify', 401, {'WWW.Authentication': 'Basic realm: \"login required\"'})",
"title": ""
},
{
"docid": "d085635d0fd13170bffa09bbfe631f95",
"score": "0.5851649",
"text": "def test_user_in_login(self):\n response = self.client.post('/accounts/login/', self.usercreate, follow=True)\n self.assertTrue(response.context['user'].is_active)",
"title": ""
},
{
"docid": "80b36adac91b91a782b532e7a7b0d70e",
"score": "0.5849562",
"text": "def before_request():\n g.user = None\n if 'user_id' in session:\n g.user = User.query.get(session['user_id'])",
"title": ""
},
{
"docid": "e2a037bfc1b2853634ea8cc975eb8d94",
"score": "0.584342",
"text": "def login():\n\tglobal logged_in\n\tusername = input(\"Username: \")\n\tpassword = getpass.getpass(\"Password: \")\n\n\tuser = session.query(User).filter(User.username == username, User.password == password).first()\n\t# if user == None:\n\t# \tprint(\"User is not registered, please register.\")\n\n\tlogged_in = user\n\n\treturn user",
"title": ""
},
{
"docid": "7024cd8d28b0b6bb8f06dbc6cd2b1d79",
"score": "0.58419806",
"text": "def load_user(user_id):\n return User.get_by_id(int(user_id))",
"title": ""
},
{
"docid": "7024cd8d28b0b6bb8f06dbc6cd2b1d79",
"score": "0.58419806",
"text": "def load_user(user_id):\n return User.get_by_id(int(user_id))",
"title": ""
},
{
"docid": "7024cd8d28b0b6bb8f06dbc6cd2b1d79",
"score": "0.58419806",
"text": "def load_user(user_id):\n return User.get_by_id(int(user_id))",
"title": ""
},
{
"docid": "1af727859f07fea83e4c3f102ad99a6f",
"score": "0.58376396",
"text": "def user_loader(user_id):\n return Member.query.get(user_id)",
"title": ""
},
{
"docid": "1265127fea8b69168664e6f6b4731f35",
"score": "0.58294654",
"text": "def load_user(user_id):\n user = session.query(User).filter_by(id=user_id).first()\n if not user:\n flash('invalid username or password')\n abort(400)\n return user",
"title": ""
},
{
"docid": "07926a0481cdbee46f7260bf1b066d5a",
"score": "0.582542",
"text": "def handle_login():\n data = get_request_data()\n email = str(data['email']).lower()\n password = str(data['password'])\n user = User.query.filter_by(_email=email).first()\n\n if not user:\n raise BadRequest('UserNotFound', 'user not found')\n\n if not user.check_password(password):\n raise BadRequest('WrongPassword', 'incorrect password')\n\n login_user(user, remember=False)\n\n return successful_response()",
"title": ""
},
{
"docid": "a69e9a408574ed7345653f03655cb837",
"score": "0.5823755",
"text": "def _handle_successful_authentication_and_login(user, request):\n if LoginFailures.is_feature_enabled():\n LoginFailures.clear_lockout_counter(user)\n\n _track_user_login(user, request)\n\n try:\n django_login(request, user)\n request.session.set_expiry(604800 * 4)\n log.debug(\"Setting user session expiry to 4 weeks\")\n\n # Announce user's login\n SESSION_LOGIN_COMPLETED.send_event(\n user=UserData(\n pii=UserPersonalData(\n username=user.username,\n email=user.email,\n name=user.profile.name,\n ),\n id=user.id,\n is_active=user.is_active,\n ),\n )\n except Exception as exc:\n AUDIT_LOG.critical(\"Login failed - Could not create session. Is memcached running?\")\n log.critical(\"Login failed - Could not create session. Is memcached running?\")\n log.exception(exc)\n raise",
"title": ""
},
{
"docid": "a9471cb7330959a88fe293958689fb73",
"score": "0.58202374",
"text": "def login(self, user):\n self.set_secure_cookie('user_id', str(user.key.id()))",
"title": ""
},
{
"docid": "f18e083930f73bfeb7bba8fd3ad15e3f",
"score": "0.58194053",
"text": "def test_load_user(self):\n\n load_user = services.load_user\n\n user = makeOne()\n user_id = user.id\n self.assertEqual(user, load_user(unicode(user_id)))\n\n # A non existing user should return None.\n self.assertEqual(None, load_user(unicode(50)))",
"title": ""
},
{
"docid": "ba41f070295a78085fc6a2f330a0d236",
"score": "0.5814357",
"text": "def user_required(handler):\n\n def check_login(self, *args, **kwargs):\n # Make sure there is a provider and credentials stored in session\n provider = session.get('provider')\n credentials = session.get('credentials')\n if not credentials or not provider:\n return redirect(\"/login\")\n\n # check if there is a user\n if not self.user:\n return redirect(\"/login\")\n\n return handler(self, *args, **kwargs)\n\n return check_login",
"title": ""
},
{
"docid": "273888bdf1fc6e49b4bad20149b1e991",
"score": "0.5813547",
"text": "async def _load_data(self) -> typing.Optional['user.User']:\n LOGGER.debug('Loading session %s', self.id)\n result = await self._redis.get(self._redis_key)\n if not result:\n LOGGER.info('Session %r not found', self.id)\n return\n data = json.loads(result.decode('utf-8'))\n self.last_save = data['last_save']\n self.start = data['start']\n if not data.get('user'):\n return\n\n password = data['user'].pop('password', None)\n if password is not None:\n password = self._application.decrypt_value(password)\n\n user_obj = user.User(self._handler.application, password=password)\n for key, value in data['user'].items():\n setattr(user_obj, key, value)\n return user_obj",
"title": ""
},
{
"docid": "653b2df4c9c85634b89f14a02eca56ac",
"score": "0.58096874",
"text": "def on_login(self, request, user):\n # logger.info(\"20130923 on_login(%s)\" % user)\n\n request.user = user\n\n user_language = user.language or settings.SITE.get_default_language()\n\n if request.method == 'GET':\n rqdata = request.GET\n elif request.method in ('PUT', 'DELETE'):\n # raw_post_data before Django 1.4\n rqdata = http.QueryDict(request.body)\n elif request.method == 'POST':\n rqdata = request.POST\n else:\n # e.g. OPTIONS, HEAD\n if len(settings.SITE.languages) > 1:\n translation.activate(user_language)\n request.LANGUAGE_CODE = translation.get_language()\n #~ logger.info(\"20121205 on_login %r\",translation.get_language())\n request.requesting_panel = None\n request.subst_user = None\n return\n # ~ else: # DELETE\n #~ request.subst_user = None\n #~ request.requesting_panel = None\n #~ return\n\n if len(settings.SITE.languages) > 1:\n\n user_language = rqdata.get(\n constants.URL_PARAM_USER_LANGUAGE, user_language)\n translation.activate(user_language)\n request.LANGUAGE_CODE = translation.get_language()\n\n su = rqdata.get(constants.URL_PARAM_SUBST_USER, None)\n if su is not None:\n if su:\n try:\n su = settings.SITE.user_model.objects.get(id=int(su))\n #~ logger.info(\"20120714 su is %s\",su.username)\n except settings.SITE.user_model.DoesNotExist:\n su = None\n else:\n su = None # e.g. when it was an empty string \"su=\"\n request.subst_user = su\n request.requesting_panel = rqdata.get(\n constants.URL_PARAM_REQUESTING_PANEL, None)\n #~ logger.info(\"20121228 subst_user is %r\",request.subst_user)\n #~ if request.subst_user is not None and not isinstance(request.subst_user,settings.SITE.user_model):\n #~ raise Exception(\"20121228\")",
"title": ""
},
{
"docid": "de6adbd2448ba725f2c9813d91a463b0",
"score": "0.5800675",
"text": "def process_login():\n\n # Get user's login credentials from login form\n email = request.form.get('email')\n password = request.form.get('password')\n \n # Look up if user exists in system\n user = customers.get_by_email(email)\n if user != None:\n\n # Verify if user provided correct password\n if user.is_correct_password(password):\n flash('Successfully logged in.')\n session['email'] = email\n return redirect('/melons')\n\n # Handle incorrect password\n flash('Incorrect username and/or password.')\n return redirect('/login')\n \n # Handle users not in system\n flash('Username does not exist.')\n return redirect('/login')",
"title": ""
},
{
"docid": "62560ab3365be4509e55da6f6dadf01e",
"score": "0.57992184",
"text": "def user_login():\n global curr_user, user_database\n curr_user = \"\"\n username_input = input(\"\\nenter your username: \")\n password_input = getpass(prompt=\"enter your password: \", stream = None)\n if(not user_database.user_in_DB(username_input)): # checking if the user in the databse\n user_database.register_user(username_input, password_input)\n curr_user = User(username_input, password_input, user_database.getID(username_input)) # creating a User object with its credentials\n return user_database.confirm_User(username_input, password_input)",
"title": ""
},
{
"docid": "625b5d89730d3e2d2f949a7cb5daa9ef",
"score": "0.57826453",
"text": "def lfs_login(request, user):\n if user is None:\n user = request.user\n # TODO: It would be nice to support different login methods, like signed cookies.\n user.last_login = datetime.datetime.now()\n user.save()\n\n if SESSION_KEY in request.session:\n if request.session[SESSION_KEY] != user.id:\n # To avoid reusing another user's session, create a new, empty\n # session if the existing session corresponds to a different\n # authenticated user.\n request.session.flush()\n else:\n pass\n # request.session.cycle_key()\n request.session[SESSION_KEY] = user.id\n request.session[BACKEND_SESSION_KEY] = user.backend\n if hasattr(request, 'user'):\n request.user = user\n\n ### LFS stuff\n cart_utils.update_cart_after_login(request)\n customer_utils.update_customer_after_login(request)",
"title": ""
},
{
"docid": "638800daf502303db6da0dee63ed5da4",
"score": "0.57755417",
"text": "def get_user_from_login(login):\n try:\n # Make sure the user exist\n if not FacadeUser.is_exist_by_login(login):\n return None\n return FacadeUser.get_by_login(login)\n except Exception as e:\n raise ServiceException('USER_DATABASE_QUERY_FAIL', \"Unable to load the user information.\", str(e))",
"title": ""
},
{
"docid": "446280c20ac78904baac3adfc0a92ec4",
"score": "0.576996",
"text": "def inject_user():\n if session.get('logged_in', None):\n return dict(user=User.objects.get(email=session['logged_in']))\n else:\n return dict(user=None)",
"title": ""
},
{
"docid": "24f5887be17070a895a63a0c47d1433e",
"score": "0.57607996",
"text": "def test_authenticated_user_can_load_profile(self):\n create_user('ron', 'Ron')\n self.client.login(username='ron', password='123123')\n response = self.client.get(reverse('user_api'))\n self.assertEqual(response.status_code, 200)\n self.assertTrue(response.content)\n self.assertEqual(json.loads(response.content),\n {'first_name': 'Ron',\n 'last_name': 'Bobski'})",
"title": ""
},
{
"docid": "bc7471d9cc707c17c44d1c3887f8a57e",
"score": "0.57559454",
"text": "def login_admin(cls):\n utils.check_or_try_logout_user(cls) # in case user changed during another test\n cls.headers, cls.cookies = utils.check_or_try_login_user(cls, username=cls.usr, password=cls.pwd)\n assert cls.headers and cls.cookies, cls.require # nosec\n cls.headers.update(cls.json_headers)",
"title": ""
},
{
"docid": "77336a0827d71f23aebefc54afd8e153",
"score": "0.5749649",
"text": "def auto_login(self, user):\n self.set_login_field_text(user.login)\n self.set_password_field_text(user.password)\n return self.click_submit_button()",
"title": ""
},
{
"docid": "cf1636cc0dd2a81fccf05c7079fff940",
"score": "0.574934",
"text": "def load_user_from_token(token):\n #The Token was encrypted using itsdangerous.URLSafeTimedSerializer which\n #allows us to have a max_age on the token itself. When the cookie is stored\n #on the users computer it also has a expiry date, but could be changed by\n #the user, so this feature allows us to enforce the expiry date of the token\n #server side and not rely on the users cookie to expire.\n max_age = app.config[\"REMEMBER_COOKIE_DURATION\"].total_seconds()\n\n try:\n #Decrypt the Security Token, data = [username, hashpass]\n data = login_serializer.loads(token, max_age=max_age)\n user_data = user_service_proxy.get_user(\n user_id=data[0],\n auth_token_username=data[0]\n )\n except:\n return None\n if user_data:\n return AuthUser(user_data)\n else:\n return None",
"title": ""
},
{
"docid": "d1dcfe372233520c693ab38e9c100a93",
"score": "0.5746156",
"text": "def load_user(id):\n if id in USERS:\n return User(id)\n return None",
"title": ""
},
{
"docid": "b5c3e10e3a51752b38073e4b8d29902f",
"score": "0.5744994",
"text": "def get_user(self):\n return TsUser.objects.get(user_name=self.validated_data[\"user_name\"])",
"title": ""
},
{
"docid": "283405a0ab88725ac25ffac789008782",
"score": "0.5741192",
"text": "def test_user_authentication(self):\n response = self.client.get(self.home_url)\n user = response.context.get('user')\n self.assertTrue(user.is_authenticated)",
"title": ""
},
{
"docid": "ba7427846f6be12bda38d7fb7757142c",
"score": "0.573319",
"text": "def authenticate(self, request):\n session = request.session\n if not session:\n return None\n user_id = session.get('auth_user_id', None)\n user = OSFUser.load(user_id)\n if not user:\n return None\n if waffle.switch_is_active(features.ENFORCE_CSRF):\n self.enforce_csrf(request)\n # CSRF passed with authenticated user\n check_user(user)\n return user, None",
"title": ""
}
]
f262780237a318b08ee6533c0c2b718c
The initial requested URI cannot be modified during the user interactive authentication session.
[
{
"docid": "89a8b1d7679d46e64d3283cc34e9200c",
"score": "0.5436516",
"text": "def test_cannot_change_uri(self) -> None:\n # Create a second login.\n self.login(\"test\", self.user_pass, \"dev2\")\n\n # Attempt to delete the first device.\n # Returns a 401 as per the spec\n channel = self.delete_device(\n self.user_tok, self.device_id, HTTPStatus.UNAUTHORIZED\n )\n\n # Grab the session\n session = channel.json_body[\"session\"]\n # Ensure that flows are what is expected.\n self.assertIn({\"stages\": [\"m.login.password\"]}, channel.json_body[\"flows\"])\n\n # Make another request providing the UI auth flow, but try to delete the\n # second device. This results in an error.\n #\n # This makes use of the fact that the device ID is embedded into the URL.\n self.delete_device(\n self.user_tok,\n \"dev2\",\n HTTPStatus.FORBIDDEN,\n {\n \"auth\": {\n \"type\": \"m.login.password\",\n \"identifier\": {\"type\": \"m.id.user\", \"user\": self.user},\n \"password\": self.user_pass,\n \"session\": session,\n },\n },\n )",
"title": ""
}
]
Returns the UID, the UID where the Brick is connected to, the position, the hardware and firmware version as well as the device identifier. The position can be '0''8' (stack position).
[
{
"docid": "f66162ef1cde617aa750947290fd4e76",
"score": "0.0",
"text": "def get_packages_async_2(filename):\n pool = Pool(processes=cpu_count() + 2)\n num = 0\n for packages in pool.map(_get_packages, _iter(filename)):\n if packages:\n for package in packages:\n package.set_num(num)\n num += 1\n yield package",
"title": ""
}
]
[
{
"docid": "69f75f8ea60b3debaabd6fc32d0dabc1",
"score": "0.66556424",
"text": "def parse(self, filename):\n pass # implemented in Ada",
"title": ""
},
{
"docid": "e39c948dfe9585ded737137cfc2c7717",
"score": "0.6559397",
"text": "def get_packages(filename):\n num = 0\n pcap_num = 0\n with open(filename) as f:\n pcap = dpkt.pcap.Reader(f)\n if pcap.datalink() == dpkt.pcap.DLT_LINUX_SLL:\n for ts, buf in pcap:\n pcap_num += 1\n sll = dpkt.sll.SLL(buf)\n ip = sll.data\n if type(ip.data) != TCP and type(ip.data) != UDP:\n continue\n if ip.data.data:\n package_type = determine_package_type(ip.data.data)\n if package_type:\n try:\n for package in package_type.parse(ip.data.data):\n num += 1\n package.set_ts(ts)\n package.set_pcap_package(sll)\n package.set_num(num)\n package.set_pcap_num(pcap_num)\n package.set_ip_dst(socket.inet_ntoa(ip.dst))\n package.set_ip_src(socket.inet_ntoa(ip.src))\n yield package\n except ParseException:\n continue\n else:\n raise WrongFileFormatException(filename)",
"title": ""
},
{
"docid": "3bd19dcf3eecda355695906edf67e8bd",
"score": "0.65228814",
"text": "def parse(self, filename):\n if filename.endswith('.pbf'):\n return self.parse_pbf_file(filename)\n elif filename.endswith(('.osm', '.osm.bz2')):\n return self.parse_xml_file(filename)\n else:\n raise NotImplementedError('unknown file extension')",
"title": ""
},
{
"docid": "0c4d6c11ae0c61459bf128cdf43a1e2f",
"score": "0.63581765",
"text": "def parse_package(package_path, package_format='tango'):\n\n if package_format == 'tango':\n return parse_tango_package(package_path)\n else:\n raise Exception('Specified unsupported package_format {}. Supported formats: tango.'.format(package_format))",
"title": ""
},
{
"docid": "9d9890f088b21202243d7f231978c9a9",
"score": "0.6340313",
"text": "def parse_filename(filename, project_hint=None):\n for pkg_type, rgx in GOOD_PACKAGE_RGXN:\n m = rgx.match(filename)\n if m:\n return (m.group('project'), m.group('version'), pkg_type)\n if project_hint is not None:\n proj_rgx = re.sub(r'[^A-Za-z0-9]+', '[-_.]+', project_hint)\n proj_rgx = re.sub(\n r'([A-Za-z])',\n lambda m: '[' + m.group(1).upper() + m.group(1).lower() + ']',\n proj_rgx,\n )\n m = re.match(proj_rgx + r'(?=-)', filename)\n if m:\n project = m.group(0)\n rest_of_name = filename[m.end(0):]\n for pkg_type, rgx in BAD_PACKAGE_BASES:\n m = rgx.match(rest_of_name)\n if m:\n return (project, m.group('version'), pkg_type)\n for pkg_type, rgx in BAD_PACKAGE_RGXN:\n m = rgx.match(filename)\n if m:\n return (m.group('project'), m.group('version'), pkg_type)\n return (None, None, None)",
"title": ""
},
{
"docid": "87dc6a577bdc5db195da5c49955591a9",
"score": "0.61758703",
"text": "def parse(self, file_path, package_name='pdfminer'):\n implementation = self.packageMapper.get(package_name)\n return implementation(file_path)",
"title": ""
},
{
"docid": "3b908b4d4f644cb58ab73634d423c155",
"score": "0.615252",
"text": "def parse(cls, filename):\n if filename.endswith('.rpm'):\n filename = filename[:-4]\n m = cls._re.match(filename)\n if not m:\n return None\n n, v, r, a = m.groups()\n if ':' not in v:\n return n, None, v, r, a\n e, v = v.split(':', 1)\n e = int(e)\n return cls(n, e, v, r, a)",
"title": ""
},
{
"docid": "036d6fe16caa565455888ed3873b604b",
"score": "0.6119223",
"text": "def _parse_package(cls, content):\n content = content.split(\" \")\n protocol, filter_strings = None, None\n name = None # darkplaces\n\n # one of the first two is the protocol\n if content[0].isdigit():\n protocol = content[0]\n filter_strings = content[1:]\n\n elif content[1].isdigit():\n # darkplaces\n name = content[0]\n protocol = content[1]\n filter_strings = content[2:]\n\n return protocol, filter_strings, name",
"title": ""
},
{
"docid": "a6f2c27c8606935cea753786f19f682d",
"score": "0.6114801",
"text": "def load_package_list(packages_file):\n with open(packages_file) as pkgs:\n packages = pkgs.read().splitlines()\n return packages",
"title": ""
},
{
"docid": "f1c65c907751261301502490c4a0bd16",
"score": "0.6110994",
"text": "def parse(self, fname):",
"title": ""
},
{
"docid": "55d9d88dbb2271564654b0c993967593",
"score": "0.60790217",
"text": "def parse_requirements(filename):\n\n # Get absolute filepath\n filepath = os.path.join(os.getcwd(), filename)\n\n # Check if file exists\n if not os.path.exists(filepath):\n print(\"[!] File {} not found\".format(filename))\n return []\n\n # Parse install requirements\n with open(filepath, encoding=\"utf-8\") as f:\n return [requires.strip() for requires in f.readlines()]",
"title": ""
},
{
"docid": "c7a30c9c0e019bd90bf340c972230a6d",
"score": "0.59785664",
"text": "def dmfile(filename):\n result = []\n with open(filename, 'r') as fp:\n for line in fp:\n line = line.strip()\n comment_pos = comment_find(line)\n\n if comment_pos >= 0:\n line = line[:comment_pos].strip()\n\n if not line:\n continue\n\n match = DMFILE_RE.match(line)\n if match is None:\n raise InvalidPackageSpec(f\"'{line}'\")\n\n pkg = match.groupdict()\n if pkg['version']:\n invalid = DMFILE_INVALID_VERSION_RE.match(pkg['version'])\n if invalid:\n raise InvalidPackageSpec(f\"'{line}'\")\n\n pkg['fullspec'] = line\n result.append(pkg)\n\n if not result:\n raise EmptyPackageSpec(\"Nothing to do\")\n\n return result",
"title": ""
},
{
"docid": "e73b8ecf46b9d21258deb3a2952677a2",
"score": "0.5970222",
"text": "def parse(path):\n fmt = path.split(\".\")[-1]\n with open(path, 'r') as parsefile:\n return parsers[fmt](parsefile.read())",
"title": ""
},
{
"docid": "65c65e4b4170e94089f0c794228aca2d",
"score": "0.5903436",
"text": "def parse_requirements(file):\n required_packages = []\n with open(os.path.join(os.path.dirname(__file__), file)) as req_file:\n for line in req_file:\n if '/' not in line:\n required_packages.append(line.strip())\n return required_packages",
"title": ""
},
{
"docid": "3ff005220688b99976c899996db5d76b",
"score": "0.5872778",
"text": "def _parse_from_file(self, filepath, fname,\n dependencies, recursive, greedy):\n #Now that we have the file contents, we can parse them using the parsers\n string = self.tramp.read(filepath)\n pmodules = self.modulep.parse(string, self, filepath=filepath)\n file_mtime = self._get_mod_mtime(filepath)\n\n for module in pmodules:\n module.change_time = file_mtime\n self.modules[module.name.lower()] = module\n self._modulefiles[fname].append(module.name.lower())\n\n pprograms = self.modulep.parse(string, self, False)\n for program in pprograms:\n program.change_time = file_mtime\n self.programs[program.name.lower()] = program\n self._programfiles[fname].append(program.name.lower())\n\n #There may be xml files for the docstrings that also need to be parsed.\n self._parse_docstrings(filepath)\n \n return (pmodules, pprograms)",
"title": ""
},
{
"docid": "8c0427d89e517552eb101aaab15362ff",
"score": "0.5868963",
"text": "def parse_filenames(self, filenames):\n for inputfile in filenames:\n if inputfile.endswith('.lib') or inputfile.endswith('.off'):\n self.process_library_file(inputfile)\n elif inputfile.endswith('.dat'):\n self.process_dat_file(inputfile)\n elif inputfile.endswith(\"mol2\"):\n self.process_mol2_file(inputfile)\n else:\n self.process_frc_file(inputfile)\n\n self.reduce_atomtypes()",
"title": ""
},
{
"docid": "d76124eee9f4567c1247e7b91f7c68d5",
"score": "0.58236897",
"text": "def parse_requirements(fname='requirements.txt'):\n require_fpath = join(dirname(__file__), fname)\n\n def parse_line(line):\n \"\"\"\n Parse information from a line in a requirements text file\n \"\"\"\n info = {}\n if line.startswith('-e '):\n info['package'] = line.split('#egg=')[1]\n else:\n # Remove versioning from the package\n pat = '(' + '|'.join(['>=', '==', '>']) + ')'\n parts = re.split(pat, line, maxsplit=1)\n parts = [p.strip() for p in parts]\n\n info['package'] = parts[0]\n if len(parts) > 1:\n # FIXME: This breaks if the package doesnt have a version num\n op, rest = parts[1:]\n if ';' in rest:\n # Handle platform specific dependencies\n # http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies\n version, platform_deps = map(str.strip, rest.split(';'))\n info['platform_deps'] = platform_deps\n else:\n version = rest # NOQA\n info['version'] = (op, version)\n return info\n\n # This breaks on pip install, so check that it exists.\n if exists(require_fpath):\n with open(require_fpath, 'r') as f:\n packages = []\n for line in f.readlines():\n line = line.strip()\n if line and not line.startswith('#'):\n info = parse_line(line)\n package = info['package']\n if not sys.version.startswith('3.4'):\n # apparently package_deps are broken in 3.4\n platform_deps = info.get('platform_deps')\n if platform_deps is not None:\n package += ';' + platform_deps\n packages.append(package)\n return packages\n return []",
"title": ""
},
{
"docid": "0fee4ea6b6a7e8d44887031aa903be35",
"score": "0.58157295",
"text": "def _parse_requirements(filename: str) -> List[str]:\n # Ref: https://stackoverflow.com/a/42033122/\n return distutils.text_file.TextFile(filename=str(Path(__file__).with_name(filename))).readlines()",
"title": ""
},
{
"docid": "f8166eea4d675efb49ea29ce7ca30c98",
"score": "0.57794666",
"text": "def parse_file(self, filename):\n gfile = open(filename, 'r')\n gstr = gfile.readlines()\n gfile.close()\n self.parse_lines(gstr)",
"title": ""
},
{
"docid": "0da83c587815d8399f13d94f936c70d7",
"score": "0.573872",
"text": "def parse(self, filename):\r\n\r\n new_components = []\r\n\r\n with open(filename) as f:\r\n for line in f:\r\n if line.startswith('DEF '):\r\n new_components.append(ComponentParser(line).parse(f))\r\n\r\n for new_component in new_components:\r\n new_component.name = new_component.name.upper()\r\n if new_component.name not in self.name2cpt:\r\n self.name2cpt[new_component.name] = new_component\r\n self.components.append(new_component)\r\n if 'kicad_alias' in new_component.attributes:\r\n for name in new_component.attributes['kicad_alias'].split():\r\n self.name2cpt[name.upper()] = new_component",
"title": ""
},
{
"docid": "1e2c70386c70c4adb2fc706b0f0f87ac",
"score": "0.57234955",
"text": "def parse_from_file(filename, parser, splitter=split_on_vnums, validate=None):\n with open(filename) as f:\n file_text = f.read()\n\n if validate:\n validate(file_text)\n\n file_text = file_text.rstrip('$\\n') # world files\n file_text = file_text.rstrip('$~\\n') # shop files\n\n return parse_from_string(file_text, parser, splitter=splitter)",
"title": ""
},
{
"docid": "99aaddcdeb7e0178c37df9982ce0f010",
"score": "0.57223636",
"text": "def parse_requirements(filename: str) -> List[str]:\n # Ref: https://stackoverflow.com/a/42033122/\n requirements = (Path(__file__).parent / filename).read_text().strip().split('\\n')\n requirements = [r.strip() for r in requirements]\n requirements = [r for r in sorted(requirements) if r and not r.startswith('#')]\n return requirements",
"title": ""
},
{
"docid": "017563706adb6a703d7507171bc8f7e4",
"score": "0.5690299",
"text": "def parse_file(self, filename):\n efile = open(filename, 'r')\n estr = efile.readlines()\n efile.close()\n self.parse_lines(estr)",
"title": ""
},
{
"docid": "02ae437673d7e8e0db8b5553bbda069f",
"score": "0.56521785",
"text": "def parse_input_file(filename):\n modname = os.path.splitext(os.path.basename(filename))[0]\n spec = importlib.util.spec_from_file_location(modname, filename)\n mod = importlib.util.module_from_spec(spec)\n spec.loader.exec_module(mod)\n assert 'A' not in mod.all_mss\n assert type(mod.all_mss) == set\n return mod.struct, mod.all_mss",
"title": ""
},
{
"docid": "71afb66bcc02b57db7f04298febf78c8",
"score": "0.5607634",
"text": "def main( packageName, filenames=(), force=0):\n packageModule = __import__(\n packageName, {}, {},\n string.split(packageName, '.')\n )\n if not hasattr( packageModule, 'package' ):\n # build the default package object...\n from resourcepackage import package\n packageObject = package.Package(\n packageModule.__name__,\n directory = os.path.dirname( os.path.abspath(packageModule.__file__) ),\n )\n else:\n packageObject = packageModule.package\n if filenames:\n for filename in filenames:\n packageObject.scanFile( source = filename, force=force )\n else:\n packageObject.scan( force=force )",
"title": ""
},
{
"docid": "0a03fee77d200f66d46accafe6613f47",
"score": "0.558873",
"text": "def parse(filename):\n stream = open(filename, 'r')\n attributes = parse_stream(stream)\n stream.close()\n return attributes",
"title": ""
},
{
"docid": "861b15a5256adad33d29c0841c11ed50",
"score": "0.55766684",
"text": "def test_package_parse(self):\r\n\r\n# TODO embedded name\r\n\r\n# external name\r\n _valid_chunk = b''.join((b\"\\x1e\\x00\\x0d\\x00\\x7c\\xfe\\xb5\\xff\",\r\n b\"\\x84\\x01\\x97\\x00\\x00\\x7f\\x34\\xe3\",\r\n b\"\\x2a\\x09\\x7f\\x2b\\xe3\\x2a\\x09\\x00\"))\r\n _package = Eagle.Package.parse(_valid_chunk)\r\n\r\n self.assertEqual(_package.name, 'name_a')\r\n self.assertEqual(_package.desc, 'name_b')\r\n self.assertEqual(_package.numofshapes, 13)\r\n\r\n return",
"title": ""
},
{
"docid": "7478d291f5ead83c65d1aa0065c6c654",
"score": "0.5562234",
"text": "def _ParsePkgmapLine(self, line):\n if line.startswith(\"#\"):\n return None\n parts = re.split(c.WS_RE, line.strip())\n # logging.debug(\"_ParsePkgmapLine(): %s\", parts)\n if len(parts) < 4:\n raise ParsingError(\"Line does not have enough fields: %s\"\n % repr(parts))\n file_type = parts[1]\n f_path = None\n f_target = None\n f_type = None\n f_class = None\n f_major = None\n f_minor = None\n f_mode = None\n f_owner = None\n f_group = None\n f_size = None\n f_cksum = None\n f_modtime = None\n f_pkgname = None\n pkgnames = []\n if file_type == 's':\n # ftype s: path=rpath s class package\n #\n # Spotted in real life:\n # ['/opt/csw/man=share/man', 's', 'none', 'CSWschilybase',\n # 'CSWschilyutils', 'CSWstar', 'CSWcommon']\n (f_path_rpath, f_type, f_class) = parts[:3]\n pkgnames.extend(parts[3:])\n f_path, f_target = f_path_rpath.split(\"=\")\n elif file_type == 'l':\n # ftype l: path l class package\n f_path, f_type, f_class, f_pkgname = parts\n elif file_type in ('d', 'x', 'p'):\n # ftype d: path d class mode owner group package(s)\n # ftype x: path x class mode owner group package\n f_path, f_type, f_class, f_mode, f_owner, f_group = parts[:6]\n pkgnames.extend(parts[6:])\n elif file_type == '?':\n # Does not follow the specfication. A specimen:\n # /opt/csw/gcc3/lib/gcc/sparc-sun-solaris2.8/3.4.6/include \n # ? none CSWgcc3g77 CSWgcc3core\n logging.warning(\"File type of %s is '?', assuming it's a directory.\",\n parts[0])\n f_type = 'd'\n f_path, unused_type, f_class = parts[:3]\n pkgnames.extend(parts[3:])\n elif file_type in ('b', 'c'):\n # ftype b: path b class major minor mode owner group package\n # ftype c: path c class major minor mode owner group package\n (f_path, f_type, f_class, f_major, f_minor, f_mode, f_owner,\n f_group, f_pkgname) = parts\n elif file_type in ('f', 'v', 'e'):\n # ftype f: path f class mode owner group size cksum modtime package\n # ftype v: path v class mode owner group size cksum modtime package\n # ftype e: path e class mode owner group size cksum modtime package\n #\n # Spotted in real life:\n # ['/etc/.java/.systemPrefs/.system.lock', 'e', 'preserve',\n # '0644', 'root', 'bin', '0', '0', '1265116929', 'SUNWj6cfg',\n # 'SUNWj5cfg']\n (f_path, f_type, f_class, f_mode, f_owner, f_group, f_size,\n f_cksum, f_modtime) = parts[:9]\n pkgnames.extend(parts[9:])\n else:\n raise ParsingError(\"Wrong file type: %s in %s\"\n % (repr(file_type), repr(line)))\n if f_pkgname:\n pkgnames.append(f_pkgname)\n\n static_parts = parts[:9]\n dynamic_parts = parts[9:]\n # ['/usr/lib/sparcv9/libpthread.so.1', 'f', 'none', '0755', 'root',\n # 'bin', '41296', '28258', '1018129099', 'SUNWcslx']\n d = {\n \"path\": f_path,\n \"target\": f_target,\n \"type\": f_type,\n \"class\": f_class,\n \"major\": f_major,\n \"minor\": f_minor,\n \"mode\": f_mode,\n \"owner\": f_owner,\n \"group\": f_group,\n \"size\": f_size,\n \"cksum\": f_cksum,\n \"modtime\": f_modtime,\n \"pkgnames\": pkgnames,\n \"line\": line,\n }\n return d",
"title": ""
},
{
"docid": "e2863f7678fd12749a269fb0e3f84131",
"score": "0.55536395",
"text": "def parse_file_name(self, path):\n pass",
"title": ""
},
{
"docid": "c7517fa6812b71bf3332db81e124d028",
"score": "0.55464613",
"text": "def parseFilename(filename, verbose=False):\n\n fname_patt = r\"(?P<product>[qomens]\\w\\w\\w)-(?P<itype>[aV])\"\n fname_patt = fname_patt + r\"-(?P<region>\\w\\w\\w)(?P<year>\\d\\d)\"\n fname_patt = fname_patt + r\"-(?P<doy_start>\\d\\d\\d)\"\n fname_patt = fname_patt + r\"-(?P<doy_end>\\d\\d\\d)\\.\"\n\n m = re.match(fname_patt, filename)\n if m is not None:\n year2 = int(m.group(\"year\"))\n if year2 > 80:\n year4 = year2 + 1900\n else:\n year4 = 2000 + year2\n fparts = {\n \"product\": m.group(\"product\"),\n \"itype\": m.group(\"itype\"),\n \"region\": m.group(\"region\"),\n \"year\": year4,\n \"doy_start\": int(m.group(\"doy_start\")),\n \"doy_end\": int(m.group(\"doy_end\")),\n }\n return fparts\n else:\n return None",
"title": ""
},
{
"docid": "181d267a83e86e776a0d23cf59fa8773",
"score": "0.5531865",
"text": "def _parse_from_file_path(self, path):\n return self._parse_from_file_name(os.path.basename(path))",
"title": ""
},
{
"docid": "65fab89e846a43aef0239d59f9185953",
"score": "0.5527418",
"text": "def parse(self, filename: Path) -> Metadata:\n raise NotImplementedError()",
"title": ""
},
{
"docid": "9c93af09ade5a806578fc9fbf02ea8b6",
"score": "0.5521588",
"text": "def parse_info_from_filename(self, files):\n filename = os.path.basename(self.files[\".out\"])\n rogue_string = \".zib.de\"\n file_path_clean = filename.replace(rogue_string, \"\")\n fnparts = file_path_clean.split(\".\")\n if len(fnparts) != 8:\n return {}\n\n info = {\n \"test_set\": fnparts[1], # short, bug, etc,\n \"settings_short_name\": fnparts[-2],\n \"run_environment\": fnparts[-3],\n \"opt_flag\": fnparts[-5],\n \"architecture\": fnparts[-7],\n \"os\": fnparts[-8]}\n return info",
"title": ""
},
{
"docid": "0eb51c8a5ef68c086bbb99ec26058eb8",
"score": "0.5517156",
"text": "def fetch(self, filename):\n return self.packages.get(filename)",
"title": ""
},
{
"docid": "5b54f2ae75c211d113b86f87e9b7c63d",
"score": "0.5515779",
"text": "def parse_file(filename, comments=';', skiprows=0, opener='(', closer=')'):\n text = Parser._readFileContents(filename, comments=comments, skiprows=skiprows)\n # Run the parsing and get a nest list of the results\n return Parser.parse_string(text, opener=opener, closer=closer)",
"title": ""
},
{
"docid": "f304ebaf975bef7fc03b66fad5f6a482",
"score": "0.5514139",
"text": "def parse(cls, filename):\n return cls(et.parse(filename))",
"title": ""
},
{
"docid": "a4f7f31cacadfe281bf974348f97c635",
"score": "0.54876864",
"text": "def parse_requirements(args):\n # pip version needs to be greater than 19.3.1 to run this script\n # see https://github.com/pypa/pip/issues/6070\n pip_version = str(subprocess.check_output([\"pip\", \"--version\"]))\n pip_version_major = int(str(pip_version).split(\".\")[0].split(\"pip\")[1].strip())\n if pip_version_major < 20:\n sys.exit(\n \"pip version is lower or equal to 19.3.1. Please upgrade the pip version to run this script.\"\n )\n\n # Start a requests session to reuse HTTP connections\n session = requests.Session()\n\n with open(args.file) as f:\n cache_path = os.path.realpath(args.cache_path)\n cache_path = check_cache_path_writable(cache_path)\n for line in f:\n char_list = line.split(\"==\")\n if len(char_list) == 2:\n # Parse PyPi and Piwheels pages to install package according to\n # its name and version\n parse_pypi_and_piwheels(\n char_list[0].strip(), char_list[1].strip(), cache_path, session\n )\n # Ignore comments\n elif not line.startswith(\"#\"):\n sys.exit(\n \"\\nName format in cext.txt is incorrect. Should be 'packageName==packageVersion'.\\n\"\n )",
"title": ""
},
{
"docid": "af7be10db8e2dfdb2519a5bbbcb06b27",
"score": "0.54868144",
"text": "def _parse(filename):\n try:\n with open(\"{0}.json\".format(filename)) as file:\n return json.load(file)\n except FileNotFoundError:\n try:\n with open(\"{0}.yml\".format(filename)) as file:\n return yaml.load(file, Loader=yaml.FullLoader)\n except yaml.scanner.ScannerError:\n print(f'Invalid Yaml for {filename}.yml')\n except FileNotFoundError:\n print(f'Could not open {filename}.yml')",
"title": ""
},
{
"docid": "ee408e490dce6aacd4128c36be4fa6f6",
"score": "0.5485742",
"text": "def parse_requirements(file_name):\n requirements = []\n for line in open(file_name, \"r\").read().split(\"\\n\"):\n if re.match(r\"(\\s*#)|(\\s*$)\", line) or line.startswith(\"-\"):\n continue\n elif \"://\" in line or line.startswith(\"-e\"):\n # TODO support version numbers\n if \"egg\" in line:\n requirements.append(re.sub(r\".*#egg=(.*)$\", r\"\\1\", line))\n elif \"file\" in line:\n requirements.append(line.strip().rsplit(\"/\", 1)[1])\n else:\n pass\n elif re.match(r\"\\s*-f\\s+\", line):\n pass\n else:\n requirements.append(line)\n return requirements",
"title": ""
},
{
"docid": "087173793729735f65d177da4d34d01d",
"score": "0.54575944",
"text": "def _parse_file(file_name):\n with open(file_name, 'r') as f:\n return json.load(f)",
"title": ""
},
{
"docid": "94914eaa069ee41b962988b54b9a6f03",
"score": "0.5445027",
"text": "def parser_from_file(filename):\n if not os.path.isfile(filename):\n raise ValueError(\"The specified file does not exist\")\n parsed = parse_file(filename)\n parsed.map_leaves(tokenize_leaf)\n return parsed",
"title": ""
},
{
"docid": "fe2697c6b5fb6d33c08bdfc38712f3e6",
"score": "0.5420339",
"text": "def parse_requirements(fname='requirements.txt', with_version=True):\n import sys\n from os.path import exists\n import re\n require_fpath = fname\n\n def parse_line(line):\n \"\"\"\n Parse information from a line in a requirements text file\n \"\"\"\n if line.startswith('-r '):\n # Allow specifying requirements in other files\n target = line.split(' ')[1]\n for info in parse_require_file(target):\n yield info\n else:\n info = {'line': line}\n if line.startswith('-e '):\n info['package'] = line.split('#egg=')[1]\n else:\n # Remove versioning from the package\n pat = '(' + '|'.join(['>=', '==', '>']) + ')'\n parts = re.split(pat, line, maxsplit=1)\n parts = [p.strip() for p in parts]\n\n info['package'] = parts[0]\n if len(parts) > 1:\n op, rest = parts[1:]\n if ';' in rest:\n # Handle platform specific dependencies\n # http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies\n version, platform_deps = map(str.strip,\n rest.split(';'))\n info['platform_deps'] = platform_deps\n else:\n version = rest # NOQA\n info['version'] = (op, version)\n yield info\n\n def parse_require_file(fpath):\n with open(fpath, 'r') as f:\n for line in f.readlines():\n line = line.strip()\n if line and not line.startswith('#'):\n for info in parse_line(line):\n yield info\n\n def gen_packages_items():\n if exists(require_fpath):\n for info in parse_require_file(require_fpath):\n parts = [info['package']]\n if with_version and 'version' in info:\n parts.extend(info['version'])\n if not sys.version.startswith('3.4'):\n # apparently package_deps are broken in 3.4\n platform_deps = info.get('platform_deps')\n if platform_deps is not None:\n parts.append(';' + platform_deps)\n item = ''.join(parts)\n yield item\n\n packages = list(gen_packages_items())\n return packages",
"title": ""
},
{
"docid": "c98fac07d682f8a879f15082bae5270d",
"score": "0.54199517",
"text": "def read_pipfile(file: str) -> dict:\n flag = ''\n pattern = re.compile(r\"([\\w\\-_]+)\\s=\\s\\\"(.*?)\\\"\")\n packages = {}\n dev_packages = {}\n with open(file, 'r') as pipfile:\n for line in pipfile:\n line = line.strip()\n if line == '[packages]':\n flag = 'packages'\n continue\n if line == '[dev-packages]':\n flag = 'dev-packages'\n continue\n if line == '':\n flag = ''\n if flag == 'packages':\n package = pattern.match(line).group(1)\n version = pattern.match(line).group(2)\n packages[package] = version\n elif flag == 'dev-packages':\n package = pattern.match(line).group(1)\n version = pattern.match(line).group(2)\n dev_packages[package] = version\n\n return {'packages': packages,\n 'dev-packages': dev_packages}",
"title": ""
},
{
"docid": "2175108e0e78c6ff4cc46e456744c27c",
"score": "0.54165584",
"text": "def parse_requirements(filename):\n # type: (str) -> List[str]\n lineiter = (line.strip() for line in open(filename))\n return [line for line in lineiter if line and not line.startswith(\"#\")]",
"title": ""
},
{
"docid": "96736157960f0cd4689b0a4f16db166a",
"score": "0.54133344",
"text": "def specs_from_text_file(filename, concretize=False):\n with open(filename, \"r\") as f:\n specs_in_file = f.readlines()\n specs_in_file = [s.strip() for s in specs_in_file]\n return spack.cmd.parse_specs(\" \".join(specs_in_file), concretize=concretize)",
"title": ""
},
{
"docid": "9ac2fe71b701c2b15f174da2491d3064",
"score": "0.54005617",
"text": "def tokenize_package_name(package_name):\n return PACKAGE_NAME_TOKENIZATION_PATTERN.split(package_name)",
"title": ""
},
{
"docid": "fe16d7fadb57df970be695ab433027a9",
"score": "0.53911173",
"text": "def loadManifestFile(filename):\n manifest = xmlparser.parseFile(filename, xmlparser.NS_FORMATS_MANIFEST)\n if manifest.name != 'bakefile-manifest':\n raise errors.ReaderError(manifest, 'invalid manifest file')\n for fmt in manifest.children:\n if fmt.name != 'format':\n raise errors.ReaderError(fmt, 'invalid manifest file')\n info = FormatInfo()\n info.name = fmt.props['id'] \n for node in fmt.children:\n if node.name == 'description':\n info.desc = node.value\n elif node.name == 'default-filename':\n info.defaultFile = node.value\n else:\n raise errors.ReaderError(node, 'invalid format description')\n if info.name == None or info.desc == None or info.defaultFile == None:\n raise errors.ReaderError(fmt, 'format not fully described')\n formats[info.name] = info",
"title": ""
},
{
"docid": "453be84be753d56e0f62565c888b2379",
"score": "0.53778034",
"text": "def parse(filename):\n\tglobal _catalogAddress, _catalogPort\n\n\ttry:\n\t\twith open(filename, 'r') as startupFile:\n\t\t\tstartupContent = startupFile.read()\n\t\t\tstartupContent = startupContent.split(ENDL)\n\n\t\t\t# First two lines : server address and port\n\t\t\t_catalogAddress = startupContent[0].split(': ')[1]\n\t\t\t_catalogPort = int(startupContent[1].split(': ')[1])\n\n\t\t\t# All other lines : path to media descriptor files\n\t\t\tmediaDescriptors = startupContent[2:]\n\t\t\tfor mediaDescriptor in mediaDescriptors:\n\t\t\t\ttry:\n\t\t\t\t\tmedia = _parseMediaDescriptor(mediaDescriptor)\n\t\t\t\t\t# Add this media to the catalog, but only if at least one of his images were readable\n\t\t\t\t\tif len(media['files']) > 0:\n\t\t\t\t\t\t_catalog.append(media)\n\t\t\t\t\telse:\n\t\t\t\t\t\traise EnvironmentError()\n\t\t\t\texcept EnvironmentError:\n\t\t\t\t\tprint('The media descriptor', mediaDescriptor, 'could not be read, ignoring this media.')\n\n\t\t\treturn (_catalogAddress, _catalogPort)\n\texcept EnvironmentError:\n\t\tprint('The catalog descriptor', filename, 'could not be read, server cannot run.')\n\t\texit()",
"title": ""
},
{
"docid": "823c4650183f15a8843633b98e164199",
"score": "0.5370685",
"text": "def _parse_package_list(self, data):\n try:\n data_string = data.decode('utf-8')\n parsed_data = yaml.safe_load(data_string)\n\n if isinstance(parsed_data, dict):\n self.package_list = self._extract_package_list(parsed_data)\n else:\n raise errors.InvalidPackageListFormat(\n \"Package data should have a top-level mapping/object.\")\n except yaml.YAMLError as ex:\n raise errors.InvalidPackageListFormat(\n \"Invalid YAML in package list: %s\" % str(ex))",
"title": ""
},
{
"docid": "e9073187ca7e0d7117d40e88f26fe1f8",
"score": "0.5342954",
"text": "def readsrgs(filename):\n\n packages=[]; classes=[]; methods=[]; fields=[]\n srgsdata = open(filename,'r').read().splitlines()\n\n for row in srgsdata:\n row = row.strip()\n #HINT: We check for comments and whitelines\n if not row or row[0] == '#':continue\n\n #HINT: We extract the flag for the row (first element of a split on ':')\n if not len(row.split(':')) == 2: raise Exception(\"The row is not conforming to TAG:ENTRY syntax. [%s]\"%row)\n flag = row.split(':')[0]\n row = row.split(':')[1].strip()\n if not flag in ['PK', 'CL', 'FD', 'MD']:raise Exception(\"Flag not recognized : %s\"%flag)\n\n #HINT: We check the value of the flag, and append the corresponding list\n #The way we are splitting, the file does support comments after the useful text\n if flag == 'PK': packages.append([row.split()[0], row.split()[1]])\n if flag == 'CL': classes.append([row.split()[0], row.split()[1]])\n if flag == 'FD': fields.append([row.split()[0], row.split()[1]])\n if flag == 'MD': methods.append([' '.join(row.split()[0:2]), ' '.join(row.split()[2:4])])\n\n return {'PK':packages, 'CL':classes, 'FD':fields, 'MD':methods}",
"title": ""
},
{
"docid": "930a37b3ed3bcbe1a48aaf4b2f2d86fa",
"score": "0.5342714",
"text": "def parse_file(filename):\n # Prep the result with none values in case of error.\n result = {}\n for key in KEYS:\n result[key] = None\n\n result['major_release'] = None\n\n try:\n with open(filename, 'r') as f:\n lines = f.readlines()\n\n # Iterate lines in file.\n for line in lines:\n if line.startswith('DISTRIB'):\n key, value = line.split('=', 1)\n # Remove white space\n key = key.strip()\n value = value.strip()\n # Remove quotes\n value = value.strip('\"')\n if key in KEYS:\n result[key] = value\n\n # Get major release from DISTRIB_RELEASE\n if result.get('DISTRIB_RELEASE') is not None:\n result['major_release'] = major_release(result['DISTRIB_RELEASE'])\n except IOError:\n # Host probably does not have osa.\n pass\n\n return result",
"title": ""
},
{
"docid": "45618b928f0f950e9c5785fe2e8094bf",
"score": "0.53414226",
"text": "def parse_filename(filename):\n\n beta_value = filename.split('_beta_')[1].split('_')[0]\n ionfrac_value = filename.split('_ionfrac_')[-1].split('.txt')[0]\n\n return float(beta_value), float(ionfrac_value)",
"title": ""
},
{
"docid": "09568c00edf9f522bb757237afdb56c4",
"score": "0.5341071",
"text": "def parsedata(filename):\n return report.parse(os.path.join(os.path.dirname(__file__), \"data\", filename))",
"title": ""
},
{
"docid": "5fda65c549e8a63cf621acddea1a6c07",
"score": "0.5338784",
"text": "def parse_requirements(filename):\n lineiter = (line.strip() for line in open(filename))\n return [line for line in lineiter if line and not line.startswith(\"#\")]",
"title": ""
},
{
"docid": "5fda65c549e8a63cf621acddea1a6c07",
"score": "0.5338784",
"text": "def parse_requirements(filename):\n lineiter = (line.strip() for line in open(filename))\n return [line for line in lineiter if line and not line.startswith(\"#\")]",
"title": ""
},
{
"docid": "5fda65c549e8a63cf621acddea1a6c07",
"score": "0.5338784",
"text": "def parse_requirements(filename):\n lineiter = (line.strip() for line in open(filename))\n return [line for line in lineiter if line and not line.startswith(\"#\")]",
"title": ""
},
{
"docid": "5fda65c549e8a63cf621acddea1a6c07",
"score": "0.5338784",
"text": "def parse_requirements(filename):\n lineiter = (line.strip() for line in open(filename))\n return [line for line in lineiter if line and not line.startswith(\"#\")]",
"title": ""
},
{
"docid": "5fda65c549e8a63cf621acddea1a6c07",
"score": "0.5338784",
"text": "def parse_requirements(filename):\n lineiter = (line.strip() for line in open(filename))\n return [line for line in lineiter if line and not line.startswith(\"#\")]",
"title": ""
},
{
"docid": "5fda65c549e8a63cf621acddea1a6c07",
"score": "0.5338784",
"text": "def parse_requirements(filename):\n lineiter = (line.strip() for line in open(filename))\n return [line for line in lineiter if line and not line.startswith(\"#\")]",
"title": ""
},
{
"docid": "5fda65c549e8a63cf621acddea1a6c07",
"score": "0.5338784",
"text": "def parse_requirements(filename):\n lineiter = (line.strip() for line in open(filename))\n return [line for line in lineiter if line and not line.startswith(\"#\")]",
"title": ""
},
{
"docid": "6599054120d62e811f0480125a09993a",
"score": "0.5331465",
"text": "def parse_input(filename, url):\n print(\"Processing\", filename, \"\\n\")\n try:\n if os.path.isdir(filename): # if it's a directory\n files = os.listdir(filename)\n for file in files:\n new_path = os.path.join(filename,file)\n parse_input(new_path, url)\n return\n\n # check for known file extensions\n file, ext = os.path.splitext(filename)\n if ext == \".json\": \n parse_json(filename, url)\n\n elif ext == \".csv\":\n parse_csv(filename, url)\n\n else: # otherwise, log and move on\n print(\"Not a supported file: \" +filename+ \"\\n\")\n except:\n print(\"Unexpected error:\", sys.exc_info()[0])",
"title": ""
},
{
"docid": "af32075ddb1a582c17a7aa298e306fb0",
"score": "0.5315196",
"text": "def parse_package_page(files, pk_version, index_url, cache_path):\n\n result = []\n for file in files.find_all(\"a\"):\n # Skip if not a whl file\n if not file.string.endswith(\"whl\"):\n continue\n\n file_name_chunks = file.string.split(\"-\")\n\n package_version = file_name_chunks[1]\n package_name = file_name_chunks[0]\n python_version = file_name_chunks[2][2:]\n platform = file_name_chunks[4].split(\".\")[0]\n implementation = file_name_chunks[2][:2]\n abi = file_name_chunks[3]\n\n if package_version != pk_version:\n continue\n if python_version == \"26\" or python_version == \"34\" or python_version == \"35\":\n continue\n if \"macosx\" in platform:\n continue\n if \"win_amd64\" in platform and python_version != \"39\":\n continue\n\n # Cryptography builds for Linux target Python 3.4+ but the only existing\n # build is labeled 3.4 (the lowest version supported).\n # Expand the abi3 tag here. e.g. cp34 abi3 is expanded to cp34m, cp35m, cp36m, cp37m\n # https://cryptography.io/en/latest/faq/#why-are-there-no-wheels-for-my-python3-x-version\n if abi == \"abi3\":\n for actual_version in range(int(python_version), 38):\n actual_version = str(actual_version)\n actual_abi = \"\".join([implementation, actual_version, \"m\"])\n info = {\n \"platform\": platform,\n \"implementation\": implementation,\n \"version\": actual_version,\n \"abi\": actual_abi,\n }\n result.append(info)\n else:\n info = {\n \"platform\": platform,\n \"implementation\": implementation,\n \"version\": python_version,\n \"abi\": abi,\n }\n result.append(info)\n\n install_package(package_name, pk_version, index_url, result, cache_path)",
"title": ""
},
{
"docid": "592a0f3201ad5b54da583c80d50ab47b",
"score": "0.5304188",
"text": "def parseFile(filename):\n\tfilepath = root + filename\n\ttry:\n\t\tif filename.endswith('.txt'):\n\t\t\tfile = open(filepath)\n\t\t\treturn file.read().splitlines()\n\t\telif filename.endswith('.csv'):\n\t\t\treader = csv.reader(open(filepath))\n\t\t\tresult = {}\n\t\t\tfor row in reader:\n\t\t\t key = row[0]\n\t\t\t result[key] = row[1]\n\t\t\treturn result\n\texcept FileNotFoundError:\n\t\topen(filepath,\"w+\").close()",
"title": ""
},
{
"docid": "e6bb291d0c1050811b9dcab12f2315f5",
"score": "0.53000575",
"text": "def parsefile(self, filename):\n g = dict()\n return g",
"title": ""
},
{
"docid": "2acf56483cd6625ca12f778a799bfa1f",
"score": "0.5293795",
"text": "def parse(self, filename):\r\n log.debug('Starting parse of %s', filename)\r\n with open(filename) as f:\r\n read = json.loads(f.read())\r\n\r\n self.parse_components(read.get('components'))\r\n self.parse_component_instances(read.get('component_instances'))\r\n if read.get('shapes') is not None:\r\n self.parse_sch_shapes(read.get('shapes'))\r\n self.parse_design_attributes(read.get('design_attributes'))\r\n self.parse_nets(read.get('nets'))\r\n self.parse_version(read.get('version'))\r\n\r\n # layout aspects\r\n self.parse_layer_options(read.get('layer_options'))\r\n self.parse_trace_segments(read.get('trace_segments'))\r\n self.parse_layout_objects(read.get('gen_objs'))\r\n self.parse_paths(read.get('paths'))\r\n self.parse_pours(read.get('pours'))\r\n self.parse_pcb_text(read.get('text'))\r\n\r\n return self.design",
"title": ""
},
{
"docid": "452fb45a344976c57c4fa857b9a8b248",
"score": "0.52927643",
"text": "def get_packages():\n line = split_words(run('rospack','list'))\n packages = [{'name': name, 'path':path} for name, path in line]\n return packages",
"title": ""
},
{
"docid": "11d106b34a06a5a58a2c185e4b9890b1",
"score": "0.529203",
"text": "def parse(self, file):\n raise NotImplementedError",
"title": ""
},
{
"docid": "d0f8e0568cc908d38a47afd2e3f55aff",
"score": "0.52908236",
"text": "def testable_packages(filename, prefix):\n pkgdir = os.path.join(prefix, 'pkgs')\n paths = []\n yaml = YAML(typ='safe')\n\n for record in dmfile(filename):\n # Reconstruct ${package}-${version} format (when possible)\n pattern = f\"{record['name']}-\"\n if record['version']:\n pattern += record['version']\n pattern += '*'\n\n # Record path to extracted package\n path = ''.join([x for x in glob(os.path.join(pkgdir, pattern))\n if os.path.isdir(x)])\n paths.append(path)\n\n for root in paths:\n info_d = os.path.join(root, 'info')\n recipe_d = os.path.join(info_d, 'recipe')\n git_log = os.path.join(info_d, 'git')\n\n if not os.path.exists(git_log):\n continue\n\n git_log_data = open(git_log).readlines()\n\n if not git_log_data:\n continue\n\n with open(os.path.join(recipe_d, 'meta.yaml')) as yaml_data:\n source = yaml.load(yaml_data)['source']\n\n if not isinstance(source, dict):\n continue\n\n repository = source['git_url']\n head = git_log_data[1].split()[1]\n yield dict(repo=repository, commit=head)",
"title": ""
},
{
"docid": "3b31af5ad6d7270ec4b68d9d43e5a07f",
"score": "0.5283741",
"text": "def _parse_build_names(file_path: Path) -> List[ReleaseInfo]:\n file_name = file_path.name\n is_nightly = NIGHTLY_NAME_SUFFIX.casefold() in file_name.casefold()\n\n if is_nightly:\n version = \"nightly\"\n else:\n version = file_path.with_suffix(\"\").name\n\n formatted_version = None if is_nightly else format_release_str(version)\n\n with open(file_path) as handle:\n # Strip and get non-empty lines\n parsed_file_names = [line.strip() for line in handle if line.strip()]\n\n # All non-nightly files have the date as their first line and they will all be on the same date\n if not is_nightly:\n release_date = datetime.date.fromisoformat(parsed_file_names.pop(0))\n # For an official release we have a single ReleaseInfo and multiple packages\n package_details: List[PackageDetails] = []\n for executable_name in parsed_file_names:\n package_details.append(\n PackageDetails(os_details=get_os(executable_name, version),\n download_url=get_download_url(executable_name, version, is_nightly)))\n releases = [\n ReleaseInfo(date=release_date,\n version=version,\n formatted_version=formatted_version,\n package_details=package_details)\n ]\n else:\n # For nightlies we have separate releases per file as sometimes not all builds complete\n releases: List[ReleaseInfo] = []\n for filename in parsed_file_names:\n releases.append(\n ReleaseInfo(date=get_nightly_date(filename),\n version=version,\n package_details=[\n PackageDetails(os_details=get_os(filename, version),\n download_url=get_download_url(\n filename, version, is_nightly))\n ]))\n\n return releases",
"title": ""
},
{
"docid": "0601a121b5808d8d35cd6ff7e2ab624d",
"score": "0.52721345",
"text": "def parse_requirements_file(*args, **kwargs):\n return (\n Parser.parser_map.get(Config.parser, PipParser)\n .parse_requirements_file(*args, **kwargs)\n )",
"title": ""
},
{
"docid": "07613738cba0ba6d09b6b5215efd450b",
"score": "0.526506",
"text": "def LoadPackageFile(self, package_file, skip_missing=False):\n archive_names = None\n self._archive_list = []\n\n # TODO(dyen): Support old format temporarily when it was a list of archives.\n if isinstance(package_file, list) or isinstance(package_file, dict):\n if isinstance(package_file, list):\n self._package_version = 0\n archive_list = package_file\n else:\n self._package_version = package_file[PACKAGE_KEY_VERSION]\n archive_list = package_file[PACKAGE_KEY_ARCHIVES]\n\n if archive_list:\n if isinstance(archive_list[0], archive_info.ArchiveInfo):\n # Setting a list of ArchiveInfo objects, no need to interpret JSON.\n self._archive_list = archive_list\n else:\n # Assume to be JSON.\n for archive_json in archive_list:\n archive = archive_info.ArchiveInfo(archive_info_file=archive_json)\n self._archive_list.append(archive)\n\n elif isinstance(package_file, str) or isinstance(package_file, unicode):\n package_data = ReadPackageFile(package_file)\n self._package_version = package_data[PACKAGE_KEY_VERSION]\n archive_names = package_data[PACKAGE_KEY_ARCHIVES]\n\n package_name = GetLocalPackageName(package_file)\n archive_dir = os.path.join(os.path.dirname(package_file), package_name)\n for archive in archive_names:\n arch_file = archive + '.json'\n arch_path = os.path.join(archive_dir, arch_file)\n if not os.path.isfile(arch_path):\n if not skip_missing:\n raise error.Error(\n 'Package (%s) points to invalid archive file (%s).' %\n (package_file, arch_path))\n archive_desc = archive_info.ArchiveInfo(name=archive)\n else:\n archive_desc = archive_info.ArchiveInfo(archive_info_file=arch_path)\n self._archive_list.append(archive_desc)\n else:\n raise error.Error('Invalid load package file type (%s): %s.' %\n (type(package_file), package_file))",
"title": ""
},
{
"docid": "a93ee9340ab0b2d51f99810977c842a6",
"score": "0.5262331",
"text": "def parse_file(self, path):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "8551e97b63b6b09cf974cd7f0cf372ec",
"score": "0.5239013",
"text": "def parse_file(filename):\n try:\n fname = open(filename, 'r')\n except:\n logger.error('File do no exists %s' % filename)\n exit()\n\n with fname as f:\n for line in f:\n if line.strip()[0] == \"#\":\n continue\n\n command = line.strip().upper().split(' ')\n\n yield (command[0], command[1:])",
"title": ""
},
{
"docid": "3d8696f015bc04d6db571cce9115d881",
"score": "0.5235695",
"text": "def parse_requirements(filename):\n lineiter = (line.strip() for line in open(filename))\n return [line for line in lineiter if line and not line.startswith('#')]",
"title": ""
},
{
"docid": "a934ef8abb8d532f6c013632bec09d28",
"score": "0.5232518",
"text": "def parse_filename(self, fname):\n\n m = re.match(self.pattern, fname, re.IGNORECASE)\n if m is not None:\n m = m.groupdict()\n if m[\"name\"] is None:\n m[\"name\"] = \"Unknown\"\n if m[\"date\"] is None:\n m[\"date\"] = \"000101\"\n else:\n m[\"date\"] = m[\"date\"].replace(\" \", \"0\")\n if m[\"time\"] is None:\n m[\"time\"] = \"000000\"\n else:\n m[\"time\"] = m[\"time\"].replace(\" \", \"0\")\n if m[\"file\"] is None:\n m[\"file\"] = \"unknown\"\n else:\n m[\"file\"] = m[\"file\"].lower()\n\n return m",
"title": ""
},
{
"docid": "b046b2a6fe1d59282e975821ca3e1f33",
"score": "0.52197796",
"text": "def parse_file(self):\n # Read in the entire file\n contents = Path(self.file).read_text()\n\n # Find the name of the network\n self.parse_network(contents)\n\n # Find all the variables\n self.parse_variables(contents)\n\n # Find all the probabilities\n self.parse_probabilities(contents)",
"title": ""
},
{
"docid": "d5e3e308024390b095850d07f176ee59",
"score": "0.5206896",
"text": "def handle_package(self, response):\n\n package_path = response.url.replace('file://', '')\n self.log('Handling package: %s' % package_path, logging.INFO)\n\n # extract the name of the package without extension\n filename = os.path.basename(response.url).rstrip(\"A.tar\").rstrip('.zip')\n\n # create temporary directory to extract zip packages:\n target_folder = mkdtemp(prefix=filename + \"_\", dir=ELSEVIER_UNPACK_FOLDER)\n\n # uncompress files to temp directory\n files = uncompress(package_path, target_folder)\n\n self.log('Files uncompressed to: %s' % target_folder, logging.INFO)\n\n for f in files:\n if 'dataset.xml' in f:\n return self.parse_dataset(target_folder, filename, package_path, f)",
"title": ""
},
{
"docid": "765fa3762c8c744ce80a71c37d7bc430",
"score": "0.5203204",
"text": "def parse_list(self, this_list, ignore_packages=None):\n packages = defaultdict(list)\n # TO-DO pass ignored and error to result\n # packages_ignored = ()\n packages_error = ()\n for item in this_list:\n # sanitize\n ignored = False\n if \"metadata expiration\" in item:\n continue\n try:\n nvr = utils.splitFilename(item)\n # <lon> labelcompare functions expect (epoch, version, release)\n epoch = nvr[3]\n version = nvr[1]\n release = nvr[2]\n if ignore_packages:\n search_term = \"{}\".format(nvr[0])\n for ignore in ignore_packages:\n if \".*\" in ignore:\n r = re.compile(ignore)\n match = r.match(search_term)\n if match:\n logging.info(\n \"Ignoring Package: {}\".format(search_term))\n ignored = True\n else:\n if ignore in search_term:\n logging.info(\n \"Ignoring Package: {}\".format(search_term))\n ignored = True\n if not ignored:\n packages[nvr[0]].append((epoch, version, release))\n except Exception as e:\n logging.error(e)\n logging.warning(\n \"error found getting package name/version for {}\"\n \" OR the ignore package regex {}\".format(str(item), ignore))\n is_alpha = re.match('[a-zA-Z]', nvr[0])\n if not is_alpha:\n packages_error.add(item)\n logging.warning(\"package name does not meet criteria \"\n \"for item: {}\".format(item))\n continue\n continue\n return packages",
"title": ""
},
{
"docid": "1e5beacfa145757fb877c255cac880c6",
"score": "0.51924807",
"text": "def parse_config(file_path):\n pass",
"title": ""
},
{
"docid": "708380378263e95599952e7aafb3910e",
"score": "0.51904434",
"text": "def ReadPackageFile(package_file):\n with open(package_file, 'rt') as f:\n json_value = json.load(f)\n\n # TODO(dyen): Support old format temporarily when it was a list of archives.\n if isinstance(json_value, list):\n return { PACKAGE_KEY_ARCHIVES: json_value, PACKAGE_KEY_VERSION: 0 }\n else:\n return json_value",
"title": ""
},
{
"docid": "f0cfd78c74d8e44194b320f40ee0fbb3",
"score": "0.518023",
"text": "def unmarshal(self, filename):\n rule_a = re.compile(\n r\"^(.+) -> (.+) if the (.+) of words i([+-]\\d+)...i([+-]\\d+) is '(.+)'$\",\n re.UNICODE,\n )\n rule_b = re.compile(\n r\"^(.+) -> (.+) if the (.+) of the (.+) word is '(.+)'$\", re.UNICODE\n )\n\n # erase any previous rules\n self._rules = []\n\n # load from file\n handler = file(filename, \"r\")\n lines = handler.readlines()\n handler.close()\n\n # remove '\\n's, even though $ would catch them\n lines = [line[:-1] for line in lines]\n # remove empty lines\n lines = [line for line in lines if len(line) > 0]\n\n # parse rules\n for rule in lines:\n match = re.match(rule_b, rule)\n if match:\n groups = list(match.groups())\n if groups[3] == \"preceding\":\n groups.pop(3)\n groups.insert(3, \"-1\")\n groups.insert(4, \"-1\")\n else:\n groups.pop(3)\n groups.insert(3, \"1\")\n groups.insert(4, \"1\")\n else:\n match = re.match(rule_a, rule)\n groups = list(match.groups())\n\n conditions = (int(groups[3]), int(groups[4]), groups[5])\n if groups[2] == \"tag\":\n r = ProximateTagsRule(groups[0], groups[1], conditions)\n else:\n r = ProximateWordsRule(groups[0], groups[1], conditions)\n\n self._rules.append(r)",
"title": ""
},
{
"docid": "61396350116d480ea062fbe6372ef107",
"score": "0.51705",
"text": "def LoadPackageData(path):\n data_list = []\n with open(path, 'r') as f:\n reader = csv.reader(f)\n for row in reader:\n data_list.append(Package(row[0], row[1], row[2], row[3], row[4],\n row[5], row[6], row[7]))\n\n return data_list",
"title": ""
},
{
"docid": "f6056ce5c78261ea82ac23bc039eb74e",
"score": "0.5151144",
"text": "def _parse_file(self, filename, file_url):\n file_set = set()\n with open(filename, 'r') as plug_in:\n lines = plug_in.readlines()\n for plug in self.plugins:\n res = plug.run(lines)\n if len(res) > 0:\n for i in res:\n i = file_url + i\n file_set.add(i)\n return file_set",
"title": ""
},
{
"docid": "1112d3093286a1222fe0a95669d8110e",
"score": "0.51428115",
"text": "def parse_requirements(filename):\n with open(filename, 'r') as f:\n lineiter = (line.strip() for line in f.readlines())\n return [line for line in lineiter if line and not line.startswith(\"#\")]",
"title": ""
},
{
"docid": "b68e3ee59d6e6f1dbedb6da6d9a3ad3f",
"score": "0.5141832",
"text": "def parse_xml_file(self, filename):\n from imposm.parser.xml.multiproc import XMLMultiProcParser\n\n with fileinput(filename) as input:\n return self._parse(input, XMLMultiProcParser)",
"title": ""
},
{
"docid": "137017c66d8fec8e664e8e4cc3e66ca8",
"score": "0.51341844",
"text": "def parse_file(self, filename: str) -> str:\n self.add_path(os.path.dirname(os.path.abspath(filename)))\n self.includes = [filename]\n\n with open(filename) as f:\n data = f.read()\n\n # TODO: No stream wrapper here? I don't really need it \n # but it was in the example implementations.\n output = io.StringIO()\n self.parse(data)\n self.write(output)\n result = output.getvalue()\n output.close()\n return result",
"title": ""
},
{
"docid": "1713330e52f4d2ab91655ed48b2821ef",
"score": "0.5130838",
"text": "def full_parse(self):\n match = re.search(self.FILE_REGEX, self.filename)\n if not match:\n raise ValueError('Invalid {} filename: {}'.format(self.__class__.__name__,\n self.filename))\n else:\n return match.groups()",
"title": ""
},
{
"docid": "09f903f9b47b3f4c8572db9104660101",
"score": "0.5129876",
"text": "def parseFile(filename):\n people = []\n f = file(filename)\n for line in f:\n pieces = line.split(':')\n name = pieces[0].strip()\n if name:\n priorities = pieces[1].strip().split(',')\n for i in range(len(priorities)):\n priorities[i] = priorities[i].strip()\n people.append((name,priorities))\n f.close()\n return people",
"title": ""
},
{
"docid": "cd4c206b91d57d4fb31dd94c80201159",
"score": "0.5118317",
"text": "def get_parser(file):\n\tif file[-4:] == \".png\":\n\t\tparser = pngParse(file)\n\telif file[-4:] == \".bmp\":\n\t\tparser = bmpParse(file)\n\telif file[-4:] == \".gif\":\n\t\tparser = gifParse(file)\n\telif file[-4:] == \".tif\" or file[-4:] == \"tiff\":\n\t\tparser = tiffParse(file)\n\telif file[-4:] in [\".pgm\", \".ppm\"]:\n\t\tparser = pgmParse(file)\n\telif \"genericParse\" in globals()\n\t\tparser = genericParse(file)\n\t\twarn(\"Using generic parser for \"+file[-4:])\n\telse: # Catch when PIL or numpy not installed\n\t\twarn(\"Couldn't parse this file at all\", 2)\n\treturn parser",
"title": ""
},
{
"docid": "65e31c99308f84c95cd4098ed29ba5be",
"score": "0.511791",
"text": "def load(klass, filename):\n p = PipfileParser(filename=filename)\n pipfile = klass(filename=filename)\n pipfile.data = p.parse()\n return pipfile",
"title": ""
},
{
"docid": "c3aade5623e5edb18716aa9690b64556",
"score": "0.5114332",
"text": "def parse_config_file(filename):\n from rbtools.config.loader import parse_config_file as _parse_config_file\n\n return _parse_config_file(filename)",
"title": ""
},
{
"docid": "4305140f1a438ad32ff05478eb3de62d",
"score": "0.5113209",
"text": "def __add_package(self, directory, filename, packages):\n file_ = utils.absolute_file_path(filename, directory)\n package = self.system.packager.package(file_)\n\n #for now skip source packages let's deal with only with binary packages\n if package.is_source():\n return\n\n if not self.package_for_arch(package):\n return\n\n if package.name in packages and \\\n package <= packages[package.name]:\n return\n\n packages[package.name] = package",
"title": ""
},
{
"docid": "f8d89027e3aef2364fb69ebca9d8ee9c",
"score": "0.5111192",
"text": "def GetPackageList(path):\n\n pattern = re.compile(\"^[A-Za-z0-9\\-\\_]*.m?pkg$\", re.IGNORECASE)\n packages = []\n for package in os.listdir(path):\n if pattern.match(package):\n packages.append(package)\n packages.sort()\n return packages",
"title": ""
},
{
"docid": "f29e8ad13c84c383ef42b291b0f38a84",
"score": "0.5109514",
"text": "def parse_requirements(\n filename, # type: str\n finder=None, # type: Optional[PackageFinder]\n comes_from=None, # type: Optional[str]\n options=None, # type: Optional[optparse.Values]\n session=None, # type: Optional[PipSession]\n constraint=False, # type: bool\n wheel_cache=None, # type: Optional[WheelCache]\n use_pep517=None # type: Optional[bool]\n):\n # type: (...) -> Iterator[InstallRequirement]\n if session is None:\n raise TypeError(\n \"parse_requirements() missing 1 required keyword argument: \"\n \"'session'\"\n )\n\n _, content = get_file_content(\n filename, comes_from=comes_from, session=session\n )\n\n lines_enum = preprocess(content, options)\n\n for line_number, line in lines_enum:\n req_iter = process_line(line, filename, line_number, finder,\n comes_from, options, session, wheel_cache,\n use_pep517=use_pep517, constraint=constraint)\n for req in req_iter:\n yield req",
"title": ""
},
{
"docid": "6bcae7febfbd790b301c393db5b7d2d2",
"score": "0.5105408",
"text": "def ParsePkginfo(lines):\n d = {}\n for line in lines:\n try:\n # Can't use split, because there might be multiple '=' characters.\n line = line.strip()\n # Skip empty and commented lines\n if not line: continue\n if line.startswith(\"#\"): continue\n var_name, var_value = line.split(\"=\", 1)\n d[var_name] = var_value\n except ValueError, e:\n raise PackageError(\"Can't parse %s: %s\" % (repr(line), e))\n return d",
"title": ""
},
{
"docid": "6bcae7febfbd790b301c393db5b7d2d2",
"score": "0.5105408",
"text": "def ParsePkginfo(lines):\n d = {}\n for line in lines:\n try:\n # Can't use split, because there might be multiple '=' characters.\n line = line.strip()\n # Skip empty and commented lines\n if not line: continue\n if line.startswith(\"#\"): continue\n var_name, var_value = line.split(\"=\", 1)\n d[var_name] = var_value\n except ValueError, e:\n raise PackageError(\"Can't parse %s: %s\" % (repr(line), e))\n return d",
"title": ""
},
{
"docid": "0daff9aaa962acdb62c10392050fc700",
"score": "0.5098932",
"text": "def _load(self, filename):\n if filename[-4:].lower() == '.npz':\n data = np.load(filename)\n self.paths = [data[k] for k in sorted(data.keys())]\n data.close()\n elif filename[-4:].lower() == '.svg':\n self.paths = []\n self._add_from_svg(filename)\n else:\n raise RuntimeError('Bad file suffix, npz or svg expected.')",
"title": ""
},
{
"docid": "d339532c4d6a008d31c18838691e8b18",
"score": "0.50980526",
"text": "def parse(file_path: str) -> List[Dict[str, Union[str, Sequence[str]]]]:\n try:\n with open(file_path) as rfile:\n deps = [line.rstrip(\"\\n\") for line in rfile]\n return [\n {\"version\": RequirementsFile.clean_dependency(dep), \"hashes\": []}\n for dep in deps\n if not any(\n [dep.strip().startswith(s) for s in INVALID_REQUIREMENTS_LINES]\n )\n ]\n except OSError as ex:\n raise OchronaFileException(f\"OS error when parsing {file_path}\") from ex",
"title": ""
},
{
"docid": "4a9e12b566380b6ea43faeeaac692631",
"score": "0.50866497",
"text": "def parse_file(filename, start):\n with open(filename) as fd:\n events = parse_lines(fd, start)\n return events",
"title": ""
},
{
"docid": "d2a245ebc1b93c92ae15151414302a40",
"score": "0.5079447",
"text": "def read_cfg_file(filename):\n rval = {'root': None,\n 'packages': {}}\n with open(filename, 'r') as rbl:\n cpkg = None\n for line in rbl:\n # throw away any comment at the end of the line\n # the '#' must be followed by whitespace to be a valid comment\n line = re.sub(r\"\\s*#\\s.*$\", \"\", line)\n # ignore blank lines\n if re.match(r\"^\\s*$\", line):\n continue\n (key, val) = line.split()\n if ':' in key:\n msg = \"Syntax error in config file: colons not allowed\"\n raise pyppi_error(msg)\n key = key.strip()\n val = val.strip()\n if key == 'root':\n if rval['root'] is None:\n rval['root'] = tbx.expand(val)\n else:\n raise pyppi_error(\"root was already set\")\n elif key == 'package':\n rval['packages'][val] = []\n cpkg = rval['packages'][val]\n elif key == 'version':\n cpkg.append({'version': val})\n elif key == 'url':\n cpkg[-1]['url'] = val\n elif key == 'minpy':\n cpkg[-1]['minpy'] = val\n return rval",
"title": ""
},
{
"docid": "6b34d11c2df925c09521cb44e74a52c7",
"score": "0.5075152",
"text": "def file_parser(file):\n\twith open(file, \"r\") as vmf:\n\n\t\tindent = 0\n\t\tprevious_line = \"versioninfo\\n\" # We only know it's a category the next line (the curly brackets open)\n\t\textracted = []\n\n\t\tfor line in vmf.readlines()[1:]:\n\t\t\tif \"}\" in line:\n\t\t\t\tindent -= 1\n\t\t\t\tif indent == 0: # If indent is not 0 we know it's a child category and not a main category\n\t\t\t\t\textracted.append(t)\n\t\t\t\tcontinue\n\n\t\t\tif \"{\" in line:\n\t\t\t\tif indent > 0: # If indent is not 0 we know it's a child category and not a main category\n\t\t\t\t\t# Open curly brackets ALWAYS follow a category, so we know the previous line is the category name\n\t\t\t\t\tt.add_child(previous_line, indent)\n\t\t\t\telse:\n\t\t\t\t\tt = TempCategory(previous_line, indent) # This is a main category (not a child category)\n\t\t\t\tindent += 1\n\t\t\t\tcontinue\n\n\t\t\tif \"\\\"\" in line: t.add_line(line, indent) # ALL lines with data have double quotes in them\n\n\t\t\tprevious_line = line\n\n\tfor c in extracted:\n\t\t# clean_up is a recursive function we only need to call it on the main categories\n\t\tc.clean_up()\n\n\treturn extracted # This is used when creating a VMT class",
"title": ""
}
]
f5cf5fc9fafde2916e90572ab278c0bf
Creates a MongoDB filter for new documents. By "new", we mean documents in this Store that were last updated later than any document in targets.
[
{
"docid": "f6226e52defd180c7c9cbc3640c0ea6b",
"score": "0.59591556",
"text": "def lu_filter(self, targets):\n if isinstance(targets, Store):\n targets = [targets]\n\n lu_list = [t.last_updated for t in targets]\n return {self.lu_field: {\"$gt\": self.lu_key[1](max(lu_list))}}",
"title": ""
}
]
[
{
"docid": "202637605fe0d6c4cd9b4f70374b6863",
"score": "0.54216784",
"text": "def changesWithFilter(self, filter, limit=1000, since=-1):\n if since < 0:\n since = self.last_seq\n data = self.get('/%s/_changes?limit=%s&since=%s&filter=%s' % (self.name, limit, since, filter))\n self.last_seq = data['last_seq']\n return data",
"title": ""
},
{
"docid": "0a079424f872943400b8498eb6dbef9f",
"score": "0.5367098",
"text": "def get_new_by_field(self, old, new, deps, fields):\n\t\tfound_objs = self.get_new_objects(old, new, deps)\n\t\tnew_objs = []\n\t\t\n\t\tfor o in found_objs:\n\t\t\texist = False\n\t\t\tfor o2 in old[deps[0]]['data']:\n\t\t\t\tsimilar = True\n\t\t\t\tfor f in fields:\n\t\t\t\t\tif not o[f] == o2[f]:\n\t\t\t\t\t\tsimilar = False\n\t\t\t\t\t\tbreak\n\t\t\t\tif similar:\n\t\t\t\t\texist = True\n\t\t\t\t\tbreak\n\t\t\tif not exist:\n\t\t\t\tnew_objs.append(o)\n\n\t\treturn new_objs",
"title": ""
},
{
"docid": "12173978af170f5e00e2250e65104765",
"score": "0.5290616",
"text": "def _fixer_apply_fix_to_all(self,old,new):\n col = self.collection\n count = col.find({\"sender\":old}).count()\n col.update({\"sender\":old},{\"$set\":{\"sender\":new}},safe=True,multi=True)\n debug(\"Fixed %d documents\\n(%s -> %s)\\n\" % (count, old, new))",
"title": ""
},
{
"docid": "6009b4d6bc1202aad70aed76cf0afc4e",
"score": "0.5076374",
"text": "def only_new_news(self):\n last_date = self._database_client.get_last_posted_date()\n last_news = self.news\n if last_date:\n # filter only new news by posted date\n last_news = [news for news in self.news if news.posted > last_date]\n return last_news",
"title": ""
},
{
"docid": "39b347e690703728f063d45dea3ff7c2",
"score": "0.49514052",
"text": "def index_all_documents(self, new_index):\n collection = self.db_client[self.config['collection']]\n results = collection.find(\n self.config['collectionFilter'],\n list(self.config['fields'].keys())\n ).sort('createdAt', pymongo.DESCENDING).limit(10)\n ct = 0\n for result in results:\n try:\n self.index_document(result, new_index)\n ct += 1\n # TODO: what are the possible specific exceptions\n except Exception as exc:\n logging.warning(\n 'Failed to create document for %s. Error: %s',\n result['_id'], exc\n )\n logging.info('Index all complete: {} docs'.format(ct))",
"title": ""
},
{
"docid": "87e69021f8648dd1bc041043937c66f4",
"score": "0.4876476",
"text": "def trackit(self, *args, **kwargs):\n\n t = Tracking.objects.filter(**kwargs)\n # update existing...refresher..\n if len(t) > 0:\n t = t[0]\n if (datetime.now() - t.created).seconds <= 300:\n setattr(t, 'counter', (getattr(t, 'counter', 0) + 1))\n t.save()\n else:\n # or add new record\n kwargs['created'] = datetime.now()\n t = Tracking(**kwargs)\n t.save()\n else:\n # or add new record\n kwargs['created'] = datetime.now()\n t = Tracking(**kwargs)\n t.save()",
"title": ""
},
{
"docid": "deefbcea0dfdc71205b85ec705ed2f10",
"score": "0.48670688",
"text": "def get_new_objects(self, old, new, deps):\n\t\thashable_united_old = set([])\n\t\thashable_united_new = set([])\n\t\tfor dep in deps:\n\t\t\thashable_united_old = hashable_united_old | set([hashabledict(d) for d in old[dep]['data']])\n\t\t\thashable_united_new = hashable_united_new | set([hashabledict(d) for d in new[dep]['data']])\n\t\treturn list(set(hashable_united_new) - set(hashable_united_old))",
"title": ""
},
{
"docid": "533de09a0520ad27ff81cbb895a3a677",
"score": "0.47890252",
"text": "def is_new(self):\n if self.first_extraction_date:\n job_age = datetime.datetime.utcnow() - self.first_extraction_date\n job_age_days = job_age.days\n return job_age_days < settings.NUM_DAYS_NEW_JOBS\n else:\n return False",
"title": ""
},
{
"docid": "7787ac955e45985d957ac30742d14c1f",
"score": "0.4786153",
"text": "def pull_changes(self, fields=None, add_query=None):\n\n query = self._get_query_dict()\n query.update(add_query or {})\n\n db_document = self.collection().find_one(query, projection=fields)\n if not db_document:\n raise self.NotFound(self.id)\n\n if not fields:\n fields = set(list(db_document) + list(self))\n\n db_document = MongoObject(db_document)\n\n for field in fields:\n try:\n db_document[field]\n except KeyError:\n try:\n self[field]\n except KeyError:\n pass\n else:\n del self[field]\n else:\n self[field] = db_document[field]",
"title": ""
},
{
"docid": "175e7bd301f5977ead3c8ed6296e976c",
"score": "0.47576076",
"text": "def filter_documents(self, filters: Optional[Dict[str, Any]] = None) -> List[Document]:",
"title": ""
},
{
"docid": "313f7f5ef5782ce3695b2e6778ebc055",
"score": "0.47341478",
"text": "def groups_with_new_post(self):\n\t\treturn GroupSubscriptions.objects.filter(person=self , isNewPublication=True)",
"title": ""
},
{
"docid": "9e5bd37f0b7ca63b4e57da6ad41eb32b",
"score": "0.46846232",
"text": "def get_new_timers(self, old, new, deps):\n\t\treturn self.get_new_by_field(old, new, deps, ['offset'])",
"title": ""
},
{
"docid": "f81002333fb2cde822ac2e5429b7ca9b",
"score": "0.46810097",
"text": "def new_ids(self):\n return self._ids(self.new_records)",
"title": ""
},
{
"docid": "f8ddb706130147e198aee3f2674c3399",
"score": "0.4670848",
"text": "def merge_none_updated_fields(self,\n new_document,\n old_document):\n pass",
"title": ""
},
{
"docid": "db4e64c349177f254e1de132785e1af2",
"score": "0.46506092",
"text": "def get_recent(self, model_cls, destination_hostname=None):\n source_instances = model_cls.objects.none()\n if not destination_hostname:\n destination_hostname = socket.gethostname()\n options = self.get_last_modified_options(model_cls)\n if options:\n qset = Q()\n for dct in options:\n qset.add(Q(**dct), Q.OR)\n source_instances = model_cls.objects.using(self.get_using_source()).filter(qset).order_by('id')\n else:\n source_instances = model_cls.objects.using(self.get_using_source()).all().order_by('id')\n return source_instances",
"title": ""
},
{
"docid": "573e97a11c363632a3dced1701909054",
"score": "0.46124834",
"text": "def new_or_changed(self):\n # return set(k for k, v in self.current_dict.items()\n # if k not in self.past_keys or v != self.past_dict[k])\n return self.added().union(self.changed())",
"title": ""
},
{
"docid": "b566f0895ec60e1b3318413c370224cf",
"score": "0.46086276",
"text": "def filter_modified_since(request, objects):\n since = isoparse(request.GET.get(\"changes-since\"))\n if since:\n modified_objs = objects.filter(updated__gte=since)\n if not modified_objs:\n raise faults.NotModified()\n return modified_objs\n else:\n return objects.filter(deleted=False)",
"title": ""
},
{
"docid": "700950607e364a6ddd4494c9e9a693e9",
"score": "0.45968056",
"text": "def add(self, name, query, **kwargs):\n obj = models.Filter({\"name\": name, \"query\": query}, self.api)\n obj.temp_id = obj[\"id\"] = self.api.generate_uuid()\n obj.data.update(kwargs)\n self.state[self.state_name].append(obj)\n cmd = {\n \"type\": \"filter_add\",\n \"temp_id\": obj.temp_id,\n \"uuid\": self.api.generate_uuid(),\n \"args\": {key: obj.data[key] for key in obj.data if key != \"id\"},\n }\n self.queue.append(cmd)\n return obj",
"title": ""
},
{
"docid": "36828dc75c451b7b9dc15db2faa51522",
"score": "0.45955482",
"text": "def new_ids(self):\n return self._ids(\"new\")",
"title": ""
},
{
"docid": "45c4d7d7b73870cc33323fc001dd3f22",
"score": "0.4589334",
"text": "def check_last_modified(self, queryset):\n modified = self.request.META.get('HTTP_IF_MODIFIED_SINCE')\n last_count = self.request.META.get('HTTP_X_COUNT')\n if modified and last_count and last_count.isdigit():\n\n # Check to see if any records have been updated since the last query\n kw = {}\n kw['%s__gt' % self.audit_field] = date_parser.parse(modified)\n if queryset.filter(**kw).exists():\n return\n\n # Check to make sure the total is the same (no deletions)\n last_count = int(last_count)\n if queryset.count() != last_count:\n return\n raise NotModified",
"title": ""
},
{
"docid": "5e6318c702e79cd23260f82b3b476026",
"score": "0.45117402",
"text": "def test_Collection_init_collection_filter(coll_id, start_date, end_date):\n # The target collection ID should be removed from the collections lists\n args = default_coll_args()\n args['collections'] = [coll_id]\n args['start_date'] = start_date\n args['end_date'] = end_date\n assert model.Collection(**args).collections == []",
"title": ""
},
{
"docid": "27d50817b24f94405d1b01ceccb48c5e",
"score": "0.44911405",
"text": "def gen_news(self, target_period, old_data, new_data):\n return self.modelling.gen_news(self, target_period, old_data, new_data)",
"title": ""
},
{
"docid": "01cd57251ac1cf94e797910f49d83bbc",
"score": "0.4477826",
"text": "def process_record(self, new, old=None):\n new = super(Record, self).process_record(new, old)\n\n schema = self._collection.get('schema')\n settings = self.request.registry.settings\n schema_validation = 'experimental_collection_schema_validation'\n if not schema or not asbool(settings.get(schema_validation)):\n return new\n\n collection_timestamp = self._collection[self.model.modified_field]\n\n try:\n stripped = copy.deepcopy(new)\n stripped.pop(self.model.id_field, None)\n stripped.pop(self.model.modified_field, None)\n stripped.pop(self.model.permissions_field, None)\n stripped.pop(self.schema_field, None)\n jsonschema.validate(stripped, schema)\n except jsonschema_exceptions.ValidationError as e:\n try:\n field = e.path.pop() if e.path else e.validator_value.pop()\n except AttributeError:\n field = None\n raise_invalid(self.request, name=field, description=e.message)\n\n new[self.schema_field] = collection_timestamp\n return new",
"title": ""
},
{
"docid": "24f231586e50f5f4fd51541eb006e974",
"score": "0.44752893",
"text": "def get_events(source_index: str, dest_index: str):\n\n res = es.search(index=source_index, body={\"query\": {\"match_all\": {}}})\n source_hits = [hit[\"_source\"] for hit in res[\"hits\"][\"hits\"]]\n source_set = set([hit[\"collection_id\"] for hit in source_hits])\n source_drs_set = set([tuple(hit['drsId']) for hit in source_hits if hit.get('drsId')])\n\n res = es.search(index=dest_index, body={\"query\": {\"match_all\": {}}})\n dest_hits = [hit[\"_source\"] for hit in res[\"hits\"][\"hits\"]]\n dest_set = set([hit[\"collection_id\"] for hit in dest_hits])\n dest_drs_set = set([tuple(hit['drsId']) for hit in source_hits if hit.get('drsId')])\n\n added = dest_set - source_set\n removed = source_set - dest_set\n updated = dest_drs_set - source_drs_set\n\n # creation of record of events.\n events = []\n\n for additions in added:\n res = es.search(index=dest_index, body={\"query\": {\"term\": {'_id': additions}}})\n hit = res['hits']['hits'][0]['_source']\n events.append(\n {\n \"collection_id\": hit[\"collection_id\"],\n \"collection_title\": hit[\"title\"],\n \"action\": \"added\",\n \"datetime\": datetime.now().isoformat(),\n\n }\n )\n\n for removals in removed:\n res = es.search(index=source_index, body={\"query\": {\"term\": {'_id': removals}}})\n hit = res['hits']['hits'][0]['_source']\n events.append(\n {\n \"collection_id\": hit[\"collection_id\"],\n \"collection_title\": hit[\"title\"],\n \"action\": \"removed\",\n \"datetime\": datetime.now().isoformat(),\n\n }\n )\n\n for update in updated:\n res = es.search(index=dest_index, body={\"query\": {\"term\": {'drsId': update}}})\n collection_id = res['hits']['hits'][0]['_id']\n res = es.search(index=source_index, body={\"query\": {\"term\": {'_id': collection_id}}})\n if res['hits']['hits']:\n hit = res['hits']['hits'][0]['_source']\n events.append(\n {\n \"collection_id\": hit[\"collection_id\"],\n \"collection_title\": hit[\"title\"],\n \"action\": \"updated\",\n \"datetime\": datetime.now().isoformat(),\n\n }\n )\n\n events_json = json.dumps(events, indent=4)\n # print(events_json)\n r = requests.post(\n url,\n data=events_json,\n headers=headers,\n )\n print(f'HTTP Response {r.status_code}') # HTTP response",
"title": ""
},
{
"docid": "ac6c1e24bbc0f2c64c8b490968dcf572",
"score": "0.44725478",
"text": "def aging_filter(days):\n now: datetime = pendulum.now(tz='UTC')\n\n def mr_aging_filter(project: Project) -> Optional[Project]:\n if not project:\n return None\n\n project.merge_requests = list(filter(lambda mr: (now - mr.created_at).days > days,\n project.merge_requests))\n\n return project if project.merge_requests else None\n\n return mr_aging_filter",
"title": ""
},
{
"docid": "ace3bc9b0c7daf109a30118c8265a9c6",
"score": "0.44568688",
"text": "def ref_overwrite(oldref, newref, days):\n if len(oldref) != len(newref):\n return True\n oldref_minus_retrieved = [x for x in oldref if x.get_prop_nr() != retrieved_pid]\n newref_minus_retrieved = [x for x in newref if x.get_prop_nr() != retrieved_pid]\n if not all(x in oldref_minus_retrieved for x in newref_minus_retrieved):\n return True\n oldref_retrieved = [x for x in oldref if x.get_prop_nr() == retrieved_pid]\n newref_retrieved = [x for x in newref if x.get_prop_nr() == retrieved_pid]\n if (len(newref_retrieved) != len(oldref_retrieved)) or not (\n len(newref_retrieved) == len(oldref_retrieved) == 1):\n return True\n datefmt = '+%Y-%m-%dT%H:%M:%SZ'\n retold = list([datetime.strptime(r.get_value()[0], datefmt) for r in oldref if r.get_prop_nr() == retrieved_pid])[0]\n retnew = list([datetime.strptime(r.get_value()[0], datefmt) for r in newref if r.get_prop_nr() == retrieved_pid])[0]\n return (retnew - retold).days >= days",
"title": ""
},
{
"docid": "9129a007a71f8ff4350363b2e4c0a561",
"score": "0.4452713",
"text": "def _prepare_new_events_for_datastore(df_new_log_events: pd.DataFrame) -> pd.DataFrame:\n # create a copy of the DataFrame\n df_new_log_events = df_new_log_events.copy(deep=True)\n\n # extract the EventId and EventTemplate columns\n df_new_log_events = df_new_log_events[[\"EventId\", \"EventTemplate\"]]\n\n # drop duplicates\n df_new_log_events = df_new_log_events.drop_duplicates()\n\n return df_new_log_events",
"title": ""
},
{
"docid": "94f7d701fff3bc9da707d0b19b4ef7b9",
"score": "0.44456246",
"text": "def test_new_filter_existing_default(self):\r\n self.build(\r\n 'ir.filters',\r\n dict(name='a', user_id=self.USER_ID, model_id='ir.filters'),\r\n dict(name='b', is_default=True, user_id=self.USER_ID, model_id='ir.filters'),\r\n )\r\n\r\n Filters = self.registry('ir.filters')\r\n Filters.create_or_replace(self.cr, self.USER_ID, {\r\n 'name': 'c',\r\n 'model_id': 'ir.filters',\r\n 'user_id': self.USER_ID,\r\n 'is_default': True,\r\n })\r\n filters = Filters.get_filters(self.cr, self.USER_ID, 'ir.filters')\r\n\r\n self.assertItemsEqual(map(noid, filters), [\r\n dict(name='a', user_id=self.USER, is_default=False, domain='[]', context='{}'),\r\n dict(name='b', user_id=self.USER, is_default=False, domain='[]', context='{}'),\r\n dict(name='c', user_id=self.USER, is_default=True, domain='[]', context='{}'),\r\n ])",
"title": ""
},
{
"docid": "d60bc9782037ddc413b44f2178010022",
"score": "0.44298473",
"text": "def retain(self, **terms):\n self.l = self.filter(**terms)\n return self",
"title": ""
},
{
"docid": "615da49516b3c5b788b504992ce5b9ba",
"score": "0.44183674",
"text": "def filter(self):\n if self.is_search:\n self.query[\"name\"] = {\"$regex\": self.dt_search[\"value\"], \"$options\": \"i\" }",
"title": ""
},
{
"docid": "148981904164813b891e0e80043e7741",
"score": "0.44177032",
"text": "def test_latest_added_with_future_product_and_past_product(self):\n time = timezone.now() - datetime.timedelta(days=30)\n Product.objects.create(name='Now', description='now', created_at=timezone.now(), price=30)\n Product.objects.create(name='Past', description='past', created_at=time, price=30)\n response = self.client.get(reverse('last_added'))\n self.assertQuerysetEqual(\n response.context['last_added_pr'], ['<Product: Now>'])",
"title": ""
},
{
"docid": "a628e9f4e3c72a787430dc11392782ad",
"score": "0.44123897",
"text": "def test_new_no_filter(self):\r\n Filters = self.registry('ir.filters')\r\n Filters.create_or_replace(self.cr, self.USER_ID, {\r\n 'name': 'a',\r\n 'model_id': 'ir.filters',\r\n 'user_id': self.USER_ID,\r\n 'is_default': True,\r\n })\r\n filters = Filters.get_filters(self.cr, self.USER_ID, 'ir.filters')\r\n\r\n self.assertItemsEqual(map(noid, filters), [\r\n dict(name='a', user_id=self.USER, is_default=True,\r\n domain='[]', context='{}')\r\n ])",
"title": ""
},
{
"docid": "e0450525714e0c7090b4a8ae8cc6ad2a",
"score": "0.44090873",
"text": "def search_new(self, n=10):\n\n return quickview(self._bibdb.sort_values(by='import_date')[-n:])",
"title": ""
},
{
"docid": "959e290dd06d726020d9bf60b58d06e2",
"score": "0.4406169",
"text": "def new_objects(self):\n return [self]",
"title": ""
},
{
"docid": "2f1c89d2e646ad6a89e74cd310cbda2f",
"score": "0.44010144",
"text": "def test_find_one_latest():\n collection = mongomock.MongoClient().db.collection\n obj1 = {'_id': 1, 'name': 'first'}\n obj2 = {'_id': 2, 'name': 'seond'}\n obj3 = {'_id': 3, 'name': 'third'}\n\n collection.insert_many([obj1, obj2, obj3])\n res = find_one_latest(collection)\n assert res == {'name': 'third'}",
"title": ""
},
{
"docid": "77e4d886ea925e79075f06906acdbf50",
"score": "0.43964005",
"text": "def query_add_filters(self, query):\n return query",
"title": ""
},
{
"docid": "43416a64b9a1b2d98e1d83c05674e9d8",
"score": "0.439603",
"text": "def mergeFilter(self, target):\r\n pass",
"title": ""
},
{
"docid": "43416a64b9a1b2d98e1d83c05674e9d8",
"score": "0.439603",
"text": "def mergeFilter(self, target):\r\n pass",
"title": ""
},
{
"docid": "ad6677e42ba8f98224175530fa255cb3",
"score": "0.43942013",
"text": "def populate_collection(collection: Collection, documents: List[Dict[str, Any]], filter_field: str) -> None:\n logger.debug(f\"Populating/updating '{collection.full_name}' using '{filter_field}' as the filter\")\n\n for document in documents:\n _ = collection.find_one_and_update({filter_field: document[filter_field]}, {\"$set\": document}, upsert=True)",
"title": ""
},
{
"docid": "e7acf8ed05e07ba5e1aa67a9b9280c30",
"score": "0.43924293",
"text": "def new_hits(self): \n return self._new_hits",
"title": ""
},
{
"docid": "1e385a246616def42200172fc2c938d3",
"score": "0.4385931",
"text": "def get_new_devices(self, old, new, deps):\n\t\told_drivers = old[deps[0]]['data']\n\t\tnew_drivers = new[deps[0]]['data']\n\t\tfound_devs = []\n\t\tfor new_driver in new_drivers:\n\t\t\tnew_devices = new_driver[\"devices\"]\n\t\t\tfor new_device in new_devices:\n\t\t\t\tis_new = True\n\t\t\t\tfor old_driver in old_drivers:\n\t\t\t\t\told_devices = old_driver[\"devices\"]\n\t\t\t\t\tfor old_device in old_devices:\n\t\t\t\t\t\tif old_device[\"device_name\"] == new_device[\"device_name\"] and \\\n\t\t\t\t\t\t old_device[\"device_type\"] == new_device[\"device_type\"]:\n\t\t\t\t\t\t\tis_new = False\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\tif not is_new:\n\t\t\t\t\t\tbreak\n\t\t\t\tif is_new:\n\t\t\t\t\tfound_devs.append(new_device)\n\t\treturn found_devs",
"title": ""
},
{
"docid": "b78dcc3c5a8329645f9ea5971e5188fe",
"score": "0.43826064",
"text": "def get_new_services(self, old, new, deps):\n\t\treturn self.get_new_by_field(old, new, deps, ['service_name'])",
"title": ""
},
{
"docid": "8a91edae33a6c4dcd71bf58f47b7fd0e",
"score": "0.43811777",
"text": "def startCreation(self):\r\n nids = []\r\n newNids = mw.col.findNotes(\"is:new\")\r\n\r\n for nid in newNids:\r\n\t if mw.col.getNote(nid).model()['name'] == self.vocabNote['VModel']:\r\n\t nids.append(nid)\r\n \r\n if nids != []:\r\n\t self.addCardsBulk(nids)",
"title": ""
},
{
"docid": "8c8458de93516d9881be35cfe3afe987",
"score": "0.4379371",
"text": "def test_latest_added_with_a_past_product(self):\n self.client.force_login(User.objects.get_or_create(username='test')[0])\n time = timezone.now() - datetime.timedelta(days=30)\n Product.objects.create(name='Past', description='past', created_at=time, price=30)\n response = self.client.get(reverse('last_added'))\n self.assertContains(response, \"No items are available.\", status_code=200)\n self.assertQuerysetEqual(response.context['last_added_pr'], [])",
"title": ""
},
{
"docid": "33229abbc088493431c10821fd3685e3",
"score": "0.43776506",
"text": "def after_new(self, form, instance):\n\n # Set the 'afternew' URL\n self.afternewurl = reverse('search_gold')\n\n # Create a new equality set to which we add this Gold sermon\n if instance.equal == None:\n geq = EqualGold.create_empty()\n instance.equal = geq\n instance.save()\n\n # Return positively\n return True, \"\"",
"title": ""
},
{
"docid": "a6336f3f5c0b25376fc9acd9a754227d",
"score": "0.43725342",
"text": "def mergeFilter(self, target):\r\n self.entries = [entry for entry in self.entries if entry.listId.ValidateFormID(target)]",
"title": ""
},
{
"docid": "a6336f3f5c0b25376fc9acd9a754227d",
"score": "0.43725342",
"text": "def mergeFilter(self, target):\r\n self.entries = [entry for entry in self.entries if entry.listId.ValidateFormID(target)]",
"title": ""
},
{
"docid": "a6336f3f5c0b25376fc9acd9a754227d",
"score": "0.43725342",
"text": "def mergeFilter(self, target):\r\n self.entries = [entry for entry in self.entries if entry.listId.ValidateFormID(target)]",
"title": ""
},
{
"docid": "a6336f3f5c0b25376fc9acd9a754227d",
"score": "0.43725342",
"text": "def mergeFilter(self, target):\r\n self.entries = [entry for entry in self.entries if entry.listId.ValidateFormID(target)]",
"title": ""
},
{
"docid": "a6336f3f5c0b25376fc9acd9a754227d",
"score": "0.43725342",
"text": "def mergeFilter(self, target):\r\n self.entries = [entry for entry in self.entries if entry.listId.ValidateFormID(target)]",
"title": ""
},
{
"docid": "a6336f3f5c0b25376fc9acd9a754227d",
"score": "0.43725342",
"text": "def mergeFilter(self, target):\r\n self.entries = [entry for entry in self.entries if entry.listId.ValidateFormID(target)]",
"title": ""
},
{
"docid": "90aec0c1378eadcf98ed80d7ac1923a4",
"score": "0.43724656",
"text": "def create_watcher_and_commit_if_needed(self, new_responsible):\n watcher = Watcher.query.get_by_actorid(new_responsible)\n if not watcher:\n\n if txn_is_dirty():\n # Creating and committing the watcher should always be the\n # first thing that's being done in the txn when reassigning.\n # Otherwise we would be committing unrelated, unexpected\n # changes.\n #\n # Detect if that happens, but still proceed and log to sentry.\n msg = 'Dirty transaction when creating and committing watcher'\n logger.warn(msg)\n logger.warn('Registered objects: %r' % registered_objects())\n log_msg_to_sentry(msg, level='warning', extra={\n 'registered_objects': repr(registered_objects())}\n )\n\n session = create_session()\n watcher = Watcher(actorid=new_responsible)\n session.add(watcher)\n transaction.commit()\n transaction.begin()",
"title": ""
},
{
"docid": "7134cf56ac2621464b9b48be993c1bf3",
"score": "0.43698126",
"text": "def potentially_expired_records(repo,\n days_to_expire=DEFAULT_EXPIRATION_DAYS):\n import utils\n cutoff_date = utils.get_utcnow() - timedelta(days_to_expire)\n return Person.all(filter_expired=False).filter(\n 'source_date <=',cutoff_date).filter(\n 'repo =', repo)",
"title": ""
},
{
"docid": "183d6b4d161855c36c202097a9f6b087",
"score": "0.43619275",
"text": "def view_update(self, context):\n\n for collection in self._watch_list:\n collection_name = get_collection_name(collection)\n collection_set = set(collection)\n tracking_set = self._tracking_sets[collection_name]\n\n # Check for new items\n add_set = collection_set - tracking_set\n self.add_delta[collection_name] = add_set\n tracking_set |= add_set\n\n # Check for removed items\n remove_set = tracking_set - collection_set\n self.remove_delta[collection_name] = remove_set\n tracking_set -= remove_set\n\n # Check for updates\n update_set = {item for item in collection if item.is_updated}\n self.update_delta[collection_name] = update_set",
"title": ""
},
{
"docid": "b1626abbeb20ccfeb37b58d5fa63e5ab",
"score": "0.43597463",
"text": "def create_watcher_and_commit_if_needed(self, new_responsible):\n watcher = Watcher.query.get_by_actorid(new_responsible)\n if not watcher:\n if txn_is_dirty():\n # Creating and committing the watcher should always be the\n # first thing that's being done in the txn when reassigning.\n # Otherwise we would be committing unrelated, unexpected\n # changes.\n #\n # Detect if that happens, but still proceed and log to sentry.\n msg = 'Dirty transaction when creating and committing watcher'\n logger.warn(msg)\n logger.warn('Registered objects: %r' % registered_objects())\n log_msg_to_sentry(msg, level='warning', extra={\n 'registered_objects': repr(registered_objects())}\n )\n\n session = create_session()\n watcher = Watcher(actorid=new_responsible)\n session.add(watcher)\n transaction.commit()\n transaction.begin()",
"title": ""
},
{
"docid": "99933fbe235a4a40a2756ab9ae967c7e",
"score": "0.43514693",
"text": "def last_remote_addr_actions(self, request, seconds):\r\n queryset = self.get_same_remote_addr(request)\r\n\r\n timedelta = datetime.timedelta(seconds=seconds)\r\n point_in_time = datetime.datetime.now() - timedelta\r\n queryset = queryset.filter(createtime__gte=point_in_time)\r\n\r\n return queryset",
"title": ""
},
{
"docid": "111afa825ee50c080f30f40b61deba39",
"score": "0.43379867",
"text": "def test_filter_latest(self):\n fs = self.bound(MultiValueDict())\n\n qs = Mock()\n fs.filter(qs)\n\n qs.filter.assert_called_with(latest=True)",
"title": ""
},
{
"docid": "16e70fbc0cc1645b0b4f9a825da0e893",
"score": "0.43332693",
"text": "def test_new_filter_not_default(self):\r\n self.build(\r\n 'ir.filters',\r\n dict(name='a', user_id=self.USER_ID, model_id='ir.filters'),\r\n dict(name='b', user_id=self.USER_ID, model_id='ir.filters'),\r\n )\r\n\r\n Filters = self.registry('ir.filters')\r\n Filters.create_or_replace(self.cr, self.USER_ID, {\r\n 'name': 'c',\r\n 'model_id': 'ir.filters',\r\n 'user_id': self.USER_ID,\r\n 'is_default': True,\r\n })\r\n filters = Filters.get_filters(self.cr, self.USER_ID, 'ir.filters')\r\n\r\n self.assertItemsEqual(map(noid, filters), [\r\n dict(name='a', user_id=self.USER, is_default=False, domain='[]', context='{}'),\r\n dict(name='b', user_id=self.USER, is_default=False, domain='[]', context='{}'),\r\n dict(name='c', user_id=self.USER, is_default=True, domain='[]', context='{}'),\r\n ])",
"title": ""
},
{
"docid": "b1e08fedc40da914f1a7de309596bf09",
"score": "0.43327948",
"text": "def new_users(self):\n if 'new_users' not in self.cache:\n self.cache['new_users'] = self._user_event_counter(EventTypes.NEW_USER)\n return self.cache['new_users']",
"title": ""
},
{
"docid": "ea22be4d67d7720869d940115c4bba21",
"score": "0.4332354",
"text": "def new_query(self):\n self._ext_queries += 1",
"title": ""
},
{
"docid": "cba20063393162c3c6e85feb247f5c7d",
"score": "0.43227044",
"text": "def keep(self, **kwargs):\n matches = [all(value == getattr(event, key) if key in Event.exact_matches else value in getattr(event, key) for key, value in kwargs.items()) if len(kwargs) > 0 else True for event in self.schedule]\n\n sched = Schedule([event for index, event in enumerate(self.schedule) if not self.__inner_filter[index] or matches[index]], name=self.name)\n \n self.__inner_filter = [True for _ in range(len(sched))]\n\n if VERBOSE:\n print(f'Filtered {self} to {len(sched)} events')\n\n return sched",
"title": ""
},
{
"docid": "e20ee497b71259461c15f742537efbae",
"score": "0.4322676",
"text": "def test_new_filter_not_default(self):\r\n self.build(\r\n 'ir.filters',\r\n dict(name='a', user_id=False, model_id='ir.filters'),\r\n dict(name='b', user_id=False, model_id='ir.filters'),\r\n )\r\n\r\n Filters = self.registry('ir.filters')\r\n Filters.create_or_replace(self.cr, self.USER_ID, {\r\n 'name': 'c',\r\n 'model_id': 'ir.filters',\r\n 'user_id': False,\r\n 'is_default': True,\r\n })\r\n filters = Filters.get_filters(self.cr, self.USER_ID, 'ir.filters')\r\n\r\n self.assertItemsEqual(map(noid, filters), [\r\n dict(name='a', user_id=False, is_default=False, domain='[]', context='{}'),\r\n dict(name='b', user_id=False, is_default=False, domain='[]', context='{}'),\r\n dict(name='c', user_id=False, is_default=True, domain='[]', context='{}'),\r\n ])",
"title": ""
},
{
"docid": "692f5e6a148421eee5d2449545e79e4b",
"score": "0.43191355",
"text": "def add(query, params, filters):\n for fltr in filters:\n fltr.add(query, params)",
"title": ""
},
{
"docid": "351f441945bf9908a35d63825b9d2d73",
"score": "0.43181813",
"text": "def get_new_connections(self, old, new, deps):\n\t\tfound_connections = self.get_new_objects(old, new, deps)\n\t\tnew_connections = []\n\t\tcuckoo_conf = Config(os.path.join(CUCKOO_ROOT, \"conf\", \"cuckoo.conf\"))\n\t\tremote_port = cuckoo_conf.resultserver.port\n\t\tremote_address = cuckoo_conf.resultserver.ip\n\t\tfor c in found_connections:\n\t\t\tif not (c[\"local_port\"] == CUCKOO_GUEST_PORT or \\\n\t\t\t c['remote_port'] == remote_port or \\\n\t\t\t c[\"remote_address\"]\t== remort_address):\n\t\t\t\tnew_connections.append(c)\n\t\treturn new_connections",
"title": ""
},
{
"docid": "3ad82fb8cb46c8ef697102e0edd357ef",
"score": "0.43161115",
"text": "def rerun_filters(dpi=None):\n\n # get the database links.\n match_db = database.get_collection('match')\n filter_db = database.get_collection('filter')\n\n # create the object.\n cbio = CBioEngine(settings.MONGO_URI,\n settings.MONGO_DBNAME,\n data_model.match_schema,\n muser=settings.MONGO_USERNAME,\n mpass=settings.MONGO_PASSWORD,\n collection_clinical=settings.COLLECTION_CLINICAL,\n collection_genomic=settings.COLLECTION_GENOMIC)\n\n query = {'status': 1, 'temporary': False, 'trial_watch': {'$exists': False}}\n filters = list(filter_db.find(query))\n for filter_ in filters:\n\n # lots of logging.\n logging.info(\"rerun_filters: filter: %s\" % filter_['_id'])\n\n # prepare the filters.\n c, g, txt = prepare_criteria(filter_)\n\n # execute the match.\n cbio.match(c=c, g=g)\n\n if cbio.match_df is not None and cbio.genomic_df is not None and cbio.clinical_df is not None:\n logging.info(\"rerun_filters: new matches: match=%d, genomic=%d, clinical=%d\" % (\n len(cbio.match_df), len(cbio.genomic_df), len(cbio.clinical_df)))\n\n # get existing matches for this filter.\n matches = list(match_db.find({'FILTER_ID': ObjectId(filter_['_id'])}))\n\n rec_cnt = 0\n for m in matches:\n rec_cnt += len(m['VARIANTS'])\n\n logging.info(\"rerun_filters: exisiting: %d %d\" % (len(matches), rec_cnt))\n\n # parse the old matches.\n clinical_old_id = set()\n old_lu = {}\n match_lu = {}\n for match in matches:\n\n # get the clincal id.\n clinical_id = match['CLINICAL_ID']\n\n # now build tuples of variants.\n for genomic_id in match['VARIANTS']:\n\n # make pair\n pair = (clinical_id, genomic_id)\n clinical_old_id.add(pair)\n\n # build id lookup.\n old_lu[pair] = match['_id']\n\n # cache matches.\n match_lu[pair] = match\n\n # parse the new matches.\n clinical_new_id = set()\n new_lu = {}\n i = 0\n for match in cbio.match_iter():\n\n # simplify.\n clinical_id = match['CLINICAL_ID']\n genomic_id = match['GENOMIC_ID']\n\n # build set.\n pair = (clinical_id, genomic_id)\n clinical_new_id.add(pair)\n\n # cache matches.\n match_lu[pair] = match\n\n # build lookup.\n new_lu[pair] = i\n i += 1\n\n # find the ones which need to be deleted and delete them.\n to_delete = clinical_old_id - clinical_new_id\n logging.info(\"rerun_filters: removing: %d\" % len(to_delete))\n updated = list()\n for pair in to_delete:\n\n # extract ids\n match_id = old_lu[pair]\n match = match_lu[pair]\n\n # find the variant.\n good = list()\n hit = False\n for v in match['VARIANTS']:\n if v != pair[1]:\n good.append(v)\n else:\n hit = True\n\n # update it if necessary.\n if hit:\n\n # check if will empty this.\n if len(good) == 0:\n\n # delete it.\n match_db.delete_one({'_id': match_id})\n else:\n\n # just update it.\n match_db.update({\"_id\": match_id}, {\"$set\": {\"VARIANTS\": good}})\n\n # update the local one to make sure we delete all variants\n match['VARIANTS'] = good\n\n # find the intersection and remove them from data frame.\n remove_frame = clinical_new_id.intersection(clinical_old_id)\n bad_list = []\n for pair in remove_frame:\n\n # lookup index.\n idx = new_lu[pair]\n bad_list.append(idx)\n\n logging.info(\"rerun_filters: skipping: %d\" % len(bad_list))\n\n # remove them.\n if cbio.match_df is not None and len(cbio.match_df) > 0:\n cbio.match_df.drop(cbio.match_df.index[bad_list], inplace=True)\n\n # insert the counts.\n count_matches(cbio, filter_)\n\n # insert the matches if not temporary.\n insert_matches(cbio, filter_, from_filter=False, dpi=dpi)",
"title": ""
},
{
"docid": "cc6c9583961accd484391452b742401b",
"score": "0.43107936",
"text": "def update_retrieved_if_new(olditem, newitem, days=180, retrieved_pid='P813'):\n def ref_overwrite(oldref, newref, days):\n \"\"\"\n If the newref is the same as the oldref except the retrieved date is `days` newer, return True\n the retrieved date is NOT `days` newer, return False\n the refs are different, return True\n \"\"\"\n if len(oldref) != len(newref):\n return True\n oldref_minus_retrieved = [x for x in oldref if x.get_prop_nr() != retrieved_pid]\n newref_minus_retrieved = [x for x in newref if x.get_prop_nr() != retrieved_pid]\n if not all(x in oldref_minus_retrieved for x in newref_minus_retrieved):\n return True\n oldref_retrieved = [x for x in oldref if x.get_prop_nr() == retrieved_pid]\n newref_retrieved = [x for x in newref if x.get_prop_nr() == retrieved_pid]\n if (len(newref_retrieved) != len(oldref_retrieved)) or not (\n len(newref_retrieved) == len(oldref_retrieved) == 1):\n return True\n datefmt = '+%Y-%m-%dT%H:%M:%SZ'\n retold = list([datetime.strptime(r.get_value()[0], datefmt) for r in oldref if r.get_prop_nr() == retrieved_pid])[0]\n retnew = list([datetime.strptime(r.get_value()[0], datefmt) for r in newref if r.get_prop_nr() == retrieved_pid])[0]\n return (retnew - retold).days >= days\n\n newrefs = newitem.references\n oldrefs = olditem.references\n if not (len(newrefs) == len(oldrefs) == 1):\n #print(\"overwriting refs, not 1\")\n olditem.references = copy.deepcopy(newitem.references)\n return None\n overwrite = ref_overwrite(oldrefs[0], newrefs[0], days)\n if overwrite:\n print(\"updating ref\")\n olditem.references = newrefs\n else:\n print(\"don't change\")\n pass",
"title": ""
},
{
"docid": "53fd50eb7ad9d9da17f61c0410f769d4",
"score": "0.43082923",
"text": "def filter_for_new_sensor_updates(aurora_creds: dict, new_sensor_list: List) -> List:\n with UseDatabase(aurora_creds) as cursor:\n sql_check = \"\"\"SELECT EXISTS (SELECT 1 FROM pg_tables\n WHERE tablename = 'all_sensor_info');\"\"\"\n cursor.execute(sql_check)\n response = cursor.fetchone()\n if response[0] == True:\n sql_collect = \"\"\"SELECT row_to_json(all_sensor_info)\n FROM all_sensor_info;\"\"\"\n cursor.execute(sql_collect)\n all_stored_sensors = cursor.fetchall()\n else:\n # If 'all_sensor_info' table does not exist\n all_stored_sensors = []\n stored_sensor_ids = []\n for i in all_stored_sensors:\n stored_sensor_ids.append(i[0]['sensor_id'])\n new_sensor_info = []\n for i in new_sensor_list:\n # If sensor is not already stored\n if i[0] not in stored_sensor_ids:\n new_sensor_info.append(i)\n else:\n for stored_sensor in all_stored_sensors:\n # If sensor has new data not yet stored\n if stored_sensor[0]['sensor_id'] == i[0] and \\\n stored_sensor[0]['end_date'].replace('-','').replace(':','') != i[3]:\n new_sensor_info.append(i)\n return new_sensor_info, stored_sensor_ids",
"title": ""
},
{
"docid": "4ddc395193ce066fcc35eb3969c54651",
"score": "0.43050253",
"text": "def _test_stream_events_before_target(self, target_timestamp):\n records = []\n for tag, r in self._readers.items():\n # The target_timestamp is the maximum timestamp that was read from the\n # stream. Some readers may have elements that are less than this. Thus,\n # we skip all readers that already have elements that are at this\n # timestamp so that we don't read everything into memory.\n if self._stream_times[tag] >= target_timestamp:\n continue\n try:\n record = next(r).recorded_event\n if record.HasField('processing_time_event'):\n self._stream_times[tag] += timestamp.Duration(\n micros=record.processing_time_event.advance_duration)\n records.append((tag, record, self._stream_times[tag]))\n except StopIteration:\n pass\n return records",
"title": ""
},
{
"docid": "998012bd237e2bd6e6f1f5a85f2cee3c",
"score": "0.43020946",
"text": "def getMongoFilter(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "3a57735da602b605765fb47fa5346855",
"score": "0.4300831",
"text": "def create_comparison(comparison_update: schemas.FilterComparisonCreate,\n db: Session = Depends(deps.get_db),\n current_user: schemas.UserVerify = Depends(\n deps.get_current_user)) -> JSONResponse:\n \n comparison = schemas.FilterComparison(\n text_sample_id_1 = comparison_update.text_sample_id_1, \n text_sample_id_2 = comparison_update.text_sample_id_2, \n item_1_is_better = comparison_update.item_1_is_better,\n user_id = current_user.id,\n id = str(uuid.uuid4().hex)\n )\n\n\n data = crud_filter_comparisons.create_comparison(comparison=comparison, db=db)\n if data is None:\n return JSONResponse(status_code=500,\n content={\"message\": \"Internal Server Error\"})\n return JSONResponse(status_code=200,\n content={\"message\": \"success\"})",
"title": ""
},
{
"docid": "2d2b94ae69ba5e405654e6e1be2c7236",
"score": "0.4300264",
"text": "def remove_all_by_source_and_date(self, source, month_delta=-2):\n d_minus_2m = self.monthdelta(datetime.now(), month_delta)\n results = self.all_dns_collection.find({'sources.source': source,\n 'sources.updated': {\"$lt\": d_minus_2m}}\n ).batch_size(30)\n\n for result in results:\n if len(result['sources']) > 1:\n self.all_dns_collection.update({'_id': ObjectId(result['_id'])},\n {\"$pull\": {\"sources\": {\"source\": source}}})\n else:\n self.all_dns_collection.remove({'_id': ObjectId(result['_id'])})\n\n return True",
"title": ""
},
{
"docid": "a287ab109944ccdb2739ad858430a8c2",
"score": "0.42931166",
"text": "def get_new_processes(self, old, new, deps):\n\t\treturn self.get_new_by_field(old, new, deps, ['process_id'])",
"title": ""
},
{
"docid": "43b0c368ed3ca8a013f5a500a9e786e6",
"score": "0.42913648",
"text": "def get_new(self):\n\n # Refresh file series\n self.refresh()\n\n # Load current and previous set of files\n new_file_series = self._load(update_path=False)\n old_file_series = self._load(prev_version=True, update_path=False)\n\n # Select files that are in the new series and not the old series\n new_files = new_file_series[-new_file_series.isin(old_file_series)]\n\n return new_files",
"title": ""
},
{
"docid": "cc00b7369e38815d1e12193d7f61f767",
"score": "0.42837262",
"text": "def __FilterAndSampleSearchTerms(self, existingSearchTerms, newSearchAttributes, sampleSize, pullDate):\n # Normalize the existing search terms:\n existingSearchTerms = { re.sub('[^\\w\\d]', '', term.lower()) : True for term in existingSearchTerms }\n # Filter out already sampled search terms, simple words etc:\n filtered = [re.sub('[^\\w\\d]', '', row[0].lower()) for row in newSearchAttributes.values if row[0] not in existingSearchTerms and row[1] <= pullDate.date()]\n # Randomly sample:\n sampleSize = min(len(filtered), sampleSize)\n indices = choose(range(0, len(filtered)), sampleSize, replace = False)\n return [filtered[i] for i in indices]",
"title": ""
},
{
"docid": "66ca8e3e47f19f1efeb414bc7e78aaf1",
"score": "0.42815423",
"text": "def update_or_create(\n self, filters: Optional[Dict[str, Any]] = None, **kwargs\n ) -> Tuple[bool, Model]:\n created = False\n instance = None\n if filters:\n instance = self.find(**filters).first()\n\n if instance is None:\n created = True\n\n return created, self.__create(instance, partial=True, **kwargs)",
"title": ""
},
{
"docid": "e9564f73fd19de0d3be77b7ade465d1f",
"score": "0.42791924",
"text": "def test_from_logs(self):\n params = deepcopy(self.DEFAULT_PARAMS)\n params['filters']['type'].append('cloudify_log')\n Events._build_select_query(**params)\n self.assertGreater(\n self.db.session.query().filter().union.call_count,\n 1,\n )",
"title": ""
},
{
"docid": "a3b2cf0a5e78dce85dbe171096849252",
"score": "0.42666724",
"text": "def _merge_report(self, target, new):\n query_millis = int(new['parsed']['millis'])\n current_sig = self._get_existing_query(target, new['queryAnalysis'])\n\n if current_sig is not None:\n current_sig['totalTimeMillis'] += query_millis\n current_sig['queryCount'] += 1\n current_sig['avgTimeMillis'] = current_sig['totalTimeMillis'] / current_sig['queryCount']\n\n else:\n initial_query_detail = self._get_initial_query_detail(new)\n target['queryDetails'].append(initial_query_detail)\n target['queries'].append(initial_query_detail['queryMask'])\n\n target['totalTimeMillis'] += query_millis\n target['queryCount'] += 1\n target['avgTimeMillis'] = target['totalTimeMillis'] / \\\n target['queryCount']",
"title": ""
},
{
"docid": "81a2b44c253985c84c7c94889f220207",
"score": "0.4264802",
"text": "def refresh_history(old, new):\n keys_list = ['val_loss', 'val_acc', 'loss', 'acc']\n for key in keys_list:\n new_entry = new[key]\n if len(new_entry) > 0:\n for elem in new_entry:\n old[key].append(elem)\n else:\n old[key].append(elem)\n return(old)",
"title": ""
},
{
"docid": "49a2076165a9003aa6d137115e2d9ea4",
"score": "0.42604247",
"text": "def make_filter(dbstate, uistate, objclass, gramps_ids, title=None):\n FilterClass = GenericFilterFactory(objclass)\n rule = getattr(getattr(rules, objclass),'RegExpIdOf')\n filter = FilterClass()\n if title is None:\n title = _(\"Filter %s from Clipboard\") % objclass\n if isinstance(title, collections.Callable):\n title = title()\n filter.set_name(title)\n struct_time = time.localtime()\n filter.set_comment( _(\"Created on %(year)4d/%(month)02d/%(day)02d\") % { \n 'year': struct_time.tm_year,\n 'month': struct_time.tm_mon,\n 'day': struct_time.tm_mday})\n re = \"|\".join([\"^%s$\" % gid for gid in sorted(gramps_ids)])\n filter.add_rule(rule([re]))\n filterdb = FilterList(CUSTOM_FILTERS)\n filterdb.load()\n EditFilter(objclass, dbstate, uistate, [],\n filter, filterdb,\n lambda : edit_filter_save(uistate, filterdb, objclass))",
"title": ""
},
{
"docid": "733f1d77640c103fc96759879a6773f4",
"score": "0.4258774",
"text": "def index_queryset(self, using=None):\n return self.get_model().objects.filter(created__lte=datetime.datetime.now())",
"title": ""
},
{
"docid": "733f1d77640c103fc96759879a6773f4",
"score": "0.4258774",
"text": "def index_queryset(self, using=None):\n return self.get_model().objects.filter(created__lte=datetime.datetime.now())",
"title": ""
},
{
"docid": "6411cb608331f15c16fd65774e03dd3b",
"score": "0.4257283",
"text": "def test_get_filtered_changes_list(self):\n self.login()\n self.set_session_user_tokens()\n qs = \"?validated=True&{}=1234&action={}\".format(\n settings.IDENTITY_FIELD, settings.ACTIONS[0][0])\n self.add_changes_callback(num=10, qs=qs)\n\n qs = \"?mother_id=1234&action={}&validated=True\".format(\n settings.ACTIONS[0][0])\n response = self.client.get('{}{}'.format(reverse('changes'), qs))\n context = response.context\n\n self.assertEqual(response.status_code, 200)\n self.assertEqual(len(context['changes']), 5)",
"title": ""
},
{
"docid": "b7d6a2bc08dfbbbb680863253331ccc4",
"score": "0.4253213",
"text": "def is_new(self):\r\n return self._new",
"title": ""
},
{
"docid": "47439e4e441dc5f200bc6f7567699361",
"score": "0.42502186",
"text": "def history_bullshit_filter(self, old_jobs):\n\n self.job_index = 0\n refreshed_jobs = []\n print('RECHECKING HISTORY')\n\n for this_job in old_jobs:\n\n self.job_index += 1\n print(self.name + \" processing job # %d\" % self.job_index)\n\n if this_job.rejection_identifier in ('a', 'r'): # already marked as removed, don't filter\n refreshed_jobs.append(this_job)\n continue\n\n this_job.approve()\n this_job.good_hits = []\n\n self.filter_title(this_job)\n\n if this_job.is_relevant:\n self.filter_body(this_job)\n\n if this_job.is_relevant:\n print('job # %d approved O' % self.job_index)\n\n refreshed_jobs.append(this_job)\n\n return refreshed_jobs",
"title": ""
},
{
"docid": "d0e112468a4ba2633a0dbe1de61263ac",
"score": "0.42451245",
"text": "def listNewFiles(self):\n return self.newfiles",
"title": ""
},
{
"docid": "4bf5ff9af7afa77835836560ed6e0f5d",
"score": "0.4244852",
"text": "def filter(cls, args, latest=True, offset=None, count=None):\n all_versions = super().filter(args, offset, count)\n if not latest:\n return all_versions\n return cls._filter_latest_versions(all_versions)",
"title": ""
},
{
"docid": "34d8a574e68eabdcb4a303992767ff78",
"score": "0.42433754",
"text": "def filter(self, *args, **kwargs):\n\n for arg in args:\n if isinstance(arg, Q) and arg.action == \"end\":\n self.qes.append(arg)\n elif isinstance(arg, Q) and arg.action == \"start\":\n self.qs.append(arg)\n elif isinstance(arg, In):\n self.relation.append(arg)\n else:\n raise Exception(\"unsupported type:{},value:{}\".format(type(arg), arg))\n\n filter_origin, filter_destination = process_filter_args(self.manager.source_class, kwargs)\n if filter_origin:\n self.filters.append(filter_origin)\n if filter_destination:\n self.filters_destination.append(filter_destination)\n return self",
"title": ""
},
{
"docid": "9c2a1a6833f9ae0fc64204b115b0b75c",
"score": "0.4231966",
"text": "def is_stale(self, version=None):\n if self.updated is None:\n return True\n else:\n return self.updated < datetime.datetime.now() - datetime.timedelta(seconds=settings.AGGREGATE_STALE)",
"title": ""
},
{
"docid": "2cbd2fe032830261d2fecc28d1a0d81d",
"score": "0.42293826",
"text": "def __init__(self,\n append_documents=None,\n ddl_only_recovery=None,\n documents_filter_type=None,\n filter_expression=None,\n id_regex=None,\n overwrite_users=None,\n suffix=None,\n ):\n\n # Initialize members of the class\n self.append_documents = append_documents\n self.ddl_only_recovery = ddl_only_recovery\n self.documents_filter_type = documents_filter_type\n self.filter_expression = filter_expression\n self.id_regex = id_regex\n self.overwrite_users = overwrite_users\n self.suffix = suffix",
"title": ""
},
{
"docid": "dfaceeb411184ded7c08c47faae5b78e",
"score": "0.4227273",
"text": "def testCreateWithFilter(self):\n self._MakeEventType()\n self._MakeSource(self.source_crd)\n self._MakeTrigger(self.source, self.event_type.type)\n\n self.operations.CreateTrigger.return_value = self.trigger\n self.Run('events triggers create my-trigger --platform=gke '\n '--cluster=cluster-1 --cluster-location=us-central1-a '\n '--target-service=my-service --type=google.source.my.type '\n '--trigger-filters key=value1,key2=value2')\n trigger_ref = self._TriggerRef('my-trigger', 'default')\n self.validate_params.assert_called_once_with(mock.ANY, self.event_type)\n self.source.name = 'source-for-my-trigger'\n self.operations.CreateTrigger.assert_called_once_with(\n trigger_ref, self.source, self.event_type.type,\n collections.OrderedDict([('key', 'value1'), ('key2', 'value2')]),\n 'my-service', 'default')",
"title": ""
},
{
"docid": "f74b5d22e9afaed32b97f60877dbfe44",
"score": "0.42246795",
"text": "def find_one_and_delete(self, filter, projection=None, sort=None, _deadline=None):\n return self._new_find_and_modify(filter, projection, sort, remove=True,\n _deadline=_deadline)",
"title": ""
},
{
"docid": "62b67fc985d847f220e8fa8acc58e3d1",
"score": "0.42183864",
"text": "def _existing(self, attr, value):\n return Document.uncached.filter(locale=self.locale,\n **{attr: value})",
"title": ""
},
{
"docid": "a4cfffbe710266e12ac68aa5e46bdae9",
"score": "0.42128938",
"text": "def test_new_readings(self):\n query = db.session.query(MeterReading).filter_by(meter=self.meter_id)\n self.assertEqual(7, query.count(), \"7 readings from setup\")\n dt = (datetime.today() - timedelta(days=3)).date()\n start_dt = dt\n readings: Dict[str, List[float]] = {}\n for idx in range(3):\n readings[dt.strftime(\"%Y-%m-%d\")] = [2.0] * 96\n dt += timedelta(days=1)\n MeterReading.merge_readings(MeterReading.from_json(self.meter_id, readings))\n db.session.flush()\n self.assertEqual(10, query.count(), \"10 readings after save\")\n for row in query.filter(MeterReading.occurred < start_dt):\n self.assertEqual(\n 96.0,\n sum(row.readings),\n \"existing readings for %s unchanged\" % row.occurred,\n )\n self.assertEqual(row.occurred, row.modified.date())\n for row in query.filter(MeterReading.occurred >= start_dt):\n self.assertEqual(\n 96.0 * 2, sum(row.readings), \"new readings added for %s\" % row.occurred\n )\n self.assertEqual(date.today(), row.modified.date())",
"title": ""
},
{
"docid": "eca9286b757a9aa8048856399fa434c4",
"score": "0.42085913",
"text": "def set_for_events(self, delete=True, **event_filter):\n\n # Delete existing EventResults (using denormalized Event fields)\n if delete:\n er_filter = {\n 'event_' + key: val for key, val in event_filter.items()\n }\n self.filter(**er_filter).delete()\n\n # Filter results (using JOIN through Report to Event)\n Result = swapper.load_model('vera', 'Result')\n result_filter = {\n 'report__event__' + key: val for key, val in event_filter.items()\n }\n ers = []\n results = Result.objects.valid_results(\n **result_filter\n ).select_related('report__event')\n for result in results:\n er = self.model(\n event=result.report.event,\n result=result\n )\n er.denormalize()\n ers.append(er)\n self.bulk_create(ers)",
"title": ""
},
{
"docid": "dff57a0cfa2c31a0d9576ce2b14d3e7b",
"score": "0.4207803",
"text": "def find_docs_to_update(coll,\n condition=None,\n progress_path=None,\n logger=None):\n if not condition or condition == [] or condition == {}:\n return coll.find()\n\n logger = logger or LOGGER\n progress_path = progress_path or PROGRESS_FILE\n method = condition['method']\n name = coll.name\n\n create_file_if_not_exists(progress_path, yaml.dump({}))\n\n if method == 'object_id':\n # Find all documents having IDs greater than the saved Object ID\n with open(progress_path, 'r') as input:\n start_id = yaml.load(input).get(name, '')\n\n if start_id == '':\n return coll.find().sort('_id')\n else:\n logger.info('starting from ObjectId: %s', start_id)\n return coll.find({ \"_id\": { \"$gt\": ObjectId(start_id) }}).sort('_id')\n\n elif method == 'date_delta':\n # Find all documents having 'date' field ≥ now() - delta\n delta = timedelta(**{ condition['unit']: condition['value']})\n start_date = (dt.now().date() - delta).strftime('%Y-%m-%d')\n\n logger.info('starting from date: %s', start_date)\n return coll.find({ 'date': { \"$gte\": start_date } })",
"title": ""
},
{
"docid": "ab82d8705fbb2d284798c5eb8e6f3087",
"score": "0.42074516",
"text": "def changes(self, since=-1):\n if since < 0:\n since = self.last_seq\n data = self.get('/%s/_changes/?since=%s' % (self.name, since))\n self.last_seq = data['last_seq']\n return data",
"title": ""
},
{
"docid": "924376f9e3cd792acbb6f9ff7ba2b93c",
"score": "0.41954598",
"text": "def insert_or_replace(self, collection, filter_data, data, by_user=None):\n if filter_data is None:\n \"\"\"\n for safety, just insert a new document when filter not provide\n \"\"\"\n result = None\n else:\n if isinstance(filter_data, str):\n filter_data = {self.PYRENEES_GUID_KEY: filter_data}\n result = self.find_one(collection, filter_data)\n if result is not None:\n self.delete(collection, {self.PYRENEES_GUID_KEY: result[self.PYRENEES_GUID_KEY]})\n data.update({\n '_db_created_time': result['_db_created_time'],\n '_db_created_by': result['_db_created_by'],\n '_db_updated_time': get_25(),\n '_db_updated_by': by_user.username if by_user else None,\n '_db_deleted': result['_db_deleted'],\n '_db_owner': result['_db_owner'],\n self.PYRENEES_GUID_KEY: result[self.PYRENEES_GUID_KEY],\n })\n self.insert(collection, data, by_user)\n result = data\n else:\n result = self.insert(collection, data, by_user)\n return result",
"title": ""
},
{
"docid": "3925065665166b820d854dae74bd45c3",
"score": "0.41932076",
"text": "def update_locations_filter(self):\n locations = []\n for doc in self.list_events():\n locations.append(doc[\"location\"])\n locations = list(set(locations))\n return self._update_filter(\"locations\", locations)",
"title": ""
},
{
"docid": "9295980659972baba3c6baac660131d0",
"score": "0.41906235",
"text": "def test_remote_local_new(self):\n results = get_diff_results(\n [self._run_names['test_project_2']], [self._report_dir_orig],\n '--new', 'json',\n ['--url', self._url])[0]\n\n self.assertEqual(\n len(results), 7)\n self.assertEqual(\n len(self.filter_by_checker(results, 'core.CallAndMessage')), 5)\n self.assertEqual(\n len(self.filter_by_checker(results, 'core.DivideZero')), 1)\n self.assertEqual(\n len(self.filter_by_checker(results, 'deadcode.DeadStores')), 1)",
"title": ""
},
{
"docid": "27fd64f24c3880e487147ea28d2da966",
"score": "0.41902626",
"text": "def test_post_confirm_relevance_with_old_date(self):\n house, *_, flat = self.init_house_structure()\n post, *_ = self.init_post(house, flat)\n\n post = Post.objects.first()\n post.created = datetime.datetime(2021, 6, 10, tzinfo=pytz.UTC)\n post.save()\n self.assertEqual(Post.objects.first().created.month, 6)\n\n url = reverse('main:posts-detail', args=[post.pk])\n response = self.client.patch(url, data={'created': True})\n self.assertEqual(response.status_code, 200)\n post = Post.objects.first()\n today = datetime.datetime.now(tz=pytz.UTC)\n self.assertEqual(post.created.month, today.month)\n self.assertEqual(post.created.day, today.day)",
"title": ""
}
]
4cfcf17fb7ed12a4a17e4ecac43019fe
Get version number from the pkmodel module. The easiest way would be to just ``import pkmodel ``, but note that this may fail if the dependencies have not been installed yet. Instead, we've put the version number in a simple version_info module, that we'll import here by temporarily adding the oxrse directory to the pythonpath using sys.path.
[
{
"docid": "1f2fc9112122d6cd672fa39a911acc3b",
"score": "0.8605629",
"text": "def get_version():\n import os\n import sys\n\n sys.path.append(os.path.abspath('pkmodel'))\n from version_info import VERSION as version\n sys.path.pop()\n\n return version",
"title": ""
}
]
Global application exception handler. For "basic" errors (I/O errors, keyboard interrupt, etc.) just the error message is printed as there's generally no need to confuse the user with a complete stack trace when it's just a missing file. Other exceptions, however, are logged with the usual full stack trace.
[
{
"docid": "59fb904953040acebe4029f7cf6808ee",
"score": "0.0",
"text": "def error_handler(exc_type, exc_value, exc_trace):\n if issubclass(exc_type, (SystemExit,)):\n # Exit with 0 (\"success\") for system exit (as it was intentional)\n return 0\n elif issubclass(exc_type, (KeyboardInterrupt,)):\n # Exit with 2 if the user deliberately terminates with Ctrl+C\n return 2\n elif issubclass(exc_type, (configargparse.ArgumentError,)):\n # For option parser errors output the error along with a message\n # indicating how the help page can be displayed\n logging.critical(str(exc_value))\n logging.critical('Try the --help option for more information.')\n return 2\n elif issubclass(exc_type, (IOError,)):\n # For simple errors like IOError just output the message which\n # should be sufficient for the end user (no need to confuse them\n # with a full stack trace)\n logging.critical(str(exc_value))\n return 1\n else:\n # Otherwise, log the stack trace and the exception into the log\n # file for debugging purposes\n for line in traceback.format_exception(exc_type, exc_value, exc_trace):\n for msg in line.rstrip().split('\\n'):\n logging.critical(msg.replace('%', '%%'))\n return 1",
"title": ""
}
]
[
{
"docid": "cfc6405b65231024c8845a59cdfa5f6b",
"score": "0.74804026",
"text": "def global_exception_handler(exception: Exception) -> None:\n log.error('occur %s %s', exception.__class__.__name__, str(exception.args))\n print(exception)",
"title": ""
},
{
"docid": "9d3b6a24b4f6e9bea2a7dac70411c627",
"score": "0.7275831",
"text": "def log_exception_as_error():\n AppLogger._app.logger.error(traceback.format_exc())",
"title": ""
},
{
"docid": "85bd91358c39b96b24777a13389269c2",
"score": "0.71941674",
"text": "def handle_general_exception(self):\n self._session_handler.ui().error(\"An error occured: %s\\n%s\" % (\n sys.exc_info()[0], traceback.format_exc(5)))",
"title": ""
},
{
"docid": "d77c2dc9c8b2552efc5a9d8a6d198f60",
"score": "0.68699837",
"text": "def _exception_handler (self, message, e=None, fatal=False):\n\n if e:\n v, t, tb = sys.exc_info()\n stack_trace = \"\".join(traceback.format_tb(tb))\n\n self._err(\"%s\\n\\n%s\\n\\n%s\" % (message, e, stack_trace))\n else:\n self._err(\"%s\" % message)\n\n if fatal:\n # we perform a hard exit here to kill all threads.\n os._exit(1)",
"title": ""
},
{
"docid": "c5ac4ac075f549057a2a6810bfe20a40",
"score": "0.681249",
"text": "def my_error_handler(exc_info):\n traceback.print_exception(*exc_info)",
"title": ""
},
{
"docid": "b7bb7e9bed26b7f1844d7ea4a27959b4",
"score": "0.68049604",
"text": "def _log_exceptions_in_root_logger() -> None:\n root_logger = logging.getLogger()\n def handle_exception(exc_type, exc_value, exc_traceback):\n root_logger.error(\"Uncaught exception\", exc_info=(exc_type, exc_value, exc_traceback))\n sys.excepthook = handle_exception",
"title": ""
},
{
"docid": "3cdecb26f681d995ac404e6de4546f0c",
"score": "0.6766457",
"text": "def exception_handler(app):\r\n mail_handler = SMTPHandler((app.config.get('MAIL_SERVER'),\r\n app.config.get('MAIL_PORT')),\r\n app.config.get('MAIL_USERNAME'),\r\n [app.config.get('MAIL_USERNAME')],\r\n '[Exception handler] A 5xx was thrown',\r\n (app.config.get('MAIL_USERNAME'),\r\n app.config.get('MAIL_PASSWORD')),\r\n secure=())\r\n\r\n mail_handler.setLevel(logging.ERROR)\r\n mail_handler.setFormatter(logging.Formatter(\"\"\"\r\n Time: %(asctime)s\r\n Message type: %(levelname)s\r\n\r\n\r\n Message:\r\n\r\n %(message)s\r\n \"\"\"))\r\n app.logger.addHandler(mail_handler)\r\n\r\n return None",
"title": ""
},
{
"docid": "e7ce1e82b25862d7fd0057d2685aaaad",
"score": "0.6723797",
"text": "def log_error_with_exception(self, message):\n self._devide_app.log_error_with_exception(message)",
"title": ""
},
{
"docid": "a0e988fac525d4dd1ebccc3355169ef6",
"score": "0.66745836",
"text": "def _log_error(self):\n pymsg = \"PYTHON ERRORS:\\nTraceback info:\\n{1}\\nError Info:\\n{0}\".format(str(sys.exc_info()[1]), \"\".join(traceback.format_tb(sys.exc_info()[2])))\n self._log_message(pymsg, True)",
"title": ""
},
{
"docid": "1548d9067b902f6df561610109129045",
"score": "0.66662747",
"text": "def handle_exception(exc):\n current_app.logger.exception(exc)\n return u.err_rep(500)",
"title": ""
},
{
"docid": "5c4473a1492ebf9ee1a484441a840031",
"score": "0.6644709",
"text": "def except_hook(exc_type, exc_value, traceback_obj):\r\n separator = '-' * 80\r\n log_file = \"error.log\"\r\n notice = '''An unhandled exception occurred. Please report the problem\r\n via email to <a href=\"mailto:[email protected]\">[email protected]</a>.<br>\r\n A log has been written to \"<i>error.log</i>\" in your application folder.<br><br>\r\n Error information:\\n'''\r\n time_string = time.strftime(\"%Y-%m-%d, %H:%M:%S\")\r\n machine_name = os.getenv('COMPUTERNAME')\r\n user_name = os.getenv('USERNAME')\r\n\r\n tb_info_file = StringIO()\r\n traceback.print_tb(traceback_obj, None, tb_info_file)\r\n tb_info_file.seek(0)\r\n tb_info = tb_info_file.read()\r\n error_message = '{0:s}: \\n{1:s}'.format(str(exc_type), str(exc_value))\r\n sections = [separator, time_string,\r\n 'Username: {0:s}'.format(user_name),\r\n 'Machine: {0:s}'.format(machine_name),\r\n 'Version: {0:s}'.format(VERSION),\r\n separator, error_message,\r\n separator, tb_info]\r\n msg = '\\n'.join(sections)\r\n try:\r\n with open(log_file, 'w') as f:\r\n f.write(msg)\r\n f.write(VERSION)\r\n except IOError:\r\n pass\r\n message_box_error(notice, str(msg))",
"title": ""
},
{
"docid": "9254a554c094ba62f25c930118365add",
"score": "0.66414803",
"text": "def exception(self, msg, *args, **kwargs):\n msg, kwargs = self.process(msg, kwargs)\n kwargs[\"exc_info\"] = 1\n self.logger.error(msg, *args, **kwargs)",
"title": ""
},
{
"docid": "edd7d972c99bde32211d920c34e0ac1f",
"score": "0.6636891",
"text": "def set_global_exception():\n def handle_exception(loop, context):\n import sys\n sys.print_exception(context[\"exception\"])\n sys.exit()\n loop = asyncio.get_event_loop()\n loop.set_exception_handler(handle_exception)",
"title": ""
},
{
"docid": "61b5fb584fb1868d5ee661b9b76c0272",
"score": "0.65881336",
"text": "def exception_error_handler(error):\r\n log4py.error('## exception_error_handler ##')\r\n log4py.error(error)",
"title": ""
},
{
"docid": "7e87d5dda8fa1214ab575b26d49e5a9d",
"score": "0.6557283",
"text": "def init_globexc(filename=None, context=None):\n\tglobal DEFAULT_STACK_TRACE_FILENAME\n\tglobal CONTEXT_LINES\n\n\tif filename is not None:\n\t\tDEFAULT_STACK_TRACE_FILENAME = filename\n\n\tif context is not None:\n\t\tCONTEXT_LINES = context\n\n\tsys.excepthook = global_exception",
"title": ""
},
{
"docid": "56003dc28cf6754c5ba50011946321ce",
"score": "0.654906",
"text": "def __global_logging_exception_handler(\n exc_type,\n exc_value,\n exc_traceback,\n _logger=logging.getLogger(__name__),\n _stderr=sys.__stderr__,\n _format_exception=traceback.format_exception,\n):\n if exc_type.__name__ == \"KeyboardInterrupt\":\n # Do not log the exception or display the traceback on Keyboard Interrupt\n # Stop the logging queue listener thread\n if is_mp_logging_listener_configured():\n shutdown_multiprocessing_logging_listener()\n return\n\n # Log the exception\n msg = \"An un-handled exception was caught by salt's global exception handler:\"\n try:\n msg = \"{}\\n{}: {}\\n{}\".format(\n msg,\n exc_type.__name__,\n exc_value,\n \"\".join(_format_exception(exc_type, exc_value, exc_traceback)).strip(),\n )\n except Exception: # pylint: disable=broad-except\n msg = \"{}\\n{}: {}\\n(UNABLE TO FORMAT TRACEBACK)\".format(\n msg,\n exc_type.__name__,\n exc_value,\n )\n try:\n _logger.error(msg)\n except Exception: # pylint: disable=broad-except\n # Python is shutting down and logging has been set to None already\n try:\n _stderr.write(msg + \"\\n\")\n except Exception: # pylint: disable=broad-except\n # We have also lost reference to sys.__stderr__ ?!\n print(msg)\n\n # Call the original sys.excepthook\n try:\n sys.__excepthook__(exc_type, exc_value, exc_traceback)\n except Exception: # pylint: disable=broad-except\n # Python is shutting down and sys has been set to None already\n pass",
"title": ""
},
{
"docid": "dbe370a30240ba10e40cd54ad7d14a9c",
"score": "0.65303767",
"text": "def error_handler(error):\n\n\t# Get the traceback\n\ttrace = str(traceback.format_exc())\n\tif app.debug:\n\t\tdebug = trace\n\telse:\n\t\tdebug = \"Ask your system administrator to consult the error log for this application.\"\n\n\tif 'username' in session:\n\t\tusername = session['username']\n\telse:\n\t\tusername = 'Not logged in'\n\n\t## Log the critical error (so that it goes to e-mail)\n\tapp.logger.error(\"\"\"Fatal Error\nHTTP Path: %s\nHTTP Method: %s\nClient IP Address: %s\nUser Agent: %s\nUser Platform: %s\nUser Browser: %s\nUser Browser Version: %s\nUsername: %s\nTraceback:\n%s\n\"\"\" % (\n\n\t\t\trequest.path,\n\t\t\trequest.method,\n\t\t\trequest.remote_addr,\n\t\t\trequest.user_agent.string,\n\t\t\trequest.user_agent.platform,\n\t\t\trequest.user_agent.browser,\n\t\t\trequest.user_agent.version,\n\t\t\tusername,\n\t\t\ttrace,\t\t\t\n\t\t))\n\n\treturn bargate.lib.errors.fatalerr(debug=debug)",
"title": ""
},
{
"docid": "d6b39b3fdbcf6930c1003ad1138770b7",
"score": "0.6508926",
"text": "def logUnhandledException(exc_type, exc_value, exc_traceback):\n\n filename, line, dummy, dummy = \\\n traceback.extract_tb(exc_traceback).pop()\n filename = os.path.basename(filename)\n error = \"%s: %s\" % (str(exc_type).split(\".\")[-1], exc_value)\n msg = error + \" on line %d, file %s\" % (line, filename) \n errorbox = PyQt4.QtGui.QMessageBox()\n errorbox.setText(\"Unhandled exception:\\n\"+msg)\n errorbox.exec_()\n file(\"/tmp/procexp.log\",\"ab\").write(msg+\"\\n\")",
"title": ""
},
{
"docid": "d86775a8139889e89b2655d118b161f0",
"score": "0.6508741",
"text": "def init_email_error_handler(application):\r\n if application.debug: return # Do not send error emails while developing\r\n\r\n # Retrieve email settings from application.config\r\n host = application.config['MAIL_SERVER']\r\n port = application.config['MAIL_PORT']\r\n from_addr = application.config['MAIL_DEFAULT_SENDER']\r\n username = application.config['MAIL_USERNAME']\r\n password = application.config['MAIL_PASSWORD']\r\n secure = () if application.config.get('MAIL_USE_TLS') else None\r\n\r\n # Retrieve application settings from application.config\r\n to_addr_list = application.config['ADMINS']\r\n subject = application.config.get('application_SYSTEM_ERROR_SUBJECT_LINE', 'System Error')\r\n\r\n # Setup an SMTP mail handler for error-level messages\r\n import logging\r\n from logging.handlers import SMTPHandler\r\n\r\n mail_handler = SMTPHandler(\r\n mailhost=(host, port), # Mail host and port\r\n fromaddr=from_addr, # From address\r\n toaddrs=to_addr_list, # To address\r\n subject=subject, # Subject line\r\n credentials=(username, password), # Credentials\r\n secure=secure,\r\n )\r\n mail_handler.setLevel(logging.ERROR)\r\n application.logger.addHandler(mail_handler)\r\n\r\n # Log errors using: application.logger.error('Some error message')\r",
"title": ""
},
{
"docid": "6600e91d11f01e372eaa17535aa9fa05",
"score": "0.6507649",
"text": "def dump_exception() -> None:\n import traceback\n print(traceback.format_exc())\n terminate_app()",
"title": ""
},
{
"docid": "8881d705ec3a4eec7a85beb9a474cfa8",
"score": "0.6501494",
"text": "def ucx_exception_handler(loop, context):\n msg = context.get(\"exception\", context[\"message\"])\n print(msg)",
"title": ""
},
{
"docid": "5e29a3928d27494a8e6f913f9a82d730",
"score": "0.6486939",
"text": "def uncaught_error_handler(e):\n\n app.logger.exception(e)\n error_message = 'Internal Server Error: {}'.format(repr(e))\n return (jsonify(status='error', message=error_message), 500)",
"title": ""
},
{
"docid": "9b4514b4da1d5dfe1f951977003cbd18",
"score": "0.6445176",
"text": "def application_error(e):\n return 'Sorry, unexpected error: {}'.format(e), 500",
"title": ""
},
{
"docid": "9b4514b4da1d5dfe1f951977003cbd18",
"score": "0.6445176",
"text": "def application_error(e):\n return 'Sorry, unexpected error: {}'.format(e), 500",
"title": ""
},
{
"docid": "9b4514b4da1d5dfe1f951977003cbd18",
"score": "0.6445176",
"text": "def application_error(e):\n return 'Sorry, unexpected error: {}'.format(e), 500",
"title": ""
},
{
"docid": "9b4514b4da1d5dfe1f951977003cbd18",
"score": "0.6445176",
"text": "def application_error(e):\n return 'Sorry, unexpected error: {}'.format(e), 500",
"title": ""
},
{
"docid": "b5063f33b7c2cb6393acae2e345cc9a6",
"score": "0.64441085",
"text": "def on_import_error(\n self, exc: Exception, module_path: Text, context: DictObject\n ):\n self.log.exception(f'encountered import error in {module_path}')",
"title": ""
},
{
"docid": "ce9a66ca8ea6bb73656a37122399caef",
"score": "0.63835645",
"text": "def _log_exception():\n exc = traceback.format_exception(*sys.exc_info())\n rospy.logerr(''.join(exc))",
"title": ""
},
{
"docid": "f7c8a7a49f13c7b442072e2660b02edb",
"score": "0.63798857",
"text": "def handleException(e,path):\r\n if isinstance(e,EnvironmentError):\r\n if os.name=='nt' and isinstance(e,WindowsError):\r\n if e.errno in [2,3]:\r\n _raisePathNotFoundError(e,path)\r\n \r\n if isinstance(e,OSError) or isinstance(e,IOError):\r\n if e.errno==errno.ENOENT:\r\n _raisePathNotFoundError(e,path)\r\n if e.errno==errno.EINVAL:\r\n raise ufsi.InvalidArgumentError('Invalid argument.',e)\r\n\r\n\r\n raise e",
"title": ""
},
{
"docid": "b9990a93c98ceac71a0c16fffd707904",
"score": "0.6372835",
"text": "def print_uncaught_exception():\n print traceback.format_exc()",
"title": ""
},
{
"docid": "01a7ae5833cb5b2fefe5f9ae0ffadceb",
"score": "0.6362596",
"text": "def handle_error(e):\n logging.error(str(e) + '\\n...Exiting program...')\n sys.exit(0)",
"title": ""
},
{
"docid": "031dfcc793f3ed59c478cc9f03d6a961",
"score": "0.6352487",
"text": "def defualt_error_handler(errr):\n s = StringIO()\n traceback.print_exc(file=s)\n text = s.getvalue()\n print(text)\n us = get_user_session()\n model.Error.new(us.id, subject='Python Error', text=text)",
"title": ""
},
{
"docid": "676dbee5f3379b8a5b3a007a8d76d6e4",
"score": "0.63474107",
"text": "def unhandled_error_handler(self, error):\n # TODO: decide how to pass this error to the app\n # TODO: if there's an error in the app handler, print it and exit\n pass",
"title": ""
},
{
"docid": "2f67e96db07e75f6170e4b0fb88b8d0b",
"score": "0.6336021",
"text": "def _error_handler(msg, system_exit=True):\n LOG.error(msg)\n if system_exit is True:\n raise SystemExit(msg)",
"title": ""
},
{
"docid": "189f957e0f01fdaca8e46e7b077c9c47",
"score": "0.6316678",
"text": "def on_exception(self, context, exception):\n try:\n raise exception\n except LibraryError, e:\n tag = 'gchecky'\n error = 'Gchecky bug'\n except SystemError, e:\n tag = 'system'\n error = 'System failure'\n except HandlerError, e:\n tag = 'handler'\n error = 'Error in user handler method'\n except DataError, e:\n tag = 'data'\n error = 'Error converting data to/from XML'\n except:\n # Should never happen...\n tag = 'unknown'\n error = 'Unknown error'\n\n description = \"%s:\\n%s\\n\\nOriginal Error:\\n%s\\n%s\" % (error,\n exception,\n exception.origin,\n exception.traceback)\n # TODO: exception trace\n self.__log(context=context, tag=tag, error=error, description=description)\n return \"%s\\n\\n%s\" % (error, description)",
"title": ""
},
{
"docid": "2937bfbbe1fe284fbff2c0f508ff68f1",
"score": "0.6286468",
"text": "def record_exception():\r\n import traceback\r\n traceback.print_exc()",
"title": ""
},
{
"docid": "6ee7d87290912984018b2153f5089723",
"score": "0.62625086",
"text": "def setup_exceptions():\n # first set up the variables needed by the _excepthook function\n # pylint: disable=global-statement\n global _print_traceback, _drill\n local_print_traceback = os.getenv(\"PYLOGCONF_PRINT_TRACEBACK\")\n if local_print_traceback is not None:\n _print_traceback = _str2bool(local_print_traceback)\n local_drill = os.getenv(\"PYLOGCONF_DRILL\")\n if local_drill is not None:\n _drill = _str2bool(local_drill)\n # now that everything is ready attach the hook\n sys.excepthook = _excepthook",
"title": ""
},
{
"docid": "a0e2f25a546985c8d2b497e3e82637a2",
"score": "0.6249556",
"text": "def exception(self, msg, *args, **kwargs):\r\n\r\n kwargs[\"exc_info\"] = 1\r\n self.log(logging.ERROR, msg, *args, **kwargs)",
"title": ""
},
{
"docid": "1b4b57fb4c77b5a179ff9a4b41660732",
"score": "0.62373877",
"text": "def handle_error(self, request, client_address):\n print '-' * 40\n print 'Exception happened during processing of request from',\n print client_address\n import traceback\n traceback.print_exc() # XXX But this goes to stderr!\n print '-' * 40",
"title": ""
},
{
"docid": "51ace34acf8d4cccb703d69da914d239",
"score": "0.62038326",
"text": "def exception ():\n \n try:\n type, value, tb = sys.exc_info ()\n info = traceback.extract_tb (tb)\n filename, lineno, function, text = info[-1] # last line only\n print_err (\"Exception: %s:%d: %s: %s (in %s)\" %\\\n (filename, lineno, type.__name__, str (value), function))\n finally:\n type = value = tb = None # clean up",
"title": ""
},
{
"docid": "4230da2047f3cc0094863ecbe2b9db7a",
"score": "0.6197903",
"text": "def ErrorHandler(app, global_conf, **errorware):\n if asbool(global_conf.get('debug')):\n from pylons.error import PylonsEvalException\n app = PylonsEvalException(app, global_conf, **errorware)\n else:\n from paste.exceptions.errormiddleware import ErrorMiddleware\n if 'error_template' in errorware:\n del errorware['error_template']\n app = ErrorMiddleware(app, global_conf, **errorware)\n return app",
"title": ""
},
{
"docid": "6d7cdf181edef58e7b2a5a5575814e8d",
"score": "0.61955386",
"text": "def log_exception(self, typ, value, tb):\r\n if isinstance(value, HTTPError):\r\n if value.log_message:\r\n format = \"%d %s: \" + value.log_message\r\n args = ([value.status_code, self._request_summary()] +\r\n list(value.args))\r\n gen_log.warning(format, *args)\r\n else:\r\n app_log.error(\"Uncaught exception %s\\n%r\", self._request_summary(),\r\n self.request, exc_info=(typ, value, tb))",
"title": ""
},
{
"docid": "22a3b37f5382519f59b1022d211735d1",
"score": "0.6169691",
"text": "def traceback(self):\n self.log.error(\"There are something error appear.\")\n traceback.print_exc()",
"title": ""
},
{
"docid": "f27451eed171d256da1f74cfcec377b8",
"score": "0.6168399",
"text": "def exceptions(e):\n ts = strftime('[%Y-%b-%d %H:%M]')\n tb = traceback.format_exc()\n logger.info('%s %s %s %s %s 5xx INTERNAL SERVER ERROR\\n%s',\n ts,\n request.remote_addr,\n request.method,\n request.scheme,\n request.full_path,\n tb)\n return \"Internal Server Error\", 500",
"title": ""
},
{
"docid": "b209c85ae054be5bc9d4b5f605c00766",
"score": "0.6168318",
"text": "def exception(self, msg, *args, **kwargs):\r\n self.log.exception(msg, *args, **kwargs)",
"title": ""
},
{
"docid": "3c64b64416776b83f2d95ed282330d68",
"score": "0.6156911",
"text": "def error_handler(e):\n logging.error(\"error_handler for socketio. An error has occurred: \" + str(e))",
"title": ""
},
{
"docid": "23ddbd8869f6d1cee21db2ba5be1cfb8",
"score": "0.6133701",
"text": "def internal_error_handler(self, exception):\n\n logging.exception(exception)\n\n if self._app.debug:\n return ErrorResponse(message=str(exception), debug_data=format_exception(exception))\n\n return ErrorResponse(message=\"Internal server error\")",
"title": ""
},
{
"docid": "2b0687118ae33e5cb70d99021b671e35",
"score": "0.6126399",
"text": "def _exception_handler(error_type, error_value, error_traceback):\n logging.exception(\"Uncaught exception {} {}\".format(\n str(error_type), str(error_value)))\n tb = traceback.format_exception(\n error_type, error_value, error_traceback)\n traceback_string = ''\n for ln in tb:\n traceback_string += ln\n logging.exception(traceback_string)",
"title": ""
},
{
"docid": "c019f8045acea1f9d318ae55b50392c0",
"score": "0.6105268",
"text": "def application_error(e):\n return 'Sorry, unexpected error: {}'.format(e) + \"\\n\" + request.form['text'], 500",
"title": ""
},
{
"docid": "91d78d5b5a68583b39b7639d4c56eac3",
"score": "0.6099875",
"text": "def exceptions(e):\n ts = strftime('[%Y-%b-%d %H:%M]')\n tb = traceback.format_exc()\n logger.error('%s %s %s %s %s 5xx INTERNAL SERVER ERROR\\n%s',\n ts,\n request.remote_addr,\n request.method,\n request.scheme,\n request.full_path,\n tb)\n return \"Internal Server Error\", 500",
"title": ""
},
{
"docid": "91d78d5b5a68583b39b7639d4c56eac3",
"score": "0.6099875",
"text": "def exceptions(e):\n ts = strftime('[%Y-%b-%d %H:%M]')\n tb = traceback.format_exc()\n logger.error('%s %s %s %s %s 5xx INTERNAL SERVER ERROR\\n%s',\n ts,\n request.remote_addr,\n request.method,\n request.scheme,\n request.full_path,\n tb)\n return \"Internal Server Error\", 500",
"title": ""
},
{
"docid": "1a6ab76d3ef1331479191b6f643d9b36",
"score": "0.6095173",
"text": "def exceptions(e):\n ts = strftime('[%Y-%b-%d %H:%M]')\n tb = traceback.format_exc()\n logger.error('%s %s %s %s %s 5xx INTERNAL SERVER ERROR\\n%s',\n ts,\n request.remote_addr,\n request.method,\n request.scheme,\n request.full_path,\n tb)\n return \"Internal Server Error\", 500",
"title": ""
},
{
"docid": "80c30afb950692f90f0b211d98df9e9e",
"score": "0.6085739",
"text": "def setup_error_handler(app):\n @app.errorhandler(Exception)\n def handle_error(e):\n code = 500\n if isinstance(e, HTTPException):\n code = e.code\n error_dict = dict(code=code, message=str(e))\n response = jsonify(error_dict)\n response.status_code = code\n\n # Re-raise server errors during debugging\n if code == 500 and app.debug:\n raise\n return response",
"title": ""
},
{
"docid": "5f712192dad76132e05f4a5723cc9865",
"score": "0.60839593",
"text": "def custom_exception_handler(exc, context):\n response = exception_handler(exc, context)\n response = _build_error_response(response, exc, context)\n custom_errlog(exc, context, response.data)\n return response",
"title": ""
},
{
"docid": "f9288e6f597ca7fc112cf463ad3fc96b",
"score": "0.6070603",
"text": "def handle_exception(self, e, *args, **kwargs):\n raise",
"title": ""
},
{
"docid": "e67e01b925c2c9dfa5ef3b90abf12a87",
"score": "0.6062612",
"text": "def log_error(e):\n print(e)",
"title": ""
},
{
"docid": "2a4940c717cffbd0b19fcc92751cb99b",
"score": "0.60462797",
"text": "def test_python_unhandled_exception(self):\n data = self._read_test_data('python_unhandled_exception.txt')\n expected_type = 'Uncaught exception'\n expected_address = ''\n expected_state = '_read_exact\\n_read_gzip_header\\nread\\n'\n\n expected_stacktrace = data\n expected_security_flag = False\n self._validate_get_crash_data(data, expected_type, expected_address,\n expected_state, expected_stacktrace,\n expected_security_flag)",
"title": ""
},
{
"docid": "4e512f4581bcd540ca9dfb36a9e74171",
"score": "0.60423297",
"text": "def log_error(e):\n\tprint(e)",
"title": ""
},
{
"docid": "d1b4db53288bd49320fddf02e1ffffc0",
"score": "0.60336524",
"text": "async def on_error(self, event, *args, **kwargs):\n\n with open('err.log', 'a') as f:\n if event == 'on_message':\n f.write(f'Unhandled message: {args[0]}\\n')\n else:\n f.write(f'Error: {args[0]}\\n')\n raise",
"title": ""
},
{
"docid": "0859d20e49d15ecaebd9f78c3ed32f27",
"score": "0.60303277",
"text": "def exception_handler(self, url=None, ex=None):\n print(\"An error occurred. Exception: {}\".format(ex))\n self._stop = True",
"title": ""
},
{
"docid": "95e7d6cd5c0b6ddc199f69390191f3ba",
"score": "0.60263324",
"text": "def handle_exception(self):\r\n self._exceptions.append(sys.exc_info()[1])",
"title": ""
},
{
"docid": "fc77ada59b000258512fd371adf2740b",
"score": "0.6020354",
"text": "def err_500():\n # return '500 page', 500\n import json, os, errno, random, traceback\n try:\n raise ValueError('value')\n except Exception as e:\n data = {\n \"serviceContext\": {\n \"service\": \"default\",\n \"version\": os.environ.get('GAE_MODULE_VERSION', 'default')\n },\n \"message\": traceback.format_exc(),\n }\n filename = '/var/log/app_engine/custom_logs/errors.log.json'\n if not os.path.exists(os.path.dirname(filename)):\n try:\n os.makedirs(os.path.dirname(filename), 0777)\n except OSError as exc:\n if exc.errno != errno.EEXIST:\n raise\n with open(filename, 'a+') as file:\n file.write('\\n'+json.dumps(data))\n raise\n # raise\n return '500 page', 500",
"title": ""
},
{
"docid": "03cfa14ee87cb53013d2aa060fa5b8bf",
"score": "0.6020314",
"text": "def handle_exec_exception(self, e):\n if self.parser.debug:\n raise\n # output user error if one exists otherwise show debugging traceback\n exc = find_user_exception(e)\n if exc is not None:\n # allow exception attribute to override user verbosity level\n if getattr(exc, \"_verbosity\", None) is not None:\n verbosity = exc._verbosity\n else:\n verbosity = getattr(self.parser, \"verbosity\", 0)\n # output verbose error message if it exists\n if verbosity > 0:\n msg = exc.msg(verbosity).strip(\"\\n\")\n if msg:\n self.err.write(msg)\n raise SystemExit\n self.parser.error(exc)\n raise",
"title": ""
},
{
"docid": "a1d459a9fbc6a0545cbfd3cca45539f7",
"score": "0.6017624",
"text": "def exception(self, msg, *args, exc_info=True, **kwargs):\n self.logger.exception(msg, *args, exc_info=True, **kwargs)",
"title": ""
},
{
"docid": "ba7c31dfe0d23e87bc2d6011c3deb227",
"score": "0.6011726",
"text": "def fileError():\n\n\t# print error message\n\tprint('[-] %s%sAn error occured with a file operation.%s' % (fg(233), bg(9), attr(0)))\n\n\t# exit program\n\texit(0)",
"title": ""
},
{
"docid": "1c4aff85ed0c257ec5b7290a57528b6f",
"score": "0.6011141",
"text": "def log_error(e):\r\n print(e)",
"title": ""
},
{
"docid": "1c4aff85ed0c257ec5b7290a57528b6f",
"score": "0.6011141",
"text": "def log_error(e):\r\n print(e)",
"title": ""
},
{
"docid": "26a992f44fedb06d3ad3d3786c7421ca",
"score": "0.60001796",
"text": "def errorHandler(self, msg):\n print \"ERROR:\", msg",
"title": ""
},
{
"docid": "bd82197556fb943986bc7f5de01e36a3",
"score": "0.59972346",
"text": "def exceptions(e):\n\n # NOTE: Capture werkzeug exceptions and propagate them to sentry.\n capture_exception(e)\n\n # NOTE: Capture traceback for dumping it to the log.\n tb = traceback.format_exc()\n\n if hasattr(e, \"code\") and e.code == 404:\n service_log.error(\n \"{} {} {} {} 404 NOT FOUND\\n{}\".format(\n request.remote_addr, request.method, request.scheme, request.full_path, tb\n )\n )\n return error_response(HTTP_SERVER_ERROR - e.code, e.name)\n\n if hasattr(e, \"code\") and e.code >= 500:\n service_log.error(\n \"{} {} {} {} 5xx INTERNAL SERVER ERROR\\n{}\".format(\n request.remote_addr, request.method, request.scheme, request.full_path, tb\n )\n )\n return error_response(HTTP_SERVER_ERROR - e.code, e.name)\n\n # NOTE: Werkzeug exceptions should be covered above, following line is for unexpected HTTP server errors.\n return error_response(HTTP_SERVER_ERROR, str(e))",
"title": ""
},
{
"docid": "54312ad70630fdb4c0c3dbf014ccd267",
"score": "0.59959984",
"text": "def configure_exceptions(app, api, custom):\n for code in default_exceptions:\n app.errorhandler(code)(handle_error)\n if api:\n api.handle_error = handle_error\n for exc in custom:\n app.errorhandler(exc)(handle_error)",
"title": ""
},
{
"docid": "fdbd9928e0c95e3f7c41d6d93e3348ba",
"score": "0.5995416",
"text": "def handle_exception(self):\n self._exceptions.append(sys.exc_info()[1])",
"title": ""
},
{
"docid": "f3d14d11c9b62ddba94cafae5764199d",
"score": "0.5993878",
"text": "def server_error(e):\n app.logger.error(f\"Server error: {e}, route: {request.url}\")\n return render_template('500.html'), 500",
"title": ""
},
{
"docid": "663132f511b96290041f278b95d98616",
"score": "0.5991694",
"text": "def global_exception(exc_type, exc_value, exc_traceback):\n\n\t# Walk to the end of the stack trace to find the line that actually raised the exception\n\tacc = exc_traceback\n\twhile acc is not None:\n\t\tfilename = acc.tb_frame.f_code.co_filename\n\t\tlineno = acc.tb_lineno\n\t\tacc = acc.tb_next\n\n\t# Print a brief summary of the exception to the log.\n\t# We suppress this if the exception was broken pipe because there is a good\n\t# change all that happened is the user was piping to more/less and pressed 'q'\n\tif not (exc_type is IOError and exc_value.errno == 32):\n\t\tlogger.critical('Exception {cls} ({filename}:{lineno}) {message}. '\n\t\t\t\t\t\t'Trace file written to \\'{trace}\\'.'.format(\n\t\t\t\tcls=exc_type,\n\t\t\t\t# cls=str(exc_type).replace('<class \\'', '').replace('\\'>', ''),\n\t\t\t\tmessage=unicode(exc_value),\n\t\t\t\tfilename=filename,\n\t\t\t\tlineno=lineno,\n\t\t\t\ttrace=expanded_filename()))\n\t\t# be very paranoid here as we are reading undocumented attributes of logging\n\t\tif hasattr(logger, 'manager') and \\\n\t\t\t\tgetattr(logger.manager, 'emittedNoHandlerWarning') is True:\n\t\t\t# make sure the user sees something even if logging is not initialised\n\t\t\t# (as of python 2.7.5 the logging module suppresses all output if not\n\t\t\t# initialised\n\t\t\tprint('Trace file written to {trace}'.format(\n\t\t\t\t\ttrace=expanded_filename()))\n\n\t# Print an even briefer version to stderr if it is an interactive terminal\n\tif sys.stderr.isatty():\n\t\tprint('\\n{message}\\n'.format(message=str(exc_value)))\n\n\t# Now write a full dump and stack trace to the trace file\n\twith open(expanded_filename(), 'w') as h:\n\t\th.write('Exception object:\\n'\n\t\t\t\t'{indent}Class: {cls}\\n'\n\t\t\t\t'{indent}Text: {value}\\n'\n\t\t\t\t'{indent}Attributes:\\n'.format(\n\t\t\t\t# cls=str(exc_type).replace('<class \\'', '').replace('<type \\'', '').replace(\n\t\t\t\t\t# '\\'>', ''),\n\t\t\t\tcls=exc_type,\n\t\t\t\tvalue=exc_value,\n\t\t\t\tindent=INDENTATION))\n\t\t# list all the attributes of the exception object\n\t\tfor key in dir(exc_value):\n\t\t\tif not key.startswith('_'):\n\t\t\t\th.write('{indent}{indent}{key}: {value}\\n'.format(\n\t\t\t\t\t\tindent=INDENTATION, key=key, value=getattr(exc_value, key)))\n\n\t\th.write('\\nCall stack:\\n')\n\t\tdisplay_tb(exc_traceback, indent=' ', target=h)",
"title": ""
},
{
"docid": "bb7f09aaf1f8baa906087633b67fbffe",
"score": "0.5989594",
"text": "def email_error(exp, message):\n # Always log a message about our fun\n cstr = StringIO.StringIO()\n traceback.print_exc(file=cstr)\n cstr.seek(0)\n tbstr = cstr.read()\n log.err( exp )\n log.msg( message )\n\n # Prevent a parser from sending tons of spam\n if int(os.environ.get('EMAILS', 10)) < 0:\n return\n os.environ['EMAILS'] = str( int(os.environ.get(\"EMAILS\",10)) - 1 )\n\n msg = MIMEText(\"\"\"\nEmails Left: %s Host: %s\n\nException:\n%s\n%s\n\nMessage:\n%s\"\"\" % (os.environ[\"EMAILS\"], socket.gethostname(), tbstr, exp, message))\n # Send the email already!\n msg['subject'] = '%s Traceback' % (sys.argv[0],)\n msg['From'] = settings.get('pywwa_errors_from', 'ldm@localhost')\n msg['To'] = settings.get('pywwa_errors_to', 'ldm@localhost')\n smtp.sendmail(\"smtp\", msg[\"From\"], msg[\"To\"], msg)",
"title": ""
},
{
"docid": "b841c561e5dcf453f7d46ef77a9be490",
"score": "0.59805",
"text": "def errorHandler(self, msg=''):\n print(\"Server Error: %s\", msg)",
"title": ""
},
{
"docid": "fe1eae43f7154afa0ab4a00d1e15c9ba",
"score": "0.5980101",
"text": "def together_exception_handler(self, config):",
"title": ""
},
{
"docid": "0f76476634212ad65d3e7411053db23e",
"score": "0.597248",
"text": "def log_exception(*args, **kwds):\r\n cls, err = sys.exc_info()[:2]\r\n logging.exception('Exception in request: %s: %s', cls.__name__, err)",
"title": ""
},
{
"docid": "7d99adbe00b14c9d6f9c2f14a28f06d2",
"score": "0.5967447",
"text": "def error_handler(self, exception):\n\n if self._app.debug:\n logging.exception(exception)\n return ErrorResponse(message=str(exception), debug_data=format_exception(exception))\n\n return ErrorResponse(message=str(exception))",
"title": ""
},
{
"docid": "b71d37a5ac2290e68c861cc4229f05a5",
"score": "0.59670025",
"text": "def unhandled_exception(error):\n app.logger.exception('Unhandled Exception: %s', error)\n return {\"message\": str(error)}, 400",
"title": ""
},
{
"docid": "853c22063470554b630571087e9acb00",
"score": "0.5966997",
"text": "def log_exception(self, context):\n exception_message = self.exception_format % context.log_vars\n self.logger.exception(exception_message)",
"title": ""
},
{
"docid": "1e89feb4523d2db249fca324476fff00",
"score": "0.5958501",
"text": "def internal_error(Exception):\n # log an error for flask\n APP.logger.error(Exception)\n # return rendered template\n return(flask.render_template(\"500.html\"))",
"title": ""
},
{
"docid": "cbedd114cd70623f6a437790cacdcc03",
"score": "0.59583366",
"text": "def handle_exception(self, e):\n handler = self.error_handlers.get(500)\n if self.debug:\n raise\n self.logger.exception('Exception on %s [%s]' % (\n request.path,\n request.method\n ))\n if handler is None:\n return InternalServerError()\n return handler(e)",
"title": ""
},
{
"docid": "f12a0a4fb01968dcacb07e98da5a131b",
"score": "0.59582615",
"text": "def print_exc(self):\n msg = self.format_exc()\n sys.stderr.write(msg)",
"title": ""
},
{
"docid": "162546b720485ae2e3ff09d3269146fa",
"score": "0.5951869",
"text": "def log_exception(*args, **kwds):\n logging.exception(\"Exception in request:\")",
"title": ""
},
{
"docid": "7004e70dc3261d6a54f73200ab8da297",
"score": "0.5941553",
"text": "def exception(__self, __message, *args, **kwargs): # noqa: N805\n options = (True,) + __self._options[1:]\n __self._log(\"ERROR\", False, options, __message, args, kwargs)",
"title": ""
},
{
"docid": "c95ac52503655dedc2f31d90fba61dd9",
"score": "0.59378076",
"text": "def log_error(self, e):\n print(e)",
"title": ""
},
{
"docid": "541f0509158dcfd60308a2414c98de34",
"score": "0.59342915",
"text": "def handle_error(self):\n type, v, t = sys.exc_info()\n if type == SystemExit:\n raise\n else:\n asynchat.async_chat.handle_error(self)",
"title": ""
},
{
"docid": "88c9e4dd84d0191ac13d335e705a0c53",
"score": "0.5919085",
"text": "def error_view(request):\n # Get the latest exception from Python system service\n (type, value, traceback1) = sys.exc_info()\n traceback1 = '.'.join(traceback.format_exception(type, value, traceback1))\n\n # Send email to admins\n subject = 'Error in ieeetags: %s, %s' % (str(type), value)\n message = traceback1\n mail_admins(subject, message, True)\n\n title = None\n message = None\n\n if type is util.EndUserException:\n title, message = value\n\n return render(request, '500.html', {\n 'title': title,\n 'message': message,\n })",
"title": ""
},
{
"docid": "514e6b8100fa5eb28a660d2a5797ce52",
"score": "0.59042305",
"text": "def log_error(e):\n print(e)",
"title": ""
},
{
"docid": "514e6b8100fa5eb28a660d2a5797ce52",
"score": "0.59042305",
"text": "def log_error(e):\n print(e)",
"title": ""
},
{
"docid": "514e6b8100fa5eb28a660d2a5797ce52",
"score": "0.59042305",
"text": "def log_error(e):\n print(e)",
"title": ""
},
{
"docid": "514e6b8100fa5eb28a660d2a5797ce52",
"score": "0.59042305",
"text": "def log_error(e):\n print(e)",
"title": ""
},
{
"docid": "514e6b8100fa5eb28a660d2a5797ce52",
"score": "0.59042305",
"text": "def log_error(e):\n print(e)",
"title": ""
},
{
"docid": "514e6b8100fa5eb28a660d2a5797ce52",
"score": "0.59042305",
"text": "def log_error(e):\n print(e)",
"title": ""
},
{
"docid": "514e6b8100fa5eb28a660d2a5797ce52",
"score": "0.59042305",
"text": "def log_error(e):\n print(e)",
"title": ""
},
{
"docid": "514e6b8100fa5eb28a660d2a5797ce52",
"score": "0.59042305",
"text": "def log_error(e):\n print(e)",
"title": ""
},
{
"docid": "514e6b8100fa5eb28a660d2a5797ce52",
"score": "0.59042305",
"text": "def log_error(e):\n print(e)",
"title": ""
},
{
"docid": "514e6b8100fa5eb28a660d2a5797ce52",
"score": "0.59042305",
"text": "def log_error(e):\n print(e)",
"title": ""
},
{
"docid": "514e6b8100fa5eb28a660d2a5797ce52",
"score": "0.59042305",
"text": "def log_error(e):\n print(e)",
"title": ""
},
{
"docid": "e16d8441ca7e4b918408c795934e3ce0",
"score": "0.59039974",
"text": "def catch_unhandled_exceptions(self):\n if not getattr(sys.excepthook, \"_wrapped\", False): # skip if already modified\n orig_hook = sys.excepthook\n\n def handle_exception(exc_type, exc_value, exc_traceback):\n if issubclass(exc_type, KeyboardInterrupt):\n sys.__excepthook__(exc_type, exc_value, exc_traceback)\n return\n logging.getLogger(\"Client\").exception(\"Uncaught exception\",\n exc_info=(exc_type, exc_value, exc_traceback))\n return orig_hook(exc_type, exc_value, exc_traceback)\n\n handle_exception._wrapped = True\n\n sys.excepthook = handle_exception",
"title": ""
},
{
"docid": "2a74e29512e47287459ced32d6c0e1e5",
"score": "0.59002215",
"text": "def error_handler(msg):\r\n print \"Server Error: %s\" % msg",
"title": ""
}
]
2c51f84424129b9c3d172d5617a4d18d
This endpoint does a special onetime bootstrap of the ACL system, making the first management token if the acl_master_token is not specified in the Consul server configuration, and if the cluster has not been bootstrapped previously. This is available in Consul 0.9.1 and later, and requires all Consul servers to be upgraded in order to operate. You can detect if something has interfered with the ACL bootstrapping by the response of this method. If you get a string response with the ``ID``, the bootstrap was a success. If the method raises a
[
{
"docid": "acecd658cbf3c6f2e8fd53588ee88c6c",
"score": "0.77933437",
"text": "def seq_swap_first_last(seq):\n return (seq if (len(seq) < 2) else seq[-1:] + seq[1:-1] + seq[0:1])",
"title": ""
},
{
"docid": "8085456ed54e7f29145e05cbbaaf882d",
"score": "0.76585096",
"text": "def exchange_first_last(seq):\n return seq[-1::] + seq[1:-1] + seq[0:1]",
"title": ""
},
{
"docid": "a9713b905368c7fbb586d0c50fd51237",
"score": "0.7462185",
"text": "def exchange_first_last(seq):\n if len(seq) <= 1:\n return seq\n else:\n return seq[-1:] + seq[1:-1] + seq[:1]",
"title": ""
},
{
"docid": "db82d92eab720b52a2f97c24cc76dc04",
"score": "0.6979666",
"text": "def seq_reverse(seq):\n return seq[::-1]",
"title": ""
},
{
"docid": "e44b2898e2a7385b03725781c400087c",
"score": "0.69596833",
"text": "def exchange_first_last(n):\n\n return n[-1:] + n[1:-1] + n[:1]",
"title": ""
},
{
"docid": "f7bc3ac0bc6a5df9212b01bd0bd55098",
"score": "0.6916659",
"text": "def reverse(seq):\n return seq[::-1]",
"title": ""
},
{
"docid": "f7bc3ac0bc6a5df9212b01bd0bd55098",
"score": "0.6916659",
"text": "def reverse(seq):\n return seq[::-1]",
"title": ""
},
{
"docid": "f7bc3ac0bc6a5df9212b01bd0bd55098",
"score": "0.6916659",
"text": "def reverse(seq):\n return seq[::-1]",
"title": ""
},
{
"docid": "78320931cf2e5fcd7e5720f81eab3be2",
"score": "0.6902493",
"text": "def reverse(sequence: str) -> str:\r\n return sequence[::-1]",
"title": ""
},
{
"docid": "30968fa2707b74211164c49553b5af6a",
"score": "0.6880139",
"text": "def mirror(s):\n mirrored = s[-1::-1]\n return s + mirrored",
"title": ""
},
{
"docid": "c54fa7a5704cefedf6816cdfe3c2bc08",
"score": "0.6778374",
"text": "def reverse_my_animals(sequence):\n rev_sequence = sequence[::-1]\n return rev_sequence",
"title": ""
},
{
"docid": "e56bcf1a041c78a0c57b0c5a5771f57c",
"score": "0.672243",
"text": "def reverse_pair(t):\n new = t.split()\n number = len(new)\n changed = []\n while number >=1:\n changed.append(new[number-1])\n number = number - 1\n changed = \" \".join(changed)\n return changed",
"title": ""
},
{
"docid": "305efa42996f5a717b1709738e460098",
"score": "0.6704497",
"text": "def reverse(s):\r\n rev=''\r\n \r\n for letter in s:\r\n # push each letter onto the front of rev\r\n rev=letter+rev\r\n \r\n return rev",
"title": ""
},
{
"docid": "8cffb7317b2d41cca86b9573bb43e2df",
"score": "0.6684242",
"text": "def reverseString(self, s):\n for i in range(len(s) // 2):\n swapping = len(s) - (i + 1)\n s[i], s[swapping] = s[swapping], s[i]",
"title": ""
},
{
"docid": "6f393ad7bd7749409a7c9604044f0c8b",
"score": "0.66659486",
"text": "def reverse(characters):\n\n left_index = 0\n right_index = len(characters) - 1\n\n while left_index < right_index:\n\n characters[left_index], characters[right_index] = characters[right_index], characters[left_index]\n\n left_index += 1\n right_index -= 1\n\n return characters",
"title": ""
},
{
"docid": "2efa807ec9922899eb9a6bf7aeddf39e",
"score": "0.6593083",
"text": "def reverseString(self, s: List[str]) -> None:\r\n i,j=0,len(s)-1\r\n while(i<j):\r\n s[i],s[j]=s[j],s[i] #swap elements\r\n i+=1\r\n j-=1",
"title": ""
},
{
"docid": "1d7b6d52e7f6d8ad881a95aebdc89889",
"score": "0.6565496",
"text": "def base_pair(seq):\n trans_5 = str.maketrans('THVthv', 'UGUugu')\n trans_3 = str.maketrans('ACGUTHVacguthv', 'UGCAAUGugcaaug')\n\n return seq.translate(trans_5), seq.translate(trans_3)[::-1]",
"title": ""
},
{
"docid": "c782324ca52a5bcaceb1806964afafdd",
"score": "0.64779764",
"text": "def mirror(s):\r\n\r\n mirrored_string = s + s[::-1]\r\n\r\n return mirrored_string",
"title": ""
},
{
"docid": "7f1460fe692a7c3d1b37efba3199c54a",
"score": "0.6458073",
"text": "def reverse_compliment(seq):\n return_strand = ''\n for nt in seq:\n if nt.upper() == 'A':\n return_strand += 'T'\n if nt.upper() == 'G':\n return_strand += 'C'\n if nt.upper() == 'C':\n return_strand += 'G'\n if nt.upper() == 'T':\n return_strand += 'A'\n if nt.upper() == 'N':\n return_strand == 'N'\n assert 'unexpected nt!!'\n return return_strand[::-1]",
"title": ""
},
{
"docid": "a38899762e14a11e7c99a4e69f5126d0",
"score": "0.64548266",
"text": "def reversed_by_slicing(seq):\n return seq[::-1]",
"title": ""
},
{
"docid": "9781433b6d419c0d50ca936670ec0614",
"score": "0.6442965",
"text": "def omkeer(s):\n return s[::-1]",
"title": ""
},
{
"docid": "d2486480d799f0541b16ca2dd0fd2b92",
"score": "0.6438348",
"text": "def reverse_complement(seq):\n return \"\".join(BASE_COMPLEMENTS[base] for base in reversed(seq))",
"title": ""
},
{
"docid": "b5ee408638376830d124daaa4289a348",
"score": "0.6427647",
"text": "def reverseString(self, s: List[str]) -> None:\n length = len(s)\n for i in range(length // 2):\n s[i], s[length-i-1] = s[length-i-1], s[i]",
"title": ""
},
{
"docid": "f99d7ecf6a62610491203a381ca56450",
"score": "0.6421205",
"text": "def reverse_complement(seq):\n return seq.translate(COMPLEMENT)[::-1]",
"title": ""
},
{
"docid": "d8ab6d50f6aa2c0e67f7fc431ca5304e",
"score": "0.64208585",
"text": "def reverseString(self, s: List[str]) -> None:\n start = 0\n end = len(s) - 1\n while start <= end:\n s[start], s[end] = s[end], s[start]\n start += 1\n end -= 1",
"title": ""
},
{
"docid": "5d8046cdaab90abb481d53860d31b45e",
"score": "0.63993514",
"text": "def reverseCompSequence(sequence):\n tmp = \"\"\n\n for base in sequence[::-1]:\n tmp += rclookup[base]\n\n return tmp",
"title": ""
},
{
"docid": "fb9fc788cfd807f8d8ece06480037f86",
"score": "0.63982475",
"text": "def rev_comp(seq):\n comp_dict = {'A':'T', 'T':'A', 'G':'C', 'C':'G', 'N':'N'}\n return \"\".join([comp_dict.get(i.upper()) for i in seq[::-1]])",
"title": ""
},
{
"docid": "3870fb68d938bbc83f4184395a543cfa",
"score": "0.6392788",
"text": "def revcomp(self):\n\n return self.translate(self.bp)[::-1]",
"title": ""
},
{
"docid": "cfcae8f83d6a84bf2d07e98f2d14ae38",
"score": "0.63794583",
"text": "def reverse_complement(sequence: str) -> str:\r\n sequence = reverse(sequence)\r\n sequence = complement(sequence)\r\n return sequence",
"title": ""
},
{
"docid": "314d598dd02416bae8ec37b925cabe56",
"score": "0.6378279",
"text": "def exchange(str):\n # if length of string is > 1, we switch characters, else just return str\n if len(str) > 1:\n # store the first char and last char, then switch them\n last_char = str[len(str) - 1]\n first_char = str[0]\n str = last_char + str[1:len(str) - 1] + first_char\n return str\n else:\n return str",
"title": ""
},
{
"docid": "686cc018f7a0c1dfffae95c66c115216",
"score": "0.63408107",
"text": "def reverse(st):\n sl = st.split() # ['Hello', 'World']\n r = sl.reverse() # ['World', 'Hello']\n rev_st = ' '.join(sl) # \"World Hello\"\n return rev_st",
"title": ""
},
{
"docid": "53eaace9b6ffb534673663082536d774",
"score": "0.6339083",
"text": "def reverseString(self, s: List[str]) -> None:\n for i in range (0, len(s)//2):\n s[i] , s[size-1-i] = s[size-1-i], s[i]",
"title": ""
},
{
"docid": "c76cc190c090b24eadb63f10cc10fbf8",
"score": "0.6312462",
"text": "def firstlast(sequence):\n return sequence[:1] + sequence[-1:]",
"title": ""
},
{
"docid": "3292433b5c1b9081b689b42d4de3924e",
"score": "0.62813103",
"text": "def reversecomplement(sequence): \n\n complement = {\"A\":\"T\", \"T\":\"A\", \"C\":\"G\", \"G\":\"C\", \"N\":\"N\"}\n \n reverse_complement_sequence = \"\"\n\n sequence_list = list(sequence)\n sequence_list.reverse()\n\n for letter in sequence_list:\n reverse_complement_sequence += complement[letter.upper()]\n \n return reverse_complement_sequence",
"title": ""
},
{
"docid": "860e437490d693104b79ab7df637287a",
"score": "0.62798786",
"text": "def function4(seq):\n\tseq1 = seq[::-1]\n\treturn seq1",
"title": ""
},
{
"docid": "7d09a20f51db2416dd30aaed39a5f9b2",
"score": "0.62715834",
"text": "def mirror(s): \r\n str = \"\"\r\n for i in s:\r\n str = i + str\r\n return s + str",
"title": ""
},
{
"docid": "4bc83958836c3c46694628ff15ca1633",
"score": "0.626788",
"text": "def reverseString(self, s: List[str]) -> None:\n \n N=len(s)\n \n i=0\n j=N-1\n \n while i<j:\n s[i],s[j]=s[j],s[i]\n i+=1\n j-=1\n \n return s",
"title": ""
},
{
"docid": "876efb83eba7fdaa6197921ad6f5e6c7",
"score": "0.6265012",
"text": "def reverseString(self, s: 'List[str]') -> None:\n ls = len(s)\n for i in range(ls // 2):\n s[i], s[ls - i - 1] = s[ls - i - 1], s[i]",
"title": ""
},
{
"docid": "aa2940bd0991e9d0336ddcf45d34e6ac",
"score": "0.6260102",
"text": "def reverseString(self, s: List[str]) -> None:\r\n if len(s) < 2:\r\n return\r\n i, j = 0, len(s) - 1\r\n while i < j:\r\n s[i], s[j] = s[j], s[i]\r\n i += 1\r\n j -= 1\r\n return",
"title": ""
},
{
"docid": "c26acd3cafe98688512caaed9343a9b1",
"score": "0.622819",
"text": "def reverseString(self, s: List[str]) -> None:\n left, right = 0, len(s) - 1\n while left < right:\n s[left], s[right] = s[right], s[left]\n left, right = left + 1, right - 1",
"title": ""
},
{
"docid": "9efffa4ce210b37261ad212696d683db",
"score": "0.6226025",
"text": "def reverseString(self, s: List[str]) -> None:\n left = 0\n right = len(s) - 1\n\n while (left < right):\n s[left], s[right] = s[right], s[left]\n left, right = left + 1, right - 1",
"title": ""
},
{
"docid": "4c92a0c48d7fb604d24f0a1295b2134e",
"score": "0.6225713",
"text": "def reverseSequence(Sequence):\n reverse = \"\"\n nucleotides = {'A' : 'T',\n 'T' : 'A',\n 'C' : 'G',\n 'G' : 'C'}\n for n in Sequence:\n if n in nucleotides:\n tmp = nucleotides[n]\n else :\n tmp = n # in some sequences there is many N or other letter\n reverse += tmp\n return reverse",
"title": ""
},
{
"docid": "d17c4752b81508ae6a66e51850e39636",
"score": "0.6215869",
"text": "def reverse(s):\r\n \r\n str = \"\"\r\n for i in s:\r\n str = i + str\r\n return str",
"title": ""
},
{
"docid": "8a9e71aefe60f29cc6618733f4f319a4",
"score": "0.6201891",
"text": "def reverse_complement(seq_str: str) -> str:\n return str(Seq(seq_str).reverse_complement())",
"title": ""
},
{
"docid": "d535dcc4c4a82b343b7f9928b50a4a7b",
"score": "0.61934096",
"text": "def reverse_pair(asd):#This function returns the reverse pair of the input sentence.\n sabai=asd.split(' ')\n newlist=sabai[::-1]\n newstring=\" \".join(newlist)\n return newstring",
"title": ""
},
{
"docid": "169bda040c8832372eb1fea6cdcef6a3",
"score": "0.6191223",
"text": "def reverseString(self, s: List[str]) -> None:\n start=0\n endl=len(s)-1\n \n while(start<endl):\n s[start] , s[endl] = s[endl] , s[start]\n start+=1\n endl-=1",
"title": ""
},
{
"docid": "8d42c3b4c31bc1bf1539ce90dec0b418",
"score": "0.61867106",
"text": "def reverse(s):\n if len(s) == 0: # basic case\n return \"\"\n return reverse(s[1:]) + s[0]",
"title": ""
},
{
"docid": "09c3444333b953819e0400712f891b86",
"score": "0.6177581",
"text": "def reverse_pair(sentence):\n list = sentence.split()\n l = []\n for s in list:\n l = [s] + l\n return ' '.join(l)",
"title": ""
},
{
"docid": "b9fac644a4d9fd69bd56e4c2a99836a3",
"score": "0.6175182",
"text": "def reverseString(self, s):\n length = len(s)\n if length <= 1:\n return\n\n start, end = 0, len(s) - 1\n while start <= end:\n s[start], s[end] = s[end], s[start]\n start, end = start + 1, end - 1\n print(s)",
"title": ""
},
{
"docid": "c23c8a2e0122f06100ffaab336a99319",
"score": "0.61726683",
"text": "def rev_comp(seq):\r\n rev_seq = seq[::-1]\r\n compliment_dict = {'A':'T', 'T':'A', 'C':'G', 'G':'C'}\r\n rev_comp_seq = ''\r\n for nuc in rev_seq:\r\n if nuc in ['A', 'T', 'G', 'C']:\r\n rev_comp_seq += compliment_dict[nuc]\r\n return rev_comp_seq",
"title": ""
},
{
"docid": "4c815001b13fa8bf473cb0d345271e79",
"score": "0.6168838",
"text": "def flipside(s):\n z = len(s)/2\n return s[z::]+s[0:z]",
"title": ""
},
{
"docid": "ca1295ac5848559d32377bee43cd4572",
"score": "0.6166178",
"text": "def reverseString(self, s: List[str]) -> None:\n # test case - [\"h\",\"e\",\"l\",\"l\",\"o\"]\n # first method - bacis python inbuild list manipulation\n # computional time - 56ms\n s.reverse()\n \n \n \"\"\"\n # second method - simple list manipulation with insert and pop\n # computional time - 40ms\n for ii in range(0, len(s)):\n s.insert(ii, s[len(s)-1])\n s.pop()\n \"\"\" \n \n # 2.1 method - shortened version of the second method with \n # simultaneously removing the last characted and then adding \n # it at the desired postion\n # computional time - 56ms\n \"\"\"\n for ii in range(0, len(s)):\n s.insert(ii, s.pop())\n \"\"\"\n \n # third method - list comprehension with lambda function\n # computional time - 86ms\n \"\"\"\n annfunc = lambda ind: s.insert(ind, s.pop()) \n [annfunc(ind) for ind in range(0, len(s))]\n \"\"\"",
"title": ""
},
{
"docid": "fd5f23d37e7ea2a1660254c5469d0326",
"score": "0.61632305",
"text": "def reverseString(self, s: List[str]) -> None:\n l,r=0,len(s)-1\n while l<r:\n s[l],s[r]=s[r],s[l]\n l+=1\n r-=1",
"title": ""
},
{
"docid": "dca16dbccdc5cc15f8c782f5a7b43493",
"score": "0.6158762",
"text": "def rev_complement(seq):\r\n return ''.join([COMP_DICT[x] for x in seq][::-1])",
"title": ""
},
{
"docid": "16020ee2dcf9f1d0c44913b27ca4b7d8",
"score": "0.6157503",
"text": "def rc(seq):\n revdict = {\"A\":\"T\",\n \"T\":\"A\",\n \"G\":\"C\",\n \"C\":\"G\",\n \"a\":\"t\",\n \"t\":\"a\",\n \"g\":\"c\",\n \"c\":\"g\"}\n return ''.join([revdict[a] for a in seq][::-1])",
"title": ""
},
{
"docid": "3f360f77419594038f087bee2567b621",
"score": "0.61558914",
"text": "def reverse(s):\n\n rev = ''\n for letter in s:\n rev = letter + rev\n return rev",
"title": ""
},
{
"docid": "76948d3d441042b4975c0f52d6c97d56",
"score": "0.61461294",
"text": "def reverse(sequence, str_table=SIMPLE_COMPLEMENTS_STR):\r\n base_mapping = get_base_mapping(str_table)\r\n return _clean_sequence(sequence[::-1], base_mapping)",
"title": ""
},
{
"docid": "54c958a638f3c5081dc053cd0ab63345",
"score": "0.61435217",
"text": "def reverse(sequence, str_table=SIMPLE_COMPLEMENTS_STR):\r\n return _clean_sequence(sequence, str_table)[::-1]",
"title": ""
},
{
"docid": "60c62b78ec20d995e857fc67786e3ffa",
"score": "0.61310756",
"text": "def adjust(s):\n # This does not need to be terribly efficient as the code is\n # only run once, during initialization\n s = s.encode('latin-1')\n for i in range(1, len(s) - 1):\n rotate(s[i], s[i - 1])",
"title": ""
},
{
"docid": "ddc07c74ea46b58046ee850e32a99bd4",
"score": "0.61263466",
"text": "def reverse(sequence, str_table=SIMPLE_COMPLEMENTS_STR):\n return _clean_sequence(sequence, str_table)[::-1]",
"title": ""
},
{
"docid": "0d9e7c22596ebda948ea3d2391bc6e4c",
"score": "0.61242956",
"text": "def first_reverse(str_param):\r\n\r\n result = ''\r\n length = len(str_param)\r\n\r\n for i in range(length - 1, -1, -1):\r\n result += str_param[i]\r\n\r\n return result",
"title": ""
},
{
"docid": "28d15b54fa02226a3fdffe3689ba2dae",
"score": "0.61200035",
"text": "def reverse_complement(sequence: str) -> str:\n return complement(sequence)[::-1]",
"title": ""
},
{
"docid": "8f67aff40541f2750a112793314859bc",
"score": "0.6118087",
"text": "def flipsides(s):\n x = len(s)//2\n return s[-x:] + s[0:x+1]",
"title": ""
},
{
"docid": "4a373f22b36f15928b3fd43d1fccf71e",
"score": "0.6114611",
"text": "def reverseString(self, s: List[str]) -> None:\n s.reverse()",
"title": ""
},
{
"docid": "6eb2ef57e175aaa63cab30068b158f92",
"score": "0.6102878",
"text": "def reverseComp(seq):\n temp = seq.translate(transTable)\n rc = temp[::-1]\n return rc",
"title": ""
},
{
"docid": "af65bc57639d6b749a5c086a23a27bba",
"score": "0.6101014",
"text": "def firstLast(seq):\n\tseq1 = seq[-1:] + seq[1:-1] + seq[:1]\n\treturn seq1",
"title": ""
},
{
"docid": "b2f6b9e9f99e63ec2f0ca8e6eeac9dac",
"score": "0.6099781",
"text": "def reverseString(self, s) -> None:\n l, r = 0, len(s) - 1\n while l < r:\n s[l], s[r] = s[r], s[l]\n l += 1\n r -= 1",
"title": ""
},
{
"docid": "ccd2a287420f6bd6c16d950433d11990",
"score": "0.6090153",
"text": "def reverse_complement(seq):\n seq = reverse(seq)\n seq = complement(seq)\n return seq",
"title": ""
},
{
"docid": "c21cfc36bff38d43ec2c17951e0d7806",
"score": "0.6089183",
"text": "def rev_comp(seq):\n rev_seq = seq[::-1]\n compliment_dict = {'A':'T', 'T':'A', 'C':'G', 'G':'C'}\n rev_comp_seq = ''\n for nuc in rev_seq:\n if nuc in ['A', 'T', 'G', 'C']:\n rev_comp_seq += compliment_dict[nuc]\n return rev_comp_seq",
"title": ""
},
{
"docid": "d4e8eae559bf926934f0e5ab7db19f04",
"score": "0.6088372",
"text": "def make_reverse_complement(seq):\n\n reverse_seq = seq[::-1]\n complement = {'A': 'T', 'C': 'G', 'G': 'C', 'T': 'A'}\n rev_complement = \"\".join(complement.get(base, base) for base in reverse_seq)\n\n return rev_complement",
"title": ""
},
{
"docid": "8348866db837910179b4452a5adec327",
"score": "0.6084004",
"text": "def revcomp(seq: str) -> str:\n\n rc_nuc = {\n 'A': 'T',\n 'C': 'G',\n 'T': 'A',\n 'G': 'C',\n }\n\n seq_rev = seq[::-1]\n seq_rev_comp = ''.join([rc_nuc[n] for n in list(seq_rev)])\n\n return seq_rev_comp",
"title": ""
},
{
"docid": "1a96e67baa27203ffb49957276a9d270",
"score": "0.6076847",
"text": "def both_ends(s): \n l = len(s)\n new_s = '' \n \n if l < 2:\n return new_s\n else:\n new_s = s[0] + s[1] + s[l-2] + s[l-1]\n return new_s",
"title": ""
},
{
"docid": "08b70379ec996fbb5d8d50c586616953",
"score": "0.60657656",
"text": "def flip(data: List[str]) -> List[str]:\n return [line[::-1] for line in data]",
"title": ""
},
{
"docid": "4bbe1ca99fad292c3614dcd3f5f4dce6",
"score": "0.6063451",
"text": "def reverseString(self, s: List[str]) -> None:\n # one-line solution\n # s.reverse() \n \n # two pointer\n # time complexity: o(n)\n # space complexity: o(1) in place\n left, right = 0, len(s) - 1\n while left < right:\n s[left], s[right] = s[right], s[left]\n left, right = left + 1, right - 1",
"title": ""
},
{
"docid": "6247818c5583298e987c0a353cd98381",
"score": "0.60550517",
"text": "def reverseString(self, s: List[str]) -> None:\r\n s.reverse()",
"title": ""
},
{
"docid": "e5d0258f51fa673df22446456a9e1c0b",
"score": "0.6041294",
"text": "def reverse(self) -> None:\n\n for i in range(self.length() // 2):\n self.swap_pairs(i, (self.length() - 1 - i))",
"title": ""
},
{
"docid": "e3a5ce824eee46475864277ff6e0637b",
"score": "0.6037553",
"text": "def reverse(str):\n\n new_str = \"\"\n for i in range(len(str), 0, -1):\n \tnew_str += str[i-1]\n return new_str",
"title": ""
},
{
"docid": "d11c87bb9d7339be3bb7a4028b477d28",
"score": "0.6032004",
"text": "def SwapString(str):\n s = []\n for x in range(0, len(str) - 1, 2):\n s.append(str[x+1])\n s.append(str[x])\n return ''.join(s).strip()",
"title": ""
},
{
"docid": "196dcf2dad1c02343e5fb810382c1a0d",
"score": "0.6030722",
"text": "def get_reverse_complement(dna):\n #empty string for eventual reverse sequence\n new_dna = ''\n #we want the reverse, so the while loop will count down\n i = len(dna) - 1\n while i >= 0:\n #if last one is A, changes to T and counts down one and then moves on\n if dna[i] == 'A':\n new_dna += ('T')\n i -= 1\n elif dna[i] == 'T':\n new_dna += ('A')\n i -= 1\n elif dna[i] == 'C':\n new_dna += ('G')\n i -= 1\n elif dna[i] == 'G':\n new_dna += ('C')\n i -= 1\n #return the resulting sequence\n return new_dna",
"title": ""
},
{
"docid": "dc3b7ec559df6df0fb76820af99a2d61",
"score": "0.60285246",
"text": "def string_reverse(s):\n if len(s) == 0:\n return s\n else:\n return string_reverse(s[1:]) + s[0]",
"title": ""
},
{
"docid": "a8b0ed80a8689341ff9dddc0f3b254a1",
"score": "0.6021835",
"text": "def swap_ends(bar):\n bar[0], bar[-1] = bar[-1], bar[0]",
"title": ""
},
{
"docid": "51f916526dce2bcee4db3f0680811029",
"score": "0.6021005",
"text": "def reverseString(s: List[str]) -> None:\n s.reverse()",
"title": ""
},
{
"docid": "ec1eb4183c2649ec253ae567f2c14d73",
"score": "0.6013185",
"text": "def backward(my_string):\n return my_string[::-1]",
"title": ""
},
{
"docid": "ecbed43e1acaa20c14e10109456626f5",
"score": "0.6011235",
"text": "def reverse(s):\n\n rev = ''\n\n # For each character in s, add that char to the beginning of rev.\n\n for ch in s:\n rev = ch + rev\n\n return rev",
"title": ""
},
{
"docid": "b4a450a32de5e4834081b8d018e534e8",
"score": "0.6008765",
"text": "def word_flipper(our_string):\n\n word_list = our_string.split(\" \")\n\n for idx in range(len(word_list)):\n word_list[idx] = word_list[idx][::-1] # [index1:index2:step]\n\n return \" \".join(word_list)",
"title": ""
},
{
"docid": "bcb0b106d56c75c2e9b4b9be475b6ffd",
"score": "0.60058576",
"text": "def sequentize(string):\n first, last, result = None, None, []\n for char in map(ord, string):\n if last is None:\n first = last = char\n elif last + 1 == char:\n last = char\n else:\n result.append((first, last))\n first = last = char\n if last is not None:\n result.append((first, last))\n return ''.join([\n '%s%s%s' % (chr(first), last > first + 1 and '-'\n or '', last != first and chr(last) or '')\n for first, last in result\n ]) # noqa",
"title": ""
},
{
"docid": "85619c268636fd5fe3e5af3f03d8c69f",
"score": "0.60008776",
"text": "def Complement(Sequence):\n Sequence=Sequence.upper()\n Trans=maketrans('ACTG','TGAC')\n return Sequence.translate(Trans)",
"title": ""
},
{
"docid": "1b28048fef1f0a0d5bff440955886de3",
"score": "0.59947234",
"text": "def make_palindrome(s):\n return s[::-1]",
"title": ""
},
{
"docid": "912bf8833b0ee09c3c0ed4c9b68550a4",
"score": "0.59922177",
"text": "def revcomp(seq):\n trantab = str.maketrans(\"ATCG\", \"TAGC\")\n return seq.translate(trantab)[::-1]",
"title": ""
},
{
"docid": "6236792d21bcefa0546b3662a0c555d0",
"score": "0.5991127",
"text": "def swap(s: str, i: int, j: int):\n l1 = list(s)\n temp = l1[i]\n l1[i] = l1[j]\n l1[j] = temp\n return ''.join(l1)",
"title": ""
},
{
"docid": "35d5284d77aff7d6e618143e847555e7",
"score": "0.59785914",
"text": "def reverseString(self, s) -> None:\n\t\tlength = len(s)\n\t\tleft = 0\n\t\tright = int((length-1))\n\t\twhile(left < right):\n\t\t\ttemp = s[left]\n\t\t\ts[left] = s[right]\n\t\t\ts[right] = temp\n\t\t\tleft +=1\n\t\t\tright -=1\n\t\treturn s",
"title": ""
},
{
"docid": "5df74448cdaf9f8a54beaead0b693649",
"score": "0.5978086",
"text": "def strReverseR(s):\r\n if s == 1:\r\n return s\r\n elif len(s) == 0:\r\n return s\r\n else:\r\n return s[-1] + strReverseR(s[:-1])\r\n return s",
"title": ""
},
{
"docid": "fa3c7c96dc396023a8e3633bc6bd4377",
"score": "0.59762144",
"text": "def reverse_complement(seq): \n\t\n\tbasecomplement = {'a':'t', 'c':'g', 't':'a', 'g':'c', 'A':'T', 'C':'G', 'T':'A', 'G':'C'}\n\treverse_seq = seq[::-1]\n\n\tdna = '' # initialize the variable dna as an empty string\n\tfor nt in reverse_seq:\n\t\tdna += basecomplement[nt] \n\treturn dna",
"title": ""
},
{
"docid": "3b1a4f3d1c1aa46754e5840a7b983105",
"score": "0.59623384",
"text": "def makePalindrome(s):\n\treturn s + s[::-1]",
"title": ""
},
{
"docid": "c94e18e60764df9a64c2c61ba061e835",
"score": "0.5961592",
"text": "def reverse_complement(bases):\r\n replacements = [('A', 't'), ('T', 'a'), ('C', 'g'), ('G', 'c')]\r\n for ch1, ch2 in replacements:\r\n bases = re.sub(ch1, ch2, bases)\r\n return bases[::-1].upper()",
"title": ""
},
{
"docid": "4ac0f13e625b1190412a19279170a8da",
"score": "0.59601516",
"text": "def reverse_item_characters(list):\n temp = []\n for fruit in list[:]:\n temp.append( fruit[::-1] )\n return temp",
"title": ""
},
{
"docid": "3515ad57459bf44d33a80110e2c5e024",
"score": "0.5958538",
"text": "def reverseString(self, s: List[str]) -> None:\r\n s = s.reverse()",
"title": ""
},
{
"docid": "74bdfb77c338654b898bc4ff4a4da0e1",
"score": "0.5954003",
"text": "def reverseString(self, s: List[str]) -> None:\n s.reverse()",
"title": ""
},
{
"docid": "a5c39a42a87ee161eda4c319978ac588",
"score": "0.5950327",
"text": "def reverseString(self, s) -> None:\n l, r = 0, len(s) - 1\n while l < r:\n s[l] ^= s[r]\n s[r] ^= s[l]\n s[l] ^= s[r]\n l += 1\n r -= 1",
"title": ""
},
{
"docid": "6333c4e59d4e1f6638bd4b628fe5546f",
"score": "0.5948949",
"text": "def reverseString(self, s: List[str]) -> None:\n # 方法一：双指针\n left, right = 0, len(s) - 1\n while left < right:\n s[left], s[right] = s[right], s[left]\n left += 1\n right -= 1\n\n # 方法二：递归，空间复杂度不符合要求\n # def helper(left, right):\n # if left < right:\n # s[left], s[right] = s[right], s[left]\n # helper(left + 1, right - 1)\n # helper(0, len(nums) - 1)",
"title": ""
}
]
4a365ce584eee01290f5a160284042b9
Returns the tests in |tests| that have at least one of their compile targets in |compile_targets|.
[
{
"docid": "26fe9329c821b898fee803f75a5b3580",
"score": "0.80282277",
"text": "def tests_in_compile_targets(api, compile_targets, tests):\n result = []\n for test in tests:\n test_compile_targets = test.compile_targets(api)\n\n # Always return tests that don't require compile. Otherwise we'd never\n # run them.\n if ((set(compile_targets).intersection(set(test_compile_targets))) or\n not test_compile_targets):\n result.append(test)\n\n return result",
"title": ""
}
]
[
{
"docid": "7c1f73c51d4e05474cc4179520270cfc",
"score": "0.64364606",
"text": "def all_compile_targets(api, tests):\n return sorted(set(x\n for test in tests\n for x in test.compile_targets(api)))",
"title": ""
},
{
"docid": "77041d9d085c52ac692244cfa9099c7a",
"score": "0.59145314",
"text": "def try_matches(target, tests):\n return _try_all(target, tests, try_match)",
"title": ""
},
{
"docid": "2562b0f5c15983d4101a6c2f7f18aa42",
"score": "0.58373857",
"text": "def _get_test_targets(self):\n\n test_targets = list(filter(self._test_target_filter(), self._get_targets()))\n return test_targets",
"title": ""
},
{
"docid": "26bdd241afeb03467ef7305f7f604c7f",
"score": "0.5755654",
"text": "def runner_tests_ok(tests: RunnerTestResults) -> bool:\n return False not in [result for test, result in tests]",
"title": ""
},
{
"docid": "1aa05a890569a1daa54c57e98d613a85",
"score": "0.54295397",
"text": "def try_non_matches(target, tests):\n return not any([_try_match_quietly(target, test) for test in tests])",
"title": ""
},
{
"docid": "990953a30df8344fae241dcdcb8fc744",
"score": "0.5347448",
"text": "def make(self, build_only=False, rebuild=False, local_builds_only=False):\n\n all_tests = []\n for test_batch in self.make_iter(build_only, rebuild, local_builds_only):\n all_tests.extend(test_batch)\n\n return all_tests",
"title": ""
},
{
"docid": "f6e3ebc5cc1157565c79f79116299eaa",
"score": "0.5336644",
"text": "def BuildFileTargets(target_list, build_file):\n return [p for p in target_list if\n BuildFileAndTarget('', p)[0] == build_file]",
"title": ""
},
{
"docid": "05e0df0614569042fdfc5e9569b79677",
"score": "0.5330824",
"text": "def filter_tests(self, tests: unittest.TestSuite) -> unittest.TestSuite:\n tests_to_be_run = unittest.TestSuite()\n for test in tests:\n added = False\n for _filter_ in self.filters:\n if getattr(test, _filter_, False):\n tests_to_be_run.addTest(test)\n added = True\n\n elif isinstance(test, unittest.TestCase):\n # noinspection PyProtectedMember\n if getattr(getattr(test.__class__, test._testMethodName), \"unittest\", False):\n tests_to_be_run.addTest(test)\n added = True\n\n if not added and isinstance(test, unittest.TestSuite):\n new_test_suite = self.filter_tests(test)\n if new_test_suite.countTestCases() > 0:\n tests_to_be_run.addTest(new_test_suite)\n\n return tests_to_be_run",
"title": ""
},
{
"docid": "c9affc2a07a06fa1ea7541eeffd85eb3",
"score": "0.5323092",
"text": "def collect_tests(self):\n tester = self.project.project_tester()\n # Collect tests\n tester.collect_tests()\n # find all tests relating to this model\n # return the set of test ids\n apply_ids = []\n for test_id in tester.test_ids:\n test = tester.get_test(test_id)\n\n did = None\n if self.design is not None:\n did = self.design.id\n\n mid = os.path.basename(self.model_path)\n if test.applies_to_model(mid, design_id=did) and not len(test.children):\n # We only care about bottom level tests\n apply_ids.append(test_id)\n\n return apply_ids",
"title": ""
},
{
"docid": "9934ca94b167ecb526184447dc1caf9a",
"score": "0.53026605",
"text": "def contains_any(self, test_components):\n for test_comp in test_components:\n if self.contains(test_comp):\n return True\n return False",
"title": ""
},
{
"docid": "eb4c50740e5377427f13181c5d4ec182",
"score": "0.5277089",
"text": "def contains_all(self, test_components):\n for test_comp in test_components:\n if not self.contains(test_comp):\n return False\n return True;",
"title": ""
},
{
"docid": "c0bab68fbea22be2dfcef1cbcbb5f3ac",
"score": "0.5267896",
"text": "def _find_matches(self) -> None:\n targets = set()\n for inspector in self._inspectors:\n targets.update(inspector.report())\n\n for root, _, files in os.walk(self._start_directory):\n for f in files:\n if fnmatch(f, self._pattern):\n full_path = os.path.join(root, f)\n tree = ast.parse(open(full_path).read())\n visitor = ImportFromVisitor(list(targets))\n visitor.visit(tree)\n if visitor.contains_target:\n self._test_filenames.add((root, f))",
"title": ""
},
{
"docid": "d0723fc891ea9c73f2dab219e105025f",
"score": "0.52182966",
"text": "def has_test_files(self):\n return (self.archive.get_files_re('test_.*.py') +\n self.archive.get_files_re('.*_test.py')) != []",
"title": ""
},
{
"docid": "9adb73b9d97baa483b9cb309c627e450",
"score": "0.51056117",
"text": "def all_successful(self):\n for target in self.targets:\n if not self.builds[target].passed():\n return False\n\n return True",
"title": ""
},
{
"docid": "d1a4cef6f7d3ec588105855f8360b894",
"score": "0.5090099",
"text": "def get_test_classes_to_run(self):\n tcs_to_run = []\n for test_class in self.test_classes:\n if not test_class.is_finished():\n tcs_to_run.append(test_class)\n return tcs_to_run",
"title": ""
},
{
"docid": "c239b82f5cd0024a533fce879c0bda1b",
"score": "0.5058649",
"text": "def _get_test_targets_for_spawn(self):\n return self._get_test_targets()",
"title": ""
},
{
"docid": "657bd06ed259f66343e1786cf6b0c121",
"score": "0.50443745",
"text": "def eval_targets(self, targets):\n\n for target in targets:\n assert target in self.test_inst.data.keys(), \\\n \"{:s} not found in data\".format(target)\n assert not np.isnan(self.test_inst[target]).any(), \\\n \"NaN values found in {:s}\".format(target)\n assert target in self.test_inst.meta.data.index, \\\n \"{:s} not found in metadata\".format(target)\n return",
"title": ""
},
{
"docid": "8a8bd787ce451933b836e6bd056d8e2b",
"score": "0.5033227",
"text": "def _CheckTests(self, expected_tests):\n for master in _MOCK_DATA[0]:\n for bot in _MOCK_DATA[1]:\n expected = ['%s/%s/%s' % (master, bot, t) for t in expected_tests]\n tests = graph_data.TestMetadata.query(\n graph_data.TestMetadata.master_name == master,\n graph_data.TestMetadata.bot_name == bot).fetch()\n actual = [t.test_path for t in tests]\n self.assertEqual(expected, actual)",
"title": ""
},
{
"docid": "4846bcf7de991bc233ba10fdbc647a47",
"score": "0.49683335",
"text": "def suite(self, filenames=None):\n filenames = filenames or self.files\n\n return {test\n for filename in filenames\n for test in self[filename].all_affected_tests}",
"title": ""
},
{
"docid": "576596d76cb6fc8f92ec2754ba41ee6e",
"score": "0.49384516",
"text": "def contains_all_tests(self):\n return not self.partial_result",
"title": ""
},
{
"docid": "18d03cc081f500d6cdf1c45590314859",
"score": "0.49305618",
"text": "def testlist(self, test_prefix=DEFAULT_TEST_PREFIX):\n if self.build_path is None:\n return []\n\n watchedfiles = self.filelist.values()\n # Create dict of expected test binary names to the relative path of the\n # source files that they were compiled from. This is to make\n # identification of test binaries easier during build tree scanning.\n testfiles = {\n os.path.splitext(w.name)[0] + EXE_SUFFIX: w.relpath\n for w in watchedfiles\n if w.name.startswith(test_prefix)\n }\n # Scan the build tree. If an expected test binary is encountered, add a\n # GTest().\n return [\n GTest(testfiles[f], os.path.join(d, f), term=self.term)\n for d, f, m, t in walk(self.build_path)\n if f in testfiles and m & stat.S_IXUSR\n ]",
"title": ""
},
{
"docid": "fbb5143578b0a41146a525a0739c9def",
"score": "0.4910435",
"text": "def _try_all(target, tests, try_method):\n return all([try_method(target, test) for test in tests])",
"title": ""
},
{
"docid": "54d73745f727b794682e6664fcf118e5",
"score": "0.48727047",
"text": "def gatherTests(self):\n tests, content = [], {}\n execution = ConfigurationManager().getConfiguration('execution').configuration\n\n if execution.scripts.enabled == 'true':\n if not os.path.exists(execution.scripts.PCDATA):\n raise Exception('Invalid configuration. Executions scripts do not exist')\n content['tools'] = execution.scripts.PCDATA\n\n if len(self.__testCreators) == 0:\n raise Exception('No enabled test creators found')\n\n for creator in self.__testCreators:\n newTests = creator.createTests()\n if len(newTests) == 0:\n raise Exception('%s provided no tests!' % creator.__class__.__name__)\n\n tests += newTests\n content[creator.execScriptName] = creator.execScriptLocation\n content[creator.srcName] = creator.srcLocation\n\n defGroup = Group(self.DEFAULT_GROUP_NAME,\n self.DEFAULT_GROUP_DESC, tests)\n source = Source(self.DEFAULT_LOCATION, defGroup)\n\n descBuffer = StringIO()\n for testFilter in self.__testFilters:\n testFilter.filterTests(source)\n descBuffer.write(testFilter.getAppliedFilterDescription())\n desc = descBuffer.getvalue()\n descBuffer.close()\n\n if len(tests) == 0:\n raise Exception('No tests found!')\n\n testIds = set()\n for test in tests:\n if test.testId in testIds:\n raise Exception('Duplicate test ID %s' % test.testId)\n testIds.add(test.testId)\n\n # TODO: where does this come from?\n testSuiteName = \"FIXME\"\n dateTime = datetime.fromtimestamp(time.time()).strftime('%Y-%m-%dT%H:%M:%S')\n macAddress = Network.getMacAddress()\n executionName = \"%s_%s_%s\" % (testSuiteName, dateTime, macAddress)\n\n self.__packDetails = (\n testSuiteName,\n str(desc),\n self.__getShortFilterDesc(),\n executionName,\n dateTime)\n\n self.__makeTar(**content)\n return source, desc",
"title": ""
},
{
"docid": "de548e1b1d120b44e199814ea947dea1",
"score": "0.48380068",
"text": "def get_test_sets() -> []:\n tests = ConfigManager.get_test_dict()\n return tests",
"title": ""
},
{
"docid": "d3e306aa8bf204fa37c444546edf484f",
"score": "0.4831065",
"text": "def tested(self):\n return self._tests",
"title": ""
},
{
"docid": "e9bc4a81b573e99420921bc3a8a68f82",
"score": "0.4807907",
"text": "def has_any(self, sources):\n return any(x in list(self.keys()) for x in sources)",
"title": ""
},
{
"docid": "d020bc84cf15b4d377d036612986267d",
"score": "0.4804515",
"text": "def match(*specs, only_installed=False):\n\n\tinst = set(installed())\n\tds = envs.match(*specs, installed=inst)\n\n\tif only_installed:\n\t\tds = ds.intersection(inst)\n\n\treturn sorted(ds)",
"title": ""
},
{
"docid": "2a052d24cf1758eec3fa932fa5283770",
"score": "0.47930267",
"text": "def contained_in(self, test_components):\n for component in self.components:\n if component not in test_components:\n return False\n return True",
"title": ""
},
{
"docid": "e7cab1ba42e2b4023bb02809fffb67b8",
"score": "0.4792717",
"text": "def all_files_present(self):\n if self._source_files is None:\n self._source_files = self._process_source_files()\n return all(x[2] for x in self._source_files)",
"title": ""
},
{
"docid": "a01c68988cda3aee270c93463315476d",
"score": "0.4789014",
"text": "def _is_test_build_target_valid(self, t_info):\n # If the cached build target can be found in current module-info, then\n # it is a valid build targets of the test.\n for build_target in t_info.build_targets:\n if str(build_target).startswith(constants.MODULES_IN):\n continue\n if not self.module_info.is_module(build_target):\n logging.debug('%s is not a valid build target.', build_target)\n return False\n return True",
"title": ""
},
{
"docid": "98655888d7824467efc9b0ddb12d2d6a",
"score": "0.47846225",
"text": "def test_xs_needs_c_compiler(testing_config):\n # This uses Sub::Identify=0.14 since it includes no .c files but a .xs file.\n api.skeletonize(\"Sub::Identify\", version=\"0.14\", repo=\"cpan\", config=testing_config)\n m = api.render(\"perl-sub-identify/0.14\", finalize=False, bypass_env_check=True)[0][\n 0\n ]\n build_requirements = m.get_value(\"requirements/build\")\n assert compiler(\"c\", testing_config) in build_requirements",
"title": ""
},
{
"docid": "2c753be57ae236eb3c49d1676e13f3ea",
"score": "0.47836363",
"text": "def load_and_run_tests(test_names, failfast, report_coverage, ask_before_running_tests, tests_mode=None):\n\n modules_to_be_loaded = sorted({test.split(\".\")[0].strip() for test in test_names})\n modules_to_be_tested = [ModuleTests(module) for module in modules_to_be_loaded]\n\n modes = set()\n tested_ioc_directories = set()\n\n for module in modules_to_be_tested:\n # Add tests that are either the module or a subset of the module i.e. module.TestClass\n module.tests = [test for test in test_names if test == module.name or test.startswith(module.name + \".\")]\n modes.update(module.modes)\n\n test_results = []\n\n arch = get_build_architecture()\n print(\"Running tests for arch {}\".format(arch.name))\n\n for mode in modes:\n if tests_mode is not None and mode != tests_mode:\n continue\n\n modules_to_be_tested_in_current_mode = [module for module in modules_to_be_tested if mode in module.modes]\n\n for module in modules_to_be_tested_in_current_mode:\n # Skip tests that cannot be run with a 32-bit architecture\n if arch not in module.architectures:\n print(f\"Skipped module tests.{module.name} in {TestModes.name(mode)}: suite not available with a {BuildArchitectures.archname(arch)} build architecture\")\n continue\n\n clean_environment()\n device_launchers, device_directories = make_device_launchers_from_module(module.file, mode)\n tested_ioc_directories.update(device_directories)\n test_results.append(\n run_tests(arguments.prefix, module.name, module.tests, device_collection_launcher(device_launchers),\n failfast, ask_before_running_tests))\n\n if report_coverage:\n report_test_coverage_for_devices(tested_ioc_directories)\n\n return all(test_result is True for test_result in test_results)",
"title": ""
},
{
"docid": "8756161d274e8730d24115fe14a82931",
"score": "0.47765338",
"text": "def test_build_test_execution_commands_no_test_suites(self):\n exp = ([\"starcluster -c sc_config start nightly_tests\"], [],\n [\"starcluster -c sc_config terminate -c nightly_tests\"])\n obs = _build_test_execution_commands([], 'sc_config', 'ubuntu',\n 'nightly_tests')\n self.assertEqual(obs, exp)",
"title": ""
},
{
"docid": "46064d60db2c3ea2147a4270bf2fd063",
"score": "0.47702372",
"text": "def contains_blocks(project, opcodes):\n return any(any((isinstance(block, dict) and block[\"opcode\"] in opcodes) for block in target[\"blocks\"].values())\n for target in project)",
"title": ""
},
{
"docid": "cd6e461a5866aa0d20fea6ef74c3f0cc",
"score": "0.47683987",
"text": "def find_containments(seed_polygon, target_polygons):\n contain_booleans = []\n for _, poly in enumerate(target_polygons):\n contain_booleans.append(seed_polygon.contains(poly))\n\n return contain_booleans",
"title": ""
},
{
"docid": "b6bac4eb1d993a84b045ca397ed4fadf",
"score": "0.475926",
"text": "def compile_all_projects(projects, extra_args=''):\r\n failed_clients = []\r\n for project, clients in enumerate_projects().iteritems():\r\n for client in clients:\r\n project_client = '%s.%s' % (project, client)\r\n if not compile_and_install_client(project_client, extra_args):\r\n failed_clients.append(project_client)\r\n\r\n return failed_clients",
"title": ""
},
{
"docid": "be24ba1aa0288716c11d3cdcbfe3dd1e",
"score": "0.47574547",
"text": "def _host_run_test_dependencies(meta_yaml: \"MetaYamlTypedDict\") -> Set[\"PackageName\"]:\n _ = meta_yaml[\"requirements\"]\n rq = (_[\"host\"] or _[\"build\"]) | _[\"run\"] | _[\"test\"]\n return typing.cast(\"Set[PackageName]\", rq)",
"title": ""
},
{
"docid": "4939e444f2f13e83997834ff7a87fb65",
"score": "0.47528726",
"text": "def is_project_set_exist(self, test_project_set):\n rst = False\n # iter in all the project set\n for project_set in self.project_set:\n if set(project_set) == set(test_project_set):\n rst = True\n break\n return rst",
"title": ""
},
{
"docid": "8d927899efd8c972542fe99c9bb18a8f",
"score": "0.47489983",
"text": "def generate(self) -> Set[Test]:\n\n tests = set()\n\n # find all functions which have not been tested\n constants = filter(lambda c: not c.properties.get('presence_tested', False),\n Database().get_constants())\n\n # for all applicable functions\n for constant in constants:\n test = self.generate_test(constant)\n\n tests.add(test)\n\n return tests",
"title": ""
},
{
"docid": "98900feed1e6d37dc2a5b101426ac51d",
"score": "0.47420177",
"text": "def targets(self):\n return filter(lambda t: isinstance(t, self.claimed_target_types), self.context.target_roots)",
"title": ""
},
{
"docid": "28d496cc7bc407db766fb3463efdd8d2",
"score": "0.47395974",
"text": "def _calculate_isortable_python_sources(self, targets):\n sources = set()\n for target in targets:\n sources.update(\n source for source in target.sources_relative_to_buildroot()\n if os.path.splitext(source)[1] == self._PYTHON_SOURCE_EXTENSION\n )\n return list(sources)",
"title": ""
},
{
"docid": "3e27eac6cf8ccdb3a53b80dbf190b5cc",
"score": "0.47389108",
"text": "def test_targets():\n # Build Ref with flags\n tempdir = None\n try:\n fprime_root = os.path.join(\n os.path.dirname(__file__), \"..\", \"..\", \"..\", \"..\", \"..\"\n )\n # Create a temp directory and register its deletion at the end of the program run\n tempdir = tempfile.mkdtemp()\n get_cmake_builder().generate_build(\n os.path.join(fprime_root, \"Ref\"), tempdir, {\"CMAKE_BUILD_TYPE\": \"Testing\"}\n )\n test_data = [\n (os.path.join(fprime_root, \"Ref\"), \"\"),\n (os.path.join(fprime_root, \"Svc\", \"CmdDispatcher\"), \"\"),\n (os.path.join(fprime_root, \"Svc\", \"CmdDispatcher\"), \"ut_exe\"),\n (os.path.join(fprime_root, \"Svc\", \"CmdDispatcher\"), \"check\"),\n ]\n # Loop over all directories and target pairs ensuing things work\n for path, target in test_data:\n get_cmake_builder().execute_known_target(target, tempdir, path)\n test_data = [\n (os.path.join(fprime_root, \"Svc\", \"CmdDispatcher\"), \"nontarget1\"),\n (os.path.join(fprime_root, \"Svc\", \"CmdDispatcher3Not\"), \"\"),\n ]\n # Loop over all paths and target pairs looking for expected Exceptions\n for path, target in test_data:\n with pytest.raises(fprime.fbuild.cmake.CMakeException):\n get_cmake_builder().execute_known_target(target, tempdir, path)\n # Clean up when all done\n finally:\n if tempdir is not None:\n shutil.rmtree(tempdir, ignore_errors=True)",
"title": ""
},
{
"docid": "c65b2ecc9a25efdfd4205e8d635ba287",
"score": "0.4736587",
"text": "def _filter_tests(classes, args):\n test_name_filters = args['--filter']\n if test_name_filters:\n test_name_filters = test_name_filters.split(',')\n test_name_res = [re.compile(p, re.I) for p in test_name_filters]\n def pred(name):\n return any(f.search(name) for f in test_name_res)\n for cls in classes:\n cls = _filter_test_methods(cls, pred)\n return classes",
"title": ""
},
{
"docid": "9679a857142bafbba24546a2b70beb6b",
"score": "0.47267923",
"text": "def setup_projects(targets):\n\n actual = []\n invalid = []\n\n allowed = get_projects()\n\n # Keep those project names which are found in the path\n # elements of the targets.\n # XXX: This is not really reliable. If a project name is\n # part of the path of another project it will be falsely\n # accepted.\n actual = []\n for project in allowed :\n for target in targets:\n elements = target.split(\"/\")\n if project in elements:\n actual += [ project ]\n\n if not actual:\n actual = allowed\n\n return actual",
"title": ""
},
{
"docid": "7c5bbdacd0a57c4059f9dad5a51e5a1a",
"score": "0.4723135",
"text": "def filter_suite(suite, test_names):\n new_suite = unittest.suite.TestSuite()\n for name in test_names:\n new_suite.addTests(cls for cls, class_name in list_classes(suite) if name in class_name)\n return new_suite",
"title": ""
},
{
"docid": "ff80ca6c429f59aa29f8ea47c1844469",
"score": "0.47174424",
"text": "def filter_targets(self, targets_names):\n\n result = []\n\n for name in targets_names:\n result += self.filter_name(name, True)\n\n return set(result)",
"title": ""
},
{
"docid": "622762a8834020818f35a8621163cd20",
"score": "0.47155708",
"text": "def has_test_suite(self):\n return (self.has_test_files or self.metadata['test_suite'] or\n self.metadata['tests_require'] != [])",
"title": ""
},
{
"docid": "3071bc50599676883c01d9b0657bfb86",
"score": "0.4712481",
"text": "def find_and_run_tests(start, options):\r\n if options.module_list:\r\n modules = []\r\n for m in options.module_list:\r\n modules.append(m.split('.'))\r\n else:\r\n modules = scan_for_modules(start, options)\r\n\r\n print_header('Number of test modules found: %d' % len(modules))\r\n\r\n functions = {}\r\n for module_names in modules:\r\n # Create a function that'll test a particular module. module=module\r\n # is a hack to force python to evaluate the params now. We then\r\n # rename the function to make error reporting nicer.\r\n run_module = lambda module=module_names: run_test(module, options)\r\n name = '.'.join(module_names)\r\n run_module.__name__ = name\r\n functions[run_module] = set()\r\n\r\n try:\r\n dargs = {}\r\n if options.debug:\r\n dargs['max_simultaneous_procs'] = 1\r\n pe = parallel.ParallelExecute(functions, **dargs)\r\n pe.run_until_completion()\r\n except parallel.ParallelError, err:\r\n return err.errors\r\n return []",
"title": ""
},
{
"docid": "b873db6d38f7489a73c06d2b3bb1e125",
"score": "0.47031468",
"text": "def test(self, selectors: Mapping[str, Any]) -> bool:",
"title": ""
},
{
"docid": "ba1f8744e245b4a2f407f6ef1bd70cab",
"score": "0.46903393",
"text": "def tests(self):\n return [test for test in self._sequence]",
"title": ""
},
{
"docid": "c42ec9ada3715f0771edb49bf1f58b11",
"score": "0.46785706",
"text": "def _get_fuzz_targets(project):\n fuzz_targets = []\n for name in os.listdir(project.out):\n if name.startswith('afl-'):\n continue\n if name == 'centipede':\n continue\n if name.startswith('jazzer_'):\n continue\n if name == 'llvm-symbolizer':\n continue\n\n path = os.path.join(project.out, name)\n # Python and JVM fuzz targets are only executable for the root user, so\n # we can't use os.access.\n if os.path.isfile(path) and (os.stat(path).st_mode & 0o111):\n fuzz_targets.append(name)\n\n return fuzz_targets",
"title": ""
},
{
"docid": "a7540ec4b310f0bbf44fe6b765ae520f",
"score": "0.46682423",
"text": "def has_testcases(xml):\n\n # if testsuite has a child, it means a testcase is present\n if len(xml) > 0:\n return True\n\n return False",
"title": ""
},
{
"docid": "be92f9d1e83b0f871b729d19b688e335",
"score": "0.46534136",
"text": "def test_multi_build(self):\n\n arg_parser = arguments.get_parser()\n args = arg_parser.parse_args([\n 'build',\n '-H', 'this',\n 'build_parallel'\n ])\n\n build_cmd = commands.get_command(args.command_name)\n build_ret = build_cmd.run(self.pav_cfg, args)\n\n build_cmd.outfile.seek(0)\n self.assertEqual(build_ret, 0, msg=build_cmd.outfile.read())\n\n for test in build_cmd.last_tests:\n test.wait(timeout=10)\n\n # Make sure we actually built separate builds\n builds = [test.builder for test in build_cmd.last_tests]\n build_names = set([b.name for b in builds])\n self.assertEqual(len(build_names), 4)\n\n for test in build_cmd.last_tests:\n if not test.skipped:\n self.assertEqual(test.status.current().state, STATES.BUILD_DONE,\n msg='Test {} status: {}'\n .format(test.id, test.status.current()))",
"title": ""
},
{
"docid": "9870fb90df561bf876272e46c68c1e9b",
"score": "0.46417305",
"text": "def test_targets(self) -> None:\n # TODO: Test the targets returned from the Excel connector\n pass",
"title": ""
},
{
"docid": "13e1d37a69731c4f68d173ebeb198d86",
"score": "0.46407396",
"text": "def check_files(self, files):\n\n result = []\n\n for file in files:\n pattern = self._filename_pattern(file)\n\n if pattern in self.found:\n found = self.found[pattern]\n\n else:\n found = self.found[pattern] = self.files_by_pattern(pattern)\n\n (base, dot, suffix) = file.partition('.')\n\n result.append(base in found)\n\n return result",
"title": ""
},
{
"docid": "5d06c1a5e9618d7137df9e9698ef11e4",
"score": "0.4619066",
"text": "def list_tests(self):\n return ['check', 'syntax-check']",
"title": ""
},
{
"docid": "0048ed06dd7f5865e0e5044e9c48bac6",
"score": "0.46176124",
"text": "def test_needed_functions():\n needed_funcs = [\n \"get_include_info\",\n \"execute_known_target\",\n \"get_include_locations\",\n \"get_fprime_configuration\",\n ]\n for func in needed_funcs:\n assert hasattr(get_cmake_builder(), func)",
"title": ""
},
{
"docid": "a94780dea2424d71b64ee45c2b0b85ee",
"score": "0.46039826",
"text": "def retrieve_required_inputs(cgpms, topo, targets, constraints, extraneous):\n required = set(targets)\n for l in reversed(topo):\n outputs_l = cgpms[l].outputs\n inputs_l = cgpms[l].inputs\n if any(i in required or i in constraints for i in outputs_l):\n required.update(inputs_l)\n return [\n target for target in required if\n all(target not in x for x in [targets, constraints, extraneous])\n ]",
"title": ""
},
{
"docid": "537eee76df34bc6a7ee8c4cae38940bf",
"score": "0.45769072",
"text": "def test_compilation(cfile, compiler, **compiler_attrs):\n\n efile, ext = os.path.splitext(cfile)\n\n cpreargs = lpreargs = []\n if sys.platform == 'darwin':\n # use appropriate arch for compiler\n if platform.architecture()[0] == '32bit':\n if platform.processor() == 'powerpc':\n cpu = 'ppc'\n else:\n cpu = 'i386'\n cpreargs = ['-arch', cpu]\n lpreargs = ['-arch', cpu, '-undefined', 'dynamic_lookup']\n else:\n # allow for missing UB arch, since it will still work:\n lpreargs = ['-undefined', 'dynamic_lookup']\n if sys.platform == 'sunos5':\n if platform.architecture()[0] == '32bit':\n lpreargs = ['-m32']\n else:\n lpreargs = ['-m64']\n extra = compiler_attrs.get('extra_compile_args', None)\n extra_link = compiler_attrs.get('extra_link_args', [])\n lpreargs.extend(extra_link)\n\n objs = compiler.compile([cfile], extra_preargs=cpreargs, extra_postargs=extra)\n compiler.link_executable(objs, efile, extra_preargs=lpreargs)\n return efile",
"title": ""
},
{
"docid": "efa3efe559b727e62440a024fc18e385",
"score": "0.45765036",
"text": "def suiteFull():\r\n \r\n return unittest.TestLoader().loadTestsFromTestCase( MatchingCompoundTests )",
"title": ""
},
{
"docid": "58afd4103accddeef52a07dc1c45f3ed",
"score": "0.45699888",
"text": "def Cost(outputs,targets):\r\n errors = np.zeros((len(targets),1))\r\n \r\n t=0\r\n for target in targets:\r\n output = outputs[t]\r\n errors[t] = output!= target\r\n t+=1\r\n \r\n return errors",
"title": ""
},
{
"docid": "440fccfba9f33a5e399a42883f36cb1f",
"score": "0.4568942",
"text": "def contains_test_passer(t, test):\n return test(t.value) or any([test(c.value) for c in t.children])",
"title": ""
},
{
"docid": "3a989679b397d02fa8b20727a9ad3371",
"score": "0.45676422",
"text": "def filter_targets(self, targets, exclude): # type: (t.List[IntegrationTarget], t.Set[str]) -> None\n if self.controller and self.args.host_settings.controller_fallback and targets:\n affected_targets = [target.name for target in targets]\n reason = self.args.host_settings.controller_fallback.reason\n\n if reason == FallbackReason.ENVIRONMENT:\n exclude.update(affected_targets)\n display.warning(f'Excluding {self.host_type} tests since a fallback controller is in use: {\", \".join(affected_targets)}')\n elif reason == FallbackReason.PYTHON:\n display.warning(f'Some {self.host_type} tests may be redundant since a fallback python is in use: {\", \".join(affected_targets)}')\n\n if not self.allow_destructive and not self.config.is_managed:\n override_destructive = set(target for target in self.include_targets if target.startswith('destructive/'))\n override = [target.name for target in targets if override_destructive & set(target.aliases)]\n\n self.skip('destructive', 'which require --allow-destructive or prefixing with \"destructive/\" to run on unmanaged hosts', targets, exclude, override)\n\n if not self.args.allow_disabled:\n override_disabled = set(target for target in self.args.include if target.startswith('disabled/'))\n override = [target.name for target in targets if override_disabled & set(target.aliases)]\n\n self.skip('disabled', 'which require --allow-disabled or prefixing with \"disabled/\"', targets, exclude, override)\n\n if not self.args.allow_unsupported:\n override_unsupported = set(target for target in self.args.include if target.startswith('unsupported/'))\n override = [target.name for target in targets if override_unsupported & set(target.aliases)]\n\n self.skip('unsupported', 'which require --allow-unsupported or prefixing with \"unsupported/\"', targets, exclude, override)\n\n if not self.args.allow_unstable:\n override_unstable = set(target for target in self.args.include if target.startswith('unstable/'))\n\n if self.args.allow_unstable_changed:\n override_unstable |= set(self.args.metadata.change_description.focused_targets or [])\n\n override = [target.name for target in targets if override_unstable & set(target.aliases)]\n\n self.skip('unstable', 'which require --allow-unstable or prefixing with \"unstable/\"', targets, exclude, override)",
"title": ""
},
{
"docid": "e13879015fcedfca8bdaee8333d1e6b7",
"score": "0.45642173",
"text": "def make_tests(input_dir, msg_dir, filter_rgx, callbacks):\n if filter_rgx:\n is_to_run = re.compile(filter_rgx).search\n else:\n is_to_run = lambda x: 1\n tests = []\n for module_file, messages_file in (\n get_tests_info(input_dir, msg_dir, 'func_', '')\n ):\n if not is_to_run(module_file) or module_file.endswith(('.pyc', \"$py.class\")):\n continue\n base = module_file.replace('func_', '').replace('.py', '')\n\n dependencies = get_tests_info(input_dir, msg_dir, base, '.py')\n\n for callback in callbacks:\n test = callback(input_dir, msg_dir, module_file, messages_file,\n dependencies)\n if test:\n tests.append(test)\n return tests",
"title": ""
},
{
"docid": "68157585d88e9e7139268fdce99c937e",
"score": "0.4555055",
"text": "def has_any_contender_results(self) -> bool:\n for run_comparison in self.run_comparisons:\n if run_comparison.contender_benchmark_result_info:\n return True\n return False",
"title": ""
},
{
"docid": "898f150259e29797b953e8c5222e0eba",
"score": "0.45541653",
"text": "def run_all_tests(skip_expensive_tests, verbose, setup_deps=True):\n\n start = time.time()\n\n # Prepare tasks.\n task_to_test = {}\n tasks = []\n test_classes = {}\n test_classes.update(ALL_TEST_CLASSES)\n test_classes.update(all_third_party_tests())\n\n for test_class_name in test_classes:\n if skip_expensive_tests and test_class_name in EXPENSIVE_TESTS:\n continue\n test = FunctionalTestTask(test_class_name, verbose)\n task = TaskThread(test.run, name='testing %s' % test_class_name)\n task_to_test[task] = test\n tasks.append(task)\n\n # order tests by their size largest to smallest\n tasks = sorted(\n tasks,\n key=lambda task: test_classes.get(task_to_test[task].test_class_name),\n reverse=True)\n\n # setup dependencies\n if setup_deps:\n setup_all_dependencies()\n\n # execute all tasks\n log('Executing all %s test suites' % len(tasks))\n runtimes_sec = []\n TaskThread.execute_task_list(\n tasks, chunk_size=16, runtimes_sec=runtimes_sec)\n\n # map durations to names\n name_durations = []\n for index, duration in enumerate(runtimes_sec):\n name_durations.append((\n round(duration, 2), task_to_test[tasks[index]].test_class_name))\n\n # report all longest first\n log('Reporting execution times for 10 longest tests')\n for duration, name in sorted(\n name_durations, key=lambda name_duration: name_duration[0],\n reverse=True)[:10]:\n log('Took %ss for %s' % (int(duration), name))\n\n # Check we ran all tests as expected.\n total_count = 0\n for task in tasks:\n test = task_to_test[task]\n # Check that no unexpected tests were picked up via automatic discovery,\n # and that the number of tests run in a particular suite.py invocation\n # matches the expected number of tests.\n test_count = test_classes.get(test.test_class_name, None)\n expected_text = 'INFO: All %s tests PASSED!' % test_count\n if test_count is None:\n log('%s\\n\\nERROR: ran unexpected test class %s' % (\n test.output, test.test_class_name))\n if expected_text not in test.output:\n log('%s\\n\\nERROR: Expected %s tests to be run for the test class '\n '%s, but found some other number.' % (\n test.output, test_count, test.test_class_name))\n raise Exception()\n total_count += test_count\n\n log('Ran %s tests in %s test classes; took %ss' % (\n total_count, len(tasks), int(time.time() - start)))",
"title": ""
},
{
"docid": "136a960c1efccb1cb433b39083e6246f",
"score": "0.4550851",
"text": "def filter_targets(self, targets, exclude): # type: (t.List[IntegrationTarget], t.Set[str]) -> None\n super().filter_targets(targets, exclude)\n\n if not self.allow_root and not self.config.have_root:\n self.skip('needs/root', 'which require --allow-root or running as root', targets, exclude)\n\n self.skip(f'skip/python{self.config.python.version}', f'which are not supported by Python {self.config.python.version}', targets, exclude)\n self.skip(f'skip/python{self.config.python.major_version}', f'which are not supported by Python {self.config.python.major_version}', targets, exclude)",
"title": ""
},
{
"docid": "8a31aa97388111f8054134897472d5a4",
"score": "0.45327947",
"text": "def has_any_contender_runs(self) -> bool:\n return bool(self.run_comparisons)",
"title": ""
},
{
"docid": "e902f7aa4b21b38720d396b0493d703f",
"score": "0.45303938",
"text": "def testscftargets(self):\r\n assert len(self.data.scftargets[0]) == self.num_scf_criteria",
"title": ""
},
{
"docid": "2526845b69ad398531a73c9a2670587d",
"score": "0.4527421",
"text": "def select_samples_from_doit(dependencies, targets):\n\n for dep_file in dependencies:\n dep_ext = dep_file.split('.')[-1]\n\n if dep_ext == 'table':\n segregation_path = dep_file\n elif dep_ext == 'txt':\n sample_names_path = dep_file\n\n assert len(targets) == 1\n\n select_samples_from_file(\n segregation_path,\n sample_names_path,\n list(targets)[0])",
"title": ""
},
{
"docid": "4cec99c70ad1a5f0d0cb1b47b167af0b",
"score": "0.45267114",
"text": "def AllTargets(target_list, target_dicts, build_file):\n bftargets = BuildFileTargets(target_list, build_file)\n deptargets = DeepDependencyTargets(target_dicts, bftargets)\n return bftargets + deptargets",
"title": ""
},
{
"docid": "9ea3086c0d114276f74e44f917059d25",
"score": "0.4524205",
"text": "def get_all_tests():\n return _get_tests('all.xml')",
"title": ""
},
{
"docid": "c3de9957fffd219bb60da7bcf8222082",
"score": "0.45203453",
"text": "def find_host_unit_tests(module_info, path):\n logging.debug('finding unit tests under %s', path)\n found_unit_tests = []\n unit_test_names = module_info.get_all_unit_tests()\n logging.debug('All the unit tests: %s', unit_test_names)\n for unit_test_name in unit_test_names:\n for test_path in module_info.get_paths(unit_test_name):\n if test_path.find(path) == 0:\n found_unit_tests.append(unit_test_name)\n return found_unit_tests",
"title": ""
},
{
"docid": "2d1ee6428883d93cebd6e47c5aa1fc0c",
"score": "0.45135447",
"text": "def all_tests(cls):\n return dict(_tests.ATest.test_classes)",
"title": ""
},
{
"docid": "a8894f925b45b3316dea143383cdb5b9",
"score": "0.45129296",
"text": "def countMulttargets(self):\n return sum(cf._multTarget is not None for cf in self._cashFlows)",
"title": ""
},
{
"docid": "6820d4d41903b22f0458744674feaaf2",
"score": "0.45128325",
"text": "def getTests(self):\n return self.__tests",
"title": ""
},
{
"docid": "85e9cd6af0195e4c0f24f3ef5512fce8",
"score": "0.45081648",
"text": "def get_selected_tests(self, filter_func):\n\n j2env = (\n jinja2.Environment(\n loader=jinja2.FileSystemLoader(self.suite_path),\n keep_trailing_newline=True,\n )\n if USE_JINJA\n else None\n )\n\n for test_name in os.listdir(self.suite_path):\n if not is_test_from_dir(self.suite_path, test_name):\n continue\n if self.args.test and not any(\n re.search(pattern, test_name) for pattern in self.args.test\n ):\n continue\n if USE_JINJA and test_name.endswith(\".gen.sql\"):\n continue\n if not filter_func(test_name):\n continue\n test_name = self.render_test_template(j2env, self.suite_path, test_name)\n yield test_name",
"title": ""
},
{
"docid": "f352a21391ee3aa04d5fb12e872df565",
"score": "0.4498331",
"text": "def get_tests_in_browsertest(file: str) -> Dict[str, Set[TestPlatform]]:\n tests: Dict[str, Set[TestPlatform]] = {}\n # Attempts to only match test test name in a test declaration, where the\n # name contains the test id prefix. Purposefully allows any prefixes on\n # the test name (like MAYBE_ or DISABLED_).\n for match in re.finditer(fr'{CoverageTest.TEST_ID_PREFIX}(\\w+)\\)', file):\n test_id = match.group(1)\n tests[test_id] = set(TestPlatform)\n test_name = f\"{CoverageTest.TEST_ID_PREFIX}{test_id}\"\n if f\"DISABLED_{test_name}\" not in file:\n continue\n enabled_platforms: Set[TestPlatform] = tests[test_id]\n for platform in TestPlatform:\n # Search for macro that specifies the given platform before\n # the string \"DISABLED_<test_name>\".\n macro_for_regex = re.escape(platform.macro)\n # This pattern ensures that there aren't any '{' or '}' characters\n # between the macro and the disabled test name, which ensures that\n # the macro is applying to the correct test.\n if re.search(fr\"{macro_for_regex}[^{{}}]+DISABLED_{test_name}\",\n file):\n enabled_platforms.remove(platform)\n if len(enabled_platforms) == len(TestPlatform):\n enabled_platforms.clear()\n return tests",
"title": ""
},
{
"docid": "ae526ebc0f858a43f95621155b04b4fd",
"score": "0.44975027",
"text": "def FindTestModules():\n tests = []\n start_dir = os.path.dirname(os.path.abspath(__file__))\n for dir, subdirs, files in os.walk(start_dir):\n if dir.endswith('/.svn') or '/.svn/' in dir:\n continue\n tests.extend(ModuleName(os.path.join(dir, f), start_dir) for f \n in files if f.endswith('_test.py'))\n return tests",
"title": ""
},
{
"docid": "3be626db7c4878e79956c890633ad620",
"score": "0.449362",
"text": "def has_test_case(view):\n for php_class in find_php_classes(view):\n if php_class[-4:] == 'Test':\n return True\n return False",
"title": ""
},
{
"docid": "40fc8696f872550456185eb9309283ac",
"score": "0.44888446",
"text": "def filter_targets(self, targets, exclude): # type: (t.List[IntegrationTarget], t.Set[str]) -> None\n super().filter_targets(targets, exclude)\n\n arch = detect_architecture(self.config.python.path)\n\n if arch:\n self.skip(f'skip/{arch}', f'which are not supported by {arch}', targets, exclude)",
"title": ""
},
{
"docid": "a0374bb0e42f8e8c6d71ba531692cfea",
"score": "0.44878712",
"text": "def compute_correct(self, outputs, targets, tolerance=None):\n tolerance = tolerance if tolerance is not None else self.tolerance\n correct = []\n for r in range(len(outputs[0])):\n row = []\n for c in range(len(outputs)):\n row.extend(list(map(lambda v: v <= tolerance, np.abs(outputs[c][r] - targets[c][r]))))\n correct.append(all(row))\n return correct",
"title": ""
},
{
"docid": "a60841f6c043b23b9d649ddb3964259e",
"score": "0.4482388",
"text": "def is_suite_to_be_executed(name, testSuites):\r\n if len(testSuites) == 0:\r\n return True\r\n else:\r\n for test_case in testSuites:\r\n if str(test_case) == name:\r\n return True\r\n\r\n return False",
"title": ""
},
{
"docid": "3a18f6ca5b40d0338a2163df7946d01d",
"score": "0.44671527",
"text": "def get_testable_functions():\n # Check the files that aren't the current file.\n curr_file = os.path.basename(__file__)\n files_to_check = [\n f for f in os.listdir() if f.endswith('.py') and f != curr_file\n ]\n\n # Collect a list of functions to test\n output = []\n for file in files_to_check:\n module_name = file[:-3]\n module = __import__(module_name)\n\n try:\n output += module.TESTABLE\n except AttributeError:\n # No testable functions in the file\n pass\n\n return output",
"title": ""
},
{
"docid": "3c71baba1ba10731c0df83fe5d6604ed",
"score": "0.44659096",
"text": "def _find_framework_tests(rows):\r\n job_test_pattern = re.compile('SERVER|CLIENT\\\\_JOB\\.[\\d]')\r\n test_jobs = []\r\n for r in rows:\r\n test_name = r[0]\r\n if job_test_pattern.match(test_name):\r\n test_jobs.append(test_name)\r\n\r\n return test_jobs",
"title": ""
},
{
"docid": "adda17059b4d04b248ebc251717c70c6",
"score": "0.4462767",
"text": "def has_passed(self):\n\n # Look through the test classes for this device,\n # and if any of them have no passed (i.e. not passed\n # required\n for test_class in self.test_classes:\n if test_class.is_required() and not test_class.has_passed():\n return False\n return True",
"title": ""
},
{
"docid": "62717f425117151325c75bb236c1a66a",
"score": "0.4461657",
"text": "def targets(self):\n targets = list()\n if self.isValid():\n target_list = self.pbx_root_object.get(PBX_Constants.kPBX_PROJECT_targets, None)\n if target_list:\n targets.extend(target_list)\n return targets",
"title": ""
},
{
"docid": "639e80cd6da61e445ccece0292f92a3a",
"score": "0.4454716",
"text": "def find_json():\n matches = []\n for root, _dirnames, filenames in os.walk('tests'):\n for filename in fnmatch.filter(filenames, '*.json'):\n matches.append(os.path.join(root, filename))\n return matches",
"title": ""
},
{
"docid": "9385d81084997adb2b261566882095dc",
"score": "0.4448311",
"text": "def get_test_names(): \n tests = [] \n for root, dirnames, filenames in os.walk(tp.tests_to_run_dir): # @UnusedVariable\n for filename in fnmatch.filter(filenames, 'test_*.py'):\n tests.append(os.path.join(root, filename))\n return tests",
"title": ""
},
{
"docid": "8716760daff9b25c04ff5653937769b6",
"score": "0.44403902",
"text": "def collect_tests(paths, logger=None, setup=None, teardown=None):\n if logger is None:\n logger = j.logger.logging\n\n result = []\n logger.info('Collecting tests from paths {}'.format(paths))\n for path in paths:\n if not j.sal.fs.exists(path):\n logger.error('Path {} does not exist'.format(path))\n continue\n if j.sal.fs.isFile(path):\n name = j.sal.fs.getBaseName(path)\n result.append(AYSTest(name=name, path=path))\n continue\n for dir_ in j.sal.fs.listDirsInDir(path):\n logger.debug('Creating group test for path {}'.format(dir_))\n result.append(AYSGroupTest(name=j.sal.fs.getBaseName(dir_), path=dir_))\n for file_ in sorted([file__ for file__ in j.sal.fs.listFilesInDir(path) if not j.sal.fs.getBaseName(file__).startswith('_') and\n (file__.endswith('{}yaml'.format(os.path.extsep)) or\n file__.endswith('{}bp'.format(os.path.extsep)))]):\n logger.debug('Creating test for path {}'.format(file_))\n result.append(AYSTest(name=j.sal.fs.getBaseName(file_), path=file_, setup=setup, teardown=teardown))\n return result",
"title": ""
},
{
"docid": "e743d441b76d01e1895d65240b0de78c",
"score": "0.4438747",
"text": "def additional_tests():\r\n return make_suite('tests.')",
"title": ""
},
{
"docid": "8f91f10a74ca7bb3a0d1b426e1134a4d",
"score": "0.443836",
"text": "def test_local_builds_only(self):\n\n arg_parser = arguments.get_parser()\n args = arg_parser.parse_args([\n 'build',\n '-H', 'this',\n '--local-builds-only',\n 'build_parallel'\n ])\n\n build_cmd = commands.get_command(args.command_name)\n build_ret = build_cmd.run(self.pav_cfg, args)\n\n build_cmd.outfile.seek(0)\n self.assertEqual(build_ret, 0, msg=build_cmd.outfile.read())\n\n for test in build_cmd.last_tests:\n test.wait(timeout=10)\n\n # Make sure we actually built separate builds\n builds = [test.builder for test in build_cmd.last_tests]\n build_names = set([b.name for b in builds])\n self.assertEqual(len(build_names), 2)\n\n for test in build_cmd.last_tests:\n if not test.skipped:\n self.assertEqual(test.status.current().state, STATES.BUILD_DONE,\n msg='Test {} status: {}'\n .format(test.id, test.status.current()))",
"title": ""
},
{
"docid": "e961a0dd43f1ce08b7d416b6a636b473",
"score": "0.4437065",
"text": "def _get_test_classes_from_modules(modules):\n classes = []\n for module in modules:\n for attr in dir(module):\n val = getattr(module, attr)\n not_base_itself = not val == seltest.Base\n instance_of_base = (hasattr(val, '__bases__')\n and seltest.Base in val.__bases__)\n if not_base_itself and instance_of_base:\n classes.append(val)\n return classes",
"title": ""
},
{
"docid": "72d2f1ed1b2e13e367e8d663e9903b8e",
"score": "0.44370463",
"text": "def get_test_methods(self, filter_required=None):\n res = []\n for test_class in self.test_classes:\n if test_class.is_required() == filter_required:\n continue\n for test_method in test_class.get_methods():\n res.append(test_method)\n return res",
"title": ""
},
{
"docid": "d14b133a7e171a32f9aae76692e94ac2",
"score": "0.44324496",
"text": "def has(*commands):\n r = True\n for c in commands:\n if runp('which ' + c, check=True)[0] != 0:\n r = False\n break\n return r",
"title": ""
},
{
"docid": "a47da154a950a22c9dd6116fe41288a3",
"score": "0.4420631",
"text": "def generate_rules() -> List[str]:\n cmake_rules = []\n\n for platform in test_definitions.CMakePlatform:\n cmake_rule = cmake_builder.rules.build_set(\n variable_name=f\"IREE_MODULE_COMPILE_CONFIG_ID_{platform.value.upper()}\",\n value=f'\"{test_definitions.PLATFORM_COMPILE_CONFIG_MAP[platform].id}\"')\n cmake_rules.append(cmake_rule)\n\n for test_config in test_definitions.TEST_CONFIGS:\n model = test_config.model\n runner_args = run_module_utils.build_run_flags_for_model(\n model=model,\n model_input_data=test_config.input_data) + test_config.extra_test_flags\n # TODO(#11136): Currently the DRIVER is a separate field in the CMake rule (\n # and has effect on test labels). Rules should be generated in another way\n # to avoid that. Generates the flags without the driver for now.\n runner_args += run_module_utils.build_run_flags_for_execution_config(\n test_config.execution_config, with_driver=False)\n cmake_rule = cmake_builder.rules.build_iree_benchmark_suite_module_test(\n target_name=test_config.name,\n model=f\"{model.id}_{model.name}\",\n driver=test_config.execution_config.driver.value,\n expected_output=test_config.expected_output,\n runner_args=runner_args,\n xfail_platforms=[\n platform.value for platform in test_config.xfail_platforms\n ],\n unsupported_platforms=[\n platform.value for platform in test_config.unsupported_platforms\n ])\n cmake_rules.append(cmake_rule)\n\n return cmake_rules",
"title": ""
},
{
"docid": "3314476a8d96c5cb43b56ed75b718627",
"score": "0.44169393",
"text": "def test_output_checks(self):\n with self._isolate() as f:\n test_artifact = os.path.join(TEST_DATA_DIR, \"output_tests_tool.cwl\")\n test_command = [\n \"test\",\n \"--no-container\",\n \"--engine\", \"cwltool\",\n test_artifact,\n ]\n self._check_exit_code(test_command, exit_code=1)\n output_json_path = os.path.join(f, \"tool_test_output.json\")\n with open(output_json_path, \"r\") as f:\n output = json.load(f)\n assert \"tests\" in output\n tests = output[\"tests\"]\n # check out tests/data/output_tests_tool_test.yml\n expected_statuses = [\n \"success\",\n \"failure\",\n \"success\",\n \"success\",\n \"failure\",\n \"success\",\n \"failure\",\n \"success\",\n \"failure\",\n \"success\",\n \"failure\",\n ]\n for i in range(len(expected_statuses)):\n test_i = tests[i]\n data = test_i[\"data\"]\n expected_status = expected_statuses[i]\n assert data[\"status\"] == expected_status",
"title": ""
},
{
"docid": "4fe142df74275f3677c0400e8f01df71",
"score": "0.44075513",
"text": "def test_contrib_tests_exist(self):\n\n contrib_tests_dir = os.path.dirname(os.path.realpath(contrib_tests.__file__))\n contrib_test_files = os.listdir(contrib_tests_dir)\n\n # Find all python files in the contrib dir and assert there's a corresponding test file\n for filename in os.listdir(self.CONTRIB_DIR):\n if filename.endswith(\".py\") and filename not in [\"__init__.py\"]:\n expected_test_file = f\"test_{filename}\"\n error_msg = (\n \"Every Contrib Rule must have associated tests. \"\n f\"Expected test file {os.path.join(contrib_tests_dir, expected_test_file)} not found.\"\n )\n self.assertIn(expected_test_file, contrib_test_files, error_msg)",
"title": ""
},
{
"docid": "82e7b9b1036db0da616f01e0c154b0a7",
"score": "0.44047672",
"text": "def load_tests(loader, tests=None, patterns=None,excludes=None):\n classes = [cls for name, cls in inspect.getmembers(sys.modules[__name__],\n inspect.isclass)\n if issubclass(cls, unittest.TestCase)]\n\n suite = OrderedTestSuite()\n for test_class in classes:\n tests = loader.loadTestsFromTestCase(test_class)\n if patterns:\n tests = [test for test in tests if all(re.search(pattern, test.id()) for pattern in patterns)]\n if excludes:\n tests = [test for test in tests if not any(re.search(exclude_pattern,test.id()) for exclude_pattern in excludes)]\n suite.addTests(tests)\n return suite",
"title": ""
},
{
"docid": "0ec3b90a66532279324e1ab53c1ece55",
"score": "0.44033256",
"text": "def matches_source_commit(self, builds):\n source_details = self.config.content.source.git\n alias = self.config.content.source.alias\n if source_details is Missing and alias is not Missing:\n source_details = self.runtime.group_config.sources[alias]\n if source_details is Missing:\n commit_hash = None # source is in distgit; just check if it has built\n else:\n _, commit_hash = self.runtime.detect_remote_source_branch(dict(source_details))\n return self._matches_commit(commit_hash, builds)",
"title": ""
}
]
45b3623ba61966a94edbc20b2bdf4fe8
Convenience method for merging replicates. Merge will extrapolate times to `number_per_run=1` and will not transfer any metadata. (Since it might differ between replicates)
[
{
"docid": "4a14c333dc41744af2af5f93dce71346",
"score": "0.44534874",
"text": "def merge(measurements): # type: (Iterable[Measurement]) -> List[Measurement]\r\n grouped_measurements: DefaultDict[TaskSpec, List[Measurement]] = collections.defaultdict(list)\r\n for m in measurements:\r\n grouped_measurements[m.task_spec].append(m)\r\n\r\n def merge_group(task_spec: TaskSpec, group: List[Measurement]) -> Measurement:\r\n times: List[float] = []\r\n for m in group:\r\n # Different measurements could have different `number_per_run`,\r\n # so we call `.times` which normalizes the results.\r\n times.extend(m.times)\r\n\r\n return Measurement(\r\n number_per_run=1,\r\n raw_times=times,\r\n task_spec=task_spec,\r\n metadata=None,\r\n )\r\n\r\n return [merge_group(t, g) for t, g in grouped_measurements.items()]",
"title": ""
}
]
[
{
"docid": "5c48bdd3d85f9a61a258cddfddf4a793",
"score": "0.57867146",
"text": "def merge_runs(ctx, args):\n from . import merge_impl\n\n merge_impl.main(args, ctx)",
"title": ""
},
{
"docid": "9061efa9a9fc791c8dbbeddcbf57705d",
"score": "0.57148755",
"text": "def run_merge(self):\n log = self.app.timing_get_full_list()\n if len(log) < 2:\n return\n self.load(log[-1].code)\n if not log[-2].can_merge(log[-1]):\n raise QError('Cannot merge latest two work entries.')\n self.ticket.work_timing_merge()\n self.ticket.save()",
"title": ""
},
{
"docid": "0ad5977298cb403442b92d8b3ad8f935",
"score": "0.5713215",
"text": "def perform_duplication():\n pass",
"title": ""
},
{
"docid": "a05639b5310c22cb1bb702be5a2a9ac9",
"score": "0.5424366",
"text": "def merge_similar_runs():\n # create/ensure existence of log directories\n target_path_base = MERGED_LOGS_PATH\n source_path_base = \"./logs\"\n if not Path(source_path_base).is_dir():\n print(f\"Logfile directory {source_path_base} not found! Run experiments before using the evaluation tool\")\n raise SystemExit\n if not Path(target_path_base).is_dir():\n Path(target_path_base).mkdir()\n source_path_base = Path(source_path_base)\n # find all json files in ./logs\n log_files = list(source_path_base.glob('*.json'))\n log_map = defaultdict(lambda: [])\n # create list of logs for each unique settings key\n for idx, log in enumerate(log_files):\n with log.open() as json_file:\n json_data = json.load(json_file)\n key = (json_data[\"Strategy\"], json_data[\"Budget\"], json_data[\"Initial Split\"],\n json_data[\"Iterations\"], json_data[\"Batch Size\"], json_data[\"Target Layer\"], json_data[\"Model\"],\n json_data[\"Data Augmentation\"])\n log_map[key].append(json_data)\n # loop over each setting key\n for key, log_list in log_map.items():\n # initialize data containers\n acc = np.empty(shape=(0, key[3]+1))\n class_dist = np.empty(shape=(0, key[3]+1, 10))\n conf_mat = np.empty(shape=(0, key[3]+1, 10, 10))\n info_gain = np.empty(shape=(0, key[3]+1))\n # accumulate date over multiple runs\n for log in log_list:\n\n acc = np.append(acc, np.expand_dims(np.asarray(log[\"Accuracy\"]), axis=0), axis=0)\n class_dist = np.append(class_dist, np.expand_dims(np.asarray(log[\"Class Distribution\"]), axis=0), axis=0)\n conf_mat = np.append(conf_mat, np.expand_dims(np.asarray(log[\"Confusion Matrix\"]), axis=0), axis=0)\n info_gain = np.append(info_gain,\n np.asarray([scipy.stats.entropy(x, np.ones(10)/10)\n for x in np.asarray(log[\"Class Distribution\"])]).reshape((1, -1)), axis=0)\n # create mean and standard deviation info over runs\n acc_mean = np.mean(acc, axis=0)\n class_dist_mean = np.mean(class_dist, axis=0)\n acc_std = np.std(acc, axis=0)\n class_dist_std = np.std(class_dist, axis=0)\n conf_mat_mean = np.mean(conf_mat, axis=0)\n conf_mat_std = np.std(conf_mat, axis=0)\n info_gain_mean = np.mean(info_gain, axis=0)\n info_gain_std = np.std(info_gain, axis=0)\n # turn back to serializable format\n acc = acc.tolist()\n acc_mean = acc_mean.tolist()\n acc_std = acc_std.tolist()\n class_dist = class_dist.tolist()\n class_dist_mean = class_dist_mean.tolist()\n class_dist_std = class_dist_std.tolist()\n conf_mat = conf_mat.tolist()\n conf_mat_mean = conf_mat_mean.tolist()\n conf_mat_std = conf_mat_std.tolist()\n info_gain = info_gain.tolist()\n info_gain_mean = info_gain_mean.tolist()\n info_gain_std = info_gain_std.tolist()\n # create json structure\n merged_dict = {\"Strategy\": key[0], \"Budget\": key[1], \"Initial Split\": key[2],\n \"Iterations\": key[3], \"Batch Size\": key[4], \"Target Layer\": key[5], \"Model\": key[6],\n \"Data Augmentation\": key[7], \"Accuracy All\": acc,\n \"Accuracy Mean\": acc_mean, \"Accuracy Std\": acc_std,\n \"Class Distribution All\": class_dist, \"Class Distribution Mean\": class_dist_mean,\n \"Class Distribution Std\": class_dist_std, \"Confusion Matrix All\": conf_mat,\n \"Confusion Matrix Mean\": conf_mat_mean, \"Confusion Matrix Std\": conf_mat_std,\n \"Information Gain All\": info_gain, \"Information Gain Mean\": info_gain_mean,\n \"Information Gain Std\": info_gain_std}\n # generate a filename by settings\n target_file = Path(f\"{key[0]}_{key[1]}_{key[2]}_{key[3]}_{key[4]}_{key[5]}_{key[6]}\"\n f\"{'_data_augmentation' if key[7] else ''}.json\")\n # create json file\n with Path.joinpath(Path(target_path_base, target_file)).open('w', encoding='utf-8') as file:\n json.dump(merged_dict, file, ensure_ascii=False)",
"title": ""
},
{
"docid": "1710fa9e72200696997e6931a86a9532",
"score": "0.5357749",
"text": "def _merge_data(self):\n self.logger_info(\"Merging into a single dataset.\")\n interim_path = self._get_data_name_folders_path(\"interim\")\n interim_agg_path = os.path.join(interim_path, self.aggregation_level)\n filenames = self._get_files_in_dir(interim_agg_path)\n list_data = [\n pd.read_csv(\n os.path.join(interim_agg_path, filename), low_memory=True\n ).infer_objects()\n for filename in tqdm(filenames, desc=\"Loading data\", leave=False)\n ]\n df_geo = list_data[0].copy()\n geo_col = [c for c in df_geo.columns if GEO_TAG in c]\n df_geo = df_geo[geo_col]\n df_geo.set_index(self._get_aggregation_level_id_col(), inplace=True)\n for df in list_data:\n df.set_index(self._get_aggregation_level_id_col(), inplace=True)\n df.drop(columns=geo_col, errors=\"ignore\", inplace=True)\n\n list_data.insert(0, df_geo)\n self.__processed_data = pd.concat(list_data, axis=1)\n self.__processed_data.reset_index(inplace=True)\n self._drop_duplicated_col_from_merge()",
"title": ""
},
{
"docid": "c052211611bc75a335ca4d9367e37657",
"score": "0.5354194",
"text": "def split_count_matrix_by_replicate(\n count_matrix_file,\n rep1_file,\n rep2_file,\n pooled_file):\n assert \".mat\" in count_matrix_file\n \n # rep1\n pull_single_replicate(count_matrix_file, rep1_file, rep=\"b1\")\n\n # rep2\n pull_single_replicate(count_matrix_file, rep2_file, rep=\"b2\")\n\n # pooled\n pool_replicates(count_matrix_file, pooled_file)\n\n return None",
"title": ""
},
{
"docid": "b977569157910b99f450b563a177fa3e",
"score": "0.5276833",
"text": "def test_pooling(self):\n directory = TempDirectory()\n dna = \"AAAAAAAAAA\"\n original_file_path = write_fixed_dna_fasta(dna, directory.path, \"original.fasta\")\n args = make_default_args(original_file_path)\n args.random_seed = 1\n args.num_sims = 2\n args.subset_len = 1\n\n args.num_subs = 1\n args.num_insertions = 0\n args.num_deletions = 0\n snpmutator.run_from_args(args)\n mutated_seq_record1 = read_fasta_seq_record(\"original_mutated_1.fasta\")\n mutated_seq_record2 = read_fasta_seq_record(\"original_mutated_2.fasta\")\n self.assertEqual(str(mutated_seq_record1.seq), 'AATAAAAAAA', \"Pooling SNP replicate 1 test failed, dna=%s mutated seq1=%s\" % (dna, str(mutated_seq_record1.seq)))\n self.assertEqual(str(mutated_seq_record2.seq), 'AACAAAAAAA', \"Pooling SNP replicate 2 test failed, dna=%s mutated seq2=%s\" % (dna, str(mutated_seq_record2.seq)))\n\n args.num_subs = 0\n args.num_insertions = 1\n args.num_deletions = 0\n snpmutator.run_from_args(args)\n mutated_seq_record1 = read_fasta_seq_record(\"original_mutated_1.fasta\")\n mutated_seq_record2 = read_fasta_seq_record(\"original_mutated_2.fasta\")\n self.assertEqual(str(mutated_seq_record1.seq), 'AAAGAAAAAAA', \"Pooling INS replicate 1 test failed, dna=%s mutated seq=%s\" % (dna, str(mutated_seq_record1.seq)))\n self.assertEqual(str(mutated_seq_record2.seq), 'AAACAAAAAAA', \"Pooling INS replicate 2 test failed, dna=%s mutated seq=%s\" % (dna, str(mutated_seq_record2.seq)))\n\n args.num_subs = 0\n args.num_insertions = 0\n args.num_deletions = 1\n snpmutator.run_from_args(args)\n mutated_seq_record1 = read_fasta_seq_record(\"original_mutated_1.fasta\")\n mutated_seq_record2 = read_fasta_seq_record(\"original_mutated_2.fasta\")\n self.assertEqual(str(mutated_seq_record1.seq), 'AAAAAAAAA', \"Pooling DEL replicate 1 test failed, dna=%s mutated seq=%s\" % (dna, str(mutated_seq_record1.seq)))\n self.assertEqual(str(mutated_seq_record2.seq), 'AAAAAAAAA', \"Pooling DEL replicate 2 test failed, dna=%s mutated seq=%s\" % (dna, str(mutated_seq_record1.seq)))",
"title": ""
},
{
"docid": "b200fe13341ba077e267fad73d0175b6",
"score": "0.52248764",
"text": "def merge(self):\n return",
"title": ""
},
{
"docid": "3d4f95fb2a70e17f50dcfd2b4f066657",
"score": "0.52225995",
"text": "def _merge_one_build(cls, aggregated_json, incremental_json,\n incremental_index, num_runs):\n\n for key in incremental_json.keys():\n # Merge json results except \"tests\" properties (results, times etc).\n # \"tests\" properties will be handled separately.\n if key == JSON_RESULTS_TESTS:\n continue\n\n if key in aggregated_json:\n aggregated_json[key].insert(\n 0, incremental_json[key][incremental_index])\n aggregated_json[key] = \\\n aggregated_json[key][:num_runs]\n else:\n aggregated_json[key] = incremental_json[key]",
"title": ""
},
{
"docid": "8b6f9b1b71549d22fac6badd78f72f2e",
"score": "0.5194163",
"text": "def merge_time(self):\n\n self.run()\n\n if type(self.current) is not list:\n warnings.warn(message=\"There is only file in the dataset. No need to merge!\")\n return None\n\n cdo_command = \"cdo --sortname -mergetime\"\n\n run_this(cdo_command, self, output=\"one\")\n\n if session_info[\"lazy\"]:\n self._merged = True\n\n if cdo_version() in [\"1.9.3\"]:\n self.run()",
"title": ""
},
{
"docid": "dda289223d7dc4df8e5a204efab72052",
"score": "0.5129586",
"text": "def merge(*args) -> None:",
"title": ""
},
{
"docid": "a0acfb75d734633227caa5bd5c039bb0",
"score": "0.5111439",
"text": "def merge(self, int: int) -> None:\n ...",
"title": ""
},
{
"docid": "bc67dcdeeb9a218c5c5a92c979ec183c",
"score": "0.5107162",
"text": "def merge(self, other):\n ...",
"title": ""
},
{
"docid": "3458d3c93a93844b1aa13f482a9bfee5",
"score": "0.50817776",
"text": "def merge(self, other: 'Saver'):\n # No merging is supported for ONNX. self.path must be unique\n raise RuntimeError('merging not supported for ONNX exporter')",
"title": ""
},
{
"docid": "1d569a1a685fe7d4a542d185121446c1",
"score": "0.5050246",
"text": "def merge_duplicates(self):\n if len(self.entries) == 0:\n self.log.error(\"WARNING: `entries` is empty, loading stubs\")\n if self.args.update:\n self.log.warning(\n \"No sources changed, entry files unchanged in update.\"\n \" Skipping merge.\")\n return\n self.entries = self.load_stubs()\n\n task_str = self.get_current_task_str()\n\n keys = list(sorted(self.entries.keys()))\n n1 = 0\n mainpbar = tqdm(total=len(keys), desc=task_str)\n while n1 < len(keys):\n name1 = keys[n1]\n if name1 not in self.entries:\n self.log.info(\"Entry for {} not found, likely already \"\n \"deleted in merging process.\".format(name1))\n n1 = n1 + 1\n mainpbar.update(1)\n continue\n allnames1 = set(self.entries[name1].get_aliases() +\n self.entries[name1].extra_aliases())\n\n # Search all later names\n for name2 in keys[n1 + 1:]:\n if name1 == name2:\n continue\n if name1 not in self.entries:\n self.log.info(\"Entry for {} not found, likely already \"\n \"deleted in merging process.\".format(name1))\n continue\n if name2 not in self.entries:\n self.log.info(\"Entry for {} not found, likely already \"\n \"deleted in merging process.\".format(name2))\n continue\n\n allnames2 = set(self.entries[name2].get_aliases() +\n self.entries[name2].extra_aliases())\n\n # If there are any common names or aliases, merge\n if len(allnames1 & allnames2):\n self.log.warning(\n \"Found two entries with common aliases \"\n \"('{}' and '{}'), merging.\".format(name1, name2))\n\n load1 = self.proto.init_from_file(\n self, name=name1)\n load2 = self.proto.init_from_file(\n self, name=name2)\n if load1 is not None and load2 is not None:\n # Delete old files\n self._delete_entry_file(entry=load1)\n self._delete_entry_file(entry=load2)\n self.entries[name1] = load1\n self.entries[name2] = load2\n priority1 = 0\n priority2 = 0\n for an in allnames1:\n if an.startswith(self.entries[name1]\n .priority_prefixes()):\n priority1 += 1\n for an in allnames2:\n if an.startswith(self.entries[name2]\n .priority_prefixes()):\n priority2 += 1\n\n if priority1 > priority2:\n self.copy_to_entry_in_catalog(name2, name1)\n keys.append(name1)\n del self.entries[name2]\n else:\n self.copy_to_entry_in_catalog(name1, name2)\n keys.append(name2)\n del self.entries[name1]\n else:\n self.log.warning('Duplicate already deleted')\n\n # if len(self.entries) != 1:\n # self.log.error(\n # \"WARNING: len(entries) = {}, expected 1. \"\n # \"Still journaling...\".format(len(self.entries)))\n self.journal_entries()\n\n if self.args.travis and n1 > self.TRAVIS_QUERY_LIMIT:\n break\n n1 = n1 + 1\n mainpbar.update(1)\n mainpbar.close()",
"title": ""
},
{
"docid": "9ea82f635a4215dfa8d3cfef68c3b923",
"score": "0.5047663",
"text": "def merge_neuron_sets_repeatedly(new_neuron_set_1,\n new_neuron_set_2,\n alignment_min_threshold,\n overlap_min_threshold,\n **parameters):\n\n if new_neuron_set_1.size:\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_neuron_set_1\"] = new_neuron_set_1\n\n if new_neuron_set_2.size:\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_neuron_set_2\"] = new_neuron_set_2\n\n assert (new_neuron_set_1.dtype == new_neuron_set_2.dtype)\n\n new_neuron_set = numpy.hstack([new_neuron_set_1, new_neuron_set_2])\n\n if len(new_neuron_set_1) and len(new_neuron_set_2):\n logger.debug(\"Have 2 sets of neurons to merge.\")\n elif len(new_neuron_set_1) or len(new_neuron_set_2):\n logger.debug(\n \"Have 1 set of neurons to merge. Only the first set has neurons.\"\n )\n else:\n logger.debug(\"Have 0 sets of neurons to merge.\")\n\n original_new_neuron_set_size = 0\n\n while (new_neuron_set.size != 1) and \\\n (original_new_neuron_set_size != new_neuron_set.size):\n original_new_neuron_set_size = new_neuron_set.size\n\n new_neuron_set_flattened_image = xnumpy.array_to_matrix(\n new_neuron_set[\"image\"]\n )\n\n new_neuron_set_flattened_mask = xnumpy.array_to_matrix(\n new_neuron_set[\"mask\"]\n )\n\n # Measure the normalized dot product between any two neurons (i.e.\n # related to the angle of separation)\n new_neuron_set_angle = xnumpy.pair_dot_product_normalized(\n new_neuron_set_flattened_image,\n ord=2\n )\n new_neuron_set_angle = numpy.triu(new_neuron_set_angle, k=1)\n\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_neuron_set_angle\"] = new_neuron_set_angle\n\n # Measure the distance between the two masks\n # (note distance relative to the total mask content of each mask\n # individually)\n new_neuron_set_masks_overlaid = xnumpy.pair_dot_product_partially_normalized(\n new_neuron_set_flattened_mask,\n ord=1,\n float_type=numpy.float32\n )\n numpy.fill_diagonal(new_neuron_set_masks_overlaid, 0)\n\n new_neuron_set_masks_overlaid_1 = new_neuron_set_masks_overlaid\n new_neuron_set_masks_overlaid_2 = new_neuron_set_masks_overlaid.T\n\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_neuron_set_masks_overlaid_1\"] = new_neuron_set_masks_overlaid_1\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_neuron_set_masks_overlaid_2\"] = new_neuron_set_masks_overlaid_2\n\n # Now that the three measures for the correlation method have been\n # found, we want to know, which are the best correlated neurons between\n # the two sets using these measures. This done to find the neuron in\n # new_neuron_set_1 that best matches each neuron in new_neuron_set_2.\n new_neuron_set_angle_all_optimal_i = new_neuron_set_angle.argmax(\n axis=0\n )\n new_neuron_set_masks_overlaid_1_all_optimal_i = new_neuron_set_masks_overlaid_1.argmax(\n axis=0\n )\n new_neuron_set_masks_overlaid_2_all_optimal_i = new_neuron_set_masks_overlaid_2.argmax(\n axis=0\n )\n\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_neuron_set_angle_all_optimal_i\"] = new_neuron_set_angle_all_optimal_i\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_neuron_set_masks_overlaid_1_all_optimal_i\"] = \\\n new_neuron_set_masks_overlaid_1_all_optimal_i\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_neuron_set_masks_overlaid_2_all_optimal_i\"] = \\\n new_neuron_set_masks_overlaid_2_all_optimal_i\n\n # Get all the j indices\n new_neuron_set_all_j = numpy.arange(len(new_neuron_set))\n\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_neuron_set_all_j\"] = new_neuron_set_all_j\n\n # Get the maximum corresponding to the best matched pairs from before\n new_neuron_set_angle_maxes = new_neuron_set_angle[\n (new_neuron_set_angle_all_optimal_i, new_neuron_set_all_j,)\n ]\n new_neuron_set_masks_overlaid_1_maxes = new_neuron_set_masks_overlaid_1[\n (new_neuron_set_masks_overlaid_1_all_optimal_i, new_neuron_set_all_j,)\n ]\n new_neuron_set_masks_overlaid_2_maxes = new_neuron_set_masks_overlaid_2[\n (new_neuron_set_masks_overlaid_2_all_optimal_i, new_neuron_set_all_j,)\n ]\n\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_neuron_set_angle_maxes\"] = new_neuron_set_angle_maxes\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_neuron_set_masks_overlaid_1_maxes\"] = new_neuron_set_masks_overlaid_1_maxes\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_neuron_set_masks_overlaid_2_maxes\"] = new_neuron_set_masks_overlaid_2_maxes\n\n # Store a list of the optimal neurons in the existing set to fuse with\n # (by default set all values to -1)\n new_neuron_set_all_optimal_i = numpy.zeros(\n (len(new_neuron_set),), dtype=int\n )\n new_neuron_set_all_optimal_i -= 1\n\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_neuron_set_all_optimal_i_0\"] = new_neuron_set_all_optimal_i\n\n # Create the masks to use for getting the proper indices\n new_neuron_set_angle_maxes_significant = numpy.zeros(\n (len(new_neuron_set),), dtype=bool\n )\n new_neuron_set_masks_overlaid_1_maxes_significant = numpy.zeros(\n (len(new_neuron_set),), dtype=bool\n )\n new_neuron_set_masks_overlaid_2_maxes_significant = numpy.zeros(\n (len(new_neuron_set),), dtype=bool\n )\n\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_neuron_set_angle_maxes_significant_0\"] = new_neuron_set_angle_maxes_significant\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_neuron_set_masks_overlaid_1_maxes_significant_0\"] = \\\n new_neuron_set_masks_overlaid_1_maxes_significant\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_neuron_set_masks_overlaid_2_maxes_significant_0\"] = \\\n new_neuron_set_masks_overlaid_2_maxes_significant\n\n already_matched = numpy.zeros((len(new_neuron_set),), dtype=bool)\n\n # Get masks that indicate which measurements have the best matching\n # neuron\n new_neuron_set_angle_maxes_significant[new_neuron_set_angle_maxes > alignment_min_threshold] = True\n\n already_matched |= new_neuron_set_angle_maxes_significant\n already_matched[\n new_neuron_set_angle_all_optimal_i[new_neuron_set_angle_maxes_significant]\n ] |= True\n\n new_neuron_set_masks_overlaid_1_maxes_significant[\n ~already_matched &\n (new_neuron_set_masks_overlaid_1_maxes > overlap_min_threshold)\n ] = True\n\n already_matched |= new_neuron_set_masks_overlaid_1_maxes_significant\n already_matched[\n new_neuron_set_masks_overlaid_1_all_optimal_i[new_neuron_set_masks_overlaid_1_maxes_significant]\n ] |= True\n\n new_neuron_set_masks_overlaid_2_maxes_significant[\n ~already_matched &\n (new_neuron_set_masks_overlaid_2_maxes_significant > overlap_min_threshold)\n ] = True\n\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_neuron_set_angle_maxes_significant_1\"] = new_neuron_set_angle_maxes_significant\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_neuron_set_masks_overlaid_1_maxes_significant_1\"] = new_neuron_set_masks_overlaid_1_maxes_significant\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_neuron_set_masks_overlaid_2_maxes_significant_1\"] = new_neuron_set_masks_overlaid_2_maxes_significant\n\n # Using the masks construct the best match neuron index for each case.\n new_neuron_set_all_optimal_i[new_neuron_set_angle_maxes_significant] = new_neuron_set_angle_all_optimal_i[new_neuron_set_angle_maxes_significant]\n\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_neuron_set_all_optimal_i_1\"] = new_neuron_set_all_optimal_i\n\n new_neuron_set_all_optimal_i[new_neuron_set_masks_overlaid_1_maxes_significant] = new_neuron_set_masks_overlaid_1_all_optimal_i[new_neuron_set_masks_overlaid_1_maxes_significant]\n\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_neuron_set_all_optimal_i_2\"] = new_neuron_set_all_optimal_i\n\n new_neuron_set_all_optimal_i[new_neuron_set_masks_overlaid_2_maxes_significant] = new_neuron_set_masks_overlaid_2_all_optimal_i[new_neuron_set_masks_overlaid_2_maxes_significant]\n\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_neuron_set_all_optimal_i_3\"] = new_neuron_set_all_optimal_i\n\n\n # Separate all the best matches that were found from those that were\n # not. Also, remove the -1 as they have served their purpose.\n new_neuron_set_all_optimal_i_found = (\n new_neuron_set_all_optimal_i != -1\n )\n new_neuron_set_all_j_fuse = new_neuron_set_all_j[new_neuron_set_all_optimal_i_found]\n new_neuron_set_all_j_append = new_neuron_set_all_j[~new_neuron_set_all_optimal_i_found]\n new_neuron_set_all_optimal_i = new_neuron_set_all_optimal_i[new_neuron_set_all_optimal_i_found]\n\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_neuron_set_all_optimal_i_found\"] = new_neuron_set_all_optimal_i_found\n\n if new_neuron_set_all_j_fuse.size:\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_neuron_set_all_j_fuse\"] = new_neuron_set_all_j_fuse\n\n if new_neuron_set_all_j_append.size:\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_neuron_set_all_j_append\"] = new_neuron_set_all_j_append\n\n if new_neuron_set_all_optimal_i.size:\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_neuron_set_all_optimal_i_3\"] = new_neuron_set_all_optimal_i\n\n new_neuron_set_kept = numpy.ones(new_neuron_set.shape, dtype=bool)\n\n # Fuse all the neurons that can be from new_neuron_set_2 to the\n # new_neuron_set (composed of new_neuron_set_1)\n for i, j in iters.izip(\n new_neuron_set_all_optimal_i, new_neuron_set_all_j_fuse\n ):\n #fuse_neurons.recorders.array_debug_recorder = hdf5.record.HDF5EnumeratedArrayRecorder(\n # merge_neuron_sets_repeatedly.recorders.array_debug_recorder.hdf5_handle\n #)\n fuse_neurons.recorders.array_debug_recorder = merge_neuron_sets_repeatedly.recorders.array_debug_recorder\n\n new_neuron_set[i] = fuse_neurons(\n new_neuron_set[i],\n new_neuron_set[j],\n **parameters[\"fuse_neurons\"]\n )\n\n new_neuron_set_kept[j] = False\n\n new_neuron_set = new_neuron_set[new_neuron_set_kept]\n\n logger.debug(\n \"Fused \\\"\" + repr(len(new_neuron_set_all_j_fuse)) +\n \"\\\" neurons to the existing set.\"\n )\n\n if new_neuron_set.size:\n merge_neuron_sets_repeatedly.recorders.array_debug_recorder[\"new_merged_neurons_set\"] = new_neuron_set\n\n return(new_neuron_set)",
"title": ""
},
{
"docid": "b35b6fb234bc2d1f74e097de01b3a888",
"score": "0.50226676",
"text": "def merge(alist, overwrite_=True):\n def verify(): \n n_rows = 0 \n row_names = []\n for i, p_obj in enumerate(alist): \n table = p_obj.table\n if i == 0: \n n_rows = table.shape[0] \n row_names = table.index \n else: \n assert table.shape[0] == n_rows\n assert all(row_names == table.index)\n return row_names\n import copy\n\n # emtpy list\n if len(alist) == 0:\n # do nothing \n print('(merge) No input data. Exiting ...')\n return PerformanceMetrics() # return a dummy object \n\n # design: assume that each df references different methods\n row_names = verify()\n\n # todo: do it according to the class protocol\n\n # create a new table and merge all the algorithm metrics (columns)\n p_new = PerformanceMetrics() # DataFrame()\n records = {} # \n for i, p_obj in enumerate(alist): \n table = p_obj.table\n for col in table.columns: \n if overwrite_ or (not col in p_new.table.columns): # add only if not existed\n p_new.table[col] = table[col] # if not incr_update, then update no matter what\n p_new.records.update(p_obj.records) # but this overwrites the key (and its value)\n \n p_new.table.index = row_names\n\n ## this operation does not merge the following base attributes\n # p_new.op \n # p_new.records \n \n return p_new",
"title": ""
},
{
"docid": "65920feb16e7dd35a5a97f43086353c8",
"score": "0.50209785",
"text": "def set_number_of_identical_mdruns (multiples) :\n self.multiples = multiples",
"title": ""
},
{
"docid": "999aa9d615fd8b254d41b9d03ed3f014",
"score": "0.4987291",
"text": "def duplicate_batch_id_csv(year, month, day, start, num):\r\n\r\n for i in range(start, start + num):\r\n with open(\"MED_DATA_\" + df.year_format(year,month,day) + df.time_format(i) + \".csv\", \"w\", newline=\"\") as csvfile:\r\n mywriter = csv.writer(csvfile, delimiter=\",\", quotechar='\"', quoting=csv.QUOTE_NONNUMERIC)\r\n\r\n header = [\"batch_id\", \"timestamp\", \"reading1\", \"reading2\", \"reading3\", \"reading4\", \"reading5\", \"reading6\",\r\n \"reading7\", \"reading8\", \"reading9\", \"reading10\"]\r\n choices = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n duplicate_id1 = ran.randint(1, 10)\r\n choices.remove(duplicate_id1)\r\n duplicate_index = ran.randint(0, 8)\r\n duplicate_id2 = choices[duplicate_index]\r\n dup1 = min(duplicate_id1, duplicate_id2)\r\n dup2 = max(duplicate_id1, duplicate_id2)\r\n\r\n mywriter.writerow(header)\r\n for n in range(0, 10):\r\n row = [ran.randint(1, 199), \"00:00:00\"]\r\n if n == dup1:\r\n dup_id = row[0]\r\n elif n == dup2:\r\n row[0] = dup_id\r\n\r\n for m in range(0, 10):\r\n row.append(round(ran.randint(0, 9900)/1000, 3))\r\n\r\n mywriter.writerow(row)",
"title": ""
},
{
"docid": "b2c0b631254d856eb8d4da4735daf4cb",
"score": "0.49846694",
"text": "def merge(self, other):\n self.documents = self.documents + other.documents\n frames = [self.metadata, other.metadata]\n self._metadata = pd.concat(frames, ignore_index=True)\n self.dic.merge_with(other.dic)\n self._reset_index()",
"title": ""
},
{
"docid": "001f95a0b1a9a612e1148703f02904c8",
"score": "0.49611795",
"text": "def merge(cls, to_merge):\n samples = to_merge[0].samples\n inst_class = to_merge[0].__class__\n \n for o in to_merge[1:]:\n if not isinstance(o, inst_class):\n raise ValueError(\"Operation not permitted for other object of type %s.\" % type(other))\n #fi\n \n if not all([ p[0] == p[1] for p in zip(samples, o.samples) ]):\n raise ValueError(\"The VCF objects do not describe the same samples!!!\")\n #fi\n #efor\n\n records = []\n for o in to_merge:\n records.extend(o.records)\n #efor\n \n records_obj = VCF2_records(records, samples)\n filt = VCF_filter(\"MERGE\", [\"%d objects\" % len(to_merge)])\n return inst_class(vcf_object=records_obj, filter_stack=[filt])",
"title": ""
},
{
"docid": "2c746a5f9ecbb2cab7fad2d24931cc1c",
"score": "0.4942957",
"text": "def set_is_replicated(self, should_replicate):\n return None",
"title": ""
},
{
"docid": "4f51416714f61d27c22e8b87545ee2af",
"score": "0.4939774",
"text": "def merge(self, ts: 'Timestamp') -> None:\n\n for (id, value) in ts.replicas.items():\n\n if id not in self.replicas:\n\n self.replicas[id] = value\n\n elif value > self.replicas[id]:\n\n self.replicas[id] = ts.replicas[id]",
"title": ""
},
{
"docid": "ce677eb801cc08a5bb7de1b7bfa3f118",
"score": "0.4930495",
"text": "def merge(self) -> bool:\n ...",
"title": ""
},
{
"docid": "563dda42dba3ce8d6c6418159aaac5fe",
"score": "0.48937684",
"text": "def merge(self, other):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "a2dac1e5154a62bf2037a469e3056dc9",
"score": "0.4890549",
"text": "def merged(cls, benches: ['MiniNASTabularBenchmark'], merge_fun=np.mean):\n b0 = benches[0]\n all_results = []\n for bench in benches:\n all_results.extend(bench.results.values())\n merged_results = MiniResult.merge_result_list(all_results, merge_fun=merge_fun, ensure_same_size=True)\n\n # update indices and reference dicts\n results, arch_to_idx, tuple_to_str, tuple_to_idx = {}, {}, {}, {}\n for i, r in enumerate(merged_results):\n r.arch_index = i\n results[i] = r\n arch_to_idx[r.arch_str] = i\n tuple_to_str[r.arch_tuple] = r.arch_index\n tuple_to_idx[r.arch_tuple] = i\n\n return cls(default_data_set=b0.default_data_set, default_result_type=b0.default_result_type,\n bench_name=\"Merged(%s)\" % \", \".join([b.bench_name for b in benches]),\n bench_description=b0.bench_description,\n value_space=b0.value_space, results=results, arch_to_idx=arch_to_idx,\n tuple_to_str=tuple_to_str, tuple_to_idx=tuple_to_idx)",
"title": ""
},
{
"docid": "3858bc7d7e08ccef18f8983ad6d9ff0e",
"score": "0.48817542",
"text": "def merge_duplicates(data):\n prev = data[0]\n mark = []\n for entry in data[1:]:\n if check_duplicate(prev, entry):\n prev[\"metadata\"][\"played_duration\"] = (\n prev[\"metadata\"][\"played_duration\"]\n + entry[\"metadata\"][\"played_duration\"]\n )\n # mark entry for removal\n mark.append(entry)\n else:\n prev = entry\n # remove marked entries\n for entry in mark:\n data.remove(entry)\n return data",
"title": ""
},
{
"docid": "6fd5d87c0514013bbbf13e04e34b63d3",
"score": "0.48810023",
"text": "def repeated_sim(n_runs=5, aggregate=True, **kwargs):\n sim_func = create_experiment_func(**kwargs)\n avg_results = rerun_experiment(sim_func, n_runs=n_runs, aggregate=aggregate)\n return avg_results",
"title": ""
},
{
"docid": "bd35562109fc379b190cce147c804957",
"score": "0.48596787",
"text": "def merged(self, *others):\n species_dict = {}; share_dict = {}\n for deme in (self,)+others:\n for i in xrange(len(deme.species)):\n species = deme.species[i]\n name = str(species)\n if species_dict.has_key(name):\n share_dict[name] += deme.distribution[i]\n else:\n species_dict[name] = species\n share_dict[name] = deme.distribution[i]\n species = [s for s in self.setup.strategyList \\\n if species_dict.has_key(s.name)] # keep species order!\n dist = array([share_dict[s.name] for s in species])\n return self.new(species, dist)",
"title": ""
},
{
"docid": "f273f1b44b0b86431a9af77cc8e20286",
"score": "0.48587766",
"text": "def merge_outcomes(self, label_merge_dict, recordZeroCnts=True):\n\n #static_self = self.copy()\n #static_self.done_adding_data() # makes static, so we can assume this below\n\n # strings -> tuple outcome labels in keys and values of label_merge_dict\n to_outcome = _ld.OutcomeLabelDict.to_outcome # shorthand\n label_merge_dict = {to_outcome(key): list(map(to_outcome, val))\n for key, val in label_merge_dict.items()}\n\n merge_dict_old_outcomes = [outcome for sublist in label_merge_dict.values() for outcome in sublist]\n if not set(self.get_outcome_labels()).issubset(merge_dict_old_outcomes):\n raise ValueError(\n \"`label_merge_dict` must account for all the outcomes in original dataset.\"\n \" It's missing directives for:\\n%s\" %\n '\\n'.join(set(map(str, self.get_outcome_labels())) - set(map(str, merge_dict_old_outcomes)))\n )\n\n new_outcomes = sorted(list(label_merge_dict.keys()))\n new_outcome_indices = _OrderedDict([(ol, i) for i, ol in enumerate(new_outcomes)])\n nNewOutcomes = len(new_outcomes)\n\n #Count the number of time steps so we allocate enough space\n nSteps = 0\n for key, dsrow in self.items():\n cur_t = None\n for t in dsrow.time:\n if t != cur_t:\n nSteps += 1\n cur_t = t\n\n #idea is that we create oliData, timeData, repData, and circuitIndices for the\n # merged dataset rather than looping over insertion, as this is faster\n oliData = _np.empty(nSteps * nNewOutcomes, self.oliType)\n repData = _np.empty(nSteps * nNewOutcomes, self.repType)\n timeData = _np.empty(nSteps * nNewOutcomes, self.timeType)\n\n oli_map = {} # maps old outcome label indices to new ones\n for new_outcome, old_outcome_list in label_merge_dict.items():\n new_index = new_outcome_indices[new_outcome]\n for old_outcome in old_outcome_list:\n oli_map[self.olIndex[old_outcome]] = new_index\n\n #Future - when recordZeroCnts=False these may not need to be so large\n new_olis = _np.array(range(nNewOutcomes), _np.int64)\n new_cnts = _np.zeros(nNewOutcomes, self.repType)\n\n if recordZeroCnts:\n def add_cnts(t, cnts, offset): # cnts is an array here\n new_cnts[:] = 0\n for nonzero_oli, cnt in cnts.items():\n new_cnts[nonzero_oli] = cnt\n timeData[offset:offset + nNewOutcomes] = t\n oliData[offset:offset + nNewOutcomes] = new_olis\n repData[offset:offset + nNewOutcomes] = new_cnts # a length-nNewOutcomes array\n return nNewOutcomes\n\n else:\n def add_cnts(t, cnts, offset): # cnts is a dict here\n nNewCnts = len(cnts)\n #new_olis = _np.empty(nNewCnts, _np.int64)\n #new_cnts = _np.empty(nNewCnts, self.repType)\n for ii, (nonzero_oli, cnt) in enumerate(cnts.items()):\n new_olis[ii] = nonzero_oli\n new_cnts[ii] = cnt\n timeData[offset:offset + nNewCnts] = t\n oliData[offset:offset + nNewCnts] = new_olis[0:nNewCnts]\n repData[offset:offset + nNewCnts] = new_cnts[0:nNewCnts]\n return nNewCnts # return the number of added counts\n\n k = 0 # beginning of current circuit data in 1D arrays: oliData, timeData, repData\n circuitIndices = _OrderedDict()\n for key, dsrow in self.items():\n\n last_t = dsrow.time[0]\n\n #Below code is faster version of: mapped_oli = [oli_map[x] for x in dsrow.oli]\n mapped_oli = dsrow.oli.copy()\n for from_oli, to_oli in oli_map.items():\n mapped_oli[dsrow.oli == from_oli] = to_oli\n\n reps = _np.ones(len(dsrow.time), self.timeType) if (self.repData is None) else dsrow.reps\n cnts = _DefaultDict(lambda: 0)\n\n i = 0 # offset to current timeslice\n for oli, t, reps in zip(mapped_oli, dsrow.time, reps):\n if t != last_t:\n i += add_cnts(last_t, cnts, k + i)\n last_t = t; cnts.clear()\n cnts[oli] += reps\n if len(cnts) > 0:\n i += add_cnts(last_t, cnts, k + i)\n\n circuitIndices[key] = slice(k, k + i)\n k += i\n\n merged_dataset = DataSet(oliData[0:k], timeData[0:k], repData[0:k], circuitIndices=circuitIndices,\n outcomeLabelIndices=new_outcome_indices, bStatic=True)\n return merged_dataset",
"title": ""
},
{
"docid": "81e3ed6c391f2624f7af2662893a0c33",
"score": "0.4857831",
"text": "def _merge_parallel_results(self, key, max_samples=None):\n numbers = [name.split('.')[-2].split('_')[-1]\n for name in os.listdir(self.directory)\n if 'mem_{}'.format(key) in name]\n mem = np.zeros((0, len(self.train_set)))\n n_sources = len(self.train_set)\n idxs = np.zeros((0, n_sources), int)\n vals = np.zeros(len(self.train_set))\n counter = 0.\n for number in numbers:\n if max_samples is not None:\n if counter > max_samples:\n break\n samples_dir = os.path.join(\n self.directory,\n 'mem_{}_{}.pkl'.format(key, number)\n )\n print(samples_dir)\n dic = pkl.load(open(samples_dir, 'rb'))\n if not len(dic['mem_{}'.format(key)]):\n continue\n mem = np.concatenate([mem, dic['mem_{}'.format(key)]])\n idxs = np.concatenate([idxs, dic['idxs_{}'.format(key)]])\n counter += len(dic['mem_{}'.format(key)])\n vals *= (counter - len(dic['mem_{}'.format(key)])) / counter\n vals += len(dic['mem_{}'.format(key)]) / counter * np.mean(mem, 0)\n os.remove(samples_dir)\n merged_dir = os.path.join(\n self.directory,\n 'mem_{}_0000.pkl'.format(key)\n )\n pkl.dump({'mem_{}'.format(key): mem, 'idxs_{}'.format(key): idxs},\n open(merged_dir, 'wb'))\n return mem, idxs, vals",
"title": ""
},
{
"docid": "ac8123df1b31d2e4b41c2a4837991507",
"score": "0.4849359",
"text": "def merge(self, other):\n e1 = self.epoch\n e2 = other.epoch if isinstance(other, PrecessingCoordinates) else None\n precess, e1, e2 = self.precession_required(e1, e2)\n\n self.epoch = e1 # Update the epoch if required\n if not precess:\n super().merge(other)\n return\n\n if e1.singular: # Precess epoch 2 onto epoch 1\n precessed = self.copy()\n self.convert(other, precessed)\n super().merge(precessed)\n return\n\n # If epoch 1 is an array and epoch 2 is not\n if e2.singular:\n # Need to convert all epoch2 times to an array.\n e2 = e2.copy()\n e2.equinox = Time(np.full(other.size, e2.equinox.value),\n scale=e2.equinox.scale,\n format=e2.equinox.format)\n\n size_1 = self.size\n size_2 = other.size\n super().merge(other)\n\n new_time_values = np.empty(size_1 + size_2, dtype=float)\n\n t_format = 'byear' if isinstance(e1, BesselianEpoch) else 'jyear'\n new_time_values[:size_1] = getattr(e1.equinox, t_format)\n new_time_values[size_1:] = getattr(e2.equinox, t_format)\n self.epoch.equinox = Time(new_time_values, scale=e1.equinox.scale,\n format=t_format)",
"title": ""
},
{
"docid": "c24e20766b31078d7d240b40b639879f",
"score": "0.48343483",
"text": "def pool_replicates(mat_file, out_file):\n assert \".mat\" in mat_file\n data = pd.read_table(mat_file, index_col=0)\n\n samples = sorted(\n list(set([colname.split(\"_\")[0]\n for colname in data.columns])))\n for sample in samples:\n data[sample] = data[\"{}_b1\".format(sample)] + data[\"{}_b2\".format(sample)]\n data_pooled = data[samples].astype(int)\n data_pooled.to_csv(out_file, sep='\\t', compression=\"gzip\")\n\n return None",
"title": ""
},
{
"docid": "aa3044db089c428e3471b5af5785d07b",
"score": "0.4816423",
"text": "def plan_merges(merge_pairs, body_sizes, size_threshold=1e7):\n assert isinstance(merge_pairs, np.ndarray)\n assert isinstance(body_sizes, pd.Series)\n assert body_sizes.index.name == 'body'\n assert body_sizes.name == 'size'\n simulated_sizes = body_sizes.to_dict()\n\n merge_pairs.sort(axis=1)\n merge_pairs = pd.DataFrame(merge_pairs, columns=['body1', 'body2'])\n merge_pairs = merge_pairs.drop_duplicates(['body1', 'body2']).copy()\n\n merges = defaultdict(lambda: [])\n remaps = {}\n merge_indexes = []\n\n for i, b1, b2 in tqdm_proxy(merge_pairs[['body1', 'body2']].itertuples(), total=len(merge_pairs)):\n b1 = remaps.get(b1, b1)\n b2 = remaps.get(b2, b2)\n if b1 == b2:\n continue\n s1, s2 = simulated_sizes[b1], simulated_sizes[b2]\n if s1 < size_threshold or s2 < size_threshold:\n merge_indexes.append(i)\n if s1 > s2:\n # Merge body2 into body 1\n remaps[b2] = b1\n for b in merges[b2]:\n remaps[b] = b1\n merges[b1].extend([b2, *merges[b2]])\n del merges[b2]\n simulated_sizes[b1] += s2\n else:\n # Merge body1 into body2\n remaps[b1] = b2\n for b in merges[b1]:\n remaps[b] = b2\n merges[b2].extend([b1, *merges[b1]])\n del merges[b1]\n simulated_sizes[b2] += s1\n\n return merge_indexes, merges, remaps, merge_pairs, simulated_sizes",
"title": ""
},
{
"docid": "6a4457ea7b90fc3d209bec99457cabd2",
"score": "0.4815523",
"text": "def merge(cls, builder, aggregated, incremental, num_runs, sort_keys=False):\n\n if not incremental:\n logging.warning(\"Nothing to merge.\")\n return None\n\n logging.info(\"Loading incremental json...\")\n incremental_json = cls._load_json(incremental)\n if not incremental_json:\n return None\n\n logging.info(\"Checking incremental json...\")\n if not cls._check_json(builder, incremental_json):\n return None\n\n logging.info(\"Loading existing aggregated json...\")\n aggregated_json = cls._load_json(aggregated)\n if not aggregated_json:\n return incremental\n\n logging.info(\"Checking existing aggregated json...\")\n if not cls._check_json(builder, aggregated_json):\n return incremental\n\n logging.info(\"Merging json results...\")\n try:\n if not cls._merge_json(aggregated_json[builder], incremental_json[builder], num_runs):\n return None\n except Exception, err:\n logging.error(\"Failed to merge json results: %s\", str(err))\n return None\n\n aggregated_json[JSON_RESULTS_VERSION_KEY] = JSON_RESULTS_VERSION\n\n return cls._generate_file_data(aggregated_json, sort_keys)",
"title": ""
},
{
"docid": "60d449679485f8fc6e17c80680c6f9d4",
"score": "0.48126644",
"text": "def _frame_merger(data, frames):\n fail = 0\n for idx, frame in enumerate(frames[1:], start=2):\n try:\n # concatenation handles 99% of the cases\n with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n data = xr.concat([data, frame], dim='time')\n except (ValueError, NotImplementedError):\n try:\n # try merging the data, usually one of the data files is missing a variable from a co-located\n # sensor that the system was unable to find\n _, index = np.unique(data['time'], return_index=True)\n data = data.isel(time=index)\n with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n data = data.merge(frame, compat='override')\n except (ValueError, NotImplementedError):\n # something is just not right with this data file\n fail += 1\n\n return data, fail",
"title": ""
},
{
"docid": "bddac254f036f46c0d9a2108d5b85ae2",
"score": "0.48090795",
"text": "def _mergeResults ( self, result ) :\n self.output.update ( result ) \n print 'FIT-MERGE: ', len ( self.output )",
"title": ""
},
{
"docid": "6527c8d23a4c8042d765492517e660e5",
"score": "0.48043308",
"text": "def merge(self, guid, diff, increment_seqno=True):\n record = self._storage.get(guid)\n seqno = None\n merged = False\n\n for prop, meta in diff.items():\n orig_meta = record.get(prop)\n if orig_meta is not None and orig_meta['mtime'] >= meta['mtime']:\n continue\n if increment_seqno:\n if not seqno:\n seqno = self._seqno.next()\n meta['seqno'] = seqno\n else:\n meta['seqno'] = (orig_meta or {}).get('seqno') or 0\n record.set(prop, **meta)\n merged = True\n\n if merged and record.consistent:\n props = {}\n if seqno:\n props['seqno'] = seqno\n self._index.store(guid, props, False,\n self._pre_store, self._post_store,\n # No need in after-merge event, further commit event\n # is enough to avoid events flow on nodes synchronization\n None, False)\n\n return seqno",
"title": ""
},
{
"docid": "818811284ae5d2abc964c81b6085273e",
"score": "0.47912434",
"text": "def set_replicates(self, replicates):\n return None",
"title": ""
},
{
"docid": "8fce1b69c35abc6cffd86ea26941a39d",
"score": "0.4785784",
"text": "def try_merge(self, other: MergedGroup) -> bool:\n if self._get_group_similarity(other) >= 0.5:\n self.members.update(other.members)\n # periods must be merged one by one to ensure time cohesion\n for new_period in other.periods:\n has_merged = False\n for period in self.periods:\n if period.try_merge(new_period):\n has_merged = True\n break\n if not has_merged:\n self.periods.append(new_period)\n return True\n return False",
"title": ""
},
{
"docid": "a2b5f3f7f8c96550f7976975d542ccb2",
"score": "0.47750998",
"text": "def merge_overlapping_images(metadata,inputs):\n\n # only for Sentinel-2 at this stage (not sure if this is needed for Landsat images)\n sat = 'S2'\n filepath = os.path.join(inputs['filepath'], inputs['sitename'])\n filenames = metadata[sat]['filenames']\n # find the pairs of images that are within 5 minutes of each other\n time_delta = 5*60 # 5 minutes in seconds\n dates = metadata[sat]['dates'].copy()\n pairs = []\n for i,date in enumerate(metadata[sat]['dates']):\n # dummy value so it does not match it again\n dates[i] = pytz.utc.localize(datetime(1,1,1) + timedelta(days=i+1))\n # calculate time difference\n time_diff = np.array([np.abs((date - _).total_seconds()) for _ in dates])\n # find the matching times and add to pairs list\n boolvec = time_diff <= time_delta\n if np.sum(boolvec) == 0:\n continue\n else:\n idx_dup = np.where(boolvec)[0][0]\n pairs.append([i,idx_dup])\n # because they could be triplicates in S2 images, adjust the for consecutive merges\n for i in range(1,len(pairs)):\n if pairs[i-1][1] == pairs[i][0]:\n pairs[i][0] = pairs[i-1][0]\n\n # for each pair of image, create a mask and add no_data into the .tif file (this is needed before merging .tif files)\n for i,pair in enumerate(pairs):\n fn_im = []\n for index in range(len(pair)):\n # get filenames of all the files corresponding to the each image in the pair\n fn_im.append([os.path.join(filepath, 'S2', '10m', filenames[pair[index]]),\n os.path.join(filepath, 'S2', '20m', filenames[pair[index]].replace('10m','20m')),\n os.path.join(filepath, 'S2', '60m', filenames[pair[index]].replace('10m','60m')),\n os.path.join(filepath, 'S2', 'meta', filenames[pair[index]].replace('_10m','').replace('.tif','.txt'))])\n # read that image\n im_ms, georef, cloud_mask, im_extra, im_QA, im_nodata = SDS_preprocess.preprocess_single(fn_im[index], sat, False)\n # im_RGB = SDS_preprocess.rescale_image_intensity(im_ms[:,:,[2,1,0]], cloud_mask, 99.9)\n\n # in Sentinel2 images close to the edge of the image there are some artefacts,\n # that are squares with constant pixel intensities. They need to be masked in the\n # raster (GEOTIFF). It can be done using the image standard deviation, which\n # indicates values close to 0 for the artefacts.\n if len(im_ms) > 0:\n # calculate image std for the first 10m band\n im_std = SDS_tools.image_std(im_ms[:,:,0],1)\n # convert to binary\n im_binary = np.logical_or(im_std < 1e-6, np.isnan(im_std))\n # dilate to fill the edges (which have high std)\n mask10 = morphology.dilation(im_binary, morphology.square(3))\n # mask all 10m bands\n for k in range(im_ms.shape[2]):\n im_ms[mask10,k] = np.nan\n # mask the 10m .tif file (add no_data where mask is True)\n SDS_tools.mask_raster(fn_im[index][0], mask10)\n # create another mask for the 20m band (SWIR1)\n im_std = SDS_tools.image_std(im_extra,1)\n im_binary = np.logical_or(im_std < 1e-6, np.isnan(im_std))\n mask20 = morphology.dilation(im_binary, morphology.square(3))\n im_extra[mask20] = np.nan\n # mask the 20m .tif file (im_extra)\n SDS_tools.mask_raster(fn_im[index][1], mask20)\n # use the 20m mask to create a mask for the 60m QA band (by resampling)\n mask60 = ndimage.zoom(mask20,zoom=1/3,order=0)\n mask60 = transform.resize(mask60, im_QA.shape, mode='constant', order=0,\n preserve_range=True)\n mask60 = mask60.astype(bool)\n # mask the 60m .tif file (im_QA)\n SDS_tools.mask_raster(fn_im[index][2], mask60)\n else:\n continue\n\n # make a figure for quality control\n # fig,ax= plt.subplots(2,2,tight_layout=True)\n # ax[0,0].imshow(im_RGB)\n # ax[0,0].set_title('RGB original')\n # ax[1,0].imshow(mask10)\n # ax[1,0].set_title('Mask 10m')\n # ax[0,1].imshow(mask20)\n # ax[0,1].set_title('Mask 20m')\n # ax[1,1].imshow(mask60)\n # ax[1,1].set_title('Mask 60 m')\n\n # once all the pairs of .tif files have been masked with no_data, merge the using gdal_merge\n fn_merged = os.path.join(filepath, 'merged.tif')\n\n # merge masked 10m bands and remove duplicate file\n gdal_merge.main(['', '-o', fn_merged, '-n', '0', fn_im[0][0], fn_im[1][0]])\n os.chmod(fn_im[0][0], 0o777)\n os.remove(fn_im[0][0])\n os.chmod(fn_im[1][0], 0o777)\n os.remove(fn_im[1][0])\n os.chmod(fn_merged, 0o777)\n os.rename(fn_merged, fn_im[0][0])\n\n # merge masked 20m band (SWIR band)\n gdal_merge.main(['', '-o', fn_merged, '-n', '0', fn_im[0][1], fn_im[1][1]])\n os.chmod(fn_im[0][1], 0o777)\n os.remove(fn_im[0][1])\n os.chmod(fn_im[1][1], 0o777)\n os.remove(fn_im[1][1])\n os.chmod(fn_merged, 0o777)\n os.rename(fn_merged, fn_im[0][1])\n\n # merge QA band (60m band)\n gdal_merge.main(['', '-o', fn_merged, '-n', '0', fn_im[0][2], fn_im[1][2]])\n os.chmod(fn_im[0][2], 0o777)\n os.remove(fn_im[0][2])\n os.chmod(fn_im[1][2], 0o777)\n os.remove(fn_im[1][2])\n os.chmod(fn_merged, 0o777)\n os.rename(fn_merged, fn_im[0][2])\n\n # remove the metadata .txt file of the duplicate image\n os.chmod(fn_im[1][3], 0o777)\n os.remove(fn_im[1][3])\n\n print('%d Sentinel-2 images were merged (overlapping or duplicate)' % len(pairs))\n\n # update the metadata dict\n metadata_updated = copy.deepcopy(metadata)\n idx_removed = []\n idx_kept = []\n for pair in pairs: idx_removed.append(pair[1])\n for idx in np.arange(0,len(metadata[sat]['dates'])):\n if not idx in idx_removed: idx_kept.append(idx)\n for key in metadata_updated[sat].keys():\n metadata_updated[sat][key] = [metadata_updated[sat][key][_] for _ in idx_kept]\n\n return metadata_updated",
"title": ""
},
{
"docid": "4ab24cf4dd99a590131e1c8f19e2a5aa",
"score": "0.47450465",
"text": "def testMergeBuildStats(self) -> None:\n base_map = data_types.TestExpectationMap({\n 'foo':\n data_types.ExpectationBuilderMap({\n data_types.Expectation('foo', ['win'], 'Failure'):\n data_types.BuilderStepMap({\n 'builder':\n data_types.StepBuildStatsMap({\n 'step': data_types.BuildStats(),\n }),\n }),\n }),\n })\n merge_stats = data_types.BuildStats()\n merge_stats.AddFailedBuild('1', frozenset())\n merge_map = data_types.TestExpectationMap({\n 'foo':\n data_types.ExpectationBuilderMap({\n data_types.Expectation('foo', ['win'], 'Failure'):\n data_types.BuilderStepMap({\n 'builder':\n data_types.StepBuildStatsMap({\n 'step': merge_stats,\n }),\n }),\n }),\n })\n expected_stats = data_types.BuildStats()\n expected_stats.AddFailedBuild('1', frozenset())\n expected_base_map = {\n 'foo': {\n data_types.Expectation('foo', ['win'], 'Failure'): {\n 'builder': {\n 'step': expected_stats,\n },\n },\n },\n }\n base_map.Merge(merge_map)\n self.assertEqual(base_map, expected_base_map)",
"title": ""
},
{
"docid": "ad08e2c615b6409af023ab6d2f74b642",
"score": "0.47434458",
"text": "def merge_batches_and_save_dataset():\n\n path = '/Users/andreidm/ETH/projects/normalization/data/'\n\n batches = []\n merged_mz = set()\n\n for bid in bids:\n\n data = get_all_data_from_h5(path + 'harm_4_{}_DATA.h5'.format(bid))\n batches.append({'data': data, 'id': bid})\n merged_mz.update(data['samples']['mzs'])\n\n merged_mz = sorted(list(merged_mz))\n annotation = []\n\n shared_mz_df = pandas.DataFrame()\n for mz in merged_mz:\n\n mz_df = pandas.DataFrame()\n for batch in batches:\n\n columns, names = get_shared_perturbations_ids_for_batch(batch)\n\n if mz in batch['data']['samples']['mzs']:\n # if this mz appears in batch, use intensities\n index = batch['data']['samples']['mzs'].index(mz)\n bdf = pandas.DataFrame([batch['data']['samples']['data'][index, columns]], columns=names)\n\n # each mz appears in one batch only, so annotation can be assigned only here\n anno_index = batch['data']['annotation']['mzs'].index(round(mz, 4))\n annotation.append(batch['data']['annotation']['names'][anno_index])\n else:\n # if not, fill up with zeros\n bdf = pandas.DataFrame([numpy.zeros(len(columns))], columns=names)\n\n mz_df = pandas.concat([mz_df, bdf], axis=1)\n\n shared_mz_df = pandas.concat([shared_mz_df, mz_df], ignore_index=True)\n\n assert len(merged_mz) == len(annotation)\n\n all_data = pandas.DataFrame({'name': annotation, 'mz': merged_mz, 'rt': [0 for x in merged_mz]})\n all_data = pandas.concat([all_data, shared_mz_df], axis=1)\n\n # collapse the same mzs\n all_data = collapse_same_mzs(all_data)\n\n # filter out small intensities\n filtered_data = all_data[(all_data.iloc[:, 3:] > min_relevant_intensity).all(axis=1)]\n\n # save\n all_data.to_csv(path + \"all_data.csv\", index=False)\n filtered_data.to_csv(path + \"filtered_data.csv\", index=False)",
"title": ""
},
{
"docid": "9ed5e75297a344a31ddf90ba7f9bdc75",
"score": "0.47304523",
"text": "def _merge_single_child(self):\n assert len(self) == 1, \"bug: _merge_single_child called on loop with len != 1\"\n child = cast(Loop, self[0])\n\n # if the child has a fixed repetition count of 1 the measurements can be merged\n mergable_measurements = child.repetition_count == 1 and not child.volatile_repetition\n\n assert not self._measurements or mergable_measurements, \"bug: _merge_single_child called on loop with measurements\"\n assert not self._waveform, \"bug: _merge_single_child called on loop with children and waveform\"\n\n measurements = child._measurements\n if self._measurements:\n if measurements:\n measurements.extend(self._measurements)\n else:\n measurements = self._measurements\n\n if not self.volatile_repetition and not child.volatile_repetition:\n # simple integer multiplication\n repetition_definition = self.repetition_count * child.repetition_count\n elif not self.volatile_repetition:\n repetition_definition = child._repetition_definition * self.repetition_count\n elif not child.volatile_repetition:\n repetition_definition = self._repetition_definition * child.repetition_count\n else:\n # create a new expression that depends on both\n expression = 'parent_repetition_count * child_repetition_count'\n repetition_definition = VolatileRepetitionCount.operation(\n expression=expression,\n parent_repetition_count=self._repetition_definition,\n child_repetition_count=child._repetition_definition)\n\n self[:] = iter(child)\n self._waveform = child._waveform\n self._repetition_definition = repetition_definition\n self._measurements = measurements\n self._invalidate_duration()\n return True",
"title": ""
},
{
"docid": "f2a8fc77a8c868356baf997ca41dc6fb",
"score": "0.47293442",
"text": "def run_replicates(iterations, n_replicates, test, parallel=False):\n df = pd.DataFrame(index=np.arange(len(iterations) * n_replicates))\n df['iterations'] = np.repeat(iterations, n_replicates)\n df['replicate'] = np.tile(np.arange(n_replicates), len(iterations))\n\n results = pints.evaluate(test, list(df['iterations']), parallel=parallel)\n assert len(results) > 0, 'Empty result set generated'\n for key in results[0].keys():\n df[key] = np.array([r[key] for r in results], copy=False)\n\n return df",
"title": ""
},
{
"docid": "d7456688e34ffd581ad2a3a52347a38d",
"score": "0.4712553",
"text": "def merge_and_dedup(bams):\n\n if len(bams) == 1:\n return bams\n\n merged = tempfile.NamedTemporaryFile(delete=False, prefix='merged', suffix='.bam').name\n merged_and_deduped = tempfile.NamedTemporaryFile(delete=False, prefix='merged_and_duped', suffix='.bam').name\n metrics = tempfile.NamedTemporaryFile(delete=False, prefix='metrics', suffix='.txt').name\n\n shell('echo \"tempfiles created by merge_and_dedup: {merged} {merged_and_deduped} {metrics}\" {log}')\n\n if not keep_tempfiles:\n registered_for_deletion.extend([merged, merged_and_deduped, metrics])\n\n bams = ' '.join(bams)\n shell(\n 'samtools merge '\n '-f '\n '-@ {snakemake.threads} '\n '{merged} '\n '{bams} '\n '{log} '\n )\n shell(\n 'picard '\n '{java_args} '\n 'MarkDuplicates '\n 'INPUT={merged} '\n 'OUTPUT={merged_and_deduped} '\n 'METRICS_FILE={metrics} '\n 'REMOVE_DUPLICATES=true '\n '{log} '\n )\n return merged_and_deduped",
"title": ""
},
{
"docid": "d8d4b2d8567999712bbada24312b16a1",
"score": "0.4705641",
"text": "def merge(args):\n import nifti_mrs.tools as nmrs_tools\n from nifti_mrs.nifti_mrs import NIFTI_MRS\n # 1. Load the files\n if len(args.files) < 2:\n raise ValueError('Files argument must provide two or more files to merge.')\n\n to_concat = []\n concat_names = []\n for fp in args.files:\n concat_names.append(fp.with_suffix('').with_suffix('').name)\n curr_file = NIFTI_MRS(fp)\n\n # Merging along a new axis\n if args.newaxis:\n if args.dim in curr_file.dim_tags:\n raise ValueError(f'--dim ({args.dim}) must be different from existing tags: {curr_file.dim_tags}.')\n if curr_file.ndim == 7:\n raise ValueError('Inputs use all three higher dimension already, cannot add new axis.')\n new_order = curr_file.dim_tags\n new_order[curr_file.ndim - 4] = args.dim\n curr_file = nmrs_tools.reorder(curr_file, new_order)\n to_concat.append(curr_file)\n\n # 2. Merge the files\n merged = nmrs_tools.merge(to_concat, args.dim)\n\n # 3. Save the output file\n if args.filename:\n file_out = args.output / args.filename\n else:\n file_out = args.output / ('_'.join(concat_names) + '_merged')\n merged.save(file_out)",
"title": ""
},
{
"docid": "ddd6595e1011468914f6ffcdfb54dffc",
"score": "0.47011703",
"text": "def merge(self):\n self.ensure_has_same_campaigns()\n self._merge()",
"title": ""
},
{
"docid": "bce74fd2d891688ed9b0486a0fca0949",
"score": "0.46962276",
"text": "def merge(self, session, source, dest):\n\n raise NotImplementedError()",
"title": ""
},
{
"docid": "ac14df03347dc17ee9c2513bf39de371",
"score": "0.46901122",
"text": "def merge_shards(StreamName=None, ShardToMerge=None, AdjacentShardToMerge=None):\n pass",
"title": ""
},
{
"docid": "d0f06e5ec3a697711b4342b4938596e1",
"score": "0.4689531",
"text": "def augment_photos_folder(self, batch_size=64, new_size=None):\n default_logger.info(\n f'Started augmentation with {self.workers} workers'\n )\n default_logger.info(f'Total images to augment: {self.total_images}')\n default_logger.info(f'Session assigned id: {self.session_id}')\n with ThreadPoolExecutor(max_workers=self.workers) as executor:\n while self.image_paths_copy:\n current_batch, current_paths = self.load_batch(\n new_size, batch_size\n )\n future_augmentations = {\n executor.submit(self.augment_image, image, path): path\n for image, path in zip(current_batch, current_paths)\n }\n for future_augmented in as_completed(future_augmentations):\n future_augmented.result()\n default_logger.info(f'Augmentation completed')\n augmentation_frame = pd.DataFrame(\n self.augmentation_data, columns=self.mapping.columns\n )\n saving_path = os.path.join(\n '..', 'Output', 'Data', f'augmented_data_plus_original.csv'\n )\n combined = pd.concat([self.mapping, augmentation_frame])\n for item in ['bx', 'by', 'bw', 'bh']:\n combined = combined.drop(combined[combined[item] > 1].index)\n combined.to_csv(saving_path, index=False)\n default_logger.info(f'Saved old + augmented labels to {saving_path}')\n adjusted_combined = adjust_non_voc_csv(\n saving_path, self.image_folder, self.image_width, self.image_height\n )\n adjusted_saving_path = saving_path.replace('augmented', 'adjusted_aug')\n adjusted_combined.to_csv(adjusted_saving_path, index=False)\n default_logger.info(\n f'Saved old + augmented (adjusted) labels to {adjusted_saving_path}'\n )\n return adjusted_combined",
"title": ""
},
{
"docid": "0224e49cc7266ddea7a8cfe1769afa8e",
"score": "0.4686847",
"text": "def aggregate(self, data_source: str, agg_rep: str, agg_time: str = \"mean\") -> \"Measurement\":\n aggs = [x.aggregate(data_source, agg_time) for x in self.repetitions] # [:-1]\n new_df = pd.DataFrame(aggs)\n if agg_rep:\n new_df = new_df.aggregate(agg_rep)\n\n return new_df",
"title": ""
},
{
"docid": "afd101da36fc603063563f357571fc6b",
"score": "0.4683037",
"text": "def merge(self, other):\n return CountersTracker(\n count=self.count + other.count,\n true_count=self.true_count + other.true_count,\n )",
"title": ""
},
{
"docid": "b2c7b6aa99d4ce7983c78e29439249c4",
"score": "0.46735698",
"text": "def merge_templates_parallel(self, pairs):\n n_samples = 2000\n p_val_threshold = 0.9\n merge_pairs = []\n\n for pair in pairs:\n unit1, unit2 = pair\n\n fname_out = os.path.join(\n self.save_dir,\n 'unit_{}_{}.npz'.format(unit1, unit2))\n\n if os.path.exists(fname_out):\n if np.load(fname_out)['merge']:\n merge_pairs.append(pair)\n\n else:\n \n # get spikes times and soft assignment\n idx1 = self.spike_train[:, 1] == unit1\n spt1 = self.spike_train[idx1, 0]\n prob1 = self.soft_assignment[idx1]\n shift1 = self.shifts[idx1]\n scale1 = self.scales[idx1]\n n_spikes1 = self.n_spikes_soft[unit1]\n \n idx2 = self.spike_train[:, 1] == unit2\n spt2 = self.spike_train[idx2, 0]\n prob2 = self.soft_assignment[idx2]\n shift2 = self.shifts[idx2]\n scale2 = self.scales[idx2]\n n_spikes2 = self.n_spikes_soft[unit2]\n \n # randomly subsample\n if n_spikes1 + n_spikes2 > n_samples:\n ratio1 = n_spikes1/float(n_spikes1+n_spikes2)\n n_samples1 = np.min((int(n_samples*ratio1), n_spikes1))\n n_samples2 = n_samples - n_samples1\n\n else:\n n_samples1 = n_spikes1\n n_samples2 = n_spikes2\n idx1_ = np.random.choice(len(spt1), n_samples1, replace=False,\n p=prob1/np.sum(prob1))\n idx2_ = np.random.choice(len(spt2), n_samples2, replace=False,\n p=prob2/np.sum(prob2))\n spt1 = spt1[idx1_]\n spt2 = spt2[idx2_]\n shift1 = shift1[idx1_]\n shift2 = shift2[idx2_]\n scale1 = scale1[idx1_]\n scale2 = scale2[idx2_]\n\n ptp_max = self.ptps[[unit1, unit2]].max(0)\n mc = ptp_max.argmax()\n vis_chan = np.where(ptp_max > 1)[0]\n\n # align two units\n shift_temp = (self.templates[unit2, :, mc].argmin() - \n self.templates[unit1, :, mc].argmin())\n spt2 += shift_temp\n \n # load residuals\n wfs1, skipped_idx1 = self.reader_residual.read_waveforms(\n spt1, self.spike_size, vis_chan)\n spt1 = np.delete(spt1, skipped_idx1)\n shift1 = np.delete(shift1, skipped_idx1)\n scale1 = np.delete(scale1, skipped_idx1)\n \n wfs2, skipped_idx2 = self.reader_residual.read_waveforms(\n spt2, self.spike_size, vis_chan)\n spt2 = np.delete(spt2, skipped_idx1)\n shift2 = np.delete(shift2, skipped_idx2)\n scale2 = np.delete(scale2, skipped_idx2)\n \n # align residuals\n wfs1 = shift_chans(wfs1, -shift1)\n wfs2 = shift_chans(wfs2, -shift2)\n\n # make clean waveforms\n wfs1 += scale1[:, None, None]*self.templates[[unit1], :, vis_chan].T\n if shift_temp > 0:\n temp_2_shfted = self.templates[[unit2], shift_temp:, vis_chan].T\n wfs2[:, :-shift_temp] += scale2[:, None, None]*temp_2_shfted\n elif shift_temp < 0:\n temp_2_shfted = self.templates[[unit2], :shift_temp, vis_chan].T\n wfs2[:, -shift_temp:] += scale2[:, None, None]*temp_2_shfted\n else:\n wfs2 += scale2[:, None, None]*self.templates[[unit2],:,vis_chan].T\n\n \n # compute spatial covariance\n spatial_whitener = self.get_spatial_whitener(vis_chan)\n # whiten\n wfs1_w = np.matmul(wfs1, spatial_whitener)\n wfs2_w = np.matmul(wfs2, spatial_whitener)\n wfs1_w = np.matmul(wfs1_w.transpose(0,2,1),\n self.temporal_whitener).transpose(0,2,1)\n wfs2_w = np.matmul(wfs2_w.transpose(0,2,1),\n self.temporal_whitener).transpose(0,2,1)\n\n\n temp_diff_w = np.mean(wfs1_w, 0) - np.mean(wfs2_w,0)\n c_w = np.sum(0.5*(np.mean(wfs1_w, 0) + np.mean(wfs2_w,0))*temp_diff_w)\n dat1_w = np.sum(wfs1_w*temp_diff_w, (1,2))\n dat2_w = np.sum(wfs2_w*temp_diff_w, (1,2))\n dat_all = np.hstack((dat1_w, dat2_w))\n p_val = dp(dat_all)[1]\n\n if p_val > p_val_threshold:\n merge = True\n else:\n merge= False\n\n centers_dist = np.linalg.norm(temp_diff_w)\n\n\n if p_val > p_val_threshold:\n merge = True\n else:\n merge= False\n \n centers_dist = np.linalg.norm(temp_diff_w)\n np.savez(fname_out,\n merge=merge,\n dat1_w=dat1_w,\n dat2_w=dat2_w,\n centers_dist=centers_dist,\n p_val=p_val)\n\n if merge:\n merge_pairs.append(pair)\n\n return merge_pairs",
"title": ""
},
{
"docid": "e92f449d20c92494bbd28d5c072dd12c",
"score": "0.46673",
"text": "def merge_replicates(regions_a, comps_a, regions_b, comps_b, contrib_a=1.0, contrib_b=1.0,\n offset_max=4.0, offset_step=0.5, scale_max=2.0, scale_step=0.1):\n \n best_score = -1.0\n best_offset = 0.0\n best_scale_start = -1.0\n best_scale_end = -1.0\n best_comps = None\n best_scores = None\n best_regions = None\n\n n_b = len(regions_b)\n points_b = np.arange(n_b)\n \n min_scale = 1.0/scale_max\n \n for offset in np.arange(-offset_max, offset_max, offset_step):\n\n for scale_start in np.arange(min_scale, scale_max, scale_step):\n \n for scale_end in np.arange(min_scale, scale_max, scale_step):\n \n stretch = np.interp(points_b, [0, n_b], [scale_start, scale_end])\n \n stretch_regions = (regions_b * stretch) + offset \n \n d = overlap_region_comps(regions_a, comps_a,\n stretch_regions, comps_b,\n contrib_a, contrib_b)\n \n sim_scores, merge_regions, merge_comps, sim_score, score_width, p = d\n \n if sim_score > best_score:\n best_scores = sim_scores\n best_score = sim_score\n best_scale_start = scale_start\n best_scale_end = scale_end\n best_offset = offset\n best_comps = merge_comps\n best_regions = merge_regions\n\n info(' best score: %5.2f at offset: %5.2f start scale: %5.2f end scale: %5.2f' % (best_score, best_offset, best_scale_start, best_scale_end))\n \n return best_regions, best_comps, best_score",
"title": ""
},
{
"docid": "42369da84a5a5c15f30b04732a243739",
"score": "0.46667022",
"text": "def merge(self):\n with open(f\"{self.file_name}.mp4\", \"wb\") as merged_file:\n for ts_file in [\n open(f\"chunks\\/{self.file_name}-{chunk_number}.chunk.ts\")\n for chunk_number in range(self.total_chunks)\n ]:\n shutil.copyfileobj(ts_file, merged_file)",
"title": ""
},
{
"docid": "9dc321b4c8efc9ef55b1637819911057",
"score": "0.46645865",
"text": "def test_merge_options(self):\n self.time.change_system_time(\"next friday 0800\")\n # m (The merge window) (Starts 1 day ago with a window size of 2)\n # <--->\n # Time/Day 1 2 3 4 5\n # 02:00 b b b b b (The last backup before the merge is the 5th Task)\n # 06:00 m (The merge is the 6th Task)\n\n # Define a schedule\n schedule = \\\n [\n (1, 'DAYS', '02:00'),\n (5, 'DAYS', '06:00'),\n ]\n\n # Make the second task a merge with offset_start = 1, offset_end = 2\n merge_map = {1: (1, 2)}\n\n schedule_test = ScheduleTest(self, [schedule], merge_map=merge_map)\n\n schedule_test.run(5)\n self.sleep(60) # Takes a moment to update the backups\n backups_before_merge = self.get_backups(\"active\", \"repo_name0\")\n self.assertEqual(len(backups_before_merge), 5)\n\n schedule_test.run(1)\n self.sleep(60) # Takes a moment to update the backups\n backups_after_merge = self.get_backups(\"active\", \"repo_name0\")\n\n # Check the merge backed up the correct backups\n self.assertEqual(len(backups_after_merge), 3)\n self.assertIn(backups_before_merge[0]._date, [backup._date for backup in backups_after_merge]) # Check backup 1 is not part of the merge\n self.assertIn(backups_before_merge[4]._date, [backup._date for backup in backups_after_merge]) # Check backup 5 is not part of the merge",
"title": ""
},
{
"docid": "d54a21a3f46c5639b0ef7bf91a95f75c",
"score": "0.46634975",
"text": "def replicate(self, source, target, opts={}, **kwargs):\n\n params = {\n 'source': source,\n 'target': target\n }\n\n params.update(opts)\n if 'params' in kwargs:\n params.update(kwargs['params'])\n del kwargs['params']\n\n return self.post('_replicate', params=params, **kwargs)",
"title": ""
},
{
"docid": "1ccfe1018eb7781938db12f74bb826e0",
"score": "0.46618715",
"text": "def parallel_merge(self, that):\n self.cov_sens = weighted_average(self.cov_sens, self.num_samples, that.cov_sens, that.num_samples)\n self.mean_sens = weighted_average(self.mean_sens, self.num_samples, that.mean_sens, that.num_samples) \n self.num_samples += that.num_samples",
"title": ""
},
{
"docid": "7eea7ea3ed5c03af7e8c0ff0861d6fe5",
"score": "0.46494043",
"text": "def merge(self, match=[\"year\", \"month\", \"day\"]):\n\n if type(match) is str:\n match = [match]\n\n if type(match) is not list:\n raise TypeError(\"match supplied is not a list\")\n\n for mm in match:\n if type(mm) is not str:\n raise TypeError(f\"{mm} from match is not a list\")\n\n if type(match) is list:\n match = [y.lower() for y in match]\n\n if len([x for x in match if x not in [\"year\", \"month\", \"day\"]]) > 0:\n raise ValueError(\"match supplied is not valid\")\n\n # Force a release if needed\n self.run()\n\n if type(self.current) is not list:\n warnings.warn(\n message=\"There is only one file in the dataset. No need to merge!\"\n )\n return None\n\n # Make sure the times in the files are compatiable, based on the match criteria\n\n all_times = []\n for ff in self:\n cdo_result = subprocess.run(\n f\"cdo ntime {ff}\",\n shell=True,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n ).stdout\n cdo_result = str(cdo_result).replace(\"b'\", \"\").strip()\n ntime = int(cdo_result.split(\"\\\\\")[0])\n all_times.append(ntime)\n if len(set(all_times)) > 1:\n warnings.warn(\n message=\"The files to merge do not have the same number of time steps!\"\n )\n\n all_grids = []\n for ff in self:\n cdo_result = subprocess.run(\n f\"cdo griddes {ff}\",\n shell=True,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n ).stdout\n all_grids.append(cdo_result)\n\n if len(set(all_grids)) > 1:\n raise ValueError(\n \"The files in the dataset to do not have the same grid. Consider using regrid!\"\n )\n\n all_times = []\n for ff in self:\n cdo_result = subprocess.run(\n f\"cdo showtimestamp {ff}\",\n shell=True,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n ).stdout\n cdo_result = str(cdo_result).replace(\"b'\", \"\").strip()\n cdo_result = cdo_result.split()\n cdo_result = pd.Series((v for v in cdo_result))\n all_times.append(cdo_result)\n\n for i in range(1, len(all_times)):\n if (len(all_times[i]) != len(all_times[0])) and (len(all_times[i]) > 1):\n raise ValueError(\n \"You are trying to merge data sets with an incompatible number of time steps\"\n )\n\n # remove files with more than one time step in it\n all_times = [x for x in all_times if len(x) > 1]\n\n all_df = []\n if len(all_times) > 1:\n for i in range(0, len(all_times)):\n month = [datetime.strptime(v[0:10], \"%Y-%m-%d\").month for v in all_times[i]]\n year = [datetime.strptime(v[0:10], \"%Y-%m-%d\").year for v in all_times[i]]\n day = [datetime.strptime(v[0:10], \"%Y-%m-%d\").day for v in all_times[i]]\n i_data = pd.DataFrame({\"year\": year, \"month\": month, \"day\": day})\n i_data = i_data.loc[:, match]\n all_df.append(i_data)\n\n for i in range(1, len(all_df)):\n if all_df[0].equals(all_df[i]) == False:\n raise ValueError(\"Dates of data sets do not satisfy matching criteria!\")\n\n cdo_command = \"cdo -merge\"\n\n run_this(cdo_command, self, output=\"one\")\n\n if session_info[\"lazy\"]:\n self._merged = True\n\n if cdo_version() in [\"1.9.3\"]:\n self.run()",
"title": ""
},
{
"docid": "f0ae060f81101cb5457805213c86bf42",
"score": "0.46367767",
"text": "def compete(self, num_runs):\n LOGGER.info('GAME {!r}\\nNUM RUNS {}'.format(self, num_runs))\n m = num_runs // 2\n self.runs(m)\n self.swap_agents()\n self.runs(num_runs-m, index_offset=m+1)\n self.swap_agents()\n log_info = 'GAME {!r}\\nNUM RUNS {} complete!'\n log_info += '\\nRESULTS\\nAGENT1 {!r}\\nAGENT2 {!r}\\n'\n LOGGER.info(log_info.format(self, num_runs, self._agent1, self._agent2))",
"title": ""
},
{
"docid": "69bd9fbb36c815e713b858119767aa83",
"score": "0.46341488",
"text": "def merge_results(images, submissions, output):\n\n df_image = gpd.read_file(images)\n df_image.index = df_image['key']\n df_image = df_image.drop(columns=['key'])\n\n damage_features = labelling_options.keys()\n\n os.makedirs(submissions+'-processed', exist_ok=True)\n sub_proc_dir = submissions+'-processed'\n\n num_batches = len(list(set([re.findall(r\".*?_(\\d+)_.*\", x)[0] for x in os.listdir(submissions)])))\n print('found', num_batches, 'batches')\n\n # loop over batches\n for batch in range(num_batches):\n\n # initialize geodatframe for results\n df_image_batch = df_image.copy()\n\n # get files and annotator names of this batch\n files = [x for x in os.listdir(submissions) if str(batch) == re.findall(r\".*?_(\\d+)_.*\", x)[0]]\n names = list(set([x.split('.')[0].split('_')[-1] for x in files]))\n num_annotators = len(names)\n map_names_ids = {name: i for i, name in enumerate(names)}\n print('processing batch', batch, 'unique annotators', names)\n batch_keys = []\n\n # initialize empty columns in results geodatframe\n for i in range(num_annotators):\n for damage_feature in damage_features:\n df_image_batch[damage_feature + '_' + str(i)] = False\n\n # merge results from different files\n for file in files:\n\n name = file.split('.')[0].split('_')[-1]\n annotator_id = map_names_ids[name]\n\n with open(submissions+'/'+file) as json_file:\n data = json.load(json_file)\n\n for image in data['_via_img_metadata']:\n labels = data['_via_img_metadata'][image]['file_attributes']['damage_labels']\n for damage_feature in damage_features:\n if damage_feature in labels.keys():\n df_image_batch.at[image, damage_feature+'_'+str(annotator_id)] = True\n batch_keys = list(data['_via_img_metadata'])\n\n # keep only images in batch\n df_image_batch = df_image_batch[df_image_batch.index.isin(batch_keys)]\n\n # merge results from the diferrent annotators\n for damage_feature in damage_features:\n df_image_batch[damage_feature+'_merged'] = 'no damage'\n\n for image in df_image_batch.index:\n df = df_image_batch.loc[image].copy()\n for damage_feature in damage_features:\n labels = [damage_feature+'_'+str(i) for i in range(num_annotators)]\n values = df[labels].values\n if any(values):\n df_image_batch.at[image, damage_feature + '_merged'] = 'possible damage'\n if all(values):\n df_image_batch.at[image, damage_feature + '_merged'] = 'confirmed damage'\n\n # save processed batch results\n print('finished processing batch', batch, 'with', len(df_image_batch), 'entries')\n if len(df_image_batch) > 0:\n df_image_batch.to_file(sub_proc_dir+'/results_batch_'+str(batch)+'.geojson', driver='GeoJSON')\n\n # merge results from all batches\n gdf_results = gpd.GeoDataFrame()\n for file in os.listdir(sub_proc_dir):\n gdf_batch = gpd.read_file(sub_proc_dir+'/'+file)\n gdf_results = gdf_results.append(gdf_batch, ignore_index=True)\n gdf_merged = gpd.GeoDataFrame()\n gdf_merged['key'] = gdf_results['key']\n gdf_merged['geometry'] = gdf_results['geometry']\n gdf_merged['captured_at'] = gdf_results['captured_at']\n\n # save one layer per damage feature\n for damage_feature in damage_features:\n gdf_results_feature = gdf_results[['key', damage_feature+'_merged', 'geometry', 'captured_at']]\n gdf_results_feature = gdf_results_feature.rename(columns={damage_feature+'_merged': damage_feature})\n gdf_results_feature.to_file(output+'/results_'+damage_feature+'.geojson', driver='GeoJSON')\n gdf_merged[damage_feature] = gdf_results_feature[damage_feature]\n\n # merge damage features in one layer, save it\n gdf_merged['damage'] = \"no damage\"\n for ix, row in gdf_merged.iterrows():\n if any(['confirmed damage' in row[x] for x in light_features]) or any(['possible damage' in row[x] for x in medium_features]):\n gdf_merged.at[ix, 'damage'] = \"light damage\"\n if any(['confirmed damage' in row[x] for x in medium_features]) or any(['possible damage' in row[x] for x in heavy_features]):\n gdf_merged.at[ix, 'damage'] = \"moderate damage\"\n if any(['confirmed damage' in row[x] for x in heavy_features]):\n gdf_merged.at[ix, 'damage'] = \"severe damage\"\n\n gdf_merged = gdf_merged.drop(columns=light_features+medium_features+heavy_features)\n gdf_merged = gdf_merged[['key', 'damage', 'geometry', 'captured_at']]\n gdf_merged.to_file(output+'/results_merged.geojson', driver='GeoJSON')",
"title": ""
},
{
"docid": "4ec086395bd313cb7719d2ba9815324d",
"score": "0.46333608",
"text": "def merge(items, target):\n item = items.pop()\n merged = {}\n k = '{}+{}'.format(item.remote_address, item.remote_port)\n merged[k] = item.copy()\n for item in items:\n k = '{}+{}'.format(item.remote_address, item.remote_port)\n if k not in merged:\n merged[k] = item.copy()\n continue\n if item.is_targeted(target):\n merged[k].client_address = item.client_address\n merged[k].count += item.count\n for t in item.METADATA_TYPES:\n merged[k].metadata[t] |= item.metadata_for(t)\n return merged.values()",
"title": ""
},
{
"docid": "f7b6d91429bcadbd8cea35024841dacd",
"score": "0.4631474",
"text": "def merge_records(self, dest, record_style, includetar=True, overwrite=False, dryrun=False):\n \n # Get records in source database\n self_records, self_df = self.get_records(record_style, return_df=True)\n print(len(self_records), 'records in source')\n \n # Get records in destination database\n dest_records, dest_df = dest.get_records(record_style, return_df=True)\n print(len(dest_records), 'records in destination')\n \n # Identify records missing from destination\n if len(dest_records) > 0:\n missing = set(self_df.name[~self_df.name.isin(dest_df.name.tolist())])\n else:\n missing = set(self_df.name)\n print(len(missing), 'records missing from destination')\n \n if overwrite is True:\n print('comparing content of records')\n # Identify records that have changed\n changed = []\n for name in tqdm(self_df.name[~self_df.name.isin(missing)], desc=\"Comparing records\", ascii=True):\n self_record = self_records[self_df.name == name][0].model.json(ensure_ascii=False)\n dest_record = dest_records[dest_df.name == name][0].model.json(ensure_ascii=False)\n if self_record != dest_record:\n changed.append(name)\n print(len(changed), 'records in destination different in source')\n missing = missing.union(changed)\n \n if includetar is True:\n \n # Get metadata for tars in source database\n self_tar = []\n for frecord in self.mongodb[f'{record_style}.files'].find():\n self_tar.append(frecord)\n self_tar = pd.DataFrame(self_tar)\n print(len(self_tar), 'tars in source')\n \n # Get metadata for tars in destination database\n dest_tar = []\n for frecord in dest.mongodb[f'{record_style}.files'].find():\n dest_tar.append(frecord)\n dest_tar = pd.DataFrame(dest_tar)\n print(len(dest_tar), 'tars in destination') \n \n # Identify records missing from destination\n if len(dest_records) > 0:\n missingtar = set(self_tar.recordname[~self_tar.recordname.isin(dest_tar.recordname.tolist())])\n else:\n missingtar = set(self_tar.recordname)\n print(len(missingtar), 'tars missing from destination')\n missing = missing.union(missingtar)\n \n if len(missing) > 0:\n records = self_records[self_df.name.isin(missing)]\n if dryrun:\n print(len(records), 'to copy')\n return records\n else:\n self.copy_records(dest, records=records, includetar=includetar, overwrite=overwrite)\n else:\n print('No records to copy')\n if dryrun:\n return []",
"title": ""
},
{
"docid": "2b353cf359933fa574641c26b724e9ae",
"score": "0.46280736",
"text": "def map_reduce_aggregation(self, last_update):\n try:\n # define the mapper,\n # emit the key and value to be used in reducer\n # the mapper should have the same form with reduced value\n mapper = Code(\n '''\n function () {\n // key is the text, value map \"documents\"\n // to the object with\n // documentId as key and \"document\"\n // in collection1 as content\n // also, move add timestamp into the \"document\" field\n var key = this.text;\n var value = {\"documents\": {}}\n value[\"documents\"][this.documentId] = this.document;\n value[\"documents\"][this.documentId][\"ts\"] = this.ts;\n\n // emit the mapped key and value\n emit(key, value);\n }\n '''\n )\n\n # define the reducer,\n # the reducer should always remain the same format no matter\n # how many times map reduce applied\n reducer = Code(\n '''\n // receive the key and value from the mapper\n function (key, values) {\n // define new object to receive\n // and add the newly reduced content\n var obj = {};\n obj[\"documents\"] = {};\n\n // merge function check the timestamp\n // of given documentId and document\n // replace the existing content if the timestamp is larger\n function merge(documentId, document) {\n // check if the documentId is in the object\n // if does, replace with new content\n if (documentId in obj[\"documents\"] &&\n obj[\"documents\"][documentId][\"ts\"] >\n document[\"ts\"]) {\n return;\n }\n obj[\"documents\"][documentId] = document;\n }\n\n // loop through the value collected,\n // and apply merge on each documentId\n for (var i = 0; i < values.length; i ++) {\n var value = values[i];\n for(let documentId in value[\"documents\"]) {\n merge(documentId, value[\"documents\"][documentId])\n }\n }\n return obj;\n }\n '''\n )\n\n # apply map reduce using the mapper and reducer,\n # with specified newly the time stamp\n return self.collection.map_reduce(\n mapper,\n reducer,\n out={\"reduce\": self.mr_collection.name},\n query={\"ts\": {\"$gte\": last_update}},\n )\n except Exception:\n raise DataBaseAggregationFail()",
"title": ""
},
{
"docid": "311d447378a12be050664ca4db29486c",
"score": "0.4611131",
"text": "def merge(cls, batches, batch_size=None):\n batches = [batch for batch in batches if batch is not None]\n if len(batches) == 0:\n return None, None\n total_len = np.sum([len(batch) for batch in batches])\n if batch_size is None:\n batch_size = total_len\n elif not isinstance(batch_size, int) or batch_size < 1:\n raise ValueError(\"Batch size must be positive int\")\n indices = np.arange(total_len)\n\n data = []\n for comp in batches[0].components:\n data.append(np.concatenate([batch.get(component=comp) for batch in batches]))\n data = copy.deepcopy(data)\n\n new_indices = indices[:batch_size]\n new_batch = cls(bf.DatasetIndex(new_indices), unique_labels=batches[0].unique_labels)\n new_batch._data = tuple(comp[:batch_size] for comp in data) # pylint: disable=protected-access, attribute-defined-outside-init, line-too-long\n if total_len <= batch_size:\n rest_batch = None\n else:\n rest_indices = indices[batch_size:]\n rest_batch = cls(bf.DatasetIndex(rest_indices), unique_labels=batches[0].unique_labels)\n rest_batch._data = tuple(comp[batch_size:] for comp in data) # pylint: disable=protected-access, attribute-defined-outside-init, line-too-long\n return new_batch, rest_batch",
"title": ""
},
{
"docid": "046b8b23e7576eecd051d84028cdc131",
"score": "0.46095175",
"text": "def merger(filepath, src, dst):\n # Load and merge the multitrack pianoroll\n multitrack = Multitrack(filepath)\n merged = get_merged(multitrack)\n\n # Save the merged multitrack pianoroll\n result_path = change_prefix(filepath, src, dst)\n make_sure_path_exists(os.path.dirname(result_path))\n merged.save(result_path)",
"title": ""
},
{
"docid": "8d83c776e3d88c7be862176e4d080aa2",
"score": "0.46063486",
"text": "def merge(self, prefix):\n world_size = get_world_size()\n merged_file = prefix.rsplit(\".\", 1)[0] + \".all\"\n logger.info(f\"concat all results into:{merged_file}\")\n merged_fd = open(merged_file, \"w\")\n for rank in range(world_size):\n res_file = prefix + str(rank)\n assert op.exists(res_file), f\"No such file or directory: {res_file}\"\n with open(res_file, \"r\") as fin:\n for line_idx, line in enumerate(fin):\n merged_fd.write(line)\n logger.info(f\"merging {res_file} {line_idx+1} results\")\n merged_fd.close()\n return merged_file",
"title": ""
},
{
"docid": "4c49a49707b56530215030a6638bd257",
"score": "0.45907712",
"text": "def replicate(self, args):\n (options, args) = MethodOptionParser(fixed=[\"glob...\"]).parse_args(args)\n\n self._Locations().forAllLocal([\"replicate\"] + args).collect(labelPrint=True)",
"title": ""
},
{
"docid": "b618d4b2bb0491ffc4585b991c58b567",
"score": "0.45877486",
"text": "def merge(self, *args, max_concurrent=None):\n from ..operators.observable.merge import merge\n return merge(self, *args, max_concurrent=max_concurrent)",
"title": ""
},
{
"docid": "12b88061064efbd011aa01365f815721",
"score": "0.45815164",
"text": "def merge(self):\n \n # Merge self.loc_df and self.pbp_df on playbyplayorder_id\n self.pbp_loc_merged_df = pd.merge(self.pbp_df, self.loc_df, on = [\"playbyplayorder_id\", \"row_type\", \"game_id\"])\n \n # Make changes in actiondescription columns to simplify\n self.pbp_loc_merged_df[\"actiondescription\"] = self.pbp_loc_merged_df[\"actiondescription\"].apply(lambda x: 'jump_shot' if 'Jump' in x else ('layup' if 'Layup' in x else ('other'))) \n \n # Inspect the data\n \"\"\"\n print (self.pbp_loc_merged_df.info())\n self.pbp_loc_merged_df.hist(bins = 50, figsize = (20, 15))\n plt.show()\n \"\"\"\n \n \n return self.pbp_loc_merged_df",
"title": ""
},
{
"docid": "0532514b5df6ecaa1a92520d98f522a6",
"score": "0.45799404",
"text": "def merge(self, otherArray, acceptDuplicates=False):\n otherArray = otherArray.toFormat(self)\n if acceptDuplicates:\n cannotAppend = self.max() > otherArray.min()\n else:\n cannotAppend = self.max() >= otherArray.min()\n if cannotAppend:\n print self\n print otherArray\n raise Exception('Time ranges cannot be concatenated')\n self.array = np.hstack((self.array, otherArray.array))",
"title": ""
},
{
"docid": "96ecf3c3898e4e97cbe273a75429fee2",
"score": "0.45793575",
"text": "def test_two_batches_binary_duplicate(self):\n community = DebugCommunity.create_community(self._dispersy, self._my_member)\n\n # create node and ensure that SELF knows the node address\n node = DebugNode(community)\n node.init_socket()\n node.init_my_member()\n\n global_time = 10\n # first batch\n message = node.create_full_sync_text(\"duplicates\", global_time)\n node.give_packets([message.packet for _ in xrange(10)])\n\n # only one message may be in the database\n times = [x for x, in self._dispersy.database.execute(u\"SELECT global_time FROM sync WHERE community = ? AND member = ? AND meta_message = ?\", (community.database_id, node.my_member.database_id, message.database_id))]\n self.assertEqual(times, [global_time])\n\n # second batch\n node.give_packets([message.packet for _ in xrange(10)])\n\n # only one message may be in the database\n times = [x for x, in self._dispersy.database.execute(u\"SELECT global_time FROM sync WHERE community = ? AND member = ? AND meta_message = ?\", (community.database_id, node.my_member.database_id, message.database_id))]\n self.assertEqual(times, [global_time])\n\n # cleanup\n community.create_dispersy_destroy_community(u\"hard-kill\")\n self._dispersy.get_community(community.cid).unload_community()",
"title": ""
},
{
"docid": "83ec724a7715315b7820c50ed04e30cb",
"score": "0.45779082",
"text": "def merge(target, **kwargs):\n subspec = kwargs.pop('spec', T)\n init = kwargs.pop('init', dict)\n op = kwargs.pop('op', None)\n if kwargs:\n raise TypeError('unexpected keyword args: %r' % sorted(kwargs.keys()))\n spec = Merge(subspec, init, op)\n return glom(target, spec)",
"title": ""
},
{
"docid": "1cff8787cfa049476f5e1a2a3cc8c620",
"score": "0.45739704",
"text": "def merge_wrapper(merge_config: str, source: List, destination: List, processes: int):\n try:\n merge(merge_config, source, destination, processes)\n exit(0)\n except Exception as me:\n get_logger().error(f\"kgx.merge error: {str(me)}\")\n exit(1)",
"title": ""
},
{
"docid": "2c50d7801d79170c235c1dad174ff672",
"score": "0.45722777",
"text": "def test_should_generate_correct_result_if_original_document_modified_in_parallel(self):\n # original document\n lww = LWWElementSet()\n abc = \"ABCDEFG\"\n t = 0\n for s in abc:\n t = t + 1\n lww.add(Element(s, t))\n\n # person 1's updates\n lww1 = LWWElementSet()\n added = \"HIJKL\"\n removed = \"ACEG\"\n i = len(abc)\n for a in added:\n i = i + 1\n lww1.add(Element(a, i))\n\n for r in removed:\n i = i + 1\n lww1.remove(Element(r, i))\n\n # merge original and person 1's update\n result1 = lww + lww1\n # Original: ABCDEFG\n # Added: HIJKL Removed: ACEG\n self.assertEqual(str(''.join([x.item for x in result1.get_resultant_document()])), 'BDFHIJKL')\n\n # person 2's parallel updates\n lww2 = LWWElementSet()\n added = \"KLMNOP\"\n removed = \"BFG\"\n i = len(abc) # to simulate parallel time, starting i with same as person 1's i\n for a in added:\n i = i + 1\n lww2.add(Element(a, i))\n\n for r in removed:\n i = i + 1\n lww2.remove(Element(r, i))\n\n # merge original and person 2's update\n result2 = lww + lww2\n # Original: ABCDEFG\n # Added: KLMNOP Removed: BFG\n self.assertEqual(str(''.join([x.item for x in result2.get_resultant_document()])), 'ACDEKLMNOP')\n\n # final state of original document after, merge of both person's updates\n final = result1 + result2\n # Original: ABCDEFG\n # Person 1 : Added : HIJKL Removed : ACEG\n # Person 2 : Added : KLMNOP Removed : BFG\n self.assertEqual(str(''.join([x.item for x in final.get_resultant_document()])), 'DHIJKLMNOP')",
"title": ""
},
{
"docid": "ce0ef033fd5c68e008c08731a0323569",
"score": "0.45706043",
"text": "def merge_whole_observations(observations: Iterable[WholeObs]) -> pd.DataFrame:\n return pd.DataFrame(dict(observations), index=[0])",
"title": ""
},
{
"docid": "f45101aef97a6de68a097ecf1a44e2c7",
"score": "0.45690897",
"text": "def testMergeBuildStats(self):\n base_map = {\n 'foo': {\n data_types.Expectation('foo', ['win'], 'Failure'): {\n 'builder': {\n 'step': data_types.BuildStats(),\n },\n },\n },\n }\n merge_stats = data_types.BuildStats()\n merge_stats.AddFailedBuild('1')\n merge_map = {\n 'foo': {\n data_types.Expectation('foo', ['win'], 'Failure'): {\n 'builder': {\n 'step': merge_stats,\n },\n },\n },\n }\n expected_stats = data_types.BuildStats()\n expected_stats.AddFailedBuild('1')\n expected_base_map = {\n 'foo': {\n data_types.Expectation('foo', ['win'], 'Failure'): {\n 'builder': {\n 'step': expected_stats,\n },\n },\n },\n }\n queries._MergeExpectationMaps(base_map, merge_map)\n self.assertEqual(base_map, expected_base_map)",
"title": ""
},
{
"docid": "5bd9543f8e7a47167d0391ce1c4ec2aa",
"score": "0.4567705",
"text": "def _process_repeats_impl(reads, repeats_dict, work_dir, all_labels,\n initial_file_names, return_queue):\n MIN_MULT = trestle_config.vals[\"min_mult\"]\n MAX_MULT = trestle_config.vals[\"max_mult\"]\n FLANKING_LEN = trestle_config.vals[\"flanking_len\"]\n ORIENT_CONFIG = trestle_config.vals[\"orientations_to_run\"]\n \n repeat_label, side_labels = all_labels\n (template_name, extended_name, repeat_reads_name, \n pre_partitioning_name) = initial_file_names\n \n reads_dict = {}\n for read_file in reads:\n reads_dict.update(fp.read_sequence_dict(read_file))\n #orig_graph = fp.read_sequence_dict(graph_edges)\n #graph_dict = {int(h.split('_')[1]):orig_graph[h] for h in orig_graph}\n \n if not reads_dict:\n raise ProcessingException(\"No reads found from {0}\".format(reads))\n #if not graph_dict:\n # raise ProcessingException(\"No edges found from {0}\".format(\n # graph_edges))\n \n repeat_list = []\n repeat_edges = {}\n all_edge_headers = {}\n for rep in sorted(repeats_dict, reverse=True):\n #Checks multiplicity of repeat and presence of reverse strand\n #One run processes both forward and reverse strand of repeat\n \n if rep <= 0:\n continue\n \n valid_repeat = True\n if -rep not in repeats_dict:\n logger.debug(\"Repeat {0} missing reverse strand\".format(rep))\n valid_repeat = False\n elif (repeats_dict[rep].multiplicity < MIN_MULT or\n repeats_dict[rep].multiplicity > MAX_MULT or\n repeats_dict[-rep].multiplicity < MIN_MULT or\n repeats_dict[-rep].multiplicity > MAX_MULT):\n logger.debug(\"Repeat {0} multiplicity not in range: {1}\".format(\n rep, repeats_dict[rep].multiplicity))\n valid_repeat = False\n #if rep not in graph_dict:\n # logger.debug(\"Repeat {0} missing from graph file\".format(rep))\n # valid_repeat = False\n if not valid_repeat:\n continue\n \n #Makes repeat dirs\n repeat_dir = os.path.join(work_dir, repeat_label.format(rep))\n if not os.path.isdir(repeat_dir):\n os.mkdir(repeat_dir)\n repeat_list.append(rep)\n \n run_orientations = []\n if ORIENT_CONFIG == \"forward\":\n run_orientations = [(\"forward\", rep)]\n elif ORIENT_CONFIG == \"reverse\":\n run_orientations = [(\"reverse\", -rep)]\n elif ORIENT_CONFIG == \"both\":\n run_orientations = [(\"forward\", rep), (\"reverse\", -rep)]\n for curr_label, curr_rep in run_orientations:\n orient_path = os.path.join(repeat_dir, curr_label)\n if not os.path.isdir(orient_path):\n os.mkdir(orient_path)\n template_path = os.path.join(orient_path, template_name)\n extended_path = os.path.join(orient_path, extended_name)\n repeat_reads_path = os.path.join(orient_path, repeat_reads_name)\n partitioning_path = os.path.join(orient_path, \n pre_partitioning_name)\n \n in_label = side_labels[0]\n out_label = side_labels[1]\n repeat_edges[curr_rep] = {in_label:[], out_label:[]}\n \n #(mult, all_reads_list, inputs_dict,\n # outputs_dict) = repeats_dict[curr_rep]\n #mult = repeats_dict[curr_rep].multiplicity\n all_reads_list = repeats_dict[curr_rep].all_reads\n inputs_dict = repeats_dict[curr_rep].in_reads\n outputs_dict = repeats_dict[curr_rep].out_reads\n \n template_dict = {}\n extended_dicts = {}\n repeat_reads_dict = {}\n #Partitioning parts: id_num, Partitioned/Tied/None, \n #edge_id, top_score, total_score, Header\n partitioning = {in_label:[], out_label:[]}\n read_id = 0\n \n template_seq = repeats_dict[curr_rep].sequences[\"template\"]\n #if curr_label == \"reverse\":\n # template_seq = fp.reverse_complement(graph_dict[rep])\n template_dict[curr_rep] = template_seq\n \n all_edge_headers[curr_rep] = {}\n out_headers = set()\n #Headers will be in the form -h or +h,\n #edge_dict is in the form >[Input,Output]_edge##_h,\n #rev_comp of read will be written if the header is -h\n for edge_id in inputs_dict: \n repeat_edges[curr_rep][in_label].append(edge_id)\n extended_dicts[(in_label, edge_id)] = {}\n \n headers = inputs_dict[edge_id]\n for header in headers:\n if (not header) or (header[0] != '+' and header[0] != '-'):\n raise ProcessingException(\n \"Input read format not recognized: {0}\".format(\n header))\n if header[1:] not in reads_dict:\n raise ProcessingException(\n \"Read header {0} not in any of {1}\".format(\n header[1:], reads))\n \n if header[1:] not in all_edge_headers[curr_rep]:\n status_label = \"Partitioned\"\n edge_label = str(edge_id)\n score = 1\n total_score = 0\n partitioning[in_label].append((read_id, status_label, \n edge_label, score, \n total_score, \n header[1:]))\n all_edge_headers[curr_rep][header[1:]] = read_id\n read_id += 1\n \n extend_in_header = \"Extended_Template_Input_{0}\".format(\n edge_id)\n #if edge_id > 0:\n # edge_seq = graph_dict[edge_id]\n #elif edge_id < 0:\n # edge_seq = fp.reverse_complement(graph_dict[-edge_id])\n edge_seq = repeats_dict[curr_rep].sequences[edge_id]\n extended_seq = edge_seq[-FLANKING_LEN:]\n extended_dicts[(in_label, edge_id)][extend_in_header] = (\n extended_seq + template_seq)\n \n \n for edge_id in outputs_dict: \n repeat_edges[curr_rep][out_label].append(edge_id)\n extended_dicts[(out_label, edge_id)] = {}\n \n headers = outputs_dict[edge_id]\n for header in headers:\n if (not header) or (header[0] != '+' and header[0] != '-'):\n raise ProcessingException(\n \"Output read format not recognized: {0}\".format(\n header))\n if header[1:] not in reads_dict:\n raise ProcessingException(\n \"Read header {0} not in any of {1}\".format(\n header[1:], reads))\n \n curr_read_id = read_id\n if header[1:] not in all_edge_headers[curr_rep]:\n status_label = \"None\"\n edge_label = \"NA\"\n score = 0\n total_score = 0\n partitioning[in_label].append((read_id, status_label, \n edge_label, score, \n total_score,\n header[1:]))\n \n all_edge_headers[curr_rep][header[1:]] = read_id\n read_id += 1\n else:\n curr_read_id = all_edge_headers[curr_rep][header[1:]]\n \n if header[1:] not in out_headers:\n status_label = \"Partitioned\"\n edge_label = str(edge_id)\n score = 1\n total_score = 0\n partitioning[out_label].append((curr_read_id, \n status_label, \n edge_label, score, \n total_score, \n header[1:]))\n out_headers.add(header[1:]) \n \n extend_out_header = \"Extended_Template_Output_{0}\".format(\n edge_id)\n #if edge_id > 0:\n # edge_seq = graph_dict[edge_id]\n #elif edge_id < 0:\n # edge_seq = fp.reverse_complement(graph_dict[-edge_id])\n edge_seq = repeats_dict[curr_rep].sequences[edge_id]\n extended_seq = edge_seq[:FLANKING_LEN]\n extended_dicts[(out_label, edge_id)][extend_out_header] = (\n template_seq + extended_seq)\n \n #Need to reiterate over in_headers to add in_headers to \n #out-partitioning while avoiding double-adding ones in both\n for edge_id in inputs_dict:\n headers = inputs_dict[edge_id]\n for header in headers:\n if header[1:] not in out_headers:\n curr_read_id = all_edge_headers[curr_rep][header[1:]]\n status_label = \"None\"\n edge_label = \"NA\"\n score = 0\n total_score = 0\n partitioning[out_label].append((curr_read_id, \n status_label, \n edge_label, score, \n total_score, \n header[1:]))\n \n \n for header in all_reads_list:\n if (not header) or (header[0] != '+' and header[0] != '-'):\n raise ProcessingException(\n \"All reads format not recognized: {0}\".format(header))\n if header[1:] not in reads_dict:\n raise ProcessingException(\n \"Read header {0} not in any of {1}\".format(\n header[1:], reads))\n \n seq = reads_dict[header[1:]]\n if header[0] == '-':\n seq = fp.reverse_complement(seq)\n repeat_reads_dict[header[1:]] = seq\n \n curr_read_id = read_id\n if header[1:] not in all_edge_headers[curr_rep]:\n all_edge_headers[curr_rep][header[1:]] = read_id\n read_id += 1\n \n status_label = \"None\"\n edge_label = \"NA\"\n score = 0\n total_score = 0\n partitioning[in_label].append((curr_read_id, status_label, \n edge_label, score, \n total_score, header[1:]))\n \n status_label = \"None\"\n edge_label = \"NA\"\n score = 0\n total_score = 0\n partitioning[out_label].append((curr_read_id, status_label, \n edge_label, score, \n total_score, header[1:]))\n \n if template_dict and template_dict.values()[0]:\n fp.write_fasta_dict(template_dict, template_path)\n for edge in extended_dicts:\n if extended_dicts[edge] and extended_dicts[edge].values()[0]:\n extended_edge_path = extended_path.format(edge[0], \n edge[1])\n fp.write_fasta_dict(extended_dicts[edge], \n extended_edge_path)\n if repeat_reads_dict and repeat_reads_dict.values()[0]:\n fp.write_fasta_dict(repeat_reads_dict, repeat_reads_path)\n for side in side_labels:\n _write_partitioning_file(partitioning[side], \n partitioning_path.format(side))\n \n if not template_dict:\n raise ProcessingException(\"No template {0} found\".format(\n curr_rep))\n for edge in extended_dicts:\n if not template_dict:\n raise ProcessingException(\n \"No extended template {0} {1} {2} found\".format(\n curr_rep, edge[0], edge[1]))\n if not repeat_reads_dict:\n raise ProcessingException(\"No repeat reads {0} found\".format(\n curr_rep))\n for side in side_labels:\n if not partitioning[side]:\n raise ProcessingException(\n \"Empty partitioning file {0}\".format(\n partitioning_path.format(side)))\n\n return_queue.put((repeat_list, repeat_edges, all_edge_headers))",
"title": ""
},
{
"docid": "e468c8d3cbe67536990b1100cfb49d7a",
"score": "0.45671993",
"text": "def test_resample_iterations_same(PM_ds_initialized_1d, chunk, replace):\n ds = PM_ds_initialized_1d.isel(lead=range(3), init=range(5))\n if chunk:\n ds = ds.chunk()\n ds_r_idx = _resample_iterations_idx(ds, ITERATIONS, \"member\", replace=replace)\n ds_r = _resample_iterations(ds, ITERATIONS, \"member\", replace=replace)\n for d in ds.dims:\n xr.testing.assert_identical(ds_r[d], ds_r_idx[d])\n assert ds_r.tos.size == ds_r_idx.tos.size",
"title": ""
},
{
"docid": "f6d5ea3f530d39e15f7e1d689b00b853",
"score": "0.4566746",
"text": "def duplicate(self, **kwargs):\n\n return self.api_request(self._get_method_fullname(\"duplicate\"), kwargs)",
"title": ""
},
{
"docid": "cf5a7dbded0db7bc743fd6800dc2299f",
"score": "0.45605308",
"text": "def duplicate(*args, **kwargs):\n\n pass",
"title": ""
},
{
"docid": "e46d145a364469d25136f51cd2a1c2f4",
"score": "0.45585206",
"text": "def mosaic(*rasters, **kwargs):\n # use cellsize of first raster if not manually assigned\n## firstraster = rasters[0]\n## cellwidth = kwargs.get(\"cellwidth\")\n## cellheight = kwargs.get(\"cellheight\")\n## if not (cellwidth and cellheight):\n## cellwidth = firstraster.info[\"cellwidth\"]\n## cellheight = firstraster.info[\"cellheight\"]\n \n # align first band of all rasters and resample to output\n aligned = align_rasters(*rasters)\n #aligned = [resample(rast, cellwidth=cellwidth, cellheight=cellheight)\n # for rast in aligned]\n firstalign,firstmask = aligned[0]\n merged = firstalign.copy()\n for rast,mask in aligned[1:]:\n merged.grids[0].img.paste(rast.grids[0].img, (0,0), mask)\n\n return merged",
"title": ""
},
{
"docid": "16ba118a41df66c31ebd35fea1616156",
"score": "0.4554724",
"text": "def merger(filepath, src, dst):\r\n # Load and merge the multitrack pianoroll\r\n multitrack = Multitrack(filepath)\r\n merged = get_merged(multitrack)\r\n\r\n # Save the merged multitrack pianoroll\r\n result_path = change_prefix(filepath, src, dst)\r\n make_sure_path_exists(os.path.dirname(result_path))\r\n merged.save(result_path)",
"title": ""
},
{
"docid": "df4b293caefa2e12aa2eea261ab004b8",
"score": "0.4552576",
"text": "def do_task_merge (self, *unnamed_args, **named_args):\n # extra dereference because we are only interested in the first (only) job\n return list(p1 for (p1, ps) in self.get_param_iterator (*unnamed_args, **named_args)(None))[0]",
"title": ""
},
{
"docid": "ffbdb4c61bdcd5e57924f26de3731f23",
"score": "0.45515713",
"text": "def test_merge_ins_with_duplicates(self, dir_paths):\n\n with pytest.raises(ValueError):\n list(\n merge.merge_samples(\n dir_paths, sample_names=['a', 'a'],\n with_expression=False)[0]) # yapf: disable",
"title": ""
},
{
"docid": "cd306e22083867de6e94ee929366ce28",
"score": "0.4550894",
"text": "def _incorporate_dup(self):\n seq, cds_start, cds_stop, start, end = self._setup_incorporate()\n\n dup_seq = seq[start:end]\n seq[end:end] = dup_seq\n\n is_frameshift = len(dup_seq) % 3 != 0\n variant_start_aa = int(math.ceil((self._var_c.posedit.pos.end.base + 1) / 3.0))\n\n alt_data = AltTranscriptData(\n seq,\n cds_start,\n cds_stop,\n is_frameshift,\n variant_start_aa,\n self._transcript_data.protein_accession,\n is_ambiguous=self._ref_has_multiple_stops,\n )\n return alt_data",
"title": ""
},
{
"docid": "637e8c8f0b23eb614d315e61f8342ca4",
"score": "0.45382947",
"text": "def resolve_duplicate(self, task, found_duplicates):\n raise NotImplementedError",
"title": ""
},
{
"docid": "2adf6262c87afd19894f8e737a505d16",
"score": "0.45308942",
"text": "def _merge_non_test_data(cls, aggregated_json, incremental_json, num_runs):\n\n incremental_builds = incremental_json[JSON_RESULTS_BUILD_NUMBERS]\n aggregated_builds = aggregated_json[JSON_RESULTS_BUILD_NUMBERS]\n aggregated_build_number = int(aggregated_builds[0])\n # Loop through all incremental builds, start from the oldest run.\n for index in reversed(range(len(incremental_builds))):\n build_number = int(incremental_builds[index])\n logging.debug(\"Merging build %s, incremental json index: %d.\",\n build_number, index)\n\n # Return if not all build numbers in the incremental json results\n # are newer than the most recent build in the aggregated results.\n # FIXME: make this case work.\n if build_number < aggregated_build_number:\n logging.warning((\"Build %d in incremental json is older than \"\n \"the most recent build in aggregated results: %d\"),\n build_number, aggregated_build_number)\n return False\n\n # Return if the build number is duplicated.\n # FIXME: skip the duplicated build and merge rest of the results.\n # Need to be careful on skiping the corresponding value in\n # _merge_tests because the property data for each test could\n # be accumulated.\n if build_number == aggregated_build_number:\n logging.warning(\"Duplicate build %d in incremental json\",\n build_number)\n return False\n\n # Merge this build into aggreagated results.\n cls._merge_one_build(aggregated_json, incremental_json, index, num_runs)\n\n return True",
"title": ""
},
{
"docid": "3f4bb9b1585cf64c6bda74d4022291e5",
"score": "0.4530067",
"text": "def merge_subreddit_threads(subreddit, file):\n RedditService.merge_csv_bulk(subreddit, file)",
"title": ""
},
{
"docid": "28d7b3bb45c1b2ed71ced30da470ba9d",
"score": "0.45209795",
"text": "def test_not_all_same_pool(self):\n directory = TempDirectory()\n original_file_path, dna = write_random_dna_fasta(directory.path, \"original.fasta\", 1000)\n args = make_default_args(original_file_path)\n args.random_seed = 1\n args.num_sims = 3\n args.num_subs = 2\n args.num_insertions = 2\n args.num_deletions = 2\n args.subset_len = 500\n\n snpmutator.run_from_args(args)\n mutated_seq_record1 = read_fasta_seq_record(\"original_mutated_1.fasta\")\n mutated_seq_record2 = read_fasta_seq_record(\"original_mutated_2.fasta\")\n mutated_seq_record3 = read_fasta_seq_record(\"original_mutated_3.fasta\")\n self.assertNotEqual(str(mutated_seq_record1.seq), str(mutated_seq_record2.seq), \"Generated sequences 1 and 2 should be different.\")\n self.assertNotEqual(str(mutated_seq_record2.seq), str(mutated_seq_record3.seq), \"Generated sequences 2 and 3 should be different.\")\n self.assertNotEqual(str(mutated_seq_record1.seq), str(mutated_seq_record3.seq), \"Generated sequences 1 and 3 should be different.\")",
"title": ""
},
{
"docid": "eaedcfab63725aa38e043baf37ce931d",
"score": "0.45205456",
"text": "def average_duplicates(args: Args):\n print('Loading data')\n header = get_header(args.data_path)\n data = get_data(path=args.data_path, smiles_columns=args.smiles_columns, target_columns=args.target_columns)\n print(f'Data size = {len(data):,}')\n\n # Map SMILES string to lists of targets\n smiles_in_order = []\n smiles_to_targets = defaultdict(list)\n for smiles, targets in zip(data.smiles(flatten=True), data.targets()):\n smiles_to_targets[smiles].append(targets)\n if len(smiles_to_targets[smiles]) == 1:\n smiles_in_order.append(smiles)\n\n # Find duplicates\n duplicate_count = 0\n stds = []\n new_data = []\n for smiles in smiles_in_order:\n all_targets = smiles_to_targets[smiles]\n duplicate_count += len(all_targets) - 1\n num_tasks = len(all_targets[0])\n\n targets_by_task = [[] for _ in range(num_tasks)]\n for task in range(num_tasks):\n for targets in all_targets:\n if targets[task] is not None:\n targets_by_task[task].append(targets[task])\n\n stds.append([np.std(task_targets) if len(task_targets) > 0 else 0.0 for task_targets in targets_by_task])\n means = [np.mean(task_targets) if len(task_targets) > 0 else None for task_targets in targets_by_task]\n new_data.append((smiles, means))\n\n print(f'Number of duplicates = {duplicate_count:,}')\n print(f'Duplicate standard deviation per task = {\", \".join(f\":{std:.4e}\" for std in np.mean(stds, axis=0))}')\n print(f'New data size = {len(new_data):,}')\n\n # Save new data\n with open(args.save_path, 'w') as f:\n f.write(','.join(header) + '\\n')\n\n for smiles, avg_targets in new_data:\n f.write(smiles + ',' + ','.join(str(value) if value is not None else '' for value in avg_targets) + '\\n')",
"title": ""
},
{
"docid": "8de712a8f2bc6e9bf7c8e857f147baa0",
"score": "0.45054013",
"text": "def merge(self):\n if len(self.n_best) <= 1:\n return\n else:\n new_n_best = self.n_best[:1]\n\n for cur_idx in xrange(1, len(self.n_best)):\n cur_hyp = self.n_best[cur_idx]\n for new_idx, new_hyp in enumerate(new_n_best):\n if new_hyp[1] == cur_hyp[1]:\n # Merge, add the probabilities.\n new_hyp[0] += cur_hyp[0]\n break\n else:\n new_n_best.append(cur_hyp)\n\n self.n_best = sorted(new_n_best, reverse=True)\n return self",
"title": ""
},
{
"docid": "13712eb74386e4f571d859c2d300e423",
"score": "0.44971138",
"text": "def get_merged(multitrack):\r\n track_lists_to_merge = [[] for _ in range(17)]\r\n for idx, track in enumerate(multitrack.tracks):\r\n if track.is_drum:\r\n track_lists_to_merge[0].append(idx)\r\n else:\r\n track_lists_to_merge[track.program//8 + 1].append(idx)\r\n\r\n tracks = []\r\n for idx, track_list_to_merge in enumerate(track_lists_to_merge):\r\n if track_list_to_merge:\r\n merged = multitrack[track_list_to_merge].get_merged_pianoroll('max')\r\n tracks.append(Track(merged, TRACK_INFO[idx][1], (idx == 0),\r\n TRACK_INFO[idx][0]))\r\n else:\r\n tracks.append(Track(None, TRACK_INFO[idx][1], (idx == 0),\r\n TRACK_INFO[idx][0]))\r\n return Multitrack(None, tracks, multitrack.tempo, multitrack.downbeat,\r\n multitrack.beat_resolution, multitrack.name)",
"title": ""
},
{
"docid": "d71d2406211439375a6d54e94c0c8a6f",
"score": "0.44917613",
"text": "def get_merged(multitrack):\n track_lists_to_merge = [[] for _ in range(5)]\n for idx, track in enumerate(multitrack.tracks):\n if track.is_drum:\n track_lists_to_merge[0].append(idx)\n elif track.program//8 == 0:\n track_lists_to_merge[1].append(idx)\n elif track.program//8 == 3:\n track_lists_to_merge[2].append(idx)\n elif track.program//8 == 4:\n track_lists_to_merge[3].append(idx)\n elif track.program < 96 or 104 <= track.program < 112:\n track_lists_to_merge[4].append(idx)\n\n tracks = []\n for idx, track_list_to_merge in enumerate(track_lists_to_merge):\n if track_list_to_merge:\n merged = multitrack[track_list_to_merge].get_merged_pianoroll('max')\n tracks.append(Track(merged, TRACK_INFO[idx][1], (idx == 0),\n TRACK_INFO[idx][0]))\n else:\n tracks.append(Track(None, TRACK_INFO[idx][1], (idx == 0),\n TRACK_INFO[idx][0]))\n return Multitrack(None, tracks, multitrack.tempo, multitrack.downbeat,\n multitrack.beat_resolution, multitrack.name)",
"title": ""
},
{
"docid": "ca1012b2aa5bbe2223394002e5651ee4",
"score": "0.4489591",
"text": "def aggregate(self, data_source: str, agg_rep: str = \"mean\", agg_time: str = \"mean\") -> pd.DataFrame:\n data = []\n for name, run in self.runs.items():\n tmp = run.aggregate(data_source, agg_rep=agg_rep, agg_time=agg_time)\n tmp[\"run\"] = name\n data.append(tmp)\n\n if agg_rep:\n data = pd.DataFrame(data)\n else:\n data = pd.concat(data)\n data = data.set_index(\"run\")\n return data",
"title": ""
},
{
"docid": "b6ce7f1435bb9e4a51594ecd8dc341c8",
"score": "0.4485769",
"text": "def combine(self, source1, source2):\n \n \n i = 1\n while source1._values != [] or source2._values != []:\n if i == 1:\n self._values.append(deepcopy(source1._values.pop()))\n if source2._values != []:\n i = 2\n elif i == 2:\n self._values.append(deepcopy(source2._values.pop()))\n if source1._values != []:\n i = 1\n \n return",
"title": ""
},
{
"docid": "0d8092f12b31cbb9fa3147ba0be55773",
"score": "0.44767585",
"text": "def build_repetition_counts(self):\n if self.repData is not None: return\n if self.bStatic:\n raise ValueError(\"Cannot build repetition counts in a static DataSet object\")\n self.repData = []\n for oliAr in self.oliData:\n self.repData.append(_np.ones(len(oliAr), self.repType))",
"title": ""
},
{
"docid": "bf73fb6878d461f97bcb990f8a806967",
"score": "0.44681942",
"text": "def combine(self, dataBatch):\n self.randomMasks()\n self.data[\"frame\"][\"A\"][self.mask[\"frame\"][\"A\"]] = dataBatch.data[\"frame\"][\"A\"][\n self.mask[\"frame\"][\"A\"]\n ]\n self.data[\"frame\"][\"B\"][self.mask[\"frame\"][\"B\"]] = dataBatch.data[\"frame\"][\"B\"][\n self.mask[\"frame\"][\"B\"]\n ]\n self.data[\"latent\"][\"A\"][self.mask[\"latent\"][\"A\"]] = dataBatch.data[\"latent\"][\n \"A\"\n ][self.mask[\"latent\"][\"A\"]]\n self.data[\"latent\"][\"B\"][self.mask[\"latent\"][\"B\"]] = dataBatch.data[\"latent\"][\n \"B\"\n ][self.mask[\"latent\"][\"B\"]]\n self.data[\"location\"][\"A\"][self.mask[\"location\"][\"A\"]] = dataBatch.data[\n \"location\"\n ][\"A\"][self.mask[\"location\"][\"A\"]]\n self.data[\"location\"][\"B\"][self.mask[\"location\"][\"B\"]] = dataBatch.data[\n \"location\"\n ][\"B\"][self.mask[\"location\"][\"B\"]]\n # self.data[\"output\"][self.mask[\"output\"]] = dataBatch.data[\"output\"][self.mask[\"output\"]]",
"title": ""
},
{
"docid": "5c7363d2d01355485af9117a5a7f5697",
"score": "0.44589022",
"text": "def merge(self, datasets=None, separate_datasets=False):\n self.logger.info(\"merging\")\n if separate_datasets:\n warnings.warn(\"The option seperate_datasets=True is\"\n \"not implemented yet. Performing merging, but\"\n \"neglecting the option.\")\n else:\n if datasets is None:\n datasets = list(range(len(self.datasets)))\n first = True\n for dataset_number in datasets:\n if first:\n dataset = self.datasets[dataset_number]\n first = False\n else:\n dataset = self._append(dataset, self.datasets[dataset_number])\n for raw_data_file, file_size in zip(self.datasets[dataset_number].raw_data_files,\n self.datasets[dataset_number].raw_data_files_length):\n dataset.raw_data_files.append(raw_data_file)\n dataset.raw_data_files_length.append(file_size)\n self.datasets = [dataset]\n self.number_of_datasets = 1\n return self",
"title": ""
}
]
a2705f464cacd4cac18658eb5c83b576
Get the abbreviated metaedges for an abbreviated metapath. Pass a hetio.MetaGraph object to `standardize_by` to standardize metaedge abbreviations based on the noninverted orietatation. Pass `text` to standardize by alphabetical/forwarddirection arrangment of the abbreviation. Default (`None`) does not standardize.
[
{
"docid": "d03621972f35fb52df1ff13fc44441a9",
"score": "0.7505954",
"text": "def metaedges_from_metapath(abbreviation, standardize_by=None):\n if isinstance(standardize_by, hetio.hetnet.MetaGraph):\n metapath = standardize_by.metapath_from_abbrev(abbreviation)\n return [metaedge.get_standard_abbrev() for metaedge in metapath]\n # Note that this is a valid regex module pattern but will not work in the\n # re module due to \"look-behind requires fixed-width pattern\".\n regex_string = r'(?<=^|[a-z<>])[A-Z][A-Z0-9]*[a-z<>]+[A-Z][A-Z0-9]*'\n pattern = regex.compile(regex_string)\n metaedge_abbrevs = pattern.findall(abbreviation, overlapped=True)\n if standardize_by is None:\n return metaedge_abbrevs\n elif standardize_by == 'text':\n metaedge_abbrevs = [arrange_metaedge(x) for x in metaedge_abbrevs]\n return metaedge_abbrevs\n else:\n raise ValueError('Invalid value for standardize_by')",
"title": ""
}
]
[
{
"docid": "f621f2899b05db266c51a54b3830006c",
"score": "0.5728711",
"text": "def create_abbreviations(metagraph):\n kind_to_abbrev = find_abbrevs(metagraph.node_dict.keys())\n kind_to_abbrev = {kind: abbrev.upper()\n for kind, abbrev in kind_to_abbrev.items()}\n\n edge_set_to_keys = dict()\n for edge in list(metagraph.edge_dict.keys()):\n key = frozenset(list(map(str.lower, edge[:2])))\n value = edge[2]\n edge_set_to_keys.setdefault(key, list()).append(value)\n\n for edge_set, keys in list(edge_set_to_keys.items()):\n key_to_abbrev = find_abbrevs(keys)\n for key, abbrev in list(key_to_abbrev.items()):\n previous_abbrev = kind_to_abbrev.get(key)\n if previous_abbrev and len(abbrev) <= len(previous_abbrev):\n continue\n kind_to_abbrev[key] = abbrev\n\n return kind_to_abbrev",
"title": ""
},
{
"docid": "2097d7789cb5d239344d5b74b592d78b",
"score": "0.5544935",
"text": "def arrange_metaedge(abbreviation):\n source, target = regex.split('[a-z<>]+', abbreviation)\n edge = regex.search('[a-z]+', abbreviation).group()\n if '<' in abbreviation or (source > target):\n source, target = target, source\n return '{}{}{}'.format(source, edge, target)",
"title": ""
},
{
"docid": "35d9cf412a587d9f4ad373e1efc163bc",
"score": "0.5312294",
"text": "def get_abbreviations(self) -> dict:\n\n return list(filter(lambda l: l.get('heading') == \"Abbreviations\", self.get_sections_texts())).pop()",
"title": ""
},
{
"docid": "c0d240365993df9e91a3fa5daea363d1",
"score": "0.50795704",
"text": "def validate_abbreviations(metagraph):\n valid = True\n metanodes = set(metagraph.get_nodes())\n metaedges = set(metagraph.get_edges(exclude_inverts=False))\n\n # Duplicated metanode and metaedge kinds\n metanode_kinds = {metanode.identifier for metanode in metanodes}\n metaedge_kinds = {metaedge.kind for metaedge in metaedges}\n duplicated_kinds = metanode_kinds & metaedge_kinds\n if duplicated_kinds:\n msg = 'Duplicated kinds between metanodes and metaedges: {}'\n print(msg.format(duplicated_kinds))\n valid = False\n\n # Check that metanodes do not have any duplicated abbreviations\n kind_to_abbrev = metagraph.kind_to_abbrev\n metanode_kind_to_abbrev = {\n k: v for k, v in kind_to_abbrev.items() if k in metanode_kinds}\n duplicated_metanode_abbrevs = get_duplicates(\n metanode_kind_to_abbrev.values())\n if duplicated_metanode_abbrevs:\n print('Duplicated metanode abbrevs:', duplicated_metanode_abbrevs)\n valid = False\n\n # Check metanode abbreviation violations\n for metanode in metanodes:\n abbrev = metanode.abbrev\n # metanode abbreviations should be uppercase\n if not abbrev.isupper():\n print('lowercase metanode abbreviation:', abbrev)\n valid = False\n # metanode abbreviation should not start with a digit\n if abbrev[0].isdigit():\n print('digit leading metanode abbreviation:', abbrev)\n valid = False\n\n # Check metaedge abbreviation violations\n for metaedge in metaedges:\n abbrev = metaedge.kind_abbrev\n # metaedge abbreviations should be lowercase\n if not abbrev.islower():\n print('uppercase metaedge abbreviation:', abbrev)\n valid = False\n # metaedge abbreviations should not contain digits\n if any(character.isdigit() for character in abbrev):\n print('digit in metaedge abbreviation:', abbrev)\n valid = False\n\n # Check that metaedges are not ambigious\n metaedge_abbrevs = [metaedge.abbrev for metaedge in metaedges]\n duplicated_meataedge_abbrevs = get_duplicates(metaedge_abbrevs)\n if duplicated_meataedge_abbrevs:\n msg = 'Duplicated metaedge abbreviations: {}'\n print(msg.format(duplicated_meataedge_abbrevs))\n valid = False\n\n return valid",
"title": ""
},
{
"docid": "2f055d622863aa5fa66b6b2bca0be090",
"score": "0.5062466",
"text": "def knowledge_area_abbreviation(ka):\n\n return {\n 'abbr': ka.title,\n 'title': ka.title[ka.title.find('(')+1:-1]\n }",
"title": ""
},
{
"docid": "100427076ad24cb402e00eb491b354d2",
"score": "0.4881498",
"text": "def _create_unit_abbreviations(text, *args):\n\n def _split(part):\n return [a for a, b in re.findall(r'(\\w+?)(\\b|(?<=[a-zäö])(?=[A-ZÄÖ]))', part)]\n\n def _variations(part):\n inner_parts = _split(part) + ['']\n\n spacecombos = list(itertools.product(['.', '. '], repeat=len(inner_parts) - 1))\n combined = [tuple(zip(inner_parts, spacecombo)) for spacecombo in spacecombos]\n combined_strings = [''.join(cc[0] + cc[1] for cc in inner).strip() for inner in combined]\n\n variations = list(set(combined_strings))\n variations += [' '.join(inner_parts)]\n variations += [''.join(inner_parts)]\n return sorted(variations)\n\n variation_lists = [_variations(part) + [part] for part in text.split('/')]\n\n combined_variations = sorted(set(['/'.join(combined).strip().replace(' /', '/')\n for combined in sorted(set(itertools.product(*variation_lists)))]))\n\n variationset = set(variation.strip() for var_list in variation_lists for variation in var_list\n if not re.search(r'^[0-9](\\.)?$', variation.strip()))\n\n return ' # '.join(combined_variations) + ' # ' + ' # '.join(sorted(variationset))",
"title": ""
},
{
"docid": "badfc61b32a9af6dc5537ec839085433",
"score": "0.48322722",
"text": "def _handle_abbreviations(s):\n # Format: abbrev = \"meaning\" gender (kk|kvk|hk)\n a = s.split('=', maxsplit=1)\n abbrev = a[0].strip()\n m = a[1].strip().split('\\\"')\n par = \"\"\n if len(m) >= 3:\n # Something follows the last quote\n par = m[-1].strip()\n gender = \"hk\" # Default gender is neutral\n fl = None # Default word category is None\n if par:\n p = par.split(' ')\n if len(p) >= 1:\n gender = p[0].strip()\n if len(p) >= 2:\n fl = p[1].strip()\n Abbreviations.add(abbrev, m[1], gender, fl)",
"title": ""
},
{
"docid": "c9dcd29c11145de62fbd00504d303d3c",
"score": "0.47770673",
"text": "def metaedge_id_from_abbreviation(metagraph, abbreviation):\n source_abbrev, target_abbrev = regex.split('[a-z<>]+', abbreviation)\n edge_abbrev = regex.search('[a-z<>]+', abbreviation).group()\n abbrev_to_kind = {v: k for k, v in metagraph.kind_to_abbrev.items()}\n source_kind = abbrev_to_kind[source_abbrev]\n target_kind = abbrev_to_kind[target_abbrev]\n metanode = metagraph.get_node(source_kind)\n for edge in metanode.edges:\n if edge.target.identifier != target_kind:\n continue\n if edge.kind_abbrev == edge_abbrev:\n kind = edge.kind\n break\n else:\n raise KeyError('edge abbreviation not found: {}'.format(edge_abbrev))\n if '>' in abbreviation:\n direction = 'forward'\n elif '<' in abbreviation:\n direction = 'backward'\n else:\n direction = 'both'\n return source_kind, target_kind, kind, direction",
"title": ""
},
{
"docid": "8d6d0616dcb6e1d8bc83c9ceafd77e15",
"score": "0.47619233",
"text": "def normalized_concept_name(lang, text):\n if lang == 'en':\n stem = normalize_english(text) or text\n return normalize_text(stem)\n else:\n return normalize_text(text)",
"title": ""
},
{
"docid": "6e2a056f75dccdefcf78a636fb8b68fd",
"score": "0.47287285",
"text": "def acronym(self, text):\r\n # Find the acronyms.\r\n acronyms = r'''(?P<acronym>[\\w]+)\\((?P<definition>[^\\(\\)]+?)\\)'''\r\n\r\n # Check all acronyms.\r\n for acronym, definition in re.findall(acronyms, text):\r\n caps_acronym = ''.join(re.findall('[A-Z\\d]+', acronym))\r\n caps_definition = ''.join(re.findall('[A-Z\\d]+', definition))\r\n if caps_acronym and caps_acronym == caps_definition:\r\n text = text.replace('%s(%s)' % (acronym, definition), '<acronym title=\"%s\">%s</acronym>' % (definition, acronym))\r\n \r\n text = html_replace(r'''(^|\\s)([A-Z]{3,})\\b(?!\\()''', r'''\\1<span class=\"caps\">\\2</span>''', text)\r\n\r\n return text",
"title": ""
},
{
"docid": "7b50836766b5307fdc8e08cc0db807c3",
"score": "0.45906082",
"text": "def extract_abbreviation(text):\n\tcur_offset = len(text)\n\tstart_index = -1\n\tgroup_count = 0\n\tbrace_count = 0\n\ttext_count = 0\n\t\n\twhile True:\n\t\tcur_offset -= 1\n\t\tif cur_offset < 0:\n\t\t\t# moved at string start\n\t\t\tstart_index = 0\n\t\t\tbreak\n\t\t\n\t\tch = text[cur_offset]\n\t\t\n\t\tif ch == ']':\n\t\t\tbrace_count += 1\n\t\telif ch == '[':\n\t\t\tif brace_count == 0: # unexpected brace\n\t\t\t\tstart_index = cur_offset + 1\n\t\t\t\tbreak\n\t\t\tbrace_count -= 1\n\t\telif ch == '}':\n\t\t\ttext_count += 1\n\t\telif ch == '{':\n\t\t\tif text_count == 0: # unexpected brace\n\t\t\t\tstart_index = cur_offset + 1\n\t\t\t\tbreak\n\t\t\ttext_count -= 1\n\t\telif ch == ')':\n\t\t\tgroup_count += 1\n\t\telif ch == '(':\n\t\t\tif group_count == 0: # unexpected brace\n\t\t\t\tstart_index = cur_offset + 1\n\t\t\t\tbreak\n\t\t\tgroup_count -= 1\n\t\telse:\n\t\t\tif brace_count or text_count:\n\t\t\t\t# respect all characters inside attribute sets\n\t\t\t\tcontinue\n\t\t\tif not is_allowed_char(ch) or (ch == '>' and is_ends_with_tag(text[0:cur_offset + 1])):\n\t\t\t\t# found stop symbol\n\t\t\t\tstart_index = cur_offset + 1\n\t\t\t\tbreak\n\t\t\n\tif start_index != -1 and start_index < len(text) and text_count == brace_count == group_count == 0:\n\t\treturn text[start_index:]\n\telse:\n\t\treturn ''",
"title": ""
},
{
"docid": "7882f9436793ea14552680ef4428aefd",
"score": "0.45717865",
"text": "def normalize(text):\n text = araby.strip_harakat(text)\n text = araby.strip_tashkeel(text)\n text = araby.strip_small(text)\n text = araby.strip_tatweel(text)\n text = araby.strip_shadda(text)\n text = araby.strip_diacritics(text)\n text = araby.normalize_ligature(text)\n text = araby.normalize_teh(text)\n text = araby.normalize_alef(text)\n return text",
"title": ""
},
{
"docid": "5bdf4f621d806277efbfb532c267668c",
"score": "0.45079258",
"text": "def _parse_abbreviation(self, abbr):\n if 'cfb/schools' not in str(abbr):\n return None\n abbr = re.sub(r'.*/schools/', '', str(abbr))\n abbr = re.sub(r'/.*', '', abbr)\n return abbr",
"title": ""
},
{
"docid": "8e64303ee44eda48ea2edff8d211f6a4",
"score": "0.4425654",
"text": "def standardize_adata_gene_names(adata_in, sp_2_letter):\n assert sp_2_letter in [\"hs\",\"mm\"]\n geneset, _ = retrieve_TADMap_by_species(sp_2_letter)\n return _match_adata_to_geneset(adata_in, geneset)",
"title": ""
},
{
"docid": "a20ee56c9ff80472faabe485053d094b",
"score": "0.44184145",
"text": "def clean_text(text):\n trantab = maketrans('%*@!$^+,:\\\"><&[()]\\';_=', ' ' * 21)\n # Hardcoded dictionary of some common abbreviations.\n hardcoded_dict = \\\n {'INV': 'Invasive',\n 'Inv': 'Invasive', 'AvDO2': 'avdo2', #'arteriojugular venous difference of oxygen'\n 'FiO2': 'fio2', 'CaO2': 'cao2', #'fraction inspired oxygen','arterial oxygen concentration'\n 's/p': 'status post', 'S/P': 'status post',\n 'D/C': 'discontinue', 'RLQ': 'Right Lower Quadrant of Abdomen',\n 'RUQ': 'Right Upper Quadrant of Abdomen',\n 'LLQ': 'Left Lower Quadrant of Abdomen',\n 'LUQ': 'Left Upper Quadrant of Abdomen',\n 'RUE': 'Right Upper Extremity',\n 'RLE': 'Right Lower Extremity', 'PO': 'by mouth',\n 'po': 'by mouth', 'airleak': 'air leak',\n 'mlns': 'ml normal saline solution', 'NS': 'normal saline solution',\n 'NSS': 'normal saline solution', 'nss': 'normal saline solution',\n 'ns': 'normal saline solution', '02sat': 'oxygen saturation',\n 'sat02': 'oxygen saturation', 'satO2': 'oxygen saturation',\n 'O2sat': 'oxygen saturation', 'LDH': 'Lactate dehydrogenase enzyme',\n 'art': 'arterial', 'ART': 'arterial', 'bp': 'blood pressure',\n 'BP': 'Blood Pressure',\n 'angio': 'angiography', 'NeoSure': 'neosure', 'NeoCate': 'neocate',\n 'PRBCS': 'Packed Red Blood Cells', 'PRBCs': 'Packed Red Blood Cells',\n 'PRBC\\'S': 'Packed Red Blood Cells',\n 'PRBC': 'Packed Red Blood Cells', 'prbc': 'Packed Red Blood Cells',\n 'BNP': 'Brain natiuretic Peptide',\n 'High PIP': 'High Peak Inspiratory Pressure',\n 'PIP': 'Peak Inspiratory Pressure',\n 'high pip': 'high peak inspiratory pressure',\n 'trach': 'tracheal', 'Trach': 'tracheal',\n 'baedp': 'Balloon Aortic End-Diastolic Pressure',\n 'baedp': 'balloon aortic end-diastolic pressure',\n 'kvo': 'keep vein open',\n 'KVO': 'Keep Vein Open', 'PTT': 'Partial Thromboplastin Time',\n 'ed': 'emergency department',\n 'LR': 'lactated Ringer\\'s solution',\n 'lr': 'lactated Ringer\\'s solution',\n '(L)': '(Left)', '(R)': '(Right)', 'Neg': 'negative',\n 'Insp': 'Inspiratory',\n 'Insp.': 'Inspiratory', 'LCW': 'Left Cardiac Work',\n 'LCWI': 'Left Cardiac Work Index',\n 'LVSW': 'Left Ventricular Stroke Work'}\n indx = []\n text_all = []\n acronyms = []\n for row in range(len(text)):\n if text.iloc[row] != '' and isinstance(text.iloc[row], str):\n indx.append(text.index.values[row])\n tmp = text.iloc[row].split(' ')\n for word in tmp:\n if sum(1 for c in word if c.isupper()) == len(word):\n acronyms.append(word)\n tmp = \" \".join(tmp)\n tmp = re.sub(r'\\b[a-z]', lambda m: m.group().upper(), tmp)\n text_all.append(tmp)\n text_split = pd.Series(text_all, index=indx)\n cleaned_text = text_split.str.split(\" \").apply(translate_words, args=[hardcoded_dict,])\n cleaned_text = cleaned_text.str.join(\" \").str.translate(trantab, '#').str.lower()\n return cleaned_text",
"title": ""
},
{
"docid": "a2e8ceb335fff945717e2ab7a1aee2e1",
"score": "0.43994313",
"text": "def _calc_abbrev(transitions):\n for transition in transitions:\n format = transition.format\n delta_seconds = transition.deltaSeconds\n\n index = format.find('/')\n if index >= 0:\n if delta_seconds == 0:\n abbrev = format[:index]\n else:\n abbrev = format[index + 1:]\n elif format.find('%s') >= 0:\n letter = transition.letter\n if letter == '-': letter = ''\n abbrev = format % letter\n else:\n abbrev = format\n\n transition.abbrev = abbrev",
"title": ""
},
{
"docid": "1bd6a77b4e43307d0eb503d88734453a",
"score": "0.43914795",
"text": "def _replace_abbreviations(self, text, split_multipart_abbrevs=True):\n replacements = {}\n text = self._replace_regex(text, self.single_letter_ellipsis, \"abbreviation\")\n text = self._replace_regex(text, self.and_cetera, \"abbreviation\")\n text = self._replace_regex(text, self.str_abbreviations, \"abbreviation\")\n text = self._replace_regex(text, self.nr_abbreviations, \"abbreviation\")\n text = self._replace_regex(text, self.single_token_abbreviation, \"abbreviation\")\n text = self._replace_regex(text, self.single_letter_abbreviation, \"abbreviation\")\n text = self.spaces.sub(\" \", text)\n text = self._replace_regex(text, self.ps, \"abbreviation\")\n\n def repl(match):\n instance = match.group(0)\n if instance not in replacements:\n # check if it is a multipart abbreviation\n if split_multipart_abbrevs and self.multipart_abbreviation.fullmatch(instance):\n parts = [p.strip() + \".\" for p in instance.strip(\".\").split(\".\")]\n replacements[instance] = self._multipart_replace(instance, parts, \"abbreviation\")\n else:\n replacement = replacements.setdefault(instance, self._get_unique_string())\n self.mapping[replacement] = Token(instance, \"abbreviation\")\n return \" %s \" % replacements[instance]\n text = self.abbreviation.sub(repl, text)\n # text = self._replace_set(text, self.simple_abbreviation_candidates, self.simple_abbreviations, \"abbreviation\", ignore_case=True)\n return text",
"title": ""
},
{
"docid": "8558de506d06d52de9d7d428c52507bb",
"score": "0.43890435",
"text": "def combine_bolds(graph_text):\n if graph_text.startswith(\"(\"):\n graph_text = (\n graph_text.replace(\" \", \" \")\n .replace(\"(\", \"**(\", 1)\n .replace(\")\", \")**\", 1)\n .replace(\"** **\", \" \", 1)\n )\n return graph_text",
"title": ""
},
{
"docid": "316e8d51f387390c203885819aba8fc0",
"score": "0.43853647",
"text": "def setUseAbbreviations(self, value):\n return self._set(useAbbreviations=value)",
"title": ""
},
{
"docid": "a5ec5109322b3878b7f20b125c0ff180",
"score": "0.43607435",
"text": "def get_abbrev(self):\n return self.__abbrev",
"title": ""
},
{
"docid": "84fcb33276077ad967e35272770d2137",
"score": "0.43299747",
"text": "def find_abbrevs(kinds):\n kind_to_abbrev = {kind: kind[0].lower() for kind in kinds}\n duplicates = get_duplicates(kind_to_abbrev.values())\n while duplicates:\n for kind, abbrev in list(kind_to_abbrev.items()):\n if abbrev in duplicates and len(abbrev) < len(kind):\n abbrev += kind[len(abbrev)].lower()\n kind_to_abbrev[kind] = abbrev\n duplicates = get_duplicates(kind_to_abbrev.values())\n return kind_to_abbrev",
"title": ""
},
{
"docid": "c507b09a10d74174ed34ff6d7c54701e",
"score": "0.429816",
"text": "def handle_disambig(text):\n # find titles of the form Foo (bar)\n text = text.replace('_', ' ').replace('/', ' ')\n while ' ' in text:\n text = text.replace(' ', ' ')\n match = re.match(r'([^(]+) \\((.+)\\)', text)\n if not match:\n return text, None\n else:\n return match.group(1), 'n/' + match.group(2).strip(' _')",
"title": ""
},
{
"docid": "bc8afb02c06fc27c2b9284fad40f3570",
"score": "0.4142849",
"text": "def __get__(self, c: \"NamingConventions\", owner) -> Optional[str]:\n\n p = c.metadata.platforms\n if not p:\n return None\n\n if not self.allow_unknown_abbreviations:\n unknowns = p.difference(self.known_abbreviations)\n if unknowns:\n raise ValueError(\n f\"We don't know the DEA abbreviation for platforms {unknowns!r}. \"\n f\"We'd love to add more! Raise an issue on Github: \"\n f\"https://github.com/GeoscienceAustralia/eo-datasets/issues/new' \"\n )\n\n abbreviations = sorted(\n self.known_abbreviations.get(s, s.replace(\"-\", \"\")) for s in p\n )\n\n if self.show_specific_platform and len(abbreviations) == 1:\n return abbreviations[0]\n\n # If all abbreviations are in a group, name it using that group.\n # (eg. \"ls\" instead of \"ls5-ls7-ls8\")\n for group_name, pattern in self.grouped_abbreviations.items():\n if all(pattern.match(a) for a in abbreviations):\n return group_name\n\n # Otherwise, there's a mix of platforms.\n\n # Is there a common constellation?\n constellation = c.metadata.properties.get(\"constellation\")\n if constellation:\n return constellation\n\n # Don't bother to include platform in name for un-groupable mixes of them.\n if not self.allow_unknown_abbreviations:\n raise NotImplementedError(\n f\"Satellite constellation abbreviation is not known for platforms {p}. \"\n f\"(for DEA derivative naming conventions.)\"\n f\" Is this a mistake? We'd love to add more! Raise an issue on Github: \"\n f\"https://github.com/GeoscienceAustralia/eo-datasets/issues/new' \"\n )\n return None",
"title": ""
},
{
"docid": "44547b32c006301d879e7ad27bb7e368",
"score": "0.413793",
"text": "def bold_first_italics(graph_text):\n if graph_text.count(\"*\") > 1:\n return graph_text.replace(\"*\", \"**\", 2)\n else:\n return graph_text",
"title": ""
},
{
"docid": "b2fc6339456fae323a4561a2624d7d3b",
"score": "0.41293293",
"text": "def normalize(self, text):\n if text is None:\n return None\n text = self.fix_text(text)\n\n # Normalize to canonical unicode (using NFKC by default)\n # if self.form is not None:\n # text = unicodedata.normalize(self.form, text)\n\n if self._remove_control_chars:\n text = self.remove_control_chars(text)\n\n # if self.fix_line_breaks:\n # text = text.replace('\\u2028', '\\n').replace('\\u2029', '\\n').replace('\\r\\n', '\\n').replace('\\r', '\\n')\n\n if self._fix_hyphens:\n text = self.fix_hyphens(text)\n\n if self._uncurl_quotes:\n text = self.uncurl_quotes(text)\n\n if self._fix_ellipsis:\n text = self.fix_ellipsis(text)\n\n if self._fix_slashes:\n text = self.fix_slashes(text)\n\n if self._fix_tildes:\n text = self.fix_tildes(text)\n\n if self._replace_tabs:\n text = self.replace_tabs(text, replacement_spaces=self._replacement_spaces)\n\n if self._fix_whitespaces:\n text = self.fix_whitespaces(text)\n\n if self._collapse_whitespaces:\n text = self.collapse_whitespaces(text)\n\n if self._strip:\n text = text.strip()\n\n if self._single_quotes_only:\n text = self.single_quotes_only(text)\n\n if self._regular_parentheses_only:\n text = self.regular_parentheses_only(text)\n\n if self._hanja2hangle:\n text = self.hanja2hangle(text)\n\n if self._fix_emoticons:\n text = self.emoticon_normalize(text, num_repeats=self._num_repeats)\n\n return text",
"title": ""
},
{
"docid": "fdb5aa00eb2b2f4a0dac037fec227052",
"score": "0.412086",
"text": "def replaced_abbreviations(sentence: str,\n abbreviation_pattern_to_full_text_map: Dict[str, str]) -> str:\n assert sentence is not None\n assert abbreviation_pattern_to_full_text_map is not None\n\n return _replaced('abbreviation', sentence, abbreviation_pattern_to_full_text_map)",
"title": ""
},
{
"docid": "5465138209d2f94b9a882c3524a1158b",
"score": "0.41156703",
"text": "def build_suffix_tree(text):\r\n result = []\r\n # Implement this function yourself\r\n tree = suffix_tree(text)\r\n for s in tree:\r\n result.append(s.label)\r\n \r\n return result",
"title": ""
},
{
"docid": "71aa5d2c926b2de30434914e32385e84",
"score": "0.41088402",
"text": "def _parse_team_abbreviation(self, stats):\n team_tag = stats(PLAYER_SCHEME['team_abbreviation'])\n team = re.sub(r'.*/cbb/schools/', '', str(team_tag('a')))\n team = re.sub(r'/.*', '', team)\n return team",
"title": ""
},
{
"docid": "e9971deaf5f7f34f3de4283dccbfa346",
"score": "0.41034454",
"text": "def test_abbreviate_returns_short_word(expected):\n # Setup - none necessary\n\n # Exercise\n result = wtlw.abbreviate(expected)\n\n # Verify\n assert result == expected\n\n # Cleanup - none",
"title": ""
},
{
"docid": "ae81cf8715bf29b8b4592d329ed7f386",
"score": "0.40943274",
"text": "def _get_abbrev(self, field_id, raw_value):\n values = self.abbreviations.get(field_id, None)\n\n # convert raw_value to a string since that's always what we want for line name input,\n # and for easy comparison against abbreviation keys which are converted to strings in\n # __init__() to avoid problems on the JS side (can't create string values as numbers)\n raw_value = str(raw_value)\n if not values:\n return raw_value\n\n abbreviation = values.get(raw_value)\n if abbreviation:\n # tolerate values that may have been provided as ints, for example\n return str(abbreviation)\n return raw_value",
"title": ""
},
{
"docid": "3793bb7a8e297fa442fd7a475acd0187",
"score": "0.4085441",
"text": "def title_except(\n s,\n exceptions=('the', 'of', 'for', 'and'),\n acronyms=('OIM', 'IT', 'PVS', 'SFM', 'OT', 'NP', 'FMDP', 'VRM', 'TEC', 'GIS', 'ODG'),\n):\n words = s.split()\n\n if words[0].startswith('A/'):\n words_title = ['A/' + words[0].replace('A/', '').capitalize()]\n elif words[0] in acronyms:\n words_title = [words[0]]\n else:\n words_title = [words[0].capitalize()]\n\n for word in words[1:]:\n word = word.lower()\n\n if word.startswith('('):\n pre = '('\n word = word.replace('(', '')\n else:\n pre = ''\n\n if word.endswith(')'):\n post = ')'\n word = word.replace(')', '')\n else:\n post = ''\n\n if word.upper() in acronyms:\n word = word.upper()\n elif word in exceptions:\n pass\n else:\n word = word.capitalize()\n\n words_title.append(pre + word + post)\n\n return ' '.join(words_title)",
"title": ""
},
{
"docid": "cbec6f5e15acb9ab855a27f85ec70d60",
"score": "0.4071853",
"text": "def func_normalize_text(text):\n # type: (str)->str\n if six.PY2:\n if isinstance(text, str):\n text = text.decode('utf-8')\n return jaconv.h2z(text=re.sub(r'\\s', '', string=text), kana=True, ascii=True, digit=True)\n else:\n return jaconv.h2z(text=re.sub(r'\\s', '', string=text), kana=True, ascii=True, digit=True)",
"title": ""
},
{
"docid": "dc997957fb59eb4600e1f0272a3b5e37",
"score": "0.40477467",
"text": "def normalize(self, text):\n return self.lemma_split(text)[0]",
"title": ""
},
{
"docid": "c20dc1efee0a3775010034b1674f0a5c",
"score": "0.40417555",
"text": "def map_abbrev_info(abbrev):\n \n if PLOT_ABBREV_MAP.has_key(abbrev):\n plot_type, subtitle, offsets_scale, orientation = PLOT_ABBREV_MAP[abbrev]\n else:\n plot_type, subtitle, offsets_scale, orientation = abbrev, 'NoMatch', [-4.85, 1.25, 0.76], 'landscape'\n return plot_type, subtitle, offsets_scale, orientation",
"title": ""
},
{
"docid": "242080837589c567c4e84c4b3c1ca5e6",
"score": "0.4035622",
"text": "def normalize_answer_allen(s):\n def remove_articles(text):\n return re.sub(r'\\b(a|an|the)\\b', ' ', text)\n\n def white_space_fix(text):\n return ' '.join(text.split())\n\n def remove_punc(text):\n exclude = set(string.punctuation)\n return ''.join(ch for ch in text if ch not in exclude)\n\n def lower(text):\n return text.lower()\n\n return white_space_fix(remove_articles(remove_punc(lower(s))))",
"title": ""
},
{
"docid": "7eef699801725fedeea546196cf68918",
"score": "0.40242097",
"text": "def generateAbbreviations(self, word: str):\n length = len(word)\n up = 1 << length\n res = []\n for num in range(up):\n tmp_str = ''\n counts = 0\n tmp_num = num\n for i in range(length-1, -1, -1):\n if tmp_num & 1:\n counts += 1\n else:\n tmp_str += str(counts)[::-1] if counts > 0 else ''\n tmp_str += word[i]\n counts = 0\n tmp_num = tmp_num >> 1\n tmp_str += str(counts)[::-1] if counts > 0 else ''\n res.append(tmp_str[::-1])\n return res",
"title": ""
},
{
"docid": "a95b3776038009a7db839dbaacdc307d",
"score": "0.40203413",
"text": "def get_tcga_abbr(self, long_name):\n with self.cadb:\n cur = self.cadb.cursor()\n\n name = cur.execute(\"SELECT Abbr FROM TCGA WHERE longName = ?\", (long_name,)).fetchone()\n\n if not name:\n return None\n\n return name[0]",
"title": ""
},
{
"docid": "d4cef77e6ce1040cdfd0e8da6665af60",
"score": "0.4015586",
"text": "def deanogham(string, alphabet=None):\n oghamdict = {\" \": \" \", \"-\": \" \", \"[\": \"[\", \"]\": \"]\", \"/\": \"/\", \".\": \".\", \"̣\": \"̣\", \"?\": \"?\", \"(\": \"(\", \")\": \")\",\n \"vac.\": \"vac.\", \"{\": \"{\", \"}\": \"}\", \"<\": \"<\", \">\": \">\", \"〚\": \"〚\", \"〛\": \"〛\"}\n if alphabet is None:\n alphabet = \"orthodox\"\n if alphabet != \"scholastic\":\n alphabet = \"orthodox\"\n if alphabet == \"orthodox\":\n oghamdict.update(aicmi)\n oghamdict.update(extendedaicmi)\n oghamdict.update(fada)\n elif alphabet == \"scholastic\":\n oghamdict.update(aicmi)\n oghamdict.update(forfeda)\n oghamdict.update(fada)\n \"\"\"Ogham does not distinguish between upper and lower case letters, all letters are upper case\"\"\"\n string = string.upper()\n \"\"\"all strings of ogham begin and end with designated markers\"\"\"\n string = (\"᚛\" + string[:] + \"᚜\")\n \"\"\"Identifies a list of characters which will not be removed during the conversion to ogham\"\"\"\n allowed = [\"᚛\", \" \", \"...\", \"…\", \"-\", \"\\n\", \"(\", \"᚜\", \"[\", \"]\", \".\"]\n for let in oghamdict:\n allowed.append(oghamdict.get(let))\n for item in removables:\n allowed.append(item)\n \"\"\"Finds and changes letter combinations which represent one ogham letter from latin alphabet to ogham\"\"\"\n for combo in lettercombos:\n if combo in oghamdict:\n if combo in string:\n comcount = string.count(combo)\n for i in range(comcount):\n compos = string.find(combo)\n string = string[:compos] + oghamdict.get(combo) + string[compos + len(combo):]\n \"\"\"Changes characters which exist in ogham from latin alphabet to ogham\"\"\"\n for character in string:\n charpos = string.find(character)\n if character in oghamdict:\n string = (string[:charpos]+oghamdict.get(character)+string[charpos+1:])\n \"\"\"Removes select non-letter characters\"\"\"\n if character in removables:\n string = (string[:charpos]+string[charpos+1:])\n \"\"\"Brackets off substrings of characters which cannot be translated to ogham\"\"\"\n newstring1 = string\n done1lets = []\n for character in newstring1:\n if character not in allowed:\n done1lets.append(character)\n charitir1 = done1lets.count(character)\n charno1 = charitir1 - 1\n charpos1 = findnth(newstring1, character, charno1)\n if newstring1[charpos1 - 1] in allowed:\n newstring1 = newstring1[:charpos1] + \"(\" + newstring1[charpos1:]\n newstring2 = newstring1\n done2lets = []\n for character in newstring2:\n if character not in allowed:\n done2lets.append(character)\n charitir2 = done2lets.count(character)\n charno2 = charitir2 - 1\n charpos2 = findnth(newstring2, character, charno2)\n if newstring2[charpos2 + 1] in allowed:\n newstring2 = newstring2[:charpos2 + 1] + \")\" + newstring2[charpos2 + 1:]\n if \"…\" in newstring2:\n splitstring = newstring2.split(\"…\")\n newstring2 = \"[...]\".join(splitstring)\n if \"[ ... ]\" in newstring2:\n splitstring = newstring2.split(\"[ ... ]\")\n newstring2 = \"[...]\".join(splitstring)\n if \"...\" in newstring2:\n splitstring = newstring2.split(\"...\")\n newstring2 = \"[...]\".join(splitstring)\n if \"\\n\" in newstring2:\n splitstring = newstring2.split(\"\\n\")\n newstring2 = \"᚜\\n᚛\".join(splitstring)\n if \"[[[\" in newstring2:\n splitstring = newstring2.split(\"[[[\")\n newstring2 = \"[\".join(splitstring)\n if \"]]]\" in newstring2:\n splitstring = newstring2.split(\"]]]\")\n newstring2 = \"]\".join(splitstring)\n if \"[[\" in newstring2:\n splitstring = newstring2.split(\"[[\")\n newstring2 = \"[\".join(splitstring)\n if \"]]\" in newstring2:\n splitstring = newstring2.split(\"]]\")\n newstring2 = \"]\".join(splitstring)\n if \".\" in newstring2:\n stoptest = \"\".join(newstring2.split(\"[...]\"))\n if \".\" in stoptest:\n splitstring = newstring2.split(\".\")\n newstring2 = \"(.)\".join(splitstring)\n if \"ᚃᚐᚉ(.)\" in newstring2:\n splitstring = newstring2.split(\"ᚃᚐᚉ(.)\")\n newstring2 = \"vac.\".join(splitstring)\n finalstring = newstring2\n return finalstring",
"title": ""
},
{
"docid": "f36a62b3e63835e4386eeba30e430e28",
"score": "0.4003113",
"text": "def parse(text):\n\n return [tokens for tokens in G(text).summarize() if tokens is not None]",
"title": ""
},
{
"docid": "d8ec716a9b4f4f26e85cc2f50e999880",
"score": "0.4002351",
"text": "def winning_abbr(self):\n if self.winner == HOME:\n if 'cfb/schools' not in str(self._home_name):\n return self._home_name.text()\n return utils._parse_abbreviation(self._home_name)\n if 'cfb/schools' not in str(self._away_name):\n return self._away_name.text()\n return utils._parse_abbreviation(self._away_name)",
"title": ""
},
{
"docid": "051f1d7c15d5118c469dccf9d0f90fe7",
"score": "0.3995614",
"text": "def abbrev(branch):\n if isinstance(branch, Branch):\n return p4gf_util.abbrev(branch.branch_id)\n return p4gf_util.abbrev(branch)",
"title": ""
},
{
"docid": "e1cb514557072800872316dfbd65d587",
"score": "0.3995111",
"text": "def ruleset_text(path):\n out = []\n for xpath, rules in standard_ruleset.items():\n try:\n # Use slice 1: to ensure we match /budget/ but not /total-budget/\n reduced_path = path.split(xpath[1:] + '/')[1]\n except IndexError:\n continue\n out += rules_text(rules, reduced_path)\n return out",
"title": ""
},
{
"docid": "8b37dbcd334a7a90f18bdcf6bbd622e8",
"score": "0.39807242",
"text": "def Akiyama():\n\n return FreeGroupAutomorphism(\"a->b,b->ac,c->a\")",
"title": ""
},
{
"docid": "56cd8a5ef8a9d7a54d2c29953fa0990a",
"score": "0.39721972",
"text": "def normalizeTitles (\r\n\r\n self,\r\n text = None\r\n ) :\r\n\r\n if utilities.isEmpty( text ) : return \"\"\r\n\r\n index = 0\r\n\r\n while True :\r\n\r\n index = text.find( self.headingCode + \" \", index )\r\n\r\n if index < 0 : break\r\n\r\n text = text.replace( self.headingCode + \" \", self.headingCode )\r\n\r\n index = 0\r\n\r\n while True :\r\n\r\n index = text.find( \" \" + self.headingCode, index )\r\n\r\n if index < 0 : break\r\n\r\n text = text.replace( \" \" + self.headingCode, self.headingCode )\r\n\r\n while True :\r\n\r\n index = text.find( \"=\" + self.heading5Code, index )\r\n\r\n if index < 0 : break\r\n\r\n text = text.replace( \"=\" + self.heading5Code, self.heading5Code )\r\n\r\n \r\n return text",
"title": ""
},
{
"docid": "03bcbd1d2fb54cd0f378c582bd4093fe",
"score": "0.39705688",
"text": "def generateAbbreviations(self, word: str):\n length = len(word)\n up = 1 << length\n res = []\n for num in range(up):\n tmp_str = ''\n counts = 0\n tmp_num = num\n for i in range(length):\n if tmp_num & 1:\n counts += 1\n else:\n tmp_str += str(counts) if counts > 0 else ''\n tmp_str += word[i]\n counts = 0\n tmp_num = tmp_num >> 1\n tmp_str += str(counts) if counts > 0 else ''\n res.append(tmp_str)\n return res",
"title": ""
},
{
"docid": "7c1a39a1c870bcea19c31cfcbc1cb14c",
"score": "0.39614338",
"text": "def get_abbreviation(syntax, abbr):\n\treturn zen_resources.get_abbreviation(syntax, abbr)",
"title": ""
},
{
"docid": "493ccb1af3bc6e29d2c07f38b110e675",
"score": "0.39538717",
"text": "def free_text_to_search_labels(self, text: str) -> List[str]:\n candidates = text.split(self._distinction_word)\n candidates = [c.strip() for c in candidates]\n\n joined_subsets = list()\n for c in candidates:\n text_list = [t for t in c.split(' ') if t not in self._distinction_word]\n subsets = list(chain.from_iterable(combinations(text_list, r) for r in range(len(text_list) + 1)))\n joined_subsets += [' '.join(s) for s in subsets if len(s) > 0]\n\n joined_subsets.append(text)\n return list(set(joined_subsets)) # prevent repeated elements",
"title": ""
},
{
"docid": "f000bc7591490b9ce489707740175350",
"score": "0.3952965",
"text": "def fix_hyphens(self, text):\n # TODO: Better normalization of em/en dashes to '--' if surrounded by spaces or start/end?\n if text is None:\n return None\n for hyphen in HYPHENS | MINUSES:\n text = text.replace(hyphen, \"-\")\n text = text.replace(\"\\u00ad\", \"\")\n return text",
"title": ""
},
{
"docid": "6ef834ec35c68462544c2a27b82a6bef",
"score": "0.39505887",
"text": "def _cleanup_label(label):\n conjunctions = ['and', 'but', 'yet', 'for', 'nor', 'so']\n little_preps = [\n 'at', 'by', 'in', 'of', 'on', 'to', 'up', 'as', 'it', 'or']\n articles = ['a', 'an', 'the']\n\n # remove the abbreviation\n lbl = label.split(r';')[0]\n\n fixedwords = []\n i = 0\n for wrd in lbl.split():\n i += 1\n # convert the roman numerals to numbers,\n # but assume that the first word is not\n # a roman numeral (this permits things like \"X inactivation\"\n if i > 1 and re.match(romanNumeralPattern, wrd):\n num = fromRoman(wrd)\n # make the assumption that the number of syndromes are <100\n # this allows me to retain \"SYNDROME C\"\n # and not convert it to \"SYNDROME 100\"\n if 0 < num < 100:\n # get the non-roman suffix, if present.\n # for example, IIIB or IVA\n suffix = wrd.replace(toRoman(num), '', 1)\n fixed = ''.join((str(num), suffix))\n wrd = fixed\n\n # capitalize first letter\n wrd = wrd.title()\n\n # replace interior conjunctions, prepositions,\n # and articles with lowercase\n if wrd.lower() in (conjunctions+little_preps+articles) and i != 1:\n wrd = wrd.lower()\n\n fixedwords.append(wrd)\n\n lbl = ' '.join(fixedwords)\n # print (label, '-->', lbl)\n return lbl",
"title": ""
},
{
"docid": "f67336597d6bd84d0c038d4dfe53fd1e",
"score": "0.39502653",
"text": "def graph_text_summarization(self, max_sents=config[\"app\"][\"text_summarization\"][\"max_num_sentences\"], min_sents=config[\"app\"][\"text_summarization\"][\"min_num_sentences\"], measure=config[\"app\"][\"text_summarization\"][\"measure\"], order_by_occurence=config[\"app\"][\"text_summarization\"][\"order_by_occurence\"]):\n item = config[self.show][\"text_summ_name\"][self.doc_type]\n results = aws.get_dynamo_data(item=item, table=config[\"aws\"][\"Dynamo_Table\"], resource=self.dynamo)\n # print(results)\n if results == \"No Response\" or self.clean_sents:\n partition_key = config[\"aws\"][\"partition_key\"]\n results = {partition_key:item}\n self.get_sentence_embeddings()\n for title, sent_embed in self.sentence_embeddings.items():\n # do graph stuff\n dists = 1-pairwise_distances(list(sent_embed.values()), metric=\"cosine\")\n dists = pd.DataFrame(dists, index = list(sent_embed.keys()), columns=list(sent_embed.keys()))\n G = nx.Graph()\n G.add_nodes_from(list(dists.columns))\n for i in list(dists.columns):\n for j in list(dists.index):\n if i==j or math.isnan(dists[i][j]) or dists[i][j] < self.sent_threshold:\n pass\n else:\n G.add_edge(i, j, weight=dists[i][j])\n dists[j][i] = None\n if measure == \"pagerank\":\n gr_dict = nx.pagerank(G)\n elif measure == \"betweenness centrality\":\n gr_dict = nx.betweenness_centrality(G, weight=\"weight\")\n elif measure == \"load centrality\":\n gr_dict = nx.load_centrality(G, weight=\"weight\")\n\n for key, value in gr_dict.items():\n gr_dict[key] = value*SemanticAlgos.sentence_length_multiplier(key)\n\n temp_dict = sort_dict(gr_dict)\n\n doc_embed=self.doc_embeddings[title]\n sorted_gr_dict = temp_dict[0:max_sents]\n summ_embedding_dict={}\n\n # finding optimal length for each summarization\n print(\"Finding Optimal n...\")\n for i in range(min_sents, max_sents+1):\n test_dict = dict(sorted_gr_dict[0:i])\n # print(list(self.sentence_embeddings.items()))\n test_dict = {key:self.sentence_embeddings[title][key] for key in list(test_dict.keys())}\n summ_embedding = np.mean(np.array(list(test_dict.values())), axis = 0)\n summ_embedding_dict.update({str(i):summ_embedding})\n \n embed_list = [doc_embed]+list(summ_embedding_dict.values())\n dists = 1-pairwise_distances(embed_list, metric=\"cosine\")[0]\n sim_dict = dict(zip(range(min_sents, max_sents+1), dists[1:len(dists)]))\n loss = [SemanticAlgos.get_loss(key, val) for key, val in sim_dict.items()]\n n = list(sim_dict.keys())[loss.index(min(loss))]\n if isinstance(n, list):\n n = n[0]\n print(\"Optimal Number of Sentences: \" + str(n))\n sorted_gr_dict = dict(temp_dict[0:n])\n if order_by_occurence:\n summary_dict = {x:gr_dict[x] for x in list(gr_dict.keys()) if x in list(sorted_gr_dict.keys())}\n else:\n summary_dict = sorted_gr_dict\n results.update({title:list(summary_dict.keys())})\n dynamo_table = self.dynamo.Table(config[\"aws\"][\"Dynamo_Table\"])\n dynamo_table.put_item(Item=results)\n return(results)",
"title": ""
},
{
"docid": "b030b2f0b4ce1441f4ca6bc8aad13271",
"score": "0.3941901",
"text": "def get_frequency(self, text):\n if not isinstance(text, unicode): text = text.decode('utf-8')\n words = self.tokenize(text).split(' ')\n for word in words:\n if self.canonicalize(word) in self.frequencies:\n return self.canonicalize(word)\n return None",
"title": ""
},
{
"docid": "b3b697e6a1f29e7d03828db80b399f32",
"score": "0.39381593",
"text": "def get_hyponyms(word, max_depth=None):\n synset = make_synset(word)\n if synset:\n hyponyms = synsets_words(all_hyponyms(synset, max_depth))\n else:\n hyponyms = None\n return hyponyms",
"title": ""
},
{
"docid": "396bfe742da53241e6f9657429669f7f",
"score": "0.39328012",
"text": "def __call__(self, text):\n return self.normalize(text)",
"title": ""
},
{
"docid": "1cecf7fe048c0008c29af21fcbf47685",
"score": "0.39302558",
"text": "def antonyms(word):\n \n la = []\n \n syn = _load(word)\n dWord = syn.get(word)\n if dWord is not None:\n ctxKeys = dWord.keys()\n relev = 3\n while relev > 0:\n for key in ctxKeys:\n dCtx = dWord.get(key)\n if isinstance(dCtx,dict):\n dSyn = dCtx.get(\"antonyms\")\n if dSyn.has_key(str(relev)):\n la.extend(dSyn.get(str(relev)))\n else:\n la.extend(antonyms(dCtx.encode(\"utf-8\")))\n relev = 0\n relev -= 1\n \n return _removeDuplicates(la)",
"title": ""
},
{
"docid": "f4410f126ad6159c7e86193d57ff7f7f",
"score": "0.392774",
"text": "def match_desc_all(self, string):\n ngram_descs = []\n node = self.desc_actrie\n for ch in string:\n node = node.move(ch)\n if node is None:\n node = self.desc_actrie\n else:\n ngram_descs.extend([match_string for match_string in \n node.generate_all_suffix_nodes_values()])\n return ngram_descs",
"title": ""
},
{
"docid": "b47f5a6f97fabe75a2fe7395c446e302",
"score": "0.39236796",
"text": "def getBiTrigrams(self, text):\n bigram_measures = cl.BigramAssocMeasures()\n trigram_measures = cl.TrigramAssocMeasures()\n tk = RegexpTokenizer(r'\\w+')\n\n st = LancasterStemmer()\n\n text = tk.tokenize(text.lower())\n job_vec = [st.stem(word) for word in text if word not in self.stopWords]\n\n bigrams = []\n trigrams = []\n collocations = cl.BigramCollocationFinder.from_words(job_vec)\n tri_collocations = cl.TrigramCollocationFinder.from_words(job_vec)\n top10 = collocations.score_ngrams(bigram_measures.raw_freq)\n top10 = sorted(bigram for bigram,score in top10)\n tri_top10 = tri_collocations.score_ngrams(bigram_measures.raw_freq)\n tri_top10 = sorted(trigram for trigram,score in tri_top10)\n for coll in top10:\n bigrams.append(coll[0] + ' ' + coll[1])\n for tri_coll in tri_top10:\n trigrams.append(tri_coll[0] + ' ' + tri_coll[1] + ' ' + tri_coll[2])\n\n return bigrams, trigrams",
"title": ""
},
{
"docid": "c8370e3255c5f4749ca3cb9130b1cbe4",
"score": "0.39226186",
"text": "def get_structural_variants(g):\n\n # get the genes.\n hugo = g['TRUE_HUGO_SYMBOL']\n k = list(hugo.keys())[0]\n genes = hugo[k]\n\n if not isinstance(genes, list):\n genes = [genes]\n\n # TODO add synonyms\n\n # encode as full search criteria.\n sv_clauses = []\n for gene in genes:\n abc = \"(.*\\W{0}\\W.*)|(^{0}\\W.*)|(.*\\W{0}$)\".format(gene)\n sv_clauses.append(re.compile(abc, re.IGNORECASE))\n\n # add it to filter and remove gene criteria.\n del g['TRUE_HUGO_SYMBOL']\n g['STRUCTURAL_VARIANT_COMMENT'] = {\"$in\": sv_clauses}\n\n return g",
"title": ""
},
{
"docid": "614bf08d0b656d2c6b40941335e1396e",
"score": "0.39219946",
"text": "def build_suffix_tree(text):\n result = []\n stree = suffixTree(text)\n\n for node in nodes:\n result.append(node.label)\n\n return result",
"title": ""
},
{
"docid": "62877535d02e176e6f6fd8db96ecfff6",
"score": "0.39149085",
"text": "def normalize(text, lang='', remove_articles=True):",
"title": ""
},
{
"docid": "c6ce067c36b24aeaee7042edd83b78b3",
"score": "0.3912082",
"text": "def normalize_text(self, text: str) -> str:\n assert text is not None, \"row is None\"\n\n # text = row.text\n # logger.debug(row.describe())\n logger.info(f'normalizing text: {text}')\n\n if text is not None and len(text) > 0:\n\n text = remove_amazon_tags(text)\n text = remove_http_links(text)\n\n if self.to_lowercase:\n text = tu.make_lowercase(text)\n if self.remove_newlines:\n text = tu.remove_newlines(text)\n if self.remove_html_tags:\n text = tu.remove_html_tags(text)\n if self.remove_accented_chars:\n text = tu.remove_accented_chars(text)\n if self.expand_contractions:\n text = tu.expand_contractions(text)\n if self.remove_special_chars:\n text = tu.remove_special_chars(text)\n # we have to do this after expanding contractions so it doesn't remove words like don't or shouldn't\n if self.remove_alphanumeric_words:\n text = tu.remove_alphanumeric_words(text)\n if self.remove_stop_words:\n text = tu.remove_stop_words(text)\n if self.stem_text:\n text = tu.stem_text(text)\n if self.lemmatize_text:\n text = tu.lemmatize_text(text)\n\n text = expand_star_ratings(text)\n\n logger.info(f'finished normalizing text: {text}')\n return text",
"title": ""
},
{
"docid": "88729d551a88a936da3f03940dfda93b",
"score": "0.3909781",
"text": "def home_away_abbrev(self):\n return 'H' if self.is_home else 'A'",
"title": ""
},
{
"docid": "703b1e0dcc6298cc97246a039ad028d3",
"score": "0.3907746",
"text": "def extract_abbreviation(view, loc):\n pt = -1\n region = None\n\n if isinstance(loc, (list, tuple)):\n loc = to_region(loc)\n\n if isinstance(loc, int):\n # Character location is passed, extract from line\n pt = loc\n region = view.line(pt)\n elif isinstance(loc, sublime.Region):\n # Extract from given range\n pt = loc.end()\n region = loc\n else:\n return None\n\n text = view.substr(region)\n begin = region.begin()\n opt = get_options(view, pt)\n\n if opt['type'] == 'stylesheet':\n # No look-ahead for stylesheets: they do not support brackets syntax\n # and enabled look-ahead produces false matches\n opt['lookAhead'] = False\n\n if opt['syntax'] == 'jsx':\n opt['prefix'] = view.settings().get('emmet_jsx_prefix', None)\n\n abbr_data = extract(text, pt - begin, opt)\n\n if abbr_data:\n abbr_data['start'] += begin\n abbr_data['end'] += begin\n abbr_data['location'] += begin\n return abbr_data, opt",
"title": ""
},
{
"docid": "ff0f53c90ab4a514a2579bbb439095a7",
"score": "0.39073408",
"text": "def team_abbreviation(self):\n return self._team_abbreviation",
"title": ""
},
{
"docid": "1c9236a6f2d54c4b41c115ad0c661b65",
"score": "0.39065203",
"text": "def _run_strip_accents(self, text):\n text = unicodedata.normalize(\"NFD\", text)\n output = []\n for char in text:\n cat = unicodedata.category(char)\n if cat == \"Mn\":\n continue\n output.append(char)\n return \"\".join(output)",
"title": ""
},
{
"docid": "f3fe86f315a33b74f1f4939d9b25bf6f",
"score": "0.38968197",
"text": "def read_MAG_taxonomy(args):\n\n #gtdb_file = os.path.join(args.b,'nc_gtdbtk_MQNC/gtdbtk.bac120.summary.tsv')\n gtdb_file = '07_binannotation/bacteria/nc_gtdbtk_MQNC/mag_taxonomy.txt'\n lineage = ['superkingdom','phylum','class','order','family','genus','species']\n #dlineage = {lin:None for lin in lineage}\n\n if not os.path.exists:\n print('No GTDBTK taxonomy available? Generate this file', gtdb_file)\n sys.exit(1)\n parsed_motherbins = set()\n MAG_tax = dict()\n with open(gtdb_file,'r') as infile:\n header = infile.readline()\n for line in infile:\n line = line.strip().split('\\t')\n genomeid = line[0]\n motherbin = genomeid.split('_')[1]\n if motherbin in parsed_motherbins:\n continue\n parsed_motherbins.add(motherbin)\n MAG_tax[motherbin] = {lin:None for lin in lineage}\n tax = line[2]\n \n tax = tax.split(';')\n tax = [ x.split('__')[1] for x in tax ]\n for i,lin in enumerate(lineage):\n entry = tax[i]\n if entry == '':\n entry = 'NA'\n if entry == 'Bacteroidota':\n entry = 'Bacteroidetes'\n \n MAG_tax[motherbin][lin] = entry\n return MAG_tax",
"title": ""
},
{
"docid": "5bd47f4ae79a5a8948e4725b51b5b4cb",
"score": "0.38956478",
"text": "def _run_strip_accents(self, text):\n text = unicodedata.normalize(\"NFD\", text)\n output = []\n for char in text:\n cat = unicodedata.category(char)\n if cat == \"Mn\":\n continue\n output.append(char)\n return \"\".join(output)",
"title": ""
},
{
"docid": "991ba8b3a8321e058451cccfe9d1ccc6",
"score": "0.38897467",
"text": "def _categorize(text, first, force=False):\n # If it's a regular word in second category, just add an 's'\n if force and not first and \"[\" not in text:\n return \"{}s\".format(text)\n\n # Handle words with optional suffices like `close[s]` and `sword[s]`\n while True:\n match = _RE_OPTIONAL_SUFFIX.search(text)\n if match is None:\n break\n\n before = text[0:match.start()]\n after = text[match.end():]\n if first:\n # Omit the optional part\n text = '%s%s' % (before, after)\n else:\n # Include the optional part\n text = '%s%s%s' % (before, match.group(1), after)\n\n # Handle irregular words like `[are|is]` and `sta[ff|aves]`\n while True:\n match = _RE_IRREGULAR.search(text)\n if match is None:\n break\n\n before = text[0:match.start()]\n after = text[match.end():]\n if first:\n # Use the first form\n text = '%s%s%s' % (before, match.group(1), after)\n else:\n # Use the second form\n text = '%s%s%s' % (before, match.group(2), after)\n\n return text",
"title": ""
},
{
"docid": "ae6e3934ce42bc6aff6bfc9cf24be2ec",
"score": "0.38846642",
"text": "def process_fasta(text):\n\n lines = text.split('\\n')\n lines = filter(lambda x: not x.startswith('>'), lines)\n lines = [line.strip() for line in lines]\n text = ''.join(lines)\n text = text.upper()\n return re.sub(r'[^ACGT]', 'T', text)",
"title": ""
},
{
"docid": "63e0efb65c721ab01eb80880b8155dde",
"score": "0.3875231",
"text": "def choose_title(text_blocks, config):\n ## Have to encode output when piping script. See: http://goo.gl/h0ql0\n for tb in text_blocks:\n if config.multiline:\n return ' '.join([t['text'] for t in tb['blockText']]).encode('utf-8')\n else:\n return tb['blockText'][0]['text'].encode('utf-8')\n return None",
"title": ""
},
{
"docid": "d04541a4bd6ffc0b2f2275f5adc82109",
"score": "0.3867616",
"text": "def normalize_answer(s):\n def remove_articles(text):\n return re.sub(r'\\b(a|an|the)\\b', ' ', text)\n\n def white_space_fix(text):\n return ' '.join(text.split())\n\n def remove_punc(text):\n exclude = set(string.punctuation)\n return ''.join(ch for ch in text if ch not in exclude)\n\n def lower(text):\n return text.lower()\n\n return white_space_fix(remove_articles(remove_punc(lower(s))))",
"title": ""
},
{
"docid": "d04541a4bd6ffc0b2f2275f5adc82109",
"score": "0.3867616",
"text": "def normalize_answer(s):\n def remove_articles(text):\n return re.sub(r'\\b(a|an|the)\\b', ' ', text)\n\n def white_space_fix(text):\n return ' '.join(text.split())\n\n def remove_punc(text):\n exclude = set(string.punctuation)\n return ''.join(ch for ch in text if ch not in exclude)\n\n def lower(text):\n return text.lower()\n\n return white_space_fix(remove_articles(remove_punc(lower(s))))",
"title": ""
},
{
"docid": "d04541a4bd6ffc0b2f2275f5adc82109",
"score": "0.3867616",
"text": "def normalize_answer(s):\n def remove_articles(text):\n return re.sub(r'\\b(a|an|the)\\b', ' ', text)\n\n def white_space_fix(text):\n return ' '.join(text.split())\n\n def remove_punc(text):\n exclude = set(string.punctuation)\n return ''.join(ch for ch in text if ch not in exclude)\n\n def lower(text):\n return text.lower()\n\n return white_space_fix(remove_articles(remove_punc(lower(s))))",
"title": ""
},
{
"docid": "d04541a4bd6ffc0b2f2275f5adc82109",
"score": "0.3867616",
"text": "def normalize_answer(s):\n def remove_articles(text):\n return re.sub(r'\\b(a|an|the)\\b', ' ', text)\n\n def white_space_fix(text):\n return ' '.join(text.split())\n\n def remove_punc(text):\n exclude = set(string.punctuation)\n return ''.join(ch for ch in text if ch not in exclude)\n\n def lower(text):\n return text.lower()\n\n return white_space_fix(remove_articles(remove_punc(lower(s))))",
"title": ""
},
{
"docid": "a7a22a15ac518dcd8eaea3c9502c3b4a",
"score": "0.3867443",
"text": "def parse(grammar, text, keep_posleafs=False):\n chart = build_chart(grammar, text)\n ret_trees = chart[1, len(text), \"S\"]\n if not keep_posleafs:\n for tree in ret_trees:\n replace_leafs_by_words(tree, text)\n return ret_trees",
"title": ""
},
{
"docid": "8a58070d1985fa536e0e8407835993b9",
"score": "0.3866564",
"text": "def is_abbreviation(self, word=\"\"):\n # Consider adding trim and remove last period to make the is_abbreviation function more universal\n return True if word.lower() in self.abbreviations else False",
"title": ""
},
{
"docid": "e386ce1464c8b9ee21d6e0d3ae523041",
"score": "0.3862285",
"text": "def normalize_answer(s):\n\n def remove_articles(text):\n return re.sub(r'\\b(a|an|the)\\b', ' ', text)\n\n def white_space_fix(text):\n return ' '.join(text.split())\n\n def remove_punc(text):\n exclude = set(string.punctuation)\n return ''.join(ch for ch in text if ch not in exclude)\n\n def lower(text):\n return text.lower()\n\n return white_space_fix(remove_articles(remove_punc(lower(s))))",
"title": ""
},
{
"docid": "e386ce1464c8b9ee21d6e0d3ae523041",
"score": "0.3862285",
"text": "def normalize_answer(s):\n\n def remove_articles(text):\n return re.sub(r'\\b(a|an|the)\\b', ' ', text)\n\n def white_space_fix(text):\n return ' '.join(text.split())\n\n def remove_punc(text):\n exclude = set(string.punctuation)\n return ''.join(ch for ch in text if ch not in exclude)\n\n def lower(text):\n return text.lower()\n\n return white_space_fix(remove_articles(remove_punc(lower(s))))",
"title": ""
},
{
"docid": "0e1d4986683a43d8b142f8f9249fd8cc",
"score": "0.38542402",
"text": "def normalize(self, text):\n return self.stem_tokens(nltk.word_tokenize(text.lower().translate(remove_punctuation_map)))",
"title": ""
},
{
"docid": "57b742da64a073217ad2653fb218fb0f",
"score": "0.38534823",
"text": "def run_strip_accents(self, text):\n text = unicodedata.normalize(\"NFD\", text)\n output = []\n for char in text:\n cat = unicodedata.category(char)\n if cat == \"Mn\":\n continue\n output.append(char)\n return \"\".join(output)",
"title": ""
},
{
"docid": "6342ed9b4ba9c8d447dc06481885a797",
"score": "0.3851088",
"text": "def normalize_answer(s):\n\n def remove_articles(text):\n return re.sub(r\"\\b(a|an|the)\\b\", \" \", text)\n\n def white_space_fix(text):\n return \" \".join(text.split())\n\n def remove_punc(text):\n exclude = set(string.punctuation)\n return \"\".join(ch for ch in text if ch not in exclude)\n\n def lower(text):\n return text.lower()\n\n return white_space_fix(remove_articles(remove_punc(lower(s))))",
"title": ""
},
{
"docid": "c1f867680295579a4b724bb77d6d1b05",
"score": "0.3847006",
"text": "def make_abbrevs(dbpath):\n with closing(sqlite3.connect(dbpath)) as dbconn:\n db = dbconn.cursor()\n if db.execute(\"select count(*) from sqlite_master where type='table' and name='abbrev'\").fetchone()[0] > 0:\n if ask_yes_no(sys.stderr, \"Table 'abbrev' already exists; do you wish to drop it?\"):\n db.execute('drop table abbrev')\n else:\n print >>sys.stderr, 'Aborting'\n return\n db.execute(\"\"\"create table abbrev (article_id integer,\n abbr text not null,\n exp text not null,\n foreign key (article_id) references article(id),\n unique(article_id, abbr, exp))\"\"\")\n db.execute('create index abbrev_article_id on abbrev(article_id)')\n db.execute('create index abbrev_abbr on abbrev(abbr)')\n db.execute('create index abbrev_exp on abbrev(exp)')\n\n\n # A generator function that yields abbreviations:\n def abbrevs():\n db2 = dbconn.cursor() # the other cursor is being used for inserting, must use a different one!\n\n # Go through all links and collect abbreviation--expansion pairs.\n\n links = db2.execute('select distinct L.text, A.title, A.id from link L join article a on L.tgt_id = A.id')\n\n n_done = 0\n for (a, b, idx) in links:\n if check_abbr(a, b):\n yield get_abbr(a, b, idx)\n n_done += 1\n\n if n_done % 1000 == 0:\n print >>sys.stderr, n_done, 'abbrevs collected'\n sys.stdout.flush()\n print >>sys.stderr, 'Done processing links'\n\n # Go through redirects.\n\n redirs = db2.execute('select distinct A.title, R.title, A.id from article A join article R on A.redirect_id = R.id')\n\n for (a, b, idx) in redirs:\n if check_abbr(a, b):\n yield get_abbr(a, b, idx)\n n_done += 1\n\n if n_done % 1000 == 0:\n print >>sys.stderr, n_done, 'abbrevs collected'\n print >>sys.stderr, 'Done processing redirects'\n\n db.executemany('insert or ignore into abbrev values(?,?,?)', abbrevs())\n dbconn.commit()",
"title": ""
},
{
"docid": "cf027a9ccba026c3114cefa08075a1d7",
"score": "0.3845125",
"text": "def normalize_answer(self, s):\n def remove_articles(text):\n return re.sub(r'\\b(a|an|the)\\b', ' ', text)\n\n def white_space_fix(text):\n return ' '.join(text.split())\n\n def remove_punc(text):\n exclude = set(string.punctuation)\n return ''.join(ch for ch in text if ch not in exclude)\n\n def lower(text):\n return text.lower()\n\n return white_space_fix(remove_articles(remove_punc(lower(s))))",
"title": ""
},
{
"docid": "cf027a9ccba026c3114cefa08075a1d7",
"score": "0.3845125",
"text": "def normalize_answer(self, s):\n def remove_articles(text):\n return re.sub(r'\\b(a|an|the)\\b', ' ', text)\n\n def white_space_fix(text):\n return ' '.join(text.split())\n\n def remove_punc(text):\n exclude = set(string.punctuation)\n return ''.join(ch for ch in text if ch not in exclude)\n\n def lower(text):\n return text.lower()\n\n return white_space_fix(remove_articles(remove_punc(lower(s))))",
"title": ""
},
{
"docid": "363e907b4dbc62fed3dc6a155b66c28c",
"score": "0.38446075",
"text": "def normalize_answer(s):\r\n\r\n def remove_articles(text):\r\n return re.sub(r'\\b(a|an|the)\\b', ' ', text)\r\n\r\n def white_space_fix(text):\r\n return ' '.join(text.split())\r\n\r\n def remove_punc(text):\r\n exclude = set(string.punctuation)\r\n return ''.join(ch for ch in text if ch not in exclude)\r\n\r\n def lower(text):\r\n return text.lower()\r\n\r\n return white_space_fix(remove_articles(remove_punc(lower(s))))",
"title": ""
},
{
"docid": "c466e4642be6020411d35b3987f2e164",
"score": "0.38414463",
"text": "def __get__(self, c: \"NamingConventions\", owner) -> Optional[str]:\n if not c.metadata.producer:\n return None\n\n try:\n return self.known_abbreviations[c.metadata.producer]\n except KeyError:\n raise NotImplementedError(\n f\"We don't know how to abbreviate organisation domain name {c.metadata.producer!r}. \"\n f\"We'd love to add more orgs! Raise an issue on Github: \"\n f\"https://github.com/GeoscienceAustralia/eo-datasets/issues/new' \"\n )",
"title": ""
},
{
"docid": "b75023a02bef57c74c7677d70551afab",
"score": "0.3837186",
"text": "def _run_strip_accents(text):\n text = unicodedata.normalize(\"NFD\", text)\n output = []\n for char in text:\n cat = unicodedata.category(char)\n if cat == \"Mn\":\n continue\n output.append(char)\n return \"\".join(output)",
"title": ""
},
{
"docid": "1f4402714565dfd2b05e57ea33b264ab",
"score": "0.38370943",
"text": "def create_tags(text,a):\n a = a.lower()\n tags = []\n inside = False\n for w in text.split():\n w_stripped = w.strip()\n if w_stripped == 'BEG____END':\n continue\n if w_stripped.startswith(\"BEG__\") and w_stripped.endswith(\"__END\"):\n concept = w_stripped.split(\"_\")[2]\n if a in concept.lower():\n tags.append('B-ans')\n if inside: # something went wrong, leave as is\n print(\"Inconsistent markup.\")\n else:\n tags.append('O')\n elif w_stripped.startswith(\"BEG__\"):\n assert not inside\n inside = True\n concept = [w_stripped.split(\"_\", 2)[-1]]\n\n elif w_stripped.endswith(\"__END\"):\n if not inside:\n if a in w_stripped[:-5].lower():\n tags.append('I') #might be B\n else:\n tags.append('O')\n else:\n concept.append(w_stripped.rsplit(\"_\", 2)[0])\n if a in ' '.join(concept).lower():\n tags.append('B-ans')\n for w in concept:\n tags.append('I-ans')\n tags.pop(-1)\n else:\n for w in concept:\n tags.append('O')\n inside = False\n else:\n if inside:\n concept.append(w_stripped)\n else:\n tags.append('O')\n\n return ' '.join(tags)",
"title": ""
},
{
"docid": "a9d4e3996d0614c5f72d66e061f441ed",
"score": "0.38356382",
"text": "def losing_abbr(self):\n if self.winner == HOME:\n if 'cfb/schools' not in str(self._away_name):\n return self._away_name.text()\n return utils._parse_abbreviation(self._away_name)\n if 'cfb/schools' not in str(self._home_name):\n return self._home_name.text()\n return utils._parse_abbreviation(self._home_name)",
"title": ""
},
{
"docid": "b7e4d95cf6d2fe54591a0fab6005e583",
"score": "0.3832994",
"text": "def convertBezToOutline(ufoFontData, glyphName, bezString):\n # convert bez data to a UFO glif XML representation\n #\n # Convert all bez ops to simplest UFO equivalent\n # Add all hints to vertical and horizontal hint lists as encountered;\n # insert a HintMask class whenever a new set of hints is encountered\n # after all operators have been processed, convert HintMask items into\n # hintmask ops and hintmask bytes add all hints as prefix review operator\n # list to optimize T2 operators.\n # if useStem3 == 1, then any counter hints must be processed as stem3\n # hints, else the opposite.\n # Counter hints are used only in LanguageGroup 1 glyphs, aka ideographs\n\n bezString = re.sub(r\"%.+?\\n\", \"\", bezString) # supress comments\n bezList = re.findall(r\"(\\S+)\", bezString)\n if not bezList:\n return \"\", None, None\n flexList = []\n # Create an initial hint mask. We use this if\n # there is no explicit initial hint sub.\n hintMask = HintMask(0)\n hintMaskList = [hintMask]\n vStem3Args = []\n hStem3Args = []\n argList = []\n opList = []\n newHintMaskName = None\n inPreFlex = False\n hintInfoDict = None\n opIndex = 0\n curX = 0\n curY = 0\n newOutline = XMLElement(\"outline\")\n outlineItem = None\n seenHints = False\n\n for token in bezList:\n try:\n val = float(token)\n argList.append(val)\n continue\n except ValueError:\n pass\n if token == \"newcolors\":\n pass\n elif token in [\"beginsubr\", \"endsubr\"]:\n pass\n elif token in [\"snc\"]:\n hintMask = HintMask(opIndex)\n # If the new hints precedes any marking operator,\n # then we want throw away the initial hint mask we\n # made, and use the new one as the first hint mask.\n if opIndex == 0:\n hintMaskList = [hintMask]\n else:\n hintMaskList.append(hintMask)\n newHintMaskName = hintMask.pointName\n elif token in [\"enc\"]:\n pass\n elif token == \"div\":\n value = argList[-2] / float(argList[-1])\n argList[-2:] = [value]\n elif token == \"rb\":\n if newHintMaskName is None:\n newHintMaskName = hintMask.pointName\n hintMask.hList.append(argList)\n argList = []\n seenHints = True\n elif token == \"ry\":\n if newHintMaskName is None:\n newHintMaskName = hintMask.pointName\n hintMask.vList.append(argList)\n argList = []\n seenHints = True\n elif token == \"rm\": # vstem3's are vhints\n if newHintMaskName is None:\n newHintMaskName = hintMask.pointName\n seenHints = True\n vStem3Args.append(argList)\n argList = []\n if len(vStem3Args) == 3:\n hintMask.vstem3List.append(vStem3Args)\n vStem3Args = []\n\n elif token == \"rv\": # hstem3's are hhints\n seenHints = True\n hStem3Args.append(argList)\n argList = []\n if len(hStem3Args) == 3:\n hintMask.hstem3List.append(hStem3Args)\n hStem3Args = []\n\n elif token == \"preflx1\":\n # the preflx1/preflx2 sequence provides the same i as the flex\n # sequence; the difference is that the preflx1/preflx2 sequence\n # provides the argument values needed for building a Type1 string\n # while the flex sequence is simply the 6 rcurveto points. Both\n # sequences are always provided.\n argList = []\n # need to skip all move-tos until we see the \"flex\" operator.\n inPreFlex = True\n elif token == \"preflx2a\":\n argList = []\n elif token == \"preflx2\":\n argList = []\n elif token == \"flxa\": # flex with absolute coords.\n inPreFlex = False\n flexPointName = kBaseFlexName + str(opIndex).zfill(4)\n flexList.append(flexPointName)\n curveCnt = 2\n i = 0\n # The first 12 args are the 6 args for each of\n # the two curves that make up the flex feature.\n while i < curveCnt:\n curX = argList[0]\n curY = argList[1]\n showX, showY = convertCoords(curX, curY)\n newPoint = XMLElement(\n \"point\", {\"x\": \"%s\" % showX, \"y\": \"%s\" % showY})\n outlineItem.append(newPoint)\n curX = argList[2]\n curY = argList[3]\n showX, showY = convertCoords(curX, curY)\n newPoint = XMLElement(\n \"point\", {\"x\": \"%s\" % showX, \"y\": \"%s\" % showY})\n outlineItem.append(newPoint)\n curX = argList[4]\n curY = argList[5]\n showX, showY = convertCoords(curX, curY)\n opName = 'curve'\n newPoint = XMLElement(\n \"point\", {\"x\": \"%s\" % showX, \"y\": \"%s\" % showY,\n \"type\": opName})\n outlineItem.append(newPoint)\n opList.append([opName, curX, curY])\n opIndex += 1\n if i == 0:\n argList = argList[6:12]\n i += 1\n # attach the point name to the first point of the first curve.\n outlineItem[-6].set(kPointName, flexPointName)\n if newHintMaskName is not None:\n # We have a hint mask that we want to attach to the first\n # point of the flex op. However, there is already a flex\n # name in that attribute. What we do is set the flex point\n # name into the hint mask.\n hintMask.pointName = flexPointName\n newHintMaskName = None\n argList = []\n elif token == \"flx\":\n inPreFlex = False\n flexPointName = kBaseFlexName + str(opIndex).zfill(4)\n flexList.append(flexPointName)\n curveCnt = 2\n i = 0\n # The first 12 args are the 6 args for each of\n # the two curves that make up the flex feature.\n while i < curveCnt:\n curX += argList[0]\n curY += argList[1]\n showX, showY = convertCoords(curX, curY)\n newPoint = XMLElement(\n \"point\", {\"x\": \"%s\" % showX, \"y\": \"%s\" % showY})\n outlineItem.append(newPoint)\n curX += argList[2]\n curY += argList[3]\n showX, showY = convertCoords(curX, curY)\n newPoint = XMLElement(\n \"point\", {\"x\": \"%s\" % showX, \"y\": \"%s\" % showY})\n outlineItem.append(newPoint)\n curX += argList[4]\n curY += argList[5]\n showX, showY = convertCoords(curX, curY)\n opName = 'curve'\n newPoint = XMLElement(\n \"point\", {\"x\": \"%s\" % showX, \"y\": \"%s\" % showY,\n \"type\": opName})\n outlineItem.append(newPoint)\n opList.append([opName, curX, curY])\n opIndex += 1\n if i == 0:\n argList = argList[6:12]\n i += 1\n # attach the point name to the first point of the first curve.\n outlineItem[-6].set(kPointName, flexPointName)\n if newHintMaskName is not None:\n # We have a hint mask that we want to attach to the first\n # point of the flex op. However, there is already a flex\n # name in that attribute. What we do is set the flex point\n # name into the hint mask.\n hintMask.pointName = flexPointName\n newHintMaskName = None\n argList = []\n elif token == \"sc\":\n pass\n elif token == \"cp\":\n pass\n elif token == \"ed\":\n pass\n else:\n if inPreFlex and (token[-2:] == \"mt\"):\n continue\n\n if token[-2:] in [\"mt\", \"dt\", \"ct\", \"cv\"]:\n opIndex += 1\n else:\n raise BezParseError(\n \"Unhandled operation: '%s' '%s'.\" % (argList, token))\n dx = dy = 0\n opName = bezToUFOPoint[token]\n if token[-2:] in [\"mt\", \"dt\"]:\n if token in [\"mt\", \"dt\"]:\n curX = argList[0]\n curY = argList[1]\n else:\n if token in [\"rmt\", \"rdt\"]:\n dx = argList[0]\n dy = argList[1]\n elif token in [\"hmt\", \"hdt\"]:\n dx = argList[0]\n elif token in [\"vmt\", \"vdt\"]:\n dy = argList[0]\n curX += dx\n curY += dy\n showX, showY = convertCoords(curX, curY)\n newPoint = XMLElement(\n \"point\", {\"x\": \"%s\" % showX, \"y\": \"%s\" % showY,\n \"type\": \"%s\" % opName})\n\n if opName == \"move\":\n if outlineItem is not None:\n if len(outlineItem) == 1:\n # Just in case we see two moves in a row,\n # delete the previous outlineItem if it has\n # only the move-to\n log.info(\"Deleting moveto: %s adding %s\",\n xmlToString(newOutline[-1]),\n xmlToString(outlineItem))\n del newOutline[-1]\n else:\n # Fix the start/implied end path\n # of the previous path.\n fixStartPoint(outlineItem, opList)\n opList = []\n outlineItem = XMLElement('contour')\n newOutline.append(outlineItem)\n\n if newHintMaskName is not None:\n newPoint.set(kPointName, newHintMaskName)\n newHintMaskName = None\n outlineItem.append(newPoint)\n opList.append([opName, curX, curY])\n else:\n if token in [\"ct\", \"cv\"]:\n curX = argList[0]\n curY = argList[1]\n showX, showY = convertCoords(curX, curY)\n newPoint = XMLElement(\n \"point\", {\"x\": \"%s\" % showX, \"y\": \"%s\" % showY})\n outlineItem.append(newPoint)\n curX = argList[2]\n curY = argList[3]\n showX, showY = convertCoords(curX, curY)\n newPoint = XMLElement(\n \"point\", {\"x\": \"%s\" % showX, \"y\": \"%s\" % showY})\n outlineItem.append(newPoint)\n curX = argList[4]\n curY = argList[5]\n showX, showY = convertCoords(curX, curY)\n newPoint = XMLElement(\n \"point\", {\"x\": \"%s\" % showX, \"y\": \"%s\" % showY,\n \"type\": \"%s\" % opName})\n outlineItem.append(newPoint)\n else:\n if token in [\"rct\", \"rcv\"]:\n curX += argList[0]\n curY += argList[1]\n showX, showY = convertCoords(curX, curY)\n newPoint = XMLElement(\n \"point\", {\"x\": \"%s\" % showX, \"y\": \"%s\" % showY})\n outlineItem.append(newPoint)\n curX += argList[2]\n curY += argList[3]\n showX, showY = convertCoords(curX, curY)\n newPoint = XMLElement(\n \"point\", {\"x\": \"%s\" % showX, \"y\": \"%s\" % showY})\n outlineItem.append(newPoint)\n curX += argList[4]\n curY += argList[5]\n showX, showY = convertCoords(curX, curY)\n newPoint = XMLElement(\n \"point\", {\"x\": \"%s\" % showX, \"y\": \"%s\" % showY,\n \"type\": \"%s\" % opName})\n outlineItem.append(newPoint)\n elif token == \"vhct\":\n curY += argList[0]\n showX, showY = convertCoords(curX, curY)\n newPoint = XMLElement(\n \"point\", {\"x\": \"%s\" % showX, \"y\": \"%s\" % showY})\n outlineItem.append(newPoint)\n curX += argList[1]\n curY += argList[2]\n showX, showY = convertCoords(curX, curY)\n newPoint = XMLElement(\n \"point\", {\"x\": \"%s\" % showX, \"y\": \"%s\" % showY})\n outlineItem.append(newPoint)\n curX += argList[3]\n showX, showY = convertCoords(curX, curY)\n newPoint = XMLElement(\n \"point\", {\"x\": \"%s\" % showX, \"y\": \"%s\" % showY,\n \"type\": \"%s\" % opName})\n outlineItem.append(newPoint)\n elif token == \"hvct\":\n curX += argList[0]\n showX, showY = convertCoords(curX, curY)\n newPoint = XMLElement(\n \"point\", {\"x\": \"%s\" % showX, \"y\": \"%s\" % showY})\n outlineItem.append(newPoint)\n curX += argList[1]\n curY += argList[2]\n showX, showY = convertCoords(curX, curY)\n newPoint = XMLElement(\n \"point\", {\"x\": \"%s\" % showX, \"y\": \"%s\" % showY})\n outlineItem.append(newPoint)\n curY += argList[3]\n showX, showY = convertCoords(curX, curY)\n newPoint = XMLElement(\n \"point\", {\"x\": \"%s\" % showX, \"y\": \"%s\" % showY,\n \"type\": \"%s\" % opName})\n outlineItem.append(newPoint)\n if newHintMaskName is not None:\n # attach the pointName to the first point of the curve.\n outlineItem[-3].set(kPointName, newHintMaskName)\n newHintMaskName = None\n opList.append([opName, curX, curY])\n argList = []\n\n if outlineItem is not None:\n if len(outlineItem) == 1:\n # Just in case we see two moves in a row, delete\n # the previous outlineItem if it has zero length.\n del newOutline[-1]\n else:\n fixStartPoint(outlineItem, opList)\n\n # add hints, if any\n # Must be done at the end of op processing to make sure we have seen\n # all the hints in the bez string.\n # Note that the hintmasks are identified in the opList by the point name.\n # We will follow the T1 spec: a glyph may have stem3 counter hints or\n # regular hints, but not both.\n\n if (seenHints) or (len(flexList) > 0):\n hintInfoDict = XMLElement(\"dict\")\n\n idItem = XMLElement(\"key\")\n idItem.text = \"id\"\n hintInfoDict.append(idItem)\n\n idString = XMLElement(\"string\")\n idString.text = \"id\"\n hintInfoDict.append(idString)\n\n hintSetListItem = XMLElement(\"key\")\n hintSetListItem.text = kHintSetListName\n hintInfoDict.append(hintSetListItem)\n\n hintSetListArray = XMLElement(\"array\")\n hintInfoDict.append(hintSetListArray)\n # Convert the rest of the hint masks\n # to a hintmask op and hintmask bytes.\n for hintMask in hintMaskList:\n hintMask.addHintSet(hintSetListArray)\n\n if len(flexList) > 0:\n hintSetListItem = XMLElement(\"key\")\n hintSetListItem.text = kFlexIndexListName\n hintInfoDict.append(hintSetListItem)\n\n flexArray = XMLElement(\"array\")\n hintInfoDict.append(flexArray)\n addFlexHint(flexList, flexArray)\n\n return newOutline, hintInfoDict",
"title": ""
},
{
"docid": "9070cbbabad88583cfc4e070ee679808",
"score": "0.38319367",
"text": "def given_names_or_middle_abbrev(tok):\r\n gnames = given_names(tok)\r\n if gnames is not None:\r\n return gnames\r\n if tok.kind != TOK.WORD:\r\n return None\r\n wrd = tok.txt\r\n if wrd.startswith('['):\r\n # Abbreviation: Cut off the brackets & trailing period\r\n wrd = wrd[1:-2]\r\n if len(wrd) > 2 or not wrd[0].isupper():\r\n if wrd not in { \"van\", \"de\", \"of\" }:\r\n # Accept \"Thomas de Broglie\", \"Ruud van Nistelroy\", \"Mary of Canterbury\"\r\n return None\r\n # One or two letters, capitalized: accept as middle name abbrev,\r\n # all genders and cases possible\r\n return [PersonName(name = wrd, gender = None, case = None)]",
"title": ""
},
{
"docid": "f5d2587f91c5cb90845d2adbdef9db53",
"score": "0.3831338",
"text": "def semantic_head(self, text):\n return str(list(self.nlp(text).sents)[0].root)",
"title": ""
},
{
"docid": "41447acb31f8332874b6c94d118e357f",
"score": "0.38295537",
"text": "def normalize_answer(s):\n def remove_articles(text):\n regex = re.compile(r'\\b(a|an|the)\\b', re.UNICODE)\n return re.sub(regex, ' ', text)\n def white_space_fix(text):\n return ' '.join(text.split())\n def remove_punc(text):\n exclude = set(string.punctuation)\n return ''.join(ch for ch in text if ch not in exclude)\n def lower(text):\n return text.lower()\n return white_space_fix(remove_articles(remove_punc(lower(s))))",
"title": ""
},
{
"docid": "ba6e05aa4cbbb517b1a56b2d549689e9",
"score": "0.3819811",
"text": "def _str_title(self):\n tmp = \"a\"\n if isinstance(self.graph, UndirectedGraph):\n tmp = \"an\"\n return \"GMRF model on {} {}\".format(tmp, self.graph)",
"title": ""
},
{
"docid": "77e39fce3500d51f478fcc6408fd277a",
"score": "0.3816846",
"text": "def normalize_answer(s):\n\n def remove_articles(text):\n regex = re.compile(r\"\\b(a|an|the)\\b\", re.UNICODE)\n return re.sub(regex, \" \", text)\n\n def white_space_fix(text):\n return \" \".join(text.split())\n\n def remove_punc(text):\n exclude = set(string.punctuation)\n return \"\".join(ch for ch in text if ch not in exclude)\n\n def lower(text):\n return text.lower()\n\n return white_space_fix(remove_articles(remove_punc(lower(s))))",
"title": ""
},
{
"docid": "77e39fce3500d51f478fcc6408fd277a",
"score": "0.3816846",
"text": "def normalize_answer(s):\n\n def remove_articles(text):\n regex = re.compile(r\"\\b(a|an|the)\\b\", re.UNICODE)\n return re.sub(regex, \" \", text)\n\n def white_space_fix(text):\n return \" \".join(text.split())\n\n def remove_punc(text):\n exclude = set(string.punctuation)\n return \"\".join(ch for ch in text if ch not in exclude)\n\n def lower(text):\n return text.lower()\n\n return white_space_fix(remove_articles(remove_punc(lower(s))))",
"title": ""
},
{
"docid": "77e39fce3500d51f478fcc6408fd277a",
"score": "0.3816846",
"text": "def normalize_answer(s):\n\n def remove_articles(text):\n regex = re.compile(r\"\\b(a|an|the)\\b\", re.UNICODE)\n return re.sub(regex, \" \", text)\n\n def white_space_fix(text):\n return \" \".join(text.split())\n\n def remove_punc(text):\n exclude = set(string.punctuation)\n return \"\".join(ch for ch in text if ch not in exclude)\n\n def lower(text):\n return text.lower()\n\n return white_space_fix(remove_articles(remove_punc(lower(s))))",
"title": ""
},
{
"docid": "2ec76bdd51bc3b8dd1dbafcb1d150ed6",
"score": "0.38141522",
"text": "def punkt(self, text):\n\t\tpunkt_param = PunktParameters()\n\t\tabbreviation = ['f', 'fr', 'k'] # depending on where sentence tokenizer is likely to split on abbrevations\n\t\tpunkt_param.abbrev_types = set(abbreviation)\n\t\ttokenizer = PunktSentenceTokenizer(punkt_param)\n\t\ttokenizer.train(text)\n\t\tsegmentedText = tokenizer.tokenize(text)\n\t\treturn segmentedText",
"title": ""
},
{
"docid": "2fb16d18de7f6e0aa0488e6489f41c53",
"score": "0.38106847",
"text": "def constructTerm(term, label, id):\n termlist = []\n if term != '': \n listitem = {}\n listitem['label'] = label\n listitem['id'] = id\n patterns = []\n #for labels that are tree species or alternate names for tree species\n if label in ['TREE SPECIES', 'ALT TREE SPECIES']:\n for s in term.split(' '):\n pattern = {}\n #we will lowercase all words in the text and in patterns so we don't have to worry abut case matching\n pattern['LOWER'] = s.lower()\n patterns.append(pattern)\n listitem['pattern'] = patterns\n termlist.append(listitem.copy())\n #create a pattern with genus abbreviated, ex. \"p. strobus\"\n altitem = {}\n altitem['label'] = label\n altitem['id'] = id\n altpatterns = []\n for i, s in enumerate(term.split(' ')):\n altpattern = {}\n if i == 0:\n altpattern['LOWER'] = s[0].lower() + '.'\n altpatterns.append(altpattern)\n else:\n altpattern['LOWER'] = s.lower()\n altpatterns.append(altpattern)\n altitem['pattern'] = altpatterns\n termlist.append(altitem.copy())\n #for the labels that are common names, add patterns for matching pluralized form in addition to singular \n elif label == 'TREE COMMON NAME':\n for i, s in enumerate(term.split(' ')):\n if '-' in s:\n hyphenpatterns = tokenHyphen(s)\n patterns.extend(hyphenpatterns)\n else:\n pattern = {}\n pattern['LOWER'] = s.lower()\n patterns.append(pattern)\n listitem['pattern'] = patterns\n termlist.append(listitem.copy())\n patterns = []\n for i, s in enumerate(term.split()):\n pattern = {}\n #pluralize only the last token in the word\n if i != len(term.split())-1:\n if '-' in s:\n hyphenpatterns = tokenHyphen(s)\n patterns.extend(hyphenpatterns)\n else:\n pattern['LOWER'] = s.lower()\n patterns.append(pattern)\n else:\n pattern['LOWER'] = pluralize(s.lower())\n patterns.append(pattern)\n listitem['pattern'] = patterns\n termlist.append(listitem)\n else:\n listitem = None\n return termlist",
"title": ""
},
{
"docid": "2a73f7fac50f276b40729910e45d9fe9",
"score": "0.38031164",
"text": "def compute_max_term_similarity(df):\n human_similarity, wup_term_sim, shp_term_sim, lch_term_sim = [], [], [], []\n # depth_max = 20 # Wordnet 3.0 depth\n depth_max = max_dataset_depth(df) # dataset taxonomy depth\n for index, row in df.iterrows():\n s_1 = wn.synsets(row['Word 1'])\n s_2 = wn.synsets(row['Word 2'])\n human_similarity.append(float(row['Human']))\n max_wup_similarity = -1\n max_shp_similarity = -1\n max_lch_similarity = -1\n for synset_1 in s_1:\n for synset_2 in s_2:\n lcs_tuple = lcs_path(synset_1, synset_2, 0)\n if lcs_tuple is not None:\n least_common_subsumer = lcs_tuple[0]\n synsets_shortest_path = lcs_tuple[1]\n wu_sim = wu_palmer_similarity(synset_1, synset_2, least_common_subsumer)\n shp_sim = shortest_path_similarity(synset_1, synset_2, synsets_shortest_path, depth_max)\n lch_sim = lc_similarity(synset_1, synset_2, synsets_shortest_path, depth_max)\n if wu_sim is not None:\n if wu_sim > max_wup_similarity:\n max_wup_similarity = wu_sim\n if shp_sim is not None:\n if shp_sim > max_shp_similarity:\n max_shp_similarity = shp_sim\n if lch_sim is not None:\n if lch_sim > max_lch_similarity:\n max_lch_similarity = lch_sim\n wup_term_sim.append(max_wup_similarity)\n shp_term_sim.append(max_shp_similarity)\n lch_term_sim.append(max_lch_similarity)\n return human_similarity, wup_term_sim, shp_term_sim, lch_term_sim",
"title": ""
},
{
"docid": "59b59d72e55b349d638f3e7080069d80",
"score": "0.38001633",
"text": "def ambn_legacy(longer_set, shorter_set, nwords):\n if shorter_set == set() or nwords <= 0:\n shorter_set.clear()\n return None\n sd = form_subdict(shorter_set, nwords)\n for longer_tuple in longer_set:\n for sub_longer_string in it.combinations(longer_tuple, nwords):\n if sub_longer_string in sd:\n for shorter_tuple in sd[sub_longer_string]:\n shorter_set.discard(shorter_tuple)\n return None",
"title": ""
}
]
e7a0978044c28389b858fd2862c54aaa
Wrapper for main function
[
{
"docid": "e696deb90cf7e8b40da734a53d2bb0d6",
"score": "0.0",
"text": "def main():\n # Constants\n y_label = \"Survived\"\n factor_cols = [\"Embarked\", \"Sex\"]\n\n # Load and clean training data\n print(\"Loading, cleaning training data...\")\n training_data = extract_clean_data(\"train.csv\")\n print(\"Done\")\n print()\n\n # These are the features we will feed back into the estimator to\n # yield predictions\n features = training_data.drop(y_label, axis=1).columns\n # train the rf model\n print(\"Training random forest classifier with test data...\")\n estimator = train_model_rf(training_data, y_label)\n print(\"Done\")\n print()\n\n # test the irf model on the testing data\n print(\"Loading test data...\")\n test_df = load_test_data(\"test.csv\", factor_cols)\n print(\"Done\")\n print()\n\n ####### RANDOM FOREST ######\n print(\"--Random forest classifier--\")\n print()\n\n # Create predictions from the rf model using the testing data\n print(\"Making predictions with RF model...\")\n predictions = predict_model(estimator, test_df, features)\n\n # Convert prediction nparray to pandas dataframe\n predict_df = pd.DataFrame(\n data=predictions,\n columns=[\"Survived\"],\n )\n print(\"Done\")\n print()\n\n # Write predictions to a CSV file based on the ID\n print(\"Writing random forest predictions to csv...\")\n write_pred(predict_df, test_df, \"model_output_rf.csv\",\n \"PassengerId\", \"Survived\")\n print(\"Done\")\n print()\n\n ###### Multilayer perceptron algorithm with backpropagation ######\n print(\"--MLP backprop--\")\n print()\n\n # train the MLP net\n print(\"Training MLP...\")\n mlp_model = train_model_mlp(training_data, y_label)\n print(\"Done\")\n print()\n\n # Create predictions with the MLP net\n print(\"Making predictions with the MLP...\")\n mlp_predictions = predict_model(mlp_model, test_df, features)\n print(\"Done\")\n print()\n\n # Convert predictions from multilayer perceptron to a pandas array\n mlp_predictions = pd.DataFrame(\n data=mlp_predictions,\n columns=[\"Survived\"],\n )\n\n # Write MLP predictions to csv file\n print(\"Writing MLP predictions to csv...\")\n write_pred(mlp_predictions, test_df, \"model_output_mlp.csv\",\n \"PassengerId\", \"Survived\")\n print(\"Done\")\n print()",
"title": ""
}
]
Execute the decorated test only if running in v2 mode. This function is intended to be applied to tests that exercise v2 only functionality. If the test is run in v1 mode it will simply be skipped. See go/tftestdecoratorcheatsheet for the decorators to use in different v1/v2/eager/graph combinations.
[
{
"docid": "da0fa67a1dfe1d3c0c9f4710dd11ae72",
"score": "0.8331696",
"text": "def run_v2_only(func=None):\n\n def decorator(f):\n if tf_inspect.isclass(f):\n raise ValueError('`run_v2_only` only supports test methods.')\n\n def decorated(self, *args, **kwargs):\n if not tf2.enabled():\n self.skipTest('Test is only compatible with v2')\n\n return f(self, *args, **kwargs)\n\n return decorated\n\n if func is not None:\n return decorator(func)\n\n return decorator",
"title": ""
}
]
[
{
"docid": "aac29693e6b53172179e0026e37e8d2c",
"score": "0.60816467",
"text": "def decorated(test):\n\n @wraps(test)\n def test_with_version(self):\n \"Only run the test if ES version is not less than specified.\"\n actual_version = self.get_es().info()['version']['number']\n\n if LooseVersion(actual_version) >= LooseVersion(minimum_version):\n test(self)\n else:\n raise SkipTest\n\n return test_with_version",
"title": ""
},
{
"docid": "c8dc78299146570896d62215deb620a9",
"score": "0.54928935",
"text": "def skip_test():\n if not tvm.get_global_func(\"relay.ext.verilator\", True):\n print(\"Skip test because Verilator codegen is not available.\")\n return True\n if sys.platform == \"win32\":\n print(\"Skip test on Windows for now\")\n return True\n return False",
"title": ""
},
{
"docid": "1ea8833179d348cdc9336c77890b39c1",
"score": "0.53972596",
"text": "def test_require_version_good(self):\n def func() :\n pass\n sys.version_info = (2, 5, 5, 'final', 4)\n current = sys.version_info[:3]\n compare = ('2.4', '2.5', '2.5.4', '2.5.5')\n for version in compare:\n decorator = require_version(version)\n self.assertEqual(func, decorator(func), '%s =< %s : function \\\n return by the decorator should be the same.' % (version,\n '.'.join([str(element) for element in current])))",
"title": ""
},
{
"docid": "7db82c049f94c7b04bc29727886f94ee",
"score": "0.5322788",
"text": "def testrun_run2(self):\n self.maxDiff = None\n self.dsr_runtest(2, '-r', remlines=[])",
"title": ""
},
{
"docid": "bfe2411e0d46d282fa793be73927d86f",
"score": "0.5282247",
"text": "def test_two_layers(self):\n with override_quantized_engine('fbgemm'):\n model = AnnotatedTwoLayerLinearModel()\n model = prepare(model)\n\n self.checkNoPrepModules(model)\n self.checkObservers(model)\n self.checkNoPrepModules(model.fc1)\n self.checkHasPrepModules(model.fc2)\n\n test_only_eval_fn(model, self.calib_data)\n model = convert(model)\n\n def checkQuantized(model):\n self.checkNoPrepModules(model)\n self.checkNoPrepModules(model.fc1)\n self.checkHasPrepModules(model.fc2)\n self.assertEqual(type(model.fc1), torch.nn.Linear)\n self.checkWrappedQuantizedLinear(model.fc2)\n test_only_eval_fn(model, self.calib_data)\n self.checkScriptable(model, self.calib_data)\n\n checkQuantized(model)\n\n # test one line API\n model = quantize(AnnotatedTwoLayerLinearModel(), test_only_eval_fn,\n self.calib_data)\n checkQuantized(model)",
"title": ""
},
{
"docid": "6077b57dbb6ebdafa9be1d3b745bdd92",
"score": "0.52524143",
"text": "def test_2(self):\n pass",
"title": ""
},
{
"docid": "6077b57dbb6ebdafa9be1d3b745bdd92",
"score": "0.52524143",
"text": "def test_2(self):\n pass",
"title": ""
},
{
"docid": "52de41269bbb4235d7cceeb98c9c69b2",
"score": "0.5246232",
"text": "def require_version(minimum_version):\n\n def decorated(test):\n \"\"\"Decorator to only run the test if ES version is greater or\n equal than specified.\n\n \"\"\"\n\n @wraps(test)\n def test_with_version(self):\n \"Only run the test if ES version is not less than specified.\"\n actual_version = self.get_es().info()['version']['number']\n\n if LooseVersion(actual_version) >= LooseVersion(minimum_version):\n test(self)\n else:\n raise SkipTest\n\n return test_with_version\n\n return decorated",
"title": ""
},
{
"docid": "e01fb4cf2a74d01b31386b144dd76e0e",
"score": "0.5226082",
"text": "def test_single_notebook_v2(self):\n self._copy_file(join(\"files\", \"test-v2.ipynb\"), \"p2.ipynb\")\n run_nbgrader([\"update\", \"p2.ipynb\"])",
"title": ""
},
{
"docid": "f149365abb89e7ab04458f93e6ddd826",
"score": "0.5168513",
"text": "def test_2(self):\n\n pass",
"title": ""
},
{
"docid": "db207465bf0f5723eb2e7111e83639ab",
"score": "0.51523286",
"text": "def test_2(self):\n tests = [\n ('model1', True),\n ('model2', True),\n ('model3', False)\n ]\n\n for test in tests:\n result = self._usecase.get_performance_metrics(test[0])\n self.assertEqual(result['success'], test[1])",
"title": ""
},
{
"docid": "e9e273ba8be5bf83a2ee55f5f16a49a7",
"score": "0.5058622",
"text": "def test_tidy2(self):\n self.__check_analyzer_result('tidy2.out', 'test2.cpp_clang-tidy.plist',\n ['files/test2.cpp'], 'tidy2.plist')",
"title": ""
},
{
"docid": "f6f120075f6594187ebe82c2e22b69e2",
"score": "0.5049639",
"text": "def pytest_tavern_before_runtest(spec):",
"title": ""
},
{
"docid": "562df03e3997b672c3274dff8b94511d",
"score": "0.50399023",
"text": "def skip_if_not_4gpu():\n\n def _wrapper(func):\n if GPU_COUNT < 4:\n # skip if not enabled\n print(\"Skip {}\".format(func.__name__))\n func.benchmark_name = \"skip_\" + func.__name__\n return func\n\n return _wrapper",
"title": ""
},
{
"docid": "dc9406d1aaa25b4734b05d91cbaede89",
"score": "0.503286",
"text": "def test_meta_block_stop_method(self, pytester: pytest.Pytester, adaptavist_mock: AdaptavistMock):\n\n pytester.makepyfile(\n \"\"\"\n import pytest\n\n def test_TEST_T123(meta_block):\n with meta_block(1) as mb_1:\n mb_1.check(False, action_on_fail=mb_1.Action.STOP_METHOD)\n mb_1.check(False, message_on_fail=\"THIS SHOULD NOT BE DISPLAYED\")\n with meta_block(2) as mb_2:\n mb_2.check(True)\n\n def test_TEST_T124(meta_block):\n with meta_block(1) as mb_1:\n mb_1.check(True)\n \"\"\"\n )\n pytester.runpytest(\"--adaptavist\")\n _, _, etss = adaptavist_mock\n assert etss.call_count == 2\n for call in etss.call_args_list:\n assert \"THIS SHOULD NOT BE DISPLAYED\" not in call.kwargs[\"comment\"]\n assert call.test_case_key != \"TEST-T123\" or call.step != 2",
"title": ""
},
{
"docid": "13d211f882237877b3726b59741636d0",
"score": "0.5016975",
"text": "def test_T2():",
"title": ""
},
{
"docid": "39428ba5b915a3ed18032afcc19d47a5",
"score": "0.50154203",
"text": "def for_testing_only(func, _testing_check=lambda: \"pytest\" in sys.modules):\n\n @functools.wraps(func)\n def _wrapper(*args, **kwargs):\n \"\"\"\n Function wrapper for `testing_only` decorator.\n \"\"\"\n if not _testing_check():\n warnings.warn(f\"{func.__name__} should only be used for testing purposes.\")\n return func(*args, **kwargs)\n\n return _wrapper",
"title": ""
},
{
"docid": "166b6f14dbb4b5a7ffdb41bb581b3122",
"score": "0.50115246",
"text": "def test_skipif_markeval_namespace_multiple(self, pytester: Pytester) -> None:\n root = pytester.mkdir(\"root\")\n root.joinpath(\"__init__.py\").touch()\n root.joinpath(\"conftest.py\").write_text(\n textwrap.dedent(\n \"\"\"\\\n import pytest\n\n def pytest_markeval_namespace():\n return {\"arg\": \"root\"}\n \"\"\"\n ),\n encoding=\"utf-8\",\n )\n root.joinpath(\"test_root.py\").write_text(\n textwrap.dedent(\n \"\"\"\\\n import pytest\n\n @pytest.mark.skipif(\"arg == 'root'\")\n def test_root():\n assert False\n \"\"\"\n ),\n encoding=\"utf-8\",\n )\n foo = root.joinpath(\"foo\")\n foo.mkdir()\n foo.joinpath(\"__init__.py\").touch()\n foo.joinpath(\"conftest.py\").write_text(\n textwrap.dedent(\n \"\"\"\\\n import pytest\n\n def pytest_markeval_namespace():\n return {\"arg\": \"foo\"}\n \"\"\"\n ),\n encoding=\"utf-8\",\n )\n foo.joinpath(\"test_foo.py\").write_text(\n textwrap.dedent(\n \"\"\"\\\n import pytest\n\n @pytest.mark.skipif(\"arg == 'foo'\")\n def test_foo():\n assert False\n \"\"\"\n ),\n encoding=\"utf-8\",\n )\n bar = root.joinpath(\"bar\")\n bar.mkdir()\n bar.joinpath(\"__init__.py\").touch()\n bar.joinpath(\"conftest.py\").write_text(\n textwrap.dedent(\n \"\"\"\\\n import pytest\n\n def pytest_markeval_namespace():\n return {\"arg\": \"bar\"}\n \"\"\"\n ),\n encoding=\"utf-8\",\n )\n bar.joinpath(\"test_bar.py\").write_text(\n textwrap.dedent(\n \"\"\"\\\n import pytest\n\n @pytest.mark.skipif(\"arg == 'bar'\")\n def test_bar():\n assert False\n \"\"\"\n ),\n encoding=\"utf-8\",\n )\n\n reprec = pytester.inline_run(\"-vs\", \"--capture=no\")\n reprec.assertoutcome(skipped=3)",
"title": ""
},
{
"docid": "86efda8f8f73c207ef2591396923f49d",
"score": "0.500848",
"text": "def new_test_decorator(path, ti, args):\n return self.on_test_visit(path, ti, args)",
"title": ""
},
{
"docid": "c6f14d99bac5e68991162706a9999ede",
"score": "0.5005335",
"text": "def pytest_runtest_call(item):\n _check_latest_version_mark(item)",
"title": ""
},
{
"docid": "3f7ca35d3ab8a9ab59721ce7cad5c811",
"score": "0.5000307",
"text": "def test_func_one_line_2():\n pass",
"title": ""
},
{
"docid": "54d825588efd551dce34f56dadbabec8",
"score": "0.49965397",
"text": "def test_workload_migration_from_v2(self) -> None:\n self._assert_migrates(\n \"workload_v2_tuned.json\",\n \"workload_v3_tuned.json\",\n )",
"title": ""
},
{
"docid": "43071de07db4dac4948dffc36e773429",
"score": "0.49735293",
"text": "def test_meta_block_stop_context(self, pytester: pytest.Pytester, adaptavist_mock: AdaptavistMock):\n pytester.makepyfile(\n \"\"\"\n import pytest\n\n def test_TEST_T123(meta_block):\n with meta_block(1) as mb_1:\n mb_1.check(False, action_on_fail=mb_1.Action.STOP_CONTEXT)\n mb_1.check(False, message_on_fail=\"THIS SHOULD NOT BE DISPLAYED\")\n with meta_block(2) as mb_2:\n mb_2.check(True)\n \"\"\"\n )\n report = pytester.runpytest(\"--adaptavist\", \"-vv\")\n _, _, etss = adaptavist_mock\n for call in etss.call_args_list:\n assert \"THIS SHOULD NOT BE DISPLAYED\" not in call.kwargs[\"comment\"]\n assert etss.call_count == 2\n assert etss.call_args.kwargs[\"step\"] == 2\n assert etss.call_args.kwargs[\"status\"] == \"Pass\"\n report.assert_outcomes(skipped=1)",
"title": ""
},
{
"docid": "0ee1194507548bad5bc88d750f1312e1",
"score": "0.49451458",
"text": "def test__decorate_functions():\n pass",
"title": ""
},
{
"docid": "b87dc36dc779585d59e0dfe7d7a33700",
"score": "0.49425277",
"text": "def decorated2(debugger, args, exe_ctx, result, dict):\n print(\"hello from decorated2\", file=result)",
"title": ""
},
{
"docid": "4416636361457254311822dcc1145908",
"score": "0.493362",
"text": "def test_prevent_double_vote(test_app):\n pass",
"title": ""
},
{
"docid": "51700f2c0c6ee3ce0eedb5c60372e5b1",
"score": "0.49065796",
"text": "def _GetTestConfigsV2():\n convert_offline = False\n # TODO(laigd): add support for static_engine.\n dynamic_engine = True\n # TODO(laigd): add support for calibration.\n no_calibration = False\n use_calibration = True\n\n # Add all possible test cases and let the derived test class to decide\n # whether to run specific ones with ShouldRunTest().\n #\n # Note:\n # - In TF2.0 the conversion always produce dynamic engine, and we don't test\n # the offline mode here.\n # - For simplicity we don't test online conversion which requires setting the\n # Grappler config in default eager context.\n # - INT8 without calibration behaves like FP32/FP16.\n opts = list(\n itertools.product([FP32, FP16], [convert_offline], [dynamic_engine],\n [no_calibration], [False, True]))\n # We always run calibration with offline tool.\n opts.append((INT8, convert_offline, dynamic_engine, use_calibration, False))\n opts.append((INT8, convert_offline, dynamic_engine, use_calibration, True))\n return opts",
"title": ""
},
{
"docid": "97e0ad0eaf4ac2e97e3b48e107ac40c7",
"score": "0.48922426",
"text": "def custom_test_func(self):\n return True",
"title": ""
},
{
"docid": "5d7947294088fc0e4f918149def067be",
"score": "0.48794407",
"text": "def Step2(self):\n if self.inRange('Step2', 'argument'): \n r = self.__impl.step2Behav(argument=self.actdict['Step2']['argument']['tok_list'])\n r['status'] = ['ProcedureExample', 'Step2', 'on']\n \n self.clear_tok('Step2')\n self.parseResult(r)\n return True \n else:\n insuff_pins = self.wrong_tok('Step2')\n for pin in insuff_pins:\n LOG.debug('Incorrect token number on input %s of action %s: %s'\n % (pin, \"Step2\", self.actdict['Step2'][pin]['tok_list']))\n return False",
"title": ""
},
{
"docid": "499562d2c19edf0854866a01e6961f7e",
"score": "0.48695183",
"text": "def benchmark_graph_2_gpu(self):\n self._setup()\n FLAGS.num_gpus = 2\n FLAGS.enable_eager = False\n FLAGS.run_eagerly = False\n FLAGS.distribution_strategy = 'mirrored'\n FLAGS.model_dir = self._get_model_dir('benchmark_graph_2_gpu')\n FLAGS.batch_size = 128 * 2 # 2 GPUs\n self._run_and_report_benchmark()",
"title": ""
},
{
"docid": "8aeb1bddd349b9f0a94f13bbbcf58617",
"score": "0.48569664",
"text": "def test_app_test(self):\n out = _run(\"entrypoint.sh test\")\n self.assertTrue(\"Ran 2 tests\" in out)",
"title": ""
},
{
"docid": "8a7544598491a6251feb55a478548709",
"score": "0.4852445",
"text": "def test_should_not_run_product_version_default_filler(self):\n self.run_test('test_should_not_run', True, test_case_prefix='tinctest.test.test_version_check.MockTINCTestCaseWithGetProductVersion')",
"title": ""
},
{
"docid": "bcf0dc93f13b6f174ecd93cc3404ec3f",
"score": "0.48497897",
"text": "def skip_wo_symlink_capability(func):\n @wraps(func)\n @attr('skip_wo_symlink_capability')\n def _wrap_skip_wo_symlink_capability(*args, **kwargs):\n if not has_symlink_capability():\n pytest.skip(\"no symlink capabilities\")\n return func(*args, **kwargs)\n return _wrap_skip_wo_symlink_capability",
"title": ""
},
{
"docid": "1353e26d42e463a43206b0207d844346",
"score": "0.4838966",
"text": "def _set_v2_dtype_behavior(fn, enabled):\n @functools.wraps(fn)\n def wrapper(*args, **kwargs):\n v2_dtype_behavior = base_layer_utils.V2_DTYPE_BEHAVIOR\n base_layer_utils.V2_DTYPE_BEHAVIOR = enabled\n try:\n return fn(*args, **kwargs)\n finally:\n base_layer_utils.V2_DTYPE_BEHAVIOR = v2_dtype_behavior\n\n return tf_decorator.make_decorator(fn, wrapper)",
"title": ""
},
{
"docid": "4252e02421a3bdc73d435c0106bfcda8",
"score": "0.4826109",
"text": "def hasAttrMocks_2( self, *args, **kwargs ): #pylint: disable=unused-argument\n print(\"arguments\", args, kwargs)\n opsDict={\n 'extraCLIArguments': False,\n 'detectorModel': False,\n 'detectortype': True,\n 'eventType': True,\n 'outputFile': True,\n 'datatype': True,\n 'setOutputRecFile': False,\n 'setOutputDstFile': False,\n }\n self.assertIn( args[1], opsDict )\n return opsDict[ args[1] ]",
"title": ""
},
{
"docid": "87ed44a3e5d59a77995a1fcb40e3975b",
"score": "0.48225012",
"text": "def _non_regression_test():\n\timport doctest\n\tdoctest.testmod()\n\treturn",
"title": ""
},
{
"docid": "7ddc6b8408e577e0ef25dbc4be5ee272",
"score": "0.481237",
"text": "def run_through_wrapper(args):\n\n test_function = args.test_function.lower()\n if test_function not in test_dict.keys():\n raise Exception(\"'test_function' {0} not found in test suite\", format(test_function))\n tf = test_dict[test_function]\n try:\n input_df = pd.read_csv(args.input_file)\n except Exception as e:\n raise Exception(\"error reading input file {0}:{1}\".format(args.input_file, str(e)))\n # if len(input_df.columns) != tf.number_decision_vars():\n # raise Exception(\"Incorrect number of decision variables. Should be {} d. vars. got {} d. vars.\"\n # .format(len(input_df.columns), tf.number_decision_vars()))\n f1 = tf.f1(input_df.parval1)\n f2 = tf.f2(input_df.parval1)\n with open(args.output_file, 'w') as f:\n f.write(\"f1 {0:20.8E}\\n\".format(f1))\n f.write(\"f2 {0:20.8E}\\n\".format(f2))",
"title": ""
},
{
"docid": "7d2265ab7e0a07f85934a21bf42137f5",
"score": "0.47986087",
"text": "def process3Dot2(self):\n if self.inRange('process3Dot2', 'argument'):\n r = self.__impl.process3Dot2Behav(argument=self.actdict['process3Dot2']['argument']['tok_list'])\n r['status'] = ['CapabilityExample', 'process3Dot2', 'on']\n \n self.clear_tok('process3Dot2')\n self.parseResult(r)\n return True \n else:\n insuff_pins = self.wrong_tok('process3Dot2')\n for pin in insuff_pins:\n LOG.debug('Incorrect token number on input %s of action %s: %s'\n % (pin, \"process3Dot2\", self.actdict['process3Dot2'][pin]['tok_list']))\n return False",
"title": ""
},
{
"docid": "ce4103cb7f58d5a1e0dc8d817fd5549b",
"score": "0.47566283",
"text": "def skip_if_not_supported(is_route_flow_counter_supported):\n pytest_require(is_route_flow_counter_supported, 'route flow counter is not supported')",
"title": ""
},
{
"docid": "c0832097ad55aecd37ff86a0e1e1d6fa",
"score": "0.47554904",
"text": "def test_func_true():\n return",
"title": ""
},
{
"docid": "e43626b3c28d1c13c77ce35a448d4742",
"score": "0.47539562",
"text": "def test_multiple_decorators():\n assert decorated_with_mulitiple_decorators() == '^third^-second-*first*test*first*-second-^third^'",
"title": ""
},
{
"docid": "76e268a065661b10c16b16acdcc03f7c",
"score": "0.47493154",
"text": "def test_meta_block_fail_method(self, pytester: pytest.Pytester, adaptavist_mock: AdaptavistMock):\n pytester.makepyfile(\n \"\"\"\n import pytest\n\n def test_TEST_T123(meta_block):\n with meta_block(1) as mb_1:\n mb_1.check(False, action_on_fail=mb_1.Action.FAIL_METHOD)\n with meta_block(2) as mb_2:\n mb_2.check(True)\n \"\"\"\n )\n pytester.runpytest(\"--adaptavist\")\n _, _, etss = adaptavist_mock\n assert etss.call_count == 1",
"title": ""
},
{
"docid": "13d70a2fc67b54be036857986c5992f5",
"score": "0.47421676",
"text": "def test_no_cross_test_side_effects_part2(self, estimator_instance):\n assert not hasattr(estimator_instance, \"test__attr\")",
"title": ""
},
{
"docid": "b4f8fdd2a09854cd9cd666bcf66e9649",
"score": "0.47362146",
"text": "def main():\n\n defaults = {\n \"TEST_STACK\": \"tst\",\n \"TEST_APP\": \"kubv2cache\" + KubeV2CacheTestScenario.DEFAULT_TEST_ID,\n }\n\n return citest.base.TestRunner.main(\n parser_inits=[KubeV2CacheTestScenario.initArgumentParser],\n default_binding_overrides=defaults,\n test_case_list=[KubeV2CacheTest],\n )",
"title": ""
},
{
"docid": "04c160a44ee95909200182703d98775f",
"score": "0.4727578",
"text": "def test_trace__decorator():\n set_trace(helper_decorator, trace_func)\n with pytest.warns(UserWarning, match=\"^helper_decorator:100$\"):\n helper_decorator()",
"title": ""
},
{
"docid": "bf4a626aa6ab62b59eab70ae1a525497",
"score": "0.47220868",
"text": "def benchmark_2_gpu(self):\n self._setup()\n FLAGS.num_gpus = 2\n FLAGS.data_dir = self.data_dir\n FLAGS.batch_size = 128\n FLAGS.train_epochs = 182\n FLAGS.model_dir = self._get_model_dir('benchmark_2_gpu')\n FLAGS.dtype = 'fp32'\n FLAGS.enable_eager = True\n self._run_and_report_benchmark()",
"title": ""
},
{
"docid": "0072db3be970fe397b8863804df915bc",
"score": "0.47206366",
"text": "def skip_from_python(*ver):\r\n def skip_from_python_(f):\r\n def skip_from_python__(self):\r\n if sys.version_info[:len(ver)] >= ver:\r\n return self.skipTest(\"skipped because Python %s\"\r\n % \".\".join(map(str, sys.version_info[:len(ver)])))\r\n else:\r\n return f(self)\r\n\r\n return skip_from_python__\r\n return skip_from_python_",
"title": ""
},
{
"docid": "6e46224b4062ea7c7b58c3c3daa3e922",
"score": "0.47187048",
"text": "def test_part2():",
"title": ""
},
{
"docid": "72fdc080b424c465c1958e6bb21d199b",
"score": "0.47152025",
"text": "def skip_if_gpu():\n device = os.environ.get(\"DGL_BENCH_DEVICE\", \"cpu\")\n\n def _wrapper(func):\n if device == \"gpu\":\n # skip if not enabled\n func.benchmark_name = \"skip_\" + func.__name__\n return func\n\n return _wrapper",
"title": ""
},
{
"docid": "8b9d8ac1103f88d0f50319d88ceddfc8",
"score": "0.47080207",
"text": "def test_with_python_api(self):\n self.build()\n self.inline_stepping()",
"title": ""
},
{
"docid": "625f8c5fe1a30777b1ba613d27b7a2f0",
"score": "0.46953195",
"text": "def _verify_decorated_result(self, expected, args, result):\n self.assertTrue(result == expected, \"caching decorator spoil function. Arguments %s. Expected %s, got %s\" %\n (args, expected, result))",
"title": ""
},
{
"docid": "756ff0570fc557d63ab026cb08d657f0",
"score": "0.46647426",
"text": "def skip_before_python(*ver):\r\n def skip_before_python_(f):\r\n def skip_before_python__(self):\r\n if sys.version_info[:len(ver)] < ver:\r\n return self.skipTest(\"skipped because Python %s\"\r\n % \".\".join(map(str, sys.version_info[:len(ver)])))\r\n else:\r\n return f(self)\r\n\r\n return skip_before_python__\r\n return skip_before_python_",
"title": ""
},
{
"docid": "c5fa4e130d28bd81983c8146c879790a",
"score": "0.4643112",
"text": "def test_meta_block_check_stop_session(self, pytester: pytest.Pytester):\n pytester.makepyfile(\n \"\"\"\n import pytest\n\n def test_TEST_T121(meta_block):\n with meta_block(1) as mb_1:\n mb_1.check(True)\n\n def test_TEST_T123(meta_block):\n with meta_block(1) as mb_1:\n mb_1.check(False, action_on_fail=mb_1.Action.STOP_SESSION)\n\n def test_TEST_T124(meta_block):\n with meta_block(1) as mb_1:\n mb_1.check(True)\n \"\"\"\n )\n outcome = pytester.runpytest(\"--adaptavist\").parseoutcomes()\n assert outcome[\"passed\"] == 1\n assert outcome[\"blocked\"] == 2",
"title": ""
},
{
"docid": "d7c1f6427058a36d4a66795334fea06e",
"score": "0.46429762",
"text": "def inception_twoview(pretrained=False, **kwargs):\n\n return InceptionTwoView(**kwargs)",
"title": ""
},
{
"docid": "7e3fa0ddb4bbb08c7dc0379b60b46f10",
"score": "0.46385658",
"text": "def two_sided_test(x1, x2, test='t_test', value=0):\n\n x1 = np.asarray(x1)\n nobs1 = x1.shape[0]\n x1_mean = x1.mean(0)\n x1_var = x1.var(0) # sample variance\n x2 = np.asarray(x2)\n nobs2 = x2.shape[0]\n x2_mean = x2.mean(0)\n x2_var = x2.var(0) # sample variance\n var_pooled = nobs1 * x1_var + nobs2 * x2_var\n\n if test == 't_test':\n dof = nobs1 + nobs2 - 2\n elif test == 'z_test':\n dof = nobs1 + nobs2\n else:\n raise ValueError('Either t_test or z_test!')\n\n var_pooled = var_pooled/dof\n var_pooled = var_pooled*(1.0 / nobs1 + 1.0 / nobs2)\n std_diff = np.sqrt(var_pooled)\n\n if test == 't_test:':\n stat, pval = _two_sided_tstat_generic(x1_mean, x2_mean, std_diff, dof, diff=value)\n else:\n stat, pval = _two_sided_zstat_generic(x1_mean, x2_mean, std_diff, diff=value)\n\n return stat, pval",
"title": ""
},
{
"docid": "e2285fd46964b6768a94cabf9ed10941",
"score": "0.4635681",
"text": "def test(self, strategy: S.Strategy, version=None):\n raise NotImplementedError",
"title": ""
},
{
"docid": "700829de63c141a9071d773be9dd1671",
"score": "0.46305203",
"text": "def test_meta_block_assume(self, pytester: pytest.Pytester, adaptavist_mock: AdaptavistMock):\n pytester.makepyfile(\n \"\"\"\n import pytest\n\n def test_TEST_T123(meta_block):\n with meta_block(1) as mb_1:\n mb_1.check(False)\n with meta_block(2) as mb_2:\n mb_2.check(True)\n \"\"\"\n )\n pytester.runpytest(\"--adaptavist\")\n _, _, etss = adaptavist_mock\n assert etss.call_count == 2",
"title": ""
},
{
"docid": "21699896a626c713c54c523da0414859",
"score": "0.46058947",
"text": "def _AddTests(test_class):\n _AddTestsFor(test_class, is_v2=False)\n _AddTestsFor(test_class, is_v2=True)",
"title": ""
},
{
"docid": "fe4cbefff195f2bff40d629022f67553",
"score": "0.46010798",
"text": "def test_run_coverage_fail_under2(self):\n STATS = r\"\\s+8\\s+5\\s+38%\\s+1, 7-10\"\n TOTAL_STATS = r\"\\s+8\\s+5\\s+38%\\s\"\n\n proc = self.runIn(\"scenario/coverage_config_fail_under2\", \"-v\")\n self.assertProcOutputPattern(\n proc,\n \"part_covered_lib\",\n STATS,\n total_stats=TOTAL_STATS,\n assert_exit_status=1,\n )",
"title": ""
},
{
"docid": "761bbaa8828877558e8de0b187f5cc4a",
"score": "0.45935196",
"text": "def test(self, label='fast', verbose=1, extra_argv=['--exe'],\n doctests=True, coverage=False):\n return super(_NoseTester, self).test(label=label, verbose=verbose,\n extra_argv=extra_argv,\n doctests=doctests, coverage=coverage)",
"title": ""
},
{
"docid": "c09cc26f83b27ef3145c1964ab2efcda",
"score": "0.45920393",
"text": "def test(self, label='fast', verbose=1, extra_argv=['--exe'],\n doctests=True, coverage=False):\n return super(NoseTester, self).test(label=label, verbose=verbose,\n extra_argv=extra_argv,\n doctests=doctests, coverage=coverage)",
"title": ""
},
{
"docid": "0f5532c513b9eeb466493c10407bf585",
"score": "0.45915064",
"text": "def Process2(self):\n if self.inRange('Process2', 'argument'):\n r = self.__impl.process2Behav(argument=self.actdict['Process2']['argument']['tok_list'])\n r['status'] = ['CapabilityExample', 'Process2', 'on']\n \n self.clear_tok('Process2')\n self.parseResult(r)\n return True \n else:\n insuff_pins = self.wrong_tok('Process2')\n for pin in insuff_pins:\n LOG.debug('Incorrect token number on input %s of action %s: %s'\n % (pin, \"Process2\", self.actdict['Process2'][pin]['tok_list']))\n return False",
"title": ""
},
{
"docid": "035a46a599ce8db2565c988864421431",
"score": "0.45821315",
"text": "def _AddTestsFor(test_class, is_v2):\n opts = _GetTestConfigsV2() if is_v2 else _GetTestConfigsV1()\n for (precision_mode, convert_online, dynamic_engine, use_calibration,\n dynamic_shape) in opts:\n conversion = \"OnlineConversion\" if convert_online else \"OfflineConversion\"\n engine_type = \"DynamicEngine\" if dynamic_engine else \"StaticEngine\"\n calibration_type = \"UseCalibration\" if use_calibration else \"NoCalibration\"\n dynamic_shape_type = \"DynamicShape\" if dynamic_shape else \"ImplicitBatch\"\n test_name = \"%s_%s_%s_%s_%s_%s\" % (\"testTfTrtV2\" if is_v2 else \"testTfTrt\",\n conversion, engine_type, precision_mode,\n calibration_type, dynamic_shape_type)\n run_params = RunParams(\n convert_online=convert_online,\n precision_mode=precision_mode,\n dynamic_engine=dynamic_engine,\n test_name=test_name,\n use_calibration=use_calibration,\n is_v2=is_v2,\n dynamic_shape=dynamic_shape)\n if is_v2:\n setattr(test_class, test_name,\n test_util.run_v2_only(_GetTest(run_params)))\n else:\n setattr(test_class, test_name,\n test_util.run_v1_only(\"\", _GetTest(run_params)))",
"title": ""
},
{
"docid": "6f37e50655c4b5b6ce1e19c55bd7cbee",
"score": "0.45816487",
"text": "def test_method_decorators_incorrect(\n assert_errors,\n parse_ast_tree,\n code,\n default_options,\n mode,\n):\n tree = parse_ast_tree(mode(code))\n\n visitor = FunctionDefinitionVisitor(default_options, tree=tree)\n visitor.run()\n\n assert_errors(visitor, [WrongDescriptorDecoratorViolation])",
"title": ""
},
{
"docid": "be0f4f93a24bfe2673794b67731d608a",
"score": "0.45760384",
"text": "def test_strict_option_is_deprecated(pytester: Pytester) -> None:\n pytester.makepyfile(\n \"\"\"\n import pytest\n\n @pytest.mark.unknown\n def test_foo(): pass\n \"\"\"\n )\n result = pytester.runpytest(\"--strict\", \"-Wdefault::pytest.PytestRemovedIn8Warning\")\n result.stdout.fnmatch_lines(\n [\n \"'unknown' not found in `markers` configuration option\",\n \"*PytestRemovedIn8Warning: The --strict option is deprecated, use --strict-markers instead.\",\n ]\n )",
"title": ""
},
{
"docid": "ab8a93fdf4e4d18c0172e39c7f1fe60e",
"score": "0.45744228",
"text": "def test_view_even(self):\n self.__base_view_test(self.object, 100, 10)\n self.__base_view_test(self.object, 100, 100)\n self.__base_view_test(self.object, 10, 9)\n self.__base_view_test(self.object, 9, 10)",
"title": ""
},
{
"docid": "d80257266e946468aad174a20829d28b",
"score": "0.4563983",
"text": "def test_cam_rotor2(self):\n\n # The flags, execute the script, and then check the chi2 value.\n self.flags(opt=True)\n self.interpreter.run(script_file=self.cam_path+'rotor2.py')\n self.check_chi2(0.24293662685639067)",
"title": ""
},
{
"docid": "e134f2319ecd8a3f5855917f483eb755",
"score": "0.4560073",
"text": "def test_step_over_with_python_api(self):\n self.build()\n self.inline_stepping_step_over()",
"title": ""
},
{
"docid": "0d37925cdf2cb36c21839c459f07bad1",
"score": "0.45436767",
"text": "def test_2(self):\n self.assertTrue(False, \"This is a failing test\")",
"title": ""
},
{
"docid": "f6d4ce1d42c5eef68eea704ba026c24e",
"score": "0.45401827",
"text": "def skip_if_tpc_disabled(f):\r\n def skip_if_tpc_disabled_(self):\r\n from psycopg2 import ProgrammingError\r\n cnn = self.connect()\r\n cur = cnn.cursor()\r\n try:\r\n cur.execute(\"SHOW max_prepared_transactions;\")\r\n except ProgrammingError:\r\n return self.skipTest(\r\n \"server too old: two phase transactions not supported.\")\r\n else:\r\n mtp = int(cur.fetchone()[0])\r\n cnn.close()\r\n\r\n if not mtp:\r\n return self.skipTest(\r\n \"server not configured for two phase transactions. \"\r\n \"set max_prepared_transactions to > 0 to run the test\")\r\n return f(self)\r\n\r\n skip_if_tpc_disabled_.__name__ = f.__name__\r\n return skip_if_tpc_disabled_",
"title": ""
},
{
"docid": "fc40e51257ebc576a53aff7cdfe7b8b7",
"score": "0.4538359",
"text": "def test84_unrechable_func_3rd(self):\n input = \"\"\"\n void foo(){}\n void foo1(){foo();}\n void foo2(boolean f){foo2(foo3());}\n boolean foo3(){return foo3();}\n int main(){\n foo1();\n float main;\n main = 1e10-10e1;\n string foo2;\n foo2(true);\n do\n return 1;\n while(true);\n }\n \"\"\"\n expect = \"Type Mismatch In Expression: CallExpr(Id(foo2),[BooleanLiteral(true)])\"\n self.assertTrue(TestChecker.test(input,expect,484))",
"title": ""
},
{
"docid": "9722af49e83711731b1c58715c190982",
"score": "0.45316827",
"text": "def test_meta_block_stop_exit_session(self, pytester: pytest.Pytester, adaptavist_mock: AdaptavistMock):\n pytester.makepyfile(\n \"\"\"\n import pytest\n\n def test_TEST_T123(meta_block):\n with meta_block(1) as mb_1:\n mb_1.check(False, action_on_fail=mb_1.Action.STOP_EXIT_SESSION)\n mb_1.check(False, message_on_fail=\"THIS SHOULD NOT BE DISPLAYED\")\n with meta_block(2) as mb_2:\n mb_2.check(True)\n\n def test_TEST_T124(meta_block):\n with meta_block(1) as mb_1:\n mb_1.check(True)\n \"\"\"\n )\n pytester.runpytest(\"--adaptavist\")\n _, _, etss = adaptavist_mock\n assert etss.call_count == 1\n assert etss.call_args.kwargs[\"status\"] == \"Blocked\"",
"title": ""
},
{
"docid": "3aaf74e527af77be7059ddb7044ce2ae",
"score": "0.45248854",
"text": "def test_doc_directive2_md(testfile_creator, testfile_tester, testfile_checker):\n testfile = testfile_creator(\"doc/directive2.md\")\n result = testfile_tester(\n contents=testfile, pytest_options=[\"-v\", \"--doctest-modules\"]\n )\n nofail_noerror_nowarn(result)\n testfile_checker(\"doc/test_directive2.py\", testfile)",
"title": ""
},
{
"docid": "7ea1ded73f0984658ed774f03fdc8480",
"score": "0.4516789",
"text": "def test_optimizer_from_api2():\n g = Generator(\n 1,\n 3,\n 2,\n 500,\n GD=400,\n GDD=400,\n TOD=800,\n FOD=7000,\n QOD=70000,\n resolution=0.05,\n normalize=True,\n )\n g.generate_freq()\n\n cf = CosFitMethod(*g.data)\n cf.guess_GD(900)\n with patch.object(FitOptimizer, \"update_plot\") as patched_obj:\n res = cf.optimizer(2, order=5, initial_region_ratio=0.01, extend_by=0.01)\n patched_obj.assert_called()\n np.testing.assert_array_almost_equal(res, [900, 400, 800, 7000, 70000])",
"title": ""
},
{
"docid": "48388a21311e867fc39ef63c2e33eba1",
"score": "0.4514874",
"text": "def test_hidden_tests_autotest(self, db, course_dir):\n run_nbgrader([\"db\", \"assignment\", \"add\", \"ps1\", \"--db\", db, \"--duedate\",\n \"2015-02-02 14:58:23.948203 America/Los_Angeles\"])\n run_nbgrader([\"db\", \"student\", \"add\", \"foo\", \"--db\", db])\n run_nbgrader([\"db\", \"student\", \"add\", \"bar\", \"--db\", db])\n run_nbgrader([\"db\", \"student\", \"add\", \"baz\", \"--db\", db])\n with open(\"nbgrader_config.py\", \"a\") as fh:\n fh.write(\"\"\"c.ClearSolutions.code_stub=dict(python=\"# YOUR CODE HERE\")\"\"\")\n\n self._copy_file(join(\"files\", \"autotest-hidden.ipynb\"), join(course_dir, \"source\", \"ps1\", \"p1.ipynb\"))\n self._copy_file(join(\"files\", \"autotests.yml\"), join(course_dir, \"autotests.yml\"))\n run_nbgrader([\"generate_assignment\", \"ps1\", \"--db\", db])\n\n self._copy_file(join(\"files\", \"autotest-hidden-unchanged.ipynb\"), join(course_dir, \"submitted\", \"foo\", \"ps1\", \"p1.ipynb\"))\n self._copy_file(join(\"files\", \"autotest-hidden-changed-wrong.ipynb\"), join(course_dir, \"submitted\", \"bar\", \"ps1\", \"p1.ipynb\"))\n self._copy_file(join(\"files\", \"autotest-hidden-changed-right.ipynb\"), join(course_dir, \"submitted\", \"baz\", \"ps1\", \"p1.ipynb\"))\n\n # make sure submitted validates for both bar and baz (should only fail on hidden tests), but not foo (missing any input and visible type checks will fail)\n output = run_nbgrader([\n \"validate\", join(course_dir, \"submitted\", \"foo\", \"ps1\", \"p1.ipynb\"),\n ], stdout=True)\n assert output.splitlines()[0] == (\n \"VALIDATION FAILED ON 1 CELL(S)! If you submit your assignment \"\n \"as it is, you WILL NOT\"\n )\n output = run_nbgrader([\n \"validate\", join(course_dir, \"submitted\", \"bar\", \"ps1\", \"p1.ipynb\")\n ], stdout=True)\n assert output.strip() == \"Success! Your notebook passes all the tests.\"\n\n output = run_nbgrader([\n \"validate\", join(course_dir, \"submitted\", \"baz\", \"ps1\", \"p1.ipynb\")\n ], stdout=True)\n assert output.strip() == \"Success! Your notebook passes all the tests.\"\n\n # autograde\n run_nbgrader([\"autograde\", \"ps1\", \"--db\", db])\n assert os.path.exists(join(course_dir, \"autograded\", \"foo\", \"ps1\", \"p1.ipynb\"))\n assert os.path.exists(join(course_dir, \"autograded\", \"bar\", \"ps1\", \"p1.ipynb\"))\n assert os.path.exists(join(course_dir, \"autograded\", \"baz\", \"ps1\", \"p1.ipynb\"))\n\n # make sure hidden tests are placed back in autograded\n sub_nb = join(course_dir, \"autograded\", \"foo\", \"ps1\", \"p1.ipynb\")\n with io.open(sub_nb, mode='r', encoding='utf-8') as nb:\n source = nb.read()\n assert \"BEGIN HIDDEN TESTS\" in source\n sub_nb = join(course_dir, \"autograded\", \"bar\", \"ps1\", \"p1.ipynb\")\n with io.open(sub_nb, mode='r', encoding='utf-8') as nb:\n source = nb.read()\n assert \"BEGIN HIDDEN TESTS\" in source\n sub_nb = join(course_dir, \"autograded\", \"baz\", \"ps1\", \"p1.ipynb\")\n with io.open(sub_nb, mode='r', encoding='utf-8') as nb:\n source = nb.read()\n assert \"BEGIN HIDDEN TESTS\" in source\n\n # make sure autograded for foo does not validate, should fail on visible and hidden tests\n output = run_nbgrader([\n \"validate\", join(course_dir, \"autograded\", \"foo\", \"ps1\", \"p1.ipynb\"),\n ], stdout=True)\n assert output.splitlines()[0] == (\n \"VALIDATION FAILED ON 1 CELL(S)! If you submit your assignment \"\n \"as it is, you WILL NOT\"\n )\n # make sure autograded for bar does not, should fail on hidden tests\n output = run_nbgrader([\n \"validate\", join(course_dir, \"autograded\", \"bar\", \"ps1\", \"p1.ipynb\"),\n ], stdout=True)\n assert output.splitlines()[0] == (\n \"VALIDATION FAILED ON 1 CELL(S)! If you submit your assignment \"\n \"as it is, you WILL NOT\"\n )\n # make sure autograded for bar validates, should succeed on hidden tests\n output = run_nbgrader([\n \"validate\", join(course_dir, \"autograded\", \"baz\", \"ps1\", \"p1.ipynb\"),\n ], stdout=True)\n assert output.strip() == \"Success! Your notebook passes all the tests.\"\n\n with Gradebook(db) as gb:\n submission = gb.find_submission(\"ps1\", \"foo\")\n nb1 = submission.notebooks[0]\n assert nb1.score == 0\n submission = gb.find_submission(\"ps1\", \"bar\")\n nb1 = submission.notebooks[0]\n assert nb1.score == 0\n submission = gb.find_submission(\"ps1\", \"baz\")\n nb1 = submission.notebooks[0]\n assert nb1.score == 1",
"title": ""
},
{
"docid": "f5484e6c3a0cb4e933fc56f47e4bb76e",
"score": "0.45074752",
"text": "async def test_properties_v2(event_loop, v2_server):\n async with v2_server:\n async with aiohttp.ClientSession(loop=event_loop) as websession:\n api = await API.login_via_credentials(TEST_EMAIL, TEST_PASSWORD, websession)\n systems = await api.get_systems()\n system = systems[TEST_SYSTEM_ID]\n\n keypad = system.sensors[\"195\"]\n assert keypad.data == 0\n assert not keypad.error\n assert not keypad.low_battery\n assert keypad.settings == 1\n\n # Ensure that attempting to access the triggered of anything but\n # an entry sensor in a V2 system throws an error:\n with pytest.raises(SimplipyError):\n assert keypad.triggered == 42\n\n entry_sensor = system.sensors[\"609\"]\n assert entry_sensor.data == 130\n assert not entry_sensor.error\n assert not entry_sensor.low_battery\n assert entry_sensor.settings == 1\n assert not entry_sensor.trigger_instantly\n assert not entry_sensor.triggered",
"title": ""
},
{
"docid": "9b6dca19da4942f625c769bd1ca80be7",
"score": "0.44941473",
"text": "def test2():\n # Step one checkdpendencies\n # this will fetch and build all dependencies\n if not TaskingBenchmarks.check_dependencies():\n print(\"Dependency check failed\")\n return\n # Step2: Fetch the specified repo\n if not TaskingBenchmarks.fetch():\n print(\"Unable to fetch\")\n return\n if not TaskingBenchmarks.build():\n print(\"Unable to build\")",
"title": ""
},
{
"docid": "fdd1e23d4903c77c51006da06b93c630",
"score": "0.4490546",
"text": "def skip_other_tests():\n import unittest\n from unittest import TextTestRunner as _TextTestRunner\n\n class CustomTestRunner(_TextTestRunner):\n def run(self, test):\n if test._tests:\n for t1 in test._tests:\n for t2 in t1._tests:\n if t2._testMethodName == self._special_name:\n return _TextTestRunner.run(self, t2)\n raise RuntimeError(\"couldn't isolate test\")\n\n def outer(fun, *args, **kwargs):\n # monkey patch unittest module\n unittest.TextTestRunner = CustomTestRunner\n if hasattr(unittest, 'runner'): # unittest2\n unittest.runner.TextTestRunner = CustomTestRunner\n CustomTestRunner._special_name = fun.__name__\n\n def inner(self):\n return fun(self, *args, **kwargs)\n return inner\n\n return outer",
"title": ""
},
{
"docid": "a1bea1358896deb1a8bd0323381dfacb",
"score": "0.44868478",
"text": "def test_game_over_2(self):\n self.assertMain(\"main_game_over_2.in\", \"main_game_over_2.out\", stop_early=False)",
"title": ""
},
{
"docid": "99bf04e63a7ba7dd4cff9f53341c77af",
"score": "0.4483516",
"text": "def test(args: Dict[str, Any], unknown: Optional[List[str]]) -> None:\n estimator = _get_estimator(args, unknown)\n estimator.test(summary=args['summary'], eager=args['eager'])",
"title": ""
},
{
"docid": "a4316e3670458cf1b76f743f7ddbf8a4",
"score": "0.44782817",
"text": "def test_func(self):\n return True",
"title": ""
},
{
"docid": "c7956a90cfc940154713321a85b67278",
"score": "0.4477657",
"text": "def test_1(self):\n pass",
"title": ""
},
{
"docid": "c7956a90cfc940154713321a85b67278",
"score": "0.4477657",
"text": "def test_1(self):\n pass",
"title": ""
},
{
"docid": "c7956a90cfc940154713321a85b67278",
"score": "0.4477657",
"text": "def test_1(self):\n pass",
"title": ""
},
{
"docid": "642ead6ae647a310d017f7f50640361f",
"score": "0.44763717",
"text": "def skip_on_fail(func):\n @functools.wraps(func)\n def wrapper(*args, **kwargs):\n instance = args[0]\n try:\n return func(*args, **kwargs)\n except Exception as e:\n log_message(instance, \"Skipping Test : %s\" % repr(e))\n instance.skipTest(e)\n\n return wrapper",
"title": ""
},
{
"docid": "885686a329fb41ec60ccc94d5af04516",
"score": "0.4473928",
"text": "def test_2_tensorflow_mnist(self):\n classifier, sess = get_image_classifier_tf()\n self._test_attack(classifier, self.x_test_mnist, self.y_test_mnist, False)",
"title": ""
},
{
"docid": "6171beb8e8ed3ca7b469475db1a38bfb",
"score": "0.44624028",
"text": "def test_version():\n with patch.object(firewalld, \"__firewall_cmd\", return_value=2):\n assert firewalld.version() == 2",
"title": ""
},
{
"docid": "76fb681d55fc2efeac2dac6cd310874e",
"score": "0.44618356",
"text": "def test_meta_block_fail_exit_session(self, pytester: pytest.Pytester, adaptavist_mock: AdaptavistMock):\n pytester.makepyfile(\n \"\"\"\n import pytest\n\n def test_TEST_T123(meta_block):\n with meta_block(1) as mb_1:\n mb_1.check(False, action_on_fail=mb_1.Action.FAIL_EXIT_SESSION)\n mb_1.check(False, message_on_fail=\"THIS SHOULD NOT BE DISPLAYED\")\n with meta_block(2) as mb_2:\n mb_2.check(True)\n\n def test_TEST_T124(meta_block):\n with meta_block(1) as mb_1:\n mb_1.check(True)\n \"\"\"\n )\n pytester.runpytest(\"--adaptavist\")\n _, _, etss = adaptavist_mock\n assert etss.call_count == 1\n assert etss.call_args.kwargs[\"status\"] == \"Fail\"",
"title": ""
},
{
"docid": "083774f46d48b811349db4e3dd659d13",
"score": "0.4460597",
"text": "def test_scenario():\r\n pass",
"title": ""
},
{
"docid": "83b0ed59ba7db0d3a0c31921d84b781f",
"score": "0.44539884",
"text": "def test_profile_training_data(self, model_version):\n pd = pytest.importorskip(\"pandas\")\n np = pytest.importorskip(\"numpy\")\n\n cont_col = np.random.random(100)\n discrete_col = np.random.choice(5, 100)\n string_discrete_col = np.random.choice([\"a\", \"b\", \"c\", \"d\", \"e\"], size=100)\n string_freeform_col = [uuid.uuid4().hex.upper()[0:10] for _ in range(100)]\n other_col = [datetime.datetime.now() for x in range(100)]\n output_col = np.random.choice(2, 100)\n\n col_names = [\n \"Continuous_Numeric\",\n \"Discrete_Numeric\",\n \"Discrete_String\",\n \"Freeform_String\",\n \"Other\",\n \"Output_Col\",\n ]\n supported_col_names = [\"Continuous_Numeric\", \"Discrete_Numeric\", \"Output_Col\"]\n\n # create dataframes\n df = pd.DataFrame(\n list(\n zip(\n cont_col,\n discrete_col,\n string_discrete_col,\n string_freeform_col,\n other_col,\n output_col,\n )\n ),\n columns=col_names,\n )\n\n # log to model version with new method\n model_version.log_training_data_profile(\n df.loc[:, df.columns != \"Output_Col\"],\n pd.DataFrame(df[\"Output_Col\"]),\n )\n\n # get back attributes to validate\n attributes = model_version.get_attributes()\n key = _deployable_entity._FEATURE_DATA_ATTR_PREFIX + \"{}\"\n discrete_col_missing_summary = _utils.json_to_proto(\n model_version.get_attribute(key.format(\"2\")),\n FeatureDataInModelVersion, # missing value\n )\n discrete_col_distribution_summary = _utils.json_to_proto(\n model_version.get_attribute(key.format(\"3\")),\n FeatureDataInModelVersion, # missing value\n )\n\n # missing value, distribution summary for each supported column +\n # equal number of attributes for visualization\n assert len(attributes.keys()) == len(supported_col_names) * 2 * 2\n assert (\n discrete_col_distribution_summary.summary_type_name\n == \"verta.discreteHistogram.v1\"\n )\n assert (\n discrete_col_distribution_summary.profiler_name == \"BinaryHistogramProfiler\"\n )\n assert (\n len(\n json.loads(discrete_col_distribution_summary.content)[\n \"discreteHistogram\"\n ][\"buckets\"]\n )\n <= 5\n )\n\n assert (\n discrete_col_missing_summary.summary_type_name\n == \"verta.discreteHistogram.v1\"\n )\n assert discrete_col_missing_summary.profiler_name == \"MissingValuesProfiler\"\n assert (\n len(\n json.loads(discrete_col_missing_summary.content)[\"discreteHistogram\"][\n \"buckets\"\n ]\n )\n == 2\n )\n\n # reference distribution attributes can be fetched back as histograms\n for col in supported_col_names:\n key = _deployable_entity._TRAINING_DATA_ATTR_PREFIX + col + \"Distribution\"\n histogram = model_version.get_attribute(key)\n assert isinstance(histogram, _verta_data_type._VertaDataType)",
"title": ""
},
{
"docid": "9f09cc56038c8f7eb4d4cf2b127ec5af",
"score": "0.44474134",
"text": "def check_admin_access_if_oss_fuzz(func):\n\n @functools.wraps(func)\n def wrapper(self):\n \"\"\"Wrapper.\"\"\"\n if utils.is_oss_fuzz():\n return check_admin_access(func)(self)\n\n return func(self)\n\n return wrapper",
"title": ""
},
{
"docid": "8154041f6136e6ecfa4235eb81bab8bf",
"score": "0.4443883",
"text": "def test_user1_allowed_by_policy2(self):\n perm_cache = perm.PermissionCache(self.env, 'user2')\n self.assertIn('ACTION_2', perm_cache)\n self.assertEqual(1, self.ps.policies[0].call_count)\n self.assertEqual(1, self.ps.policies[1].call_count)\n self.assertEqual([\n ('policy1', 'ACTION_2', None),\n ('policy2', 'ACTION_2', True),\n ], self.decisions)",
"title": ""
},
{
"docid": "41d0e5a516ab7ff4c7a472142a9c4a7b",
"score": "0.4443878",
"text": "def test_doc_example2_md(testfile_creator, testfile_tester, testfile_checker):\n testfile = testfile_creator(\"doc/example2.md\", skips=[\"Python 3.7\", \"LAST\"])\n result = testfile_tester(\n contents=testfile, pytest_options=[\"-v\", \"--doctest-modules\"]\n )\n nofail_noerror_nowarn(result)\n testfile_checker(\"doc/test_example2.py\", testfile)",
"title": ""
},
{
"docid": "53fd2fb48439b0138236795795b17b28",
"score": "0.44356787",
"text": "def test__before_after__parametrize__many_functions(testdir):\n testdir.makepyfile(\"\"\"\n import pytest\n data = {p1: {p2: {\"before\": [0]*3, \"after\": [0]*3} for p2 in [7, 8]} for p1 in [1, 2]}\n data2 = {p1: {p2: {\"before\": [0]*3, \"after\": [0]*3} for p2 in [7, 8]} for p1 in [1, 2]}\n \n def teardown_module():\n assert data == {p1: {p2: {\"before\": [3, 2, 1], \"after\": [1, 2, 3]} for p2 in [7, 8]} for p1 in [1, 2]}\n assert data2 == {p1: {p2: {\"before\": [3, 2, 1], \"after\": [1, 2, 3]} for p2 in [7, 8]} for p1 in [1, 2]}\n\n @pytest.mark.parametrize(\"param1\", [1, 2])\n @pytest.mark.deployment_test\n @pytest.mark.parametrize(\"param2\", [7, 8])\n async def test_parametrize(param1, param2, deploy_status):\n data[param1][param2][deploy_status.status][0] += 1\n yield\n data[param1][param2][deploy_status.status][1] += 1\n yield\n data[param1][param2][deploy_status.status][2] += 1\n \n @pytest.mark.parametrize(\"param1\", [1, 2])\n @pytest.mark.deployment_test\n @pytest.mark.parametrize(\"param2\", [7, 8])\n async def test_parametrize2(param1, param2, deploy_status):\n data2[param1][param2][deploy_status.status][0] += 1\n yield\n data2[param1][param2][deploy_status.status][1] += 1\n yield\n data2[param1][param2][deploy_status.status][2] += 1\n \"\"\")\n result = testdir.runpytest('--deployment_test=echo 1')\n result.assert_outcomes(failed=0, error=0, passed=65, skipped=0)",
"title": ""
},
{
"docid": "0a31b6417cdfa7ea254ed810bede0803",
"score": "0.44356555",
"text": "def test_hacking_support_python2():\n code = dedent(\"\"\"\\\n import ConfigParser\n import re\n import StringIO\n\n import thirdparty\n \"\"\")\n\n assert format_(code, formatter=hacking) == code",
"title": ""
},
{
"docid": "35419cc9ac33e6f93de1c50dadac33b2",
"score": "0.44351637",
"text": "def callDeprecated(self, version, f, *args, **kwargs):\n result = f(*args, **kwargs)\n warningsShown = self.flushWarnings([self.callDeprecated])\n try:\n info = list(version)\n except TypeError:\n since = version\n replacement = None\n else:\n [since, replacement] = info\n\n if len(warningsShown) == 0:\n self.fail('%r is not deprecated.' % (f,))\n\n observedWarning = warningsShown[0]['message']\n expectedWarning = getDeprecationWarningString(\n f, since, replacement=replacement)\n self.assertEqual(expectedWarning, observedWarning)\n\n return result",
"title": ""
},
{
"docid": "f263d4050f97a892a4bfa609d3e81dbd",
"score": "0.44349003",
"text": "def test_RestrictingNodeTransformer__visit_FunctionDef__2():\n result = compile_restricted_exec(\"def foo(_bad=1): pass\")\n assert result.errors == (functiondef_err_msg,)",
"title": ""
},
{
"docid": "069f4afe28a06a33fd553efe3af27f0f",
"score": "0.442796",
"text": "def is_even(arg):\n if arg % 2 == 1:\n print(\"not even\")\n else:\n print(\"even\")",
"title": ""
},
{
"docid": "f3acd0e2a125fbe7673f3b4c677e9e2d",
"score": "0.4423457",
"text": "def test_catch_violation(test_app):\n pass",
"title": ""
},
{
"docid": "75b2a9fac3c57511880383b4012f005e",
"score": "0.44215012",
"text": "def test(self, *args, **kwargs):\n pass",
"title": ""
}
]
8815029ce05be0962e676d03a39cb105
get GS states behind and other states at front
[
{
"docid": "f0fd1d1e4410d79516a30bb815aeb5d6",
"score": "0.0",
"text": "def ordered_states(state_list):\n labels = [s.label for s in state_list]\n indices = np.argsort(labels)\n state_list = np.array(state_list)[indices].tolist()\n\n ordered_list = []\n for state in state_list:\n if state.label == _GS_.label:\n ordered_list.append(state)\n else:\n ordered_list.insert(0, state)\n\n return tuple(ordered_list)",
"title": ""
}
]
Like get_observations() but handles pagination so you get all the results in one shot.
[
{
"docid": "56a7866671c7575d9da7ba58c8febe31",
"score": "0.7313944",
"text": "def get_all_observations(params: Dict, user_agent: str = None) -> List[Dict[str, Any]]:\n\n # According to the doc: \"The large size of the observations index prevents us from supporting the page parameter\n # when retrieving records from large result sets. If you need to retrieve large numbers of records, use the\n # per_page and id_above or id_below parameters instead.\n\n results = [] # type: List[Dict[str, Any]]\n id_above = 0\n\n while True:\n iteration_params = merge_two_dicts(\n params,\n {\"order_by\": \"id\", \"order\": \"asc\", \"per_page\": PER_PAGE_RESULTS, \"id_above\": id_above,},\n )\n\n page_obs = get_observations(params=iteration_params, user_agent=user_agent)\n results = results + page_obs[\"results\"]\n\n if page_obs[\"total_results\"] <= PER_PAGE_RESULTS:\n return results\n\n sleep(THROTTLING_DELAY)\n id_above = results[-1][\"id\"]",
"title": ""
}
]
[
{
"docid": "da7f164f0dfe32607542dab96d5e45d8",
"score": "0.72840744",
"text": "def observations(self):\n ro = self._create_basic_request_object('observations_get')\n\n ro.set_owner(self.owner_name)\n ro.set_description('retrieve observations for {}'.format(self._reference_indicator))\n\n for item in self._tc.result_pagination(ro, 'observation'):\n yield parse_observation(item)",
"title": ""
},
{
"docid": "4d0415fb292780f619556091104e09d7",
"score": "0.68137586",
"text": "def get_observations():\n \n max_results = request.args.get('max_results', 10, type=int)\n page = request.args.get('page', 1, type=int)\n sort_tmp = request.args.get('sort', '_updated', type=str)\n \n sort = {}\n \n if sort_tmp[0] == '-':\n sort['field'] = sort_tmp[1:]\n sort['direction'] = -1\n else:\n sort['field'] = sort_tmp\n sort['direction'] = 1\n \n \n col = app.data.driver.db['observations']\n #db.companies.find().skip(NUMBER_OF_ITEMS * (PAGE_NUMBER - 1)).limit(NUMBER_OF_ITEMS )\n cursor = col.find({'$and': [{'workflow.state': {'$nin': ['closed', 'withdrawn']}}, \\\n {'$or': [{'acl.execute.users': {'$in': [app.globals['user_id']]}}, \\\n {'acl.execute.groups': {'$in': app.globals['acl']['groups']}}, \\\n {'acl.execute.roles': {'$in': app.globals['acl']['roles']}} ] } ] } ).sort(sort['field'], sort['direction'])\n total_items = cursor.count()\n \n _items = list(cursor.skip(max_results * (page - 1)).limit(max_results))\n \n \"\"\"\n #hateos\n _links = {\"self\": {\"title\": \"observations/todo\", \"href\": \"observations/todo?max_results=%i&page=%i\" % (max_results, page), \n \"next\": {},\n \"previous\": {},\n \"last\": {},\n \"first\": {},\n \"parent\": {}}}\n \"\"\" \n _meta = { 'page': page, 'max_results' : max_results, 'total' : total_items} \n result = {'_items' : _items, '_meta': _meta} \n return Response(json.dumps(result, default=json_util.default), mimetype='application/json')",
"title": ""
},
{
"docid": "17698e94b79d1828613b962ba8f06bfa",
"score": "0.67402494",
"text": "def get_observations(self):\n return self.observations",
"title": ""
},
{
"docid": "7f281678ac1953ea36fe7c9174690a20",
"score": "0.66541165",
"text": "def current_observations(cls):\n try:\n response = requests.get(cls.API_URL, headers=cls.API_HEADERS, timeout=15)\n response.raise_for_status()\n response.encoding = \"UTF8\"\n return csv.DictReader(\n response.text.splitlines(), delimiter=\";\", quotechar='\"'\n )\n except requests.exceptions.HTTPError:\n _LOGGER.error(\"While fetching data\")",
"title": ""
},
{
"docid": "ad076bfe0e50e8ba908b1bbc9768011d",
"score": "0.6569353",
"text": "def cli_observations(ctx):\n pass",
"title": ""
},
{
"docid": "dbbe09a2bb298e407cf9643b2bfc43d1",
"score": "0.6232293",
"text": "def collect_observations(observations_request):\r\n\r\n try:\r\n request = requests.get(url=observations_request) # json object\r\n except requests.HTTPError:\r\n print('HTTP error for the request: ' + str(observations_request))\r\n else:\r\n # make first request\r\n\r\n response_json = request.json()\r\n # collect data\r\n observations = response_json[\"value\"] # collect things from all responses. A list\r\n # print('type of observations', type(observations))\r\n # print(observations)\r\n make_request = True\r\n # retrieve data from all pages\r\n while make_request:\r\n if '@iot.nextLink' in response_json:\r\n request = requests.get(response_json['@iot.nextLink'])\r\n response_json = request.json()\r\n observations = observations + response_json['value']\r\n else:\r\n make_request = False\r\n return observations",
"title": ""
},
{
"docid": "77a02e5cb2a8a277397336f3393862f0",
"score": "0.61225486",
"text": "def get_observations(self, *args: Any, **kwargs: Any) -> Observations:\n return Observations(self.sensors, *args, **kwargs)",
"title": ""
},
{
"docid": "9d7ff1ed9c0ea03fe9c1d7eb568b7b89",
"score": "0.60767525",
"text": "def _get_obs(self):\n # TODO\n return observations",
"title": ""
},
{
"docid": "d7ebf94d024624491d1557e78f5fd912",
"score": "0.6041073",
"text": "def get_obs(self):\n return self.observations",
"title": ""
},
{
"docid": "eb8d42777424b498185338668f26fdd7",
"score": "0.59841275",
"text": "def get_observations(self, *args: Any, **kwargs: Any) -> Observations:\n return Observations(self.sensors, *args, **kwargs)",
"title": ""
},
{
"docid": "f28fc5f288b003fe7ea29673231c9906",
"score": "0.5980575",
"text": "def observations(self):\n return self._observations",
"title": ""
},
{
"docid": "0713bfac78af3bbdcd27d3ed9b9aa2c0",
"score": "0.5959873",
"text": "def scrap_obs(self):",
"title": ""
},
{
"docid": "5c727a184ed392b244cd0ce19acab932",
"score": "0.590692",
"text": "def dot_observation_range(self):\n if self.single_submit is None:\n case_id = self.patient_casedoc._id\n observations = query_observations(case_id, self.start_date, self.end_date)\n else:\n observations = query_observations_singledoc(self.single_submit)\n\n return sorted(observations, key=lambda x: x['observed_date'])",
"title": ""
},
{
"docid": "a3083883e5121b3a435f5908c25ac5f3",
"score": "0.5822204",
"text": "def __iter__(self):\n return self._observations.__iter__()",
"title": ""
},
{
"docid": "ac79082e61132a8813933a1542598498",
"score": "0.5698318",
"text": "def observations(self):\n return self.agent_manager.get_observations()",
"title": ""
},
{
"docid": "84934c12c86384a0242d9b730867938a",
"score": "0.5675413",
"text": "def collect_table_data():\n first_response = requests.get(\n \"https://www.ebi.ac.uk/ebisearch/ws/rest/embl-covid19/\"\n \"?size=1000&format=JSON&facetcount=11&query=id%3A%5B*%20TO%20*%5D\"\n ).json()\n results = list()\n for i in range(0, first_response['hitCount'], 1000):\n if i == 0:\n for record in first_response['entries']:\n results.append(record)\n else:\n response = requests.get(\n f\"https://www.ebi.ac.uk/ebisearch/ws/rest/embl-covid19/\"\n f\"?start={i}&size=1000&format=JSON&facetcount=11\"\n f\"&query=id%3A%5B*%20TO%20*%5D\").json()\n for record in response['entries']:\n results.append(record)\n return results",
"title": ""
},
{
"docid": "5ae9330eb106be923991f1a64c37c41f",
"score": "0.56370836",
"text": "def get_observations(latitude, longitude):\n\n try:\n payload = dict()\n payload['latitude'] = latitude\n payload['longitude'] = longitude\n response = call_openwhisk('observations', payload)\n except Exception as e:\n raise APIException('KO', internal_details=str(e))\n\n return response",
"title": ""
},
{
"docid": "e28f3f265381e6b947b14bf0a91f873a",
"score": "0.5616497",
"text": "def get_data(self):\n url = os.path.join(BASE_SW_API, \"people/\")\n json_response = self._fetch(url).json()\n total_count = json_response[\"count\"]\n all_results = json_response[\"results\"]\n\n # got thought all the page next by next until we get all objects or next_url is None\n while len(all_results) < total_count:\n next_url = json_response[\"next\"]\n if next_url is None:\n break\n json_response = self._fetch(next_url).json()\n all_results += json_response[\"results\"]\n return all_results",
"title": ""
},
{
"docid": "29f837adcb57c49fcb4b62821dcffc70",
"score": "0.561592",
"text": "def get_allsamples(self):\n\n import web.apps.web_copo.templatetags.html_tags as htags\n\n df = pd.DataFrame()\n\n if self.accession:\n if isinstance(self.accession, str):\n self.accession = self.accession.split(\",\")\n\n object_ids = [ObjectId(x) for x in self.accession if x.strip()]\n records = cursor_to_list(Sample().get_collection_handle().find({\"_id\": {\"$in\": object_ids}}))\n\n if records:\n df = pd.DataFrame(records)\n df['accession'] = df._id.astype(str)\n df['label'] = df['name']\n df['desc'] = df['accession'].apply(lambda x: htags.generate_attributes(\"sample\", x))\n df['description'] = df['desc'].apply(lambda x: self.format_description(x))\n df['server-side'] = True # ...to request callback to server for resolving item description\n elif self.search_term:\n projection = dict(name=1)\n filter_by = dict()\n filter_by[\"name\"] = {'$regex': self.search_term, \"$options\": 'i'}\n\n sort_by = 'name'\n sort_direction = -1\n\n records = Sample(profile_id=self.profile_id).get_all_records_columns(filter_by=filter_by,\n projection=projection,\n sort_by=sort_by,\n sort_direction=sort_direction)\n if not records:\n # try getting all records\n del filter_by['name']\n records = Sample(profile_id=self.profile_id).get_all_records_columns(filter_by=filter_by,\n projection=projection,\n sort_by=sort_by,\n sort_direction=sort_direction)\n\n if records:\n df = pd.DataFrame(records)\n df['accession'] = df._id.astype(str)\n df['label'] = df['name']\n df['description'] = ''\n df['server-side'] = True # ...to request callback to server for resolving item description\n\n result = list()\n\n if not df.empty:\n df = df[['accession', 'label', 'description', 'server-side']]\n result = df.to_dict('records')\n\n return result",
"title": ""
},
{
"docid": "2541fac64bdb547f0ccee103b56ebfda",
"score": "0.56017536",
"text": "def __next__(self):\n return self._observations.__next__()",
"title": ""
},
{
"docid": "e037cdf107326dfcee7f4f12de325387",
"score": "0.56010973",
"text": "def get_observations(self, id):\n\n query_string = \"\"\"SELECT L.SUBJECT_ID, L.HADM_ID, L.ITEMID, COUNT(L.ITEMID) as TOTAL_ITEMCOUNT, COUNT(L.FLAG) AS TOTAL_ABNORMAL, \n MAX(DL.LABEL) AS LABEL, MAX(DL.FLUID) AS SPECIMEN, MAX(DL.CATEGORY) AS CATEGORY FROM `green-gasket-256323.mimiciii_fullyautomated.LABEVENTS` AS L \n JOIN `green-gasket-256323.mimiciii_fullyautomated.D_LABITEMS` AS DL ON L.ITEMID = DL.ITEMID WHERE HADM_ID IS NOT NULL AND SUBJECT_ID = {} \n GROUP BY SUBJECT_ID, HADM_ID, ITEMID;\"\"\"\n query_string = query_string.format(id)\n results = self.cl.queryRecords(query_string)\n\n patient_observation = []\n for row in results:\n res = {}\n for i in row.keys():\n if i not in res:\n res[i] = None\n if i in res and res[i] == None:\n res[i] = row[i]\n patient_observation.append(res)\n \n patient_observation_res = []\n\n for res in patient_observation:\n observation_json = {\n \"resourceType\" : \"Observation\",\n # from Resource: id, meta, implicitRules, and language\n # from DomainResource: text, contained, extension, and modifierExtension\n \"identifier\" : None, # Business Identifier for observation\n \"basedOn\" : None, # Fulfills plan, proposal or order\n \"partOf\" : None, # Part of referenced event\n \"status\" : \"registered\", # R! registered | preliminary | final | amended +\n \"category\" : res[\"CATEGORY\"], # Classification of type of observation\n \"code\" : res[\"ITEMID\"], # R! Type of observation (code / type)\n \"subject\" : res[\"SUBJECT_ID\"], # Who and/or what the observation is about\n \"focus\" : res[\"LABEL\"], # What the observation is about, when it is not about the subject of record\n \"encounter\" : res[\"HADM_ID\"], # Healthcare event during which this observation is made\n # effective[x]: Clinically relevant time/time-period for observation. One of these 4:\n \"effectiveDateTime\" : None,\n \"effectivePeriod\" : None,\n \"effectiveTiming\" : None,\n \"effectiveInstant\" : None,\n \"issued\" : None, # Date/Time this version was made available\n \"performer\" : None, # Who is responsible for the observation\n # value[x]: Actual result. One of these 11:\n \"valueQuantity\" : res[\"TOTAL_ITEMCOUNT\"],\n \"valueCodeableConcept\" : None,\n \"valueString\" : None,\n \"valueBoolean\" : None,\n \"valueInteger\" : res[\"TOTAL_ABNORMAL\"],\n \"valueRange\" : None,\n \"valueRatio\" : None,\n \"valueSampledData\" : None,\n \"valueTime\" : None,\n \"valueDateTime\" : None,\n \"valuePeriod\" : None,\n \"dataAbsentReason\" : None, # C? Why the result is missing\n \"interpretation\" : None, # High, low, normal, etc.\n \"note\" : None, # Comments about the observation\n \"bodySite\" : None, # Observed body part\n \"method\" : None, # How it was done\n \"specimen\" : res[\"SPECIMEN\"], # Specimen used for this observation\n \"device\" : None, # (Measurement) Device\n \"referenceRange\" : [{ # Provides guide for interpretation\n \"low\" : None, # C? Low Range, if relevant\n \"high\" : None, # C? High Range, if relevant\n \"type\" : None, # Reference range qualifier\n \"appliesTo\" :None, # Reference range population\n \"age\" : None, # Applicable age range, if relevant\n \"text\" : None # Text based reference range in an observation\n }],\n \"hasMember\" : None, # Related resource that belongs to the Observation group\n \"derivedFrom\" :None, # Related measurements the observation is made from\n \"component\" : [{ # Component results\n \"code\" : res[\"ITEMID\"], # R! Type of component observation (code / type)\n # value[x]: Actual component result. One of these 11:\n \"valueQuantity\" : None,\n \"valueCodeableConcept\" : None,\n \"valueString\" : None,\n \"valueBoolean\" : None,\n \"valueInteger\" : None,\n \"valueRange\" : None,\n \"valueRatio\" : None,\n \"valueSampledData\" : None,\n \"valueTime\" : None,\n \"valueDateTime\" : None,\n \"valuePeriod\" : None,\n \"dataAbsentReason\" : None, # C? Why the component result is missing\n \"interpretation\" : None, # High, low, normal, etc.\n \"referenceRange\" : None # Provides guide for interpretation of component result\n }]\n }\n patient_observation_res.append(observation_json)\n return patient_observation_res",
"title": ""
},
{
"docid": "4591fdd8b7e579c70d27db83c8adc579",
"score": "0.55799055",
"text": "def get_observations(self, obs_type):\n return self.obs[obs_type]",
"title": ""
},
{
"docid": "df981c12944d4180669c1c0ff7f16863",
"score": "0.55657864",
"text": "def get_data(self, limit=500):",
"title": ""
},
{
"docid": "1c1e5113182724ff9f9363566b43f757",
"score": "0.5517853",
"text": "def prepare_observations_request(sensor_api_root, extent, phenomenon, page_size=200):\r\n\r\n if is_valid_wkt_polygon(extent):\r\n observations_request = sensor_api_root + \"/Things?$top=\"+ str(page_size) + \\\r\n \"&$filter=geo.intersects(Things/Locations/location,\" + \\\r\n \"geography'\" + extent + \"')\" + \\\r\n \" and Datastreams/ObservedProperty/name eq '\" + phenomenon + \"'\" + \\\r\n \"&$select=name,@iot.id&$expand=Datastreams([email protected],unitOfMeasurement;$filter=ObservedProperty/name eq '\" + phenomenon + \"';\" + \\\r\n \"$expand=Observations($orderby=phenomenonTime desc;$top=1)),Locations($select=location;$expand=HistoricalLocations($select=time;$orderby=time desc;$top=1))\"\r\n\r\n else:\r\n print('Extent definition is not valid')\r\n return\r\n return observations_request",
"title": ""
},
{
"docid": "099c59bc3c28e07344dbe5bf9210fa55",
"score": "0.5509803",
"text": "def list_survey_observations(self, survey):\n observations = Observation.objects.filter(survey=survey)\n\n return observations",
"title": ""
},
{
"docid": "166524ab52ff6dbde4c322e9e8acf176",
"score": "0.5490312",
"text": "def paging_results(self):\n\n raise NotImplementedError()",
"title": ""
},
{
"docid": "1accd376abbb176208d7117b4c7337f9",
"score": "0.5487095",
"text": "def getObservations(self):\n \n # Sometimes the first station does not have any observations so\n # we have to loop until we get at least the temperature.\n stations = self.getStations()\n for stationId,stationName in stations:\n print(stationName)\n r = requests.get(baseuri + \"stations/%s/observations/latest\" % stationId)\n if r.status_code != 200: \n continue\n j = geojson.loads(r.text)\n #print(geojson.dumps(j,indent=4))\n temperature = None\n conditions = None\n try:\n properties = j[\"properties\"]\n temperature_c = (properties[\"temperature\"])[\"value\"]\n temperature = c_to_f(temperature_c)\n windspeed = ms_to_mph((properties[\"windSpeed\"])[\"value\"])\n description = properties[\"textDescription\"]\n conditions = \"%s, %s, %s.\" % (description, temperature, windspeed)\n self.stationName = stationName\n self.conditions = conditions\n pass\n except KeyError as e:\n print(stationName,\"empty\")\n pass\n if temperature:\n # Windspeed and description would be good but not worth waiting for\n break\n\n return conditions",
"title": ""
},
{
"docid": "2fedef6fcd584d845217d2523ba304c5",
"score": "0.548324",
"text": "def get_samples(sample_per_client=1):\n page = 1\n total_pages = 1\n results = []\n # Multiple pages of results\n while page <= total_pages:\n params = {\n 'sample': sample_per_client,\n 'sample-group': 'data-center',\n 'page[size]': 1000,\n 'url': '*'\n }\n\n # Build the request to get the sample dois\n payload = urllib.parse.urlencode(params)\n url = API_ENDPOINT + '?%s' % payload\n with urllib.request.urlopen(url) as f:\n data = json.loads(f.read())\n\n # Extract just doi and url\n for work in data['data']:\n result = {\n 'doi': work['attributes']['doi'],\n 'url': work['attributes']['url']\n }\n results.append(result)\n\n total_pages = data['meta']['total-pages']\n\n page += 1\n\n return results",
"title": ""
},
{
"docid": "950ab961078f2f31de0fba268ded310c",
"score": "0.5476006",
"text": "def get_observations(self, series):\n return series.findall(\".//Obs\")",
"title": ""
},
{
"docid": "6bd3f6c292b09079bd3908b14b0068a2",
"score": "0.54727274",
"text": "def get_observations(self, track_id: str) -> Dict[str, pymap.Observation]:\n pass",
"title": ""
},
{
"docid": "3f6a1b4e25a266e566f5a766d53f3c11",
"score": "0.54560524",
"text": "def all():\n url_params = connexion.request.args\n page = url_params.get('page', 0)\n limit = url_params.get('limit', 20)\n return jsonify({\"data\": [c.serialize() for c in models.Interviewer.query.offset(page).limit(limit).all()]})",
"title": ""
},
{
"docid": "dbf8071600025542f8ea7b5649ef3572",
"score": "0.54555935",
"text": "def _fetch_data(\n self,\n included_mrns: Optional[Set] = None,\n limit: Optional[int] = None\n ):\n data = self._data\n if included_mrns:\n data = data[data.medical_record_number.isin(included_mrns)]\n return data[:limit]",
"title": ""
},
{
"docid": "6ba91e901c8e4f0eb219d99bfe2cf861",
"score": "0.5446574",
"text": "def get_all(self, start=0, count=-1, filter='', sort='', scope_uris=''):\n return self._helper.get_all(start, count, filter=filter, sort=sort, scope_uris=scope_uris)",
"title": ""
},
{
"docid": "e63be33386b44b4709f9edf4203ec052",
"score": "0.5442254",
"text": "def _get_data(self, paginate):\n queryset = self._get_queryset()\n\n if paginate and self.pagination:\n queryset = queryset[self.pagination.start:self.pagination.start + self.pagination.count]\n return list(queryset)",
"title": ""
},
{
"docid": "b0773ab7029e01ef0c3c6990aa5e8d4c",
"score": "0.54409903",
"text": "def get_values(self,\n observations):\n\n return NotImplemented",
"title": ""
},
{
"docid": "b10df6a62c25009f6d4423146e4dff23",
"score": "0.54276747",
"text": "def get_all_samples():\n response = {}\n for num,doc in enumerate(mongo_db['samples'].find(None,{'_id':0}).limit(5)):\n response[num] = doc\n return response",
"title": ""
},
{
"docid": "5c17394fa4ae674f35973e97dca7ef92",
"score": "0.5409065",
"text": "def get(endpoint, **parameters):\n page_number = 1\n dataset = pd.DataFrame()\n\n while True:\n page = get_page(endpoint, page_number, **parameters)\n dataset = pd.concat([dataset, page])\n\n if len(page) == 0 or page_number == page.total_pages:\n return dataset\n\n page_number += 1",
"title": ""
},
{
"docid": "b184f30cbba77e12b942916b63f80e5e",
"score": "0.54063785",
"text": "def get_observations_for_date_interval(self, start_date, end_date):\n\n # TODO: add exception handling when dates do not have records for in data_df\n start_row_id = self.data_df[self.data_df['Timestamp'] == start_date].index.tolist()[0]\n end_row_id = self.data_df[self.data_df['Timestamp'] == end_date].index.tolist()[0]\n\n df = self.data_df[start_row_id:end_row_id]\n\n # filter out production data from the result set to return\n df = df.filter(['Timestamp',\n 'Temperature, C (observation)',\n 'Wind speed, km/h (observation)',\n 'Wind direction, degrees (observation)'],\n axis=1)\n return df",
"title": ""
},
{
"docid": "609283ab9019c266ce710b73b149dbb8",
"score": "0.5394708",
"text": "def test_get_series_observations(self):\n\n response = self.series_services.get_series_observations(\n series_id='GNPCA'\n )\n self.assertIsNotNone(response)",
"title": ""
},
{
"docid": "1fcc9e92e8a2e3eafe107ca6ba00c477",
"score": "0.53858954",
"text": "def get_isasamples(self):\n\n import web.apps.web_copo.templatetags.html_tags as htags\n\n df = pd.DataFrame()\n\n if self.accession:\n if isinstance(self.accession, str):\n self.accession = self.accession.split(\",\")\n\n object_ids = [ObjectId(x) for x in self.accession if x.strip()]\n records = cursor_to_list(Sample().get_collection_handle().find({\"_id\": {\"$in\": object_ids}}))\n\n if records:\n df = pd.DataFrame(records)\n df['accession'] = df._id.astype(str)\n df['label'] = df['name']\n df['desc'] = df['accession'].apply(lambda x: htags.generate_attributes(\"sample\", x))\n df['description'] = df['desc'].apply(lambda x: self.format_description(x))\n df['server-side'] = True # ...to request callback to server for resolving item description\n\n elif self.search_term:\n projection = dict(name=1)\n filter_by = dict(sample_type=\"isasample\")\n filter_by[\"name\"] = {'$regex': self.search_term, \"$options\": 'i'}\n\n sort_by = 'name'\n sort_direction = -1\n\n records = Sample(profile_id=self.profile_id).get_all_records_columns(filter_by=filter_by,\n projection=projection,\n sort_by=sort_by,\n sort_direction=sort_direction)\n if not records:\n # try getting all records\n del filter_by['name']\n records = Sample(profile_id=self.profile_id).get_all_records_columns(filter_by=filter_by,\n projection=projection,\n sort_by=sort_by,\n sort_direction=sort_direction)\n if records:\n df = pd.DataFrame(records)\n df['accession'] = df._id.astype(str)\n df['label'] = df['name']\n df['description'] = ''\n df['server-side'] = True # ...to request callback to server for resolving item description\n\n result = list()\n\n if not df.empty:\n df = df[['accession', 'label', 'description', 'server-side']]\n result = df.to_dict('records')\n\n return result",
"title": ""
},
{
"docid": "343b607e0c8567c2b3280916496e7391",
"score": "0.5383245",
"text": "def get_all(self, start=0, count=-1, filter='', sort='', query='', scope_uris=''):\n return self._helper.get_all(start, count, filter=filter, sort=sort, query=query, scope_uris=scope_uris)",
"title": ""
},
{
"docid": "671644eb0bdc4d971672b84cbf3170e9",
"score": "0.5362104",
"text": "def test_observations_method(self):\n\n try:\n awx = AerisWeather(app_id=app_id,\n client_id=client_id,\n client_secret=client_secret)\n\n obs_sum_list = awx.observations_summary(location=None,\n action=RequestAction.OBSERVATIONS_SUMMARY.CLOSEST,\n filter_=[RequestFilter.OBSERVATIONS_SUMMARY.ALL_STATIONS],\n sort=None,\n params={ParameterType.OBSERVATIONS_SUMMARY.P: \"54601\"},\n query=None)\n\n for obs_sum in obs_sum_list: # type: ObservationsSummaryResponse\n assert obs_sum.id is not None\n\n loc = obs_sum.loc\n assert loc is not None\n assert type(loc) is AerisLocation\n assert obs_sum.loc.lat > 43\n\n place = obs_sum.place\n assert place is not None\n # assert place.name == \"la crosse\"\n assert place.state == \"wi\"\n\n periods = obs_sum.periods\n assert periods is not None\n\n temp = periods[0].temp\n assert type(temp) is ObservationsSummaryTemp\n assert temp.avgF > -10\n\n profile = obs_sum.profile\n assert profile is not None\n assert profile.elevFT > 600\n\n except URLError as url_err:\n print(\"URL Error: \" + url_err.reason)\n raise url_err\n\n except AerisError as aeris_err:\n print(\"AerisError: \" + str(aeris_err))\n raise aeris_err\n\n except Exception as ex:\n print(ex.args)\n raise ex",
"title": ""
},
{
"docid": "20f040034505a17cce83ee6d9e7b6032",
"score": "0.5349634",
"text": "def getDocuments(n=100, start=0):\n\n base_url = \"https://cn.dataone.org/cn/v1/query/solr/\"\n fields = \",\".join([\"identifier\"])\n query_params = \"formatType:METADATA+AND+(datasource:*LTER+OR+datasource:*\"\\\n \"KNB+OR+datasource:*PISCO+OR+datasource:*GOA)+AND+-\"\\\n \"obsoletedBy:*\"\n rows = n\n start = start\n\n query_string = \"%s?fl=%s&q=%s&rows=%s&start=%s\" % (base_url,\n fields,\n query_params,\n rows,\n start)\n print(query_string)\n\n xmldoc = getXML(query_string)\n\n return(xmldoc)",
"title": ""
},
{
"docid": "d294f0c318208a341af8fff5b8664d2b",
"score": "0.53440946",
"text": "def GenerateObservations(self, num_reads):\n n, prevalences = self.suite.SamplePosterior()\n\n names = self.GetNames()\n name_iter = SpeciesGenerator(names, n)\n\n items = zip(name_iter, prevalences)\n\n cdf = thinkbayes2.Cdf(dict(items))\n observations = cdf.Sample(num_reads)\n\n #for ob in observations:\n # print ob\n\n return n, observations",
"title": ""
},
{
"docid": "58e3ab3b733bce7f73f89422e9553975",
"score": "0.5340551",
"text": "def _return_data(self, limit):\n data = copy.deepcopy(self._data)\n return data[\"results\"][:limit]",
"title": ""
},
{
"docid": "c2b1434737fe1fbfc511249c6478eb59",
"score": "0.5339006",
"text": "def handle_get_dataset_paginated(project_id, experiment_id, operator_id, pagination_parameters):\n datasets = get_dataset_pagination(project_id=project_id, experiment_id=experiment_id, operator_id=operator_id,\n page=pagination_parameters.page, page_size=pagination_parameters.page_size)\n\n return datasets",
"title": ""
},
{
"docid": "a5c68a16e0759286a582966e19a39011",
"score": "0.5330591",
"text": "def sample(self, n=10) -> List[dict]:\n return self.client._get_records_sync(\n self.name, params={\"with_meta\": \"yes\", \"count\": n, \"flatten\": \"yes\"}\n )",
"title": ""
},
{
"docid": "919108741325028b2f8e93a45783bcc6",
"score": "0.53253525",
"text": "def fetch(self):\n self.total_pages = self.get_total_pages()\n for i in range(self.page, self.total_pages + 1):\n for item in self.get_data(i):\n yield item",
"title": ""
},
{
"docid": "95da89fdc984821f24875585bb5c4c30",
"score": "0.5324761",
"text": "def observations(identifier, **kwargs):\r\n kwargs['series_id'] = identifier\r\n return Fred().series('observations', **kwargs)",
"title": ""
},
{
"docid": "ba98e45cc1bec44da6332e95b6604a40",
"score": "0.53190106",
"text": "def _fetch_page(self) -> list:\n self.filters['offset'] = self._current_offset\n self._total_count, new_page = self._fetch_analyses_history(\n self._request_url_path, self.filters)\n\n if new_page:\n self._current_page = new_page\n self._update_current_page_metadata()\n return self._current_page",
"title": ""
},
{
"docid": "041232468c18eb1eb3c4b54a26bb9806",
"score": "0.52738976",
"text": "def sel_observations(self, idx):\n raise NotImplementedError(\"sel_observations must be implemented on a model basis\")",
"title": ""
},
{
"docid": "2f665cbf0cacfd92ad50a1bdbd2f4d3a",
"score": "0.52661055",
"text": "def _build_observations(self, state: np.array) -> List[np.array]:\n pass",
"title": ""
},
{
"docid": "0eb37953c797fe848d0f078ab7f2e759",
"score": "0.5260021",
"text": "def process_observations(results):\n\n processed_map = {}\n for result in results:\n\n # Skip results where the publication state is working\n if result.get(\"publicationState\") == \"working\":\n continue\n\n # Skip where the result_field value is None\n if result[\"result_field\"] is None:\n continue\n\n data_path = result[\"result_field\"][\"dataPath\"].rstrip(\"/\")\n\n try:\n processed_map[data_path] = {\n \"title\": result[\"title\"],\n \"url\": f'https://catalogue.ceda.ac.uk/uuid/{result[\"uuid\"]}',\n \"record_type\": \"Dataset\",\n }\n except TypeError:\n continue\n\n return processed_map",
"title": ""
},
{
"docid": "8637426c39daff22ddd89d16a371e719",
"score": "0.5257472",
"text": "async def latest_observations(\n place: str,\n starttime: Optional[str] = None,\n timestep: int = 10,\n aiohttp_kwargs: Any = None,\n) -> List[Observation]:\n sensor_parameters = \",\".join(OBSERVATION_PARAMS.keys())\n\n params = {\n \"request\": \"getFeature\",\n \"storedquery_id\": \"fmi::observations::weather::simple\",\n \"place\": place,\n \"parameters\": sensor_parameters,\n }\n\n if timestep % 10 != 0:\n raise ValueError(\"timestep must be divisable by 10\")\n\n if timestep != 10:\n params[\"timestep\"] = str(timestep)\n\n if starttime:\n params[\"starttime\"] = starttime\n\n url = WFS_URL + urlencode(params)\n unparsed_gml = await fetch(url, aiohttp_kwargs)\n\n return parse_latest_observations(unparsed_gml)",
"title": ""
},
{
"docid": "25efe8a885d1ee6410fccbd8f095bd7d",
"score": "0.5256837",
"text": "def get_all(self):\n last_index = self.last_index\n self.last_index = 0\n res = self.get_next_batch(64)\n self.last_index = last_index\n \n return res",
"title": ""
},
{
"docid": "5fb090d12d046c83eb4c123b135a083e",
"score": "0.52458596",
"text": "def get_results(self, request, term, page, context):\n raise NotImplementedError",
"title": ""
},
{
"docid": "128698f0642ad3836a53456e2dcf4563",
"score": "0.5245216",
"text": "def observations(identifier, **kwargs):\n kwargs['series_id'] = identifier\n return Fred().series('observations', **kwargs)",
"title": ""
},
{
"docid": "72be29c230633c4d1c3e7c2cea571da8",
"score": "0.5220634",
"text": "def sel_observations(self, idx):\n raise NotImplementedError(\"sel_observations method must be implemented for each subclass\")",
"title": ""
},
{
"docid": "eca14ccd20c91e174ac3d753bfc83a0f",
"score": "0.52206016",
"text": "def observations(self):\n observations = []\n for observed_data in self._env.query(Filter('type', '=', 'observed-data')):\n if not self.revoked(observed_data):\n observations.append(observed_data)\n return observations",
"title": ""
},
{
"docid": "5d7f15e6748cadd4b3d63b4998241062",
"score": "0.51923966",
"text": "def retrieve_reads():\n reads = query_premises.get_reads_for_gas_south_customers(YEAR, MONTH)\n reads = reads.append(query_premises.get_reads_for_gas_south_customers(YEAR, MONTH, year_prior=True))\n # # export as csv\n # reads.to_csv('reads.csv', index=False)\n reads = reads.reset_index(drop=True)\n return reads",
"title": ""
},
{
"docid": "0570caef43e0929d4c2a2bea08c8b340",
"score": "0.51902467",
"text": "def get_all(self, start=0, count=-1, filter='', sort=''):\n return self._client.get_all(start, count, filter=filter, sort=sort)",
"title": ""
},
{
"docid": "62398f5a08738907fc9d6fd287d43a43",
"score": "0.51882225",
"text": "def explore_dataset(request):\n \n response = query_individual_dataset('00000')\n\n return JsonResponse(response)",
"title": ""
},
{
"docid": "e631fe5e79ea68fde485e3a644b68e8e",
"score": "0.5181932",
"text": "def get_all(method, *args, **kwargs):\n res = []\n kwargs['page'] = 1\n\n while True:\n response = method(*args, **kwargs)\n\n if not response:\n break\n\n res.extend(response)\n kwargs['page'] += 1\n\n return res",
"title": ""
},
{
"docid": "335883fcfc21e7b3e1f3142eafbaf6b6",
"score": "0.51795",
"text": "def get_all(self, start=0, count=-1, filter='', sort=''):\n return self._client.get_all(start=start, count=count, filter=filter, sort=sort)",
"title": ""
},
{
"docid": "17a18204c2c82c3c2ab72415e3521d3a",
"score": "0.51743394",
"text": "def get_geojson_observations(properties: List[str] = None, **kwargs) -> Dict[str, Any]:\n kwargs[\"mappable\"] = True\n observations = get_all_observations(kwargs)\n return as_geojson_feature_collection(\n (flatten_nested_params(obs) for obs in observations),\n properties=properties if properties is not None else DEFAULT_OBSERVATION_ATTRS,\n )",
"title": ""
},
{
"docid": "b8da1da84b676e50fcffe956c3f2836a",
"score": "0.5166958",
"text": "def get(self,count=500):\n\n return self.filter_proxies()[:count]",
"title": ""
},
{
"docid": "f81be40393f2c4d6f97b57f2def8730f",
"score": "0.5163707",
"text": "def fetchall(self):\n out = self._results[:]\n self._results = []\n return out",
"title": ""
},
{
"docid": "7166bea11ea241b40fa515c344b601d8",
"score": "0.5155878",
"text": "def iter_records(self, **kwargs):\n return self._iter_records(**kwargs)",
"title": ""
},
{
"docid": "78b5ee66b949bed8a6cc69dbe20a5ade",
"score": "0.51556313",
"text": "def get_objects(self):\n\n query = self.get_basequery()\n\n if getattr(self, 'paginate', True):\n page = self.get_page()\n pagination = query.paginate(page, per_page=self.get_per_page())\n\n return pagination.items, pagination\n\n return query.all(), None",
"title": ""
},
{
"docid": "66bc3d445eb0a3b794e48cb853e10900",
"score": "0.51527894",
"text": "def feed(self, n=20, page=0):\n _objects = [i for i in self.posts.all()]\n for i in self.campaigns.all():\n _objects.append(i)\n _objects.extend(p for p in i.posts.all())\n _objects.sort(key=lambda o: o.pub_date, reverse=True)\n\n start = page * n\n end = start + n\n # this will never return an `IndexError`\n # slicing a list out-of-bounds returns a truncated slice, or an empty list\n return _objects[start:end]",
"title": ""
},
{
"docid": "5d104086064a452946afa2e5861fc878",
"score": "0.5152389",
"text": "def observations_count(self):\n\n qs = Observation.objects.all()\n observations = set()\n\n for observation in qs:\n observation: Observation\n\n observations.add(observation.specimen_label)\n\n return observations",
"title": ""
},
{
"docid": "a19d16431e4f32b184273b07c7896b33",
"score": "0.5142812",
"text": "def download_all(self):\n parser = PageParser(self.query,self.numResults,self.subcorpus,\n self.mode,self.nLeft,self.nRight)\n all_res = parser.extract_results()\n n_results = min(self.numResults,parser.occurrences)\n for i in range(self.per_page,n_results,self.per_page):\n parser.pagenum = i\n all_res += parser.extract_results()\n if len(all_res) > self.numResults:\n all_res = all_res[:self.numResults]\n return all_res",
"title": ""
},
{
"docid": "cd926c005ff344fbee36cfbf72cb4cd0",
"score": "0.51314485",
"text": "def _fetch_all(self):\n if self._result_cache is None:\n self._result_cache, self._pagination_item = self.model.get(self.request_options)",
"title": ""
},
{
"docid": "3df7e8b0ad4ccc49fabfb096c487f146",
"score": "0.51308376",
"text": "def get_all_pred_records():\n session = controller.connect_to_database()\n result = session.query(Prediction).all()\n preds = convert_pred_results(result)\n session.close()\n return preds",
"title": ""
},
{
"docid": "acefbb95634855a960aa2b06fe50733a",
"score": "0.5130649",
"text": "def get_all_records(self):\n pass",
"title": ""
},
{
"docid": "29d0a74cb95cabfb4f5e181ef36696a5",
"score": "0.512669",
"text": "def test_count_observations(self):\n parser = WebObsResultsParser(RESULTS_HTML)\n observations = parser.get_observations()\n self.assertEqual(len(observations), 1)",
"title": ""
},
{
"docid": "a051f66df8b9d388076b1d5b714825cd",
"score": "0.5122111",
"text": "def get_all(self, q=None, limit=None):\r\n q = q or []\r\n\r\n if limit and limit < 0:\r\n raise ClientSideError(_(\"Limit must be positive\"))\r\n kwargs = _query_to_kwargs(q, storage.SampleFilter.__init__)\r\n f = storage.SampleFilter(**kwargs)\r\n return map(Sample.from_db_model,\r\n pecan.request.storage_conn.get_samples(f, limit=limit))",
"title": ""
},
{
"docid": "f3240c57005d7f136ea27dfcf65b4f31",
"score": "0.5119736",
"text": "def get_all(self, start=0, count=-1, filter='', sort='', scope_uris=''):\n result = self._helper.get_all(start=start,\n count=count,\n filter=filter,\n sort=sort,\n scope_uris=scope_uris)\n\n return result",
"title": ""
},
{
"docid": "dc4491b54f9637db755690a2497fce95",
"score": "0.5110192",
"text": "def observations(self, main_type, sub_type, unique_id, owner=None, params=None):\n params = params or {}\n\n if owner:\n params['owner'] = owner\n\n if not sub_type:\n url = f'/v2/{main_type}/{unique_id}/observations'\n else:\n url = f'/v2/{type}/{sub_type}/{unique_id}/observations'\n\n r = self._get(url, params)\n self.log.debug(f'status code: {r.status_code}')\n self.log.trace(f'url: {r.request.url}')\n return r",
"title": ""
},
{
"docid": "12de52d51ccf25cd11e657bd2dec7111",
"score": "0.508577",
"text": "def get_socrata_dataset_in_chunks(soda_api_url, limit=10000, order_by=\":id\"):\n offset = 0\n data_set_df = None\n\n while True:\n dataset_url = soda_api_url + \"&$order=\" + order_by + \"&$offset=\" + str(offset) + \"&$limit=\" + str(limit) + \"&$$exclude_system_fields=false\"\n print(dataset_url)\n slice_df = pd.read_json(dataset_url)\n record_count = slice_df[order_by].count()\n if data_set_df is None:\n data_set_df = slice_df\n else:\n data_set_df = data_set_df.append(slice_df)\n\n if record_count < limit:\n break\n else:\n data_set_df\n\n offset += limit\n\n return data_set_df",
"title": ""
},
{
"docid": "84f4dda8f08ed0332444be0e26073db9",
"score": "0.5080683",
"text": "def data_fetch(self):\n\t\tcurrent_date = date.today().strftime(\"%Y-%m-%d\")\n\t\tmaster_URL = \"\"\"{main_url}&end_at={end_dt_filter}\"\"\".format(main_url = self.URL, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t end_dt_filter = current_date)\n\t\t\n\t\tself.LOGGER.info(\"URL to fetch records --> {master_url}\\n\".format(master_url = master_URL))\n\t\t\n\t\ttry:\n\t\t\tself.LOGGER.info(\"Get request --> hitting the URL to fetch data\")\n\t\t\tresponse = requests.get(url = master_URL)\n\t\t\tself.LOGGER.info(\"Get request ::: Status --> Successful\\n\")\n\t\texcept Exception as e:\n\t\t\tself.LOGGER.info(\"Get request ::: Status --> Failed\")\n\t\t\tself.LOGGER.info(\"Reason ::: {failure_logs}\".format(failure_logs = str(e)))\n\t\t\traise \n\t\t\t\n\t\thistorical_records_json = response.json()\n\t\tself.LOGGER.info(\"Display sample records fetched from URL\\n\")\n\t\tprint(historical_records_json['rates'][list(historical_records_json['rates'].keys())[0]])\n\t\tprint(' ')\n\t\t\n\t\treturn historical_records_json",
"title": ""
},
{
"docid": "35300ecf05e1564b8da63cb06dfd16be",
"score": "0.50719196",
"text": "def fetch_data(self):\n for t in [HOURLY, DAILY, MONTHLY, YEARLY]:\n self.get_data_per_period(t)",
"title": ""
},
{
"docid": "496a9e23601670a04cf89d520120127d",
"score": "0.5069737",
"text": "def get_observations(self, input_image):\n output_image_default, observations = self.model.calculate(input_image, suppress=True)\n return output_image_default, self.transform_observations(observations)",
"title": ""
},
{
"docid": "0c1983f50251ae73cfd9d38e66f512a7",
"score": "0.5067351",
"text": "def dates():\r\n # Query all dates and temperature observations for last year\r\n results = session.query(Measurement.date, Measurement.tobs).\\\r\n filter(Measurement.date.between('2017-01-01', '2017-12-31')).all()\r\n\r\n #Convert query results to dictionary\r\n all_observations = []\r\n for temp in results:\r\n temp_dict = {}\r\n temp_dict[\"date\"] = temp.date\r\n temp_dict[\"tobs\"] = temp.tobs\r\n all_observations.append(temp_dict)\r\n\r\n # Convert list of tuples into normal list\r\n return jsonify(all_observations)",
"title": ""
},
{
"docid": "f2c44252487fa2f59a5d8f3e6a858284",
"score": "0.50631493",
"text": "def test_get_documents_offset_optional_params(index_with_documents):\n index = index_with_documents()\n response = index.get_documents()\n assert isinstance(response, list)\n assert len(response) == 20\n response_offset_limit = index.get_documents({\n 'limit': 3,\n 'offset': 1,\n 'attributesToRetrieve': 'title'\n })\n assert len(response_offset_limit) == 3\n assert response_offset_limit[0]['title'] == response[1]['title']",
"title": ""
},
{
"docid": "bba4f577965bb164fa7aeb8833b7a741",
"score": "0.50628805",
"text": "def displayCurrentObservations(self):\n return self.__currentState.availableObservations()",
"title": ""
},
{
"docid": "c51e88e9f78c002e44e6f3a94cce8d32",
"score": "0.5060686",
"text": "def generate_observations_rough(self, conditions):\n self.reward = self.calc_reward_function(conditions)\n\n # Check if we need to spin the tesselation\n if self.dither & (conditions.night != self.night):\n self._spin_fields()\n self.night = conditions.night.copy()\n\n # Let's find the best N from the fields\n order = np.argsort(self.reward)[::-1]\n # Crop off any NaNs\n order = order[~np.isnan(self.reward[order])]\n\n iter = 0\n while True:\n best_hp = order[iter*self.block_size:(iter+1)*self.block_size]\n best_fields = np.unique(self.hp2fields[best_hp])\n observations = []\n for field in best_fields:\n obs = empty_observation()\n obs['RA'] = self.fields['RA'][field]\n obs['dec'] = self.fields['dec'][field]\n obs['rotSkyPos'] = 0.\n obs['filter'] = self.filtername\n obs['nexp'] = self.nexp\n obs['exptime'] = self.exptime\n obs['field_id'] = -1\n obs['note'] = self.survey_name\n\n observations.append(obs)\n break\n iter += 1\n if len(observations) > 0 or (iter+2)*self.block_size > len(order):\n break\n return observations",
"title": ""
},
{
"docid": "30b47084f734e6563987122c8bef6fc6",
"score": "0.50575334",
"text": "def get_historical_data():",
"title": ""
},
{
"docid": "f401932db0f0d00b59bb5733a382891f",
"score": "0.5055959",
"text": "def retrieve_results(self):\n for a in data.get_user_top_albums(self.name,\"overall\"):\n self.all_albums.add((a[\"artist\"],a[\"name\"]))\n self.recent_albums = data.get_user_top_albums(self.name,\"12month\")\n return self._get_results()",
"title": ""
},
{
"docid": "ce93bc6604d366fd82b2fe13be0cc16c",
"score": "0.5038485",
"text": "def __query_all_data(self, query_func, reset_data=True, **query_params):\n data = []\n\n self.params[\"page\"] = 1\n self.params[\"per_page\"] = 100\n\n while not self.try_query(query_func, **query_params):\n pass\n\n data.extend(self.query_result[\"data\"])\n\n while(self.__update_page_request()):\n while not self.try_query(query_func, **query_params):\n pass\n\n data.extend(self.query_result[\"data\"])\n\n if reset_data:\n self.data = data\n else:\n self.data.extend(data)",
"title": ""
},
{
"docid": "7f1085ec73c83b089b8cb7d723ead536",
"score": "0.50355464",
"text": "def search(self, debug=True):\n inc=10000\n for price in range(0, 3000000, inc):\n if debug:\n print(\"From \" + str(price) + \" to \" + str(price + inc))\n res = self.__search(1, set_page_count=True, min_price=price, max_price=price + inc);\n if self.results is not None:\n print(self.results.shape)\n self.results = self.results.append(res)\n print(self.results.shape)\n else:\n self.results = res\n\n print(self.page_count)\n for page_number in range(2, self.page_count + 1):\n if debug:\n print(\"Getting page: \" + str(page_number))\n res = self.__search(page_number, min_price=price, max_price=price + inc)\n if self.results is not None:\n print(self.results.shape)\n self.results = self.results.append(res)\n print(self.results.shape)\n else:\n self.results = res\n\n self.results = self.results.drop_duplicates(subset=['id'], keep=False)\n self.results = self.results.reset_index(drop=True)\n self.results = self.results.sort_values(by='price')\n return self.results",
"title": ""
},
{
"docid": "6b7fdb6e934f785dbcc9c3688c119029",
"score": "0.502903",
"text": "def fetchall(self, api: Airtable, **kwargs) -> GenAny:\n for page in api.get_iter(**kwargs):\n for record in page:\n yield Model.from_airtable(record)",
"title": ""
},
{
"docid": "a244d0003c8d46d8be3844ca44ddccfa",
"score": "0.50193834",
"text": "def get_datarecords():\n try:\n return record_models.Dataset.objects.all()\n except:\n raise",
"title": ""
},
{
"docid": "6df9be311a6101b53aeadf02ab9b1f37",
"score": "0.50192374",
"text": "def get_all(self): # noqa\n if self.number_calls is None:\n total_resources = self._get_count(self.url)\n logger.info(f\"there are {total_resources} matching resources for {self.url}\")\n self.number_calls = int(np.ceil(total_resources / PAGE_SIZE))\n\n if self.number_calls == 0:\n return self._get_data([])\n\n if self.parallel_requests:\n urls = []\n for i in range(self.number_calls):\n urls.append(\n (\n self.auth.token,\n f\"{self.url}&_getpagesoffset={i*PAGE_SIZE}&_count={PAGE_SIZE}\",\n )\n )\n\n p = multiprocessing.Pool()\n results = p.starmap(process_function, urls)\n p.close()\n self.pbar.update(self.bar_frac)\n else:\n results = []\n\n next_url = self.url\n while next_url:\n next_url = self._fix_next_url(next_url)\n response = self._get_response(next_url)\n page_results = self._get_data(response.results)\n\n results.append(page_results)\n next_url = response.next_url\n\n self._concat(results)\n\n return self.df",
"title": ""
},
{
"docid": "4032bcc05ae057854fd4f8dc68c4b58b",
"score": "0.5015355",
"text": "def retrieve_documents(self, assessor, query_id, doc_retrieval_count):\n\n dao = DAO()\n\n if not dao.index_exists():\n dao.create_index()\n existing_doc_count = 0\n else:\n existing_doc_count = dao.get_doc_count(assessor, query_id)\n\n print 'existing count', existing_doc_count\n\n if existing_doc_count < doc_retrieval_count:\n dao.add_additional_docs(existing_doc_count, doc_retrieval_count, assessor, query_id)\n\n assessment_list = dao.retrieve_docs(assessor, query_id, doc_retrieval_count)\n\n doc_list = []\n\n for doc in assessment_list:\n\n doc_index_id = doc.get_index_id()\n doc_query_id = doc.get_query_id()\n doc_assessor_ = doc.get_assessor()\n doc_url = doc.get_document()\n doc_grade = doc.get_grade()\n\n doc_list.append({'index_id': doc_index_id,\n 'query_id': doc_query_id,\n 'assessor': doc_assessor_,\n 'document': doc_url,\n 'grade': doc_grade})\n\n return doc_list",
"title": ""
},
{
"docid": "9c31f5097481405d172d8993e3ff3ed5",
"score": "0.50085855",
"text": "def _fetchResults(self, limit, context, obj, token=None):\n from google.appengine.ext.ndb import query as ndb_query\n\n query = self.getQuery(context, obj)\n assert isinstance(query, ndb_query.Query)\n\n kwargs = {\n 'produce_cursors': True,\n 'keys_only': self.getKeysOnly(context, obj),\n 'deadline': self.getDeadline(context, obj),\n 'read_policy': self.getReadPolicy(context, obj),\n }\n\n if token:\n kwargs['start_cursor'] = ndb_query.Cursor.from_websafe_string(token)\n\n results, cursor, more = query.fetch_page(limit, **kwargs)\n\n obj[CONTINUATION_MORE_RESULTS_KEY] = more\n obj[self.__NEXT_TOKEN] = more and cursor.to_websafe_string() or None\n return results",
"title": ""
},
{
"docid": "6e99ebab3e00d753f99bd48feffc0af8",
"score": "0.5001199",
"text": "def get_entire_dataset(self, dataset_id, order_by):\n count = self.get_dataset_count(dataset_id)\n limit = 100000\n dataset = []\n for offset in range(0, count, limit):\n dataset.extend(self.client.get(dataset_id, order=order_by,\n offset=offset, limit=limit))\n return dataset",
"title": ""
},
{
"docid": "5245604d0fd6ed5c7058c342a5c81f15",
"score": "0.49992168",
"text": "def get_sales():\n response = requests.get(base_url + \"/api/v1/sales?page=1\")\n data = response.json()\n sales_df = pd.DataFrame(data['payload']['sales'])\n \n total_pages = data['payload']['max_page']\n\n for i in range(2, total_pages+1): \n print(f\"Retrieving sales from page {i}\")\n response = requests.get(base_url + f\"/api/v1/sales?page={i}\")\n data = response.json()\n sales_df = pd.concat([sales_df, pd.DataFrame(data['payload']['sales'])])\n \n return sales_df.reset_index()",
"title": ""
},
{
"docid": "8d5893867be77d59ae9da37669831492",
"score": "0.49958277",
"text": "def fetch_stix_objects_from_api(self, test: bool = False, **kwargs):\n data = []\n\n server = Server(url=self._base_url, auth=TokenAuth(key=self._api_key), verify=self._verify,\n proxies=self._proxies)\n\n for api_root in server.api_roots:\n for collection in api_root.collections:\n for bundle in as_pages(collection.get_objects, per_request=100, **kwargs):\n data.extend(bundle.get('objects'))\n if test:\n return data\n\n self.objects_data[kwargs.get('type')] = data",
"title": ""
},
{
"docid": "dd56f6e661837a2fac6e2227dc68b366",
"score": "0.49912074",
"text": "def get_data(self):\n\n parameters = {\n \"action\": \"query\",\n \"prop\": \"extracts|info\",\n \"inprop\": \"url\",\n \"explaintext\": True,\n \"exsentences\": 2,\n \"exlimit\": 1,\n \"generator\": \"geosearch\",\n \"ggsradius\": 10000,\n \"ggscoord\": f\"{self.latitude}|{self.longitude}\",\n \"format\": \"json\",\n }\n\n headers = {\"date\": DATE, \"user-agent\": APP_NAME}\n response = requests.get(\n self.api_url, params=parameters, headers=headers, timeout=3\n )\n\n if response.status_code == 200:\n content = response.json()\n if content.get(\"query\", \"\") != \"\":\n if content.get(\"query\").get(\"pages\", \"\") != \"\":\n places = content.get(\"query\").get(\"pages\")\n places_list = []\n\n # select the nearest place\n for place in places:\n # select the smallest index\n places_list.append(\n (\n places.get(place).get(\"index\"),\n places.get(place).get(\"pageid\"),\n )\n )\n\n place_selected = min(places_list)\n pageid_selected = str(place_selected[1])\n\n return {\n \"title\": content.get(\"query\")\n .get(\"pages\")\n .get(pageid_selected)\n .get(\"title\", \"\"),\n \"extract\": content.get(\"query\")\n .get(\"pages\")\n .get(pageid_selected)\n .get(\"extract\", \"\"),\n \"fullurl\": content.get(\"query\")\n .get(\"pages\")\n .get(pageid_selected)\n .get(\"fullurl\", \"\"),\n }\n\n else:\n err = f\"Mediawiki API : '{response.status_code}' error occurred\"\n print(err)",
"title": ""
}
]
[
{
"docid": "671597d49d82516294ea16dbefe37f43",
"score": "0.58281344",
"text": "def match_ndc(ndc):\n\n def find_recent_db():\n \"\"\" returns most recent db csv file. Gives error pop up if no db file found \"\"\"\n files = glob.glob('ndcdb-*.csv')\n if len(files) == 0:\n error_msg = \"No NDC database file found (prefixed with ndcdb-[date].csv). This means that either \" \\\n \"the file doesn't exist or was renamed to something else. Please generate that file\"\n popupmsg(error_msg)\n print(error_msg)\n else:\n pattern = re.compile(r\"^ndcdb-(\\d{4}-\\d{2}-\\d{2}).csv$\")\n dates_list = []\n for file in files:\n str_date = pattern.match(file).groups()[0]\n date = datetime.strptime(str_date, '%Y-%m-%d')\n dates_list.append(date)\n\n formatted_date = datetime.strftime(max(dates_list).date(), '%Y-%m-%d')\n\n recent_db_file = 'ndcdb-' + formatted_date + '.csv'\n lastupdated_label[\"text\"] = \"Using data last updated on: \" + formatted_date\n return recent_db_file\n\n recent_file = find_recent_db()\n df = pd.read_csv(recent_file)\n print('Using db from this file: ', recent_file)\n\n # print(\"First entry of ndc package code no hyphen: \", df['NDCPACKAGECODE_nohyphen'][0], \", and type: \", type(df['NDCPACKAGECODE_nohyphen'][0]))\n # print(\"First entry of ALL_NDC: \", df['ALL_NDC'][0], \", and type: \", type(df['ALL_NDC'][0]))\n\n # below will be if ndc in df['NDCPACKAGECODE_nohyphen'].values: -- if ndc is found in a list of NDCs with no hyphen\n\n # 012037086010010821TXZ811R4ZKNC\t1723022810MT005\n # if df['ALL_NDC'].apply(lambda x: ndc in x).sum() == 1:\n # str_to_list = lambda x: x.strip('][').split(', ')\n if df['ALL_NDC_NO_HYPHEN'].apply(lambda x: ndc in x).any():\n index = df.loc[df['ALL_NDC_NO_HYPHEN'].apply(lambda x: ndc in x)].index[0]\n\n # assign \"raw_ndc\" with hyphens by looking through column with list of all ndcs under a package\n ndc_list = str_to_list(df['ALL_NDC'][index])\n for check_ndc in ndc_list:\n if ndc == remove_hyphen(check_ndc):\n raw_ndc = check_ndc\n\n pack_desc = df['PACKAGEDESCRIPTION'][index]\n brand = df['PROPRIETARYNAME'][index]\n generic = df['NONPROPRIETARYNAME'][index]\n dosage_form = df['DOSAGEFORMNAME'][index]\n route = df['ROUTENAME'][index]\n mfg = df['LABELERNAME'][index]\n strength = df['ACTIVE_NUMERATOR_STRENGTH'][index]\n str_units = df['ACTIVE_INGRED_UNIT'][index]\n pharm_class = df['PHARM_CLASSES'][index]\n\n def eleven_dig(ten_digit):\n \"\"\" changes 10 digit ndc to 11 digit ndc adding the zero to the appropriate place \"\"\"\n lst = ten_digit.split(\"-\")\n lst[0] = lst[0].zfill(5)\n lst[1] = lst[1].zfill(4)\n lst[2] = lst[2].zfill(2)\n return ''.join(lst)\n\n ten_dig = eleven_dig(raw_ndc)\n return True, raw_ndc, ten_dig, pack_desc, brand, generic, dosage_form, route, mfg, strength, \\\n str_units, pharm_class\n else:\n error_msg = \"NDC doesn't exist in database? If drug is new or unofficial (or maybe OTC? not sure if those are\" \\\n \" in there.., may require updating database?\"\n print(error_msg)\n popupmsg(error_msg)",
"title": ""
}
]
[
{
"docid": "e0c3d258c19d4d7fa03901c154ec7bfa",
"score": "0.5899773",
"text": "def inspect_db(self):",
"title": ""
},
{
"docid": "4c75ae12209357a13cd26ecb99d48a79",
"score": "0.56975514",
"text": "def getdb(self):\n if self.cl._db is not None:\n return self.cl._db\n return discretezoo.DEFAULT_DB",
"title": ""
},
{
"docid": "2d7600e1e532f9e6b12b0494dbf3a5dd",
"score": "0.5669286",
"text": "def ReadNDCDatabase(SearchText, SearchType, SearchSize):\n\n NDCRecords = []\n NDCEnhancedArray = []\n\n\n if SearchType==\"ndc\":\n SearchText = SearchText.upper()\n SearchText = SearchText.strip()\n SearchError = False\n CleanNDC = SearchText.replace(\"-\", \"\")\n if len(CleanNDC) == 9:\n qNDC = NDCLookup.query(NDCLookup.ninedigitndc == CleanNDC)\n else:\n qNDC = NDCLookup.query(NDCLookup.ndc == CleanNDC)\n\n elif SearchType == \"name\":\n SearchText = SearchText.upper()\n SearchText = SearchText.strip()\n SearchError = len(SearchText) == 0\n qNDC = NDCLookup.query(NDCLookup.proprietaryname == SearchText)\n\n elif SearchType == \"ingredient\":\n QueryString = IngredientQuery(SearchText)\n logging.info(\"Executing Ingredient Search: %s\", QueryString)\n SearchError = len(QueryString) == 0 \n qNDC= NDCLookup.gql(QueryString)\n\n elif SearchType == \"image\":\n SearchText = SearchText.upper()\n SearchText = SearchText.strip()\n QueryString = NDC9ArrayQuery(SearchText)\n logging.info(\"Executing NDC9 Search: %s\", QueryString)\n SearchError = len(QueryString) == 0\n qNDC= NDCLookup.gql(QueryString)\n\n elif SearchType == \"active\":\n SearchText = SearchText.upper()\n SearchText = SearchText.strip()\n SearchError = len(SearchText) == 0\n qNDC = NDCLookup.query(NDCLookup.substancename == SearchText)\n \n else:\n SearchError = True\n\n if not(SearchError) : NDCRecords = qNDC.fetch(limit=int(SearchSize))\n\n for NDCRecord in NDCRecords:\n NDCRecord.ndc = NDCTenDigitFormat(NDCRecord.ndc, NDCRecord.format)\n NDCRecord.proprietaryname = NDCRecord.proprietaryname.title()\n NDCRecord.nonproprietaryname = NDCRecord.nonproprietaryname.title()\n NDCRecord.packagedescription = NDCRecord.packagedescription.title()\n\n substancelist = []\n\n for substance in NDCRecord.substancelist:\n substancelist.append(substance.title())\n\n NDCRecord.substancelist = substancelist\n\n\n TemplateValues = {\"NDCRecordArray\": NDCRecords,\n \"SearchText\": SearchText,\n \"SearchType\": SearchType,\n \"SearchSize\": SearchSize}\n\n return TemplateValues",
"title": ""
},
{
"docid": "808d9a25b42b6f3d15a009c3bbb5e49e",
"score": "0.5438229",
"text": "def getMainDbDN(self):\n \n \n if self.conn.search(search_base=\"cn=config\", search_scope=LEVEL,\n search_filter=\"(olcDbDirectory=/opt/gluu/data/main_db)\",\n attributes='*'):\n if self.conn.response:\n return self.conn.response[0]['dn']",
"title": ""
},
{
"docid": "faa84fb710c5caa6a1b2549f07ff4aea",
"score": "0.53561217",
"text": "def db_for_read(self, model, **hints):\n return random.choice(['replica_1'])",
"title": ""
},
{
"docid": "e00622aa8d0d49fff6a6a11e28c41937",
"score": "0.5321135",
"text": "def db(self) -> ConfigNodePropertyString:\n return self._db",
"title": ""
},
{
"docid": "abef234fddbac44609b1aafb0e2cce6d",
"score": "0.53086776",
"text": "def get_db():\n if not hasattr(g, 'mongo_db'):\n g.mongo_db = pymongo.MongoClient('db')\n return g.mongo_db.filterforge",
"title": ""
},
{
"docid": "6099d13b58ec8776840d902d254ade31",
"score": "0.5238932",
"text": "def get_db():\n pass",
"title": ""
},
{
"docid": "ef33563c491377b11678fe20f4dfc96f",
"score": "0.5220712",
"text": "def db_for_read(self, model, **hints):\n if model._meta.app_label == 'fblog':\n return 'fblog_db'\n return None",
"title": ""
},
{
"docid": "389799e6ff7064b6ebf88447c09f4e1e",
"score": "0.52157825",
"text": "def _db_exist(self, source):\n\t\tpass",
"title": ""
},
{
"docid": "b72f57ab10518ae657f2cbf0f2e1abe7",
"score": "0.51940084",
"text": "def dispatch_database():\n pass",
"title": ""
},
{
"docid": "75c9dd6cbc580743d1a9d43792f6ae33",
"score": "0.51888806",
"text": "def db(self):\r\n return self.__db",
"title": ""
},
{
"docid": "fd2ed4dd2b09b95e6f22bfbd3c33c0b1",
"score": "0.5169626",
"text": "def get_db_info(db_coll_dict,config_path):\n ip,username,password=load_mongo_account(config_path)\n client=MongoClient(ip,username=username,password=password)\n detail_dict={}\n for db in db_coll_dict.keys():\n meta_client=client[db][db+\".meta\"]\n detail_dict[db]={}\n\n detail_dict[db]['episodes']=[i for i in client[db].list_collection_names() if \".meta\" not in i and \".files\" not in i and \".chunks\" not in i and \".vis\" not in i and \".scans\" not in i ]\n\n # Add task description\n detail_dict[db]['task_description']=get_task_description(meta_client)\n\n # Add class and id info for entities\n detail_dict[db]['entities']={}\n class_info=get_all_class(meta_client)\n for each_object in class_info:\n obj_id=each_object['id']\n obj_class=each_object['class']\n if obj_class not in detail_dict[db]['entities'].keys():\n detail_dict[db]['entities'][obj_class]=[obj_id]\n else:\n detail_dict[db]['entities'][obj_class].append(obj_id)\n \n # Add skel entities\n detail_dict[db]['skels']={}\n bone_info=get_bone(meta_client)\n for each_bone in bone_info:\n bone=each_bone['bone']\n skel=each_bone['skel']\n if skel not in detail_dict[db]['skels'].keys():\n detail_dict[db]['skels'][skel]=[bone]\n else:\n detail_dict[db]['skels'][skel].append(bone)\n\n # Add camera view\n detail_dict[db]['camera_views']=get_camera_view(meta_client)\n # pprint.pprint(detail_dict)\n \n return detail_dict",
"title": ""
},
{
"docid": "cea77859580f584522b8b98ba0892c08",
"score": "0.5169591",
"text": "def legacy_db_configured(self):\n if (\n self.kv.get(\"db_host\")\n and self.kv.get(\"db_port\")\n and self.kv.get(\"db_db\")\n and self.kv.get(\"db_user\")\n and self.kv.get(\"db_pass\")\n ):\n return True\n return False",
"title": ""
},
{
"docid": "acb9ce2f999a8235e840fd94d2128e30",
"score": "0.5157214",
"text": "def dbc_db(self):\n return self.__dbc_db",
"title": ""
},
{
"docid": "c986b9db2d7cd8d6e5c55fb2b65759b5",
"score": "0.5132187",
"text": "def db(self):\n return self._conf['db']",
"title": ""
},
{
"docid": "59c589aa63afdf12dde81088a8648e97",
"score": "0.51253587",
"text": "def get_db_state():\n print \"\\nreference implementation of get_db_state\\n\"\n return None",
"title": ""
},
{
"docid": "f8b6921814cd96eaf5c4e4836d56d0d0",
"score": "0.5109895",
"text": "def CGD(cls):\n cls.Namespace(\"http://www.candidagenome.org/cgi-bin/locus.pl?dbid=\")",
"title": ""
},
{
"docid": "30d7f1379c3f7f0f80343538209cc154",
"score": "0.5109482",
"text": "def check_mongodb_state(config_path):\n ip,username,password=load_mongo_account(config_path)\n client=pymongo.MongoClient(ip,username=username,password=password,serverSelectionTimeoutMS=100)\n try:\n print(client.list_database_names())\n return True\n except Exception as e:\n return False",
"title": ""
},
{
"docid": "5a52a415663f3ab4721fe938d3b92013",
"score": "0.5103297",
"text": "def test_ngen_with_known_db(runner):\n result = runner.invoke(cli.main, ['list', '-d' , '~/.keepassxc/worth.kdbx'])\n assert result.exit_code == 0",
"title": ""
},
{
"docid": "5fc530f67300c30c79dc8432d0146653",
"score": "0.5096329",
"text": "def db_for_read(self, model, **hints):\n if model._meta.app_label == 'auth':\n return 'auth_db'\n return None",
"title": ""
},
{
"docid": "0db5d66430443a9b54386c3076b54d8b",
"score": "0.5089351",
"text": "def __init__(self,db):\n self.db = db",
"title": ""
},
{
"docid": "d1d22064ce85a9d9e5421703f72a124a",
"score": "0.5073341",
"text": "def create_db_from_dict(self, ref_dict, database = 'smart_meter'):\n\n\n\n\t\tprint(\"Database {} has been created from the dictionary\".format(database))",
"title": ""
},
{
"docid": "e7129aa5fee58918ea3e721d4b5062e8",
"score": "0.50717753",
"text": "def __init__(self, db):\n self._db = db",
"title": ""
},
{
"docid": "e7129aa5fee58918ea3e721d4b5062e8",
"score": "0.50717753",
"text": "def __init__(self, db):\n self._db = db",
"title": ""
},
{
"docid": "6e422fbc834c5ce8795ad8afb0331382",
"score": "0.50672495",
"text": "def _get_database_id(self, db_name):\n for expert_database in Database.objects.all():\n if re.match(expert_database.label, db_name, re.IGNORECASE):\n return expert_database.id\n return None",
"title": ""
},
{
"docid": "be694c1d9a541835fe2b57be9e5d36ea",
"score": "0.506357",
"text": "def _init_db(self):\n raise NotImplementedError(\"Use subclasses of Database.\")",
"title": ""
},
{
"docid": "14377ae4a1264e70fb40459e55696168",
"score": "0.5059923",
"text": "def checkAccesslogDB(self):\n \n return self.conn.search(search_base='cn=config',\n search_filter='(olcSuffix=cn=accesslog)',\n search_scope=SUBTREE, attributes=[\"*\"])",
"title": ""
},
{
"docid": "f884bbe9255899e5ccad50d1aa7da105",
"score": "0.50476664",
"text": "def db():\n pass",
"title": ""
},
{
"docid": "4ec067f043854af130dc68d6310bb290",
"score": "0.5025044",
"text": "def db_for_read(self, model, **hints):\n if model._meta.app_label == 'mapdata':\n return 'msemap_db'\n return None",
"title": ""
},
{
"docid": "782deb8c0adabe9849cfdf86e50738ea",
"score": "0.501365",
"text": "def db_show(self, name) -> bool:\n found = False\n dbs = self._index_local_databases()\n\n for db in dbs:\n if db == name:\n found = True;\n\n updated = datetime.datetime.fromtimestamp(dbs[db]['last modified']).strftime('%Y-%m-%d %H:%M:%S')\n checked = datetime.datetime.fromtimestamp(dbs[db]['last checked']).strftime('%Y-%m-%d %H:%M:%S')\n self.logger.info(f\"Database: {db}\")\n if dbs[db]['last modified'] == 0:\n self.logger.info(\" last modified: not downloaded\")\n else:\n self.logger.info(f\" last modified: {updated}\")\n if dbs[db]['last checked'] == 0:\n self.logger.info(\" last checked: n/a\")\n else:\n self.logger.info(f\" last checked: {checked}\")\n self.logger.info(f\" url: {dbs[db]['url']}\")\n if db.endswith(\".cvd\"):\n self.logger.info(f\" local version: {dbs[db]['local version']}\")\n if len(dbs[db]['CDIFFs']) > 0:\n self.logger.info(f\" CDIFFs: \\n{json.dumps(dbs[db]['CDIFFs'], indent=4)}\")\n return True\n\n if not found:\n self.logger.error(f\"No such database: {name}\")\n return found",
"title": ""
},
{
"docid": "bebd245ae8ad61090e8d6a3ec3a0f552",
"score": "0.4993931",
"text": "def test_003_dbtype(self) -> None:\n self.assertEqual(self.ds.dbtype, \"dynamo\")",
"title": ""
},
{
"docid": "e536412c7386152d4bae645d367cb220",
"score": "0.4969846",
"text": "def db():\n raise NotImplementedError()",
"title": ""
},
{
"docid": "1061a95e9c40d3e60d39cc5f11b0033a",
"score": "0.49446747",
"text": "def get_db():\n if not hasattr(g, 'db_con'):\n g.db_con = connect_db()\n return g.db_con",
"title": ""
},
{
"docid": "3bca35078fe59913957c9f19e658127a",
"score": "0.49268067",
"text": "def set_candb(self, db):\n self.candb = db",
"title": ""
},
{
"docid": "9bcdaf6f50aeeb2c3ed60f75fc79ce09",
"score": "0.49224553",
"text": "def db(self):\n# log.info('fetching request.db')\n return DBSession",
"title": ""
},
{
"docid": "aec98e1603bb3a5d602c25f3edde63ad",
"score": "0.4914233",
"text": "def mogo_db_info(self) -> str:\n return self.config['database']['mongo']['information']",
"title": ""
},
{
"docid": "cf985bf73df44a6316c55391fcc69e3e",
"score": "0.4912167",
"text": "def db_variables():\n\n return (\"azurehack.n8sw8.gcp.mongodb.net\",\"backend\",\"azure2020\",\"admin\",\"azurehack\")",
"title": ""
},
{
"docid": "a465304cf06a63ebd646ac049c4a6702",
"score": "0.490552",
"text": "def dbformat(self):\n\n pass",
"title": ""
},
{
"docid": "7ebea65531837f781815f8a5d7cdeae1",
"score": "0.48924413",
"text": "def getDbCount(self):\n\t\tc= self.ds.value(\"dbCounter\",0).toInt()[0]\n\t\treturn c",
"title": ""
},
{
"docid": "c8a44427758ee3855d217be560bc8631",
"score": "0.4883515",
"text": "def db_for_write(self, model, **hints):\n if model._meta.app_label == 'fblog':\n return 'fblog_db'\n return None",
"title": ""
},
{
"docid": "3e2aff155f9b1b697a164fdacc3ab9f6",
"score": "0.4879035",
"text": "def get_vesync_database_info_from_nacos(self) -> dict:\n nacos_client = nacos.NacosClient(self.nacos_server.host, namespace=self.stage_namespace_id)\n self.set_nacos_client_debug(nacos_client)\n configs = nacos_client.get_config(settings.VESYNC_DATABASE_DATA_ID,\n settings.VESYNC_DATABASE_GROUP,\n no_snapshot=True)\n logger.debug(f\"configs (data id: {settings.VESYNC_DATABASE_DATA_ID}, group: {settings.VESYNC_DATABASE_GROUP}): \"\n f\"{configs}\")\n configs = common.load_properties_from_string(configs)\n database_info = {\n \"host\": configs[settings.KEY_TO_VESYNC_DATABASE_HOST],\n \"port\": int(configs[settings.KEY_TO_VESYNC_DATABASE_PORT]),\n \"user\": configs[settings.KEY_TO_VESYNC_DATABASE_USER],\n \"password\": configs[settings.KEY_TO_VESYNC_DATABASE_PASSWORD],\n \"database\": configs[settings.KEY_TO_VESYNC_DATABASE_NAME],\n \"charset\": \"utf8\",\n \"cursorclass\": pymysql.cursors.DictCursor\n }\n logger.info(f\"database info used to connect: {database_info}\")\n return database_info",
"title": ""
},
{
"docid": "ee111f5320870999c82485c6eb76104f",
"score": "0.48773244",
"text": "def internalDB_process(self, *args, **kwargs):\n d_ret = self.dcm.internalDB_process(*args, **kwargs)\n return d_ret",
"title": ""
},
{
"docid": "300a2074558c1aa272dc718eae3183ec",
"score": "0.48739982",
"text": "def preferred_dc(self):\n return self._preferred_dc",
"title": ""
},
{
"docid": "a779cf9d9baf7039fa83e9cf136e8ffe",
"score": "0.4869784",
"text": "def _get_db(cls):\r\n return get_db(cls._meta.get(\"db_alias\", DEFAULT_CONNECTION_NAME ))",
"title": ""
},
{
"docid": "d5c97ce33139911dad3ffd7a0a2253c7",
"score": "0.48628843",
"text": "def __init__(self, db) -> None:\n self._db = db",
"title": ""
},
{
"docid": "214f1e1b15658dd10a2111c5317b104e",
"score": "0.48577946",
"text": "def filter_databases(rc):\n dbs = rc.databases\n public_only = rc._get(\"public_only\", False)\n if public_only:\n dbs = [db for db in dbs if db[\"public\"]]\n dbname = rc._get(\"db\")\n if dbname is not None:\n dbs = [db for db in dbs if db[\"name\"] == dbname]\n elif len(dbs) == 1:\n rc.db = dbs[0][\"name\"]\n rc.databases = dbs",
"title": ""
},
{
"docid": "7079b600de1b490e5a7b5d5d76912821",
"score": "0.485489",
"text": "def _initdb(self, db=None):\n if self._db is not None:\n return\n if db is None:\n self._db = discretezoo.DEFAULT_DB\n else:\n self._db = db",
"title": ""
},
{
"docid": "d05dfa6c864d6c3db4bc0ae5b5620b9e",
"score": "0.48525262",
"text": "def load(self, db):\n\t\tpass",
"title": ""
},
{
"docid": "b10a78718803ce0ec49b89b80a3f64c4",
"score": "0.48498744",
"text": "def db(self) -> Any:\n return self._db",
"title": ""
},
{
"docid": "a688e70742fe7fb640e8b7e42eed47fc",
"score": "0.48465395",
"text": "def get_master_mongo_conn(env='dev'):\n if env == 'prod':\n MONGO_URI = 'mongodb://chefd_prod:[email protected]:35459/'\n MONGO_URI += 'prod_chefd_1_gb?replicaSet=rs-ds135459'\n else:\n MONGO_URI = 'mongodb://staging_chefd:[email protected]:41098/'\n MONGO_URI += 'staging-chefd-replica'\n client = pymongo.MongoClient(MONGO_URI)\n db = client.get_default_database()\n return db",
"title": ""
},
{
"docid": "c1a9648f2d860cb95a6f5dd2859d1061",
"score": "0.48406836",
"text": "def db(self, db: ConfigNodePropertyString):\n\n self._db = db",
"title": ""
},
{
"docid": "7c40713744c3a79de6b7d1dc95549911",
"score": "0.4830416",
"text": "def _get_db():\n global db_conn\n\n if not db_conn:\n db_conn = _connect_db(DSN)\n return db_conn",
"title": ""
},
{
"docid": "45adfa4a8a41cc5ced4962b86c5acad3",
"score": "0.4827729",
"text": "def db_for_read(self, model, **hints):\n if model._meta.db_table in self.call_center_tables:\n return 'call_center'\n return None",
"title": ""
},
{
"docid": "6bf6b0932ec62e7aa0d6b98cf3ea7b1a",
"score": "0.48261854",
"text": "def get_database_for(self, id):\n url=\"https://app.crepc.sk/oai\"\n params={\"verb\":\"GetRecord\",\n \"metadataPrefix\":\"xml-crepc2\",\n \"identifier\":\"oai:crepc.sk:database/\"+id\n }\n r = requests.get(url = url, params = params)\n return r.content.decode(\"utf-8\")",
"title": ""
},
{
"docid": "31a7f676bab2ed16a7cf35919b03e346",
"score": "0.4823183",
"text": "def _get_nsd_catalog(self):\n return self.__nsd_catalog",
"title": ""
},
{
"docid": "e2178508a7adc4d9478519f598f8e8e0",
"score": "0.48205787",
"text": "def before_request():\n g.db = connect_db(app.config['ACCOUNT'], app.config['DBNAME'])\n #Entry.set_db(g.db)",
"title": ""
},
{
"docid": "1049a65383a146966505e8e8bcc12bd6",
"score": "0.48180845",
"text": "def connect_db():\n pass",
"title": ""
},
{
"docid": "6c25bd4fb73f28e625785e71da259f68",
"score": "0.4813999",
"text": "def db(self) -> Database[DBRecord]:\n raise NotImplementedError",
"title": ""
},
{
"docid": "31240513e1360995e5ebc17db19072c7",
"score": "0.4811872",
"text": "def db_for_read(self, model, **hints):\n if model._meta.app_label == 'travel_buddy':\n return 'travel_buddy_db'\n return None",
"title": ""
},
{
"docid": "5e7792b1126518cc230e39697c64c0ad",
"score": "0.48093012",
"text": "def test_db(config_parser):\n mongo_host = config_parser.get('mongo','db-host')\n mongo_db = config_parser.get('mongo','db-name')\n mongo_port = config_parser.getint('mongo','db-port')\n mongo_user = config_parser.get('mongo','username')\n mongo_pwd = config_parser.get('mongo','password')\n mongo_auth = config_parser.get('mongo','auth-db')\n client = MongoClient(mongo_host)\n db = client[mongo_db]\n db.authenticate(mongo_user,mongo_pwd,source=mongo_auth)\n logger.debug(\"Successfully connected to %s\"%db)\n return db",
"title": ""
},
{
"docid": "bbabd11b6efa7da267c65435dd760e60",
"score": "0.4806655",
"text": "def get_uri_db_tny(self,id_db_tny):\n \n if id_db_tny:\n if id_db_tny in [\"softrans_sb\",\"softlog\"]:\n db = self.get_session.query(self.stringconexoes_model).filter(self.stringconexoes_model.banco_dados==id_db_tny).first()\n else:\n db = self.get_session.query(self.stringconexoes_model).filter(self.stringconexoes_model.banco_dados==\"softlog_\" + id_db_tny).first()\n \n if db is not None:\n return db.db_uri_db_pg()\n\n if id_db_tny:\n db = self.get_session.query(self.stringconexoes_model).filter(self.stringconexoes_model.id_string_conexao==id_db_tny).first()\n else:\n return None\n\n return db.db_uri_db_pg()",
"title": ""
},
{
"docid": "ae7dc1377dd58edfea824a5393775a31",
"score": "0.4789945",
"text": "def db_for_write(self, model, **hints):\n if model._meta.app_label == 'auth':\n return 'auth_db'\n return None",
"title": ""
},
{
"docid": "f5cd05e589f912f524c8410fc99add35",
"score": "0.47840783",
"text": "def __init__(self, db_con_str=\"dbname=catalogdb\"):\n self.conn = psycopg2.connect(db_con_str)",
"title": ""
},
{
"docid": "8776bb80d80432ccdeee4801baba1114",
"score": "0.47730455",
"text": "def get_db():\n if not hasattr(g, 'db_conn'):\n g.db_conn = db_connect()\n return g.db_conn",
"title": ""
},
{
"docid": "adf227e79ef42ab8883ed455430fb593",
"score": "0.4772139",
"text": "def _get_nsd(self):\n return self.__nsd",
"title": ""
},
{
"docid": "76847efaba7e5a8392258df064fcac5b",
"score": "0.47715753",
"text": "def get_database(bases_list, database_name, desired_key):\n\tthere = False\n\tfor base in bases_list:\n#\t\tprint base.name\n#\t\tprint database_name\n\t\tif str(base.name) == database_name:\n\t\t\td = base\n\t\t\tthere = True\n\tif not there:\n#\t\tprint 'not there'\n\t\td = Database(parent = desired_key)\n\t\td.name = database_name\n\t\td.content = db.Text('')\n\treturn d",
"title": ""
},
{
"docid": "5b1703b356641fa0f1b0d8fdf2659578",
"score": "0.47707888",
"text": "def db_for_read(self, model, **hints):\n if model._meta.app_label in DATABASE_DICT:\n return DATABASE_DICT[model._meta.app_label]\n return None",
"title": ""
},
{
"docid": "db556679b20f0c272a5b0281b319a053",
"score": "0.47697708",
"text": "def test_db_table(self):\n\n\t\tself.assertEqual(self.record._meta.db_table,\"reviews\")",
"title": ""
},
{
"docid": "249e6e635ff6c09ab7eec0bc32fde578",
"score": "0.47613108",
"text": "def get_connection(**kwargs):\n try:\n logging.debug(\"Connecting to mapd db...\")\n con = pymapd.connect(\n user=kwargs[\"db_user\"],\n password=kwargs[\"db_passwd\"],\n host=kwargs[\"db_server\"],\n port=kwargs[\"db_port\"],\n dbname=kwargs[\"db_name\"],\n )\n logging.info(\"Succesfully connected to mapd db\")\n return con\n except (pymapd.exceptions.OperationalError, pymapd.exceptions.Error):\n logging.exception(\"Error connecting to database.\")\n return False",
"title": ""
},
{
"docid": "b24b310e971351286e27d62cd9fbf645",
"score": "0.47595018",
"text": "def dc(self):\n pass\n # TODO Client disconnection",
"title": ""
},
{
"docid": "26de2e1fb255c2a5a43aef5fa1145655",
"score": "0.4756963",
"text": "def testDetermineDatabaseType(self):\n test_extractor = catalog_extractor.EseDbCatalogExtractor()\n\n database_type = test_extractor._DetermineDatabaseType([\n 'SystemIndex_0A', 'SystemIndex_Gthr'])\n self.assertEqual(database_type, 'search')",
"title": ""
},
{
"docid": "b5470e0f8db47cbfa374940c7064db0c",
"score": "0.47476617",
"text": "def accesslogDBEntry(self, replicator_dn, \n log_dir=\"/opt/gluu/data/accesslog\"):\n\n attributes = {'objectClass': ['olcDatabaseConfig', 'olcMdbConfig'],\n 'olcDatabase': '{2}mdb',\n 'olcDbDirectory': log_dir,\n 'OlcDbMaxSize': 1073741824,\n 'olcSuffix': 'cn=accesslog',\n 'olcRootDN': 'cn=admin, cn=accesslog',\n 'olcRootPW': ldap_encode(self.passwd),\n 'olcDbIndex': ['default eq', 'objectClass,entryCSN,entryUUID,reqEnd,reqResult,reqStart,reqDN'],\n 'olcLimits': 'dn.exact=\"{0}\" time.soft=unlimited time.hard=unlimited size.soft=unlimited size.hard=unlimited'.format(replicator_dn),\n\n }\n #check if accesslogdb entry is allread exists. If not exists, create it.\n if not self.checkAccesslogDBEntry():\n return self.conn.add('olcDatabase={2}mdb,cn=config',\n attributes=attributes)",
"title": ""
},
{
"docid": "17eb67d6b93aec15e1ad14cdb6106aad",
"score": "0.47474527",
"text": "def configureDBSnapshot(self):\n tag = { \"DDDB\": self.getProp('DDDBtag')\n , \"LHCBCOND\": self.getProp('CondDBtag')\n , \"SIMCOND\" : self.getProp('CondDBtag')\n , \"ONLINE\" : 'fake'\n }\n\n # https://savannah.cern.ch/bugs/?94454#comment12\n from Configurables import MagneticFieldSvc\n MagneticFieldSvc().UseSetCurrent = True\n\n from Configurables import CondDB\n cdb = CondDB()\n cdb.Tags = tag\n cdb.setProp('IgnoreHeartBeat', True)\n self.setOtherProps( cdb, [ 'UseDBSnapshot',\n 'DBSnapshotDirectory',\n 'PartitionName' ])\n\n # So, here is the problem: we don't want to run the risk that\n # the CondDB() configurable (which configures /after/ us)\n # overwrites our conditions. Yet, we don't want to miss the\n # default conditions (e.g. velo stepper motor, magnetic field)\n # either. if we add our conditions to its\n # RunChangeHandlerConditions list, then we a) need to fix the\n # path and b) don't know what happens for conditions that\n # appear twice, because we don't control the ordering of the\n # list. So, the hack is:\n # - don't set 'EnableRunChangeHandler'\n # - copy what is hidden behind that flag in CondDB()._configureDBSnapshot\n # - do the test of the RunChangeHandler configuration ourselves:\n cdb.setProp('EnableRunChangeHandler', False)\n from Configurables import RunChangeHandlerSvc\n rch = RunChangeHandlerSvc()\n ApplicationMgr().ExtSvc.append(rch)\n baseloc = self.getProp( \"DBSnapshotDirectory\" )\n rch.Conditions = dict( (c,'/'.join([baseloc,f])) for f,cs in cdb.getProp(\"RunChangeHandlerConditions\").iteritems() for c in cs )\n \n #path = self.getProp('DBSnapshotDirectory') + \"/..\"*4 + \"/group/online/AligWork/current/\"\n allconds = {\n 'Velo' : [\n 'Conditions/Alignment/Velo/VeloSystem',\n 'Conditions/Alignment/Velo/VeloRight',\n 'Conditions/Alignment/Velo/VeloLeft']\n + ['Conditions/Alignment/Velo/Module%02d'%i for i in range(0, 42)] \n + ['Conditions/Alignment/Velo/Detector%02d-%02d' % (i, (1 + i / 2) % 2) for i in range(0, 42)],\n 'IT' : []\n + [ 'Conditions/Alignment/IT/ITSystem' ]\n + [ 'Conditions/Alignment/IT/ITT%d' % i for i in range(1,4) ]\n + [ 'Conditions/Alignment/IT/ITT%d%sBox' % (i,b) for i in range(1,4) for b in ['Top','Bottom','ASide','CSide' ] ]\n + [ 'Conditions/Alignment/IT/ITT%d%sLayer%s' % (i,b,l) for i in range(1,4) for b in ['Top','Bottom','ASide','CSide' ] for l in ['X1','U','V','X2' ] ]\n + [ 'Conditions/Alignment/IT/ITT%d%sLayer%sLadder%d' % (i,b,l,a) for i in range(1,4) for b in ['Top','Bottom','ASide','CSide' ] for l in ['X1','U','V','X2' ] for a in range(1,8) ],\n # + [ 'Conditions/Alignment/IT/ITT%d%sLayer%sLadder%dSector' % (i,b,l,a) for i in range(1,4) for b in ['Top','Bottom','ASide','CSide' ] for l in ['X1','U','V','X2' ] for a in range(1,8) ]\n # + [ 'Conditions/Alignment/IT/ITT%d%sLayer%sLadder%dSector_Sensor1' % (i,b,l,a) for i in range(1,4) for b in ['Top','Bottom','ASide','CSide' ] for l in ['X1','U','V','X2' ] for a in range(1,8) ]\n # + [ 'Conditions/Alignment/IT/ITT%d%sLayer%sLadder%dSector_Sensor2' % (i,b,l,a) for i in range(1,4) for b in ['ASide','CSide' ] for l in ['X1','U','V','X2' ] for a in range(1,8) ] ,\n 'OT' : []\n + [ 'Conditions/Alignment/OT/OTSystem' ]\n + [ 'Conditions/Alignment/OT/T%d' %i for i in range(1,4) ]\n + [ 'Conditions/Alignment/OT/T%d%s' % (i,l) for i in range(1,4) for l in ['X1','U','V','X2' ] ]\n + [ 'Conditions/Alignment/OT/T%d%sQ%d' % (i,l,q) for i in range(1,4) for l in ['X1','U','V','X2' ] for q in range(0,4) ]\n + [ 'Conditions/Alignment/OT/T%d%sQ%dM%d' % (i,l,q,m) for i in range(1,4) for l in ['X1','U','V','X2' ] for q in range(0,4) for m in range(1,10) ],\n 'TT' : []\n + [ 'Conditions/Alignment/TT/TTSystem' ]\n + [ 'Conditions/Alignment/TT/TT%s' % i for i in ['a','b' ] ]\n + [ 'Conditions/Alignment/TT/TT%sLayer' % (l) for l in ['aX','aU','bV','bX' ] ]\n + [ 'Conditions/Alignment/TT/TT%sLayerR%dModule%d%s' % (l,r,m,w) for w in ['T','B'] for l in ['aX','aU','bV','bX'] for r in range(1,4) for m in range(1,4)]\n + [ 'Conditions/Alignment/TT/TT%sLayerR%dModule%d%s' % (l,r,m,w) for w in ['T','B'] for l in ['bV','bX'] for r in range(1,4) for m in range(4,6)]\n + [ 'Conditions/Alignment/TT/TT%sLayerR%dModule%d%s' % (l,r,m,w) for w in ['T','B'] for l in ['aX','aU','bV','bX'] for r in [1,3] for m in range(6,7)]\n + [ 'Conditions/Alignment/TT/TT%sLayerR%dModule%d%s' % (l,r,m,w) for w in ['T','B'] for l in ['aX','aU'] for r in [1,3] for m in range(4,6)],\n 'Muon' : []\n + ['Conditions/Alignment/Muon/MuonSystem']\n + [ 'Conditions/Alignment/Muon/M%sStation' % i for i in range(1,6) ]\n + [ 'Conditions/Alignment/Muon/M%sASide' % i for i in range(1,6) ]\n + [ 'Conditions/Alignment/Muon/M%sCSide' % i for i in range(1,6) ]\n }\n\n ## This is a bit dirty, since we're supposed to control TAlignment. We\n ## know that this is set from top level, so let's give it a try anyway\n ta = TAlignment()\n sdToWrite = set(ta.getProp(\"WriteCondSubDetList\"))\n pat = self.getProp(\"OnlineAligWorkDir\") + \"/xml/%s.xml\" \n conditionmap = dict((pat % sd, f) for (sd, f) in allconds.iteritems() if sd in sdToWrite)\n \n # add to the existing map\n rch.Conditions = dict(rch.Conditions.items() + dict( (c,f) for f,cs in conditionmap.iteritems() for c in cs ).items() )\n\n from Configurables import MagneticFieldSvc\n MagneticFieldSvc().UseSetCurrent = True",
"title": ""
},
{
"docid": "157a6f85df20d249486b84e76c7046f1",
"score": "0.47464782",
"text": "def mongo_list_dbs():\n client = mongo_connect()\n for dbname in client.list_database_names():\n if dbname in ['admin', 'local']:\n continue\n db = client[dbname]\n gpup = db['gpu_procs'].find_one()\n if gpup is not None:\n gpup = gpup['gpu_procs']\n print(f\"DB: {dbname}\\n GPU proc count: {gpup}\")\n for colname in db.list_collection_names():\n col = db[colname]\n print(f\" Col: {colname}, count: {col.count()}\")\n client.close()",
"title": ""
},
{
"docid": "c1c546ff3533607a7c08e2103a8e3296",
"score": "0.47407824",
"text": "def get_kdb():\n return current_app.extensions['kdb'].connection",
"title": ""
},
{
"docid": "2be622382abe530d362506be550627f9",
"score": "0.47388348",
"text": "def checkAccesslogDBEntry(self):\n \n return self.conn.search(search_base='cn=config',\n search_filter='(olcSuffix=cn=accesslog)',\n search_scope=SUBTREE, attributes=[\"*\"])",
"title": ""
},
{
"docid": "07435a72d7afb184ec87d740dcaa0776",
"score": "0.47269005",
"text": "def get_graph_db(self):\n return self.get_variable(\"general\", \"graph_db\")",
"title": ""
},
{
"docid": "6a7daa68cfeb920c088725c5d078cf2c",
"score": "0.47193885",
"text": "def __init__(self, dsn, dsnDict, console):\n super(MySQLdbStorage, self).__init__(dsn, dsnDict, console)",
"title": ""
},
{
"docid": "17388a380ba9911fda750bbbcd80851e",
"score": "0.47184587",
"text": "def __init__(self):\n self.db = database.db_connection()",
"title": ""
},
{
"docid": "397399c38248591d8b2403a3d550fa74",
"score": "0.47183996",
"text": "def exist_care(mongoconn,db_name,carer_id,cared_id):\n factor = {'carer_id':carer_id,'cared_id':cared_id}\n res = mongoconn[db_name]['care_record'].find_one(factor)\n if res is not None:\n return res['_id']\n return None",
"title": ""
},
{
"docid": "22ea32a1bbe0a4a2789cd49b063ec39e",
"score": "0.47158706",
"text": "def allow_syncdb(self, db, model):\n\n if db in DATABASE_DICT.values():\n return DATABASE_DICT.get(model._meta.app_label) == db\n elif model._meta.app_label in DATABASE_DICT:\n return False\n return None",
"title": ""
},
{
"docid": "efcd17c7e244e8ec6c9d39fa2d9120a0",
"score": "0.47150096",
"text": "def database(self):\n return self.info_query(\"SELECT DATABASE()\")[0]",
"title": ""
},
{
"docid": "5894d4b1c01c5091c9cb8d691e58dcd9",
"score": "0.47148642",
"text": "def get_db():\n if not hasattr(g, 'mongo'):\n g.mongo = mongo # Flask-Mongo connection\n return g.mongo.db",
"title": ""
},
{
"docid": "aed0cd135cb4c801742f2e69a2f69c51",
"score": "0.47099882",
"text": "def _db_read_nonprimary(self, cur=None):\n return False",
"title": ""
},
{
"docid": "123972ebf820beee8b10c334341d80ce",
"score": "0.4706701",
"text": "def ready(self, dbname):\r\n return dbname in self",
"title": ""
},
{
"docid": "c60a20f7558c1cc5b1e8c76a71b50a9e",
"score": "0.47008118",
"text": "def get_custom_db(firmware_version, _db):\r\n if _db:\r\n if firmware_version in _db:\r\n return _db[firmware_version]\r\n return None",
"title": ""
},
{
"docid": "9e94f29ce5612aafc0907a72795eaea6",
"score": "0.4698479",
"text": "def _configure_nve_db(self, vni, device_id, mcast_group, host_id):\n host_connections = self._get_switch_info(host_id)\n for switch_ip, intf_type, nexus_port in host_connections:\n nxos_db.add_nexusnve_binding(vni, switch_ip, device_id,\n mcast_group)",
"title": ""
},
{
"docid": "76312512dc2ded0f5ad963b73f24aaad",
"score": "0.4696835",
"text": "def __init__(self):\n self._db_params = self._configuration_database()",
"title": ""
},
{
"docid": "9c8bb710b4a166859cc2182dcc78304d",
"score": "0.46957707",
"text": "def get_db(server_id):\n DATABASE = \"DATABASE\" + str(server_id)\n print(DATABASE)\n \"\"\"The internal LocalStack that holds AppContext instances...application context binds an application object implicitly to the current thread\"\"\"\n tracktop = _app_ctx_stack.top\n \"\"\"If the database connection is not initiated then initiate connection. We are using sqllite3 for the database connection.\"\"\"\n \"\"\"detect_types in connect makes the sqlite3 module parse the declared type for each column it returns.\"\"\"\n if not hasattr(tracktop, 'track_db0') and server_id == 0:\n tracktop.track_db0 = sqlite3.connect(app.config[DATABASE], detect_types=sqlite3.PARSE_DECLTYPES)\n tracktop.track_db0.row_factory = sqlite3.Row\n if not hasattr(tracktop, 'track_db1') and server_id == 1:\n tracktop.track_db1 = sqlite3.connect(app.config[DATABASE], detect_types=sqlite3.PARSE_DECLTYPES)\n tracktop.track_db1.row_factory = sqlite3.Row\n if not hasattr(tracktop, 'track_db2') and server_id == 2:\n tracktop.track_db2 = sqlite3.connect(app.config[DATABASE], detect_types=sqlite3.PARSE_DECLTYPES)\n tracktop.track_db2.row_factory = sqlite3.Row\n \"\"\" check server id return the track database accordingly\"\"\"\n if server_id == 0:\n return tracktop.track_db0\n elif server_id == 1:\n return tracktop.track_db1\n else:\n return tracktop.track_db2",
"title": ""
},
{
"docid": "27e1ffbd14a947894b7936437351383b",
"score": "0.4693095",
"text": "def test_get_db():\n db_test = User._get_db() # Get pymongo.database.Database instance\n assert isinstance(db_test, Database)",
"title": ""
},
{
"docid": "7364862c404d1c3dc4e933539b7d50ea",
"score": "0.468888",
"text": "def get_db():\n if not hasattr(g, 'db'):\n g.db = connect_db()\n return g.db",
"title": ""
},
{
"docid": "305a8e9dcd23d67e92986ad73c269c1e",
"score": "0.46874708",
"text": "def get_mongo_conn():\n MONGO_URI = 'mongodb://saran:[email protected]:13736/ingredientmaster'\n client = pymongo.MongoClient(MONGO_URI)\n db = client.get_database('ingredientmaster')\n return db",
"title": ""
},
{
"docid": "f4a815af9118297f580ce6106690b9df",
"score": "0.46834752",
"text": "def test_ngen_with_unknown_db(runner):\n result = runner.invoke(cli.main, ['list', 'fake.'])\n assert result.exit_code == 2",
"title": ""
},
{
"docid": "37d90dc6bdb91cbc0cf53e1ed35489fd",
"score": "0.46785578",
"text": "def db_for_read(self, model, **hints):\r\n if model._meta.app_label == 'virvo':\r\n return 'virvo'\r\n return None",
"title": ""
},
{
"docid": "9c227f6c0dcc3bdd23a06f9623fb35d2",
"score": "0.4678507",
"text": "def test_db_host_get(self):\r\n\r\n db_path = os.path.join(self.res_dir, 'regular.sqlite')\r\n db_obj = db.DataBaseHost.get(db_path)\r\n\r\n self.assertEqual(db.DataBaseHost.get(db_path), db_obj)\r\n\r\n db_obj.close()",
"title": ""
},
{
"docid": "c5f02e637e32b8ccef59b854bba5f422",
"score": "0.46758056",
"text": "def connections(self):\n tag, has_dimension = {}, False\n try:\n self.es_tag = pd.read_sql(\"SELECT * FROM es_connection\", con).to_dict('results')[-1]\n dimensions = pd.read_sql(\"SELECT * FROM data_connection\", con).to_dict('results')[0]\n if dimensions['dimension'] not in ['None', None]:\n has_dimension = True\n except Exception as e:\n print(e)\n return has_dimension",
"title": ""
},
{
"docid": "4bdf84a3c72f6ab54c9d1a3335be458a",
"score": "0.4670276",
"text": "def load_db(self, cursor, connection):",
"title": ""
},
{
"docid": "0560115b04f42f82e10f0b7c43c687eb",
"score": "0.46691918",
"text": "def _lookup_cddb(self):\n import DiscID, CDDB\n if self.cddevice: dev = DiscID.open(self.cddevice)\n else: dev = DiscID.open()\n cdid = DiscID.disc_id(dev)\n tracks = cdid[1]\n (status, info) = CDDB.query(cdid)\n if status == 200:\n (status, info) = CDDB.read(info['category'], \\\n info['disc_id'])\n elif status == 210 or status == 211:\n (status, info) = CDDB.read(info[0]['category'], \\\n info[0]['disc_id'])\n else:\n return None\n\n ret = cdinfo()\n for key in info.keys():\n if key.startswith('TTITLE'):\n n = int(re.findall(\"TTITLE([0-9]+)\", key)[0])\n ret.titles[n] = info[key].encode('ascii', errors='replace')\n elif key == 'DTITLE':\n (artist, album) = info[key].split('/')\n ret.artist = artist.strip()\n ret.album = album.strip()\n elif key == 'DYEAR':\n ret.releasedate = info[key]\n wx.LogMessage(\"cddb succeeded\")\n return ret",
"title": ""
},
{
"docid": "cc3e31a4747600f576f65d8a3a1a9855",
"score": "0.46684363",
"text": "def db_for_write(self, model, **hints):\n if model._meta.app_label == 'mapdata':\n return 'msemap_db'\n return None",
"title": ""
}
]
c6ccb384e1ab09d3b8f9ca491cfa70c2
Fetch and format documents screen. Content is supplied from self.DATA_INDEX. App handles formatting and display
