paulo037/starcoder · Datasets at Hugging Face

max_stars_repo_path stringlengths 4 237	max_stars_repo_name stringlengths 6 117	max_stars_count int64 0 95.2k	id stringlengths 1 7	content stringlengths 12 593k	input_ids sequencelengths 7 549k
py_projects_boilerplates/sphinx_loguru_pytest_boiler/app/modules/hello_world/tools/tools.py	rypaik/PYTHON	0	45525	<gh_stars>0 #from config import console, log from config import console from loguru import logger import sys #TODO: factory function to create logger # setting up Logger logger.add(sys.stderr, format="{time} {level} {message}", level="INFO") logger.add("./common/loguru_log/Tools_{time}.log", rotation="500 MB") # @logger.catch # @logger.catch def say_hello(message): # print(message) console.print(message, style="bold blue") return message logger.bind(special=True).info("This message, though, is logged to the file!")	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 3166, 2295, 1053, 2991, 29892, 1480, 13, 3166, 2295, 1053, 2991, 13, 3166, 1480, 20144, 1053, 17927, 13, 5215, 10876, 13, 29937, 4986, 3970, 29901, 12529, 740, 304, 1653, 17927, 13, 13, 29937, 4444, 701, 28468, 13, 21707, 29889, 1202, 29898, 9675, 29889, 303, 20405, 29892, 3402, 10724, 2230, 29913, 426, 5563, 29913, 426, 4906, 17671, 3233, 543, 11690, 1159, 13, 21707, 29889, 1202, 703, 6904, 9435, 29914, 1188, 20144, 29918, 1188, 29914, 24183, 648, 2230, 1836, 1188, 613, 13733, 543, 29945, 29900, 29900, 13232, 1159, 13, 13, 29937, 732, 21707, 29889, 12510, 29871, 13, 29937, 732, 21707, 29889, 12510, 13, 1753, 1827, 29918, 12199, 29898, 4906, 1125, 13, 1678, 396, 1596, 29898, 4906, 29897, 13, 1678, 2991, 29889, 2158, 29898, 4906, 29892, 3114, 543, 8934, 7254, 1159, 13, 1678, 736, 2643, 13, 1678, 17927, 29889, 5355, 29898, 18732, 29922, 5574, 467, 3888, 703, 4013, 2643, 29892, 2466, 29892, 338, 13817, 304, 278, 934, 29991, 1159, 2 ]
geniepy/src/geniepy/classmgmt/classifiers.py	geralddzx/genie	1	61252	"""Implementation of predictive classifiers.""" from abc import ABC, abstractmethod import pandas as pd from google_drive_downloader import GoogleDriveDownloader as gdd from joblib import load as jload from geniepy.errors import ClassifierError import geniepy.config as gc ERROR_SCORE = float(-1) PCPCLSFR_NAME = "pub_score" CTCLSFR_NAME = "ct_score" class BaseClassifier(ABC): """Base Classifier Abstract Class.""" __slots__ = [ "_is_trained", # True if classifier is trained "_model", # The classifier model "_name", # The name of the classifier (used as name of prediction col) ] def __init__(self, name): """ Construct classifier obj. Arguments: name {[type]} -- The classifier's name """ self._name = name self._is_trained = False self._model = None @property def name(self): """Return name prediction column.""" return self._name @property def is_trained(self): """Return true is model is trained.""" return self._is_trained @abstractmethod def predict(self, features: pd.DataFrame) -> float: """ Calculate publication count label. It is import for this method to always produce results and handle exceptions internally, so execution isn't halted due to exceptions. Arguments: features {pd.DataFrame} -- Pandas series necessary prediction data Returns: float -- the classifier prediction """ def load(self): """ Load classifier model. Raises: ClassifierError -- If model doesn't load successfully """ # Download model from google drive model_path = gc.TMP_DIR.joinpath("gene_disease_gbc.joblib") model_id = gc.get_model() try: gdd.download_file_from_google_drive( file_id=model_id, dest_path=model_path, unzip=True, ) self._model = jload(model_path) self._is_trained = True model_path.unlink() except Exception: # If load fail raise ClassifierError("Unable to load model") class Classifier(BaseClassifier): """Implementation of Publication Count Predictive Classifier.""" def predict(self, features: pd.DataFrame): """ Calculate publication count label. It is import for this method to always produce results and handle exceptions internally, so execution isn't halted due to exceptions. Arguments: features {pd.DataFrame} -- Pandas series necessary prediction data Returns: float -- the classifier prediction """ if (not self._is_trained) or (features is None): # TODO log event "Received None features to predict" # TODO log event "Untrained classifier can't calculate predictions" return ERROR_SCORE # Only keep expected columns features.dropna(inplace=True) scores_df = pd.DataFrame(features["gene_disease_relationship"]) filtered_features = features[ [ "num_publications", "citations_cum_sum", "authors_cum_sum", "chemicals_cum_sum", "cum_sum_journals", "num_languages", "sjr", "h_index", "us_published", "us_uk_published", ] ] # Predictions and probabilities predictions = self._model.predict(filtered_features) probs = self._model.predict_proba(filtered_features) prob_1 = [item[0] for item in probs] # Get positiive probs only # Add new fields into original df scores_df["classifier_prediction"] = predictions scores_df["classifier_prob"] = prob_1 return scores_df	[ 1, 9995, 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ants/core/__init__.py	ncullen93/ANTsPy	3	1603901	from .ants_image import * from .ants_transform import * from .image_io import * from .transform_io import *	[ 1, 29871, 13, 3166, 869, 1934, 29918, 3027, 1053, 334, 13, 3166, 869, 1934, 29918, 9067, 1053, 334, 13, 13, 3166, 869, 3027, 29918, 601, 1053, 334, 13, 3166, 869, 9067, 29918, 601, 1053, 334, 13, 2 ]
DNN_Experiments/MaskRCNN/convert.py	wmjpillow/FlameDetectionAPP	2	23770	#!/usr/bin/env python # convert jpg tp png from glob import glob import cv2 pngs = glob('./.jpg') for j in pngs: img = cv2.imread(j) cv2.imwrite(j[:-3] + 'png', img) # delete jpg files import glob import os dir = "/Users/wangmeijie/ALLImportantProjects/FlameDetectionAPP/Models/MaskRCNN/02_26_2020/Mask_RCNN/dataset/train" for jpgpath in glob.iglob(os.path.join(dir, '.jpg')): os.remove(jpgpath)	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 3588, 432, 4061, 260, 29886, 282, 865, 13, 3166, 13149, 1053, 13149, 13, 5215, 13850, 29906, 13, 2732, 29879, 353, 13149, 12839, 5515, 29889, 6173, 1495, 13, 13, 1454, 432, 297, 282, 865, 29879, 29901, 13, 1678, 10153, 353, 13850, 29906, 29889, 326, 949, 29898, 29926, 29897, 13, 1678, 13850, 29906, 29889, 326, 3539, 29898, 29926, 7503, 29899, 29941, 29962, 718, 525, 2732, 742, 10153, 29897, 13, 13, 13, 29937, 5217, 432, 4061, 2066, 13, 5215, 13149, 13, 5215, 2897, 13, 13, 3972, 353, 5591, 5959, 29914, 29893, 574, 1004, 823, 347, 29914, 9818, 17518, 424, 25119, 29914, 8754, 420, 29928, 2650, 428, 20576, 29914, 23785, 29914, 19832, 10363, 10262, 29914, 29900, 29906, 29918, 29906, 29953, 29918, 29906, 29900, 29906, 29900, 29914, 19832, 29918, 10363, 10262, 29914, 24713, 29914, 14968, 29908, 13, 13, 1454, 432, 4061, 2084, 297, 13149, 29889, 335, 2127, 29898, 359, 29889, 2084, 29889, 7122, 29898, 3972, 29892, 525, 10521, 6173, 8785, 29901, 13, 1678, 2897, 29889, 5992, 29898, 6173, 2084, 29897, 2 ]
linkcopy.py	priximmo/scripts-scripts	1	166173	# This script is for Windows # import sys import pathlib import subprocess as sub if len(sys.argv) < 3: print("{} - Make symbolic links for input's contents into output's contents" .format(sys.argv[0])) print("Usage: {} INPUT_DIRECTORY OUTPUT_DIRECTORY".format(sys.argv[0])) sys.exit(1) INPUT_DIRECTORY = pathlib.Path(sys.argv[1]) OUTPUT_DIRECTORY = pathlib.Path(sys.argv[2]) try: for child in INPUT_DIRECTORY.iterdir(): # child is the FQ'ed path of below variable current_file = child.parts[len(child.parts)-1] new_path = OUTPUT_DIRECTORY new_file = OUTPUT_DIRECTORY / current_file command = "MKLINK \"%s\" \"%s\"" % (new_file, child) ret = sub.call(command, shell=True) if ret != 0: raise OSError print("INPUT: {}".format(child)) print("OUTPUT: {}".format(new_file)) except OSError: print("The command was not completed successfully. :(") sys.exit(1)	[ 1, 396, 910, 2471, 338, 363, 3852, 30004, 13, 29937, 30004, 13, 5215, 10876, 30004, 13, 5215, 2224, 1982, 30004, 13, 5215, 1014, 5014, 408, 1014, 30004, 13, 30004, 13, 361, 7431, 29898, 9675, 29889, 19218, 29897, 529, 29871, 29941, 29901, 30004, 13, 1678, 1596, 703, 8875, 448, 8561, 5829, 293, 2988, 363, 1881, 29915, 29879, 8118, 964, 1962, 29915, 29879, 8118, 19451, 13, 1678, 869, 4830, 29898, 9675, 29889, 19218, 29961, 29900, 12622, 30004, 13, 1678, 1596, 703, 27573, 29901, 6571, 2672, 12336, 29918, 4571, 26282, 18929, 19474, 12336, 29918, 4571, 26282, 18929, 1642, 4830, 29898, 9675, 29889, 19218, 29961, 29900, 12622, 30004, 13, 1678, 10876, 29889, 13322, 29898, 29896, 8443, 13, 30004, 13, 1177, 12336, 29918, 4571, 26282, 18929, 353, 2224, 1982, 29889, 2605, 29898, 9675, 29889, 19218, 29961, 29896, 2314, 30004, 13, 12015, 12336, 29918, 4571, 26282, 18929, 353, 2224, 1982, 29889, 2605, 29898, 9675, 29889, 19218, 29961, 29906, 2314, 30004, 13, 30004, 13, 2202, 29901, 30004, 13, 1678, 363, 2278, 297, 2672, 12336, 29918, 4571, 26282, 18929, 29889, 1524, 3972, 7295, 30004, 13, 4706, 396, 2278, 338, 278, 383, 29984, 29915, 287, 2224, 310, 2400, 2286, 30004, 13, 4706, 1857, 29918, 1445, 353, 2278, 29889, 20895, 29961, 2435, 29898, 5145, 29889, 20895, 6817, 29896, 29962, 30004, 13, 4706, 716, 29918, 2084, 353, 19474, 12336, 29918, 4571, 26282, 18929, 30004, 13, 4706, 716, 29918, 1445, 353, 19474, 12336, 29918, 4571, 26282, 18929, 847, 1857, 29918, 1445, 30004, 13, 4706, 1899, 353, 376, 29924, 29968, 23714, 29968, 13218, 29995, 29879, 5931, 13218, 29995, 29879, 5931, 29908, 1273, 313, 1482, 29918, 1445, 29892, 2278, 8443, 13, 4706, 3240, 353, 1014, 29889, 4804, 29898, 6519, 29892, 6473, 29922, 5574, 8443, 13, 4706, 565, 3240, 2804, 29871, 29900, 29901, 30004, 13, 9651, 12020, 438, 29173, 30004, 13, 4706, 1596, 703, 1177, 12336, 29901, 6571, 1642, 4830, 29898, 5145, 876, 30004, 13, 4706, 1596, 703, 12015, 12336, 29901, 6571, 1642, 4830, 29898, 1482, 29918, 1445, 876, 30004, 13, 19499, 438, 29173, 29901, 30004, 13, 1678, 1596, 703, 1576, 1899, 471, 451, 8676, 8472, 29889, 584, 703, 8443, 13, 1678, 10876, 29889, 13322, 29898, 29896, 8443, 13, 2 ]
predict_with_ssd7.py	esp32wrangler/ssd_keras	0	24775	<gh_stars>0 from keras.optimizers import Adam from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau, TerminateOnNaN, CSVLogger from keras import backend as K from keras.models import load_model from math import ceil import numpy as np from matplotlib import pyplot as plt from models.keras_ssd7 import build_model from keras_loss_function.keras_ssd_loss import SSDLoss from keras_layers.keras_layer_AnchorBoxes import AnchorBoxes from keras_layers.keras_layer_DecodeDetections import DecodeDetections from keras_layers.keras_layer_DecodeDetectionsFast import DecodeDetectionsFast from ssd_encoder_decoder.ssd_input_encoder import SSDInputEncoder from ssd_encoder_decoder.ssd_output_decoder import decode_detections, decode_detections_fast from data_generator.object_detection_2d_data_generator import DataGenerator from data_generator.object_detection_2d_misc_utils import apply_inverse_transforms from data_generator.data_augmentation_chain_variable_input_size import DataAugmentationVariableInputSize from data_generator.data_augmentation_chain_constant_input_size import DataAugmentationConstantInputSize from data_generator.data_augmentation_chain_original_ssd import SSDDataAugmentation import cv2 img_height = 416 # Height of the input images img_width = 416 # Width of the input images img_channels = 3 # Number of color channels of the input images intensity_mean = 127.5 # Set this to your preference (maybe `None`). The current settings transform the input pixel values to the interval `[-1,1]`. intensity_range = 127.5 # Set this to your preference (maybe `None`). The current settings transform the input pixel values to the interval `[-1,1]`. n_classes = 3 # Number of positive classes scales = [0.08, 0.16, 0.32, 0.64, 0.96] # An explicit list of anchor box scaling factors. If this is passed, it will override `min_scale` and `max_scale`. aspect_ratios = [0.5, 1.0, 2.0] # The list of aspect ratios for the anchor boxes two_boxes_for_ar1 = True # Whether or not you want to generate two anchor boxes for aspect ratio 1 steps = None # In case you'd like to set the step sizes for the anchor box grids manually; not recommended offsets = None # In case you'd like to set the offsets for the anchor box grids manually; not recommended clip_boxes = False # Whether or not to clip the anchor boxes to lie entirely within the image boundaries variances = [1.0, 1.0, 1.0, 1.0] # The list of variances by which the encoded target coordinates are scaled normalize_coords = True # Whether or not the model is supposed to use coordinates relative to the image size model_path = 'ssd7_epoch-07_loss-0.9988_val_loss-0.6916.h5' # We need to create an SSDLoss object in order to pass that to the model loader. ssd_loss = SSDLoss(neg_pos_ratio=3, alpha=1.0) K.clear_session() # Clear previous models from memory. model = build_model(image_size=(img_height, img_width, img_channels), n_classes=n_classes, mode='training', l2_regularization=0.0005, scales=scales, aspect_ratios_global=aspect_ratios, aspect_ratios_per_layer=None, two_boxes_for_ar1=two_boxes_for_ar1, steps=steps, offsets=offsets, clip_boxes=clip_boxes, variances=variances, normalize_coords=normalize_coords, subtract_mean=intensity_mean, divide_by_stddev=intensity_range) # 2: Optional: Load some weights model.load_weights(model_path, by_name=True) train_dataset = DataGenerator(load_images_into_memory=False, hdf5_dataset_path=None) val_dataset = DataGenerator(load_images_into_memory=False, hdf5_dataset_path=None) # 2: Parse the image and label lists for the training and validation datasets. # TODO: Set the paths to your dataset here. # Images images_dir = './infiles' ''' # Ground truth train_labels_filename = 'onsite-images-export.csv' val_labels_filename = 'onsite-images-valid.csv' train_dataset.parse_csv(images_dir=images_dir, labels_filename=train_labels_filename, input_format=['image_name', 'xmin', 'ymin', 'xmax', 'ymax', 'class_id'], # This is the order of the first six columns in the CSV file that contains the labels for your dataset. If your labels are in XML format, maybe the XML parser will be helpful, check the documentation. include_classes='all') val_dataset.parse_csv(images_dir=images_dir, labels_filename=val_labels_filename, input_format=['image_name', 'xmin', 'ymin', 'xmax', 'ymax', 'class_id'], include_classes='all') train_dataset.create_hdf5_dataset(file_path='train_imgs.h5', resize=False, variable_image_size=True, verbose=True) val_dataset.create_hdf5_dataset(file_path='val_imgs.h5', resize=False, variable_image_size=True, verbose=True) train_dataset_size = train_dataset.get_dataset_size() val_dataset_size = val_dataset.get_dataset_size() predict_generator = val_dataset.generate(batch_size=1, shuffle=True, transformations=[], label_encoder=None, returns={'processed_images', 'processed_labels', 'filenames'}, keep_images_without_gt=False) batch_images, batch_labels, batch_filenames = next(predict_generator) i = 0 # Which batch item to look at print("Image:", batch_filenames[i]) print() print("Ground truth boxes:\n") print(batch_labels[i]) ''' import glob import os fnames = glob.glob(images_dir+"/*.png") #fnames = [images_dir+"/yellow834159457.png"] for fn in fnames: img = cv2.imread(fn) img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) imgs = np.reshape(img_rgb, (1, 416, 416, 3)) y_pred = model.predict(imgs) y_pred_decoded = decode_detections(y_pred, confidence_thresh=0.7, iou_threshold=0.45, top_k=200, normalize_coords=normalize_coords, img_height=img_height, img_width=img_width) boxColors = [(0, 255, 255), (0, 0, 255), (0, 255, 0)] #print y_pred_decoded if len(y_pred_decoded) > 0: y_pred_list = y_pred_decoded[0].tolist() for sss in y_pred_list: #print sss cls, conf, xmin, ymin, xmax, ymax = sss boxColor = boxColors[int(cls)-1] cv2.rectangle(img, (int(xmin), int(ymin)), (int(xmax), int(ymax)), boxColor, 2) cv2.imwrite("outfiles/" + os.path.basename(fn), img) ''' y_pred = model.predict(batch_images) y_pred_decoded = decode_detections(y_pred, confidence_thresh=0.2, iou_threshold=0.45, top_k=200, normalize_coords=normalize_coords, img_height=img_height, img_width=img_width) np.set_printoptions(precision=2, suppress=True, linewidth=90) print("Predicted boxes:\n") print(' class conf xmin ymin xmax ymax') print(y_pred_decoded[i]) '''	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 13023, 294, 29889, 20640, 19427, 1053, 11783, 13, 3166, 13023, 294, 29889, 14035, 29879, 1053, 8125, 5596, 3149, 29892, 11095, 20754, 3262, 29892, 4367, 24551, 29931, 1672, 29876, 3247, 403, 585, 29892, 11814, 16976, 2951, 19377, 29892, 16874, 16363, 13, 3166, 13023, 294, 1053, 14998, 408, 476, 13, 3166, 13023, 294, 29889, 9794, 1053, 2254, 29918, 4299, 13, 3166, 5844, 1053, 2257, 309, 13, 5215, 12655, 408, 7442, 13, 3166, 22889, 1053, 11451, 5317, 408, 14770, 13, 13, 3166, 4733, 29889, 3946, 294, 29918, 893, 29881, 29955, 1053, 2048, 29918, 4299, 13, 3166, 13023, 294, 29918, 6758, 29918, 2220, 29889, 3946, 294, 29918, 893, 29881, 29918, 6758, 1053, 5886, 19558, 2209, 13, 3166, 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src/basic_example/catkin_generated/installspace/eye_node.py	juliojn/eye-saccade	0	123294	#!/usr/bin/python3.7 from std_msgs.msg import String import opensim as osim from basic_example.srv import * import rospy import sys import os # ---------------------------------------------------------------------- # Load the musculoskeletal model from a file. # ---------------------------------------------------------------------- path = os.path.dirname(os.path.abspath(__file__)) model = osim.Model(path + "/../model/UPAT_Eye_Model_Passive_Pulleys_v2.osim") # ---------------------------------------------------------------------- # Add a controller to the model's muscles. # ---------------------------------------------------------------------- actuator_set = model.getActuators() lateral_rectus = actuator_set.get("r_Lateral_Rectus") medial_rectus = actuator_set.get("r_Medial_Rectus") brain = osim.PrescribedController() brain.addActuator(lateral_rectus) brain.addActuator(medial_rectus) brain.prescribeControlForActuator("r_Lateral_Rectus", osim.Constant(0.0)) brain.prescribeControlForActuator("r_Medial_Rectus", osim.Constant(0.0)) model.addController(brain) # ---------------------------------------------------------------------- # Add a console reporter to print the following values: # Position and speed of the adduction-abduction rotational Degree of # Freedom (y-axis). # Current fiber force applied to the Lateral Rectus and the # Medial Rectus tendons. # ---------------------------------------------------------------------- coordinate_set = model.getCoordinateSet() eye_add_abd = coordinate_set.get("r_eye_add_abd") reporter = osim.ConsoleReporter() reporter.set_report_time_interval(0.002) reporter.addToReport(eye_add_abd.getOutput("value"), "position") reporter.addToReport(eye_add_abd.getOutput("speed"), "speed") reporter.addToReport(lateral_rectus.getOutput("fiber_force"), "lateral_force") reporter.addToReport(medial_rectus.getOutput("fiber_force"), "medial_force") model.addComponent(reporter) # -------------------------------------------------------------------------- # Configure the simulation of the model # -------------------------------------------------------------------------- state = model.initSystem() model.equilibrateMuscles(state) manager = osim.Manager(model) state.setTime(0) manager.initialize(state) # -------------------------------------------------------------------------- # Get the control signals of the Lateral Rectus an the Medial Rectus # -------------------------------------------------------------------------- def getControlSignal(current_pos, time): rospy.wait_for_service("get_control_signal") try: get_control_signal = rospy.ServiceProxy("get_control_signal", GetControlSignal) response = get_control_signal(current_pos, time) return response except rospy.ServiceException as e: print("Service call failed: %s"%e) if __name__ == '__main__': time = 0.0 # in seconds sim_time = 0.002 # in seconds while time < 5.0: current_pos = 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jpp_boosted_django/jpp_boosted/website/templatetags/project_overview_list.py	QualmandDriven/jpp_boosted	1	45847	from django import template from django.template.loader import get_template register = template.Library() @register.inclusion_tag('project_overview_list.html') def project_overview_list(project_list): return {'project_list': project_list}	[ 1, 515, 9557, 1053, 4472, 13, 3166, 9557, 29889, 6886, 29889, 12657, 1053, 679, 29918, 6886, 13, 9573, 353, 4472, 29889, 12284, 580, 13, 13, 29992, 9573, 29889, 262, 10085, 29918, 4039, 877, 4836, 29918, 957, 1493, 29918, 1761, 29889, 1420, 1495, 13, 1753, 2060, 29918, 957, 1493, 29918, 1761, 29898, 4836, 29918, 1761, 1125, 13, 1678, 736, 11117, 4836, 29918, 1761, 2396, 2060, 29918, 1761, 29913, 2 ]
analysis_helpers/general.py	davidcrowland/layer_vb_tagging	0	177270	from __future__ import print_function # Python 2.x import os import numpy as np import pandas as pd import h5py import sys import math from fnmatch import fnmatch # helper functions klusta analysis pipeline def get_param_file(filename,params_folder): found = False params = [] for path, subdirs, files in os.walk(params_folder): for name in files: for string in filename.split("/"): if string in name: found = True params = name return found, params return found, params def n_user(filename): #find n-drive username n_drive_user = 'non_identified' if 'MosersServer' in filename: # assuming osx/linux: idx = [i for i,x in enumerate(filename.split('/')) if x == 'MosersServer'][0] n_drive_user = filename.split('/')[idx+1] else: # assuming windows: idx = [i for i,x in enumerate(filename.split('/')) if x == 'N:'][0] n_drive_user = filename.split('/')[idx+1] return n_drive_user def create_export_folders(filename): # create export folders export_folder = "/".join(filename.split("/")[:-1])+"/KLUSTA/" if not os.path.exists(export_folder): os.makedirs(export_folder) print('Created export folder: {:s}'.format(export_folder)) export_folder_basesession= "/".join(filename.split("/")[:-1])+"/KLUSTA/base_session" if not os.path.exists(export_folder_basesession): os.makedirs(export_folder_basesession) print('Created export_folder_basesession: {:s}'.format(export_folder_basesession)) export_folder_othersessions= "/".join(filename.split("/")[:-1])+"/KLUSTA/other_session" if not os.path.exists(export_folder_othersessions): os.makedirs(export_folder_othersessions) print('Created export_folder_othersessions: {:s}'.format(export_folder_othersessions)) export_folder_othersessions= "/".join(filename.split("/")[:-1])+"/KLUSTA/other_session" if not os.path.exists(export_folder_othersessions): os.makedirs(export_folder_othersessions) print('Created export_folder_othersessions: {:s}'.format(export_folder_othersessions)) export_folder_lfp = "/".join(filename.split("/")[:-1])+"/KLUSTA/LFP" if not os.path.exists(export_folder_lfp): os.makedirs(export_folder_lfp) print('Created export_folder_lfp: {:s}'.format(export_folder_lfp)) return export_folder, export_folder_basesession, export_folder_othersessions, export_folder_lfp def get_clusters(filename,key=None): if not key: sys.stdout.write('No cluster group key given.') sys.exit() cluster_group_names = [] key_clusters =[] with h5py.File(filename, mode="r") as f: cluster_groups = f['channel_groups/1/cluster_groups/main/'].keys() for clusterg in cluster_groups: name = f['channel_groups/1/cluster_groups/main/'+clusterg].attrs.values() cluster_group_names.append(name[0][0]) for cluster in f['/channel_groups/1/clusters/main'].iteritems(): name = f['channel_groups/1/clusters/main/'+cluster[0]].attrs.get('cluster_group') if cluster_group_names[int(name)] == key: key_clusters.append(int(cluster[0])) print('{} clusters: {}'.format(key,key_clusters)) if not key_clusters: print('None found :(') sys.exit() # apparently this is a bad way to abort the execution ... but it does the job. return key_clusters def get_clusters_dont_exit(filename,key=None): ''' Had to add this function to debug stimulus artefacts ... it serves no other purpose... ''' if not key: sys.stdout.write('No cluster group key given.') sys.exit() cluster_group_names = [] key_clusters =[] try: with h5py.File(filename, mode="r") as f: cluster_groups = f['channel_groups/1/cluster_groups/main/'].keys() for clusterg in cluster_groups: name = f['channel_groups/1/cluster_groups/main/'+clusterg].attrs.values() cluster_group_names.append(name[0][0]) for cluster in f['/channel_groups/1/clusters/main'].iteritems(): name = f['channel_groups/1/clusters/main/'+cluster[0]].attrs.get('cluster_group') if cluster_group_names[int(name)] == key: key_clusters.append(int(cluster[0])) print('{} clusters: {}'.format(key,key_clusters)) if not key_clusters: print('None found :(') except KeyError as err: print('No valid cluster groups found.') return key_clusters def get_basenames(filename): with h5py.File(filename, mode="r") as f: basenames = f['basenames'][:] return basenames def extract_times(filename): with h5py.File(filename, mode="r") as f: spiketimes = np.array(f['/channel_groups/1/spikes/time_samples'][:],dtype=float) sample_rate = float(f['/application_data/spikedetekt'].attrs.get('sample_rate')) time_stamps = np.array(f['/event_types/sessions/events/time_samples'][:],dtype=float) # extract the time stamps yet another way ... time_stamps_sessions = np.cumsum(np.array(f['/time_stamps_sessions'][:], dtype=float)) time_stamps_sessions_sample_rate = 96000. # cannot extract that from hdf5??? print('Extracted spiketimes.') return spiketimes,sample_rate,time_stamps, time_stamps_sessions, time_stamps_sessions_sample_rate def extract_input(filename): with h5py.File(filename, mode="r") as f: input_data = f['/input/input_data'][:] sample_rate_inp = f['/input'].attrs.get('sample_rate_inp') status=True try: sample_rate_inp = float(sample_rate_inp) except TypeError as error: status=False if not status: input_data=np.nan;time_inp=np.nan;time_stamps_sessions_input=np.nan; sample_rate_inp=np.nan;num_inp_samples=np.nan;duration_inp=np.nan return input_data,time_inp,time_stamps_sessions_input,sample_rate_inp,num_inp_samples,duration_inp # process: time_inp = np.array(input_data['time'],dtype=float) time_inp = time_inp/sample_rate_inp1000. # in ms num_inp_samples = len(input_data) time_stamps_sessions_input = f['/input/time_stamps_sessions_input'][:] time_stamps_sessions_input = np.array(time_stamps_sessions_input,dtype=np.float64) time_stamps_sessions_input = np.cumsum(time_stamps_sessions_input) time_stamps_sessions_input = time_stamps_sessions_input/sample_rate_inp1000. # in ms duration_inp = (time_stamps_sessions_input)[-1] # in ms print('Extracted input data.') return input_data,time_inp,time_stamps_sessions_input,sample_rate_inp,num_inp_samples,duration_inp def extract_waveforms(filename): # extract waveforms: kwx_filename = filename[:-5] + '.kwx' file_kwx = h5py.File(kwx_filename, 'r') with h5py.File(kwx_filename, mode="r") as f: waveforms = f['/channel_groups/1/waveforms_raw'][:] print('Extracted waveforms.') return waveforms def extract_positions(filename): with h5py.File(filename, mode="r") as f: data_pos = f['/positions/data_pos'][:] time_stamps_sessions_pos = np.cumsum(np.array(f['/positions/time_stamps_sessions_pos'][:]),dtype=float) timebase_pos = float(f['/positions/'].attrs.get('timebase_pos')) print('Extracted tracking data.') return data_pos, time_stamps_sessions_pos, timebase_pos #print('Found position samples at {} Hz ({} seconds over {} session(s))'.format(timebase_pos,time_stamps_sessions_pos[-1]/timebase_pos,len(time_stamps_sessions_pos)-1)) def extract_lfp(filename): with h5py.File(filename, mode="r") as f: eeg_raw = f['/eeg/data_eeg'][:] samples_sessions_eeg = f['/eeg/samples_sessions_eeg'][:] sample_rate_eeg = f['/eeg/'].attrs.get('sample_rate_eeg') samples_sessions_eeg = np.cumsum(samples_sessions_eeg) print('Extracted LFP data.') #print('Found EEG: {} samples at {} Hz ({} s). Separate LFPs recorded: {}'.format(eeg_raw.shape[1],sample_rate_eeg,len_eeg_s,len(eeg_raw))) return eeg_raw,samples_sessions_eeg,sample_rate_eeg def eeg_make_df(eeg_raw,data_pos_df,sample_rate_eeg,timebase_pos): eeg_df = pd.DataFrame() for eeg_no in xrange(eeg_raw['eeg'].shape[0]): eeg_df['eeg{}'.format(eeg_no)] = eeg_raw['eeg'][eeg_no] eeg_df['eeg_mean'] = np.mean(eeg_raw['eeg'],axis=0) eeg_df['speed'] = np.repeat(data_pos_df['speed_filtered'].values,sample_rate_eeg/timebase_pos) eeg_df['time'] = eeg_df.index.values.astype(float)/float(sample_rate_eeg) # in sec eeg_df.set_index('time', drop=True, append=False, inplace=True, verify_integrity=False) return eeg_df def sanity_check(spiketimes=None,sample_rate=None,time_stamps=None,time_stamps_sessions=None,time_stamps_sessions_sample_rate=None, waveforms=None,time_stamps_sessions_pos=None,timebase_pos=None,data_pos=None,time_stamps_sessions_input=None, samples_sessions_eeg=None,sample_rate_eeg=None): # performs a check of length of sessions etc. try: sys.stdout.write('\nComparing recorded session lengths...') if time_stamps is not None and sample_rate and time_stamps_sessions is not None and time_stamps_sessions_sample_rate: length_session1 = float(time_stamps[-1])/sample_rate length_session2 = float(time_stamps_sessions[-1])/time_stamps_sessions_sample_rate if length_session1 != length_session2: sys.stdout.write('\rInconsistency in calculated session lengths.') sys.exit() else: sys.stdout.write('Success.') else: sys.stdout.write('\rNo basic session information found.') except AttributeError: sys.stdout.write('\rNo basic session information found.') except TypeError: sys.stdout.write('\rNo basic session information found.') try: sys.stdout.write('\nComparing waveform and spike numbers...') if waveforms.shape[0] != len(spiketimes): # as many waveforms recorded as spikes? sys.stdout.write('\rNumber of recorded waveforms does not match length of recorded spikes.') sys.exit() else: sys.stdout.write('Success.') except AttributeError: sys.stdout.write('\rNo waveforms loaded.') except TypeError: sys.stdout.write('\rNo waveforms loaded.') try: sys.stdout.write('\nComparing recorded session lengths and position record...') if time_stamps_sessions_pos is not None and timebase_pos and time_stamps is not None and sample_rate and data_pos is not None: if not (time_stamps/sample_rate == time_stamps_sessions_pos/timebase_pos).all(): sys.stdout.write('\rLength of sessions and position file do not match.') sys.exit() elif not len(data_pos)/timebase_pos == (time_stamps/sample_rate)[-1]: sys.stdout.write('\rLength of sessions and position file do not match.') sys.exit() else: sys.stdout.write('Success.') else: sys.stdout.write('\rNo position data loaded.') except AttributeError: sys.stdout.write('\rNo position data loaded.') except TypeError: sys.stdout.write('\rNo position data loaded.') # input data: try: sys.stdout.write('\nComparing recorded session lengths and input data...') if time_stamps is not None and sample_rate and time_stamps_sessions_input is not None: time_stamps_session = np.cumsum(time_stamps)/sample_rate if (time_stamps_session != np.cumsum(time_stamps_sessions_input)/1000).any(): sys.stdout.write('\rInconsistency in calculated session lengths (session vs. input)') sys.exit() else: sys.stdout.write('Success.') else: sys.stdout.write('\rNo basic session or input information found! ') except AttributeError: sys.stdout.write('\rNo basic session or input information found! ') sys.exit() # this is fatal ... except TypeError: sys.stdout.write('\rNo basic session or input information found! ') sys.exit() # this is fatal ... # LFP data: try: sys.stdout.write('\nComparing recorded session lengths and LFP data...') if time_stamps is not None and sample_rate and samples_sessions_eeg is not None and sample_rate_eeg: if (time_stamps/sample_rate != samples_sessions_eeg/sample_rate_eeg).all(): sys.stdout.write('\rInconsistency in calculated session lengths (session vs. LFP)') sys.exit() else: sys.stdout.write('Success.') else: sys.stdout.write('\rNo basic session or LFP information found!') except AttributeError: sys.stdout.write('\rNo basic session or LFP information found!') sys.exit() # this is fatal ... except TypeError: sys.stdout.write('\rNo basic session or LFP information found!') sys.exit() # this is fatal ... def find_nearest(array,value): idx = np.searchsorted(array, value, side="left") if idx > 0 and (idx == len(array) or math.fabs(value - array[idx-1]) < math.fabs(value - array[idx])): return idx-1 else: return idx print('Loaded analysis helpers: General')	[ 1, 515, 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42_Google_Hash_Code/pizza_ml_0/ingredients.py	gguarnay/42	1	94104	import numpy as np class Ingredients: ''' Class for calculations of ingredients inside an area. ''' def __init__(self, pizza_lines): self._lines = [list(l) for l in pizza_lines] self._unique, self._map = np.unique(self._lines, return_inverse=True) self._map = self._map.reshape((len(self._lines),len(self._lines[0]))) self.shape = self._map.shape self.total = self.shape[0]self.shape[1] self.total_unique = np.max(self._map)+1 self.initialize() def initialize(self): ''' Create an array for faster calculation of ingredients inside an area. ''' from_origin = np.zeros((self.shape, self.total_unique)) for r in range(self.shape[0]): for c in range(self.shape[1]): ingredient_id = self._map[r][c] from_origin[r][c][ingredient_id] = 1 if r > 0: from_origin[r][c] += from_origin[r-1][c] if c > 0: from_origin[r][c] += from_origin[r][c-1] if r > 0 and c > 0: from_origin[r][c] -= from_origin[r-1][c-1] self._from_origin = from_origin def of(self, slice): ''' Return 1d array of number of ingredients, so i-th element is the number of ingredient with id i inside specified slice. ''' ingredients_inside_slice = np.copy(self._from_origin[ slice.r1, slice.c1]) if slice.r0 > 0: ingredients_inside_slice -= self._from_origin[slice.r0-1][slice.c1] if slice.c0 > 0: ingredients_inside_slice -= self._from_origin[slice.r1][slice.c0-1] if slice.r0 > 0 and slice.c0 > 0: ingredients_inside_slice += self._from_origin[slice.r0-1][slice.c0-1] return ingredients_inside_slice	[ 1, 1053, 12655, 408, 7442, 13, 13, 1990, 22607, 1127, 10070, 29901, 13, 1678, 14550, 13, 1678, 4134, 363, 17203, 310, 2348, 1127, 10070, 2768, 385, 4038, 29889, 13, 1678, 14550, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 282, 24990, 29918, 9012, 1125, 13, 4706, 1583, 3032, 9012, 353, 518, 1761, 29898, 29880, 29897, 363, 301, 297, 282, 24990, 29918, 9012, 29962, 13, 4706, 1583, 3032, 13092, 29892, 1583, 3032, 1958, 353, 7442, 29889, 13092, 29898, 1311, 3032, 9012, 29892, 736, 29918, 262, 3901, 29922, 5574, 29897, 13, 4706, 1583, 3032, 1958, 353, 1583, 3032, 1958, 29889, 690, 14443, 3552, 2435, 29898, 1311, 3032, 9012, 511, 2435, 29898, 1311, 3032, 9012, 29961, 29900, 29962, 4961, 13, 4706, 1583, 29889, 12181, 353, 1583, 3032, 1958, 29889, 12181, 13, 13, 4706, 1583, 29889, 7827, 353, 1583, 29889, 12181, 29961, 29900, 14178, 1311, 29889, 12181, 29961, 29896, 29962, 13, 4706, 1583, 29889, 7827, 29918, 13092, 353, 7442, 29889, 3317, 29898, 1311, 3032, 1958, 7240, 29896, 13, 13, 4706, 1583, 29889, 24926, 580, 13, 13, 1678, 822, 11905, 29898, 1311, 1125, 13, 4706, 14550, 13, 4706, 6204, 385, 1409, 363, 8473, 13944, 310, 2348, 1127, 10070, 2768, 385, 4038, 29889, 13, 4706, 14550, 13, 13, 4706, 515, 29918, 12574, 353, 7442, 29889, 3298, 359, 3552, 29930, 1311, 29889, 12181, 29892, 1583, 29889, 7827, 29918, 13092, 876, 13, 13, 4706, 363, 364, 297, 3464, 29898, 1311, 29889, 12181, 29961, 29900, 29962, 1125, 13, 9651, 363, 274, 297, 3464, 29898, 1311, 29889, 12181, 29961, 29896, 29962, 1125, 13, 18884, 2348, 1127, 993, 29918, 333, 353, 1583, 3032, 1958, 29961, 29878, 3816, 29883, 29962, 13, 18884, 515, 29918, 12574, 29961, 29878, 3816, 29883, 3816, 292, 1127, 993, 29918, 333, 29962, 353, 29871, 29896, 13, 18884, 565, 364, 1405, 29871, 29900, 29901, 13, 462, 1678, 515, 29918, 12574, 29961, 29878, 3816, 29883, 29962, 4619, 515, 29918, 12574, 29961, 29878, 29899, 29896, 3816, 29883, 29962, 13, 18884, 565, 274, 1405, 29871, 29900, 29901, 13, 462, 1678, 515, 29918, 12574, 29961, 29878, 3816, 29883, 29962, 4619, 515, 29918, 12574, 29961, 29878, 3816, 29883, 29899, 29896, 29962, 13, 18884, 565, 364, 1405, 29871, 29900, 322, 274, 1405, 29871, 29900, 29901, 13, 462, 1678, 515, 29918, 12574, 29961, 29878, 3816, 29883, 29962, 22361, 515, 29918, 12574, 29961, 29878, 29899, 29896, 3816, 29883, 29899, 29896, 29962, 13, 13, 4706, 1583, 3032, 3166, 29918, 12574, 353, 515, 29918, 12574, 13, 13, 1678, 822, 310, 29898, 1311, 29892, 22780, 1125, 13, 4706, 14550, 13, 4706, 7106, 29871, 29896, 29881, 1409, 310, 1353, 310, 2348, 1127, 10070, 29892, 577, 474, 29899, 386, 1543, 338, 278, 1353, 310, 13, 4706, 2348, 1127, 993, 411, 1178, 474, 2768, 6790, 22780, 29889, 13, 4706, 14550, 13, 13, 4706, 2348, 1127, 10070, 29918, 26102, 29918, 18337, 353, 7442, 29889, 8552, 29898, 1311, 3032, 3166, 29918, 12574, 29961, 13, 18884, 22780, 29889, 29878, 29896, 29892, 13, 18884, 22780, 29889, 29883, 29896, 2314, 13, 13, 4706, 565, 22780, 29889, 29878, 29900, 1405, 29871, 29900, 29901, 13, 9651, 2348, 1127, 10070, 29918, 26102, 29918, 18337, 22361, 1583, 3032, 3166, 29918, 12574, 29961, 18337, 29889, 29878, 29900, 29899, 29896, 3816, 18337, 29889, 29883, 29896, 29962, 13, 4706, 565, 22780, 29889, 29883, 29900, 1405, 29871, 29900, 29901, 13, 9651, 2348, 1127, 10070, 29918, 26102, 29918, 18337, 22361, 1583, 3032, 3166, 29918, 12574, 29961, 18337, 29889, 29878, 29896, 3816, 18337, 29889, 29883, 29900, 29899, 29896, 29962, 13, 4706, 565, 22780, 29889, 29878, 29900, 1405, 29871, 29900, 322, 22780, 29889, 29883, 29900, 1405, 29871, 29900, 29901, 13, 9651, 2348, 1127, 10070, 29918, 26102, 29918, 18337, 4619, 1583, 3032, 3166, 29918, 12574, 29961, 18337, 29889, 29878, 29900, 29899, 29896, 3816, 18337, 29889, 29883, 29900, 29899, 29896, 29962, 13, 13, 4706, 736, 2348, 1127, 10070, 29918, 26102, 29918, 18337, 13, 2 ]
nested_admin/tests/gfk/models.py	mionch/django-nested-admin	0	74097	from __future__ import unicode_literals from django.contrib.contenttypes.fields import GenericRelation, GenericForeignKey from django.contrib.contenttypes.models import ContentType from django.db import models from django.db.models import ForeignKey, CASCADE from django.utils.encoding import python_2_unicode_compatible @python_2_unicode_compatible class GFKB(models.Model): name = models.CharField(max_length=255) position = models.PositiveIntegerField() content_type = ForeignKey(ContentType, on_delete=CASCADE) object_id = models.PositiveIntegerField() content_object = GenericForeignKey() class Meta: ordering = ['object_id', 'position'] def __str__(self): parts = ["%s[%d]" % (self.name, self.position)] if self.content_object: parts.insert(0, "%s" % self.content_object) return "/".join(parts) @python_2_unicode_compatible class GFKA(models.Model): slug = models.CharField(max_length=255) position = models.PositiveIntegerField() content_type = ForeignKey(ContentType, on_delete=CASCADE) object_id = models.PositiveIntegerField() content_object = GenericForeignKey() b_set = GenericRelation(GFKB) class Meta: ordering = ['object_id', 'position'] def __str__(self): parts = ["%s[%d]" % (self.slug, self.position)] if self.content_object: parts.insert(0, "%s" % self.content_object) return "/".join(parts) @python_2_unicode_compatible class GFKRoot(models.Model): slug = models.CharField(max_length=255) a_set = GenericRelation(GFKA) def __str__(self): return self.slug	[ 1, 515, 4770, 29888, 9130, 1649, 1053, 29104, 29918, 20889, 1338, 13, 13, 3166, 9557, 29889, 21570, 29889, 3051, 8768, 29889, 9621, 1053, 3251, 293, 9662, 362, 29892, 3251, 293, 27755, 2558, 13, 3166, 9557, 29889, 21570, 29889, 3051, 8768, 29889, 9794, 1053, 10576, 1542, 13, 3166, 9557, 29889, 2585, 1053, 4733, 13, 3166, 9557, 29889, 2585, 29889, 9794, 1053, 19358, 2558, 29892, 315, 3289, 5454, 2287, 13, 3166, 9557, 29889, 13239, 29889, 22331, 1053, 3017, 29918, 29906, 29918, 2523, 356, 29918, 23712, 13, 13, 13, 29992, 4691, 29918, 29906, 29918, 2523, 356, 29918, 23712, 13, 1990, 402, 29943, 26067, 29898, 9794, 29889, 3195, 1125, 13, 1678, 1024, 353, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29906, 29945, 29945, 29897, 13, 1678, 2602, 353, 4733, 29889, 9135, 3321, 7798, 3073, 580, 13, 1678, 2793, 29918, 1853, 353, 19358, 2558, 29898, 3916, 1542, 29892, 373, 29918, 8143, 29922, 29907, 3289, 5454, 2287, 29897, 13, 1678, 1203, 29918, 333, 353, 4733, 29889, 9135, 3321, 7798, 3073, 580, 13, 1678, 2793, 29918, 3318, 353, 3251, 293, 27755, 2558, 580, 13, 13, 1678, 770, 20553, 29901, 13, 4706, 20520, 353, 6024, 3318, 29918, 333, 742, 525, 3283, 2033, 13, 13, 1678, 822, 4770, 710, 12035, 1311, 1125, 13, 4706, 5633, 353, 6796, 29995, 29879, 29961, 29995, 29881, 18017, 1273, 313, 1311, 29889, 978, 29892, 1583, 29889, 3283, 4638, 13, 4706, 565, 1583, 29889, 3051, 29918, 3318, 29901, 13, 9651, 5633, 29889, 7851, 29898, 29900, 29892, 11860, 29879, 29908, 1273, 1583, 29889, 3051, 29918, 3318, 29897, 13, 4706, 736, 5591, 1642, 7122, 29898, 20895, 29897, 13, 13, 13, 29992, 4691, 29918, 29906, 29918, 2523, 356, 29918, 23712, 13, 1990, 402, 29943, 29968, 29909, 29898, 9794, 29889, 3195, 1125, 13, 1678, 2243, 688, 353, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29906, 29945, 29945, 29897, 13, 1678, 2602, 353, 4733, 29889, 9135, 3321, 7798, 3073, 580, 13, 1678, 2793, 29918, 1853, 353, 19358, 2558, 29898, 3916, 1542, 29892, 373, 29918, 8143, 29922, 29907, 3289, 5454, 2287, 29897, 13, 1678, 1203, 29918, 333, 353, 4733, 29889, 9135, 3321, 7798, 3073, 580, 13, 1678, 2793, 29918, 3318, 353, 3251, 293, 27755, 2558, 580, 13, 1678, 289, 29918, 842, 353, 3251, 293, 9662, 362, 29898, 29954, 29943, 26067, 29897, 13, 13, 1678, 770, 20553, 29901, 13, 4706, 20520, 353, 6024, 3318, 29918, 333, 742, 525, 3283, 2033, 13, 13, 1678, 822, 4770, 710, 12035, 1311, 1125, 13, 4706, 5633, 353, 6796, 29995, 29879, 29961, 29995, 29881, 18017, 1273, 313, 1311, 29889, 29517, 29892, 1583, 29889, 3283, 4638, 13, 4706, 565, 1583, 29889, 3051, 29918, 3318, 29901, 13, 9651, 5633, 29889, 7851, 29898, 29900, 29892, 11860, 29879, 29908, 1273, 1583, 29889, 3051, 29918, 3318, 29897, 13, 4706, 736, 5591, 1642, 7122, 29898, 20895, 29897, 13, 13, 13, 29992, 4691, 29918, 29906, 29918, 2523, 356, 29918, 23712, 13, 1990, 402, 29943, 29968, 10303, 29898, 9794, 29889, 3195, 1125, 13, 1678, 2243, 688, 353, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29906, 29945, 29945, 29897, 13, 1678, 263, 29918, 842, 353, 3251, 293, 9662, 362, 29898, 29954, 29943, 29968, 29909, 29897, 13, 13, 1678, 822, 4770, 710, 12035, 1311, 1125, 13, 4706, 736, 1583, 29889, 29517, 13, 2 ]
src/spectrabuster/Spectrum/spectrum.py	eduardosprp/spectrabuster	0	146853	import numpy as np from time import sleep from math import exp import matplotlib.pyplot as plt from scipy.integrate import trapz from os import path import struct import spectrabuster.functions as sbf from importlib import import_module from datetime import date, datetime from functools import partial class Spectrum(object): """ Class variables. Mainly default values that are used whenever their instance counterparts are not specified when initializing a Spectrum object. """ # {{{ to_save = { "int_time", "correct_nl", "correct_dc", "UV_index", "capture_date", "capture_time", "temp", } # fields that will be written to the file samples = 1 # how many samples to average optimize = True # whether or not to call optimize_int_time when sensor saturation is detected UV_index = None # }}} def __init__(self, int_time=None, kwargs): # {{{ """ Initializes the Spectrum with the values specified by kwargs. Absence of a key results in the attribute taking a default value. """ # First of all, define a backend backend = kwargs["backend"] if "backend" in kwargs else None try: self.backend = sbf.get_backend(backend) except RuntimeError: self._warn("No backend specified. Using none by default.") self.backend = sbf.get_backend("none") # Then get the device if "device" in kwargs: self.device = self.backend.Device(kwargs["device"]) else: self.device = self.backend.first_available_device() # Some features of the device if self.backend.features["int_time_limits"]: self.int_time_limits = self.device.int_time_limits else: self.int_time_limits = None if self.backend.features["sat_intensity"]: self.sat_intensity = self.device.sat_intensity else: self.sat_intensity = None # Bunch of optional parameters self.from_index = kwargs["from_index"] if "from_index" in kwargs else None self.to_index = kwargs["to_index"] if "to_index" in kwargs else None self.correct_nl = kwargs["correct_nl"] if "correct_nl" in kwargs else False self.correct_dc = kwargs["correct_dc"] if "correct_dc" in kwargs else False self.samples = kwargs["samples"] if "samples" in kwargs else None self.int_time = int_time # Then the wavelengths and the intensities. It'll get each from the # device unless provided at the instantiation. if "wavelengths" in kwargs: self.wavel = np.array(kwargs["wavelengths"]) elif self.backend.features["measure"]: self.wavel = self.device.wavelengths() else: raise RuntimeError( f"No wavelengths array passed, and the {self.backend} cannot make measurements." ) if "intensities" in kwargs: self.inten = np.array(kwargs["intensities"]) elif self.backend.features["measure"]: self.inten = self.measure_inten(int_time=self.int_time) else: raise RuntimeError( f"No intensities array passed, and the {self.backend} cannot make masurements." ) # Finally, slice the intensities and wavelengths arrays. self.wavel, self.inten, self.slice = self.slice_array( self.wavel, self.inten, (self.from_index, self.to_index) ) # }}} def measure_inten(self, int_time=None, samples=None, kwargs): # {{{ samples = samples if samples is not None else self.samples int_time = int_time if int_time is not None else self.int_time correct_dc = kwargs["correct_dc"] if "correct_dc" in kwargs else self.correct_dc correct_nl = kwargs["correct_nl"] if "correct_nl" in kwargs else self.correct_nl if samples is None: samples = 1 elif type(samples) is not int or samples < 0: raise ValueError( f"Invalid value of {self.samples} for the number of samples to average." ) if int_time is None: pass else: try: self.device.set_int_time(float(int_time)) except (TypeError, ValueError) as e: raise ValueError( f"Invalid type or value of {int_time} for integration time" ) measure = partial( self.device.measure, correct_dc=correct_dc, correct_nl=correct_nl ) inten_avg = np.average([measure() for i in range(samples)], axis=0) return inten_avg # }}} def write_to_file(self, file_path=None, save_fields=True, *kwargs): # {{{ """ Stores spectrum in a .dat text file, using a format that is easy to parse in gnu octave, R or any other programming language, or visualize in gnuplot, or any spreadsheet program. """ overwrite = kwargs["overwrite"] if "overwrite" in kwargs else None if path.exists(file_path): if overwrite: self._warn(f"WARNING: File {file_path} exists. Overwriting it.") else: raise RuntimeError( f"File {file_path} already exists. Pass 'overwrite=True' if you are sure you want to overwrite it." ) only_wavelengths = ( kwargs["only_wavelengths"] if "only_wavelengths" in kwargs else False ) only_intensities = ( kwargs["only_intensities"] if "only_intensities" in kwargs else False ) to_save = self.to_save # fields that will be written to the file if not file_path or not isinstance(file_path, str): raise ValueError( "Please pass a string as the file path wherein to save the spectrum." ) with open(file_path, "w+") as arq: gen_comments = ( f"# {name} = {value}\n" for name, value in vars(self).items() if name in to_save ) arq.writelines(gen_comments) if only_wavelengths: gen_wavel_inten = (f"{wavel}\n" for wavel in self.wavel) elif only_intensities: gen_wavel_inten = (f"{inten}\n" for inten in self.inten) else: gen_wavel_inten = ( f"{wavel}\t{inten}\n" for wavel, inten in zip(self.spectrum) ) arq.writelines(gen_wavel_inten) # }}} def to_spectral_irrad(self, calibration_file=None, int_time=None): # {{{ """ Applies the spectral irradiance calibration and returns another Spectrum object for the irradiance spectrum. It also has to be a file with the wavelengths and spectral sensitivity, by the way. And note that this function assumes that the current Spectrum and the calibration file have the same wavelengths array, maybe just sliced differently """ if not calibration_file: raise RuntimeError( "Please pass the path to the calibration file as an argument." ) if not int_time and not self.int_time: raise ValueError( "No integration time argument passed, and this spectrum's int_time field is empty." ) elif not int_time and self.int_time: int_time = self.int_time calib_wavel, calib_inten, _ = self._read_file(calibration_file) if self.wavel.size > calib_wavel.size: from_index = self.find_wavel_index(self.wavel, calib_wavel[0]) to_index = self.find_wavel_index(self.wavel, calib_wavel[-1]) wavel_array = self.wavel[from_index : to_index + 1] inten_array = self.inten[from_index : to_index + 1] elif calib_wavel.size > self.wavel.size: from_index = self.find_wavel_index(calib_wavel, self.wavel[0]) to_index = self.find_wavel_index(calib_wavel, self.wavel[-1]) calib_inten = calib_inten[from_index : to_index + 1] wavel_array = calib_wavel[from_index : to_index + 1] inten_array = self.inten else: inten_array = self.inten wavel_array = self.wavel apply_calibration = lambda counts, calib: counts / (int_time * calib * 0.000001) inten_array = apply_calibration(inten_array, calib_inten) return Spectrum( intensities=inten_array, wavelengths=wavel_array, int_time=int_time, from_index=self.from_index, to_index=self.to_index, ) self_params = vars(self).copy() self_params.update({"intensities": inten_array, "wavelengths": wavel_array}) return Spectrum(*self_params) # }}} def to_count_rate(self): # {{{ """ Divides the spectrum by its integration time and that's it. """ if self.int_time: return self / (self.int_time 0.000001) else: raise ValueError( "Integration time undefined for calculation of count rate." ) # }}} def calc_uv_index(self, from_wavel=286.0): # {{{ """ Calculates the UV index based on Mckinley-Diffey's action spectra for erythema. """ weighted_irrad = np.array( [ self.weight_irrad(wavel, irrad, from_wavel) for wavel, irrad in zip(self.spectrum) ] ) self.UV_index = round(0.04 trapz(weighted_irrad, self.wavel), 2) return self.UV_index # just for convenience # }}} def optimize_int_time(self, initial=None, limits=(0.8, 1), max_tries=5): # {{{ """ Attemps to find an integration time that maximizes signal to noise ratio while avoiding sensor saturation. This could probably be done more elegantly with recursion, but I haven't got time to think about that. Also BEWARE that this will overwrite the current spectrum. """ if initial is None: initial = self.int_time min_int_time, max_int_time = self.device.int_time_limits max_counts = self.device.sat_intensity target_counts = abs((limits[1] + limits[0]) / 2) * max_counts int_time = initial self.int_time = int_time print("Optimizing integration time...") i = 0 while i < max_tries or inten_max == max_counts: self.inten = self.measure_inten( correct_nl=False, correct_dc=False, samples=1 ) inten_max = np.amax(self.inten) ratio = inten_max / target_counts print(f"{i} {self.int_time} {ratio} {inten_max}") if limits[0] <= ratio <= limits[1]: break elif limits[1] <= ratio <= 1: int_time = ratio * 2 elif ratio > 1: int_time /= ratio 2 else: int_time /= ratio while int_time < min_int_time or int_time > max_int_time: int_time /= 2 self.int_time = int_time i += 1 self.inten = self.measure_inten()[self.slice] return int_time # just for convenience # }}} def join(self, other): # {{{ """ Joins two spectra. It will give preference to itself when resolving overlaps. Probably one of the first functions to get a rewrite. """ if not isinstance(other, Spectrum): raise TypeError("join takes only spectra as arguments") self_wavel_max = self.wavel[-1] self_wavel_min = self.wavel[0] other_wavel_max = other.wavel[-1] other_wavel_min = other.wavel[0] # other.wavel starts before self.wavel ends if np.isclose(other.wavel, self_wavel_max).any(): # NOTE: These variables are indexes referring to self.wavelengths and # other.wavelengths respectively! start_overlap = np.argmax(np.isclose(self.wavel, other_wavel_min)) end_overlap = np.argmax(np.isclose(other.wavel, self_wavel_max)) Spectrum._warn( f"WARNING: The spectra overlap from {other_wavel_min} to {self_wavel_max}" ) # For some god forsaken reason np.concatenate will only work if you pass # a tuple of arrays... new_wavels = np.copy(self.wavel) new_wavels = np.concatenate( (new_wavels, np.copy(other.wavel[end_overlap + 1 :])) ) new_intens = np.copy(self.inten) new_intens = np.concatenate( (new_intens, np.copy(other.inten[end_overlap + 1 :])) ) # self.wavelengths starts before other.wavelengths ends elif np.isclose(self.wavel, other_wavel_max).any(): # NOTE: These variables are indexes referring to other.wavel and # self.wavel respectively! start_overlap = np.argmax(np.isclose(other.wavel, self_wavel_min)) end_overlap = np.argmax(np.isclose(self.wavel, other_wavel_max)) Spectrum._warn( f"WARNING: The spectra overlap from {self_wavel_min} to {other_wavel_max}" ) # You see, the preference is always given to self new_wavels = np.copy(other.wavel[:start_overlap]) new_wavels = np.concatenate((new_wavels, np.copy(self.wavel))) new_intens = np.copy(other.inten[:start_overlap]) new_intens = np.concatenate((new_intens, np.copy(self.inten))) # There is no overlap else: if other_wavel_min > self_wavel_min: new_wavels = np.concatenate( (np.copy(self.wavel), np.copy(other.wavel)) ) new_intens = np.concatenate( (np.copy(self.inten), np.copy(other.inten)) ) elif other_wavel_min < self_wavel_min: new_wavels = np.concatenate( (np.copy(other.wavel), np.copy(self.wavel)) ) new_intens = np.concatenate( (np.copy(other.inten), np.copy(self.inten)) ) self_params = vars(self).copy() self_params.update( { "intensities": new_intens, "wavelengths": new_wavels, "from_index": None, "to_index": None, "backend": "none", } ) return Spectrum(self_params) # }}} # Esse um é para que o animal lembre de consertar quando saporra # inevitavelmente der erro @property def max_counts(self): return np.amax(self.inten) @property def uv(self): # {{{ # Lmao... if self.UV_index: return self.UV_index else: return self.calc_uv_index() # }}} @property def spectrum(self): return self.wavel, self.inten @property def wavelengths(self): return self.wavel @property def intensities(self): return self.inten @classmethod def from_file(cls, inten_wavel_file=None, kwargs): # {{{ """ Creates a spectrum instance with the wavelengths and/or intensities read from a text file. Additionally, it looks for key-word arguments at the first few lines of the file. If the same kwargs are passed to this function, they take precedence. When retrieving a Spectrum from a file, it will always be assigned the backend 'none'. """ wavel_file = kwargs["wavel_file"] if "wavel_file" in kwargs else None inten_file = kwargs["inten_file"] if "inten_file" in kwargs else None inten_wavel_file = ( kwargs["inten_wavel_file"] if "inten_wavel_file" in kwargs else inten_wavel_file ) inten_array = None wavel_array = None new_kwargs = {} if inten_wavel_file: wavel_array, inten_array, new_kwargs = cls._read_file(inten_wavel_file) if wavel_file: wavel_array, _, new_kwargs = cls._read_file(wavel_file) if inten_file: inten_array, _, new_kwargs = cls._read_file(inten_file) if not inten_file and not inten_wavel_file and not wavel_file: cls._warn( "WARNING: Instantiating a spectrum with function from_file, but no file path arguments were passed." ) new_kwargs["intensities"] = inten_array new_kwargs["wavelengths"] = wavel_array # The backend 'none' will always be used when loading a # Spectrum from a file new_kwargs["backend"] = "none" new_kwargs.update(kwargs) return cls(new_kwargs) # }}} @staticmethod def _read_file(text_file): # {{{ """ Used internally by the class method from_file. Returns as many numpy arrays as there are columns in the file, and a dictionary with whatever comments (prefaced by #) it finds. """ dict_args = {} col1 = [] col2 = [] with open(text_file, "r") as arq: # Generator for the lines in the archive gen_lines = (line.split() for line in arq) for line_split in gen_lines: if ( line_split[0] == "#" ): # comment, presumably containing arguments for __init__ dict_args[line_split[1]] = line_split[3] elif ( len(line_split) > 1 ): # 2 or more columns. Will ignore anything after the second column col1.append(float(line_split[0])) col2.append(float(line_split[1])) elif len(line_split) == 1: # 1 column col1.append(float(line_split[0])) if not dict_args and not col1 and not col2: # Check if they're all empty raise RuntimeError( f"No arguments, wavelengths and intensities found in {text_file}. Please check if this is a valid file." ) return np.array(col1), np.array(col2), dict_args # }}} @staticmethod def weight_irrad(wavel, irrad, from_wavel=286.0): # {{{ """ Simple implementation of Mckinley-Diffey's action spectrum. """ if from_wavel <= wavel < 298: return irrad elif 298 <= wavel < 328: return exp(0.216 * (298 - wavel)) * irrad elif 328 <= wavel < 400: return exp(0.034 * (139 - wavel)) * irrad else: return 0 # }}} @staticmethod def find_wavel_index(wavel_array, wavel, margin=0.5): # {{{ """ Attempts to find 'wavel' in 'wavel_array'. Will try using the closest wavelength at most 0.5 units from 'wavel' """ array_diffs = np.abs(wavel_array - wavel) closest_index = array_diffs.argmin() if np.isclose(wavel_array[closest_index], wavel): return closest_index elif array_diffs[closest_index] < 0.5: Spectrum._warn( f"Exact match for {wavel} not found. Using {wavel_array[closest_index]} instead." ) return closest_index else: raise ValueError( f"A close enough {wavel} wasn't found. Closest value is {wavel_array[closest_index]}." ) # }}} @staticmethod def slice_array(wavel, inten, indices, *kwargs): # {{{ """ Takes in two arrays and returns them sliced according to indices=(from_index, to_index). If the indeces are integers, it takes them to be literal indeces for the array. If they are floats, then it'll assume they are wavelengths whose literal indeces must be found before slicing. This behaviour can be overriden by passing literal_indices=True or False """ literal = kwargs["literal"] if "literal" in kwargs else None len_array = len(wavel) if len(inten) != len_array: raise ValueError("The arrays must be of equal length.") new_indices = [] for index in indices: if index is None: new_indices.append(index) elif type(index) is int or literal is True: if not (0 <= abs(index) <= len_array): raise IndexError( f"Invalid index of {index} for array of size {len_array}." ) else: new_indices.append(index) elif type(index) in (float, np.float64) or literal is False: index_wavel = Spectrum.find_wavel_index(wavel, index) new_indices.append(index_wavel) array_slice = slice(new_indices[0], new_indices[1]) return wavel[array_slice], inten[array_slice], array_slice # }}} @staticmethod def _warn(string): # {{{ """ Warnings can be disabled by setting the class variable 'opt_warnings' to False """ print(string) # }}} # Magic methods start here def __iter__(self): return zip(self.spectrum) def __add__(self, other): # {{{ """ Adds the first spectrum's intensities with the second's. It can add spectrums with numpy arrays and lists as well, as long as they are the same length as the spectrum's wavelengths array. This operation will always return another spectrum with the added intensities. """ if isinstance(other, Spectrum): if np.isclose(self.wavel, other.wavel).all(): new_inten = self.inten + other.inten else: raise ValueError( "The divided spectrums must have the same wavelengths array." ) elif isinstance(other, (np.ndarray, list)): if len(other) == self.wavel.size or len(other) == 1: new_inten = self.inten + other else: raise ( ValueError( "The other operand must have the same size as the spectrum's wavelengths array, or size 1." ) ) elif isinstance(other, (float, int)): new_inten = self.inten + other else: raise (TypeError("Incompatible types for addition.")) self_params = vars(self).copy() self_params.update( { "intensities": new_inten, "wavelengths": self.wavel, "from_index": None, "to_index": None, "backend": "none", } ) return Spectrum(*self_params) # }}} def __radd__(self, other): return self + other def __sub__(self, other): # {{{ if isinstance(other, Spectrum): if np.isclose(self.wavel, other.wavel).all(): return self + np.negative(other.intensities) else: raise ValueError( "The subtracted spectrums must have the same wavelengths array." ) else: return self + np.negative(other) # }}} def __rsub__(self, other): raise NotImplementedError def __mul__(self, other): # {{{ """ Multiplies the first spectrum's intensities by the second. It can multiply spectrums with numpy arrays and lists as well, as long as they are the same length as the spectrum's wavelengths array. This operation will always return another spectrum with the multiplied intensities. """ if isinstance(other, Spectrum): if np.isclose(self.wavel, other.wavel).all(): new_inten = self.inten other.inten else: raise ValueError( "The divided spectrums must have the same wavelengths array." ) elif isinstance(other, (np.ndarray, list)): if len(other) == self.wavel.size or len(other) == 1: new_inten = self.inten * other else: raise ( ValueError( "The other operand must have the same size as the spectrum's wavelengths array, or size 1." ) ) elif isinstance(other, (float, int)): new_inten = self.inten * other else: raise (TypeError("Incompatible types for multiplication.")) self_params = vars(self).copy() self_params.update( { "intensities": new_inten, "wavelengths": self.wavel, "from_index": None, "to_index": None, "backend": "none", } ) return Spectrum(*self_params) # }}} def __rmul__(self, other): return self other def __truediv__(self, other): # {{{ """ Divides the first spectrum's intensities by the second. I makes no checks whether division by zero is being requested, that I leave to numpy. This operation will always return another spectrum with the divided intensities. The new spectrum's fields will be inherited from the first operand. """ if isinstance(other, Spectrum): if np.isclose(self.wavel, other.wavel).all(): new_inten = self.inten / other.inten else: raise ValueError( "The divided spectrums must have the same wavelengths array." ) elif isinstance(other, (np.ndarray, list)): if len(other) == self.wavel.size or len(other) == 1: new_inten = self.inten / other else: raise ( ValueError( "The other operand must have the same size as the spectrum's wavelengths array, or size 1." ) ) elif isinstance(other, (float, int)): new_inten = self.inten / other else: raise (TypeError("Incompatible types for division.")) self_params = vars(self).copy() self_params.update( { "intensities": new_inten, "wavelengths": self.wavel, "from_index": None, "to_index": None, "backend": "none", } ) return Spectrum(**self_params) # }}} def __rdiv__(self, other): raise NotImplementedError def __getitem__(self, key): # {{{ """ Takes the key to be a proper index if it is an integer, and as a wavelength if it is float. It also accepts numpy slices and regular slices, of course. """ if isinstance(key, (int, list, np.ndarray)): return self.inten[key] elif isinstance(key, float): int_index = self.find_wavel_index(self.wavel, key) return self.inten[int_index] else: raise TypeError( "Invalid type for index. Please enter an integer, list, numpy array or a float." ) # }}} def __setitem__(self, key, val): # {{{ """ Changes the intensity with index 'key' to 'val'. The new value must be a number, a tuple, list or numpy array. In the latter 3 cases, numpy will handle the assignment. """ if isinstance(key, (list, tuple, np.ndarray)): # Melhorar isto. Adicionar gerenciamento de exceções key = [ self.find_wavel_index(self.wavel, x) if isinstance(x, float) else x for x in key ] elif isinstance(key, float): key = self.find_wavel_index(self.wavel, key) elif isinstance(key, int): if abs(key) > self.wavel.size: raise IndexError( f"Invalid index of {val} for wavelengths array of size {self.wavel.size}" ) else: raise TypeError( "Invalid type for index. Please enter an integer, list, numpy array or a float." ) if isinstance(val, (tuple, list, np.ndarray)): try: val = [float(x) for x in val] except (TypeError, ValueError) as exception: raise ValueError(f"The {type(val)} {val} must contain only numbers.") else: try: val = float(val) except: raise ValueError( f"Invalid value of {val} for intensity. Please enter something convertible to float." ) self.inten[key] = val # }}} def __contains__(self, value): raise NotImplementedError def __repr__(self): return "Spectrum({}, {})".format(self.wavel, self.inten) def __len__(self): return self.wavel.size	[ 1, 1053, 12655, 408, 7442, 13, 3166, 931, 1053, 8709, 13, 3166, 5844, 1053, 1518, 13, 5215, 22889, 29889, 2272, 5317, 408, 14770, 13, 3166, 4560, 2272, 29889, 14146, 403, 1053, 26505, 29920, 13, 3166, 2897, 1053, 2224, 13, 5215, 2281, 13, 5215, 6683, 4201, 5402, 29889, 12171, 408, 269, 1635, 13, 3166, 1053, 1982, 1053, 1053, 29918, 5453, 13, 3166, 12865, 1053, 2635, 29892, 12865, 13, 3166, 2090, 312, 8789, 1053, 7687, 13, 13, 13, 1990, 27738, 5848, 29898, 3318, 1125, 13, 13, 1678, 9995, 13, 1678, 4134, 3651, 29889, 4241, 368, 2322, 1819, 393, 526, 1304, 10940, 1009, 13, 1678, 2777, 6795, 20895, 526, 451, 6790, 746, 2847, 5281, 263, 27738, 5848, 13, 1678, 1203, 29889, 13, 1678, 9995, 13, 13, 1678, 396, 426, 6224, 13, 1678, 304, 29918, 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bbc1/libs/bbclib_msgtype.py	imony/py-bbclib	1	1601752	<reponame>imony/py-bbclib # -- coding: utf-8 -- """ Copyright (c) 2018 beyond-blockchain.org. 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. """ class MsgType: """Message types for between core node and client""" REQUEST_SETUP_DOMAIN = 0 RESPONSE_SETUP_DOMAIN = 1 REQUEST_SET_STATIC_NODE = 4 RESPONSE_SET_STATIC_NODE = 5 REQUEST_GET_CONFIG = 8 RESPONSE_GET_CONFIG = 9 REQUEST_MANIP_LEDGER_SUBSYS = 10 RESPONSE_MANIP_LEDGER_SUBSYS = 11 DOMAIN_PING = 12 REQUEST_GET_DOMAINLIST = 13 RESPONSE_GET_DOMAINLIST = 14 REQUEST_INSERT_NOTIFICATION = 15 CANCEL_INSERT_NOTIFICATION = 16 REQUEST_GET_STATS = 17 RESPONSE_GET_STATS = 18 NOTIFY_DOMAIN_KEY_UPDATE = 19 REQUEST_GET_NEIGHBORLIST = 21 RESPONSE_GET_NEIGHBORLIST = 22 REQUEST_GET_USERS = 23 RESPONSE_GET_USERS = 24 REQUEST_GET_FORWARDING_LIST = 25 RESPONSE_GET_FORWARDING_LIST = 26 REQUEST_GET_NODEID = 27 RESPONSE_GET_NODEID = 28 REQUEST_GET_NOTIFICATION_LIST = 29 RESPONSE_GET_NOTIFICATION_LIST = 30 REQUEST_CLOSE_DOMAIN = 31 RESPONSE_CLOSE_DOMAIN = 32 REQUEST_ECDH_KEY_EXCHANGE = 33 RESPONSE_ECDH_KEY_EXCHANGE = 34 REGISTER = 64 UNREGISTER = 65 MESSAGE = 66 REQUEST_GATHER_SIGNATURE = 67 RESPONSE_GATHER_SIGNATURE = 68 REQUEST_SIGNATURE = 69 RESPONSE_SIGNATURE = 70 REQUEST_INSERT = 71 RESPONSE_INSERT = 72 NOTIFY_INSERTED = 73 NOTIFY_CROSS_REF = 74 REQUEST_SEARCH_TRANSACTION = 82 RESPONSE_SEARCH_TRANSACTION = 83 REQUEST_SEARCH_WITH_CONDITIONS = 86 RESPONSE_SEARCH_WITH_CONDITIONS = 87 REQUEST_TRAVERSE_TRANSACTIONS = 88 RESPONSE_TRAVERSE_TRANSACTIONS = 89 REQUEST_CROSS_REF_VERIFY = 90 RESPONSE_CROSS_REF_VERIFY = 91 REQUEST_CROSS_REF_LIST = 92 RESPONSE_CROSS_REF_LIST = 93 REQUEST_REPAIR = 94 REQUEST_COUNT_TRANSACTIONS = 95 RESPONSE_COUNT_TRANSACTIONS = 95 REQUEST_REGISTER_HASH_IN_SUBSYS = 128 RESPONSE_REGISTER_HASH_IN_SUBSYS = 129 REQUEST_VERIFY_HASH_IN_SUBSYS = 130 RESPONSE_VERIFY_HASH_IN_SUBSYS = 131	[ 1, 529, 276, 1112, 420, 29958, 326, 2592, 29914, 2272, 29899, 1327, 29883, 1982, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 15945, 29908, 13, 11882, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29896, 29947, 8724, 29899, 1271, 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src/psqlsync/ghmoteqlync/sync.py	tiptenbrink/psqlsync	0	88447	from pathlib import Path from functools import partial import tempfile import logging import tomli import trio from dirgh import find_download import psqlsync.actions logger = logging.getLogger(__name__) def prepare(target, owner, repo, repo_dir, overwrite, config, token, verbose): logger.info("Downloading backup files...") run = partial(find_download, owner, repo, repo_dir, target, overwrite=overwrite, token=token) trio.run(run) latest_save = find_latest(target) temp_dir = Path(tempfile.gettempdir()).absolute() restore_filename = temp_dir.joinpath('restore.dump.gz') manager_config = { 'BACKUP_PATH': temp_dir, 'LOCAL_BACKUP_PATH': target } with open(config, "rb") as f: cfg = tomli.load(f) restore_uncompressed = temp_dir.joinpath('restore.dump') postgresql_cfg = cfg.get('postgresql') postgres_host = postgresql_cfg.get('host') postgres_port = postgresql_cfg.get('port') postgres_db = postgresql_cfg.get('db') postgres_restore = "{}_psqlsync_temp_restore".format(postgres_db) postgres_user = postgresql_cfg.get('user') postgres_password = postgresql_cfg.get('password') psqlsync.actions.restore(latest_save, restore_filename, 'LOCAL', manager_config, postgres_restore, None, postgres_db, postgres_host, postgres_port, postgres_user, postgres_password, restore_uncompressed, verbose) logger.info("Sync finished successfully.") def find_latest(target: str): db_path = Path(target) globbed = db_path.glob("*") backup_files = [] for glb in globbed: if glb.is_file(): if "backup" in glb.name and "gz" in glb.suffix: dash_split = glb.name.split('-') backup_time = '-'.join([p for p in dash_split if p.isnumeric()]) backup_files.append(backup_time) if not backup_files: raise ValueError("No backup files could be found, did download succeed?") # ascending order backup_files = sorted(backup_files) return backup_files[-1]	[ 1, 515, 2224, 1982, 1053, 10802, 13, 3166, 2090, 312, 8789, 1053, 7687, 13, 5215, 5694, 1445, 13, 5215, 12183, 13, 5215, 6454, 492, 13, 5215, 3367, 29877, 13, 3166, 4516, 12443, 1053, 1284, 29918, 10382, 13, 13, 5215, 282, 2850, 16593, 29889, 7387, 13, 13, 13, 21707, 353, 12183, 29889, 657, 16363, 22168, 978, 1649, 29897, 13, 13, 13, 1753, 19012, 29898, 5182, 29892, 12271, 29892, 13761, 29892, 13761, 29918, 3972, 29892, 26556, 29892, 2295, 29892, 5993, 29892, 26952, 1125, 13, 1678, 17927, 29889, 3888, 703, 6767, 13234, 16199, 2066, 856, 1159, 13, 13, 1678, 1065, 353, 7687, 29898, 2886, 29918, 10382, 29892, 12271, 29892, 13761, 29892, 13761, 29918, 3972, 29892, 3646, 29892, 26556, 29922, 957, 3539, 29892, 13, 462, 29871, 5993, 29922, 6979, 29897, 13, 1678, 3367, 29877, 29889, 3389, 29898, 3389, 29897, 13, 1678, 9281, 29918, 7620, 353, 1284, 29918, 12333, 29898, 5182, 29897, 13, 13, 1678, 5694, 29918, 3972, 353, 10802, 29898, 7382, 1445, 29889, 657, 7382, 3972, 16655, 23552, 580, 13, 1678, 17749, 29918, 9507, 353, 5694, 29918, 3972, 29889, 7122, 2084, 877, 5060, 487, 29889, 15070, 29889, 18828, 1495, 13, 1678, 8455, 29918, 2917, 353, 426, 13, 4706, 525, 29933, 11375, 4897, 29918, 10145, 2396, 5694, 29918, 3972, 29892, 13, 4706, 525, 16652, 1964, 29918, 29933, 11375, 4897, 29918, 10145, 2396, 3646, 13, 1678, 500, 13, 1678, 411, 1722, 29898, 2917, 29892, 376, 6050, 1159, 408, 285, 29901, 13, 4706, 274, 16434, 353, 6454, 492, 29889, 1359, 29898, 29888, 29897, 13, 1678, 17749, 29918, 348, 510, 13120, 353, 5694, 29918, 3972, 29889, 7122, 2084, 877, 5060, 487, 29889, 15070, 1495, 13, 1678, 27035, 29918, 16859, 353, 274, 16434, 29889, 657, 877, 29272, 1495, 13, 1678, 1400, 7201, 29918, 3069, 353, 27035, 29918, 16859, 29889, 657, 877, 3069, 1495, 13, 1678, 1400, 7201, 29918, 637, 353, 27035, 29918, 16859, 29889, 657, 877, 637, 1495, 13, 1678, 1400, 7201, 29918, 2585, 353, 27035, 29918, 16859, 29889, 657, 877, 2585, 1495, 13, 1678, 1400, 7201, 29918, 5060, 487, 353, 29850, 2403, 567, 1519, 16593, 29918, 7382, 29918, 5060, 487, 1642, 4830, 29898, 2490, 7201, 29918, 2585, 29897, 13, 1678, 1400, 7201, 29918, 1792, 353, 27035, 29918, 16859, 29889, 657, 877, 1792, 1495, 13, 1678, 1400, 7201, 29918, 5630, 353, 27035, 29918, 16859, 29889, 657, 877, 5630, 1495, 13, 1678, 282, 2850, 16593, 29889, 7387, 29889, 5060, 487, 29898, 12333, 29918, 7620, 29892, 17749, 29918, 9507, 29892, 525, 16652, 1964, 742, 8455, 29918, 2917, 29892, 1400, 7201, 29918, 5060, 487, 29892, 6213, 29892, 13, 462, 632, 1400, 7201, 29918, 2585, 29892, 13, 462, 632, 1400, 7201, 29918, 3069, 29892, 13, 462, 632, 1400, 7201, 29918, 637, 29892, 13, 462, 632, 1400, 7201, 29918, 1792, 29892, 13, 462, 632, 1400, 7201, 29918, 5630, 29892, 13, 462, 632, 17749, 29918, 348, 510, 13120, 29892, 26952, 29897, 13, 1678, 17927, 29889, 3888, 703, 21077, 7743, 8472, 23157, 13, 13, 13, 1753, 1284, 29918, 12333, 29898, 5182, 29901, 851, 1125, 13, 1678, 4833, 29918, 2084, 353, 10802, 29898, 5182, 29897, 13, 1678, 15482, 1327, 287, 353, 4833, 29918, 2084, 29889, 23705, 703, 29930, 1159, 13, 1678, 16199, 29918, 5325, 353, 5159, 13, 13, 1678, 363, 3144, 29890, 297, 15482, 1327, 287, 29901, 13, 4706, 565, 3144, 29890, 29889, 275, 29918, 1445, 7295, 13, 9651, 565, 376, 1627, 786, 29908, 297, 3144, 29890, 29889, 978, 322, 376, 18828, 29908, 297, 3144, 29890, 29889, 2146, 600, 861, 29901, 13, 18884, 12569, 29918, 5451, 353, 3144, 29890, 29889, 978, 29889, 5451, 877, 29899, 1495, 13, 18884, 16199, 29918, 2230, 353, 17411, 4286, 7122, 4197, 29886, 363, 282, 297, 12569, 29918, 5451, 565, 282, 29889, 275, 21574, 580, 2314, 13, 18884, 16199, 29918, 5325, 29889, 4397, 29898, 1627, 786, 29918, 2230, 29897, 13, 13, 1678, 565, 451, 16199, 29918, 5325, 29901, 13, 4706, 12020, 7865, 2392, 703, 3782, 16199, 2066, 1033, 367, 1476, 29892, 1258, 5142, 9269, 29973, 1159, 13, 13, 1678, 396, 12066, 2548, 1797, 13, 1678, 16199, 29918, 5325, 353, 12705, 29898, 1627, 786, 29918, 5325, 29897, 13, 13, 1678, 736, 16199, 29918, 5325, 14352, 29896, 29962, 13, 2 ]
PDA_Transport/multi_period_aam.py	gaufung/LMDI	4	72334	<filename>PDA_Transport/multi_period_aam.py # -- encoding:utf-8 -- from single_period_aam import Spaam class Mpaam(object): def __init__(self, dmus_s, name, global_dmus, cefs, years): self._period_count = len(dmus_s) self._dmus_s = dmus_s self._province_count = len(dmus_s[0]) self._province_names = [item.name for item in dmus_s[0]] self._name = name self._cache = {} self._global_dmus = global_dmus self._cefs = cefs self._years = years @property def name(self): return self._name @property def province_names(self): return self._province_names def _get_spaam(self, left, right): assert left != right label = str(left) + '-' + str(right) if label not in self._cache: self._cache[label] = Spaam.build(self._dmus_s[left], self._dmus_s[right], self._years[left] + '-' + self._years[right], self._global_dmus, self._cefs) return self._cache[label] def _index_t(self, t, index_name): result = 1.0 for i in range(1, t + 1): result = getattr(self._get_spaam(i - 1, i), index_name) return result def _index(self, index_name): result = [] for i in range(self._province_count): value = 0.0 for t in range(1, self._period_count): aggerate_value = self._index_t(t - 1, index_name) spaam_t_1_t = self._get_spaam(t - 1, t) contribution = (getattr(spaam_t_1_t, 'r' + index_name)()[i] getattr(spaam_t_1_t, index_name + '_ratio')()[i]) value += aggerate_value * contribution result.append(value) return result # emx def emx(self): return self._index('emx') def emx_t(self, t): return self._index_t(t, 'emx') # cef def cef(self): return self._index('cef') def cef_t(self, t): return self._index_t(t, 'cef') # pei def pei_t(self, t): return self._index_t(t, 'pei') def pei(self): return self._index('pei') # est def est_t(self, t): return self._index_t(t, 'est') def est(self): return self._index('est') # eue def eue_t(self, t): return self._index_t(t, 'eue') def eue(self): return self._index('eue') # pti def pti_t(self, t): return self._index_t(t, 'pti') def pti(self): return self._index('pti') # yoe def yoe_t(self, t): return self._index_t(t, 'yoe') def yoe(self): return self._index('yoe') # yct def yct_t(self, t): return self._index_t(t, 'yct') def yct(self): return self._index('yct') # rts def rts_t(self, t): return self._index_t(t, 'rts') def rts(self): return self._index('rts') def indexes(self, t): return [self.cef_t(t), self.emx_t(t), self.pei_t(t), self.est_t(t), self.eue_t(t), self.pti_t(t), self.yoe_t(t), self.yct_t(t), self.rts_t(t)]	[ 1, 529, 9507, 29958, 29925, 7698, 29918, 27395, 29914, 9910, 29918, 19145, 29918, 29874, 314, 29889, 2272, 13, 29937, 448, 29930, 29899, 8025, 29901, 9420, 29899, 29947, 448, 29930, 29899, 13, 3166, 2323, 29918, 19145, 29918, 29874, 314, 1053, 1706, 29874, 314, 13, 13, 13, 1990, 341, 3274, 314, 29898, 3318, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 270, 8366, 29918, 29879, 29892, 1024, 29892, 5534, 29918, 29881, 8366, 29892, 274, 1389, 29879, 29892, 2440, 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ivy/test/afterinit1.py	b1f6c1c4/cfg-enum	113	42268	from ivy import ivy_module as im from ivy.ivy_compiler import ivy_from_string from ivy.tk_ui import new_ui from ivy import ivy_utils as iu prog = """#lang ivy1.6 type t individual x(X:t) : t object foo(me:t) = { after init { x(me) := me; assert false } } isolate iso_foo(me:t) = foo(me) with x(me) """ with im.Module(): iu.set_parameters({'mode':'induction','isolate':'iso_foo','show_compiled':'true'}) main_ui = new_ui() ui = main_ui.add(ivy_from_string(prog)) main_ui.tk.update_idletasks() main_ui.answer("OK") ui.check_safety_node(ui.node(0)) assert not ui.node(0).safe # ui.check_inductiveness() # # ui = ui.cti # cg = ui.current_concept_graph # cg.show_relation(cg.relation('link(X,Y)'),'+') # cg.gather() # main_ui.answer("OK") # cg.strengthen() # main_ui.answer("OK") # ui.check_inductiveness() # # cg.show_relation(cg.relation('semaphore'),'+') # cg.gather() # main_ui.answer("View") # cg.bmc_conjecture(bound=1) # main_ui.mainloop()	[ 1, 29871, 13, 3166, 20444, 29891, 1053, 20444, 29891, 29918, 5453, 408, 527, 13, 3166, 20444, 29891, 29889, 440, 29891, 29918, 21789, 1053, 20444, 29891, 29918, 3166, 29918, 1807, 13, 3166, 20444, 29891, 29889, 11178, 29918, 1481, 1053, 716, 29918, 1481, 13, 3166, 20444, 29891, 1053, 20444, 29891, 29918, 13239, 408, 474, 29884, 13, 13, 29097, 353, 9995, 29937, 3893, 20444, 29891, 29896, 29889, 29953, 13, 13, 1853, 260, 13, 13, 513, 23352, 921, 29898, 29990, 29901, 29873, 29897, 584, 260, 13, 13, 3318, 7953, 29898, 1004, 29901, 29873, 29897, 353, 426, 13, 1678, 1156, 2069, 426, 13, 4706, 921, 29898, 1004, 29897, 3490, 592, 29936, 13, 4706, 4974, 2089, 13, 1678, 500, 13, 29913, 13, 13, 275, 23167, 338, 29877, 29918, 5431, 29898, 1004, 29901, 29873, 29897, 353, 7953, 29898, 1004, 29897, 411, 921, 29898, 1004, 29897, 13, 13, 15945, 29908, 13, 13, 2541, 527, 29889, 7355, 7295, 13, 1678, 474, 29884, 29889, 842, 29918, 16744, 3319, 29915, 8513, 22099, 19910, 428, 3788, 275, 23167, 22099, 10718, 29918, 5431, 3788, 4294, 29918, 2388, 2356, 22099, 3009, 29915, 1800, 13, 1678, 1667, 29918, 1481, 353, 716, 29918, 1481, 580, 13, 1678, 14313, 353, 1667, 29918, 1481, 29889, 1202, 29898, 440, 29891, 29918, 3166, 29918, 1807, 29898, 29097, 876, 13, 1678, 1667, 29918, 1481, 29889, 11178, 29889, 5504, 29918, 333, 1026, 1278, 29879, 580, 268, 13, 1678, 1667, 29918, 1481, 29889, 12011, 703, 8949, 1159, 13, 1678, 14313, 29889, 3198, 29918, 29879, 2142, 3305, 29918, 3177, 29898, 1481, 29889, 3177, 29898, 29900, 876, 13, 1678, 4974, 451, 14313, 29889, 3177, 29898, 29900, 467, 11177, 13, 29937, 268, 14313, 29889, 3198, 29918, 513, 5313, 20193, 580, 13, 29937, 396, 1678, 14313, 353, 14313, 29889, 312, 29875, 13, 29937, 268, 274, 29887, 353, 14313, 29889, 3784, 29918, 535, 1547, 29918, 4262, 13, 29937, 268, 274, 29887, 29889, 4294, 29918, 23445, 29898, 29883, 29887, 29889, 23445, 877, 2324, 29898, 29990, 29892, 29979, 29897, 5477, 18717, 1495, 13, 29937, 268, 274, 29887, 29889, 29887, 1624, 580, 13, 29937, 268, 1667, 29918, 1481, 29889, 12011, 703, 8949, 1159, 13, 29937, 268, 274, 29887, 29889, 710, 1477, 264, 580, 13, 29937, 268, 1667, 29918, 1481, 29889, 12011, 703, 8949, 1159, 13, 29937, 268, 14313, 29889, 3198, 29918, 513, 5313, 20193, 580, 13, 29937, 396, 1678, 274, 29887, 29889, 4294, 29918, 23445, 29898, 29883, 29887, 29889, 23445, 877, 12846, 12451, 487, 5477, 18717, 1495, 13, 29937, 268, 274, 29887, 29889, 29887, 1624, 580, 13, 29937, 268, 1667, 29918, 1481, 29889, 12011, 703, 1043, 1159, 13, 29937, 268, 274, 29887, 29889, 5838, 29883, 29918, 535, 622, 545, 29898, 9917, 29922, 29896, 29897, 13, 29937, 1678, 1667, 29918, 1481, 29889, 3396, 7888, 580, 13, 13, 13, 2 ]
grammar.py	ankieter-gui/release	0	79750	import typing import error REQUEST_TABLE = { 'get': [[str]], 'as': [str], 'by': ([str], 'optional'), 'if': ([list], 'optional'), 'except': ([list], 'optional'), 'join': ([ { 'name': str, 'of': [str], } ], 'optional'), 'macro': ([str], 'optional'), } REQUEST_TABLE_QUESTION_COUNT = { 'get': [[str]], 'if': ([list], 'optional'), 'except': ([list], 'optional'), 'macro': [str], } REQUEST_CREATE_SURVEY = { 'surveyId': int, 'title': str, } REQUEST_CHANGE_PERMISSIONS = { 'r': ([int], 'optional'), 'w': ([int], 'optional'), 'n': ([int], 'optional'), } REQUEST_GROUP = { 'group': str, } REQUEST_USER = { 'userId': int, } REQUEST_SURVEY_LINK = { 'permission': str, 'surveyId': int } REQUEST_REPORT_LINK = { 'permission': str, 'reportId': int } def analyze(tp: typing.Any, obj: typing.Any) -> str: """Analyze object structure. Keyword arguments: tp -- expected object structure obj -- given object Return value: returns message after analyze """ # Check if obj is of the desired type if type(tp) is type: if type(obj) is tp: return '' else: return f'expected {tp.__name__}, got {type(obj).__name__}' # If the desired type is a list, check types of each of its elements if type(tp) is list: if type(obj) is not list: return f'expected {type(tp).__name__}, got {type(obj).__name__}' for i, o in enumerate(obj): if msg := analyze(tp[0], o): return f'in element [{i}]: {msg}' return '' # If the desired type is a dict, check types of values under each key if type(tp) is dict: if type(obj) is not dict: return f'expected {type(tp).__name__}, got {type(obj).__name__}' for k, t in tp.items(): if type(t) is tuple: t, *params = t else: params = [] if k not in obj: if 'optional' in params: continue return f'expected key \'{k}\'' if msg := analyze(t, obj[k]): return f'in element \'{k}\': {msg}' return '' return 'unexpected object type' def check(tp: typing.Any, obj: typing.Any): """Validate object structure. 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python2.7/site-packages/zope/interface/tests/test_verify.py	84KaliPleXon3/sslstrip-hsts-openwrt	4	1609893	############################################################################## # # Copyright (c) 2001, 2002 Zope Corporation and Contributors. # All Rights Reserved. # # This software is subject to the provisions of the Zope Public License, # Version 2.1 (ZPL). A copy of the ZPL should accompany this distribution. # THIS SOFTWARE IS PROVIDED "AS IS" AND ANY AND ALL EXPRESS OR IMPLIED # WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS # FOR A PARTICULAR PURPOSE. # ############################################################################## """Interface Verify tests $Id: test_verify.py 26866 2004-08-02 20:57:00Z jim $ """ from zope.interface import Interface, implements, classImplements, Attribute from zope.interface.verify import verifyClass, verifyObject from zope.interface.exceptions import DoesNotImplement, BrokenImplementation from zope.interface.exceptions import BrokenMethodImplementation import unittest class Test(unittest.TestCase): def testNotImplemented(self): class C(object): pass class I(Interface): pass self.assertRaises(DoesNotImplement, verifyClass, I, C) classImplements(C, I) verifyClass(I, C) def testMissingAttr(self): class I(Interface): def f(): pass class C(object): implements(I) self.assertRaises(BrokenImplementation, verifyClass, I, C) C.f=lambda self: None verifyClass(I, C) def testMissingAttr_with_Extended_Interface(self): class II(Interface): def f(): pass class I(II): pass class C(object): implements(I) self.assertRaises(BrokenImplementation, verifyClass, I, C) C.f=lambda self: None verifyClass(I, C) def testWrongArgs(self): class I(Interface): def f(a): pass class C(object): def f(self, b): pass implements(I) # We no longer require names to match. #self.assertRaises(BrokenMethodImplementation, verifyClass, I, C) C.f=lambda self, a: None verifyClass(I, C) C.f=lambda self, *kw: None self.assertRaises(BrokenMethodImplementation, verifyClass, I, C) C.f=lambda self, a, args: None verifyClass(I, C) C.f=lambda self, a, args, kw: None verifyClass(I, C) C.f=lambda self, args: None verifyClass(I, C) def testExtraArgs(self): class I(Interface): def f(a): pass class C(object): def f(self, a, b): pass implements(I) self.assertRaises(BrokenMethodImplementation, verifyClass, I, C) C.f=lambda self, a: None verifyClass(I, C) C.f=lambda self, a, b=None: None verifyClass(I, C) def testNoVar(self): class I(Interface): def f(a, args): pass class C(object): def f(self, a): pass implements(I) self.assertRaises(BrokenMethodImplementation, verifyClass, I, C) C.f=lambda self, a, foo: None verifyClass(I, C) def testNoKW(self): class I(Interface): def f(a, args): pass class C(object): def f(self, a): pass implements(I) self.assertRaises(BrokenMethodImplementation, verifyClass, I, C) C.f=lambda self, a, foo: None verifyClass(I, C) def testModule(self): from zope.interface.tests.ifoo import IFoo from zope.interface.tests import dummy verifyObject(IFoo, dummy) def testMethodForAttr(self): class IFoo(Interface): foo = Attribute("The foo Attribute") class Foo: implements(IFoo) def foo(self): pass verifyClass(IFoo, Foo) def testNonMethodForMethod(self): class IBar(Interface): def foo(): pass class Bar: 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databasetools/projectiontest.py	salhasalman/DatabaseTools	0	145558	# pew in databasetools-venv python /home/hayj/wm-dist-tmp/DatabaseTools/databasetools/projectiontest.py import sys, os; sys.path.append("/".join(os.path.abspath(__file__).split("/")[0:-2])) import pymongo import random import string import time from systemtools.system import * from systemtools.duration import * # We init db name, host...: dbTest = "student" host = "localhost" port = "27017" # We init users: if isHostname("hjlat"): user = None password = None elif isHostname("datascience01"): user = "student" password = "<PASSWORD>" else: print("Pls execute on hjlat or datascience01.") ; exit() # We make the mongo scheme: mongoConnectionScheme = "mongodb://" + host + ":" + port if user is not None: mongoConnectionScheme = "mongodb://" + user + ":" + password + "@" + host + ":" + port myclient = pymongo.MongoClient(mongoConnectionScheme) # The function which create a fake collection: def createFakeCollection(alwaysRecreate=True): collectionName = "usercrawl" mydb = myclient[dbTest] mycol = mydb[collectionName] if alwaysRecreate or (mycol.count({}) < 100 and (collectionName == "test" or collectionName == "student")): mycol.create_index("user_id") mycol.delete_many({}) for i in range(1000): text = "" for u in range(1000): text += ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(10)) o = {"timestamp": time.time(), "text": text, "user_id": i + 5000} mycol.insert_one(o) return mycol def getUserCrawl(): if not isHostname("datascience01"): print("Pls execute on datascience01.") ; exit() mydb = myclient["twitter"] return mydb["usercrawl"] def copyUserCrawlCollection(copyCount=1000, alwaysRecreate=False): mydb = myclient["twitter"] userCrawl = mydb["usercrawl"] mydbTest = myclient[dbTest] testCol = mydbTest[dbTest] testCol.create_index("user_id") if alwaysRecreate or testCol.count() < copyCount: testCol.delete_many({}) i = 0 for row in userCrawl.find({}): del row["_id"] testCol.insert_one(row) if i >= copyCount: break if i % int(copyCount / 50) == 0: print(str(int(i / copyCount * 100)) + "%") i += 1 return testCol # for row in mycol.find({}): # if not dictContains(row, "user_id"): # print("FUCK") # print(row["url"]) # exit() tt = TicToc() tt.tic() mycol = createFakeCollection(alwaysRecreate=False) # mycol = createFakeCollection(alwaysRecreate=True) # mycol = getUserCrawl() # mycol = copyUserCrawlCollection() tt.tic("collection init DONE") iterationMaxCount = 20 print("Test started.") i = 0 for row in mycol.find({}): if i % int(iterationMaxCount / 3) == 0: print(str(i) + ": " + str(lts(row)[0:60])) if i > iterationMaxCount: break i += 1 tt.tic("Find done.") iterationMaxCount = 200 i = 0 for row in mycol.distinct("user_id"): if i % int(iterationMaxCount / 3) == 0: print(str(i) + ": " + str(row)[0:60]) if i > 200: break i += 1 tt.tic("Find distincts done.") i = 0 for row in mycol.find({}, projection={"user_id": True}): if i % int(iterationMaxCount / 3) == 0: print(str(i) + ": " + str(lts(row)[0:60])) if i > 200: break i += 1 tt.tic("Find with projection done.")	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dev/otu_handler.py	michaelwiest/microbiome_rnn	0	185844	<filename>dev/otu_handler.py<gh_stars>0 import copy import pandas as pd import numpy as np from scipy.stats.mstats import gmean, zscore ''' This is the main object that handles the OTU data. It has a few main functions: 1. Managing data normalization. 2. Managing training, validation, and test data. 3. Data sampling. All of the different models use this data handler to get data samples. ''' def clr(some_array, axis): ''' Had to reimplement this because the server is python2 and skbio is only python3 ''' gms = gmean(some_array, axis=axis) gms = np.expand_dims(gms, axis=axis) gms = np.repeat(gms, some_array.shape[axis], axis=axis) return np.log(some_array / gms) class OTUHandler(object): ''' Class for handling OTU data. It generates samples and keeps track of training and validation data. ''' def __init__(self, files, test_files=None): # Read in the sample data. self.samples = [] for f in files: self.samples.append(pd.read_csv(f, index_col=0)) # If test_files are provided then read them in. if test_files is not None: self.test_data = [] for f in test_files: self.test_data.append(pd.read_csv(f, index_col=0)) else: self.test_data = None # Helper variables that are used elsewhere in the code. self.strains = list(self.samples[0].index.values) self.num_strains = len(self.strains) # These get set when calling set_train_val self.train_data = None self.val_data = None def set_train_val(self, percent=0.8, minsize=20): ''' Set the training and validation data for each sample. Can include a lower bound on size of train/validation ''' # For keeping track of the minimum size that the train/val data need to be. self.min_len = minsize # To be populated. self.train_data = [] self.val_data = [] temp_sizes = [] # Go through each of the samples. for i, sample in enumerate(self.samples): # Where in the sample to set the split. index = int(percent * sample.shape[1]) # If not enough examples, skip this file. if not ((sample.iloc[:, :index].shape[1]) < minsize or (sample.iloc[:, index:].shape[1]) < minsize): # Append the new data to the appropriate location. self.train_data.append(sample.iloc[:, :index]) self.val_data.append(sample.iloc[:, index:]) else: # If it's too small only use it as training data. print('Skipping file, {}, because it\'s of shape: {}'.format(i, sample.shape)) self.train_data.append(sample) def normalize_data(self, method='zscore'): ''' Method for normalizing the input data. This can currently zscore and clr the data. Other methods could be added. ''' method = method.lower() if method not in ['zscore', 'clr']: raise ValueError('Specify "zscore" or "clr" for method') if method == 'zscore': m = zscore else: m = clr # Keep track of the new data. new_vals = [] for i, which_data in enumerate([self.samples, self.test_data]): # This is in case the test data is not supplied. if which_data is not None: new_vals = [] # For each of the dataframes. for s in which_data: # Apply the normalization method. new_vals.append(pd.DataFrame(m(s.values, axis=0), index=s.index, columns=s.columns)) # This is sort of a hack which is annoying. # Basically set the data to be the normalized version. if i == 0: self.samples = new_vals elif i == 1: self.test_data = new_vals def get_N_samples_and_targets(self, N, input_slice_size, target_slice_size, which_data='train', target_slice=True, slice_offset=1, which_donor=None): ''' This is the main function for generating samples of data with which to train the neural network. Returns two things, an input and a target (both numpy arrays): input:[N x num_otus x input_slice_size] target: [N x num_otus x target_slice_size] Selects N random examples from all possible training samples. It selects from them based upon the number of timepoints present in each donor. So donors with more data get sampled more often. ''' # What data source to sample from. which_data = which_data.lower() if which_data not in ['train', 'validation', 'test']: raise ValueError('Please specify either: train, validaiton, or test' ' for argument "which_sample"') if self.test_data is None and which_data == 'test': raise ValueError('Do not select for test data when none is set.') # Set the appropriate location. if which_data == 'train': data_source = self.train_data elif which_data == 'validation': data_source = self.val_data elif which_data == 'test': data_source = self.test_data samples = [] # Samples to feed to the model. targets = [] # Targets to compare predictions against. # If train validation split hasn't been specified. if self.train_data is None: raise AttributeError('Please specify train and val data before ' 'calling this function.') # This flag is for generating samples from a particular donor. # During training this is always set as None so that it samples from # a distribution. if which_donor is None: # Samples from data based on number of samples. Ie, samples with more # data points get more selection. all_sizes = [d.shape[1] for d in data_source] probs = [s / (1.0 * sum(all_sizes)) for s in all_sizes] # Get the samples based on sizes. which_samples = np.random.choice(len(data_source), N, p=probs) else: # If we know what donor we want then only sample from that one. which_samples = [which_donor] * N # For each of the specified samples to pick. for ws in which_samples: sample = data_source[ws] # Pick a random starting point in the example. Get the data in # that slice and then the values immediately after. start_index = np.random.randint(sample.shape[1] - input_slice_size - target_slice_size) data = sample.iloc[:, start_index: start_index + input_slice_size].values # For the LSTM and EncoderDecoer we want a whole slice of values # to compare against. 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pos_tagger/pos_replace_v5.py	ZirconiumZr/UOB_SIT	0	1617223	<gh_stars>0 # -- coding: utf-8 -- """pos_replace_v5.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/1Mdfqrn2Nc8xE6xFlAstCmzPeEMCgguP- """ import nltk from nltk import word_tokenize, pos_tag import pandas as pd import io from nltk.tokenize.treebank import TreebankWordDetokenizer try: nltk.data.find('tokenizers/punkt') except LookupError: nltk.download('punkt') try: nltk.data.find('taggers/averaged_perceptron_tagger') except LookupError: nltk.download('averaged_perceptron_tagger') try: nltk.data.find('taggers/universal_tagset') except LookupError: nltk.download('universal_tagset') template = pd.read_csv('/content/replace_template.csv') # template=template.fillna('') text1 = "i'm fine at your bb and u o p is a bank" text2 = "i'm from uob and i'm fine in u o b" text3 = "you can get youll be credit card" def pos_replace(l, template): ''' l: the stt sentence template: DataFrame, read as csv from stt_replace_template.txt pos_tag reference: https://universaldependencies.org/u/pos/all.html#al-u-pos/ ''' flag_change = "" words = word_tokenize(l) # l_r = TreebankWordDetokenizer().detokenize(words) l_r = " ".join(words) for i in range(len(template)): row = template.iloc[i] pos_before = str(row['pos_before']).split(';') pos_after = str(row['pos_after']).split(';') replace_list = str(row['replace']).split(';') flag_direct_replace = str(row['flag_direct_replace']) key = word_tokenize(row['key']) for p in replace_list: p_w = word_tokenize(p) # p_s = TreebankWordDetokenizer().detokenize(p_w) p_s = " ".join(p_w) if p_s in l_r: # p_w = word_tokenize(p) lenth_p = len(p_w) words = word_tokenize(l_r) words_pos = pos_tag(words, tagset='universal') lenth_w = len(words) # r_len = lenth_w-lenth_p+1 if flag_direct_replace.lower() == "x": i = 0 # for i in range(lenth_w-lenth_p+1): while i < lenth_w-lenth_p+1: # print(i) if words[i:i+lenth_p]==p_w: print('directly replace', p, 'with',row[0]) words[i:i+lenth_p] = key words_pos = pos_tag(words, tagset='universal') lenth_w = len(words) # r_len = lenth_w-lenth_p+1 flag_change = "x" i += 1 else: # for i in range(lenth_w-lenth_p+1): i = 0 while i < lenth_w-lenth_p+1: # print(i) if words[i:i+lenth_p]==p_w: if words_pos[i-1][1] in pos_before: print('the pos of the word before', p, 'is',words_pos[i-1][1]) words[i:i+lenth_p] = key words_pos = pos_tag(words, tagset='universal') lenth_w = len(words) # r_len = lenth_w-lenth_p+1 flag_change = "x" # l_r=l_r.replace(p, row[0]) # l_r = " ".join(words) # print(l_r) elif i+lenth_p<len(words) and words_pos[i+lenth_p][1] in pos_after: print('the pos of the word after', p, 'is',words_pos[i+lenth_p][1]) words[i:i+lenth_p] = key words_pos = pos_tag(words, tagset='universal') lenth_w = len(words) # r_len = lenth_w-lenth_p+1 flag_change = "x" # l_r=l_r.replace(p, row[0]) # l_r = " ".join(words) # print(l_r) i += 1 if flag_change == "x": print(l_r) l_r = " ".join(words) # l_r = TreebankWordDetokenizer().detokenize(words) print(l_r) flag_change = "" # l_r = l # for i in range(len(template)): # get every row in the template # row = template.iloc[i] # pos_before = str(row['pos_before']).split(';') # pos_after = str(row['pos_after']).split(';') # replace_list = str(row['replace']).split(';') # print('replace_list:',replace_list) # for p in replace_list: # get each element in replace_list of template row[i] # print('find p for=', p) # if p in l_r: # print('find p if=', p) # # y = p.replace(' ','') # y = re.sub(r'[^\w\s]','',p) # replace punctuations to blank str # print('y: ',y) # interim = l_r.replace(p, y) # words = word_tokenize(interim) # interim_pos = pos_tag(words, tagset='universal') # index_=[i for i,x in enumerate(interim_pos) if x[0]==y] # for i in index_: # if interim_pos[i-1][1] in pos_before: # print('the pos of the word before', p, 'is',interim_pos[i-1][1]) # words[i] = row[0] # # l_r=l_r.replace(p, row[0]) # l_r = " ".join(words) # print(l_r) # elif interim_pos[i+1][1] in pos_after: # print('the pos of the word after', p, 'is',interim_pos[i+1][1]) # words[i] = row[0] # # l_r=l_r.replace(p, row[0]) # 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helpers/labml_helpers/datasets/remote/test/mnist_server.py	mcx/labml	174	113625	<reponame>mcx/labml<gh_stars>100-1000 from labml import lab from labml_helpers.datasets.remote import DatasetServer from torchvision import datasets, transforms def main(): transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ]) train_dataset = datasets.MNIST(str(lab.get_data_path()), train=True, download=True, transform=transform) valid_dataset = datasets.MNIST(str(lab.get_data_path()), train=False, download=True, transform=transform) ds = DatasetServer() ds.add_dataset('mnist_train', train_dataset) ds.add_dataset('mnist_valid', valid_dataset) ds.start() if __name__ == '__main__': main()	[ 1, 529, 276, 1112, 420, 29958, 14047, 29916, 29914, 8205, 828, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29900, 29899, 29896, 29900, 29900, 29900, 13, 3166, 9775, 828, 1053, 9775, 13, 3166, 9775, 828, 29918, 3952, 6774, 29889, 14538, 1691, 29889, 16674, 1053, 13373, 24541, 6004, 13, 3166, 4842, 305, 4924, 1053, 20035, 29892, 4327, 29879, 13, 13, 13, 1753, 1667, 7295, 13, 1678, 4327, 353, 4327, 29879, 29889, 1523, 4220, 4197, 13, 4706, 4327, 29879, 29889, 1762, 29911, 6073, 3285, 13, 4706, 4327, 29879, 29889, 19077, 675, 3552, 29900, 29889, 29896, 29941, 29900, 29955, 29892, 511, 313, 29900, 29889, 29941, 29900, 29947, 29896, 29892, 876, 13, 268, 2314, 13, 13, 1678, 7945, 29918, 24713, 353, 20035, 29889, 29924, 29940, 9047, 29898, 710, 29898, 8205, 29889, 657, 29918, 1272, 29918, 2084, 25739, 13, 462, 462, 259, 7945, 29922, 5574, 29892, 13, 462, 462, 259, 5142, 29922, 5574, 29892, 13, 462, 462, 259, 4327, 29922, 9067, 29897, 13, 13, 1678, 2854, 29918, 24713, 353, 20035, 29889, 29924, 29940, 9047, 29898, 710, 29898, 8205, 29889, 657, 29918, 1272, 29918, 2084, 25739, 13, 462, 462, 259, 7945, 29922, 8824, 29892, 13, 462, 462, 259, 5142, 29922, 5574, 29892, 13, 462, 462, 259, 4327, 29922, 9067, 29897, 13, 13, 1678, 18031, 353, 13373, 24541, 6004, 580, 13, 1678, 18031, 29889, 1202, 29918, 24713, 877, 23521, 391, 29918, 14968, 742, 7945, 29918, 24713, 29897, 13, 1678, 18031, 29889, 1202, 29918, 24713, 877, 23521, 391, 29918, 3084, 742, 2854, 29918, 24713, 29897, 13, 13, 1678, 18031, 29889, 2962, 580, 13, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 1667, 580, 13, 2 ]
costflow/config.py	StdioA/costflow	0	183685	<filename>costflow/config.py<gh_stars>0 from dataclasses import dataclass, field @dataclass class Config: default_currency: str = "CNY" formulas: dict = field(default_factory=dict) def get_formula(self, name): return self.formulas.get(name, "") # TODO: Beancount loader for default currency # Global store config = Config()	[ 1, 529, 9507, 29958, 18253, 1731, 29914, 2917, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 848, 13203, 1053, 848, 1990, 29892, 1746, 13, 13, 13, 29992, 1272, 1990, 13, 1990, 12782, 29901, 13, 1678, 2322, 29918, 26095, 29901, 851, 353, 376, 13778, 29979, 29908, 13, 1678, 26760, 29901, 9657, 353, 1746, 29898, 4381, 29918, 14399, 29922, 8977, 29897, 13, 13, 1678, 822, 679, 29918, 689, 2497, 29898, 1311, 29892, 1024, 1125, 13, 4706, 736, 1583, 29889, 689, 15173, 29889, 657, 29898, 978, 29892, 20569, 13, 13, 1678, 396, 14402, 29901, 1522, 273, 2798, 23466, 363, 2322, 27550, 13, 13, 13, 29937, 12002, 3787, 13, 2917, 353, 12782, 580, 13, 2 ]
django/dashboard/views/index.py	aistairc/voteclustering_aist	0	69666	from django.db.models import Count, Q from django.shortcuts import redirect, render from django.utils import timezone from AIST_survey.models import Enquete, Respondent, Question, Choice, Evaluation def index(request): if "enquete_id" not in request.session: return redirect("login") enquete_id = request.session["enquete_id"] enquete = Enquete.objects.get(id=int(enquete_id)) params = get_index_params(enquete) return render(request, 'dashboard/index.html', params) def get_index_params(enquete): respondents = Respondent.objects.filter(enquete_id=enquete.id) questions = Question.objects.filter(enquete_id=enquete.id) choices = [] for q in questions: choice_data = Choice.objects.filter(question_id=q.id) choices.extend(choice_data) params = { 'enquete': enquete, 'respondent_num': len(respondents), 'today_respondents_num': get_today_respondents_num(enquete), 'remaining_days': get_remaining_days(enquete), 'respondent_transition_data': get_respondent_transition_data(enquete), 'question_graph_data': get_question_graph_data(enquete) } return params def get_question_graph_data(enquete): questions = Question.objects.filter(enquete_id=enquete.id) graph_data = [] for q in questions: if q.type == Question.SINGLE or q.type == Question.MULTI: data = { "question_text": q.text, "type": "selection", "graph_data": get_selection_data(q) } graph_data.append(data) elif q.type == Question.QUESTION: data = { "question_text": q.text, "type": "open_end", "graph_data": get_question_data(q) } graph_data.append(data) return graph_data def get_selection_data(question): choices = Choice.objects.filter(question=question) selection_labels = [] selection_data = [] for c in choices: choice_num = Evaluation.objects.filter(choice=c).count() selection_labels.append(c.text) selection_data.append(choice_num) return {"labels": selection_labels, "data": selection_data} def get_question_data(question): choices = Choice.objects.filter(question=question) question_data = { "like": [], "time": [], } choices_timestamp = choices.order_by("-timestamp")[0:5] for choice in choices_timestamp: question_data["time"].append({ "time": choice.timestamp, "text": choice.text }) choices_like = choices\ .annotate( count=Count( "evaluation", filter=Q(evaluation__assessment=Evaluation.LIKED) \| Q(evaluation__assessment=Evaluation.PROPOSED) ) )\ .order_by("-count")[0:5] for choice in choices_like: question_data["like"].append({ "text": choice.text, "like": choice.count, "timestamp": choice.timestamp }) return question_data def get_today_respondents_num(enquete): today_respondents = Respondent.objects.filter(enquete_id=enquete.id, finishTime__date=timezone.now()) return today_respondents.count() def get_remaining_days(enquete): remaining_days = (enquete.expired_at - timezone.now()).days return remaining_days if remaining_days > 0 else 0 def get_respondent_transition_data(enquete): today = timezone.now().date() date_list = [] respondent_count_list = [] publish_day = enquete.published_at.astimezone().replace(hour=23, minute=59, second=59, microsecond=999999) while publish_day.date() <= today: respondent_count = Respondent.objects.filter(finishTime__date__lte=publish_day, enquete_id=enquete.id).count() date_list.append(publish_day.strftime("%Y/%m/%d")) respondent_count_list.append(respondent_count) publish_day += timezone.timedelta(days=1) return {'labels': date_list, 'values': respondent_count_list}	[ 1, 515, 9557, 29889, 2585, 29889, 9794, 1053, 3917, 29892, 660, 13, 3166, 9557, 29889, 12759, 7582, 29879, 1053, 6684, 29892, 4050, 13, 3166, 9557, 29889, 13239, 1053, 29431, 13, 13, 3166, 319, 9047, 29918, 7610, 6950, 29889, 9794, 1053, 1174, 339, 2650, 29892, 2538, 2818, 296, 29892, 894, 29892, 14542, 625, 29892, 382, 4387, 362, 13, 13, 13, 1753, 2380, 29898, 3827, 1125, 13, 1678, 565, 376, 264, 339, 2650, 29918, 333, 29908, 451, 297, 2009, 29889, 7924, 29901, 13, 4706, 736, 6684, 703, 7507, 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test/test_server.py	sebastianludwig/SensationDriver	0	116373	<reponame>sebastianludwig/SensationDriver<filename>test/test_server.py from utils import * from sensationdriver.server import Server class TestServer(unittest.TestCase): class MemoryHandler(object): def __init__(self): self.set_up_called_counter = 0 self.tear_down_called_counter = 0 @asyncio.coroutine def set_up(self): self.set_up_called_counter += 1 @asyncio.coroutine def tear_down(self): self.tear_down_called_counter += 1 def test_handler_torn_down_after_server_usage(self): server = Server() handler = self.MemoryHandler() server.handler = handler with server: pass self.assertEqual(handler.tear_down_called_counter, 1) def test_handler_set_up_called_on_restart(self): server = Server() handler = self.MemoryHandler() server.handler = handler with server: pass with server: self.assertEqual(handler.set_up_called_counter, 2) def test_handler_set_up_not_called_twice_on_start(self): server = Server() handler = self.MemoryHandler() server.handler = handler with server: self.assertEqual(handler.set_up_called_counter, 1) if __name__ == '__main__': unittest.main()	[ 1, 529, 276, 1112, 420, 29958, 344, 29890, 579, 713, 29880, 566, 9192, 29914, 29903, 575, 362, 12376, 29966, 9507, 29958, 1688, 29914, 1688, 29918, 2974, 29889, 2272, 13, 3166, 3667, 29879, 1053, 334, 13, 13, 3166, 4771, 362, 9465, 29889, 2974, 1053, 5656, 13, 13, 13, 1990, 4321, 6004, 29898, 348, 27958, 29889, 3057, 8259, 1125, 13, 1678, 770, 18914, 4598, 29898, 3318, 1125, 13, 4706, 822, 4770, 2344, 12035, 1311, 1125, 13, 9651, 1583, 29889, 842, 29918, 786, 29918, 13998, 29918, 11808, 353, 29871, 29900, 13, 9651, 1583, 29889, 371, 279, 29918, 3204, 29918, 13998, 29918, 11808, 353, 29871, 29900, 13, 13, 4706, 732, 294, 948, 3934, 29889, 2616, 449, 457, 13, 4706, 822, 731, 29918, 786, 29898, 1311, 1125, 13, 9651, 1583, 29889, 842, 29918, 786, 29918, 13998, 29918, 11808, 4619, 29871, 29896, 13, 13, 4706, 732, 294, 948, 3934, 29889, 2616, 449, 457, 13, 4706, 822, 734, 279, 29918, 3204, 29898, 1311, 1125, 13, 9651, 1583, 29889, 371, 279, 29918, 3204, 29918, 13998, 29918, 11808, 4619, 29871, 29896, 13, 13, 1678, 822, 1243, 29918, 13789, 29918, 29873, 1398, 29918, 3204, 29918, 7045, 29918, 2974, 29918, 21125, 29898, 1311, 1125, 13, 4706, 1923, 353, 5656, 580, 13, 4706, 7834, 353, 1583, 29889, 16015, 4598, 580, 13, 4706, 1923, 29889, 13789, 353, 7834, 13, 4706, 411, 1923, 29901, 13, 9651, 1209, 13, 4706, 1583, 29889, 9294, 9843, 29898, 13789, 29889, 371, 279, 29918, 3204, 29918, 13998, 29918, 11808, 29892, 29871, 29896, 29897, 13, 13, 1678, 822, 1243, 29918, 13789, 29918, 842, 29918, 786, 29918, 13998, 29918, 265, 29918, 5060, 442, 29898, 1311, 1125, 13, 4706, 1923, 353, 5656, 580, 13, 4706, 7834, 353, 1583, 29889, 16015, 4598, 580, 13, 4706, 1923, 29889, 13789, 353, 7834, 13, 4706, 411, 1923, 29901, 13, 9651, 1209, 13, 13, 4706, 411, 1923, 29901, 13, 9651, 1583, 29889, 9294, 9843, 29898, 13789, 29889, 842, 29918, 786, 29918, 13998, 29918, 11808, 29892, 29871, 29906, 29897, 13, 13, 13, 1678, 822, 1243, 29918, 13789, 29918, 842, 29918, 786, 29918, 1333, 29918, 13998, 29918, 7516, 625, 29918, 265, 29918, 2962, 29898, 1311, 1125, 13, 4706, 1923, 353, 5656, 580, 13, 4706, 7834, 353, 1583, 29889, 16015, 4598, 580, 13, 4706, 1923, 29889, 13789, 353, 7834, 13, 4706, 411, 1923, 29901, 13, 9651, 1583, 29889, 9294, 9843, 29898, 13789, 29889, 842, 29918, 786, 29918, 13998, 29918, 11808, 29892, 29871, 29896, 29897, 13, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 443, 27958, 29889, 3396, 580, 13, 2 ]
statsWaveletFilt/threshold.py	Tiarles/Statistical-Wavelet-Filtering	3	61809	<reponame>Tiarles/Statistical-Wavelet-Filtering<filename>statsWaveletFilt/threshold.py<gh_stars>1-10 # -- coding: utf-8 -- ''' Wavelet Based in CUSUM control chart for filtering signals Project (module ``statsWaveletFilt.threshold`` ): Statistic functions for obtain threshold values for wavelet coefficients based in some referenced works. Created by <NAME>, 2018 ''' def lambdasVisuShrink(wavCoeff): ''' Computes the threshold value (lambda) by VisuShrink [1] method. Parameters --------- wavCoeff: list of lists or array-like Wavelet coefficients Returns ------- list of float: The threshold values for each wavelet coefficients vector. See also -------- filtration: Function that use this function for filter via wavelet coefficients pywt.wavedec: Function that decomposes the signal in wavelet and scale coefficients pywt.waverec: Function that recomposes the signal from wavelet and scale coefficients References ---------- .. [1] <NAME>.; <NAME>. Ideal spatial adaptation via wavelet shrinkage. Biometrika, v. 81, p. 425–455, 1994. ''' import numpy as np # Coefficients Vector from the bigger resolution d_m1 = list(wavCoeff[-1]) # Make a copy d_m1 = np.array(d_m1) # Turns a numpy.array estDeviation = np.median(np.abs(d_m1))/.6745 lambdaValues = [estDeviation * np.sqrt(2np.log10(d_m1.size))] \ len(wavCoeff) return lambdaValues def _sure(vector, ti): ''' Internal function, for lambdasSureShrink method. .. note:: After the test (via pytest) the fuction was changed for better performance. Parameters --------- vector: list or array-like vector of coefficients who lambdasSureShrink function needs for the computes. t: list or array-like Intern parameter set by lambdasSureShrink method. Returns ------- list of float: Based on work of Stein [1]. See also -------- filtration: Function that use this function to filter via wavelet coefficients lambdasSureShrink: Function that use this function for sureshrink algorithm References ---------- .. [1] STEIN, C. Estimation of the mean of a multivariate normal distribution. Annals of Statistics, v. 9, p. 1135–1151, 1981. ''' import numpy as np vector2 = np.array(list(vector)) soma1 = np.sum(vector2 <= ti) ti_list = [[ti]]vector2.size soma2 = np.sum(np.power(np.minimum(vector2, np.concatenate(ti_list)), 2)) return vector2.size - 2 soma1 + soma2 def lambdasSureShrink(wavCoeff, dim_t=1024): ''' Computes the threshold value (lambda) by SureShrink [1] method. It's showed also in [2]. .. note:: After the test (via pytest) the fuction was changed for better performance. Parameters --------- wavCoeff: list of lists or array-like Wavelet coefficients dim_t: optional, 1024 by default. t-dimension. Input vector from internal function _sure(vector, dim_t). Returns ------- list of float: The threshold values for each wavelet coefficients vector. See also -------- filtration: Function that use this function to filter via wavelet coefficients pywt.wavedec: Function that decomposes the signal in wavelet and scale coefficients pywt.waverec: Function that recomposes the signal from wavelet and scale coefficients References ---------- .. [1] <NAME>.; <NAME>. Ideal spatial adaptation via wavelet shrinkage. Biometrika, v. 81, p. 425–455, 1994. .. [2] <NAME>.; <NAME>. Filtragem de sinais via limiarização de coeficientes wavelet. Ciência e Natura, v. 36, p. 37–51, 2014. In portuguese. ''' import numpy as np wavCoeff2 = [np.array(list(wavCoeff_i)) for wavCoeff_i in wavCoeff] lambdaValues = [] for coeff in wavCoeff2: estDeviation = np.median(np.abs(coeff))/.6745 tmax = estDeviationnp.sqrt(2np.log10(coeff.size)) t = np.linspace(0, tmax, dim_t) res_sure = [_sure(coeff, ti) for ti in t] lambdaValues.append(t[np.argmin(res_sure)]) return lambdaValues def lambdasBayesShrink(wavCoeff): ''' Computes the threshold value (lambda) by BayesShrink [1] method. It's showed also in [2]. Parameters --------- wavCoeff: list of lists or array-like Wavelet coefficients Returns ------- list of float: The threshold values for each wavelet coefficients vector. See also -------- filtration: Function that use this function to filter via wavelet coefficients pywt.wavedec: Function that decomposes the signal in wavelet and scale coefficients pywt.waverec: Function that recomposes the signal from wavelet and scale coefficients References ---------- .. [1] <NAME>.; <NAME>.; <NAME>. Adaptive wavelet thresholding for image denoising and compression. IEEE Transactions on Image Processing, v. 9, p. 1532–1546, 2000. .. [2] <NAME>.; <NAME>. Filtragem de sinais via limiarização de coeficientes wavelet. Ciência e Natura, v. 36, p. 37–51, 2014. In portuguese. ''' import numpy as np wavCoeff2 = [np.array(list(wavCoeff_i)) for wavCoeff_i in wavCoeff] d_m1 = wavCoeff2[-1] deviation_square = np.power(np.median(np.abs(d_m1))/0.6745, 2) lambdaValues = [] for wavCoeff_i in wavCoeff2: deviation2_wavCoeff_i = np.sum(np.power(wavCoeff_i, 2))/wavCoeff_i.size deviation_Xj = np.sqrt( np.maximum(deviation2_wavCoeff_i - deviation_square, 0)) lambdaValues.append(deviation_square/deviation_Xj) return lambdaValues def lambdasSPC_Threshold(wavCoeff, p=3): ''' Computes the threshold value (lambda) by SPC-Threshold [1], [2] method .. note:: After the test (via pytest) the fuction was changed for better performance. Parameters --------- wavCoeff: list of lists or array-like Wavelet coefficients p: int or float Optional, 3 by default. Parameter for the algorithm [1], generally is used 2 or 3. Returns ------- list of float: The threshold values for each wavelet coefficients vector. See also -------- filtration: Function that use this function to filter via wavelet coefficients pywt.wavedec: Function that decomposes the signal in wavelet and scale coefficients pywt.waverec: Function that recomposes the signal from wavelet and scale coefficients References ---------- .. [1] <NAME>.; <NAME>. SPC-threshold: uma proposta de limiarização para filtragem adaptativa de sinais. Tendências em Matemática Aplicada e Computacional, v. 11, n. 2, p. 121–132, 2010. In portuguese. .. [2] <NAME>.; <NAME>. Filtragem de sinais via limiarização de coeficientes wavelet. Ciência e Natura, v. 36, p. 37–51, 2014. In portuguese. ''' import numpy as np wavCoeff2 = [np.array(list(wavCoeff_i)) for wavCoeff_i in wavCoeff] lambdaValues = [] for wavCoeff_i in wavCoeff2: Sj = np.sqrt(1./(wavCoeff_i.size - 1) * np.sum(np.power(wavCoeff_i - wavCoeff_i.mean(), 2))) while (np.abs(wavCoeff_i) >= pSj).any(): wavCoeff_i = wavCoeff_i[np.abs(wavCoeff_i) < pSj] Sj = np.sqrt(1./(wavCoeff_i.size - 1) * np.sum(np.power(wavCoeff_i - wavCoeff_i.mean(), 2))) lambdaValues.append(p*Sj) return lambdaValues	[ 1, 529, 276, 1112, 420, 29958, 29911, 4447, 793, 29914, 9513, 391, 936, 29899, 29956, 1351, 1026, 29899, 5072, 292, 29966, 9507, 29958, 16202, 29956, 1351, 1026, 29943, 2782, 29914, 386, 12268, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 13, 12008, 13, 1068, 29956, 1351, 1026, 16564, 297, 315, 3308, 5005, 2761, 8727, 363, 21166, 18470, 8010, 313, 5453, 1068, 13, 16159, 16202, 29956, 1351, 1026, 29943, 2782, 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school/eCornell/Python Cert/Currency Converter/src/exchangeit.py	KyleGortych/sample_work	0	1605067	""" User interface for module currency When run as a script, this module prompts the user for two currencies and amount. It prints out the result of converting the first currency to the second. Author: <NAME> Date: 02/16/2022 """ import currency src = input("3-letter code for original currency: ") src = str(src) dst = input("3-letter code for the new currency: ") dst = str(dst) amt = input("Amount of the original currency: ") amt = float(amt) json = currency.service_response(src,dst,amt) # print(json) src_result = currency.before_space(currency.get_src(json)) sub_str = src dst_result = currency.exchange(src,dst,amt) dst_rounded = round(dst_result, 3) sub_str2 = str(dst_rounded) sub_str3 = dst print("You can exchange " + src_result + " " + sub_str + " for " + sub_str2 + " " + sub_str3 + ".")	[ 1, 9995, 13, 2659, 5067, 363, 3883, 27550, 13, 13, 10401, 1065, 408, 263, 2471, 29892, 445, 3883, 9508, 29879, 278, 1404, 363, 1023, 16256, 15942, 322, 5253, 29889, 13, 3112, 14677, 714, 278, 1121, 310, 17415, 278, 937, 27550, 304, 278, 1473, 29889, 13, 13, 13720, 29901, 529, 5813, 29958, 13, 2539, 29901, 1678, 29900, 29906, 29914, 29896, 29953, 29914, 29906, 29900, 29906, 29906, 13, 15945, 29908, 13, 5215, 27550, 13, 13, 4351, 353, 1881, 703, 29941, 29899, 15670, 775, 363, 2441, 27550, 29901, 16521, 13, 4351, 353, 851, 29898, 4351, 29897, 13, 13, 22992, 353, 1881, 703, 29941, 29899, 15670, 775, 363, 278, 716, 27550, 29901, 16521, 13, 22992, 353, 851, 29898, 22992, 29897, 13, 13, 8035, 353, 1881, 703, 18087, 310, 278, 2441, 27550, 29901, 16521, 13, 8035, 353, 5785, 29898, 8035, 29897, 13, 13, 3126, 353, 27550, 29889, 5509, 29918, 5327, 29898, 4351, 29892, 22992, 29892, 8035, 29897, 13, 29937, 1596, 29898, 3126, 29897, 13, 13, 4351, 29918, 2914, 353, 27550, 29889, 11083, 29918, 3493, 29898, 26095, 29889, 657, 29918, 4351, 29898, 3126, 876, 13, 1491, 29918, 710, 353, 4765, 13, 13, 22992, 29918, 2914, 353, 27550, 29889, 6543, 29898, 4351, 29892, 22992, 29892, 8035, 29897, 13, 22992, 29918, 29878, 7261, 353, 4513, 29898, 22992, 29918, 2914, 29892, 29871, 29941, 29897, 13, 1491, 29918, 710, 29906, 353, 851, 29898, 22992, 29918, 29878, 7261, 29897, 13, 13, 1491, 29918, 710, 29941, 353, 29743, 13, 13, 2158, 703, 3492, 508, 14523, 376, 718, 4765, 29918, 2914, 718, 376, 376, 718, 1014, 29918, 710, 718, 376, 363, 376, 718, 1014, 29918, 710, 29906, 718, 376, 376, 718, 1014, 29918, 710, 29941, 718, 11393, 1159, 13, 2 ]
PythonMPI/computepi/ComputePiSeq.py	terasakisatoshi/MPI	1	177540	import math import time #compute \pi using formula shown below: #\pi=\int_{0}^{1}\frac{4}{1+x^2}dx \sim =\frac{1}{n}\sum_{i=0}^{n-1}\frac{4}{1+(\frac{i+0.5}{n})^2} # def compute_pi(num_step): h=1.0/num_step s=0.0 for i in range(num_step): x=h(i+0.5) s+=4.0/(1.0+x2) return sh def main(): n=int(1.0e8) start=time.time() pi=compute_pi(n) diff=abs(pi-math.pi) print("pi is approximately %.16f,diff=compute_pi-math.pi is %.16f" % (pi,diff)) elapsed_time=time.time()-start print("elapsed_time=%f" % elapsed_time ) if __name__ == '__main__': main()	[ 1, 1053, 5844, 13, 5215, 931, 13, 29937, 26017, 320, 1631, 773, 7063, 4318, 2400, 29901, 13, 29937, 29905, 1631, 2013, 524, 648, 29900, 2844, 29896, 1012, 1154, 29912, 29946, 1157, 29896, 29974, 29916, 29985, 29906, 29913, 8235, 320, 3601, 17313, 1154, 29912, 29896, 1157, 29876, 1012, 2083, 648, 29875, 29922, 29900, 2844, 29876, 29899, 29896, 1012, 1154, 29912, 29946, 1157, 29896, 29974, 1194, 1154, 29912, 29875, 29974, 29900, 29889, 29945, 1157, 29876, 28813, 29906, 29913, 13, 29937, 13, 1753, 10272, 29918, 1631, 29898, 1949, 29918, 10568, 1125, 13, 1678, 298, 29922, 29896, 29889, 29900, 29914, 1949, 29918, 10568, 13, 1678, 269, 29922, 29900, 29889, 29900, 13, 1678, 363, 474, 297, 3464, 29898, 1949, 29918, 10568, 1125, 13, 4706, 921, 29922, 29882, 16395, 29875, 29974, 29900, 29889, 29945, 29897, 13, 4706, 269, 23661, 29946, 29889, 29900, 14571, 29896, 29889, 29900, 29974, 29916, 1068, 29906, 29897, 13, 1678, 736, 269, 29930, 29882, 13, 13, 1753, 1667, 7295, 13, 1678, 302, 29922, 524, 29898, 29896, 29889, 29900, 29872, 29947, 29897, 13, 1678, 1369, 29922, 2230, 29889, 2230, 580, 13, 1678, 2930, 29922, 26017, 29918, 1631, 29898, 29876, 29897, 13, 1678, 2923, 29922, 6897, 29898, 1631, 29899, 755, 29889, 1631, 29897, 13, 1678, 1596, 703, 1631, 338, 14235, 18695, 29896, 29953, 29888, 29892, 12765, 29922, 26017, 29918, 1631, 29899, 755, 29889, 1631, 338, 18695, 29896, 29953, 29888, 29908, 1273, 313, 1631, 29892, 12765, 876, 13, 1678, 560, 28170, 29918, 2230, 29922, 2230, 29889, 2230, 580, 29899, 2962, 13, 1678, 1596, 703, 295, 28170, 29918, 2230, 16328, 29888, 29908, 1273, 560, 28170, 29918, 2230, 1723, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 1667, 580, 2 ]
code/tensorflow/tests/test_image_generator.py	siduojiang/BERTVision	5	106254	<reponame>siduojiang/BERTVision<gh_stars>1-10 import unittest import h5py #git repo specific setup import sys sys.path.append('../utils') from training_utils import * from evaluation import * class TestBERTImageGenerator(unittest.TestCase): def __init__(self, args, kwargs): super().__init__(args, **kwargs) self.model = PretrainedBertSquad2Faster('../../bert_base_squad_1e-5_adam_4batchsize_2epochs_weights_BERT_ONLY.h5').model train = h5py.File('../../../SQuADv2/train_386.h5', 'r') self.start_ids = train['input_start'] self.end_ids = train['input_end'] self.train_inputs = np.array(train['input_ids']) self.train_masks = np.array(train['attention_mask']) self.labels = np.vstack([self.start_ids, self.end_ids]).T self.offset = 2 self.start_idx = 14325 self.end_idx = 14642 self.images = BERTImageGenerator('../../../data/train/', self.labels, batch_size=1, start_idx = self.start_idx, end_idx = self.end_idx, shuffle = False) self.subset = self.images[self.offset] def setUp(self): self.idx = self.start_idx + self.offset def test_embeddings_shape(self): #extract image 2 with starting index 14325, leading to image 14325 self.assertEqual(len(self.subset), 2) self.assertEqual(self.subset[0].shape, (1, 24, 386, 1024)) self.assertEqual(self.subset[1].shape, (1, 2)) def test_offset(self): manual_load = h5py.File('../../../data/train/%d.h5' %self.idx, 'r') self.assertTrue((np.array(manual_load['hidden_state_activations'])[0][-1] == self.subset[0][0][0][-1]).all()) def test_label_ids(self): self.assertEqual(self.subset[1][0][0], self.start_ids[self.idx]) self.assertEqual(self.subset[1][0][1], self.end_ids[self.idx]) def test_embeddings_with_model(self): embeddings, outputs = self.model.predict([self.train_inputs[[self.idx]], self.train_masks[[self.idx]]]) non_zero_idx = np.sum(self.train_inputs[[self.idx]] != 0) self.assertTrue((embeddings[12][0][:non_zero_idx][-1] == self.subset[0][0][12][-1]).all()) def test_shuffle_ids(self): pass def test_shuffled_labels(self): pass if __name__ == '__main__': unittest.main()	[ 1, 529, 276, 1112, 420, 29958, 29879, 333, 25608, 2397, 574, 29914, 13635, 8050, 2459, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 5215, 443, 27958, 13, 5215, 298, 29945, 2272, 13, 13, 29937, 5559, 13761, 2702, 6230, 13, 5215, 10876, 13, 9675, 29889, 2084, 29889, 4397, 877, 6995, 13239, 1495, 13, 13, 3166, 6694, 29918, 13239, 1053, 334, 13, 3166, 17983, 1053, 334, 13, 13, 1990, 4321, 13635, 29911, 2940, 21575, 29898, 348, 27958, 29889, 3057, 8259, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 334, 5085, 29892, 3579, 19290, 1125, 13, 4706, 2428, 2141, 1649, 2344, 1649, 10456, 5085, 29892, 3579, 19290, 29897, 13, 4706, 1583, 29889, 4299, 353, 349, 2267, 22042, 29933, 814, 29903, 3425, 29906, 29943, 1901, 877, 21546, 2151, 29918, 3188, 29918, 29879, 3425, 29918, 29896, 29872, 29899, 29945, 29918, 328, 314, 29918, 29946, 16175, 2311, 29918, 29906, 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Crypto-hardRSA/flag.py	JSW2020/hsctf-2019-freshmen	16	14930	flag = "flag{b3453333-9da9-49ae-b4ed-0017c392d58e}" e1 = 65537 e2 = 368273	[ 1, 7353, 353, 376, 15581, 29912, 29890, 29941, 29946, 29945, 29941, 29941, 29941, 29941, 29899, 29929, 1388, 29929, 29899, 29946, 29929, 3660, 29899, 29890, 29946, 287, 29899, 29900, 29900, 29896, 29955, 29883, 29941, 29929, 29906, 29881, 29945, 29947, 29872, 5038, 13, 29872, 29896, 353, 29871, 29953, 29945, 29945, 29941, 29955, 13, 29872, 29906, 353, 29871, 29941, 29953, 29947, 29906, 29955, 29941, 2 ]
tests/service/ai/test_not_killing_itself_ai.py	jonashellmann/informaticup21-team-chillow	3	11942	import unittest from datetime import datetime, timezone from typing import List from chillow.service.ai.not_killing_itself_ai import NotKillingItselfAI from chillow.model.action import Action from chillow.model.cell import Cell from chillow.model.direction import Direction from chillow.model.game import Game from chillow.model.player import Player from chillow.service.game_service import GameService class NotKillingItselfAITest(unittest.TestCase): def test_ai_should_choose_the_own_non_killing_itself_action(self): player1 = Player(1, 0, 0, Direction.up, 1, True, "") player2 = Player(2, 4, 4, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell([player1]), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell([player2])]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) sut = NotKillingItselfAI(player1, [], 3, 0, 3) actions: List[Action] = sut.find_surviving_actions(game_service, 3) self.assertTrue(Action.turn_right in actions) self.assertTrue(len(actions) == 1) def test_ai_should_choose_the_correct_list_of_actions_non_killing_itself(self): player1 = Player(1, 0, 1, Direction.up, 1, True, "") player2 = Player(2, 4, 4, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player1]), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell([player2])]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) sut = NotKillingItselfAI(player1, [], 3, 0, 3) actions: List[Action] = sut.find_surviving_actions(game_service, 3) self.assertTrue(Action.change_nothing in actions) self.assertTrue(Action.turn_right in actions) self.assertTrue(len(actions) == 2) def test_ai_should_choose_the_correct_list_of_actions_non_killing_itself2(self): player1 = Player(1, 1, 2, Direction.up, 1, True, "") player2 = Player(2, 1, 1, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell(), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell([player2]), Cell(), Cell(), Cell()], [Cell(), Cell([player1]), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) sut = NotKillingItselfAI(player1, [], 3, 0, 3) actions: List[Action] = sut.find_surviving_actions(game_service, 3) self.assertTrue(Action.turn_left in actions) self.assertTrue(Action.turn_right in actions) self.assertTrue(len(actions) == 2) def test_ai_should_choose_the_correct_list_of_actions_non_killing_itself_in_turn_6(self): player1 = Player(1, 0, 4, Direction.up, 3, True, "") player2 = Player(2, 0, 1, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player2]), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player1]), Cell(), Cell(), Cell(), Cell()]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) game_service.turn.turn_ctr = 6 sut = NotKillingItselfAI(player1, [], 4, 0, 3) actions: List[Action] = sut.find_surviving_actions(game_service, 1) self.assertTrue(Action.slow_down in actions) self.assertTrue(Action.turn_right in actions) self.assertTrue(Action.speed_up in actions) self.assertTrue(len(actions) == 3) def test_ai_should_not_choose_speed_up_if_max_speed_is_allready_reached(self): MAX_SPEED = 3 player1 = Player(1, 0, 4, Direction.up, MAX_SPEED, True, "") player2 = Player(2, 0, 1, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player2]), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player1]), Cell(), Cell(), Cell(), Cell()]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) sut = NotKillingItselfAI(player1, [], MAX_SPEED, 0, 3) actions: List[Action] = sut.find_surviving_actions(game_service, 1) self.assertTrue(Action.slow_down in actions) self.assertTrue(Action.turn_right in actions) self.assertTrue(len(actions) == 2) def test_ai_should_calc_action_with_max_distance(self): player1 = Player(1, 0, 4, Direction.up, 1, True, "") player2 = Player(2, 0, 1, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player2]), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player1]), Cell(), Cell(), Cell(), Cell()]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) sut = NotKillingItselfAI(player1, [], 3, 0, 3) actions: List[Action] = sut.calc_action_with_max_distance_to_visited_cells(game_service, [Action.speed_up, Action.change_nothing, Action.turn_right]) self.assertTrue(Action.turn_right in actions) self.assertTrue(len(actions) == 1) def test_ai_should_calc_all_action_with_max_distance_with_max_worse_distance(self): MAX_WORSE_DISTANCE = 1 player1 = Player(1, 0, 4, Direction.up, 1, True, "") player2 = Player(2, 4, 4, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell(), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player1]), Cell(), Cell(), Cell(), Cell([player2])]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) sut = NotKillingItselfAI(player1, [], 3, MAX_WORSE_DISTANCE, 3) actions: List[Action] = sut.calc_action_with_max_distance_to_visited_cells(game_service, [Action.speed_up, Action.change_nothing, Action.turn_right]) self.assertTrue(Action.speed_up in actions) self.assertTrue(Action.change_nothing in actions) self.assertTrue(Action.turn_right in actions) self.assertTrue(len(actions) == 3) def test_get_information(self): player = Player(1, 0, 4, Direction.up, 1, True, "") sut = NotKillingItselfAI(player, [], 3, 1, 3) expected = "max_speed=3, max_worse_distance=1, depth=3" result = sut.get_information() self.assertEqual(expected, result) def test_ai_should_choose_the_correct_list_of_actions_non_killing_itself_with_depth_greater_than_one(self): player1 = Player(1, 1, 2, Direction.up, 1, True, "") player2 = Player(2, 1, 1, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player2]), Cell([player2]), Cell(), Cell(), Cell()], [Cell(), Cell([player1]), Cell(), Cell(), Cell()], [Cell([player2]), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) sut = NotKillingItselfAI(player1, [], 3, 0, 2) actions: List[Action] = sut.find_surviving_actions(game_service, 2) self.assertTrue(Action.turn_right in actions) self.assertTrue(len(actions) == 1) def test_ai_should_choose_empty_list_with_depth_greater_than_one_and_no_surviving_action(self): player1 = Player(1, 1, 2, Direction.up, 1, True, "") player2 = Player(2, 1, 1, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player2]), Cell([player2]), Cell([player2]), Cell(), Cell()], [Cell(), Cell([player1]), Cell(), Cell([player2]), Cell()], [Cell([player2]), Cell(), Cell([player2]), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) sut = NotKillingItselfAI(player1, [], 3, 0, 2) actions: List[Action] = sut.find_surviving_actions(game_service, 2) self.assertTrue(len(actions) == 0) def test_ai_should_choose_correct_list_with_depth_three_and_surviving_action(self): player1 = Player(1, 1, 2, Direction.up, 1, True, "") player2 = Player(2, 1, 1, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player2]), Cell([player2]), Cell([player2]), Cell(), Cell()], [Cell(), Cell([player1]), Cell(), Cell([player2]), Cell()], [Cell([player2]), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) sut = NotKillingItselfAI(player1, [], 3, 0, 3) actions: List[Action] = sut.find_surviving_actions(game_service, 3) self.assertTrue(Action.turn_right in actions) self.assertTrue(len(actions) == 1) def test_ai_should_choose_empty_list_with_depth_three_and_no_surviving_action(self): player1 = Player(1, 1, 2, Direction.up, 1, True, "") player2 = Player(2, 1, 1, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player2]), Cell([player2]), Cell([player2]), Cell(), Cell()], [Cell(), Cell([player1]), Cell(), Cell([player2]), Cell()], [Cell([player2]), Cell([player2]), Cell(), Cell([player2]), Cell()], [Cell(), Cell(), Cell([player2]), Cell(), Cell()]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) sut = NotKillingItselfAI(player1, [], 3, 0, 3) actions: List[Action] = sut.find_surviving_actions(game_service, 3) self.assertTrue(len(actions) == 0) def test_ai_should_choose_best_list_of_actions_by_depth_from_lower_depth(self): player1 = Player(1, 1, 2, Direction.up, 1, True, "") player2 = Player(2, 1, 1, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player2]), Cell([player2]), Cell([player2]), Cell(), Cell()], [Cell(), Cell([player1]), Cell(), Cell([player2]), Cell()], [Cell([player2]), Cell([player2]), Cell(), Cell([player2]), Cell()], [Cell(), Cell(), Cell([player2]), Cell(), Cell()]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) sut = NotKillingItselfAI(player1, [], 3, 0, 5) actions: List[Action] = sut.find_surviving_actions_with_best_depth(game_service) self.assertTrue(Action.turn_right in actions) self.assertTrue(len(actions) == 1) def test_ai_should_choose_best_list_of_actions_by_depth(self): player1 = Player(1, 1, 2, Direction.up, 1, True, "") player2 = Player(2, 1, 1, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player2]), Cell([player2]), Cell([player2]), Cell(), Cell()], [Cell(), Cell([player1]), Cell(), Cell([player2]), Cell()], [Cell([player2]), Cell(), Cell(), Cell([player2]), Cell()], [Cell(), Cell(), Cell([player2]), Cell(), Cell()]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) sut = NotKillingItselfAI(player1, [], 3, 0, 5) actions: List[Action] = sut.find_surviving_actions_with_best_depth(game_service) self.assertTrue(Action.turn_right in actions) self.assertTrue(len(actions) == 1) def test_ai_should_choose_best_list_of_actions_in_lowest_possible_depth(self): player1 = Player(1, 1, 2, Direction.up, 1, True, "") player2 = Player(2, 1, 1, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player2]), Cell([player2]), Cell([player2]), Cell(), Cell()], [Cell(), Cell([player1]), Cell(), Cell([player2]), Cell()], [Cell([player2]), Cell(), Cell([player2]), Cell([player2]), Cell()], [Cell(), Cell(), Cell([player2]), Cell(), Cell()]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) sut = NotKillingItselfAI(player1, [], 3, 0, 5) actions: List[Action] = sut.find_surviving_actions_with_best_depth(game_service) self.assertTrue(Action.turn_left in actions) self.assertTrue(Action.turn_right in actions) self.assertTrue(len(actions) == 2)	[ 1, 1053, 443, 27958, 13, 3166, 12865, 1053, 12865, 29892, 29431, 13, 3166, 19229, 1053, 2391, 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ebl/tests/corpus/test_line.py	ElectronicBabylonianLiterature/ebl-api	4	111754	<gh_stars>1-10 from typing import Sequence import pytest from ebl.corpus.domain.line import Line, LineVariant, ManuscriptLine from ebl.transliteration.domain.atf import Ruling, Surface from ebl.transliteration.domain.dollar_line import RulingDollarLine from ebl.transliteration.domain.enclosure_tokens import BrokenAway from ebl.transliteration.domain.labels import ColumnLabel, Label, SurfaceLabel from ebl.transliteration.domain.line_number import LineNumber from ebl.transliteration.domain.markup import StringPart from ebl.transliteration.domain.normalized_akkadian import AkkadianWord from ebl.transliteration.domain.note_line import NoteLine from ebl.transliteration.domain.parallel_line import ParallelComposition from ebl.transliteration.domain.sign_tokens import Reading from ebl.transliteration.domain.text_line import TextLine from ebl.transliteration.domain.tokens import ValueToken from ebl.transliteration.domain.translation_line import Extent, TranslationLine from ebl.transliteration.domain.word_tokens import Word LINE_NUMBER = LineNumber(1) LINE_RECONSTRUCTION = (AkkadianWord.of((ValueToken.of("buāru"),)),) NOTE = None MANUSCRIPT_ID = 9001 LABELS = (SurfaceLabel.from_label(Surface.OBVERSE),) MANUSCRIPT_TEXT = TextLine(LINE_NUMBER, (Word.of([Reading.of([ValueToken.of("ku")])]),)) PARATEXT = (NoteLine((StringPart("note"),)), RulingDollarLine(Ruling.SINGLE)) OMITTED_WORDS = (1,) PARALLEL_LINES = (ParallelComposition(False, "a composition", LineNumber(7)),) INTERTEXT = (StringPart("foo"),) LINE_VARIANT = LineVariant( LINE_RECONSTRUCTION, NOTE, (ManuscriptLine(MANUSCRIPT_ID, LABELS, MANUSCRIPT_TEXT, PARATEXT, OMITTED_WORDS),), PARALLEL_LINES, INTERTEXT, ) def test_invalid_extent() -> None: translation = TranslationLine( tuple(), extent=Extent(LineNumber(1), (SurfaceLabel(tuple(), Surface.OBVERSE),)) ) with pytest.raises( ValueError, match="Labels are not allowed in line translations." ): Line(LineNumber(1), tuple(), translation=(translation,)), def test_line_variant_constructor(): assert LINE_VARIANT.reconstruction == LINE_RECONSTRUCTION assert LINE_VARIANT.note == NOTE assert LINE_VARIANT.parallel_lines == PARALLEL_LINES assert LINE_VARIANT.intertext == INTERTEXT assert LINE_VARIANT.manuscripts[0].manuscript_id == MANUSCRIPT_ID assert LINE_VARIANT.manuscripts[0].labels == LABELS assert LINE_VARIANT.manuscripts[0].line == MANUSCRIPT_TEXT assert LINE_VARIANT.manuscripts[0].paratext == PARATEXT assert LINE_VARIANT.manuscripts[0].omitted_words == OMITTED_WORDS @pytest.mark.parametrize( # pyre-ignore[56] "labels", [ (ColumnLabel.from_label("i"), ColumnLabel.from_label("ii")), ( SurfaceLabel.from_label(Surface.OBVERSE), SurfaceLabel.from_label(Surface.REVERSE), ), (ColumnLabel.from_label("i"), SurfaceLabel.from_label(Surface.REVERSE)), ], ) def test_invalid_labels(labels: Sequence[Label]): with pytest.raises(ValueError): ManuscriptLine(manuscript_id=1, labels=labels, line=TextLine(LineNumber(1))) def test_invalid_reconstruction(): with pytest.raises(ValueError): Line( LINE_NUMBER, (LineVariant((AkkadianWord.of((BrokenAway.open(),)),), NOTE, tuple()),), False, False, ) def test_update_manuscript_alignment(): word1 = Word.of( [Reading.of_name("ku")], alignment=0, variant=Word.of([Reading.of_name("uk")]) ) word2 = Word.of( [Reading.of_name("ra")], alignment=1, variant=Word.of([Reading.of_name("ar")]) ) word3 = Word.of( [Reading.of_name("pa")], alignment=2, variant=Word.of([Reading.of_name("ap")]) ) manuscript = ManuscriptLine( MANUSCRIPT_ID, LABELS, TextLine(LineNumber(1), (word1, word2, word3)), PARATEXT, (1, 3), ) expected = ManuscriptLine( MANUSCRIPT_ID, LABELS, TextLine( LineNumber(1), ( word1.set_alignment(None, None), word2.set_alignment(0, word2.variant), word3.set_alignment(None, None), ), ), PARATEXT, (0,), ) assert manuscript.update_alignments([None, 0]) == expected	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 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plottwist/bootstrap/maya/userSetup.py	Plot-Twist-Short-Film/plottwist-bootstrap	0	152413	#!/usr/bin/env python # -- coding: utf-8 -- """ Initialization for Plot Twist Tools """ from __future__ import print_function, division, absolute_import __author__ = "<NAME>" __license__ = "MIT" __maintainer__ = "<NAME>" __email__ = "<EMAIL>" import maya.cmds as cmds # Do not remove def init(): print('=' * 100) print('\| Plot Twist ArtellaPipe \| > Loading Plot Twist Tools') try: # Initialize tpDcc library import tpDcc.loader print(tpDcc.loader) tpDcc.loader.init(dev=False) # Initialize artellapipe library import artellapipe.loader artellapipe.loader.init(dev=False) # Initialize plottwist project import plottwist.loader plottwist.loader.init(dev=False) print('\| Plot Twist Pipeline \| Plot Twist loaded successfully!') print('=' * 100) except Exception as exc: import traceback print('ERROR: Impossible to load Plot Twist Tools, contact TD!') print('{} \| {}'.format(exc, traceback.format_exc())) # We must launch it with low priority, otherwise USD plugin loading operations will fail cmds.evalDeferred(init, lp=True)	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 13, 15945, 29908, 13, 15514, 2133, 363, 18399, 8168, 391, 27564, 13, 15945, 29908, 13, 13, 3166, 4770, 29888, 9130, 1649, 1053, 1596, 29918, 2220, 29892, 8542, 29892, 8380, 29918, 5215, 13, 13, 1649, 8921, 1649, 353, 9872, 5813, 11903, 13, 1649, 506, 1947, 1649, 353, 376, 26349, 29908, 13, 1649, 29885, 2365, 4008, 1649, 353, 9872, 5813, 11903, 13, 1649, 5269, 1649, 353, 9872, 26862, 6227, 11903, 13, 13, 5215, 1122, 29874, 29889, 9006, 29879, 408, 9920, 29879, 4706, 396, 1938, 451, 3349, 13, 13, 13, 1753, 2069, 7295, 13, 13, 1678, 1596, 877, 2433, 334, 29871, 29896, 29900, 29900, 29897, 13, 1678, 1596, 877, 29989, 18399, 8168, 391, 3012, 3547, 12197, 412, 891, 1405, 4309, 9382, 18399, 8168, 391, 27564, 1495, 13, 13, 1678, 1018, 29901, 13, 4706, 396, 25455, 260, 29886, 29928, 617, 3489, 13, 4706, 1053, 260, 29886, 29928, 617, 29889, 12657, 13, 4706, 1596, 29898, 9392, 29928, 617, 29889, 12657, 29897, 13, 4706, 260, 29886, 29928, 617, 29889, 12657, 29889, 2344, 29898, 3359, 29922, 8824, 29897, 13, 13, 4706, 396, 25455, 1616, 3547, 17760, 3489, 13, 4706, 1053, 1616, 3547, 17760, 29889, 12657, 13, 4706, 1616, 3547, 17760, 29889, 12657, 29889, 2344, 29898, 3359, 29922, 8824, 29897, 13, 13, 4706, 396, 25455, 715, 1501, 29893, 391, 2060, 13, 4706, 1053, 715, 1501, 29893, 391, 29889, 12657, 13, 4706, 715, 1501, 29893, 391, 29889, 12657, 29889, 2344, 29898, 3359, 29922, 8824, 29897, 13, 13, 4706, 1596, 877, 29989, 18399, 8168, 391, 349, 23828, 891, 18399, 8168, 391, 7500, 8472, 29991, 1495, 13, 4706, 1596, 877, 2433, 334, 29871, 29896, 29900, 29900, 29897, 13, 1678, 5174, 8960, 408, 5566, 29901, 13, 4706, 1053, 9637, 1627, 13, 4706, 1596, 877, 11432, 29901, 1954, 27338, 304, 2254, 18399, 8168, 391, 27564, 29892, 6958, 323, 29928, 29991, 1495, 13, 4706, 1596, 877, 8875, 891, 6571, 4286, 4830, 29898, 735, 29883, 29892, 9637, 1627, 29889, 4830, 29918, 735, 29883, 22130, 13, 13, 13, 29937, 1334, 1818, 6826, 372, 411, 4482, 20136, 29892, 6467, 3148, 29928, 7079, 8363, 6931, 674, 4418, 13, 9006, 29879, 29889, 14513, 2772, 14373, 29898, 2344, 29892, 301, 29886, 29922, 5574, 29897, 13, 2 ]
kensu/client/models/field_def.py	vidma/kensu-py	16	46838	<reponame>vidma/kensu-py # coding: utf-8 """ No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) OpenAPI spec version: beta Generated by: https://github.com/swagger-api/swagger-codegen.git """ from pprint import pformat from six import iteritems class FieldDef(object): """ NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_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. """ swagger_types = { 'name': 'str', 'field_type': 'str', 'nullable': 'bool' } attribute_map = { 'name': 'name', 'field_type': 'fieldType', 'nullable': 'nullable' } def __init__(self, name=None, field_type=None, nullable=None): """ FieldDef - a model defined in Swagger """ self._name = None self._field_type = None self._nullable = None self.name = name self.field_type = field_type self.nullable = nullable @property def name(self): """ Gets the name of this FieldDef. :return: The name of this FieldDef. :rtype: str """ return self._name @name.setter def name(self, name): """ Sets the name of this FieldDef. :param name: The name of this FieldDef. :type: str """ if name is None: raise ValueError("Invalid value for `name`, must not be `None`") self._name = name @property def field_type(self): """ Gets the field_type of this FieldDef. :return: The field_type of this FieldDef. :rtype: str """ return self._field_type @field_type.setter def field_type(self, field_type): """ Sets the field_type of this FieldDef. :param field_type: The field_type of this FieldDef. :type: str """ if field_type is None: raise ValueError("Invalid value for `field_type`, must not be `None`") self._field_type = field_type @property def nullable(self): """ Gets the nullable of this FieldDef. :return: The nullable of this FieldDef. :rtype: bool """ return self._nullable @nullable.setter def nullable(self, nullable): """ Sets the nullable of this FieldDef. :param nullable: The nullable of this FieldDef. :type: bool """ if nullable is None: raise ValueError("Invalid value for `nullable`, must not be `None`") self._nullable = nullable def to_dict(self): """ Returns the model properties as a dict """ result = {} for attr, _ in iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] 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psi/controller/plugin.py	bburan/psiexperiment	5	193981	<reponame>bburan/psiexperiment import logging log = logging.getLogger(__name__) from functools import partial import operator as op import threading import numpy as np from atom.api import Enum, Bool, Typed, Property from enaml.application import deferred_call from enaml.qt.QtCore import QTimer from enaml.workbench.api import Extension from enaml.workbench.plugin import Plugin from .calibration.util import load_calibration from .channel import Channel, OutputMixin, InputMixin from .engine import Engine from .output import Output, Synchronized from .input import Input from .device import Device from .experiment_action import (ExperimentAction, ExperimentActionBase, ExperimentCallback, ExperimentEvent, ExperimentState) from .output import ContinuousOutput, EpochOutput IO_POINT = 'psi.controller.io' ACTION_POINT = 'psi.controller.actions' def get_inputs(input): inputs = [] for child in input.children: inputs.extend(get_inputs(child)) inputs.append(input) return inputs def get_outputs(output): outputs = [] for child in output.children: outputs.extend(get_outputs(child)) outputs.append(output) return outputs def find_devices(point): devices = {} for extension in point.extensions: for device in extension.get_children(Device): devices[device.name] = device return devices engine_error = ''' More than one engine named "{}" To fix this, please review the IO manifest (i.e., hardware configuration) you selected and verify that all engines have unique names. ''' def find_engines(point): master_engine = None engines = {} for extension in point.extensions: for e in extension.get_children(Engine): if e.name in engines: raise ValueError(engine_error.strip().format(e.name)) engines[e.name] = e if e.master_clock: if master_engine is not None: m = 'Only one engine can be defined as the master' raise ValueError(m) master_engine = e return engines, master_engine def find_channels(engines): channels = {} for e in engines.values(): for c in e.get_channels(active=False): channels[c.name] = c return channels def find_outputs(channels, point): outputs = {} supporting = {} # Find all the outputs already connected to a channel for c in channels.values(): if isinstance(c, OutputMixin): for o in c.children: for oi in get_outputs(o): outputs[oi.name] = oi # Find unconnected outputs and inputs (these are allowed so that we can # split processing hierarchies across multiple manifests). for extension in point.extensions: for o in extension.get_children(Output): outputs[o.name] = o for s in extension.get_children(Synchronized): supporting[s.name] = s for o in s.outputs: outputs[o.name] = o return outputs, supporting def find_inputs(channels, point): inputs = {} # Find all the outputs already connected to a channel for c in channels.values(): if isinstance(c, InputMixin): for i in c.children: for ci in get_inputs(i): inputs[ci.name] = ci for extension in point.extensions: for i in extension.get_children(Input): # Recurse through input tree. Currently we assume that # inputs can be nested/hierarchial while outputs are not. for ci in get_inputs(i): inputs[ci.name] = ci return inputs class ControllerPlugin(Plugin): # Tracks the state of the controller. experiment_state = Enum('initialized', 'running', 'paused', 'stopped') # Provides direct access to plugins rather than going through the core # command system. Right now the context plugin is so fundamentally important # to the controller that it would be cumbersome to use the core command # system. core = Typed(Plugin) context = Typed(Plugin) data = Typed(Plugin) # We should not respond to changes during the course of a trial. These # flags indicate changes or requests from the user are pending and should # be processed when the opportunity arises (e.g., at the end of the trial). _apply_requested = Bool(False) _remind_requested = Bool(False) _pause_requested = Bool(False) # Can the experiment be paused? _pause_ok = Bool(False) # Available engines _engines = Typed(dict, {}) # Available channels _channels = Typed(dict, {}) # Available outputs _outputs = Typed(dict, {}) # Available supporting classes for outputs (right now only Synchronized) _supporting = Typed(dict, {}) # Available inputs _inputs = Typed(dict, {}) # Available devices _devices = Typed(dict, {}) # This determines which engine is responsible for the clock _master_engine = Typed(Engine) # List of events and actions that can be associated with the event _events = Typed(dict, {}) _states = Typed(dict, {}) _action_context = Typed(dict, {}) _timers = Typed(dict, {}) # Plugin actions are automatically registered when the manifests are # loaded. In contrast, registered actions are registered by setup code # (e.g., when one doesn't know in advance which output/input the user wants # to record from). _plugin_actions = Typed(list, {}) _registered_actions = Typed(list, {}) _actions = Property() def _get__actions(self): return self._registered_actions + self._plugin_actions def start(self): log.debug('Starting controller plugin') self._refresh_io() self._refresh_actions() self._bind_observers() self.core = self.workbench.get_plugin('enaml.workbench.core') self.context = self.workbench.get_plugin('psi.context') self.data = self.workbench.get_plugin('psi.data') def stop(self): self._unbind_observers() def _bind_observers(self): self.workbench.get_extension_point(IO_POINT) \ .observe('extensions', self._refresh_io) self.workbench.get_extension_point(ACTION_POINT) \ .observe('extensions', self._refresh_actions) def _unbind_observers(self): self.workbench.get_extension_point(IO_POINT) \ .unobserve('extensions',self._refresh_io) self.workbench.get_extension_point(ACTION_POINT) \ .unobserve('extensions', self._refresh_actions) def _refresh_io(self, event=None): # TODO: Allow disabling of devices. log.debug('Loading IO') point = self.workbench.get_extension_point(IO_POINT) self._devices = find_devices(point) self._engines, self._master_engine = find_engines(point) self._channels = find_channels(self._engines) self._outputs, self._supporting = find_outputs(self._channels, point) self._inputs = find_inputs(self._channels, point) for c in self._channels.values(): c.load_manifest(self.workbench) for d in self._devices.values(): d.load_manifest(self.workbench) for s in self._supporting.values(): s.load_manifest(self.workbench) for e in self._engines.values(): e.load_manifest(self.workbench) for o in self._outputs.values(): o.load_manifest(self.workbench) for i in self._inputs.values(): i.load_manifest(self.workbench) log.warn('*********************************************') log.warn(str(self._outputs.keys())) def _connect_outputs(self): for o in self._outputs.values(): # First, make sure that the output is connected to a target. Check # to see if the target is named. If not, then check to see if it # has a parent one can use. if o.target is None and o.target_name: self.connect_output(o.name, o.target_name) elif o.target is None and not isinstance(o.parent, Extension): o.parent.add_output(o) elif o.target is None: log.warn('Unconnected output %s', o.name) def _connect_inputs(self): for i in self._inputs.values(): # First, make sure the input is connected to a source try: if i.source is None and i.source_name: self.connect_input(i.name, i.source_name) elif i.source is None and not isinstance(i.parent, Extension): i.parent.add_input(i) elif i.source is None: log.warn('Unconnected input %s', i.name) except: pass def connect_output(self, output_name, target_name): # Link up outputs with channels if needed. TODO: Can another output be # the target (e.g., if one wanted to combine multiple tokens into a # single stream)? if target_name in self._channels: target = self._channels[target_name] else: m = "Unknown target {} specified for output {}" \ .format(target_name, output_name) raise ValueError(m) o = self._outputs[output_name] target.add_output(o) m = 'Connected output %s to target %s' log.debug(m, output_name, target_name) def connect_input(self, input_name, source_name): if source_name in self._inputs: source = self._inputs[source_name] elif source_name in self._channels: source = self._channels[source_name] else: m = "Unknown source {}".format(source_name) raise ValueError(m) i = self._inputs[input_name] source.add_input(i) m = 'Connected input %s to source %s' log.debug(m, input_name, source_name) def _refresh_actions(self, event=None): actions = [] events = {} states = {} point = self.workbench.get_extension_point(ACTION_POINT) for extension in point.extensions: found_states = extension.get_children(ExperimentState) found_events = extension.get_children(ExperimentEvent) found_actions = extension.get_children(ExperimentActionBase) for state in found_states: if state.name in states: m = '{} state already exists'.format(state.name) raise ValueError(m) states[state.name] = state found_events.extend(state._generate_events()) for event in found_events: if event.name in events: m = '{} event already exists'.format(event.name) raise ValueError(m) events[event.name] = event actions.extend(found_actions) context = {} for state_name in states: context[state_name + '_active'] = False for event_name in events: context[event_name] = False actions.sort(key=lambda a: a.weight) self._states = states self._events = events self._plugin_actions = actions self._action_context = context def register_action(self, event, command, kwargs=None): if kwargs is None: kwargs = {} if isinstance(command, str): action = ExperimentAction(event=event, command=command, kwargs=kwargs) else: action = ExperimentCallback(event=event, callback=command, kwargs=kwargs) self._registered_actions.append(action) def finalize_io(self): self._connect_outputs() self._connect_inputs() def configure_engines(self): log.debug('Configuring engines') for engine in self._engines.values(): # Check to see if engine is being used if engine.get_channels(): engine.configure() cb = partial(self.invoke_actions, '{}_end'.format(engine.name)) engine.register_done_callback(cb) self.invoke_actions('engines_configured') def start_engines(self): log.debug('Starting engines') for engine in self._engines.values(): engine.start() def stop_engines(self): for name, timer in list(self._timers.items()): timer.timeout.disconnect() timer.stop() del self._timers[name] for engine in self._engines.values(): engine.stop() def reset_engines(self): for engine in self._engines.values(): engine.reset() def get_output(self, output_name): return self._outputs[output_name] def get_input(self, input_name): return self._inputs[input_name] def set_input_attr(self, input_name, attr_name, value): setattr(self._inputs[input_name], attr_name, value) def get_channel(self, channel_name): return self._channels[channel_name] def get_channels(self, mode=None, direction=None, timing=None, active=True): ''' Return channels matching criteria across all engines Parameters ---------- mode : {None, 'analog', 'digital' Type of channel direction : {None, 'input, 'output'} Direction timing : {None, 'hardware', 'software'} Hardware or software-timed channel. Hardware-timed channels have a sampling frequency greater than 0. active : bool If True, return only channels that have configured inputs or outputs. ''' channels = [] for engine in self._engines.values(): ec = engine.get_channels(mode, direction, timing, active) channels.extend(ec) return channels def load_calibration(self, calibration_file): channels = list(self._channels.values()) load_calibration(calibration_file, channels) def invoke_actions(self, event_name, timestamp=None, delayed=False, cancel_existing=True, kw=None): log.debug('Invoking actions for %s', event_name) if cancel_existing: deferred_call(self.stop_timer, event_name) if delayed: delay = timestamp-self.get_ts() if delay > 0: cb = lambda: self._invoke_actions(event_name, timestamp) deferred_call(self.start_timer, event_name, delay, cb) return self._invoke_actions(event_name, timestamp, kw) def event_used(self, event_name): ''' Returns true if the experiment event is bound to an experiment action. This is typically used internally as a performance-optimization so we don't configure callbacks for events that are unused. For example, we can attach actions to the <input_name>_acquired event. However, this event typically occurs several times a second for each input. This would result in unecessary calls to `invoke_actions`. Parameters ---------- event_name : str Name of event Returns ------- used : bool True if event is bound to an action, False otherwise. ''' for action in self._actions: if event_name in action.dependencies: return True return False def _invoke_actions(self, event_name, timestamp=None, kw=None): log.debug('Triggering event {}'.format(event_name)) if timestamp is not None: data = {'event': event_name, 'timestamp': timestamp} self.invoke_actions('experiment_event', kw={'data': data}) # If this is a stateful event, update the associated state. if event_name.endswith('_start'): key = event_name[:-6] self._action_context[key + '_active'] = True elif event_name.endswith('_end'): key = event_name[:-4] self._action_context[key + '_active'] = False # Make a copy of the context and set the event to True. We don't want # to set the state on the main context since it may affect recursive # notifications. context = self._action_context.copy() context[event_name] = True for action in self._actions: if action.match(context): log.debug('... invoking action %s', action) self._invoke_action(action, event_name, timestamp, kw) def _invoke_action(self, action, event_name, timestamp, kw): # Add the event name and timestamp to the parameters passed to the # command. kwargs = action.kwargs.copy() kwargs['timestamp'] = timestamp kwargs['event'] = event_name if kw is not None: kwargs.update(kw) if isinstance(action, ExperimentAction): self.core.invoke_command(action.command, parameters=kwargs) elif isinstance(action, ExperimentCallback): action.callback(kwargs) def request_apply(self): if not self.apply_changes(): log.debug('Apply requested') deferred_call(lambda: setattr(self, '_apply_requested', True)) def request_remind(self): deferred_call(lambda: setattr(self, '_remind_requested', True)) def request_pause(self): if not self.pause_experiment(): log.debug('Pause requested') deferred_call(lambda: setattr(self, '_pause_requested', True)) def request_resume(self): self._pause_requested = False deferred_call(lambda: setattr(self, 'experiment_state', 'running')) def apply_changes(self): raise NotImplementedError def start_experiment(self): self.invoke_actions('experiment_initialize') self.invoke_actions('experiment_prepare') self.invoke_actions('experiment_start') deferred_call(lambda: setattr(self, 'experiment_state', 'running')) def stop_experiment(self): try: self.invoke_actions('experiment_end', self.get_ts()) except Exception as e: log.exception(e) deferred_call(lambda: setattr(self, 'experiment_state', 'stopped')) def pause_experiment(self): raise NotImplementedError def get_ts(self): return self._master_engine.get_ts() def set_pause_ok(self, value): deferred_call(lambda: setattr(self, '_pause_ok', value)) def start_timer(self, name, duration, callback): log.debug('Starting 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stable-baselines/tests/test_deterministic.py	princeton-vl/PackIt	49	9989	import pytest from stable_baselines import A2C, ACER, ACKTR, DeepQ, DDPG, PPO1, PPO2, TRPO from stable_baselines.ddpg import AdaptiveParamNoiseSpec from stable_baselines.common.identity_env import IdentityEnv, IdentityEnvBox from stable_baselines.common.vec_env import DummyVecEnv PARAM_NOISE_DDPG = AdaptiveParamNoiseSpec(initial_stddev=float(0.2), desired_action_stddev=float(0.2)) # Hyperparameters for learning identity for each RL model LEARN_FUNC_DICT = { 'a2c': lambda e: A2C(policy="MlpPolicy", env=e).learn(total_timesteps=1000), 'acer': lambda e: ACER(policy="MlpPolicy", env=e).learn(total_timesteps=1000), 'acktr': lambda e: ACKTR(policy="MlpPolicy", env=e).learn(total_timesteps=1000), 'deepq': lambda e: DeepQ(policy="MlpPolicy", env=e).learn(total_timesteps=1000), 'ddpg': lambda e: DDPG(policy="MlpPolicy", env=e, param_noise=PARAM_NOISE_DDPG).learn(total_timesteps=1000), 'ppo1': lambda e: PPO1(policy="MlpPolicy", env=e).learn(total_timesteps=1000), 'ppo2': lambda e: PPO2(policy="MlpPolicy", env=e).learn(total_timesteps=1000), 'trpo': lambda e: TRPO(policy="MlpPolicy", env=e).learn(total_timesteps=1000), } @pytest.mark.slow @pytest.mark.parametrize("model_name", ['a2c', 'acer', 'acktr', 'deepq', 'ppo1', 'ppo2', 'trpo']) def test_identity(model_name): """ Test if the algorithm (with a given policy) can learn an identity transformation (i.e. return observation as an action) :param model_name: (str) Name of the RL model """ env = DummyVecEnv([lambda: IdentityEnv(10)]) model = LEARN_FUNC_DICT[model_name](env) n_trials = 1000 obs = env.reset() action_shape = model.predict(obs, deterministic=False)[0].shape action, _ = model.predict(obs, deterministic=True) assert action.shape == action_shape for _ in range(n_trials): new_action = model.predict(obs, deterministic=True)[0] assert action == model.predict(obs, deterministic=True)[0] assert new_action.shape == action_shape # Free memory del model, env @pytest.mark.slow @pytest.mark.parametrize("model_name", ['a2c', 'ddpg', 'ppo1', 'ppo2', 'trpo']) def test_identity_continuous(model_name): """ Test if the algorithm (with a given policy) can learn an identity transformation (i.e. return observation as an action) :param model_name: (str) Name of the RL model """ env = DummyVecEnv([lambda: IdentityEnvBox(eps=0.5)]) model = LEARN_FUNC_DICT[model_name](env) n_trials = 1000 obs = env.reset() action_shape = model.predict(obs, deterministic=False)[0].shape action, _ = model.predict(obs, deterministic=True) assert action.shape == action_shape for _ in range(n_trials): new_action = model.predict(obs, deterministic=True)[0] assert action == model.predict(obs, deterministic=True)[0] assert new_action.shape == action_shape	[ 1, 1053, 11451, 1688, 13, 13, 3166, 13714, 29918, 6500, 24210, 1053, 319, 29906, 29907, 29892, 14614, 1001, 29892, 319, 7077, 5659, 29892, 21784, 29984, 29892, 360, 11191, 29954, 29892, 349, 13152, 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bot/utils/github.py	mtrsk/PUGMA-bot	9	119869	""" Modulo dedicado a classes/funções sem nenhuma outra dependência ou que não sejam parte do core do Bot. """ import json import requests def photo_meetup(repo_data, matches): """ Recebe como entrada uma Dataclass com dados do nosso repositório no Github e retorna a url de uma imagem do meetup. """ contents_url = f"{repo_data.url}/contents/palestras?ref=master" response = requests.get(contents_url, headers=repo_data.headers) content = json.loads(response.content) content_length = len(content) if len(matches) == 1: # /meetup retorna sempre o último return content[-1].get("download_url"), (content_length) elif int(matches[1]) < content_length: # /meetup \d{1,2} event_num = int(matches[1]) return content[event_num - 1].get("download_url"), event_num else: # Numero invalido return None, None	[ 1, 9995, 13, 2111, 7207, 8856, 912, 263, 4413, 29914, 7692, 5616, 3031, 302, 264, 29882, 10859, 714, 336, 8839, 10544, 2123, 13, 802, 8145, 409, 29926, 314, 3810, 437, 7136, 437, 11273, 29889, 13, 15945, 29908, 13, 5215, 4390, 13, 13, 5215, 7274, 13, 13, 13, 1753, 15373, 29918, 1004, 300, 786, 29898, 20095, 29918, 1272, 29892, 7087, 1125, 13, 1678, 9995, 13, 1678, 24328, 915, 1986, 9953, 1114, 3672, 3630, 1990, 419, 270, 2255, 437, 7814, 578, 13, 1678, 17573, 277, 29980, 5378, 694, 402, 2985, 321, 3240, 272, 1056, 263, 3142, 316, 3672, 6382, 331, 13, 1678, 437, 5870, 786, 29889, 13, 1678, 9995, 13, 1678, 8118, 29918, 2271, 353, 285, 29908, 29912, 20095, 29918, 1272, 29889, 2271, 6822, 10853, 29914, 7830, 342, 3417, 29973, 999, 29922, 6207, 29908, 13, 13, 1678, 2933, 353, 7274, 29889, 657, 29898, 10853, 29918, 2271, 29892, 9066, 29922, 20095, 29918, 1272, 29889, 13662, 29897, 13, 1678, 2793, 353, 4390, 29889, 18132, 29898, 5327, 29889, 3051, 29897, 13, 1678, 2793, 29918, 2848, 353, 7431, 29898, 3051, 29897, 13, 13, 1678, 565, 7431, 29898, 20317, 29897, 1275, 29871, 29896, 29901, 13, 4706, 396, 847, 1004, 300, 786, 3240, 272, 1056, 14472, 288, 20195, 13, 4706, 736, 2793, 14352, 29896, 1822, 657, 703, 10382, 29918, 2271, 4968, 313, 3051, 29918, 2848, 29897, 13, 1678, 25342, 938, 29898, 20317, 29961, 29896, 2314, 529, 2793, 29918, 2848, 29901, 13, 4706, 396, 847, 1004, 300, 786, 320, 29881, 29912, 29896, 29892, 29906, 29913, 13, 4706, 1741, 29918, 1949, 353, 938, 29898, 20317, 29961, 29896, 2314, 13, 4706, 736, 2793, 29961, 3696, 29918, 1949, 448, 29871, 29896, 1822, 657, 703, 10382, 29918, 2271, 4968, 1741, 29918, 1949, 13, 1678, 1683, 29901, 13, 4706, 396, 11848, 1489, 297, 791, 1941, 13, 4706, 736, 6213, 29892, 6213, 13, 2 ]
src/experiments/random_projections_aistats_ridge_regression.py	c-dickens/FrequentDirectionsRidgeRegression	0	116922	# Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF 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. import numpy as np from timeit import default_timer as timer class RPRidge: def __init__(self,rp_dim:int,rp_mode='Sparse',gamma=1.0): self.rp_dim = rp_dim self.rp_mode = rp_mode self.gamma = gamma def iterate_single(self,X,y,iterations=10,seed=100): ''' Fits the iterated ridge model with FD ''' d = X.shape[1] w = np.zeros((d,1),dtype=float) all_w = np.zeros((d,iterations)) XTy = (X.T@y).reshape(-1,1) # Fit the FD SA = self._get_sketch(X,seed) H = SA.T@SA + (self.gamma)np.eye(d) H_inv = np.linalg.pinv(H) for it in range(iterations): grad = X.T@(X@w) + self.gammaw - XTy w += - H_inv@grad all_w[:,it] = np.squeeze(w) return np.squeeze(w), all_w def iterate_multiple(self,X,y,iterations=10): ''' Fits the iterated ridge model with FD ''' d = X.shape[1] w = np.zeros((d,1),dtype=float) all_w = np.zeros((d,iterations)) XTy = (X.T@y).reshape(-1,1) for it in range(iterations): SA = self._get_sketch(X,seed=100it) H = SA.T@SA + (self.gamma)np.eye(d) H_inv = np.linalg.pinv(H) grad = X.T@(X@w) + self.gammaw - XTy w += - H_inv@grad all_w[:,it] = np.squeeze(w) return np.squeeze(w), all_w def _naive_hessian_update(self, sketched_data, vec): """ Naively performs the hessian update by evaluating the approximate Hessian and inverting. """ H = sketched_data.T@sketched_data + (self.gamma)np.eye(sketched_data.shape[1]) H_inv = np.linalg.pinv(H) return H_inv@vec def _linear_solve_hessian_update(self, sketched_data, vec): """ Implements the hessian_inv@gradient efficiently by using Woodbury. """ K = sketched_data@sketched_data.T / self.gamma + np.eye(sketched_data.shape[0]) u = (1/self.gamma)vec - (sketched_data.T / self.gamma2)@(np.linalg.solve(K,sketched_data@vec)) return u def iterate_multiple_timing(self, X, y, iterations=10): """ Fits the iterated ridge model with a new sketch every iteration """ # Initialisation not timed d = X.shape[1] w = np.zeros((d,1),dtype=float) all_w = np.zeros((d,iterations)) if self.rp_dim < d: update_method = self._linear_solve_hessian_update else: update_method = self._naive_hessian_update measurables = { 'sketch time' : None, 'all_times' : np.zeros(iterations+1,dtype=float), 'gradients' : np.zeros((d,iterations),dtype=float), 'updates' : np.zeros((d,iterations),dtype=float), 'sketch' : None } SKETCH_TIME = 0.0 TIMER_START = timer() XTy = (X.T@y).reshape(-1,1) for it in range(iterations): # * Timing the sketch separately SKETCH_TIMER_START = timer() SA = self._get_sketch(X,seed=10it) SKETCH_TIME += timer() - SKETCH_TIMER_START # Into grad updates grad = X.T@(X@w) + self.gammaw - XTy update = update_method(SA, grad) w += - update all_w[:,it] = np.squeeze(w) measurables['all_times'][it+1] = timer() - TIMER_START measurables['sketch time'] = SKETCH_TIME return np.squeeze(w), all_w, measurables def iterate_single_timing(self, X, y, iterations=10, seed=100): """ Fits the iterated ridge model with a new sketch every iteration """ # Initialisation not timed d = X.shape[1] w = np.zeros((d,1),dtype=float) all_w = np.zeros((d,iterations)) if self.rp_dim < d: update_method = self._linear_solve_hessian_update else: update_method = self._naive_hessian_update measurables = { 'sketch time' : None, 'all_times' : np.zeros(iterations+1,dtype=float), 'gradients' : np.zeros((d,iterations),dtype=float), 'updates' : np.zeros((d,iterations),dtype=float), 'sketch' : None } TIMER_START = timer() XTy = (X.T@y).reshape(-1,1) SA = self._get_sketch(X,seed) SKETCH_TIME = timer() - TIMER_START for it in range(iterations): grad = X.T@(X@w) + self.gammaw - XTy update = update_method(SA, grad) w += - update all_w[:,it] = np.squeeze(w) measurables['all_times'][it+1] = timer() - TIMER_START measurables['sketch time'] = SKETCH_TIME return np.squeeze(w), all_w, measurables def _get_sketch(self,data,seed=10): ''' Performs the sketch depending on the chosen mode. ''' if self.rp_mode == 'Gaussian': return self._gaussian_projection(data,seed) elif self.rp_mode == 'SJLT': return self._sparse_projection(data,10,seed) else: raise NotImplementedError def fit_classical(self,X,y): ''' Fits the ridge regression model on data X with targets y ''' d = X.shape[1] data = np.c_[X,y] #S_data = self._sparse_projection(data,self.rp_dim) #S_data = self._gaussian_projection(data,self.rp_dim) S_data = self._get_sketch(data) SX = S_data[:,:-1] Sy = S_data[:,-1] H_est = SX.T@SX + self.gammanp.eye(d) self.H = H_est self.classical_coef_ = np.linalg.solve(H_est,SX.T@Sy) def fit_hessian_sketch(self,X,y): ''' Fits the ridge regression model on data X with targets y ''' d = X.shape[1] #SX = self._gaussian_projection(X,self.rp_dim) #SX = self._sparse_projection(X,self.rp_dim) SX = self._get_sketch(X) H_est = SX.T@SX + self.gammanp.eye(d) self.hessian_coef_ = np.linalg.solve(H_est,X.T@y) def get_classical_bias(self,X,w0): ''' Returns the bias of the estimate ''' return (self.gamma)np.linalg.norm(np.linalg.pinv(self.H)@w0) #return (np.linalg.pinv(self.H)@(self.H - self.gammanp.eye(X.shape[1])))@w0 - w0 def get_classical_variance(self,X): ''' Returns the variance term: \|\|S.T@S@A H_gamma^{-1}\|\|_F^2 ''' S = self.sketch_mat #return np.linalg.norm(np.linalg.pinv(self.H)@(X.T@(S.T@S)),ord='fro')2 return np.linalg.norm( S.T@(S@([email protected](self.H))) ,ord='fro')2 def get_hessian_sketch_bias(self,X,w0): ''' Returns the bias of the Hessian sketch method for regression ''' return np.linalg.pinv(self.H)@(X.T@(X@w0)) - w0 def get_hessian_sketch_variance(self,X): ''' Returns the variance term: \|\|A H_gamma^{-1}\|\|_F^2 ''' return np.linalg.norm([email protected](self.H),ord='fro')2 def _sparse_projection(self,mat,sparsity=1,random_seed=10): """ Performs the sparse johnson lindenstrauss transform of Kane and Nelson """ [n,_] = mat.shape sketch = np.zeros((self.rp_dim ,n),dtype=float) for i in range(n): nnz_loc = np.random.choice(self.rp_dim ,size=sparsity,replace=False) nnz_sign = np.random.choice([-1,1],size=sparsity,replace=True) sketch[nnz_loc,i] = nnz_sign self.sketch_mat = sketch return (1./np.sqrt(sparsity))sketch@mat def _gaussian_projection(self,mat,random_seed=10): """ Performs the dense gaussian random projection. 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emoticonvis/apps/api/tests/test_serializers.py	nanchenchen/emoticon-analysis	0	127553	from django.test import TestCase from django.utils.timezone import now, timedelta from emoticonvis.apps.corpus import models as corpus_models from emoticonvis.apps.corpus import utils as corpus_utils from emoticonvis.apps.coding import models as coding_models from emoticonvis.apps.api import serializers from django.contrib.auth.models import User from emoticonvis.apps.api.tests import api_time_format class CodeSerializerTest(TestCase): """ """ def test_code_definition_serialization(self): master = User.objects.create_user(username='master') user1 = User.objects.create_user(username='user1') code = corpus_models.Code.objects.create(text='testcode') dataset = corpus_models.Dataset.objects.create(name='test', description='test') messages = [corpus_models.Message.objects.create(dataset=dataset, text='msg0'), corpus_models.Message.objects.create(dataset=dataset, text='msg1'), corpus_models.Message.objects.create(dataset=dataset, text='msg2')] code_definition1 = coding_models.CodeDefinition.objects.create(code=code, source=master, text='master_def') code_definition1.examples.add(messages[0]) code_definition1.examples.add(messages[2]) code_definition2 = coding_models.CodeDefinition.objects.create(code=code, source=user1, text='user1_def') code_definition2.examples.add(messages[1]) code_definition2.examples.add(messages[2]) desired_result = [ { "code": code.text, "source": "master", "text": "master_def", "examples": [serializers.MessageSerializer(messages[0]).data, serializers.MessageSerializer(messages[2]).data] }, { "code": code.text, "source": "user1", "text": "user1_def", "examples": [serializers.MessageSerializer(messages[1]).data, serializers.MessageSerializer(messages[2]).data] } ] code_definitions = [code.get_definition(master), code.get_definition(user1)] serializer = serializers.CodeDefinitionSerializer(code_definitions, many=True) try: result = serializer.data self.assertListEqual(result, desired_result) except: import pdb pdb.set_trace() def test_code_message_serialization(self): master = User.objects.create_user(username='master') code = corpus_models.Code.objects.create(text='testcode') code2 = corpus_models.Code.objects.create(text='testcode2') dataset = corpus_models.Dataset.objects.create(name='test', description='test') messages = [corpus_models.Message.objects.create(dataset=dataset, text='msg0'), corpus_models.Message.objects.create(dataset=dataset, text='msg1'), corpus_models.Message.objects.create(dataset=dataset, text='msg2')] code_assignment1 = coding_models.CodeAssignment.objects.create(code=code, source=master, message=messages[0]) code_assignment2 = coding_models.CodeAssignment.objects.create(code=code, source=master, message=messages[1]) code_assignment3 = coding_models.CodeAssignment.objects.create(code=code2, source=master, message=messages[1]) desired_result = [ { "code": code.text, "source": "master", "messages": [serializers.MessageSerializer(messages[0]).data, serializers.MessageSerializer(messages[1]).data] }, { "code": code2.text, "source": "master", "messages": [serializers.MessageSerializer(messages[1]).data] } ] source = master codes = [code.text, code2.text] code_messages = [] for code in codes: if corpus_models.Code.objects.filter(text=code).exists(): code_obj = corpus_models.Code.objects.get(text=code) messages = corpus_models.Message.objects.filter(code_assignments__valid=True, code_assignments__source=source, code_assignments__code=code_obj).all() code_messages.append({ "code": code, "source": source, "messages": messages, }) serializer = serializers.CodeMessageSerializer(code_messages, many=True) try: result = serializer.data self.assertListEqual(result, desired_result) except: import pdb pdb.set_trace()	[ 1, 515, 9557, 29889, 1688, 1053, 4321, 8259, 13, 3166, 9557, 29889, 13239, 29889, 2230, 8028, 1053, 1286, 29892, 5335, 287, 2554, 13, 3166, 23023, 4144, 1730, 29889, 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migrations/versions/1fba1a39c681_initial_data.py	eubr-bigsea/thorn	0	114245	<filename>migrations/versions/1fba1a39c681_initial_data.py """initial data Revision ID: <KEY> Revises: <PASSWORD> Create Date: 2020-03-02 09:28:48.614317 """ import datetime import bcrypt from alembic import context from alembic import op from sqlalchemy import String, Integer, DateTime from sqlalchemy.orm import sessionmaker from sqlalchemy.sql import table, column from thorn.migration_utils import is_mysql # revision identifiers, used by Alembic. revision = '<KEY>' down_revision = '2f93dbc61a5d' branch_labels = None depends_on = None def _insert_permissions(): tb = table( 'permission', column('id', Integer), column('name', String), column('applicable_to', String), column('enabled', String), ) columns = [c.name for c in tb.columns] data = [ (1, 'WORKFLOW_VIEW_ANY', 'WORKFLOW', True), (2, 'WORKFLOW_EDIT_ANY', 'WORKFLOW', True), (3, 'WORKFLOW_EXECUTE_ANY', 'WORKFLOW', True), (4, 'WORKFLOW_VIEW', 'WORKFLOW', True), (5, 'WORKFLOW_EDIT', 'WORKFLOW', True), (6, 'WORKFLOW_EXECUTE', 'WORKFLOW', True), (7, 'DATA_SOURCE_VIEW_ANY', 'DATA_SOURCE', True), (8, 'DATA_SOURCE_EDIT_ANY', 'DATA_SOURCE', True), (9, 'DATA_SOURCE_USE_ANY', 'DATA_SOURCE', True), (10, 'DATA_SOURCE_VIEW', 'DATA_SOURCE', True), (11, 'DATA_SOURCE_EDIT', 'DATA_SOURCE', True), (12, 'DATA_SOURCE_USE', 'DATA_SOURCE', True), (13, 'DASHBOARD_VIEW_ANY', 'DASHBOARD', True), (14, 'DASHBOARD_EDIT_ANY', 'DASHBOARD', True), (15, 'DASHBOARD_VIEW', 'DASHBOARD', True), (16, 'DASHBOARD_EDIT', 'DASHBOARD', True), (100, 'USER_MANAGE', 'USER', True), (101, 'STORAGE_MANAGE', 'SYSTEM', True), (102, 'CLUSTER_MANAGE', 'SYSTEM', True), (1000, 'ADMINISTRATOR', 'SYSTEM', True), ] rows = [dict(list(zip(columns, row))) for row in data] op.bulk_insert(tb, rows) def _insert_permission_translations(): tb = table( 'permission_translation', column('id', Integer), column('locale', String), column('description', String) ) columns = [c.name for c in tb.columns] data = [ (1, 'pt', 'Visualizar qualquer fluxo de trabalho'), (1, 'en', 'View any workflow'), (2, 'pt', 'Editar qualquer fluxo de trabalho'), (2, 'en', 'Edit any workflow'), (3, 'pt', 'Executar qualquer fluxo de trabalho'), (3, 'en', 'Execute any workflow'), (4, 'pt', 'Visualizar fluxo de trabalho'), (4, 'en', 'View workflow'), (5, 'pt', 'Editar fluxo de trabalho'), (5, 'en', 'Edit workflow'), (6, 'pt', 'Executar fluxo de trabalho'), (6, 'en', 'Execute workflow'), (7, 'pt', 'Visualizar qualquer fonte de dados'), (7, 'en', 'View any data source'), (8, 'pt', 'Editar qualquer fonte de dados'), (8, 'en', 'Edit any data source'), (9, 'pt', 'Usar qualquer fonte de dados'), (9, 'en', 'Use any data source'), (10, 'pt', 'Visualizar fonte de dados'), (10, 'en', 'View data source'), (11, 'pt', 'Editar fonte de dados'), (11, 'en', 'Edit data source'), (12, 'pt', 'Usar fonte de dados'), (12, 'en', 'Use data source'), (13, 'pt', 'Visualizar qualquer dashboard'), (13, 'en', 'View any dashboard'), (14, 'pt', 'Editar qualquer dashboard'), (14, 'en', 'Edit any dashboard'), (15, 'pt', 'Visualizar dashboard'), (15, 'en', 'View dashboard'), (16, 'pt', 'Editar dashboard'), (16, 'en', 'Edit dashboard'), (100, 'pt', 'Gerenciar usuários'), (100, 'en', 'Manage users'), (101, 'pt', 'Gerenciar armazenamentos'), (101, 'en', 'Manage stores'), (102, 'pt', 'Gerenciar clusters'), (102, 'en', 'Manage clusters'), (1000, 'en', 'Administrator'), (1000, 'pt', 'Administrador'), ] rows = [dict(list(zip(columns, row))) for row in data] op.bulk_insert(tb, rows) def _insert_admin(): tb = table( 'user', column('id', Integer), column('login', String), column('email', String), column('encrypted_password', String), column('created_at', DateTime), column('first_name', String), column('last_name', String), column('locale', String), column('enabled', Integer), column('authentication_type', String) ) columns = [c.name for c in tb.columns] hashed = bcrypt.hashpw('admin'.encode('utf8'), bcrypt.gensalt(12)).decode('utf8') data = [ (1, '<EMAIL>', '<EMAIL>', hashed, datetime.datetime.now(), 'Admin', '', 'pt', True, 'INTERNAL'), ] rows = [dict(list(zip(columns, row))) for row in data] op.bulk_insert(tb, rows) def _insert_roles(): tb = table( 'role', column('id', Integer), column('name', String), column('all_user', Integer), column('enabled', Integer), ) columns = [c.name for c in tb.columns] data = [ (1, 'admin', False, True), (100, 'public', True, True), ] rows = [dict(list(zip(columns, row))) for row in data] op.bulk_insert(tb, rows) def _insert_role_translations(): tb = table( 'role_translation', column('id', Integer), column('locale', String), column('description', String) ) columns = [c.name for c in tb.columns] data = [ (1, 'pt', 'Administrador'), (1, 'en', 'Administrator'), (100, 'pt', 'Público'), (100, 'en', 'Public'), ] rows = [dict(list(zip(columns, row))) for row in data] op.bulk_insert(tb, rows) def _insert_user_roles(): tb = table( 'user_role', column('user_id', Integer), column('role_id', Integer), ) columns = [c.name for c in tb.columns] data = [ (1, 1), ] rows = [dict(list(zip(columns, row))) for row in data] op.bulk_insert(tb, rows) def _insert_role_permission(): tb = table( 'role_permission', column('role_id', Integer), column('permission_id', Integer), ) columns = [c.name for c in tb.columns] data = [ (1, 1000), ] rows = [dict(list(zip(columns, row))) for row in data] op.bulk_insert(tb, rows) def get_commands(): user_table = 'user' if is_mysql() else '"user"' all_commands = [ (_insert_permissions, 'DELETE FROM permission WHERE id BETWEEN 1 AND 16 OR ' 'id BETWEEN 100 AND 102 OR id BETWEEN 1000 AND 1000'), (_insert_permission_translations, 'DELETE FROM permission_translation WHERE id BETWEEN 1 AND 16 OR ' 'id BETWEEN 100 AND 102 OR id BETWEEN 1000 AND 1000'), (_insert_admin, f'DELETE FROM {user_table} WHERE id BETWEEN 1 AND 1 '), (_insert_roles, 'DELETE FROM role WHERE id BETWEEN 1 AND 1 OR id ' 'BETWEEN 100 AND 100'), (_insert_role_translations, 'DELETE FROM role_translation WHERE id BETWEEN 1 AND 1 OR id ' 'BETWEEN 100 AND 100'), (_insert_user_roles, 'DELETE FROM user_role WHERE user_id BETWEEN 1 AND 1'), (_insert_role_permission, 'DELETE FROM role_permission WHERE role_id = 1 ' 'AND permission_id = 1000') ] return all_commands def upgrade(): ctx = context.get_context() session = sessionmaker(bind=ctx.bind)() connection = session.connection() all_commands = get_commands() try: for cmd in all_commands: if isinstance(cmd[0], str): connection.execute(cmd[0]) elif isinstance(cmd[0], list): for row in cmd[0]: connection.execute(row) else: cmd[0]() except: session.rollback() raise session.commit() def downgrade(): ctx = context.get_context() session = sessionmaker(bind=ctx.bind)() connection = session.connection() all_commands = get_commands() try: for cmd in reversed(all_commands): if isinstance(cmd[1], str): connection.execute(cmd[1]) elif isinstance(cmd[1], list): for row in cmd[1]: 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effect_of_vanishing_photos/effect_of_vanishing_photos.py	gil9red/SimplePyScripts	117	144049	#!/usr/bin/env python3 # -- coding: utf-8 -- __author__ = 'ipetrash' """Эффект исчезновения фотографии Кликая на области на фотографии запускаются процессы плавного увеличения прозрачности пикселей, эффект как круги воды, будут расходиться пока не закончатся непрозрачные пиксели""" import sys import traceback try: from PyQt5.QtWidgets import * from PyQt5.QtGui import * from PyQt5.QtCore import * except: try: from PyQt4.QtGui import * from PyQt4.QtCore import * except: from PySide.QtGui import * from PySide.QtCore import * def log_uncaught_exceptions(ex_cls, ex, tb): text = '{}: {}:\n'.format(ex_cls.__name__, ex) text += ''.join(traceback.format_tb(tb)) print(text) QMessageBox.critical(None, 'Error', text) sys.exit(1) sys.excepthook = log_uncaught_exceptions class Timer(QTimer): class Circle: def __init__(self, pos_center): self.pos_center = pos_center self.radii = 1 def next(self): self.radii += 1 def __init__(self, widget, image): super().__init__() self.circle_list = list() self.widget = widget self.setInterval(60) self.timeout.connect(self.tick) self.painter = QPainter(image) self.painter.setRenderHint(QPainter.Antialiasing) self.painter.setCompositionMode(QPainter.CompositionMode_Clear) self.painter.setPen(Qt.NoPen) self.painter.setBrush(Qt.transparent) def add(self, pos_center): self.circle_list.append(Timer.Circle(pos_center)) def tick(self): for circle in self.circle_list: self.painter.drawEllipse(circle.pos_center, circle.radii, circle.radii) circle.next() self.widget.update() class Widget(QWidget): def __init__(self): super().__init__() self.setWindowTitle('effect_of_vanishing_photos.py') self.im = QImage('im.png') self.resize(self.im.size()) self.timer = Timer(self, self.im) self.timer.start() def mouseReleaseEvent(self, event): super().mouseReleaseEvent(event) self.timer.add(event.pos()) def paintEvent(self, event): super().paintEvent(event) p = QPainter(self) p.setBrush(Qt.white) p.drawRect(self.rect()) p.setBrush(Qt.yellow) p.drawRect(self.width() // 6, self.width() // 5, self.width() // 3, self.height() // 4) p.drawImage(0, 0, self.im) if __name__ == '__main__': app = QApplication(sys.argv) w = Widget() w.show() app.exec_()	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 13, 1649, 8921, 1649, 353, 525, 666, 18184, 1161, 29915, 13, 13, 13, 15945, 29908, 30082, 30011, 18201, 29932, 9971, 1093, 29972, 27290, 1587, 26592, 14694, 29917, 13, 30014, 644, 22972, 665, 9308, 665, 26592, 14694, 29917, 1077, 2968, 2494, 9480, 14852, 29935, 5551, 469, 5206, 1868, 863, 19630, 12395, 13, 5945, 29972, 494, 4913, 1415, 6712, 29951, 1502, 6188, 29892, 2352, 30011, 18201, 29932, 5413, 1186, 1086, 1892, 1786, 4184, 29892, 22770, 29932, 7421, 4647, 4199, 25693, 1538, 13, 1902, 6587, 1282, 7489, 1538, 5945, 29972, 494, 20125, 6712, 29951, 1502, 644, 15945, 29908, 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tests/fixtures/test_product.py	oldarmyc/cap	1	22815	<reponame>oldarmyc/cap # Copyright 2016 <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. sample_product = { "title": "Test", "us_url": "http://us.test.com", "uk_url": "http://uk.test.com", "active": True, "db_name": "test", "require_region": True, "doc_url": "http://doc.test.com", "pitchfork_url": "https://pitchfork/url" } sample_limit = { "product": "test", "title": "Test", "uri": "/limits", "slug": "test", "active": True, "absolute_path": "test/path", "absolute_type": "list", "limit_key": "test_limit", "value_key": "test_value" } sample_log = { "queried": ["dns"], "queried_by": "skeletor", "region": "dfw", "ddi": "123456", 'query_results': [] } sample_auth_failure = { 'message': ( '<strong>Error!</strong> Authentication has failed due to' ' incorrect token or DDI. Please check the token and DDI ' 'and try again.' ) } """ DNS Tests """ dns = { "title": "DNS", "us_url": "https://us.test.com", "uk_url": "https://uk.test.com", "active": True, "db_name": "dns", "require_region": True, "doc_url": "https://doc.test.com", "pitchfork_url": "https://pitchfork.url", "limit_maps": [] } dns_limit = { "product": "dns", "title": "Domains", "uri": "/limits", "slug": "domains", "active": True, "absolute_path": "limits.absolute", "absolute_type": "dict", "value_key": "", "limit_key": "domains" } dns_limit_return = { "limits": { "rate": [ { "regex": "./v\\d+\\.\\d+/(\\d+/domains/search).", "limit": [ { "value": 20, "verb": "GET", "next-available": "2016-01-12T13:56:11.450Z", "remaining": 20, "unit": "MINUTE" } ], "uri": "/domains/search" } ], "absolute": { "domains": 500, "records per domain": 500 } } } dns_list_return = { "domains": [ { "comment": "Test", "updated": "2015-12-08T20:47:02.000+0000", "name": "test.net", "created": "2015-04-09T15:42:49.000+0000", "emailAddress": "<EMAIL>", "id": 123465798, "accountId": 1234567 } ], "totalEntries": 1 } dns_full_return = { 'dns': { 'values': {'Domains': 1}, 'limits': {'Domains': 500} } } """ Autoscale """ autoscale = { "title": "Autoscale", "us_url": "https://us.test.com", "uk_url": "https://uk.test.com", "active": True, "db_name": "autoscale", "require_region": True, "doc_url": "https://doc.test.com", "pitchfork_url": "https://pitchfork.url", "limit_maps": [] } autoscale_limit = { "product": "autoscale", "title": "Max Groups", "absolute_path": "limits.absolute", "uri": "/v1.0/{ddi}/limits", "slug": "max_groups", "value_key": "", "absolute_type": "dict", "active": True, "limit_key": "maxGroups" } autoscale_limit_return = { "limits": { "rate": [ { "regex": "/v1\\.0/execute/(.)", "limit": [ { "value": 10, "verb": "ALL", "next-available": "2016-01-12T14:51:13.402Z", "remaining": 10, "unit": "SECOND" } ], "uri": "/v1.0/execute/" } ], "absolute": { "maxGroups": 1000, "maxPoliciesPerGroup": 100, "maxWebhooksPerPolicy": 25 } } } autoscale_list_return = { "groups": [ { "state": { "status": "ACTIVE", "desiredCapacity": 0, "paused": False, "active": [], "pendingCapacity": 0, "activeCapacity": 0, "name": "test" }, "id": "d446f3c2-612f-41b8-92dc-4d6e1422bde2", "links": [ { "href": ( 'https://dfw.autoscale.api.rackspacecloud.com/v1.0' '/1234567/groups/d446f3c2-612f-41b8-92dc-4d6e1422bde2/' ), "rel": "self" } ] } ], "groups_links": [] } autoscale_full_return = { 'autoscale': { 'values': {'Max Groups': 1}, 'limits': {'Max Groups': 1000} } } """ Big Data """ big_data = { "title": "Big Data", "us_url": "https://us.test.com", "uk_url": "https://uk.test.com", "active": True, "db_name": "big_data", "require_region": True, "doc_url": "https://doc.test.com", "pitchfork_url": "https://pitchfork.url", "limit_maps": [] } big_data_limit = [ { "product": "big_data", "title": "Node Count", "absolute_path": "limits.absolute.node_count", "uri": "/v2/{ddi}/limits", "slug": "node_count", "value_key": "remaining", "absolute_type": "dict", "active": True, "limit_key": "limit" }, { "product": "big_data", "title": "Disk - MB", "absolute_path": "limits.absolute.disk", "uri": "/v2/{ddi}/limits", "slug": "disk_-_mb", "value_key": "remaining", "absolute_type": "dict", "active": True, "limit_key": "limit" } ] big_data_limit_return = { "limits": { "absolute": { "node_count": { "limit": 15, "remaining": 8 }, "disk": { "limit": 50000, "remaining": 25000 }, "ram": { "limit": 655360, "remaining": 555360 }, "vcpus": { "limit": 200, "remaining": 120 } } } } big_data_full_return = { 'big_data': { 'values': {'Node Count': 7, 'Disk - MB': 25000}, 'limits': {'Node Count': 15, 'Disk - MB': 50000} } } """ CBS """ cbs = { "title": "CBS", "us_url": "https://us.test.com", "uk_url": "https://uk.test.com", "active": True, "db_name": "cbs", "require_region": True, "doc_url": "https://doc.test.com", "pitchfork_url": "https://pitchfork.url", "limit_maps": [] } cbs_limit = { "product": "cbs", "title": "SATA - GB", "absolute_path": "quota_set.gigabytes_SATA", "uri": "/v1/{ddi}/os-quota-sets/{ddi}?usage=True", "slug": "sata_-_gb", "value_key": "in_use", "absolute_type": "dict", "active": True, "limit_key": "limit" } cbs_limit_return = { "quota_set": { "volumes": { "limit": -1, "reserved": 0, "in_use": 3 }, "gigabytes_SATA": { "limit": 10240, "reserved": 0, "in_use": 325 }, "gigabytes_SSD": { "limit": 10240, "reserved": 0, "in_use": 50 } } } cbs_full_return = { 'cbs': { 'values': {'SATA - GB': 9915}, 'limits': {'SATA - GB': 10240} } } """ Load Balancers """ clb = { "title": "Load Balancers", "us_url": "https://us.test.com", "uk_url": "https://uk.test.com", "active": True, "db_name": "load_balancers", "require_region": True, "doc_url": "https://doc.test.com", "pitchfork_url": "https://pitchfork.url", "limit_maps": [] } clb_limit = [ { "product": "load_balancers", "title": "Total Load Balancers", "uri": "/v1.0/{ddi}/loadbalancers/absolutelimits", "slug": "total_load_balancers", "active": True, "path": "absolute['LOADBALANCER_LIMIT']", "absolute_path": "absolute", "value_key": "", "absolute_type": "list", "limit_key": "LOADBALANCER_LIMIT" }, { "product": "load_balancers", "title": "Nodes per LB", "uri": "/v1.0/{ddi}/loadbalancers/absolutelimits", "slug": "nodes_per_lb", "active": True, "path": "absolute['NODE_LIMIT']", "absolute_path": "absolute", "value_key": "", "absolute_type": "list", "limit_key": "NODE_LIMIT" } ] clb_limit_return = { "absolute": [ { "name": "IPV6_LIMIT", "value": 25 }, { "name": "LOADBALANCER_LIMIT", "value": 25 }, { "name": "BATCH_DELETE_LIMIT", "value": 10 }, { "name": "ACCESS_LIST_LIMIT", "value": 100 }, { "name": "NODE_LIMIT", "value": 25 }, { "name": "NODE_META_LIMIT", "value": 25 }, { "name": "LOADBALANCER_META_LIMIT", "value": 25 }, { "name": "CERTIFICATE_MAPPING_LIMIT", "value": 20 } ] } clb_list_return = { "loadBalancers": [ { "status": "ACTIVE", "updated": { "time": "2016-01-12T16:04:44Z" }, "protocol": "HTTP", "name": "test", "algorithm": "LEAST_CONNECTIONS", "created": { "time": "2016-01-12T16:04:44Z" }, "virtualIps": [ { "ipVersion": "IPV4", "type": "PUBLIC", "id": 19875, "address": "172.16.31.10" }, { "ipVersion": "IPV6", "type": "PUBLIC", "id": 9318325, "address": "fd00:c2b6:b24b:be67:2827:688d:e6a1:6a3b" } ], "id": 506497, "timeout": 30, "nodeCount": 0, "port": 80 } ] } clb_full_return = { 'load_balancers': { 'values': {'Total Load Balancers': 1}, 'limits': {'Total Load Balancers': 25, 'Nodes per LB': 25} } } """ Servers """ server = { "title": "Servers", "us_url": "https://us.test.com", "uk_url": "https://uk.test.com", "active": True, "db_name": "servers", "require_region": True, "doc_url": "https://doc.test.com", "pitchfork_url": "https://pitchfork.url", "limit_maps": [] } server_limit = [ { "product": "servers", "title": "Servers", "uri": "/v2/{ddi}/limits", "slug": "servers", "active": True, "path": "absolute['maxTotalInstances']", "absolute_path": "limits.absolute", "value_key": "", "absolute_type": "dict", "limit_key": "maxTotalInstances" }, { "product": "servers", "title": "Private Networks", "uri": "/v2/{ddi}/limits", "slug": "private_networks", "active": True, "path": "absolute['maxTotalPrivateNetworks']", "absolute_path": "limits.absolute", "value_key": "", "absolute_type": "dict", "limit_key": "maxTotalPrivateNetworks" }, { "product": "servers", "title": "Ram - MB", "uri": "/v2/{ddi}/limits", "slug": "ram_-_mb", "active": True, "path": "absolute['maxTotalRAMSize']", "absolute_path": "limits.absolute", "value_key": "", "absolute_type": "dict", "limit_key": "maxTotalRAMSize" } ] server_limit_return = { "limits": { "rate": [ { "regex": "/[^/]/?$", "limit": [ { "next-available": "2016-01-12T16:14:47.624Z", "unit": "MINUTE", "verb": "GET", "remaining": 2200, "value": 2200 } ], "uri": "" }, { "regex": ( "/v[^/]+/[^/]+/servers/([^/]+)/rax-si-image-schedule" ), "limit": [ { "next-available": "2016-01-12T16:14:47.624Z", "unit": "SECOND", "verb": "POST", "remaining": 10, "value": 10 } ], "uri": "/servers/{id}/rax-si-image-schedule" } ], "absolute": { "maxPersonalitySize": 1000, "maxTotalCores": -1, "maxPersonality": 5, "totalPrivateNetworksUsed": 1, "maxImageMeta": 40, "maxTotalPrivateNetworks": 10, "maxSecurityGroupRules": -1, "maxTotalKeypairs": 100, "totalRAMUsed": 4096, "maxSecurityGroups": -1, "totalFloatingIpsUsed": 0, "totalInstancesUsed": 3, "totalSecurityGroupsUsed": 0, "maxServerMeta": 40, "maxTotalFloatingIps": -1, "maxTotalInstances": 200, "totalCoresUsed": 4, "maxTotalRAMSize": 256000 } } } server_list_return = { "servers": [ { "OS-EXT-STS:task_state": None, "addresses": { "public": [ { "version": 4, "addr": "192.168.3.11" }, { "version": 6, "addr": "fc00:e968:6179::de52:7100" } ], "private": [ { "version": 4, "addr": "10.176.205.68" } ] }, "flavor": { "id": "general1-1", "links": [ { "href": ( "https://iad.servers.api.rackspacecloud.com" "/766030/flavors/general1-1" ), "rel": "bookmark" } ] }, "id": "3290e50d-888f-4500-a934-16c10f3b8a10", "user_id": "284275", "OS-DCF:diskConfig": "MANUAL", "accessIPv4": "192.168.3.11", "accessIPv6": "fc00:e968:6179::de52:7100", "progress": 100, "OS-EXT-STS:power_state": 1, "config_drive": "", "status": "ACTIVE", "updated": "2016-01-12T15:16:37Z", "name": "test-server", "created": "2016-01-12T15:15:39Z", "tenant_id": "1234567", "metadata": { "build_config": "", "rax_service_level_automation": "Complete" } } ] } server_list_processed_return = [ { 'status': 'ACTIVE', 'updated': '2016-01-12T15:16:37Z', 'OS-EXT-STS:task_state': None, 'user_id': '284275', 'addresses': { 'public': [ { 'version': 4, 'addr': '192.168.3.11' }, { 'version': 6, 'addr': 'fc00:e968:6179::de52:7100' } ], 'private': [ { 'version': 4, 'addr': '10.176.205.68' } ] }, 'created': '2016-01-12T15:15:39Z', 'tenant_id': '1234567', 'OS-DCF:diskConfig': 'MANUAL', 'id': '3290e50d-888f-4500-a934-16c10f3b8a10', 'accessIPv4': '192.168.3.11', 'accessIPv6': 'fc00:e968:6179::de52:7100', 'config_drive': '', 'progress': 100, 'OS-EXT-STS:power_state': 1, 'metadata': { 'build_config': '', 'rax_service_level_automation': 'Complete' }, 'flavor': { 'id': 'general1-1', 'links': [ { 'href': ( 'https://iad.servers.api.rackspacecloud.com' '/766030/flavors/general1-1' ), 'rel': 'bookmark' } ] }, 'name': 'test-server' } ] network_list_return = { "networks": [ { "status": "ACTIVE", "subnets": [ "879ff280-6f17-4fd8-b684-19237d88fc45" ], "name": "test-network", "admin_state_up": True, "tenant_id": "1234567", "shared": False, "id": "e737483a-00d7-4517-afc3-bd1fbbbd4cd3" } ] } network_processed_list = [ { 'status': 'ACTIVE', 'subnets': [ '879ff280-6f17-4fd8-b684-19237d88fc45' ], 'name': 'test-network', 'admin_state_up': True, 'tenant_id': '1234567', 'shared': False, 'id': 'e737483a-00d7-4517-afc3-bd1fbbbd4cd3' } ] server_flavor_return = { "flavor": { "ram": 1024, "name": "1 GB General Purpose v1", "OS-FLV-WITH-EXT-SPECS:extra_specs": { "number_of_data_disks": "0", "class": "general1", "disk_io_index": "40", 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models.py	danger-ahead/notes-backend-service	1	134936	from sqlalchemy.orm import relationship from sqlalchemy.sql.functions import now from sqlalchemy.sql.schema import ForeignKey from sqlalchemy.sql.sqltypes import TIMESTAMP from database import Base from sqlalchemy import Column, Integer, String class Note(Base): __tablename__ = "notes" id = Column(Integer, primary_key=True, nullable=False) title = Column(String, nullable=False) content = Column(String, nullable=True) created_at = Column(TIMESTAMP(timezone=True), nullable=False, server_default=now()) last_updated_at = Column( TIMESTAMP(timezone=True), nullable=False, server_default=now() ) username = Column( String, ForeignKey("users.username", ondelete="CASCADE"), nullable=False ) user = relationship("User") class User(Base): __tablename__ = "users" username = Column(String, nullable=False, unique=True, primary_key=True) password = Column(String, nullable=False) created_at = Column(TIMESTAMP(timezone=True), nullable=False, server_default=now())	[ 1, 515, 4576, 284, 305, 6764, 29889, 555, 1053, 9443, 13, 3166, 4576, 284, 305, 6764, 29889, 2850, 29889, 12171, 1053, 1286, 13, 3166, 4576, 284, 305, 6764, 29889, 2850, 29889, 11010, 1053, 19358, 2558, 13, 3166, 4576, 284, 305, 6764, 29889, 2850, 29889, 2850, 8768, 1053, 323, 8890, 1254, 19297, 13, 3166, 2566, 1053, 7399, 13, 3166, 4576, 284, 305, 6764, 1053, 12481, 29892, 8102, 29892, 1714, 13, 13, 13, 1990, 3940, 29898, 5160, 1125, 13, 1678, 4770, 3891, 2435, 420, 1649, 353, 376, 16953, 29908, 13, 13, 1678, 1178, 353, 12481, 29898, 7798, 29892, 7601, 29918, 1989, 29922, 5574, 29892, 1870, 519, 29922, 8824, 29897, 13, 1678, 3611, 353, 12481, 29898, 1231, 29892, 1870, 519, 29922, 8824, 29897, 13, 1678, 2793, 353, 12481, 29898, 1231, 29892, 1870, 519, 29922, 5574, 29897, 13, 1678, 2825, 29918, 271, 353, 12481, 29898, 15307, 1254, 19297, 29898, 2230, 8028, 29922, 5574, 511, 1870, 519, 29922, 8824, 29892, 1923, 29918, 4381, 29922, 3707, 3101, 13, 1678, 1833, 29918, 21402, 29918, 271, 353, 12481, 29898, 13, 4706, 323, 8890, 1254, 19297, 29898, 2230, 8028, 29922, 5574, 511, 1870, 519, 29922, 8824, 29892, 1923, 29918, 4381, 29922, 3707, 580, 13, 1678, 1723, 13, 1678, 8952, 353, 12481, 29898, 13, 4706, 1714, 29892, 19358, 2558, 703, 7193, 29889, 6786, 613, 373, 8143, 543, 29907, 3289, 5454, 2287, 4968, 1870, 519, 29922, 8824, 13, 1678, 1723, 13, 13, 1678, 1404, 353, 9443, 703, 2659, 1159, 13, 13, 13, 1990, 4911, 29898, 5160, 1125, 13, 1678, 4770, 3891, 2435, 420, 1649, 353, 376, 7193, 29908, 13, 13, 1678, 8952, 353, 12481, 29898, 1231, 29892, 1870, 519, 29922, 8824, 29892, 5412, 29922, 5574, 29892, 7601, 29918, 1989, 29922, 5574, 29897, 13, 1678, 4800, 353, 12481, 29898, 1231, 29892, 1870, 519, 29922, 8824, 29897, 13, 1678, 2825, 29918, 271, 353, 12481, 29898, 15307, 1254, 19297, 29898, 2230, 8028, 29922, 5574, 511, 1870, 519, 29922, 8824, 29892, 1923, 29918, 4381, 29922, 3707, 3101, 13, 2 ]
gui/verifyseed.py	roshan-c/shoeshive.tv	2	162861	import PySimpleGUI as sg sg.theme('SandyBeach') layout = [ [sg.Text('Please enter your Username and the Seed provided by the Host')], [sg.Text('Username', size =(15, 1)), sg.InputText()], [sg.Text('Server Seed', size =(15, 1)), sg.InputText()], [sg.Submit(), sg.Cancel()] ] window = sg.Window('Shoeshine.tv', layout) event, values = window.read() window.close() username = values[0] seedinput = values[1] print(username) print(seedinput) connect = False with open('seed.txt') as f: seed = f.read() print(seed) if seed == seedinput: connect = True print('True') f.close() else: connect = False print('False') f.close()	[ 1, 1053, 10772, 15427, 29954, 3120, 408, 269, 29887, 30004, 13, 30004, 13, 5311, 29889, 18193, 877, 29903, 13910, 3629, 496, 1495, 418, 6756, 13, 2680, 353, 518, 30004, 13, 1678, 518, 5311, 29889, 1626, 877, 12148, 3896, 596, 4911, 978, 322, 278, 922, 287, 4944, 491, 278, 16956, 1495, 1402, 30004, 13, 1678, 518, 5311, 29889, 1626, 877, 20249, 742, 2159, 353, 29898, 29896, 29945, 29892, 29871, 29896, 8243, 269, 29887, 29889, 4290, 1626, 580, 1402, 30004, 13, 1678, 518, 5311, 29889, 1626, 877, 6004, 922, 287, 742, 2159, 353, 29898, 29896, 29945, 29892, 29871, 29896, 8243, 269, 29887, 29889, 4290, 1626, 580, 1402, 30004, 13, 1678, 518, 5311, 29889, 16228, 3285, 269, 29887, 29889, 19420, 580, 29962, 30004, 13, 29962, 30004, 13, 29871, 6756, 13, 7165, 353, 269, 29887, 29889, 5907, 877, 29903, 1251, 12094, 457, 29889, 12427, 742, 5912, 8443, 13, 3696, 29892, 1819, 353, 3474, 29889, 949, 26471, 13, 7165, 29889, 5358, 26471, 13, 30004, 13, 6786, 353, 1819, 29961, 29900, 29962, 30004, 13, 26776, 2080, 353, 1819, 29961, 29896, 29962, 30004, 13, 30004, 13, 2158, 29898, 6786, 8443, 13, 2158, 29898, 26776, 2080, 8443, 13, 30004, 13, 6915, 353, 7700, 30004, 13, 2541, 1722, 877, 26776, 29889, 3945, 1495, 408, 285, 29901, 30004, 13, 1678, 16717, 353, 285, 29889, 949, 26471, 13, 1678, 1596, 29898, 26776, 8443, 13, 30004, 13, 361, 16717, 1275, 16717, 2080, 29901, 30004, 13, 1678, 4511, 353, 5852, 30004, 13, 1678, 1596, 877, 5574, 1495, 30004, 13, 1678, 285, 29889, 5358, 26471, 13, 2870, 29901, 30004, 13, 1678, 4511, 353, 7700, 30004, 13, 1678, 1596, 877, 8824, 1495, 30004, 13, 1678, 285, 29889, 5358, 580, 2 ]
Curso_Coursera/Ex1_ParImpar.py	shirleyguimaraes/Linguagem_Python	0	33098	def main(): n = int(input("Digite um número inteiro: ")) resto = n % 2 if resto == 0: print("Par") else: print("Impar") main()	[ 1, 6756, 13, 1753, 1667, 7295, 30004, 13, 30004, 13, 1678, 302, 353, 938, 29898, 2080, 703, 14991, 568, 1922, 13831, 2293, 3350, 29901, 376, 876, 30004, 13, 30004, 13, 1678, 25814, 353, 302, 1273, 29871, 29906, 30004, 13, 30004, 13, 1678, 565, 25814, 1275, 29871, 29900, 29901, 30004, 13, 4706, 1596, 703, 2177, 1159, 30004, 13, 1678, 1683, 29901, 30004, 13, 4706, 1596, 703, 1888, 862, 1159, 30004, 13, 30004, 13, 30004, 13, 3396, 26471, 13, 2 ]
cttools/config.py	nik849/ct-tools	0	74928	import numpy as np from .parse import parse_xtekct_file class Config(object): def __init__(self): """ Configuration object which contains all settings neccessary for the forward projection and tomographic reconstruction using the axitom algorithm. """ self.n_voxels_x = 2000 self.n_voxels_y = 2000 self.n_voxels_z = 2000 self.object_size_x = 27.229675726 self.object_size_y = 27.229675726 self.object_size_z = 27.229675726 self.n_pixels_u = 2000 self.n_pixels_v = 2000 self.detector_size_u = 400. self.detector_size_v = 400. self.source_to_detector_dist = 797.8693 self.source_to_object_dist = 95.1665735244751 self.angular_inc = 1. self.pixel_offset_u = 0 self.pixel_offset_v = 0 self.center_of_rot_y = 0 self.projection_angs = np.arange(0., 360, self.angular_inc) self.n_projections = len(self.projection_angs) self.voxel_size_x = self.object_size_x / self.n_voxels_x self.voxel_size_y = self.object_size_y / self.n_voxels_y self.voxel_size_z = self.object_size_z / self.n_voxels_z self.pixel_size_u = self.detector_size_u / self.n_pixels_u self.pixel_size_v = self.detector_size_v / self.n_pixels_v self.object_xs = (np.arange(self.n_voxels_x, dtype=np.float32) - self.n_voxels_x / 2.) * self.voxel_size_x self.object_ys = (np.arange(self.n_voxels_y, dtype=np.float32) - self.n_voxels_y / 2.) * self.voxel_size_y self.object_zs = (np.arange(self.n_voxels_z, dtype=np.float32) - self.n_voxels_z / 2.) * self.voxel_size_z self.detector_us = (np.arange(self.n_pixels_u, dtype=np.float32) - self.n_pixels_u / 2.) * self.pixel_size_u + self.pixel_offset_u * self.pixel_size_u self.detector_vs = (np.arange(self.n_pixels_v, dtype=np.float32) - self.n_pixels_v / 2.) * self.pixel_size_v + self.pixel_offset_v * self.pixel_size_v def update(self): self.projection_angs = np.arange(0., 360, self.angular_inc) self.n_projections = len(self.projection_angs) self.voxel_size_x = self.object_size_x / self.n_voxels_x self.voxel_size_y = self.object_size_y / self.n_voxels_y self.voxel_size_z = self.object_size_z / self.n_voxels_z self.pixel_size_u = self.detector_size_u / self.n_pixels_u self.pixel_size_v = self.detector_size_v / self.n_pixels_v self.object_xs = (np.arange(self.n_voxels_x, dtype=np.float32) - self.n_voxels_x / 2.) * self.voxel_size_x self.object_ys = (np.arange(self.n_voxels_y, dtype=np.float32) - self.n_voxels_y / 2.) * self.voxel_size_y self.object_zs = (np.arange(self.n_voxels_z, dtype=np.float32) - self.n_voxels_z / 2.) * self.voxel_size_z self.detector_us = (np.arange(self.n_pixels_u, dtype=np.float32) - self.n_pixels_u / 2.) * self.pixel_size_u + self.pixel_offset_u * self.pixel_size_u self.detector_vs = (np.arange(self.n_pixels_v, dtype=np.float32) - self.n_pixels_v / 2.) * self.pixel_size_v + self.pixel_offset_v * self.pixel_size_v def config_from_xtekct(file_path): """ Make config object from a Nikon X-tek CT input file The .xtekct file is parsed and a config file containing all relevant settings is returned. Parameters ---------- file_path : str The path to the .xtekct file Returns ------- obj Config object """ inputfile = parse_xtekct_file(file_path) conf = Config() try: conf.n_voxels_x = inputfile["VoxelsX"] conf.n_voxels_y = inputfile["VoxelsY"] conf.n_voxels_z = inputfile["VoxelsZ"] conf.object_size_x = inputfile["VoxelSizeX"] * conf.n_voxels_x conf.object_size_y = inputfile["VoxelSizeY"] * conf.n_voxels_y conf.object_size_z = inputfile["VoxelSizeZ"] * conf.n_voxels_z conf.n_pixels_u = inputfile["DetectorPixelsX"] conf.n_pixels_v = inputfile["DetectorPixelsY"] conf.detector_size_u = inputfile["DetectorPixelSizeX"] * conf.n_pixels_u conf.detector_size_v = inputfile["DetectorPixelSizeY"] * conf.n_pixels_v conf.source_to_detector_dist = inputfile["SrcToDetector"] conf.source_to_object_dist = inputfile["SrcToObject"] except Exception as e: raise IOError("Parsing of X-tec file failed with key: ", e) return conf	[ 1, 1053, 12655, 408, 7442, 13, 3166, 869, 5510, 1053, 6088, 29918, 29916, 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tests/basic_store.py	fmarczin/simplekv	0	134401	<gh_stars>0 # coding: utf8 import os import time import tempfile from tempdir import TempDir import pytest from simplekv._compat import BytesIO, xrange, text_type from simplekv.decorator import PrefixDecorator from simplekv.crypt import HMACDecorator from simplekv.idgen import UUIDDecorator, HashDecorator from simplekv import CopyMixin class BasicStore(object): def test_store(self, store, key, value): key = store.put(key, value) assert isinstance(key, text_type) def test_unicode_store(self, store, key, unicode_value): with pytest.raises(IOError): store.put(key, unicode_value) def test_store_and_retrieve(self, store, key, value): store.put(key, value) assert store.get(key) == value def test_store_and_retrieve_filelike(self, store, key, value): store.put_file(key, BytesIO(value)) assert store.get(key) == value def test_store_and_retrieve_overwrite(self, store, key, value, value2): store.put_file(key, BytesIO(value)) assert store.get(key) == value store.put(key, value2) assert store.get(key) == value2 def test_store_and_open(self, store, key, value): store.put_file(key, BytesIO(value)) assert store.open(key).read() == value def test_store_and_copy(self, store, key, key2, value): if not isinstance(store, CopyMixin): pytest.skip() store.put(key, value) assert store.get(key) == value store.copy(key, key2) assert store.get(key) == value assert store.get(key2) == value def test_store_and_copy_overwrite(self, store, key, key2, value, value2): if not isinstance(store, CopyMixin): pytest.skip() store.put(key, value) store.put(key2, value2) assert store.get(key) == value assert store.get(key2) == value2 store.copy(key, key2) assert store.get(key) == value assert store.get(key2) == value def test_open_incremental_read(self, store, key, long_value): store.put_file(key, BytesIO(long_value)) ok = store.open(key) assert long_value[:3] == ok.read(3) assert long_value[3:5] == ok.read(2) assert long_value[5:8] == ok.read(3) def test_bytestring_key_store(self, store, bytestring_key, value): with pytest.raises(ValueError): store.put(bytestring_key, value) def test_key_error_on_nonexistant_get(self, store, key): with pytest.raises(KeyError): store.get(key) def test_key_error_on_nonexistant_copy(self, store, key, key2): if not isinstance(store, CopyMixin): pytest.skip() with pytest.raises(KeyError): store.copy(key, key2) def test_key_error_on_nonexistant_open(self, store, key): with pytest.raises(KeyError): store.open(key) def test_key_error_on_nonexistant_get_file(self, store, key): with pytest.raises(KeyError): store.get_file(key, BytesIO()) def test_key_error_on_nonexistant_get_filename(self, store, key): with pytest.raises(KeyError): store.get_file(key, '/dev/null') def test_exception_on_invalid_key_get(self, store, invalid_key): with pytest.raises(ValueError): store.get(invalid_key) def test_exception_on_invalid_key_copy(self, store, invalid_key, key): if not isinstance(store, CopyMixin): pytest.skip() with pytest.raises(ValueError): store.copy(invalid_key, key) with pytest.raises(ValueError): store.copy(key, invalid_key) def test_exception_on_invalid_key_get_file(self, store, invalid_key): with pytest.raises(ValueError): store.get_file(invalid_key, '/dev/null') def test_exception_on_invalid_key_delete(self, store, invalid_key): with pytest.raises(ValueError): store.delete(invalid_key) def test_put_file(self, store, key, value): tmp = tempfile.NamedTemporaryFile(delete=False) try: tmp.write(value) tmp.close() store.put_file(key, tmp.name) assert store.get(key) == value finally: if os.path.exists(tmp.name): os.unlink(tmp.name) def test_put_opened_file(self, store, key, value): with tempfile.NamedTemporaryFile() as tmp: tmp.write(value) tmp.flush() store.put_file(key, open(tmp.name, 'rb')) assert store.get(key) == value def test_get_into_file(self, store, key, value): with TempDir() as tmpdir: store.put(key, value) out_filename = os.path.join(tmpdir, 'output') store.get_file(key, out_filename) assert open(out_filename, 'rb').read() == value def test_get_into_stream(self, store, key, value): store.put(key, value) output = BytesIO() store.get_file(key, output) assert output.getvalue() == value def test_put_return_value(self, store, key, value): assert key == store.put(key, value) def test_put_file_return_value(self, store, key, value): assert key == store.put_file(key, BytesIO(value)) def test_put_filename_return_value(self, store, key, value): tmp = tempfile.NamedTemporaryFile(delete=False) try: tmp.write(value) tmp.close() assert key == store.put_file(key, tmp.name) finally: if os.path.exists(tmp.name): os.unlink(tmp.name) def test_delete(self, store, key, value): store.put(key, value) assert value == store.get(key) store.delete(key) with pytest.raises(KeyError): store.get(key) def test_multiple_delete_fails_without_error(self, store, key, value): store.put(key, value) store.delete(key) store.delete(key) store.delete(key) def test_can_delete_key_that_never_exists(self, store, key): store.delete(key) def test_key_iterator(self, store, key, key2, value, value2): store.put(key, value) store.put(key2, value2) l = [] for k in store.iter_keys(): assert isinstance(k, text_type) l.append(k) l.sort() assert l == sorted([key, key2]) def test_key_iterator_with_prefix(self, store, key, key2, value): prefix = key key_prefix_1 = prefix + '_key1' key_prefix_2 = prefix + '_key2' store.put(key_prefix_1, value) store.put(key_prefix_2, value) store.put(key2, value) l = [] for k in store.iter_keys(): l.append(k) l.sort() assert l == sorted([key_prefix_1, key_prefix_2, key2]) l = [] for k in store.iter_keys(prefix): l.append(k) l.sort() assert l == sorted([key_prefix_1, key_prefix_2]) def test_keys(self, store, key, key2, value, value2): store.put(key, value) store.put(key2, value2) l = sorted(store.keys()) for k in l: assert isinstance(k, text_type) assert l == sorted([key, key2]) def test_keys_with_prefix(self, store, key, key2, value): prefix = key key_prefix_1 = prefix + '_key1' key_prefix_2 = prefix + '_key2' store.put(key_prefix_1, value) store.put(key_prefix_2, value) store.put(key2, value) l = sorted(store.keys()) assert l == sorted([key_prefix_1, key_prefix_2, key2]) l = sorted(store.keys(prefix)) assert l == sorted([key_prefix_1, key_prefix_2]) def test_has_key(self, store, key, key2, value): store.put(key, value) assert key in store assert key2 not in store def test_has_key_with_delete(self, store, key, value): assert key not in store store.put(key, value) assert key in store store.delete(key) assert key not in store store.put(key, value) assert key in store def test_get_with_delete(self, store, key, value): with pytest.raises(KeyError): store.get(key) store.put(key, value) store.get(key) store.delete(key) with pytest.raises(KeyError): store.get(key) store.put(key, value) store.get(key) def test_max_key_length(self, store, max_key, value): new_key = store.put(max_key, value) assert new_key == max_key assert value == store.get(max_key) def test_a_lot_of_puts(self, store, key, value): a_lot = 20 for i in xrange(a_lot): key = key + '_{}'.format(i) store.put(key, value) # small extra time added to account for variance TTL_MARGIN = 1 class TTLStore(object): @pytest.fixture def ustore(self, store): return UUIDDecorator(store) @pytest.fixture(params=['hash', 'uuid', 'hmac', 'prefix']) def dstore(self, request, store, secret_key): if request.param == 'hash': return HashDecorator(store) elif request.param == 'uuid': return self.ustore(store) elif request.param == 'hmac': return HMACDecorator(secret_key, store) elif request.param == 'prefix': return PrefixDecorator('SaMpLe_PrEfIX', store) @pytest.fixture(params=[0.4, 1]) def small_ttl(self, request): return request.param def test_put_with_negative_ttl_throws_error(self, store, key, value): with pytest.raises(ValueError): store.put(key, value, ttl_secs=-1) def test_put_with_non_numeric_ttl_throws_error(self, store, key, value): with pytest.raises(ValueError): store.put(key, value, ttl_secs='badttl') def test_put_with_ttl_argument(self, store, key, value, small_ttl): store.put(key, value, small_ttl) time.sleep(small_ttl + TTL_MARGIN) with pytest.raises(KeyError): store.get(key) def test_put_set_default(self, store, key, value, small_ttl): store.default_ttl_secs = small_ttl store.put(key, value) time.sleep(small_ttl + TTL_MARGIN) with pytest.raises(KeyError): store.get(key) def test_put_file_with_ttl_argument(self, store, key, value, small_ttl): store.put_file(key, BytesIO(value), small_ttl) time.sleep(small_ttl + TTL_MARGIN) with pytest.raises(KeyError): store.get(key) def test_put_file_set_default(self, store, key, value, small_ttl): store.default_ttl_secs = small_ttl store.put_file(key, BytesIO(value)) time.sleep(small_ttl + TTL_MARGIN) with pytest.raises(KeyError): store.get(key) def test_uuid_decorator(self, ustore, value): key = ustore.put(None, value) assert key def test_advertises_ttl_features(self, store): 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usermess/migrations/0002_auto_20181111_2211.py	HemanthJella/ewallet	0	138956	# Generated by Django 2.1.2 on 2018-11-11 22:11 import datetime from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('usermess', '0001_initial'), ] operations = [ migrations.AlterField( model_name='couponsbought', name='boughtTime', field=models.DateTimeField(default=datetime.datetime(2018, 11, 11, 22, 11, 23, 928786)), ), migrations.AlterField( model_name='couponsbought', name='couponId', field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, to='mess.Coupons'), ), ]	[ 1, 396, 3251, 630, 491, 15337, 29871, 29906, 29889, 29896, 29889, 29906, 373, 29871, 29906, 29900, 29896, 29947, 29899, 29896, 29896, 29899, 29896, 29896, 29871, 29906, 29906, 29901, 29896, 29896, 30004, 13, 30004, 13, 5215, 12865, 30004, 13, 3166, 9557, 29889, 2585, 1053, 9725, 800, 29892, 4733, 30004, 13, 5215, 9557, 29889, 2585, 29889, 9794, 29889, 311, 1026, 291, 30004, 13, 30004, 13, 30004, 13, 1990, 341, 16783, 29898, 26983, 800, 29889, 29924, 16783, 1125, 30004, 13, 30004, 13, 1678, 9962, 353, 518, 30004, 13, 4706, 6702, 375, 837, 404, 742, 525, 29900, 29900, 29900, 29896, 29918, 11228, 5477, 30004, 13, 1678, 4514, 30004, 13, 30004, 13, 1678, 6931, 353, 518, 30004, 13, 4706, 9725, 800, 29889, 2499, 357, 3073, 29898, 30004, 13, 9651, 1904, 29918, 978, 2433, 29883, 1132, 787, 29890, 1774, 23592, 13, 9651, 1024, 2433, 29890, 1774, 2481, 23592, 13, 9651, 1746, 29922, 9794, 29889, 11384, 3073, 29898, 4381, 29922, 12673, 29889, 12673, 29898, 29906, 29900, 29896, 29947, 29892, 29871, 29896, 29896, 29892, 29871, 29896, 29896, 29892, 29871, 29906, 29906, 29892, 29871, 29896, 29896, 29892, 29871, 29906, 29941, 29892, 29871, 29929, 29906, 29947, 29955, 29947, 29953, 8243, 30004, 13, 4706, 10353, 30004, 13, 4706, 9725, 800, 29889, 2499, 357, 3073, 29898, 30004, 13, 9651, 1904, 29918, 978, 2433, 29883, 1132, 787, 29890, 1774, 23592, 13, 9651, 1024, 2433, 16589, 1112, 1204, 23592, 13, 9651, 1746, 29922, 9794, 29889, 27755, 2558, 29898, 265, 29918, 8143, 29922, 14095, 29889, 2585, 29889, 9794, 29889, 311, 1026, 291, 29889, 8618, 4330, 1783, 29892, 304, 2433, 12062, 29889, 29907, 1132, 787, 5477, 30004, 13, 4706, 10353, 30004, 13, 1678, 4514, 30004, 13, 2 ]
sdk/python/pulumi_aiven/get_service_user.py	pulumi/pulumi-aiven	7	180868	# coding=utf-8 # * WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. * # * Do not edit by hand unless you're certain you know what you are doing! * import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from . import _utilities __all__ = [ 'GetServiceUserResult', 'AwaitableGetServiceUserResult', 'get_service_user', ] @pulumi.output_type class GetServiceUserResult: """ A collection of values returned by getServiceUser. """ def __init__(__self__, access_cert=None, access_key=None, authentication=None, id=None, password=<PASSWORD>, project=None, redis_acl_categories=None, redis_acl_channels=None, redis_acl_commands=None, redis_acl_keys=None, service_name=None, type=None, username=None): if access_cert and not isinstance(access_cert, str): raise TypeError("Expected argument 'access_cert' to be a str") pulumi.set(__self__, "access_cert", access_cert) if access_key and not isinstance(access_key, str): raise TypeError("Expected argument 'access_key' to be a str") pulumi.set(__self__, "access_key", access_key) if authentication and not isinstance(authentication, str): raise TypeError("Expected argument 'authentication' to be a str") pulumi.set(__self__, "authentication", authentication) if id and not isinstance(id, str): raise TypeError("Expected argument 'id' to be a str") pulumi.set(__self__, "id", id) if password and not isinstance(password, str): raise TypeError("Expected argument 'password' to be a str") pulumi.set(__self__, "password", password) if project and not isinstance(project, str): raise TypeError("Expected argument 'project' to be a str") pulumi.set(__self__, "project", project) if redis_acl_categories and not isinstance(redis_acl_categories, list): raise TypeError("Expected argument 'redis_acl_categories' to be a list") pulumi.set(__self__, "redis_acl_categories", redis_acl_categories) if redis_acl_channels and not isinstance(redis_acl_channels, list): raise TypeError("Expected argument 'redis_acl_channels' to be a list") pulumi.set(__self__, "redis_acl_channels", redis_acl_channels) if redis_acl_commands and not isinstance(redis_acl_commands, list): raise TypeError("Expected argument 'redis_acl_commands' to be a list") pulumi.set(__self__, "redis_acl_commands", redis_acl_commands) if redis_acl_keys and not isinstance(redis_acl_keys, list): raise TypeError("Expected argument 'redis_acl_keys' to be a list") pulumi.set(__self__, "redis_acl_keys", redis_acl_keys) if service_name and not isinstance(service_name, str): raise TypeError("Expected argument 'service_name' to be a str") pulumi.set(__self__, "service_name", service_name) if type and not isinstance(type, str): raise TypeError("Expected argument 'type' to be a str") pulumi.set(__self__, "type", type) if username and not isinstance(username, str): raise TypeError("Expected argument 'username' to be a str") pulumi.set(__self__, "username", username) @property @pulumi.getter(name="accessCert") def access_cert(self) -> str: """ is the access certificate of the user (not applicable for all services). """ return pulumi.get(self, "access_cert") @property @pulumi.getter(name="accessKey") def access_key(self) -> str: """ is the access key of the user (not applicable for all services). """ return pulumi.get(self, "access_key") @property @pulumi.getter def authentication(self) -> Optional[str]: return pulumi.get(self, "authentication") @property @pulumi.getter def id(self) -> str: """ The provider-assigned unique ID for this managed resource. """ return pulumi.get(self, "id") @property @pulumi.getter def password(self) -> str: """ is the password of the user (not applicable for all services). """ return pulumi.get(self, "password") @property @pulumi.getter def project(self) -> str: return pulumi.get(self, "project") @property @pulumi.getter(name="redisAclCategories") def redis_acl_categories(self) -> Optional[Sequence[str]]: """ Redis specific field, defines command category rules. """ return pulumi.get(self, "redis_acl_categories") @property @pulumi.getter(name="redisAclChannels") def redis_acl_channels(self) -> Optional[Sequence[str]]: return pulumi.get(self, "redis_acl_channels") @property @pulumi.getter(name="redisAclCommands") def redis_acl_commands(self) -> Optional[Sequence[str]]: """ Redis specific field, defines rules for individual commands. """ return pulumi.get(self, "redis_acl_commands") @property @pulumi.getter(name="redisAclKeys") def redis_acl_keys(self) -> Optional[Sequence[str]]: """ Redis specific field, defines key access rules. """ return pulumi.get(self, "redis_acl_keys") @property @pulumi.getter(name="serviceName") def service_name(self) -> str: return pulumi.get(self, "service_name") @property @pulumi.getter def type(self) -> str: """ tells whether the user is primary account or regular account. """ return pulumi.get(self, "type") @property @pulumi.getter def username(self) -> str: return pulumi.get(self, "username") class AwaitableGetServiceUserResult(GetServiceUserResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return GetServiceUserResult( access_cert=self.access_cert, access_key=self.access_key, authentication=self.authentication, id=self.id, password=<PASSWORD>, project=self.project, redis_acl_categories=self.redis_acl_categories, redis_acl_channels=self.redis_acl_channels, redis_acl_commands=self.redis_acl_commands, redis_acl_keys=self.redis_acl_keys, service_name=self.service_name, type=self.type, username=self.username) def get_service_user(access_cert: Optional[str] = None, access_key: Optional[str] = None, authentication: Optional[str] = None, password: Optional[str] = None, project: Optional[str] = None, redis_acl_categories: Optional[Sequence[str]] = None, redis_acl_channels: Optional[Sequence[str]] = None, redis_acl_commands: Optional[Sequence[str]] = None, redis_acl_keys: Optional[Sequence[str]] = None, service_name: Optional[str] = None, type: Optional[str] = None, username: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetServiceUserResult: """ ## # Service User Data Source The Service User data source provides information about the existing Aiven Service User. ## Example Usage ```python import pulumi import pulumi_aiven as aiven myserviceuser = aiven.get_service_user(project=aiven_project["myproject"]["project"], service_name=aiven_service["myservice"]["service_name"], username="<USERNAME>") ``` > Note The service user data source is not supported for Aiven Grafana services. :param str access_cert: is the access certificate of the user (not applicable for all services). :param str access_key: is the access key of the user (not applicable for all services). :param str password: is the password of the user (not applicable for all services). :param str project: and `service_name` - (Required) define the project and service the user belongs to. They should be defined using reference as shown above to set up dependencies correctly. :param Sequence[str] redis_acl_categories: Redis specific field, defines command category rules. :param Sequence[str] redis_acl_commands: Redis specific field, defines rules for individual commands. :param Sequence[str] redis_acl_keys: Redis specific field, defines key access rules. :param str type: tells whether the user is primary account or regular account. :param str username: is the actual name of the user account. """ __args__ = dict() __args__['accessCert'] = access_cert __args__['accessKey'] = access_key __args__['authentication'] = authentication __args__['password'] = password __args__['project'] = project __args__['redisAclCategories'] = redis_acl_categories __args__['redisAclChannels'] = redis_acl_channels __args__['redisAclCommands'] = redis_acl_commands __args__['redisAclKeys'] = redis_acl_keys __args__['serviceName'] = service_name __args__['type'] = type __args__['username'] = username if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('aiven:index/getServiceUser:getServiceUser', __args__, opts=opts, typ=GetServiceUserResult).value return AwaitableGetServiceUserResult( access_cert=__ret__.access_cert, access_key=__ret__.access_key, authentication=__ret__.authentication, id=__ret__.id, password=__<PASSWORD>, project=__ret__.project, redis_acl_categories=__ret__.redis_acl_categories, 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gwk/constants.py	aixcyi/gwk	1	181388	# -- coding: utf-8 -- __all__ = [ 'GachaType', 'WishType', 'JsonStruct', 'CEILINGS', 'UNIFORM_TIME_FORMAT', 'DT_STAMP_OFFSET_CHANGE', 'DT_VERSION_START_2_3', ] from datetime import datetime from enum import Enum from gwk.typing import Choices class GachaType(Choices): """祈愿的卡池类型。""" BEGINNERS_WISH = '100', '新手祈愿' WANDERLUST_INVOCATION = '200', '常驻祈愿' CHARACTER_EVENT_WISH = '301', '角色活动祈愿' WEAPON_EVENT_WISH = '302', '武器活动祈愿' CHARACTER_EVENT_WISH_2 = '400', '角色活动祈愿-2' class WishType(Choices): """祈愿卡池的类型。""" BEGINNERS_WISH = '100', '新手祈愿' WANDERLUST_INVOCATION = '200', '常驻祈愿' CHARACTER_EVENT_WISH = '301', '角色活动祈愿' WEAPON_EVENT_WISH = '302', '武器活动祈愿' class JsonStruct(Enum): """导出的祈愿记录JSON文件的格式。""" GWK = 'GWK' UIGF = 'UIGF' CEILINGS = { WishType.BEGINNERS_WISH: 90, # 新手祈愿 WishType.WANDERLUST_INVOCATION: 90, # 常驻祈愿 WishType.CHARACTER_EVENT_WISH: 90, # 角色活动祈愿 WishType.WEAPON_EVENT_WISH: 80, # 武器活动祈愿 # WishType.CHARACTER_EVENT_WISH_2: 90, # 角色活动祈愿-2 } # Time Format UNIFORM_TIME_FORMAT = '%Y-%m-%d %H:%M:%S' # Datetime DT_STAMP_OFFSET_CHANGE = datetime(2020, 12, 31) DT_VERSION_START_2_3 = datetime(2021, 11, 24, 7, 0, 0)	[ 1, 396, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 13, 1649, 497, 1649, 353, 518, 13, 1678, 525, 29954, 496, 29874, 1542, 742, 13, 1678, 525, 29956, 728, 1542, 742, 13, 1678, 525, 8148, 19560, 742, 13, 1678, 525, 4741, 6227, 4214, 29903, 742, 13, 1678, 525, 3904, 6545, 12054, 29918, 15307, 29918, 19094, 1299, 742, 13, 1678, 525, 12972, 29918, 1254, 19297, 29918, 27681, 10490, 29918, 3210, 24336, 742, 13, 1678, 525, 12972, 29918, 16358, 29918, 25826, 29918, 29906, 29918, 29941, 742, 13, 29962, 13, 13, 3166, 12865, 1053, 12865, 13, 3166, 14115, 1053, 1174, 398, 13, 13, 3166, 330, 29893, 29895, 29889, 1017, 15702, 1053, 14542, 1575, 13, 13, 13, 1990, 402, 496, 29874, 1542, 29898, 15954, 1575, 1125, 13, 1678, 9995, 234, 168, 139, 233, 135, 194, 30210, 232, 144, 164, 31853, 30832, 30883, 30267, 15945, 29908, 13, 13, 1678, 22815, 13865, 29903, 29918, 29956, 3235, 29950, 353, 525, 29896, 29900, 29900, 742, 525, 30374, 30880, 234, 168, 139, 233, 135, 194, 29915, 13, 1678, 399, 2190, 8032, 29931, 17321, 29918, 1177, 29963, 20166, 8098, 353, 525, 29906, 29900, 29900, 742, 525, 31190, 236, 172, 190, 234, 168, 139, 233, 135, 194, 29915, 13, 1678, 26871, 17923, 1001, 29918, 22240, 3919, 29918, 29956, 3235, 29950, 353, 525, 29941, 29900, 29896, 742, 525, 31432, 31085, 31704, 30846, 234, 168, 139, 233, 135, 194, 29915, 13, 1678, 399, 29923, 3301, 1164, 29918, 22240, 3919, 29918, 29956, 3235, 29950, 353, 525, 29941, 29900, 29906, 742, 525, 30819, 30943, 31704, 30846, 234, 168, 139, 233, 135, 194, 29915, 13, 1678, 26871, 17923, 1001, 29918, 22240, 3919, 29918, 29956, 3235, 29950, 29918, 29906, 353, 525, 29946, 29900, 29900, 742, 525, 31432, 31085, 31704, 30846, 234, 168, 139, 233, 135, 194, 29899, 29906, 29915, 13, 13, 13, 1990, 399, 728, 1542, 29898, 15954, 1575, 1125, 13, 1678, 9995, 234, 168, 139, 233, 135, 194, 232, 144, 164, 31853, 30210, 30832, 30883, 30267, 15945, 29908, 13, 13, 1678, 22815, 13865, 29903, 29918, 29956, 3235, 29950, 353, 525, 29896, 29900, 29900, 742, 525, 30374, 30880, 234, 168, 139, 233, 135, 194, 29915, 13, 1678, 399, 2190, 8032, 29931, 17321, 29918, 1177, 29963, 20166, 8098, 353, 525, 29906, 29900, 29900, 742, 525, 31190, 236, 172, 190, 234, 168, 139, 233, 135, 194, 29915, 13, 1678, 26871, 17923, 1001, 29918, 22240, 3919, 29918, 29956, 3235, 29950, 353, 525, 29941, 29900, 29896, 742, 525, 31432, 31085, 31704, 30846, 234, 168, 139, 233, 135, 194, 29915, 13, 1678, 399, 29923, 3301, 1164, 29918, 22240, 3919, 29918, 29956, 3235, 29950, 353, 525, 29941, 29900, 29906, 742, 525, 30819, 30943, 31704, 30846, 234, 168, 139, 233, 135, 194, 29915, 13, 13, 13, 1990, 14355, 19560, 29898, 16854, 1125, 13, 1678, 9995, 31943, 30544, 30210, 234, 168, 139, 233, 135, 194, 31410, 31283, 7249, 30333, 30631, 30210, 31168, 30607, 30267, 15945, 29908, 13, 13, 1678, 402, 29956, 29968, 353, 525, 29954, 29956, 29968, 29915, 13, 1678, 3740, 29954, 29943, 353, 525, 3120, 29954, 29943, 29915, 13, 13, 13, 4741, 6227, 4214, 29903, 353, 426, 13, 1678, 399, 728, 1542, 29889, 29933, 17958, 13865, 29903, 29918, 29956, 3235, 29950, 29901, 29871, 29929, 29900, 29892, 29871, 396, 29871, 30374, 30880, 234, 168, 139, 233, 135, 194, 13, 1678, 399, 728, 1542, 29889, 29956, 2190, 8032, 29931, 17321, 29918, 1177, 29963, 20166, 8098, 29901, 29871, 29929, 29900, 29892, 29871, 396, 29871, 31190, 236, 172, 190, 234, 168, 139, 233, 135, 194, 13, 1678, 399, 728, 1542, 29889, 11282, 17923, 1001, 29918, 22240, 3919, 29918, 29956, 3235, 29950, 29901, 29871, 29929, 29900, 29892, 29871, 396, 29871, 31432, 31085, 31704, 30846, 234, 168, 139, 233, 135, 194, 13, 1678, 399, 728, 1542, 29889, 8851, 3301, 1164, 29918, 22240, 3919, 29918, 29956, 3235, 29950, 29901, 29871, 29947, 29900, 29892, 29871, 396, 29871, 30819, 30943, 31704, 30846, 234, 168, 139, 233, 135, 194, 13, 1678, 396, 399, 728, 1542, 29889, 11282, 17923, 1001, 29918, 22240, 3919, 29918, 29956, 3235, 29950, 29918, 29906, 29901, 29871, 29929, 29900, 29892, 29871, 396, 29871, 31432, 31085, 31704, 30846, 234, 168, 139, 233, 135, 194, 29899, 29906, 13, 29913, 13, 13, 29937, 5974, 19191, 13, 3904, 6545, 12054, 29918, 15307, 29918, 19094, 1299, 353, 14210, 29979, 19222, 29885, 19222, 29881, 1273, 29950, 16664, 29924, 16664, 29903, 29915, 13, 13, 29937, 13373, 5410, 13, 12972, 29918, 1254, 19297, 29918, 27681, 10490, 29918, 3210, 24336, 353, 12865, 29898, 29906, 29900, 29906, 29900, 29892, 29871, 29896, 29906, 29892, 29871, 29941, 29896, 29897, 13, 12972, 29918, 16358, 29918, 25826, 29918, 29906, 29918, 29941, 353, 12865, 29898, 29906, 29900, 29906, 29896, 29892, 29871, 29896, 29896, 29892, 29871, 29906, 29946, 29892, 29871, 29955, 29892, 29871, 29900, 29892, 29871, 29900, 29897, 13, 2 ]
Python/Code/Python3-Base/11_Advance/Generator.py	hiloWang/notes	2	40781	<filename>Python/Code/Python3-Base/11_Advance/Generator.py ######################### # 生成器 ######################### # 创建一个生成器 L = (x * 2 for x in range(5)) print(type(L)) print(L) for value in L: print(value, end=' ') print() # 创建一个函数生成器 def fib(times): n = 0 a, b = 0, 1 while n < times: """ 在循环过程中不断调用 yield ，就会不断中断。当然要给循环设置一个条件来退出循环，不然就会产生一个无限数列出来。同样的，把函数改成generator后，我们基本上从来不会用 next() 来获取下一个返回值，而是直接使用 for 循环来迭代 """ yield b a, b = b, a + b n += 1 return 'done' for value in fib(10): print(value, end=' ') print() # 如果想要拿到返回值，必须捕获StopIteration错误，返回值包含在StopIteration的value中 try: fibSqu = fib(10) while True: print(next(fibSqu), end=' ') except StopIteration as e: print("生成器返回值：%s" % e.value, end='') print() # 使用send def gen(): i = 0 while i < 5: # temp为外部调用send函数时发送的值 temp = yield i print('gen ', temp, end=' ') i += 1 f = gen() print(f.__next__()) # 必须先调用 __next__或 send(None) print('send ', f.send('a')) print('send ', f.send('b')) print('send ', f.send('c'))	[ 1, 529, 9507, 29958, 11980, 29914, 3399, 29914, 11980, 29941, 29899, 5160, 29914, 29896, 29896, 29918, 3253, 29894, 749, 29914, 21575, 29889, 2272, 13, 13383, 7346, 29937, 13, 29937, 259, 30486, 30494, 30943, 13, 13383, 7346, 29937, 13, 13, 29937, 29871, 31441, 30886, 30287, 30502, 30486, 30494, 30943, 13, 29931, 353, 313, 29916, 334, 29871, 29906, 363, 921, 297, 3464, 29898, 29945, 876, 13, 2158, 29898, 1853, 29898, 29931, 876, 13, 2158, 29898, 29931, 29897, 13, 13, 1454, 995, 297, 365, 29901, 13, 1678, 1596, 29898, 1767, 29892, 1095, 2433, 25710, 13, 2158, 580, 13, 13, 13, 29937, 29871, 31441, 30886, 30287, 30502, 31629, 30354, 30486, 30494, 30943, 13, 13, 1753, 18755, 29898, 3706, 1125, 13, 1678, 302, 353, 29871, 29900, 13, 1678, 263, 29892, 289, 353, 29871, 29900, 29892, 29871, 29896, 13, 1678, 1550, 302, 529, 3064, 29901, 13, 4706, 9995, 13, 308, 30505, 232, 193, 173, 234, 145, 178, 31138, 31101, 30275, 30413, 31683, 31268, 30406, 7709, 29871, 30214, 31238, 30437, 30413, 31683, 30275, 31683, 30267, 30948, 31516, 30698, 31999, 232, 193, 173, 234, 145, 178, 30872, 30669, 30287, 30502, 31217, 30631, 30805, 236, 131, 131, 30544, 232, 193, 173, 234, 145, 178, 30214, 30413, 31516, 31238, 30437, 231, 189, 170, 30486, 30287, 30502, 31352, 31175, 30354, 31025, 30544, 30805, 30267, 13, 308, 30980, 31819, 30210, 30214, 233, 141, 141, 31629, 30354, 31264, 30494, 27959, 30822, 30214, 30672, 31381, 31359, 30346, 30429, 31594, 30805, 30413, 30437, 30406, 2446, 580, 29871, 30805, 31024, 30683, 30557, 30287, 30502, 31086, 30742, 30959, 30214, 31325, 30392, 31157, 31092, 30785, 30406, 363, 29871, 232, 193, 173, 234, 145, 178, 30805, 235, 194, 176, 30690, 13, 4706, 9995, 13, 4706, 7709, 289, 13, 4706, 263, 29892, 289, 353, 289, 29892, 263, 718, 289, 13, 4706, 302, 4619, 29871, 29896, 13, 1678, 736, 525, 15091, 29915, 13, 13, 13, 1454, 995, 297, 18755, 29898, 29896, 29900, 1125, 13, 1678, 1596, 29898, 1767, 29892, 1095, 2433, 25710, 13, 2158, 580, 13, 13, 29937, 29871, 30847, 30801, 31522, 30698, 233, 142, 194, 30780, 31086, 30742, 30959, 30214, 31641, 236, 164, 190, 233, 144, 152, 31024, 16329, 13463, 362, 31745, 235, 178, 178, 30214, 31086, 30742, 30959, 31473, 232, 147, 174, 30505, 16329, 13463, 362, 30210, 1767, 30275, 13, 2202, 29901, 13, 1678, 18755, 29903, 339, 353, 18755, 29898, 29896, 29900, 29897, 13, 1678, 1550, 5852, 29901, 13, 4706, 1596, 29898, 4622, 29898, 29888, 747, 29903, 339, 511, 1095, 2433, 25710, 13, 19499, 22303, 13463, 362, 408, 321, 29901, 13, 1678, 1596, 703, 30486, 30494, 30943, 31086, 30742, 30959, 30383, 29995, 29879, 29908, 1273, 321, 29889, 1767, 29892, 1095, 2433, 1495, 13, 2158, 580, 13, 13, 13, 29937, 29871, 30785, 30406, 6717, 13, 13, 1753, 2531, 7295, 13, 1678, 474, 353, 29871, 29900, 13, 1678, 1550, 474, 529, 29871, 29945, 29901, 13, 4706, 396, 5694, 30573, 31066, 30636, 31268, 30406, 6717, 31629, 30354, 30594, 30910, 31545, 30210, 30959, 13, 4706, 5694, 353, 7709, 474, 13, 4706, 1596, 877, 1885, 13420, 5694, 29892, 1095, 2433, 25710, 13, 4706, 474, 4619, 29871, 29896, 13, 13, 13, 29888, 353, 2531, 580, 13, 2158, 29898, 29888, 17255, 4622, 1649, 3101, 396, 29871, 31641, 236, 164, 190, 31244, 31268, 30406, 4770, 4622, 1649, 31391, 3638, 29898, 8516, 29897, 13, 2158, 877, 6717, 13420, 285, 29889, 6717, 877, 29874, 8785, 13, 2158, 877, 6717, 13420, 285, 29889, 6717, 877, 29890, 8785, 13, 2158, 877, 6717, 13420, 285, 29889, 6717, 877, 29883, 8785, 13, 2 ]
Language Skills/Python/Unit 11/2-Classes/Using Classes/7-Creating class methods.py	vpstudios/codecademy-exercise-answers	1	197192	<reponame>vpstudios/codecademy-exercise-answers<gh_stars>1-10 class Car(object): condition = "new" def __init__(self, model, color, mpg): self.model = model self.color = color self.mpg = mpg def display_car(self): return "This is a %s %s with %s MPG." % (self.color, self.model, str(self.mpg)) my_car = Car("DeLorean","silver", 88) print my_car.display_car()	[ 1, 529, 276, 1112, 420, 29958, 29894, 29886, 18082, 2363, 29914, 401, 9567, 29899, 735, 6269, 895, 29899, 550, 17538, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 1990, 1704, 29898, 3318, 1125, 13, 1678, 4195, 353, 376, 1482, 29908, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 1904, 29892, 2927, 29892, 286, 4061, 1125, 13, 4706, 1583, 29889, 4299, 353, 1904, 13, 4706, 1583, 29889, 2780, 353, 2927, 13, 4706, 1583, 29889, 1526, 29887, 259, 353, 286, 4061, 13, 13, 1678, 822, 2479, 29918, 4287, 29898, 1311, 1125, 13, 4706, 736, 376, 4013, 338, 263, 1273, 29879, 1273, 29879, 411, 1273, 29879, 16379, 29954, 1213, 1273, 313, 1311, 29889, 2780, 29892, 1583, 29889, 4299, 29892, 851, 29898, 1311, 29889, 1526, 29887, 876, 13, 13, 1357, 29918, 4287, 353, 1704, 703, 2772, 29931, 487, 273, 3284, 25590, 369, 613, 29871, 29947, 29947, 29897, 13, 2158, 590, 29918, 4287, 29889, 4990, 29918, 4287, 580, 13, 2 ]
training/pretrainav/weightsampling.py	rub-ksv/-lrs_avsr1_local-	1	170559	<reponame>rub-ksv/-lrs_avsr1_local- import torch from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding class Conv2dSubsampling(torch.nn.Module): """Convolutional 2D subsampling (to 1/4 length) :param int idim: input dim :param int odim: output dim :param flaot dropout_rate: dropout rate """ def __init__(self, idim, odim, dropout_rate): super(Conv2dSubsampling, self).__init__() self.out = torch.nn.Sequential(torch.nn.Linear(idim, odim)) def forward(self, x, x_mask): """Subsample x :param torch.Tensor x: input tensor :param torch.Tensor x_mask: input mask :return: subsampled x and mask :rtype Tuple[torch.Tensor, torch.Tensor] """ x = self.out(x) if x_mask is None: return x, None return x, x_mask	[ 1, 529, 276, 1112, 420, 29958, 29878, 431, 29899, 2039, 29894, 24028, 29880, 2288, 29918, 485, 21935, 29896, 29918, 2997, 29899, 13, 5215, 4842, 305, 13, 13, 3166, 5152, 1212, 29889, 1212, 29879, 29889, 2272, 7345, 305, 29918, 27852, 29889, 9067, 261, 29889, 17987, 8497, 1053, 10321, 3245, 14934, 13, 13, 13, 1990, 1281, 29894, 29906, 29881, 4035, 13445, 10335, 29898, 7345, 305, 29889, 15755, 29889, 7355, 1125, 13, 1678, 9995, 1168, 4068, 284, 29871, 29906, 29928, 11684, 314, 10335, 313, 517, 29871, 29896, 29914, 29946, 3309, 29897, 13, 13, 1678, 584, 3207, 938, 1178, 326, 29901, 1881, 3964, 13, 1678, 584, 3207, 938, 2413, 326, 29901, 1962, 3964, 13, 1678, 584, 3207, 17422, 327, 5768, 449, 29918, 10492, 29901, 5768, 449, 6554, 13, 1678, 9995, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 1178, 326, 29892, 2413, 326, 29892, 5768, 449, 29918, 10492, 1125, 13, 4706, 2428, 29898, 1168, 29894, 29906, 29881, 4035, 13445, 10335, 29892, 1583, 467, 1649, 2344, 1649, 580, 13, 4706, 1583, 29889, 449, 353, 4842, 305, 29889, 15755, 29889, 16941, 2556, 29898, 7345, 305, 29889, 15755, 29889, 12697, 29898, 333, 326, 29892, 2413, 326, 876, 13, 13, 1678, 822, 6375, 29898, 1311, 29892, 921, 29892, 921, 29918, 13168, 1125, 13, 4706, 9995, 4035, 11249, 921, 13, 13, 4706, 584, 3207, 4842, 305, 29889, 29911, 6073, 921, 29901, 1881, 12489, 13, 4706, 584, 3207, 4842, 305, 29889, 29911, 6073, 921, 29918, 13168, 29901, 1881, 11105, 13, 4706, 584, 2457, 29901, 1014, 11249, 29881, 921, 322, 11105, 13, 4706, 584, 29878, 1853, 12603, 552, 29961, 7345, 305, 29889, 29911, 6073, 29892, 4842, 305, 29889, 29911, 6073, 29962, 13, 4706, 9995, 13, 13, 4706, 921, 353, 1583, 29889, 449, 29898, 29916, 29897, 13, 4706, 565, 921, 29918, 13168, 338, 6213, 29901, 13, 9651, 736, 921, 29892, 6213, 13, 4706, 736, 921, 29892, 921, 29918, 13168, 13, 2 ]
csbiginteger/BigIntegerNet.py	NeoResearch/csBigInteger.py	2	30829	<gh_stars>1-10 #!/usr/bin/python3 from csbiginteger.BigInteger import BigInteger from functools import total_ordering # requires: pip install msl-loadlib pycparser pythonnet from msl.loadlib import LoadLibrary # remember to execute first: cd csbiginteger/dotnet && dotnet build -c Release net = LoadLibrary('csbiginteger/dotnet/bin/Release/netstandard2.0/publish/csbiginteger.dll', 'net') biglib = net.lib.csbiglib.BigIntegerLib() z = biglib.zero() print(bytearray(z.ToByteArray())) m1 = biglib.from_int32(-1) print(bytearray(m1.ToByteArray())) i255 = biglib.from_int32(255) print(bytearray(i255.ToByteArray())) b2 = biglib.from_bytes(bytearray(i255.ToByteArray())) print(biglib.to_int32(b2)) b3 = biglib.from_string("0xff", 16) print(biglib.to_int32(b3)) print(biglib.to_string(b3, 16)) print(biglib.to_string(b3, 10)) print(bytearray(biglib.to_bytes(b3))) @total_ordering class BigIntegerNet(BigInteger): # param may be: int, bytearray, bytes, string (parsed with base) # bytes and bytearray should be received in little-endian format (same as to_bytearray() returns) def __init__(self, param=0, base=10): if type(param) is int: param = str(param) # convert to base-10 integer base = 10 # force base 10 if type(param) is bytearray: param = bytes(param) # bytearray to bytes if type(param) is bytes: self._big = biglib.from_bytes(bytearray(param)) if type(param) is str: self._big = biglib.from_string(param, base) # returns value in signed int32 limit (or exception) def to_int(self): return biglib.to_int32(self._big) def to_long(self): return biglib.to_int64(self._big) # bytearray is returned in little-endian format def to_bytearray(self): return bytearray(biglib.to_bytes(self._big)) def to_str(self, base=16): return str(biglib.to_string(self._big, base)) def add(self, other): if type(other) is int: other = BigIntegerNet(other) big3 = BigIntegerNet() big3._big = self._big.Add(self._big, other._big) return big3 def sub(self, other): if type(other) is int: other = BigIntegerNet(other) big3 = BigIntegerNet() big3._big = self._big.Subtract(self._big, other._big) return big3 def mul(self, other): if type(other) is int: other = BigIntegerNet(other) big3 = BigIntegerNet() big3._big = self._big.Multiply(self._big, other._big) return big3 def div(self, other): if type(other) is int: other = BigIntegerNet(other) big3 = BigIntegerNet() big3._big = self._big.Divide(self._big, other._big) return big3 def mod(self, other): if type(other) is int: other = BigIntegerNet(other) big3 = BigIntegerNet() big3._big = self._big.DivRem(self._big, other._big) return big3 def shl(self, other): if type(other) is int: other = BigIntegerNet(other) big3 = BigIntegerNet() big3._big = self._big.op_LeftShift(other._big) return big3 def shr(self, other): if type(other) is int: other = BigIntegerNet(other) big3 = BigIntegerNet() big3._big = self._big.op_RightShift(other._big) return big3 def eq(self, other): if type(other) is int: other = BigIntegerNet(other) return self._big.op_Equality(other._big) def lt(self, other): if type(other) is int: other = BigIntegerNet(other) return self._big.op_LessThan(other._big) def __repr__(self): return str(self) def __str__(self): return self.to_str(10) def __len__(self): return len(self.to_bytearray()) # --------- # operators # --------- def __add__(self, other): return self.add(other) def __sub__(self, other): return self.sub(other) def __mul__(self, other): return self.mul(other) # note that python usually follows 'pure floor' operation here, on a // b => floor(a/b) # example: -5 // 2 => -3 (standard int on python) # however, this library follows hardware-standard (from c/c++/java/fortran), of truncating positive or negative # so, here: BigInteger(-5) // BigInteger(2) => -2 ("rounding" up, not down) # floordiv is thus not a good name, since it's only floor for positive division, but ceil for negative, but that's what we have :) def __floordiv__(self, other): return 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drs2.py	gling07/Text2DRS	3	1608128	<filename>drs2.py # MIT License # # Copyright (c) [2018] [<NAME> (<EMAIL>), # <NAME> (<EMAIL>)] # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import verbnetsrl import pprint drs_dict = dict() verb_pos = ['VBD', 'VB', 'VBN', 'VBG', 'VBP'] noun_lst = ['NNP', 'NN', 'PRP', 'NNS'] def drs_generator(data_dct_lst, coref_dictionary): omit_list = get_omit_entities(coref_dictionary) entities = get_all_entities(data_dct_lst, omit_list) entities_map = mapping_entity(entities) property = retrieve_property(entities_map) (events_map, event_property) = retrieve_event(data_dct_lst) event_type = retrieve_event_type(data_dct_lst) event_time = retrieve_event_time(events_map) event_argument = retrieve_event_argument(data_dct_lst, property, event_type, event_property) drs_dict['entity'] = [k for k in entities_map.keys()] drs_dict['property'] = property drs_dict['event'] = [k for k in events_map.keys()] drs_dict['eventType'] = event_type drs_dict['eventTime'] = event_time drs_dict['eventArgument'] = event_argument return drs_dict def get_omit_entities(coref_dictionary): special = "'"+'s' omit_list = list() for key, value in coref_dictionary.items(): if ' ' in key and special not in key: entity = key.split(' ')[-1] for v in value[1:]: omit_list.append((entity, v)) else: for v in value[1:]: omit_list.append((key, v)) return omit_list def get_all_entities(data_dct_lst, omit_list): entities = list() num = 0 for sentences in data_dct_lst: num += 1 for sen in sentences: if sen.get('PPOS') in noun_lst: tmp = (sen.get('Form'), num) if tmp not in omit_list: entities.append(sen.get('Form')) return entities def mapping_entity(entities): entities_dictionary = dict() count = 1 for entity in entities: entities_dictionary['r'+ str(count)] = entity count += 1 return entities_dictionary def retrieve_property(entities_map): properties = list() for key, entity in entities_map.items(): temp = (key, entity) properties.append(temp) return properties def retrieve_event(data_dct_lst): events_dictionary = dict() events_property = dict() count = 1 sentence_id = 1 for sentences in data_dct_lst: verb = list() for sen in sentences: if sen.get('Pred') != '_' and sen.get('PPOS') in verb_pos: events_dictionary['e' + str(count)] = sen.get('PLemma') verb.append((sen.get('PLemma'), 'e' + str(count))) count += 1 events_property[sentence_id] = verb sentence_id += 1 return (events_dictionary, events_property) # include picking first vn-class if multiple returns def retrieve_event_type(data_dct_lst): event_type_dictionary = dict() count = 1 for sentence in data_dct_lst: for item in sentence: if item.get('PPOS') in verb_pos: pred = item.get('Pred') if item.get(pred + ':vb-class') is not None: event_type_dictionary['e' + str(count)] = item.get(pred + ':vb-class')[0] count += 1 event_type_list = [(k, v) for k, v in event_type_dictionary.items()] return event_type_list def retrieve_event_time(events_map): event_time_dictionary = dict() count = 0 for event, value in events_map.items(): event_time_dictionary[event] = count count += 1 event_time_list = [(k, v) for k, v in event_time_dictionary.items()] return event_time_list def retrieve_event_argument(data_dct_lst, property, event_type, event_property): event_argument_list = list() sentence_id = 1 for sentence in data_dct_lst: predicates = verbnetsrl.get_predicates(sentence) events = event_type[0:len(predicates)] event_type = event_type[len(predicates):] for (pred, event) in zip(predicates, events): event_ref = event[0] for sent in sentence: if sent.get('Args:' + pred) != '_': # use first verb class as vn class vn_role = sent.get(pred + ':vn-class')[0][1] if sent.get('PPOS') in noun_lst: for (ref, ent) in property: if ent == sent.get('Form'): event_argument_list.append((event_ref, vn_role, ref)) break elif sent.get('PPOS') in verb_pos: verb_property = event_property.get(sentence_id) for (plemma, eref) in 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hrl_dynamic_mpc/src/opt_traj_tools.py	gt-ros-pkg/hrl-haptic-manip	1	75566	# # # Copyright (c) 2013, Georgia Tech Research Corporation # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # * Neither the name of the Georgia Tech Research Corporation nor the # names of its contributors may be used to endorse or promote products # derived from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY GEORGIA TECH RESEARCH CORPORATION ''AS IS'' AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL GEORGIA TECH BE LIABLE FOR ANY DIRECT, INDIRECT, # INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, # OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF # LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE # OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF # ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # # \authors: <NAME> (Healthcare Robotics Lab, Georgia Tech.) # \adviser: <NAME> (Healthcare Robotics Lab, Georgia Tech.) import itertools import matplotlib.pyplot as pp import roslib; roslib.load_manifest('hrl_dynamic_mpc') import rospy import hrl_lib.util as ut import hrl_lib.matplotlib_util as mpu from matplotlib import pyplot as pl from numpy import matrix, cos, arange, zeros, ones, asarray, zeros, mat, array, transpose, vstack, min, max from numpy import dot, radians, degrees, size, shape, cov, log, linalg, random as rd import numpy as np import random import math import sys import time ###################################################################################### ## Joint Limits ## # create joint limit dicts # if arm == 'r': # max_lim = np.radians([ 120.00, 122.15, 77.5, 144., 122., 45., 45.]) # min_lim = np.radians([ -47.61, -20., -77.5, 0., -80., -45., -45.]) # else: # max_lim = np.radians([ 120.00, 20., 77.5, 144., 80., 45., 45.]) # min_lim = np.radians([ -47.61, -122.15, -77.5, 0., -122., -45., -45.]) ## Velocity Limits ## # 5 deg/s to 15 deg/s # Optimization Criteria # d-optimality criterion : -log(det(M)) : M = covariance matrix of regressor matrix ##################################################################################### def get_init_condition(vl, vh, tf, wf, N): qdot_list = [] t = arange(0,tf,0.1) M = size(t) A = zeros((M,2N+1)) for i in range(M): qdot_list.append(radians(random.randrange(vl,vh))) for i in range(M): j = 0 k = 1 while (j < 2N): A[i][j] = (math.sin(wft[i]k))/(wfk) A[i][j+1] = (math.cos(wft[i]k))/(wfk) k=k+1 j=j+2 A[i][j] = 1 ans = dot((array(((matrix(A).T)matrix(A)).I)(matrix(A).T)),array(qdot_list)) return ans.tolist() def get_ref_traj_at_time(x, time, num_dof, N): wf = 0.12math.pi q = [] qd = [] qdd = [] for j in xrange(num_dof): q_buff = x[(j+1)(2N)] qd_buff = 0. qdd_buff = 0. for k in xrange(N): t = time a = x[j(2N+1)+2k] b = x[j(2N+1)+2k+1] q_buff = q_buff + a(math.sin(wft(k+1)))/(wf(k+1)) - b(math.cos(wft(k+1)))/(wf(k+1)) qd_buff = qd_buff + a(math.cos(wft(k+1))) - b(math.sin(wft(k+1))) qdd_buff = qdd_buff + a(wf(k+1))(math.sin(wft(k+1))) + b(wf(k+1))(math.cos(wft(k+1))) q.append(q_buff) qd.append(qd_buff) qdd.append(qdd_buff) return q, qd, qdd def get_ref_traj(x, t_total, rate, num_dof, N): wf = 0.12math.pi num_samples = int(t_total/float(rate)) q = [] qd = [] qdd = [] # print "x is :", x # print "num_samples is :", num_samples for i in xrange(num_samples): q_cur = [] qd_cur = [] qdd_cur = [] for j in xrange(num_dof): q_buff = x[(j+1)(2N)] # print "j is :", j # print "x[(j+1)(2N)]", x[(j+1)(2N)] qd_buff = 0. qdd_buff = 0. for k in xrange(N): t = irate a = x[j(2N+1)+2k] b = x[j(2N+1)+2k+1] if False: print "t is :", t print "a is :", a print "b is :", b print "wf is :", wf print "k is :", k print "(wft(k+1))", (wft(k+1)) q_buff = q_buff + a(math.sin(wft(k+1)))/(wf(k+1)) - b(math.cos(wft(k+1)))/(wf(k+1)) qd_buff = qd_buff + a(math.cos(wft(k+1))) - b(math.sin(wft(k+1))) qdd_buff = qdd_buff + a(wf(k+1))(math.sin(wft(k+1))) + b(wf(k+1))(math.cos(wft(k+1))) #raw_input() q_cur.append(q_buff) qd_cur.append(qd_buff) qdd_cur.append(qdd_buff) q.append(q_cur) qd.append(qd_cur) qdd.append(qdd_cur) # pp.figure() # pp.plot(q) # pp.show() #time.sleep(5) return q, qd, qdd # def get_ref_traj(init_delta, t, num_dof): # wf = 0.1 # N = 5 # numJoints = num_dof # q_list = [] # qdot_list = [] # qddot_list = [] # rows = numJoints # columns = 2N + 1 # temp_delta = zeros((rows, columns)) # for row in range(rows): # for column in range(columns): # temp_delta[row][column] = init_delta[rowcolumns+column] # for row in range(rows): # delta = temp_delta[row].tolist() # q_element = 0.0 # qdot_element = 0.0 # qddot_element = 0.0 # j = 0 # k = 1 # while (j < 2N): # q_element = q_element + delta[j](math.sin(wftk))/(wfk) - delta[j+1](math.cos(wftk))/(wfk) # qdot_element = qdot_element + delta[j](math.cos(wftk)) + delta[j+1](math.sin(wftk)) # qdfdot_element = qddot_element - delta[j](wfk)(math.sin(wftk)) + delta[j+1](wfk)(math.cos(wftk)) # k=k+1 # j=j+2 # q_list.append(q_element + delta[2*N]) # qdot_list.append(qdot_element) # qddot_list.append(qddot_element) # return q_list, qdot_list, qddot_list if __name__ == '__main__': print "hello. world."	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General/serverInfo.py	Aggis15/T4NK0R	0	195310	import logging import discord from discord.ext import commands from discord.commands import slash_command import json import requests from PIL import Image, ImageDraw, ImageFont from resizeimage import resizeimage # Logging logging.basicConfig( filename="./logs/discordlogs.log", filemode="w", format="%(name)s - %(levelname)s - %(message)s", ) logger = logging.getLogger() logger.setLevel(logging.INFO) # Initiate json file = open("config.json") data = json.load(file) # Public variables guildID = data["guildID"][0] class serverInfo(commands.Cog): def __init__(self, bot): self.bot = bot @slash_command( guild_ids=[guildID], description="A command to check the server information!" ) async def server(self, ctx): guild = ctx.guild serverLevel = guild.premium_tier serverBoosts = guild.premium_subscription_count serverMembers = len(guild.members) serverTextChannels = len(guild.text_channels) serverVoiceChannels = len(guild.voice_channels) serverEmojis = len(guild.emojis) serverRoles = len(guild.roles) # Download the avatar getGuildAvatar = requests.get(guild.icon.url) with open(f"./Images/avatarCache/{guild.id}.png", "wb") as outfile: outfile.write(getGuildAvatar.content) guildAvatarImage = Image.open(f"./Images/avatarCache/{guild.id}.png") # Crop the avatar to make it a circle width, height = guildAvatarImage.size x = width - height img_cropped = guildAvatarImage.crop((x, 0, x + height, height)) mask = Image.new("L", img_cropped.size) mask_draw = ImageDraw.Draw(mask) width, height = img_cropped.size mask_draw.ellipse((0, 0, width, height), fill=255) img_cropped.putalpha(mask) img_cropped.save(f"./Images/avatarCache/{guild.id}.png") # Resize the avatar to fit the image resizeAvatar = Image.open(f"./Images/avatarCache/{guild.id}.png") resizeAvatar = resizeimage.resize_width(resizeAvatar, 100) resizeAvatar.save(f"./Images/avatarCache/{guild.id}.png", resizeAvatar.format) defaultImage = Image.open("./Images/serverInfo.png") draw = ImageDraw.Draw(defaultImage) font = ImageFont.truetype("Bungee-Regular.ttf", 36) draw.text((438, 38), str(serverLevel), (157, 156, 157), font=font) draw.text((332, 85), str(serverBoosts), (157, 156, 157), font=font) draw.text((246, 148), str(serverMembers), (157, 156, 157), font=font) draw.text((370, 199), str(serverTextChannels), (157, 156, 157), font=font) draw.text((394, 243), str(serverVoiceChannels), (157, 156, 157), font=font) draw.text((388, 288), str(serverEmojis), (157, 156, 157), font=font) draw.text((357, 334), str(serverRoles), (157, 156, 157), font=font) avatarImage = Image.open(f"./Images/avatarCache/{guild.id}.png") defaultImage.paste(avatarImage, (40, 40), avatarImage) defaultImage.save("./Images/serverImageReady.png") await ctx.respond(file=discord.File("./Images/serverImageReady.png")) logger.info( f"{ctx.author.name} with ID: {ctx.author.id} has used the info slash command" ) @server.error async def server_error(self, ctx, 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tests/test_mangling.py	ecoinvent/brightway2-parameters	0	23145	from bw2parameters import * def test_mangle_formula(): given = "log(foo * bar) + 7 / baz" prefix = "pre" assert mangle_formula(given, prefix, ['bar']) == '(log((pre__foo * bar)) + (7 / pre__baz))' def test_prefix_parameter_dict(): given = { 'a': {'formula': 'a + b / c', 'foo': True}, 'b': {'formula': '2 * a - exp(7 - b)'}, 'catch': {} } expected = { 't_a': {'formula': '(t_a + (t_b / c))', 'foo': True, 'original': 'a'}, 't_b': {'formula': '((2 * t_a) - exp((7 - t_b)))', 'original': 'b'}, 't_catch': {'original': 'catch'} } substitutions = {'a': 't_a', 'b': 't_b', 'catch': 't_catch'} assert prefix_parameter_dict(given, "t_") == (expected, substitutions) def test_chain_prefix_parameter_dict(): given = {'a': {'formula': 'a + b / c'}} g_copy = {'a': {'formula': 'a + b / c'}} expected = { 't_a': {'formula': '(t_a + (b / c))', 'original': 'a'}, } substitutions = {'a': 't_a'} assert prefix_parameter_dict(given, "t_") == (expected, substitutions) assert given == g_copy given, _ = prefix_parameter_dict(given, "t_") s1 = {'b': 'dog'} r1 = substitute_in_formulas(given, s1) expected = {'t_a': {'formula': '(t_a + (dog / c))', 'original': 'a'}} assert r1 == expected s2 = {'c': 'cat'} r2 = substitute_in_formulas(r1, s2) expected = {'t_a': {'formula': '(t_a + (dog / cat))', 'original': 'a'}} assert r2 == expected	[ 1, 515, 289, 29893, 29906, 16744, 1053, 334, 13, 13, 13, 1753, 1243, 29918, 29885, 2521, 29918, 689, 2497, 7295, 13, 1678, 2183, 353, 376, 1188, 29898, 5431, 334, 2594, 29897, 718, 29871, 29955, 847, 12741, 29908, 13, 1678, 10944, 353, 376, 1457, 29908, 13, 1678, 4974, 286, 2521, 29918, 689, 2497, 29898, 29887, 5428, 29892, 10944, 29892, 6024, 1646, 11287, 1275, 525, 29898, 1188, 3552, 1457, 1649, 5431, 334, 2594, 876, 718, 313, 29955, 847, 758, 1649, 27975, 876, 29915, 13, 13, 1753, 1243, 29918, 13506, 29918, 15501, 29918, 8977, 7295, 13, 1678, 2183, 353, 426, 13, 4706, 525, 29874, 2396, 11117, 689, 2497, 2396, 525, 29874, 718, 289, 847, 274, 742, 525, 5431, 2396, 5852, 1118, 13, 4706, 525, 29890, 2396, 11117, 689, 2497, 2396, 525, 29906, 334, 263, 448, 1518, 29898, 29955, 448, 289, 16029, 1118, 13, 4706, 525, 12510, 2396, 6571, 13, 1678, 500, 13, 1678, 3806, 353, 426, 13, 4706, 525, 29873, 29918, 29874, 2396, 11117, 689, 2497, 2396, 525, 29898, 29873, 29918, 29874, 718, 313, 29873, 29918, 29890, 847, 274, 876, 742, 525, 5431, 2396, 5852, 29892, 525, 13492, 2396, 525, 29874, 16675, 13, 4706, 525, 29873, 29918, 29890, 2396, 11117, 689, 2497, 2396, 525, 3552, 29906, 334, 260, 29918, 29874, 29897, 448, 1518, 3552, 29955, 448, 260, 29918, 29890, 4961, 742, 525, 13492, 2396, 525, 29890, 16675, 13, 4706, 525, 29873, 29918, 12510, 2396, 11117, 13492, 2396, 525, 12510, 10827, 13, 1678, 500, 13, 1678, 23697, 29879, 353, 11117, 29874, 2396, 525, 29873, 29918, 29874, 742, 525, 29890, 2396, 525, 29873, 29918, 29890, 742, 525, 12510, 2396, 525, 29873, 29918, 12510, 10827, 13, 1678, 4974, 10944, 29918, 15501, 29918, 8977, 29898, 29887, 5428, 29892, 376, 29873, 29918, 1159, 1275, 313, 9684, 29892, 23697, 29879, 29897, 13, 13, 1753, 1243, 29918, 14153, 29918, 13506, 29918, 15501, 29918, 8977, 7295, 13, 1678, 2183, 353, 11117, 29874, 2396, 11117, 689, 2497, 2396, 525, 29874, 718, 289, 847, 274, 29915, 930, 13, 1678, 330, 29918, 8552, 353, 11117, 29874, 2396, 11117, 689, 2497, 2396, 525, 29874, 718, 289, 847, 274, 29915, 930, 13, 1678, 3806, 353, 426, 13, 4706, 525, 29873, 29918, 29874, 2396, 11117, 689, 2497, 2396, 525, 29898, 29873, 29918, 29874, 718, 313, 29890, 847, 274, 876, 742, 525, 13492, 2396, 525, 29874, 16675, 13, 1678, 500, 13, 1678, 23697, 29879, 353, 11117, 29874, 2396, 525, 29873, 29918, 29874, 10827, 13, 1678, 4974, 10944, 29918, 15501, 29918, 8977, 29898, 29887, 5428, 29892, 376, 29873, 29918, 1159, 1275, 313, 9684, 29892, 23697, 29879, 29897, 13, 1678, 4974, 2183, 1275, 330, 29918, 8552, 13, 1678, 2183, 29892, 903, 353, 10944, 29918, 15501, 29918, 8977, 29898, 29887, 5428, 29892, 376, 29873, 29918, 1159, 13, 1678, 269, 29896, 353, 11117, 29890, 2396, 525, 26169, 10827, 13, 1678, 364, 29896, 353, 23764, 29918, 262, 29918, 689, 15173, 29898, 29887, 5428, 29892, 269, 29896, 29897, 13, 1678, 3806, 353, 11117, 29873, 29918, 29874, 2396, 11117, 689, 2497, 2396, 525, 29898, 29873, 29918, 29874, 718, 313, 26169, 847, 274, 876, 742, 525, 13492, 2396, 525, 29874, 29915, 930, 13, 1678, 4974, 364, 29896, 1275, 3806, 13, 13, 1678, 269, 29906, 353, 11117, 29883, 2396, 525, 4117, 10827, 13, 1678, 364, 29906, 353, 23764, 29918, 262, 29918, 689, 15173, 29898, 29878, 29896, 29892, 269, 29906, 29897, 13, 1678, 3806, 353, 11117, 29873, 29918, 29874, 2396, 11117, 689, 2497, 2396, 525, 29898, 29873, 29918, 29874, 718, 313, 26169, 847, 6635, 876, 742, 525, 13492, 2396, 525, 29874, 29915, 930, 13, 1678, 4974, 364, 29906, 1275, 3806, 13, 2 ]
designer/new_dialog.py	katnino/kivy-designer	0	100068	from functools import partial from os.path import join from designer.helper_functions import get_kd_dir from kivy.adapters.listadapter import ListAdapter from kivy.core.window import Keyboard, Window from kivy.factory import Factory from kivy.properties import NumericProperty, ObjectProperty from kivy.uix.boxlayout import BoxLayout from kivy.uix.image import Image from kivy.uix.listview import ListView NEW_PROJECTS = { 'FloatLayout': ('template_floatlayout_kv', 'template_floatlayout_py'), 'BoxLayout': ('template_boxlayout_kv', 'template_boxlayout_py'), 'ScreenManager': ('template_screen_manager_kv', 'template_screen_manager_py'), 'ActionBar': ('template_actionbar_kv', 'template_actionbar_py'), 'Carousel and ActionBar': ('template_actionbar_carousel_kv', 'template_actionbar_carousel_py'), 'ScreenManager and ActionBar': ('template_screen_manager_actionbar_kv', 'template_screen_manager_actionbar_py'), 'TabbedPanel': ('template_tabbed_panel_kv', 'template_tabbed_panel_py'), 'TextInput and ScrollView': ('template_textinput_scrollview_kv', 'template_textinput_scrollview_py')} NEW_TEMPLATES_DIR = 'new_templates' NEW_TEMPLATE_IMAGE_PATH = join(NEW_TEMPLATES_DIR, 'images') class NewProjectDialog(BoxLayout): listview = ObjectProperty(None) ''':class:`~kivy.uix.listview.ListView` used for showing file paths. :data:`listview` is a :class:`~kivy.properties.ObjectProperty` ''' select_button = ObjectProperty(None) ''':class:`~kivy.uix.button.Button` used to select the list item. :data:`select_button` is a :class:`~kivy.properties.ObjectProperty` ''' cancel_button = ObjectProperty(None) ''':class:`~kivy.uix.button.Button` to cancel the dialog. :data:`cancel_button` is a :class:`~kivy.properties.ObjectProperty` ''' adapter = ObjectProperty(None) ''':class:`~kivy.uix.listview.ListAdapter` used for selecting files. :data:`adapter` is a :class:`~kivy.properties.ObjectProperty` ''' image = ObjectProperty(None) '''Type of :class:`~kivy.uix.image.Image` to display image of selected new template. :data:`image` is a :class:`~kivy.properties.ObjectProperty` ''' list_parent = ObjectProperty(None) '''Parent of listview. :data:`list_parent` is a :class:`~kivy.properties.ObjectProperty` ''' prev_selection = NumericProperty(0) '''to memorize the previous selection. :attr:`prev_selection` is a :class: `~kivy.properties.NumericProperty`, defaults to (0). ''' __events__ = ('on_select', 'on_cancel') def __init__(self, kwargs): super(NewProjectDialog, self).__init__(kwargs) item_strings = list(NEW_PROJECTS.keys()) item_strings.sort() self.adapter = ListAdapter(cls=Factory.DesignerListItemButton, data=item_strings, selection_mode='single', allow_empty_selection=False) self.adapter.check_for_empty_selection = self.check_for_empty_selection self.adapter.bind(on_selection_change=self.on_adapter_selection_change) self.listview = ListView(adapter=self.adapter) self.listview.size_hint = (0.5, 1) self.listview.pos_hint = {'top': 1} self.list_parent.add_widget(self.listview, 1) self.on_adapter_selection_change(self.adapter) def on_parent(self, args): if self.parent: Window.bind(on_key_down=self._on_keyboard_down) else: Window.unbind(on_key_down=self._on_keyboard_down) def _on_keyboard_down(self, keyboard, key, codepoint, text, modifier, args): '''To detect which key is pressed ''' if modifier: return False key_str = Keyboard.keycode_to_string(Window._system_keyboard, key) if key_str == 'up': v = self.adapter.get_view(self.prev_selection - 1) if v is not None: self.adapter.handle_selection(v) return True if key_str == 'down': v = self.adapter.get_view(self.prev_selection + 1) if v is not None: self.adapter.handle_selection(v) return True if key_str == 'enter': self.dispatch('on_select') return True def check_for_empty_selection(self, args): if not self.adapter.allow_empty_selection: if len(self.adapter.selection) == 0: # Select the first item if we have it. v = self.adapter.get_view(self.prev_selection) if v is not None: self.adapter.handle_selection(v) def on_adapter_selection_change(self, adapter): '''Event handler for 'on_selection_change' event of adapter. ''' name = adapter.selection[0].text.lower() + '.png' name = name.replace(' and ', '_') image_source = join(NEW_TEMPLATE_IMAGE_PATH, name) _dir = get_kd_dir() image_source = join(_dir, image_source) parent = self.image.parent parent.remove_widget(self.image) self.image = Image(source=image_source) parent.add_widget(self.image) self.prev_selection = adapter.data.index(adapter.selection[0].text) def on_touch_down(self, touch): '''Used to determine where touch is down and to detect double tap. ''' if touch.is_double_tap: self.dispatch('on_select') return super(NewProjectDialog, self).on_touch_down(touch) def on_select(self, args): '''Default Event Handler for 'on_select' event ''' pass def on_cancel(self, args): '''Default Event Handler for 'on_cancel' event ''' pass def on_select_button(self, args): '''Event Handler for 'on_release' of select button. 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Extensions/Dependencies/tools/extract_objects.py	kylefmohr/Pyto	0	1605021	from subprocess import check_output import sys objects = check_output(["ar", "-t", sys.argv[1]]).decode().split("\n") for object in objects: if not object.endswith(".o") or object.endswith("lso.o"): continue check_output(["ar", "-xv", sys.argv[1], object])	[ 1, 515, 1014, 5014, 1053, 1423, 29918, 4905, 13, 5215, 10876, 13, 13, 12650, 353, 1423, 29918, 4905, 29898, 3366, 279, 613, 11663, 29873, 613, 10876, 29889, 19218, 29961, 29896, 5262, 467, 13808, 2141, 5451, 14182, 29876, 1159, 13, 13, 1454, 1203, 297, 3618, 29901, 13, 1678, 565, 451, 1203, 29889, 1975, 2541, 17350, 29877, 1159, 470, 1203, 29889, 1975, 2541, 703, 29880, 578, 29889, 29877, 29908, 1125, 13, 4706, 6773, 13, 268, 13, 1678, 1423, 29918, 4905, 29898, 3366, 279, 613, 29871, 11663, 29916, 29894, 613, 10876, 29889, 19218, 29961, 29896, 1402, 1203, 2314, 13, 13, 2 ]
node-api/get-block-transfers/request.py	Venoox/casper-integrations	5	11051	<reponame>Venoox/casper-integrations import json import os import pycspr # A known casper test-net node address. _NODE_ADDRESS = os.getenv("CASPER_NODE_ADDRESS", "192.168.127.12") # A known block hash. _BLOCK_HASH: bytes = bytes.fromhex("c7148e1e2e115d8fba357e04be2073d721847c982dc70d5c36b5f6d3cf66331c") # A known block height. _BLOCK_HEIGHT: int = 20652 def main(): """Retrieves transfers by block. """ # Set client. client = pycspr.NodeClient(pycspr.NodeConnectionInfo(host=_NODE_ADDRESS)) # Set block by known hash. block_transers_1: tuple = client.queries.get_block_transfers(_BLOCK_HASH) # Set block by known height. block_transers_2: tuple = client.queries.get_block_transfers(_BLOCK_HEIGHT) # Verify block information equivalence. assert block_transers_1 == block_transers_2 print("-----------------------------------------------------------------------------------------------------") print(f"QUERIED TEST-NET NODE {_NODE_ADDRESS}") print("-----------------------------------------------------------------------------------------------------") print(f"Block transfers = {json.dumps(block_transers_1, indent=4)}") print("-----------------------------------------------------------------------------------------------------") if __name__ == "__main__": try: main() except Exception as err: print(f"API ERROR @ NODE {_NODE_ADDRESS} :: {err}")	[ 1, 529, 276, 1112, 420, 29958, 29963, 8154, 2251, 29914, 9398, 546, 29899, 14146, 800, 13, 5215, 4390, 13, 5215, 2897, 13, 13, 5215, 11451, 2395, 558, 13, 13, 13, 13, 29937, 319, 2998, 3209, 546, 1243, 29899, 1212, 2943, 3211, 29889, 13, 29918, 6632, 2287, 29918, 17744, 26785, 353, 2897, 29889, 657, 6272, 703, 29907, 3289, 13171, 29918, 6632, 2287, 29918, 17744, 26785, 613, 376, 29896, 29929, 29906, 29889, 29896, 29953, 29947, 29889, 29896, 29906, 29955, 29889, 29896, 29906, 1159, 13, 13, 29937, 319, 2998, 2908, 6608, 29889, 13, 29918, 29933, 21339, 29918, 29950, 24943, 29901, 6262, 353, 6262, 29889, 3166, 20970, 703, 29883, 29955, 29896, 29946, 29947, 29872, 29896, 29872, 29906, 29872, 29896, 29896, 29945, 29881, 29947, 29888, 2291, 29941, 29945, 29955, 29872, 29900, 29946, 915, 29906, 29900, 29955, 29941, 29881, 29955, 29906, 29896, 29947, 29946, 29955, 29883, 29929, 29947, 29906, 13891, 29955, 29900, 29881, 29945, 29883, 29941, 29953, 29890, 29945, 29888, 29953, 29881, 29941, 6854, 29953, 29953, 29941, 29941, 29896, 29883, 1159, 13, 13, 29937, 319, 2998, 2908, 3171, 29889, 13, 29918, 29933, 21339, 29918, 9606, 22530, 29901, 938, 353, 29871, 29906, 29900, 29953, 29945, 29906, 13, 13, 13, 1753, 1667, 7295, 13, 1678, 9995, 8015, 2546, 1960, 1301, 25534, 491, 2908, 29889, 13, 268, 13, 1678, 9995, 13, 1678, 396, 3789, 3132, 29889, 13, 1678, 3132, 353, 11451, 2395, 558, 29889, 4247, 4032, 29898, 2272, 2395, 558, 29889, 4247, 5350, 3401, 29898, 3069, 29922, 29918, 6632, 2287, 29918, 17744, 26785, 876, 13, 13, 1678, 396, 3789, 2908, 491, 2998, 6608, 29889, 13, 1678, 2908, 29918, 3286, 414, 29918, 29896, 29901, 18761, 353, 3132, 29889, 339, 6358, 29889, 657, 29918, 1271, 29918, 3286, 25534, 7373, 29933, 21339, 29918, 29950, 24943, 29897, 13, 13, 1678, 396, 3789, 2908, 491, 2998, 3171, 29889, 13, 1678, 2908, 29918, 3286, 414, 29918, 29906, 29901, 18761, 353, 3132, 29889, 339, 6358, 29889, 657, 29918, 1271, 29918, 3286, 25534, 7373, 29933, 21339, 29918, 9606, 22530, 29897, 13, 13, 1678, 396, 1798, 1598, 2908, 2472, 24796, 29889, 13, 1678, 4974, 2908, 29918, 3286, 414, 29918, 29896, 1275, 2908, 29918, 3286, 414, 29918, 29906, 13, 268, 13, 1678, 1596, 703, 2683, 2683, 2683, 2683, 2683, 2683, 23648, 1159, 13, 1678, 1596, 29898, 29888, 29908, 13356, 1001, 29902, 3352, 17067, 1254, 29899, 6006, 11698, 2287, 426, 29918, 6632, 2287, 29918, 17744, 26785, 27195, 13, 1678, 1596, 703, 2683, 2683, 2683, 2683, 2683, 2683, 23648, 1159, 13, 1678, 1596, 29898, 29888, 29908, 7445, 1301, 25534, 353, 426, 3126, 29889, 29881, 17204, 29898, 1271, 29918, 3286, 414, 29918, 29896, 29892, 29536, 29922, 29946, 2915, 1159, 13, 1678, 1596, 703, 2683, 2683, 2683, 2683, 2683, 2683, 23648, 1159, 13, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 1018, 29901, 13, 4706, 1667, 580, 13, 1678, 5174, 8960, 408, 4589, 29901, 13, 4706, 1596, 29898, 29888, 29908, 8787, 14431, 732, 11698, 2287, 426, 29918, 6632, 2287, 29918, 17744, 26785, 29913, 4761, 426, 3127, 27195, 13, 2 ]
setup.py	saquib-mehmood/pyproblib	1	186089	<reponame>saquib-mehmood/pyproblib from setuptools import setup setup(name='pyproblib', version='1.1', description='Probability Distribution Functions', packages=['pyproblib'], author='<NAME>', author_email='<EMAIL>', zip_safe=False)	[ 1, 529, 276, 1112, 420, 29958, 4977, 339, 747, 29899, 1004, 7184, 2092, 29914, 2272, 22795, 1982, 13, 13, 3166, 731, 21245, 8789, 1053, 6230, 13, 13, 14669, 29898, 978, 2433, 2272, 22795, 1982, 742, 13, 418, 1873, 2433, 29896, 29889, 29896, 742, 13, 418, 6139, 2433, 1184, 29890, 3097, 17740, 6680, 29879, 742, 13, 418, 9741, 29922, 1839, 2272, 22795, 1982, 7464, 13, 418, 4148, 2433, 29966, 5813, 29958, 742, 13, 418, 4148, 29918, 5269, 2433, 29966, 26862, 6227, 29958, 742, 13, 418, 14319, 29918, 11177, 29922, 8824, 29897, 2 ]
aioinject/__init__.py	ThirVondukr/aioinject	4	176713	from .containers import Container from .context import InjectionContext, SyncInjectionContext from .decorators import inject from .markers import Inject from .providers import Callable, Object, Provider, Singleton __all__ = [ "Container", "InjectionContext", "SyncInjectionContext", "inject", "Inject", "Callable", "Provider", "Singleton", "Object", ]	[ 1, 515, 869, 1285, 475, 414, 1053, 21679, 13, 3166, 869, 4703, 1053, 512, 6929, 2677, 29892, 317, 2720, 797, 6929, 2677, 13, 3166, 869, 19557, 4097, 1053, 11658, 13, 3166, 869, 3502, 414, 1053, 512, 622, 13, 3166, 869, 771, 29454, 1053, 8251, 519, 29892, 4669, 29892, 1019, 5489, 29892, 6106, 11285, 13, 13, 1649, 497, 1649, 353, 518, 13, 1678, 376, 7895, 613, 13, 1678, 376, 797, 6929, 2677, 613, 13, 1678, 376, 21077, 797, 6929, 2677, 613, 13, 1678, 376, 21920, 613, 13, 1678, 376, 28329, 613, 13, 1678, 376, 5594, 519, 613, 13, 1678, 376, 6980, 613, 13, 1678, 376, 10873, 11285, 613, 13, 1678, 376, 2061, 613, 13, 29962, 13, 2 ]
zinc/utils/validation.py	PressLabs/zinc	29	37359	<gh_stars>10-100 import ipaddress def is_ipv6(ip_addr): try: ipaddress.IPv6Address(ip_addr) return True except ipaddress.AddressValueError: return False	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 5215, 10377, 7328, 13, 13, 13, 1753, 338, 29918, 666, 29894, 29953, 29898, 666, 29918, 10030, 1125, 13, 1678, 1018, 29901, 13, 4706, 10377, 7328, 29889, 5690, 29894, 29953, 7061, 29898, 666, 29918, 10030, 29897, 13, 4706, 736, 5852, 13, 1678, 5174, 10377, 7328, 29889, 7061, 1917, 2392, 29901, 13, 4706, 736, 7700, 13, 2 ]
resistics/window.py	resistics/resistics	38	193431	""" Module for calculating window related data. Windows can be indexed relative to two starting indices. - Local window index - Window index relative to the TimeData is called "local_win" - Local window indices always start at 0 - Global window index - The global window index is relative to the project reference time - The 0 index window begins at the reference time - This window indexing is to synchronise data across sites The global window index is considered the default and sometimes referred to as the window. Local windows should be explicitly referred to as local_win in all cases. The window module includes functionality to do the following: - Windowing utility functions to calculate window and overlap sizes - Functions to map windows to samples in TimeData - Converting a global index array to datetime Usually with windowing, there is a window size and windows overlap with each other for a set number of samples. As an illustrative examples, consider a signal sampled at 10 Hz (dt=0.1 seconds) with 24 samples. This will be windowed using a window size of 8 samples per window and a 2 sample overlap. .. plot:: >>> import numpy as np >>> import matplotlib.pyplot as plt >>> fs = 10 >>> n_samples = 24 >>> win_size = 8 >>> olap_size = 2 >>> times = np.arange(0, n_samples) * (1/fs) The first window >>> start_win1 = 0 >>> end_win1 = win_size >>> win1_times = times[start_win1:end_win1] The second window >>> start_win2 = end_win1 - olap_size >>> end_win2 = start_win2 + win_size >>> win2_times = times[start_win2:end_win2] The third window >>> start_win3 = end_win2 - olap_size >>> end_win3 = start_win3 + win_size >>> win3_times = times[start_win3:end_win3] The fourth window >>> start_win4= end_win3 - olap_size >>> end_win4 = start_win4 + win_size >>> win4_times = times[start_win4:end_win4] Let's look at the actual window times for each window >>> win1_times array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]) >>> win2_times array([0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2, 1.3]) >>> win3_times array([1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9]) >>> win4_times array([1.8, 1.9, 2. , 2.1, 2.2, 2.3]) The duration and increments of windows can be calculated using provided methods >>> from resistics.window import win_duration, inc_duration >>> print(win_duration(win_size, fs)) 0:00:00.7 >>> print(inc_duration(win_size, olap_size, fs)) 0:00:00.6 Plot the windows to give an illustration of how it works >>> plt.plot(win1_times, np.ones_like(win1_times), "bo", label="window1") # doctest: +SKIP >>> plt.plot(win2_times, np.ones_like(win2_times)2, "ro", label="window2") # doctest: +SKIP >>> plt.plot(win3_times, np.ones_like(win3_times)3, "go", label="window3") # doctest: +SKIP >>> plt.plot(win4_times, np.ones_like(win4_times)4, "co", label="window4") # doctest: +SKIP >>> plt.xlabel("Time [s]") # doctest: +SKIP >>> plt.legend() # doctest: +SKIP >>> plt.grid() # doctest: +SKIP >>> plt.tight_layout() # doctest: +SKIP >>> plt.show() # doctest: +SKIP """ from loguru import logger from pathlib import Path from typing import Optional, List, Tuple, Dict, Union, Any from pydantic import PositiveInt import numpy as np import pandas as pd from resistics.errors import ProcessRunError from resistics.common import History, ResisticsModel, ResisticsData, ResisticsProcess from resistics.common import ResisticsWriter, Metadata, WriteableMetadata from resistics.sampling import RSDateTime, RSTimeDelta, HighResDateTime from resistics.time import ChanMetadata from resistics.decimate import DecimatedLevelMetadata, DecimatedData def win_duration(win_size: int, fs: float) -> RSTimeDelta: """ Get the window duration Parameters ---------- win_size : int Window size in samples fs : float Sampling frequency Hz Returns ------- RSTimeDelta Duration Examples -------- A few examples with different sampling frequencies and window sizes >>> from resistics.window import win_duration >>> duration = win_duration(512, 512) >>> print(duration) 0:00:00.998046875 >>> duration = win_duration(520, 512) >>> print(duration) 0:00:01.013671875 >>> duration = win_duration(4096, 16_384) >>> print(duration) 0:00:00.24993896484375 >>> duration = win_duration(200, 0.05) >>> print(duration) 1:06:20 """ from resistics.sampling import to_timedelta return to_timedelta(1 / fs) float(win_size - 1) def inc_duration(win_size: int, olap_size: int, fs: float) -> RSTimeDelta: """ Get the increment between window start times If the overlap size = 0, then the time increment between windows is simply the window duration. However, when there is an overlap, the increment between window start times has to be adjusted by the overlap size Parameters ---------- win_size : int The window size in samples olap_size : int The overlap size in samples fs : float The sample frequency Hz Returns ------- RSTimeDelta The duration of the window Examples -------- >>> from resistics.window import inc_duration >>> increment = inc_duration(128, 32, 128) >>> print(increment) 0:00:00.75 >>> increment = inc_duration(1283600, 12860, 128) >>> print(increment) 0:59:00 """ from resistics.sampling import to_timedelta return to_timedelta(1 / fs) * float(win_size - olap_size) def win_to_datetime( ref_time: RSDateTime, global_win: int, increment: RSTimeDelta ) -> RSDateTime: """ Convert reference window index to start time of window Parameters ---------- ref_time : RSDateTime Reference time global_win : int Window index relative to reference time increment : RSTimeDelta The increment duration Returns ------- RSDateTime Start time of window Examples -------- An example with sampling at 1 Hz, a window size of 100 and an overlap size of 25. >>> from resistics.sampling import to_datetime >>> from resistics.window import inc_duration, win_to_datetime >>> ref_time = to_datetime("2021-01-01 00:00:00") >>> fs = 1 >>> win_size = 60 >>> olap_size = 15 >>> increment = inc_duration(win_size, olap_size, fs) >>> print(increment) 0:00:45 The increment is the time increment between the start of time one window and the succeeding window. >>> print(win_to_datetime(ref_time, 0, increment)) 2021-01-01 00:00:00 >>> print(win_to_datetime(ref_time, 1, increment)) 2021-01-01 00:00:45 >>> print(win_to_datetime(ref_time, 2, increment)) 2021-01-01 00:01:30 >>> print(win_to_datetime(ref_time, 3, increment)) 2021-01-01 00:02:15 """ return ref_time + (global_win * increment) def datetime_to_win( ref_time: RSDateTime, time: RSDateTime, increment: RSTimeDelta, method: str = "round", ) -> int: """ Convert a datetime to a global window index Parameters ---------- ref_time : RSDateTime Reference time time : RSDateTime Datetime to convert increment : RSTimeDelta The increment duration method : str, optional Method for dealing with float results, by default "round" Returns ------- int The global window index i.e. the window index relative to the reference time Raises ------ ValueError If time < ref_time Examples -------- A simple example to show the logic >>> from resistics.sampling import to_datetime, to_timedelta >>> from resistics.window import datetime_to_win, win_to_datetime, inc_duration >>> ref_time = to_datetime("2021-01-01 00:00:00") >>> time = to_datetime("2021-01-01 00:01:00") >>> increment = to_timedelta(60) >>> global_win = datetime_to_win(ref_time, time, increment) >>> global_win 1 >>> print(win_to_datetime(ref_time, global_win, increment)) 2021-01-01 00:01:00 A more complex logic with window sizes, overlap sizes and sampling frequencies >>> fs = 128 >>> win_size = 256 >>> olap_size = 64 >>> ref_time = to_datetime("2021-03-15 00:00:00") >>> time = to_datetime("2021-04-17 18:00:00") >>> increment = inc_duration(win_size, olap_size, fs) >>> print(increment) 0:00:01.5 >>> global_win = datetime_to_win(ref_time, time, increment) >>> global_win 1944000 >>> print(win_to_datetime(ref_time, global_win, increment)) 2021-04-17 18:00:00 In this scenario, explore the use of rounding >>> time = to_datetime("2021-04-17 18:00:00.50") >>> global_win = datetime_to_win(ref_time, time, increment, method = "floor") >>> global_win 1944000 >>> print(win_to_datetime(ref_time, global_win, increment)) 2021-04-17 18:00:00 >>> global_win = datetime_to_win(ref_time, time, increment, method = "ceil") >>> global_win 1944001 >>> print(win_to_datetime(ref_time, global_win, increment)) 2021-04-17 18:00:01.5 >>> global_win = datetime_to_win(ref_time, time, increment, method = "round") >>> global_win 1944000 >>> print(win_to_datetime(ref_time, global_win, increment)) 2021-04-17 18:00:00 Another example with a window duration of greater than a day >>> fs = 4.8828125e-05 >>> win_size = 64 >>> olap_size = 16 >>> ref_time = to_datetime("1985-07-18 01:00:20") >>> time = to_datetime("1985-09-22 23:00:00") >>> increment = inc_duration(win_size, olap_size, fs) >>> print(increment) 11 days, 9:04:00 >>> global_win = datetime_to_win(ref_time, time, increment) >>> global_win 6 >>> print(win_to_datetime(ref_time, global_win, increment)) 1985-09-24 07:24:20 This time is greater than the time that was transformed to global window, 1985-09-22 23:00:00. Try again, this time with the floor option. >>> global_win = datetime_to_win(ref_time, time, increment, method="floor") >>> global_win 5 >>> print(win_to_datetime(ref_time, global_win, increment)) 1985-09-12 22:20:20 """ from math import floor, ceil from resistics.sampling import to_seconds if time < ref_time: raise ValueError(f"Time {str(time)} < reference time {str(ref_time)}") increment_days_in_seconds, increment_remaining_in_seconds = to_seconds(increment) increment_seconds = increment_days_in_seconds + increment_remaining_in_seconds delta_days_in_seconds, delta_remaining_in_seconds = to_seconds(time - ref_time) delta_total_in_seconds = delta_days_in_seconds + delta_remaining_in_seconds n_increments = delta_total_in_seconds / increment_seconds if n_increments.is_integer(): n_increments = int(n_increments) elif method == "floor": n_increments = int(floor(n_increments)) elif method == "ceil": n_increments = int(ceil(n_increments)) else: n_increments = int(round(n_increments)) return n_increments def get_first_and_last_win( ref_time: RSDateTime, metadata: DecimatedLevelMetadata, win_size: int, olap_size: int, ) -> Tuple[int, int]: """ Get first and last window for a decimated data level .. note:: For the last window, on initial calculation this may be one or a maximum of two windows beyond the last time. The last window is adjusted in this function. Two windows may occur when the time of the last sample is in the overlap of the final two windows. Parameters ---------- ref_time : RSDateTime The reference time metadata : DecimatedLevelMetadata Metadata for the decimation level win_size : int Window size in samples olap_size : int Overlap size in samples Returns ------- Tuple[int, int] First and last global windows. This is window indices relative to the reference time Raises ------ ValueError If unable to calculate the last window correctly as this will result in an incorrect number of windows Examples -------- Get the first and last window for the first decimation level in a decimated data instance. >>> from resistics.testing import decimated_data_random >>> from resistics.sampling import to_datetime >>> from resistics.window import get_first_and_last_win, win_to_datetime >>> from resistics.window import win_duration, inc_duration >>> ref_time = to_datetime("2021-01-01 00:00:00") >>> dec_data = decimated_data_random(fs=0.1, first_time="2021-01-01 00:05:10", n_samples=100, factor=10) Get the metadata for decimation level 0 >>> level_metadata = dec_data.metadata.levels_metadata[0] >>> level_metadata.summary() { 'fs': 10.0, 'n_samples': 10000, 'first_time': '2021-01-01 00:05:10.000000_000000_000000_000000', 'last_time': '2021-01-01 00:21:49.899999_999999_977300_000000' } .. note:: As a point of interest, note how the last time is actually slightly incorrect. This is due to machine precision issues described in more detail here https://docs.python.org/3/tutorial/floatingpoint.html. Whilst there is value in using the high resolution datetime format for high sampling rates, there is a tradeoff. Such are the perils of floating point arithmetic. The next step is to calculate the first and last window, relative to the reference time >>> win_size = 100 >>> olap_size = 25 >>> first_win, last_win = get_first_and_last_win(ref_time, level_metadata, win_size, olap_size) >>> print(first_win, last_win) 42 173 These window indices can be converted to start times of the windows. The last window is checked to make sure it does not extend past the end of the time data. First get the window duration and increments. >>> duration = win_duration(win_size, level_metadata.fs) >>> print(duration) 0:00:09.9 >>> increment = inc_duration(win_size, olap_size, level_metadata.fs) >>> print(increment) 0:00:07.5 Now calculate the times of the windows >>> first_win_start_time = win_to_datetime(ref_time, 42, increment) >>> last_win_start_time = win_to_datetime(ref_time, 173, increment) >>> print(first_win_start_time, last_win_start_time) 2021-01-01 00:05:15 2021-01-01 00:21:37.5 >>> print(last_win_start_time + duration) 2021-01-01 00:21:47.4 >>> print(level_metadata.last_time) 2021-01-01 00:21:49.8999999999999773 >>> level_metadata.last_time > last_win_start_time + increment True """ duration = win_duration(win_size, metadata.fs) increment = inc_duration(win_size, olap_size, metadata.fs) first_win = datetime_to_win(ref_time, metadata.first_time, increment, method="ceil") last_win = datetime_to_win(ref_time, metadata.last_time, increment, method="floor") # adjust if there is not enough date to complete the last window last_win_time = win_to_datetime(ref_time, last_win, increment) for attempt in range(2): if metadata.last_time >= last_win_time + duration: break logger.debug(f"Adjusting last window attempt {attempt + 1}") last_win -= 1 last_win_time = win_to_datetime(ref_time, last_win, increment) if metadata.last_time < last_win_time + duration: raise ValueError("Unable to correctly get the last window") return first_win, last_win def get_win_starts( ref_time: RSDateTime, win_size: int, olap_size: int, fs: float, n_wins: int, index_offset: int, ) -> pd.DatetimeIndex: """ Get window start times This is a useful for getting the timestamps for the windows in a dataset Parameters ---------- ref_time : RSDateTime The reference time win_size : int The window size olap_size : int The overlap size fs : float The sampling frequency n_wins : int The number of windows index_offset : int The index offset from the reference time Returns ------- pd.DatetimeIndex The start times of the windows Examples -------- >>> import pandas as pd >>> from resistics.sampling import to_datetime >>> from resistics.window import get_win_starts >>> ref_time = to_datetime("2021-01-01 00:00:00") >>> win_size = 100 >>> olap_size = 25 >>> fs = 10 >>> n_wins = 3 >>> index_offset = 480 >>> starts = get_win_starts(ref_time, win_size, olap_size, fs, n_wins, index_offset) >>> pd.Series(starts) 0 2021-01-01 01:00:00.000 1 2021-01-01 01:00:07.500 2 2021-01-01 01:00:15.000 dtype: datetime64[ns] """ from resistics.sampling import datetime_array_estimate increment = inc_duration(win_size, olap_size, fs) first_win_time = win_to_datetime(ref_time, index_offset, increment) increment_size = win_size - olap_size return datetime_array_estimate(first_win_time, fs / increment_size, n_wins) def get_win_ends( starts: pd.DatetimeIndex, win_size: int, fs: float, ) -> pd.DatetimeIndex: """ Get window end times Parameters ---------- starts : RSDateTime The start times of the windows win_size : int The window size fs : float The sampling frequency Returns ------- pd.DatetimeIndex The end times of the windows Examples -------- >>> import pandas as pd >>> from resistics.sampling import to_datetime >>> from resistics.window import get_win_starts, get_win_ends >>> ref_time = to_datetime("2021-01-01 00:00:00") >>> win_size = 100 >>> olap_size = 25 >>> fs = 10 >>> n_wins = 3 >>> index_offset = 480 >>> starts = get_win_starts(ref_time, win_size, olap_size, fs, n_wins, index_offset) >>> pd.Series(starts) 0 2021-01-01 01:00:00.000 1 2021-01-01 01:00:07.500 2 2021-01-01 01:00:15.000 dtype: datetime64[ns] >>> ends = get_win_ends(starts, win_size, fs) >>> pd.Series(ends) 0 2021-01-01 01:00:09.900 1 2021-01-01 01:00:17.400 2 2021-01-01 01:00:24.900 dtype: datetime64[ns] """ return starts + pd.Timedelta((win_size - 1) * (1 / fs), "s") def get_win_table( ref_time: RSDateTime, metadata: DecimatedLevelMetadata, win_size: int, olap_size: int, ) -> pd.DataFrame: """ Get a DataFrame with Parameters ---------- ref_time : RSDateTime Reference metadata : DecimatedLevelMetadata Metadata for the decimation level win_size : int The window size olap_size : int The overlap size Returns ------- pd.DataFrame A pandas DataFrame with details about each window Examples -------- .. plot:: :width: 90% >>> import matplotlib.pyplot as plt >>> from resistics.decimate import DecimatedLevelMetadata >>> from resistics.sampling import to_datetime, to_timedelta >>> from resistics.window import get_win_table >>> ref_time = to_datetime("2021-01-01 00:00:00") >>> fs = 10 >>> n_samples = 1000 >>> first_time = to_datetime("2021-01-01 01:00:00") >>> last_time = first_time + to_timedelta((n_samples-1)/fs) >>> metadata = DecimatedLevelMetadata(fs=10, n_samples=1000, first_time=first_time, last_time=last_time) >>> print(metadata.fs, metadata.first_time, metadata.last_time) 10.0 2021-01-01 01:00:00 2021-01-01 01:01:39.9 >>> win_size = 100 >>> olap_size = 25 >>> df = get_win_table(ref_time, metadata, win_size, olap_size) >>> print(df.to_string()) global local from_sample to_sample win_start win_end 0 480 0 0 99 2021-01-01 01:00:00.000 2021-01-01 01:00:09.900 1 481 1 75 174 2021-01-01 01:00:07.500 2021-01-01 01:00:17.400 2 482 2 150 249 2021-01-01 01:00:15.000 2021-01-01 01:00:24.900 3 483 3 225 324 2021-01-01 01:00:22.500 2021-01-01 01:00:32.400 4 484 4 300 399 2021-01-01 01:00:30.000 2021-01-01 01:00:39.900 5 485 5 375 474 2021-01-01 01:00:37.500 2021-01-01 01:00:47.400 6 486 6 450 549 2021-01-01 01:00:45.000 2021-01-01 01:00:54.900 7 487 7 525 624 2021-01-01 01:00:52.500 2021-01-01 01:01:02.400 8 488 8 600 699 2021-01-01 01:01:00.000 2021-01-01 01:01:09.900 9 489 9 675 774 2021-01-01 01:01:07.500 2021-01-01 01:01:17.400 10 490 10 750 849 2021-01-01 01:01:15.000 2021-01-01 01:01:24.900 11 491 11 825 924 2021-01-01 01:01:22.500 2021-01-01 01:01:32.400 12 492 12 900 999 2021-01-01 01:01:30.000 2021-01-01 01:01:39.900 Plot six windows to illustrate the overlap >>> plt.figure(figsize=(8, 3)) # doctest: +SKIP >>> for idx, row in df.iterrows(): ... color = "red" if idx%2 == 0 else "blue" ... plt.axvspan(row.loc["win_start"], row.loc["win_end"], alpha=0.5, color=color) # doctest: +SKIP ... if idx > 5: ... break >>> plt.tight_layout() # doctest: +SKIP >>> plt.show() # doctest: +SKIP """ from resistics.sampling import to_n_samples, datetime_array_estimate increment_size = win_size - olap_size increment = inc_duration(win_size, olap_size, metadata.fs) fs = metadata.fs first_time = metadata.first_time first_win, last_win = get_first_and_last_win( ref_time, metadata, win_size, olap_size ) first_win_time = win_to_datetime(ref_time, first_win, increment) n_wins = last_win - first_win + 1 local_wins = np.arange(n_wins).astype(int) # samples - use the floor here to avoid a situation where it is rounded up # and then there are insufficient samples first_sample = to_n_samples(first_win_time - first_time, fs, method="floor") - 1 starts = datetime_array_estimate(first_win_time, fs / increment_size, n_wins) ends = get_win_ends(starts, win_size, fs) df_dict = { "global": np.arange(first_win, last_win + 1), "local": local_wins, "from_sample": first_sample + (local_wins * increment_size), "to_sample": first_sample + win_size - 1 + (local_wins * increment_size), "win_start": starts, "win_end": ends, } return pd.DataFrame(data=df_dict) class WindowParameters(ResisticsModel): """ Windowing parameters per decimation level Windowing parameters are the window and overlap size for each decimation level. Parameters ---------- n_levels : int The number of decimation levels min_n_wins : int Minimum number of windows win_sizes : List[int] The window sizes per decimation level olap_sizes : List[int] The overlap sizes per decimation level Examples -------- Generate decimation and windowing parameters for data sampled at 4096 Hz. Note that requesting window sizes or overlap sizes for decimation levels that do not exist will raise a ValueError. >>> from resistics.decimate import DecimationSetup >>> from resistics.window import WindowSetup >>> dec_setup = DecimationSetup(n_levels=3, per_level=3) >>> dec_params = dec_setup.run(4096) >>> dec_params.summary() { 'fs': 4096.0, 'n_levels': 3, 'per_level': 3, 'min_samples': 256, 'eval_freqs': [ 1024.0, 724.0773439350246, 512.0, 362.0386719675123, 256.0, 181.01933598375615, 128.0, 90.50966799187808, 64.0 ], 'dec_factors': [1, 2, 8], 'dec_increments': [1, 2, 4], 'dec_fs': [4096.0, 2048.0, 512.0] } >>> win_params = WindowSetup().run(dec_params.n_levels, dec_params.dec_fs) >>> win_params.summary() { 'n_levels': 3, 'min_n_wins': 5, 'win_sizes': [1024, 512, 128], 'olap_sizes': [256, 128, 32] } >>> win_params.get_win_size(0) 1024 >>> win_params.get_olap_size(0) 256 >>> win_params.get_olap_size(3) Traceback (most recent call last): ... ValueError: Level 3 must be 0 <= level < 3 """ n_levels: int min_n_wins: int win_sizes: List[int] olap_sizes: List[int] def check_level(self, level: int): """Check the decimation level is within range""" if level < 0 or level >= self.n_levels: raise ValueError(f"Level {level} must be 0 <= level < {self.n_levels}") def get_win_size(self, level: int) -> int: """Get window size for a decimation level""" self.check_level(level) return self.win_sizes[level] def get_olap_size(self, level: int) -> int: """Get overlap size for a decimation level""" self.check_level(level) return self.olap_sizes[level] class WindowSetup(ResisticsProcess): """ Setup WindowParameters WindowSetup outputs the WindowParameters to use for windowing decimated time data. Window parameters are simply the window and overlap sizes for each decimation level. Parameters ---------- min_size : int, optional Minimum window size, by default 128 min_olap : int, optional Minimum overlap size, by default 32 win_factor : int, optional Window factor, by default 4. Window sizes are calculated by sampling frequency / 4 to ensure sufficient frequency resolution. If the sampling frequency is small, window size will be adjusted to min_size olap_proportion : float, optional The proportion of the window size to use as the overlap, by default 0.25. For example, for a window size of 128, the overlap would be 0.25 * 128 = 32 min_n_wins : int, optional The minimum number of windows needed in a decimation level, by default 5 win_sizes : Optional[List[int]], optional Explicit define window sizes, by default None. Must have the same length as number of decimation levels olap_sizes : Optional[List[int]], optional Explicitly define overlap sizes, by default None. Must have the same length as number of decimation levels Examples -------- Generate decimation and windowing parameters for data sampled at 0.05 Hz or 20 seconds sampling period >>> from resistics.decimate import DecimationSetup >>> from resistics.window import WindowSetup >>> dec_params = DecimationSetup(n_levels=3, per_level=3).run(0.05) >>> dec_params.summary() { 'fs': 0.05, 'n_levels': 3, 'per_level': 3, 'min_samples': 256, 'eval_freqs': [ 0.0125, 0.008838834764831844, 0.00625, 0.004419417382415922, 0.003125, 0.002209708691207961, 0.0015625, 0.0011048543456039805, 0.00078125 ], 'dec_factors': [1, 2, 8], 'dec_increments': [1, 2, 4], 'dec_fs': [0.05, 0.025, 0.00625] } >>> win_params = WindowSetup().run(dec_params.n_levels, dec_params.dec_fs) >>> win_params.summary() { 'n_levels': 3, 'min_n_wins': 5, 'win_sizes': [128, 128, 128], 'olap_sizes': [32, 32, 32] } Window parameters can also be explicitly defined >>> from resistics.decimate import DecimationSetup >>> from resistics.window import WindowSetup >>> dec_setup = DecimationSetup(n_levels=3, per_level=3) >>> dec_params = dec_setup.run(0.05) >>> win_setup = WindowSetup(win_sizes=[1000, 578, 104]) >>> win_params = win_setup.run(dec_params.n_levels, dec_params.dec_fs) >>> win_params.summary() { 'n_levels': 3, 'min_n_wins': 5, 'win_sizes': [1000, 578, 104], 'olap_sizes': [250, 144, 32] } """ min_size: int = 128 min_olap: int = 32 win_factor: int = 4 olap_proportion: float = 0.25 min_n_wins: int = 5 win_sizes: Optional[List[int]] = None olap_sizes: Optional[List[int]] = None def run(self, n_levels: int, dec_fs: List[float]) -> WindowParameters: """ Calculate window and overlap sizes for each decimation level based on decimation level sampling frequency and minimum allowable parameters The window and overlap sizes (number of samples) are calculated based in the following way: - window size = frequency at decimation level / window factor - overlap size = window size * overlap proportion This is to ensure good frequency resolution at high frequencies. At low sampling frequencies, this would result in very small window sizes, therefore, there a minimum allowable sizes for both windows and overlap defined by min_size and min_olap in the initialiser. If window sizes or overlaps size are calculated below these respecitively, they will be set to the minimum values. Parameters ---------- n_levels : int The number of decimation levels dec_fs : List[float] The sampling frequencies for each decimation level Returns ------- WindowParameters The window parameters, the window sizes and overlaps for each decimation level Raises ------ ValueError If the number of windows does not match the number of levels ValueError If the number of overlaps does not match the number of levels """ if self.win_sizes is None: win_sizes = self._get_win_sizes(n_levels, dec_fs) else: win_sizes = list(self.win_sizes) if len(win_sizes) < n_levels: raise ValueError(f"Num. windows {len(win_sizes)} < n_levels {n_levels}") if len(win_sizes) > n_levels: # this may happen with user input windows # but decimated data has fewer levels win_sizes = win_sizes[:n_levels] if self.olap_sizes is None: olap_sizes = self._get_olap_sizes(win_sizes) else: olap_sizes = self.olap_sizes if len(olap_sizes) < n_levels: raise ValueError(f"Num. overlaps {len(olap_sizes)} < n_levels {n_levels}") if len(olap_sizes) > n_levels: # this may happen with user input windows # but decimated data has fewer levels olap_sizes = olap_sizes[:n_levels] return WindowParameters( n_levels=n_levels, min_n_wins=self.min_n_wins, win_sizes=win_sizes, olap_sizes=olap_sizes, ) def _get_win_sizes(self, n_levels: int, dec_fs: List[float]) -> List[int]: """ Get the window sizes Parameters ---------- n_levels : int The number of decimation levels dec_fs : List[float] The sampling frequencies for each decimation level Returns ------- List[int] Window sizes """ win_sizes = [] for ilevel in range(n_levels): win_size = dec_fs[ilevel] // self.win_factor if win_size < self.min_size: win_size = self.min_size win_sizes.append(int(win_size)) return win_sizes def _get_olap_sizes(self, win_sizes: List[int]) -> List[int]: """ Get overlap sizes Parameters ---------- win_sizes : List[int] The window sizes Returns ------- List[int] The overlap sizes """ olap_sizes = [] for win_size in win_sizes: olap_size = int(win_size * self.olap_proportion) if olap_size < self.min_olap: olap_size = self.min_olap olap_sizes.append(olap_size) return olap_sizes class WindowedLevelMetadata(Metadata): """Metadata for a windowed level""" fs: float """The sampling frequency for the decimation level""" n_wins: int """The number of windows""" win_size: PositiveInt """The window size in samples""" olap_size: PositiveInt """The overlap size in samples""" index_offset: int """The global window offset for local window 0""" @property def dt(self): return 1 / self.fs class WindowedMetadata(WriteableMetadata): """Metadata for windowed data""" fs: List[float] chans: List[str] n_chans: Optional[int] = None n_levels: int first_time: HighResDateTime last_time: HighResDateTime system: str = "" serial: str = "" wgs84_latitude: float = -999.0 wgs84_longitude: float = -999.0 easting: float = -999.0 northing: float = -999.0 elevation: float = -999.0 chans_metadata: Dict[str, ChanMetadata] levels_metadata: List[WindowedLevelMetadata] ref_time: HighResDateTime history: History = History() class Config: extra = "ignore" class WindowedData(ResisticsData): """ Windows of a DecimatedData object The windowed data is stored in a dictionary attribute named data. This is a dictionary with an entry for each decimation level. The shape for a single decimation level is as follows: n_wins x n_chans x n_samples """ def __init__( self, metadata: WindowedMetadata, data: Dict[int, np.ndarray], ): """ Initialise the WindowedData Parameters ---------- metadata : WindowedDataMetadata The metadata for the windowed data data : Dict[int, WindowedTimeData] The windowed data """ logger.debug(f"Creating WindowedData with data type {data[0].dtype}") self.metadata = metadata self.data = data def get_level(self, level: int) -> np.ndarray: """ Get windows for a decimation level Parameters ---------- level : int The decimation level Returns ------- np.ndarray The window array Raises ------ ValueError If decimation level is not within range """ if level >= self.metadata.n_levels: raise ValueError(f"Level {level} not <= max {self.metadata.n_levels - 1}") return self.data[level] def get_local(self, level: int, local_win: int) -> np.ndarray: """ Get window using local index Parameters ---------- level : int The decimation level local_win : int Local window index Returns ------- np.ndarray Window data Raises ------ ValueError If local window index is out of range """ n_wins = self.metadata.levels_metadata[level].n_wins if local_win < 0 or local_win >= n_wins: raise ValueError(f"Local window {local_win} not 0 <= local_win < {n_wins}") return self.get_level(level)[local_win] def get_global(self, level: int, global_win: int) -> np.ndarray: """ Get window using global index Parameters ---------- level : int The decimation level global_win : int Global window index Returns ------- np.ndarray Window data """ index_offset = self.metadata.levels_metadata[level].index_offset return self.get_local(level, global_win + index_offset) def get_chan(self, level: int, chan: str) -> np.ndarray: """ Get all the windows for a channel Parameters ---------- level : int The decimation level chan : str The channel Returns ------- np.ndarray The data for the channels Raises ------ ChannelNotFoundError If the channel is not found in the data """ from resistics.errors import ChannelNotFoundError if chan not in self.metadata.chans: raise ChannelNotFoundError(chan, self.metadata.chans) idx = self.metadata.chans.index(chan) return self.get_level(level)[..., idx, :] def to_string(self) -> str: """Class information as a string""" return self.metadata.to_string() class Windower(ResisticsProcess): """ Windows DecimatedData This is the primary window making process for resistics and should be used when alignment of windows with a site or across sites is required. This method uses numpy striding to produce window views into the decimated data. See Also -------- WindowerTarget : A windower to make a target number of windows Examples -------- The Windower windows a DecimatedData object given a reference time and some window parameters. There's quite a few imports needed for this example. Begin by doing the imports, defining a reference time and generating random decimated data. >>> from resistics.sampling import to_datetime >>> from resistics.testing import decimated_data_linear >>> from resistics.window import WindowSetup, Windower >>> dec_data = decimated_data_linear(fs=128) >>> ref_time = dec_data.metadata.first_time >>> print(dec_data.to_string()) <class 'resistics.decimate.DecimatedData'> fs dt n_samples first_time last_time level 0 2048.0 0.000488 16384 2021-01-01 00:00:00 2021-01-01 00:00:07.99951171875 1 512.0 0.001953 4096 2021-01-01 00:00:00 2021-01-01 00:00:07.998046875 2 128.0 0.007812 1024 2021-01-01 00:00:00 2021-01-01 00:00:07.9921875 Next, initialise the window parameters. For this example, use small windows, which will make inspecting them easier. >>> win_params = WindowSetup(win_sizes=[16,16,16], min_olap=4).run(dec_data.metadata.n_levels, dec_data.metadata.fs) >>> win_params.summary() { 'n_levels': 3, 'min_n_wins': 5, 'win_sizes': [16, 16, 16], 'olap_sizes': [4, 4, 4] } Perform the windowing. This actually creates views into the decimated data using the numpy.lib.stride_tricks.sliding_window_view function. The shape for a data array at a decimation level is: n_wins x n_chans x win_size. The information about each level is also in the levels_metadata attribute of WindowedMetadata. >>> win_data = Windower().run(ref_time, win_params, dec_data) >>> win_data.data[0].shape (1365, 2, 16) >>> for level_metadata in win_data.metadata.levels_metadata: ... level_metadata.summary() { 'fs': 2048.0, 'n_wins': 1365, 'win_size': 16, 'olap_size': 4, 'index_offset': 0 } { 'fs': 512.0, 'n_wins': 341, 'win_size': 16, 'olap_size': 4, 'index_offset': 0 } { 'fs': 128.0, 'n_wins': 85, 'win_size': 16, 'olap_size': 4, 'index_offset': 0 } Let's look at an example of data from the first decimation level for the first channel. This is simply a linear set of data ranging from 0...16_383. >>> dec_data.data[0][0] array([ 0, 1, 2, ..., 16381, 16382, 16383]) Inspecting the first few windows shows they are as expected including the overlap. >>> win_data.data[0][0, 0] array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]) >>> win_data.data[0][1, 0] array([12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]) >>> win_data.data[0][2, 0] array([24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39]) """ def run( self, ref_time: RSDateTime, win_params: WindowParameters, dec_data: DecimatedData, ) -> WindowedData: """ Perform windowing of DecimatedData Parameters ---------- ref_time : RSDateTime The reference time win_params : WindowParameters The window parameters dec_data : DecimatedData The decimated data Returns ------- WindowedData Windows for decimated data Raises ------ ProcessRunError If the number of windows calculated in the window table does not match the size of the array views """ metadata_dict = dec_data.metadata.dict() data = {} win_levels_metadata = [] messages = [] for ilevel in range(0, dec_data.metadata.n_levels): logger.info(f"Windowing decimation level {ilevel}") win_size = win_params.get_win_size(ilevel) olap_size = win_params.get_olap_size(ilevel) level_metadata = dec_data.metadata.levels_metadata[ilevel] win_table = get_win_table(ref_time, level_metadata, win_size, olap_size) n_wins = len(win_table.index) logger.info(f"{n_wins} windows, size {win_size}, overlap {olap_size}") messages.append(f"Level {ilevel}, generated {n_wins} windows") messages.append(f"Window size {win_size}, olap_size {olap_size}") if n_wins < win_params.min_n_wins: logger.debug(f"Number windows {n_wins} < min. {win_params.min_n_wins}") messages.append(f"Num. windows {n_wins} < min. {win_params.min_n_wins}") messages.append(f"Level {ilevel} incomplete, terminating windowing") break win_level_data = self._get_level_data( dec_data.get_level(ilevel), win_table, dec_data.metadata.n_chans, win_size, olap_size, ) if win_level_data.shape[0] != n_wins: raise ProcessRunError( self.name, f"Num. windows mismatch {win_level_data.shape[0]} != {n_wins}", ) win_level_metadata = self._get_level_metadata( level_metadata, win_table, win_size, olap_size, ) data[ilevel] = win_level_data win_levels_metadata.append(win_level_metadata) metadata_dict["ref_time"] = ref_time metadata = self._get_metadata(metadata_dict, win_levels_metadata) metadata.history.add_record(self._get_record(messages)) logger.info("Windowing completed") return WindowedData(metadata, data) def _get_level_data( self, data: np.ndarray, win_table: pd.DataFrame, n_chans: int, win_size: int, olap_size: int, ) -> np.ndarray: """ Get window data for a decimation level Parameters ---------- data : np.ndarray The decimated time data for the level win_table : pd.DataFrame The window table n_chans : int The number of channels win_size : int The window size olap_size : int The overlap size Returns ------- np.ndarray Sliding window views in an array for the decimation level """ from numpy.lib.stride_tricks import sliding_window_view from_sample = win_table.loc[0, "from_sample"] to_sample = win_table.loc[win_table.index[-1], "from_sample"] increment_size = win_size - olap_size view = np.squeeze( sliding_window_view(data, window_shape=(n_chans, win_size), writeable=True) ) return view[from_sample : to_sample + 1 : increment_size] def _get_level_metadata( self, level_metadata: DecimatedLevelMetadata, win_table: pd.DataFrame, win_size: int, olap_size: int, ) -> WindowedLevelMetadata: """Get the windowed metadata for a decimation level""" offset = (win_table["global"] - win_table["local"]).unique() if len(offset) != 1: raise ValueError("Malformed window table, varying local to global offset") return WindowedLevelMetadata( fs=level_metadata.fs, n_wins=len(win_table.index), win_size=win_size, olap_size=olap_size, index_offset=offset[0], ) def _get_metadata( self, metadata_dict: Dict[str, Any], levels_metadata: List[WindowedLevelMetadata], ) -> WindowedMetadata: """Get the metadata for the windowed data""" metadata_dict.pop("file_info") metadata_dict["n_levels"] = len(levels_metadata) metadata_dict["levels_metadata"] = levels_metadata return WindowedMetadata(*metadata_dict) class WindowerTarget(Windower): """ Windower that selects window sizes to meet a target number of windows The minimum window size in window parameters will be respected even if the generated number of windows is below the target. This is to avoid situations where excessively small windows sizes are selected. .. warning:: This process is primarily useful for quick processing of a single measurement and should not be used when any alignment of windows is required within a site or across sites. Parameters ---------- target : int The target number of windows for each decimation level olap_proportion : float The overlap proportion of the window size See Also -------- Windower : The window making process to use when alignment is required """ target: int = 1000 min_size: int = 64 olap_proportion: float = 0.25 def run( self, ref_time: RSDateTime, win_params: WindowParameters, dec_data: DecimatedData, ) -> WindowedData: metadata_dict = dec_data.metadata.dict() data = {} win_levels_metadata = [] messages = [] for ilevel in range(0, dec_data.metadata.n_levels): logger.info(f"Windowing decimation level {ilevel}") level_metadata = dec_data.metadata.levels_metadata[ilevel] win_size = self._get_win_size(level_metadata) olap_size = int(np.floor(self.olap_proportion win_size)) win_table = get_win_table(ref_time, level_metadata, win_size, olap_size) n_wins = len(win_table.index) logger.info(f"{n_wins} windows, size {win_size}, overlap {olap_size}") messages.append(f"Level {ilevel}, generated {n_wins} windows") messages.append(f"Window size {win_size}, olap_size {olap_size}") if n_wins < win_params.min_n_wins: logger.debug(f"Number windows {n_wins} < min. {win_params.min_n_wins}") messages.append(f"Num. windows {n_wins} < min. {win_params.min_n_wins}") messages.append(f"Level {ilevel} incomplete, terminating windowing") break win_level_data = self._get_level_data( dec_data.get_level(ilevel), win_table, dec_data.metadata.n_chans, win_size, olap_size, ) win_level_metadata = self._get_level_metadata( level_metadata, win_table, win_size, olap_size, ) data[ilevel] = win_level_data win_levels_metadata.append(win_level_metadata) metadata_dict["ref_time"] = metadata_dict["first_time"] metadata = self._get_metadata(metadata_dict, win_levels_metadata) metadata.history.add_record(self._get_record(messages)) logger.info("Windowing completed") return WindowedData(metadata, data) def _get_win_size(self, level_metadata: DecimatedLevelMetadata) -> int: r""" Get window size that gives close to the target number of windows Windows increment by (window size - overlap size), therefore the follwing equation is solved, .. math:: n_{samples} / ((1 - n_{overlap})n_{window}) = target Rearrangning, get, .. math:: n_{window} = n_{samples} / ((1 - n_{overlap})target) Parameters ---------- level_metadata : DecimatedLevelMetadata The metadata for the decimation level Returns ------- int The window size """ win_size = level_metadata.n_samples / ((1 - self.olap_proportion) * self.target) win_size = int(np.floor(win_size)) if win_size < self.min_size: return self.min_size return win_size class WindowedDataWriter(ResisticsWriter): """Writer of resistics windowed data""" def run(self, dir_path: Path, win_data: WindowedData) -> None: """ Write out WindowedData Parameters ---------- dir_path : Path The directory path to write to win_data : WindowedData Windowed data to write out Raises ------ WriteError If unable to write to the directory """ from resistics.errors import WriteError if not self._check_dir(dir_path): raise WriteError(dir_path, "Unable to write to directory, check logs") logger.info(f"Writing windowed data to {dir_path}") metadata_path = dir_path / "metadata.json" data_path = dir_path / "data" np.savez_compressed(data_path, **{str(x): y for x, y in win_data.data.items()}) metadata = win_data.metadata.copy() metadata.history.add_record(self._get_record(dir_path, type(win_data))) metadata.write(metadata_path) class WindowedDataReader(ResisticsProcess): """Reader of resistics windowed data""" def run( self, dir_path: Path, metadata_only: bool = False ) -> Union[WindowedMetadata, WindowedData]: """ Read WindowedData Parameters ---------- dir_path : Path The directory path to read from metadata_only : bool, optional Flag for getting metadata only, by default False Returns ------- Union[WindowedMetadata, WindowedData] The WindowedData or WindowedMetadata if metadata_only is True Raises ------ ReadError If the directory does not exist """ from 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acmicpc/python/14405.py	hyeongyun0916/Algorithm	1	1600248	<reponame>hyeongyun0916/Algorithm import sys # chupikachupipichu s = sys.stdin.readline().strip() i = 0 while i < len(s): if s[i] == 'p': if i+1 < len(s) and s[i+1] == 'i': i += 1 else: print('NO') exit() elif s[i] == 'k': if i+1 < len(s) and s[i+1] == 'a': i += 1 else: print('NO') exit() elif s[i] == 'c': if i+2 < len(s) and s[i+1] == 'h' and s[i+2] == 'u': i += 2 else: print('NO') exit() else: print('NO') exit() i += 1 print('YES')	[ 1, 529, 276, 1112, 420, 29958, 29882, 4099, 549, 29891, 348, 29900, 29929, 29896, 29953, 29914, 22461, 4540, 13, 5215, 10876, 13, 29937, 521, 786, 638, 496, 786, 666, 436, 29884, 13, 13, 29879, 353, 10876, 29889, 4172, 262, 29889, 949, 1220, 2141, 17010, 580, 13, 13, 29875, 353, 29871, 29900, 13, 8000, 474, 529, 7431, 29898, 29879, 1125, 13, 1678, 565, 269, 29961, 29875, 29962, 1275, 525, 29886, 2396, 13, 4706, 565, 474, 29974, 29896, 529, 7431, 29898, 29879, 29897, 322, 269, 29961, 29875, 29974, 29896, 29962, 1275, 525, 29875, 2396, 13, 9651, 474, 4619, 29871, 29896, 13, 4706, 1683, 29901, 13, 9651, 1596, 877, 6632, 1495, 13, 9651, 6876, 580, 13, 1678, 25342, 269, 29961, 29875, 29962, 1275, 525, 29895, 2396, 13, 4706, 565, 474, 29974, 29896, 529, 7431, 29898, 29879, 29897, 322, 269, 29961, 29875, 29974, 29896, 29962, 1275, 525, 29874, 2396, 13, 9651, 474, 4619, 29871, 29896, 13, 4706, 1683, 29901, 13, 9651, 1596, 877, 6632, 1495, 13, 9651, 6876, 580, 13, 1678, 25342, 269, 29961, 29875, 29962, 1275, 525, 29883, 2396, 13, 4706, 565, 474, 29974, 29906, 529, 7431, 29898, 29879, 29897, 322, 269, 29961, 29875, 29974, 29896, 29962, 1275, 525, 29882, 29915, 322, 269, 29961, 29875, 29974, 29906, 29962, 1275, 525, 29884, 2396, 13, 9651, 474, 4619, 29871, 29906, 13, 4706, 1683, 29901, 13, 9651, 1596, 877, 6632, 1495, 13, 9651, 6876, 580, 1678, 13, 1678, 1683, 29901, 13, 4706, 1596, 877, 6632, 1495, 13, 4706, 6876, 580, 13, 1678, 474, 4619, 29871, 29896, 13, 2158, 877, 21143, 1495, 2 ]
save_restore/check_frozen_model.py	scotthuang1989/mytensorflow_example_code	32	141193	import tensorflow as tf import argparse def load_graph(frozen_graph_filename): # We load the protobuf file from the disk and parse it to retrieve the # unserialized graph_def with tf.gfile.GFile(frozen_graph_filename, "rb") as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) # Then, we import the graph_def into a new Graph and returns it with tf.Graph().as_default() as graph: # The name var will prefix every op/nodes in your graph # Since we load everything in a new graph, this is not needed tf.import_graph_def(graph_def, name="") return graph if __name__ == '__main__': # Let's allow the user to pass the filename as an argument parser = argparse.ArgumentParser() # parser.add_argument("--frozen_model_filename", default="/home/scott/Downloads/ssd_mobilenet_v1_coco_11_06_2017/frozen_inference_graph.pb", type=str, help="Frozen model file to import") parser.add_argument("--frozen_model_filename", default="/home/scott/Downloads/faster_rcnn_inception_resnet_v2_atrous_coco_11_06_2017/frozen_inference_graph.pb", type=str, help="Frozen model file to import") args = parser.parse_args() # We use our "load_graph" function graph = load_graph(args.frozen_model_filename) # We can verify that we can access the list of operations in the graph for op in graph.get_operations(): print(op.name) # prefix/Placeholder/inputs_placeholder # ... # prefix/Accuracy/predictions # y = graph.get_tensor_by_name('prefix/Accuracy/predictions:0')	[ 1, 1053, 26110, 408, 15886, 13, 5215, 1852, 5510, 13, 13, 1753, 2254, 29918, 4262, 29898, 29888, 307, 2256, 29918, 4262, 29918, 9507, 1125, 13, 1678, 396, 1334, 2254, 278, 17814, 9721, 934, 515, 278, 8086, 322, 6088, 372, 304, 10563, 278, 13, 1678, 396, 443, 15550, 1891, 3983, 29918, 1753, 13, 1678, 411, 15886, 29889, 29887, 1445, 29889, 29954, 2283, 29898, 29888, 307, 2256, 29918, 4262, 29918, 9507, 29892, 376, 6050, 1159, 408, 285, 29901, 13, 4706, 3983, 29918, 1753, 353, 15886, 29889, 9527, 3206, 580, 13, 4706, 3983, 29918, 1753, 29889, 12914, 4591, 1231, 29898, 29888, 29889, 949, 3101, 13, 13, 1678, 396, 1987, 29892, 591, 1053, 278, 3983, 29918, 1753, 964, 263, 716, 12367, 322, 3639, 372, 13, 1678, 411, 15886, 29889, 9527, 2141, 294, 29918, 4381, 580, 408, 3983, 29901, 13, 4706, 396, 450, 1024, 722, 674, 10944, 1432, 1015, 29914, 18010, 297, 596, 3983, 13, 4706, 396, 4001, 591, 2254, 4129, 297, 263, 716, 3983, 29892, 445, 338, 451, 4312, 13, 4706, 15886, 29889, 5215, 29918, 4262, 29918, 1753, 29898, 4262, 29918, 1753, 29892, 1024, 543, 1159, 13, 1678, 736, 3983, 13, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 396, 2803, 29915, 29879, 2758, 278, 1404, 304, 1209, 278, 10422, 408, 385, 2980, 13, 1678, 13812, 353, 1852, 5510, 29889, 15730, 11726, 580, 13, 1678, 396, 13812, 29889, 1202, 29918, 23516, 703, 489, 29888, 307, 2256, 29918, 4299, 29918, 9507, 613, 2322, 13802, 5184, 29914, 1557, 1501, 29914, 6767, 18132, 29914, 893, 29881, 29918, 29885, 12213, 264, 300, 29918, 29894, 29896, 29918, 29883, 6235, 29918, 29896, 29896, 29918, 29900, 29953, 29918, 29906, 29900, 29896, 29955, 29914, 29888, 307, 2256, 29918, 262, 1659, 29918, 4262, 29889, 24381, 613, 1134, 29922, 710, 29892, 1371, 543, 29943, 307, 2256, 1904, 934, 304, 1053, 1159, 13, 1678, 13812, 29889, 1202, 29918, 23516, 703, 489, 29888, 307, 2256, 29918, 4299, 29918, 9507, 613, 2322, 13802, 5184, 29914, 1557, 1501, 29914, 6767, 18132, 29914, 29888, 1901, 29918, 2214, 15755, 29918, 1239, 683, 29918, 690, 1212, 29918, 29894, 29906, 29918, 8141, 681, 29918, 29883, 6235, 29918, 29896, 29896, 29918, 29900, 29953, 29918, 29906, 29900, 29896, 29955, 29914, 29888, 307, 2256, 29918, 262, 1659, 29918, 4262, 29889, 24381, 613, 1134, 29922, 710, 29892, 1371, 543, 29943, 307, 2256, 1904, 934, 304, 1053, 1159, 13, 1678, 6389, 353, 13812, 29889, 5510, 29918, 5085, 580, 13, 13, 1678, 396, 1334, 671, 1749, 376, 1359, 29918, 4262, 29908, 740, 13, 1678, 3983, 353, 2254, 29918, 4262, 29898, 5085, 29889, 29888, 307, 2256, 29918, 4299, 29918, 9507, 29897, 13, 13, 1678, 396, 1334, 508, 11539, 393, 591, 508, 2130, 278, 1051, 310, 6931, 297, 278, 3983, 13, 1678, 363, 1015, 297, 3983, 29889, 657, 29918, 3372, 800, 7295, 13, 4706, 1596, 29898, 459, 29889, 978, 29897, 13, 4706, 396, 10944, 29914, 22150, 7694, 29914, 2080, 29879, 29918, 27074, 13, 4706, 396, 2023, 13, 4706, 396, 10944, 29914, 7504, 332, 4135, 29914, 27711, 1080, 13, 13, 1678, 396, 343, 353, 3983, 29889, 657, 29918, 20158, 29918, 1609, 29918, 978, 877, 13506, 29914, 7504, 332, 4135, 29914, 27711, 1080, 29901, 29900, 1495, 13, 2 ]
learning/LearnPython3TheHardWay/InteractiveFictionClass/GoogleColabStarterCode/Parser.py	AlexanderTN/Python	1	47793	<filename>learning/LearnPython3TheHardWay/InteractiveFictionClass/GoogleColabStarterCode/Parser.py<gh_stars>1-10 class Parser: """The Parser is the class that handles the player's input. The player writes commands, and the parser performs natural language understanding in order to interpret what the player intended, and how that intent is reflected in the simulated world. """ def __init__(self, game): # A list of all of the commands that the player has issued. self.command_history = [] # A pointer to the game. self.game = game def get_player_intent(self,command): command = command.lower() if "," in command: # Let the player type in a comma separted sequence of commands return "sequence" elif self.get_direction(command): # Check for the direction intent return "direction" elif command.lower() == "look" or command.lower() == "l": # when the user issues a "look" command, re-describe what they see return "redescribe" elif "examine " in command or command.lower().startswith("x "): return "examine" elif "take " in command or "get " in command: return "take" elif "drop " in command: return "drop" elif "inventory" in command or command.lower() == "i": return "inventory" else: for item in self.game.get_items_in_scope(): special_commands = item.get_commands() for special_command in special_commands: if command == special_command.lower(): return "special" def parse_command(self, command): # add this command to the history self.command_history.append(command) # By default, none of the intents end the game. The following are ways this # flag can be changed to True. # * Going to a certain place. # * Entering a certain special command # * Picking up a certain object. end_game = False # Intents are functions that can be executed intent = self.get_player_intent(command) if intent == "direction": end_game = self.go_in_direction(command) elif intent == "redescribe": self.game.describe() elif intent == "examine": self.examine(command) elif intent == "take": end_game = self.take(command) elif intent == "drop": self.drop(command) elif intent == "inventory": self.check_inventory() #Tam: Actually there is no usage of the command in here, so I will remove it out of the parameters list passed in the function, old: self.check_inventory(command) elif intent == "special": end_game = self.run_special_command(command) elif intent == "sequence": #end_game = self.execute_sequence(command) self.execute_sequence(command) #Tam3 change it as this function dont return value else: print("I'm not sure what you want to do.") return end_game ### Intent Functions ### def go_in_direction(self, command): """ The user wants to in some direction """ direction = self.get_direction(command) if direction: if direction in self.game.curr_location.connections: if self.game.curr_location.is_blocked(direction, self.game): # check to see whether that direction is blocked. print(self.game.curr_location.get_block_description(direction)) else: # if it's not blocked, then move there self.game.curr_location = self.game.curr_location.connections[direction] # If moving to this location ends the game, only describe the location # and not the available items or actions. if self.game.curr_location.end_game: self.game.describe_current_location() else: self.game.describe() else: print("You can't go %s from here." % direction.capitalize()) return self.game.curr_location.end_game def check_inventory(self): #Tam: Actually there is no usage of the command in here, so I will remove it out of the parameters list passed in the function, old: def check_inventory(self,command): """ The player wants to check their inventory""" if len(self.game.inventory) == 0: print("You don't have anything.") else: descriptions = [] for item_name in self.game.inventory: item = self.game.inventory[item_name] descriptions.append(item.description) print("You have: ", end = '') print(*descriptions, sep = ", ",) def examine(self, command): """ The player wants to examine something """ command = command.lower() matched_item = False # check whether any of the items at this location match the command for item_name in self.game.curr_location.items: if item_name in command: item = self.game.curr_location.items[item_name] if item.examine_text: print(item.examine_text) matched_item = True break # check whether any of the items in the inventory match the command for item_name in self.game.inventory: if item_name in command: item = self.game.inventory[item_name] if item.examine_text: print(item.examine_text) matched_item = True # fail if not matched_item: print("You don't see anything special.") def take(self, command): """ The player wants to put something in their inventory """ command = command.lower() matched_item = False # This gets set to True if posession of this object ends the game. end_game = False # check whether any of the items at this location match the command for item_name in self.game.curr_location.items: if item_name in command: item = self.game.curr_location.items[item_name] if item.gettable: self.game.add_to_inventory(item) self.game.curr_location.remove_item(item) print(item.take_text) end_game = item.end_game else: print("You cannot take the %s." % item_name) matched_item = True break # check whether any of the items in the inventory match the command if not matched_item: for item_name in self.game.inventory: if item_name in command: print("You already have the %s." % item_name) matched_item = True # fail if not matched_item: print("You can't find it.") return end_game def drop(self, command): """ The player wants to remove something from their inventory """ command = command.lower() matched_item = False # check whether any of the items in the inventory match the command if not matched_item: for item_name in self.game.inventory: if item_name in command: matched_item = True item = self.game.inventory[item_name] self.game.curr_location.add_item(item_name, item) self.game.inventory.pop(item_name) print("You drop the %s." % item_name) break # fail if not matched_item: print("You don't have that.") def run_special_command(self, command): """Run a special command associated with one of the items in this location or in the player's inventory""" for item in self.game.get_items_in_scope(): special_commands = item.get_commands() for special_command in special_commands: if command == special_command.lower(): return item.do_action(special_command, self.game) def execute_sequence(self, command): for cmd in command.split(","): cmd = cmd.strip() self.parse_command(cmd) #return 1 #Tam add to pass the error def get_direction(self, command): command = command.lower() if command == "n" or "north" in command: return "north" if command == "s" or "south" in command: return "south" if command == "e" or "east" in command: return "east" if command == "w" or "west" in command: return "west" if command == "up": return "up" if command == "down": return "down" if command.startswith("go out"): return "out" if command.startswith("go in"): return "in" for exit in self.game.curr_location.connections.keys(): if command == exit.lower() or command == "go " + exit.lower(): return exit return None	[ 1, 529, 9507, 29958, 21891, 29914, 29931, 799, 29876, 11980, 29941, 1576, 29950, 538, 29956, 388, 29914, 4074, 4925, 29943, 2463, 2385, 29914, 14207, 1625, 370, 855, 4254, 3399, 29914, 11726, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 1990, 1459, 643, 29901, 13, 1678, 9995, 1576, 1459, 643, 338, 278, 770, 393, 17766, 278, 4847, 29915, 29879, 1881, 29889, 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run.py	zgoda/kyfood	0	193311	from argparse import ArgumentParser from dotenv import find_dotenv, load_dotenv from werkzeug.serving import run_simple from api._local import API def get_options(): parser = ArgumentParser() parser.add_argument( "-o", "--host", default="127.0.0.1", help="host name or IP address to bind" ) parser.add_argument( "-p", "--port", type=int, default=5000, help="port number to listen" ) parser.add_argument( "--no-reload", action="store_true", default=False, help="disable hot reload" ) return parser.parse_args() def main(options): app = API() reload_ = not options.no_reload run_simple(options.host, options.port, app, use_reloader=reload_) if __name__ == "__main__": load_dotenv(find_dotenv()) opts = get_options() main(opts)	[ 1, 515, 1852, 5510, 1053, 23125, 11726, 13, 13, 3166, 8329, 6272, 1053, 1284, 29918, 6333, 6272, 29892, 2254, 29918, 6333, 6272, 13, 3166, 23085, 13289, 29889, 643, 1747, 1053, 1065, 29918, 12857, 13, 13, 3166, 7882, 3032, 2997, 1053, 3450, 13, 13, 13, 1753, 679, 29918, 6768, 7295, 13, 1678, 13812, 353, 23125, 11726, 580, 13, 1678, 13812, 29889, 1202, 29918, 23516, 29898, 13, 4706, 11663, 29877, 613, 376, 489, 3069, 613, 2322, 543, 29896, 29906, 29955, 29889, 29900, 29889, 29900, 29889, 29896, 613, 1371, 543, 3069, 1024, 470, 5641, 3211, 304, 7868, 29908, 13, 1678, 1723, 13, 1678, 13812, 29889, 1202, 29918, 23516, 29898, 13, 4706, 11663, 29886, 613, 376, 489, 637, 613, 1134, 29922, 524, 29892, 2322, 29922, 29945, 29900, 29900, 29900, 29892, 1371, 543, 637, 1353, 304, 11621, 29908, 13, 1678, 1723, 13, 1678, 13812, 29889, 1202, 29918, 23516, 29898, 13, 4706, 376, 489, 1217, 29899, 28120, 613, 3158, 543, 8899, 29918, 3009, 613, 2322, 29922, 8824, 29892, 1371, 543, 20472, 7375, 19763, 29908, 13, 1678, 1723, 13, 1678, 736, 13812, 29889, 5510, 29918, 5085, 580, 13, 13, 13, 1753, 1667, 29898, 6768, 1125, 13, 1678, 623, 353, 3450, 580, 13, 1678, 19763, 29918, 353, 451, 3987, 29889, 1217, 29918, 28120, 13, 1678, 1065, 29918, 12857, 29898, 6768, 29889, 3069, 29892, 3987, 29889, 637, 29892, 623, 29892, 671, 29918, 276, 12657, 29922, 28120, 19925, 13, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 2254, 29918, 6333, 6272, 29898, 2886, 29918, 6333, 6272, 3101, 13, 1678, 29111, 353, 679, 29918, 6768, 580, 13, 1678, 1667, 29898, 25707, 29897, 13, 2 ]
question_bank/merge-two-binary-trees/merge-two-binary-trees.py	yatengLG/leetcode-python	9	143442	# -- coding: utf-8 -- # @Author : LG """ 执行用时：104 ms, 在所有 Python3 提交中击败了64.10% 的用户内存消耗：14.4 MB, 在所有 Python3 提交中击败了79.35% 的用户解题思路：具体实现见代码注释 """ class Solution: def mergeTrees(self, t1: TreeNode, t2: TreeNode) -> TreeNode: def find(root1, root2): # 两棵树的对应节点 if root1 and root2: # 如果节点都存在 node = TreeNode(root1.val + root2.val) # 合并后的当前节点为两树节点和 node.left = find(root1.left, root2.left) # 处理两树的对应左子树，返回的节点为当前合并子树的左子树 node.right = find(root1.right, root2.right) # 处理两树的对应右子树 elif root1 or root2: # 只存在一个节点 node = TreeNode(root1.val if root1 else root2.val) # 当前节点等于存在节点值 node.left = find(root1.left if root1 else None, root2.left if root2 else None) # 遍历存在节点的左子树 node.right = find(root1.right if root1 else None, root2.right if root2 else None) # 遍历存在节点的右子树 else: return None # 均不存在返回None return node t = find(t1, t2) return t """ 执行用时：100 ms, 在所有 Python3 提交中击败了91.61% 的用户内存消耗：14.5 MB, 在所有 Python3 提交中击败了46.33% 的用户解题思路：同上，但不新建树 """ class Solution: def mergeTrees(self, t1: TreeNode, t2: TreeNode) -> TreeNode: def find(root1, root2): # 两棵树的对应节点 if root1 and root2: # 如果节点都存在 root1.val = root1.val + root2.val # 合并后的当前节点为两树节点和 root1.left = find(root1.left, root2.left) # 处理两树的对应左子树，返回的节点为当前合并子树的左子树 root1.right = find(root1.right, root2.right) # 处理两树的对应右子树 elif root1 or root2: # 只存在一个节点, 直接返回存在的节点即可 return root1 if root1 else root2 else: return None # 均不存在返回None return root1 t = find(t1, t2) return t	[ 1, 396, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 29937, 732, 13720, 29871, 584, 365, 29954, 13, 13, 15945, 29908, 13, 233, 140, 170, 30448, 30406, 30594, 30383, 29896, 29900, 29946, 10887, 29892, 29871, 30505, 30744, 30417, 5132, 29941, 29871, 31302, 31398, 30275, 31768, 31955, 30743, 29953, 29946, 29889, 29896, 29900, 29995, 29871, 30210, 30406, 31229, 13, 30728, 30946, 31276, 235, 131, 154, 30383, 29896, 29946, 29889, 29946, 13232, 29892, 29871, 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233, 163, 148, 31669, 30940, 30503, 13, 18884, 3876, 29896, 29889, 1563, 353, 1284, 29898, 4632, 29896, 29889, 1563, 29892, 3876, 29906, 29889, 1563, 29897, 1678, 396, 29871, 31548, 30687, 31977, 233, 163, 148, 30210, 30783, 31370, 31651, 30319, 233, 163, 148, 30214, 29871, 31086, 30742, 30210, 31669, 30940, 30573, 30948, 30658, 30733, 31666, 30319, 233, 163, 148, 30210, 31651, 30319, 233, 163, 148, 13, 18884, 3876, 29896, 29889, 1266, 353, 1284, 29898, 4632, 29896, 29889, 1266, 29892, 3876, 29906, 29889, 1266, 29897, 396, 29871, 31548, 30687, 31977, 233, 163, 148, 30210, 30783, 31370, 31803, 30319, 233, 163, 148, 13, 13, 9651, 25342, 3876, 29896, 470, 3876, 29906, 29901, 1678, 396, 29871, 31557, 30946, 30505, 30287, 30502, 31669, 30940, 29892, 29871, 31157, 31092, 31086, 30742, 30946, 30505, 30210, 31669, 30940, 232, 144, 182, 30682, 13, 18884, 736, 3876, 29896, 565, 3876, 29896, 1683, 3876, 29906, 13, 9651, 1683, 29901, 13, 18884, 736, 6213, 396, 29871, 232, 160, 138, 30413, 30946, 30505, 31086, 30742, 8516, 13, 9651, 736, 3876, 29896, 13, 4706, 260, 353, 1284, 29898, 29873, 29896, 29892, 260, 29906, 29897, 13, 4706, 736, 260, 2 ]
Ex021-Tocando_um_mp3.py	Diego-Sil/Python_do_Curso_em_Video	0	110403	import pygame pygame.mixer.init() pygame.init() pygame.mixer.music.load('asus-yamete-kudasai.mp3') pygame.mixer.music.play() pygame.event.wait()	[ 1, 1053, 22028, 13, 2272, 11802, 29889, 28084, 261, 29889, 2344, 580, 13, 2272, 11802, 29889, 2344, 580, 13, 2272, 11802, 29889, 28084, 261, 29889, 23596, 29889, 1359, 877, 294, 375, 29899, 29891, 314, 2650, 29899, 29895, 566, 294, 1794, 29889, 1526, 29941, 1495, 13, 2272, 11802, 29889, 28084, 261, 29889, 23596, 29889, 1456, 580, 13, 2272, 11802, 29889, 3696, 29889, 10685, 580, 13, 13, 2 ]
tests/test_search_core.py	dinghino/searchengine	0	75747	<gh_stars>0 """ Testing module for the search core functionalities """ from search import core from tests.helpers import Item class TestCore: @classmethod def setup_class(cls): cls.items = Item.setup() cls.search = core.SearchEngine(['words'], limit=10) def test_single_words(self): seq = Item.get_by_length(max_length=1) res = self.search('inconvene', seq) assert res == ['inconvenience'] res = self.search('laugh', seq) assert res == ['laughter'] def test_short_phrases(self): seq = Item.get_by_length(3, 5) results = self.search('sherlock holmes', seq) reversed_query_results = self.search('holmes sherlock', seq) expected = [ 'sherlock holmes clapped upon knee', 'sherlock holmes foretold exquisite mouth', 'sherlock holmes upon grey walls', 'sherlock holmes wrote english capital', ] assert results == expected assert reversed_query_results == expected results = self.search('watson', seq) assert results == [ "watson have learned something", "what wanted what doctor watson", ] def test_search_with_params(self): seq = Item.get_by_length(3, 5) res = self.search('sherlock holmes', seq, limit=2, threshold=.95) expected = [ 'sherlock holmes clapped upon knee', 'sherlock holmes foretold exquisite mouth', ] assert res == expected def test_search_long_phrases(self): seq = Item.get_by_length(12, 15) results = self.search('sherlock holmes', seq) expected = [ 'facts these about four clock when sherlock holmes took step between wharf ruffian pursued', # noqa: E501 'moment intimacy there were other traces sherlock holmes stopped front aberdeen shipping company', # noqa: E501 'rushed down just have taken mind understand from some small dealings with sherlock holmes', # noqa: E501 'sherlock holmes brought gush hope which sank into desultory chat with over despair', # noqa: E501 'sherlock holmes great many scattered papers have whatever bears upon stone professional work attend' # noqa: E501 ] assert results == expected	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 15945, 29908, 13, 3057, 292, 3883, 363, 278, 2740, 7136, 13303, 1907, 13, 15945, 29908, 13, 3166, 2740, 1053, 7136, 13, 3166, 6987, 29889, 3952, 6774, 1053, 10976, 13, 13, 13, 1990, 4321, 9203, 29901, 13, 1678, 732, 1990, 5696, 13, 1678, 822, 6230, 29918, 1990, 29898, 25932, 1125, 13, 4706, 1067, 29879, 29889, 7076, 353, 10976, 29889, 14669, 580, 13, 4706, 1067, 29879, 29889, 4478, 353, 7136, 29889, 7974, 12412, 18959, 9303, 7464, 4046, 29922, 29896, 29900, 29897, 13, 13, 1678, 822, 1243, 29918, 14369, 29918, 9303, 29898, 1311, 1125, 13, 4706, 19359, 353, 10976, 29889, 657, 29918, 1609, 29918, 2848, 29898, 3317, 29918, 2848, 29922, 29896, 29897, 13, 13, 4706, 620, 353, 1583, 29889, 4478, 877, 262, 535, 854, 29872, 742, 19359, 29897, 13, 4706, 4974, 620, 1275, 6024, 262, 535, 854, 5597, 2033, 13, 13, 4706, 620, 353, 1583, 29889, 4478, 877, 433, 6129, 742, 19359, 29897, 13, 4706, 4974, 620, 1275, 6024, 433, 6129, 357, 2033, 13, 13, 1678, 822, 1243, 29918, 12759, 29918, 24588, 2129, 29898, 1311, 1125, 13, 4706, 19359, 353, 10976, 29889, 657, 29918, 1609, 29918, 2848, 29898, 29941, 29892, 29871, 29945, 29897, 13, 4706, 2582, 353, 1583, 29889, 4478, 877, 845, 261, 908, 8753, 4467, 742, 19359, 29897, 13, 4706, 18764, 287, 29918, 1972, 29918, 9902, 353, 1583, 29889, 4478, 877, 5391, 4467, 528, 261, 908, 742, 19359, 29897, 13, 4706, 3806, 353, 518, 13, 9651, 525, 845, 261, 908, 8753, 4467, 3711, 2986, 2501, 17905, 29872, 742, 13, 9651, 525, 845, 261, 908, 8753, 4467, 363, 300, 1025, 429, 7680, 568, 13394, 742, 13, 9651, 525, 845, 261, 908, 8753, 4467, 2501, 18345, 14603, 742, 13, 9651, 525, 845, 261, 908, 8753, 4467, 5456, 3033, 1674, 7483, 742, 13, 4706, 4514, 13, 13, 4706, 4974, 2582, 1275, 3806, 13, 4706, 4974, 18764, 287, 29918, 1972, 29918, 9902, 1275, 3806, 13, 4706, 2582, 353, 1583, 29889, 4478, 877, 29893, 271, 1100, 742, 19359, 29897, 13, 4706, 4974, 2582, 1275, 518, 13, 9651, 376, 29893, 271, 1100, 505, 10972, 1554, 613, 13, 9651, 376, 5816, 5131, 825, 11619, 16699, 1100, 613, 13, 4706, 4514, 13, 13, 1678, 822, 1243, 29918, 4478, 29918, 2541, 29918, 7529, 29898, 1311, 1125, 13, 4706, 19359, 353, 10976, 29889, 657, 29918, 1609, 29918, 2848, 29898, 29941, 29892, 29871, 29945, 29897, 13, 4706, 620, 353, 1583, 29889, 4478, 877, 845, 261, 908, 8753, 4467, 742, 19359, 29892, 4046, 29922, 29906, 29892, 16897, 21098, 29929, 29945, 29897, 13, 4706, 3806, 353, 518, 13, 9651, 525, 845, 261, 908, 8753, 4467, 3711, 2986, 2501, 17905, 29872, 742, 13, 9651, 525, 845, 261, 908, 8753, 4467, 363, 300, 1025, 429, 7680, 568, 13394, 742, 13, 4706, 4514, 13, 4706, 4974, 620, 1275, 3806, 13, 13, 1678, 822, 1243, 29918, 4478, 29918, 5426, 29918, 24588, 2129, 29898, 1311, 1125, 13, 4706, 19359, 353, 10976, 29889, 657, 29918, 1609, 29918, 2848, 29898, 29896, 29906, 29892, 29871, 29896, 29945, 29897, 13, 4706, 2582, 353, 1583, 29889, 4478, 877, 845, 261, 908, 8753, 4467, 742, 19359, 29897, 13, 4706, 3806, 353, 518, 13, 9651, 525, 17028, 29879, 1438, 1048, 3023, 12006, 746, 528, 261, 908, 8753, 4467, 3614, 4331, 1546, 377, 29293, 364, 3096, 713, 12359, 6742, 742, 9651, 396, 694, 25621, 29901, 382, 29945, 29900, 29896, 13, 9651, 525, 29885, 2932, 938, 326, 4135, 727, 892, 916, 26695, 528, 261, 908, 8753, 4467, 11084, 4565, 6126, 311, 264, 528, 17347, 5001, 742, 418, 396, 694, 25621, 29901, 382, 29945, 29900, 29896, 13, 9651, 525, 29878, 15392, 1623, 925, 505, 4586, 3458, 2274, 515, 777, 2319, 5376, 886, 411, 528, 261, 908, 8753, 4467, 742, 9651, 396, 694, 25621, 29901, 382, 29945, 29900, 29896, 13, 9651, 525, 845, 261, 908, 8753, 4467, 6296, 330, 1878, 4966, 607, 269, 804, 964, 553, 499, 706, 13563, 411, 975, 8913, 1466, 742, 462, 259, 396, 694, 25621, 29901, 382, 29945, 29900, 29896, 13, 9651, 525, 845, 261, 908, 8753, 4467, 2107, 1784, 29574, 15055, 505, 6514, 367, 1503, 2501, 12565, 10257, 664, 14333, 29915, 259, 396, 694, 25621, 29901, 382, 29945, 29900, 29896, 13, 4706, 4514, 13, 4706, 4974, 2582, 1275, 3806, 13, 2 ]
stian/day_thirteen/python/day_thirteen.py	stian-eng/advent-2021	0	65965	<reponame>stian-eng/advent-2021 from requests.api import get import time import aoc_frame def get_points(data) : points = [] instr = [] flag = True for i in data : if i : if flag : x, y = i.split(',') points.append((int(x), int(y))) else : y = i.split() x = y[-1] axis, num = x.split('=') instr.append((axis, int(num))) else : flag = False return points, instr def fold(points, fold) : if fold[0] == 'y' : for i in range(len(points)) : curr = points[i] if curr[1] > fold[1] : points[i] = (curr[0], fold[1] - (curr[1] - fold[1])) elif fold[0] == 'x' : for i in range(len(points)) : curr = points[i] if curr[0] > fold[1] : points[i] = (fold[1] - (curr[0] - fold[1]), curr[1]) return points def part_one(data, flag2) : points, instr = get_points(data) for f in instr : points = fold(points, f) if flag2 : seen = set() count = 0 for i in points : if i not in seen : seen.add(i) count += 1 return count return points def part_two(data) : points = part_one(data, False) max_x = max([i[0] for i in points]) max_y = max([i[1] for i in points]) matrix = [[' ' for i in range(max_x + 1)] for i in range(max_y + 1)] seen = set() for p in points : if p not in seen : matrix[p[1]][p[0]] = '#' seen.add(p) for i in matrix : print(i) # answer: """ [[' ', '#', '#', ' ', ' ', '#', '#', '#', ' ', ' ', '#', ' ', ' ', '#', ' ', '#', '#', '#', '#', ' ', '#', '#', '#', ' ', ' ', ' ', '#', '#', ' ', ' ', '#', ' ', ' ', '#', ' ', '#', ' ', ' ', '#'], ['#', ' ', ' ', '#', ' ', '#', ' ', ' ', '#', ' ', '#', ' ', ' ', '#', ' ', ' ', ' ', ' ', '#', ' ', '#', ' ', ' ', '#', ' ', '#', ' ', ' ', '#', ' ', '#', ' ', ' ', '#', ' ', '#', ' ', ' ', '#'], ['#', ' ', ' ', '#', ' ', '#', ' ', ' ', '#', ' ', '#', '#', '#', '#', ' ', ' ', ' ', '#', ' ', ' ', '#', ' ', ' ', '#', ' ', '#', ' ', ' ', ' ', ' ', '#', ' ', ' ', '#', ' ', '#', '#', '#', '#'], ['#', '#', '#', '#', ' ', '#', '#', '#', ' ', ' ', '#', ' ', ' ', '#', ' ', ' ', '#', ' ', ' ', ' ', '#', '#', '#', ' ', ' ', '#', ' ', ' ', ' ', ' ', '#', ' ', ' ', '#', ' ', '#', ' ', ' ', '#'], ['#', ' ', ' ', '#', ' ', '#', ' ', '#', ' ', ' ', '#', ' ', ' ', '#', ' ', '#', ' ', ' ', ' ', ' ', '#', ' ', ' ', ' ', ' ', '#', ' ', ' ', '#', ' ', '#', ' ', ' ', '#', ' ', '#', ' ', ' ', '#'], ['#', ' ', ' ', '#', ' ', '#', ' ', ' ', '#', ' ', '#', ' ', ' ', '#', ' ', '#', '#', '#', '#', ' ', '#', ' ', ' ', ' ', ' ', ' ', '#', '#', ' ', ' ', ' ', '#', '#', ' ', ' ', '#', ' ', ' ', '#']] """ if __name__ == '__main__' : data = [] test = [] with open('day_thirteen.txt', 'r') as f : for line in f.readlines() : data.append(line.strip('\n')) with open('test.txt', 'r') as f : for line in f.readlines() : test.append(line.strip('\n')) t0 = time.time() aoc_frame.format_strings(part_one(data, True), part_one(test, True), 1, t0) t0 = time.time() aoc_frame.format_strings(part_two(data), part_two(test), 2, t0)	[ 1, 529, 276, 1112, 420, 29958, 303, 713, 29899, 996, 29914, 328, 794, 29899, 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queue_services/common/tests/integration/test_queue_connections.py	argush3/lear	8	139742	<reponame>argush3/lear # Copyright © 2019 Province of British Columbia # # 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. """The Test Suite to ensure queue connections are working as expected.""" import pytest from nats.aio.client import Client as Nats from stan.aio.client import Client as Stan from stan.aio.errors import StanError @pytest.mark.asyncio async def test_queue_connection(stan_server, event_loop, client_id): """Assert that we connect to the queue configuration used by the tests.""" nc = Nats() await nc.connect(loop=event_loop) sc = Stan() await sc.connect('test-cluster', client_id, nats=nc) assert sc._pub_prefix # pylint: disable=protected-access; sc does not expose a connection check assert sc._conn_id # pylint: disable=protected-access; sc does not expose a connection check await sc.close() # should not close the connection assert nc.is_connected with pytest.raises(StanError): await sc.close() await nc.close() assert not nc.is_connected	[ 1, 529, 276, 1112, 420, 29958, 1191, 1878, 29941, 29914, 1945, 13, 29937, 14187, 1266, 29871, 30211, 29871, 29906, 29900, 29896, 29929, 17325, 310, 4908, 15411, 13, 29937, 13, 29937, 10413, 21144, 1090, 278, 13380, 19245, 29892, 10079, 29871, 29906, 29889, 29900, 313, 1552, 376, 29931, 293, 1947, 1496, 13, 29937, 366, 1122, 451, 671, 445, 934, 5174, 297, 752, 13036, 411, 278, 19245, 29889, 13, 29937, 887, 1122, 4017, 263, 3509, 310, 278, 19245, 472, 13, 29937, 13, 29937, 268, 1732, 597, 1636, 29889, 4288, 29889, 990, 29914, 506, 11259, 29914, 27888, 1430, 1660, 29899, 29906, 29889, 29900, 13, 29937, 13, 29937, 25870, 3734, 491, 22903, 4307, 470, 15502, 304, 297, 5007, 29892, 7047, 13, 29937, 13235, 1090, 278, 19245, 338, 13235, 373, 385, 376, 3289, 8519, 29908, 350, 3289, 3235, 29892, 13, 29937, 399, 1806, 8187, 2692, 399, 1718, 29934, 13566, 29059, 6323, 8707, 29928, 22122, 29903, 8079, 13764, 29979, 476, 22255, 29892, 2845, 4653, 470, 2411, 2957, 29889, 13, 29937, 2823, 278, 19245, 363, 278, 2702, 4086, 14765, 1076, 11239, 322, 13, 29937, 27028, 1090, 278, 19245, 29889, 13, 15945, 29908, 1576, 4321, 2166, 568, 304, 9801, 9521, 12368, 526, 1985, 408, 3806, 1213, 15945, 13, 5215, 11451, 1688, 13, 3166, 302, 1446, 29889, 29874, 601, 29889, 4645, 1053, 12477, 408, 405, 1446, 13, 3166, 11075, 29889, 29874, 601, 29889, 4645, 1053, 12477, 408, 7813, 13, 3166, 11075, 29889, 29874, 601, 29889, 12523, 1053, 7813, 2392, 13, 13, 13, 29992, 2272, 1688, 29889, 3502, 29889, 294, 948, 3934, 13, 12674, 822, 1243, 29918, 9990, 29918, 9965, 29898, 14411, 29918, 2974, 29892, 1741, 29918, 7888, 29892, 3132, 29918, 333, 1125, 13, 1678, 9995, 14697, 393, 591, 4511, 304, 278, 9521, 5285, 1304, 491, 278, 6987, 1213, 15945, 13, 1678, 302, 29883, 353, 405, 1446, 580, 13, 1678, 7272, 302, 29883, 29889, 6915, 29898, 7888, 29922, 3696, 29918, 7888, 29897, 13, 13, 1678, 885, 353, 7813, 580, 13, 1678, 7272, 885, 29889, 6915, 877, 1688, 29899, 19594, 742, 3132, 29918, 333, 29892, 302, 1446, 29922, 17608, 29897, 13, 13, 1678, 4974, 885, 3032, 5467, 29918, 13506, 29871, 396, 282, 2904, 524, 29901, 11262, 29922, 24681, 29899, 5943, 29936, 885, 947, 451, 24396, 263, 3957, 1423, 13, 1678, 4974, 885, 3032, 13082, 29918, 333, 29871, 396, 282, 2904, 524, 29901, 11262, 29922, 24681, 29899, 5943, 29936, 885, 947, 451, 24396, 263, 3957, 1423, 13, 13, 1678, 7272, 885, 29889, 5358, 580, 29871, 396, 881, 451, 3802, 278, 3957, 13, 1678, 4974, 302, 29883, 29889, 275, 29918, 18045, 13, 13, 1678, 411, 11451, 1688, 29889, 336, 4637, 29898, 855, 273, 2392, 1125, 13, 4706, 7272, 885, 29889, 5358, 580, 13, 13, 1678, 7272, 302, 29883, 29889, 5358, 580, 13, 1678, 4974, 451, 302, 29883, 29889, 275, 29918, 18045, 13, 2 ]
docs/generate_example_images.py	KhaledSharif/kornia	0	8661	<reponame>KhaledSharif/kornia<gh_stars>0 import importlib import math import os from pathlib import Path from typing import Optional, Tuple import cv2 import numpy as np import requests import torch import kornia as K def read_img_from_url(url: str, resize_to: Optional[Tuple[int, int]] = None) -> torch.Tensor: # perform request response = requests.get(url).content # convert to array of ints nparr = np.frombuffer(response, np.uint8) # convert to image array and resize img: np.ndarray = cv2.imdecode(nparr, cv2.IMREAD_UNCHANGED)[..., :3] # convert the image to a tensor img_t: torch.Tensor = K.utils.image_to_tensor(img, keepdim=False) # 1xCxHXW img_t = img_t.float() / 255.0 if resize_to is None: img_t = K.geometry.resize(img_t, 184) else: img_t = K.geometry.resize(img_t, resize_to) return img_t def main(): # load the images BASE_IMAGE_URL1: str = "https://raw.githubusercontent.com/kornia/data/main/panda.jpg" # augmentation BASE_IMAGE_URL2: str = "https://raw.githubusercontent.com/kornia/data/main/simba.png" # color BASE_IMAGE_URL3: str = "https://raw.githubusercontent.com/kornia/data/main/girona.png" # enhance BASE_IMAGE_URL4: str = "https://raw.githubusercontent.com/kornia/data/main/baby_giraffe.png" # morphology BASE_IMAGE_URL5: str = "https://raw.githubusercontent.com/kornia/data/main/persistencia_memoria.jpg" # filters BASE_IMAGE_URL6: str = "https://raw.githubusercontent.com/kornia/data/main/delorean.png" # geometry OUTPUT_PATH = Path(__file__).absolute().parent / "source/_static/img" os.makedirs(OUTPUT_PATH, exist_ok=True) print(f"Pointing images to path {OUTPUT_PATH}.") img1 = read_img_from_url(BASE_IMAGE_URL1) img2 = read_img_from_url(BASE_IMAGE_URL2, img1.shape[-2:]) img3 = read_img_from_url(BASE_IMAGE_URL3, img1.shape[-2:]) img4 = read_img_from_url(BASE_IMAGE_URL4) img5 = read_img_from_url(BASE_IMAGE_URL5, (234, 320)) img6 = read_img_from_url(BASE_IMAGE_URL6) # TODO: make this more generic for modules out of kornia.augmentation # Dictionary containing the transforms to generate the sample images: # Key: Name of the transform class. # Value: (parameters, num_samples, seed) mod = importlib.import_module("kornia.augmentation") augmentations_list: dict = { "CenterCrop": ((184, 184), 1, 2018), "ColorJitter": ((0.3, 0.3, 0.3, 0.3), 2, 2018), "RandomAffine": (((-15.0, 20.0), (0.1, 0.1), (0.7, 1.3), 20), 2, 2019), "RandomBoxBlur": (((7, 7),), 1, 2020), "RandomCrop": ((img1.shape[-2:], (50, 50)), 2, 2020), "RandomChannelShuffle": ((), 1, 2020), "RandomElasticTransform": (((63, 63), (32, 32), (2.0, 2.0)), 2, 2018), "RandomEqualize": ((), 1, 2020), "RandomErasing": (((0.2, 0.4), (0.3, 1 / 0.3)), 2, 2017), "RandomFisheye": ((torch.tensor([-0.3, 0.3]), torch.tensor([-0.3, 0.3]), torch.tensor([0.9, 1.0])), 2, 2020), "RandomGaussianBlur": (((3, 3), (0.1, 2.0)), 1, 2020), "RandomGaussianNoise": ((0.0, 0.05), 1, 2020), "RandomGrayscale": ((), 1, 2020), "RandomHorizontalFlip": ((), 1, 2020), "RandomInvert": ((), 1, 2020), "RandomMotionBlur": ((7, 35.0, 0.5), 2, 2020), "RandomPerspective": ((0.2,), 2, 2020), "RandomPlanckianJitter": ((), 2, 2022), "RandomPosterize": (((1, 4),), 2, 2016), "RandomResizedCrop": ((img1.shape[-2:], (1.0, 2.0), (1.0, 2.0)), 2, 2020), "RandomRotation": ((45.0,), 2, 2019), "RandomSharpness": ((16.0,), 1, 2019), "RandomSolarize": ((0.2, 0.2), 2, 2019), "RandomVerticalFlip": ((), 1, 2020), "RandomThinPlateSpline": ((), 1, 2020), } # ITERATE OVER THE TRANSFORMS for aug_name, (args, num_samples, seed) in augmentations_list.items(): img_in = img1.repeat(num_samples, 1, 1, 1) # dynamically create the class instance cls = getattr(mod, aug_name) aug = cls(args, p=1.0) # set seed torch.manual_seed(seed) # apply the augmentaiton to the image and concat out = aug(img_in) if aug_name == "CenterCrop": h, w = img1.shape[-2:] h_new, w_new = out.shape[-2:] h_dif, w_dif = int(h - h_new), int(w - w_new) out = torch.nn.functional.pad(out, (w_dif // 2, w_dif // 2, 0, h_dif)) out = torch.cat([img_in[0], (out[i] for i in range(out.size(0)))], dim=-1) # save the output image out_np = K.utils.tensor_to_image((out * 255.0).byte()) cv2.imwrite(str(OUTPUT_PATH / f"{aug_name}.png"), out_np) sig = f"{aug_name}({', '.join([str(a) for a in args])}, p=1.0)" print(f"Generated image example for {aug_name}. {sig}") mod = importlib.import_module("kornia.augmentation") mix_augmentations_list: dict = { "RandomMixUp": (((0.3, 0.4),), 2, 20), "RandomCutMix": ((img1.shape[-2], img1.shape[-1]), 2, 2019), } # ITERATE OVER THE TRANSFORMS for aug_name, (args, num_samples, seed) in mix_augmentations_list.items(): img_in = torch.cat([img1, img2]) # dynamically create the class instance cls = getattr(mod, aug_name) aug = cls(args, p=1.0) # set seed torch.manual_seed(seed) # apply the augmentaiton to the image and concat out, _ = aug(img_in, torch.tensor([0, 1])) out = torch.cat([img_in[0], img_in[1], (out[i] for i in range(out.size(0)))], dim=-1) # save the output image out_np = K.utils.tensor_to_image((out * 255.0).byte()) cv2.imwrite(str(OUTPUT_PATH / f"{aug_name}.png"), out_np) sig = f"{aug_name}({', '.join([str(a) for a in args])}, p=1.0)" print(f"Generated image example for {aug_name}. {sig}") mod = importlib.import_module("kornia.color") color_transforms_list: dict = { "grayscale_to_rgb": ((), 3), "rgb_to_bgr": ((), 1), "rgb_to_grayscale": ((), 1), "rgb_to_hsv": ((), 1), "rgb_to_hls": ((), 1), "rgb_to_luv": ((), 1), "rgb_to_lab": ((), 1), # "rgb_to_rgba": ((1.,), 1), "rgb_to_xyz": ((), 1), "rgb_to_ycbcr": ((), 1), "rgb_to_yuv": ((), 1), "rgb_to_linear_rgb": ((), 1), } # ITERATE OVER THE TRANSFORMS for fn_name, (args, num_samples) in color_transforms_list.items(): # import function and apply fn = getattr(mod, fn_name) if fn_name == "grayscale_to_rgb": out = fn(K.color.rgb_to_grayscale(img2), args) else: out = fn(img2, args) # perform normalization to visualize if fn_name == "rgb_to_lab": out = out[:, :1] / 100.0 elif fn_name == "rgb_to_hsv": out[:, :1] = out[:, :1] / 2 * math.pi elif fn_name == "rgb_to_luv": out = out[:, :1] / 116.0 # repeat channels for grayscale if out.shape[1] != 3: out = out.repeat(1, 3, 1, 1) # save the output image if fn_name == "grayscale_to_rgb": out = torch.cat( [K.color.rgb_to_grayscale(img2[0]).repeat(3, 1, 1), (out[i] for i in range(out.size(0)))], dim=-1 ) else: out = torch.cat([img2[0], (out[i] for i in range(out.size(0)))], dim=-1) out_np = K.utils.tensor_to_image((out * 255.0).byte()) cv2.imwrite(str(OUTPUT_PATH / f"{fn_name}.png"), out_np) sig = f"{fn_name}({', '.join([str(a) for a in args])})" print(f"Generated image example for {fn_name}. {sig}") # korna.enhance module mod = importlib.import_module("kornia.enhance") transforms: dict = { "adjust_brightness": ((torch.tensor([0.25, 0.5]),), 2), "adjust_contrast": ((torch.tensor([0.65, 0.5]),), 2), "adjust_gamma": ((torch.tensor([0.85, 0.75]), 2.0), 2), "adjust_hue": ((torch.tensor([-math.pi / 4, math.pi / 4]),), 2), "adjust_saturation": ((torch.tensor([1.0, 2.0]),), 2), "solarize": ((torch.tensor([0.8, 0.5]), torch.tensor([-0.25, 0.25])), 2), "posterize": ((torch.tensor([4, 2]),), 2), "sharpness": ((torch.tensor([1.0, 2.5]),), 2), "equalize": ((), 1), "invert": ((), 1), "equalize_clahe": ((), 1), "add_weighted": ((0.75, 0.25, 2.0), 1), } # ITERATE OVER THE TRANSFORMS for fn_name, (args, num_samples) in transforms.items(): img_in = img3.repeat(num_samples, 1, 1, 1) if fn_name == "add_weighted": args_in = (img_in, args[0], img2, args[1], args[2]) else: args_in = (img_in, args) # import function and apply fn = getattr(mod, fn_name) out = fn(args_in) # save the output image out = torch.cat([img_in[0], (out[i] for i in range(out.size(0)))], dim=-1) out_np = K.utils.tensor_to_image((out 255.0).byte()) cv2.imwrite(str(OUTPUT_PATH / f"{fn_name}.png"), out_np) sig = f"{fn_name}({', '.join([str(a) for a in args])})" print(f"Generated image example for {fn_name}. {sig}") # korna.morphology module mod = importlib.import_module("kornia.morphology") kernel = torch.tensor([[0, 1, 0], [1, 1, 1], [0, 1, 0]]) transforms: dict = { "dilation": ((kernel,), 1), "erosion": ((kernel,), 1), "opening": ((kernel,), 1), "closing": ((kernel,), 1), "gradient": ((kernel,), 1), "top_hat": ((kernel,), 1), "bottom_hat": ((kernel,), 1), } # ITERATE OVER THE TRANSFORMS for fn_name, (args, num_samples) in transforms.items(): img_in = img4.repeat(num_samples, 1, 1, 1) args_in = (img_in, args) # import function and apply # import pdb;pdb.set_trace() fn = getattr(mod, fn_name) out = fn(args_in) # save the output image out = torch.cat([img_in[0], (out[i] for i in range(out.size(0)))], dim=-1) out_np = K.utils.tensor_to_image((out 255.0).byte()) cv2.imwrite(str(OUTPUT_PATH / f"{fn_name}.png"), out_np) sig = f"{fn_name}({', '.join([str(a) for a in args])})" print(f"Generated image example for {fn_name}. {sig}") # korna.filters module mod = importlib.import_module("kornia.filters") kernel = torch.tensor([[0, 1, 0], [1, 1, 1], [0, 1, 0]]) transforms: dict = { "box_blur": (((5, 5),), 1), "median_blur": (((5, 5),), 1), "gaussian_blur2d": (((5, 5), (1.5, 1.5)), 1), "motion_blur": ((5, 90.0, 1.0), 1), "max_blur_pool2d": ((5,), 1), "blur_pool2d": ((5,), 1), "unsharp_mask": (((5, 5), (1.5, 1.5)), 1), "laplacian": ((5,), 1), "sobel": ((), 1), "spatial_gradient": ((), 1), "canny": ((), 1), } # ITERATE OVER THE TRANSFORMS for fn_name, (args, num_samples) in transforms.items(): img_in = img5.repeat(num_samples, 1, 1, 1) args_in = (img_in, args) # import function and apply fn = getattr(mod, fn_name) out = fn(args_in) if fn_name in ("max_blur_pool2d", "blur_pool2d"): out = K.geometry.resize(out, img_in.shape[-2:]) if fn_name == "canny": out = out[1].repeat(1, 3, 1, 1) if isinstance(out, torch.Tensor): out = out.clamp(min=0.0, max=1.0) if fn_name in ("laplacian", "sobel", "spatial_gradient", "canny"): out = K.enhance.normalize_min_max(out) if fn_name == "spatial_gradient": out = out.permute(2, 1, 0, 3, 4).squeeze() # save the output image out = torch.cat([img_in[0], (out[i] for i in range(out.size(0)))], dim=-1) out_np = K.utils.tensor_to_image((out 255.0).byte()) cv2.imwrite(str(OUTPUT_PATH / f"{fn_name}.png"), out_np) sig = f"{fn_name}({', '.join([str(a) for a in args])})" print(f"Generated image example for {fn_name}. {sig}") # korna.geometry.transform module mod = importlib.import_module("kornia.geometry.transform") h, w = img6.shape[-2:] def _get_tps_args(): src = torch.tensor([[[-1.0, -1.0], [-1.0, 1.0], [1.0, -1.0], [1.0, -1.0], [0.0, 0.0]]]).repeat(2, 1, 1) # Bx5x2 dst = src + torch.distributions.Uniform(-0.2, 0.2).rsample((2, 5, 2)) kernel, affine = K.geometry.transform.get_tps_transform(dst, src) return src, kernel, affine transforms: dict = { "warp_affine": ( ( K.geometry.transform.get_affine_matrix2d( translations=torch.zeros(2, 2), center=(torch.tensor([w, h]) / 2).repeat(2, 1), scale=torch.distributions.Uniform(0.5, 1.5).rsample((2, 2)), angle=torch.tensor([-25.0, 25.0]), )[:, :2, :3], (h, w), ), 2, ), "remap": ( ( (K.utils.create_meshgrid(h, w, normalized_coordinates=True) - 0.25).unbind(-1), 'bilinear', 'zeros', True, True, ), 1, ), "warp_image_tps": ((_get_tps_args()), 2), "rotate": ((torch.tensor([-15.0, 25.0]),), 2), "translate": ((torch.tensor([[10.0, -15], [50.0, -25.0]]),), 2), "scale": ((torch.tensor([[0.5, 1.25], [1.0, 1.5]]),), 2), "shear": ((torch.tensor([[0.1, -0.2], [-0.2, 0.1]]),), 2), "rot180": ((), 1), "hflip": ((), 1), "vflip": ((), 1), "resize": (((120, 220),), 1), "rescale": ((0.5,), 1), "elastic_transform2d": ((torch.rand(1, 2, h, w) 2 - 1, (63, 63), (32, 32), (4.0, 4.0)), 1), "pyrdown": ((), 1), "pyrup": ((), 1), "build_pyramid": ((3,), 1), } # ITERATE OVER THE TRANSFORMS for fn_name, (args, num_samples) in transforms.items(): img_in = img6.repeat(num_samples, 1, 1, 1) args_in = (img_in, args) # import function and apply fn = getattr(mod, fn_name) out = fn(args_in) if fn_name in ("resize", "rescale", "pyrdown", "pyrup"): h_new, w_new = out.shape[-2:] out = torch.nn.functional.pad(out, (0, (w - w_new), 0, (h - h_new))) if fn_name == "build_pyramid": _out = [] for pyr in out[1:]: h_new, w_new = pyr.shape[-2:] out_tmp = torch.nn.functional.pad(pyr, (0, (w - w_new), 0, (h - h_new))) _out.append(out_tmp) out = torch.cat(_out) # save the output image out = torch.cat([img_in[0], (out[i] for i in range(out.size(0)))], dim=-1) out_np = K.utils.tensor_to_image((out 255.0).byte()) cv2.imwrite(str(OUTPUT_PATH / f"{fn_name}.png"), out_np) sig = f"{fn_name}({', '.join([str(a) for a in args])})" print(f"Generated image example for {fn_name}. {sig}") if __name__ == "__main__": main()	[ 1, 529, 276, 1112, 420, 29958, 29968, 29882, 7943, 2713, 279, 361, 29914, 29895, 1398, 423, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 5215, 1053, 1982, 13, 5215, 5844, 13, 5215, 2897, 13, 3166, 2224, 1982, 1053, 10802, 13, 3166, 19229, 1053, 28379, 29892, 12603, 552, 13, 13, 5215, 13850, 29906, 13, 5215, 12655, 408, 7442, 13, 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userprofile/migrations/0014_auto_20190430_2023.py	Riphiphip/website	25	167600	<gh_stars>10-100 # Generated by Django 2.0.10 on 2019-04-30 20:23 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('userprofile', '0013_auto_20190408_1825'), ] operations = [ migrations.RemoveField( model_name='profile', name='social_slack', ), migrations.AlterField( model_name='profile', name='social_battlenet', field=models.CharField(blank=True, max_length=30, null=True, verbose_name='Battle.net-tag'), ), migrations.AlterField( model_name='profile', name='social_git', field=models.CharField(blank=True, max_length=30, null=True, verbose_name='Git navn'), ), migrations.AlterField( model_name='profile', name='social_steam', field=models.CharField(blank=True, max_length=30, null=True, verbose_name='Steam navn'), ), ]	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 29937, 3251, 630, 491, 15337, 29871, 29906, 29889, 29900, 29889, 29896, 29900, 373, 29871, 29906, 29900, 29896, 29929, 29899, 29900, 29946, 29899, 29941, 29900, 29871, 29906, 29900, 29901, 29906, 29941, 13, 13, 3166, 9557, 29889, 2585, 1053, 9725, 800, 29892, 4733, 13, 13, 13, 1990, 341, 16783, 29898, 26983, 800, 29889, 29924, 16783, 1125, 13, 13, 1678, 9962, 353, 518, 13, 4706, 6702, 1792, 10185, 742, 525, 29900, 29900, 29896, 29941, 29918, 6921, 29918, 29906, 29900, 29896, 29929, 29900, 29946, 29900, 29947, 29918, 29896, 29947, 29906, 29945, 5477, 13, 1678, 4514, 13, 13, 1678, 6931, 353, 518, 13, 4706, 9725, 800, 29889, 15941, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 10185, 742, 13, 9651, 1024, 2433, 24911, 29918, 29879, 2364, 742, 13, 4706, 10353, 13, 4706, 9725, 800, 29889, 2499, 357, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 10185, 742, 13, 9651, 1024, 2433, 24911, 29918, 29890, 1131, 2435, 300, 742, 13, 9651, 1746, 29922, 9794, 29889, 27890, 29898, 19465, 29922, 5574, 29892, 4236, 29918, 2848, 29922, 29941, 29900, 29892, 1870, 29922, 5574, 29892, 26952, 29918, 978, 2433, 29933, 5315, 29889, 1212, 29899, 4039, 5477, 13, 4706, 10353, 13, 4706, 9725, 800, 29889, 2499, 357, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 10185, 742, 13, 9651, 1024, 2433, 24911, 29918, 5559, 742, 13, 9651, 1746, 29922, 9794, 29889, 27890, 29898, 19465, 29922, 5574, 29892, 4236, 29918, 2848, 29922, 29941, 29900, 29892, 1870, 29922, 5574, 29892, 26952, 29918, 978, 2433, 28712, 6283, 29876, 5477, 13, 4706, 10353, 13, 4706, 9725, 800, 29889, 2499, 357, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 10185, 742, 13, 9651, 1024, 2433, 24911, 29918, 1655, 314, 742, 13, 9651, 1746, 29922, 9794, 29889, 27890, 29898, 19465, 29922, 5574, 29892, 4236, 29918, 2848, 29922, 29941, 29900, 29892, 1870, 29922, 5574, 29892, 26952, 29918, 978, 2433, 7789, 314, 6283, 29876, 5477, 13, 4706, 10353, 13, 1678, 4514, 13, 2 ]
python2/probe_yd.py	Nzen/run_ydl	0	22214	<filename>python2/probe_yd.py<gh_stars>0 from sys import argv from subprocess import call try : link = argv[ 1 ] except IndexError: link = raw_input( " - which url interests you? " ) try: ydl_answ = call( "youtube-dl -F "+ link, shell = True ) if ydl_answ is not 0 : print "-- failed "+ link + " code "+ str(ydl_answ) except OSError as ose : print "Execution failed:", ose	[ 1, 529, 9507, 29958, 4691, 29906, 29914, 771, 915, 29918, 2941, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 10876, 1053, 1852, 29894, 13, 3166, 1014, 5014, 1053, 1246, 13, 13, 13, 2202, 584, 13, 12, 2324, 353, 1852, 29894, 29961, 29871, 29896, 4514, 13, 19499, 11374, 2392, 29901, 13, 12, 2324, 353, 10650, 29918, 2080, 29898, 376, 448, 607, 3142, 20017, 366, 29973, 376, 1723, 13, 13, 2202, 29901, 13, 12, 2941, 29880, 29918, 550, 29893, 353, 1246, 29898, 376, 19567, 29899, 11671, 448, 29943, 15691, 1544, 29892, 6473, 353, 5852, 1723, 13, 12, 361, 343, 11671, 29918, 550, 29893, 338, 451, 29871, 29900, 584, 13, 12, 12, 2158, 376, 489, 5229, 15691, 1544, 718, 376, 775, 15691, 851, 29898, 2941, 29880, 29918, 550, 29893, 29897, 13, 19499, 438, 29173, 408, 288, 344, 584, 13, 12, 2158, 376, 20418, 5229, 29901, 613, 288, 344, 13, 2 ]
lib/ldapconn.py	ArcaniteSolutions/zabbix-ldap-sync	0	1615065	import ldap import ldap.filter import logging class LDAPConn(object): """ LDAP connector class Defines methods for retrieving users and groups from LDAP server. """ def __init__(self, config): self.conn = None self.disabled_filter = config.ad_filterdisabled self.uri = config.ldap_uri self.base = config.ldap_base self.ldap_accountids = config.ldap_accountids self.ldap_user = config.ldap_user self.ldap_pass = config.ldap_passwd self.ldap_type = config.ldap_type self.group_member_attribute = config.ldap_group_member_attribute self.group_filter = config.ldap_group_filter self.uid_attribute = config.ldap_uid_attribute self.active_directory = config.ldap_active_directory self.recursive = config.ldap_recursive if self.recursive and self.active_directory: self.memberof_filter = config.ldap_memberof_filter self.skipdisabled = config.ldap_skipdisabled self.user_filter = config.ldap_user_filter self.verbose = config.verbose self.openldap_type = config.openldap_type self.logger = logging.getLogger(self.__class__.__name__) # Log from pyldap log = logging.getLogger('ldap') if self.verbose: log.setLevel(logging.DEBUG) ldap.set_option(ldap.OPT_DEBUG_LEVEL, 4095) if config.ldap_ignore_tls_errors: ldap.set_option(ldap.OPT_X_TLS_REQUIRE_CERT, ldap.OPT_X_TLS_NEVER) def connect(self): """ Establish a connection to the LDAP server. Raises: SystemExit """ ldap.set_option(ldap.OPT_X_TLS_REQUIRE_CERT, ldap.OPT_X_TLS_NEVER) self.conn = ldap.initialize(self.uri) self.conn.set_option(ldap.OPT_REFERRALS, ldap.OPT_OFF) try: self.conn.simple_bind_s(self.ldap_user, self.ldap_pass) except ldap.SERVER_DOWN as e: raise SystemExit('Cannot connect to LDAP server: %s' % e) def disconnect(self): """ Disconnect from the LDAP server. """ self.conn.unbind() def remove_ad_referrals(self, result: list): """ Remove referrals from AD query result """ return [i for i in result if i[0] is not None] def get_group_members(self, group: str): """ Retrieves the members of an LDAP group Args: group (str): The LDAP group name Returns: A list of all users in the LDAP group """ attrlist = [self.group_member_attribute] filter = self.group_filter % group self.logger.debug('Searching LDAP with filter >>>%s<<<' % filter) result = self.conn.search_s(base=self.base, scope=ldap.SCOPE_SUBTREE, filterstr=filter, attrlist=attrlist) if not result: self.logger.info('Unable to find group "%s" with filter "%s", skipping group' % (group, filter)) return None # Get DN for each user in the group if self.active_directory: return self.get_group_members_active_directory(result) else: return self.get_group_members_ldap(result) def get_group_members_ldap(self, result: list): dn, users = result.pop() if not users: return {} final_listing = {} group_members = [] # Get info for each user in the group for memberid in users[self.group_member_attribute]: memberid = memberid.decode("utf-8") if self.openldap_type == "groupofnames": filter = "(objectClass=*)" # memberid is user dn base = memberid else: # memberid is user attribute, most likely uid filter = self.user_filter % memberid base = self.base attrlist = [self.uid_attribute] # get the actual LDAP object for each group member self.logger.debug('Searching LDAP with filter >>>%s<<<' % filter) uid = self.conn.search_s(base=base, scope=ldap.SCOPE_SUBTREE, filterstr=filter, attrlist=attrlist) for item in uid: group_members.append(item) # Fill dictionary with usernames and corresponding DNs for item in group_members: dn = item[0] username = item[1][self.uid_attribute] user = ''.join(username[0].decode('utf-8')) final_listing[user] = dn return final_listing def get_group_members_active_directory(self, result: list): result = self.remove_ad_referrals(result) final_listing = {} for members in result: result_dn = members[0] result_attrs = members[1] group_members = [] attrlist = [self.uid_attribute] if self.recursive: # Get a DN for all users in a group (recursive) # It's available only on domain controllers with Windows Server 2003 SP2 or later member_of_filter_dn = self.memberof_filter % result_dn if self.skipdisabled: filter = "(&%s%s%s)" % (self.user_filter, member_of_filter_dn, self.disabled_filter) else: filter = "(&%s%s)" % (self.user_filter, member_of_filter_dn) self.logger.debug('Searching LDAP with filter >>>%s<<<' % filter) uid = self.conn.search_s(base=self.base, scope=ldap.SCOPE_SUBTREE, filterstr=filter, attrlist=attrlist) for item in self.remove_ad_referrals(uid): group_members.append(item) else: # Otherwise, just get a DN for each user in the group for member in result_attrs[self.group_member_attribute]: if self.skipdisabled: filter = "(&%s%s)" % (self.user_filter, self.disabled_filter) else: filter = "(&%s)" % self.user_filter self.logger.debug('Searching LDAP with filter >>>%s<<<' % filter) uid = self.conn.search_s(base=member.decode('utf8'), scope=ldap.SCOPE_BASE, filterstr=filter, attrlist=attrlist) for item in uid: group_members.append(item) # Fill dictionary with usernames and corresponding DNs for item in group_members: dn = item[0] username = item[1][self.uid_attribute] if self.ldap_accountids: username = username[0].decode('utf8') else: username = username[0].decode('utf8').lower() final_listing[username] = dn return final_listing def get_user_media(self, dn: str, ldap_media: list): """ Retrieves the 'media' attribute of an LDAP user Args: dn (str): The LDAP distinguished name to lookup ldap_media (str): The name of the field containing the media address Returns: The user's media attribute value """ attrlist = [ldap_media] result = self.conn.search_s(base=dn, scope=ldap.SCOPE_BASE, attrlist=attrlist) if not result: return None dn, data = result.pop() mail = data.get(ldap_media) if not mail: return None return mail.pop() def get_user_sn(self, dn: str): """ Retrieves the 'sn' attribute of an LDAP user Args: dn (str): The LDAP distinguished name to lookup Returns: The user's surname attribute """ attrlist = ['sn'] result = self.conn.search_s(base=dn, scope=ldap.SCOPE_BASE, attrlist=attrlist) if not result: return None dn, data = result.pop() sn = data.get('sn') if not sn: return None return sn.pop() def get_user_givenName(self, dn: str): """ Retrieves the 'givenName' attribute of an LDAP user Args: dn (str): The LDAP distinguished name to lookup Returns: The user's given name attribute """ attrlist = ['givenName'] result = self.conn.search_s(base=dn, scope=ldap.SCOPE_BASE, attrlist=attrlist) if not result: return None dn, data = result.pop() name = data.get('givenName') if not name: return None return name.pop() def get_groups_with_wildcard(self, groups_wildcard: str): filters = [] for wildcard in groups_wildcard: self.logger.info("Search groups with wildcard: %s" % wildcard) filters.append(self.group_filter % wildcard) ldap_filter = "(\| %s)" % (" ".join(filters)) result_groups = [] self.logger.debug('Searching LDAP with filter >>>%s<<<' % ldap_filter) result = self.conn.search_s(base=self.base, scope=ldap.SCOPE_SUBTREE, filterstr=ldap_filter) for group in result: # Skip refldap (when Active Directory used) # [0]==None if group[0]: group_name = group[1]['name'][0].decode() self.logger.info("Found group %s" % group_name) result_groups.append(group_name) if not result_groups: self.logger.info('Unable to find group "%s", skipping group wildcard' % groups_wildcard) return result_groups	[ 1, 1053, 301, 29881, 481, 13, 5215, 301, 29881, 481, 29889, 4572, 13, 5215, 12183, 13, 13, 13, 1990, 365, 29928, 3301, 1168, 29876, 29898, 3318, 1125, 13, 1678, 9995, 13, 1678, 365, 29928, 3301, 1826, 2801, 770, 13, 13, 1678, 5282, 1475, 3519, 363, 5663, 15387, 4160, 322, 6471, 515, 365, 29928, 3301, 1923, 29889, 13, 13, 1678, 9995, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 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google/cloud/bigquery/reservation_v1/types/reservation.py	shollyman/python-bigquery-reservation	0	137874	<gh_stars>0 # -- coding: utf-8 -- # Copyright 2020 Google LLC # # 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 proto # type: ignore from google.protobuf import field_mask_pb2 as field_mask # type: ignore from google.protobuf import timestamp_pb2 as timestamp # type: ignore from google.rpc import status_pb2 as status # type: ignore __protobuf__ = proto.module( package="google.cloud.bigquery.reservation.v1", manifest={ "Reservation", "CapacityCommitment", "CreateReservationRequest", "ListReservationsRequest", "ListReservationsResponse", "GetReservationRequest", "DeleteReservationRequest", "UpdateReservationRequest", "CreateCapacityCommitmentRequest", "ListCapacityCommitmentsRequest", "ListCapacityCommitmentsResponse", "GetCapacityCommitmentRequest", "DeleteCapacityCommitmentRequest", "UpdateCapacityCommitmentRequest", "SplitCapacityCommitmentRequest", "SplitCapacityCommitmentResponse", "MergeCapacityCommitmentsRequest", "Assignment", "CreateAssignmentRequest", "ListAssignmentsRequest", "ListAssignmentsResponse", "DeleteAssignmentRequest", "SearchAssignmentsRequest", "SearchAssignmentsResponse", "MoveAssignmentRequest", "BiReservation", "GetBiReservationRequest", "UpdateBiReservationRequest", }, ) class Reservation(proto.Message): r"""A reservation is a mechanism used to guarantee slots to users. Attributes: name (str): The resource name of the reservation, e.g., ``projects//locations//reservations/team1-prod``. slot_capacity (int): Minimum slots available to this reservation. A slot is a unit of computational power in BigQuery, and serves as the unit of parallelism. Queries using this reservation might use more slots during runtime if ignore_idle_slots is set to false. If the new reservation's slot capacity exceed the parent's slot capacity or if total slot capacity of the new reservation and its siblings exceeds the parent's slot capacity, the request will fail with ``google.rpc.Code.RESOURCE_EXHAUSTED``. ignore_idle_slots (bool): If false, any query using this reservation will use idle slots from other reservations within the same admin project. If true, a query using this reservation will execute with the slot capacity specified above at most. """ name = proto.Field(proto.STRING, number=1) slot_capacity = proto.Field(proto.INT64, number=2) ignore_idle_slots = proto.Field(proto.BOOL, number=4) class CapacityCommitment(proto.Message): r"""Capacity commitment is a way to purchase compute capacity for BigQuery jobs (in the form of slots) with some committed period of usage. Annual commitments renew by default. Commitments can be removed after their commitment end time passes. In order to remove annual commitment, its plan needs to be changed to monthly or flex first. A capacity commitment resource exists as a child resource of the admin project. Attributes: name (str): Output only. The resource name of the capacity commitment, e.g., ``projects/myproject/locations/US/capacityCommitments/123`` slot_count (int): Number of slots in this commitment. plan (~.gcbr_reservation.CapacityCommitment.CommitmentPlan): Capacity commitment commitment plan. state (~.gcbr_reservation.CapacityCommitment.State): Output only. State of the commitment. commitment_end_time (~.timestamp.Timestamp): Output only. The end of the current commitment period. It is applicable only for ACTIVE capacity commitments. failure_status (~.status.Status): Output only. For FAILED commitment plan, provides the reason of failure. renewal_plan (~.gcbr_reservation.CapacityCommitment.CommitmentPlan): The plan this capacity commitment is converted to after commitment_end_time passes. Once the plan is changed, committed period is extended according to commitment plan. Only applicable for ANNUAL and TRIAL commitments. """ class CommitmentPlan(proto.Enum): r"""Commitment plan defines the current committed period. Capacity commitment cannot be deleted during it's committed period. """ COMMITMENT_PLAN_UNSPECIFIED = 0 FLEX = 3 TRIAL = 5 MONTHLY = 2 ANNUAL = 4 class State(proto.Enum): r"""Capacity commitment can either become ACTIVE right away or transition from PENDING to ACTIVE or FAILED. """ STATE_UNSPECIFIED = 0 PENDING = 1 ACTIVE = 2 FAILED = 3 name = proto.Field(proto.STRING, number=1) slot_count = proto.Field(proto.INT64, number=2) plan = proto.Field(proto.ENUM, number=3, enum=CommitmentPlan) state = proto.Field(proto.ENUM, number=4, enum=State) commitment_end_time = proto.Field( proto.MESSAGE, number=5, message=timestamp.Timestamp ) failure_status = proto.Field(proto.MESSAGE, number=7, message=status.Status) renewal_plan = proto.Field(proto.ENUM, number=8, enum=CommitmentPlan) class CreateReservationRequest(proto.Message): r"""The request for [ReservationService.CreateReservation][google.cloud.bigquery.reservation.v1.ReservationService.CreateReservation]. Attributes: parent (str): Required. Project, location. E.g., ``projects/myproject/locations/US`` reservation_id (str): The reservation ID. This field must only contain lower case alphanumeric characters or dash. Max length is 64 characters. reservation (~.gcbr_reservation.Reservation): Definition of the new reservation to create. """ parent = proto.Field(proto.STRING, number=1) reservation_id = proto.Field(proto.STRING, number=2) reservation = proto.Field(proto.MESSAGE, number=3, message=Reservation) class ListReservationsRequest(proto.Message): r"""The request for [ReservationService.ListReservations][google.cloud.bigquery.reservation.v1.ReservationService.ListReservations]. Attributes: parent (str): Required. The parent resource name containing project and location, e.g.: "projects/myproject/locations/US". page_size (int): The maximum number of items to return per page. page_token (str): The next_page_token value returned from a previous List request, if any. """ parent = proto.Field(proto.STRING, number=1) page_size = proto.Field(proto.INT32, number=2) page_token = proto.Field(proto.STRING, number=3) class ListReservationsResponse(proto.Message): r"""The response for [ReservationService.ListReservations][google.cloud.bigquery.reservation.v1.ReservationService.ListReservations]. Attributes: reservations (Sequence[~.gcbr_reservation.Reservation]): List of reservations visible to the user. next_page_token (str): Token to retrieve the next page of results, or empty if there are no more results in the list. """ @property def raw_page(self): return self reservations = proto.RepeatedField(proto.MESSAGE, number=1, message=Reservation) next_page_token = proto.Field(proto.STRING, number=2) class GetReservationRequest(proto.Message): r"""The request for [ReservationService.GetReservation][google.cloud.bigquery.reservation.v1.ReservationService.GetReservation]. Attributes: name (str): Required. Resource name of the reservation to retrieve. E.g., ``projects/myproject/locations/US/reservations/team1-prod`` """ name = proto.Field(proto.STRING, number=1) class DeleteReservationRequest(proto.Message): r"""The request for [ReservationService.DeleteReservation][google.cloud.bigquery.reservation.v1.ReservationService.DeleteReservation]. Attributes: name (str): Required. Resource name of the reservation to retrieve. E.g., ``projects/myproject/locations/US/reservations/team1-prod`` """ name = proto.Field(proto.STRING, number=1) class UpdateReservationRequest(proto.Message): r"""The request for [ReservationService.UpdateReservation][google.cloud.bigquery.reservation.v1.ReservationService.UpdateReservation]. Attributes: reservation (~.gcbr_reservation.Reservation): Content of the reservation to update. update_mask (~.field_mask.FieldMask): Standard field mask for the set of fields to be updated. """ reservation = proto.Field(proto.MESSAGE, number=1, message=Reservation) update_mask = proto.Field(proto.MESSAGE, number=2, message=field_mask.FieldMask) class CreateCapacityCommitmentRequest(proto.Message): r"""The request for [ReservationService.CreateCapacityCommitment][google.cloud.bigquery.reservation.v1.ReservationService.CreateCapacityCommitment]. Attributes: parent (str): Required. Resource name of the parent reservation. E.g., projects/myproject/locations/US capacity_commitment (~.gcbr_reservation.CapacityCommitment): Content of the capacity commitment to create. enforce_single_admin_project_per_org (bool): If true, fail the request if another project in the organization has a capacity commitment. """ parent = proto.Field(proto.STRING, number=1) capacity_commitment = proto.Field( proto.MESSAGE, number=2, message=CapacityCommitment ) enforce_single_admin_project_per_org = proto.Field(proto.BOOL, number=4) class ListCapacityCommitmentsRequest(proto.Message): r"""The request for [ReservationService.ListCapacityCommitments][google.cloud.bigquery.reservation.v1.ReservationService.ListCapacityCommitments]. Attributes: parent (str): Required. Resource name of the parent reservation. E.g., projects/myproject/locations/US page_size (int): The maximum number of items to return. page_token (str): The next_page_token value returned from a previous List request, if any. """ parent = proto.Field(proto.STRING, number=1) page_size = proto.Field(proto.INT32, number=2) page_token = proto.Field(proto.STRING, number=3) class ListCapacityCommitmentsResponse(proto.Message): r"""The response for [ReservationService.ListCapacityCommitments][google.cloud.bigquery.reservation.v1.ReservationService.ListCapacityCommitments]. Attributes: capacity_commitments (Sequence[~.gcbr_reservation.CapacityCommitment]): List of capacity commitments visible to the user. next_page_token (str): Token to retrieve the next page of results, or empty if there are no more results in the list. """ @property def raw_page(self): return self capacity_commitments = proto.RepeatedField( proto.MESSAGE, number=1, message=CapacityCommitment ) next_page_token = proto.Field(proto.STRING, number=2) class GetCapacityCommitmentRequest(proto.Message): r"""The request for [ReservationService.GetCapacityCommitment][google.cloud.bigquery.reservation.v1.ReservationService.GetCapacityCommitment]. Attributes: name (str): Required. Resource name of the capacity commitment to retrieve. E.g., projects/myproject/locations/US/capacityCommitments/123 """ name = proto.Field(proto.STRING, number=1) class DeleteCapacityCommitmentRequest(proto.Message): r"""The request for [ReservationService.DeleteCapacityCommitment][google.cloud.bigquery.reservation.v1.ReservationService.DeleteCapacityCommitment]. Attributes: name (str): Required. Resource name of the capacity commitment to delete. E.g., projects/myproject/locations/US/capacityCommitments/123 """ name = proto.Field(proto.STRING, number=1) class UpdateCapacityCommitmentRequest(proto.Message): r"""The request for [ReservationService.UpdateCapacityCommitment][google.cloud.bigquery.reservation.v1.ReservationService.UpdateCapacityCommitment]. Attributes: capacity_commitment (~.gcbr_reservation.CapacityCommitment): Content of the capacity commitment to update. update_mask (~.field_mask.FieldMask): Standard field mask for the set of fields to be updated. """ capacity_commitment = proto.Field( proto.MESSAGE, number=1, message=CapacityCommitment ) update_mask = proto.Field(proto.MESSAGE, number=2, message=field_mask.FieldMask) class SplitCapacityCommitmentRequest(proto.Message): r"""The request for [ReservationService.SplitCapacityCommitment][google.cloud.bigquery.reservation.v1.ReservationService.SplitCapacityCommitment]. Attributes: name (str): Required. The resource name e.g.,: projects/myproject/locations/US/capacityCommitments/123 slot_count (int): Number of slots in the capacity commitment after the split. """ name = proto.Field(proto.STRING, number=1) slot_count = proto.Field(proto.INT64, number=2) class SplitCapacityCommitmentResponse(proto.Message): r"""The response for [ReservationService.SplitCapacityCommitment][google.cloud.bigquery.reservation.v1.ReservationService.SplitCapacityCommitment]. Attributes: first (~.gcbr_reservation.CapacityCommitment): First capacity commitment, result of a split. second (~.gcbr_reservation.CapacityCommitment): Second capacity commitment, result of a split. """ first = proto.Field(proto.MESSAGE, number=1, message=CapacityCommitment) second = proto.Field(proto.MESSAGE, number=2, message=CapacityCommitment) class MergeCapacityCommitmentsRequest(proto.Message): r"""The request for [ReservationService.MergeCapacityCommitments][google.cloud.bigquery.reservation.v1.ReservationService.MergeCapacityCommitments]. Attributes: parent (str): Parent resource that identifies admin project and location e.g., projects/myproject/locations/us capacity_commitment_ids (Sequence[str]): Ids of capacity commitments to merge. These capacity commitments must exist under admin project and location specified in the parent. """ parent = proto.Field(proto.STRING, number=1) capacity_commitment_ids = proto.RepeatedField(proto.STRING, number=2) class Assignment(proto.Message): r"""A Assignment allows a project to submit jobs of a certain type using slots from the specified reservation. Attributes: name (str): Output only. Name of the resource. E.g.: ``projects/myproject/locations/US/reservations/team1-prod/assignments/123``. assignee (str): The resource which will use the reservation. E.g. ``projects/myproject``, ``folders/123``, or ``organizations/456``. job_type (~.gcbr_reservation.Assignment.JobType): Which type of jobs will use the reservation. state (~.gcbr_reservation.Assignment.State): Output only. State of the assignment. """ class JobType(proto.Enum): r"""Types of job, which could be specified when using the reservation. """ JOB_TYPE_UNSPECIFIED = 0 PIPELINE = 1 QUERY = 2 class State(proto.Enum): r"""Assignment will remain in PENDING state if no active capacity commitment is present. It will become ACTIVE when some capacity commitment becomes active. """ STATE_UNSPECIFIED = 0 PENDING = 1 ACTIVE = 2 name = proto.Field(proto.STRING, number=1) assignee = proto.Field(proto.STRING, number=4) job_type = proto.Field(proto.ENUM, number=3, enum=JobType) state = proto.Field(proto.ENUM, number=6, enum=State) class CreateAssignmentRequest(proto.Message): r"""The request for [ReservationService.CreateAssignment][google.cloud.bigquery.reservation.v1.ReservationService.CreateAssignment]. Note: "bigquery.reservationAssignments.create" permission is required on the related assignee. Attributes: parent (str): Required. The parent resource name of the assignment E.g. ``projects/myproject/locations/US/reservations/team1-prod`` assignment (~.gcbr_reservation.Assignment): Assignment resource to create. """ parent = proto.Field(proto.STRING, number=1) assignment = proto.Field(proto.MESSAGE, number=2, message=Assignment) class ListAssignmentsRequest(proto.Message): r"""The request for [ReservationService.ListAssignments][google.cloud.bigquery.reservation.v1.ReservationService.ListAssignments]. Attributes: parent (str): Required. The parent resource name e.g.: ``projects/myproject/locations/US/reservations/team1-prod`` Or: ``projects/myproject/locations/US/reservations/-`` page_size (int): The maximum number of items to return per page. page_token (str): The next_page_token value returned from a previous List request, if any. """ parent = proto.Field(proto.STRING, number=1) page_size = proto.Field(proto.INT32, number=2) page_token = proto.Field(proto.STRING, number=3) class ListAssignmentsResponse(proto.Message): r"""The response for [ReservationService.ListAssignments][google.cloud.bigquery.reservation.v1.ReservationService.ListAssignments]. Attributes: assignments (Sequence[~.gcbr_reservation.Assignment]): List of assignments visible to the user. next_page_token (str): Token to retrieve the next page of results, or empty if there are no more results in the list. """ @property def raw_page(self): return self assignments = proto.RepeatedField(proto.MESSAGE, number=1, message=Assignment) next_page_token = proto.Field(proto.STRING, number=2) class DeleteAssignmentRequest(proto.Message): r"""The request for [ReservationService.DeleteAssignment][google.cloud.bigquery.reservation.v1.ReservationService.DeleteAssignment]. Note: "bigquery.reservationAssignments.delete" permission is required on the related assignee. Attributes: name (str): Required. Name of the resource, e.g. ``projects/myproject/locations/US/reservations/team1-prod/assignments/123`` """ name = proto.Field(proto.STRING, number=1) class SearchAssignmentsRequest(proto.Message): r"""The request for [ReservationService.SearchAssignments][google.cloud.bigquery.reservation.v1.ReservationService.SearchAssignments]. Note: "bigquery.reservationAssignments.search" permission is required on the related assignee. Attributes: parent (str): Required. The resource name of the admin project(containing project and location), e.g.: "projects/myproject/locations/US". query (str): Please specify resource name as assignee in the query. Examples: - ``assignee=projects/myproject`` - ``assignee=folders/123`` - ``assignee=organizations/456`` page_size (int): The maximum number of items to return per page. page_token (str): The next_page_token value returned from a previous List request, if any. """ parent = proto.Field(proto.STRING, number=1) query = proto.Field(proto.STRING, number=2) page_size = proto.Field(proto.INT32, number=3) page_token = proto.Field(proto.STRING, number=4) class SearchAssignmentsResponse(proto.Message): r"""The response for [ReservationService.SearchAssignments][google.cloud.bigquery.reservation.v1.ReservationService.SearchAssignments]. Attributes: assignments (Sequence[~.gcbr_reservation.Assignment]): List of assignments visible to the user. next_page_token (str): Token to retrieve the next page of results, or empty if there are no more results in the list. """ @property def raw_page(self): return self assignments = proto.RepeatedField(proto.MESSAGE, number=1, message=Assignment) next_page_token = proto.Field(proto.STRING, number=2) class MoveAssignmentRequest(proto.Message): r"""The request for [ReservationService.MoveAssignment][google.cloud.bigquery.reservation.v1.ReservationService.MoveAssignment]. Note: "bigquery.reservationAssignments.create" permission is required on the destination_id. Note: "bigquery.reservationAssignments.create" and "bigquery.reservationAssignments.delete" permission are required on the related assignee. Attributes: name (str): Required. The resource name of the assignment, e.g. ``projects/myproject/locations/US/reservations/team1-prod/assignments/123`` destination_id (str): The new reservation ID, e.g.: ``projects/myotherproject/locations/US/reservations/team2-prod`` """ name = proto.Field(proto.STRING, number=1) destination_id = proto.Field(proto.STRING, number=3) class BiReservation(proto.Message): r"""Represents a BI Reservation. Attributes: name (str): The resource name of the singleton BI reservation. Reservation names have the form ``projects/{project_id}/locations/{location_id}/bireservation``. update_time (~.timestamp.Timestamp): Output only. The last update timestamp of a reservation. size (int): Size of a reservation, in bytes. """ name = proto.Field(proto.STRING, number=1) update_time = proto.Field(proto.MESSAGE, number=3, message=timestamp.Timestamp) size = proto.Field(proto.INT64, number=4) class GetBiReservationRequest(proto.Message): r"""A request to get a singleton BI reservation. Attributes: name (str): Required. Name of the requested reservation, for example: ``projects/{project_id}/locations/{location_id}/bireservation`` """ name = proto.Field(proto.STRING, number=1) class UpdateBiReservationRequest(proto.Message): r"""A request to update a BI reservation. Attributes: bi_reservation (~.gcbr_reservation.BiReservation): A reservation to update. update_mask (~.field_mask.FieldMask): A list of fields to be updated in this request. """ bi_reservation = proto.Field(proto.MESSAGE, number=1, message=BiReservation) update_mask = proto.Field(proto.MESSAGE, number=2, message=field_mask.FieldMask) __all__ = tuple(sorted(__protobuf__.manifest))	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 13, 29937, 14187, 1266, 29871, 29906, 29900, 29906, 29900, 5087, 365, 12182, 13, 29937, 13, 29937, 10413, 21144, 1090, 278, 13380, 19245, 29892, 10079, 29871, 29906, 29889, 29900, 313, 1552, 376, 29931, 293, 1947, 1496, 13, 29937, 366, 1122, 451, 671, 445, 934, 5174, 297, 752, 13036, 411, 278, 19245, 29889, 13, 29937, 887, 1122, 4017, 263, 3509, 310, 278, 19245, 472, 13, 29937, 13, 29937, 268, 1732, 597, 1636, 29889, 4288, 29889, 990, 29914, 506, 11259, 29914, 27888, 1430, 1660, 29899, 29906, 29889, 29900, 13, 29937, 13, 29937, 25870, 3734, 491, 22903, 4307, 470, 15502, 304, 297, 5007, 29892, 7047, 13, 29937, 13235, 1090, 278, 19245, 338, 13235, 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2060, 29898, 1285, 17225, 2060, 322, 4423, 511, 321, 29889, 29887, 4898, 13, 9651, 376, 16418, 29914, 1357, 4836, 29914, 2029, 800, 29914, 3308, 1642, 13, 4706, 2346, 313, 710, 1125, 13, 9651, 3529, 6084, 6503, 1024, 408, 1223, 4895, 29872, 297, 278, 2346, 29889, 13, 13, 9651, 1222, 9422, 29901, 13, 13, 9651, 448, 29871, 4954, 465, 4895, 29872, 29922, 16418, 29914, 1357, 4836, 16159, 13, 9651, 448, 29871, 4954, 465, 4895, 29872, 29922, 8771, 414, 29914, 29896, 29906, 29941, 16159, 13, 9651, 448, 29871, 4954, 465, 4895, 29872, 29922, 6388, 17063, 29914, 29946, 29945, 29953, 16159, 13, 4706, 1813, 29918, 2311, 313, 524, 1125, 13, 9651, 450, 7472, 1353, 310, 4452, 304, 736, 639, 13, 9651, 1813, 29889, 13, 4706, 1813, 29918, 6979, 313, 710, 1125, 13, 9651, 450, 2446, 29918, 3488, 29918, 6979, 995, 4133, 515, 263, 3517, 2391, 13, 9651, 2009, 29892, 565, 738, 29889, 13, 1678, 9995, 13, 13, 1678, 3847, 353, 17814, 29889, 3073, 29898, 17529, 29889, 20785, 29892, 1353, 29922, 29896, 29897, 13, 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310, 2582, 29892, 13, 9651, 470, 4069, 565, 727, 526, 694, 901, 2582, 297, 278, 13, 9651, 1051, 29889, 13, 1678, 9995, 13, 13, 1678, 732, 6799, 13, 1678, 822, 10650, 29918, 3488, 29898, 1311, 1125, 13, 4706, 736, 1583, 13, 13, 1678, 3566, 1860, 353, 17814, 29889, 1123, 412, 630, 3073, 29898, 17529, 29889, 2303, 1799, 10461, 29892, 1353, 29922, 29896, 29892, 2643, 29922, 7900, 10194, 29897, 13, 1678, 2446, 29918, 3488, 29918, 6979, 353, 17814, 29889, 3073, 29898, 17529, 29889, 20785, 29892, 1353, 29922, 29906, 29897, 13, 13, 13, 1990, 25249, 7900, 10194, 3089, 29898, 17529, 29889, 3728, 1125, 13, 1678, 364, 15945, 29908, 1576, 2009, 363, 13, 1678, 518, 1666, 20525, 3170, 29889, 16619, 7900, 10194, 3816, 3608, 29889, 9274, 29889, 3752, 1972, 29889, 690, 20525, 29889, 29894, 29896, 29889, 1666, 20525, 3170, 29889, 16619, 7900, 10194, 1822, 13, 13, 1678, 3579, 9842, 1068, 29901, 376, 3752, 1972, 29889, 690, 20525, 7900, 647, 1860, 29889, 3258, 29908, 10751, 338, 13, 1678, 3734, 373, 278, 12551, 29918, 333, 29889, 13, 13, 1678, 3579, 9842, 1068, 29901, 376, 3752, 1972, 29889, 690, 20525, 7900, 647, 1860, 29889, 3258, 29908, 322, 13, 1678, 376, 3752, 1972, 29889, 690, 20525, 7900, 647, 1860, 29889, 8143, 29908, 10751, 526, 3734, 373, 13, 1678, 278, 4475, 1223, 4895, 29872, 29889, 13, 13, 1678, 6212, 5026, 29901, 13, 4706, 1024, 313, 710, 1125, 13, 9651, 830, 5958, 29889, 450, 6503, 1024, 310, 278, 12827, 29892, 321, 29889, 29887, 29889, 13, 9651, 4954, 16418, 29914, 1357, 4836, 29914, 2029, 800, 29914, 3308, 29914, 690, 6972, 800, 29914, 14318, 29896, 29899, 10633, 29914, 16645, 1860, 29914, 29896, 29906, 29941, 16159, 13, 4706, 12551, 29918, 333, 313, 710, 1125, 13, 9651, 450, 716, 620, 20525, 3553, 29892, 321, 29889, 29887, 4898, 13, 9651, 4954, 16418, 29914, 1357, 1228, 4836, 29914, 2029, 800, 29914, 3308, 29914, 690, 6972, 800, 29914, 14318, 29906, 29899, 10633, 16159, 13, 1678, 9995, 13, 13, 1678, 1024, 353, 17814, 29889, 3073, 29898, 17529, 29889, 20785, 29892, 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350, 29902, 620, 20525, 29889, 13, 13, 1678, 6212, 5026, 29901, 13, 4706, 4768, 29918, 690, 20525, 313, 30022, 29889, 27354, 1182, 29918, 690, 20525, 29889, 20517, 1666, 20525, 1125, 13, 9651, 319, 620, 20525, 304, 2767, 29889, 13, 4706, 2767, 29918, 13168, 313, 30022, 29889, 2671, 29918, 13168, 29889, 3073, 19832, 1125, 13, 9651, 319, 1051, 310, 4235, 304, 367, 4784, 297, 445, 13, 9651, 2009, 29889, 13, 1678, 9995, 13, 13, 1678, 4768, 29918, 690, 20525, 353, 17814, 29889, 3073, 29898, 17529, 29889, 2303, 1799, 10461, 29892, 1353, 29922, 29896, 29892, 2643, 29922, 20517, 1666, 20525, 29897, 13, 1678, 2767, 29918, 13168, 353, 17814, 29889, 3073, 29898, 17529, 29889, 2303, 1799, 10461, 29892, 1353, 29922, 29906, 29892, 2643, 29922, 2671, 29918, 13168, 29889, 3073, 19832, 29897, 13, 13, 13, 1649, 497, 1649, 353, 18761, 29898, 24582, 22168, 17529, 9721, 26914, 29135, 876, 13, 2 ]
job-queue-portal/postgres_django_queue/djangoenv/lib/python3.8/site-packages/django_celery_results/utils.py	Sruthi-Ganesh/postgres-django-queue	0	185979	"""Utilities.""" # -- XXX This module must not use translation as that causes # -- a recursive loader import! from django.conf import settings from django.utils import timezone # see Issue celery/django-celery#222 now_localtime = getattr(timezone, 'template_localtime', timezone.localtime) def now(): """Return the current date and time.""" if getattr(settings, 'USE_TZ', False): return now_localtime(timezone.now()) else: return timezone.now()	[ 1, 9995, 7270, 1907, 1213, 15945, 13, 29937, 1192, 22615, 910, 3883, 1818, 451, 671, 13962, 408, 393, 9946, 13, 29937, 1192, 263, 16732, 23466, 1053, 29991, 13, 13, 3166, 9557, 29889, 5527, 1053, 6055, 13, 3166, 9557, 29889, 13239, 1053, 29431, 13, 13, 29937, 1074, 26246, 6432, 708, 29914, 14095, 29899, 2242, 708, 29937, 29906, 29906, 29906, 13, 3707, 29918, 2997, 2230, 353, 679, 5552, 29898, 2230, 8028, 29892, 525, 6886, 29918, 2997, 2230, 742, 29431, 29889, 2997, 2230, 29897, 13, 13, 13, 1753, 1286, 7295, 13, 1678, 9995, 11609, 278, 1857, 2635, 322, 931, 1213, 15945, 13, 1678, 565, 679, 5552, 29898, 11027, 29892, 525, 17171, 29918, 29911, 29999, 742, 7700, 1125, 13, 4706, 736, 1286, 29918, 2997, 2230, 29898, 2230, 8028, 29889, 3707, 3101, 13, 1678, 1683, 29901, 13, 4706, 736, 29431, 29889, 3707, 580, 13, 2 ]
code/Dictionary/key_of_min.py	jumploop/30-seconds-of-python	0	197308	#!/usr/bin/env python # -- coding: utf-8 -- """ 功能实现：在字典中查找最小值的键。解读：使用min()并将key参数设置为dict.get()来查找并返回给定字典中最小值的键。 """ def key_of_min(d): return min(d, key=d.get) # Examples print(key_of_min({'a': 4, 'b': 0, 'c': 13})) # output: # b	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 15945, 29908, 13, 31134, 30815, 31195, 31424, 30383, 30505, 30578, 31259, 30275, 31213, 233, 140, 193, 30878, 30446, 30959, 30210, 236, 151, 177, 30267, 13, 13, 31201, 235, 178, 190, 30383, 13, 30785, 30406, 1195, 580, 31666, 30998, 1989, 31125, 30354, 30872, 30669, 30573, 8977, 29889, 657, 580, 30805, 31213, 233, 140, 193, 31666, 31086, 30742, 31999, 30495, 30578, 31259, 30275, 30878, 30446, 30959, 30210, 236, 151, 177, 30267, 13, 15945, 29908, 13, 13, 13, 1753, 1820, 29918, 974, 29918, 1195, 29898, 29881, 1125, 13, 1678, 736, 1375, 29898, 29881, 29892, 1820, 29922, 29881, 29889, 657, 29897, 13, 13, 13, 29937, 1222, 9422, 13, 13, 2158, 29898, 1989, 29918, 974, 29918, 1195, 3319, 29915, 29874, 2396, 29871, 29946, 29892, 525, 29890, 2396, 29871, 29900, 29892, 525, 29883, 2396, 29871, 29896, 29941, 20073, 13, 29937, 1962, 29901, 13, 29937, 289, 13, 2 ]
dronesym-python/flask-api/src/dronepool.py	dilinade/DroneSym	1	3946	<reponame>dilinade/DroneSym<filename>dronesym-python/flask-api/src/dronepool.py #DronePool module which handles interaction with SITLs from dronekit import Vehicle, VehicleMode, connect from dronekit_sitl import SITL from threading import Lock import node, time import mavparser import threadrunner drone_pool = {} instance_count = 0 env_test = False q = None mq = None lock = Lock() class Sim(SITL, object): def __init__(self, instance=1, home=None): super(Sim, self).download("copter", "3.3", verbose=not env_test) self.instance = instance if home: self.home = home else: self.home = {"lat":6.9271, "lon":79.8612, "alt": 1} self.p = None return def connection_string(self): return super(Sim, self).connection_string()[:-4] + str(5760 + self.instance * 10) def launch(self): home_str = str(self.home['lat']) + ',' + str(self.home['lon']) + ',0,353' super(Sim, self).launch(["--instance", str(self.instance), "--home", home_str], await_ready=True, verbose=not env_test) def get_sitl_status(self): return { 'id': self.instance, 'home': self.home } def initialize(): global q, mq, instance_count q = threadrunner.q mq = threadrunner.mq drones = node.get_drones()['drones'] if not drones: return for drone_id in drones: if drone_id not in list(drone_pool.keys()): drone = node.get_drone_by_id(drone_id) location = drone['location'] q.put((create_new_drone, { "db_key" : drone_id, "home" : location })) if 'status' in list(drone.keys()) and drone['status'] == 'FLYING': q.put((resume_flight, { "drone_id" : drone_id })) def resume_flight(kwargs): drone_id = kwargs.get("drone_id", None) drone = node.get_drone_by_id(drone_id) waypoints = [] for wp in sorted(drone['waypoints']): waypoints.append(drone['waypoints'][wp]) next_waypoint = waypoints.index(drone['waypoint']) print (next_waypoint) q.put((takeoff_drone, { "drone_id" : drone_id, "waypoints" : waypoints[next_waypoint:] })) def create_new_drone(kwargs): global instance_count instance_count += 1 home = kwargs.get("home", None) db_key = kwargs.get("db_key", None) retries = 3 drone = Sim(instance_count, home) drone.launch() while retries > 0: try: drone_conn = connect(drone.connection_string(), wait_ready=True) break except: print ("Retrying...") retries -= 1 drone_pool[db_key] = drone_conn res = { "status" : "OK", "id" : db_key } return res def remove_drone(kwargs): drone_id = kwargs.get("drone_id", None) if drone_id not in drone_pool: return { "status" : "ERROR", "msg" : "Drone instance not found" } drone = drone_pool[drone_id] if drone.mode == VehicleMode('AUTO'): return { "status" : "ERROR", "msg" : "Drone in operation" } del drone_pool[drone_id] return { "status" : "OK", "id" : drone_id } def run_mission(drone, target_height, waypoints): while True: print(("Reaching target alt : " + str(drone.location.global_relative_frame.alt))) if drone.location.global_relative_frame.alt >= target_height * 0.9: break print ('target alt reached') mavparser.create_mission(drone, waypoints) print ('mission acquired') drone.mode = VehicleMode('AUTO') print ('initiating sequence') print ('in mission') def attach_listener(kwargs): attr = kwargs.get('attr', None) fn = kwargs.get('fn', None) attach_fn = kwargs.get('attach_fn', None) if not fn == None and not attr == None and not attach_fn == None: attach_fn(attr, fn) def takeoff_drone(kwargs): global q drone_id = kwargs.get("drone_id", None) target_height = kwargs.get("target_height", 10) waypoints = kwargs.get("waypoints", None) try: drone = drone_pool[drone_id] except: raise drone.initialize() drone.mode = VehicleMode('GUIDED') drone.armed = True while not drone.armed: time.sleep(1) drone.simple_takeoff(target_height) print (waypoints) if waypoints: run_mission(drone, target_height, waypoints) def detach_event_listeners(drone, value, status): drone.remove_attribute_listener('location', update_location) drone.remove_attribute_listener('airspeed', update_airspeed) drone.remove_attribute_listener('attitude', udpate_attitude) drone.remove_attribute_listener('heading', update_heading) node.update_drone(drone_id, { "location" : {"lat": value.global_relative_frame.lat, "lon": value.global_relative_frame.lon, "alt": value.global_relative_frame.alt}, "status": status}) return def update_location(self, attr_name, value): node.update_drone(drone_id, { "location" : {"lat": value.global_relative_frame.lat, "lon": value.global_relative_frame.lon, "alt": value.global_relative_frame.alt}, "status": "FLYING"}) command_len = len(drone.commands) wp_len = len(waypoints) if command_len >= wp_len : diff = command_len - wp_len next_wp = max(drone.commands.__next__ - diff, 0) % len(waypoints) waypoint = waypoints[next_wp] # print "df: " + `diff` # print next_wp node.update_drone(drone_id, { "waypoint" : waypoint }) if drone.mode == VehicleMode('LAND') and drone.location.global_relative_frame.alt <= 0.1: detach_event_listeners(drone, value, "HALTED") return if drone.commands.__next__ == len(drone.commands): detach_event_listeners(drone, value, "FINISHED") return def update_airspeed(self, attr_name, value): node.update_drone(drone_id, {"airspeed": value}) def udpate_attitude(self, attr_name, value): node.update_drone(drone_id, { "pitch": value.pitch, 'roll': value.roll, 'yaw': value.yaw }) def update_heading(self, attr_name, value): node.update_drone(drone_id, { "heading": value }) mq.put((attach_listener, { "attach_fn" : drone.add_attribute_listener, "attr" : 'location', "fn" : update_location })) mq.put((attach_listener, { "attach_fn" : drone.add_attribute_listener, "attr" : 'airspeed', "fn" : update_airspeed })) mq.put((attach_listener, { "attach_fn" : drone.add_attribute_listener, "attr" : 'attitude', "fn" : udpate_attitude })) mq.put((attach_listener, { "attach_fn" : drone.add_attribute_listener, "attr" : 'heading', "fn" : update_heading })) print ('took off') return True def land_drone(kwargs): drone_id = kwargs.get("drone_id", None) 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python3/hackerrank_leetcode/coin_change/main.py	seLain/codesnippets	0	183246	''' Hackerrank: https://www.hackerrank.com/challenges/coin-change/problem this solution will get timeout in many test cases however, this solution can get all the combination during computation ''' import sys from collections import Counter, defaultdict sys.setrecursionlimit(10000) memory = defaultdict(list) def recur_getWays(n, c): #print('enter: n='+str(n)) if n < 0: return False, [] elif n == 0: return True, [Counter()] if n in memory.keys(): return True, memory[n] all_combs_count = [] for coin in c: #print('choose coin:'+str(coin)) valid, combs = recur_getWays(n-coin, c) if valid: for comb in combs: comb = comb + Counter({coin:1}) existed = False for existed_comb in all_combs_count: if existed_comb == comb: existed = True break if not existed: all_combs_count.append(comb) #print('memorize: n='+str(n)+' count:'+str(all_combs_count)) memory[n] = all_combs_count return True, all_combs_count def getWays(n, c): # Complete this function valid, combs = recur_getWays(n, c) #print(memory) return len(combs) n, m = input().strip().split(' ') n, m = [int(n), int(m)] c = list(map(int, input().strip().split(' '))) # Print the number of ways of making change for 'n' units using coins having the values given by 'c' ways = getWays(n, c) print(ways)	[ 1, 14550, 13, 29950, 28940, 10003, 29901, 2045, 597, 1636, 29889, 29882, 28940, 10003, 29889, 510, 29914, 305, 16047, 267, 29914, 1111, 262, 29899, 3167, 29914, 17199, 13, 1366, 1650, 674, 679, 11815, 297, 1784, 1243, 4251, 13, 3525, 1310, 29892, 445, 1650, 508, 679, 599, 278, 10296, 2645, 16287, 13, 12008, 13, 5215, 10876, 13, 13, 3166, 16250, 1053, 315, 5336, 29892, 2322, 8977, 13, 9675, 29889, 842, 3757, 1295, 291, 13400, 29898, 29896, 29900, 29900, 29900, 29900, 29897, 13, 13, 14834, 353, 2322, 8977, 29898, 1761, 29897, 13, 13, 1753, 1162, 332, 29918, 657, 29956, 1036, 29898, 29876, 29892, 274, 1125, 13, 1678, 396, 2158, 877, 5893, 29901, 302, 2433, 29974, 710, 29898, 29876, 876, 13, 1678, 565, 302, 529, 29871, 29900, 29901, 13, 4706, 736, 7700, 29892, 5159, 13, 1678, 25342, 302, 1275, 29871, 29900, 29901, 13, 4706, 736, 5852, 29892, 518, 17779, 580, 29962, 13, 268, 13, 1678, 565, 302, 297, 3370, 29889, 8149, 7295, 13, 4706, 736, 5852, 29892, 3370, 29961, 29876, 29962, 13, 268, 13, 1678, 599, 29918, 510, 5824, 29918, 2798, 353, 5159, 13, 1678, 363, 19480, 297, 274, 29901, 13, 4706, 396, 2158, 877, 21803, 19480, 11283, 29974, 710, 29898, 1111, 262, 876, 13, 4706, 2854, 29892, 4145, 29879, 353, 1162, 332, 29918, 657, 29956, 1036, 29898, 29876, 29899, 1111, 262, 29892, 274, 29897, 13, 4706, 565, 2854, 29901, 13, 9651, 363, 4145, 297, 4145, 29879, 29901, 13, 18884, 4145, 353, 4145, 718, 315, 5336, 3319, 1111, 262, 29901, 29896, 1800, 13, 18884, 22856, 353, 7700, 13, 18884, 363, 22856, 29918, 17743, 297, 599, 29918, 510, 5824, 29918, 2798, 29901, 13, 462, 1678, 565, 22856, 29918, 17743, 1275, 4145, 29901, 13, 462, 4706, 22856, 353, 5852, 13, 462, 4706, 2867, 13, 18884, 565, 451, 22856, 29901, 13, 462, 1678, 599, 29918, 510, 5824, 29918, 2798, 29889, 4397, 29898, 17743, 29897, 13, 1678, 396, 2158, 877, 6954, 272, 675, 29901, 302, 2433, 29974, 710, 29898, 29876, 7240, 29915, 2302, 11283, 29974, 710, 29898, 497, 29918, 510, 5824, 29918, 2798, 876, 13, 1678, 3370, 29961, 29876, 29962, 353, 599, 29918, 510, 5824, 29918, 2798, 13, 268, 13, 1678, 736, 5852, 29892, 599, 29918, 510, 5824, 29918, 2798, 13, 13, 1753, 679, 29956, 1036, 29898, 29876, 29892, 274, 1125, 13, 1678, 396, 25034, 445, 740, 13, 1678, 2854, 29892, 4145, 29879, 353, 1162, 332, 29918, 657, 29956, 1036, 29898, 29876, 29892, 274, 29897, 13, 1678, 396, 2158, 29898, 14834, 29897, 13, 1678, 736, 7431, 29898, 510, 5824, 29897, 13, 13, 29876, 29892, 286, 353, 1881, 2141, 17010, 2141, 5451, 877, 25710, 13, 29876, 29892, 286, 353, 518, 524, 29898, 29876, 511, 938, 29898, 29885, 4638, 13, 29883, 353, 1051, 29898, 1958, 29898, 524, 29892, 1881, 2141, 17010, 2141, 5451, 877, 525, 4961, 13, 29937, 13905, 278, 1353, 310, 5837, 310, 3907, 1735, 363, 525, 29876, 29915, 10340, 773, 1302, 1144, 2534, 278, 1819, 2183, 491, 525, 29883, 29915, 13, 1994, 353, 679, 29956, 1036, 29898, 29876, 29892, 274, 29897, 13, 2158, 29898, 1994, 29897, 2 ]
src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py	LeonardoWang/nni	0	123083	<gh_stars>0 from torch import Tensor from torch.nn import Module, Parameter from ruamel.yaml import YAML from typing import List import logging logger = logging.getLogger('torch_compressor') __all__ = [ 'TorchCompressor', 'TorchPruner', 'TorchQuantizer', '_torch_detect_prunable_layers', '_torch_default_get_configure', '_torch_default_load_configure_file' ] class TorchCompressor: """ Base compressor for pytorch """ def __init__(self): self._bound_model = None def compress(self, model): """ Compress the model with algorithm implemented by subclass. The model will be instrumented and user should never edit it after calling this method. """ assert self._bound_model is None, "Each NNI compressor instance can only compress one model" self._bound_model = model self.bind_model(model) def bind_model(self, model): """ This method is called when a model is bound to the compressor. Users can optionally overload this method to do model-specific initialization. It is guaranteed that only one model will be bound to each compressor instance. """ pass def update_epoch(self, epoch): """ if user want to update model every epoch, user can override this method """ pass def step(self): """ if user want to update model every step, user can override this method """ pass class TorchLayerInfo: """ layer info for pytorch """ def __init__(self, name, layer): self.name = name self.layer = layer self._forward = None def _torch_detect_prunable_layers(model): # search for all layers which have parameter "weight" ret = [] for name, layer in model.named_modules(): try: if isinstance(layer.weight, Parameter) and isinstance(layer.weight.data, Tensor): ret.append(TorchLayerInfo(name, layer)) except AttributeError: pass return ret def _torch_default_get_configure(configure_list, layer_info): """ Get configure for input layer defaultly the later config will cover front config WARNING: please mask sure default configure is the first in the list """ if not configure_list: logger.warning('WARNING: configure list is None') configure = {} for config in configure_list: if config.get('support_type', '') == 'default': configure = config elif type(layer_info.layer).__name__ in config.get('support_type', []): configure = config elif layer_info.name in config.get('support_op', []): configure = config if not configure: logger.warning('WARNING: can not get configure, default NONE!!!') return configure def _torch_default_load_configure_file(config_path, class_name): logger.info('load CLASS:{0} from PATH:{1}'.format(class_name, config_path)) assert config_path is not None and config_path.endswith('yaml') file = open(config_path, 'r') yaml = YAML(typ='safe') yaml_text = yaml.load(file.read()) configure_file = yaml_text.get(class_name, {}) if not configure_file: logger.warning('WARNING: load Nothing from configure file, Default { }') return configure_file class TorchPruner(TorchCompressor): """ Base pruner for pytorch pruner """ def __init__(self): super().__init__() def __call__(self, model): self.compress(model) return model def calc_mask(self, layer_info, weight): """ Pruners should overload this method to provide mask for weight tensors. The mask must have the same shape and type comparing to the weight. It will be applied with `mul()` operation. This method is effectively hooked to `forward()` method of the model. """ raise NotImplementedError("Pruners must overload calc_mask()") def compress(self, model): super().compress(model) # TODO: configurable whitelist for layer_info in _torch_detect_prunable_layers(model): self._instrument_layer(layer_info) def _instrument_layer(self, layer_info): # TODO: bind additional properties to layer_info instead of layer # create a wrapper forward function to replace the original one assert layer_info._forward is None, 'Each model can only be compressed once' layer_info._forward = layer_info.layer.forward def new_forward(input): # apply mask to weight mask = self.calc_mask(layer_info, layer_info.layer.weight.data) layer_info._backup_weight = layer_info.layer.weight.data layer_info.layer.weight.data = layer_info.layer.weight.data.mul(mask) # calculate forward ret = layer_info._forward(input) # recover original weight layer_info.layer.weight.data = layer_info._backup_weight return ret layer_info.layer.forward = new_forward class TorchQuantizer(TorchCompressor): """ Base quantizer for pytorch quantizer """ def __init__(self): super().__init__() def __call__(self, model): self.compress(model) return model def quantize_weight(self, layer_info, weight): """ user should know where dequantize goes and implement it in quantize method we now do not provide dequantize method """ raise NotImplementedError("Quantizer must overload quantize_weight()") def compress(self, model): super().compress(model) count = 0 for layer_info in _torch_detect_prunable_layers(model): if count == 0: count = count +1 continue self._instrument_layer(layer_info) def _instrument_layer(self, layer_info): assert layer_info._forward is None layer_info._forward = layer_info.layer.forward def new_forward(input): layer_info.layer.weight.data = self.quantize_weight(layer_info, layer_info.layer.weight.data) return layer_info._forward(input) layer_info.layer.forward = new_forward	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 4842, 305, 1053, 323, 6073, 13, 3166, 4842, 305, 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Python/problem0050.py	1050669722/LeetCode-Answers	0	43814	# -- coding: utf-8 -- """ Created on Fri May 31 10:11:51 2019 @author: Administrator """ class Solution: def myPow(self, x: float, n: int) -> float: # if n == 1: # return x # if x!=0 and n == 0: # return 1 # if x == 0 and n <= 0: # return None # if n>0: # if n%2 == 0: # return self.myPow(x, n//2) * self.myPow(x, n//2) # else: # return self.myPow(x, n//2) * self.myPow(x, n//2) * x # if n<0: # if n%2 == 0: # return 1. / ( self.myPow(x, -n//2) * self.myPow(x, -n//2) ) # else: # return 1. / ( self.myPow(x, -n//2) * self.myPow(x, -n//2) * x ) ## if n == 1: ## return x ## if n == 0: ## return 1 # if n == 1: # return x # if x!=0 and n == 0: # return 1 # if x == 0 and n <= 0: # return None # if n>0: # temp = self.myPow(x, n//2) # if n%2 == 0: # return temp * temp # else: # return temp * temp * x # if n<0: # temp = self.myPow(x, -n//2) # if n%2 == 0: # return 1. / ( temp * temp ) # else: # return 1. / ( temp * temp * x ) # if n == 1: return x # if n == 0: return 1 # t = self.myPow(x, abs(n) // 2) # if n % 2 == 0: # t = t * t # else: t = x * t * t # if n < 0: # return 1.0 /t # return t if n<0: n = -n x = 1. / x ans = 1 cp = x while n>=1: ans = anscp(n%2) # cp = 2 cp = cp n //= 2 return ans solu = Solution() x, n = 2.00000, 10 #x, n = 2.00000, 4 #x, n = 2.10000, 3 #x, n = 2.00000, -2 #x, n = 0.00001, 2147483647#2**31-1# #x, n = 0.00000, -1 print(solu.myPow(x, n))	[ 1, 396, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 15945, 29908, 13, 20399, 373, 11169, 2610, 29871, 29941, 29896, 29871, 29896, 29900, 29901, 29896, 29896, 29901, 29945, 29896, 29871, 29906, 29900, 29896, 29929, 13, 13, 29992, 8921, 29901, 24510, 1061, 13, 15945, 29908, 13, 13, 1990, 24380, 29901, 13, 1678, 822, 590, 29925, 340, 29898, 1311, 29892, 921, 29901, 5785, 29892, 302, 29901, 938, 29897, 1599, 5785, 29901, 13, 29937, 4706, 565, 302, 1275, 29871, 29896, 29901, 13, 29937, 9651, 736, 921, 13, 29937, 4706, 565, 921, 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ConvertShellcode.py	rvrsh3ll/CPLResourceRunner	232	110036	<reponame>rvrsh3ll/CPLResourceRunner<filename>ConvertShellcode.py #!/usr/bin/env python import binascii import sys file_name = sys.argv[1] with open (file_name) as f: hexdata = binascii.hexlify(f.read()) hexlist = map(''.join, zip(hexdata[::2], hexdata[1::2])) shellcode = '' for i in hexlist: shellcode += "0x{},".format(i) shellcode = shellcode[:-1] output = open('shellcode.txt', 'w') output.write(shellcode) output.close() print "Shellcode written to shellcode.txt"	[ 1, 529, 276, 1112, 420, 29958, 29878, 13416, 845, 29941, 645, 29914, 6271, 29931, 6848, 16802, 29966, 9507, 29958, 18455, 16037, 401, 29889, 2272, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 5215, 9016, 294, 18869, 13, 5215, 10876, 13, 13, 1445, 29918, 978, 353, 10876, 29889, 19218, 29961, 29896, 29962, 13, 2541, 1722, 313, 1445, 29918, 978, 29897, 408, 285, 29901, 13, 12, 20970, 1272, 353, 9016, 294, 18869, 29889, 354, 15524, 1598, 29898, 29888, 29889, 949, 3101, 13, 12, 20970, 1761, 353, 2910, 877, 4286, 7122, 29892, 14319, 29898, 20970, 1272, 29961, 1057, 29906, 1402, 15090, 1272, 29961, 29896, 1057, 29906, 12622, 13, 12, 15903, 401, 353, 6629, 13, 12, 1454, 474, 297, 15090, 1761, 29901, 13, 12, 12, 15903, 401, 4619, 376, 29900, 29916, 29912, 1118, 1642, 4830, 29898, 29875, 29897, 13, 12, 15903, 401, 353, 6473, 401, 7503, 29899, 29896, 29962, 13, 12, 4905, 353, 1722, 877, 15903, 401, 29889, 3945, 742, 525, 29893, 1495, 13, 12, 4905, 29889, 3539, 29898, 15903, 401, 29897, 13, 12, 4905, 29889, 5358, 580, 13, 13, 2158, 376, 16037, 401, 3971, 304, 6473, 401, 29889, 3945, 29908, 2 ]
rnn.py	peterwilliams97/deidentification	0	70497	<reponame>peterwilliams97/deidentification import tensorflow as tf import numpy as np from tensorflow.contrib import rnn from os.path import expanduser from glob import glob import re import random import time import os import spacy from collections import defaultdict # Parameters seed = 112 use_spacy = True MAX_VOCAB = 40000 MAX_WORDS = 1000000 SMALL_TEXT = False learning_rate = 0.001 n_input = 3 batch_size = 100 test_frac = .2 n_epochs = 1000 n_epochs_report = 1 patience = 100 model_folder = 'models' # number of units in RNN cell n_hidden = 512 UNKNOWN = '<UNKNOWN>' random.seed(seed) # Target log path logs_path = '/tmp/tensorflow/rnn_words' writer = tf.summary.FileWriter(logs_path) start_time = time.time() def elapsed(): sec = time.time() - start_time if sec < 60: return "%.2f sec" % sec elif sec < 60 * 60: return "%.2f min" % (sec / 60.0) else: return "%.2f hour" % (sec / (60 * 60)) def show(name, x): try: v = '%s:%s' % (list(x.shape), x.dtype) except AttributeError: if isinstance(x, (list, tuple)): v = '%d:%s' % (len(x), type(x[0])) else: v = type(x) print('"%s"=%s' % (name, v)) def read_text(path): with open(path, 'rt') as f: return f.read() def make_text(): if SMALL_TEXT: return read_text('belling_the_cat.txt') # mask = expanduser('~/testdata.clean/deploy/PDF32000_2008.txt') mask = expanduser('~/testdata.clean/deploy/The_Block_Cipher_Companion.txt') file_list = sorted(glob(mask)) print('%d files' % len(file_list)) return '\n'.join(read_text(path) for path in file_list) RE_SPACE = re.compile(r'[\s,\.:;!\?\-\(\)]+', re.DOTALL \| re.MULTILINE) RE_NUMBER = re.compile(r'[^A-Z^a-z]') def tokenize_simple(text, max_words): words = RE_SPACE.split(text) words = [w for w in words if len(w) > 1 and not RE_NUMBER.search(w)] if max_words > 0: words = words[:max_words] return [words] spacy_nlp = spacy.load('en') punct = {',', '.', ';', ':', '\n', '\t', '\f', ''} def tokenize_spacy(text, n_input, max_words): document = spacy_nlp(text) doc_sents = list(document.sents) print(len(doc_sents)) # for i, sent in enumerate(doc_sents[:20]): # print(i, type(sent), len(sent), sent[:5]) # sentences sentences = [] n_words = 0 for span in document.sents: sent = [token.text.strip() for token in span] sent = [w for w in sent if w not in punct] if len(sent) < n_input + 1: continue sentences.append(sent) n_words += len(sent) if max_words > 0 and n_words >= max_words: break # for token in sentence: # print(token) # token_dict = {} # token_dict['start'], token_dict['end'] = get_start_and_end_offset_of_token_from_spacy(token) # token_dict['text'] = text[token_dict['start']:token_dict['end']] # if token_dict['text'].strip() in ['\n', '\t', ' ', '']: # continue # # Make sure that the token text does not contain any space # if len(token_dict['text'].split(' ')) != 1: # print("WARNING: the text of the token contains space character, replaced with " # "hyphen\n\t{0}\n\t{1}".format(token_dict['text'], token_dict['text'].replace(' ', '-'))) # token_dict['text'] = token_dict['text'].replace(' ', '-') # sentence_tokens.append(token_dict) # sentences.append(sentence_tokens) print('!!!', len(sentences)) for i, sent in enumerate(sentences[:5]): print(i, type(sent), len(sent), sent[:5]) return sentences def tokenize(text, n_input, max_words): if use_spacy: return tokenize_spacy(text, n_input, max_words) return tokenize_simple(text, max_words) def train_test(sentences, n_input, test_frac): random.shuffle(sentences) n_samples = sum((len(sent) - n_input) for sent in sentences) n_test = int(n_samples * test_frac) test = [] i_test = 0 for sent in sentences: if i_test + len(sent) - n_input > n_test: break test.append(sent) i_test += len(sent) - n_input train = sentences[len(test):] assert train and test, (len(sentences), len(test), len(train), test_frac) return train, test def build_indexes(sentences, max_vocab): word_counts = defaultdict(int) for sent in sentences: for w in sent: word_counts[w] += 1 vocabulary_list = sorted(word_counts, key=lambda x: (-word_counts[x], x))[:max_vocab - 1] vocabulary_list.append(UNKNOWN) vocabulary = set(vocabulary_list) # for i, w in enumerate(words[:5]): # marker = '*' if w in vocabulary else '' # print('%3d: %-20s %s' % (i, w, marker)) vocab_size = len(vocabulary) unk_index = vocab_size - 1 word_index = {w: i for i, w in enumerate(vocabulary_list)} word_index[UNKNOWN] = unk_index index_word = {i: w for w, i in word_index.items()} # for i in sorted(index_word)[:5]: # print(i, index_word[i], type(i)) # for i in range(vocab_size): # assert i in index_word, i return word_index, index_word, unk_index def build_embeddings(word_index): embeddings = {w: np.zeros(len(word_index), dtype=np.float32) for w in word_index} for w, i in word_index.items(): embeddings[w][i] = 1.0 unk_embedding = embeddings[UNKNOWN] return embeddings, unk_embedding text = make_text() print('%7d bytes' % len(text)) sentences = tokenize(text, n_input, MAX_WORDS) print('ALL') print('%7d sentences' % len(sentences)) print('%7d words' % sum(len(sent) for sent in sentences)) train, test_sentences = train_test(sentences, n_input, test_frac) sentences = train print('TRAIN') print('%7d sentences' % len(sentences)) print('%7d words' % sum(len(sent) for sent in sentences)) word_index, index_word, unk_index = build_indexes(sentences, MAX_VOCAB) vocab_size = len(word_index) print('%7d vocab' % vocab_size) embeddings, unk_embedding = build_embeddings(word_index) n_samples = sum((len(sent) - n_input) for sent in sentences) print('%7d samples' % n_samples) def batch_getter(sentences, n_input, batch_size): """Generator that returns x, y, oneh_y in `batch_size` batches phrase is a random phrase of length n_input + 1 from words x = indexes of first n_input words y = index of last word returns x, y, one hot encoding of y """ sequence_numbers = [] for i, sent in enumerate(sentences): for j in range(len(sent) - n_input - 1): sequence_numbers.append((i, j)) random.shuffle(sequence_numbers) for k0 in range(0, len(sequence_numbers), batch_size): n = min(len(sequence_numbers) - k0, batch_size) # print('*', k, n, len(sequence_numbers)) oneh_y = np.empty((n, vocab_size), dtype=np.float32) indexes_x = np.empty((n, n_input), dtype=int) indexes_y = np.empty((n), dtype=int) for k in range(n): i, j = sequence_numbers[k0 + k] words = sentences[i] assert j < len(words) - n_input - 1, (i, j, len(words), n_input) assert j + n_input < len(words), 'i=%d j=%d words=%d sequence_numbers n_input=%d' % ( i, j, len(words), len(sequence_numbers), n_input) phrase_x = words[j:j + n_input] phrase_y = words[j + n_input] wx = [word_index.get(w, unk_index) for w in phrase_x] wy = word_index.get(phrase_y, unk_index) indexes_x[k] = np.array(wx) indexes_y[k] = np.array(wy) oneh_y[k] = embeddings.get(phrase_y, unk_embedding) # show('indexes_x', indexes_y) yield indexes_x, indexes_y, oneh_y # tf Graph inputs # x = indexes of first n_input words in phrase # y = one hot encoding of last word in phrase x = tf.placeholder("float", [None, n_input]) y = tf.placeholder("float", [None, vocab_size]) # RNN output node weights and biases weights = tf.Variable(tf.random_normal([n_hidden, vocab_size])) biases = tf.Variable(tf.random_normal([vocab_size])) def RNN(x, weights, biases): """RNN predicts y (one hot) from x (indexes), weights and biases y = g(x) W + b, where g = LSTM x = indexes of input words y = one-hot encoding of output word """ show('x', x) # Generate a n_input-element sequence of inputs # (eg. [had] [a] [general] -> [20] [6] [33]) x = tf.split(x, n_input, 1) show('x split', x) # 1-layer LSTM with n_hidden units. rnn_cell = rnn.BasicLSTMCell(n_hidden) # generate prediction outputs, states = rnn.static_rnn(rnn_cell, x, dtype=tf.float32) # there are n_input outputs but we only want the last output y = tf.matmul(outputs[-1], weights) + biases show('y', y) return y pred = RNN(x, weights, biases) # Loss and optimizer cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate).minimize(cost) # Model evaluation correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # Initializing the variables init = tf.global_variables_initializer() os.makedirs(model_folder, exist_ok=True) saver = tf.train.Saver(max_to_keep=3) def demonstrate(indexes_x, indexes_y, onehot_pred, step, i): indexes = [int(indexes_x[i, j]) for j in range(indexes_x.shape[1])] symbols_in = [index_word.get(j, UNKNOWN) for j in indexes] symbols_out = index_word.get(indexes_y[i], UNKNOWN) v = tf.argmax(onehot_pred, 1).eval() symbols_out_pred = index_word[int(v[i])] mark = '**' if symbols_out_pred == symbols_out else '' return "%5d: %60s -> [%s] -- [%s] %s" % (step, symbols_in, symbols_out, symbols_out_pred, mark) def make_prediction(session, x): y_pred = tf.run([y], feed_dict={x: x}) def test_model(session, x): return tf.run([accuracy], feed_dict={x: x}) def test_results(session): batch_size = 1000 acc_total = 0.0 loss_total = 0.0 predictions = [] source = batch_getter(test_sentences, n_input, batch_size) step = 0 # Process minibatches of size `batch_size` for _, (indexes_x, indexes_y, onehot_y) in enumerate(source): # print('* %s %d %d' % (list(indexes_y.shape), n_samples, batch_size)) frac = len(indexes_y) / n_samples # Update the model acc, loss, onehot_pred = session.run([accuracy, cost, pred], feed_dict={x: indexes_x, y: onehot_y}) loss_total += loss * frac acc_total += acc * frac assert acc <= 1.0, (acc, loss, frac) assert frac <= 1.0, (acc, loss, frac) assert acc_total <= 1.0, (acc, loss) for i in range(len(indexes_y)): if step < 10: predictions.append(demonstrate(indexes_x, indexes_y, onehot_pred, step, i)) step += 1 return loss_total, acc_total, predictions # Launch the graph with tf.Session() as session: session.run(init) writer.add_graph(session.graph) best_acc = 0.0 best_epoch = -1 new_best = False if False: devices = session.list_devices() for d in devices: print(d.name) assert False for epoch in range(n_epochs): accuracies = [] train_acc = 0.0 train_loss = 0.0 source = batch_getter(sentences, n_input, batch_size) # Process minibatches of size `batch_size` for step, (indexes_x, indexes_y, onehot_y) in enumerate(source): # print('*** %s %d %d' % (list(indexes_y.shape), n_samples, batch_size)) frac = len(indexes_y) / n_samples # Update the model _, acc, loss, onehot_pred = session.run([optimizer, accuracy, cost, pred], feed_dict={x: indexes_x, y: onehot_y}) train_loss += loss * frac train_acc += acc * frac accuracies.append(acc) assert acc <= 1.0, (acc, loss, frac, accuracies) assert frac <= 1.0, (acc, loss, frac, accuracies) assert train_acc <= 1.0, (acc, loss, frac, accuracies) # assert (len(accuracies) + 1) * batch_size > n_samples, (len(accuracies), batch_size, n_samples) test_loss, test_acc, predictions = test_results(session) # Show some progress statistics if test_acc > best_acc: best_epoch = epoch best_acc = test_acc best_predictions = predictions new_best = True # saver.save(session, os.path.join(model_folder, 'model_%06d.ckpt' % step)) print('epoch=%3d train_acc=%.2f test_acc=%.2f' % (epoch + 1, 100.0 * train_acc, 100.0 * test_acc)) done = epoch > best_epoch + patience or epoch == n_epochs - 1 if (epoch + 1) % n_epochs_report == 0 or done: print("epoch=%3d (best=%3d): Train Loss=%9.6f Accuracy=%5.2f%% -- " "Test Loss=%9.6f Accuracy=%5.2f%%" % (epoch + 1, best_epoch + 1, train_loss, 100.0 * train_acc, test_loss, 100.0 * test_acc)) if new_best: print('\n'.join(best_predictions)) new_best = False # indexes = [int(indexes_x[0, i]) for i in range(indexes_x.shape[1])] # symbols_in = [index_word.get(i, UNKNOWN) for i in indexes] # symbols_out = index_word.get(indexes_y[0], UNKNOWN) # v = tf.argmax(onehot_pred, 1).eval() # symbols_out_pred = index_word[int(v[0])] # print("%s -> [%s] predicted [%s]" % (symbols_in, symbols_out, symbols_out_pred)) if done: break print("Optimization Finished!") print("Elapsed time: ", elapsed()) print("Run on command line.") print("\ttensorboard --logdir=%s" % (logs_path)) print("Point your web browser to: http://localhost:6006/") # while True: # prompt = "%s words: " % n_input # sentence = input(prompt) # sentence = sentence.strip() # words = sentence.split(' ') # if len(words) != n_input: # continue # try: # indexes = [word_index.get(w, unk_index) for w in words] # for i in range(32): # keys = np.reshape(np.array(indexes), 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account_verification_flask/services/twilio_services.py	TwilioDevEd/account-verification-flask	8	191141	<reponame>TwilioDevEd/account-verification-flask<gh_stars>1-10 import account_verification_flask.utilities from account_verification_flask.utilities.settings import TwilioSettings from twilio.rest import Client class TwilioServices: twilio_client = None def __init__(self): if TwilioServices.twilio_client is None: TwilioServices.twilio_client = Client( TwilioSettings.api_key(), TwilioSettings.api_secret(), TwilioSettings.account_sid(), ) def send_registration_success_sms(self, to_number): self.twilio_client.messages.create( body=account_verification_flask.utilities.Signup_Complete, to=to_number, from_=TwilioSettings.phone_number(), )	[ 1, 529, 276, 1112, 420, 29958, 27418, 14876, 16618, 3853, 29914, 10149, 29899, 369, 2450, 29899, 1579, 1278, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 30143, 5215, 3633, 29918, 369, 2450, 29918, 1579, 1278, 29889, 4422, 1907, 13, 3166, 3633, 29918, 369, 2450, 29918, 1579, 1278, 29889, 4422, 1907, 29889, 11027, 1053, 8168, 14876, 9585, 13, 3166, 3252, 14876, 29889, 5060, 1053, 12477, 13, 13, 13, 1990, 8168, 14876, 13779, 29901, 13, 1678, 3252, 14876, 29918, 4645, 353, 6213, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 1125, 13, 4706, 565, 8168, 14876, 13779, 29889, 7516, 14876, 29918, 4645, 338, 6213, 29901, 13, 9651, 8168, 14876, 13779, 29889, 7516, 14876, 29918, 4645, 353, 12477, 29898, 13, 18884, 8168, 14876, 9585, 29889, 2754, 29918, 1989, 3285, 13, 18884, 8168, 14876, 9585, 29889, 2754, 29918, 19024, 3285, 13, 18884, 8168, 14876, 9585, 29889, 10149, 29918, 29879, 333, 3285, 13, 9651, 1723, 13, 13, 1678, 822, 3638, 29918, 1727, 8306, 29918, 8698, 29918, 29879, 1516, 29898, 1311, 29892, 304, 29918, 4537, 1125, 13, 4706, 1583, 29889, 7516, 14876, 29918, 4645, 29889, 19158, 29889, 3258, 29898, 13, 9651, 3573, 29922, 10149, 29918, 369, 2450, 29918, 1579, 1278, 29889, 4422, 1907, 29889, 10140, 786, 29918, 17813, 29892, 13, 9651, 304, 29922, 517, 29918, 4537, 29892, 13, 9651, 515, 29918, 29922, 27418, 14876, 9585, 29889, 6710, 29918, 4537, 3285, 13, 4706, 1723, 13, 2 ]
utils/glove.py	MirunaPislar/Word2vec	13	5371	import numpy as np DEFAULT_FILE_PATH = "utils/datasets/glove.6B.50d.txt" def loadWordVectors(tokens, filepath=DEFAULT_FILE_PATH, dimensions=50): """Read pretrained GloVe vectors""" wordVectors = np.zeros((len(tokens), dimensions)) with open(filepath) as ifs: for line in ifs: line = line.strip() if not line: continue row = line.split() token = row[0] if token not in tokens: continue data = [float(x) for x in row[1:]] if len(data) != dimensions: raise RuntimeError("wrong number of dimensions") wordVectors[tokens[token]] = np.asarray(data) return wordVectors	[ 1, 1053, 12655, 408, 7442, 13, 13, 23397, 29918, 7724, 29918, 10145, 353, 376, 13239, 29914, 14538, 1691, 29914, 29887, 417, 345, 29889, 29953, 29933, 29889, 29945, 29900, 29881, 29889, 3945, 29908, 13, 13, 1753, 2254, 14463, 29963, 11142, 29898, 517, 12360, 29892, 934, 2084, 29922, 23397, 29918, 7724, 29918, 10145, 29892, 13391, 29922, 29945, 29900, 1125, 13, 1678, 9995, 6359, 758, 3018, 1312, 21806, 29963, 29872, 12047, 15945, 29908, 13, 1678, 1734, 29963, 11142, 353, 7442, 29889, 3298, 359, 3552, 2435, 29898, 517, 12360, 511, 13391, 876, 13, 1678, 411, 1722, 29898, 1445, 2084, 29897, 408, 565, 29879, 29901, 13, 4706, 363, 1196, 297, 565, 29879, 29901, 13, 9651, 1196, 353, 1196, 29889, 17010, 580, 13, 9651, 565, 451, 1196, 29901, 13, 18884, 6773, 13, 9651, 1948, 353, 1196, 29889, 5451, 580, 13, 9651, 5993, 353, 1948, 29961, 29900, 29962, 13, 9651, 565, 5993, 451, 297, 18897, 29901, 13, 18884, 6773, 13, 9651, 848, 353, 518, 7411, 29898, 29916, 29897, 363, 921, 297, 1948, 29961, 29896, 29901, 5262, 13, 9651, 565, 7431, 29898, 1272, 29897, 2804, 13391, 29901, 13, 18884, 12020, 24875, 2392, 703, 15866, 549, 1353, 310, 13391, 1159, 13, 9651, 1734, 29963, 11142, 29961, 517, 12360, 29961, 6979, 5262, 353, 7442, 29889, 294, 2378, 29898, 1272, 29897, 13, 1678, 736, 1734, 29963, 11142, 13, 2 ]
to_excel_demo.py	46319943/geopandas_demo	0	190712	<filename>to_excel_demo.py<gh_stars>0 from pathlib2 import Path import geopandas # 存放所有城市的目录 source_dir = Path(r'D:\Document\HousePricing\公司') # 获取所有目录名字 city_dir_list = [x for x in source_dir.iterdir() if x.is_dir()] # 输出目录 output_dir = Path(r'D:\Document\HousePricing\公司') for city_dir in city_dir_list: if (city_dir/'公司.shp').exists(): geodf = geopandas.read_file(str(city_dir/'公司.shp'),encoding="UTF-8") print(geodf.head()) geodf.iloc[:, 0:-1].to_excel(str(city_dir/'公司.xlsx'),index=False)	[ 1, 529, 9507, 29958, 517, 29918, 24633, 29918, 17482, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 2224, 1982, 29906, 1053, 10802, 13, 5215, 1737, 459, 7086, 13, 13, 29937, 29871, 30946, 31182, 30744, 30417, 30626, 30461, 30210, 30895, 31283, 13, 4993, 29918, 3972, 353, 10802, 29898, 29878, 29915, 29928, 3583, 6268, 29905, 29950, 1709, 29925, 2200, 292, 29905, 30539, 30931, 1495, 13, 29937, 29871, 31024, 30683, 30744, 30417, 30895, 31283, 30548, 30578, 13, 12690, 29918, 3972, 29918, 1761, 353, 518, 29916, 363, 921, 297, 2752, 29918, 3972, 29889, 1524, 3972, 580, 565, 921, 29889, 275, 29918, 3972, 580, 29962, 13, 29937, 29871, 31573, 30544, 30895, 31283, 13, 4905, 29918, 3972, 353, 10802, 29898, 29878, 29915, 29928, 3583, 6268, 29905, 29950, 1709, 29925, 2200, 292, 29905, 30539, 30931, 1495, 13, 13, 1454, 4272, 29918, 3972, 297, 4272, 29918, 3972, 29918, 1761, 29901, 13, 1678, 565, 313, 12690, 29918, 3972, 22208, 30539, 30931, 29889, 845, 29886, 2824, 9933, 7295, 13, 4706, 1737, 397, 29888, 353, 1737, 459, 7086, 29889, 949, 29918, 1445, 29898, 710, 29898, 12690, 29918, 3972, 22208, 30539, 30931, 29889, 845, 29886, 5477, 22331, 543, 10496, 29899, 29947, 1159, 13, 4706, 1596, 29898, 479, 397, 29888, 29889, 2813, 3101, 13, 4706, 1737, 397, 29888, 29889, 309, 542, 7503, 29892, 29871, 29900, 13018, 29896, 1822, 517, 29918, 24633, 29898, 710, 29898, 12690, 29918, 3972, 22208, 30539, 30931, 29889, 20267, 29916, 5477, 2248, 29922, 8824, 29897, 13, 2 ]
bin/lmi-add-cat.py	mkelley/dct-redux	0	44897	#!/usr/bin/env python3 from astropy.modeling.models import Const1D, Const2D, Gaussian1D, Gaussian2D from astropy.modeling.fitting import LevMarLSQFitter from astropy.modeling import Fittable2DModel, Parameter import sys import logging import argparse import warnings from datetime import datetime from glob import glob import numpy as np import scipy.ndimage as nd from matplotlib.patches import Ellipse import matplotlib.pyplot as plt from astropy.io import fits from astropy.stats import sigma_clipped_stats from astropy.wcs import WCS from astropy.table import Table from astropy.wcs import FITSFixedWarning import photutils as pu import sep parser = argparse.ArgumentParser() parser.add_argument('files', nargs='', help='files to process') parser.add_argument('--reprocess', action='store_true') parser.add_argument('--verbose', '-v', action='store_true', help='verbose logging') args = parser.parse_args() ###################################################################### class GaussianConst2D(Fittable2DModel): """A model for a 2D Gaussian plus a constant. Code from photutils (Copyright (c) 2011, Photutils developers). Parameters ---------- constant : float Value of the constant. amplitude : float Amplitude of the Gaussian. x_mean : float Mean of the Gaussian in x. y_mean : float Mean of the Gaussian in y. x_stddev : float Standard deviation of the Gaussian in x. ``x_stddev`` and ``y_stddev`` must be specified unless a covariance matrix (``cov_matrix``) is input. y_stddev : float Standard deviation of the Gaussian in y. ``x_stddev`` and ``y_stddev`` must be specified unless a covariance matrix (``cov_matrix``) is input. theta : float, optional Rotation angle in radians. The rotation angle increases counterclockwise. """ constant = Parameter(default=1) amplitude = Parameter(default=1) x_mean = Parameter(default=0) y_mean = Parameter(default=0) x_stddev = Parameter(default=1) y_stddev = Parameter(default=1) theta = Parameter(default=0) @staticmethod def evaluate(x, y, constant, amplitude, x_mean, y_mean, x_stddev, y_stddev, theta): """Two dimensional Gaussian plus constant function.""" model = Const2D(constant)(x, y) + Gaussian2D(amplitude, x_mean, y_mean, x_stddev, y_stddev, theta)(x, y) return model def fit_2dgaussian(data): """Fit a 2D Gaussian plus a constant to a 2D image. Based on code from photutils (Copyright (c) 2011, Photutils developers). Parameters ---------- data : array_like The 2D array of the image. Returns ------- result : A `GaussianConst2D` model instance. The best-fitting Gaussian 2D model. """ if np.ma.count(data) < 7: raise ValueError('Input data must have a least 7 unmasked values to ' 'fit a 2D Gaussian plus a constant.') data.fill_value = 0. data = data.filled() # Subtract the minimum of the data as a rough background estimate. # This will also make the data values positive, preventing issues with # the moment estimation in data_properties. Moments from negative data # values can yield undefined Gaussian parameters, e.g., x/y_stddev. data = data - np.min(data) guess_y, guess_x = np.array(data.shape) / 2 init_amplitude = np.ptp(data) g_init = GaussianConst2D(constant=0, amplitude=init_amplitude, x_mean=guess_x, y_mean=guess_y, x_stddev=3, y_stddev=3, theta=0) fitter = LevMarLSQFitter() y, x = np.indices(data.shape) gfit = fitter(g_init, x, y, data) return gfit ###################################################################### # setup logging logger = logging.Logger('LMI Add Catalog') logger.setLevel(logging.DEBUG) # this allows logging to work when lmi-add-cat is run multiple times from # ipython if len(logger.handlers) == 0: formatter = logging.Formatter('%(levelname)s: %(message)s') level = logging.DEBUG if args.verbose else logging.INFO console = logging.StreamHandler(sys.stdout) console.setLevel(level) console.setFormatter(formatter) logger.addHandler(console) logfile = logging.FileHandler('lmi-add-cat.log') logfile.setLevel(level) logfile.setFormatter(formatter) logger.addHandler(logfile) logger.info('#' 70) logger.info(datetime.now().isoformat()) logger.info('Command line: ' + ' '.join(sys.argv[1:])) ###################################################################### # suppress unnecessary warnings warnings.simplefilter('ignore', FITSFixedWarning) ###################################################################### def show_objects(im, objects): # plot background-subtracted image fig, ax = plt.subplots() m, s = np.mean(im), np.std(im) im = ax.imshow(im, interpolation='nearest', cmap='gray', vmin=m-s, vmax=m+s, origin='lower') # plot an ellipse for each object for i in range(len(objects)): e = Ellipse(xy=(objects['x'][i], objects['y'][i]), width=6objects['a'][i], height=6objects['b'][i], angle=objects['theta'][i] * 180. / np.pi) e.set_facecolor('none') e.set_edgecolor('red') ax.add_artist(e) for f in args.files: if fits.getheader(f)['IMAGETYP'] != 'OBJECT': continue logger.debug(f) with fits.open(f, mode='update') as hdu: im = hdu[0].data + 0 h = hdu[0].header if h['IMAGETYP'].upper() != 'OBJECT': logger.warning( f'Refusing to measure {f} with image type {h["imagetyp"]}.') continue if 'MASK' in hdu: mask = hdu['MASK'].data.astype(bool) else: mask = np.zeros_like(im, bool) if 'cat' in hdu: if not args.reprocess: continue else: del hdu['cat'] det = np.zeros_like(mask) for iteration in range(3): bkg = sep.Background(im, mask=det \| mask, bw=64, bh=64, fw=3, fh=3) # mask potential sources det = ((im - bkg) / bkg.globalrms) > 3 # remove isolated pixels det = nd.binary_closing(det) bkg = sep.Background(im, mask=det \| mask, bw=64, bh=64, fw=3, fh=3) if 'bg' in hdu: del hdu['bg'] hdu.append(fits.ImageHDU(bkg.back(), name='bg')) hdu['bg'].header['bg'] = bkg.globalback hdu['bg'].header['rms'] = bkg.globalrms data = im - bkg data[mask] = 0 try: objects, labels = sep.extract(data, 3, err=bkg.globalrms, segmentation_map=True) except Exception as e: logger.error(f'{f}: Object detection failed - {str(e)}') continue hdu[0].header['ncat'] = len(objects), 'number of objects in catalog' if len(objects) == 0: continue #show_objects(data, objects) # estimate seeing fwhms = [] segmap = pu.SegmentationImage(labels) for i in np.random.choice(len(segmap.segments), 50): obj = segmap.segments[i].make_cutout(data, masked_array=True) try: g = fit_2dgaussian(obj) except: continue fwhm = np.mean((g.x_stddev.value, g.y_stddev.value)) * 2.35 if fwhm < 1: continue fwhms.append(fwhm) fwhm = sigma_clipped_stats(fwhms)[1] rap = fwhm * 2 if np.isfinite(fwhm) else 10 flux, fluxerr, flag = sep.sum_circle( data, objects['x'], objects['y'], rap, err=bkg.globalrms, gain=h['gain']) kronrad, krflag = sep.kron_radius(data, objects['x'], objects['y'], objects['a'], objects['b'], objects['theta'], 6.0) krflux, krfluxerr, _flag = sep.sum_ellipse( data, objects['x'], objects['y'], objects['a'], objects['b'], np.minimum(objects['theta'], np.pi / 2.00001), 2.5 * kronrad, subpix=1, err=bkg.globalrms, gain=h['gain']) krflag \|= _flag # combine flags wcs = WCS(h) ra, dec = wcs.all_pix2world(objects['x'], objects['y'], 0) tab = Table((objects['x'], objects['y'], ra, dec, flux, fluxerr, flag, objects['a'], objects['b'], objects['theta'], kronrad, krflux, krfluxerr, krflag), names=('x', 'y', 'ra', 'dec', 'flux', 'fluxerr', 'flag', 'a', 'b', 'theta', 'kronrad', 'krflux', 'krfluxerr', 'krflag')) if 'cat' in hdu: del hdu['cat'] hdu.append(fits.BinTableHDU(tab, name='cat')) hdu['cat'].header['FWHM'] = fwhm, 'estimated median FWHM' hdu['cat'].header['RADIUS'] = 2 * fwhm, 'aperture photometry radius' logger.info(f"{f}: {len(tab)} objects, seeing = {fwhm:.1f}, background mean/rms = " f"{hdu['bg'].header['bg']:.1f}/{hdu['bg'].header['rms']:.1f}")	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 3166, 8717, 14441, 29889, 4299, 292, 29889, 9794, 1053, 5798, 29896, 29928, 29892, 5798, 29906, 29928, 29892, 22477, 29896, 29928, 29892, 22477, 29906, 29928, 13, 3166, 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lib/evaluation/sort_class_mean_accuracy.py	YerongLi2/LTVRR	13	196691	<filename>lib/evaluation/sort_class_mean_accuracy.py ''' # How can we do the long tail with the soft? # we pick the winner from our table (most likely a hubness) and the baseline. # I want a csv that tells me for every predition (rel. or sbj.) # a) its class, (optionally the class-frequency), and the top-1 or top-5 [choose] # score per a soft matrix. ''' import pandas as pd import os.path as osp #method_name = '_hubness10k' method_name = '_hubness' #method_name = '_baseline' workon = 'relation' # or 'relations' #in_csv_file = '/ibex/scratch/x_abdelks/MetricAnalysis/VQA/_focal_loss_g025/test/subjects_data_GQA.csv' # method in_csv_file = '/ibex/scratch/x_abdelks/MetricAnalysis/VQA/{}/test/predicates_data_GQA.csv'.format(method_name) # method #similarities_used = ['word2vec_GNews'] # pick one similarities_used = ['word2vec_visualVG'] # pick one top_aux_data_dir = './' # where are the freq. dictionaries print(method_name, similarities_used, workon) df = pd.read_csv(in_csv_file) ## Clip similarities to [-1, 1] range. for sim in similarities_used: too_large = df[sim] > 1 df.loc[too_large, sim] = 1 too_small = df[sim] < -1 df.loc[too_small, sim] = -1 freq_info = pd.read_csv(osp.join(top_aux_data_dir, 'gvqa_{}_to_train_freq.csv'.format(workon))) freq_info_dict = dict() if workon == 'subject': x, y = freq_info.gt_sbj, freq_info.sbj_freq_gt elif workon == 'relation': x, y = freq_info.gt_rel, freq_info.rel_freq_gt for k, v in zip(x, y): freq_info_dict[k] = v ndf = df[df['i'] == 0][[sim, 'gold']] # top-1 g = ndf.groupby('gold') average_per_class = g[sim].mean().reset_index() average_per_class['frequency'] = average_per_class['gold'].apply(lambda x: freq_info_dict[x]) average_per_class = average_per_class.sort_values('frequency', ascending=False) average_per_class.to_csv('{}_vgqa_sorted_mean_acc_per_class_on_{}_with_{}.csv'.format(method_name, workon, sim))	[ 1, 529, 9507, 29958, 1982, 29914, 24219, 362, 29914, 6605, 29918, 1990, 29918, 12676, 29918, 562, 2764, 4135, 29889, 2272, 13, 12008, 13, 29937, 1128, 508, 591, 437, 278, 1472, 12464, 411, 278, 4964, 29973, 29871, 13, 29937, 591, 5839, 278, 19576, 515, 1749, 1591, 313, 3242, 5517, 263, 19766, 2264, 29897, 322, 278, 2362, 5570, 29889, 13, 29937, 306, 864, 263, 11799, 393, 10603, 592, 363, 1432, 4450, 654, 313, 2674, 29889, 470, 17444, 29926, 1846, 13, 29937, 263, 29897, 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supervisor/medusa/text_socket.py	infoxchange/supervisor	1	100089	# -- Mode: Python -- __author__ = '<NAME>' from supervisor.compat import PY3 from supervisor.compat import as_string, as_bytes from socket import * # relied on to be imported from elsewhere if PY3: bin_socket = socket class text_socket(socket): def __init__(self, family=AF_INET, type=SOCK_STREAM, proto=0, fileno=None): bin_socket.__init__(self, family, type, proto, fileno) def recv(self, args, kwargs): return as_string(bin_socket.recv(self, args, *kwargs)) def recvfrom(self, args, *kwargs): reply, whence = bin_socket.recvfrom(self, args, *kwargs) reply = as_string(reply) return reply, whence def send(self, data, args, *kwargs): b = as_bytes(data) return bin_socket.send(self, b, args, *kwargs) def sendall(self, data, args, *kwargs): return bin_socket.sendall(self, as_bytes(data), args, *kwargs) def sendto(self, data, args, *kwargs): return bin_socket.sendto(self, as_bytes(data), args, **kwargs) def accept(self): # sock, addr = bin_socket.accept(self) # sock = text_socket(self.family, self.type, self.proto, fileno=sock.fileno()) fd, addr = self._accept() sock = text_socket(self.family, self.type, self.proto, fileno=fd) # Issue #7995: if no default timeout is set and the listening # socket had a (non-zero) timeout, force the new socket in blocking # mode to override platform-specific socket flags inheritance. if getdefaulttimeout() is None and self.gettimeout(): sock.setblocking(True) return sock, addr text_socket.__init__.__doc__ = bin_socket.__init__.__doc__ socket = text_socket	[ 1, 396, 448, 29930, 29899, 21864, 29901, 5132, 448, 29930, 29899, 13, 13, 1649, 8921, 1649, 353, 12801, 5813, 16299, 13, 13, 3166, 2428, 19188, 29889, 12667, 1053, 349, 29979, 29941, 13, 3166, 2428, 19188, 29889, 12667, 1053, 408, 29918, 1807, 29892, 408, 29918, 13193, 13, 13, 3166, 9909, 1053, 334, 396, 337, 2957, 373, 304, 367, 19673, 515, 17551, 13, 13, 361, 349, 29979, 29941, 29901, 13, 1678, 9016, 29918, 11514, 353, 9909, 13, 1678, 770, 1426, 29918, 11514, 29898, 11514, 1125, 13, 4706, 822, 4770, 2344, 12035, 1311, 29892, 3942, 29922, 5098, 29918, 1177, 2544, 29892, 1134, 29922, 6156, 7077, 29918, 1254, 1525, 5194, 29892, 17814, 29922, 29900, 29892, 977, 8154, 29922, 8516, 1125, 13, 9651, 9016, 29918, 11514, 17255, 2344, 12035, 1311, 29892, 3942, 29892, 1134, 29892, 17814, 29892, 977, 8154, 29897, 13, 13, 4706, 822, 1162, 29894, 29898, 1311, 29892, 334, 5085, 29892, 3579, 19290, 1125, 13, 9651, 736, 408, 29918, 1807, 29898, 2109, 29918, 11514, 29889, 3757, 29894, 29898, 1311, 29892, 334, 5085, 29892, 3579, 19290, 876, 13, 13, 4706, 822, 1162, 29894, 3166, 29898, 1311, 29892, 334, 5085, 29892, 3579, 19290, 1125, 13, 9651, 8908, 29892, 377, 663, 353, 9016, 29918, 11514, 29889, 3757, 29894, 3166, 29898, 1311, 29892, 334, 5085, 29892, 3579, 19290, 29897, 13, 9651, 8908, 353, 408, 29918, 1807, 29898, 3445, 368, 29897, 13, 9651, 736, 8908, 29892, 377, 663, 13, 13, 4706, 822, 3638, 29898, 1311, 29892, 848, 29892, 334, 5085, 29892, 3579, 19290, 1125, 13, 9651, 289, 353, 408, 29918, 13193, 29898, 1272, 29897, 13, 9651, 736, 9016, 29918, 11514, 29889, 6717, 29898, 1311, 29892, 289, 29892, 334, 5085, 29892, 3579, 19290, 29897, 13, 13, 4706, 822, 3638, 497, 29898, 1311, 29892, 848, 29892, 334, 5085, 29892, 3579, 19290, 1125, 13, 9651, 736, 9016, 29918, 11514, 29889, 6717, 497, 29898, 1311, 29892, 408, 29918, 13193, 29898, 1272, 511, 334, 5085, 29892, 3579, 19290, 29897, 13, 13, 4706, 822, 3638, 517, 29898, 1311, 29892, 848, 29892, 334, 5085, 29892, 3579, 19290, 1125, 13, 9651, 736, 9016, 29918, 11514, 29889, 6717, 517, 29898, 1311, 29892, 408, 29918, 13193, 29898, 1272, 511, 334, 5085, 29892, 3579, 19290, 29897, 13, 13, 4706, 822, 3544, 29898, 1311, 1125, 13, 29937, 9651, 577, 384, 29892, 28915, 353, 9016, 29918, 11514, 29889, 16044, 29898, 1311, 29897, 13, 29937, 9651, 577, 384, 353, 1426, 29918, 11514, 29898, 1311, 29889, 11922, 29892, 1583, 29889, 1853, 29892, 1583, 29889, 17529, 29892, 977, 8154, 29922, 21852, 29889, 1777, 8154, 3101, 13, 9651, 285, 29881, 29892, 28915, 353, 1583, 3032, 16044, 580, 13, 9651, 577, 384, 353, 1426, 29918, 11514, 29898, 1311, 29889, 11922, 29892, 1583, 29889, 1853, 29892, 1583, 29889, 17529, 29892, 977, 8154, 29922, 11512, 29897, 13, 9651, 396, 26246, 396, 29955, 29929, 29929, 29945, 29901, 565, 694, 2322, 11815, 338, 731, 322, 278, 19866, 13, 9651, 396, 9909, 750, 263, 313, 5464, 29899, 9171, 29897, 11815, 29892, 4889, 278, 716, 9909, 297, 23473, 13, 9651, 396, 4464, 304, 5712, 7481, 29899, 14940, 9909, 13449, 20328, 29889, 13, 9651, 565, 679, 4381, 15619, 580, 338, 6213, 322, 1583, 29889, 657, 15619, 7295, 13, 18884, 577, 384, 29889, 842, 1271, 292, 29898, 5574, 29897, 13, 9651, 736, 577, 384, 29892, 28915, 13, 13, 1678, 1426, 29918, 11514, 17255, 2344, 1649, 17255, 1514, 1649, 353, 9016, 29918, 11514, 17255, 2344, 1649, 17255, 1514, 1649, 13, 1678, 9909, 353, 1426, 29918, 11514, 13, 2 ]
irc/bot.py	suut/psychic-happiness	1	1607972	<gh_stars>1-10 # -- coding: utf-8 -- # Copyright (C) 1999-2002 <NAME> # Portions Copyright © 2011-2012 <NAME> """ Simple IRC bot library. This module contains a single-server IRC bot class that can be used to write simpler bots. """ from __future__ import absolute_import import sys import irc.client import irc.modes from .dict import IRCDict class ServerSpec(object): """ An IRC server specification. >>> spec = ServerSpec('localhost') >>> spec.host 'localhost' >>> spec.port 6667 >>> spec.password >>> spec = ServerSpec('127.0.0.1', 6697, '<PASSWORD>') >>> spec.password '<PASSWORD>' """ def __init__(self, host, port=6667, password=None): self.host = host self.port = port self.password = password class SingleServerIRCBot(irc.client.SimpleIRCClient): """A single-server IRC bot class. The bot tries to reconnect if it is disconnected. The bot keeps track of the channels it has joined, the other clients that are present in the channels and which of those that have operator or voice modes. The "database" is kept in the self.channels attribute, which is an IRCDict of Channels. """ def __init__(self, server_list, nickname, username, realname, reconnection_interval=60, connect_params): """Constructor for SingleServerIRCBot objects. Arguments: server_list -- A list of ServerSpec objects or tuples of parameters suitable for constructing ServerSpec objects. Defines the list of servers the bot will use (in order). nickname -- The bot's nickname. realname -- The bot's realname. reconnection_interval -- How long the bot should wait before trying to reconnect. dcc_connections -- A list of initiated/accepted DCC connections. connect_params -- parameters to pass through to the connect method. """ super(SingleServerIRCBot, self).__init__() self.__connect_params = connect_params self.channels = IRCDict() self.server_list = [ ServerSpec(server) if isinstance(server, (tuple, list)) else server for server in server_list ] assert all( isinstance(server, ServerSpec) for server in self.server_list ) if not reconnection_interval or reconnection_interval < 0: reconnection_interval = 2 * 31 self.reconnection_interval = reconnection_interval self._nickname = nickname self._realname = realname self._username = username for i in ["disconnect", "join", "kick", "mode", "namreply", "nick", "part", "quit"]: self.connection.add_global_handler(i, getattr(self, "_on_" + i), -20) def _connected_checker(self): """[Internal]""" if not self.connection.is_connected(): self.connection.execute_delayed(self.reconnection_interval, self._connected_checker) self.jump_server() def _connect(self): """ Establish a connection to the server at the front of the server_list. """ server = self.server_list[0] try: self.connect(server.host, server.port, self._nickname, server.password, self._username, self._realname, *self.__connect_params) except irc.client.ServerConnectionError: pass def _on_disconnect(self, c, e): self.channels = IRCDict() self.connection.execute_delayed(self.reconnection_interval, self._connected_checker) def _on_join(self, c, e): ch = e.target nick = e.source.nick if nick == c.get_nickname(): self.channels[ch] = Channel() self.channels[ch].add_user(nick) def _on_kick(self, c, e): nick = e.arguments[0] channel = e.target if nick == c.get_nickname(): del self.channels[channel] else: self.channels[channel].remove_user(nick) def _on_mode(self, c, e): modes = irc.modes.parse_channel_modes(" ".join(e.arguments)) t = e.target if irc.client.is_channel(t): ch = self.channels[t] for mode in modes: if mode[0] == "+": f = ch.set_mode else: f = ch.clear_mode f(mode[1], mode[2]) else: # Mode on self... XXX pass def _on_namreply(self, c, e): # e.arguments[0] == "@" for secret channels, # "" for private channels, # "=" for others (public channels) # e.arguments[1] == channel # e.arguments[2] == nick list ch_type, channel, nick_list = e.arguments if channel == '*': # User is not in any visible channel # http://tools.ietf.org/html/rfc2812#section-3.2.5 return for nick in nick_list.split(): nick_modes = [] if nick[0] in self.connection.features.prefix: nick_modes.append(self.connection.features.prefix[nick[0]]) nick = nick[1:] for mode in nick_modes: self.channels[channel].set_mode(mode, nick) self.channels[channel].add_user(nick) def _on_nick(self, c, e): before = e.source.nick after = e.target for ch in self.channels.values(): if ch.has_user(before): ch.change_nick(before, after) def _on_part(self, c, e): nick = e.source.nick channel = e.target if nick == c.get_nickname(): del self.channels[channel] else: self.channels[channel].remove_user(nick) def _on_quit(self, c, e): nick = e.source.nick for ch in self.channels.values(): if ch.has_user(nick): ch.remove_user(nick) def die(self, msg="Bye, cruel world!"): """Let the bot die. Arguments: msg -- Quit message. """ self.connection.disconnect(msg) sys.exit(0) def disconnect(self, msg="I'll be back!"): """Disconnect the bot. The bot will try to reconnect after a while. Arguments: msg -- Quit message. """ self.connection.disconnect(msg) def get_version(self): """Returns the bot version. Used when answering a CTCP VERSION request. """ return "Python irc.bot ({version})".format( version=irc.client.VERSION_STRING) def jump_server(self, msg="Changing servers"): """Connect to a new server, possibly disconnecting from the current. The bot will skip to next server in the server_list each time jump_server is called. """ if self.connection.is_connected(): self.connection.disconnect(msg) self.server_list.append(self.server_list.pop(0)) self._connect() def on_ctcp(self, c, e): """Default handler for ctcp events. Replies to VERSION and PING requests and relays DCC requests to the on_dccchat method. """ nick = e.source.nick if e.arguments[0] == "VERSION": c.ctcp_reply(nick, "VERSION " + self.get_version()) elif e.arguments[0] == "PING": if len(e.arguments) > 1: c.ctcp_reply(nick, "PING " + e.arguments[1]) elif e.arguments[0] == "DCC" and e.arguments[1].split(" ", 1)[0] == "CHAT": self.on_dccchat(c, e) def on_dccchat(self, c, e): pass def start(self): """Start the bot.""" self._connect() super(SingleServerIRCBot, self).start() class Channel(object): """A class for keeping information about an IRC channel. This class can be improved a lot. """ def __init__(self): self.userdict = IRCDict() self.operdict = IRCDict() self.voiceddict = IRCDict() self.ownerdict = IRCDict() self.halfopdict = IRCDict() self.modes = {} def users(self): """Returns an unsorted list of the channel's users.""" return self.userdict.keys() def opers(self): """Returns an unsorted list of the channel's operators.""" return self.operdict.keys() def voiced(self): """Returns an unsorted list of the persons that have voice mode set in the channel.""" return self.voiceddict.keys() def owners(self): """Returns an unsorted list of the channel's owners.""" return self.ownerdict.keys() def halfops(self): """Returns an unsorted list of the channel's half-operators.""" return self.halfopdict.keys() def has_user(self, nick): """Check whether the channel has a user.""" return nick in self.userdict def is_oper(self, nick): """Check whether a user has operator status in the channel.""" return nick in self.operdict def is_voiced(self, nick): """Check whether a user has voice mode set in the channel.""" return nick in self.voiceddict def is_owner(self, nick): """Check whether a user has owner status in the channel.""" return nick in self.ownerdict def is_halfop(self, nick): """Check whether a user has half-operator status in the channel.""" return nick in self.halfopdict def add_user(self, nick): self.userdict[nick] = 1 def remove_user(self, nick): for d in self.userdict, self.operdict, self.voiceddict: if nick in d: del d[nick] def change_nick(self, before, after): self.userdict[after] = self.userdict.pop(before) if before in self.operdict: self.operdict[after] = self.operdict.pop(before) if before in self.voiceddict: self.voiceddict[after] = self.voiceddict.pop(before) def set_userdetails(self, nick, details): if nick in self.userdict: self.userdict[nick] = details def set_mode(self, mode, value=None): """Set mode on the channel. Arguments: mode -- The mode (a single-character string). value -- Value """ if mode == "o": self.operdict[value] = 1 elif mode == "v": self.voiceddict[value] = 1 elif mode == "q": self.ownerdict[value] = 1 elif mode == "h": self.halfopdict[value] = 1 else: self.modes[mode] = value def clear_mode(self, mode, value=None): """Clear mode on the channel. Arguments: mode -- The mode (a single-character string). value -- Value """ try: if mode == "o": del self.operdict[value] elif mode == "v": del self.voiceddict[value] elif mode == "q": del self.ownerdict[value] elif mode == "h": del self.halfopdict[value] else: del self.modes[mode] except KeyError: pass def has_mode(self, mode): return mode in self.modes def is_moderated(self): return self.has_mode("m") def is_secret(self): return self.has_mode("s") def is_protected(self): return self.has_mode("p") def has_topic_lock(self): return self.has_mode("t") def is_invite_only(self): return self.has_mode("i") def has_allow_external_messages(self): return self.has_mode("n") def has_limit(self): return self.has_mode("l") def limit(self): if self.has_limit(): return self.modes["l"] else: return None def has_key(self): return self.has_mode("k")	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 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schema/test_connection.py	ramrod-project/database-brain	0	166709	<filename>schema/test_connection.py<gh_stars>0 """ Pytest file for the connection wrapper """ from os import environ from pytest import fixture, raises import docker from .brain import connect, r from .brain.connection import DefaultConnection, BrainNotReady CLIENT = docker.from_env() @fixture(scope='module') def rethink(): try: tag = environ.get("TRAVIS_BRANCH", "dev").replace("master", "latest") except KeyError: tag = "latest" container_name = "brainmoduletest" CLIENT.containers.run( "ramrodpcp/database-brain:{}".format(tag), name=container_name, detach=True, ports={"28015/tcp": 28015}, remove=True ) yield True # Teardown for module tests containers = CLIENT.containers.list() for container in containers: if container.name == container_name: container.stop() break def test_brain_interface(rethink): return connect() def test_brain_interface_with_host(rethink): import rethinkdb return connect("localhost", rethinkdb.DEFAULT_PORT) def test_direct_interface(rethink): r.connect() if __name__ == "__main__": a = rethink() c = test_brain_interface(a.__next__()) try: a.__next__() except StopIteration: pass	[ 1, 529, 9507, 29958, 11010, 29914, 1688, 29918, 9965, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 15945, 29908, 13, 19737, 1688, 934, 363, 278, 3957, 14476, 13, 15945, 29908, 13, 13, 3166, 2897, 1053, 12471, 13, 3166, 11451, 1688, 1053, 5713, 15546, 29892, 1153, 4637, 13, 5215, 10346, 13, 13, 3166, 869, 2634, 262, 1053, 4511, 29892, 364, 13, 3166, 869, 2634, 262, 29889, 9965, 1053, 13109, 5350, 29892, 5032, 262, 3664, 28181, 13, 27205, 3919, 353, 10346, 29889, 3166, 29918, 6272, 580, 13, 13, 13, 29992, 7241, 15546, 29898, 6078, 2433, 5453, 1495, 13, 1753, 337, 386, 682, 7295, 13, 1678, 1018, 29901, 13, 4706, 4055, 353, 12471, 29889, 657, 703, 29911, 4717, 28607, 29918, 15176, 2190, 3210, 613, 376, 3359, 2564, 6506, 703, 6207, 613, 376, 12333, 1159, 13, 1678, 5174, 7670, 2392, 29901, 13, 4706, 4055, 353, 376, 12333, 29908, 13, 1678, 5639, 29918, 978, 353, 376, 2634, 262, 1545, 29884, 1026, 342, 29908, 13, 1678, 24492, 3919, 29889, 1285, 475, 414, 29889, 3389, 29898, 13, 4706, 376, 2572, 5964, 6739, 29886, 29914, 9803, 29899, 2634, 262, 29901, 8875, 1642, 4830, 29898, 4039, 511, 13, 4706, 1024, 29922, 7611, 29918, 978, 29892, 13, 4706, 1439, 496, 29922, 5574, 29892, 13, 4706, 16169, 3790, 29908, 29906, 29947, 29900, 29896, 29945, 29914, 23981, 1115, 29871, 29906, 29947, 29900, 29896, 29945, 1118, 13, 4706, 3349, 29922, 5574, 13, 1678, 1723, 13, 1678, 7709, 5852, 13, 1678, 396, 1920, 538, 776, 363, 3883, 6987, 13, 1678, 22637, 353, 24492, 3919, 29889, 1285, 475, 414, 29889, 1761, 580, 13, 1678, 363, 5639, 297, 22637, 29901, 13, 4706, 565, 5639, 29889, 978, 1275, 5639, 29918, 978, 29901, 13, 9651, 5639, 29889, 9847, 580, 13, 9651, 2867, 13, 13, 13, 13, 1753, 1243, 29918, 2634, 262, 29918, 13248, 29898, 276, 386, 682, 1125, 13, 1678, 736, 4511, 580, 13, 13, 1753, 1243, 29918, 2634, 262, 29918, 13248, 29918, 2541, 29918, 3069, 29898, 276, 386, 682, 1125, 13, 1678, 1053, 337, 386, 682, 2585, 13, 1678, 736, 4511, 703, 7640, 613, 337, 386, 682, 2585, 29889, 23397, 29918, 15082, 29897, 13, 13, 13, 1753, 1243, 29918, 11851, 29918, 13248, 29898, 276, 386, 682, 1125, 13, 1678, 364, 29889, 6915, 580, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 263, 353, 337, 386, 682, 580, 13, 1678, 274, 353, 1243, 29918, 2634, 262, 29918, 13248, 29898, 29874, 17255, 4622, 1649, 3101, 13, 1678, 1018, 29901, 13, 4706, 263, 17255, 4622, 1649, 580, 13, 1678, 5174, 22303, 13463, 362, 29901, 13, 4706, 1209, 13, 2 ]
Main/migrations/0072_auto_20210506_0016.py	Muhammet-Yildiz/Ecommerce_Website-HepsiOrada	10	8383	# Generated by Django 3.1.4 on 2021-05-05 21:16 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('Main', '0071_auto_20210506_0004'), ] operations = [ migrations.RemoveField( model_name='product', name='chooseColor', ), migrations.RemoveField( model_name='product', name='chooseSize', ), ]	[ 1, 396, 3251, 630, 491, 15337, 29871, 29941, 29889, 29896, 29889, 29946, 373, 29871, 29906, 29900, 29906, 29896, 29899, 29900, 29945, 29899, 29900, 29945, 29871, 29906, 29896, 29901, 29896, 29953, 13, 13, 3166, 9557, 29889, 2585, 1053, 9725, 800, 13, 13, 13, 1990, 341, 16783, 29898, 26983, 800, 29889, 29924, 16783, 1125, 13, 13, 1678, 9962, 353, 518, 13, 4706, 6702, 6330, 742, 525, 29900, 29900, 29955, 29896, 29918, 6921, 29918, 29906, 29900, 29906, 29896, 29900, 29945, 29900, 29953, 29918, 29900, 29900, 29900, 29946, 5477, 13, 1678, 4514, 13, 13, 1678, 6931, 353, 518, 13, 4706, 9725, 800, 29889, 15941, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 4704, 742, 13, 9651, 1024, 2433, 21803, 3306, 742, 13, 4706, 10353, 13, 4706, 9725, 800, 29889, 15941, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 4704, 742, 13, 9651, 1024, 2433, 21803, 3505, 742, 13, 4706, 10353, 13, 1678, 4514, 13, 2 ]
tools/c7n_mailer/tests/common.py	gowthamsubbu/cloud-custodian	0	131359	<filename>tools/c7n_mailer/tests/common.py<gh_stars>0 # Copyright 2017 Capital One Services, LLC # # 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 fakeredis import logging from c7n_mailer.ldap_lookup import LdapLookup, Redis from ldap3 import Server, Connection, MOCK_SYNC from ldap3.strategy import mockBase logger = logging.getLogger('custodian.mailer') PETER = ( 'uid=peter,cn=users,dc=initech,dc=com', { 'uid': ['peter'], 'manager': 'uid=bill_lumbergh,cn=users,dc=initech,dc=com', 'mail': '<EMAIL>', 'displayName': 'Peter', 'objectClass': 'person' } ) BILL = ( 'uid=bill_lumbergh,cn=users,dc=initech,dc=com', { 'uid': ['bill_lumbergh'], 'mail': '<EMAIL>', 'displayName': '<NAME>', 'objectClass': 'person' } ) MAILER_CONFIG = { 'smtp_port': 25, 'from_address': '<EMAIL>', 'contact_tags': ['OwnerEmail', 'SupportEmail'], 'queue_url': 'https://sqs.us-east-1.amazonaws.com/xxxx/cloudcustodian-mailer', 'region': 'us-east-1', 'ldap_uri': 'ldap.initech.com', 'smtp_server': 'smtp.inittech.com', 'cache_engine': 'sqlite', 'role': 'arn:aws:iam::xxxx:role/cloudcustodian-mailer', 'ldap_uid_tags': ['CreatorName', 'Owner'], } RESOURCE_1 = { 'AvailabilityZone': 'us-east-1a', 'Attachments': [], 'Tags': [ { 'Value': '<EMAIL>', 'Key': 'SupportEmail' }, { 'Value': 'peter', 'Key': 'CreatorName' } ], 'VolumeId': 'vol-01a0e6ea6b89f0099' } RESOURCE_2 = { 'AvailabilityZone': 'us-east-1c', 'Attachments': [], 'Tags': [ { 'Value': '<EMAIL>', 'Key': 'SupportEmail' }, { 'Value': 'peter', 'Key': 'CreatorName' } ], 'VolumeId': 'vol-21a0e7ea9b19f0043', 'Size': 8 } SQS_MESSAGE_1 = { 'account': 'core-services-dev', 'account_id': '000000000000', 'region': 'us-east-1', 'action': { 'to': ['resource-owner', 'ldap_uid_tags'], 'email_ldap_username_manager': True, 'template': '', 'priority_header': '1', 'type': 'notify', 'transport': {'queue': 'xxx', 'type': 'sqs'}, 'subject': '{{ account }} AWS EBS Volumes will be DELETED in 15 DAYS!' }, 'policy': { 'filters': [{'Attachments': []}, {'tag:maid_status': 'absent'}], 'resource': 'ebs', 'actions': [ { 'type': 'mark-for-op', 'days': 15, 'op': 'delete' }, { 'to': ['resource-owner', 'ldap_uid_tags'], 'email_ldap_username_manager': True, 'template': '', 'priority_header': '1', 'type': 'notify', 'subject': 'EBS Volumes will be DELETED in 15 DAYS!' } ], 'comments': 'We are deleting your EBS volumes.', 'name': 'ebs-mark-unattached-deletion' }, 'event': None, 'resources': [RESOURCE_1] } SQS_MESSAGE_2 = { 'account': 'core-services-dev', 'account_id': '000000000000', 'region': 'us-east-1', 'action': { 'type': 'notify', 'to': ['datadog://?metric_name=EBS_volume.available.size'] }, 'policy': { 'filters': [{'Attachments': []}, {'tag:maid_status': 'absent'}], 'resource': 'ebs', 'actions': [ { 'type': 'mark-for-op', 'days': 15, 'op': 'delete' }, { 'type': 'notify', 'to': ['datadog://?metric_name=EBS_volume.available.size'] } ], 'comments': 'We are deleting your EBS volumes.', 'name': 'ebs-mark-unattached-deletion' }, 'event': None, 'resources': [RESOURCE_1, RESOURCE_2] } SQS_MESSAGE_3 = { 'account': 'core-services-dev', 'account_id': '000000000000', 'region': 'us-east-1', 'action': { 'type': 'notify', 'to': ['datadog://?metric_name=EBS_volume.available.size&metric_value_tag=Size'] }, 'policy': { 'filters': [{'Attachments': []}, {'tag:maid_status': 'absent'}], 'resource': 'ebs', 'actions': [ { 'type': 'mark-for-op', 'days': 15, 'op': 'delete' }, { 'type': 'notify', 'to': ['datadog://?metric_name=EBS_volume.available.size&metric_value_tag=Size'] } ], 'comments': 'We are deleting your EBS volumes.', 'name': 'ebs-mark-unattached-deletion' }, 'event': None, 'resources': [RESOURCE_2] } # Monkey-patch ldap3 to work around a bytes/text handling bug. _safe_rdn = mockBase.safe_rdn def safe_rdn(a, kw): return [(k, mockBase.to_raw(v)) for k, v in _safe_rdn(a, **kw)] mockBase.safe_rdn = safe_rdn def get_fake_ldap_connection(): server = Server('my_fake_server') connection = Connection( server, client_strategy=MOCK_SYNC ) connection.bind() connection.strategy.add_entry(PETER[0], PETER[1]) connection.strategy.add_entry(BILL[0], BILL[1]) return connection def get_ldap_lookup(cache_engine=None, uid_regex=None): if cache_engine == 'sqlite': config = { 'cache_engine': 'sqlite', 'ldap_cache_file': ':memory:' } elif cache_engine == 'redis': config = { 'cache_engine': 'redis', 'redis_host': 'localhost' } if uid_regex: config['ldap_uid_regex'] = uid_regex ldap_lookup = MockLdapLookup(config, logger) michael_bolton = { 'dn': 'CN=<NAME>,cn=users,dc=initech,dc=com', 'mail': '<EMAIL>', 'manager': 'CN=Milton,cn=users,dc=initech,dc=com', 'displayName': '<NAME>' } milton = { 'uid': '123456', 'dn': 'CN=Milton,cn=users,dc=initech,dc=com', 'mail': '<EMAIL>', 'manager': 'CN=cthulhu,cn=users,dc=initech,dc=com', 'displayName': 'Milton' } bob_porter = { 'dn': 'CN=<NAME>,cn=users,dc=initech,dc=com', 'mail': '<EMAIL>', 'manager': 'CN=<NAME>,cn=users,dc=initech,dc=com', 'displayName': '<NAME>' } ldap_lookup.base_dn = 'cn=users,dc=initech,dc=com' ldap_lookup.uid_key = 'uid' ldap_lookup.attributes.append('uid') ldap_lookup.caching.set('michael_bolton', michael_bolton) ldap_lookup.caching.set(bob_porter['dn'], bob_porter) ldap_lookup.caching.set('123456', milton) ldap_lookup.caching.set(milton['dn'], milton) return ldap_lookup class MockLdapLookup(LdapLookup): # allows us to instantiate this object and not need a redis daemon def get_redis_connection(self, redis_host, 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pymic/transform/threshold.py	HiLab-git/PyMIC	147	22249	<reponame>HiLab-git/PyMIC # -- coding: utf-8 -- from __future__ import print_function, division import torch import json import math import random import numpy as np from scipy import ndimage from pymic.transform.abstract_transform import AbstractTransform from pymic.util.image_process import * class ChannelWiseThreshold(AbstractTransform): """Threshold the image (shape [C, D, H, W] or [C, H, W]) for each channel """ def __init__(self, params): """ channels (tuple/list/None): the list of specified channels for thresholding. Default value is all the channels. threshold_lower (tuple/list/None): The lower threshold values for specified channels. threshold_upper (tuple/list/None): The uppoer threshold values for specified channels. replace_lower (tuple/list/None): new values for pixels with intensity smaller than threshold_lower. Default value is replace_upper (tuple/list/None): new values for pixels with intensity larger than threshold_upper. """ super(ChannelWiseThreshold, self).__init__(params) self.channlels = params['ChannelWiseThreshold_channels'.lower()] self.threshold_lower = params['ChannelWiseThreshold_threshold_lower'.lower()] self.threshold_upper = params['ChannelWiseThreshold_threshold_upper'.lower()] self.replace_lower = params['ChannelWiseThreshold_replace_lower'.lower()] self.replace_upper = params['ChannelWiseThreshold_replace_upper'.lower()] self.inverse = params.get('ChannelWiseThreshold_inverse'.lower(), False) def __call__(self, sample): image= sample['image'] channels = range(image.shape[0]) if self.channlels is None else self.channlels for i in range(len(channels)): chn = channels[i] if((self.threshold_lower is not None) and (self.threshold_lower[i] is not None)): t_lower = self.threshold_lower[i] r_lower = self.threshold_lower[i] if((self.replace_lower is not None) and (self.replace_lower[i] is not None)): r_lower = self.replace_lower[i] image[chn][image[chn] < t_lower] = r_lower if((self.threshold_upper is not None) and (self.threshold_upper[i] is not None)): t_upper = self.threshold_upper[i] r_upper = self.threshold_upper[i] if((self.replace_upper is not None) and (self.replace_upper[i] is not None)): r_upper= self.replace_upper[i] image[chn][image[chn] > t_upper] = r_upper sample['image'] = image return sample class ChannelWiseThresholdWithNormalize(AbstractTransform): """ Note that this can be replaced by ChannelWiseThreshold + NormalizeWithMinMax Threshold the image (shape [C, D, H, W] or [C, H, W]) for each channel and then normalize the image based on remaining pixels """ def __init__(self, params): """ :param threshold_lower: (tuple/list/None) The lower threshold value along each channel. :param threshold_upper: (typle/list/None) The upper threshold value along each channel. :param mean_std_mode: (bool) If true, nomalize the image based on mean and std values, and pixels values outside the threshold value are replaced random number. If false, use the min and max values for normalization. """ super(ChannelWiseThresholdWithNormalize, self).__init__(params) self.threshold_lower = params['ChannelWiseThresholdWithNormalize_threshold_lower'.lower()] self.threshold_upper = params['ChannelWiseThresholdWithNormalize_threshold_upper'.lower()] self.mean_std_mode = params['ChannelWiseThresholdWithNormalize_mean_std_mode'.lower()] self.inverse = params.get('ChannelWiseThresholdWithNormalize_inverse'.lower(), False) def __call__(self, sample): image= sample['image'] for chn in range(image.shape[0]): v0 = self.threshold_lower[chn] v1 = self.threshold_upper[chn] if(self.mean_std_mode == True): mask = np.ones_like(image[chn]) if(v0 is not None): mask = mask * np.asarray(image[chn] > v0) if(v1 is not None): mask = mask * np.asarray(image[chn] < v1) pixels = image[chn][mask > 0] chn_mean = pixels.mean() chn_std = pixels.std() chn_norm = (image[chn] - chn_mean)/chn_std chn_random = np.random.normal(0, 1, size = chn_norm.shape) chn_norm[mask == 0] = chn_random[mask == 0] image[chn] = chn_norm else: img_chn = image[chn] if(v0 is not None): img_chn[img_chn < v0] = v0 min_value = v0 else: min_value = img_chn.min() if(v1 is not None): img_chn[img_chn > v1] = v1 max_value = img_chn.max() else: max_value = img_chn.max() img_chn = (img_chn - min_value) / (max_value - min_value) image[chn] = img_chn sample['image'] = image return sample	[ 1, 529, 276, 1112, 420, 29958, 18567, 28632, 29899, 5559, 29914, 19737, 29924, 2965, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 3166, 4770, 29888, 9130, 1649, 1053, 1596, 29918, 2220, 29892, 8542, 13, 13, 5215, 4842, 305, 13, 5215, 4390, 13, 5215, 5844, 13, 5215, 4036, 13, 5215, 12655, 408, 7442, 13, 3166, 4560, 2272, 1053, 29871, 299, 3027, 13, 3166, 282, 962, 293, 29889, 9067, 29889, 16595, 29918, 9067, 1053, 25513, 13372, 13, 3166, 282, 962, 293, 29889, 4422, 29889, 3027, 29918, 5014, 1053, 334, 13, 13, 13, 1990, 17368, 29956, 895, 1349, 12268, 29898, 9118, 13372, 1125, 13, 1678, 9995, 1349, 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Node_Utility.py	SBCV/PythonBlenderUtility	1	74203	import bpy import os from collections import defaultdict from Utility.Logging_Extension import logger # http://blender.stackexchange.com/questions/8936/does-switching-from-blender-render-to-cycles-mess-things-up # * All matierals in cycles use nodes (even if you set up the material in the Properties panel, it will create # nodes in the node editor). # * Since BI materials don't use nodes by default, when you switch to cycles from BI there won't be any BI # nodes in the node tree, yet nodes will be enabled. This will make the material render as transparent. # One must switch / toggle between use_shader nodes # Remark: # info pannel in cycles: # the info pannel in cycles misses a lot of different commands # http://blender.stackexchange.com/questions/18020/print-all-commands-in-the-info-view # https://www.blender.org/api/blender_python_api_2_72_release/info_api_reference.html#operators # Most key-strokes and buttons in Blender call an operator which is also exposed to python via bpy.ops, # To see the Python equivalent hover your mouse over the button and see the tool-tip, # eg Python: bpy.ops.render.render(), If there is no tool-tip or the Python: line is missing then this button # is not using an operator and can't be accessed from Python. # If you want to use this in a script you can press Control-C while your mouse is over the button to copy it to the # clipboard. # ================= # alternative approach (not tested yet) # http://blender.stackexchange.com/questions/364/how-do-i-convert-materials-from-blender-internal-to-cycles # https://blenderartists.org/forum/showthread.php?247271-Cycles-Automatic-Material-Textures-Node def rearrange_nodes(): logger.info('rearrange_nodes: ...') # TODO # https://www.blendernation.com/2015/11/03/development-cleaning-up-node-trees/ # https://github.com/JuhaW/NodeArrange/blob/master/__init__.py assert False def create_viewer_node(scene, preceeding_node_name, preceeding_channel_name): """ For debug purposes. Allows to visualize intermediate nodes. :param scene: :param preceeding_node_name: :param preceeding_channel_name: :return: """ logger.info('create_viewer_node: ...') scene_nodes = scene.node_tree.nodes scene_links = scene.node_tree.links mask_id_node = scene_nodes.get(preceeding_node_name) viewer_node = scene_nodes.new('CompositorNodeViewer') scene_links.new(mask_id_node.outputs[preceeding_channel_name], viewer_node.inputs['Image']) logger.info('create_viewer_node: Done') def create_depth_viewer_node(scene): """ This will save the z buffer in the Viewer Node after rendering bpy.ops.render.render() rendered_image = bpy.data.images['Viewer Node'] pixels = rendered_image.pixels :param scene: :return: """ logger.info('create_depth_output_nodes: ...') scene.use_nodes = True scene_nodes = scene.node_tree.nodes scene_links = scene.node_tree.links default_render_layers_node = scene_nodes.get('Render Layers') # output_value = default_render_layers_node.outputs[output_type] # print(type(output_value)) viewer_node = scene_nodes.get('Depth Viewer') if viewer_node is None: viewer_node = scene_nodes.new('CompositorNodeViewer') viewer_node.name = 'Depth Viewer' logger.vinfo('viewer_node.name', viewer_node.name) viewer_node.use_alpha = False output_type = 'Depth' scene_links.new( default_render_layers_node.outputs[output_type], viewer_node.inputs[0]) # link Z to output logger.info('create_depth_output_nodes: Done') def create_additional_optical_flow_output_nodes(scene, output_path=None, image_stem=None, leading_zeroes_template='#####'): logger.info('create_additional_optical_flow_output_nodes: ...') default_render_layer = scene.render.layers.get(scene.render.layers.active.name) default_render_layer.use_pass_vector = True default_render_layer.pass_alpha_threshold = 0 scene.use_nodes = True scene_links = scene.node_tree.links scene_nodes = scene.node_tree.nodes default_render_layers_node = scene_nodes.get('Render Layers') optical_flow_output_node = scene_nodes.new('CompositorNodeOutputFile') optical_flow_output_node.format.file_format = 'OPEN_EXR' #optical_flow_output_node.format.use_zbuffer = True # Store floats if output_path is not None: optical_flow_output_node.base_path = output_path if image_stem is not None: optical_flow_output_node.file_slots[0].path = image_stem + leading_zeroes_template scene_links.new(default_render_layers_node.outputs['Vector'], optical_flow_output_node.inputs['Image']) logger.info('create_additional_optical_flow_output_nodes: Done') return optical_flow_output_node def create_additional_depth_output_nodes(scene, output_path=None, image_stem=None, leading_zeroes_template='#####'): logger.info('create_additional_depth_output_nodes: ...') default_render_layer = scene.render.layers.get(scene.render.layers.active.name) default_render_layer.pass_alpha_threshold = 0 scene.use_nodes = True scene_nodes = scene.node_tree.nodes scene_links = scene.node_tree.links default_render_layers_node = scene_nodes.get('Render Layers') depth_image_output_node = scene_nodes.new('CompositorNodeOutputFile') depth_image_output_node.format.file_format = 'OPEN_EXR' depth_image_output_node.format.use_zbuffer = True # Store floats if output_path is not None: depth_image_output_node.base_path = output_path if image_stem is not None: depth_image_output_node.file_slots[0].path = image_stem + leading_zeroes_template scene_links.new(default_render_layers_node.outputs['Depth'], depth_image_output_node.inputs['Image']) logger.info('create_additional_depth_output_nodes: Done') return depth_image_output_node def create_additional_mask_output_nodes(scene, object_index, output_path=None, image_stem=None, leading_zeroes_template='#####'): logger.info('create_additional_mask_output_nodes: ...') # Make sure that the render layer passes the object index default_render_layer = scene.render.layers.get(scene.render.layers.active.name) # Add additional pass values # default_render_layer.use_pass_combined = True # default_render_layer.use_pass_mist = True # default_render_layer.use_pass_normal = True # default_render_layer.use_pass_vector = True # default_render_layer.use_pass_uv = True default_render_layer.use_pass_object_index = True # default_render_layer.use_pass_material_index = True # default_render_layer.use_pass_shadow = True # ========== IMPORTANT FOR TRANSPARENT MATERIALS ========= default_render_layer.pass_alpha_threshold = 0 scene.use_nodes = True scene_nodes = scene.node_tree.nodes scene_links = scene.node_tree.links default_render_layers_node = scene_nodes.get('Render Layers') mask_node = scene_nodes.new('CompositorNodeIDMask') mask_node.index = object_index mask_node.use_antialiasing = True image_output_node = scene_nodes.new('CompositorNodeOutputFile') if output_path is not None: image_output_node.base_path = output_path if image_stem is not None: image_output_node.file_slots[0].path = image_stem + leading_zeroes_template scene_links.new( default_render_layers_node.outputs['IndexOB'], mask_node.inputs['ID value']) scene_links.new( mask_node.outputs['Alpha'], image_output_node.inputs['Image']) logger.info('create_additional_mask_output_nodes: Done') return mask_node, image_output_node def create_simple_material(): logger.info('Create Simple Material: ...') simple_material = bpy.data.materials.new('simple_material') simple_material.use_nodes = True simple_material_nodes = simple_material.node_tree.nodes simple_material_links = simple_material.node_tree.links shader_node_diffuse_bsdf = simple_material_nodes.get(NodeUtility.DIFFUSE_BSDF) shader_node_diffuse_bsdf.inputs[0].default_value = [255,0,0, 1] return simple_material def enable_backdrop(enable=True): logger.info('enable_backdrop: ...') # Enable backdrop for area in bpy.context.screen.areas: if area.type == 'NODE_EDITOR': for space in area.spaces: if space.type == 'NODE_EDITOR': logger.info('Backdrop Enabled') space.show_backdrop = enable break logger.info('enable_backdrop: Done') class NodeUtility: USE_MAP_COLOR_DIFFUSE = 'use_map_color_diffuse' USE_MAP_NORMAL = 'use_map_normal' DIFFUSE_BSDF = 'Diffuse BSDF' GLOSSY_BSDF = 'Glossy BSDF' TRANSPARENT_BSDF = 'Transparent BSDF' GLASS_BSDF = 'Glass BSDF' EMISSION = 'Emission' OBJECT_INFO = 'Object Info' MATERIAL_OUTPUT = 'Material Output' SHADER_NODE_RGB = 'ShaderNodeRGB' SHADER_NODE_MIX_RGB = 'ShaderNodeMixRGB' SHADER_NODE_EMISSION = 'ShaderNodeEmission' SHADER_NODE_BSDF_GLASS = 'ShaderNodeBsdfGlass' SHADER_NODE_OBJECT_INFO = 'ShaderNodeObjectInfo' @staticmethod def _collect_texture(type_to_texture_file_path, use_map_type, filepath): logger.debug('filepath: ' + filepath) if type_to_texture_file_path[use_map_type] is None: type_to_texture_file_path[use_map_type] = filepath else: logger.warning('Two Textures with the same use_type:') logger.warning('First: ' + use_map_type + ', ' + type_to_texture_file_path[use_map_type]) logger.warning('Second: ' + use_map_type + ', ' + filepath) logger.warning('We use the first texture as : ' + use_map_type) @staticmethod def _get_blender_internal_texture_type_to_file_paths(material): some_other_name = material.name logger.debug(some_other_name) # fprint('material: ' + material.name) texture_name_set = set() texture_type_to_file_path = defaultdict(lambda: None) for texture_slot in material.texture_slots: if texture_slot: texture = texture_slot.texture texture_name_set.add(texture) # fprint('texture: ' + texture.name) if hasattr(texture, 'image'): logger.debug('Material: ' + material.name + ', Texture: ' + texture.name) logger.debug('use_map_color_diffuse: ' + str(texture_slot.use_map_color_diffuse)) logger.debug('use_map_normal: ' + str(texture_slot.use_map_normal)) # ==== Remark ==== # Relative paths start with '//' and are relative to the blend file. # The prefix of paths to textures packed inside the .blend file are dependent on the original # file path. For example <blend_file_folder>/textures/texture_file.ext, i.e. look like the # following '//textures/<texturename>.<textureextension>' if texture.image.packed_file is not None: logger.debug('Image is packed') # If the texture is packed, the file is definitively valid, otherwise check the file image_is_valid = True else: logger.debug('Image is an external source') image_is_valid = os.path.isfile(bpy.path.abspath(texture.image.filepath)) if image_is_valid: if texture_slot.use_map_color_diffuse: NodeUtility._collect_texture(texture_type_to_file_path, NodeUtility.USE_MAP_COLOR_DIFFUSE, texture.image.filepath) elif texture_slot.use_map_normal: NodeUtility._collect_texture(texture_type_to_file_path, NodeUtility.USE_MAP_NORMAL, texture.image.filepath) logger.info('texture_type_to_file_path: ' + str(texture_type_to_file_path)) return texture_type_to_file_path @staticmethod def replace_bsdf_node_in_material(material, old_node, new_node, preceding_node=None, next_node=None): nodes = material.node_tree.nodes links = material.node_tree.links # we replace the oldf bsdf with a new one nodes.remove(old_node) if preceding_node is not None: links.new(preceding_node.outputs[0], new_node.inputs[0]) if next_node is not None: links.new(new_node.outputs[0], next_node.inputs[0]) @staticmethod def create_material_nodes_for_cycle_using_blender_internal_textures(material_default_bsdf_type=DIFFUSE_BSDF, transparent_default_bsdf_type=TRANSPARENT_BSDF): """ :param material_default_bsdf_type: DIFFUSE_BSDF or GLOSSY_BSDF :param transparent_default_bsdf_type: TRANSPARENT_BSDF or GLASS_BSDF :return: """ logger.info('create_material_nodes_for_cycle_using_blender_internal_textures: ...') bpy.context.scene.render.engine = 'CYCLES' # # each object has several material slots, which link to the materials provided in bpy.data.materials # for material in bpy.data.materials: for object in bpy.data.objects: logger.debug('object.name: ' + object.name) for material_slot in object.material_slots: material = material_slot.material # https://wiki.blender.org/index.php/Dev:Py/Scripts/Cookbook/Code_snippets/Nodes logger.info('material.name: ' + material.name) # change only blender internal materials (keep cycle materials as is) if not material.use_nodes: logger.debug('Adding nodes ...') # this adds by default a node "Material Output" and a node "Diffuse BSDF" material.use_nodes = True # get the "Diffuse BSDF" node nodes = material.node_tree.nodes links = material.node_tree.links # this diffuse node does automatically inherit the color of the material shader_node_diffuse_bsdf = nodes.get(NodeUtility.DIFFUSE_BSDF) shader_node_material_output = nodes.get("Material Output") # These texture file path should be valid texture_type_to_file_path = NodeUtility._get_blender_internal_texture_type_to_file_paths(material) # 1 Case: Material is just a texture # Image Texture -> Diffuse BSDF/Glossy BSDF -> Material Output color_texture_file_path = texture_type_to_file_path[NodeUtility.USE_MAP_COLOR_DIFFUSE] logger.debug('color_texture_file_path: ' + str(color_texture_file_path)) if color_texture_file_path is not None: logger.debug('Converting Material With Texture: ' + color_texture_file_path) logger.debug('Texture path is valid') # test if the image texture node has already been created shader_node_tex_image = nodes.get("Image Texture") if not shader_node_tex_image: shader_node_tex_image = nodes.new(type='ShaderNodeTexImage') shader_node_tex_image.image = bpy.data.images.load(color_texture_file_path) # link the nodes links.new(shader_node_tex_image.outputs[0], shader_node_diffuse_bsdf.inputs[0]) # if material_default_bsdf_type == BICyclesMaterialConverter.GLOSSY_BSDF: # # logger.debug('Replace Diffuse Material Node with Glossy Material Node' ) # shader_node_glossy_bsdf = nodes.get(BICyclesMaterialConverter.GLOSSY_BSDF) # if not shader_node_glossy_bsdf: # # shader_node_glossy_bsdf = nodes.new(type='ShaderNodeBsdfGlossy') # # BICyclesMaterialConverter._replace_bsdf_node(material, # old_node=shader_node_diffuse_bsdf, # new_node=shader_node_glossy_bsdf, # preceding_node=shader_node_tex_image, # next_node=shader_node_material_output) # 2 Case: Material is transparent # RGB -> Transparent BSDF/Glass BSDF -> Material Output elif material.use_transparency: logger.debug('Converting Transparent Material') shader_node_transparent_or_glass_bsdf = nodes.get(transparent_default_bsdf_type) if not shader_node_transparent_or_glass_bsdf: if transparent_default_bsdf_type == NodeUtility.GLASS_BSDF: shader_node_transparent_or_glass_bsdf = nodes.new(type='ShaderNodeBsdfGlass') else: shader_node_transparent_or_glass_bsdf = nodes.new(type='ShaderNodeBsdfTransparent') shader_node_RGB = nodes.new(type='ShaderNodeRGB') NodeUtility.replace_bsdf_node_in_material(material, old_node=shader_node_diffuse_bsdf, new_node=shader_node_transparent_or_glass_bsdf, preceding_node=shader_node_RGB, next_node=shader_node_material_output) else: logger.debug('Converting Material With Simple Color') # by default there is just a diffuse bsdf created using the color of the material if material_default_bsdf_type == NodeUtility.GLOSSY_BSDF: logger.debug('Replace Diffuse Material Node with Glossy Material Node') shader_node_glossy_bsdf = nodes.get(NodeUtility.GLOSSY_BSDF) if not shader_node_glossy_bsdf: shader_node_glossy_bsdf = nodes.new(type='ShaderNodeBsdfGlossy') NodeUtility.replace_bsdf_node_in_material(material, old_node=shader_node_diffuse_bsdf, new_node=shader_node_glossy_bsdf, preceding_node=None, next_node=shader_node_material_output) else: logger.debug('Material has already a node ...') logger.info('create_material_nodes_for_cycle_using_blender_internal_textures: Done')	[ 1, 29871, 13, 5215, 289, 2272, 13, 5215, 2897, 13, 13, 3166, 16250, 1053, 2322, 8977, 13, 3166, 22310, 537, 29889, 3403, 3460, 29918, 17657, 1053, 17927, 13, 13, 29937, 1732, 597, 2204, 1581, 29889, 7041, 29889, 510, 29914, 2619, 29914, 29947, 29929, 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trivector/trivector.py	nklapste/trivector	1	70706	#!/usr/bin/env python # -- coding: utf-8 -- """Image conversion functionality for trivector""" from enum import Enum import numpy as np import svgwrite import cv2 import progressbar def upper_tri_sum(d3array: np.ndarray) -> np.ndarray: """Get a 3D image array's upper diagonal's pixel color average :param d3array: 3D image array derived from :func:`cv2.imread` Treat the 3D array as 2d array. Having the innermost array (pixel BGR values) be considered base values to be averaged. :return: BGR array of the average color of the upper diagonal of the 3D image array """ x, y, _ = d3array.shape tri = [] for i in range(x): if i > y: break for j in range(y - i): tri.append(d3array[i][i + j]) return np.sum(tri, axis=0) // len(tri) def lower_tri_sum(d3array: np.ndarray) -> np.ndarray: """Get a 3D image array's lower diagonal's pixel color average :param d3array: 3D image array derived from :func:`cv2.imread` Treat the 3D array as 2d array. Having the innermost array (pixel BGR values) be considered base values to be averaged. .. note:: If the lower diagonal cannot be computed (eg: flat/malformed 3D array) use the 3D image array's upper diagonal's pixel color average instead. :return: BGR array of the average color of the lower diagonal of the 3D image array """ x, y, _ = d3array.shape tri = [] for i in range(x): if i > y: break for j in range(i): tri.append(d3array[i][j]) # if bottom tri is empty use the upper tri's sum if not tri: return upper_tri_sum(d3array) return np.sum(tri, axis=0) // len(tri) def vectorize_sector_left(sub_img: np.ndarray, svg_drawing: svgwrite.Drawing, x: int, y: int, cut_size: int): """Add two triangles to ``svg_drawing`` whose colors are derived from the color averages from the top and bottom diagonals of the 3D BGR image array of the sub image""" b, g, r = upper_tri_sum(sub_img) svg_drawing.add( svg_drawing.polygon( [(x, y), (x + cut_size, y), (x + cut_size, y + cut_size)], fill=svgwrite.rgb(r, g, b, "RGB") ) ) b, g, r = lower_tri_sum(sub_img) svg_drawing.add( svg_drawing.polygon( [(x, y), (x, y + cut_size), (x + cut_size, y + cut_size)], fill=svgwrite.rgb(r, g, b, "RGB") ) ) def vectorize_sector_right(sub_img: np.ndarray, svg_drawing: svgwrite.Drawing, x: int, y: int, cut_size: int): """Add two triangles to ``svg_drawing`` whose colors are derived from the color averages from the top and bottom diagonals of the 3D BGR image array of the sub image""" b, g, r = upper_tri_sum(sub_img) svg_drawing.add( svg_drawing.polygon( [(x, y + cut_size), (x + cut_size, y + cut_size), (x + cut_size, y)], fill=svgwrite.rgb(r, g, b, "RGB") ) ) b, g, r = lower_tri_sum(sub_img) svg_drawing.add( svg_drawing.polygon( [(x, y + cut_size), (x, y), (x + cut_size, y)], fill=svgwrite.rgb(r, g, b, "RGB") ) ) class DiagonalStyle(Enum): """Styling options noting the diagonal arrangement of the triangle sectors""" right = "right" left = "left" alternating = "alternating" def __str__(self): return self.value def trivector(image_path: str, cut_size: int, output_path: str, diagonal_style: DiagonalStyle = DiagonalStyle.alternating): """Convert an image into a SVG vector image composed of triangular sectors :param image_path: path to the image to trivector :param cut_size: size in pixels for each triangle sector :param diagonal_style: diagonal arrangement of the triangle sectors :param output_path: path to write the trivectored image """ image = cv2.imread(image_path) # pylint:disable=no-member height, width, _ = image.shape width_slices = range(0, width, cut_size) height_slices = range(0, height, cut_size) svg_drawing = svgwrite.Drawing( output_path, profile="full", size=(len(width_slices)cut_size, len(height_slices)cut_size) ) # start up the progress bar # each image sector is one tick one the progress bar bar = progressbar.ProgressBar(max_value=len(width_slices)*len(height_slices)) counter_2 = 0 sector_num = 0 for y in height_slices: counter_1 = counter_2 counter_2 += 1 for x in 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py_projects_boilerplates/sphinx_loguru_pytest_boiler/app/modules/hello_world/tools/tools.py

rypaik/PYTHON

0

45525

<gh_stars>0 #from config import console, log from config import console from loguru import logger import sys #TODO: factory function to create logger # setting up Logger logger.add(sys.stderr, format="{time} {level} {message}", level="INFO") logger.add("./common/loguru_log/Tools_{time}.log", rotation="500 MB") # @logger.catch # @logger.catch def say_hello(message): # print(message) console.print(message, style="bold blue") return message logger.bind(special=True).info("This message, though, is logged to the file!")

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geniepy/src/geniepy/classmgmt/classifiers.py

geralddzx/genie

1

61252

"""Implementation of predictive classifiers.""" from abc import ABC, abstractmethod import pandas as pd from google_drive_downloader import GoogleDriveDownloader as gdd from joblib import load as jload from geniepy.errors import ClassifierError import geniepy.config as gc ERROR_SCORE = float(-1) PCPCLSFR_NAME = "pub_score" CTCLSFR_NAME = "ct_score" class BaseClassifier(ABC): """Base Classifier Abstract Class.""" __slots__ = [ "_is_trained", # True if classifier is trained "_model", # The classifier model "_name", # The name of the classifier (used as name of prediction col) ] def __init__(self, name): """ Construct classifier obj. Arguments: name {[type]} -- The classifier's name """ self._name = name self._is_trained = False self._model = None @property def name(self): """Return name prediction column.""" return self._name @property def is_trained(self): """Return true is model is trained.""" return self._is_trained @abstractmethod def predict(self, features: pd.DataFrame) -> float: """ Calculate publication count label. It is import for this method to always produce results and handle exceptions internally, so execution isn't halted due to exceptions. Arguments: features {pd.DataFrame} -- Pandas series necessary prediction data Returns: float -- the classifier prediction """ def load(self): """ Load classifier model. Raises: ClassifierError -- If model doesn't load successfully """ # Download model from google drive model_path = gc.TMP_DIR.joinpath("gene_disease_gbc.joblib") model_id = gc.get_model() try: gdd.download_file_from_google_drive( file_id=model_id, dest_path=model_path, unzip=True, ) self._model = jload(model_path) self._is_trained = True model_path.unlink() except Exception: # If load fail raise ClassifierError("Unable to load model") class Classifier(BaseClassifier): """Implementation of Publication Count Predictive Classifier.""" def predict(self, features: pd.DataFrame): """ Calculate publication count label. It is import for this method to always produce results and handle exceptions internally, so execution isn't halted due to exceptions. Arguments: features {pd.DataFrame} -- Pandas series necessary prediction data Returns: float -- the classifier prediction """ if (not self._is_trained) or (features is None): # TODO log event "Received None features to predict" # TODO log event "Untrained classifier can't calculate predictions" return ERROR_SCORE # Only keep expected columns features.dropna(inplace=True) scores_df = pd.DataFrame(features["gene_disease_relationship"]) filtered_features = features[ [ "num_publications", "citations_cum_sum", "authors_cum_sum", "chemicals_cum_sum", "cum_sum_journals", "num_languages", "sjr", "h_index", "us_published", "us_uk_published", ] ] # Predictions and probabilities predictions = self._model.predict(filtered_features) probs = self._model.predict_proba(filtered_features) prob_1 = [item[0] for item in probs] # Get positiive probs only # Add new fields into original df scores_df["classifier_prediction"] = predictions scores_df["classifier_prob"] = prob_1 return scores_df

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ants/core/__init__.py

ncullen93/ANTsPy

3

1603901

from .ants_image import * from .ants_transform import * from .image_io import * from .transform_io import *

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DNN_Experiments/MaskRCNN/convert.py

wmjpillow/FlameDetectionAPP

2

23770

#!/usr/bin/env python # convert jpg tp png from glob import glob import cv2 pngs = glob('./*.jpg') for j in pngs: img = cv2.imread(j) cv2.imwrite(j[:-3] + 'png', img) # delete jpg files import glob import os dir = "/Users/wangmeijie/ALLImportantProjects/FlameDetectionAPP/Models/MaskRCNN/02_26_2020/Mask_RCNN/dataset/train" for jpgpath in glob.iglob(os.path.join(dir, '*.jpg')): os.remove(jpgpath)

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linkcopy.py

priximmo/scripts-scripts

1

166173

# This script is for Windows # import sys import pathlib import subprocess as sub if len(sys.argv) < 3: print("{} - Make symbolic links for input's contents into output's contents" .format(sys.argv[0])) print("Usage: {} INPUT_DIRECTORY OUTPUT_DIRECTORY".format(sys.argv[0])) sys.exit(1) INPUT_DIRECTORY = pathlib.Path(sys.argv[1]) OUTPUT_DIRECTORY = pathlib.Path(sys.argv[2]) try: for child in INPUT_DIRECTORY.iterdir(): # child is the FQ'ed path of below variable current_file = child.parts[len(child.parts)-1] new_path = OUTPUT_DIRECTORY new_file = OUTPUT_DIRECTORY / current_file command = "MKLINK \"%s\" \"%s\"" % (new_file, child) ret = sub.call(command, shell=True) if ret != 0: raise OSError print("INPUT: {}".format(child)) print("OUTPUT: {}".format(new_file)) except OSError: print("The command was not completed successfully. :(") sys.exit(1)

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predict_with_ssd7.py

esp32wrangler/ssd_keras

0

24775

<gh_stars>0 from keras.optimizers import Adam from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau, TerminateOnNaN, CSVLogger from keras import backend as K from keras.models import load_model from math import ceil import numpy as np from matplotlib import pyplot as plt from models.keras_ssd7 import build_model from keras_loss_function.keras_ssd_loss import SSDLoss from keras_layers.keras_layer_AnchorBoxes import AnchorBoxes from keras_layers.keras_layer_DecodeDetections import DecodeDetections from keras_layers.keras_layer_DecodeDetectionsFast import DecodeDetectionsFast from ssd_encoder_decoder.ssd_input_encoder import SSDInputEncoder from ssd_encoder_decoder.ssd_output_decoder import decode_detections, decode_detections_fast from data_generator.object_detection_2d_data_generator import DataGenerator from data_generator.object_detection_2d_misc_utils import apply_inverse_transforms from data_generator.data_augmentation_chain_variable_input_size import DataAugmentationVariableInputSize from data_generator.data_augmentation_chain_constant_input_size import DataAugmentationConstantInputSize from data_generator.data_augmentation_chain_original_ssd import SSDDataAugmentation import cv2 img_height = 416 # Height of the input images img_width = 416 # Width of the input images img_channels = 3 # Number of color channels of the input images intensity_mean = 127.5 # Set this to your preference (maybe `None`). The current settings transform the input pixel values to the interval `[-1,1]`. intensity_range = 127.5 # Set this to your preference (maybe `None`). The current settings transform the input pixel values to the interval `[-1,1]`. n_classes = 3 # Number of positive classes scales = [0.08, 0.16, 0.32, 0.64, 0.96] # An explicit list of anchor box scaling factors. If this is passed, it will override `min_scale` and `max_scale`. aspect_ratios = [0.5, 1.0, 2.0] # The list of aspect ratios for the anchor boxes two_boxes_for_ar1 = True # Whether or not you want to generate two anchor boxes for aspect ratio 1 steps = None # In case you'd like to set the step sizes for the anchor box grids manually; not recommended offsets = None # In case you'd like to set the offsets for the anchor box grids manually; not recommended clip_boxes = False # Whether or not to clip the anchor boxes to lie entirely within the image boundaries variances = [1.0, 1.0, 1.0, 1.0] # The list of variances by which the encoded target coordinates are scaled normalize_coords = True # Whether or not the model is supposed to use coordinates relative to the image size model_path = 'ssd7_epoch-07_loss-0.9988_val_loss-0.6916.h5' # We need to create an SSDLoss object in order to pass that to the model loader. ssd_loss = SSDLoss(neg_pos_ratio=3, alpha=1.0) K.clear_session() # Clear previous models from memory. model = build_model(image_size=(img_height, img_width, img_channels), n_classes=n_classes, mode='training', l2_regularization=0.0005, scales=scales, aspect_ratios_global=aspect_ratios, aspect_ratios_per_layer=None, two_boxes_for_ar1=two_boxes_for_ar1, steps=steps, offsets=offsets, clip_boxes=clip_boxes, variances=variances, normalize_coords=normalize_coords, subtract_mean=intensity_mean, divide_by_stddev=intensity_range) # 2: Optional: Load some weights model.load_weights(model_path, by_name=True) train_dataset = DataGenerator(load_images_into_memory=False, hdf5_dataset_path=None) val_dataset = DataGenerator(load_images_into_memory=False, hdf5_dataset_path=None) # 2: Parse the image and label lists for the training and validation datasets. # TODO: Set the paths to your dataset here. # Images images_dir = './infiles' ''' # Ground truth train_labels_filename = 'onsite-images-export.csv' val_labels_filename = 'onsite-images-valid.csv' train_dataset.parse_csv(images_dir=images_dir, labels_filename=train_labels_filename, input_format=['image_name', 'xmin', 'ymin', 'xmax', 'ymax', 'class_id'], # This is the order of the first six columns in the CSV file that contains the labels for your dataset. If your labels are in XML format, maybe the XML parser will be helpful, check the documentation. include_classes='all') val_dataset.parse_csv(images_dir=images_dir, labels_filename=val_labels_filename, input_format=['image_name', 'xmin', 'ymin', 'xmax', 'ymax', 'class_id'], include_classes='all') train_dataset.create_hdf5_dataset(file_path='train_imgs.h5', resize=False, variable_image_size=True, verbose=True) val_dataset.create_hdf5_dataset(file_path='val_imgs.h5', resize=False, variable_image_size=True, verbose=True) train_dataset_size = train_dataset.get_dataset_size() val_dataset_size = val_dataset.get_dataset_size() predict_generator = val_dataset.generate(batch_size=1, shuffle=True, transformations=[], label_encoder=None, returns={'processed_images', 'processed_labels', 'filenames'}, keep_images_without_gt=False) batch_images, batch_labels, batch_filenames = next(predict_generator) i = 0 # Which batch item to look at print("Image:", batch_filenames[i]) print() print("Ground truth boxes:\n") print(batch_labels[i]) ''' import glob import os fnames = glob.glob(images_dir+"/*.png") #fnames = [images_dir+"/yellow834159457.png"] for fn in fnames: img = cv2.imread(fn) img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) imgs = np.reshape(img_rgb, (1, 416, 416, 3)) y_pred = model.predict(imgs) y_pred_decoded = decode_detections(y_pred, confidence_thresh=0.7, iou_threshold=0.45, top_k=200, normalize_coords=normalize_coords, img_height=img_height, img_width=img_width) boxColors = [(0, 255, 255), (0, 0, 255), (0, 255, 0)] #print y_pred_decoded if len(y_pred_decoded) > 0: y_pred_list = y_pred_decoded[0].tolist() for sss in y_pred_list: #print sss cls, conf, xmin, ymin, xmax, ymax = sss boxColor = boxColors[int(cls)-1] cv2.rectangle(img, (int(xmin), int(ymin)), (int(xmax), int(ymax)), boxColor, 2) cv2.imwrite("outfiles/" + os.path.basename(fn), img) ''' y_pred = model.predict(batch_images) y_pred_decoded = decode_detections(y_pred, confidence_thresh=0.2, iou_threshold=0.45, top_k=200, normalize_coords=normalize_coords, img_height=img_height, img_width=img_width) np.set_printoptions(precision=2, suppress=True, linewidth=90) print("Predicted boxes:\n") print(' class conf xmin ymin xmax ymax') print(y_pred_decoded[i]) '''

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src/basic_example/catkin_generated/installspace/eye_node.py

juliojn/eye-saccade

0

123294

#!/usr/bin/python3.7 from std_msgs.msg import String import opensim as osim from basic_example.srv import * import rospy import sys import os # ---------------------------------------------------------------------- # Load the musculoskeletal model from a file. # ---------------------------------------------------------------------- path = os.path.dirname(os.path.abspath(__file__)) model = osim.Model(path + "/../model/UPAT_Eye_Model_Passive_Pulleys_v2.osim") # ---------------------------------------------------------------------- # Add a controller to the model's muscles. # ---------------------------------------------------------------------- actuator_set = model.getActuators() lateral_rectus = actuator_set.get("r_Lateral_Rectus") medial_rectus = actuator_set.get("r_Medial_Rectus") brain = osim.PrescribedController() brain.addActuator(lateral_rectus) brain.addActuator(medial_rectus) brain.prescribeControlForActuator("r_Lateral_Rectus", osim.Constant(0.0)) brain.prescribeControlForActuator("r_Medial_Rectus", osim.Constant(0.0)) model.addController(brain) # ---------------------------------------------------------------------- # Add a console reporter to print the following values: # Position and speed of the adduction-abduction rotational Degree of # Freedom (y-axis). # Current fiber force applied to the Lateral Rectus and the # Medial Rectus tendons. # ---------------------------------------------------------------------- coordinate_set = model.getCoordinateSet() eye_add_abd = coordinate_set.get("r_eye_add_abd") reporter = osim.ConsoleReporter() reporter.set_report_time_interval(0.002) reporter.addToReport(eye_add_abd.getOutput("value"), "position") reporter.addToReport(eye_add_abd.getOutput("speed"), "speed") reporter.addToReport(lateral_rectus.getOutput("fiber_force"), "lateral_force") reporter.addToReport(medial_rectus.getOutput("fiber_force"), "medial_force") model.addComponent(reporter) # -------------------------------------------------------------------------- # Configure the simulation of the model # -------------------------------------------------------------------------- state = model.initSystem() model.equilibrateMuscles(state) manager = osim.Manager(model) state.setTime(0) manager.initialize(state) # -------------------------------------------------------------------------- # Get the control signals of the Lateral Rectus an the Medial Rectus # -------------------------------------------------------------------------- def getControlSignal(current_pos, time): rospy.wait_for_service("get_control_signal") try: get_control_signal = rospy.ServiceProxy("get_control_signal", GetControlSignal) response = get_control_signal(current_pos, time) return response except rospy.ServiceException as e: print("Service call failed: %s"%e) if __name__ == '__main__': time = 0.0 # in seconds sim_time = 0.002 # in seconds while time < 5.0: current_pos = eye_add_abd.getValue(state) res = getControlSignal(current_pos, time) brain.prescribeControlForActuator("r_Lateral_Rectus", osim.Constant(res.lateral_control)) brain.prescribeControlForActuator("r_Medial_Rectus", osim.Constant(res.medial_control)) time += sim_time state = manager.integrate(time)

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jpp_boosted_django/jpp_boosted/website/templatetags/project_overview_list.py

QualmandDriven/jpp_boosted

1

45847

from django import template from django.template.loader import get_template register = template.Library() @register.inclusion_tag('project_overview_list.html') def project_overview_list(project_list): return {'project_list': project_list}

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analysis_helpers/general.py

davidcrowland/layer_vb_tagging

0

177270

from __future__ import print_function # Python 2.x import os import numpy as np import pandas as pd import h5py import sys import math from fnmatch import fnmatch # helper functions klusta analysis pipeline def get_param_file(filename,params_folder): found = False params = [] for path, subdirs, files in os.walk(params_folder): for name in files: for string in filename.split("/"): if string in name: found = True params = name return found, params return found, params def n_user(filename): #find n-drive username n_drive_user = 'non_identified' if 'MosersServer' in filename: # assuming osx/linux: idx = [i for i,x in enumerate(filename.split('/')) if x == 'MosersServer'][0] n_drive_user = filename.split('/')[idx+1] else: # assuming windows: idx = [i for i,x in enumerate(filename.split('/')) if x == 'N:'][0] n_drive_user = filename.split('/')[idx+1] return n_drive_user def create_export_folders(filename): # create export folders export_folder = "/".join(filename.split("/")[:-1])+"/KLUSTA/" if not os.path.exists(export_folder): os.makedirs(export_folder) print('Created export folder: {:s}'.format(export_folder)) export_folder_basesession= "/".join(filename.split("/")[:-1])+"/KLUSTA/base_session" if not os.path.exists(export_folder_basesession): os.makedirs(export_folder_basesession) print('Created export_folder_basesession: {:s}'.format(export_folder_basesession)) export_folder_othersessions= "/".join(filename.split("/")[:-1])+"/KLUSTA/other_session" if not os.path.exists(export_folder_othersessions): os.makedirs(export_folder_othersessions) print('Created export_folder_othersessions: {:s}'.format(export_folder_othersessions)) export_folder_othersessions= "/".join(filename.split("/")[:-1])+"/KLUSTA/other_session" if not os.path.exists(export_folder_othersessions): os.makedirs(export_folder_othersessions) print('Created export_folder_othersessions: {:s}'.format(export_folder_othersessions)) export_folder_lfp = "/".join(filename.split("/")[:-1])+"/KLUSTA/LFP" if not os.path.exists(export_folder_lfp): os.makedirs(export_folder_lfp) print('Created export_folder_lfp: {:s}'.format(export_folder_lfp)) return export_folder, export_folder_basesession, export_folder_othersessions, export_folder_lfp def get_clusters(filename,key=None): if not key: sys.stdout.write('No cluster group key given.') sys.exit() cluster_group_names = [] key_clusters =[] with h5py.File(filename, mode="r") as f: cluster_groups = f['channel_groups/1/cluster_groups/main/'].keys() for clusterg in cluster_groups: name = f['channel_groups/1/cluster_groups/main/'+clusterg].attrs.values() cluster_group_names.append(name[0][0]) for cluster in f['/channel_groups/1/clusters/main'].iteritems(): name = f['channel_groups/1/clusters/main/'+cluster[0]].attrs.get('cluster_group') if cluster_group_names[int(name)] == key: key_clusters.append(int(cluster[0])) print('{} clusters: {}'.format(key,key_clusters)) if not key_clusters: print('None found :(') sys.exit() # apparently this is a bad way to abort the execution ... but it does the job. return key_clusters def get_clusters_dont_exit(filename,key=None): ''' Had to add this function to debug stimulus artefacts ... it serves no other purpose... ''' if not key: sys.stdout.write('No cluster group key given.') sys.exit() cluster_group_names = [] key_clusters =[] try: with h5py.File(filename, mode="r") as f: cluster_groups = f['channel_groups/1/cluster_groups/main/'].keys() for clusterg in cluster_groups: name = f['channel_groups/1/cluster_groups/main/'+clusterg].attrs.values() cluster_group_names.append(name[0][0]) for cluster in f['/channel_groups/1/clusters/main'].iteritems(): name = f['channel_groups/1/clusters/main/'+cluster[0]].attrs.get('cluster_group') if cluster_group_names[int(name)] == key: key_clusters.append(int(cluster[0])) print('{} clusters: {}'.format(key,key_clusters)) if not key_clusters: print('None found :(') except KeyError as err: print('No valid cluster groups found.') return key_clusters def get_basenames(filename): with h5py.File(filename, mode="r") as f: basenames = f['basenames'][:] return basenames def extract_times(filename): with h5py.File(filename, mode="r") as f: spiketimes = np.array(f['/channel_groups/1/spikes/time_samples'][:],dtype=float) sample_rate = float(f['/application_data/spikedetekt'].attrs.get('sample_rate')) time_stamps = np.array(f['/event_types/sessions/events/time_samples'][:],dtype=float) # extract the time stamps yet another way ... time_stamps_sessions = np.cumsum(np.array(f['/time_stamps_sessions'][:], dtype=float)) time_stamps_sessions_sample_rate = 96000. # cannot extract that from hdf5??? print('Extracted spiketimes.') return spiketimes,sample_rate,time_stamps, time_stamps_sessions, time_stamps_sessions_sample_rate def extract_input(filename): with h5py.File(filename, mode="r") as f: input_data = f['/input/input_data'][:] sample_rate_inp = f['/input'].attrs.get('sample_rate_inp') status=True try: sample_rate_inp = float(sample_rate_inp) except TypeError as error: status=False if not status: input_data=np.nan;time_inp=np.nan;time_stamps_sessions_input=np.nan; sample_rate_inp=np.nan;num_inp_samples=np.nan;duration_inp=np.nan return input_data,time_inp,time_stamps_sessions_input,sample_rate_inp,num_inp_samples,duration_inp # process: time_inp = np.array(input_data['time'],dtype=float) time_inp = time_inp/sample_rate_inp*1000. # in ms num_inp_samples = len(input_data) time_stamps_sessions_input = f['/input/time_stamps_sessions_input'][:] time_stamps_sessions_input = np.array(time_stamps_sessions_input,dtype=np.float64) time_stamps_sessions_input = np.cumsum(time_stamps_sessions_input) time_stamps_sessions_input = time_stamps_sessions_input/sample_rate_inp*1000. # in ms duration_inp = (time_stamps_sessions_input)[-1] # in ms print('Extracted input data.') return input_data,time_inp,time_stamps_sessions_input,sample_rate_inp,num_inp_samples,duration_inp def extract_waveforms(filename): # extract waveforms: kwx_filename = filename[:-5] + '.kwx' file_kwx = h5py.File(kwx_filename, 'r') with h5py.File(kwx_filename, mode="r") as f: waveforms = f['/channel_groups/1/waveforms_raw'][:] print('Extracted waveforms.') return waveforms def extract_positions(filename): with h5py.File(filename, mode="r") as f: data_pos = f['/positions/data_pos'][:] time_stamps_sessions_pos = np.cumsum(np.array(f['/positions/time_stamps_sessions_pos'][:]),dtype=float) timebase_pos = float(f['/positions/'].attrs.get('timebase_pos')) print('Extracted tracking data.') return data_pos, time_stamps_sessions_pos, timebase_pos #print('Found position samples at {} Hz ({} seconds over {} session(s))'.format(timebase_pos,time_stamps_sessions_pos[-1]/timebase_pos,len(time_stamps_sessions_pos)-1)) def extract_lfp(filename): with h5py.File(filename, mode="r") as f: eeg_raw = f['/eeg/data_eeg'][:] samples_sessions_eeg = f['/eeg/samples_sessions_eeg'][:] sample_rate_eeg = f['/eeg/'].attrs.get('sample_rate_eeg') samples_sessions_eeg = np.cumsum(samples_sessions_eeg) print('Extracted LFP data.') #print('Found EEG: {} samples at {} Hz ({} s). Separate LFPs recorded: {}'.format(eeg_raw.shape[1],sample_rate_eeg,len_eeg_s,len(eeg_raw))) return eeg_raw,samples_sessions_eeg,sample_rate_eeg def eeg_make_df(eeg_raw,data_pos_df,sample_rate_eeg,timebase_pos): eeg_df = pd.DataFrame() for eeg_no in xrange(eeg_raw['eeg'].shape[0]): eeg_df['eeg{}'.format(eeg_no)] = eeg_raw['eeg'][eeg_no] eeg_df['eeg_mean'] = np.mean(eeg_raw['eeg'],axis=0) eeg_df['speed'] = np.repeat(data_pos_df['speed_filtered'].values,sample_rate_eeg/timebase_pos) eeg_df['time'] = eeg_df.index.values.astype(float)/float(sample_rate_eeg) # in sec eeg_df.set_index('time', drop=True, append=False, inplace=True, verify_integrity=False) return eeg_df def sanity_check(spiketimes=None,sample_rate=None,time_stamps=None,time_stamps_sessions=None,time_stamps_sessions_sample_rate=None, waveforms=None,time_stamps_sessions_pos=None,timebase_pos=None,data_pos=None,time_stamps_sessions_input=None, samples_sessions_eeg=None,sample_rate_eeg=None): # performs a check of length of sessions etc. try: sys.stdout.write('\nComparing recorded session lengths...') if time_stamps is not None and sample_rate and time_stamps_sessions is not None and time_stamps_sessions_sample_rate: length_session1 = float(time_stamps[-1])/sample_rate length_session2 = float(time_stamps_sessions[-1])/time_stamps_sessions_sample_rate if length_session1 != length_session2: sys.stdout.write('\rInconsistency in calculated session lengths.') sys.exit() else: sys.stdout.write('Success.') else: sys.stdout.write('\rNo basic session information found.') except AttributeError: sys.stdout.write('\rNo basic session information found.') except TypeError: sys.stdout.write('\rNo basic session information found.') try: sys.stdout.write('\nComparing waveform and spike numbers...') if waveforms.shape[0] != len(spiketimes): # as many waveforms recorded as spikes? sys.stdout.write('\rNumber of recorded waveforms does not match length of recorded spikes.') sys.exit() else: sys.stdout.write('Success.') except AttributeError: sys.stdout.write('\rNo waveforms loaded.') except TypeError: sys.stdout.write('\rNo waveforms loaded.') try: sys.stdout.write('\nComparing recorded session lengths and position record...') if time_stamps_sessions_pos is not None and timebase_pos and time_stamps is not None and sample_rate and data_pos is not None: if not (time_stamps/sample_rate == time_stamps_sessions_pos/timebase_pos).all(): sys.stdout.write('\rLength of sessions and position file do not match.') sys.exit() elif not len(data_pos)/timebase_pos == (time_stamps/sample_rate)[-1]: sys.stdout.write('\rLength of sessions and position file do not match.') sys.exit() else: sys.stdout.write('Success.') else: sys.stdout.write('\rNo position data loaded.') except AttributeError: sys.stdout.write('\rNo position data loaded.') except TypeError: sys.stdout.write('\rNo position data loaded.') # input data: try: sys.stdout.write('\nComparing recorded session lengths and input data...') if time_stamps is not None and sample_rate and time_stamps_sessions_input is not None: time_stamps_session = np.cumsum(time_stamps)/sample_rate if (time_stamps_session != np.cumsum(time_stamps_sessions_input)/1000).any(): sys.stdout.write('\rInconsistency in calculated session lengths (session vs. input)') sys.exit() else: sys.stdout.write('Success.') else: sys.stdout.write('\rNo basic session or input information found! ') except AttributeError: sys.stdout.write('\rNo basic session or input information found! ') sys.exit() # this is fatal ... except TypeError: sys.stdout.write('\rNo basic session or input information found! ') sys.exit() # this is fatal ... # LFP data: try: sys.stdout.write('\nComparing recorded session lengths and LFP data...') if time_stamps is not None and sample_rate and samples_sessions_eeg is not None and sample_rate_eeg: if (time_stamps/sample_rate != samples_sessions_eeg/sample_rate_eeg).all(): sys.stdout.write('\rInconsistency in calculated session lengths (session vs. LFP)') sys.exit() else: sys.stdout.write('Success.') else: sys.stdout.write('\rNo basic session or LFP information found!') except AttributeError: sys.stdout.write('\rNo basic session or LFP information found!') sys.exit() # this is fatal ... except TypeError: sys.stdout.write('\rNo basic session or LFP information found!') sys.exit() # this is fatal ... def find_nearest(array,value): idx = np.searchsorted(array, value, side="left") if idx > 0 and (idx == len(array) or math.fabs(value - array[idx-1]) < math.fabs(value - array[idx])): return idx-1 else: return idx print('Loaded analysis helpers: General')

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414, 29901, 4593, 1495, 13, 2 ]

42_Google_Hash_Code/pizza_ml_0/ingredients.py

gguarnay/42

1

94104

import numpy as np class Ingredients: ''' Class for calculations of ingredients inside an area. ''' def __init__(self, pizza_lines): self._lines = [list(l) for l in pizza_lines] self._unique, self._map = np.unique(self._lines, return_inverse=True) self._map = self._map.reshape((len(self._lines),len(self._lines[0]))) self.shape = self._map.shape self.total = self.shape[0]*self.shape[1] self.total_unique = np.max(self._map)+1 self.initialize() def initialize(self): ''' Create an array for faster calculation of ingredients inside an area. ''' from_origin = np.zeros((*self.shape, self.total_unique)) for r in range(self.shape[0]): for c in range(self.shape[1]): ingredient_id = self._map[r][c] from_origin[r][c][ingredient_id] = 1 if r > 0: from_origin[r][c] += from_origin[r-1][c] if c > 0: from_origin[r][c] += from_origin[r][c-1] if r > 0 and c > 0: from_origin[r][c] -= from_origin[r-1][c-1] self._from_origin = from_origin def of(self, slice): ''' Return 1d array of number of ingredients, so i-th element is the number of ingredient with id i inside specified slice. ''' ingredients_inside_slice = np.copy(self._from_origin[ slice.r1, slice.c1]) if slice.r0 > 0: ingredients_inside_slice -= self._from_origin[slice.r0-1][slice.c1] if slice.c0 > 0: ingredients_inside_slice -= self._from_origin[slice.r1][slice.c0-1] if slice.r0 > 0 and slice.c0 > 0: ingredients_inside_slice += self._from_origin[slice.r0-1][slice.c0-1] return ingredients_inside_slice

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nested_admin/tests/gfk/models.py

mionch/django-nested-admin

0

74097

from __future__ import unicode_literals from django.contrib.contenttypes.fields import GenericRelation, GenericForeignKey from django.contrib.contenttypes.models import ContentType from django.db import models from django.db.models import ForeignKey, CASCADE from django.utils.encoding import python_2_unicode_compatible @python_2_unicode_compatible class GFKB(models.Model): name = models.CharField(max_length=255) position = models.PositiveIntegerField() content_type = ForeignKey(ContentType, on_delete=CASCADE) object_id = models.PositiveIntegerField() content_object = GenericForeignKey() class Meta: ordering = ['object_id', 'position'] def __str__(self): parts = ["%s[%d]" % (self.name, self.position)] if self.content_object: parts.insert(0, "%s" % self.content_object) return "/".join(parts) @python_2_unicode_compatible class GFKA(models.Model): slug = models.CharField(max_length=255) position = models.PositiveIntegerField() content_type = ForeignKey(ContentType, on_delete=CASCADE) object_id = models.PositiveIntegerField() content_object = GenericForeignKey() b_set = GenericRelation(GFKB) class Meta: ordering = ['object_id', 'position'] def __str__(self): parts = ["%s[%d]" % (self.slug, self.position)] if self.content_object: parts.insert(0, "%s" % self.content_object) return "/".join(parts) @python_2_unicode_compatible class GFKRoot(models.Model): slug = models.CharField(max_length=255) a_set = GenericRelation(GFKA) def __str__(self): return self.slug

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src/spectrabuster/Spectrum/spectrum.py

eduardosprp/spectrabuster

0

146853

import numpy as np from time import sleep from math import exp import matplotlib.pyplot as plt from scipy.integrate import trapz from os import path import struct import spectrabuster.functions as sbf from importlib import import_module from datetime import date, datetime from functools import partial class Spectrum(object): """ Class variables. Mainly default values that are used whenever their instance counterparts are not specified when initializing a Spectrum object. """ # {{{ to_save = { "int_time", "correct_nl", "correct_dc", "UV_index", "capture_date", "capture_time", "temp", } # fields that will be written to the file samples = 1 # how many samples to average optimize = True # whether or not to call optimize_int_time when sensor saturation is detected UV_index = None # }}} def __init__(self, int_time=None, **kwargs): # {{{ """ Initializes the Spectrum with the values specified by kwargs. Absence of a key results in the attribute taking a default value. """ # First of all, define a backend backend = kwargs["backend"] if "backend" in kwargs else None try: self.backend = sbf.get_backend(backend) except RuntimeError: self._warn("No backend specified. Using none by default.") self.backend = sbf.get_backend("none") # Then get the device if "device" in kwargs: self.device = self.backend.Device(kwargs["device"]) else: self.device = self.backend.first_available_device() # Some features of the device if self.backend.features["int_time_limits"]: self.int_time_limits = self.device.int_time_limits else: self.int_time_limits = None if self.backend.features["sat_intensity"]: self.sat_intensity = self.device.sat_intensity else: self.sat_intensity = None # Bunch of optional parameters self.from_index = kwargs["from_index"] if "from_index" in kwargs else None self.to_index = kwargs["to_index"] if "to_index" in kwargs else None self.correct_nl = kwargs["correct_nl"] if "correct_nl" in kwargs else False self.correct_dc = kwargs["correct_dc"] if "correct_dc" in kwargs else False self.samples = kwargs["samples"] if "samples" in kwargs else None self.int_time = int_time # Then the wavelengths and the intensities. It'll get each from the # device unless provided at the instantiation. if "wavelengths" in kwargs: self.wavel = np.array(kwargs["wavelengths"]) elif self.backend.features["measure"]: self.wavel = self.device.wavelengths() else: raise RuntimeError( f"No wavelengths array passed, and the {self.backend} cannot make measurements." ) if "intensities" in kwargs: self.inten = np.array(kwargs["intensities"]) elif self.backend.features["measure"]: self.inten = self.measure_inten(int_time=self.int_time) else: raise RuntimeError( f"No intensities array passed, and the {self.backend} cannot make masurements." ) # Finally, slice the intensities and wavelengths arrays. self.wavel, self.inten, self.slice = self.slice_array( self.wavel, self.inten, (self.from_index, self.to_index) ) # }}} def measure_inten(self, int_time=None, samples=None, **kwargs): # {{{ samples = samples if samples is not None else self.samples int_time = int_time if int_time is not None else self.int_time correct_dc = kwargs["correct_dc"] if "correct_dc" in kwargs else self.correct_dc correct_nl = kwargs["correct_nl"] if "correct_nl" in kwargs else self.correct_nl if samples is None: samples = 1 elif type(samples) is not int or samples < 0: raise ValueError( f"Invalid value of {self.samples} for the number of samples to average." ) if int_time is None: pass else: try: self.device.set_int_time(float(int_time)) except (TypeError, ValueError) as e: raise ValueError( f"Invalid type or value of {int_time} for integration time" ) measure = partial( self.device.measure, correct_dc=correct_dc, correct_nl=correct_nl ) inten_avg = np.average([measure() for i in range(samples)], axis=0) return inten_avg # }}} def write_to_file(self, file_path=None, save_fields=True, **kwargs): # {{{ """ Stores spectrum in a .dat text file, using a format that is easy to parse in gnu octave, R or any other programming language, or visualize in gnuplot, or any spreadsheet program. """ overwrite = kwargs["overwrite"] if "overwrite" in kwargs else None if path.exists(file_path): if overwrite: self._warn(f"WARNING: File {file_path} exists. Overwriting it.") else: raise RuntimeError( f"File {file_path} already exists. Pass 'overwrite=True' if you are sure you want to overwrite it." ) only_wavelengths = ( kwargs["only_wavelengths"] if "only_wavelengths" in kwargs else False ) only_intensities = ( kwargs["only_intensities"] if "only_intensities" in kwargs else False ) to_save = self.to_save # fields that will be written to the file if not file_path or not isinstance(file_path, str): raise ValueError( "Please pass a string as the file path wherein to save the spectrum." ) with open(file_path, "w+") as arq: gen_comments = ( f"# {name} = {value}\n" for name, value in vars(self).items() if name in to_save ) arq.writelines(gen_comments) if only_wavelengths: gen_wavel_inten = (f"{wavel}\n" for wavel in self.wavel) elif only_intensities: gen_wavel_inten = (f"{inten}\n" for inten in self.inten) else: gen_wavel_inten = ( f"{wavel}\t{inten}\n" for wavel, inten in zip(*self.spectrum) ) arq.writelines(gen_wavel_inten) # }}} def to_spectral_irrad(self, calibration_file=None, int_time=None): # {{{ """ Applies the spectral irradiance calibration and returns another Spectrum object for the irradiance spectrum. It also has to be a file with the wavelengths and spectral sensitivity, by the way. And note that this function assumes that the current Spectrum and the calibration file have the same wavelengths array, maybe just sliced differently """ if not calibration_file: raise RuntimeError( "Please pass the path to the calibration file as an argument." ) if not int_time and not self.int_time: raise ValueError( "No integration time argument passed, and this spectrum's int_time field is empty." ) elif not int_time and self.int_time: int_time = self.int_time calib_wavel, calib_inten, _ = self._read_file(calibration_file) if self.wavel.size > calib_wavel.size: from_index = self.find_wavel_index(self.wavel, calib_wavel[0]) to_index = self.find_wavel_index(self.wavel, calib_wavel[-1]) wavel_array = self.wavel[from_index : to_index + 1] inten_array = self.inten[from_index : to_index + 1] elif calib_wavel.size > self.wavel.size: from_index = self.find_wavel_index(calib_wavel, self.wavel[0]) to_index = self.find_wavel_index(calib_wavel, self.wavel[-1]) calib_inten = calib_inten[from_index : to_index + 1] wavel_array = calib_wavel[from_index : to_index + 1] inten_array = self.inten else: inten_array = self.inten wavel_array = self.wavel apply_calibration = lambda counts, calib: counts / (int_time * calib * 0.000001) inten_array = apply_calibration(inten_array, calib_inten) return Spectrum( intensities=inten_array, wavelengths=wavel_array, int_time=int_time, from_index=self.from_index, to_index=self.to_index, ) self_params = vars(self).copy() self_params.update({"intensities": inten_array, "wavelengths": wavel_array}) return Spectrum(**self_params) # }}} def to_count_rate(self): # {{{ """ Divides the spectrum by its integration time and that's it. """ if self.int_time: return self / (self.int_time * 0.000001) else: raise ValueError( "Integration time undefined for calculation of count rate." ) # }}} def calc_uv_index(self, from_wavel=286.0): # {{{ """ Calculates the UV index based on Mckinley-Diffey's action spectra for erythema. """ weighted_irrad = np.array( [ self.weight_irrad(wavel, irrad, from_wavel) for wavel, irrad in zip(*self.spectrum) ] ) self.UV_index = round(0.04 * trapz(weighted_irrad, self.wavel), 2) return self.UV_index # just for convenience # }}} def optimize_int_time(self, initial=None, limits=(0.8, 1), max_tries=5): # {{{ """ Attemps to find an integration time that maximizes signal to noise ratio while avoiding sensor saturation. This could probably be done more elegantly with recursion, but I haven't got time to think about that. Also BEWARE that this will overwrite the current spectrum. """ if initial is None: initial = self.int_time min_int_time, max_int_time = self.device.int_time_limits max_counts = self.device.sat_intensity target_counts = abs((limits[1] + limits[0]) / 2) * max_counts int_time = initial self.int_time = int_time print("Optimizing integration time...") i = 0 while i < max_tries or inten_max == max_counts: self.inten = self.measure_inten( correct_nl=False, correct_dc=False, samples=1 ) inten_max = np.amax(self.inten) ratio = inten_max / target_counts print(f"{i} {self.int_time} {ratio} {inten_max}") if limits[0] <= ratio <= limits[1]: break elif limits[1] <= ratio <= 1: int_time *= ratio ** 2 elif ratio > 1: int_time /= ratio ** 2 else: int_time /= ratio while int_time < min_int_time or int_time > max_int_time: int_time /= 2 self.int_time = int_time i += 1 self.inten = self.measure_inten()[self.slice] return int_time # just for convenience # }}} def join(self, other): # {{{ """ Joins two spectra. It will give preference to itself when resolving overlaps. Probably one of the first functions to get a rewrite. """ if not isinstance(other, Spectrum): raise TypeError("join takes only spectra as arguments") self_wavel_max = self.wavel[-1] self_wavel_min = self.wavel[0] other_wavel_max = other.wavel[-1] other_wavel_min = other.wavel[0] # other.wavel starts before self.wavel ends if np.isclose(other.wavel, self_wavel_max).any(): # NOTE: These variables are indexes referring to self.wavelengths and # other.wavelengths respectively! start_overlap = np.argmax(np.isclose(self.wavel, other_wavel_min)) end_overlap = np.argmax(np.isclose(other.wavel, self_wavel_max)) Spectrum._warn( f"WARNING: The spectra overlap from {other_wavel_min} to {self_wavel_max}" ) # For some god forsaken reason np.concatenate will only work if you pass # a tuple of arrays... new_wavels = np.copy(self.wavel) new_wavels = np.concatenate( (new_wavels, np.copy(other.wavel[end_overlap + 1 :])) ) new_intens = np.copy(self.inten) new_intens = np.concatenate( (new_intens, np.copy(other.inten[end_overlap + 1 :])) ) # self.wavelengths starts before other.wavelengths ends elif np.isclose(self.wavel, other_wavel_max).any(): # NOTE: These variables are indexes referring to other.wavel and # self.wavel respectively! start_overlap = np.argmax(np.isclose(other.wavel, self_wavel_min)) end_overlap = np.argmax(np.isclose(self.wavel, other_wavel_max)) Spectrum._warn( f"WARNING: The spectra overlap from {self_wavel_min} to {other_wavel_max}" ) # You see, the preference is always given to self new_wavels = np.copy(other.wavel[:start_overlap]) new_wavels = np.concatenate((new_wavels, np.copy(self.wavel))) new_intens = np.copy(other.inten[:start_overlap]) new_intens = np.concatenate((new_intens, np.copy(self.inten))) # There is no overlap else: if other_wavel_min > self_wavel_min: new_wavels = np.concatenate( (np.copy(self.wavel), np.copy(other.wavel)) ) new_intens = np.concatenate( (np.copy(self.inten), np.copy(other.inten)) ) elif other_wavel_min < self_wavel_min: new_wavels = np.concatenate( (np.copy(other.wavel), np.copy(self.wavel)) ) new_intens = np.concatenate( (np.copy(other.inten), np.copy(self.inten)) ) self_params = vars(self).copy() self_params.update( { "intensities": new_intens, "wavelengths": new_wavels, "from_index": None, "to_index": None, "backend": "none", } ) return Spectrum(**self_params) # }}} # Esse um é para que o animal lembre de consertar quando saporra # inevitavelmente der erro @property def max_counts(self): return np.amax(self.inten) @property def uv(self): # {{{ # Lmao... if self.UV_index: return self.UV_index else: return self.calc_uv_index() # }}} @property def spectrum(self): return self.wavel, self.inten @property def wavelengths(self): return self.wavel @property def intensities(self): return self.inten @classmethod def from_file(cls, inten_wavel_file=None, **kwargs): # {{{ """ Creates a spectrum instance with the wavelengths and/or intensities read from a text file. Additionally, it looks for key-word arguments at the first few lines of the file. If the same kwargs are passed to this function, they take precedence. When retrieving a Spectrum from a file, it will always be assigned the backend 'none'. """ wavel_file = kwargs["wavel_file"] if "wavel_file" in kwargs else None inten_file = kwargs["inten_file"] if "inten_file" in kwargs else None inten_wavel_file = ( kwargs["inten_wavel_file"] if "inten_wavel_file" in kwargs else inten_wavel_file ) inten_array = None wavel_array = None new_kwargs = {} if inten_wavel_file: wavel_array, inten_array, new_kwargs = cls._read_file(inten_wavel_file) if wavel_file: wavel_array, _, new_kwargs = cls._read_file(wavel_file) if inten_file: inten_array, _, new_kwargs = cls._read_file(inten_file) if not inten_file and not inten_wavel_file and not wavel_file: cls._warn( "WARNING: Instantiating a spectrum with function from_file, but no file path arguments were passed." ) new_kwargs["intensities"] = inten_array new_kwargs["wavelengths"] = wavel_array # The backend 'none' will always be used when loading a # Spectrum from a file new_kwargs["backend"] = "none" new_kwargs.update(kwargs) return cls(**new_kwargs) # }}} @staticmethod def _read_file(text_file): # {{{ """ Used internally by the class method from_file. Returns as many numpy arrays as there are columns in the file, and a dictionary with whatever comments (prefaced by #) it finds. """ dict_args = {} col1 = [] col2 = [] with open(text_file, "r") as arq: # Generator for the lines in the archive gen_lines = (line.split() for line in arq) for line_split in gen_lines: if ( line_split[0] == "#" ): # comment, presumably containing arguments for __init__ dict_args[line_split[1]] = line_split[3] elif ( len(line_split) > 1 ): # 2 or more columns. Will ignore anything after the second column col1.append(float(line_split[0])) col2.append(float(line_split[1])) elif len(line_split) == 1: # 1 column col1.append(float(line_split[0])) if not dict_args and not col1 and not col2: # Check if they're all empty raise RuntimeError( f"No arguments, wavelengths and intensities found in {text_file}. Please check if this is a valid file." ) return np.array(col1), np.array(col2), dict_args # }}} @staticmethod def weight_irrad(wavel, irrad, from_wavel=286.0): # {{{ """ Simple implementation of Mckinley-Diffey's action spectrum. """ if from_wavel <= wavel < 298: return irrad elif 298 <= wavel < 328: return exp(0.216 * (298 - wavel)) * irrad elif 328 <= wavel < 400: return exp(0.034 * (139 - wavel)) * irrad else: return 0 # }}} @staticmethod def find_wavel_index(wavel_array, wavel, margin=0.5): # {{{ """ Attempts to find 'wavel' in 'wavel_array'. Will try using the closest wavelength at most 0.5 units from 'wavel' """ array_diffs = np.abs(wavel_array - wavel) closest_index = array_diffs.argmin() if np.isclose(wavel_array[closest_index], wavel): return closest_index elif array_diffs[closest_index] < 0.5: Spectrum._warn( f"Exact match for {wavel} not found. Using {wavel_array[closest_index]} instead." ) return closest_index else: raise ValueError( f"A close enough {wavel} wasn't found. Closest value is {wavel_array[closest_index]}." ) # }}} @staticmethod def slice_array(wavel, inten, indices, **kwargs): # {{{ """ Takes in two arrays and returns them sliced according to indices=(from_index, to_index). If the indeces are integers, it takes them to be literal indeces for the array. If they are floats, then it'll assume they are wavelengths whose literal indeces must be found before slicing. This behaviour can be overriden by passing literal_indices=True or False """ literal = kwargs["literal"] if "literal" in kwargs else None len_array = len(wavel) if len(inten) != len_array: raise ValueError("The arrays must be of equal length.") new_indices = [] for index in indices: if index is None: new_indices.append(index) elif type(index) is int or literal is True: if not (0 <= abs(index) <= len_array): raise IndexError( f"Invalid index of {index} for array of size {len_array}." ) else: new_indices.append(index) elif type(index) in (float, np.float64) or literal is False: index_wavel = Spectrum.find_wavel_index(wavel, index) new_indices.append(index_wavel) array_slice = slice(new_indices[0], new_indices[1]) return wavel[array_slice], inten[array_slice], array_slice # }}} @staticmethod def _warn(string): # {{{ """ Warnings can be disabled by setting the class variable 'opt_warnings' to False """ print(string) # }}} # Magic methods start here def __iter__(self): return zip(*self.spectrum) def __add__(self, other): # {{{ """ Adds the first spectrum's intensities with the second's. It can add spectrums with numpy arrays and lists as well, as long as they are the same length as the spectrum's wavelengths array. This operation will always return another spectrum with the added intensities. """ if isinstance(other, Spectrum): if np.isclose(self.wavel, other.wavel).all(): new_inten = self.inten + other.inten else: raise ValueError( "The divided spectrums must have the same wavelengths array." ) elif isinstance(other, (np.ndarray, list)): if len(other) == self.wavel.size or len(other) == 1: new_inten = self.inten + other else: raise ( ValueError( "The other operand must have the same size as the spectrum's wavelengths array, or size 1." ) ) elif isinstance(other, (float, int)): new_inten = self.inten + other else: raise (TypeError("Incompatible types for addition.")) self_params = vars(self).copy() self_params.update( { "intensities": new_inten, "wavelengths": self.wavel, "from_index": None, "to_index": None, "backend": "none", } ) return Spectrum(**self_params) # }}} def __radd__(self, other): return self + other def __sub__(self, other): # {{{ if isinstance(other, Spectrum): if np.isclose(self.wavel, other.wavel).all(): return self + np.negative(other.intensities) else: raise ValueError( "The subtracted spectrums must have the same wavelengths array." ) else: return self + np.negative(other) # }}} def __rsub__(self, other): raise NotImplementedError def __mul__(self, other): # {{{ """ Multiplies the first spectrum's intensities by the second. It can multiply spectrums with numpy arrays and lists as well, as long as they are the same length as the spectrum's wavelengths array. This operation will always return another spectrum with the multiplied intensities. """ if isinstance(other, Spectrum): if np.isclose(self.wavel, other.wavel).all(): new_inten = self.inten * other.inten else: raise ValueError( "The divided spectrums must have the same wavelengths array." ) elif isinstance(other, (np.ndarray, list)): if len(other) == self.wavel.size or len(other) == 1: new_inten = self.inten * other else: raise ( ValueError( "The other operand must have the same size as the spectrum's wavelengths array, or size 1." ) ) elif isinstance(other, (float, int)): new_inten = self.inten * other else: raise (TypeError("Incompatible types for multiplication.")) self_params = vars(self).copy() self_params.update( { "intensities": new_inten, "wavelengths": self.wavel, "from_index": None, "to_index": None, "backend": "none", } ) return Spectrum(**self_params) # }}} def __rmul__(self, other): return self * other def __truediv__(self, other): # {{{ """ Divides the first spectrum's intensities by the second. I makes no checks whether division by zero is being requested, that I leave to numpy. This operation will always return another spectrum with the divided intensities. The new spectrum's fields will be inherited from the first operand. """ if isinstance(other, Spectrum): if np.isclose(self.wavel, other.wavel).all(): new_inten = self.inten / other.inten else: raise ValueError( "The divided spectrums must have the same wavelengths array." ) elif isinstance(other, (np.ndarray, list)): if len(other) == self.wavel.size or len(other) == 1: new_inten = self.inten / other else: raise ( ValueError( "The other operand must have the same size as the spectrum's wavelengths array, or size 1." ) ) elif isinstance(other, (float, int)): new_inten = self.inten / other else: raise (TypeError("Incompatible types for division.")) self_params = vars(self).copy() self_params.update( { "intensities": new_inten, "wavelengths": self.wavel, "from_index": None, "to_index": None, "backend": "none", } ) return Spectrum(**self_params) # }}} def __rdiv__(self, other): raise NotImplementedError def __getitem__(self, key): # {{{ """ Takes the key to be a proper index if it is an integer, and as a wavelength if it is float. It also accepts numpy slices and regular slices, of course. """ if isinstance(key, (int, list, np.ndarray)): return self.inten[key] elif isinstance(key, float): int_index = self.find_wavel_index(self.wavel, key) return self.inten[int_index] else: raise TypeError( "Invalid type for index. Please enter an integer, list, numpy array or a float." ) # }}} def __setitem__(self, key, val): # {{{ """ Changes the intensity with index 'key' to 'val'. The new value must be a number, a tuple, list or numpy array. In the latter 3 cases, numpy will handle the assignment. """ if isinstance(key, (list, tuple, np.ndarray)): # Melhorar isto. Adicionar gerenciamento de exceções key = [ self.find_wavel_index(self.wavel, x) if isinstance(x, float) else x for x in key ] elif isinstance(key, float): key = self.find_wavel_index(self.wavel, key) elif isinstance(key, int): if abs(key) > self.wavel.size: raise IndexError( f"Invalid index of {val} for wavelengths array of size {self.wavel.size}" ) else: raise TypeError( "Invalid type for index. Please enter an integer, list, numpy array or a float." ) if isinstance(val, (tuple, list, np.ndarray)): try: val = [float(x) for x in val] except (TypeError, ValueError) as exception: raise ValueError(f"The {type(val)} {val} must contain only numbers.") else: try: val = float(val) except: raise ValueError( f"Invalid value of {val} for intensity. Please enter something convertible to float." ) self.inten[key] = val # }}} def __contains__(self, value): raise NotImplementedError def __repr__(self): return "Spectrum({}, {})".format(self.wavel, self.inten) def __len__(self): return self.wavel.size

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bbc1/libs/bbclib_msgtype.py

imony/py-bbclib

1

1601752

<reponame>imony/py-bbclib # -*- coding: utf-8 -*- """ Copyright (c) 2018 beyond-blockchain.org. 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. """ class MsgType: """Message types for between core node and client""" REQUEST_SETUP_DOMAIN = 0 RESPONSE_SETUP_DOMAIN = 1 REQUEST_SET_STATIC_NODE = 4 RESPONSE_SET_STATIC_NODE = 5 REQUEST_GET_CONFIG = 8 RESPONSE_GET_CONFIG = 9 REQUEST_MANIP_LEDGER_SUBSYS = 10 RESPONSE_MANIP_LEDGER_SUBSYS = 11 DOMAIN_PING = 12 REQUEST_GET_DOMAINLIST = 13 RESPONSE_GET_DOMAINLIST = 14 REQUEST_INSERT_NOTIFICATION = 15 CANCEL_INSERT_NOTIFICATION = 16 REQUEST_GET_STATS = 17 RESPONSE_GET_STATS = 18 NOTIFY_DOMAIN_KEY_UPDATE = 19 REQUEST_GET_NEIGHBORLIST = 21 RESPONSE_GET_NEIGHBORLIST = 22 REQUEST_GET_USERS = 23 RESPONSE_GET_USERS = 24 REQUEST_GET_FORWARDING_LIST = 25 RESPONSE_GET_FORWARDING_LIST = 26 REQUEST_GET_NODEID = 27 RESPONSE_GET_NODEID = 28 REQUEST_GET_NOTIFICATION_LIST = 29 RESPONSE_GET_NOTIFICATION_LIST = 30 REQUEST_CLOSE_DOMAIN = 31 RESPONSE_CLOSE_DOMAIN = 32 REQUEST_ECDH_KEY_EXCHANGE = 33 RESPONSE_ECDH_KEY_EXCHANGE = 34 REGISTER = 64 UNREGISTER = 65 MESSAGE = 66 REQUEST_GATHER_SIGNATURE = 67 RESPONSE_GATHER_SIGNATURE = 68 REQUEST_SIGNATURE = 69 RESPONSE_SIGNATURE = 70 REQUEST_INSERT = 71 RESPONSE_INSERT = 72 NOTIFY_INSERTED = 73 NOTIFY_CROSS_REF = 74 REQUEST_SEARCH_TRANSACTION = 82 RESPONSE_SEARCH_TRANSACTION = 83 REQUEST_SEARCH_WITH_CONDITIONS = 86 RESPONSE_SEARCH_WITH_CONDITIONS = 87 REQUEST_TRAVERSE_TRANSACTIONS = 88 RESPONSE_TRAVERSE_TRANSACTIONS = 89 REQUEST_CROSS_REF_VERIFY = 90 RESPONSE_CROSS_REF_VERIFY = 91 REQUEST_CROSS_REF_LIST = 92 RESPONSE_CROSS_REF_LIST = 93 REQUEST_REPAIR = 94 REQUEST_COUNT_TRANSACTIONS = 95 RESPONSE_COUNT_TRANSACTIONS = 95 REQUEST_REGISTER_HASH_IN_SUBSYS = 128 RESPONSE_REGISTER_HASH_IN_SUBSYS = 129 REQUEST_VERIFY_HASH_IN_SUBSYS = 130 RESPONSE_VERIFY_HASH_IN_SUBSYS = 131

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src/psqlsync/ghmoteqlync/sync.py

tiptenbrink/psqlsync

0

88447

from pathlib import Path from functools import partial import tempfile import logging import tomli import trio from dirgh import find_download import psqlsync.actions logger = logging.getLogger(__name__) def prepare(target, owner, repo, repo_dir, overwrite, config, token, verbose): logger.info("Downloading backup files...") run = partial(find_download, owner, repo, repo_dir, target, overwrite=overwrite, token=token) trio.run(run) latest_save = find_latest(target) temp_dir = Path(tempfile.gettempdir()).absolute() restore_filename = temp_dir.joinpath('restore.dump.gz') manager_config = { 'BACKUP_PATH': temp_dir, 'LOCAL_BACKUP_PATH': target } with open(config, "rb") as f: cfg = tomli.load(f) restore_uncompressed = temp_dir.joinpath('restore.dump') postgresql_cfg = cfg.get('postgresql') postgres_host = postgresql_cfg.get('host') postgres_port = postgresql_cfg.get('port') postgres_db = postgresql_cfg.get('db') postgres_restore = "{}_psqlsync_temp_restore".format(postgres_db) postgres_user = postgresql_cfg.get('user') postgres_password = postgresql_cfg.get('password') psqlsync.actions.restore(latest_save, restore_filename, 'LOCAL', manager_config, postgres_restore, None, postgres_db, postgres_host, postgres_port, postgres_user, postgres_password, restore_uncompressed, verbose) logger.info("Sync finished successfully.") def find_latest(target: str): db_path = Path(target) globbed = db_path.glob("*") backup_files = [] for glb in globbed: if glb.is_file(): if "backup" in glb.name and "gz" in glb.suffix: dash_split = glb.name.split('-') backup_time = '-'.join([p for p in dash_split if p.isnumeric()]) backup_files.append(backup_time) if not backup_files: raise ValueError("No backup files could be found, did download succeed?") # ascending order backup_files = sorted(backup_files) return backup_files[-1]

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PDA_Transport/multi_period_aam.py

gaufung/LMDI

4

72334

<filename>PDA_Transport/multi_period_aam.py # -*- encoding:utf-8 -*- from single_period_aam import Spaam class Mpaam(object): def __init__(self, dmus_s, name, global_dmus, cefs, years): self._period_count = len(dmus_s) self._dmus_s = dmus_s self._province_count = len(dmus_s[0]) self._province_names = [item.name for item in dmus_s[0]] self._name = name self._cache = {} self._global_dmus = global_dmus self._cefs = cefs self._years = years @property def name(self): return self._name @property def province_names(self): return self._province_names def _get_spaam(self, left, right): assert left != right label = str(left) + '-' + str(right) if label not in self._cache: self._cache[label] = Spaam.build(self._dmus_s[left], self._dmus_s[right], self._years[left] + '-' + self._years[right], self._global_dmus, self._cefs) return self._cache[label] def _index_t(self, t, index_name): result = 1.0 for i in range(1, t + 1): result *= getattr(self._get_spaam(i - 1, i), index_name) return result def _index(self, index_name): result = [] for i in range(self._province_count): value = 0.0 for t in range(1, self._period_count): aggerate_value = self._index_t(t - 1, index_name) spaam_t_1_t = self._get_spaam(t - 1, t) contribution = (getattr(spaam_t_1_t, 'r' + index_name)()[i] * getattr(spaam_t_1_t, index_name + '_ratio')()[i]) value += aggerate_value * contribution result.append(value) return result # emx def emx(self): return self._index('emx') def emx_t(self, t): return self._index_t(t, 'emx') # cef def cef(self): return self._index('cef') def cef_t(self, t): return self._index_t(t, 'cef') # pei def pei_t(self, t): return self._index_t(t, 'pei') def pei(self): return self._index('pei') # est def est_t(self, t): return self._index_t(t, 'est') def est(self): return self._index('est') # eue def eue_t(self, t): return self._index_t(t, 'eue') def eue(self): return self._index('eue') # pti def pti_t(self, t): return self._index_t(t, 'pti') def pti(self): return self._index('pti') # yoe def yoe_t(self, t): return self._index_t(t, 'yoe') def yoe(self): return self._index('yoe') # yct def yct_t(self, t): return self._index_t(t, 'yct') def yct(self): return self._index('yct') # rts def rts_t(self, t): return self._index_t(t, 'rts') def rts(self): return self._index('rts') def indexes(self, t): return [self.cef_t(t), self.emx_t(t), self.pei_t(t), self.est_t(t), self.eue_t(t), self.pti_t(t), self.yoe_t(t), self.yct_t(t), self.rts_t(t)]

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ivy/test/afterinit1.py

b1f6c1c4/cfg-enum

113

42268

from ivy import ivy_module as im from ivy.ivy_compiler import ivy_from_string from ivy.tk_ui import new_ui from ivy import ivy_utils as iu prog = """#lang ivy1.6 type t individual x(X:t) : t object foo(me:t) = { after init { x(me) := me; assert false } } isolate iso_foo(me:t) = foo(me) with x(me) """ with im.Module(): iu.set_parameters({'mode':'induction','isolate':'iso_foo','show_compiled':'true'}) main_ui = new_ui() ui = main_ui.add(ivy_from_string(prog)) main_ui.tk.update_idletasks() main_ui.answer("OK") ui.check_safety_node(ui.node(0)) assert not ui.node(0).safe # ui.check_inductiveness() # # ui = ui.cti # cg = ui.current_concept_graph # cg.show_relation(cg.relation('link(X,Y)'),'+') # cg.gather() # main_ui.answer("OK") # cg.strengthen() # main_ui.answer("OK") # ui.check_inductiveness() # # cg.show_relation(cg.relation('semaphore'),'+') # cg.gather() # main_ui.answer("View") # cg.bmc_conjecture(bound=1) # main_ui.mainloop()

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grammar.py

ankieter-gui/release

0

79750

import typing import error REQUEST_TABLE = { 'get': [[str]], 'as': [str], 'by': ([str], 'optional'), 'if': ([list], 'optional'), 'except': ([list], 'optional'), 'join': ([ { 'name': str, 'of': [str], } ], 'optional'), 'macro': ([str], 'optional'), } REQUEST_TABLE_QUESTION_COUNT = { 'get': [[str]], 'if': ([list], 'optional'), 'except': ([list], 'optional'), 'macro': [str], } REQUEST_CREATE_SURVEY = { 'surveyId': int, 'title': str, } REQUEST_CHANGE_PERMISSIONS = { 'r': ([int], 'optional'), 'w': ([int], 'optional'), 'n': ([int], 'optional'), } REQUEST_GROUP = { 'group': str, } REQUEST_USER = { 'userId': int, } REQUEST_SURVEY_LINK = { 'permission': str, 'surveyId': int } REQUEST_REPORT_LINK = { 'permission': str, 'reportId': int } def analyze(tp: typing.Any, obj: typing.Any) -> str: """Analyze object structure. Keyword arguments: tp -- expected object structure obj -- given object Return value: returns message after analyze """ # Check if obj is of the desired type if type(tp) is type: if type(obj) is tp: return '' else: return f'expected {tp.__name__}, got {type(obj).__name__}' # If the desired type is a list, check types of each of its elements if type(tp) is list: if type(obj) is not list: return f'expected {type(tp).__name__}, got {type(obj).__name__}' for i, o in enumerate(obj): if msg := analyze(tp[0], o): return f'in element [{i}]: {msg}' return '' # If the desired type is a dict, check types of values under each key if type(tp) is dict: if type(obj) is not dict: return f'expected {type(tp).__name__}, got {type(obj).__name__}' for k, t in tp.items(): if type(t) is tuple: t, *params = t else: params = [] if k not in obj: if 'optional' in params: continue return f'expected key \'{k}\'' if msg := analyze(t, obj[k]): return f'in element \'{k}\': {msg}' return '' return 'unexpected object type' def check(tp: typing.Any, obj: typing.Any): """Validate object structure. Keyword arguments: tp -- expected object structure obj -- given object """ if msg := analyze(tp, obj): raise error.API(msg)

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python2.7/site-packages/zope/interface/tests/test_verify.py

84KaliPleXon3/sslstrip-hsts-openwrt

4

1609893

############################################################################## # # Copyright (c) 2001, 2002 Zope Corporation and Contributors. # All Rights Reserved. # # This software is subject to the provisions of the Zope Public License, # Version 2.1 (ZPL). A copy of the ZPL should accompany this distribution. # THIS SOFTWARE IS PROVIDED "AS IS" AND ANY AND ALL EXPRESS OR IMPLIED # WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS # FOR A PARTICULAR PURPOSE. # ############################################################################## """Interface Verify tests $Id: test_verify.py 26866 2004-08-02 20:57:00Z jim $ """ from zope.interface import Interface, implements, classImplements, Attribute from zope.interface.verify import verifyClass, verifyObject from zope.interface.exceptions import DoesNotImplement, BrokenImplementation from zope.interface.exceptions import BrokenMethodImplementation import unittest class Test(unittest.TestCase): def testNotImplemented(self): class C(object): pass class I(Interface): pass self.assertRaises(DoesNotImplement, verifyClass, I, C) classImplements(C, I) verifyClass(I, C) def testMissingAttr(self): class I(Interface): def f(): pass class C(object): implements(I) self.assertRaises(BrokenImplementation, verifyClass, I, C) C.f=lambda self: None verifyClass(I, C) def testMissingAttr_with_Extended_Interface(self): class II(Interface): def f(): pass class I(II): pass class C(object): implements(I) self.assertRaises(BrokenImplementation, verifyClass, I, C) C.f=lambda self: None verifyClass(I, C) def testWrongArgs(self): class I(Interface): def f(a): pass class C(object): def f(self, b): pass implements(I) # We no longer require names to match. #self.assertRaises(BrokenMethodImplementation, verifyClass, I, C) C.f=lambda self, a: None verifyClass(I, C) C.f=lambda self, **kw: None self.assertRaises(BrokenMethodImplementation, verifyClass, I, C) C.f=lambda self, a, *args: None verifyClass(I, C) C.f=lambda self, a, *args, **kw: None verifyClass(I, C) C.f=lambda self, *args: None verifyClass(I, C) def testExtraArgs(self): class I(Interface): def f(a): pass class C(object): def f(self, a, b): pass implements(I) self.assertRaises(BrokenMethodImplementation, verifyClass, I, C) C.f=lambda self, a: None verifyClass(I, C) C.f=lambda self, a, b=None: None verifyClass(I, C) def testNoVar(self): class I(Interface): def f(a, *args): pass class C(object): def f(self, a): pass implements(I) self.assertRaises(BrokenMethodImplementation, verifyClass, I, C) C.f=lambda self, a, *foo: None verifyClass(I, C) def testNoKW(self): class I(Interface): def f(a, **args): pass class C(object): def f(self, a): pass implements(I) self.assertRaises(BrokenMethodImplementation, verifyClass, I, C) C.f=lambda self, a, **foo: None verifyClass(I, C) def testModule(self): from zope.interface.tests.ifoo import IFoo from zope.interface.tests import dummy verifyObject(IFoo, dummy) def testMethodForAttr(self): class IFoo(Interface): foo = Attribute("The foo Attribute") class Foo: implements(IFoo) def foo(self): pass verifyClass(IFoo, Foo) def testNonMethodForMethod(self): class IBar(Interface): def foo(): pass class Bar: implements(IBar) foo = 1 self.assertRaises(BrokenMethodImplementation, verifyClass, IBar, Bar) def test_suite(): loader=unittest.TestLoader() return loader.loadTestsFromTestCase(Test) if __name__=='__main__': unittest.TextTestRunner().run(test_suite())

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databasetools/projectiontest.py

salhasalman/DatabaseTools

0

145558

# pew in databasetools-venv python /home/hayj/wm-dist-tmp/DatabaseTools/databasetools/projectiontest.py import sys, os; sys.path.append("/".join(os.path.abspath(__file__).split("/")[0:-2])) import pymongo import random import string import time from systemtools.system import * from systemtools.duration import * # We init db name, host...: dbTest = "student" host = "localhost" port = "27017" # We init users: if isHostname("hjlat"): user = None password = None elif isHostname("datascience01"): user = "student" password = "<PASSWORD>" else: print("Pls execute on hjlat or datascience01.") ; exit() # We make the mongo scheme: mongoConnectionScheme = "mongodb://" + host + ":" + port if user is not None: mongoConnectionScheme = "mongodb://" + user + ":" + password + "@" + host + ":" + port myclient = pymongo.MongoClient(mongoConnectionScheme) # The function which create a fake collection: def createFakeCollection(alwaysRecreate=True): collectionName = "usercrawl" mydb = myclient[dbTest] mycol = mydb[collectionName] if alwaysRecreate or (mycol.count({}) < 100 and (collectionName == "test" or collectionName == "student")): mycol.create_index("user_id") mycol.delete_many({}) for i in range(1000): text = "" for u in range(1000): text += ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(10)) o = {"timestamp": time.time(), "text": text, "user_id": i + 5000} mycol.insert_one(o) return mycol def getUserCrawl(): if not isHostname("datascience01"): print("Pls execute on datascience01.") ; exit() mydb = myclient["twitter"] return mydb["usercrawl"] def copyUserCrawlCollection(copyCount=1000, alwaysRecreate=False): mydb = myclient["twitter"] userCrawl = mydb["usercrawl"] mydbTest = myclient[dbTest] testCol = mydbTest[dbTest] testCol.create_index("user_id") if alwaysRecreate or testCol.count() < copyCount: testCol.delete_many({}) i = 0 for row in userCrawl.find({}): del row["_id"] testCol.insert_one(row) if i >= copyCount: break if i % int(copyCount / 50) == 0: print(str(int(i / copyCount * 100)) + "%") i += 1 return testCol # for row in mycol.find({}): # if not dictContains(row, "user_id"): # print("FUCK") # print(row["url"]) # exit() tt = TicToc() tt.tic() mycol = createFakeCollection(alwaysRecreate=False) # mycol = createFakeCollection(alwaysRecreate=True) # mycol = getUserCrawl() # mycol = copyUserCrawlCollection() tt.tic("collection init DONE") iterationMaxCount = 20 print("Test started.") i = 0 for row in mycol.find({}): if i % int(iterationMaxCount / 3) == 0: print(str(i) + ": " + str(lts(row)[0:60])) if i > iterationMaxCount: break i += 1 tt.tic("Find done.") iterationMaxCount = 200 i = 0 for row in mycol.distinct("user_id"): if i % int(iterationMaxCount / 3) == 0: print(str(i) + ": " + str(row)[0:60]) if i > 200: break i += 1 tt.tic("Find distincts done.") i = 0 for row in mycol.find({}, projection={"user_id": True}): if i % int(iterationMaxCount / 3) == 0: print(str(i) + ": " + str(lts(row)[0:60])) if i > 200: break i += 1 tt.tic("Find with projection done.")

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dev/otu_handler.py

michaelwiest/microbiome_rnn

0

185844

<filename>dev/otu_handler.py<gh_stars>0 import copy import pandas as pd import numpy as np from scipy.stats.mstats import gmean, zscore ''' This is the main object that handles the OTU data. It has a few main functions: 1. Managing data normalization. 2. Managing training, validation, and test data. 3. Data sampling. All of the different models use this data handler to get data samples. ''' def clr(some_array, axis): ''' Had to reimplement this because the server is python2 and skbio is only python3 ''' gms = gmean(some_array, axis=axis) gms = np.expand_dims(gms, axis=axis) gms = np.repeat(gms, some_array.shape[axis], axis=axis) return np.log(some_array / gms) class OTUHandler(object): ''' Class for handling OTU data. It generates samples and keeps track of training and validation data. ''' def __init__(self, files, test_files=None): # Read in the sample data. self.samples = [] for f in files: self.samples.append(pd.read_csv(f, index_col=0)) # If test_files are provided then read them in. if test_files is not None: self.test_data = [] for f in test_files: self.test_data.append(pd.read_csv(f, index_col=0)) else: self.test_data = None # Helper variables that are used elsewhere in the code. self.strains = list(self.samples[0].index.values) self.num_strains = len(self.strains) # These get set when calling set_train_val self.train_data = None self.val_data = None def set_train_val(self, percent=0.8, minsize=20): ''' Set the training and validation data for each sample. Can include a lower bound on size of train/validation ''' # For keeping track of the minimum size that the train/val data need to be. self.min_len = minsize # To be populated. self.train_data = [] self.val_data = [] temp_sizes = [] # Go through each of the samples. for i, sample in enumerate(self.samples): # Where in the sample to set the split. index = int(percent * sample.shape[1]) # If not enough examples, skip this file. if not ((sample.iloc[:, :index].shape[1]) < minsize or (sample.iloc[:, index:].shape[1]) < minsize): # Append the new data to the appropriate location. self.train_data.append(sample.iloc[:, :index]) self.val_data.append(sample.iloc[:, index:]) else: # If it's too small only use it as training data. print('Skipping file, {}, because it\'s of shape: {}'.format(i, sample.shape)) self.train_data.append(sample) def normalize_data(self, method='zscore'): ''' Method for normalizing the input data. This can currently zscore and clr the data. Other methods could be added. ''' method = method.lower() if method not in ['zscore', 'clr']: raise ValueError('Specify "zscore" or "clr" for method') if method == 'zscore': m = zscore else: m = clr # Keep track of the new data. new_vals = [] for i, which_data in enumerate([self.samples, self.test_data]): # This is in case the test data is not supplied. if which_data is not None: new_vals = [] # For each of the dataframes. for s in which_data: # Apply the normalization method. new_vals.append(pd.DataFrame(m(s.values, axis=0), index=s.index, columns=s.columns)) # This is sort of a hack which is annoying. # Basically set the data to be the normalized version. if i == 0: self.samples = new_vals elif i == 1: self.test_data = new_vals def get_N_samples_and_targets(self, N, input_slice_size, target_slice_size, which_data='train', target_slice=True, slice_offset=1, which_donor=None): ''' This is the main function for generating samples of data with which to train the neural network. Returns two things, an input and a target (both numpy arrays): input:[N x num_otus x input_slice_size] target: [N x num_otus x target_slice_size] Selects N random examples from all possible training samples. It selects from them based upon the number of timepoints present in each donor. So donors with more data get sampled more often. ''' # What data source to sample from. which_data = which_data.lower() if which_data not in ['train', 'validation', 'test']: raise ValueError('Please specify either: train, validaiton, or test' ' for argument "which_sample"') if self.test_data is None and which_data == 'test': raise ValueError('Do not select for test data when none is set.') # Set the appropriate location. if which_data == 'train': data_source = self.train_data elif which_data == 'validation': data_source = self.val_data elif which_data == 'test': data_source = self.test_data samples = [] # Samples to feed to the model. targets = [] # Targets to compare predictions against. # If train validation split hasn't been specified. if self.train_data is None: raise AttributeError('Please specify train and val data before ' 'calling this function.') # This flag is for generating samples from a particular donor. # During training this is always set as None so that it samples from # a distribution. if which_donor is None: # Samples from data based on number of samples. Ie, samples with more # data points get more selection. all_sizes = [d.shape[1] for d in data_source] probs = [s / (1.0 * sum(all_sizes)) for s in all_sizes] # Get the samples based on sizes. which_samples = np.random.choice(len(data_source), N, p=probs) else: # If we know what donor we want then only sample from that one. which_samples = [which_donor] * N # For each of the specified samples to pick. for ws in which_samples: sample = data_source[ws] # Pick a random starting point in the example. Get the data in # that slice and then the values immediately after. start_index = np.random.randint(sample.shape[1] - input_slice_size - target_slice_size) data = sample.iloc[:, start_index: start_index + input_slice_size].values # For the LSTM and EncoderDecoer we want a whole slice of values # to compare against. Not just a single target like in the FFN. # Now this just increments the input by one position for the target. if not target_slice: target = sample.iloc[:, start_index + input_slice_size].values else: target = sample.iloc[:, start_index + slice_offset: start_index + target_slice_size + slice_offset].values # Store all the values samples.append(data) targets.append(target) samples = np.array(samples) targets = np.array(targets) return samples, targets

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pos_tagger/pos_replace_v5.py

ZirconiumZr/UOB_SIT

0

1617223

<gh_stars>0 # -*- coding: utf-8 -*- """pos_replace_v5.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/1Mdfqrn2Nc8xE6xFlAstCmzPeEMCgguP- """ import nltk from nltk import word_tokenize, pos_tag import pandas as pd import io from nltk.tokenize.treebank import TreebankWordDetokenizer try: nltk.data.find('tokenizers/punkt') except LookupError: nltk.download('punkt') try: nltk.data.find('taggers/averaged_perceptron_tagger') except LookupError: nltk.download('averaged_perceptron_tagger') try: nltk.data.find('taggers/universal_tagset') except LookupError: nltk.download('universal_tagset') template = pd.read_csv('/content/replace_template.csv') # template=template.fillna('') text1 = "i'm fine at your bb and u o p is a bank" text2 = "i'm from uob and i'm fine in u o b" text3 = "you can get youll be credit card" def pos_replace(l, template): ''' l: the stt sentence template: DataFrame, read as csv from stt_replace_template.txt pos_tag reference: https://universaldependencies.org/u/pos/all.html#al-u-pos/ ''' flag_change = "" words = word_tokenize(l) # l_r = TreebankWordDetokenizer().detokenize(words) l_r = " ".join(words) for i in range(len(template)): row = template.iloc[i] pos_before = str(row['pos_before']).split(';') pos_after = str(row['pos_after']).split(';') replace_list = str(row['replace']).split(';') flag_direct_replace = str(row['flag_direct_replace']) key = word_tokenize(row['key']) for p in replace_list: p_w = word_tokenize(p) # p_s = TreebankWordDetokenizer().detokenize(p_w) p_s = " ".join(p_w) if p_s in l_r: # p_w = word_tokenize(p) lenth_p = len(p_w) words = word_tokenize(l_r) words_pos = pos_tag(words, tagset='universal') lenth_w = len(words) # r_len = lenth_w-lenth_p+1 if flag_direct_replace.lower() == "x": i = 0 # for i in range(lenth_w-lenth_p+1): while i < lenth_w-lenth_p+1: # print(i) if words[i:i+lenth_p]==p_w: print('directly replace', p, 'with',row[0]) words[i:i+lenth_p] = key words_pos = pos_tag(words, tagset='universal') lenth_w = len(words) # r_len = lenth_w-lenth_p+1 flag_change = "x" i += 1 else: # for i in range(lenth_w-lenth_p+1): i = 0 while i < lenth_w-lenth_p+1: # print(i) if words[i:i+lenth_p]==p_w: if words_pos[i-1][1] in pos_before: print('the pos of the word before', p, 'is',words_pos[i-1][1]) words[i:i+lenth_p] = key words_pos = pos_tag(words, tagset='universal') lenth_w = len(words) # r_len = lenth_w-lenth_p+1 flag_change = "x" # l_r=l_r.replace(p, row[0]) # l_r = " ".join(words) # print(l_r) elif i+lenth_p<len(words) and words_pos[i+lenth_p][1] in pos_after: print('the pos of the word after', p, 'is',words_pos[i+lenth_p][1]) words[i:i+lenth_p] = key words_pos = pos_tag(words, tagset='universal') lenth_w = len(words) # r_len = lenth_w-lenth_p+1 flag_change = "x" # l_r=l_r.replace(p, row[0]) # l_r = " ".join(words) # print(l_r) i += 1 if flag_change == "x": print(l_r) l_r = " ".join(words) # l_r = TreebankWordDetokenizer().detokenize(words) print(l_r) flag_change = "" # l_r = l # for i in range(len(template)): # get every row in the template # row = template.iloc[i] # pos_before = str(row['pos_before']).split(';') # pos_after = str(row['pos_after']).split(';') # replace_list = str(row['replace']).split(';') # print('replace_list:',replace_list) # for p in replace_list: # get each element in replace_list of template row[i] # print('find p for=', p) # if p in l_r: # print('find p if=', p) # # y = p.replace(' ','') # y = re.sub(r'[^\w\s]','',p) # replace punctuations to blank str # print('y: ',y) # interim = l_r.replace(p, y) # words = word_tokenize(interim) # interim_pos = pos_tag(words, tagset='universal') # index_=[i for i,x in enumerate(interim_pos) if x[0]==y] # for i in index_: # if interim_pos[i-1][1] in pos_before: # print('the pos of the word before', p, 'is',interim_pos[i-1][1]) # words[i] = row[0] # # l_r=l_r.replace(p, row[0]) # l_r = " ".join(words) # print(l_r) # elif interim_pos[i+1][1] in pos_after: # print('the pos of the word after', p, 'is',interim_pos[i+1][1]) # words[i] = row[0] # # l_r=l_r.replace(p, row[0]) # l_r = " ".join(words) # print(l_r) return l_r pos_replace(text1, template) pos_replace(text2, template) pos_replace(text3, template)

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helpers/labml_helpers/datasets/remote/test/mnist_server.py

mcx/labml

174

113625

<reponame>mcx/labml<gh_stars>100-1000 from labml import lab from labml_helpers.datasets.remote import DatasetServer from torchvision import datasets, transforms def main(): transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ]) train_dataset = datasets.MNIST(str(lab.get_data_path()), train=True, download=True, transform=transform) valid_dataset = datasets.MNIST(str(lab.get_data_path()), train=False, download=True, transform=transform) ds = DatasetServer() ds.add_dataset('mnist_train', train_dataset) ds.add_dataset('mnist_valid', valid_dataset) ds.start() if __name__ == '__main__': main()

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costflow/config.py

StdioA/costflow

0

183685

<filename>costflow/config.py<gh_stars>0 from dataclasses import dataclass, field @dataclass class Config: default_currency: str = "CNY" formulas: dict = field(default_factory=dict) def get_formula(self, name): return self.formulas.get(name, "") # TODO: Beancount loader for default currency # Global store config = Config()

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django/dashboard/views/index.py

aistairc/voteclustering_aist

0

69666

from django.db.models import Count, Q from django.shortcuts import redirect, render from django.utils import timezone from AIST_survey.models import Enquete, Respondent, Question, Choice, Evaluation def index(request): if "enquete_id" not in request.session: return redirect("login") enquete_id = request.session["enquete_id"] enquete = Enquete.objects.get(id=int(enquete_id)) params = get_index_params(enquete) return render(request, 'dashboard/index.html', params) def get_index_params(enquete): respondents = Respondent.objects.filter(enquete_id=enquete.id) questions = Question.objects.filter(enquete_id=enquete.id) choices = [] for q in questions: choice_data = Choice.objects.filter(question_id=q.id) choices.extend(choice_data) params = { 'enquete': enquete, 'respondent_num': len(respondents), 'today_respondents_num': get_today_respondents_num(enquete), 'remaining_days': get_remaining_days(enquete), 'respondent_transition_data': get_respondent_transition_data(enquete), 'question_graph_data': get_question_graph_data(enquete) } return params def get_question_graph_data(enquete): questions = Question.objects.filter(enquete_id=enquete.id) graph_data = [] for q in questions: if q.type == Question.SINGLE or q.type == Question.MULTI: data = { "question_text": q.text, "type": "selection", "graph_data": get_selection_data(q) } graph_data.append(data) elif q.type == Question.QUESTION: data = { "question_text": q.text, "type": "open_end", "graph_data": get_question_data(q) } graph_data.append(data) return graph_data def get_selection_data(question): choices = Choice.objects.filter(question=question) selection_labels = [] selection_data = [] for c in choices: choice_num = Evaluation.objects.filter(choice=c).count() selection_labels.append(c.text) selection_data.append(choice_num) return {"labels": selection_labels, "data": selection_data} def get_question_data(question): choices = Choice.objects.filter(question=question) question_data = { "like": [], "time": [], } choices_timestamp = choices.order_by("-timestamp")[0:5] for choice in choices_timestamp: question_data["time"].append({ "time": choice.timestamp, "text": choice.text }) choices_like = choices\ .annotate( count=Count( "evaluation", filter=Q(evaluation__assessment=Evaluation.LIKED) | Q(evaluation__assessment=Evaluation.PROPOSED) ) )\ .order_by("-count")[0:5] for choice in choices_like: question_data["like"].append({ "text": choice.text, "like": choice.count, "timestamp": choice.timestamp }) return question_data def get_today_respondents_num(enquete): today_respondents = Respondent.objects.filter(enquete_id=enquete.id, finishTime__date=timezone.now()) return today_respondents.count() def get_remaining_days(enquete): remaining_days = (enquete.expired_at - timezone.now()).days return remaining_days if remaining_days > 0 else 0 def get_respondent_transition_data(enquete): today = timezone.now().date() date_list = [] respondent_count_list = [] publish_day = enquete.published_at.astimezone().replace(hour=23, minute=59, second=59, microsecond=999999) while publish_day.date() <= today: respondent_count = Respondent.objects.filter(finishTime__date__lte=publish_day, enquete_id=enquete.id).count() date_list.append(publish_day.strftime("%Y/%m/%d")) respondent_count_list.append(respondent_count) publish_day += timezone.timedelta(days=1) return {'labels': date_list, 'values': respondent_count_list}

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test/test_server.py

sebastianludwig/SensationDriver

0

116373

<reponame>sebastianludwig/SensationDriver<filename>test/test_server.py from utils import * from sensationdriver.server import Server class TestServer(unittest.TestCase): class MemoryHandler(object): def __init__(self): self.set_up_called_counter = 0 self.tear_down_called_counter = 0 @asyncio.coroutine def set_up(self): self.set_up_called_counter += 1 @asyncio.coroutine def tear_down(self): self.tear_down_called_counter += 1 def test_handler_torn_down_after_server_usage(self): server = Server() handler = self.MemoryHandler() server.handler = handler with server: pass self.assertEqual(handler.tear_down_called_counter, 1) def test_handler_set_up_called_on_restart(self): server = Server() handler = self.MemoryHandler() server.handler = handler with server: pass with server: self.assertEqual(handler.set_up_called_counter, 2) def test_handler_set_up_not_called_twice_on_start(self): server = Server() handler = self.MemoryHandler() server.handler = handler with server: self.assertEqual(handler.set_up_called_counter, 1) if __name__ == '__main__': unittest.main()

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statsWaveletFilt/threshold.py

Tiarles/Statistical-Wavelet-Filtering

3

61809

<reponame>Tiarles/Statistical-Wavelet-Filtering<filename>statsWaveletFilt/threshold.py<gh_stars>1-10 # -*- coding: utf-8 -*- ''' **Wavelet Based in CUSUM control chart for filtering signals Project (module** ``statsWaveletFilt.threshold`` **):** Statistic functions for obtain threshold values for wavelet coefficients based in some referenced works. *Created by <NAME>, 2018* ''' def lambdasVisuShrink(wavCoeff): ''' Computes the threshold value (lambda) by VisuShrink [1] method. Parameters --------- wavCoeff: list of lists or array-like Wavelet coefficients Returns ------- list of float: The threshold values for each wavelet coefficients vector. See also -------- filtration: Function that use this function for filter via wavelet coefficients pywt.wavedec: Function that decomposes the signal in wavelet and scale coefficients pywt.waverec: Function that recomposes the signal from wavelet and scale coefficients References ---------- .. [1] <NAME>.; <NAME>. Ideal spatial adaptation via wavelet shrinkage. Biometrika, v. 81, p. 425–455, 1994. ''' import numpy as np # Coefficients Vector from the bigger resolution d_m1 = list(wavCoeff[-1]) # Make a copy d_m1 = np.array(d_m1) # Turns a numpy.array estDeviation = np.median(np.abs(d_m1))/.6745 lambdaValues = [estDeviation * np.sqrt(2*np.log10(d_m1.size))] * \ len(wavCoeff) return lambdaValues def _sure(vector, ti): ''' Internal function, for lambdasSureShrink method. .. note:: After the test (via **pytest**) the fuction was changed for better performance. Parameters --------- vector: list or array-like vector of coefficients who lambdasSureShrink function needs for the computes. t: list or array-like Intern parameter set by lambdasSureShrink method. Returns ------- list of float: Based on work of Stein [1]. See also -------- filtration: Function that use this function to filter via wavelet coefficients lambdasSureShrink: Function that use this function for sureshrink algorithm References ---------- .. [1] STEIN, C. Estimation of the mean of a multivariate normal distribution. Annals of Statistics, v. 9, p. 1135–1151, 1981. ''' import numpy as np vector2 = np.array(list(vector)) soma1 = np.sum(vector2 <= ti) ti_list = [[ti]]*vector2.size soma2 = np.sum(np.power(np.minimum(vector2, np.concatenate(ti_list)), 2)) return vector2.size - 2 * soma1 + soma2 def lambdasSureShrink(wavCoeff, dim_t=1024): ''' Computes the threshold value (lambda) by SureShrink [1] method. It's showed also in [2]. .. note:: After the test (via **pytest**) the fuction was changed for better performance. Parameters --------- wavCoeff: list of lists or array-like Wavelet coefficients dim_t: optional, 1024 by default. t-dimension. Input vector from internal function _sure(vector, dim_t). Returns ------- list of float: The threshold values for each wavelet coefficients vector. See also -------- filtration: Function that use this function to filter via wavelet coefficients pywt.wavedec: Function that decomposes the signal in wavelet and scale coefficients pywt.waverec: Function that recomposes the signal from wavelet and scale coefficients References ---------- .. [1] <NAME>.; <NAME>. Ideal spatial adaptation via wavelet shrinkage. Biometrika, v. 81, p. 425–455, 1994. .. [2] <NAME>.; <NAME>. Filtragem de sinais via limiarização de coeficientes wavelet. Ciência e Natura, v. 36, p. 37–51, 2014. In portuguese. ''' import numpy as np wavCoeff2 = [np.array(list(wavCoeff_i)) for wavCoeff_i in wavCoeff] lambdaValues = [] for coeff in wavCoeff2: estDeviation = np.median(np.abs(coeff))/.6745 tmax = estDeviation*np.sqrt(2*np.log10(coeff.size)) t = np.linspace(0, tmax, dim_t) res_sure = [_sure(coeff, ti) for ti in t] lambdaValues.append(t[np.argmin(res_sure)]) return lambdaValues def lambdasBayesShrink(wavCoeff): ''' Computes the threshold value (lambda) by BayesShrink [1] method. It's showed also in [2]. Parameters --------- wavCoeff: list of lists or array-like Wavelet coefficients Returns ------- list of float: The threshold values for each wavelet coefficients vector. See also -------- filtration: Function that use this function to filter via wavelet coefficients pywt.wavedec: Function that decomposes the signal in wavelet and scale coefficients pywt.waverec: Function that recomposes the signal from wavelet and scale coefficients References ---------- .. [1] <NAME>.; <NAME>.; <NAME>. Adaptive wavelet thresholding for image denoising and compression. IEEE Transactions on Image Processing, v. 9, p. 1532–1546, 2000. .. [2] <NAME>.; <NAME>. Filtragem de sinais via limiarização de coeficientes wavelet. Ciência e Natura, v. 36, p. 37–51, 2014. In portuguese. ''' import numpy as np wavCoeff2 = [np.array(list(wavCoeff_i)) for wavCoeff_i in wavCoeff] d_m1 = wavCoeff2[-1] deviation_square = np.power(np.median(np.abs(d_m1))/0.6745, 2) lambdaValues = [] for wavCoeff_i in wavCoeff2: deviation2_wavCoeff_i = np.sum(np.power(wavCoeff_i, 2))/wavCoeff_i.size deviation_Xj = np.sqrt( np.maximum(deviation2_wavCoeff_i - deviation_square, 0)) lambdaValues.append(deviation_square/deviation_Xj) return lambdaValues def lambdasSPC_Threshold(wavCoeff, p=3): ''' Computes the threshold value (lambda) by SPC-Threshold [1], [2] method .. note:: After the test (via **pytest**) the fuction was changed for better performance. Parameters --------- wavCoeff: list of lists or array-like Wavelet coefficients p: int or float Optional, 3 by default. Parameter for the algorithm [1], generally is used 2 or 3. Returns ------- list of float: The threshold values for each wavelet coefficients vector. See also -------- filtration: Function that use this function to filter via wavelet coefficients pywt.wavedec: Function that decomposes the signal in wavelet and scale coefficients pywt.waverec: Function that recomposes the signal from wavelet and scale coefficients References ---------- .. [1] <NAME>.; <NAME>. SPC-threshold: uma proposta de limiarização para filtragem adaptativa de sinais. Tendências em Matemática Aplicada e Computacional, v. 11, n. 2, p. 121–132, 2010. In portuguese. .. [2] <NAME>.; <NAME>. Filtragem de sinais via limiarização de coeficientes wavelet. Ciência e Natura, v. 36, p. 37–51, 2014. In portuguese. ''' import numpy as np wavCoeff2 = [np.array(list(wavCoeff_i)) for wavCoeff_i in wavCoeff] lambdaValues = [] for wavCoeff_i in wavCoeff2: Sj = np.sqrt(1./(wavCoeff_i.size - 1) * np.sum(np.power(wavCoeff_i - wavCoeff_i.mean(), 2))) while (np.abs(wavCoeff_i) >= p*Sj).any(): wavCoeff_i = wavCoeff_i[np.abs(wavCoeff_i) < p*Sj] Sj = np.sqrt(1./(wavCoeff_i.size - 1) * np.sum(np.power(wavCoeff_i - wavCoeff_i.mean(), 2))) lambdaValues.append(p*Sj) return lambdaValues

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school/eCornell/Python Cert/Currency Converter/src/exchangeit.py

KyleGortych/sample_work

0

1605067

""" User interface for module currency When run as a script, this module prompts the user for two currencies and amount. It prints out the result of converting the first currency to the second. Author: <NAME> Date: 02/16/2022 """ import currency src = input("3-letter code for original currency: ") src = str(src) dst = input("3-letter code for the new currency: ") dst = str(dst) amt = input("Amount of the original currency: ") amt = float(amt) json = currency.service_response(src,dst,amt) # print(json) src_result = currency.before_space(currency.get_src(json)) sub_str = src dst_result = currency.exchange(src,dst,amt) dst_rounded = round(dst_result, 3) sub_str2 = str(dst_rounded) sub_str3 = dst print("You can exchange " + src_result + " " + sub_str + " for " + sub_str2 + " " + sub_str3 + ".")

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PythonMPI/computepi/ComputePiSeq.py

terasakisatoshi/MPI

1

177540

import math import time #compute \pi using formula shown below: #\pi=\int_{0}^{1}\frac{4}{1+x^2}dx \sim =\frac{1}{n}\sum_{i=0}^{n-1}\frac{4}{1+(\frac{i+0.5}{n})^2} # def compute_pi(num_step): h=1.0/num_step s=0.0 for i in range(num_step): x=h*(i+0.5) s+=4.0/(1.0+x**2) return s*h def main(): n=int(1.0e8) start=time.time() pi=compute_pi(n) diff=abs(pi-math.pi) print("pi is approximately %.16f,diff=compute_pi-math.pi is %.16f" % (pi,diff)) elapsed_time=time.time()-start print("elapsed_time=%f" % elapsed_time ) if __name__ == '__main__': main()

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code/tensorflow/tests/test_image_generator.py

siduojiang/BERTVision

5

106254

<reponame>siduojiang/BERTVision<gh_stars>1-10 import unittest import h5py #git repo specific setup import sys sys.path.append('../utils') from training_utils import * from evaluation import * class TestBERTImageGenerator(unittest.TestCase): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.model = PretrainedBertSquad2Faster('../../bert_base_squad_1e-5_adam_4batchsize_2epochs_weights_BERT_ONLY.h5').model train = h5py.File('../../../SQuADv2/train_386.h5', 'r') self.start_ids = train['input_start'] self.end_ids = train['input_end'] self.train_inputs = np.array(train['input_ids']) self.train_masks = np.array(train['attention_mask']) self.labels = np.vstack([self.start_ids, self.end_ids]).T self.offset = 2 self.start_idx = 14325 self.end_idx = 14642 self.images = BERTImageGenerator('../../../data/train/', self.labels, batch_size=1, start_idx = self.start_idx, end_idx = self.end_idx, shuffle = False) self.subset = self.images[self.offset] def setUp(self): self.idx = self.start_idx + self.offset def test_embeddings_shape(self): #extract image 2 with starting index 14325, leading to image 14325 self.assertEqual(len(self.subset), 2) self.assertEqual(self.subset[0].shape, (1, 24, 386, 1024)) self.assertEqual(self.subset[1].shape, (1, 2)) def test_offset(self): manual_load = h5py.File('../../../data/train/%d.h5' %self.idx, 'r') self.assertTrue((np.array(manual_load['hidden_state_activations'])[0][-1] == self.subset[0][0][0][-1]).all()) def test_label_ids(self): self.assertEqual(self.subset[1][0][0], self.start_ids[self.idx]) self.assertEqual(self.subset[1][0][1], self.end_ids[self.idx]) def test_embeddings_with_model(self): embeddings, outputs = self.model.predict([self.train_inputs[[self.idx]], self.train_masks[[self.idx]]]) non_zero_idx = np.sum(self.train_inputs[[self.idx]] != 0) self.assertTrue((embeddings[12][0][:non_zero_idx][-1] == self.subset[0][0][12][-1]).all()) def test_shuffle_ids(self): pass def test_shuffled_labels(self): pass if __name__ == '__main__': unittest.main()

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Crypto-hardRSA/flag.py

JSW2020/hsctf-2019-freshmen

16

14930

flag = "flag{b3453333-9da9-49ae-b4ed-0017c392d58e}" e1 = 65537 e2 = 368273

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tests/service/ai/test_not_killing_itself_ai.py

jonashellmann/informaticup21-team-chillow

3

11942

import unittest from datetime import datetime, timezone from typing import List from chillow.service.ai.not_killing_itself_ai import NotKillingItselfAI from chillow.model.action import Action from chillow.model.cell import Cell from chillow.model.direction import Direction from chillow.model.game import Game from chillow.model.player import Player from chillow.service.game_service import GameService class NotKillingItselfAITest(unittest.TestCase): def test_ai_should_choose_the_own_non_killing_itself_action(self): player1 = Player(1, 0, 0, Direction.up, 1, True, "") player2 = Player(2, 4, 4, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell([player1]), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell([player2])]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) sut = NotKillingItselfAI(player1, [], 3, 0, 3) actions: List[Action] = sut.find_surviving_actions(game_service, 3) self.assertTrue(Action.turn_right in actions) self.assertTrue(len(actions) == 1) def test_ai_should_choose_the_correct_list_of_actions_non_killing_itself(self): player1 = Player(1, 0, 1, Direction.up, 1, True, "") player2 = Player(2, 4, 4, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player1]), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell([player2])]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) sut = NotKillingItselfAI(player1, [], 3, 0, 3) actions: List[Action] = sut.find_surviving_actions(game_service, 3) self.assertTrue(Action.change_nothing in actions) self.assertTrue(Action.turn_right in actions) self.assertTrue(len(actions) == 2) def test_ai_should_choose_the_correct_list_of_actions_non_killing_itself2(self): player1 = Player(1, 1, 2, Direction.up, 1, True, "") player2 = Player(2, 1, 1, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell(), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell([player2]), Cell(), Cell(), Cell()], [Cell(), Cell([player1]), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) sut = NotKillingItselfAI(player1, [], 3, 0, 3) actions: List[Action] = sut.find_surviving_actions(game_service, 3) self.assertTrue(Action.turn_left in actions) self.assertTrue(Action.turn_right in actions) self.assertTrue(len(actions) == 2) def test_ai_should_choose_the_correct_list_of_actions_non_killing_itself_in_turn_6(self): player1 = Player(1, 0, 4, Direction.up, 3, True, "") player2 = Player(2, 0, 1, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player2]), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player1]), Cell(), Cell(), Cell(), Cell()]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) game_service.turn.turn_ctr = 6 sut = NotKillingItselfAI(player1, [], 4, 0, 3) actions: List[Action] = sut.find_surviving_actions(game_service, 1) self.assertTrue(Action.slow_down in actions) self.assertTrue(Action.turn_right in actions) self.assertTrue(Action.speed_up in actions) self.assertTrue(len(actions) == 3) def test_ai_should_not_choose_speed_up_if_max_speed_is_allready_reached(self): MAX_SPEED = 3 player1 = Player(1, 0, 4, Direction.up, MAX_SPEED, True, "") player2 = Player(2, 0, 1, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player2]), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player1]), Cell(), Cell(), Cell(), Cell()]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) sut = NotKillingItselfAI(player1, [], MAX_SPEED, 0, 3) actions: List[Action] = sut.find_surviving_actions(game_service, 1) self.assertTrue(Action.slow_down in actions) self.assertTrue(Action.turn_right in actions) self.assertTrue(len(actions) == 2) def test_ai_should_calc_action_with_max_distance(self): player1 = Player(1, 0, 4, Direction.up, 1, True, "") player2 = Player(2, 0, 1, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player2]), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player1]), Cell(), Cell(), Cell(), Cell()]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) sut = NotKillingItselfAI(player1, [], 3, 0, 3) actions: List[Action] = sut.calc_action_with_max_distance_to_visited_cells(game_service, [Action.speed_up, Action.change_nothing, Action.turn_right]) self.assertTrue(Action.turn_right in actions) self.assertTrue(len(actions) == 1) def test_ai_should_calc_all_action_with_max_distance_with_max_worse_distance(self): MAX_WORSE_DISTANCE = 1 player1 = Player(1, 0, 4, Direction.up, 1, True, "") player2 = Player(2, 4, 4, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell(), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player1]), Cell(), Cell(), Cell(), Cell([player2])]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) sut = NotKillingItselfAI(player1, [], 3, MAX_WORSE_DISTANCE, 3) actions: List[Action] = sut.calc_action_with_max_distance_to_visited_cells(game_service, [Action.speed_up, Action.change_nothing, Action.turn_right]) self.assertTrue(Action.speed_up in actions) self.assertTrue(Action.change_nothing in actions) self.assertTrue(Action.turn_right in actions) self.assertTrue(len(actions) == 3) def test_get_information(self): player = Player(1, 0, 4, Direction.up, 1, True, "") sut = NotKillingItselfAI(player, [], 3, 1, 3) expected = "max_speed=3, max_worse_distance=1, depth=3" result = sut.get_information() self.assertEqual(expected, result) def test_ai_should_choose_the_correct_list_of_actions_non_killing_itself_with_depth_greater_than_one(self): player1 = Player(1, 1, 2, Direction.up, 1, True, "") player2 = Player(2, 1, 1, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player2]), Cell([player2]), Cell(), Cell(), Cell()], [Cell(), Cell([player1]), Cell(), Cell(), Cell()], [Cell([player2]), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) sut = NotKillingItselfAI(player1, [], 3, 0, 2) actions: List[Action] = sut.find_surviving_actions(game_service, 2) self.assertTrue(Action.turn_right in actions) self.assertTrue(len(actions) == 1) def test_ai_should_choose_empty_list_with_depth_greater_than_one_and_no_surviving_action(self): player1 = Player(1, 1, 2, Direction.up, 1, True, "") player2 = Player(2, 1, 1, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player2]), Cell([player2]), Cell([player2]), Cell(), Cell()], [Cell(), Cell([player1]), Cell(), Cell([player2]), Cell()], [Cell([player2]), Cell(), Cell([player2]), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) sut = NotKillingItselfAI(player1, [], 3, 0, 2) actions: List[Action] = sut.find_surviving_actions(game_service, 2) self.assertTrue(len(actions) == 0) def test_ai_should_choose_correct_list_with_depth_three_and_surviving_action(self): player1 = Player(1, 1, 2, Direction.up, 1, True, "") player2 = Player(2, 1, 1, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player2]), Cell([player2]), Cell([player2]), Cell(), Cell()], [Cell(), Cell([player1]), Cell(), Cell([player2]), Cell()], [Cell([player2]), Cell(), Cell(), Cell(), Cell()], [Cell(), Cell(), Cell(), Cell(), Cell()]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) sut = NotKillingItselfAI(player1, [], 3, 0, 3) actions: List[Action] = sut.find_surviving_actions(game_service, 3) self.assertTrue(Action.turn_right in actions) self.assertTrue(len(actions) == 1) def test_ai_should_choose_empty_list_with_depth_three_and_no_surviving_action(self): player1 = Player(1, 1, 2, Direction.up, 1, True, "") player2 = Player(2, 1, 1, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player2]), Cell([player2]), Cell([player2]), Cell(), Cell()], [Cell(), Cell([player1]), Cell(), Cell([player2]), Cell()], [Cell([player2]), Cell([player2]), Cell(), Cell([player2]), Cell()], [Cell(), Cell(), Cell([player2]), Cell(), Cell()]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) sut = NotKillingItselfAI(player1, [], 3, 0, 3) actions: List[Action] = sut.find_surviving_actions(game_service, 3) self.assertTrue(len(actions) == 0) def test_ai_should_choose_best_list_of_actions_by_depth_from_lower_depth(self): player1 = Player(1, 1, 2, Direction.up, 1, True, "") player2 = Player(2, 1, 1, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player2]), Cell([player2]), Cell([player2]), Cell(), Cell()], [Cell(), Cell([player1]), Cell(), Cell([player2]), Cell()], [Cell([player2]), Cell([player2]), Cell(), Cell([player2]), Cell()], [Cell(), Cell(), Cell([player2]), Cell(), Cell()]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) sut = NotKillingItselfAI(player1, [], 3, 0, 5) actions: List[Action] = sut.find_surviving_actions_with_best_depth(game_service) self.assertTrue(Action.turn_right in actions) self.assertTrue(len(actions) == 1) def test_ai_should_choose_best_list_of_actions_by_depth(self): player1 = Player(1, 1, 2, Direction.up, 1, True, "") player2 = Player(2, 1, 1, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player2]), Cell([player2]), Cell([player2]), Cell(), Cell()], [Cell(), Cell([player1]), Cell(), Cell([player2]), Cell()], [Cell([player2]), Cell(), Cell(), Cell([player2]), Cell()], [Cell(), Cell(), Cell([player2]), Cell(), Cell()]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) sut = NotKillingItselfAI(player1, [], 3, 0, 5) actions: List[Action] = sut.find_surviving_actions_with_best_depth(game_service) self.assertTrue(Action.turn_right in actions) self.assertTrue(len(actions) == 1) def test_ai_should_choose_best_list_of_actions_in_lowest_possible_depth(self): player1 = Player(1, 1, 2, Direction.up, 1, True, "") player2 = Player(2, 1, 1, Direction.down, 3, True, "") players = [player1, player2] cells = [[Cell(), Cell(), Cell(), Cell(), Cell()], [Cell([player2]), Cell([player2]), Cell([player2]), Cell(), Cell()], [Cell(), Cell([player1]), Cell(), Cell([player2]), Cell()], [Cell([player2]), Cell(), Cell([player2]), Cell([player2]), Cell()], [Cell(), Cell(), Cell([player2]), Cell(), Cell()]] time = datetime(2020, 10, 1, 12, 5, 13, 0, timezone.utc) game = Game(5, 5, cells, players, 2, True, time) game_service = GameService(game) sut = NotKillingItselfAI(player1, [], 3, 0, 5) actions: List[Action] = sut.find_surviving_actions_with_best_depth(game_service) self.assertTrue(Action.turn_left in actions) self.assertTrue(Action.turn_right in actions) self.assertTrue(len(actions) == 2)

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ebl/tests/corpus/test_line.py

ElectronicBabylonianLiterature/ebl-api

4

111754

<gh_stars>1-10 from typing import Sequence import pytest from ebl.corpus.domain.line import Line, LineVariant, ManuscriptLine from ebl.transliteration.domain.atf import Ruling, Surface from ebl.transliteration.domain.dollar_line import RulingDollarLine from ebl.transliteration.domain.enclosure_tokens import BrokenAway from ebl.transliteration.domain.labels import ColumnLabel, Label, SurfaceLabel from ebl.transliteration.domain.line_number import LineNumber from ebl.transliteration.domain.markup import StringPart from ebl.transliteration.domain.normalized_akkadian import AkkadianWord from ebl.transliteration.domain.note_line import NoteLine from ebl.transliteration.domain.parallel_line import ParallelComposition from ebl.transliteration.domain.sign_tokens import Reading from ebl.transliteration.domain.text_line import TextLine from ebl.transliteration.domain.tokens import ValueToken from ebl.transliteration.domain.translation_line import Extent, TranslationLine from ebl.transliteration.domain.word_tokens import Word LINE_NUMBER = LineNumber(1) LINE_RECONSTRUCTION = (AkkadianWord.of((ValueToken.of("buāru"),)),) NOTE = None MANUSCRIPT_ID = 9001 LABELS = (SurfaceLabel.from_label(Surface.OBVERSE),) MANUSCRIPT_TEXT = TextLine(LINE_NUMBER, (Word.of([Reading.of([ValueToken.of("ku")])]),)) PARATEXT = (NoteLine((StringPart("note"),)), RulingDollarLine(Ruling.SINGLE)) OMITTED_WORDS = (1,) PARALLEL_LINES = (ParallelComposition(False, "a composition", LineNumber(7)),) INTERTEXT = (StringPart("foo"),) LINE_VARIANT = LineVariant( LINE_RECONSTRUCTION, NOTE, (ManuscriptLine(MANUSCRIPT_ID, LABELS, MANUSCRIPT_TEXT, PARATEXT, OMITTED_WORDS),), PARALLEL_LINES, INTERTEXT, ) def test_invalid_extent() -> None: translation = TranslationLine( tuple(), extent=Extent(LineNumber(1), (SurfaceLabel(tuple(), Surface.OBVERSE),)) ) with pytest.raises( ValueError, match="Labels are not allowed in line translations." ): Line(LineNumber(1), tuple(), translation=(translation,)), def test_line_variant_constructor(): assert LINE_VARIANT.reconstruction == LINE_RECONSTRUCTION assert LINE_VARIANT.note == NOTE assert LINE_VARIANT.parallel_lines == PARALLEL_LINES assert LINE_VARIANT.intertext == INTERTEXT assert LINE_VARIANT.manuscripts[0].manuscript_id == MANUSCRIPT_ID assert LINE_VARIANT.manuscripts[0].labels == LABELS assert LINE_VARIANT.manuscripts[0].line == MANUSCRIPT_TEXT assert LINE_VARIANT.manuscripts[0].paratext == PARATEXT assert LINE_VARIANT.manuscripts[0].omitted_words == OMITTED_WORDS @pytest.mark.parametrize( # pyre-ignore[56] "labels", [ (ColumnLabel.from_label("i"), ColumnLabel.from_label("ii")), ( SurfaceLabel.from_label(Surface.OBVERSE), SurfaceLabel.from_label(Surface.REVERSE), ), (ColumnLabel.from_label("i"), SurfaceLabel.from_label(Surface.REVERSE)), ], ) def test_invalid_labels(labels: Sequence[Label]): with pytest.raises(ValueError): ManuscriptLine(manuscript_id=1, labels=labels, line=TextLine(LineNumber(1))) def test_invalid_reconstruction(): with pytest.raises(ValueError): Line( LINE_NUMBER, (LineVariant((AkkadianWord.of((BrokenAway.open(),)),), NOTE, tuple()),), False, False, ) def test_update_manuscript_alignment(): word1 = Word.of( [Reading.of_name("ku")], alignment=0, variant=Word.of([Reading.of_name("uk")]) ) word2 = Word.of( [Reading.of_name("ra")], alignment=1, variant=Word.of([Reading.of_name("ar")]) ) word3 = Word.of( [Reading.of_name("pa")], alignment=2, variant=Word.of([Reading.of_name("ap")]) ) manuscript = ManuscriptLine( MANUSCRIPT_ID, LABELS, TextLine(LineNumber(1), (word1, word2, word3)), PARATEXT, (1, 3), ) expected = ManuscriptLine( MANUSCRIPT_ID, LABELS, TextLine( LineNumber(1), ( word1.set_alignment(None, None), word2.set_alignment(0, word2.variant), word3.set_alignment(None, None), ), ), PARATEXT, (0,), ) assert manuscript.update_alignments([None, 0]) == expected

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plottwist/bootstrap/maya/userSetup.py

Plot-Twist-Short-Film/plottwist-bootstrap

0

152413

#!/usr/bin/env python # -*- coding: utf-8 -*- """ Initialization for Plot Twist Tools """ from __future__ import print_function, division, absolute_import __author__ = "<NAME>" __license__ = "MIT" __maintainer__ = "<NAME>" __email__ = "<EMAIL>" import maya.cmds as cmds # Do not remove def init(): print('=' * 100) print('| Plot Twist ArtellaPipe | > Loading Plot Twist Tools') try: # Initialize tpDcc library import tpDcc.loader print(tpDcc.loader) tpDcc.loader.init(dev=False) # Initialize artellapipe library import artellapipe.loader artellapipe.loader.init(dev=False) # Initialize plottwist project import plottwist.loader plottwist.loader.init(dev=False) print('| Plot Twist Pipeline | Plot Twist loaded successfully!') print('=' * 100) except Exception as exc: import traceback print('ERROR: Impossible to load Plot Twist Tools, contact TD!') print('{} | {}'.format(exc, traceback.format_exc())) # We must launch it with low priority, otherwise USD plugin loading operations will fail cmds.evalDeferred(init, lp=True)

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kensu/client/models/field_def.py

vidma/kensu-py

16

46838

<reponame>vidma/kensu-py # coding: utf-8 """ No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) OpenAPI spec version: beta Generated by: https://github.com/swagger-api/swagger-codegen.git """ from pprint import pformat from six import iteritems class FieldDef(object): """ NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_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. """ swagger_types = { 'name': 'str', 'field_type': 'str', 'nullable': 'bool' } attribute_map = { 'name': 'name', 'field_type': 'fieldType', 'nullable': 'nullable' } def __init__(self, name=None, field_type=None, nullable=None): """ FieldDef - a model defined in Swagger """ self._name = None self._field_type = None self._nullable = None self.name = name self.field_type = field_type self.nullable = nullable @property def name(self): """ Gets the name of this FieldDef. :return: The name of this FieldDef. :rtype: str """ return self._name @name.setter def name(self, name): """ Sets the name of this FieldDef. :param name: The name of this FieldDef. :type: str """ if name is None: raise ValueError("Invalid value for `name`, must not be `None`") self._name = name @property def field_type(self): """ Gets the field_type of this FieldDef. :return: The field_type of this FieldDef. :rtype: str """ return self._field_type @field_type.setter def field_type(self, field_type): """ Sets the field_type of this FieldDef. :param field_type: The field_type of this FieldDef. :type: str """ if field_type is None: raise ValueError("Invalid value for `field_type`, must not be `None`") self._field_type = field_type @property def nullable(self): """ Gets the nullable of this FieldDef. :return: The nullable of this FieldDef. :rtype: bool """ return self._nullable @nullable.setter def nullable(self, nullable): """ Sets the nullable of this FieldDef. :param nullable: The nullable of this FieldDef. :type: bool """ if nullable is None: raise ValueError("Invalid value for `nullable`, must not be `None`") self._nullable = nullable def to_dict(self): """ Returns the model properties as a dict """ result = {} for attr, _ in iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """ Returns the string representation of the model """ return 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, FieldDef): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """ Returns true if both objects are not equal """ return not self == other

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psi/controller/plugin.py

bburan/psiexperiment

5

193981

<reponame>bburan/psiexperiment import logging log = logging.getLogger(__name__) from functools import partial import operator as op import threading import numpy as np from atom.api import Enum, Bool, Typed, Property from enaml.application import deferred_call from enaml.qt.QtCore import QTimer from enaml.workbench.api import Extension from enaml.workbench.plugin import Plugin from .calibration.util import load_calibration from .channel import Channel, OutputMixin, InputMixin from .engine import Engine from .output import Output, Synchronized from .input import Input from .device import Device from .experiment_action import (ExperimentAction, ExperimentActionBase, ExperimentCallback, ExperimentEvent, ExperimentState) from .output import ContinuousOutput, EpochOutput IO_POINT = 'psi.controller.io' ACTION_POINT = 'psi.controller.actions' def get_inputs(input): inputs = [] for child in input.children: inputs.extend(get_inputs(child)) inputs.append(input) return inputs def get_outputs(output): outputs = [] for child in output.children: outputs.extend(get_outputs(child)) outputs.append(output) return outputs def find_devices(point): devices = {} for extension in point.extensions: for device in extension.get_children(Device): devices[device.name] = device return devices engine_error = ''' More than one engine named "{}" To fix this, please review the IO manifest (i.e., hardware configuration) you selected and verify that all engines have unique names. ''' def find_engines(point): master_engine = None engines = {} for extension in point.extensions: for e in extension.get_children(Engine): if e.name in engines: raise ValueError(engine_error.strip().format(e.name)) engines[e.name] = e if e.master_clock: if master_engine is not None: m = 'Only one engine can be defined as the master' raise ValueError(m) master_engine = e return engines, master_engine def find_channels(engines): channels = {} for e in engines.values(): for c in e.get_channels(active=False): channels[c.name] = c return channels def find_outputs(channels, point): outputs = {} supporting = {} # Find all the outputs already connected to a channel for c in channels.values(): if isinstance(c, OutputMixin): for o in c.children: for oi in get_outputs(o): outputs[oi.name] = oi # Find unconnected outputs and inputs (these are allowed so that we can # split processing hierarchies across multiple manifests). for extension in point.extensions: for o in extension.get_children(Output): outputs[o.name] = o for s in extension.get_children(Synchronized): supporting[s.name] = s for o in s.outputs: outputs[o.name] = o return outputs, supporting def find_inputs(channels, point): inputs = {} # Find all the outputs already connected to a channel for c in channels.values(): if isinstance(c, InputMixin): for i in c.children: for ci in get_inputs(i): inputs[ci.name] = ci for extension in point.extensions: for i in extension.get_children(Input): # Recurse through input tree. Currently we assume that # inputs can be nested/hierarchial while outputs are not. for ci in get_inputs(i): inputs[ci.name] = ci return inputs class ControllerPlugin(Plugin): # Tracks the state of the controller. experiment_state = Enum('initialized', 'running', 'paused', 'stopped') # Provides direct access to plugins rather than going through the core # command system. Right now the context plugin is so fundamentally important # to the controller that it would be cumbersome to use the core command # system. core = Typed(Plugin) context = Typed(Plugin) data = Typed(Plugin) # We should not respond to changes during the course of a trial. These # flags indicate changes or requests from the user are pending and should # be processed when the opportunity arises (e.g., at the end of the trial). _apply_requested = Bool(False) _remind_requested = Bool(False) _pause_requested = Bool(False) # Can the experiment be paused? _pause_ok = Bool(False) # Available engines _engines = Typed(dict, {}) # Available channels _channels = Typed(dict, {}) # Available outputs _outputs = Typed(dict, {}) # Available supporting classes for outputs (right now only Synchronized) _supporting = Typed(dict, {}) # Available inputs _inputs = Typed(dict, {}) # Available devices _devices = Typed(dict, {}) # This determines which engine is responsible for the clock _master_engine = Typed(Engine) # List of events and actions that can be associated with the event _events = Typed(dict, {}) _states = Typed(dict, {}) _action_context = Typed(dict, {}) _timers = Typed(dict, {}) # Plugin actions are automatically registered when the manifests are # loaded. In contrast, registered actions are registered by setup code # (e.g., when one doesn't know in advance which output/input the user wants # to record from). _plugin_actions = Typed(list, {}) _registered_actions = Typed(list, {}) _actions = Property() def _get__actions(self): return self._registered_actions + self._plugin_actions def start(self): log.debug('Starting controller plugin') self._refresh_io() self._refresh_actions() self._bind_observers() self.core = self.workbench.get_plugin('enaml.workbench.core') self.context = self.workbench.get_plugin('psi.context') self.data = self.workbench.get_plugin('psi.data') def stop(self): self._unbind_observers() def _bind_observers(self): self.workbench.get_extension_point(IO_POINT) \ .observe('extensions', self._refresh_io) self.workbench.get_extension_point(ACTION_POINT) \ .observe('extensions', self._refresh_actions) def _unbind_observers(self): self.workbench.get_extension_point(IO_POINT) \ .unobserve('extensions',self._refresh_io) self.workbench.get_extension_point(ACTION_POINT) \ .unobserve('extensions', self._refresh_actions) def _refresh_io(self, event=None): # TODO: Allow disabling of devices. log.debug('Loading IO') point = self.workbench.get_extension_point(IO_POINT) self._devices = find_devices(point) self._engines, self._master_engine = find_engines(point) self._channels = find_channels(self._engines) self._outputs, self._supporting = find_outputs(self._channels, point) self._inputs = find_inputs(self._channels, point) for c in self._channels.values(): c.load_manifest(self.workbench) for d in self._devices.values(): d.load_manifest(self.workbench) for s in self._supporting.values(): s.load_manifest(self.workbench) for e in self._engines.values(): e.load_manifest(self.workbench) for o in self._outputs.values(): o.load_manifest(self.workbench) for i in self._inputs.values(): i.load_manifest(self.workbench) log.warn('***********************************************') log.warn(str(self._outputs.keys())) def _connect_outputs(self): for o in self._outputs.values(): # First, make sure that the output is connected to a target. Check # to see if the target is named. If not, then check to see if it # has a parent one can use. if o.target is None and o.target_name: self.connect_output(o.name, o.target_name) elif o.target is None and not isinstance(o.parent, Extension): o.parent.add_output(o) elif o.target is None: log.warn('Unconnected output %s', o.name) def _connect_inputs(self): for i in self._inputs.values(): # First, make sure the input is connected to a source try: if i.source is None and i.source_name: self.connect_input(i.name, i.source_name) elif i.source is None and not isinstance(i.parent, Extension): i.parent.add_input(i) elif i.source is None: log.warn('Unconnected input %s', i.name) except: pass def connect_output(self, output_name, target_name): # Link up outputs with channels if needed. TODO: Can another output be # the target (e.g., if one wanted to combine multiple tokens into a # single stream)? if target_name in self._channels: target = self._channels[target_name] else: m = "Unknown target {} specified for output {}" \ .format(target_name, output_name) raise ValueError(m) o = self._outputs[output_name] target.add_output(o) m = 'Connected output %s to target %s' log.debug(m, output_name, target_name) def connect_input(self, input_name, source_name): if source_name in self._inputs: source = self._inputs[source_name] elif source_name in self._channels: source = self._channels[source_name] else: m = "Unknown source {}".format(source_name) raise ValueError(m) i = self._inputs[input_name] source.add_input(i) m = 'Connected input %s to source %s' log.debug(m, input_name, source_name) def _refresh_actions(self, event=None): actions = [] events = {} states = {} point = self.workbench.get_extension_point(ACTION_POINT) for extension in point.extensions: found_states = extension.get_children(ExperimentState) found_events = extension.get_children(ExperimentEvent) found_actions = extension.get_children(ExperimentActionBase) for state in found_states: if state.name in states: m = '{} state already exists'.format(state.name) raise ValueError(m) states[state.name] = state found_events.extend(state._generate_events()) for event in found_events: if event.name in events: m = '{} event already exists'.format(event.name) raise ValueError(m) events[event.name] = event actions.extend(found_actions) context = {} for state_name in states: context[state_name + '_active'] = False for event_name in events: context[event_name] = False actions.sort(key=lambda a: a.weight) self._states = states self._events = events self._plugin_actions = actions self._action_context = context def register_action(self, event, command, kwargs=None): if kwargs is None: kwargs = {} if isinstance(command, str): action = ExperimentAction(event=event, command=command, kwargs=kwargs) else: action = ExperimentCallback(event=event, callback=command, kwargs=kwargs) self._registered_actions.append(action) def finalize_io(self): self._connect_outputs() self._connect_inputs() def configure_engines(self): log.debug('Configuring engines') for engine in self._engines.values(): # Check to see if engine is being used if engine.get_channels(): engine.configure() cb = partial(self.invoke_actions, '{}_end'.format(engine.name)) engine.register_done_callback(cb) self.invoke_actions('engines_configured') def start_engines(self): log.debug('Starting engines') for engine in self._engines.values(): engine.start() def stop_engines(self): for name, timer in list(self._timers.items()): timer.timeout.disconnect() timer.stop() del self._timers[name] for engine in self._engines.values(): engine.stop() def reset_engines(self): for engine in self._engines.values(): engine.reset() def get_output(self, output_name): return self._outputs[output_name] def get_input(self, input_name): return self._inputs[input_name] def set_input_attr(self, input_name, attr_name, value): setattr(self._inputs[input_name], attr_name, value) def get_channel(self, channel_name): return self._channels[channel_name] def get_channels(self, mode=None, direction=None, timing=None, active=True): ''' Return channels matching criteria across all engines Parameters ---------- mode : {None, 'analog', 'digital' Type of channel direction : {None, 'input, 'output'} Direction timing : {None, 'hardware', 'software'} Hardware or software-timed channel. Hardware-timed channels have a sampling frequency greater than 0. active : bool If True, return only channels that have configured inputs or outputs. ''' channels = [] for engine in self._engines.values(): ec = engine.get_channels(mode, direction, timing, active) channels.extend(ec) return channels def load_calibration(self, calibration_file): channels = list(self._channels.values()) load_calibration(calibration_file, channels) def invoke_actions(self, event_name, timestamp=None, delayed=False, cancel_existing=True, kw=None): log.debug('Invoking actions for %s', event_name) if cancel_existing: deferred_call(self.stop_timer, event_name) if delayed: delay = timestamp-self.get_ts() if delay > 0: cb = lambda: self._invoke_actions(event_name, timestamp) deferred_call(self.start_timer, event_name, delay, cb) return self._invoke_actions(event_name, timestamp, kw) def event_used(self, event_name): ''' Returns true if the experiment event is bound to an experiment action. This is typically used internally as a performance-optimization so we don't configure callbacks for events that are unused. For example, we can attach actions to the <input_name>_acquired event. However, this event typically occurs several times a second for each input. This would result in unecessary calls to `invoke_actions`. Parameters ---------- event_name : str Name of event Returns ------- used : bool True if event is bound to an action, False otherwise. ''' for action in self._actions: if event_name in action.dependencies: return True return False def _invoke_actions(self, event_name, timestamp=None, kw=None): log.debug('Triggering event {}'.format(event_name)) if timestamp is not None: data = {'event': event_name, 'timestamp': timestamp} self.invoke_actions('experiment_event', kw={'data': data}) # If this is a stateful event, update the associated state. if event_name.endswith('_start'): key = event_name[:-6] self._action_context[key + '_active'] = True elif event_name.endswith('_end'): key = event_name[:-4] self._action_context[key + '_active'] = False # Make a copy of the context and set the event to True. We don't want # to set the state on the main context since it may affect recursive # notifications. context = self._action_context.copy() context[event_name] = True for action in self._actions: if action.match(context): log.debug('... invoking action %s', action) self._invoke_action(action, event_name, timestamp, kw) def _invoke_action(self, action, event_name, timestamp, kw): # Add the event name and timestamp to the parameters passed to the # command. kwargs = action.kwargs.copy() kwargs['timestamp'] = timestamp kwargs['event'] = event_name if kw is not None: kwargs.update(kw) if isinstance(action, ExperimentAction): self.core.invoke_command(action.command, parameters=kwargs) elif isinstance(action, ExperimentCallback): action.callback(**kwargs) def request_apply(self): if not self.apply_changes(): log.debug('Apply requested') deferred_call(lambda: setattr(self, '_apply_requested', True)) def request_remind(self): deferred_call(lambda: setattr(self, '_remind_requested', True)) def request_pause(self): if not self.pause_experiment(): log.debug('Pause requested') deferred_call(lambda: setattr(self, '_pause_requested', True)) def request_resume(self): self._pause_requested = False deferred_call(lambda: setattr(self, 'experiment_state', 'running')) def apply_changes(self): raise NotImplementedError def start_experiment(self): self.invoke_actions('experiment_initialize') self.invoke_actions('experiment_prepare') self.invoke_actions('experiment_start') deferred_call(lambda: setattr(self, 'experiment_state', 'running')) def stop_experiment(self): try: self.invoke_actions('experiment_end', self.get_ts()) except Exception as e: log.exception(e) deferred_call(lambda: setattr(self, 'experiment_state', 'stopped')) def pause_experiment(self): raise NotImplementedError def get_ts(self): return self._master_engine.get_ts() def set_pause_ok(self, value): deferred_call(lambda: setattr(self, '_pause_ok', value)) def start_timer(self, name, duration, callback): log.debug('Starting %f sec. timer %s', duration, name) timer = QTimer() timer.timeout.connect(callback) timer.setSingleShot(True) timer.start(duration*1e3) self._timers[name] = timer def stop_timer(self, name): if self._timers.get(name) is not None: self._timers[name].timeout.disconnect() self._timers[name].stop() del self._timers[name] log.debug('Disabled deferred event %s', name)

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stable-baselines/tests/test_deterministic.py

princeton-vl/PackIt

49

9989

import pytest from stable_baselines import A2C, ACER, ACKTR, DeepQ, DDPG, PPO1, PPO2, TRPO from stable_baselines.ddpg import AdaptiveParamNoiseSpec from stable_baselines.common.identity_env import IdentityEnv, IdentityEnvBox from stable_baselines.common.vec_env import DummyVecEnv PARAM_NOISE_DDPG = AdaptiveParamNoiseSpec(initial_stddev=float(0.2), desired_action_stddev=float(0.2)) # Hyperparameters for learning identity for each RL model LEARN_FUNC_DICT = { 'a2c': lambda e: A2C(policy="MlpPolicy", env=e).learn(total_timesteps=1000), 'acer': lambda e: ACER(policy="MlpPolicy", env=e).learn(total_timesteps=1000), 'acktr': lambda e: ACKTR(policy="MlpPolicy", env=e).learn(total_timesteps=1000), 'deepq': lambda e: DeepQ(policy="MlpPolicy", env=e).learn(total_timesteps=1000), 'ddpg': lambda e: DDPG(policy="MlpPolicy", env=e, param_noise=PARAM_NOISE_DDPG).learn(total_timesteps=1000), 'ppo1': lambda e: PPO1(policy="MlpPolicy", env=e).learn(total_timesteps=1000), 'ppo2': lambda e: PPO2(policy="MlpPolicy", env=e).learn(total_timesteps=1000), 'trpo': lambda e: TRPO(policy="MlpPolicy", env=e).learn(total_timesteps=1000), } @pytest.mark.slow @pytest.mark.parametrize("model_name", ['a2c', 'acer', 'acktr', 'deepq', 'ppo1', 'ppo2', 'trpo']) def test_identity(model_name): """ Test if the algorithm (with a given policy) can learn an identity transformation (i.e. return observation as an action) :param model_name: (str) Name of the RL model """ env = DummyVecEnv([lambda: IdentityEnv(10)]) model = LEARN_FUNC_DICT[model_name](env) n_trials = 1000 obs = env.reset() action_shape = model.predict(obs, deterministic=False)[0].shape action, _ = model.predict(obs, deterministic=True) assert action.shape == action_shape for _ in range(n_trials): new_action = model.predict(obs, deterministic=True)[0] assert action == model.predict(obs, deterministic=True)[0] assert new_action.shape == action_shape # Free memory del model, env @pytest.mark.slow @pytest.mark.parametrize("model_name", ['a2c', 'ddpg', 'ppo1', 'ppo2', 'trpo']) def test_identity_continuous(model_name): """ Test if the algorithm (with a given policy) can learn an identity transformation (i.e. return observation as an action) :param model_name: (str) Name of the RL model """ env = DummyVecEnv([lambda: IdentityEnvBox(eps=0.5)]) model = LEARN_FUNC_DICT[model_name](env) n_trials = 1000 obs = env.reset() action_shape = model.predict(obs, deterministic=False)[0].shape action, _ = model.predict(obs, deterministic=True) assert action.shape == action_shape for _ in range(n_trials): new_action = model.predict(obs, deterministic=True)[0] assert action == model.predict(obs, deterministic=True)[0] assert new_action.shape == action_shape

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bot/utils/github.py

mtrsk/PUGMA-bot

9

119869

""" Modulo dedicado a classes/funções sem nenhuma outra dependência ou que não sejam parte do core do Bot. """ import json import requests def photo_meetup(repo_data, matches): """ Recebe como entrada uma Dataclass com dados do nosso repositório no Github e retorna a url de uma imagem do meetup. """ contents_url = f"{repo_data.url}/contents/palestras?ref=master" response = requests.get(contents_url, headers=repo_data.headers) content = json.loads(response.content) content_length = len(content) if len(matches) == 1: # /meetup retorna sempre o último return content[-1].get("download_url"), (content_length) elif int(matches[1]) < content_length: # /meetup \d{1,2} event_num = int(matches[1]) return content[event_num - 1].get("download_url"), event_num else: # Numero invalido return None, None

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src/experiments/random_projections_aistats_ridge_regression.py

c-dickens/FrequentDirectionsRidgeRegression

0

116922

# Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF 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. import numpy as np from timeit import default_timer as timer class RPRidge: def __init__(self,rp_dim:int,rp_mode='Sparse',gamma=1.0): self.rp_dim = rp_dim self.rp_mode = rp_mode self.gamma = gamma def iterate_single(self,X,y,iterations=10,seed=100): ''' Fits the iterated ridge model with FD ''' d = X.shape[1] w = np.zeros((d,1),dtype=float) all_w = np.zeros((d,iterations)) XTy = (X.T@y).reshape(-1,1) # Fit the FD SA = self._get_sketch(X,seed) H = SA.T@SA + (self.gamma)*np.eye(d) H_inv = np.linalg.pinv(H) for it in range(iterations): grad = X.T@(X@w) + self.gamma*w - XTy w += - H_inv@grad all_w[:,it] = np.squeeze(w) return np.squeeze(w), all_w def iterate_multiple(self,X,y,iterations=10): ''' Fits the iterated ridge model with FD ''' d = X.shape[1] w = np.zeros((d,1),dtype=float) all_w = np.zeros((d,iterations)) XTy = (X.T@y).reshape(-1,1) for it in range(iterations): SA = self._get_sketch(X,seed=100*it) H = SA.T@SA + (self.gamma)*np.eye(d) H_inv = np.linalg.pinv(H) grad = X.T@(X@w) + self.gamma*w - XTy w += - H_inv@grad all_w[:,it] = np.squeeze(w) return np.squeeze(w), all_w def _naive_hessian_update(self, sketched_data, vec): """ Naively performs the hessian update by evaluating the approximate Hessian and inverting. """ H = sketched_data.T@sketched_data + (self.gamma)*np.eye(sketched_data.shape[1]) H_inv = np.linalg.pinv(H) return H_inv@vec def _linear_solve_hessian_update(self, sketched_data, vec): """ Implements the hessian_inv@gradient efficiently by using Woodbury. """ K = sketched_data@sketched_data.T / self.gamma + np.eye(sketched_data.shape[0]) u = (1/self.gamma)*vec - (sketched_data.T / self.gamma**2)@(np.linalg.solve(K,sketched_data@vec)) return u def iterate_multiple_timing(self, X, y, iterations=10): """ Fits the iterated ridge model with a new sketch every iteration """ # * Initialisation not timed d = X.shape[1] w = np.zeros((d,1),dtype=float) all_w = np.zeros((d,iterations)) if self.rp_dim < d: update_method = self._linear_solve_hessian_update else: update_method = self._naive_hessian_update measurables = { 'sketch time' : None, 'all_times' : np.zeros(iterations+1,dtype=float), 'gradients' : np.zeros((d,iterations),dtype=float), 'updates' : np.zeros((d,iterations),dtype=float), 'sketch' : None } SKETCH_TIME = 0.0 TIMER_START = timer() XTy = (X.T@y).reshape(-1,1) for it in range(iterations): # * Timing the sketch separately SKETCH_TIMER_START = timer() SA = self._get_sketch(X,seed=10*it) SKETCH_TIME += timer() - SKETCH_TIMER_START # * Into grad updates grad = X.T@(X@w) + self.gamma*w - XTy update = update_method(SA, grad) w += - update all_w[:,it] = np.squeeze(w) measurables['all_times'][it+1] = timer() - TIMER_START measurables['sketch time'] = SKETCH_TIME return np.squeeze(w), all_w, measurables def iterate_single_timing(self, X, y, iterations=10, seed=100): """ Fits the iterated ridge model with a new sketch every iteration """ # * Initialisation not timed d = X.shape[1] w = np.zeros((d,1),dtype=float) all_w = np.zeros((d,iterations)) if self.rp_dim < d: update_method = self._linear_solve_hessian_update else: update_method = self._naive_hessian_update measurables = { 'sketch time' : None, 'all_times' : np.zeros(iterations+1,dtype=float), 'gradients' : np.zeros((d,iterations),dtype=float), 'updates' : np.zeros((d,iterations),dtype=float), 'sketch' : None } TIMER_START = timer() XTy = (X.T@y).reshape(-1,1) SA = self._get_sketch(X,seed) SKETCH_TIME = timer() - TIMER_START for it in range(iterations): grad = X.T@(X@w) + self.gamma*w - XTy update = update_method(SA, grad) w += - update all_w[:,it] = np.squeeze(w) measurables['all_times'][it+1] = timer() - TIMER_START measurables['sketch time'] = SKETCH_TIME return np.squeeze(w), all_w, measurables def _get_sketch(self,data,seed=10): ''' Performs the sketch depending on the chosen mode. ''' if self.rp_mode == 'Gaussian': return self._gaussian_projection(data,seed) elif self.rp_mode == 'SJLT': return self._sparse_projection(data,10,seed) else: raise NotImplementedError def fit_classical(self,X,y): ''' Fits the ridge regression model on data X with targets y ''' d = X.shape[1] data = np.c_[X,y] #S_data = self._sparse_projection(data,self.rp_dim) #S_data = self._gaussian_projection(data,self.rp_dim) S_data = self._get_sketch(data) SX = S_data[:,:-1] Sy = S_data[:,-1] H_est = SX.T@SX + self.gamma*np.eye(d) self.H = H_est self.classical_coef_ = np.linalg.solve(H_est,SX.T@Sy) def fit_hessian_sketch(self,X,y): ''' Fits the ridge regression model on data X with targets y ''' d = X.shape[1] #SX = self._gaussian_projection(X,self.rp_dim) #SX = self._sparse_projection(X,self.rp_dim) SX = self._get_sketch(X) H_est = SX.T@SX + self.gamma*np.eye(d) self.hessian_coef_ = np.linalg.solve(H_est,X.T@y) def get_classical_bias(self,X,w0): ''' Returns the bias of the estimate ''' return (self.gamma)*np.linalg.norm(np.linalg.pinv(self.H)@w0) #return (np.linalg.pinv(self.H)@(self.H - self.gamma*np.eye(X.shape[1])))@w0 - w0 def get_classical_variance(self,X): ''' Returns the variance term: ||S.T@S@A H_gamma^{-1}||_F^2 ''' S = self.sketch_mat #return np.linalg.norm(np.linalg.pinv(self.H)@(X.T@(S.T@S)),ord='fro')**2 return np.linalg.norm( S.T@(S@([email protected](self.H))) ,ord='fro')**2 def get_hessian_sketch_bias(self,X,w0): ''' Returns the bias of the Hessian sketch method for regression ''' return np.linalg.pinv(self.H)@(X.T@(X@w0)) - w0 def get_hessian_sketch_variance(self,X): ''' Returns the variance term: ||A H_gamma^{-1}||_F^2 ''' return np.linalg.norm([email protected](self.H),ord='fro')**2 def _sparse_projection(self,mat,sparsity=1,random_seed=10): """ Performs the sparse johnson lindenstrauss transform of Kane and Nelson """ [n,_] = mat.shape sketch = np.zeros((self.rp_dim ,n),dtype=float) for i in range(n): nnz_loc = np.random.choice(self.rp_dim ,size=sparsity,replace=False) nnz_sign = np.random.choice([-1,1],size=sparsity,replace=True) sketch[nnz_loc,i] = nnz_sign self.sketch_mat = sketch return (1./np.sqrt(sparsity))*sketch@mat def _gaussian_projection(self,mat,random_seed=10): """ Performs the dense gaussian random projection. """ [n,_] = mat.shape np.random.seed(random_seed) S = np.random.randn(self.rp_dim,n) / np.sqrt(self.rp_dim) self.sketch_mat = S return S@mat

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emoticonvis/apps/api/tests/test_serializers.py

nanchenchen/emoticon-analysis

0

127553

from django.test import TestCase from django.utils.timezone import now, timedelta from emoticonvis.apps.corpus import models as corpus_models from emoticonvis.apps.corpus import utils as corpus_utils from emoticonvis.apps.coding import models as coding_models from emoticonvis.apps.api import serializers from django.contrib.auth.models import User from emoticonvis.apps.api.tests import api_time_format class CodeSerializerTest(TestCase): """ """ def test_code_definition_serialization(self): master = User.objects.create_user(username='master') user1 = User.objects.create_user(username='user1') code = corpus_models.Code.objects.create(text='testcode') dataset = corpus_models.Dataset.objects.create(name='test', description='test') messages = [corpus_models.Message.objects.create(dataset=dataset, text='msg0'), corpus_models.Message.objects.create(dataset=dataset, text='msg1'), corpus_models.Message.objects.create(dataset=dataset, text='msg2')] code_definition1 = coding_models.CodeDefinition.objects.create(code=code, source=master, text='master_def') code_definition1.examples.add(messages[0]) code_definition1.examples.add(messages[2]) code_definition2 = coding_models.CodeDefinition.objects.create(code=code, source=user1, text='user1_def') code_definition2.examples.add(messages[1]) code_definition2.examples.add(messages[2]) desired_result = [ { "code": code.text, "source": "master", "text": "master_def", "examples": [serializers.MessageSerializer(messages[0]).data, serializers.MessageSerializer(messages[2]).data] }, { "code": code.text, "source": "user1", "text": "user1_def", "examples": [serializers.MessageSerializer(messages[1]).data, serializers.MessageSerializer(messages[2]).data] } ] code_definitions = [code.get_definition(master), code.get_definition(user1)] serializer = serializers.CodeDefinitionSerializer(code_definitions, many=True) try: result = serializer.data self.assertListEqual(result, desired_result) except: import pdb pdb.set_trace() def test_code_message_serialization(self): master = User.objects.create_user(username='master') code = corpus_models.Code.objects.create(text='testcode') code2 = corpus_models.Code.objects.create(text='testcode2') dataset = corpus_models.Dataset.objects.create(name='test', description='test') messages = [corpus_models.Message.objects.create(dataset=dataset, text='msg0'), corpus_models.Message.objects.create(dataset=dataset, text='msg1'), corpus_models.Message.objects.create(dataset=dataset, text='msg2')] code_assignment1 = coding_models.CodeAssignment.objects.create(code=code, source=master, message=messages[0]) code_assignment2 = coding_models.CodeAssignment.objects.create(code=code, source=master, message=messages[1]) code_assignment3 = coding_models.CodeAssignment.objects.create(code=code2, source=master, message=messages[1]) desired_result = [ { "code": code.text, "source": "master", "messages": [serializers.MessageSerializer(messages[0]).data, serializers.MessageSerializer(messages[1]).data] }, { "code": code2.text, "source": "master", "messages": [serializers.MessageSerializer(messages[1]).data] } ] source = master codes = [code.text, code2.text] code_messages = [] for code in codes: if corpus_models.Code.objects.filter(text=code).exists(): code_obj = corpus_models.Code.objects.get(text=code) messages = corpus_models.Message.objects.filter(code_assignments__valid=True, code_assignments__source=source, code_assignments__code=code_obj).all() code_messages.append({ "code": code, "source": source, "messages": messages, }) serializer = serializers.CodeMessageSerializer(code_messages, many=True) try: result = serializer.data self.assertListEqual(result, desired_result) except: import pdb pdb.set_trace()

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migrations/versions/1fba1a39c681_initial_data.py

eubr-bigsea/thorn

0

114245

<filename>migrations/versions/1fba1a39c681_initial_data.py """initial data Revision ID: <KEY> Revises: <PASSWORD> Create Date: 2020-03-02 09:28:48.614317 """ import datetime import bcrypt from alembic import context from alembic import op from sqlalchemy import String, Integer, DateTime from sqlalchemy.orm import sessionmaker from sqlalchemy.sql import table, column from thorn.migration_utils import is_mysql # revision identifiers, used by Alembic. revision = '<KEY>' down_revision = '2f93dbc61a5d' branch_labels = None depends_on = None def _insert_permissions(): tb = table( 'permission', column('id', Integer), column('name', String), column('applicable_to', String), column('enabled', String), ) columns = [c.name for c in tb.columns] data = [ (1, 'WORKFLOW_VIEW_ANY', 'WORKFLOW', True), (2, 'WORKFLOW_EDIT_ANY', 'WORKFLOW', True), (3, 'WORKFLOW_EXECUTE_ANY', 'WORKFLOW', True), (4, 'WORKFLOW_VIEW', 'WORKFLOW', True), (5, 'WORKFLOW_EDIT', 'WORKFLOW', True), (6, 'WORKFLOW_EXECUTE', 'WORKFLOW', True), (7, 'DATA_SOURCE_VIEW_ANY', 'DATA_SOURCE', True), (8, 'DATA_SOURCE_EDIT_ANY', 'DATA_SOURCE', True), (9, 'DATA_SOURCE_USE_ANY', 'DATA_SOURCE', True), (10, 'DATA_SOURCE_VIEW', 'DATA_SOURCE', True), (11, 'DATA_SOURCE_EDIT', 'DATA_SOURCE', True), (12, 'DATA_SOURCE_USE', 'DATA_SOURCE', True), (13, 'DASHBOARD_VIEW_ANY', 'DASHBOARD', True), (14, 'DASHBOARD_EDIT_ANY', 'DASHBOARD', True), (15, 'DASHBOARD_VIEW', 'DASHBOARD', True), (16, 'DASHBOARD_EDIT', 'DASHBOARD', True), (100, 'USER_MANAGE', 'USER', True), (101, 'STORAGE_MANAGE', 'SYSTEM', True), (102, 'CLUSTER_MANAGE', 'SYSTEM', True), (1000, 'ADMINISTRATOR', 'SYSTEM', True), ] rows = [dict(list(zip(columns, row))) for row in data] op.bulk_insert(tb, rows) def _insert_permission_translations(): tb = table( 'permission_translation', column('id', Integer), column('locale', String), column('description', String) ) columns = [c.name for c in tb.columns] data = [ (1, 'pt', 'Visualizar qualquer fluxo de trabalho'), (1, 'en', 'View any workflow'), (2, 'pt', 'Editar qualquer fluxo de trabalho'), (2, 'en', 'Edit any workflow'), (3, 'pt', 'Executar qualquer fluxo de trabalho'), (3, 'en', 'Execute any workflow'), (4, 'pt', 'Visualizar fluxo de trabalho'), (4, 'en', 'View workflow'), (5, 'pt', 'Editar fluxo de trabalho'), (5, 'en', 'Edit workflow'), (6, 'pt', 'Executar fluxo de trabalho'), (6, 'en', 'Execute workflow'), (7, 'pt', 'Visualizar qualquer fonte de dados'), (7, 'en', 'View any data source'), (8, 'pt', 'Editar qualquer fonte de dados'), (8, 'en', 'Edit any data source'), (9, 'pt', 'Usar qualquer fonte de dados'), (9, 'en', 'Use any data source'), (10, 'pt', 'Visualizar fonte de dados'), (10, 'en', 'View data source'), (11, 'pt', 'Editar fonte de dados'), (11, 'en', 'Edit data source'), (12, 'pt', 'Usar fonte de dados'), (12, 'en', 'Use data source'), (13, 'pt', 'Visualizar qualquer dashboard'), (13, 'en', 'View any dashboard'), (14, 'pt', 'Editar qualquer dashboard'), (14, 'en', 'Edit any dashboard'), (15, 'pt', 'Visualizar dashboard'), (15, 'en', 'View dashboard'), (16, 'pt', 'Editar dashboard'), (16, 'en', 'Edit dashboard'), (100, 'pt', 'Gerenciar usuários'), (100, 'en', 'Manage users'), (101, 'pt', 'Gerenciar armazenamentos'), (101, 'en', 'Manage stores'), (102, 'pt', 'Gerenciar clusters'), (102, 'en', 'Manage clusters'), (1000, 'en', 'Administrator'), (1000, 'pt', 'Administrador'), ] rows = [dict(list(zip(columns, row))) for row in data] op.bulk_insert(tb, rows) def _insert_admin(): tb = table( 'user', column('id', Integer), column('login', String), column('email', String), column('encrypted_password', String), column('created_at', DateTime), column('first_name', String), column('last_name', String), column('locale', String), column('enabled', Integer), column('authentication_type', String) ) columns = [c.name for c in tb.columns] hashed = bcrypt.hashpw('admin'.encode('utf8'), bcrypt.gensalt(12)).decode('utf8') data = [ (1, '<EMAIL>', '<EMAIL>', hashed, datetime.datetime.now(), 'Admin', '', 'pt', True, 'INTERNAL'), ] rows = [dict(list(zip(columns, row))) for row in data] op.bulk_insert(tb, rows) def _insert_roles(): tb = table( 'role', column('id', Integer), column('name', String), column('all_user', Integer), column('enabled', Integer), ) columns = [c.name for c in tb.columns] data = [ (1, 'admin', False, True), (100, 'public', True, True), ] rows = [dict(list(zip(columns, row))) for row in data] op.bulk_insert(tb, rows) def _insert_role_translations(): tb = table( 'role_translation', column('id', Integer), column('locale', String), column('description', String) ) columns = [c.name for c in tb.columns] data = [ (1, 'pt', 'Administrador'), (1, 'en', 'Administrator'), (100, 'pt', 'Público'), (100, 'en', 'Public'), ] rows = [dict(list(zip(columns, row))) for row in data] op.bulk_insert(tb, rows) def _insert_user_roles(): tb = table( 'user_role', column('user_id', Integer), column('role_id', Integer), ) columns = [c.name for c in tb.columns] data = [ (1, 1), ] rows = [dict(list(zip(columns, row))) for row in data] op.bulk_insert(tb, rows) def _insert_role_permission(): tb = table( 'role_permission', column('role_id', Integer), column('permission_id', Integer), ) columns = [c.name for c in tb.columns] data = [ (1, 1000), ] rows = [dict(list(zip(columns, row))) for row in data] op.bulk_insert(tb, rows) def get_commands(): user_table = 'user' if is_mysql() else '"user"' all_commands = [ (_insert_permissions, 'DELETE FROM permission WHERE id BETWEEN 1 AND 16 OR ' 'id BETWEEN 100 AND 102 OR id BETWEEN 1000 AND 1000'), (_insert_permission_translations, 'DELETE FROM permission_translation WHERE id BETWEEN 1 AND 16 OR ' 'id BETWEEN 100 AND 102 OR id BETWEEN 1000 AND 1000'), (_insert_admin, f'DELETE FROM {user_table} WHERE id BETWEEN 1 AND 1 '), (_insert_roles, 'DELETE FROM role WHERE id BETWEEN 1 AND 1 OR id ' 'BETWEEN 100 AND 100'), (_insert_role_translations, 'DELETE FROM role_translation WHERE id BETWEEN 1 AND 1 OR id ' 'BETWEEN 100 AND 100'), (_insert_user_roles, 'DELETE FROM user_role WHERE user_id BETWEEN 1 AND 1'), (_insert_role_permission, 'DELETE FROM role_permission WHERE role_id = 1 ' 'AND permission_id = 1000') ] return all_commands def upgrade(): ctx = context.get_context() session = sessionmaker(bind=ctx.bind)() connection = session.connection() all_commands = get_commands() try: for cmd in all_commands: if isinstance(cmd[0], str): connection.execute(cmd[0]) elif isinstance(cmd[0], list): for row in cmd[0]: connection.execute(row) else: cmd[0]() except: session.rollback() raise session.commit() def downgrade(): ctx = context.get_context() session = sessionmaker(bind=ctx.bind)() connection = session.connection() all_commands = get_commands() try: for cmd in reversed(all_commands): if isinstance(cmd[1], str): connection.execute(cmd[1]) elif isinstance(cmd[1], list): for row in cmd[1]: connection.execute(row) else: cmd[1]() except: session.rollback() raise session.commit()

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effect_of_vanishing_photos/effect_of_vanishing_photos.py

gil9red/SimplePyScripts

117

144049

#!/usr/bin/env python3 # -*- coding: utf-8 -*- __author__ = 'ipetrash' """Эффект исчезновения фотографии Кликая на области на фотографии запускаются процессы плавного увеличения прозрачности пикселей, эффект как круги воды, будут расходиться пока не закончатся непрозрачные пиксели""" import sys import traceback try: from PyQt5.QtWidgets import * from PyQt5.QtGui import * from PyQt5.QtCore import * except: try: from PyQt4.QtGui import * from PyQt4.QtCore import * except: from PySide.QtGui import * from PySide.QtCore import * def log_uncaught_exceptions(ex_cls, ex, tb): text = '{}: {}:\n'.format(ex_cls.__name__, ex) text += ''.join(traceback.format_tb(tb)) print(text) QMessageBox.critical(None, 'Error', text) sys.exit(1) sys.excepthook = log_uncaught_exceptions class Timer(QTimer): class Circle: def __init__(self, pos_center): self.pos_center = pos_center self.radii = 1 def next(self): self.radii += 1 def __init__(self, widget, image): super().__init__() self.circle_list = list() self.widget = widget self.setInterval(60) self.timeout.connect(self.tick) self.painter = QPainter(image) self.painter.setRenderHint(QPainter.Antialiasing) self.painter.setCompositionMode(QPainter.CompositionMode_Clear) self.painter.setPen(Qt.NoPen) self.painter.setBrush(Qt.transparent) def add(self, pos_center): self.circle_list.append(Timer.Circle(pos_center)) def tick(self): for circle in self.circle_list: self.painter.drawEllipse(circle.pos_center, circle.radii, circle.radii) circle.next() self.widget.update() class Widget(QWidget): def __init__(self): super().__init__() self.setWindowTitle('effect_of_vanishing_photos.py') self.im = QImage('im.png') self.resize(self.im.size()) self.timer = Timer(self, self.im) self.timer.start() def mouseReleaseEvent(self, event): super().mouseReleaseEvent(event) self.timer.add(event.pos()) def paintEvent(self, event): super().paintEvent(event) p = QPainter(self) p.setBrush(Qt.white) p.drawRect(self.rect()) p.setBrush(Qt.yellow) p.drawRect(self.width() // 6, self.width() // 5, self.width() // 3, self.height() // 4) p.drawImage(0, 0, self.im) if __name__ == '__main__': app = QApplication(sys.argv) w = Widget() w.show() app.exec_()

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tests/fixtures/test_product.py

oldarmyc/cap

1

22815

<reponame>oldarmyc/cap # Copyright 2016 <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. sample_product = { "title": "Test", "us_url": "http://us.test.com", "uk_url": "http://uk.test.com", "active": True, "db_name": "test", "require_region": True, "doc_url": "http://doc.test.com", "pitchfork_url": "https://pitchfork/url" } sample_limit = { "product": "test", "title": "Test", "uri": "/limits", "slug": "test", "active": True, "absolute_path": "test/path", "absolute_type": "list", "limit_key": "test_limit", "value_key": "test_value" } sample_log = { "queried": ["dns"], "queried_by": "skeletor", "region": "dfw", "ddi": "123456", 'query_results': [] } sample_auth_failure = { 'message': ( '<strong>Error!</strong> Authentication has failed due to' ' incorrect token or DDI. Please check the token and DDI ' 'and try again.' ) } """ DNS Tests """ dns = { "title": "DNS", "us_url": "https://us.test.com", "uk_url": "https://uk.test.com", "active": True, "db_name": "dns", "require_region": True, "doc_url": "https://doc.test.com", "pitchfork_url": "https://pitchfork.url", "limit_maps": [] } dns_limit = { "product": "dns", "title": "Domains", "uri": "/limits", "slug": "domains", "active": True, "absolute_path": "limits.absolute", "absolute_type": "dict", "value_key": "", "limit_key": "domains" } dns_limit_return = { "limits": { "rate": [ { "regex": ".*/v\\d+\\.\\d+/(\\d+/domains/search).*", "limit": [ { "value": 20, "verb": "GET", "next-available": "2016-01-12T13:56:11.450Z", "remaining": 20, "unit": "MINUTE" } ], "uri": "*/domains/search*" } ], "absolute": { "domains": 500, "records per domain": 500 } } } dns_list_return = { "domains": [ { "comment": "Test", "updated": "2015-12-08T20:47:02.000+0000", "name": "test.net", "created": "2015-04-09T15:42:49.000+0000", "emailAddress": "<EMAIL>", "id": 123465798, "accountId": 1234567 } ], "totalEntries": 1 } dns_full_return = { 'dns': { 'values': {'Domains': 1}, 'limits': {'Domains': 500} } } """ Autoscale """ autoscale = { "title": "Autoscale", "us_url": "https://us.test.com", "uk_url": "https://uk.test.com", "active": True, "db_name": "autoscale", "require_region": True, "doc_url": "https://doc.test.com", "pitchfork_url": "https://pitchfork.url", "limit_maps": [] } autoscale_limit = { "product": "autoscale", "title": "Max Groups", "absolute_path": "limits.absolute", "uri": "/v1.0/{ddi}/limits", "slug": "max_groups", "value_key": "", "absolute_type": "dict", "active": True, "limit_key": "maxGroups" } autoscale_limit_return = { "limits": { "rate": [ { "regex": "/v1\\.0/execute/(.*)", "limit": [ { "value": 10, "verb": "ALL", "next-available": "2016-01-12T14:51:13.402Z", "remaining": 10, "unit": "SECOND" } ], "uri": "/v1.0/execute/*" } ], "absolute": { "maxGroups": 1000, "maxPoliciesPerGroup": 100, "maxWebhooksPerPolicy": 25 } } } autoscale_list_return = { "groups": [ { "state": { "status": "ACTIVE", "desiredCapacity": 0, "paused": False, "active": [], "pendingCapacity": 0, "activeCapacity": 0, "name": "test" }, "id": "d446f3c2-612f-41b8-92dc-4d6e1422bde2", "links": [ { "href": ( 'https://dfw.autoscale.api.rackspacecloud.com/v1.0' '/1234567/groups/d446f3c2-612f-41b8-92dc-4d6e1422bde2/' ), "rel": "self" } ] } ], "groups_links": [] } autoscale_full_return = { 'autoscale': { 'values': {'Max Groups': 1}, 'limits': {'Max Groups': 1000} } } """ Big Data """ big_data = { "title": "Big Data", "us_url": "https://us.test.com", "uk_url": "https://uk.test.com", "active": True, "db_name": "big_data", "require_region": True, "doc_url": "https://doc.test.com", "pitchfork_url": "https://pitchfork.url", "limit_maps": [] } big_data_limit = [ { "product": "big_data", "title": "Node Count", "absolute_path": "limits.absolute.node_count", "uri": "/v2/{ddi}/limits", "slug": "node_count", "value_key": "remaining", "absolute_type": "dict", "active": True, "limit_key": "limit" }, { "product": "big_data", "title": "Disk - MB", "absolute_path": "limits.absolute.disk", "uri": "/v2/{ddi}/limits", "slug": "disk_-_mb", "value_key": "remaining", "absolute_type": "dict", "active": True, "limit_key": "limit" } ] big_data_limit_return = { "limits": { "absolute": { "node_count": { "limit": 15, "remaining": 8 }, "disk": { "limit": 50000, "remaining": 25000 }, "ram": { "limit": 655360, "remaining": 555360 }, "vcpus": { "limit": 200, "remaining": 120 } } } } big_data_full_return = { 'big_data': { 'values': {'Node Count': 7, 'Disk - MB': 25000}, 'limits': {'Node Count': 15, 'Disk - MB': 50000} } } """ CBS """ cbs = { "title": "CBS", "us_url": "https://us.test.com", "uk_url": "https://uk.test.com", "active": True, "db_name": "cbs", "require_region": True, "doc_url": "https://doc.test.com", "pitchfork_url": "https://pitchfork.url", "limit_maps": [] } cbs_limit = { "product": "cbs", "title": "SATA - GB", "absolute_path": "quota_set.gigabytes_SATA", "uri": "/v1/{ddi}/os-quota-sets/{ddi}?usage=True", "slug": "sata_-_gb", "value_key": "in_use", "absolute_type": "dict", "active": True, "limit_key": "limit" } cbs_limit_return = { "quota_set": { "volumes": { "limit": -1, "reserved": 0, "in_use": 3 }, "gigabytes_SATA": { "limit": 10240, "reserved": 0, "in_use": 325 }, "gigabytes_SSD": { "limit": 10240, "reserved": 0, "in_use": 50 } } } cbs_full_return = { 'cbs': { 'values': {'SATA - GB': 9915}, 'limits': {'SATA - GB': 10240} } } """ Load Balancers """ clb = { "title": "Load Balancers", "us_url": "https://us.test.com", "uk_url": "https://uk.test.com", "active": True, "db_name": "load_balancers", "require_region": True, "doc_url": "https://doc.test.com", "pitchfork_url": "https://pitchfork.url", "limit_maps": [] } clb_limit = [ { "product": "load_balancers", "title": "Total Load Balancers", "uri": "/v1.0/{ddi}/loadbalancers/absolutelimits", "slug": "total_load_balancers", "active": True, "path": "absolute['LOADBALANCER_LIMIT']", "absolute_path": "absolute", "value_key": "", "absolute_type": "list", "limit_key": "LOADBALANCER_LIMIT" }, { "product": "load_balancers", "title": "Nodes per LB", "uri": "/v1.0/{ddi}/loadbalancers/absolutelimits", "slug": "nodes_per_lb", "active": True, "path": "absolute['NODE_LIMIT']", "absolute_path": "absolute", "value_key": "", "absolute_type": "list", "limit_key": "NODE_LIMIT" } ] clb_limit_return = { "absolute": [ { "name": "IPV6_LIMIT", "value": 25 }, { "name": "LOADBALANCER_LIMIT", "value": 25 }, { "name": "BATCH_DELETE_LIMIT", "value": 10 }, { "name": "ACCESS_LIST_LIMIT", "value": 100 }, { "name": "NODE_LIMIT", "value": 25 }, { "name": "NODE_META_LIMIT", "value": 25 }, { "name": "LOADBALANCER_META_LIMIT", "value": 25 }, { "name": "CERTIFICATE_MAPPING_LIMIT", "value": 20 } ] } clb_list_return = { "loadBalancers": [ { "status": "ACTIVE", "updated": { "time": "2016-01-12T16:04:44Z" }, "protocol": "HTTP", "name": "test", "algorithm": "LEAST_CONNECTIONS", "created": { "time": "2016-01-12T16:04:44Z" }, "virtualIps": [ { "ipVersion": "IPV4", "type": "PUBLIC", "id": 19875, "address": "172.16.31.10" }, { "ipVersion": "IPV6", "type": "PUBLIC", "id": 9318325, "address": "fd00:c2b6:b24b:be67:2827:688d:e6a1:6a3b" } ], "id": 506497, "timeout": 30, "nodeCount": 0, "port": 80 } ] } clb_full_return = { 'load_balancers': { 'values': {'Total Load Balancers': 1}, 'limits': {'Total Load Balancers': 25, 'Nodes per LB': 25} } } """ Servers """ server = { "title": "Servers", "us_url": "https://us.test.com", "uk_url": "https://uk.test.com", "active": True, "db_name": "servers", "require_region": True, "doc_url": "https://doc.test.com", "pitchfork_url": "https://pitchfork.url", "limit_maps": [] } server_limit = [ { "product": "servers", "title": "Servers", "uri": "/v2/{ddi}/limits", "slug": "servers", "active": True, "path": "absolute['maxTotalInstances']", "absolute_path": "limits.absolute", "value_key": "", "absolute_type": "dict", "limit_key": "maxTotalInstances" }, { "product": "servers", "title": "Private Networks", "uri": "/v2/{ddi}/limits", "slug": "private_networks", "active": True, "path": "absolute['maxTotalPrivateNetworks']", "absolute_path": "limits.absolute", "value_key": "", "absolute_type": "dict", "limit_key": "maxTotalPrivateNetworks" }, { "product": "servers", "title": "Ram - MB", "uri": "/v2/{ddi}/limits", "slug": "ram_-_mb", "active": True, "path": "absolute['maxTotalRAMSize']", "absolute_path": "limits.absolute", "value_key": "", "absolute_type": "dict", "limit_key": "maxTotalRAMSize" } ] server_limit_return = { "limits": { "rate": [ { "regex": "/[^/]*/?$", "limit": [ { "next-available": "2016-01-12T16:14:47.624Z", "unit": "MINUTE", "verb": "GET", "remaining": 2200, "value": 2200 } ], "uri": "*" }, { "regex": ( "/v[^/]+/[^/]+/servers/([^/]+)/rax-si-image-schedule" ), "limit": [ { "next-available": "2016-01-12T16:14:47.624Z", "unit": "SECOND", "verb": "POST", "remaining": 10, "value": 10 } ], "uri": "/servers/{id}/rax-si-image-schedule" } ], "absolute": { "maxPersonalitySize": 1000, "maxTotalCores": -1, "maxPersonality": 5, "totalPrivateNetworksUsed": 1, "maxImageMeta": 40, "maxTotalPrivateNetworks": 10, "maxSecurityGroupRules": -1, "maxTotalKeypairs": 100, "totalRAMUsed": 4096, "maxSecurityGroups": -1, "totalFloatingIpsUsed": 0, "totalInstancesUsed": 3, "totalSecurityGroupsUsed": 0, "maxServerMeta": 40, "maxTotalFloatingIps": -1, "maxTotalInstances": 200, "totalCoresUsed": 4, "maxTotalRAMSize": 256000 } } } server_list_return = { "servers": [ { "OS-EXT-STS:task_state": None, "addresses": { "public": [ { "version": 4, "addr": "192.168.3.11" }, { "version": 6, "addr": "fc00:e968:6179::de52:7100" } ], "private": [ { "version": 4, "addr": "10.176.205.68" } ] }, "flavor": { "id": "general1-1", "links": [ { "href": ( "https://iad.servers.api.rackspacecloud.com" "/766030/flavors/general1-1" ), "rel": "bookmark" } ] }, "id": "3290e50d-888f-4500-a934-16c10f3b8a10", "user_id": "284275", "OS-DCF:diskConfig": "MANUAL", "accessIPv4": "192.168.3.11", "accessIPv6": "fc00:e968:6179::de52:7100", "progress": 100, "OS-EXT-STS:power_state": 1, "config_drive": "", "status": "ACTIVE", "updated": "2016-01-12T15:16:37Z", "name": "test-server", "created": "2016-01-12T15:15:39Z", "tenant_id": "1234567", "metadata": { "build_config": "", "rax_service_level_automation": "Complete" } } ] } server_list_processed_return = [ { 'status': 'ACTIVE', 'updated': '2016-01-12T15:16:37Z', 'OS-EXT-STS:task_state': None, 'user_id': '284275', 'addresses': { 'public': [ { 'version': 4, 'addr': '192.168.3.11' }, { 'version': 6, 'addr': 'fc00:e968:6179::de52:7100' } ], 'private': [ { 'version': 4, 'addr': '10.176.205.68' } ] }, 'created': '2016-01-12T15:15:39Z', 'tenant_id': '1234567', 'OS-DCF:diskConfig': 'MANUAL', 'id': '3290e50d-888f-4500-a934-16c10f3b8a10', 'accessIPv4': '192.168.3.11', 'accessIPv6': 'fc00:e968:6179::de52:7100', 'config_drive': '', 'progress': 100, 'OS-EXT-STS:power_state': 1, 'metadata': { 'build_config': '', 'rax_service_level_automation': 'Complete' }, 'flavor': { 'id': 'general1-1', 'links': [ { 'href': ( 'https://iad.servers.api.rackspacecloud.com' '/766030/flavors/general1-1' ), 'rel': 'bookmark' } ] }, 'name': 'test-server' } ] network_list_return = { "networks": [ { "status": "ACTIVE", "subnets": [ "879ff280-6f17-4fd8-b684-19237d88fc45" ], "name": "test-network", "admin_state_up": True, "tenant_id": "1234567", "shared": False, "id": "e737483a-00d7-4517-afc3-bd1fbbbd4cd3" } ] } network_processed_list = [ { 'status': 'ACTIVE', 'subnets': [ '879ff280-6f17-4fd8-b684-19237d88fc45' ], 'name': 'test-network', 'admin_state_up': True, 'tenant_id': '1234567', 'shared': False, 'id': 'e737483a-00d7-4517-afc3-bd1fbbbd4cd3' } ] server_flavor_return = { "flavor": { "ram": 1024, "name": "1 GB General Purpose v1", "OS-FLV-WITH-EXT-SPECS:extra_specs": { "number_of_data_disks": "0", "class": "general1", "disk_io_index": "40", "policy_class": "general_flavor" }, "vcpus": 1, "swap": "", "rxtx_factor": 200.0, "OS-FLV-EXT-DATA:ephemeral": 0, "disk": 20, "id": "general1-1" } } server_full_return = { 'servers': { 'values': { 'Private Networks': 1, 'Ram - MB': 1024, 'Servers': 1 }, 'limits': { 'Private Networks': 10, 'Ram - MB': 256000, 'Servers': 200 } } }

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models.py

danger-ahead/notes-backend-service

1

134936

from sqlalchemy.orm import relationship from sqlalchemy.sql.functions import now from sqlalchemy.sql.schema import ForeignKey from sqlalchemy.sql.sqltypes import TIMESTAMP from database import Base from sqlalchemy import Column, Integer, String class Note(Base): __tablename__ = "notes" id = Column(Integer, primary_key=True, nullable=False) title = Column(String, nullable=False) content = Column(String, nullable=True) created_at = Column(TIMESTAMP(timezone=True), nullable=False, server_default=now()) last_updated_at = Column( TIMESTAMP(timezone=True), nullable=False, server_default=now() ) username = Column( String, ForeignKey("users.username", ondelete="CASCADE"), nullable=False ) user = relationship("User") class User(Base): __tablename__ = "users" username = Column(String, nullable=False, unique=True, primary_key=True) password = Column(String, nullable=False) created_at = Column(TIMESTAMP(timezone=True), nullable=False, server_default=now())

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gui/verifyseed.py

roshan-c/shoeshive.tv

2

162861

import PySimpleGUI as sg sg.theme('SandyBeach') layout = [ [sg.Text('Please enter your Username and the Seed provided by the Host')], [sg.Text('Username', size =(15, 1)), sg.InputText()], [sg.Text('Server Seed', size =(15, 1)), sg.InputText()], [sg.Submit(), sg.Cancel()] ] window = sg.Window('Shoeshine.tv', layout) event, values = window.read() window.close() username = values[0] seedinput = values[1] print(username) print(seedinput) connect = False with open('seed.txt') as f: seed = f.read() print(seed) if seed == seedinput: connect = True print('True') f.close() else: connect = False print('False') f.close()

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Curso_Coursera/Ex1_ParImpar.py

shirleyguimaraes/Linguagem_Python

0

33098

def main(): n = int(input("Digite um número inteiro: ")) resto = n % 2 if resto == 0: print("Par") else: print("Impar") main()

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cttools/config.py

nik849/ct-tools

0

74928

import numpy as np from .parse import parse_xtekct_file class Config(object): def __init__(self): """ Configuration object which contains all settings neccessary for the forward projection and tomographic reconstruction using the axitom algorithm. """ self.n_voxels_x = 2000 self.n_voxels_y = 2000 self.n_voxels_z = 2000 self.object_size_x = 27.229675726 self.object_size_y = 27.229675726 self.object_size_z = 27.229675726 self.n_pixels_u = 2000 self.n_pixels_v = 2000 self.detector_size_u = 400. self.detector_size_v = 400. self.source_to_detector_dist = 797.8693 self.source_to_object_dist = 95.1665735244751 self.angular_inc = 1. self.pixel_offset_u = 0 self.pixel_offset_v = 0 self.center_of_rot_y = 0 self.projection_angs = np.arange(0., 360, self.angular_inc) self.n_projections = len(self.projection_angs) self.voxel_size_x = self.object_size_x / self.n_voxels_x self.voxel_size_y = self.object_size_y / self.n_voxels_y self.voxel_size_z = self.object_size_z / self.n_voxels_z self.pixel_size_u = self.detector_size_u / self.n_pixels_u self.pixel_size_v = self.detector_size_v / self.n_pixels_v self.object_xs = (np.arange(self.n_voxels_x, dtype=np.float32) - self.n_voxels_x / 2.) * self.voxel_size_x self.object_ys = (np.arange(self.n_voxels_y, dtype=np.float32) - self.n_voxels_y / 2.) * self.voxel_size_y self.object_zs = (np.arange(self.n_voxels_z, dtype=np.float32) - self.n_voxels_z / 2.) * self.voxel_size_z self.detector_us = (np.arange(self.n_pixels_u, dtype=np.float32) - self.n_pixels_u / 2.) * self.pixel_size_u + self.pixel_offset_u * self.pixel_size_u self.detector_vs = (np.arange(self.n_pixels_v, dtype=np.float32) - self.n_pixels_v / 2.) * self.pixel_size_v + self.pixel_offset_v * self.pixel_size_v def update(self): self.projection_angs = np.arange(0., 360, self.angular_inc) self.n_projections = len(self.projection_angs) self.voxel_size_x = self.object_size_x / self.n_voxels_x self.voxel_size_y = self.object_size_y / self.n_voxels_y self.voxel_size_z = self.object_size_z / self.n_voxels_z self.pixel_size_u = self.detector_size_u / self.n_pixels_u self.pixel_size_v = self.detector_size_v / self.n_pixels_v self.object_xs = (np.arange(self.n_voxels_x, dtype=np.float32) - self.n_voxels_x / 2.) * self.voxel_size_x self.object_ys = (np.arange(self.n_voxels_y, dtype=np.float32) - self.n_voxels_y / 2.) * self.voxel_size_y self.object_zs = (np.arange(self.n_voxels_z, dtype=np.float32) - self.n_voxels_z / 2.) * self.voxel_size_z self.detector_us = (np.arange(self.n_pixels_u, dtype=np.float32) - self.n_pixels_u / 2.) * self.pixel_size_u + self.pixel_offset_u * self.pixel_size_u self.detector_vs = (np.arange(self.n_pixels_v, dtype=np.float32) - self.n_pixels_v / 2.) * self.pixel_size_v + self.pixel_offset_v * self.pixel_size_v def config_from_xtekct(file_path): """ Make config object from a Nikon X-tek CT input file The .xtekct file is parsed and a config file containing all relevant settings is returned. Parameters ---------- file_path : str The path to the .xtekct file Returns ------- obj Config object """ inputfile = parse_xtekct_file(file_path) conf = Config() try: conf.n_voxels_x = inputfile["VoxelsX"] conf.n_voxels_y = inputfile["VoxelsY"] conf.n_voxels_z = inputfile["VoxelsZ"] conf.object_size_x = inputfile["VoxelSizeX"] * conf.n_voxels_x conf.object_size_y = inputfile["VoxelSizeY"] * conf.n_voxels_y conf.object_size_z = inputfile["VoxelSizeZ"] * conf.n_voxels_z conf.n_pixels_u = inputfile["DetectorPixelsX"] conf.n_pixels_v = inputfile["DetectorPixelsY"] conf.detector_size_u = inputfile["DetectorPixelSizeX"] * conf.n_pixels_u conf.detector_size_v = inputfile["DetectorPixelSizeY"] * conf.n_pixels_v conf.source_to_detector_dist = inputfile["SrcToDetector"] conf.source_to_object_dist = inputfile["SrcToObject"] except Exception as e: raise IOError("Parsing of X-tec file failed with key: ", e) return conf

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tests/basic_store.py

fmarczin/simplekv

0

134401

<gh_stars>0 # coding: utf8 import os import time import tempfile from tempdir import TempDir import pytest from simplekv._compat import BytesIO, xrange, text_type from simplekv.decorator import PrefixDecorator from simplekv.crypt import HMACDecorator from simplekv.idgen import UUIDDecorator, HashDecorator from simplekv import CopyMixin class BasicStore(object): def test_store(self, store, key, value): key = store.put(key, value) assert isinstance(key, text_type) def test_unicode_store(self, store, key, unicode_value): with pytest.raises(IOError): store.put(key, unicode_value) def test_store_and_retrieve(self, store, key, value): store.put(key, value) assert store.get(key) == value def test_store_and_retrieve_filelike(self, store, key, value): store.put_file(key, BytesIO(value)) assert store.get(key) == value def test_store_and_retrieve_overwrite(self, store, key, value, value2): store.put_file(key, BytesIO(value)) assert store.get(key) == value store.put(key, value2) assert store.get(key) == value2 def test_store_and_open(self, store, key, value): store.put_file(key, BytesIO(value)) assert store.open(key).read() == value def test_store_and_copy(self, store, key, key2, value): if not isinstance(store, CopyMixin): pytest.skip() store.put(key, value) assert store.get(key) == value store.copy(key, key2) assert store.get(key) == value assert store.get(key2) == value def test_store_and_copy_overwrite(self, store, key, key2, value, value2): if not isinstance(store, CopyMixin): pytest.skip() store.put(key, value) store.put(key2, value2) assert store.get(key) == value assert store.get(key2) == value2 store.copy(key, key2) assert store.get(key) == value assert store.get(key2) == value def test_open_incremental_read(self, store, key, long_value): store.put_file(key, BytesIO(long_value)) ok = store.open(key) assert long_value[:3] == ok.read(3) assert long_value[3:5] == ok.read(2) assert long_value[5:8] == ok.read(3) def test_bytestring_key_store(self, store, bytestring_key, value): with pytest.raises(ValueError): store.put(bytestring_key, value) def test_key_error_on_nonexistant_get(self, store, key): with pytest.raises(KeyError): store.get(key) def test_key_error_on_nonexistant_copy(self, store, key, key2): if not isinstance(store, CopyMixin): pytest.skip() with pytest.raises(KeyError): store.copy(key, key2) def test_key_error_on_nonexistant_open(self, store, key): with pytest.raises(KeyError): store.open(key) def test_key_error_on_nonexistant_get_file(self, store, key): with pytest.raises(KeyError): store.get_file(key, BytesIO()) def test_key_error_on_nonexistant_get_filename(self, store, key): with pytest.raises(KeyError): store.get_file(key, '/dev/null') def test_exception_on_invalid_key_get(self, store, invalid_key): with pytest.raises(ValueError): store.get(invalid_key) def test_exception_on_invalid_key_copy(self, store, invalid_key, key): if not isinstance(store, CopyMixin): pytest.skip() with pytest.raises(ValueError): store.copy(invalid_key, key) with pytest.raises(ValueError): store.copy(key, invalid_key) def test_exception_on_invalid_key_get_file(self, store, invalid_key): with pytest.raises(ValueError): store.get_file(invalid_key, '/dev/null') def test_exception_on_invalid_key_delete(self, store, invalid_key): with pytest.raises(ValueError): store.delete(invalid_key) def test_put_file(self, store, key, value): tmp = tempfile.NamedTemporaryFile(delete=False) try: tmp.write(value) tmp.close() store.put_file(key, tmp.name) assert store.get(key) == value finally: if os.path.exists(tmp.name): os.unlink(tmp.name) def test_put_opened_file(self, store, key, value): with tempfile.NamedTemporaryFile() as tmp: tmp.write(value) tmp.flush() store.put_file(key, open(tmp.name, 'rb')) assert store.get(key) == value def test_get_into_file(self, store, key, value): with TempDir() as tmpdir: store.put(key, value) out_filename = os.path.join(tmpdir, 'output') store.get_file(key, out_filename) assert open(out_filename, 'rb').read() == value def test_get_into_stream(self, store, key, value): store.put(key, value) output = BytesIO() store.get_file(key, output) assert output.getvalue() == value def test_put_return_value(self, store, key, value): assert key == store.put(key, value) def test_put_file_return_value(self, store, key, value): assert key == store.put_file(key, BytesIO(value)) def test_put_filename_return_value(self, store, key, value): tmp = tempfile.NamedTemporaryFile(delete=False) try: tmp.write(value) tmp.close() assert key == store.put_file(key, tmp.name) finally: if os.path.exists(tmp.name): os.unlink(tmp.name) def test_delete(self, store, key, value): store.put(key, value) assert value == store.get(key) store.delete(key) with pytest.raises(KeyError): store.get(key) def test_multiple_delete_fails_without_error(self, store, key, value): store.put(key, value) store.delete(key) store.delete(key) store.delete(key) def test_can_delete_key_that_never_exists(self, store, key): store.delete(key) def test_key_iterator(self, store, key, key2, value, value2): store.put(key, value) store.put(key2, value2) l = [] for k in store.iter_keys(): assert isinstance(k, text_type) l.append(k) l.sort() assert l == sorted([key, key2]) def test_key_iterator_with_prefix(self, store, key, key2, value): prefix = key key_prefix_1 = prefix + '_key1' key_prefix_2 = prefix + '_key2' store.put(key_prefix_1, value) store.put(key_prefix_2, value) store.put(key2, value) l = [] for k in store.iter_keys(): l.append(k) l.sort() assert l == sorted([key_prefix_1, key_prefix_2, key2]) l = [] for k in store.iter_keys(prefix): l.append(k) l.sort() assert l == sorted([key_prefix_1, key_prefix_2]) def test_keys(self, store, key, key2, value, value2): store.put(key, value) store.put(key2, value2) l = sorted(store.keys()) for k in l: assert isinstance(k, text_type) assert l == sorted([key, key2]) def test_keys_with_prefix(self, store, key, key2, value): prefix = key key_prefix_1 = prefix + '_key1' key_prefix_2 = prefix + '_key2' store.put(key_prefix_1, value) store.put(key_prefix_2, value) store.put(key2, value) l = sorted(store.keys()) assert l == sorted([key_prefix_1, key_prefix_2, key2]) l = sorted(store.keys(prefix)) assert l == sorted([key_prefix_1, key_prefix_2]) def test_has_key(self, store, key, key2, value): store.put(key, value) assert key in store assert key2 not in store def test_has_key_with_delete(self, store, key, value): assert key not in store store.put(key, value) assert key in store store.delete(key) assert key not in store store.put(key, value) assert key in store def test_get_with_delete(self, store, key, value): with pytest.raises(KeyError): store.get(key) store.put(key, value) store.get(key) store.delete(key) with pytest.raises(KeyError): store.get(key) store.put(key, value) store.get(key) def test_max_key_length(self, store, max_key, value): new_key = store.put(max_key, value) assert new_key == max_key assert value == store.get(max_key) def test_a_lot_of_puts(self, store, key, value): a_lot = 20 for i in xrange(a_lot): key = key + '_{}'.format(i) store.put(key, value) # small extra time added to account for variance TTL_MARGIN = 1 class TTLStore(object): @pytest.fixture def ustore(self, store): return UUIDDecorator(store) @pytest.fixture(params=['hash', 'uuid', 'hmac', 'prefix']) def dstore(self, request, store, secret_key): if request.param == 'hash': return HashDecorator(store) elif request.param == 'uuid': return self.ustore(store) elif request.param == 'hmac': return HMACDecorator(secret_key, store) elif request.param == 'prefix': return PrefixDecorator('SaMpLe_PrEfIX', store) @pytest.fixture(params=[0.4, 1]) def small_ttl(self, request): return request.param def test_put_with_negative_ttl_throws_error(self, store, key, value): with pytest.raises(ValueError): store.put(key, value, ttl_secs=-1) def test_put_with_non_numeric_ttl_throws_error(self, store, key, value): with pytest.raises(ValueError): store.put(key, value, ttl_secs='badttl') def test_put_with_ttl_argument(self, store, key, value, small_ttl): store.put(key, value, small_ttl) time.sleep(small_ttl + TTL_MARGIN) with pytest.raises(KeyError): store.get(key) def test_put_set_default(self, store, key, value, small_ttl): store.default_ttl_secs = small_ttl store.put(key, value) time.sleep(small_ttl + TTL_MARGIN) with pytest.raises(KeyError): store.get(key) def test_put_file_with_ttl_argument(self, store, key, value, small_ttl): store.put_file(key, BytesIO(value), small_ttl) time.sleep(small_ttl + TTL_MARGIN) with pytest.raises(KeyError): store.get(key) def test_put_file_set_default(self, store, key, value, small_ttl): store.default_ttl_secs = small_ttl store.put_file(key, BytesIO(value)) time.sleep(small_ttl + TTL_MARGIN) with pytest.raises(KeyError): store.get(key) def test_uuid_decorator(self, ustore, value): key = ustore.put(None, value) assert key def test_advertises_ttl_features(self, store): assert store.ttl_support is True assert hasattr(store, 'ttl_support') assert getattr(store, 'ttl_support') is True def test_advertises_ttl_features_through_decorator(self, dstore): assert dstore.ttl_support is True assert hasattr(dstore, 'ttl_support') assert getattr(dstore, 'ttl_support') is True def test_can_pass_ttl_through_decorator(self, dstore, key, value): dstore.put(key, value, ttl_secs=10)

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usermess/migrations/0002_auto_20181111_2211.py

HemanthJella/ewallet

0

138956

# Generated by Django 2.1.2 on 2018-11-11 22:11 import datetime from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('usermess', '0001_initial'), ] operations = [ migrations.AlterField( model_name='couponsbought', name='boughtTime', field=models.DateTimeField(default=datetime.datetime(2018, 11, 11, 22, 11, 23, 928786)), ), migrations.AlterField( model_name='couponsbought', name='couponId', field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, to='mess.Coupons'), ), ]

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sdk/python/pulumi_aiven/get_service_user.py

pulumi/pulumi-aiven

7

180868

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from . import _utilities __all__ = [ 'GetServiceUserResult', 'AwaitableGetServiceUserResult', 'get_service_user', ] @pulumi.output_type class GetServiceUserResult: """ A collection of values returned by getServiceUser. """ def __init__(__self__, access_cert=None, access_key=None, authentication=None, id=None, password=<PASSWORD>, project=None, redis_acl_categories=None, redis_acl_channels=None, redis_acl_commands=None, redis_acl_keys=None, service_name=None, type=None, username=None): if access_cert and not isinstance(access_cert, str): raise TypeError("Expected argument 'access_cert' to be a str") pulumi.set(__self__, "access_cert", access_cert) if access_key and not isinstance(access_key, str): raise TypeError("Expected argument 'access_key' to be a str") pulumi.set(__self__, "access_key", access_key) if authentication and not isinstance(authentication, str): raise TypeError("Expected argument 'authentication' to be a str") pulumi.set(__self__, "authentication", authentication) if id and not isinstance(id, str): raise TypeError("Expected argument 'id' to be a str") pulumi.set(__self__, "id", id) if password and not isinstance(password, str): raise TypeError("Expected argument 'password' to be a str") pulumi.set(__self__, "password", password) if project and not isinstance(project, str): raise TypeError("Expected argument 'project' to be a str") pulumi.set(__self__, "project", project) if redis_acl_categories and not isinstance(redis_acl_categories, list): raise TypeError("Expected argument 'redis_acl_categories' to be a list") pulumi.set(__self__, "redis_acl_categories", redis_acl_categories) if redis_acl_channels and not isinstance(redis_acl_channels, list): raise TypeError("Expected argument 'redis_acl_channels' to be a list") pulumi.set(__self__, "redis_acl_channels", redis_acl_channels) if redis_acl_commands and not isinstance(redis_acl_commands, list): raise TypeError("Expected argument 'redis_acl_commands' to be a list") pulumi.set(__self__, "redis_acl_commands", redis_acl_commands) if redis_acl_keys and not isinstance(redis_acl_keys, list): raise TypeError("Expected argument 'redis_acl_keys' to be a list") pulumi.set(__self__, "redis_acl_keys", redis_acl_keys) if service_name and not isinstance(service_name, str): raise TypeError("Expected argument 'service_name' to be a str") pulumi.set(__self__, "service_name", service_name) if type and not isinstance(type, str): raise TypeError("Expected argument 'type' to be a str") pulumi.set(__self__, "type", type) if username and not isinstance(username, str): raise TypeError("Expected argument 'username' to be a str") pulumi.set(__self__, "username", username) @property @pulumi.getter(name="accessCert") def access_cert(self) -> str: """ is the access certificate of the user (not applicable for all services). """ return pulumi.get(self, "access_cert") @property @pulumi.getter(name="accessKey") def access_key(self) -> str: """ is the access key of the user (not applicable for all services). """ return pulumi.get(self, "access_key") @property @pulumi.getter def authentication(self) -> Optional[str]: return pulumi.get(self, "authentication") @property @pulumi.getter def id(self) -> str: """ The provider-assigned unique ID for this managed resource. """ return pulumi.get(self, "id") @property @pulumi.getter def password(self) -> str: """ is the password of the user (not applicable for all services). """ return pulumi.get(self, "password") @property @pulumi.getter def project(self) -> str: return pulumi.get(self, "project") @property @pulumi.getter(name="redisAclCategories") def redis_acl_categories(self) -> Optional[Sequence[str]]: """ Redis specific field, defines command category rules. """ return pulumi.get(self, "redis_acl_categories") @property @pulumi.getter(name="redisAclChannels") def redis_acl_channels(self) -> Optional[Sequence[str]]: return pulumi.get(self, "redis_acl_channels") @property @pulumi.getter(name="redisAclCommands") def redis_acl_commands(self) -> Optional[Sequence[str]]: """ Redis specific field, defines rules for individual commands. """ return pulumi.get(self, "redis_acl_commands") @property @pulumi.getter(name="redisAclKeys") def redis_acl_keys(self) -> Optional[Sequence[str]]: """ Redis specific field, defines key access rules. """ return pulumi.get(self, "redis_acl_keys") @property @pulumi.getter(name="serviceName") def service_name(self) -> str: return pulumi.get(self, "service_name") @property @pulumi.getter def type(self) -> str: """ tells whether the user is primary account or regular account. """ return pulumi.get(self, "type") @property @pulumi.getter def username(self) -> str: return pulumi.get(self, "username") class AwaitableGetServiceUserResult(GetServiceUserResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return GetServiceUserResult( access_cert=self.access_cert, access_key=self.access_key, authentication=self.authentication, id=self.id, password=<PASSWORD>, project=self.project, redis_acl_categories=self.redis_acl_categories, redis_acl_channels=self.redis_acl_channels, redis_acl_commands=self.redis_acl_commands, redis_acl_keys=self.redis_acl_keys, service_name=self.service_name, type=self.type, username=self.username) def get_service_user(access_cert: Optional[str] = None, access_key: Optional[str] = None, authentication: Optional[str] = None, password: Optional[str] = None, project: Optional[str] = None, redis_acl_categories: Optional[Sequence[str]] = None, redis_acl_channels: Optional[Sequence[str]] = None, redis_acl_commands: Optional[Sequence[str]] = None, redis_acl_keys: Optional[Sequence[str]] = None, service_name: Optional[str] = None, type: Optional[str] = None, username: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetServiceUserResult: """ ## # Service User Data Source The Service User data source provides information about the existing Aiven Service User. ## Example Usage ```python import pulumi import pulumi_aiven as aiven myserviceuser = aiven.get_service_user(project=aiven_project["myproject"]["project"], service_name=aiven_service["myservice"]["service_name"], username="<USERNAME>") ``` > **Note** The service user data source is not supported for Aiven Grafana services. :param str access_cert: is the access certificate of the user (not applicable for all services). :param str access_key: is the access key of the user (not applicable for all services). :param str password: is the password of the user (not applicable for all services). :param str project: and `service_name` - (Required) define the project and service the user belongs to. They should be defined using reference as shown above to set up dependencies correctly. :param Sequence[str] redis_acl_categories: Redis specific field, defines command category rules. :param Sequence[str] redis_acl_commands: Redis specific field, defines rules for individual commands. :param Sequence[str] redis_acl_keys: Redis specific field, defines key access rules. :param str type: tells whether the user is primary account or regular account. :param str username: is the actual name of the user account. """ __args__ = dict() __args__['accessCert'] = access_cert __args__['accessKey'] = access_key __args__['authentication'] = authentication __args__['password'] = password __args__['project'] = project __args__['redisAclCategories'] = redis_acl_categories __args__['redisAclChannels'] = redis_acl_channels __args__['redisAclCommands'] = redis_acl_commands __args__['redisAclKeys'] = redis_acl_keys __args__['serviceName'] = service_name __args__['type'] = type __args__['username'] = username if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('aiven:index/getServiceUser:getServiceUser', __args__, opts=opts, typ=GetServiceUserResult).value return AwaitableGetServiceUserResult( access_cert=__ret__.access_cert, access_key=__ret__.access_key, authentication=__ret__.authentication, id=__ret__.id, password=__<PASSWORD>, project=__ret__.project, redis_acl_categories=__ret__.redis_acl_categories, redis_acl_channels=__ret__.redis_acl_channels, redis_acl_commands=__ret__.redis_acl_commands, redis_acl_keys=__ret__.redis_acl_keys, service_name=__ret__.service_name, type=__ret__.type, username=__ret__.username)

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gwk/constants.py

aixcyi/gwk

1

181388

# -*- coding: utf-8 -*- __all__ = [ 'GachaType', 'WishType', 'JsonStruct', 'CEILINGS', 'UNIFORM_TIME_FORMAT', 'DT_STAMP_OFFSET_CHANGE', 'DT_VERSION_START_2_3', ] from datetime import datetime from enum import Enum from gwk.typing import Choices class GachaType(Choices): """祈愿的卡池类型。""" BEGINNERS_WISH = '100', '新手祈愿' WANDERLUST_INVOCATION = '200', '常驻祈愿' CHARACTER_EVENT_WISH = '301', '角色活动祈愿' WEAPON_EVENT_WISH = '302', '武器活动祈愿' CHARACTER_EVENT_WISH_2 = '400', '角色活动祈愿-2' class WishType(Choices): """祈愿卡池的类型。""" BEGINNERS_WISH = '100', '新手祈愿' WANDERLUST_INVOCATION = '200', '常驻祈愿' CHARACTER_EVENT_WISH = '301', '角色活动祈愿' WEAPON_EVENT_WISH = '302', '武器活动祈愿' class JsonStruct(Enum): """导出的祈愿记录JSON文件的格式。""" GWK = 'GWK' UIGF = 'UIGF' CEILINGS = { WishType.BEGINNERS_WISH: 90, # 新手祈愿 WishType.WANDERLUST_INVOCATION: 90, # 常驻祈愿 WishType.CHARACTER_EVENT_WISH: 90, # 角色活动祈愿 WishType.WEAPON_EVENT_WISH: 80, # 武器活动祈愿 # WishType.CHARACTER_EVENT_WISH_2: 90, # 角色活动祈愿-2 } # Time Format UNIFORM_TIME_FORMAT = '%Y-%m-%d %H:%M:%S' # Datetime DT_STAMP_OFFSET_CHANGE = datetime(2020, 12, 31) DT_VERSION_START_2_3 = datetime(2021, 11, 24, 7, 0, 0)

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Python/Code/Python3-Base/11_Advance/Generator.py

hiloWang/notes

2

40781

<filename>Python/Code/Python3-Base/11_Advance/Generator.py ######################### # 生成器 ######################### # 创建一个生成器 L = (x * 2 for x in range(5)) print(type(L)) print(L) for value in L: print(value, end=' ') print() # 创建一个函数生成器 def fib(times): n = 0 a, b = 0, 1 while n < times: """ 在循环过程中不断调用 yield ，就会不断中断。当然要给循环设置一个条件来退出循环，不然就会产生一个无限数列出来。同样的，把函数改成generator后，我们基本上从来不会用 next() 来获取下一个返回值，而是直接使用 for 循环来迭代 """ yield b a, b = b, a + b n += 1 return 'done' for value in fib(10): print(value, end=' ') print() # 如果想要拿到返回值，必须捕获StopIteration错误，返回值包含在StopIteration的value中 try: fibSqu = fib(10) while True: print(next(fibSqu), end=' ') except StopIteration as e: print("生成器返回值：%s" % e.value, end='') print() # 使用send def gen(): i = 0 while i < 5: # temp为外部调用send函数时发送的值 temp = yield i print('gen ', temp, end=' ') i += 1 f = gen() print(f.__next__()) # 必须先调用 __next__或 send(None) print('send ', f.send('a')) print('send ', f.send('b')) print('send ', f.send('c'))

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Language Skills/Python/Unit 11/2-Classes/Using Classes/7-Creating class methods.py

vpstudios/codecademy-exercise-answers

1

197192

<reponame>vpstudios/codecademy-exercise-answers<gh_stars>1-10 class Car(object): condition = "new" def __init__(self, model, color, mpg): self.model = model self.color = color self.mpg = mpg def display_car(self): return "This is a %s %s with %s MPG." % (self.color, self.model, str(self.mpg)) my_car = Car("DeLorean","silver", 88) print my_car.display_car()

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training/pretrainav/weightsampling.py

rub-ksv/-lrs_avsr1_local-

1

170559

<reponame>rub-ksv/-lrs_avsr1_local- import torch from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding class Conv2dSubsampling(torch.nn.Module): """Convolutional 2D subsampling (to 1/4 length) :param int idim: input dim :param int odim: output dim :param flaot dropout_rate: dropout rate """ def __init__(self, idim, odim, dropout_rate): super(Conv2dSubsampling, self).__init__() self.out = torch.nn.Sequential(torch.nn.Linear(idim, odim)) def forward(self, x, x_mask): """Subsample x :param torch.Tensor x: input tensor :param torch.Tensor x_mask: input mask :return: subsampled x and mask :rtype Tuple[torch.Tensor, torch.Tensor] """ x = self.out(x) if x_mask is None: return x, None return x, x_mask

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csbiginteger/BigIntegerNet.py

NeoResearch/csBigInteger.py

2

30829

<gh_stars>1-10 #!/usr/bin/python3 from csbiginteger.BigInteger import BigInteger from functools import total_ordering # requires: pip install msl-loadlib pycparser pythonnet from msl.loadlib import LoadLibrary # remember to execute first: cd csbiginteger/dotnet && dotnet build -c Release net = LoadLibrary('csbiginteger/dotnet/bin/Release/netstandard2.0/publish/csbiginteger.dll', 'net') biglib = net.lib.csbiglib.BigIntegerLib() z = biglib.zero() print(bytearray(z.ToByteArray())) m1 = biglib.from_int32(-1) print(bytearray(m1.ToByteArray())) i255 = biglib.from_int32(255) print(bytearray(i255.ToByteArray())) b2 = biglib.from_bytes(bytearray(i255.ToByteArray())) print(biglib.to_int32(b2)) b3 = biglib.from_string("0xff", 16) print(biglib.to_int32(b3)) print(biglib.to_string(b3, 16)) print(biglib.to_string(b3, 10)) print(bytearray(biglib.to_bytes(b3))) @total_ordering class BigIntegerNet(BigInteger): # param may be: int, bytearray, bytes, string (parsed with base) # bytes and bytearray should be received in little-endian format (same as to_bytearray() returns) def __init__(self, param=0, base=10): if type(param) is int: param = str(param) # convert to base-10 integer base = 10 # force base 10 if type(param) is bytearray: param = bytes(param) # bytearray to bytes if type(param) is bytes: self._big = biglib.from_bytes(bytearray(param)) if type(param) is str: self._big = biglib.from_string(param, base) # returns value in signed int32 limit (or exception) def to_int(self): return biglib.to_int32(self._big) def to_long(self): return biglib.to_int64(self._big) # bytearray is returned in little-endian format def to_bytearray(self): return bytearray(biglib.to_bytes(self._big)) def to_str(self, base=16): return str(biglib.to_string(self._big, base)) def add(self, other): if type(other) is int: other = BigIntegerNet(other) big3 = BigIntegerNet() big3._big = self._big.Add(self._big, other._big) return big3 def sub(self, other): if type(other) is int: other = BigIntegerNet(other) big3 = BigIntegerNet() big3._big = self._big.Subtract(self._big, other._big) return big3 def mul(self, other): if type(other) is int: other = BigIntegerNet(other) big3 = BigIntegerNet() big3._big = self._big.Multiply(self._big, other._big) return big3 def div(self, other): if type(other) is int: other = BigIntegerNet(other) big3 = BigIntegerNet() big3._big = self._big.Divide(self._big, other._big) return big3 def mod(self, other): if type(other) is int: other = BigIntegerNet(other) big3 = BigIntegerNet() big3._big = self._big.DivRem(self._big, other._big) return big3 def shl(self, other): if type(other) is int: other = BigIntegerNet(other) big3 = BigIntegerNet() big3._big = self._big.op_LeftShift(other._big) return big3 def shr(self, other): if type(other) is int: other = BigIntegerNet(other) big3 = BigIntegerNet() big3._big = self._big.op_RightShift(other._big) return big3 def eq(self, other): if type(other) is int: other = BigIntegerNet(other) return self._big.op_Equality(other._big) def lt(self, other): if type(other) is int: other = BigIntegerNet(other) return self._big.op_LessThan(other._big) def __repr__(self): return str(self) def __str__(self): return self.to_str(10) def __len__(self): return len(self.to_bytearray()) # --------- # operators # --------- def __add__(self, other): return self.add(other) def __sub__(self, other): return self.sub(other) def __mul__(self, other): return self.mul(other) # note that python usually follows 'pure floor' operation here, on a // b => floor(a/b) # example: -5 // 2 => -3 (standard int on python) # however, this library follows hardware-standard (from c/c++/java/fortran), of truncating positive or negative # so, here: BigInteger(-5) // BigInteger(2) => -2 ("rounding" up, not down) # floordiv is thus not a good name, since it's only floor for positive division, but ceil for negative, but that's what we have :) def __floordiv__(self, other): return self.div(other) # truediv does not exist (using a // b) def __truediv__(self, other): return self.div(other) def __mod__(self, other): return self.mod(other) def __rshift__(self, other): return self.shr(other) def __lshift__(self, other): return self.shl(other) # comparisons # ----------- def __eq__(self, other): return self.eq(other) # def __gt__(self, other): # return self.gt(other) def __lt__(self, other): return self.lt(other)

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drs2.py

gling07/Text2DRS

3

1608128

<filename>drs2.py # MIT License # # Copyright (c) [2018] [<NAME> (<EMAIL>), # <NAME> (<EMAIL>)] # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import verbnetsrl import pprint drs_dict = dict() verb_pos = ['VBD', 'VB', 'VBN', 'VBG', 'VBP'] noun_lst = ['NNP', 'NN', 'PRP', 'NNS'] def drs_generator(data_dct_lst, coref_dictionary): omit_list = get_omit_entities(coref_dictionary) entities = get_all_entities(data_dct_lst, omit_list) entities_map = mapping_entity(entities) property = retrieve_property(entities_map) (events_map, event_property) = retrieve_event(data_dct_lst) event_type = retrieve_event_type(data_dct_lst) event_time = retrieve_event_time(events_map) event_argument = retrieve_event_argument(data_dct_lst, property, event_type, event_property) drs_dict['entity'] = [k for k in entities_map.keys()] drs_dict['property'] = property drs_dict['event'] = [k for k in events_map.keys()] drs_dict['eventType'] = event_type drs_dict['eventTime'] = event_time drs_dict['eventArgument'] = event_argument return drs_dict def get_omit_entities(coref_dictionary): special = "'"+'s' omit_list = list() for key, value in coref_dictionary.items(): if ' ' in key and special not in key: entity = key.split(' ')[-1] for v in value[1:]: omit_list.append((entity, v)) else: for v in value[1:]: omit_list.append((key, v)) return omit_list def get_all_entities(data_dct_lst, omit_list): entities = list() num = 0 for sentences in data_dct_lst: num += 1 for sen in sentences: if sen.get('PPOS') in noun_lst: tmp = (sen.get('Form'), num) if tmp not in omit_list: entities.append(sen.get('Form')) return entities def mapping_entity(entities): entities_dictionary = dict() count = 1 for entity in entities: entities_dictionary['r'+ str(count)] = entity count += 1 return entities_dictionary def retrieve_property(entities_map): properties = list() for key, entity in entities_map.items(): temp = (key, entity) properties.append(temp) return properties def retrieve_event(data_dct_lst): events_dictionary = dict() events_property = dict() count = 1 sentence_id = 1 for sentences in data_dct_lst: verb = list() for sen in sentences: if sen.get('Pred') != '_' and sen.get('PPOS') in verb_pos: events_dictionary['e' + str(count)] = sen.get('PLemma') verb.append((sen.get('PLemma'), 'e' + str(count))) count += 1 events_property[sentence_id] = verb sentence_id += 1 return (events_dictionary, events_property) # include picking first vn-class if multiple returns def retrieve_event_type(data_dct_lst): event_type_dictionary = dict() count = 1 for sentence in data_dct_lst: for item in sentence: if item.get('PPOS') in verb_pos: pred = item.get('Pred') if item.get(pred + ':vb-class') is not None: event_type_dictionary['e' + str(count)] = item.get(pred + ':vb-class')[0] count += 1 event_type_list = [(k, v) for k, v in event_type_dictionary.items()] return event_type_list def retrieve_event_time(events_map): event_time_dictionary = dict() count = 0 for event, value in events_map.items(): event_time_dictionary[event] = count count += 1 event_time_list = [(k, v) for k, v in event_time_dictionary.items()] return event_time_list def retrieve_event_argument(data_dct_lst, property, event_type, event_property): event_argument_list = list() sentence_id = 1 for sentence in data_dct_lst: predicates = verbnetsrl.get_predicates(sentence) events = event_type[0:len(predicates)] event_type = event_type[len(predicates):] for (pred, event) in zip(predicates, events): event_ref = event[0] for sent in sentence: if sent.get('Args:' + pred) != '_': # use first verb class as vn class vn_role = sent.get(pred + ':vn-class')[0][1] if sent.get('PPOS') in noun_lst: for (ref, ent) in property: if ent == sent.get('Form'): event_argument_list.append((event_ref, vn_role, ref)) break elif sent.get('PPOS') in verb_pos: verb_property = event_property.get(sentence_id) for (plemma, eref) in verb_property: if sent.get('PLemma') == plemma: event_argument_list.append((event_ref, vn_role, eref)) sentence_id += 1 return event_argument_list

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hrl_dynamic_mpc/src/opt_traj_tools.py

gt-ros-pkg/hrl-haptic-manip

1

75566

# # # Copyright (c) 2013, Georgia Tech Research Corporation # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # * Neither the name of the Georgia Tech Research Corporation nor the # names of its contributors may be used to endorse or promote products # derived from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY GEORGIA TECH RESEARCH CORPORATION ''AS IS'' AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL GEORGIA TECH BE LIABLE FOR ANY DIRECT, INDIRECT, # INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, # OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF # LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE # OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF # ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # # \authors: <NAME> (Healthcare Robotics Lab, Georgia Tech.) # \adviser: <NAME> (Healthcare Robotics Lab, Georgia Tech.) import itertools import matplotlib.pyplot as pp import roslib; roslib.load_manifest('hrl_dynamic_mpc') import rospy import hrl_lib.util as ut import hrl_lib.matplotlib_util as mpu from matplotlib import pyplot as pl from numpy import matrix, cos, arange, zeros, ones, asarray, zeros, mat, array, transpose, vstack, min, max from numpy import dot, radians, degrees, size, shape, cov, log, linalg, random as rd import numpy as np import random import math import sys import time ###################################################################################### ## Joint Limits ## # create joint limit dicts # if arm == 'r': # max_lim = np.radians([ 120.00, 122.15, 77.5, 144., 122., 45., 45.]) # min_lim = np.radians([ -47.61, -20., -77.5, 0., -80., -45., -45.]) # else: # max_lim = np.radians([ 120.00, 20., 77.5, 144., 80., 45., 45.]) # min_lim = np.radians([ -47.61, -122.15, -77.5, 0., -122., -45., -45.]) ## Velocity Limits ## # 5 deg/s to 15 deg/s # Optimization Criteria # d-optimality criterion : -log(det(M)) : M = covariance matrix of regressor matrix ##################################################################################### def get_init_condition(vl, vh, tf, wf, N): qdot_list = [] t = arange(0,tf,0.1) M = size(t) A = zeros((M,2*N+1)) for i in range(M): qdot_list.append(radians(random.randrange(vl,vh))) for i in range(M): j = 0 k = 1 while (j < 2*N): A[i][j] = (math.sin(wf*t[i]*k))/(wf*k) A[i][j+1] = (math.cos(wf*t[i]*k))/(wf*k) k=k+1 j=j+2 A[i][j] = 1 ans = dot((array(((matrix(A).T)*matrix(A)).I)*(matrix(A).T)),array(qdot_list)) return ans.tolist() def get_ref_traj_at_time(x, time, num_dof, N): wf = 0.1*2*math.pi q = [] qd = [] qdd = [] for j in xrange(num_dof): q_buff = x[(j+1)*(2*N)] qd_buff = 0. qdd_buff = 0. for k in xrange(N): t = time a = x[j*(2*N+1)+2*k] b = x[j*(2*N+1)+2*k+1] q_buff = q_buff + a*(math.sin(wf*t*(k+1)))/(wf*(k+1)) - b*(math.cos(wf*t*(k+1)))/(wf*(k+1)) qd_buff = qd_buff + a*(math.cos(wf*t*(k+1))) - b*(math.sin(wf*t*(k+1))) qdd_buff = qdd_buff + a*(wf*(k+1))*(math.sin(wf*t*(k+1))) + b*(wf*(k+1))*(math.cos(wf*t*(k+1))) q.append(q_buff) qd.append(qd_buff) qdd.append(qdd_buff) return q, qd, qdd def get_ref_traj(x, t_total, rate, num_dof, N): wf = 0.1*2*math.pi num_samples = int(t_total/float(rate)) q = [] qd = [] qdd = [] # print "x is :", x # print "num_samples is :", num_samples for i in xrange(num_samples): q_cur = [] qd_cur = [] qdd_cur = [] for j in xrange(num_dof): q_buff = x[(j+1)*(2*N)] # print "j is :", j # print "x[(j+1)*(2*N)]", x[(j+1)*(2*N)] qd_buff = 0. qdd_buff = 0. for k in xrange(N): t = i*rate a = x[j*(2*N+1)+2*k] b = x[j*(2*N+1)+2*k+1] if False: print "t is :", t print "a is :", a print "b is :", b print "wf is :", wf print "k is :", k print "(wf*t*(k+1))", (wf*t*(k+1)) q_buff = q_buff + a*(math.sin(wf*t*(k+1)))/(wf*(k+1)) - b*(math.cos(wf*t*(k+1)))/(wf*(k+1)) qd_buff = qd_buff + a*(math.cos(wf*t*(k+1))) - b*(math.sin(wf*t*(k+1))) qdd_buff = qdd_buff + a*(wf*(k+1))*(math.sin(wf*t*(k+1))) + b*(wf*(k+1))*(math.cos(wf*t*(k+1))) #raw_input() q_cur.append(q_buff) qd_cur.append(qd_buff) qdd_cur.append(qdd_buff) q.append(q_cur) qd.append(qd_cur) qdd.append(qdd_cur) # pp.figure() # pp.plot(q) # pp.show() #time.sleep(5) return q, qd, qdd # def get_ref_traj(init_delta, t, num_dof): # wf = 0.1 # N = 5 # numJoints = num_dof # q_list = [] # qdot_list = [] # qddot_list = [] # rows = numJoints # columns = 2*N + 1 # temp_delta = zeros((rows, columns)) # for row in range(rows): # for column in range(columns): # temp_delta[row][column] = init_delta[row*columns+column] # for row in range(rows): # delta = temp_delta[row].tolist() # q_element = 0.0 # qdot_element = 0.0 # qddot_element = 0.0 # j = 0 # k = 1 # while (j < 2*N): # q_element = q_element + delta[j]*(math.sin(wf*t*k))/(wf*k) - delta[j+1]*(math.cos(wf*t*k))/(wf*k) # qdot_element = qdot_element + delta[j]*(math.cos(wf*t*k)) + delta[j+1]*(math.sin(wf*t*k)) # qdfdot_element = qddot_element - delta[j]*(wf*k)*(math.sin(wf*t*k)) + delta[j+1]*(wf*k)*(math.cos(wf*t*k)) # k=k+1 # j=j+2 # q_list.append(q_element + delta[2*N]) # qdot_list.append(qdot_element) # qddot_list.append(qddot_element) # return q_list, qdot_list, qddot_list if __name__ == '__main__': print "hello. world."

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General/serverInfo.py

Aggis15/T4NK0R

0

195310

import logging import discord from discord.ext import commands from discord.commands import slash_command import json import requests from PIL import Image, ImageDraw, ImageFont from resizeimage import resizeimage # Logging logging.basicConfig( filename="./logs/discordlogs.log", filemode="w", format="%(name)s - %(levelname)s - %(message)s", ) logger = logging.getLogger() logger.setLevel(logging.INFO) # Initiate json file = open("config.json") data = json.load(file) # Public variables guildID = data["guildID"][0] class serverInfo(commands.Cog): def __init__(self, bot): self.bot = bot @slash_command( guild_ids=[guildID], description="A command to check the server information!" ) async def server(self, ctx): guild = ctx.guild serverLevel = guild.premium_tier serverBoosts = guild.premium_subscription_count serverMembers = len(guild.members) serverTextChannels = len(guild.text_channels) serverVoiceChannels = len(guild.voice_channels) serverEmojis = len(guild.emojis) serverRoles = len(guild.roles) # Download the avatar getGuildAvatar = requests.get(guild.icon.url) with open(f"./Images/avatarCache/{guild.id}.png", "wb") as outfile: outfile.write(getGuildAvatar.content) guildAvatarImage = Image.open(f"./Images/avatarCache/{guild.id}.png") # Crop the avatar to make it a circle width, height = guildAvatarImage.size x = width - height img_cropped = guildAvatarImage.crop((x, 0, x + height, height)) mask = Image.new("L", img_cropped.size) mask_draw = ImageDraw.Draw(mask) width, height = img_cropped.size mask_draw.ellipse((0, 0, width, height), fill=255) img_cropped.putalpha(mask) img_cropped.save(f"./Images/avatarCache/{guild.id}.png") # Resize the avatar to fit the image resizeAvatar = Image.open(f"./Images/avatarCache/{guild.id}.png") resizeAvatar = resizeimage.resize_width(resizeAvatar, 100) resizeAvatar.save(f"./Images/avatarCache/{guild.id}.png", resizeAvatar.format) defaultImage = Image.open("./Images/serverInfo.png") draw = ImageDraw.Draw(defaultImage) font = ImageFont.truetype("Bungee-Regular.ttf", 36) draw.text((438, 38), str(serverLevel), (157, 156, 157), font=font) draw.text((332, 85), str(serverBoosts), (157, 156, 157), font=font) draw.text((246, 148), str(serverMembers), (157, 156, 157), font=font) draw.text((370, 199), str(serverTextChannels), (157, 156, 157), font=font) draw.text((394, 243), str(serverVoiceChannels), (157, 156, 157), font=font) draw.text((388, 288), str(serverEmojis), (157, 156, 157), font=font) draw.text((357, 334), str(serverRoles), (157, 156, 157), font=font) avatarImage = Image.open(f"./Images/avatarCache/{guild.id}.png") defaultImage.paste(avatarImage, (40, 40), avatarImage) defaultImage.save("./Images/serverImageReady.png") await ctx.respond(file=discord.File("./Images/serverImageReady.png")) logger.info( f"{ctx.author.name} with ID: {ctx.author.id} has used the info slash command" ) @server.error async def server_error(self, ctx, error): await ctx.respond(f"`{error}`") def setup(bot): bot.add_cog(serverInfo(bot))

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tests/test_mangling.py

ecoinvent/brightway2-parameters

0

23145

from bw2parameters import * def test_mangle_formula(): given = "log(foo * bar) + 7 / baz" prefix = "pre" assert mangle_formula(given, prefix, ['bar']) == '(log((pre__foo * bar)) + (7 / pre__baz))' def test_prefix_parameter_dict(): given = { 'a': {'formula': 'a + b / c', 'foo': True}, 'b': {'formula': '2 * a - exp(7 - b)'}, 'catch': {} } expected = { 't_a': {'formula': '(t_a + (t_b / c))', 'foo': True, 'original': 'a'}, 't_b': {'formula': '((2 * t_a) - exp((7 - t_b)))', 'original': 'b'}, 't_catch': {'original': 'catch'} } substitutions = {'a': 't_a', 'b': 't_b', 'catch': 't_catch'} assert prefix_parameter_dict(given, "t_") == (expected, substitutions) def test_chain_prefix_parameter_dict(): given = {'a': {'formula': 'a + b / c'}} g_copy = {'a': {'formula': 'a + b / c'}} expected = { 't_a': {'formula': '(t_a + (b / c))', 'original': 'a'}, } substitutions = {'a': 't_a'} assert prefix_parameter_dict(given, "t_") == (expected, substitutions) assert given == g_copy given, _ = prefix_parameter_dict(given, "t_") s1 = {'b': 'dog'} r1 = substitute_in_formulas(given, s1) expected = {'t_a': {'formula': '(t_a + (dog / c))', 'original': 'a'}} assert r1 == expected s2 = {'c': 'cat'} r2 = substitute_in_formulas(r1, s2) expected = {'t_a': {'formula': '(t_a + (dog / cat))', 'original': 'a'}} assert r2 == expected

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designer/new_dialog.py

katnino/kivy-designer

0

100068

from functools import partial from os.path import join from designer.helper_functions import get_kd_dir from kivy.adapters.listadapter import ListAdapter from kivy.core.window import Keyboard, Window from kivy.factory import Factory from kivy.properties import NumericProperty, ObjectProperty from kivy.uix.boxlayout import BoxLayout from kivy.uix.image import Image from kivy.uix.listview import ListView NEW_PROJECTS = { 'FloatLayout': ('template_floatlayout_kv', 'template_floatlayout_py'), 'BoxLayout': ('template_boxlayout_kv', 'template_boxlayout_py'), 'ScreenManager': ('template_screen_manager_kv', 'template_screen_manager_py'), 'ActionBar': ('template_actionbar_kv', 'template_actionbar_py'), 'Carousel and ActionBar': ('template_actionbar_carousel_kv', 'template_actionbar_carousel_py'), 'ScreenManager and ActionBar': ('template_screen_manager_actionbar_kv', 'template_screen_manager_actionbar_py'), 'TabbedPanel': ('template_tabbed_panel_kv', 'template_tabbed_panel_py'), 'TextInput and ScrollView': ('template_textinput_scrollview_kv', 'template_textinput_scrollview_py')} NEW_TEMPLATES_DIR = 'new_templates' NEW_TEMPLATE_IMAGE_PATH = join(NEW_TEMPLATES_DIR, 'images') class NewProjectDialog(BoxLayout): listview = ObjectProperty(None) ''':class:`~kivy.uix.listview.ListView` used for showing file paths. :data:`listview` is a :class:`~kivy.properties.ObjectProperty` ''' select_button = ObjectProperty(None) ''':class:`~kivy.uix.button.Button` used to select the list item. :data:`select_button` is a :class:`~kivy.properties.ObjectProperty` ''' cancel_button = ObjectProperty(None) ''':class:`~kivy.uix.button.Button` to cancel the dialog. :data:`cancel_button` is a :class:`~kivy.properties.ObjectProperty` ''' adapter = ObjectProperty(None) ''':class:`~kivy.uix.listview.ListAdapter` used for selecting files. :data:`adapter` is a :class:`~kivy.properties.ObjectProperty` ''' image = ObjectProperty(None) '''Type of :class:`~kivy.uix.image.Image` to display image of selected new template. :data:`image` is a :class:`~kivy.properties.ObjectProperty` ''' list_parent = ObjectProperty(None) '''Parent of listview. :data:`list_parent` is a :class:`~kivy.properties.ObjectProperty` ''' prev_selection = NumericProperty(0) '''to memorize the previous selection. :attr:`prev_selection` is a :class: `~kivy.properties.NumericProperty`, defaults to (0). ''' __events__ = ('on_select', 'on_cancel') def __init__(self, **kwargs): super(NewProjectDialog, self).__init__(**kwargs) item_strings = list(NEW_PROJECTS.keys()) item_strings.sort() self.adapter = ListAdapter(cls=Factory.DesignerListItemButton, data=item_strings, selection_mode='single', allow_empty_selection=False) self.adapter.check_for_empty_selection = self.check_for_empty_selection self.adapter.bind(on_selection_change=self.on_adapter_selection_change) self.listview = ListView(adapter=self.adapter) self.listview.size_hint = (0.5, 1) self.listview.pos_hint = {'top': 1} self.list_parent.add_widget(self.listview, 1) self.on_adapter_selection_change(self.adapter) def on_parent(self, *args): if self.parent: Window.bind(on_key_down=self._on_keyboard_down) else: Window.unbind(on_key_down=self._on_keyboard_down) def _on_keyboard_down(self, keyboard, key, codepoint, text, modifier, *args): '''To detect which key is pressed ''' if modifier: return False key_str = Keyboard.keycode_to_string(Window._system_keyboard, key) if key_str == 'up': v = self.adapter.get_view(self.prev_selection - 1) if v is not None: self.adapter.handle_selection(v) return True if key_str == 'down': v = self.adapter.get_view(self.prev_selection + 1) if v is not None: self.adapter.handle_selection(v) return True if key_str == 'enter': self.dispatch('on_select') return True def check_for_empty_selection(self, *args): if not self.adapter.allow_empty_selection: if len(self.adapter.selection) == 0: # Select the first item if we have it. v = self.adapter.get_view(self.prev_selection) if v is not None: self.adapter.handle_selection(v) def on_adapter_selection_change(self, adapter): '''Event handler for 'on_selection_change' event of adapter. ''' name = adapter.selection[0].text.lower() + '.png' name = name.replace(' and ', '_') image_source = join(NEW_TEMPLATE_IMAGE_PATH, name) _dir = get_kd_dir() image_source = join(_dir, image_source) parent = self.image.parent parent.remove_widget(self.image) self.image = Image(source=image_source) parent.add_widget(self.image) self.prev_selection = adapter.data.index(adapter.selection[0].text) def on_touch_down(self, touch): '''Used to determine where touch is down and to detect double tap. ''' if touch.is_double_tap: self.dispatch('on_select') return super(NewProjectDialog, self).on_touch_down(touch) def on_select(self, *args): '''Default Event Handler for 'on_select' event ''' pass def on_cancel(self, *args): '''Default Event Handler for 'on_cancel' event ''' pass def on_select_button(self, *args): '''Event Handler for 'on_release' of select button. 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Extensions/Dependencies/tools/extract_objects.py

kylefmohr/Pyto

0

1605021

from subprocess import check_output import sys objects = check_output(["ar", "-t", sys.argv[1]]).decode().split("\n") for object in objects: if not object.endswith(".o") or object.endswith("lso.o"): continue check_output(["ar", "-xv", sys.argv[1], object])

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node-api/get-block-transfers/request.py

Venoox/casper-integrations

5

11051

<reponame>Venoox/casper-integrations import json import os import pycspr # A known casper test-net node address. _NODE_ADDRESS = os.getenv("CASPER_NODE_ADDRESS", "192.168.127.12") # A known block hash. _BLOCK_HASH: bytes = bytes.fromhex("c7148e1e2e115d8fba357e04be2073d721847c982dc70d5c36b5f6d3cf66331c") # A known block height. _BLOCK_HEIGHT: int = 20652 def main(): """Retrieves transfers by block. """ # Set client. client = pycspr.NodeClient(pycspr.NodeConnectionInfo(host=_NODE_ADDRESS)) # Set block by known hash. block_transers_1: tuple = client.queries.get_block_transfers(_BLOCK_HASH) # Set block by known height. block_transers_2: tuple = client.queries.get_block_transfers(_BLOCK_HEIGHT) # Verify block information equivalence. assert block_transers_1 == block_transers_2 print("-----------------------------------------------------------------------------------------------------") print(f"QUERIED TEST-NET NODE {_NODE_ADDRESS}") print("-----------------------------------------------------------------------------------------------------") print(f"Block transfers = {json.dumps(block_transers_1, indent=4)}") print("-----------------------------------------------------------------------------------------------------") if __name__ == "__main__": try: main() except Exception as err: print(f"API ERROR @ NODE {_NODE_ADDRESS} :: {err}")

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setup.py

saquib-mehmood/pyproblib

1

186089

<reponame>saquib-mehmood/pyproblib from setuptools import setup setup(name='pyproblib', version='1.1', description='Probability Distribution Functions', packages=['pyproblib'], author='<NAME>', author_email='<EMAIL>', zip_safe=False)

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aioinject/__init__.py

ThirVondukr/aioinject

4

176713

from .containers import Container from .context import InjectionContext, SyncInjectionContext from .decorators import inject from .markers import Inject from .providers import Callable, Object, Provider, Singleton __all__ = [ "Container", "InjectionContext", "SyncInjectionContext", "inject", "Inject", "Callable", "Provider", "Singleton", "Object", ]

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zinc/utils/validation.py

PressLabs/zinc

29

37359

<gh_stars>10-100 import ipaddress def is_ipv6(ip_addr): try: ipaddress.IPv6Address(ip_addr) return True except ipaddress.AddressValueError: return False

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resistics/window.py

resistics/resistics

38

193431

""" Module for calculating window related data. Windows can be indexed relative to two starting indices. - Local window index - Window index relative to the TimeData is called "local_win" - Local window indices always start at 0 - Global window index - The global window index is relative to the project reference time - The 0 index window begins at the reference time - This window indexing is to synchronise data across sites The global window index is considered the default and sometimes referred to as the window. Local windows should be explicitly referred to as local_win in all cases. The window module includes functionality to do the following: - Windowing utility functions to calculate window and overlap sizes - Functions to map windows to samples in TimeData - Converting a global index array to datetime Usually with windowing, there is a window size and windows overlap with each other for a set number of samples. As an illustrative examples, consider a signal sampled at 10 Hz (dt=0.1 seconds) with 24 samples. This will be windowed using a window size of 8 samples per window and a 2 sample overlap. .. plot:: >>> import numpy as np >>> import matplotlib.pyplot as plt >>> fs = 10 >>> n_samples = 24 >>> win_size = 8 >>> olap_size = 2 >>> times = np.arange(0, n_samples) * (1/fs) The first window >>> start_win1 = 0 >>> end_win1 = win_size >>> win1_times = times[start_win1:end_win1] The second window >>> start_win2 = end_win1 - olap_size >>> end_win2 = start_win2 + win_size >>> win2_times = times[start_win2:end_win2] The third window >>> start_win3 = end_win2 - olap_size >>> end_win3 = start_win3 + win_size >>> win3_times = times[start_win3:end_win3] The fourth window >>> start_win4= end_win3 - olap_size >>> end_win4 = start_win4 + win_size >>> win4_times = times[start_win4:end_win4] Let's look at the actual window times for each window >>> win1_times array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]) >>> win2_times array([0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2, 1.3]) >>> win3_times array([1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9]) >>> win4_times array([1.8, 1.9, 2. , 2.1, 2.2, 2.3]) The duration and increments of windows can be calculated using provided methods >>> from resistics.window import win_duration, inc_duration >>> print(win_duration(win_size, fs)) 0:00:00.7 >>> print(inc_duration(win_size, olap_size, fs)) 0:00:00.6 Plot the windows to give an illustration of how it works >>> plt.plot(win1_times, np.ones_like(win1_times), "bo", label="window1") # doctest: +SKIP >>> plt.plot(win2_times, np.ones_like(win2_times)*2, "ro", label="window2") # doctest: +SKIP >>> plt.plot(win3_times, np.ones_like(win3_times)*3, "go", label="window3") # doctest: +SKIP >>> plt.plot(win4_times, np.ones_like(win4_times)*4, "co", label="window4") # doctest: +SKIP >>> plt.xlabel("Time [s]") # doctest: +SKIP >>> plt.legend() # doctest: +SKIP >>> plt.grid() # doctest: +SKIP >>> plt.tight_layout() # doctest: +SKIP >>> plt.show() # doctest: +SKIP """ from loguru import logger from pathlib import Path from typing import Optional, List, Tuple, Dict, Union, Any from pydantic import PositiveInt import numpy as np import pandas as pd from resistics.errors import ProcessRunError from resistics.common import History, ResisticsModel, ResisticsData, ResisticsProcess from resistics.common import ResisticsWriter, Metadata, WriteableMetadata from resistics.sampling import RSDateTime, RSTimeDelta, HighResDateTime from resistics.time import ChanMetadata from resistics.decimate import DecimatedLevelMetadata, DecimatedData def win_duration(win_size: int, fs: float) -> RSTimeDelta: """ Get the window duration Parameters ---------- win_size : int Window size in samples fs : float Sampling frequency Hz Returns ------- RSTimeDelta Duration Examples -------- A few examples with different sampling frequencies and window sizes >>> from resistics.window import win_duration >>> duration = win_duration(512, 512) >>> print(duration) 0:00:00.998046875 >>> duration = win_duration(520, 512) >>> print(duration) 0:00:01.013671875 >>> duration = win_duration(4096, 16_384) >>> print(duration) 0:00:00.24993896484375 >>> duration = win_duration(200, 0.05) >>> print(duration) 1:06:20 """ from resistics.sampling import to_timedelta return to_timedelta(1 / fs) * float(win_size - 1) def inc_duration(win_size: int, olap_size: int, fs: float) -> RSTimeDelta: """ Get the increment between window start times If the overlap size = 0, then the time increment between windows is simply the window duration. However, when there is an overlap, the increment between window start times has to be adjusted by the overlap size Parameters ---------- win_size : int The window size in samples olap_size : int The overlap size in samples fs : float The sample frequency Hz Returns ------- RSTimeDelta The duration of the window Examples -------- >>> from resistics.window import inc_duration >>> increment = inc_duration(128, 32, 128) >>> print(increment) 0:00:00.75 >>> increment = inc_duration(128*3600, 128*60, 128) >>> print(increment) 0:59:00 """ from resistics.sampling import to_timedelta return to_timedelta(1 / fs) * float(win_size - olap_size) def win_to_datetime( ref_time: RSDateTime, global_win: int, increment: RSTimeDelta ) -> RSDateTime: """ Convert reference window index to start time of window Parameters ---------- ref_time : RSDateTime Reference time global_win : int Window index relative to reference time increment : RSTimeDelta The increment duration Returns ------- RSDateTime Start time of window Examples -------- An example with sampling at 1 Hz, a window size of 100 and an overlap size of 25. >>> from resistics.sampling import to_datetime >>> from resistics.window import inc_duration, win_to_datetime >>> ref_time = to_datetime("2021-01-01 00:00:00") >>> fs = 1 >>> win_size = 60 >>> olap_size = 15 >>> increment = inc_duration(win_size, olap_size, fs) >>> print(increment) 0:00:45 The increment is the time increment between the start of time one window and the succeeding window. >>> print(win_to_datetime(ref_time, 0, increment)) 2021-01-01 00:00:00 >>> print(win_to_datetime(ref_time, 1, increment)) 2021-01-01 00:00:45 >>> print(win_to_datetime(ref_time, 2, increment)) 2021-01-01 00:01:30 >>> print(win_to_datetime(ref_time, 3, increment)) 2021-01-01 00:02:15 """ return ref_time + (global_win * increment) def datetime_to_win( ref_time: RSDateTime, time: RSDateTime, increment: RSTimeDelta, method: str = "round", ) -> int: """ Convert a datetime to a global window index Parameters ---------- ref_time : RSDateTime Reference time time : RSDateTime Datetime to convert increment : RSTimeDelta The increment duration method : str, optional Method for dealing with float results, by default "round" Returns ------- int The global window index i.e. the window index relative to the reference time Raises ------ ValueError If time < ref_time Examples -------- A simple example to show the logic >>> from resistics.sampling import to_datetime, to_timedelta >>> from resistics.window import datetime_to_win, win_to_datetime, inc_duration >>> ref_time = to_datetime("2021-01-01 00:00:00") >>> time = to_datetime("2021-01-01 00:01:00") >>> increment = to_timedelta(60) >>> global_win = datetime_to_win(ref_time, time, increment) >>> global_win 1 >>> print(win_to_datetime(ref_time, global_win, increment)) 2021-01-01 00:01:00 A more complex logic with window sizes, overlap sizes and sampling frequencies >>> fs = 128 >>> win_size = 256 >>> olap_size = 64 >>> ref_time = to_datetime("2021-03-15 00:00:00") >>> time = to_datetime("2021-04-17 18:00:00") >>> increment = inc_duration(win_size, olap_size, fs) >>> print(increment) 0:00:01.5 >>> global_win = datetime_to_win(ref_time, time, increment) >>> global_win 1944000 >>> print(win_to_datetime(ref_time, global_win, increment)) 2021-04-17 18:00:00 In this scenario, explore the use of rounding >>> time = to_datetime("2021-04-17 18:00:00.50") >>> global_win = datetime_to_win(ref_time, time, increment, method = "floor") >>> global_win 1944000 >>> print(win_to_datetime(ref_time, global_win, increment)) 2021-04-17 18:00:00 >>> global_win = datetime_to_win(ref_time, time, increment, method = "ceil") >>> global_win 1944001 >>> print(win_to_datetime(ref_time, global_win, increment)) 2021-04-17 18:00:01.5 >>> global_win = datetime_to_win(ref_time, time, increment, method = "round") >>> global_win 1944000 >>> print(win_to_datetime(ref_time, global_win, increment)) 2021-04-17 18:00:00 Another example with a window duration of greater than a day >>> fs = 4.8828125e-05 >>> win_size = 64 >>> olap_size = 16 >>> ref_time = to_datetime("1985-07-18 01:00:20") >>> time = to_datetime("1985-09-22 23:00:00") >>> increment = inc_duration(win_size, olap_size, fs) >>> print(increment) 11 days, 9:04:00 >>> global_win = datetime_to_win(ref_time, time, increment) >>> global_win 6 >>> print(win_to_datetime(ref_time, global_win, increment)) 1985-09-24 07:24:20 This time is greater than the time that was transformed to global window, 1985-09-22 23:00:00. Try again, this time with the floor option. >>> global_win = datetime_to_win(ref_time, time, increment, method="floor") >>> global_win 5 >>> print(win_to_datetime(ref_time, global_win, increment)) 1985-09-12 22:20:20 """ from math import floor, ceil from resistics.sampling import to_seconds if time < ref_time: raise ValueError(f"Time {str(time)} < reference time {str(ref_time)}") increment_days_in_seconds, increment_remaining_in_seconds = to_seconds(increment) increment_seconds = increment_days_in_seconds + increment_remaining_in_seconds delta_days_in_seconds, delta_remaining_in_seconds = to_seconds(time - ref_time) delta_total_in_seconds = delta_days_in_seconds + delta_remaining_in_seconds n_increments = delta_total_in_seconds / increment_seconds if n_increments.is_integer(): n_increments = int(n_increments) elif method == "floor": n_increments = int(floor(n_increments)) elif method == "ceil": n_increments = int(ceil(n_increments)) else: n_increments = int(round(n_increments)) return n_increments def get_first_and_last_win( ref_time: RSDateTime, metadata: DecimatedLevelMetadata, win_size: int, olap_size: int, ) -> Tuple[int, int]: """ Get first and last window for a decimated data level .. note:: For the last window, on initial calculation this may be one or a maximum of two windows beyond the last time. The last window is adjusted in this function. Two windows may occur when the time of the last sample is in the overlap of the final two windows. Parameters ---------- ref_time : RSDateTime The reference time metadata : DecimatedLevelMetadata Metadata for the decimation level win_size : int Window size in samples olap_size : int Overlap size in samples Returns ------- Tuple[int, int] First and last global windows. This is window indices relative to the reference time Raises ------ ValueError If unable to calculate the last window correctly as this will result in an incorrect number of windows Examples -------- Get the first and last window for the first decimation level in a decimated data instance. >>> from resistics.testing import decimated_data_random >>> from resistics.sampling import to_datetime >>> from resistics.window import get_first_and_last_win, win_to_datetime >>> from resistics.window import win_duration, inc_duration >>> ref_time = to_datetime("2021-01-01 00:00:00") >>> dec_data = decimated_data_random(fs=0.1, first_time="2021-01-01 00:05:10", n_samples=100, factor=10) Get the metadata for decimation level 0 >>> level_metadata = dec_data.metadata.levels_metadata[0] >>> level_metadata.summary() { 'fs': 10.0, 'n_samples': 10000, 'first_time': '2021-01-01 00:05:10.000000_000000_000000_000000', 'last_time': '2021-01-01 00:21:49.899999_999999_977300_000000' } .. note:: As a point of interest, note how the last time is actually slightly incorrect. This is due to machine precision issues described in more detail here https://docs.python.org/3/tutorial/floatingpoint.html. Whilst there is value in using the high resolution datetime format for high sampling rates, there is a tradeoff. Such are the perils of floating point arithmetic. The next step is to calculate the first and last window, relative to the reference time >>> win_size = 100 >>> olap_size = 25 >>> first_win, last_win = get_first_and_last_win(ref_time, level_metadata, win_size, olap_size) >>> print(first_win, last_win) 42 173 These window indices can be converted to start times of the windows. The last window is checked to make sure it does not extend past the end of the time data. First get the window duration and increments. >>> duration = win_duration(win_size, level_metadata.fs) >>> print(duration) 0:00:09.9 >>> increment = inc_duration(win_size, olap_size, level_metadata.fs) >>> print(increment) 0:00:07.5 Now calculate the times of the windows >>> first_win_start_time = win_to_datetime(ref_time, 42, increment) >>> last_win_start_time = win_to_datetime(ref_time, 173, increment) >>> print(first_win_start_time, last_win_start_time) 2021-01-01 00:05:15 2021-01-01 00:21:37.5 >>> print(last_win_start_time + duration) 2021-01-01 00:21:47.4 >>> print(level_metadata.last_time) 2021-01-01 00:21:49.8999999999999773 >>> level_metadata.last_time > last_win_start_time + increment True """ duration = win_duration(win_size, metadata.fs) increment = inc_duration(win_size, olap_size, metadata.fs) first_win = datetime_to_win(ref_time, metadata.first_time, increment, method="ceil") last_win = datetime_to_win(ref_time, metadata.last_time, increment, method="floor") # adjust if there is not enough date to complete the last window last_win_time = win_to_datetime(ref_time, last_win, increment) for attempt in range(2): if metadata.last_time >= last_win_time + duration: break logger.debug(f"Adjusting last window attempt {attempt + 1}") last_win -= 1 last_win_time = win_to_datetime(ref_time, last_win, increment) if metadata.last_time < last_win_time + duration: raise ValueError("Unable to correctly get the last window") return first_win, last_win def get_win_starts( ref_time: RSDateTime, win_size: int, olap_size: int, fs: float, n_wins: int, index_offset: int, ) -> pd.DatetimeIndex: """ Get window start times This is a useful for getting the timestamps for the windows in a dataset Parameters ---------- ref_time : RSDateTime The reference time win_size : int The window size olap_size : int The overlap size fs : float The sampling frequency n_wins : int The number of windows index_offset : int The index offset from the reference time Returns ------- pd.DatetimeIndex The start times of the windows Examples -------- >>> import pandas as pd >>> from resistics.sampling import to_datetime >>> from resistics.window import get_win_starts >>> ref_time = to_datetime("2021-01-01 00:00:00") >>> win_size = 100 >>> olap_size = 25 >>> fs = 10 >>> n_wins = 3 >>> index_offset = 480 >>> starts = get_win_starts(ref_time, win_size, olap_size, fs, n_wins, index_offset) >>> pd.Series(starts) 0 2021-01-01 01:00:00.000 1 2021-01-01 01:00:07.500 2 2021-01-01 01:00:15.000 dtype: datetime64[ns] """ from resistics.sampling import datetime_array_estimate increment = inc_duration(win_size, olap_size, fs) first_win_time = win_to_datetime(ref_time, index_offset, increment) increment_size = win_size - olap_size return datetime_array_estimate(first_win_time, fs / increment_size, n_wins) def get_win_ends( starts: pd.DatetimeIndex, win_size: int, fs: float, ) -> pd.DatetimeIndex: """ Get window end times Parameters ---------- starts : RSDateTime The start times of the windows win_size : int The window size fs : float The sampling frequency Returns ------- pd.DatetimeIndex The end times of the windows Examples -------- >>> import pandas as pd >>> from resistics.sampling import to_datetime >>> from resistics.window import get_win_starts, get_win_ends >>> ref_time = to_datetime("2021-01-01 00:00:00") >>> win_size = 100 >>> olap_size = 25 >>> fs = 10 >>> n_wins = 3 >>> index_offset = 480 >>> starts = get_win_starts(ref_time, win_size, olap_size, fs, n_wins, index_offset) >>> pd.Series(starts) 0 2021-01-01 01:00:00.000 1 2021-01-01 01:00:07.500 2 2021-01-01 01:00:15.000 dtype: datetime64[ns] >>> ends = get_win_ends(starts, win_size, fs) >>> pd.Series(ends) 0 2021-01-01 01:00:09.900 1 2021-01-01 01:00:17.400 2 2021-01-01 01:00:24.900 dtype: datetime64[ns] """ return starts + pd.Timedelta((win_size - 1) * (1 / fs), "s") def get_win_table( ref_time: RSDateTime, metadata: DecimatedLevelMetadata, win_size: int, olap_size: int, ) -> pd.DataFrame: """ Get a DataFrame with Parameters ---------- ref_time : RSDateTime Reference metadata : DecimatedLevelMetadata Metadata for the decimation level win_size : int The window size olap_size : int The overlap size Returns ------- pd.DataFrame A pandas DataFrame with details about each window Examples -------- .. plot:: :width: 90% >>> import matplotlib.pyplot as plt >>> from resistics.decimate import DecimatedLevelMetadata >>> from resistics.sampling import to_datetime, to_timedelta >>> from resistics.window import get_win_table >>> ref_time = to_datetime("2021-01-01 00:00:00") >>> fs = 10 >>> n_samples = 1000 >>> first_time = to_datetime("2021-01-01 01:00:00") >>> last_time = first_time + to_timedelta((n_samples-1)/fs) >>> metadata = DecimatedLevelMetadata(fs=10, n_samples=1000, first_time=first_time, last_time=last_time) >>> print(metadata.fs, metadata.first_time, metadata.last_time) 10.0 2021-01-01 01:00:00 2021-01-01 01:01:39.9 >>> win_size = 100 >>> olap_size = 25 >>> df = get_win_table(ref_time, metadata, win_size, olap_size) >>> print(df.to_string()) global local from_sample to_sample win_start win_end 0 480 0 0 99 2021-01-01 01:00:00.000 2021-01-01 01:00:09.900 1 481 1 75 174 2021-01-01 01:00:07.500 2021-01-01 01:00:17.400 2 482 2 150 249 2021-01-01 01:00:15.000 2021-01-01 01:00:24.900 3 483 3 225 324 2021-01-01 01:00:22.500 2021-01-01 01:00:32.400 4 484 4 300 399 2021-01-01 01:00:30.000 2021-01-01 01:00:39.900 5 485 5 375 474 2021-01-01 01:00:37.500 2021-01-01 01:00:47.400 6 486 6 450 549 2021-01-01 01:00:45.000 2021-01-01 01:00:54.900 7 487 7 525 624 2021-01-01 01:00:52.500 2021-01-01 01:01:02.400 8 488 8 600 699 2021-01-01 01:01:00.000 2021-01-01 01:01:09.900 9 489 9 675 774 2021-01-01 01:01:07.500 2021-01-01 01:01:17.400 10 490 10 750 849 2021-01-01 01:01:15.000 2021-01-01 01:01:24.900 11 491 11 825 924 2021-01-01 01:01:22.500 2021-01-01 01:01:32.400 12 492 12 900 999 2021-01-01 01:01:30.000 2021-01-01 01:01:39.900 Plot six windows to illustrate the overlap >>> plt.figure(figsize=(8, 3)) # doctest: +SKIP >>> for idx, row in df.iterrows(): ... color = "red" if idx%2 == 0 else "blue" ... plt.axvspan(row.loc["win_start"], row.loc["win_end"], alpha=0.5, color=color) # doctest: +SKIP ... if idx > 5: ... break >>> plt.tight_layout() # doctest: +SKIP >>> plt.show() # doctest: +SKIP """ from resistics.sampling import to_n_samples, datetime_array_estimate increment_size = win_size - olap_size increment = inc_duration(win_size, olap_size, metadata.fs) fs = metadata.fs first_time = metadata.first_time first_win, last_win = get_first_and_last_win( ref_time, metadata, win_size, olap_size ) first_win_time = win_to_datetime(ref_time, first_win, increment) n_wins = last_win - first_win + 1 local_wins = np.arange(n_wins).astype(int) # samples - use the floor here to avoid a situation where it is rounded up # and then there are insufficient samples first_sample = to_n_samples(first_win_time - first_time, fs, method="floor") - 1 starts = datetime_array_estimate(first_win_time, fs / increment_size, n_wins) ends = get_win_ends(starts, win_size, fs) df_dict = { "global": np.arange(first_win, last_win + 1), "local": local_wins, "from_sample": first_sample + (local_wins * increment_size), "to_sample": first_sample + win_size - 1 + (local_wins * increment_size), "win_start": starts, "win_end": ends, } return pd.DataFrame(data=df_dict) class WindowParameters(ResisticsModel): """ Windowing parameters per decimation level Windowing parameters are the window and overlap size for each decimation level. Parameters ---------- n_levels : int The number of decimation levels min_n_wins : int Minimum number of windows win_sizes : List[int] The window sizes per decimation level olap_sizes : List[int] The overlap sizes per decimation level Examples -------- Generate decimation and windowing parameters for data sampled at 4096 Hz. Note that requesting window sizes or overlap sizes for decimation levels that do not exist will raise a ValueError. >>> from resistics.decimate import DecimationSetup >>> from resistics.window import WindowSetup >>> dec_setup = DecimationSetup(n_levels=3, per_level=3) >>> dec_params = dec_setup.run(4096) >>> dec_params.summary() { 'fs': 4096.0, 'n_levels': 3, 'per_level': 3, 'min_samples': 256, 'eval_freqs': [ 1024.0, 724.0773439350246, 512.0, 362.0386719675123, 256.0, 181.01933598375615, 128.0, 90.50966799187808, 64.0 ], 'dec_factors': [1, 2, 8], 'dec_increments': [1, 2, 4], 'dec_fs': [4096.0, 2048.0, 512.0] } >>> win_params = WindowSetup().run(dec_params.n_levels, dec_params.dec_fs) >>> win_params.summary() { 'n_levels': 3, 'min_n_wins': 5, 'win_sizes': [1024, 512, 128], 'olap_sizes': [256, 128, 32] } >>> win_params.get_win_size(0) 1024 >>> win_params.get_olap_size(0) 256 >>> win_params.get_olap_size(3) Traceback (most recent call last): ... ValueError: Level 3 must be 0 <= level < 3 """ n_levels: int min_n_wins: int win_sizes: List[int] olap_sizes: List[int] def check_level(self, level: int): """Check the decimation level is within range""" if level < 0 or level >= self.n_levels: raise ValueError(f"Level {level} must be 0 <= level < {self.n_levels}") def get_win_size(self, level: int) -> int: """Get window size for a decimation level""" self.check_level(level) return self.win_sizes[level] def get_olap_size(self, level: int) -> int: """Get overlap size for a decimation level""" self.check_level(level) return self.olap_sizes[level] class WindowSetup(ResisticsProcess): """ Setup WindowParameters WindowSetup outputs the WindowParameters to use for windowing decimated time data. Window parameters are simply the window and overlap sizes for each decimation level. Parameters ---------- min_size : int, optional Minimum window size, by default 128 min_olap : int, optional Minimum overlap size, by default 32 win_factor : int, optional Window factor, by default 4. Window sizes are calculated by sampling frequency / 4 to ensure sufficient frequency resolution. If the sampling frequency is small, window size will be adjusted to min_size olap_proportion : float, optional The proportion of the window size to use as the overlap, by default 0.25. For example, for a window size of 128, the overlap would be 0.25 * 128 = 32 min_n_wins : int, optional The minimum number of windows needed in a decimation level, by default 5 win_sizes : Optional[List[int]], optional Explicit define window sizes, by default None. Must have the same length as number of decimation levels olap_sizes : Optional[List[int]], optional Explicitly define overlap sizes, by default None. Must have the same length as number of decimation levels Examples -------- Generate decimation and windowing parameters for data sampled at 0.05 Hz or 20 seconds sampling period >>> from resistics.decimate import DecimationSetup >>> from resistics.window import WindowSetup >>> dec_params = DecimationSetup(n_levels=3, per_level=3).run(0.05) >>> dec_params.summary() { 'fs': 0.05, 'n_levels': 3, 'per_level': 3, 'min_samples': 256, 'eval_freqs': [ 0.0125, 0.008838834764831844, 0.00625, 0.004419417382415922, 0.003125, 0.002209708691207961, 0.0015625, 0.0011048543456039805, 0.00078125 ], 'dec_factors': [1, 2, 8], 'dec_increments': [1, 2, 4], 'dec_fs': [0.05, 0.025, 0.00625] } >>> win_params = WindowSetup().run(dec_params.n_levels, dec_params.dec_fs) >>> win_params.summary() { 'n_levels': 3, 'min_n_wins': 5, 'win_sizes': [128, 128, 128], 'olap_sizes': [32, 32, 32] } Window parameters can also be explicitly defined >>> from resistics.decimate import DecimationSetup >>> from resistics.window import WindowSetup >>> dec_setup = DecimationSetup(n_levels=3, per_level=3) >>> dec_params = dec_setup.run(0.05) >>> win_setup = WindowSetup(win_sizes=[1000, 578, 104]) >>> win_params = win_setup.run(dec_params.n_levels, dec_params.dec_fs) >>> win_params.summary() { 'n_levels': 3, 'min_n_wins': 5, 'win_sizes': [1000, 578, 104], 'olap_sizes': [250, 144, 32] } """ min_size: int = 128 min_olap: int = 32 win_factor: int = 4 olap_proportion: float = 0.25 min_n_wins: int = 5 win_sizes: Optional[List[int]] = None olap_sizes: Optional[List[int]] = None def run(self, n_levels: int, dec_fs: List[float]) -> WindowParameters: """ Calculate window and overlap sizes for each decimation level based on decimation level sampling frequency and minimum allowable parameters The window and overlap sizes (number of samples) are calculated based in the following way: - window size = frequency at decimation level / window factor - overlap size = window size * overlap proportion This is to ensure good frequency resolution at high frequencies. At low sampling frequencies, this would result in very small window sizes, therefore, there a minimum allowable sizes for both windows and overlap defined by min_size and min_olap in the initialiser. If window sizes or overlaps size are calculated below these respecitively, they will be set to the minimum values. Parameters ---------- n_levels : int The number of decimation levels dec_fs : List[float] The sampling frequencies for each decimation level Returns ------- WindowParameters The window parameters, the window sizes and overlaps for each decimation level Raises ------ ValueError If the number of windows does not match the number of levels ValueError If the number of overlaps does not match the number of levels """ if self.win_sizes is None: win_sizes = self._get_win_sizes(n_levels, dec_fs) else: win_sizes = list(self.win_sizes) if len(win_sizes) < n_levels: raise ValueError(f"Num. windows {len(win_sizes)} < n_levels {n_levels}") if len(win_sizes) > n_levels: # this may happen with user input windows # but decimated data has fewer levels win_sizes = win_sizes[:n_levels] if self.olap_sizes is None: olap_sizes = self._get_olap_sizes(win_sizes) else: olap_sizes = self.olap_sizes if len(olap_sizes) < n_levels: raise ValueError(f"Num. overlaps {len(olap_sizes)} < n_levels {n_levels}") if len(olap_sizes) > n_levels: # this may happen with user input windows # but decimated data has fewer levels olap_sizes = olap_sizes[:n_levels] return WindowParameters( n_levels=n_levels, min_n_wins=self.min_n_wins, win_sizes=win_sizes, olap_sizes=olap_sizes, ) def _get_win_sizes(self, n_levels: int, dec_fs: List[float]) -> List[int]: """ Get the window sizes Parameters ---------- n_levels : int The number of decimation levels dec_fs : List[float] The sampling frequencies for each decimation level Returns ------- List[int] Window sizes """ win_sizes = [] for ilevel in range(n_levels): win_size = dec_fs[ilevel] // self.win_factor if win_size < self.min_size: win_size = self.min_size win_sizes.append(int(win_size)) return win_sizes def _get_olap_sizes(self, win_sizes: List[int]) -> List[int]: """ Get overlap sizes Parameters ---------- win_sizes : List[int] The window sizes Returns ------- List[int] The overlap sizes """ olap_sizes = [] for win_size in win_sizes: olap_size = int(win_size * self.olap_proportion) if olap_size < self.min_olap: olap_size = self.min_olap olap_sizes.append(olap_size) return olap_sizes class WindowedLevelMetadata(Metadata): """Metadata for a windowed level""" fs: float """The sampling frequency for the decimation level""" n_wins: int """The number of windows""" win_size: PositiveInt """The window size in samples""" olap_size: PositiveInt """The overlap size in samples""" index_offset: int """The global window offset for local window 0""" @property def dt(self): return 1 / self.fs class WindowedMetadata(WriteableMetadata): """Metadata for windowed data""" fs: List[float] chans: List[str] n_chans: Optional[int] = None n_levels: int first_time: HighResDateTime last_time: HighResDateTime system: str = "" serial: str = "" wgs84_latitude: float = -999.0 wgs84_longitude: float = -999.0 easting: float = -999.0 northing: float = -999.0 elevation: float = -999.0 chans_metadata: Dict[str, ChanMetadata] levels_metadata: List[WindowedLevelMetadata] ref_time: HighResDateTime history: History = History() class Config: extra = "ignore" class WindowedData(ResisticsData): """ Windows of a DecimatedData object The windowed data is stored in a dictionary attribute named data. This is a dictionary with an entry for each decimation level. The shape for a single decimation level is as follows: n_wins x n_chans x n_samples """ def __init__( self, metadata: WindowedMetadata, data: Dict[int, np.ndarray], ): """ Initialise the WindowedData Parameters ---------- metadata : WindowedDataMetadata The metadata for the windowed data data : Dict[int, WindowedTimeData] The windowed data """ logger.debug(f"Creating WindowedData with data type {data[0].dtype}") self.metadata = metadata self.data = data def get_level(self, level: int) -> np.ndarray: """ Get windows for a decimation level Parameters ---------- level : int The decimation level Returns ------- np.ndarray The window array Raises ------ ValueError If decimation level is not within range """ if level >= self.metadata.n_levels: raise ValueError(f"Level {level} not <= max {self.metadata.n_levels - 1}") return self.data[level] def get_local(self, level: int, local_win: int) -> np.ndarray: """ Get window using local index Parameters ---------- level : int The decimation level local_win : int Local window index Returns ------- np.ndarray Window data Raises ------ ValueError If local window index is out of range """ n_wins = self.metadata.levels_metadata[level].n_wins if local_win < 0 or local_win >= n_wins: raise ValueError(f"Local window {local_win} not 0 <= local_win < {n_wins}") return self.get_level(level)[local_win] def get_global(self, level: int, global_win: int) -> np.ndarray: """ Get window using global index Parameters ---------- level : int The decimation level global_win : int Global window index Returns ------- np.ndarray Window data """ index_offset = self.metadata.levels_metadata[level].index_offset return self.get_local(level, global_win + index_offset) def get_chan(self, level: int, chan: str) -> np.ndarray: """ Get all the windows for a channel Parameters ---------- level : int The decimation level chan : str The channel Returns ------- np.ndarray The data for the channels Raises ------ ChannelNotFoundError If the channel is not found in the data """ from resistics.errors import ChannelNotFoundError if chan not in self.metadata.chans: raise ChannelNotFoundError(chan, self.metadata.chans) idx = self.metadata.chans.index(chan) return self.get_level(level)[..., idx, :] def to_string(self) -> str: """Class information as a string""" return self.metadata.to_string() class Windower(ResisticsProcess): """ Windows DecimatedData This is the primary window making process for resistics and should be used when alignment of windows with a site or across sites is required. This method uses numpy striding to produce window views into the decimated data. See Also -------- WindowerTarget : A windower to make a target number of windows Examples -------- The Windower windows a DecimatedData object given a reference time and some window parameters. There's quite a few imports needed for this example. Begin by doing the imports, defining a reference time and generating random decimated data. >>> from resistics.sampling import to_datetime >>> from resistics.testing import decimated_data_linear >>> from resistics.window import WindowSetup, Windower >>> dec_data = decimated_data_linear(fs=128) >>> ref_time = dec_data.metadata.first_time >>> print(dec_data.to_string()) <class 'resistics.decimate.DecimatedData'> fs dt n_samples first_time last_time level 0 2048.0 0.000488 16384 2021-01-01 00:00:00 2021-01-01 00:00:07.99951171875 1 512.0 0.001953 4096 2021-01-01 00:00:00 2021-01-01 00:00:07.998046875 2 128.0 0.007812 1024 2021-01-01 00:00:00 2021-01-01 00:00:07.9921875 Next, initialise the window parameters. For this example, use small windows, which will make inspecting them easier. >>> win_params = WindowSetup(win_sizes=[16,16,16], min_olap=4).run(dec_data.metadata.n_levels, dec_data.metadata.fs) >>> win_params.summary() { 'n_levels': 3, 'min_n_wins': 5, 'win_sizes': [16, 16, 16], 'olap_sizes': [4, 4, 4] } Perform the windowing. This actually creates views into the decimated data using the numpy.lib.stride_tricks.sliding_window_view function. The shape for a data array at a decimation level is: n_wins x n_chans x win_size. The information about each level is also in the levels_metadata attribute of WindowedMetadata. >>> win_data = Windower().run(ref_time, win_params, dec_data) >>> win_data.data[0].shape (1365, 2, 16) >>> for level_metadata in win_data.metadata.levels_metadata: ... level_metadata.summary() { 'fs': 2048.0, 'n_wins': 1365, 'win_size': 16, 'olap_size': 4, 'index_offset': 0 } { 'fs': 512.0, 'n_wins': 341, 'win_size': 16, 'olap_size': 4, 'index_offset': 0 } { 'fs': 128.0, 'n_wins': 85, 'win_size': 16, 'olap_size': 4, 'index_offset': 0 } Let's look at an example of data from the first decimation level for the first channel. This is simply a linear set of data ranging from 0...16_383. >>> dec_data.data[0][0] array([ 0, 1, 2, ..., 16381, 16382, 16383]) Inspecting the first few windows shows they are as expected including the overlap. >>> win_data.data[0][0, 0] array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]) >>> win_data.data[0][1, 0] array([12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]) >>> win_data.data[0][2, 0] array([24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39]) """ def run( self, ref_time: RSDateTime, win_params: WindowParameters, dec_data: DecimatedData, ) -> WindowedData: """ Perform windowing of DecimatedData Parameters ---------- ref_time : RSDateTime The reference time win_params : WindowParameters The window parameters dec_data : DecimatedData The decimated data Returns ------- WindowedData Windows for decimated data Raises ------ ProcessRunError If the number of windows calculated in the window table does not match the size of the array views """ metadata_dict = dec_data.metadata.dict() data = {} win_levels_metadata = [] messages = [] for ilevel in range(0, dec_data.metadata.n_levels): logger.info(f"Windowing decimation level {ilevel}") win_size = win_params.get_win_size(ilevel) olap_size = win_params.get_olap_size(ilevel) level_metadata = dec_data.metadata.levels_metadata[ilevel] win_table = get_win_table(ref_time, level_metadata, win_size, olap_size) n_wins = len(win_table.index) logger.info(f"{n_wins} windows, size {win_size}, overlap {olap_size}") messages.append(f"Level {ilevel}, generated {n_wins} windows") messages.append(f"Window size {win_size}, olap_size {olap_size}") if n_wins < win_params.min_n_wins: logger.debug(f"Number windows {n_wins} < min. {win_params.min_n_wins}") messages.append(f"Num. windows {n_wins} < min. {win_params.min_n_wins}") messages.append(f"Level {ilevel} incomplete, terminating windowing") break win_level_data = self._get_level_data( dec_data.get_level(ilevel), win_table, dec_data.metadata.n_chans, win_size, olap_size, ) if win_level_data.shape[0] != n_wins: raise ProcessRunError( self.name, f"Num. windows mismatch {win_level_data.shape[0]} != {n_wins}", ) win_level_metadata = self._get_level_metadata( level_metadata, win_table, win_size, olap_size, ) data[ilevel] = win_level_data win_levels_metadata.append(win_level_metadata) metadata_dict["ref_time"] = ref_time metadata = self._get_metadata(metadata_dict, win_levels_metadata) metadata.history.add_record(self._get_record(messages)) logger.info("Windowing completed") return WindowedData(metadata, data) def _get_level_data( self, data: np.ndarray, win_table: pd.DataFrame, n_chans: int, win_size: int, olap_size: int, ) -> np.ndarray: """ Get window data for a decimation level Parameters ---------- data : np.ndarray The decimated time data for the level win_table : pd.DataFrame The window table n_chans : int The number of channels win_size : int The window size olap_size : int The overlap size Returns ------- np.ndarray Sliding window views in an array for the decimation level """ from numpy.lib.stride_tricks import sliding_window_view from_sample = win_table.loc[0, "from_sample"] to_sample = win_table.loc[win_table.index[-1], "from_sample"] increment_size = win_size - olap_size view = np.squeeze( sliding_window_view(data, window_shape=(n_chans, win_size), writeable=True) ) return view[from_sample : to_sample + 1 : increment_size] def _get_level_metadata( self, level_metadata: DecimatedLevelMetadata, win_table: pd.DataFrame, win_size: int, olap_size: int, ) -> WindowedLevelMetadata: """Get the windowed metadata for a decimation level""" offset = (win_table["global"] - win_table["local"]).unique() if len(offset) != 1: raise ValueError("Malformed window table, varying local to global offset") return WindowedLevelMetadata( fs=level_metadata.fs, n_wins=len(win_table.index), win_size=win_size, olap_size=olap_size, index_offset=offset[0], ) def _get_metadata( self, metadata_dict: Dict[str, Any], levels_metadata: List[WindowedLevelMetadata], ) -> WindowedMetadata: """Get the metadata for the windowed data""" metadata_dict.pop("file_info") metadata_dict["n_levels"] = len(levels_metadata) metadata_dict["levels_metadata"] = levels_metadata return WindowedMetadata(**metadata_dict) class WindowerTarget(Windower): """ Windower that selects window sizes to meet a target number of windows The minimum window size in window parameters will be respected even if the generated number of windows is below the target. This is to avoid situations where excessively small windows sizes are selected. .. warning:: This process is primarily useful for quick processing of a single measurement and should not be used when any alignment of windows is required within a site or across sites. Parameters ---------- target : int The target number of windows for each decimation level olap_proportion : float The overlap proportion of the window size See Also -------- Windower : The window making process to use when alignment is required """ target: int = 1000 min_size: int = 64 olap_proportion: float = 0.25 def run( self, ref_time: RSDateTime, win_params: WindowParameters, dec_data: DecimatedData, ) -> WindowedData: metadata_dict = dec_data.metadata.dict() data = {} win_levels_metadata = [] messages = [] for ilevel in range(0, dec_data.metadata.n_levels): logger.info(f"Windowing decimation level {ilevel}") level_metadata = dec_data.metadata.levels_metadata[ilevel] win_size = self._get_win_size(level_metadata) olap_size = int(np.floor(self.olap_proportion * win_size)) win_table = get_win_table(ref_time, level_metadata, win_size, olap_size) n_wins = len(win_table.index) logger.info(f"{n_wins} windows, size {win_size}, overlap {olap_size}") messages.append(f"Level {ilevel}, generated {n_wins} windows") messages.append(f"Window size {win_size}, olap_size {olap_size}") if n_wins < win_params.min_n_wins: logger.debug(f"Number windows {n_wins} < min. {win_params.min_n_wins}") messages.append(f"Num. windows {n_wins} < min. {win_params.min_n_wins}") messages.append(f"Level {ilevel} incomplete, terminating windowing") break win_level_data = self._get_level_data( dec_data.get_level(ilevel), win_table, dec_data.metadata.n_chans, win_size, olap_size, ) win_level_metadata = self._get_level_metadata( level_metadata, win_table, win_size, olap_size, ) data[ilevel] = win_level_data win_levels_metadata.append(win_level_metadata) metadata_dict["ref_time"] = metadata_dict["first_time"] metadata = self._get_metadata(metadata_dict, win_levels_metadata) metadata.history.add_record(self._get_record(messages)) logger.info("Windowing completed") return WindowedData(metadata, data) def _get_win_size(self, level_metadata: DecimatedLevelMetadata) -> int: r""" Get window size that gives close to the target number of windows Windows increment by (window size - overlap size), therefore the follwing equation is solved, .. math:: n_{samples} / ((1 - n_{overlap})*n_{window}) = target Rearrangning, get, .. math:: n_{window} = n_{samples} / ((1 - n_{overlap})*target) Parameters ---------- level_metadata : DecimatedLevelMetadata The metadata for the decimation level Returns ------- int The window size """ win_size = level_metadata.n_samples / ((1 - self.olap_proportion) * self.target) win_size = int(np.floor(win_size)) if win_size < self.min_size: return self.min_size return win_size class WindowedDataWriter(ResisticsWriter): """Writer of resistics windowed data""" def run(self, dir_path: Path, win_data: WindowedData) -> None: """ Write out WindowedData Parameters ---------- dir_path : Path The directory path to write to win_data : WindowedData Windowed data to write out Raises ------ WriteError If unable to write to the directory """ from resistics.errors import WriteError if not self._check_dir(dir_path): raise WriteError(dir_path, "Unable to write to directory, check logs") logger.info(f"Writing windowed data to {dir_path}") metadata_path = dir_path / "metadata.json" data_path = dir_path / "data" np.savez_compressed(data_path, **{str(x): y for x, y in win_data.data.items()}) metadata = win_data.metadata.copy() metadata.history.add_record(self._get_record(dir_path, type(win_data))) metadata.write(metadata_path) class WindowedDataReader(ResisticsProcess): """Reader of resistics windowed data""" def run( self, dir_path: Path, metadata_only: bool = False ) -> Union[WindowedMetadata, WindowedData]: """ Read WindowedData Parameters ---------- dir_path : Path The directory path to read from metadata_only : bool, optional Flag for getting metadata only, by default False Returns ------- Union[WindowedMetadata, WindowedData] The WindowedData or WindowedMetadata if metadata_only is True Raises ------ ReadError If the directory does not exist """ from resistics.errors import ReadError if not dir_path.exists(): raise ReadError(dir_path, "Directory does not exist") logger.info(f"Reading windowed data from {dir_path}") metadata_path = dir_path / "metadata.json" metadata = WindowedMetadata.parse_file(metadata_path) if metadata_only: return metadata data_path = dir_path / "data.npz" npz_file = np.load(data_path) data = {int(level): npz_file[level] for level in npz_file.files} messages = [f"Windowed data read from {dir_path}"] metadata.history.add_record(self._get_record(messages)) return WindowedData(metadata, data)

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acmicpc/python/14405.py

hyeongyun0916/Algorithm

1

1600248

<reponame>hyeongyun0916/Algorithm import sys # chupikachupipichu s = sys.stdin.readline().strip() i = 0 while i < len(s): if s[i] == 'p': if i+1 < len(s) and s[i+1] == 'i': i += 1 else: print('NO') exit() elif s[i] == 'k': if i+1 < len(s) and s[i+1] == 'a': i += 1 else: print('NO') exit() elif s[i] == 'c': if i+2 < len(s) and s[i+1] == 'h' and s[i+2] == 'u': i += 2 else: print('NO') exit() else: print('NO') exit() i += 1 print('YES')

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save_restore/check_frozen_model.py

scotthuang1989/mytensorflow_example_code

32

141193

import tensorflow as tf import argparse def load_graph(frozen_graph_filename): # We load the protobuf file from the disk and parse it to retrieve the # unserialized graph_def with tf.gfile.GFile(frozen_graph_filename, "rb") as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) # Then, we import the graph_def into a new Graph and returns it with tf.Graph().as_default() as graph: # The name var will prefix every op/nodes in your graph # Since we load everything in a new graph, this is not needed tf.import_graph_def(graph_def, name="") return graph if __name__ == '__main__': # Let's allow the user to pass the filename as an argument parser = argparse.ArgumentParser() # parser.add_argument("--frozen_model_filename", default="/home/scott/Downloads/ssd_mobilenet_v1_coco_11_06_2017/frozen_inference_graph.pb", type=str, help="Frozen model file to import") parser.add_argument("--frozen_model_filename", default="/home/scott/Downloads/faster_rcnn_inception_resnet_v2_atrous_coco_11_06_2017/frozen_inference_graph.pb", type=str, help="Frozen model file to import") args = parser.parse_args() # We use our "load_graph" function graph = load_graph(args.frozen_model_filename) # We can verify that we can access the list of operations in the graph for op in graph.get_operations(): print(op.name) # prefix/Placeholder/inputs_placeholder # ... # prefix/Accuracy/predictions # y = graph.get_tensor_by_name('prefix/Accuracy/predictions:0')

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learning/LearnPython3TheHardWay/InteractiveFictionClass/GoogleColabStarterCode/Parser.py

AlexanderTN/Python

1

47793

<filename>learning/LearnPython3TheHardWay/InteractiveFictionClass/GoogleColabStarterCode/Parser.py<gh_stars>1-10 class Parser: """The Parser is the class that handles the player's input. The player writes commands, and the parser performs natural language understanding in order to interpret what the player intended, and how that intent is reflected in the simulated world. """ def __init__(self, game): # A list of all of the commands that the player has issued. self.command_history = [] # A pointer to the game. self.game = game def get_player_intent(self,command): command = command.lower() if "," in command: # Let the player type in a comma separted sequence of commands return "sequence" elif self.get_direction(command): # Check for the direction intent return "direction" elif command.lower() == "look" or command.lower() == "l": # when the user issues a "look" command, re-describe what they see return "redescribe" elif "examine " in command or command.lower().startswith("x "): return "examine" elif "take " in command or "get " in command: return "take" elif "drop " in command: return "drop" elif "inventory" in command or command.lower() == "i": return "inventory" else: for item in self.game.get_items_in_scope(): special_commands = item.get_commands() for special_command in special_commands: if command == special_command.lower(): return "special" def parse_command(self, command): # add this command to the history self.command_history.append(command) # By default, none of the intents end the game. The following are ways this # flag can be changed to True. # * Going to a certain place. # * Entering a certain special command # * Picking up a certain object. end_game = False # Intents are functions that can be executed intent = self.get_player_intent(command) if intent == "direction": end_game = self.go_in_direction(command) elif intent == "redescribe": self.game.describe() elif intent == "examine": self.examine(command) elif intent == "take": end_game = self.take(command) elif intent == "drop": self.drop(command) elif intent == "inventory": self.check_inventory() #Tam: Actually there is no usage of the command in here, so I will remove it out of the parameters list passed in the function, old: self.check_inventory(command) elif intent == "special": end_game = self.run_special_command(command) elif intent == "sequence": #end_game = self.execute_sequence(command) self.execute_sequence(command) #Tam3 change it as this function dont return value else: print("I'm not sure what you want to do.") return end_game ### Intent Functions ### def go_in_direction(self, command): """ The user wants to in some direction """ direction = self.get_direction(command) if direction: if direction in self.game.curr_location.connections: if self.game.curr_location.is_blocked(direction, self.game): # check to see whether that direction is blocked. print(self.game.curr_location.get_block_description(direction)) else: # if it's not blocked, then move there self.game.curr_location = self.game.curr_location.connections[direction] # If moving to this location ends the game, only describe the location # and not the available items or actions. if self.game.curr_location.end_game: self.game.describe_current_location() else: self.game.describe() else: print("You can't go %s from here." % direction.capitalize()) return self.game.curr_location.end_game def check_inventory(self): #Tam: Actually there is no usage of the command in here, so I will remove it out of the parameters list passed in the function, old: def check_inventory(self,command): """ The player wants to check their inventory""" if len(self.game.inventory) == 0: print("You don't have anything.") else: descriptions = [] for item_name in self.game.inventory: item = self.game.inventory[item_name] descriptions.append(item.description) print("You have: ", end = '') print(*descriptions, sep = ", ",) def examine(self, command): """ The player wants to examine something """ command = command.lower() matched_item = False # check whether any of the items at this location match the command for item_name in self.game.curr_location.items: if item_name in command: item = self.game.curr_location.items[item_name] if item.examine_text: print(item.examine_text) matched_item = True break # check whether any of the items in the inventory match the command for item_name in self.game.inventory: if item_name in command: item = self.game.inventory[item_name] if item.examine_text: print(item.examine_text) matched_item = True # fail if not matched_item: print("You don't see anything special.") def take(self, command): """ The player wants to put something in their inventory """ command = command.lower() matched_item = False # This gets set to True if posession of this object ends the game. end_game = False # check whether any of the items at this location match the command for item_name in self.game.curr_location.items: if item_name in command: item = self.game.curr_location.items[item_name] if item.gettable: self.game.add_to_inventory(item) self.game.curr_location.remove_item(item) print(item.take_text) end_game = item.end_game else: print("You cannot take the %s." % item_name) matched_item = True break # check whether any of the items in the inventory match the command if not matched_item: for item_name in self.game.inventory: if item_name in command: print("You already have the %s." % item_name) matched_item = True # fail if not matched_item: print("You can't find it.") return end_game def drop(self, command): """ The player wants to remove something from their inventory """ command = command.lower() matched_item = False # check whether any of the items in the inventory match the command if not matched_item: for item_name in self.game.inventory: if item_name in command: matched_item = True item = self.game.inventory[item_name] self.game.curr_location.add_item(item_name, item) self.game.inventory.pop(item_name) print("You drop the %s." % item_name) break # fail if not matched_item: print("You don't have that.") def run_special_command(self, command): """Run a special command associated with one of the items in this location or in the player's inventory""" for item in self.game.get_items_in_scope(): special_commands = item.get_commands() for special_command in special_commands: if command == special_command.lower(): return item.do_action(special_command, self.game) def execute_sequence(self, command): for cmd in command.split(","): cmd = cmd.strip() self.parse_command(cmd) #return 1 #Tam add to pass the error def get_direction(self, command): command = command.lower() if command == "n" or "north" in command: return "north" if command == "s" or "south" in command: return "south" if command == "e" or "east" in command: return "east" if command == "w" or "west" in command: return "west" if command == "up": return "up" if command == "down": return "down" if command.startswith("go out"): return "out" if command.startswith("go in"): return "in" for exit in self.game.curr_location.connections.keys(): if command == exit.lower() or command == "go " + exit.lower(): return exit return None

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run.py

zgoda/kyfood

0

193311

from argparse import ArgumentParser from dotenv import find_dotenv, load_dotenv from werkzeug.serving import run_simple from api._local import API def get_options(): parser = ArgumentParser() parser.add_argument( "-o", "--host", default="127.0.0.1", help="host name or IP address to bind" ) parser.add_argument( "-p", "--port", type=int, default=5000, help="port number to listen" ) parser.add_argument( "--no-reload", action="store_true", default=False, help="disable hot reload" ) return parser.parse_args() def main(options): app = API() reload_ = not options.no_reload run_simple(options.host, options.port, app, use_reloader=reload_) if __name__ == "__main__": load_dotenv(find_dotenv()) opts = get_options() main(opts)

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question_bank/merge-two-binary-trees/merge-two-binary-trees.py

yatengLG/leetcode-python

9

143442

# -*- coding: utf-8 -*- # @Author : LG """ 执行用时：104 ms, 在所有 Python3 提交中击败了64.10% 的用户内存消耗：14.4 MB, 在所有 Python3 提交中击败了79.35% 的用户解题思路：具体实现见代码注释 """ class Solution: def mergeTrees(self, t1: TreeNode, t2: TreeNode) -> TreeNode: def find(root1, root2): # 两棵树的对应节点 if root1 and root2: # 如果节点都存在 node = TreeNode(root1.val + root2.val) # 合并后的当前节点为两树节点和 node.left = find(root1.left, root2.left) # 处理两树的对应左子树，返回的节点为当前合并子树的左子树 node.right = find(root1.right, root2.right) # 处理两树的对应右子树 elif root1 or root2: # 只存在一个节点 node = TreeNode(root1.val if root1 else root2.val) # 当前节点等于存在节点值 node.left = find(root1.left if root1 else None, root2.left if root2 else None) # 遍历存在节点的左子树 node.right = find(root1.right if root1 else None, root2.right if root2 else None) # 遍历存在节点的右子树 else: return None # 均不存在返回None return node t = find(t1, t2) return t """ 执行用时：100 ms, 在所有 Python3 提交中击败了91.61% 的用户内存消耗：14.5 MB, 在所有 Python3 提交中击败了46.33% 的用户解题思路：同上，但不新建树 """ class Solution: def mergeTrees(self, t1: TreeNode, t2: TreeNode) -> TreeNode: def find(root1, root2): # 两棵树的对应节点 if root1 and root2: # 如果节点都存在 root1.val = root1.val + root2.val # 合并后的当前节点为两树节点和 root1.left = find(root1.left, root2.left) # 处理两树的对应左子树，返回的节点为当前合并子树的左子树 root1.right = find(root1.right, root2.right) # 处理两树的对应右子树 elif root1 or root2: # 只存在一个节点, 直接返回存在的节点即可 return root1 if root1 else root2 else: return None # 均不存在返回None return root1 t = find(t1, t2) return t

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Ex021-Tocando_um_mp3.py

Diego-Sil/Python_do_Curso_em_Video

0

110403

import pygame pygame.mixer.init() pygame.init() pygame.mixer.music.load('asus-yamete-kudasai.mp3') pygame.mixer.music.play() pygame.event.wait()

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tests/test_search_core.py

dinghino/searchengine

0

75747

<gh_stars>0 """ Testing module for the search core functionalities """ from search import core from tests.helpers import Item class TestCore: @classmethod def setup_class(cls): cls.items = Item.setup() cls.search = core.SearchEngine(['words'], limit=10) def test_single_words(self): seq = Item.get_by_length(max_length=1) res = self.search('inconvene', seq) assert res == ['inconvenience'] res = self.search('laugh', seq) assert res == ['laughter'] def test_short_phrases(self): seq = Item.get_by_length(3, 5) results = self.search('sherlock holmes', seq) reversed_query_results = self.search('holmes sherlock', seq) expected = [ 'sherlock holmes clapped upon knee', 'sherlock holmes foretold exquisite mouth', 'sherlock holmes upon grey walls', 'sherlock holmes wrote english capital', ] assert results == expected assert reversed_query_results == expected results = self.search('watson', seq) assert results == [ "watson have learned something", "what wanted what doctor watson", ] def test_search_with_params(self): seq = Item.get_by_length(3, 5) res = self.search('sherlock holmes', seq, limit=2, threshold=.95) expected = [ 'sherlock holmes clapped upon knee', 'sherlock holmes foretold exquisite mouth', ] assert res == expected def test_search_long_phrases(self): seq = Item.get_by_length(12, 15) results = self.search('sherlock holmes', seq) expected = [ 'facts these about four clock when sherlock holmes took step between wharf ruffian pursued', # noqa: E501 'moment intimacy there were other traces sherlock holmes stopped front aberdeen shipping company', # noqa: E501 'rushed down just have taken mind understand from some small dealings with sherlock holmes', # noqa: E501 'sherlock holmes brought gush hope which sank into desultory chat with over despair', # noqa: E501 'sherlock holmes great many scattered papers have whatever bears upon stone professional work attend' # noqa: E501 ] assert results == expected

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stian/day_thirteen/python/day_thirteen.py

stian-eng/advent-2021

0

65965

<reponame>stian-eng/advent-2021 from requests.api import get import time import aoc_frame def get_points(data) : points = [] instr = [] flag = True for i in data : if i : if flag : x, y = i.split(',') points.append((int(x), int(y))) else : y = i.split() x = y[-1] axis, num = x.split('=') instr.append((axis, int(num))) else : flag = False return points, instr def fold(points, fold) : if fold[0] == 'y' : for i in range(len(points)) : curr = points[i] if curr[1] > fold[1] : points[i] = (curr[0], fold[1] - (curr[1] - fold[1])) elif fold[0] == 'x' : for i in range(len(points)) : curr = points[i] if curr[0] > fold[1] : points[i] = (fold[1] - (curr[0] - fold[1]), curr[1]) return points def part_one(data, flag2) : points, instr = get_points(data) for f in instr : points = fold(points, f) if flag2 : seen = set() count = 0 for i in points : if i not in seen : seen.add(i) count += 1 return count return points def part_two(data) : points = part_one(data, False) max_x = max([i[0] for i in points]) max_y = max([i[1] for i in points]) matrix = [[' ' for i in range(max_x + 1)] for i in range(max_y + 1)] seen = set() for p in points : if p not in seen : matrix[p[1]][p[0]] = '#' seen.add(p) for i in matrix : print(i) # answer: """ [[' ', '#', '#', ' ', ' ', '#', '#', '#', ' ', ' ', '#', ' ', ' ', '#', ' ', '#', '#', '#', '#', ' ', '#', '#', '#', ' ', ' ', ' ', '#', '#', ' ', ' ', '#', ' ', ' ', '#', ' ', '#', ' ', ' ', '#'], ['#', ' ', ' ', '#', ' ', '#', ' ', ' ', '#', ' ', '#', ' ', ' ', '#', ' ', ' ', ' ', ' ', '#', ' ', '#', ' ', ' ', '#', ' ', '#', ' ', ' ', '#', ' ', '#', ' ', ' ', '#', ' ', '#', ' ', ' ', '#'], ['#', ' ', ' ', '#', ' ', '#', ' ', ' ', '#', ' ', '#', '#', '#', '#', ' ', ' ', ' ', '#', ' ', ' ', '#', ' ', ' ', '#', ' ', '#', ' ', ' ', ' ', ' ', '#', ' ', ' ', '#', ' ', '#', '#', '#', '#'], ['#', '#', '#', '#', ' ', '#', '#', '#', ' ', ' ', '#', ' ', ' ', '#', ' ', ' ', '#', ' ', ' ', ' ', '#', '#', '#', ' ', ' ', '#', ' ', ' ', ' ', ' ', '#', ' ', ' ', '#', ' ', '#', ' ', ' ', '#'], ['#', ' ', ' ', '#', ' ', '#', ' ', '#', ' ', ' ', '#', ' ', ' ', '#', ' ', '#', ' ', ' ', ' ', ' ', '#', ' ', ' ', ' ', ' ', '#', ' ', ' ', '#', ' ', '#', ' ', ' ', '#', ' ', '#', ' ', ' ', '#'], ['#', ' ', ' ', '#', ' ', '#', ' ', ' ', '#', ' ', '#', ' ', ' ', '#', ' ', '#', '#', '#', '#', ' ', '#', ' ', ' ', ' ', ' ', ' ', '#', '#', ' ', ' ', ' ', '#', '#', ' ', ' ', '#', ' ', ' ', '#']] """ if __name__ == '__main__' : data = [] test = [] with open('day_thirteen.txt', 'r') as f : for line in f.readlines() : data.append(line.strip('\n')) with open('test.txt', 'r') as f : for line in f.readlines() : test.append(line.strip('\n')) t0 = time.time() aoc_frame.format_strings(part_one(data, True), part_one(test, True), 1, t0) t0 = time.time() aoc_frame.format_strings(part_two(data), part_two(test), 2, t0)

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queue_services/common/tests/integration/test_queue_connections.py

argush3/lear

8

139742

<reponame>argush3/lear # Copyright © 2019 Province of British Columbia # # 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. """The Test Suite to ensure queue connections are working as expected.""" import pytest from nats.aio.client import Client as Nats from stan.aio.client import Client as Stan from stan.aio.errors import StanError @pytest.mark.asyncio async def test_queue_connection(stan_server, event_loop, client_id): """Assert that we connect to the queue configuration used by the tests.""" nc = Nats() await nc.connect(loop=event_loop) sc = Stan() await sc.connect('test-cluster', client_id, nats=nc) assert sc._pub_prefix # pylint: disable=protected-access; sc does not expose a connection check assert sc._conn_id # pylint: disable=protected-access; sc does not expose a connection check await sc.close() # should not close the connection assert nc.is_connected with pytest.raises(StanError): await sc.close() await nc.close() assert not nc.is_connected

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docs/generate_example_images.py

KhaledSharif/kornia

0

8661

<reponame>KhaledSharif/kornia<gh_stars>0 import importlib import math import os from pathlib import Path from typing import Optional, Tuple import cv2 import numpy as np import requests import torch import kornia as K def read_img_from_url(url: str, resize_to: Optional[Tuple[int, int]] = None) -> torch.Tensor: # perform request response = requests.get(url).content # convert to array of ints nparr = np.frombuffer(response, np.uint8) # convert to image array and resize img: np.ndarray = cv2.imdecode(nparr, cv2.IMREAD_UNCHANGED)[..., :3] # convert the image to a tensor img_t: torch.Tensor = K.utils.image_to_tensor(img, keepdim=False) # 1xCxHXW img_t = img_t.float() / 255.0 if resize_to is None: img_t = K.geometry.resize(img_t, 184) else: img_t = K.geometry.resize(img_t, resize_to) return img_t def main(): # load the images BASE_IMAGE_URL1: str = "https://raw.githubusercontent.com/kornia/data/main/panda.jpg" # augmentation BASE_IMAGE_URL2: str = "https://raw.githubusercontent.com/kornia/data/main/simba.png" # color BASE_IMAGE_URL3: str = "https://raw.githubusercontent.com/kornia/data/main/girona.png" # enhance BASE_IMAGE_URL4: str = "https://raw.githubusercontent.com/kornia/data/main/baby_giraffe.png" # morphology BASE_IMAGE_URL5: str = "https://raw.githubusercontent.com/kornia/data/main/persistencia_memoria.jpg" # filters BASE_IMAGE_URL6: str = "https://raw.githubusercontent.com/kornia/data/main/delorean.png" # geometry OUTPUT_PATH = Path(__file__).absolute().parent / "source/_static/img" os.makedirs(OUTPUT_PATH, exist_ok=True) print(f"Pointing images to path {OUTPUT_PATH}.") img1 = read_img_from_url(BASE_IMAGE_URL1) img2 = read_img_from_url(BASE_IMAGE_URL2, img1.shape[-2:]) img3 = read_img_from_url(BASE_IMAGE_URL3, img1.shape[-2:]) img4 = read_img_from_url(BASE_IMAGE_URL4) img5 = read_img_from_url(BASE_IMAGE_URL5, (234, 320)) img6 = read_img_from_url(BASE_IMAGE_URL6) # TODO: make this more generic for modules out of kornia.augmentation # Dictionary containing the transforms to generate the sample images: # Key: Name of the transform class. # Value: (parameters, num_samples, seed) mod = importlib.import_module("kornia.augmentation") augmentations_list: dict = { "CenterCrop": ((184, 184), 1, 2018), "ColorJitter": ((0.3, 0.3, 0.3, 0.3), 2, 2018), "RandomAffine": (((-15.0, 20.0), (0.1, 0.1), (0.7, 1.3), 20), 2, 2019), "RandomBoxBlur": (((7, 7),), 1, 2020), "RandomCrop": ((img1.shape[-2:], (50, 50)), 2, 2020), "RandomChannelShuffle": ((), 1, 2020), "RandomElasticTransform": (((63, 63), (32, 32), (2.0, 2.0)), 2, 2018), "RandomEqualize": ((), 1, 2020), "RandomErasing": (((0.2, 0.4), (0.3, 1 / 0.3)), 2, 2017), "RandomFisheye": ((torch.tensor([-0.3, 0.3]), torch.tensor([-0.3, 0.3]), torch.tensor([0.9, 1.0])), 2, 2020), "RandomGaussianBlur": (((3, 3), (0.1, 2.0)), 1, 2020), "RandomGaussianNoise": ((0.0, 0.05), 1, 2020), "RandomGrayscale": ((), 1, 2020), "RandomHorizontalFlip": ((), 1, 2020), "RandomInvert": ((), 1, 2020), "RandomMotionBlur": ((7, 35.0, 0.5), 2, 2020), "RandomPerspective": ((0.2,), 2, 2020), "RandomPlanckianJitter": ((), 2, 2022), "RandomPosterize": (((1, 4),), 2, 2016), "RandomResizedCrop": ((img1.shape[-2:], (1.0, 2.0), (1.0, 2.0)), 2, 2020), "RandomRotation": ((45.0,), 2, 2019), "RandomSharpness": ((16.0,), 1, 2019), "RandomSolarize": ((0.2, 0.2), 2, 2019), "RandomVerticalFlip": ((), 1, 2020), "RandomThinPlateSpline": ((), 1, 2020), } # ITERATE OVER THE TRANSFORMS for aug_name, (args, num_samples, seed) in augmentations_list.items(): img_in = img1.repeat(num_samples, 1, 1, 1) # dynamically create the class instance cls = getattr(mod, aug_name) aug = cls(*args, p=1.0) # set seed torch.manual_seed(seed) # apply the augmentaiton to the image and concat out = aug(img_in) if aug_name == "CenterCrop": h, w = img1.shape[-2:] h_new, w_new = out.shape[-2:] h_dif, w_dif = int(h - h_new), int(w - w_new) out = torch.nn.functional.pad(out, (w_dif // 2, w_dif // 2, 0, h_dif)) out = torch.cat([img_in[0], *(out[i] for i in range(out.size(0)))], dim=-1) # save the output image out_np = K.utils.tensor_to_image((out * 255.0).byte()) cv2.imwrite(str(OUTPUT_PATH / f"{aug_name}.png"), out_np) sig = f"{aug_name}({', '.join([str(a) for a in args])}, p=1.0)" print(f"Generated image example for {aug_name}. {sig}") mod = importlib.import_module("kornia.augmentation") mix_augmentations_list: dict = { "RandomMixUp": (((0.3, 0.4),), 2, 20), "RandomCutMix": ((img1.shape[-2], img1.shape[-1]), 2, 2019), } # ITERATE OVER THE TRANSFORMS for aug_name, (args, num_samples, seed) in mix_augmentations_list.items(): img_in = torch.cat([img1, img2]) # dynamically create the class instance cls = getattr(mod, aug_name) aug = cls(*args, p=1.0) # set seed torch.manual_seed(seed) # apply the augmentaiton to the image and concat out, _ = aug(img_in, torch.tensor([0, 1])) out = torch.cat([img_in[0], img_in[1], *(out[i] for i in range(out.size(0)))], dim=-1) # save the output image out_np = K.utils.tensor_to_image((out * 255.0).byte()) cv2.imwrite(str(OUTPUT_PATH / f"{aug_name}.png"), out_np) sig = f"{aug_name}({', '.join([str(a) for a in args])}, p=1.0)" print(f"Generated image example for {aug_name}. {sig}") mod = importlib.import_module("kornia.color") color_transforms_list: dict = { "grayscale_to_rgb": ((), 3), "rgb_to_bgr": ((), 1), "rgb_to_grayscale": ((), 1), "rgb_to_hsv": ((), 1), "rgb_to_hls": ((), 1), "rgb_to_luv": ((), 1), "rgb_to_lab": ((), 1), # "rgb_to_rgba": ((1.,), 1), "rgb_to_xyz": ((), 1), "rgb_to_ycbcr": ((), 1), "rgb_to_yuv": ((), 1), "rgb_to_linear_rgb": ((), 1), } # ITERATE OVER THE TRANSFORMS for fn_name, (args, num_samples) in color_transforms_list.items(): # import function and apply fn = getattr(mod, fn_name) if fn_name == "grayscale_to_rgb": out = fn(K.color.rgb_to_grayscale(img2), *args) else: out = fn(img2, *args) # perform normalization to visualize if fn_name == "rgb_to_lab": out = out[:, :1] / 100.0 elif fn_name == "rgb_to_hsv": out[:, :1] = out[:, :1] / 2 * math.pi elif fn_name == "rgb_to_luv": out = out[:, :1] / 116.0 # repeat channels for grayscale if out.shape[1] != 3: out = out.repeat(1, 3, 1, 1) # save the output image if fn_name == "grayscale_to_rgb": out = torch.cat( [K.color.rgb_to_grayscale(img2[0]).repeat(3, 1, 1), *(out[i] for i in range(out.size(0)))], dim=-1 ) else: out = torch.cat([img2[0], *(out[i] for i in range(out.size(0)))], dim=-1) out_np = K.utils.tensor_to_image((out * 255.0).byte()) cv2.imwrite(str(OUTPUT_PATH / f"{fn_name}.png"), out_np) sig = f"{fn_name}({', '.join([str(a) for a in args])})" print(f"Generated image example for {fn_name}. {sig}") # korna.enhance module mod = importlib.import_module("kornia.enhance") transforms: dict = { "adjust_brightness": ((torch.tensor([0.25, 0.5]),), 2), "adjust_contrast": ((torch.tensor([0.65, 0.5]),), 2), "adjust_gamma": ((torch.tensor([0.85, 0.75]), 2.0), 2), "adjust_hue": ((torch.tensor([-math.pi / 4, math.pi / 4]),), 2), "adjust_saturation": ((torch.tensor([1.0, 2.0]),), 2), "solarize": ((torch.tensor([0.8, 0.5]), torch.tensor([-0.25, 0.25])), 2), "posterize": ((torch.tensor([4, 2]),), 2), "sharpness": ((torch.tensor([1.0, 2.5]),), 2), "equalize": ((), 1), "invert": ((), 1), "equalize_clahe": ((), 1), "add_weighted": ((0.75, 0.25, 2.0), 1), } # ITERATE OVER THE TRANSFORMS for fn_name, (args, num_samples) in transforms.items(): img_in = img3.repeat(num_samples, 1, 1, 1) if fn_name == "add_weighted": args_in = (img_in, args[0], img2, args[1], args[2]) else: args_in = (img_in, *args) # import function and apply fn = getattr(mod, fn_name) out = fn(*args_in) # save the output image out = torch.cat([img_in[0], *(out[i] for i in range(out.size(0)))], dim=-1) out_np = K.utils.tensor_to_image((out * 255.0).byte()) cv2.imwrite(str(OUTPUT_PATH / f"{fn_name}.png"), out_np) sig = f"{fn_name}({', '.join([str(a) for a in args])})" print(f"Generated image example for {fn_name}. {sig}") # korna.morphology module mod = importlib.import_module("kornia.morphology") kernel = torch.tensor([[0, 1, 0], [1, 1, 1], [0, 1, 0]]) transforms: dict = { "dilation": ((kernel,), 1), "erosion": ((kernel,), 1), "opening": ((kernel,), 1), "closing": ((kernel,), 1), "gradient": ((kernel,), 1), "top_hat": ((kernel,), 1), "bottom_hat": ((kernel,), 1), } # ITERATE OVER THE TRANSFORMS for fn_name, (args, num_samples) in transforms.items(): img_in = img4.repeat(num_samples, 1, 1, 1) args_in = (img_in, *args) # import function and apply # import pdb;pdb.set_trace() fn = getattr(mod, fn_name) out = fn(*args_in) # save the output image out = torch.cat([img_in[0], *(out[i] for i in range(out.size(0)))], dim=-1) out_np = K.utils.tensor_to_image((out * 255.0).byte()) cv2.imwrite(str(OUTPUT_PATH / f"{fn_name}.png"), out_np) sig = f"{fn_name}({', '.join([str(a) for a in args])})" print(f"Generated image example for {fn_name}. {sig}") # korna.filters module mod = importlib.import_module("kornia.filters") kernel = torch.tensor([[0, 1, 0], [1, 1, 1], [0, 1, 0]]) transforms: dict = { "box_blur": (((5, 5),), 1), "median_blur": (((5, 5),), 1), "gaussian_blur2d": (((5, 5), (1.5, 1.5)), 1), "motion_blur": ((5, 90.0, 1.0), 1), "max_blur_pool2d": ((5,), 1), "blur_pool2d": ((5,), 1), "unsharp_mask": (((5, 5), (1.5, 1.5)), 1), "laplacian": ((5,), 1), "sobel": ((), 1), "spatial_gradient": ((), 1), "canny": ((), 1), } # ITERATE OVER THE TRANSFORMS for fn_name, (args, num_samples) in transforms.items(): img_in = img5.repeat(num_samples, 1, 1, 1) args_in = (img_in, *args) # import function and apply fn = getattr(mod, fn_name) out = fn(*args_in) if fn_name in ("max_blur_pool2d", "blur_pool2d"): out = K.geometry.resize(out, img_in.shape[-2:]) if fn_name == "canny": out = out[1].repeat(1, 3, 1, 1) if isinstance(out, torch.Tensor): out = out.clamp(min=0.0, max=1.0) if fn_name in ("laplacian", "sobel", "spatial_gradient", "canny"): out = K.enhance.normalize_min_max(out) if fn_name == "spatial_gradient": out = out.permute(2, 1, 0, 3, 4).squeeze() # save the output image out = torch.cat([img_in[0], *(out[i] for i in range(out.size(0)))], dim=-1) out_np = K.utils.tensor_to_image((out * 255.0).byte()) cv2.imwrite(str(OUTPUT_PATH / f"{fn_name}.png"), out_np) sig = f"{fn_name}({', '.join([str(a) for a in args])})" print(f"Generated image example for {fn_name}. {sig}") # korna.geometry.transform module mod = importlib.import_module("kornia.geometry.transform") h, w = img6.shape[-2:] def _get_tps_args(): src = torch.tensor([[[-1.0, -1.0], [-1.0, 1.0], [1.0, -1.0], [1.0, -1.0], [0.0, 0.0]]]).repeat(2, 1, 1) # Bx5x2 dst = src + torch.distributions.Uniform(-0.2, 0.2).rsample((2, 5, 2)) kernel, affine = K.geometry.transform.get_tps_transform(dst, src) return src, kernel, affine transforms: dict = { "warp_affine": ( ( K.geometry.transform.get_affine_matrix2d( translations=torch.zeros(2, 2), center=(torch.tensor([w, h]) / 2).repeat(2, 1), scale=torch.distributions.Uniform(0.5, 1.5).rsample((2, 2)), angle=torch.tensor([-25.0, 25.0]), )[:, :2, :3], (h, w), ), 2, ), "remap": ( ( *(K.utils.create_meshgrid(h, w, normalized_coordinates=True) - 0.25).unbind(-1), 'bilinear', 'zeros', True, True, ), 1, ), "warp_image_tps": ((_get_tps_args()), 2), "rotate": ((torch.tensor([-15.0, 25.0]),), 2), "translate": ((torch.tensor([[10.0, -15], [50.0, -25.0]]),), 2), "scale": ((torch.tensor([[0.5, 1.25], [1.0, 1.5]]),), 2), "shear": ((torch.tensor([[0.1, -0.2], [-0.2, 0.1]]),), 2), "rot180": ((), 1), "hflip": ((), 1), "vflip": ((), 1), "resize": (((120, 220),), 1), "rescale": ((0.5,), 1), "elastic_transform2d": ((torch.rand(1, 2, h, w) * 2 - 1, (63, 63), (32, 32), (4.0, 4.0)), 1), "pyrdown": ((), 1), "pyrup": ((), 1), "build_pyramid": ((3,), 1), } # ITERATE OVER THE TRANSFORMS for fn_name, (args, num_samples) in transforms.items(): img_in = img6.repeat(num_samples, 1, 1, 1) args_in = (img_in, *args) # import function and apply fn = getattr(mod, fn_name) out = fn(*args_in) if fn_name in ("resize", "rescale", "pyrdown", "pyrup"): h_new, w_new = out.shape[-2:] out = torch.nn.functional.pad(out, (0, (w - w_new), 0, (h - h_new))) if fn_name == "build_pyramid": _out = [] for pyr in out[1:]: h_new, w_new = pyr.shape[-2:] out_tmp = torch.nn.functional.pad(pyr, (0, (w - w_new), 0, (h - h_new))) _out.append(out_tmp) out = torch.cat(_out) # save the output image out = torch.cat([img_in[0], *(out[i] for i in range(out.size(0)))], dim=-1) out_np = K.utils.tensor_to_image((out * 255.0).byte()) cv2.imwrite(str(OUTPUT_PATH / f"{fn_name}.png"), out_np) sig = f"{fn_name}({', '.join([str(a) for a in args])})" print(f"Generated image example for {fn_name}. {sig}") if __name__ == "__main__": main()

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userprofile/migrations/0014_auto_20190430_2023.py

Riphiphip/website

25

167600

<gh_stars>10-100 # Generated by Django 2.0.10 on 2019-04-30 20:23 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('userprofile', '0013_auto_20190408_1825'), ] operations = [ migrations.RemoveField( model_name='profile', name='social_slack', ), migrations.AlterField( model_name='profile', name='social_battlenet', field=models.CharField(blank=True, max_length=30, null=True, verbose_name='Battle.net-tag'), ), migrations.AlterField( model_name='profile', name='social_git', field=models.CharField(blank=True, max_length=30, null=True, verbose_name='Git navn'), ), migrations.AlterField( model_name='profile', name='social_steam', field=models.CharField(blank=True, max_length=30, null=True, verbose_name='Steam navn'), ), ]

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python2/probe_yd.py

Nzen/run_ydl

0

22214

<filename>python2/probe_yd.py<gh_stars>0 from sys import argv from subprocess import call try : link = argv[ 1 ] except IndexError: link = raw_input( " - which url interests you? " ) try: ydl_answ = call( "youtube-dl -F "+ link, shell = True ) if ydl_answ is not 0 : print "-- failed "+ link + " code "+ str(ydl_answ) except OSError as ose : print "Execution failed:", ose

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lib/ldapconn.py

ArcaniteSolutions/zabbix-ldap-sync

0

1615065

import ldap import ldap.filter import logging class LDAPConn(object): """ LDAP connector class Defines methods for retrieving users and groups from LDAP server. """ def __init__(self, config): self.conn = None self.disabled_filter = config.ad_filterdisabled self.uri = config.ldap_uri self.base = config.ldap_base self.ldap_accountids = config.ldap_accountids self.ldap_user = config.ldap_user self.ldap_pass = config.ldap_passwd self.ldap_type = config.ldap_type self.group_member_attribute = config.ldap_group_member_attribute self.group_filter = config.ldap_group_filter self.uid_attribute = config.ldap_uid_attribute self.active_directory = config.ldap_active_directory self.recursive = config.ldap_recursive if self.recursive and self.active_directory: self.memberof_filter = config.ldap_memberof_filter self.skipdisabled = config.ldap_skipdisabled self.user_filter = config.ldap_user_filter self.verbose = config.verbose self.openldap_type = config.openldap_type self.logger = logging.getLogger(self.__class__.__name__) # Log from pyldap log = logging.getLogger('ldap') if self.verbose: log.setLevel(logging.DEBUG) ldap.set_option(ldap.OPT_DEBUG_LEVEL, 4095) if config.ldap_ignore_tls_errors: ldap.set_option(ldap.OPT_X_TLS_REQUIRE_CERT, ldap.OPT_X_TLS_NEVER) def connect(self): """ Establish a connection to the LDAP server. Raises: SystemExit """ ldap.set_option(ldap.OPT_X_TLS_REQUIRE_CERT, ldap.OPT_X_TLS_NEVER) self.conn = ldap.initialize(self.uri) self.conn.set_option(ldap.OPT_REFERRALS, ldap.OPT_OFF) try: self.conn.simple_bind_s(self.ldap_user, self.ldap_pass) except ldap.SERVER_DOWN as e: raise SystemExit('Cannot connect to LDAP server: %s' % e) def disconnect(self): """ Disconnect from the LDAP server. """ self.conn.unbind() def remove_ad_referrals(self, result: list): """ Remove referrals from AD query result """ return [i for i in result if i[0] is not None] def get_group_members(self, group: str): """ Retrieves the members of an LDAP group Args: group (str): The LDAP group name Returns: A list of all users in the LDAP group """ attrlist = [self.group_member_attribute] filter = self.group_filter % group self.logger.debug('Searching LDAP with filter >>>%s<<<' % filter) result = self.conn.search_s(base=self.base, scope=ldap.SCOPE_SUBTREE, filterstr=filter, attrlist=attrlist) if not result: self.logger.info('Unable to find group "%s" with filter "%s", skipping group' % (group, filter)) return None # Get DN for each user in the group if self.active_directory: return self.get_group_members_active_directory(result) else: return self.get_group_members_ldap(result) def get_group_members_ldap(self, result: list): dn, users = result.pop() if not users: return {} final_listing = {} group_members = [] # Get info for each user in the group for memberid in users[self.group_member_attribute]: memberid = memberid.decode("utf-8") if self.openldap_type == "groupofnames": filter = "(objectClass=*)" # memberid is user dn base = memberid else: # memberid is user attribute, most likely uid filter = self.user_filter % memberid base = self.base attrlist = [self.uid_attribute] # get the actual LDAP object for each group member self.logger.debug('Searching LDAP with filter >>>%s<<<' % filter) uid = self.conn.search_s(base=base, scope=ldap.SCOPE_SUBTREE, filterstr=filter, attrlist=attrlist) for item in uid: group_members.append(item) # Fill dictionary with usernames and corresponding DNs for item in group_members: dn = item[0] username = item[1][self.uid_attribute] user = ''.join(username[0].decode('utf-8')) final_listing[user] = dn return final_listing def get_group_members_active_directory(self, result: list): result = self.remove_ad_referrals(result) final_listing = {} for members in result: result_dn = members[0] result_attrs = members[1] group_members = [] attrlist = [self.uid_attribute] if self.recursive: # Get a DN for all users in a group (recursive) # It's available only on domain controllers with Windows Server 2003 SP2 or later member_of_filter_dn = self.memberof_filter % result_dn if self.skipdisabled: filter = "(&%s%s%s)" % (self.user_filter, member_of_filter_dn, self.disabled_filter) else: filter = "(&%s%s)" % (self.user_filter, member_of_filter_dn) self.logger.debug('Searching LDAP with filter >>>%s<<<' % filter) uid = self.conn.search_s(base=self.base, scope=ldap.SCOPE_SUBTREE, filterstr=filter, attrlist=attrlist) for item in self.remove_ad_referrals(uid): group_members.append(item) else: # Otherwise, just get a DN for each user in the group for member in result_attrs[self.group_member_attribute]: if self.skipdisabled: filter = "(&%s%s)" % (self.user_filter, self.disabled_filter) else: filter = "(&%s)" % self.user_filter self.logger.debug('Searching LDAP with filter >>>%s<<<' % filter) uid = self.conn.search_s(base=member.decode('utf8'), scope=ldap.SCOPE_BASE, filterstr=filter, attrlist=attrlist) for item in uid: group_members.append(item) # Fill dictionary with usernames and corresponding DNs for item in group_members: dn = item[0] username = item[1][self.uid_attribute] if self.ldap_accountids: username = username[0].decode('utf8') else: username = username[0].decode('utf8').lower() final_listing[username] = dn return final_listing def get_user_media(self, dn: str, ldap_media: list): """ Retrieves the 'media' attribute of an LDAP user Args: dn (str): The LDAP distinguished name to lookup ldap_media (str): The name of the field containing the media address Returns: The user's media attribute value """ attrlist = [ldap_media] result = self.conn.search_s(base=dn, scope=ldap.SCOPE_BASE, attrlist=attrlist) if not result: return None dn, data = result.pop() mail = data.get(ldap_media) if not mail: return None return mail.pop() def get_user_sn(self, dn: str): """ Retrieves the 'sn' attribute of an LDAP user Args: dn (str): The LDAP distinguished name to lookup Returns: The user's surname attribute """ attrlist = ['sn'] result = self.conn.search_s(base=dn, scope=ldap.SCOPE_BASE, attrlist=attrlist) if not result: return None dn, data = result.pop() sn = data.get('sn') if not sn: return None return sn.pop() def get_user_givenName(self, dn: str): """ Retrieves the 'givenName' attribute of an LDAP user Args: dn (str): The LDAP distinguished name to lookup Returns: The user's given name attribute """ attrlist = ['givenName'] result = self.conn.search_s(base=dn, scope=ldap.SCOPE_BASE, attrlist=attrlist) if not result: return None dn, data = result.pop() name = data.get('givenName') if not name: return None return name.pop() def get_groups_with_wildcard(self, groups_wildcard: str): filters = [] for wildcard in groups_wildcard: self.logger.info("Search groups with wildcard: %s" % wildcard) filters.append(self.group_filter % wildcard) ldap_filter = "(| %s)" % (" ".join(filters)) result_groups = [] self.logger.debug('Searching LDAP with filter >>>%s<<<' % ldap_filter) result = self.conn.search_s(base=self.base, scope=ldap.SCOPE_SUBTREE, filterstr=ldap_filter) for group in result: # Skip refldap (when Active Directory used) # [0]==None if group[0]: group_name = group[1]['name'][0].decode() self.logger.info("Found group %s" % group_name) result_groups.append(group_name) if not result_groups: self.logger.info('Unable to find group "%s", skipping group wildcard' % groups_wildcard) return result_groups

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google/cloud/bigquery/reservation_v1/types/reservation.py

shollyman/python-bigquery-reservation

0

137874

<gh_stars>0 # -*- coding: utf-8 -*- # Copyright 2020 Google LLC # # 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 proto # type: ignore from google.protobuf import field_mask_pb2 as field_mask # type: ignore from google.protobuf import timestamp_pb2 as timestamp # type: ignore from google.rpc import status_pb2 as status # type: ignore __protobuf__ = proto.module( package="google.cloud.bigquery.reservation.v1", manifest={ "Reservation", "CapacityCommitment", "CreateReservationRequest", "ListReservationsRequest", "ListReservationsResponse", "GetReservationRequest", "DeleteReservationRequest", "UpdateReservationRequest", "CreateCapacityCommitmentRequest", "ListCapacityCommitmentsRequest", "ListCapacityCommitmentsResponse", "GetCapacityCommitmentRequest", "DeleteCapacityCommitmentRequest", "UpdateCapacityCommitmentRequest", "SplitCapacityCommitmentRequest", "SplitCapacityCommitmentResponse", "MergeCapacityCommitmentsRequest", "Assignment", "CreateAssignmentRequest", "ListAssignmentsRequest", "ListAssignmentsResponse", "DeleteAssignmentRequest", "SearchAssignmentsRequest", "SearchAssignmentsResponse", "MoveAssignmentRequest", "BiReservation", "GetBiReservationRequest", "UpdateBiReservationRequest", }, ) class Reservation(proto.Message): r"""A reservation is a mechanism used to guarantee slots to users. Attributes: name (str): The resource name of the reservation, e.g., ``projects/*/locations/*/reservations/team1-prod``. slot_capacity (int): Minimum slots available to this reservation. A slot is a unit of computational power in BigQuery, and serves as the unit of parallelism. Queries using this reservation might use more slots during runtime if ignore_idle_slots is set to false. If the new reservation's slot capacity exceed the parent's slot capacity or if total slot capacity of the new reservation and its siblings exceeds the parent's slot capacity, the request will fail with ``google.rpc.Code.RESOURCE_EXHAUSTED``. ignore_idle_slots (bool): If false, any query using this reservation will use idle slots from other reservations within the same admin project. If true, a query using this reservation will execute with the slot capacity specified above at most. """ name = proto.Field(proto.STRING, number=1) slot_capacity = proto.Field(proto.INT64, number=2) ignore_idle_slots = proto.Field(proto.BOOL, number=4) class CapacityCommitment(proto.Message): r"""Capacity commitment is a way to purchase compute capacity for BigQuery jobs (in the form of slots) with some committed period of usage. Annual commitments renew by default. Commitments can be removed after their commitment end time passes. In order to remove annual commitment, its plan needs to be changed to monthly or flex first. A capacity commitment resource exists as a child resource of the admin project. Attributes: name (str): Output only. The resource name of the capacity commitment, e.g., ``projects/myproject/locations/US/capacityCommitments/123`` slot_count (int): Number of slots in this commitment. plan (~.gcbr_reservation.CapacityCommitment.CommitmentPlan): Capacity commitment commitment plan. state (~.gcbr_reservation.CapacityCommitment.State): Output only. State of the commitment. commitment_end_time (~.timestamp.Timestamp): Output only. The end of the current commitment period. It is applicable only for ACTIVE capacity commitments. failure_status (~.status.Status): Output only. For FAILED commitment plan, provides the reason of failure. renewal_plan (~.gcbr_reservation.CapacityCommitment.CommitmentPlan): The plan this capacity commitment is converted to after commitment_end_time passes. Once the plan is changed, committed period is extended according to commitment plan. Only applicable for ANNUAL and TRIAL commitments. """ class CommitmentPlan(proto.Enum): r"""Commitment plan defines the current committed period. Capacity commitment cannot be deleted during it's committed period. """ COMMITMENT_PLAN_UNSPECIFIED = 0 FLEX = 3 TRIAL = 5 MONTHLY = 2 ANNUAL = 4 class State(proto.Enum): r"""Capacity commitment can either become ACTIVE right away or transition from PENDING to ACTIVE or FAILED. """ STATE_UNSPECIFIED = 0 PENDING = 1 ACTIVE = 2 FAILED = 3 name = proto.Field(proto.STRING, number=1) slot_count = proto.Field(proto.INT64, number=2) plan = proto.Field(proto.ENUM, number=3, enum=CommitmentPlan) state = proto.Field(proto.ENUM, number=4, enum=State) commitment_end_time = proto.Field( proto.MESSAGE, number=5, message=timestamp.Timestamp ) failure_status = proto.Field(proto.MESSAGE, number=7, message=status.Status) renewal_plan = proto.Field(proto.ENUM, number=8, enum=CommitmentPlan) class CreateReservationRequest(proto.Message): r"""The request for [ReservationService.CreateReservation][google.cloud.bigquery.reservation.v1.ReservationService.CreateReservation]. Attributes: parent (str): Required. Project, location. E.g., ``projects/myproject/locations/US`` reservation_id (str): The reservation ID. This field must only contain lower case alphanumeric characters or dash. Max length is 64 characters. reservation (~.gcbr_reservation.Reservation): Definition of the new reservation to create. """ parent = proto.Field(proto.STRING, number=1) reservation_id = proto.Field(proto.STRING, number=2) reservation = proto.Field(proto.MESSAGE, number=3, message=Reservation) class ListReservationsRequest(proto.Message): r"""The request for [ReservationService.ListReservations][google.cloud.bigquery.reservation.v1.ReservationService.ListReservations]. Attributes: parent (str): Required. The parent resource name containing project and location, e.g.: "projects/myproject/locations/US". page_size (int): The maximum number of items to return per page. page_token (str): The next_page_token value returned from a previous List request, if any. """ parent = proto.Field(proto.STRING, number=1) page_size = proto.Field(proto.INT32, number=2) page_token = proto.Field(proto.STRING, number=3) class ListReservationsResponse(proto.Message): r"""The response for [ReservationService.ListReservations][google.cloud.bigquery.reservation.v1.ReservationService.ListReservations]. Attributes: reservations (Sequence[~.gcbr_reservation.Reservation]): List of reservations visible to the user. next_page_token (str): Token to retrieve the next page of results, or empty if there are no more results in the list. """ @property def raw_page(self): return self reservations = proto.RepeatedField(proto.MESSAGE, number=1, message=Reservation) next_page_token = proto.Field(proto.STRING, number=2) class GetReservationRequest(proto.Message): r"""The request for [ReservationService.GetReservation][google.cloud.bigquery.reservation.v1.ReservationService.GetReservation]. Attributes: name (str): Required. Resource name of the reservation to retrieve. E.g., ``projects/myproject/locations/US/reservations/team1-prod`` """ name = proto.Field(proto.STRING, number=1) class DeleteReservationRequest(proto.Message): r"""The request for [ReservationService.DeleteReservation][google.cloud.bigquery.reservation.v1.ReservationService.DeleteReservation]. Attributes: name (str): Required. Resource name of the reservation to retrieve. E.g., ``projects/myproject/locations/US/reservations/team1-prod`` """ name = proto.Field(proto.STRING, number=1) class UpdateReservationRequest(proto.Message): r"""The request for [ReservationService.UpdateReservation][google.cloud.bigquery.reservation.v1.ReservationService.UpdateReservation]. Attributes: reservation (~.gcbr_reservation.Reservation): Content of the reservation to update. update_mask (~.field_mask.FieldMask): Standard field mask for the set of fields to be updated. """ reservation = proto.Field(proto.MESSAGE, number=1, message=Reservation) update_mask = proto.Field(proto.MESSAGE, number=2, message=field_mask.FieldMask) class CreateCapacityCommitmentRequest(proto.Message): r"""The request for [ReservationService.CreateCapacityCommitment][google.cloud.bigquery.reservation.v1.ReservationService.CreateCapacityCommitment]. Attributes: parent (str): Required. Resource name of the parent reservation. E.g., projects/myproject/locations/US capacity_commitment (~.gcbr_reservation.CapacityCommitment): Content of the capacity commitment to create. enforce_single_admin_project_per_org (bool): If true, fail the request if another project in the organization has a capacity commitment. """ parent = proto.Field(proto.STRING, number=1) capacity_commitment = proto.Field( proto.MESSAGE, number=2, message=CapacityCommitment ) enforce_single_admin_project_per_org = proto.Field(proto.BOOL, number=4) class ListCapacityCommitmentsRequest(proto.Message): r"""The request for [ReservationService.ListCapacityCommitments][google.cloud.bigquery.reservation.v1.ReservationService.ListCapacityCommitments]. Attributes: parent (str): Required. Resource name of the parent reservation. E.g., projects/myproject/locations/US page_size (int): The maximum number of items to return. page_token (str): The next_page_token value returned from a previous List request, if any. """ parent = proto.Field(proto.STRING, number=1) page_size = proto.Field(proto.INT32, number=2) page_token = proto.Field(proto.STRING, number=3) class ListCapacityCommitmentsResponse(proto.Message): r"""The response for [ReservationService.ListCapacityCommitments][google.cloud.bigquery.reservation.v1.ReservationService.ListCapacityCommitments]. Attributes: capacity_commitments (Sequence[~.gcbr_reservation.CapacityCommitment]): List of capacity commitments visible to the user. next_page_token (str): Token to retrieve the next page of results, or empty if there are no more results in the list. """ @property def raw_page(self): return self capacity_commitments = proto.RepeatedField( proto.MESSAGE, number=1, message=CapacityCommitment ) next_page_token = proto.Field(proto.STRING, number=2) class GetCapacityCommitmentRequest(proto.Message): r"""The request for [ReservationService.GetCapacityCommitment][google.cloud.bigquery.reservation.v1.ReservationService.GetCapacityCommitment]. Attributes: name (str): Required. Resource name of the capacity commitment to retrieve. E.g., projects/myproject/locations/US/capacityCommitments/123 """ name = proto.Field(proto.STRING, number=1) class DeleteCapacityCommitmentRequest(proto.Message): r"""The request for [ReservationService.DeleteCapacityCommitment][google.cloud.bigquery.reservation.v1.ReservationService.DeleteCapacityCommitment]. Attributes: name (str): Required. Resource name of the capacity commitment to delete. E.g., projects/myproject/locations/US/capacityCommitments/123 """ name = proto.Field(proto.STRING, number=1) class UpdateCapacityCommitmentRequest(proto.Message): r"""The request for [ReservationService.UpdateCapacityCommitment][google.cloud.bigquery.reservation.v1.ReservationService.UpdateCapacityCommitment]. Attributes: capacity_commitment (~.gcbr_reservation.CapacityCommitment): Content of the capacity commitment to update. update_mask (~.field_mask.FieldMask): Standard field mask for the set of fields to be updated. """ capacity_commitment = proto.Field( proto.MESSAGE, number=1, message=CapacityCommitment ) update_mask = proto.Field(proto.MESSAGE, number=2, message=field_mask.FieldMask) class SplitCapacityCommitmentRequest(proto.Message): r"""The request for [ReservationService.SplitCapacityCommitment][google.cloud.bigquery.reservation.v1.ReservationService.SplitCapacityCommitment]. Attributes: name (str): Required. The resource name e.g.,: projects/myproject/locations/US/capacityCommitments/123 slot_count (int): Number of slots in the capacity commitment after the split. """ name = proto.Field(proto.STRING, number=1) slot_count = proto.Field(proto.INT64, number=2) class SplitCapacityCommitmentResponse(proto.Message): r"""The response for [ReservationService.SplitCapacityCommitment][google.cloud.bigquery.reservation.v1.ReservationService.SplitCapacityCommitment]. Attributes: first (~.gcbr_reservation.CapacityCommitment): First capacity commitment, result of a split. second (~.gcbr_reservation.CapacityCommitment): Second capacity commitment, result of a split. """ first = proto.Field(proto.MESSAGE, number=1, message=CapacityCommitment) second = proto.Field(proto.MESSAGE, number=2, message=CapacityCommitment) class MergeCapacityCommitmentsRequest(proto.Message): r"""The request for [ReservationService.MergeCapacityCommitments][google.cloud.bigquery.reservation.v1.ReservationService.MergeCapacityCommitments]. Attributes: parent (str): Parent resource that identifies admin project and location e.g., projects/myproject/locations/us capacity_commitment_ids (Sequence[str]): Ids of capacity commitments to merge. These capacity commitments must exist under admin project and location specified in the parent. """ parent = proto.Field(proto.STRING, number=1) capacity_commitment_ids = proto.RepeatedField(proto.STRING, number=2) class Assignment(proto.Message): r"""A Assignment allows a project to submit jobs of a certain type using slots from the specified reservation. Attributes: name (str): Output only. Name of the resource. E.g.: ``projects/myproject/locations/US/reservations/team1-prod/assignments/123``. assignee (str): The resource which will use the reservation. E.g. ``projects/myproject``, ``folders/123``, or ``organizations/456``. job_type (~.gcbr_reservation.Assignment.JobType): Which type of jobs will use the reservation. state (~.gcbr_reservation.Assignment.State): Output only. State of the assignment. """ class JobType(proto.Enum): r"""Types of job, which could be specified when using the reservation. """ JOB_TYPE_UNSPECIFIED = 0 PIPELINE = 1 QUERY = 2 class State(proto.Enum): r"""Assignment will remain in PENDING state if no active capacity commitment is present. It will become ACTIVE when some capacity commitment becomes active. """ STATE_UNSPECIFIED = 0 PENDING = 1 ACTIVE = 2 name = proto.Field(proto.STRING, number=1) assignee = proto.Field(proto.STRING, number=4) job_type = proto.Field(proto.ENUM, number=3, enum=JobType) state = proto.Field(proto.ENUM, number=6, enum=State) class CreateAssignmentRequest(proto.Message): r"""The request for [ReservationService.CreateAssignment][google.cloud.bigquery.reservation.v1.ReservationService.CreateAssignment]. Note: "bigquery.reservationAssignments.create" permission is required on the related assignee. Attributes: parent (str): Required. The parent resource name of the assignment E.g. ``projects/myproject/locations/US/reservations/team1-prod`` assignment (~.gcbr_reservation.Assignment): Assignment resource to create. """ parent = proto.Field(proto.STRING, number=1) assignment = proto.Field(proto.MESSAGE, number=2, message=Assignment) class ListAssignmentsRequest(proto.Message): r"""The request for [ReservationService.ListAssignments][google.cloud.bigquery.reservation.v1.ReservationService.ListAssignments]. Attributes: parent (str): Required. The parent resource name e.g.: ``projects/myproject/locations/US/reservations/team1-prod`` Or: ``projects/myproject/locations/US/reservations/-`` page_size (int): The maximum number of items to return per page. page_token (str): The next_page_token value returned from a previous List request, if any. """ parent = proto.Field(proto.STRING, number=1) page_size = proto.Field(proto.INT32, number=2) page_token = proto.Field(proto.STRING, number=3) class ListAssignmentsResponse(proto.Message): r"""The response for [ReservationService.ListAssignments][google.cloud.bigquery.reservation.v1.ReservationService.ListAssignments]. Attributes: assignments (Sequence[~.gcbr_reservation.Assignment]): List of assignments visible to the user. next_page_token (str): Token to retrieve the next page of results, or empty if there are no more results in the list. """ @property def raw_page(self): return self assignments = proto.RepeatedField(proto.MESSAGE, number=1, message=Assignment) next_page_token = proto.Field(proto.STRING, number=2) class DeleteAssignmentRequest(proto.Message): r"""The request for [ReservationService.DeleteAssignment][google.cloud.bigquery.reservation.v1.ReservationService.DeleteAssignment]. Note: "bigquery.reservationAssignments.delete" permission is required on the related assignee. Attributes: name (str): Required. Name of the resource, e.g. ``projects/myproject/locations/US/reservations/team1-prod/assignments/123`` """ name = proto.Field(proto.STRING, number=1) class SearchAssignmentsRequest(proto.Message): r"""The request for [ReservationService.SearchAssignments][google.cloud.bigquery.reservation.v1.ReservationService.SearchAssignments]. Note: "bigquery.reservationAssignments.search" permission is required on the related assignee. Attributes: parent (str): Required. The resource name of the admin project(containing project and location), e.g.: "projects/myproject/locations/US". query (str): Please specify resource name as assignee in the query. Examples: - ``assignee=projects/myproject`` - ``assignee=folders/123`` - ``assignee=organizations/456`` page_size (int): The maximum number of items to return per page. page_token (str): The next_page_token value returned from a previous List request, if any. """ parent = proto.Field(proto.STRING, number=1) query = proto.Field(proto.STRING, number=2) page_size = proto.Field(proto.INT32, number=3) page_token = proto.Field(proto.STRING, number=4) class SearchAssignmentsResponse(proto.Message): r"""The response for [ReservationService.SearchAssignments][google.cloud.bigquery.reservation.v1.ReservationService.SearchAssignments]. Attributes: assignments (Sequence[~.gcbr_reservation.Assignment]): List of assignments visible to the user. next_page_token (str): Token to retrieve the next page of results, or empty if there are no more results in the list. """ @property def raw_page(self): return self assignments = proto.RepeatedField(proto.MESSAGE, number=1, message=Assignment) next_page_token = proto.Field(proto.STRING, number=2) class MoveAssignmentRequest(proto.Message): r"""The request for [ReservationService.MoveAssignment][google.cloud.bigquery.reservation.v1.ReservationService.MoveAssignment]. **Note**: "bigquery.reservationAssignments.create" permission is required on the destination_id. **Note**: "bigquery.reservationAssignments.create" and "bigquery.reservationAssignments.delete" permission are required on the related assignee. Attributes: name (str): Required. The resource name of the assignment, e.g. ``projects/myproject/locations/US/reservations/team1-prod/assignments/123`` destination_id (str): The new reservation ID, e.g.: ``projects/myotherproject/locations/US/reservations/team2-prod`` """ name = proto.Field(proto.STRING, number=1) destination_id = proto.Field(proto.STRING, number=3) class BiReservation(proto.Message): r"""Represents a BI Reservation. Attributes: name (str): The resource name of the singleton BI reservation. Reservation names have the form ``projects/{project_id}/locations/{location_id}/bireservation``. update_time (~.timestamp.Timestamp): Output only. The last update timestamp of a reservation. size (int): Size of a reservation, in bytes. """ name = proto.Field(proto.STRING, number=1) update_time = proto.Field(proto.MESSAGE, number=3, message=timestamp.Timestamp) size = proto.Field(proto.INT64, number=4) class GetBiReservationRequest(proto.Message): r"""A request to get a singleton BI reservation. Attributes: name (str): Required. Name of the requested reservation, for example: ``projects/{project_id}/locations/{location_id}/bireservation`` """ name = proto.Field(proto.STRING, number=1) class UpdateBiReservationRequest(proto.Message): r"""A request to update a BI reservation. Attributes: bi_reservation (~.gcbr_reservation.BiReservation): A reservation to update. update_mask (~.field_mask.FieldMask): A list of fields to be updated in this request. """ bi_reservation = proto.Field(proto.MESSAGE, number=1, message=BiReservation) update_mask = proto.Field(proto.MESSAGE, number=2, message=field_mask.FieldMask) __all__ = tuple(sorted(__protobuf__.manifest))

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job-queue-portal/postgres_django_queue/djangoenv/lib/python3.8/site-packages/django_celery_results/utils.py

Sruthi-Ganesh/postgres-django-queue

0

185979

"""Utilities.""" # -- XXX This module must not use translation as that causes # -- a recursive loader import! from django.conf import settings from django.utils import timezone # see Issue celery/django-celery#222 now_localtime = getattr(timezone, 'template_localtime', timezone.localtime) def now(): """Return the current date and time.""" if getattr(settings, 'USE_TZ', False): return now_localtime(timezone.now()) else: return timezone.now()

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code/Dictionary/key_of_min.py

jumploop/30-seconds-of-python

0

197308

#!/usr/bin/env python # -*- coding: utf-8 -*- """ 功能实现：在字典中查找最小值的键。解读：使用min()并将key参数设置为dict.get()来查找并返回给定字典中最小值的键。 """ def key_of_min(d): return min(d, key=d.get) # Examples print(key_of_min({'a': 4, 'b': 0, 'c': 13})) # output: # b

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dronesym-python/flask-api/src/dronepool.py

dilinade/DroneSym

1

3946

<reponame>dilinade/DroneSym<filename>dronesym-python/flask-api/src/dronepool.py #DronePool module which handles interaction with SITLs from dronekit import Vehicle, VehicleMode, connect from dronekit_sitl import SITL from threading import Lock import node, time import mavparser import threadrunner drone_pool = {} instance_count = 0 env_test = False q = None mq = None lock = Lock() class Sim(SITL, object): def __init__(self, instance=1, home=None): super(Sim, self).download("copter", "3.3", verbose=not env_test) self.instance = instance if home: self.home = home else: self.home = {"lat":6.9271, "lon":79.8612, "alt": 1} self.p = None return def connection_string(self): return super(Sim, self).connection_string()[:-4] + str(5760 + self.instance * 10) def launch(self): home_str = str(self.home['lat']) + ',' + str(self.home['lon']) + ',0,353' super(Sim, self).launch(["--instance", str(self.instance), "--home", home_str], await_ready=True, verbose=not env_test) def get_sitl_status(self): return { 'id': self.instance, 'home': self.home } def initialize(): global q, mq, instance_count q = threadrunner.q mq = threadrunner.mq drones = node.get_drones()['drones'] if not drones: return for drone_id in drones: if drone_id not in list(drone_pool.keys()): drone = node.get_drone_by_id(drone_id) location = drone['location'] q.put((create_new_drone, { "db_key" : drone_id, "home" : location })) if 'status' in list(drone.keys()) and drone['status'] == 'FLYING': q.put((resume_flight, { "drone_id" : drone_id })) def resume_flight(kwargs): drone_id = kwargs.get("drone_id", None) drone = node.get_drone_by_id(drone_id) waypoints = [] for wp in sorted(drone['waypoints']): waypoints.append(drone['waypoints'][wp]) next_waypoint = waypoints.index(drone['waypoint']) print (next_waypoint) q.put((takeoff_drone, { "drone_id" : drone_id, "waypoints" : waypoints[next_waypoint:] })) def create_new_drone(kwargs): global instance_count instance_count += 1 home = kwargs.get("home", None) db_key = kwargs.get("db_key", None) retries = 3 drone = Sim(instance_count, home) drone.launch() while retries > 0: try: drone_conn = connect(drone.connection_string(), wait_ready=True) break except: print ("Retrying...") retries -= 1 drone_pool[db_key] = drone_conn res = { "status" : "OK", "id" : db_key } return res def remove_drone(kwargs): drone_id = kwargs.get("drone_id", None) if drone_id not in drone_pool: return { "status" : "ERROR", "msg" : "Drone instance not found" } drone = drone_pool[drone_id] if drone.mode == VehicleMode('AUTO'): return { "status" : "ERROR", "msg" : "Drone in operation" } del drone_pool[drone_id] return { "status" : "OK", "id" : drone_id } def run_mission(drone, target_height, waypoints): while True: print(("Reaching target alt : " + str(drone.location.global_relative_frame.alt))) if drone.location.global_relative_frame.alt >= target_height * 0.9: break print ('target alt reached') mavparser.create_mission(drone, waypoints) print ('mission acquired') drone.mode = VehicleMode('AUTO') print ('initiating sequence') print ('in mission') def attach_listener(kwargs): attr = kwargs.get('attr', None) fn = kwargs.get('fn', None) attach_fn = kwargs.get('attach_fn', None) if not fn == None and not attr == None and not attach_fn == None: attach_fn(attr, fn) def takeoff_drone(kwargs): global q drone_id = kwargs.get("drone_id", None) target_height = kwargs.get("target_height", 10) waypoints = kwargs.get("waypoints", None) try: drone = drone_pool[drone_id] except: raise drone.initialize() drone.mode = VehicleMode('GUIDED') drone.armed = True while not drone.armed: time.sleep(1) drone.simple_takeoff(target_height) print (waypoints) if waypoints: run_mission(drone, target_height, waypoints) def detach_event_listeners(drone, value, status): drone.remove_attribute_listener('location', update_location) drone.remove_attribute_listener('airspeed', update_airspeed) drone.remove_attribute_listener('attitude', udpate_attitude) drone.remove_attribute_listener('heading', update_heading) node.update_drone(drone_id, { "location" : {"lat": value.global_relative_frame.lat, "lon": value.global_relative_frame.lon, "alt": value.global_relative_frame.alt}, "status": status}) return def update_location(self, attr_name, value): node.update_drone(drone_id, { "location" : {"lat": value.global_relative_frame.lat, "lon": value.global_relative_frame.lon, "alt": value.global_relative_frame.alt}, "status": "FLYING"}) command_len = len(drone.commands) wp_len = len(waypoints) if command_len >= wp_len : diff = command_len - wp_len next_wp = max(drone.commands.__next__ - diff, 0) % len(waypoints) waypoint = waypoints[next_wp] # print "df: " + `diff` # print next_wp node.update_drone(drone_id, { "waypoint" : waypoint }) if drone.mode == VehicleMode('LAND') and drone.location.global_relative_frame.alt <= 0.1: detach_event_listeners(drone, value, "HALTED") return if drone.commands.__next__ == len(drone.commands): detach_event_listeners(drone, value, "FINISHED") return def update_airspeed(self, attr_name, value): node.update_drone(drone_id, {"airspeed": value}) def udpate_attitude(self, attr_name, value): node.update_drone(drone_id, { "pitch": value.pitch, 'roll': value.roll, 'yaw': value.yaw }) def update_heading(self, attr_name, value): node.update_drone(drone_id, { "heading": value }) mq.put((attach_listener, { "attach_fn" : drone.add_attribute_listener, "attr" : 'location', "fn" : update_location })) mq.put((attach_listener, { "attach_fn" : drone.add_attribute_listener, "attr" : 'airspeed', "fn" : update_airspeed })) mq.put((attach_listener, { "attach_fn" : drone.add_attribute_listener, "attr" : 'attitude', "fn" : udpate_attitude })) mq.put((attach_listener, { "attach_fn" : drone.add_attribute_listener, "attr" : 'heading', "fn" : update_heading })) print ('took off') return True def land_drone(kwargs): drone_id = kwargs.get("drone_id", None) try: drone = drone_pool[drone_id] except: raise if not drone.armed: return False cmds = drone.commands cmds.wait_ready() cmds.clear() drone.mode = VehicleMode('LAND') print((drone.mode)) return True

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python3/hackerrank_leetcode/coin_change/main.py

seLain/codesnippets

0

183246

''' Hackerrank: https://www.hackerrank.com/challenges/coin-change/problem this solution will get timeout in many test cases however, this solution can get all the combination during computation ''' import sys from collections import Counter, defaultdict sys.setrecursionlimit(10000) memory = defaultdict(list) def recur_getWays(n, c): #print('enter: n='+str(n)) if n < 0: return False, [] elif n == 0: return True, [Counter()] if n in memory.keys(): return True, memory[n] all_combs_count = [] for coin in c: #print('choose coin:'+str(coin)) valid, combs = recur_getWays(n-coin, c) if valid: for comb in combs: comb = comb + Counter({coin:1}) existed = False for existed_comb in all_combs_count: if existed_comb == comb: existed = True break if not existed: all_combs_count.append(comb) #print('memorize: n='+str(n)+' count:'+str(all_combs_count)) memory[n] = all_combs_count return True, all_combs_count def getWays(n, c): # Complete this function valid, combs = recur_getWays(n, c) #print(memory) return len(combs) n, m = input().strip().split(' ') n, m = [int(n), int(m)] c = list(map(int, input().strip().split(' '))) # Print the number of ways of making change for 'n' units using coins having the values given by 'c' ways = getWays(n, c) print(ways)

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src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py

LeonardoWang/nni

0

123083

<gh_stars>0 from torch import Tensor from torch.nn import Module, Parameter from ruamel.yaml import YAML from typing import List import logging logger = logging.getLogger('torch_compressor') __all__ = [ 'TorchCompressor', 'TorchPruner', 'TorchQuantizer', '_torch_detect_prunable_layers', '_torch_default_get_configure', '_torch_default_load_configure_file' ] class TorchCompressor: """ Base compressor for pytorch """ def __init__(self): self._bound_model = None def compress(self, model): """ Compress the model with algorithm implemented by subclass. The model will be instrumented and user should never edit it after calling this method. """ assert self._bound_model is None, "Each NNI compressor instance can only compress one model" self._bound_model = model self.bind_model(model) def bind_model(self, model): """ This method is called when a model is bound to the compressor. Users can optionally overload this method to do model-specific initialization. It is guaranteed that only one model will be bound to each compressor instance. """ pass def update_epoch(self, epoch): """ if user want to update model every epoch, user can override this method """ pass def step(self): """ if user want to update model every step, user can override this method """ pass class TorchLayerInfo: """ layer info for pytorch """ def __init__(self, name, layer): self.name = name self.layer = layer self._forward = None def _torch_detect_prunable_layers(model): # search for all layers which have parameter "weight" ret = [] for name, layer in model.named_modules(): try: if isinstance(layer.weight, Parameter) and isinstance(layer.weight.data, Tensor): ret.append(TorchLayerInfo(name, layer)) except AttributeError: pass return ret def _torch_default_get_configure(configure_list, layer_info): """ Get configure for input layer defaultly the later config will cover front config WARNING: please mask sure default configure is the first in the list """ if not configure_list: logger.warning('WARNING: configure list is None') configure = {} for config in configure_list: if config.get('support_type', '') == 'default': configure = config elif type(layer_info.layer).__name__ in config.get('support_type', []): configure = config elif layer_info.name in config.get('support_op', []): configure = config if not configure: logger.warning('WARNING: can not get configure, default NONE!!!') return configure def _torch_default_load_configure_file(config_path, class_name): logger.info('load CLASS:{0} from PATH:{1}'.format(class_name, config_path)) assert config_path is not None and config_path.endswith('yaml') file = open(config_path, 'r') yaml = YAML(typ='safe') yaml_text = yaml.load(file.read()) configure_file = yaml_text.get(class_name, {}) if not configure_file: logger.warning('WARNING: load Nothing from configure file, Default { }') return configure_file class TorchPruner(TorchCompressor): """ Base pruner for pytorch pruner """ def __init__(self): super().__init__() def __call__(self, model): self.compress(model) return model def calc_mask(self, layer_info, weight): """ Pruners should overload this method to provide mask for weight tensors. The mask must have the same shape and type comparing to the weight. It will be applied with `mul()` operation. This method is effectively hooked to `forward()` method of the model. """ raise NotImplementedError("Pruners must overload calc_mask()") def compress(self, model): super().compress(model) # TODO: configurable whitelist for layer_info in _torch_detect_prunable_layers(model): self._instrument_layer(layer_info) def _instrument_layer(self, layer_info): # TODO: bind additional properties to layer_info instead of layer # create a wrapper forward function to replace the original one assert layer_info._forward is None, 'Each model can only be compressed once' layer_info._forward = layer_info.layer.forward def new_forward(*input): # apply mask to weight mask = self.calc_mask(layer_info, layer_info.layer.weight.data) layer_info._backup_weight = layer_info.layer.weight.data layer_info.layer.weight.data = layer_info.layer.weight.data.mul(mask) # calculate forward ret = layer_info._forward(*input) # recover original weight layer_info.layer.weight.data = layer_info._backup_weight return ret layer_info.layer.forward = new_forward class TorchQuantizer(TorchCompressor): """ Base quantizer for pytorch quantizer """ def __init__(self): super().__init__() def __call__(self, model): self.compress(model) return model def quantize_weight(self, layer_info, weight): """ user should know where dequantize goes and implement it in quantize method we now do not provide dequantize method """ raise NotImplementedError("Quantizer must overload quantize_weight()") def compress(self, model): super().compress(model) count = 0 for layer_info in _torch_detect_prunable_layers(model): if count == 0: count = count +1 continue self._instrument_layer(layer_info) def _instrument_layer(self, layer_info): assert layer_info._forward is None layer_info._forward = layer_info.layer.forward def new_forward(*input): layer_info.layer.weight.data = self.quantize_weight(layer_info, layer_info.layer.weight.data) return layer_info._forward(*input) layer_info.layer.forward = new_forward

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Python/problem0050.py

1050669722/LeetCode-Answers

0

43814

# -*- coding: utf-8 -*- """ Created on Fri May 31 10:11:51 2019 @author: Administrator """ class Solution: def myPow(self, x: float, n: int) -> float: # if n == 1: # return x # if x!=0 and n == 0: # return 1 # if x == 0 and n <= 0: # return None # if n>0: # if n%2 == 0: # return self.myPow(x, n//2) * self.myPow(x, n//2) # else: # return self.myPow(x, n//2) * self.myPow(x, n//2) * x # if n<0: # if n%2 == 0: # return 1. / ( self.myPow(x, -n//2) * self.myPow(x, -n//2) ) # else: # return 1. / ( self.myPow(x, -n//2) * self.myPow(x, -n//2) * x ) ## if n == 1: ## return x ## if n == 0: ## return 1 # if n == 1: # return x # if x!=0 and n == 0: # return 1 # if x == 0 and n <= 0: # return None # if n>0: # temp = self.myPow(x, n//2) # if n%2 == 0: # return temp * temp # else: # return temp * temp * x # if n<0: # temp = self.myPow(x, -n//2) # if n%2 == 0: # return 1. / ( temp * temp ) # else: # return 1. / ( temp * temp * x ) # if n == 1: return x # if n == 0: return 1 # t = self.myPow(x, abs(n) // 2) # if n % 2 == 0: # t = t * t # else: t = x * t * t # if n < 0: # return 1.0 /t # return t if n<0: n = -n x = 1. / x ans = 1 cp = x while n>=1: ans = ans*cp**(n%2) # cp **= 2 cp *= cp n //= 2 return ans solu = Solution() x, n = 2.00000, 10 #x, n = 2.00000, 4 #x, n = 2.10000, 3 #x, n = 2.00000, -2 #x, n = 0.00001, 2147483647#2**31-1# #x, n = 0.00000, -1 print(solu.myPow(x, n))

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313, 5694, 334, 5694, 1723, 13, 29937, 9651, 1683, 29901, 13, 29937, 18884, 736, 29871, 29896, 29889, 847, 313, 5694, 334, 5694, 334, 921, 1723, 13, 308, 13, 29937, 4706, 565, 302, 1275, 29871, 29896, 29901, 736, 921, 13, 29937, 4706, 565, 302, 1275, 29871, 29900, 29901, 736, 29871, 29896, 13, 29937, 4706, 260, 353, 1583, 29889, 1357, 29925, 340, 29898, 29916, 29892, 6425, 29898, 29876, 29897, 849, 29871, 29906, 29897, 13, 29937, 4706, 565, 302, 1273, 29871, 29906, 1275, 29871, 29900, 29901, 13, 29937, 9651, 260, 353, 260, 334, 260, 13, 29937, 4706, 1683, 29901, 260, 353, 29871, 921, 334, 260, 334, 260, 13, 29937, 4706, 565, 302, 529, 29871, 29900, 29901, 13, 29937, 9651, 736, 29871, 29896, 29889, 29900, 29871, 847, 29873, 13, 29937, 4706, 736, 260, 13, 308, 13, 4706, 565, 302, 29966, 29900, 29901, 13, 9651, 302, 353, 448, 29876, 13, 9651, 921, 353, 29871, 29896, 29889, 847, 921, 13, 4706, 6063, 353, 29871, 29896, 13, 4706, 21447, 353, 921, 13, 4706, 1550, 302, 18572, 29896, 29901, 13, 9651, 6063, 353, 6063, 29930, 6814, 1068, 29898, 29876, 29995, 29906, 29897, 13, 29937, 9651, 21447, 3579, 29922, 29871, 29906, 13, 9651, 21447, 334, 29922, 21447, 13, 9651, 302, 849, 29922, 29871, 29906, 13, 4706, 736, 6063, 13, 308, 13, 2929, 29884, 353, 24380, 580, 13, 29916, 29892, 302, 353, 29871, 29906, 29889, 29900, 29900, 29900, 29900, 29900, 29892, 29871, 29896, 29900, 13, 29937, 29916, 29892, 302, 353, 29871, 29906, 29889, 29900, 29900, 29900, 29900, 29900, 29892, 29871, 29946, 13, 29937, 29916, 29892, 302, 353, 29871, 29906, 29889, 29896, 29900, 29900, 29900, 29900, 29892, 29871, 29941, 13, 29937, 29916, 29892, 302, 353, 29871, 29906, 29889, 29900, 29900, 29900, 29900, 29900, 29892, 448, 29906, 13, 29937, 29916, 29892, 302, 353, 29871, 29900, 29889, 29900, 29900, 29900, 29900, 29896, 29892, 29871, 29906, 29896, 29946, 29955, 29946, 29947, 29941, 29953, 29946, 29955, 29937, 29906, 1068, 29941, 29896, 29899, 29896, 29937, 13, 29937, 29916, 29892, 302, 353, 29871, 29900, 29889, 29900, 29900, 29900, 29900, 29900, 29892, 448, 29896, 13, 2158, 29898, 2929, 29884, 29889, 1357, 29925, 340, 29898, 29916, 29892, 302, 876, 2 ]

ConvertShellcode.py

rvrsh3ll/CPLResourceRunner

232

110036

<reponame>rvrsh3ll/CPLResourceRunner<filename>ConvertShellcode.py #!/usr/bin/env python import binascii import sys file_name = sys.argv[1] with open (file_name) as f: hexdata = binascii.hexlify(f.read()) hexlist = map(''.join, zip(hexdata[::2], hexdata[1::2])) shellcode = '' for i in hexlist: shellcode += "0x{},".format(i) shellcode = shellcode[:-1] output = open('shellcode.txt', 'w') output.write(shellcode) output.close() print "Shellcode written to shellcode.txt"

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rnn.py

peterwilliams97/deidentification

0

70497

<reponame>peterwilliams97/deidentification import tensorflow as tf import numpy as np from tensorflow.contrib import rnn from os.path import expanduser from glob import glob import re import random import time import os import spacy from collections import defaultdict # Parameters seed = 112 use_spacy = True MAX_VOCAB = 40000 MAX_WORDS = 1000000 SMALL_TEXT = False learning_rate = 0.001 n_input = 3 batch_size = 100 test_frac = .2 n_epochs = 1000 n_epochs_report = 1 patience = 100 model_folder = 'models' # number of units in RNN cell n_hidden = 512 UNKNOWN = '<UNKNOWN>' random.seed(seed) # Target log path logs_path = '/tmp/tensorflow/rnn_words' writer = tf.summary.FileWriter(logs_path) start_time = time.time() def elapsed(): sec = time.time() - start_time if sec < 60: return "%.2f sec" % sec elif sec < 60 * 60: return "%.2f min" % (sec / 60.0) else: return "%.2f hour" % (sec / (60 * 60)) def show(name, x): try: v = '%s:%s' % (list(x.shape), x.dtype) except AttributeError: if isinstance(x, (list, tuple)): v = '%d:%s' % (len(x), type(x[0])) else: v = type(x) print('"%s"=%s' % (name, v)) def read_text(path): with open(path, 'rt') as f: return f.read() def make_text(): if SMALL_TEXT: return read_text('belling_the_cat.txt') # mask = expanduser('~/testdata.clean/deploy/PDF32000_2008.txt') mask = expanduser('~/testdata.clean/deploy/The_Block_Cipher_Companion.txt') file_list = sorted(glob(mask)) print('%d files' % len(file_list)) return '\n'.join(read_text(path) for path in file_list) RE_SPACE = re.compile(r'[\s,\.:;!\?\-]+', re.DOTALL | re.MULTILINE) RE_NUMBER = re.compile(r'[^A-Z^a-z]') def tokenize_simple(text, max_words): words = RE_SPACE.split(text) words = [w for w in words if len(w) > 1 and not RE_NUMBER.search(w)] if max_words > 0: words = words[:max_words] return [words] spacy_nlp = spacy.load('en') punct = {',', '.', ';', ':', '\n', '\t', '\f', ''} def tokenize_spacy(text, n_input, max_words): document = spacy_nlp(text) doc_sents = list(document.sents) print(len(doc_sents)) # for i, sent in enumerate(doc_sents[:20]): # print(i, type(sent), len(sent), sent[:5]) # sentences sentences = [] n_words = 0 for span in document.sents: sent = [token.text.strip() for token in span] sent = [w for w in sent if w not in punct] if len(sent) < n_input + 1: continue sentences.append(sent) n_words += len(sent) if max_words > 0 and n_words >= max_words: break # for token in sentence: # print(token) # token_dict = {} # token_dict['start'], token_dict['end'] = get_start_and_end_offset_of_token_from_spacy(token) # token_dict['text'] = text[token_dict['start']:token_dict['end']] # if token_dict['text'].strip() in ['\n', '\t', ' ', '']: # continue # # Make sure that the token text does not contain any space # if len(token_dict['text'].split(' ')) != 1: # print("WARNING: the text of the token contains space character, replaced with " # "hyphen\n\t{0}\n\t{1}".format(token_dict['text'], token_dict['text'].replace(' ', '-'))) # token_dict['text'] = token_dict['text'].replace(' ', '-') # sentence_tokens.append(token_dict) # sentences.append(sentence_tokens) print('!!!', len(sentences)) for i, sent in enumerate(sentences[:5]): print(i, type(sent), len(sent), sent[:5]) return sentences def tokenize(text, n_input, max_words): if use_spacy: return tokenize_spacy(text, n_input, max_words) return tokenize_simple(text, max_words) def train_test(sentences, n_input, test_frac): random.shuffle(sentences) n_samples = sum((len(sent) - n_input) for sent in sentences) n_test = int(n_samples * test_frac) test = [] i_test = 0 for sent in sentences: if i_test + len(sent) - n_input > n_test: break test.append(sent) i_test += len(sent) - n_input train = sentences[len(test):] assert train and test, (len(sentences), len(test), len(train), test_frac) return train, test def build_indexes(sentences, max_vocab): word_counts = defaultdict(int) for sent in sentences: for w in sent: word_counts[w] += 1 vocabulary_list = sorted(word_counts, key=lambda x: (-word_counts[x], x))[:max_vocab - 1] vocabulary_list.append(UNKNOWN) vocabulary = set(vocabulary_list) # for i, w in enumerate(words[:5]): # marker = '***' if w in vocabulary else '' # print('%3d: %-20s %s' % (i, w, marker)) vocab_size = len(vocabulary) unk_index = vocab_size - 1 word_index = {w: i for i, w in enumerate(vocabulary_list)} word_index[UNKNOWN] = unk_index index_word = {i: w for w, i in word_index.items()} # for i in sorted(index_word)[:5]: # print(i, index_word[i], type(i)) # for i in range(vocab_size): # assert i in index_word, i return word_index, index_word, unk_index def build_embeddings(word_index): embeddings = {w: np.zeros(len(word_index), dtype=np.float32) for w in word_index} for w, i in word_index.items(): embeddings[w][i] = 1.0 unk_embedding = embeddings[UNKNOWN] return embeddings, unk_embedding text = make_text() print('%7d bytes' % len(text)) sentences = tokenize(text, n_input, MAX_WORDS) print('ALL') print('%7d sentences' % len(sentences)) print('%7d words' % sum(len(sent) for sent in sentences)) train, test_sentences = train_test(sentences, n_input, test_frac) sentences = train print('TRAIN') print('%7d sentences' % len(sentences)) print('%7d words' % sum(len(sent) for sent in sentences)) word_index, index_word, unk_index = build_indexes(sentences, MAX_VOCAB) vocab_size = len(word_index) print('%7d vocab' % vocab_size) embeddings, unk_embedding = build_embeddings(word_index) n_samples = sum((len(sent) - n_input) for sent in sentences) print('%7d samples' % n_samples) def batch_getter(sentences, n_input, batch_size): """Generator that returns x, y, oneh_y in `batch_size` batches phrase is a random phrase of length n_input + 1 from words x = indexes of first n_input words y = index of last word returns x, y, one hot encoding of y """ sequence_numbers = [] for i, sent in enumerate(sentences): for j in range(len(sent) - n_input - 1): sequence_numbers.append((i, j)) random.shuffle(sequence_numbers) for k0 in range(0, len(sequence_numbers), batch_size): n = min(len(sequence_numbers) - k0, batch_size) # print('****', k, n, len(sequence_numbers)) oneh_y = np.empty((n, vocab_size), dtype=np.float32) indexes_x = np.empty((n, n_input), dtype=int) indexes_y = np.empty((n), dtype=int) for k in range(n): i, j = sequence_numbers[k0 + k] words = sentences[i] assert j < len(words) - n_input - 1, (i, j, len(words), n_input) assert j + n_input < len(words), 'i=%d j=%d words=%d sequence_numbers n_input=%d' % ( i, j, len(words), len(sequence_numbers), n_input) phrase_x = words[j:j + n_input] phrase_y = words[j + n_input] wx = [word_index.get(w, unk_index) for w in phrase_x] wy = word_index.get(phrase_y, unk_index) indexes_x[k] = np.array(wx) indexes_y[k] = np.array(wy) oneh_y[k] = embeddings.get(phrase_y, unk_embedding) # show('indexes_x', indexes_y) yield indexes_x, indexes_y, oneh_y # tf Graph inputs # x = indexes of first n_input words in phrase # y = one hot encoding of last word in phrase x = tf.placeholder("float", [None, n_input]) y = tf.placeholder("float", [None, vocab_size]) # RNN output node weights and biases weights = tf.Variable(tf.random_normal([n_hidden, vocab_size])) biases = tf.Variable(tf.random_normal([vocab_size])) def RNN(x, weights, biases): """RNN predicts y (one hot) from x (indexes), weights and biases y = g(x) * W + b, where g = LSTM x = indexes of input words y = one-hot encoding of output word """ show('x', x) # Generate a n_input-element sequence of inputs # (eg. [had] [a] [general] -> [20] [6] [33]) x = tf.split(x, n_input, 1) show('x split', x) # 1-layer LSTM with n_hidden units. rnn_cell = rnn.BasicLSTMCell(n_hidden) # generate prediction outputs, states = rnn.static_rnn(rnn_cell, x, dtype=tf.float32) # there are n_input outputs but we only want the last output y = tf.matmul(outputs[-1], weights) + biases show('y', y) return y pred = RNN(x, weights, biases) # Loss and optimizer cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate).minimize(cost) # Model evaluation correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # Initializing the variables init = tf.global_variables_initializer() os.makedirs(model_folder, exist_ok=True) saver = tf.train.Saver(max_to_keep=3) def demonstrate(indexes_x, indexes_y, onehot_pred, step, i): indexes = [int(indexes_x[i, j]) for j in range(indexes_x.shape[1])] symbols_in = [index_word.get(j, UNKNOWN) for j in indexes] symbols_out = index_word.get(indexes_y[i], UNKNOWN) v = tf.argmax(onehot_pred, 1).eval() symbols_out_pred = index_word[int(v[i])] mark = '****' if symbols_out_pred == symbols_out else '' return "%5d: %60s -> [%s] -- [%s] %s" % (step, symbols_in, symbols_out, symbols_out_pred, mark) def make_prediction(session, x): y_pred = tf.run([y], feed_dict={x: x}) def test_model(session, x): return tf.run([accuracy], feed_dict={x: x}) def test_results(session): batch_size = 1000 acc_total = 0.0 loss_total = 0.0 predictions = [] source = batch_getter(test_sentences, n_input, batch_size) step = 0 # Process minibatches of size `batch_size` for _, (indexes_x, indexes_y, onehot_y) in enumerate(source): # print('*** %s %d %d' % (list(indexes_y.shape), n_samples, batch_size)) frac = len(indexes_y) / n_samples # Update the model acc, loss, onehot_pred = session.run([accuracy, cost, pred], feed_dict={x: indexes_x, y: onehot_y}) loss_total += loss * frac acc_total += acc * frac assert acc <= 1.0, (acc, loss, frac) assert frac <= 1.0, (acc, loss, frac) assert acc_total <= 1.0, (acc, loss) for i in range(len(indexes_y)): if step < 10: predictions.append(demonstrate(indexes_x, indexes_y, onehot_pred, step, i)) step += 1 return loss_total, acc_total, predictions # Launch the graph with tf.Session() as session: session.run(init) writer.add_graph(session.graph) best_acc = 0.0 best_epoch = -1 new_best = False if False: devices = session.list_devices() for d in devices: print(d.name) assert False for epoch in range(n_epochs): accuracies = [] train_acc = 0.0 train_loss = 0.0 source = batch_getter(sentences, n_input, batch_size) # Process minibatches of size `batch_size` for step, (indexes_x, indexes_y, onehot_y) in enumerate(source): # print('*** %s %d %d' % (list(indexes_y.shape), n_samples, batch_size)) frac = len(indexes_y) / n_samples # Update the model _, acc, loss, onehot_pred = session.run([optimizer, accuracy, cost, pred], feed_dict={x: indexes_x, y: onehot_y}) train_loss += loss * frac train_acc += acc * frac accuracies.append(acc) assert acc <= 1.0, (acc, loss, frac, accuracies) assert frac <= 1.0, (acc, loss, frac, accuracies) assert train_acc <= 1.0, (acc, loss, frac, accuracies) # assert (len(accuracies) + 1) * batch_size > n_samples, (len(accuracies), batch_size, n_samples) test_loss, test_acc, predictions = test_results(session) # Show some progress statistics if test_acc > best_acc: best_epoch = epoch best_acc = test_acc best_predictions = predictions new_best = True # saver.save(session, os.path.join(model_folder, 'model_%06d.ckpt' % step)) print('epoch=%3d train_acc=%.2f test_acc=%.2f' % (epoch + 1, 100.0 * train_acc, 100.0 * test_acc)) done = epoch > best_epoch + patience or epoch == n_epochs - 1 if (epoch + 1) % n_epochs_report == 0 or done: print("epoch=%3d (best=%3d): Train Loss=%9.6f Accuracy=%5.2f%% -- " "Test Loss=%9.6f Accuracy=%5.2f%%" % (epoch + 1, best_epoch + 1, train_loss, 100.0 * train_acc, test_loss, 100.0 * test_acc)) if new_best: print('\n'.join(best_predictions)) new_best = False # indexes = [int(indexes_x[0, i]) for i in range(indexes_x.shape[1])] # symbols_in = [index_word.get(i, UNKNOWN) for i in indexes] # symbols_out = index_word.get(indexes_y[0], UNKNOWN) # v = tf.argmax(onehot_pred, 1).eval() # symbols_out_pred = index_word[int(v[0])] # print("%s -> [%s] predicted [%s]" % (symbols_in, symbols_out, symbols_out_pred)) if done: break print("Optimization Finished!") print("Elapsed time: ", elapsed()) print("Run on command line.") print("\ttensorboard --logdir=%s" % (logs_path)) print("Point your web browser to: http://localhost:6006/") # while True: # prompt = "%s words: " % n_input # sentence = input(prompt) # sentence = sentence.strip() # words = sentence.split(' ') # if len(words) != n_input: # continue # try: # indexes = [word_index.get(w, unk_index) for w in words] # for i in range(32): # keys = np.reshape(np.array(indexes), [-1, n_input]) # onehot_pred = session.run(pred, feed_dict={x: keys}) # indexes_pred = int(tf.argmax(onehot_pred, 1).eval()) # sentence = "%s %s" % (sentence, index_word[indexes_pred]) # indexes = indexes[1:] # indexes.append(indexes_pred) # print(sentence) # except KeyError: # print("Word not in dictionary")

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account_verification_flask/services/twilio_services.py

TwilioDevEd/account-verification-flask

8

191141

<reponame>TwilioDevEd/account-verification-flask<gh_stars>1-10 import account_verification_flask.utilities from account_verification_flask.utilities.settings import TwilioSettings from twilio.rest import Client class TwilioServices: twilio_client = None def __init__(self): if TwilioServices.twilio_client is None: TwilioServices.twilio_client = Client( TwilioSettings.api_key(), TwilioSettings.api_secret(), TwilioSettings.account_sid(), ) def send_registration_success_sms(self, to_number): self.twilio_client.messages.create( body=account_verification_flask.utilities.Signup_Complete, to=to_number, from_=TwilioSettings.phone_number(), )

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utils/glove.py

MirunaPislar/Word2vec

13

5371

import numpy as np DEFAULT_FILE_PATH = "utils/datasets/glove.6B.50d.txt" def loadWordVectors(tokens, filepath=DEFAULT_FILE_PATH, dimensions=50): """Read pretrained GloVe vectors""" wordVectors = np.zeros((len(tokens), dimensions)) with open(filepath) as ifs: for line in ifs: line = line.strip() if not line: continue row = line.split() token = row[0] if token not in tokens: continue data = [float(x) for x in row[1:]] if len(data) != dimensions: raise RuntimeError("wrong number of dimensions") wordVectors[tokens[token]] = np.asarray(data) return wordVectors

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to_excel_demo.py

46319943/geopandas_demo

0

190712

<filename>to_excel_demo.py<gh_stars>0 from pathlib2 import Path import geopandas # 存放所有城市的目录 source_dir = Path(r'D:\Document\HousePricing\公司') # 获取所有目录名字 city_dir_list = [x for x in source_dir.iterdir() if x.is_dir()] # 输出目录 output_dir = Path(r'D:\Document\HousePricing\公司') for city_dir in city_dir_list: if (city_dir/'公司.shp').exists(): geodf = geopandas.read_file(str(city_dir/'公司.shp'),encoding="UTF-8") print(geodf.head()) geodf.iloc[:, 0:-1].to_excel(str(city_dir/'公司.xlsx'),index=False)

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bin/lmi-add-cat.py

mkelley/dct-redux

0

44897

#!/usr/bin/env python3 from astropy.modeling.models import Const1D, Const2D, Gaussian1D, Gaussian2D from astropy.modeling.fitting import LevMarLSQFitter from astropy.modeling import Fittable2DModel, Parameter import sys import logging import argparse import warnings from datetime import datetime from glob import glob import numpy as np import scipy.ndimage as nd from matplotlib.patches import Ellipse import matplotlib.pyplot as plt from astropy.io import fits from astropy.stats import sigma_clipped_stats from astropy.wcs import WCS from astropy.table import Table from astropy.wcs import FITSFixedWarning import photutils as pu import sep parser = argparse.ArgumentParser() parser.add_argument('files', nargs='*', help='files to process') parser.add_argument('--reprocess', action='store_true') parser.add_argument('--verbose', '-v', action='store_true', help='verbose logging') args = parser.parse_args() ###################################################################### class GaussianConst2D(Fittable2DModel): """A model for a 2D Gaussian plus a constant. Code from photutils (Copyright (c) 2011, Photutils developers). Parameters ---------- constant : float Value of the constant. amplitude : float Amplitude of the Gaussian. x_mean : float Mean of the Gaussian in x. y_mean : float Mean of the Gaussian in y. x_stddev : float Standard deviation of the Gaussian in x. ``x_stddev`` and ``y_stddev`` must be specified unless a covariance matrix (``cov_matrix``) is input. y_stddev : float Standard deviation of the Gaussian in y. ``x_stddev`` and ``y_stddev`` must be specified unless a covariance matrix (``cov_matrix``) is input. theta : float, optional Rotation angle in radians. The rotation angle increases counterclockwise. """ constant = Parameter(default=1) amplitude = Parameter(default=1) x_mean = Parameter(default=0) y_mean = Parameter(default=0) x_stddev = Parameter(default=1) y_stddev = Parameter(default=1) theta = Parameter(default=0) @staticmethod def evaluate(x, y, constant, amplitude, x_mean, y_mean, x_stddev, y_stddev, theta): """Two dimensional Gaussian plus constant function.""" model = Const2D(constant)(x, y) + Gaussian2D(amplitude, x_mean, y_mean, x_stddev, y_stddev, theta)(x, y) return model def fit_2dgaussian(data): """Fit a 2D Gaussian plus a constant to a 2D image. Based on code from photutils (Copyright (c) 2011, Photutils developers). Parameters ---------- data : array_like The 2D array of the image. Returns ------- result : A `GaussianConst2D` model instance. The best-fitting Gaussian 2D model. """ if np.ma.count(data) < 7: raise ValueError('Input data must have a least 7 unmasked values to ' 'fit a 2D Gaussian plus a constant.') data.fill_value = 0. data = data.filled() # Subtract the minimum of the data as a rough background estimate. # This will also make the data values positive, preventing issues with # the moment estimation in data_properties. Moments from negative data # values can yield undefined Gaussian parameters, e.g., x/y_stddev. data = data - np.min(data) guess_y, guess_x = np.array(data.shape) / 2 init_amplitude = np.ptp(data) g_init = GaussianConst2D(constant=0, amplitude=init_amplitude, x_mean=guess_x, y_mean=guess_y, x_stddev=3, y_stddev=3, theta=0) fitter = LevMarLSQFitter() y, x = np.indices(data.shape) gfit = fitter(g_init, x, y, data) return gfit ###################################################################### # setup logging logger = logging.Logger('LMI Add Catalog') logger.setLevel(logging.DEBUG) # this allows logging to work when lmi-add-cat is run multiple times from # ipython if len(logger.handlers) == 0: formatter = logging.Formatter('%(levelname)s: %(message)s') level = logging.DEBUG if args.verbose else logging.INFO console = logging.StreamHandler(sys.stdout) console.setLevel(level) console.setFormatter(formatter) logger.addHandler(console) logfile = logging.FileHandler('lmi-add-cat.log') logfile.setLevel(level) logfile.setFormatter(formatter) logger.addHandler(logfile) logger.info('#' * 70) logger.info(datetime.now().isoformat()) logger.info('Command line: ' + ' '.join(sys.argv[1:])) ###################################################################### # suppress unnecessary warnings warnings.simplefilter('ignore', FITSFixedWarning) ###################################################################### def show_objects(im, objects): # plot background-subtracted image fig, ax = plt.subplots() m, s = np.mean(im), np.std(im) im = ax.imshow(im, interpolation='nearest', cmap='gray', vmin=m-s, vmax=m+s, origin='lower') # plot an ellipse for each object for i in range(len(objects)): e = Ellipse(xy=(objects['x'][i], objects['y'][i]), width=6*objects['a'][i], height=6*objects['b'][i], angle=objects['theta'][i] * 180. / np.pi) e.set_facecolor('none') e.set_edgecolor('red') ax.add_artist(e) for f in args.files: if fits.getheader(f)['IMAGETYP'] != 'OBJECT': continue logger.debug(f) with fits.open(f, mode='update') as hdu: im = hdu[0].data + 0 h = hdu[0].header if h['IMAGETYP'].upper() != 'OBJECT': logger.warning( f'Refusing to measure {f} with image type {h["imagetyp"]}.') continue if 'MASK' in hdu: mask = hdu['MASK'].data.astype(bool) else: mask = np.zeros_like(im, bool) if 'cat' in hdu: if not args.reprocess: continue else: del hdu['cat'] det = np.zeros_like(mask) for iteration in range(3): bkg = sep.Background(im, mask=det | mask, bw=64, bh=64, fw=3, fh=3) # mask potential sources det = ((im - bkg) / bkg.globalrms) > 3 # remove isolated pixels det = nd.binary_closing(det) bkg = sep.Background(im, mask=det | mask, bw=64, bh=64, fw=3, fh=3) if 'bg' in hdu: del hdu['bg'] hdu.append(fits.ImageHDU(bkg.back(), name='bg')) hdu['bg'].header['bg'] = bkg.globalback hdu['bg'].header['rms'] = bkg.globalrms data = im - bkg data[mask] = 0 try: objects, labels = sep.extract(data, 3, err=bkg.globalrms, segmentation_map=True) except Exception as e: logger.error(f'{f}: Object detection failed - {str(e)}') continue hdu[0].header['ncat'] = len(objects), 'number of objects in catalog' if len(objects) == 0: continue #show_objects(data, objects) # estimate seeing fwhms = [] segmap = pu.SegmentationImage(labels) for i in np.random.choice(len(segmap.segments), 50): obj = segmap.segments[i].make_cutout(data, masked_array=True) try: g = fit_2dgaussian(obj) except: continue fwhm = np.mean((g.x_stddev.value, g.y_stddev.value)) * 2.35 if fwhm < 1: continue fwhms.append(fwhm) fwhm = sigma_clipped_stats(fwhms)[1] rap = fwhm * 2 if np.isfinite(fwhm) else 10 flux, fluxerr, flag = sep.sum_circle( data, objects['x'], objects['y'], rap, err=bkg.globalrms, gain=h['gain']) kronrad, krflag = sep.kron_radius(data, objects['x'], objects['y'], objects['a'], objects['b'], objects['theta'], 6.0) krflux, krfluxerr, _flag = sep.sum_ellipse( data, objects['x'], objects['y'], objects['a'], objects['b'], np.minimum(objects['theta'], np.pi / 2.00001), 2.5 * kronrad, subpix=1, err=bkg.globalrms, gain=h['gain']) krflag |= _flag # combine flags wcs = WCS(h) ra, dec = wcs.all_pix2world(objects['x'], objects['y'], 0) tab = Table((objects['x'], objects['y'], ra, dec, flux, fluxerr, flag, objects['a'], objects['b'], objects['theta'], kronrad, krflux, krfluxerr, krflag), names=('x', 'y', 'ra', 'dec', 'flux', 'fluxerr', 'flag', 'a', 'b', 'theta', 'kronrad', 'krflux', 'krfluxerr', 'krflag')) if 'cat' in hdu: del hdu['cat'] hdu.append(fits.BinTableHDU(tab, name='cat')) hdu['cat'].header['FWHM'] = fwhm, 'estimated median FWHM' hdu['cat'].header['RADIUS'] = 2 * fwhm, 'aperture photometry radius' logger.info(f"{f}: {len(tab)} objects, seeing = {fwhm:.1f}, background mean/rms = " f"{hdu['bg'].header['bg']:.1f}/{hdu['bg'].header['rms']:.1f}")

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lib/evaluation/sort_class_mean_accuracy.py

YerongLi2/LTVRR

13

196691

<filename>lib/evaluation/sort_class_mean_accuracy.py ''' # How can we do the long tail with the soft? # we pick the winner from our table (most likely a hubness) and the baseline. # I want a csv that tells me for every predition (rel. or sbj.) # a) its class, (optionally the class-frequency), and the top-1 or top-5 [choose] # score per a soft matrix. ''' import pandas as pd import os.path as osp #method_name = '_hubness10k' method_name = '_hubness' #method_name = '_baseline' workon = 'relation' # or 'relations' #in_csv_file = '/ibex/scratch/x_abdelks/MetricAnalysis/VQA/_focal_loss_g025/test/subjects_data_GQA.csv' # method in_csv_file = '/ibex/scratch/x_abdelks/MetricAnalysis/VQA/{}/test/predicates_data_GQA.csv'.format(method_name) # method #similarities_used = ['word2vec_GNews'] # pick one similarities_used = ['word2vec_visualVG'] # pick one top_aux_data_dir = './' # where are the freq. dictionaries print(method_name, similarities_used, workon) df = pd.read_csv(in_csv_file) ## Clip similarities to [-1, 1] range. for sim in similarities_used: too_large = df[sim] > 1 df.loc[too_large, sim] = 1 too_small = df[sim] < -1 df.loc[too_small, sim] = -1 freq_info = pd.read_csv(osp.join(top_aux_data_dir, 'gvqa_{}_to_train_freq.csv'.format(workon))) freq_info_dict = dict() if workon == 'subject': x, y = freq_info.gt_sbj, freq_info.sbj_freq_gt elif workon == 'relation': x, y = freq_info.gt_rel, freq_info.rel_freq_gt for k, v in zip(x, y): freq_info_dict[k] = v ndf = df[df['i'] == 0][[sim, 'gold']] # top-1 g = ndf.groupby('gold') average_per_class = g[sim].mean().reset_index() average_per_class['frequency'] = average_per_class['gold'].apply(lambda x: freq_info_dict[x]) average_per_class = average_per_class.sort_values('frequency', ascending=False) average_per_class.to_csv('{}_vgqa_sorted_mean_acc_per_class_on_{}_with_{}.csv'.format(method_name, workon, sim))

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supervisor/medusa/text_socket.py

infoxchange/supervisor

1

100089

# -*- Mode: Python -*- __author__ = '<NAME>' from supervisor.compat import PY3 from supervisor.compat import as_string, as_bytes from socket import * # relied on to be imported from elsewhere if PY3: bin_socket = socket class text_socket(socket): def __init__(self, family=AF_INET, type=SOCK_STREAM, proto=0, fileno=None): bin_socket.__init__(self, family, type, proto, fileno) def recv(self, *args, **kwargs): return as_string(bin_socket.recv(self, *args, **kwargs)) def recvfrom(self, *args, **kwargs): reply, whence = bin_socket.recvfrom(self, *args, **kwargs) reply = as_string(reply) return reply, whence def send(self, data, *args, **kwargs): b = as_bytes(data) return bin_socket.send(self, b, *args, **kwargs) def sendall(self, data, *args, **kwargs): return bin_socket.sendall(self, as_bytes(data), *args, **kwargs) def sendto(self, data, *args, **kwargs): return bin_socket.sendto(self, as_bytes(data), *args, **kwargs) def accept(self): # sock, addr = bin_socket.accept(self) # sock = text_socket(self.family, self.type, self.proto, fileno=sock.fileno()) fd, addr = self._accept() sock = text_socket(self.family, self.type, self.proto, fileno=fd) # Issue #7995: if no default timeout is set and the listening # socket had a (non-zero) timeout, force the new socket in blocking # mode to override platform-specific socket flags inheritance. if getdefaulttimeout() is None and self.gettimeout(): sock.setblocking(True) return sock, addr text_socket.__init__.__doc__ = bin_socket.__init__.__doc__ socket = text_socket

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irc/bot.py

suut/psychic-happiness

1

1607972

<gh_stars>1-10 # -*- coding: utf-8 -*- # Copyright (C) 1999-2002 <NAME> # Portions Copyright © 2011-2012 <NAME> """ Simple IRC bot library. This module contains a single-server IRC bot class that can be used to write simpler bots. """ from __future__ import absolute_import import sys import irc.client import irc.modes from .dict import IRCDict class ServerSpec(object): """ An IRC server specification. >>> spec = ServerSpec('localhost') >>> spec.host 'localhost' >>> spec.port 6667 >>> spec.password >>> spec = ServerSpec('127.0.0.1', 6697, '<PASSWORD>') >>> spec.password '<PASSWORD>' """ def __init__(self, host, port=6667, password=None): self.host = host self.port = port self.password = password class SingleServerIRCBot(irc.client.SimpleIRCClient): """A single-server IRC bot class. The bot tries to reconnect if it is disconnected. The bot keeps track of the channels it has joined, the other clients that are present in the channels and which of those that have operator or voice modes. The "database" is kept in the self.channels attribute, which is an IRCDict of Channels. """ def __init__(self, server_list, nickname, username, realname, reconnection_interval=60, **connect_params): """Constructor for SingleServerIRCBot objects. Arguments: server_list -- A list of ServerSpec objects or tuples of parameters suitable for constructing ServerSpec objects. Defines the list of servers the bot will use (in order). nickname -- The bot's nickname. realname -- The bot's realname. reconnection_interval -- How long the bot should wait before trying to reconnect. dcc_connections -- A list of initiated/accepted DCC connections. **connect_params -- parameters to pass through to the connect method. """ super(SingleServerIRCBot, self).__init__() self.__connect_params = connect_params self.channels = IRCDict() self.server_list = [ ServerSpec(*server) if isinstance(server, (tuple, list)) else server for server in server_list ] assert all( isinstance(server, ServerSpec) for server in self.server_list ) if not reconnection_interval or reconnection_interval < 0: reconnection_interval = 2 ** 31 self.reconnection_interval = reconnection_interval self._nickname = nickname self._realname = realname self._username = username for i in ["disconnect", "join", "kick", "mode", "namreply", "nick", "part", "quit"]: self.connection.add_global_handler(i, getattr(self, "_on_" + i), -20) def _connected_checker(self): """[Internal]""" if not self.connection.is_connected(): self.connection.execute_delayed(self.reconnection_interval, self._connected_checker) self.jump_server() def _connect(self): """ Establish a connection to the server at the front of the server_list. """ server = self.server_list[0] try: self.connect(server.host, server.port, self._nickname, server.password, self._username, self._realname, **self.__connect_params) except irc.client.ServerConnectionError: pass def _on_disconnect(self, c, e): self.channels = IRCDict() self.connection.execute_delayed(self.reconnection_interval, self._connected_checker) def _on_join(self, c, e): ch = e.target nick = e.source.nick if nick == c.get_nickname(): self.channels[ch] = Channel() self.channels[ch].add_user(nick) def _on_kick(self, c, e): nick = e.arguments[0] channel = e.target if nick == c.get_nickname(): del self.channels[channel] else: self.channels[channel].remove_user(nick) def _on_mode(self, c, e): modes = irc.modes.parse_channel_modes(" ".join(e.arguments)) t = e.target if irc.client.is_channel(t): ch = self.channels[t] for mode in modes: if mode[0] == "+": f = ch.set_mode else: f = ch.clear_mode f(mode[1], mode[2]) else: # Mode on self... XXX pass def _on_namreply(self, c, e): # e.arguments[0] == "@" for secret channels, # "*" for private channels, # "=" for others (public channels) # e.arguments[1] == channel # e.arguments[2] == nick list ch_type, channel, nick_list = e.arguments if channel == '*': # User is not in any visible channel # http://tools.ietf.org/html/rfc2812#section-3.2.5 return for nick in nick_list.split(): nick_modes = [] if nick[0] in self.connection.features.prefix: nick_modes.append(self.connection.features.prefix[nick[0]]) nick = nick[1:] for mode in nick_modes: self.channels[channel].set_mode(mode, nick) self.channels[channel].add_user(nick) def _on_nick(self, c, e): before = e.source.nick after = e.target for ch in self.channels.values(): if ch.has_user(before): ch.change_nick(before, after) def _on_part(self, c, e): nick = e.source.nick channel = e.target if nick == c.get_nickname(): del self.channels[channel] else: self.channels[channel].remove_user(nick) def _on_quit(self, c, e): nick = e.source.nick for ch in self.channels.values(): if ch.has_user(nick): ch.remove_user(nick) def die(self, msg="Bye, cruel world!"): """Let the bot die. Arguments: msg -- Quit message. """ self.connection.disconnect(msg) sys.exit(0) def disconnect(self, msg="I'll be back!"): """Disconnect the bot. The bot will try to reconnect after a while. Arguments: msg -- Quit message. """ self.connection.disconnect(msg) def get_version(self): """Returns the bot version. Used when answering a CTCP VERSION request. """ return "Python irc.bot ({version})".format( version=irc.client.VERSION_STRING) def jump_server(self, msg="Changing servers"): """Connect to a new server, possibly disconnecting from the current. The bot will skip to next server in the server_list each time jump_server is called. """ if self.connection.is_connected(): self.connection.disconnect(msg) self.server_list.append(self.server_list.pop(0)) self._connect() def on_ctcp(self, c, e): """Default handler for ctcp events. Replies to VERSION and PING requests and relays DCC requests to the on_dccchat method. """ nick = e.source.nick if e.arguments[0] == "VERSION": c.ctcp_reply(nick, "VERSION " + self.get_version()) elif e.arguments[0] == "PING": if len(e.arguments) > 1: c.ctcp_reply(nick, "PING " + e.arguments[1]) elif e.arguments[0] == "DCC" and e.arguments[1].split(" ", 1)[0] == "CHAT": self.on_dccchat(c, e) def on_dccchat(self, c, e): pass def start(self): """Start the bot.""" self._connect() super(SingleServerIRCBot, self).start() class Channel(object): """A class for keeping information about an IRC channel. This class can be improved a lot. """ def __init__(self): self.userdict = IRCDict() self.operdict = IRCDict() self.voiceddict = IRCDict() self.ownerdict = IRCDict() self.halfopdict = IRCDict() self.modes = {} def users(self): """Returns an unsorted list of the channel's users.""" return self.userdict.keys() def opers(self): """Returns an unsorted list of the channel's operators.""" return self.operdict.keys() def voiced(self): """Returns an unsorted list of the persons that have voice mode set in the channel.""" return self.voiceddict.keys() def owners(self): """Returns an unsorted list of the channel's owners.""" return self.ownerdict.keys() def halfops(self): """Returns an unsorted list of the channel's half-operators.""" return self.halfopdict.keys() def has_user(self, nick): """Check whether the channel has a user.""" return nick in self.userdict def is_oper(self, nick): """Check whether a user has operator status in the channel.""" return nick in self.operdict def is_voiced(self, nick): """Check whether a user has voice mode set in the channel.""" return nick in self.voiceddict def is_owner(self, nick): """Check whether a user has owner status in the channel.""" return nick in self.ownerdict def is_halfop(self, nick): """Check whether a user has half-operator status in the channel.""" return nick in self.halfopdict def add_user(self, nick): self.userdict[nick] = 1 def remove_user(self, nick): for d in self.userdict, self.operdict, self.voiceddict: if nick in d: del d[nick] def change_nick(self, before, after): self.userdict[after] = self.userdict.pop(before) if before in self.operdict: self.operdict[after] = self.operdict.pop(before) if before in self.voiceddict: self.voiceddict[after] = self.voiceddict.pop(before) def set_userdetails(self, nick, details): if nick in self.userdict: self.userdict[nick] = details def set_mode(self, mode, value=None): """Set mode on the channel. Arguments: mode -- The mode (a single-character string). value -- Value """ if mode == "o": self.operdict[value] = 1 elif mode == "v": self.voiceddict[value] = 1 elif mode == "q": self.ownerdict[value] = 1 elif mode == "h": self.halfopdict[value] = 1 else: self.modes[mode] = value def clear_mode(self, mode, value=None): """Clear mode on the channel. Arguments: mode -- The mode (a single-character string). value -- Value """ try: if mode == "o": del self.operdict[value] elif mode == "v": del self.voiceddict[value] elif mode == "q": del self.ownerdict[value] elif mode == "h": del self.halfopdict[value] else: del self.modes[mode] except KeyError: pass def has_mode(self, mode): return mode in self.modes def is_moderated(self): return self.has_mode("m") def is_secret(self): return self.has_mode("s") def is_protected(self): return self.has_mode("p") def has_topic_lock(self): return self.has_mode("t") def is_invite_only(self): return self.has_mode("i") def has_allow_external_messages(self): return self.has_mode("n") def has_limit(self): return self.has_mode("l") def limit(self): if self.has_limit(): return self.modes["l"] else: return None def has_key(self): return self.has_mode("k")

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schema/test_connection.py

ramrod-project/database-brain

0

166709

<filename>schema/test_connection.py<gh_stars>0 """ Pytest file for the connection wrapper """ from os import environ from pytest import fixture, raises import docker from .brain import connect, r from .brain.connection import DefaultConnection, BrainNotReady CLIENT = docker.from_env() @fixture(scope='module') def rethink(): try: tag = environ.get("TRAVIS_BRANCH", "dev").replace("master", "latest") except KeyError: tag = "latest" container_name = "brainmoduletest" CLIENT.containers.run( "ramrodpcp/database-brain:{}".format(tag), name=container_name, detach=True, ports={"28015/tcp": 28015}, remove=True ) yield True # Teardown for module tests containers = CLIENT.containers.list() for container in containers: if container.name == container_name: container.stop() break def test_brain_interface(rethink): return connect() def test_brain_interface_with_host(rethink): import rethinkdb return connect("localhost", rethinkdb.DEFAULT_PORT) def test_direct_interface(rethink): r.connect() if __name__ == "__main__": a = rethink() c = test_brain_interface(a.__next__()) try: a.__next__() except StopIteration: pass

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Main/migrations/0072_auto_20210506_0016.py

Muhammet-Yildiz/Ecommerce_Website-HepsiOrada

10

8383

# Generated by Django 3.1.4 on 2021-05-05 21:16 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('Main', '0071_auto_20210506_0004'), ] operations = [ migrations.RemoveField( model_name='product', name='chooseColor', ), migrations.RemoveField( model_name='product', name='chooseSize', ), ]

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tools/c7n_mailer/tests/common.py

gowthamsubbu/cloud-custodian

0

131359

<filename>tools/c7n_mailer/tests/common.py<gh_stars>0 # Copyright 2017 Capital One Services, LLC # # 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 fakeredis import logging from c7n_mailer.ldap_lookup import LdapLookup, Redis from ldap3 import Server, Connection, MOCK_SYNC from ldap3.strategy import mockBase logger = logging.getLogger('custodian.mailer') PETER = ( 'uid=peter,cn=users,dc=initech,dc=com', { 'uid': ['peter'], 'manager': 'uid=bill_lumbergh,cn=users,dc=initech,dc=com', 'mail': '<EMAIL>', 'displayName': 'Peter', 'objectClass': 'person' } ) BILL = ( 'uid=bill_lumbergh,cn=users,dc=initech,dc=com', { 'uid': ['bill_lumbergh'], 'mail': '<EMAIL>', 'displayName': '<NAME>', 'objectClass': 'person' } ) MAILER_CONFIG = { 'smtp_port': 25, 'from_address': '<EMAIL>', 'contact_tags': ['OwnerEmail', 'SupportEmail'], 'queue_url': 'https://sqs.us-east-1.amazonaws.com/xxxx/cloudcustodian-mailer', 'region': 'us-east-1', 'ldap_uri': 'ldap.initech.com', 'smtp_server': 'smtp.inittech.com', 'cache_engine': 'sqlite', 'role': 'arn:aws:iam::xxxx:role/cloudcustodian-mailer', 'ldap_uid_tags': ['CreatorName', 'Owner'], } RESOURCE_1 = { 'AvailabilityZone': 'us-east-1a', 'Attachments': [], 'Tags': [ { 'Value': '<EMAIL>', 'Key': 'SupportEmail' }, { 'Value': 'peter', 'Key': 'CreatorName' } ], 'VolumeId': 'vol-01a0e6ea6b89f0099' } RESOURCE_2 = { 'AvailabilityZone': 'us-east-1c', 'Attachments': [], 'Tags': [ { 'Value': '<EMAIL>', 'Key': 'SupportEmail' }, { 'Value': 'peter', 'Key': 'CreatorName' } ], 'VolumeId': 'vol-21a0e7ea9b19f0043', 'Size': 8 } SQS_MESSAGE_1 = { 'account': 'core-services-dev', 'account_id': '000000000000', 'region': 'us-east-1', 'action': { 'to': ['resource-owner', 'ldap_uid_tags'], 'email_ldap_username_manager': True, 'template': '', 'priority_header': '1', 'type': 'notify', 'transport': {'queue': 'xxx', 'type': 'sqs'}, 'subject': '{{ account }} AWS EBS Volumes will be DELETED in 15 DAYS!' }, 'policy': { 'filters': [{'Attachments': []}, {'tag:maid_status': 'absent'}], 'resource': 'ebs', 'actions': [ { 'type': 'mark-for-op', 'days': 15, 'op': 'delete' }, { 'to': ['resource-owner', 'ldap_uid_tags'], 'email_ldap_username_manager': True, 'template': '', 'priority_header': '1', 'type': 'notify', 'subject': 'EBS Volumes will be DELETED in 15 DAYS!' } ], 'comments': 'We are deleting your EBS volumes.', 'name': 'ebs-mark-unattached-deletion' }, 'event': None, 'resources': [RESOURCE_1] } SQS_MESSAGE_2 = { 'account': 'core-services-dev', 'account_id': '000000000000', 'region': 'us-east-1', 'action': { 'type': 'notify', 'to': ['datadog://?metric_name=EBS_volume.available.size'] }, 'policy': { 'filters': [{'Attachments': []}, {'tag:maid_status': 'absent'}], 'resource': 'ebs', 'actions': [ { 'type': 'mark-for-op', 'days': 15, 'op': 'delete' }, { 'type': 'notify', 'to': ['datadog://?metric_name=EBS_volume.available.size'] } ], 'comments': 'We are deleting your EBS volumes.', 'name': 'ebs-mark-unattached-deletion' }, 'event': None, 'resources': [RESOURCE_1, RESOURCE_2] } SQS_MESSAGE_3 = { 'account': 'core-services-dev', 'account_id': '000000000000', 'region': 'us-east-1', 'action': { 'type': 'notify', 'to': ['datadog://?metric_name=EBS_volume.available.size&metric_value_tag=Size'] }, 'policy': { 'filters': [{'Attachments': []}, {'tag:maid_status': 'absent'}], 'resource': 'ebs', 'actions': [ { 'type': 'mark-for-op', 'days': 15, 'op': 'delete' }, { 'type': 'notify', 'to': ['datadog://?metric_name=EBS_volume.available.size&metric_value_tag=Size'] } ], 'comments': 'We are deleting your EBS volumes.', 'name': 'ebs-mark-unattached-deletion' }, 'event': None, 'resources': [RESOURCE_2] } # Monkey-patch ldap3 to work around a bytes/text handling bug. _safe_rdn = mockBase.safe_rdn def safe_rdn(*a, **kw): return [(k, mockBase.to_raw(v)) for k, v in _safe_rdn(*a, **kw)] mockBase.safe_rdn = safe_rdn def get_fake_ldap_connection(): server = Server('my_fake_server') connection = Connection( server, client_strategy=MOCK_SYNC ) connection.bind() connection.strategy.add_entry(PETER[0], PETER[1]) connection.strategy.add_entry(BILL[0], BILL[1]) return connection def get_ldap_lookup(cache_engine=None, uid_regex=None): if cache_engine == 'sqlite': config = { 'cache_engine': 'sqlite', 'ldap_cache_file': ':memory:' } elif cache_engine == 'redis': config = { 'cache_engine': 'redis', 'redis_host': 'localhost' } if uid_regex: config['ldap_uid_regex'] = uid_regex ldap_lookup = MockLdapLookup(config, logger) michael_bolton = { 'dn': 'CN=<NAME>,cn=users,dc=initech,dc=com', 'mail': '<EMAIL>', 'manager': 'CN=Milton,cn=users,dc=initech,dc=com', 'displayName': '<NAME>' } milton = { 'uid': '123456', 'dn': 'CN=Milton,cn=users,dc=initech,dc=com', 'mail': '<EMAIL>', 'manager': 'CN=cthulhu,cn=users,dc=initech,dc=com', 'displayName': 'Milton' } bob_porter = { 'dn': 'CN=<NAME>,cn=users,dc=initech,dc=com', 'mail': '<EMAIL>', 'manager': 'CN=<NAME>,cn=users,dc=initech,dc=com', 'displayName': '<NAME>' } ldap_lookup.base_dn = 'cn=users,dc=initech,dc=com' ldap_lookup.uid_key = 'uid' ldap_lookup.attributes.append('uid') ldap_lookup.caching.set('michael_bolton', michael_bolton) ldap_lookup.caching.set(bob_porter['dn'], bob_porter) ldap_lookup.caching.set('123456', milton) ldap_lookup.caching.set(milton['dn'], milton) return ldap_lookup class MockLdapLookup(LdapLookup): # allows us to instantiate this object and not need a redis daemon def get_redis_connection(self, redis_host, redis_port): return MockRedisLookup() # us to instantiate this object and not have ldap3 try to connect # to anything or raise exception in unit tests, we replace connection with a mock def get_connection(self, ignore, these, params): return get_fake_ldap_connection() class MockRedisLookup(Redis): def __init__(self): self.connection = fakeredis.FakeStrictRedis()

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pymic/transform/threshold.py

HiLab-git/PyMIC

147

22249

<reponame>HiLab-git/PyMIC # -*- coding: utf-8 -*- from __future__ import print_function, division import torch import json import math import random import numpy as np from scipy import ndimage from pymic.transform.abstract_transform import AbstractTransform from pymic.util.image_process import * class ChannelWiseThreshold(AbstractTransform): """Threshold the image (shape [C, D, H, W] or [C, H, W]) for each channel """ def __init__(self, params): """ channels (tuple/list/None): the list of specified channels for thresholding. Default value is all the channels. threshold_lower (tuple/list/None): The lower threshold values for specified channels. threshold_upper (tuple/list/None): The uppoer threshold values for specified channels. replace_lower (tuple/list/None): new values for pixels with intensity smaller than threshold_lower. Default value is replace_upper (tuple/list/None): new values for pixels with intensity larger than threshold_upper. """ super(ChannelWiseThreshold, self).__init__(params) self.channlels = params['ChannelWiseThreshold_channels'.lower()] self.threshold_lower = params['ChannelWiseThreshold_threshold_lower'.lower()] self.threshold_upper = params['ChannelWiseThreshold_threshold_upper'.lower()] self.replace_lower = params['ChannelWiseThreshold_replace_lower'.lower()] self.replace_upper = params['ChannelWiseThreshold_replace_upper'.lower()] self.inverse = params.get('ChannelWiseThreshold_inverse'.lower(), False) def __call__(self, sample): image= sample['image'] channels = range(image.shape[0]) if self.channlels is None else self.channlels for i in range(len(channels)): chn = channels[i] if((self.threshold_lower is not None) and (self.threshold_lower[i] is not None)): t_lower = self.threshold_lower[i] r_lower = self.threshold_lower[i] if((self.replace_lower is not None) and (self.replace_lower[i] is not None)): r_lower = self.replace_lower[i] image[chn][image[chn] < t_lower] = r_lower if((self.threshold_upper is not None) and (self.threshold_upper[i] is not None)): t_upper = self.threshold_upper[i] r_upper = self.threshold_upper[i] if((self.replace_upper is not None) and (self.replace_upper[i] is not None)): r_upper= self.replace_upper[i] image[chn][image[chn] > t_upper] = r_upper sample['image'] = image return sample class ChannelWiseThresholdWithNormalize(AbstractTransform): """ Note that this can be replaced by ChannelWiseThreshold + NormalizeWithMinMax Threshold the image (shape [C, D, H, W] or [C, H, W]) for each channel and then normalize the image based on remaining pixels """ def __init__(self, params): """ :param threshold_lower: (tuple/list/None) The lower threshold value along each channel. :param threshold_upper: (typle/list/None) The upper threshold value along each channel. :param mean_std_mode: (bool) If true, nomalize the image based on mean and std values, and pixels values outside the threshold value are replaced random number. If false, use the min and max values for normalization. """ super(ChannelWiseThresholdWithNormalize, self).__init__(params) self.threshold_lower = params['ChannelWiseThresholdWithNormalize_threshold_lower'.lower()] self.threshold_upper = params['ChannelWiseThresholdWithNormalize_threshold_upper'.lower()] self.mean_std_mode = params['ChannelWiseThresholdWithNormalize_mean_std_mode'.lower()] self.inverse = params.get('ChannelWiseThresholdWithNormalize_inverse'.lower(), False) def __call__(self, sample): image= sample['image'] for chn in range(image.shape[0]): v0 = self.threshold_lower[chn] v1 = self.threshold_upper[chn] if(self.mean_std_mode == True): mask = np.ones_like(image[chn]) if(v0 is not None): mask = mask * np.asarray(image[chn] > v0) if(v1 is not None): mask = mask * np.asarray(image[chn] < v1) pixels = image[chn][mask > 0] chn_mean = pixels.mean() chn_std = pixels.std() chn_norm = (image[chn] - chn_mean)/chn_std chn_random = np.random.normal(0, 1, size = chn_norm.shape) chn_norm[mask == 0] = chn_random[mask == 0] image[chn] = chn_norm else: img_chn = image[chn] if(v0 is not None): img_chn[img_chn < v0] = v0 min_value = v0 else: min_value = img_chn.min() if(v1 is not None): img_chn[img_chn > v1] = v1 max_value = img_chn.max() else: max_value = img_chn.max() img_chn = (img_chn - min_value) / (max_value - min_value) image[chn] = img_chn sample['image'] = image return sample

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Node_Utility.py

SBCV/PythonBlenderUtility

1

74203

import bpy import os from collections import defaultdict from Utility.Logging_Extension import logger # http://blender.stackexchange.com/questions/8936/does-switching-from-blender-render-to-cycles-mess-things-up # * All matierals in cycles use nodes (even if you set up the material in the Properties panel, it will create # nodes in the node editor). # * Since BI materials don't use nodes by default, when you switch to cycles from BI there won't be any BI # nodes in the node tree, yet nodes will be enabled. This will make the material render as transparent. # One must switch / toggle between use_shader nodes # Remark: # info pannel in cycles: # the info pannel in cycles misses a lot of different commands # http://blender.stackexchange.com/questions/18020/print-all-commands-in-the-info-view # https://www.blender.org/api/blender_python_api_2_72_release/info_api_reference.html#operators # Most key-strokes and buttons in Blender call an operator which is also exposed to python via bpy.ops, # To see the Python equivalent hover your mouse over the button and see the tool-tip, # eg Python: bpy.ops.render.render(), If there is no tool-tip or the Python: line is missing then this button # is not using an operator and can't be accessed from Python. # If you want to use this in a script you can press Control-C while your mouse is over the button to copy it to the # clipboard. # ================= # alternative approach (not tested yet) # http://blender.stackexchange.com/questions/364/how-do-i-convert-materials-from-blender-internal-to-cycles # https://blenderartists.org/forum/showthread.php?247271-Cycles-Automatic-Material-Textures-Node def rearrange_nodes(): logger.info('rearrange_nodes: ...') # TODO # https://www.blendernation.com/2015/11/03/development-cleaning-up-node-trees/ # https://github.com/JuhaW/NodeArrange/blob/master/__init__.py assert False def create_viewer_node(scene, preceeding_node_name, preceeding_channel_name): """ For debug purposes. Allows to visualize intermediate nodes. :param scene: :param preceeding_node_name: :param preceeding_channel_name: :return: """ logger.info('create_viewer_node: ...') scene_nodes = scene.node_tree.nodes scene_links = scene.node_tree.links mask_id_node = scene_nodes.get(preceeding_node_name) viewer_node = scene_nodes.new('CompositorNodeViewer') scene_links.new(mask_id_node.outputs[preceeding_channel_name], viewer_node.inputs['Image']) logger.info('create_viewer_node: Done') def create_depth_viewer_node(scene): """ This will save the z buffer in the Viewer Node after rendering bpy.ops.render.render() rendered_image = bpy.data.images['Viewer Node'] pixels = rendered_image.pixels :param scene: :return: """ logger.info('create_depth_output_nodes: ...') scene.use_nodes = True scene_nodes = scene.node_tree.nodes scene_links = scene.node_tree.links default_render_layers_node = scene_nodes.get('Render Layers') # output_value = default_render_layers_node.outputs[output_type] # print(type(output_value)) viewer_node = scene_nodes.get('Depth Viewer') if viewer_node is None: viewer_node = scene_nodes.new('CompositorNodeViewer') viewer_node.name = 'Depth Viewer' logger.vinfo('viewer_node.name', viewer_node.name) viewer_node.use_alpha = False output_type = 'Depth' scene_links.new( default_render_layers_node.outputs[output_type], viewer_node.inputs[0]) # link Z to output logger.info('create_depth_output_nodes: Done') def create_additional_optical_flow_output_nodes(scene, output_path=None, image_stem=None, leading_zeroes_template='#####'): logger.info('create_additional_optical_flow_output_nodes: ...') default_render_layer = scene.render.layers.get(scene.render.layers.active.name) default_render_layer.use_pass_vector = True default_render_layer.pass_alpha_threshold = 0 scene.use_nodes = True scene_links = scene.node_tree.links scene_nodes = scene.node_tree.nodes default_render_layers_node = scene_nodes.get('Render Layers') optical_flow_output_node = scene_nodes.new('CompositorNodeOutputFile') optical_flow_output_node.format.file_format = 'OPEN_EXR' #optical_flow_output_node.format.use_zbuffer = True # Store floats if output_path is not None: optical_flow_output_node.base_path = output_path if image_stem is not None: optical_flow_output_node.file_slots[0].path = image_stem + leading_zeroes_template scene_links.new(default_render_layers_node.outputs['Vector'], optical_flow_output_node.inputs['Image']) logger.info('create_additional_optical_flow_output_nodes: Done') return optical_flow_output_node def create_additional_depth_output_nodes(scene, output_path=None, image_stem=None, leading_zeroes_template='#####'): logger.info('create_additional_depth_output_nodes: ...') default_render_layer = scene.render.layers.get(scene.render.layers.active.name) default_render_layer.pass_alpha_threshold = 0 scene.use_nodes = True scene_nodes = scene.node_tree.nodes scene_links = scene.node_tree.links default_render_layers_node = scene_nodes.get('Render Layers') depth_image_output_node = scene_nodes.new('CompositorNodeOutputFile') depth_image_output_node.format.file_format = 'OPEN_EXR' depth_image_output_node.format.use_zbuffer = True # Store floats if output_path is not None: depth_image_output_node.base_path = output_path if image_stem is not None: depth_image_output_node.file_slots[0].path = image_stem + leading_zeroes_template scene_links.new(default_render_layers_node.outputs['Depth'], depth_image_output_node.inputs['Image']) logger.info('create_additional_depth_output_nodes: Done') return depth_image_output_node def create_additional_mask_output_nodes(scene, object_index, output_path=None, image_stem=None, leading_zeroes_template='#####'): logger.info('create_additional_mask_output_nodes: ...') # Make sure that the render layer passes the object index default_render_layer = scene.render.layers.get(scene.render.layers.active.name) # Add additional pass values # default_render_layer.use_pass_combined = True # default_render_layer.use_pass_mist = True # default_render_layer.use_pass_normal = True # default_render_layer.use_pass_vector = True # default_render_layer.use_pass_uv = True default_render_layer.use_pass_object_index = True # default_render_layer.use_pass_material_index = True # default_render_layer.use_pass_shadow = True # ========== IMPORTANT FOR TRANSPARENT MATERIALS ========= default_render_layer.pass_alpha_threshold = 0 scene.use_nodes = True scene_nodes = scene.node_tree.nodes scene_links = scene.node_tree.links default_render_layers_node = scene_nodes.get('Render Layers') mask_node = scene_nodes.new('CompositorNodeIDMask') mask_node.index = object_index mask_node.use_antialiasing = True image_output_node = scene_nodes.new('CompositorNodeOutputFile') if output_path is not None: image_output_node.base_path = output_path if image_stem is not None: image_output_node.file_slots[0].path = image_stem + leading_zeroes_template scene_links.new( default_render_layers_node.outputs['IndexOB'], mask_node.inputs['ID value']) scene_links.new( mask_node.outputs['Alpha'], image_output_node.inputs['Image']) logger.info('create_additional_mask_output_nodes: Done') return mask_node, image_output_node def create_simple_material(): logger.info('Create Simple Material: ...') simple_material = bpy.data.materials.new('simple_material') simple_material.use_nodes = True simple_material_nodes = simple_material.node_tree.nodes simple_material_links = simple_material.node_tree.links shader_node_diffuse_bsdf = simple_material_nodes.get(NodeUtility.DIFFUSE_BSDF) shader_node_diffuse_bsdf.inputs[0].default_value = [255,0,0, 1] return simple_material def enable_backdrop(enable=True): logger.info('enable_backdrop: ...') # Enable backdrop for area in bpy.context.screen.areas: if area.type == 'NODE_EDITOR': for space in area.spaces: if space.type == 'NODE_EDITOR': logger.info('Backdrop Enabled') space.show_backdrop = enable break logger.info('enable_backdrop: Done') class NodeUtility: USE_MAP_COLOR_DIFFUSE = 'use_map_color_diffuse' USE_MAP_NORMAL = 'use_map_normal' DIFFUSE_BSDF = 'Diffuse BSDF' GLOSSY_BSDF = 'Glossy BSDF' TRANSPARENT_BSDF = 'Transparent BSDF' GLASS_BSDF = 'Glass BSDF' EMISSION = 'Emission' OBJECT_INFO = 'Object Info' MATERIAL_OUTPUT = 'Material Output' SHADER_NODE_RGB = 'ShaderNodeRGB' SHADER_NODE_MIX_RGB = 'ShaderNodeMixRGB' SHADER_NODE_EMISSION = 'ShaderNodeEmission' SHADER_NODE_BSDF_GLASS = 'ShaderNodeBsdfGlass' SHADER_NODE_OBJECT_INFO = 'ShaderNodeObjectInfo' @staticmethod def _collect_texture(type_to_texture_file_path, use_map_type, filepath): logger.debug('filepath: ' + filepath) if type_to_texture_file_path[use_map_type] is None: type_to_texture_file_path[use_map_type] = filepath else: logger.warning('Two Textures with the same use_type:') logger.warning('First: ' + use_map_type + ', ' + type_to_texture_file_path[use_map_type]) logger.warning('Second: ' + use_map_type + ', ' + filepath) logger.warning('We use the first texture as : ' + use_map_type) @staticmethod def _get_blender_internal_texture_type_to_file_paths(material): some_other_name = material.name logger.debug(some_other_name) # fprint('material: ' + material.name) texture_name_set = set() texture_type_to_file_path = defaultdict(lambda: None) for texture_slot in material.texture_slots: if texture_slot: texture = texture_slot.texture texture_name_set.add(texture) # fprint('texture: ' + texture.name) if hasattr(texture, 'image'): logger.debug('Material: ' + material.name + ', Texture: ' + texture.name) logger.debug('use_map_color_diffuse: ' + str(texture_slot.use_map_color_diffuse)) logger.debug('use_map_normal: ' + str(texture_slot.use_map_normal)) # ==== Remark ==== # Relative paths start with '//' and are relative to the blend file. # The prefix of paths to textures packed inside the .blend file are dependent on the original # file path. For example <blend_file_folder>/textures/texture_file.ext, i.e. look like the # following '//textures/<texturename>.<textureextension>' if texture.image.packed_file is not None: logger.debug('Image is packed') # If the texture is packed, the file is definitively valid, otherwise check the file image_is_valid = True else: logger.debug('Image is an external source') image_is_valid = os.path.isfile(bpy.path.abspath(texture.image.filepath)) if image_is_valid: if texture_slot.use_map_color_diffuse: NodeUtility._collect_texture(texture_type_to_file_path, NodeUtility.USE_MAP_COLOR_DIFFUSE, texture.image.filepath) elif texture_slot.use_map_normal: NodeUtility._collect_texture(texture_type_to_file_path, NodeUtility.USE_MAP_NORMAL, texture.image.filepath) logger.info('texture_type_to_file_path: ' + str(texture_type_to_file_path)) return texture_type_to_file_path @staticmethod def replace_bsdf_node_in_material(material, old_node, new_node, preceding_node=None, next_node=None): nodes = material.node_tree.nodes links = material.node_tree.links # we replace the oldf bsdf with a new one nodes.remove(old_node) if preceding_node is not None: links.new(preceding_node.outputs[0], new_node.inputs[0]) if next_node is not None: links.new(new_node.outputs[0], next_node.inputs[0]) @staticmethod def create_material_nodes_for_cycle_using_blender_internal_textures(material_default_bsdf_type=DIFFUSE_BSDF, transparent_default_bsdf_type=TRANSPARENT_BSDF): """ :param material_default_bsdf_type: DIFFUSE_BSDF or GLOSSY_BSDF :param transparent_default_bsdf_type: TRANSPARENT_BSDF or GLASS_BSDF :return: """ logger.info('create_material_nodes_for_cycle_using_blender_internal_textures: ...') bpy.context.scene.render.engine = 'CYCLES' # # each object has several material slots, which link to the materials provided in bpy.data.materials # for material in bpy.data.materials: for object in bpy.data.objects: logger.debug('object.name: ' + object.name) for material_slot in object.material_slots: material = material_slot.material # https://wiki.blender.org/index.php/Dev:Py/Scripts/Cookbook/Code_snippets/Nodes logger.info('material.name: ' + material.name) # change only blender internal materials (keep cycle materials as is) if not material.use_nodes: logger.debug('Adding nodes ...') # this adds by default a node "Material Output" and a node "Diffuse BSDF" material.use_nodes = True # get the "Diffuse BSDF" node nodes = material.node_tree.nodes links = material.node_tree.links # this diffuse node does automatically inherit the color of the material shader_node_diffuse_bsdf = nodes.get(NodeUtility.DIFFUSE_BSDF) shader_node_material_output = nodes.get("Material Output") # These texture file path should be valid texture_type_to_file_path = NodeUtility._get_blender_internal_texture_type_to_file_paths(material) # 1 Case: Material is just a texture # Image Texture -> Diffuse BSDF/Glossy BSDF -> Material Output color_texture_file_path = texture_type_to_file_path[NodeUtility.USE_MAP_COLOR_DIFFUSE] logger.debug('color_texture_file_path: ' + str(color_texture_file_path)) if color_texture_file_path is not None: logger.debug('Converting Material With Texture: ' + color_texture_file_path) logger.debug('Texture path is valid') # test if the image texture node has already been created shader_node_tex_image = nodes.get("Image Texture") if not shader_node_tex_image: shader_node_tex_image = nodes.new(type='ShaderNodeTexImage') shader_node_tex_image.image = bpy.data.images.load(color_texture_file_path) # link the nodes links.new(shader_node_tex_image.outputs[0], shader_node_diffuse_bsdf.inputs[0]) # if material_default_bsdf_type == BICyclesMaterialConverter.GLOSSY_BSDF: # # logger.debug('Replace Diffuse Material Node with Glossy Material Node' ) # shader_node_glossy_bsdf = nodes.get(BICyclesMaterialConverter.GLOSSY_BSDF) # if not shader_node_glossy_bsdf: # # shader_node_glossy_bsdf = nodes.new(type='ShaderNodeBsdfGlossy') # # BICyclesMaterialConverter._replace_bsdf_node(material, # old_node=shader_node_diffuse_bsdf, # new_node=shader_node_glossy_bsdf, # preceding_node=shader_node_tex_image, # next_node=shader_node_material_output) # 2 Case: Material is transparent # RGB -> Transparent BSDF/Glass BSDF -> Material Output elif material.use_transparency: logger.debug('Converting Transparent Material') shader_node_transparent_or_glass_bsdf = nodes.get(transparent_default_bsdf_type) if not shader_node_transparent_or_glass_bsdf: if transparent_default_bsdf_type == NodeUtility.GLASS_BSDF: shader_node_transparent_or_glass_bsdf = nodes.new(type='ShaderNodeBsdfGlass') else: shader_node_transparent_or_glass_bsdf = nodes.new(type='ShaderNodeBsdfTransparent') shader_node_RGB = nodes.new(type='ShaderNodeRGB') NodeUtility.replace_bsdf_node_in_material(material, old_node=shader_node_diffuse_bsdf, new_node=shader_node_transparent_or_glass_bsdf, preceding_node=shader_node_RGB, next_node=shader_node_material_output) else: logger.debug('Converting Material With Simple Color') # by default there is just a diffuse bsdf created using the color of the material if material_default_bsdf_type == NodeUtility.GLOSSY_BSDF: logger.debug('Replace Diffuse Material Node with Glossy Material Node') shader_node_glossy_bsdf = nodes.get(NodeUtility.GLOSSY_BSDF) if not shader_node_glossy_bsdf: shader_node_glossy_bsdf = nodes.new(type='ShaderNodeBsdfGlossy') NodeUtility.replace_bsdf_node_in_material(material, old_node=shader_node_diffuse_bsdf, new_node=shader_node_glossy_bsdf, preceding_node=None, next_node=shader_node_material_output) else: logger.debug('Material has already a node ...') logger.info('create_material_nodes_for_cycle_using_blender_internal_textures: Done')

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trivector/trivector.py

nklapste/trivector

1

70706

#!/usr/bin/env python # -*- coding: utf-8 -*- """Image conversion functionality for trivector""" from enum import Enum import numpy as np import svgwrite import cv2 import progressbar def upper_tri_sum(d3array: np.ndarray) -> np.ndarray: """Get a 3D image array's upper diagonal's pixel color average :param d3array: 3D image array derived from :func:`cv2.imread` Treat the 3D array as 2d array. Having the innermost array (pixel BGR values) be considered base values to be averaged. :return: BGR array of the average color of the upper diagonal of the 3D image array """ x, y, _ = d3array.shape tri = [] for i in range(x): if i > y: break for j in range(y - i): tri.append(d3array[i][i + j]) return np.sum(tri, axis=0) // len(tri) def lower_tri_sum(d3array: np.ndarray) -> np.ndarray: """Get a 3D image array's lower diagonal's pixel color average :param d3array: 3D image array derived from :func:`cv2.imread` Treat the 3D array as 2d array. Having the innermost array (pixel BGR values) be considered base values to be averaged. .. note:: If the lower diagonal cannot be computed (eg: flat/malformed 3D array) use the 3D image array's upper diagonal's pixel color average instead. :return: BGR array of the average color of the lower diagonal of the 3D image array """ x, y, _ = d3array.shape tri = [] for i in range(x): if i > y: break for j in range(i): tri.append(d3array[i][j]) # if bottom tri is empty use the upper tri's sum if not tri: return upper_tri_sum(d3array) return np.sum(tri, axis=0) // len(tri) def vectorize_sector_left(sub_img: np.ndarray, svg_drawing: svgwrite.Drawing, x: int, y: int, cut_size: int): """Add two triangles to ``svg_drawing`` whose colors are derived from the color averages from the top and bottom diagonals of the 3D BGR image array of the sub image""" b, g, r = upper_tri_sum(sub_img) svg_drawing.add( svg_drawing.polygon( [(x, y), (x + cut_size, y), (x + cut_size, y + cut_size)], fill=svgwrite.rgb(r, g, b, "RGB") ) ) b, g, r = lower_tri_sum(sub_img) svg_drawing.add( svg_drawing.polygon( [(x, y), (x, y + cut_size), (x + cut_size, y + cut_size)], fill=svgwrite.rgb(r, g, b, "RGB") ) ) def vectorize_sector_right(sub_img: np.ndarray, svg_drawing: svgwrite.Drawing, x: int, y: int, cut_size: int): """Add two triangles to ``svg_drawing`` whose colors are derived from the color averages from the top and bottom diagonals of the 3D BGR image array of the sub image""" b, g, r = upper_tri_sum(sub_img) svg_drawing.add( svg_drawing.polygon( [(x, y + cut_size), (x + cut_size, y + cut_size), (x + cut_size, y)], fill=svgwrite.rgb(r, g, b, "RGB") ) ) b, g, r = lower_tri_sum(sub_img) svg_drawing.add( svg_drawing.polygon( [(x, y + cut_size), (x, y), (x + cut_size, y)], fill=svgwrite.rgb(r, g, b, "RGB") ) ) class DiagonalStyle(Enum): """Styling options noting the diagonal arrangement of the triangle sectors""" right = "right" left = "left" alternating = "alternating" def __str__(self): return self.value def trivector(image_path: str, cut_size: int, output_path: str, diagonal_style: DiagonalStyle = DiagonalStyle.alternating): """Convert an image into a SVG vector image composed of triangular sectors :param image_path: path to the image to trivector :param cut_size: size in pixels for each triangle sector :param diagonal_style: diagonal arrangement of the triangle sectors :param output_path: path to write the trivectored image """ image = cv2.imread(image_path) # pylint:disable=no-member height, width, _ = image.shape width_slices = range(0, width, cut_size) height_slices = range(0, height, cut_size) svg_drawing = svgwrite.Drawing( output_path, profile="full", size=(len(width_slices)*cut_size, len(height_slices)*cut_size) ) # start up the progress bar # each image sector is one tick one the progress bar bar = progressbar.ProgressBar(max_value=len(width_slices)*len(height_slices)) counter_2 = 0 sector_num = 0 for y in height_slices: counter_1 = counter_2 counter_2 += 1 for x in width_slices: sector_image = image[y:y + cut_size, x:x + cut_size] if (diagonal_style == DiagonalStyle.left) or \ (diagonal_style == DiagonalStyle.alternating and counter_1 % 2): vectorize_sector_left(sector_image, svg_drawing, x, y, cut_size) else: sector_image = np.rot90(sector_image, axes=(0, 1)) vectorize_sector_right(sector_image, svg_drawing, x, y, cut_size) sector_num += 1 counter_1 += 1 bar.update(sector_num) svg_drawing.save()

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