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tests/test_persistent_object.py
samuraisam/pynamo
4
199025
<gh_stars>1-10 import unittest, random, uuid from boto.exception import DynamoDBResponseError from boto.dynamodb.table import Table from pynamo import * from .common import TestPersistentObject, TestPersistentObjectPreparedKey class PersistentObjectClassTests(unittest.TestCase): def test_no_hash_key(self): er = 'At least one field must be marked a hash_key \(even if ' \ 'hash_key_format is defined\) for \(class A\)' with self.assertRaisesRegexp(TypeError, er): class A(PersistentObject): table_name = Meta('t1') def test_too_many_hash_keys(self): er = 'Only one Field is allowed to be marked hash_key per class. ' \ '\(class A\)' with self.assertRaisesRegexp(TypeError, er): class A(PersistentObject): table_name = Meta('t1') hk1 = StringField(hash_key=True) hk2 = StringField(hash_key=True) def test_too_many_range_keys(self): er = 'Only one Field is allowed to be marked range_key per class ' \ '\(class B\)' with self.assertRaisesRegexp(TypeError, er): class B(PersistentObject): table_name = Meta('t1') rk1 = IntegerField(range_key=True) rk2 = IntegerField(range_key=True) def test_fmt_wrong_key_type(self): er = 'If defining a hash_key_format, the field marked as hash_key ' \ 'must be a StringField. \(class B\)' with self.assertRaisesRegexp(TypeError, er): class B(PersistentObject): table_name = Meta('t1') hash_key_format = Meta('{key1}:{key2}') key = IntegerField(hash_key=True) def test_fmt_missing_piece(self): er = 'hash_key_format definition requires key2 but it is not an ' \ 'attribute on the class. \(C\)' with self.assertRaisesRegexp(TypeError, er): class C(PersistentObject): table_name = Meta('t1') hash_key_format = Meta('{key1}:{key2}') key = StringField(hash_key=True) key1 = StringField() def test_no_table_name(self): er = 'Must define a table_name for class C' with self.assertRaisesRegexp(TypeError, er): class C(PersistentObject): key = StringField(hash_key=True) def test_ops_on_base_class(self): class C(PersistentObject): table_name = Meta('t1') key = StringField(hash_key=True) er = 'Can not perform that operation on the base class. ' \ 'Please subclass PersistentObject to do that.' with self.assertRaisesRegexp(TypeError, er): C.create({'key': 'lol'}) # these tests take unbearibly long to run # dynamodb takes forever to create/destroy tables class PersistentObjectTableTests(unittest.TestCase): def setUp(self): Configure.with_ini_file() def test_create_wait_drop(self): # waits for creation TestPersistentObject.create_table(wait=True) conn = Configure.get_connection() self.assertTrue(isinstance(conn.get_table( TestPersistentObject._full_table_name), Table)) TestPersistentObject.drop_table(wait=True) self.assertRaises(DynamoDBResponseError, conn.get_table, TestPersistentObject._full_table_name) def test_reset_table(self): # create a table TestPersistentObject.create_table(wait=True) # create some objects in it TestPersistentObject.create(key='lol').save() TestPersistentObject.create(key='wut').save() # make sure they're really there self.assertTrue(isinstance(TestPersistentObject.get('lol'), TestPersistentObject)) conn = Configure.get_connection() TestPersistentObject.reset_table(wait=True) # now ensure they're really gone self.assertRaises(NotFoundError, TestPersistentObject.get, 'lol') self.assertRaises(NotFoundError, TestPersistentObject.get, 'wut') # and that the table is recreated self.assertTrue(isinstance(conn.get_table( TestPersistentObject._full_table_name), Table)) TestPersistentObject.drop_table(wait=True) self.assertRaises(DynamoDBResponseError, conn.get_table, TestPersistentObject._full_table_name) class PersistentObjectTests(unittest.TestCase): @staticmethod def setUpClass(): Configure.with_ini_file() return TestPersistentObject.create_table(wait=True) TestPersistentObjectPreparedKey.create_table(wait=True) @staticmethod def tearDownClass(): return TestPersistentObject.drop_table(wait=True) TestPersistentObjectPreparedKey.drop_table(wait=True) def test_prepare_key(self): # first test that prepare_key does not do anything if it's not set up self.assertEquals(TestPersistentObject.prepare_key('lol'), 'lol') # then test that prepare_key works with all the attribtues self.assertEquals(TestPersistentObjectPreparedKey.prepare_key( dict(key_1='hello', key_2=1, key_3='wut')), 'hello:1') # and test that it throws the proper error when they aren't there with self.assertRaises(ValueError): TestPersistentObjectPreparedKey.prepare_key( dict(key_1='hello', key_string='wut')) # and test that it works if you just provide the key self.assertEquals(TestPersistentObjectPreparedKey.prepare_key( {'key': 'key111'}), 'key111') # test that it throws a ValidationError if it's the wrong key type with self.assertRaises(ValidationError): TestPersistentObject.prepare_key({'key': 1}) def test_creation(self): le_id1 = uuid.uuid1().hex r1 = TestPersistentObject.create(key=le_id1).save() self.assertEquals(TestPersistentObject.get(le_id1).key, le_id1) d2 = { # this is only a subset of the keys 'key_1': uuid.uuid1().hex, 'key_2': random.randint(50000, 500000000), 'key_string': uuid.uuid1().hex, } r2 = TestPersistentObjectPreparedKey.create(d2).save() self.assertEquals(d2['key_1'], r2.key_1) self.assertEquals(d2['key_2'], r2.key_2) self.assertEquals(d2['key_string'], r2.key_string) res2 = TestPersistentObjectPreparedKey.get(d2) self.assertEquals(r2.key_1, res2.key_1) self.assertEquals(r2.key_2, res2.key_2) self.assertEquals(r2.key_string, res2.key_string) # throws the corect error when not creating it with the right args with self.assertRaises(ValueError): TestPersistentObjectPreparedKey.create(key_1='lolomg', key_string='hay so') def test_get(self): le_id1 = uuid.uuid1().hex def d(): return dict(key_1=uuid.uuid1().hex, key_2=random.randint(500000,500000000)) # works normally, with keyword arguments d1 = d() r1 = TestPersistentObjectPreparedKey.create(**d1).save() self.assertEquals(r1.key_1, d1['key_1']) # throws the proper error if not ixists with self.assertRaises(NotFoundError): TestPersistentObject.get(uuid.uuid1().hex) def test_get_or_create(self): def d(): return dict(key_1=uuid.uuid1().hex, key_2=random.randint(500000,500000000), key_list=[1,2,3]) # normal d1 = d() c1 = TestPersistentObjectPreparedKey.get_or_create(d1).save() self.assertEquals(c1.key_list, [1,2,3]) r1 = TestPersistentObjectPreparedKey.get(d1) self.assertEquals(r1.key_list, [1,2,3]) # keywords d2 = d() c2 = TestPersistentObjectPreparedKey.get_or_create(**d2).save() self.assertEquals(c2.key_1, d2['key_1']) r2 = TestPersistentObjectPreparedKey.get(d2) self.assertEquals(r2.key_list, [1,2,3]) # existing r3 = TestPersistentObjectPreparedKey.get_or_create(**d2).save() self.assertEquals(c3.key_list, [1,2,3])
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codes/train.py
mengzhu0308/Text-emotion-classification-based-on-CNN
0
108582
#! -*- coding:utf-8 -*- ''' @Author: ZM @Date and Time: 2020/12/13 16:28 @File: train.py ''' import math import numpy as np import pandas as pd from gensim import corpora from keras.layers import Input from keras import Model from keras.callbacks import Callback from keras.optimizers import Adam from keras import backend as K from Dataset import Dataset from get_dataset import get_dataset from generator import generator from utils import str2id, sequence_padding from Loss import Loss from ToOneHot import ToOneHot from CNN_model import CNN_Model class CrossEntropy(Loss): def compute_loss(self, inputs): y_true, y_pred = inputs loss = K.categorical_crossentropy(y_true, K.softmax(y_pred)) return K.mean(loss) if __name__ == '__main__': num_classes = 4 vocab_size = 33106 max_length = 181 hidden_dim = 64 train_batch_size = 128 val_batch_size = 500 (X_train, Y_train), (X_val, Y_val) = get_dataset() dictionary = corpora.Dictionary(pd.concat([X_train, X_val])) X_train = [str2id(x, dictionary.token2id) for x in X_train] X_val = [str2id(x, dictionary.token2id) for x in X_val] X_train = sequence_padding(X_train, max_length=max_length) Y_train = np.array(Y_train, dtype='int32') X_val = sequence_padding(X_val, max_length=max_length) Y_val = np.array(Y_val, dtype='int32') train_dataset = Dataset(X_train, Y_train, label_transform=ToOneHot(num_classes)) val_dataset = Dataset(X_val, Y_val, label_transform=ToOneHot(num_classes)) train_generator = generator(train_dataset, batch_size=train_batch_size, shuffle=True) val_generator = generator(val_dataset, batch_size=val_batch_size, shuffle=False) text_input = Input(shape=(max_length, ), name='text_input', dtype='int32') y_true = Input(shape=(num_classes, ), dtype='int32') out = CNN_Model(text_input, vocab_size, max_length, hidden_dim=hidden_dim, num_classes=num_classes) out = CrossEntropy(-1)([y_true, out]) model = Model([y_true, text_input], out) opt = Adam() model.compile(opt) num_train_batches = math.ceil(len(Y_train) / train_batch_size) num_val_examples = len(Y_val) num_val_batches = math.ceil(num_val_examples / val_batch_size) def evaluate(model): total_loss = 0. total_corrects = 0 for _ in range(num_val_batches): batch_data, _ = next(val_generator) val_loss, predict = model.test_on_batch(batch_data, y=None), model.predict_on_batch(batch_data) total_loss += val_loss total_corrects += np.sum(np.argmax(batch_data[0], axis=-1) == np.argmax(predict, axis=-1)) val_loss = total_loss / num_val_batches val_acc = (total_corrects / num_val_examples) * 100 return val_loss, val_acc class Evaluator(Callback): def __init__(self): super(Evaluator, self).__init__() def on_epoch_end(self, epoch, logs=None): val_loss, val_acc = evaluate(self.model) print(f'val_loss = {val_loss:.5f}, val_acc = {val_acc:.2f}') evaluator = Evaluator() model.fit_generator( train_generator, steps_per_epoch=num_train_batches, epochs=10, callbacks=[evaluator], shuffle=False, initial_epoch=0 )
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lib/dataset/InterHandDataset.py
ZJULiHongxin/two-hand-pose-est
0
72931
# Copyright (c) Facebook, Inc. and its affiliates. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. # import sys import os import time currentUrl = os.path.dirname(__file__) parentUrl = os.path.abspath(os.path.join(currentUrl, os.pardir)) sys.path.append(parentUrl) import argparse import numpy as np import torch import torch.utils.data from torch.utils.data import DataLoader from torchvision.transforms import functional as F import matplotlib.pyplot as plt import cv2 from glob import glob import os.path as osp from utils.preprocessing import load_img, load_skeleton, get_bbox, process_bbox, augmentation, transform_input_to_output_space, trans_point2d from utils.transforms import world2cam, cam2pixel, pixel2cam from utils.vis import vis_keypoints, vis_3d_keypoints, plot_hand from utils.standard_legends import idx_InterHand from PIL import Image, ImageDraw import random import json import math from pycocotools.coco import COCO import scipy.io as sio class InterHandDataset(torch.utils.data.Dataset): def __init__(self, cfg, transforms, mode): self.cfg = cfg self.name = 'InterHand' self.mode = mode # train, test, val self.img_path = osp.join(cfg.DATASET.DATA_DIR, 'images') # '../data/InterHand2.6M/images' self.annot_path = osp.join(cfg.DATASET.DATA_DIR, 'annotations') # '../data/InterHand2.6M/annotations' # if self.mode == 'val': # self.rootnet_output_path = '../data/InterHand2.6M/rootnet_output/rootnet_interhand2.6m_output_val.json' # else: # self.rootnet_output_path = '../data/InterHand2.6M/rootnet_output/rootnet_interhand2.6m_output_test.json' self.transform = transforms self.joint_num = cfg.DATASET.NUM_JOINTS # 21 # single hand self.root_joint_idx = {'right': 0, 'left': 21} # Please modify this idx after changing the order of joints self.joint_type = {'right': np.arange(0,self.joint_num), 'left': np.arange(self.joint_num,self.joint_num*2)} self.skeleton = load_skeleton(osp.join(self.annot_path, 'skeleton.txt'), self.joint_num*2) self.datalist = [] self.datalist_sh = [] self.datalist_ih = [] self.sequence_names = [] # load annotation print("Load annotation from " + osp.join(self.annot_path, self.mode)) t1 = time.time() prefix = 'simple_' db = COCO(osp.join(self.annot_path, self.mode, prefix+'InterHand2.6M_' + self.mode + '_data.json')) with open(osp.join(self.annot_path, self.mode, 'InterHand2.6M_' + self.mode + '_camera.json')) as f: cameras = json.load(f) with open(osp.join(self.annot_path, self.mode, 'InterHand2.6M_' + self.mode + '_joint_3d.json')) as f: joints = json.load(f) print("Annotation loading spent {}s".format(time.time()-t1)) # if (self.mode == 'val' or self.mode == 'test') and cfg.trans_test == 'rootnet': # print("Get bbox and root depth from " + self.rootnet_output_path) # rootnet_result = {} # with open(self.rootnet_output_path) as f: # annot = json.load(f) # for i in range(len(annot)): # rootnet_result[str(annot[i]['annot_id'])] = annot[i] # else: # print("Get bbox and root depth from groundtruth annotation") for aid in db.anns.keys(): ann = db.anns[aid] image_id = ann['image_id'] img = db.loadImgs(image_id)[0] capture_id = img['capture'] seq_name = img['seq_name'] cam = img['camera'] frame_idx = img['frame_idx'] img_path = osp.join(self.img_path, self.mode, img['file_name']) campos, camrot = np.array(cameras[str(capture_id)]['campos'][str(cam)], dtype=np.float32), np.array(cameras[str(capture_id)]['camrot'][str(cam)], dtype=np.float32) focal, princpt = np.array(cameras[str(capture_id)]['focal'][str(cam)], dtype=np.float32), np.array(cameras[str(capture_id)]['princpt'][str(cam)], dtype=np.float32) # get the groundtruth pose and reorder it joint_world = np.array(joints[str(capture_id)][str(frame_idx)]['world_coord'], dtype=np.float32)[idx_InterHand] # 42 x 3 joint_cam = world2cam(joint_world.transpose(1,0), camrot, campos.reshape(3,1)).transpose(1,0) joint_img = cam2pixel(joint_cam, focal, princpt) # 42 x 2 [u,v] # 1 if a joint is annotated and inside of image. 0 otherwise joint_valid = np.array(ann['joint_valid'],dtype=np.float32).reshape(self.joint_num*2) # if root is not valid -> root-relative 3D pose is also not valid. Therefore, mark all joints as invalid joint_valid[self.joint_type['right']] *= joint_valid[self.root_joint_idx['right']] joint_valid[self.joint_type['left']] *= joint_valid[self.root_joint_idx['left']] hand_type = ann['hand_type'] # 1 if hand_type in ('right', 'left') or hand_type == 'interacting' and np.sum(joint_valid) > 30, 0 otherwise hand_type_valid = np.array((ann['hand_type_valid']), dtype=np.float32) # if (self.mode == 'val' or self.mode == 'test') and cfg.trans_test == 'rootnet': # bbox = np.array(rootnet_result[str(aid)]['bbox'],dtype=np.float32) # abs_depth = {'right': rootnet_result[str(aid)]['abs_depth'][0], 'left': rootnet_result[str(aid)]['abs_depth'][1]} # else: img_width, img_height = img['width'], img['height'] # original image size 344(w) x 512(h) bbox = np.array(ann['bbox'],dtype=np.float32) # x,y,w,h bbox = process_bbox(bbox, (img_height, img_width)) abs_depth = {'right': joint_cam[self.root_joint_idx['right'],2], 'left': joint_cam[self.root_joint_idx['left'],2]} cam_param = {'focal': focal, 'princpt': princpt} joint = {'cam_coord': joint_cam, 'img_coord': joint_img, 'valid': joint_valid} data = {'img_path': img_path, 'seq_name': seq_name, 'cam_param': cam_param, 'bbox': bbox, 'joint': joint, 'hand_type': hand_type, 'hand_type_valid': hand_type_valid, 'abs_depth': abs_depth, 'file_name': img['file_name'], 'capture': capture_id, 'cam': cam, 'frame': frame_idx} if hand_type == 'right' or hand_type == 'left': self.datalist_sh.append(data) else: self.datalist_ih.append(data) if seq_name not in self.sequence_names: self.sequence_names.append(seq_name) self.datalist = self.datalist_sh + self.datalist_ih print('Number of annotations in single hand sequences: ' + str(len(self.datalist_sh))) print('Number of annotations in interacting hand sequences: ' + str(len(self.datalist_ih))) def handtype_str2array(self, hand_type): if hand_type == 'right': return np.array([1,0], dtype=np.float32) elif hand_type == 'left': return np.array([0,1], dtype=np.float32) elif hand_type == 'interacting': return np.array([1,1], dtype=np.float32) else: assert 0, print('Not supported hand type: ' + hand_type) def __len__(self): return len(self.datalist) def __getitem__(self, idx): data = self.datalist[idx] img_path, bbox, joint, hand_type, hand_type_valid = data['img_path'], data['bbox'], data['joint'], data['hand_type'], data['hand_type_valid'] joint_cam = joint['cam_coord'].copy() joint_img = joint['img_coord'].copy() joint_valid = joint['valid'].copy() # 1 if inside the image, o other wise. # 42 hand_type_vec = self.handtype_str2array(hand_type) joint_coord = np.concatenate((joint_img, joint_cam[:,2,None]),1) # 42 x 3 [u,v,z] # input(joint_valid) # input(joint_coord) # image load try: img = cv2.cvtColor(cv2.imread(img_path, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION), cv2.COLOR_BGR2RGB) # 512 x 334 x 3 except: print('[Warning] Invalid image path:', img_path) # DEBUG # f = plt.figure() # ax1 = f.add_subplot(1,1,1) # ax1.imshow(img) # for k in range(joint_coord.shape[0]): # print('[{:.4f}, {:.4f}, {:.4f}],'.format(*joint_coord[k])) # print(hand_type_vec) # if hand_type_vec[0] == 1: # plot_hand(ax1, joint_coord[0:21,0:2], vis=joint_valid[0:21], order = 'uv') # elif hand_type_vec[1] == 1: # plot_hand(ax1, joint_coord[21:42,0:2], vis=joint_valid[21:42], order = 'uv') # ax1.set_title(hand_type) # plt.show() # augmentation img, joint_coord, joint_valid, hand_type_vec, inv_trans = augmentation(img, bbox, joint_coord, joint_valid, hand_type_vec, self.mode, self.joint_type, self.cfg.MODEL.INPUT_SIZE) # f1 = plt.figure() # ax1 = f1.add_subplot(1,1,1) # ax1.imshow(img.astype(int)) # for k in range(joint_coord.shape[0]): # print('[{:.4f}, {:.4f}, {:.4f}],'.format(*joint_coord[k])) # print(joint_coord) # if hand_type_vec[0] == 1: # plot_hand(ax1, joint_coord[0:21,0:2], vis=joint_valid[0:21], order = 'uv') # elif hand_type_vec[1] == 1: # plot_hand(ax1, joint_coord[21:42,0:2], vis=joint_valid[21:42], order = 'uv') # ax1.set_title(hand_type) # plt.show() #rel_root_depth = np.array([joint_coord[self.root_joint_idx['left'],2] - joint_coord[self.root_joint_idx['right'],2]],dtype=np.float32).reshape(1) #root_valid = np.array([joint_valid[self.root_joint_idx['right']] * joint_valid[self.root_joint_idx['left']]],dtype=np.float32).reshape(1) if hand_type_vec[0]*hand_type_vec[1] == 1 else np.zeros((1),dtype=np.float32) # transform to output heatmap space (this line of code is useless for anchor-based estimation) #joint_coord, joint_valid, rel_root_depth, root_valid = transform_input_to_output_space(self.cfg, joint_coord, joint_valid, rel_root_depth, root_valid, self.root_joint_idx, self.joint_type) img = self.transform(img.astype(np.float32) / 255.) # inputs = {'img': img} # targets = {'pose2d_gt': joint_coord, 'rel_root_depth': rel_root_depth, 'hand_type': hand_type_vec} # meta_info = {'joint_valid': joint_valid, 'root_valid': root_valid, 'hand_type_valid': hand_type_valid, 'inv_trans': inv_trans, 'capture': int(data['capture']), 'cam': int(data['cam']), 'frame': int(data['frame'])} return {'imgs': img, 'pose2d_gt': joint_coord, 'joint_valid': joint_valid, 'hand_type': hand_type_vec} #return inputs, targets, meta_info def evaluate(self, preds): print() print('Evaluation start...') gts = self.datalist preds_joint_coord, preds_rel_root_depth, preds_hand_type, inv_trans = preds['joint_coord'], preds['rel_root_depth'], preds['hand_type'], preds['inv_trans'] assert len(gts) == len(preds_joint_coord) sample_num = len(gts) mpjpe_sh = [[] for _ in range(self.joint_num*2)] mpjpe_ih = [[] for _ in range(self.joint_num*2)] mrrpe = [] acc_hand_cls = 0 hand_cls_cnt = 0 for n in range(sample_num): data = gts[n] bbox, cam_param, joint, gt_hand_type, hand_type_valid = data['bbox'], data['cam_param'], data['joint'], data['hand_type'], data['hand_type_valid'] focal = cam_param['focal'] princpt = cam_param['princpt'] gt_joint_coord = joint['cam_coord'] joint_valid = joint['valid'] # restore xy coordinates to original image space pred_joint_coord_img = preds_joint_coord[n].copy() pred_joint_coord_img[:,0] = pred_joint_coord_img[:,0]/cfg.output_hm_shape[2]*cfg.input_img_shape[1] pred_joint_coord_img[:,1] = pred_joint_coord_img[:,1]/cfg.output_hm_shape[1]*cfg.input_img_shape[0] for j in range(self.joint_num*2): pred_joint_coord_img[j,:2] = trans_point2d(pred_joint_coord_img[j,:2],inv_trans[n]) # restore depth to original camera space pred_joint_coord_img[:,2] = (pred_joint_coord_img[:,2]/cfg.output_hm_shape[0] * 2 - 1) * (cfg.bbox_3d_size/2) # mrrpe if gt_hand_type == 'interacting' and joint_valid[self.root_joint_idx['left']] and joint_valid[self.root_joint_idx['right']]: pred_rel_root_depth = (preds_rel_root_depth[n]/cfg.output_root_hm_shape * 2 - 1) * (cfg.bbox_3d_size_root/2) pred_left_root_img = pred_joint_coord_img[self.root_joint_idx['left']].copy() pred_left_root_img[2] += data['abs_depth']['right'] + pred_rel_root_depth pred_left_root_cam = pixel2cam(pred_left_root_img[None,:], focal, princpt)[0] pred_right_root_img = pred_joint_coord_img[self.root_joint_idx['right']].copy() pred_right_root_img[2] += data['abs_depth']['right'] pred_right_root_cam = pixel2cam(pred_right_root_img[None,:], focal, princpt)[0] pred_rel_root = pred_left_root_cam - pred_right_root_cam gt_rel_root = gt_joint_coord[self.root_joint_idx['left']] - gt_joint_coord[self.root_joint_idx['right']] mrrpe.append(float(np.sqrt(np.sum((pred_rel_root - gt_rel_root)**2)))) # add root joint depth pred_joint_coord_img[self.joint_type['right'],2] += data['abs_depth']['right'] pred_joint_coord_img[self.joint_type['left'],2] += data['abs_depth']['left'] # back project to camera coordinate system pred_joint_coord_cam = pixel2cam(pred_joint_coord_img, focal, princpt) # root joint alignment for h in ('right', 'left'): pred_joint_coord_cam[self.joint_type[h]] = pred_joint_coord_cam[self.joint_type[h]] - pred_joint_coord_cam[self.root_joint_idx[h],None,:] gt_joint_coord[self.joint_type[h]] = gt_joint_coord[self.joint_type[h]] - gt_joint_coord[self.root_joint_idx[h],None,:] # mpjpe for j in range(self.joint_num*2): if joint_valid[j]: if gt_hand_type == 'right' or gt_hand_type == 'left': mpjpe_sh[j].append(np.sqrt(np.sum((pred_joint_coord_cam[j] - gt_joint_coord[j])**2))) else: mpjpe_ih[j].append(np.sqrt(np.sum((pred_joint_coord_cam[j] - gt_joint_coord[j])**2))) # handedness accuray if hand_type_valid: if gt_hand_type == 'right' and preds_hand_type[n][0] > 0.5 and preds_hand_type[n][1] < 0.5: acc_hand_cls += 1 elif gt_hand_type == 'left' and preds_hand_type[n][0] < 0.5 and preds_hand_type[n][1] > 0.5: acc_hand_cls += 1 elif gt_hand_type == 'interacting' and preds_hand_type[n][0] > 0.5 and preds_hand_type[n][1] > 0.5: acc_hand_cls += 1 hand_cls_cnt += 1 vis = False if vis: img_path = data['img_path'] cvimg = cv2.imread(img_path, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION) _img = cvimg[:,:,::-1].transpose(2,0,1) vis_kps = pred_joint_coord_img.copy() vis_valid = joint_valid.copy() capture = str(data['capture']) cam = str(data['cam']) frame = str(data['frame']) filename = 'out_' + str(n) + '_' + gt_hand_type + '.jpg' vis_keypoints(_img, vis_kps, vis_valid, self.skeleton, filename) vis = False if vis: filename = 'out_' + str(n) + '_3d.jpg' vis_3d_keypoints(pred_joint_coord_cam, joint_valid, self.skeleton, filename) if hand_cls_cnt > 0: print('Handedness accuracy: ' + str(acc_hand_cls / hand_cls_cnt)) if len(mrrpe) > 0: print('MRRPE: ' + str(sum(mrrpe)/len(mrrpe))) print() tot_err = [] eval_summary = 'MPJPE for each joint: \n' for j in range(self.joint_num*2): tot_err_j = np.mean(np.concatenate((np.stack(mpjpe_sh[j]), np.stack(mpjpe_ih[j])))) joint_name = self.skeleton[j]['name'] eval_summary += (joint_name + ': %.2f, ' % tot_err_j) tot_err.append(tot_err_j) print(eval_summary) print('MPJPE for all hand sequences: %.2f' % (np.mean(tot_err))) print() eval_summary = 'MPJPE for each joint: \n' for j in range(self.joint_num*2): mpjpe_sh[j] = np.mean(np.stack(mpjpe_sh[j])) joint_name = self.skeleton[j]['name'] eval_summary += (joint_name + ': %.2f, ' % mpjpe_sh[j]) print(eval_summary) print('MPJPE for single hand sequences: %.2f' % (np.mean(mpjpe_sh))) print() eval_summary = 'MPJPE for each joint: \n' for j in range(self.joint_num*2): mpjpe_ih[j] = np.mean(np.stack(mpjpe_ih[j])) joint_name = self.skeleton[j]['name'] eval_summary += (joint_name + ': %.2f, ' % mpjpe_ih[j]) print(eval_summary) print('MPJPE for interacting hand sequences: %.2f' % (np.mean(mpjpe_ih))) if __name__ == "__main__": def add_path(path): if path not in sys.path: sys.path.insert(0, path) this_dir = osp.dirname(__file__) lib_path = osp.join(this_dir, '..', '..','lib') add_path(lib_path) mm_path = osp.join(this_dir, '..', 'lib/poseeval/py-motmetrics') add_path(mm_path) from torchvision import transforms from config.default import _C as cfg from config.default import update_config from utils.vis import plot_hand parser = argparse.ArgumentParser(description='Train keypoints network') parser.add_argument('--cfg', help='experiment configure file name', default='../../experiments/exp_test.yaml', type=str) parser.add_argument('opts', help="Modify config options using the command-line", default=None, nargs=argparse.REMAINDER) args = parser.parse_args() args.cfg = "../../experiments/exp_test.yaml" update_config(cfg, args) cfg.defrost() dataset = InterHandDataset(cfg, transforms.ToTensor(), "train") batch_generator = DataLoader(dataset=dataset, batch_size=1, shuffle=False, num_workers=0, pin_memory=True) for itr, (inputs, targets, meta_info) in enumerate(batch_generator): img = inputs['imgs'].numpy().squeeze()*255 # 1 x 3 x 256 x 256 joint_coord = targets['pose2d_gt'].numpy().squeeze() # [42, 3] # u,v pixel, z root-relative discretized depth joint_valid = meta_info['joint_valid'].numpy().squeeze() # [42] filename = 'result_2d.jpg' for k in range(joint_coord.shape[0]): print('[{},{}],'.format(joint_coord[k,0],joint_coord[k,1])) vis_img = vis_keypoints(img, joint_coord, joint_valid, dataset.skeleton, filename, save_path='.') filename = 'result_3d' vis_3d_keypoints(joint_coord, joint_valid, dataset.skeleton, filename)
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inventory/categories/api/tests/test_categories_api.py
cnobile2012/inventory
10
133364
# -*- coding: utf-8 -*- # # inventory/categories/api/tests/test_categories_api.py # from django.contrib.auth import get_user_model from rest_framework.reverse import reverse from rest_framework import status from rest_framework.test import APITestCase from inventory.categories.models import Category from inventory.common.api.tests.base_test import BaseTest from inventory.projects.models import Membership UserModel = get_user_model() class TestCategoryAPI(BaseTest, APITestCase): DEFAULT_USER = UserModel.ROLE_MAP[UserModel.DEFAULT_USER] PROJECT_USER = Membership.ROLE_MAP[Membership.PROJECT_USER] def __init__(self, name): super().__init__(name) def setUp(self): super().setUp() # Create an InventoryType and Project. self.in_type = self._create_inventory_type() members = [ {'user': self.user, 'role_text': self.PROJECT_USER} ] self.project = self._create_project(self.in_type, members=members) kwargs = {'public_id': self.project.public_id} self.project_uri = reverse('project-detail', kwargs=kwargs) def get_category_field(self, uri, field): """ Get a category and return the value of the provided field. """ response = self.client.get(uri, format='json') return response.data.get(field) def test_GET_category_list_with_invalid_permissions(self): """ Test the category_list endpoint with no permissions. """ #self.skipTest("Temporarily skipped") method = 'get' category = self._create_category(self.project, "Test Root Category") uri = reverse('category-list') self._test_users_with_invalid_permissions(uri, method) self._test_project_users_with_invalid_permissions(uri, method) def test_GET_category_list_with_valid_permissions(self): """ Test the category_list endpoint with valid permissions. """ #self.skipTest("Temporarily skipped") method = 'get' category = self._create_category(self.project, "Test Root Category") uri = reverse('category-list') self._test_users_with_valid_permissions( uri, method, default_user=False) self._test_project_users_with_valid_permissions(uri, method) def test_POST_category_list_with_invalid_permissions(self): """ Test that a POST to category_list fails with invalid permissions. """ #self.skipTest("Temporarily skipped") method = 'post' uri = reverse('category-list') data = {} su = data.setdefault('SU', {}) su['name'] = 'TestCategory-01' su['project'] = self.project_uri data.setdefault('AD', su.copy()) data.setdefault('DU', su.copy()) self._test_users_with_invalid_permissions( uri, method, request_data=data) data.setdefault('POW', su.copy()) data.setdefault('PMA', su.copy()) data.setdefault('PDU', su.copy()) self._test_project_users_with_invalid_permissions( uri, method, request_data=data) def test_POST_category_list_with_valid_permissions(self): """ Test that a POST to category_list passes with valid permissions. """ #self.skipTest("Temporarily skipped") method = 'post' uri = reverse('category-list') data = {} su = data.setdefault('SU', {}) su['name'] = 'TestCategory-01' su['project'] = self.project_uri ad = data.setdefault('AD', su.copy()) ad['name'] = 'TestCategory-02' du = data.setdefault('DU', su.copy()) du['name'] = 'TestCategory-03' self._test_users_with_valid_permissions( uri, method, request_data=data) pow = data.setdefault('POW', su.copy()) pow['name'] = 'TestCategory-04' pma = data.setdefault('PMA', su.copy()) pma['name'] = 'TestCategory-05' pdu = data.setdefault('PDU', su.copy()) pdu['name'] = 'TestCategory-06' self._test_project_users_with_valid_permissions( uri, method, project_user=False, request_data=data) def test_OPTIONS_category_list_with_invalid_permissions(self): """ Test that the method OPTIONS fails with invald permissions. """ #self.skipTest("Temporarily skipped") method = 'options' uri = reverse('category-list') self._test_users_with_invalid_permissions(uri, method) self._test_project_users_with_invalid_permissions(uri, method) def test_OPTIONS_category_list_with_valid_permissions(self): """ Test that the method OPTIONS brings back the correct data. """ method = 'options' uri = reverse('category-list') self._test_users_with_valid_permissions(uri, method) self._test_project_users_with_valid_permissions(uri, method) def test_GET_category_detail_with_invalid_permissions(self): """ Test that a GET on the category_detail fails with invalid permissions. """ #self.skipTest("Temporarily skipped") category = self._create_category(self.project, "Test Root Category") uri = reverse('category-detail', kwargs={'public_id': category.public_id}) method = 'get' self._test_users_with_invalid_permissions(uri, method) self._test_project_users_with_invalid_permissions(uri, method) def test_GET_category_detail_with_valid_permissions(self): """ Test that a GET to category_detail passes with valid permissions. """ #self.skipTest("Temporarily skipped") category = self._create_category(self.project, "Test Root Category") uri = reverse('category-detail', kwargs={'public_id': category.public_id}) method = 'get' self._test_users_with_valid_permissions(uri, method) self._test_project_users_with_valid_permissions(uri, method) def test_PUT_category_detail_with_invalid_permissions(self): """ Test that a PUT to category_detail fails with invalid permissions. """ #self.skipTest("Temporarily skipped") category = self._create_category(self.project, "Test Root Category") uri = reverse('category-detail', kwargs={'public_id': category.public_id}) method = 'put' data = {} su = data.setdefault('SU', {}) su['name'] = 'TestCategory-01' su['project'] = self.project_uri data.setdefault('AD', su.copy()) data.setdefault('DU', su.copy()) self._test_users_with_invalid_permissions( uri, method, request_data=data) data.setdefault('POW', su.copy()) data.setdefault('PMA', su.copy()) data.setdefault('PDU', su.copy()) self._test_project_users_with_invalid_permissions( uri, method, request_data=data) def test_PUT_category_detail_with_valid_permissions(self): """ Test that a PUT to category_detail passes with valid permissions. """ #self.skipTest("Temporarily skipped") category = self._create_category(self.project, "Test Root Category") uri = reverse('category-detail', kwargs={'public_id': category.public_id}) method = 'put' data = {} su = data.setdefault('SU', {}) su['name'] = 'TestCategory-01' su['project'] = self.project_uri ad = data.setdefault('AD', su.copy()) ad['name'] = 'TestCategory-02' du = data.setdefault('DU', su.copy()) du['name'] = 'TestCategory-03' self._test_users_with_valid_permissions( uri, method, request_data=data) pow = data.setdefault('POW', su.copy()) pow['name'] = 'TestCategory-04' pma = data.setdefault('PMA', su.copy()) pma['name'] = 'TestCategory-05' pdu = data.setdefault('PDU', su.copy()) pdu['name'] = 'TestCategory-06' self._test_project_users_with_valid_permissions( uri, method, project_user=False, request_data=data) def test_PATCH_category_detail_with_invalid_permissions(self): """ Test that a PATCH to category_detail fails with invalid permissions. """ #self.skipTest("Temporarily skipped") category = self._create_category(self.project, "Test Root Category") uri = reverse('category-detail', kwargs={'public_id': category.public_id}) method = 'patch' data = {} su = data.setdefault('SU', {}) su['name'] = 'TestCategory-01' su['project'] = self.project_uri data.setdefault('AD', su.copy()) data.setdefault('DU', su.copy()) self._test_users_with_invalid_permissions( uri, method, request_data=data) data.setdefault('POW', su.copy()) data.setdefault('PMA', su.copy()) data.setdefault('PDU', su.copy()) self._test_project_users_with_invalid_permissions( uri, method, request_data=data) def test_PATCH_category_detail_with_valid_permissions(self): """ Test that a PATCH to category_detail passes with valid permissions. """ #self.skipTest("Temporarily skipped") category = self._create_category(self.project, "Test Root Category") uri = reverse('category-detail', kwargs={'public_id': category.public_id}) method = 'patch' data = {} su = data.setdefault('SU', {}) su['name'] = 'TestCategory-01' su['project'] = self.project_uri ad = data.setdefault('AD', {}) ad['name'] = 'TestCategory-02' du = data.setdefault('DU', {}) du['name'] = 'TestCategory-03' self._test_users_with_valid_permissions( uri, method, request_data=data) pow = data.setdefault('POW', {}) pow['name'] = 'TestCategory-04' pma = data.setdefault('PMA', {}) pma['name'] = 'TestCategory-05' pdu = data.setdefault('PDU', {}) pdu['name'] = 'TestCategory-06' self._test_project_users_with_valid_permissions( uri, method, project_user=False, request_data=data) def test_DELETE_category_detail_with_invalid_permissions(self): """ Test that a DELETE to category_detail fails with invalid permissions. """ #self.skipTest("Temporarily skipped") method = 'delete' category = self._create_category(self.project, "Test Root Category") uri = reverse('category-detail', kwargs={'public_id': category.public_id}) self._test_users_with_invalid_permissions(uri, method) self._test_project_users_with_invalid_permissions(uri, method) def test_DELETE_category_detail_with_valid_permissions(self): """ Test that a DELETE to category_detail pass' with valid permissions. """ #self.skipTest("Temporarily skipped") method = 'delete' # Test SUPERUSER category = self._create_category(self.project, "Test Root Category") uri = reverse('category-detail', kwargs={'public_id': category.public_id}) self._test_superuser_with_valid_permissions(uri, method) self._test_valid_GET_with_errors(uri) # Test ADMINISTRATOR category = self._create_category(self.project, "Test Root Category") uri = reverse('category-detail', kwargs={'public_id': category.public_id}) self._test_administrator_with_valid_permissions(uri, method) self._test_valid_GET_with_errors(uri) # Test DEFAULT_USER ## This is an invalid test since the DEFAULT_USER has no access. # Test PROJECT_OWNER category = self._create_category(self.project, "Test Root Category") uri = reverse('category-detail', kwargs={'public_id': category.public_id}) self._test_project_owner_with_valid_permissions(uri, method) self._test_valid_GET_with_errors(uri) # Test PROJECT_MANAGER category = self._create_category(self.project, "Test Root Category") uri = reverse('category-detail', kwargs={'public_id': category.public_id}) self._test_project_manager_with_valid_permissions(uri, method) self._test_valid_GET_with_errors(uri) # Test PROJECT_USER ## This is an invalid test since the PROJECT_USER has no access. def test_OPTIONS_category_detail_with_invalid_permissions(self): """ Test that the method OPTIONS fails with invald permissions. """ #self.skipTest("Temporarily skipped") method = 'options' category = self._create_category(self.project, "Test Root Category") uri = reverse('category-detail', kwargs={'public_id': category.public_id}) self._test_users_with_invalid_permissions(uri, method) self._test_project_users_with_invalid_permissions(uri, method) def test_OPTIONS_category_detail_with_valid_permissions(self): """ Test that the method OPTIONS brings back the correct data. """ method = 'options' category = self._create_category(self.project, "Test Root Category") uri = reverse('category-detail', kwargs={'public_id': category.public_id}) self._test_users_with_valid_permissions(uri, method) self._test_project_users_with_valid_permissions(uri, method) def test_create_category_twice_to_same_parent(self): """ Test that a category is not created twice with the same composite key. """ #self.skipTest("Temporarily skipped") # Create Category one. uri = reverse('category-list') new_data = {'name': 'TestCategory-1', 'project': self.project_uri} response = self.client.post(uri, new_data, format='json') msg = "Response: {} should be {}, content: {}".format( response.status_code, status.HTTP_201_CREATED, response.data) self.assertEqual(response.status_code, status.HTTP_201_CREATED, msg) # Create Category two. parent_uri = response.data.get('href') uri = reverse('category-list') new_data = {'name': 'TestCategory-2', 'parent': parent_uri, 'project': self.project_uri} response = self.client.post(uri, new_data, format='json') msg = "Response: {} should be {}, content: {}".format( response.status_code, status.HTTP_201_CREATED, response.data) self.assertEqual(response.status_code, status.HTTP_201_CREATED, msg) # Create Category two again--should fail. uri = reverse('category-list') new_data = {'name': 'TestCategory-2', 'parent': parent_uri, 'project': self.project_uri} response = self.client.post(uri, new_data, format='json') msg = "Response: {} should be {}, content: {}".format( response.status_code, status.HTTP_400_BAD_REQUEST, response.data) self.assertEqual( response.status_code, status.HTTP_400_BAD_REQUEST, msg) def test_delimitor_in_category_name(self): """ Test that the delimitor is not in the category name. """ #self.skipTest("Temporarily skipped") # Create Category one. uri = reverse('category-list') new_data = {'name': 'Test{}Category-1'.format( Category.DEFAULT_SEPARATOR), 'project': self.project_uri} response = self.client.post(uri, new_data, format='json') msg = "Response: {} should be {}, content: {}".format( response.status_code, status.HTTP_400_BAD_REQUEST, response.data) self.assertEqual( response.status_code, status.HTTP_400_BAD_REQUEST, msg) self.assertTrue(self._has_error(response, 'name'), msg) self._test_errors(response, tests={ 'name': u"A category name cannot ", }) def test_category_is_not_parent(self): """ Test that this category does not exist in the current tree. """ #self.skipTest("Temporarily skipped") # Create three catagories. name = "Test Category 1" cat0 = self._create_category(self.project, name=name) name = "Test Category 2" cat1 = self._create_category(self.project, name=name, parent=cat0) name = "Test Category 3" cat2 = self._create_category(self.project, name=name, parent=cat1) # Try adding 'Test Category 2' to the tree using the API. uri = reverse('category-list') cat2_uri = reverse('category-detail', kwargs={'public_id': cat2.public_id}) new_data = {'name': "Test Category 2", 'project': self.project_uri, 'parent': cat2_uri} response = self.client.post(uri, new_data, format='json') msg = "Response: {} should be {}, content: {}".format( response.status_code, status.HTTP_400_BAD_REQUEST, response.data) self.assertEqual( response.status_code, status.HTTP_400_BAD_REQUEST, msg) self.assertTrue(self._has_error(response, 'name'), msg) self._test_errors(response, tests={ 'name': u"A category in this tree ", }) def test_root_level_category_exists(self): """ Test that there are no root level categories with this name that already exist for this owner. """ #self.skipTest("Temporarily skipped") # Create a catagory. name = "Duplicate Name" cat = self._create_category(self.project, name=name) # Create a category through the API. new_data = {'name': name, 'project': self.project_uri} uri = reverse('category-list') response = self.client.post(uri, new_data, format='json') msg = "Response: {} should be {}, content: {}".format( response.status_code, status.HTTP_400_BAD_REQUEST, response.data) self.assertEqual( response.status_code, status.HTTP_400_BAD_REQUEST, msg) self.assertTrue(self._has_error(response, 'name'), msg) self._test_errors(response, tests={ 'name': u"A root level category name ", }) def test_wrong_user_gets_no_results(self): """ Test that the request user gets no results of categories that are in a different project. This test determines that a user of one project does not have access to another project's objects. """ #self.skipTest("Temporarily skipped") # Create new user user, client = self._create_user( username="SecondUser", password="<PASSWORD>") # Create a new project p_name = "Test Project_1" project = self._create_project(self.in_type, name=p_name) # Create a category c_name = "Test Category 1" category = self._create_category(project, name=c_name) # GET category on the API uri = reverse('category-list') response = client.get(uri, format='json', **self.HEADERS) msg = "Response: {} should be {}, content: {}".format( response.status_code, status.HTTP_403_FORBIDDEN, response.data) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN, msg) self.assertTrue(self._has_error(response), msg) self._test_errors(response, tests={ 'detail': "You do not have permission to perform this action.", }) # Test GET on a category detail uri = reverse('category-detail', kwargs={'public_id': category.public_id}) response = client.get(uri, format='json', **self.HEADERS) msg = "Response: {} should be {}, content: {}".format( response.status_code, status.HTTP_403_FORBIDDEN, response.data) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN, msg) self.assertTrue(self._has_error(response), msg) self._test_errors(response, tests={ 'detail': "You do not have permission to perform this action.", }) # Test PUT to a specific category data = {'name': 'Changed Category'} response = client.patch(uri, data=data, format='json', **self.HEADERS) msg = ("Response: {} should be {}, content: {}, user: {}, " "project creator: {}").format( response.status_code, status.HTTP_403_FORBIDDEN, response.data, user, project.creator) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN, msg) self.assertTrue(self._has_error(response), msg) self._test_errors(response, tests={ 'detail': "You do not have permission to perform this action.", }) class TestCategoryCloneAPI(BaseTest): def __init__(self, name): super().__init__(name) def setUp(self): super().setUp() # Create an InventoryType and Project. self.in_type = self._create_inventory_type() self.project = self._create_project(self.in_type, members=[self.user]) kwargs = {'public_id': self.project.public_id} self.project_uri = reverse('project-detail', kwargs=kwargs) def flatten(self, items): """ Given a list, possibly nested to any level, return it flattened. http://code.activestate.com/recipes/578948-flattening-an-arbitrarily-nested-list-in-python/ """ flattened = [] for item in items: if isinstance(item, list): flattened.extend(self.flatten(item)) else: flattened.append(item) return flattened def test_GET_category_clone_with_invalid_permissions(self): """ Test the category_clone endpoint with no permissions. """ #self.skipTest("Temporarily skipped") method = 'get' category = self._create_category(self.project, "Test Root Category") uri = reverse('category-clone') data = {} su = data.setdefault('SU', {}) su['categories'] = [category.public_id] su['project'] = self.project.public_id data.setdefault('AD', su.copy()) data.setdefault('DU', su.copy()) self._test_users_with_invalid_permissions( uri, method, request_data=data) data.setdefault('POW', su.copy()) data.setdefault('PMA', su.copy()) data.setdefault('PDU', su.copy()) self._test_project_users_with_invalid_permissions( uri, method, request_data=data) def test_GET_category_clone_with_valid_permissions(self): """ Test the category_clone endpoint with valid permissions. """ #self.skipTest("Temporarily skipped") method = 'get' create_list = [['TestLevel-0', (('TestLevel-1', 'TestLevel-2',), ('TestLevel-1a', 'TestLevel-2a',))]] categories = Category.objects.create_category_tree( self.project, create_list) uri = reverse('category-clone') data = {} su = data.setdefault('SU', {}) su['categories'] = [categories[0][0].public_id] # 'TestLevel-0' su['project'] = self.project.public_id data.setdefault('AD', su.copy()) data.setdefault('DU', su.copy()) self._test_users_with_valid_permissions( uri, method, request_data=data, default_user=False) data.setdefault('POW', su.copy()) data.setdefault('PMA', su.copy()) data.setdefault('PDU', su.copy()) self._test_project_users_with_valid_permissions( uri, method, request_data=data) def test_GET_category_clone_with_parameters(self): """ Test the location_clone endpoint with various parameters. """ #self.skipTest("Temporarily skipped") method = 'get' create_list = [['TestLevel-0', (('TestLevel-1', 'TestLevel-2',), ('TestLevel-1a', 'TestLevel-2a',))]] categories = Category.objects.create_category_tree( self.project, create_list) uri = reverse('category-clone') data = {} data['categories'] = [categories[0][0].public_id] # 'TestLevel-0' data['project'] = self.project.public_id # Test with default arguments. response = self.client.get(uri, data=data, format='json', **self.HEADERS) res_data = self.flatten(response.data) msg = ("data: {}, found '{}' records , should be 5 records" ).format(res_data, len(res_data)) self.assertEqual(len(res_data), 5, msg) # Test with with_root=False data['with_root'] = False response = self.client.get(uri, data=data, format='json', **self.HEADERS) res_data = self.flatten(response.data) msg = ("data: {}, found '{}' records , should be 4 records" ).format(res_data, len(res_data)) self.assertEqual(len(res_data), 4, msg) def test_POST_category_clone_with_invalid_permissions(self): """ Test the category_clone endpoint with no permissions. """ #self.skipTest("Temporarily skipped") method = 'post' create_list = [['TestLevel-0', (('TestLevel-1', 'TestLevel-2',), ('TestLevel-1a', 'TestLevel-2a',))]] uri = reverse('category-clone') data = {} su = data.setdefault('SU', {}) su['categories'] = create_list su['project'] = self.project.public_id data.setdefault('AD', su.copy()) data.setdefault('DU', su.copy()) self._test_users_with_invalid_permissions( uri, method, request_data=data) data.setdefault('POW', su.copy()) data.setdefault('PMA', su.copy()) data.setdefault('PDU', su.copy()) self._test_project_users_with_invalid_permissions( uri, method, request_data=data) categories = Category.objects.all() msg = "categories: {}, count: {}, should be 0".format( categories, categories.count()) self.assertEqual(categories.count(), 0, msg) def test_POST_category_clone_with_valid_permissions(self): """ Test the category_clone endpoint with valid permissions. """ #self.skipTest("Temporarily skipped") method = 'post' create_list = [['TestLevel-0', (('TestLevel-1', 'TestLevel-2',), ('TestLevel-1a', 'TestLevel-2a',))]] uri = reverse('category-clone') data = {} su = data.setdefault('SU', {}) su['categories'] = create_list su['project'] = self.project.public_id data.setdefault('AD', su.copy()) data.setdefault('DU', su.copy()) self._test_users_with_valid_permissions( uri, method, request_data=data, default_user=False) data.setdefault('POW', su.copy()) data.setdefault('PMA', su.copy()) data.setdefault('PDU', su.copy()) self._test_project_users_with_valid_permissions( uri, method, request_data=data, project_user=False) categories = Category.objects.all() msg = "categories: {}, count: {}, should be 5".format( categories, categories.count()) self.assertEqual(categories.count(), 5, msg) def test_DELETE_category_clone_with_invalid_permissions(self): """ Test the category_clone endpoint with no permissions. """ #self.skipTest("Temporarily skipped") method = 'delete' create_list = [['TestLevel-0', (('TestLevel-1', 'TestLevel-2',), ('TestLevel-1a', 'TestLevel-2a',))]] categories = Category.objects.create_category_tree( self.project, create_list) uri = reverse('category-clone') data = {} su = data.setdefault('SU', {}) su['categories'] = [categories[0][0].public_id] su['project'] = self.project.public_id data.setdefault('AD', su.copy()) data.setdefault('DU', su.copy()) self._test_users_with_invalid_permissions( uri, method, request_data=data) data.setdefault('POW', su.copy()) data.setdefault('PMA', su.copy()) data.setdefault('PDU', su.copy()) self._test_project_users_with_invalid_permissions( uri, method, request_data=data) categories = Category.objects.all() msg = "categories: {}, count: {}, should be 5".format( categories, categories.count()) self.assertEqual(categories.count(), 5, msg) def test_DELETE_category_clone_with_valid_permissions(self): """ Test the category_clone endpoint with valid permissions. """ #self.skipTest("Temporarily skipped") method = 'delete' create_list = [['TestLevel-0', (('TestLevel-1', 'TestLevel-2',), ('TestLevel-1a', 'TestLevel-2a',))]] categories = Category.objects.create_category_tree( self.project, create_list) uri = reverse('category-clone') data = {} su = data.setdefault('SU', {}) su['categories'] = [categories[0][0].public_id] su['project'] = self.project.public_id data.setdefault('AD', su.copy()) data.setdefault('DU', su.copy()) self._test_users_with_valid_permissions( uri, method, request_data=data, default_user=False) data.setdefault('POW', su.copy()) data.setdefault('PMA', su.copy()) data.setdefault('PDU', su.copy()) self._test_project_users_with_valid_permissions( uri, method, request_data=data, project_user=False) categories = Category.objects.all() msg = "categories: {}, count: {}, should be 0".format( categories, categories.count()) self.assertEqual(categories.count(), 0, msg) def test_category_clone_serializer_validation_errors_on_project(self): """ Test that invalid data causes validation errors. """ #self.skipTest("Temporarily skipped") create_list = [['TestLevel-0', (('TestLevel-1', 'TestLevel-2',), ('TestLevel-1a', 'TestLevel-2a',))]] categories = Category.objects.create_category_tree( self.project, create_list) uri = reverse('category-clone') data = {} data['categories'] = [categories[0][0].public_id] data['project'] = 'junk' kwargs = self._setup_user_credentials() kwargs['login'] = True kwargs['is_superuser'] = False kwargs['role'] = self.DEFAULT_USER user, client = self._create_user(**kwargs) self.project.process_members([self.user, user]) self.project.set_role(user, self.PROJECT_USER) response = client.get(uri, data=data, format='json', **self.HEADERS) msg = "Response: {} should be {}, content: {}".format( response.status_code, status.HTTP_400_BAD_REQUEST, response.data) self.assertEqual( response.status_code, status.HTTP_400_BAD_REQUEST, msg) self.assertTrue(self._has_error(response, error_key='project'), msg) self._test_errors(response, tests={ 'project': "A project with the public_id 'junk' does not exist.", }) def test_category_clone_serializer_validation_errors_on_categories(self): """ Test that invalid data causes validation errors. """ #self.skipTest("Temporarily skipped") create_list = [['TestLevel-0', (('TestLevel-1', 'TestLevel-2',), ('TestLevel-1a', 'TestLevel-2a',))]] categories = Category.objects.create_category_tree( self.project, create_list) uri = reverse('category-clone') data = {} data['categories'] = [] data['project'] = self.project.public_id kwargs = self._setup_user_credentials() kwargs['login'] = True kwargs['is_superuser'] = False kwargs['role'] = self.DEFAULT_USER user, client = self._create_user(**kwargs) self.project.process_members([self.user, user]) self.project.set_role(user, self.PROJECT_USER) status_code = status.HTTP_400_BAD_REQUEST response = client.get(uri, data=data, **self.HEADERS) msg = "Response: {} should be {}, content: {}".format( response.status_code, status_code, response.data) self.assertEqual(response.status_code, status_code, msg) self.assertTrue(self._has_error(response, error_key='categories'), msg) self._test_errors(response, tests={ 'categories': "This field is required.", })
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src/features/build_features.py
weasysolutions/skin-lesion-dataset-cookiecutter
0
39475
# -*- coding: utf-8 -*- import click import os import logging import sys import pandas as pd import os, sys, inspect cmd_subfolder = os.path.realpath(os.path.abspath(os.path.join(os.path.split(inspect.getfile( inspect.currentframe() ))[0],"images_in_features_subdirs"))) if cmd_subfolder not in sys.path: sys.path.append(cmd_subfolder) cmd_subfolder = os.path.realpath(os.path.abspath(os.path.join(os.path.split(inspect.getfile( inspect.currentframe() ))[0],"non_duplicate_lesion_id"))) if cmd_subfolder not in sys.path: sys.path.append(cmd_subfolder) cmd_subfolder = os.path.realpath(os.path.abspath(os.path.join(os.path.split(inspect.getfile( inspect.currentframe() ))[0],"training_and_validation_sets"))) if cmd_subfolder not in sys.path: sys.path.append(cmd_subfolder) from images_in_features_subdirs import images_in_features_subdirs from non_duplicate_lesion_id import non_duplicate_lesion_id from training_and_validation_sets import training_and_validation_sets def build_features(**kwargs): metadata_csv = kwargs['metadata_csv'] train_dir = kwargs['train_dir'] images_dir = kwargs['images_dir'] val_dir = kwargs['val_dir'] """ Takes data in ../data/interim. Splits training and validation data. Stores splitted sets in ../data/processed/base_dir/train_dir and ../data/processed/base_dir/val_dir """ #load meta-data-set df = pd.read_csv(metadata_csv) #Create non_duplicate column. # Make a new df with unique_ids df, df_unique_id = non_duplicate_lesion_id(df) #split in training and validation dataframes df, df_train, df_val = training_and_validation_sets(df,df_unique_id) #place images in named attributes directories images_in_features_subdirs(df, images_dir = images_dir, train_dir = train_dir, val_dir = val_dir \ ) logger = logging.getLogger(__name__) logger.info('Features added. Data is ready for modelling.') if __name__ == '__main__': import json log_fmt = '%(asctime)s - %(name)s - %(levelname)s - %(message)s' logging.basicConfig(level=logging.INFO, format=log_fmt) data=json.loads(argv[1]) build_features(data)
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test/com/facebook/buck/parser/testdata/load_twice_from_bzl/b.bzl
jasonnam/buck
0
68113
<filename>test/com/facebook/buck/parser/testdata/load_twice_from_bzl/b.bzl z = 3 w = 4
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python/src/problem/leetcode/easy/leetcode_189.py
yipwinghong/Algorithm
9
22684
# coding=utf-8 from typing import List class Solution: """ 旋转数组 """ def reverse(self, nums: List[int], i: int, j: int) -> None: """ :param nums: :param i: :param j: :return: """ while i < j: nums[i], nums[j] = nums[j], nums[i] i += 1 j -= 1 def rotate(self, nums: List[int], k: int) -> None: """ Time: O(n), Space: O(1) :param nums: :param k: :return: """ if not nums or k <= 0: return n, m = len(nums), k % len(nums) self.reverse(nums, 0, n - 1) self.reverse(nums, 0, m - 1) self.reverse(nums, m, n - 1)
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doomrnn/model.py
yumaloop/predwm
494
67920
<gh_stars>100-1000 import numpy as np import random #from scipy.fftpack import dct import json import sys import config from env import make_env import time final_mode = True render_mode = True RENDER_DELAY = False def make_model(game): # can be extended in the future. model = Model(game) return model def sigmoid(x): return 1 / (1 + np.exp(-x)) def relu(x): return np.maximum(x, 0) def passthru(x): return x def softmax(x): e_x = np.exp(x - np.max(x)) return e_x / e_x.sum(axis=0) def sample(p): return np.argmax(np.random.multinomial(1, p)) class Model: ''' simple feedforward model ''' def __init__(self, game): self.noise_level = 0.0 self.env_name = game.env_name self.input_size = game.input_size self.output_size = game.output_size self.shapes = [ (game.input_size, game.output_size) ] self.sample_output = False if game.activation == 'relu': self.activations = [relu] elif game.activation == 'sigmoid': self.activations = [sigmoid] elif game.activation == 'passthru': self.activations = [np.tanh] else: self.activations = [np.tanh] self.weight = [] self.param_count = game.input_size * game.output_size for shape in self.shapes: self.weight.append(np.zeros(shape=shape)) self.render_mode = False def make_env(self, seed=-1, render_mode=False, load_model=True): self.render_mode = render_mode self.env = make_env(self.env_name, seed=seed, render_mode=render_mode, load_model=load_model) def get_action(self, x): # if mean_mode = True, ignore sampling. h = np.array(x).flatten() num_layers = len(self.weight) for i in range(num_layers): w = self.weight[i] h = np.matmul(h, w) h = self.activations[i](h + np.random.randn()*self.noise_level) return h def set_model_params(self, model_params): pointer = 0 for i in range(len(self.shapes)): w_shape = self.shapes[i] s_w = np.product(w_shape) s = s_w chunk = np.array(model_params[pointer:pointer+s]) self.weight[i] = chunk[:s_w].reshape(w_shape) pointer += s def load_model(self, filename): with open(filename) as f: data = json.load(f) print('loading file %s' % (filename)) self.data = data model_params = np.array(data[0]) # assuming other stuff is in data self.set_model_params(model_params) # also load the vae and rnn self.env.vae.load_json('tf_models/vae.json') self.env.rnn.load_json('tf_models/rnn.json') def get_random_model_params(self, stdev=0.1): #return np.random.randn(self.param_count)*stdev return np.random.standard_cauchy(self.param_count)*stdev # spice things up! def init_random_model_params(self, stdev=0.1): params = self.get_random_model_params(stdev=stdev) self.set_model_params(params) vae_params = self.env.vae.get_random_model_params(stdev=stdev) self.env.vae.set_model_params(vae_params) rnn_params = self.env.rnn.get_random_model_params(stdev=stdev) self.env.rnn.set_model_params(rnn_params) def evaluate(model): # run 100 times and average score, according to the reles. model.env.seed(0) total_reward = 0.0 N = 100 for i in range(N): reward, t = simulate(model, train_mode=False, render_mode=False, num_episode=1) total_reward += reward[0] return (total_reward / float(N)) def simulate(model, train_mode=False, render_mode=True, num_episode=5, seed=-1, max_len=-1): reward_list = [] t_list = [] max_episode_length = 2100 if train_mode and max_len > 0: max_episode_length = max_len if (seed >= 0): random.seed(seed) np.random.seed(seed) model.env.seed(seed) for episode in range(num_episode): obs = model.env.reset() if obs is None: obs = np.zeros(model.input_size) total_reward = 0.0 for t in range(max_episode_length): if render_mode: model.env.render("human") if RENDER_DELAY: time.sleep(0.01) action = model.get_action(obs) prev_obs = obs obs, reward, done, info = model.env.step(action) if (render_mode): pass #print("action", action, "step reward", reward) #print("step reward", reward) total_reward += reward if done: break if render_mode: print("reward", total_reward, "timesteps", t) model.env.close() reward_list.append(total_reward) t_list.append(t) return reward_list, t_list def main(): global RENDER_DELAY global final_mode assert len(sys.argv) > 2, 'python model.py gamename render/norender path_to_model.json [seed]' gamename = sys.argv[1] game = config.games[gamename] final_mode_string = str(sys.argv[2]) if (final_mode_string == "render"): final_mode = False # don't run 100 times, just visualize results. use_model = False if (len(sys.argv) > 3): use_model = True filename = sys.argv[3] print("filename", filename) the_seed = np.random.randint(10000) if len(sys.argv) > 4: the_seed = int(sys.argv[4]) print("seed", the_seed) model = make_model(game) print('model size', model.param_count) if (use_model): model.make_env(render_mode=render_mode) model.load_model(filename) else: model.make_env(render_mode=render_mode, load_model=False) model.init_random_model_params(stdev=np.random.rand()*0.01) if final_mode: total_reward = 0.0 np.random.seed(the_seed) model.env.seed(the_seed) reward_list = [] for i in range(100): reward, steps_taken = simulate(model, train_mode=False, render_mode=False, num_episode=1) print("iteration", i, "reward", reward[0]) total_reward += reward[0] reward_list.append(reward[0]) print("seed", the_seed, "average_reward", total_reward/100, "stdev", np.std(reward_list)) else: reward, steps_taken = simulate(model, train_mode=False, render_mode=render_mode, num_episode=1) print ("terminal reward", reward, "average steps taken", np.mean(steps_taken)+1) if __name__ == "__main__": main()
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test-simple-searcher.py
t83714/test-bert
0
188893
<reponame>t83714/test-bert import torch import pandas as pd from transformers import BertTokenizer, BertModel from SimpleSearcher import SimpleSearcher from format_attn_tuple import format_attn_tuple torch.set_printoptions(profile="full") gth_file = 'gth_part1' sentences = pd.read_csv('{0}.csv'.format(gth_file))[ 'Sentences'].values.tolist() tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertModel.from_pretrained('bert-base-uncased') def process_sentence(sentence): print("Receive sentence: ", sentence) inputs = tokenizer(sentence, return_tensors="pt", add_special_tokens=False) outputs = model(**inputs, output_attentions=True, return_dict=False) searcher = SimpleSearcher(tokenizer, inputs.input_ids, outputs[-1]) fact = searcher.search() print("Found fact: ", " -> ".join(map(lambda item: str(item[0]) + "("+str(item[1])+")", list(fact))), "\n") for s in sentences: process_sentence(s)
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dassh/correlations/flowsplit_ctd.py
khurrumsaleem/dassh
11
25771
######################################################################## # Copyright 2021, UChicago Argonne, LLC # # Licensed under the BSD-3 License (the "License"); you may not use # this file except in compliance with the License. You may obtain a # copy of the License at # # https://opensource.org/licenses/BSD-3-Clause # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or # implied. See the License for the specific language governing # permissions and limitations under the License. ######################################################################## """ date: 2021-08-12 author: matz Cheng-Todreas correlation for flow split (1986) """ ######################################################################## import numpy as np from . import friction_ctd as ctd applicability = ctd.applicability ######################################################################## # MODULE-WIDE CONSTANTS _GAMMA = 1 / 3.0 _M = ctd._m _EXP1 = {} _EXP2 = {} for regime in ctd._m.keys(): _EXP1[regime] = (1 + ctd._m[regime]) / (2 - ctd._m[regime]) _EXP2[regime] = 1 / (2 - ctd._m[regime]) ######################################################################## def calculate_flow_split(asm_obj, regime=None, beta=1.0): """Calculate the flow split into the different types of subchannels based on the Cheng-Todreas model Parameters ---------- asm_obj : DASSH Assembly object Contains the geometric description of the assembly regime : str or NoneType Indicate flow regime for which to calculate flow split {'turbulent', 'laminar', None}; default = None beta : float Beta is a factor used to combine the laminar and turbulent flowpslit terms in the transition region. It comes from Cheng's 1984 thesis in which he recommends a value of 0.05. There, Figure 4.19 shows the edge flowsplit assuming beta=0.05. However, in reality beta=0.05 gives weird results and beta=1.0 matches what's shown in the figure. Therefore, it'set to 1.0 here by default. Returns ------- numpy.ndarray Flow split between interior, edge, and corner coolant subchannels """ try: Re_bnds = asm_obj.corr_constants['fs']['Re_bnds'] except (KeyError, AttributeError): Re_bnds = ctd.calculate_Re_bounds(asm_obj) try: Cf = asm_obj.corr_constants['fs']['Cf_sc'] except (KeyError, AttributeError): Cf = ctd.calculate_subchannel_friction_factor_const(asm_obj) if regime is not None: return _calculate_flow_split(asm_obj, Cf, regime, Re_bnds, beta=beta) elif asm_obj.coolant_int_params['Re'] <= Re_bnds[0]: return _calculate_flow_split(asm_obj, Cf, 'laminar') elif asm_obj.coolant_int_params['Re'] >= Re_bnds[1]: return _calculate_flow_split(asm_obj, Cf, 'turbulent') else: return _calculate_flow_split(asm_obj, Cf, 'transition', Re_bnds, beta) def _calculate_flow_split(asm_obj, Cf_dict, regime, Re_bnds=None, beta=1.0): """Worker function to calculate the flow split into the different types of subchannels based on the Cheng-Todreas model. Parameters ---------- asm_obj : DASSH Assembly object Contains the geometric description of the assembly Cf_dict : dict Dictionary containing subchannel friction factor constants; keys: ['laminar', 'turbulent'] regime : str {'laminar', 'turbulent', 'transition'} Flow regime with which to evaluate flow split ratios Re_bnds : list (optional) Reynolds number flow regime boundaries for calculating intermittency factor in transition regime beta : float Beta is a factor used to combine the laminar and turbulent flowpslit terms in the transition region. It comes from Cheng's 1984 thesis in which he recommends a value of 0.05. There, Figure 4.19 shows the edge flowsplit assuming beta=0.05. However, in reality beta=0.05 gives weird results and beta=1.0 matches what's shown in the figure. Therefore, it'set to 1.0 here by default. Returns ------- numpy.ndarray Flow split between interior, edge, and corner coolant subchannels Notes ----- This method is imported by the flow split model in the Upgraded Cheng-Todreas correlation (flowsplit_uctd) """ if regime == 'transition': try: na = asm_obj.corr_constants['fs']['na'] except (KeyError, AttributeError): na = [asm_obj.subchannel.n_sc['coolant']['interior'] * asm_obj.params['area'][0], asm_obj.subchannel.n_sc['coolant']['edge'] * asm_obj.params['area'][1], asm_obj.subchannel.n_sc['coolant']['corner'] * asm_obj.params['area'][2]] flow_split = np.zeros(3) intf_b = ctd.calc_intermittency_factor( asm_obj, Re_bnds[0], Re_bnds[1]) xratio_t = asm_obj.corr_constants['fs']['xr']['transition'].copy() xratio_t[0] = (xratio_t[0] * (1 - intf_b)**_GAMMA / asm_obj.coolant_int_params['Re']) xratio_t[1] = (xratio_t[1] * intf_b**_GAMMA / asm_obj.coolant_int_params['Re']**_M['turbulent'] )**_EXP2['turbulent'] # xratio = xratio_t1 + beta * xratio_t2 xratio = xratio_t[0] + beta * xratio_t[1] x1x2 = xratio[1] / xratio[0] # Equation 4.51 in Cheng 1984 x3x2 = xratio[1] / xratio[2] # Equation 4.51 in Cheng 1984 flow_split[1] = (asm_obj.bundle_params['area'] / (na[1] + x1x2 * na[0] + x3x2 * na[2])) flow_split[0] = x1x2 * flow_split[1] flow_split[2] = x3x2 * flow_split[1] else: flow_split = asm_obj.corr_constants['fs']['fs'][regime] # x1x2 = asm_obj.corr_constants['fs']['xr'][regime][0] # x3x2 = asm_obj.corr_constants['fs']['xr'][regime][1] # # # Flow split to subchannel type 2 # flow_split[1] = (asm_obj.bundle_params['area'] # / (na[1] + x1x2 * na[0] + x3x2 * na[2])) # flow_split[0] = x1x2 * flow_split[1] # flow_split[2] = x3x2 * flow_split[1] return flow_split def calc_constants(asm_obj): """Calculate constants needed by the CTD flowsplit calculation""" const = ctd.calc_constants(asm_obj) del const['Cf_b'] # Total subchannel area for each subchannel type const['na'] = [asm_obj.subchannel.n_sc['coolant']['interior'] * asm_obj.params['area'][0], asm_obj.subchannel.n_sc['coolant']['edge'] * asm_obj.params['area'][1], asm_obj.subchannel.n_sc['coolant']['corner'] * asm_obj.params['area'][2]] # REGIME RATIO CONSTANTS const['xr'] = _calc_regime_ratio_constants(asm_obj, const['Cf_sc']) # # Transition regime # const['xr'] = {} # const['xr']['transition'] = np.array([ # (const['Cf_sc']['laminar'] # * asm_obj.bundle_params['de'] # / asm_obj.params['de']**2), # (const['Cf_sc']['turbulent'] # * asm_obj.bundle_params['de']**_M['turbulent'] # / asm_obj.params['de']**(_M['turbulent'] + 1)) # ]) # # # Laminar/turbulent regime # for k in ['laminar', 'turbulent']: # const['xr'][k] = np.array([ # ((asm_obj.params['de'][0] / asm_obj.params['de'][1])**_EXP1[k] # * (const['Cf_sc'][k][1] / const['Cf_sc'][k][0])**_EXP2[k]), # ((asm_obj.params['de'][2] / asm_obj.params['de'][1])**_EXP1[k] # * (const['Cf_sc'][k][1] / const['Cf_sc'][k][2])**_EXP2[k]) # ]) # Laminar/turbulent: constant flow split! const['fs'] = _calc_constant_flowsplits(asm_obj, const) # const['fs'] = {} # for k in ['laminar', 'turbulent']: # const['fs'][k] = np.zeros(3) # const['fs'][k][1] = (asm_obj.bundle_params['area'] # / (const['na'][1] # + const['xr'][k][0] * const['na'][0] # + const['xr'][k][1] * const['na'][2])) # const['fs'][k][0] = const['xr'][k][0] * const['fs'][k][1] # const['fs'][k][2] = const['xr'][k][1] * const['fs'][k][1] return const def _calc_regime_ratio_constants(asm_obj, Cf_sc): """Constant ratios for laminar, turbulent, and transition regimes""" xr = {} xr['transition'] = np.array([ (Cf_sc['laminar'] * asm_obj.bundle_params['de'] / asm_obj.params['de']**2), (Cf_sc['turbulent'] * asm_obj.bundle_params['de']**_M['turbulent'] / asm_obj.params['de']**(_M['turbulent'] + 1)) ]) # Laminar/turbulent regime for k in ['laminar', 'turbulent']: xr[k] = np.array([ ((asm_obj.params['de'][0] / asm_obj.params['de'][1])**_EXP1[k] * (Cf_sc[k][1] / Cf_sc[k][0])**_EXP2[k]), ((asm_obj.params['de'][2] / asm_obj.params['de'][1])**_EXP1[k] * (Cf_sc[k][1] / Cf_sc[k][2])**_EXP2[k]) ]) return xr def _calc_constant_flowsplits(asm_obj, const): """Laminar and turbulent flowsplits are constant""" fs = {} for k in ['laminar', 'turbulent']: fs[k] = np.zeros(3) fs[k][1] = (asm_obj.bundle_params['area'] / (const['na'][1] + const['xr'][k][0] * const['na'][0] + const['xr'][k][1] * const['na'][2])) fs[k][0] = const['xr'][k][0] * fs[k][1] fs[k][2] = const['xr'][k][1] * fs[k][1] return fs
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scripts/geo/geo_process_LCDB.py
mdmeadows/DSM-to-DTM
36
189742
<gh_stars>10-100 # Process: Manaaki Whenua Land Cover Database (LCDB v5.0) # Import required packages import sys, subprocess import numpy as np import pandas as pd import matplotlib.pyplot as plt # Import helper functions relevant to this script sys.path.append('E:/mdm123/D/scripts/geo/') from geo_helpers import extract_projection_info # List paths to GDAL scripts ogr2ogr = 'C:/Anaconda3/envs/geo/Library/bin/ogr2ogr.exe' gdal_warp = 'C:/Anaconda3/envs/geo/Library/bin/gdalwarp.exe' gdal_rasterise = 'C:/Anaconda3/envs/geo/Library/bin/gdal_rasterize.exe' # Define paths to SRTM & LCDB folders folder_srtm = 'E:/mdm123/D/data/DSM/SRTM/' folder_lcdb = 'E:/mdm123/D/data/LRIS/lris-lcdb-v50' folder_inputs = 'E:/mdm123/D/ML/inputs/1D' folder_fig = 'E:/mdm123/D/figures' # Define list of zones to be processed (separate LiDAR coverage areas) zones = ['MRL18_WPE', 'MRL18_WVL', 'MRL18_WKW', 'MRL18_FGA', 'TSM17_STA', 'TSM17_LDM', 'TSM17_GLB', 'TSM16_ATG'] # Define dictionary to hold information relating to each zone covered by the Marlborough (2018) survey dtm_dict = {'MRL18_WPE':{'label':'Wairau Plains East (Marlborough 2018)', 'year':'2018'}, 'MRL18_WVL':{'label':'Wairau Valley (Marlborough 2018)', 'year':'2018'}, 'MRL18_WKW':{'label':'Picton - Waikawa (Marlborough 2018)', 'year':'2018'}, 'MRL18_FGA':{'label':'Flaxbourne, Grassmere & Lower Awatere (Marlborough 2018)', 'year':'2018'}, 'TSM17_STA':{'label':'St Arnaud (Tasman 2017)', 'year':'2017'}, 'TSM17_LDM':{'label':'Lee Dam (Tasman 2017)', 'year':'2017'}, 'TSM17_GLB':{'label':'Golden Bay & Farewell Spit (Tasman 2017)', 'year':'2017'}, 'TSM16_ATG':{'label':'Abel Tasman & Golden Bay (Tasman 2016)', 'year':'2016'}} # Define the number of cells of padding to add along each raster boundary pad = 44 ############################################################################### # 1. Reproject LCDB SHP (from NZTM2000 to WGS84) # ############################################################################### # Reproject the Manaaki Whenua land cover SHP from NZTM2000 to WGS84 (EPSG:4326) LCDB_shp_NZTM2000 = '{}/raw/lcdb-v50-land-cover-database-version-50-mainland-new-zealand.shp'.format(folder_lcdb) LCDB_shp_WGS84 = '{}/proc/LCDB_v50_WGS84.shp'.format(folder_lcdb) WGS84 = 'EPSG:4326' reproject_command = [ogr2ogr, LCDB_shp_WGS84, LCDB_shp_NZTM2000, '-t_srs', WGS84, '-overwrite'] reproject_result = subprocess.run(reproject_command, stdout=subprocess.PIPE) if reproject_result.returncode != 0: print(reproject_result.stdout) ############################################################################### # 2. ArcMap codeblocks for reclassification of sub-classes to main groupings # ############################################################################### # Used for Python codeblock of field calculation in ArcMap (based on LCDB v4+ definitions of CLASS & NAME) def classify_group(c): # Artificial surfaces if c in [1, 2, 5, 6]: return 'Artificial Surfaces' # Bare or Lightly-vegetated Surfaces elif c in [10, 12, 14, 15, 16]: return 'Bare or Lightly-vegetated Surfaces' # Water Bodies elif c in [20, 21, 22]: return 'Water Bodies' # Cropland elif c in [30, 33]: return 'Cropland' # Grassland, Sedgeland and Marshland elif c in [40, 41, 43, 44, 45, 46, 47]: return 'Grassland, Sedgeland and Marshland' # Scrub and Shrubland elif c in [50, 51, 52, 54, 55, 56, 58, 80, 81]: return 'Scrub and Shrubland' # Forest elif c in [64, 68, 69, 70, 71]: return 'Forest' # Other else: return 'Other' # Used for Python codeblock of field calculation in ArcMap (based on LCDB v4+ definitions of CLASS & NAME) def classify_ID(c): # Artificial surfaces = 1 if c in [1, 2, 5, 6]: return 1 # Bare or Lightly-vegetated Surfaces = 2 elif c in [10, 12, 14, 15, 16]: return 2 # Water Bodies = 3 elif c in [20, 21, 22]: return 3 # Cropland = 4 elif c in [30, 33]: return 4 # Grassland, Sedgeland and Marshland = 5 elif c in [40, 41, 43, 44, 45, 46, 47]: return 5 # Scrub and Shrubland = 6 elif c in [50, 51, 52, 54, 55, 56, 58, 80, 81]: return 6 # Forest = 7 elif c in [64, 68, 69, 70, 71]: return 7 # Other = 8 else: return 8 ############################################################################### # 3. Resample LCDB rasters to match padded SRTM grids for each zone # ############################################################################### # Loop through all available DTM survey zones for zone in zones: print('\nProcessing {} zone:'.format(zone)) # Open a template raster (DEM for that zone, with pad=44) & extract its properties print(' - Analysing zonal SRTM raster to align grids...') srtm_filename = '{}proc/{}/SRTM_{}_Z.tif'.format(folder_srtm, zone, zone) srtm_proj, srtm_res_x, srtm_res_y, srtm_x_min, srtm_x_max, srtm_y_min, srtm_y_max, srtm_width, srtm_height = extract_projection_info(srtm_filename) # Define a new bounding box, including the padding required for the 2D convnet data pre-processing pad_x_min = srtm_x_min - pad*srtm_res_x pad_x_max = srtm_x_max + pad*srtm_res_x pad_y_min = srtm_y_min - pad*-srtm_res_y pad_y_max = srtm_y_max + pad*-srtm_res_y pad_width = srtm_width + 2*pad pad_height = srtm_height + 2*pad # Rasterise the Manaaki Whenua land cover SHP to a raster (aligning it with the others, in terms of resolution & extent) print(' - Rasterising land cover SHP to zone GeoTIFF...') LCDB_shp = '{}/proc/LCDB_v50_WGS84.shp'.format(folder_lcdb) LCDB_tif = '{}/proc/LCDB_GroupID_{}_Pad44.tif'.format(folder_lcdb, zone) rasterise_command = [gdal_rasterise, '-a', 'GrpID_2018', '-l', LCDB_shp.split('/')[-1][:-4], LCDB_shp, LCDB_tif, '-a_nodata', '-9999', '-tr', str(srtm_res_x), str(-srtm_res_y), '-te', str(pad_x_min), str(pad_y_min), str(pad_x_max), str(pad_y_max)] rasterise_result = subprocess.run(rasterise_command, stdout=subprocess.PIPE) if rasterise_result.returncode != 0: print('\nProcess failed, with error message: {}\n'.format(rasterise_result.stdout)) break ############################################################################### # 4. More processing of LCDB rasters within the geo_process_LiDAR_SRTM script # ############################################################################### # Further processing & visualisation of the LCDB raster data was done in the "geo_process_LiDAR_SRTM.py" script ############################################################################### # 5. Generate thumbnail histograms for each zone, to include in LCDB map # ############################################################################### # Set up a dictionary of properties for each Manaaki Whenua landclass type present lcdb_dict = {1:{'label':'Artificial\nsurfaces', 'colour':(78/255, 78/255, 78/255)}, 2:{'label':'Bare or Lightly-\nvegetated Surfaces', 'colour':(255/255, 235/255, 190/255)}, 3:{'label':'Water\nBodies', 'colour':(0/255, 197/255, 255/255)}, 4:{'label':'Cropland', 'colour':(255/255, 170/255, 0/255)}, 5:{'label':'Grassland, Sedgeland\nand Marshland', 'colour':(255/255, 255/255, 115/255)}, 6:{'label':'Scrub and\nShrubland', 'colour':(137/255, 205/255, 102/255)}, 7:{'label':'Forest', 'colour':(38/255, 115/255, 0/255)}, 8:{'label':'Other', 'colour':'red'}} # Set up list of bin edges & colours lcdb_bins = np.linspace(0.5, 7.5, num=8) lcdb_colours = [lcdb_dict[l]['colour'] for l in range(1,8)] # Loop through each zone, generating a very simple histogram (colours only) of the land cover classes present for zone in zones: # Read 1D vector of processed input data for that zone df = pd.read_csv('{}/Input1D_ByZone_{}.csv'.format(folder_inputs, zone)) # Get a list of LCDB class codes for all valid pixels lcdb_list = df['lcdb'].loc[df['diff'] != -9999].tolist() lcdb_list = [l for l in lcdb_list if (not np.isnan(l) and l != None and l != -9999)] # Generate histogram manually, to ensure all classes covered (even if not present in that zone) fig, axes = plt.subplots(figsize=(2,0.8)) _,_,patches = axes.hist(lcdb_list, bins=lcdb_bins, edgecolor='dimgrey', linewidth=0.3) for patch, colour in zip(patches, lcdb_colours): patch.set_facecolor(colour) # Tidy up figure & save [axes.spines[edge].set_visible(False) for edge in ['left','top','right']] axes.spines['bottom'].set_color('black') axes.spines['bottom'].set_linewidth(0.5) axes.yaxis.set_visible(False) axes.set_xticklabels([]) axes.set_xticks([]) fig.tight_layout() fig.savefig('{}/All/Distributions/LCDB/landcover_hist_{}.png'.format(folder_fig, zone), dpi=150, transparent=True, bbox='tight') plt.close()
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jms_oidc_rp/utils.py
BaiJiangJie/jumpserver-django-oidc-rp
1
129610
""" OpenID Connect relying party (RP) utilities =========================================== This modules defines utilities allowing to manipulate ID tokens and other common helpers. """ import logging import datetime as dt from calendar import timegm from urllib.parse import urlparse, urljoin from django.core.exceptions import SuspiciousOperation from django.utils.encoding import force_bytes, smart_bytes from jwkest import JWKESTException from jwkest.jwk import KEYS from jwkest.jws import JWS from .conf.settings import dynamic_setting as oidc_rp_settings def get_logger(name=''): return logging.getLogger('jumpserver.{}'.format(name)) logger = get_logger(__file__) def validate_and_return_id_token(jws, nonce=None, validate_nonce=True): """ Validates the id_token according to the OpenID Connect specification. """ log_prompt = "Validate ID Token: {}" logger.debug(log_prompt.format('Get shared key')) shared_key = oidc_rp_settings.CLIENT_SECRET \ if oidc_rp_settings.PROVIDER_SIGNATURE_ALG == 'HS256' \ else oidc_rp_settings.PROVIDER_SIGNATURE_KEY # RS256 try: # Decodes the JSON Web Token and raise an error if the signature is invalid. logger.debug(log_prompt.format('Verify compact jwk')) id_token = JWS().verify_compact(force_bytes(jws), _get_jwks_keys(shared_key)) except JWKESTException as e: logger.debug(log_prompt.format('Verify compact jwkest exception: {}'.format(str(e)))) return # Validates the claims embedded in the id_token. logger.debug(log_prompt.format('Validate claims')) _validate_claims(id_token, nonce=nonce, validate_nonce=validate_nonce) return id_token def _get_jwks_keys(shared_key): """ Returns JWKS keys used to decrypt id_token values. """ # The OpenID Connect Provider (OP) uses RSA keys to sign/enrypt ID tokens and generate public # keys allowing to decrypt them. These public keys are exposed through the 'jwks_uri' and should # be used to decrypt the JWS - JSON Web Signature. log_prompt = "Get jwks keys: {}" logger.debug(log_prompt.format('Start')) jwks_keys = KEYS() logger.debug(log_prompt.format('Load from provider jwks endpoint')) jwks_keys.load_from_url(oidc_rp_settings.PROVIDER_JWKS_ENDPOINT) # Adds the shared key (which can correspond to the client_secret) as an oct key so it can be # used for HMAC signatures. logger.debug(log_prompt.format('Add key')) jwks_keys.add({'key': smart_bytes(shared_key), 'kty': 'oct'}) logger.debug(log_prompt.format('End')) return jwks_keys def _validate_claims(id_token, nonce=None, validate_nonce=True): """ Validates the claims embedded in the JSON Web Token. """ log_prompt = "Validate claims: {}" logger.debug(log_prompt.format('Start')) iss_parsed_url = urlparse(id_token['iss']) provider_parsed_url = urlparse(oidc_rp_settings.PROVIDER_ENDPOINT) if iss_parsed_url.netloc != provider_parsed_url.netloc: logger.debug(log_prompt.format('Invalid issuer')) raise SuspiciousOperation('Invalid issuer') if isinstance(id_token['aud'], str): id_token['aud'] = [id_token['aud']] if oidc_rp_settings.CLIENT_ID not in id_token['aud']: logger.debug(log_prompt.format('Invalid audience')) raise SuspiciousOperation('Invalid audience') if len(id_token['aud']) > 1 and 'azp' not in id_token: logger.debug(log_prompt.format('Incorrect id_token: azp')) raise SuspiciousOperation('Incorrect id_token: azp') if 'azp' in id_token and id_token['azp'] != oidc_rp_settings.CLIENT_ID: raise SuspiciousOperation('Incorrect id_token: azp') utc_timestamp = timegm(dt.datetime.utcnow().utctimetuple()) if utc_timestamp > id_token['exp']: logger.debug(log_prompt.format('Signature has expired')) raise SuspiciousOperation('Signature has expired') if 'nbf' in id_token and utc_timestamp < id_token['nbf']: logger.debug(log_prompt.format('Incorrect id_token: nbf')) raise SuspiciousOperation('Incorrect id_token: nbf') # Verifies that the token was issued in the allowed timeframe. if utc_timestamp > id_token['iat'] + oidc_rp_settings.ID_TOKEN_MAX_AGE: logger.debug(log_prompt.format('Incorrect id_token: iat')) raise SuspiciousOperation('Incorrect id_token: iat') # Validate the nonce to ensure the request was not modified if applicable. id_token_nonce = id_token.get('nonce', None) if validate_nonce and oidc_rp_settings.USE_NONCE and id_token_nonce != nonce: logger.debug(log_prompt.format('Incorrect id_token: nonce')) raise SuspiciousOperation('Incorrect id_token: nonce') logger.debug(log_prompt.format('End')) def build_absolute_uri(request, path=None): """ Build absolute redirect uri """ if path is None: path = '/' if oidc_rp_settings.BASE_SITE_URL: redirect_uri = urljoin(oidc_rp_settings.BASE_SITE_URL, path) else: redirect_uri = request.build_absolute_uri(path) return redirect_uri
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plot_maps.py
denadai2/A-Tale-of-Cities---code
1
111210
__author__ = '<NAME>' __license__ = "MIT" import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from math import sqrt import datetime from mpl_toolkits.basemap import Basemap from matplotlib.colors import LinearSegmentedColormap # Set the plot styles parse = lambda x: datetime.datetime.fromtimestamp(float(x)/1000) fig_width_pt = 345 # Get this from LaTeX using \showthe\columnwidth inches_per_pt = 1.0/72.27 # Convert pt to inches golden_mean = (sqrt(5)-1.0)/2.0 # Aesthetic ratio fig_width = fig_width_pt*inches_per_pt # width in inches fig_height = fig_width*golden_mean # height in inches fig_size = [fig_width, fig_height] sns.set_style("ticks") sns.set_context("paper") # Import the dataset Telecommunications sliceSum = pd.DataFrame({}) for index in range(4,11): sliceSum2 = pd.read_csv('nature/sms-call-internet-tn-2013-11-'+str(index).zfill(2) +'.txt', sep='\t', encoding="utf-8-sig", names=['CellID', 'datetime', 'countrycode', 'smsin', 'smsout', 'callin', 'callout', 'internet'], parse_dates=['datetime'], date_parser=parse)#, parse_dates=['datetime']) sliceSum2 = sliceSum2.set_index('datetime') sliceSum2['hour'] = sliceSum2.index.hour sliceSum2['weekday'] = sliceSum2.index.weekday sliceSum2 = sliceSum2.groupby(['hour','weekday', 'CellID'], as_index=False).sum() sliceSum = sliceSum.append(sliceSum2) sliceSum['idx'] = sliceSum['hour'] + (sliceSum['weekday']*24) sliceSum.head() # Import the dataset - precipitations precipitation_df = pd.read_csv('nature/precipitation-trentino.csv', sep=',', names=['datetime','CellID','intensity'], encoding="utf-8-sig", parse_dates=['datetime'], date_parser=parse) precipitation_df = precipitation_df.set_index(['datetime'], drop=False) precipitation_df = precipitation_df.groupby(['CellID'], as_index=False).mean() precipitation_df.head() # Import the dataset - news news_df = pd.read_csv('nature/news.csv', sep=',', encoding="utf-8-sig", parse_dates=['date']) news_df.head() # Import the grid, obtained from the geojson. The CSV has X, Y coordinates and CellID csv_df = pd.read_csv('nature/grid_trentino.csv') # Merge csv_df with Telecommunications, grouped by cellID merge = pd.merge(sliceSum.groupby(['CellID'], as_index=False).sum(), csv_df, on='CellID') # Extract the points for hexbin points_x = np.array([x['X'] for i,x in merge.iterrows()]) points_y = np.array([x['Y'] for i,x in merge.iterrows()]) c = np.array([x['internet'] for i,x in merge.iterrows()]) # Trentino's boundingbox a = (45.6730682227551, 10.4521594968354) b = (46.5327699992773, 11.9627133503828) # Trentino shapefile # http://dati.trentino.it/dataset/limite-comprensoriale-027140/resource/ff1f1687-3f8f-427e-84d9-cf40c8b9b98a m = Basemap(lat_0 = (a[0]+b[0])/2, lon_0 = (a[1]+b[1])/2, epsg=4326, llcrnrlon=a[1],llcrnrlat=a[0],urcrnrlon=b[1],urcrnrlat=b[0],) m.readshapefile('nature/amm','Trentino_shapefile', color='0.35') cmap = LinearSegmentedColormap.from_list("skil", sns.color_palette("RdBu_r", 7)[1:]) plt.register_cmap(cmap=cmap) m.hexbin(points_x, points_y, cmap="skil", gridsize=50, C=c, bins='log', mincnt=1) sns.despine(left=True, bottom=True) plt.savefig('map.pdf', format='pdf', dpi=330, bbox_inches='tight') # Import the dataset - social pulse, obtained from the geojson social_df = pd.read_csv('nature/result2.csv', sep=',', encoding="utf-8-sig", parse_dates=['created']) points_x = np.array([x['geomPoint.geom/coordinates/0'] for i,x in social_df.iterrows()]) points_y = np.array([x['geomPoint.geom/coordinates/1'] for i,x in social_df.iterrows()]) m = Basemap(lat_0 = (a[0]+b[0])/2, lon_0 = (a[1]+b[1])/2, epsg=4326, llcrnrlon=a[1], llcrnrlat=a[0], urcrnrlon=b[1], urcrnrlat=b[0],) m.readshapefile('nature/amm', 'Trentino_shapefile', color='0.35') m.hexbin(points_x, points_y, cmap="skil", gridsize=50, bins='log', mincnt=1) sns.despine(left=True, bottom=True) plt.savefig('map_social.pdf', format='pdf', dpi=330,bbox_inches='tight') # Energy map line_df = pd.read_csv('nature/line.csv', sep=',', encoding="utf-8-sig") line_df['CellID'] = line_df['SQUAREID'] merge = pd.merge(line_df.groupby(['CellID'], as_index=False).sum(), csv_df, on='CellID') points_x = np.array([x['X'] for i,x in merge.iterrows()]) points_y = np.array([x['Y'] for i,x in merge.iterrows()]) c = np.array([x['NR_UBICAZIONI'] for i,x in merge.iterrows()]) m = Basemap(lat_0 = (a[0]+b[0])/2, lon_0 = (a[1]+b[1])/2, epsg=4326, llcrnrlon=a[1],llcrnrlat=a[0],urcrnrlon=b[1],urcrnrlat=b[0],) m.readshapefile('nature/amm','Trentino_shapefile', color='0.35') m.hexbin(points_x, points_y, cmap="skil", gridsize=50, bins='log', C=c, mincnt=1) sns.despine(left=True, bottom=True) plt.savefig('map_line.pdf', format='pdf', dpi=330,bbox_inches='tight') # Precipitation map merge = pd.merge(precipitation_df, csv_df, on='CellID') points_x = np.array([x['X'] for i,x in merge.iterrows()]) points_y = np.array([x['Y'] for i,x in merge.iterrows()]) c = np.array([x['intensity'] for i,x in merge.iterrows()]) m = Basemap(lat_0 = (a[0]+b[0])/2, lon_0 = (a[1]+b[1])/2, epsg=4326, llcrnrlon=a[1], llcrnrlat=a[0], urcrnrlon=b[1], urcrnrlat=b[0],) m.readshapefile('nature/amm', 'Trentino_shapefile', color='0.35') m.hexbin(points_x, points_y, cmap="skil", gridsize=50, bins='log', C=c, mincnt=1) sns.despine(left=True, bottom=True) plt.savefig('map_precipitation.pdf', format='pdf', dpi=330, bbox_inches='tight') # News map points_x = np.array([x['geomPoint.geom/coordinates/0'] for i,x in news_df.iterrows()]) points_y = np.array([x['geomPoint.geom/coordinates/1'] for i,x in news_df.iterrows()]) m = Basemap(lat_0 = (a[0]+b[0])/2, lon_0 = (a[1]+b[1])/2, epsg=4326, llcrnrlon=a[1],llcrnrlat=a[0],urcrnrlon=b[1],urcrnrlat=b[0],) m.readshapefile('nature/amm','Trentino_shapefile', color='0.35') m.hexbin(points_x, points_y, cmap="skil", gridsize=35, bins='log', mincnt=1) sns.despine(left=True, bottom=True) plt.savefig('map_news.pdf', format='pdf', dpi=330,bbox_inches='tight')
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scripts/extract_user_sudo_privileges.py
worr/sysops-api
40
27665
#!/usr/bin/python2.6 # (c) [2013] LinkedIn Corp. All rights reserved. # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. # You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. from optparse import OptionParser import sys sys.path.append("/usr/local/admin") import sysopsapi.cache_extractor ########################################################################## def main(): parser = OptionParser(usage="usage: %prog [options]", version="%prog 1.0") parser.add_option("--verbose", action="store_true", dest="verbose", default=False, help="Enable verbose execution") parser.add_option("--range-query", action="store", dest="range_query", help="Specify a range cluster of hosts you which to use to use to make queries against.") parser.add_option("--user", action="store", dest="user", help="Specify a unix user uid or id that you are interested in searching for.") (options, args) = parser.parse_args() ina_groups = [] ina_group_file = open( '/etc/sudo.d/sudoers-USERS_GROUP_WORLD_READABLE', 'r').readlines() for line in ina_group_file: if options.user in line: ina_groups.append(line.split()[1]) sudoers_rules = {} sudoers_file = open('/etc/sudo.d/sudoers_WORLD_READABLE', 'r').readlines() for line in sudoers_file: if " = " in line: ina_group = line.split()[0].strip() machine_group = line.split()[1].strip() privs = line.split('=')[1].strip() if ina_group in ina_groups: sudoers_rules[machine_group] = privs cache_results = sysopsapi.cache_extractor.CacheExtractor(verbose=options.verbose, scope='global', contents=True, range_query=options.range_query, search_string='sudoers-MACHINE_GROUP') for key in cache_results._gold.iterkeys(): host = key.split('#')[0] for line in cache_results._gold[key].splitlines(): if "Host_Alias" in line: system_machine_group = line.split()[1] if sudoers_rules.get(system_machine_group): print ("user: " + options.user).ljust(0) + \ ("privs: " + sudoers_rules.get(system_machine_group)).center(40) + \ ("host: " + host).center(40) + \ ("machine_group: " + system_machine_group).rjust(10) ########################################################################## if __name__ == '__main__': main()
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tests/unit/test_validate.py
DavidBakerEffendi/nlapi-python
36
196859
<reponame>DavidBakerEffendi/nlapi-python<filename>tests/unit/test_validate.py # Copyright (c) 2020 original authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from unittest.mock import patch from expertai.nlapi.common.errors import ParameterError from expertai.nlapi.cloud.validate import ExpertAiValidation from tests import BaseTestCase class ValidationTests(BaseTestCase): def setUp(self): super().setUp() self.validation_klass = ExpertAiValidation() def test_the_language_code_is_not_among_those_permitted(self): """ ...then the value should not be considered correct """ self.assertFalse( self.validation_klass.language_value_is_correct(language="sv") ) def test_the_resource_is_not_among_those_permitted(self): """ ...then the value should not be considered correct """ self.assertFalse( self.validation_klass.resource_value_is_correct( resource="document" ) ) @patch("expertai.nlapi.cloud.validate.ExpertAiValidation.language_value_is_correct") def test_the_parameter_to_be_validated_is_the_language( self, patched_method ): """ ...then verify language_value_is_correct() is called """ self.validation_klass.check_value(param_name="language", value="it") patched_method.assert_called_once_with(language="it") @patch("expertai.nlapi.cloud.validate.ExpertAiValidation.resource_value_is_correct") def test_the_parameter_to_be_validated_is_the_resource( self, patched_method ): """ ...then verify resource_value_is_correct() is called """ self.validation_klass.check_value( param_name="resource", value="disambiguation" ) patched_method.assert_called_once_with(resource="disambiguation") def test_parameter_name_is_wrong(self): """ ...then the validation should fail immediately """ self.assertRaises( ParameterError, self.validation_klass.check_name, param_name="lang" ) def test_when_parameter_name_is_correct_but_the_value_is_wrong(self): """ ...then the validation should still fail This test implicitly verifies that the two check_name and check_value methods are sequentially invoked """ self.assertRaises( ParameterError, self.validation_klass.check_value, param_name="language", value="sv", )
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python/chronos/test/bigdl/chronos/data/experimental/test_xshardstsdataset.py
sgwhat/BigDL
0
6322
<reponame>sgwhat/BigDL # # Copyright 2016 The BigDL Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import pytest import numpy as np import pandas as pd import random import os from unittest import TestCase from bigdl.chronos.data import TSDataset from bigdl.chronos.data.experimental import XShardsTSDataset from bigdl.orca.data.pandas import read_csv from bigdl.orca.common import init_orca_context, stop_orca_context, OrcaContext from pandas.testing import assert_frame_equal from numpy.testing import assert_array_almost_equal def generate_spark_df(): init_orca_context(cores=8) sc = OrcaContext.get_spark_context() rdd = sc.range(0, 100) from pyspark.ml.linalg import DenseVector df = rdd.map(lambda x: (DenseVector(np.random.randn(1, ).astype(np.float)), int(np.random.randint(0, 2, size=())), int(x))).toDF(["feature", "id", "date"]) return df def get_ugly_ts_df(): data = np.random.random_sample((100, 5)) mask = np.random.random_sample((100, 5)) newmask = mask.copy() mask[newmask >= 0.4] = 2 mask[newmask < 0.4] = 1 mask[newmask < 0.2] = 0 data[mask == 0] = None data[mask == 1] = np.nan df = pd.DataFrame(data, columns=['a', 'b', 'c', 'd', 'e']) df['a'][0] = np.nan # make sure column 'a' has a N/A df["datetime"] = pd.date_range('1/1/2019', periods=100) df.loc[50:100, "datetime"] = pd.date_range('1/1/2019', periods=50) df["id"] = np.array(['00']*50 + ['01']*50) return df class TestXShardsTSDataset(TestCase): def setUp(self): self.resource_path = os.path.join(os.path.split(__file__)[0], "../../resources/") def tearDown(self): pass @classmethod def tearDownClass(cls): # stop possible active_spark_context from pyspark import SparkContext from bigdl.orca.ray import OrcaRayContext if SparkContext._active_spark_context is not None: print("Stopping spark_orca context") sc = SparkContext.getOrCreate() if sc.getConf().get("spark.master").startswith("spark://"): from bigdl.dllib.nncontext import stop_spark_standalone stop_spark_standalone() sc.stop() def test_xshardstsdataset_initialization(self): shards_single = read_csv(os.path.join(self.resource_path, "single.csv")) tsdata = XShardsTSDataset.from_xshards(shards_single, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id") assert tsdata._id_list == [0] assert tsdata.feature_col == ["extra feature"] assert tsdata.target_col == ["value"] assert tsdata.dt_col == "datetime" assert tsdata.shards.num_partitions() == 1 tsdata = XShardsTSDataset.from_xshards(shards_single, dt_col="datetime", target_col=["value"], extra_feature_col="extra feature", id_col="id") assert tsdata._id_list == [0] assert tsdata.feature_col == ["extra feature"] assert tsdata.target_col == ["value"] assert tsdata.dt_col == "datetime" assert tsdata.shards.num_partitions() == 1 tsdata = XShardsTSDataset.from_xshards(shards_single, dt_col="datetime", target_col=["value"], extra_feature_col="extra feature") assert tsdata._id_list == ["0"] assert tsdata.feature_col == ["extra feature"] assert tsdata.target_col == ["value"] assert tsdata.dt_col == "datetime" assert tsdata.shards.num_partitions() == 1 def test_xshardstsdataset_initialization_multiple(self): shards_multiple = read_csv(os.path.join(self.resource_path, "multiple.csv")) # legal input tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id") assert tsdata._id_list == [0, 1] assert tsdata.feature_col == ["extra feature"] assert tsdata.target_col == ["value"] assert tsdata.dt_col == "datetime" assert tsdata.shards.num_partitions() == 2 tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col="datetime", target_col=["value"], extra_feature_col="extra feature", id_col="id") assert tsdata._id_list == [0, 1] assert tsdata.feature_col == ["extra feature"] assert tsdata.target_col == ["value"] assert tsdata.dt_col == "datetime" assert tsdata.shards.num_partitions() == 2 tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col="datetime", target_col=["value"], extra_feature_col="extra feature") assert tsdata._id_list == ['0'] assert tsdata.feature_col == ["extra feature"] assert tsdata.target_col == ["value"] assert tsdata.dt_col == "datetime" assert tsdata.shards.num_partitions() == 1 def test_xshardstsdataset_split(self): shards_multiple = read_csv(os.path.join(self.resource_path, "multiple.csv")) # only train and test tsdata_train, tsdata_valid, tsdata_test =\ XShardsTSDataset.from_xshards(shards_multiple, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id", with_split=True, val_ratio=0, test_ratio=0.1) # standard split with all three sets tsdata_train, tsdata_valid, tsdata_test =\ XShardsTSDataset.from_xshards(shards_multiple, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id", with_split=True, val_ratio=0.1, test_ratio=0.1, largest_look_back=5, largest_horizon=2) tsdata_train.feature_col.append("new extra feature") assert len(tsdata_train.feature_col) == 2 assert len(tsdata_valid.feature_col) == 1 assert len(tsdata_test.feature_col) == 1 tsdata_train.target_col[0] = "new value" assert tsdata_train.target_col[0] == "new value" assert tsdata_valid.target_col[0] != "new value" assert tsdata_test.target_col[0] != "new value" def test_xshardstsdataset_roll_multiple_id(self): shards_multiple = read_csv(os.path.join(self.resource_path, "multiple.csv")) horizon = random.randint(1, 10) lookback = random.randint(1, 20) tsdata = XShardsTSDataset.from_xshards(shards_multiple, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id") with pytest.raises(RuntimeError): tsdata.to_xshards() # roll train tsdata.roll(lookback=lookback, horizon=horizon) shards_numpy = tsdata.to_xshards() collected_numpy = shards_numpy.collect() # collect and valid x = np.concatenate([collected_numpy[i]['x'] for i in range(len(collected_numpy))], axis=0) y = np.concatenate([collected_numpy[i]['y'] for i in range(len(collected_numpy))], axis=0) assert x.shape == ((50-lookback-horizon+1)*2, lookback, 2) assert y.shape == ((50-lookback-horizon+1)*2, horizon, 1) tsdata.roll(lookback=lookback, horizon=horizon, feature_col=["extra feature"], target_col="value") shards_numpy = tsdata.to_xshards() collected_numpy = shards_numpy.collect() # collect and valid x = np.concatenate([collected_numpy[i]['x'] for i in range(len(collected_numpy))], axis=0) y = np.concatenate([collected_numpy[i]['y'] for i in range(len(collected_numpy))], axis=0) assert x.shape == ((50-lookback-horizon+1)*2, lookback, 2) assert y.shape == ((50-lookback-horizon+1)*2, horizon, 1) tsdata.roll(lookback=lookback, horizon=horizon, feature_col=[], target_col="value") shards_numpy = tsdata.to_xshards() collected_numpy = shards_numpy.collect() # collect and valid x = np.concatenate([collected_numpy[i]['x'] for i in range(len(collected_numpy))], axis=0) y = np.concatenate([collected_numpy[i]['y'] for i in range(len(collected_numpy))], axis=0) assert x.shape == ((50-lookback-horizon+1)*2, lookback, 1) assert y.shape == ((50-lookback-horizon+1)*2, horizon, 1) # roll test horizon = 0 lookback = random.randint(1, 20) tsdata.roll(lookback=lookback, horizon=horizon) shards_numpy = tsdata.to_xshards() collected_numpy = shards_numpy.collect() # collect and valid x = np.concatenate([collected_numpy[i]['x'] for i in range(len(collected_numpy))], axis=0) assert x.shape == ((50-lookback-horizon+1)*2, lookback, 2) def test_xshardstsdataset_impute(self): from tempfile import TemporaryDirectory tmp_df = get_ugly_ts_df() with TemporaryDirectory() as tmpdir: file_name = os.path.join(tmpdir, 'impute.csv') tmp_df.to_csv(file_name, index=False) shards_tmp = read_csv(file_name) for val in ["last", "const", "linear"]: tsdata = XShardsTSDataset.from_xshards(shards_tmp, dt_col="datetime", target_col="e", extra_feature_col=["a", "b", "c", "d"], id_col="id") tsdata.impute(mode=val) collected_df = tsdata.shards.collect() collected_df = pd.concat(collected_df, axis=0) assert collected_df.isna().sum().sum() == 0 assert len(collected_df) == 100 def test_xshardstsdataset_sparkdf(self): df = generate_spark_df() # with id tsdata = XShardsTSDataset.from_sparkdf(df, dt_col="date", target_col="feature", id_col="id") tsdata.roll(lookback=4, horizon=2) data = tsdata.to_xshards().collect() assert data[0]['x'].shape[1] == 4 assert data[0]['x'].shape[2] == 1 assert data[0]['y'].shape[1] == 2 assert data[0]['y'].shape[2] == 1 assert tsdata.shards.num_partitions() == 2 # with only 1 id tsdata = XShardsTSDataset.from_sparkdf(df, dt_col="date", target_col="feature") tsdata.roll(lookback=4, horizon=2) data = tsdata.to_xshards().collect() assert data[0]['x'].shape[1] == 4 assert data[0]['x'].shape[2] == 1 assert data[0]['y'].shape[1] == 2 assert data[0]['y'].shape[2] == 1 assert tsdata.shards.num_partitions() == 1
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shopkit/order/app.py
sakkada/django-shopkit
0
160295
from django.contrib.auth.decorators import login_required from django.shortcuts import get_object_or_404, render from django.utils.decorators import method_decorator from django.conf.urls import url from ..core.app import ShopKitApp class OrderApp(ShopKitApp): app_name = 'order' namespace = 'order' Order = None DeliveryGroup = None OrderLine = None order_list_templates = [ 'shopkit/order/list.html', ] order_details_templates = [ 'shopkit/order/view.html', ] def __init__(self, **kwargs): super(OrderApp, self).__init__(**kwargs) assert self.Order and self.DeliveryGroup and self.OrderLine, ( 'You need to subclass OrderApp and provide Order,' ' DeliveryGroup and OrderLine.' ) def get_order(self, request, order_token): orders = self.Order.objects.filter( user=request.user if request.user.is_authenticated else None) return get_object_or_404(orders, token=order_token) # Views methods section # --------------------- def get_urls(self): return [ url(r'^$', self.index, name='index'), url(r'^(?P<order_token>[\w-]+)/$', self.details, name='details'), ] @method_decorator(login_required) def index(self, request, **kwargs): orders = self.Order.objects.filter(user=request.user) context = self.get_context_data(request, orders=orders) return render(request, self.order_list_templates, context) def details(self, request, order_token, **kwargs): order = self.get_order(request, order_token=order_token) context = self.get_context_data(request, order=order) return render(request, self.order_details_templates, context)
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config-server/test.py
wtsi-hgi/webhook-router
2
19778
import json from configserver import ConfigServer, get_postgres_db from configserver.errors import InvalidRouteUUIDError from flask.testing import FlaskClient import pytest from peewee import SqliteDatabase import logging from uuid import uuid4 import functools from typing import Iterable @pytest.fixture(autouse=True) def no_logs(): logging.getLogger().setLevel(logging.WARNING) @pytest.fixture() def webhook_server(): with open("config.json") as config_file: config_JSON = json.load(config_file) server = ConfigServer( use_test_auth=True, db=get_postgres_db(), config_JSON=config_JSON ) yield server server.close() @pytest.fixture() def user_auth(): return { "headers": { "user": f"test_user{uuid4()}@<EMAIL>" } } @pytest.fixture() def router_app(webhook_server, user_auth): test_client = webhook_server.app.app.test_client() # type: FlaskClient class PatchedFlaskClient: get = functools.partialmethod(test_client.get, **user_auth) delete = functools.partialmethod(test_client.delete, **user_auth) post = functools.partialmethod(test_client.post, **user_auth) patch = functools.partialmethod(test_client.patch, **user_auth) return PatchedFlaskClient @pytest.fixture() def test_route_uuid(webhook_server: ConfigServer, router_app: FlaskClient) -> Iterable[str]: create_route_resp = router_app.post( "/create-route", data=json.dumps({ "name": "route", "destination": "http://127.0.0.1" }), content_type='application/json' ) uuid = json.loads(create_route_resp.data)["uuid"] try: yield uuid finally: router_app.delete(f"/routes/{uuid}") def test_create_route(router_app: FlaskClient): create_route_resp = router_app.post( "/create-route", data=json.dumps({ "name": "route", "destination": "http://127.0.0.1" }), content_type='application/json' ) assert create_route_resp.status_code == 201 def test_get(router_app: FlaskClient, test_route_uuid: str): assert router_app.get(f"/routes/{test_route_uuid}").status_code == 200 def test_get_by_token(router_app: FlaskClient, test_route_uuid: str): token = json.loads(router_app.get(f"/routes/{test_route_uuid}").data)["token"] assert router_app.get(f"/routes/token/{token}").status_code == 200 def test_patch(router_app: FlaskClient, test_route_uuid: str): assert router_app.patch( f"/routes/{test_route_uuid}", data=json.dumps({ "name": "new-name" }), content_type='application/json', ).status_code == 204 assert json.loads(router_app.get(f"/routes/{test_route_uuid}").data)["name"] == "new-name" @pytest.mark.usefixtures("test_route_uuid") def test_get_all(router_app: FlaskClient): all_routes_resp = router_app.get("/routes") assert all_routes_resp.status_code == 200 data = json.loads(all_routes_resp.data) assert len(data) == 1 and data[0]["name"] == "route" def test_delete(router_app: FlaskClient, test_route_uuid: str): assert router_app.delete(f"/routes/{test_route_uuid}").status_code == 204 assert router_app.get(f"/routes/{test_route_uuid}").status_code == 404 def test_regenerate(router_app: FlaskClient, test_route_uuid: str): prev_token = json.loads(router_app.get(f"/routes/{test_route_uuid}").data)["token"] resp = router_app.post(f"/routes/{test_route_uuid}/regenerate") assert resp.status_code == 200 assert json.loads(resp.data)["token"] != prev_token def test_add_user_link(router_app: FlaskClient, test_route_uuid: str): test_auth = { "headers": { "user": "<EMAIL>" } } assert router_app.post(f"/links/{test_route_uuid}", **test_auth).status_code == 201 assert len(json.loads(router_app.get("/routes", **test_auth).data)) == 1 def test_get_user_link(router_app: FlaskClient, test_route_uuid: str): test_auth = { "headers": { "user": "<EMAIL>" } } assert router_app.get(f"/links/{test_route_uuid}", **test_auth).status_code == 404 assert router_app.get(f"/links/{test_route_uuid}").status_code == 200 def test_remove_user_link(router_app: FlaskClient, test_route_uuid: str): test_auth = { "headers": { "user": "<EMAIL>" } } test_add_user_link(router_app, test_route_uuid) assert router_app.delete(f"/links/{test_route_uuid}", **test_auth).status_code == 204 assert len(json.loads(router_app.get("/routes", **test_auth).data)) == 0 def test_get_route_stats(router_app: FlaskClient, test_route_uuid: str): assert router_app.get(f"/routes/{test_route_uuid}/statistics").status_code == 200 def test_get_route_logs(router_app: FlaskClient, test_route_uuid: str): assert router_app.get(f"/routes/{test_route_uuid}/logs").status_code == 200 @pytest.mark.usefixtures("test_route_uuid") def test_all_routes_stats(router_app: FlaskClient): assert router_app.get(f"/routes/statistics").status_code == 200 def test_all_routes_stats_with_no_stats(router_app: FlaskClient): assert router_app.get(f"/routes/statistics").status_code == 200
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Bidding/forms.py
Ishikashah2510/nirvaas_main
0
114737
<reponame>Ishikashah2510/nirvaas_main from django import forms from welcome.models import Users from django.contrib.auth.models import User from Bidding.models import * class Sell_Form(forms.Form): item_name = forms.CharField(label='Item Name') item_description = forms.CharField(max_length=1000, default='',label='Item Description') item_photo = forms.ImageField(label='Upload Photo of Item') threshold_value = forms.FloatField(label='Base Value') class Make_Bid(forms.Form): bid_value = forms.FloatField(label='Enter Bid Value')
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pytools/unit.py
ry-shika/Geister-cpp-lib
8
26743
<gh_stars>1-10 #!/usr/bin/env python # -*- coding: utf-8 -*- class Unit: def __init__(self, x, y, color, name): self.x = x self.y = y self.color = color self.name = name self.taken = False class OpUnit(Unit): def __init__(self, x, y, color, name): super().__init__(x, y, color, name) self.blue = 0.0
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tools/get_rig_info.py
bionicles/neuromax
0
119446
from subprocess import check_output from collections import Iterable from pprint import pprint import itertools import platform import inspect GPU_QUERIES = [ 'gpu_name', 'driver_version', # 'vbios_version', # 'pstate', 'memory.total', 'temperature.gpu', # 'temperature.memory', # 'power.draw', 'power.limit', 'clocks.gr', 'clocks.sm', 'clocks.mem', 'clocks.video' ] COMMANDS = { q: f'nvidia-smi --query-gpu={q} --format=csv,noheader' for q in GPU_QUERIES} BLACKLIST = [ 'linux_distribution', 'python_version_tuple', 'java_ver', 'uname', 'version', 'dist' ] # https://stackoverflow.com/questions/9727688/how-to-get-the-cuda-version def get_cuda_version(): return open('/usr/local/cuda/version.txt').readlines()[0][13:-2] def get_numbers(S): return [int(s) for s in S.split() if s.isdigit()] # https://stackoverflow.com/questions/31326015/how-to-verify-cudnn-installation def get_cudnn_version(): # cudnn_h_path = '/usr/local/cuda/include/cudnn.h' cudnn_h_path = '/usr/include/cudnn.h' N = get_numbers( check_output( f'cat {cudnn_h_path} | grep CUDNN_MAJOR -A 2', shell=True)) V = f'{N[0]}.{N[1]}.{N[2]}' return V def get_ubuntu_version(): return ' '.join( check_output('lsb_release -sdc', shell=True).decode().split()) def get_memory_info(): S = check_output('head -1 /proc/meminfo', shell=True).decode() return f'{get_numbers(S)[0] / 1e6} GB' def get_cpu_info(): cpu_info = {} for key in ['model name', 'cpu cores', 'cache size']: cpu_info[key] = decode(check_output( f"cat /proc/cpuinfo | grep '{key}' | head -n 1 | cut -d' ' -f 3-", shell=True)) return cpu_info def no_underscore(string): try: return '_' not in list(string[:2]) except Exception as e: print(e, string) return '_' not in list(string) def _flatten(l): for el in l: if isinstance(el, Iterable) and not isinstance(el, (str, bytes)): yield from flatten(el) else: yield el def flatten(l): if isinstance(l, Iterable) and not isinstance(l, str): return " ".join(list(filter(None, _flatten(l)))) else: return l def drop_falsy(l): return list(itertools.filterfalse(True, flatten(l))) def empty(x): if isinstance(x, str) and x: return False elif isinstance(x, Iterable): return len(drop_falsy(x)) else: return False def decode(bytes): return bytes.decode().rstrip("\n") def invoke(key, module_or_command): if inspect.ismodule(module_or_command): module_or_command = getattr(module_or_command, key) if isinstance(module_or_command, str): if no_underscore(module_or_command): output = check_output(module_or_command, shell=True) if isinstance(output, bytes): output = decode(output) # print("invoke output", output) return output elif callable(module_or_command): return module_or_command() # print(f"INVOKE FAILED ON {key}") return None def try_to_set(key, info, module_or_command): if key not in BLACKLIST and no_underscore(key): try: value = invoke(key, module_or_command) # print('invoke output type', type(value)) value = flatten(value) if value: info[key] = value except Exception as e: print('exception in try_to_set', key, module_or_command, e) return info def remove_redundant_values(d): d2 = {} for key, value in d.items(): if value not in d2.values(): d2[key] = value return d2 def delete_stuff_in_platform_string(d): d2 = {k: v for k, v in d.items() if v not in d['platform']} d2['platform'] = d['platform'] return d2 def get_rig_info(): platform_info = {} for key in dir(platform): platform_info = try_to_set(key, platform_info, platform) platform_info = remove_redundant_values(platform_info) platform_info = delete_stuff_in_platform_string(platform_info) platform_info['os'] = get_ubuntu_version(), gpu_info = {'cuda': get_cuda_version(), 'cudnn': get_cudnn_version()} for key, command in COMMANDS.items(): gpu_info = try_to_set(key, gpu_info, command) info = { 'ram': get_memory_info(), 'cpu': get_cpu_info(), 'platform': platform_info, 'gpu': remove_redundant_values(gpu_info)} pprint(info) return info get_rig_info() # get_memory_info() # get_cpu_info() # get_ubuntu_version() # get_cudnn_version()
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criterion.py
ash368/FaceParsing
138
76936
# -*- coding: utf-8 -*- # @Author: luoling # @Date: 2019-12-06 10:41:34 # @Last Modified by: luoling # @Last Modified time: 2019-12-18 17:52:49 import torch import torch.nn.functional as F import torch.nn as nn def cross_entropy2d(input, target, weight=None, reduction='none'): n, c, h, w = input.size() nt, ht, wt = target.size() # Handle inconsistent size between input and target if h != ht or w != wt: input = F.interpolate(input, size=( ht, wt), mode="bilinear", align_corners=True) # https://zhuanlan.zhihu.com/p/76583143 input = input.transpose(1, 2).transpose(2, 3).contiguous().view(-1, c) target = target.view(-1) # https://www.cnblogs.com/marsggbo/p/10401215.html loss = F.cross_entropy( input, target, weight=weight, reduction=reduction, ignore_index=250 ) return loss def bootstrapped_cross_entropy2d(input, target, K=100000, weight=None, size_average=True): """High-performance semantic segmentation using very deep fully convolutional networks""" batch_size = input.size()[0] def _bootstrap_xentropy_single(input, target, K, weight=None, size_average=True): n, c, h, w = input.size() input = input.transpose(1, 2).transpose(2, 3).contiguous().view(-1, c) target = target.view(-1) loss = F.cross_entropy( input, target, weight=weight, reduce=False, size_average=False, ignore_index=250 ) topk_loss, _ = loss.topk(K) reduced_topk_loss = topk_loss.sum() / K return reduced_topk_loss loss = 0.0 # Bootstrap from each image not entire batch for i in range(batch_size): loss += _bootstrap_xentropy_single( input=torch.unsqueeze(input[i], 0), target=torch.unsqueeze(target[i], 0), K=K, weight=weight, size_average=size_average, ) return loss / float(batch_size) class DiceLoss(nn.Module): def __init__(self, smooth=1., ignore_index=255): super(DiceLoss, self).__init__() self.ignore_index = ignore_index self.smooth = smooth @staticmethod def make_one_hot(labels, classes): one_hot = torch.cuda.FloatTensor(labels.size()[0], classes, labels.size()[ 2], labels.size()[3]).zero_() target = one_hot.scatter_(1, labels.data, 1) return target def forward(self, output, target): if self.ignore_index not in range(target.min(), target.max()): if (target == self.ignore_index).sum() > 0: target[target == self.ignore_index] = target.min() target = self.make_one_hot( target.unsqueeze(dim=1), classes=output.size()[1]) output = F.softmax(output, dim=1) output_flat = output.contiguous().view(-1) target_flat = target.contiguous().view(-1) intersection = (output_flat * target_flat).sum() loss = 1 - ((2. * intersection + self.smooth) / (output_flat.sum() + target_flat.sum() + self.smooth)) return loss class CriterionAll(nn.Module): """Segmentation aware and Edge aware loss.""" def __init__(self, alpha=50, ignore_index=255): super(CriterionAll, self).__init__() self.ignore_index = ignore_index self.criterion = nn.CrossEntropyLoss(ignore_index=ignore_index) self.weighted_criterion = nn.CrossEntropyLoss( ignore_index=ignore_index, reduction='none') self.alpha = alpha def parsing_loss(self, preds, target): h, w = target[0].size(1), target[0].size(2) pos_num = torch.sum(target[1] == 1, dtype=torch.float) neg_num = torch.sum(target[1] == 0, dtype=torch.float) weight_pos = neg_num / (pos_num + neg_num) weight_neg = pos_num / (pos_num + neg_num) weights = torch.tensor([weight_neg, weight_pos]) loss = 0 # Edge-aware branch preds_edge = preds[1][0] scale_pred = F.interpolate(input=preds_edge, size=(h, w), mode='bilinear', align_corners=True) loss += F.cross_entropy(scale_pred, target[1], weights.cuda(), ignore_index=self.ignore_index) # Segmentation-aware branch preds_parsing = preds[0] if isinstance(preds_parsing, list): for idx, pred_parsing in enumerate(preds_parsing): scale_pred = F.interpolate(input=pred_parsing, size=(h, w), mode='bilinear', align_corners=True) if idx == len(preds_parsing) - 1: # Is that the last term ? loss += (torch.mul(self.weighted_criterion(scale_pred, target[0]), torch.where( target[1] == 0, torch.Tensor([1]).cuda(), torch.Tensor([1 + self.alpha]).cuda()))).mean() else: loss += self.criterion(scale_pred, target[0]) else: scale_pred = F.interpolate(input=preds_parsing, size=(h, w), mode='bilinear', align_corners=True) loss += self.criterion(scale_pred, target[0]) return loss def forward(self, preds, target): loss = self.parsing_loss(preds, target) return loss
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29892, 3646, 29897, 13, 4706, 736, 6410, 13, 2 ]
config/constants.py
flopezag/fiware-tsc-dashboard
0
11779
#!/usr/bin/env python # -*- encoding: utf-8 -*- ## # Copyright 2017 FIWARE Foundation, e.V. # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. ## __author__ = 'fla' GOOGLE_ACCOUNTS_BASE_URL = 'https://accounts.google.com' APPLICATION_NAME = 'TSC Enablers Dashboard' CREDENTIAL_DIR = '.credentials' CREDENTIAL_FILE = 'sheets.googleapis.com.json' DB_NAME = 'enablers-dashboard.db' DB_FOLDER = 'dbase' LOG_FILE = 'tsc-dashboard.log' # We need to add 16 rows in the number of enablers list corresponding to: # - Title # - Report date # - Data sources updated on # - Source # - Units # - Enabler Impl # - INCUBATED # - DEVELOPMENT # - SUPPORT # - DEPRECATED # - And 6 extra blank rows between them FIXED_ROWS = 16 # We keep the firsts row without change in the sheet (sheet title) INITIAL_ROW = 2 # The number of columns to delete corresponds to: # Source, Catalogue, ReadTheDocs, Docker, GitHub, Coverall, Academy, HelpDesk, Backlog, GitHub_Open_Issues, # GitHub_Closed_Issues, GitHub_Adopters, GitHub_Adopters_Open_Issues, GitHub_Adopters_Closed_Issues, # GitHub_Comits, GitHub_Forks, GitHub_Watchers, GitHub_Stars, Jira_WorkItem_Not_Closed, Jira_WorkItem_Closed # + Extra 2 = 22 FIXED_COLUMNS = 22 # We start to delete from the initial column INITIAL_COLUMN = 1
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data/datasets.py
shirgur/UMIS
67
172635
import torch from torch.utils.data import Dataset import glob import tifffile as T from libtiff import TIFF import numpy as np def range_normalize(v): v = (v - v.mean(axis=(1, 2), keepdims=True)) / (v.std(axis=(1, 2), keepdims=True) + 1e-12) v_min, v_max = v.min(axis=(1, 2), keepdims=True), v.max(axis=(1, 2), keepdims=True) v = (v - v_min) / (v_max - v_min + 1e-5) return v def smart_padding(img, data_shape, lables_shape, stride): if img.shape[0] < data_shape[0]: img = np.pad(img, ((0, data_shape[0] - img.shape[0]), (0, 0), (0, 0)), mode='reflect') if img.shape[1] < data_shape[1]: img = np.pad(img, ((0, 0), (0, data_shape[1] - img.shape[1]), (0, 0)), mode='reflect') if img.shape[2] < data_shape[2]: img = np.pad(img, ((0, 0), (0, 0), (0, data_shape[2] - img.shape[1])), mode='reflect') dz = int(np.floor((img.shape[0] - data_shape[0]) / stride[0] + 1)) dy = int(np.floor((img.shape[1] - data_shape[1]) / stride[1] + 1)) dx = int(np.floor((img.shape[2] - data_shape[2]) / stride[2] + 1)) effective_data_shape = ( data_shape[0] * dz - (data_shape[0] - stride[0]) * (dz - 1), data_shape[1] * dy - (data_shape[1] - stride[1]) * (dy - 1), data_shape[2] * dx - (data_shape[2] - stride[2]) * (dx - 1) ) if effective_data_shape[0] < img.shape[0]: img = np.pad(img, ( (0, (data_shape[0] * (dz + 1) - (data_shape[0] - stride[0]) * dz) - img.shape[0]), (0, 0), (0, 0)), mode='reflect') if effective_data_shape[1] < img.shape[1]: img = np.pad(img, ( (0, 0), (0, (data_shape[1] * (dy + 1) - (data_shape[1] - stride[1]) * dy) - img.shape[1]), (0, 0)), mode='reflect') if effective_data_shape[2] < img.shape[2]: img = np.pad(img, ( (0, 0), (0, 0), (0, (data_shape[2] * (dx + 1) - (data_shape[2] - stride[2]) * dx) - img.shape[2])), mode='reflect') effective_data_shape = img.shape effective_lable_shape = ( effective_data_shape[0] - (data_shape[0] - lables_shape[0]), effective_data_shape[1] - (data_shape[1] - lables_shape[1]), effective_data_shape[2] - (data_shape[2] - lables_shape[2]) ) if effective_lable_shape[0] < img.shape[0]: img = np.pad(img, (((data_shape[0] - lables_shape[0]) // 2, (data_shape[0] - lables_shape[0]) // 2 + (data_shape[0] - lables_shape[0]) % 2), (0, 0), (0, 0)), mode='reflect') if effective_lable_shape[1] < img.shape[1]: img = np.pad(img, ((0, 0), ((data_shape[1] - lables_shape[1]) // 2, (data_shape[1] - lables_shape[1]) // 2 + (data_shape[1] - lables_shape[1]) % 2), (0, 0)), mode='reflect') if effective_lable_shape[2] < img.shape[2]: img = np.pad(img, ((0, 0), (0, 0), ((data_shape[2] - lables_shape[2]) // 2, (data_shape[2] - lables_shape[2]) // 2 + ( data_shape[2] - lables_shape[2]) % 2)), mode='reflect') return img class Single_Image_Eval(Dataset): def __init__(self, image_path='HaftJavaherian_DeepVess2018_GroundTruthImage.tif', label_path='HaftJavaherian_DeepVess2018_GroundTruthLabel.tif', data_shape=(7, 33, 33), lables_shape=(1, 4, 4), stride=(1, 1, 1), range_norm=False): self.range_norm = range_norm try: img = T.imread(image_path) except: img = [] tif = TIFF.open(image_path) for _image in tif.iter_images(): img.append(_image) img = np.stack(img, 0) try: lbl = T.imread(label_path) except: lbl = [] tif = TIFF.open(label_path) for _lable in tif.iter_images(): lbl.append(_lable) lbl = np.stack(lbl, 0) img = smart_padding(img, data_shape, lables_shape, stride) lbl = smart_padding(lbl, data_shape, lables_shape, stride) self.org_shape = img.shape self.img = img.astype(np.float32) self.lbl = lbl.astype(np.float32) self.shape = self.img.shape self.data_shape = data_shape self.lables_shape = lables_shape self.stride = stride self.dz = int(np.floor((self.shape[0] - data_shape[0]) / stride[0] + 1)) self.dy = int(np.floor((self.shape[1] - data_shape[1]) / stride[1] + 1)) self.dx = int(np.floor((self.shape[2] - data_shape[2]) / stride[2] + 1)) self.effective_data_shape = ( data_shape[0] * self.dz - (data_shape[0] - stride[0]) * (self.dz - 1), data_shape[1] * self.dy - (data_shape[1] - stride[1]) * (self.dy - 1), data_shape[2] * self.dx - (data_shape[2] - stride[2]) * (self.dx - 1) ) self.effective_lable_shape = ( self.effective_data_shape[0] - (data_shape[0] - lables_shape[0]), self.effective_data_shape[1] - (data_shape[1] - lables_shape[1]), self.effective_data_shape[2] - (data_shape[2] - lables_shape[2]) ) self.effective_lable_idx = ( ((data_shape[0] - lables_shape[0]) // 2, self.effective_data_shape[0] - ( (data_shape[0] - lables_shape[0]) // 2 + (data_shape[0] - lables_shape[0]) % 2)), ((data_shape[1] - lables_shape[1]) // 2, self.effective_data_shape[1] - ( (data_shape[1] - lables_shape[1]) // 2 + (data_shape[1] - lables_shape[1]) % 2)), ((data_shape[2] - lables_shape[2]) // 2, self.effective_data_shape[2] - ( (data_shape[2] - lables_shape[2]) // 2 + (data_shape[2] - lables_shape[2]) % 2)) ) self.lbl_z = ((data_shape[0] - lables_shape[0]) // 2, (data_shape[0] - lables_shape[0]) // 2 + lables_shape[0]) self.lbl_y = ((data_shape[1] - lables_shape[1]) // 2, (data_shape[1] - lables_shape[1]) // 2 + lables_shape[1]) self.lbl_x = ((data_shape[2] - lables_shape[2]) // 2, (data_shape[2] - lables_shape[2]) // 2 + lables_shape[2]) self.max_iter = self.dz * self.dy * self.dx def __len__(self): return self.max_iter def __getitem__(self, index): z, y, x = np.unravel_index(index, (self.dz, self.dy, self.dx)) z = z * self.stride[0] y = y * self.stride[1] x = x * self.stride[2] v = self.img[z: z + self.data_shape[0], y: y + self.data_shape[1], x: x + self.data_shape[2]] lbl = self.lbl[z: z + self.data_shape[0], y: y + self.data_shape[1], x: x + self.data_shape[2]] lbl = lbl[self.lbl_z[0]: self.lbl_z[1], self.lbl_y[0]: self.lbl_y[1], self.lbl_x[0]: self.lbl_x[1]] # Normalize if self.range_norm: v = range_normalize(v) else: v = (v - v.mean(axis=(1, 2), keepdims=True)) / (v.std(axis=(1, 2), keepdims=True) + 1e-12) # To Tensor data = torch.Tensor(v).unsqueeze(0) lables = torch.Tensor(lbl // self.lbl.max()).long() return data, lables class Directory_Image_Train(Dataset): def __init__(self, images_path, labels_path, max_iter=1000, data_shape=(7, 33, 33), lables_shape=(1, 4, 4), stride=(1, 1, 1), range_norm=False): self.range_norm = range_norm images = sorted(glob.glob(images_path + '/*tif')) labels = sorted(glob.glob(labels_path + '/*tif')) self.org_shape = [] self.shape = [] self.img = [] self.lbl = [] self.data_shape = data_shape self.lables_shape = lables_shape self.stride = stride self.dz = [] self.dy = [] self.dx = [] self.effective_data_shape = [] self.effective_lable_shape = [] self.effective_lable_idx = [] self.lbl_z = [] self.lbl_y = [] self.lbl_x = [] for img_path, lbl_path in zip(images, labels): try: img = T.imread(img_path) except: img = [] tif = TIFF.open(img_path) for _image in tif.iter_images(): img.append(_image) img = np.stack(img, 0) try: lbl = T.imread(lbl_path) except: lbl = [] tif = TIFF.open(lbl_path) for _lable in tif.iter_images(): lbl.append(_lable) lbl = np.stack(lbl, 0) img = smart_padding(img, data_shape, lables_shape, stride) lbl = smart_padding(lbl, data_shape, lables_shape, stride) self.org_shape.append(img.shape) self.img.append(img.astype(np.float32)) self.lbl.append(lbl.astype(np.float32)) shape = img.shape self.shape.append(shape) dz = int(np.floor((shape[0] - data_shape[0]) / stride[0] + 1)) dy = int(np.floor((shape[1] - data_shape[1]) / stride[1] + 1)) dx = int(np.floor((shape[2] - data_shape[2]) / stride[2] + 1)) effective_data_shape = ( data_shape[0] * dz - (data_shape[0] - stride[0]) * (dz - 1), data_shape[1] * dy - (data_shape[1] - stride[1]) * (dy - 1), data_shape[2] * dx - (data_shape[2] - stride[2]) * (dx - 1) ) effective_lable_shape = ( effective_data_shape[0] - (data_shape[0] - lables_shape[0]), effective_data_shape[1] - (data_shape[1] - lables_shape[1]), effective_data_shape[2] - (data_shape[2] - lables_shape[2]) ) effective_lable_idx = ( ((data_shape[0] - lables_shape[0]) // 2, effective_data_shape[0] - ( (data_shape[0] - lables_shape[0]) // 2 + (data_shape[0] - lables_shape[0]) % 2)), ((data_shape[1] - lables_shape[1]) // 2, effective_data_shape[1] - ( (data_shape[1] - lables_shape[1]) // 2 + (data_shape[1] - lables_shape[1]) % 2)), ((data_shape[2] - lables_shape[2]) // 2, effective_data_shape[2] - ( (data_shape[2] - lables_shape[2]) // 2 + (data_shape[2] - lables_shape[2]) % 2)) ) lbl_z = ((data_shape[0] - lables_shape[0]) // 2, (data_shape[0] - lables_shape[0]) // 2 + lables_shape[0]) lbl_y = ((data_shape[1] - lables_shape[1]) // 2, (data_shape[1] - lables_shape[1]) // 2 + lables_shape[1]) lbl_x = ((data_shape[2] - lables_shape[2]) // 2, (data_shape[2] - lables_shape[2]) // 2 + lables_shape[2]) self.dz.append(dz) self.dy.append(dy) self.dx.append(dx) self.effective_data_shape.append(effective_data_shape) self.effective_lable_shape.append(effective_lable_shape) self.effective_lable_idx.append(effective_lable_idx) self.lbl_z.append(lbl_z) self.lbl_y.append(lbl_y) self.lbl_x.append(lbl_x) self.max_iter = max_iter def __len__(self): return self.max_iter def __getitem__(self, index): i = np.random.randint(0, len(self.img)) z = np.random.randint(0, self.dz[i]) y = np.random.randint(0, self.dy[i]) x = np.random.randint(0, self.dx[i]) z = z * self.stride[0] y = y * self.stride[1] x = x * self.stride[2] v = self.img[i][z: z + self.data_shape[0], y: y + self.data_shape[1], x: x + self.data_shape[2]] lbl = self.lbl[i][z: z + self.data_shape[0], y: y + self.data_shape[1], x: x + self.data_shape[2]] lbl = lbl[self.lbl_z[i][0]: self.lbl_z[i][1], self.lbl_y[i][0]: self.lbl_y[i][1], self.lbl_x[i][0]: self.lbl_x[i][1]] # Normalize if self.range_norm: v = range_normalize(v) else: v = (v - v.mean(axis=(1, 2), keepdims=True)) / (v.std(axis=(1, 2), keepdims=True) + 1e-12) # To Tensor data = torch.Tensor(v).unsqueeze(0) lables = torch.Tensor(lbl // 255).long() return data, lables
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src/scripts/merge_pandas_conll.py
acoli-repo/OpenIE_Stanovsky_Dagan
117
117220
""" Usage: merge_pandas_conll --out=OUTPUT_FN <filenames>... Merge a list of data frames in csv format and print to output file. """ from docopt import docopt import pandas as pd import logging logging.basicConfig(level = logging.DEBUG) if __name__ == "__main__": args = docopt(__doc__) logging.debug(args) input_fns = args["<filenames>"] out_fn = args["--out"] pd.concat([pd.read_csv(fn, sep = '\t', header = 0) for fn in input_fns]).to_csv(out_fn, sep = '\t', header = True, index = False)
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nixui/utils/store.py
yater/nix-gui
254
177322
<gh_stars>100-1000 import os def get_store_path(): if 'XDG_CONFIG_HOME' in os.environ: config_home = os.environ['XDG_CONFIG_HOME'] else: config_home = os.path.join( os.getenv("HOME"), '.config' ) return os.path.join( config_home, 'nixgui' )
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apps/useradmin/src/useradmin/models.py
ajay25/hue
0
129303
#!/usr/bin/env python # Licensed to Cloudera, Inc. under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. Cloudera, Inc. licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ The core of this module adds permissions functionality to Hue applications. A "Hue Permission" (colloquially, appname.action, but stored in the HuePermission model) is a way to specify some action whose control may be restricted. Every Hue application, by default, has an "access" action. To specify extra actions, applications can specify them in appname.settings.PERMISSION_ACTIONS, as pairs of (action_name, description). Several mechanisms enforce permission. First of all, the "access" permission is controlled by LoginAndPermissionMiddleware. For eligible views within an application, the access permission is checked. Second, views may use @desktop.decorators.hue_permission_required("action", "app") to annotate their function, and this decorator will check a permission. Thirdly, you may wish to do so manually, by using something akin to: app = desktop.lib.apputil.get_current_app() # a string dp = HuePermission.objects.get(app=pp, action=action) request.user.has_hue_permission(dp) Permissions may be granted to groups, but not, currently, to users. A user's abilities is the union of all permissions the group has access to. Note that Django itself has a notion of users, groups, and permissions. We re-use Django's notion of users and groups, but ignore its notion of permissions. The permissions notion in Django is strongly tied to what models you may or may not edit, and there are elaborations (especially in Django 1.2) to manipulate this row by row. This does not map nicely onto actions which may not relate to database models. """ import collections import json import logging from datetime import datetime from enum import Enum from django.db import connection, models, transaction from django.contrib.auth import models as auth_models from django.contrib.auth.models import AbstractUser, BaseUserManager from django.core.cache import cache from django.utils import timezone as dtz from django.utils.translation import ugettext_lazy as _t from desktop import appmanager from desktop.conf import ENABLE_ORGANIZATIONS, ENABLE_CONNECTORS from desktop.lib.connectors.models import _get_installed_connectors from desktop.lib.exceptions_renderable import PopupException from desktop.lib.idbroker.conf import is_idbroker_enabled from desktop.monkey_patches import monkey_patch_username_validator from useradmin.conf import DEFAULT_USER_GROUP if ENABLE_ORGANIZATIONS.get(): from useradmin.models2 import OrganizationUser as User, OrganizationGroup as Group, Organization, default_organization, get_organization else: from django.contrib.auth.models import User, Group class Organization(): pass def default_organization(): pass def get_organization(): pass monkey_patch_username_validator() LOG = logging.getLogger(__name__) class UserProfile(models.Model): """ Extra settings / properties to store for each user. """ class CreationMethod(Enum): HUE = 1 EXTERNAL = 2 user = models.OneToOneField(User, unique=True) home_directory = models.CharField(editable=True, max_length=1024, null=True) creation_method = models.CharField(editable=True, null=False, max_length=64, default=CreationMethod.HUE.name) first_login = models.BooleanField(default=True, verbose_name=_t('First Login'), help_text=_t('If this is users first login.')) last_activity = models.DateTimeField(auto_now=True, db_index=True) json_data = models.TextField(default='{}') def get_groups(self): return self.user.groups.all() def _lookup_permission(self, app, action): # We cache it instead of doing HuePermission.objects.get(app=app, action=action). To revert with Django 1.6 perms = cache.get('perms') if not perms: perms = dict([('%s:%s' % (p.app, p.action), p) for p in HuePermission.objects.all()]) cache.set('perms', perms, 60 * 60) return perms.get('%s:%s' % (app, action)) def has_hue_permission(self, action=None, app=None, perm=None): if perm is None: try: perm = self._lookup_permission(app, action) except HuePermission.DoesNotExist: LOG.exception("Permission object %s - %s not available. Was Django migrate command run after installation?" % (app, action)) return self.user.is_superuser if self.user.is_superuser: return True if ENABLE_CONNECTORS.get() and app in ('jobbrowser', 'metastore', 'filebrowser', 'indexer'): return True group_ids = self.user.groups.values_list('id', flat=True) return GroupPermission.objects.filter(group__id__in=group_ids, hue_permission=perm).exists() def get_permissions(self): return HuePermission.objects.filter(groups__user=self.user) def check_hue_permission(self, perm=None, app=None, action=None): """ Raises a PopupException if permission is denied. Either perm or both app and action are required. """ if perm is None: perm = self._lookup_permission(app, action) if self.has_hue_permission(perm): return else: raise PopupException(_t("You do not have permissions to %(description)s.") % {'description': perm.description}) @property def data(self): if not self.json_data: self.json_data = json.dumps({}) return json.loads(self.json_data) def update_data(self, val): data_dict = self.data data_dict.update(val) self.json_data = json.dumps(data_dict) def get_profile(user): """ Caches the profile, to avoid DB queries at every call. """ if hasattr(user, "_cached_userman_profile"): return user._cached_userman_profile else: # Lazily create profile. try: profile = UserProfile.objects.get(user=user) except UserProfile.DoesNotExist as e: profile = create_profile_for_user(user) user._cached_userman_profile = profile return profile def group_has_permission(group, perm): return GroupPermission.objects.filter(group=group, hue_permission=perm).exists() def group_permissions(group): return HuePermission.objects.filter(grouppermission__group=group).all() # Create a user profile for the given user def create_profile_for_user(user): p = UserProfile() p.user = user p.last_activity = dtz.now() p.home_directory = "/user/%s" % p.user.username try: p.save() return p except: LOG.exception("Failed to automatically create user profile.") return None class LdapGroup(models.Model): """ Groups that come from LDAP originally will have an LdapGroup record generated at creation time. """ group = models.ForeignKey(Group, related_name="group") class GroupPermission(models.Model): """ Represents the permissions a group has. """ group = models.ForeignKey(Group) hue_permission = models.ForeignKey("HuePermission") class HuePermission(models.Model): """ Set of non-object specific permissions that an app supports. Currently only assign permissions to groups (not users or roles). Could be move to support Apache Ranger permissions, AWS IAM... for Hue and connector access. """ app = models.CharField(max_length=30) action = models.CharField(max_length=100) description = models.CharField(max_length=255) groups = models.ManyToManyField(Group, through=GroupPermission) def __str__(self): return "%s.%s:%s(%d)" % (self.app, self.action, self.description, self.pk) @classmethod def get_app_permission(cls, hue_app, action): return HuePermission.objects.get(app=hue_app, action=action) def get_default_user_group(**kwargs): default_user_group = DEFAULT_USER_GROUP.get() if default_user_group is None: return None if ENABLE_ORGANIZATIONS.get(): group, created = Group.objects.get_or_create(name=default_user_group, organization=default_organization()) else: group, created = Group.objects.get_or_create(name=default_user_group) if created: group.save() return group def update_app_permissions(**kwargs): """ Keep in sync apps and connectors permissions into the database table. Map app + action to a HuePermission. v2 Based on the connectors. Permissions are either based on connectors instances or Hue specific actions. Permissions can be deleted or added dynamically. v1 This is a 'migrate' callback. We never delete permissions automatically, because apps might come and go. Note that signing up to the "migrate" signal is not necessarily the best thing we can do, since some apps might not have models, but nonetheless, "migrate" is typically run when apps are installed. """ created_tables = connection.introspection.table_names() if u'useradmin_huepermission' in created_tables: # Check if Useradmin has been installed. current = {} try: for dp in HuePermission.objects.all(): current.setdefault(dp.app, {})[dp.action] = dp except: LOG.exception('failed to get permissions') return updated = 0 uptodate = 0 added = [] if ENABLE_CONNECTORS.get(): old_apps = list(current.keys()) ConnectorPerm = collections.namedtuple('ConnectorPerm', 'name nice_name settings') apps = [ ConnectorPerm(name=connector['name'], nice_name=connector['nice_name'], settings=[]) for connector in _get_installed_connectors() ] else: old_apps = [] apps = appmanager.DESKTOP_APPS for app in apps: app_name = app.name permission_description = "Access the %s connection" % app.nice_name if ENABLE_CONNECTORS.get() else "Launch this application" actions = set([("access", permission_description)]) actions.update(getattr(app.settings, "PERMISSION_ACTIONS", [])) if app_name not in current: current[app_name] = {} if app_name in old_apps: old_apps.remove(app_name) for action, description in actions: c = current[app_name].get(action) if c: if c.description != description: c.description = description c.save() updated += 1 else: uptodate += 1 else: new_dp = HuePermission(app=app_name, action=action, description=description) new_dp.save() added.append(new_dp) # Only with v2 deleted, _ = HuePermission.objects.filter(app__in=old_apps).delete() # Add all permissions to default group except some. default_group = get_default_user_group() if default_group: for new_dp in added: if not (new_dp.app == 'useradmin' and new_dp.action == 'access') and \ not (new_dp.app == 'useradmin' and new_dp.action == 'superuser') and \ not (new_dp.app == 'metastore' and new_dp.action == 'write') and \ not (new_dp.app == 'hbase' and new_dp.action == 'write') and \ not (new_dp.app == 'security' and new_dp.action == 'impersonate') and \ not (new_dp.app == 'filebrowser' and new_dp.action == 's3_access' and not is_idbroker_enabled('s3a')) and \ not (new_dp.app == 'filebrowser' and new_dp.action == 'gs_access' and not is_idbroker_enabled('gs')) and \ not (new_dp.app == 'filebrowser' and new_dp.action == 'adls_access') and \ not (new_dp.app == 'filebrowser' and new_dp.action == 'abfs_access') and \ not (new_dp.app == 'oozie' and new_dp.action == 'disable_editor_access'): GroupPermission.objects.create(group=default_group, hue_permission=new_dp) available = HuePermission.objects.count() stale = available - len(added) - updated - uptodate if len(added) or updated or stale or deleted: LOG.info("HuePermissions: %d added, %d updated, %d up to date, %d stale, %d deleted" % ( len(added), updated, uptodate, stale, deleted ) ) models.signals.post_migrate.connect(update_app_permissions) # models.signals.post_migrate.connect(get_default_user_group) def install_sample_user(django_user=None): """ Setup the de-activated sample user with a certain id. Do not create a user profile. """ from desktop.models import SAMPLE_USER_ID, get_sample_user_install from hadoop import cluster user = None django_username = get_sample_user_install(django_user) if ENABLE_ORGANIZATIONS.get(): lookup = {'email': django_username} django_username_short = django_user.username_short else: lookup = {'username': django_username} django_username_short = django_username try: if User.objects.filter(id=SAMPLE_USER_ID).exists(): user = User.objects.get(id=SAMPLE_USER_ID) LOG.info('Sample user found with username "%s" and User ID: %s' % (user.username, user.id)) elif User.objects.filter(**lookup).exists(): user = User.objects.get(**lookup) LOG.info('Sample user found: %s' % lookup) else: user_attributes = lookup.copy() if ENABLE_ORGANIZATIONS.get(): user_attributes['organization'] = get_organization(user=django_user) user_attributes.update({ 'password': '!', 'is_active': False, 'is_superuser': False, 'id': SAMPLE_USER_ID, 'pk': SAMPLE_USER_ID }) user, created = User.objects.get_or_create(**user_attributes) if created: LOG.info('Installed a user "%s"' % lookup) if user.username != django_username and not ENABLE_ORGANIZATIONS.get(): LOG.warn('Sample user does not have username "%s", will attempt to modify the username.' % django_username) with transaction.atomic(): user = User.objects.get(id=SAMPLE_USER_ID) user.username = django_username user.save() except Exception as ex: LOG.exception('Failed to get or create sample user') # If sample user doesn't belong to default group, add to default group default_group = get_default_user_group() if user is not None and default_group is not None and default_group not in user.groups.all(): user.groups.add(default_group) user.save() fs = cluster.get_hdfs() # If home directory doesn't exist for sample user, create it try: if not fs.do_as_user(django_username_short, fs.get_home_dir): fs.do_as_user(django_username_short, fs.create_home_dir) LOG.info('Created home directory for user: %s' % django_username_short) else: LOG.info('Home directory already exists for user: %s' % django_username) except Exception as ex: LOG.exception('Failed to create home directory for user %s: %s' % (django_username, str(ex))) return user def orm_user_lookup(): return 'email' if ENABLE_ORGANIZATIONS.get() else 'username'
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app/concat_unmask.py
torond/unmasking
0
197571
<filename>app/concat_unmask.py import os import subprocess import time import yaml import argparse """ Given a directory of PAN20 formatted datasets, runs `unmask.py run` on all of them. Inputs: - job.yml with <output_dir> and <transcription> placeholders - path to directory with PAN20 formatted datasets """ def now(): return time.strftime("%Y-%m-%d_%H-%M-%S") def main(): parser = argparse.ArgumentParser( prog="concat_unmask", description="Automate unmasking for multiple datasets", add_help=True) parser.add_argument('--input', '-i', help='Path to directory of PAN20 formatted datasets') args = parser.parse_args() directory = [d for d in os.scandir(args.input)] print(f'Found {len(directory)} PAN20 data files.') output_folder = f'unmasking_curves_{now()}/' for i, dir_entry in enumerate(directory): print(f'Unmasking {dir_entry.name}... ({i+1}/{len(directory)})') # Workaround, as there is no --input option for `unmask.py run` with open(os.path.join('app', 'job.yml')) as f: doc = yaml.load(f, Loader=yaml.FullLoader) doc['job%']['input']['parser']['parameters']['corpus_path'] = dir_entry.path with open(os.path.join('app', 'temp_job.yml'), 'w') as f: yaml.dump(doc, f) # Trigger Unmasking subprocess.run(['./unmask', 'run', '-o', os.path.join('..', 'data', output_folder, dir_entry.name), os.path.join('app', 'temp_job.yml')]) os.remove(os.path.join('app', 'temp_job.yml')) if __name__ == "__main__": main()
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ftd_main/forms.py
meatware/FixedTermDepositGrapher
0
71687
from flask_wtf import FlaskForm from wtforms import StringField, SelectField, DecimalField, IntegerField, SubmitField, PasswordField, BooleanField, DateField from wtforms.validators import DataRequired, NumberRange, Length, ValidationError, DataRequired, Email, EqualTo from ftd_main.models import User #from ftd_main import date_pick #def picker(self, id=".datepicker", # identifier will be passed to Jquery to select element # dateFormat='yy-mm-dd', # can't be explained more ! # maxDate='2018-12-90', # maximum date to select from. Make sure to follow the same format yy-mm-dd # minDate='2017-12-01', # btnsId='.btnId'): # minimum date # id assigned to instigating buttons if needed # return #TODO: fix form flashing class AddFixedDepositForm(FlaskForm): ischemes = [('Monthly', 'Monthly'), ('Quarterly', 'Quarterly'), ('Yearly', 'Yearly')] ac_no = StringField('Acc No', validators=[DataRequired(), Length(5, 50, "Length range from 5 to 50")]) # TODO: Fix so autoincrements for user start_date = DateField('Start Date dd/mm/yyyy', validators=[DataRequired()], format='%d-%m-%Y') #, widget=DatePickerWidget()) #TODO: add validators end_date = StringField('End Date dd/mm/yyyy') #TODO: Calculate end_date interest_rate = DecimalField('Interest Rate (%)', validators=[DataRequired(), NumberRange(0, 100, "Please enter percentage range 0 to 100%")]) interest_scheme = SelectField('Interest Scheme', choices=ischemes, validators=[DataRequired()]) period = IntegerField('Deposit time period (days for now)') initial_deposit = DecimalField('Initial Deposit') final_deposit = DecimalField('Final Deposit') #TODO: Calculate final_deposit submit = SubmitField('Submits') def create_deposit(deposit, form, new=False): """ Save the changes to the database """ # Get data from form and assign it to the correct attributes # of the SQLAlchemy table object deposit.ac_no = form.ac_no.data deposit.start_date = form.start_date.data deposit.end_date = form.end_date.data deposit.interest_rate = form.interest_rate.data deposit.interest_scheme = form.interest_scheme.data deposit.period = form.period.data deposit.initial_deposit = form.initial_deposit.data deposit.final_deposit = form.final_deposit.data return deposit class Loginform(FlaskForm) : username = StringField('Username', validators=[DataRequired()]) password = PasswordField('Password', validators=[DataRequired()]) remember_me = BooleanField('Remember Me') submit = SubmitField('Sign In') class RegistrationForm(FlaskForm): username = StringField('Username', validators=[DataRequired()]) email = StringField('Email', validators=[DataRequired(), Email()]) password = PasswordField('Password', validators=[DataRequired()]) password2 = PasswordField( 'Repeat Password', validators=[DataRequired(), EqualTo('password')]) submit = SubmitField('Register') def validate_username(self, username): user = User.query.filter_by(username=username.data).first() if user is not None: raise ValidationError('Please use a different username.') def validate_email(self, email): user = User.query.filter_by(email=email.data).first() if user is not None: raise ValidationError('Please use a different email address.')
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vizsgaremek/test_conduit_logged_in.py
femese/conduit
0
34011
from selenium import webdriver from selenium.webdriver.chrome.options import Options from webdriver_manager.chrome import ChromeDriverManager from pages.home_page import HomePage from pages.profile_page import ProfilePage from pages.login_page import LoginPage from pages.registration_page import RegistrationPage from pages.article_page import ArticlePage from pages.new_article_page import NewArticlePage from pages.navigation_bar import NavigationBar import pytest import csv browser_options = Options() browser_options.add_experimental_option("excludeSwitches", ["enable-logging"]) browser_options.headless = True URL = 'http://localhost:1667' class Test_Conduit_Logged_In: def setup_method(self, method): self.browser = webdriver.Chrome(ChromeDriverManager().install(), options=browser_options) self.browser.maximize_window() self.browser.get(URL) self.homepage = HomePage(driver=self.browser) self.homepage.login_button.click() login_page = LoginPage(driver=self.browser) login_page.fill_login_details('<EMAIL>', 'Teszt1teszt') login_page.signin_button.click() def teardown_method(self, method): self.browser.close() def test_one_article(self): self.homepage = HomePage(driver=self.browser) self.homepage.logout_button.find() self.homepage.article_button.click() new_article_page = NewArticlePage(driver=self.browser) new_article_page.title_input.send_text_to_input("Title") new_article_page.summary_input.send_text_to_input("Summary") new_article_page.main_body_input.send_text_to_input("Main article") new_article_page.tags_input.send_text_to_input("nonsense") new_article_page.publish_button.click() article_page = ArticlePage(driver=self.browser) assert article_page.main_textfield.text() == "Main article" def test_new_articles(self): number_of_paginator = len(self.homepage.page_list_buttons) reader = csv.reader(open('./vizsgaremek/articles.csv', 'r'), delimiter=';') for row in reader: navigation_bar = NavigationBar(driver=self.browser) navigation_bar.logout_button.find() navigation_bar.article_button.click() new_article_page = NewArticlePage(driver=self.browser) new_article_page.title_input.send_text_to_input(row[0]) new_article_page.summary_input.send_text_to_input(row[1]) new_article_page.main_body_input.send_text_to_input(row[2]) new_article_page.tags_input.send_text_to_input(row[3]) new_article_page.publish_button.click() navigation_bar.home_button.click() assert len(self.homepage.page_list_buttons) > number_of_paginator def test_page_list(self): self.homepage = HomePage(driver=self.browser) for x in self.homepage.page_list_buttons: x.click() self.homepage = HomePage(driver=self.browser) assert self.homepage.is_last_page_active() def test_list_articles(self): assert len(self.homepage.article_list) > 0 def test_change_article(self): article_page = self.create_article() txt_to_change = article_page.main_textfield.text() article_page.edit_button.find() article_page.edit_button.click() article_edit_page = NewArticlePage(self.browser) article_edit_page.main_body_input.send_text_to_input(txt_to_change[:len(txt_to_change)//2].strip() + "changed") article_edit_page.publish_button.click() assert article_page.main_textfield.text() == txt_to_change[:len(txt_to_change)//2].strip() + "changed" def test_save_to_file(self): self.homepage.profile_button.click() profile_page = ProfilePage(self.browser) self.homepage.article_list[0].click() article_page = ArticlePage(self.browser) txt_to_save = article_page.main_textfield.text() txt_file = open("./vizsgaremek/test.txt", "w") txt_file.write(txt_to_save) txt_file.close() txt_file = open("./vizsgaremek/test.txt", "r") assert txt_file.read() == txt_to_save txt_file.close() def test_delete_article(self): article_page = self.create_article() article_page.delete_button.find() article_page.delete_button.click() assert (article_page.delete_popup.text() == "Deleted the article. Going home...") def test_logout(self): self.homepage.logout_button.click() assert self.homepage.login_button.text().strip() == "Sign in" def create_article(self): self.homepage.logout_button.find() self.homepage.article_button.click() new_article_page = NewArticlePage(driver=self.browser) new_article_page.title_input.send_text_to_input("Test article title") new_article_page.summary_input.send_text_to_input("Test article summary") new_article_page.main_body_input.send_text_to_input("Test article main text") new_article_page.tags_input.send_text_to_input("test, article, tags") new_article_page.publish_button.click() return ArticlePage(driver=self.browser)
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models/spach/spach.py
ritwikraha/SPACH
104
60160
# Copyright (c) Microsoft Corporation. # Licensed under the MIT License. from functools import partial import torch from torch import nn from timm.models.layers import DropPath from einops.layers.torch import Reduce from .layers import DWConv, SPATIAL_FUNC, ChannelMLP, STEM_LAYER from .misc import reshape2n class MixingBlock(nn.Module): def __init__(self, dim, spatial_func=None, scaled=True, init_values=1e-4, shared_spatial_func=False, norm_layer=partial(nn.LayerNorm, eps=1e-6), act_layer=nn.GELU, drop_path=0., cpe=True, num_heads=None, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., # attn in_features=None, hidden_features=None, drop=0., # mlp channel_ratio=2.0 ): super(MixingBlock, self).__init__() spatial_kwargs = dict(act_layer=act_layer, in_features=in_features, hidden_features=hidden_features, drop=drop, # mlp dim=dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=proj_drop # attn ) self.valid_spatial_func = True if spatial_func is not None: if shared_spatial_func: self.spatial_func = spatial_func else: self.spatial_func = spatial_func(**spatial_kwargs) self.norm1 = norm_layer(dim) if scaled: self.gamma_1 = nn.Parameter(init_values * torch.ones(1, 1, dim), requires_grad=True) else: self.gamma_1 = 1. else: self.valid_spatial_func = False self.channel_func = ChannelMLP(in_features=dim, hidden_features=int(dim*channel_ratio), act_layer=act_layer, drop=drop) self.norm2 = norm_layer(dim) self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.cpe = cpe if cpe: self.cpe_net = DWConv(dim) def forward(self, x): in_x = x if self.valid_spatial_func: x = x + self.drop_path(self.gamma_1 * self.spatial_func(self.norm1(in_x))) if self.cpe: x = x + self.cpe_net(in_x) x = x + self.drop_path(self.channel_func(self.norm2(x))) return x def flops(self, input_shape): _, N, C = input_shape flops = 0 if self.valid_spatial_func: flops += self.spatial_func.flops(input_shape) flops += N * C * 2 # norm + skip if self.cpe: flops += self.cpe_net.flops(input_shape) flops += self.channel_func.flops(input_shape) flops += N * C * 2 return flops class Spach(nn.Module): def __init__(self, num_classes=1000, img_size=224, in_chans=3, hidden_dim=384, patch_size=16, net_arch=None, act_layer=nn.GELU, norm_layer=partial(nn.LayerNorm, eps=1e-6), stem_type='conv1', scaled=True, init_values=1e-4, drop_path_rate=0., cpe=True, shared_spatial_func=False, # mixing block num_heads=12, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0., # attn token_ratio=0.5, channel_ratio=2.0, drop_rate=0., # mlp downstream=False, **kwargs ): super(Spach, self).__init__() self.num_classes = num_classes self.hidden_dim = hidden_dim self.downstream = downstream self.stem = STEM_LAYER[stem_type]( img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=hidden_dim, downstream=downstream) self.norm1 = norm_layer(hidden_dim) block_kwargs = dict(dim=hidden_dim, scaled=scaled, init_values=init_values, cpe=cpe, shared_spatial_func=shared_spatial_func, norm_layer=norm_layer, act_layer=act_layer, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=proj_drop, # attn in_features=self.stem.num_patches, hidden_features=int(self.stem.num_patches * token_ratio), channel_ratio=channel_ratio, drop=drop_rate) # mlp self.blocks = self.make_blocks(net_arch, block_kwargs, drop_path_rate, shared_spatial_func) self.norm2 = norm_layer(hidden_dim) if not downstream: self.pool = Reduce('b n c -> b c', reduction='mean') self.head = nn.Linear(hidden_dim, self.num_classes) self.init_weights() def make_blocks(self, net_arch, block_kwargs, drop_path, shared_spatial_func): if shared_spatial_func: assert len(net_arch) == 1, '`shared_spatial_func` only support unitary spatial function' assert net_arch[0][0] != 'pass', '`shared_spatial_func` do not support pass' spatial_func = SPATIAL_FUNC[net_arch[0][0]](**block_kwargs) else: spatial_func = None blocks = [] for func_type, depth in net_arch: for i in range(depth): blocks.append(MixingBlock(spatial_func=spatial_func or SPATIAL_FUNC[func_type], drop_path=drop_path, **block_kwargs)) return nn.Sequential(*blocks) def init_weights(self): for n, m in self.named_modules(): _init_weights(m, n) def forward_features(self, x): x = self.stem(x) x = reshape2n(x) x = self.norm1(x) x = self.blocks(x) x = self.norm2(x) return x def forward(self, x): x = self.forward_features(x) x = self.pool(x) x = self.head(x) return x def flops(self): flops = 0 shape = (1, self.stem.num_patches, self.hidden_dim) # stem flops += self.stem.flops() flops += sum(shape) # blocks flops += sum([i.flops(shape) for i in self.blocks]) flops += sum(shape) # head flops += self.hidden_dim * self.num_classes return flops def _init_weights(m, n: str): if isinstance(m, nn.Linear): if n.startswith('head'): nn.init.zeros_(m.weight) nn.init.zeros_(m.bias) else: nn.init.xavier_uniform_(m.weight) if m.bias is not None: if 'mlp' in n: nn.init.normal_(m.bias, std=1e-6) else: nn.init.zeros_(m.bias) elif isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') if m.bias is not None: nn.init.zeros_(m.bias) elif isinstance(m, (nn.LayerNorm, nn.BatchNorm2d, nn.GroupNorm)): nn.init.ones_(m.weight) nn.init.zeros_(m.bias)
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apertools/log.py
scottstanie/apertools
8
32333
""" This module exports a Log class that wraps the logging python package Uses the standard python logging utilities, just provides nice formatting out of the box. Usage: from apertools.log import get_log logger = get_log() logger.info("Something happened") logger.warning("Something concerning happened") logger.error("Something bad happened") logger.critical("Something just awful happened") logger.debug("Extra printing we often don't need to see.") # Custom output for this module: logger.success("Something great happened: highlight this success") """ import logging import time from functools import wraps try: from colorlog import ColoredFormatter COLORS = True except ImportError: from logging import Formatter COLORS = False def get_log(debug=False, name=__file__, verbose=False): """Creates a nice log format for use across multiple files. Default logging level is INFO Args: name (Optional[str]): The name the logger will use when printing statements debug (Optional[bool]): If true, sets logging level to DEBUG """ logger = logging.getLogger(name) return format_log(logger, debug=debug, verbose=verbose) def format_log(logger, debug=False, verbose=False): """Makes the logging output pretty and colored with times""" log_level = logging.DEBUG if debug else logging.INFO log_colors = { "DEBUG": "blue", "INFO": "cyan", "WARNING": "yellow", "ERROR": "red", "CRITICAL": "black,bg_red", "SUCCESS": "white,bg_blue", } if COLORS: format_ = "[%(asctime)s] [%(log_color)s%(levelname)s %(filename)s%(reset)s] %(message)s%(reset)s" formatter = ColoredFormatter( format_, datefmt="%m/%d %H:%M:%S", log_colors=log_colors ) else: format_ = "[%(asctime)s] [%(levelname)s %(filename)s] %(message)s" formatter = Formatter(format_, datefmt="%m/%d %H:%M:%S") handler = logging.StreamHandler() handler.setFormatter(formatter) logging.SUCCESS = 25 # between WARNING and INFO logging.addLevelName(logging.SUCCESS, "SUCCESS") setattr( logger, "success", lambda message, *args: logger._log(logging.SUCCESS, message, args), ) if not logger.handlers: logger.addHandler(handler) logger.setLevel(log_level) if verbose: logger.info("Logger initialized: %s" % (logger.name,)) if debug: logger.setLevel(debug) return logger logger = get_log() def log_runtime(f): """ Logs how long a decorated function takes to run Args: f (function): The function to wrap Returns: function: The wrapped function Example: >>> @log_runtime ... def test_func(): ... return 2 + 4 >>> test_func() 6 This prints out to stderr the following in addition to the answer: [05/26 10:05:51] [INFO log.py] Total elapsed time for test_func (minutes): 0.00 """ @wraps(f) def wrapper(*args, **kwargs): t1 = time.time() result = f(*args, **kwargs) t2 = time.time() elapsed_time = t2 - t1 time_string = "Total elapsed time for {} : {} minutes ({} seconds)".format( f.__name__, "{0:.2f}".format(elapsed_time / 60.0), "{0:.2f}".format(elapsed_time), ) logger.info(time_string) return result return wrapper
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mymodule/menu.py
Alex-quickcoder/Notebook
0
84385
<gh_stars>0 """Work with a notebook with an interface.""" import sys from notebook import Notebook, Note from time import sleep from os.path import isfile class Menu: """Display a menu and respond to choices when run. :var file_name: a file name where to store notes. """ file_name = "notes.txt" def __init__(self): """Initialize a notebook and menu choices.""" self.notebook = Notebook() self.choices = {"1": self.show_notes, "2": self.search_notes, "3": self.add_note, "4": self.modify_note, "5": self.clear, "6": self.save, "7": self.quit} def display_menu(self): """Display the choices to the user.""" print("Menu Choices:") for key in self.choices: choice = self.choices[key].__name__ choice = choice.replace("_", " ").title() print(f"{key}: {choice}") def run(self): """Display the menu and respond to the choices.""" choice_quant = len(self.choices) if isfile(self.file_name): notes = self.notebook.notes with open("notes.txt") as file: for line in file: line = line.split("\t\t") line[1] = line[1][1:-1].replace("'", "") line[1] = line[1].split(", ") if line[1][0] == "": notes.append(Note(line[0], [])) else: notes.append(Note(line[0], line[1])) notes[-1].creation_date = line[2][:-1] print("Added saved notes.") self.show_notes() sleep(0.5) while True: self.display_menu() choice = input(f"Enter a valid option(1-{choice_quant}): ") action = self.choices.get(choice) if action: action() sleep(0.5) else: print(f"'{choice}' is invalid, try again...") def show_notes(self, notes: list =None): """Show all stored notes. :param notes: notes to show if available (if None shows all notes) """ if notes is None: notes = self.notebook.notes elif not notes: print("Search didn't return anything.") for note in notes: print(note) def search_notes(self): """Search for a note by memo and tags.""" search_filter = input("Search for (enter separated tags " "or memo fragment): ") notes = self.notebook.search(search_filter) self.show_notes(notes) def add_note(self): """Add a note to the notebook.""" memo = input("Enter a memo: ") tags = input("Enter comma-separated tags or press Enter to skip: ") if tags: self.notebook.new_note(memo, tags.split(",")) else: self.notebook.new_note(memo) print("Your note has been added.") def modify_note(self): """Modify a note.""" try: n_id = int(input("Enter a note id: ")) except (TypeError, ValueError): print("Operation has failed.") return None while True: delete = input("Enter whether to delete a note(y/n): ").lower() if delete in {"y", "yes", "1", "true"}: if self.notebook.modify(n_id, delete=True): print("Operation successful.") else: print("Note with this id doesn't exist.") return None elif delete in {"n", "no", "0", "false"}: break print("Response unclear, try again...") memo = input("Enter a memo (press Enter to skip): ") tags = input("Enter tags (press Enter to skip): ") oper1, oper2 = None, None if memo: oper1 = self.notebook.modify(n_id, n_memo=memo) if tags: oper2 = self.notebook.modify(n_id, n_tags=tags.split(",")) if oper1 or oper2: print("Operation successful.") else: print("Note with this id not found or not modified.") def clear(self): """Delete all notes.""" while True: clear = input("Are you sure you want " "to clear all notes(y/n): ").lower() if clear in {"y", "yes", "1", "true"}: self.notebook.notes = [] Note._id = 1 break elif clear in {"n", "no", "0", "false"}: break else: print("Response unclear, try again...") def save(self): """Save notes into a file.""" with open(self.file_name, mode="w+") as file: for note in self.notebook.notes: file.write(f"{note.memo}\t\t{note.tags}" f"\t\t{note.creation_date}\n") print("Notes saved in notes.txt.") def quit(self): """Quit the program.""" print("Thank you for using your notebook today.") sys.exit(0) if __name__ == "__main__": Menu().run()
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setup.py
CWE0/rainflow
0
149348
from setuptools import setup import os this_dir = os.path.dirname(__file__) with open(os.path.join(this_dir, "README.md"), "rb") as fo: long_description = fo.read().decode("utf8") setup( name='rainflow', package_dir={"": "src"}, py_modules=['rainflow'], description='Implementation of ASTM E1049-85 rainflow cycle counting algorithm', install_requires=["importlib_metadata ; python_version < '3.8'"], extras_require={"dev": ["pytest ~= 4.6"]}, python_requires=">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, <4", setup_requires=["setuptools_scm"], use_scm_version=True, long_description=long_description, long_description_content_type='text/markdown', author='<NAME>', url='https://github.com/iamlikeme/rainflow/', license="MIT", classifiers=[ "Development Status :: 5 - Production/Stable", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Topic :: Scientific/Engineering", "Topic :: Software Development :: Libraries :: Python Modules", ], )
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sample-apps/statsd/webserver.py
alanwest/aws-otel-test-framework
0
159241
from http.server import HTTPServer, BaseHTTPRequestHandler import os import socket import logging import json class httpHandler(BaseHTTPRequestHandler): def do_GET(self): if self.path == '/a': self.wfile.write('Helloa! '.encode()) elif self.path == '/statsd': send_statsd_metrics() self.send_response(200) self.send_header('Content-type', 'application/json') self.end_headers() self.wfile.write(json.dumps({'traceId': 'dummy'}).encode()) elif self.path == '/': self.send_response(200) self.end_headers() else: self.wfile.write('Hello! '.encode()) def send_statsd_metrics(): INSTANCE_ID = os.getenv('INSTANCE_ID') RECEIVER_ADDRESS = os.getenv('COLLECTOR_UDP_ADDRESS') logging.warning(INSTANCE_ID) logging.warning(RECEIVER_ADDRESS) if INSTANCE_ID is not None and RECEIVER_ADDRESS is not None: statsD_metric = bytes('statsdTestMetric1g_%s:1|g|#mykey1:myvalue1,mykey2:myvalue2\nstatsdTestMetric1c_%s:1|c|#mykey3:myvalue3' % (INSTANCE_ID, INSTANCE_ID),'utf-8') opened_socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) opened_socket.sendto(statsD_metric, (RECEIVER_ADDRESS.split(':')[0], int(RECEIVER_ADDRESS.split(':')[1]))) return def main(): ADDRESS = os.getenv('LISTEN_ADDRESS') if ADDRESS is not None: HOST = ADDRESS.split(':')[0] PORT = int(ADDRESS.split(':')[1]) else: HOST = '0.0.0.0' PORT = 4321 server = HTTPServer((HOST, PORT), httpHandler) server.serve_forever() if __name__ == '__main__': main()
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src/util/error.py
aYukiYoshida/xisscfeffect
0
79541
<reponame>aYukiYoshida/xisscfeffect<filename>src/util/error.py # -*- coding: utf-8 -*- class InsufficientInputError(Exception): """入力が不足していることを知らせる例外クラス""" pass class InvalidInputError(Exception): """入力が相応しくないことを知らせる例外クラス""" pass
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basic/Pyshop/products/models.py
IsAlbertLiu/Python-basics
0
22827
<filename>basic/Pyshop/products/models.py from django.db import models # Create your models here. class Product(models.Model): name = models.CharField(max_length=255) price = models.FloatField() stack = models.IntegerField() image_url = models.CharField(2083)
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tests/predictors/relation_classifier_predictor_test.py
DFKI-NLP/RelEx
16
123755
# pylint: disable=no-self-use,invalid-name,unused-import from unittest import TestCase from pytest import approx from allennlp.models.archival import load_archive from allennlp.predictors import Predictor import relex class RelationClassifierPredictorTest(TestCase): def test_uses_named_inputs(self): inputs = { "text": ( "The system as described above has its greatest " "application in an arrayed configuration of antenna elements ." ), "head": [12, 13], "tail": [15, 16], } archive = load_archive("tests/fixtures/model.tar.gz") predictor = Predictor.from_archive(archive, "relation_classifier") result = predictor.predict_json(inputs) label = result.get("label") assert label in { "Other", "Entity-Destination(e1,e2)", "Component-Whole(e2,e1)", "Instrument-Agency(e2,e1)", "Member-Collection(e1,e2)", "Cause-Effect(e2,e1)", "Content-Container(e1,e2)", } class_probabilities = result.get("class_probabilities") assert class_probabilities is not None assert all(cp > 0 for cp in class_probabilities) assert sum(class_probabilities) == approx(1.0)
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hsf_website_helpers/bin/hsf_reformat_training_events.py
HSF/website-helpers
0
26148
#!/usr/bin/env python3 """ Quick script to read all training schools from data file and write them out again to e.g. update the formatting. """ import argparse from hsf_website_helpers.events.event import EventDatabase from hsf_website_helpers.util.cli import add_website_home_option def get_parser() -> argparse.ArgumentParser: d = ( "Quick script to read all training schools from data file and write " "them out again to e.g. update the formatting." ) parser = argparse.ArgumentParser(description=d) add_website_home_option(parser) return parser if __name__ == "__main__": parser = get_parser() args = parser.parse_args() path = args.home / "_data" / "training-schools.yml" if path.is_file(): edb = EventDatabase.from_file(path) print(f"Loaded {len(edb.events)} events from database.") else: print(f"Did not find database at {path}. Initializing empty one.") edb = EventDatabase() edb.write(path) print( "Reformated database. Please commit and submit a PR to add it to " "the webpage." )
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Sudoku Solver/sudoku.py
Ch-V3nU/Projects
0
64479
#!/usr/bin/py2 import cv2 import imutils import numpy as np from solver import Solver from Recognizer import OCR from skimage.segmentation import clear_border from imutils.perspective import four_point_transform class Sudoku(object): def __init__(self, image): self.image = image self.gray = None def initialize_image(self): self.image = cv2.imread(self.image) self.image = imutils.resize(self.image, width=600) return def fetch_rectangle(self): self.gray = cv2.cvtColor(self.image, cv2.COLOR_BGR2GRAY) blurred = cv2.GaussianBlur(self.gray, (7, 7), 3) thresh = cv2.adaptiveThreshold(blurred, 255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2) thresh = cv2.bitwise_not(thresh) cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) cnts = imutils.grab_contours(cnts) cnts = sorted(cnts, key=cv2.contourArea, reverse=True) puzzleCnt = None for c in cnts: peri = cv2.arcLength(c, True) approx = cv2.approxPolyDP(c, 0.02 * peri, True) if len(approx) == 4: puzzleCnt = approx break return puzzleCnt def extract_sudoku_board(self,board): original_image = four_point_transform(self.image, board.reshape(4, 2)) gray_image = four_point_transform(self.gray, board.reshape(4, 2)) return gray_image def split_board(self,board): return board.shape[1] // 9, board.shape[0] // 9 def extract_digit(self,cell): thresh = cv2.threshold(cell, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1] thresh = clear_border(thresh) cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts = imutils.grab_contours(cnts) if len(cnts) == 0: return None c = max(cnts, key=cv2.contourArea) mask = np.zeros(thresh.shape, dtype="uint8") cv2.drawContours(mask, [c], -1, 255, -1) (h, w) = thresh.shape percentFilled = cv2.countNonZero(mask) / float(w * h) if percentFilled < 0.03: return None digit = cv2.bitwise_and(thresh, thresh, mask=mask) return digit def process_cells(self,stepX,stepY,board): ocr = OCR() sudoku_array = np.zeros((9, 9), dtype="int") cellLocs = [] boolean = True for y in range(0, 9): row = [] for x in range(0, 9): startX = x * stepX startY = y * stepY endX = (x + 1) * stepX endY = (y + 1) * stepY row.append((startX, startY, endX, endY)) cell = board[startY:endY, startX:endX] digit = self.extract_digit(cell) if (digit is not None): cv2.imwrite("img-"+str(y)+str(x)+".png",digit) sudoku_array[y][x] = ocr.prediction(digit) return sudoku_array def solve(self): self.initialize_image() board = self.fetch_rectangle() if board is None: return board = self.extract_sudoku_board(board) x,y = self.split_board(board) final_board = self.process_cells(x,y,board) return final_board def manipulate(board): canShow = True while (canShow): decision = raw_input("\nWanna make any corrections to the board? Press y if yes:-") if (decision and decision[0].lower() == "y"): canShow = False break values = raw_input("\nEnter Row,Column and Value. Ex: 2,2,3: ").split(",") try: row,col,val = list(map(int,values[:3])) check = lambda x: x>0 and x<10 if (all([check(i) for i in [row,col,val]])): board[row-1][col-1] = val print("\nUpdated Board\n") print(board) else: print("\nInvalid input") except: print("\nInvalid input") return board sudoku_board = Sudoku("Images/sudoku.jpg").solve() print(sudoku_board) updated_board = manipulate(sudoku_board) Solver().solution(updated_board)
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discovery/zenodo/zenodo_discovery.py
VIDA-NYU/auctus
15
103615
import asyncio import elasticsearch import json import logging import requests import time from urllib.parse import urlencode from datamart_core import Discoverer from datamart_core.common import setup_logging logger = logging.getLogger(__name__) class ZenodoDiscoverer(Discoverer): EXTENSIONS = ('.xls', '.xlsx', '.csv', '.sav') FILE_TYPES = ['csv', 'xlsx', 'sav'] def __init__(self, *args, **kwargs): super(ZenodoDiscoverer, self).__init__(*args, **kwargs) with open('zenodo.json') as fp: obj = json.load(fp) self.keyword_query = obj.pop('keyword_query', '') if obj: logger.warning("Unknown keys in configuration: %s", ', '.join(obj)) logger.info("Loaded keyword from zenodo.json: %s", self.keyword_query) def discover_datasets(self): seen = set() url = ( 'https://zenodo.org/api/records/' '?' + urlencode( dict( page=1, size=200, q=self.keyword_query, file_type=self.FILE_TYPES, type='dataset', ), doseq=True ) ) while url: logger.info("Getting %s", url) headers = {'Accept': 'application/json'} response = requests.get(url, headers=headers) response.raise_for_status() obj = response.json() for record in obj: self.process_record(record) seen.add(record['id']) if 'next' in response.links: url = response.links['next']['url'] time.sleep(2) else: url = None # Clean up the datasets we didn't see deleted = 0 size = 10000 query = { 'query': { 'term': { 'materialize.identifier': self.identifier, }, }, } hits = self.elasticsearch.scan( index='datasets,pending', query=query, size=size, _source=['materialize.zenodo_record_id'], ) for h in hits: if h['_source']['materialize']['zenodo_record_id'] not in seen: self.delete_dataset(full_id=h['_id']) deleted += 1 if deleted: logger.info("Deleted %d missing datasets", deleted) def process_record(self, record): # Get metadata common for the whole deposit record_metadata = dict( name=record['title'], source='zenodo.org', source_url='https://zenodo.org/record/%d' % record['id'], ) if 'license' in record['metadata']: record_metadata['license'] = record['metadata']['license'] description = '' if record['metadata'].get('description'): description += record['metadata']['description'] if record['metadata'].get('keywords'): description += '\n\n' + ', '.join(record['metadata']['keywords']) if description: record_metadata['description'] = description logger.info("Processing record %s %r", record['id'], record['title']) # Process each file for file in record['files']: if not file['filename'].lower().endswith(self.EXTENSIONS): continue dataset_id = '%s.%s' % (record['id'], file['id']) # See if we've ingested this file try: self.elasticsearch.get( 'datasets', '%s.%s' % (self.identifier, dataset_id), _source=False, ) except elasticsearch.NotFoundError: pass else: logger.info("Dataset already in index") return try: hit = self.elasticsearch.get( 'pending', '%s.%s' % (self.identifier, dataset_id), _source=['status'], )['_source'] except elasticsearch.NotFoundError: pass else: logger.info( "Dataset already in pending index, status=%s", hit.get('status'), ) return logger.info("File %s", file['filename']) file_metadata = dict( record_metadata, name='%s - %s' % ( record_metadata['name'], file['filename'], ), size=file['filesize'], ) direct_url = file['links']['download'] # Discover this dataset self.record_dataset( dict( zenodo_record_id=record['id'], zenodo_file_id=file['id'], zenodo_record_updated=record['modified'], direct_url=direct_url, ), file_metadata, dataset_id=dataset_id, ) if __name__ == '__main__': setup_logging() asyncio.get_event_loop().run_until_complete( ZenodoDiscoverer('datamart.zenodo').run() )
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tools/extract_from_multialign.py
dvalenzu/vcf2multialign
2
67277
<filename>tools/extract_from_multialign.py # # Copyright (c) 2017 <NAME> # This code is licensed under MIT license (see LICENSE for details). # import argparse import codecs import os import sys # Read from file into a buffer. # Used the idea from https://stackoverflow.com/a/26209275/856976 def chunks(fp, bufsize): while True: chunk = fp.read(bufsize) if not chunk: break yield chunk def chars(fp, bufsize = 4096): for chunk in chunks(fp, bufsize): for char in chunk: yield char def handle_ref_input(src, offset, length): """Find the offset of the given gene in the reference input.""" src.seek(0, 0) i = 0 j = 0 outputting = False for char in chars(src): if char != '-': if i == offset: return j i += 1 j += 1 return None def write_sequence(src, dst, fname, file_offset, length): k = 0 src.seek(file_offset, 0) for char in chars(src): if '-' == char: continue dst.write(char) k += 1 if k == length: break def handle_files(ref_input, co_ordinate_input, seq_file_names): write_flags = os.O_CREAT | os.O_EXCL | os.O_WRONLY for line in co_ordinate_input: fields = line.strip().split("\t") fields = fields[0:6] (chrom, chrom_start, chrom_end, name, score, strand) = fields chrom_start = int(chrom_start) chrom_end = int(chrom_end) length = chrom_end - chrom_start print("Handling sequence '%s'…" % name, file = sys.stderr) # Find the offset from the reference file. file_offset = handle_ref_input(ref_input, chrom_start, length) print("Found the requested substring at file offset %d" % file_offset, file = sys.stderr) # Handle the source files. fd = os.open("%s.fa" % name, write_flags) with os.fdopen(fd, 'w') as dst: for fname in seq_file_names: with open(fname, 'r') as src: print("\tHandling source file '%s'…" % fname, file = sys.stderr) dst.write(">%s\n" % fname) write_sequence(src, dst, fname, file_offset, length) dst.write("\n") if __name__ == "__main__": parser = argparse.ArgumentParser("Extract subsequences from vcf2multialign output.") parser.add_argument('--aligned-reference', type = argparse.FileType('rU'), required = True) parser.add_argument('--extracted-co-ordinates', type = argparse.FileType('rU'), required = True) parser.add_argument('source-files', nargs = '*') args = parser.parse_args() # Output UTF-8, https://stackoverflow.com/a/4374457/856976 #sys.stdout = codecs.getwriter("utf-8")(sys.stdout.detach()) #sys.stderr = codecs.getwriter("utf-8")(sys.stderr.detach()) handle_files( args.aligned_reference, args.extracted_co_ordinates, vars(args)['source-files'], )
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utils/imutils.py
Youwang-Kim/sds-dataset
0
139679
<gh_stars>0 import numpy as np from skimage.transform import resize import cv2 import math import torch import torchvision import torchvision.transforms as transforms def read_cv2_img(path): """ Read color images :param path: Path to image :return: Only returns color images """ img = cv2.imread(path, 1) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) return img def random_crop_range(crop_min=0.5, crop_max=0.7): while True: rand_nums = torch.rand(2) crop_range = torch.abs(rand_nums[0]-rand_nums[1]) if crop_min <= crop_range <= crop_max: break return crop_range def random_crop(img): h,w,_ = np.shape(img) h_crop_range = random_crop_range() h_size = math.ceil(h*h_crop_range) w_crop_range = random_crop_range() w_size = math.ceil(w * w_crop_range) i,j,h,w = transforms.RandomCrop.get_params(transforms.ToPILImage()(img), output_size=(h_size, w_size)) cropped_img = torchvision.transforms.functional.crop(transforms.ToPILImage()(img),i,j,h,w) #crop_oper = transforms.RandomCrop((h_size, w_size)) #cropped_img = crop_oper(transforms.ToPILImage()(img)) cropped_img = transforms.ToTensor()(cropped_img) return cropped_img, [i,j,h,w] def flip_img(img): """Flip rgb images or masks. channels come last, e.g. (256,256,3). """ img = np.fliplr(img) return img def get_transform(center, scale, res, rot=0): """Generate transformation matrix.""" h = 200 * scale t = np.zeros((3, 3)) t[0, 0] = float(res[1]) / h t[1, 1] = float(res[0]) / h t[0, 2] = res[1] * (-float(center[0]) / h + .5) t[1, 2] = res[0] * (-float(center[1]) / h + .5) t[2, 2] = 1 if not rot == 0: rot = -rot # To match direction of rotation from cropping rot_mat = np.zeros((3, 3)) rot_rad = rot * np.pi / 180 sn, cs = np.sin(rot_rad), np.cos(rot_rad) rot_mat[0, :2] = [cs, -sn] rot_mat[1, :2] = [sn, cs] rot_mat[2, 2] = 1 # Need to rotate around center t_mat = np.eye(3) t_mat[0, 2] = -res[1] / 2 t_mat[1, 2] = -res[0] / 2 t_inv = t_mat.copy() t_inv[:2, 2] *= -1 t = np.dot(t_inv, np.dot(rot_mat, np.dot(t_mat, t))) return t def transform(pt, center, scale, res, invert=0, rot=0): """Transform pixel location to different reference.""" t = get_transform(center, scale, res, rot=rot) if invert: t = np.linalg.inv(t) new_pt = np.array([pt[0] - 1, pt[1] - 1, 1.]).T new_pt = np.dot(t, new_pt) return new_pt[:2].astype(int) + 1 def crop(img, center, scale, res, rot=0): """Crop image according to the supplied bounding box.""" # Upper left point ul = np.array(transform([1, 1], center, scale, res, invert=1)) - 1 # Bottom right point br = np.array(transform([res[0] + 1, res[1] + 1], center, scale, res, invert=1)) - 1 # Padding so that when rotated proper amount of context is included pad = int(np.linalg.norm(br - ul) / 2 - float(br[1] - ul[1]) / 2) if not rot == 0: ul -= pad br += pad new_shape = [br[1] - ul[1], br[0] - ul[0]] if len(img.shape) > 2: new_shape += [img.shape[2]] new_img = np.zeros(new_shape) # Range to fill new array new_x = max(0, -ul[0]), min(br[0], len(img[0])) - ul[0] new_y = max(0, -ul[1]), min(br[1], len(img)) - ul[1] # Range to sample from original image old_x = max(0, ul[0]), min(len(img[0]), br[0]) old_y = max(0, ul[1]), min(len(img), br[1]) new_img[new_y[0]:new_y[1], new_x[0]:new_x[1]] = img[old_y[0]:old_y[1], old_x[0]:old_x[1]] # if not rot == 0: # # Remove padding # #new_img = scipy.misc.imrotate(new_img, rot) # new_img = rotate(new_img, rot) # new_img = new_img[pad:-pad, pad:-pad] # new_img = scipy.misc.imresize(new_img, res) new_img = resize(new_img, res) return new_img def rgb_processing(rgb_img, center, scale, flip, rot=0): """"Process rgb image and do augmentation.""" img_res = [224, 224] rgb_img = crop(rgb_img, center, scale, img_res, rot=rot) # flip the image if flip: rgb_img = flip_img(rgb_img) # in the rgb image we add pixel noise in a channel-wise manner # (3,224,224),float,[0,1] rgb_img = np.transpose(rgb_img.astype('float32'), (2, 0, 1)) / 255.0 return rgb_img
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exercicios/PythonExercicios/ex059.py
Roberto-Sartore/Python
0
74199
from time import sleep n1 = int(input('Primeiro número:')) n2 = int(input('Segundo número')) opcao = 0 while opcao != 5: print(''' [ 1 ] Somar [ 2 ] Multiplicar [ 3 ] Maior [ 4 ] Novos números [ 5 ] Sair do programa''') opcao = int(input('\033[33m Qual é a sua opção? \033[m')) if opcao == 1: soma = n1 + n2 print('\033[34m A soma entre {} e {} é {} \033[m'.format(n1, n2, soma)) elif opcao == 2: produto = n1 * n2 print('\033[34m O produto de {} X {} é {} \033[m'.format(n1, n2, produto)) elif opcao == 3: if n1 > n2: maior = n1 else: maior = n2 print('\033[34m Entre {} e {} o maior valor é {} \033[m'.format(n1, n2, maior)) elif opcao == 4: print('\033[34m Informe os números novamente:\033[m') n1 = int(input('Primeiro valor:')) n2 = int(input('Segundo valor:')) elif opcao == 5: print('\033[32m Finalizando...\033[m') else: print('\033[31m Opção inválida. Tente novamente. \033[m') print('=-=' * 12) sleep(2) print('\033[33m Fim do programa! Volte sempre! \033[m')
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RLBotPack/Beast from the East/behaviour.py
DaCoolOne/RLBotPack
0
182769
import predict import render from info import Field, Ball from moves import AimCone from plans import DodgePlan from rlmath import * class Carry: def __init__(self): self.is_dribbling = False self.flick_timer = 0 # Constants self.extra_utility_bias = 0.2 self.wait_before_flick = 0.26 self.flick_init_jump_duration = 0.07 self.required_distance_to_ball_for_flick = 173 self.offset_bias = 36 def utility(self, bot): car = bot.info.my_car ball = bot.info.ball car_to_ball = car.pos - ball.pos bouncing_b = ball.pos.z > 130 or abs(ball.vel.z) > 300 if not bouncing_b: return 0 dist_01 = clip01(1 - norm(car_to_ball) / 3000) head_dir = lerp(Vec3(0, 0, 1), car.forward(), 0.1) ang = angle_between(head_dir, car_to_ball) ang_01 = clip01(1 - ang / (math.pi / 2)) return clip01(0.6 * ang_01 + 0.4 * dist_01 # - 0.3 * bot.analyzer.team_mate_has_ball_01 + self.is_dribbling * self.extra_utility_bias) def execute(self, bot): self.is_dribbling = True car = bot.info.my_car ball = bot.info.ball ball_landing = predict.next_ball_landing(bot) ball_to_goal = bot.info.enemy_goal - ball.pos # Decide on target pos and speed target = ball_landing.data["obj"].pos - self.offset_bias * normalize(ball_to_goal) dist = norm(target - bot.info.my_car.pos) speed = 1400 if ball_landing.time == 0 else dist / ball_landing.time # Do a flick? car_to_ball = ball.pos - car.pos dist = norm(car_to_ball) if dist <= self.required_distance_to_ball_for_flick: self.flick_timer += 0.016666 if self.flick_timer > self.wait_before_flick: bot.plan = DodgePlan(bot.info.enemy_goal) # use flick_init_jump_duration? else: self.flick_timer = 0 # dodge on far distances if dist > 2450 and speed > 1410: ctt_n = normalize(target - car.pos) vtt = dot(bot.info.my_car.vel, ctt_n) / dot(ctt_n, ctt_n) if vtt > 750: bot.plan = DodgePlan(target) controls = bot.drive.go_towards_point(bot, target, target_vel=speed, slide=False, boost=False, can_keep_speed=False, can_dodge=True, wall_offset_allowed=0) bot.controls = controls if bot.do_rendering: bot.renderer.draw_line_3d(car.pos, target, bot.renderer.pink()) def reset(self): self.is_dribbling = False self.flick_timer = 0 class ShootAtGoal: def __init__(self): self.aim_cone = None self.ball_to_goal_right = None self.ball_to_goal_left = None def utility(self, bot): ball_soon = predict.ball_predict(bot, 1) arena_length2 = bot.info.team_sign * Field.LENGTH / 2 own_half_01 = clip01(remap(arena_length2, -arena_length2, 0.0, 1.1, ball_soon.pos.y)) reachable_ball = predict.ball_predict(bot, predict.time_till_reach_ball(bot.info.my_car, bot.info.ball)) self.ball_to_goal_right = bot.info.enemy_goal_right - reachable_ball.pos self.ball_to_goal_left = bot.info.enemy_goal_left - reachable_ball.pos self.aim_cone = AimCone(self.ball_to_goal_right, self.ball_to_goal_left) car_to_ball = reachable_ball.pos - bot.info.my_car.pos in_position = self.aim_cone.contains_direction(car_to_ball) return clip01(own_half_01 + 0.1 * in_position) def execute(self, bot): car = bot.info.my_car ball = bot.info.ball shoot_controls = bot.shoot.with_aiming(bot, self.aim_cone, predict.time_till_reach_ball(car, ball)) if bot.do_rendering: self.aim_cone.draw(bot, bot.shoot.ball_when_hit.pos, b=0) hit_pos = bot.shoot.ball_when_hit.pos dist = norm(car.pos - hit_pos) closest_enemy, enemy_dist = bot.info.closest_enemy(0.5 * (hit_pos + ball.pos)) if not bot.shoot.can_shoot and is_closer_to_goal_than(car.pos, hit_pos, bot.info.team): # Can't shoot but or at least on the right side: Chase goal_to_ball = normalize(hit_pos - bot.info.enemy_goal) offset_ball = hit_pos + goal_to_ball * Ball.RADIUS * 0.9 bot.controls = bot.drive.go_towards_point(bot, offset_ball, target_vel=2200, slide=False, boost=True) if bot.do_rendering: bot.renderer.draw_line_3d(car.pos, offset_ball, bot.renderer.yellow()) elif not bot.shoot.aim_is_ok and hit_pos.y * -bot.info.team_sign > 4350 and abs(hit_pos.x) > 900 and not dist < 450: # hit_pos is an enemy corner and we are not close: Avoid enemy corners and just wait enemy_to_ball = normalize(hit_pos - closest_enemy.pos) wait_point = hit_pos + enemy_to_ball * enemy_dist # a point 50% closer to the center of the field wait_point = lerp(wait_point, ball.pos + Vec3(0, bot.info.team_sign * 3000, 0), 0.5) bot.controls = bot.drive.go_towards_point(bot, wait_point, norm(car.pos - wait_point), slide=False, boost=False, can_keep_speed=True, can_dodge=False) if bot.do_rendering: bot.renderer.draw_line_3d(car.pos, wait_point, bot.renderer.yellow()) elif not bot.shoot.can_shoot: # return home bot.controls = bot.drive.go_home(bot) else: # Shoot ! bot.controls = shoot_controls if bot.shoot.using_curve and bot.do_rendering: render.draw_bezier(bot, [car.pos, bot.shoot.curve_point, hit_pos]) class ClearBall: def __init__(self, bot): if bot.team == 0: # blue self.aim_cone = AimCone(.8 * math.pi, .2 * math.pi) else: # orange self.aim_cone = AimCone(-.1 * math.pi, -.9 * math.pi) def utility(self, bot): team_sign = bot.info.team_sign length = team_sign * Field.LENGTH / 2 ball_own_half_01 = clip01(remap(-length, length, -0.2, 1.2, bot.info.ball.pos.y)) reachable_ball = predict.ball_predict(bot, predict.time_till_reach_ball(bot.info.my_car, bot.info.ball)) car_to_ball = reachable_ball.pos - bot.info.my_car.pos in_position = self.aim_cone.contains_direction(car_to_ball, math.pi / 8) return ball_own_half_01 * in_position def execute(self, bot): car = bot.info.my_car shoot_controls = bot.shoot.with_aiming(bot, self.aim_cone, predict.time_till_reach_ball(bot.info.my_car, bot.info.ball)) hit_pos = bot.shoot.ball_when_hit.pos if bot.do_rendering: self.aim_cone.draw(bot, hit_pos, r=0, g=170, b=255) if bot.shoot.can_shoot: bot.controls = shoot_controls if bot.shoot.using_curve and bot.do_rendering: render.draw_bezier(bot, [car.pos, bot.shoot.curve_point, hit_pos]) else: # go home-ish own_goal = lerp(bot.info.own_goal, bot.info.ball.pos, 0.5) bot.controls = bot.drive.go_towards_point(bot, own_goal, target_vel=1460, slide=True, boost=True, can_keep_speed=True) class SaveGoal: def __init__(self, bot): team_sign = bot.info.team_sign self.own_goal_right = Vec3(-820 * team_sign, 5120 * team_sign, 0) self.own_goal_left = Vec3(820 * team_sign, 5120 * team_sign, 0) self.aim_cone = None self.ball_to_goal_right = None self.ball_to_goal_left = None def utility(self, bot): team_sign = bot.info.team_sign ball = bot.info.ball ball_to_goal = bot.info.own_goal - ball.pos too_close = norm(ball_to_goal) < Field.GOAL_WIDTH / 2 + Ball.RADIUS hits_goal_prediction = predict.will_ball_hit_goal(bot) hits_goal = hits_goal_prediction.happens and sign(ball.vel.y) == team_sign and hits_goal_prediction.time < 3 return hits_goal or too_close def execute(self, bot): car = bot.info.my_car ball = bot.info.ball hits_goal_prediction = predict.will_ball_hit_goal(bot) reach_time = clip(predict.time_till_reach_ball(car, ball), 0, hits_goal_prediction.time - 0.5) reachable_ball = predict.ball_predict(bot, reach_time) self.ball_to_goal_right = self.own_goal_right - reachable_ball.pos self.ball_to_goal_left = self.own_goal_left - reachable_ball.pos self.aim_cone = AimCone(self.ball_to_goal_left, self.ball_to_goal_right) self.aim_cone.draw(bot, reachable_ball.pos, r=200, g=0, b=160) shoot_controls = bot.shoot.with_aiming(bot, self.aim_cone, reach_time) if not bot.shoot.can_shoot: # Go home bot.controls = bot.drive.go_home(bot) else: bot.controls = shoot_controls
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Code/Alestle.py
SIUE-Library/alestleMetadata
0
87622
<reponame>SIUE-Library/alestleMetadata import datetime import re class Alestle(): def __init__(self, fileURL): #Title := "The Alestle, <Month> <Day>, <Year>" self.title = "" self.url = fileURL self.author = "Southern Illinois University Edwardsville" self.publisher = "Southern Illinois University Edwardsville" self.city = "Edwardsville" self.docType = "book" self.abstract = "" self.pubdate = "" self.fillClass() #return the object with its comma delineated values, including needed default values #for the columns not included here. A collection alestle.toString()'s should be a valid csv def toString(self): t="" d="" if self.pubdate == "": t += "Error: Date is wrong at:\n" if self.abstract == "": t += "Error: Edition is wrong at:\n" try: d = datetime.datetime.strptime(self.pubdate, "%B%d,%Y").strftime("%Y-%m-%d") t += "The Alestle, "+d self.title = "The Alestle, "+d self.pubdate = d except: t += "Error AT: "+self.url+"\n" print("EVERYTHING IS ON FIRE") url = "192.168.3.11/alestle/"+self.url[16:-3]+"pdf" return self.title+"|"+url+"||"+self.abstract+"|Southern Illinois University Edwardsville||||||FALSE|||||||FALSE|||||||FALSE|||||||FALSE||||||"+self.pubdate+"|\n" def fillClass(self): #Fils in the variables for the alestle class, given a string containing the entire newspaper. testString = open(self.url, "r") testString = testString.read() testString = testString.replace(" ", "") testString = testString.replace("\n", "") testString = testString.replace("\r", "") dateMatch = re.search("(January|February|March|April|May|June|July|August|September|October|November|December)(\d|\d\d,)(\d\d\d\d)",testString) if(dateMatch): self.pubdate = dateMatch.group(0) issueMatch = re.search("Vol(,|\.)(\d\d|\d)(,|\.)No(,|\.)(\d\d|\d)",testString) if(issueMatch): self.abstract = issueMatch.group(0) self.abstract = self.abstract.replace(",", " ") print("UPDATED")
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tests/test_stackimages.py
BodenmillerGroup/ImcPluginsCP
10
108231
<reponame>BodenmillerGroup/ImcPluginsCP """test_stackimages.py - test the stackimages module """ import unittest import numpy as np from cellprofiler_core.preferences import set_headless set_headless() import cellprofiler_core.workspace as cpw import cellprofiler_core.image as cpi import cellprofiler_core.object as cpo import cellprofiler_core.pipeline as cpp import cellprofiler_core.measurement as cpmeas import plugins.stackimages as S INPUT_IMAGE_BASENAME = "myimage" OUTPUT_IMAGE_NAME = "mystackedimage" class TestStackImages(unittest.TestCase): def make_workspace(self, images): """Make a workspace """ module = S.StackImages() pipeline = cpp.Pipeline() object_set = cpo.ObjectSet() image_set_list = cpi.ImageSetList() image_set = image_set_list.get_image_set(0) workspace = cpw.Workspace( pipeline, module, image_set, object_set, cpmeas.Measurements(), image_set_list, ) # setup the input images names = [INPUT_IMAGE_BASENAME + str(i) for i, img in enumerate(images)] for img, nam in zip(images, names): image_set.add(nam, cpi.Image(img)) # setup the input images settings module.stack_image_name.value = OUTPUT_IMAGE_NAME nimgs = len(images) while len(module.stack_channels) < nimgs: module.add_stack_channel_cb() for sc, imname in zip(module.stack_channels, names): sc.image_name.value = imname return workspace, module def assert_stack(self, images, result): # test if this is equal to the stacked images lb = 0 for im in images: if len(im.shape) > 2: offset = im.shape[2] else: offset = 1 ub = lb + offset np.testing.assert_equal(np.squeeze(result.pixel_data[:, :, lb:ub]), im) lb = ub def assert_shape(self, images, result): new_shape = list(images[0].shape)[:2] c = 0 for im in images: if len(im.shape) > 2: c += im.shape[2] else: c += 1 new_shape += [c] np.testing.assert_equal(result.pixel_data.shape, new_shape) def test_stack_multichannel(self): img_shape = (10, 10, 5) image1 = np.zeros(img_shape) image2 = np.copy(image1) image2[:] = 1 input_imgs = [image1, image2] workspace, module = self.make_workspace(input_imgs) module.run(workspace) result = workspace.image_set.get_image(OUTPUT_IMAGE_NAME) self.assert_stack(input_imgs, result) self.assert_shape(input_imgs, result) def test_stack_multi_gray(self): img_shape = (10, 10, 5) image1 = np.zeros(img_shape) image1[:] = 1 img_shape2 = (10, 10) image2 = np.zeros(img_shape2) input_imgs = [image1, image2] workspace, module = self.make_workspace(input_imgs) module.run(workspace) result = workspace.image_set.get_image(OUTPUT_IMAGE_NAME) self.assert_stack(input_imgs, result) self.assert_shape(input_imgs, result) def test_stack_gray(self): img_shape = (10, 10) image1 = np.zeros(img_shape) image2 = np.copy(image1) image2[:] = 1 input_imgs = [image1, image2] workspace, module = self.make_workspace(input_imgs) module.run(workspace) result = workspace.image_set.get_image(OUTPUT_IMAGE_NAME) self.assert_stack(input_imgs, result) self.assert_shape(input_imgs, result) def test_stack_3multichannel(self): nimgs = 5 img_shape = (10, 10, 5) image = np.zeros(img_shape) input_imgs = [] for i in range(nimgs): img = np.copy(image) img[:] = i input_imgs.append(img) workspace, module = self.make_workspace(input_imgs) module.run(workspace) result = workspace.image_set.get_image(OUTPUT_IMAGE_NAME) self.assert_stack(input_imgs, result) self.assert_shape(input_imgs, result)
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bin/validate_plug_ins.py
warp5tw/openbmc-test-automation
0
67783
<gh_stars>0 #!/usr/bin/env python import sys try: import __builtin__ except ImportError: import builtins as __builtin__ import os # python puts the program's directory path in sys.path[0]. In other words, the user ordinarily has no way # to override python's choice of a module from its own dir. We want to have that ability in our environment. # However, we don't want to break any established python modules that depend on this behavior. So, we'll # save the value from sys.path[0], delete it, import our modules and then restore sys.path to its original # value. save_path_0 = sys.path[0] del sys.path[0] from gen_print import * from gen_arg import * from gen_plug_in import * # Restore sys.path[0]. sys.path.insert(0, save_path_0) # Create parser object to process command line parameters and args. # Create parser object. parser = argparse.ArgumentParser( usage='%(prog)s [OPTIONS] [PLUG_IN_DIR_PATHS]', description="%(prog)s will validate the plug-in packages passed to it." + " It will also print a list of the absolute plug-in" + " directory paths for use by the calling program.", formatter_class=argparse.ArgumentDefaultsHelpFormatter, prefix_chars='-+') # Create arguments. parser.add_argument( 'plug_in_dir_paths', nargs='?', default="", help=plug_in_dir_paths_help_text + default_string) parser.add_argument( '--mch_class', default="obmc", help=mch_class_help_text + default_string) # The stock_list will be passed to gen_get_options. We populate it with the names of stock parm options we # want. These stock parms are pre-defined by gen_get_options. stock_list = [("test_mode", 0), ("quiet", 1), ("debug", 0)] def exit_function(signal_number=0, frame=None): r""" Execute whenever the program ends normally or with the signals that we catch (i.e. TERM, INT). """ dprint_executing() dprint_var(signal_number) qprint_pgm_footer() def signal_handler(signal_number, frame): r""" Handle signals. Without a function to catch a SIGTERM or SIGINT, our program would terminate immediately with return code 143 and without calling our exit_function. """ # Our convention is to set up exit_function with atexit.registr() so there is no need to explicitly call # exit_function from here. dprint_executing() # Calling exit prevents us from returning to the code that was running when we received the signal. exit(0) def validate_parms(): r""" Validate program parameters, etc. Return True or False accordingly. """ gen_post_validation(exit_function, signal_handler) return True def main(): r""" This is the "main" function. The advantage of having this function vs just doing this in the true mainline is that you can: - Declare local variables - Use "return" instead of "exit". - Indent 4 chars like you would in any function. This makes coding more consistent, i.e. it's easy to move code from here into a function and vice versa. """ if not gen_get_options(parser, stock_list): return False if not validate_parms(): return False qprint_pgm_header() # Access program parameter globals. global plug_in_dir_paths global mch_class plug_in_packages_list = return_plug_in_packages_list(plug_in_dir_paths, mch_class) qprint_var(plug_in_packages_list) # As stated in the help text, this program must print the full paths of each selected plug in. for plug_in_dir_path in plug_in_packages_list: print(plug_in_dir_path) return True # Main if not main(): exit(1)
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tests/bicep/graph/graph_builder/test_local_graph.py
pmalkki/checkov
0
188859
from collections import Counter from pathlib import Path from checkov.bicep.graph_builder.graph_components.block_types import BlockType from checkov.bicep.graph_builder.local_graph import BicepLocalGraph from checkov.bicep.parser import Parser EXAMPLES_DIR = Path(__file__).parent.parent.parent / "examples" def test_build_graph(): # given test_file = EXAMPLES_DIR / "playground.bicep" template, _ = Parser().parse(test_file) local_graph = BicepLocalGraph(definitions={test_file: template}) # when local_graph.build_graph(render_variables=False) # then assert len(local_graph.vertices) == 24 assert len(local_graph.edges) == 29 assert len(local_graph.vertices_by_block_type[BlockType.TARGET_SCOPE]) == 1 assert len(local_graph.vertices_by_block_type[BlockType.PARAM]) == 5 assert len(local_graph.vertices_by_block_type[BlockType.VAR]) == 10 assert len(local_graph.vertices_by_block_type[BlockType.RESOURCE]) == 8 assert len(local_graph.vertices_by_block_type[BlockType.MODULE]) == 0 assert len(local_graph.vertices_by_block_type[BlockType.OUTPUT]) == 0 out_edge_counts = Counter([e.origin for e in local_graph.edges]) in_edge_counts = Counter([e.dest for e in local_graph.edges]) assert out_edge_counts == Counter( { 16: 9, 19: 5, 20: 4, 17: 3, 18: 2, 21: 2, 22: 2, 23: 2, } ) assert in_edge_counts == Counter( { 5: 8, 6: 2, 2: 1, 3: 1, 1: 1, 20: 1, 21: 1, 17: 1, 13: 1, 4: 1, 15: 1, 9: 1, 10: 1, 11: 1, 18: 1, 7: 1, 22: 1, 23: 1, 8: 1, 12: 1, 14: 1, } )
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utils/nlp.py
splovyt/SFPython-Project-Night
1
6500
import ssl import nltk from textblob import TextBlob from nltk.corpus import stopwords # set SSL try: _create_unverified_https_context = ssl._create_unverified_context except AttributeError: pass else: ssl._create_default_https_context = _create_unverified_https_context # download noun data (if required) nltk.download('brown') nltk.download('punkt') nltk.download('stopwords') def extract_nouns(sentence): """Extract the nouns from a sentence using the 'textblob' library.""" blob = TextBlob(sentence) return blob.noun_phrases def remove_stopwords(sentence): """Remove stopwords from a sentence and return the list of words.""" blob = TextBlob(sentence) return [word for word in blob.words if word not in stopwords.words('english') and len(word)>2]
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interface/python/test.py
gaubert/nessDB
1
27667
<reponame>gaubert/nessDB #!/usr/bin/env python #-*- coding:utf-8 -*- # author : KDr2 # BohuTANG @2012 # import sys import random import string import time import nessdb def gen_random_str(len): return ''.join([random.choice('abcdefghijklmnoprstuvwyxzABCDEFGHIJKLMNOPRSTUVWXYZ') for i in range(len)]) def ness_open(db_name): return nessdb.NessDB(db_name) def ness_write(db, c): s_time = time.time() for i in range(0, c): key = gen_random_str(16) db.db_add(key, "abcd") if (i % 10000) == 0: sys.stdout.write("\r\x1b[K ....write finished " + i.__str__()) sys.stdout.flush() e_time = time.time() print "" print "---->count:<%i>,cost time:<%i>, %i/sec\n" %(c, e_time - s_time, c / (e_time - s_time)) if __name__ == '__main__': if (len(sys.argv) > 2): if (sys.argv[1] == "write"): db = ness_open("test") ness_write(db, int(sys.argv[2])) db.db_close() else: print "test.py write <count>"
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app/run.py
bollwyvl/blockd3
33
101699
<filename>app/run.py #!/usr/bin/env python # -*- coding: utf-8 -*- import unittest import logging from ghost import GhostTestCase, Ghost from app import make_app app = make_app("test") PORT = 5000 base_url = "http://localhost:%s/dist/" % PORT class Blockd3GhostTest(GhostTestCase): port = PORT display = False log_level = logging.INFO def __new__(cls, *args, **kwargs): """Creates Ghost instance.""" if not hasattr(cls, 'ghost'): cls.ghost = Ghost(display=cls.display, wait_timeout=10, viewport_size=cls.viewport_size, log_level=cls.log_level) return super(Blockd3GhostTest, cls).__new__(cls, *args, **kwargs) @classmethod def create_app(cls): return app def test_open(self): """ Test that the page loads """ page, resources = self.ghost.open(base_url) self.assertEqual(page.url, base_url) self.ghost.click("#run") if __name__ == "__main__": unittest.main()
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preprocessing/generate_landmarks.py
huangjiadidi/dfdc_deepfake_challenge
499
1614671
import argparse import os from functools import partial from multiprocessing.pool import Pool os.environ["MKL_NUM_THREADS"] = "1" os.environ["NUMEXPR_NUM_THREADS"] = "1" os.environ["OMP_NUM_THREADS"] = "1" from tqdm import tqdm import cv2 cv2.ocl.setUseOpenCL(False) cv2.setNumThreads(0) from preprocessing.utils import get_original_video_paths from PIL import Image from facenet_pytorch.models.mtcnn import MTCNN import numpy as np detector = MTCNN(margin=0, thresholds=[0.65, 0.75, 0.75], device="cpu") def save_landmarks(ori_id, root_dir): ori_id = ori_id[:-4] ori_dir = os.path.join(root_dir, "crops", ori_id) landmark_dir = os.path.join(root_dir, "landmarks", ori_id) os.makedirs(landmark_dir, exist_ok=True) for frame in range(320): if frame % 10 != 0: continue for actor in range(2): image_id = "{}_{}.png".format(frame, actor) landmarks_id = "{}_{}".format(frame, actor) ori_path = os.path.join(ori_dir, image_id) landmark_path = os.path.join(landmark_dir, landmarks_id) if os.path.exists(ori_path): try: image_ori = cv2.imread(ori_path, cv2.IMREAD_COLOR)[...,::-1] frame_img = Image.fromarray(image_ori) batch_boxes, conf, landmarks = detector.detect(frame_img, landmarks=True) if landmarks is not None: landmarks = np.around(landmarks[0]).astype(np.int16) np.save(landmark_path, landmarks) except Exception as e: print(e) pass def parse_args(): parser = argparse.ArgumentParser( description="Extract image landmarks") parser.add_argument("--root-dir", help="root directory", default="/mnt/sota/datasets/deepfake") args = parser.parse_args() return args def main(): args = parse_args() ids = get_original_video_paths(args.root_dir, basename=True) os.makedirs(os.path.join(args.root_dir, "landmarks"), exist_ok=True) with Pool(processes=os.cpu_count()) as p: with tqdm(total=len(ids)) as pbar: func = partial(save_landmarks, root_dir=args.root_dir) for v in p.imap_unordered(func, ids): pbar.update() if __name__ == '__main__': main()
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run.py
jonasstenling/bumper
0
90593
''' This module is used to test the project implementation from the command line ''' from ruleset import RuleSet from ciscoconfparse import CiscoConfParse def load_config(config_file): '''Returns parse configuration as CiscoConfParse object.''' return CiscoConfParse(config_file) def main(): '''Main entry point for module.''' config = load_config('test.conf') myrules = RuleSet() myrules.load_rules('syntax.yml') myresult = [] for rule in myrules.rules: result = rule.apply(config) for element in result: if element.result == False: print "Rule evaluation failed:\n Cfgline: '{}'\n Rule: {}".format( element.cfgline.text, element.rule) myresult.append(result) return myresult if __name__ == '__main__': main()
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sandbox/lib/jumpscale/JumpScale9Lib/clients/atyourservice/setup.py
Jumpscale/sandbox_linux
2
158923
from setuptools import setup, find_packages # To use a consistent encoding from codecs import open from os import path here = path.abspath(path.dirname(__file__)) # Get the long description from the README file with open(path.join(here, 'README.md'), encoding='utf-8') as f: long_description = f.read() setup ( name='ays', version='0.9', description='Python client for the AYS RESTful API', long_description=long_description, url='https://github.com/jumpscale/lib9', author='<NAME>', author_email='<EMAIL>', license='Apache 2.0', packages=['ays'], install_requires=[], )
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stroylux/main/export_import/__init__.py
vladkoblynsky/shop
0
131952
class ExportObjStatus: SUCCESS = 'success' ERROR = 'error' IN_PROGRESS = 'in_progress' CHOICES = [ (SUCCESS, 'Success'), (ERROR, 'Error'), (IN_PROGRESS, 'In progress'), ]
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hello/migrations/0002_auto_20201116_1409.py
chenyuan99/OwlSavesCats
0
24542
# Generated by Django 3.0.8 on 2020-11-16 19:09 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('hello', '0001_initial'), ] operations = [ migrations.CreateModel( name='Author', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('realname', models.CharField(max_length=64)), ('phone', models.CharField(max_length=16)), ('email', models.EmailField(max_length=254)), ('sign', models.BooleanField()), ('create_time', models.DateTimeField(auto_now=True)), ], options={ 'ordering': ['-id'], }, ), migrations.CreateModel( name='paperclip', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=200)), ('abstract', models.CharField(max_length=200)), ('publish_time', models.DateTimeField()), ('create_time', models.DateTimeField(auto_now=True)), ('pid', models.CharField(max_length=16)), ], ), migrations.AlterUniqueTogether( name='guest', unique_together=None, ), migrations.RemoveField( model_name='guest', name='event', ), migrations.DeleteModel( name='Event', ), migrations.DeleteModel( name='Guest', ), migrations.AddField( model_name='author', name='paperclip', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='hello.paperclip'), ), migrations.AlterUniqueTogether( name='author', unique_together={('phone', 'paperclip')}, ), ]
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Chapter08/example3.py
jpgacrama/Mastering-Concurrency-in-Python
0
170972
<filename>Chapter08/example3.py # ch8/example3.py import cv2 import os # define our clear function def clear(): # for windows if os.name == 'nt': _ = os.system('cls') # for mac and linux(here, os.name is 'posix') else: _ = os.system('clear') if __name__ == '__main__': clear() cwd = os.getcwd() if os.name == 'nt': im = cv2.imread(os.path.join(cwd, 'Chapter08\input\ship.jpg')) else: im = cv2.imread(os.path.join(cwd, 'Chapter08/input/ship.jpg')) gray_im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) ret, custom_thresh_im = cv2.threshold(gray_im, 127, 255, cv2.THRESH_BINARY) cv2.imwrite('output/custom_thresh_ship.jpg', custom_thresh_im) print('Done.')
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ACM ICPC/Math/gcd/gcd.py
shreejitverma/GeeksforGeeks
2
174727
<filename>ACM ICPC/Math/gcd/gcd.py def gcd(a,b): if a==0: return b else: return gcd(b%a,a) c = gcd(100,35) # c = 5 print(c)
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validation.py
Jianxiang-Wang/Pytorch-code-for-time-series-classification
2
25149
from sklearn import metrics import torch from models import * import torch.backends.cudnn as cudnn import seaborn as sns import matplotlib.pyplot as plt from dataset import load #define the net device = 'cuda' if torch.cuda.is_available() else 'cpu' net = LSTM(3, 10, 2, 3) net = net.to(device) if device == 'cuda': net = torch.nn.DataParallel(net) cudnn.benchmark = True net.load_state_dict(torch.load('./checkpoint/ckpt.pth')) net = net.module #loading data _, _, valloader, classes = load() def validation(): print(net.classifier) #print(net) net.eval() correct = 0 total = 0 for batch_idx, (inputs, targets) in enumerate(valloader): inputs, targets = inputs.to(device).float(), targets.to(device) inputs = inputs.view(-1,300,3) outputs = net(inputs) # Confusion Matrix print("Confusion Matrix...") _, predicted = outputs.max(1) total += targets.size(0) correct += predicted.eq(targets).sum().item() Accuracy = 100.*correct/total predicted = predicted.cpu().numpy() targets = targets.data.cpu().numpy() cm = metrics.confusion_matrix(targets, predicted) print(cm) print('Accuracy=',Accuracy,"%") figure = plt.figure(figsize=(8, 8)) sns.heatmap(cm, annot=True, cmap='Blues') plt.ylim(0, 10) plt.xlabel('Predicted labels') plt.ylabel('True labels') plt.show() if __name__=='__main__': validation()
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tests/test_agents.py
RamanKishoreSingh/pyriad
1
1604124
import torch from . import factories def test_base_agent_initialize(): agent = factories.PSOAgentFactory.create() swarm = factories.SwarmFactory.create() data = torch.tensor([ [3.0, 3.0, 3.0], [1.0, 1.0, 1.0], [2.0, 2.0, 2.0], ]) agent.initialize(data=data, swarm=swarm) if not agent.swarm == swarm: raise AssertionError() if not len(agent.centroids) == agent.n_clusters: raise AssertionError() def test_base_agent_memorize(): agent = factories.PSOAgentFactory.create() tensor = torch.tensor([3.0, 3.0, 3.0]) agent.memorize(score=1, position=tensor) if not torch.all(torch.eq(tensor, agent.memory.memory[0][1])) == True: raise AssertionError() def test_base_agent_topk(): agent = factories.PSOAgentFactory.create() tensor = torch.tensor([3.0, 3.0, 3.0]) agent.memorize(score=1, position=tensor) topk = agent.topk(k=1)[0] if not torch.all(torch.eq(tensor, topk)) == True: raise AssertionError()
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sdk/luminesce/models/background_query_cancel_response.py
finbourne/luminesce-sdk-python-preview
0
130961
# coding: utf-8 """ FINBOURNE Honeycomb Web API FINBOURNE Technology # noqa: E501 The version of the OpenAPI document: 1.9.129 Contact: <EMAIL> Generated by: https://openapi-generator.tech """ try: from inspect import getfullargspec except ImportError: from inspect import getargspec as getfullargspec import pprint import re # noqa: F401 import six from luminesce.configuration import Configuration class BackgroundQueryCancelResponse(object): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. """ """ Attributes: openapi_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. required_map (dict): The key is attribute name and the value is whether it is 'required' or 'optional'. """ openapi_types = { 'had_data': 'bool', 'previous_status': 'TaskStatus', 'previous_state': 'BackgroundQueryState', 'progress': 'str' } attribute_map = { 'had_data': 'hadData', 'previous_status': 'previousStatus', 'previous_state': 'previousState', 'progress': 'progress' } required_map = { 'had_data': 'optional', 'previous_status': 'optional', 'previous_state': 'optional', 'progress': 'optional' } def __init__(self, had_data=None, previous_status=None, previous_state=None, progress=None, local_vars_configuration=None): # noqa: E501 """BackgroundQueryCancelResponse - a model defined in OpenAPI" :param had_data: :type had_data: bool :param previous_status: :type previous_status: luminesce.TaskStatus :param previous_state: :type previous_state: luminesce.BackgroundQueryState :param progress: :type progress: str """ # noqa: E501 if local_vars_configuration is None: local_vars_configuration = Configuration.get_default_copy() self.local_vars_configuration = local_vars_configuration self._had_data = None self._previous_status = None self._previous_state = None self._progress = None self.discriminator = None if had_data is not None: self.had_data = had_data if previous_status is not None: self.previous_status = previous_status if previous_state is not None: self.previous_state = previous_state self.progress = progress @property def had_data(self): """Gets the had_data of this BackgroundQueryCancelResponse. # noqa: E501 :return: The had_data of this BackgroundQueryCancelResponse. # noqa: E501 :rtype: bool """ return self._had_data @had_data.setter def had_data(self, had_data): """Sets the had_data of this BackgroundQueryCancelResponse. :param had_data: The had_data of this BackgroundQueryCancelResponse. # noqa: E501 :type had_data: bool """ self._had_data = had_data @property def previous_status(self): """Gets the previous_status of this BackgroundQueryCancelResponse. # noqa: E501 :return: The previous_status of this BackgroundQueryCancelResponse. # noqa: E501 :rtype: luminesce.TaskStatus """ return self._previous_status @previous_status.setter def previous_status(self, previous_status): """Sets the previous_status of this BackgroundQueryCancelResponse. :param previous_status: The previous_status of this BackgroundQueryCancelResponse. # noqa: E501 :type previous_status: luminesce.TaskStatus """ self._previous_status = previous_status @property def previous_state(self): """Gets the previous_state of this BackgroundQueryCancelResponse. # noqa: E501 :return: The previous_state of this BackgroundQueryCancelResponse. # noqa: E501 :rtype: luminesce.BackgroundQueryState """ return self._previous_state @previous_state.setter def previous_state(self, previous_state): """Sets the previous_state of this BackgroundQueryCancelResponse. :param previous_state: The previous_state of this BackgroundQueryCancelResponse. # noqa: E501 :type previous_state: luminesce.BackgroundQueryState """ self._previous_state = previous_state @property def progress(self): """Gets the progress of this BackgroundQueryCancelResponse. # noqa: E501 :return: The progress of this BackgroundQueryCancelResponse. # noqa: E501 :rtype: str """ return self._progress @progress.setter def progress(self, progress): """Sets the progress of this BackgroundQueryCancelResponse. :param progress: The progress of this BackgroundQueryCancelResponse. # noqa: E501 :type progress: str """ self._progress = progress def to_dict(self, serialize=False): """Returns the model properties as a dict""" result = {} def convert(x): if hasattr(x, "to_dict"): args = getfullargspec(x.to_dict).args if len(args) == 1: return x.to_dict() else: return x.to_dict(serialize) else: return x for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) attr = self.attribute_map.get(attr, attr) if serialize else attr if isinstance(value, list): result[attr] = list(map( lambda x: convert(x), value )) elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], convert(item[1])), value.items() )) else: result[attr] = convert(value) return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, BackgroundQueryCancelResponse): return False return self.to_dict() == other.to_dict() def __ne__(self, other): """Returns true if both objects are not equal""" if not isinstance(other, BackgroundQueryCancelResponse): return True return self.to_dict() != other.to_dict()
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main_app/needs/__init__.py
sashalavrus/cost_app
0
43350
from flask import Blueprint needs = Blueprint('needs', __name__) from . import views from ..models import Permission @needs.app_context_processor def inject_permissions(): return dict(Permission=Permission)
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include/visualize.py
MLI-lab/overparameterized_convolutional_generators
13
80083
<filename>include/visualize.py<gh_stars>10-100 import matplotlib.pyplot as plt from torch.autograd import Variable import torch import torch.optim import numpy as np from collections import Iterable dtype = torch.cuda.FloatTensor #dtype = torch.FloatTensor def save_np_img(img,filename): if(img.shape[0] == 1): plt.imshow(np.clip(img[0],0,1)) else: plt.imshow(np.clip(img.transpose(1, 2, 0),0,1)) plt.axis('off') plt.savefig(filename, bbox_inches='tight') plt.close() def apply_until(net_input,net,n = 100): # applies function by funtion of a network for i,fun in enumerate(net): if i>=n: break if i==0: out = fun(net_input.type(dtype)) else: out = fun(out) print(i, "last func. applied:", net[i-1]) if n == 0: return net_input else: return out from math import ceil # given a lists of images as np-arrays, plot them as a row# given def plot_image_grid(imgs,nrows=10): ncols = ceil( len(imgs)/nrows ) nrows = min(nrows,len(imgs)) fig, axes = plt.subplots(nrows=nrows, ncols=ncols, sharex=True, sharey=True,figsize=(ncols,nrows),squeeze=False) for i, row in enumerate(axes): for j, ax in enumerate(row): ax.imshow(imgs[j*nrows+i], cmap='Greys_r', interpolation='none') ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) fig.tight_layout(pad=0.1) return fig def save_tensor(out,filename,nrows=8): imgs = [img for img in out.data.cpu().numpy()[0]] fig = plot_image_grid(imgs,nrows=nrows) plt.savefig(filename) plt.close() def plot_kernels(tensor): if not len(tensor.shape)==4: raise Exception("assumes a 4D tensor") num_kernels = tensor.shape[0] fig = plt.figure(figsize=(tensor.shape[0],tensor.shape[1])) for i in range(tensor.shape[0]): for j in range(tensor.shape[1]): ax1 = fig.add_subplot(tensor.shape[0],tensor.shape[1],1+i*tensor.shape[0]+j) ax1.imshow(tensor[i][j]) ax1.axis('off') ax1.set_xticklabels([]) ax1.set_yticklabels([]) plt.subplots_adjust(wspace=0.1, hspace=0.1) plt.show() def plot_tensor(out,nrows=8): imgs = [img for img in out.data.cpu().numpy()[0]] fig = plot_image_grid(imgs,nrows=nrows) plt.show()
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postman1.py
Natalia1957/stepik-auto-test-course_
0
111699
import requests URL_AUTH = 'https://developers.lingvolive.com/api/v1.1/authenticate' URL_TRANSLATE = 'https://developers.lingvolive.com/api/v1/Minicard' KEY = "<KEY>" headers_auth = {'Authorization': "Basic" + " " + KEY} auth = requests.post(URL_AUTH, headers=headers_auth) print(auth.status_code) print(auth.text) if auth.status_code == 200: token = auth.text while True: word = input("input word for translate: ") if word: headers_translate = { 'Authorization': "Bearer " + " " + token } params = { "text": word, "srcLang": 1033, "dstLang": 1049 } r = requests.get(URL_TRANSLATE, headers=headers_translate, params=params) res = r.json() try: print(res["Translation"] ["Translation"]) except: print("net word") else: print("Error")
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src/rez/utils/graph_utils.py
maxnbk/rez
0
197575
<reponame>maxnbk/rez # Copyright Contributors to the Rez project # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Functions for manipulating dot-based resolve graphs. """ from __future__ import print_function import os.path import sys import tempfile from ast import literal_eval from rez.config import config from rez.vendor.pydot import pydot from rez.utils.execution import Popen from rez.utils.formatting import PackageRequest from rez.exceptions import PackageRequestError from rez.vendor.pygraph.readwrite.dot import read as read_dot from rez.vendor.pygraph.algorithms.accessibility import accessibility from rez.vendor.pygraph.classes.digraph import digraph from rez.vendor.six import six basestring = six.string_types[0] def read_graph_from_string(txt): """Read a graph from a string, either in dot format, or our own compressed format. Returns: `pygraph.digraph`: Graph object. """ if not txt.startswith('{'): return read_dot(txt) # standard dot format def conv(value): if isinstance(value, basestring): return '"' + value + '"' else: return value # our compacted format doc = literal_eval(txt) g = digraph() for attrs, values in doc.get("nodes", []): attrs = [(k, conv(v)) for k, v in attrs] for value in values: if isinstance(value, basestring): node_name = value attrs_ = attrs else: node_name, label = value attrs_ = attrs + [("label", conv(label))] g.add_node(node_name, attrs=attrs_) for attrs, values in doc.get("edges", []): attrs_ = [(k, conv(v)) for k, v in attrs] for value in values: if len(value) == 3: edge = value[:2] label = value[-1] else: edge = value label = '' g.add_edge(edge, label=label, attrs=attrs_) return g def write_compacted(g): """Write a graph in our own compacted format. Returns: str. """ d_nodes = {} d_edges = {} def conv(value): if isinstance(value, basestring): return value.strip('"') else: return value for node in g.nodes(): label = None attrs = [] for k, v in sorted(g.node_attributes(node)): v_ = conv(v) if k == "label": label = v_ else: attrs.append((k, v_)) value = (node, label) if label else node d_nodes.setdefault(tuple(attrs), []).append(value) for edge in g.edges(): attrs = [(k, conv(v)) for k, v in sorted(g.edge_attributes(edge))] label = str(g.edge_label(edge)) value = tuple(list(edge) + [label]) if label else edge d_edges.setdefault(tuple(attrs), []).append(tuple(value)) doc = dict(nodes=list(d_nodes.items()), edges=list(d_edges.items())) contents = str(doc) return contents def write_dot(g): """Replacement for pygraph.readwrite.dot.write, which is dog slow. Note: This isn't a general replacement. It will work for the graphs that Rez generates, but there are no guarantees beyond that. Args: g (`pygraph.digraph`): Input graph. Returns: str: Graph in dot format. """ lines = ["digraph g {"] def attrs_txt(items): if items: txt = ", ".join(('%s="%s"' % (k, str(v).strip('"'))) for k, v in items) return '[' + txt + ']' else: return '' for node in g.nodes(): atxt = attrs_txt(g.node_attributes(node)) txt = "%s %s;" % (node, atxt) lines.append(txt) for e in g.edges(): edge_from, edge_to = e attrs = g.edge_attributes(e) label = str(g.edge_label(e)) if label: attrs.append(("label", label)) atxt = attrs_txt(attrs) txt = "%s -> %s %s;" % (edge_from, edge_to, atxt) lines.append(txt) lines.append("}") return '\n'.join(lines) def prune_graph(graph_str, package_name): """Prune a package graph so it only contains nodes accessible from the given package. Args: graph_str (str): Dot-language graph string. package_name (str): Name of package of interest. Returns: Pruned graph, as a string. """ # find nodes of interest g = read_dot(graph_str) nodes = set() for node, attrs in g.node_attr.items(): attr = [x for x in attrs if x[0] == "label"] if attr: label = attr[0][1] try: req_str = _request_from_label(label) request = PackageRequest(req_str) except PackageRequestError: continue if request.name == package_name: nodes.add(node) if not nodes: raise ValueError("The package %r does not appear in the graph." % package_name) # find nodes upstream from these nodes g_rev = g.reverse() accessible_nodes = set() access = accessibility(g_rev) for node in nodes: nodes_ = access.get(node, []) accessible_nodes |= set(nodes_) # remove inaccessible nodes inaccessible_nodes = set(g.nodes()) - accessible_nodes for node in inaccessible_nodes: g.del_node(node) return write_dot(g) def save_graph(graph_str, dest_file, fmt=None, image_ratio=None): """Render a graph to an image file. Args: graph_str (str): Dot-language graph string. dest_file (str): Filepath to save the graph to. fmt (str): Format, eg "png", "jpg". image_ratio (float): Image ratio. Returns: String representing format that was written, such as 'png'. """ # Disconnected edges can result in multiple graphs. We should never see # this - it's a bug in graph generation if we do. # graphs = pydot.graph_from_dot_data(graph_str) if not graphs: raise RuntimeError("No graph generated") if len(graphs) > 1: path, ext = os.path.splitext(dest_file) dest_files = [] for i, g in enumerate(graphs): try: dest_file_ = "%s.%d%s" % (path, i + 1, ext) save_graph_object(g, dest_file_, fmt, image_ratio) dest_files.append(dest_file_) except: pass raise RuntimeError( "More than one graph was generated; this probably indicates a bug " "in graph generation. Graphs were written to %r" % dest_files ) # write the graph return save_graph_object(graphs[0], dest_file, fmt, image_ratio) def save_graph_object(g, dest_file, fmt=None, image_ratio=None): """Like `save_graph`, but takes a pydot Dot object. """ # determine the dest format if fmt is None: fmt = os.path.splitext(dest_file)[1].lower().strip('.') or "png" if hasattr(g, "write_" + fmt): write_fn = getattr(g, "write_" + fmt) else: raise RuntimeError("Unsupported graph format: '%s'" % fmt) if image_ratio: g.set_ratio(str(image_ratio)) write_fn(dest_file) return fmt def view_graph(graph_str, dest_file=None): """View a dot graph in an image viewer.""" from rez.system import system from rez.config import config if (system.platform == "linux") and (not os.getenv("DISPLAY")): print("Unable to open display.", file=sys.stderr) sys.exit(1) dest_file = _write_graph(graph_str, dest_file=dest_file) # view graph viewed = False prog = config.image_viewer or 'browser' print("loading image viewer (%s)..." % prog) if config.image_viewer: with Popen([config.image_viewer, dest_file]) as p: p.wait() viewed = not bool(p.returncode) if not viewed: import webbrowser webbrowser.open_new("file://" + dest_file) def _write_graph(graph_str, dest_file=None): if not dest_file: tmpf = tempfile.mkstemp(prefix='resolve-dot-', suffix='.' + config.dot_image_format) os.close(tmpf[0]) dest_file = tmpf[1] print("rendering image to " + dest_file + "...") save_graph(graph_str, dest_file) return dest_file # converts string like '"PyQt-4.8.0[1]"' to 'PyQt-4.8.0' def _request_from_label(label): return label.strip('"').strip("'").rsplit('[', 1)[0]
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model.py
DishantIsrani/SmartCCTV
0
116995
<reponame>DishantIsrani/SmartCCTV from sklearn.svm import LinearSVC import numpy as np import cv2 import PIL import camera class Model: def __init__(self): self.model = LinearSVC() def train_model(self, counters): img_list = np.array([]) class_list = np.array([]) for i in range(1, counters[0]): img = cv2.imread(f"1/frame{i}.jpg")[:,:,0] img = img.reshape(16950) img_list = np.append(img_list, [img]) class_list = np.append(class_list, 1) for i in range(1, counters[1]): img = cv2.imread(f"2/frame{i}.jpg")[:,:,0] img = img.reshape(16950) img_list = np.append(img_list, [img]) class_list = np.append(class_list, 2) img_list = img_list.reshape(counters[0]-1 + counters[1]-1, 16950) self.model.fit(img_list, class_list) print("Model successfully Trained!") def predict(self, frame): frame = frame[1] cv2.imwrite('frame.jpg', cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)) img = PIL.Image.open('frame.jpg') img.thumbnail((150, 150), PIL.Image.ANTIALIAS) img.save("frame.jpg") img = cv2.imread('frame.jpg')[:,:,0] img = img.reshape(16950) prediction = self.model.predict([img]) return prediction[0]
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core.py
iBug/OmniAE
0
28131
# LOL. Hope you're not fooled by the name of this file import sys import os from configparser import ConfigParser # Note: All classes here have N801 (CapWords naming convention) disabled. # They're intended to be singletons class Object(object): def __init__(self, _default=None, **kwargs): self.__dict__["_data"] = dict(kwargs) self.__dict__["_default"] = _default def __getattr__(self, attr): try: return self._data[attr] except KeyError: if self._default: self._data[attr] = self._default() return self._data[attr] raise AttributeError("Object has no attribute {!r}".format(attr)) from None def __setattr__(self, attr, value): self._data[attr] = value return value def __getitem__(self, index): return self._data[index] def __setitem__(self, index, value): self._data[index] = value return value def set_default(self, default=None): if default is True: default = Object self._default = default config = Object(_default=type(None)) class obj: # noqa: N801 site_list = None sews = None tasker = None post_storage = None class worker: # noqa: N801 sews = None scanner = None handler = None check = Object( development=None, ) config_parser = ConfigParser() def load(): global config_parser if "pytest" in sys.modules: config_parser.read("config.ci", encoding="utf-8") elif os.path.isfile("config"): config_parser.read("config", encoding="utf-8") else: config_parser.read("config.sample", encoding="utf-8") conf = config_parser['Config'] config.read_key = conf.get('read_key', "<KEY>) config.write_key = conf.get('write_key') config.write_token = conf.get('write_token') config.site = conf.get('site', "android.stackexchange.com") config.log_level = int(conf.get('log_level', 1)) config.file_log_level = int(conf.get('file_log_level', 3)) config.log_file = conf.get('log_file', "log.txt") config.db_file = conf.get('db_file', "androidoverflow.db") config.repo_slug = conf.get('repo_slug', "iBug/AndroidOverflow")
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test_memory.py
dbstein/python_examples
0
32652
import numpy as np from numexpr_kernel import numexpr_kernel from numba_kernel import numba_kernel N = 10000 x = np.random.rand(N) y = np.random.rand(N) z = np.random.rand(N) tau = np.random.rand(N) r1 = numexpr_kernel(x, y, z, tau) r1 = numexpr_kernel(x, y, z, tau) r2 = np.zeros(N, dtype=float) numba_kernel(x, y, z, tau, r2, N) numba_kernel(x, y, z, tau, r2, N)
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bostaSDK/delivery/create/CreateDeliveryRequest.py
bostaapp/bosta-python
0
77483
import json from bostaSDK.utils.Address import Address from bostaSDK.utils.Receiver import Receiver from bostaSDK.utils.DeliverySpecs import DeliverySpecs class CreateDeliveryRequest: def __init__( self, deliveryType, cod, dropOffAddress: Address, receiver: Receiver, deliverySpecs=None, notes=None, businessReference=None, ): """ Initialize new instance from CreateDeliveryRequest class Parameters: deliveryType (int) cod (int): Cash on delivery amount dropOffAddress (Address) cashOnDelivery (int): Cash on delivery amount receiver (Receiver) deliverySpecs (DeliverySpecs) notes (str) businessReference (str) Returns: instance from CreateDeliveryRequest """ self.type = deliveryType self.cod = cod self.dropOffAddress = dropOffAddress self.receiver = receiver def toJSONPayload(self): """ Returns: JSON object from current instance """ return json.dumps({ "type": self.type, "cod": self.cod, "dropOffAddress": self.dropOffAddress.toJSON(), "receiver": self.receiver.toJSON() })
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Important_data/Thesis figure scripts/six_sigmoids.py
haakonvt/LearningTensorFlow
5
21290
<gh_stars>1-10 from matplotlib import rc rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']}) rc('text', usetex=True) rc('legend',**{'fontsize':11}) # Font size for legend from mpl_toolkits.axes_grid.axislines import SubplotZero import matplotlib as mpl mpl.rcParams['lines.linewidth'] = 2.5 import matplotlib.pyplot as plt from math import erf,sqrt import numpy as np xmin = -4; xmax = 4 x = np.linspace(xmin,xmax,1001) y1 = lambda x: np.array([erf(0.5*i*sqrt(np.pi)) for i in x]) y2 = lambda x: np.tanh(x) y3 = lambda x: 4./np.pi*np.arctan(np.tanh(np.pi*x/4.)) y4 = lambda x: x/np.sqrt(1.+x**2) y5 = lambda x: 2.0/np.pi*np.arctan(np.pi/2.0 * x) y6 = lambda x: x/(1+np.abs(x)) fig = plt.figure(1) ax = SubplotZero(fig, 111) fig.add_subplot(ax) plt.subplots_adjust(left = 0.125, # the left side of the subplots of the figure right = 0.9, # the right side of the subplots of the figure bottom = 0.1, # the bottom of the subplots of the figure top = 0.9, # the top of the subplots of the figure wspace = 0., # the amount of width reserved for blank space between subplots hspace = 0.) # the amount of height reserved for white space between subplots plt.setp(ax, xticks=[-3,-2,-1,1,2,3], xticklabels=[" "," "," "," "," "," ",], yticks=[-1,1], yticklabels=[" "," ",]) # Make coordinate axes with "arrows" for direction in ["xzero", "yzero"]: ax.axis[direction].set_visible(True) # Coordinate axes with arrow (guess what, these are the arrows) plt.arrow(2.65, 0.0, 0.5, 0.0, color="k", clip_on=False, head_length=0.06, head_width=0.08) plt.arrow(0.0, 1.03, 0.0, 0.1, color="k", clip_on=False, head_length=0.06, head_width=0.08) # Remove edge around the entire plot for direction in ["left", "right", "bottom", "top"]: ax.axis[direction].set_visible(False) plt.rc('text', usetex=True) plt.rc('font', family='serif') colormap = plt.cm.Spectral #nipy_spectral # Other possible colormaps: Set1, Accent, nipy_spectral, Paired colors = [colormap(i) for i in np.linspace(0, 1, 6)] plt.title("Six sigmoid functions", fontsize=18, y=1.08) leg_list = [r"$\mathrm{erf}\left(\frac{\sqrt{\pi}}{2}x \right)$", r"$\tanh(x)$", r"$\frac{2}{\pi}\mathrm{gd}\left( \frac{\pi}{2}x \right)$", r"$x\left(1+x^2\right)^{-\frac{1}{2}}$", r"$\frac{2}{\pi}\mathrm{arctan}\left( \frac{\pi}{2}x \right)$", r"$x\left(1+|x|\right)^{-1}$"] for i in range(1,7): s = "ax.plot(x,y%s(x),color=colors[i-1])" %(str(i)) eval(s) ax.legend(leg_list,loc="best", ncol=2, fancybox=True) # title="Legend", fontsize=12 # ax.grid(True, which='both') ax.set_aspect('equal') ax.set_xlim([-3.1,3.1]) ax.set_ylim([-1.1,1.1]) ax.annotate('1', xy=(0.08, 1-0.02)) ax.annotate('0', xy=(0.08, -0.2)) ax.annotate('-1', xy=(0.08, -1-0.03)) for i in [-3,-2,-1,1,2,3]: ax.annotate('%s' %str(i), xy=(i-0.03, -0.2)) maybe = raw_input("\nUpdate figure directly in master thesis?\nEnter 'YES' (anything else = ONLY show to screen) ") if maybe == "YES": # Only save to disc if need to be updated filenameWithPath = "/Users/haakonvt/Dropbox/uio/master/latex-master/Illustrations/six_sigmoids.pdf" plt.savefig(filenameWithPath, bbox_inches='tight') #, pad_inches=0.2) print 'Saved over previous file in location:\n "%s"' %filenameWithPath else: print 'Figure was only shown on screen.' plt.show()
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nbx/cli/client.py
dalejung/nbx
2
83294
<filename>nbx/cli/client.py import requests class IPythonService(object): def __init__(self, host, port, token=None): self.host = host self.port = port self.token = token def _get(self, path, **kwargs): token = self.token if token: kwargs['headers'] = {'Authorization': f'token {token}'} url = 'http://{host}:{port}/{path}'.format(path=path, **self.__dict__) r = requests.get(url, **kwargs) return r.json() def sessions(self): return self._get('api/sessions') def server_info(self): return self._get('server-info') def kernel_info(self, kernel_id): return self._get('api/kernel-info/{kernel_id}'.format(kernel_id=kernel_id)) class IPythonClient(object): def __init__(self, service): self.service = service self.sessions_cache = None def _list_sessions(self): sessions = self.service.sessions() self.sessions_cache = sessions lines = [self._format_session(i, session) for i, session in enumerate(sessions)] return lines def list_sessions(self): lines = [] lines.append("Active Kernels:") lines.append("====================") lines.extend(self._list_sessions()) print('\n'.join(lines)) def _format_session(self, i, session): name = session['notebook']['path'].rsplit('/')[-1] return "[{i}] {name}".format(i=i, name=name) def _get_session(self, pos): session = self.sessions_cache[pos] return session def attach(self, pos): info = self.service.server_info() profile = info['profile'] pos = int(pos) session = self._get_session(pos) print("=" * 80) name = session['notebook']['path'].rsplit('/')[-1] print("Attaching to {name}".format(name=name)) print("=" * 80) return attach_session(session, profile=profile) def kernel_path(self, pos): info = self.service.server_info() profile = info['profile'] pos = int(pos) session = self._get_session(pos) kernel_id = session['kernel']['id'] kernel_info = self.service.kernel_info(kernel_id) kernel_path = kernel_info['kernel_path'] return kernel_path def attach_session(session, profile='default'): """ Start a terminal app attached to a notebook """ from jupyter_console import app kernel = 'kernel-{0}.json'.format(session['kernel']['id']) # TODO support other submodules like qtconsole argv = ['console', '--existing', kernel, '--profile={0}'.format(profile)] return app.launch_new_instance(argv=argv) def client(host="127.0.0.1", port="8888", token=None): service = IPythonService(host, port, token) client = IPythonClient(service) return client
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viewer/bitmap_from_array.py
TiankunZhou/dials
2
12196
from __future__ import absolute_import, division, print_function import numpy as np import wx from dials.array_family import flex from dials_viewer_ext import rgb_img class wxbmp_from_np_array(object): def __init__( self, lst_data_in, show_nums=True, palette="black2white", lst_data_mask_in=None ): self.wx_bmp_arr = rgb_img() if lst_data_in is None and lst_data_mask_in is None: self._ini_wx_bmp_lst = None else: self._ini_wx_bmp_lst = [] for lst_pos in range(len(lst_data_in)): data_3d_in = lst_data_in[lst_pos] xmax = data_3d_in.shape[1] ymax = data_3d_in.shape[2] # remember to put here some assertion to check that # both arrays have the same shape if lst_data_mask_in is not None: data_3d_in_mask = lst_data_mask_in[lst_pos] self.vl_max = float(np.amax(data_3d_in)) self.vl_min = float(np.amin(data_3d_in)) tmp_data2d = np.zeros((xmax, ymax), "double") tmp_data2d_mask = np.zeros((xmax, ymax), "double") z_dp = data_3d_in.shape[0] single_block_lst_01 = [] for z in range(z_dp): # print "z =", z tmp_data2d[:, :] = data_3d_in[z : z + 1, :, :] if lst_data_mask_in is not None: tmp_data2d_mask[:, :] = data_3d_in_mask[z : z + 1, :, :] else: tmp_data2d_mask = None data_sigle_img = self._wx_img_w_cpp( tmp_data2d, show_nums, palette, tmp_data2d_mask ) single_block_lst_01.append(data_sigle_img) self._ini_wx_bmp_lst.append(single_block_lst_01) def bmp_lst_scaled(self, scale=1.0): if self._ini_wx_bmp_lst is None: NewW = 350 wx_image = wx.Image(NewW, NewW) wxBitmap = wx_image.ConvertToBitmap() dc = wx.MemoryDC(wxBitmap) text = "No Shoebox data" w, h = dc.GetSize() tw, th = dc.GetTextExtent(text) dc.Clear() dc.DrawText(text, (w - tw) / 2, (h - th) / 2) # display text in center dc.SelectObject(wxBitmap) del dc wx_bmp_lst = [[wxBitmap]] else: wx_bmp_lst = [] for data_3d in self._ini_wx_bmp_lst: single_block_lst = [] for sigle_img_data in data_3d: single_block_lst.append(self._wx_bmp_scaled(sigle_img_data, scale)) wx_bmp_lst.append(single_block_lst) return wx_bmp_lst def _wx_img_w_cpp(self, np_2d_tmp, show_nums, palette, np_2d_mask=None): xmax = np_2d_tmp.shape[1] ymax = np_2d_tmp.shape[0] if np_2d_mask is None: np_2d_mask = np.zeros((ymax, xmax), "double") transposed_data = np.zeros((ymax, xmax), "double") transposed_mask = np.zeros((ymax, xmax), "double") transposed_data[:, :] = np_2d_tmp transposed_mask[:, :] = np_2d_mask flex_data_in = flex.double(transposed_data) flex_mask_in = flex.double(transposed_mask) if palette == "black2white": palette_num = 1 elif palette == "white2black": palette_num = 2 elif palette == "hot ascend": palette_num = 3 else: # assuming "hot descend" palette_num = 4 img_array_tmp = self.wx_bmp_arr.gen_bmp( flex_data_in, flex_mask_in, show_nums, palette_num ) np_img_array = img_array_tmp.as_numpy_array() height = np.size(np_img_array[:, 0:1, 0:1]) width = np.size(np_img_array[0:1, :, 0:1]) img_array = np.empty((height, width, 3), "uint8") img_array[:, :, :] = np_img_array[:, :, :] self._wx_image = wx.Image(width, height) self._wx_image.SetData(img_array.tostring()) data_to_become_bmp = (self._wx_image, width, height) return data_to_become_bmp def _wx_bmp_scaled(self, data_to_become_bmp, scale): to_become_bmp = data_to_become_bmp[0] width = data_to_become_bmp[1] height = data_to_become_bmp[2] NewW = int(width * scale) NewH = int(height * scale) to_become_bmp = to_become_bmp.Scale(NewW, NewH, wx.IMAGE_QUALITY_NORMAL) wxBitmap = to_become_bmp.ConvertToBitmap() return wxBitmap
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ibeis/viz/viz_name.py
brmscheiner/ibeis
0
73428
# -*- coding: utf-8 -*- from __future__ import absolute_import, division, print_function, unicode_literals import plottool.draw_func2 as df2 import numpy as np from ibeis.other import ibsfuncs from plottool import plot_helpers as ph import plottool as pt import utool as ut from ibeis.viz import viz_chip (print, rrr, profile) = ut.inject2(__name__) def show_name_of(ibs, aid, **kwargs): nid = ibs.get_annot_names(aid) show_name(ibs, nid, sel_aids=[aid], **kwargs) def testdata_showname(): import ibeis ibs = ibeis.opendb(defaultdb='testdb1') default = None if ibs.dbname == 'testdb1': default = 'easy' name_text = ut.get_argval('--name', type_=str, default=default) if name_text is None: nid = 1 else: nid = ibs.get_name_rowids_from_text(name_text) in_image = not ut.get_argflag('--no-inimage') index_list = ut.get_argval('--index_list', type_=list, default=None) return ibs, nid, in_image, index_list def testdata_multichips(): import ibeis ibs = ibeis.opendb(defaultdb='testdb1') nid = ut.get_argval('--nid', type_=int, default=None) tags = ut.get_argval('--tags', type_=list, default=None) if nid is not None: aid_list = ibs.get_name_aids(nid) elif tags is not None: index = ut.get_argval('--index', default=0) aid_list = ibs.filter_aidpairs_by_tags(any_tags=tags)[index] else: #aid_list = ut.get_argval('--aids', type_=list, default=[1, 2, 3]) aid_list = ibeis.testdata_aids(default_aids=[1, 2, 3], ibs=ibs) in_image = not ut.get_argflag('--no-inimage') return ibs, aid_list, in_image #9108 and 9180 def show_multiple_chips(ibs, aid_list, in_image=True, fnum=0, sel_aids=[], subtitle='', annote=False, **kwargs): """ CommandLine: python -m ibeis.viz.viz_name --test-show_multiple_chips --show --no-inimage python -m ibeis.viz.viz_name --test-show_multiple_chips --show --db NNP_Master3 --aids=6435,9861,137,6563,9167,12547,9332,12598,13285 --no-inimage --notitle python -m ibeis.viz.viz_name --test-show_multiple_chips --show --db NNP_Master3 --aids=137,6563,12547,9332,12598,13285 --no-inimage --notitle --adjust=.05 python -m ibeis.viz.viz_name --test-show_multiple_chips --show --db NNP_Master3 --aids=6563,9332,13285,12598 --no-inimage --notitle --adjust=.05 --rc=1,4 python -m ibeis.viz.viz_name --test-show_multiple_chips --show --db PZ_Master0 --aids=1288 --no-inimage --notitle --adjust=.05 python -m ibeis.viz.viz_name --test-show_multiple_chips --show --db PZ_Master0 --aids=4020,4839 --no-inimage --notitle --adjust=.05 python -m ibeis.viz.viz_name --test-show_multiple_chips --db NNP_Master3 --aids=6524,6540,6571,6751 --no-inimage --notitle --adjust=.05 --diskshow python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST -a default:index=0:4 --show --aids=1 --doboth --show --no-inimage python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST --aids=1 --doboth --show --no-inimage python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST --aids=1 --doboth --rc=2,1 --show --no-inimage python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST --aids=1 --doboth --rc=2,1 --show --notitle --trydrawline --no-draw_lbls python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST --aids=1,2 --doboth --show --notitle --trydrawline python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST --aids=1,2,3,4,5 --doboth --rc=2,5 --show --chrlbl --trydrawline --qualtitle --no-figtitle --notitle --doboth --doboth --show python -m ibeis.viz.viz_name --test-show_multiple_chips --db NNP_Master3 --aids=15419 --doboth --rc=2,1 --show --notitle --trydrawline --no-draw_lbls Example: >>> # DISABLE_DOCTEST >>> from ibeis.viz.viz_name import * # NOQA >>> import ibeis >>> ibs, aid_list, in_image = testdata_multichips() >>> if True: >>> import matplotlib as mpl >>> from ibeis.scripts.thesis import TMP_RC >>> mpl.rcParams.update(TMP_RC) >>> fnum = 0 >>> sel_aids = [] >>> subtitle = '' >>> annote = False >>> fig = show_multiple_chips(ibs, aid_list, in_image, fnum, sel_aids, subtitle, annote) >>> ut.quit_if_noshow() >>> fig.canvas.draw() >>> ut.show_if_requested() """ fnum = pt.ensure_fnum(fnum) nAids = len(aid_list) if nAids == 0: fig = df2.figure(fnum=fnum, pnum=(1, 1, 1), **kwargs) df2.imshow_null(fnum=fnum, **kwargs) return fig # Trigger computation of all chips in parallel ibsfuncs.ensure_annotation_data(ibs, aid_list, chips=(not in_image or annote), feats=annote) print('[viz_name] * annot_vuuid=%r' % ((ibs.get_annot_visual_uuids(aid_list),))) print('[viz_name] * aid_list=%r' % ((aid_list,))) DOBOTH = ut.get_argflag('--doboth') rc = ut.get_argval('--rc', type_=list, default=None) if rc is None: nRows, nCols = ph.get_square_row_cols(nAids * (2 if DOBOTH else 1)) else: nRows, nCols = rc notitle = ut.get_argflag('--notitle') draw_lbls = not ut.get_argflag('--no-draw_lbls') show_chip_kw = dict(annote=annote, in_image=in_image, notitle=notitle, draw_lbls=draw_lbls) #print('[viz_name] * r=%r, c=%r' % (nRows, nCols)) #gs2 = gridspec.GridSpec(nRows, nCols) pnum_ = df2.get_pnum_func(nRows, nCols) fig = df2.figure(fnum=fnum, pnum=pnum_(0), **kwargs) fig.clf() ax_list1 = [] for px, aid in enumerate(aid_list): print('px = %r' % (px,)) _fig, _ax1 = viz_chip.show_chip(ibs, aid=aid, pnum=pnum_(px), **show_chip_kw) print('other_aids = %r' % (ibs.get_annot_contact_aids(aid),)) ax = df2.gca() ax_list1.append(_ax1) if aid in sel_aids: df2.draw_border(ax, df2.GREEN, 4) if ut.get_argflag('--chrlbl') and not DOBOTH: ax.set_xlabel('(' + chr(ord('a') - 1 + px) + ')') elif ut.get_argflag('--numlbl') and not DOBOTH: ax.set_xlabel('(' + str(px + 1) + ')') #plot_aid3(ibs, aid) # HACK to show in image and not in image if DOBOTH: #ut.embed() #ph.get_plotdat_dict(ax_list1[1]) #ph.get_plotdat_dict(ax_list2[1]) ax_list2 = [] show_chip_kw['in_image'] = not show_chip_kw['in_image'] start = px + 1 for px, aid in enumerate(aid_list, start=start): _fig, _ax2 = viz_chip.show_chip(ibs, aid=aid, pnum=pnum_(px), **show_chip_kw) ax = df2.gca() ax_list2.append(_ax2) if ut.get_argflag('--chrlbl'): ax.set_xlabel('(' + chr(ord('a') - start + px) + ')') elif ut.get_argflag('--numlbl'): ax.set_xlabel('(' + str(px - start + 1) + ')') if ut.get_argflag('--qualtitle'): qualtext = ibs.get_annot_quality_texts(aid) ax.set_title(qualtext) if aid in sel_aids: df2.draw_border(ax, df2.GREEN, 4) if in_image: ax_list1, ax_list2 = ax_list2, ax_list1 if ut.get_argflag('--trydrawline'): # Unfinished #ut.embed() # Draw lines between corresponding axes # References: # http://stackoverflow.com/questions/17543359/drawing-lines-between-two-plots-in-matplotlib import matplotlib as mpl import vtool as vt # !!! #http://matplotlib.org/users/transforms_tutorial.html #invTransFigure_fn1 = fig.transFigure.inverted().transform #invTransFigure_fn2 = fig.transFigure.inverted().transform #print(ax_list1) #print(ax_list2) assert len(ax_list1) == len(ax_list2) for ax1, ax2 in zip(ax_list1, ax_list2): #_ = ax1.get_window_extent().transformed(fig.dpi_scale_trans.inverted()) #bbox1 = (0, 0, _.width * fig.dpi, _.height * fig.dpi) # returns in figure coordinates #bbox1 = df2.get_axis_bbox(ax=ax1) #if bbox1[-1] < 0: # # Weird bug # bbox1 = bbox1[1] print('--') print('ax1 = %r' % (ax1,)) print('ax2 = %r' % (ax2,)) chipshape = ph.get_plotdat(ax1, 'chipshape') #_bbox1 = ax1.get_window_extent().transformed(fig.dpi_scale_trans.inverted()) #bbox1 = (0, 0, _bbox1.width * fig.dpi, _bbox1.height * fig.dpi) bbox1 = (0, 0, chipshape[1], chipshape[0]) aid_ = ph.get_plotdat(ax2, 'aid') aid_list_ = ph.get_plotdat(ax2, 'aid_list') index = aid_list_.index(aid_) annotation_bbox_list = ph.get_plotdat(ax2, 'annotation_bbox_list') bbox2 = annotation_bbox_list[index] print('bbox1 = %r' % (bbox1,)) print('bbox2 = %r' % (bbox2,)) vert_list1 = np.array(vt.verts_from_bbox(bbox1)) vert_list2 = np.array(vt.verts_from_bbox(bbox2)) print('vert_list1 = %r' % (vert_list1,)) print('vert_list2 = %r' % (vert_list2,)) #for vx in [0, 1, 2, 3]: for vx in [0, 1]: vert1 = vert_list1[vx].tolist() vert2 = vert_list2[vx].tolist() print(' ***') print(' * vert1 = %r' % (vert1,)) print(' * vert2 = %r' % (vert2,)) coordsA = coordsB = 'data' #coords = 'axes points' #'axes fraction' #'axes pixels' #coordsA = 'axes pixels' #coordsB = 'data' #'figure fraction' #'figure pixels' #'figure pixels' #'figure points' #'polar' #'offset points' con = mpl.patches.ConnectionPatch( xyA=vert1, xyB=vert2, coordsA=coordsA, coordsB=coordsB, axesA=ax1, axesB=ax2, linewidth=1, color='k') #, arrowstyle="-") #ut.embed() #con.set_zorder(None) ax1.add_artist(con) #ax2.add_artist(con) #ut.embed() #verts2.T[1] -= bbox2[-1] #bottom_left1, bottom_right1 = verts1[1:3].tolist() #bottom_left2, bottom_right2 = verts2[1:3].tolist() ##transAxes1 = ax1.transData.inverted() #transAxes1_fn = ax1.transData.transform #transAxes2_fn = ax2.transData.transform #transAxes1_fn = ut.identity #transAxes2_fn = ut.identity #coord_bl1 = transFigure.transform(transAxes1.transform(bottom_left1)) #coord_br1 = transFigure.transform(transAxes1.transform(bottom_right1)) #coord_bl1 = invTransFigure_fn1(transAxes1_fn(bottom_left1)) #print('bottom_left2 = %r' % (bottom_left2,)) #coord_bl1 = (5, 5) #coord_bl2 = invTransFigure_fn2(transAxes2_fn(bottom_left2)) #print('coord_bl2 = %r' % (coord_bl2,)) #coord_br1 = invTransFigure_fn1(transAxes1_fn(bottom_right1)) #coord_br2 = invTransFigure_fn2(transAxes2_fn(bottom_right2)) ##print('coord_bl1 = %r' % (coord_bl1,)) #line_coords1 = np.vstack([coord_bl1, coord_bl2]) #line_coords2 = np.vstack([coord_br1, coord_br2]) #print('line_coords1 = %r' % (line_coords1,)) #line1 = mpl.lines.Line2D((line_coords1[0]), (line_coords1[1]), transform=fig.transFigure) #line2 = mpl.lines.Line2D((line_coords2[0]), (line_coords2[1]), transform=fig.transFigure) #xs1, ys1 = line_coords1.T #xs2, ys2 = line_coords2.T #linekw = dict(transform=fig.transFigure) #linekw = dict() #print('xs1 = %r' % (xs1,)) #print('ys1 = %r' % (ys1,)) #line1 = mpl.lines.Line2D(xs1, ys1, **linekw) #line2 = mpl.lines.Line2D(xs2, ys2, **linekw) # NOQA #shrinkA=5, shrinkB=5, mutation_scale=20, fc="w") #ax2.add_artist(con) #fig.lines.append(line1) #fig.lines.append(line2) pass return fig <EMAIL> def show_name(ibs, nid, in_image=True, fnum=0, sel_aids=[], subtitle='', annote=False, aid_list=None, index_list=None, **kwargs): r""" Args: ibs (IBEISController): ibeis controller object nid (?): in_image (bool): fnum (int): figure number sel_aids (list): subtitle (str): annote (bool): CommandLine: python -m ibeis.viz.viz_name --test-show_name --dpath ~/latex/crall-candidacy-2015 --save 'figures/{name}.jpg' --no-figtitle --notitle --db NNP_Master3 --figsize=9,4 --clipwhite --dpi=180 --adjust=.05 --index_list=[0,1,2,3] --rc=2,4 --append temp_out_figure.tex --name=IBEIS_PZ_0739 --no-draw_lbls --doboth --no-inimage --diskshow python -m ibeis.viz.viz_name --test-show_name --no-figtitle --notitle --db NNP_Master3 --figsize=9,4 --clipwhite --dpi=180 --adjust=.05 --index_list=[0,1,2,3] --rc=2,4 --append temp_out_figure.tex --name=IBEIS_PZ_0739 --no-draw_lbls --doboth --no-inimage --show python -m ibeis.viz.viz_name --test-show_name --show Example: >>> # DISABLE_DOCTEST >>> from ibeis.viz.viz_name import * # NOQA >>> ibs, nid, in_image, index_list = testdata_showname() >>> fnum = 0 >>> sel_aids = [] >>> subtitle = '' >>> annote = False >>> # execute function >>> show_name(ibs, nid, in_image, fnum, sel_aids, subtitle, annote, index_list=index_list) >>> ut.show_if_requested() """ print('[viz_name] show_name nid=%r, index_list=%r, aid_list=%r' % (nid, index_list, aid_list)) if aid_list is None: aid_list = ibs.get_name_aids(nid) else: assert ut.list_all_eq_to(ibs.get_annot_nids(aid_list), nid) if index_list is not None: aid_list = ut.take(aid_list, index_list) name = ibs.get_name_texts((nid,)) print('[viz_name] * name=%r aid_list=%r' % (name, aid_list)) show_multiple_chips(ibs, aid_list, in_image=in_image, fnum=fnum, sel_aids=sel_aids, annote=annote, **kwargs) if isinstance(nid, np.ndarray): nid = nid[0] if isinstance(name, np.ndarray): name = name[0] use_figtitle = not ut.get_argflag('--no-figtitle') if use_figtitle: figtitle = 'Name View nid=%r name=%r' % (nid, name) df2.set_figtitle(figtitle) #if not annote: # title += ' noannote' #gs2.tight_layout(fig) #gs2.update(top=df2.TOP_SUBPLOT_ADJUST) #df2.set_figtitle(title, subtitle) if __name__ == '__main__': """ CommandLine: python -m ibeis.viz.viz_name python -m ibeis.viz.viz_name --allexamples python -m ibeis.viz.viz_name --allexamples --noface --nosrc """ import multiprocessing multiprocessing.freeze_support() # for win32 import utool as ut # NOQA ut.doctest_funcs()
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predict_traffic.py
clankster99/NN_project
0
54397
<filename>predict_traffic.py from PIL import Image import numpy as np import os def show_masked_img(roads_tensor, cars_tensor, raw_img, img_name): # Convert to numpy roads_pred = roads_tensor.cpu().detach().numpy() cars_pred = cars_tensor.cpu().detach().numpy() # Extract roads roads_img_r = roads_pred[1] roads_img_b = roads_pred[0] # Extract cars cars_img_r = cars_pred[1] cars_img_b = cars_pred[0] # Setting true valeus where the pixel is more likely to be a road roads_pred = roads_img_r > roads_img_b # Setting true valeus where the pixel is more likely to be a car cars_pred = cars_img_r > cars_img_b # Only care about the cars predicted on roads cars_pred *= roads_pred # Remove car pixels from road pixels roads_pred *= np.logical_not(cars_pred) # Convert raw_img to numpy array img_array = np.array(raw_img).astype(np.uint8) # Make the image more opaque img_array //= 2 # img_array *= 2 # Create array of 255 colored_array = np.zeros_like(img_array[:,:,0]) + 255 # Creating masks roads_mask = colored_array - img_array[:,:,0] cars_mask = colored_array - img_array[:,:,2] roads_mask *= roads_pred.astype(np.uint8) cars_mask *= cars_pred.astype(np.uint8) # Create image img_array[:,:,0] += roads_mask img_array[:,:,2] += cars_mask image = Image.fromarray(img_array, 'RGB') image.save('dataset/traffic/' + img_name) image.show() def get_traffic_level(roads_tensor, cars_tensor): # Convert to numpy roads_pred = roads_tensor.cpu().detach().numpy() cars_pred = cars_tensor.cpu().detach().numpy() # Extract roads roads_img_r = roads_pred[1] roads_img_b = roads_pred[0] # Extract cars cars_img_r = cars_pred[1] cars_img_b = cars_pred[0] # Setting true valeus where the pixel is more likely to be a road roads_pred = roads_img_r > roads_img_b # Setting true valeus where the pixel is more likely to be a car cars_pred = cars_img_r > cars_img_b # Only care about the cars predicted on roads cars_pred *= roads_pred # Check if enough roads road_pixels = np.sum(roads_pred) total_pixels = roads_pred.shape[0] * roads_pred.shape[1] if road_pixels / total_pixels < 0.1: return "Not enough roads" #Check number of car pixels car_pixels = np.sum(cars_pred) traffic = car_pixels / road_pixels # print("Traffic", traffic) return traffic # if traffic < 0.2: # return "No traffic" # if traffic < 0.5: # return "Moderate traffic" # if traffic < 0.8: # return "Traffic" # return "Heavy traffic" if __name__ == "__main__": import pickle directory = 'dataset/car_preds' num_of_files = len([name for name in os.listdir(directory) if os.path.isfile(os.path.join(directory, name))]) for idx in range(1, num_of_files + 1): str_idx = str(idx) with open('dataset/car_preds/prediction_' + str_idx + '.pred', 'rb') as handle: cars_pred, _ = pickle.load(handle) with open('dataset/road_preds/prediction_' + str_idx + '.pred', 'rb') as handle: roads_pred, raw_img = pickle.load(handle) img_name = ('0' * (7 - len(str_idx) + 1)) + str_idx + '.png' show_masked_img(roads_pred, cars_pred, raw_img, img_name) print(get_traffic_level(roads_pred, cars_pred))
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Image/Histrogram.py
pection/InternshipProject
0
170138
import cv2 import numpy as np from matplotlib import pyplot as plt img = cv2.imread('C:\Code_python\Image\Picture\Tiger.jpg',0) # img2 = cv2.equalizeHist(img) hist,bins = np.histogram(img.flatten(),256,[0,256]) cdf = hist.cumsum() cdf_normalized = cdf * hist.max()/ cdf.max() cdf_m = np.ma.masked_equal(cdf,0) cdf_m = (cdf_m - cdf_m.min())*255/(cdf_m.max()-cdf_m.min()) cdf = np.ma.filled(cdf_m,0).astype('uint8') img2 = cdf[img] cv2.namedWindow('before') cv2.imshow('before',img) cv2.namedWindow('after') cv2.imshow('after',img2) cv2.waitKey(0) cv2.destroyAllWindows()
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mysite/models.py
zurawiki/thecrimsom
1
103790
<filename>mysite/models.py from django.core.urlresolvers import reverse from django.db import models from django.template.loader import render_to_string from django.utils.html import strip_tags from solo.models import SingletonModel class Section(models.Model): name = models.CharField(blank=False, max_length=50, db_index=True) def __str__(self): return self.name class Author(models.Model): first_name = models.CharField(blank=False, null=True, max_length=70) last_name = models.CharField(blank=False, null=True, max_length=100) middle_name = models.CharField(blank=True, null=True, max_length=70) created_on = models.DateField(auto_now_add=True) def get_absolute_url(self): return reverse('mysite.views.writer_detail', args=[self.pk, self.first_name, self.middle_name, self.last_name]) def __unicode__(self): if self.middle_name: return "%s %s. %s" % (self.first_name, self.middle_name[0], self.last_name) else: return "%s %s" % (self.first_name, self.last_name) class Article(models.Model): PUB_CHOICES = ( (0, 'Draft'), (1, 'Published'), (-1, 'Deleted'), ) fake = models.BooleanField(default=False) title = models.CharField(max_length=200, blank=False, null=False, default='') subtitle = models.CharField(max_length=255, blank=True, null=False, default='') authors = models.ManyToManyField(Author, null=True, related_name='content') section = models.ForeignKey(Section, null=False, related_name='content') image = models.ImageField(null=True, blank=True, upload_to='images') content = models.TextField(blank=True, null=False, default='') slug = models.SlugField(max_length=70, unique=True, db_index=True, help_text=""" The text that will be displayed in the URL of this article. Can only contain letters, numbers, and dashes (-). """ ) pub_status = models.IntegerField(null=False, choices=PUB_CHOICES, default=0, db_index=True) created_on = models.DateTimeField(auto_now_add=True, db_index=True) modified_on = models.DateTimeField(auto_now=True) def list_preview(self): return render_to_string('article/list_preview.html', {'article': self}) def __unicode__(self): if self.fake: return "(FAKE) %s" % self.title else: return self.title def __str__(self): return "".join([x if ord(x) < 128 else '?' for x in unicode(self)]) def get_absolute_url(self): if self.fake: return '#' year = self.created_on.year month = self.created_on.month day = self.created_on.day return reverse('mysite.views.article_detail', args=[year, month, day, self.slug]) def teaser(self): if self.subtitle: return self.subtitle else: return strip_tags(self.content) class BreakingNews(SingletonModel): description = models.TextField(blank=False, null=False, default='') link = models.CharField(max_length=255, blank=False, null=False, default='#') active = models.BooleanField() when = models.DateTimeField() class HomePage(SingletonModel): primary1 = models.ForeignKey(Article, null=True, related_name='+') primary2 = models.ForeignKey(Article, null=True, related_name='+') primary3 = models.ForeignKey(Article, null=True, related_name='+') primary4 = models.ForeignKey(Article, null=True, related_name='+') primary5 = models.ForeignKey(Article, null=True, related_name='+') primary6 = models.ForeignKey(Article, null=True, related_name='+') def __unicode__(self): return u"The Home Page" # something like this will make admin message strings more coherent class Meta: verbose_name = "Home Page" # once again this will make sure your admin UI doesn't have illogical text verbose_name_plural = "Home Page" class NewsSection(SingletonModel): featured = models.ForeignKey(Article, null=True, related_name='+') image1 = models.ForeignKey(Article, null=True, related_name='+') image2 = models.ForeignKey(Article, null=True, related_name='+') image3 = models.ForeignKey(Article, null=True, related_name='+') image4 = models.ForeignKey(Article, null=True, related_name='+') image5 = models.ForeignKey(Article, null=True, related_name='+') def __unicode__(self): return u"News Section" # something like this will make admin message strings more coherent class Meta: verbose_name = u"News Section" # once again this will make sure your admin UI doesn't have illogical text verbose_name_plural = u"News Section" class OpinionSection(SingletonModel): comic = models.ImageField(upload_to='images') ed1 = models.ForeignKey(Article, null=True, related_name='+') ed2 = models.ForeignKey(Article, null=True, related_name='+') ed3 = models.ForeignKey(Article, null=True, related_name='+') ed4 = models.ForeignKey(Article, null=True, related_name='+') oped1 = models.ForeignKey(Article, null=True, related_name='+') oped2 = models.ForeignKey(Article, null=True, related_name='+') oped3 = models.ForeignKey(Article, null=True, related_name='+') oped4 = models.ForeignKey(Article, null=True, related_name='+') def __unicode__(self): return u"Opinion Section" # something like this will make admin message strings more coherent class Meta: verbose_name = u"Opinion Section" # once again this will make sure your admin UI doesn't have illogical text verbose_name_plural = u"Opinion Section" class MostRead(SingletonModel): n1 = models.ForeignKey(Article, null=True, related_name='+') n2 = models.ForeignKey(Article, null=True, related_name='+') n3 = models.ForeignKey(Article, null=True, related_name='+') n4 = models.ForeignKey(Article, null=True, related_name='+') n5 = models.ForeignKey(Article, null=True, related_name='+') def __unicode__(self): return u"Most Read" # something like this will make admin message strings more coherent class Meta: verbose_name = "Most Read" # once again this will make sure your admin UI doesn't have illogical text verbose_name_plural = "Most Read"
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day06/python/mschlund/solution.py
jamhocken/aoc-2020
16
171627
<gh_stars>10-100 import re def get_answers_of_group(group): answers = {x for x in group if re.match('[a-z]', x)} return answers def get_unanimously_answered_questions(group): persons = group.split('\n') questions_answered = [{q for q in person} for person in persons] return set.intersection(*questions_answered) def main(): input_filename = 'input.txt' with open(input_filename, 'r') as input: all_custom_forms = input.read() groups_answers = re.split('\n\n', all_custom_forms) nr_answered = [len(get_answers_of_group(g)) for g in groups_answers] print('Sum of # of questions answered: {}'.format(sum(nr_answered))) unanimously_answered = [len(get_unanimously_answered_questions(g)) for g in groups_answers] print('Sum of questions answered unanimously: {}'.format(sum(unanimously_answered))) if __name__ == "__main__": main()
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ninjia/transformers/category_group.py
taohu88/ninjia
0
185819
from collections import defaultdict from sklearn.base import BaseEstimator, TransformerMixin class CategoryGrouper(BaseEstimator, TransformerMixin): """A tranformer for combining low count observations for categorical features. This transformer will preserve category values that are above a certain threshold, while bucketing together all the other values. This will fix issues where new data may have an unobserved category value that the training data did not have. """ def __init__(self, threshold=0.05): """Initialize method. Args: threshold (float): The threshold to apply the bucketing when categorical values drop below that threshold. """ self.d = defaultdict(list) self.threshold = threshold def transform(self, X, **transform_params): """Transforms X with new buckets. Args: X (obj): The dataset to pass to the transformer. Returns: The transformed X with grouped buckets. """ X_copy = X.copy() for col in X_copy.columns: X_copy[col] = X_copy[col].apply(lambda x: x if x in self.d[col] else 'CategoryGrouperOther') return X_copy def fit(self, X, y=None, **fit_params): """Fits transformer over X. Builds a dictionary of lists where the lists are category values of the column key for preserving, since they meet the threshold. """ df_rows = len(X.index) for col in X.columns: calc_col = X.groupby(col)[col].agg(lambda x: (len(x) * 1.0) / df_rows) self.d[col] = calc_col[calc_col >= self.threshold].index.tolist() return self if __name__ == "__main__": import pandas as pd cgr = CategoryGrouper(threshold=0.1) colors = 3*['red'] + 11 * ['green'] + 5 *['blue'] + ['yellow'] print(f"Colors = {colors}") df = pd.DataFrame.from_dict({'color': colors}) print(df.head()) X = cgr.fit_transform(df) print(X)
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dfm/net.py
127t6937/chainer-gan-lib
449
147917
<filename>dfm/net.py import chainer.functions as F import chainer.links as L import numpy as np import chainer class Discriminator(chainer.Chain): def __init__(self, bottom_width=2, ch=512, wscale=0.02): w = chainer.initializers.Normal(wscale) super(Discriminator, self).__init__() with self.init_scope(): self.c0 = L.Convolution2D(3, ch // 16, 3, 1, 1, initialW=w) self.c1 = L.Convolution2D(ch // 16, ch // 8, 4, 2, 1, initialW=w) self.c2 = L.Convolution2D(ch // 8, ch // 4, 4, 2, 1, initialW=w) self.c3 = L.Convolution2D(ch // 4, ch // 2, 4, 2, 1, initialW=w) self.c4 = L.Convolution2D(ch // 2, ch // 1, 4, 2, 1, initialW=w) self.l5 = L.Linear(bottom_width * bottom_width * ch, 1, initialW=w) self.bn1 = L.BatchNormalization(ch // 8) self.bn2 = L.BatchNormalization(ch // 4) self.bn3 = L.BatchNormalization(ch // 2) self.bn4 = L.BatchNormalization(ch // 1, use_gamma=False) def __call__(self, x): h = x h = F.leaky_relu(self.c0(h)) h = F.leaky_relu(self.bn1(self.c1(h))) h = F.leaky_relu(self.bn2(self.c2(h))) h = F.leaky_relu(self.bn3(self.c3(h))) feature = self.bn4(self.c4(h)) h = F.leaky_relu(feature) return feature, self.l5(h) class Denoiser(chainer.Chain): def __init__(self): super(Denoiser, self).__init__() with self.init_scope(): self.l0 = L.Linear(2048, 2048) self.l1 = L.Linear(2048, 2048) self.l2 = L.Linear(2048, 2048) self.l3 = L.Linear(2048, 2048) self.l4 = L.Linear(2048, 2048) self.l5 = L.Linear(2048, 2048) self.l6 = L.Linear(2048, 2048) self.l7 = L.Linear(2048, 2048) self.l8 = L.Linear(2048, 2048) self.l9 = L.Linear(2048, 2048) self.bn0 = L.BatchNormalization(2048) self.bn1 = L.BatchNormalization(2048) self.bn2 = L.BatchNormalization(2048) self.bn3 = L.BatchNormalization(2048) self.bn4 = L.BatchNormalization(2048) self.bn5 = L.BatchNormalization(2048) self.bn6 = L.BatchNormalization(2048) self.bn7 = L.BatchNormalization(2048) self.bn8 = L.BatchNormalization(2048) def __call__(self, x): h = F.reshape(x, (len(x), 2048)) h = F.leaky_relu(self.bn0(self.l0(h))) h = F.leaky_relu(self.bn1(self.l1(h))) h = F.leaky_relu(self.bn2(self.l2(h))) h = F.leaky_relu(self.bn3(self.l3(h))) h = F.leaky_relu(self.bn4(self.l4(h))) h = F.leaky_relu(self.bn5(self.l5(h))) h = F.leaky_relu(self.bn6(self.l6(h))) h = F.leaky_relu(self.bn7(self.l7(h))) h = F.leaky_relu(self.bn8(self.l8(h))) return F.reshape(self.l9(h), (len(x), 512, 2, 2))
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unet/generatePatches.py
MartimChaves/ret_detect
0
54945
<filename>unet/generatePatches.py # this needs to be in the previous directory import cv2.cv2 as cv2 import numpy as np import os from skimage.measure import label, regionprops os.chdir('C:/Users/Martim_Pc/Desktop/DACO/PROJECT_DACO/convNet/Unet') def myShowImage(img,name = "from_show_function"): cv2.imshow(name, img) cv2.waitKey(0) # waits until a key is pressed cv2.destroyAllWindows() # destroys the window showing image return def getBBox(img_mask): labels, numRegs = label(img_mask, neighbors=8, background = 0, return_num = True) regionsLog = regionprops(labels) bbxs = [regionsLog[x]['bbox'] for x in range(numRegs)] return bbxs def getLargestAreaEcentroid(img_pred_Log): # returns mask with the regions with the largest area, coords of centroid and radius labelsLog, numLog = label(img_pred_Log, neighbors=8, background = 0, return_num = True) regionsLog = regionprops(labelsLog) areasLog = [region['area'] for region in regionsLog] areasLogArr = np.array(areasLog) maxIndex = np.argmax(areasLogArr) value = labelsLog[regionsLog[maxIndex]['coords'][0][0],regionsLog[maxIndex]['coords'][0][1]] labelsLog[labelsLog != value] = 0 labelsLog[labelsLog == value] = 1 centreCoords = np.round(regionsLog[maxIndex]['centroid']) centreCoords = centreCoords.astype(np.uint) radius = (regionsLog[maxIndex]['major_axis_length'] + regionsLog[maxIndex]['minor_axis_length']) / 4 colsCoord = [regionsLog[maxIndex]['bbox'][1],regionsLog[maxIndex]['bbox'][3]] labelsArr = np.array(labelsLog) return labelsArr, centreCoords, radius, colsCoord testImage = [17,18,19]#20,21,22] testRun = True imageNumberName = 1 testImgNumber = 1 for i in range(1,41): if testRun: if i not in testImage: continue imgPath = 'Datasets/IDRID training/IDRiD_' + str(i).zfill(2) + '.jpg' imgPathOD_Masks = 'Datasets/IDRID training/IDRiD_' + str(i).zfill(2) + '_OD.tif' imgPathEX_Masks = 'Datasets/IDRID training/IDRiD_' + str(i).zfill(2) + '_EX.tif' img = cv2.imread(imgPath)#, cv2.CV_8UC1) Od_mask = cv2.imread(imgPathOD_Masks, cv2.CV_8UC1) Ex_mask = cv2.imread(imgPathEX_Masks, cv2.CV_8UC1) maskRed = img[...,0]>30 maskGreen = img[...,1]>30 maskBlue = img[...,2]>30 mask1 = np.logical_or(maskRed,maskGreen) maskFinal = np.logical_or(mask1,maskBlue) zeros = np.zeros(img.shape) zeros[maskFinal] = img[maskFinal] zeros = zeros.astype(np.uint8) img = np.copy(zeros) firstCol = np.min(np.where(img!=0)[1]) lastCol = np.max(np.where(img!=0)[1]) #print(np.max(img)) #myShowImage(cv2.resize(img, (416,416), interpolation = cv2.INTER_AREA)) finalWidth = int(lastCol-firstCol) offsetRows = int(round((finalWidth-img.shape[0])/2)) #Get a squared image #Retina image squareToBePatched = np.zeros([finalWidth,finalWidth,3]) squareToBePatched[offsetRows:img.shape[0]+offsetRows,::] = np.copy(img[::,firstCol:lastCol]) #OD mask ODsquareToBePatched = np.zeros([finalWidth,finalWidth]) ODsquareToBePatched[offsetRows:img.shape[0]+offsetRows,::] = np.copy(Od_mask[::,firstCol:lastCol]) _, centreCoords, radius, _ = getLargestAreaEcentroid(ODsquareToBePatched) print(radius) #Ex mask EXsquareToBePatched = np.zeros([finalWidth,finalWidth]) EXsquareToBePatched[offsetRows:img.shape[0]+offsetRows,::] = np.copy(Ex_mask[::,firstCol:lastCol]) # Defining patch size patchSize = int(finalWidth/7) createGeneralPatches = False if createGeneralPatches: if not testRun: if i not in testImage: for j in range(7): for k in range(7): # build patch print("Creating patch: ",str(imageNumberName)) # select region patchImg = np.copy(squareToBePatched[j*patchSize:(j+1)*patchSize,k*patchSize:(k+1)*patchSize]) patchOD = np.copy(ODsquareToBePatched[j*patchSize:(j+1)*patchSize,k*patchSize:(k+1)*patchSize]) patchEX = np.copy(EXsquareToBePatched[j*patchSize:(j+1)*patchSize,k*patchSize:(k+1)*patchSize]) # resize to 416x416 patchImg = cv2.resize(patchImg, (416,416), interpolation = cv2.INTER_AREA) patchOD = cv2.resize(patchOD, (416,416), interpolation = cv2.INTER_AREA) patchEX = cv2.resize(patchEX, (416,416), interpolation = cv2.INTER_AREA) # threshold patchOD = np.multiply(patchOD,255/np.max(patchOD)) patchOD[patchOD>220] = 255 patchOD[patchOD<=220] = 0 patchOD = patchOD.astype(np.uint8) patchEX = np.multiply(patchEX,255/np.max(patchEX)) patchEX[patchEX>220] = 255 patchEX[patchEX<=220] = 0 patchEX = patchEX.astype(np.uint8) # if black -> remove indxsBlack = np.where(patchImg < 5) if len(indxsBlack[0]) > 259584: continue #get bboxs bbxsOd = getBBox(patchOD) bbxsEx = getBBox(patchEX) #save img and info to annot file # if a lot of stuff - data augment seenClasses = False if len(bbxsOd) > 0: seenClasses = True # do more of these # 4 crops for each flip, 3 flips - 16 cropAssistValues = {0:[-30,-30],1:[-30,+30],2:[+30,-30],3:[+30,+30], 4:[0,-30],5:[-30,0],6:[0,+30],7:[+30,0]} #cropAssistValues[c][0 ou 1] for c in range(8): # crops try: patchImg = np.copy(squareToBePatched[(j*patchSize)+cropAssistValues[c][0]:((j+1)*patchSize)+cropAssistValues[c][0], (k*patchSize+cropAssistValues[c][1]):((k+1)*patchSize)+cropAssistValues[c][0]]) patchOD = np.copy(ODsquareToBePatched[(j*patchSize)+cropAssistValues[c][0]:((j+1)*patchSize)+cropAssistValues[c][0], (k*patchSize+cropAssistValues[c][1]):((k+1)*patchSize)+cropAssistValues[c][0]]) for f in range(4): # flips patchImg = np.rot90(patchImg,axes=(0,1)) patchOD = np.rot90(patchOD,axes=(0,1)) patchImg = cv2.resize(patchImg, (416,416), interpolation = cv2.INTER_AREA) patchOD = cv2.resize(patchOD, (416,416), interpolation = cv2.INTER_AREA) # threshold patchOD = np.multiply(patchOD,255/np.max(patchOD)) patchOD[patchOD>220] = 255 patchOD[patchOD<=220] = 0 patchOD = patchOD.astype(np.uint8) bbxsOd = getBBox(patchOD) if len(bbxsOd) > 0: image_path = os.path.realpath(os.path.join("YOLOV3/data/dataset/train", "%06d.jpg" %(imageNumberName))) cv2.imwrite(image_path,patchImg) annotation = image_path for k in range(len(bbxsOd)): xmin = str(bbxsOd[k][1]) ymin = str(bbxsOd[k][0]) xmax = str(bbxsOd[k][3]) ymax = str(bbxsOd[k][2]) class_ind = str(0) annotation += ' ' + ','.join([xmin, ymin, xmax, ymax, str(class_ind)]) wf.write(annotation + "\n") imageNumberName += 1 except: print("Error croping.") if len(bbxsEx) > 0: seenClasses = True # do more of these # do more of these # 4 crops for each flip, 3 flips - 16 cropAssistValues = {0:[-30,-30],1:[-30,+30],2:[+30,-30],3:[+30,+30]} #cropAssistValues[c][0 ou 1] for c in range(4): # crops try: patchImg = np.copy(squareToBePatched[(j*patchSize)+cropAssistValues[c][0]:((j+1)*patchSize)+cropAssistValues[c][0], (k*patchSize+cropAssistValues[c][1]):((k+1)*patchSize)+cropAssistValues[c][0]]) patchOD = np.copy(EXsquareToBePatched[(j*patchSize)+cropAssistValues[c][0]:((j+1)*patchSize)+cropAssistValues[c][0], (k*patchSize+cropAssistValues[c][1]):((k+1)*patchSize)+cropAssistValues[c][0]]) for f in range(4): # flips patchImg = np.rot90(patchImg,axes=(0,1)) patchOD = np.rot90(patchOD,axes=(0,1)) patchImg = cv2.resize(patchImg, (416,416), interpolation = cv2.INTER_AREA) patchOD = cv2.resize(patchOD, (416,416), interpolation = cv2.INTER_AREA) # threshold patchOD = np.multiply(patchOD,255/np.max(patchOD)) patchOD[patchOD>220] = 255 patchOD[patchOD<=220] = 0 patchOD = patchOD.astype(np.uint8) bbxsOd = getBBox(patchOD) if len(bbxsOd) > 0: image_path = os.path.realpath(os.path.join("YOLOV3/data/dataset/train", "%06d.jpg" %(imageNumberName))) cv2.imwrite(image_path,patchImg) annotation = image_path for k in range(len(bbxsOd)): xmin = str(bbxsOd[k][1]) ymin = str(bbxsOd[k][0]) xmax = str(bbxsOd[k][3]) ymax = str(bbxsOd[k][2]) class_ind = str(1) annotation += ' ' + ','.join([xmin, ymin, xmax, ymax, str(class_ind)]) wf.write(annotation + "\n") imageNumberName += 1 except: print("Error croping.") if seenClasses: continue image_path = os.path.realpath(os.path.join("YOLOV3/data/dataset/train", "%06d.jpg" %(imageNumberName))) cv2.imwrite(image_path,patchImg) annotation = image_path for k in range(len(bbxsOd)): xmin = str(bbxsOd[k][1]) ymin = str(bbxsOd[k][0]) xmax = str(bbxsOd[k][3]) ymax = str(bbxsOd[k][2]) class_ind = str(0) annotation += ' ' + ','.join([xmin, ymin, xmax, ymax, str(class_ind)]) for k in range(len(bbxsEx)): xmin = str(bbxsEx[k][1]) ymin = str(bbxsEx[k][0]) xmax = str(bbxsEx[k][3]) ymax = str(bbxsEx[k][2]) class_ind = str(1) annotation += ' ' + ','.join([xmin, ymin, xmax, ymax, str(class_ind)]) wf.write(annotation + "\n") imageNumberName += 1 else: if i in testImage: for j in range(7): for k in range(7): #build patch print("Creating patch.") #select region patchImg = np.copy(squareToBePatched[j*patchSize:(j+1)*patchSize,k*patchSize:(k+1)*patchSize]) patchOD = np.copy(ODsquareToBePatched[j*patchSize:(j+1)*patchSize,k*patchSize:(k+1)*patchSize]) patchEX = np.copy(EXsquareToBePatched[j*patchSize:(j+1)*patchSize,k*patchSize:(k+1)*patchSize]) # resize to 416x416 patchImg = cv2.resize(patchImg, (416,416), interpolation = cv2.INTER_AREA) patchOD = cv2.resize(patchOD, (416,416), interpolation = cv2.INTER_AREA) patchEX = cv2.resize(patchEX, (416,416), interpolation = cv2.INTER_AREA) # threshold patchOD = np.multiply(patchOD,255/np.max(patchOD)) patchOD[patchOD>220] = 255 patchOD[patchOD<=220] = 0 patchOD = patchOD.astype(np.uint8) patchEX = np.multiply(patchEX,255/np.max(patchEX)) patchEX[patchEX>220] = 255 patchEX[patchEX<=220] = 0 patchEX = patchEX.astype(np.uint8) # if black -> remove indxsBlack = np.where(patchImg < 5) if len(indxsBlack[0]) > 259584: continue #get bboxs bbxsOd = getBBox(patchOD) bbxsEx = getBBox(patchEX) #save img and info to annot file image_path = os.path.realpath(os.path.join("YOLOV3/data/dataset/test", "%06d.jpg" %(imageNumberName))) cv2.imwrite(image_path,patchImg) annotation = image_path for k in range(len(bbxsOd)): xmin = str(bbxsOd[k][1]) ymin = str(bbxsOd[k][0]) xmax = str(bbxsOd[k][3]) ymax = str(bbxsOd[k][2]) class_ind = str(0) annotation += ' ' + ','.join([xmin, ymin, xmax, ymax, str(class_ind)]) for k in range(len(bbxsEx)): xmin = str(bbxsEx[k][1]) ymin = str(bbxsEx[k][0]) xmax = str(bbxsEx[k][3]) ymax = str(bbxsEx[k][2]) class_ind = str(1) annotation += ' ' + ','.join([xmin, ymin, xmax, ymax, str(class_ind)]) wf.write(annotation + "\n") imageNumberName += 1 createODPatches = True if createODPatches: if not testRun: if i not in testImage: cropAssistValues = {0:[0,-296],1:[0,-592],2:[0,-888],3:[0,-1184],4:[0,-1480], 5:[-296,-296],6:[-296,-592],7:[-296,-888],8:[-296,-1184],9:[-296,-1480], 10:[-592,-296],11:[-592,-592],12:[-592,-888],13:[-592,-1184],14:[-592,-1480], 15:[-888,-296],16:[-888,-592],17:[-888,-888],18:[-888,-1184],19:[-888,-1480], 20:[-1184,-296],21:[-1184,-592],22:[-1184,-888],23:[-1184,-1184],24:[-1184,-1480], 25:[-1480,-296],26:[-1480,-592],27:[-1480,-888],28:[-1480,-1184],29:[-1480,-1480], 30:[-148,-296],31:[-148,-592],32:[-148,-888],33:[-148,-1184],34:[-148,-1480], 35:[-700,-296],36:[-700,-592],37:[-700,-888],38:[-700,-1184],39:[-700,-1480]} #cropAssistValues[c][0 ou 1] for w in range(40): try: patchImg = np.copy(squareToBePatched[centreCoords[0]+cropAssistValues[w][0]:centreCoords[0]+1480+cropAssistValues[w][0], (centreCoords[1]+cropAssistValues[w][1]):(centreCoords[1]+1480+cropAssistValues[w][1])]) patchOD = np.copy(ODsquareToBePatched[centreCoords[0]+cropAssistValues[w][0]:centreCoords[0]+1480+cropAssistValues[w][0], (centreCoords[1]+cropAssistValues[w][1]):(centreCoords[1]+1480+cropAssistValues[w][1])]) if patchImg.shape[0] != 1480 or patchImg.shape[1] != 1480: continue #myShowImage(cv2.resize(patchOD, (416,416), interpolation = cv2.INTER_AREA).astype(np.uint8)) for f in range(4): # flips patchImg = np.rot90(patchImg,axes=(0,1)) patchOD = np.rot90(patchOD,axes=(0,1)) patchImg = cv2.resize(patchImg, (256,256), interpolation = cv2.INTER_AREA) patchOD = cv2.resize(patchOD, (256,256), interpolation = cv2.INTER_AREA) # threshold patchOD = np.multiply(patchOD,255/np.max(patchOD)) patchOD[patchOD>220] = 255 patchOD[patchOD<=220] = 0 patchOD = patchOD.astype(np.uint8) bbxsOd = getBBox(patchOD) '''image_path = os.path.realpath(os.path.join("YOLOV3/data/dataset/train", "%06d.jpg" %(imageNumberName))) cv2.imwrite(image_path,patchImg.astype(np.uint8))''' if len(bbxsOd) > 0: #image_path = os.path.realpath(os.path.join("YOLOV3/data/dataset/train", "%06d.jpg" %(imageNumberName))) image_path = os.path.realpath(os.path.join("train/images", str(imageNumberName)+".jpg")) cv2.imwrite(image_path,patchImg.astype(np.uint8)) label_path = os.path.realpath(os.path.join("train/labels", str(imageNumberName)+".jpg")) cv2.imwrite(label_path,patchOD) annotation = image_path '''for k in range(len(bbxsOd)): xmin = str(bbxsOd[k][1]) ymin = str(bbxsOd[k][0]) xmax = str(bbxsOd[k][3]) ymax = str(bbxsOd[k][2]) class_ind = str(0) annotation += ' ' + ','.join([xmin, ymin, xmax, ymax, str(class_ind)]) wf.write(annotation + "\n")''' print("Generating patch: ",str(imageNumberName)) imageNumberName += 1 except: print("Failed to crop.") else: if i in testImage: cropAssistValues = {0:[0,-296],1:[0,-592],2:[0,-888],3:[0,-1184],4:[0,-1480], 5:[-296,-296],6:[-296,-592],7:[-296,-888],8:[-296,-1184],9:[-296,-1480], 10:[-592,-296],11:[-592,-592],12:[-592,-888],13:[-592,-1184],14:[-592,-1480], 15:[-888,-296],16:[-888,-592],17:[-888,-888],18:[-888,-1184],19:[-888,-1480], 20:[-1184,-296],21:[-1184,-592],22:[-1184,-888],23:[-1184,-1184],24:[-1184,-1480], 25:[-1480,-296],26:[-1480,-592],27:[-1480,-888],28:[-1480,-1184],29:[-1480,-1480], 30:[-148,-296],31:[-148,-592],32:[-148,-888],33:[-148,-1184],34:[-148,-1480], 35:[-700,-296],36:[-700,-592],37:[-700,-888],38:[-700,-1184],39:[-700,-1480]} #cropAssistValues[c][0 ou 1] for w in range(40): try: patchImg = np.copy(squareToBePatched[centreCoords[0]+cropAssistValues[w][0]:centreCoords[0]+1480+cropAssistValues[w][0], (centreCoords[1]+cropAssistValues[w][1]):(centreCoords[1]+1480+cropAssistValues[w][1])]) patchOD = np.copy(ODsquareToBePatched[centreCoords[0]+cropAssistValues[w][0]:centreCoords[0]+1480+cropAssistValues[w][0], (centreCoords[1]+cropAssistValues[w][1]):(centreCoords[1]+1480+cropAssistValues[w][1])]) if patchImg.shape[0] != 1480 or patchImg.shape[1] != 1480: continue #myShowImage(cv2.resize(patchOD, (416,416), interpolation = cv2.INTER_AREA).astype(np.uint8)) for f in range(4): # flips patchImg = np.rot90(patchImg,axes=(0,1)) patchOD = np.rot90(patchOD,axes=(0,1)) patchImg = cv2.resize(patchImg, (256,256), interpolation = cv2.INTER_AREA) patchOD = cv2.resize(patchOD, (256,256), interpolation = cv2.INTER_AREA) # threshold patchOD = np.multiply(patchOD,255/np.max(patchOD)) patchOD[patchOD>220] = 255 patchOD[patchOD<=220] = 0 patchOD = patchOD.astype(np.uint8) bbxsOd = getBBox(patchOD) if len(bbxsOd) > 0: #image_path = os.path.realpath(os.path.join("YOLOV3/data/dataset/test", "%06d.jpg" %(imageNumberName))) image_path = os.path.realpath(os.path.join("test/images", str(imageNumberName)+".jpg")) cv2.imwrite(image_path,patchImg.astype(np.uint8)) label_path = os.path.realpath(os.path.join("test/labels", str(imageNumberName)+".jpg")) cv2.imwrite(label_path,patchOD) annotation = image_path '''for k in range(len(bbxsOd)): xmin = str(bbxsOd[k][1]) ymin = str(bbxsOd[k][0]) xmax = str(bbxsOd[k][3]) ymax = str(bbxsOd[k][2]) class_ind = str(0) annotation += ' ' + ','.join([xmin, ymin, xmax, ymax, str(class_ind)]) wf.write(annotation + "\n")''' print("Generating patch: ",str(imageNumberName)) imageNumberName += 1 except: print("Failed to crop.") # Divide image in 4x3 regions # check if in each region there are exudates # if there are, get largest one and do data aug based on that one - crops and rotations createExPatches = False kernel_ones = np.ones([25,25]) if createExPatches: for g in range(4): for t in range(3): #get patch: patchImg = np.copy(squareToBePatched[int(round(g*finalWidth/4)):int(round((g+1)*finalWidth/4)),int(round(t*finalWidth/3)):int(round((t+1)*finalWidth/3))]) patchEX = np.copy(EXsquareToBePatched[int(round(g*finalWidth/4)):int(round((g+1)*finalWidth/4)),int(round(t*finalWidth/3)):int(round((t+1)*finalWidth/3))]) patchEX = cv2.morphologyEx(patchEX, cv2.MORPH_CLOSE, kernel_ones) bbxsEx = getBBox(patchEX) if len(bbxsEx) > 0: _, centreCoords, radius, _ = getLargestAreaEcentroid(patchEX) else: continue cropAssistValues = {0:[0,-296],1:[0,-592],2:[0,-888],3:[0,-1184],4:[0,-1480], 5:[-296,-296],6:[-296,-592],7:[-296,-888],8:[-296,-1184],9:[-296,-1480], 10:[-592,-296],11:[-592,-592],12:[-592,-888],13:[-592,-1184],14:[-592,-1480], 15:[-888,-296],16:[-888,-592],17:[-888,-888],18:[-888,-1184],19:[-888,-1480], 20:[-1184,-296],21:[-1184,-592],22:[-1184,-888],23:[-1184,-1184],24:[-1184,-1480], 25:[-1480,-296],26:[-1480,-592],27:[-1480,-888],28:[-1480,-1184],29:[-1480,-1480], 30:[-148,-296],31:[-148,-592],32:[-148,-888],33:[-148,-1184],34:[-148,-1480], 35:[-700,-296],36:[-700,-592],37:[-700,-888],38:[-700,-1184],39:[-700,-1480]} #cropAssistValues[c][0 ou 1] for w in range(40): try: patchImg = np.copy(squareToBePatched[centreCoords[0]+cropAssistValues[w][0]:centreCoords[0]+1480+cropAssistValues[w][0], (centreCoords[1]+cropAssistValues[w][1]):(centreCoords[1]+1480+cropAssistValues[w][1])]) patchEX = np.copy(EXsquareToBePatched[centreCoords[0]+cropAssistValues[w][0]:centreCoords[0]+1480+cropAssistValues[w][0], (centreCoords[1]+cropAssistValues[w][1]):(centreCoords[1]+1480+cropAssistValues[w][1])]) if patchImg.shape[0] != 1480 or patchImg.shape[1] != 1480: continue #myShowImage(cv2.resize(patchOD, (416,416), interpolation = cv2.INTER_AREA).astype(np.uint8)) #myShowImage(cv2.resize(patchImg, (416,416), interpolation = cv2.INTER_AREA).astype(np.uint8)) patchEX = cv2.morphologyEx(patchEX, cv2.MORPH_CLOSE, kernel_ones) for f in range(4): # flips patchImg = np.rot90(patchImg,axes=(0,1)) patchOD = np.rot90(patchEX,axes=(0,1)) patchImg = cv2.resize(patchImg, (416,416), interpolation = cv2.INTER_AREA) patchOD = cv2.resize(patchOD, (416,416), interpolation = cv2.INTER_AREA) # threshold patchOD = np.multiply(patchOD,255/np.max(patchOD)) patchOD[patchOD>220] = 255 patchOD[patchOD<=220] = 0 patchOD = patchOD.astype(np.uint8) bbxsOd = getBBox(patchOD) if len(bbxsOd) > 0: image_path = os.path.realpath(os.path.join("YOLOV3/data/dataset/test", "%06d.jpg" %(imageNumberName))) cv2.imwrite(image_path,patchImg.astype(np.uint8)) annotation = image_path for k in range(len(bbxsOd)): xmin = str(bbxsOd[k][1]) ymin = str(bbxsOd[k][0]) xmax = str(bbxsOd[k][3]) ymax = str(bbxsOd[k][2]) class_ind = str(0) annotation += ' ' + ','.join([xmin, ymin, xmax, ymax, str(class_ind)]) wf.write(annotation + "\n") print("Generating patch: ",str(imageNumberName)) imageNumberName += 1 except: print("Failed to crop.")
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nxontology_data/commands.py
related-sciences/nxontology-data
0
69641
import logging import fire from nxontology import NXOntology from nxontology_data.mesh.mesh import MeshLoader from nxontology_data.pubchem.classifications import export_all_heirarchies from nxontology_data.utils import get_output_dir, write_ontology def write_test_output() -> None: """Export an empty test ontology, useful for testing CI deployment.""" output_dir = get_output_dir().joinpath("test") output_dir.mkdir(parents=True, exist_ok=True) nxo: NXOntology[str] = NXOntology() nxo.graph.graph["name"] = "test" write_ontology(nxo, output_dir) def cli() -> None: """ Run like `poetry run nxontology_data` """ logging.basicConfig() logging.getLogger().setLevel(logging.INFO) commands = { "mesh": MeshLoader.export_mesh_outputs, "pubchem": export_all_heirarchies, "test": write_test_output, } fire.Fire(commands)
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log/use_handl_format.py
nkzmsb/howto
0
174507
<reponame>nkzmsb/howto<gh_stars>0 import logging logger=logging.getLogger(__name__) logger.setLevel(logging.INFO) # create formatter formatter = logging.Formatter('%(asctime)s\t%(name)-12s\t%(funcName)s\t%(levelname)-8s\t%(message)s' # , datefmt='%y%m%d_%H:%M:%S' # asftimeの形式を指定することも可能 ) # create console handler handler = logging.StreamHandler() # add formatter to ch handler.setFormatter(formatter) # add handler to logger logger.addHandler(handler) logger.info("info!!")
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george/basic.py
kastnerkyle/george
1
46525
# -*- coding: utf-8 -*- from __future__ import division, print_function __all__ = ["GP"] try: from itertools import izip except ImportError: izip = zip import numpy as np import scipy.optimize as op from scipy.linalg import cho_factor, cho_solve, LinAlgError from .utils import multivariate_gaussian_samples, nd_sort_samples # MAGIC: tiny epsilon to add on the diagonal of the matrices in the absence # of observational uncertainties. Needed for computational stability. TINY = 1.25e-12 class GP(object): """ The basic Gaussian Process object. :param kernel: An instance of a subclass of :class:`kernels.Kernel`. :param mean: (optional) A description of the mean function; can be a callable or a scalar. If scalar, the mean is assumed constant. Otherwise, the function will be called with the array of independent coordinates as the only argument. (default: ``0.0``) """ def __init__(self, kernel, mean=None): self.kernel = kernel self._computed = False if mean is None: self.mean = _default_mean(0.) else: try: val = float(mean) except TypeError: self.mean = mean else: self.mean = _default_mean(val) @property def computed(self): """ Has the processes been computed since the last update of the kernel? """ return self._computed and not self.kernel.dirty @computed.setter def computed(self, v): self._computed = v if v: self.kernel.dirty = False def parse_samples(self, t, sort=False): """ Parse a list of samples to make sure that it has the correct dimensions and optionally sort it. In one dimension, the samples will be sorted in the logical order. In higher dimensions, a kd-tree is built and the samples are sorted in increasing distance from the *first* sample. :param t: ``(nsamples,)`` or ``(nsamples, ndim)`` The list of samples. If 1-D, this is assumed to be a list of one-dimensional samples otherwise, the size of the second dimension is assumed to be the dimension of the input space. :param sort: A boolean flag indicating whether or not the samples should be sorted. Returns a tuple ``(samples, inds)`` where * **samples** is an array with shape ``(nsamples, ndim)`` and if ``sort`` was ``True``, it will also be sorted, and * **inds** is an ``(nsamples,)`` list of integer permutations used to sort the list of samples. Raises a ``RuntimeError`` if the input dimension doesn't match the dimension of the kernel. """ t = np.atleast_1d(t) if len(t.shape) == 1: # Deal with one-dimensional data. if sort: inds = np.argsort(t) else: inds = np.arange(len(t), dtype=int) t = np.atleast_2d(t).T elif sort: # Sort the data using a KD-tree. inds = nd_sort_samples(t) else: # Otherwise, assume that the samples are sorted. inds = np.arange(t.shape[0], dtype=int) # Double check the dimensions against the kernel. if len(t.shape) != 2 or t.shape[1] != self.kernel.ndim: raise ValueError("Dimension mismatch") return t[inds], inds def _check_dimensions(self, y): n, ndim = self._x.shape y = np.atleast_1d(y) if len(y.shape) > 1: raise ValueError("The predicted dimension must be 1-D") if len(y) != n: raise ValueError("Dimension mismatch") return y def compute(self, x, yerr=TINY, sort=True, **kwargs): """ Pre-compute the covariance matrix and factorize it for a set of times and uncertainties. :param x: ``(nsamples,)`` or ``(nsamples, ndim)`` The independent coordinates of the data points. :param yerr: (optional) ``(nsamples,)`` or scalar The Gaussian uncertainties on the data points at coordinates ``x``. These values will be added in quadrature to the diagonal of the covariance matrix. :param sort: (optional) Should the samples be sorted before computing the covariance matrix? This can lead to more numerically stable results and with some linear algebra libraries this can more computationally efficient. Either way, this flag is passed directly to :func:`parse_samples`. (default: ``True``) """ # Parse the input coordinates. self._x, self.inds = self.parse_samples(x, sort) try: self._yerr = float(yerr) * np.ones(len(x)) except TypeError: self._yerr = self._check_dimensions(yerr)[self.inds] self._do_compute(**kwargs) def _do_compute(self, _scale=0.5*np.log(2*np.pi)): # Compute the kernel matrix. K = self.kernel(self._x[:, None], self._x[None, :]) K[np.diag_indices_from(K)] += self._yerr ** 2 # Factor the matrix and compute the log-determinant. factor, _ = self._factor = cho_factor(K, overwrite_a=True) self._const = -(np.sum(np.log(np.diag(factor))) + _scale*len(self._x)) # Save the computed state. self.computed = True def recompute(self, sort=False, **kwargs): """ Re-compute a previously computed model. You might want to do this if the kernel parameters change and the kernel is labeled as ``dirty``. :params sort: (optional) Should the samples be sorted before computing the covariance matrix? (default: ``False``) """ if not (hasattr(self, "_x") and hasattr(self, "_yerr")): raise RuntimeError("You need to compute the model first") return self.compute(self._x, self._yerr, sort=sort, **kwargs) def _compute_lnlike(self, r): return self._const - 0.5*np.dot(r.T, cho_solve(self._factor, r)) def lnlikelihood(self, y, quiet=False): """ Compute the ln-likelihood of a set of observations under the Gaussian process model. You must call ``compute`` before this function. :param y: ``(nsamples, )`` The observations at the coordinates provided in the ``compute`` step. :param quiet: If ``True`` return negative infinity instead of raising an exception when there is an invalid kernel or linear algebra failure. (default: ``False``) """ if not self.computed: try: self.recompute() except (ValueError, LinAlgError): if quiet: return -np.inf raise r = self._check_dimensions(y)[self.inds] - self.mean(self._x) ll = self._compute_lnlike(r) return ll if np.isfinite(ll) else -np.inf def grad_lnlikelihood(self, y, dims=None, quiet=False): """ Compute the gradient of the ln-likelihood function as a function of the kernel parameters. :param y: ``(nsamples,)`` The list of observations at coordinates ``x`` provided to the :func:`compute` function. :param dims: (optional) If you only want to compute the gradient in some dimensions, list them here. :param quiet: If ``True`` return a gradient of zero instead of raising an exception when there is an invalid kernel or linear algebra failure. (default: ``False``) """ # By default, compute the gradient in all dimensions. if dims is None: dims = np.ones(len(self.kernel), dtype=bool) # Make sure that the model is computed and try to recompute it if it's # dirty. if not self.computed: try: self.recompute() except (ValueError, LinAlgError): if quiet: return np.zeros_like(dims, dtype=float) raise # Parse the input sample list. r = self._check_dimensions(y)[self.inds] - self.mean(self._x) # Pre-compute some factors. alpha = cho_solve(self._factor, r) Kg = self.kernel.grad(self._x[:, None], self._x[None, :])[dims] # Loop over dimensions and compute the gradient in each one. g = np.empty(len(Kg)) for i, k in enumerate(Kg): d = sum(map(lambda r: np.dot(alpha, r), alpha[:, None] * k)) d -= np.sum(np.diag(cho_solve(self._factor, k))) g[i] = 0.5 * d return g def predict(self, y, t): """ Compute the conditional predictive distribution of the model. :param y: ``(nsamples,)`` The observations to condition the model on. :param t: ``(ntest,)`` or ``(ntest, ndim)`` The coordinates where the predictive distribution should be computed. Returns a tuple ``(mu, cov)`` where * **mu** ``(ntest,)`` is the mean of the predictive distribution, and * **cov** ``(ntest, ntest)`` is the predictive covariance. """ if not self.computed: self.recompute() r = self._check_dimensions(y)[self.inds] - self.mean(self._x) xs, i = self.parse_samples(t, False) alpha = cho_solve(self._factor, r) # Compute the predictive mean. Kxs = self.kernel(self._x[None, :], xs[:, None]) mu = np.dot(Kxs, alpha) + self.mean(xs) # Compute the predictive covariance. cov = self.kernel(xs[:, None], xs[None, :]) cov -= np.dot(Kxs, cho_solve(self._factor, Kxs.T)) return mu, cov def sample_conditional(self, y, t, size=1): """ Draw samples from the predictive conditional distribution. :param y: ``(nsamples, )`` The observations to condition the model on. :param t: ``(ntest, )`` or ``(ntest, ndim)`` The coordinates where the predictive distribution should be computed. :param size: (optional) The number of samples to draw. (default: ``1``) Returns **samples** ``(N, ntest)``, a list of predictions at coordinates given by ``t``. """ mu, cov = self.predict(y, t) return multivariate_gaussian_samples(cov, size, mean=mu) def sample(self, t, size=1): """ Draw samples from the prior distribution. :param t: ``(ntest, )`` or ``(ntest, ndim)`` The coordinates where the model should be sampled. :param size: (optional) The number of samples to draw. (default: ``1``) Returns **samples** ``(N, ntest)``, a list of predictions at coordinates given by ``t``. """ x, _ = self.parse_samples(t, False) cov = self.get_matrix(x) return multivariate_gaussian_samples(cov, size, mean=self.mean(x)) def get_matrix(self, t): """ Get the covariance matrix at a given set of independent coordinates. :param t: ``(nsamples,)`` or ``(nsamples, ndim)`` The list of samples. """ r, _ = self.parse_samples(t, False) return self.kernel(r[:, None], r[None, :]) def optimize(self, x, y, yerr=TINY, sort=True, dims=None, in_log=True, verbose=True, **kwargs): """ A simple and not terribly robust non-linear optimization algorithm for the kernel hyperpararmeters. :param x: ``(nsamples,)`` or ``(nsamples, ndim)`` The independent coordinates of the data points. :param y: ``(nsamples, )`` The observations at the coordinates ``x``. :param yerr: (optional) ``(nsamples,)`` or scalar The Gaussian uncertainties on the data points at coordinates ``x``. These values will be added in quadrature to the diagonal of the covariance matrix. :param sort: (optional) Should the samples be sorted before computing the covariance matrix? :param dims: (optional) If you only want to optimize over some parameters, list their indices here. :param in_log: (optional) ``(len(kernel),)``, ``(len(dims),)`` or bool If you want to fit the parameters in the log (this can be useful for parameters that shouldn't go negative) specify that here. This can be a single boolean---in which case it is assumed to apply to every dimension---or it can be an array of booleans, one for each dimension. :param verbose: (optional) Display the results of the call to :func:`scipy.optimize.minimize`? (default: ``True``) Returns ``(pars, results)`` where ``pars`` is the list of optimized parameters and ``results`` is the results object returned by :func:`scipy.optimize.minimize`. """ self.compute(x, yerr, sort=sort) # By default, optimize all the hyperparameters. if dims is None: dims = np.ones(len(self.kernel), dtype=bool) dims = np.arange(len(self.kernel))[dims] # Deal with conversion functions. try: len(in_log) except TypeError: in_log = in_log * np.ones_like(dims, dtype=bool) else: if len(in_log) != len(dims): raise RuntimeError("Dimension list and log mask mismatch") # Build the conversion functions. conv = np.array([lambda x: x for i in range(len(dims))]) iconv = np.array([lambda x: x for i in range(len(dims))]) conv[in_log] = np.exp iconv[in_log] = np.log # Define the objective function and gradient. def nll(pars): for i, f, p in izip(dims, conv, pars): self.kernel[i] = f(p) ll = self.lnlikelihood(y, quiet=True) if not np.isfinite(ll): return 1e25 # The optimizers can't deal with infinities. return -ll def grad_nll(pars): for i, f, p in izip(dims, conv, pars): self.kernel[i] = f(p) return -self.grad_lnlikelihood(y, dims=dims, quiet=True) # Run the optimization. p0 = [f(p) for f, p in izip(iconv, self.kernel.pars[dims])] results = op.minimize(nll, p0, jac=grad_nll, **kwargs) if verbose: print(results.message) # Update the kernel. for i, f, p in izip(dims, conv, results.x): self.kernel[i] = f(p) return self.kernel.pars[dims], results class _default_mean(object): def __init__(self, value): self.value = value def __call__(self, t): return self.value + np.zeros(len(t), dtype=float)
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scaffoldgraph/vis/base.py
UCLCheminformatics/ScaffoldGraph
121
92361
<filename>scaffoldgraph/vis/base.py """ scaffoldgraph.vis.base """ import networkx as nx from abc import ABC from scaffoldgraph.core import ScaffoldGraph from scaffoldgraph.utils import canonize_smiles from .utils import remove_node_mol_images class Visualizer(ABC): """Base class for ScaffoldGraph visualizers. A Visualizer contains functions for creating visualizations of ScaffoldGraphs. See Also -------- scaffoldgraph.vis.notebook.cytoscape.CytoscapeVisualizer """ def __init__(self, graph, requires_tree=False, refresh_images=False): """Initialize the visualizer. Parameters ---------- graph : ScaffoldGraph ScaffoldGraph to visualize requires_tree : bool, optional Whether the visualizer requires a tree structure to create a visualization. refresh_images: bool, optional If True remove all embeded images from the input graph and regenerate when required. The default is False. """ self._requires_tree = requires_tree self._refresh = refresh_images self._graph = self._validate_graph(graph) @property def graph(self): """ScaffoldGraph: return the graph associated with the visualizer.""" return self._graph @graph.setter def graph(self, graph): self._graph = self._validate_graph(graph) def _validate_graph(self, graph): """Private: Validate a graph is suitable for visualizer.""" if not issubclass(type(graph), ScaffoldGraph): raise ValueError( f'{graph} must be a subclass of ScaffoldGraph' ) if self._requires_tree: if not nx.is_tree(graph) or nx.is_forest(graph): msg = '{} requires a tree/forest structured graph' msg.format(self.__class__.__name__) raise ValueError(msg) if self._refresh is True: remove_node_mol_images(graph) return graph def _subgraph_from_mol(self, molecule): """Private: Select a subgraph starting at a molecule node. Parameters ---------- molecule : str Molecule node identifier. Returns ------- subgraph : ScaffoldGraph A subgraph starting at `molecule`. """ G = self._graph if not G.molecule_in_graph(molecule): raise ValueError(f'molecule: {molecule} not in graph {G}') scaffolds = G.get_scaffolds_for_molecule(molecule) subgraph = G.subgraph([molecule] + scaffolds) return subgraph def _subgraph_from_scf(self, scaffold, traversal): """Private: Select a subgraph starting at a scaffold node. Parameters ---------- scaffold : str Scaffold node identifier. traversal : str {'parent', 'child', 'bidirectional'} The direction of traversal to create the subgraph. If 'bidirectional' both directions are considered. Returns ------- subgraph : ScaffoldGraph A subgraph starting at `scaffold`. """ G = self._graph query = canonize_smiles(scaffold) if not G.scaffold_in_graph(query): raise ValueError(f'scaffold: {query} not in graph {G}') if traversal == 'parent': nodes = G.get_parent_scaffolds(query) elif traversal == 'child': nodes = list(nx.descendants(G, query)) elif traversal == 'bidirectional': nodes = G.get_parent_scaffolds(query) nodes += list(nx.descendants(G, query)) else: msg = 'traversal must be one of {child, parent, bidirectional}' raise ValueError(msg) subgraph = G.subgraph([query] + nodes) return subgraph def __repr__(self): return '<{_cls} at {address}>'.format( _cls=self.__class__.__name__, address=hex(id(self)) )
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xicam/core/tests/test_workflow.py
mrakitin/Xi-cam.core
0
176108
<gh_stars>0 from xicam.core.execution.workflow import Workflow from xicam.core.execution.daskexecutor import DaskExecutor from xicam.plugins import Input, Output, ProcessingPlugin from pyFAI.detectors import Pilatus2M import numpy as np from pyFAI import AzimuthalIntegrator, units from scipy.ndimage import morphology import fabio class ThresholdMaskPlugin(ProcessingPlugin): data = Input(description="Frame image data", type=np.ndarray) minimum = Input(description="Threshold floor", type=int) maximum = Input(description="Threshold ceiling", type=int) neighborhood = Input( description="Neighborhood size in pixels for morphological opening. Only clusters of this size" " that fail the threshold are masked", type=int, ) mask = Output(description="Thresholded mask (1 is masked)", type=np.ndarray) def evaluate(self): self.mask.value = np.logical_or(self.data.value < self.minimum.value, self.data.value > self.maximum.value) y, x = np.ogrid[ -self.neighborhood.value : self.neighborhood.value + 1, -self.neighborhood.value : self.neighborhood.value + 1 ] kernel = x ** 2 + y ** 2 <= self.neighborhood.value ** 2 morphology.binary_opening(self.mask.value, kernel, output=self.mask.value) # write-back to mask class QIntegratePlugin(ProcessingPlugin): integrator = Input(description="A PyFAI.AzimuthalIntegrator object", type=AzimuthalIntegrator) data = Input(description="2d array representing intensity for each pixel", type=np.ndarray) npt = Input(description="Number of bins along q") polz_factor = Input(description="Polarization factor for correction", type=float) unit = Input(description="Output units for q", type=[str, units.Unit], default="q_A^-1") radial_range = Input( description="The lower and upper range of the radial unit. If not provided, range is simply " "(data.min(), data.max()). Values outside the range are ignored.", type=tuple, ) azimuth_range = Input( description="The lower and upper range of the azimuthal angle in degree. If not provided, " "range is simply (data.min(), data.max()). Values outside the range are ignored." ) mask = Input(description="Array (same size as image) with 1 for masked pixels, and 0 for valid pixels", type=np.ndarray) dark = Input(description="Dark noise image", type=np.ndarray) flat = Input(description="Flat field image", type=np.ndarray) method = Input( description='Can be "numpy", "cython", "BBox" or "splitpixel", "lut", "csr", "nosplit_csr", ' '"full_csr", "lut_ocl" and "csr_ocl" if you want to go on GPU. To Specify the device: ' '"csr_ocl_1,2"', type=str, ) normalization_factor = Input(description="Value of a normalization monitor", type=float) q = Output(description="Q bin center positions", type=np.array) I = Output(description="Binned/pixel-split integrated intensity", type=np.array) def evaluate(self): self.q.value, self.I.value = self.integrator.value().integrate1d( data=self.data.value, npt=self.npt.value, radial_range=self.radial_range.value, azimuth_range=self.azimuth_range.value, mask=self.mask.value, polarization_factor=self.polz_factor.value, dark=self.dark.value, flat=self.flat.value, method=self.method.value, unit=self.unit.value, normalization_factor=self.normalization_factor.value, ) def test_SAXSWorkflow(): # create processes thresholdmask = ThresholdMaskPlugin() qintegrate = QIntegratePlugin() # set values AI = AzimuthalIntegrator( 0.283, 5.24e-3, 4.085e-3, 0, 0, 0, 1.72e-4, 1.72e-4, detector=Pilatus2M(), wavelength=1.23984e-10 ) thresholdmask.data.value = fabio.open("/Users/hari/Downloads/AGB_5S_USE_2_2m.edf").data def AI_func(): from pyFAI.detectors import Pilatus2M from pyFAI import AzimuthalIntegrator, units return AzimuthalIntegrator( 0.283, 5.24e-3, 4.085e-3, 0, 0, 0, 1.72e-4, 1.72e-4, detector=Pilatus2M(), wavelength=1.23984e-10 ) qintegrate.integrator.value = AI_func qintegrate.npt.value = 1000 thresholdmask.minimum.value = 30 thresholdmask.maximum.value = 1e12 qintegrate.data.value = fabio.open("/Users/hari/Downloads/AGB_5S_USE_2_2m.edf").data thresholdmask.neighborhood.value = 1 qintegrate.normalization_factor.value = 0.5 qintegrate.method.value = "numpy" # connect processes thresholdmask.mask.connect(qintegrate.mask) # add processes to workflow wf = Workflow("QIntegrate") wf.addProcess(thresholdmask) wf.addProcess(qintegrate) dsk = DaskExecutor() result = dsk.execute(wf) print(result) def test_autoconnect(): # create processes thresholdmask = ThresholdMaskPlugin() qintegrate = QIntegratePlugin() # set values AI = AzimuthalIntegrator( 0.283, 5.24e-3, 4.085e-3, 0, 0, 0, 1.72e-4, 1.72e-4, detector=Pilatus2M(), wavelength=1.23984e-10 ) thresholdmask.data.value = fabio.open("/Users/hari/Downloads/AGB_5S_USE_2_2m.edf").data qintegrate.integrator.value = AI qintegrate.npt.value = 1000 thresholdmask.minimum.value = 30 thresholdmask.maximum.value = 1e12 # add process to workflow
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main.py
Matthewk01/Snake-AI
0
5836
import pygame from game.game_logic.game import Game import matplotlib.pyplot as plt def main(): scores_history = [] GAME_COUNT = 2 for i in range(GAME_COUNT): game = Game(400, "Snake AI") score = game.start() scores_history.append(score) print("Game:", i) plt.ylim(0, 36) plt.plot(range(len(scores_history)), scores_history) plt.ylabel('Snake length') plt.xlabel('Game count') plt.show() if __name__ == "__main__": main()
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mutagen/dsf.py
lucienimmink/scanner.py
2
1609436
<filename>mutagen/dsf.py # -*- coding: utf-8 -*- # Copyright (C) 2017 <NAME> # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. """Read and write DSF audio stream information and tags.""" import sys import struct from io import BytesIO from mutagen import FileType, StreamInfo from mutagen._util import cdata, MutagenError, loadfile, \ convert_error, reraise, endswith from mutagen.id3 import ID3 from mutagen.id3._util import ID3NoHeaderError, error as ID3Error __all__ = ["DSF", "Open", "delete"] class error(MutagenError): pass class DSFChunk(object): """A generic chunk of a DSFFile.""" chunk_offset = 0 chunk_header = " " chunk_size = -1 def __init__(self, fileobj, create=False): self.fileobj = fileobj if not create: self.chunk_offset = fileobj.tell() self.load() def load(self): raise NotImplementedError def write(self): raise NotImplementedError class DSDChunk(DSFChunk): """Represents the first chunk of a DSF file""" CHUNK_SIZE = 28 total_size = 0 offset_metdata_chunk = 0 def __init__(self, fileobj, create=False): super(DSDChunk, self).__init__(fileobj, create) if create: self.chunk_header = b"DSD " self.chunk_size = DSDChunk.CHUNK_SIZE def load(self): data = self.fileobj.read(DSDChunk.CHUNK_SIZE) if len(data) != DSDChunk.CHUNK_SIZE: raise error("DSF chunk truncated") self.chunk_header = data[0:4] if self.chunk_header != b"DSD ": raise error("DSF dsd header not found") self.chunk_size = cdata.ulonglong_le(data[4:12]) if self.chunk_size != DSDChunk.CHUNK_SIZE: raise error("DSF dsd header size mismatch") self.total_size = cdata.ulonglong_le(data[12:20]) self.offset_metdata_chunk = cdata.ulonglong_le(data[20:28]) def write(self): f = BytesIO() f.write(self.chunk_header) f.write(struct.pack("<Q", DSDChunk.CHUNK_SIZE)) f.write(struct.pack("<Q", self.total_size)) f.write(struct.pack("<Q", self.offset_metdata_chunk)) self.fileobj.seek(self.chunk_offset) self.fileobj.write(f.getvalue()) def pprint(self): return (u"DSD Chunk (Total file size = %d, " u"Pointer to Metadata chunk = %d)" % ( self.total_size, self.offset_metdata_chunk)) class FormatChunk(DSFChunk): CHUNK_SIZE = 52 VERSION = 1 FORMAT_DSD_RAW = 0 """Format ID: DSD Raw""" format_version = VERSION format_id = FORMAT_DSD_RAW channel_type = 1 channel_num = 1 sampling_frequency = 2822400 bits_per_sample = 1 sample_count = 0 block_size_per_channel = 4096 def __init__(self, fileobj, create=False): super(FormatChunk, self).__init__(fileobj, create) if create: self.chunk_header = b"fmt " self.chunk_size = FormatChunk.CHUNK_SIZE def load(self): data = self.fileobj.read(FormatChunk.CHUNK_SIZE) if len(data) != FormatChunk.CHUNK_SIZE: raise error("DSF chunk truncated") self.chunk_header = data[0:4] if self.chunk_header != b"fmt ": raise error("DSF fmt header not found") self.chunk_size = cdata.ulonglong_le(data[4:12]) if self.chunk_size != FormatChunk.CHUNK_SIZE: raise error("DSF dsd header size mismatch") self.format_version = cdata.uint_le(data[12:16]) if self.format_version != FormatChunk.VERSION: raise error("Unsupported format version") self.format_id = cdata.uint_le(data[16:20]) if self.format_id != FormatChunk.FORMAT_DSD_RAW: raise error("Unsupported format ID") self.channel_type = cdata.uint_le(data[20:24]) self.channel_num = cdata.uint_le(data[24:28]) self.sampling_frequency = cdata.uint_le(data[28:32]) self.bits_per_sample = cdata.uint_le(data[32:36]) self.sample_count = cdata.ulonglong_le(data[36:44]) def pprint(self): return u"fmt Chunk (Channel Type = %d, Channel Num = %d, " \ u"Sampling Frequency = %d, %.2f seconds)" % \ (self.channel_type, self.channel_num, self.sampling_frequency, self.length) class DataChunk(DSFChunk): CHUNK_SIZE = 12 data = "" def __init__(self, fileobj, create=False): super(DataChunk, self).__init__(fileobj, create) if create: self.chunk_header = b"data" self.chunk_size = DataChunk.CHUNK_SIZE def load(self): data = self.fileobj.read(DataChunk.CHUNK_SIZE) if len(data) != DataChunk.CHUNK_SIZE: raise error("DSF chunk truncated") self.chunk_header = data[0:4] if self.chunk_header != b"data": raise error("DSF data header not found") self.chunk_size = cdata.ulonglong_le(data[4:12]) if self.chunk_size < DataChunk.CHUNK_SIZE: raise error("DSF data header size mismatch") def pprint(self): return u"data Chunk (Chunk Offset = %d, Chunk Size = %d)" % ( self.chunk_offset, self.chunk_size) class _DSFID3(ID3): """A DSF file with ID3v2 tags""" @convert_error(IOError, error) def _pre_load_header(self, fileobj): fileobj.seek(0) id3_location = DSDChunk(fileobj).offset_metdata_chunk if id3_location == 0: raise ID3NoHeaderError("File has no existing ID3 tag") fileobj.seek(id3_location) @convert_error(IOError, error) @loadfile(writable=True) def save(self, filething=None, v2_version=4, v23_sep='/', padding=None): """Save ID3v2 data to the DSF file""" fileobj = filething.fileobj fileobj.seek(0) dsd_header = DSDChunk(fileobj) if dsd_header.offset_metdata_chunk == 0: # create a new ID3 chunk at the end of the file fileobj.seek(0, 2) # store reference to ID3 location dsd_header.offset_metdata_chunk = fileobj.tell() dsd_header.write() try: data = self._prepare_data( fileobj, dsd_header.offset_metdata_chunk, self.size, v2_version, v23_sep, padding) except ID3Error as e: reraise(error, e, sys.exc_info()[2]) fileobj.seek(dsd_header.offset_metdata_chunk) fileobj.write(data) fileobj.truncate() # Update total file size dsd_header.total_size = fileobj.tell() dsd_header.write() class DSFInfo(StreamInfo): """DSF audio stream information. Information is parsed from the fmt chunk of the DSF file. Attributes: length (`float`): audio length, in seconds. channels (`int`): The number of audio channels. sample_rate (`int`): Sampling frequency, in Hz. (2822400, 5644800, 11289600, or 22579200) bits_per_sample (`int`): The audio sample size. bitrate (`int`): The audio bitrate. """ def __init__(self, fmt_chunk): self.fmt_chunk = fmt_chunk @property def length(self): return float(self.fmt_chunk.sample_count) / self.sample_rate @property def channels(self): return self.fmt_chunk.channel_num @property def sample_rate(self): return self.fmt_chunk.sampling_frequency @property def bits_per_sample(self): return self.fmt_chunk.bits_per_sample @property def bitrate(self): return self.sample_rate * self.bits_per_sample * self.channels def pprint(self): return u"%d channel DSF @ %d bits, %s Hz, %.2f seconds" % ( self.channels, self.bits_per_sample, self.sample_rate, self.length) class DSFFile(object): dsd_chunk = None fmt_chunk = None data_chunk = None def __init__(self, fileobj): self.dsd_chunk = DSDChunk(fileobj) self.fmt_chunk = FormatChunk(fileobj) self.data_chunk = DataChunk(fileobj) class DSF(FileType): """An DSF audio file. Arguments: filething (filething) Attributes: info (`DSFInfo`) tags (`mutagen.id3.ID3Tags` or `None`) """ _mimes = ["audio/dsf"] @staticmethod def score(filename, fileobj, header): return header.startswith(b"DSD ") * 2 + \ endswith(filename.lower(), ".dsf") def add_tags(self): """Add a DSF tag block to the file.""" if self.tags is None: self.tags = _DSFID3() else: raise error("an ID3 tag already exists") @convert_error(IOError, error) @loadfile() def load(self, filething, **kwargs): dsf_file = DSFFile(filething.fileobj) try: self.tags = _DSFID3(filething.fileobj, **kwargs) except ID3NoHeaderError: self.tags = None except ID3Error as e: raise error(e) else: self.tags.filename = self.filename self.info = DSFInfo(dsf_file.fmt_chunk) @loadfile(writable=True) def delete(self, filething=None): self.tags = None delete(filething) @convert_error(IOError, error) @loadfile(method=False, writable=True) def delete(filething): """Remove tags from a file. Args: filething (filething) Raises: mutagen.MutagenError """ dsf_file = DSFFile(filething.fileobj) if dsf_file.dsd_chunk.offset_metdata_chunk != 0: id3_location = dsf_file.dsd_chunk.offset_metdata_chunk dsf_file.dsd_chunk.offset_metdata_chunk = 0 dsf_file.dsd_chunk.write() filething.fileobj.seek(id3_location) filething.fileobj.truncate() Open = DSF
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src/python/watch-wasm.py
Dante83/A-Terra-Incognito
23
160172
import os, time cpp_directories = ['../cpp/state-engine/', '../cpp/' '../cpp/interpolation-engine/'] cpp_files = [['SkyState.cpp',\ 'autoexposure/LightingAnalyzer.cpp',\ 'world_state/AstroTime.cpp',\ 'world_state/Location.cpp',\ 'astro_bodies/SkyManager.cpp',\ 'astro_bodies/AstronomicalBody.cpp',\ 'astro_bodies/Sun.cpp',\ 'astro_bodies/Moon.cpp',\ 'astro_bodies/Planet.cpp',\ 'astro_bodies/planets/Earth.cpp',\ 'astro_bodies/OtherPlanet.cpp',\ 'astro_bodies/planets/Mercury.cpp',\ 'astro_bodies/planets/Venus.cpp',\ 'astro_bodies/planets/Mars.cpp',\ 'astro_bodies/planets/Jupiter.cpp',\ 'astro_bodies/planets/Saturn.cpp'],\ ['SkyInterpolator.cpp',\ 'color/ColorInterpolator.cpp' ]] module_file = ['state-engine.js', 'interpolation-engine.js'] exported_functions = [['_main', '_setupSky', '_updateSky', '_initializeMeteringAndLightingDependencies',\ '_updateMeteringData', '_updateDirectLighting', '_updateHemisphericalLightingData'],\ ['_main', '_setupInterpolators', '_updateFinalAstronomicalValues', '_updateAstronomicalTimeData',\ '_tick_astronomicalInterpolations', '_setSunAndMoonTimeTo', '_denormalizeSkyIntensity0', '_updateLightingValues',\ '_tick_lightingInterpolations', '_bolometricMagnitudeToLuminosity', '_luminosityToAtmosphericIntensity',\ '_initializeLightingValues']] cpp_update_date = {} def recursivelyWalkDirectories(absolute_cpp_directory, file_check_callback): for root, dirs, files in os.walk(absolute_cpp_directory): for file in files: call_back_status = file_check_callback(root, file) if call_back_status: return True for dir in dirs: call_back_status = recursivelyWalkDirectories('{}/{}'.format(absolute_cpp_directory, dir), file_check_callback) if call_back_status: return True return False for i, cpp_directory in enumerate(cpp_directories): absolute_cpp_directory = os.path.abspath(cpp_directory) def intializeFileUpdateTimes(root, file): if file.endswith('.h') or file.endswith('.cpp'): cpp_update_date[file] = os.path.getmtime('{}/{}'.format(root, file)) #Never break on this return False recursivelyWalkDirectories(absolute_cpp_directory, intializeFileUpdateTimes) def CPPWatcher(): #This should run forever, repeatedly creating our files from the updated structure every time for i, cpp_directory in enumerate(cpp_directories): os.system('clear') os.chdir(cpp_directory) os.system("emcc {} -s WASM=1 -s EXPORTED_FUNCTIONS='[{}]' -o {} -s ALLOW_MEMORY_GROWTH=1;".format(' '.join(cpp_files[i]), ', '.join(exported_functions[i]), module_file[i])) os.chdir('../../python') print 'Watching for updates...' #Watch loop while True: break_loops = True for i, cpp_directory in enumerate(cpp_directories): absolute_cpp_directory = os.path.abspath(cpp_directory) def checkForUpdates(root, file): #Only check cpp and header files and determine if the file had an update absolute_file_path = '{}/{}'.format(root, file) if (file.endswith('.h') or file.endswith('.cpp')) and (cpp_update_date[file] < os.path.getmtime(absolute_file_path)): os.system('clear') print 'Change found in file {}, updating.'.format(file) cpp_update_date[file] = os.path.getmtime(absolute_file_path) os.chdir(cpp_directory) os.system("emcc {} -s WASM=1 -s EXPORTED_FUNCTIONS='[{}]' -O3 {} -s ALLOW_MEMORY_GROWTH=1;".format(' '.join(cpp_files[i]), ', '.join(exported_functions[i]), module_file[i])) os.chdir('../../python') print "WASM update at: {}".format(time.strftime('%H:%M %Y-%m-%d')) print "-"*15 return True return False #Update our file list and check for any changes to any of our files recursivelyWalkDirectories(absolute_cpp_directory, checkForUpdates) #And we only do this every five seconds because this is a little more costly time.sleep(1) #Run the main application! :D CPPWatcher()
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libpmf-1.41/python/libpmf.py
zhenv5/atp
7
198405
#!/usr/bin/env python from ctypes import * from os import path libpmf = CDLL(path.join(path.dirname(__file__),'pmf_py.so.1')) def genFields(names, types): return list(zip(names, types)) def fillprototype(f, restype, argtypes): f.restype = restype f.argtypes = argtypes class parameter(Structure): _names = ["solver_type", "k", "threads", "maxiter", "maxinneriter", "lambda", "rho", "eps", "eta0", "betaup", "betadown", "lrate_method", "num_blocks", "do_predict", "verbose", "do_nmf"] _types = [c_int, c_int, c_int, c_int, c_int, c_double, c_double, c_double, c_double, c_double, c_double, c_int, c_int, c_int, c_int, c_int] _fields_ = genFields(_names, _types) fillprototype(libpmf.training_option, c_char_p, []) fillprototype(libpmf.parse_training_command_line, parameter, [c_char_p]) fillprototype(libpmf.pmf_train, None, [c_int, c_int, c_long, POINTER(c_int), POINTER(c_int), POINTER(c_double), POINTER(parameter), POINTER(c_double), POINTER(c_double)]) import numpy as np import scipy.sparse as sparse def train(A=None, param_str='', zero_as_missing = True): if A is None: print('train(A, param_str="", zero_as_missing = True)\n' ' A: a numpy array or a scipy.sparse.matrix\n' ' zero_as_missing: whether treat zero as missing (Default True)\n' ' param_str:\n' '%s' %(libpmf.training_option().split('\n',1)[-1])) return None m, n = A.shape if not zero_as_missing and isinstance(A, sparse.spmatrix): A = A.toarray() if isinstance(A, sparse.spmatrix): coo = sparse.coo_matrix(A) return train_coo(row_idx=coo.row, col_idx=coo.col, obs_val=coo.data, m=m, n=n, param_str=param_str) elif isinstance(A, np.ndarray): if zero_as_missing: row_idx, col_idx = np.nonzero(np.isfinite(A) & (A !=0)) else : row_idx, col_idx = np.nonzero(np.isfinite(A)) val = A[(row_idx, col_idx)] return train_coo(row_idx=row_idx, col_idx=col_idx, obs_val=val, m=m, n=n, param_str=param_str) else : print('type(A) = %s is not supported' % (type(A))) return None def train_coo(row_idx=None, col_idx=None, obs_val=None, obs_weight=None, m=None, n=None, param_str = ''): ''' if None in [row_idx, col_idx, obs_val, m, n]: print ( 'train_coo(row_idx, col_idx, obs_val, obs_weight, m, n, param_str="")\n' ' row_idx : a numpy.ndarray with dtype = numpy.int32\n' ' col_idx : a numpy.ndarray with dtype = numpy.int32\n' ' obs_val : a numpy.ndarray with dtype = numpy.float64\n' ' obs_weight : a numpy.ndarray with dtype = numpy.float64 (optional)\n' ' m, n : # of rows, # of cols\n' ' param_str: \n' '%s' % (libpmf.training_option().split('\n',1)[-1])) return None ''' param = libpmf.parse_training_command_line(param_str) row_idx = np.array(row_idx, dtype=np.int32, copy=False) col_idx = np.array(col_idx, dtype=np.int32, copy=False) obs_val = np.array(obs_val, dtype=np.float64, copy=False) if not (obs_weight is None): obs_weight = np.array(obs_weight, dtype=np.float64, copy=False) if row_idx.max() >= m or col_idx.max() >= n: print row_idx.max(), col_idx.max(), m, n raise ValueError('row_idx or col_idx contains an index in wrong range') W = np.zeros((param.k, m), dtype=np.float64) H = np.zeros((param.k, n), dtype=np.float64) nnz = len(row_idx) libpmf.pmf_train(m, n, nnz, row_idx.ctypes.data_as(POINTER(c_int)), col_idx.ctypes.data_as(POINTER(c_int)), obs_val.ctypes.data_as(POINTER(c_double)), param, W.ctypes.data_as(POINTER(c_double)), H.ctypes.data_as(POINTER(c_double))) return {'W':W.T, 'H':H.T}
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QWeb/internal/user.py
janmaterne/qweb
1
129339
# -*- coding: utf-8 -*- # -------------------------- # Copyright © 2014 - Qentinel Group. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # --------------------------- import os def is_root(): try: # Windows doesn't have getuid. We just assume that user is not root. We # most likely won't need proper Windows support here anyway. uid = os.getuid() # pylint: disable=no-member except AttributeError: return False # User id 0 is reserved for superuser aka root if uid == 0: return True return False
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Breakingtheloop ML Repository/ml_explore.py
sghoseWI/Machine-Learning
0
138755
import pandas as pd import numpy as np from matplotlib import pyplot as plt import seaborn as sns import math import re from ml_pipeline_lch import isolate_categoricals, is_category def view_dist(df, geo_columns = True, fig_size=(20,15), labels = None): ''' Plot distributions of non-categorical columns in a given dataframe Inputs: df: pandas dataframe geo_columns: list of column names corresponding to columns with numeric geographical information (ex: zipcodes) labels: list of labels to apply to plot: title, xlable, ylabel, respectively ''' non_categoricals = isolate_categoricals(df, categoricals_fcn = is_category, ret_categoricals = False, geos_indicator = geo_columns) # non_categoricals = isolate_noncategoricals(df, ret_categoricals = False, # geo_cols = geo_columns) df[non_categoricals].hist(bins = 10, figsize=fig_size, color = 'blue') if labels: plt.title(labels[0]) plt.xlabel(labels[1]) plt.ylabel(labels[2]) plt.show() def plot_value_counts(df, type_dict, category, norm = False, plot_kind = 'bar'): for col in type_dict[category]: plot_title = col + " distribution" df[col].value_counts(normalize = norm).plot(kind=plot_kind) plt.title(plot_title) plt.xlabel(col) plt.ylabel("frequency") plt.show() def check_corr(df, geo_columns = True, cat_cols = None): ''' Display heatmap of linear correlation between non-categorical columns in a given dataframe Inputs: df: pandas dataframe geo_columns: list of column names corresponding to columns with numeric geographical information (ex: zipcodes) Attribution: Colormap Attribution: adapted from gradiated dataframe at https://www.datascience.com/blog/introduction-to-correlation-learn-data-science-tutorials and correlation heatmap at https://stackoverflow.com/questions/29432629/correlation-matrix-using-pandas ''' try: non_categoricals = isolate_categoricals(df, categoricals_fcn = is_category, ret_categoricals = False, geos_indicator = geo_columns) fig, ax = plt.subplots(figsize=(12, 12)) corr = df[non_categoricals].corr(method="pearson") sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap=plt.get_cmap("coolwarm"), square=True, ax=ax, annot=True) ax.set_xticks(range(len(non_categoricals))) ax.set_yticks(range(len(non_categoricals))) ax.tick_params(direction='inout') ax.set_xticklabels(non_categoricals, rotation=45, ha='right') ax.set_yticklabels(non_categoricals, rotation=45, va='top') plt.title('Feature Correlation') plt.show() except: if cat_cols: cat_df = df[df.columns] for col in cat_cols: cat_df[col] = cat_df[col].astype('categorical') fig, ax = plt.subplots(figsize=(12, 12)) corr = cat_df.corr(method="pearson") sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap=plt.get_cmap("coolwarm"), square=True, ax=ax, annot=True) ax.set_xticks(range(len(cat_df.columns))) ax.set_yticks(range(len(cat_df.columns))) ax.tick_params(direction='inout') ax.set_xticklabels(cat_df.columns, rotation=45, ha='right') ax.set_yticklabels(cat_df.columns, rotation=45, va='top') plt.title('Feature Correlation') plt.show() def discretize_cols(df, num_bins, geo_columns=True, specific_cols = False, split = False): ''' Add columns to discretize and classify non-categorical columns in a given data frame Inputs: df: pandas dataframe geo_columns: list of column names corresponding to columns with numeric geographical information (ex: zipcodes) num_bins: number of groups into which column values should be discretized ''' if specific_cols: non_categoricals = specific_cols else: non_categoricals = isolate_categoricals(df, categoricals_fcn = is_category, ret_categoricals = False, geos_indicator = geo_columns) for col in non_categoricals: bin_col = col + "_bin" if col == "age": age_bins = math.ceil((df[col].max() - df[col].min()) / 10) if split: df[bin_col], train_bins = pd.cut(df[col], bins = age_bins, right = False, precision=0, retbins=split) else: df[bin_col] = pd.cut(df[col], bins = age_bins, right = False, precision=0, retbins=split) else: if split: df[bin_col], train_bins = pd.cut(df[col], bins = num_bins, precision=0, retbins=split) else: df[bin_col] = pd.cut(df[col], bins = num_bins, precision=0, retbins=split) if split: return train_bins def discretize_train_test(train_test_tuples, still_blanks): for i, (train, test) in enumerate(train_test_tuples): fill_cols = still_blanks[i] for col in fill_cols: grouped = col + '_bin' train[grouped], train_bins = pd.cut(train[col], bins = 4, precision = 0, retbins = True) test[grouped] = pd.cut(test[col], bins = train_bins, precision = 0) def confirm_train_test_discretization(train_test_tuples, still_blanks): for i, (train, test) in enumerate(train_test_tuples): for col in still_blanks[i]: grouped = col grouped = col + '_bin' print("set {} {} train: {}.".format(i, col, train[grouped].unique())) print() print("set {} {} test: {}.".format(i, col, test[grouped].unique())) print() def drop_tt_binned(train_test_tuples, to_drop): ''' Drop columns from both train and test sets. Inputs: train_test_tuples: list of tupled dataframes to_drop: list of columns to drop ''' for train, test in train_test_tuples: train.drop(to_drop, axis = 1, inplace = True) test.drop(to_drop, axis = 1, inplace = True) def create_binary_vars(df, cols_to_dummy, keyword_list): ''' Create columns of binary values corresponding to values above zero for selected columns in a given dataframe based on common keywords Inputs: df: pandas dataframe cols_to_dummy: (list of strings) columns in data frame to be evaluated into dummy variables keyword_list: (list of strings) words or phrases included in columns to be evaluated indicating a dummy variable should be created based on its values ''' keyword_string = ("|").join(keyword_list) for col in cols_to_dummy: colname_trunc = re.sub(keyword_string, '', col) binary_col_name = 'tf_' + colname_trunc df[binary_col_name] = df[col].apply(lambda x: x > 0) def plot_corr(df, color_category, geo_columns=True): ''' Observe distributions and correlations of features for non-categorical Inputs: df: pandas dataframe categoricals_list: list of strings corresponding to categorical columns (ex: zip codes) ''' non_categoricals = isolate_categoricals(df, categoricals_fcn = is_category, ret_categoricals = False, geos_indicator = geo_columns) plot_list = non_categoricals + [color_category] corr = sns.pairplot(df[plot_list], hue = color_category, palette = "Set2") def plot_relationship(df, feature_x, xlabel,feature_y, ylabel, xlimit = None, ylimit = None, color_cat = None, filter_col = None, filter_criteria = None, filter_param = None, filter_param2 = None): ''' Plot two features in a given data frame against each other to view relationship and outliers. Attribution: adapted from https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Seaborn_Cheat_Sheet.pdf ''' if filter_col and filter_criteria and filter_param: if filter_criteria == 'geq': use_df = df[df[filter_col] >= filter_param] elif filter_criteria == 'gt': use_df = df[df[filter_col] > filter_param] elif filter_criteria == 'leq': use_df = df[df[filter_col] <= filter_param] elif filter_criteria == 'lt': use_df = df[df[filter_col] < filter_param] elif filter_criteria == 'eq': use_df = df[df[filter_col] == filter_param] elif filter_criteria == 'neq': use_df = df[df[filter_col] != filter_param] elif filter_criteria == 'between': use_df = df[(df[filter_col] > filter_param) and (df[filter_col] < filter_param2)] g = sns.lmplot(x = feature_x, y = feature_y, data = use_df, aspect = 3, hue = color_cat) g = (g.set_axis_labels(xlabel,ylabel)).set(xlim = xlimit , ylim = ylimit) plot_title = ylabel + " by " + xlabel plt.title(plot_title) plt.show(g) else: g = sns.lmplot(x = feature_x, y = feature_y, data = df, aspect = 3, hue = color_cat) g = (g.set_axis_labels(xlabel,ylabel)).set(xlim = xlimit , ylim = ylimit) plot_title = ylabel + " by " + xlabel plt.title(plot_title) plt.show(g) def eval_ratios(df, include_cols, category_cols, method = "count", pct = False): ''' Evaluate specific features via grouping on one or more category Inputs: df: (dataframe) pandas dataframe include_cols: (list of strings) column names to be aggregated or grouped category_cols: (list of strings) column name(s) for variable(s) used for grouping method: (string) groupby aggregation method for column values Output: ratio_df: pandas data frame of grouped data ''' if method == "count": ratio_df = df[include_cols].groupby(category_cols).count() if pct: single_col = include_cols[-1] + " Percentage" ratio_df[single_col] = ((df[include_cols].groupby(category_cols).count() / df[include_cols].groupby(category_cols).count().sum()) * 100) elif method == "sum": ratio_df = df[include_cols].groupby(category_cols).sum() if pct: single_col = include_cols[-1] + " Percentage" ratio_df[single_col] = ((df[include_cols].groupby(category_cols).sum() / df[include_cols].groupby(category_cols).sum().sum()) * 100) return ratio_df def feature_by_geo(df, geo, expl_var, num_var, method = "median"): ''' Evaluate specific features by geography (ex: zip code) Inputs: df: (dataframe) pandas dataframe geo: (string) column name corresponding to geography used for grouping expl_var: (string) column name for exploratory variable used for grouping num_var: (string) column name for numeric variable/ feature to be aggregated method: (string) groupby aggregation method for column values Output: geo_features: pandas data frame of grouped data ''' df_geo = df[(df[geo] != 0)] groupby_list = [geo] + expl_var if method == "median": geo_features = df_geo.groupby(groupby_list)[num_var].median().unstack(level = 1) if method == "count": geo_features = df_geo.groupby(groupby_list)[num_var].count().unstack(level = 1) geo_features.fillna(value = "", inplace = True) return geo_features def plot_top_distros(train_test_tuples, var_dict, set_num): for i, col in enumerate(var_dict['tops']): train, test = train_test_tuples[set_num] plot_title = "Projects by {} for Training Set {}".format(col, set_num) train[col].value_counts().sort_index().plot(kind='bar', title = plot_title) plt.show()
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src/dataclay/communication/grpc/messages/logicmodule/__init__.py
kpavel/pyclay
1
14479
""" Class description goes here. """ """Package containing gRPC classes.""" __author__ = '<NAME> <<EMAIL>>' __copyright__ = '2017 Barcelona Supercomputing Center (BSC-CNS)'
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irida_uploader_cl/parsers/nextseq/parser.py
duanjunhyq/irida_uploader_cl
0
1608448
<filename>irida_uploader_cl/parsers/nextseq/parser.py import logging import os import irida_uploader_cl.model as model import irida_uploader_cl.progress as progress from irida_uploader_cl.parsers import exceptions from irida_uploader_cl.parsers.nextseq import sample_parser, validation class Parser: SAMPLE_SHEET_FILE_NAME = 'SampleSheet.csv' UPLOAD_COMPLETE_FILE_NAME = 'RTAComplete.txt' @staticmethod def get_required_file_list(): """ Returns a list of files that are required for a run directory to be considered valid :return: [files_names] """ logging.warning("NOTE: If bcl2fastq has not finished, run may return as invalid, " "or incomplete files could be uploaded!") return [ Parser.SAMPLE_SHEET_FILE_NAME, Parser.UPLOAD_COMPLETE_FILE_NAME ] @staticmethod def _find_directory_list(directory): """Find and return all directories in the specified directory. Arguments: directory -- the directory to find directories in Returns: a list of directories including current directory """ # Checks if we can access to the given directory, return empty and log a warning if we cannot. if not os.access(directory, os.W_OK): raise exceptions.DirectoryError("The directory is not writeable, " "can not upload samples from this directory {}".format(directory), directory) dir_list = next(os.walk(directory))[1] # Gets the list of directories in the directory full_dir_list = [] for d in dir_list: full_dir_list.append(os.path.join(directory, d)) return full_dir_list @staticmethod def find_runs(directory): """ find a list of run directories in the directory given :param directory: :return: list of DirectoryStatus objects """ logging.info("looking for runs in {}".format(directory)) runs = [] directory_list = Parser._find_directory_list(directory) for d in directory_list: runs.append(progress.get_directory_status(d, Parser.get_required_file_list())) return runs @staticmethod def find_single_run(directory): """ Find a run in the base directory given :param directory: :return: DirectoryStatus object """ logging.info("looking for run in {}".format(directory)) return progress.get_directory_status(directory, Parser.get_required_file_list()) @staticmethod def get_sample_sheet(directory): """ gets the sample sheet file path from a given run directory :param directory: :return: """ logging.info("Looking for sample sheet in {}".format(directory)) # Checks if we can access to the given directory, return empty and log a warning if we cannot. if not os.access(directory, os.W_OK): logging.error(("The directory is not accessible, can not parse samples from this directory {}" "".format(directory), directory)) raise exceptions.DirectoryError("The directory is not accessible, " "can not parse samples from this directory {}".format(directory), directory) sample_sheet_file_name = Parser.SAMPLE_SHEET_FILE_NAME file_list = next(os.walk(directory))[2] # Gets the list of files in the directory if sample_sheet_file_name not in file_list: logging.error("No sample sheet file in the NextSeq format found") raise exceptions.DirectoryError("The directory {} has no sample sheet file in the NextSeq format" " with the name {}" .format(directory, sample_sheet_file_name), directory) else: logging.debug("Sample sheet found") return os.path.join(directory, sample_sheet_file_name) @staticmethod def get_sequencing_run(sample_sheet): """ Does local validation on the integrety of the run directory / sample sheet Throws a ValidationError with a valadation result attached if it cannot make a sequencing run :param sample_sheet: :return: SequencingRun """ # Try to get the sample sheet, validate that the sample sheet is valid validation_result = validation.validate_sample_sheet(sample_sheet) if not validation_result.is_valid(): logging.error("Errors occurred while getting sample sheet") raise exceptions.ValidationError("Errors occurred while getting sample sheet", validation_result) # Try to parse the meta data from the sample sheet, throw validation error if errors occur validation_result = model.ValidationResult() try: run_metadata = sample_parser.parse_metadata(sample_sheet) except exceptions.SampleSheetError as error: validation_result.add_error(error) logging.error("Errors occurred while parsing metadata") raise exceptions.ValidationError("Errors occurred while parsing metadata", validation_result) # Try to build sequencing run from sample sheet & meta data, raise validation error if errors occur try: sequencing_run = sample_parser.build_sequencing_run_from_samples(sample_sheet, run_metadata) except exceptions.SequenceFileError as error: validation_result.add_error(error) logging.error("Errors occurred while building sequence run from sample sheet") raise exceptions.ValidationError("Errors occurred while building sequence run from sample sheet", validation_result) return sequencing_run
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slack_resurrect/settings.py
halkeye/slacktalker
1
1610932
import os class Config: @classmethod def toJSON(cls): import json data = dict() for item in dir(cls): if item.isupper(): data[item] = getattr(cls, item) return json.dumps(data) DEBUG = False TESTING = False DEBUG_SQL = False SQLALCHEMY_TRACK_MODIFICATIONS = False SENTRY_TOKEN = os.environ.get('SENTRY_TOKEN') SENTRY_ENVIRONMENT = None SLACK_BOT_TOKEN = os.environ.get('SLACK_BOT_TOKEN') # Webhooks token SLACK_TOKEN = os.environ.get('SLACK_TOKEN') PORT = 3000 class DevelopmentConfig(Config): DEBUG = True DEBUG_SQL = True SENTRY_ENVIRONMENT = 'development' SQLALCHEMY_DATABASE_URI = 'sqlite:///development.sqlite' class TestingConfig(DevelopmentConfig): TESTING = True DEBUG_SQL = False SQLALCHEMY_DATABASE_URI = 'sqlite://' SENTRY_TOKEN = None SENTRY_ENVIRONMENT = 'testing' SLACK_TOKEN = '3rm7Ngf57te4ncHKwSvWhseb' SLACK_BOT_TOKEN = None class ProductionConfig(Config): DEBUG = False TESTING = False SQLALCHEMY_DATABASE_URI = os.environ.get('DATABASE_URL') SENTRY_ENVIRONMENT = 'production' ENV = os.environ.get('APP_ENV', os.environ.get('FLASK_ENV', 'prod')) CONFIG = None if ENV == 'dev' or ENV == 'development': CONFIG = DevelopmentConfig elif ENV == 'test': CONFIG = TestingConfig elif ENV == 'prod': CONFIG = ProductionConfig else: raise ValueError('Invalid environment name')
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bindings/python/v1/empowerparser.py
Nibamot/empower-enb-proto
0
1606323
<filename>bindings/python/v1/empowerparser.py #!/usr/bin/env python3 """Empower-Parser""" # Copyright (c) 2018 FBK-CREATENET # AUTHOR- <NAME> # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. import ctypes as ct from v1 import MessageType from v1 import ActionType from v1.lte.ltemsg import LTEMsg class EmpowerParser: """Wrapper around direct python bindings""" """@staticmethod def parseversion(buf, size): #function to parse any message to be parsed by #LTEMsg or its child classes _proto = ct.CDLL("libemproto.so") parseversion = _proto.epp_ve return ltemsg.parse(buf, size)""" @staticmethod def parseevent(buf, size): """function to parse any message to be parsed by LTEMsg or its child classes""" _proto = ct.CDLL("libemproto.so") parseevent = _proto.epp_msg_type parseevent.restype = MessageType parseevent.argtypes = [ct.c_char_p, ct.c_uint] return parseevent(buf, size) @staticmethod def parseaction(buf, size): """function to parse any message to be parsed by LTEMsg or its child classes""" _proto = ct.CDLL("libemproto.so") parseaction = _proto.epp_msg_type #generic action type? parseaction.restype = ActionType parseaction.argtypes = [ct.c_char_p, ct.c_uint] return parseaction(buf, size) @staticmethod def parsedirection(buf, size): """function to parse any message to be parsed by LTEMsg or its child classes""" _proto = ct.CDLL("libemproto.so") parsedirection = _proto.epp_dir parsedirection.restype = ct.c_uint parsedirection.argtypes = [ct.c_char_p, ct.c_uint] return parsedirection(buf, size)
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looker_api/__init__.py
nofeet/looker-api
1
107710
<reponame>nofeet/looker-api<filename>looker_api/__init__.py from looker_api import *
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tools/mytools/ARIA/src/py/aria/legacy/FortranFormat.py
fmareuil/Galaxy_test_pasteur
0
56675
<filename>tools/mytools/ARIA/src/py/aria/legacy/FortranFormat.py # This module defines a class that handles I/O using # Fortran-compatible format specifications. # # # Warning: Fortran formatting is a complex business and I don't # claim that this module works for anything complicated. It knows # only the most frequent formatting options. Known limitations: # # 1) Only A, D, E, F, G, I, and X formats are supported (plus string constants # for output). # 2) No direct support for complex numbers. You have to split them into # real and imaginary parts before output, and for input you get # two float numbers anyway. # 3) No overflow check. If an output field gets too large, it will # take more space, instead of being replaced by stars. # # # Written by <NAME> <<EMAIL>> # last revision: 1996-9-27 # """Fortran-compatible input/output This module provides two classes that aid in reading and writing Fortran-formatted text files. Only a subset of formatting options is supported: A, D, E, F, G, I, and X formats, plus string constants for output. Repetition (e.g. 4I5 or 3(1X,A4)) is supported. Complex numbers are not supported; you have to treat real and imaginary parts separately. Examples: ========= Input: ------ >> s = ' 59999' >> format = FortranFormat('2I4') >> line = FortranLine(s, format) >> line[0] 5 >> line[1] 9999 Output: ------- >> format = FortranFormat('2D15.5') >> line = FortranLine([3.1415926, 2.71828], format) >> str(line) ' 3.14159D+00 2.71828D+00' The second argumet to FortranLine can be a format object or a string (that is then converted into a format object). If the same format is to be used several times, it is more efficient to convert it into a format object once - parsing the format string is a relatively expensive operation. """ import string # # The class FortranLine represents a single line of input/output, # which can be accessed as text or as a list of items. # class FortranLine: def __init__(self, line, format, length = 80): if type(line) == type(''): self.text = line self.data = None else: self.text = None self.data = line if type(format) == type(''): self.format = FortranFormat(format) else: self.format = format self.length = length if self.text is None: self._output() if self.data is None: self._input() def __len__(self): return len(self.data) def __getitem__(self, i): return self.data[i] def __getslice__(self, i, j): return self.data[i:j] def __str__(self): return self.text def isBlank(self): return len(string.strip(self.text)) == 0 def _input(self): text = self.text if len(text) < self.length: text = text + (self.length-len(text))*' ' self.data = [] for field in self.format: l = field[1] s = text[:l] text = text[l:] type = field[0] value = None if type == 'A': value = s elif type == 'I': s = string.strip(s) if len(s) == 0: value = 0 else: value = string.atoi(s) elif type == 'D' or type == 'E' or type == 'F' or type == 'G': s = string.lower(string.strip(s)) n = string.find(s, 'd') if n >= 0: s = s[:n] + 'e' + s[n+1:] if len(s) == 0: value = 0. else: value = string.atof(s) if value is not None: self.data.append(value) def _output(self): data = self.data self.text = '' for field in self.format: type = field[0] if type == "'": self.text = self.text + field[1] elif type == 'X': self.text = self.text + field[1]*' ' else: # fields that use input data length = field[1] if len(field) > 2: fraction = field[2] value = data[0] data = data[1:] if type == 'A': self.text = self.text + (value+length*' ')[:length] else: # numeric fields if type == 'I': s = `value` elif type == 'D': s = ('%'+`length`+'.'+`fraction`+'e') % value n = string.find(s, 'e') s = s[:n] + 'D' + s[n+1:] elif type == 'E': s = ('%'+`length`+'.'+`fraction`+'e') % value elif type == 'F': s = ('%'+`length`+'.'+`fraction`+'f') % value elif type == 'G': s = ('%'+`length`+'.'+`fraction`+'g') % value else: raise ValueError, 'Not yet implemented' s = string.upper(s) self.text = self.text + ((length*' ')+s)[-length:] # # The class FortranFormat represents a format specification. # It ought to work for correct specifications, but there is # little error checking. # class FortranFormat: def __init__(self, format, nested = 0): fields = [] format = string.strip(format) while format and format[0] != ')': n = 0 while format[0] in string.digits: n = 10*n + string.atoi(format[0]) format = format[1:] if n == 0: n = 1 type = string.upper(format[0]) if type == "'": eof = string.find(format, "'", 1) text = format[1:eof] format = format[eof+1:] else: format = string.strip(format[1:]) if type == '(': subformat = FortranFormat(format, 1) fields = fields + n*subformat.fields format = subformat.rest eof = string.find(format, ',') if eof >= 0: format = format[eof+1:] else: eof = string.find(format, ',') if eof >= 0: field = format[:eof] format = format[eof+1:] else: eof = string.find(format, ')') if eof >= 0: field = format[:eof] format = format[eof+1:] else: field = format format = '' if type == "'": field = (type, text) else: dot = string.find(field, '.') if dot > 0: length = string.atoi(field[:dot]) fraction = string.atoi(field[dot+1:]) field = (type, length, fraction) else: if field: length = string.atoi(field) else: length = 1 field = (type, length) fields = fields + n*[field] self.fields = fields if nested: self.rest = format def __len__(self): return len(self.fields) def __getitem__(self, i): return self.fields[i] # Test code if __name__ == '__main__': f = FortranFormat("'!!',D10.3,F10.3,G10.3,'!!'") l = FortranLine([1.5707963, 3.14159265358, 2.71828], f) print str(l) f = FortranFormat("F12.0") l = FortranLine('2.1D2', f) print l[0]
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src/test/resources/outputs/blueprints/openstack/lamp/wrapper/mysql/org.alien4cloud.interfaces.cfy.lifecycle/NodeInit/_a4c_NodeInit.py
alien4cloud/alien4cloud-cloudify4-provider
0
1607952
<reponame>alien4cloud/alien4cloud-cloudify4-provider<filename>src/test/resources/outputs/blueprints/openstack/lamp/wrapper/mysql/org.alien4cloud.interfaces.cfy.lifecycle/NodeInit/_a4c_NodeInit.py<gh_stars>0 from cloudify import ctx from cloudify import utils from cloudify.exceptions import NonRecoverableError from StringIO import StringIO import base64 import os import platform import re import subprocess import sys import time import threading import platform import json def convert_env_value_to_string(envDict): for key, value in envDict.items(): envDict[str(key)] = str(envDict.pop(key)) def get_attribute_user(ctx): if get_attribute_from_top_host(ctx, 'user'): return get_attribute_from_top_host(ctx, 'user') if get_attribute(ctx, 'cloudify_agent'): return get_attribute(ctx, 'cloudify_agent').get('user', None) if get_attribute(ctx, 'agent_config'): return get_attribute(ctx, 'agent_config').get('user', None) return None def get_attribute_key(ctx): if get_attribute_from_top_host(ctx, 'key'): return get_attribute_from_top_host(ctx, 'key') if get_attribute(ctx, 'cloudify_agent'): return get_attribute(ctx, 'cloudify_agent').get('key', None) if get_attribute(ctx, 'agent_config'): return get_attribute(ctx, 'agent_config').get('key', None) return None def get_host(entity): if entity.instance.relationships: for relationship in entity.instance.relationships: if 'cloudify.relationships.contained_in' in relationship.type_hierarchy: return relationship.target return None def has_attribute_mapping(entity, attribute_name): # ctx.logger.debug('Check if it exists mapping for attribute {0} in {1}'.format(attribute_name,json.dumps(entity.node.properties))) mapping_configuration = entity.node.properties.get('_a4c_att_' + attribute_name, None) if mapping_configuration is not None: if mapping_configuration['parameters'][0] == 'SELF' and mapping_configuration['parameters'][1] == attribute_name: return False else: return True return False def process_attribute_mapping(entity, attribute_name, data_retriever_function): # This is where attribute mapping is defined in the cloudify type mapping_configuration = entity.node.properties['_a4c_att_' + attribute_name] # ctx.logger.debug('Mapping configuration found for attribute {0} is {1}'.format(attribute_name, json.dumps(mapping_configuration))) # If the mapping configuration exist and if it concerns SELF then just get attribute of the mapped attribute name # Else if it concerns TARGET then follow the relationship and retrieved the mapped attribute name from the TARGET if mapping_configuration['parameters'][0] == 'SELF': return data_retriever_function(entity, mapping_configuration['parameters'][1]) elif mapping_configuration['parameters'][0] == 'TARGET' and entity.instance.relationships: for relationship in entity.instance.relationships: if mapping_configuration['parameters'][1] in relationship.type_hierarchy: return data_retriever_function(relationship.target, mapping_configuration['parameters'][2]) return "" def get_nested_attribute(entity, attribute_names): deep_properties = get_attribute(entity, attribute_names[0]) attribute_names_iter = iter(attribute_names) next(attribute_names_iter) for attribute_name in attribute_names_iter: if deep_properties is None: return "" else: deep_properties = deep_properties.get(attribute_name, None) return deep_properties def _all_instances_get_nested_attribute(entity, attribute_names): return None def get_attribute(entity, attribute_name): if has_attribute_mapping(entity, attribute_name): # First check if any mapping exist for attribute mapped_value = process_attribute_mapping(entity, attribute_name, get_attribute) # ctx.logger.debug('Mapping exists for attribute {0} with value {1}'.format(attribute_name, json.dumps(mapped_value))) return mapped_value # No mapping exist, try to get directly the attribute from the entity attribute_value = entity.instance.runtime_properties.get(attribute_name, None) if attribute_value is not None: # ctx.logger.debug('Found the attribute {0} with value {1} on the node {2}'.format(attribute_name, json.dumps(attribute_value), entity.node.id)) return attribute_value # Attribute retrieval fails, fall back to property property_value = entity.node.properties.get(attribute_name, None) if property_value is not None: return property_value # Property retrieval fails, fall back to host instance host = get_host(entity) if host is not None: # ctx.logger.debug('Attribute not found {0} go up to the parent node {1}'.format(attribute_name, host.node.id)) return get_attribute(host, attribute_name) # Nothing is found return "" def get_target_capa_or_node_attribute(entity, capability_attribute_name, attribute_name): attribute_value = entity.instance.runtime_properties.get(capability_attribute_name, None) if attribute_value is not None: # ctx.logger.debug('Found the capability attribute {0} with value {1} on the node {2}'.format(attribute_name, json.dumps(attribute_value), entity.node.id)) return attribute_value return get_attribute(entity, attribute_name) def _all_instances_get_attribute(entity, attribute_name): result_map = {} # get all instances data using cfy rest client # we have to get the node using the rest client with node_instance.node_id # then we will have the relationships node = client.nodes.get(ctx.deployment.id, entity.node.id) all_node_instances = client.node_instances.list(ctx.deployment.id, entity.node.id) for node_instance in all_node_instances: prop_value = __recursively_get_instance_data(node, node_instance, attribute_name) if prop_value is not None: # ctx.logger.debug('Found the property/attribute {0} with value {1} on the node {2} instance {3}'.format(attribute_name, json.dumps(prop_value), entity.node.id, # node_instance.id)) result_map[node_instance.id + '_'] = prop_value return result_map # Same as previous method but will first try to find the attribute on the capability. def _all_instances_get_target_capa_or_node_attribute(entity, capability_attribute_name, attribute_name): result_map = {} node = client.nodes.get(ctx.deployment.id, entity.node.id) all_node_instances = client.node_instances.list(ctx.deployment.id, entity.node.id) for node_instance in all_node_instances: attribute_value = node_instance.runtime_properties.get(capability_attribute_name, None) if attribute_value is not None: prop_value = attribute_value else: prop_value = __recursively_get_instance_data(node, node_instance, attribute_name) if prop_value is not None: # ctx.logger.debug('Found the property/attribute {0} with value {1} on the node {2} instance {3}'.format(attribute_name, json.dumps(prop_value), entity.node.id, # node_instance.id)) result_map[node_instance.id + '_'] = prop_value return result_map def get_property(entity, property_name): # Try to get the property value on the node property_value = entity.node.properties.get(property_name, None) if property_value is not None: # ctx.logger.debug('Found the property {0} with value {1} on the node {2}'.format(property_name, json.dumps(property_value), entity.node.id)) return property_value # No property found on the node, fall back to the host host = get_host(entity) if host is not None: # ctx.logger.debug('Property not found {0} go up to the parent node {1}'.format(property_name, host.node.id)) return get_property(host, property_name) return "" def get_instance_list(node_id): result = '' all_node_instances = client.node_instances.list(ctx.deployment.id, node_id) for node_instance in all_node_instances: if len(result) > 0: result += ',' result += node_instance.id return result def get_host_node_name(instance): for relationship in instance.relationships: if 'cloudify.relationships.contained_in' in relationship.type_hierarchy: return relationship.target.node.id return None def __get_relationship(node, target_name, relationship_type): for relationship in node.relationships: if relationship.get('target_id') == target_name and relationship_type in relationship.get('type_hierarchy'): return relationship return None def __has_attribute_mapping(node, attribute_name): # ctx.logger.debug('Check if it exists mapping for attribute {0} in {1}'.format(attribute_name, json.dumps(node.properties))) mapping_configuration = node.properties.get('_a4c_att_' + attribute_name, None) if mapping_configuration is not None: if mapping_configuration['parameters'][0] == 'SELF' and mapping_configuration['parameters'][1] == attribute_name: return False else: return True return False def __process_attribute_mapping(node, node_instance, attribute_name, data_retriever_function): # This is where attribute mapping is defined in the cloudify type mapping_configuration = node.properties['_a4c_att_' + attribute_name] # ctx.logger.debug('Mapping configuration found for attribute {0} is {1}'.format(attribute_name, json.dumps(mapping_configuration))) # If the mapping configuration exist and if it concerns SELF then just get attribute of the mapped attribute name # Else if it concerns TARGET then follow the relationship and retrieved the mapped attribute name from the TARGET if mapping_configuration['parameters'][0] == 'SELF': return data_retriever_function(node, node_instance, mapping_configuration['parameters'][1]) elif mapping_configuration['parameters'][0] == 'TARGET' and node_instance.relationships: for rel in node_instance.relationships: relationship = __get_relationship(node, rel.get('target_name'), rel.get('type')) if mapping_configuration['parameters'][1] in relationship.get('type_hierarchy'): target_instance = client.node_instances.get(rel.get('target_id')) target_node = client.nodes.get(ctx.deployment.id, target_instance.node_id) return data_retriever_function(target_node, target_instance, mapping_configuration['parameters'][2]) return None def __recursively_get_instance_data(node, node_instance, attribute_name): if __has_attribute_mapping(node, attribute_name): return __process_attribute_mapping(node, node_instance, attribute_name, __recursively_get_instance_data) attribute_value = node_instance.runtime_properties.get(attribute_name, None) if attribute_value is not None: return attribute_value elif node_instance.relationships: for rel in node_instance.relationships: # on rel we have target_name, target_id (instanceId), type relationship = __get_relationship(node, rel.get('target_name'), rel.get('type')) if 'cloudify.relationships.contained_in' in relationship.get('type_hierarchy'): parent_instance = client.node_instances.get(rel.get('target_id')) parent_node = client.nodes.get(ctx.deployment.id, parent_instance.node_id) return __recursively_get_instance_data(parent_node, parent_instance, attribute_name) return None else: return None def get_public_or_private_ip(entity): public_ip = get_attribute(entity, 'public_ip_address') if not public_ip: return get_attribute(entity, 'ip_address') return public_ip def get_attribute_from_top_host(entity, attribute_name): host = get_host(entity) while host is not None: entity = host host = get_host(entity) return get_attribute(entity, attribute_name) ctx.instance.runtime_properties['tosca_id'] = ctx.instance.id ctx.instance.runtime_properties['tosca_name'] = ctx.node.id ctx.instance.runtime_properties['port'] = r'3306' ctx.instance.runtime_properties['name'] = r'wordpress' ctx.instance.runtime_properties['db_user'] = r'pass' ctx.instance.runtime_properties['db_password'] = r'<PASSWORD>' ctx.instance.runtime_properties['bind_address'] = r'true' ctx.instance.runtime_properties['storage_path'] = r'/mountedStorage' ctx.instance.runtime_properties['capabilities.database_endpoint.protocol'] = r'tcp' ctx.instance.runtime_properties['capabilities.database_endpoint.secure'] = r'false' ctx.instance.runtime_properties['capabilities.database_endpoint.network_name'] = r'PRIVATE' ctx.instance.runtime_properties['capabilities.database_endpoint.initiator'] = r'source' ctx.instance.runtime_properties['capabilities.database_endpoint.ip_address'] = get_attribute(ctx, 'ip_address')
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sockfilter/real.py
cardforcoin/sockfilter
1
189695
<filename>sockfilter/real.py __all__ = ['restore', 'socket', 'socks', 'socket__socket'] import socket socket__socket = socket.socket socks__socksocket = None try: import socks socks__socksocket = socks.socksocket except ImportError: socks = None from .util import apply_attr_and_dict def restore(): apply_attr_and_dict(socket, 'socket', socket__socket) apply_attr_and_dict(socket, 'SocketType', socket__socket) apply_attr_and_dict(socket, '_socketobject', socket__socket) apply_attr_and_dict(socks, 'socksocket', socks__socksocket)
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