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
flask_atomic/ext.py	kmjbyrne/flask-kbpc	0	103791	<reponame>kmjbyrne/flask-kbpc from datetime import datetime from flask import Flask from flask import Response from flask import jsonify from flask.json import JSONEncoder from sqlalchemy.orm.collections import InstrumentedList from sqlalchemy_blender import serialize from sqlalchemy_blender import iserialize from handyhttp import HTTPException class ModelEncoder(JSONEncoder): def default(self, data): if isinstance(data, HTTPException): return data.pack() if isinstance(data, InstrumentedList): return serialize(data) elif isinstance(data, datetime): return data.isoformat() else: data = iserialize(data) return data class JSONResponse(Response): @classmethod def force_type(cls, rv, environ=None): if isinstance(rv, HTTPException): return rv.pack() if isinstance(rv, dict): rv = jsonify(rv) elif isinstance(rv, tuple): rv = jsonify(rv[0]), rv[1] return super(JSONResponse, cls).force_type(rv, environ) class FlaskJSON(Flask): response_class = JSONResponse json_encoder = ModelEncoder	[ 1, 529, 276, 1112, 420, 29958, 8848, 29926, 1609, 29878, 484, 29914, 1579, 1278, 29899, 21066, 6739, 13, 3166, 12865, 1053, 12865, 13, 13, 3166, 29784, 1053, 2379, 1278, 13, 3166, 29784, 1053, 13291, 13, 3166, 29784, 1053, 4390, 1598, 13, 3166, 29784, 29889, 3126, 1053, 4663, 8566, 6119, 13, 13, 3166, 4576, 284, 305, 6764, 29889, 555, 29889, 29027, 1053, 2799, 15461, 287, 1293, 13, 13, 3166, 4576, 284, 305, 6764, 29918, 2204, 1581, 1053, 28755, 13, 3166, 4576, 284, 305, 6764, 29918, 2204, 1581, 1053, 338, 261, 6646, 13, 3166, 1361, 29891, 1124, 1053, 7331, 2451, 13, 13, 13, 1990, 8125, 8566, 6119, 29898, 7249, 8566, 6119, 1125, 13, 1678, 822, 2322, 29898, 1311, 29892, 848, 1125, 13, 4706, 565, 338, 8758, 29898, 1272, 29892, 7331, 2451, 1125, 13, 9651, 736, 848, 29889, 4058, 580, 13, 4706, 565, 338, 8758, 29898, 1272, 29892, 2799, 15461, 287, 1293, 1125, 13, 9651, 736, 28755, 29898, 1272, 29897, 13, 4706, 25342, 338, 8758, 29898, 1272, 29892, 12865, 1125, 13, 9651, 736, 848, 29889, 10718, 4830, 580, 13, 4706, 1683, 29901, 13, 9651, 848, 353, 338, 261, 6646, 29898, 1272, 29897, 13, 4706, 736, 848, 13, 13, 13, 1990, 4663, 5103, 29898, 5103, 1125, 13, 1678, 732, 1990, 5696, 13, 1678, 822, 4889, 29918, 1853, 29898, 25932, 29892, 364, 29894, 29892, 12471, 29922, 8516, 1125, 13, 4706, 565, 338, 8758, 29898, 15291, 29892, 7331, 2451, 1125, 13, 9651, 736, 364, 29894, 29889, 4058, 580, 13, 4706, 565, 338, 8758, 29898, 15291, 29892, 9657, 1125, 13, 9651, 364, 29894, 353, 4390, 1598, 29898, 15291, 29897, 13, 4706, 25342, 338, 8758, 29898, 15291, 29892, 18761, 1125, 13, 9651, 364, 29894, 353, 4390, 1598, 29898, 15291, 29961, 29900, 11724, 364, 29894, 29961, 29896, 29962, 13, 4706, 736, 2428, 29898, 7249, 5103, 29892, 1067, 29879, 467, 10118, 29918, 1853, 29898, 15291, 29892, 12471, 29897, 13, 13, 13, 1990, 2379, 1278, 7249, 29898, 8754, 1278, 1125, 13, 1678, 2933, 29918, 1990, 353, 4663, 5103, 13, 1678, 4390, 29918, 3977, 6119, 353, 8125, 8566, 6119, 13, 2 ]
chapter7/exercises/EG7-09 get_value investigation 2.py	munnep/begin_to_code_with_python	0	191459	<gh_stars>0 # EG7-09 get_value investigation 2 def get_value(prompt, value_min, value_max): return ride_number=get_value(prompt='Please enter the ride number you want:',value_min=1,value_max=5) print('You have selected ride:',ride_number)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 382, 29954, 29955, 29899, 29900, 29929, 679, 29918, 1767, 22522, 29871, 29906, 13, 1753, 679, 29918, 1767, 29898, 14032, 415, 29892, 995, 29918, 1195, 29892, 995, 29918, 3317, 1125, 13, 1678, 736, 29871, 13, 13, 2426, 29918, 4537, 29922, 657, 29918, 1767, 29898, 14032, 415, 2433, 12148, 3896, 278, 22203, 1353, 366, 864, 29901, 742, 1767, 29918, 1195, 29922, 29896, 29892, 1767, 29918, 3317, 29922, 29945, 29897, 13, 2158, 877, 3492, 505, 4629, 22203, 29901, 742, 2426, 29918, 4537, 29897, 13, 2 ]
boned_html/__init__.py	alexgarel/boned-html	0	1608097	<gh_stars>0 from .version import __version__ # noqa: F401 from .boner import HtmlBoner # noqa: F401	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 869, 3259, 1053, 4770, 3259, 1649, 29871, 396, 694, 25621, 29901, 383, 29946, 29900, 29896, 13, 3166, 869, 6718, 261, 1053, 24726, 29933, 265, 261, 29871, 396, 694, 25621, 29901, 383, 29946, 29900, 29896, 13, 2 ]
trainer.py	thedesertm/leapmotion_training_svm	0	18431	import pandas as pd import os from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler from sklearn.metrics import classification_report, confusion_matrix from sklearn.model_selection import train_test_split import pickle BASE_PATH = os.path.join(os.getcwd() , "dataset") df = None i = 0 for file_name in os.listdir(BASE_PATH): file_path = os.path.join(BASE_PATH , file_name) print(file_path) data_frame = pd.read_csv(file_path , header=None) data_frame.pop(178) data_frame.pop(0) dat = pd.DataFrame({'result': [i for k in range(data_frame.shape[1])]}) data_frame = data_frame.join(dat) if not df is None : df = df.append(data_frame , ignore_index=True) else: df = data_frame i += 1 scaler = StandardScaler() y = df.pop("result") scalled_data = scaler.fit_transform(df) X_train, X_test, y_train, y_test = train_test_split(scalled_data , y, test_size = 0.20) svclassifier = SVC(kernel='linear') svclassifier.fit(X_train, y_train) y_pred = svclassifier.predict(X_test) print(confusion_matrix(y_test,y_pred)) print(classification_report(y_test,y_pred)) pickle.dump(svclassifier , open("classifier.pkl" , 'wb')) pickle.dump(scaler , open("scaler.pkl" , 'wb'))	[ 1, 1053, 11701, 408, 10518, 13, 5215, 2897, 13, 3166, 2071, 19668, 29889, 4501, 29885, 1053, 317, 8257, 13, 3166, 2071, 19668, 29889, 1457, 19170, 1053, 10117, 29636, 261, 13, 3166, 2071, 19668, 29889, 2527, 10817, 1053, 12965, 29918, 12276, 29892, 14679, 29918, 5344, 13, 3166, 2071, 19668, 29889, 4299, 29918, 21731, 1053, 7945, 29918, 1688, 29918, 5451, 13, 5215, 5839, 280, 13, 13, 25416, 29918, 10145, 353, 2897, 29889, 2084, 29889, 7122, 29898, 359, 29889, 657, 29883, 9970, 580, 1919, 376, 24713, 1159, 13, 2176, 353, 6213, 13, 29875, 353, 29871, 29900, 13, 1454, 934, 29918, 978, 297, 2897, 29889, 1761, 3972, 29898, 25416, 29918, 10145, 1125, 13, 1678, 934, 29918, 2084, 353, 2897, 29889, 2084, 29889, 7122, 29898, 25416, 29918, 10145, 1919, 934, 29918, 978, 29897, 13, 1678, 1596, 29898, 1445, 29918, 2084, 29897, 13, 1678, 848, 29918, 2557, 353, 10518, 29889, 949, 29918, 7638, 29898, 1445, 29918, 2084, 1919, 29871, 4839, 29922, 8516, 29897, 13, 1678, 848, 29918, 2557, 29889, 7323, 29898, 29896, 29955, 29947, 29897, 13, 1678, 848, 29918, 2557, 29889, 7323, 29898, 29900, 29897, 13, 1678, 1418, 353, 10518, 29889, 17271, 3319, 29915, 2914, 2396, 518, 29875, 363, 413, 297, 3464, 29898, 1272, 29918, 2557, 29889, 12181, 29961, 29896, 2314, 29962, 1800, 13, 1678, 848, 29918, 2557, 353, 848, 29918, 2557, 29889, 7122, 29898, 4130, 29897, 13, 1678, 565, 451, 4489, 338, 6213, 584, 13, 4706, 4489, 353, 4489, 29889, 4397, 29898, 1272, 29918, 2557, 1919, 11455, 29918, 2248, 29922, 5574, 29897, 13, 1678, 1683, 29901, 13, 4706, 4489, 353, 848, 29918, 2557, 13, 1678, 474, 4619, 29871, 29896, 13, 13, 13, 19529, 261, 353, 10117, 29636, 261, 580, 13, 29891, 353, 4489, 29889, 7323, 703, 2914, 1159, 13, 29879, 13998, 29918, 1272, 353, 8716, 261, 29889, 9202, 29918, 9067, 29898, 2176, 29897, 13, 13, 29990, 29918, 14968, 29892, 1060, 29918, 1688, 29892, 343, 29918, 14968, 29892, 343, 29918, 1688, 353, 7945, 29918, 1688, 29918, 5451, 29898, 29879, 13998, 29918, 1272, 1919, 343, 29892, 1243, 29918, 2311, 353, 29871, 29900, 29889, 29906, 29900, 29897, 13, 13, 4501, 1990, 3709, 353, 317, 8257, 29898, 17460, 2433, 10660, 1495, 13, 4501, 1990, 3709, 29889, 9202, 29898, 29990, 29918, 14968, 29892, 343, 29918, 14968, 29897, 13, 29891, 29918, 11965, 353, 3731, 1990, 3709, 29889, 27711, 29898, 29990, 29918, 1688, 29897, 13, 2158, 29898, 5527, 3958, 29918, 5344, 29898, 29891, 29918, 1688, 29892, 29891, 29918, 11965, 876, 13, 2158, 29898, 1990, 2450, 29918, 12276, 29898, 29891, 29918, 1688, 29892, 29891, 29918, 11965, 876, 13, 23945, 280, 29889, 15070, 29898, 4501, 1990, 3709, 1919, 1722, 703, 1990, 3709, 29889, 29886, 6321, 29908, 1919, 525, 29893, 29890, 8785, 13, 23945, 280, 29889, 15070, 29898, 19529, 261, 1919, 1722, 703, 19529, 261, 29889, 29886, 6321, 29908, 1919, 525, 29893, 29890, 8785, 2 ]
pythia/opal/content/KeyboardAttributes.py	willic3/pythia	1	47028	<filename>pythia/opal/content/KeyboardAttributes.py #!/usr/bin/env python # # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # <NAME> # California Institute of Technology # (C) 1998-2005 All Rights Reserved # # {LicenseText} # # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # def KeyboardAttributes(object): def identify(self, inspector): return inspector.onKeyboardAttributes(self) def __init__(self): self.accesskey = '' self.tabindex = '' return # version __id__ = "$Id: KeyboardAttributes.py,v 1.1 2005/03/20 07:22:58 aivazis Exp $" # End of file	[ 1, 529, 9507, 29958, 29886, 1541, 423, 29914, 459, 284, 29914, 3051, 29914, 2558, 3377, 15801, 29889, 2272, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 13, 29937, 3695, 26594, 26594, 26594, 26594, 26594, 26594, 26594, 26594, 26594, 14087, 30022, 13, 29937, 13, 29937, 462, 632, 529, 5813, 29958, 13, 29937, 462, 418, 8046, 8907, 310, 17968, 13, 29937, 462, 418, 313, 29907, 29897, 29871, 29896, 29929, 29929, 29947, 29899, 29906, 29900, 29900, 29945, 29871, 2178, 26863, 2538, 9841, 13, 29937, 13, 29937, 426, 29931, 293, 1947, 1626, 29913, 13, 29937, 13, 29937, 3695, 26594, 26594, 26594, 26594, 26594, 26594, 26594, 26594, 26594, 14087, 30022, 13, 29937, 13, 13, 13, 1753, 7670, 3377, 15801, 29898, 3318, 1125, 13, 13, 13, 1678, 822, 12439, 29898, 1311, 29892, 16096, 272, 1125, 13, 4706, 736, 16096, 272, 29889, 265, 2558, 3377, 15801, 29898, 1311, 29897, 13, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 1125, 13, 4706, 1583, 29889, 5943, 1989, 353, 6629, 13, 4706, 1583, 29889, 3891, 2248, 353, 6629, 13, 4706, 736, 13, 13, 13, 29937, 1873, 13, 1649, 333, 1649, 353, 3908, 1204, 29901, 7670, 3377, 15801, 29889, 2272, 29892, 29894, 29871, 29896, 29889, 29896, 29871, 29906, 29900, 29900, 29945, 29914, 29900, 29941, 29914, 29906, 29900, 29871, 29900, 29955, 29901, 29906, 29906, 29901, 29945, 29947, 263, 440, 834, 275, 12027, 395, 29908, 13, 13, 29937, 2796, 310, 934, 29871, 13, 2 ]
classes/aSimple.py	IMTorgTestCode/testprj_Python_aSimple	0	1617897	<filename>classes/aSimple.py import Animal, Dog, Cat if __name__ == '__main__': print "Hello World!" dogName = raw_input("\nWhat is your dog's name?" ) if dogName: Fido = Dog.Dog(dogName) Fido.getSound() Fluffy = Cat.Cat("Fluffy") Fluffy.getSound()	[ 1, 529, 9507, 29958, 13203, 29914, 29874, 15427, 29889, 2272, 13, 30004, 13, 30004, 13, 30004, 13, 30004, 13, 5215, 22809, 29892, 18776, 29892, 10459, 12, 12, 30004, 13, 30004, 13, 30004, 13, 30004, 13, 30004, 13, 30004, 13, 30004, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 30004, 13, 12, 2158, 376, 10994, 2787, 3850, 6756, 13, 12, 26169, 1170, 353, 10650, 29918, 2080, 14182, 29876, 5618, 338, 596, 11203, 29915, 29879, 1024, 3026, 1723, 6756, 13, 12, 30004, 13, 30004, 13, 12, 361, 11203, 1170, 29901, 30004, 13, 12, 12, 29943, 1941, 353, 18776, 29889, 29928, 468, 29898, 26169, 1170, 8443, 13, 12, 12, 29943, 1941, 29889, 657, 29456, 26471, 13, 12, 30004, 13, 12, 8754, 3096, 29891, 353, 10459, 29889, 9694, 703, 8754, 3096, 29891, 1159, 30004, 13, 12, 8754, 3096, 29891, 29889, 657, 29456, 26471, 13, 30004, 13, 30004, 13, 30004, 13, 30004, 13, 2 ]
medtrialextractor/formatting/read_annotations.py	juanmoo/MedTrialExtractor	1	41636	<filename>medtrialextractor/formatting/read_annotations.py ''' Read Annotations file and save in struct ''' import pandas as pd import json import codecs import multiprocessing from contextlib import contextmanager # Utils def match_ids_by_filename(ann, struct): fname_to_id = {} for doc_id in pd.unique(ann['doc_id']): doc_name = ann.loc[ann['doc_id'] == doc_id].iloc[0]['doc_name'] fname_to_id[doc_name] = doc_id new_struct = struct.copy() new_struct['documents'] = {} for old_id in struct['documents']: doc_struct = struct['documents'][old_id].copy() fname = doc_struct['file_name'] new_id = fname_to_id.get(fname, old_id) doc_struct['document_id'] = new_id new_struct['documents'][new_id] = doc_struct return new_struct def get_spans(spans_str): idxs = [int(e) for e in spans_str.split(',') if len(e) > 0] assert(len(idxs)%2 == 0) return list(zip(idxs[::2], idxs[1::2])) @contextmanager def poolcontext(args, kwargs): pool = multiprocessing.Pool(args, *kwargs) yield pool pool.terminate() # Main def read_annotations(args): # Read input files annotation_file_path = args.annotation_file_path struct_base_path = args.struct_base_path annotations = pd.read_csv(annotation_file_path, keep_default_na=False) struct_base = None with codecs.open(struct_base_path, 'r', encoding='utf-8', errors='replace') as struct_base_file: struct_base = json.load(struct_base_file) # Spacing for output readability print('\n' 2) # Match ids by file name if flag present if args.match_ids_by_filename: print('Matching document ids by file name ...') struct_base = match_ids_by_filename(annotations, struct_base) print('done.\n') struct_doc_ids = set(struct_base['documents'].keys()) annotated_doc_ids = set(pd.unique(annotations['doc_id'])) annotated_docs = annotated_doc_ids & struct_doc_ids not_annotated_docs = struct_doc_ids - annotated_doc_ids unknown_annotated_docs = annotated_doc_ids - struct_doc_ids if args.verbose: if len(annotated_docs) > 0: print('Annotated Documents:') for j, id in enumerate(annotated_docs): print(f'{j + 1}. {id}') print() print('-' * 100) else: print(f'No annotations for documents in {struct_base_path} were found.') print() print('-' * 100) if len(not_annotated_docs) > 0: print('No annotations found for the following documents:') for j, id in enumerate(not_annotated_docs): print(f'{j + 1}. {id}') print() print('-' * 100) if len(unknown_annotated_docs) > 0: print('These files were found in the annotaiton file but are not present in the base struct file:') print('No annotations found for the following documents:') for j, id in enumerate(unknown_annotated_docs): print(f'{j + 1}. {id}') print() print('-' * 100) # for doc_id in annotated_docs: annotation_args = [(struct_base['documents'][id], annotations) for id in annotated_docs] with poolcontext(processes=args.num_workers) as pool: doc_structs = pool.starmap(read_document_annotation, annotation_args) for ds in doc_structs: id = ds['document_id'] struct_base['documents'][id] = ds if 'annotated_docs' not in struct_base: struct_base['annotated_docs'] = [] struct_base['annotated_docs'].extend(list(annotated_docs)) # Save annotated struct if args.struct_annotated_path is not None: struct_annotated_path = args.struct_annotated_path else: struct_annotated_path = args.struct_base_path with codecs.open(struct_annotated_path, 'w', encoding='utf-8', errors='replace') as output_struct: json.dump(struct_base, output_struct, indent=4) def read_document_annotation(doc_struct, annotations): doc_id = doc_struct['document_id'] doc_df = annotations.loc[annotations['doc_id'] == doc_id] print(f'Processing annotations for {doc_id} ...') if len(doc_df) == 0: raise Exception(f'Unable to find annotations for document: {doc_id}') non_arm_cols = ['title', 'authors', 'study_type'] arm_non_numbered = ['arm_efficacy_metric', 'arm_efficacy_results'] arm_numbered = ['arm_dosage', 'arm_description'] # Iterate over doc_struct paragraphs for par in doc_struct['paragraphs']: ann_par = doc_df.loc[doc_df['description'] == par['text']] for j, row in ann_par.iterrows(): par['annotated'] = True par['annotations_spans'] = {} par['annotations_span_arms'] = {} # Get arm number arm_count = int(row['arm_number']) # Get annotations for non-arm columns for k in non_arm_cols: for kp in row.index: if k in kp: ann_spans = par['annotations_spans'] ann_span_arms = par['annotations_span_arms'] tags_col = f'{k}-tag' span_str = row[tags_col] spans = get_spans(span_str) ann_spans[k] = spans ann_span_arms = [('', '')] * len(spans) # Don't duplicate cols break # Get annotations for arm non-numbered columns for k in arm_non_numbered: for kp in row.index: if k in kp: ann_spans = par['annotations_spans'] ann_span_arms = par['annotations_span_arms'] tags_col = f'{k}-tag' span_str = row[tags_col] spans = get_spans(span_str) ann_spans[k] = spans ann_span_arms[k] = [(arm_count, '')] len(spans) # Don't duplicate cols break # Get annotations for arm columns for k in arm_numbered: for kp in row.index: if k in kp: ann_spans = par['annotations_spans'] ann_span_arms = par['annotations_span_arms'] desc_idxs = [] for c in row.index: if c.startswith(k) and c.endswith('-tag'): c_idx = int(c[len(k) + 1:-4]) desc_idxs.append(c_idx) k_spans = [] k_arms = [] for d_idx in desc_idxs: tag_col = f'{k}-{d_idx}-tag' span_str = row[tag_col] d_spans = get_spans(span_str) d_arms = [(arm_count, d_idx)] * len(d_spans) k_spans.extend(d_spans) k_arms.extend(d_arms) ann_spans[k] = k_spans ann_span_arms[k] = k_arms # Don't duplicate cols break return doc_struct	[ 1, 529, 9507, 29958, 2168, 3626, 744, 486, 28891, 29914, 689, 23980, 29914, 949, 29918, 6735, 800, 29889, 2272, 13, 12008, 13, 6359, 530, 1333, 800, 934, 322, 4078, 297, 2281, 13, 12008, 13, 13, 5215, 11701, 408, 10518, 13, 5215, 4390, 13, 5215, 775, 2395, 13, 5215, 6674, 307, 985, 292, 13, 3166, 3030, 1982, 1053, 3030, 12847, 13, 13, 13, 29937, 22310, 29879, 13, 1753, 1993, 29918, 4841, 29918, 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event.py	Wired-Square/homeassistant-twc-director	0	112142	"""The Tesla Wall Charger Director integration.""" import logging from homeassistant.core import HomeAssistant from homeassistant.const import CONF_EVENT, CONF_ID, CONF_DEVICE_ID from twcdirector.device import TWCPeripheral from .const import ( DOMAIN_EVENT ) from .device import ( TWCDeviceEntity ) _LOGGER = logging.getLogger(__name__) class TWCDeviceEvent(TWCDeviceEntity): def __init__(self, hass: HomeAssistant, twc_device: TWCPeripheral): super().__init__(twc_device) self.hass = hass self._name = self._device_name self._twc_device.register_device_data_updated_callback({ "TWC_CAR_CONNECTED": self._connected_event }) async def _connected_event(self): event_data = { CONF_ID: self.unique_id, CONF_DEVICE_ID: self.entity_id, CONF_EVENT: "car_connected" if self._twc_device.is_car_connected() else "car_disconnected" } self.hass.bus.async_fire(DOMAIN_EVENT, event_data)	[ 1, 9995, 1576, 323, 267, 433, 14406, 2896, 914, 15944, 13465, 1213, 15945, 13, 5215, 12183, 13, 13, 3166, 3271, 465, 22137, 29889, 3221, 1053, 8778, 7900, 22137, 13, 3166, 3271, 465, 22137, 29889, 3075, 1053, 8707, 29943, 29918, 22240, 3919, 29892, 8707, 29943, 29918, 1367, 29892, 8707, 29943, 29918, 2287, 19059, 29918, 1367, 13, 13, 3166, 3252, 2252, 1088, 272, 29889, 10141, 1053, 323, 29956, 29907, 5894, 29875, 8096, 284, 13, 13, 3166, 869, 3075, 1053, 313, 13, 1678, 11662, 29032, 29918, 22240, 3919, 13, 29897, 13, 13, 3166, 869, 10141, 1053, 313, 13, 1678, 323, 29956, 29907, 11501, 6691, 13, 29897, 13, 13, 29918, 14480, 17070, 353, 12183, 29889, 657, 16363, 22168, 978, 1649, 29897, 13, 13, 13, 1990, 323, 29956, 29907, 11501, 2624, 29898, 16240, 29907, 11501, 6691, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 298, 465, 29901, 8778, 7900, 22137, 29892, 3252, 29883, 29918, 10141, 29901, 323, 29956, 29907, 5894, 29875, 8096, 284, 1125, 13, 4706, 2428, 2141, 1649, 2344, 12035, 7516, 29883, 29918, 10141, 29897, 13, 4706, 1583, 29889, 29882, 465, 353, 298, 465, 13, 4706, 1583, 3032, 978, 353, 1583, 3032, 10141, 29918, 978, 13, 13, 4706, 1583, 3032, 7516, 29883, 29918, 10141, 29889, 9573, 29918, 10141, 29918, 1272, 29918, 21402, 29918, 14035, 3319, 13, 9651, 376, 16240, 29907, 29918, 29907, 1718, 29918, 6007, 8186, 1783, 3352, 1115, 1583, 3032, 18045, 29918, 3696, 13, 4706, 5615, 13, 13, 1678, 7465, 822, 903, 18045, 29918, 3696, 29898, 1311, 1125, 13, 4706, 1741, 29918, 1272, 353, 426, 13, 9651, 8707, 29943, 29918, 1367, 29901, 1583, 29889, 13092, 29918, 333, 29892, 13, 9651, 8707, 29943, 29918, 2287, 19059, 29918, 1367, 29901, 1583, 29889, 10041, 29918, 333, 29892, 13, 9651, 8707, 29943, 29918, 22240, 3919, 29901, 376, 4287, 29918, 18045, 29908, 565, 1583, 3032, 7516, 29883, 29918, 10141, 29889, 275, 29918, 4287, 29918, 18045, 580, 1683, 376, 4287, 29918, 2218, 18045, 29908, 13, 4706, 500, 13, 13, 4706, 1583, 29889, 29882, 465, 29889, 8262, 29889, 12674, 29918, 8696, 29898, 3970, 29032, 29918, 22240, 3919, 29892, 1741, 29918, 1272, 29897, 13, 2 ]
data_access/qso_data.py	yishayv/lyacorr	2	149840	<filename>data_access/qso_data.py """ Helper classes for encapsulating QSO metadata and spectrum data. """ import astropy.table as table class QSORecord: def __init__(self, index, spec_obj_id, z, ra, dec, plate, mjd, fiber_id, extinction_g): self.index = index self.specObjID = spec_obj_id self.z = z self.ra = ra self.dec = dec self.plate = plate self.mjd = mjd self.fiberID = fiber_id self.extinction_g = extinction_g @classmethod def from_row(cls, qso_row): assert isinstance(qso_row, table.Row) return cls(qso_row['index'], qso_row['specObjID'], qso_row['z'], qso_row['ra'], qso_row['dec'], qso_row['plate'], qso_row['mjd'], qso_row['fiberID'], qso_row['extinction_g']) def __str__(self): return " ".join([str(self.index), str(self.specObjID), str(self.z), str(self.ra), str(self.dec), str(self.plate), str(self.mjd), str(self.fiberID)]) class QSOData: def __init__(self, qso_rec, ar_wavelength, ar_flux, ar_ivar): """ :type qso_rec: QSORecord :type ar_wavelength: np.multiarray.ndarray :type ar_flux: np.multiarray.ndarray :type ar_ivar: np.multiarray.ndarray """ self.qso_rec = qso_rec self.ar_wavelength = ar_wavelength self.ar_flux = ar_flux self.ar_ivar = ar_ivar	[ 1, 529, 9507, 29958, 1272, 29918, 5943, 29914, 29939, 578, 29918, 1272, 29889, 2272, 13, 15945, 29908, 13, 10739, 4413, 363, 2094, 2547, 18099, 660, 6156, 15562, 322, 18272, 848, 29889, 13, 15945, 29908, 13, 13, 5215, 8717, 14441, 29889, 2371, 408, 1591, 13, 13, 13, 1990, 660, 29903, 1955, 687, 536, 29901, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 2380, 29892, 1580, 29918, 5415, 29918, 333, 29892, 503, 29892, 1153, 29892, 1602, 29892, 15284, 29892, 286, 26012, 29892, 5713, 495, 29918, 333, 29892, 1294, 16807, 29918, 29887, 1125, 13, 4706, 1583, 29889, 2248, 353, 2380, 13, 4706, 1583, 29889, 6550, 9930, 1367, 353, 1580, 29918, 5415, 29918, 333, 13, 4706, 1583, 29889, 29920, 353, 503, 13, 4706, 1583, 29889, 336, 353, 1153, 13, 4706, 1583, 29889, 7099, 353, 1602, 13, 4706, 1583, 29889, 2341, 353, 15284, 13, 4706, 1583, 29889, 29885, 26012, 353, 286, 26012, 13, 4706, 1583, 29889, 7241, 495, 1367, 353, 5713, 495, 29918, 333, 13, 4706, 1583, 29889, 1062, 16807, 29918, 29887, 353, 1294, 16807, 29918, 29887, 13, 13, 1678, 732, 1990, 5696, 13, 1678, 822, 515, 29918, 798, 29898, 25932, 29892, 3855, 578, 29918, 798, 1125, 13, 4706, 4974, 338, 8758, 29898, 29939, 578, 29918, 798, 29892, 1591, 29889, 4301, 29897, 13, 4706, 736, 1067, 29879, 29898, 29939, 578, 29918, 798, 1839, 2248, 7464, 3855, 578, 29918, 798, 1839, 6550, 9930, 1367, 7464, 3855, 578, 29918, 798, 1839, 29920, 7464, 13, 462, 259, 3855, 578, 29918, 798, 1839, 336, 7464, 3855, 578, 29918, 798, 1839, 7099, 7464, 13, 462, 259, 3855, 578, 29918, 798, 1839, 2341, 7464, 3855, 578, 29918, 798, 1839, 29885, 26012, 7464, 3855, 578, 29918, 798, 1839, 7241, 495, 1367, 7464, 13, 462, 259, 3855, 578, 29918, 798, 1839, 1062, 16807, 29918, 29887, 11287, 13, 13, 1678, 822, 4770, 710, 12035, 1311, 1125, 13, 4706, 736, 376, 11393, 7122, 4197, 710, 29898, 1311, 29889, 2248, 511, 851, 29898, 1311, 29889, 6550, 9930, 1367, 511, 851, 29898, 1311, 29889, 29920, 511, 851, 29898, 1311, 29889, 336, 511, 851, 29898, 1311, 29889, 7099, 511, 13, 462, 308, 851, 29898, 1311, 29889, 2341, 511, 851, 29898, 1311, 29889, 29885, 26012, 511, 851, 29898, 1311, 29889, 7241, 495, 1367, 29897, 2314, 13, 13, 13, 1990, 660, 6156, 1469, 29901, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 3855, 578, 29918, 3757, 29892, 564, 29918, 29893, 6447, 1477, 29892, 564, 29918, 1579, 1314, 29892, 564, 29918, 440, 279, 1125, 13, 4706, 9995, 13, 13, 4706, 584, 1853, 3855, 578, 29918, 3757, 29901, 660, 29903, 1955, 687, 536, 13, 4706, 584, 1853, 564, 29918, 29893, 6447, 1477, 29901, 7442, 29889, 9910, 2378, 29889, 299, 2378, 13, 4706, 584, 1853, 564, 29918, 1579, 1314, 29901, 7442, 29889, 9910, 2378, 29889, 299, 2378, 13, 4706, 584, 1853, 564, 29918, 440, 279, 29901, 7442, 29889, 9910, 2378, 29889, 299, 2378, 13, 4706, 9995, 13, 4706, 1583, 29889, 29939, 578, 29918, 3757, 353, 3855, 578, 29918, 3757, 13, 4706, 1583, 29889, 279, 29918, 29893, 6447, 1477, 353, 564, 29918, 29893, 6447, 1477, 13, 4706, 1583, 29889, 279, 29918, 1579, 1314, 353, 564, 29918, 1579, 1314, 13, 4706, 1583, 29889, 279, 29918, 440, 279, 353, 564, 29918, 440, 279, 13, 2 ]
bin/foldx_tsv.py	allydunham/mutfunc_sars_cov_2	1	148693	<reponame>allydunham/mutfunc_sars_cov_2 #!/usr/bin/env python3 """ Generate overall table of FoldX results across all genes """ import argparse import logging from pathlib import Path def get_template(path, model): """ Identify template for a given model based on the model table """ with open(path, 'r') as models_file: for line in models_file: line = line.strip().split('\t') if line[0] == model: return line[1] return None def main(args): """ Main """ print('uniprot', 'name', 'model', 'template', 'chain', 'position', 'wt', 'mut', 'sd', 'total_energy', 'backbone_hbond', 'sidechain_hbond', 'van_der_waals', 'electrostatics', 'solvation_polar', 'solvation_hydrophobic', 'van_der_waals_clashes', 'entropy_sidechain', 'entropy_mainchain', 'sloop_entropy', 'mloop_entropy', 'cis_bond', 'torsional_clash', 'backbone_clash', 'helix_dipole', 'water_bridge', 'disulfide', 'electrostatic_kon', 'partial_covalent_bonds', 'energy_ionisation', 'entropy_complex', sep='\t') for foldx_path in args.foldx: uniprot, protein, model = Path(foldx_path).parent.stem.split('_') try: template = get_template(f'{args.models}/{uniprot}_{protein}.models', model) except FileNotFoundError: logging.warning('Not .models file found for %s_%s', uniprot, protein) template = None with open(foldx_path, 'r') as foldx_file: next(foldx_file) for line in foldx_file: print(uniprot, protein, model, template if template is not None else 'NA', line.strip(), sep='\t') def parse_args(): """ Parse arguments """ parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('foldx', metavar='F', nargs='+', help="FoldX output files") parser.add_argument('--models', '-m', default='data/swissmodel', help="Directory containing .models tables") return parser.parse_args() if __name__ == '__main__': main(parse_args())	[ 1, 529, 276, 1112, 420, 29958, 635, 29881, 348, 3391, 29914, 6149, 9891, 29918, 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grievance/urls.py	AdarshNandanwar/CMS	0	31166	from django.urls import path from django.conf.urls import url from django.urls import path,include import grievance.views as VIEWS from django.conf.urls.static import static from django.conf import settings app_name = 'grievance' urlpatterns =[ # path('', VIEWS.HomeView.as_view()) path('level1/', VIEWS.level1HomeView.as_view()), path('level1/type/<type>',VIEWS.level1RequestView.as_view()), path('level1/student/<student_id>', VIEWS.level1StudentView.as_view()), path('level1/psd-student-status/student/<student_id>',VIEWS.ViewOnlyPSDStudentPageView.as_view()), path('', include('django.contrib.auth.urls')), path('redirect/', VIEWS.RedirectView.as_view()), path('student/', VIEWS.studentHomeView.as_view()), path('level2/', VIEWS.level2HomeView.as_view()), path('level2/type/<type>', VIEWS.level2RequestView.as_view()), path('level2/student/<student_id>', VIEWS.level2StudentView.as_view()), path('<level>/student-status/student/<student_id>', VIEWS.ViewOnlyStudentPageView.as_view()), path('website-admin', VIEWS.websiteAdminHomePageView.as_view()), # path('website-admin/change-deadline', VIEWS.changeDeadlineView.as_view()), path('website-admin/add-user', VIEWS.addUser.as_view()), path('get-deadline', VIEWS.showDeadlineInHeader.as_view()), path('dev_page', VIEWS.devPageView.as_view()), ] urlpatterns+= static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)	[ 1, 515, 9557, 29889, 26045, 1053, 2224, 13, 3166, 9557, 29889, 5527, 29889, 26045, 1053, 3142, 13, 3166, 9557, 29889, 26045, 1053, 2224, 29892, 2856, 13, 5215, 867, 10384, 749, 29889, 7406, 408, 5473, 29923, 7811, 13, 3166, 9557, 29889, 5527, 29889, 26045, 29889, 7959, 1053, 2294, 13, 3166, 9557, 29889, 5527, 1053, 6055, 13, 13, 13, 932, 29918, 978, 353, 525, 629, 10384, 749, 29915, 13, 2271, 11037, 29879, 353, 29961, 13, 12, 29937, 2224, 877, 742, 5473, 29923, 7811, 29889, 11184, 1043, 29889, 294, 29918, 1493, 3101, 13, 12, 2084, 877, 5563, 29896, 29914, 742, 5473, 29923, 7811, 29889, 5563, 29896, 11184, 1043, 29889, 294, 29918, 1493, 25739, 13, 12, 2084, 877, 5563, 29896, 29914, 1853, 29914, 29966, 1853, 29958, 742, 29963, 8673, 7811, 29889, 5563, 29896, 3089, 1043, 29889, 294, 29918, 1493, 25739, 13, 12, 2084, 877, 5563, 29896, 29914, 18945, 29914, 29966, 18945, 29918, 333, 29958, 742, 5473, 29923, 7811, 29889, 5563, 29896, 20791, 1043, 29889, 294, 29918, 1493, 25739, 13, 12, 2084, 877, 5563, 29896, 29914, 567, 29881, 29899, 18945, 29899, 4882, 29914, 18945, 29914, 29966, 18945, 29918, 333, 29958, 742, 29963, 8673, 7811, 29889, 1043, 11730, 7024, 29928, 20791, 5074, 1043, 29889, 294, 29918, 1493, 25739, 13, 12, 2084, 877, 742, 3160, 877, 14095, 29889, 21570, 29889, 5150, 29889, 26045, 1495, 511, 13, 12, 2084, 877, 17886, 29914, 742, 5473, 29923, 7811, 29889, 24735, 1043, 29889, 294, 29918, 1493, 25739, 13, 12, 2084, 877, 18945, 29914, 742, 5473, 29923, 7811, 29889, 18945, 11184, 1043, 29889, 294, 29918, 1493, 25739, 13, 12, 2084, 877, 5563, 29906, 29914, 742, 5473, 29923, 7811, 29889, 5563, 29906, 11184, 1043, 29889, 294, 29918, 1493, 25739, 13, 12, 2084, 877, 5563, 29906, 29914, 1853, 29914, 29966, 1853, 29958, 742, 5473, 29923, 7811, 29889, 5563, 29906, 3089, 1043, 29889, 294, 29918, 1493, 25739, 13, 12, 2084, 877, 5563, 29906, 29914, 18945, 29914, 29966, 18945, 29918, 333, 29958, 742, 5473, 29923, 7811, 29889, 5563, 29906, 20791, 1043, 29889, 294, 29918, 1493, 25739, 13, 12, 2084, 877, 29966, 5563, 20690, 18945, 29899, 4882, 29914, 18945, 29914, 29966, 18945, 29918, 333, 29958, 742, 5473, 29923, 7811, 29889, 1043, 11730, 20791, 5074, 1043, 29889, 294, 29918, 1493, 25739, 13, 12, 2084, 877, 22942, 29899, 6406, 742, 5473, 29923, 7811, 29889, 22942, 12754, 11184, 5074, 1043, 29889, 294, 29918, 1493, 25739, 13, 12, 29937, 2224, 877, 22942, 29899, 6406, 29914, 3167, 29899, 311, 328, 1220, 742, 5473, 29923, 7811, 29889, 3167, 29928, 1479, 1220, 1043, 29889, 294, 29918, 1493, 25739, 13, 12, 2084, 877, 22942, 29899, 6406, 29914, 1202, 29899, 1792, 742, 5473, 29923, 7811, 29889, 1202, 2659, 29889, 294, 29918, 1493, 25739, 13, 12, 2084, 877, 657, 29899, 311, 328, 1220, 742, 5473, 29923, 7811, 29889, 4294, 29928, 1479, 1220, 797, 7850, 29889, 294, 29918, 1493, 25739, 13, 12, 2084, 877, 3359, 29918, 3488, 742, 5473, 29923, 7811, 29889, 3359, 5074, 1043, 29889, 294, 29918, 1493, 25739, 13, 13, 29962, 13, 13, 2271, 11037, 29879, 23661, 2294, 29898, 11027, 29889, 2303, 4571, 29909, 29918, 4219, 29892, 1842, 29918, 4632, 29922, 11027, 29889, 2303, 4571, 29909, 29918, 21289, 29897, 13, 13, 2 ]
backend/Day.py	tkyaw1/lotus	0	116318	from Block import * """ This is the class for the day view of the journal. Each day will contain multiple blocks """ class Day: def __init__(self): self.blocks = [] self.date = "" #YYYYMMDD self.weather = "" self.heading = "" # not too sure what will populate this self.keywords = [] def __str__(self): return str({ "blocks": [block.return_json() for block in self.blocks], "date": self.date, "weather": self.weather, "heading": self.heading, "keywords": self.keywords }) def return_day_json(self): return str({ "blocks": [block.return_json() for block in self.blocks], "date": self.date, "weather": self.weather, "heading": self.heading, "keywords": self.keywords }) def add_block(self, block): """ Appends a block to the list of blocks """ self.blocks.append(block) # TODO: how to get blocks or return as a json? is this the __str__ function? def add_keywords(self, block): [self.keywords.append(keyword) for keyword in block.get_keywords()] def add_all_keywords(self): # for block in self.blocks: # self.keywords.append(block.get_keywords()) for block in self.blocks: [self.keywords.append(keyword) for keyword in block.get_keywords()] # 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src/training/experiments_ecfp_linearModel_MLP_only/experiments_ecfp_linearModel_MLP_molnet_NS.py	XieResearchGroup/PLANS	0	174696	from ...models.linear import Linear_S, Linear_M, Linear_L from ..experiment_base import ExperimentBase from ..train_model import ns_linear_teacher_model, ns_linear_student_model from ..train_model import predict_and_mix from ..training_args import LMMixupArgs from ...utils.training_utils import init_model, callback_list class Experiment(ExperimentBase): def train_teacher( self, model, x_train, y_train, x_test, y_test, x_pred, batch_size, epochs, log_f, log_path, n_repeat, activation="sigmoid", loss="categorical_crossentropy", out_len=32, ): model = init_model( model, drop_rate=self.drop_rate, loss=loss, out_len=out_len, activation=activation, ) cb_list = callback_list(log_path, self.es_patience, model) trained_model, histories = ns_linear_teacher_model( model=model, x_train=x_train, y_train=y_train, x_test=x_test, y_test=y_test, x_pred=x_pred, batch_size=batch_size, epochs=epochs, cb_list=cb_list, log_f=log_f, log_path=log_path, n_repeat=n_repeat, ) return trained_model, histories def train_student( self, student_model, teacher_model, x_train, y_train, x_test, y_test, x_pred, batch_size, epochs, log_f, log_path, n_repeat, activation="sigmoid", loss="categorical_crossentropy", out_len=32, ): x_mix, y_mix = predict_and_mix( teacher_model, x_pred, x_train, y_train, shuffle=True ) if self.mixup is not None: x_mix, y_mix = self._mixup(x_mix, y_mix) # init model model = init_model( student_model, drop_rate=self.drop_rate, loss=loss, out_len=out_len, activation=activation, ) # callbacks cb_list = callback_list(log_path, self.es_patience, model) # fit Linear_M model to mixed dataset trained_model, histories = ns_linear_student_model( model=model, x_train=x_train, y_train=y_train, x_mix=x_mix, y_mix=y_mix, x_test=x_test, y_test=y_test, x_pred=x_pred, batch_size=batch_size, epochs=epochs, cb_list=cb_list, log_f=log_f, log_path=log_path, n_repeat=n_repeat, ) return trained_model, histories def run_experiment(self): # load training and testing data x_train, y_train, x_test, y_test, x_unlabeled = self.load_data( ["ECFP", "Label"] ) # open log log_f, log_path = self.open_log_(self.log_path) # train the teacher model trained_model, histories = self.train_teacher( model=Linear_S, x_train=x_train, y_train=y_train, x_test=x_test, y_test=y_test, x_pred=x_unlabeled, batch_size=self.batch_size, epochs=self.epochs, log_f=log_f, log_path=log_path, n_repeat=self.n_repeat, activation="sigmoid", loss="binary_crossentropy", out_len=12, ) # log results self.log_training(trained_model, histories, log_path) # train student models for student in [Linear_M, Linear_L]: trained_model, histories = self.train_student( student_model=student, teacher_model=trained_model, x_train=x_train, y_train=y_train, x_test=x_test, y_test=y_test, x_pred=x_unlabeled, batch_size=self.batch_size, epochs=self.epochs, log_f=log_f, log_path=log_path, n_repeat=self.n_repeat, activation="sigmoid", loss="binary_crossentropy", out_len=12, ) # log results self.log_training(trained_model, histories, log_path) log_f.write("best losses:\n {}\n".format(str(self.best_loss))) log_f.write("best accuracies:\n {}\n".format(str(self.best_acc))) self.log_predictions(trained_model, x_test, y_test, log_path) if __name__ == "__main__": parser = LMMixupArgs() args = parser.parse_args() experiment = Experiment( data_path=args.data_path, log_path=args.log_path, es_patience=args.es_patience, batch_size=args.batch_size, epochs=args.epochs, n_repeat=args.repeat, mixup=args.mixup, mixup_repeat=args.mixup_repeat, rand_seed=args.rand_seed, ) experiment.run_experiment()	[ 1, 515, 2023, 9794, 29889, 10660, 1053, 22985, 29918, 29903, 29892, 22985, 29918, 29924, 29892, 22985, 29918, 29931, 13, 3166, 6317, 735, 15362, 29918, 3188, 1053, 1222, 15362, 5160, 13, 3166, 6317, 14968, 29918, 4299, 1053, 17534, 29918, 10660, 29918, 371, 11665, 29918, 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bin/nf.genome.py	orionzhou/nf	0	188562	#!/usr/bin/env python # -- coding: utf-8 -- import os import os.path as op import sys import pandas as pd import logging #import simplejson as json import yaml from jcvi.apps.base import sh, mkdir def get_gsize(fs): cl = pd.read_csv(fs, sep="\t", header=None, names=['chrom','size']) return sum(cl['size']) def tsv2yml(args): cvts = dict(genome=str,species=str,source=str) gl = pd.read_csv(args.fi, sep="\t", header=0, converters=cvts, true_values=['1','Y','Yes','T','True'], false_values=['0','N','No','F','False']) jd = dict() for i in range(len(gl)): genome, species, source, status = \ gl['genome'][i], gl['species'][i], gl['source'][i], gl['status'][i] #if not status in ['C','T']: continue jd1 = dict() pre = "%s/%s" % (args.dirg, genome) jd1['alias'] = gl['alias'][i] jd1['prefix'] = gl['prefix'][i] jd1['fasta'] = "%s/10.fasta" % pre jd1['fasta_idx'] = "%s/10.fasta.fai" % pre jd1['genome_bed'] = "%s/15_intervals/01.chrom.bed" % pre jd1['genome_sizes'] = "%s/15_intervals/01.chrom.sizes" % pre jd1['gap_bed'] = "%s/15_intervals/11.gap.bed" % pre if op.isfile(jd1['genome_sizes']): jd1['macs_gsize'] = get_gsize(jd1['genome_sizes']) # annotation jd1['gff'] = "%s/50_annotation/10.gff" % pre jd1['gff_db'] = "%s/50_annotation/10.gff.db" % pre jd1['gtf'] = "%s/50_annotation/10.gtf" % pre jd1['bed'] = "%s/50_annotation/10.bed" % pre jd1['fna'] = "%s/50_annotation/10.nt.fasta" % pre jd1['faa'] = "%s/50_annotation/10.aa.fasta" % pre jd1['tss'] = "%s/50_annotation/10.tss.bed" % pre jd1['pgff'] = "%s/50_annotation/15.gff" % pre jd1['pgff_db'] = "%s/50_annotation/15.gff.db" % pre jd1['pgtf'] = "%s/50_annotation/15.gtf" % pre jd1['pbed'] = "%s/50_annotation/15.bed" % pre jd1['pfna'] = "%s/50_annotation/15.nt.fasta" % pre jd1['pfaa'] = "%s/50_annotation/15.aa.fasta" % pre if gl['blat'][i]: jd1['blat'] = "%s/21_dbs/blat/db.2bit" % pre if gl['gatk'][i]: jd1['gatk'] = f"{pre}/21_dbs/gatk/" if gl['star'][i]: jd1['star'] = "%s/21_dbs/star/" % pre if gl['hisat2'][i]: jd1['hisat2'] = "%s/21_dbs/hisat2/db" % pre if genome in ['Zmays_B73']: jd1['hisat2'] = "%s/21_dbs/hisat2/B73_vt01/db" % pre if gl['bwa'][i]: jd1['bwa'] = "%s/21_dbs/bwa/db" % pre if gl['bismark'][i]: jd1['bismark'] = "%s/21_dbs/bismark" % pre if gl['salmon'][i]: jd1['salmon'] = "%s/21_dbs/salmon/db" % pre jd1['tx2gene'] = "%s/21_dbs/salmon/tx2gene.csv" % pre if gl['rcfg'][i]: jd1['rcfg'] = "%s/55.rds" % pre if gl['blast'][i]: jd1['blastp'] = f"{pre}/21_dbs/blastp" jd1['blastn'] = f"{pre}/21_dbs/blastn" win11 = "%s/15_intervals/20.win11.tsv" % pre win56 = "%s/15_intervals/20.win56.tsv" % pre win127 = "%s/15_intervals/20.win127.tsv" % pre if op.isfile(win11): jd1['win11'] = win11 if op.isfile(win56): jd1['win56'] = win56 if op.isfile(win127): jd1['win127'] = win127 jd1['fc_group_features'] = 'gene_id' jd1['fc_group_features_type'] = 'gene_biotype' jd[genome] = jd1 #j = dict(params = dict(genomes = jd)) j = dict(genomes = jd) with open(args.fo, 'w') as outfile: yaml.dump(j, outfile) # with open(args.json, 'w') as outfile: # json.dump(j, outfile) def download(args): cvts = dict(genome=str,species=str,source=str) gl = pd.read_csv(args.cfg, sep="\t", header=0, converters=cvts, true_values=['1','Y','Yes','T','True'], false_values=['0','N','No','F','False']) url_pre = "http://ftp.ebi.ac.uk/ensemblgenomes/pub" for i in range(len(gl)): if pd.isna(gl['status'][i]) or not gl['status'][i]: logging.warning(f"{gl['genome'][i]}: skipped") continue genome,species,source,version,assembly,url_fas,url_gff = \ gl['genome'][i], gl['species'][i], gl['source'][i], gl['version'][i], \ gl['assembly'][i], gl['url_fas'][i], gl['url_gff'][i] dirw = f"{args.dirg}/{genome}/raw" if not op.isdir(dirw): mkdir(dirw) os.chdir(dirw) if source == 'ensembl': version = int(version) species = species.replace(" ", "_") assembly = assembly.replace(" ", "_") url_fas = f"{url_pre}/release-{version}/plants/fasta/{species.lower()}/dna/{species}.{assembly}.dna.toplevel.fa.gz" url_gff = f"{url_pre}/release-{version}/plants/gff3/{species.lower()}/{species}.{assembly}.{version}.gff3.gz" fn1, fn2 = op.basename(url_fas), op.basename(url_gff) comp1, fn1c = False, fn1 comp2, fn2c = False, fn2 if not fn1.endswith(".gz"): comp1 = True fn1c = f"{fn1}.gz" if not fn2.endswith(".gz"): comp2 = True fn2c = f"{fn2}.gz" if op.isfile(fn1c) and os.stat(fn1c).st_size > 0 and op.isfile(fn2c) and os.stat(fn2c).st_size > 0: logging.warning(f"{genome}: already done") continue else: logging.debug(f"{genome}: working") if source == 'local': if not op.isfile(url_fas): url_fas = op.join(args.dirg, url_fas) if not op.isfile(url_gff): url_gff = op.join(args.dirg, url_gff) if not op.isfile(url_fas) or not op.isfile(url_gff): logging.error(f"no fasta/gff found: {url_fas} {url_gff}") sh(f"cp {url_fas} {fn1}") sh(f"cp {url_gff} {fn2}") else: sh(f"axel -n {args.thread} {url_fas} -o {fn1}") sh(f"axel -n {args.thread} {url_gff} -o {fn2}") if comp1: sh(f"gzip {fn1}") if comp2: sh(f"gzip {fn2}") sh(f"ln -sf {fn1c} raw.fasta.gz") sh(f"ln -sf {fn2c} raw.gff.gz") if __name__ == "__main__": import argparse ps = argparse.ArgumentParser( formatter_class = argparse.ArgumentDefaultsHelpFormatter, description = "help utilities to run the nextflow genome pipeline" ) sp = ps.add_subparsers(title = 'available commands', dest = 'command') sp1 = sp.add_parser("conv", formatter_class = argparse.ArgumentDefaultsHelpFormatter, help = "prepare genome config file for nextflow pipelines") sp1.add_argument('fi', help = 'input genome tsv') sp1.add_argument('fo', help = 'output yaml file') sp1.add_argument('--dirg', default="%s/zhoup-genome" % os.environ["s3"], help='genome directory') # sp1.add_argument('--json', default='genomes.json', help = 'output json file') sp1.set_defaults(func = tsv2yml) sp1 = sp.add_parser("download", formatter_class = argparse.ArgumentDefaultsHelpFormatter, help = "download raw genome (fasta+gff) files") sp1.add_argument('--cfg', default=f"{os.environ['genome']}/nf/genomes.tsv", help='genome config table') sp1.add_argument('--dirg', default="%s/zhoup-genome" % os.environ["s3"], help='genome directory') sp1.add_argument('--thread', type=int, default=4, help='downloading threads') sp1.set_defaults(func = download) args = ps.parse_args() if args.command: args.func(args) else: print('Error: need to specify a sub command\n') ps.print_help()	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 13, 5215, 2897, 13, 5215, 2897, 29889, 2084, 408, 1015, 13, 5215, 10876, 13, 5215, 11701, 408, 10518, 13, 5215, 12183, 13, 29937, 5215, 2560, 3126, 408, 4390, 13, 5215, 343, 8807, 13, 13, 3166, 432, 29883, 1403, 29889, 13371, 29889, 3188, 1053, 528, 29892, 29356, 13, 13, 1753, 679, 29918, 29887, 2311, 29898, 5847, 1125, 13, 1678, 1067, 353, 10518, 29889, 949, 29918, 7638, 29898, 5847, 29892, 16345, 543, 29905, 29873, 613, 4839, 29922, 8516, 29892, 2983, 29922, 1839, 27433, 3788, 2311, 11287, 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Other groups/Owlolf/Yuuki Yuuna wa Yuusha de Aru Churutto!/churuttoo_06.py	Ichunjo/encode-script	36	133285	"""YuYuYu Churutto script""" # noqa from __future__ import annotations __author__ = 'Vardë' import G41Fun as gf import havsfunc as hvf import lvsfunc as lvf import vardefunc as vdf import xvs from vardautomation import FileInfo, PresetEAC3, PresetWEB from vsutil import depth, get_y from churutto_common import EncodeGoBrrr import vapoursynth as vs core = vs.core NUM = __file__[-5:-3] WEB = FileInfo(f'{NUM}/Yuuki Yuuna wa Yuusha de Aru Churutto! - {NUM} (Amazon Prime VBR 1080p).mkv', 24, -22, preset=[PresetWEB, PresetEAC3]) class Filtering(): # noqa def main(self: Filtering) -> vs.VideoNode: # noqa """Vapoursynth filtering""" src = WEB.clip_cut src = src.std.AssumeFPS(src) src = depth(src, 16) out = src denoise = hvf.SMDegrain(out, tr=1, thSAD=100, thSADC=100) out = denoise dering = hvf.EdgeCleaner(out, 15, smode=1, hot=True) dering = gf.MaskedDHA(dering, darkstr=0.05, brightstr=0.75) out = dering aaa = vdf.scale.nnedi3_upscale(get_y(out), pscrn=1, correct_shift=False) aaa = aaa.resize.Bicubic(1920, 1080, src_left=0.5, src_top=0.5, filter_param_a=-0.5, filter_param_b=0.25) out = vdf.misc.merge_chroma(aaa, out) cwarp = xvs.WarpFixChromaBlend(out, 64, depth=4) out = cwarp detail_mask = lvf.mask.detail_mask(out, brz_a=2250, brz_b=1200) deband = vdf.deband.dumb3kdb(out, 16, threshold=33, grain=[24, 0]) # deband_a = vdf.deband.dumb3kdb(out, 20, threshold=89, grain=[24, 0]) # deband = lvf.rfs(deband, deband_a, [(1970, out.num_frames - 1)]) deband = core.std.MaskedMerge(deband, out, detail_mask) out = deband return depth(out, 10).std.Limiter(16 << 2, [235 << 2, 240 << 2], [0, 1, 2])[:100] # return detail_mask if __name__ == '__main__': wizardry = EncodeGoBrrr(Filtering().main(), WEB) wizardry.run() else: WEB.clip_cut.set_output(0) FILTERED = Filtering().main() FILTERED.set_output(1) # FILTERED[0].set_output(1) # FILTERED[1].set_output(2)	[ 1, 9995, 29979, 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custom_addons/books_log/models/quotations.py	MonwarAdeeb/Bista_Solutions	0	92870	from odoo import _, api, fields, models class Quotations(models.Model): _inherit = "sale.order" note_on_customer = fields.Text("Note on Customer", help="Add Notes on Customers!")	[ 1, 515, 288, 1867, 29877, 1053, 17117, 7882, 29892, 4235, 29892, 4733, 13, 13, 13, 1990, 751, 327, 800, 29898, 9794, 29889, 3195, 1125, 13, 1678, 903, 262, 27069, 353, 376, 29879, 744, 29889, 2098, 29908, 13, 13, 1678, 4443, 29918, 265, 29918, 15539, 353, 4235, 29889, 1626, 703, 9842, 373, 21886, 613, 13, 462, 462, 259, 1371, 543, 2528, 8695, 373, 8701, 414, 29991, 1159, 13, 2 ]
topicextractor/__init__.py	kjchung495/topicextractor	0	31900	<gh_stars>0 import nltk from nltk.tokenize import word_tokenize #nltk.download('punkt') #nltk.download('averaged_perceptron_tagger') from collections import Counter def extract_noun_counts(doc_list): nouns_pdoc = [] for i in range(len(doc_list)): pos = nltk.pos_tag(nltk.word_tokenize(doc_list[i])) print('working on doc ' + str(i+1)) nouns = [] for word, pos in pos: if pos.startswith('NN'): nouns.append(word) nouns_pdoc.append(nouns) freq = [] for nouns in nouns_pdoc: freq.append(dict(Counter(nouns))) return freq def tfidf(count, count_container): #'count' in dict type tfidf = {} doc_max = 0 for key, val in count.items(): if doc_max < val: doc_max = val all_doc_max = [] for i in range(len(count_container)): temp_max = 0 for key, val in count_container[i].items(): if temp_max < val: temp_max = val all_doc_max.append(temp_max) for key, val in count.items(): idf_sum = 0 idf = 0 for i in range(len(count_container)): if key in count_container[i].keys(): idf_sum += count_container[i][key]/all_doc_max[i] else: idf_sum += 0 idf = idf_sum/len(count_container) + 1e-10 #'idf' will have a value between 0~1 tfidf[key] = round((val/doc_max)/idf, 4) return Counter(tfidf).most_common()	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 5215, 302, 1896, 29895, 13, 3166, 302, 1896, 29895, 29889, 6979, 675, 1053, 1734, 29918, 6979, 675, 13, 29937, 29876, 1896, 29895, 29889, 10382, 877, 19294, 1495, 13, 29937, 29876, 1896, 29895, 29889, 10382, 877, 12483, 4063, 29918, 546, 1547, 1617, 29918, 4039, 914, 1495, 13, 13, 3166, 16250, 1053, 315, 5336, 13, 13, 1753, 6597, 29918, 29876, 1309, 29918, 2798, 29879, 29898, 1514, 29918, 1761, 1125, 29871, 13, 268, 13, 1678, 302, 1309, 29879, 29918, 29886, 1514, 353, 5159, 13, 268, 13, 1678, 363, 474, 297, 3464, 29898, 2435, 29898, 1514, 29918, 1761, 22164, 13, 4706, 926, 353, 302, 1896, 29895, 29889, 1066, 29918, 4039, 29898, 29876, 1896, 29895, 29889, 1742, 29918, 6979, 675, 29898, 1514, 29918, 1761, 29961, 29875, 12622, 13, 308, 13, 4706, 1596, 877, 22899, 373, 1574, 525, 718, 851, 29898, 29875, 29974, 29896, 876, 13, 4706, 302, 1309, 29879, 353, 5159, 13, 4706, 363, 1734, 29892, 926, 297, 926, 29901, 13, 9651, 565, 926, 29889, 27382, 2541, 877, 10262, 29374, 13, 18884, 302, 1309, 29879, 29889, 4397, 29898, 1742, 29897, 13, 308, 13, 4706, 302, 1309, 29879, 29918, 29886, 1514, 29889, 4397, 29898, 29876, 1309, 29879, 29897, 13, 268, 13, 1678, 3005, 29939, 353, 5159, 13, 13, 1678, 363, 302, 1309, 29879, 297, 302, 1309, 29879, 29918, 29886, 1514, 29901, 13, 4706, 3005, 29939, 29889, 4397, 29898, 8977, 29898, 17779, 29898, 29876, 1309, 29879, 4961, 13, 308, 13, 1678, 736, 3005, 29939, 13, 13, 13, 1753, 15886, 333, 29888, 29898, 2798, 29892, 2302, 29918, 7611, 1125, 29871, 396, 29915, 2798, 29915, 297, 9657, 1134, 13, 308, 13, 1678, 15886, 333, 29888, 353, 6571, 13, 268, 13, 1678, 1574, 29918, 3317, 353, 29871, 29900, 13, 1678, 363, 1820, 29892, 659, 297, 2302, 29889, 7076, 7295, 13, 4706, 565, 1574, 29918, 3317, 529, 659, 29901, 13, 9651, 1574, 29918, 3317, 353, 659, 13, 13, 1678, 599, 29918, 1514, 29918, 3317, 353, 5159, 13, 1678, 363, 474, 297, 3464, 29898, 2435, 29898, 2798, 29918, 7611, 22164, 13, 4706, 5694, 29918, 3317, 353, 29871, 29900, 13, 13, 4706, 363, 1820, 29892, 659, 297, 2302, 29918, 7611, 29961, 29875, 1822, 7076, 7295, 13, 9651, 565, 5694, 29918, 3317, 529, 659, 29901, 13, 18884, 5694, 29918, 3317, 353, 659, 13, 4706, 599, 29918, 1514, 29918, 3317, 29889, 4397, 29898, 7382, 29918, 3317, 29897, 13, 13, 1678, 363, 1820, 29892, 659, 297, 2302, 29889, 7076, 7295, 13, 13, 4706, 1178, 29888, 29918, 2083, 353, 29871, 29900, 13, 4706, 1178, 29888, 353, 29871, 29900, 13, 4706, 363, 474, 297, 3464, 29898, 2435, 29898, 2798, 29918, 7611, 22164, 13, 13, 9651, 565, 1820, 297, 2302, 29918, 7611, 29961, 29875, 1822, 8149, 7295, 13, 18884, 1178, 29888, 29918, 2083, 4619, 2302, 29918, 7611, 29961, 29875, 3816, 1989, 16261, 497, 29918, 1514, 29918, 3317, 29961, 29875, 29962, 13, 9651, 1683, 29901, 13, 18884, 1178, 29888, 29918, 2083, 4619, 29871, 29900, 13, 13, 4706, 1178, 29888, 353, 1178, 29888, 29918, 2083, 29914, 2435, 29898, 2798, 29918, 7611, 29897, 718, 29871, 29896, 29872, 29899, 29896, 29900, 396, 29915, 333, 29888, 29915, 674, 505, 263, 995, 1546, 29871, 29900, 30022, 29896, 13, 4706, 15886, 333, 29888, 29961, 1989, 29962, 353, 4513, 3552, 791, 29914, 1514, 29918, 3317, 6802, 333, 29888, 29892, 29871, 29946, 29897, 13, 13, 1678, 736, 315, 5336, 29898, 13264, 333, 29888, 467, 3242, 29918, 9435, 580, 1678, 2 ]
StockAndFlowInPython/depreciated/SFD_display_0.py	Rutherford1895/SFD_Canvas	3	54899	<reponame>Rutherford1895/SFD_Canvas import math from tkinter import * # matplotlib.use('TkAgg') from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg from StockAndFlowInPython.depreciated.classes import global_model as glbele class SFDCanvas(Frame): def __init__(self, master, stocks, flows, auxs, connectors): super().__init__(master) self.master = master self.xmost = 400 self.ymost = 400 self.canvas = Canvas(master=self.master) self.canvas.configure(background='white') #self.canvas.pack(side=BOTTOM, fill=BOTH, expand=1) self.hbar = Scrollbar(master=self.master, orient=HORIZONTAL) self.hbar.pack(side=BOTTOM, fill=X) self.hbar.config(command=self.canvas.xview) self.vbar = Scrollbar(master=self.master, orient=VERTICAL) self.vbar.pack(side=RIGHT, fill=Y) self.vbar.config(command=self.canvas.yview) # self.create_widgets() self.pack(fill=BOTH, expand=1) # self.filename = '' self.stocks = stocks self.flows = flows self.auxs = auxs self.connectors = connectors self.modelDrawer() def create_stock(self, x, y, w, h, label): """ :param x: Center x :param y: Center y :param w: width :param h: height :param label: label :return: """ self.canvas.create_rectangle(x - w * 0.5, y - h * 0.5, x + w * 0.5, y + h * 0.5, fill="#fff") self.canvas.create_text(x, y + 30, anchor=CENTER, font=("Arial", 13), text=label) def create_flow(self, x, y, pts, r, label): ''' Starting point x, y, ending point x, y, length, circle radius, label ''' for i in range(len(pts)-1): if i != len(pts)-2: self.canvas.create_line(pts[i][0],pts[i][1],pts[i+1][0],pts[i+1][1]) else: self.canvas.create_line(pts[i][0],pts[i][1],pts[i+1][0],pts[i+1][1],arrow=LAST) # self.canvas.create_line(xA, yA, xB, yB, arrow=LAST) self.canvas.create_oval(x - r, y - r, x + r, y + r, fill="#fff") self.canvas.create_text(x, y + r + 10, anchor=CENTER, font=("Arial", 13), text=label) def create_aux(self, x, y, r, label): ''' Central point x, y, radius, label ''' self.canvas.create_oval(x - r, y - r, x + r, y + r, fill="#fff") self.canvas.create_text(x, y + r + 10, anchor=CENTER, font=("Arial", 13), text=label) def create_alias(self, x, y, r, label): ''' Central point x, y, radius, label ''' self.canvas.create_oval(x - r, y - r, x + r, y + r, fill="gray70") self.canvas.create_text(x, y, anchor=CENTER, font=("Arial", 10), text="G") self.canvas.create_text(x, y + r + 10, anchor=CENTER, font=("Arial", 13, "italic"), text=label) def create_connector(self, xA, yA, xB, yB, angle, color='black'): # self.create_dot(xA,yA,3,'black') # self.create_dot(xB,yB,3,'black') alpha = math.radians(angle) if math.pi < alpha < math.pi * 2: alpha -= math.pi * 2 beta = math.atan2((yA - yB), (xB - xA)) # angle between A->B and x-positive print('alpha in degrees, ', math.degrees(alpha), 'beta in degrees, ', math.degrees(beta)) # calculate the center of circle rad_radiusA = alpha - math.pi * 0.5 # radiant of radius of the circle going out from A print('rad_radiusA (degrees), ', math.degrees(rad_radiusA), 'radians, ', rad_radiusA) gA = math.tan(rad_radiusA) print('gradiantA, ', gA) if xB != xA: gAB = (yA - yB) / (xB - xA) # y axis inversed, could be 'zero division' else: gAB = 99.99 print('gradiantAB, ', gAB) gM = (-1) / gAB print('gradiantM, ', gM) xM = (xA + xB) / 2 yM = (yA + yB) / 2 print("M's coordinate", xM, yM) xC = (yA + gA * xA - gM * xM - yM) / (gA - gM) yC = yA - gA * (xC - xA) print("A's coordinate: ", xA, yA) print("C's coordinate: ", xC, yC) #self.create_dot(xC, yC, 2, color, str(angle)) # draw center of the circle # TODO: when C and A are calculated to be the same point (and in fact not) rad_CA = math.atan2((yC - yA), (xA - xC)) rad_CB = math.atan2((yC - yB), (xB - xC)) print('rad_CA, ', rad_CA, 'rad_CB, ', rad_CB) # calculate radius radius = (pow((xB - xC), 2) + pow((yC - yB), 2)) ** 0.5 baseArc = math.atan2(yC - yA, xA - xC) print('baseArc in degrees, ', math.degrees(baseArc)) print("checking youhu or liehu") # vectors, this part seems to be correct vecStarting = [math.cos(alpha), math.sin(alpha)] vecAtoB = [xB - xA, yA - yB] print('vecStarting, ', vecStarting, 'vecAtoB, ', vecAtoB) angleCos = self.cosFormula(vecStarting, vecAtoB) print('Cosine of the angle in Between, ', angleCos) # checking youhu or liehu the direction inverse = 1 if angleCos < 0: # you hu print('deg_CA, ', math.degrees(rad_CA),'deg_CB',math.degrees(rad_CB)) diff = rad_CB-rad_CA print('youhu') else: # lie hu if rad_CArad_CB<0 and rad_CA <= rad_CB: # yi hao diff = rad_CB-rad_CA if diff > math.pi: diff = abs(diff) - math.pi2 elif rad_CArad_CB<0 and rad_CA > rad_CB: diff = math.pi2-rad_CA+rad_CB if diff > math.pi: diff = diff - math.pi2 elif rad_CArad_CB>0 and rad_CA > rad_CB: diff = rad_CB-rad_CA if diff > math.pi: diff = math.pi2 - diff elif rad_CArad_CB>0 and rad_CA < rad_CB: diff = rad_CB-rad_CA if diff > math.pi: diff = math.pi2 - diff else: diff = rad_CB-rad_CA print('liehu') print('final diff in degrees, ', math.degrees(diff)) # generate new points x = [xA] y = [yA] n = 7 for i in range(n): baseArc = baseArc + diff / (n + 1) inverse x1 = xC + radius * math.cos(baseArc) x.append(x1) y1 = yC - radius * math.sin(baseArc) y.append(y1) # Draw dots of the connectors, if you would like #self.create_dot(x1,y1,2,'gray',str(i)) x.append(xB) y.append(yB) self.canvas.create_line(x[0], y[0], x[1], y[1], x[2], y[2], x[3], y[3], x[4], y[4], x[5], y[5], x[6], y[6], x[7], y[7], x[8], y[8], smooth=True, fill='maroon2', arrow=LAST) print('\n') def create_dot(self, x, y, r, color, label=''): self.canvas.create_oval(x - r, y - r, x + r, y + r, outline=color, fill=color) self.canvas.create_text(x, y - 10, text=label) def cosFormula(self, a, b): ''' calculate the cosine value of a angle between 2 vectors. :param a: :param b: :return: ''' l = 0 m = 0 n = 0 for i in range(2): l += a[i] * b[i] m += a[i] 2 n += b[i] 2 return l / ((m * n) ** 0.5) def locateVar(self, name): name = name.replace("_", " ") # print(name) for s in self.modelHandler1.stocks: nameWithoutN = s.name.replace("\\n", " ") if nameWithoutN == name: return [s.x, s.y] for f in self.modelHandler1.flows: nameWithoutN = f.name.replace("\\n", " ") if nameWithoutN == name: return [f.x, f.y] for a in self.modelHandler1.auxs: nameWithoutN = a.name.replace("\\n", " ") # print(nameWithoutN) if nameWithoutN == name: return [a.x, a.y] def locateAlias(self, uid): # print("locate_alias is called") for al in self.modelHandler1.aliases: if al.uid == uid: return [al.x, al.y] # Here starts Widgets and Commands def modelDrawer(self): # now starts the 'drawing' part self.canvas.config(width = self.xmost, height = self.ymost, scrollregion = (0,0,self.xmost,self.ymost)) #self.canvas.config(width = wid, height = hei) self.canvas.config(xscrollcommand = self.hbar.set, yscrollcommand = self.vbar.set) # set common parameters width1 = 46 height1 = 35 length1 = 115 radius1 = 5 # draw connectors for c in self.connectors: print("\n") print(c.uid) ''' # from print("c.from_var:", c.from_var, "childNodes:", c.from_var.childNodes) if len(c.from_var.childNodes) > 1: # if this end is on an alias print("it has more than 1 childNodes, so alias") from_cord = self.locate_alias(c.from_var.childNodes[1].getAttribute("uid")) else: print("it has childNodes, so normal variable") print("c.from_var.childNodes[0].data: ", c.from_var.childNodes[0].data) from_cord = self.locate_var(c.from_var.childNodes[0].data) print("from_cord: ", from_cord) # to print("c.to_var", c.to_var, "childNodes:", c.to_var.childNodes) if len(c.to_var.childNodes) > 1: # if this end is no an alias print("it has more than 1 childNodes, so alias") to_cord = self.locate_alias(c.to_var.childNodes[1].getAttribute("uid")) else: print("it has childNodes, so normal variable") print("c.to_var.childNodes[0].data: ", c.to_var.childNodes[0].data) to_cord = self.locate_var(c.to_var.childNodes[0].data) print("to_cord: ", to_cord) ''' from_cord = [glbele.get_value(c.from_var).x, glbele.get_value(c.from_var).y] to_cord = [glbele.get_value(c.to_var).x, glbele.get_value(c.to_var).y] from_to_cord = from_cord + to_cord self.create_connector(from_to_cord[0], from_to_cord[1], from_to_cord[2], from_to_cord[3] - 8, c.angle) # minus 8: the arrow it self not consumed # draw stocks # for s in self.session_handler1.stocks: for s in self.stocks: self.create_stock(s.x, s.y, width1, height1, s.name) if s.x> self.xmost: self.xmost = s.x if s.y> self.ymost: self.ymost = s.y # draw flows for f in self.flows: self.create_flow(f.x, f.y, f.pts, radius1, f.name) if f.x > self.xmost: self.xmost = f.x if f.y > self.ymost: self.ymost = f.y # draw auxs for a in self.auxs: self.create_aux(a.x, a.y, radius1, a.name) if a.x > self.xmost: self.xmost = a.x if a.y > self.ymost: self.ymost = a.y ''' # draw aliases for al in self.session_handler1.aliases: self.create_alias(al.x, al.y, radius1, al.of.replace('_', ' ')) if al.x > self.xmost: self.xmost = al.x if al.y > self.ymost: self.ymost = al.y ''' print('Xmost,',self.xmost,'Ymost,',self.ymost) self.canvas.config(width = self.xmost, height = self.ymost, scrollregion = (0,0,self.xmost,self.ymost)) self.canvas.pack(side = LEFT, expand=1, fill=BOTH) # Here starts the simulation part ''' def simulation_handler(self): import pysd if self.filename != '': new_name = self.filename[:-5]+".xmile" shutil.copy(self.filename,new_name) self.model_run = pysd.read_xmile(new_name) os.remove(new_name) os.remove(new_name[:-6]+".py") self.results = self.model_run.run() print("Simulation Finished.") self.variables_in_model = self.results.columns.values.tolist() self.variables_in_model.remove("TIME") self.comboxlist["values"] = self.variables_in_model def select_variable(self,*args): self.selected_variable = self.comboxlist.get() def show_figure(self): f = Figure(figsize=(5, 4), dpi=100) a = f.add_subplot(111) x = self.results['TIME'].tolist() y = self.results[self.selected_variable].tolist() a.plot(x,y) a.set_title(self.selected_variable) a.set_xlabel('Time') a.set_ylabel(self.selected_variable) figure_0 = GraphWindow(self.selected_variable, f) ''' class GraphWindow(): def __init__(self,title,figure): top = Toplevel() top.title = title graph = FigureCanvasTkAgg(figure, master=top) graph.draw() graph._tkcanvas.pack()	[ 1, 529, 276, 1112, 420, 29958, 29934, 14107, 4006, 29896, 29947, 29929, 29945, 29914, 20322, 29928, 29918, 21960, 13, 5215, 5844, 13, 3166, 18883, 1639, 1053, 334, 13, 13, 29937, 22889, 29889, 1509, 877, 29911, 29895, 29909, 1505, 1495, 13, 13, 3166, 22889, 29889, 1627, 1975, 29889, 27852, 29918, 11178, 16170, 1053, 11479, 21960, 29911, 29895, 29909, 1505, 13, 3166, 10224, 2855, 17907, 797, 11980, 29889, 311, 1457, 455, 630, 29889, 13203, 1053, 5534, 29918, 4299, 408, 3144, 915, 280, 13, 13, 13, 1990, 317, 26453, 21960, 29898, 4308, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 5835, 29892, 10961, 29879, 29892, 24536, 29892, 3479, 29879, 29892, 4511, 943, 1125, 13, 4706, 2428, 2141, 1649, 2344, 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ingenico/connect/sdk/param_request.py	festicket/connect-sdk-python3	12	194710	<filename>ingenico/connect/sdk/param_request.py class ParamRequest(object): """ Represents a set of request parameters. """ def to_request_parameters(self): """ :return: list[:class:`ingenico.connect.sdk.RequestParam`] representing the HTTP request parameters """ raise NotImplementedError	[ 1, 529, 9507, 29958, 4289, 1417, 29914, 6915, 29914, 15348, 29914, 3207, 29918, 3827, 29889, 2272, 13, 1990, 12662, 3089, 29898, 3318, 1125, 13, 1678, 9995, 13, 1678, 830, 4569, 1237, 263, 731, 310, 2009, 4128, 29889, 13, 1678, 9995, 13, 13, 1678, 822, 304, 29918, 3827, 29918, 16744, 29898, 1311, 1125, 13, 4706, 9995, 13, 4706, 584, 2457, 29901, 1051, 7503, 1990, 18078, 4289, 1417, 29889, 6915, 29889, 15348, 29889, 3089, 4736, 29952, 29962, 15783, 278, 7331, 2009, 4128, 13, 4706, 9995, 13, 4706, 12020, 2216, 1888, 2037, 287, 2392, 13, 2 ]
scripts/8-pacotes-modulos-python/ex107/teste.py	dev-alissonalves/python-codes	1	110049	import moeda p = float(input("Informe um valor qualquer: ")) print("\n") print("-=-=-=-=- INÍCIO =-=-=-=") print(f"A metade do valor {p:.2f} é: {moeda.metade(p)}") print(f"O dobro do valor {p:.2f} é: {moeda.dobro(p)}") print(f"Aumentando 10%, temos: {moeda.aumentar(p, 10)}") print(f"Diminuindo 10%, temos: {moeda.diminuir(p, 10)}") print("-=-=-=-=- FIM =-=-=-=")	[ 1, 1053, 2730, 8710, 13, 29886, 353, 5785, 29898, 2080, 703, 797, 689, 29872, 1922, 16497, 4021, 7808, 29901, 376, 876, 13, 2158, 14182, 29876, 1159, 13, 2158, 703, 29899, 10457, 10457, 10457, 10457, 2672, 30175, 29907, 5971, 353, 29899, 10457, 10457, 543, 29897, 13, 2158, 29898, 29888, 29908, 29909, 12700, 311, 437, 16497, 426, 29886, 29901, 29889, 29906, 29888, 29913, 904, 29901, 426, 4346, 8710, 29889, 7299, 311, 29898, 29886, 2915, 1159, 13, 13, 2158, 29898, 29888, 29908, 29949, 437, 6729, 437, 16497, 426, 29886, 29901, 29889, 29906, 29888, 29913, 904, 29901, 426, 4346, 8710, 29889, 11152, 307, 29898, 29886, 2915, 1159, 13, 13, 2158, 29898, 29888, 29908, 29909, 942, 1743, 29871, 29896, 29900, 13667, 1350, 359, 29901, 426, 4346, 8710, 29889, 585, 358, 279, 29898, 29886, 29892, 29871, 29896, 29900, 2915, 1159, 13, 13, 2158, 29898, 29888, 29908, 29928, 8332, 29884, 15036, 29871, 29896, 29900, 13667, 1350, 359, 29901, 426, 4346, 8710, 29889, 6229, 8675, 381, 29898, 29886, 29892, 29871, 29896, 29900, 2915, 1159, 13, 13, 2158, 703, 29899, 10457, 10457, 10457, 10457, 383, 7833, 353, 29899, 10457, 10457, 543, 29897, 13, 13, 2 ]
transfer_learning/data_handling.py	E-Fumi/VariationalAutoEncoder	1	188939	<filename>transfer_learning/data_handling.py import hyperparameters as hp import tensorflow as tf from tensorflow.keras.preprocessing.image import ImageDataGenerator data_augmenter = ImageDataGenerator( rescale=1.0/255, horizontal_flip=True, rotation_range=25, brightness_range=[0.8, 1.2], zoom_range=0.2, shear_range=0.1 ) def build_generator(augmenter, directory): generator = augmenter.flow_from_directory( directory='C://Data/2nd_pass/' + directory, target_size=(300, 300), color_mode='rgb', batch_size=hp.batch_size, class_mode='binary', shuffle=True) return generator training_generator = build_generator(data_augmenter, 'train') val_ds = tf.keras.preprocessing.image_dataset_from_directory( directory='C://Data/2nd_pass/validation', labels='inferred', label_mode='binary', batch_size=32, image_size=(300, 300), shuffle=True) val_ds = val_ds.map(lambda a, b: (a / 255.0, tf.cast(b, dtype=tf.int8)))	[ 1, 529, 9507, 29958, 3286, 571, 29918, 21891, 29914, 1272, 29918, 3179, 1847, 29889, 2272, 13, 5215, 11266, 16744, 408, 298, 29886, 30004, 13, 5215, 26110, 408, 15886, 30004, 13, 3166, 26110, 29889, 3946, 294, 29889, 1457, 19170, 29889, 3027, 1053, 7084, 1469, 21575, 30004, 13, 30004, 13, 30004, 13, 1272, 29918, 2987, 358, 261, 353, 7084, 1469, 21575, 29898, 30004, 13, 1678, 620, 29883, 744, 29922, 29896, 29889, 29900, 29914, 29906, 29945, 29945, 11167, 13, 1678, 14698, 29918, 29888, 3466, 29922, 5574, 11167, 13, 1678, 13733, 29918, 3881, 29922, 29906, 29945, 11167, 13, 1678, 11785, 2264, 29918, 3881, 11759, 29900, 29889, 29947, 29892, 29871, 29896, 29889, 29906, 1402, 30004, 13, 1678, 19342, 29918, 3881, 29922, 29900, 29889, 29906, 11167, 13, 1678, 1183, 279, 29918, 3881, 29922, 29900, 29889, 29896, 30004, 13, 8443, 13, 30004, 13, 30004, 13, 1753, 2048, 29918, 27959, 29898, 2987, 358, 261, 29892, 3884, 1125, 30004, 13, 1678, 15299, 353, 18765, 261, 29889, 1731, 29918, 3166, 29918, 12322, 29898, 30004, 13, 4706, 3884, 2433, 29907, 597, 1469, 29914, 29906, 299, 29918, 3364, 22208, 718, 3884, 11167, 13, 4706, 3646, 29918, 2311, 7607, 29941, 29900, 29900, 29892, 29871, 29941, 29900, 29900, 511, 30004, 13, 4706, 2927, 29918, 8513, 2433, 23973, 23592, 13, 4706, 9853, 29918, 2311, 29922, 28887, 29889, 16175, 29918, 2311, 11167, 13, 4706, 770, 29918, 8513, 2433, 19541, 23592, 13, 4706, 528, 21897, 29922, 5574, 8443, 13, 1678, 736, 15299, 30004, 13, 30004, 13, 30004, 13, 26495, 29918, 27959, 353, 2048, 29918, 27959, 29898, 1272, 29918, 2987, 358, 261, 29892, 525, 14968, 1495, 30004, 13, 30004, 13, 30004, 13, 791, 29918, 6289, 353, 15886, 29889, 3946, 294, 29889, 1457, 19170, 29889, 3027, 29918, 24713, 29918, 3166, 29918, 12322, 29898, 30004, 13, 4706, 3884, 2433, 29907, 597, 1469, 29914, 29906, 299, 29918, 3364, 29914, 18157, 23592, 13, 4706, 11073, 2433, 262, 14373, 23592, 13, 4706, 3858, 29918, 8513, 2433, 19541, 23592, 13, 4706, 9853, 29918, 2311, 29922, 29941, 29906, 11167, 13, 4706, 1967, 29918, 2311, 7607, 29941, 29900, 29900, 29892, 29871, 29941, 29900, 29900, 511, 30004, 13, 4706, 528, 21897, 29922, 5574, 8443, 13, 30004, 13, 791, 29918, 6289, 353, 659, 29918, 6289, 29889, 1958, 29898, 2892, 263, 29892, 289, 29901, 313, 29874, 847, 29871, 29906, 29945, 29945, 29889, 29900, 29892, 15886, 29889, 4384, 29898, 29890, 29892, 26688, 29922, 13264, 29889, 524, 29947, 4961, 2 ]
excel_with_python.py	wahidupal/excel_with_python	1	154158	<reponame>wahidupal/excel_with_python import dash import plotly.express as px import pandas as pd # Data exploration with Pandas #----------------------------- df = pd.read_csv("vgsales.csv") # print(df[:5]) #print(df.iloc[:5, [2,3,5,10]]) # print(df.Genre.nunique()) # print(df.Genre.unique()) # print(sorted(df.Year.unique())) # Data Visualization with Plotly # ------------------------------ # fig_pie = px.pie(data_frame=df, names='Genre', values='Japan Sales') # fig_pie = px.pie(data_frame=df, names='Genre', values='North American Sales') # fig_pie.show() # fig_bar = px.bar(data_frame=df, x='Genre', y='Japan Sales') # fig_bar.show() # fig_hist = px.histogram(data_frame=df, x='Genre', y='Japan Sales') # fig_hist.show() # fig_hist = px.histogram(data_frame=df, x='Year', y='Japan Sales') # fig_hist.show() # Interactive Graphs with Dash # ---------------------------- import dash_core_components as dcc import dash_html_components as html from dash.dependencies import Output, Input app = dash.Dash(__name__) app.layout=html.Div([ html.H1("Graph Analysis for Game released from 1980 to 2016"), dcc.Dropdown(id='genre-choice', options=[{'label':x, 'value':x} for x in sorted(df.Genre.unique())], value='Action' ), dcc.Graph(id= 'my-graph' , figure = {} ) ]) @app.callback( Output(component_id='my-graph', component_property='figure'), Input(component_id='genre-choice', component_property='value') ) def interactive_graphs(value_genre): print(value_genre) dff = df[df.Genre==value_genre] fig = px.bar(data_frame=dff, x='Year', y='Japan Sales') return fig if __name__ == '__main__': app.run_server()	[ 1, 529, 276, 1112, 420, 29958, 29893, 801, 333, 786, 284, 29914, 24633, 29918, 2541, 29918, 4691, 13, 5215, 12569, 13, 5215, 6492, 368, 29889, 17073, 408, 282, 29916, 13, 5215, 11701, 408, 10518, 13, 13, 29937, 3630, 3902, 12418, 411, 349, 7086, 13, 29937, 2683, 9072, 29899, 13, 13, 2176, 353, 10518, 29889, 949, 29918, 7638, 703, 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src/systemmanager.py	WinterLicht/Chaos-Projectile	59	149276	<filename>src/systemmanager.py """ .. module:: systemmanager :Platform: Unix, Windows :Synopsis: system manager contains all game systems. """ import rendersystem import collisionsystem import inputsystem import animationsystem import combatsystem import statesystem import soundsystem class SystemManager(object): """System manager is a container of all game systems. :Attributes: - systems (systems list): all systems are stored here """ def __init__(self, event_manager, gameWorld): """ :param event_manager: event manager :type event_manager: events.EventManager :param gameWorld: game world :type gameWorld: gameWorld.GameWorld """ self.systems = list() self.systems.append(collisionsystem.CollisionSystem(event_manager, gameWorld)) self.systems.append(animationsystem.AnimationSystem(event_manager, gameWorld)) self.systems.append(inputsystem.InputSystem(event_manager, gameWorld)) self.systems.append(statesystem.StateSystem(event_manager, gameWorld)) self.systems.append(rendersystem.RenderSystem(event_manager, gameWorld)) self.systems.append(combatsystem.CombatSystem(event_manager, gameWorld)) #self.systems.append(soundsystem.SoundSystem(event_manager, gameWorld))	[ 1, 529, 9507, 29958, 4351, 29914, 5205, 12847, 29889, 2272, 13, 15945, 29908, 13, 636, 3883, 1057, 1788, 12847, 13, 259, 584, 21889, 29901, 26663, 29892, 3852, 13, 259, 584, 29216, 15368, 29901, 1788, 8455, 3743, 599, 3748, 6757, 29889, 13, 15945, 29908, 13, 13, 5215, 7697, 414, 973, 13, 5215, 5321, 12112, 973, 13, 5215, 1881, 5205, 13, 5215, 3778, 800, 973, 13, 5215, 4145, 1446, 973, 13, 5215, 5922, 973, 13, 5215, 10083, 973, 13, 13, 13, 1990, 2184, 3260, 29898, 3318, 1125, 13, 1678, 9995, 3924, 8455, 338, 263, 5639, 310, 599, 3748, 6757, 29889, 13, 13, 1678, 584, 15801, 29901, 13, 4706, 448, 334, 5205, 29879, 29930, 313, 5205, 29879, 1051, 1125, 599, 6757, 526, 6087, 1244, 13, 1678, 9995, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 1741, 29918, 12847, 29892, 3748, 14058, 1125, 13, 4706, 9995, 13, 4706, 584, 3207, 1741, 29918, 12847, 29901, 1741, 8455, 13, 4706, 584, 1853, 1741, 29918, 12847, 29901, 4959, 29889, 2624, 3260, 13, 4706, 584, 3207, 3748, 14058, 29901, 3748, 3186, 13, 4706, 584, 1853, 3748, 14058, 29901, 3748, 14058, 29889, 14199, 14058, 13, 4706, 9995, 13, 4706, 1583, 29889, 5205, 29879, 353, 1051, 580, 13, 4706, 1583, 29889, 5205, 29879, 29889, 4397, 29898, 22017, 12112, 973, 29889, 28377, 2459, 3924, 29898, 3696, 29918, 12847, 29892, 3748, 14058, 876, 13, 4706, 1583, 29889, 5205, 29879, 29889, 4397, 29898, 11576, 800, 973, 29889, 13579, 3924, 29898, 3696, 29918, 12847, 29892, 3748, 14058, 876, 13, 4706, 1583, 29889, 5205, 29879, 29889, 4397, 29898, 2080, 5205, 29889, 4290, 3924, 29898, 3696, 29918, 12847, 29892, 3748, 14058, 876, 13, 4706, 1583, 29889, 5205, 29879, 29889, 4397, 29898, 28631, 973, 29889, 2792, 3924, 29898, 3696, 29918, 12847, 29892, 3748, 14058, 876, 13, 4706, 1583, 29889, 5205, 29879, 29889, 4397, 29898, 29878, 21043, 973, 29889, 10716, 3924, 29898, 3696, 29918, 12847, 29892, 3748, 14058, 876, 13, 4706, 1583, 29889, 5205, 29879, 29889, 4397, 29898, 17743, 1446, 973, 29889, 1523, 10222, 3924, 29898, 3696, 29918, 12847, 29892, 3748, 14058, 876, 13, 4706, 396, 1311, 29889, 5205, 29879, 29889, 4397, 29898, 29879, 3885, 973, 29889, 29456, 3924, 29898, 3696, 29918, 12847, 29892, 3748, 14058, 876, 13, 2 ]
desktop/core/ext-py/SQLAlchemy-1.3.16/examples/elementtree/optimized_al.py	e11it/hue-1	2	152222	"""Uses the same strategy as ``adjacency_list.py``, but associates each DOM row with its owning document row, so that a full document of DOM nodes can be loaded using O(1) queries - the construction of the "hierarchy" is performed after the load in a non-recursive fashion and is more efficient. """ # PART I - Imports/Configuration from __future__ import print_function import os import re from xml.etree import ElementTree from sqlalchemy import and_ from sqlalchemy import Column from sqlalchemy import create_engine from sqlalchemy import ForeignKey from sqlalchemy import Integer from sqlalchemy import MetaData from sqlalchemy import String from sqlalchemy import Table from sqlalchemy import Unicode from sqlalchemy.orm import lazyload from sqlalchemy.orm import mapper from sqlalchemy.orm import relationship from sqlalchemy.orm import Session e = create_engine("sqlite://") meta = MetaData() # PART II - Table Metadata # stores a top level record of an XML document. documents = Table( "documents", meta, Column("document_id", Integer, primary_key=True), Column("filename", String(30), unique=True), ) # stores XML nodes in an adjacency list model. This corresponds to # Element and SubElement objects. elements = Table( "elements", meta, Column("element_id", Integer, primary_key=True), Column("parent_id", Integer, ForeignKey("elements.element_id")), Column("document_id", Integer, ForeignKey("documents.document_id")), Column("tag", Unicode(30), nullable=False), Column("text", Unicode), Column("tail", Unicode), ) # stores attributes. This corresponds to the dictionary of attributes # stored by an Element or SubElement. attributes = Table( "attributes", meta, Column( "element_id", Integer, ForeignKey("elements.element_id"), primary_key=True, ), Column("name", Unicode(100), nullable=False, primary_key=True), Column("value", Unicode(255)), ) meta.create_all(e) # PART III - Model # our document class. contains a string name, # and the ElementTree root element. class Document(object): def __init__(self, name, element): self.filename = name self.element = element # PART IV - Persistence Mapping # Node class. a non-public class which will represent the DB-persisted # Element/SubElement object. We cannot create mappers for ElementTree elements # directly because they are at the very least not new-style classes, and also # may be backed by native implementations. so here we construct an adapter. class _Node(object): pass # Attribute class. also internal, this will represent the key/value attributes # stored for a particular Node. class _Attribute(object): def __init__(self, name, value): self.name = name self.value = value # setup mappers. Document will eagerly load a list of _Node objects. # they will be ordered in primary key/insert order, so that we can reconstruct # an ElementTree structure from the list. mapper( Document, documents, properties={ "_nodes": relationship( _Node, lazy="joined", cascade="all, delete-orphan" ) }, ) # the _Node objects change the way they load so that a list of _Nodes will # organize themselves hierarchically using the ElementTreeMarshal. this # depends on the ordering of nodes being hierarchical as well; relationship() # always applies at least ROWID/primary key ordering to rows which will # suffice. mapper( _Node, elements, properties={ "children": relationship( _Node, lazy=None ), # doesnt load; used only for the save relationship "attributes": relationship( _Attribute, lazy="joined", cascade="all, delete-orphan" ), # eagerly load attributes }, ) mapper(_Attribute, attributes) # define marshalling functions that convert from _Node/_Attribute to/from # ElementTree objects. this will set the ElementTree element as # "document._element", and append the root _Node object to the "_nodes" mapped # collection. class ElementTreeMarshal(object): def __get__(self, document, owner): if document is None: return self if hasattr(document, "_element"): return document._element nodes = {} root = None for node in document._nodes: if node.parent_id is not None: parent = nodes[node.parent_id] elem = ElementTree.SubElement(parent, node.tag) nodes[node.element_id] = elem else: parent = None elem = root = ElementTree.Element(node.tag) nodes[node.element_id] = root for attr in node.attributes: elem.attrib[attr.name] = attr.value elem.text = node.text elem.tail = node.tail document._element = ElementTree.ElementTree(root) return document._element def __set__(self, document, element): def traverse(node): n = _Node() n.tag = str(node.tag) n.text = str(node.text) n.tail = str(node.tail) document._nodes.append(n) n.children = [traverse(n2) for n2 in node] n.attributes = [ _Attribute(str(k), str(v)) for k, v in node.attrib.items() ] return n traverse(element.getroot()) document._element = element def __delete__(self, document): del document._element document._nodes = [] # override Document's "element" attribute with the marshaller. Document.element = ElementTreeMarshal() # PART V - Basic Persistence Example line = "\n--------------------------------------------------------" # save to DB session = Session(e) # get ElementTree documents for file in ("test.xml", "test2.xml", "test3.xml"): filename = os.path.join(os.path.dirname(__file__), file) doc = ElementTree.parse(filename) session.add(Document(file, doc)) print("\nSaving three documents...", line) session.commit() print("Done.") print("\nFull text of document 'text.xml':", line) document = session.query(Document).filter_by(filename="test.xml").first() ElementTree.dump(document.element) # PART VI - Searching for Paths # manually search for a document which contains "/somefile/header/field1:hi" print("\nManual search for /somefile/header/field1=='hi':", line) d = ( session.query(Document) .join("_nodes", aliased=True) .filter(and_(_Node.parent_id == None, _Node.tag == "somefile")) .join("children", aliased=True, from_joinpoint=True) .filter(_Node.tag == "header") .join("children", aliased=True, from_joinpoint=True) .filter(and_(_Node.tag == "field1", _Node.text == "hi")) .one() ) ElementTree.dump(d.element) # generalize the above approach into an extremely impoverished xpath function: def find_document(path, compareto): query = session.query(Document) first = True for i, match in enumerate( re.finditer(r"/([\w_]+)(?:\[@([\w_]+)(?:=(.*))?\])?", path) ): (token, attrname, attrvalue) = match.group(1, 2, 3) if first: query = query.join("_nodes", aliased=True).filter( _Node.parent_id == None ) first = False else: query = query.join("children", aliased=True, from_joinpoint=True) query = query.filter(_Node.tag == token) if attrname: query = query.join("attributes", aliased=True, from_joinpoint=True) if attrvalue: query = query.filter( and_( _Attribute.name == attrname, _Attribute.value == attrvalue, ) ) else: query = query.filter(_Attribute.name == attrname) return ( query.options(lazyload("_nodes")).filter(_Node.text == compareto).all() ) for path, compareto in ( ("/somefile/header/field1", "hi"), ("/somefile/field1", "hi"), ("/somefile/header/field2", "there"), ("/somefile/header/field2[@attr=foo]", "there"), ): print("\nDocuments containing '%s=%s':" % (path, compareto), line) print([d.filename for d in find_document(path, compareto)])	[ 1, 9995, 15922, 267, 278, 1021, 13705, 408, 13, 29871, 4954, 26859, 562, 3819, 29918, 1761, 29889, 2272, 29952, 1673, 541, 4067, 1078, 1269, 259, 12369, 1948, 411, 967, 8152, 1076, 13, 29871, 1842, 1948, 29892, 577, 393, 263, 2989, 1842, 310, 12369, 7573, 508, 367, 7500, 13, 29871, 773, 438, 29898, 29896, 29897, 9365, 448, 278, 7632, 310, 278, 376, 29882, 631, 12040, 29908, 338, 8560, 13, 29871, 1156, 278, 2254, 297, 263, 1661, 29899, 3757, 25397, 13460, 322, 338, 901, 13, 29871, 8543, 29889, 13, 13, 15945, 29908, 13, 13, 29937, 349, 8322, 306, 448, 1954, 4011, 29914, 8614, 13, 13, 3166, 4770, 29888, 9130, 1649, 1053, 1596, 29918, 2220, 13, 13, 5215, 2897, 13, 5215, 337, 13, 3166, 4903, 29889, 300, 929, 1053, 10619, 9643, 13, 13, 3166, 4576, 284, 305, 6764, 1053, 322, 29918, 13, 3166, 4576, 284, 305, 6764, 1053, 12481, 13, 3166, 4576, 284, 305, 6764, 1053, 1653, 29918, 10599, 13, 3166, 4576, 284, 305, 6764, 1053, 19358, 2558, 13, 3166, 4576, 284, 305, 6764, 1053, 8102, 13, 3166, 4576, 284, 305, 6764, 1053, 20553, 1469, 13, 3166, 4576, 284, 305, 6764, 1053, 1714, 13, 3166, 4576, 284, 305, 6764, 1053, 6137, 13, 3166, 4576, 284, 305, 6764, 1053, 23862, 13, 3166, 4576, 284, 305, 6764, 29889, 555, 1053, 17366, 1359, 13, 3166, 4576, 284, 305, 6764, 29889, 555, 1053, 611, 2496, 13, 3166, 4576, 284, 305, 6764, 29889, 555, 1053, 9443, 13, 3166, 4576, 284, 305, 6764, 29889, 555, 1053, 16441, 13, 13, 13, 29872, 353, 1653, 29918, 10599, 703, 22793, 597, 1159, 13, 7299, 353, 20553, 1469, 580, 13, 13, 29937, 349, 8322, 1944, 448, 6137, 4737, 7221, 13, 13, 29937, 14422, 263, 2246, 3233, 2407, 310, 385, 6560, 1842, 29889, 13, 3225, 29879, 353, 6137, 29898, 13, 1678, 376, 3225, 29879, 613, 13, 1678, 12700, 29892, 13, 1678, 12481, 703, 3225, 29918, 333, 613, 8102, 29892, 7601, 29918, 1989, 29922, 5574, 511, 13, 1678, 12481, 703, 9507, 613, 1714, 29898, 29941, 29900, 511, 5412, 29922, 5574, 511, 13, 29897, 13, 13, 29937, 14422, 6560, 7573, 297, 385, 12109, 562, 3819, 1051, 1904, 29889, 29871, 910, 16161, 304, 13, 29937, 10619, 322, 3323, 2642, 3618, 29889, 13, 17664, 353, 6137, 29898, 13, 1678, 376, 17664, 613, 13, 1678, 12700, 29892, 13, 1678, 12481, 703, 5029, 29918, 333, 613, 8102, 29892, 7601, 29918, 1989, 29922, 5574, 511, 13, 1678, 12481, 703, 3560, 29918, 333, 613, 8102, 29892, 19358, 2558, 703, 17664, 29889, 5029, 29918, 333, 1159, 511, 13, 1678, 12481, 703, 3225, 29918, 333, 613, 8102, 29892, 19358, 2558, 703, 3225, 29879, 29889, 3225, 29918, 333, 1159, 511, 13, 1678, 12481, 703, 4039, 613, 23862, 29898, 29941, 29900, 511, 1870, 519, 29922, 8824, 511, 13, 1678, 12481, 703, 726, 613, 23862, 511, 13, 1678, 12481, 703, 18237, 613, 23862, 511, 13, 29897, 13, 13, 29937, 14422, 8393, 29889, 29871, 910, 16161, 304, 278, 8600, 310, 8393, 13, 29937, 6087, 491, 385, 10619, 470, 3323, 2642, 29889, 13, 15697, 353, 6137, 29898, 13, 1678, 376, 15697, 613, 13, 1678, 12700, 29892, 13, 1678, 12481, 29898, 13, 4706, 376, 5029, 29918, 333, 613, 13, 4706, 8102, 29892, 13, 4706, 19358, 2558, 703, 17664, 29889, 5029, 29918, 333, 4968, 13, 4706, 7601, 29918, 1989, 29922, 5574, 29892, 13, 1678, 10353, 13, 1678, 12481, 703, 978, 613, 23862, 29898, 29896, 29900, 29900, 511, 1870, 519, 29922, 8824, 29892, 7601, 29918, 1989, 29922, 5574, 511, 13, 1678, 12481, 703, 1767, 613, 23862, 29898, 29906, 29945, 29945, 8243, 13, 29897, 13, 13, 7299, 29889, 3258, 29918, 497, 29898, 29872, 29897, 13, 13, 29937, 349, 8322, 4786, 448, 8125, 13, 13, 29937, 1749, 1842, 770, 29889, 29871, 3743, 263, 1347, 1024, 29892, 13, 29937, 322, 278, 10619, 9643, 3876, 1543, 29889, 13, 13, 13, 1990, 10854, 29898, 3318, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 1024, 29892, 1543, 1125, 13, 4706, 1583, 29889, 9507, 353, 1024, 13, 4706, 1583, 29889, 5029, 353, 1543, 13, 13, 13, 29937, 349, 8322, 6599, 448, 9034, 11416, 341, 20304, 13, 13, 29937, 9071, 770, 29889, 29871, 263, 1661, 29899, 3597, 770, 607, 674, 2755, 278, 6535, 29899, 6774, 12652, 13, 29937, 10619, 29914, 4035, 2642, 1203, 29889, 29871, 1334, 2609, 1653, 611, 22437, 363, 10619, 9643, 3161, 13, 29937, 4153, 1363, 896, 526, 472, 278, 1407, 3203, 451, 716, 29899, 3293, 4413, 29892, 322, 884, 13, 29937, 1122, 367, 1250, 287, 491, 7531, 20240, 29889, 577, 1244, 591, 3386, 385, 13304, 29889, 13, 13, 13, 1990, 903, 4247, 29898, 3318, 1125, 13, 1678, 1209, 13, 13, 13, 29937, 23833, 770, 29889, 29871, 884, 7463, 29892, 445, 674, 2755, 278, 1820, 29914, 1767, 8393, 13, 29937, 6087, 363, 263, 3153, 9071, 29889, 13, 13, 13, 1990, 903, 6708, 29898, 3318, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 1024, 29892, 995, 1125, 13, 4706, 1583, 29889, 978, 353, 1024, 13, 4706, 1583, 29889, 1767, 353, 995, 13, 13, 13, 29937, 6230, 611, 22437, 29889, 29871, 10854, 674, 19888, 368, 2254, 263, 1051, 310, 903, 4247, 3618, 29889, 13, 29937, 896, 674, 367, 10372, 297, 7601, 1820, 29914, 7851, 1797, 29892, 577, 393, 591, 508, 337, 11433, 13, 29937, 385, 10619, 9643, 3829, 515, 278, 1051, 29889, 13, 655, 2496, 29898, 13, 1678, 10854, 29892, 13, 1678, 10701, 29892, 13, 1678, 4426, 3790, 13, 4706, 11119, 18010, 1115, 9443, 29898, 13, 9651, 903, 4247, 29892, 17366, 543, 2212, 1312, 613, 3209, 6332, 543, 497, 29892, 5217, 29899, 5676, 273, 29908, 13, 4706, 1723, 13, 1678, 2981, 13, 29897, 13, 13, 29937, 278, 903, 4247, 3618, 1735, 278, 982, 896, 2254, 577, 393, 263, 1051, 310, 903, 20284, 674, 13, 29937, 2894, 675, 6053, 6128, 1279, 1711, 773, 278, 10619, 9643, 7083, 23258, 29889, 29871, 445, 13, 29937, 7111, 373, 278, 20520, 310, 7573, 1641, 6128, 1279, 936, 408, 1532, 29936, 9443, 580, 13, 29937, 2337, 16058, 472, 3203, 390, 9806, 1367, 29914, 16072, 1820, 20520, 304, 4206, 607, 674, 13, 29937, 9378, 625, 29889, 13, 655, 2496, 29898, 13, 1678, 903, 4247, 29892, 13, 1678, 3161, 29892, 13, 1678, 4426, 3790, 13, 4706, 376, 11991, 1115, 9443, 29898, 13, 9651, 903, 4247, 29892, 17366, 29922, 8516, 13, 4706, 10353, 29871, 396, 19403, 2254, 29936, 1304, 871, 363, 278, 4078, 9443, 13, 4706, 376, 15697, 1115, 9443, 29898, 13, 9651, 903, 6708, 29892, 17366, 543, 2212, 1312, 613, 3209, 6332, 543, 497, 29892, 5217, 29899, 5676, 273, 29908, 13, 4706, 10353, 29871, 396, 19888, 368, 2254, 8393, 13, 1678, 2981, 13, 29897, 13, 13, 655, 2496, 7373, 6708, 29892, 8393, 29897, 13, 13, 29937, 4529, 1766, 845, 27855, 3168, 393, 3588, 515, 903, 4247, 19891, 6708, 304, 29914, 3166, 13, 29937, 10619, 9643, 3618, 29889, 445, 674, 731, 278, 10619, 9643, 1543, 408, 13, 29937, 376, 3225, 3032, 5029, 613, 322, 9773, 278, 3876, 903, 4247, 1203, 304, 278, 11119, 18010, 29908, 20545, 13, 29937, 4333, 29889, 13, 13, 13, 1990, 10619, 9643, 7083, 23258, 29898, 3318, 1125, 13, 1678, 822, 4770, 657, 12035, 1311, 29892, 1842, 29892, 12271, 1125, 13, 4706, 565, 1842, 338, 6213, 29901, 13, 9651, 736, 1583, 13, 13, 4706, 565, 756, 5552, 29898, 3225, 29892, 11119, 5029, 29908, 1125, 13, 9651, 736, 1842, 3032, 5029, 13, 13, 4706, 7573, 353, 6571, 13, 4706, 3876, 353, 6213, 13, 4706, 363, 2943, 297, 1842, 3032, 18010, 29901, 13, 9651, 565, 2943, 29889, 3560, 29918, 333, 338, 451, 6213, 29901, 13, 18884, 3847, 353, 7573, 29961, 3177, 29889, 3560, 29918, 333, 29962, 13, 18884, 21268, 353, 10619, 9643, 29889, 4035, 2642, 29898, 3560, 29892, 2943, 29889, 4039, 29897, 13, 18884, 7573, 29961, 3177, 29889, 5029, 29918, 333, 29962, 353, 21268, 13, 9651, 1683, 29901, 13, 18884, 3847, 353, 6213, 13, 18884, 21268, 353, 3876, 353, 10619, 9643, 29889, 2642, 29898, 3177, 29889, 4039, 29897, 13, 18884, 7573, 29961, 3177, 29889, 5029, 29918, 333, 29962, 353, 3876, 13, 9651, 363, 12421, 297, 2943, 29889, 15697, 29901, 13, 18884, 21268, 29889, 1131, 1091, 29961, 5552, 29889, 978, 29962, 353, 12421, 29889, 1767, 13, 9651, 21268, 29889, 726, 353, 2943, 29889, 726, 13, 9651, 21268, 29889, 18237, 353, 2943, 29889, 18237, 13, 13, 4706, 1842, 3032, 5029, 353, 10619, 9643, 29889, 2642, 9643, 29898, 4632, 29897, 13, 4706, 736, 1842, 3032, 5029, 13, 13, 1678, 822, 4770, 842, 12035, 1311, 29892, 1842, 29892, 1543, 1125, 13, 4706, 822, 29370, 29898, 3177, 1125, 13, 9651, 302, 353, 903, 4247, 580, 13, 9651, 302, 29889, 4039, 353, 851, 29898, 3177, 29889, 4039, 29897, 13, 9651, 302, 29889, 726, 353, 851, 29898, 3177, 29889, 726, 29897, 13, 9651, 302, 29889, 18237, 353, 851, 29898, 3177, 29889, 18237, 29897, 13, 9651, 1842, 3032, 18010, 29889, 4397, 29898, 29876, 29897, 13, 9651, 302, 29889, 11991, 353, 518, 3018, 3901, 29898, 29876, 29906, 29897, 363, 302, 29906, 297, 2943, 29962, 13, 9651, 302, 29889, 15697, 353, 518, 13, 18884, 903, 6708, 29898, 710, 29898, 29895, 511, 851, 29898, 29894, 876, 363, 413, 29892, 325, 297, 2943, 29889, 1131, 1091, 29889, 7076, 580, 13, 9651, 4514, 13, 9651, 736, 302, 13, 13, 4706, 29370, 29898, 5029, 29889, 657, 4632, 3101, 13, 4706, 1842, 3032, 5029, 353, 1543, 13, 13, 1678, 822, 4770, 8143, 12035, 1311, 29892, 1842, 1125, 13, 4706, 628, 1842, 3032, 5029, 13, 4706, 1842, 3032, 18010, 353, 5159, 13, 13, 13, 29937, 5712, 10854, 29915, 29879, 376, 5029, 29908, 5352, 411, 278, 1766, 845, 12572, 29889, 13, 6268, 29889, 5029, 353, 10619, 9643, 7083, 23258, 580, 13, 13, 29937, 349, 8322, 478, 448, 19219, 9034, 11416, 8741, 13, 13, 1220, 353, 6634, 29876, 2683, 2683, 2683, 1378, 29908, 13, 13, 29937, 4078, 304, 6535, 13, 7924, 353, 16441, 29898, 29872, 29897, 13, 13, 29937, 679, 10619, 9643, 10701, 13, 1454, 934, 297, 4852, 1688, 29889, 3134, 613, 376, 1688, 29906, 29889, 3134, 613, 376, 1688, 29941, 29889, 3134, 29908, 1125, 13, 1678, 10422, 353, 2897, 29889, 2084, 29889, 7122, 29898, 359, 29889, 2084, 29889, 25721, 22168, 1445, 1649, 511, 934, 29897, 13, 1678, 1574, 353, 10619, 9643, 29889, 5510, 29898, 9507, 29897, 13, 1678, 4867, 29889, 1202, 29898, 6268, 29898, 1445, 29892, 1574, 876, 13, 13, 2158, 14182, 29876, 29903, 5555, 2211, 10701, 856, 613, 1196, 29897, 13, 7924, 29889, 15060, 580, 13, 2158, 703, 25632, 23157, 13, 13, 2158, 14182, 29876, 13658, 1426, 310, 1842, 525, 726, 29889, 3134, 2396, 613, 1196, 29897, 13, 3225, 353, 4867, 29889, 1972, 29898, 6268, 467, 4572, 29918, 1609, 29898, 9507, 543, 1688, 29889, 3134, 2564, 4102, 580, 13, 13, 2642, 9643, 29889, 15070, 29898, 3225, 29889, 5029, 29897, 13, 13, 29937, 349, 8322, 5473, 448, 11856, 292, 363, 10802, 29879, 13, 13, 29937, 7522, 2740, 363, 263, 1842, 607, 3743, 5591, 5372, 1445, 29914, 6672, 29914, 2671, 29896, 29901, 2918, 29908, 13, 2158, 14182, 29876, 2517, 950, 2740, 363, 847, 5372, 1445, 29914, 6672, 29914, 2671, 29896, 1360, 29915, 2918, 2396, 613, 1196, 29897, 13, 29881, 353, 313, 13, 1678, 4867, 29889, 1972, 29898, 6268, 29897, 13, 1678, 869, 7122, 703, 29918, 18010, 613, 14430, 1463, 29922, 5574, 29897, 13, 1678, 869, 4572, 29898, 392, 29918, 7373, 4247, 29889, 3560, 29918, 333, 1275, 6213, 29892, 903, 4247, 29889, 4039, 1275, 376, 5372, 1445, 5783, 13, 1678, 869, 7122, 703, 11991, 613, 14430, 1463, 29922, 5574, 29892, 515, 29918, 7122, 3149, 29922, 5574, 29897, 13, 1678, 869, 4572, 7373, 4247, 29889, 4039, 1275, 376, 6672, 1159, 13, 1678, 869, 7122, 703, 11991, 613, 14430, 1463, 29922, 5574, 29892, 515, 29918, 7122, 3149, 29922, 5574, 29897, 13, 1678, 869, 4572, 29898, 392, 29918, 7373, 4247, 29889, 4039, 1275, 376, 2671, 29896, 613, 903, 4247, 29889, 726, 1275, 376, 2918, 5783, 13, 1678, 869, 650, 580, 13, 29897, 13, 2642, 9643, 29889, 15070, 29898, 29881, 29889, 5029, 29897, 13, 13, 29937, 2498, 675, 278, 2038, 2948, 964, 385, 14154, 2411, 957, 3276, 921, 2084, 740, 29901, 13, 13, 13, 1753, 1284, 29918, 3225, 29898, 2084, 29892, 7252, 517, 1125, 13, 1678, 2346, 353, 4867, 29889, 1972, 29898, 6268, 29897, 13, 1678, 937, 353, 5852, 13, 1678, 363, 474, 29892, 1993, 297, 26985, 29898, 13, 4706, 337, 29889, 2886, 1524, 29898, 29878, 23901, 4197, 29905, 29893, 29918, 10062, 5033, 29973, 3583, 17548, 4197, 29905, 29893, 29918, 10062, 5033, 25825, 7607, 5575, 876, 29973, 29905, 2314, 29973, 613, 2224, 29897, 13, 268, 1125, 13, 4706, 313, 6979, 29892, 12421, 978, 29892, 12421, 1767, 29897, 353, 1993, 29889, 2972, 29898, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29897, 13, 4706, 565, 937, 29901, 13, 9651, 2346, 353, 2346, 29889, 7122, 703, 29918, 18010, 613, 14430, 1463, 29922, 5574, 467, 4572, 29898, 13, 18884, 903, 4247, 29889, 3560, 29918, 333, 1275, 6213, 13, 9651, 1723, 13, 9651, 937, 353, 7700, 13, 4706, 1683, 29901, 13, 9651, 2346, 353, 2346, 29889, 7122, 703, 11991, 613, 14430, 1463, 29922, 5574, 29892, 515, 29918, 7122, 3149, 29922, 5574, 29897, 13, 4706, 2346, 353, 2346, 29889, 4572, 7373, 4247, 29889, 4039, 1275, 5993, 29897, 13, 4706, 565, 12421, 978, 29901, 13, 9651, 2346, 353, 2346, 29889, 7122, 703, 15697, 613, 14430, 1463, 29922, 5574, 29892, 515, 29918, 7122, 3149, 29922, 5574, 29897, 13, 9651, 565, 12421, 1767, 29901, 13, 18884, 2346, 353, 2346, 29889, 4572, 29898, 13, 462, 1678, 322, 23538, 13, 462, 4706, 903, 6708, 29889, 978, 1275, 12421, 978, 29892, 13, 462, 4706, 903, 6708, 29889, 1767, 1275, 12421, 1767, 29892, 13, 462, 1678, 1723, 13, 18884, 1723, 13, 9651, 1683, 29901, 13, 18884, 2346, 353, 2346, 29889, 4572, 7373, 6708, 29889, 978, 1275, 12421, 978, 29897, 13, 1678, 736, 313, 13, 4706, 2346, 29889, 6768, 29898, 433, 1537, 1359, 703, 29918, 18010, 1159, 467, 4572, 7373, 4247, 29889, 726, 1275, 7252, 517, 467, 497, 580, 13, 1678, 1723, 13, 13, 13, 1454, 2224, 29892, 7252, 517, 297, 313, 13, 1678, 4852, 29914, 5372, 1445, 29914, 6672, 29914, 2671, 29896, 613, 376, 2918, 4968, 13, 1678, 4852, 29914, 5372, 1445, 29914, 2671, 29896, 613, 376, 2918, 4968, 13, 1678, 4852, 29914, 5372, 1445, 29914, 6672, 29914, 2671, 29906, 613, 376, 12711, 4968, 13, 1678, 4852, 29914, 5372, 1445, 29914, 6672, 29914, 2671, 29906, 17548, 5552, 29922, 5431, 29962, 613, 376, 12711, 4968, 13, 1125, 13, 1678, 1596, 14182, 29876, 20128, 6943, 14210, 29879, 16328, 29879, 2396, 29908, 1273, 313, 2084, 29892, 7252, 517, 511, 1196, 29897, 13, 1678, 1596, 4197, 29881, 29889, 9507, 363, 270, 297, 1284, 29918, 3225, 29898, 2084, 29892, 7252, 517, 29897, 2314, 13, 2 ]
sem/features/arityfeatures.py	YoannDupont/SEM	22	165125	<filename>sem/features/arityfeatures.py<gh_stars>10-100 # -- coding: utf-8 -- """ file: arityfeatures.py Description: features that could not be categorized and are thus defined by the number of arguments they take (their arity). author: <NAME> MIT License Copyright (c) 2018 <NAME> 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 re from .feature import Feature from .getterfeatures import DEFAULT_GETTER class ArityFeature(Feature): def __init__(self, args, kwargs): super(ArityFeature, self).__init__(args, *kwargs) self._getter = kwargs.pop("getter", DEFAULT_GETTER) class NullaryFeature(ArityFeature): def __init__(self, args, *kwargs): super(NullaryFeature, self).__init__(args, *kwargs) class BOSFeature(NullaryFeature): def __init__(self, args, *kwargs): super(BOSFeature, self).__init__(args, *kwargs) self._is_boolean = True def __call__(self, list2dict, position, args, *kwargs): return position == 0 class EOSFeature(NullaryFeature): def __init__(self, args, *kwargs): super(EOSFeature, self).__init__(args, *kwargs) self._is_boolean = True def __call__(self, list2dict, position, args, *kwargs): return position == len(list2dict)-1 class LowerFeature(NullaryFeature): def __init__(self, args, *kwargs): super(LowerFeature, self).__init__(args, *kwargs) def __call__(self, args, *kwargs): return self._getter(args, kwargs).lower() class SubstringFeature(NullaryFeature): def __init__(self, from_index=0, to_index=231, args, kwargs): super(SubstringFeature, self).__init__(from_index, to_index, args, *kwargs) self._default = kwargs.pop("default", '""') self._from_index = int(from_index) self._to_index = int(to_index) def __call__(self, args, *kwargs): s = self._getter(args, *kwargs)[self._from_index : self._to_index] if s: return s else: return self._default class UnaryFeature(ArityFeature): def __init__(self, element, args, *kwargs): super(UnaryFeature, self).__init__(args, *kwargs) self._is_boolean = False self._element = element class IsUpperFeature(UnaryFeature): def __init__(self, index, args, *kwargs): super(IsUpperFeature, self).__init__(index, args, *kwargs) self._is_boolean = True self._index = self._element def __call__(self, args, *kwargs): return self._getter(args, *kwargs)[self._index].isupper() class BinaryFeature(ArityFeature): def __init__(self, element1, element2, args, *kwargs): super(BinaryFeature, self).__init__(args, *kwargs) self._element1 = element1 self._element2 = element2 class SubstitutionFeature(BinaryFeature): def __init__(self, pattern, replacement, args, *kwargs): super(SubstitutionFeature, self).__init__(pattern, replacement, args, *kwargs) flags = kwargs.pop("flags", 0) self._replacer = re.compile(pattern, flags) self._replacement = replacement def __call__(self, args, *kwargs): return self._replacer.sub(self._replacement, self._getter(args, *kwargs)) class NaryFeature(ArityFeature): def __init__(self, args, *kwargs): super(NaryFeature, self).__init__(self, args, *kwargs) class SequencerFeature(NaryFeature): def __init__(self, args, *kwargs): super(SequencerFeature, self).__init__(args, *kwargs) if len(args) == 0: raise ValueError("{0} cannot have less than 1 feature".format(self.__class__.__name__)) self._features = args for i in range(1, len(self._features)): # ensuring that we can call the different features sequentially 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src/platforms/platform_abs.py	Brickyblossom/DT2021	1	155766	<filename>src/platforms/platform_abs.py<gh_stars>1-10 '''Abstract class for platform''' from abc import ABC from typing import Dict import disnake from src import cfg class PlatForm(ABC): '''Abstract base class for a platform''' RANKCOLOR = {} PLATFORM_NAME = "Nothing" HANDLE_FILE_NAME = "/abc" def __init__(self) -> None: self.bot = cfg.bot async def verify(self, member: disnake.Member, handle: str) -> bool: # pylint: disable=no-self-use,unused-argument '''Default verification procedure (returns True)''' return True async def generate_user_embed(self, handle: str, member: disnake.Member) -> disnake.Embed: # pylint: disable=no-self-use,unused-argument '''Generates a blank embed''' return disnake.Embed() async def generate_dict_of_rank(self, user_list) -> Dict: # pylint: disable=no-self-use,unused-argument '''Generate blank dict''' return {}	[ 1, 529, 9507, 29958, 4351, 29914, 12120, 29879, 29914, 12120, 29918, 6897, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 12008, 9118, 770, 363, 7481, 12008, 13, 3166, 25638, 1053, 16417, 13, 3166, 19229, 1053, 360, 919, 13, 5215, 766, 21040, 13, 3166, 4765, 1053, 274, 16434, 13, 1990, 27201, 2500, 29898, 19658, 1125, 13, 1678, 14550, 9118, 2967, 770, 363, 263, 7481, 12008, 13, 1678, 390, 2190, 29968, 15032, 1955, 353, 6571, 13, 1678, 16507, 1299, 19094, 29918, 5813, 353, 376, 26521, 29908, 13, 1678, 379, 9468, 1307, 29918, 7724, 29918, 5813, 353, 5591, 10736, 29908, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29897, 1599, 6213, 29901, 13, 4706, 1583, 29889, 7451, 353, 274, 16434, 29889, 7451, 13, 13, 1678, 7465, 822, 11539, 29898, 1311, 29892, 4509, 29901, 766, 21040, 29889, 13404, 29892, 4386, 29901, 851, 29897, 1599, 6120, 29901, 29871, 396, 282, 2904, 524, 29901, 11262, 29922, 1217, 29899, 1311, 29899, 1509, 29892, 348, 3880, 29899, 23516, 13, 4706, 14550, 4592, 1147, 2450, 8792, 313, 18280, 5852, 29897, 12008, 13, 4706, 736, 5852, 13, 13, 1678, 7465, 822, 5706, 29918, 1792, 29918, 17987, 29898, 1311, 29892, 4386, 29901, 851, 29892, 4509, 29901, 766, 21040, 29889, 13404, 29897, 1599, 766, 21040, 29889, 6026, 2580, 29901, 29871, 396, 282, 2904, 524, 29901, 11262, 29922, 1217, 29899, 1311, 29899, 1509, 29892, 348, 3880, 29899, 23516, 13, 4706, 14550, 5631, 1078, 263, 9654, 8297, 12008, 13, 4706, 736, 766, 21040, 29889, 6026, 2580, 580, 13, 13, 1678, 7465, 822, 5706, 29918, 8977, 29918, 974, 29918, 10003, 29898, 1311, 29892, 1404, 29918, 1761, 29897, 1599, 360, 919, 29901, 396, 282, 2904, 524, 29901, 11262, 29922, 1217, 29899, 1311, 29899, 1509, 29892, 348, 3880, 29899, 23516, 13, 4706, 14550, 5631, 403, 9654, 9657, 12008, 13, 4706, 736, 6571, 13, 2 ]
Image_detection_codes/Keras_training/test2.py	pasadyash/CitizenServiceApp	0	12732	import numpy as np np.random.seed(123) # for reproducibility from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Convolution2D, MaxPooling2D from keras.utils import np_utils from dataset_pothole import pothole from keras.models import model_from_json # 4. Load pre-shuffled MNIST data into train and test sets (X_train, y_train), (X_test, y_test) = pothole.load_data() print(X_train.shape) print() print (y_train.shape) print() # 5. Preprocess input data X_train = X_train.reshape(X_train.shape[0], 200, 200, 1) X_test = X_test.reshape(X_test.shape[0], 200, 200, 1) X_train = X_train.astype('float32') X_test = X_test.astype('float32') X_train /= 3380 X_test /= 3380 # 6. Preprocess class labels Y_train = np_utils.to_categorical(y_train, 4) Y_test = np_utils.to_categorical(y_test, 4) # 7. Define model architecture nb_classes = 4 # number of epochs to train # number of convolutional filters to use nb_filters = 32 # size of pooling area for max pooling nb_pool = 2 # convolution kernel size nb_conv = 3 model = Sequential() model.add(Convolution2D(nb_filters, nb_conv, nb_conv, border_mode='valid', input_shape=(200, 200, 1))) convout1 = Activation('relu') model.add(convout1) model.add(Convolution2D(nb_filters, nb_conv, nb_conv)) convout2 = Activation('relu') model.add(convout2) model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool))) model.add(Dropout(0.5)) model.add(Flatten()) model.add(Dense(128)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(nb_classes)) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='adadelta') # 9. Fit model on training data model.fit(X_train, Y_train, batch_size=32, nb_epoch=2, verbose=1) # 10. Evaluate model on test data score = model.evaluate(X_test, Y_test, verbose=0) # serialize model to JSON model_json = model.to_json() with open("model.json", "w") as json_file: json_file.write(model_json) # serialize weights to HDF5 model.save_weights("model.h5") print("Saved model to disk") print('Test loss: ', score[0]) print('Test accuracy: ', score[1])	[ 1, 1053, 12655, 408, 7442, 13, 9302, 29889, 8172, 29889, 26776, 29898, 29896, 29906, 29941, 29897, 29871, 396, 363, 9483, 455, 29890, 1793, 13, 29871, 13, 3166, 13023, 294, 29889, 9794, 1053, 922, 339, 2556, 13, 3166, 13023, 294, 29889, 29277, 1053, 360, 1947, 29892, 20724, 449, 29892, 21775, 362, 29892, 2379, 8606, 13, 3166, 13023, 294, 29889, 29277, 1053, 1281, 4068, 29906, 29928, 29892, 5918, 11426, 292, 29906, 29928, 13, 3166, 13023, 294, 29889, 13239, 1053, 7442, 29918, 13239, 13, 3166, 8783, 29918, 29886, 720, 1772, 1053, 282, 720, 1772, 13, 3166, 13023, 294, 29889, 9794, 1053, 1904, 29918, 3166, 29918, 3126, 13, 13, 29937, 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pulse2percept/datasets/tests/test_nanduri2012.py	narenberg/pulse2percept	40	29857	<reponame>narenberg/pulse2percept import pandas as pd import numpy.testing as npt from pulse2percept.datasets import load_nanduri2012 def test_load_nanduri2012(): data = load_nanduri2012(shuffle=False) npt.assert_equal(isinstance(data, pd.DataFrame), True) columns = ['subject', 'implant', 'electrode', 'task', 'stim_class', 'freq', 'amp_factor', 'ref_stim_class', 'ref_amp_factor', 'ref_freq', 'brightness', 'size', 'pulse_dur', 'interphase_dur', 'pulse_type', 'varied_param'] for expected_col in columns: npt.assert_equal(expected_col in data.columns, True) npt.assert_equal(data.shape, (128, 17)) npt.assert_equal(data.subject.unique(), ['S06']) # Shuffle dataset (index will always be range(552), but rows are shuffled): data = load_nanduri2012(shuffle=True, random_state=42) npt.assert_equal(data.loc[0, 'subject'], 'S06') npt.assert_equal(data.loc[0, 'electrode'], 'B1') npt.assert_equal(data.loc[94, 'subject'], 'S06') npt.assert_equal(data.loc[94, 'electrode'], 'A4') # Select electrodes: data = load_nanduri2012(electrodes='A2') npt.assert_equal(data.shape, (16, 17)) npt.assert_equal(data.electrode.unique(), 'A2') npt.assert_equal(data.subject.unique(), 'S06') data = load_nanduri2012(electrodes=['A1', 'A9']) # 'A9' doesn't exist npt.assert_equal(data.shape, (0, 17)) npt.assert_equal(data.electrode.unique(), 'A1') npt.assert_equal(data.subject.unique(), 'S06') # Select task data = load_nanduri2012(task='rate') npt.assert_equal(data.shape, (88, 17)) npt.assert_equal(data.task.unique(), 'rate') data = load_nanduri2012(task='size') npt.assert_equal(data.shape, (40, 17)) npt.assert_equal(data.task.unique(), 'size')	[ 1, 529, 276, 1112, 420, 29958, 29876, 8326, 2552, 29914, 29886, 19994, 29906, 546, 1547, 13, 5215, 11701, 408, 10518, 13, 5215, 12655, 29889, 13424, 408, 302, 415, 13, 13, 3166, 9505, 344, 29906, 546, 1547, 29889, 14538, 1691, 1053, 2254, 29918, 29876, 392, 5338, 29906, 29900, 29896, 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src/notelist/schemas/search.py	jajimenez/notelist	1	114418	<gh_stars>1-10 """Search schemas module.""" from marshmallow import Schema from marshmallow.fields import Str from marshmallow.validate import Length class SearchSchema(Schema): """Search schema.""" search = Str( validate=Length(min=2, max=200), required=True, error_messages={"required": "Field is required."})	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 15945, 29908, 7974, 1364, 8609, 3883, 1213, 15945, 13, 13, 3166, 1766, 845, 29885, 9536, 1053, 1102, 2603, 13, 3166, 1766, 845, 29885, 9536, 29889, 9621, 1053, 3767, 13, 3166, 1766, 845, 29885, 9536, 29889, 15480, 1053, 365, 1477, 13, 13, 13, 1990, 11856, 12763, 29898, 12763, 1125, 13, 1678, 9995, 7974, 10938, 1213, 15945, 13, 13, 1678, 2740, 353, 3767, 29898, 13, 4706, 12725, 29922, 6513, 29898, 1195, 29922, 29906, 29892, 4236, 29922, 29906, 29900, 29900, 511, 13, 4706, 3734, 29922, 5574, 29892, 13, 4706, 1059, 29918, 19158, 3790, 29908, 12403, 1115, 376, 3073, 338, 3734, 1213, 1800, 13, 2 ]
test/test_notification_target_api.py	chbndrhnns/ahoi-client	0	186576	# coding: utf-8 """ [AHOI cookbook](/ahoi/docs/cookbook/index.html) [Data Privacy](/sandboxmanager/#/privacy) [Terms of Service](/sandboxmanager/#/terms) [Imprint](https://sparkassen-hub.com/impressum/) © 2016&dash;2017 Starfinanz - Ein Unternehmen der Finanz Informatik # noqa: E501 OpenAPI spec version: 2.1.0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import swagger_client from swagger_client.api.notification_target_api import NotificationTargetApi # noqa: E501 from swagger_client.rest import ApiException class TestNotificationTargetApi(unittest.TestCase): """NotificationTargetApi unit test stubs""" def setUp(self): self.api = swagger_client.api.notification_target_api.NotificationTargetApi() # noqa: E501 def tearDown(self): pass def test_delete_notification_target(self): """Test case for delete_notification_target Delete notification target # noqa: E501 """ pass def test_get_notification_target(self): """Test case for get_notification_target Get notification target # noqa: E501 """ pass def test_get_notification_targets(self): """Test case for get_notification_targets List notification targets # noqa: E501 """ pass def test_post_notification_target(self): """Test case for post_notification_target Create a new notification target # noqa: E501 """ pass def test_put_notification_target(self): """Test case for put_notification_target Update notification target # noqa: E501 """ pass if __name__ == '__main__': unittest.main()	[ 1, 396, 14137, 29901, 23616, 29899, 29947, 13, 13, 15945, 29908, 13, 268, 13, 13, 1678, 518, 29909, 8187, 29902, 7984, 2909, 26909, 801, 7768, 29914, 2640, 29914, 15108, 2909, 29914, 2248, 29889, 1420, 29897, 29871, 518, 1469, 18936, 4135, 26909, 29879, 26738, 12847, 8484, 29914, 22534, 4135, 29897, 29871, 518, 14343, 29879, 310, 6692, 26909, 29879, 26738, 12847, 8484, 29914, 357, 1516, 29897, 29871, 518, 1888, 2158, 850, 991, 597, 12597, 11257, 29899, 29882, 431, 29889, 510, 29914, 326, 2139, 398, 4551, 29871, 669, 8552, 29936, 29871, 29906, 29900, 29896, 29953, 29987, 14592, 29936, 29906, 29900, 29896, 29955, 7828, 4951, 4096, 448, 2694, 24014, 589, 4231, 4096, 512, 4830, 638, 29871, 396, 694, 25621, 29901, 382, 29945, 29900, 29896, 13, 13, 1678, 4673, 8787, 1580, 1873, 29901, 29871, 29906, 29889, 29896, 29889, 29900, 13, 268, 13, 1678, 3251, 630, 491, 29901, 2045, 597, 3292, 29889, 510, 29914, 2774, 9921, 29899, 2754, 29914, 2774, 9921, 29899, 401, 1885, 29889, 5559, 13, 15945, 29908, 13, 13, 13, 3166, 4770, 29888, 9130, 1649, 1053, 8380, 29918, 5215, 13, 13, 5215, 443, 27958, 13, 13, 5215, 2381, 9921, 29918, 4645, 13, 3166, 2381, 9921, 29918, 4645, 29889, 2754, 29889, 24671, 29918, 5182, 29918, 2754, 1053, 28578, 8667, 11713, 29871, 396, 694, 25621, 29901, 382, 29945, 29900, 29896, 13, 3166, 2381, 9921, 29918, 4645, 29889, 5060, 1053, 29749, 2451, 13, 13, 13, 1990, 4321, 12958, 8667, 11713, 29898, 348, 27958, 29889, 3057, 8259, 1125, 13, 1678, 9995, 12958, 8667, 11713, 5190, 1243, 19281, 29879, 15945, 29908, 13, 13, 1678, 822, 731, 3373, 29898, 1311, 1125, 13, 4706, 1583, 29889, 2754, 353, 2381, 9921, 29918, 4645, 29889, 2754, 29889, 24671, 29918, 5182, 29918, 2754, 29889, 12958, 8667, 11713, 580, 29871, 396, 694, 25621, 29901, 382, 29945, 29900, 29896, 13, 13, 1678, 822, 734, 279, 6767, 29898, 1311, 1125, 13, 4706, 1209, 13, 13, 1678, 822, 1243, 29918, 8143, 29918, 24671, 29918, 5182, 29898, 1311, 1125, 13, 4706, 9995, 3057, 1206, 363, 5217, 29918, 24671, 29918, 5182, 13, 13, 4706, 21267, 12519, 3646, 29871, 396, 694, 25621, 29901, 382, 29945, 29900, 29896, 13, 4706, 9995, 13, 4706, 1209, 13, 13, 1678, 822, 1243, 29918, 657, 29918, 24671, 29918, 5182, 29898, 1311, 1125, 13, 4706, 9995, 3057, 1206, 363, 679, 29918, 24671, 29918, 5182, 13, 13, 4706, 3617, 12519, 3646, 29871, 396, 694, 25621, 29901, 382, 29945, 29900, 29896, 13, 4706, 9995, 13, 4706, 1209, 13, 13, 1678, 822, 1243, 29918, 657, 29918, 24671, 29918, 5182, 29879, 29898, 1311, 1125, 13, 4706, 9995, 3057, 1206, 363, 679, 29918, 24671, 29918, 5182, 29879, 13, 13, 4706, 2391, 12519, 22525, 29871, 396, 694, 25621, 29901, 382, 29945, 29900, 29896, 13, 4706, 9995, 13, 4706, 1209, 13, 13, 1678, 822, 1243, 29918, 2490, 29918, 24671, 29918, 5182, 29898, 1311, 1125, 13, 4706, 9995, 3057, 1206, 363, 1400, 29918, 24671, 29918, 5182, 13, 13, 4706, 6204, 263, 716, 12519, 3646, 29871, 396, 694, 25621, 29901, 382, 29945, 29900, 29896, 13, 4706, 9995, 13, 4706, 1209, 13, 13, 1678, 822, 1243, 29918, 649, 29918, 24671, 29918, 5182, 29898, 1311, 1125, 13, 4706, 9995, 3057, 1206, 363, 1925, 29918, 24671, 29918, 5182, 13, 13, 4706, 10318, 12519, 3646, 29871, 396, 694, 25621, 29901, 382, 29945, 29900, 29896, 13, 4706, 9995, 13, 4706, 1209, 13, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 443, 27958, 29889, 3396, 580, 13, 2 ]
internetradio.py	pathakraul/internetradio-client	0	116085	<reponame>pathakraul/internetradio-client #!/usr/bin/python # Internet Radio Client using Shoutcast service # to search and play random channels with genre search # Usage ./internetradio.py --genre <GENRE> import os import logging import argparse import requests as rq import xml.etree.ElementTree as et #---------------------------------------------------------------------------------------- # Set your shoutcast devId here #---------------------------------------------------------------------------------------- devId = "" #---------------------------------------------------------------------------------------- logging.basicConfig(format='%(levelname)s %(asctime)s %(message)s', level=logging.INFO) log = logging.getLogger(__name__) #---------------------------------------------------------------------------------------- baseUrl = "http://api.shoutcast.com/" tuneinUrl = "http://yp.shoutcast.com/" base = "sbin/tunein-station.pls" randomSearch = "station/randomstations?k={0}&f=xml&mt=audio/mpeg&limit=1&genre=".format(devId) #---------------------------------------------------------------------------------------- playerCommand = "mplayer -quiet {0}\n" #---------------------------------------------------------------------------------------- def keywordSearch(keyword): log.debug("Making Keyword Search\n") searchurl=baseUrl+randomSearch+keyword log.debug("Searching for {0} on Shoutcast".format(keyword)) res = rq.get(searchurl, proxies=proxies) if res.status_code == 200: srtContent = res.content return str(res.content) else: log.error("Search response failure from server {0}".format(res.status_code)) #---------------------------------------------------------------------------------------- ''' <response> <statusCode>200</statusCode> #tree[0] <statusText>Ok</statusText> #tree[1] <data> #tree[2] <stationlist> #tree[2][0] <tunein base="/sbin/tunein-station.pls" base-m3u="/sbin/tunein-station.m3u" base-xspf="/sbin/tunein-station.xspf"/> #tree[2][0][0] <station name="Bollywood Hits" genre="Hindi" ct="Baamulaiza - Mika <NAME>" mt="audio/mpeg" id="312131" br="96" lc="13" ml="100"/> #tree[2][0][1] </stationlist> </data> </response> ''' def getRandomChannel(data): tree = et.fromstring(data) cid = tree[2][0][1].attrib['id'] cn = tree[2][0][1].attrib['name'] log.info("Playing Channel [{0}], Channel ID [{1}]".format(cn, cid)) return cid #---------------------------------------------------------------------------------------- def playChannel(channelId): channelurl = tuneinUrl+base+"?id="+ str(channelId) channelstream = parseStreamUrl(channelurl) print channelstream command = playerCommand.format(channelstream) print command os.system(command) #---------------------------------------------------------------------------------------- def searchChannelAndPlay(genreKeyword): responsedata = keywordSearch(genreKeyword) channelid = getRandomChannel(responsedata) playChannel(channelid) #---------------------------------------------------------------------------------------- def parseStreamUrl(channelurl): response = rq.get(channelurl, stream=True, proxies=proxies) with open("stream.pls", "wb") as handle: for data in response.iter_content(): handle.write(data) x = [] datafile = file('./stream.pls') for line in datafile: if "File1=" in line: x = str(line) return x[6:-1] #---------------------------------------------------------------------------------------- if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--genre", help="Search random channel of particular genre") args = parser.parse_args() if args.genre: genreKeyword = args.genre else: log.error("Provide any genre to search a random channel") searchChannelAndPlay(genreKeyword) #----------------------------------------------------------------------------------------	[ 1, 529, 276, 1112, 420, 29958, 2084, 557, 336, 352, 29914, 14168, 300, 13399, 29899, 4645, 13, 29937, 14708, 4855, 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larpWikiAnomalyScanner.py	AllanWegan/larpWikiAnomalyScanner	0	46081	#!/usr/bin/env python3 import time import re import glob import os import platform import sys import unicodedata import urllib.parse import codecs import queue from multiprocessing import Process, Event, Queue from collections import Counter baseDir = os.path.dirname(__file__) sourceDir = os.path.join(baseDir, 'backup') blacklist = ( 'HilfeZurCreoleSyntax.txt', ) class AnomalyFormatter: """ Formats found anomalies and buffers the resuklting text. The text buffer is returned and erased by getText(). Also counts found anomalies. """ def __init__(self, textEscaper, textDecorator, maxPartLength=70): self._buffer = [] self._escaper = textEscaper self._decorator = textDecorator self.maxPartLength = maxPartLength self.qoute = '"' self.ellipsis = '…' self.sol = '\|' self.eol = '\|' self.minAfterLength = 20 self.counts = Counter() self._lastPath = '' self._lastLineNr = 0 def out(self, path, lineNr, startColumn, endColumn, line, anomaly): b = self._buffer d = self._decorator q = self.qoute if self._lastPath != path: self._lastPath = path self._lastLineNr = 0 self.counts['pathCount'] += 1 ePath = d.decorateText(self._escaper.escape(path), d.textBCyan) pageName = os.path.basename(path).replace(' - ', '/') if pageName[-4:] == '.txt': pageName = pageName[0:-4] url = 'https://larpwiki.de/' + urllib.parse.quote(pageName) eUrl = d.decorateText(url, d.textWhite) b.extend(('\n', ePath, ':\n')) b.extend((' ', eUrl, '\n')) if self._lastLineNr != lineNr: if self._lastLineNr != lineNr: self.counts['lineCount'] += 1 self._lastLineNr = lineNr eLineNr = d.decorateText(str(lineNr + 1), d.textBYellow) b.extend((' Line ', eLineNr, ':\n')) self.counts['anomalyCount'] += 1 self.counts[anomaly] += 1 eColumn = d.decorateText(str(startColumn + 1), d.textBYellow) ml = self.maxPartLength # Extract as much of the anomaly as allowed and selected: t = self._escaper.escapeLimitRight(line[startColumn:endColumn], ml) part = t[0] partCpLength = t[1] partComplete = ((endColumn - startColumn - partCpLength) == 0) ml = max(0, ml - len(part)) # Extract leading text but reserve some quota for trailing: if partComplete: mal = min(len(line) - endColumn, int(ml / 2), self.minAfterLength) else: mal = 0 bLength = min(startColumn, ml - mal) t = self._escaper.escapeLimitLeft(line[:startColumn], bLength) before = t[0] beforeCpLength = t[1] ml = max(0, ml - len(before)) # Extract as much of trailing text as available and quota left: if partComplete: t = self._escaper.escapeLimitRight(line[endColumn:], ml) after = t[0] afterCpLength = t[1] else: after = '' afterCpLength = 0 if startColumn - beforeCpLength > 0: sol = self.ellipsis else: sol = self.sol if (startColumn + partCpLength + afterCpLength) < len(line): eol = self.ellipsis else: eol = self.eol before = d.decorateText(before, d.textYellow) part = d.decorateText(part, d.textBYellow, d.textUnderline) after = d.decorateText(after, d.textYellow) b.extend((' Column ', eColumn, ', anomaly ', q, anomaly, q, ':\n')) b.extend((' ', sol, q, before, part, after, q, eol, '\n')) def getText(self): text = ''.join(self._buffer) self._buffer = [] return text def getCounts(self): counts = self.counts self.counts = Counter() return counts class AnsiTextDecorator: """ Colorizes output for ANSI terminals """ textBlack = '30' textRed = '31' textGreen = '32' textYellow = '33' textBlue = '34' textMagenta = '35' textCyan = '36' textGrey = '37' textBGrey = '30;1' textBRed = '31;1' textBGreen = '32;1' textBYellow = '33;1' textBBlue = '34;1' textBMagenta = '35;1' textBCyan = '36;1' textWhite = '37;1' textBold = '1' textItalic = '3' textUnderline = '4' backgroundBlack = '40' backgroundRed = '41' backgroundGreen = '42' backgroundYellow = '43' backgroundBlue = '44' backgroundMagenta = '45' backgroundCyan = '46' backgroundGrey = '47' def decorateText(self, text, codes): if not len(codes): return text codesStr = ''.join(('\x1B[' + code + 'm' for code in codes)) return '{0}{1}\x1B[0m'.format(codesStr, text) class DummyTextDecorator(AnsiTextDecorator): def decorateText(self, text, codes): return text def makeTextDecorator(useAnsi=False): if useAnsi: return AnsiTextDecorator() return DummyTextDecorator() class TextEscaper: """ Escapes non-printable code points except space (0x20). """ def escape(self, text): return repr(text)[1:-1].replace('"', r'\"') def escapeLimitRight(self, text, maxLength): if maxLength <= 0: return '', 0 text = text[:maxLength] textEsc = self.escape(text) while len(textEsc) > maxLength: text = text[0:-1] textEsc = self.escape(text) return textEsc, len(text) def escapeLimitLeft(self, text, maxLength): if maxLength <= 0: return '', 0 text = text[-maxLength:] textEsc = self.escape(text) while len(textEsc) > maxLength: text = text[1:] textEsc = self.escape(text) return textEsc, len(text) _detectSmilieRe = re.compile(r'''(?:^\|(?<=\s)) [:;,8B][-~]?(?:[)}\]\|({[]{1,2}\|[pPD])[=\#]? (?:\s\|$)''', re.VERBOSE) def detectSmilie(line, offset): """ Detects simple western LTR ASCII smilies like ";~P=" A smilie starts with a symbol for the eyes, followed by an optional symbol for the nose and a symbol for the mouth. A symbol for the beard may follow. The smilie has to begin and end at the start/end of line or after/before whitespace. """ return _detectSmilieRe.match(line, offset) is not None def checkForInvalidCodePoints(escaper, outputter, path, lineNr, line): markAllowed = False for cpIndex, cp in enumerate(line): anomaly = True unexpectedMark = False cpCat = unicodedata.category(cp) cpCatMain = cpCat[0] # Don't report letters, numbers, punctuation, symbols, # whitespace and some miscategorized whitespace: if cpCatMain in 'LNPSZ' or cp in ( '\t', '\xad', # SOFT HYPHEN, category Cf '\u200d', # ZERO WIDTH JOINER, category Cf '\u200e', # LEFT-TO-RIGHT MARK, category Cf None ): anomaly = False # But report REPLACEMENT CHARACTER from category So, because # it most likely is a character set conversion artifact: if cp == '�': anomaly = True # Don't report marks following letters or other marks: if cpCatMain == 'M': if markAllowed: anomaly = False else: # Not in letter cluster. anomaly, unexpectedMark = True, True elif cpCatMain == 'L': markAllowed = True else: markAllowed = False if anomaly: cpName = unicodedata.name(cp, 'unnamed') if unexpectedMark: suffix = ' not preceded by a letter' else: suffix = '' msg = 'Unicode {0} ({1}, category {2}){3}' msg = msg.format(escaper.escape(cp), cpName, cpCat, suffix) outputter.out(path, lineNr, cpIndex, cpIndex + 1, line, msg) _checkForUseModListRe = re.compile(r'(\\|#(\\|#([#])))[#]') def checkForUseModList(outputter, path, lineNr, line, isDirective, isComment): match = _checkForUseModListRe.match(line) if match: isDirective, isComment = False, False start = match.start() end = match.end() outputter.out(path, lineNr, start, end, line, 'UseMod list') return isDirective, isComment _checkForNonCommentAfterRedirectRe = re.compile(r'\s(\S.?)\s$') def detectNonCommentAfterRedirect(outputter, path, lineNr, line): match = _checkForNonCommentAfterRedirectRe.match(line) if match: start = match.start(1) end = match.end(1) msg = 'Non-empty non-comment line after valid redirect' outputter.out(path, lineNr, start, end, line, msg) return True return False _detectRedirect = re.compile(r'#REDIRECT(\s)(?P<name>.)') def detectRedirect(outputter, path, lineNr, line, firstDirectiveLine , validRedirectPresent): match = _detectRedirect.match(line) if match: if firstDirectiveLine: name = match.group('name') if not name: msg = 'Redirect without target' outputter.out(path, lineNr, 0, len(line), line, msg) else: validRedirectPresent = True else: msg = 'Redirect in non-first line' outputter.out(path, lineNr, 0, len(line), line, msg) return validRedirectPresent, True return validRedirectPresent, False def detectUseModIndent(outputter, path, lineNr, line): if line[0:1] != ':' or detectSmilie(line, 0): return False end = len(line) - len(line.lstrip(':')) outputter.out(path, lineNr, 0, end, line, 'UseMod indentation') return True def detectUseModDefinitionList(outputter, path, lineNr, line): if line[0:1] != ';' or detectSmilie(line, 0): return False outputter.out(path, lineNr, 0, 1, line, 'UseMod definition list') return True _detectUseModTagsRe = re.compile(r'''<(?P<close>[/]?) (?P<name>(b\|i\|nowiki\|pre\|toc\|tt)) >''', re.IGNORECASE \| re.VERBOSE) def detectUseModTags(outputter, path, lineNr, line): matches = _detectUseModTagsRe.finditer(line) for match in matches: start = match.start() end = match.end() closing = match.group('close') tagName = match.group('name').lower() tagType = 'close' if closing else 'open' msg = 'UseMod tag {0} {1}'.format(tagName, tagType) outputter.out(path, lineNr, start, end, line, msg) return False _checkBrTagsRe = re.compile(r''' (?P<open><[<`]) (?P<name>br) (?P<close>[>`]>) ''', re.IGNORECASE \| re.VERBOSE) def checkBrTags(outputter, path, lineNr, line): """ UseMod forced linebreak: <br> MoinMoin forced linebreak: <<BR>> """ matches = _checkBrTagsRe.finditer(line) for match in matches: start = match.start() end = match.end() tagOpen = match.group('open') tagName = match.group('name') tagClose = match.group('close') if (tagOpen == '<') and (tagClose == '>'): msg = 'UseMod forced linebreak' outputter.out(path, lineNr, start, end, line, msg) return True if ((tagOpen == '<<') and (tagClose[0:2] == '>>') and (tagName != 'BR')): msg = 'Invalid MoinMoin forced linebreak' outputter.out(path, lineNr, start, end, line, msg) return True return False _checkHeadlinesRe = re.compile(r''' (?P<spaceBeforOpen>\s) # Illegal. (?P<openTag>[=]+) # Headline open tag. (?P<spaceAfterOpen>\s) # Required. (?P<nIndicator>[\#])\s* # Numbering from old wiki. (?P<text>.?) # Required headline text (non-greedy). (?P<spaceBeforClose>\s) # Required. (?P<closeTag>[=]) # Has to be same as open tag. (?P<spaceAfterClose>\s) # Illegal trailing whitespace. $''', re.VERBOSE) def checkHeadlines(outputter, path, lineNr, line): match = _checkHeadlinesRe.match(line) if match is None: return False spaceBeforOpen = match.group('spaceBeforOpen') openTag = match.group('openTag') openTagStart = match.start('openTag') openTagEnd = match.end('openTag') spaceAfterOpen = match.group('spaceAfterOpen') nIndicator = match.group('nIndicator') text = match.group('text') spaceBeforClose = match.group('spaceBeforClose') closeTag = match.group('closeTag') spaceAfterClose = match.group('spaceAfterClose') if spaceBeforOpen: end = len(spaceBeforOpen) msg = 'Headline after whitespace' outputter.out(path, lineNr, 0, end, line, msg) if len(openTag) > 5: start = openTagStart end = openTagEnd msg = 'Headline of level > 5' outputter.out(path, lineNr, start, end, line, msg) if text: iMatches = re.finditer(r"[`']{2,}", text) for iMatch in iMatches: start = match.start('text') + iMatch.start() end = match.start('text') + iMatch.end() msg = 'Headline contains markup' outputter.out(path, lineNr, start, end, line, msg) else: end = len(line) start = openTagEnd - 1 msg = 'Headline contains no text' outputter.out(path, lineNr, start, end, line, msg) return True if not spaceAfterOpen: if nIndicator: start = match.start('nIndicator') else: start = match.start('text') msg = 'Headline without whitespace after open tag' outputter.out(path, lineNr, start, start + 1, line, msg) if nIndicator: start = match.start('nIndicator') end = match.end('nIndicator') msg = 'Headline with UseMod numbering indicator' outputter.out(path, lineNr, start, end, line, msg) if closeTag: if len(openTag) != len(closeTag): start = match.start('closeTag') end = match.end('closeTag') msg = ('Headline with different length open and close' + ' tags') outputter.out(path, lineNr, start, end, line, msg) if not spaceBeforClose: start = match.start('closeTag') msg = 'Headline without whitespace before close tag' outputter.out(path, lineNr, start, start + 1, line, msg) if spaceAfterClose: start = match.start('spaceAfterClose') end = match.end('spaceAfterClose') msg = 'Headline ends with whitespace' outputter.out(path, lineNr, start, end, line, msg) else: msg = 'Headline without close tag' outputter.out(path, lineNr, len(line)-1, len(line), line, msg) # Skip following checks when no close tag present. return True _detectUseModAnchorsRe = re.compile(r'(?:^\|[^[])(\[#[^#\]]+\])(?:$\|[^]])') def detectUseModAnchors(outputter, path, lineNr, line): matches = _detectUseModAnchorsRe.finditer(line) for match in matches: start = match.start(1) end = match.end(1) msg = 'UseMod anchor' outputter.out(path, lineNr, start, end, line, msg) return False _checkLinksRe = re.compile(r''' (?P<openBrackets>\[[\[`]) # Valid links got 2 brackets (?P<openQuote>"?) # Artifact from old wiki conversion \s (?P<linkUrl>.?) # Link URL (not greedy) \s (?P<closeQuote>"?) # Artifact from old wiki conversion (?P<closeBrackets>[\]`]\]) # Valid links got 2 brackets ''', re.IGNORECASE \| re.VERBOSE) def checkLinks(outputter, path, lineNr, line): matches = _checkLinksRe.finditer(line) for match in matches: start = match.start() end = match.end() openBrackets = match.group('openBrackets') openQuote = match.group('openQuote') linkUrl = match.group('linkUrl') if openQuote: msg = 'Fail-converted unnamed internal UseMod link' outputter.out(path, lineNr, start, end, line, msg) continue if (len(openBrackets) == 1) and re.search(r':', linkUrl): msg = 'Fail-converted external UseMod link' outputter.out(path, lineNr, start, end, line, msg) continue return False _detectUseModUploadsRe = re.compile(r'(^\|\s)(?P<link>upload:\S+)(\s\|$)', re.I) def detectUseModUploads(outputter, path, lineNr, line): matches = _detectUseModUploadsRe.finditer(line) for match in matches: start = match.start('link') end = match.end('link') msg = 'UseMod upload link' outputter.out(path, lineNr, start, end, line, msg) return False # noinspection PyUnusedLocal def detectMoinMoinComment(outputter, path, lineNr, line): return line.startswith('##') def makeCheckFile(checkFuns, cols, useAnsi): escaper = TextEscaper() decorator = makeTextDecorator(useAnsi) maxPartLength = cols - 11 outputter = AnomalyFormatter(escaper, decorator, maxPartLength) def checkFile(path): # Read file and report broken UTF-8 encoding: with open(path, 'rb') as file: textBytes = file.read() decoder = codecs.getincrementaldecoder('utf-8')() lines, line, invalidEncoding = [], [], False lastI = len(textBytes) + 1 for i in range(0, len(textBytes)): try: cp = decoder.decode(textBytes[i:i+1], i == lastI) if len(cp) != 0: if cp == '\n': if line[-1:] == ['\r']: del line[-1] lines.append(''.join(line)) line = [] else: line.append(cp) except ValueError: invalidEncoding = True lineNr, cpIndex = len(lines) + 1, len(line) lineStr = ''.join(line) msg = 'UTF-8 invalid byte while decoding line!' outputter.out(path, lineNr, cpIndex, cpIndex + 1, lineStr, msg) break if invalidEncoding: return outputter.getText(), tuple(outputter.getCounts().items()) lines.append(''.join(line)) firstDirectiveLine = 1 validRedirectPresent = False for lineNr, line in enumerate(lines): isComment = detectMoinMoinComment(outputter, path, lineNr, line) isDirective = not isComment and line.startswith('#') checkForInvalidCodePoints(escaper, outputter, path, lineNr , line) isDirective, isComment = checkForUseModList(outputter, path , lineNr, line, isDirective, isComment) # No further wiki syntax checks for comments: if isComment: continue # Determine first directive line if (firstDirectiveLine == lineNr) and isComment: firstDirectiveLine += 1 # Detect extra non-comment markup after valid redirect: if validRedirectPresent and not isDirective: skipRemaining = detectNonCommentAfterRedirect(outputter, path , lineNr, line) if skipRemaining: continue validRedirectPresent, skipRemaining = detectRedirect(outputter, path , lineNr, line, firstDirectiveLine, validRedirectPresent) if skipRemaining: continue if isDirective: # Skip other directives. continue for checkFun in checkFuns: skipRemaining = checkFun(outputter, path, lineNr, line) if skipRemaining: continue return outputter.getText(), tuple(outputter.getCounts().items()) return checkFile def workerProc(termE:Event, jobs:Queue, results:Queue, workerFactory, args): try: workFun = workerFactory(args) while not termE.is_set(): try: job = jobs.get(True, 0.02) except queue.Empty: continue result = job, workFun(job) results.put(result, True) except KeyboardInterrupt: pass def handleResults(results:Queue, counts:Counter): while True: try: job, (rText, rCounts) = results.get(False) except queue.Empty: return counts['fileCount'] += 1 if len(rText) != 0: print(rText, end='') for name, count in rCounts: counts[name] += count def main(): checkFuns = ( detectUseModIndent, detectUseModDefinitionList, detectUseModTags, checkBrTags, checkHeadlines, checkLinks, detectUseModAnchors, detectUseModUploads, ) if sys.stdout.isatty() and (platform.system() != 'Windows'): import subprocess cols = int(subprocess.Popen(('tput', 'cols'), stdout=subprocess.PIPE).stdout.read()) if cols <= 0: cols = 80 useAnsi = True else: cols, useAnsi = 80, False workerCount = max(1, len(os.sched_getaffinity(0))) termE = Event() jobs = Queue(maxsize=2workerCount) results = Queue(maxsize=2workerCount) workerArgs = termE, jobs, results, makeCheckFile, checkFuns, cols, useAnsi workerPool = [Process(target=workerProc, args=workerArgs) for _ in range(0, workerCount)] for worker in workerPool: worker.start() counts = Counter() blistedCount = 0 try: print('Scanning files...') paths = glob.iglob(os.path.join(sourceDir, ".txt")) for path in paths: if not os.path.isfile(path): continue if path in blacklist: blistedCount += 1 continue while True: handleResults(results, counts) try: jobs.put(path, True, 0.02) break except queue.Full: pass while not jobs.empty(): handleResults(results, counts) time.sleep(0.02) except KeyboardInterrupt: print('') print('Processing interrupted by user!') termE.set() while any(worker.is_alive() for worker in workerPool): handleResults(results, counts) time.sleep(0.02) for worker in workerPool: worker.join() handleResults(results, counts) decorator = makeTextDecorator(useAnsi) fileCount, anomalyCount = counts['fileCount'], counts['anomalyCount'] pathCount, lineCount = counts['pathCount'], counts['lineCount'] del counts['fileCount'] del counts['anomalyCount'] del counts['pathCount'] del counts['lineCount'] eFileCount = decorator.decorateText(str(fileCount), decorator.textBYellow) eBlistedCount = decorator.decorateText(str(blistedCount), decorator.textBYellow) if anomalyCount != 0: eAnomalyCount = decorator.decorateText(str(anomalyCount), decorator.textBYellow) eLineCount = decorator.decorateText(str(lineCount), decorator.textBYellow) ePathCount = decorator.decorateText(str(pathCount), decorator.textBYellow) msg = ('Found {0} anomalies in {1} lines from {2} files' + ' ({3} scanned, {4} excluded):') print('') print(msg.format(eAnomalyCount, eLineCount, ePathCount, eFileCount , eBlistedCount)) maxValueLen = len(str(max(counts.values()))) for name, count in sorted(counts.items()): eCount = '{0:{1}}'.format(count, maxValueLen) eCount = decorator.decorateText(eCount, decorator.textBYellow) print(' {0} {1}'.format(eCount, name)) else: msg = 'Found no anomalies in {0} files ({1} excluded).' print('') print(msg.format(fileCount, eBlistedCount)) if __name__ == '__main__': main()	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 13, 5215, 931, 13, 5215, 337, 13, 5215, 13149, 13, 5215, 2897, 13, 5215, 7481, 13, 5215, 10876, 13, 5215, 443, 293, 6797, 532, 13, 5215, 3142, 1982, 29889, 5510, 13, 5215, 775, 2395, 13, 5215, 9521, 13, 3166, 6674, 307, 985, 292, 1053, 10554, 29892, 6864, 29892, 5462, 434, 13, 3166, 16250, 1053, 315, 5336, 13, 13, 3188, 9170, 353, 2897, 29889, 2084, 29889, 25721, 22168, 1445, 1649, 29897, 13, 13, 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discord/ext/vbu/cogs/utils/converters/filtered_user.py	6days9weeks/Novus	2	17850	from discord.ext import commands class FilteredUser(commands.UserConverter): """ A simple :class:`discord.ext.commands.UserConverter` that doesn't allow bots or the author to be passed into the function. """ def __init__(self, , allow_author: bool = False, allow_bots: bool = False): super().__init__() self.allow_author = allow_author self.allow_bots = allow_bots async def convert(self, ctx: commands.Context, argument: str): m = await super().convert(ctx, argument) if self.allow_author is False and ctx.author.id == m.id: raise commands.BadArgument("You can't run this command on yourself.") if self.allow_bots is False and m.bot: raise commands.BadArgument("You can't run this command on bots.") return m class FilteredMember(commands.MemberConverter): """ A simple :class:`discord.ext.commands.MemberConverter` that doesn't allow bots or the author to be passed into the function. """ def __init__(self, , allow_author: bool = False, allow_bots: bool = False): super().__init__() self.allow_author = allow_author self.allow_bots = allow_bots async def convert(self, ctx: commands.Context, argument: str): m = await super().convert(ctx, argument) if self.allow_author is False and ctx.author.id == m.id: raise commands.BadArgument("You can't run this command on yourself.") if self.allow_bots is False and m.bot: raise commands.BadArgument("You can't run this command on bots.") return m	[ 1, 515, 2313, 536, 29889, 1062, 1053, 8260, 13, 13, 13, 1990, 19916, 287, 2659, 29898, 26381, 29889, 2659, 18545, 1125, 13, 1678, 9995, 13, 1678, 319, 2560, 584, 1990, 18078, 2218, 16090, 29889, 1062, 29889, 26381, 29889, 2659, 18545, 29952, 393, 1838, 29915, 29873, 2758, 289, 1862, 13, 1678, 470, 278, 4148, 304, 367, 4502, 964, 278, 740, 29889, 13, 1678, 9995, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 334, 29892, 2758, 29918, 8921, 29901, 6120, 353, 7700, 29892, 2758, 29918, 29890, 1862, 29901, 6120, 353, 7700, 1125, 13, 4706, 2428, 2141, 1649, 2344, 1649, 580, 13, 4706, 1583, 29889, 9536, 29918, 8921, 353, 2758, 29918, 8921, 13, 4706, 1583, 29889, 9536, 29918, 29890, 1862, 353, 2758, 29918, 29890, 1862, 13, 13, 1678, 7465, 822, 3588, 29898, 1311, 29892, 12893, 29901, 8260, 29889, 2677, 29892, 2980, 29901, 851, 1125, 13, 4706, 286, 353, 7272, 2428, 2141, 13441, 29898, 13073, 29892, 2980, 29897, 13, 4706, 565, 1583, 29889, 9536, 29918, 8921, 338, 7700, 322, 12893, 29889, 8921, 29889, 333, 1275, 286, 29889, 333, 29901, 13, 9651, 12020, 8260, 29889, 22050, 15730, 703, 3492, 508, 29915, 29873, 1065, 445, 1899, 373, 7535, 23157, 13, 4706, 565, 1583, 29889, 9536, 29918, 29890, 1862, 338, 7700, 322, 286, 29889, 7451, 29901, 13, 9651, 12020, 8260, 29889, 22050, 15730, 703, 3492, 508, 29915, 29873, 1065, 445, 1899, 373, 289, 1862, 23157, 13, 4706, 736, 286, 13, 13, 13, 1990, 19916, 287, 13404, 29898, 26381, 29889, 13404, 18545, 1125, 13, 1678, 9995, 13, 1678, 319, 2560, 584, 1990, 18078, 2218, 16090, 29889, 1062, 29889, 26381, 29889, 13404, 18545, 29952, 393, 1838, 29915, 29873, 2758, 289, 1862, 13, 1678, 470, 278, 4148, 304, 367, 4502, 964, 278, 740, 29889, 13, 1678, 9995, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 334, 29892, 2758, 29918, 8921, 29901, 6120, 353, 7700, 29892, 2758, 29918, 29890, 1862, 29901, 6120, 353, 7700, 1125, 13, 4706, 2428, 2141, 1649, 2344, 1649, 580, 13, 4706, 1583, 29889, 9536, 29918, 8921, 353, 2758, 29918, 8921, 13, 4706, 1583, 29889, 9536, 29918, 29890, 1862, 353, 2758, 29918, 29890, 1862, 13, 13, 1678, 7465, 822, 3588, 29898, 1311, 29892, 12893, 29901, 8260, 29889, 2677, 29892, 2980, 29901, 851, 1125, 13, 4706, 286, 353, 7272, 2428, 2141, 13441, 29898, 13073, 29892, 2980, 29897, 13, 4706, 565, 1583, 29889, 9536, 29918, 8921, 338, 7700, 322, 12893, 29889, 8921, 29889, 333, 1275, 286, 29889, 333, 29901, 13, 9651, 12020, 8260, 29889, 22050, 15730, 703, 3492, 508, 29915, 29873, 1065, 445, 1899, 373, 7535, 23157, 13, 4706, 565, 1583, 29889, 9536, 29918, 29890, 1862, 338, 7700, 322, 286, 29889, 7451, 29901, 13, 9651, 12020, 8260, 29889, 22050, 15730, 703, 3492, 508, 29915, 29873, 1065, 445, 1899, 373, 289, 1862, 23157, 13, 4706, 736, 286, 13, 2 ]
fold_cur_trans.py	lucasforever24/arcface_noonan	0	5358	import cv2 from PIL import Image import argparse from pathlib import Path from multiprocessing import Process, Pipe,Value,Array import torch from config import get_config from mtcnn import MTCNN from Learner_trans_tf import face_learner from utils import load_facebank, draw_box_name, prepare_facebank, save_label_score, label_binarize from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score from sklearn.model_selection import KFold import os import glob import shutil import numpy as np import matplotlib.pyplot as plt import matplotlib.colors as mcolors import datetime if __name__ == '__main__': parser = argparse.ArgumentParser(description='for face verification') parser.add_argument("-ds", "--dataset_dir", help="where to get data", default="noonan", type=str) parser.add_argument('-sd','--stored_result_dir',help='where to store data as np arrays', default="results/trans/", type=str) parser.add_argument("-k", "--kfold", help="returns the number of splitting iterations in the cross-validator.", default=10, type=int) parser.add_argument("-e", "--epochs", help="training epochs", default=20, type=int) parser.add_argument("-n", "--names_considered", help="names for different types considered, separated by commas", default="normal,noonan,others", type=str) parser.add_argument("-g", "--gpu_id", help="gpu id to use", default="", type=str) parser.add_argument("-s", "--use_shuffled_kfold", help="whether to use shuffled kfold.", action="store_true") parser.add_argument("-rs", "--random_seed", help="random seed used for k-fold split.", default=6, type=int) parser.add_argument("-tta", "--tta", help="whether test time augmentation",action="store_true") parser.add_argument("-a", "--additional_data_dir", help="where to get the additional data", default="", type=str) parser.add_argument("-ta", "--additional_test_or_train", help="use additional data in only train, or test, or both", default="", type=str) parser.add_argument("-as", "--stylegan_data_dir", help="where to get the additional data", default="", type=str) parser.add_argument("-ts", "--stylegan_test_or_train", help="use stylegan data in only train, or test, or both", default="", type=str) parser.add_argument("-tf", "--transfer", help="how many layer(s) used for transfer learning, " "but 0 means retraining the whole network.", default=0, type=int) parser.add_argument("-ac", "--arch", help="types of model used for encoder", default="mobile", type=str) args = parser.parse_args() for arg in vars(args): print(arg+':', getattr(args, arg)) emore_dir = 'faces_emore' conf = get_config(True, args) conf.emore_folder = conf.data_path/emore_dir mtcnn = MTCNN() print('mtcnn loaded') names_considered = args.names_considered.strip().split(',') exp_name = args.dataset_dir[:4] if args.additional_data_dir: if 'LAG' in args.additional_data_dir: exp_name += '_lag' elif 'literature' in args.additional_data_dir: exp_name += '_ltr' if args.kfold != 10: exp_name += ('_k' + str(args.kfold)) if args.epochs != 20: exp_name += ('_e' + str(args.epochs)) if args.transfer != 0 and args.transfer != 1: exp_name += ('_td' + str(args.transfer)) if args.use_shuffled_kfold: exp_name += ('_s' + str(args.random_seed)) print(exp_name) # prepare folders raw_dir = 'raw_112' verify_type = 'trans' if args.use_shuffled_kfold: verify_type += '_shuffled' # train_dir = conf.facebank_path/args.dataset_dir/verify_type/'train' train_dir = conf.emore_folder/'imgs' test_dir = conf.emore_folder/'test' conf.facebank_path = train_dir if os.path.exists(train_dir): shutil.rmtree(train_dir) if os.path.exists(test_dir): shutil.rmtree(test_dir) os.mkdir(train_dir) os.mkdir(test_dir) for name in names_considered: os.makedirs(str(train_dir) + '/' + name, exist_ok=True) os.makedirs(str(test_dir) + '/' + name, exist_ok=True) if args.stylegan_data_dir: #e.g. smile_refine_mtcnn_112_divi full_stylegan_dir = str(conf.data_path/'facebank'/'stylegan'/args.stylegan_data_dir) stylegan_folders = os.listdir(full_stylegan_dir) if args.additional_data_dir: full_additional_dir = str(conf.data_path/'facebank'/args.additional_data_dir) # init kfold if args.use_shuffled_kfold: kf = KFold(n_splits=args.kfold, shuffle=True, random_state=args.random_seed) else: kf = KFold(n_splits=args.kfold, shuffle=False, random_state=None) # collect and split raw data data_dict = {} idx_gen = {} for name in names_considered: tmp_list = glob.glob(str(conf.data_path/'facebank'/args.dataset_dir/raw_dir) + '/' + name + '') if 'innm' in args.stylegan_data_dir: tmp_list = tmp_list + glob.glob(str(full_stylegan_dir) + '/' + name + '') stylegan_folders = [] print(str(conf.data_path/'facebank'/args.dataset_dir/raw_dir)) data_dict[name] = np.array(tmp_list) idx_gen[name] = kf.split(data_dict[name]) if 'literature' in args.additional_data_dir: data_dict['ltr'] = np.array(glob.glob(str(full_additional_dir) + '/')) idx_gen['ltr'] = kf.split(data_dict['ltr']) score_names = [] scores = [] wrong_names = [] args.stored_result_path = args.stored_result_dir + os.sep + str(datetime.datetime.now())[:19] if not os.path.exists(args.stored_result_path): os.mkdir(args.stored_result_path) # for fold_idx, (train_index, test_index) in enumerate(kf.split(data_dict[names_considered[0]])): for fold_idx in range(args.kfold): train_set = {} test_set = {} for name in names_considered: (train_index, test_index) = next(idx_gen[name]) train_set[name], test_set[name] = data_dict[name][train_index], data_dict[name][test_index] if 'ltr' in data_dict.keys(): (train_index, test_index) = next(idx_gen['ltr']) train_set['ltr'], test_set['ltr'] = data_dict['ltr'][train_index], data_dict['ltr'][test_index] if 'train' in args.additional_test_or_train: train_set['noonan'] = np.concatenate((train_set['noonan'], train_set['ltr'])) if 'test' in args.additional_test_or_train: test_set['noonan'] = np.concatenate((test_set['noonan'], test_set['ltr'])) # remove previous data prev = glob.glob(str(train_dir) + '//') for p in prev: os.remove(p) prev = glob.glob(str(test_dir) + '//') for p in prev: os.remove(p) # save trains to conf.facebank_path/args.dataset_dir/'train' and # tests to conf.data_path/'facebank'/args.dataset_dir/'test' # count unbalanced data train_count = {} test_count = {} for name in names_considered: train_count[name] = 0 for i in range(len(train_set[name])): img_folder = str(train_set[name][i]) for img in os.listdir(img_folder): shutil.copy(img_folder + os.sep + str(img), os.path.join(str(train_dir), name, str(img))) train_count[name] += 1 # addition data from stylegan if 'interp' not in data_dict.keys(): folder = os.path.basename(train_set[name][i]) if args.stylegan_data_dir and ('train' in args.stylegan_test_or_train) and (folder in stylegan_folders): for img in os.listdir(full_stylegan_dir + os.sep + folder): shutil.copy(os.path.join(full_stylegan_dir, folder, str(img)), os.path.join(str(train_dir), name, str(img))) # ('/'.join(train_set[name][i].strip().split('/')[:-2]) + # '/' + verify_type + '/train/' + name + os.sep + img)) train_count[name] += 1 # test for i in range(len(test_set[name])): test_count[name] = 0 img_folder = str(test_set[name][i]) for img in os.listdir(img_folder): shutil.copy(img_folder + os.sep + str(img), os.path.join(str(test_dir), name, str(img))) test_count[name] += 1 # addition data from stylegan if 'interp' not in data_dict.keys(): folder = os.path.basename(test_set[name][i]) if args.stylegan_data_dir and ('test' in args.stylegan_test_or_train) and (folder in stylegan_folders): # and # (folder not in ['noonan7','noonan19','noonan23','normal9','normal20','normal23'])): for img in os.listdir(full_stylegan_dir + os.sep + folder): shutil.copy(os.path.join(full_stylegan_dir, folder, str(img)), os.path.join(str(test_dir), name, str(img))) test_count[name] += 1 print(train_count, test_count) # deal with unbalanced data """ if train_count['normal'] // train_count['noonan'] > 1: aug_num = train_count['normal'] // train_count['noonan'] - 1 for img in os.listdir(os.path.join(str(train_dir), 'noonan')): for aug_idx in range(aug_num): aug_img = img[:img.rfind('.')] + '_' + str(aug_idx) + img[img.rfind('.'):] shutil.copy(os.path.join(str(train_dir), 'noonan', img), os.path.join(str(train_dir), 'noonan', aug_img)) """ if 'fake' in args.additional_data_dir: fake_dict = {'noonan':'normal', 'normal':'noonan'} full_additional_dir = conf.data_path/'facebank'/'noonan+normal'/args.additional_data_dir add_data = glob.glob(str(full_additional_dir) + os.sep + '.png') print('additional:', args.additional_data_dir, len(add_data)) for name in names_considered: for img_f in add_data: if name in img_f.strip().split(os.sep)[-1]: # print('source:', img_f) # print('copy to:', img_f.replace(str(full_additional_dir), # str(train_dir) + os.sep + fake_dict[name])) # print('copy to:', img_f.replace(args.additional_data_dir, # verify_type + '/train/' + name)) shutil.copy(img_f, os.path.join(str(train_dir), fake_dict[name], os.path.basename(img_f))) print(fold_idx) print('datasets ready') conf_train = get_config(True, args) conf_train.emore_folder = conf.data_path/emore_dir conf_train.stored_result_dir = args.stored_result_path learner = face_learner(conf=conf_train, transfer=args.transfer, ext=exp_name+'_'+str(fold_idx)) # conf, inference=False, transfer=0 if args.transfer != 0: learner.load_state(conf.save_path, False, True) print('learner loaded') learner.train(conf_train, args.epochs) print('learner retrained.') learner.save_state() print('Model is saved') # prepare_facebank targets, names, names_idx = prepare_facebank(conf, learner.model, mtcnn, tta = args.tta) print('names_classes:', names) noonan_idx = names_idx['noonan'] print('facebank updated') for path in test_dir.iterdir(): if path.is_file(): continue # print(path) for fil in path.iterdir(): # print(fil) orig_name = ''.join([i for i in fil.name.strip().split('.')[0].split('_')[0] if not i.isdigit()]) for name in names_idx.keys(): if name in orig_name: score_names.append(names_idx[name]) """ if orig_name not in names_considered: print("Un-considered name:", fil.name) continue """ frame = cv2.imread(str(fil)) image = Image.fromarray(frame) faces = [image,] distance = learner.binfer(conf, faces, targets, args.tta) label = score_names[-1] score = np.exp(distance.dot(-1)) pred = np.argmax(score, 1) if pred != label: wrong_names.append(orig_name) scores.append(score) score_names = np.array(score_names) wrong_names = np.array(wrong_names) score_np = np.squeeze(np.array(scores)) n_classes = score_np.shape[1] score_names = label_binarize(score_names, classes=range(n_classes)) score_sum = np.zeros([score_np.shape[0], 1]) for i in range(n_classes): score_sum += score_np[:, i, None] # keep the dimension relative_scores = (score_np / score_sum) total_scores = relative_scores.ravel() total_names = score_names.ravel() name_path = os.path.join(args.stored_result_path, 'wrong_names.npy') save_label_score(name_path, wrong_names) label_path = os.path.join(args.stored_result_path, 'labels_trans.npy') save_label_score(label_path, score_names) score_path = os.path.join(args.stored_result_path, 'scores_trans.npy') save_label_score(score_path, relative_scores) print('saved!') # Compute ROC curve and ROC area for noonan fpr, tpr, _ = roc_curve(total_names, total_scores) #scores_np[:, noonan_idx] roc_auc = auc(fpr, tpr) # For PR curve precision, recall, _ = precision_recall_curve(total_names, total_scores) average_precision = average_precision_score(total_names, total_scores) # plots plt.figure() colors = list(mcolors.TABLEAU_COLORS) lw = 2 plt.plot(fpr, tpr, color='darkorange', lw=lw, label='ROC curve (area = %0.4f)' % roc_auc) plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('ROC_{}'.format(exp_name)) plt.legend(loc="lower right") plt.savefig(args.stored_result_path + os.sep + '/fp_tp_{}.png'.format(exp_name)) plt.close() # plt.show() plt.figure() plt.step(recall, precision, where='post') plt.xlabel('Recall') plt.ylabel('Precision') plt.ylim([0.0, 1.05]) plt.xlim([0.0, 1.0]) plt.title('Average precision score ({}): AP={:0.4f}'.format(exp_name, average_precision)) plt.savefig(args.stored_result_path + os.sep + '/pr_{}.png'.format(exp_name)) plt.close()	[ 1, 1053, 13850, 29906, 13, 3166, 349, 6227, 1053, 7084, 13, 5215, 1852, 5510, 13, 3166, 2224, 1982, 1053, 10802, 13, 3166, 6674, 307, 985, 292, 1053, 10554, 29892, 7362, 412, 29892, 1917, 29892, 2588, 13, 5215, 4842, 305, 13, 3166, 2295, 1053, 679, 29918, 2917, 13, 3166, 286, 14246, 15755, 1053, 341, 9472, 10262, 13, 3166, 19530, 1089, 29918, 3286, 29918, 13264, 1053, 3700, 29918, 1945, 1089, 13, 3166, 3667, 29879, 1053, 2254, 29918, 2161, 9157, 29892, 4216, 29918, 1884, 29918, 978, 29892, 19012, 29918, 2161, 9157, 29892, 4078, 29918, 1643, 29918, 13628, 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hood/urls.py	wadi-1000/Vicinity	0	3242	<reponame>wadi-1000/Vicinity from django.urls import path,include from . import views urlpatterns = [ path('home/', views.home, name = 'home'), path('add_hood/',views.uploadNeighbourhood, name = 'add_hood'), path('viewhood/',views.viewHood, name = 'viewhood'), path('hood/<int:pk>/',views.hood, name = 'hood'), path('add_bizna/',views.uploadBuisness, name = 'add_bizna'), path('bizna/',views.viewBizna, name = 'view_bizna'), path('viewbizna/<int:pk>/',views.bizna, name = 'bizna'), path('post/',views.create_post, name = 'post'), path('posts/',views.viewPost, name = 'posts'), path('searchbizna/', views.searchBizna, name="search_results"), path('searchhood/', views.searchHood, name="search_res"), path('join_hood/<id>', views.join_neighbourhood, name='join-hood'), path('leave_hood/<id>', views.leave_neighbourhood, name='leave-hood'), ]	[ 1, 529, 276, 1112, 420, 29958, 29893, 10129, 29899, 29896, 29900, 29900, 29900, 29914, 29963, 293, 13593, 13, 3166, 9557, 29889, 26045, 1053, 2224, 29892, 2856, 13, 3166, 869, 1053, 8386, 13, 13, 2271, 11037, 29879, 353, 518, 13, 1678, 2224, 877, 5184, 29914, 742, 8386, 29889, 5184, 29892, 1024, 353, 525, 5184, 5477, 13, 1678, 2224, 877, 1202, 29918, 6614, 29914, 742, 7406, 29889, 9009, 8139, 1141, 6526, 6614, 29892, 1024, 353, 525, 1202, 29918, 6614, 5477, 13, 1678, 2224, 877, 1493, 6614, 29914, 742, 7406, 29889, 1493, 29950, 2092, 29892, 1024, 353, 525, 1493, 6614, 5477, 13, 1678, 2224, 877, 6614, 29914, 29966, 524, 29901, 20571, 20690, 742, 7406, 29889, 6614, 29892, 1024, 353, 525, 6614, 5477, 13, 1678, 2224, 877, 1202, 29918, 29890, 466, 1056, 29914, 742, 7406, 29889, 9009, 3727, 275, 2264, 29892, 1024, 353, 525, 1202, 29918, 29890, 466, 1056, 5477, 13, 1678, 2224, 877, 29890, 466, 1056, 29914, 742, 7406, 29889, 1493, 29933, 466, 1056, 29892, 1024, 353, 525, 1493, 29918, 29890, 466, 1056, 5477, 13, 1678, 2224, 877, 1493, 29890, 466, 1056, 29914, 29966, 524, 29901, 20571, 20690, 742, 7406, 29889, 29890, 466, 1056, 29892, 1024, 353, 525, 29890, 466, 1056, 5477, 13, 1678, 2224, 877, 2490, 29914, 742, 7406, 29889, 3258, 29918, 2490, 29892, 1024, 353, 525, 2490, 5477, 13, 1678, 2224, 877, 14080, 29914, 742, 7406, 29889, 1493, 6747, 29892, 1024, 353, 525, 14080, 5477, 13, 1678, 2224, 877, 4478, 29890, 466, 1056, 29914, 742, 8386, 29889, 4478, 29933, 466, 1056, 29892, 1024, 543, 4478, 29918, 9902, 4968, 13, 1678, 2224, 877, 4478, 6614, 29914, 742, 8386, 29889, 4478, 29950, 2092, 29892, 1024, 543, 4478, 29918, 690, 4968, 13, 1678, 2224, 877, 7122, 29918, 6614, 29914, 29966, 333, 29958, 742, 8386, 29889, 7122, 29918, 484, 1141, 6526, 6614, 29892, 1024, 2433, 7122, 29899, 6614, 5477, 13, 1678, 2224, 877, 280, 1351, 29918, 6614, 29914, 29966, 333, 29958, 742, 8386, 29889, 280, 1351, 29918, 484, 1141, 6526, 6614, 29892, 1024, 2433, 280, 1351, 29899, 6614, 5477, 13, 29962, 2 ]
setup.py	jacobschaer/qt_compat	0	13246	<filename>setup.py<gh_stars>0 from setuptools import setup, find_packages setup( name="QtCompat", version="0.1", packages=find_packages(), scripts=[], # Project uses reStructuredText, so ensure that the docutils get # installed or upgraded on the target machine install_requires=[], package_data={ }, # metadata for upload to PyPI author="<NAME>", author_email="", description="PyQt4, 5 and Pyside Compatibility Library", license="MIT", keywords="pyqt4 pyqt5 pyside compatibility", url="https://github.com/jacobschaer/qt_compat/", # project home page, if any # could also include long_description, download_url, classifiers, etc. )	[ 1, 529, 9507, 29958, 14669, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 731, 21245, 8789, 1053, 6230, 29892, 1284, 29918, 8318, 13, 14669, 29898, 13, 1678, 1024, 543, 17303, 14644, 613, 13, 1678, 1873, 543, 29900, 29889, 29896, 613, 13, 1678, 9741, 29922, 2886, 29918, 8318, 3285, 13, 1678, 12078, 11759, 1402, 13, 13, 1678, 396, 8010, 3913, 337, 19560, 2955, 1626, 29892, 577, 9801, 393, 278, 1574, 13239, 679, 13, 1678, 396, 5130, 470, 20337, 287, 373, 278, 3646, 4933, 13, 1678, 2601, 29918, 276, 339, 2658, 11759, 1402, 13, 13, 1678, 3577, 29918, 1272, 3790, 13, 1678, 2981, 13, 13, 1678, 396, 15562, 363, 6441, 304, 10772, 2227, 13, 1678, 4148, 543, 29966, 5813, 28341, 13, 1678, 4148, 29918, 5269, 543, 613, 13, 1678, 6139, 543, 19737, 17303, 29946, 29892, 29871, 29945, 322, 349, 952, 680, 3831, 271, 4127, 9538, 613, 13, 1678, 19405, 543, 26349, 613, 13, 1678, 29361, 543, 2272, 17915, 29946, 11451, 17915, 29945, 282, 952, 680, 24521, 613, 13, 1678, 3142, 543, 991, 597, 3292, 29889, 510, 29914, 29926, 562, 711, 816, 29874, 261, 29914, 17915, 29918, 12667, 29914, 613, 259, 396, 2060, 3271, 1813, 29892, 565, 738, 13, 13, 1678, 396, 1033, 884, 3160, 1472, 29918, 8216, 29892, 5142, 29918, 2271, 29892, 770, 14903, 29892, 2992, 29889, 13, 29897, 2 ]
examples/simple_example.py	alexkyllo/torch-wtte	1	100975	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: percent # format_version: '1.3' # jupytext_version: 1.11.4 # kernelspec: # display_name: wtte-dev # language: python # name: wtte-dev # --- # %% [markdown] # # WTTE-RNN in PyTorch # # <NAME> # # Based on original Keras version written by <NAME>: # https://github.com/ragulpr/wtte-rnn/blob/master/examples/keras/simple_example.ipynb # MIT license # # For details, check out # https://ragulpr.github.io/2016/12/22/WTTE-RNN-Hackless-churn-modeling/ # https://github.com/ragulpr/wtte-rnn # %% # %matplotlib inline import sys import numpy as np import torch from torch import nn, optim from torch.utils.data import TensorDataset, DataLoader import matplotlib.pyplot as plt sys.path.append("..") from torch_wtte import losses np.random.seed(11) torch.manual_seed(11) # %% def get_data(n_timesteps, every_nth, n_repeats, noise_level, n_features, use_censored=True): def get_equal_spaced(n, every_nth): # create some simple data of evenly spaced events recurring every_nth step # Each is on (time,batch)-format events = np.array([np.array(range(n)) for _ in range(every_nth)]) events = events + np.array(range(every_nth)).reshape(every_nth, 1) + 1 tte_actual = every_nth - 1 - events % every_nth was_event = (events % every_nth == 0) * 1.0 was_event[:, 0] = 0.0 events = tte_actual == 0 is_censored = (events[:, ::-1].cumsum(1)[:, ::-1] == 0) * 1 tte_censored = is_censored[:, ::-1].cumsum(1)[:, ::-1] * is_censored tte_censored = tte_censored + (1 - is_censored) * tte_actual events = np.copy(events.T * 1.0) tte_actual = np.copy(tte_actual.T * 1.0) tte_censored = np.copy(tte_censored.T * 1.0) was_event = np.copy(was_event.T * 1.0) not_censored = 1 - np.copy(is_censored.T * 1.0) return tte_censored, not_censored, was_event, events, tte_actual tte_censored, not_censored, was_event, events, tte_actual = get_equal_spaced( n=n_timesteps, every_nth=every_nth ) # From https://keras.io/layers/recurrent/ # input shape rnn recurrent if return_sequences: (nb_samples, timesteps, input_dim) u_train = not_censored.T.reshape(n_sequences, n_timesteps, 1) x_train = was_event.T.reshape(n_sequences, n_timesteps, 1) tte_censored = tte_censored.T.reshape(n_sequences, n_timesteps, 1) y_train = np.append(tte_censored, u_train, axis=2) # (n_sequences,n_timesteps,2) u_test = np.ones(shape=(n_sequences, n_timesteps, 1)) x_test = np.copy(x_train) tte_actual = tte_actual.T.reshape(n_sequences, n_timesteps, 1) y_test = np.append(tte_actual, u_test, axis=2) # (n_sequences,n_timesteps,2) if not use_censored: x_train = np.copy(x_test) y_train = np.copy(y_test) # Since the above is deterministic perfect fit is feasible. # More noise->more fun so add noise to the training data: x_train = np.tile(x_train.T, n_repeats).T y_train = np.tile(y_train.T, n_repeats).T # Try with more than one feature TODO x_train_new = np.zeros([x_train.shape[0], x_train.shape[1], n_features]) x_test_new = np.zeros([x_test.shape[0], x_test.shape[1], n_features]) for f in range(n_features): x_train_new[:, :, f] = x_train[:, :, 0] x_test_new[:, :, f] = x_test[:, :, 0] x_train = x_train_new x_test = x_test_new # xtrain is signal XOR noise with probability noise_level noise = np.random.binomial(1, noise_level, size=x_train.shape) x_train = x_train + noise - x_train * noise return y_train, x_train, y_test, x_test, events # %% [markdown] # ### Generate some data # # * The true event-sequence is evenly spaced points (but we start anywhere in the sequence) # * The true feature is (binary) if there was an event in last step # * In the training data the feature has added noise # * Training TTE is censored. Testing TTE is uncensored. # %% n_timesteps = 200 n_sequences = every_nth = 80 n_features = 1 n_repeats = 1000 noise_level = 0.005 use_censored = True y_train, x_train, y_test, x_test, events = get_data( n_timesteps, every_nth, n_repeats, noise_level, n_features, use_censored ) # %% #### Plots print("test shape", x_test.shape, y_test.shape) plt.imshow(x_test[:, :, :].sum(axis=2) > 0, interpolation="none", cmap="Accent", aspect="auto") plt.title("x_test (lagged/deterministic event indicator)") plt.show() plt.imshow(y_test[:, :, 0], interpolation="none", cmap="jet", aspect="auto") plt.title("y_test[:,:,0] actual tte") plt.show() print("train shape", x_train.shape, y_train.shape) plt.imshow( x_train[:every_nth, :, :].mean(axis=2), interpolation="none", cmap="Accent", aspect="auto" ) plt.title("x_train[:every_nth,:,0] (lagged/noisy event indicator)") plt.show() plt.imshow(y_train[:every_nth, :, 0], interpolation="none", cmap="jet", aspect="auto") plt.title("y_train[:every_nth,:,0] censored tte") plt.show() plt.imshow(y_train[:every_nth, :, 1], interpolation="none", cmap="Accent", aspect="auto") plt.title("y_train[:every_nth,:,1] u (non-censoring indicator)") plt.show() ## Example TTE: print("Example TTEs") plt.plot( y_train[every_nth // 4, :, 0], label="censored tte (train)", color="black", linestyle="dashed", linewidth=2, drawstyle="steps-post", ) plt.plot( y_test[every_nth // 4, :, 0], label="actual tte (test)", color="black", linestyle="solid", linewidth=2, drawstyle="steps-post", ) plt.xlim(0, n_timesteps) plt.xlabel("time") plt.ylabel("time to event") plt.title("Example TTEs") plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0) plt.show() # %% [markdown] # # Train a WTTE-RNN # ### Things to try out: # * have fun with data paramaters: # * every_nth to control event frequency # * noise_level to make it more noisy # * n_timesteps # * n_features to get more noisy input # * Generate more interesting temporal relationships # * Here we use the smallest possible GRU. Try different learning rates, network architectures, initializations. # * Try Implementing multivariate distributions, other distributions, data pipelines etc. # * Invent better output activation layer # * Invent ways to overcome instability with lots of censoring # * ETC and have fun! # %% # Paramaeters for output activation layer initialization. # Start at naive geometric (beta=1) MLE: tte_mean_train = np.nanmean(y_train[:, :, 0]) init_alpha = -1.0 / np.log(1.0 - 1.0 / (tte_mean_train + 1.0)) mean_u = np.nanmean(y_train[:, :, 1]) init_alpha = init_alpha / mean_u print("init_alpha: ", init_alpha, "mean uncensored: ", mean_u) ### Uncomment if you have varying length sequences that is nanpadded to the right: # mask_value = -1.3371337 # Use some improbable but not nan-causing telltale value # x_train[:,:,:][np.isnan(x_train)] = mask_value # y_train[:,:,0][np.isnan(y_train[:,:,0])] = tte_mean_train # y_train[:,:,1][np.isnan(y_train[:,:,1])] = 0.5 # sample_weights = (x_train[:,:,0]!=mask_value)*1. # %% class WTTERNN(nn.Module): def __init__(self, discrete): super().__init__() self.epoch = 0 self.layers = nn.ModuleList( [ nn.GRU(input_size=n_features, hidden_size=2, batch_first=True), nn.Tanh(), losses.WeibullActivation(init_alpha=init_alpha, max_beta=4.0), ] ) self.criterion = losses.WeibullCensoredNLLLoss(discrete=discrete) def forward(self, x): x, _ = self.layers[0](x) # discard GRU hidden state output x = self.layers[1](x) x = self.layers[2](x) return x def fit(self, optimizer, train_loader, device="cpu"): num_batches = ( len(train_loader.dataset) + train_loader.batch_size - 1 ) // train_loader.batch_size self.to(device) self.train() train_losses = [] for batch_idx, (data, labels) in enumerate(train_loader): data, labels = data.to(device), labels.to(device) optimizer.zero_grad() output = self(data).squeeze() loss = self.criterion(output, labels).sum() loss.backward() optimizer.step() train_losses.append(loss.item()) avg_loss = loss / len(data) print( f"Epoch: {self.epoch} [batch {batch_idx+1}/{num_batches}]\tLoss: {loss.item():.6f} Avg Loss: {avg_loss:.6f}", end="\r", ) avg_train_loss = np.sum(train_losses) / len(train_loader.dataset) print() self.epoch += 1 return avg_train_loss def score(self, valid_loader, device="cpu"): self.to(device) self.eval() valid_loss = 0 correct = 0 with torch.no_grad(): for data, labels in valid_loader: data, labels = data.to(device), labels.to(device) output = self(data).squeeze() tte = labels[..., 0] uncensored = labels[..., 1] alpha = output[..., 0] beta = output[..., 1] valid_loss = self.criterion(tte, uncensored, alpha, beta).sum().item() pred = output.data.round() correct += pred.eq(labels.data.view_as(pred)).sum() n = len(valid_loader.dataset) valid_loss /= n print(f"Validation: avg loss: {valid_loss:.4f}") return valid_loss # %% def run(x_train, y_train, x_test, y_test, epochs, device): print(f"Using device {device}") x_train = torch.from_numpy(x_train.astype("float32")).to(device) x_test = torch.from_numpy(x_test.astype("float32")).to(device) y_train = torch.from_numpy(y_train.astype("float32")).to(device) y_test = torch.from_numpy(y_test.astype("float32")).to(device) train_data = TensorDataset(x_train, y_train) test_data = TensorDataset(x_test, y_test) batch_size = x_train.shape[0] // 10 train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True) test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=False) model = WTTERNN(discrete=True) optimizer = optim.Adam(model.parameters(), lr=0.01) train_loss_history = [] valid_loss_history = [] for _ in range(epochs): train_loss = model.fit(optimizer, train_loader, device=device) train_loss_history.append(train_loss) valid_loss = model.score(test_loader, device=device) valid_loss_history.append(valid_loss) return model, train_loss_history, valid_loss_history # %% device = "cuda:0" if torch.cuda.is_available() else "cpu" model, train_losses, valid_losses = run(x_train, y_train, x_test, y_test, epochs=60, device=device) # %% plt.plot(train_losses, label="training") plt.plot(valid_losses, label="validation") plt.title("loss") plt.legend() # %% [markdown] # # Predictions # Try out training the model with different levels of noise. With more noise confidence gets lower (smaller beta). With less noise beta goes to maximum value and the predicted mode/peak probability is centered around the actual TTE. # %% # Make some parametric predictions print("TESTING (no noise in features)") print("(each horizontal line is a sequence)") predicted = model(torch.from_numpy(x_test.astype("float32")).to(device)).detach().cpu().numpy() print(predicted.shape) plt.imshow(predicted[:, :, 0], interpolation="none", cmap="jet", aspect="auto") plt.title("predicted[:,:,0] (alpha)") plt.colorbar(orientation="horizontal") plt.show() plt.imshow(predicted[:, :, 1], interpolation="none", cmap="jet", aspect="auto") plt.title("predicted[:,:,1] (beta)") plt.colorbar(orientation="horizontal") plt.show() print("TRAINING (Noisy features)") predicted = ( model(torch.from_numpy(x_train[:every_nth, :, :].astype("float32")).to(device)) .detach() .cpu() .numpy() ) print(predicted.shape) plt.imshow(predicted[:, :, 0], interpolation="none", cmap="jet", aspect="auto") plt.title("predicted[:,:,0] (alpha)") 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tests/test_solver/test_sensitivities.py	dbankmann/python-mloc	0	118749	<reponame>dbankmann/python-mloc # # Copyright (c) 2019-2020 # # @author: <NAME> # @company: Technische Universität Berlin # # This file is part of the python package pymloc # (see https://gitlab.tubit.tu-berlin.de/bankmann91/python-mloc ) # # License: 3-clause BSD, see https://opensource.org/licenses/BSD-3-Clause # from copy import deepcopy import jax.numpy as jnp import numpy as np import pytest from pymloc.model.domains import RNDomain from pymloc.model.dynamical_system.flow_problem import LinearFlow from pymloc.model.dynamical_system.initial_value_problem import InitialValueProblem from pymloc.model.dynamical_system.parameter_bvp import ParameterBoundaryValueProblem from pymloc.model.dynamical_system.parameter_bvp import ParameterBoundaryValues from pymloc.model.dynamical_system.parameter_dae import LinearParameterDAE from pymloc.model.sensitivities.boundary_dae import BVPSensitivities from pymloc.model.variables import NullVariables from pymloc.model.variables import ParameterContainer from pymloc.model.variables import StateVariablesContainer from pymloc.model.variables.time_function import Time from pymloc.solvers.dynamical_systems.adjoint_sensitivities import AdjointSensitivitiesSolver @pytest.fixture def bvp_sens_object(linear_param_bvp): return BVPSensitivities(linear_param_bvp, n_param=1) @pytest.fixture def sens_solver_sine(bvp_sens_object): return AdjointSensitivitiesSolver(bvp_sens_object, rel_tol=1e-1, abs_tol=1e-1) @pytest.fixture def sens_solver_sine2(bvp_sens_object): bvpsens2 = deepcopy(bvp_sens_object) bvpsens2.dynamical_system._a = lambda p, t: jnp.array([[0., 1.], [-p*2, 0.]]) bvpsens2.boundary_value_problem.boundary_values.inhomogeneity = lambda p: jnp.array( [0., 2.]) return AdjointSensitivitiesSolver(bvpsens2, rel_tol=1e-6, abs_tol=1e-6) @pytest.fixture def sens_solver_sine3(bvp_sens_object): bvpsens2 = deepcopy(bvp_sens_object) bound_f = lambda p: jnp.array([[0., 0.], [p, 0.]]) # noqa: E731 bvpsens2.boundary_value_problem.boundary_values.boundary_f = bound_f bvs = bvpsens2.boundary_value_problem.boundary_values.boundary_values bvs = list(bvs) bvs[1] = bound_f bvpsens2.boundary_value_problem.boundary_values._boundary_values = bvs bvpsens2.boundary_value_problem.boundary_values.inhomogeneity = lambda p: jnp.array( [0., 2.]) # noqa: E731 return AdjointSensitivitiesSolver(bvpsens2, rel_tol=1e-1, abs_tol=1e-1) @pytest.fixture def linear_param_bvp(linear_param_dae, param_vars): initial_value = lambda p: jnp.array([[1., 0.], [0., 0.]]) # noqa: E731 final_value = lambda p: jnp.array([[0., 0.], [1., 0.]]) # noqa: E731 inhomogeneity = lambda p: jnp.array([0., p]) # noqa: E731 t = Time(0., np.pi / 2., time_grid=np.linspace(0., np.pi / 2., 10)) bvs = ParameterBoundaryValues(param_vars, initial_value, final_value, inhomogeneity, 2) return ParameterBoundaryValueProblem(param_vars, t, linear_param_dae, bvs) @pytest.fixture def linear_param_dae(param_vars, e_lin_param_dae, a_lin_param_dae, f_lin_param_dae): return LinearParameterDAE(param_vars, e_lin_param_dae, a_lin_param_dae, f_lin_param_dae, 2) @pytest.fixture def param_vars(): states = StateVariablesContainer(2) domain = RNDomain(1) parameters = ParameterContainer(1, domain) null_vars = NullVariables() return null_vars, parameters, states @pytest.fixture def e_lin_param_dae(): def e(p, t): return np.identity(2) return e @pytest.fixture def a_lin_param_dae(): def a(p, t): return jnp.array([[0., 1.], [-1., 0.]]) return a @pytest.fixture def f_lin_param_dae(): def f(p, t): return np.array([0., 0.]) return f class TestBVPSensitivitiesSolver: def test_compute_adjoint_boundary_values(self, sens_solver, localized_bvp): sens_solver._compute_adjoint_boundary_values(localized_bvp) def test_get_adjoint_dae_coeffs(self, sens_solver, localized_bvp): sens_solver._get_adjoint_dae_coeffs(localized_bvp) @pytest.mark.parametrize("tau", np.arange(0.1, 1., 0.4)) def test_solve_adjoint_dae(self, sens_solver, localized_bvp, tau): parameters = localized_bvp._localization_parameters[0] time = deepcopy(localized_bvp.time_interval) stepsize = 0.5e-0 time.grid = np.hstack( (np.arange(time.t_0, tau, stepsize), np.arange(tau, time.t_f, stepsize))) sol = sens_solver._get_adjoint_solution(localized_bvp, parameters, tau, time) assert sol @pytest.mark.parametrize("tau", np.arange(0.1, 1., 0.4)) def test_f_tilde(self, sens_solver, localized_bvp, tau): time = localized_bvp.time_interval flow_prob = LinearFlow(time, localized_bvp.dynamical_system) ivp_prob = InitialValueProblem(np.zeros((3, )), time, localized_bvp.dynamical_system) nodes = np.linspace(time.t_0, time.t_f, 3) stepsize = 0.5e-0 localized_bvp.init_solver(flow_prob, ivp_prob, nodes, stepsize) sens_solver._get_capital_fs(localized_bvp, localized_bvp.solve(time), localized_bvp._localization_parameters[0]) @pytest.mark.parametrize("tau", np.arange(0.1, 1., 0.4)) def test_run(self, sens_solver, localized_bvp, tau): sens_solver.run(localized_bvp._localization_parameters[0], tau) def test_sens_sine(self, sens_solver_sine): sol = sens_solver_sine.run(np.array(2.), 1.) assert np.allclose(sol.solution, np.array([np.sin(1.), np.cos(1.)]), atol=0.5, rtol=1e-4) def test_sens_sine2(self, sens_solver_sine2): sol = sens_solver_sine2.run(np.array(1.), 1.) assert np.allclose( sol(1.).T, np.array([2 * np.cos(1.), 2 * np.cos(1.) - 2 * 1. * np.sin(1.)]), atol=1e-1, rtol=1e-9) def test_sens_sine3(self, sens_solver_sine3): sol = sens_solver_sine3.run(np.array(2.), 1.) assert np.allclose(sol.solution, -2 * .25 * np.array([np.sin(1.), np.cos(1.)]), atol=0.5, rtol=1e-7)	[ 1, 529, 276, 1112, 420, 29958, 2585, 804, 4403, 29914, 4691, 29899, 29885, 2029, 13, 29937, 13, 29937, 14187, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29896, 29929, 29899, 29906, 29900, 29906, 29900, 13, 29937, 13, 29937, 732, 8921, 29901, 529, 5813, 29958, 13, 29937, 732, 14518, 29901, 8364, 2010, 12855, 5115, 13, 29937, 13, 29937, 910, 934, 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tests/test_node.py	flipcoder/qork	3	168303	#!/usr/bin/env pytest import pytest import sys sys.path.append("..") from glm import vec3 from qork.util import * from qork.node import Node from qork.minimal import MinimalCore from qork.util import walk def test_node(): world = Node() # move/position assert fcmp(world.position, vec3(0)) world.move(vec3(1, 2, 3)) assert fcmp(world.position, vec3(1, 2, 3)) world.move(vec3(1, 2, 3)) assert fcmp(world.position, vec3(2, 4, 6)) # reset world.pos = vec3(0) assert fcmp(world.position, vec3(0)) # attach child = Node() world.position = vec3(0) assert not world.children world.attach(child) world.position = (1, 2, 3) child.position = vec3(1) assert fcmp(child.world_pos, vec3(2, 3, 4)) world.position = vec3(0) assert fcmp(child.world_pos, vec3(1)) world.position = vec3(1) assert fcmp(child.world_pos, vec3(2)) assert child.parent == world child.detach() assert child.parent is None assert fcmp(child.world_pos, vec3(2)) world.attach(child) assert child in world world.children._blocked += 1 child.detach() world.children._blocked -= 1 assert child in world assert world.children._blocked == 0 world.children.refresh() assert child not in world def test_node_velocity(): world = Node() world.velocity = (1, 2, 3) # use tuple assert fcmp(world.position, vec3(0)) world.update(1) assert fcmp(world.position, vec3(1, 2, 3)) assert fcmp(world.position, vec3(1, 2, 3)) world.update(1) assert fcmp(world.position, vec3(2, 4, 6)) def test_node_walk(): a = Node() b = a.add(5) assert len(a) == 5 assert len(list(a.walk())) == 5 def test_node_find(): a = Node() b = a.add("foo") c = a.add("bar") assert list(a.find("foo")) == [b] assert list(a.find("bar")) == [c] b.tag("baz") assert b.tags == set(["baz"]) assert list(a.find("baz")) == [] assert list(a.find("#baz")) == [b] def test_node_detach(): # given a tree like this: # root # a # b # c # a.detach() does this: # root root = Node("root") a = root.add(Node("a")) b = a.add(Node("b")) c = a.add(Node("c")) assert tuple(map(lambda x: x.name, root.children)) == ("a",) a.detach() # a is gone # print(tuple(map(lambda x: x.name, root.children))) assert tuple(map(lambda x: x.name, root.children)) == tuple() # b and c are still on detached a assert tuple(map(lambda x: x.name, a.children)) == ("b", "c") def test_node_detach_collapse(): # given a tree like this: # root # a # b # c # and a.detach(collapse=True) does this: # root # b # c root = Node("root") a = root.add(Node("a")) b = a.add(Node("b")) c = a.add(Node("c")) assert tuple(map(lambda x: x.name, root.children)) == ("a",) # detach and reattach collapse b and c to root a.detach(collapse=True) # b and c moved to root? assert tuple(map(lambda x: x.name, root.children)) == ("b", "c") # children were removed from a? assert tuple(map(lambda x: x.name, a.children)) == tuple()	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 11451, 1688, 13, 5215, 11451, 1688, 13, 5215, 10876, 13, 13, 9675, 29889, 2084, 29889, 4397, 703, 636, 1159, 13, 13, 3166, 3144, 29885, 1053, 9649, 29941, 13, 3166, 3855, 548, 29889, 4422, 1053, 334, 13, 3166, 3855, 548, 29889, 3177, 1053, 9071, 13, 3166, 3855, 548, 29889, 1195, 3039, 1053, 3080, 3039, 9203, 13, 3166, 3855, 548, 29889, 4422, 1053, 6686, 13, 13, 13, 1753, 1243, 29918, 3177, 7295, 13, 1678, 3186, 353, 9071, 580, 13, 13, 1678, 396, 4337, 29914, 3283, 13, 1678, 4974, 285, 21058, 29898, 11526, 29889, 3283, 29892, 9649, 29941, 29898, 29900, 876, 13, 1678, 3186, 29889, 11631, 29898, 2003, 29941, 29898, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 876, 13, 1678, 4974, 285, 21058, 29898, 11526, 29889, 3283, 29892, 9649, 29941, 29898, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 876, 13, 1678, 3186, 29889, 11631, 29898, 2003, 29941, 29898, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 876, 13, 1678, 4974, 285, 21058, 29898, 11526, 29889, 3283, 29892, 9649, 29941, 29898, 29906, 29892, 29871, 29946, 29892, 29871, 29953, 876, 13, 13, 1678, 396, 10092, 13, 1678, 3186, 29889, 1066, 353, 9649, 29941, 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1678, 4974, 2278, 29889, 3560, 1275, 3186, 13, 1678, 2278, 29889, 4801, 496, 580, 13, 1678, 4974, 2278, 29889, 3560, 338, 6213, 13, 1678, 4974, 285, 21058, 29898, 5145, 29889, 11526, 29918, 1066, 29892, 9649, 29941, 29898, 29906, 876, 13, 1678, 3186, 29889, 14930, 29898, 5145, 29897, 13, 1678, 4974, 2278, 297, 3186, 13, 1678, 3186, 29889, 11991, 3032, 1271, 287, 4619, 29871, 29896, 13, 1678, 2278, 29889, 4801, 496, 580, 13, 1678, 3186, 29889, 11991, 3032, 1271, 287, 22361, 29871, 29896, 13, 1678, 4974, 2278, 297, 3186, 13, 1678, 4974, 3186, 29889, 11991, 3032, 1271, 287, 1275, 29871, 29900, 13, 1678, 3186, 29889, 11991, 29889, 22379, 580, 13, 1678, 4974, 2278, 451, 297, 3186, 13, 13, 13, 1753, 1243, 29918, 3177, 29918, 955, 25245, 7295, 13, 1678, 3186, 353, 9071, 580, 13, 1678, 3186, 29889, 955, 25245, 353, 313, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29897, 29871, 396, 671, 18761, 13, 1678, 4974, 285, 21058, 29898, 11526, 29889, 3283, 29892, 9649, 29941, 29898, 29900, 876, 13, 1678, 3186, 29889, 5504, 29898, 29896, 29897, 13, 1678, 4974, 285, 21058, 29898, 11526, 29889, 3283, 29892, 9649, 29941, 29898, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 876, 13, 1678, 4974, 285, 21058, 29898, 11526, 29889, 3283, 29892, 9649, 29941, 29898, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 876, 13, 1678, 3186, 29889, 5504, 29898, 29896, 29897, 13, 1678, 4974, 285, 21058, 29898, 11526, 29889, 3283, 29892, 9649, 29941, 29898, 29906, 29892, 29871, 29946, 29892, 29871, 29953, 876, 13, 13, 13, 1753, 1243, 29918, 3177, 29918, 20919, 7295, 13, 1678, 263, 353, 9071, 580, 13, 1678, 289, 353, 263, 29889, 1202, 29898, 29945, 29897, 13, 1678, 4974, 7431, 29898, 29874, 29897, 1275, 29871, 29945, 13, 1678, 4974, 7431, 29898, 1761, 29898, 29874, 29889, 20919, 22130, 1275, 29871, 29945, 13, 13, 13, 1753, 1243, 29918, 3177, 29918, 2886, 7295, 13, 1678, 263, 353, 9071, 580, 13, 1678, 289, 353, 263, 29889, 1202, 703, 5431, 1159, 13, 1678, 274, 353, 263, 29889, 1202, 703, 1646, 1159, 13, 13, 1678, 4974, 1051, 29898, 29874, 29889, 2886, 703, 5431, 5783, 1275, 518, 29890, 29962, 13, 1678, 4974, 1051, 29898, 29874, 29889, 2886, 703, 1646, 5783, 1275, 518, 29883, 29962, 13, 13, 1678, 289, 29889, 4039, 703, 27975, 1159, 13, 1678, 4974, 289, 29889, 11338, 1275, 731, 29898, 3366, 27975, 20068, 13, 1678, 4974, 1051, 29898, 29874, 29889, 2886, 703, 27975, 5783, 1275, 5159, 13, 1678, 4974, 1051, 29898, 29874, 29889, 2886, 14822, 27975, 5783, 1275, 518, 29890, 29962, 13, 13, 13, 1753, 1243, 29918, 3177, 29918, 4801, 496, 7295, 13, 1678, 396, 2183, 263, 5447, 763, 445, 29901, 13, 1678, 396, 259, 3876, 13, 1678, 396, 268, 263, 13, 1678, 396, 539, 289, 13, 1678, 396, 539, 274, 13, 1678, 396, 263, 29889, 4801, 496, 580, 947, 445, 29901, 13, 1678, 396, 259, 3876, 13, 13, 1678, 3876, 353, 9071, 703, 4632, 1159, 13, 1678, 263, 353, 3876, 29889, 1202, 29898, 4247, 703, 29874, 5783, 13, 1678, 289, 353, 263, 29889, 1202, 29898, 4247, 703, 29890, 5783, 13, 1678, 274, 353, 263, 29889, 1202, 29898, 4247, 703, 29883, 5783, 13, 13, 1678, 4974, 18761, 29898, 1958, 29898, 2892, 921, 29901, 921, 29889, 978, 29892, 3876, 29889, 11991, 876, 1275, 4852, 29874, 613, 29897, 13, 13, 1678, 263, 29889, 4801, 496, 580, 13, 13, 1678, 396, 263, 338, 7695, 13, 1678, 396, 1596, 29898, 23583, 29898, 1958, 29898, 2892, 921, 29901, 921, 29889, 978, 29892, 3876, 29889, 11991, 4961, 13, 1678, 4974, 18761, 29898, 1958, 29898, 2892, 921, 29901, 921, 29889, 978, 29892, 3876, 29889, 11991, 876, 1275, 18761, 580, 13, 13, 1678, 396, 289, 322, 274, 526, 1603, 373, 1439, 3791, 263, 13, 1678, 4974, 18761, 29898, 1958, 29898, 2892, 921, 29901, 921, 29889, 978, 29892, 263, 29889, 11991, 876, 1275, 4852, 29890, 613, 376, 29883, 1159, 13, 13, 13, 1753, 1243, 29918, 3177, 29918, 4801, 496, 29918, 27756, 7295, 13, 1678, 396, 2183, 263, 5447, 763, 445, 29901, 13, 1678, 396, 259, 3876, 13, 1678, 396, 268, 263, 13, 1678, 396, 539, 289, 13, 1678, 396, 539, 274, 13, 1678, 396, 322, 263, 29889, 4801, 496, 29898, 27756, 29922, 5574, 29897, 947, 445, 29901, 13, 1678, 396, 259, 3876, 13, 1678, 396, 268, 289, 13, 1678, 396, 268, 274, 13, 13, 1678, 3876, 353, 9071, 703, 4632, 1159, 13, 1678, 263, 353, 3876, 29889, 1202, 29898, 4247, 703, 29874, 5783, 13, 1678, 289, 353, 263, 29889, 1202, 29898, 4247, 703, 29890, 5783, 13, 1678, 274, 353, 263, 29889, 1202, 29898, 4247, 703, 29883, 5783, 13, 13, 1678, 4974, 18761, 29898, 1958, 29898, 2892, 921, 29901, 921, 29889, 978, 29892, 3876, 29889, 11991, 876, 1275, 4852, 29874, 613, 29897, 13, 13, 1678, 396, 1439, 496, 322, 337, 14930, 24382, 289, 322, 274, 304, 3876, 13, 1678, 263, 29889, 4801, 496, 29898, 27756, 29922, 5574, 29897, 13, 13, 1678, 396, 289, 322, 274, 6153, 304, 3876, 29973, 13, 1678, 4974, 18761, 29898, 1958, 29898, 2892, 921, 29901, 921, 29889, 978, 29892, 3876, 29889, 11991, 876, 1275, 4852, 29890, 613, 376, 29883, 1159, 13, 13, 1678, 396, 4344, 892, 6206, 515, 263, 29973, 13, 1678, 4974, 18761, 29898, 1958, 29898, 2892, 921, 29901, 921, 29889, 978, 29892, 263, 29889, 11991, 876, 1275, 18761, 580, 13, 2 ]
Media/common/Build/build_assets.py	dmacka/MultiverseClientServer	5	149341	import sha import time from patch_tool import * def add_assets(asset_tree): asset_tree.add_ignore("mv.patch") asset_tree.add_ignore("mv.patch.cur") # Directories for main assets asset_tree.add_asset_path("Base", "Fonts") asset_tree.add_asset_path("Base", "GpuPrograms") asset_tree.add_asset_path("Base", "Icons") asset_tree.add_asset_path("Base", "Imagefiles") asset_tree.add_asset_path("Base", "Interface/FrameXML") asset_tree.add_asset_path("Base", "Interface/Imagesets") asset_tree.add_asset_path("Base", "Materials") asset_tree.add_asset_path("Base", "Meshes") asset_tree.add_asset_path("Base", "Misc") asset_tree.add_asset_path("Base", "Particles") asset_tree.add_asset_path("Base", "Physics") asset_tree.add_asset_path("Base", "Scripts") asset_tree.add_asset_path("Base", "Skeletons") asset_tree.add_asset_path("Base", "Sounds") asset_tree.add_asset_path("Base", "SpeedTree") asset_tree.add_asset_path("Base", "Textures") # Directories for addon (local) assets asset_tree.add_asset_path("Base", "AddOns/Fonts") asset_tree.add_asset_path("Base", "AddOns/GpuPrograms") asset_tree.add_asset_path("Base", "AddOns/Icons") asset_tree.add_asset_path("Base", "AddOns/Imagefiles") asset_tree.add_asset_path("Base", "AddOns/Interface/FrameXML") asset_tree.add_asset_path("Base", "AddOns/Interface/Imagesets") asset_tree.add_asset_path("Base", "AddOns/Materials") asset_tree.add_asset_path("Base", "AddOns/Meshes") asset_tree.add_asset_path("Base", "AddOns/Misc") asset_tree.add_asset_path("Base", "AddOns/Particles") asset_tree.add_asset_path("Base", "AddOns/Physics") asset_tree.add_asset_path("Base", "AddOns/Scripts") asset_tree.add_asset_path("Base", "AddOns/Skeletons") asset_tree.add_asset_path("Base", "AddOns/Sounds") asset_tree.add_asset_path("Base", "AddOns/SpeedTree") asset_tree.add_asset_path("Base", "AddOns/Textures") asset_tree.add_ignore("AddOns/Fonts/.") asset_tree.add_ignore("AddOns/GpuPrograms/.") asset_tree.add_ignore("AddOns/Icons/.") asset_tree.add_ignore("AddOns/Imagefiles/.") asset_tree.add_ignore("AddOns/Interface/FrameXML/.") asset_tree.add_ignore("AddOns/Interface/Imagesets/.") asset_tree.add_ignore("AddOns/Materials/.") asset_tree.add_ignore("AddOns/Meshes/.") asset_tree.add_ignore("AddOns/Misc/.") asset_tree.add_ignore("AddOns/Physics/.") asset_tree.add_ignore("AddOns/Scripts/.") asset_tree.add_ignore("AddOns/Skeletons/.") asset_tree.add_ignore("AddOns/Sounds/.") asset_tree.add_ignore("AddOns/SpeedTree/.") asset_tree.add_ignore("AddOns/Textures/.*") asset_tree.add_asset_path("Base", "Scripts/Standalone.py") # client gpu programs asset_tree.add_asset_path("Base", "GpuPrograms/DetailVeg.cg") asset_tree.add_asset_path("Base", "GpuPrograms/DiffuseBump.cg") asset_tree.add_asset_path("Base", "GpuPrograms/Compound_Basic_Skinned.cg") asset_tree.add_asset_path("Base", "GpuPrograms/Multiverse.program") # common to the tools package asset_tree.add_asset_path("Base", "GpuPrograms/Ocean.cg") asset_tree.add_asset_path("Base", "GpuPrograms/Terrain.cg") asset_tree.add_asset_path("Base", "GpuPrograms/Trees.cg") # end common # physics meshes asset_tree.add_asset_path("Base", "Meshes/unit_box.mesh") asset_tree.add_asset_path("Base", "Meshes/unit_cylinder.mesh") asset_tree.add_asset_path("Base", "Meshes/unit_sphere.mesh") # end physics # tool mesh # end tool # non-physics and non-tool meshes asset_tree.add_asset_path("Base", "Meshes/bang.mesh") asset_tree.add_asset_path("Base", "Meshes/tiny_cube.mesh") # end non-physics and non-tool meshes # key materials asset_tree.add_asset_path("Base", "Materials/bang.material") asset_tree.add_asset_path("Base", "Materials/DetailVeg.material") asset_tree.add_asset_path("Base", "Materials/MVSMTerrain.material") asset_tree.add_asset_path("Base", "Materials/Multiverse.material") asset_tree.add_asset_path("Base", "Materials/Ocean.material") asset_tree.add_asset_path("Base", "Materials/tiny_cube.material") asset_tree.add_asset_path("Base", "Materials/Trees.material") asset_tree.add_asset_path("Base", "Materials/Water.material") # end key materials # physics asset_tree.add_asset_path("Base", "Materials/unit_box.material") asset_tree.add_asset_path("Base", "Materials/unit_cylinder.material") asset_tree.add_asset_path("Base", "Materials/unit_sphere.material") # end physics # base sound files #asset_tree.add_asset_path("Base", "Sounds/ambient_hills_day.ogg") #asset_tree.add_asset_path("Base", "Sounds/human_run_grass.ogg") # key textures asset_tree.add_asset_path("Base", "Textures/DetailVegAtlas.dds") # imageset for the DetailVegAtlas asset_tree.add_asset_path("Base", "Textures/DetailVeg.imageset") asset_tree.add_asset_path("Base", "Textures/MultiverseImageset.png") asset_tree.add_asset_path("Base", "Textures/loadscreen.dds") asset_tree.add_asset_path("Base", "Textures/mv_skybox0001.dds") asset_tree.add_asset_path("Base", "Textures/mv_skybox0002.dds") asset_tree.add_asset_path("Base", "Textures/mv_skybox0003.dds") asset_tree.add_asset_path("Base", "Textures/mv_skybox0004.dds") asset_tree.add_asset_path("Base", "Textures/mv_skybox0005.dds") asset_tree.add_asset_path("Base", "Textures/mv_skybox0006.dds") asset_tree.add_asset_path("Base", "Textures/noon_j_back.dds") asset_tree.add_asset_path("Base", "Textures/noon_j_front.dds") asset_tree.add_asset_path("Base", "Textures/noon_j_left.dds") asset_tree.add_asset_path("Base", "Textures/noon_j_right.dds") asset_tree.add_asset_path("Base", "Textures/noon_j_top.dds") asset_tree.add_asset_path("Base", "Textures/sandy_path.dds") asset_tree.add_asset_path("Base", "Textures/splatting_grass.dds") asset_tree.add_asset_path("Base", "Textures/splatting_rock.dds") asset_tree.add_asset_path("Base", "Textures/splatting_sand.dds") asset_tree.add_asset_path("Base", "Textures/splatting_snow.dds") asset_tree.add_asset_path("Base", "Textures/Water02.dds") asset_tree.add_asset_path("Base", "Textures/waves.dds") asset_tree.add_asset_path("Base", "Textures/White.dds") #end key textures # SpeedTree textures # common to the tools package asset_tree.add_asset_path("Base", "Textures/AmericanBoxwood_Composite.dds") asset_tree.add_asset_path("Base", "Textures/AmericanBoxwoodCluster_Composite.dds") asset_tree.add_asset_path("Base", "Textures/AppleTree_SelfShadow.dds") asset_tree.add_asset_path("Base", "Textures/AppleTreeBark.dds") asset_tree.add_asset_path("Base", "Textures/AppleTreeBarkNormals.dds") asset_tree.add_asset_path("Base", "Textures/Azalea_Composite.dds") asset_tree.add_asset_path("Base", "Textures/AzaleaPatch_Composite.dds") asset_tree.add_asset_path("Base", "Textures/AzaleaPatchPink_Composite.dds") asset_tree.add_asset_path("Base", "Textures/AzaleaPink_Composite.dds") asset_tree.add_asset_path("Base", "Textures/Beech_Composite.dds") asset_tree.add_asset_path("Base", "Textures/Beech_SelfShadow.dds") asset_tree.add_asset_path("Base", "Textures/BeechBark.dds") asset_tree.add_asset_path("Base", "Textures/BeechBarkNormals.dds") asset_tree.add_asset_path("Base", "Textures/BeechFall_Composite.dds") asset_tree.add_asset_path("Base", "Textures/BeechWinter_Composite.dds") asset_tree.add_asset_path("Base", "Textures/CurlyPalm_Composite.dds") asset_tree.add_asset_path("Base", "Textures/CurlyPalm_SelfShadow.dds") asset_tree.add_asset_path("Base", "Textures/CurlyPalmBark.dds") asset_tree.add_asset_path("Base", "Textures/CurlyPalmBarkNormals.dds") asset_tree.add_asset_path("Base", "Textures/CurlyPalmCluster_Composite.dds") asset_tree.add_asset_path("Base", "Textures/FraserFir_Composite.dds") asset_tree.add_asset_path("Base", "Textures/FraserFir_SelfShadow.dds") asset_tree.add_asset_path("Base", "Textures/FraserFirBark.dds") asset_tree.add_asset_path("Base", "Textures/FraserFirBarkNormals.dds") asset_tree.add_asset_path("Base", "Textures/FraserFirCluster_Composite.dds") asset_tree.add_asset_path("Base", "Textures/FraserFirCluster_SelfShadow.dds") asset_tree.add_asset_path("Base", "Textures/FraserFirClusterSnow_Composite.dds") asset_tree.add_asset_path("Base", "Textures/FraserFirSnow_Composite.dds") asset_tree.add_asset_path("Base", "Textures/RDApple_Composite.dds") asset_tree.add_asset_path("Base", "Textures/RDAppleApples_Composite.dds") asset_tree.add_asset_path("Base", "Textures/RDAppleSpring_Composite.dds") asset_tree.add_asset_path("Base", "Textures/RDAppleWinter_Composite.dds") asset_tree.add_asset_path("Base", "Textures/SugarPine_Composite.dds") asset_tree.add_asset_path("Base", "Textures/SugarPine_SelfShadow.dds") asset_tree.add_asset_path("Base", "Textures/SugarPineBark.dds") asset_tree.add_asset_path("Base", "Textures/SugarPineBarkNormals.dds") asset_tree.add_asset_path("Base", "Textures/SugarPineWinter_Composite.dds") asset_tree.add_asset_path("Base", "Textures/UmbrellaThorn_Composite.dds") asset_tree.add_asset_path("Base", "Textures/UmbrellaThorn_SelfShadow.dds") asset_tree.add_asset_path("Base", "Textures/UmbrellaThornBark.dds") asset_tree.add_asset_path("Base", "Textures/UmbrellaThornBarkNormals.dds") asset_tree.add_asset_path("Base", "Textures/UmbrellaThornDead_Composite.dds") asset_tree.add_asset_path("Base", "Textures/UmbrellaThornFlowers_Composite.dds") asset_tree.add_asset_path("Base", "Textures/VenusTree_Composite.dds") asset_tree.add_asset_path("Base", "Textures/VenusTree_SelfShadow.dds") asset_tree.add_asset_path("Base", "Textures/VenusTreeBark.dds") asset_tree.add_asset_path("Base", "Textures/VenusTreeBarkNormals.dds") asset_tree.add_asset_path("Base", "Textures/WeepingWillow_Composite.dds") asset_tree.add_asset_path("Base", "Textures/WeepingWillow_SelfShadow.dds") asset_tree.add_asset_path("Base", "Textures/WeepingWillowBark.dds") asset_tree.add_asset_path("Base", "Textures/WeepingWillowBarkNormals.dds") asset_tree.add_asset_path("Base", "Textures/WeepingWillowFall_Composite.dds") asset_tree.add_asset_path("Base", "Textures/WeepingWillowWinter_Composite.dds") # end common # end speedtree textures # SpeedTree trees # common to the tools package asset_tree.add_asset_path("Base", "SpeedTree/AmericanBoxwood_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/AmericanBoxwoodCluster_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/Azalea_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/Azalea_RT_Pink.spt") asset_tree.add_asset_path("Base", "SpeedTree/AzaleaPatch_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/AzaleaPatch_RT_Pink.spt") asset_tree.add_asset_path("Base", "SpeedTree/Beech_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/Beech_RT_Fall.spt") asset_tree.add_asset_path("Base", "SpeedTree/Beech_RT_Winter.spt") asset_tree.add_asset_path("Base", "SpeedTree/CurlyPalm_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/CurlyPalmCluster_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/FraserFir_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/FraserFir_RT_Snow.spt") asset_tree.add_asset_path("Base", "SpeedTree/FraserFirCluster_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/FraserFirCluster_RT_Snow.spt") asset_tree.add_asset_path("Base", "SpeedTree/RDApple_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/RDApple_RT_Apples.spt") asset_tree.add_asset_path("Base", "SpeedTree/RDApple_RT_Spring.spt") asset_tree.add_asset_path("Base", "SpeedTree/RDApple_RT_Winter.spt") asset_tree.add_asset_path("Base", "SpeedTree/SugarPine_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/SugarPine_RT_Winter.spt") asset_tree.add_asset_path("Base", "SpeedTree/UmbrellaThorn_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/UmbrellaThorn_RT_Dead.spt") asset_tree.add_asset_path("Base", "SpeedTree/UmbrellaThorn_RT_Flowers.spt") asset_tree.add_asset_path("Base", "SpeedTree/VenusTree_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/WeepingWillow_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/WeepingWillow_RT_Fall.spt") asset_tree.add_asset_path("Base", "SpeedTree/WeepingWillow_RT_Winter.spt") asset_tree.add_asset_path("Base", "SpeedTree/demoWind.ini") # end common # end trees # Interface asset_tree.add_asset_path("Base", "Fonts/MUFN____.TTF") asset_tree.add_asset_path("Base", "Imagefiles/Buttons.tga") asset_tree.add_asset_path("Base", "Imagefiles/CharacterFrame.tga") asset_tree.add_asset_path("Base", "Imagefiles/ChatFrame.tga") asset_tree.add_asset_path("Base", "Imagefiles/Common.tga") asset_tree.add_asset_path("Base", "Imagefiles/ContainerFrame.tga") asset_tree.add_asset_path("Base", "Imagefiles/Cursor.tga") asset_tree.add_asset_path("Base", "Imagefiles/DialogFrame.tga") asset_tree.add_asset_path("Base", "Imagefiles/Icons.tga") asset_tree.add_asset_path("Base", "Imagefiles/MvButtons.tga") asset_tree.add_asset_path("Base", "Imagefiles/MvChatFrame.tga") asset_tree.add_asset_path("Base", "Imagefiles/MvQuestFrame.tga") asset_tree.add_asset_path("Base", "Imagefiles/PaperDollInfoFrame.tga") asset_tree.add_asset_path("Base", "Imagefiles/QuestFrame.tga") asset_tree.add_asset_path("Base", "Imagefiles/TargetingFrame.tga") asset_tree.add_asset_path("Base", "Imagefiles/Tooltips.tga") asset_tree.add_asset_path("Base", "Interface/FrameXML/Library.py") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvActionBar.py") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvActionBar.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvActionButton.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvCharacter.py") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvCharacter.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvChat.py") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvChat.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvContainer.py") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvContainer.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvDialog.py") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvDialog.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvFonts.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvFrame.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvItemButton.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvPlayer.py") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvPlayer.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvSocialBar.py") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvSocialBar.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvStatus.py") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvStatus.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvTarget.py") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvTarget.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvTooltip.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvUnit.py") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvUnit.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/basic.toc") asset_tree.add_asset_path("Base", "Interface/FrameXML/social.toc") asset_tree.add_asset_path("Base", "Interface/FrameXML/betaworld.toc") asset_tree.add_asset_path("Base", "Interface/Imagesets/Buttons.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/CharacterFrame.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/ChatFrame.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/Common.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/ContainerFrame.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/Cursor.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/DialogFrame.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/Icons.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/MvButtons.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/MvChat.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/MvQuestFrame.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/PaperDollInfoFrame.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/QuestFrame.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/TargetingFrame.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/Tooltips.xml") # Misc. asset_tree.add_asset_path("Base", "Icons/AxiomIcon.ico") # Mars files asset_tree.add_asset_path("Mars", "Interface/FrameXML/mars.toc") asset_tree.add_asset_path("Mars", "Interface/FrameXML/MarsPlayer.py") asset_tree.add_asset_path("Mars", "Interface/FrameXML/MarsPlayer.xml") asset_tree.add_asset_path("Mars", "Interface/FrameXML/MarsTarget.py") asset_tree.add_asset_path("Mars", "Interface/FrameXML/MarsTarget.xml") asset_tree.add_asset_path("Mars", "Interface/FrameXML/MarsUnit.py") asset_tree.add_asset_path("Mars", "Interface/FrameXML/MarsUnit.xml") asset_tree.add_asset_path("Particles", "Materials/aureola.material") asset_tree.add_asset_path("Particles", "Materials/bigflame.material") asset_tree.add_asset_path("Particles", "Materials/droplet.material") asset_tree.add_asset_path("Particles", "Materials/explosion.material") asset_tree.add_asset_path("Particles", "Materials/flare.material") asset_tree.add_asset_path("Particles", "Materials/flare2.material") asset_tree.add_asset_path("Particles", "Materials/flaretrail.material") asset_tree.add_asset_path("Particles", "Materials/lensflare.material") asset_tree.add_asset_path("Particles", "Materials/rain.material") asset_tree.add_asset_path("Particles", "Materials/ringflare.material") asset_tree.add_asset_path("Particles", "Materials/ringflare2.material") asset_tree.add_asset_path("Particles", "Materials/smoke.material") asset_tree.add_asset_path("Particles", "Materials/smoke2.material") asset_tree.add_asset_path("Particles", "Materials/streak.material") asset_tree.add_asset_path("Particles", "Materials/waterfountain.material") asset_tree.add_asset_path("Particles", "Particles/aureola.particle"); asset_tree.add_asset_path("Particles", "Particles/blast.particle"); asset_tree.add_asset_path("Particles", "Particles/blast2.particle"); asset_tree.add_asset_path("Particles", "Particles/downpour.particle"); asset_tree.add_asset_path("Particles", "Particles/eruption.particle"); asset_tree.add_asset_path("Particles", "Particles/flame.particle"); asset_tree.add_asset_path("Particles", "Particles/floatyGreeny.particle"); asset_tree.add_asset_path("Particles", "Particles/fountain.particle"); asset_tree.add_asset_path("Particles", "Particles/greenyNimbus.particle"); asset_tree.add_asset_path("Particles", "Particles/jetengine1.particle"); asset_tree.add_asset_path("Particles", "Particles/jetengine2.particle"); asset_tree.add_asset_path("Particles", "Particles/pentagram.particle"); asset_tree.add_asset_path("Particles", "Particles/purpleFountain.particle"); asset_tree.add_asset_path("Particles", "Particles/rain.particle"); asset_tree.add_asset_path("Particles", "Particles/ringOfFire.particle"); asset_tree.add_asset_path("Particles", "Particles/smoke.particle"); asset_tree.add_asset_path("Particles", "Particles/smoke2.particle"); asset_tree.add_asset_path("Particles", "Particles/space.particle"); asset_tree.add_asset_path("Particles", "Particles/waterfall.particle"); asset_tree.add_asset_path("Particles", "Particles/waterfountain.particle"); asset_tree.add_asset_path("Particles", "Textures/aureola.dds") asset_tree.add_asset_path("Particles", "Textures/basic_droplet.dds") asset_tree.add_asset_path("Particles", "Textures/bigflame.png") asset_tree.add_asset_path("Particles", "Textures/blue_flare.dds") asset_tree.add_asset_path("Particles", "Textures/blue_flare.jpg") asset_tree.add_asset_path("Particles", "Textures/explosion.dds") asset_tree.add_asset_path("Particles", "Textures/Flare.dds") asset_tree.add_asset_path("Particles", "Textures/Flare.jpg") asset_tree.add_asset_path("Particles", "Textures/flaretrail.dds") asset_tree.add_asset_path("Particles", "Textures/ring_flare.dds") asset_tree.add_asset_path("Particles", "Textures/ring_flare2.dds") asset_tree.add_asset_path("Particles", "Textures/smoke.dds") asset_tree.add_asset_path("Particles", "Textures/smoke.dds") asset_tree.add_asset_path("Particles", "Textures/smokecolors.dds") asset_tree.add_asset_path("Particles", "Textures/smokecolors.dds") asset_tree.add_asset_path("Particles", "Textures/streak.dds") # Social World Assets asset_tree.add_asset_path("SocialWorld", "Imagefiles/ActionbarIcons.dds") asset_tree.add_asset_path("SocialWorld", "Interface/Imagesets/ActionbarIcons.xml") asset_tree.add_asset_path("SocialWorld", "Materials/bench.material") asset_tree.add_asset_path("SocialWorld", "Materials/cobblestone.material") asset_tree.add_asset_path("SocialWorld", "Materials/cobblestone_marble.material") asset_tree.add_asset_path("SocialWorld", "Materials/colonnade.material") asset_tree.add_asset_path("SocialWorld", "Materials/gazebo_green.material") asset_tree.add_asset_path("SocialWorld", "Materials/gazebo_sand.material") asset_tree.add_asset_path("SocialWorld", "Materials/pool.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_cdance_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_cdance_drk_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_cdance_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_cdance_lt_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_dagger_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_dagger_drk_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_dagger_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_dagger_lt_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_dance_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_dance_drk_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_dance_drk_shade_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_dance_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_dance_lt_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_kneel_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_kneel_drk_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_kneel_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_kneel_lt_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_lean_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_lean_drk_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_lean_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_lean_lt_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_sword_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_sword_drk_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_sword_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_sword_lt_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_m_lean_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_m_lean_drk_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_m_lean_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_m_lean_lt_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_m_throw_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_m_throw_drk_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_m_throw_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_m_throw_lt_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/stage.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_f_cdance_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_f_cdance_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_f_dagger_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_f_dagger_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_f_dance_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_f_dance_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_f_kneel_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_f_kneel_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_f_lean_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_f_lean_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_f_sword_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_f_sword_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_m_lean_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_m_lean_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_m_throw_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_m_throw_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/sword.material") asset_tree.add_asset_path("SocialWorld", "Materials/water_trim_corner_sqr.material") asset_tree.add_asset_path("SocialWorld", "Materials/water_trim_corner_tri_eq.material") asset_tree.add_asset_path("SocialWorld", "Materials/water_trim_corner_tri_odd.material") asset_tree.add_asset_path("SocialWorld", "Materials/water_trim_long_block.material") asset_tree.add_asset_path("SocialWorld", "Materials/water_trim_octagon.material") asset_tree.add_asset_path("SocialWorld", "Meshes/bench.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/bench_01.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/cobblestone.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/cobblestone_marble.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/colonnade.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/gazebo_green.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/gazebo_sand.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/pool.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_cdance_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_cdance_drk_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_cdance_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_cdance_lt_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_dagger_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_dagger_drk_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_dagger_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_dagger_lt_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_dance_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_dance_drk_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_dance_drk_shade_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_dance_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_dance_lt_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_kneel_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_kneel_drk_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_kneel_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_kneel_lt_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_lean_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_lean_drk_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_lean_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_lean_lt_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_sword_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_sword_drk_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_sword_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_sword_lt_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_m_lean_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_m_lean_drk_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_m_lean_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_m_lean_lt_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_m_throw_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_m_throw_drk_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_m_throw_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_m_throw_lt_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stage.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_f_cdance_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_f_cdance_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_f_dagger_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_f_dagger_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_f_dance_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_f_dance_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_f_kneel_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_f_kneel_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_f_lean_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_f_lean_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_f_sword_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_f_sword_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_m_lean_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_m_lean_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_m_throw_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_m_throw_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/sword.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/water_trim_corner_sqr.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/water_trim_corner_tri_eq.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/water_trim_corner_tri_odd.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/water_trim_long_block.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/water_trim_octagon.mesh") asset_tree.add_asset_path("SocialWorld", "Misc/SocialWorld_AssetConfig.xml", "AssetConfig.xml") asset_tree.add_asset_path("SocialWorld", "Materials/waterfountain.material") asset_tree.add_asset_path("SocialWorld", "Particles/waterfountain.particle"); asset_tree.add_asset_path("SocialWorld", "Textures/Flare.dds") asset_tree.add_asset_path("SocialWorld", "Physics/bench.physics") asset_tree.add_asset_path("SocialWorld", "Physics/cobblestone.physics") asset_tree.add_asset_path("SocialWorld", "Physics/cobblestone_marble.physics") asset_tree.add_asset_path("SocialWorld", "Physics/colonnade.physics") asset_tree.add_asset_path("SocialWorld", "Physics/gazebo_green.physics") asset_tree.add_asset_path("SocialWorld", "Physics/gazebo_sand.physics") asset_tree.add_asset_path("SocialWorld", "Physics/pool.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_cdance_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_cdance_drk_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_cdance_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_cdance_lt_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_dagger_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_dagger_drk_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_dagger_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_dagger_lt_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_dance_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_dance_drk_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_dance_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_dance_lt_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_kneel_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_kneel_drk_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_kneel_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_kneel_lt_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_lean_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_lean_drk_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_lean_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_lean_lt_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_sword_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_sword_drk_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_sword_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_sword_lt_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_m_lean_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_m_lean_drk_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_m_lean_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_m_lean_lt_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_m_throw_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_m_throw_drk_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_m_throw_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_m_throw_lt_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stage.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_f_cdance_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_f_cdance_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_f_dagger_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_f_dagger_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_f_dance_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_f_dance_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_f_kneel_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_f_kneel_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_f_lean_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_f_lean_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_f_sword_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_f_sword_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_m_lean_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_m_lean_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_m_throw_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_m_throw_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/water_trim_corner_sqr.physics") asset_tree.add_asset_path("SocialWorld", "Physics/water_trim_corner_tri_eq.physics") asset_tree.add_asset_path("SocialWorld", "Physics/water_trim_corner_tri_odd.physics") asset_tree.add_asset_path("SocialWorld", "Physics/water_trim_long_block.physics") asset_tree.add_asset_path("SocialWorld", "Physics/water_trim_octagon.physics") #asset_tree.add_asset_path("SocialWorld", "Sounds/social_ambient.ogg") #asset_tree.add_asset_path("SocialWorld", "Sounds/stage.ogg") asset_tree.add_asset_path("SocialWorld", "Textures/bench_marble.dds") asset_tree.add_asset_path("SocialWorld", "Textures/cobblestone_marble.dds") asset_tree.add_asset_path("SocialWorld", "Textures/cobblestones_seemless.dds") asset_tree.add_asset_path("SocialWorld", "Textures/colonnade.dds") asset_tree.add_asset_path("SocialWorld", "Textures/female_face_marble_blue_grey.dds") asset_tree.add_asset_path("SocialWorld", "Textures/female_face_marble_white_rev_darker.dds") asset_tree.add_asset_path("SocialWorld", "Textures/female_marble_blue_grey.dds") asset_tree.add_asset_path("SocialWorld", "Textures/female_marble_white_rev_darker.dds") asset_tree.add_asset_path("SocialWorld", "Textures/fountain_water.dds") asset_tree.add_asset_path("SocialWorld", "Textures/gazebo_a.dds") asset_tree.add_asset_path("SocialWorld", "Textures/gazebo_a_floor.dds") asset_tree.add_asset_path("SocialWorld", "Textures/gazebo_b.dds") asset_tree.add_asset_path("SocialWorld", "Textures/gazebo_b_floor.dds") asset_tree.add_asset_path("SocialWorld", "Textures/male_head_marble_blue_grey.dds") asset_tree.add_asset_path("SocialWorld", "Textures/male_marble_blue_grey.dds") asset_tree.add_asset_path("SocialWorld", "Textures/male_marble_white_rev_darker.dds") asset_tree.add_asset_path("SocialWorld", "Textures/marble_blue_grey.dds") asset_tree.add_asset_path("SocialWorld", "Textures/marble_white_rev_darker.dds") asset_tree.add_asset_path("SocialWorld", "Textures/pool_marble.dds") asset_tree.add_asset_path("SocialWorld", "Textures/Stage_01.dds") asset_tree.add_asset_path("SocialWorld", "Textures/sword_005.dds") asset_tree.add_asset_path("SocialWorld", "Textures/water_edging.dds") asset_tree.add_asset_path("SocialWorld", "Textures/loadscreen_social.dds", "Textures/loadscreen.dds") # male rocketbox assets asset_tree.add_asset_path("Rocketbox", "Skeletons/rocketbox_f_mediumpoly.skeleton") asset_tree.add_asset_path("Rocketbox", "Skeletons/rocketbox_f_lowpoly.skeleton") asset_tree.add_asset_path("Rocketbox", "Skeletons/rocketbox_m_mediumpoly.skeleton") asset_tree.add_asset_path("Rocketbox", "Skeletons/rocketbox_m_lowpoly.skeleton") asset_tree.add_asset_path("Rocketbox", "Materials/business03_m_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/business03_m_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/business03_m_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/business03_m_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/business03_m_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/business03_m_35.dds") asset_tree.add_asset_path("Rocketbox", "Materials/business05_m_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/business05_m_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/business05_m_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/business05_m_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/business05_m_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/business05_m_25.dds") asset_tree.add_asset_path("Rocketbox", "Materials/casual03_m_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/casual03_m_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/casual03_m_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/casual03_m_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/casual03_m_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/casual03_m_25.dds") asset_tree.add_asset_path("Rocketbox", "Materials/casual04_m_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/casual04_m_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/casual04_m_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/casual04_m_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/casual04_m_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/casual04_m_25.dds") asset_tree.add_asset_path("Rocketbox", "Materials/casual07_m_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/casual07_m_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/casual07_m_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/casual07_m_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/casual07_m_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/casual07_m_25.dds") asset_tree.add_asset_path("Rocketbox", "Materials/casual10_m_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/casual10_m_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/casual10_m_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/casual10_m_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/casual10_m_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/casual10_m_30.dds") asset_tree.add_asset_path("Rocketbox", "Materials/casual16_m_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/casual16_m_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/casual16_m_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/casual16_m_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/casual16_m_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/casual16_m_35.dds") asset_tree.add_asset_path("Rocketbox", "Materials/casual21_m_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/casual21_m_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/casual21_m_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/casual21_m_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/casual21_m_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/casual21_m_35.dds") asset_tree.add_asset_path("Rocketbox", "Materials/sportive01_m_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/sportive01_m_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/sportive01_m_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/sportive01_m_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/sportive01_m_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/sportive01_m_20.dds") asset_tree.add_asset_path("Rocketbox", "Materials/sportive09_m_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/sportive09_m_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/sportive09_m_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/sportive09_m_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/sportive09_m_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/sportive09_m_25.dds") # female rocketbox assets asset_tree.add_asset_path("Rocketbox", "Materials/business04_f_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/business04_f_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/business04_f_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/business04_f_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/business04_f_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/business04_f_25.dds") asset_tree.add_asset_path("Rocketbox", "Textures/business04_f_25_hair.dds") asset_tree.add_asset_path("Rocketbox", "Materials/casual06_f_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/casual06_f_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/casual06_f_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/casual06_f_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/casual06_f_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/casual06_f_20.dds") asset_tree.add_asset_path("Rocketbox", "Textures/casual06_f_hairalpha.dds") asset_tree.add_asset_path("Rocketbox", "Materials/casual07_f_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/casual07_f_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/casual07_f_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/casual07_f_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/casual07_f_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/casual07_f_20.dds") asset_tree.add_asset_path("Rocketbox", "Textures/casual07_f_20_hair.dds") asset_tree.add_asset_path("Rocketbox", "Materials/casual08_f_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/casual08_f_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/casual08_f_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/casual08_f_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/casual08_f_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/casual08_f_25.dds") asset_tree.add_asset_path("Rocketbox", "Textures/casual08_f_25_hair.dds") asset_tree.add_asset_path("Rocketbox", "Materials/casual15_f_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/casual15_f_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/casual15_f_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/casual15_f_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/casual15_f_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/casual15_f_20.dds") asset_tree.add_asset_path("Rocketbox", "Textures/casual15_f_20_hair_alpha.dds") asset_tree.add_asset_path("Rocketbox", "Materials/casual19_f_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/casual19_f_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/casual19_f_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/casual19_f_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/casual19_f_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/casual19_f_20.dds") asset_tree.add_asset_path("Rocketbox", "Textures/casual19_f_20_hair.dds") asset_tree.add_asset_path("Rocketbox", "Materials/casual21_f_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/casual21_f_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/casual21_f_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/casual21_f_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/casual21_f_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/casual21_f_30.dds") asset_tree.add_asset_path("Rocketbox", "Textures/casual21_f_30_hair.dds") asset_tree.add_asset_path("Rocketbox", "Materials/sportive01_f_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/sportive01_f_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/sportive01_f_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/sportive01_f_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/sportive01_f_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/sportive01_f_20.dds") asset_tree.add_asset_path("Rocketbox", "Textures/sportive01_f_20_hair.dds") asset_tree.add_asset_path("Rocketbox", "Materials/sportive02_f_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/sportive02_f_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/sportive02_f_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/sportive02_f_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/sportive02_f_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/sportive02_f_35.dds") asset_tree.add_asset_path("Rocketbox", "Textures/sportive02_f_35_hair.dds") asset_tree.add_asset_path("Rocketbox", "Materials/sportive05_f_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/sportive05_f_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/sportive05_f_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/sportive05_f_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/sportive05_f_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/sportive05_f_25.dds") asset_tree.add_asset_path("Rocketbox", "Textures/sportive05_f_25_hair.dds") asset_tree.add_asset_path("Rocketbox", "Materials/sportive07_f_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/sportive07_f_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/sportive07_f_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/sportive07_f_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/sportive07_f_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/sportive07_f_20.dds") # human_female assets asset_tree.add_asset_path("HumanFemale", "Materials/human_female.material") asset_tree.add_asset_path("HumanFemale", "Meshes/human_female.mesh") asset_tree.add_asset_path("HumanFemale", "Physics/human_female.physics") asset_tree.add_asset_path("HumanFemale", "Skeletons/human_female.skeleton") asset_tree.add_asset_path("HumanFemale", "Textures/ARMOR_col.dds") asset_tree.add_asset_path("HumanFemale", "Textures/ARMOR_revised_1024_nm.tga") asset_tree.add_asset_path("HumanFemale", "Textures/blank.dds") asset_tree.add_asset_path("HumanFemale", "Textures/human_female_face.dds") asset_tree.add_asset_path("HumanFemale", "Textures/human_female_hair_a.dds") asset_tree.add_asset_path("HumanFemale", "Textures/human_female_leather_a.dds") asset_tree.add_asset_path("HumanFemale", "Textures/human_female_skin.dds") # human_male assets asset_tree.add_asset_path("HumanMale", "Materials/human_male.material") asset_tree.add_asset_path("HumanMale", "Meshes/human_male.mesh") asset_tree.add_asset_path("HumanMale", "Meshes/human_male_bones.mesh") asset_tree.add_asset_path("HumanMale", "Physics/human_male.physics") asset_tree.add_asset_path("HumanMale", "Skeletons/human_male.skeleton") asset_tree.add_asset_path("HumanMale", "Textures/human_male_apron.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_body.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_body2.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_body_shorts.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_face.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_hair1.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_hair2.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_head.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_leather_a.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_leather_a.jpg") asset_tree.add_asset_path("HumanMale", "Textures/human_male_leather_armor.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_mustache1.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_peasant.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_plate_armor.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_plate_armor1.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_plate_armor2.dds") # SampleWorld assets asset_tree.add_asset_path("SampleWorld", "Materials/hall.material") asset_tree.add_asset_path("SampleWorld", "Materials/house_tall.material") asset_tree.add_asset_path("SampleWorld", "Materials/human_shack_tall.material") asset_tree.add_asset_path("SampleWorld", "Materials/shack.material") asset_tree.add_asset_path("SampleWorld", "Materials/wolf.material") asset_tree.add_asset_path("SampleWorld", "Materials/zombie.material") asset_tree.add_asset_path("SampleWorld", "Materials/human_house_wishbone.material") asset_tree.add_asset_path("SampleWorld", "Materials/human_house_open.material") asset_tree.add_asset_path("SampleWorld", "Materials/human_meeting_house.material") asset_tree.add_asset_path("SampleWorld", "Materials/sword.material") asset_tree.add_asset_path("SampleWorld", "Meshes/hall.mesh") asset_tree.add_asset_path("SampleWorld", "Meshes/house_tall.mesh") asset_tree.add_asset_path("SampleWorld", "Meshes/human_shack_tall.mesh") asset_tree.add_asset_path("SampleWorld", "Meshes/shack.mesh") asset_tree.add_asset_path("SampleWorld", "Meshes/wolf.mesh") asset_tree.add_asset_path("SampleWorld", "Meshes/zombie.mesh") asset_tree.add_asset_path("SampleWorld", "Meshes/human_house_wishbone.mesh") asset_tree.add_asset_path("SampleWorld", "Meshes/human_house_open.mesh") asset_tree.add_asset_path("SampleWorld", "Meshes/human_meeting_house.mesh") asset_tree.add_asset_path("SampleWorld", "Meshes/sword.mesh") asset_tree.add_asset_path("SampleWorld", "Misc/SampleWorld_AssetConfig.xml", "AssetConfig.xml") # asset_tree.add_asset_path("SampleWorld", "Misc/sampleworld.terrain") asset_tree.add_asset_path("SampleWorld", "Physics/human_shack_tall.physics") asset_tree.add_asset_path("SampleWorld", "Physics/wolf.physics") asset_tree.add_asset_path("SampleWorld", "Physics/zombie.physics") asset_tree.add_asset_path("SampleWorld", "Physics/human_house_wishbone.physics") asset_tree.add_asset_path("SampleWorld", "Physics/human_house_open.physics") asset_tree.add_asset_path("SampleWorld", "Physics/human_meeting_house.physics") asset_tree.add_asset_path("SampleWorld", "Skeletons/wolf.skeleton") asset_tree.add_asset_path("SampleWorld", "Skeletons/zombie.skeleton") asset_tree.add_asset_path("SampleWorld", "Textures/hall.dds") asset_tree.add_asset_path("SampleWorld", "Textures/house_tall.dds") asset_tree.add_asset_path("SampleWorld", "Textures/human_shack_tall_texture.dds") asset_tree.add_asset_path("SampleWorld", "Textures/shack.dds") asset_tree.add_asset_path("SampleWorld", "Textures/wolf_re.dds") asset_tree.add_asset_path("SampleWorld", "Textures/Zombie_Body_Diffuse.tga") asset_tree.add_asset_path("SampleWorld", "Textures/Zombie_Body_Normal.tga") asset_tree.add_asset_path("SampleWorld", "Textures/Zombie_Body_Specular.tga") asset_tree.add_asset_path("SampleWorld", "Textures/Zombie_Clothes_Diffuse.tga") asset_tree.add_asset_path("SampleWorld", "Textures/Zombie_Clothes_Normal.tga") asset_tree.add_asset_path("SampleWorld", "Textures/Zombie_Clothes_Specular.tga") asset_tree.add_asset_path("SampleWorld", "Textures/human_wishbone_house.dds") asset_tree.add_asset_path("SampleWorld", "Textures/human_house_open_texture.dds") asset_tree.add_asset_path("SampleWorld", "Textures/human_meeting_house.dds") asset_tree.add_asset_path("SampleWorld", "Textures/sword_005.dds") # FCworld assets asset_tree.add_asset_path("FCworld", "Meshes/casual07_f_lowpoly.mesh") asset_tree.add_asset_path("FCworld", "Meshes/casual07_f_mediumpoly.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_dividerWalls.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_foundation.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_large_side_building.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_middle_building.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_pillar.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_Pillar_tangentIssues.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_railing.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_railing_endcap.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_railing_stairs.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_small_side_building.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_stairs_back.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_stairs_center.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_stairs_fourth.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_stairs_main.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_stairs_third.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_Wall_dragon_spout.mesh") asset_tree.add_asset_path("FCworld", "Materials/casual07_f_lowpoly.material") asset_tree.add_asset_path("FCworld", "Materials/casual07_f_mediumpoly.material") asset_tree.add_asset_path("FCworld", "Materials/FC_dividerWalls.material") asset_tree.add_asset_path("FCworld", "Materials/FC_foundation.material") asset_tree.add_asset_path("FCworld", "Materials/FC_large_side_building.material") asset_tree.add_asset_path("FCworld", "Materials/FC_middle_building.material") asset_tree.add_asset_path("FCworld", "Materials/FC_pillar.material") asset_tree.add_asset_path("FCworld", "Materials/FC_Pillar_tangentIssues.material") asset_tree.add_asset_path("FCworld", "Materials/FC_railing.material") asset_tree.add_asset_path("FCworld", "Materials/FC_railing_endcap.material") asset_tree.add_asset_path("FCworld", "Materials/FC_railing_stairs.material") asset_tree.add_asset_path("FCworld", "Materials/FC_small_side_building.material") asset_tree.add_asset_path("FCworld", "Materials/FC_stairs_back.material") asset_tree.add_asset_path("FCworld", "Materials/FC_stairs_center.material") asset_tree.add_asset_path("FCworld", "Materials/FC_stairs_fourth.material") asset_tree.add_asset_path("FCworld", "Materials/FC_stairs_main.material") asset_tree.add_asset_path("FCworld", "Materials/FC_stairs_third.material") asset_tree.add_asset_path("FCworld", "Materials/FC_Wall_dragon_spout.material") asset_tree.add_asset_path("FCworld", "Physics/casual07_f_mediumpoly.physics") asset_tree.add_asset_path("FCworld", "Physics/FC_foundation.physics") asset_tree.add_asset_path("FCworld", "Physics/FC_large_side_building.physics") asset_tree.add_asset_path("FCworld", "Physics/FC_stairs_back.physics") asset_tree.add_asset_path("FCworld", "Skeletons/rocketbox_f_lowpoly.skeleton") asset_tree.add_asset_path("FCworld", "Skeletons/rocketbox_f_mediumpoly.skeleton") asset_tree.add_asset_path("FCworld", "Textures/blank.dds") asset_tree.add_asset_path("FCworld", "Textures/casual07_f_20.dds") asset_tree.add_asset_path("FCworld", "Textures/casual07_f_20_hair.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_Beam01_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_Beam01_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_Beam01_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_beam02_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_beam02_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_beam02_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_beam_hang_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_beam_hang_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_beam_hang_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_beamRed_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_beamRed_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_beamRed_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_building_floor_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_building_floor_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_building_floor_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_column_wing_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_column_wing_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_column_wing_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_door_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_door_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_door_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_dragon_head_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_dragon_head_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_dragon_head_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_endWall_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_endWall_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_endWall_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_foundationStairs_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_foundationStairs_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_foundationStairs_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_foundationStairSide_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_foundationStairSide_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_foundationStairSide_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_foundationWall_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_foundationWall_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_foundationWall_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_foundationWalls.JPG") asset_tree.add_asset_path("FCworld", "Textures/FC_midLowLevel_foundation.JPG") asset_tree.add_asset_path("FCworld", "Textures/FC_midLowLevelFoundation_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_midLowLevelFoundation_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_midLowLevelFoundation_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_peak_dragon_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_peak_dragon_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_peak_dragon_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_pillars_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_pillars_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_pillars_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_railings.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_railings_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_railings_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_railings_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_roof_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_roof_edge_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_roof_edge_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_roof_edge_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_roof_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_roof_normal_detail.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_roof_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_roofPeaks_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_roofPeaks_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_roofPeaks_specularity.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_sign.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_skybox_east.bmp") asset_tree.add_asset_path("FCworld", "Textures/FC_skybox_east.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_skybox_north.bmp") asset_tree.add_asset_path("FCworld", "Textures/FC_skybox_north.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_skybox_south.bmp") asset_tree.add_asset_path("FCworld", "Textures/FC_skybox_south.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_skybox_top.bmp") asset_tree.add_asset_path("FCworld", "Textures/FC_skybox_top.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_skybox_west.bmp") asset_tree.add_asset_path("FCworld", "Textures/FC_skybox_west.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_StairCenter_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_topLevelFoundation_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_topLevelFoundation_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_topLevelFoundation_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_window_mantle_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_window_mantle_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_window_mantle_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_windows_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_windows_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_windows_specular.dds") # Tools assets asset_tree.add_asset_path("Tools", "Materials/axe.material") asset_tree.add_asset_path("Tools", "Materials/barrel.material") asset_tree.add_asset_path("Tools", "Materials/barrel_broken.material") asset_tree.add_asset_path("Tools", "Materials/barrel_half_open.material") asset_tree.add_asset_path("Tools", "Materials/brax.material") asset_tree.add_asset_path("Tools", "Materials/corral_01.material") asset_tree.add_asset_path("Tools", "Materials/corral_02.material") asset_tree.add_asset_path("Tools", "Materials/corral_03.material") asset_tree.add_asset_path("Tools", "Materials/directional_marker.material") asset_tree.add_asset_path("Tools", "Materials/fountainEmpty.material") asset_tree.add_asset_path("Tools", "Materials/gate.material") asset_tree.add_asset_path("Tools", "Materials/hall.material") asset_tree.add_asset_path("Tools", "Materials/HMETerrain.material") asset_tree.add_asset_path("Tools", "Materials/house_open.material") asset_tree.add_asset_path("Tools", "Materials/house_stilt.material") asset_tree.add_asset_path("Tools", "Materials/house_tall.material") asset_tree.add_asset_path("Tools", "Materials/house_wishbone.material") asset_tree.add_asset_path("Tools", "Materials/human_female.material") asset_tree.add_asset_path("Tools", "Materials/human_male.material") asset_tree.add_asset_path("Tools", "Materials/MVSMTerrain.material") asset_tree.add_asset_path("Tools", "Materials/Multiverse.material") asset_tree.add_asset_path("Tools", "Materials/orc.material") asset_tree.add_asset_path("Tools", "Materials/orc_house.material") asset_tree.add_asset_path("Tools", "Materials/orcsword.material") asset_tree.add_asset_path("Tools", "Materials/pick.material") asset_tree.add_asset_path("Tools", "Materials/rock_group_01.material") asset_tree.add_asset_path("Tools", "Materials/rock_group_02.material") asset_tree.add_asset_path("Tools", "Materials/shack.material") asset_tree.add_asset_path("Tools", "Materials/shield.material") asset_tree.add_asset_path("Tools", "Materials/spear.material") asset_tree.add_asset_path("Tools", "Materials/sword.material") asset_tree.add_asset_path("Tools", "Materials/tent_orc.material") asset_tree.add_asset_path("Tools", "Materials/tower.material") asset_tree.add_asset_path("Tools", "Materials/wall.material") asset_tree.add_asset_path("Tools", "Materials/wall_corner.material") asset_tree.add_asset_path("Tools", "Materials/wall_corner_walkway.material") asset_tree.add_asset_path("Tools", "Materials/wall_walkway.material") asset_tree.add_asset_path("Tools", "Materials/wolf.material") asset_tree.add_asset_path("Tools", "Materials/zombie.material") asset_tree.add_asset_path("Tools", "Materials/Ocean.material") asset_tree.add_asset_path("Tools", "Materials/Trees.material") asset_tree.add_asset_path("Tools", "Materials/Water.material") asset_tree.add_asset_path("Tools", "Meshes/axe.mesh") asset_tree.add_asset_path("Tools", "Meshes/barrel.mesh") asset_tree.add_asset_path("Tools", "Meshes/barrel_broken.mesh") asset_tree.add_asset_path("Tools", "Meshes/barrel_half_open.mesh") asset_tree.add_asset_path("Tools", "Meshes/brax.mesh") asset_tree.add_asset_path("Tools", "Meshes/corral_01.mesh") asset_tree.add_asset_path("Tools", "Meshes/corral_02.mesh") asset_tree.add_asset_path("Tools", "Meshes/corral_03.mesh") asset_tree.add_asset_path("Tools", "Meshes/directional_marker.mesh") asset_tree.add_asset_path("Tools", "Meshes/fountainEmpty.mesh") asset_tree.add_asset_path("Tools", "Meshes/gate.mesh") asset_tree.add_asset_path("Tools", "Meshes/hall.mesh") asset_tree.add_asset_path("Tools", "Meshes/house_open.mesh") asset_tree.add_asset_path("Tools", "Meshes/house_stilt.mesh") asset_tree.add_asset_path("Tools", "Meshes/house_tall.mesh") asset_tree.add_asset_path("Tools", "Meshes/house_wishbone.mesh") asset_tree.add_asset_path("Tools", "Meshes/human_female.mesh") asset_tree.add_asset_path("Tools", "Meshes/human_male.mesh") asset_tree.add_asset_path("Tools", "Meshes/orc.mesh") asset_tree.add_asset_path("Tools", "Meshes/orc_house.mesh") asset_tree.add_asset_path("Tools", "Meshes/orcsword.mesh") asset_tree.add_asset_path("Tools", "Meshes/pick.mesh") asset_tree.add_asset_path("Tools", "Meshes/rock_group_01.mesh") asset_tree.add_asset_path("Tools", "Meshes/rock_group_02.mesh") asset_tree.add_asset_path("Tools", "Meshes/shack.mesh") asset_tree.add_asset_path("Tools", "Meshes/shield.mesh") asset_tree.add_asset_path("Tools", "Meshes/spear.mesh") asset_tree.add_asset_path("Tools", "Meshes/sword.mesh") asset_tree.add_asset_path("Tools", "Meshes/tent_orc.mesh") asset_tree.add_asset_path("Tools", "Meshes/tower.mesh") asset_tree.add_asset_path("Tools", "Meshes/wall.mesh") asset_tree.add_asset_path("Tools", "Meshes/wall_corner.mesh") asset_tree.add_asset_path("Tools", "Meshes/wall_corner_walkway.mesh") asset_tree.add_asset_path("Tools", "Meshes/wall_walkway.mesh") asset_tree.add_asset_path("Tools", "Meshes/wolf.mesh") asset_tree.add_asset_path("Tools", "Meshes/zombie.mesh") asset_tree.add_asset_path("Tools", "Textures/Zombie_Body_Diffuse.tga") asset_tree.add_asset_path("Tools", "Textures/Zombie_Body_Normal.tga") asset_tree.add_asset_path("Tools", "Textures/Zombie_Body_Specular.tga") asset_tree.add_asset_path("Tools", "Textures/Zombie_Clothes_Diffuse.tga") asset_tree.add_asset_path("Tools", "Textures/Zombie_Clothes_Normal.tga") asset_tree.add_asset_path("Tools", "Textures/Zombie_Clothes_Specular.tga") asset_tree.add_asset_path("Tools", "Textures/axe_003.dds") asset_tree.add_asset_path("Tools", "Textures/bodyUV_DI_RED_1024.dds") asset_tree.add_asset_path("Tools", "Textures/dino2.dds") asset_tree.add_asset_path("Tools", "Textures/dino_top2.dds") asset_tree.add_asset_path("Tools", "Textures/faceUV_DI_RED_1024.dds") asset_tree.add_asset_path("Tools", "Textures/fence.dds") asset_tree.add_asset_path("Tools", "Textures/hall.dds") asset_tree.add_asset_path("Tools", "Textures/house_open.dds") asset_tree.add_asset_path("Tools", "Textures/house_tall.dds") asset_tree.add_asset_path("Tools", "Textures/human_female_face.dds") asset_tree.add_asset_path("Tools", "Textures/human_female_hair_a.dds") asset_tree.add_asset_path("Tools", "Textures/human_female_leather_a.dds") asset_tree.add_asset_path("Tools", "Textures/human_female_skin.dds") asset_tree.add_asset_path("Tools", "Textures/human_male_apron.dds") asset_tree.add_asset_path("Tools", "Textures/human_male_body2.dds") asset_tree.add_asset_path("Tools", "Textures/human_male_face.dds") asset_tree.add_asset_path("Tools", "Textures/human_male_hair2.dds") asset_tree.add_asset_path("Tools", "Textures/human_male_leather_a.dds") asset_tree.add_asset_path("Tools", "Textures/human_male_leather_armor.dds") asset_tree.add_asset_path("Tools", "Textures/human_male_mustache1.dds") asset_tree.add_asset_path("Tools", "Textures/human_male_peasant.dds") asset_tree.add_asset_path("Tools", "Textures/human_male_plate_armor1.dds") asset_tree.add_asset_path("Tools", "Textures/human_male_plate_armor2.dds") asset_tree.add_asset_path("Tools", "Textures/lezhanka.dds") asset_tree.add_asset_path("Tools", "Textures/orc_house_007.dds") asset_tree.add_asset_path("Tools", "Textures/pick_003.dds") asset_tree.add_asset_path("Tools", "Textures/ramp.dds") asset_tree.add_asset_path("Tools", "Textures/shack.dds") asset_tree.add_asset_path("Tools", "Textures/shield_006.dds") asset_tree.add_asset_path("Tools", "Textures/skull.dds") asset_tree.add_asset_path("Tools", "Textures/spear_004.dds") asset_tree.add_asset_path("Tools", "Textures/sword_005.dds") asset_tree.add_asset_path("Tools", "Textures/town_fountain.dds") asset_tree.add_asset_path("Tools", "Textures/weap_rack.dds") asset_tree.add_asset_path("Tools", "Textures/wolf_re.dds") asset_tree.add_asset_path("Tools", "Materials/directional_marker.material") asset_tree.add_asset_path("Tools", "GpuPrograms/HMETerrain.cg") asset_tree.add_asset_path("Tools", "Meshes/directional_marker.mesh") 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Easy/1018-binary-prefix-divisible-by-5.py	sandychn/LeetCode-Solutions	0	94899	<gh_stars>0 class Solution: def prefixesDivBy5(self, A: List[int]) -> List[bool]: val = 0 res = [None] * len(A) for i, v in enumerate(A): val = ((val << 1) + v) % 5 res[i] = (val == 0) return res	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 1990, 24380, 29901, 30004, 13, 1678, 822, 10944, 267, 12596, 2059, 29945, 29898, 1311, 29892, 319, 29901, 2391, 29961, 524, 2314, 1599, 2391, 29961, 11227, 5387, 30004, 13, 4706, 659, 353, 29871, 29900, 30004, 13, 4706, 620, 353, 518, 8516, 29962, 334, 7431, 29898, 29909, 8443, 13, 4706, 363, 474, 29892, 325, 297, 26985, 29898, 29909, 1125, 30004, 13, 9651, 659, 353, 5135, 791, 3532, 29871, 29896, 29897, 718, 325, 29897, 1273, 29871, 29945, 30004, 13, 9651, 620, 29961, 29875, 29962, 353, 313, 791, 1275, 29871, 29900, 8443, 13, 4706, 736, 620, 30004, 13, 2 ]
attributes_and_methods/lab/project/hotel.py	BoyanPeychinov/object_oriented_programming	0	1610794	<reponame>BoyanPeychinov/object_oriented_programming class Hotel: def __init__(self, name): self.name = name self.rooms = [] self.guests = 0 @staticmethod def find_room(rooms, room_number): return list(filter(lambda room: room.number == room_number, rooms))[0] @classmethod def from_stars(cls, stars_count): return cls(f"{stars_count} stars Hotel") def add_room(self, room): self.rooms.append(room) def take_room(self, room_number, people): result = self.find_room(self.rooms, room_number).take_room(people) if result: return result self.guests += people def free_room(self, room_number): room = self.find_room(self.rooms, room_number) guests_to_remove = room.guests result = room.free_room() if result: return result self.guests -= guests_to_remove def print_status(self): free_rooms_numbers = [str(room) for room in self.rooms if not room.is_taken] taken_rooms_numbers = [str(room) for room in self.rooms if room.is_taken] print(f"Hotel {self.name} has {self.guests} total guests") print(f"Free rooms: {', '.join(free_rooms_numbers)}") print(f"Taken rooms: {', '.join(taken_rooms_numbers)}")	[ 1, 529, 276, 1112, 420, 29958, 8431, 10094, 29925, 1032, 24338, 586, 29914, 3318, 29918, 12236, 287, 29918, 28426, 13, 1990, 16923, 29901, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 1024, 1125, 13, 4706, 1583, 29889, 978, 353, 1024, 13, 4706, 1583, 29889, 18901, 353, 5159, 13, 4706, 1583, 29889, 2543, 9197, 353, 29871, 29900, 13, 13, 1678, 732, 7959, 5696, 13, 1678, 822, 1284, 29918, 8345, 29898, 18901, 29892, 5716, 29918, 4537, 1125, 13, 4706, 736, 1051, 29898, 4572, 29898, 2892, 5716, 29901, 5716, 29889, 4537, 1275, 5716, 29918, 4537, 29892, 19600, 876, 29961, 29900, 29962, 13, 13, 1678, 732, 1990, 5696, 13, 1678, 822, 515, 29918, 303, 1503, 29898, 25932, 29892, 10819, 29918, 2798, 1125, 13, 4706, 736, 1067, 29879, 29898, 29888, 29908, 29912, 303, 1503, 29918, 2798, 29913, 10819, 16923, 1159, 13, 13, 1678, 822, 788, 29918, 8345, 29898, 1311, 29892, 5716, 1125, 13, 4706, 1583, 29889, 18901, 29889, 4397, 29898, 8345, 29897, 13, 13, 1678, 822, 2125, 29918, 8345, 29898, 1311, 29892, 5716, 29918, 4537, 29892, 2305, 1125, 13, 4706, 1121, 353, 1583, 29889, 2886, 29918, 8345, 29898, 1311, 29889, 18901, 29892, 5716, 29918, 4537, 467, 19730, 29918, 8345, 29898, 25719, 29897, 13, 13, 4706, 565, 1121, 29901, 13, 9651, 736, 1121, 13, 4706, 1583, 29889, 2543, 9197, 4619, 2305, 13, 13, 1678, 822, 3889, 29918, 8345, 29898, 1311, 29892, 5716, 29918, 4537, 1125, 13, 4706, 5716, 353, 1583, 29889, 2886, 29918, 8345, 29898, 1311, 29889, 18901, 29892, 5716, 29918, 4537, 29897, 13, 4706, 28865, 29918, 517, 29918, 5992, 353, 5716, 29889, 2543, 9197, 13, 4706, 1121, 353, 5716, 29889, 9021, 29918, 8345, 580, 13, 13, 4706, 565, 1121, 29901, 13, 9651, 736, 1121, 13, 4706, 1583, 29889, 2543, 9197, 22361, 28865, 29918, 517, 29918, 5992, 13, 13, 1678, 822, 1596, 29918, 4882, 29898, 1311, 1125, 13, 4706, 3889, 29918, 18901, 29918, 20326, 353, 518, 710, 29898, 8345, 29897, 363, 5716, 297, 1583, 29889, 18901, 565, 451, 5716, 29889, 275, 29918, 29873, 9424, 29962, 13, 4706, 4586, 29918, 18901, 29918, 20326, 353, 518, 710, 29898, 8345, 29897, 363, 5716, 297, 1583, 29889, 18901, 565, 5716, 29889, 275, 29918, 29873, 9424, 29962, 13, 13, 4706, 1596, 29898, 29888, 29908, 28917, 295, 426, 1311, 29889, 978, 29913, 756, 426, 1311, 29889, 2543, 9197, 29913, 3001, 28865, 1159, 13, 4706, 1596, 29898, 29888, 29908, 20475, 19600, 29901, 426, 742, 15300, 7122, 29898, 9021, 29918, 18901, 29918, 20326, 2915, 1159, 13, 4706, 1596, 29898, 29888, 29908, 29911, 9424, 19600, 29901, 426, 742, 15300, 7122, 29898, 29873, 9424, 29918, 18901, 29918, 20326, 2915, 1159, 2 ]
day2/demo1.py	PickHeBin/2020-2-25	0	125620	#!/usr/bin/env python # -- coding: utf-8 -- # File : demo1.py # Author: MuNian # Date : 2020-01-08 ''' Python爬虫假装浏览器 ''' import requests proxie = { 'http':'http://localhost:5555/random' } print(proxie) url = 'https://www.baidu.com' response = requests.get(url, proxies=proxie) print(response)	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 29937, 3497, 29871, 584, 13455, 29896, 29889, 2272, 13, 29937, 13361, 29901, 8229, 29940, 713, 13, 29937, 4712, 29871, 584, 29871, 29906, 29900, 29906, 29900, 29899, 29900, 29896, 29899, 29900, 29947, 13, 13, 12008, 13, 11980, 234, 139, 175, 235, 156, 174, 232, 132, 138, 31905, 233, 184, 146, 235, 170, 139, 30943, 13, 13, 13, 12008, 13, 13, 5215, 7274, 13, 13, 771, 29916, 347, 353, 426, 13, 1678, 525, 1124, 22099, 1124, 597, 7640, 29901, 29945, 29945, 29945, 29945, 29914, 8172, 29915, 13, 29913, 13, 13, 13, 2158, 29898, 771, 29916, 347, 29897, 13, 2271, 353, 525, 991, 597, 1636, 29889, 2291, 333, 29884, 29889, 510, 29915, 13, 13, 5327, 353, 7274, 29889, 657, 29898, 2271, 29892, 410, 29916, 583, 29922, 771, 29916, 347, 29897, 13, 2158, 29898, 5327, 29897, 2 ]
libcst/codemod/tests/test_codemod_cli.py	lpetre/LibCST	2	142100	# Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. # import subprocess import sys from pathlib import Path from libcst.testing.utils import UnitTest class TestCodemodCLI(UnitTest): def test_codemod_formatter_error_input(self) -> None: rlt = subprocess.run( [ "python", "-m", "libcst.tool", "codemod", "remove_unused_imports.RemoveUnusedImportsCommand", str(Path(__file__).parent / "codemod_formatter_error_input.py.txt"), ], stdout=subprocess.PIPE, stderr=subprocess.PIPE, ) version = sys.version_info if version[0] == 3 and version[1] == 6: self.assertIn( "ParserSyntaxError: Syntax Error @ 14:11.", rlt.stderr.decode("utf-8"), ) else: self.assertIn( "error: cannot format -: Cannot parse: 13:10: async with AsyncExitStack() as stack:", rlt.stderr.decode("utf-8"), )	[ 1, 396, 14187, 1266, 313, 29883, 29897, 13327, 29892, 9266, 29889, 322, 967, 23736, 1078, 29889, 13, 29937, 13, 29937, 910, 2752, 775, 338, 7794, 21144, 1090, 278, 341, 1806, 19405, 1476, 297, 278, 13, 29937, 365, 2965, 1430, 1660, 934, 297, 278, 3876, 3884, 310, 445, 2752, 5447, 29889, 13, 29937, 13, 13, 13, 5215, 1014, 5014, 13, 5215, 10876, 13, 3166, 2224, 1982, 1053, 10802, 13, 13, 3166, 4303, 29883, 303, 29889, 13424, 29889, 13239, 1053, 13223, 3057, 13, 13, 13, 1990, 4321, 3399, 1545, 27205, 29898, 8325, 3057, 1125, 13, 1678, 822, 1243, 29918, 401, 1545, 29918, 689, 2620, 29918, 2704, 29918, 2080, 29898, 1311, 29897, 1599, 6213, 29901, 13, 4706, 364, 1896, 353, 1014, 5014, 29889, 3389, 29898, 13, 9651, 518, 13, 18884, 376, 4691, 613, 13, 18884, 11663, 29885, 613, 13, 18884, 376, 1982, 29883, 303, 29889, 10154, 613, 13, 18884, 376, 401, 1545, 613, 13, 18884, 376, 5992, 29918, 348, 3880, 29918, 326, 4011, 29889, 15941, 2525, 3880, 1888, 4011, 6255, 613, 13, 18884, 851, 29898, 2605, 22168, 1445, 1649, 467, 3560, 847, 376, 401, 1545, 29918, 689, 2620, 29918, 2704, 29918, 2080, 29889, 2272, 29889, 3945, 4968, 13, 9651, 21251, 13, 9651, 27591, 29922, 1491, 5014, 29889, 2227, 4162, 29892, 13, 9651, 380, 20405, 29922, 1491, 5014, 29889, 2227, 4162, 29892, 13, 4706, 1723, 13, 4706, 1873, 353, 10876, 29889, 3259, 29918, 3888, 13, 4706, 565, 1873, 29961, 29900, 29962, 1275, 29871, 29941, 322, 1873, 29961, 29896, 29962, 1275, 29871, 29953, 29901, 13, 9651, 1583, 29889, 9294, 797, 29898, 13, 18884, 376, 11726, 16676, 2392, 29901, 21306, 4829, 732, 29871, 29896, 29946, 29901, 29896, 29896, 19602, 13, 18884, 364, 1896, 29889, 303, 20405, 29889, 13808, 703, 9420, 29899, 29947, 4968, 13, 9651, 1723, 13, 4706, 1683, 29901, 13, 9651, 1583, 29889, 9294, 797, 29898, 13, 18884, 376, 2704, 29901, 2609, 3402, 448, 29901, 15808, 6088, 29901, 29871, 29896, 29941, 29901, 29896, 29900, 29901, 268, 7465, 411, 20688, 24365, 7264, 580, 408, 5096, 29901, 613, 13, 18884, 364, 1896, 29889, 303, 20405, 29889, 13808, 703, 9420, 29899, 29947, 4968, 13, 9651, 1723, 13, 2 ]
lib/bes/text/text_fit.py	reconstruir/bes	0	51321	<gh_stars>0 #-- coding:utf-8; mode:python; indent-tabs-mode: nil; c-basic-offset: 2; tab-width: 2 -- from .string_lexer import string_lexer as lexer from bes.compat.StringIO import StringIO class text_fit(object): 'Fit text into bounded areas keeping words intact.' @classmethod def fit_text(clazz, text, width): lines = [] for line in text.split('\n'): lines.extend(clazz.fit_line(text, width)) return lines @classmethod def fit_line(clazz, text, width): assert '\n' not in text lines = [] buf = StringIO() for token in lexer.tokenize(text, 'text_fit', options = lexer.KEEP_QUOTES \| lexer.IGNORE_COMMENTS): if token.token_type == lexer.TOKEN_SPACE: if (buf.tell() + len(token.value)) > width: lines.append(buf.getvalue().strip()) buf = StringIO() else: buf.write(token.value) if token.token_type == lexer.TOKEN_STRING: if (buf.tell() + len(token.value)) > width: lines.append(buf.getvalue().strip()) buf = StringIO() buf.write(token.value) elif token.token_type == lexer.TOKEN_DONE: if buf.tell() > 0: lines.append(buf.getvalue().strip()) return lines	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 29899, 29930, 29899, 14137, 29901, 9420, 29899, 29947, 29936, 4464, 29901, 4691, 29936, 29536, 29899, 21175, 29899, 8513, 29901, 4263, 29936, 274, 29899, 16121, 29899, 10289, 29901, 29871, 29906, 29936, 4434, 29899, 2103, 29901, 29871, 29906, 448, 29930, 29899, 13, 13, 3166, 869, 1807, 29918, 2506, 261, 1053, 1347, 29918, 2506, 261, 408, 19566, 261, 13, 3166, 3008, 29889, 12667, 29889, 1231, 5971, 1053, 1714, 5971, 13, 13, 1990, 1426, 29918, 9202, 29898, 3318, 1125, 13, 29871, 525, 29943, 277, 1426, 964, 12635, 10161, 12515, 3838, 938, 627, 6169, 13, 13, 29871, 732, 1990, 5696, 13, 29871, 822, 6216, 29918, 726, 29898, 16398, 5617, 29892, 1426, 29892, 2920, 1125, 13, 1678, 3454, 353, 5159, 13, 1678, 363, 1196, 297, 1426, 29889, 5451, 28909, 29876, 29374, 13, 418, 3454, 29889, 21843, 29898, 16398, 5617, 29889, 9202, 29918, 1220, 29898, 726, 29892, 2920, 876, 13, 1678, 736, 3454, 13, 539, 13, 29871, 732, 1990, 5696, 13, 29871, 822, 6216, 29918, 1220, 29898, 16398, 5617, 29892, 1426, 29892, 2920, 1125, 13, 1678, 4974, 11297, 29876, 29915, 451, 297, 1426, 13, 1678, 3454, 353, 5159, 13, 1678, 18392, 353, 1714, 5971, 580, 13, 268, 13, 1678, 363, 5993, 297, 19566, 261, 29889, 6979, 675, 29898, 726, 29892, 525, 726, 29918, 9202, 742, 3987, 353, 19566, 261, 29889, 6059, 15488, 29918, 13356, 2891, 2890, 891, 19566, 261, 29889, 6259, 6632, 1525, 29918, 3217, 7428, 3919, 29903, 1125, 13, 418, 565, 5993, 29889, 6979, 29918, 1853, 1275, 19566, 261, 29889, 4986, 29968, 1430, 29918, 5550, 11538, 29901, 13, 4706, 565, 313, 9721, 29889, 29873, 514, 580, 718, 7431, 29898, 6979, 29889, 1767, 876, 1405, 2920, 29901, 13, 3986, 3454, 29889, 4397, 29898, 9721, 29889, 657, 1767, 2141, 17010, 3101, 13, 3986, 18392, 353, 1714, 5971, 580, 13, 4706, 1683, 29901, 13, 3986, 18392, 29889, 3539, 29898, 6979, 29889, 1767, 29897, 13, 418, 565, 5993, 29889, 6979, 29918, 1853, 1275, 19566, 261, 29889, 4986, 29968, 1430, 29918, 20785, 29901, 13, 4706, 565, 313, 9721, 29889, 29873, 514, 580, 718, 7431, 29898, 6979, 29889, 1767, 876, 1405, 2920, 29901, 13, 3986, 3454, 29889, 4397, 29898, 9721, 29889, 657, 1767, 2141, 17010, 3101, 13, 3986, 18392, 353, 1714, 5971, 580, 13, 4706, 18392, 29889, 3539, 29898, 6979, 29889, 1767, 29897, 13, 418, 25342, 5993, 29889, 6979, 29918, 1853, 1275, 19566, 261, 29889, 4986, 29968, 1430, 29918, 29928, 12413, 29901, 13, 4706, 565, 18392, 29889, 29873, 514, 580, 1405, 29871, 29900, 29901, 13, 3986, 3454, 29889, 4397, 29898, 9721, 29889, 657, 1767, 2141, 17010, 3101, 13, 1678, 736, 3454, 13, 2 ]
reverse-string/reverse_string.py	Kalpavrikshika/Exercism-tests	0	77619	<filename>reverse-string/reverse_string.py def reverse(input): #reverse as string return input[::-1] #An alternative approach I wass using - string to list '''myinput = input mylist = list(myinput) #print (mylist) mylist.reverse() return (mylist)''' '''while True: if input == None: print (None) break else: #find length of list l=len(mylist)-1 #while loop to reverse while l >= 0: print (mylist[l]) l -= 1 #actual reverse function happens here #return(mylist) #not working: '' input and '' puctuated sentence.''' reverse(input = 'robot') reverse(input = 'Ramen') reverse(input = 'I\'m hungry!') reverse(input = 'racecar')	[ 1, 529, 9507, 29958, 24244, 29899, 1807, 29914, 24244, 29918, 1807, 29889, 2272, 13, 13, 1753, 11837, 29898, 2080, 1125, 13, 268, 13, 1678, 396, 24244, 408, 1347, 13, 1678, 736, 1881, 29961, 1057, 29899, 29896, 29962, 13, 13, 29937, 2744, 8671, 2948, 306, 281, 465, 773, 448, 1347, 304, 1051, 13, 1678, 14550, 1357, 2080, 353, 1881, 13, 1678, 590, 1761, 353, 1051, 29898, 1357, 2080, 29897, 13, 1678, 396, 2158, 313, 1357, 1761, 29897, 13, 1678, 590, 1761, 29889, 24244, 580, 13, 1678, 736, 313, 1357, 1761, 29897, 12008, 13, 13, 1678, 14550, 8000, 5852, 29901, 13, 4706, 565, 1881, 1275, 6213, 29901, 13, 9651, 1596, 313, 8516, 29897, 13, 9651, 2867, 13, 4706, 1683, 29901, 13, 1678, 396, 2886, 3309, 310, 1051, 13, 1678, 301, 29922, 2435, 29898, 1357, 1761, 6817, 29896, 13, 1678, 396, 8000, 2425, 304, 11837, 13, 1678, 1550, 301, 6736, 29871, 29900, 29901, 13, 4706, 1596, 313, 1357, 1761, 29961, 29880, 2314, 13, 4706, 301, 22361, 29871, 29896, 13, 29937, 19304, 11837, 740, 5930, 1244, 13, 268, 13, 1678, 396, 2457, 29898, 1357, 1761, 29897, 13, 1678, 396, 1333, 1985, 29901, 6629, 1881, 322, 6629, 2653, 22999, 630, 10541, 29889, 12008, 13, 13, 24244, 29898, 2080, 353, 525, 307, 7451, 1495, 13, 24244, 29898, 2080, 353, 525, 29934, 5071, 1495, 13, 24244, 29898, 2080, 353, 525, 29902, 20333, 29885, 9074, 14793, 29991, 1495, 13, 24244, 29898, 2080, 353, 525, 25525, 4287, 1495, 13, 13, 13, 13, 2 ]
subparsers/list_custom_images.py	dman777/image_share	1	59033	<reponame>dman777/image_share # List custom images def list_custom_images_subparser(subparser): list_images = subparser.add_parser( 'list-custom-images', description=('***List custom' ' saved images of' ' producers/consumers' ' account'), help=('List custom' ' saved images of' ' producers/consumers' ' account')) group_key = list_images.add_mutually_exclusive_group(required=True) group_key.add_argument('-pn', '--producer-username', help="Producer\'s(source account) username") group_key.add_argument('-cn', '--consumer-username', dest='producer_username', metavar='CONSUMER_USERNAME', help="Consumer\'s(destination account) username") group_apikey = list_images.add_mutually_exclusive_group(required=True) group_apikey.add_argument('-pa', '--producer-apikey', help="Producer\'s(source account) apikey") group_apikey.add_argument('-ca', '--consumer-apikey', dest='producer_apikey', metavar='CONSUMER_APIKEY', help="Consumer\'s(destination account) apikey") list_images.add_argument('-u', '--uuid', help="Image ID number")	[ 1, 529, 276, 1112, 420, 29958, 29881, 1171, 29955, 29955, 29955, 29914, 3027, 29918, 13653, 13, 29937, 2391, 2888, 4558, 13, 1753, 1051, 29918, 6341, 29918, 8346, 29918, 1491, 16680, 29898, 1491, 16680, 1125, 13, 1678, 1051, 29918, 8346, 353, 1014, 16680, 29889, 1202, 29918, 16680, 29898, 13, 462, 462, 539, 525, 1761, 29899, 6341, 29899, 8346, 742, 13, 462, 462, 539, 6139, 29922, 877, 17435, 1293, 2888, 29915, 13, 462, 462, 462, 1678, 525, 7160, 4558, 310, 29915, 13, 462, 462, 462, 1678, 525, 1391, 22543, 29914, 25978, 414, 29915, 13, 462, 462, 462, 1678, 525, 3633, 5477, 13, 462, 462, 539, 1371, 29922, 877, 1293, 2888, 29915, 13, 462, 462, 632, 525, 7160, 4558, 310, 29915, 13, 462, 462, 632, 525, 1391, 22543, 29914, 25978, 414, 29915, 13, 462, 462, 632, 525, 3633, 8785, 13, 1678, 2318, 29918, 1989, 353, 1051, 29918, 8346, 29889, 1202, 29918, 6149, 1474, 29918, 735, 7009, 573, 29918, 2972, 29898, 12403, 29922, 5574, 29897, 13, 1678, 2318, 29918, 1989, 29889, 1202, 29918, 23516, 877, 29899, 21257, 742, 13, 462, 965, 525, 489, 5498, 2265, 29899, 6786, 742, 13, 462, 965, 1371, 543, 23665, 2265, 20333, 29879, 29898, 4993, 3633, 29897, 8952, 1159, 13, 1678, 2318, 29918, 1989, 29889, 1202, 29918, 23516, 877, 29899, 18038, 742, 13, 462, 965, 525, 489, 25978, 261, 29899, 6786, 742, 13, 462, 965, 2731, 2433, 5498, 2265, 29918, 6786, 742, 13, 462, 965, 1539, 485, 279, 2433, 6007, 25021, 1001, 29918, 11889, 5813, 742, 13, 462, 965, 1371, 543, 13696, 4680, 20333, 29879, 29898, 23848, 3633, 29897, 8952, 1159, 13, 1678, 2318, 29918, 2754, 1989, 353, 1051, 29918, 8346, 29889, 1202, 29918, 6149, 1474, 29918, 735, 7009, 573, 29918, 2972, 29898, 12403, 29922, 5574, 29897, 13, 1678, 2318, 29918, 2754, 1989, 29889, 1202, 29918, 23516, 877, 29899, 3274, 742, 13, 462, 795, 525, 489, 5498, 2265, 29899, 2754, 1989, 742, 13, 462, 795, 1371, 543, 23665, 2265, 20333, 29879, 29898, 4993, 3633, 29897, 7882, 1989, 1159, 13, 1678, 2318, 29918, 2754, 1989, 29889, 1202, 29918, 23516, 877, 29899, 1113, 742, 13, 462, 795, 525, 489, 25978, 261, 29899, 2754, 1989, 742, 13, 462, 795, 2731, 2433, 5498, 2265, 29918, 2754, 1989, 742, 13, 462, 795, 1539, 485, 279, 2433, 6007, 25021, 1001, 29918, 8787, 10818, 742, 13, 462, 795, 1371, 543, 13696, 4680, 20333, 29879, 29898, 23848, 3633, 29897, 7882, 1989, 1159, 13, 1678, 1051, 29918, 8346, 29889, 1202, 29918, 23516, 877, 29899, 29884, 742, 13, 462, 632, 525, 489, 25118, 742, 13, 462, 632, 1371, 543, 2940, 3553, 1353, 1159, 13, 2 ]
python/ack.py	catseye/Dipple	5	42633	#!/usr/bin/env python import sys def ack(m, n): if m == 0: return n + 1 elif n == 0: return ack(m-1, 1) else: return ack(m-1, ack(m, n-1)) sys.setrecursionlimit(12000) for m in range(0, 4): for n in range(0, 10): print "ack(%s,%s)=%s" % (m, n, ack(m, n)) m = 4 n = 0 print "ack(%s,%s)=%s" % (m, n, ack(m, n)) # m = 4 # n = 1 # print "ack(%s,%s)=%s" % (m, n, ack(m, n))	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 13, 5215, 10876, 13, 13, 1753, 263, 384, 29898, 29885, 29892, 302, 1125, 13, 1678, 565, 286, 1275, 29871, 29900, 29901, 13, 4706, 736, 302, 718, 29871, 29896, 13, 1678, 25342, 302, 1275, 29871, 29900, 29901, 13, 4706, 736, 263, 384, 29898, 29885, 29899, 29896, 29892, 29871, 29896, 29897, 13, 1678, 1683, 29901, 13, 4706, 736, 263, 384, 29898, 29885, 29899, 29896, 29892, 263, 384, 29898, 29885, 29892, 302, 29899, 29896, 876, 13, 13, 9675, 29889, 842, 3757, 1295, 291, 13400, 29898, 29896, 29906, 29900, 29900, 29900, 29897, 13, 13, 1454, 286, 297, 3464, 29898, 29900, 29892, 29871, 29946, 1125, 13, 29871, 363, 302, 297, 3464, 29898, 29900, 29892, 29871, 29896, 29900, 1125, 13, 1678, 1596, 376, 547, 29414, 29879, 24163, 29879, 3892, 29995, 29879, 29908, 1273, 313, 29885, 29892, 302, 29892, 263, 384, 29898, 29885, 29892, 302, 876, 13, 13, 29885, 353, 29871, 29946, 13, 29876, 353, 29871, 29900, 13, 2158, 376, 547, 29414, 29879, 24163, 29879, 3892, 29995, 29879, 29908, 1273, 313, 29885, 29892, 302, 29892, 263, 384, 29898, 29885, 29892, 302, 876, 13, 13, 29937, 286, 353, 29871, 29946, 13, 29937, 302, 353, 29871, 29896, 13, 29937, 1596, 376, 547, 29414, 29879, 24163, 29879, 3892, 29995, 29879, 29908, 1273, 313, 29885, 29892, 302, 29892, 263, 384, 29898, 29885, 29892, 302, 876, 13, 2 ]
src/Diagnostics.AIProjects/SearchAPI/SearchModule/TfIdfSearchModel.py	yiningwei/Azure-AppServices-Diagnostics	0	86060	<reponame>yiningwei/Azure-AppServices-Diagnostics<filename>src/Diagnostics.AIProjects/SearchAPI/SearchModule/TfIdfSearchModel.py import json, os from SearchModule.ModelInfo import ModelInfo from SearchModule.TokenizerModule import getAllNGrams from gensim.models import TfidfModel from gensim import corpora, similarities from SearchModule.Exceptions import * from SearchModule.Utilities import absPath, verifyFile from SearchModule.MessageStrings import fileMissingMessage class TfIdfSearchModel: def __init__(self, modelpackagepath): packageFiles = { "dictionaryFile": "dictionary.dict", "m1ModelFile": "m1.model", "m1IndexFile": "m1.index", "m2ModelFile": "m2.model", "m2IndexFile": "m2.index", "detectorsFile": "Detectors.json", "sampleUtterancesFile": "SampleUtterances.json", "mappingsFile": "Mappings.json", "modelInfo": "ModelInfo.json" } for key in packageFiles.keys(): packageFiles[key] = absPath(os.path.join(modelpackagepath, packageFiles[key])) self.packageFiles = packageFiles self.optionalFiles = ["mappingsFile"] if not self.verifyModelFiles(): raise ModelFileVerificationFailed(fileMissingMessage) self.models = {"dictionary": None, "m1Model": None, "m1Index": None, "m2Model": None, "m2Index": None, "detectors": None, "sampleUtterances": None, "mappings": None, "modelInfo": None} try: with open(self.packageFiles["modelInfo"], "r") as fp: self.models["modelInfo"] = ModelInfo(json.loads(fp.read())) fp.close() except: self.models["modelInfo"] = ModelInfo({}) try: self.models["dictionary"] = corpora.Dictionary.load(self.packageFiles["dictionaryFile"]) except: raise ModelFileLoadFailed("Failed to load dictionary from file " + self.packageFiles["dictionaryFile"]) try: self.models["m1Model"] = TfidfModel.load(self.packageFiles["m1ModelFile"]) except: raise ModelFileLoadFailed("Failed to load model from file " + self.packageFiles["m1ModelFile"]) try: self.models["m1Index"] = similarities.MatrixSimilarity.load(self.packageFiles["m1IndexFile"]) except: raise ModelFileLoadFailed("Failed to load index from file " + self.packageFiles["m1IndexFile"]) try: self.models["m2Model"] = TfidfModel.load(self.packageFiles["m2ModelFile"]) self.models["m2Model"] = None del self.models["m2Model"] except: raise ModelFileLoadFailed("Failed to load model from file " + self.packageFiles["m2ModelFile"]) try: self.models["m2Index"] = similarities.MatrixSimilarity.load(self.packageFiles["m2IndexFile"]) self.models["m2Index"] = None del self.models["m2Index"] except: raise ModelFileLoadFailed("Failed to load index from file " + self.packageFiles["m2IndexFile"]) try: with open(self.packageFiles["detectorsFile"], "r") as f: self.models["detectors"] = json.loads(f.read()) f.close() except: raise ModelFileLoadFailed("Failed to parse json from file " + self.packageFiles["detectorsFile"]) if self.models["modelInfo"].detectorContentSplitted: try: with open(self.packageFiles["mappingsFile"], "r") as f: self.models["mappings"] = json.loads(f.read()) f.close() except: raise ModelFileLoadFailed("Failed to parse json from file " + self.packageFiles["mappingsFile"]) try: with open(self.packageFiles["sampleUtterancesFile"], "r") as f: self.models["sampleUtterances"] = json.loads(f.read()) f.close() self.models["sampleUtterances"] = None del self.models["sampleUtterances"] except: raise ModelFileLoadFailed("Failed to parse json from file " + self.packageFiles["sampleUtterancesFile"]) def verifyModelFiles(self): for key in self.packageFiles.keys(): if key not in self.optionalFiles and not verifyFile(self.packageFiles[key], absolute=True, prelogMessage="TfIdfSearchModel: "): return False return True def getDetectorByIndex(self, index): detector = [x for x in self.models["mappings"] if (x["startindex"] <= index <= x["endindex"])] if detector and detector[0]: return detector[0]["id"] else: return None def queryDetectors(self, query=None): if query: try: vector = self.models["m1Model"][self.models["dictionary"].doc2bow(getAllNGrams(query, self.models["modelInfo"].textNGrams))] if self.models["modelInfo"].detectorContentSplitted: similar_doc_indices = sorted(enumerate(self.models["m1Index"][vector]), key=lambda item: -item[1])[:10] similar_docs = [] for x in similar_doc_indices: detector = self.getDetectorByIndex(x[0]) if detector and (not (detector in [p["detector"] for p in similar_docs])): similar_docs.append({"detector": self.getDetectorByIndex(x[0]), "score": str(x[1])}) else: similar_doc_indices = sorted(enumerate(self.models["m1Index"][vector]), key=lambda item: -item[1]) similar_docs = list(map(lambda x: {"detector": self.models["detectors"][x[0]]["id"], "score": str(x[1])}, similar_doc_indices)) return {"query": query, "results": similar_docs} except Exception as e: return {"query": query, "results": [], "exception": str(e)} return None def loadUtteranceModel(self): self.models["m2Model"] = TfidfModel.load(self.packageFiles["m2ModelFile"]) self.models["m2Index"] = similarities.MatrixSimilarity.load(self.packageFiles["m2IndexFile"]) with open(self.packageFiles["sampleUtterancesFile"], "r") as f: self.models["sampleUtterances"] = json.loads(f.read()) f.close() def unloadUtteranceModel(self): del self.models["m2Model"] del self.models["m2Index"] del self.models["sampleUtterances"] def queryUtterances(self, query=None, existing_utterances=[]): if query: query = query + " ".join(existing_utterances) self.loadUtteranceModel() try: vector = self.models["m2Model"][self.models["dictionary"].doc2bow(getAllNGrams(query, self.models["modelInfo"].textNGrams))] similar_doc_indices = sorted(enumerate(self.models["m2Index"][vector]), key=lambda item: -item[1]) similar_doc_indices = [x for x in similar_doc_indices if self.models["sampleUtterances"][x[0]]["text"].lower() not in existing_utterances][:10] similar_docs = list(map(lambda x: {"sampleUtterance": self.models["sampleUtterances"][x[0]], "score": str(x[1])}, similar_doc_indices)) return {"query": query, "results": similar_docs} except Exception as e: return {"query": query, "results": None} self.unloadUtteranceModel() return None	[ 1, 529, 276, 1112, 420, 29958, 29891, 2827, 26599, 29914, 28413, 29899, 2052, 13779, 29899, 12130, 20921, 29966, 9507, 29958, 4351, 29914, 12130, 20921, 29889, 23869, 25119, 29914, 7974, 8787, 29914, 7974, 7355, 29914, 29911, 29888, 1204, 29888, 7974, 3195, 29889, 2272, 13, 5215, 4390, 29892, 2897, 13, 3166, 11856, 7355, 29889, 3195, 3401, 1053, 8125, 3401, 13, 3166, 11856, 7355, 29889, 6066, 3950, 7355, 1053, 679, 3596, 29940, 3338, 2232, 13, 3166, 26943, 326, 29889, 9794, 1053, 323, 29888, 333, 29888, 3195, 13, 3166, 26943, 326, 1053, 17266, 29874, 29892, 2788, 1907, 13, 3166, 11856, 7355, 29889, 2451, 29879, 1053, 334, 13, 3166, 11856, 7355, 29889, 7270, 1907, 1053, 6425, 2605, 29892, 11539, 2283, 13, 3166, 11856, 7355, 29889, 3728, 26545, 1053, 934, 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python/errnames.py	amirgeva/redrobot	1	123789	<gh_stars>1-10 import errno def get_error_name(e): if e==errno.EPERM: return 'Operation not permitted' if e==errno.ENOENT: return 'No such file or directory' if e==errno.ESRCH: return 'No such process' if e==errno.EINTR: return 'Interrupted system call' if e==errno.EIO: return 'I/O error' if e==errno.ENXIO: return 'No such device or address' if e==errno.E2BIG: return 'Arg list too long' if e==errno.ENOEXEC: return 'Exec format error' if e==errno.EBADF: return 'Bad file number' if e==errno.ECHILD: return 'No child processes' if e==errno.EAGAIN: return 'Try again' if e==errno.ENOMEM: return 'Out of memory' if e==errno.EACCES: return 'Permission denied' if e==errno.EFAULT: return 'Bad address' if e==errno.ENOTBLK: return 'Block device required' if e==errno.EBUSY: return 'Device or resource busy' if e==errno.EEXIST: return 'File exists' if e==errno.EXDEV: return 'Cross-device link' if e==errno.ENODEV: return 'No such device' if e==errno.ENOTDIR: return 'Not a directory' if e==errno.EISDIR: return 'Is a directory' if e==errno.EINVAL: return 'Invalid argument' if e==errno.ENFILE: return 'File table overflow' if e==errno.EMFILE: return 'Too many open files' if e==errno.ENOTTY: return 'Not a typewriter' if e==errno.ETXTBSY: return 'Text file busy' if e==errno.EFBIG: return 'File too large' if e==errno.ENOSPC: return 'No space left on device' if e==errno.ESPIPE: return 'Illegal seek' if e==errno.EROFS: return 'Read-only file system' if e==errno.EMLINK: return 'Too many links' if e==errno.EPIPE: return 'Broken pipe' if e==errno.EDOM: return 'Math argument out of domain of func' if e==errno.ERANGE: return 'Math result not representable' if e==errno.EDEADLK: return 'Resource deadlock would occur' if e==errno.ENAMETOOLONG: return 'File name too long' if e==errno.ENOLCK: return 'No record locks available' if e==errno.ENOSYS: return 'Function not implemented' if e==errno.ENOTEMPTY: return 'Directory not empty' if e==errno.ELOOP: return 'Too many symbolic links encountered' if e==errno.EWOULDBLOCK: return 'Operation would block' if e==errno.ENOMSG: return 'No message of desired type' if e==errno.EIDRM: return 'Identifier removed' if e==errno.ECHRNG: return 'Channel number out of range' if e==errno.EL2NSYNC: return 'Level 2 not synchronized' if e==errno.EL3HLT: return 'Level 3 halted' if e==errno.EL3RST: return 'Level 3 reset' if e==errno.ELNRNG: return 'Link number out of range' if e==errno.EUNATCH: return 'Protocol driver not attached' if e==errno.ENOCSI: return 'No CSI structure available' if e==errno.EL2HLT: return 'Level 2 halted' if e==errno.EBADE: return 'Invalid exchange' if e==errno.EBADR: return 'Invalid request descriptor' if e==errno.EXFULL: return 'Exchange full' if e==errno.ENOANO: return 'No anode' if e==errno.EBADRQC: return 'Invalid request code' if e==errno.EBADSLT: return 'Invalid slot' if e==errno.EDEADLOCK: return 'File locking deadlock error' if e==errno.EBFONT: return 'Bad font file format' if e==errno.ENOSTR: return 'Device not a stream' if e==errno.ENODATA: return 'No data available' if e==errno.ETIME: return 'Timer expired' if e==errno.ENOSR: return 'Out of streams resources' if e==errno.ENONET: return 'Machine is not on the network' if e==errno.ENOPKG: return 'Package not installed' if e==errno.EREMOTE: return 'Object is remote' if e==errno.ENOLINK: return 'Link has been severed' if e==errno.EADV: return 'Advertise error' if e==errno.ESRMNT: return 'Srmount error' if e==errno.ECOMM: return 'Communication error on send' if e==errno.EPROTO: return 'Protocol error' if e==errno.EMULTIHOP: return 'Multihop attempted' if e==errno.EDOTDOT: return 'RFS specific error' if e==errno.EBADMSG: return 'Not a data message' if e==errno.EOVERFLOW: return 'Value too large for defined data type' if e==errno.ENOTUNIQ: return 'Name not unique on network' if e==errno.EBADFD: return 'File descriptor in bad state' if e==errno.EREMCHG: return 'Remote address changed' if e==errno.ELIBACC: return 'Can not access a needed shared library' if e==errno.ELIBBAD: return 'Accessing a corrupted shared library' if e==errno.ELIBSCN: return '.lib section in a.out corrupted' if e==errno.ELIBMAX: return 'Attempting to link in too many shared libraries' if e==errno.ELIBEXEC: return 'Cannot exec a shared library directly' if e==errno.EILSEQ: return 'Illegal byte sequence' if e==errno.ERESTART: return 'Interrupted system call should be restarted' if e==errno.ESTRPIPE: return 'Streams pipe error' if e==errno.EUSERS: return 'Too many users' if e==errno.ENOTSOCK: return 'Socket operation on non-socket' if e==errno.EDESTADDRREQ: return 'Destination address required' if e==errno.EMSGSIZE: return 'Message too long' if e==errno.EPROTOTYPE: return 'Protocol wrong type for socket' if e==errno.ENOPROTOOPT: return 'Protocol not available' if e==errno.EPROTONOSUPPORT: return 'Protocol not supported' if e==errno.ESOCKTNOSUPPORT: return 'Socket type not supported' if e==errno.EOPNOTSUPP: return 'Operation not supported on transport endpoint' if e==errno.EPFNOSUPPORT: return 'Protocol family not supported' if e==errno.EAFNOSUPPORT: return 'Address family not supported by protocol' if e==errno.EADDRINUSE: return 'Address already in use' if e==errno.EADDRNOTAVAIL: return 'Cannot assign requested address' if e==errno.ENETDOWN: return 'Network is down' if e==errno.ENETUNREACH: return 'Network is unreachable' if e==errno.ENETRESET: return 'Network dropped connection because of reset' if e==errno.ECONNABORTED: return 'Software caused connection abort' if e==errno.ECONNRESET: return 'Connection reset by peer' if e==errno.ENOBUFS: return 'No buffer space available' if e==errno.EISCONN: return 'Transport endpoint is already connected' if e==errno.ENOTCONN: return 'Transport endpoint is not connected' if e==errno.ESHUTDOWN: return 'Cannot send after transport endpoint shutdown' if e==errno.ETOOMANYREFS: return 'Too many references: cannot splice' if e==errno.ETIMEDOUT: return 'Connection timed out' if e==errno.ECONNREFUSED: return 'Connection refused' if e==errno.EHOSTDOWN: return 'Host is down' if e==errno.EHOSTUNREACH: return 'No route to host' if e==errno.EALREADY: return 'Operation already in progress' if e==errno.EINPROGRESS: return 'Operation now in progress' if e==errno.ESTALE: return 'Stale NFS file handle' if e==errno.EUCLEAN: return 'Structure needs cleaning' if e==errno.ENOTNAM: return 'Not a XENIX named type file' if e==errno.ENAVAIL: return 'No XENIX semaphores available' if e==errno.EISNAM: return 'Is a named type file' if e==errno.EREMOTEIO: return 'Remote I/O error' if e==errno.EDQUOT: return 'Quota exceeded' return "Unknown"	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 5215, 4589, 1217, 13, 13, 1753, 679, 29918, 2704, 29918, 978, 29898, 29872, 1125, 13, 1678, 565, 321, 1360, 3127, 1217, 29889, 29923, 13171, 29924, 29901, 13, 4706, 736, 525, 10925, 451, 21905, 29915, 13, 1678, 565, 321, 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bin/calc_post_prob_codivergence.py	phyletica/gekgo	1	196643	<filename>bin/calc_post_prob_codivergence.py #! /usr/bin/env python """ Calculate the posterior probability that the Gekko pairs Panay-Masbate and Sabtang-Batan co-diverged. """ import os import sys import glob import logging import gekgo_util import pycoevolity _LOG = gekgo_util.RunLogger(name=__file__, log_to_stderr=True, log_to_file=False) def main(): paths = glob.glob(os.path.join(gekgo_util.ECOEVOLITY_OUTPUT_DIR, "run-?-gekko-rate2000-state-run-1.log")) posterior = pycoevolity.posterior.PosteriorSummary(paths, burnin = 101) post_prob_shared = 0.0 sys.stdout.write("model\tpost_prob\n") for model, prob in posterior.get_models(): sys.stdout.write("{model}\t{post_prob}\n".format( model = model, post_prob = prob)) if model[3] == model[5]: post_prob_shared += prob sys.stdout.write("{0}\n".format(post_prob_shared)) if __name__ == '__main__': main()	[ 1, 529, 9507, 29958, 2109, 29914, 28667, 29918, 2490, 29918, 22795, 29918, 19284, 2147, 10238, 29889, 2272, 13, 29937, 29991, 847, 4855, 29914, 2109, 29914, 6272, 3017, 13, 13, 15945, 29908, 13, 27065, 403, 278, 13446, 6976, 393, 278, 402, 1416, 2901, 11000, 6518, 388, 29899, 29924, 294, 29890, 403, 322, 13, 29903, 370, 29873, 574, 29899, 29933, 23402, 1302, 29899, 29881, 2147, 3192, 29889, 13, 15945, 29908, 13, 13, 5215, 2897, 13, 5215, 10876, 13, 5215, 13149, 13, 5215, 12183, 13, 13, 5215, 14865, 1484, 29918, 4422, 13, 13, 5215, 11451, 1111, 29872, 1555, 537, 13, 13, 29918, 14480, 353, 14865, 1484, 29918, 4422, 29889, 6558, 16363, 29898, 978, 29922, 1649, 1445, 1649, 29892, 13, 4706, 1480, 29918, 517, 29918, 303, 20405, 29922, 5574, 29892, 13, 4706, 1480, 29918, 517, 29918, 1445, 29922, 8824, 29897, 13, 13, 1753, 1667, 7295, 13, 1678, 10898, 353, 13149, 29889, 23705, 29898, 359, 29889, 2084, 29889, 7122, 29898, 479, 29895, 1484, 29918, 4422, 29889, 29923, 3217, 22240, 5607, 11937, 29918, 12015, 12336, 29918, 9464, 29892, 13, 9651, 376, 3389, 29899, 29973, 29899, 479, 29895, 2901, 29899, 10492, 29906, 29900, 29900, 29900, 29899, 3859, 29899, 3389, 29899, 29896, 29889, 1188, 5783, 13, 1678, 13446, 353, 11451, 1111, 29872, 1555, 537, 29889, 2490, 261, 1611, 29889, 6747, 261, 1611, 26289, 29898, 24772, 29892, 12138, 262, 353, 29871, 29896, 29900, 29896, 29897, 13, 1678, 1400, 29918, 22795, 29918, 12366, 353, 29871, 29900, 29889, 29900, 13, 1678, 10876, 29889, 25393, 29889, 3539, 703, 4299, 29905, 29873, 2490, 29918, 22795, 29905, 29876, 1159, 13, 1678, 363, 1904, 29892, 2070, 297, 13446, 29889, 657, 29918, 9794, 7295, 13, 4706, 10876, 29889, 25393, 29889, 3539, 703, 29912, 4299, 1012, 29873, 29912, 2490, 29918, 22795, 1012, 29876, 1642, 4830, 29898, 13, 18884, 1904, 353, 1904, 29892, 13, 18884, 1400, 29918, 22795, 353, 2070, 876, 13, 4706, 565, 1904, 29961, 29941, 29962, 1275, 1904, 29961, 29945, 5387, 13, 9651, 1400, 29918, 22795, 29918, 12366, 4619, 2070, 13, 1678, 10876, 29889, 25393, 29889, 3539, 703, 29912, 29900, 1012, 29876, 1642, 4830, 29898, 2490, 29918, 22795, 29918, 12366, 876, 13, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 1667, 580, 13, 2 ]
python/after/banking/controller.py	vritb/command-pattern-undo-redo	5	98853	from dataclasses import dataclass, field from banking.transaction import Transaction @dataclass class BankController: undo_stack: list[Transaction] = field(default_factory=list) redo_stack: list[Transaction] = field(default_factory=list) def execute(self, transaction: Transaction) -> None: transaction.execute() self.redo_stack.clear() self.undo_stack.append(transaction) def undo(self) -> None: if not self.undo_stack: return transaction = self.undo_stack.pop() transaction.undo() self.redo_stack.append(transaction) def redo(self) -> None: if not self.redo_stack: return transaction = self.redo_stack.pop() transaction.execute() self.undo_stack.append(transaction)	[ 1, 515, 848, 13203, 1053, 848, 1990, 29892, 1746, 13, 13, 3166, 9124, 292, 29889, 20736, 1053, 4103, 2467, 13, 13, 13, 29992, 1272, 1990, 13, 1990, 10253, 2956, 29901, 13, 1678, 563, 29877, 29918, 1429, 29901, 1051, 29961, 12460, 29962, 353, 1746, 29898, 4381, 29918, 14399, 29922, 1761, 29897, 13, 1678, 337, 1867, 29918, 1429, 29901, 1051, 29961, 12460, 29962, 353, 1746, 29898, 4381, 29918, 14399, 29922, 1761, 29897, 13, 13, 1678, 822, 6222, 29898, 1311, 29892, 10804, 29901, 4103, 2467, 29897, 1599, 6213, 29901, 13, 4706, 10804, 29889, 7978, 580, 13, 4706, 1583, 29889, 1127, 29877, 29918, 1429, 29889, 8551, 580, 13, 4706, 1583, 29889, 6201, 29918, 1429, 29889, 4397, 29898, 20736, 29897, 13, 13, 1678, 822, 563, 29877, 29898, 1311, 29897, 1599, 6213, 29901, 13, 4706, 565, 451, 1583, 29889, 6201, 29918, 1429, 29901, 13, 9651, 736, 13, 4706, 10804, 353, 1583, 29889, 6201, 29918, 1429, 29889, 7323, 580, 13, 4706, 10804, 29889, 6201, 580, 13, 4706, 1583, 29889, 1127, 29877, 29918, 1429, 29889, 4397, 29898, 20736, 29897, 13, 13, 1678, 822, 337, 1867, 29898, 1311, 29897, 1599, 6213, 29901, 13, 4706, 565, 451, 1583, 29889, 1127, 29877, 29918, 1429, 29901, 13, 9651, 736, 13, 4706, 10804, 353, 1583, 29889, 1127, 29877, 29918, 1429, 29889, 7323, 580, 13, 4706, 10804, 29889, 7978, 580, 13, 4706, 1583, 29889, 6201, 29918, 1429, 29889, 4397, 29898, 20736, 29897, 13, 2 ]
data/tools/replay/ui.py	ASGuard-UCI/ld-metric	0	66438	<gh_stars>0 #!/usr/bin/env python import argparse import os import sys os.environ["OMP_NUM_THREADS"] = "1" import cv2 import numpy as np import pygame import zmq from common.basedir import BASEDIR from common.transformations.camera import FULL_FRAME_SIZE, eon_intrinsics from common.transformations.model import (MODEL_CX, MODEL_CY, MODEL_INPUT_SIZE, get_camera_frame_from_model_frame) from selfdrive.car.toyota.interface import CarInterface as ToyotaInterface from selfdrive.config import UIParams as UP from selfdrive.controls.lib.vehicle_model import VehicleModel from selfdrive.messaging import SubMaster, recv_one from selfdrive.services import service_list from tools.replay.lib.ui_helpers import (_BB_TO_FULL_FRAME, BLACK, BLUE, GREEN, YELLOW, RED, CalibrationTransformsForWarpMatrix, draw_lead_car, draw_lead_on, draw_mpc, extract_model_data, get_blank_lid_overlay, init_plots, maybe_update_radar_points, plot_model, pygame_modules_have_loaded, warp_points) os.environ['BASEDIR'] = BASEDIR ANGLE_SCALE = 5.0 HOR = os.getenv("HORIZONTAL") is not None def ui_thread(addr, frame_address): context = zmq.Context.instance() # TODO: Detect car from replay and use that to select carparams CP = ToyotaInterface.get_params("TOYOTA PRIUS 2017") VM = VehicleModel(CP) CalP = np.asarray([[0, 0], [MODEL_INPUT_SIZE[0], 0], [MODEL_INPUT_SIZE[0], MODEL_INPUT_SIZE[1]], [0, MODEL_INPUT_SIZE[1]]]) vanishing_point = np.asarray([[MODEL_CX, MODEL_CY]]) pygame.init() pygame.font.init() assert pygame_modules_have_loaded() if HOR: size = (640+384+640, 960) write_x = 5 write_y = 680 else: size = (640+384, 960+300) write_x = 645 write_y = 970 pygame.display.set_caption("openpilot debug UI") screen = pygame.display.set_mode(size, pygame.DOUBLEBUF) alert1_font = pygame.font.SysFont("arial", 30) alert2_font = pygame.font.SysFont("arial", 20) info_font = pygame.font.SysFont("arial", 15) camera_surface = pygame.surface.Surface((640, 480), 0, 24).convert() cameraw_surface = pygame.surface.Surface(MODEL_INPUT_SIZE, 0, 24).convert() cameraw_test_surface = pygame.surface.Surface(MODEL_INPUT_SIZE, 0, 24) top_down_surface = pygame.surface.Surface((UP.lidar_x, UP.lidar_y),0,8) frame = context.socket(zmq.SUB) frame.setsockopt(zmq.CONFLATE, 1) frame.connect(frame_address or "tcp://%s:%d" % (addr, service_list['frame'].port)) frame.setsockopt(zmq.SUBSCRIBE, b"") sm = SubMaster(['carState', 'plan', 'carControl', 'radarState', 'liveCalibration', 'controlsState', 'liveTracks', 'model', 'liveMpc', 'liveParameters', 'pathPlan']) calibration = None img = np.zeros((480, 640, 3), dtype='uint8') imgff = np.zeros((FULL_FRAME_SIZE[1], FULL_FRAME_SIZE[0], 3), dtype=np.uint8) imgw = np.zeros((160, 320, 3), dtype=np.uint8) # warped image lid_overlay_blank = get_blank_lid_overlay(UP) # plots name_to_arr_idx = { "gas": 0, "computer_gas": 1, "user_brake": 2, "computer_brake": 3, "v_ego": 4, "v_pid": 5, "angle_steers_des": 6, "angle_steers": 7, "angle_steers_k": 8, "steer_torque": 9, "v_override": 10, "v_cruise": 11, "a_ego": 12, "a_target": 13, "accel_override": 14} plot_arr = np.zeros((100, len(name_to_arr_idx.values()))) plot_xlims = [(0, plot_arr.shape[0]), (0, plot_arr.shape[0]), (0, plot_arr.shape[0]), (0, plot_arr.shape[0])] plot_ylims = [(-0.1, 1.1), (-ANGLE_SCALE, ANGLE_SCALE), (0., 75.), (-3.0, 2.0)] plot_names = [["gas", "computer_gas", "user_brake", "computer_brake", "accel_override"], ["angle_steers", "angle_steers_des", "angle_steers_k", "steer_torque"], ["v_ego", "v_override", "v_pid", "v_cruise"], ["a_ego", "a_target"]] plot_colors = [["b", "b", "g", "r", "y"], ["b", "g", "y", "r"], ["b", "g", "r", "y"], ["b", "r"]] plot_styles = [["-", "-", "-", "-", "-"], ["-", "-", "-", "-"], ["-", "-", "-", "-"], ["-", "-"]] draw_plots = init_plots(plot_arr, name_to_arr_idx, plot_xlims, plot_ylims, plot_names, plot_colors, plot_styles, bigplots=True) counter = 0 while 1: list(pygame.event.get()) screen.fill((64,64,64)) lid_overlay = lid_overlay_blank.copy() top_down = top_down_surface, lid_overlay # *** frame *** fpkt = recv_one(frame) rgb_img_raw = fpkt.frame.image if fpkt.frame.transform: img_transform = np.array(fpkt.frame.transform).reshape(3,3) else: # assume frame is flipped img_transform = np.array([ [-1.0, 0.0, FULL_FRAME_SIZE[0]-1], [ 0.0, -1.0, FULL_FRAME_SIZE[1]-1], [ 0.0, 0.0, 1.0] ]) if rgb_img_raw and len(rgb_img_raw) == FULL_FRAME_SIZE[0] * FULL_FRAME_SIZE[1] * 3: imgff = np.frombuffer(rgb_img_raw, dtype=np.uint8).reshape((FULL_FRAME_SIZE[1], FULL_FRAME_SIZE[0], 3)) imgff = imgff[:, :, ::-1] # Convert BGR to RGB cv2.warpAffine(imgff, np.dot(img_transform, _BB_TO_FULL_FRAME)[:2], (img.shape[1], img.shape[0]), dst=img, flags=cv2.WARP_INVERSE_MAP) intrinsic_matrix = eon_intrinsics else: img.fill(0) intrinsic_matrix = np.eye(3) if calibration is not None: transform = np.dot(img_transform, calibration.model_to_full_frame) imgw = cv2.warpAffine(imgff, transform[:2], (MODEL_INPUT_SIZE[0], MODEL_INPUT_SIZE[1]), flags=cv2.WARP_INVERSE_MAP) else: imgw.fill(0) sm.update() w = sm['controlsState'].lateralControlState.which() if w == 'lqrState': angle_steers_k = sm['controlsState'].lateralControlState.lqrState.steerAngle elif w == 'indiState': angle_steers_k = sm['controlsState'].lateralControlState.indiState.steerAngle else: angle_steers_k = np.inf plot_arr[:-1] = plot_arr[1:] plot_arr[-1, name_to_arr_idx['angle_steers']] = sm['controlsState'].angleSteers plot_arr[-1, name_to_arr_idx['angle_steers_des']] = sm['carControl'].actuators.steerAngle plot_arr[-1, name_to_arr_idx['angle_steers_k']] = angle_steers_k plot_arr[-1, name_to_arr_idx['gas']] = sm['carState'].gas plot_arr[-1, name_to_arr_idx['computer_gas']] = sm['carControl'].actuators.gas plot_arr[-1, name_to_arr_idx['user_brake']] = sm['carState'].brake plot_arr[-1, name_to_arr_idx['steer_torque']] = sm['carControl'].actuators.steer * ANGLE_SCALE plot_arr[-1, name_to_arr_idx['computer_brake']] = sm['carControl'].actuators.brake plot_arr[-1, name_to_arr_idx['v_ego']] = sm['controlsState'].vEgo plot_arr[-1, name_to_arr_idx['v_pid']] = sm['controlsState'].vPid plot_arr[-1, name_to_arr_idx['v_override']] = sm['carControl'].cruiseControl.speedOverride plot_arr[-1, name_to_arr_idx['v_cruise']] = sm['carState'].cruiseState.speed plot_arr[-1, name_to_arr_idx['a_ego']] = sm['carState'].aEgo plot_arr[-1, name_to_arr_idx['a_target']] = sm['plan'].aTarget plot_arr[-1, name_to_arr_idx['accel_override']] = sm['carControl'].cruiseControl.accelOverride # *** model model_data = extract_model_data(sm['model']) plot_model(model_data, VM, sm['controlsState'].vEgo, sm['controlsState'].curvature, imgw, calibration, top_down, np.array(sm['pathPlan'].dPoly)) # MPC if sm.updated['liveMpc']: draw_mpc(sm['liveMpc'], top_down) # draw all radar points maybe_update_radar_points(sm['liveTracks'], top_down[1]) if sm.updated['liveCalibration']: extrinsic_matrix = np.asarray(sm['liveCalibration'].extrinsicMatrix).reshape(3, 4) ke = intrinsic_matrix.dot(extrinsic_matrix) warp_matrix = get_camera_frame_from_model_frame(ke) calibration = CalibrationTransformsForWarpMatrix(warp_matrix, intrinsic_matrix, extrinsic_matrix) # draw red pt for lead car in the main img for lead in [sm['radarState'].leadOne, sm['radarState'].leadTwo]: if lead.status: if calibration is not None: draw_lead_on(img, lead.dRel, lead.yRel, calibration, color=(192,0,0)) draw_lead_car(lead.dRel, top_down) # * blits *** pygame.surfarray.blit_array(camera_surface, img.swapaxes(0,1)) screen.blit(camera_surface, (0, 0)) # display alerts alert_line1 = alert1_font.render(sm['controlsState'].alertText1, True, (255,0,0)) alert_line2 = alert2_font.render(sm['controlsState'].alertText2, True, (255,0,0)) screen.blit(alert_line1, (180, 150)) screen.blit(alert_line2, (180, 190)) if calibration is not None and img is not None: cpw = warp_points(CalP, calibration.model_to_bb) vanishing_pointw = warp_points(vanishing_point, calibration.model_to_bb) pygame.draw.polygon(screen, BLUE, tuple(map(tuple, cpw)), 1) pygame.draw.circle(screen, BLUE, list(map(int, map(round, vanishing_pointw[0]))), 2) if HOR: screen.blit(draw_plots(plot_arr), (640+384, 0)) else: screen.blit(draw_plots(plot_arr), (0, 600)) pygame.surfarray.blit_array(cameraw_surface, imgw.swapaxes(0, 1)) screen.blit(cameraw_surface, (320, 480)) pygame.surfarray.blit_array(top_down) screen.blit(top_down[0], (640,0)) i = 0 SPACING = 25 lines = [ info_font.render("ENABLED", True, GREEN if sm['controlsState'].enabled else BLACK), info_font.render("BRAKE LIGHTS", True, RED if sm['carState'].brakeLights else BLACK), info_font.render("SPEED: " + str(round(sm['carState'].vEgo, 1)) + " m/s", True, YELLOW), info_font.render("LONG CONTROL STATE: " + str(sm['controlsState'].longControlState), True, YELLOW), info_font.render("LONG MPC SOURCE: " + str(sm['plan'].longitudinalPlanSource), True, YELLOW), None, info_font.render("ANGLE OFFSET (AVG): " + str(round(sm['liveParameters'].angleOffsetAverage, 2)) + " deg", True, YELLOW), info_font.render("ANGLE OFFSET (INSTANT): " + str(round(sm['liveParameters'].angleOffset, 2)) + " deg", True, YELLOW), info_font.render("STIFFNESS: " + str(round(sm['liveParameters'].stiffnessFactor 100., 2)) + " %", True, YELLOW), info_font.render("STEER RATIO: " + str(round(sm['liveParameters'].steerRatio, 2)), True, YELLOW) ] for i, line in enumerate(lines): if line is not None: screen.blit(line, (write_x, write_y + i * SPACING)) # this takes time...vsync or something pygame.display.flip() def get_arg_parser(): parser = argparse.ArgumentParser( description="Show replay data in a UI.", formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument("ip_address", nargs="?", default="127.0.0.1", help="The ip address on which to receive zmq messages.") parser.add_argument("--frame-address", default=None, help="The frame address (fully qualified ZMQ endpoint for frames) on which to receive zmq messages.") return parser if __name__ == "__main__": args = get_arg_parser().parse_args(sys.argv[1:]) ui_thread(args.ip_address, args.frame_address)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 5215, 1852, 5510, 13, 5215, 2897, 13, 5215, 10876, 13, 13, 359, 29889, 21813, 3366, 29949, 3580, 29918, 13967, 29918, 4690, 16310, 29903, 3108, 353, 376, 29896, 29908, 13, 13, 5215, 13850, 29906, 13, 5215, 12655, 408, 7442, 13, 5215, 22028, 13, 5215, 12162, 29939, 13, 13, 3166, 3619, 29889, 6707, 381, 1053, 350, 8127, 9464, 13, 3166, 3619, 29889, 9067, 800, 29889, 26065, 1053, 383, 3299, 29918, 29943, 4717, 2303, 29918, 14226, 29892, 321, 265, 29918, 262, 509, 1144, 1199, 13, 3166, 3619, 29889, 9067, 800, 29889, 4299, 1053, 313, 20387, 29931, 29918, 29907, 29990, 29892, 16999, 2287, 29931, 29918, 29907, 29979, 29892, 16999, 2287, 29931, 29918, 1177, 12336, 29918, 14226, 29892, 13, 462, 462, 3986, 679, 29918, 26065, 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ecom/prep.py	leocnj/ecom-rakuten	23	150223	<reponame>leocnj/ecom-rakuten #!/usr/bin/env python from . import data, vocab import collections import pandas as pd from sklearn.model_selection import train_test_split def char_encoder(items): freq = collections.Counter(ch for item in items for ch in item) itos = [o for o, c in freq.most_common() if c >= 10] itos.insert(0, '_PAD_') itos.insert(1, '_BOS_') itos.insert(2, '_EOS_') itos.insert(3, '_UNK_') stoi = collections.defaultdict(lambda: 3, vocab.mk_stoi(itos)) return vocab.CharEncoder(itos, stoi, freq) def category_encoder(cats): freq = collections.Counter(cats) itos = [o for o, c in freq.most_common()] return vocab.CategoryEncoder(itos, vocab.mk_stoi(itos), freq) def validation_set(all_train): freq = all_train.groupby('cat', as_index=False).count() singletons = freq[freq.item == 1].cat single_train = all_train[all_train.cat.isin(singletons)] multi_train = all_train[~all_train.cat.isin(singletons)] most_train, val = train_test_split( multi_train, test_size=200000, random_state=42, stratify=multi_train['cat'], ) train = pd.concat([most_train, single_train]) return train, val def main(): train, val = validation_set(data.load_all_train()) data.save_train_val(train, val) enc = char_encoder(train.item) cenc = category_encoder(train.cat) data.save_encoders(enc, cenc) trn_enc = enc.encode(train.item) val_enc = enc.encode(val.item) trn_ds = data.RakDataset(trn_enc, cenc.encode(train.cat)) val_ds = data.RakDataset(val_enc, cenc.encode(val.cat)) data.save_datasets(trn_ds, val_ds) if __name__ == '__main__': main()	[ 1, 529, 276, 1112, 420, 29958, 280, 542, 29876, 29926, 29914, 29872, 510, 29899, 4442, 6935, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 13, 3166, 869, 1053, 848, 29892, 7931, 370, 13, 13, 5215, 16250, 13, 5215, 11701, 408, 10518, 13, 13, 3166, 2071, 19668, 29889, 4299, 29918, 21731, 1053, 7945, 29918, 1688, 29918, 5451, 13, 13, 13, 1753, 1373, 29918, 3977, 6119, 29898, 7076, 1125, 13, 1678, 3005, 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tests/r/test_utilities2.py	hajime9652/observations	199	97811	from __future__ import absolute_import from __future__ import division from __future__ import print_function import shutil import sys import tempfile from observations.r.utilities2 import utilities2 def test_utilities2(): """Test module utilities2.py by downloading utilities2.csv and testing shape of extracted data has 117 rows and 19 columns """ test_path = tempfile.mkdtemp() x_train, metadata = utilities2(test_path) try: assert x_train.shape == (117, 19) except: shutil.rmtree(test_path) raise()	[ 1, 515, 4770, 29888, 9130, 1649, 1053, 8380, 29918, 5215, 13, 3166, 4770, 29888, 9130, 1649, 1053, 8542, 13, 3166, 4770, 29888, 9130, 1649, 1053, 1596, 29918, 2220, 13, 13, 5215, 528, 4422, 13, 5215, 10876, 13, 5215, 5694, 1445, 13, 13, 3166, 13917, 29889, 29878, 29889, 4422, 1907, 29906, 1053, 3667, 1907, 29906, 13, 13, 13, 1753, 1243, 29918, 4422, 1907, 29906, 7295, 13, 29871, 9995, 3057, 3883, 3667, 1907, 29906, 29889, 2272, 491, 28536, 13, 259, 3667, 1907, 29906, 29889, 7638, 322, 6724, 8267, 310, 13, 259, 23892, 848, 756, 29871, 29896, 29896, 29955, 4206, 322, 29871, 29896, 29929, 4341, 13, 29871, 9995, 13, 29871, 1243, 29918, 2084, 353, 5694, 1445, 29889, 11256, 29881, 7382, 580, 13, 29871, 921, 29918, 14968, 29892, 15562, 353, 3667, 1907, 29906, 29898, 1688, 29918, 2084, 29897, 13, 29871, 1018, 29901, 13, 1678, 4974, 921, 29918, 14968, 29889, 12181, 1275, 313, 29896, 29896, 29955, 29892, 29871, 29896, 29929, 29897, 13, 29871, 5174, 29901, 13, 1678, 528, 4422, 29889, 1758, 8336, 29898, 1688, 29918, 2084, 29897, 13, 1678, 12020, 580, 13, 2 ]
log/image_log.py	HELLORPG/GraduationProjec	1	1613475	""" 该文件中包括了一些图像输出的调试信息例如，输出生成图像和原始图像的对比等等 """ import matplotlib.pyplot as plt import torchvision.transforms as transforms import log.tb_log as tb_log import torchvision.utils from config.trecgnet_config import TRECGNET_CONFIG import os class ImageLog: def __init__(self): return @classmethod def image_compare_to_file(cls, source_images, generate_images, standard_images, path, title="Image Compare"): """ 输出图像对比 :param source_images: 源数据，Tensor :param generate_images: :param standard_images: :param path: :param title: :return: """ # tb_log.tb_test(images=torchvision.utils.make_grid(generate_images.clone().cpu().data, 3, # normalize=True), path=os.path.join(TRECGNET_CONFIG.MODEL_IMAGE_LOG_DIR, "tb")) plt.clf() plt.close("all") plt.suptitle(title) plt.figure(figsize=(40, 40)) image_len = source_images.shape[0] for i in range(0, image_len): plt.subplot(3, image_len, i + 1) source_image = transforms.ToPILImage()(cls.trans(source_images[i])).convert("RGB") plt.imshow(source_image) plt.subplot(3, image_len, image_len + i + 1) generate_image = transforms.ToPILImage()(cls.trans(generate_images[i])).convert("RGB") plt.imshow(generate_image) plt.subplot(3, image_len, 2 * image_len + i + 1) standard_image = transforms.ToPILImage()(cls.trans(standard_images[i])).convert("RGB") plt.imshow(standard_image) plt.savefig(path) plt.clf() plt.close("all") @classmethod def trans(cls, image, mean=[0.5,0.5,0.5], std=[0.5,0.5,0.5]): for i in range(0, len(mean)): image[i] = image[i] * std[i] + mean[i] return image	[ 1, 9995, 13, 31751, 30333, 30631, 30275, 31473, 233, 142, 175, 30743, 30287, 31959, 30861, 31551, 31573, 30544, 30210, 31268, 31787, 30689, 31021, 13, 31507, 30847, 30214, 31573, 30544, 30486, 30494, 30861, 31551, 30503, 30667, 31020, 30861, 31551, 30210, 30783, 31419, 31184, 31184, 13, 15945, 29908, 13, 13, 13, 5215, 22889, 29889, 2272, 5317, 408, 14770, 13, 5215, 4842, 305, 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Store/Store/settings/dev.py	BreezeDawn/DjangoStore	0	135493	""" Django settings for Store project. Generated by 'django-admin startproject' using Django 1.11.11. For more information on this file, see https://docs.djangoproject.com/en/1.11/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.11/ref/settings/ """ import datetime import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # 将sys.path中添加apps路径,以便寻找子应用 import sys sys.path.insert(0, os.path.join(BASE_DIR, 'apps')) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.11/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '<KEY>' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True # 允许访问的host ALLOWED_HOSTS = ['127.0.0.1','www.xingtu.info'] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'users.apps.UsersConfig', 'QQ.apps.QqConfig', 'verifications.apps.VerificationsConfig', 'areas.apps.AreasConfig', 'rest_framework', 'corsheaders' ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', 'corsheaders.middleware.CorsMiddleware' ] # 如果修改目录名,该配置需要被修改 ROOT_URLCONF = 'Store.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] # 如果修改目录名,该配置需要被修改 WSGI_APPLICATION = 'Store.wsgi.application' # Database # https://docs.djangoproject.com/en/1.11/ref/settings/#databases # 数据库配置 DATABASES = { 'default': { 'ENGINE': 'django.db.backends.mysql', 'HOST': '192.168.128.128', 'PORT': 3306, 'USER': 'maimai', 'PASSWORD': '<PASSWORD>', 'NAME': 'Store' } } # Password validation # https://docs.djangoproject.com/en/1.11/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/1.11/topics/i18n/ # 修改语言 LANGUAGE_CODE = 'zh-hans' # 修改时区 TIME_ZONE = 'Asia/Shanghai' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.11/howto/static-files/ STATIC_URL = '/static/' # 设置缓存存入redis CACHES = { "default": { "BACKEND": "django_redis.cache.RedisCache", "LOCATION": "redis://192.168.128.128:6379/0", "OPTIONS": { "CLIENT_CLASS": "django_redis.client.default.DefaultClient" } }, # 存放session "session": { "BACKEND": "django_redis.cache.RedisCache", "LOCATION": "redis://192.168.128.128:6379/1", "OPTIONS": { "CLIENT_CLASS": "django_redis.client.default.DefaultClient" } }, # 存放短信验证码 "verify_codes": { "BACKEND": "django_redis.cache.RedisCache", "LOCATION": "redis://192.168.128.128:6379/2", "OPTIONS": { "CLIENT_CLASS": "django_redis.client.default.DefaultClient" } }, # 3号为celery中间人 # 存放三级联动 "areas": { "BACKEND": "django_redis.cache.RedisCache", "LOCATION": "redis://192.168.128.128:6379/4", "OPTIONS": { "CLIENT_CLASS": "django_redis.client.default.DefaultClient" } }, } # 设置session用缓存保存,而上面设置缓存用redis保存,所以session的缓存保存在了redis中 SESSION_ENGINE = "django.contrib.sessions.backends.cache" # 使用名为'session'的redis配置 SESSION_CACHE_ALIAS = "session" LOGGING = { # 保留字段 'version': 1, # 是否禁用已经存在的日志器 'disable_existing_loggers': False, # 日志信息显示的格式 'formatters': { # 标准版 'standard': { 'format': '[%(asctime)s][%(threadName)s:%(thread)d][task_id:%(name)s]' '[%(filename)s:%(lineno)d][%(levelname)s][%(message)s]' }, # 冗长版 'verbose': { 'format': '%(levelname)s %(asctime)s %(module)s %(lineno)d %(message)s' }, # 简单版 'simple': { 'format': '%(levelname)s %(module)s %(lineno)d %(message)s' }, }, # 对日志进行过滤 'filters': { 'require_debug_true': { # django在debug模式下才输出日志 '()': 'django.utils.log.RequireDebugTrue', }, }, # 日志流的处理方法 'handlers': { # 把日志打印到终端 'console': { 'level': 'INFO', 'filters': ['require_debug_true'], 'class': 'logging.StreamHandler', 'formatter': 'simple' }, # 向文件中输出日志 'file': { 'level': 'INFO', 'class': 'logging.handlers.RotatingFileHandler', 'filename': os.path.join(os.path.dirname(BASE_DIR), "logs/store.log"), # 日志文件的位置 'maxBytes': 300 * 1024 * 1024, 'backupCount': 10, 'formatter': 'verbose' }, }, # 日志器 'loggers': { 'django': { # 定义了一个名为django的日志器 'handlers': ['console', 'file'], # 可以同时向终端与文件中输出日志 'propagate': True, # 是否继续传递日志信息 'level': 'INFO', # 日志器接收的最低日志级别 }, }, } REST_FRAMEWORK = { # 异常处理 'EXCEPTION_HANDLER': 'Store.utils.exceptions.drf_exception_handler', # 设置认证方案 'DEFAULT_AUTHENTICATION_CLASSES': ( # JWT认证:json_web_token认证 'rest_framework_jwt.authentication.JSONWebTokenAuthentication', # Session:session_id认证 'rest_framework.authentication.SessionAuthentication', # Basic:账号密码验证 'rest_framework.authentication.BasicAuthentication', ), } # 指明用户模型 AUTH_USER_MODEL = 'users.User' # CORS跨域请求(只针对异步请求如ajax) # CORS白名单 CORS_ORIGIN_WHITELIST = ( '127.0.0.1:8080', 'www.xingtu.info:8080', ) # CORS允许携带cookie访问 CORS_ALLOW_CREDENTIALS = True # JWT扩展设置 JWT_AUTH = { # 设置生成jwt-token数据时,token数据的有效时间 'JWT_EXPIRATION_DELTA': datetime.timedelta(days=1), # 修改JWT登录视图返回值调用的函数,自定义相应函数 'JWT_RESPONSE_PAYLOAD_HANDLER': 'users.utils.jwt_response_payload_handler', } AUTHENTICATION_BACKENDS = [ "users.utils.UsernameMobileModelBackend", ] # QQ登录开发设置 QQ_CLIENT_ID = '101507886' # APP ID QQ_CLIENT_SECRET = '9c9915eb20bd631bdf11b528360cdc28' # APP Key QQ_REDIRECT_URI = 'http://www.xingtu.info:8080/qq-callback.html' # 网站回调地址 QQ_STATE = '/' # QQ成功登录之后跳转页面地址 # 邮件发送设置 EMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend' # SMTP服务器地址 EMAIL_HOST = 'smtp.qq.com' # SMTP服务端口号 EMAIL_PORT = 587 # 发送邮件的邮箱 EMAIL_HOST_USER = '<EMAIL>' # 在邮箱中设置的客户端授权密码 EMAIL_HOST_PASSWORD = '<PASSWORD>' # 收件人看到的发件人 EMAIL_FROM = 'Store<<EMAIL>>' EMAIL_USE_TLS = True #与SMTP服务器通信时，是否启动TLS链接(安全链接) # DRF扩展 REST_FRAMEWORK_EXTENSIONS = { # 三级联动缓存 # 缓存时间 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web_app/services/bascilica_services2.py	worldwidekatie/flask_app	0	86464	import basilica import os from dotenv import load_dotenv load_dotenv() BASCILICA_API_KEY=os.getenv("BASCILICA_API_KEY", default="oops") sentences = [ "This is a sentence!", "This is a similar sentence!", "I don't think this sentence is very similar at all...", ] with basilica.Connection(BASCILICA_API_KEY) as connection: #connection = Connection(BASCILICA_API_KEY) print(type(connection)) embeddings = list(connection.embed_sentences(sentences)) for embed in embeddings: print("---------") print(embed) # a list of 786 floats from -1 to 1 breakpoint() embedding = connection.embed_sentence("Hello World") print(embedding)	[ 1, 1053, 2362, 309, 983, 13, 13, 5215, 2897, 13, 3166, 8329, 6272, 1053, 2254, 29918, 6333, 6272, 13, 1359, 29918, 6333, 6272, 580, 13, 13, 29933, 28599, 6227, 2965, 29909, 29918, 8787, 29918, 10818, 29922, 359, 29889, 657, 6272, 703, 29933, 28599, 6227, 2965, 29909, 29918, 8787, 29918, 10818, 613, 2322, 543, 29877, 3554, 1159, 13, 18616, 2063, 353, 518, 13, 1678, 376, 4013, 338, 263, 10541, 29991, 613, 13, 1678, 376, 4013, 338, 263, 2788, 10541, 29991, 613, 13, 1678, 376, 29902, 1016, 29915, 29873, 1348, 445, 10541, 338, 1407, 2788, 472, 599, 856, 613, 13, 29962, 13, 2541, 2362, 309, 983, 29889, 5350, 29898, 29933, 28599, 6227, 2965, 29909, 29918, 8787, 29918, 10818, 29897, 408, 3957, 29901, 13, 29937, 9965, 353, 15160, 29898, 29933, 28599, 6227, 2965, 29909, 29918, 8787, 29918, 10818, 29897, 13, 1678, 1596, 29898, 1853, 29898, 9965, 876, 13, 13, 13, 17987, 29881, 886, 353, 1051, 29898, 9965, 29889, 17987, 29918, 18616, 2063, 29898, 18616, 2063, 876, 13, 1454, 8297, 297, 8297, 29881, 886, 29901, 13, 1678, 1596, 703, 1378, 29899, 1159, 13, 1678, 1596, 29898, 17987, 29897, 396, 263, 1051, 310, 29871, 29955, 29947, 29953, 5685, 1446, 515, 448, 29896, 304, 29871, 29896, 13, 13, 8690, 3149, 580, 13, 13, 17987, 8497, 353, 3957, 29889, 17987, 29918, 18616, 663, 703, 10994, 2787, 1159, 13, 2158, 29898, 17987, 8497, 29897, 2 ]
proj4/part2/train.py	chengxuxin/CS194_projects	0	148717	import os import time import cv2 import matplotlib.pyplot as plt import numpy as np import png import torch import torch.nn as nn import torch.optim as optim import torchvision from colormap.colors import Color, hex2rgb from sklearn.metrics import average_precision_score as ap_score from torch.utils.data import DataLoader from torchvision import datasets, models, transforms from tqdm import tqdm from dataset import FacadeDataset N_CLASS=5 class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.n_class = N_CLASS self.layers = nn.Sequential( ######################################### ### TODO: Add more layers ### ######################################### nn.Conv2d(3, self.n_class, 1, padding=0), nn.ReLU(inplace=True) ) def forward(self, x): x = self.layers(x) return x def save_label(label, path): ''' Function for ploting labels. ''' colormap = [ '#000000', '#0080FF', '#80FF80', '#FF8000', '#FF0000', ] assert(np.max(label)<len(colormap)) colors = [hex2rgb(color, normalise=False) for color in colormap] w = png.Writer(label.shape[1], label.shape[0], palette=colors, bitdepth=4) with open(path, 'wb') as f: w.write(f, label) def train(trainloader, net, criterion, optimizer, device, epoch): ''' Function for training. ''' start = time.time() running_loss = 0.0 net = net.train() for images, labels in tqdm(trainloader): images = images.to(device) labels = labels.to(device) optimizer.zero_grad() output = net(images) loss = criterion(output, labels) loss.backward() optimizer.step() running_loss = loss.item() end = time.time() print('[epoch %d] loss: %.3f elapsed time %.3f' % (epoch, running_loss, end-start)) def test(testloader, net, criterion, device): ''' Function for testing. ''' losses = 0. cnt = 0 with torch.no_grad(): net = net.eval() for images, labels in tqdm(testloader): images = images.to(device) labels = labels.to(device) output = net(images) loss = criterion(output, labels) losses += loss.item() cnt += 1 print(losses / cnt) return (losses/cnt) def cal_AP(testloader, net, criterion, device): ''' Calculate Average Precision ''' losses = 0. cnt = 0 with torch.no_grad(): net = net.eval() preds = [[] for _ in range(5)] heatmaps = [[] for _ in range(5)] for images, labels in tqdm(testloader): images = images.to(device) labels = labels.to(device) output = net(images).cpu().numpy() for c in range(5): preds[c].append(output[:, c].reshape(-1)) heatmaps[c].append(labels[:, c].cpu().numpy().reshape(-1)) aps = [] for c in range(5): preds[c] = np.concatenate(preds[c]) heatmaps[c] = np.concatenate(heatmaps[c]) if heatmaps[c].max() == 0: ap = float('nan') else: ap = ap_score(heatmaps[c], preds[c]) aps.append(ap) print("AP = {}".format(ap)) # print(losses / cnt) return None def get_result(testloader, net, device, folder='output_train'): result = [] cnt = 1 with torch.no_grad(): net = net.eval() cnt = 0 for images, labels in tqdm(testloader): images = images.to(device) labels = labels.to(device) output = net(images)[0].cpu().numpy() c, h, w = output.shape assert(c == N_CLASS) y = np.zeros((h,w)).astype('uint8') for i in range(N_CLASS): mask = output[i]>0.5 y[mask] = i gt = labels.cpu().data.numpy().squeeze(0).astype('uint8') save_label(y, './{}/y{}.png'.format(folder, cnt)) save_label(gt, './{}/gt{}.png'.format(folder, cnt)) plt.imsave( './{}/x{}.png'.format(folder, cnt), ((images[0].cpu().data.numpy()+1)*128).astype(np.uint8).transpose(1,2,0)) cnt += 1 def main(): device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # TODO change data_range to include all train/evaluation/test data. # TODO adjust batch_size. train_data = FacadeDataset(flag='train', data_range=(0,20), onehot=False) train_loader = DataLoader(train_data, batch_size=1) test_data = FacadeDataset(flag='test_dev', data_range=(0,114), onehot=False) test_loader = DataLoader(test_data, batch_size=1) ap_data = FacadeDataset(flag='test_dev', data_range=(0,114), onehot=True) ap_loader = DataLoader(ap_data, batch_size=1) name = 'starter_net' net = Net().to(device) criterion = nn.CrossEntropyLoss() #TODO decide loss optimizer = torch.optim.Adam(net.parameters(), 1e-3, weight_decay=1e-5) print('\nStart training') for epoch in range(10): #TODO decide epochs print('-----------------Epoch = %d-----------------' % (epoch+1)) train(train_loader, net, criterion, optimizer, device, epoch+1) # TODO create your evaluation set, load the evaluation set and test on evaluation set evaluation_loader = train_loader test(evaluation_loader, net, criterion, device) print('\nFinished Training, Testing on test set') test(test_loader, net, criterion, device) print('\nGenerating Unlabeled Result') result = get_result(test_loader, net, device, folder='output_test') 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cadet_app/views/text.py	standoff-nlp/cadet	0	125472	<gh_stars>0 from django.shortcuts import render, get_object_or_404, redirect from django.contrib.auth.decorators import login_required, permission_required from django.contrib.auth import authenticate, login, logout from django.contrib import messages from django.contrib.admin.views.decorators import staff_member_required from django.http import HttpResponse from django.core.paginator import Paginator from cadet_app.lang_utils import create_spacy_language, clone_spacy_language, create_stop_words, create_examples from pathlib import Path from django.conf import settings import sys import json import gzip from cadet_app.utils import ( make_dict, update_state, get_state, get_sentences, matcher, update_spacy_langs, add_annotations, export_tei, get_previous_and_next_text, put_spans_around_tokens, blank_examples, ) from cadet_app.handle_uploaded_file import handle_uploaded_file, handle_url_file from cadet_app.models import * from cadet_app.forms import ( ProjectForm, ProjectLanguageForm, TextForm, AnnotationForm, EditAnnotationForm, AnnotationTypeForm, TokenTestForm, AddExampleSentenceForm, ) from social_django.utils import psa import spacy def set_text(request, id): text = Text.objects.get(pk=id) request.session["text_id"] = text.id request.session["text_title"] = text.title request.session["text_slug"] = text.text_slug return redirect(data) #return redirect(a, request.session.get("project_slug"), text.text_slug) def delete_text(request, id): text = Text.objects.get(pk=id) text.delete() return redirect(data) @login_required(redirect_field_name="", login_url="login/") def edit_text(request, id): if request.method == "POST": text = get_object_or_404(Text, pk=id) form = TextForm(request.POST or None, instance=text) if form.is_valid(): form.save() return redirect(data) else: text = get_object_or_404(Text, pk=id) form = TextForm(request.POST or None, instance=text) context = {} context["form"] = form return render(request, "add_project.html", context) def data(request): all_texts = Text.objects.all() project_texts = Text.objects.filter(projects__id=request.session.get("project_id")) if request.method == "POST": form = TextForm(request.POST, request.FILES) if form.is_valid(): text = form.save() text.author = request.user text.projects.add(request.session.get("project_id")) text.save() context = {} context["form"] = form context["all_texts"] = all_texts context["project_texts"] = project_texts title = request.POST["title"] project_language = get_object_or_404(Project, id=request.session.get("project_id")).spacy_language if request.FILES["file"]: context["message"] = handle_uploaded_file( request, project_language, text, title ) if request.session.get("url", None): context["message"] = handle_url_file(request, project_language, text, title) if request.user.is_staff: return render(request, "data.html", context) else: return render(request, "data_public.html", context) else: form = TextForm() 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chromium/tools/telemetry/telemetry/internal/image_processing/video.py	wedataintelligence/vivaldi-source	925	12138	<gh_stars>100-1000 # Copyright 2014 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. import subprocess from catapult_base import cloud_storage from telemetry.core import platform from telemetry.util import image_util from telemetry.util import rgba_color HIGHLIGHT_ORANGE_FRAME = rgba_color.WEB_PAGE_TEST_ORANGE class BoundingBoxNotFoundException(Exception): pass class Video(object): """Utilities for storing and interacting with the video capture.""" def __init__(self, video_file_obj): assert video_file_obj.delete assert not video_file_obj.close_called self._video_file_obj = video_file_obj self._tab_contents_bounding_box = None def UploadToCloudStorage(self, bucket, target_path): """Uploads video file to cloud storage. Args: target_path: Path indicating where to store the file in cloud storage. """ cloud_storage.Insert(bucket, target_path, self._video_file_obj.name) def GetVideoFrameIter(self): """Returns the iteration for processing the video capture. This looks for the initial color flash in the first frame to establish the tab content boundaries and then omits all frames displaying the flash. Yields: (time_ms, image) tuples representing each video keyframe. Only the first frame is a run of sequential duplicate bitmaps is typically included. time_ms is milliseconds since navigationStart. image may be a telemetry.core.Bitmap, or a numpy array depending on whether numpy is installed. """ frame_generator = self._FramesFromMp4(self._video_file_obj.name) # Flip through frames until we find the initial tab contents flash. content_box = None for _, bmp in frame_generator: content_box = self._FindHighlightBoundingBox( bmp, HIGHLIGHT_ORANGE_FRAME) if content_box: break if not content_box: raise BoundingBoxNotFoundException( 'Failed to identify tab contents in video capture.') # Flip through frames until the flash goes away and emit that as frame 0. timestamp = 0 for timestamp, bmp in frame_generator: if not self._FindHighlightBoundingBox(bmp, HIGHLIGHT_ORANGE_FRAME): yield 0, image_util.Crop(bmp, content_box) break start_time = timestamp for timestamp, bmp in frame_generator: yield timestamp - start_time, image_util.Crop(bmp, content_box) def _FindHighlightBoundingBox(self, bmp, color, bounds_tolerance=8, color_tolerance=8): """Returns the bounding box of the content highlight of the given color. Raises: BoundingBoxNotFoundException if the hightlight could not be found. """ content_box, pixel_count = image_util.GetBoundingBox(bmp, color, tolerance=color_tolerance) if not content_box: return None # We assume arbitrarily that tabs are all larger than 200x200. If this # fails it either means that assumption has changed or something is # awry with our bounding box calculation. if content_box[2] < 200 or content_box[3] < 200: raise BoundingBoxNotFoundException('Unexpectedly small tab contents.') # TODO(tonyg): Can this threshold be increased? if pixel_count < 0.9 * content_box[2] * content_box[3]: raise BoundingBoxNotFoundException( 'Low count of pixels in tab contents matching expected color.') # Since we allow some fuzziness in bounding box finding, we want to make # sure that the bounds are always stable across a run. So we cache the # first box, whatever it may be. # # This relies on the assumption that since Telemetry doesn't know how to # resize the window, we should always get the same content box for a tab. # If this assumption changes, this caching needs to be reworked. if not self._tab_contents_bounding_box: self._tab_contents_bounding_box = content_box # Verify that there is only minor variation in the bounding box. If it's # just a few pixels, we can assume it's due to compression artifacts. for x, y in zip(self._tab_contents_bounding_box, content_box): if abs(x - y) > bounds_tolerance: # If this fails, it means either that either the above assumption has # changed or something is awry with our bounding box calculation. raise BoundingBoxNotFoundException( 'Unexpected change in tab contents box.') return self._tab_contents_bounding_box def _FramesFromMp4(self, mp4_file): host_platform = platform.GetHostPlatform() if not host_platform.CanLaunchApplication('avconv'): host_platform.InstallApplication('avconv') def GetDimensions(video): proc = subprocess.Popen(['avconv', '-i', video], stderr=subprocess.PIPE) dimensions = None output = '' for line in proc.stderr.readlines(): output += line if 'Video:' in line: dimensions = line.split(',')[2] dimensions = map(int, dimensions.split()[0].split('x')) break proc.communicate() assert dimensions, ('Failed to determine video dimensions. output=%s' % output) return dimensions def GetFrameTimestampMs(stderr): """Returns the frame timestamp in integer milliseconds from the dump log. The expected line format is: ' dts=1.715 pts=1.715\n' We have to be careful to only read a single timestamp per call to avoid deadlock because avconv interleaves its writes to stdout and stderr. """ while True: line = '' next_char = '' while next_char != '\n': next_char = stderr.read(1) line += next_char if 'pts=' in line: return int(1000 * float(line.split('=')[-1])) dimensions = GetDimensions(mp4_file) frame_length = dimensions[0] * dimensions[1] * 3 frame_data = bytearray(frame_length) # Use rawvideo so that we don't need any external library to parse frames. proc = subprocess.Popen(['avconv', '-i', mp4_file, '-vcodec', 'rawvideo', '-pix_fmt', 'rgb24', '-dump', '-loglevel', 'debug', '-f', 'rawvideo', '-'], stderr=subprocess.PIPE, stdout=subprocess.PIPE) while True: num_read = proc.stdout.readinto(frame_data) if not num_read: raise StopIteration assert num_read == len(frame_data), 'Unexpected frame size: %d' % num_read yield (GetFrameTimestampMs(proc.stderr), image_util.FromRGBPixels(dimensions[0], dimensions[1], frame_data))	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29900, 29899, 29896, 29900, 29900, 29900, 13, 29937, 14187, 1266, 29871, 29906, 29900, 29896, 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standards/_dev_/regex.py	CambridgeSemiticsLab/nena_data	3	190153	""" version 0.04 beta This module contains standard regex patterns and functions for handling strings in the NENA text corpus. """ import unicodedata def normalize_string(string): """Normalize string for tokenization and matching. Characters can come in composed or decomposed varieties. We normalize to decomposed characters in order to allow fine-grained control of accent patterns. The string is converted to lowercase. Argument: string: str to be normalized Returns: str decomposed with NFD and lowercased NB: normalized text should not replace any display/stored texts because capitalization is stripped and also decomposed strings are unsightly.""" return unicodedata.normalize('NFD', string).lower() def tokenize_string(string): """Split a string up into analyzable characters. Returns a list of individual characters that can then be matched with the regex patterns. Note that all accent characters can be found with the range: \u0300-\u036F. Thus, strings are split by [any_character][any_accent]. """ norm_string = normalize_string(string) return re.findall('.[\u0300-\u036F]', norm_string) """ The following regex patterns only work with normalized characters. Use `tokenize_string` to split the string into analyzable characters that can be identified with the regex below. """ # Regex for punctuation (includes spaces) # NB: ellipsis (...) requires a separate pattern # as defined in punctuation.json punctuation = '[ .⁺\-=ˈ,.:—;?!"]' # Regex for foreign letters wrapped with a language tag # e.g. <P>Some foreign letters<P> foreign_letters = '[a-zA-ZðÐɟəƏɛƐʾʿθΘ][\u0300-\u033d]*' # Regex for NENA letters base_chars = '[a-zðɟəɛʾʿθ]' unaccented = base_chars + '(?![\u0300-\u033d])' cons_accented = '[dhlmprstzð]\u0323\|[ckpt]\u032d\|[csz]\u030c\|c[\u0323\u032d]\u030c\|g\u0307' vowel_accented = '[aeiouəɛ][\u0300\u0301]\|[aeiouɛ]\u0304\|[aeiou]\u0304[\u0300\u0301]\|[au]\u0306[\u0300\u0301]?' nena_letters = '\|'.join([unaccented, cons_accented, vowel_accented]) # i.e. "One Regex to Rule Them All"	[ 1, 9995, 13, 3259, 29871, 29900, 29889, 29900, 29946, 21762, 13, 13, 4013, 3883, 3743, 3918, 6528, 15038, 322, 3168, 363, 29871, 13, 3179, 1847, 6031, 297, 278, 405, 1430, 29909, 1426, 1034, 13364, 29889, 13, 15945, 29908, 13, 13, 5215, 443, 293, 6797, 532, 13, 13, 1753, 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PP4E/Examples/PP4E/Gui/Intro/defaults.py	BeacherHou/Python-_Markdown-	0	1612553	def simple(): spam = 'ni' def action(): print(spam) # name maps to enclosing function return action act = simple() # make and return nested function act() # then call it: prints 'ni' def normal(): def action(): return spam # really, looked up when used spam = 'ni' return action act = normal() print(act()) # also prints 'ni' def weird(): spam = 42 return (lambda: spam * 2) # remembers spam in enclosing scope act = weird() print(act()) # prints 84 def weird(): tmp = (lambda: spam * 2) # remembers spam spam = 42 # even though not set till here return tmp act = weird() print(act()) # prints 84 def weird(): spam = 42 handler = (lambda: spam * 2) # func doesn't save 42 now spam = 50 print(handler()) # prints 100: spam looked up now spam = 60 print(handler()) # prints 120: spam looked up again now weird() def odd(): funcs = [] for c in 'abcdefg': funcs.append((lambda: c)) # c will be looked up later return funcs # does not remember current c for func in odd(): print(func(), end=' ') # print 7 g's, not a,b,c,... ! print() def odd(): funcs = [] for c in 'abcdefg': funcs.append((lambda c=c: c)) # force to remember c now return funcs # defaults eval now for func in odd(): print(func(), end=' ') # OK: now prints a,b,c,... print() funcs = [] # enclosing scope is module for c in 'abcdefg': # force to remember c now funcs.append((lambda c=c: c)) # else prints 7 g's again for func in funcs: print(func(), end=' ') # OK: prints a,b,c,...	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nq_eval.py	Franck-Dernoncourt/natural-questions	0	78415	<reponame>Franck-Dernoncourt/natural-questions<filename>nq_eval.py # coding=utf-8 # Copyright 2018 The Google AI Language Team Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. r"""Official evaluation script for Natural Questions. https://ai.google.com/research/NaturalQuestions ------------------------------------------------------------------------------ Example usage: nq_eval --gold_path=<path-to-gold-files> --predictions_path=<path_to_json> This will compute both the official F1 scores as well as recall@precision tables for both long and short answers. Note that R@P are only meaningful if your model populates the score fields of the prediction JSON format. gold_path should point to the five way annotated dev data in the original download format (gzipped jsonlines). predictions_path should point to a json file containing the predictions in the format given below. ------------------------------------------------------------------------------ Prediction format: {'predictions': [ { 'example_id': -2226525965842375672, 'long_answer': { 'start_byte': 62657, 'end_byte': 64776, 'start_token': 391, 'end_token': 604 }, 'long_answer_score': 13.5, 'short_answers': [ {'start_byte': 64206, 'end_byte': 64280, 'start_token': 555, 'end_token': 560}, ...], 'short_answers_score': 26.4, 'yes_no_answer': 'NONE' }, ... ] } The prediction format mirrors the annotation format in defining each long or short answer span both in terms of byte offsets and token offsets. We do not expect participants to supply both. The order of preference is: if start_byte >= 0 and end_byte >=0, use byte offsets, else if start_token >= 0 and end_token >= 0, use token offsets, else no span is defined (null answer). The short answer metric takes both short answer spans, and the yes/no answer into account. If the 'short_answers' list contains any non/null spans, then 'yes_no_answer' should be set to 'NONE'. ----------------------------------------------------------------------------- Metrics: If >= 2 of the annotators marked a non-null long answer, then the prediction must match any one of the non-null long answers to be considered correct. If >= 2 of the annotators marked a non-null set of short answers, or a yes/no answer, then the short answers prediction must match any one of the non-null sets of short answers or the yes/no prediction must match one of the non-null yes/no answer labels. All span comparisons are exact and each individual prediction can be fully correct, or incorrect. Each prediction should be provided with a long answer score, and a short answers score. At evaluation time, the evaluation script will find a score threshold at which F1 is maximized. All predictions with scores below this threshold are ignored (assumed to be null). If the score is not provided, the evaluation script considers all predictions to be valid. The script will also output the maximum recall at precision points of >= 0.5, >= 0.75, and >= 0.9. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from collections import OrderedDict import json import os import pickle from absl import app from absl import flags from absl import logging import eval_utils as util flags.DEFINE_string( 'gold_path', None, 'Path to the gzip JSON data. For ' 'multiple files, should be a glob ' 'pattern (e.g. "/path/to/files-"') flags.DEFINE_string('predictions_path', None, 'Path to prediction JSON.') flags.DEFINE_bool( 'cache_gold_data', False, 'Whether to cache gold data in Pickle format to speed up ' 'multiple evaluations.') flags.DEFINE_integer('num_threads', 10, 'Number of threads for reading.') flags.DEFINE_bool('pretty_print', False, 'Whether to pretty print output.') FLAGS = flags.FLAGS def safe_divide(x, y): """Compute x / y, but return 0 if y is zero.""" if y == 0: return 0 else: return x / y def score_long_answer(gold_label_list, pred_label): """Scores a long answer as correct or not. 1) First decide if there is a gold long answer with LONG_NO_NULL_THRESHOLD. 2) The prediction will get a match if: a. There is a gold long answer. b. The prediction span match exactly with one* of the non-null gold long answer span. Args: gold_label_list: A list of NQLabel, could be None. pred_label: A single NQLabel, could be None. Returns: gold_has_answer, pred_has_answer, is_correct, score """ gold_has_answer = util.gold_has_long_answer(gold_label_list) pred_has_answer = pred_label and ( not pred_label.long_answer_span.is_null_span()) is_correct = False score = pred_label.long_score # Both sides are non-null spans. if gold_has_answer and pred_has_answer: for gold_label in gold_label_list: # while the voting results indicate there is an long answer, each # annotator might still say there is no long answer. if gold_label.long_answer_span.is_null_span(): continue if util.nonnull_span_equal(gold_label.long_answer_span, pred_label.long_answer_span): is_correct = True break return gold_has_answer, pred_has_answer, is_correct, score def score_short_answer(gold_label_list, pred_label): """Scores a short answer as correct or not. 1) First decide if there is a gold short answer with SHORT_NO_NULL_THRESHOLD. 2) The prediction will get a match if: a. There is a gold short answer. b. The prediction span set match exactly with one of the non-null gold short answer span set. Args: gold_label_list: A list of NQLabel. pred_label: A single NQLabel. Returns: gold_has_answer, pred_has_answer, is_correct, score """ # There is a gold short answer if gold_label_list not empty and non null # answers is over the threshold (sum over annotators). gold_has_answer = util.gold_has_short_answer(gold_label_list) # There is a pred long answer if pred_label is not empty and short answer # set is not empty. pred_has_answer = pred_label and ( (not util.is_null_span_list(pred_label.short_answer_span_list)) or pred_label.yes_no_answer != 'none') is_correct = False score = pred_label.short_score # Both sides have short answers, which contains yes/no questions. if gold_has_answer and pred_has_answer: if pred_label.yes_no_answer != 'none': # System thinks its y/n questions. for gold_label in gold_label_list: if pred_label.yes_no_answer == gold_label.yes_no_answer: is_correct = True break else: for gold_label in gold_label_list: if util.span_set_equal(gold_label.short_answer_span_list, pred_label.short_answer_span_list): is_correct = True break return gold_has_answer, pred_has_answer, is_correct, score def score_answers(gold_annotation_dict, pred_dict): """Scores all answers for all documents. Args: gold_annotation_dict: a dict from example id to list of NQLabels. pred_dict: a dict from example id to list of NQLabels. Returns: long_answer_stats: List of scores for long answers. short_answer_stats: List of scores for short answers. """ gold_id_set = set(gold_annotation_dict.keys()) pred_id_set = set(pred_dict.keys()) if gold_id_set.symmetric_difference(pred_id_set): raise ValueError('ERROR: the example ids in gold annotations and example ' 'ids in the prediction are not equal.') long_answer_stats = [] short_answer_stats = [] for example_id in gold_id_set: gold = gold_annotation_dict[example_id] pred = pred_dict[example_id] long_answer_stats.append(score_long_answer(gold, pred)) short_answer_stats.append(score_short_answer(gold, pred)) # use the 'score' column, which is last long_answer_stats.sort(key=lambda x: x[-1], reverse=True) short_answer_stats.sort(key=lambda x: x[-1], reverse=True) return long_answer_stats, short_answer_stats def compute_f1(answer_stats, prefix=''): """Computes F1, precision, recall for a list of answer scores. Args: answer_stats: List of per-example scores. prefix (''): Prefix to prepend to score dictionary. Returns: Dictionary mapping string names to scores. """ has_gold, has_pred, is_correct, _ = zip(answer_stats) precision = safe_divide(sum(is_correct), sum(has_pred)) recall = safe_divide(sum(is_correct), sum(has_gold)) f1 = safe_divide(2 precision * recall, precision + recall) return OrderedDict({ prefix + 'n': len(answer_stats), prefix + 'f1': f1, prefix + 'precision': precision, prefix + 'recall': recall }) def compute_final_f1(long_answer_stats, short_answer_stats): """Computes overall F1 given long and short answers, ignoring scores. Note: this assumes that the answers have been thresholded. Arguments: long_answer_stats: List of long answer scores. short_answer_stats: List of short answer scores. Returns: Dictionary of name (string) -> score. """ scores = compute_f1(long_answer_stats, prefix='long-answer-') scores.update(compute_f1(short_answer_stats, prefix='short-answer-')) return scores def compute_pr_curves(answer_stats, targets=None): """Computes PR curve and returns R@P for specific targets. The values are computed as follows: find the (precision, recall) point with maximum recall and where precision > target. Arguments: answer_stats: List of statistic tuples from the answer scores. targets (None): List of precision thresholds to target. Returns: List of table with rows: [target, r, p, score]. """ total_correct = 0 total_has_pred = 0 total_has_gold = 0 # Count the number of gold annotations. for has_gold, _, _, _ in answer_stats: total_has_gold += has_gold # Keep track of the point of maximum recall for each target. max_recall = [0 for _ in targets] max_precision = [0 for _ in targets] max_scores = [None for _ in targets] # Only keep track of unique thresholds in this dictionary. scores_to_stats = OrderedDict() # Loop through every possible threshold and compute precision + recall. for has_gold, has_pred, is_correct, score in answer_stats: total_correct += is_correct total_has_pred += has_pred precision = safe_divide(total_correct, total_has_pred) recall = safe_divide(total_correct, total_has_gold) # If there are any ties, this will be updated multiple times until the # ties are all counted. scores_to_stats[score] = [precision, recall] best_f1 = 0.0 best_precision = 0.0 best_recall = 0.0 best_threshold = 0.0 for threshold, (precision, recall) in scores_to_stats.iteritems(): # Match the thresholds to the find the closest precision above some target. for t, target in enumerate(targets): if precision >= target and recall > max_recall[t]: max_recall[t] = recall max_precision[t] = precision max_scores[t] = threshold # Compute optimal threshold. f1 = safe_divide(2 * precision * recall, precision + recall) if f1 > best_f1: best_f1 = f1 best_precision = precision best_recall = recall best_threshold = threshold return ((best_f1, best_precision, best_recall, best_threshold), zip(targets, max_recall, max_precision, max_scores)) def print_r_at_p_table(answer_stats): """Pretty prints the R@P table for default targets.""" opt_result, pr_table = compute_pr_curves( answer_stats, targets=[0.5, 0.75, 0.9]) f1, precision, recall, threshold = opt_result print('Optimal threshold: {:.5}'.format(threshold)) print(' F1 / P / R') print('{: >7.2%} / {: >7.2%} / {: >7.2%}'.format(f1, precision, recall)) for target, recall, precision, row in pr_table: print('R@P={}: {:.2%} (actual p={:.2%}, score threshold={:.4})'.format( target, recall, precision, row)) def get_metrics_as_dict(gold_path, prediction_path, num_threads=10): """Library version of the end-to-end evaluation. Arguments: gold_path: Path to the gzip JSON data. For multiple files, should be a glob pattern (e.g. "/path/to/files-") prediction_path: Path to the JSON prediction data. num_threads (10): Number of threads to use when parsing multiple files. Returns: metrics: A dictionary mapping string names to metric scores. """ nq_gold_dict = util.read_annotation(gold_path, n_threads=num_threads) nq_pred_dict = util.read_prediction_json(prediction_path) long_answer_stats, short_answer_stats = score_answers(nq_gold_dict, nq_pred_dict) return get_metrics_with_answer_stats(long_answer_stats, short_answer_stats) def get_metrics_with_answer_stats(long_answer_stats, short_answer_stats): """Generate metrics dict using long and short answer stats.""" def _get_metric_dict(answer_stats, prefix=''): """Compute all metrics for a set of answer statistics.""" opt_result, pr_table = compute_pr_curves( answer_stats, targets=[0.5, 0.75, 0.9]) f1, precision, recall, threshold = opt_result metrics = OrderedDict({ 'best-threshold-f1': f1, 'best-threshold-precision': precision, 'best-threshold-recall': recall, 'best-threshold': threshold, }) for target, recall, precision, _ in pr_table: metrics['recall-at-precision>={:.2}'.format(target)] = recall metrics['precision-at-precision>={:.2}'.format(target)] = precision # Add prefix before returning. return dict([(prefix + k, v) for k, v in metrics.iteritems()]) metrics = _get_metric_dict(long_answer_stats, 'long-') metrics.update(_get_metric_dict(short_answer_stats, 'short-')) return metrics def main(_): cache_path = os.path.join(os.path.dirname(FLAGS.gold_path), 'cache') if FLAGS.cache_gold_data and os.path.exists(cache_path): logging.info('Reading from cache: %s', format(cache_path)) nq_gold_dict = pickle.load(open(cache_path, 'r')) else: nq_gold_dict = util.read_annotation( FLAGS.gold_path, n_threads=FLAGS.num_threads) if FLAGS.cache_gold_data: logging.info('Caching gold data for next time to: %s', format(cache_path)) pickle.dump(nq_gold_dict, open(cache_path, 'w')) nq_pred_dict = util.read_prediction_json(FLAGS.predictions_path) long_answer_stats, short_answer_stats = score_answers(nq_gold_dict, nq_pred_dict) if FLAGS.pretty_print: print('' * 20) print('LONG ANSWER R@P TABLE:') print_r_at_p_table(long_answer_stats) print('' 20) print('SHORT ANSWER R@P TABLE:') print_r_at_p_table(short_answer_stats) scores = compute_final_f1(long_answer_stats, short_answer_stats) print('' 20) print('METRICS IGNORING SCORES (n={}):'.format(scores['long-answer-n'])) print(' F1 / P / R') print('Long answer {: >7.2%} / {: >7.2%} / {: >7.2%}'.format( scores['long-answer-f1'], scores['long-answer-precision'], scores['long-answer-recall'])) print('Short answer {: >7.2%} / {: >7.2%} / {: >7.2%}'.format( scores['short-answer-f1'], scores['short-answer-precision'], scores['short-answer-recall'])) else: metrics = get_metrics_with_answer_stats(long_answer_stats, short_answer_stats) print(json.dumps(metrics)) if __name__ == '__main__': flags.mark_flag_as_required('gold_path') flags.mark_flag_as_required('predictions_path') app.run(main)	[ 1, 529, 276, 1112, 420, 29958, 7675, 384, 29899, 29928, 824, 24746, 14316, 29914, 25047, 29899, 2619, 29966, 9507, 29958, 29876, 29939, 29918, 14513, 29889, 2272, 13, 29937, 14137, 29922, 9420, 29899, 29947, 13, 29937, 14187, 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examples/matmul/matmul_automatic.py	UTexas-PSAAP/Parla.py	0	142453	""" Multi-device matrix multiplication using parla with cupy as the kernel engine. """ import sys import time import numpy as np import cupy as cp from parla import Parla, get_all_devices from parla.array import copy, clone_here from parla.cpu import cpu from parla.cuda import gpu from parla.function_decorators import specialized from parla.ldevice import LDeviceSequenceBlocked from parla.tasks import spawn, TaskSpace, CompletedTaskSpace, reserve_persistent_memory from parla.parray import asarray_batch def main(): @spawn(placement=cpu) async def main_task(): ngpus = cp.cuda.runtime.getDeviceCount() repetitions = int(sys.argv[1]) # set up two n x n arrays to multiply together. # n is chosen so that all three can be # stored within the memory of a single GPU # so that strong scaling numbers make sense. n = 32000 blocks = ngpus block_size = n // ngpus h_ordr = 'C' d_ordr = 'F' print("BlockSize: ", block_size, "GPUS: ", ngpus) # Overdecomposing doesn't actually seem to help in this case # with the current parla runtime. This may be related to # some weirdness within the scheduler though, so # we can leave the code for blocks in-place for further # testing later. np.random.seed(0) a_cpu = np.random.rand(n, n).astype(dtype=np.float32, order=h_ordr) b_cpu = np.random.rand(n, n).astype(dtype=np.float32, order=h_ordr) print("Finished Data Allocation", flush=True) # Partition the two arrays and set up the # partitioned array where the result will be stored. # This could also be done using a parla mapper object. a_part = [] b_part = [] c_part = [] distribute=True reset=True fixed_placement=False verbose=False sync=False time_list = list() # Start all operans from CPU memory. for i in range(blocks): if distribute: with cp.cuda.Device(i): a_part.append(cp.asarray(a_cpu[i * block_size : (i + 1) * block_size], order=d_ordr)) b_part.append(cp.asarray(b_cpu[i * block_size : (i + 1) * block_size], order=d_ordr)) cp.cuda.stream.get_current_stream().synchronize() else: a_part.append(a_cpu[i * block_size : (i + 1) * block_size]) b_part.append(b_cpu[i * block_size : (i + 1) * block_size]) for i in range(blocks): c_part.append(list()) for j in range(blocks): c_part[i].append(np.empty((0, 0), dtype=np.float32, order=h_ordr)) #print(len(c_part), len(c_part[0]), c_part[0][0].shape) # 1. NEW: convert to parray in batch a_part, b_part = asarray_batch(a_part, b_part) c_part = asarray_batch(c_part) #print(len(c_part), len(c_part[0])) for repetition in range(repetitions): #reset cblocks to None for i in range(blocks): for j in range(blocks): c_part[i][j].update(np.empty((0, 0), dtype=np.float32, order=h_ordr)) if reset: #reset coherence to only be in starting locations rspace = TaskSpace("reset") for i in range(blocks): @spawn(rspace[i], placement=gpu(i%ngpus), memory=2block_sizen, inout=[a_part[i], b_part[i]]) def reset_task(): a_part[i].update(a_part[i].array) b_part[i].update(b_part[i].array) await rspace matmul = TaskSpace("matmul") start = time.perf_counter() for i in range(blocks): for j in range(blocks): a_block = a_part[i] b_block = b_part[j] c_block = c_part[i][j] memsize = (block_size*2)4 if fixed_placement: loc = gpu(i%ngpus) else: loc = gpu @spawn(matmul[i, j], placement = loc, memory=memsize, input=[a_block, b_block], output=[c_block]) def matmul_task(): a = a_block.array b = b_block.array c = c_block.array stream = cp.cuda.get_current_stream() stream.synchronize() assert(a.device.id == b.device.id) if verbose: print(f"+({i}, {j}): ", a.shape, b.shape, c.shape, " \| On Device: ", cp.cuda.runtime.getDevice(), a.device.id, flush=True) local_start = time.perf_counter() c = a @ b.T if sync: stream.synchronize() local_end = time.perf_counter() c_block.update(c) c = c_block.array if verbose: print(f"-({i}, {j}): ", a.shape, b.shape, c.shape, " \| Elapsed: ", local_end-local_start, flush=True) await matmul stop = time.perf_counter() print(f"Iteration {repetition} \| Time elapsed: ", stop - start, flush=True) time_list.append(stop-start) mean = np.mean(np.array(time_list)) median = np.median(np.array(time_list)) print(f"Execution Time:: Average = {mean} \| Median = {median}", flush=True) if __name__ == "__main__": with Parla(): main()	[ 1, 9995, 13, 15329, 29899, 10141, 4636, 21666, 773, 610, 433, 411, 18002, 29891, 408, 278, 8466, 6012, 29889, 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components/elm/src/external_models/fates/functional_unit_testing/hydro/HydroUTestDriver.py	meng630/GMD_E3SM_SCM	0	113703	<filename>components/elm/src/external_models/fates/functional_unit_testing/hydro/HydroUTestDriver.py # ======================================================================================= # # For usage: $python HydroUTestDriver.py --help # # This script runs unit tests on the hydraulics functions. # # # ======================================================================================= import matplotlib as mpl #mpl.use('Agg') import matplotlib.pyplot as plt from datetime import datetime import argparse #from matplotlib.backends.backend_pdf import PdfPages import platform import numpy as np import os import sys import getopt import code # For development: code.interact(local=dict(globals(), *locals())) import time import imp import ctypes from ctypes import from operator import add CDLParse = imp.load_source('CDLParse','../shared/py_src/CDLParse.py') F90ParamParse = imp.load_source('F90ParamParse','../shared/py_src/F90ParamParse.py') PyF90Utils = imp.load_source('PyF90Utils','../shared/py_src/PyF90Utils.py') from CDLParse import CDLParseDims, CDLParseParam, cdl_param_type from F90ParamParse import f90_param_type, GetSymbolUsage, GetPFTParmFileSymbols, MakeListUnique from PyF90Utils import c8, ci, cchar, c8_arr, ci_arr # Load the fortran objects via CTYPES f90_unitwrap_obj = ctypes.CDLL('bld/UnitWrapMod.o',mode=ctypes.RTLD_GLOBAL) f90_constants_obj = ctypes.CDLL('bld/FatesConstantsMod.o',mode=ctypes.RTLD_GLOBAL) f90_wftfuncs_obj = ctypes.CDLL('bld/FatesHydroWTFMod.o',mode=ctypes.RTLD_GLOBAL) f90_hydrounitwrap_obj = ctypes.CDLL('bld/HydroUnitWrapMod.o',mode=ctypes.RTLD_GLOBAL) # Alias the F90 functions, specify the return type # ----------------------------------------------------------------------------------- initalloc_wtfs = f90_hydrounitwrap_obj.__hydrounitwrapmod_MOD_initallocwtfs setwrf = f90_hydrounitwrap_obj.__hydrounitwrapmod_MOD_setwrf setwkf = f90_hydrounitwrap_obj.__hydrounitwrapmod_MOD_setwkf th_from_psi = f90_hydrounitwrap_obj.__hydrounitwrapmod_MOD_wrapthfrompsi th_from_psi.restype = c_double psi_from_th = f90_hydrounitwrap_obj.__hydrounitwrapmod_MOD_wrappsifromth psi_from_th.restype = c_double dpsidth_from_th = f90_hydrounitwrap_obj.__hydrounitwrapmod_MOD_wrapdpsidth dpsidth_from_th.restype = c_double ftc_from_psi = f90_hydrounitwrap_obj.__hydrounitwrapmod_MOD_wrapftcfrompsi ftc_from_psi.restype = c_double dftcdpsi_from_psi = f90_hydrounitwrap_obj.__hydrounitwrapmod_MOD_wrapdftcdpsi dftcdpsi_from_psi.restype = c_double # Some constants rwcft = [1.0,0.958,0.958,0.958] rwccap = [1.0,0.947,0.947,0.947] pm_leaf = 1 pm_stem = 2 pm_troot = 3 pm_aroot = 4 pm_rhiz = 5 # These parameters are matched with the indices in FATES-HYDRO vg_type = 1 cch_type = 2 tfs_type = 3 isoil1 = 0 # Top soil layer parameters (@BCI) isoil2 = 1 # Bottom soil layer parameters # Constants for rhizosphere watsat = [0.567, 0.444] sucsat = [159.659, 256.094] bsw = [6.408, 9.27] unconstrained = True # ======================================================================================== # ======================================================================================== # Main # ======================================================================================== # ======================================================================================== class vg_wrf: def __init__(self,index,alpha, psd, th_sat, th_res): self.alpha = alpha self.psd = psd self.th_sat = th_sat self.th_res = th_res init_wrf_args = [self.alpha, self.psd, self.th_sat, self.th_res] iret = setwrf(ci(index),ci(vg_type),ci(len(init_wrf_args)),c8_arr(init_wrf_args)) class cch_wrf: def __init__(self,index,th_sat,psi_sat,beta): self.th_sat = th_sat self.psi_sat = psi_sat self.beta = beta init_wrf_args = [self.th_sat,self.psi_sat,self.beta] iret = setwrf(ci(index),ci(cch_type),ci(len(init_wrf_args)),c8_arr(init_wrf_args)) class vg_wkf: def __init__(self,index,alpha, psd, th_sat, th_res, tort): self.alpha = alpha self.psd = psd self.th_sat = th_sat self.th_res = th_res self.tort = tort init_wkf_args = [self.alpha, self.psd,self.th_sat,self.th_res,self.tort] iret = setwkf(ci(index),ci(vg_type),ci(len(init_wkf_args)),c8_arr(init_wkf_args)) class cch_wkf: def __init__(self,index,th_sat,psi_sat,beta): self.th_sat = th_sat self.psi_sat = psi_sat self.beta = beta init_wkf_args = [self.th_sat,self.psi_sat,self.beta] iret = setwkf(ci(index),ci(cch_type),ci(len(init_wkf_args)),c8_arr(init_wkf_args)) class tfs_wrf: def __init__(self,index,th_sat,th_res,pinot,epsil,rwc_fd,cap_corr,cap_int,cap_slp,pmedia): self.th_sat = th_sat self.th_res = th_res self.pinot = pinot self.epsil = epsil self.rwc_fd = rwc_fd self.cap_corr = cap_corr self.cap_int = cap_int self.cap_slp = cap_slp self.pmedia = pmedia init_wrf_args = [self.th_sat,self.th_res,self.pinot,self.epsil,self.rwc_fd,self.cap_corr,self.cap_int,self.cap_slp,self.pmedia] iret = setwrf(ci(index),ci(tfs_type),ci(len(init_wrf_args)),c8_arr(init_wrf_args)) class tfs_wkf: def __init__(self,index,p50,avuln): self.avuln = avuln self.p50 = p50 init_wkf_args = [self.p50,self.avuln] iret = setwkf(ci(index),ci(tfs_type),ci(len(init_wkf_args)),c8_arr(init_wkf_args)) def main(argv): # First check to make sure python 2.7 is being used version = platform.python_version() verlist = version.split('.') if( not ((verlist[0] == '2') & (verlist[1] == '7') & (int(verlist[2])>=15) ) ): print("The PARTEH driver mus be run with python 2.7") print(" with tertiary version >=15.") print(" your version is {}".format(version)) print(" exiting...") sys.exit(2) # Read in the arguments # ======================================================================================= # parser = argparse.ArgumentParser(description='Parse command line arguments to this script.') # parser.add_argument('--cdl-file', dest='cdlfile', type=str, \ # help="Input CDL filename. Required.", required=True) # args = parser.parse_args() # Set number of analysis points npts = 1000 # min_theta = np.full(shape=(2),dtype=np.float64,fill_value=np.nan) # wrf_type = [vg_type, vg_type, cch_type, cch_type] # wkf_type = [vg_type, tfs_type, cch_type, tfs_type] # th_ress = [0.01, 0.10, -9, -9] # th_sats = [0.55, 0.55, 0.65, 0.65] # alphas = [1.0, 1.0, 1.0, 1.0] # psds = [2.7, 2.7, 2.7, 2.7] # tort = [0.5, 0.5, 0.5, 0.5] # beta = [-9, -9, 6, 9] # avuln = [2.0, 2.0, 2.5, 2.5] # p50 = [-1.5, -1.5, -2.25, -2.25] ncomp= 3 rwc_fd = [1.0,0.958,0.958,0.958] rwccap = [1.0,0.947,0.947,0.947] cap_slp = [] cap_int = [] cap_corr= [] hydr_psi0 = 0.0 hydr_psicap = -0.6 for pm in range(4): if (pm == 0): cap_slp.append(0.0) cap_int.append(0.0) cap_corr.append(1.0) else: cap_slp.append((hydr_psi0 - hydr_psicap )/(1.0 - rwccap[pm])) cap_int.append(-cap_slp[pm] + hydr_psi0) cap_corr.append(-cap_int[pm]/cap_slp[pm]) # Allocate memory to our objective classes iret = initalloc_wtfs(ci(ncomp),ci(ncomp)) print('Allocated') # Define the funcions and their parameters # vg_wrf(1,alpha=1.0,psd=2.7,th_sat=0.55,th_res=0.1) # vg_wkf(1,alpha=1.0,psd=2.7,th_sat=0.55,th_res=0.1,tort=0.5) cch_wrf(1,th_sat=0.55, psi_sat=-1.56e-3, beta=6) cch_wkf(1,th_sat=0.55, psi_sat=-1.56e-3, beta=6) # cch_wrf(3,th_sat=0.55, psi_sat=-1.56e-3, beta=6) # tfs_wkf(3,p50=-2.25, avuln=2.0) names=['Soil','ARoot','Leaf'] # Absorving root tfs_wrf(2,th_sat=0.75,th_res=0.15,pinot=-1.043478, \ epsil=8,rwc_fd=rwc_fd[3],cap_corr=cap_corr[3], \ cap_int=cap_int[3],cap_slp=cap_slp[3],pmedia=4) tfs_wkf(2,p50=-2.25, avuln=2.0) # Leaf tfs_wrf(3,th_sat=0.65,th_res=0.25,pinot=-1.47, \ epsil=12,rwc_fd=rwc_fd[0],cap_corr=cap_corr[0], \ cap_int=cap_int[0],cap_slp=cap_slp[0],pmedia=1) tfs_wkf(3,p50=-2.25, avuln=2.0) print('initialized WRF') theta = np.linspace(0.10, 0.7, num=npts) psi = np.full(shape=(ncomp,len(theta)),dtype=np.float64,fill_value=np.nan) dpsidth = np.full(shape=(ncomp,len(theta)),dtype=np.float64,fill_value=np.nan) cdpsidth = np.full(shape=(ncomp,len(theta)),dtype=np.float64,fill_value=np.nan) for ic in range(ncomp): for i,th in enumerate(theta): psi[ic,i] = psi_from_th(ci(ic+1),c8(th)) # Theta vs psi plots fig0, ax1 = plt.subplots(1,1,figsize=(9,6)) for ic in range(ncomp): ax1.plot(theta,psi[ic,:],label='{}'.format(names[ic])) ax1.set_ylim((-30,5)) ax1.set_ylabel('Matric Potential [MPa]') ax1.set_xlabel('VWC [m3/m3]') ax1.legend(loc='lower right') for ic in range(ncomp): for i in range(1,len(theta)-1): dpsidth[ic,i] = dpsidth_from_th(ci(ic+1),c8(theta[i])) cdpsidth[ic,i] = (psi[ic,i+1]-psi[ic,i-1])/(theta[i+1]-theta[i-1]) # Theta vs dpsi_dth (also checks deriv versus explicit) fig1, ax1 = plt.subplots(1,1,figsize=(9,6)) for ic in range(ncomp): ax1.plot(theta,dpsidth[0,:],label='func') ax1.plot(theta,cdpsidth[0,:],label='check') ax1.set_ylim((0,1000)) ax1.set_ylabel('dPSI/dTh [MPa m3 m-3]') ax1.set_xlabel('VWC [m3/m3]') ax1.legend(loc='upper right') # Push parameters to WKF classes # ------------------------------------------------------------------------- # Generic VGs ftc = np.full(shape=(ncomp,len(theta)),dtype=np.float64,fill_value=np.nan) dftcdpsi = np.full(shape=(ncomp,len(theta)),dtype=np.float64,fill_value=np.nan) cdftcdpsi = np.full(shape=(ncomp,len(theta)),dtype=np.float64,fill_value=np.nan) for ic in range(ncomp): for i in range(0,len(theta)): ftc[ic,i] = ftc_from_psi(ci(ic+1),c8(psi[ic,i])) for ic in range(ncomp): for i in range(1,len(theta)-1): dftcdpsi[ic,i] = dftcdpsi_from_psi(ci(ic+1),c8(psi[ic,i])) cdftcdpsi[ic,i] = (ftc[ic,i+1]-ftc[ic,i-1])/(psi[ic,i+1]-psi[ic,i-1]) # FTC versus Psi fig2, ax1 = plt.subplots(1,1,figsize=(9,6)) for ic in range(ncomp): ax1.plot(psi[ic,:],ftc[ic,:],label='{}'.format(names[ic])) ax1.set_ylabel('FTC') ax1.set_xlabel('Psi [MPa]') ax1.set_xlim([-5,0]) ax1.legend(loc='upper right') # FTC versus theta fig4, ax1 = plt.subplots(1,1,figsize=(9,6)) for ic in range(ncomp): ax1.plot(theta,ftc[ic,:],label='{}'.format(names[ic])) ax1.set_ylabel('FTC') ax1.set_xlabel('Theta [m3/m3]') ax1.legend(loc='lower right') # dFTC/dPSI fig3,ax1 = plt.subplots(1,1,figsize=(9,6)) for ic in range(ncomp): # ax1.plot(psi[ic,:],abs(dftcdpsi[ic,:]-cdftcdpsi[ic,:])/abs(cdftcdpsi[ic,:]),label='{}'.format(ic)) ax1.plot(psi[ic,:],cdftcdpsi[ic,:],label='check') ax1.set_ylabel('dFTC/dPSI') ax1.set_xlabel('Psi [MPa]') # ax1.set_xlim([-30,3]) # ax1.set_ylim([0,10]) ax1.legend(loc='upper right') plt.show() # code.interact(local=dict(globals(), *locals())) # Helper code to plot negative logs def semilogneg(x): y = np.sign(x)np.log(abs(x)) return(y) def semilog10net(x): y = np.sign(x)*np.log10(abs(x)) return(y) # ======================================================================================= # This is the actual call to main if __name__ == "__main__": main(sys.argv)	[ 1, 529, 9507, 29958, 14036, 29914, 16422, 29914, 4351, 29914, 23176, 29918, 9794, 29914, 29888, 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viz/view.py	LynnHongLiu/replayed_distillation	79	138465	import numpy as np import cv2 # This file is a set of commonly used functions by the viz scripts. It # is not meant to be run on its own def unblockshaped(arr, h, w, rgb=False): if rgb: n, nrows, ncols, nchannels = arr.shape return (arr.reshape(h//nrows, -1, nrows, ncols, nchannels) .swapaxes(1,2) .reshape(h, w, 3)) n, nrows, ncols = arr.shape return (arr.reshape(h//nrows, -1, nrows, ncols) .swapaxes(1,2) .reshape(h, w)) def reshape_to_row(arr, side=28, rgb=False): if rgb: grid = np.array([np.reshape(img, (side, side, 3)) for img in arr]) else: grid = np.array([np.reshape(img, (side, side)) for img in arr]) return unblockshaped(grid, int(side), int(side * grid.shape[0]), rgb=rgb) def reshape_to_grid(arr, side=28, rgb=False): if rgb: grid = np.array([np.reshape(img, (side, side, 3)) for img in arr]) else: grid = np.array([np.reshape(img, (side, side)) for img in arr]) size = int(side * np.sqrt(grid.shape[0])) return unblockshaped(grid, size, size, rgb=rgb)	[ 1, 1053, 12655, 408, 7442, 13, 5215, 13850, 29906, 13, 13, 29937, 910, 934, 338, 263, 731, 310, 15574, 1304, 3168, 491, 278, 25294, 12078, 29889, 739, 13, 29937, 338, 451, 6839, 304, 367, 1065, 373, 967, 1914, 13, 13, 1753, 443, 1271, 845, 10501, 29898, 2749, 29892, 298, 29892, 281, 29892, 15552, 29890, 29922, 8824, 1125, 13, 1678, 565, 15552, 29890, 29901, 13, 4706, 302, 29892, 302, 5727, 29892, 302, 22724, 29892, 302, 305, 12629, 353, 3948, 29889, 12181, 13, 4706, 736, 313, 2749, 29889, 690, 14443, 29898, 29882, 458, 29876, 5727, 29892, 448, 29896, 29892, 302, 5727, 29892, 302, 22724, 29892, 302, 305, 12629, 29897, 13, 462, 1678, 869, 26276, 1165, 267, 29898, 29896, 29892, 29906, 29897, 13, 462, 1678, 869, 690, 14443, 29898, 29882, 29892, 281, 29892, 29871, 29941, 876, 13, 1678, 302, 29892, 302, 5727, 29892, 302, 22724, 353, 3948, 29889, 12181, 13, 1678, 736, 313, 2749, 29889, 690, 14443, 29898, 29882, 458, 29876, 5727, 29892, 448, 29896, 29892, 302, 5727, 29892, 302, 22724, 29897, 13, 12, 12, 29889, 26276, 1165, 267, 29898, 29896, 29892, 29906, 29897, 13, 12, 12, 29889, 690, 14443, 29898, 29882, 29892, 281, 876, 13, 13, 1753, 620, 14443, 29918, 517, 29918, 798, 29898, 2749, 29892, 2625, 29922, 29906, 29947, 29892, 15552, 29890, 29922, 8824, 1125, 13, 1678, 565, 15552, 29890, 29901, 13, 4706, 6856, 353, 7442, 29889, 2378, 4197, 9302, 29889, 690, 14443, 29898, 2492, 29892, 313, 2975, 29892, 2625, 29892, 29871, 29941, 876, 363, 10153, 297, 3948, 2314, 13, 1678, 1683, 29901, 13, 4706, 6856, 353, 7442, 29889, 2378, 4197, 9302, 29889, 690, 14443, 29898, 2492, 29892, 313, 2975, 29892, 2625, 876, 363, 10153, 297, 3948, 2314, 13, 1678, 736, 443, 1271, 845, 10501, 29898, 7720, 29892, 938, 29898, 2975, 511, 938, 29898, 2975, 334, 6856, 29889, 12181, 29961, 29900, 11724, 15552, 29890, 29922, 23973, 29897, 13, 13, 1753, 620, 14443, 29918, 517, 29918, 7720, 29898, 2749, 29892, 2625, 29922, 29906, 29947, 29892, 15552, 29890, 29922, 8824, 1125, 13, 1678, 565, 15552, 29890, 29901, 13, 4706, 6856, 353, 7442, 29889, 2378, 4197, 9302, 29889, 690, 14443, 29898, 2492, 29892, 313, 2975, 29892, 2625, 29892, 29871, 29941, 876, 363, 10153, 297, 3948, 2314, 13, 1678, 1683, 29901, 13, 4706, 6856, 353, 7442, 29889, 2378, 4197, 9302, 29889, 690, 14443, 29898, 2492, 29892, 313, 2975, 29892, 2625, 876, 363, 10153, 297, 3948, 2314, 13, 1678, 2159, 353, 938, 29898, 2975, 334, 7442, 29889, 3676, 29898, 7720, 29889, 12181, 29961, 29900, 12622, 13, 1678, 736, 443, 1271, 845, 10501, 29898, 7720, 29892, 2159, 29892, 2159, 29892, 15552, 29890, 29922, 23973, 29897, 13, 13, 2 ]
mplcyberpunk/__init__.py	story645/mplcyberpunk	1	102596	"""mplcyberpunk - A new Python package""" __version__ = '0.1.0' __author__ = '<NAME> <<EMAIL>>' __all__ = [] import matplotlib as mpl import pkg_resources from .core import add_glow_effects, make_lines_glow, add_underglow # register the included stylesheet in the mpl style library data_path = pkg_resources.resource_filename('mplcyberpunk', 'data/') cyberpunk_stylesheets = mpl.style.core.read_style_directory(data_path) mpl.style.core.update_nested_dict(mpl.style.library, cyberpunk_stylesheets)	[ 1, 9995, 29885, 572, 1270, 495, 29886, 2960, 448, 319, 716, 5132, 3577, 15945, 29908, 13, 13, 1649, 3259, 1649, 353, 525, 29900, 29889, 29896, 29889, 29900, 29915, 13, 1649, 8921, 1649, 353, 12801, 5813, 29958, 3532, 26862, 6227, 6778, 29915, 13, 1649, 497, 1649, 353, 5159, 13, 13, 13, 5215, 22889, 408, 286, 572, 13, 5215, 282, 9415, 29918, 13237, 13, 13, 3166, 869, 3221, 1053, 788, 29918, 29887, 677, 29918, 15987, 29879, 29892, 1207, 29918, 9012, 29918, 29887, 677, 29892, 788, 29918, 5062, 29887, 677, 13, 13, 29937, 6036, 278, 5134, 11949, 4155, 297, 278, 286, 572, 3114, 3489, 13, 1272, 29918, 2084, 353, 282, 9415, 29918, 13237, 29889, 10314, 29918, 9507, 877, 29885, 572, 1270, 495, 29886, 2960, 742, 525, 1272, 29914, 1495, 13, 1270, 495, 29886, 2960, 29918, 9783, 354, 1691, 353, 286, 572, 29889, 3293, 29889, 3221, 29889, 949, 29918, 3293, 29918, 12322, 29898, 1272, 29918, 2084, 29897, 13, 29885, 572, 29889, 3293, 29889, 3221, 29889, 5504, 29918, 27420, 29918, 8977, 29898, 29885, 572, 29889, 3293, 29889, 5258, 29892, 5094, 495, 29886, 2960, 29918, 9783, 354, 1691, 29897, 2 ]
example/simple.py	xiaodongxiexie/log2db	1	153393	# -- coding: utf-8 -- # @Author: xiaodong # @Date : 2021/4/17 import logging from log2db import init, Proxy, LoggingMQHandler Proxy.DBURL = "sqlite:///{}".format("mylogger.db") _ = init() logger = logging.getLogger("log2db") logger.parent = None handler = LoggingMQHandler(logging.INFO) logger.addHandler(handler) if __name__ == '__main__': import random, time from log2db import Logging session = _.session logger.info("this is a test", extra={"user": "none"}) logger.warning("this is a warn %s", "ohuo") try: 1/0 except: logger.exception("this is a exception", exc_info=True) print(session.query(Logging).count()) for i in range(1000): random.choice([logger.info, logger.warning, logger.exception, logger.debug])(i) print(session.query(Logging).count()) # time.sleep(3) print(session.query(Logging).count())	[ 1, 396, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 29937, 732, 13720, 29901, 921, 423, 397, 549, 13, 29937, 732, 2539, 29871, 584, 29871, 29906, 29900, 29906, 29896, 29914, 29946, 29914, 29896, 29955, 13, 13, 5215, 12183, 13, 3166, 1480, 29906, 2585, 1053, 2069, 29892, 1019, 3594, 29892, 4522, 3460, 25566, 4598, 13, 13, 13, 14048, 29889, 4051, 4219, 353, 376, 22793, 597, 29914, 8875, 1642, 4830, 703, 1357, 21707, 29889, 2585, 1159, 13, 13, 29918, 353, 2069, 580, 13, 13, 21707, 353, 12183, 29889, 657, 16363, 703, 1188, 29906, 2585, 1159, 13, 21707, 29889, 3560, 353, 6213, 13, 13789, 353, 4522, 3460, 25566, 4598, 29898, 21027, 29889, 11690, 29897, 13, 21707, 29889, 1202, 4598, 29898, 13789, 29897, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 13, 1678, 1053, 4036, 29892, 931, 13, 13, 1678, 515, 1480, 29906, 2585, 1053, 4522, 3460, 13, 13, 1678, 4867, 353, 903, 29889, 7924, 13, 13, 1678, 17927, 29889, 3888, 703, 1366, 338, 263, 1243, 613, 4805, 3790, 29908, 1792, 1115, 376, 9290, 29908, 1800, 13, 1678, 17927, 29889, 27392, 703, 1366, 338, 263, 29383, 1273, 29879, 613, 376, 1148, 25608, 1159, 13, 1678, 1018, 29901, 13, 308, 29896, 29914, 29900, 13, 1678, 5174, 29901, 13, 4706, 17927, 29889, 11739, 703, 1366, 338, 263, 3682, 613, 5566, 29918, 3888, 29922, 5574, 29897, 13, 13, 1678, 1596, 29898, 7924, 29889, 1972, 29898, 3403, 3460, 467, 2798, 3101, 13, 13, 1678, 363, 474, 297, 3464, 29898, 29896, 29900, 29900, 29900, 1125, 13, 4706, 4036, 29889, 16957, 4197, 21707, 29889, 3888, 29892, 17927, 29889, 27392, 29892, 17927, 29889, 11739, 29892, 17927, 29889, 8382, 2314, 29898, 29875, 29897, 13, 13, 1678, 1596, 29898, 7924, 29889, 1972, 29898, 3403, 3460, 467, 2798, 3101, 13, 1678, 396, 931, 29889, 17059, 29898, 29941, 29897, 13, 1678, 1596, 29898, 7924, 29889, 1972, 29898, 3403, 3460, 467, 2798, 3101, 13, 1678, 13, 2 ]
scanner3d/metrics/__init__.py	Belval/Scanner3D	1	184192	<reponame>Belval/Scanner3D """ Raise classes to top-level """ from .base_metric import BaseMetric from .fitness import Fitness from .rmse import RMSE	[ 1, 529, 276, 1112, 420, 29958, 21140, 791, 29914, 4421, 7310, 29941, 29928, 13, 15945, 29908, 13, 29934, 29874, 895, 4413, 304, 2246, 29899, 5563, 13, 15945, 29908, 13, 13, 3166, 869, 3188, 29918, 16414, 1053, 7399, 10095, 2200, 13, 3166, 869, 9202, 2264, 1053, 383, 277, 2264, 13, 3166, 869, 1758, 344, 1053, 390, 29924, 1660, 13, 2 ]
complex_performance_metrics/solvers/multiclass/coco_qmean_nae.py	hnarasimhan/constrained-classification	9	118432	import numpy as np from copy import copy from complex_performance_metrics.models.plugin import MulticlassPluginClassifier from complex_performance_metrics.utils import nae """ This module implements the COCO algorithm for: minimizing the (multiclass) Q-mean loss subject to (multiclass) Normalized Absolute Error <= epsilon """ def frank_wolfe(x, y, classifier, cpe_model, alpha, num_inner_iter): """ Inner Frank-Wolfe optimization in COCO Perform Lagrangian optimization over confusion matrices for Lagrange multiplier alpha Args: x (array-like, dtype = float, shape = (m,d)): Features y (array-like, dtype = int, shape = (m,)): Labels {0,...,m-1} classifier (RandomizedClassifier): A randomized classifier to which additional base classifiers are to be added cpe_model (sklearn estimator): A model with a predict_proba() function (default: None) alpha (float): Lagrange multiplier num_inner_iter (int): Number of solver iterations Returns: classifier (RandomizedClassifier): Solution classifier for inner maximization """ num_class = classifier.num_class p = np.zeros((num_class,)) for i in range(num_class): p[i] = (y == i).mean() # Intialize gain matrix W = np.eye(num_class, num_class) for i in range(num_class): W[i, i] = 1.0 / p[i] # Create binary plug-in with separate thresholds for protected attribute values plugin = MulticlassPluginClassifier(cpe_model, num_class=num_class) plugin.set_cost_matrix(W, is_cost=False) C = plugin.evaluate_conf(x, y, use_stored_prob=True) # Initialize constants/normalization terms norm_costs_const = 0.5 / (1 - np.min(p)) norm_weights_const = 1.0 # Frank-Wolfe iterations for i in range(num_inner_iter): # Compute gain matrix from objective gradient, # and construct optimal classifier for cost-sensitive learning step W = np.zeros((num_class, num_class)) for j in range(num_class): for k in range(num_class): if j == k: W[j, j] = 2 * (1 - C[j, j] / p[j]) / p[j] / num_class W[j, k] -= alpha * 2 * (C[:,k].sum() - p[k]) * norm_costs_const plugin.set_cost_matrix(W, is_cost=False) # Update confusion matrix iterate C_hat = plugin.evaluate_conf(x, y, use_stored_prob=True) C = (1 - 2.0 / (i + 2)) * C + 2.0 / (i + 2) * C_hat # Append weight and copy of plug-in classifier to randomized classifier if i == 0: classifier.append(1.0, copy(plugin)) else: norm_weights_const = 1 - 2.0 / (i + 2) classifier.append(2.0 / (i + 2) / norm_weights_const, copy(plugin)) # Normalize classifier weights to sum up to 1 classifier.weights[-num_inner_iter:-1] = [x norm_weights_const for x in classifier.weights[-num_inner_iter:-1]] classifier.weights[-1] = norm_weights_const return C, classifier def fit(x, y, classifier, cpe_model, eps, eta, num_outer_iter, num_inner_iter): """ Outer optimization in COCO Run gradient ascent over Lagrange multipliers alpha Args: x (array-like, dtype = float, shape= (m,d)): Features y (array-like, dtype = int, shape = (m,)): Labels {0,...,m-1} classifier (RandomizedClassifier): A randomized classifier to which additional base classifiers are to be added cpe_model (sklearn estimator): A model with a predict_proba() function (default: None) eps (float): Constraint function tolerance eta (float): Step-size for gradient-ascent solver num_outer_iter (int): Number of outer iterations in solver (gradient ascent) num_inner_iter (int): Number of inner iterations in solver (Frank-Wolfe) Returns: classifier (RandomizedClassifier): Final classifier """ # If the problem has no constraints, set alpha to 0 and run outer solver only for one iteration if eps == 1: alpha = 0.0 num_outer_iter = 1 else: # else initialize Lagrange multiplier alpha = 0.01 # Gradient ascent iterations for t in range(num_outer_iter): # Find confusion matrix that maximizes the Lagrangian at alpha C, _ = frank_wolfe(x, y, classifier, cpe_model, alpha, num_inner_iter) er = nae(C) # Gradient update to alpha alpha += eta 1.0 / np.sqrt(t + 1) * (er - eps) # Projection step if alpha < 0: alpha = 0 # Normalize classifier weights to sum up to 1 classifier.normalize_weights() return classifier	[ 1, 1053, 12655, 408, 7442, 13, 3166, 3509, 1053, 3509, 13, 3166, 4280, 29918, 546, 13390, 29918, 2527, 10817, 29889, 9794, 29889, 8582, 1053, 9683, 293, 605, 16288, 2385, 3709, 13, 3166, 4280, 29918, 546, 13390, 29918, 2527, 10817, 29889, 13239, 1053, 1055, 29872, 13, 13, 13, 15945, 29908, 13, 4013, 3883, 10703, 278, 4810, 3217, 5687, 363, 29901, 29871, 13, 1678, 6260, 5281, 278, 313, 4713, 293, 605, 29897, 660, 29899, 12676, 6410, 4967, 304, 313, 4713, 293, 605, 29897, 21981, 1891, 1976, 14977, 4829, 5277, 321, 3232, 13, 15945, 29908, 13, 13, 13, 1753, 2524, 29895, 29918, 24437, 1725, 29898, 29916, 29892, 343, 29892, 770, 3709, 29892, 274, 412, 29918, 4299, 29892, 15595, 29892, 954, 29918, 3993, 29918, 1524, 1125, 13, 1678, 9995, 13, 1678, 25665, 4976, 29899, 29956, 324, 1725, 13883, 297, 4810, 3217, 13, 1678, 27313, 365, 10312, 574, 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pip_packages/setup.py	Stonepia/model-gardens	0	82037	## Modified from https://github.com/tensorflow/models/blob/master/official/pip_package/setup.py """Sets up TensorFlow Official Models.""" import datetime import os import sys from setuptools import find_packages from setuptools import setup version = '2.5.0' project_name = 'tf-models-official' long_description = """The TensorFlow official models are a collection of models that use TensorFlow's high-level APIs. They are intended to be well-maintained, tested, and kept up to date with the latest TensorFlow API. They should also be reasonably optimized for fast performance while still being easy to read.""" if '--project_name' in sys.argv: project_name_idx = sys.argv.index('--project_name') project_name = sys.argv[project_name_idx + 1] sys.argv.remove('--project_name') sys.argv.pop(project_name_idx) def _get_requirements(): """Parses requirements.txt file.""" install_requires_tmp = [] dependency_links_tmp = [] with open( os.path.join(os.path.dirname(__file__), '../requirements.txt'), 'r') as f: for line in f: package_name = line.strip() # Skip empty line or comments starting with "#". if not package_name or package_name[0] == '#': continue if package_name.startswith('-e '): dependency_links_tmp.append(package_name[3:].strip()) else: install_requires_tmp.append(package_name) return install_requires_tmp, dependency_links_tmp install_requires, dependency_links = _get_requirements() if project_name == 'tf-models-nightly': version += '.dev' + datetime.datetime.now().strftime('%Y%m%d') install_requires.append('tf-nightly') install_requires.append('tensorflow-text-nightly') else: install_requires.append('tensorflow>=2.4.0') install_requires.append('tensorflow-text>=2.4.0') print('install_requires: ', install_requires) print('dependency_links: ', dependency_links) setup( name=project_name, version=version, description='Model Implementation gardens', long_description=long_description, author='Stonepia', author_email='<EMAIL>', url='https://github.com/Stonepia/model-gardens', license='Apache 2.0', packages=find_packages(exclude=[ 'research', 'official.pip_package', 'official.benchmark', 'official.colab', ]), exclude_package_data={ '': ['*_test.py',], }, install_requires=install_requires, dependency_links=dependency_links, python_requires='>=3.6', )	[ 1, 444, 3382, 2164, 515, 2045, 597, 3292, 29889, 510, 29914, 29056, 29914, 9794, 29914, 10054, 29914, 6207, 29914, 29877, 7880, 29914, 13096, 29918, 5113, 29914, 14669, 29889, 2272, 13, 13, 15945, 29908, 29903, 1691, 701, 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gorillabot/plugins/connection.py	pep7/GorillaBot	21	164254	# Copyright (c) 2013-2016 <NAME> # # 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. from plugins.util import admin @admin() def join(m): """Join a channel.""" #- !join #channel #- #- Joins the specified channel. Only joins one channel at a time. if len(m.line) == 1: m.bot.private_message(m.location, "Please specify a channel to join.") else: chan = m.line[1] if chan[0] != "#": m.bot.private_message(m.location, "Not a valid channel name.") m.bot.join([chan]) m.bot.logger.info("Joining " + chan) @admin("leave") def part(m): """Part from the specified channel.""" #- !part [#channel] [message] #- #- Parts from the specified channel, or the current channel if unspecified. Only parts from #- one channel at a time. If a message is included, this will be used as the part message. part_msg = "" if len(m.line) == 1: m.bot.send("PART " + m.location + " :" + part_msg) m.bot.logger.info("Parting from " + m.location) try: del m.bot.configuration["chans"][m.location] except KeyError: m.bot.logger("Wasn't joined to " + m.location) else: m.bot.update_configuration(m.bot.configuration) return channel = "" if len(m.line) > 2: part_msg = " ".join(m.line[2:]) if m.line[1][0] != "#": m.bot.private_message(m.location, "Not a valid channel name.") return else: channel = m.line[1] m.bot.send("PART " + channel + " :" + part_msg) m.bot.logger.info("Parting from " + channel + ".") try: del m.bot.configuration["chans"][m.location] except KeyError: m.bot.logger.info("Wasn't joined to " + m.location) else: m.bot.update_configuration(m.bot.configuration) @admin("shutdown") def quit(m): """Shut down the bot entirely.""" #- !quit [message] #- #- Quits the bot from the network and shuts down. msg = " ".join(m.line[1:]) if len(m.line) > 1 else "" m.bot.shutdown_message = msg m.bot.reconnect_time = None m.bot.shutdown.set()	[ 1, 396, 14187, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29896, 29941, 29899, 29906, 29900, 29896, 29953, 529, 5813, 29958, 13, 29937, 13, 29937, 20894, 2333, 338, 1244, 1609, 16896, 29892, 3889, 310, 8323, 29892, 304, 738, 2022, 4017, 292, 263, 3509, 310, 445, 7047, 13, 29937, 322, 6942, 5106, 2066, 313, 1552, 376, 6295, 14093, 4968, 304, 5376, 297, 278, 18540, 1728, 13, 29937, 24345, 29892, 3704, 1728, 29485, 278, 10462, 304, 671, 29892, 3509, 29892, 6623, 29892, 10366, 29892, 9805, 29892, 13, 29937, 1320, 2666, 29892, 269, 803, 1947, 29892, 322, 29914, 272, 19417, 14591, 310, 278, 18540, 29892, 322, 304, 14257, 12407, 304, 6029, 278, 13, 29937, 18540, 338, 15252, 3276, 304, 437, 577, 29892, 4967, 304, 278, 1494, 5855, 29901, 13, 29937, 13, 29937, 450, 2038, 3509, 1266, 8369, 322, 445, 10751, 8369, 4091, 367, 5134, 297, 599, 14591, 470, 13, 29937, 23228, 2011, 1080, 310, 278, 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data/databases/create_dataset_from_mongodb_collection.py	james94/driverlessai-recipes	194	129527	"""Create dataset from MonogDB""" # Author: <NAME> # Created: 31/01/2020 # Last Updated: 20/02/2020 import datatable as dt import pandas as pd from h2oaicore.data import CustomData _global_modules_needed_by_name = ["pymongo", "dnspython"] # Please fill before usage # Note that this information is logged in Driverless AI logs. MONGO_CONNECTION_STRING = "mongodb+srv://<username>:<password>@host[/[database][?options]]" MONGO_DB = "sample_mflix" MONGO_COLLECTION = "theaters" DATASET_NAME = "sample_mflix.theaters" class MongoDbData(CustomData): _modules_needed_by_name = ["pymongo", "dnspython"] @staticmethod def create_data(X: dt.Frame = None): from pymongo import MongoClient # Note: adding try clause to help pass tests internally. # can cause unexpected effect of recipe completing successfully but returning an empty dataset. try: # Initialize MongoDB python client client = MongoClient(MONGO_CONNECTION_STRING) # Use MongoDB python client to obtain list of all documents in a specific database + collection db = client.get_database(MONGO_DB) coll = db.get_collection(MONGO_COLLECTION) docs = coll.find() # Convert MongoDB documents cursor to pandas dataframe df = pd.DataFrame.from_dict(docs) # Cast all object columns as string since datatable cannot accept arbitrary objects object_cols = df.select_dtypes(include=['object']).columns df[object_cols] = df[object_cols].astype(str) # return dict where key is name of dataset and value is a datatable Frame of the data. return {DATASET_NAME: dt.Frame(df)} except Exception as e: return []	[ 1, 9995, 4391, 8783, 515, 2598, 468, 4051, 15945, 29908, 13, 13, 29937, 13361, 29901, 529, 5813, 29958, 13, 29937, 6760, 630, 29901, 29871, 29941, 29896, 29914, 29900, 29896, 29914, 29906, 29900, 29906, 29900, 13, 29937, 9208, 25723, 29901, 29871, 29906, 29900, 29914, 29900, 29906, 29914, 29906, 29900, 29906, 29900, 13, 13, 5215, 1418, 17219, 408, 11636, 13, 5215, 11701, 408, 10518, 13, 3166, 298, 29906, 19807, 293, 487, 29889, 1272, 1053, 8701, 1469, 13, 13, 29918, 10945, 29918, 7576, 29918, 484, 19226, 29918, 1609, 29918, 978, 353, 6796, 29886, 962, 7443, 613, 376, 5200, 1028, 1656, 3108, 13, 13, 29937, 3529, 5445, 1434, 8744, 13, 29937, 3940, 393, 445, 2472, 338, 13817, 297, 26391, 2222, 319, 29902, 10748, 29889, 13, 22877, 17080, 29918, 6007, 8186, 9838, 29918, 20785, 353, 376, 23264, 29974, 29879, 15291, 597, 29966, 6786, 23917, 29966, 5630, 29958, 29992, 3069, 29961, 29914, 29961, 9803, 3816, 29973, 6768, 5262, 29908, 13, 22877, 17080, 29918, 4051, 353, 376, 11249, 29918, 29885, 20157, 29916, 29908, 13, 22877, 17080, 29918, 15032, 3281, 2725, 353, 376, 1552, 10412, 29908, 13, 25832, 8127, 29911, 29918, 5813, 353, 376, 11249, 29918, 29885, 20157, 29916, 29889, 1552, 10412, 29908, 13, 13, 13, 1990, 18294, 10234, 1469, 29898, 7281, 1469, 1125, 13, 1678, 903, 7576, 29918, 484, 19226, 29918, 1609, 29918, 978, 353, 6796, 29886, 962, 7443, 613, 376, 5200, 1028, 1656, 3108, 13, 13, 1678, 732, 7959, 5696, 13, 1678, 822, 1653, 29918, 1272, 29898, 29990, 29901, 11636, 29889, 4308, 353, 6213, 1125, 13, 4706, 515, 282, 962, 7443, 1053, 18294, 4032, 13, 13, 4706, 396, 3940, 29901, 4417, 1018, 11845, 304, 1371, 1209, 6987, 25106, 29889, 13, 4706, 396, 539, 508, 4556, 15668, 2779, 310, 9522, 412, 1614, 1259, 8472, 541, 7863, 385, 4069, 8783, 29889, 13, 4706, 1018, 29901, 13, 9651, 396, 25455, 29004, 3017, 3132, 13, 9651, 3132, 353, 18294, 4032, 29898, 22877, 17080, 29918, 6007, 8186, 9838, 29918, 20785, 29897, 13, 13, 9651, 396, 4803, 29004, 3017, 3132, 304, 4017, 1051, 310, 599, 10701, 297, 263, 2702, 2566, 718, 4333, 13, 9651, 4833, 353, 3132, 29889, 657, 29918, 9803, 29898, 22877, 17080, 29918, 4051, 29897, 13, 9651, 5321, 353, 4833, 29889, 657, 29918, 10855, 29898, 22877, 17080, 29918, 15032, 3281, 2725, 29897, 13, 9651, 10561, 353, 5321, 29889, 2886, 580, 13, 13, 9651, 396, 14806, 29004, 10701, 10677, 304, 11701, 12205, 13, 9651, 4489, 353, 10518, 29889, 17271, 29889, 3166, 29918, 8977, 29898, 2640, 29897, 13, 13, 9651, 396, 4834, 599, 1203, 4341, 408, 1347, 1951, 1418, 17219, 2609, 3544, 11472, 3618, 13, 9651, 1203, 29918, 22724, 353, 4489, 29889, 2622, 29918, 29881, 8768, 29898, 2856, 29922, 1839, 3318, 2033, 467, 13099, 13, 9651, 4489, 29961, 3318, 29918, 22724, 29962, 353, 4489, 29961, 3318, 29918, 22724, 1822, 579, 668, 29898, 710, 29897, 13, 13, 9651, 396, 736, 9657, 988, 1820, 338, 1024, 310, 8783, 322, 995, 338, 263, 1418, 17219, 12218, 310, 278, 848, 29889, 13, 9651, 736, 426, 25832, 8127, 29911, 29918, 5813, 29901, 11636, 29889, 4308, 29898, 2176, 2915, 13, 4706, 5174, 8960, 408, 321, 29901, 13, 9651, 736, 5159, 13, 2 ]
records/urls.py	Glucemy/Glucemy-back	0	9680	from rest_framework.routers import DefaultRouter from records.views import RecordViewSet router = DefaultRouter() router.register('', RecordViewSet, basename='records') urlpatterns = router.urls	[ 1, 515, 1791, 29918, 4468, 29889, 27537, 2153, 1053, 13109, 23971, 13, 13, 3166, 6475, 29889, 7406, 1053, 14164, 1043, 2697, 13, 13, 15140, 353, 13109, 23971, 580, 13, 15140, 29889, 9573, 877, 742, 14164, 1043, 2697, 29892, 2362, 3871, 2433, 3757, 4339, 1495, 13, 13, 2271, 11037, 29879, 353, 12876, 29889, 26045, 13, 2 ]
src/sage/modules/module_functors.py	bopopescu/sage	1,742	160594	""" Module Functors AUTHORS: - <NAME> (2017-10): Initial implementation of :class:`QuotientModuleFunctor` """ #*************************************************************************** # Copyright (C) 2017 <NAME> <<EMAIL>> # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 2 of the License, or # (at your option) any later version. # http://www.gnu.org/licenses/ #*************************************************************************** ############################################################## # Construction functor for quotient modules ############################################################## from sage.categories.pushout import ConstructionFunctor from sage.categories.modules import Modules class QuotientModuleFunctor(ConstructionFunctor): r""" Construct the quotient of a module by a submodule. INPUT: - ``relations`` -- a module .. NOTE:: This construction functor keeps track of the basis of defining ``relations``. It can only be applied to free modules into which this basis coerces. EXAMPLES:: sage: A = (1/2)ZZ^2 sage: B = 2ZZ^2 sage: Q = A / B sage: F = Q.construction()[0] sage: F QuotientModuleFunctor sage: F(A) == Q True The modules are constructed from the cover not the ambient module:: sage: F(B.ambient_module()) == Q False We can construct quotients from different modules:: sage: F((1/2)ZZ^2) Finitely generated module V/W over Integer Ring with invariants (4, 4) sage: F(ZZ^2) Finitely generated module V/W over Integer Ring with invariants (2, 2) sage: F(2ZZ^2) Finitely generated module V/W over Integer Ring with invariants () This functor is used for constructing pushouts:: sage: A = ZZ^3 sage: x,y,z = A.basis() sage: A1 = A.submodule([x]) sage: A2 = A.submodule([y, 2x]) sage: B1 = A.submodule([]) sage: B2 = A.submodule([2x]) sage: Q1 = A1 / B1 sage: Q2 = A2 / B2 sage: q3 = Q1.an_element() + Q2.an_element() """ rank = 5 # ranking of functor, not rank of module def __init__(self, relations): """ Initialization of ``self``. TESTS:: sage: from sage.modules.module_functors import QuotientModuleFunctor sage: B = (2/3)ZZ^2 sage: F = QuotientModuleFunctor(B) sage: TestSuite(F).run() """ R = relations.category().base_ring() ConstructionFunctor.__init__(self, Modules(R), Modules(R)) self._relations = relations def relations(self): """ Return the defining relations of ``self``. EXAMPLES:: sage: A = (ZZ2) / span([[4,0],[0,3]], ZZ) sage: A.construction()[0].relations() Free module of degree 2 and rank 2 over Integer Ring Echelon basis matrix: [4 0] [0 3] """ return self._relations def _apply_functor(self, ambient): """ Apply the functor to an object of ``self``'s domain. TESTS:: sage: A = ZZ^3 sage: B = 2 A sage: C = 4 * A sage: D = B / C sage: F = D.construction()[0] sage: D == F(D.construction()[1]) True """ return ambient.quotient(self._relations) def __eq__(self, other): """ The quotient functor ``self`` is equal to ``other`` if it is a :class:`QuotientModuleFunctor` and the relations subspace are equal. EXAMPLES:: sage: F1 = ((ZZ^3) / (4ZZ^3)).construction()[0] sage: F2 = ((2ZZ^3) / (4ZZ^3)).construction()[0] sage: F1 == F2 True sage: F3 = ((ZZ^3) / (8ZZ^3)).construction()[0] sage: F1 == F3 False """ if not isinstance(other, QuotientModuleFunctor): return False return self._relations == other._relations def __ne__(self, other): r""" Check whether ``self`` is not equal to ``other``. EXAMPLES:: sage: F1 = ((ZZ^3) / (4ZZ^3)).construction()[0] sage: F2 = ((2ZZ^3) / (4ZZ^3)).construction()[0] sage: F1 != F2 False sage: F3 = ((ZZ^3) / (8ZZ^3)).construction()[0] sage: F1 != F3 True """ return not (self == other) def merge(self, other): r""" Merge the construction functors ``self`` and ``other``. EXAMPLES:: sage: A = ZZ^3 sage: x,y,z = A.basis() sage: A1 = A.submodule([x]) sage: A2 = A.submodule([y, 2x]) sage: B1 = A.submodule([]) sage: B2 = A.submodule([2x]) sage: Q1 = A1 / B1 sage: Q2 = A2 / B2 sage: F1 = Q1.construction()[0] sage: F2 = Q2.construction()[0] sage: F3 = F1.merge(F2) sage: q3 = Q1.an_element() + Q2.an_element() sage: q3.parent() == F3(A1 + A2) True sage: G = A1.construction()[0]; G SubspaceFunctor sage: F1.merge(G) sage: F2.merge(G) """ if isinstance(other, QuotientModuleFunctor): return QuotientModuleFunctor(self._relations + other._relations)	[ 1, 9995, 13, 7355, 13811, 14359, 13, 13, 20656, 29950, 24125, 29901, 13, 13, 29899, 529, 5813, 29958, 313, 29906, 29900, 29896, 29955, 29899, 29896, 29900, 1125, 17250, 5314, 310, 13, 29871, 584, 1990, 18078, 2182, 327, 993, 7355, 25394, 2801, 29952, 13, 15945, 29908, 13, 13, 29937, 7775, 7775, 7775, 7775, 4189, 2328, 29930, 13, 29937, 539, 14187, 1266, 313, 29907, 29897, 29871, 29906, 29900, 29896, 29955, 529, 5813, 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SAC2018/SAC.py	Wen2chao/RL-Algorithm-	19	192737	<reponame>Wen2chao/RL-Algorithm- import gym import torch import random import torch.nn as nn import collections import numpy as np import torch.nn.functional as F import torch.optim as optim import matplotlib.pyplot as plt from torch.distributions import Normal class ReplayBeffer(): def __init__(self, buffer_maxlen): self.buffer = collections.deque(maxlen=buffer_maxlen) def push(self, data): self.buffer.append(data) def sample(self, batch_size): state_list = [] action_list = [] reward_list = [] next_state_list = [] done_list = [] batch = random.sample(self.buffer, batch_size) for experience in batch: s, a, r, n_s, d = experience # state, action, reward, next_state, done state_list.append(s) action_list.append(a) reward_list.append(r) next_state_list.append(n_s) done_list.append(d) return torch.FloatTensor(state_list).to(device), \ torch.FloatTensor(action_list).to(device), \ torch.FloatTensor(reward_list).unsqueeze(-1).to(device), \ torch.FloatTensor(next_state_list).to(device), \ torch.FloatTensor(done_list).unsqueeze(-1).to(device) def buffer_len(self): return len(self.buffer) # Value Net class ValueNet(nn.Module): def __init__(self, state_dim, edge=3e-3): super(ValueNet, self).__init__() self.linear1 = nn.Linear(state_dim, 256) self.linear2 = nn.Linear(256, 256) self.linear3 = nn.Linear(256, 1) self.linear3.weight.data.uniform_(-edge, edge) self.linear3.bias.data.uniform_(-edge, edge) def forward(self, state): x = F.relu(self.linear1(state)) x = F.relu(self.linear2(x)) x = self.linear3(x) return x # Soft Q Net class SoftQNet(nn.Module): def __init__(self, state_dim, action_dim, edge=3e-3): super(SoftQNet, self).__init__() self.linear1 = nn.Linear(state_dim + action_dim, 256) self.linear2 = nn.Linear(256, 256) self.linear3 = nn.Linear(256, 1) self.linear3.weight.data.uniform_(-edge, edge) self.linear3.bias.data.uniform_(-edge, edge) def forward(self, state, action): x = torch.cat([state, action], 1) x = F.relu(self.linear1(x)) x = F.relu(self.linear2(x)) x = self.linear3(x) return x # Policy Net class PolicyNet(nn.Module): def __init__(self, state_dim, action_dim, log_std_min=-20, log_std_max=2, edge=3e-3): super(PolicyNet, self).__init__() self.log_std_min = log_std_min self.log_std_max = log_std_max self.linear1 = nn.Linear(state_dim, 256) self.linear2 = nn.Linear(256, 256) self.mean_linear = nn.Linear(256, action_dim) self.mean_linear.weight.data.uniform_(-edge, edge) self.mean_linear.bias.data.uniform_(-edge, edge) self.log_std_linear = nn.Linear(256, action_dim) self.log_std_linear.weight.data.uniform_(-edge, edge) self.log_std_linear.bias.data.uniform_(-edge, edge) def forward(self, state): x = F.relu(self.linear1(state)) x = F.relu(self.linear2(x)) mean = self.mean_linear(x) log_std = self.log_std_linear(x) log_std = torch.clamp(log_std, self.log_std_min, self.log_std_max) return mean, log_std def action(self, state): state = torch.FloatTensor(state).to(device) mean, log_std = self.forward(state) std = log_std.exp() normal = Normal(mean, std) z = normal.sample() action = torch.tanh(z).detach().cpu().numpy() return action # Use re-parameterization tick def evaluate(self, state, epsilon=1e-6): mean, log_std = self.forward(state) std = log_std.exp() normal = Normal(mean, std) noise = Normal(0, 1) z = noise.sample() action = torch.tanh(mean + std * z.to(device)) log_prob = normal.log_prob(mean + std * z.to(device)) - torch.log(1 - action.pow(2) + epsilon) return action, log_prob class SAC: def __init__(self, env, gamma, tau, buffer_maxlen, value_lr, q_lr, policy_lr): self.env = env self.state_dim = env.observation_space.shape[0] self.action_dim = env.action_space.shape[0] # hyperparameters self.gamma = gamma self.tau = tau # initialize networks self.value_net = ValueNet(self.state_dim).to(device) self.target_value_net = ValueNet(self.state_dim).to(device) self.q1_net = SoftQNet(self.state_dim, self.action_dim).to(device) self.q2_net = SoftQNet(self.state_dim, self.action_dim).to(device) self.policy_net = PolicyNet(self.state_dim, self.action_dim).to(device) # Load the target value network parameters for target_param, param in zip(self.target_value_net.parameters(), self.value_net.parameters()): target_param.data.copy_(self.tau * param + (1 - self.tau) * target_param) # Initialize the optimizer self.value_optimizer = optim.Adam(self.value_net.parameters(), lr=value_lr) self.q1_optimizer = optim.Adam(self.q1_net.parameters(), lr=q_lr) self.q2_optimizer = optim.Adam(self.q2_net.parameters(), lr=q_lr) self.policy_optimizer = optim.Adam(self.policy_net.parameters(), lr=policy_lr) # Initialize thebuffer self.buffer = ReplayBeffer(buffer_maxlen) def get_action(self, state): action = self.policy_net.action(state) return action def update(self, batch_size): state, action, reward, next_state, done = self.buffer.sample(batch_size) new_action, log_prob = self.policy_net.evaluate(state) # V value loss value = self.value_net(state) new_q1_value = self.q1_net(state, new_action) new_q2_value = self.q2_net(state, new_action) next_value = torch.min(new_q1_value, new_q2_value) - log_prob value_loss = F.mse_loss(value, next_value.detach()) # Soft q loss q1_value = self.q1_net(state, action) q2_value = self.q2_net(state, action) target_value = self.target_value_net(next_state) target_q_value = reward + done * self.gamma * target_value q1_value_loss = F.mse_loss(q1_value, target_q_value.detach()) q2_value_loss = F.mse_loss(q2_value, target_q_value.detach()) # Policy loss policy_loss = (log_prob - torch.min(new_q1_value, new_q2_value)).mean() # Update Policy self.policy_optimizer.zero_grad() policy_loss.backward() self.policy_optimizer.step() # Update v self.value_optimizer.zero_grad() value_loss.backward() self.value_optimizer.step() # Update Soft q self.q1_optimizer.zero_grad() self.q2_optimizer.zero_grad() q1_value_loss.backward() q2_value_loss.backward() self.q1_optimizer.step() self.q2_optimizer.step() # Update target networks for target_param, param in zip(self.target_value_net.parameters(), self.value_net.parameters()): target_param.data.copy_(self.tau * param + (1 - self.tau) * target_param) def main(env, agent, Episode, batch_size): Return = [] action_range = [env.action_space.low, env.action_space.high] for episode in range(Episode): score = 0 state = env.reset() for i in range(300): action = agent.get_action(state) # action output range[-1,1],expand to allowable range action_in = action * (action_range[1] - action_range[0]) / 2.0 + (action_range[1] + action_range[0]) / 2.0 next_state, reward, done, _ = env.step(action_in) done_mask = 0.0 if done else 1.0 agent.buffer.push((state, action, reward, next_state, done_mask)) state = next_state score += reward if done: break if agent.buffer.buffer_len() > 500: agent.update(batch_size) print("episode:{}, Return:{}, buffer_capacity:{}".format(episode, score, agent.buffer.buffer_len())) Return.append(score) score = 0 env.close() plt.plot(Return) plt.ylabel('Return') plt.xlabel("Episode") plt.grid(True) plt.show() if __name__ == '__main__': env = gym.make("Pendulum-v0") device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu") # Params tau = 0.01 gamma = 0.99 q_lr = 3e-3 value_lr = 3e-3 policy_lr = 3e-3 buffer_maxlen = 50000 Episode = 100 batch_size = 128 agent = SAC(env, gamma, tau, buffer_maxlen, value_lr, q_lr, policy_lr) main(env, agent, Episode, batch_size)	[ 1, 529, 276, 1112, 420, 29958, 29956, 264, 29906, 5815, 29877, 29914, 2241, 29899, 22461, 4540, 29899, 13, 5215, 330, 962, 30004, 13, 5215, 4842, 305, 30004, 13, 5215, 4036, 30004, 13, 5215, 4842, 305, 29889, 15755, 408, 302, 29876, 30004, 13, 5215, 16250, 30004, 13, 5215, 12655, 408, 7442, 30004, 13, 5215, 4842, 305, 29889, 15755, 29889, 2220, 284, 408, 383, 30004, 13, 5215, 4842, 305, 29889, 20640, 408, 5994, 30004, 13, 5215, 22889, 29889, 2272, 5317, 408, 14770, 30004, 13, 3166, 4842, 305, 29889, 27691, 29879, 1053, 21981, 30004, 13, 30004, 13, 30004, 13, 1990, 830, 1456, 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hera_notebook_templates/utils_h1c.py	HERA-Team/hera_notebook_templates	0	125478	<filename>hera_notebook_templates/utils_h1c.py # -- coding: utf-8 -- # Copyright 2020 the HERA Project # Licensed under the MIT License import matplotlib import matplotlib.pyplot as plt from matplotlib.ticker import FormatStrFormatter import matplotlib.patches as mpatches import matplotlib.gridspec as gridspec import numpy as np from pyuvdata import UVCal, UVData, utils import os import sys import glob import uvtools as uvt from astropy.time import Time from astropy.coordinates import EarthLocation, AltAz, Angle from astropy.coordinates import SkyCoord as sc import pandas import warnings import copy from hera_mc import cm_hookup, geo_sysdef import math from uvtools import dspec import hera_qm from hera_mc import cm_active from matplotlib.lines import Line2D from matplotlib import colors import json from hera_notebook_templates.data import DATA_PATH from astropy.io import fits import csv from astropy import units as u from astropy_healpix import HEALPix from astropy.coordinates import Galactic import healpy import yaml warnings.filterwarnings('ignore') def read_a_priori_ant_flags(a_priori_flags_yaml, ant_indices_only=False, by_ant_pol=False, ant_pols=None): '''Parse an a priori flag YAML file for a priori antenna flags. Parameters ---------- a_priori_flags_yaml : str Path to YAML file with a priori antenna flags ant_indices_only : bool If True, ignore polarizations and flag entire antennas when they appear, e.g. (1, 'Jee') --> 1. by_ant_pol : bool If True, expand all integer antenna indices into per-antpol entries using ant_pols ant_pols : list of str List of antenna polarizations strings e.g. 'Jee'. If not empty, strings in the YAML must be in here or an error is raised. Required if by_ant_pol is True. Returns ------- a_priori_antenna_flags : list List of a priori antenna flags, either integers or ant-pol tuples e.g. (0, 'Jee') ''' if ant_indices_only and by_ant_pol: raise ValueError("ant_indices_only and by_ant_pol can't both be True.") apaf = [] apf = yaml.safe_load(open(a_priori_flags_yaml, 'r')) # Load antenna flags if 'ex_ants' in apf: for ant in apf['ex_ants']: # flag antenna number if type(ant) == int: apaf.append(ant) # flag single antpol elif (type(ant) == list) and (len(ant) == 2) and (type(ant[0]) == int) and (type(ant[1]) == str): # check that antpol string is valid if ant_pols is not empty if (ant_pols is not None) and (ant[1] not in ant_pols): raise ValueError(f'{ant[1]} is not a valid ant_pol in {ant_pols}.') if ant_indices_only: apaf.append(ant[0]) else: apaf += [tuple(ant)] else: raise TypeError(f'ex_ants entires must be integers or a list of one int and one str. {ant} is not.') # Expand all integer antenna flags into antpol pairs if by_ant_pol: if ant_pols is None: raise ValueError('If by_ant_pol is True, then ant_pols must be specified.') apapf = [] for ant in apaf: if type(ant) == int: apapf += [(ant, pol) for pol in ant_pols] else: # then it's already and antpol tuple apapf.append(ant) return sorted(set(apapf)) return list(set(apaf)) def load_data(data_path,JD): HHfiles = sorted(glob.glob("{0}/zen.{1}..sum.uvh5".format(data_path,JD))) Nfiles = len(HHfiles) hhfile_bases = map(os.path.basename, HHfiles) sep = '.' x = sep.join(HHfiles[0].split('.')[-4:-2]) y = sep.join(HHfiles[-1].split('.')[-4:-2]) print(f'{len(HHfiles)} sum files found between JDs {x} and {y}') # choose one for single-file plots hhfile1 = HHfiles[len(HHfiles)//2] # Load data uvd_hh = UVData() unread = True while unread is True: try: uvd_hh.read(hhfile1, skip_bad_files=True) except: hhfile += 1 continue unread = False uvd_xx1 = uvd_hh.select(polarizations = -5, inplace = False) uvd_xx1.ants = np.unique(np.concatenate([uvd_xx1.ant_1_array, uvd_xx1.ant_2_array])) # -5: 'xx', -6: 'yy', -7: 'xy', -8: 'yx' uvd_yy1 = uvd_hh.select(polarizations = -6, inplace = False) uvd_yy1.ants = np.unique(np.concatenate([uvd_yy1.ant_1_array, uvd_yy1.ant_2_array])) return HHfiles, uvd_xx1, uvd_yy1 def plot_sky_map(uvd,ra_pad=20,dec_pad=30,clip=True,fwhm=11,nx=300,ny=200,sources=[]): map_path = f'{DATA_PATH}/haslam408_dsds_Remazeilles2014.fits' hdulist = fits.open(map_path) # Set up the HEALPix projection nside = hdulist[1].header['NSIDE'] order = hdulist[1].header['ORDERING'] hp = HEALPix(nside=nside, order=order, frame=Galactic()) #Get RA/DEC coords of observation loc = EarthLocation.from_geocentric(uvd.telescope_location, unit='m') time_array = uvd.time_array obstime_start = Time(time_array[0],format='jd',location=loc) obstime_end = Time(time_array[-1],format='jd',location=loc) zenith_start = sc(Angle(0, unit='deg'),Angle(90,unit='deg'),frame='altaz',obstime=obstime_start,location=loc) zenith_start = zenith_start.transform_to('icrs') zenith_end = sc(Angle(0, unit='deg'),Angle(90,unit='deg'),frame='altaz',obstime=obstime_end,location=loc) zenith_end = zenith_end.transform_to('icrs') lst_start = obstime_start.sidereal_time('mean').hour lst_end = obstime_end.sidereal_time('mean').hour start_coords = [zenith_start.ra.degree,zenith_start.dec.degree] if start_coords[0] > 180: start_coords[0] = start_coords[0] - 360 end_coords = [zenith_end.ra.degree,zenith_end.dec.degree] if end_coords[0] > 180: end_coords[0] = end_coords[0] - 360 # Sample a 300x200 grid in RA/Dec ra_range = [zenith_start.ra.degree-ra_pad, zenith_end.ra.degree+ra_pad] dec_range = [zenith_start.dec.degree-ra_pad, zenith_end.dec.degree+ra_pad] if clip == True: ra = np.linspace(ra_range[0],ra_range[1], nx) dec = np.linspace(dec_range[0],dec_range[1], ny) else: ra = np.linspace(-180,180,nx) dec = np.linspace(-90,zenith_start.dec.degree+90,ny) ra_grid, dec_grid = np.meshgrid(ra * u.deg, dec * u.deg) #Create alpha grid alphas = np.ones(ra_grid.shape) alphas = np.multiply(alphas,0.5) ra_min = np.argmin(np.abs(np.subtract(ra,start_coords[0]-fwhm/2))) ra_max = np.argmin(np.abs(np.subtract(ra,end_coords[0]+fwhm/2))) dec_min = np.argmin(np.abs(np.subtract(dec,start_coords[1]-fwhm/2))) dec_max = np.argmin(np.abs(np.subtract(dec,end_coords[1]+fwhm/2))) alphas[dec_min:dec_max, ra_min:ra_max] = 1 # Set up Astropy coordinate objects coords = sc(ra_grid.ravel(), dec_grid.ravel(), frame='icrs') # Interpolate values temperature = healpy.read_map(map_path) tmap = hp.interpolate_bilinear_skycoord(coords, temperature) tmap = tmap.reshape((ny, nx)) tmap = np.flip(tmap,axis=1) alphas = np.flip(alphas,axis=1) # Make a plot of the interpolated temperatures plt.figure(figsize=(12, 7)) im = plt.imshow(tmap, extent=[ra[-1], ra[0], dec[0], dec[-1]], cmap=plt.cm.viridis, aspect='auto', vmin=10,vmax=40,alpha=alphas,origin='lower') plt.xlabel('RA (ICRS)') plt.ylabel('DEC (ICRS)') plt.hlines(y=start_coords[1]-fwhm/2,xmin=ra[-1],xmax=ra[0],linestyles='dashed') plt.hlines(y=start_coords[1]+fwhm/2,xmin=ra[-1],xmax=ra[0],linestyles='dashed') plt.vlines(x=start_coords[0],ymin=start_coords[1],ymax=dec[-1],linestyles='dashed') plt.vlines(x=end_coords[0],ymin=start_coords[1],ymax=dec[-1],linestyles='dashed') plt.annotate(np.around(lst_start,2),xy=(start_coords[0],dec[-1]),xytext=(0,8), fontsize=10,xycoords='data',textcoords='offset points',horizontalalignment='center') plt.annotate(np.around(lst_end,2),xy=(end_coords[0],dec[-1]),xytext=(0,8), fontsize=10,xycoords='data',textcoords='offset points',horizontalalignment='center') plt.annotate('LST (hours)',xy=(np.average([start_coords[0],end_coords[0]]),dec[-1]), xytext=(0,22),fontsize=10,xycoords='data',textcoords='offset points',horizontalalignment='center') for s in sources: if s[1] > dec[0] and s[1] < dec[-1]: if s[0] > 180: s = (s[0]-360,s[1],s[2]) if s[2] == 'LMC' or s[2] == 'SMC': plt.annotate(s[2],xy=(s[0],s[1]),xycoords='data',fontsize=8,xytext=(20,-20), textcoords='offset points',arrowprops=dict(facecolor='black', shrink=2,width=1, headwidth=4)) else: plt.scatter(s[0],s[1],c='k',s=6) if len(s[2]) > 0: plt.annotate(s[2],xy=(s[0]+3,s[1]-4),xycoords='data',fontsize=6) plt.show() plt.close() hdulist.close() def plot_inspect_ants(uvd1,jd,badAnts=[],flaggedAnts={},tempAnts={},crossedAnts=[],use_ants='auto'): # status_use = ['RF_ok','digital_ok','calibration_maintenance','calibration_ok','calibration_triage'] if use_ants == 'auto': use_ants = uvd1.get_ants() h = cm_active.ActiveData(at_date=jd) h.load_apriori() inspectAnts = [] for ant in use_ants: # status = h.apriori[f'HH{ant}:A'].status if ant in badAnts or ant in flaggedAnts.keys() or ant in crossedAnts: inspectAnts.append(ant) inspectAnts = np.unique(inspectAnts) inspectTitles = {} for ant in inspectAnts: inspectTitles[ant] = 'Flagged by: ' if ant in badAnts: inspectTitles[ant] = f'{inspectTitles[ant]} correlation matrix,' if ant in flaggedAnts.keys(): inspectTitles[ant] = f'{inspectTitles[ant]} ant_metrics,' if ant in crossedAnts: inspectTitles[ant] = f'{inspectTitles[ant]} cross matrix,' try: for k in tempAnts.keys(): if ant in tempAnts[k]: inspectTitles[ant] = f'{inspectTitles[ant]} template - {k},' except: continue if inspectTitles[ant][-1] == ',': inspectTitles[ant] = inspectTitles[ant][:-1] print('Antennas that require further inspection are:') print(inspectAnts) for ant in inspectAnts: auto_waterfall_lineplot(uvd1,ant,jd,title=inspectTitles[ant]) return inspectAnts def auto_waterfall_lineplot(uv, ant, jd, pols=['xx','yy'], colorbar_min=1e6, colorbar_max=1e8, title=''): freq = uv.freq_array[0]1e-6 fig = plt.figure(figsize=(12,8)) gs = gridspec.GridSpec(3, 2, height_ratios=[2,0.7,1]) it = 0 pol_dirs = ['NN','EE'] for p,pol in enumerate(pols): waterfall= plt.subplot(gs[it]) jd_ax=plt.gca() times= np.unique(uv.time_array) d = np.abs(uv.get_data((ant,ant, pol))) if len(np.nonzero(d)[0])==0: print('#########################################') print(f'Data for antenna {ant} is entirely zeros') print('#########################################') plt.close() return im = plt.imshow(d,norm=colors.LogNorm(), aspect='auto') waterfall.set_title(f'{pol_dirs[p]} pol') freqs = uv.freq_array[0, :] / 1000000 xticks = np.arange(0, len(freqs), 120) plt.xticks(xticks, labels =np.around(freqs[xticks],2)) if p == 0: jd_ax.set_ylabel('JD') jd_yticks = [int(i) for i in np.linspace(0,len(times)-1,8)] jd_labels = np.around(times[jd_yticks],2) jd_ax.set_yticks(jd_yticks) jd_ax.set_yticklabels(jd_labels) jd_ax.autoscale(False) if p == 1: lst_ax = jd_ax.twinx() lst_ax.set_ylabel('LST (hours)') lsts = uv.lst_array3.819719 inds = np.unique(lsts,return_index=True)[1] lsts = [lsts[ind] for ind in sorted(inds)] lst_yticks = [int(i) for i in np.linspace(0,len(lsts)-1,8)] lst_labels = np.around([lsts[i] for i in lst_yticks],2) lst_ax.set_yticks(lst_yticks) lst_ax.set_yticklabels(lst_labels) lst_ax.set_ylim(jd_ax.get_ylim()) lst_ax.autoscale(False) jd_ax.set_yticks([]) line= plt.subplot(gs[it+2]) averaged_data= np.abs(np.average(uv.get_data((ant,ant,pol)),0)) plt.plot(freq,averaged_data) line.set_yscale('log') if p == 0: line.set_ylabel('Night Average') else: line.set_yticks([]) line.set_xlim(freq[0],freq[-1]) line.set_xticks([]) line2 = plt.subplot(gs[it+4]) dat = uv.get_data((ant,ant,pol)) dat = np.abs(dat[len(dat)//2,:]) plt.plot(freq,dat) line2.set_yscale('log') line2.set_xlabel('Frequency (MHz)') if p == 0: line2.set_ylabel('Single Slice') else: line2.set_yticks([]) line2.set_xlim(freq[0],freq[-1]) plt.setp(waterfall.get_xticklabels(), visible=False) plt.subplots_adjust(hspace=.0) cbar = plt.colorbar(im, pad= 0.2, orientation = 'horizontal') cbar.set_label('Power') it=1 fig.suptitle(f'{ant}', fontsize=10,y=0.96) plt.annotate(title, xy=(0.5,0.94), ha='center',xycoords='figure fraction') plt.show() plt.close() def plot_autos(uvdx, uvdy): ants = uvdx.get_ants() sorted_ants = sorted(ants) freqs = (uvdx.freq_array[0])10(-6) times = uvdx.time_array lsts = uvdx.lst_array Nants = len(ants) Nside = 6 Yside = Nants//6 + 1 t_index = 0 jd = times[t_index] utc = Time(jd, format='jd').datetime xlim = (np.min(freqs), np.max(freqs)) ylim = (0, 20) fig, axes = plt.subplots(Yside, Nside, figsize=(16,Yside3)) ptitle = 1.92/(Yside3) fig.suptitle("JD = {0}, time = {1} UTC".format(jd, utc), fontsize=10,y=1+ptitle) fig.tight_layout(rect=(0, 0, 1, 0.95)) fig.subplots_adjust(left=.1, bottom=.1, right=.9, top=1, wspace=0.05, hspace=0.3) k = 0 for n,a in enumerate(ants): j = n%Nside i = n//Nside ax = axes[i,j] ax.set_xlim(xlim) ax.set_ylim(ylim) px, = ax.plot(freqs, 10np.log10(np.abs(uvdx.get_data((a, a,'xx'))[t_index])), color='r', alpha=0.75, linewidth=1) py, = ax.plot(freqs, 10np.log10(np.abs(uvdy.get_data((a, a,'yy'))[t_index])), color='b', alpha=0.75, linewidth=1) ax.grid(False, which='both') ax.set_title(f'{a}', fontsize=10) if k == 0: ax.legend([px, py], ['NN', 'EE']) if i == Yside-1: [t.set_fontsize(10) for t in ax.get_xticklabels()] ax.set_xlabel('freq (MHz)', fontsize=10) else: ax.set_xticklabels([]) if j!=0: ax.set_yticklabels([]) else: [t.set_fontsize(10) for t in ax.get_yticklabels()] ax.set_ylabel(r'$10\cdot\log$(amp)', fontsize=10) j += 1 k += 1 for k in range(j,Nside): axes[i,k].axis('off') plt.show() plt.close() def plot_wfs(uvd, pol, mean_sub=False, save=False, jd=''): # amps = np.abs(uvd.data_array[:, :, :, pol].reshape(uvd.Ntimes, uvd.Nants_data, uvd.Nfreqs, 1)) ants = uvd.get_ants() sorted_ants = sorted(ants) freqs = (uvd.freq_array[0])10*(-6) times = uvd.time_array lsts = uvd.lst_array3.819719 inds = np.unique(lsts,return_index=True)[1] lsts = [lsts[ind] for ind in sorted(inds)] polnames = ['xx','yy'] Nants = len(ants) Nside = 6 Yside = Nants//6 + 1 t_index = 0 jd = times[t_index] utc = Time(jd, format='jd').datetime ptitle = 1.92/(Yside3) fig, axes = plt.subplots(Yside, Nside, figsize=(16,Yside3)) if pol == 0: fig.suptitle("North Polarization", fontsize=14, y=1+ptitle) else: fig.suptitle("East Polarization", fontsize=14, y=1+ptitle) fig.tight_layout(rect=(0, 0, 1, 0.95)) fig.subplots_adjust(left=0, bottom=.1, right=.9, top=1, wspace=0.1, hspace=0.3) vmin = 0 vmax = 2 k = 0 for n,a in enumerate(ants): j = n%Nside i = n//Nside ax = axes[i,j] dat = np.log10(np.abs(uvd.get_data(a,a,polnames[pol]))) if mean_sub == True: ms = np.subtract(dat, np.nanmean(dat,axis=0)) im = ax.imshow(ms, vmin = -0.04, vmax = 0.04, aspect='auto',interpolation='nearest') else: im = ax.imshow(dat, vmin = vmin, vmax = vmax, aspect='auto',interpolation='nearest') ax.set_title(f'{a}', fontsize=10) ax.grid(False, which='both') if i == Yside-1: xticks = [int(i) for i in np.linspace(0,len(freqs)-1,3)] xticklabels = np.around(freqs[xticks],0) ax.set_xticks(xticks) ax.set_xticklabels(xticklabels) ax.set_xlabel('Freq (MHz)', fontsize=10) [t.set_rotation(70) for t in ax.get_xticklabels()] else: ax.set_xticklabels([]) if j!=0: ax.set_yticklabels([]) else: yticks = [int(i) for i in np.linspace(0,len(lsts)-1,6)] yticklabels = [np.around(lsts[ytick],1) for ytick in yticks] [t.set_fontsize(12) for t in ax.get_yticklabels()] ax.set_ylabel('Time(LST)', fontsize=10) ax.set_yticks(yticks) ax.set_yticklabels(yticklabels) ax.set_ylabel('Time(LST)', fontsize=10) if j==Nside-1: pos = ax.get_position() cbar_ax=fig.add_axes([0.91,pos.y0,0.01,pos.height]) cbar = fig.colorbar(im, cax=cbar_ax) cbar.set_label(f'Node {n}',rotation=270, labelpad=15) j += 1 k += 1 for k in range(j,Nside): axes[i,k].axis('off') plt.show() plt.close() # for i,n in enumerate(inclNodes): # ants = nodes[n]['ants'] # j = 0 # for _,a in enumerate(sorted_ants): # if a not in ants: # continue # # status = h.apriori[f'HH{a}:A'].status # # abb = status_abbreviations[status] # ax = axes[i,j] # dat = np.log10(np.abs(uvd.get_data(a,a,polnames[pol]))) # if mean_sub == True: # ms = np.subtract(dat, np.nanmean(dat,axis=0)) # im = ax.imshow(ms, # vmin = -0.07, vmax = 0.07, aspect='auto',interpolation='nearest') # else: # im = ax.imshow(dat, # vmin = vmin, vmax = vmax, aspect='auto',interpolation='nearest') # ax.set_title(f'{a}', fontsize=10) # if i == len(inclNodes)-1: # xticks = [int(i) for i in np.linspace(0,len(freqs)-1,3)] # xticklabels = np.around(freqs[xticks],0) # ax.set_xticks(xticks) # ax.set_xticklabels(xticklabels) # ax.set_xlabel('Freq (MHz)', fontsize=10) # [t.set_rotation(70) for t in ax.get_xticklabels()] # else: # ax.set_xticklabels([]) # if j != 0: # ax.set_yticklabels([]) # else: # yticks = [int(i) for i in np.linspace(0,len(lsts)-1,6)] # yticklabels = [np.around(lsts[ytick],1) for ytick in yticks] # [t.set_fontsize(12) for t in ax.get_yticklabels()] # ax.set_ylabel('Time(LST)', fontsize=10) # ax.set_yticks(yticks) # ax.set_yticklabels(yticklabels) # ax.set_ylabel('Time(LST)', fontsize=10) # j += 1 # for k in range(j,maxants): # axes[i,k].axis('off') # pos = ax.get_position() # cbar_ax=fig.add_axes([0.91,pos.y0,0.01,pos.height]) # cbar = fig.colorbar(im, cax=cbar_ax) # cbar.set_label(f'Node {n}',rotation=270, labelpad=15) # if save is True: # plt.savefig(f'{jd}_mean_subtracted_per_node_{pol}.png',bbox_inches='tight',dpi=300) # plt.show() # plt.close() def plot_mean_subtracted_wfs(uvd, use_ants, jd, pols=['xx','yy']): freqs = (uvd.freq_array[0])1e-6 times = uvd.time_array lsts = uvd.lst_array3.819719 inds = np.unique(lsts,return_index=True)[1] lsts = [lsts[ind] for ind in sorted(inds)] ants = sorted(use_ants) Nants = len(ants) pol_labels = ['NN','EE'] # status_colors = { # 'dish_maintenance' : 'salmon', # 'dish_ok' : 'red', # 'RF_maintenance' : 'lightskyblue', # 'RF_ok' : 'royalblue', # 'digital_maintenance' : 'plum', # 'digital_ok' : 'mediumpurple', # 'calibration_maintenance' : 'lightgreen', # 'calibration_ok' : 'green', # 'calibration_triage' : 'lime'} # status_abbreviations = { # 'dish_maintenance' : 'dish-M', # 'dish_ok' : 'dish-OK', # 'RF_maintenance' : 'RF-M', # 'RF_ok' : 'RF-OK', # 'digital_maintenance' : 'dig-M', # 'digital_ok' : 'dig-OK', # 'calibration_maintenance' : 'cal-M', # 'calibration_ok' : 'cal-OK', # 'calibration_triage' : 'cal-Tri'} h = cm_active.ActiveData(at_date=jd) h.load_apriori() fig, axes = plt.subplots(Nants, 2, figsize=(7,Nants2.2)) fig.suptitle('Mean Subtracted Waterfalls') fig.tight_layout(rect=(0, 0, 1, 0.95)) fig.subplots_adjust(left=.1, bottom=.1, right=.85, top=.975, wspace=0.05, hspace=0.2) for i,ant in enumerate(ants): # status = h.apriori[f'HH{ant}:A'].status # abb = status_abbreviations[status] # color = status_colors[status] for j,pol in enumerate(pols): ax = axes[i,j] dat = np.log10(np.abs(uvd.get_data(ant,ant,pol))) ms = np.subtract(dat, np.nanmean(dat,axis=0)) im = ax.imshow(ms, vmin = -0.07, vmax = 0.07, aspect='auto',interpolation='nearest') ax.set_title(f'{ant} - {pol_labels[j]}', fontsize=10) if j != 0: ax.set_yticklabels([]) else: yticks = [int(i) for i in np.linspace(0,len(lsts)-1,6)] yticklabels = [np.around(lsts[ytick],1) for ytick in yticks] [t.set_fontsize(12) for t in ax.get_yticklabels()] ax.set_ylabel('Time(LST)', fontsize=10) ax.set_yticks(yticks) ax.set_yticklabels(yticklabels) if i != Nants-1: ax.set_xticklabels([]) else: xticks = [int(i) for i in np.linspace(0,len(freqs)-1,8)] xticklabels = np.around(freqs[xticks],0) ax.set_xticks(xticks) ax.set_xticklabels(xticklabels) ax.set_xlabel('Frequency (MHz)', fontsize=10) if j == 1: pos = ax.get_position() cbar_ax=fig.add_axes([0.88,pos.y0,0.02,pos.height]) fig.colorbar(im, cax=cbar_ax) fig.show() def plot_closure(uvd, triad_length, pol): """Plot closure phase for an example triad. Parameters ---------- files : list of strings List of data filenames triad_length : float {14., 29.} Length of the triangle segment length. Must be 14 or 29. pol : str {xx, yy} Polarization to plot. Returns ------- None """ if triad_length == 14.: triad_list = [[0, 11, 12], [0, 1, 12], [1, 12, 13], [1, 2, 13], [2, 13, 14], [11, 23, 24], [11, 12, 24], [12, 24, 25], [12, 13, 25], [13, 25, 26], [13, 14, 26], [14, 26, 27], [23, 36, 37], [23, 24, 37], [24, 37, 38], [24, 25, 38], [25, 38, 39], [25, 26, 39], [26, 39, 40], [26, 27, 40], [27, 40, 41], [36, 37, 51], [37, 51, 52], [37, 38, 52], [38, 52, 53], [38, 39, 53], [39, 53, 54], [39, 40, 54], [40, 54, 55], [40, 41, 55], [51, 66, 67], [51, 52, 67], [53, 54, 69], [54, 69, 70], [54, 55, 70], [55, 70, 71], [65, 66, 82], [66, 82, 83], [66, 67, 83], [67, 83, 84], [70, 71, 87], [120, 121, 140], [121, 140, 141], [121, 122, 141], [122, 141, 142], [122, 123, 142], [123, 142, 143], [123, 124, 143]] else: triad_list = [[0, 23, 25], [0, 2, 25], [1, 24, 26], [2, 25, 27], [11, 36, 38], [11, 13, 38], [12, 37, 39], [12, 14, 39], [13, 38, 40], [14, 39, 41], [23, 25, 52], [24, 51, 53], [24, 26, 53], [25, 52, 54], [25, 27, 54], [26, 53, 55], [36, 65, 67], [36, 38, 67], [38, 67, 69], [38, 40, 69], [39, 41, 70], [40, 69, 71], [51, 82, 84], [51, 53, 84], [52, 83, 85], [52, 54, 85], [54, 85, 87], [83, 85, 120], [85, 120, 122], [85, 87, 122], [87, 122, 124]] # Look for a triad that exists in the data for triad in triad_list: bls = [[triad[0], triad[1]], [triad[1], triad[2]], [triad[2], triad[0]]] triad_in = True for bl in bls: inds = uvd.antpair2ind(bl[0], bl[1], ordered=False) if len(inds) == 0: triad_in = False break if triad_in: break if not triad_in: raise ValueError('Could not find triad in data.') closure_ph = np.angle(uvd.get_data(triad[0], triad[1], pol) uvd.get_data(triad[1], triad[2], pol) * uvd.get_data(triad[2], triad[0], pol)) plt.imshow(closure_ph, aspect='auto', rasterized=True, interpolation='nearest', cmap = 'twilight') def plotNodeAveragedSummary(uv,HHfiles,jd,use_ants,pols=['xx','yy'],mat_pols=['xx','yy'], baseline_groups=[],removeBadAnts=False,plotRatios=False,plotSummary=True): """ Plots a summary of baseline correlations throughout a night for each baseline group specified, separated into inter-node and intra-node baselines, for each polarization specified. Parameters ---------- uv: UVData object UVData object containing any file from the desired night of observation. HHfiles: List A list of all files to be looked at for the desired night of observation. jd: String The JD of the night of observation pols: List A list containing the desired polarizations to look at. Options are any polarization strings accepted by pyuvdata. baseline_groups: [] A list containing the baseline types to look at, formatted as (length, N-S separation, label (str)). removeBadAnts: Bool Option to flag seemingly dead antennas and remove them from the per-baseline-group averaging. Returns ------- badAnts: List A list specifying the antennas flagged as dead or non-correlating. """ if baseline_groups == []: baseline_groups = [(14,0,'14m E-W'),(14,-11,'14m NW-SE'),(14,11,'14m SW-NE'),(29,0,'29m E-W'),(29,22,'29m SW-NE'), (44,0,'44m E-W'),(58.5,0,'58m E-W'),(73,0,'73m E-W'),(87.6,0,'88m E-W'), (102.3,0,'102m E-W')] nodeMedians,lsts,badAnts=get_correlation_baseline_evolutions(uv,HHfiles,jd,use_ants,pols=pols,mat_pols=mat_pols, bl_type=baseline_groups,removeBadAnts=removeBadAnts, plotRatios=plotRatios) # pols = mat_pols # if plotSummary is False: # return badAnts # if len(lsts)>1: # fig,axs = plt.subplots(len(pols),2,figsize=(16,16)) # maxLength = 0 # cmap = plt.get_cmap('Blues') # for group in baseline_groups: # if group[0] > maxLength: # maxLength = group[0] # for group in baseline_groups: # length = group[0] # data = nodeMedians[group[2]] # colorInd = float(length/maxLength) # if len(data['inter']['xx']) == 0: # continue # for i in range(len(pols)): # pol = pols[i] # axs[i][0].plot(data['inter'][pol], color=cmap(colorInd), label=group[2]) # axs[i][1].plot(data['intra'][pol], color=cmap(colorInd), label=group[2]) # axs[i][0].set_ylabel('Median Correlation Metric') # axs[i][0].set_title('Internode, Polarization %s' % pol) # axs[i][1].set_title('Intranode, Polarization %s' % pol) # xticks = np.arange(0,len(lsts),1) # axs[i][0].set_xticks(xticks) # axs[i][0].set_xticklabels([str(lst) for lst in lsts]) # axs[i][1].set_xticks(xticks) # axs[i][1].set_xticklabels([str(lst) for lst in lsts]) # axs[1][1].legend() # axs[1][0].set_xlabel('LST (hours)') # axs[1][1].set_xlabel('LST (hours)') # fig.tight_layout(pad=2) # else: # print('#############################################################################') # print('Not enough LST coverage to show metric evolution - that plot is being skipped') # print('#############################################################################') # return badAnts def plotVisibilitySpectra(file,jd,use_ants='auto',badAnts=[],pols=['xx','yy']): """ Plots visibility amplitude spectra for a set of redundant baselines, labeled by inter vs. intranode baselines. Parameters --------- file: String File to calculate the spectra from jd: String JD of the night 'file' was observed on badAnts: List A list of antennas not to include in the plot pols: List Polarizations to plot. Can include any polarization strings accepted by pyuvdata. """ pol_labels = ['NS','EW'] plt.subplots_adjust(wspace=0.25) uv = UVData() uv.read_uvh5(file) baseline_groups = get_baseline_groups(uv,use_ants="auto") freqs = uv.freq_array[0]/1000000 loc = EarthLocation.from_geocentric(uv.telescope_location, unit='m') obstime_start = Time(uv.time_array[0],format='jd',location=loc) startTime = obstime_start.sidereal_time('mean').hour JD = int(obstime_start.jd) j = 0 fig, axs = plt.subplots(len(baseline_groups),2,figsize=(12,4len(baseline_groups))) for orientation in baseline_groups: bls = baseline_groups[orientation] usable = 0 for i in range(len(bls)): ants = uv.baseline_to_antnums(bls[i]) if ants[0] in badAnts or ants[1] in badAnts: continue if ants[0] in use_ants and ants[1] in use_ants: usable += 1 if usable <=4: use_all = True print(f'Note: not enough baselines of orientation {orientation} - using all available baselines') elif usable <= 10: print(f'Note: only a small number of baselines of orientation {orientation} are available') use_all = False else: use_all = False for p in range(len(pols)): inter=False intra=False pol = pols[p] for i in range(len(bls)): ants = uv.baseline_to_antnums(bls[i]) ant1 = ants[0] ant2 = ants[1] if (ant1 in use_ants and ant2 in use_ants) or use_all == True: # key1 = 'HH%i:A' % (ant1) # n1 = x[key1].get_part_from_type('node')['E<ground'][1:] # key2 = 'HH%i:A' % (ant2) # n2 = x[key2].get_part_from_type('node')['E<ground'][1:] dat = np.mean(np.abs(uv.get_data(ant1,ant2,pol)),0) auto1 = np.mean(np.abs(uv.get_data(ant1,ant1,pol)),0) auto2 = np.mean(np.abs(uv.get_data(ant2,ant2,pol)),0) norm = np.sqrt(np.multiply(auto1,auto2)) dat = np.divide(dat,norm) if ant1 in badAnts or ant2 in badAnts: continue # if n1 == n2: # if intra is False: # axs[j][p].plot(freqs,dat,color='blue',label='intranode') # intra=True # else: # axs[j][p].plot(freqs,dat,color='blue') # else: # if inter is False: # axs[j][p].plot(freqs,dat,color='red',label='internode') # inter=True # else: # axs[j][p].plot(freqs,dat,color='red') axs[j][p].plot(freqs,dat,color='blue') axs[j][p].set_yscale('log') axs[j][p].set_title('%s: %s pol' % (orientation,pol_labels[p])) if j == 0: axs[len(baseline_groups)-1][p].set_xlabel('Frequency (MHz)') if p == 0: axs[j][p].legend() axs[j][0].set_ylabel('log(\|Vij\|)') axs[j][1].set_yticks([]) j += 1 fig.suptitle('Visibility spectra (JD: %i)' % (JD)) fig.subplots_adjust(top=.94,wspace=0.05) plt.show() plt.close() def plot_antenna_positions(uv, badAnts=[],use_ants='auto'): """ Plots the positions of all antennas that have data, colored by node. Parameters ---------- uv: UVData object Observation to extract antenna numbers and positions from badAnts: List A list of flagged or bad antennas. These will be outlined in black in the plot. flaggedAnts: Dict A dict of antennas flagged by ant_metrics with value corresponding to color in ant_metrics plot """ plt.figure(figsize=(12,10)) if badAnts == None: badAnts = [] all_ants = uv.antenna_numbers # nodes, antDict, inclNodes = generate_nodeDict(uv) # N = len(inclNodes) cmap = plt.get_cmap('tab20') i = 0 # nodePos = geo_sysdef.read_nodes() # antPos = geo_sysdef.read_antennas() # ants = geo_sysdef.read_antennas() # nodes = geo_sysdef.read_nodes() # firstNode = True firstAnt = True for i,a in enumerate(all_ants): width = 0 widthf = 0 if a in badAnts: width = 2 x = uv.antenna_positions[i,0] y = uv.antenna_positions[i,1] if a in use_ants: falpha = 0.5 else: falpha = 0.1 if firstAnt: if a in badAnts: if falpha == 0.1: plt.plot(x,y,marker="h",markersize=40,alpha=falpha,color='b', markeredgecolor='black',markeredgewidth=0) plt.annotate(a, [x-1, y]) continue plt.plot(x,y,marker="h",markersize=40,alpha=falpha,color='b', markeredgecolor='black',markeredgewidth=0) if a in badAnts: plt.plot(x,y,marker="h",markersize=40,color='b', markeredgecolor='black',markeredgewidth=width, markerfacecolor="None") else: if falpha == 0.1: plt.plot(x,y,marker="h",markersize=40,alpha=falpha,color='b', markeredgecolor='black',markeredgewidth=0) plt.annotate(a, [x-1, y]) continue plt.plot(x,y,marker="h",markersize=40,alpha=falpha,color='b', markeredgecolor='black',markeredgewidth=width) firstAnt = False else: plt.plot(x,y,marker="h",markersize=40,alpha=falpha,color='b', markeredgecolor='black',markeredgewidth=0) if a in badAnts and a in use_ants: plt.plot(x,y,marker="h",markersize=40,color='b', markeredgecolor='black',markeredgewidth=width, markerfacecolor="None") plt.annotate(a, [x-1, y]) plt.xlabel('East') plt.ylabel('North') plt.show() plt.close() def plot_lst_coverage(uvd): """ Plots the LST and JD coverage for a particular night. Parameters ---------- uvd: UVData Object Object containing a whole night of data, used to extract the time array. """ lsts = uvd.lst_array3.819719 jds = np.unique(uvd.time_array) alltimes = np.arange(np.floor(jds[0]),np.ceil(jds[0]),jds[2]-jds[1]) df = jds[2]-jds[1] truetimes = [np.min(np.abs(jds-jd))<=df0.6 for jd in alltimes] usetimes = np.tile(np.asarray(truetimes),(20,1)) fig = plt.figure(figsize=(20,2)) ax = fig.add_subplot() im = ax.imshow(usetimes, aspect='auto',cmap='RdYlGn',vmin=0,vmax=1,interpolation='nearest') fig.colorbar(im) ax.set_yticklabels([]) ax.set_yticks([]) if len(alltimes) <= 15: xticks = [int(i) for i in np.linspace(0,len(alltimes)-1,len(alltimes))] else: xticks = [int(i) for i in np.linspace(0,len(alltimes)-1,14)] ax.set_xticks(xticks) ax.set_xticklabels(np.around(alltimes[xticks],2)) ax.set_xlabel('JD') ax.set_title('LST (hours)') ax2 = ax.twiny() ax2.set_xticks(xticks) jds = alltimes[xticks] lstlabels = [] loc = EarthLocation.from_geocentric(uvd.telescope_location, unit='m') for jd in jds: t = Time(jd,format='jd',location=loc) lstlabels.append(t.sidereal_time('mean').hour) ax2.set_xticklabels(np.around(lstlabels,2)) ax2.set_label('LST (hours)') ax2.tick_params(labelsize=12) plt.show() plt.close() def calcEvenOddAmpMatrix(sm,pols=['xx','yy'],nodes='auto', badThresh=0.25, plotRatios=False): """ Calculates a matrix of phase correlations between antennas, where each pixel is calculated as (even/abs(even)) (conj(odd)/abs(odd)), and then averaged across time and frequency. Paramters: --------- sm: UVData Object Sum observation. df: UVData Object Diff observation. Must be the same time of observation as sm. pols: List Polarizations to plot. Can include any polarization strings accepted by pyuvdata. nodes: String or List Nodes to include in matrix. Default is 'auto', which generates a list of all nodes included in the provided data files. badThresh: Float Threshold correlation metric value to use for flagging bad antennas. Returns: ------- data: Dict Dictionary containing calculated values, formatted as data[polarization][ant1,ant2]. badAnts: List List of antennas that were flagged as bad based on badThresh. """ nants = len(sm.get_ants()) data = {} antnumsAll = sorted(sm.get_ants()) badAnts = [] for p in range(len(pols)): pol = pols[p] data[pol] = np.empty((nants,nants)) for i in range(len(antnumsAll)): thisAnt = [] for j in range(len(antnumsAll)): ant1 = antnumsAll[i] ant2 = antnumsAll[j] dat = sm.get_data(ant1,ant2,pol) even = dat[::2,:] # print('Even') # print(even) odd = dat[1::2,:] # s = sm.get_data(ant1,ant2,pol) # d = df.get_data(ant1,ant2,pol) # even = (s + d)/2 even = np.divide(even,np.abs(even)) # print('Even norm') # print(even) # odd = (s - d)/2 odd = np.divide(odd,np.abs(odd)) product = np.multiply(even,np.conj(odd)) # print('product') # print(product) data[pol][i,j] = np.abs(np.nanmean(product)) thisAnt.append(np.abs(np.mean(product))) pgood = np.count_nonzero(~np.isnan(thisAnt))/len(thisAnt) if (np.nanmedian(thisAnt) < badThresh or pgood<0.2) and antnumsAll[i] not in badAnts: if pol[0]==pol[1]: #Don't assign bad ants based on cross pols badAnts.append(antnumsAll[i]) if plotRatios is True: if len(pols) == 4: data['xx-xy'] = np.subtract(data['xx'],data['xy']) data['xx-yx'] = np.subtract(data['xx'],data['yx']) data['yy-xy'] = np.subtract(data['yy'],data['xy']) data['yy-yx'] = np.subtract(data['yy'],data['yx']) else: print('Can only calculate differences if cross pols were specified') polAnts = {} badAnts = [] subs = ['xx-xy','xx-yx','yy-xy','yy-yx'] for k in subs: for i,ant in enumerate(antnumsAll): dat = data[k][i,:] if np.nanmedian(dat) < 0: if ant in polAnts.keys(): polAnts[ant] = polAnts[ant] + 1 else: polAnts[ant] = 1 if polAnts[ant] == 4: badAnts.append(ant) return data, badAnts def plotCorrMatrix(uv,data,pols=['xx','yy'],vminIn=0,vmaxIn=1,nodes='auto',logScale=False,plotRatios=False): """ Plots a matrix representing the phase correlation of each baseline. Parameters: ---------- uv: UVData Object Observation used for calculating the correlation metric data: Dict Dictionary containing the correlation metric for each baseline and each polarization. Formatted as data[polarization] [ant1,ant2] pols: List Polarizations to plot. Can include any polarization strings accepted by pyuvdata. vminIn: float Lower limit of colorbar. Default is 0. vmaxIn: float Upper limit of colorbar. Default is 1. nodes: Dict Dictionary containing the nodes (and their constituent antennas) to include in the matrix. Formatted as nodes[Node #][Ant List, Snap # List, Snap Location List]. logScale: Bool Option to put colormap on a logarithmic scale. Default is False. """ # if nodes=='auto': # nodeDict, antDict, inclNodes = generate_nodeDict(uv) nantsTotal = len(uv.get_ants()) power = np.empty((nantsTotal,nantsTotal)) fig, axs = plt.subplots(2,2,figsize=(16,16)) dirs = ['NN','EE','NE','EN'] cmap='plasma' if plotRatios is True: pols = ['xx-xy','yy-xy','xx-yx','yy-yx'] dirs=pols vminIn=-1 cmap='seismic' loc = EarthLocation.from_geocentric(uv.telescope_location, unit='m') jd = uv.time_array[0] t = Time(jd,format='jd',location=loc) lst = round(t.sidereal_time('mean').hour,2) t.format='fits' antnumsAll = sorted(uv.get_ants()) i = 0 for p in range(len(pols)): if p >= 2: i=1 pol = pols[p] nants = len(antnumsAll) if logScale is True: im = axs[i][p%2].imshow(data[pol],cmap=cmap,origin='upper',extent=[0.5,nantsTotal+.5,0.5,nantsTotal+0.5],norm=LogNorm(vmin=vminIn, vmax=vmaxIn)) else: im = axs[i][p%2].imshow(data[pol],cmap=cmap,origin='upper',extent=[0.5,nantsTotal+.5,0.5,nantsTotal+0.5],vmin=vminIn, vmax=vmaxIn) axs[i][p%2].set_xticks(np.arange(0,nantsTotal)+1) axs[i][p%2].set_xticklabels(antnumsAll,rotation=90,fontsize=6) axs[i][p%2].xaxis.set_ticks_position('top') axs[i][p%2].set_title('polarization: ' + dirs[p] + '\n') # n=0 # n=0 # for node in sorted(inclNodes): # n += len(nodeDict[node]['ants']) # axs[0][1].text(nantsTotal+1,nantsTotal-n+len(nodeDict[node]['ants'])/2,node) # axs[1][1].text(nantsTotal+1,nantsTotal-n+len(nodeDict[node]['ants'])/2,node) # axs[0][1].text(1.05,0.4,'Node Number',rotation=270,transform=axs[0][1].transAxes) axs[0][1].set_yticklabels([]) axs[0][1].set_yticks([]) axs[0][0].set_yticks(np.arange(nantsTotal,0,-1)) axs[0][0].set_yticklabels(antnumsAll,fontsize=6) axs[0][0].set_ylabel('Antenna Number') # axs[1][1].text(1.05,0.4,'Node Number',rotation=270,transform=axs[1][1].transAxes) axs[1][1].set_yticklabels([]) axs[1][1].set_yticks([]) axs[1][0].set_yticks(np.arange(nantsTotal,0,-1)) axs[1][0].set_yticklabels(antnumsAll,fontsize=6) axs[1][0].set_ylabel('Antenna Number') cbar_ax = fig.add_axes([0.98,0.18,0.015,0.6]) cbar_ax.set_xlabel('\|V\|', rotation=0) cbar = fig.colorbar(im, cax=cbar_ax) fig.suptitle('Correlation Matrix - JD: %s, LST: %.0fh' % (str(jd),np.round(lst,0))) fig.subplots_adjust(top=1.28,wspace=0.05,hspace=1.1) fig.tight_layout(pad=2) plt.show() plt.close() def plot_single_matrix(uv,data,vminIn=0,vmaxIn=1,nodes='auto',logScale=False): if nodes=='auto': nodeDict, antDict, inclNodes = generate_nodeDict(uv) nantsTotal = len(uv.get_ants()) power = np.empty((nantsTotal,nantsTotal)) fig, axs = plt.subplots(1,1,figsize=(16,16)) loc = EarthLocation.from_geocentric(uv.telescope_location, unit='m') jd = uv.time_array[0] t = Time(jd,format='jd',location=loc) lst = round(t.sidereal_time('mean').hour,2) t.format='fits' antnumsAll = sort_antennas(uv) nants = len(antnumsAll) if logScale is True: im = axs[0][0].imshow(data[pol],cmap='plasma',origin='upper', extent=[0.5,nantsTotal+.5,0.5,nantsTotal+0.5],norm=LogNorm(vmin=vminIn, vmax=vmaxIn)) else: im = axs[0][0].imshow(data[pol],cmap='plasma',origin='upper',extent= [0.5,nantsTotal+.5,0.5,nantsTotal+0.5],vmin=vminIn, vmax=vmaxIn) axs[0][0].set_xticks(np.arange(0,nantsTotal)+1) axs[0][0].set_xticklabels(antnumsAll,rotation=90,fontsize=6) axs[0][0].xaxis.set_ticks_position('top') axs[0][0].set_title('polarization: ' + dirs[p] + '\n') n=0 for node in sorted(inclNodes): n += len(nodeDict[node]['ants']) axs[0][0].axhline(len(antnumsAll)-n+.5,lw=4) axs[0][0].axvline(n+.5,lw=4) axs[0][0].text(n-len(nodeDict[node]['ants'])/2,-.5,node) axs[0][0].text(.42,-.05,'Node Number',transform=axs[0][0].transAxes) n=0 for node in sorted(inclNodes): n += len(nodeDict[node]['ants']) axs[0][0].text(nantsTotal+1,nantsTotal-n+len(nodeDict[node]['ants'])/2,node) axs[0][0].text(1.05,0.4,'Node Number',rotation=270,transform=axs[0][0].transAxes) axs[0][0].set_yticks(np.arange(nantsTotal,0,-1)) axs[0][0].set_yticklabels(antnumsAll,fontsize=6) axs[0][0].set_ylabel('Antenna Number') axs[0][0].text(1.05,0.4,'Node Number',rotation=270,transform=axs[0][0].transAxes) cbar_ax = fig.add_axes([0.98,0.18,0.015,0.6]) cbar_ax.set_xlabel('\|V\|', rotation=0) cbar = fig.colorbar(im, cax=cbar_ax) fig.suptitle('Correlation Matrix - JD: %s, LST: %.0fh' % (str(jd),np.round(lst,0))) fig.subplots_adjust(top=1.28,wspace=0.05,hspace=1.1) fig.tight_layout(pad=2) plt.show() plt.close() def get_hourly_files(uv, HHfiles, jd): """ Generates a list of files spaced one hour apart throughout a night of observation, and the times those files were observed. Parameters: ---------- uv: UVData Object Sample observation from the given night, used only for grabbing the telescope location HHFiles: List List of all files from the desired night of observation jd: String JD of the night of observation Returns: ------- use_files: List List of files separated by one hour use_lsts: List List of LSTs of observations in use_files """ use_lsts = [] use_files = [] use_file_inds = [] loc = EarthLocation.from_geocentric(uv.telescope_location, unit='m') for i,file in enumerate(HHfiles): try: dat = UVData() dat.read(file, read_data=False) except KeyError: continue jd = dat.time_array[0] t = Time(jd,format='jd',location=loc) lst = round(t.sidereal_time('mean').hour,2) if np.round(lst,0) == 24: continue if np.abs((lst-np.round(lst,0)))<0.05: if len(use_lsts)>0 and np.abs(use_lsts[-1]-lst)<0.5: if np.abs((lst-np.round(lst,0))) < abs((use_lsts[-1]-np.round(lst,0))): use_lsts[-1] = lst use_files[-1] = file use_file_inds[-1] = i else: use_lsts.append(lst) use_files.append(file) use_file_inds.append(i) return use_files, use_lsts, use_file_inds def get_baseline_groups(uv, bl_groups=[(14,0,'14m E-W'),(29,0,'29m E-W'),(14,-11,'14m NW-SE'),(14,11,'14m SW-NE')], use_ants='auto'): """ Generate dictionary containing baseline groups. Parameters: ---------- uv: UVData Object Observation to extract antenna position information from bl_groups: List Desired baseline types to extract, formatted as (length (float), N-S separation (float), label (string)) Returns: -------- bls: Dict Dictionary containing list of lists of redundant baseline numbers, formatted as bls[group label] """ bls={} baseline_groups,vec_bin_centers,lengths = uv.get_redundancies(use_antpos=True,include_autos=False) for i in range(len(baseline_groups)): bl = baseline_groups[i] for group in bl_groups: if np.abs(lengths[i]-group[0])<1: ant1 = uv.baseline_to_antnums(bl[0])[0] ant2 = uv.baseline_to_antnums(bl[0])[1] if use_ants == 'auto' or (ant1 in use_ants and ant2 in use_ants): antPos1 = uv.antenna_positions[np.argwhere(uv.antenna_numbers == ant1)] antPos2 = uv.antenna_positions[np.argwhere(uv.antenna_numbers == ant2)] disp = (antPos2-antPos1)[0][0] if np.abs(disp[2]-group[1])<0.5: bls[group[2]] = bl return bls def get_correlation_baseline_evolutions(uv,HHfiles,jd,use_ants='auto',badThresh=0.35,pols=['xx','yy'],bl_type=(14,0,'14m E-W'), removeBadAnts=False, plotMatrix=True,mat_pols=['xx','yy','xy','yx'],plotRatios=False): """ Calculates the average correlation metric for a set of redundant baseline groups at one hour intervals throughout a night of observation. Parameters: ---------- uv: UVData Object Sample observation from the desired night, used only for getting telescope location information. HHfiles: List List of all files for a night of observation jd: String JD of the given night of observation badThresh: Float Threshold correlation metric value to use for flagging bad antennas. Default is 0.35. pols: List Polarizations to plot. Can include any polarization strings accepted by pyuvdata. bl_type: Tuple Redundant baseline group to calculate correlation metric for. Default is 14m E-W baselines removeBadAnts: Bool Option to exclude antennas marked as bad from calculation. Default is False. plotMatrix: Bool Option to plot the correlation matrix for observations once each hour. Default is True. Returns: ------- result: Dict Per hour correlation metric, formatted as result[baseline type]['inter' or 'intra'][polarization] lsts: List LSTs that metric was calculated for, spaced 1 hour apart. bad_antennas: List Antenna numbers flagged as bad based on badThresh parameter. """ files, lsts, inds = get_hourly_files(uv, HHfiles, jd) if use_ants == 'auto': use_ants = uv.get_ants() if plotRatios is True: files = [files[len(files)//2]] nTimes=1 else: nTimes = len(files) if nTimes > 3: plotTimes = [0,nTimes//2,nTimes-1] else: plotTimes = np.arange(0,nTimes,1) # nodeDict, antDict, inclNodes = generate_nodeDict(uv) JD = math.floor(uv.time_array[0]) bad_antennas = [] pols = mat_pols # corrSummary = generateDataTable(uv,pols=pols) result = {} for f in range(nTimes): file = files[f] ind = inds[f] sm = UVData() df = UVData() try: # print(f'Trying to read {file}') sm.read(file, skip_bad_files=True, antenna_nums=use_ants) # dffile = '%sdiff%s' % (file[0:-8],file[-5:]) # df.read(dffile, skip_bad_files=True, antenna_nums=use_ants) except: i = -5 read = False while i<5 and read==False: try: file = HHfiles[ind+i] # print(f'trying to read {file}') sm.read(file, skip_bad_files=True, antenna_nums=use_ants) # dffile = '%sdiff%s' % (file[0:-8],file[-5:]) # df.read(dffile, skip_bad_files=True, antenna_nums=use_ants) read = True except: i += 1 if read == False: print(f'WARNING: unable to read {file}') continue if f in plotTimes: # print(f) # print(mat_pols) matrix, badAnts = calcEvenOddAmpMatrix(sm,nodes='auto',pols=mat_pols,badThresh=badThresh,plotRatios=plotRatios) # print(badAnts) # print(np.shape(matrix)) # print(np.max(matrix)) # print(matrix) if plotMatrix is True and f in plotTimes: plotCorrMatrix(sm, matrix, pols=mat_pols, nodes='auto',plotRatios=plotRatios) result = None return result,lsts,bad_antennas # def generateDataTable(uv,pols=['xx','yy']): # """ # Simple helper function to generate an empty dictionary of the format desired for get_correlation_baseline_evolutions() # Parameters: # ---------- # uv: UVData Object # Sample observation to extract node and antenna information from. # pols: List # Polarizations to plot. Can include any polarization strings accepted by pyuvdata. Default is ['xx','yy']. # Returns: # ------- # dataObject: Dict # Empty dict formatted as dataObject[node #][polarization]['inter' or 'intra'] # """ # nodeDict, antDict, inclNodes = generate_nodeDict(uv) # dataObject = {} # for node in nodeDict: # dataObject[node] = {} # for pol in pols: # dataObject[node][pol] = { # 'inter' : [], # 'intra' : [] # } # return dataObject def get_baseline_type(uv,bl_type=(14,0,'14m E-W'),use_ants='auto'): """ Parameters: ---------- uv: UVData Object Sample observation to get baseline information from. bl_type: Tuple Redundant baseline group to extract baseline numbers for. Formatted as (length, N-S separation, label). Returns: ------- bl: List List of lists of redundant baseline numbers. Returns None if the provided bl_type is not found. """ baseline_groups,vec_bin_centers,lengths = uv.get_redundancies(use_antpos=True,include_autos=False) for i in range(len(baseline_groups)): bl = baseline_groups[i] if np.abs(lengths[i]-bl_type[0])<1: ant1 = uv.baseline_to_antnums(bl[0])[0] ant2 = uv.baseline_to_antnums(bl[0])[1] if (ant1 in use_ants and ant2 in use_ants) or use_ants == 'auto': antPos1 = uv.antenna_positions[np.argwhere(uv.antenna_numbers == ant1)] antPos2 = uv.antenna_positions[np.argwhere(uv.antenna_numbers == ant2)] disp = (antPos2-antPos1)[0][0] if np.abs(disp[2]-bl_type[1])<0.5: return bl return None def plot_crosses(uvd, ref_ant): ants = uvd.get_ants() freqs = (uvd.freq_array[0])10*(-6) times = uvd.time_array lsts = uvd.lst_array Nants = len(ants) # Nside = int(np.ceil(np.sqrt(Nants)))3 Nside = 4 Yside = int(np.ceil(float(Nants)/Nside)) t_index = 0 jd = times[t_index] utc = Time(jd, format='jd').datetime xlim = (np.min(freqs), np.max(freqs)) ylim = (60, 90) fig, axes = plt.subplots(Yside, Nside, figsize=(Yside2, Nside60)) fig.suptitle("JD = {0}, time = {1} UTC".format(jd, utc), fontsize=10) fig.tight_layout(rect=(0, 0, 1, 0.95)) fig.subplots_adjust(left=.1, bottom=.1, right=.9, top=.9, wspace=0.05, hspace=0.2) k = 0 for i in range(Yside): for j in range(Nside): ax = axes[i,j] ax.set_xlim(xlim) # ax.set_ylim(ylim) if k < Nants: px, = ax.plot(freqs, 10np.log10(np.abs(np.mean(uvd.get_data((ants[k], ref_ant, 'xx')),axis=0))), color='red', alpha=0.75, linewidth=1) py, = ax.plot(freqs, 10np.log10(np.abs(np.mean(uvd.get_data((ants[k], ref_ant, 'yy')),axis=0))), color='darkorange', alpha=0.75, linewidth=1) pxy, = ax.plot(freqs, 10np.log10(np.abs(np.mean(uvd.get_data((ants[k], ref_ant, 'xy')),axis=0))), color='royalblue', alpha=0.75, linewidth=1) pyx, = ax.plot(freqs, 10np.log10(np.abs(np.mean(uvd.get_data((ants[k], ref_ant, 'yx')),axis=0))), color='darkviolet', alpha=0.75, linewidth=1) ax.grid(False, which='both') ax.set_title(str(ants[k]), fontsize=14) if k == 0: ax.legend([px, py, pxy, pyx], ['XX', 'YY', 'XY','YX']) else: ax.axis('off') if j != 0: ax.set_yticklabels([]) else: [t.set_fontsize(10) for t in ax.get_yticklabels()] ax.set_ylabel(r'$10\cdot\log_{10}$ amplitude', fontsize=10) if i != Yside-1: ax.set_xticklabels([]) else: [t.set_fontsize(10) for t in ax.get_xticklabels()] ax.set_xlabel('freq (MHz)', fontsize=10) k += 1 fig.show() plt.close() def gather_source_list(): sources = [] sources.append((50.6750,-37.2083,'Fornax A')) sources.append((201.3667,-43.0192,'Cen A')) # sources.append((83.6333,22.0144,'Taurus A')) sources.append((252.7833,4.9925,'Hercules A')) sources.append((139.5250,-12.0947,'Hydra A')) sources.append((79.9583,-45.7789,'Pictor A')) sources.append((187.7042,12.3911,'Virgo A')) sources.append((83.8208,-59.3897,'Orion A')) sources.append((80.8958,-69.7561,'LMC')) sources.append((13.1875,-72.8286,'SMC')) sources.append((201.3667,-43.0192,'Cen A')) sources.append((83.6333,20.0144,'Crab Pulsar')) sources.append((128.8375,-45.1764,'Vela SNR')) cat_path = f'{DATA_PATH}/G4Jy_catalog.tsv' cat = open(cat_path) f = csv.reader(cat,delimiter='\n') for row in f: if len(row)>0 and row[0][0]=='J': s = row[0].split(';') tup = (float(s[1]),float(s[2]),'') sources.append(tup) return sources # def clean_ds(HHfiles, difffiles, bls, area=1000., tol=1e-9, skip_wgts=0.2): # uvd_ds = UVData() # uvd_ds.read(HHfiles[0], bls=bls[0], polarizations=-5, keep_all_metadata=False) # times = np.unique(uvd_ds.time_array) # Nfiles = int(1./((times[-1]-times[0])24.)) # try: # uvd_ds.read(HHfiles[:Nfiles], bls=bls, polarizations=[-5,-6], keep_all_metadata=False) # uvd_ds.flag_array = np.zeros_like(uvd_ds.flag_array) # uvd_diff = UVData() # uvd_diff.read(difffiles[:Nfiles], bls=bls, polarizations=[-5,-6], keep_all_metadata=False) # except: # uvd_ds.read(HHfiles[len(HHfiles)//2:len(HHfiles)//2+Nfiles], bls=bls, polarizations=[-5,-6], keep_all_metadata=False) # uvd_ds.flag_array = np.zeros_like(uvd_ds.flag_array) # uvd_diff = UVData() # uvd_diff.read(difffiles[len(HHfiles)//2:len(HHfiles)//2+Nfiles], bls=bls, polarizations=[-5,-6], keep_all_metadata=False) # uvf_m, uvf_fws = hera_qm.xrfi.xrfi_h1c_pipe(uvd_ds, sig_adj=1, sig_init=3) # hera_qm.xrfi.flag_apply(uvf_m, uvd_ds) # freqs = uvd_ds.freq_array[0] # FM_idx = np.searchsorted(freqs1e-6, [85,110]) # flag_FM = np.zeros(freqs.size, dtype=bool) # flag_FM[FM_idx[0]:FM_idx[1]] = True # win = dspec.gen_window('bh7', freqs.size) # pols = ['nn','ee'] # _data_sq_cleaned, data_rs = {}, {} # for bl in bls: # for i, pol in enumerate(pols): # key = (bl[0],bl[1],pol) # data = uvd_ds.get_data(key) # diff = uvd_diff.get_data(key) # wgts = (~uvd_ds.get_flags(key)~flag_FM[np.newaxis,:]).astype(float) # d_even = (data+diff)0.5 # d_odd = (data-diff)0.5 # d_even_cl, d_even_rs, _ = dspec.high_pass_fourier_filter(d_even, wgts, area1e-9, freqs[1]-freqs[0], # tol=tol, skip_wgt=skip_wgts, window='bh7') # d_odd_cl, d_odd_rs, _ = dspec.high_pass_fourier_filter(d_odd, wgts, area1e-9, freqs[1]-freqs[0], # tol=tol, skip_wgt=skip_wgts, window='bh7') # idx = np.where(np.mean(np.abs(d_even_cl), axis=1) == 0)[0] # d_even_cl[idx] = np.nan # d_even_rs[idx] = np.nan # idx = np.where(np.mean(np.abs(d_odd_cl), axis=1) == 0)[0] # d_odd_cl[idx] = np.nan # d_odd_rs[idx] = np.nan # _d_even = np.fft.fftshift(np.fft.ifft((d_even_cl+d_even_rs)win), axes=1) # _d_odd = np.fft.fftshift(np.fft.ifft((d_odd_cl+d_odd_rs)win), axes=1) # _data_sq_cleaned[key] = _d_odd.conj()_d_even # data_rs[key] = d_even_rs # return _data_sq_cleaned, data_rs, uvd_ds, uvd_diff # def plot_wfds(uvd, _data_sq, pol): # ants = uvd.get_ants() # freqs = uvd.freq_array[0] # times = uvd.time_array # lsts = uvd.lst_array # taus = np.fft.fftshift(np.fft.fftfreq(freqs.size, np.diff(freqs)[0])) # Nants = len(ants) # Nside = int(np.ceil(np.sqrt(Nants))) # Yside = int(np.ceil(float(Nants)/Nside)) # t_index = 0 # jd = times[t_index] # utc = Time(jd, format='jd').datetime # fig, axes = plt.subplots(Yside, Nside, figsize=(Yside2,Nside2)) # if pol == 'ee': # fig.suptitle("delay spectrum (auto) waterfalls from {0} -- {1} East Polarization".format(times[0], times[-1]), fontsize=14) # else: # fig.suptitle("delay spectrum (auto) waterfalls from {0} -- {1} North Polarization".format(times[0], times[-1]), fontsize=14) # fig.tight_layout(rect=(0, 0, 1, 0.95)) # fig.subplots_adjust(left=.1, bottom=.1, right=.9, top=.9, wspace=0.05, hspace=0.2) # k = 0 # for i in range(Yside): # for j in range(Nside): # ax = axes[i,j] # if k < Nants: # key = (ants[k], ants[k], pol) # ds = 10.np.log10(np.sqrt(np.abs(_data_sq[key])/np.abs(_data_sq[key]).max(axis=1)[:,np.newaxis])) # im = ax.imshow(ds, aspect='auto', rasterized=True, # interpolation='nearest', vmin = -50, vmax = -30, # extent=[taus[0]1e9, taus[-1]1e9, np.max(lsts), np.min(lsts)]) # ax.set_title(str(ants[k]), fontsize=10) # else: # ax.axis('off') # if j != 0: # ax.set_yticklabels([]) # else: # [t.set_fontsize(12) for t in ax.get_yticklabels()] # ax.set_ylabel('Time(LST)', fontsize=10) # if i != Yside-1: # ax.set_xticklabels([]) # else: # [t.set_fontsize(10) for t in ax.get_xticklabels()] # [t.set_rotation(25) for t in ax.get_xticklabels()] # ax.xaxis.set_major_formatter(FormatStrFormatter('%.3f')) # ax.set_xlabel('Delay (ns)', fontsize=10) # k += 1 # cbar_ax=fig.add_axes([0.95,0.15,0.02,0.7]) # cb = fig.colorbar(im, cax=cbar_ax) # cb.set_label('dB') # fig.show() # def plot_ds(uvd, uvd_diff, _data_sq_cleaned, data_rs, skip_wgts=0.2): # matplotlib.rcParams.update({'font.size': 8}) # freqs = uvd.freq_array[0] # taus = np.fft.fftshift(np.fft.fftfreq(freqs.size, np.diff(freqs)[0])) # FM_idx = np.searchsorted(freqs1e-6, [85,110]) # flag_FM = np.zeros(freqs.size, dtype=bool) # flag_FM[FM_idx[0]:FM_idx[1]] = True # ants = np.sort(uvd.get_ants()) # pols = ['nn','ee'] # colors = ['b','r'] # nodes, antDict, inclNodes = generate_nodeDict(uvd) # for i, ant in enumerate(ants): # i2 = i % 2 # if(i2 == 0): # fig = plt.figure(figsize=(10, 3), dpi=110) # grid = plt.GridSpec(5, 4, wspace=0.55, hspace=2) # for j, pol in enumerate(pols): # key = (ant,ant,pol) # ax = fig.add_subplot(grid[:5, 0+i22]) # ds_ave = np.sqrt(np.abs(np.nanmean(_data_sq_cleaned[key], axis=0))) # _diff = np.fft.fftshift(np.fft.ifft(uvd_diff.get_data(key)), axes=1) # ns_ave = np.abs(np.nanmean(_diff, axis=0)) # ns_ave_ = np.sqrt(np.abs(np.mean(_diff.conj()_diff, axis=0))/(2_diff.shape[0])) # norm = np.max(ds_ave) # plt.plot(taus1e9, 10np.log10(ds_ave/norm), color=colors[j], label=pols[j], linewidth=0.7) # plt.plot(taus1e9, 10np.log10(ns_ave/norm), color=colors[j], alpha=0.5, linewidth=0.5) # plt.plot(taus1e9, 10np.log10(ns_ave_/norm), color=colors[j], ls='--', linewidth=0.5) # plt.axvspan(250, 500, alpha=0.2, facecolor='y') # plt.axvspan(2500, 3000, alpha=0.2, facecolor='g') # plt.axvspan(3500, 4000, alpha=0.2, facecolor='m') # plt.legend(loc='upper right', bbox_to_anchor=(1.0, 0.95), frameon=False) # plt.xlim(0,4500) # plt.ylim(-60,0) # plt.title('ant {}'.format(ant)) # plt.xlabel(r'$\tau$ (ns)') # plt.ylabel(r'$\|\tilde{V}(\tau)\|$ in dB') # for yl in range(-50,0,10): # plt.axhline(y=yl, color='k', lw=0.5, alpha=0.1) # if(j == 0): # plt.annotate('node {} (snap {})'.format(antDict[ant]['node'],antDict[ant]['snapLocs'][0]), xy=(0.04,0.930), xycoords='axes fraction') # ax2 = fig.add_subplot(grid[:3, 1+i22]) # auto = np.abs(uvd.get_data(key)) # auto_ave = np.mean(auto/np.median(auto, axis=1)[:,np.newaxis], axis=0) # wgts = (~uvd.get_flags(key)~flag_FM[np.newaxis,:]).astype(float) # wgts = np.where(wgts > skip_wgts, wgts, 0) # auto_flagged = auto/wgts # auto_flagged[np.isinf(auto_flagged)] = np.nan # auto_flagged_ave = np.nanmean(auto_flagged/np.nanmedian(auto_flagged, axis=1)[:,np.newaxis], axis=0) # plt.plot(freqs/1e6, np.log10(auto_ave), linewidth=1.0, color=colors[j], label='autocorrelation') # plt.plot(freqs/1e6, np.log10(auto_flagged_ave0.7), linewidth=1.0, color=colors[j], alpha=0.5) # if(j == 0): # plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.18), frameon=False, handlelength=0, handletextpad=0) # plt.xlim(freqs.min()/1e6, freqs.max()/1e6) # plt.ylabel(r'log$_{10}(\|V(\nu)\|)$') # plt.ylim(-1, 0.7) # ax3 = fig.add_subplot(grid[3:5, 1+i22]) # data_rs_ave = np.nanmean(data_rs[key]/np.nanmedian(auto_flagged, axis=1)[:,np.newaxis], axis=0) # plt.plot(freqs/1e6, np.log10(np.abs(data_rs_ave)), linewidth=1.0, color=colors[j], label='clean residual') # if(j == 0): # plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.31), frameon=False, handlelength=0, handletextpad=0) # plt.xlim(freqs.min()/1e6, freqs.max()/1e6) # plt.ylim(-5,0) # plt.xlabel(r'$\nu$ (MHz)') # plt.ylabel(r'log$_{10}(\|V(\nu)\|)$') # matplotlib.rcParams.update({'font.size': 10}) # def plot_ds_diagnosis(uvd_diff, _data_sq_cleaned): # freqs = uvd_diff.freq_array[0] # taus = np.fft.fftshift(np.fft.fftfreq(freqs.size, np.diff(freqs)[0])) # ants = np.sort(uvd_diff.get_ants()) # pols = ['nn','ee'] # colors = ['b','r'] # nodes, antDict, inclNodes = generate_nodeDict(uvd_diff) # marker = ['o', 'x', 'P', 's', 'd', 'p', '<', 'h', '+', '>', 'X', ''] # nodes = [] # ds_ave_250_500_nn, ds_ave_250_500_ee = [], [] # ds_peak_2500_3000_nn, ds_peak_2500_3000_ee = [], [] # ds_ratio_3500_4000_nn, ds_ratio_3500_4000_ee = [], [] # for ant in ants: # nodes.append(int(antDict[ant]['node'])) # for pol in pols: # key = (ant,ant,pol) # ds_ave = np.sqrt(np.abs(np.nanmean(_data_sq_cleaned[key], axis=0))) # _diff = np.fft.fftshift(np.fft.ifft(uvd_diff.get_data(key)), axes=1) # ns_ave = np.sqrt(np.abs(np.mean(_diff.conj()_diff, axis=0))/(2_diff.shape[0])) # norm = np.max(ds_ave) # idx_region1 = (np.abs(taus)1e9 > 250) (np.abs(taus)1e9 < 500) # idx_region2 = (np.abs(taus)1e9 > 2500) * (np.abs(taus)1e9 < 3000) # idx_region_out = (np.abs(taus)1e9 > 3000) * (np.abs(taus)1e9 < 3200) # idx_region3 = (np.abs(taus)1e9 > 3500) * (np.abs(taus)1e9 < 4000) # if(pol == 'nn'): # ds_ave_250_500_nn.append(np.nanmean(ds_ave[idx_region1]/norm)) # ds_peak_2500_3000_nn.append(np.std(ds_ave[idx_region2])/np.std(ds_ave[idx_region_out])) # ds_ratio_3500_4000_nn.append(np.nanmean(ds_ave[idx_region3])/np.mean(ns_ave[idx_region3])) # else: # ds_ave_250_500_ee.append(np.nanmean(ds_ave[idx_region1]/norm)) # ds_peak_2500_3000_ee.append(np.std(ds_ave[idx_region2])/np.std(ds_ave[idx_region_out])) # ds_ratio_3500_4000_ee.append(np.nanmean(ds_ave[idx_region3])/np.mean(ns_ave[idx_region3])) # nodes = np.array(nodes) # N_nodes = nodes.size # ds_ave_250_500_nn = np.array(ds_ave_250_500_nn) # ds_ave_250_500_ee = np.array(ds_ave_250_500_ee) # ds_peak_2500_3000_nn = np.array(ds_peak_2500_3000_nn) # ds_peak_2500_3000_ee = np.array(ds_peak_2500_3000_ee) # ds_ratio_3500_4000_nn = np.array(ds_ratio_3500_4000_nn) # ds_ratio_3500_4000_ee = np.array(ds_ratio_3500_4000_ee) # plt.figure(figsize=(15,15)) # plt.subplot(3,1,1) # node_number = np.unique(nodes) # for i, node in enumerate(node_number): # idx_ant = np.where(nodes == node)[0] # plt.plot(ants[idx_ant], 10np.log10(ds_ave_250_500_nn[idx_ant]), 'bo', label='nn', marker=marker[i%N_nodes]) # plt.plot(ants[idx_ant], 10np.log10(ds_ave_250_500_ee[idx_ant]), 'ro', label='ee', marker=marker[i%N_nodes]) # if(i == 0): # plt.legend() # for i, ant in enumerate(ants): # plt.annotate('{}'.format(ant), xy=(ants[i], 10np.log10(ds_ave_250_500_nn[i]))) # plt.xlabel('Antenna number') # plt.ylabel('dB (relative to the peak)') # plt.title('Averaged delay spectrum at 250-500 ns') # plt.subplot(3,1,2) # for i, node in enumerate(node_number): # idx_ant = np.where(nodes == node)[0] # plt.plot(ants[idx_ant], ds_peak_2500_3000_nn[idx_ant], 'bo', label='nn', marker=marker[i%N_nodes]) # plt.plot(ants[idx_ant], ds_peak_2500_3000_ee[idx_ant], 'ro', label='ee', marker=marker[i%N_nodes]) # if(i == 0): # plt.legend() # for i, ant in enumerate(ants): # plt.annotate('{}'.format(ant), xy=(ants[i], ds_peak_2500_3000_nn[i])) # plt.axhline(y=1, ls='--', color='k') # plt.xlabel('Antenna number') # plt.ylabel('$\sigma_{2500-3000}/\sigma_{3000-3200}$') # plt.title('Standard deviation ratio between 2500-3000 ns and 3000-3200 ns') # plt.subplot(3,1,3) # for i, node in enumerate(node_number): # idx_ant = np.where(nodes == node)[0] # plt.plot(ants[idx_ant], 10np.log10(ds_ratio_3500_4000_nn[idx_ant]), 'bo', label='nn', marker=marker[i%N_nodes]) # plt.plot(ants[idx_ant], 10np.log10(ds_ratio_3500_4000_ee[idx_ant]), 'ro', label='ee', marker=marker[i%N_nodes]) # if(i == 0): # plt.legend() # for i, ant in enumerate(ants): # plt.annotate('{}'.format(ant), xy=(ants[i], 10np.log10(ds_ratio_3500_4000_nn[i]))) # plt.axhline(y=0, ls='--', color='k') # plt.xlabel('Antenna number') # plt.ylabel('Deviation from the noise level in dB') # plt.title('Deviation of averaged delay spectrum at 3500-4000 ns from the noise level') # plt.subplots_adjust(hspace=0.4) # def plot_ds_nodes(uvd_diff, _data_sq_cleaned): # freqs = uvd_diff.freq_array[0] # taus = np.fft.fftshift(np.fft.fftfreq(freqs.size, np.diff(freqs)[0])) # ants = np.sort(uvd_diff.get_ants()) # pols = ['nn','ee'] # colors = ['b','r'] # nodes, antDict, inclNodes = generate_nodeDict(uvd_diff) # nodes = [] # ds_ave_nn, ns_ave_nn = [], [] # ds_ave_ee, ns_ave_ee = [], [] # for ant in ants: # nodes.append(int(antDict[ant]['node'])) # for pol in pols: # key = (ant,ant,pol) # ds_ave = np.sqrt(np.abs(np.nanmean(_data_sq_cleaned[key], axis=0))) # _diff = np.fft.fftshift(np.fft.ifft(uvd_diff.get_data(key)), axes=1) # ns_ave = np.abs(np.nanmean(_diff, axis=0)) # norm = np.max(ds_ave) # if(pol == 'nn'): # ds_ave_nn.append(ds_ave/norm) # ns_ave_nn.append(ns_ave/norm) # else: # ds_ave_ee.append(ds_ave/norm) # ns_ave_ee.append(ns_ave/norm) # nodes = np.array(nodes) # ds_ave_nn = np.array(ds_ave_nn).reshape(ants.size,taus.size) # ns_ave_nn = np.array(ns_ave_nn).reshape(ants.size,taus.size) # ds_ave_ee = np.array(ds_ave_ee).reshape(ants.size,taus.size) # ns_ave_ee = np.array(ns_ave_ee).reshape(ants.size,taus.size) # node_number = np.unique(nodes) # for i, node in enumerate(node_number): # if(i % 2 == 0): # plt.figure(figsize=(16,4)) # for j, pol in enumerate(pols): # plt.subplot(1,4,2(i%2)+j+1) # idx_ant = np.where(nodes == node)[0] # if(pol == 'nn'): # for idx in idx_ant: # plt.plot(taus1e9, 10np.log10(ds_ave_nn[idx]), label='({},{})'.format(ants[idx],ants[idx]), linewidth=0.8) # else: # for idx in idx_ant: # plt.plot(taus1e9, 10np.log10(ds_ave_ee[idx]), label='({},{})'.format(ants[idx],ants[idx]), linewidth=0.8) # plt.xlim(0,4500) # plt.ylim(-55,0) # plt.title('node {}, {}'.format(node, pol)) # plt.xlabel(r'$\tau$ (ns)') # if(i % 2 == 0 and j == 0): # plt.ylabel(r'$\|\tilde{V}(\tau)\|$ in dB') # plt.grid(axis='y') # plt.legend(loc='upper left', ncol=2) # def plot_wfds_cr(uvd, _data_sq, pol): # bls = uvd.get_antpairs() # freqs = uvd.freq_array[0] # times = uvd.time_array # lsts = uvd.lst_array # taus = np.fft.fftshift(np.fft.fftfreq(freqs.size, np.diff(freqs)[0])) # idx_mid = np.where(taus == 0)[0] # Nants = len(uvd.get_antpairs()) # Nside = int(np.ceil(np.sqrt(Nants))) # Yside = int(np.ceil(float(Nants)/Nside)) # t_index = 0 # jd = times[t_index] # utc = Time(jd, format='jd').datetime # fig, axes = plt.subplots(Yside, Nside, figsize=(Yside2,Nside2), dpi=75) # if pol == 'ee': # fig.suptitle("delay spectrum (cross) waterfalls from {0} -- {1} East Polarization".format(times[0], times[-1]), fontsize=14) # else: # fig.suptitle("delay spectrum (cross) waterfalls from {0} -- {1} North Polarization".format(times[0], times[-1]), fontsize=14) # fig.tight_layout(rect=(0, 0, 1, 0.95)) # fig.subplots_adjust(left=.1, bottom=.1, right=.9, top=.9, wspace=0.05, hspace=0.2) # k = 0 # for i in range(Yside): # for j in range(Nside): # ax = axes[i,j] # if k < Nants: # key = (bls[k][0], bls[k][1], pol) # ds = 10.np.log10(np.sqrt(np.abs(_data_sq[key])/np.abs(_data_sq[key][:,idx_mid]))) # im = ax.imshow(ds, aspect='auto', rasterized=True, # interpolation='nearest', vmin = -30, vmax = 0, # extent=[taus[0]1e9, taus[-1]1e9, np.max(lsts), np.min(lsts)]) # ax.set_title(str(bls[k]), fontsize=10) # else: # ax.axis('off') # if j != 0: # ax.set_yticklabels([]) # else: # [t.set_fontsize(12) for t in ax.get_yticklabels()] # ax.set_ylabel('Time(LST)', fontsize=10) # if i != Yside-1: # ax.set_xticklabels([]) # else: # [t.set_fontsize(10) for t in ax.get_xticklabels()] # [t.set_rotation(25) for t in ax.get_xticklabels()] # ax.xaxis.set_major_formatter(FormatStrFormatter('%.3f')) # ax.set_xlabel('Delay (ns)', fontsize=10) # k += 1 # cbar_ax=fig.add_axes([0.95,0.15,0.02,0.7]) # cb = fig.colorbar(im, cax=cbar_ax) # cb.set_label('dB') # fig.show() # def plot_metric(metrics, ants=None, antpols=None, title='', ylabel='Modified z-Score', xlabel=''): # '''Helper function for quickly plotting an individual antenna metric.''' # if ants is None: # ants = list(set([key[0] for key in metrics.keys()])) # if antpols is None: # antpols = list(set([key[1] for key in metrics.keys()])) # for antpol in antpols: # for i,ant in enumerate(ants): # metric = 0 # if (ant,antpol) in metrics: # metric = metrics[(ant,antpol)] # plt.plot(i,metric,'.') # plt.annotate(str(ant)+antpol,xy=(i,metrics[(ant,antpol)])) # plt.gca().set_prop_cycle(None) # plt.title(title) # plt.ylabel(ylabel) # plt.xlabel(xlabel) # def show_metric(ant_metrics, antmetfiles, ants=None, antpols=None, title='', ylabel='Modified z-Score', xlabel=''): # print("Ant Metrics for {}".format(antmetfiles[1])) # plt.figure() # plot_metric(ant_metrics['final_mod_z_scores']['meanVij'], # title = 'Mean Vij Modified z-Score') # plt.figure() # plot_metric(ant_metrics['final_mod_z_scores']['redCorr'], # title = 'Redundant Visibility Correlation Modified z-Score') # plt.figure() # plot_metric(ant_metrics['final_mod_z_scores']['meanVijXPol'], antpols=['n'], # title = 'Modified z-score of (Vxy+Vyx)/(Vxx+Vyy)') # plt.figure() # plot_metric(ant_metrics['final_mod_z_scores']['redCorrXPol'], antpols=['n'], # title = 'Modified z-Score of Power Correlation Ratio Cross/Same') # plt.figure() # plot_metric(ant_metrics['final_mod_z_scores']['redCorrXPol'], antpols=['e'], # title = 'Modified z-Score of Power Correlation Ratio Cross/Same') # def all_ant_mets(antmetfiles,HHfiles): # file = HHfiles[0] # uvd_hh = UVData() # uvd_hh.read_uvh5(file) # uvdx = uvd_hh.select(polarizations = -5, inplace = False) # uvdx.ants = np.unique(np.concatenate([uvdx.ant_1_array, uvdx.ant_2_array])) # ants = uvdx.get_ants() # times = uvd_hh.time_array # Nants = len(ants) # jd_start = np.floor(times.min()) # antfinfiles = [] # for i,file in enumerate(antmetfiles): # if i%50==0: # antfinfiles.append(antmetfiles[i]) # Nfiles = len(antfinfiles) # Nfiles2 = len(antmetfiles) # xants = np.zeros((Nants2, Nfiles2)) # dead_ants = np.zeros((Nants2, Nfiles2)) # cross_ants = np.zeros((Nants2, Nfiles2)) # badants = [] # pol2ind = {'n':0, 'e':1} # times = [] # for i,file in enumerate(antfinfiles): # time = file[54:60] # times.append(time) # for i,file in enumerate(antmetfiles): # antmets = hera_qm.ant_metrics.load_antenna_metrics(file) # for j in antmets['xants']: # xants[2np.where(ants==j[0])[0]+pol2ind[j[1]], i] = 1 # badants.extend(map(lambda x: x[0], antmets['xants'])) # for j in antmets['crossed_ants']: # cross_ants[2np.where(ants==j[0])[0]+pol2ind[j[1]], i] = 1 # for j in antmets['dead_ants']: # dead_ants[2np.where(ants==j[0])[0]+pol2ind[j[1]], i] = 1 # badants = np.unique(badants) # xants[np.where(xants==1)] = np.nan # dead_ants[np.where(dead_ants==0)] = np.nan # cross_ants[np.where(cross_ants==0)] = np.nan # antslabels = [] # for i in ants: # labeln = str(i) + 'n' # labele = str(i) + 'e' # antslabels.append(labeln) # antslabels.append(labele) # fig, ax = plt.subplots(1, figsize=(16,20)) # # plotting # ax.matshow(xants, aspect='auto', cmap='RdYlGn_r', vmin=-.3, vmax=1.3, # extent=[0, len(times), Nants2, 0]) # ax.matshow(dead_ants, aspect='auto', cmap='RdYlGn_r', vmin=-.3, vmax=1.3, # extent=[0, len(times), Nants2, 0]) # ax.matshow(cross_ants, aspect='auto', cmap='RdBu', vmin=-.3, vmax=1.3, # extent=[0, len(times), Nants2, 0]) # # axes # ax.grid(color='k') # ax.xaxis.set_ticks_position('bottom') # ax.set_xticks(np.arange(len(times))+0.5) # ax.set_yticks(np.arange(Nants2)+0.5) # ax.tick_params(size=8) # if Nfiles > 20: # ticklabels = times # ax.set_xticklabels(ticklabels) # else: # ax.set_xticklabels(times) # ax.set_yticklabels(antslabels) # [t.set_rotation(30) for t in ax.get_xticklabels()] # [t.set_size(12) for t in ax.get_xticklabels()] # [t.set_rotation(0) for t in ax.get_yticklabels()] # [t.set_size(12) for t in ax.get_yticklabels()] # ax.set_title("Ant Metrics bad ants over observation", fontsize=14) # ax.set_xlabel('decimal of JD = {}'.format(int(jd_start)), fontsize=16) # ax.set_ylabel('antenna number and pol', fontsize=16) # red_ptch = mpatches.Patch(color='red') # grn_ptch = mpatches.Patch(color='green') # blu_ptch = mpatches.Patch(color='blue') # ax.legend([red_ptch, blu_ptch, grn_ptch], ['dead ant', 'cross ant', 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my_drawing/my_drawing.py	steven3614/MystanCodeprojects	0	149831	<filename>my_drawing/my_drawing.py<gh_stars>0 """ File: my_drawing Name: 張文銓 ---------------------- TODO: This function is my drawing about the famous show "squid game" including the killer and the invitation card in the story. """ from campy.graphics.gobjects import GOval, GRect, GLabel from campy.graphics.gwindow import GWindow def main(): """ TODO: I drew different items and named them by numbers because there are too many items. the whole picture is about the famous show squid game. """ window = GWindow(width=800, height=790, title='Squid Game') # this is the window of the picture one = GOval(230, 300, x=30, y=130) one.filled = True one.fill_color = 'palevioletred' one.color = 'palevioletred' window.add(one) two = GOval(230, 300, x=280, y=100) two.filled = True two.fill_color = 'palevioletred' two.color = 'palevioletred' window.add(two) three = GOval(230, 300, x=530, y=130) three.filled = True three.fill_color = 'palevioletred' three.color = 'palevioletred' window.add(three) four = GOval(210, 280, x=40, y=140) four.filled = True four.fill_color = 'black' four.color = 'black' window.add(four) five = GOval(210, 280, x=290, y=110) five.filled = True five.fill_color = 'black' five.color = 'black' window.add(five) six = GOval(210, 280, x=540, y=140) six.filled = True six.fill_color = 'black' six.color = 'black' window.add(six) # one to six are the items for the mask of the killer seven = GOval(150, 150, x=72.5, y=205) seven.color = 'white' seven.filled = True seven.fill_color = 'white' window.add(seven) eight = GOval(125, 125, x=85, y=217.5) eight.filled = True eight.fill_color = 'black' eight.color = 'black' window.add(eight) nine = GRect(150, 150, x=320, y=185) nine.filled = True nine.fill_color = 'white' nine.color = 'white' window.add(nine) ten = GRect(125, 125, x=332.5, y=197.5) ten.filled = True ten.fill_color = 'black' ten.color = 'black' window.add(ten) eleven = GLabel('^', x=585, y=490) eleven.color = 'white' eleven.font = '-300' window.add(eleven) twelve = GLabel('-', x=580, y=460) twelve.color = 'white' twelve.font = '-300' window.add(twelve) # seven to twelve are the items on the mask(square, circle and triangle) thirteen = GRect(400, 250, x=200, y=490) thirteen.filled = True thirteen.fill_color = 'khaki' window.add(thirteen) # this is the item for the invitation card fourteen = GLabel('WELCOME TO THE SQUID GAME!', x=210, y=525) fourteen.color = 'red' fourteen.font = '-25' window.add(fourteen) fifteen = GOval(90, 90, x=225, y=570) fifteen.filled = True fifteen.fill_color = 'black' fifteen.color = 'black' window.add(fifteen) sixteen = GRect(90, 90, x=350, y=570) sixteen.filled = True sixteen.fill_color = 'black' sixteen.color = 'black' window.add(sixteen) seventeen = GLabel('^', x=465, y=840) seventeen.font = "-300" seventeen.color = "black" window.add(seventeen) eighteen = GLabel('-', x=460, y=805) eighteen.font = "-300" eighteen.color = "black" window.add(eighteen) nineteen = GOval(70, 70, x=235, y=580) nineteen.filled = True nineteen.fill_color = 'khaki' nineteen.color = 'khaki' window.add(nineteen) twenty = GRect(70, 70, x=360, y=580) twenty.filled = True twenty.fill_color = 'khaki' twenty.color = 'khaki' window.add(twenty) # the rest of the items consist the image on the invitation card if __name__ == '__main__': main()	[ 1, 529, 9507, 29958, 1357, 29918, 4012, 292, 29914, 1357, 29918, 4012, 292, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 15945, 29908, 13, 2283, 29901, 590, 29918, 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src/predectorutils/analyses/localizer.py	ccdmb/predector-utils	0	71913	<filename>src/predectorutils/analyses/localizer.py #!/usr/bin/env python3 import re from typing import Optional from typing import TextIO from typing import Iterator from typing import Tuple from predectorutils.higher import fmap from predectorutils.gff import GFFRecord, GFFAttributes, Strand from predectorutils.analyses.base import Analysis, GFFAble from predectorutils.analyses.base import ( int_or_none, float_or_none, str_or_none ) from predectorutils.parsers import ( FieldParseError, LineParseError, parse_field, raise_it, parse_str, parse_regex ) # This matches strings of the form "Y (0.962\| 25-49)" TP_REGEX = re.compile( r"^Y \((?P<prob>\d?\.?\d+)\s\\|\s(?P<start>\d+)-(?P<end>\d+)\)$") NUC_REGEX = re.compile(r"^Y \((?P<sigs>.+)\)$") HEADER_REGEX = re.compile( r"^Identifier\s+Chloroplast\s+Mitochondria\s+Nucleus$") def parse_tp_field( field: str, field_name: str, ) -> Tuple[bool, Optional[float], Optional[int], Optional[int]]: field = field.strip() if field == "-": return (False, None, None, None) res = parse_field(parse_regex(TP_REGEX), field_name)(field) if isinstance(res, FieldParseError): raise res # This should be safe because we know the regex matched. return (True, float(res["prob"]), int(res["start"]), int(res["end"])) def parse_nuc_field( field: str, ) -> Tuple[bool, Optional[str]]: field = field.strip() if field == "-": return (False, None) res = parse_field(parse_regex(NUC_REGEX), "nucleus")(field) if isinstance(res, FieldParseError): raise res # This should be safe because we know the regex matched. return (True, res["sigs"]) class LOCALIZER(Analysis, GFFAble): """ """ columns = ["name", "chloroplast_decision", "chloroplast_prob", "chloroplast_start", "chloroplast_end", "mitochondria_decision", "mitochondria_prob", "mitochondria_start", "mitochondria_end", "nucleus_decision", "nucleus_signals"] types = [str, bool, float_or_none, int_or_none, int_or_none, bool, float_or_none, int_or_none, int_or_none, bool, str_or_none] analysis = "localizer" software = "LOCALIZER" def __init__( self, name: str, chloroplast_decision: bool, chloroplast_prob: Optional[float], chloroplast_start: Optional[int], chloroplast_end: Optional[int], mitochondria_decision: bool, mitochondria_prob: Optional[float], mitochondria_start: Optional[int], mitochondria_end: Optional[int], nucleus_decision: bool, nucleus_signals: Optional[str], ) -> None: self.name = name self.chloroplast_decision = chloroplast_decision self.chloroplast_prob = chloroplast_prob self.chloroplast_start = chloroplast_start self.chloroplast_end = chloroplast_end self.mitochondria_decision = mitochondria_decision self.mitochondria_prob = mitochondria_prob self.mitochondria_start = mitochondria_start self.mitochondria_end = mitochondria_end self.nucleus_decision = nucleus_decision self.nucleus_signals = nucleus_signals return @classmethod def from_line(cls, line: str) -> "LOCALIZER": """ Parse an ApoplastP line as an object. """ if line == "": raise LineParseError("The line was empty.") sline = [c.strip() for c in line.strip().split("\t")] if len(sline) != 4: raise LineParseError( "The line had the wrong number of columns. " f"Expected 4 but got {len(sline)}" ) (cp, cp_prob, cp_start, cp_end) = parse_tp_field( sline[1], "chloroplast" ) (mt, mt_prob, mt_start, mt_end) = parse_tp_field( sline[2], "mitochondria" ) (nuc, nuc_sigs) = parse_nuc_field(sline[3]) return cls( raise_it(parse_field(parse_str, "name"))(sline[0]), cp, cp_prob, fmap(lambda x: x - 1 + 20, cp_start), cp_end, mt, mt_prob, fmap(lambda x: x - 1 + 20, mt_start), mt_end, nuc, nuc_sigs ) @classmethod def from_file(cls, handle: TextIO) -> Iterator["LOCALIZER"]: for i, line in enumerate(handle): sline = line.strip() if sline.startswith("#"): continue elif sline == "": continue elif HEADER_REGEX.match(sline) is not None: continue try: yield cls.from_line(sline) except (FieldParseError, LineParseError) as e: raise e.as_parse_error(line=i).add_filename_from_handle(handle) return def as_gff( self, software_version: Optional[str] = None, database_version: Optional[str] = None, keep_all: bool = True, id_index: int = 1, ) -> Iterator[GFFRecord]: source = self.gen_source(software_version, database_version) if self.chloroplast_decision: assert self.chloroplast_start is not None assert self.chloroplast_end is not None attr = GFFAttributes( note=["Putative internal chloroplast localization peptide"], custom={ "prob": str(self.chloroplast_prob), } ) yield GFFRecord( seqid=self.name, source=source, type="peptide_localization_signal", start=self.chloroplast_start, end=self.chloroplast_end, score=self.chloroplast_prob, strand=Strand.PLUS, attributes=attr ) if self.mitochondria_decision: assert self.mitochondria_start is not None assert self.mitochondria_end is not None attr = GFFAttributes( note=["Putative internal mitochondrial localization peptide"], custom={ "prob": str(self.mitochondria_prob), } ) yield GFFRecord( seqid=self.name, source=source, type="mitochondrial_targeting_signal", start=self.mitochondria_start, end=self.mitochondria_end, score=self.mitochondria_prob, strand=Strand.UNSTRANDED, attributes=attr ) return	[ 1, 529, 9507, 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tests/deprecated/fastplt.py	davtoh/RRTools	1	151114	# -- coding: utf-8 -- from __future__ import division from __future__ import print_function import matplotlib.pyplot as plt import matplotlib.axes import matplotlib.figure from multiprocessing import Process from RRtoolbox.lib.config import FLAG_DEBUG wins = [0] # keeps track of image number through different processes def fastplt(image, cmap = None, title ="visualazor", win = None, block = False, daemon = False): """ Fast plot. :param image: image to show :param cmap: "gray" or None :param title: title of subplot :param win: title of window :param block: if True it wait for window close, else it detaches :param daemon: if True window closes if main thread ends, else windows must be closed to main thread to end :return: plt """ if FLAG_DEBUG: print("fastplt received image type: ",type(image)) def myplot(): if isinstance(image, matplotlib.axes.SubplotBase): f = image.figure elif isinstance(image, matplotlib.figure.Figure): f = image else: f = plt.figure() # Normally this will always be "Figure 1" since it's the first # figure created by this process. So do something about it. plt.imshow(image,cmap) if title: plt.title(title) plt.xticks([]), plt.yticks([]) #plt.colorbar() wins[0]+=1 if win: f.canvas.set_window_title(win) else:f.canvas.set_window_title("Figure {}".format(wins[-1])) if FLAG_DEBUG: print("showing now...") #plt.ion() plt.show() if FLAG_DEBUG: print("showed...") if block: myplot() else: if FLAG_DEBUG: print("multiprocessing...") p = Process(target=myplot) # FIXME i shoud call a miniprogram p.daemon = daemon p.start() if FLAG_DEBUG: print("left fastplt...") if __name__ == "__main__": import argparse from RRtoolbox.lib.serverServices import parseString as _parseString #import sys #if FLAG_DEBUG: print sys.argv parser = argparse.ArgumentParser(description='fast plot of images.') parser.add_argument('image', metavar='N', #action='append', help='path to image or numpy string',nargs="+") parser.add_argument('-m','--cmap', dest='cmap', action='store', help='map to use in matplotlib') parser.add_argument('-t','--title', dest='title', action='store',default="visualazor", help='title of subplot') parser.add_argument('-w','--win', dest='win', action='store', help='title of window') parser.add_argument('-n','--num', dest='num', action='store',type = int, default=0, help='number of Figure') parser.add_argument('-b','--block', dest='block', action='store_true', default=False, help='number of Figure') parser.add_argument('-d','--daemon', dest='daemon', action='store_true', default=False, help='number of Figure') v = vars(parser.parse_args()) images = _parseString(v.pop("image")) wins[-1] = v.pop("num") if FLAG_DEBUG: print("properties:", v) for image in images: fastplt(image,**v) if FLAG_DEBUG: print("leaving fastplt module...")	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website/migrations/0002_auto_20210205_1929.py	TAS-7/KDY-SITE	1	123943	# Generated by Django 3.1.2 on 2021-02-05 19:29 from django.conf import settings from django.db import migrations, models import django.db.models.deletion import django.utils.timezone class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('website', '0001_initial'), ] operations = [ migrations.AlterField( model_name='post', name='date_posted', field=models.DateTimeField(default=django.utils.timezone.now), ), migrations.CreateModel( name='Comment', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('answer', models.TextField()), ('created_date', models.DateTimeField(default=django.utils.timezone.now)), ('approved_comment', models.BooleanField(default=False)), ('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ('question', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='comments', to='website.post')), ], ), ]	[ 1, 396, 3251, 630, 491, 15337, 29871, 29941, 29889, 29896, 29889, 29906, 373, 29871, 29906, 29900, 29906, 29896, 29899, 29900, 29906, 29899, 29900, 29945, 29871, 29896, 29929, 29901, 29906, 29929, 13, 13, 3166, 9557, 29889, 5527, 1053, 6055, 13, 3166, 9557, 29889, 2585, 1053, 9725, 800, 29892, 4733, 13, 5215, 9557, 29889, 2585, 29889, 9794, 29889, 311, 1026, 291, 13, 5215, 9557, 29889, 13239, 29889, 2230, 8028, 13, 13, 13, 1990, 341, 16783, 29898, 26983, 800, 29889, 29924, 16783, 1125, 13, 13, 1678, 9962, 353, 518, 13, 4706, 9725, 800, 29889, 2774, 932, 519, 29918, 10836, 29898, 11027, 29889, 20656, 29950, 29918, 11889, 29918, 20387, 29931, 511, 13, 4706, 6702, 22942, 742, 525, 29900, 29900, 29900, 29896, 29918, 11228, 5477, 13, 1678, 4514, 13, 13, 1678, 6931, 353, 518, 13, 4706, 9725, 800, 29889, 2499, 357, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 2490, 742, 13, 9651, 1024, 2433, 1256, 29918, 2490, 287, 742, 13, 9651, 1746, 29922, 9794, 29889, 11384, 3073, 29898, 4381, 29922, 14095, 29889, 13239, 29889, 2230, 8028, 29889, 3707, 511, 13, 4706, 10353, 13, 4706, 9725, 800, 29889, 4391, 3195, 29898, 13, 9651, 1024, 2433, 20001, 742, 13, 9651, 4235, 11759, 13, 18884, 6702, 333, 742, 4733, 29889, 12300, 3073, 29898, 6921, 29918, 11600, 29922, 5574, 29892, 7601, 29918, 1989, 29922, 5574, 29892, 28755, 29922, 8824, 29892, 26952, 29918, 978, 2433, 1367, 1495, 511, 13, 18884, 6702, 12011, 742, 4733, 29889, 15778, 25739, 13, 18884, 6702, 11600, 29918, 1256, 742, 4733, 29889, 11384, 3073, 29898, 4381, 29922, 14095, 29889, 13239, 29889, 2230, 8028, 29889, 3707, 8243, 13, 18884, 6702, 9961, 1490, 29918, 9342, 742, 4733, 29889, 18146, 3073, 29898, 4381, 29922, 8824, 8243, 13, 18884, 6702, 8921, 742, 4733, 29889, 27755, 2558, 29898, 265, 29918, 8143, 29922, 14095, 29889, 2585, 29889, 9794, 29889, 311, 1026, 291, 29889, 29907, 3289, 5454, 2287, 29892, 304, 29922, 11027, 29889, 20656, 29950, 29918, 11889, 29918, 20387, 29931, 8243, 13, 18884, 6702, 12470, 742, 4733, 29889, 27755, 2558, 29898, 265, 29918, 8143, 29922, 14095, 29889, 2585, 29889, 9794, 29889, 311, 1026, 291, 29889, 29907, 3289, 5454, 2287, 29892, 4475, 29918, 978, 2433, 21032, 742, 304, 2433, 22942, 29889, 2490, 1495, 511, 13, 9651, 21251, 13, 4706, 10353, 13, 1678, 4514, 13, 2 ]
mf2cCommsLib/mf2cComms/channel/msg/__init__.py	cketley/acsl	0	86512	<gh_stars>0 from mf2cComms.channel.msg import message	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 286, 29888, 29906, 29883, 1523, 1516, 29889, 12719, 29889, 7645, 1053, 2643, 2 ]
pytorch/.circleci/cimodel/data/pytorch_build_definitions.py	raghavnauhria/whatmt	0	85804	#!/usr/bin/env python3 from collections import OrderedDict from cimodel.data.pytorch_build_data import TopLevelNode, CONFIG_TREE_DATA import cimodel.data.dimensions as dimensions import cimodel.lib.conf_tree as conf_tree import cimodel.lib.miniutils as miniutils import cimodel.lib.visualization as visualization from dataclasses import dataclass, field from typing import List, Optional DOCKER_IMAGE_PATH_BASE = "308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/" DOCKER_IMAGE_VERSION = 300 @dataclass class Conf: distro: str parms: List[str] pyver: Optional[str] = None cuda_version: Optional[str] = None # TODO expand this to cover all the USE_* that we want to test for # tesnrorrt, leveldb, lmdb, redis, opencv, mkldnn, ideep, etc. # (from https://github.com/pytorch/pytorch/pull/17323#discussion_r259453608) is_xla: bool = False restrict_phases: Optional[List[str]] = None gpu_resource: Optional[str] = None dependent_tests: List = field(default_factory=list) parent_build: Optional['Conf'] = None is_namedtensor: bool = False is_important: bool = False # TODO: Eliminate the special casing for docker paths # In the short term, we will need to support special casing as docker images are merged for caffe2 and pytorch def get_parms(self, for_docker): leading = [] # We just don't run non-important jobs on pull requests; # previously we also named them in a way to make it obvious # if self.is_important and not for_docker: # leading.append("AAA") leading.append("pytorch") if self.is_xla and not for_docker: leading.append("xla") if self.is_namedtensor and not for_docker: leading.append("namedtensor") cuda_parms = [] if self.cuda_version: cuda_parms.extend(["cuda" + self.cuda_version, "cudnn7"]) return leading + ["linux", self.distro] + cuda_parms + self.parms def gen_docker_image_path(self): parms_source = self.parent_build or self base_build_env_name = "-".join(parms_source.get_parms(True)) return miniutils.quote(DOCKER_IMAGE_PATH_BASE + base_build_env_name + ":" + str(DOCKER_IMAGE_VERSION)) def get_build_job_name_pieces(self, build_or_test): return self.get_parms(False) + [build_or_test] def gen_build_name(self, build_or_test): return ("_".join(map(str, self.get_build_job_name_pieces(build_or_test)))).replace(".", "_").replace("-", "_") def get_dependents(self): return self.dependent_tests or [] def gen_yaml_tree(self, build_or_test): build_job_name_pieces = self.get_build_job_name_pieces(build_or_test) build_env_name = "-".join(map(str, build_job_name_pieces)) env_dict = OrderedDict([ ("BUILD_ENVIRONMENT", build_env_name), ("DOCKER_IMAGE", self.gen_docker_image_path()), ]) if self.pyver: env_dict["PYTHON_VERSION"] = miniutils.quote(self.pyver) if build_or_test == "test" and self.gpu_resource: env_dict["USE_CUDA_DOCKER_RUNTIME"] = miniutils.quote("1") d = { "environment": env_dict, "<<": "" + "_".join(["pytorch", "linux", build_or_test, "defaults"]), } if build_or_test == "test": resource_class = "large" if self.gpu_resource: resource_class = "gpu." + self.gpu_resource if self.gpu_resource == "large": env_dict["MULTI_GPU"] = miniutils.quote("1") d["resource_class"] = resource_class return d def gen_workflow_yaml_item(self, phase): # All jobs require the setup job parameters = OrderedDict({"requires": ["setup"]}) if phase == "test": # TODO When merging the caffe2 and pytorch jobs, it might be convenient for a while to make a # caffe2 test job dependent on a pytorch build job. This way we could quickly dedup the repeated # build of pytorch in the caffe2 build job, and just run the caffe2 tests off of a completed # pytorch build job (from https://github.com/pytorch/pytorch/pull/17323#discussion_r259452641) dependency_build = self.parent_build or self parameters["requires"].append(dependency_build.gen_build_name("build")) if not self.is_important: # If you update this, update # caffe2_build_definitions.py too parameters["filters"] = {"branches": {"only": ["master", r"/ci-all\/./"]}} return {self.gen_build_name(phase): parameters} # TODO This is a hack to special case some configs just for the workflow list class HiddenConf(object): def __init__(self, name, parent_build=None): self.name = name self.parent_build = parent_build def gen_workflow_yaml_item(self, phase): return {self.gen_build_name(phase): {"requires": [self.parent_build.gen_build_name("build")]}} def gen_build_name(self, _): return self.name # TODO Convert these to graph nodes def gen_dependent_configs(xenial_parent_config): extra_parms = [ (["multigpu"], "large"), (["NO_AVX2"], "medium"), (["NO_AVX", "NO_AVX2"], "medium"), (["slow"], "medium"), (["nogpu"], None), ] configs = [] for parms, gpu in extra_parms: c = Conf( xenial_parent_config.distro, ["py3"] + parms, pyver="3.6", cuda_version=xenial_parent_config.cuda_version, restrict_phases=["test"], gpu_resource=gpu, parent_build=xenial_parent_config, is_important=xenial_parent_config.is_important, ) configs.append(c) for x in ["pytorch_short_perf_test_gpu", "pytorch_python_doc_push", "pytorch_cpp_doc_push"]: configs.append(HiddenConf(x, parent_build=xenial_parent_config)) return configs def get_root(): return TopLevelNode("PyTorch Builds", CONFIG_TREE_DATA) def gen_tree(): root = get_root() configs_list = conf_tree.dfs(root) return configs_list def instantiate_configs(): config_list = [] root = get_root() found_configs = conf_tree.dfs(root) restrict_phases = None for fc in found_configs: distro_name = fc.find_prop("distro_name") python_version = None if distro_name == "xenial": python_version = fc.find_prop("pyver") parms_list = [fc.find_prop("abbreviated_pyver")] else: parms_list = ["py" + fc.find_prop("pyver")] compiler_name = fc.find_prop("compiler_name") cuda_version = None if compiler_name == "cuda": cuda_version = fc.find_prop("compiler_version") elif compiler_name == "android": android_ndk_version = fc.find_prop("compiler_version") # TODO: do we need clang to compile host binaries like protoc? parms_list.append("clang5") parms_list.append("android-ndk-" + android_ndk_version) restrict_phases = ["build"] elif compiler_name: gcc_version = compiler_name + (fc.find_prop("compiler_version") or "") parms_list.append(gcc_version) # TODO: This is a nasty special case if compiler_name == "clang": parms_list.append("asan") if cuda_version in ["9.2", "10"]: # TODO The gcc version is orthogonal to CUDA version? parms_list.append("gcc7") is_xla = fc.find_prop("is_xla") or False is_namedtensor = fc.find_prop("is_namedtensor") or False is_important = fc.find_prop("is_important") or False gpu_resource = None if cuda_version and cuda_version != "10": gpu_resource = "medium" c = Conf( distro_name, parms_list, python_version, cuda_version, is_xla, restrict_phases, gpu_resource, is_namedtensor=is_namedtensor, is_important=is_important, ) if cuda_version == "9" and python_version == "3.6": c.dependent_tests = gen_dependent_configs(c) config_list.append(c) return config_list def add_build_env_defs(jobs_dict): mydict = OrderedDict() config_list = instantiate_configs() for c in config_list: phases = c.restrict_phases or dimensions.PHASES for phase in phases: # TODO why does this not have a test? if phase == "test" and c.cuda_version == "10": continue d = c.gen_yaml_tree(phase) mydict[c.gen_build_name(phase)] = d if phase == "test": for x in filter(lambda x: type(x) is not HiddenConf, c.get_dependents()): d = x.gen_yaml_tree(phase) mydict[x.gen_build_name(phase)] = d # this is the circleci api version and probably never changes jobs_dict["version"] = 2 jobs_dict["jobs"] = mydict graph = visualization.generate_graph(get_root()) graph.draw("pytorch-config-dimensions.png", prog="twopi") def get_workflow_list(): config_list = instantiate_configs() x = ["setup"] for conf_options in config_list: phases = conf_options.restrict_phases or dimensions.PHASES for phase in phases: # TODO why does this not have a test? if phase == "test" and conf_options.cuda_version == "10": continue x.append(conf_options.gen_workflow_yaml_item(phase)) # TODO convert to recursion for conf in conf_options.get_dependents(): x.append(conf.gen_workflow_yaml_item("test")) return x	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 13, 3166, 16250, 1053, 8170, 287, 21533, 13, 13, 3166, 274, 326, 27224, 29889, 1272, 29889, 2272, 7345, 305, 29918, 4282, 29918, 1272, 1053, 7488, 10108, 4247, 29892, 8707, 18667, 29918, 29911, 21661, 29918, 14573, 13, 5215, 274, 326, 27224, 29889, 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jax_dft/jax_dft/losses_test.py	deepneuralmachine/google-research	23,901	159153	<filename>jax_dft/jax_dft/losses_test.py # coding=utf-8 # Copyright 2021 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Lint as: python3 """Tests for jax_dft.losses.""" from absl.testing import absltest from jax.config import config import jax.numpy as jnp import numpy as np from jax_dft import losses # Set the default dtype as float64 config.update('jax_enable_x64', True) class LossesTest(absltest.TestCase): def test_trajectory_mse_wrong_predict_ndim(self): with self.assertRaisesRegex( ValueError, 'The size of the shape of predict should be ' 'greater or equal to 2, got 1'): losses.trajectory_mse( target=jnp.array([[0.2, 0.2, 0.2, 0.2], [0.6, 0.6, 0.6, 0.6]]), predict=jnp.array([0.6, 0.6, 0.6, 0.6]), discount=1.) def test_trajectory_mse_wrong_predict_target_ndim_difference(self): with self.assertRaisesRegex( ValueError, 'The size of the shape of predict should be greater than ' 'the size of the shape of target by 1, ' r'but got predict \(2\) and target \(2\)'): losses.trajectory_mse( target=jnp.array([[0.2, 0.2, 0.2, 0.2], [0.6, 0.6, 0.6, 0.6]]), predict=jnp.array([[0.2, 0.2, 0.2, 0.2], [0.6, 0.6, 0.6, 0.6]]), discount=1.) def test_density_mse(self): self.assertAlmostEqual( float(losses.mean_square_error( target=jnp.array([[0.2, 0.2, 0.2, 0.2], [0.6, 0.6, 0.6, 0.6]]), predict=jnp.array([[0.4, 0.5, 0.2, 0.3], [0.6, 0.6, 0.6, 0.6]]))), # (( # (0.4 - 0.2) 2 + (0.5 - 0.2) 2 # + (0.2 - 0.2) 2 + (0.3 - 0.2) 2 # ) / 4 + 0) / 2 = 0.0175 0.0175) def test_energy_mse(self): self.assertAlmostEqual( float(losses.mean_square_error( target=jnp.array([[0.2, 0.6]]), predict=jnp.array([[0.4, 0.7]]))), # ((0.4 - 0.2) 2 + (0.7 - 0.6) 2) / 2 = 0.025 0.025) def test_get_discount_coefficients(self): np.testing.assert_allclose( losses._get_discount_coefficients(num_steps=4, discount=0.8), [0.512, 0.64, 0.8, 1.]) def test_trajectory_mse_on_density(self): self.assertAlmostEqual( float(losses.trajectory_mse( target=jnp.array([[0.2, 0.2, 0.2, 0.2], [0.6, 0.6, 0.6, 0.6]]), predict=jnp.array([ [[0.4, 0.5, 0.2, 0.3], [0.3, 0.3, 0.2, 0.2], [0.3, 0.3, 0.3, 0.2]], [[0.6, 0.6, 0.6, 0.6], [0.6, 0.6, 0.6, 0.5], [0.6, 0.6, 0.6, 0.6]]]), discount=0.6)), # First sample in the batch: # ( # (0.4 - 0.2) 2 + (0.5 - 0.2) 2 # + (0.2 - 0.2) 2 + (0.3 - 0.2) 2 # ) / 4 * 0.6 * 0.6 # + ( # (0.3 - 0.2) 2 + (0.3 - 0.2) 2 # + (0.2 - 0.2) 2 + (0.2 - 0.2) 2 # ) / 4 * 0.6 # + ( # (0.3 - 0.2) 2 + (0.3 - 0.2) 2 # + (0.3 - 0.2) 2 + (0.2 - 0.2) 2 # ) / 4 = 0.0231 # Second sample in the batch: # ( # (0.6 - 0.6) 2 + (0.6 - 0.6) 2 # + (0.6 - 0.6) 2 + (0.6 - 0.6) 2 # ) / 4 * 0.6 * 0.6 # + ( # (0.6 - 0.6) 2 + (0.6 - 0.6) 2 # + (0.6 - 0.6) 2 + (0.5 - 0.6) 2 # ) / 4 * 0.6 # + ( # (0.6 - 0.6) 2 + (0.6 - 0.6) 2 # + (0.6 - 0.6) 2 + (0.6 - 0.6) 2 # ) / 4 = 0.0015 # Loss: # (0.0231 + 0.0015) / 2 = 0.0123 0.0123) def test_trajectory_mse_on_energy(self): self.assertAlmostEqual( float(losses.trajectory_mse( target=jnp.array([0.2, 0.6]), predict=jnp.array([[0.4, 0.3, 0.2], [0.7, 0.7, 0.7]]), discount=0.6)), # First sample in the batch: # ((0.4 - 0.2) ** 2 * 0.6 * 0.6 # + (0.3 - 0.2) ** 2 * 0.6 + (0.2 - 0.2) 2) = 0.0204 # Second sample in the batch: # ((0.7 - 0.6) 2 * 0.6 * 0.6 # + (0.7 - 0.6) ** 2 * 0.6 + (0.7 - 0.6) ** 2) = 0.0196 # Loss: # (0.0204 + 0.0196) / 2 = 0.02 0.02) if __name__ == '__main__': absltest.main()	[ 1, 529, 9507, 29958, 6487, 29918, 29881, 615, 29914, 6487, 29918, 29881, 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setup.py	EncoreTechnologies/py-menandmice	1	48039	# Licensed to the Encore Technologies ("Encore") 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 os.path from setuptools import setup, find_packages from pip.req import parse_requirements from menandmice import __version__ def fetch_requirements(requirements_file_path): """ Return a list of requirements and links by parsing the provided requirements file. """ links = [] reqs = [] for req in parse_requirements(requirements_file_path, session=False): if req.link: links.append(str(req.link)) reqs.append(str(req.req)) return (reqs, links) PACKAGE_NAME = 'menandmice' BASE_DIR = os.path.dirname(os.path.abspath(__file__)) REQUIREMENTS_FILE = os.path.join(BASE_DIR, 'requirements.txt') install_reqs, dep_links = fetch_requirements(REQUIREMENTS_FILE) with open('README.md') as f: readme = f.read() with open('LICENSE') as f: license = f.read() setup( name=PACKAGE_NAME, version=__version__, description='Python bindings for the Men&Mice IPAM REST API ', long_description=readme, author='<NAME>', author_email='<EMAIL>', url='https://github.com/EncoreTechnologies/py-menandmice', license=license, install_requires=install_reqs, dependency_links=dep_links, test_suite="nose.collector", tests_require=["nose", "mock"], packages=find_packages(exclude=['tests']) )	[ 1, 396, 10413, 21144, 304, 278, 1174, 3221, 8364, 11763, 4852, 2369, 3221, 1159, 1090, 697, 13, 29937, 470, 901, 17737, 3406, 19405, 8571, 4110, 29889, 29871, 2823, 278, 6058, 12107, 934, 13, 29937, 13235, 411, 445, 664, 363, 5684, 2472, 13, 29937, 11211, 3509, 1266, 27428, 29889, 29871, 450, 3339, 29943, 7794, 11259, 445, 934, 13, 29937, 304, 366, 1090, 278, 13380, 19245, 29892, 10079, 29871, 29906, 29889, 29900, 313, 1552, 13, 29937, 376, 29931, 293, 1947, 1496, 366, 1122, 451, 671, 445, 934, 5174, 297, 752, 13036, 13, 29937, 411, 278, 19245, 29889, 29871, 887, 1122, 4017, 263, 3509, 310, 278, 19245, 472, 13, 29937, 13, 29937, 259, 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, 13, 29937, 7047, 13235, 1090, 278, 19245, 338, 13235, 373, 385, 13, 29937, 376, 3289, 8519, 29908, 350, 3289, 3235, 29892, 399, 1806, 8187, 2692, 399, 1718, 29934, 13566, 29059, 6323, 8707, 29928, 22122, 29903, 8079, 13764, 29979, 13, 29937, 476, 22255, 29892, 2845, 4653, 470, 2411, 2957, 29889, 29871, 2823, 278, 19245, 363, 278, 13, 29937, 2702, 4086, 14765, 1076, 11239, 322, 27028, 13, 29937, 1090, 278, 19245, 29889, 13, 13, 5215, 2897, 29889, 2084, 13, 13, 3166, 731, 21245, 8789, 1053, 6230, 29892, 1284, 29918, 8318, 13, 13, 3166, 8450, 29889, 7971, 1053, 6088, 29918, 12277, 1860, 13, 13, 3166, 1757, 392, 29885, 625, 1053, 4770, 3259, 1649, 13, 13, 13, 1753, 6699, 29918, 12277, 1860, 29898, 12277, 1860, 29918, 1445, 29918, 2084, 1125, 13, 1678, 9995, 13, 1678, 7106, 263, 1051, 310, 11780, 322, 2988, 491, 13755, 278, 4944, 11780, 934, 29889, 13, 1678, 9995, 13, 1678, 2988, 353, 5159, 13, 1678, 12428, 29879, 353, 5159, 13, 1678, 363, 12428, 297, 6088, 29918, 12277, 1860, 29898, 12277, 1860, 29918, 1445, 29918, 2084, 29892, 4867, 29922, 8824, 1125, 13, 4706, 565, 12428, 29889, 2324, 29901, 13, 9651, 2988, 29889, 4397, 29898, 710, 29898, 7971, 29889, 2324, 876, 13, 4706, 12428, 29879, 29889, 4397, 29898, 710, 29898, 7971, 29889, 7971, 876, 13, 1678, 736, 313, 7971, 29879, 29892, 2988, 29897, 13, 13, 29925, 11375, 10461, 29918, 5813, 353, 525, 1527, 392, 29885, 625, 29915, 13, 25416, 29918, 9464, 353, 2897, 29889, 2084, 29889, 25721, 29898, 359, 29889, 2084, 29889, 370, 1028, 493, 22168, 1445, 1649, 876, 13, 1525, 29984, 3120, 1525, 13780, 29903, 29918, 7724, 353, 2897, 29889, 2084, 29889, 7122, 29898, 25416, 29918, 9464, 29892, 525, 12277, 1860, 29889, 3945, 1495, 13, 13, 6252, 29918, 7971, 29879, 29892, 1401, 29918, 4965, 353, 6699, 29918, 12277, 1860, 29898, 1525, 29984, 3120, 1525, 13780, 29903, 29918, 7724, 29897, 13, 13, 2541, 1722, 877, 16310, 2303, 29889, 3487, 1495, 408, 285, 29901, 13, 1678, 1303, 1004, 353, 285, 29889, 949, 580, 13, 13, 2541, 1722, 877, 27888, 1430, 1660, 1495, 408, 285, 29901, 13, 1678, 19405, 353, 285, 29889, 949, 580, 13, 13, 14669, 29898, 13, 1678, 1024, 29922, 29925, 11375, 10461, 29918, 5813, 29892, 13, 1678, 1873, 29922, 1649, 3259, 1649, 29892, 13, 1678, 6139, 2433, 11980, 7868, 886, 363, 278, 7567, 29987, 29924, 625, 5641, 5194, 16759, 3450, 13420, 13, 1678, 1472, 29918, 8216, 29922, 949, 1004, 29892, 13, 1678, 4148, 2433, 29966, 5813, 29958, 742, 13, 1678, 4148, 29918, 5269, 2433, 29966, 26862, 6227, 29958, 742, 13, 1678, 3142, 2433, 991, 597, 3292, 29889, 510, 29914, 2369, 3221, 7141, 3049, 11763, 29914, 2272, 29899, 1527, 392, 29885, 625, 742, 13, 1678, 19405, 29922, 506, 1947, 29892, 13, 1678, 2601, 29918, 276, 339, 2658, 29922, 6252, 29918, 7971, 29879, 29892, 13, 1678, 10609, 29918, 4965, 29922, 2716, 29918, 4965, 29892, 13, 1678, 1243, 29918, 13495, 543, 29876, 852, 29889, 15914, 272, 613, 13, 1678, 6987, 29918, 12277, 29922, 3366, 29876, 852, 613, 376, 17640, 12436, 13, 1678, 9741, 29922, 2886, 29918, 8318, 29898, 735, 2325, 29922, 1839, 21150, 11287, 13, 29897, 13, 2 ]
blocks/bricks/bn.py	janchorowski/blocks	3	199455	<gh_stars>1-10 import collections import numpy from picklable_itertools.extras import equizip import theano from theano import tensor from theano.tensor.nnet import bn from ..graph import add_annotation from ..initialization import Constant from ..roles import (BATCH_NORM_POPULATION_MEAN, BATCH_NORM_POPULATION_STDEV, BATCH_NORM_OFFSET, BATCH_NORM_DIVISOR, BATCH_NORM_MINIBATCH_ESTIMATE, BATCH_NORM_SHIFT_PARAMETER, BATCH_NORM_SCALE_PARAMETER, add_role) from ..utils import (shared_floatx_zeros, shared_floatx, shared_floatx_nans) from .base import lazy, application from .sequences import Sequence, Feedforward, MLP from .interfaces import RNGMixin def _add_batch_axis(var): """Prepend a singleton axis to a TensorVariable and name it.""" new_var = new_var = tensor.shape_padleft(var) new_var.name = 'shape_padleft({})'.format(var.name) return new_var def _add_role_and_annotate(var, role, annotations=()): """Add a role and zero or more annotations to a variable.""" add_role(var, role) for annotation in annotations: add_annotation(var, annotation) class BatchNormalization(RNGMixin, Feedforward): r"""Normalizes activations, parameterizes a scale and shift. Parameters ---------- input_dim : int or tuple Shape of a single input example. It is assumed that a batch axis will be prepended to this. broadcastable : tuple, optional Tuple the same length as `input_dim` which specifies which of the per-example axes should be averaged over to compute means and standard deviations. For example, in order to normalize over all spatial locations in a `(batch_index, channels, height, width)` image, pass `(False, True, True)`. conserve_memory : bool, optional Use an implementation that stores less intermediate state and therefore uses less memory, at the expense of 5-10% speed. Default is `True`. epsilon : float, optional The stabilizing constant for the minibatch standard deviation computation (when the brick is run in training mode). Added to the variance inside the square root, as in the batch normalization paper. scale_init : object, optional Initialization object to use for the learned scaling parameter ($\\gamma$ in [BN]_). By default, uses constant initialization of 1. shift_init : object, optional Initialization object to use for the learned shift parameter ($\\beta$ in [BN]_). By default, uses constant initialization of 0. Notes ----- In order for trained models to behave sensibly immediately upon upon deserialization, by default, this brick runs in inference mode, using a population mean and population standard deviation (initialized to zeros and ones respectively) to normalize activations. It is expected that the user will adapt these during training in some fashion, independently of the training objective, e.g. by taking a moving average of minibatch-wise statistics. In order to train with batch normalization, one must obtain a training graph by transforming the original inference graph. See :func:`~blocks.graph.apply_batch_normalization` for a routine to transform graphs, and :func:`~blocks.graph.batch_normalization` for a context manager that may enable shorter compile times (every instance of :class:`BatchNormalization` is itself a context manager, entry into which causes applications to be in minibatch "training" mode, however it is usually more convenient to use :func:`~blocks.graph.batch_normalization` to enable this behaviour for all of your graph's :class:`BatchNormalization` bricks at once). Note that training in inference mode should be avoided, as this brick introduces scales and shift parameters (tagged with the `PARAMETER` role) that, in the absence of batch normalization, usually makes things unstable. If you must do this, filter for and remove `BATCH_NORM_SHIFT_PARAMETER` and `BATCH_NORM_SCALE_PARAMETER` from the list of parameters you are training, and this brick should behave as a (somewhat expensive) no-op. This Brick accepts `scale_init` and `shift_init` arguments but is not an instance of :class:`~blocks.bricks.Initializable`, and will therefore not receive pushed initialization config from any parent brick. In almost all cases, you will probably want to stick with the defaults (unit scale and zero offset), but you can explicitly pass one or both initializers to override this. This has the necessary properties to be inserted into a :class:`blocks.bricks.conv.ConvolutionalSequence` as-is, in which case the `input_dim` should be omitted at construction, to be inferred from the layer below. """ @lazy(allocation=['input_dim']) def __init__(self, input_dim, broadcastable=None, conserve_memory=True, epsilon=1e-4, scale_init=None, shift_init=None, kwargs): self.input_dim = input_dim self.broadcastable = broadcastable self.conserve_memory = conserve_memory self.epsilon = epsilon self.scale_init = (Constant(1) if scale_init is None else scale_init) self.shift_init = (Constant(0) if shift_init is None else shift_init) self._training_mode = [] super(BatchNormalization, self).__init__(kwargs) @application(inputs=['input_'], outputs=['output']) def apply(self, input_, application_call): if self._training_mode: mean, stdev = self._compute_training_statistics(input_) else: mean, stdev = self._prepare_population_statistics() # Useful for filtration of calls that were already made in # training mode when doing graph transformations. # Very important to cast to bool, as self._training_mode is # normally a list (to support nested context managers), which would # otherwise get passed by reference and be remotely mutated. application_call.metadata['training_mode'] = bool(self._training_mode) # Useful for retrieving a list of updates for population # statistics. Ditch the broadcastable first axis, though, to # make it the same dimensions as the population mean and stdev # shared variables. application_call.metadata['offset'] = mean[0] application_call.metadata['divisor'] = stdev[0] # Give these quantities roles in the graph. _add_role_and_annotate(mean, BATCH_NORM_OFFSET, [self, application_call]) _add_role_and_annotate(stdev, BATCH_NORM_DIVISOR, [self, application_call]) scale = _add_batch_axis(self.scale) shift = _add_batch_axis(self.shift) # Heavy lifting is done by the Theano utility function. normalized = bn.batch_normalization(input_, scale, shift, mean, stdev, mode=('low_mem' if self.conserve_memory else 'high_mem')) return normalized def __enter__(self): self._training_mode.append(True) def __exit__(self, exc_info): self._training_mode.pop() def _compute_training_statistics(self, input_): axes = (0,) + tuple((i + 1) for i, b in enumerate(self.population_mean.broadcastable) if b) mean = input_.mean(axis=axes, keepdims=True) assert mean.broadcastable[1:] == self.population_mean.broadcastable stdev = tensor.sqrt(tensor.var(input_, axis=axes, keepdims=True) + numpy.cast[theano.config.floatX](self.epsilon)) assert stdev.broadcastable[1:] == self.population_stdev.broadcastable add_role(mean, BATCH_NORM_MINIBATCH_ESTIMATE) add_role(stdev, BATCH_NORM_MINIBATCH_ESTIMATE) return mean, stdev def _prepare_population_statistics(self): mean = _add_batch_axis(self.population_mean) stdev = _add_batch_axis(self.population_stdev) return mean, stdev def _allocate(self): input_dim = ((self.input_dim,) if not isinstance(self.input_dim, collections.Sequence) else self.input_dim) broadcastable = (tuple(False for _ in input_dim) if self.broadcastable is None else self.broadcastable) if len(input_dim) != len(broadcastable): raise ValueError("input_dim and broadcastable must be same length") var_dim = tuple(1 if broadcast else dim for dim, broadcast in equizip(input_dim, broadcastable)) broadcastable = broadcastable # "gamma", from the Ioffe & Szegedy manuscript. self.scale = shared_floatx_nans(var_dim, name='batch_norm_scale', broadcastable=broadcastable) # "beta", from the Ioffe & Szegedy manuscript. self.shift = shared_floatx_nans(var_dim, name='batch_norm_shift', broadcastable=broadcastable) add_role(self.scale, BATCH_NORM_SCALE_PARAMETER) add_role(self.shift, BATCH_NORM_SHIFT_PARAMETER) self.parameters.append(self.scale) self.parameters.append(self.shift) # These aren't technically parameters, in that they should not be # learned using the same cost function as other model parameters. self.population_mean = shared_floatx_zeros(var_dim, name='population_mean', broadcastable=broadcastable) self.population_stdev = shared_floatx(numpy.ones(var_dim), name='population_stdev', broadcastable=broadcastable) add_role(self.population_mean, BATCH_NORM_POPULATION_MEAN) add_role(self.population_stdev, BATCH_NORM_POPULATION_STDEV) # Normally these would get annotated by an AnnotatingList, but they # aren't in self.parameters. add_annotation(self.population_mean, self) add_annotation(self.population_stdev, self) def _initialize(self): self.shift_init.initialize(self.shift, self.rng) self.scale_init.initialize(self.scale, self.rng) # Needed for the Feedforward interface. @property def output_dim(self): return self.input_dim # The following properties allow for BatchNormalization bricks # to be used directly inside of a ConvolutionalSequence. @property def image_size(self): return self.input_dim[-2:] @image_size.setter def image_size(self, value): if not isinstance(self.input_dim, collections.Sequence): self.input_dim = (None,) + tuple(value) else: self.input_dim = (self.input_dim[0],) + tuple(value) @property def num_channels(self): return self.input_dim[0] @num_channels.setter def num_channels(self, value): if not isinstance(self.input_dim, collections.Sequence): self.input_dim = (value,) + (None, None) else: self.input_dim = (value,) + self.input_dim[-2:] def get_dim(self, name): if name in ('input', 'output'): return self.input_dim else: raise KeyError @property def num_output_channels(self): return self.num_channels class SpatialBatchNormalization(BatchNormalization): """Convenient subclass for batch normalization across spatial inputs. Parameters ---------- input_dim : int or tuple The input size of a single example. Must be length at least 2. It's assumed that the first axis of this tuple is a "channels" axis, which should not be summed over, and all remaining dimensions are spatial dimensions. Notes ----- See :class:`BatchNormalization` for more details (and additional keyword arguments). """ def _allocate(self): if not isinstance(self.input_dim, collections.Sequence) or len(self.input_dim) < 2: raise ValueError('expected input_dim to be length >= 2 ' 'e.g. (channels, height, width)') self.broadcastable = (False,) + ((True,) (len(self.input_dim) - 1)) super(SpatialBatchNormalization, self)._allocate() class BatchNormalizedMLP(MLP): """Convenient subclass for building an MLP with batch normalization. Parameters ---------- conserve_memory : bool, optional See :class:`BatchNormalization`. Notes ----- All other parameters are the same as :class:`~blocks.bricks.MLP`. Each activation brick is wrapped in a :class:`~blocks.bricks.Sequence` containing an appropriate :class:`BatchNormalization` brick and the activation that follows it. By default, the contained :class:`~blocks.bricks.Linear` bricks will not contain any biases, as they could be canceled out by the biases in the :class:`BatchNormalization` bricks being added. Pass `use_bias` with a value of `True` if you really want this for some reason. """ @lazy(allocation=['dims']) def __init__(self, activations, dims, args, kwargs): conserve_memory = kwargs.pop('conserve_memory', True) activations = [ Sequence([ BatchNormalization(conserve_memory=conserve_memory).apply, act.apply ], name='batch_norm_activation_{}'.format(i)) for i, act in enumerate(activations) ] # Batch normalization bricks incorporate a bias, so there's no # need for our Linear bricks to have them. kwargs.setdefault('use_bias', False) super(BatchNormalizedMLP, self).__init__(activations, dims, args, **kwargs) @property def conserve_memory(self): return self._conserve_memory @conserve_memory.setter def conserve_memory(self, value): self._conserve_memory = value for act in self.activations: assert isinstance(act.children[0], BatchNormalization) act.children[0].conserve_memory = value def _push_allocation_config(self): super(BatchNormalizedMLP, self)._push_allocation_config() # Do the extra allocation pushing for the BatchNormalization # bricks. They need as their input dimension the output dimension # of each linear transformation. Exclude the first dimension, # which is the input dimension. for act, dim in equizip(self.activations, self.dims[1:]): assert isinstance(act.children[0], BatchNormalization) act.children[0].input_dim = dim	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 5215, 16250, 13, 13, 5215, 12655, 13, 3166, 5839, 29880, 519, 29918, 1524, 8504, 29889, 1062, 3417, 1053, 1592, 466, 666, 13, 5215, 278, 1562, 13, 3166, 278, 1562, 1053, 12489, 13, 3166, 278, 1562, 29889, 20158, 29889, 29876, 1212, 1053, 289, 29876, 13, 13, 3166, 6317, 4262, 1053, 788, 29918, 18317, 13, 3166, 6317, 11228, 2133, 1053, 28601, 13, 3166, 6317, 307, 793, 1053, 313, 29933, 14789, 29918, 29940, 12054, 29918, 29925, 4590, 13309, 8098, 29918, 2303, 2190, 29892, 13, 462, 268, 350, 14789, 29918, 29940, 12054, 29918, 29925, 4590, 13309, 8098, 29918, 1254, 2287, 29963, 29892, 350, 14789, 29918, 29940, 12054, 29918, 27681, 10490, 29892, 13, 462, 268, 350, 14789, 29918, 29940, 12054, 29918, 4571, 28607, 1955, 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.tox/scenario/lib/python2.7/site-packages/pycadf/measurement.py	bdrich/neutron-lbaas	0	122430	<reponame>bdrich/neutron-lbaas # Copyright 2013 IBM Corp. # # 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. from pycadf import cadftype from pycadf import identifier from pycadf import metric from pycadf import resource MEASUREMENT_KEYNAME_RESULT = "result" MEASUREMENT_KEYNAME_METRIC = "metric" MEASUREMENT_KEYNAME_METRICID = "metricId" MEASUREMENT_KEYNAME_CALCBY = "calculatedBy" MEASUREMENT_KEYNAMES = [MEASUREMENT_KEYNAME_RESULT, MEASUREMENT_KEYNAME_METRICID, MEASUREMENT_KEYNAME_METRIC, MEASUREMENT_KEYNAME_CALCBY] class Measurement(cadftype.CADFAbstractType): result = cadftype.ValidatorDescriptor(MEASUREMENT_KEYNAME_RESULT) metric = cadftype.ValidatorDescriptor( MEASUREMENT_KEYNAME_METRIC, lambda x: isinstance(x, metric.Metric)) metricId = cadftype.ValidatorDescriptor(MEASUREMENT_KEYNAME_METRICID, lambda x: identifier.is_valid(x)) calculatedBy = cadftype.ValidatorDescriptor( MEASUREMENT_KEYNAME_CALCBY, (lambda x: isinstance(x, resource.Resource) and x.is_valid())) def __init__(self, result=None, metric=None, metricId=None, calculatedBy=None): """Create Measurement data type :param result: value of measurement :param metric: Metric data type of current measurement :param metricId: id of Metric data type of current measurement :param calculatedBy: Resource that calculated measurement """ # Measurement.result if result is not None: setattr(self, MEASUREMENT_KEYNAME_RESULT, result) # Measurement.metricId if metricId is not None: setattr(self, MEASUREMENT_KEYNAME_METRICID, metricId) # Measurement.metric if metric is not None: setattr(self, MEASUREMENT_KEYNAME_METRIC, metric) # Measurement.calculaedBy if calculatedBy is not None: setattr(self, MEASUREMENT_KEYNAME_CALCBY, calculatedBy) # self validate this cadf:Measurement type against schema def is_valid(self): """Validation to ensure Measurement required attributes are set. 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Problems/5_best_sum.py	v4n5h4j/dynamic-programming-python	0	152091	def bestSum(t, arr, memo=None): """ m: target sum, t n: len(arr) time = O(n^mm) space = O(mm) [in each stack frame, I am storing an array, which in worst case would be m] // Memoized complexity time = O(nmm) space = O(m*m) """ if memo is None: memo = {} if t in memo: return memo[t] if t == 0: return [] if t < 0: return None shortestCombination = None for ele in arr: r = t - ele rRes = bestSum(r, arr, memo) if rRes is not None: combination = rRes + [ele] # If the combination is shorter than current shortest, update it if (shortestCombination is None or len(combination) < len(shortestCombination)): shortestCombination = combination memo[t] = shortestCombination return memo[t] print(bestSum(7, [5,3,4,7])) print(bestSum(8, [2,3,5])) print(bestSum(8, [1,4,5])) print(bestSum(100, [1,2,5,25]))	[ 1, 822, 1900, 11139, 29898, 29873, 29892, 3948, 29892, 2626, 29877, 29922, 8516, 1125, 13, 12, 15945, 29908, 13, 12, 29885, 29901, 3646, 2533, 29892, 260, 13, 12, 29876, 29901, 7431, 29898, 2749, 29897, 13, 12, 2230, 353, 438, 29898, 29876, 29985, 29885, 29930, 29885, 29897, 13, 12, 3493, 353, 438, 29898, 29885, 29930, 29885, 29897, 518, 262, 1269, 5096, 3515, 29892, 306, 626, 15446, 385, 1409, 29892, 607, 297, 17322, 1206, 723, 367, 286, 29962, 13, 13, 12, 458, 8133, 29877, 1891, 13644, 13, 12, 2230, 353, 438, 29898, 29876, 29930, 29885, 29930, 29885, 29897, 13, 12, 3493, 353, 438, 29898, 29885, 29930, 29885, 29897, 13, 12, 15945, 29908, 13, 12, 361, 2626, 29877, 338, 6213, 29901, 13, 12, 12, 6954, 29877, 353, 6571, 13, 13, 12, 361, 260, 297, 2626, 29877, 29901, 13, 12, 12, 2457, 2626, 29877, 29961, 29873, 29962, 13, 13, 12, 361, 260, 1275, 29871, 29900, 29901, 13, 12, 12, 2457, 5159, 13, 12, 361, 260, 529, 29871, 29900, 29901, 13, 12, 12, 2457, 6213, 13, 13, 12, 12759, 342, 1523, 2109, 362, 353, 6213, 13, 12, 1454, 4552, 297, 3948, 29901, 13, 12, 12, 29878, 353, 260, 448, 4552, 13, 12, 12, 29878, 1666, 353, 1900, 11139, 29898, 29878, 29892, 3948, 29892, 2626, 29877, 29897, 13, 12, 12, 361, 364, 1666, 338, 451, 6213, 29901, 13, 12, 12, 12, 510, 2109, 362, 353, 364, 1666, 718, 518, 6146, 29962, 13, 12, 12, 12, 29937, 960, 278, 10296, 338, 20511, 1135, 1857, 3273, 342, 29892, 2767, 372, 13, 12, 12, 12, 361, 313, 12759, 342, 1523, 2109, 362, 338, 6213, 470, 7431, 29898, 510, 2109, 362, 29897, 529, 7431, 29898, 12759, 342, 1523, 2109, 362, 22164, 13, 12, 12, 12, 12, 12759, 342, 1523, 2109, 362, 353, 10296, 13, 13, 12, 6954, 29877, 29961, 29873, 29962, 353, 3273, 342, 1523, 2109, 362, 13, 12, 2457, 2626, 29877, 29961, 29873, 29962, 13, 13, 13, 2158, 29898, 13318, 11139, 29898, 29955, 29892, 518, 29945, 29892, 29941, 29892, 29946, 29892, 29955, 12622, 13, 2158, 29898, 13318, 11139, 29898, 29947, 29892, 518, 29906, 29892, 29941, 29892, 29945, 12622, 13, 2158, 29898, 13318, 11139, 29898, 29947, 29892, 518, 29896, 29892, 29946, 29892, 29945, 12622, 13, 2158, 29898, 13318, 11139, 29898, 29896, 29900, 29900, 29892, 518, 29896, 29892, 29906, 29892, 29945, 29892, 29906, 29945, 12622, 2 ]
model/VAE.py	y-x-c/electricauth	1	169447	""" PyTorch implementation of VAE found at: https://github.com/pytorch/examples/tree/master/vae. """ import torch from torch import nn import torch.nn.functional as F class VAE(nn.Module): def __init__(self, input_length, n_sensor_channel, n_hidden = 400, n_latent_features=20, no_variational=False ): super(VAE, self).__init__() self.fc1 = nn.Linear(input_lengthn_sensor_channel, n_hidden) self.fc21 = nn.Linear(n_hidden, n_latent_features) self.fc22 = nn.Linear(n_hidden, n_latent_features) self.fc3 = nn.Linear(n_latent_features, n_hidden) self.fc4 = nn.Linear(n_hidden, input_length n_sensor_channel) self.no_variational = no_variational def encode(self, x): h1 = F.relu(self.fc1(x)) return self.fc21(h1), self.fc22(h1) def reparameterize(self, mu, logvar): if self.no_variational: return mu else: std = torch.exp(0.5*logvar) eps = torch.randn_like(std) return eps.mul(std).add_(mu) def decode(self, z): h3 = F.relu(self.fc3(z)) return torch.sigmoid(self.fc4(h3)) def forward(self, x): mu, logvar = self.encode(x) z = self.reparameterize(mu, logvar) return self.decode(z), mu, logvar	[ 1, 9995, 13, 19737, 29911, 25350, 5314, 310, 478, 16036, 1476, 472, 29901, 2045, 597, 3292, 29889, 510, 29914, 2272, 7345, 305, 29914, 19057, 29914, 8336, 29914, 6207, 29914, 1564, 29872, 29889, 13, 15945, 29908, 13, 13, 5215, 4842, 305, 13, 3166, 4842, 305, 1053, 302, 29876, 13, 5215, 4842, 305, 29889, 15755, 29889, 2220, 284, 408, 383, 13, 13, 1990, 478, 16036, 29898, 15755, 29889, 7355, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 1881, 29918, 2848, 29892, 302, 29918, 29879, 6073, 29918, 12719, 29892, 13, 4706, 302, 29918, 10892, 353, 29871, 29946, 29900, 29900, 29892, 13, 4706, 302, 29918, 5066, 296, 29918, 22100, 29922, 29906, 29900, 29892, 694, 29918, 5927, 1288, 29922, 8824, 13, 268, 1125, 13, 4706, 2428, 29898, 29963, 16036, 29892, 1583, 467, 1649, 2344, 1649, 580, 13, 4706, 1583, 29889, 13801, 29896, 353, 302, 29876, 29889, 12697, 29898, 2080, 29918, 2848, 29930, 29876, 29918, 29879, 6073, 29918, 12719, 29892, 302, 29918, 10892, 29897, 13, 4706, 1583, 29889, 13801, 29906, 29896, 353, 302, 29876, 29889, 12697, 29898, 29876, 29918, 10892, 29892, 302, 29918, 5066, 296, 29918, 22100, 29897, 13, 4706, 1583, 29889, 13801, 29906, 29906, 353, 302, 29876, 29889, 12697, 29898, 29876, 29918, 10892, 29892, 302, 29918, 5066, 296, 29918, 22100, 29897, 13, 4706, 1583, 29889, 13801, 29941, 353, 302, 29876, 29889, 12697, 29898, 29876, 29918, 5066, 296, 29918, 22100, 29892, 302, 29918, 10892, 29897, 13, 4706, 1583, 29889, 13801, 29946, 353, 302, 29876, 29889, 12697, 29898, 29876, 29918, 10892, 29892, 1881, 29918, 2848, 334, 302, 29918, 29879, 6073, 29918, 12719, 29897, 13, 308, 13, 4706, 1583, 29889, 1217, 29918, 5927, 1288, 353, 694, 29918, 5927, 1288, 13, 13, 1678, 822, 19750, 29898, 1311, 29892, 921, 1125, 13, 4706, 298, 29896, 353, 383, 29889, 2674, 29884, 29898, 1311, 29889, 13801, 29896, 29898, 29916, 876, 13, 4706, 736, 1583, 29889, 13801, 29906, 29896, 29898, 29882, 29896, 511, 1583, 29889, 13801, 29906, 29906, 29898, 29882, 29896, 29897, 13, 13, 1678, 822, 337, 15501, 675, 29898, 1311, 29892, 3887, 29892, 1480, 1707, 1125, 13, 4706, 565, 1583, 29889, 1217, 29918, 5927, 1288, 29901, 13, 9651, 736, 3887, 13, 4706, 1683, 29901, 13, 9651, 3659, 353, 4842, 305, 29889, 4548, 29898, 29900, 29889, 29945, 29930, 1188, 1707, 29897, 13, 9651, 321, 567, 353, 4842, 305, 29889, 9502, 29876, 29918, 4561, 29898, 4172, 29897, 13, 9651, 736, 321, 567, 29889, 16109, 29898, 4172, 467, 1202, 23538, 2589, 29897, 13, 13, 1678, 822, 21822, 29898, 1311, 29892, 503, 1125, 13, 4706, 298, 29941, 353, 383, 29889, 2674, 29884, 29898, 1311, 29889, 13801, 29941, 29898, 29920, 876, 13, 4706, 736, 4842, 305, 29889, 18816, 29885, 3398, 29898, 1311, 29889, 13801, 29946, 29898, 29882, 29941, 876, 13, 13, 1678, 822, 6375, 29898, 1311, 29892, 921, 1125, 13, 4706, 3887, 29892, 1480, 1707, 353, 1583, 29889, 12508, 29898, 29916, 29897, 13, 4706, 503, 353, 1583, 29889, 276, 15501, 675, 29898, 2589, 29892, 1480, 1707, 29897, 13, 4706, 736, 1583, 29889, 13808, 29898, 29920, 511, 3887, 29892, 1480, 1707, 13, 13, 2 ]
training_scripts/main.py	grantsrb/planet	0	63132	<gh_stars>0 import numpy as np import os import torch import pickle import time from planet.training import * from planet.experience import ExperienceReplay from planet.agents import * from planet.models import * from planet.game_envs import GameEnv import planet.prep_fxns as prep_fxns from planet.utils import try_key import sys import json DEVICE = torch.device("cuda:0") def load_json(file_name): with open(file_name, 'r') as f: return json.load(f) def cuda_if(module): if torch.cuda.is_available(): return module.to(DEVICE) return module if __name__=="__main__": starttime = time.time() # Load hyps if len(sys.argv) < 2: hyps = load_json("hyps.json") else: hyps = load_json(sys.argv[1]) if hyps['exp_name'] == "test": hyps['n_train_loops'] = 2 hyps['n_new_sodes'] = 2 hyps['n_epochs'] = 5 if "float_params" not in hyps and "game_keys" in hyps: keys = hyps['game_keys'] hyps['float_params'] = {k:try_key(hyps,k,0) for k in keys} if "minObjLoc" not in hyps: hyps['float_params']["minObjLoc"] = 0.27 hyps['float_params']["maxObjLoc"] = 0.73 print("Float Params:", hyps['float_params']) hyps['prep_fxn'] = getattr(prep_fxns, hyps['prep_fxn']) # Initiallize saving variables save_folder = hyps['exp_name'] if not os.path.exists(save_folder): os.mkdir(save_folder) _, _, file_names = next(os.walk(save_folder)) save_num = 0 for fname in file_names: if "state_dict" in fname and "log" not in fname: num = int(fname.split("_")[-1].split(".")[0]) if num+1 > save_num: save_num = num+1 file_name = "state_dicts_"+str(save_num)+".pth" save_name = os.path.join(save_folder, file_name) # Prepare game and random agent game = GameEnv(*hyps) if len(game.obs_shape) > 1: depth = hyps['obs_depth']game.obs_shape[0] obs_shape = (depth, game.obs_shape[1:]) else: obs_shape = (hyps['obs_depth'], game.obs_shape) action_repeat = hyps['action_repeat'] discrete = hasattr(game.env.action_space, "n") rand_agent = RandnAgent(obs_shape, hyps['obs_depth'], game.a_size, action_repeat=action_repeat, discrete=discrete) # Collect Initial Data data = game.collect_sodes(rand_agent, hyps['n_sodes'], maxsize=hyps['max_steps']) if hyps['env_name'] == "Pong-v0": rew = data['rews'].sum()/(data['dones'].sum()) else: rew = data['rews'].mean() print("Baseline Rew:", rew) # Make Experience Replay exp_replay = ExperienceReplay(intl_data=data, max_size=hyps['max_steps']) # Make Models h_size = hyps['h_size'] s_size = hyps['s_size'] a_size = data['actions'].shape[-1] min_sigma = hyps['min_sigma'] obs_shape = data['observs'].shape[1:] env_name = hyps['env_name'] bnorm = hyps['bnorm'] dynamics = Dynamics(obs_shape, h_size, s_size, a_size, bnorm=bnorm, env_name=env_name, min_sigma=min_sigma) if env_name=="Pendulum-v0" or env_name == "MountainCarContinuous-v0": decoder = SimpleDecoder(dynamics.encoder.emb_shape, obs_shape, h_size, s_size, bnorm=bnorm) else: decoder = Decoder(dynamics.encoder.emb_shape, obs_shape, h_size, s_size, bnorm=bnorm) rew_model = RewardModel(h_size, s_size, bnorm=bnorm) # Put them up on the gpu dynamics = cuda_if(dynamics) decoder = cuda_if(decoder) rew_model = cuda_if(rew_model) # Make Agent obs_depth = hyps['obs_depth'] agent = DynamicsAgent(obs_shape, obs_depth, a_size, hyps, dynamics, rew_model, discrete=discrete) # Make optimizer params = list(dynamics.parameters()) + list(decoder.parameters()) + list(rew_model.parameters()) optim_type = hyps['optim_type'] lr = hyps['lr'] optimizer = getattr(torch.optim, optim_type)(params, lr=lr) # Potentially restore previous models if "load_file" in hyps and hyps['load_file'] is not None: try: state_dicts = torch.load(hyps['load_file']) dynamics.load_state_dict(state_dicts['dynamics']) decoder.load_state_dict(state_dicts['decoder']) rew_model.load_state_dict(state_dicts['rew_model']) optimizer.load_state_dict(state_dicts['optimizer']) except Exception as e: print(e) print("Failed to load state dicts") # Make Trainer trainer = Trainer(dynamics, decoder, rew_model, optimizer, exp_replay) # Training Loop best_infr_rew = -np.inf n_epochs = hyps['n_epochs'] n_train_loops = hyps['n_train_loops'] steps_seen = len(exp_replay) for epoch in range(n_epochs): log_str = "" s = "\n\nEpoch: {}".format(epoch) print(s) log_str += s + "\n" temptime = time.time() s = "Data steps: {} \| Tot running time: {}" s = s.format(steps_seen,temptime-starttime) print(s) log_str += s + "\n" for loop in range(n_train_loops): looptime = temptime losses = trainer.train(hyps) for k in losses.keys(): print(k+":", np.mean(losses[k])) temptime = time.time() print("Execution Time:", temptime-looptime) print("Saving model to:", save_name) state_dicts = { "dynamics": dynamics.state_dict(), "decoder": decoder.state_dict(), "rew_model": rew_model.state_dict(), "optimizer": optimizer.state_dict() } torch.save(state_dicts, save_name) print("\nCollecting new data...", end=" ") for _ in range(hyps['n_new_sodes']): new_sode = game.collect_sode(agent, render=hyps['render']) exp_replay.add_new_data(new_sode) steps_seen += len(new_sode['rews']) print("\| Exec Time:", time.time()-temptime) rews = new_sode['rews'] if "raw_rews" not in new_sode else new_sode['raw_rews'] infr_rew = np.mean(rews) if infr_rew > best_infr_rew: best_infr_rew = infr_rew if hyps['env_name'] == "Pong-v0": s = "Inference rews: {}".format(rews.sum()) elif hyps['env_name'] == "Pendulum-v0": quad = max(len(new_sode['rews'])//4, 1) rng = range(0,quad*4,quad) means = [str(np.mean(rews[i:i+quad])) for i in rng] strmeans = " \| ".join(means) s="Inference rews by quadrant (early->later): "+strmeans else: s = "Avg Inference Rew: {:.5f} \| Best: {}".format(infr_rew, best_infr_rew) print(s) log_str += s + "\n" s = "\nTotal running time: {}".format(time.time()-starttime) print(s) log_str += s + "\n" file_name = "log_"+str(save_num)+".txt" log_path = os.path.join(save_folder, file_name) with open(log_path,'a') as f: f.write(log_str)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 5215, 12655, 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prettyfigure/__init__.py	lue/prettyfigure	2	182459	<gh_stars>1-10 __author__ = 'sasha'	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 1649, 8921, 1649, 353, 525, 29879, 26840, 29915, 13, 2 ]
qube/postprocess/postprocess.py	ClementGeffroy/qube	1	163901	import numpy as np from scipy.signal import savgol_filter from qube.postprocess.dataset import Axis def create_name(name, suffix=None, prefix=None): elements = [] if prefix: elements.append(str(prefix)) elements.append(str(name)) if suffix: elements.append(str(suffix)) name = '_'.join(elements) return name def duplicate_dataset(dataset, suffix=None, prefix=None, custom_name=None): new_ds = dataset.copy(shallow_copy=False) if custom_name: name = custom_name else: name = dataset.name new_ds.name = create_name(name, suffix, prefix) return new_ds def remove_dim_in_axes(axes, dim=None): new_axes = [] if dim is not None: for si in axes: ax = si.copy(shallow_copy=False) if si.dim != dim and si.dim > dim: ax.dim = si.dim - 1 new_axes.append(ax) elif si.dim < dim: new_axes.append(ax) return new_axes def histogram1d(dataset, bins=10, range=None, normed=None, weights=None, density=None): ds = dataset.copy() ds.name = f'{ds.name}_hist1d' hist, bins = np.histogram( ds.value, bins=bins, range=range, normed=normed, weights=weights, density=density, ) bins = bins[:-1] # remove 1 point for ax.plot axis = Axis( name=ds.name, value=bins, unit=ds.unit, dim=0, ) ds.value = hist ds.unit = 'Counts' ds.axes = {axis.name: axis} return ds def take(dataset, indices, axis=None): ds = dataset.copy() ds.name = f'{ds.name}_take' ds.value = np.take(ds.value, indices=indices, axis=axis) old_axes = ds.get_axes(counters=False) ds.clear_axes() if axis is not None: for si in old_axes: ax = si.copy(shallow_copy=False) if si.dim == axis: ax.value = np.take(ax.value, indices=indices) ds.add_axis(ax) elif si.dim < axis or si.dim > axis: ds.add_axis(ax) return ds def mean(dataset, axis=None): ds = dataset.copy() ds.name = f'{ds.name}_mean' ds.value = np.mean(ds.value, axis=axis) old_axes = ds.get_axes(counters=False) ds.clear_axes() new_axes = remove_dim_in_axes(old_axes, axis) for ax in new_axes: ds.add_axis(ax) return ds def nanmean(dataset, axis=None): ds = dataset.copy() ds.name = f'{ds.name}_nanmean' ds.value = np.nanmean(ds.value, axis=axis) old_axes = ds.get_axes(counters=False) ds.clear_axes() new_axes = remove_dim_in_axes(old_axes, axis) for ax in new_axes: ds.add_axis(ax) return ds def subtract(dataset1, dataset2): ds1 = dataset1.copy() ds2 = dataset2 ds1.value = ds1.value - ds2.value ds1.name = f'{ds1.name}-{ds2.name}' return ds1 def gradient(dataset, axis=None, edge_order=1): ds = dataset.copy() ds.name = f'{ds.name}_grad' ds.unit = f'd {ds.unit}' ds.value = np.gradient(ds.value, axis=axis, edge_order=edge_order) old_axes = ds.get_axes(counters=False) ds.clear_axes() if axis is not None: for si in old_axes: ax = si.copy(shallow_copy=False) ds.add_axis(ax) return ds def smooth(dataset, window=5, order=3, axis=-1,kwargs): ds = dataset.copy() ds.name = f'{ds.name}_smooth' ds.value = savgol_filter(ds.value, window_length=window, polyorder=order, axis=axis,kwargs) old_axes = ds.get_axes(counters=False) ds.clear_axes() if axis is not None: for si in old_axes: ax = si.copy(shallow_copy=False) ds.add_axis(ax) return ds def fft( dataset, axis=-1, as_period=False, # return "xdata" as period instead of frequency no_dc_offset=True, # take out point at 0 frequency only_positive=True, # get only positive frequencies kwargs ): ds_amp = dataset.copy() ds_amp.name = f'{ds_amp.name}_fftamp' ds_amp.unit = f'{ds_amp.unit}' ds_pha = dataset.copy() ds_pha.name = f'{ds_pha.name}_fftpha' ds_pha.unit = f'rad' old_axes = dataset.get_axes(counters=False) ds_amp.clear_axes() ds_pha.clear_axes() if as_period: no_dc_offset = True if no_dc_offset: ind0 = 1 else: ind0 = 0 axs = [] for old_axis in old_axes: ax = old_axis.copy(shallow_copy=False) if ax.dim == axis: N = len(ax.value) Nhalf = int(N/2) if only_positive: ind1 = Nhalf else: ind1 = N xdata_freq = np.fft.fftfreq(len(ax.value), np.abs(ax.value[1] - ax.value[0]))[ind0:ind1] if not as_period: ax.name = f'{ax.name}_fftfreq' ax.unit = f'1/{ax.unit}' ax.value = xdata_freq print(ax.unit) else: ax.name = f'{ax.name}_fftper' ax.unit = f'{ax.unit}' ax.value = 1.0/xdata_freq axs.append(ax) data2analyse = np.moveaxis(dataset.value, axis, 0) value_complex = np.fft.fft(data2analyse,axis=0,kwargs) value_complex = value_complex[ind0:ind1,Ellipsis] value_complex = np.moveaxis(value_complex, 0, axis) ds_amp.value = np.abs(value_complex) ds_pha.value = np.angle(value_complex) for ax in axs: ds_amp.add_axis(ax) ds_pha.add_axis(ax) return ds_amp,ds_pha def value_mask_by_range(dataset, init, final, value, unit=None): ds = dataset.copy() ds.name = f'{ds.name}_vmasked' if init <= final: f1 = np.greater_equal f2 = np.less_equal else: f1 = np.less_equal f2 = np.greater_equal idxs_1 = f1(ds.value, init) idxs_2 = f2(ds.value, final) idxs = np.logical_and(idxs_1, idxs_2) new_value = np.array(ds.value) new_value[idxs] = value ds.value = new_value ds.unit = unit return ds def value_mask_by_bounds(dataset, bounds, values, unit=None): ds = dataset.copy() ds.name = f'{ds.name}_vmasked' " Verify length of bounds and values" bounds = np.array(bounds) values = np.array(values) valid_length = len(values) == len(bounds) + 1 if not valid_length: raise ValueError('len(values) must be len(bounds) + 1)') " Verify that bounds increase or decrease " comparison = [left < right for left, right in zip(bounds[0:-1], bounds[1:])] comparison = np.array(comparison) reduced_comp = list(set(comparison)) valid_slope = len(reduced_comp) == 1 if not valid_slope: raise ValueError('bounds must increase or decrease') " Sort bounds and values to increase " if reduced_comp[0] is False: idxs_sorted = np.argsort(bounds) bounds = np.sort(bounds) values = values[idxs_sorted] " Apply mask " n_bulk = len(values) - 2 new_values = np.zeros_like(ds.value) for i, vi in enumerate(values): if i == 0: idxs = np.less(ds.value, bounds[i]) elif i < n_bulk: idxs_left = np.greater_equal(ds.value, bounds[i - 1]) idxs_right = np.less_equal(ds.value, bounds[i]) idxs = np.logical_and(idxs_left, idxs_right) else: idxs = np.greater(ds.value, bounds[i - 1]) new_values[idxs] = vi ds.value = new_values ds.unit = unit return ds def boolmask(dataset, value, key='=='): ds = dataset.copy() ds.name = f'{ds.name}_bmasked' if key == '==': ds.value = ds.value == value if key == '>=': ds.value = ds.value >= value if key == '<=': ds.value = ds.value <= value if key == '!=': ds.value = ds.value != value if key == '>': ds.value = ds.value > value if key == '<': ds.value = ds.value < value ds.unit = 'boolean' return ds def probability(dataset, value, key='==', axis=None): ds = dataset.copy() ds.name = f'{ds.name}_prob' ds_bool = boolmask(ds, value, key=key) boolv = ds_bool.value ds.value = np.apply_along_axis(_prob, axis=axis, arr=boolv) ds.unit = '%' old_axes = ds.get_axes(counters=False) ds.clear_axes() new_axes = remove_dim_in_axes(old_axes, dim=axis) for ax in new_axes: ds.add_axis(ax) return ds def _prob(arr): total_counts = arr.size nonzero_counts = np.count_nonzero(arr) if total_counts >= 0: prob = 1. * nonzero_counts / total_counts * 100. else: prob = 0 return prob if __name__ == '__main__': pass bonds = [-1, 0, 1, 2] values = [0, 1, 2, 3, 4]	[ 1, 1053, 12655, 408, 7442, 13, 3166, 4560, 2272, 29889, 25436, 1053, 4048, 29887, 324, 29918, 4572, 13, 13, 3166, 439, 915, 29889, 2490, 5014, 29889, 24713, 1053, 319, 11497, 13, 13, 1753, 1653, 29918, 978, 29898, 978, 29892, 25557, 29922, 8516, 29892, 10944, 29922, 8516, 1125, 13, 1678, 3161, 353, 5159, 13, 1678, 565, 10944, 29901, 13, 4706, 3161, 29889, 4397, 29898, 710, 29898, 13506, 876, 13, 1678, 3161, 29889, 4397, 29898, 710, 29898, 978, 876, 13, 1678, 565, 25557, 29901, 13, 4706, 3161, 29889, 4397, 29898, 710, 29898, 2146, 600, 861, 876, 13, 1678, 1024, 353, 22868, 4286, 7122, 29898, 17664, 29897, 13, 1678, 736, 1024, 13, 13, 13, 1753, 7929, 29918, 24713, 29898, 24713, 29892, 25557, 29922, 8516, 29892, 10944, 29922, 8516, 29892, 2888, 29918, 978, 29922, 8516, 1125, 13, 1678, 716, 29918, 6289, 353, 8783, 29889, 8552, 29898, 845, 9536, 29918, 8552, 29922, 8824, 29897, 13, 1678, 565, 2888, 29918, 978, 29901, 13, 4706, 1024, 353, 2888, 29918, 978, 13, 1678, 1683, 29901, 13, 4706, 1024, 353, 8783, 29889, 978, 13, 1678, 716, 29918, 6289, 29889, 978, 353, 1653, 29918, 978, 29898, 978, 29892, 25557, 29892, 10944, 29897, 13, 1678, 736, 716, 29918, 6289, 13, 13, 13, 1753, 3349, 29918, 6229, 29918, 262, 29918, 1165, 267, 29898, 1165, 267, 29892, 3964, 29922, 8516, 1125, 13, 1678, 716, 29918, 1165, 267, 353, 5159, 13, 1678, 565, 3964, 338, 451, 6213, 29901, 13, 4706, 363, 1354, 297, 27815, 29901, 13, 9651, 4853, 353, 1354, 29889, 8552, 29898, 845, 9536, 29918, 8552, 29922, 8824, 29897, 13, 9651, 565, 1354, 29889, 6229, 2804, 3964, 322, 1354, 29889, 6229, 1405, 3964, 29901, 13, 18884, 4853, 29889, 6229, 353, 1354, 29889, 6229, 448, 29871, 29896, 13, 18884, 716, 29918, 1165, 267, 29889, 4397, 29898, 1165, 29897, 13, 9651, 25342, 1354, 29889, 6229, 529, 3964, 29901, 13, 18884, 716, 29918, 1165, 267, 29889, 4397, 29898, 1165, 29897, 13, 1678, 736, 716, 29918, 1165, 267, 13, 13, 13, 1753, 9825, 13342, 29896, 29881, 29898, 24713, 29892, 289, 1144, 29922, 29896, 29900, 29892, 3464, 29922, 8516, 29892, 6056, 287, 29922, 8516, 29892, 18177, 29922, 8516, 29892, 9027, 29922, 8516, 1125, 13, 1678, 18031, 353, 8783, 29889, 8552, 580, 13, 1678, 18031, 29889, 978, 353, 285, 29915, 29912, 6289, 29889, 978, 2403, 29882, 391, 29896, 29881, 29915, 13, 1678, 9825, 29892, 289, 1144, 353, 7442, 29889, 29882, 391, 13342, 29898, 13, 4706, 18031, 29889, 1767, 29892, 13, 4706, 289, 1144, 29922, 29890, 1144, 29892, 13, 4706, 3464, 29922, 3881, 29892, 13, 4706, 6056, 287, 29922, 12324, 287, 29892, 13, 4706, 18177, 29922, 705, 5861, 29892, 13, 4706, 9027, 29922, 21518, 537, 29892, 13, 1678, 1723, 13, 1678, 289, 1144, 353, 289, 1144, 7503, 29899, 29896, 29962, 29871, 396, 3349, 29871, 29896, 1298, 363, 4853, 29889, 5317, 13, 13, 1678, 9685, 353, 319, 11497, 29898, 13, 4706, 1024, 29922, 6289, 29889, 978, 29892, 13, 4706, 995, 29922, 29890, 1144, 29892, 13, 4706, 5190, 29922, 6289, 29889, 5441, 29892, 13, 4706, 3964, 29922, 29900, 29892, 13, 1678, 1723, 13, 1678, 18031, 29889, 1767, 353, 9825, 13, 1678, 18031, 29889, 5441, 353, 525, 3981, 29879, 29915, 13, 1678, 18031, 29889, 1165, 267, 353, 426, 8990, 29889, 978, 29901, 9685, 29913, 13, 1678, 736, 18031, 13, 13, 13, 1753, 2125, 29898, 24713, 29892, 16285, 29892, 9685, 29922, 8516, 1125, 13, 1678, 18031, 353, 8783, 29889, 8552, 580, 13, 1678, 18031, 29889, 978, 353, 285, 29915, 29912, 6289, 29889, 978, 2403, 19730, 29915, 13, 1678, 18031, 29889, 1767, 353, 7442, 29889, 19730, 29898, 6289, 29889, 1767, 29892, 16285, 29922, 513, 1575, 29892, 9685, 29922, 8990, 29897, 13, 1678, 2030, 29918, 1165, 267, 353, 18031, 29889, 657, 29918, 1165, 267, 29898, 29883, 1309, 2153, 29922, 8824, 29897, 13, 1678, 18031, 29889, 8551, 29918, 1165, 267, 580, 13, 1678, 565, 9685, 338, 451, 6213, 29901, 13, 4706, 363, 1354, 297, 2030, 29918, 1165, 267, 29901, 13, 9651, 4853, 353, 1354, 29889, 8552, 29898, 845, 9536, 29918, 8552, 29922, 8824, 29897, 13, 9651, 565, 1354, 29889, 6229, 1275, 9685, 29901, 13, 18884, 4853, 29889, 1767, 353, 7442, 29889, 19730, 29898, 1165, 29889, 1767, 29892, 16285, 29922, 513, 1575, 29897, 13, 18884, 18031, 29889, 1202, 29918, 8990, 29898, 1165, 29897, 13, 9651, 25342, 1354, 29889, 6229, 529, 9685, 470, 1354, 29889, 6229, 1405, 9685, 29901, 13, 18884, 18031, 29889, 1202, 29918, 8990, 29898, 1165, 29897, 13, 1678, 736, 18031, 13, 13, 13, 1753, 2099, 29898, 24713, 29892, 9685, 29922, 8516, 1125, 13, 1678, 18031, 353, 8783, 29889, 8552, 580, 13, 1678, 18031, 29889, 978, 353, 285, 29915, 29912, 6289, 29889, 978, 2403, 12676, 29915, 13, 1678, 18031, 29889, 1767, 353, 7442, 29889, 12676, 29898, 6289, 29889, 1767, 29892, 9685, 29922, 8990, 29897, 13, 1678, 2030, 29918, 1165, 267, 353, 18031, 29889, 657, 29918, 1165, 267, 29898, 29883, 1309, 2153, 29922, 8824, 29897, 13, 1678, 18031, 29889, 8551, 29918, 1165, 267, 580, 13, 1678, 716, 29918, 1165, 267, 353, 3349, 29918, 6229, 29918, 262, 29918, 1165, 267, 29898, 1025, 29918, 1165, 267, 29892, 9685, 29897, 13, 1678, 363, 4853, 297, 716, 29918, 1165, 267, 29901, 13, 4706, 18031, 29889, 1202, 29918, 8990, 29898, 1165, 29897, 13, 1678, 736, 18031, 13, 13, 13, 1753, 23432, 12676, 29898, 24713, 29892, 9685, 29922, 8516, 1125, 13, 1678, 18031, 353, 8783, 29889, 8552, 580, 13, 1678, 18031, 29889, 978, 353, 285, 29915, 29912, 6289, 29889, 978, 2403, 13707, 12676, 29915, 13, 1678, 18031, 29889, 1767, 353, 7442, 29889, 13707, 12676, 29898, 6289, 29889, 1767, 29892, 9685, 29922, 8990, 29897, 13, 1678, 2030, 29918, 1165, 267, 353, 18031, 29889, 657, 29918, 1165, 267, 29898, 29883, 1309, 2153, 29922, 8824, 29897, 13, 1678, 18031, 29889, 8551, 29918, 1165, 267, 580, 13, 1678, 716, 29918, 1165, 267, 353, 3349, 29918, 6229, 29918, 262, 29918, 1165, 267, 29898, 1025, 29918, 1165, 267, 29892, 9685, 29897, 13, 1678, 363, 4853, 297, 716, 29918, 1165, 267, 29901, 13, 4706, 18031, 29889, 1202, 29918, 8990, 29898, 1165, 29897, 13, 1678, 736, 18031, 13, 13, 13, 1753, 23197, 29898, 24713, 29896, 29892, 8783, 29906, 1125, 13, 1678, 18031, 29896, 353, 8783, 29896, 29889, 8552, 580, 13, 1678, 18031, 29906, 353, 8783, 29906, 13, 1678, 18031, 29896, 29889, 1767, 353, 18031, 29896, 29889, 1767, 448, 18031, 29906, 29889, 1767, 13, 1678, 18031, 29896, 29889, 978, 353, 285, 29915, 29912, 6289, 29896, 29889, 978, 7402, 29912, 6289, 29906, 29889, 978, 10162, 13, 1678, 736, 18031, 29896, 13, 13, 13, 1753, 16030, 29898, 24713, 29892, 9685, 29922, 8516, 29892, 7636, 29918, 2098, 29922, 29896, 1125, 13, 1678, 18031, 353, 8783, 29889, 8552, 580, 13, 1678, 18031, 29889, 978, 353, 285, 29915, 29912, 6289, 29889, 978, 2403, 5105, 29915, 13, 1678, 18031, 29889, 5441, 353, 285, 29915, 29881, 426, 6289, 29889, 5441, 10162, 13, 1678, 18031, 29889, 1767, 353, 7442, 29889, 24970, 29898, 6289, 29889, 1767, 29892, 9685, 29922, 8990, 29892, 7636, 29918, 2098, 29922, 12864, 29918, 2098, 29897, 13, 1678, 2030, 29918, 1165, 267, 353, 18031, 29889, 657, 29918, 1165, 267, 29898, 29883, 1309, 2153, 29922, 8824, 29897, 13, 1678, 18031, 29889, 8551, 29918, 1165, 267, 580, 13, 1678, 565, 9685, 338, 451, 6213, 29901, 13, 4706, 363, 1354, 297, 2030, 29918, 1165, 267, 29901, 13, 9651, 4853, 353, 1354, 29889, 8552, 29898, 845, 9536, 29918, 8552, 29922, 8824, 29897, 13, 9651, 18031, 29889, 1202, 29918, 8990, 29898, 1165, 29897, 13, 1678, 736, 18031, 13, 13, 1753, 10597, 29898, 24713, 29892, 3474, 29922, 29945, 29892, 1797, 29922, 29941, 29892, 9685, 10457, 29896, 29892, 1068, 19290, 1125, 13, 1678, 18031, 353, 8783, 29889, 8552, 580, 13, 1678, 18031, 29889, 978, 353, 285, 29915, 29912, 6289, 29889, 978, 2403, 3844, 6983, 29915, 13, 1678, 18031, 29889, 1767, 353, 4048, 29887, 324, 29918, 4572, 29898, 6289, 29889, 1767, 29892, 3474, 29918, 2848, 29922, 7165, 29892, 15680, 2098, 29922, 2098, 29892, 9685, 29922, 8990, 29892, 1068, 19290, 29897, 13, 1678, 2030, 29918, 1165, 267, 353, 18031, 29889, 657, 29918, 1165, 267, 29898, 29883, 1309, 2153, 29922, 8824, 29897, 13, 1678, 18031, 29889, 8551, 29918, 1165, 267, 580, 13, 1678, 565, 9685, 338, 451, 6213, 29901, 13, 4706, 363, 1354, 297, 2030, 29918, 1165, 267, 29901, 13, 9651, 4853, 353, 1354, 29889, 8552, 29898, 845, 9536, 29918, 8552, 29922, 8824, 29897, 13, 9651, 18031, 29889, 1202, 29918, 8990, 29898, 1165, 29897, 13, 1678, 736, 18031, 13, 13, 1753, 285, 615, 29898, 13, 1678, 8783, 29892, 13, 1678, 9685, 10457, 29896, 29892, 29871, 13, 1678, 408, 29918, 19145, 29922, 8824, 29892, 396, 736, 376, 29916, 1272, 29908, 408, 3785, 2012, 310, 10868, 13, 1678, 694, 29918, 13891, 29918, 10289, 29922, 5574, 29892, 396, 2125, 714, 1298, 472, 29871, 29900, 10868, 13, 1678, 871, 29918, 1066, 3321, 29922, 5574, 29892, 396, 679, 871, 6374, 29511, 13, 1678, 3579, 19290, 13, 1125, 13, 1678, 18031, 29918, 1160, 353, 8783, 29889, 8552, 580, 13, 1678, 18031, 29918, 1160, 29889, 978, 353, 285, 29915, 29912, 6289, 29918, 1160, 29889, 978, 2403, 600, 29873, 1160, 29915, 13, 1678, 18031, 29918, 1160, 29889, 5441, 353, 285, 29915, 29912, 6289, 29918, 1160, 29889, 5441, 10162, 13, 1678, 18031, 29918, 2026, 353, 8783, 29889, 8552, 580, 13, 1678, 18031, 29918, 2026, 29889, 978, 353, 285, 29915, 29912, 6289, 29918, 2026, 29889, 978, 2403, 600, 29873, 2026, 29915, 13, 1678, 18031, 29918, 2026, 29889, 5441, 353, 285, 29915, 3665, 29915, 13, 268, 13, 1678, 2030, 29918, 1165, 267, 353, 8783, 29889, 657, 29918, 1165, 267, 29898, 29883, 1309, 2153, 29922, 8824, 29897, 13, 1678, 18031, 29918, 1160, 29889, 8551, 29918, 1165, 267, 580, 13, 1678, 18031, 29918, 2026, 29889, 8551, 29918, 1165, 267, 580, 13, 268, 13, 1678, 565, 408, 29918, 19145, 29901, 13, 4706, 694, 29918, 13891, 29918, 10289, 353, 5852, 13, 268, 13, 1678, 565, 694, 29918, 13891, 29918, 10289, 29901, 13, 4706, 1399, 29900, 353, 29871, 29896, 13, 1678, 1683, 29901, 13, 4706, 1399, 29900, 353, 29871, 29900, 13, 13, 1678, 4853, 29879, 353, 5159, 13, 1678, 363, 2030, 29918, 8990, 297, 2030, 29918, 1165, 267, 29901, 13, 4706, 4853, 353, 2030, 29918, 8990, 29889, 8552, 29898, 845, 9536, 29918, 8552, 29922, 8824, 29897, 13, 4706, 565, 4853, 29889, 6229, 1275, 9685, 29901, 13, 632, 13, 632, 13, 9651, 405, 353, 7431, 29898, 1165, 29889, 1767, 29897, 13, 9651, 405, 24498, 353, 938, 29898, 29940, 29914, 29906, 29897, 13, 632, 13, 9651, 565, 871, 29918, 1066, 3321, 29901, 13, 18884, 1399, 29896, 353, 405, 24498, 13, 9651, 1683, 29901, 13, 18884, 1399, 29896, 353, 405, 13, 632, 13, 9651, 921, 1272, 29918, 29888, 7971, 353, 7442, 29889, 600, 29873, 29889, 600, 13264, 7971, 29898, 2435, 29898, 1165, 29889, 1767, 511, 7442, 29889, 6897, 29898, 1165, 29889, 1767, 29961, 29896, 29962, 448, 4853, 29889, 1767, 29961, 29900, 12622, 29961, 513, 29900, 29901, 513, 29896, 29962, 13, 9651, 565, 451, 408, 29918, 19145, 29901, 13, 18884, 4853, 29889, 978, 353, 285, 29915, 29912, 1165, 29889, 978, 2403, 600, 13264, 7971, 29915, 13, 18884, 4853, 29889, 5441, 353, 285, 29915, 29896, 19248, 1165, 29889, 5441, 10162, 13, 18884, 4853, 29889, 1767, 353, 921, 1272, 29918, 29888, 7971, 13, 18884, 1596, 29898, 1165, 29889, 5441, 29897, 13, 9651, 1683, 29901, 13, 18884, 4853, 29889, 978, 353, 285, 29915, 29912, 1165, 29889, 978, 2403, 600, 29873, 546, 29915, 13, 18884, 4853, 29889, 5441, 353, 285, 29915, 29912, 1165, 29889, 5441, 10162, 13, 18884, 4853, 29889, 1767, 353, 29871, 29896, 29889, 29900, 29914, 29916, 1272, 29918, 29888, 7971, 13, 13, 4706, 4853, 29879, 29889, 4397, 29898, 1165, 29897, 308, 13, 13, 308, 13, 1678, 848, 29906, 24209, 344, 353, 7442, 29889, 11631, 8990, 29898, 24713, 29889, 1767, 29892, 9685, 29892, 29871, 29900, 29897, 13, 1678, 995, 29918, 19676, 353, 7442, 29889, 600, 29873, 29889, 600, 29873, 29898, 1272, 29906, 24209, 344, 29892, 8990, 29922, 29900, 29892, 1068, 19290, 29897, 13, 1678, 995, 29918, 19676, 353, 995, 29918, 19676, 29961, 513, 29900, 29901, 513, 29896, 29892, 6489, 492, 567, 275, 29962, 13, 1678, 995, 29918, 19676, 353, 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picoCTF/cryptography/credstuff/examplecode.py	Estheryu991/CaptureTheFlag	0	161337	#this code is just an example from 2018 nur luege / from flask import Flask, request, render_template, abort import os, requests app = Flask(__name__) class user: def __init__(self, username, password): self.username = username self.__password = password self.files = [] def getPass(self): return self.__password users = {} users["admin"] = user("admin", os.environ["FLAG"]) def custom500(error): return str(error), 500 @app.route("/", methods=["GET", "POST"]) def mainpage(): if request.method == "POST": if request.form["action"] == "Login": if request.form["username"] in users: if request.form["password"] == users[request.form["username"]].getPass(): return render_template("index.html", user=users[request.form["username"]]) return "wrong password" return "user does not exist" elif request.form["action"] == "Signup": if request.form["username"] not in users: users[request.form["username"]] = user(request.form["username"], request.form["password"]) return render_template("index.html", user=users[request.form["username"]]) else: return "user already exists" elif request.form["action"] == "Add File": return addfile() return render_template("loggedout.html") #beta feature for viewing info about other users - still testing @app.route("/user/<username>", methods=['POST']) def getInfo(username): val = getattr(users[username], request.form['field'], None) if val != None: return val else: return "error" @app.route("/files/<path:file>", methods=["GET"]) def getFile(file): if "index.py" in file: return "no! bad user! bad!" return open(file, "rb").read() def addfile(): if users[request.form["username"]].getPass() == request.form["password"]: if request.form['url'][-1] == "/": downloadurl = request.form['url'][:-1] else: downloadurl = request.form['url'] if downloadurl.split("/")[-1] in os.listdir("."): return "file already exists" file = requests.get(downloadurl, stream=True) f = 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example/openpose/pose-paf-master/tf_data_aug.py	boldjoel/tensorlayer	0	75725	import math ####### import random import cv2 import numpy as np import matplotlib.pyplot as plt from tensorpack.dataflow.imgaug.geometry import RotationAndCropValid def crop_meta_image(image,annos,mask): _target_height=368 _target_width =368 if len(np.shape(image))==2: image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB) height,width,_=np.shape(image) # print("the size of original img is:", height, width) if height<=width: ratio=_target_height/height new_width=int(ratiowidth) if height==width: new_width=_target_height image,annos,mask=_resize_image(image,annos,mask,new_width,_target_height) for i in annos: if len(i) is not 19: print('Joints of person is not 19 ERROR FROM RESIZE') if new_width>_target_width: crop_range_x=np.random.randint(0, new_width-_target_width) else: crop_range_x=0 image= image[:, crop_range_x:crop_range_x + 368,:] mask = mask[:, crop_range_x:crop_range_x + 368] # joint_list= [] new_joints = [] #annos-pepople-joints (must be 19 or []) for people in annos: # print("number of keypoints is", np.shape(people)) new_keypoints = [] for keypoints in people: if keypoints[0] < -10 or keypoints[1] < -10: new_keypoints.append((-1000, -1000)) continue top=crop_range_x+367 if keypoints[0]>=crop_range_x and keypoints[0]<= top: # pts = (keypoints[0]-crop_range_x, keypoints[1]) pts = (int(keypoints[0] - crop_range_x),int(keypoints[1])) else: pts= (-1000,-1000) new_keypoints.append(pts) new_joints.append(new_keypoints) if len(new_keypoints) != 19: print('1:The Length of joints list should be 0 or 19 but actually:', len(new_keypoints)) annos = new_joints if height>width: ratio = _target_width / width new_height = int(ratio height) image,annos,mask = _resize_image(image,annos,mask,_target_width, new_height) for i in annos: if len(i) is not 19: print('Joints of person is not 19 ERROR') if new_height > _target_height: crop_range_y = np.random.randint(0, new_height - _target_height) else: crop_range_y = 0 image = image[crop_range_y:crop_range_y + 368, :, :] mask = mask[crop_range_y:crop_range_y + 368, :] new_joints = [] for people in annos: new_keypoints = [] for keypoints in people: # case orginal points are not usable if keypoints[0] < 0 or keypoints[1] < 0: new_keypoints.append((-1000, -1000)) continue # y axis coordinate change bot = crop_range_y + 367 if keypoints[1] >= crop_range_y and keypoints[1] <= bot: # pts = (keypoints[0], keypoints[1]-crop_range_y) pts = (int(keypoints[0]), int (keypoints[1] - crop_range_y)) # if pts[0]>367 or pts[1]>367: # print('Error2') else: pts =(-1000,-1000) new_keypoints.append(pts) new_joints.append(new_keypoints) if len(new_keypoints) != 19: print('2:The Length of joints list should be 0 or 19 but actually:', len(new_keypoints)) annos = new_joints # mask = cv2.resize(mask, (46, 46), interpolation=cv2.INTER_AREA) return image,annos,mask def _resize_image(image,annos,mask,_target_width,_target_height): # _target_height=368 # _target_width =368 #original image y,x,_=np.shape(image) ratio_y= _target_height/y ratio_x= _target_width/x new_joints=[] # update meta # meta.height=_target_height # meta.width =_target_width for people in annos: new_keypoints=[] for keypoints in people: if keypoints[0]<0 or keypoints[1]<0: new_keypoints.append((-1000, -1000)) continue pts = (int(keypoints[0] * ratio_x+0.5), int(keypoints[1] * ratio_y+0.5)) if pts[0] > _target_width-1 or pts[1] > _target_height-1: new_keypoints.append((-1000, -1000)) continue new_keypoints.append(pts) new_joints.append(new_keypoints) annos=new_joints new_image = cv2.resize(image, (_target_width, _target_height), interpolation=cv2.INTER_AREA) new_mask = cv2.resize(mask, (_target_width, _target_height), interpolation=cv2.INTER_AREA) return new_image,annos,new_mask def _rotate_coord(shape, newxy, point, angle): angle = -1 * angle / 180.0 * math.pi ox, oy = shape px, py = point ox /= 2 oy /= 2 qx = math.cos(angle) * (px - ox) - math.sin(angle) * (py - oy) qy = math.sin(angle) * (px - ox) + math.cos(angle) * (py - oy) new_x, new_y = newxy qx += ox - new_x qy += oy - new_y return int(qx + 0.5), int(qy + 0.5) def pose_rotation(image,annos,mask): img_shape=np.shape(image) height = img_shape[0] width = img_shape[1] deg = random.uniform(-15.0, 15.0) img = image center = (img.shape[1] * 0.5, img.shape[0] * 0.5) # x, y rot_m = cv2.getRotationMatrix2D((int(center[0]), int(center[1])), deg, 1) ret = cv2.warpAffine(img, rot_m, img.shape[1::-1], flags=cv2.INTER_AREA, borderMode=cv2.BORDER_CONSTANT) if img.ndim == 3 and ret.ndim == 2: ret = ret[:, :, np.newaxis] neww, newh = RotationAndCropValid.largest_rotated_rect(ret.shape[1], ret.shape[0], deg) neww = min(neww, ret.shape[1]) newh = min(newh, ret.shape[0]) newx = int(center[0] - neww * 0.5) newy = int(center[1] - newh * 0.5) # print(ret.shape, deg, newx, newy, neww, newh) img = ret[newy:newy + newh, newx:newx + neww] # adjust meta data adjust_joint_list = [] for joint in annos: adjust_joint = [] for point in joint: if point[0] < -100 or point[1] < -100: adjust_joint.append((-1000, -1000)) continue # if point[0] <= 0 or point[1] <= 0: # adjust_joint.append((-1, -1)) # continue x, y = _rotate_coord((width, height), (newx, newy), point, deg) # if x > neww or y > newh: # adjust_joint.append((-1000, -1000)) # continue if x>neww-1 or y>newh-1: adjust_joint.append((-1000, -1000)) continue if x < 0 or y < 0: adjust_joint.append((-1000, -1000)) continue adjust_joint.append((x, y)) adjust_joint_list.append(adjust_joint) joint_list = adjust_joint_list msk = mask center = (msk.shape[1] * 0.5, msk.shape[0] * 0.5) # x, y rot_m = cv2.getRotationMatrix2D((int(center[0]), int(center[1])), deg, 1) ret = cv2.warpAffine(msk, rot_m, msk.shape[1::-1], flags=cv2.INTER_AREA, borderMode=cv2.BORDER_CONSTANT) if msk.ndim == 3 and msk.ndim == 2: ret = ret[:, :, np.newaxis] neww, newh = RotationAndCropValid.largest_rotated_rect(ret.shape[1], ret.shape[0], deg) neww = min(neww, ret.shape[1]) newh = min(newh, ret.shape[0]) newx = int(center[0] - neww * 0.5) newy = int(center[1] - newh * 0.5) # print(ret.shape, deg, newx, newy, neww, newh) msk = ret[newy:newy + newh, newx:newx + neww] return img, joint_list, msk def random_flip(image,annos,mask_miss): flip_list=[0, 1, 5, 6, 7, 2, 3, 4, 11, 12, 13, 8, 9, 10, 15, 14, 17, 16, 18] prob = random.uniform(0, 1.0) if prob > 0.5: return image,annos,mask_miss _, width, _ = np.shape(image) image = cv2.flip(image, 1) mask_miss=cv2.flip(mask_miss,1) new_joints = [] for people in annos: new_keypoints = [] for k in flip_list: point=people[k] if point[0] < 0 or point[1] < 0: new_keypoints.append((-1000, -1000)) continue if point[0]>image.shape[1]-1 or point[1]>image.shape[0]-1: new_keypoints.append((-1000, -1000)) continue if (width - point[0])>image.shape[1]-1: new_keypoints.append((-1000, -1000)) continue new_keypoints.append((width - point[0], point[1])) new_joints.append(new_keypoints) annos=new_joints return image, annos, mask_miss def pose_random_scale(image,annos,mask_miss): height=image.shape[0] width =image.shape[1] scalew = np.random.uniform(0.8, 1.2) scaleh = np.random.uniform(0.8, 1.2) # scalew =scaleh=np.random.uniform(0.5, 1.1) # scaleh=0.8934042054560039 # scalew=1.0860957314059887 neww = int(width * scalew) newh = int(height * scaleh) dst = cv2.resize(image, (neww, newh), interpolation=cv2.INTER_AREA) mask_miss=cv2.resize(mask_miss, (neww, newh), interpolation=cv2.INTER_AREA) # adjust meta data adjust_joint_list = [] for joint in annos: adjust_joint = [] for point in joint: if point[0] < -100 or point[1] < -100: adjust_joint.append((-1000, -1000)) continue # if point[0] <= 0 or point[1] <= 0 or int(point[0] * scalew + 0.5) > neww or int( # point[1] * scaleh + 0.5) > newh: # adjust_joint.append((-1, -1)) # continue adjust_joint.append((int(point[0] * scalew + 0.5), int(point[1] * scaleh + 0.5))) adjust_joint_list.append(adjust_joint) return dst,adjust_joint_list,mask_miss def pose_resize_shortestedge_random(image,annos, mask): _target_height = 368 _target_width = 368 if len(np.shape(image))==2: image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB) height,width,_=np.shape(image) ratio_w = _target_width / width ratio_h = _target_height / height ratio = min(ratio_w, ratio_h) target_size = int(min(width * ratio + 0.5, height * ratio + 0.5)) random_target=random.uniform(0.95, 1.6) # random_target=1.1318003767113862 target_size = int(target_size * random_target) # target_size = int(min(_network_w, _network_h) * random.uniform(0.7, 1.5)) return pose_resize_shortestedge(image, annos, mask, target_size) def pose_resize_shortestedge(image, annos,mask,target_size): _target_height = 368 _target_width = 368 img=image height, width, _ = np.shape(image) # adjust image scale = target_size / min(height, width) if height < width: newh, neww = target_size, int(scale * width + 0.5) else: newh, neww = int(scale * height + 0.5), target_size dst = cv2.resize(img, (neww, newh), interpolation=cv2.INTER_AREA) mask = cv2.resize(mask, (neww, newh), interpolation=cv2.INTER_AREA) pw = ph = 0 if neww < _target_width or newh < _target_height: pw = max(0, (_target_width - neww) // 2) ph = max(0, (_target_height - newh) // 2) mw = (_target_width - neww) % 2 mh = (_target_height - newh) % 2 color = np.random.uniform(0.0, 1.0) dst = cv2.copyMakeBorder(dst, ph, ph + mh, pw, pw + mw, cv2.BORDER_CONSTANT, value=(0,0 ,color)) mask = cv2.copyMakeBorder(mask, ph, ph + mh, pw, pw + mw, cv2.BORDER_CONSTANT, value=1) # adjust meta data adjust_joint_list = [] for joint in annos: adjust_joint = [] for point in joint: if point[0] < -100 or point[1] < -100: adjust_joint.append((-1000, -1000)) continue # if point[0] <= 0 or point[1] <= 0 or int(point[0]scale+0.5) > neww or int(point[1]scale+0.5) > newh: # adjust_joint.append((-1, -1)) # continue adjust_joint.append((int(point[0] * scale + 0.5) + pw, int(point[1] * scale + 0.5) + ph)) adjust_joint_list.append(adjust_joint) return dst,adjust_joint_list,mask def pose_crop_random(image,annos,mask): _target_height = 368 _target_width = 368 target_size = (_target_width, _target_height) if len(np.shape(image))==2: image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB) height,width,_=np.shape(image) for _ in range(50): x = random.randrange(0, width - target_size[0]) if width > target_size[0] else 0 y = random.randrange(0, height - target_size[1]) if height > target_size[1] else 0 # check whether any face is inside the box to generate a reasonably-balanced datasets for joint in annos: if x <= joint[0][0] < x + target_size[0] and y <= joint[0][1] < y + target_size[1]: break return pose_crop(image,annos,mask, x, y, target_size[0], target_size[1]) def pose_crop(image,annos,mask, x, y, w, h): # adjust image target_size = (w, h) img = image resized = img[y:y+target_size[1], x:x+target_size[0], :] resized_mask = mask[y:y + target_size[1], x:x + target_size[0]] # adjust meta data adjust_joint_list = [] for joint in annos: adjust_joint = [] for point in joint: if point[0] < -10 or point[1] < -10: adjust_joint.append((-1000, -1000)) continue # if point[0] <= 0 or point[1] <= 0: # adjust_joint.append((-1000, -1000)) # continue new_x, new_y = point[0] - x, point[1] - y # if new_x <= 0 or new_y <= 0 or new_x > target_size[0] or new_y > target_size[1]: # adjust_joint.append((-1, -1)) # continue if new_x > 367 or new_y > 367: adjust_joint.append((-1000, -1000)) continue adjust_joint.append((new_x, new_y)) adjust_joint_list.append(adjust_joint) return resized,adjust_joint_list,resized_mask def drawing(image, annos): plt.imshow(image) for j in annos: for i in j: if i[0]>0 and i[1]>0: plt.scatter(i[0],i[1]) plt.savefig('fig/'+str(i)+'.jpg', dpi=100) plt.show()	[ 1, 1053, 5844, 13, 4136, 2277, 29937, 13, 5215, 4036, 13, 5215, 13850, 29906, 13, 5215, 12655, 408, 7442, 13, 5215, 22889, 29889, 2272, 5317, 408, 14770, 13, 3166, 12489, 4058, 29889, 1272, 1731, 29889, 2492, 2987, 29889, 19156, 1053, 9664, 362, 2855, 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website/addons/dataverse/views/widget.py	bdyetton/prettychart	0	140569	<gh_stars>0 import httplib as http from website.addons.dataverse.client import connect_from_settings_or_401, \ get_dataverse, get_dataset from website.project.decorators import must_be_contributor_or_public, \ must_have_addon @must_be_contributor_or_public @must_have_addon('dataverse', 'node') def dataverse_widget(node_addon, kwargs): node = node_addon.owner widget_url = node.api_url_for('dataverse_get_widget_contents') ret = { 'complete': node_addon.complete, 'widget_url': widget_url, } ret.update(node_addon.config.to_json()) return ret, http.OK @must_be_contributor_or_public @must_have_addon('dataverse', 'node') def dataverse_get_widget_contents(node_addon, kwargs): data = { 'connected': False, } if not node_addon.complete: return {'data': data}, http.OK doi = node_addon.dataset_doi alias = node_addon.dataverse_alias connection = connect_from_settings_or_401(node_addon) dataverse = get_dataverse(connection, alias) dataset = get_dataset(dataverse, doi) if dataset is None: return {'data': data}, http.BAD_REQUEST dataverse_host = node_addon.external_account.oauth_key dataverse_url = 'http://{0}/dataverse/{1}'.format(dataverse_host, alias) dataset_url = 'http://dx.doi.org/' + doi data.update({ 'connected': True, 'dataverse': node_addon.dataverse, 'dataverseUrl': dataverse_url, 'dataset': node_addon.dataset, 'doi': doi, 'datasetUrl': dataset_url, 'citation': dataset.citation, }) return {'data': data}, http.OK	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 5215, 1732, 1982, 408, 1732, 13, 13, 3166, 4700, 29889, 1202, 787, 29889, 1272, 3901, 29889, 4645, 1053, 4511, 29918, 3166, 29918, 11027, 29918, 272, 29918, 29946, 29900, 29896, 29892, 320, 13, 1678, 679, 29918, 1272, 3901, 29892, 679, 29918, 24713, 13, 3166, 4700, 29889, 4836, 29889, 19557, 4097, 1053, 1818, 29918, 915, 29918, 21570, 3406, 29918, 272, 29918, 3597, 29892, 320, 13, 1678, 1818, 29918, 17532, 29918, 1202, 265, 13, 13, 13, 29992, 21969, 29918, 915, 29918, 21570, 3406, 29918, 272, 29918, 3597, 13, 29992, 21969, 29918, 17532, 29918, 1202, 265, 877, 1272, 3901, 742, 525, 3177, 1495, 13, 1753, 848, 3901, 29918, 8030, 29898, 3177, 29918, 1202, 265, 29892, 3579, 19290, 1125, 13, 13, 1678, 2943, 353, 2943, 29918, 1202, 265, 29889, 20348, 13, 1678, 11109, 29918, 2271, 353, 2943, 29889, 2754, 29918, 2271, 29918, 1454, 877, 1272, 3901, 29918, 657, 29918, 8030, 29918, 10853, 1495, 13, 13, 1678, 3240, 353, 426, 13, 4706, 525, 8835, 2396, 2943, 29918, 1202, 265, 29889, 8835, 29892, 13, 4706, 525, 8030, 29918, 2271, 2396, 11109, 29918, 2271, 29892, 13, 1678, 500, 13, 1678, 3240, 29889, 5504, 29898, 3177, 29918, 1202, 265, 29889, 2917, 29889, 517, 29918, 3126, 3101, 13, 13, 1678, 736, 3240, 29892, 1732, 29889, 8949, 13, 13, 13, 29992, 21969, 29918, 915, 29918, 21570, 3406, 29918, 272, 29918, 3597, 13, 29992, 21969, 29918, 17532, 29918, 1202, 265, 877, 1272, 3901, 742, 525, 3177, 1495, 13, 1753, 848, 3901, 29918, 657, 29918, 8030, 29918, 10853, 29898, 3177, 29918, 1202, 265, 29892, 3579, 19290, 1125, 13, 13, 1678, 848, 353, 426, 13, 4706, 525, 18045, 2396, 7700, 29892, 13, 1678, 500, 13, 13, 1678, 565, 451, 2943, 29918, 1202, 265, 29889, 8835, 29901, 13, 4706, 736, 11117, 1272, 2396, 848, 1118, 1732, 29889, 8949, 13, 13, 1678, 13102, 353, 2943, 29918, 1202, 265, 29889, 24713, 29918, 1867, 29875, 13, 1678, 13995, 353, 2943, 29918, 1202, 265, 29889, 1272, 3901, 29918, 19973, 13, 13, 1678, 3957, 353, 4511, 29918, 3166, 29918, 11027, 29918, 272, 29918, 29946, 29900, 29896, 29898, 3177, 29918, 1202, 265, 29897, 13, 1678, 848, 3901, 353, 679, 29918, 1272, 3901, 29898, 9965, 29892, 13995, 29897, 13, 1678, 8783, 353, 679, 29918, 24713, 29898, 1272, 3901, 29892, 13102, 29897, 13, 13, 1678, 565, 8783, 338, 6213, 29901, 13, 4706, 736, 11117, 1272, 2396, 848, 1118, 1732, 29889, 29933, 3035, 29918, 16244, 13, 13, 1678, 848, 3901, 29918, 3069, 353, 2943, 29918, 1202, 265, 29889, 23176, 29918, 10149, 29889, 23106, 29918, 1989, 13, 1678, 848, 3901, 29918, 2271, 353, 525, 1124, 597, 29912, 29900, 6822, 1272, 3901, 19248, 29896, 29913, 4286, 4830, 29898, 1272, 3901, 29918, 3069, 29892, 13995, 29897, 13, 1678, 8783, 29918, 2271, 353, 525, 1124, 597, 8235, 29889, 1867, 29875, 29889, 990, 22208, 718, 13102, 13, 13, 1678, 848, 29889, 5504, 3319, 13, 4706, 525, 18045, 2396, 5852, 29892, 13, 4706, 525, 1272, 3901, 2396, 2943, 29918, 1202, 265, 29889, 1272, 3901, 29892, 13, 4706, 525, 1272, 3901, 5983, 2396, 848, 3901, 29918, 2271, 29892, 13, 4706, 525, 24713, 2396, 2943, 29918, 1202, 265, 29889, 24713, 29892, 13, 4706, 525, 1867, 29875, 2396, 13102, 29892, 13, 4706, 525, 24713, 5983, 2396, 8783, 29918, 2271, 29892, 13, 4706, 525, 29883, 7018, 2396, 8783, 29889, 29883, 7018, 29892, 13, 1678, 5615, 13, 1678, 736, 11117, 1272, 2396, 848, 1118, 1732, 29889, 8949, 13, 2 ]
_internal/generate.py	damz/gopherjs-feather-icons	0	183393	<reponame>damz/gopherjs-feather-icons import os import glob import lxml.etree import subprocess if __name__ == "__main__": parser = lxml.etree.XMLParser(remove_blank_text=True) def normalize_xml(data): root = lxml.etree.XML(data, parser=parser) root.insert( 0, root.makeelement("path", { "visibility": "hidden", "fill": "none", "stroke": "none", "d": "M0 0 H 24 V 24 H 0 Z", }) ) return lxml.etree.tostring(root) camelize_exceptions = { "cpu": "CPU", } def _camelize(part): try: return camelize_exceptions[part] except KeyError: return part[0].upper() + part[1:] def camelize(name): return "".join( _camelize(part) for part in name.split("_") if part != "" ) with open("icons.go", "wb") as f: f.write("package icons\n\n") f.write("const (\n") for filepath in sorted(glob.glob("feather/icons/*.svg")): name = os.path.basename(filepath)[:-len(".svg")].replace("-", "_") go_name = camelize(name) with open(filepath, "rb") as icon_f: data = icon_f.read() f.write("\t%s = `%s`\n" % ( go_name, normalize_xml(data), )) f.write(")\n") subprocess.check_call([ "gofmt", "-w", "icons.go", ])	[ 1, 529, 276, 1112, 420, 29958, 16846, 29920, 29914, 29887, 13434, 1315, 29899, 1725, 1624, 29899, 27078, 13, 5215, 2897, 13, 5215, 13149, 13, 5215, 301, 3134, 29889, 300, 929, 13, 5215, 1014, 5014, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 13812, 353, 301, 3134, 29889, 300, 929, 29889, 9165, 11726, 29898, 5992, 29918, 19465, 29918, 726, 29922, 5574, 29897, 13, 13, 1678, 822, 4226, 675, 29918, 3134, 29898, 1272, 1125, 13, 4706, 3876, 353, 301, 3134, 29889, 300, 929, 29889, 9165, 29898, 1272, 29892, 13812, 29922, 16680, 29897, 13, 4706, 3876, 29889, 7851, 29898, 13, 632, 29900, 29892, 13, 9651, 3876, 29889, 5675, 5029, 703, 2084, 613, 426, 13, 18884, 376, 28814, 1115, 376, 10892, 613, 13, 18884, 376, 5589, 1115, 376, 9290, 613, 13, 18884, 376, 25893, 1115, 376, 9290, 613, 13, 18884, 376, 29881, 1115, 376, 29924, 29900, 29871, 29900, 379, 29871, 29906, 29946, 478, 29871, 29906, 29946, 379, 29871, 29900, 796, 613, 13, 9651, 5615, 13, 4706, 1723, 13, 4706, 736, 301, 3134, 29889, 300, 929, 29889, 517, 1807, 29898, 4632, 29897, 13, 13, 1678, 3949, 295, 675, 29918, 11739, 29879, 353, 426, 13, 4706, 376, 21970, 1115, 376, 6271, 29965, 613, 13, 1678, 500, 13, 13, 1678, 822, 903, 11108, 295, 675, 29898, 1595, 1125, 13, 4706, 1018, 29901, 13, 9651, 736, 3949, 295, 675, 29918, 11739, 29879, 29961, 1595, 29962, 13, 4706, 5174, 7670, 2392, 29901, 13, 9651, 736, 760, 29961, 29900, 1822, 21064, 580, 718, 760, 29961, 29896, 17531, 13, 13, 1678, 822, 3949, 295, 675, 29898, 978, 1125, 13, 4706, 736, 376, 1642, 7122, 29898, 13, 9651, 903, 11108, 295, 675, 29898, 1595, 29897, 13, 9651, 363, 760, 297, 1024, 29889, 5451, 703, 29918, 1159, 13, 9651, 565, 760, 2804, 5124, 13, 4706, 1723, 13, 13, 1678, 411, 1722, 703, 27078, 29889, 1484, 613, 376, 29893, 29890, 1159, 408, 285, 29901, 13, 4706, 285, 29889, 3539, 703, 5113, 27673, 29905, 29876, 29905, 29876, 1159, 13, 4706, 285, 29889, 3539, 703, 3075, 3441, 29876, 1159, 13, 4706, 363, 934, 2084, 297, 12705, 29898, 23705, 29889, 23705, 703, 1725, 1624, 29914, 27078, 5515, 29889, 15120, 5783, 29901, 13, 9651, 1024, 353, 2897, 29889, 2084, 29889, 6500, 3871, 29898, 1445, 2084, 29897, 7503, 29899, 2435, 17350, 15120, 1159, 1822, 6506, 703, 29899, 613, 11119, 1159, 13, 9651, 748, 29918, 978, 353, 3949, 295, 675, 29898, 978, 29897, 13, 13, 9651, 411, 1722, 29898, 1445, 2084, 29892, 376, 6050, 1159, 408, 9849, 29918, 29888, 29901, 13, 18884, 848, 353, 9849, 29918, 29888, 29889, 949, 580, 13, 13, 9651, 285, 29889, 3539, 14182, 29873, 29995, 29879, 353, 22570, 29879, 29952, 29905, 29876, 29908, 1273, 313, 13, 18884, 748, 29918, 978, 29892, 13, 18884, 4226, 675, 29918, 3134, 29898, 1272, 511, 13, 632, 876, 13, 4706, 285, 29889, 3539, 703, 2144, 29876, 1159, 13, 13, 1678, 1014, 5014, 29889, 3198, 29918, 4804, 4197, 13, 4706, 376, 29887, 974, 4378, 613, 11663, 29893, 613, 13, 4706, 376, 27078, 29889, 1484, 613, 13, 268, 2314, 13, 2 ]
tests/test_conveniences.py	CSC-DPR/eopf-cpm	0	34346	import datetime import os import sys from cmath import inf from typing import Any import hypothesis.extra.numpy as xps import hypothesis.strategies as st import numpy import pytest from hypothesis import assume, given from eopf.product.utils import ( apply_xpath, conv, convert_to_unix_time, is_date, parse_xml, reverse_conv, translate_structure, ) @pytest.fixture def tree(EMBEDED_TEST_DATA_FOLDER: str): snippet_path = os.path.join(EMBEDED_TEST_DATA_FOLDER, "snippet_xfdumanifest.xml") with open(snippet_path) as f: return parse_xml(f) @st.composite def value_with_type(draw, elements=st.integers(), expected_type=int, expected_container_type=None): if isinstance(expected_type, st.SearchStrategy): expected_type = draw(expected_type) if expected_container_type is not None: if isinstance(expected_container_type, st.SearchStrategy): expected_container_type = draw(expected_container_type) return (draw(elements), expected_type, expected_container_type) return (draw(elements), expected_type) @st.composite def numpy_value(draw, dtype_st=xps.scalar_dtypes(), allow_infinity=True, allow_nan=True): return draw(xps.from_dtype(draw(dtype_st), allow_infinity=allow_infinity, allow_nan=allow_nan)) @pytest.mark.unit def test_parse_xml(tree): """Given an input xml, the output of the function must match the expected output""" result = "" display_namespaces = True for element in tree.iter(): tag = element.tag result += f"{tag}\n" if display_namespaces: display_namespaces = False for key, value in element.nsmap.items(): result += f"{key} : {value}\n" attributes = element.attrib for key, value in attributes.items(): result += f"{key} : {value}\n" textual_content = element.text if textual_content and textual_content.strip(): result += textual_content + "\n" file_path = os.path.join(os.path.abspath("tests/data"), "solutions.txt") with open(file_path, "r") as f: expected = f.read() assert result == expected @pytest.mark.unit def test_translate_structure(tree): """Given an input xml, the output of the function must match the expected output""" MAP = { "title": "concat('',metadataSection/metadataObject[@ID='generalProductInformation']/metadataWrap/xmlData/" "sentinel3:generalProductInformation/sentinel3:productName/text())", "Conventions": "'CF-1.9'", } NAMESPACES = { "xfdu": "urn:ccsds:schema:xfdu:1", "gml": "http://www.opengis.net/gml", "sentinel-safe": "http://www.esa.int/safe/sentinel/1.1", "sentinel3": "http://www.esa.int/safe/sentinel/sentinel-3/1.0", "olci": "http://www.esa.int/safe/sentinel/sentinel-3/olci/1.0", } result = translate_structure(MAP, tree, NAMESPACES) assert result == { "title": "S3A_OL_1_EFR____20220116T092821_20220116T093121_20220117T134858_0179_081_036_2160_LN1_O_NT_002.SEN3", "Conventions": "CF-1.9", } @pytest.mark.unit def test_apply_xpath(tree): """Given an input xml, the output of the function must match the expected output""" MAP = { "title": "concat('',metadataSection/metadataObject[@ID='generalProductInformation']/metadataWrap/xmlData/" "sentinel3:generalProductInformation/sentinel3:productName/text())", "Conventions": "'CF-1.9'", } NAMESPACES = { "xfdu": "urn:ccsds:schema:xfdu:1", "gml": "http://www.opengis.net/gml", "sentinel-safe": "http://www.esa.int/safe/sentinel/1.1", "sentinel3": "http://www.esa.int/safe/sentinel/sentinel-3/1.0", "olci": "http://www.esa.int/safe/sentinel/sentinel-3/olci/1.0", } result = {attr: apply_xpath(tree, MAP[attr], NAMESPACES) for attr in MAP} assert result == { "title": "S3A_OL_1_EFR____20220116T092821_20220116T093121_20220117T134858_0179_081_036_2160_LN1_O_NT_002.SEN3", "Conventions": "CF-1.9", } @pytest.mark.unit def test_is_date(): string_date_1 = "2020-03-31T17:19:29.230522Z" # Zulu time string_date_2 = "2020-03-31T17:19:29.230522GMT+3" # GMT+3 Time string_date_3 = "some_random_string" dt_date = datetime.datetime(2020, 3, 31, 17, 19, 29, 230522) assert is_date(string_date_1) assert is_date(string_date_2) assert is_date(str(dt_date)) assert not is_date(string_date_3) @pytest.mark.unit def test_convert_unix_time(): import pytz # Define datetime-like string and verify if conversion match with datetime object and expected unix time. (MS) string_date = "2020-03-31T17:19:29.230522Z" dt_date = datetime.datetime(2020, 3, 31, 17, 19, 29, 230522, pytz.UTC) expected_unix_time = 1585675169230522 assert convert_to_unix_time(string_date) == convert_to_unix_time(dt_date) == expected_unix_time # Define datetime-like string in Zulu Time Zone, and verify that it doesnt match with expected unix time string_date = "2020-03-31T17:19:29.230522GMT-3" assert convert_to_unix_time(string_date) != convert_to_unix_time(dt_date) assert convert_to_unix_time(string_date) != expected_unix_time # try: string_date = "a string that is not a valid date" convert_to_unix_time(string_date) except ValueError: assert True @pytest.mark.unit @given( value_and_types=st.one_of( value_with_type( st.lists(elements=st.floats(allow_infinity=False, allow_nan=False), unique=True, min_size=10), float, list, ), value_with_type(st.lists(elements=st.integers(), unique=True, min_size=10), int, list), value_with_type(st.lists(elements=st.booleans(), unique=True, min_size=2), int, list), value_with_type(st.sets(elements=st.floats(allow_infinity=False, allow_nan=False), min_size=10), float, set), value_with_type(st.sets(elements=st.integers(), min_size=10), int, set), value_with_type(st.sets(elements=st.booleans(), min_size=2), int, set), value_with_type(st.dictionaries(st.text(), st.integers(), min_size=10), int, dict), value_with_type(st.dictionaries(st.text(), st.booleans(), min_size=10), int, dict), value_with_type( st.dictionaries(st.text(), st.floats(allow_infinity=False, allow_nan=False), min_size=10), float, dict, ), value_with_type(xps.arrays(xps.floating_dtypes(), 10, unique=True), float, list), value_with_type(xps.arrays(xps.integer_dtypes(), 10, unique=True), int, list), value_with_type(xps.arrays(xps.boolean_dtypes(), 10, unique=True), int, list), ), ) def test_conv_sequences(value_and_types: tuple[Any, type, type]): values, type_, container_type = value_and_types assume(inf not in values) converted_list = conv(values) assert isinstance(converted_list, container_type) # Check if size of converted value doesn't change assert len(converted_list) == len(values) # Check if type of each item from converted value is correct if isinstance(converted_list, dict): iterator = converted_list.values() original = values.values() else: iterator = converted_list original = values for converted_value, value in zip(sorted(iterator), sorted(original)): assert isinstance(converted_value, type_) conv_value = conv(value) # check if converted values are the same or both are nan assert converted_value == conv_value or (converted_value != converted_value and conv_value != conv_value) @pytest.mark.unit @pytest.mark.parametrize("EPSILON", [0.1]) @given(value=numpy_value(xps.floating_dtypes(), allow_infinity=False, allow_nan=False)) def test_epsilon_on_fp_conv(value, EPSILON): converted_value = conv(value) assert value - converted_value < EPSILON assert converted_value - value < EPSILON @pytest.mark.unit @given( value_and_type=st.one_of( value_with_type( elements=numpy_value(xps.floating_dtypes(), allow_infinity=False, allow_nan=False), expected_type=float, ), value_with_type( elements=numpy_value(xps.integer_dtypes(), allow_infinity=False, allow_nan=False), expected_type=int, ), value_with_type( elements=st.datetimes(), expected_type=int, ), ), ) def test_conv(value_and_type): value, expected_type = value_and_type converted_value = conv(value) assert isinstance(converted_value, expected_type) @pytest.mark.unit @pytest.mark.parametrize( "sysmax, maxint", [ (numpy.int64(sys.maxsize), numpy.int64(9223372036854775807)), ], ) def test_maxint_conv(sysmax, maxint): # Robustness assert conv(sysmax) == maxint @pytest.mark.unit @given( value_and_types=st.one_of( value_with_type( st.integers(min_value=numpy.iinfo("int64").min, max_value=numpy.iinfo("int64").max), int, xps.integer_dtypes(endianness="=", sizes=(64,)), ), value_with_type( st.integers(min_value=numpy.iinfo("int32").min, max_value=numpy.iinfo("int32").max), int, xps.integer_dtypes(endianness="=", sizes=(32,)), ), value_with_type( st.integers(min_value=numpy.iinfo("int16").min, max_value=numpy.iinfo("int16").max), int, xps.integer_dtypes(endianness="=", sizes=(16,)), ), value_with_type( st.integers(min_value=numpy.iinfo("int8").min, max_value=numpy.iinfo("int8").max), int, xps.integer_dtypes(endianness="=", sizes=(8,)), ), value_with_type(st.floats(width=16), float, xps.floating_dtypes(endianness="=", sizes=(16,))), value_with_type(st.floats(width=32), float, xps.floating_dtypes(endianness="=", sizes=(32,))), value_with_type(st.floats(width=64), float, xps.floating_dtypes(endianness="=", sizes=(64,))), ), ) def test_reverse_conv(value_and_types): value, current_type, data_type = value_and_types # verify if the current data type is as expected (int or float) assert isinstance(value, current_type) # convert value to given data type (int64, int32, float64 etc .. ) converted_value = reverse_conv(data_type, 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bin/import_guest.py	GOMYWAY-NETWORKS-LLC/karesansui	54	111697	<gh_stars>10-100 #!/usr/bin/env python # -- coding: utf-8 -- # # This file is part of Karesansui. # # Copyright (C) 2012 HDE, Inc. # # 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 os import os.path import sys import signal import logging from optparse import OptionParser import glob from ksscommand import KssCommand, KssCommandException, KssCommandOptException import __cmd__ try: import karesansui from karesansui import __version__ from karesansui.lib.virt.virt import KaresansuiVirtConnection, KaresansuiVirtException from karesansui.lib.utils import load_locale from karesansui.lib.virt.config_export import ExportConfigParam except ImportError, e: print >>sys.stderr, "[Error] some packages not found. - %s" % e sys.exit(1) _ = load_locale() usage = '%prog [options]' def getopts(): optp = OptionParser(usage=usage, version=__version__) optp.add_option('-e', '--exportuuid', dest='exportuuid', help=_('Export UUID to import')) optp.add_option('-d', '--destuuid', dest='destuuid', help=_('Guest UUID to import')) optp.add_option('-q', '--quiet',dest='verbose', action="store_false", default=True, help=_("don't print status messages")) return optp.parse_args() def chkopts(opts): if not opts.exportuuid: raise KssCommandOptException('ERROR: -e or --exportuuid option is required.') if not opts.destuuid: raise KssCommandOptException('ERROR: -d or --destuuid option is required.') class ImportGuest(KssCommand): def process(self): (opts, args) = getopts() chkopts(opts) self.up_progress(10) conn = KaresansuiVirtConnection(readonly=False) try: progresscb = None if opts.verbose: try: from karesansui.lib.progress import ProgressMeter progresscb = ProgressMeter(command_object=self) except: pass else: try: from karesansui.lib.progress import ProgressMeter progresscb = ProgressMeter(command_object=self,quiet=True) except: pass try: #inactive_pool = conn.list_inactive_storage_pool() inactive_pool = [] active_pool = conn.list_active_storage_pool() pools = inactive_pool + active_pool if not pools: raise KssCommandException("Storage pool does not exist, or has been stopped.") pools.sort() export = [] for pool_name in pools: pool = conn.search_kvn_storage_pools(pool_name) path = pool[0].get_info()["target"]["path"] if os.path.exists(path): for _afile in glob.glob("%s/*/info.dat" % (path,)): e_param = ExportConfigParam() e_param.load_xml_config(_afile) if e_param.get_uuid() != opts.exportuuid: continue export.append({"dir" : os.path.dirname(_afile), "uuid" : opts.exportuuid, }) if len(export) != 1: raise KssCommandException("There are differences in the export data and real data. - uuid=%s" % opts.exportuuid) else: export = export[0] if os.path.isdir(export["dir"]) is False: raise KssCommandException("There is no real data. - dir=%s" % export["dir"]) conn.import_guest(export["dir"], uuid=opts.destuuid, progresscb=progresscb) self.up_progress(40) self.logger.info('Import guest completed. - export=%s, dest=%s' % (opts.exportuuid, opts.destuuid)) print >>sys.stdout, _('Import guest completed. - export=%s, dest=%s' % (opts.exportuuid, opts.destuuid)) return True except KaresansuiVirtException, e: raise KssCommandException('Failed to import guest. - [%s]' \ % (''.join(str(e.args)))) finally: conn.close() if __name__ == "__main__": target = ImportGuest() sys.exit(target.run())	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 29937, 13, 29937, 910, 934, 338, 760, 310, 3467, 267, 550, 1481, 29889, 13, 29937, 13, 29937, 14187, 1266, 313, 29907, 29897, 29871, 29906, 29900, 29896, 29906, 379, 2287, 29892, 9266, 29889, 13, 29937, 13, 29937, 20894, 2333, 338, 1244, 1609, 16896, 29892, 3889, 310, 8323, 29892, 304, 738, 2022, 4017, 292, 263, 3509, 13, 29937, 310, 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29889, 6605, 580, 13, 13, 18884, 5609, 353, 5159, 13, 18884, 363, 11565, 29918, 978, 297, 772, 3775, 29901, 13, 462, 1678, 11565, 353, 11009, 29889, 4478, 29918, 29895, 18564, 29918, 12925, 29918, 1129, 3775, 29898, 10109, 29918, 978, 29897, 13, 462, 1678, 2224, 353, 11565, 29961, 29900, 1822, 657, 29918, 3888, 580, 3366, 5182, 3108, 3366, 2084, 3108, 13, 462, 1678, 565, 2897, 29889, 2084, 29889, 9933, 29898, 2084, 1125, 13, 462, 4706, 363, 903, 29874, 1445, 297, 13149, 29889, 23705, 11702, 29879, 29914, 3877, 3888, 29889, 4130, 29908, 1273, 313, 2084, 29892, 22164, 13, 462, 9651, 321, 29918, 3207, 353, 1222, 637, 3991, 4736, 580, 13, 462, 9651, 321, 29918, 3207, 29889, 1359, 29918, 3134, 29918, 2917, 7373, 29874, 1445, 29897, 13, 13, 462, 9651, 565, 321, 29918, 3207, 29889, 657, 29918, 25118, 580, 2804, 29111, 29889, 15843, 25118, 29901, 13, 462, 18884, 6773, 13, 13, 462, 9651, 5609, 29889, 4397, 3319, 29908, 3972, 29908, 584, 2897, 29889, 2084, 29889, 25721, 7373, 29874, 1445, 511, 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Outputs_win_app.py	stfbnc/mtsa_py	0	197740	import sys if sys.version_info[0] == 2: import Tkinter as tk from tkFileDialog import askdirectory else: import tkinter as tk from tkinter.filedialog import askdirectory import numpy as np import matplotlib matplotlib.use("TkAgg") import matplotlib.pyplot as plt from matplotlib.figure import Figure from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg import os ### set size for figures x_size = 7 y_size = 5 ############################################################################# ############################## SAVE_FREQ #################################### ############################################################################# def save_freq(arg0,arg2,arg3,arg4): path_file = askdirectory() path_file = os.path.join(path_file,'freq_perc.txt') f = open(path_file,'w') f.write('Frequencies for file %s\n\n' % (arg0)) f.write('Filter_round Frequency Period Percentage\n\n') for i in range(len(arg2)): f.write('%d %.8f %.2f %.2f\n' % (arg2[i],1.0 / arg3[i],arg3[i],arg4[i])) f.close() ############################################################################# ############################################################################# ############################## DETREND_PLOT ################################# ############################################################################# def detrend_plot(main_win,arg0,arg1,arg2): ticks = np.arange(0,len(arg0),len(arg0) / 7,dtype = int) t = np.arange(1,len(arg0) + 1,dtype = int) time = np.array(arg2,dtype = str) ticks_vec = t[ticks] time_label = time[ticks] def save_fig(): path_tot = askdirectory() plt.rc('text',usetex = True) plt.rc('font',family = 'serif') plt.plot(t,arg0 + arg1,'r',label = 'original') if not np.isscalar(arg1): plt.plot(t,arg1,'k',label = 'trend') plt.plot(t,arg0,'b',label = 'detrended') plt.legend(loc = 0) plt.xlim(float(entries[0].get()),float(entries[1].get())) plt.ylim(float(entries[2].get()),float(entries[3].get())) plt.xlabel(entries[4].get()) plt.ylabel(entries[5].get()) plt.title(entries[6].get()) plt.xticks(ticks,time_label) plt.margins(0.2) plt.subplots_adjust(bottom = 0.2) plt.savefig(os.path.join(path_tot,'ts.pdf')) plt.close() def screen_fig(): fig_ts = Figure(figsize = (x_size,y_size)) a = fig_ts.add_subplot(111) a.plot(t,arg0 + arg1,'r',label = 'original') if not np.isscalar(arg1): a.plot(t,arg1,'k',label = 'trend') a.plot(t,arg0,'b',label = 'detrended') a.legend(loc = 0) a.set_xlim(float(entries[0].get()),float(entries[1].get())) a.set_ylim(float(entries[2].get()),float(entries[3].get())) a.set_xlabel(entries[4].get(),fontsize = 15) a.set_ylabel(entries[5].get(),fontsize = 15) a.set_title(entries[6].get(),fontsize = 15) a.set_xticks(ticks_vec) a.set_xticklabels(time_label) fig_ts.tight_layout() canvas = FigureCanvasTkAgg(fig_ts,master = frame_1) canvas.get_tk_widget().grid(row = 0,column = 0) canvas.draw() def reset_fig(): for i in range(len(entries)): entries[i].delete(0,tk.END) entries[i].insert(0,values[i]) screen_fig() top = tk.Toplevel(main_win) top.geometry("%dx%d" % (int(main_win.winfo_screenwidth() * 0.93 * 0.85), int(main_win.winfo_screenheight() * 0.65))) top.wm_title("Time Series") top.resizable(width = False,height = False) frame_1 = tk.Frame(top) frame_1.grid(row = 0,column = 0) frame_2 = tk.Frame(top) frame_2.grid(row = 0,column = 1) names = ["X Limit (left)","X Limit (right)","Y Limit (bottom)","Y Limit (top)","X Label","Y Label","Title"] if not np.isscalar(arg1): values = [t[0],t[-1],np.min([np.min(arg0[~np.isnan(arg0)]),np.min(arg1[~np.isnan(arg1)]), np.min(arg0[~np.isnan(arg0)] + arg1[~np.isnan(arg1)])]) - 1.0, np.max([np.max(arg0[~np.isnan(arg0)]),np.max(arg1[~np.isnan(arg1)]), np.max(arg0[~np.isnan(arg0)] + arg1[~np.isnan(arg1)])]) + 1.0,'t','$X_t$', 'Time Series'] else: values = [t[0],t[-1],np.min(arg0[~np.isnan(arg0)]) - 1.0,np.max(arg0[~np.isnan(arg0)]) + 1.0, 't','$X_t$','Time Series'] entries = [] for i in range(len(names)): tk.Label(frame_2,text = names[i],font = "Verdana 13 bold").grid(row = 2 * i,column = 0, padx = int(main_win.winfo_screenwidth() * 0.01)) entries.append(tk.Entry(frame_2,width = 18)) entries[-1].insert(0,values[i]) entries[-1].grid(row = 2 * i,column = 1) for i in range(len(names)): tk.Label(frame_2,text = "").grid(row = 2 * i + 1,column = 0) screen_fig() tk.Button(frame_2,text = "Replot",font = "Verdana 13 bold",command = screen_fig).grid(row = 2 * len(names),column = 0) tk.Button(frame_2,text = "Save",font = "Verdana 13 bold",command = save_fig).grid(row = 2 * len(names),column = 1) tk.Label(frame_2,text = "").grid(row = 2 * len(names) + 1,column = 0) tk.Button(frame_2,text = "Reset",font = "Verdana 13 bold",command = reset_fig).grid(row = 2 * len(names) + 2,column = 0) ############################################################################# ############################################################################# ############################## SPECTRUM_PLOT ################################ ############################################################################# def spectrum_plot(main_win,arg0,arg1,arg2): def save_fig(): path_tot = askdirectory() plt.rc('text',usetex = True) plt.rc('font',family = 'serif') plt.plot(arg1,arg0,'b') if arg2 != 0: plt.plot((arg1[0],arg1[-1]),(arg2,arg2),'r') plt.xlabel(entries[0].get()) plt.ylabel(entries[1].get()) plt.xlim(float(entries[2].get()),float(entries[3].get())) plt.ylim(float(entries[4].get()),float(entries[5].get())) plt.title(entries[6].get()) plt.savefig(os.path.join(path_tot,'spectrum_in.pdf')) plt.close() def screen_fig(): fig_ts = Figure(figsize = (x_size,y_size)) a = fig_ts.add_subplot(111) a.plot(arg1,arg0,'b') if arg2 != 0: a.plot((arg1[0],arg1[-1]),(arg2,arg2),'r') a.set_xlabel(entries[0].get(),fontsize = 15) a.set_ylabel(entries[1].get(),fontsize = 15) a.set_xlim(float(entries[2].get()),float(entries[3].get())) a.set_ylim(float(entries[4].get()),float(entries[5].get())) a.set_title(entries[6].get(),fontsize = 15) fig_ts.tight_layout() canvas = FigureCanvasTkAgg(fig_ts,master = frame_1) canvas.get_tk_widget().grid(row = 0,column = 0) canvas.draw() def reset_fig(): for i in range(len(entries)): entries[i].delete(0,tk.END) entries[i].insert(0,values[i]) screen_fig() top = tk.Toplevel(main_win) top.geometry("%dx%d" % (int(main_win.winfo_screenwidth() * 0.93 * 0.85), int(main_win.winfo_screenheight() * 0.65))) if arg2 != 0: top.wm_title("Spectrum") else: top.wm_title("Spectrum of residuals") top.resizable(width = False,height = False) frame_1 = tk.Frame(top) frame_1.grid(row = 0,column = 0) frame_2 = tk.Frame(top) frame_2.grid(row = 0,column = 1) names = ["X Label","Y Label","X Limit (left)","X Limit (right)","Y Limit (bottom)","Y Limit (top)","Title"] if arg2 != 0: values = ['$\\nu$','$P(\\nu)$',0,arg1[-1],0,np.max(arg0) + 10.0,'LS spectrum (initial)'] else: values = ['$\\nu$','$P(\\nu)$',0,arg1[-1],0,np.max(arg0) + 10.0,'LS spectrum of residuals'] entries = [] for i in range(len(names)): tk.Label(frame_2,text = names[i],font = "Verdana 13 bold").grid(row = 2 * i,column = 0, padx = int(main_win.winfo_screenwidth() * 0.01)) entries.append(tk.Entry(frame_2,width = 18)) entries[-1].insert(0,values[i]) entries[-1].grid(row = 2 * i,column = 1) for i in range(len(names)): tk.Label(frame_2,text = "").grid(row = 2 * i + 1,column = 0) screen_fig() tk.Button(frame_2,text = "Replot",font = "Verdana 13 bold",command = screen_fig).grid(row = 2 * len(names),column = 0) tk.Button(frame_2,text = "Save",font = "Verdana 13 bold",command = save_fig).grid(row = 2 * len(names),column = 1) tk.Label(frame_2,text = "").grid(row = 2 * len(names) + 1,column = 0) tk.Button(frame_2,text = "Reset",font = "Verdana 13 bold",command = reset_fig).grid(row = 2 * len(names) + 2,column = 0) ############################################################################# ############################################################################# ############################## RES_PLOT ##################################### ############################################################################# def res_plot(main_win,arg0,arg1,arg2,arg3): ticks = np.arange(0,len(arg0),len(arg0) / 7,dtype = int) t = np.arange(1,len(arg0) + 1,dtype = int) time = np.array(arg3,dtype = str) ticks_vec = t[ticks] time_label = time[ticks] pn_norm_notnan = arg2[~np.isnan(arg2)] outlier_lim = 3.0 num_outliers_max = len(pn_norm_notnan[pn_norm_notnan > outlier_lim]) num_outliers_min = len(pn_norm_notnan[pn_norm_notnan < -outlier_lim]) num_outliers = num_outliers_max + num_outliers_min def save_fig(): path_tot = askdirectory() plt.figure(figsize = (12,9)) plt.rc('text',usetex = True) plt.rc('font',family = 'serif') plt.subplot(2,1,1) plt.plot(t,arg0) plt.xlim(int(entries[0].get()),int(entries[1].get())) plt.ylim(float(entries[5].get()),float(entries[6].get())) plt.xticks(ticks,'') plt.ylabel(entries[2].get()) plt.title(entries[4].get()) plt.margins(0.2) plt.subplots_adjust(hspace = 0.0) plt.subplot(2,1,2) sigma = '%.2f' % arg1 if int(matplotlib.__version__.split('.')[0]) == 2: plt.bar(t,arg2,width = 10,label = 'num outl = ' + str(num_outliers)) else: plt.bar(t,arg2,width = 0.1,label = 'num outl = ' + str(num_outliers)) plt.plot((t[0],t[-1]),(outlier_lim,outlier_lim),'r',label = '$\sigma$ = ' + sigma) plt.plot((t[0],t[-1]),(-outlier_lim,-outlier_lim),'r') plt.legend(loc = 0) plt.xlim(int(entries[0].get()),int(entries[1].get())) plt.ylim(float(entries[7].get()),float(entries[8].get())) plt.xticks(ticks,time_label) plt.ylabel(entries[3].get()) plt.margins(0.2) plt.subplots_adjust(hspace = 0.0) plt.savefig(os.path.join(path_tot,'res.pdf')) plt.close() def screen_fig(): fig_ts = Figure(figsize = (x_size,y_size)) a = fig_ts.add_subplot(211) a.plot(t,arg0) a.set_xlim(int(entries[0].get()),int(entries[1].get())) a.set_ylim(float(entries[5].get()),float(entries[6].get())) a.set_xticks(ticks_vec) a.set_xticklabels('') a.set_ylabel(entries[2].get(),fontsize = 15) a.set_title(entries[4].get(),fontsize = 15) b = fig_ts.add_subplot(212) sigma = '%.2f' % arg1 if int(matplotlib.__version__.split('.')[0]) == 2: b.bar(t,arg2,width = 10,label = 'num outl = ' + str(num_outliers)) else: b.bar(t,arg2,width = 0.1,label = 'num outl = ' + str(num_outliers)) b.plot((t[0],t[-1]),(outlier_lim,outlier_lim),'r',label = '$\sigma$ = ' + sigma) b.plot((t[0],t[-1]),(-outlier_lim,-outlier_lim),'r') b.legend(loc = 0) b.set_xlim(int(entries[0].get()),int(entries[1].get())) b.set_ylim(float(entries[7].get()),float(entries[8].get())) b.set_xticks(ticks) b.set_xticklabels(time_label) b.set_ylabel(entries[3].get(),fontsize = 15) fig_ts.tight_layout() canvas = FigureCanvasTkAgg(fig_ts,master = frame_1) canvas.get_tk_widget().grid(row = 0,column = 0) canvas.draw() def reset_fig(): for i in range(len(entries)): entries[i].delete(0,tk.END) entries[i].insert(0,values[i]) screen_fig() top = tk.Toplevel(main_win) top.geometry("%dx%d" % (int(main_win.winfo_screenwidth() * 0.93 * 0.85), int(main_win.winfo_screenheight() * 0.65))) top.wm_title("Residuals") top.resizable(width = False,height = False) frame_1 = tk.Frame(top) frame_1.grid(row = 0,column = 0) frame_2 = tk.Frame(top) frame_2.grid(row = 0,column = 1) names = ["X Limit (left)","X Limit (right)","Y Label (top)","Y Label (bottom)","Title", "Y1 Limit (bottom)","Y1 Limit (top)","Y2 Limit (bottom)","Y2 Limit (top)"] values = [t[0],t[-1],'$N_t$','$N_t^{norm}$','Residuals / Normalised residuals',np.min(arg0[~np.isnan(arg0)]) - 10.0, np.max(arg0[~np.isnan(arg0)]) + 10.0,np.min(arg2[~np.isnan(arg0)]) - 1.0,np.max(arg2[~np.isnan(arg0)]) + 1.0] entries = [] for i in range(len(names)): tk.Label(frame_2,text = names[i],font = "Verdana 13 bold").grid(row = 2 * i,column = 0, padx = int(main_win.winfo_screenwidth() * 0.01)) entries.append(tk.Entry(frame_2,width = 18)) entries[-1].insert(0,values[i]) entries[-1].grid(row = 2 * i,column = 1) for i in range(len(names)): tk.Label(frame_2,text = "").grid(row = 2 * i + 1,column = 0) screen_fig() tk.Button(frame_2,text = "Replot",font = "Verdana 13 bold",command = screen_fig).grid(row = 2 * len(names),column = 0) tk.Button(frame_2,text = "Save",font = "Verdana 13 bold",command = save_fig).grid(row = 2 * len(names),column = 1) tk.Label(frame_2,text = "").grid(row = 2 * len(names) + 1,column = 0) tk.Button(frame_2,text = "Reset",font = "Verdana 13 bold",command = reset_fig).grid(row = 2 * len(names) + 2,column = 0) ############################################################################# ############################################################################# ############################## DFA_PLOT ##################################### ############################################################################# def dfa_plot(main_win,arg0,arg1,arg2,arg3): def save_fig(): path_tot = askdirectory() plt.rc('text',usetex = True) plt.rc('font',family = 'serif') plt.plot(np.log(arg0),np.log(arg1),'o',label = '$H$ = ' + arg3) plt.plot(np.log(arg0),arg2,'r') plt.legend(loc = 0) plt.xlim(float(entries[0].get()),float(entries[1].get())) plt.ylim(float(entries[2].get()),float(entries[3].get())) plt.xlabel(entries[4].get()) plt.ylabel(entries[5].get()) plt.title(entries[6].get()) plt.savefig(os.path.join(path_tot,'dfa.pdf')) plt.close() def screen_fig(): fig_ts = Figure(figsize = (x_size,y_size)) a = fig_ts.add_subplot(111) a.plot(np.log(arg0),np.log(arg1),'o',label = '$H$ = ' + arg3) a.plot(np.log(arg0),arg2,'r') a.legend(loc = 0) a.set_xlim(float(entries[0].get()),float(entries[1].get())) a.set_ylim(float(entries[2].get()),float(entries[3].get())) a.set_xlabel(entries[4].get()) a.set_ylabel(entries[5].get()) a.set_title(entries[6].get()) fig_ts.tight_layout() canvas = FigureCanvasTkAgg(fig_ts,master = frame_1) canvas.get_tk_widget().grid(row = 0,column = 0) canvas.draw() def reset_fig(): for i in range(len(entries)): entries[i].delete(0,tk.END) entries[i].insert(0,values[i]) screen_fig() top = tk.Toplevel(main_win) top.geometry("%dx%d" % (int(main_win.winfo_screenwidth() * 0.93 * 0.85), int(main_win.winfo_screenheight() * 0.65))) top.wm_title("DFA") top.resizable(width = False,height = False) frame_1 = tk.Frame(top) frame_1.grid(row = 0,column = 0) frame_2 = tk.Frame(top) frame_2.grid(row = 0,column = 1) names = ["X Limit (left)","X Limit (right)","Y Limit (bottom)","Y Limit (top)","X Label","Y Label","Title"] values = [np.log(arg0[0]) - 0.3,np.log(arg0[-1]) + 0.3,np.min(np.log(arg1)) - 1.0,np.max(np.log(arg1)) + 1.0, 'log$(F(n))$','log$(n)$','DFA fit'] entries = [] for i in range(len(names)): tk.Label(frame_2,text = names[i],font = "Verdana 13 bold").grid(row = 2 * i,column = 0, padx = int(main_win.winfo_screenwidth() * 0.01)) entries.append(tk.Entry(frame_2,width = 18)) entries[-1].insert(0,values[i]) entries[-1].grid(row = 2 * i,column = 1) for i in range(len(names)): tk.Label(frame_2,text = "").grid(row = 2 * i + 1,column = 0) screen_fig() tk.Button(frame_2,text = "Replot",font = "Verdana 13 bold",command = screen_fig).grid(row = 2 * len(names),column = 0) tk.Button(frame_2,text = "Save",font = "Verdana 13 bold",command = save_fig).grid(row = 2 * len(names),column = 1) tk.Label(frame_2,text = "").grid(row = 2 * len(names) + 1,column = 0) tk.Button(frame_2,text = "Reset",font = "Verdana 13 bold",command = reset_fig).grid(row = 2 * len(names) + 2,column = 0) ############################################################################# ############################################################################# ############################## MDFA_PLOT #################################### ############################################################################# def mdfa_plot(main_win,arg0,arg1,arg2,arg3,arg4,arg5,arg6,arg7): def save_fig(): path_tot = askdirectory() plt.figure(figsize = (11,11)) plt.rc('text',usetex = True) plt.rc('font',family = 'serif') plt.subplot(2,2,1) plt.plot(np.log(arg0),np.log(arg1[0,:]),'b.') plt.plot(np.log(arg0),arg2[:,0],'b',label = 'q = -3') plt.plot(np.log(arg0),np.log(arg1[50,:]),'r.') plt.plot(np.log(arg0),arg2[:,50],'r',label = 'q = 0') plt.plot(np.log(arg0),np.log(arg1[-1,:]),'g.') plt.plot(np.log(arg0),arg2[:,-1],'g',label = 'q = 3') plt.legend(loc = 0) plt.xlim(float(entries[0].get()),float(entries[1].get())) plt.ylim(float(entries[2].get()),float(entries[3].get())) plt.xlabel(entries[4].get()) plt.ylabel(entries[5].get()) plt.title(entries[6].get()) plt.margins(0.2) plt.subplots_adjust(bottom = 0.2) plt.subplot(2,2,2) plt.plot(arg3,arg4,'b',label = 'h(q)') plt.plot((arg3[0],arg3[-1]),(arg5,arg5),'k',label = 'H') plt.legend(loc = 0) plt.xlim(float(entries[7].get()),float(entries[8].get())) plt.ylim(float(entries[9].get()),float(entries[10].get())) plt.xlabel(entries[11].get()) plt.ylabel(entries[12].get()) plt.title(entries[13].get()) plt.margins(0.2) plt.subplots_adjust(bottom = 0.2) plt.subplot(2,2,3) plt.plot(arg6,arg7,'b') plt.xlim(float(entries[14].get()),float(entries[15].get())) plt.ylim(float(entries[16].get()),float(entries[17].get())) plt.xlabel(entries[18].get()) plt.ylabel(entries[19].get()) plt.title(entries[20].get()) plt.margins(0.2) plt.subplots_adjust(bottom = 0.2) plt.savefig(os.path.join(path_tot,'mdfa.pdf')) plt.close() def screen_fig(): fig_ts = Figure(figsize = (x_size,y_size)) a = fig_ts.add_subplot(221) a.plot(np.log(arg0),np.log(arg1[0,:]),'b.') a.plot(np.log(arg0),arg2[:,0],'b',label = 'q = -3') a.plot(np.log(arg0),np.log(arg1[50,:]),'r.') a.plot(np.log(arg0),arg2[:,50],'r',label = 'q = 0') a.plot(np.log(arg0),np.log(arg1[-1,:]),'g.') a.plot(np.log(arg0),arg2[:,-1],'g',label = 'q = 3') a.legend(loc = 0) a.set_xlim(float(entries[0].get()),float(entries[1].get())) a.set_ylim(float(entries[2].get()),float(entries[3].get())) a.set_xlabel(entries[4].get()) a.set_ylabel(entries[5].get()) a.set_title(entries[6].get()) b = fig_ts.add_subplot(222) b.plot(arg3,arg4,'b',label = 'H(q)') b.plot((arg3[0],arg3[-1]),(arg5,arg5),'k',label = 'H') b.legend(loc = 0) b.set_xlim(float(entries[7].get()),float(entries[8].get())) b.set_ylim(float(entries[9].get()),float(entries[10].get())) b.set_xlabel(entries[11].get()) b.set_ylabel(entries[12].get()) b.set_title(entries[13].get()) c = fig_ts.add_subplot(223) c.plot(arg6,arg7,'b') c.set_xlim(float(entries[14].get()),float(entries[15].get())) c.set_ylim(float(entries[16].get()),float(entries[17].get())) c.set_xlabel(entries[18].get()) c.set_ylabel(entries[19].get()) c.set_title(entries[20].get()) fig_ts.tight_layout() canvas = FigureCanvasTkAgg(fig_ts,master = frame_1) canvas.get_tk_widget().grid(row = 0,column = 0) canvas.draw() def reset_fig(): for i in range(len(entries)): entries[i].delete(0,tk.END) entries[i].insert(0,values[i]) screen_fig() top = tk.Toplevel(main_win) top.geometry("%dx%d" % (int(main_win.winfo_screenwidth() * 0.93 * 0.85), int(main_win.winfo_screenheight() * 0.75))) top.wm_title("MFDFA") top.resizable(width = False,height = False) frame_1 = tk.Frame(top) frame_1.grid(row = 0,column = 0) frame_2 = tk.Frame(top) frame_2.grid(row = 0,column = 1) names = ["X1 Limit (left)","X1 Limit (right)","Y1 Limit (bottom)","Y1 Limit (top)","X1 Label","Y1 Label","Title1", "X2 Limit (left)","X2 Limit (right)","Y2 Limit (bottom)","Y2 Limit (top)","X2 Label","Y2 Label","Title2", "X3 Limit (left)","X3 Limit (right)","Y3 Limit (bottom)","Y3 Limit (top)","X3 Label","Y3 Label","Title3"] values = [np.log(arg0[0]),np.log(arg0[-1]),np.min([np.min(np.log(arg1[0,:])),np.min(arg2[:,0]), np.min(np.log(arg1[50,:])),np.min(arg2[:,50]),np.min(np.log(arg1[-1,:])),np.min(arg2[:,-1])]) - 1.0, np.max([np.max(np.log(arg1[0,:])),np.max(arg2[:,0]),np.max(np.log(arg1[50,:])),np.max(arg2[:,50]), np.max(np.log(arg1[-1,:])),np.max(arg2[:,-1])]) + 1.0,'log(n)','log(F(n))','MDFA fit', arg3[0],arg3[-1],np.min(arg4) - 0.1,np.max(arg4) + 0.1,'q','H(q)','Generalised Hurst exponent', np.min(arg6) - 0.2,np.max(arg6) + 0.2,np.min(arg7) - 0.2,1.2,'$\\alpha$','$f(\\alpha)$', 'Singularity spectrum'] entries = [] for i in range(len(names)): tk.Label(frame_2,text = names[i],font = "Verdana 13 bold").grid(row = i,column = 0, padx = int(main_win.winfo_screenwidth() * 0.01)) entries.append(tk.Entry(frame_2,width = 18)) entries[-1].insert(0,values[i]) entries[-1].grid(row = i,column = 1) screen_fig() tk.Label(frame_2,text = "").grid(row = len(names),column = 0) tk.Button(frame_2,text = "Replot",font = "Verdana 13 bold",command = screen_fig).grid(row = len(names) + 1,column = 0) tk.Button(frame_2,text = "Save",font = "Verdana 13 bold",command = save_fig).grid(row = len(names) + 1,column = 1) tk.Button(frame_2,text = "Reset",font = "Verdana 13 bold",command = reset_fig).grid(row = len(names) + 2,column = 0) ############################################################################# ############################################################################# ############################## MFDFA2_PLOT ################################## ############################################################################# def MFDFA2_plot(main_win,arg0,arg1,arg2,arg3,arg4,arg5): def save_fig(): path_tot = askdirectory() plt.figure(figsize = (12,9)) plt.rc('text',usetex = True) plt.rc('font',family = 'serif') plt.subplot(2,1,1) ax = plt.gca() if int(matplotlib.__version__.split('.')[0]) == 2: ax.set_facecolor('black') else: ax.set_axis_bgcolor('black') plt.plot(arg0,'y') plt.plot(0.5 * np.ones((len(arg0),)),'w') plt.plot(np.ones((len(arg0),)),'m') plt.plot(1.5 * np.ones((len(arg0),)),'r') plt.xlim(float(entries[0].get()),float(entries[1].get())) plt.ylim(float(entries[2].get()),float(entries[3].get())) plt.xlabel(entries[4].get()) plt.ylabel(entries[5].get()) plt.title(entries[6].get()) plt.margins(0.2) plt.subplots_adjust(hspace = 0.3) plt.subplot(2,1,2) plt.plot(arg1,arg2,'b',label = '$\mu$ = ' + arg4) plt.plot(arg1,arg3,'r',linewidth = 2.0,label = '$\sigma$ = ' + arg5) plt.legend(loc = 0) plt.xlim(float(entries[7].get()),float(entries[8].get())) plt.ylim(float(entries[9].get()),float(entries[10].get())) plt.ylabel(entries[11].get()) plt.xlabel(entries[12].get()) plt.title(entries[13].get()) plt.margins(0.2) plt.subplots_adjust(hspace = 0.3) plt.savefig(os.path.join(path_tot,'MFDFA2.pdf')) plt.close() def screen_fig(): fig_ts = Figure(figsize = (x_size,y_size)) a = fig_ts.add_subplot(211) ax = fig_ts.gca() if int(matplotlib.__version__.split('.')[0]) == 2: ax.set_facecolor('black') else: ax.set_axis_bgcolor('black') a.plot(arg0,'y') a.plot(0.5 * np.ones((len(arg0),)),'w') a.plot(np.ones((len(arg0),)),'m') a.plot(1.5 * np.ones((len(arg0),)),'r') a.set_xlim(float(entries[0].get()),float(entries[1].get())) a.set_ylim(float(entries[2].get()),float(entries[3].get())) a.set_xlabel(entries[4].get()) a.set_ylabel(entries[5].get()) a.set_title(entries[6].get()) b = fig_ts.add_subplot(212) b.plot(arg1,arg2,'b',label = '$\mu$ = ' + arg4) b.plot(arg1,arg3,'r',linewidth = 2.0,label = '$\sigma$ = ' + arg5) b.legend(loc = 0) b.set_xlim(float(entries[7].get()),float(entries[8].get())) b.set_ylim(float(entries[9].get()),float(entries[10].get())) b.set_ylabel(entries[11].get()) b.set_xlabel(entries[12].get()) b.set_title(entries[13].get()) fig_ts.tight_layout() canvas = FigureCanvasTkAgg(fig_ts,master = frame_1) canvas.get_tk_widget().grid(row = 0,column = 0) canvas.draw() def reset_fig(): for i in range(len(entries)): entries[i].delete(0,tk.END) entries[i].insert(0,values[i]) screen_fig() top = tk.Toplevel(main_win) top.geometry("%dx%d" % (int(main_win.winfo_screenwidth() * 0.93 * 0.85), int(main_win.winfo_screenheight() * 0.65))) top.wm_title("DFA") top.resizable(width = False,height = False) frame_1 = tk.Frame(top) frame_1.grid(row = 0,column = 0) frame_2 = tk.Frame(top) frame_2.grid(row = 0,column = 1) names = ["X1 Limit (left)","X1 Limit (right)","Y1 Limit (bottom)","Y1 Limit (top)","X1 Label (top)", "Y1 Label (top)","Title1 (top)","X2 Limit (left)","X2 Limit (right)","Y2 Limit (bottom)", "Y2 Limit (top)","X2 Label (top)","Y2 Label (bottom)","Title2 (bottom)"] values = [0,len(arg0),0,3,'time','$H_t$','local Hurst exponent',np.min(arg1) - 0.2,np.max(arg1) + 0.2, 0,np.max(arg2) * 11 / 10,'P($H_t$)','$H_t$','Prob distr of $H_t$'] entries = [] for i in range(len(names)): tk.Label(frame_2,text = names[i],font = "Verdana 13 bold").grid(row = i,column = 0, padx = int(main_win.winfo_screenwidth() * 0.01)) entries.append(tk.Entry(frame_2,width = 18)) entries[-1].insert(0,values[i]) entries[-1].grid(row = i,column = 1) screen_fig() tk.Label(frame_2,text = "").grid(row = len(names),column = 0) tk.Button(frame_2,text = "Replot",font = "Verdana 13 bold",command = screen_fig).grid(row = len(names) + 1,column = 0) tk.Button(frame_2,text = "Save",font = "Verdana 13 bold",command = save_fig).grid(row = len(names) + 1,column = 1) tk.Label(frame_2,text = "").grid(row = len(names) + 2,column = 0) 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flask_atomic/ext.py

kmjbyrne/flask-kbpc

0

103791

<reponame>kmjbyrne/flask-kbpc from datetime import datetime from flask import Flask from flask import Response from flask import jsonify from flask.json import JSONEncoder from sqlalchemy.orm.collections import InstrumentedList from sqlalchemy_blender import serialize from sqlalchemy_blender import iserialize from handyhttp import HTTPException class ModelEncoder(JSONEncoder): def default(self, data): if isinstance(data, HTTPException): return data.pack() if isinstance(data, InstrumentedList): return serialize(data) elif isinstance(data, datetime): return data.isoformat() else: data = iserialize(data) return data class JSONResponse(Response): @classmethod def force_type(cls, rv, environ=None): if isinstance(rv, HTTPException): return rv.pack() if isinstance(rv, dict): rv = jsonify(rv) elif isinstance(rv, tuple): rv = jsonify(rv[0]), rv[1] return super(JSONResponse, cls).force_type(rv, environ) class FlaskJSON(Flask): response_class = JSONResponse json_encoder = ModelEncoder

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chapter7/exercises/EG7-09 get_value investigation 2.py

munnep/begin_to_code_with_python

0

191459

<gh_stars>0 # EG7-09 get_value investigation 2 def get_value(prompt, value_min, value_max): return ride_number=get_value(prompt='Please enter the ride number you want:',value_min=1,value_max=5) print('You have selected ride:',ride_number)

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boned_html/__init__.py

alexgarel/boned-html

0

1608097

<gh_stars>0 from .version import __version__ # noqa: F401 from .boner import HtmlBoner # noqa: F401

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trainer.py

thedesertm/leapmotion_training_svm

0

18431

import pandas as pd import os from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler from sklearn.metrics import classification_report, confusion_matrix from sklearn.model_selection import train_test_split import pickle BASE_PATH = os.path.join(os.getcwd() , "dataset") df = None i = 0 for file_name in os.listdir(BASE_PATH): file_path = os.path.join(BASE_PATH , file_name) print(file_path) data_frame = pd.read_csv(file_path , header=None) data_frame.pop(178) data_frame.pop(0) dat = pd.DataFrame({'result': [i for k in range(data_frame.shape[1])]}) data_frame = data_frame.join(dat) if not df is None : df = df.append(data_frame , ignore_index=True) else: df = data_frame i += 1 scaler = StandardScaler() y = df.pop("result") scalled_data = scaler.fit_transform(df) X_train, X_test, y_train, y_test = train_test_split(scalled_data , y, test_size = 0.20) svclassifier = SVC(kernel='linear') svclassifier.fit(X_train, y_train) y_pred = svclassifier.predict(X_test) print(confusion_matrix(y_test,y_pred)) print(classification_report(y_test,y_pred)) pickle.dump(svclassifier , open("classifier.pkl" , 'wb')) pickle.dump(scaler , open("scaler.pkl" , 'wb'))

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pythia/opal/content/KeyboardAttributes.py

willic3/pythia

1

47028

<filename>pythia/opal/content/KeyboardAttributes.py #!/usr/bin/env python # # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # <NAME> # California Institute of Technology # (C) 1998-2005 All Rights Reserved # # {LicenseText} # # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # def KeyboardAttributes(object): def identify(self, inspector): return inspector.onKeyboardAttributes(self) def __init__(self): self.accesskey = '' self.tabindex = '' return # version __id__ = "$Id: KeyboardAttributes.py,v 1.1 2005/03/20 07:22:58 aivazis Exp $" # End of file

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classes/aSimple.py

IMTorgTestCode/testprj_Python_aSimple

0

1617897

<filename>classes/aSimple.py import Animal, Dog, Cat if __name__ == '__main__': print "Hello World!" dogName = raw_input("\nWhat is your dog's name?" ) if dogName: Fido = Dog.Dog(dogName) Fido.getSound() Fluffy = Cat.Cat("Fluffy") Fluffy.getSound()

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medtrialextractor/formatting/read_annotations.py

juanmoo/MedTrialExtractor

1

41636

<filename>medtrialextractor/formatting/read_annotations.py ''' Read Annotations file and save in struct ''' import pandas as pd import json import codecs import multiprocessing from contextlib import contextmanager # Utils def match_ids_by_filename(ann, struct): fname_to_id = {} for doc_id in pd.unique(ann['doc_id']): doc_name = ann.loc[ann['doc_id'] == doc_id].iloc[0]['doc_name'] fname_to_id[doc_name] = doc_id new_struct = struct.copy() new_struct['documents'] = {} for old_id in struct['documents']: doc_struct = struct['documents'][old_id].copy() fname = doc_struct['file_name'] new_id = fname_to_id.get(fname, old_id) doc_struct['document_id'] = new_id new_struct['documents'][new_id] = doc_struct return new_struct def get_spans(spans_str): idxs = [int(e) for e in spans_str.split(',') if len(e) > 0] assert(len(idxs)%2 == 0) return list(zip(idxs[::2], idxs[1::2])) @contextmanager def poolcontext(*args, **kwargs): pool = multiprocessing.Pool(*args, **kwargs) yield pool pool.terminate() # Main def read_annotations(args): # Read input files annotation_file_path = args.annotation_file_path struct_base_path = args.struct_base_path annotations = pd.read_csv(annotation_file_path, keep_default_na=False) struct_base = None with codecs.open(struct_base_path, 'r', encoding='utf-8', errors='replace') as struct_base_file: struct_base = json.load(struct_base_file) # Spacing for output readability print('\n' * 2) # Match ids by file name if flag present if args.match_ids_by_filename: print('Matching document ids by file name ...') struct_base = match_ids_by_filename(annotations, struct_base) print('done.\n') struct_doc_ids = set(struct_base['documents'].keys()) annotated_doc_ids = set(pd.unique(annotations['doc_id'])) annotated_docs = annotated_doc_ids & struct_doc_ids not_annotated_docs = struct_doc_ids - annotated_doc_ids unknown_annotated_docs = annotated_doc_ids - struct_doc_ids if args.verbose: if len(annotated_docs) > 0: print('Annotated Documents:') for j, id in enumerate(annotated_docs): print(f'{j + 1}. {id}') print() print('-' * 100) else: print(f'No annotations for documents in {struct_base_path} were found.') print() print('-' * 100) if len(not_annotated_docs) > 0: print('No annotations found for the following documents:') for j, id in enumerate(not_annotated_docs): print(f'{j + 1}. {id}') print() print('-' * 100) if len(unknown_annotated_docs) > 0: print('These files were found in the annotaiton file but are not present in the base struct file:') print('No annotations found for the following documents:') for j, id in enumerate(unknown_annotated_docs): print(f'{j + 1}. {id}') print() print('-' * 100) # for doc_id in annotated_docs: annotation_args = [(struct_base['documents'][id], annotations) for id in annotated_docs] with poolcontext(processes=args.num_workers) as pool: doc_structs = pool.starmap(read_document_annotation, annotation_args) for ds in doc_structs: id = ds['document_id'] struct_base['documents'][id] = ds if 'annotated_docs' not in struct_base: struct_base['annotated_docs'] = [] struct_base['annotated_docs'].extend(list(annotated_docs)) # Save annotated struct if args.struct_annotated_path is not None: struct_annotated_path = args.struct_annotated_path else: struct_annotated_path = args.struct_base_path with codecs.open(struct_annotated_path, 'w', encoding='utf-8', errors='replace') as output_struct: json.dump(struct_base, output_struct, indent=4) def read_document_annotation(doc_struct, annotations): doc_id = doc_struct['document_id'] doc_df = annotations.loc[annotations['doc_id'] == doc_id] print(f'Processing annotations for {doc_id} ...') if len(doc_df) == 0: raise Exception(f'Unable to find annotations for document: {doc_id}') non_arm_cols = ['title', 'authors', 'study_type'] arm_non_numbered = ['arm_efficacy_metric', 'arm_efficacy_results'] arm_numbered = ['arm_dosage', 'arm_description'] # Iterate over doc_struct paragraphs for par in doc_struct['paragraphs']: ann_par = doc_df.loc[doc_df['description'] == par['text']] for j, row in ann_par.iterrows(): par['annotated'] = True par['annotations_spans'] = {} par['annotations_span_arms'] = {} # Get arm number arm_count = int(row['arm_number']) # Get annotations for non-arm columns for k in non_arm_cols: for kp in row.index: if k in kp: ann_spans = par['annotations_spans'] ann_span_arms = par['annotations_span_arms'] tags_col = f'{k}-tag' span_str = row[tags_col] spans = get_spans(span_str) ann_spans[k] = spans ann_span_arms = [('*', '*')] * len(spans) # Don't duplicate cols break # Get annotations for arm non-numbered columns for k in arm_non_numbered: for kp in row.index: if k in kp: ann_spans = par['annotations_spans'] ann_span_arms = par['annotations_span_arms'] tags_col = f'{k}-tag' span_str = row[tags_col] spans = get_spans(span_str) ann_spans[k] = spans ann_span_arms[k] = [(arm_count, '*')] * len(spans) # Don't duplicate cols break # Get annotations for arm columns for k in arm_numbered: for kp in row.index: if k in kp: ann_spans = par['annotations_spans'] ann_span_arms = par['annotations_span_arms'] desc_idxs = [] for c in row.index: if c.startswith(k) and c.endswith('-tag'): c_idx = int(c[len(k) + 1:-4]) desc_idxs.append(c_idx) k_spans = [] k_arms = [] for d_idx in desc_idxs: tag_col = f'{k}-{d_idx}-tag' span_str = row[tag_col] d_spans = get_spans(span_str) d_arms = [(arm_count, d_idx)] * len(d_spans) k_spans.extend(d_spans) k_arms.extend(d_arms) ann_spans[k] = k_spans ann_span_arms[k] = k_arms # Don't duplicate cols break return doc_struct

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event.py

Wired-Square/homeassistant-twc-director

0

112142

"""The Tesla Wall Charger Director integration.""" import logging from homeassistant.core import HomeAssistant from homeassistant.const import CONF_EVENT, CONF_ID, CONF_DEVICE_ID from twcdirector.device import TWCPeripheral from .const import ( DOMAIN_EVENT ) from .device import ( TWCDeviceEntity ) _LOGGER = logging.getLogger(__name__) class TWCDeviceEvent(TWCDeviceEntity): def __init__(self, hass: HomeAssistant, twc_device: TWCPeripheral): super().__init__(twc_device) self.hass = hass self._name = self._device_name self._twc_device.register_device_data_updated_callback({ "TWC_CAR_CONNECTED": self._connected_event }) async def _connected_event(self): event_data = { CONF_ID: self.unique_id, CONF_DEVICE_ID: self.entity_id, CONF_EVENT: "car_connected" if self._twc_device.is_car_connected() else "car_disconnected" } self.hass.bus.async_fire(DOMAIN_EVENT, event_data)

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data_access/qso_data.py

yishayv/lyacorr

2

149840

<filename>data_access/qso_data.py """ Helper classes for encapsulating QSO metadata and spectrum data. """ import astropy.table as table class QSORecord: def __init__(self, index, spec_obj_id, z, ra, dec, plate, mjd, fiber_id, extinction_g): self.index = index self.specObjID = spec_obj_id self.z = z self.ra = ra self.dec = dec self.plate = plate self.mjd = mjd self.fiberID = fiber_id self.extinction_g = extinction_g @classmethod def from_row(cls, qso_row): assert isinstance(qso_row, table.Row) return cls(qso_row['index'], qso_row['specObjID'], qso_row['z'], qso_row['ra'], qso_row['dec'], qso_row['plate'], qso_row['mjd'], qso_row['fiberID'], qso_row['extinction_g']) def __str__(self): return " ".join([str(self.index), str(self.specObjID), str(self.z), str(self.ra), str(self.dec), str(self.plate), str(self.mjd), str(self.fiberID)]) class QSOData: def __init__(self, qso_rec, ar_wavelength, ar_flux, ar_ivar): """ :type qso_rec: QSORecord :type ar_wavelength: np.multiarray.ndarray :type ar_flux: np.multiarray.ndarray :type ar_ivar: np.multiarray.ndarray """ self.qso_rec = qso_rec self.ar_wavelength = ar_wavelength self.ar_flux = ar_flux self.ar_ivar = ar_ivar

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bin/foldx_tsv.py

allydunham/mutfunc_sars_cov_2

1

148693

<reponame>allydunham/mutfunc_sars_cov_2 #!/usr/bin/env python3 """ Generate overall table of FoldX results across all genes """ import argparse import logging from pathlib import Path def get_template(path, model): """ Identify template for a given model based on the model table """ with open(path, 'r') as models_file: for line in models_file: line = line.strip().split('\t') if line[0] == model: return line[1] return None def main(args): """ Main """ print('uniprot', 'name', 'model', 'template', 'chain', 'position', 'wt', 'mut', 'sd', 'total_energy', 'backbone_hbond', 'sidechain_hbond', 'van_der_waals', 'electrostatics', 'solvation_polar', 'solvation_hydrophobic', 'van_der_waals_clashes', 'entropy_sidechain', 'entropy_mainchain', 'sloop_entropy', 'mloop_entropy', 'cis_bond', 'torsional_clash', 'backbone_clash', 'helix_dipole', 'water_bridge', 'disulfide', 'electrostatic_kon', 'partial_covalent_bonds', 'energy_ionisation', 'entropy_complex', sep='\t') for foldx_path in args.foldx: uniprot, protein, model = Path(foldx_path).parent.stem.split('_') try: template = get_template(f'{args.models}/{uniprot}_{protein}.models', model) except FileNotFoundError: logging.warning('Not .models file found for %s_%s', uniprot, protein) template = None with open(foldx_path, 'r') as foldx_file: next(foldx_file) for line in foldx_file: print(uniprot, protein, model, template if template is not None else 'NA', line.strip(), sep='\t') def parse_args(): """ Parse arguments """ parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('foldx', metavar='F', nargs='+', help="FoldX output files") parser.add_argument('--models', '-m', default='data/swissmodel', help="Directory containing .models tables") return parser.parse_args() if __name__ == '__main__': main(parse_args())

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grievance/urls.py

AdarshNandanwar/CMS

0

31166

from django.urls import path from django.conf.urls import url from django.urls import path,include import grievance.views as VIEWS from django.conf.urls.static import static from django.conf import settings app_name = 'grievance' urlpatterns =[ # path('', VIEWS.HomeView.as_view()) path('level1/', VIEWS.level1HomeView.as_view()), path('level1/type/<type>',VIEWS.level1RequestView.as_view()), path('level1/student/<student_id>', VIEWS.level1StudentView.as_view()), path('level1/psd-student-status/student/<student_id>',VIEWS.ViewOnlyPSDStudentPageView.as_view()), path('', include('django.contrib.auth.urls')), path('redirect/', VIEWS.RedirectView.as_view()), path('student/', VIEWS.studentHomeView.as_view()), path('level2/', VIEWS.level2HomeView.as_view()), path('level2/type/<type>', VIEWS.level2RequestView.as_view()), path('level2/student/<student_id>', VIEWS.level2StudentView.as_view()), path('<level>/student-status/student/<student_id>', VIEWS.ViewOnlyStudentPageView.as_view()), path('website-admin', VIEWS.websiteAdminHomePageView.as_view()), # path('website-admin/change-deadline', VIEWS.changeDeadlineView.as_view()), path('website-admin/add-user', VIEWS.addUser.as_view()), path('get-deadline', VIEWS.showDeadlineInHeader.as_view()), path('dev_page', VIEWS.devPageView.as_view()), ] urlpatterns+= static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)

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backend/Day.py

tkyaw1/lotus

0

116318

from Block import * """ This is the class for the day view of the journal. Each day will contain multiple blocks """ class Day: def __init__(self): self.blocks = [] self.date = "" #YYYYMMDD self.weather = "" self.heading = "" # not too sure what will populate this self.keywords = [] def __str__(self): return str({ "blocks": [block.return_json() for block in self.blocks], "date": self.date, "weather": self.weather, "heading": self.heading, "keywords": self.keywords }) def return_day_json(self): return str({ "blocks": [block.return_json() for block in self.blocks], "date": self.date, "weather": self.weather, "heading": self.heading, "keywords": self.keywords }) def add_block(self, block): """ Appends a block to the list of blocks """ self.blocks.append(block) # TODO: how to get blocks or return as a json? is this the __str__ function? def add_keywords(self, block): [self.keywords.append(keyword) for keyword in block.get_keywords()] def add_all_keywords(self): # for block in self.blocks: # self.keywords.append(block.get_keywords()) for block in self.blocks: [self.keywords.append(keyword) for keyword in block.get_keywords()] # [self.keywords.append(keyword) for keyword in block.get_keywords() for block in self.blocks] def get_keywords(self): return self.keywords def set_date(self, date): self.date = date def get_date(self): return self.date def set_weather(self, weather): self.weather = weather def get_weather(self): return self.weather def set_heading(self, heading): self.heading = heading def get_heading(self): return self.heading

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src/training/experiments_ecfp_linearModel_MLP_only/experiments_ecfp_linearModel_MLP_molnet_NS.py

XieResearchGroup/PLANS

0

174696

from ...models.linear import Linear_S, Linear_M, Linear_L from ..experiment_base import ExperimentBase from ..train_model import ns_linear_teacher_model, ns_linear_student_model from ..train_model import predict_and_mix from ..training_args import LMMixupArgs from ...utils.training_utils import init_model, callback_list class Experiment(ExperimentBase): def train_teacher( self, model, x_train, y_train, x_test, y_test, x_pred, batch_size, epochs, log_f, log_path, n_repeat, activation="sigmoid", loss="categorical_crossentropy", out_len=32, ): model = init_model( model, drop_rate=self.drop_rate, loss=loss, out_len=out_len, activation=activation, ) cb_list = callback_list(log_path, self.es_patience, model) trained_model, histories = ns_linear_teacher_model( model=model, x_train=x_train, y_train=y_train, x_test=x_test, y_test=y_test, x_pred=x_pred, batch_size=batch_size, epochs=epochs, cb_list=cb_list, log_f=log_f, log_path=log_path, n_repeat=n_repeat, ) return trained_model, histories def train_student( self, student_model, teacher_model, x_train, y_train, x_test, y_test, x_pred, batch_size, epochs, log_f, log_path, n_repeat, activation="sigmoid", loss="categorical_crossentropy", out_len=32, ): x_mix, y_mix = predict_and_mix( teacher_model, x_pred, x_train, y_train, shuffle=True ) if self.mixup is not None: x_mix, y_mix = self._mixup(x_mix, y_mix) # init model model = init_model( student_model, drop_rate=self.drop_rate, loss=loss, out_len=out_len, activation=activation, ) # callbacks cb_list = callback_list(log_path, self.es_patience, model) # fit Linear_M model to mixed dataset trained_model, histories = ns_linear_student_model( model=model, x_train=x_train, y_train=y_train, x_mix=x_mix, y_mix=y_mix, x_test=x_test, y_test=y_test, x_pred=x_pred, batch_size=batch_size, epochs=epochs, cb_list=cb_list, log_f=log_f, log_path=log_path, n_repeat=n_repeat, ) return trained_model, histories def run_experiment(self): # load training and testing data x_train, y_train, x_test, y_test, x_unlabeled = self.load_data( ["ECFP", "Label"] ) # open log log_f, log_path = self.open_log_(self.log_path) # train the teacher model trained_model, histories = self.train_teacher( model=Linear_S, x_train=x_train, y_train=y_train, x_test=x_test, y_test=y_test, x_pred=x_unlabeled, batch_size=self.batch_size, epochs=self.epochs, log_f=log_f, log_path=log_path, n_repeat=self.n_repeat, activation="sigmoid", loss="binary_crossentropy", out_len=12, ) # log results self.log_training(trained_model, histories, log_path) # train student models for student in [Linear_M, Linear_L]: trained_model, histories = self.train_student( student_model=student, teacher_model=trained_model, x_train=x_train, y_train=y_train, x_test=x_test, y_test=y_test, x_pred=x_unlabeled, batch_size=self.batch_size, epochs=self.epochs, log_f=log_f, log_path=log_path, n_repeat=self.n_repeat, activation="sigmoid", loss="binary_crossentropy", out_len=12, ) # log results self.log_training(trained_model, histories, log_path) log_f.write("best losses:\n {}\n".format(str(self.best_loss))) log_f.write("best accuracies:\n {}\n".format(str(self.best_acc))) self.log_predictions(trained_model, x_test, y_test, log_path) if __name__ == "__main__": parser = LMMixupArgs() args = parser.parse_args() experiment = Experiment( data_path=args.data_path, log_path=args.log_path, es_patience=args.es_patience, batch_size=args.batch_size, epochs=args.epochs, n_repeat=args.repeat, mixup=args.mixup, mixup_repeat=args.mixup_repeat, rand_seed=args.rand_seed, ) experiment.run_experiment()

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bin/nf.genome.py

orionzhou/nf

0

188562

#!/usr/bin/env python # -*- coding: utf-8 -*- import os import os.path as op import sys import pandas as pd import logging #import simplejson as json import yaml from jcvi.apps.base import sh, mkdir def get_gsize(fs): cl = pd.read_csv(fs, sep="\t", header=None, names=['chrom','size']) return sum(cl['size']) def tsv2yml(args): cvts = dict(genome=str,species=str,source=str) gl = pd.read_csv(args.fi, sep="\t", header=0, converters=cvts, true_values=['1','Y','Yes','T','True'], false_values=['0','N','No','F','False']) jd = dict() for i in range(len(gl)): genome, species, source, status = \ gl['genome'][i], gl['species'][i], gl['source'][i], gl['status'][i] #if not status in ['C','T']: continue jd1 = dict() pre = "%s/%s" % (args.dirg, genome) jd1['alias'] = gl['alias'][i] jd1['prefix'] = gl['prefix'][i] jd1['fasta'] = "%s/10.fasta" % pre jd1['fasta_idx'] = "%s/10.fasta.fai" % pre jd1['genome_bed'] = "%s/15_intervals/01.chrom.bed" % pre jd1['genome_sizes'] = "%s/15_intervals/01.chrom.sizes" % pre jd1['gap_bed'] = "%s/15_intervals/11.gap.bed" % pre if op.isfile(jd1['genome_sizes']): jd1['macs_gsize'] = get_gsize(jd1['genome_sizes']) # annotation jd1['gff'] = "%s/50_annotation/10.gff" % pre jd1['gff_db'] = "%s/50_annotation/10.gff.db" % pre jd1['gtf'] = "%s/50_annotation/10.gtf" % pre jd1['bed'] = "%s/50_annotation/10.bed" % pre jd1['fna'] = "%s/50_annotation/10.nt.fasta" % pre jd1['faa'] = "%s/50_annotation/10.aa.fasta" % pre jd1['tss'] = "%s/50_annotation/10.tss.bed" % pre jd1['pgff'] = "%s/50_annotation/15.gff" % pre jd1['pgff_db'] = "%s/50_annotation/15.gff.db" % pre jd1['pgtf'] = "%s/50_annotation/15.gtf" % pre jd1['pbed'] = "%s/50_annotation/15.bed" % pre jd1['pfna'] = "%s/50_annotation/15.nt.fasta" % pre jd1['pfaa'] = "%s/50_annotation/15.aa.fasta" % pre if gl['blat'][i]: jd1['blat'] = "%s/21_dbs/blat/db.2bit" % pre if gl['gatk'][i]: jd1['gatk'] = f"{pre}/21_dbs/gatk/" if gl['star'][i]: jd1['star'] = "%s/21_dbs/star/" % pre if gl['hisat2'][i]: jd1['hisat2'] = "%s/21_dbs/hisat2/db" % pre if genome in ['Zmays_B73']: jd1['hisat2'] = "%s/21_dbs/hisat2/B73_vt01/db" % pre if gl['bwa'][i]: jd1['bwa'] = "%s/21_dbs/bwa/db" % pre if gl['bismark'][i]: jd1['bismark'] = "%s/21_dbs/bismark" % pre if gl['salmon'][i]: jd1['salmon'] = "%s/21_dbs/salmon/db" % pre jd1['tx2gene'] = "%s/21_dbs/salmon/tx2gene.csv" % pre if gl['rcfg'][i]: jd1['rcfg'] = "%s/55.rds" % pre if gl['blast'][i]: jd1['blastp'] = f"{pre}/21_dbs/blastp" jd1['blastn'] = f"{pre}/21_dbs/blastn" win11 = "%s/15_intervals/20.win11.tsv" % pre win56 = "%s/15_intervals/20.win56.tsv" % pre win127 = "%s/15_intervals/20.win127.tsv" % pre if op.isfile(win11): jd1['win11'] = win11 if op.isfile(win56): jd1['win56'] = win56 if op.isfile(win127): jd1['win127'] = win127 jd1['fc_group_features'] = 'gene_id' jd1['fc_group_features_type'] = 'gene_biotype' jd[genome] = jd1 #j = dict(params = dict(genomes = jd)) j = dict(genomes = jd) with open(args.fo, 'w') as outfile: yaml.dump(j, outfile) # with open(args.json, 'w') as outfile: # json.dump(j, outfile) def download(args): cvts = dict(genome=str,species=str,source=str) gl = pd.read_csv(args.cfg, sep="\t", header=0, converters=cvts, true_values=['1','Y','Yes','T','True'], false_values=['0','N','No','F','False']) url_pre = "http://ftp.ebi.ac.uk/ensemblgenomes/pub" for i in range(len(gl)): if pd.isna(gl['status'][i]) or not gl['status'][i]: logging.warning(f"{gl['genome'][i]}: skipped") continue genome,species,source,version,assembly,url_fas,url_gff = \ gl['genome'][i], gl['species'][i], gl['source'][i], gl['version'][i], \ gl['assembly'][i], gl['url_fas'][i], gl['url_gff'][i] dirw = f"{args.dirg}/{genome}/raw" if not op.isdir(dirw): mkdir(dirw) os.chdir(dirw) if source == 'ensembl': version = int(version) species = species.replace(" ", "_") assembly = assembly.replace(" ", "_") url_fas = f"{url_pre}/release-{version}/plants/fasta/{species.lower()}/dna/{species}.{assembly}.dna.toplevel.fa.gz" url_gff = f"{url_pre}/release-{version}/plants/gff3/{species.lower()}/{species}.{assembly}.{version}.gff3.gz" fn1, fn2 = op.basename(url_fas), op.basename(url_gff) comp1, fn1c = False, fn1 comp2, fn2c = False, fn2 if not fn1.endswith(".gz"): comp1 = True fn1c = f"{fn1}.gz" if not fn2.endswith(".gz"): comp2 = True fn2c = f"{fn2}.gz" if op.isfile(fn1c) and os.stat(fn1c).st_size > 0 and op.isfile(fn2c) and os.stat(fn2c).st_size > 0: logging.warning(f"{genome}: already done") continue else: logging.debug(f"{genome}: working") if source == 'local': if not op.isfile(url_fas): url_fas = op.join(args.dirg, url_fas) if not op.isfile(url_gff): url_gff = op.join(args.dirg, url_gff) if not op.isfile(url_fas) or not op.isfile(url_gff): logging.error(f"no fasta/gff found: {url_fas} {url_gff}") sh(f"cp {url_fas} {fn1}") sh(f"cp {url_gff} {fn2}") else: sh(f"axel -n {args.thread} {url_fas} -o {fn1}") sh(f"axel -n {args.thread} {url_gff} -o {fn2}") if comp1: sh(f"gzip {fn1}") if comp2: sh(f"gzip {fn2}") sh(f"ln -sf {fn1c} raw.fasta.gz") sh(f"ln -sf {fn2c} raw.gff.gz") if __name__ == "__main__": import argparse ps = argparse.ArgumentParser( formatter_class = argparse.ArgumentDefaultsHelpFormatter, description = "help utilities to run the nextflow genome pipeline" ) sp = ps.add_subparsers(title = 'available commands', dest = 'command') sp1 = sp.add_parser("conv", formatter_class = argparse.ArgumentDefaultsHelpFormatter, help = "prepare genome config file for nextflow pipelines") sp1.add_argument('fi', help = 'input genome tsv') sp1.add_argument('fo', help = 'output yaml file') sp1.add_argument('--dirg', default="%s/zhoup-genome" % os.environ["s3"], help='genome directory') # sp1.add_argument('--json', default='genomes.json', help = 'output json file') sp1.set_defaults(func = tsv2yml) sp1 = sp.add_parser("download", formatter_class = argparse.ArgumentDefaultsHelpFormatter, help = "download raw genome (fasta+gff) files") sp1.add_argument('--cfg', default=f"{os.environ['genome']}/nf/genomes.tsv", help='genome config table') sp1.add_argument('--dirg', default="%s/zhoup-genome" % os.environ["s3"], help='genome directory') sp1.add_argument('--thread', type=int, default=4, help='downloading threads') sp1.set_defaults(func = download) args = ps.parse_args() if args.command: args.func(args) else: print('Error: need to specify a sub command\n') ps.print_help()

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Other groups/Owlolf/Yuuki Yuuna wa Yuusha de Aru Churutto!/churuttoo_06.py

Ichunjo/encode-script

36

133285

"""YuYuYu Churutto script""" # noqa from __future__ import annotations __author__ = 'Vardë' import G41Fun as gf import havsfunc as hvf import lvsfunc as lvf import vardefunc as vdf import xvs from vardautomation import FileInfo, PresetEAC3, PresetWEB from vsutil import depth, get_y from churutto_common import EncodeGoBrrr import vapoursynth as vs core = vs.core NUM = __file__[-5:-3] WEB = FileInfo(f'{NUM}/Yuuki Yuuna wa Yuusha de Aru Churutto! - {NUM} (Amazon Prime VBR 1080p).mkv', 24, -22, preset=[PresetWEB, PresetEAC3]) class Filtering(): # noqa def main(self: Filtering) -> vs.VideoNode: # noqa """Vapoursynth filtering""" src = WEB.clip_cut src = src.std.AssumeFPS(src) src = depth(src, 16) out = src denoise = hvf.SMDegrain(out, tr=1, thSAD=100, thSADC=100) out = denoise dering = hvf.EdgeCleaner(out, 15, smode=1, hot=True) dering = gf.MaskedDHA(dering, darkstr=0.05, brightstr=0.75) out = dering aaa = vdf.scale.nnedi3_upscale(get_y(out), pscrn=1, correct_shift=False) aaa = aaa.resize.Bicubic(1920, 1080, src_left=0.5, src_top=0.5, filter_param_a=-0.5, filter_param_b=0.25) out = vdf.misc.merge_chroma(aaa, out) cwarp = xvs.WarpFixChromaBlend(out, 64, depth=4) out = cwarp detail_mask = lvf.mask.detail_mask(out, brz_a=2250, brz_b=1200) deband = vdf.deband.dumb3kdb(out, 16, threshold=33, grain=[24, 0]) # deband_a = vdf.deband.dumb3kdb(out, 20, threshold=89, grain=[24, 0]) # deband = lvf.rfs(deband, deband_a, [(1970, out.num_frames - 1)]) deband = core.std.MaskedMerge(deband, out, detail_mask) out = deband return depth(out, 10).std.Limiter(16 << 2, [235 << 2, 240 << 2], [0, 1, 2])[:100] # return detail_mask if __name__ == '__main__': wizardry = EncodeGoBrrr(Filtering().main(), WEB) wizardry.run() else: WEB.clip_cut.set_output(0) FILTERED = Filtering().main() FILTERED.set_output(1) # FILTERED[0].set_output(1) # FILTERED[1].set_output(2)

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custom_addons/books_log/models/quotations.py

MonwarAdeeb/Bista_Solutions

0

92870

from odoo import _, api, fields, models class Quotations(models.Model): _inherit = "sale.order" note_on_customer = fields.Text("Note on Customer", help="Add Notes on Customers!")

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topicextractor/__init__.py

kjchung495/topicextractor

0

31900

<gh_stars>0 import nltk from nltk.tokenize import word_tokenize #nltk.download('punkt') #nltk.download('averaged_perceptron_tagger') from collections import Counter def extract_noun_counts(doc_list): nouns_pdoc = [] for i in range(len(doc_list)): pos = nltk.pos_tag(nltk.word_tokenize(doc_list[i])) print('working on doc ' + str(i+1)) nouns = [] for word, pos in pos: if pos.startswith('NN'): nouns.append(word) nouns_pdoc.append(nouns) freq = [] for nouns in nouns_pdoc: freq.append(dict(Counter(nouns))) return freq def tfidf(count, count_container): #'count' in dict type tfidf = {} doc_max = 0 for key, val in count.items(): if doc_max < val: doc_max = val all_doc_max = [] for i in range(len(count_container)): temp_max = 0 for key, val in count_container[i].items(): if temp_max < val: temp_max = val all_doc_max.append(temp_max) for key, val in count.items(): idf_sum = 0 idf = 0 for i in range(len(count_container)): if key in count_container[i].keys(): idf_sum += count_container[i][key]/all_doc_max[i] else: idf_sum += 0 idf = idf_sum/len(count_container) + 1e-10 #'idf' will have a value between 0~1 tfidf[key] = round((val/doc_max)/idf, 4) return Counter(tfidf).most_common()

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StockAndFlowInPython/depreciated/SFD_display_0.py

Rutherford1895/SFD_Canvas

3

54899

<reponame>Rutherford1895/SFD_Canvas import math from tkinter import * # matplotlib.use('TkAgg') from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg from StockAndFlowInPython.depreciated.classes import global_model as glbele class SFDCanvas(Frame): def __init__(self, master, stocks, flows, auxs, connectors): super().__init__(master) self.master = master self.xmost = 400 self.ymost = 400 self.canvas = Canvas(master=self.master) self.canvas.configure(background='white') #self.canvas.pack(side=BOTTOM, fill=BOTH, expand=1) self.hbar = Scrollbar(master=self.master, orient=HORIZONTAL) self.hbar.pack(side=BOTTOM, fill=X) self.hbar.config(command=self.canvas.xview) self.vbar = Scrollbar(master=self.master, orient=VERTICAL) self.vbar.pack(side=RIGHT, fill=Y) self.vbar.config(command=self.canvas.yview) # self.create_widgets() self.pack(fill=BOTH, expand=1) # self.filename = '' self.stocks = stocks self.flows = flows self.auxs = auxs self.connectors = connectors self.modelDrawer() def create_stock(self, x, y, w, h, label): """ :param x: Center x :param y: Center y :param w: width :param h: height :param label: label :return: """ self.canvas.create_rectangle(x - w * 0.5, y - h * 0.5, x + w * 0.5, y + h * 0.5, fill="#fff") self.canvas.create_text(x, y + 30, anchor=CENTER, font=("Arial", 13), text=label) def create_flow(self, x, y, pts, r, label): ''' Starting point x, y, ending point x, y, length, circle radius, label ''' for i in range(len(pts)-1): if i != len(pts)-2: self.canvas.create_line(pts[i][0],pts[i][1],pts[i+1][0],pts[i+1][1]) else: self.canvas.create_line(pts[i][0],pts[i][1],pts[i+1][0],pts[i+1][1],arrow=LAST) # self.canvas.create_line(xA, yA, xB, yB, arrow=LAST) self.canvas.create_oval(x - r, y - r, x + r, y + r, fill="#fff") self.canvas.create_text(x, y + r + 10, anchor=CENTER, font=("Arial", 13), text=label) def create_aux(self, x, y, r, label): ''' Central point x, y, radius, label ''' self.canvas.create_oval(x - r, y - r, x + r, y + r, fill="#fff") self.canvas.create_text(x, y + r + 10, anchor=CENTER, font=("Arial", 13), text=label) def create_alias(self, x, y, r, label): ''' Central point x, y, radius, label ''' self.canvas.create_oval(x - r, y - r, x + r, y + r, fill="gray70") self.canvas.create_text(x, y, anchor=CENTER, font=("Arial", 10), text="G") self.canvas.create_text(x, y + r + 10, anchor=CENTER, font=("Arial", 13, "italic"), text=label) def create_connector(self, xA, yA, xB, yB, angle, color='black'): # self.create_dot(xA,yA,3,'black') # self.create_dot(xB,yB,3,'black') alpha = math.radians(angle) if math.pi < alpha < math.pi * 2: alpha -= math.pi * 2 beta = math.atan2((yA - yB), (xB - xA)) # angle between A->B and x-positive print('alpha in degrees, ', math.degrees(alpha), 'beta in degrees, ', math.degrees(beta)) # calculate the center of circle rad_radiusA = alpha - math.pi * 0.5 # radiant of radius of the circle going out from A print('rad_radiusA (degrees), ', math.degrees(rad_radiusA), 'radians, ', rad_radiusA) gA = math.tan(rad_radiusA) print('gradiantA, ', gA) if xB != xA: gAB = (yA - yB) / (xB - xA) # y axis inversed, could be 'zero division' else: gAB = 99.99 print('gradiantAB, ', gAB) gM = (-1) / gAB print('gradiantM, ', gM) xM = (xA + xB) / 2 yM = (yA + yB) / 2 print("M's coordinate", xM, yM) xC = (yA + gA * xA - gM * xM - yM) / (gA - gM) yC = yA - gA * (xC - xA) print("A's coordinate: ", xA, yA) print("C's coordinate: ", xC, yC) #self.create_dot(xC, yC, 2, color, str(angle)) # draw center of the circle # TODO: when C and A are calculated to be the same point (and in fact not) rad_CA = math.atan2((yC - yA), (xA - xC)) rad_CB = math.atan2((yC - yB), (xB - xC)) print('rad_CA, ', rad_CA, 'rad_CB, ', rad_CB) # calculate radius radius = (pow((xB - xC), 2) + pow((yC - yB), 2)) ** 0.5 baseArc = math.atan2(yC - yA, xA - xC) print('baseArc in degrees, ', math.degrees(baseArc)) print("checking youhu or liehu") # vectors, this part seems to be correct vecStarting = [math.cos(alpha), math.sin(alpha)] vecAtoB = [xB - xA, yA - yB] print('vecStarting, ', vecStarting, 'vecAtoB, ', vecAtoB) angleCos = self.cosFormula(vecStarting, vecAtoB) print('Cosine of the angle in Between, ', angleCos) # checking youhu or liehu the direction inverse = 1 if angleCos < 0: # you hu print('deg_CA, ', math.degrees(rad_CA),'deg_CB',math.degrees(rad_CB)) diff = rad_CB-rad_CA print('youhu') else: # lie hu if rad_CA*rad_CB<0 and rad_CA <= rad_CB: # yi hao diff = rad_CB-rad_CA if diff > math.pi: diff = abs(diff) - math.pi*2 elif rad_CA*rad_CB<0 and rad_CA > rad_CB: diff = math.pi*2-rad_CA+rad_CB if diff > math.pi: diff = diff - math.pi*2 elif rad_CA*rad_CB>0 and rad_CA > rad_CB: diff = rad_CB-rad_CA if diff > math.pi: diff = math.pi*2 - diff elif rad_CA*rad_CB>0 and rad_CA < rad_CB: diff = rad_CB-rad_CA if diff > math.pi: diff = math.pi*2 - diff else: diff = rad_CB-rad_CA print('liehu') print('final diff in degrees, ', math.degrees(diff)) # generate new points x = [xA] y = [yA] n = 7 for i in range(n): baseArc = baseArc + diff / (n + 1) * inverse x1 = xC + radius * math.cos(baseArc) x.append(x1) y1 = yC - radius * math.sin(baseArc) y.append(y1) # Draw dots of the connectors, if you would like #self.create_dot(x1,y1,2,'gray',str(i)) x.append(xB) y.append(yB) self.canvas.create_line(x[0], y[0], x[1], y[1], x[2], y[2], x[3], y[3], x[4], y[4], x[5], y[5], x[6], y[6], x[7], y[7], x[8], y[8], smooth=True, fill='maroon2', arrow=LAST) print('\n') def create_dot(self, x, y, r, color, label=''): self.canvas.create_oval(x - r, y - r, x + r, y + r, outline=color, fill=color) self.canvas.create_text(x, y - 10, text=label) def cosFormula(self, a, b): ''' calculate the cosine value of a angle between 2 vectors. :param a: :param b: :return: ''' l = 0 m = 0 n = 0 for i in range(2): l += a[i] * b[i] m += a[i] ** 2 n += b[i] ** 2 return l / ((m * n) ** 0.5) def locateVar(self, name): name = name.replace("_", " ") # print(name) for s in self.modelHandler1.stocks: nameWithoutN = s.name.replace("\\n", " ") if nameWithoutN == name: return [s.x, s.y] for f in self.modelHandler1.flows: nameWithoutN = f.name.replace("\\n", " ") if nameWithoutN == name: return [f.x, f.y] for a in self.modelHandler1.auxs: nameWithoutN = a.name.replace("\\n", " ") # print(nameWithoutN) if nameWithoutN == name: return [a.x, a.y] def locateAlias(self, uid): # print("locate_alias is called") for al in self.modelHandler1.aliases: if al.uid == uid: return [al.x, al.y] # Here starts Widgets and Commands def modelDrawer(self): # now starts the 'drawing' part self.canvas.config(width = self.xmost, height = self.ymost, scrollregion = (0,0,self.xmost,self.ymost)) #self.canvas.config(width = wid, height = hei) self.canvas.config(xscrollcommand = self.hbar.set, yscrollcommand = self.vbar.set) # set common parameters width1 = 46 height1 = 35 length1 = 115 radius1 = 5 # draw connectors for c in self.connectors: print("\n") print(c.uid) ''' # from print("c.from_var:", c.from_var, "childNodes:", c.from_var.childNodes) if len(c.from_var.childNodes) > 1: # if this end is on an alias print("it has more than 1 childNodes, so alias") from_cord = self.locate_alias(c.from_var.childNodes[1].getAttribute("uid")) else: print("it has childNodes, so normal variable") print("c.from_var.childNodes[0].data: ", c.from_var.childNodes[0].data) from_cord = self.locate_var(c.from_var.childNodes[0].data) print("from_cord: ", from_cord) # to print("c.to_var", c.to_var, "childNodes:", c.to_var.childNodes) if len(c.to_var.childNodes) > 1: # if this end is no an alias print("it has more than 1 childNodes, so alias") to_cord = self.locate_alias(c.to_var.childNodes[1].getAttribute("uid")) else: print("it has childNodes, so normal variable") print("c.to_var.childNodes[0].data: ", c.to_var.childNodes[0].data) to_cord = self.locate_var(c.to_var.childNodes[0].data) print("to_cord: ", to_cord) ''' from_cord = [glbele.get_value(c.from_var).x, glbele.get_value(c.from_var).y] to_cord = [glbele.get_value(c.to_var).x, glbele.get_value(c.to_var).y] from_to_cord = from_cord + to_cord self.create_connector(from_to_cord[0], from_to_cord[1], from_to_cord[2], from_to_cord[3] - 8, c.angle) # minus 8: the arrow it self not consumed # draw stocks # for s in self.session_handler1.stocks: for s in self.stocks: self.create_stock(s.x, s.y, width1, height1, s.name) if s.x> self.xmost: self.xmost = s.x if s.y> self.ymost: self.ymost = s.y # draw flows for f in self.flows: self.create_flow(f.x, f.y, f.pts, radius1, f.name) if f.x > self.xmost: self.xmost = f.x if f.y > self.ymost: self.ymost = f.y # draw auxs for a in self.auxs: self.create_aux(a.x, a.y, radius1, a.name) if a.x > self.xmost: self.xmost = a.x if a.y > self.ymost: self.ymost = a.y ''' # draw aliases for al in self.session_handler1.aliases: self.create_alias(al.x, al.y, radius1, al.of.replace('_', ' ')) if al.x > self.xmost: self.xmost = al.x if al.y > self.ymost: self.ymost = al.y ''' print('Xmost,',self.xmost,'Ymost,',self.ymost) self.canvas.config(width = self.xmost, height = self.ymost, scrollregion = (0,0,self.xmost,self.ymost)) self.canvas.pack(side = LEFT, expand=1, fill=BOTH) # Here starts the simulation part ''' def simulation_handler(self): import pysd if self.filename != '': new_name = self.filename[:-5]+".xmile" shutil.copy(self.filename,new_name) self.model_run = pysd.read_xmile(new_name) os.remove(new_name) os.remove(new_name[:-6]+".py") self.results = self.model_run.run() print("Simulation Finished.") self.variables_in_model = self.results.columns.values.tolist() self.variables_in_model.remove("TIME") self.comboxlist["values"] = self.variables_in_model def select_variable(self,*args): self.selected_variable = self.comboxlist.get() def show_figure(self): f = Figure(figsize=(5, 4), dpi=100) a = f.add_subplot(111) x = self.results['TIME'].tolist() y = self.results[self.selected_variable].tolist() a.plot(x,y) a.set_title(self.selected_variable) a.set_xlabel('Time') a.set_ylabel(self.selected_variable) figure_0 = GraphWindow(self.selected_variable, f) ''' class GraphWindow(): def __init__(self,title,figure): top = Toplevel() top.title = title graph = FigureCanvasTkAgg(figure, master=top) graph.draw() graph._tkcanvas.pack()

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ingenico/connect/sdk/param_request.py

festicket/connect-sdk-python3

12

194710

<filename>ingenico/connect/sdk/param_request.py class ParamRequest(object): """ Represents a set of request parameters. """ def to_request_parameters(self): """ :return: list[:class:`ingenico.connect.sdk.RequestParam`] representing the HTTP request parameters """ raise NotImplementedError

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scripts/8-pacotes-modulos-python/ex107/teste.py

dev-alissonalves/python-codes

1

110049

import moeda p = float(input("Informe um valor qualquer: ")) print("\n") print("-=-=-=-=- INÍCIO =-=-=-=") print(f"A metade do valor {p:.2f} é: {moeda.metade(p)}") print(f"O dobro do valor {p:.2f} é: {moeda.dobro(p)}") print(f"Aumentando 10%, temos: {moeda.aumentar(p, 10)}") print(f"Diminuindo 10%, temos: {moeda.diminuir(p, 10)}") print("-=-=-=-=- FIM =-=-=-=")

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transfer_learning/data_handling.py

E-Fumi/VariationalAutoEncoder

1

188939

<filename>transfer_learning/data_handling.py import hyperparameters as hp import tensorflow as tf from tensorflow.keras.preprocessing.image import ImageDataGenerator data_augmenter = ImageDataGenerator( rescale=1.0/255, horizontal_flip=True, rotation_range=25, brightness_range=[0.8, 1.2], zoom_range=0.2, shear_range=0.1 ) def build_generator(augmenter, directory): generator = augmenter.flow_from_directory( directory='C://Data/2nd_pass/' + directory, target_size=(300, 300), color_mode='rgb', batch_size=hp.batch_size, class_mode='binary', shuffle=True) return generator training_generator = build_generator(data_augmenter, 'train') val_ds = tf.keras.preprocessing.image_dataset_from_directory( directory='C://Data/2nd_pass/validation', labels='inferred', label_mode='binary', batch_size=32, image_size=(300, 300), shuffle=True) val_ds = val_ds.map(lambda a, b: (a / 255.0, tf.cast(b, dtype=tf.int8)))

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excel_with_python.py

wahidupal/excel_with_python

1

154158

<reponame>wahidupal/excel_with_python import dash import plotly.express as px import pandas as pd # Data exploration with Pandas #----------------------------- df = pd.read_csv("vgsales.csv") # print(df[:5]) #print(df.iloc[:5, [2,3,5,10]]) # print(df.Genre.nunique()) # print(df.Genre.unique()) # print(sorted(df.Year.unique())) # Data Visualization with Plotly # ------------------------------ # fig_pie = px.pie(data_frame=df, names='Genre', values='Japan Sales') # fig_pie = px.pie(data_frame=df, names='Genre', values='North American Sales') # fig_pie.show() # fig_bar = px.bar(data_frame=df, x='Genre', y='Japan Sales') # fig_bar.show() # fig_hist = px.histogram(data_frame=df, x='Genre', y='Japan Sales') # fig_hist.show() # fig_hist = px.histogram(data_frame=df, x='Year', y='Japan Sales') # fig_hist.show() # Interactive Graphs with Dash # ---------------------------- import dash_core_components as dcc import dash_html_components as html from dash.dependencies import Output, Input app = dash.Dash(__name__) app.layout=html.Div([ html.H1("Graph Analysis for Game released from 1980 to 2016"), dcc.Dropdown(id='genre-choice', options=[{'label':x, 'value':x} for x in sorted(df.Genre.unique())], value='Action' ), dcc.Graph(id= 'my-graph' , figure = {} ) ]) @app.callback( Output(component_id='my-graph', component_property='figure'), Input(component_id='genre-choice', component_property='value') ) def interactive_graphs(value_genre): print(value_genre) dff = df[df.Genre==value_genre] fig = px.bar(data_frame=dff, x='Year', y='Japan Sales') return fig if __name__ == '__main__': app.run_server()

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src/systemmanager.py

WinterLicht/Chaos-Projectile

59

149276

<filename>src/systemmanager.py """ .. module:: systemmanager :Platform: Unix, Windows :Synopsis: system manager contains all game systems. """ import rendersystem import collisionsystem import inputsystem import animationsystem import combatsystem import statesystem import soundsystem class SystemManager(object): """System manager is a container of all game systems. :Attributes: - *systems* (systems list): all systems are stored here """ def __init__(self, event_manager, gameWorld): """ :param event_manager: event manager :type event_manager: events.EventManager :param gameWorld: game world :type gameWorld: gameWorld.GameWorld """ self.systems = list() self.systems.append(collisionsystem.CollisionSystem(event_manager, gameWorld)) self.systems.append(animationsystem.AnimationSystem(event_manager, gameWorld)) self.systems.append(inputsystem.InputSystem(event_manager, gameWorld)) self.systems.append(statesystem.StateSystem(event_manager, gameWorld)) self.systems.append(rendersystem.RenderSystem(event_manager, gameWorld)) self.systems.append(combatsystem.CombatSystem(event_manager, gameWorld)) #self.systems.append(soundsystem.SoundSystem(event_manager, gameWorld))

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desktop/core/ext-py/SQLAlchemy-1.3.16/examples/elementtree/optimized_al.py

e11it/hue-1

2

152222

"""Uses the same strategy as ``adjacency_list.py``, but associates each DOM row with its owning document row, so that a full document of DOM nodes can be loaded using O(1) queries - the construction of the "hierarchy" is performed after the load in a non-recursive fashion and is more efficient. """ # PART I - Imports/Configuration from __future__ import print_function import os import re from xml.etree import ElementTree from sqlalchemy import and_ from sqlalchemy import Column from sqlalchemy import create_engine from sqlalchemy import ForeignKey from sqlalchemy import Integer from sqlalchemy import MetaData from sqlalchemy import String from sqlalchemy import Table from sqlalchemy import Unicode from sqlalchemy.orm import lazyload from sqlalchemy.orm import mapper from sqlalchemy.orm import relationship from sqlalchemy.orm import Session e = create_engine("sqlite://") meta = MetaData() # PART II - Table Metadata # stores a top level record of an XML document. documents = Table( "documents", meta, Column("document_id", Integer, primary_key=True), Column("filename", String(30), unique=True), ) # stores XML nodes in an adjacency list model. This corresponds to # Element and SubElement objects. elements = Table( "elements", meta, Column("element_id", Integer, primary_key=True), Column("parent_id", Integer, ForeignKey("elements.element_id")), Column("document_id", Integer, ForeignKey("documents.document_id")), Column("tag", Unicode(30), nullable=False), Column("text", Unicode), Column("tail", Unicode), ) # stores attributes. This corresponds to the dictionary of attributes # stored by an Element or SubElement. attributes = Table( "attributes", meta, Column( "element_id", Integer, ForeignKey("elements.element_id"), primary_key=True, ), Column("name", Unicode(100), nullable=False, primary_key=True), Column("value", Unicode(255)), ) meta.create_all(e) # PART III - Model # our document class. contains a string name, # and the ElementTree root element. class Document(object): def __init__(self, name, element): self.filename = name self.element = element # PART IV - Persistence Mapping # Node class. a non-public class which will represent the DB-persisted # Element/SubElement object. We cannot create mappers for ElementTree elements # directly because they are at the very least not new-style classes, and also # may be backed by native implementations. so here we construct an adapter. class _Node(object): pass # Attribute class. also internal, this will represent the key/value attributes # stored for a particular Node. class _Attribute(object): def __init__(self, name, value): self.name = name self.value = value # setup mappers. Document will eagerly load a list of _Node objects. # they will be ordered in primary key/insert order, so that we can reconstruct # an ElementTree structure from the list. mapper( Document, documents, properties={ "_nodes": relationship( _Node, lazy="joined", cascade="all, delete-orphan" ) }, ) # the _Node objects change the way they load so that a list of _Nodes will # organize themselves hierarchically using the ElementTreeMarshal. this # depends on the ordering of nodes being hierarchical as well; relationship() # always applies at least ROWID/primary key ordering to rows which will # suffice. mapper( _Node, elements, properties={ "children": relationship( _Node, lazy=None ), # doesnt load; used only for the save relationship "attributes": relationship( _Attribute, lazy="joined", cascade="all, delete-orphan" ), # eagerly load attributes }, ) mapper(_Attribute, attributes) # define marshalling functions that convert from _Node/_Attribute to/from # ElementTree objects. this will set the ElementTree element as # "document._element", and append the root _Node object to the "_nodes" mapped # collection. class ElementTreeMarshal(object): def __get__(self, document, owner): if document is None: return self if hasattr(document, "_element"): return document._element nodes = {} root = None for node in document._nodes: if node.parent_id is not None: parent = nodes[node.parent_id] elem = ElementTree.SubElement(parent, node.tag) nodes[node.element_id] = elem else: parent = None elem = root = ElementTree.Element(node.tag) nodes[node.element_id] = root for attr in node.attributes: elem.attrib[attr.name] = attr.value elem.text = node.text elem.tail = node.tail document._element = ElementTree.ElementTree(root) return document._element def __set__(self, document, element): def traverse(node): n = _Node() n.tag = str(node.tag) n.text = str(node.text) n.tail = str(node.tail) document._nodes.append(n) n.children = [traverse(n2) for n2 in node] n.attributes = [ _Attribute(str(k), str(v)) for k, v in node.attrib.items() ] return n traverse(element.getroot()) document._element = element def __delete__(self, document): del document._element document._nodes = [] # override Document's "element" attribute with the marshaller. Document.element = ElementTreeMarshal() # PART V - Basic Persistence Example line = "\n--------------------------------------------------------" # save to DB session = Session(e) # get ElementTree documents for file in ("test.xml", "test2.xml", "test3.xml"): filename = os.path.join(os.path.dirname(__file__), file) doc = ElementTree.parse(filename) session.add(Document(file, doc)) print("\nSaving three documents...", line) session.commit() print("Done.") print("\nFull text of document 'text.xml':", line) document = session.query(Document).filter_by(filename="test.xml").first() ElementTree.dump(document.element) # PART VI - Searching for Paths # manually search for a document which contains "/somefile/header/field1:hi" print("\nManual search for /somefile/header/field1=='hi':", line) d = ( session.query(Document) .join("_nodes", aliased=True) .filter(and_(_Node.parent_id == None, _Node.tag == "somefile")) .join("children", aliased=True, from_joinpoint=True) .filter(_Node.tag == "header") .join("children", aliased=True, from_joinpoint=True) .filter(and_(_Node.tag == "field1", _Node.text == "hi")) .one() ) ElementTree.dump(d.element) # generalize the above approach into an extremely impoverished xpath function: def find_document(path, compareto): query = session.query(Document) first = True for i, match in enumerate( re.finditer(r"/([\w_]+)(?:\[@([\w_]+)(?:=(.*))?\])?", path) ): (token, attrname, attrvalue) = match.group(1, 2, 3) if first: query = query.join("_nodes", aliased=True).filter( _Node.parent_id == None ) first = False else: query = query.join("children", aliased=True, from_joinpoint=True) query = query.filter(_Node.tag == token) if attrname: query = query.join("attributes", aliased=True, from_joinpoint=True) if attrvalue: query = query.filter( and_( _Attribute.name == attrname, _Attribute.value == attrvalue, ) ) else: query = query.filter(_Attribute.name == attrname) return ( query.options(lazyload("_nodes")).filter(_Node.text == compareto).all() ) for path, compareto in ( ("/somefile/header/field1", "hi"), ("/somefile/field1", "hi"), ("/somefile/header/field2", "there"), ("/somefile/header/field2[@attr=foo]", "there"), ): print("\nDocuments containing '%s=%s':" % (path, compareto), line) print([d.filename for d in find_document(path, compareto)])

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sem/features/arityfeatures.py

YoannDupont/SEM

22

165125

<filename>sem/features/arityfeatures.py<gh_stars>10-100 # -*- coding: utf-8 -*- """ file: arityfeatures.py Description: features that could not be categorized and are thus defined by the number of arguments they take (their arity). author: <NAME> MIT License Copyright (c) 2018 <NAME> 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 re from .feature import Feature from .getterfeatures import DEFAULT_GETTER class ArityFeature(Feature): def __init__(self, *args, **kwargs): super(ArityFeature, self).__init__(*args, **kwargs) self._getter = kwargs.pop("getter", DEFAULT_GETTER) class NullaryFeature(ArityFeature): def __init__(self, *args, **kwargs): super(NullaryFeature, self).__init__(*args, **kwargs) class BOSFeature(NullaryFeature): def __init__(self, *args, **kwargs): super(BOSFeature, self).__init__(*args, **kwargs) self._is_boolean = True def __call__(self, list2dict, position, *args, **kwargs): return position == 0 class EOSFeature(NullaryFeature): def __init__(self, *args, **kwargs): super(EOSFeature, self).__init__(*args, **kwargs) self._is_boolean = True def __call__(self, list2dict, position, *args, **kwargs): return position == len(list2dict)-1 class LowerFeature(NullaryFeature): def __init__(self, *args, **kwargs): super(LowerFeature, self).__init__(*args, **kwargs) def __call__(self, *args, **kwargs): return self._getter(*args, **kwargs).lower() class SubstringFeature(NullaryFeature): def __init__(self, from_index=0, to_index=2**31, *args, **kwargs): super(SubstringFeature, self).__init__(from_index, to_index, *args, **kwargs) self._default = kwargs.pop("default", '""') self._from_index = int(from_index) self._to_index = int(to_index) def __call__(self, *args, **kwargs): s = self._getter(*args, **kwargs)[self._from_index : self._to_index] if s: return s else: return self._default class UnaryFeature(ArityFeature): def __init__(self, element, *args, **kwargs): super(UnaryFeature, self).__init__(*args, **kwargs) self._is_boolean = False self._element = element class IsUpperFeature(UnaryFeature): def __init__(self, index, *args, **kwargs): super(IsUpperFeature, self).__init__(index, *args, **kwargs) self._is_boolean = True self._index = self._element def __call__(self, *args, **kwargs): return self._getter(*args, **kwargs)[self._index].isupper() class BinaryFeature(ArityFeature): def __init__(self, element1, element2, *args, **kwargs): super(BinaryFeature, self).__init__(*args, **kwargs) self._element1 = element1 self._element2 = element2 class SubstitutionFeature(BinaryFeature): def __init__(self, pattern, replacement, *args, **kwargs): super(SubstitutionFeature, self).__init__(pattern, replacement, *args, **kwargs) flags = kwargs.pop("flags", 0) self._replacer = re.compile(pattern, flags) self._replacement = replacement def __call__(self, *args, **kwargs): return self._replacer.sub(self._replacement, self._getter(*args, **kwargs)) class NaryFeature(ArityFeature): def __init__(self, *args, **kwargs): super(NaryFeature, self).__init__(self, *args, **kwargs) class SequencerFeature(NaryFeature): def __init__(self, *args, **kwargs): super(SequencerFeature, self).__init__(*args, **kwargs) if len(args) == 0: raise ValueError("{0} cannot have less than 1 feature".format(self.__class__.__name__)) self._features = args for i in range(1, len(self._features)): # ensuring that we can call the different features sequentially self._features[i]._getter = DEFAULT_GETTER def __call__(self, *args, **kwargs): current = self._features[0](*args, **kwargs) for feature in self._features[1:]: current = feature(current, *args, **kwargs) return current

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src/platforms/platform_abs.py

Brickyblossom/DT2021

1

155766

<filename>src/platforms/platform_abs.py<gh_stars>1-10 '''Abstract class for platform''' from abc import ABC from typing import Dict import disnake from src import cfg class PlatForm(ABC): '''Abstract base class for a platform''' RANKCOLOR = {} PLATFORM_NAME = "Nothing" HANDLE_FILE_NAME = "/abc" def __init__(self) -> None: self.bot = cfg.bot async def verify(self, member: disnake.Member, handle: str) -> bool: # pylint: disable=no-self-use,unused-argument '''Default verification procedure (returns True)''' return True async def generate_user_embed(self, handle: str, member: disnake.Member) -> disnake.Embed: # pylint: disable=no-self-use,unused-argument '''Generates a blank embed''' return disnake.Embed() async def generate_dict_of_rank(self, user_list) -> Dict: # pylint: disable=no-self-use,unused-argument '''Generate blank dict''' return {}

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Image_detection_codes/Keras_training/test2.py

pasadyash/CitizenServiceApp

0

12732

import numpy as np np.random.seed(123) # for reproducibility from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Convolution2D, MaxPooling2D from keras.utils import np_utils from dataset_pothole import pothole from keras.models import model_from_json # 4. Load pre-shuffled MNIST data into train and test sets (X_train, y_train), (X_test, y_test) = pothole.load_data() print(X_train.shape) print() print (y_train.shape) print() # 5. Preprocess input data X_train = X_train.reshape(X_train.shape[0], 200, 200, 1) X_test = X_test.reshape(X_test.shape[0], 200, 200, 1) X_train = X_train.astype('float32') X_test = X_test.astype('float32') X_train /= 3380 X_test /= 3380 # 6. Preprocess class labels Y_train = np_utils.to_categorical(y_train, 4) Y_test = np_utils.to_categorical(y_test, 4) # 7. Define model architecture nb_classes = 4 # number of epochs to train # number of convolutional filters to use nb_filters = 32 # size of pooling area for max pooling nb_pool = 2 # convolution kernel size nb_conv = 3 model = Sequential() model.add(Convolution2D(nb_filters, nb_conv, nb_conv, border_mode='valid', input_shape=(200, 200, 1))) convout1 = Activation('relu') model.add(convout1) model.add(Convolution2D(nb_filters, nb_conv, nb_conv)) convout2 = Activation('relu') model.add(convout2) model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool))) model.add(Dropout(0.5)) model.add(Flatten()) model.add(Dense(128)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(nb_classes)) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='adadelta') # 9. Fit model on training data model.fit(X_train, Y_train, batch_size=32, nb_epoch=2, verbose=1) # 10. Evaluate model on test data score = model.evaluate(X_test, Y_test, verbose=0) # serialize model to JSON model_json = model.to_json() with open("model.json", "w") as json_file: json_file.write(model_json) # serialize weights to HDF5 model.save_weights("model.h5") print("Saved model to disk") print('Test loss: ', score[0]) print('Test accuracy: ', score[1])

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pulse2percept/datasets/tests/test_nanduri2012.py

narenberg/pulse2percept

40

29857

<reponame>narenberg/pulse2percept import pandas as pd import numpy.testing as npt from pulse2percept.datasets import load_nanduri2012 def test_load_nanduri2012(): data = load_nanduri2012(shuffle=False) npt.assert_equal(isinstance(data, pd.DataFrame), True) columns = ['subject', 'implant', 'electrode', 'task', 'stim_class', 'freq', 'amp_factor', 'ref_stim_class', 'ref_amp_factor', 'ref_freq', 'brightness', 'size', 'pulse_dur', 'interphase_dur', 'pulse_type', 'varied_param'] for expected_col in columns: npt.assert_equal(expected_col in data.columns, True) npt.assert_equal(data.shape, (128, 17)) npt.assert_equal(data.subject.unique(), ['S06']) # Shuffle dataset (index will always be range(552), but rows are shuffled): data = load_nanduri2012(shuffle=True, random_state=42) npt.assert_equal(data.loc[0, 'subject'], 'S06') npt.assert_equal(data.loc[0, 'electrode'], 'B1') npt.assert_equal(data.loc[94, 'subject'], 'S06') npt.assert_equal(data.loc[94, 'electrode'], 'A4') # Select electrodes: data = load_nanduri2012(electrodes='A2') npt.assert_equal(data.shape, (16, 17)) npt.assert_equal(data.electrode.unique(), 'A2') npt.assert_equal(data.subject.unique(), 'S06') data = load_nanduri2012(electrodes=['A1', 'A9']) # 'A9' doesn't exist npt.assert_equal(data.shape, (0, 17)) npt.assert_equal(data.electrode.unique(), 'A1') npt.assert_equal(data.subject.unique(), 'S06') # Select task data = load_nanduri2012(task='rate') npt.assert_equal(data.shape, (88, 17)) npt.assert_equal(data.task.unique(), 'rate') data = load_nanduri2012(task='size') npt.assert_equal(data.shape, (40, 17)) npt.assert_equal(data.task.unique(), 'size')

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src/notelist/schemas/search.py

jajimenez/notelist

1

114418

<gh_stars>1-10 """Search schemas module.""" from marshmallow import Schema from marshmallow.fields import Str from marshmallow.validate import Length class SearchSchema(Schema): """Search schema.""" search = Str( validate=Length(min=2, max=200), required=True, error_messages={"required": "Field is required."})

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test/test_notification_target_api.py

chbndrhnns/ahoi-client

0

186576

# coding: utf-8 """ [AHOI cookbook](/ahoi/docs/cookbook/index.html) [Data Privacy](/sandboxmanager/#/privacy) [Terms of Service](/sandboxmanager/#/terms) [Imprint](https://sparkassen-hub.com/impressum/) © 2016&dash;2017 Starfinanz - Ein Unternehmen der Finanz Informatik # noqa: E501 OpenAPI spec version: 2.1.0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import swagger_client from swagger_client.api.notification_target_api import NotificationTargetApi # noqa: E501 from swagger_client.rest import ApiException class TestNotificationTargetApi(unittest.TestCase): """NotificationTargetApi unit test stubs""" def setUp(self): self.api = swagger_client.api.notification_target_api.NotificationTargetApi() # noqa: E501 def tearDown(self): pass def test_delete_notification_target(self): """Test case for delete_notification_target Delete notification target # noqa: E501 """ pass def test_get_notification_target(self): """Test case for get_notification_target Get notification target # noqa: E501 """ pass def test_get_notification_targets(self): """Test case for get_notification_targets List notification targets # noqa: E501 """ pass def test_post_notification_target(self): """Test case for post_notification_target Create a new notification target # noqa: E501 """ pass def test_put_notification_target(self): """Test case for put_notification_target Update notification target # noqa: E501 """ pass if __name__ == '__main__': unittest.main()

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internetradio.py

pathakraul/internetradio-client

0

116085

<reponame>pathakraul/internetradio-client #!/usr/bin/python # Internet Radio Client using Shoutcast service # to search and play random channels with genre search # Usage ./internetradio.py --genre <GENRE> import os import logging import argparse import requests as rq import xml.etree.ElementTree as et #---------------------------------------------------------------------------------------- # Set your shoutcast devId here #---------------------------------------------------------------------------------------- devId = "" #---------------------------------------------------------------------------------------- logging.basicConfig(format='%(levelname)s %(asctime)s %(message)s', level=logging.INFO) log = logging.getLogger(__name__) #---------------------------------------------------------------------------------------- baseUrl = "http://api.shoutcast.com/" tuneinUrl = "http://yp.shoutcast.com/" base = "sbin/tunein-station.pls" randomSearch = "station/randomstations?k={0}&f=xml&mt=audio/mpeg&limit=1&genre=".format(devId) #---------------------------------------------------------------------------------------- playerCommand = "mplayer -quiet {0}\n" #---------------------------------------------------------------------------------------- def keywordSearch(keyword): log.debug("Making Keyword Search\n") searchurl=baseUrl+randomSearch+keyword log.debug("Searching for {0} on Shoutcast".format(keyword)) res = rq.get(searchurl, proxies=proxies) if res.status_code == 200: srtContent = res.content return str(res.content) else: log.error("Search response failure from server {0}".format(res.status_code)) #---------------------------------------------------------------------------------------- ''' <response> <statusCode>200</statusCode> #tree[0] <statusText>Ok</statusText> #tree[1] <data> #tree[2] <stationlist> #tree[2][0] <tunein base="/sbin/tunein-station.pls" base-m3u="/sbin/tunein-station.m3u" base-xspf="/sbin/tunein-station.xspf"/> #tree[2][0][0] <station name="Bollywood Hits" genre="Hindi" ct="Baamulaiza - Mika <NAME>" mt="audio/mpeg" id="312131" br="96" lc="13" ml="100"/> #tree[2][0][1] </stationlist> </data> </response> ''' def getRandomChannel(data): tree = et.fromstring(data) cid = tree[2][0][1].attrib['id'] cn = tree[2][0][1].attrib['name'] log.info("Playing Channel [{0}], Channel ID [{1}]".format(cn, cid)) return cid #---------------------------------------------------------------------------------------- def playChannel(channelId): channelurl = tuneinUrl+base+"?id="+ str(channelId) channelstream = parseStreamUrl(channelurl) print channelstream command = playerCommand.format(channelstream) print command os.system(command) #---------------------------------------------------------------------------------------- def searchChannelAndPlay(genreKeyword): responsedata = keywordSearch(genreKeyword) channelid = getRandomChannel(responsedata) playChannel(channelid) #---------------------------------------------------------------------------------------- def parseStreamUrl(channelurl): response = rq.get(channelurl, stream=True, proxies=proxies) with open("stream.pls", "wb") as handle: for data in response.iter_content(): handle.write(data) x = [] datafile = file('./stream.pls') for line in datafile: if "File1=" in line: x = str(line) return x[6:-1] #---------------------------------------------------------------------------------------- if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--genre", help="Search random channel of particular genre") args = parser.parse_args() if args.genre: genreKeyword = args.genre else: log.error("Provide any genre to search a random channel") searchChannelAndPlay(genreKeyword) #----------------------------------------------------------------------------------------

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larpWikiAnomalyScanner.py

AllanWegan/larpWikiAnomalyScanner

0

46081

#!/usr/bin/env python3 import time import re import glob import os import platform import sys import unicodedata import urllib.parse import codecs import queue from multiprocessing import Process, Event, Queue from collections import Counter baseDir = os.path.dirname(__file__) sourceDir = os.path.join(baseDir, 'backup') blacklist = ( 'HilfeZurCreoleSyntax.txt', ) class AnomalyFormatter: """ Formats found anomalies and buffers the resuklting text. The text buffer is returned and erased by getText(). Also counts found anomalies. """ def __init__(self, textEscaper, textDecorator, maxPartLength=70): self._buffer = [] self._escaper = textEscaper self._decorator = textDecorator self.maxPartLength = maxPartLength self.qoute = '"' self.ellipsis = '…' self.sol = '|' self.eol = '|' self.minAfterLength = 20 self.counts = Counter() self._lastPath = '' self._lastLineNr = 0 def out(self, path, lineNr, startColumn, endColumn, line, anomaly): b = self._buffer d = self._decorator q = self.qoute if self._lastPath != path: self._lastPath = path self._lastLineNr = 0 self.counts['pathCount'] += 1 ePath = d.decorateText(self._escaper.escape(path), d.textBCyan) pageName = os.path.basename(path).replace(' - ', '/') if pageName[-4:] == '.txt': pageName = pageName[0:-4] url = 'https://larpwiki.de/' + urllib.parse.quote(pageName) eUrl = d.decorateText(url, d.textWhite) b.extend(('\n', ePath, ':\n')) b.extend((' ', eUrl, '\n')) if self._lastLineNr != lineNr: if self._lastLineNr != lineNr: self.counts['lineCount'] += 1 self._lastLineNr = lineNr eLineNr = d.decorateText(str(lineNr + 1), d.textBYellow) b.extend((' Line ', eLineNr, ':\n')) self.counts['anomalyCount'] += 1 self.counts[anomaly] += 1 eColumn = d.decorateText(str(startColumn + 1), d.textBYellow) ml = self.maxPartLength # Extract as much of the anomaly as allowed and selected: t = self._escaper.escapeLimitRight(line[startColumn:endColumn], ml) part = t[0] partCpLength = t[1] partComplete = ((endColumn - startColumn - partCpLength) == 0) ml = max(0, ml - len(part)) # Extract leading text but reserve some quota for trailing: if partComplete: mal = min(len(line) - endColumn, int(ml / 2), self.minAfterLength) else: mal = 0 bLength = min(startColumn, ml - mal) t = self._escaper.escapeLimitLeft(line[:startColumn], bLength) before = t[0] beforeCpLength = t[1] ml = max(0, ml - len(before)) # Extract as much of trailing text as available and quota left: if partComplete: t = self._escaper.escapeLimitRight(line[endColumn:], ml) after = t[0] afterCpLength = t[1] else: after = '' afterCpLength = 0 if startColumn - beforeCpLength > 0: sol = self.ellipsis else: sol = self.sol if (startColumn + partCpLength + afterCpLength) < len(line): eol = self.ellipsis else: eol = self.eol before = d.decorateText(before, d.textYellow) part = d.decorateText(part, d.textBYellow, d.textUnderline) after = d.decorateText(after, d.textYellow) b.extend((' Column ', eColumn, ', anomaly ', q, anomaly, q, ':\n')) b.extend((' ', sol, q, before, part, after, q, eol, '\n')) def getText(self): text = ''.join(self._buffer) self._buffer = [] return text def getCounts(self): counts = self.counts self.counts = Counter() return counts class AnsiTextDecorator: """ Colorizes output for ANSI terminals """ textBlack = '30' textRed = '31' textGreen = '32' textYellow = '33' textBlue = '34' textMagenta = '35' textCyan = '36' textGrey = '37' textBGrey = '30;1' textBRed = '31;1' textBGreen = '32;1' textBYellow = '33;1' textBBlue = '34;1' textBMagenta = '35;1' textBCyan = '36;1' textWhite = '37;1' textBold = '1' textItalic = '3' textUnderline = '4' backgroundBlack = '40' backgroundRed = '41' backgroundGreen = '42' backgroundYellow = '43' backgroundBlue = '44' backgroundMagenta = '45' backgroundCyan = '46' backgroundGrey = '47' def decorateText(self, text, *codes): if not len(codes): return text codesStr = ''.join(('\x1B[' + code + 'm' for code in codes)) return '{0}{1}\x1B[0m'.format(codesStr, text) class DummyTextDecorator(AnsiTextDecorator): def decorateText(self, text, *codes): return text def makeTextDecorator(useAnsi=False): if useAnsi: return AnsiTextDecorator() return DummyTextDecorator() class TextEscaper: """ Escapes non-printable code points except space (0x20). """ def escape(self, text): return repr(text)[1:-1].replace('"', r'\"') def escapeLimitRight(self, text, maxLength): if maxLength <= 0: return '', 0 text = text[:maxLength] textEsc = self.escape(text) while len(textEsc) > maxLength: text = text[0:-1] textEsc = self.escape(text) return textEsc, len(text) def escapeLimitLeft(self, text, maxLength): if maxLength <= 0: return '', 0 text = text[-maxLength:] textEsc = self.escape(text) while len(textEsc) > maxLength: text = text[1:] textEsc = self.escape(text) return textEsc, len(text) _detectSmilieRe = re.compile(r'''(?:^|(?<=\s)) [:;,8B][-~]?(?:[)}\]|({[]{1,2}|[pPD])[=\#]? (?:\s|$)''', re.VERBOSE) def detectSmilie(line, offset): """ Detects simple western LTR ASCII smilies like ";~P=" A smilie starts with a symbol for the eyes, followed by an optional symbol for the nose and a symbol for the mouth. A symbol for the beard may follow. The smilie has to begin and end at the start/end of line or after/before whitespace. """ return _detectSmilieRe.match(line, offset) is not None def checkForInvalidCodePoints(escaper, outputter, path, lineNr, line): markAllowed = False for cpIndex, cp in enumerate(line): anomaly = True unexpectedMark = False cpCat = unicodedata.category(cp) cpCatMain = cpCat[0] # Don't report letters, numbers, punctuation, symbols, # whitespace and some miscategorized whitespace: if cpCatMain in 'LNPSZ' or cp in ( '\t', '\xad', # SOFT HYPHEN, category Cf '\u200d', # ZERO WIDTH JOINER, category Cf '\u200e', # LEFT-TO-RIGHT MARK, category Cf None ): anomaly = False # But report REPLACEMENT CHARACTER from category So, because # it most likely is a character set conversion artifact: if cp == '�': anomaly = True # Don't report marks following letters or other marks: if cpCatMain == 'M': if markAllowed: anomaly = False else: # Not in letter cluster. anomaly, unexpectedMark = True, True elif cpCatMain == 'L': markAllowed = True else: markAllowed = False if anomaly: cpName = unicodedata.name(cp, 'unnamed') if unexpectedMark: suffix = ' not preceded by a letter' else: suffix = '' msg = 'Unicode {0} ({1}, category {2}){3}' msg = msg.format(escaper.escape(cp), cpName, cpCat, suffix) outputter.out(path, lineNr, cpIndex, cpIndex + 1, line, msg) _checkForUseModListRe = re.compile(r'(\*|#(\*|#([*#])))[*#]*') def checkForUseModList(outputter, path, lineNr, line, isDirective, isComment): match = _checkForUseModListRe.match(line) if match: isDirective, isComment = False, False start = match.start() end = match.end() outputter.out(path, lineNr, start, end, line, 'UseMod list') return isDirective, isComment _checkForNonCommentAfterRedirectRe = re.compile(r'\s*(\S.*?)\s*$') def detectNonCommentAfterRedirect(outputter, path, lineNr, line): match = _checkForNonCommentAfterRedirectRe.match(line) if match: start = match.start(1) end = match.end(1) msg = 'Non-empty non-comment line after valid redirect' outputter.out(path, lineNr, start, end, line, msg) return True return False _detectRedirect = re.compile(r'#REDIRECT(\s*)(?P<name>.*)') def detectRedirect(outputter, path, lineNr, line, firstDirectiveLine , validRedirectPresent): match = _detectRedirect.match(line) if match: if firstDirectiveLine: name = match.group('name') if not name: msg = 'Redirect without target' outputter.out(path, lineNr, 0, len(line), line, msg) else: validRedirectPresent = True else: msg = 'Redirect in non-first line' outputter.out(path, lineNr, 0, len(line), line, msg) return validRedirectPresent, True return validRedirectPresent, False def detectUseModIndent(outputter, path, lineNr, line): if line[0:1] != ':' or detectSmilie(line, 0): return False end = len(line) - len(line.lstrip(':')) outputter.out(path, lineNr, 0, end, line, 'UseMod indentation') return True def detectUseModDefinitionList(outputter, path, lineNr, line): if line[0:1] != ';' or detectSmilie(line, 0): return False outputter.out(path, lineNr, 0, 1, line, 'UseMod definition list') return True _detectUseModTagsRe = re.compile(r'''<(?P<close>[/]?) (?P<name>(b|i|nowiki|pre|toc|tt)) >''', re.IGNORECASE | re.VERBOSE) def detectUseModTags(outputter, path, lineNr, line): matches = _detectUseModTagsRe.finditer(line) for match in matches: start = match.start() end = match.end() closing = match.group('close') tagName = match.group('name').lower() tagType = 'close' if closing else 'open' msg = 'UseMod tag {0} {1}'.format(tagName, tagType) outputter.out(path, lineNr, start, end, line, msg) return False _checkBrTagsRe = re.compile(r''' (?P<open><[<`]*) (?P<name>br) (?P<close>[>`]*>) ''', re.IGNORECASE | re.VERBOSE) def checkBrTags(outputter, path, lineNr, line): """ UseMod forced linebreak: <br> MoinMoin forced linebreak: <<BR>> """ matches = _checkBrTagsRe.finditer(line) for match in matches: start = match.start() end = match.end() tagOpen = match.group('open') tagName = match.group('name') tagClose = match.group('close') if (tagOpen == '<') and (tagClose == '>'): msg = 'UseMod forced linebreak' outputter.out(path, lineNr, start, end, line, msg) return True if ((tagOpen == '<<') and (tagClose[0:2] == '>>') and (tagName != 'BR')): msg = 'Invalid MoinMoin forced linebreak' outputter.out(path, lineNr, start, end, line, msg) return True return False _checkHeadlinesRe = re.compile(r''' (?P<spaceBeforOpen>\s*) # Illegal. (?P<openTag>[=]+) # Headline open tag. (?P<spaceAfterOpen>\s*) # Required. (?P<nIndicator>[\#*]*)\s* # Numbering from old wiki. (?P<text>.*?) # Required headline text (non-greedy). (?P<spaceBeforClose>\s*) # Required. (?P<closeTag>[=]*) # Has to be same as open tag. (?P<spaceAfterClose>\s*) # Illegal trailing whitespace. $''', re.VERBOSE) def checkHeadlines(outputter, path, lineNr, line): match = _checkHeadlinesRe.match(line) if match is None: return False spaceBeforOpen = match.group('spaceBeforOpen') openTag = match.group('openTag') openTagStart = match.start('openTag') openTagEnd = match.end('openTag') spaceAfterOpen = match.group('spaceAfterOpen') nIndicator = match.group('nIndicator') text = match.group('text') spaceBeforClose = match.group('spaceBeforClose') closeTag = match.group('closeTag') spaceAfterClose = match.group('spaceAfterClose') if spaceBeforOpen: end = len(spaceBeforOpen) msg = 'Headline after whitespace' outputter.out(path, lineNr, 0, end, line, msg) if len(openTag) > 5: start = openTagStart end = openTagEnd msg = 'Headline of level > 5' outputter.out(path, lineNr, start, end, line, msg) if text: iMatches = re.finditer(r"[`']{2,}", text) for iMatch in iMatches: start = match.start('text') + iMatch.start() end = match.start('text') + iMatch.end() msg = 'Headline contains markup' outputter.out(path, lineNr, start, end, line, msg) else: end = len(line) start = openTagEnd - 1 msg = 'Headline contains no text' outputter.out(path, lineNr, start, end, line, msg) return True if not spaceAfterOpen: if nIndicator: start = match.start('nIndicator') else: start = match.start('text') msg = 'Headline without whitespace after open tag' outputter.out(path, lineNr, start, start + 1, line, msg) if nIndicator: start = match.start('nIndicator') end = match.end('nIndicator') msg = 'Headline with UseMod numbering indicator' outputter.out(path, lineNr, start, end, line, msg) if closeTag: if len(openTag) != len(closeTag): start = match.start('closeTag') end = match.end('closeTag') msg = ('Headline with different length open and close' + ' tags') outputter.out(path, lineNr, start, end, line, msg) if not spaceBeforClose: start = match.start('closeTag') msg = 'Headline without whitespace before close tag' outputter.out(path, lineNr, start, start + 1, line, msg) if spaceAfterClose: start = match.start('spaceAfterClose') end = match.end('spaceAfterClose') msg = 'Headline ends with whitespace' outputter.out(path, lineNr, start, end, line, msg) else: msg = 'Headline without close tag' outputter.out(path, lineNr, len(line)-1, len(line), line, msg) # Skip following checks when no close tag present. return True _detectUseModAnchorsRe = re.compile(r'(?:^|[^[])(\[#[^#\]]+\])(?:$|[^]])') def detectUseModAnchors(outputter, path, lineNr, line): matches = _detectUseModAnchorsRe.finditer(line) for match in matches: start = match.start(1) end = match.end(1) msg = 'UseMod anchor' outputter.out(path, lineNr, start, end, line, msg) return False _checkLinksRe = re.compile(r''' (?P<openBrackets>\[[\[`]*) # Valid links got 2 brackets (?P<openQuote>"?) # Artifact from old wiki conversion \s* (?P<linkUrl>.*?) # Link URL (not greedy) \s* (?P<closeQuote>"?) # Artifact from old wiki conversion (?P<closeBrackets>[\]`]*\]) # Valid links got 2 brackets ''', re.IGNORECASE | re.VERBOSE) def checkLinks(outputter, path, lineNr, line): matches = _checkLinksRe.finditer(line) for match in matches: start = match.start() end = match.end() openBrackets = match.group('openBrackets') openQuote = match.group('openQuote') linkUrl = match.group('linkUrl') if openQuote: msg = 'Fail-converted unnamed internal UseMod link' outputter.out(path, lineNr, start, end, line, msg) continue if (len(openBrackets) == 1) and re.search(r':', linkUrl): msg = 'Fail-converted external UseMod link' outputter.out(path, lineNr, start, end, line, msg) continue return False _detectUseModUploadsRe = re.compile(r'(^|\s)(?P<link>upload:\S+)(\s|$)', re.I) def detectUseModUploads(outputter, path, lineNr, line): matches = _detectUseModUploadsRe.finditer(line) for match in matches: start = match.start('link') end = match.end('link') msg = 'UseMod upload link' outputter.out(path, lineNr, start, end, line, msg) return False # noinspection PyUnusedLocal def detectMoinMoinComment(outputter, path, lineNr, line): return line.startswith('##') def makeCheckFile(checkFuns, cols, useAnsi): escaper = TextEscaper() decorator = makeTextDecorator(useAnsi) maxPartLength = cols - 11 outputter = AnomalyFormatter(escaper, decorator, maxPartLength) def checkFile(path): # Read file and report broken UTF-8 encoding: with open(path, 'rb') as file: textBytes = file.read() decoder = codecs.getincrementaldecoder('utf-8')() lines, line, invalidEncoding = [], [], False lastI = len(textBytes) + 1 for i in range(0, len(textBytes)): try: cp = decoder.decode(textBytes[i:i+1], i == lastI) if len(cp) != 0: if cp == '\n': if line[-1:] == ['\r']: del line[-1] lines.append(''.join(line)) line = [] else: line.append(cp) except ValueError: invalidEncoding = True lineNr, cpIndex = len(lines) + 1, len(line) lineStr = ''.join(line) msg = 'UTF-8 invalid byte while decoding line!' outputter.out(path, lineNr, cpIndex, cpIndex + 1, lineStr, msg) break if invalidEncoding: return outputter.getText(), tuple(outputter.getCounts().items()) lines.append(''.join(line)) firstDirectiveLine = 1 validRedirectPresent = False for lineNr, line in enumerate(lines): isComment = detectMoinMoinComment(outputter, path, lineNr, line) isDirective = not isComment and line.startswith('#') checkForInvalidCodePoints(escaper, outputter, path, lineNr , line) isDirective, isComment = checkForUseModList(outputter, path , lineNr, line, isDirective, isComment) # No further wiki syntax checks for comments: if isComment: continue # Determine first directive line if (firstDirectiveLine == lineNr) and isComment: firstDirectiveLine += 1 # Detect extra non-comment markup after valid redirect: if validRedirectPresent and not isDirective: skipRemaining = detectNonCommentAfterRedirect(outputter, path , lineNr, line) if skipRemaining: continue validRedirectPresent, skipRemaining = detectRedirect(outputter, path , lineNr, line, firstDirectiveLine, validRedirectPresent) if skipRemaining: continue if isDirective: # Skip other directives. continue for checkFun in checkFuns: skipRemaining = checkFun(outputter, path, lineNr, line) if skipRemaining: continue return outputter.getText(), tuple(outputter.getCounts().items()) return checkFile def workerProc(termE:Event, jobs:Queue, results:Queue, workerFactory, *args): try: workFun = workerFactory(*args) while not termE.is_set(): try: job = jobs.get(True, 0.02) except queue.Empty: continue result = job, workFun(job) results.put(result, True) except KeyboardInterrupt: pass def handleResults(results:Queue, counts:Counter): while True: try: job, (rText, rCounts) = results.get(False) except queue.Empty: return counts['fileCount'] += 1 if len(rText) != 0: print(rText, end='') for name, count in rCounts: counts[name] += count def main(): checkFuns = ( detectUseModIndent, detectUseModDefinitionList, detectUseModTags, checkBrTags, checkHeadlines, checkLinks, detectUseModAnchors, detectUseModUploads, ) if sys.stdout.isatty() and (platform.system() != 'Windows'): import subprocess cols = int(subprocess.Popen(('tput', 'cols'), stdout=subprocess.PIPE).stdout.read()) if cols <= 0: cols = 80 useAnsi = True else: cols, useAnsi = 80, False workerCount = max(1, len(os.sched_getaffinity(0))) termE = Event() jobs = Queue(maxsize=2*workerCount) results = Queue(maxsize=2*workerCount) workerArgs = termE, jobs, results, makeCheckFile, checkFuns, cols, useAnsi workerPool = [Process(target=workerProc, args=workerArgs) for _ in range(0, workerCount)] for worker in workerPool: worker.start() counts = Counter() blistedCount = 0 try: print('Scanning files...') paths = glob.iglob(os.path.join(sourceDir, "*.txt")) for path in paths: if not os.path.isfile(path): continue if path in blacklist: blistedCount += 1 continue while True: handleResults(results, counts) try: jobs.put(path, True, 0.02) break except queue.Full: pass while not jobs.empty(): handleResults(results, counts) time.sleep(0.02) except KeyboardInterrupt: print('') print('Processing interrupted by user!') termE.set() while any(worker.is_alive() for worker in workerPool): handleResults(results, counts) time.sleep(0.02) for worker in workerPool: worker.join() handleResults(results, counts) decorator = makeTextDecorator(useAnsi) fileCount, anomalyCount = counts['fileCount'], counts['anomalyCount'] pathCount, lineCount = counts['pathCount'], counts['lineCount'] del counts['fileCount'] del counts['anomalyCount'] del counts['pathCount'] del counts['lineCount'] eFileCount = decorator.decorateText(str(fileCount), decorator.textBYellow) eBlistedCount = decorator.decorateText(str(blistedCount), decorator.textBYellow) if anomalyCount != 0: eAnomalyCount = decorator.decorateText(str(anomalyCount), decorator.textBYellow) eLineCount = decorator.decorateText(str(lineCount), decorator.textBYellow) ePathCount = decorator.decorateText(str(pathCount), decorator.textBYellow) msg = ('Found {0} anomalies in {1} lines from {2} files' + ' ({3} scanned, {4} excluded):') print('') print(msg.format(eAnomalyCount, eLineCount, ePathCount, eFileCount , eBlistedCount)) maxValueLen = len(str(max(counts.values()))) for name, count in sorted(counts.items()): eCount = '{0:{1}}'.format(count, maxValueLen) eCount = decorator.decorateText(eCount, decorator.textBYellow) print(' {0} {1}'.format(eCount, name)) else: msg = 'Found no anomalies in {0} files ({1} excluded).' print('') print(msg.format(fileCount, eBlistedCount)) if __name__ == '__main__': main()

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discord/ext/vbu/cogs/utils/converters/filtered_user.py

6days9weeks/Novus

2

17850

from discord.ext import commands class FilteredUser(commands.UserConverter): """ A simple :class:`discord.ext.commands.UserConverter` that doesn't allow bots or the author to be passed into the function. """ def __init__(self, *, allow_author: bool = False, allow_bots: bool = False): super().__init__() self.allow_author = allow_author self.allow_bots = allow_bots async def convert(self, ctx: commands.Context, argument: str): m = await super().convert(ctx, argument) if self.allow_author is False and ctx.author.id == m.id: raise commands.BadArgument("You can't run this command on yourself.") if self.allow_bots is False and m.bot: raise commands.BadArgument("You can't run this command on bots.") return m class FilteredMember(commands.MemberConverter): """ A simple :class:`discord.ext.commands.MemberConverter` that doesn't allow bots or the author to be passed into the function. """ def __init__(self, *, allow_author: bool = False, allow_bots: bool = False): super().__init__() self.allow_author = allow_author self.allow_bots = allow_bots async def convert(self, ctx: commands.Context, argument: str): m = await super().convert(ctx, argument) if self.allow_author is False and ctx.author.id == m.id: raise commands.BadArgument("You can't run this command on yourself.") if self.allow_bots is False and m.bot: raise commands.BadArgument("You can't run this command on bots.") return m

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fold_cur_trans.py

lucasforever24/arcface_noonan

0

5358

import cv2 from PIL import Image import argparse from pathlib import Path from multiprocessing import Process, Pipe,Value,Array import torch from config import get_config from mtcnn import MTCNN from Learner_trans_tf import face_learner from utils import load_facebank, draw_box_name, prepare_facebank, save_label_score, label_binarize from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score from sklearn.model_selection import KFold import os import glob import shutil import numpy as np import matplotlib.pyplot as plt import matplotlib.colors as mcolors import datetime if __name__ == '__main__': parser = argparse.ArgumentParser(description='for face verification') parser.add_argument("-ds", "--dataset_dir", help="where to get data", default="noonan", type=str) parser.add_argument('-sd','--stored_result_dir',help='where to store data as np arrays', default="results/trans/", type=str) parser.add_argument("-k", "--kfold", help="returns the number of splitting iterations in the cross-validator.", default=10, type=int) parser.add_argument("-e", "--epochs", help="training epochs", default=20, type=int) parser.add_argument("-n", "--names_considered", help="names for different types considered, separated by commas", default="normal,noonan,others", type=str) parser.add_argument("-g", "--gpu_id", help="gpu id to use", default="", type=str) parser.add_argument("-s", "--use_shuffled_kfold", help="whether to use shuffled kfold.", action="store_true") parser.add_argument("-rs", "--random_seed", help="random seed used for k-fold split.", default=6, type=int) parser.add_argument("-tta", "--tta", help="whether test time augmentation",action="store_true") parser.add_argument("-a", "--additional_data_dir", help="where to get the additional data", default="", type=str) parser.add_argument("-ta", "--additional_test_or_train", help="use additional data in only train, or test, or both", default="", type=str) parser.add_argument("-as", "--stylegan_data_dir", help="where to get the additional data", default="", type=str) parser.add_argument("-ts", "--stylegan_test_or_train", help="use stylegan data in only train, or test, or both", default="", type=str) parser.add_argument("-tf", "--transfer", help="how many layer(s) used for transfer learning, " "but 0 means retraining the whole network.", default=0, type=int) parser.add_argument("-ac", "--arch", help="types of model used for encoder", default="mobile", type=str) args = parser.parse_args() for arg in vars(args): print(arg+':', getattr(args, arg)) emore_dir = 'faces_emore' conf = get_config(True, args) conf.emore_folder = conf.data_path/emore_dir mtcnn = MTCNN() print('mtcnn loaded') names_considered = args.names_considered.strip().split(',') exp_name = args.dataset_dir[:4] if args.additional_data_dir: if 'LAG' in args.additional_data_dir: exp_name += '_lag' elif 'literature' in args.additional_data_dir: exp_name += '_ltr' if args.kfold != 10: exp_name += ('_k' + str(args.kfold)) if args.epochs != 20: exp_name += ('_e' + str(args.epochs)) if args.transfer != 0 and args.transfer != 1: exp_name += ('_td' + str(args.transfer)) if args.use_shuffled_kfold: exp_name += ('_s' + str(args.random_seed)) print(exp_name) # prepare folders raw_dir = 'raw_112' verify_type = 'trans' if args.use_shuffled_kfold: verify_type += '_shuffled' # train_dir = conf.facebank_path/args.dataset_dir/verify_type/'train' train_dir = conf.emore_folder/'imgs' test_dir = conf.emore_folder/'test' conf.facebank_path = train_dir if os.path.exists(train_dir): shutil.rmtree(train_dir) if os.path.exists(test_dir): shutil.rmtree(test_dir) os.mkdir(train_dir) os.mkdir(test_dir) for name in names_considered: os.makedirs(str(train_dir) + '/' + name, exist_ok=True) os.makedirs(str(test_dir) + '/' + name, exist_ok=True) if args.stylegan_data_dir: #e.g. smile_refine_mtcnn_112_divi full_stylegan_dir = str(conf.data_path/'facebank'/'stylegan'/args.stylegan_data_dir) stylegan_folders = os.listdir(full_stylegan_dir) if args.additional_data_dir: full_additional_dir = str(conf.data_path/'facebank'/args.additional_data_dir) # init kfold if args.use_shuffled_kfold: kf = KFold(n_splits=args.kfold, shuffle=True, random_state=args.random_seed) else: kf = KFold(n_splits=args.kfold, shuffle=False, random_state=None) # collect and split raw data data_dict = {} idx_gen = {} for name in names_considered: tmp_list = glob.glob(str(conf.data_path/'facebank'/args.dataset_dir/raw_dir) + '/' + name + '*') if 'innm' in args.stylegan_data_dir: tmp_list = tmp_list + glob.glob(str(full_stylegan_dir) + '/' + name + '*') stylegan_folders = [] print(str(conf.data_path/'facebank'/args.dataset_dir/raw_dir)) data_dict[name] = np.array(tmp_list) idx_gen[name] = kf.split(data_dict[name]) if 'literature' in args.additional_data_dir: data_dict['ltr'] = np.array(glob.glob(str(full_additional_dir) + '/*')) idx_gen['ltr'] = kf.split(data_dict['ltr']) score_names = [] scores = [] wrong_names = [] args.stored_result_path = args.stored_result_dir + os.sep + str(datetime.datetime.now())[:19] if not os.path.exists(args.stored_result_path): os.mkdir(args.stored_result_path) # for fold_idx, (train_index, test_index) in enumerate(kf.split(data_dict[names_considered[0]])): for fold_idx in range(args.kfold): train_set = {} test_set = {} for name in names_considered: (train_index, test_index) = next(idx_gen[name]) train_set[name], test_set[name] = data_dict[name][train_index], data_dict[name][test_index] if 'ltr' in data_dict.keys(): (train_index, test_index) = next(idx_gen['ltr']) train_set['ltr'], test_set['ltr'] = data_dict['ltr'][train_index], data_dict['ltr'][test_index] if 'train' in args.additional_test_or_train: train_set['noonan'] = np.concatenate((train_set['noonan'], train_set['ltr'])) if 'test' in args.additional_test_or_train: test_set['noonan'] = np.concatenate((test_set['noonan'], test_set['ltr'])) # remove previous data prev = glob.glob(str(train_dir) + '/*/*') for p in prev: os.remove(p) prev = glob.glob(str(test_dir) + '/*/*') for p in prev: os.remove(p) # save trains to conf.facebank_path/args.dataset_dir/'train' and # tests to conf.data_path/'facebank'/args.dataset_dir/'test' # count unbalanced data train_count = {} test_count = {} for name in names_considered: train_count[name] = 0 for i in range(len(train_set[name])): img_folder = str(train_set[name][i]) for img in os.listdir(img_folder): shutil.copy(img_folder + os.sep + str(img), os.path.join(str(train_dir), name, str(img))) train_count[name] += 1 # addition data from stylegan if 'interp' not in data_dict.keys(): folder = os.path.basename(train_set[name][i]) if args.stylegan_data_dir and ('train' in args.stylegan_test_or_train) and (folder in stylegan_folders): for img in os.listdir(full_stylegan_dir + os.sep + folder): shutil.copy(os.path.join(full_stylegan_dir, folder, str(img)), os.path.join(str(train_dir), name, str(img))) # ('/'.join(train_set[name][i].strip().split('/')[:-2]) + # '/' + verify_type + '/train/' + name + os.sep + img)) train_count[name] += 1 # test for i in range(len(test_set[name])): test_count[name] = 0 img_folder = str(test_set[name][i]) for img in os.listdir(img_folder): shutil.copy(img_folder + os.sep + str(img), os.path.join(str(test_dir), name, str(img))) test_count[name] += 1 # addition data from stylegan if 'interp' not in data_dict.keys(): folder = os.path.basename(test_set[name][i]) if args.stylegan_data_dir and ('test' in args.stylegan_test_or_train) and (folder in stylegan_folders): # and # (folder not in ['noonan7','noonan19','noonan23','normal9','normal20','normal23'])): for img in os.listdir(full_stylegan_dir + os.sep + folder): shutil.copy(os.path.join(full_stylegan_dir, folder, str(img)), os.path.join(str(test_dir), name, str(img))) test_count[name] += 1 print(train_count, test_count) # deal with unbalanced data """ if train_count['normal'] // train_count['noonan'] > 1: aug_num = train_count['normal'] // train_count['noonan'] - 1 for img in os.listdir(os.path.join(str(train_dir), 'noonan')): for aug_idx in range(aug_num): aug_img = img[:img.rfind('.')] + '_' + str(aug_idx) + img[img.rfind('.'):] shutil.copy(os.path.join(str(train_dir), 'noonan', img), os.path.join(str(train_dir), 'noonan', aug_img)) """ if 'fake' in args.additional_data_dir: fake_dict = {'noonan':'normal', 'normal':'noonan'} full_additional_dir = conf.data_path/'facebank'/'noonan+normal'/args.additional_data_dir add_data = glob.glob(str(full_additional_dir) + os.sep + '*.png') print('additional:', args.additional_data_dir, len(add_data)) for name in names_considered: for img_f in add_data: if name in img_f.strip().split(os.sep)[-1]: # print('source:', img_f) # print('copy to:', img_f.replace(str(full_additional_dir), # str(train_dir) + os.sep + fake_dict[name])) # print('copy to:', img_f.replace(args.additional_data_dir, # verify_type + '/train/' + name)) shutil.copy(img_f, os.path.join(str(train_dir), fake_dict[name], os.path.basename(img_f))) print(fold_idx) print('datasets ready') conf_train = get_config(True, args) conf_train.emore_folder = conf.data_path/emore_dir conf_train.stored_result_dir = args.stored_result_path learner = face_learner(conf=conf_train, transfer=args.transfer, ext=exp_name+'_'+str(fold_idx)) # conf, inference=False, transfer=0 if args.transfer != 0: learner.load_state(conf.save_path, False, True) print('learner loaded') learner.train(conf_train, args.epochs) print('learner retrained.') learner.save_state() print('Model is saved') # prepare_facebank targets, names, names_idx = prepare_facebank(conf, learner.model, mtcnn, tta = args.tta) print('names_classes:', names) noonan_idx = names_idx['noonan'] print('facebank updated') for path in test_dir.iterdir(): if path.is_file(): continue # print(path) for fil in path.iterdir(): # print(fil) orig_name = ''.join([i for i in fil.name.strip().split('.')[0].split('_')[0] if not i.isdigit()]) for name in names_idx.keys(): if name in orig_name: score_names.append(names_idx[name]) """ if orig_name not in names_considered: print("Un-considered name:", fil.name) continue """ frame = cv2.imread(str(fil)) image = Image.fromarray(frame) faces = [image,] distance = learner.binfer(conf, faces, targets, args.tta) label = score_names[-1] score = np.exp(distance.dot(-1)) pred = np.argmax(score, 1) if pred != label: wrong_names.append(orig_name) scores.append(score) score_names = np.array(score_names) wrong_names = np.array(wrong_names) score_np = np.squeeze(np.array(scores)) n_classes = score_np.shape[1] score_names = label_binarize(score_names, classes=range(n_classes)) score_sum = np.zeros([score_np.shape[0], 1]) for i in range(n_classes): score_sum += score_np[:, i, None] # keep the dimension relative_scores = (score_np / score_sum) total_scores = relative_scores.ravel() total_names = score_names.ravel() name_path = os.path.join(args.stored_result_path, 'wrong_names.npy') save_label_score(name_path, wrong_names) label_path = os.path.join(args.stored_result_path, 'labels_trans.npy') save_label_score(label_path, score_names) score_path = os.path.join(args.stored_result_path, 'scores_trans.npy') save_label_score(score_path, relative_scores) print('saved!') # Compute ROC curve and ROC area for noonan fpr, tpr, _ = roc_curve(total_names, total_scores) #scores_np[:, noonan_idx] roc_auc = auc(fpr, tpr) # For PR curve precision, recall, _ = precision_recall_curve(total_names, total_scores) average_precision = average_precision_score(total_names, total_scores) # plots plt.figure() colors = list(mcolors.TABLEAU_COLORS) lw = 2 plt.plot(fpr, tpr, color='darkorange', lw=lw, label='ROC curve (area = %0.4f)' % roc_auc) plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('ROC_{}'.format(exp_name)) plt.legend(loc="lower right") plt.savefig(args.stored_result_path + os.sep + '/fp_tp_{}.png'.format(exp_name)) plt.close() # plt.show() plt.figure() plt.step(recall, precision, where='post') plt.xlabel('Recall') plt.ylabel('Precision') plt.ylim([0.0, 1.05]) plt.xlim([0.0, 1.0]) plt.title('Average precision score ({}): AP={:0.4f}'.format(exp_name, average_precision)) plt.savefig(args.stored_result_path + os.sep + '/pr_{}.png'.format(exp_name)) plt.close()

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hood/urls.py

wadi-1000/Vicinity

0

3242

<reponame>wadi-1000/Vicinity from django.urls import path,include from . import views urlpatterns = [ path('home/', views.home, name = 'home'), path('add_hood/',views.uploadNeighbourhood, name = 'add_hood'), path('viewhood/',views.viewHood, name = 'viewhood'), path('hood/<int:pk>/',views.hood, name = 'hood'), path('add_bizna/',views.uploadBuisness, name = 'add_bizna'), path('bizna/',views.viewBizna, name = 'view_bizna'), path('viewbizna/<int:pk>/',views.bizna, name = 'bizna'), path('post/',views.create_post, name = 'post'), path('posts/',views.viewPost, name = 'posts'), path('searchbizna/', views.searchBizna, name="search_results"), path('searchhood/', views.searchHood, name="search_res"), path('join_hood/<id>', views.join_neighbourhood, name='join-hood'), path('leave_hood/<id>', views.leave_neighbourhood, name='leave-hood'), ]

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setup.py

jacobschaer/qt_compat

0

13246

<filename>setup.py<gh_stars>0 from setuptools import setup, find_packages setup( name="QtCompat", version="0.1", packages=find_packages(), scripts=[], # Project uses reStructuredText, so ensure that the docutils get # installed or upgraded on the target machine install_requires=[], package_data={ }, # metadata for upload to PyPI author="<NAME>", author_email="", description="PyQt4, 5 and Pyside Compatibility Library", license="MIT", keywords="pyqt4 pyqt5 pyside compatibility", url="https://github.com/jacobschaer/qt_compat/", # project home page, if any # could also include long_description, download_url, classifiers, etc. )

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examples/simple_example.py

alexkyllo/torch-wtte

1

100975

# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: percent # format_version: '1.3' # jupytext_version: 1.11.4 # kernelspec: # display_name: wtte-dev # language: python # name: wtte-dev # --- # %% [markdown] # # WTTE-RNN in PyTorch # # <NAME> # # Based on original Keras version written by <NAME>: # https://github.com/ragulpr/wtte-rnn/blob/master/examples/keras/simple_example.ipynb # MIT license # # For details, check out # https://ragulpr.github.io/2016/12/22/WTTE-RNN-Hackless-churn-modeling/ # https://github.com/ragulpr/wtte-rnn # %% # %matplotlib inline import sys import numpy as np import torch from torch import nn, optim from torch.utils.data import TensorDataset, DataLoader import matplotlib.pyplot as plt sys.path.append("..") from torch_wtte import losses np.random.seed(11) torch.manual_seed(11) # %% def get_data(n_timesteps, every_nth, n_repeats, noise_level, n_features, use_censored=True): def get_equal_spaced(n, every_nth): # create some simple data of evenly spaced events recurring every_nth step # Each is on (time,batch)-format events = np.array([np.array(range(n)) for _ in range(every_nth)]) events = events + np.array(range(every_nth)).reshape(every_nth, 1) + 1 tte_actual = every_nth - 1 - events % every_nth was_event = (events % every_nth == 0) * 1.0 was_event[:, 0] = 0.0 events = tte_actual == 0 is_censored = (events[:, ::-1].cumsum(1)[:, ::-1] == 0) * 1 tte_censored = is_censored[:, ::-1].cumsum(1)[:, ::-1] * is_censored tte_censored = tte_censored + (1 - is_censored) * tte_actual events = np.copy(events.T * 1.0) tte_actual = np.copy(tte_actual.T * 1.0) tte_censored = np.copy(tte_censored.T * 1.0) was_event = np.copy(was_event.T * 1.0) not_censored = 1 - np.copy(is_censored.T * 1.0) return tte_censored, not_censored, was_event, events, tte_actual tte_censored, not_censored, was_event, events, tte_actual = get_equal_spaced( n=n_timesteps, every_nth=every_nth ) # From https://keras.io/layers/recurrent/ # input shape rnn recurrent if return_sequences: (nb_samples, timesteps, input_dim) u_train = not_censored.T.reshape(n_sequences, n_timesteps, 1) x_train = was_event.T.reshape(n_sequences, n_timesteps, 1) tte_censored = tte_censored.T.reshape(n_sequences, n_timesteps, 1) y_train = np.append(tte_censored, u_train, axis=2) # (n_sequences,n_timesteps,2) u_test = np.ones(shape=(n_sequences, n_timesteps, 1)) x_test = np.copy(x_train) tte_actual = tte_actual.T.reshape(n_sequences, n_timesteps, 1) y_test = np.append(tte_actual, u_test, axis=2) # (n_sequences,n_timesteps,2) if not use_censored: x_train = np.copy(x_test) y_train = np.copy(y_test) # Since the above is deterministic perfect fit is feasible. # More noise->more fun so add noise to the training data: x_train = np.tile(x_train.T, n_repeats).T y_train = np.tile(y_train.T, n_repeats).T # Try with more than one feature TODO x_train_new = np.zeros([x_train.shape[0], x_train.shape[1], n_features]) x_test_new = np.zeros([x_test.shape[0], x_test.shape[1], n_features]) for f in range(n_features): x_train_new[:, :, f] = x_train[:, :, 0] x_test_new[:, :, f] = x_test[:, :, 0] x_train = x_train_new x_test = x_test_new # xtrain is signal XOR noise with probability noise_level noise = np.random.binomial(1, noise_level, size=x_train.shape) x_train = x_train + noise - x_train * noise return y_train, x_train, y_test, x_test, events # %% [markdown] # ### Generate some data # # * The true event-sequence is evenly spaced points (but we start anywhere in the sequence) # * The true feature is (binary) if there was an event in last step # * In the training data the feature has added noise # * Training TTE is censored. Testing TTE is uncensored. # %% n_timesteps = 200 n_sequences = every_nth = 80 n_features = 1 n_repeats = 1000 noise_level = 0.005 use_censored = True y_train, x_train, y_test, x_test, events = get_data( n_timesteps, every_nth, n_repeats, noise_level, n_features, use_censored ) # %% #### Plots print("test shape", x_test.shape, y_test.shape) plt.imshow(x_test[:, :, :].sum(axis=2) > 0, interpolation="none", cmap="Accent", aspect="auto") plt.title("x_test (lagged/deterministic event indicator)") plt.show() plt.imshow(y_test[:, :, 0], interpolation="none", cmap="jet", aspect="auto") plt.title("y_test[:,:,0] actual tte") plt.show() print("train shape", x_train.shape, y_train.shape) plt.imshow( x_train[:every_nth, :, :].mean(axis=2), interpolation="none", cmap="Accent", aspect="auto" ) plt.title("x_train[:every_nth,:,0] (lagged/noisy event indicator)") plt.show() plt.imshow(y_train[:every_nth, :, 0], interpolation="none", cmap="jet", aspect="auto") plt.title("y_train[:every_nth,:,0] censored tte") plt.show() plt.imshow(y_train[:every_nth, :, 1], interpolation="none", cmap="Accent", aspect="auto") plt.title("y_train[:every_nth,:,1] u (non-censoring indicator)") plt.show() ## Example TTE: print("Example TTEs") plt.plot( y_train[every_nth // 4, :, 0], label="censored tte (train)", color="black", linestyle="dashed", linewidth=2, drawstyle="steps-post", ) plt.plot( y_test[every_nth // 4, :, 0], label="actual tte (test)", color="black", linestyle="solid", linewidth=2, drawstyle="steps-post", ) plt.xlim(0, n_timesteps) plt.xlabel("time") plt.ylabel("time to event") plt.title("Example TTEs") plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0) plt.show() # %% [markdown] # # Train a WTTE-RNN # ### Things to try out: # * have fun with data paramaters: # * every_nth to control event frequency # * noise_level to make it more noisy # * n_timesteps # * n_features to get more noisy input # * Generate more interesting temporal relationships # * Here we use the smallest possible GRU. Try different learning rates, network architectures, initializations. # * Try Implementing multivariate distributions, other distributions, data pipelines etc. # * Invent better output activation layer # * Invent ways to overcome instability with lots of censoring # * ETC and have fun! # %% # Paramaeters for output activation layer initialization. # Start at naive geometric (beta=1) MLE: tte_mean_train = np.nanmean(y_train[:, :, 0]) init_alpha = -1.0 / np.log(1.0 - 1.0 / (tte_mean_train + 1.0)) mean_u = np.nanmean(y_train[:, :, 1]) init_alpha = init_alpha / mean_u print("init_alpha: ", init_alpha, "mean uncensored: ", mean_u) ### Uncomment if you have varying length sequences that is nanpadded to the right: # mask_value = -1.3371337 # Use some improbable but not nan-causing telltale value # x_train[:,:,:][np.isnan(x_train)] = mask_value # y_train[:,:,0][np.isnan(y_train[:,:,0])] = tte_mean_train # y_train[:,:,1][np.isnan(y_train[:,:,1])] = 0.5 # sample_weights = (x_train[:,:,0]!=mask_value)*1. # %% class WTTERNN(nn.Module): def __init__(self, discrete): super().__init__() self.epoch = 0 self.layers = nn.ModuleList( [ nn.GRU(input_size=n_features, hidden_size=2, batch_first=True), nn.Tanh(), losses.WeibullActivation(init_alpha=init_alpha, max_beta=4.0), ] ) self.criterion = losses.WeibullCensoredNLLLoss(discrete=discrete) def forward(self, x): x, _ = self.layers[0](x) # discard GRU hidden state output x = self.layers[1](x) x = self.layers[2](x) return x def fit(self, optimizer, train_loader, device="cpu"): num_batches = ( len(train_loader.dataset) + train_loader.batch_size - 1 ) // train_loader.batch_size self.to(device) self.train() train_losses = [] for batch_idx, (data, labels) in enumerate(train_loader): data, labels = data.to(device), labels.to(device) optimizer.zero_grad() output = self(data).squeeze() loss = self.criterion(output, labels).sum() loss.backward() optimizer.step() train_losses.append(loss.item()) avg_loss = loss / len(data) print( f"Epoch: {self.epoch} [batch {batch_idx+1}/{num_batches}]\tLoss: {loss.item():.6f} Avg Loss: {avg_loss:.6f}", end="\r", ) avg_train_loss = np.sum(train_losses) / len(train_loader.dataset) print() self.epoch += 1 return avg_train_loss def score(self, valid_loader, device="cpu"): self.to(device) self.eval() valid_loss = 0 correct = 0 with torch.no_grad(): for data, labels in valid_loader: data, labels = data.to(device), labels.to(device) output = self(data).squeeze() tte = labels[..., 0] uncensored = labels[..., 1] alpha = output[..., 0] beta = output[..., 1] valid_loss = self.criterion(tte, uncensored, alpha, beta).sum().item() pred = output.data.round() correct += pred.eq(labels.data.view_as(pred)).sum() n = len(valid_loader.dataset) valid_loss /= n print(f"Validation: avg loss: {valid_loss:.4f}") return valid_loss # %% def run(x_train, y_train, x_test, y_test, epochs, device): print(f"Using device {device}") x_train = torch.from_numpy(x_train.astype("float32")).to(device) x_test = torch.from_numpy(x_test.astype("float32")).to(device) y_train = torch.from_numpy(y_train.astype("float32")).to(device) y_test = torch.from_numpy(y_test.astype("float32")).to(device) train_data = TensorDataset(x_train, y_train) test_data = TensorDataset(x_test, y_test) batch_size = x_train.shape[0] // 10 train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True) test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=False) model = WTTERNN(discrete=True) optimizer = optim.Adam(model.parameters(), lr=0.01) train_loss_history = [] valid_loss_history = [] for _ in range(epochs): train_loss = model.fit(optimizer, train_loader, device=device) train_loss_history.append(train_loss) valid_loss = model.score(test_loader, device=device) valid_loss_history.append(valid_loss) return model, train_loss_history, valid_loss_history # %% device = "cuda:0" if torch.cuda.is_available() else "cpu" model, train_losses, valid_losses = run(x_train, y_train, x_test, y_test, epochs=60, device=device) # %% plt.plot(train_losses, label="training") plt.plot(valid_losses, label="validation") plt.title("loss") plt.legend() # %% [markdown] # # Predictions # Try out training the model with different levels of noise. With more noise confidence gets lower (smaller beta). With less noise beta goes to maximum value and the predicted mode/peak probability is centered around the actual TTE. # %% # Make some parametric predictions print("TESTING (no noise in features)") print("(each horizontal line is a sequence)") predicted = model(torch.from_numpy(x_test.astype("float32")).to(device)).detach().cpu().numpy() print(predicted.shape) plt.imshow(predicted[:, :, 0], interpolation="none", cmap="jet", aspect="auto") plt.title("predicted[:,:,0] (alpha)") plt.colorbar(orientation="horizontal") plt.show() plt.imshow(predicted[:, :, 1], interpolation="none", cmap="jet", aspect="auto") plt.title("predicted[:,:,1] (beta)") plt.colorbar(orientation="horizontal") plt.show() print("TRAINING (Noisy features)") predicted = ( model(torch.from_numpy(x_train[:every_nth, :, :].astype("float32")).to(device)) .detach() .cpu() .numpy() ) print(predicted.shape) plt.imshow(predicted[:, :, 0], interpolation="none", cmap="jet", aspect="auto") plt.title("predicted[:,:,0] (alpha)") plt.colorbar(orientation="horizontal") plt.show() plt.imshow(predicted[:, :, 1], interpolation="none", cmap="jet", aspect="auto") plt.title("predicted[:,:,1] (beta)") plt.colorbar(orientation="horizontal") plt.show()

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tests/test_solver/test_sensitivities.py

dbankmann/python-mloc

0

118749

<reponame>dbankmann/python-mloc # # Copyright (c) 2019-2020 # # @author: <NAME> # @company: Technische Universität Berlin # # This file is part of the python package pymloc # (see https://gitlab.tubit.tu-berlin.de/bankmann91/python-mloc ) # # License: 3-clause BSD, see https://opensource.org/licenses/BSD-3-Clause # from copy import deepcopy import jax.numpy as jnp import numpy as np import pytest from pymloc.model.domains import RNDomain from pymloc.model.dynamical_system.flow_problem import LinearFlow from pymloc.model.dynamical_system.initial_value_problem import InitialValueProblem from pymloc.model.dynamical_system.parameter_bvp import ParameterBoundaryValueProblem from pymloc.model.dynamical_system.parameter_bvp import ParameterBoundaryValues from pymloc.model.dynamical_system.parameter_dae import LinearParameterDAE from pymloc.model.sensitivities.boundary_dae import BVPSensitivities from pymloc.model.variables import NullVariables from pymloc.model.variables import ParameterContainer from pymloc.model.variables import StateVariablesContainer from pymloc.model.variables.time_function import Time from pymloc.solvers.dynamical_systems.adjoint_sensitivities import AdjointSensitivitiesSolver @pytest.fixture def bvp_sens_object(linear_param_bvp): return BVPSensitivities(linear_param_bvp, n_param=1) @pytest.fixture def sens_solver_sine(bvp_sens_object): return AdjointSensitivitiesSolver(bvp_sens_object, rel_tol=1e-1, abs_tol=1e-1) @pytest.fixture def sens_solver_sine2(bvp_sens_object): bvpsens2 = deepcopy(bvp_sens_object) bvpsens2.dynamical_system._a = lambda p, t: jnp.array([[0., 1.], [-p**2, 0.]]) bvpsens2.boundary_value_problem.boundary_values.inhomogeneity = lambda p: jnp.array( [0., 2.]) return AdjointSensitivitiesSolver(bvpsens2, rel_tol=1e-6, abs_tol=1e-6) @pytest.fixture def sens_solver_sine3(bvp_sens_object): bvpsens2 = deepcopy(bvp_sens_object) bound_f = lambda p: jnp.array([[0., 0.], [p, 0.]]) # noqa: E731 bvpsens2.boundary_value_problem.boundary_values.boundary_f = bound_f bvs = bvpsens2.boundary_value_problem.boundary_values.boundary_values bvs = list(bvs) bvs[1] = bound_f bvpsens2.boundary_value_problem.boundary_values._boundary_values = bvs bvpsens2.boundary_value_problem.boundary_values.inhomogeneity = lambda p: jnp.array( [0., 2.]) # noqa: E731 return AdjointSensitivitiesSolver(bvpsens2, rel_tol=1e-1, abs_tol=1e-1) @pytest.fixture def linear_param_bvp(linear_param_dae, param_vars): initial_value = lambda p: jnp.array([[1., 0.], [0., 0.]]) # noqa: E731 final_value = lambda p: jnp.array([[0., 0.], [1., 0.]]) # noqa: E731 inhomogeneity = lambda p: jnp.array([0., p]) # noqa: E731 t = Time(0., np.pi / 2., time_grid=np.linspace(0., np.pi / 2., 10)) bvs = ParameterBoundaryValues(*param_vars, initial_value, final_value, inhomogeneity, 2) return ParameterBoundaryValueProblem(*param_vars, t, linear_param_dae, bvs) @pytest.fixture def linear_param_dae(param_vars, e_lin_param_dae, a_lin_param_dae, f_lin_param_dae): return LinearParameterDAE(*param_vars, e_lin_param_dae, a_lin_param_dae, f_lin_param_dae, 2) @pytest.fixture def param_vars(): states = StateVariablesContainer(2) domain = RNDomain(1) parameters = ParameterContainer(1, domain) null_vars = NullVariables() return null_vars, parameters, states @pytest.fixture def e_lin_param_dae(): def e(p, t): return np.identity(2) return e @pytest.fixture def a_lin_param_dae(): def a(p, t): return jnp.array([[0., 1.], [-1., 0.]]) return a @pytest.fixture def f_lin_param_dae(): def f(p, t): return np.array([0., 0.]) return f class TestBVPSensitivitiesSolver: def test_compute_adjoint_boundary_values(self, sens_solver, localized_bvp): sens_solver._compute_adjoint_boundary_values(localized_bvp) def test_get_adjoint_dae_coeffs(self, sens_solver, localized_bvp): sens_solver._get_adjoint_dae_coeffs(localized_bvp) @pytest.mark.parametrize("tau", np.arange(0.1, 1., 0.4)) def test_solve_adjoint_dae(self, sens_solver, localized_bvp, tau): parameters = localized_bvp._localization_parameters[0] time = deepcopy(localized_bvp.time_interval) stepsize = 0.5e-0 time.grid = np.hstack( (np.arange(time.t_0, tau, stepsize), np.arange(tau, time.t_f, stepsize))) sol = sens_solver._get_adjoint_solution(localized_bvp, parameters, tau, time) assert sol @pytest.mark.parametrize("tau", np.arange(0.1, 1., 0.4)) def test_f_tilde(self, sens_solver, localized_bvp, tau): time = localized_bvp.time_interval flow_prob = LinearFlow(time, localized_bvp.dynamical_system) ivp_prob = InitialValueProblem(np.zeros((3, )), time, localized_bvp.dynamical_system) nodes = np.linspace(time.t_0, time.t_f, 3) stepsize = 0.5e-0 localized_bvp.init_solver(flow_prob, ivp_prob, nodes, stepsize) sens_solver._get_capital_fs(localized_bvp, localized_bvp.solve(time), localized_bvp._localization_parameters[0]) @pytest.mark.parametrize("tau", np.arange(0.1, 1., 0.4)) def test_run(self, sens_solver, localized_bvp, tau): sens_solver.run(localized_bvp._localization_parameters[0], tau) def test_sens_sine(self, sens_solver_sine): sol = sens_solver_sine.run(np.array(2.), 1.) assert np.allclose(sol.solution, np.array([np.sin(1.), np.cos(1.)]), atol=0.5, rtol=1e-4) def test_sens_sine2(self, sens_solver_sine2): sol = sens_solver_sine2.run(np.array(1.), 1.) assert np.allclose( sol(1.).T, np.array([2 * np.cos(1.), 2 * np.cos(1.) - 2 * 1. * np.sin(1.)]), atol=1e-1, rtol=1e-9) def test_sens_sine3(self, sens_solver_sine3): sol = sens_solver_sine3.run(np.array(2.), 1.) assert np.allclose(sol.solution, -2 * .25 * np.array([np.sin(1.), np.cos(1.)]), atol=0.5, rtol=1e-7)

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tests/test_node.py

flipcoder/qork

3

168303

#!/usr/bin/env pytest import pytest import sys sys.path.append("..") from glm import vec3 from qork.util import * from qork.node import Node from qork.minimal import MinimalCore from qork.util import walk def test_node(): world = Node() # move/position assert fcmp(world.position, vec3(0)) world.move(vec3(1, 2, 3)) assert fcmp(world.position, vec3(1, 2, 3)) world.move(vec3(1, 2, 3)) assert fcmp(world.position, vec3(2, 4, 6)) # reset world.pos = vec3(0) assert fcmp(world.position, vec3(0)) # attach child = Node() world.position = vec3(0) assert not world.children world.attach(child) world.position = (1, 2, 3) child.position = vec3(1) assert fcmp(child.world_pos, vec3(2, 3, 4)) world.position = vec3(0) assert fcmp(child.world_pos, vec3(1)) world.position = vec3(1) assert fcmp(child.world_pos, vec3(2)) assert child.parent == world child.detach() assert child.parent is None assert fcmp(child.world_pos, vec3(2)) world.attach(child) assert child in world world.children._blocked += 1 child.detach() world.children._blocked -= 1 assert child in world assert world.children._blocked == 0 world.children.refresh() assert child not in world def test_node_velocity(): world = Node() world.velocity = (1, 2, 3) # use tuple assert fcmp(world.position, vec3(0)) world.update(1) assert fcmp(world.position, vec3(1, 2, 3)) assert fcmp(world.position, vec3(1, 2, 3)) world.update(1) assert fcmp(world.position, vec3(2, 4, 6)) def test_node_walk(): a = Node() b = a.add(5) assert len(a) == 5 assert len(list(a.walk())) == 5 def test_node_find(): a = Node() b = a.add("foo") c = a.add("bar") assert list(a.find("foo")) == [b] assert list(a.find("bar")) == [c] b.tag("baz") assert b.tags == set(["baz"]) assert list(a.find("baz")) == [] assert list(a.find("#baz")) == [b] def test_node_detach(): # given a tree like this: # root # a # b # c # a.detach() does this: # root root = Node("root") a = root.add(Node("a")) b = a.add(Node("b")) c = a.add(Node("c")) assert tuple(map(lambda x: x.name, root.children)) == ("a",) a.detach() # a is gone # print(tuple(map(lambda x: x.name, root.children))) assert tuple(map(lambda x: x.name, root.children)) == tuple() # b and c are still on detached a assert tuple(map(lambda x: x.name, a.children)) == ("b", "c") def test_node_detach_collapse(): # given a tree like this: # root # a # b # c # and a.detach(collapse=True) does this: # root # b # c root = Node("root") a = root.add(Node("a")) b = a.add(Node("b")) c = a.add(Node("c")) assert tuple(map(lambda x: x.name, root.children)) == ("a",) # detach and reattach collapse b and c to root a.detach(collapse=True) # b and c moved to root? assert tuple(map(lambda x: x.name, root.children)) == ("b", "c") # children were removed from a? assert tuple(map(lambda x: x.name, a.children)) == tuple()

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Media/common/Build/build_assets.py

dmacka/MultiverseClientServer

5

149341

import sha import time from patch_tool import * def add_assets(asset_tree): asset_tree.add_ignore("mv.patch") asset_tree.add_ignore("mv.patch.cur") # Directories for main assets asset_tree.add_asset_path("Base", "Fonts") asset_tree.add_asset_path("Base", "GpuPrograms") asset_tree.add_asset_path("Base", "Icons") asset_tree.add_asset_path("Base", "Imagefiles") asset_tree.add_asset_path("Base", "Interface/FrameXML") asset_tree.add_asset_path("Base", "Interface/Imagesets") asset_tree.add_asset_path("Base", "Materials") asset_tree.add_asset_path("Base", "Meshes") asset_tree.add_asset_path("Base", "Misc") asset_tree.add_asset_path("Base", "Particles") asset_tree.add_asset_path("Base", "Physics") asset_tree.add_asset_path("Base", "Scripts") asset_tree.add_asset_path("Base", "Skeletons") asset_tree.add_asset_path("Base", "Sounds") asset_tree.add_asset_path("Base", "SpeedTree") asset_tree.add_asset_path("Base", "Textures") # Directories for addon (local) assets asset_tree.add_asset_path("Base", "AddOns/Fonts") asset_tree.add_asset_path("Base", "AddOns/GpuPrograms") asset_tree.add_asset_path("Base", "AddOns/Icons") asset_tree.add_asset_path("Base", "AddOns/Imagefiles") asset_tree.add_asset_path("Base", "AddOns/Interface/FrameXML") asset_tree.add_asset_path("Base", "AddOns/Interface/Imagesets") asset_tree.add_asset_path("Base", "AddOns/Materials") asset_tree.add_asset_path("Base", "AddOns/Meshes") asset_tree.add_asset_path("Base", "AddOns/Misc") asset_tree.add_asset_path("Base", "AddOns/Particles") asset_tree.add_asset_path("Base", "AddOns/Physics") asset_tree.add_asset_path("Base", "AddOns/Scripts") asset_tree.add_asset_path("Base", "AddOns/Skeletons") asset_tree.add_asset_path("Base", "AddOns/Sounds") asset_tree.add_asset_path("Base", "AddOns/SpeedTree") asset_tree.add_asset_path("Base", "AddOns/Textures") asset_tree.add_ignore("AddOns/Fonts/.*") asset_tree.add_ignore("AddOns/GpuPrograms/.*") asset_tree.add_ignore("AddOns/Icons/.*") asset_tree.add_ignore("AddOns/Imagefiles/.*") asset_tree.add_ignore("AddOns/Interface/FrameXML/.*") asset_tree.add_ignore("AddOns/Interface/Imagesets/.*") asset_tree.add_ignore("AddOns/Materials/.*") asset_tree.add_ignore("AddOns/Meshes/.*") asset_tree.add_ignore("AddOns/Misc/.*") asset_tree.add_ignore("AddOns/Physics/.*") asset_tree.add_ignore("AddOns/Scripts/.*") asset_tree.add_ignore("AddOns/Skeletons/.*") asset_tree.add_ignore("AddOns/Sounds/.*") asset_tree.add_ignore("AddOns/SpeedTree/.*") asset_tree.add_ignore("AddOns/Textures/.*") asset_tree.add_asset_path("Base", "Scripts/Standalone.py") # client gpu programs asset_tree.add_asset_path("Base", "GpuPrograms/DetailVeg.cg") asset_tree.add_asset_path("Base", "GpuPrograms/DiffuseBump.cg") asset_tree.add_asset_path("Base", "GpuPrograms/Compound_Basic_Skinned.cg") asset_tree.add_asset_path("Base", "GpuPrograms/Multiverse.program") # common to the tools package asset_tree.add_asset_path("Base", "GpuPrograms/Ocean.cg") asset_tree.add_asset_path("Base", "GpuPrograms/Terrain.cg") asset_tree.add_asset_path("Base", "GpuPrograms/Trees.cg") # end common # physics meshes asset_tree.add_asset_path("Base", "Meshes/unit_box.mesh") asset_tree.add_asset_path("Base", "Meshes/unit_cylinder.mesh") asset_tree.add_asset_path("Base", "Meshes/unit_sphere.mesh") # end physics # tool mesh # end tool # non-physics and non-tool meshes asset_tree.add_asset_path("Base", "Meshes/bang.mesh") asset_tree.add_asset_path("Base", "Meshes/tiny_cube.mesh") # end non-physics and non-tool meshes # key materials asset_tree.add_asset_path("Base", "Materials/bang.material") asset_tree.add_asset_path("Base", "Materials/DetailVeg.material") asset_tree.add_asset_path("Base", "Materials/MVSMTerrain.material") asset_tree.add_asset_path("Base", "Materials/Multiverse.material") asset_tree.add_asset_path("Base", "Materials/Ocean.material") asset_tree.add_asset_path("Base", "Materials/tiny_cube.material") asset_tree.add_asset_path("Base", "Materials/Trees.material") asset_tree.add_asset_path("Base", "Materials/Water.material") # end key materials # physics asset_tree.add_asset_path("Base", "Materials/unit_box.material") asset_tree.add_asset_path("Base", "Materials/unit_cylinder.material") asset_tree.add_asset_path("Base", "Materials/unit_sphere.material") # end physics # base sound files #asset_tree.add_asset_path("Base", "Sounds/ambient_hills_day.ogg") #asset_tree.add_asset_path("Base", "Sounds/human_run_grass.ogg") # key textures asset_tree.add_asset_path("Base", "Textures/DetailVegAtlas.dds") # imageset for the DetailVegAtlas asset_tree.add_asset_path("Base", "Textures/DetailVeg.imageset") asset_tree.add_asset_path("Base", "Textures/MultiverseImageset.png") asset_tree.add_asset_path("Base", "Textures/loadscreen.dds") asset_tree.add_asset_path("Base", "Textures/mv_skybox0001.dds") asset_tree.add_asset_path("Base", "Textures/mv_skybox0002.dds") asset_tree.add_asset_path("Base", "Textures/mv_skybox0003.dds") asset_tree.add_asset_path("Base", "Textures/mv_skybox0004.dds") asset_tree.add_asset_path("Base", "Textures/mv_skybox0005.dds") asset_tree.add_asset_path("Base", "Textures/mv_skybox0006.dds") asset_tree.add_asset_path("Base", "Textures/noon_j_back.dds") asset_tree.add_asset_path("Base", "Textures/noon_j_front.dds") asset_tree.add_asset_path("Base", "Textures/noon_j_left.dds") asset_tree.add_asset_path("Base", "Textures/noon_j_right.dds") asset_tree.add_asset_path("Base", "Textures/noon_j_top.dds") asset_tree.add_asset_path("Base", "Textures/sandy_path.dds") asset_tree.add_asset_path("Base", "Textures/splatting_grass.dds") asset_tree.add_asset_path("Base", "Textures/splatting_rock.dds") asset_tree.add_asset_path("Base", "Textures/splatting_sand.dds") asset_tree.add_asset_path("Base", "Textures/splatting_snow.dds") asset_tree.add_asset_path("Base", "Textures/Water02.dds") asset_tree.add_asset_path("Base", "Textures/waves.dds") asset_tree.add_asset_path("Base", "Textures/White.dds") #end key textures # SpeedTree textures # common to the tools package asset_tree.add_asset_path("Base", "Textures/AmericanBoxwood_Composite.dds") asset_tree.add_asset_path("Base", "Textures/AmericanBoxwoodCluster_Composite.dds") asset_tree.add_asset_path("Base", "Textures/AppleTree_SelfShadow.dds") asset_tree.add_asset_path("Base", "Textures/AppleTreeBark.dds") asset_tree.add_asset_path("Base", "Textures/AppleTreeBarkNormals.dds") asset_tree.add_asset_path("Base", "Textures/Azalea_Composite.dds") asset_tree.add_asset_path("Base", "Textures/AzaleaPatch_Composite.dds") asset_tree.add_asset_path("Base", "Textures/AzaleaPatchPink_Composite.dds") asset_tree.add_asset_path("Base", "Textures/AzaleaPink_Composite.dds") asset_tree.add_asset_path("Base", "Textures/Beech_Composite.dds") asset_tree.add_asset_path("Base", "Textures/Beech_SelfShadow.dds") asset_tree.add_asset_path("Base", "Textures/BeechBark.dds") asset_tree.add_asset_path("Base", "Textures/BeechBarkNormals.dds") asset_tree.add_asset_path("Base", "Textures/BeechFall_Composite.dds") asset_tree.add_asset_path("Base", "Textures/BeechWinter_Composite.dds") asset_tree.add_asset_path("Base", "Textures/CurlyPalm_Composite.dds") asset_tree.add_asset_path("Base", "Textures/CurlyPalm_SelfShadow.dds") asset_tree.add_asset_path("Base", "Textures/CurlyPalmBark.dds") asset_tree.add_asset_path("Base", "Textures/CurlyPalmBarkNormals.dds") asset_tree.add_asset_path("Base", "Textures/CurlyPalmCluster_Composite.dds") asset_tree.add_asset_path("Base", "Textures/FraserFir_Composite.dds") asset_tree.add_asset_path("Base", "Textures/FraserFir_SelfShadow.dds") asset_tree.add_asset_path("Base", "Textures/FraserFirBark.dds") asset_tree.add_asset_path("Base", "Textures/FraserFirBarkNormals.dds") asset_tree.add_asset_path("Base", "Textures/FraserFirCluster_Composite.dds") asset_tree.add_asset_path("Base", "Textures/FraserFirCluster_SelfShadow.dds") asset_tree.add_asset_path("Base", "Textures/FraserFirClusterSnow_Composite.dds") asset_tree.add_asset_path("Base", "Textures/FraserFirSnow_Composite.dds") asset_tree.add_asset_path("Base", "Textures/RDApple_Composite.dds") asset_tree.add_asset_path("Base", "Textures/RDAppleApples_Composite.dds") asset_tree.add_asset_path("Base", "Textures/RDAppleSpring_Composite.dds") asset_tree.add_asset_path("Base", "Textures/RDAppleWinter_Composite.dds") asset_tree.add_asset_path("Base", "Textures/SugarPine_Composite.dds") asset_tree.add_asset_path("Base", "Textures/SugarPine_SelfShadow.dds") asset_tree.add_asset_path("Base", "Textures/SugarPineBark.dds") asset_tree.add_asset_path("Base", "Textures/SugarPineBarkNormals.dds") asset_tree.add_asset_path("Base", "Textures/SugarPineWinter_Composite.dds") asset_tree.add_asset_path("Base", "Textures/UmbrellaThorn_Composite.dds") asset_tree.add_asset_path("Base", "Textures/UmbrellaThorn_SelfShadow.dds") asset_tree.add_asset_path("Base", "Textures/UmbrellaThornBark.dds") asset_tree.add_asset_path("Base", "Textures/UmbrellaThornBarkNormals.dds") asset_tree.add_asset_path("Base", "Textures/UmbrellaThornDead_Composite.dds") asset_tree.add_asset_path("Base", "Textures/UmbrellaThornFlowers_Composite.dds") asset_tree.add_asset_path("Base", "Textures/VenusTree_Composite.dds") asset_tree.add_asset_path("Base", "Textures/VenusTree_SelfShadow.dds") asset_tree.add_asset_path("Base", "Textures/VenusTreeBark.dds") asset_tree.add_asset_path("Base", "Textures/VenusTreeBarkNormals.dds") asset_tree.add_asset_path("Base", "Textures/WeepingWillow_Composite.dds") asset_tree.add_asset_path("Base", "Textures/WeepingWillow_SelfShadow.dds") asset_tree.add_asset_path("Base", "Textures/WeepingWillowBark.dds") asset_tree.add_asset_path("Base", "Textures/WeepingWillowBarkNormals.dds") asset_tree.add_asset_path("Base", "Textures/WeepingWillowFall_Composite.dds") asset_tree.add_asset_path("Base", "Textures/WeepingWillowWinter_Composite.dds") # end common # end speedtree textures # SpeedTree trees # common to the tools package asset_tree.add_asset_path("Base", "SpeedTree/AmericanBoxwood_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/AmericanBoxwoodCluster_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/Azalea_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/Azalea_RT_Pink.spt") asset_tree.add_asset_path("Base", "SpeedTree/AzaleaPatch_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/AzaleaPatch_RT_Pink.spt") asset_tree.add_asset_path("Base", "SpeedTree/Beech_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/Beech_RT_Fall.spt") asset_tree.add_asset_path("Base", "SpeedTree/Beech_RT_Winter.spt") asset_tree.add_asset_path("Base", "SpeedTree/CurlyPalm_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/CurlyPalmCluster_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/FraserFir_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/FraserFir_RT_Snow.spt") asset_tree.add_asset_path("Base", "SpeedTree/FraserFirCluster_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/FraserFirCluster_RT_Snow.spt") asset_tree.add_asset_path("Base", "SpeedTree/RDApple_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/RDApple_RT_Apples.spt") asset_tree.add_asset_path("Base", "SpeedTree/RDApple_RT_Spring.spt") asset_tree.add_asset_path("Base", "SpeedTree/RDApple_RT_Winter.spt") asset_tree.add_asset_path("Base", "SpeedTree/SugarPine_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/SugarPine_RT_Winter.spt") asset_tree.add_asset_path("Base", "SpeedTree/UmbrellaThorn_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/UmbrellaThorn_RT_Dead.spt") asset_tree.add_asset_path("Base", "SpeedTree/UmbrellaThorn_RT_Flowers.spt") asset_tree.add_asset_path("Base", "SpeedTree/VenusTree_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/WeepingWillow_RT.spt") asset_tree.add_asset_path("Base", "SpeedTree/WeepingWillow_RT_Fall.spt") asset_tree.add_asset_path("Base", "SpeedTree/WeepingWillow_RT_Winter.spt") asset_tree.add_asset_path("Base", "SpeedTree/demoWind.ini") # end common # end trees # Interface asset_tree.add_asset_path("Base", "Fonts/MUFN____.TTF") asset_tree.add_asset_path("Base", "Imagefiles/Buttons.tga") asset_tree.add_asset_path("Base", "Imagefiles/CharacterFrame.tga") asset_tree.add_asset_path("Base", "Imagefiles/ChatFrame.tga") asset_tree.add_asset_path("Base", "Imagefiles/Common.tga") asset_tree.add_asset_path("Base", "Imagefiles/ContainerFrame.tga") asset_tree.add_asset_path("Base", "Imagefiles/Cursor.tga") asset_tree.add_asset_path("Base", "Imagefiles/DialogFrame.tga") asset_tree.add_asset_path("Base", "Imagefiles/Icons.tga") asset_tree.add_asset_path("Base", "Imagefiles/MvButtons.tga") asset_tree.add_asset_path("Base", "Imagefiles/MvChatFrame.tga") asset_tree.add_asset_path("Base", "Imagefiles/MvQuestFrame.tga") asset_tree.add_asset_path("Base", "Imagefiles/PaperDollInfoFrame.tga") asset_tree.add_asset_path("Base", "Imagefiles/QuestFrame.tga") asset_tree.add_asset_path("Base", "Imagefiles/TargetingFrame.tga") asset_tree.add_asset_path("Base", "Imagefiles/Tooltips.tga") asset_tree.add_asset_path("Base", "Interface/FrameXML/Library.py") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvActionBar.py") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvActionBar.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvActionButton.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvCharacter.py") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvCharacter.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvChat.py") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvChat.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvContainer.py") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvContainer.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvDialog.py") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvDialog.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvFonts.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvFrame.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvItemButton.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvPlayer.py") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvPlayer.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvSocialBar.py") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvSocialBar.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvStatus.py") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvStatus.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvTarget.py") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvTarget.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvTooltip.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvUnit.py") asset_tree.add_asset_path("Base", "Interface/FrameXML/MvUnit.xml") asset_tree.add_asset_path("Base", "Interface/FrameXML/basic.toc") asset_tree.add_asset_path("Base", "Interface/FrameXML/social.toc") asset_tree.add_asset_path("Base", "Interface/FrameXML/betaworld.toc") asset_tree.add_asset_path("Base", "Interface/Imagesets/Buttons.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/CharacterFrame.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/ChatFrame.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/Common.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/ContainerFrame.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/Cursor.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/DialogFrame.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/Icons.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/MvButtons.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/MvChat.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/MvQuestFrame.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/PaperDollInfoFrame.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/QuestFrame.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/TargetingFrame.xml") asset_tree.add_asset_path("Base", "Interface/Imagesets/Tooltips.xml") # Misc. asset_tree.add_asset_path("Base", "Icons/AxiomIcon.ico") # Mars files asset_tree.add_asset_path("Mars", "Interface/FrameXML/mars.toc") asset_tree.add_asset_path("Mars", "Interface/FrameXML/MarsPlayer.py") asset_tree.add_asset_path("Mars", "Interface/FrameXML/MarsPlayer.xml") asset_tree.add_asset_path("Mars", "Interface/FrameXML/MarsTarget.py") asset_tree.add_asset_path("Mars", "Interface/FrameXML/MarsTarget.xml") asset_tree.add_asset_path("Mars", "Interface/FrameXML/MarsUnit.py") asset_tree.add_asset_path("Mars", "Interface/FrameXML/MarsUnit.xml") asset_tree.add_asset_path("Particles", "Materials/aureola.material") asset_tree.add_asset_path("Particles", "Materials/bigflame.material") asset_tree.add_asset_path("Particles", "Materials/droplet.material") asset_tree.add_asset_path("Particles", "Materials/explosion.material") asset_tree.add_asset_path("Particles", "Materials/flare.material") asset_tree.add_asset_path("Particles", "Materials/flare2.material") asset_tree.add_asset_path("Particles", "Materials/flaretrail.material") asset_tree.add_asset_path("Particles", "Materials/lensflare.material") asset_tree.add_asset_path("Particles", "Materials/rain.material") asset_tree.add_asset_path("Particles", "Materials/ringflare.material") asset_tree.add_asset_path("Particles", "Materials/ringflare2.material") asset_tree.add_asset_path("Particles", "Materials/smoke.material") asset_tree.add_asset_path("Particles", "Materials/smoke2.material") asset_tree.add_asset_path("Particles", "Materials/streak.material") asset_tree.add_asset_path("Particles", "Materials/waterfountain.material") asset_tree.add_asset_path("Particles", "Particles/aureola.particle"); asset_tree.add_asset_path("Particles", "Particles/blast.particle"); asset_tree.add_asset_path("Particles", "Particles/blast2.particle"); asset_tree.add_asset_path("Particles", "Particles/downpour.particle"); asset_tree.add_asset_path("Particles", "Particles/eruption.particle"); asset_tree.add_asset_path("Particles", "Particles/flame.particle"); asset_tree.add_asset_path("Particles", "Particles/floatyGreeny.particle"); asset_tree.add_asset_path("Particles", "Particles/fountain.particle"); asset_tree.add_asset_path("Particles", "Particles/greenyNimbus.particle"); asset_tree.add_asset_path("Particles", "Particles/jetengine1.particle"); asset_tree.add_asset_path("Particles", "Particles/jetengine2.particle"); asset_tree.add_asset_path("Particles", "Particles/pentagram.particle"); asset_tree.add_asset_path("Particles", "Particles/purpleFountain.particle"); asset_tree.add_asset_path("Particles", "Particles/rain.particle"); asset_tree.add_asset_path("Particles", "Particles/ringOfFire.particle"); asset_tree.add_asset_path("Particles", "Particles/smoke.particle"); asset_tree.add_asset_path("Particles", "Particles/smoke2.particle"); asset_tree.add_asset_path("Particles", "Particles/space.particle"); asset_tree.add_asset_path("Particles", "Particles/waterfall.particle"); asset_tree.add_asset_path("Particles", "Particles/waterfountain.particle"); asset_tree.add_asset_path("Particles", "Textures/aureola.dds") asset_tree.add_asset_path("Particles", "Textures/basic_droplet.dds") asset_tree.add_asset_path("Particles", "Textures/bigflame.png") asset_tree.add_asset_path("Particles", "Textures/blue_flare.dds") asset_tree.add_asset_path("Particles", "Textures/blue_flare.jpg") asset_tree.add_asset_path("Particles", "Textures/explosion.dds") asset_tree.add_asset_path("Particles", "Textures/Flare.dds") asset_tree.add_asset_path("Particles", "Textures/Flare.jpg") asset_tree.add_asset_path("Particles", "Textures/flaretrail.dds") asset_tree.add_asset_path("Particles", "Textures/ring_flare.dds") asset_tree.add_asset_path("Particles", "Textures/ring_flare2.dds") asset_tree.add_asset_path("Particles", "Textures/smoke.dds") asset_tree.add_asset_path("Particles", "Textures/smoke.dds") asset_tree.add_asset_path("Particles", "Textures/smokecolors.dds") asset_tree.add_asset_path("Particles", "Textures/smokecolors.dds") asset_tree.add_asset_path("Particles", "Textures/streak.dds") # Social World Assets asset_tree.add_asset_path("SocialWorld", "Imagefiles/ActionbarIcons.dds") asset_tree.add_asset_path("SocialWorld", "Interface/Imagesets/ActionbarIcons.xml") asset_tree.add_asset_path("SocialWorld", "Materials/bench.material") asset_tree.add_asset_path("SocialWorld", "Materials/cobblestone.material") asset_tree.add_asset_path("SocialWorld", "Materials/cobblestone_marble.material") asset_tree.add_asset_path("SocialWorld", "Materials/colonnade.material") asset_tree.add_asset_path("SocialWorld", "Materials/gazebo_green.material") asset_tree.add_asset_path("SocialWorld", "Materials/gazebo_sand.material") asset_tree.add_asset_path("SocialWorld", "Materials/pool.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_cdance_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_cdance_drk_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_cdance_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_cdance_lt_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_dagger_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_dagger_drk_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_dagger_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_dagger_lt_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_dance_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_dance_drk_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_dance_drk_shade_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_dance_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_dance_lt_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_kneel_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_kneel_drk_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_kneel_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_kneel_lt_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_lean_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_lean_drk_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_lean_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_lean_lt_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_sword_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_sword_drk_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_sword_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_f_sword_lt_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_m_lean_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_m_lean_drk_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_m_lean_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_m_lean_lt_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_m_throw_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_m_throw_drk_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_m_throw_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/st_m_throw_lt_full.material") asset_tree.add_asset_path("SocialWorld", "Materials/stage.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_f_cdance_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_f_cdance_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_f_dagger_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_f_dagger_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_f_dance_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_f_dance_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_f_kneel_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_f_kneel_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_f_lean_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_f_lean_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_f_sword_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_f_sword_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_m_lean_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_m_lean_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_m_throw_drk.material") asset_tree.add_asset_path("SocialWorld", "Materials/stb_m_throw_lt.material") asset_tree.add_asset_path("SocialWorld", "Materials/sword.material") asset_tree.add_asset_path("SocialWorld", "Materials/water_trim_corner_sqr.material") asset_tree.add_asset_path("SocialWorld", "Materials/water_trim_corner_tri_eq.material") asset_tree.add_asset_path("SocialWorld", "Materials/water_trim_corner_tri_odd.material") asset_tree.add_asset_path("SocialWorld", "Materials/water_trim_long_block.material") asset_tree.add_asset_path("SocialWorld", "Materials/water_trim_octagon.material") asset_tree.add_asset_path("SocialWorld", "Meshes/bench.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/bench_01.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/cobblestone.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/cobblestone_marble.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/colonnade.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/gazebo_green.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/gazebo_sand.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/pool.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_cdance_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_cdance_drk_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_cdance_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_cdance_lt_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_dagger_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_dagger_drk_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_dagger_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_dagger_lt_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_dance_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_dance_drk_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_dance_drk_shade_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_dance_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_dance_lt_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_kneel_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_kneel_drk_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_kneel_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_kneel_lt_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_lean_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_lean_drk_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_lean_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_lean_lt_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_sword_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_sword_drk_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_sword_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_f_sword_lt_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_m_lean_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_m_lean_drk_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_m_lean_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_m_lean_lt_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_m_throw_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_m_throw_drk_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_m_throw_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/st_m_throw_lt_full.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stage.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_f_cdance_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_f_cdance_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_f_dagger_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_f_dagger_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_f_dance_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_f_dance_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_f_kneel_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_f_kneel_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_f_lean_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_f_lean_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_f_sword_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_f_sword_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_m_lean_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_m_lean_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_m_throw_drk.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/stb_m_throw_lt.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/sword.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/water_trim_corner_sqr.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/water_trim_corner_tri_eq.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/water_trim_corner_tri_odd.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/water_trim_long_block.mesh") asset_tree.add_asset_path("SocialWorld", "Meshes/water_trim_octagon.mesh") asset_tree.add_asset_path("SocialWorld", "Misc/SocialWorld_AssetConfig.xml", "AssetConfig.xml") asset_tree.add_asset_path("SocialWorld", "Materials/waterfountain.material") asset_tree.add_asset_path("SocialWorld", "Particles/waterfountain.particle"); asset_tree.add_asset_path("SocialWorld", "Textures/Flare.dds") asset_tree.add_asset_path("SocialWorld", "Physics/bench.physics") asset_tree.add_asset_path("SocialWorld", "Physics/cobblestone.physics") asset_tree.add_asset_path("SocialWorld", "Physics/cobblestone_marble.physics") asset_tree.add_asset_path("SocialWorld", "Physics/colonnade.physics") asset_tree.add_asset_path("SocialWorld", "Physics/gazebo_green.physics") asset_tree.add_asset_path("SocialWorld", "Physics/gazebo_sand.physics") asset_tree.add_asset_path("SocialWorld", "Physics/pool.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_cdance_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_cdance_drk_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_cdance_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_cdance_lt_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_dagger_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_dagger_drk_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_dagger_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_dagger_lt_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_dance_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_dance_drk_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_dance_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_dance_lt_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_kneel_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_kneel_drk_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_kneel_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_kneel_lt_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_lean_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_lean_drk_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_lean_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_lean_lt_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_sword_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_sword_drk_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_sword_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_f_sword_lt_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_m_lean_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_m_lean_drk_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_m_lean_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_m_lean_lt_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_m_throw_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_m_throw_drk_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_m_throw_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/st_m_throw_lt_full.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stage.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_f_cdance_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_f_cdance_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_f_dagger_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_f_dagger_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_f_dance_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_f_dance_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_f_kneel_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_f_kneel_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_f_lean_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_f_lean_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_f_sword_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_f_sword_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_m_lean_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_m_lean_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_m_throw_drk.physics") asset_tree.add_asset_path("SocialWorld", "Physics/stb_m_throw_lt.physics") asset_tree.add_asset_path("SocialWorld", "Physics/water_trim_corner_sqr.physics") asset_tree.add_asset_path("SocialWorld", "Physics/water_trim_corner_tri_eq.physics") asset_tree.add_asset_path("SocialWorld", "Physics/water_trim_corner_tri_odd.physics") asset_tree.add_asset_path("SocialWorld", "Physics/water_trim_long_block.physics") asset_tree.add_asset_path("SocialWorld", "Physics/water_trim_octagon.physics") #asset_tree.add_asset_path("SocialWorld", "Sounds/social_ambient.ogg") #asset_tree.add_asset_path("SocialWorld", "Sounds/stage.ogg") asset_tree.add_asset_path("SocialWorld", "Textures/bench_marble.dds") asset_tree.add_asset_path("SocialWorld", "Textures/cobblestone_marble.dds") asset_tree.add_asset_path("SocialWorld", "Textures/cobblestones_seemless.dds") asset_tree.add_asset_path("SocialWorld", "Textures/colonnade.dds") asset_tree.add_asset_path("SocialWorld", "Textures/female_face_marble_blue_grey.dds") asset_tree.add_asset_path("SocialWorld", "Textures/female_face_marble_white_rev_darker.dds") asset_tree.add_asset_path("SocialWorld", "Textures/female_marble_blue_grey.dds") asset_tree.add_asset_path("SocialWorld", "Textures/female_marble_white_rev_darker.dds") asset_tree.add_asset_path("SocialWorld", "Textures/fountain_water.dds") asset_tree.add_asset_path("SocialWorld", "Textures/gazebo_a.dds") asset_tree.add_asset_path("SocialWorld", "Textures/gazebo_a_floor.dds") asset_tree.add_asset_path("SocialWorld", "Textures/gazebo_b.dds") asset_tree.add_asset_path("SocialWorld", "Textures/gazebo_b_floor.dds") asset_tree.add_asset_path("SocialWorld", "Textures/male_head_marble_blue_grey.dds") asset_tree.add_asset_path("SocialWorld", "Textures/male_marble_blue_grey.dds") asset_tree.add_asset_path("SocialWorld", "Textures/male_marble_white_rev_darker.dds") asset_tree.add_asset_path("SocialWorld", "Textures/marble_blue_grey.dds") asset_tree.add_asset_path("SocialWorld", "Textures/marble_white_rev_darker.dds") asset_tree.add_asset_path("SocialWorld", "Textures/pool_marble.dds") asset_tree.add_asset_path("SocialWorld", "Textures/Stage_01.dds") asset_tree.add_asset_path("SocialWorld", "Textures/sword_005.dds") asset_tree.add_asset_path("SocialWorld", "Textures/water_edging.dds") asset_tree.add_asset_path("SocialWorld", "Textures/loadscreen_social.dds", "Textures/loadscreen.dds") # male rocketbox assets asset_tree.add_asset_path("Rocketbox", "Skeletons/rocketbox_f_mediumpoly.skeleton") asset_tree.add_asset_path("Rocketbox", "Skeletons/rocketbox_f_lowpoly.skeleton") asset_tree.add_asset_path("Rocketbox", "Skeletons/rocketbox_m_mediumpoly.skeleton") asset_tree.add_asset_path("Rocketbox", "Skeletons/rocketbox_m_lowpoly.skeleton") asset_tree.add_asset_path("Rocketbox", "Materials/business03_m_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/business03_m_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/business03_m_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/business03_m_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/business03_m_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/business03_m_35.dds") asset_tree.add_asset_path("Rocketbox", "Materials/business05_m_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/business05_m_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/business05_m_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/business05_m_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/business05_m_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/business05_m_25.dds") asset_tree.add_asset_path("Rocketbox", "Materials/casual03_m_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/casual03_m_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/casual03_m_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/casual03_m_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/casual03_m_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/casual03_m_25.dds") asset_tree.add_asset_path("Rocketbox", "Materials/casual04_m_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/casual04_m_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/casual04_m_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/casual04_m_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/casual04_m_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/casual04_m_25.dds") asset_tree.add_asset_path("Rocketbox", "Materials/casual07_m_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/casual07_m_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/casual07_m_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/casual07_m_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/casual07_m_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/casual07_m_25.dds") asset_tree.add_asset_path("Rocketbox", "Materials/casual10_m_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/casual10_m_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/casual10_m_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/casual10_m_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/casual10_m_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/casual10_m_30.dds") asset_tree.add_asset_path("Rocketbox", "Materials/casual16_m_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/casual16_m_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/casual16_m_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/casual16_m_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/casual16_m_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/casual16_m_35.dds") asset_tree.add_asset_path("Rocketbox", "Materials/casual21_m_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/casual21_m_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/casual21_m_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/casual21_m_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/casual21_m_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/casual21_m_35.dds") asset_tree.add_asset_path("Rocketbox", "Materials/sportive01_m_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/sportive01_m_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/sportive01_m_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/sportive01_m_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/sportive01_m_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/sportive01_m_20.dds") asset_tree.add_asset_path("Rocketbox", "Materials/sportive09_m_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/sportive09_m_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/sportive09_m_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/sportive09_m_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/sportive09_m_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/sportive09_m_25.dds") # female rocketbox assets asset_tree.add_asset_path("Rocketbox", "Materials/business04_f_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/business04_f_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/business04_f_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/business04_f_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/business04_f_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/business04_f_25.dds") asset_tree.add_asset_path("Rocketbox", "Textures/business04_f_25_hair.dds") asset_tree.add_asset_path("Rocketbox", "Materials/casual06_f_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/casual06_f_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/casual06_f_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/casual06_f_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/casual06_f_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/casual06_f_20.dds") asset_tree.add_asset_path("Rocketbox", "Textures/casual06_f_hairalpha.dds") asset_tree.add_asset_path("Rocketbox", "Materials/casual07_f_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/casual07_f_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/casual07_f_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/casual07_f_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/casual07_f_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/casual07_f_20.dds") asset_tree.add_asset_path("Rocketbox", "Textures/casual07_f_20_hair.dds") asset_tree.add_asset_path("Rocketbox", "Materials/casual08_f_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/casual08_f_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/casual08_f_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/casual08_f_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/casual08_f_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/casual08_f_25.dds") asset_tree.add_asset_path("Rocketbox", "Textures/casual08_f_25_hair.dds") asset_tree.add_asset_path("Rocketbox", "Materials/casual15_f_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/casual15_f_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/casual15_f_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/casual15_f_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/casual15_f_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/casual15_f_20.dds") asset_tree.add_asset_path("Rocketbox", "Textures/casual15_f_20_hair_alpha.dds") asset_tree.add_asset_path("Rocketbox", "Materials/casual19_f_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/casual19_f_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/casual19_f_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/casual19_f_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/casual19_f_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/casual19_f_20.dds") asset_tree.add_asset_path("Rocketbox", "Textures/casual19_f_20_hair.dds") asset_tree.add_asset_path("Rocketbox", "Materials/casual21_f_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/casual21_f_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/casual21_f_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/casual21_f_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/casual21_f_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/casual21_f_30.dds") asset_tree.add_asset_path("Rocketbox", "Textures/casual21_f_30_hair.dds") asset_tree.add_asset_path("Rocketbox", "Materials/sportive01_f_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/sportive01_f_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/sportive01_f_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/sportive01_f_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/sportive01_f_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/sportive01_f_20.dds") asset_tree.add_asset_path("Rocketbox", "Textures/sportive01_f_20_hair.dds") asset_tree.add_asset_path("Rocketbox", "Materials/sportive02_f_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/sportive02_f_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/sportive02_f_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/sportive02_f_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/sportive02_f_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/sportive02_f_35.dds") asset_tree.add_asset_path("Rocketbox", "Textures/sportive02_f_35_hair.dds") asset_tree.add_asset_path("Rocketbox", "Materials/sportive05_f_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/sportive05_f_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/sportive05_f_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/sportive05_f_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/sportive05_f_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/sportive05_f_25.dds") asset_tree.add_asset_path("Rocketbox", "Textures/sportive05_f_25_hair.dds") asset_tree.add_asset_path("Rocketbox", "Materials/sportive07_f_mediumpoly.material") asset_tree.add_asset_path("Rocketbox", "Materials/sportive07_f_lowpoly.material") asset_tree.add_asset_path("Rocketbox", "Meshes/sportive07_f_mediumpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Meshes/sportive07_f_lowpoly.mesh") asset_tree.add_asset_path("Rocketbox", "Physics/sportive07_f_mediumpoly.physics") asset_tree.add_asset_path("Rocketbox", "Textures/sportive07_f_20.dds") # human_female assets asset_tree.add_asset_path("HumanFemale", "Materials/human_female.material") asset_tree.add_asset_path("HumanFemale", "Meshes/human_female.mesh") asset_tree.add_asset_path("HumanFemale", "Physics/human_female.physics") asset_tree.add_asset_path("HumanFemale", "Skeletons/human_female.skeleton") asset_tree.add_asset_path("HumanFemale", "Textures/ARMOR_col.dds") asset_tree.add_asset_path("HumanFemale", "Textures/ARMOR_revised_1024_nm.tga") asset_tree.add_asset_path("HumanFemale", "Textures/blank.dds") asset_tree.add_asset_path("HumanFemale", "Textures/human_female_face.dds") asset_tree.add_asset_path("HumanFemale", "Textures/human_female_hair_a.dds") asset_tree.add_asset_path("HumanFemale", "Textures/human_female_leather_a.dds") asset_tree.add_asset_path("HumanFemale", "Textures/human_female_skin.dds") # human_male assets asset_tree.add_asset_path("HumanMale", "Materials/human_male.material") asset_tree.add_asset_path("HumanMale", "Meshes/human_male.mesh") asset_tree.add_asset_path("HumanMale", "Meshes/human_male_bones.mesh") asset_tree.add_asset_path("HumanMale", "Physics/human_male.physics") asset_tree.add_asset_path("HumanMale", "Skeletons/human_male.skeleton") asset_tree.add_asset_path("HumanMale", "Textures/human_male_apron.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_body.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_body2.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_body_shorts.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_face.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_hair1.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_hair2.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_head.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_leather_a.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_leather_a.jpg") asset_tree.add_asset_path("HumanMale", "Textures/human_male_leather_armor.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_mustache1.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_peasant.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_plate_armor.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_plate_armor1.dds") asset_tree.add_asset_path("HumanMale", "Textures/human_male_plate_armor2.dds") # SampleWorld assets asset_tree.add_asset_path("SampleWorld", "Materials/hall.material") asset_tree.add_asset_path("SampleWorld", "Materials/house_tall.material") asset_tree.add_asset_path("SampleWorld", "Materials/human_shack_tall.material") asset_tree.add_asset_path("SampleWorld", "Materials/shack.material") asset_tree.add_asset_path("SampleWorld", "Materials/wolf.material") asset_tree.add_asset_path("SampleWorld", "Materials/zombie.material") asset_tree.add_asset_path("SampleWorld", "Materials/human_house_wishbone.material") asset_tree.add_asset_path("SampleWorld", "Materials/human_house_open.material") asset_tree.add_asset_path("SampleWorld", "Materials/human_meeting_house.material") asset_tree.add_asset_path("SampleWorld", "Materials/sword.material") asset_tree.add_asset_path("SampleWorld", "Meshes/hall.mesh") asset_tree.add_asset_path("SampleWorld", "Meshes/house_tall.mesh") asset_tree.add_asset_path("SampleWorld", "Meshes/human_shack_tall.mesh") asset_tree.add_asset_path("SampleWorld", "Meshes/shack.mesh") asset_tree.add_asset_path("SampleWorld", "Meshes/wolf.mesh") asset_tree.add_asset_path("SampleWorld", "Meshes/zombie.mesh") asset_tree.add_asset_path("SampleWorld", "Meshes/human_house_wishbone.mesh") asset_tree.add_asset_path("SampleWorld", "Meshes/human_house_open.mesh") asset_tree.add_asset_path("SampleWorld", "Meshes/human_meeting_house.mesh") asset_tree.add_asset_path("SampleWorld", "Meshes/sword.mesh") asset_tree.add_asset_path("SampleWorld", "Misc/SampleWorld_AssetConfig.xml", "AssetConfig.xml") # asset_tree.add_asset_path("SampleWorld", "Misc/sampleworld.terrain") asset_tree.add_asset_path("SampleWorld", "Physics/human_shack_tall.physics") asset_tree.add_asset_path("SampleWorld", "Physics/wolf.physics") asset_tree.add_asset_path("SampleWorld", "Physics/zombie.physics") asset_tree.add_asset_path("SampleWorld", "Physics/human_house_wishbone.physics") asset_tree.add_asset_path("SampleWorld", "Physics/human_house_open.physics") asset_tree.add_asset_path("SampleWorld", "Physics/human_meeting_house.physics") asset_tree.add_asset_path("SampleWorld", "Skeletons/wolf.skeleton") asset_tree.add_asset_path("SampleWorld", "Skeletons/zombie.skeleton") asset_tree.add_asset_path("SampleWorld", "Textures/hall.dds") asset_tree.add_asset_path("SampleWorld", "Textures/house_tall.dds") asset_tree.add_asset_path("SampleWorld", "Textures/human_shack_tall_texture.dds") asset_tree.add_asset_path("SampleWorld", "Textures/shack.dds") asset_tree.add_asset_path("SampleWorld", "Textures/wolf_re.dds") asset_tree.add_asset_path("SampleWorld", "Textures/Zombie_Body_Diffuse.tga") asset_tree.add_asset_path("SampleWorld", "Textures/Zombie_Body_Normal.tga") asset_tree.add_asset_path("SampleWorld", "Textures/Zombie_Body_Specular.tga") asset_tree.add_asset_path("SampleWorld", "Textures/Zombie_Clothes_Diffuse.tga") asset_tree.add_asset_path("SampleWorld", "Textures/Zombie_Clothes_Normal.tga") asset_tree.add_asset_path("SampleWorld", "Textures/Zombie_Clothes_Specular.tga") asset_tree.add_asset_path("SampleWorld", "Textures/human_wishbone_house.dds") asset_tree.add_asset_path("SampleWorld", "Textures/human_house_open_texture.dds") asset_tree.add_asset_path("SampleWorld", "Textures/human_meeting_house.dds") asset_tree.add_asset_path("SampleWorld", "Textures/sword_005.dds") # FCworld assets asset_tree.add_asset_path("FCworld", "Meshes/casual07_f_lowpoly.mesh") asset_tree.add_asset_path("FCworld", "Meshes/casual07_f_mediumpoly.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_dividerWalls.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_foundation.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_large_side_building.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_middle_building.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_pillar.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_Pillar_tangentIssues.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_railing.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_railing_endcap.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_railing_stairs.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_small_side_building.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_stairs_back.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_stairs_center.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_stairs_fourth.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_stairs_main.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_stairs_third.mesh") asset_tree.add_asset_path("FCworld", "Meshes/FC_Wall_dragon_spout.mesh") asset_tree.add_asset_path("FCworld", "Materials/casual07_f_lowpoly.material") asset_tree.add_asset_path("FCworld", "Materials/casual07_f_mediumpoly.material") asset_tree.add_asset_path("FCworld", "Materials/FC_dividerWalls.material") asset_tree.add_asset_path("FCworld", "Materials/FC_foundation.material") asset_tree.add_asset_path("FCworld", "Materials/FC_large_side_building.material") asset_tree.add_asset_path("FCworld", "Materials/FC_middle_building.material") asset_tree.add_asset_path("FCworld", "Materials/FC_pillar.material") asset_tree.add_asset_path("FCworld", "Materials/FC_Pillar_tangentIssues.material") asset_tree.add_asset_path("FCworld", "Materials/FC_railing.material") asset_tree.add_asset_path("FCworld", "Materials/FC_railing_endcap.material") asset_tree.add_asset_path("FCworld", "Materials/FC_railing_stairs.material") asset_tree.add_asset_path("FCworld", "Materials/FC_small_side_building.material") asset_tree.add_asset_path("FCworld", "Materials/FC_stairs_back.material") asset_tree.add_asset_path("FCworld", "Materials/FC_stairs_center.material") asset_tree.add_asset_path("FCworld", "Materials/FC_stairs_fourth.material") asset_tree.add_asset_path("FCworld", "Materials/FC_stairs_main.material") asset_tree.add_asset_path("FCworld", "Materials/FC_stairs_third.material") asset_tree.add_asset_path("FCworld", "Materials/FC_Wall_dragon_spout.material") asset_tree.add_asset_path("FCworld", "Physics/casual07_f_mediumpoly.physics") asset_tree.add_asset_path("FCworld", "Physics/FC_foundation.physics") asset_tree.add_asset_path("FCworld", "Physics/FC_large_side_building.physics") asset_tree.add_asset_path("FCworld", "Physics/FC_stairs_back.physics") asset_tree.add_asset_path("FCworld", "Skeletons/rocketbox_f_lowpoly.skeleton") asset_tree.add_asset_path("FCworld", "Skeletons/rocketbox_f_mediumpoly.skeleton") asset_tree.add_asset_path("FCworld", "Textures/blank.dds") asset_tree.add_asset_path("FCworld", "Textures/casual07_f_20.dds") asset_tree.add_asset_path("FCworld", "Textures/casual07_f_20_hair.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_Beam01_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_Beam01_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_Beam01_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_beam02_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_beam02_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_beam02_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_beam_hang_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_beam_hang_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_beam_hang_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_beamRed_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_beamRed_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_beamRed_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_building_floor_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_building_floor_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_building_floor_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_column_wing_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_column_wing_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_column_wing_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_door_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_door_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_door_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_dragon_head_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_dragon_head_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_dragon_head_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_endWall_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_endWall_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_endWall_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_foundationStairs_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_foundationStairs_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_foundationStairs_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_foundationStairSide_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_foundationStairSide_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_foundationStairSide_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_foundationWall_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_foundationWall_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_foundationWall_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_foundationWalls.JPG") asset_tree.add_asset_path("FCworld", "Textures/FC_midLowLevel_foundation.JPG") asset_tree.add_asset_path("FCworld", "Textures/FC_midLowLevelFoundation_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_midLowLevelFoundation_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_midLowLevelFoundation_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_peak_dragon_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_peak_dragon_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_peak_dragon_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_pillars_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_pillars_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_pillars_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_railings.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_railings_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_railings_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_railings_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_roof_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_roof_edge_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_roof_edge_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_roof_edge_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_roof_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_roof_normal_detail.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_roof_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_roofPeaks_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_roofPeaks_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_roofPeaks_specularity.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_sign.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_skybox_east.bmp") asset_tree.add_asset_path("FCworld", "Textures/FC_skybox_east.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_skybox_north.bmp") asset_tree.add_asset_path("FCworld", "Textures/FC_skybox_north.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_skybox_south.bmp") asset_tree.add_asset_path("FCworld", "Textures/FC_skybox_south.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_skybox_top.bmp") asset_tree.add_asset_path("FCworld", "Textures/FC_skybox_top.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_skybox_west.bmp") asset_tree.add_asset_path("FCworld", "Textures/FC_skybox_west.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_StairCenter_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_topLevelFoundation_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_topLevelFoundation_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_topLevelFoundation_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_window_mantle_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_window_mantle_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_window_mantle_specular.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_windows_diffuse.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_windows_normal.dds") asset_tree.add_asset_path("FCworld", "Textures/FC_windows_specular.dds") # Tools assets asset_tree.add_asset_path("Tools", "Materials/axe.material") asset_tree.add_asset_path("Tools", "Materials/barrel.material") asset_tree.add_asset_path("Tools", "Materials/barrel_broken.material") asset_tree.add_asset_path("Tools", "Materials/barrel_half_open.material") asset_tree.add_asset_path("Tools", "Materials/brax.material") asset_tree.add_asset_path("Tools", "Materials/corral_01.material") asset_tree.add_asset_path("Tools", "Materials/corral_02.material") asset_tree.add_asset_path("Tools", "Materials/corral_03.material") asset_tree.add_asset_path("Tools", "Materials/directional_marker.material") asset_tree.add_asset_path("Tools", "Materials/fountainEmpty.material") asset_tree.add_asset_path("Tools", "Materials/gate.material") asset_tree.add_asset_path("Tools", "Materials/hall.material") asset_tree.add_asset_path("Tools", "Materials/HMETerrain.material") asset_tree.add_asset_path("Tools", "Materials/house_open.material") asset_tree.add_asset_path("Tools", "Materials/house_stilt.material") asset_tree.add_asset_path("Tools", "Materials/house_tall.material") asset_tree.add_asset_path("Tools", "Materials/house_wishbone.material") asset_tree.add_asset_path("Tools", "Materials/human_female.material") asset_tree.add_asset_path("Tools", "Materials/human_male.material") asset_tree.add_asset_path("Tools", "Materials/MVSMTerrain.material") asset_tree.add_asset_path("Tools", "Materials/Multiverse.material") asset_tree.add_asset_path("Tools", "Materials/orc.material") asset_tree.add_asset_path("Tools", "Materials/orc_house.material") asset_tree.add_asset_path("Tools", "Materials/orcsword.material") asset_tree.add_asset_path("Tools", "Materials/pick.material") asset_tree.add_asset_path("Tools", "Materials/rock_group_01.material") asset_tree.add_asset_path("Tools", "Materials/rock_group_02.material") asset_tree.add_asset_path("Tools", "Materials/shack.material") asset_tree.add_asset_path("Tools", "Materials/shield.material") asset_tree.add_asset_path("Tools", "Materials/spear.material") asset_tree.add_asset_path("Tools", "Materials/sword.material") asset_tree.add_asset_path("Tools", "Materials/tent_orc.material") asset_tree.add_asset_path("Tools", "Materials/tower.material") asset_tree.add_asset_path("Tools", "Materials/wall.material") asset_tree.add_asset_path("Tools", "Materials/wall_corner.material") asset_tree.add_asset_path("Tools", "Materials/wall_corner_walkway.material") asset_tree.add_asset_path("Tools", "Materials/wall_walkway.material") asset_tree.add_asset_path("Tools", "Materials/wolf.material") asset_tree.add_asset_path("Tools", "Materials/zombie.material") asset_tree.add_asset_path("Tools", "Materials/Ocean.material") asset_tree.add_asset_path("Tools", "Materials/Trees.material") asset_tree.add_asset_path("Tools", "Materials/Water.material") asset_tree.add_asset_path("Tools", "Meshes/axe.mesh") asset_tree.add_asset_path("Tools", "Meshes/barrel.mesh") asset_tree.add_asset_path("Tools", "Meshes/barrel_broken.mesh") asset_tree.add_asset_path("Tools", "Meshes/barrel_half_open.mesh") asset_tree.add_asset_path("Tools", "Meshes/brax.mesh") asset_tree.add_asset_path("Tools", "Meshes/corral_01.mesh") asset_tree.add_asset_path("Tools", "Meshes/corral_02.mesh") asset_tree.add_asset_path("Tools", "Meshes/corral_03.mesh") asset_tree.add_asset_path("Tools", "Meshes/directional_marker.mesh") asset_tree.add_asset_path("Tools", "Meshes/fountainEmpty.mesh") asset_tree.add_asset_path("Tools", "Meshes/gate.mesh") asset_tree.add_asset_path("Tools", "Meshes/hall.mesh") asset_tree.add_asset_path("Tools", "Meshes/house_open.mesh") asset_tree.add_asset_path("Tools", "Meshes/house_stilt.mesh") asset_tree.add_asset_path("Tools", "Meshes/house_tall.mesh") asset_tree.add_asset_path("Tools", "Meshes/house_wishbone.mesh") asset_tree.add_asset_path("Tools", "Meshes/human_female.mesh") asset_tree.add_asset_path("Tools", "Meshes/human_male.mesh") asset_tree.add_asset_path("Tools", "Meshes/orc.mesh") asset_tree.add_asset_path("Tools", "Meshes/orc_house.mesh") asset_tree.add_asset_path("Tools", "Meshes/orcsword.mesh") asset_tree.add_asset_path("Tools", "Meshes/pick.mesh") asset_tree.add_asset_path("Tools", "Meshes/rock_group_01.mesh") asset_tree.add_asset_path("Tools", "Meshes/rock_group_02.mesh") asset_tree.add_asset_path("Tools", "Meshes/shack.mesh") asset_tree.add_asset_path("Tools", "Meshes/shield.mesh") asset_tree.add_asset_path("Tools", "Meshes/spear.mesh") asset_tree.add_asset_path("Tools", "Meshes/sword.mesh") asset_tree.add_asset_path("Tools", "Meshes/tent_orc.mesh") asset_tree.add_asset_path("Tools", "Meshes/tower.mesh") asset_tree.add_asset_path("Tools", "Meshes/wall.mesh") asset_tree.add_asset_path("Tools", "Meshes/wall_corner.mesh") asset_tree.add_asset_path("Tools", "Meshes/wall_corner_walkway.mesh") asset_tree.add_asset_path("Tools", "Meshes/wall_walkway.mesh") asset_tree.add_asset_path("Tools", "Meshes/wolf.mesh") asset_tree.add_asset_path("Tools", "Meshes/zombie.mesh") asset_tree.add_asset_path("Tools", "Textures/Zombie_Body_Diffuse.tga") asset_tree.add_asset_path("Tools", "Textures/Zombie_Body_Normal.tga") asset_tree.add_asset_path("Tools", "Textures/Zombie_Body_Specular.tga") asset_tree.add_asset_path("Tools", "Textures/Zombie_Clothes_Diffuse.tga") asset_tree.add_asset_path("Tools", "Textures/Zombie_Clothes_Normal.tga") asset_tree.add_asset_path("Tools", "Textures/Zombie_Clothes_Specular.tga") asset_tree.add_asset_path("Tools", "Textures/axe_003.dds") asset_tree.add_asset_path("Tools", "Textures/bodyUV_DI_RED_1024.dds") asset_tree.add_asset_path("Tools", "Textures/dino2.dds") asset_tree.add_asset_path("Tools", "Textures/dino_top2.dds") asset_tree.add_asset_path("Tools", "Textures/faceUV_DI_RED_1024.dds") asset_tree.add_asset_path("Tools", "Textures/fence.dds") asset_tree.add_asset_path("Tools", "Textures/hall.dds") asset_tree.add_asset_path("Tools", "Textures/house_open.dds") asset_tree.add_asset_path("Tools", "Textures/house_tall.dds") asset_tree.add_asset_path("Tools", "Textures/human_female_face.dds") asset_tree.add_asset_path("Tools", "Textures/human_female_hair_a.dds") asset_tree.add_asset_path("Tools", "Textures/human_female_leather_a.dds") asset_tree.add_asset_path("Tools", "Textures/human_female_skin.dds") asset_tree.add_asset_path("Tools", "Textures/human_male_apron.dds") asset_tree.add_asset_path("Tools", "Textures/human_male_body2.dds") asset_tree.add_asset_path("Tools", "Textures/human_male_face.dds") asset_tree.add_asset_path("Tools", "Textures/human_male_hair2.dds") asset_tree.add_asset_path("Tools", "Textures/human_male_leather_a.dds") asset_tree.add_asset_path("Tools", "Textures/human_male_leather_armor.dds") asset_tree.add_asset_path("Tools", "Textures/human_male_mustache1.dds") asset_tree.add_asset_path("Tools", "Textures/human_male_peasant.dds") asset_tree.add_asset_path("Tools", "Textures/human_male_plate_armor1.dds") asset_tree.add_asset_path("Tools", "Textures/human_male_plate_armor2.dds") asset_tree.add_asset_path("Tools", "Textures/lezhanka.dds") asset_tree.add_asset_path("Tools", "Textures/orc_house_007.dds") asset_tree.add_asset_path("Tools", "Textures/pick_003.dds") asset_tree.add_asset_path("Tools", "Textures/ramp.dds") asset_tree.add_asset_path("Tools", "Textures/shack.dds") asset_tree.add_asset_path("Tools", "Textures/shield_006.dds") asset_tree.add_asset_path("Tools", "Textures/skull.dds") asset_tree.add_asset_path("Tools", "Textures/spear_004.dds") asset_tree.add_asset_path("Tools", "Textures/sword_005.dds") asset_tree.add_asset_path("Tools", "Textures/town_fountain.dds") asset_tree.add_asset_path("Tools", "Textures/weap_rack.dds") asset_tree.add_asset_path("Tools", "Textures/wolf_re.dds") asset_tree.add_asset_path("Tools", "Materials/directional_marker.material") asset_tree.add_asset_path("Tools", "GpuPrograms/HMETerrain.cg") asset_tree.add_asset_path("Tools", "Meshes/directional_marker.mesh") asset_tree.add_asset_path("Tools", "Textures/crosshatch-thin.dds") asset_tree.add_asset_path("Tools", "Textures/directional_marker.dds") asset_tree.add_asset_path("Tools", "Textures/directional_marker_greyscale.dds") asset_tree.add_asset_path("Tools", "Textures/directional_marker_orange.dds") asset_tree.add_asset_path("Tools", "Textures/directional_marker_red.dds") asset_tree.add_asset_path("Tools", "Textures/directional_marker_yellow.dds") def build_assets(kinds, tar_file_name): asset_tree = AssetTree("../", "") add_assets(asset_tree) tar_file = tarfile.open(tar_file_name, "w") asset_tree.write_to_tar(kinds, tar_file) tar_file.close() def copy_assets(kinds, prefix): if (prefix[:1] != "/" and prefix[:1] != "\\"): prefix = prefix + "/" asset_tree = AssetTree("../", "") add_assets(asset_tree) asset_tree.copy_media_files(kinds, prefix)

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Easy/1018-binary-prefix-divisible-by-5.py

sandychn/LeetCode-Solutions

0

94899

<gh_stars>0 class Solution: def prefixesDivBy5(self, A: List[int]) -> List[bool]: val = 0 res = [None] * len(A) for i, v in enumerate(A): val = ((val << 1) + v) % 5 res[i] = (val == 0) return res

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attributes_and_methods/lab/project/hotel.py

BoyanPeychinov/object_oriented_programming

0

1610794

<reponame>BoyanPeychinov/object_oriented_programming class Hotel: def __init__(self, name): self.name = name self.rooms = [] self.guests = 0 @staticmethod def find_room(rooms, room_number): return list(filter(lambda room: room.number == room_number, rooms))[0] @classmethod def from_stars(cls, stars_count): return cls(f"{stars_count} stars Hotel") def add_room(self, room): self.rooms.append(room) def take_room(self, room_number, people): result = self.find_room(self.rooms, room_number).take_room(people) if result: return result self.guests += people def free_room(self, room_number): room = self.find_room(self.rooms, room_number) guests_to_remove = room.guests result = room.free_room() if result: return result self.guests -= guests_to_remove def print_status(self): free_rooms_numbers = [str(room) for room in self.rooms if not room.is_taken] taken_rooms_numbers = [str(room) for room in self.rooms if room.is_taken] print(f"Hotel {self.name} has {self.guests} total guests") print(f"Free rooms: {', '.join(free_rooms_numbers)}") print(f"Taken rooms: {', '.join(taken_rooms_numbers)}")

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day2/demo1.py

PickHeBin/2020-2-25

0

125620

#!/usr/bin/env python # -*- coding: utf-8 -*- # File : demo1.py # Author: MuNian # Date : 2020-01-08 ''' Python爬虫假装浏览器 ''' import requests proxie = { 'http':'http://localhost:5555/random' } print(proxie) url = 'https://www.baidu.com' response = requests.get(url, proxies=proxie) print(response)

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libcst/codemod/tests/test_codemod_cli.py

lpetre/LibCST

2

142100

# Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. # import subprocess import sys from pathlib import Path from libcst.testing.utils import UnitTest class TestCodemodCLI(UnitTest): def test_codemod_formatter_error_input(self) -> None: rlt = subprocess.run( [ "python", "-m", "libcst.tool", "codemod", "remove_unused_imports.RemoveUnusedImportsCommand", str(Path(__file__).parent / "codemod_formatter_error_input.py.txt"), ], stdout=subprocess.PIPE, stderr=subprocess.PIPE, ) version = sys.version_info if version[0] == 3 and version[1] == 6: self.assertIn( "ParserSyntaxError: Syntax Error @ 14:11.", rlt.stderr.decode("utf-8"), ) else: self.assertIn( "error: cannot format -: Cannot parse: 13:10: async with AsyncExitStack() as stack:", rlt.stderr.decode("utf-8"), )

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lib/bes/text/text_fit.py

reconstruir/bes

0

51321

<gh_stars>0 #-*- coding:utf-8; mode:python; indent-tabs-mode: nil; c-basic-offset: 2; tab-width: 2 -*- from .string_lexer import string_lexer as lexer from bes.compat.StringIO import StringIO class text_fit(object): 'Fit text into bounded areas keeping words intact.' @classmethod def fit_text(clazz, text, width): lines = [] for line in text.split('\n'): lines.extend(clazz.fit_line(text, width)) return lines @classmethod def fit_line(clazz, text, width): assert '\n' not in text lines = [] buf = StringIO() for token in lexer.tokenize(text, 'text_fit', options = lexer.KEEP_QUOTES | lexer.IGNORE_COMMENTS): if token.token_type == lexer.TOKEN_SPACE: if (buf.tell() + len(token.value)) > width: lines.append(buf.getvalue().strip()) buf = StringIO() else: buf.write(token.value) if token.token_type == lexer.TOKEN_STRING: if (buf.tell() + len(token.value)) > width: lines.append(buf.getvalue().strip()) buf = StringIO() buf.write(token.value) elif token.token_type == lexer.TOKEN_DONE: if buf.tell() > 0: lines.append(buf.getvalue().strip()) return lines

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reverse-string/reverse_string.py

Kalpavrikshika/Exercism-tests

0

77619

<filename>reverse-string/reverse_string.py def reverse(input): #reverse as string return input[::-1] #An alternative approach I wass using - string to list '''myinput = input mylist = list(myinput) #print (mylist) mylist.reverse() return (mylist)''' '''while True: if input == None: print (None) break else: #find length of list l=len(mylist)-1 #while loop to reverse while l >= 0: print (mylist[l]) l -= 1 #actual reverse function happens here #return(mylist) #not working: '' input and '' puctuated sentence.''' reverse(input = 'robot') reverse(input = 'Ramen') reverse(input = 'I\'m hungry!') reverse(input = 'racecar')

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subparsers/list_custom_images.py

dman777/image_share

1

59033

<reponame>dman777/image_share # List custom images def list_custom_images_subparser(subparser): list_images = subparser.add_parser( 'list-custom-images', description=('***List custom' ' saved images of' ' producers/consumers' ' account'), help=('List custom' ' saved images of' ' producers/consumers' ' account')) group_key = list_images.add_mutually_exclusive_group(required=True) group_key.add_argument('-pn', '--producer-username', help="Producer\'s(source account) username") group_key.add_argument('-cn', '--consumer-username', dest='producer_username', metavar='CONSUMER_USERNAME', help="Consumer\'s(destination account) username") group_apikey = list_images.add_mutually_exclusive_group(required=True) group_apikey.add_argument('-pa', '--producer-apikey', help="Producer\'s(source account) apikey") group_apikey.add_argument('-ca', '--consumer-apikey', dest='producer_apikey', metavar='CONSUMER_APIKEY', help="Consumer\'s(destination account) apikey") list_images.add_argument('-u', '--uuid', help="Image ID number")

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python/ack.py

catseye/Dipple

5

42633

#!/usr/bin/env python import sys def ack(m, n): if m == 0: return n + 1 elif n == 0: return ack(m-1, 1) else: return ack(m-1, ack(m, n-1)) sys.setrecursionlimit(12000) for m in range(0, 4): for n in range(0, 10): print "ack(%s,%s)=%s" % (m, n, ack(m, n)) m = 4 n = 0 print "ack(%s,%s)=%s" % (m, n, ack(m, n)) # m = 4 # n = 1 # print "ack(%s,%s)=%s" % (m, n, ack(m, n))

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src/Diagnostics.AIProjects/SearchAPI/SearchModule/TfIdfSearchModel.py

yiningwei/Azure-AppServices-Diagnostics

0

86060

<reponame>yiningwei/Azure-AppServices-Diagnostics<filename>src/Diagnostics.AIProjects/SearchAPI/SearchModule/TfIdfSearchModel.py import json, os from SearchModule.ModelInfo import ModelInfo from SearchModule.TokenizerModule import getAllNGrams from gensim.models import TfidfModel from gensim import corpora, similarities from SearchModule.Exceptions import * from SearchModule.Utilities import absPath, verifyFile from SearchModule.MessageStrings import fileMissingMessage class TfIdfSearchModel: def __init__(self, modelpackagepath): packageFiles = { "dictionaryFile": "dictionary.dict", "m1ModelFile": "m1.model", "m1IndexFile": "m1.index", "m2ModelFile": "m2.model", "m2IndexFile": "m2.index", "detectorsFile": "Detectors.json", "sampleUtterancesFile": "SampleUtterances.json", "mappingsFile": "Mappings.json", "modelInfo": "ModelInfo.json" } for key in packageFiles.keys(): packageFiles[key] = absPath(os.path.join(modelpackagepath, packageFiles[key])) self.packageFiles = packageFiles self.optionalFiles = ["mappingsFile"] if not self.verifyModelFiles(): raise ModelFileVerificationFailed(fileMissingMessage) self.models = {"dictionary": None, "m1Model": None, "m1Index": None, "m2Model": None, "m2Index": None, "detectors": None, "sampleUtterances": None, "mappings": None, "modelInfo": None} try: with open(self.packageFiles["modelInfo"], "r") as fp: self.models["modelInfo"] = ModelInfo(json.loads(fp.read())) fp.close() except: self.models["modelInfo"] = ModelInfo({}) try: self.models["dictionary"] = corpora.Dictionary.load(self.packageFiles["dictionaryFile"]) except: raise ModelFileLoadFailed("Failed to load dictionary from file " + self.packageFiles["dictionaryFile"]) try: self.models["m1Model"] = TfidfModel.load(self.packageFiles["m1ModelFile"]) except: raise ModelFileLoadFailed("Failed to load model from file " + self.packageFiles["m1ModelFile"]) try: self.models["m1Index"] = similarities.MatrixSimilarity.load(self.packageFiles["m1IndexFile"]) except: raise ModelFileLoadFailed("Failed to load index from file " + self.packageFiles["m1IndexFile"]) try: self.models["m2Model"] = TfidfModel.load(self.packageFiles["m2ModelFile"]) self.models["m2Model"] = None del self.models["m2Model"] except: raise ModelFileLoadFailed("Failed to load model from file " + self.packageFiles["m2ModelFile"]) try: self.models["m2Index"] = similarities.MatrixSimilarity.load(self.packageFiles["m2IndexFile"]) self.models["m2Index"] = None del self.models["m2Index"] except: raise ModelFileLoadFailed("Failed to load index from file " + self.packageFiles["m2IndexFile"]) try: with open(self.packageFiles["detectorsFile"], "r") as f: self.models["detectors"] = json.loads(f.read()) f.close() except: raise ModelFileLoadFailed("Failed to parse json from file " + self.packageFiles["detectorsFile"]) if self.models["modelInfo"].detectorContentSplitted: try: with open(self.packageFiles["mappingsFile"], "r") as f: self.models["mappings"] = json.loads(f.read()) f.close() except: raise ModelFileLoadFailed("Failed to parse json from file " + self.packageFiles["mappingsFile"]) try: with open(self.packageFiles["sampleUtterancesFile"], "r") as f: self.models["sampleUtterances"] = json.loads(f.read()) f.close() self.models["sampleUtterances"] = None del self.models["sampleUtterances"] except: raise ModelFileLoadFailed("Failed to parse json from file " + self.packageFiles["sampleUtterancesFile"]) def verifyModelFiles(self): for key in self.packageFiles.keys(): if key not in self.optionalFiles and not verifyFile(self.packageFiles[key], absolute=True, prelogMessage="TfIdfSearchModel: "): return False return True def getDetectorByIndex(self, index): detector = [x for x in self.models["mappings"] if (x["startindex"] <= index <= x["endindex"])] if detector and detector[0]: return detector[0]["id"] else: return None def queryDetectors(self, query=None): if query: try: vector = self.models["m1Model"][self.models["dictionary"].doc2bow(getAllNGrams(query, self.models["modelInfo"].textNGrams))] if self.models["modelInfo"].detectorContentSplitted: similar_doc_indices = sorted(enumerate(self.models["m1Index"][vector]), key=lambda item: -item[1])[:10] similar_docs = [] for x in similar_doc_indices: detector = self.getDetectorByIndex(x[0]) if detector and (not (detector in [p["detector"] for p in similar_docs])): similar_docs.append({"detector": self.getDetectorByIndex(x[0]), "score": str(x[1])}) else: similar_doc_indices = sorted(enumerate(self.models["m1Index"][vector]), key=lambda item: -item[1]) similar_docs = list(map(lambda x: {"detector": self.models["detectors"][x[0]]["id"], "score": str(x[1])}, similar_doc_indices)) return {"query": query, "results": similar_docs} except Exception as e: return {"query": query, "results": [], "exception": str(e)} return None def loadUtteranceModel(self): self.models["m2Model"] = TfidfModel.load(self.packageFiles["m2ModelFile"]) self.models["m2Index"] = similarities.MatrixSimilarity.load(self.packageFiles["m2IndexFile"]) with open(self.packageFiles["sampleUtterancesFile"], "r") as f: self.models["sampleUtterances"] = json.loads(f.read()) f.close() def unloadUtteranceModel(self): del self.models["m2Model"] del self.models["m2Index"] del self.models["sampleUtterances"] def queryUtterances(self, query=None, existing_utterances=[]): if query: query = query + " ".join(existing_utterances) self.loadUtteranceModel() try: vector = self.models["m2Model"][self.models["dictionary"].doc2bow(getAllNGrams(query, self.models["modelInfo"].textNGrams))] similar_doc_indices = sorted(enumerate(self.models["m2Index"][vector]), key=lambda item: -item[1]) similar_doc_indices = [x for x in similar_doc_indices if self.models["sampleUtterances"][x[0]]["text"].lower() not in existing_utterances][:10] similar_docs = list(map(lambda x: {"sampleUtterance": self.models["sampleUtterances"][x[0]], "score": str(x[1])}, similar_doc_indices)) return {"query": query, "results": similar_docs} except Exception as e: return {"query": query, "results": None} self.unloadUtteranceModel() return None

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python/errnames.py

amirgeva/redrobot

1

123789

<gh_stars>1-10 import errno def get_error_name(e): if e==errno.EPERM: return 'Operation not permitted' if e==errno.ENOENT: return 'No such file or directory' if e==errno.ESRCH: return 'No such process' if e==errno.EINTR: return 'Interrupted system call' if e==errno.EIO: return 'I/O error' if e==errno.ENXIO: return 'No such device or address' if e==errno.E2BIG: return 'Arg list too long' if e==errno.ENOEXEC: return 'Exec format error' if e==errno.EBADF: return 'Bad file number' if e==errno.ECHILD: return 'No child processes' if e==errno.EAGAIN: return 'Try again' if e==errno.ENOMEM: return 'Out of memory' if e==errno.EACCES: return 'Permission denied' if e==errno.EFAULT: return 'Bad address' if e==errno.ENOTBLK: return 'Block device required' if e==errno.EBUSY: return 'Device or resource busy' if e==errno.EEXIST: return 'File exists' if e==errno.EXDEV: return 'Cross-device link' if e==errno.ENODEV: return 'No such device' if e==errno.ENOTDIR: return 'Not a directory' if e==errno.EISDIR: return 'Is a directory' if e==errno.EINVAL: return 'Invalid argument' if e==errno.ENFILE: return 'File table overflow' if e==errno.EMFILE: return 'Too many open files' if e==errno.ENOTTY: return 'Not a typewriter' if e==errno.ETXTBSY: return 'Text file busy' if e==errno.EFBIG: return 'File too large' if e==errno.ENOSPC: return 'No space left on device' if e==errno.ESPIPE: return 'Illegal seek' if e==errno.EROFS: return 'Read-only file system' if e==errno.EMLINK: return 'Too many links' if e==errno.EPIPE: return 'Broken pipe' if e==errno.EDOM: return 'Math argument out of domain of func' if e==errno.ERANGE: return 'Math result not representable' if e==errno.EDEADLK: return 'Resource deadlock would occur' if e==errno.ENAMETOOLONG: return 'File name too long' if e==errno.ENOLCK: return 'No record locks available' if e==errno.ENOSYS: return 'Function not implemented' if e==errno.ENOTEMPTY: return 'Directory not empty' if e==errno.ELOOP: return 'Too many symbolic links encountered' if e==errno.EWOULDBLOCK: return 'Operation would block' if e==errno.ENOMSG: return 'No message of desired type' if e==errno.EIDRM: return 'Identifier removed' if e==errno.ECHRNG: return 'Channel number out of range' if e==errno.EL2NSYNC: return 'Level 2 not synchronized' if e==errno.EL3HLT: return 'Level 3 halted' if e==errno.EL3RST: return 'Level 3 reset' if e==errno.ELNRNG: return 'Link number out of range' if e==errno.EUNATCH: return 'Protocol driver not attached' if e==errno.ENOCSI: return 'No CSI structure available' if e==errno.EL2HLT: return 'Level 2 halted' if e==errno.EBADE: return 'Invalid exchange' if e==errno.EBADR: return 'Invalid request descriptor' if e==errno.EXFULL: return 'Exchange full' if e==errno.ENOANO: return 'No anode' if e==errno.EBADRQC: return 'Invalid request code' if e==errno.EBADSLT: return 'Invalid slot' if e==errno.EDEADLOCK: return 'File locking deadlock error' if e==errno.EBFONT: return 'Bad font file format' if e==errno.ENOSTR: return 'Device not a stream' if e==errno.ENODATA: return 'No data available' if e==errno.ETIME: return 'Timer expired' if e==errno.ENOSR: return 'Out of streams resources' if e==errno.ENONET: return 'Machine is not on the network' if e==errno.ENOPKG: return 'Package not installed' if e==errno.EREMOTE: return 'Object is remote' if e==errno.ENOLINK: return 'Link has been severed' if e==errno.EADV: return 'Advertise error' if e==errno.ESRMNT: return 'Srmount error' if e==errno.ECOMM: return 'Communication error on send' if e==errno.EPROTO: return 'Protocol error' if e==errno.EMULTIHOP: return 'Multihop attempted' if e==errno.EDOTDOT: return 'RFS specific error' if e==errno.EBADMSG: return 'Not a data message' if e==errno.EOVERFLOW: return 'Value too large for defined data type' if e==errno.ENOTUNIQ: return 'Name not unique on network' if e==errno.EBADFD: return 'File descriptor in bad state' if e==errno.EREMCHG: return 'Remote address changed' if e==errno.ELIBACC: return 'Can not access a needed shared library' if e==errno.ELIBBAD: return 'Accessing a corrupted shared library' if e==errno.ELIBSCN: return '.lib section in a.out corrupted' if e==errno.ELIBMAX: return 'Attempting to link in too many shared libraries' if e==errno.ELIBEXEC: return 'Cannot exec a shared library directly' if e==errno.EILSEQ: return 'Illegal byte sequence' if e==errno.ERESTART: return 'Interrupted system call should be restarted' if e==errno.ESTRPIPE: return 'Streams pipe error' if e==errno.EUSERS: return 'Too many users' if e==errno.ENOTSOCK: return 'Socket operation on non-socket' if e==errno.EDESTADDRREQ: return 'Destination address required' if e==errno.EMSGSIZE: return 'Message too long' if e==errno.EPROTOTYPE: return 'Protocol wrong type for socket' if e==errno.ENOPROTOOPT: return 'Protocol not available' if e==errno.EPROTONOSUPPORT: return 'Protocol not supported' if e==errno.ESOCKTNOSUPPORT: return 'Socket type not supported' if e==errno.EOPNOTSUPP: return 'Operation not supported on transport endpoint' if e==errno.EPFNOSUPPORT: return 'Protocol family not supported' if e==errno.EAFNOSUPPORT: return 'Address family not supported by protocol' if e==errno.EADDRINUSE: return 'Address already in use' if e==errno.EADDRNOTAVAIL: return 'Cannot assign requested address' if e==errno.ENETDOWN: return 'Network is down' if e==errno.ENETUNREACH: return 'Network is unreachable' if e==errno.ENETRESET: return 'Network dropped connection because of reset' if e==errno.ECONNABORTED: return 'Software caused connection abort' if e==errno.ECONNRESET: return 'Connection reset by peer' if e==errno.ENOBUFS: return 'No buffer space available' if e==errno.EISCONN: return 'Transport endpoint is already connected' if e==errno.ENOTCONN: return 'Transport endpoint is not connected' if e==errno.ESHUTDOWN: return 'Cannot send after transport endpoint shutdown' if e==errno.ETOOMANYREFS: return 'Too many references: cannot splice' if e==errno.ETIMEDOUT: return 'Connection timed out' if e==errno.ECONNREFUSED: return 'Connection refused' if e==errno.EHOSTDOWN: return 'Host is down' if e==errno.EHOSTUNREACH: return 'No route to host' if e==errno.EALREADY: return 'Operation already in progress' if e==errno.EINPROGRESS: return 'Operation now in progress' if e==errno.ESTALE: return 'Stale NFS file handle' if e==errno.EUCLEAN: return 'Structure needs cleaning' if e==errno.ENOTNAM: return 'Not a XENIX named type file' if e==errno.ENAVAIL: return 'No XENIX semaphores available' if e==errno.EISNAM: return 'Is a named type file' if e==errno.EREMOTEIO: return 'Remote I/O error' if e==errno.EDQUOT: return 'Quota exceeded' return "Unknown"

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bin/calc_post_prob_codivergence.py

phyletica/gekgo

1

196643

<filename>bin/calc_post_prob_codivergence.py #! /usr/bin/env python """ Calculate the posterior probability that the Gekko pairs Panay-Masbate and Sabtang-Batan co-diverged. """ import os import sys import glob import logging import gekgo_util import pycoevolity _LOG = gekgo_util.RunLogger(name=__file__, log_to_stderr=True, log_to_file=False) def main(): paths = glob.glob(os.path.join(gekgo_util.ECOEVOLITY_OUTPUT_DIR, "run-?-gekko-rate2000-state-run-1.log")) posterior = pycoevolity.posterior.PosteriorSummary(paths, burnin = 101) post_prob_shared = 0.0 sys.stdout.write("model\tpost_prob\n") for model, prob in posterior.get_models(): sys.stdout.write("{model}\t{post_prob}\n".format( model = model, post_prob = prob)) if model[3] == model[5]: post_prob_shared += prob sys.stdout.write("{0}\n".format(post_prob_shared)) if __name__ == '__main__': main()

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python/after/banking/controller.py

vritb/command-pattern-undo-redo

5

98853

from dataclasses import dataclass, field from banking.transaction import Transaction @dataclass class BankController: undo_stack: list[Transaction] = field(default_factory=list) redo_stack: list[Transaction] = field(default_factory=list) def execute(self, transaction: Transaction) -> None: transaction.execute() self.redo_stack.clear() self.undo_stack.append(transaction) def undo(self) -> None: if not self.undo_stack: return transaction = self.undo_stack.pop() transaction.undo() self.redo_stack.append(transaction) def redo(self) -> None: if not self.redo_stack: return transaction = self.redo_stack.pop() transaction.execute() self.undo_stack.append(transaction)

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data/tools/replay/ui.py

ASGuard-UCI/ld-metric

0

66438

<gh_stars>0 #!/usr/bin/env python import argparse import os import sys os.environ["OMP_NUM_THREADS"] = "1" import cv2 import numpy as np import pygame import zmq from common.basedir import BASEDIR from common.transformations.camera import FULL_FRAME_SIZE, eon_intrinsics from common.transformations.model import (MODEL_CX, MODEL_CY, MODEL_INPUT_SIZE, get_camera_frame_from_model_frame) from selfdrive.car.toyota.interface import CarInterface as ToyotaInterface from selfdrive.config import UIParams as UP from selfdrive.controls.lib.vehicle_model import VehicleModel from selfdrive.messaging import SubMaster, recv_one from selfdrive.services import service_list from tools.replay.lib.ui_helpers import (_BB_TO_FULL_FRAME, BLACK, BLUE, GREEN, YELLOW, RED, CalibrationTransformsForWarpMatrix, draw_lead_car, draw_lead_on, draw_mpc, extract_model_data, get_blank_lid_overlay, init_plots, maybe_update_radar_points, plot_model, pygame_modules_have_loaded, warp_points) os.environ['BASEDIR'] = BASEDIR ANGLE_SCALE = 5.0 HOR = os.getenv("HORIZONTAL") is not None def ui_thread(addr, frame_address): context = zmq.Context.instance() # TODO: Detect car from replay and use that to select carparams CP = ToyotaInterface.get_params("TOYOTA PRIUS 2017") VM = VehicleModel(CP) CalP = np.asarray([[0, 0], [MODEL_INPUT_SIZE[0], 0], [MODEL_INPUT_SIZE[0], MODEL_INPUT_SIZE[1]], [0, MODEL_INPUT_SIZE[1]]]) vanishing_point = np.asarray([[MODEL_CX, MODEL_CY]]) pygame.init() pygame.font.init() assert pygame_modules_have_loaded() if HOR: size = (640+384+640, 960) write_x = 5 write_y = 680 else: size = (640+384, 960+300) write_x = 645 write_y = 970 pygame.display.set_caption("openpilot debug UI") screen = pygame.display.set_mode(size, pygame.DOUBLEBUF) alert1_font = pygame.font.SysFont("arial", 30) alert2_font = pygame.font.SysFont("arial", 20) info_font = pygame.font.SysFont("arial", 15) camera_surface = pygame.surface.Surface((640, 480), 0, 24).convert() cameraw_surface = pygame.surface.Surface(MODEL_INPUT_SIZE, 0, 24).convert() cameraw_test_surface = pygame.surface.Surface(MODEL_INPUT_SIZE, 0, 24) top_down_surface = pygame.surface.Surface((UP.lidar_x, UP.lidar_y),0,8) frame = context.socket(zmq.SUB) frame.setsockopt(zmq.CONFLATE, 1) frame.connect(frame_address or "tcp://%s:%d" % (addr, service_list['frame'].port)) frame.setsockopt(zmq.SUBSCRIBE, b"") sm = SubMaster(['carState', 'plan', 'carControl', 'radarState', 'liveCalibration', 'controlsState', 'liveTracks', 'model', 'liveMpc', 'liveParameters', 'pathPlan']) calibration = None img = np.zeros((480, 640, 3), dtype='uint8') imgff = np.zeros((FULL_FRAME_SIZE[1], FULL_FRAME_SIZE[0], 3), dtype=np.uint8) imgw = np.zeros((160, 320, 3), dtype=np.uint8) # warped image lid_overlay_blank = get_blank_lid_overlay(UP) # plots name_to_arr_idx = { "gas": 0, "computer_gas": 1, "user_brake": 2, "computer_brake": 3, "v_ego": 4, "v_pid": 5, "angle_steers_des": 6, "angle_steers": 7, "angle_steers_k": 8, "steer_torque": 9, "v_override": 10, "v_cruise": 11, "a_ego": 12, "a_target": 13, "accel_override": 14} plot_arr = np.zeros((100, len(name_to_arr_idx.values()))) plot_xlims = [(0, plot_arr.shape[0]), (0, plot_arr.shape[0]), (0, plot_arr.shape[0]), (0, plot_arr.shape[0])] plot_ylims = [(-0.1, 1.1), (-ANGLE_SCALE, ANGLE_SCALE), (0., 75.), (-3.0, 2.0)] plot_names = [["gas", "computer_gas", "user_brake", "computer_brake", "accel_override"], ["angle_steers", "angle_steers_des", "angle_steers_k", "steer_torque"], ["v_ego", "v_override", "v_pid", "v_cruise"], ["a_ego", "a_target"]] plot_colors = [["b", "b", "g", "r", "y"], ["b", "g", "y", "r"], ["b", "g", "r", "y"], ["b", "r"]] plot_styles = [["-", "-", "-", "-", "-"], ["-", "-", "-", "-"], ["-", "-", "-", "-"], ["-", "-"]] draw_plots = init_plots(plot_arr, name_to_arr_idx, plot_xlims, plot_ylims, plot_names, plot_colors, plot_styles, bigplots=True) counter = 0 while 1: list(pygame.event.get()) screen.fill((64,64,64)) lid_overlay = lid_overlay_blank.copy() top_down = top_down_surface, lid_overlay # ***** frame ***** fpkt = recv_one(frame) rgb_img_raw = fpkt.frame.image if fpkt.frame.transform: img_transform = np.array(fpkt.frame.transform).reshape(3,3) else: # assume frame is flipped img_transform = np.array([ [-1.0, 0.0, FULL_FRAME_SIZE[0]-1], [ 0.0, -1.0, FULL_FRAME_SIZE[1]-1], [ 0.0, 0.0, 1.0] ]) if rgb_img_raw and len(rgb_img_raw) == FULL_FRAME_SIZE[0] * FULL_FRAME_SIZE[1] * 3: imgff = np.frombuffer(rgb_img_raw, dtype=np.uint8).reshape((FULL_FRAME_SIZE[1], FULL_FRAME_SIZE[0], 3)) imgff = imgff[:, :, ::-1] # Convert BGR to RGB cv2.warpAffine(imgff, np.dot(img_transform, _BB_TO_FULL_FRAME)[:2], (img.shape[1], img.shape[0]), dst=img, flags=cv2.WARP_INVERSE_MAP) intrinsic_matrix = eon_intrinsics else: img.fill(0) intrinsic_matrix = np.eye(3) if calibration is not None: transform = np.dot(img_transform, calibration.model_to_full_frame) imgw = cv2.warpAffine(imgff, transform[:2], (MODEL_INPUT_SIZE[0], MODEL_INPUT_SIZE[1]), flags=cv2.WARP_INVERSE_MAP) else: imgw.fill(0) sm.update() w = sm['controlsState'].lateralControlState.which() if w == 'lqrState': angle_steers_k = sm['controlsState'].lateralControlState.lqrState.steerAngle elif w == 'indiState': angle_steers_k = sm['controlsState'].lateralControlState.indiState.steerAngle else: angle_steers_k = np.inf plot_arr[:-1] = plot_arr[1:] plot_arr[-1, name_to_arr_idx['angle_steers']] = sm['controlsState'].angleSteers plot_arr[-1, name_to_arr_idx['angle_steers_des']] = sm['carControl'].actuators.steerAngle plot_arr[-1, name_to_arr_idx['angle_steers_k']] = angle_steers_k plot_arr[-1, name_to_arr_idx['gas']] = sm['carState'].gas plot_arr[-1, name_to_arr_idx['computer_gas']] = sm['carControl'].actuators.gas plot_arr[-1, name_to_arr_idx['user_brake']] = sm['carState'].brake plot_arr[-1, name_to_arr_idx['steer_torque']] = sm['carControl'].actuators.steer * ANGLE_SCALE plot_arr[-1, name_to_arr_idx['computer_brake']] = sm['carControl'].actuators.brake plot_arr[-1, name_to_arr_idx['v_ego']] = sm['controlsState'].vEgo plot_arr[-1, name_to_arr_idx['v_pid']] = sm['controlsState'].vPid plot_arr[-1, name_to_arr_idx['v_override']] = sm['carControl'].cruiseControl.speedOverride plot_arr[-1, name_to_arr_idx['v_cruise']] = sm['carState'].cruiseState.speed plot_arr[-1, name_to_arr_idx['a_ego']] = sm['carState'].aEgo plot_arr[-1, name_to_arr_idx['a_target']] = sm['plan'].aTarget plot_arr[-1, name_to_arr_idx['accel_override']] = sm['carControl'].cruiseControl.accelOverride # ***** model **** model_data = extract_model_data(sm['model']) plot_model(model_data, VM, sm['controlsState'].vEgo, sm['controlsState'].curvature, imgw, calibration, top_down, np.array(sm['pathPlan'].dPoly)) # MPC if sm.updated['liveMpc']: draw_mpc(sm['liveMpc'], top_down) # draw all radar points maybe_update_radar_points(sm['liveTracks'], top_down[1]) if sm.updated['liveCalibration']: extrinsic_matrix = np.asarray(sm['liveCalibration'].extrinsicMatrix).reshape(3, 4) ke = intrinsic_matrix.dot(extrinsic_matrix) warp_matrix = get_camera_frame_from_model_frame(ke) calibration = CalibrationTransformsForWarpMatrix(warp_matrix, intrinsic_matrix, extrinsic_matrix) # draw red pt for lead car in the main img for lead in [sm['radarState'].leadOne, sm['radarState'].leadTwo]: if lead.status: if calibration is not None: draw_lead_on(img, lead.dRel, lead.yRel, calibration, color=(192,0,0)) draw_lead_car(lead.dRel, top_down) # *** blits *** pygame.surfarray.blit_array(camera_surface, img.swapaxes(0,1)) screen.blit(camera_surface, (0, 0)) # display alerts alert_line1 = alert1_font.render(sm['controlsState'].alertText1, True, (255,0,0)) alert_line2 = alert2_font.render(sm['controlsState'].alertText2, True, (255,0,0)) screen.blit(alert_line1, (180, 150)) screen.blit(alert_line2, (180, 190)) if calibration is not None and img is not None: cpw = warp_points(CalP, calibration.model_to_bb) vanishing_pointw = warp_points(vanishing_point, calibration.model_to_bb) pygame.draw.polygon(screen, BLUE, tuple(map(tuple, cpw)), 1) pygame.draw.circle(screen, BLUE, list(map(int, map(round, vanishing_pointw[0]))), 2) if HOR: screen.blit(draw_plots(plot_arr), (640+384, 0)) else: screen.blit(draw_plots(plot_arr), (0, 600)) pygame.surfarray.blit_array(cameraw_surface, imgw.swapaxes(0, 1)) screen.blit(cameraw_surface, (320, 480)) pygame.surfarray.blit_array(*top_down) screen.blit(top_down[0], (640,0)) i = 0 SPACING = 25 lines = [ info_font.render("ENABLED", True, GREEN if sm['controlsState'].enabled else BLACK), info_font.render("BRAKE LIGHTS", True, RED if sm['carState'].brakeLights else BLACK), info_font.render("SPEED: " + str(round(sm['carState'].vEgo, 1)) + " m/s", True, YELLOW), info_font.render("LONG CONTROL STATE: " + str(sm['controlsState'].longControlState), True, YELLOW), info_font.render("LONG MPC SOURCE: " + str(sm['plan'].longitudinalPlanSource), True, YELLOW), None, info_font.render("ANGLE OFFSET (AVG): " + str(round(sm['liveParameters'].angleOffsetAverage, 2)) + " deg", True, YELLOW), info_font.render("ANGLE OFFSET (INSTANT): " + str(round(sm['liveParameters'].angleOffset, 2)) + " deg", True, YELLOW), info_font.render("STIFFNESS: " + str(round(sm['liveParameters'].stiffnessFactor * 100., 2)) + " %", True, YELLOW), info_font.render("STEER RATIO: " + str(round(sm['liveParameters'].steerRatio, 2)), True, YELLOW) ] for i, line in enumerate(lines): if line is not None: screen.blit(line, (write_x, write_y + i * SPACING)) # this takes time...vsync or something pygame.display.flip() def get_arg_parser(): parser = argparse.ArgumentParser( description="Show replay data in a UI.", formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument("ip_address", nargs="?", default="127.0.0.1", help="The ip address on which to receive zmq messages.") parser.add_argument("--frame-address", default=None, help="The frame address (fully qualified ZMQ endpoint for frames) on which to receive zmq messages.") return parser if __name__ == "__main__": args = get_arg_parser().parse_args(sys.argv[1:]) ui_thread(args.ip_address, args.frame_address)

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ecom/prep.py

leocnj/ecom-rakuten

23

150223

<reponame>leocnj/ecom-rakuten #!/usr/bin/env python from . import data, vocab import collections import pandas as pd from sklearn.model_selection import train_test_split def char_encoder(items): freq = collections.Counter(ch for item in items for ch in item) itos = [o for o, c in freq.most_common() if c >= 10] itos.insert(0, '_PAD_') itos.insert(1, '_BOS_') itos.insert(2, '_EOS_') itos.insert(3, '_UNK_') stoi = collections.defaultdict(lambda: 3, vocab.mk_stoi(itos)) return vocab.CharEncoder(itos, stoi, freq) def category_encoder(cats): freq = collections.Counter(cats) itos = [o for o, c in freq.most_common()] return vocab.CategoryEncoder(itos, vocab.mk_stoi(itos), freq) def validation_set(all_train): freq = all_train.groupby('cat', as_index=False).count() singletons = freq[freq.item == 1].cat single_train = all_train[all_train.cat.isin(singletons)] multi_train = all_train[~all_train.cat.isin(singletons)] most_train, val = train_test_split( multi_train, test_size=200000, random_state=42, stratify=multi_train['cat'], ) train = pd.concat([most_train, single_train]) return train, val def main(): train, val = validation_set(data.load_all_train()) data.save_train_val(train, val) enc = char_encoder(train.item) cenc = category_encoder(train.cat) data.save_encoders(enc, cenc) trn_enc = enc.encode(train.item) val_enc = enc.encode(val.item) trn_ds = data.RakDataset(trn_enc, cenc.encode(train.cat)) val_ds = data.RakDataset(val_enc, cenc.encode(val.cat)) data.save_datasets(trn_ds, val_ds) if __name__ == '__main__': main()

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tests/r/test_utilities2.py

hajime9652/observations

199

97811

from __future__ import absolute_import from __future__ import division from __future__ import print_function import shutil import sys import tempfile from observations.r.utilities2 import utilities2 def test_utilities2(): """Test module utilities2.py by downloading utilities2.csv and testing shape of extracted data has 117 rows and 19 columns """ test_path = tempfile.mkdtemp() x_train, metadata = utilities2(test_path) try: assert x_train.shape == (117, 19) except: shutil.rmtree(test_path) raise()

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log/image_log.py

HELLORPG/GraduationProjec

1

1613475

""" 该文件中包括了一些图像输出的调试信息例如，输出生成图像和原始图像的对比等等 """ import matplotlib.pyplot as plt import torchvision.transforms as transforms import log.tb_log as tb_log import torchvision.utils from config.trecgnet_config import TRECGNET_CONFIG import os class ImageLog: def __init__(self): return @classmethod def image_compare_to_file(cls, source_images, generate_images, standard_images, path, title="Image Compare"): """ 输出图像对比 :param source_images: 源数据，Tensor :param generate_images: :param standard_images: :param path: :param title: :return: """ # tb_log.tb_test(images=torchvision.utils.make_grid(generate_images.clone().cpu().data, 3, # normalize=True), path=os.path.join(TRECGNET_CONFIG.MODEL_IMAGE_LOG_DIR, "tb")) plt.clf() plt.close("all") plt.suptitle(title) plt.figure(figsize=(40, 40)) image_len = source_images.shape[0] for i in range(0, image_len): plt.subplot(3, image_len, i + 1) source_image = transforms.ToPILImage()(cls.trans(source_images[i])).convert("RGB") plt.imshow(source_image) plt.subplot(3, image_len, image_len + i + 1) generate_image = transforms.ToPILImage()(cls.trans(generate_images[i])).convert("RGB") plt.imshow(generate_image) plt.subplot(3, image_len, 2 * image_len + i + 1) standard_image = transforms.ToPILImage()(cls.trans(standard_images[i])).convert("RGB") plt.imshow(standard_image) plt.savefig(path) plt.clf() plt.close("all") @classmethod def trans(cls, image, mean=[0.5,0.5,0.5], std=[0.5,0.5,0.5]): for i in range(0, len(mean)): image[i] = image[i] * std[i] + mean[i] return image

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Store/Store/settings/dev.py

BreezeDawn/DjangoStore

0

135493

""" Django settings for Store project. Generated by 'django-admin startproject' using Django 1.11.11. For more information on this file, see https://docs.djangoproject.com/en/1.11/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.11/ref/settings/ """ import datetime import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # 将sys.path中添加apps路径,以便寻找子应用 import sys sys.path.insert(0, os.path.join(BASE_DIR, 'apps')) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.11/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '<KEY>' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True # 允许访问的host ALLOWED_HOSTS = ['127.0.0.1','www.xingtu.info'] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'users.apps.UsersConfig', 'QQ.apps.QqConfig', 'verifications.apps.VerificationsConfig', 'areas.apps.AreasConfig', 'rest_framework', 'corsheaders' ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', 'corsheaders.middleware.CorsMiddleware' ] # 如果修改目录名,该配置需要被修改 ROOT_URLCONF = 'Store.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] # 如果修改目录名,该配置需要被修改 WSGI_APPLICATION = 'Store.wsgi.application' # Database # https://docs.djangoproject.com/en/1.11/ref/settings/#databases # 数据库配置 DATABASES = { 'default': { 'ENGINE': 'django.db.backends.mysql', 'HOST': '192.168.128.128', 'PORT': 3306, 'USER': 'maimai', 'PASSWORD': '<PASSWORD>', 'NAME': 'Store' } } # Password validation # https://docs.djangoproject.com/en/1.11/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/1.11/topics/i18n/ # 修改语言 LANGUAGE_CODE = 'zh-hans' # 修改时区 TIME_ZONE = 'Asia/Shanghai' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.11/howto/static-files/ STATIC_URL = '/static/' # 设置缓存存入redis CACHES = { "default": { "BACKEND": "django_redis.cache.RedisCache", "LOCATION": "redis://192.168.128.128:6379/0", "OPTIONS": { "CLIENT_CLASS": "django_redis.client.default.DefaultClient" } }, # 存放session "session": { "BACKEND": "django_redis.cache.RedisCache", "LOCATION": "redis://192.168.128.128:6379/1", "OPTIONS": { "CLIENT_CLASS": "django_redis.client.default.DefaultClient" } }, # 存放短信验证码 "verify_codes": { "BACKEND": "django_redis.cache.RedisCache", "LOCATION": "redis://192.168.128.128:6379/2", "OPTIONS": { "CLIENT_CLASS": "django_redis.client.default.DefaultClient" } }, # 3号为celery中间人 # 存放三级联动 "areas": { "BACKEND": "django_redis.cache.RedisCache", "LOCATION": "redis://192.168.128.128:6379/4", "OPTIONS": { "CLIENT_CLASS": "django_redis.client.default.DefaultClient" } }, } # 设置session用缓存保存,而上面设置缓存用redis保存,所以session的缓存保存在了redis中 SESSION_ENGINE = "django.contrib.sessions.backends.cache" # 使用名为'session'的redis配置 SESSION_CACHE_ALIAS = "session" LOGGING = { # 保留字段 'version': 1, # 是否禁用已经存在的日志器 'disable_existing_loggers': False, # 日志信息显示的格式 'formatters': { # 标准版 'standard': { 'format': '[%(asctime)s][%(threadName)s:%(thread)d][task_id:%(name)s]' '[%(filename)s:%(lineno)d][%(levelname)s][%(message)s]' }, # 冗长版 'verbose': { 'format': '%(levelname)s %(asctime)s %(module)s %(lineno)d %(message)s' }, # 简单版 'simple': { 'format': '%(levelname)s %(module)s %(lineno)d %(message)s' }, }, # 对日志进行过滤 'filters': { 'require_debug_true': { # django在debug模式下才输出日志 '()': 'django.utils.log.RequireDebugTrue', }, }, # 日志流的处理方法 'handlers': { # 把日志打印到终端 'console': { 'level': 'INFO', 'filters': ['require_debug_true'], 'class': 'logging.StreamHandler', 'formatter': 'simple' }, # 向文件中输出日志 'file': { 'level': 'INFO', 'class': 'logging.handlers.RotatingFileHandler', 'filename': os.path.join(os.path.dirname(BASE_DIR), "logs/store.log"), # 日志文件的位置 'maxBytes': 300 * 1024 * 1024, 'backupCount': 10, 'formatter': 'verbose' }, }, # 日志器 'loggers': { 'django': { # 定义了一个名为django的日志器 'handlers': ['console', 'file'], # 可以同时向终端与文件中输出日志 'propagate': True, # 是否继续传递日志信息 'level': 'INFO', # 日志器接收的最低日志级别 }, }, } REST_FRAMEWORK = { # 异常处理 'EXCEPTION_HANDLER': 'Store.utils.exceptions.drf_exception_handler', # 设置认证方案 'DEFAULT_AUTHENTICATION_CLASSES': ( # JWT认证:json_web_token认证 'rest_framework_jwt.authentication.JSONWebTokenAuthentication', # Session:session_id认证 'rest_framework.authentication.SessionAuthentication', # Basic:账号密码验证 'rest_framework.authentication.BasicAuthentication', ), } # 指明用户模型 AUTH_USER_MODEL = 'users.User' # CORS跨域请求(只针对异步请求如ajax) # CORS白名单 CORS_ORIGIN_WHITELIST = ( '127.0.0.1:8080', 'www.xingtu.info:8080', ) # CORS允许携带cookie访问 CORS_ALLOW_CREDENTIALS = True # JWT扩展设置 JWT_AUTH = { # 设置生成jwt-token数据时,token数据的有效时间 'JWT_EXPIRATION_DELTA': datetime.timedelta(days=1), # 修改JWT登录视图返回值调用的函数,自定义相应函数 'JWT_RESPONSE_PAYLOAD_HANDLER': 'users.utils.jwt_response_payload_handler', } AUTHENTICATION_BACKENDS = [ "users.utils.UsernameMobileModelBackend", ] # QQ登录开发设置 QQ_CLIENT_ID = '101507886' # APP ID QQ_CLIENT_SECRET = '9c9915eb20bd631bdf11b528360cdc28' # APP Key QQ_REDIRECT_URI = 'http://www.xingtu.info:8080/qq-callback.html' # 网站回调地址 QQ_STATE = '/' # QQ成功登录之后跳转页面地址 # 邮件发送设置 EMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend' # SMTP服务器地址 EMAIL_HOST = 'smtp.qq.com' # SMTP服务端口号 EMAIL_PORT = 587 # 发送邮件的邮箱 EMAIL_HOST_USER = '<EMAIL>' # 在邮箱中设置的客户端授权密码 EMAIL_HOST_PASSWORD = '<PASSWORD>' # 收件人看到的发件人 EMAIL_FROM = 'Store<<EMAIL>>' EMAIL_USE_TLS = True #与SMTP服务器通信时，是否启动TLS链接(安全链接) # DRF扩展 REST_FRAMEWORK_EXTENSIONS = { # 三级联动缓存 # 缓存时间 'DEFAULT_CACHE_RESPONSE_TIMEOUT': 60 * 60, # 缓存存储 'DEFAULT_USE_CACHE': 'areas', }

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web_app/services/bascilica_services2.py

worldwidekatie/flask_app

0

86464

import basilica import os from dotenv import load_dotenv load_dotenv() BASCILICA_API_KEY=os.getenv("BASCILICA_API_KEY", default="oops") sentences = [ "This is a sentence!", "This is a similar sentence!", "I don't think this sentence is very similar at all...", ] with basilica.Connection(BASCILICA_API_KEY) as connection: #connection = Connection(BASCILICA_API_KEY) print(type(connection)) embeddings = list(connection.embed_sentences(sentences)) for embed in embeddings: print("---------") print(embed) # a list of 786 floats from -1 to 1 breakpoint() embedding = connection.embed_sentence("Hello World") print(embedding)

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proj4/part2/train.py

chengxuxin/CS194_projects

0

148717

import os import time import cv2 import matplotlib.pyplot as plt import numpy as np import png import torch import torch.nn as nn import torch.optim as optim import torchvision from colormap.colors import Color, hex2rgb from sklearn.metrics import average_precision_score as ap_score from torch.utils.data import DataLoader from torchvision import datasets, models, transforms from tqdm import tqdm from dataset import FacadeDataset N_CLASS=5 class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.n_class = N_CLASS self.layers = nn.Sequential( ######################################### ### TODO: Add more layers ### ######################################### nn.Conv2d(3, self.n_class, 1, padding=0), nn.ReLU(inplace=True) ) def forward(self, x): x = self.layers(x) return x def save_label(label, path): ''' Function for ploting labels. ''' colormap = [ '#000000', '#0080FF', '#80FF80', '#FF8000', '#FF0000', ] assert(np.max(label)<len(colormap)) colors = [hex2rgb(color, normalise=False) for color in colormap] w = png.Writer(label.shape[1], label.shape[0], palette=colors, bitdepth=4) with open(path, 'wb') as f: w.write(f, label) def train(trainloader, net, criterion, optimizer, device, epoch): ''' Function for training. ''' start = time.time() running_loss = 0.0 net = net.train() for images, labels in tqdm(trainloader): images = images.to(device) labels = labels.to(device) optimizer.zero_grad() output = net(images) loss = criterion(output, labels) loss.backward() optimizer.step() running_loss = loss.item() end = time.time() print('[epoch %d] loss: %.3f elapsed time %.3f' % (epoch, running_loss, end-start)) def test(testloader, net, criterion, device): ''' Function for testing. ''' losses = 0. cnt = 0 with torch.no_grad(): net = net.eval() for images, labels in tqdm(testloader): images = images.to(device) labels = labels.to(device) output = net(images) loss = criterion(output, labels) losses += loss.item() cnt += 1 print(losses / cnt) return (losses/cnt) def cal_AP(testloader, net, criterion, device): ''' Calculate Average Precision ''' losses = 0. cnt = 0 with torch.no_grad(): net = net.eval() preds = [[] for _ in range(5)] heatmaps = [[] for _ in range(5)] for images, labels in tqdm(testloader): images = images.to(device) labels = labels.to(device) output = net(images).cpu().numpy() for c in range(5): preds[c].append(output[:, c].reshape(-1)) heatmaps[c].append(labels[:, c].cpu().numpy().reshape(-1)) aps = [] for c in range(5): preds[c] = np.concatenate(preds[c]) heatmaps[c] = np.concatenate(heatmaps[c]) if heatmaps[c].max() == 0: ap = float('nan') else: ap = ap_score(heatmaps[c], preds[c]) aps.append(ap) print("AP = {}".format(ap)) # print(losses / cnt) return None def get_result(testloader, net, device, folder='output_train'): result = [] cnt = 1 with torch.no_grad(): net = net.eval() cnt = 0 for images, labels in tqdm(testloader): images = images.to(device) labels = labels.to(device) output = net(images)[0].cpu().numpy() c, h, w = output.shape assert(c == N_CLASS) y = np.zeros((h,w)).astype('uint8') for i in range(N_CLASS): mask = output[i]>0.5 y[mask] = i gt = labels.cpu().data.numpy().squeeze(0).astype('uint8') save_label(y, './{}/y{}.png'.format(folder, cnt)) save_label(gt, './{}/gt{}.png'.format(folder, cnt)) plt.imsave( './{}/x{}.png'.format(folder, cnt), ((images[0].cpu().data.numpy()+1)*128).astype(np.uint8).transpose(1,2,0)) cnt += 1 def main(): device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # TODO change data_range to include all train/evaluation/test data. # TODO adjust batch_size. train_data = FacadeDataset(flag='train', data_range=(0,20), onehot=False) train_loader = DataLoader(train_data, batch_size=1) test_data = FacadeDataset(flag='test_dev', data_range=(0,114), onehot=False) test_loader = DataLoader(test_data, batch_size=1) ap_data = FacadeDataset(flag='test_dev', data_range=(0,114), onehot=True) ap_loader = DataLoader(ap_data, batch_size=1) name = 'starter_net' net = Net().to(device) criterion = nn.CrossEntropyLoss() #TODO decide loss optimizer = torch.optim.Adam(net.parameters(), 1e-3, weight_decay=1e-5) print('\nStart training') for epoch in range(10): #TODO decide epochs print('-----------------Epoch = %d-----------------' % (epoch+1)) train(train_loader, net, criterion, optimizer, device, epoch+1) # TODO create your evaluation set, load the evaluation set and test on evaluation set evaluation_loader = train_loader test(evaluation_loader, net, criterion, device) print('\nFinished Training, Testing on test set') test(test_loader, net, criterion, device) print('\nGenerating Unlabeled Result') result = get_result(test_loader, net, device, folder='output_test') torch.save(net.state_dict(), './models/model_{}.pth'.format(name)) cal_AP(ap_loader, net, criterion, device) if __name__ == "__main__": main()

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cadet_app/views/text.py

standoff-nlp/cadet

0

125472

<gh_stars>0 from django.shortcuts import render, get_object_or_404, redirect from django.contrib.auth.decorators import login_required, permission_required from django.contrib.auth import authenticate, login, logout from django.contrib import messages from django.contrib.admin.views.decorators import staff_member_required from django.http import HttpResponse from django.core.paginator import Paginator from cadet_app.lang_utils import create_spacy_language, clone_spacy_language, create_stop_words, create_examples from pathlib import Path from django.conf import settings import sys import json import gzip from cadet_app.utils import ( make_dict, update_state, get_state, get_sentences, matcher, update_spacy_langs, add_annotations, export_tei, get_previous_and_next_text, put_spans_around_tokens, blank_examples, ) from cadet_app.handle_uploaded_file import handle_uploaded_file, handle_url_file from cadet_app.models import * from cadet_app.forms import ( ProjectForm, ProjectLanguageForm, TextForm, AnnotationForm, EditAnnotationForm, AnnotationTypeForm, TokenTestForm, AddExampleSentenceForm, ) from social_django.utils import psa import spacy def set_text(request, id): text = Text.objects.get(pk=id) request.session["text_id"] = text.id request.session["text_title"] = text.title request.session["text_slug"] = text.text_slug return redirect(data) #return redirect(a, request.session.get("project_slug"), text.text_slug) def delete_text(request, id): text = Text.objects.get(pk=id) text.delete() return redirect(data) @login_required(redirect_field_name="", login_url="login/") def edit_text(request, id): if request.method == "POST": text = get_object_or_404(Text, pk=id) form = TextForm(request.POST or None, instance=text) if form.is_valid(): form.save() return redirect(data) else: text = get_object_or_404(Text, pk=id) form = TextForm(request.POST or None, instance=text) context = {} context["form"] = form return render(request, "add_project.html", context) def data(request): all_texts = Text.objects.all() project_texts = Text.objects.filter(projects__id=request.session.get("project_id")) if request.method == "POST": form = TextForm(request.POST, request.FILES) if form.is_valid(): text = form.save() text.author = request.user text.projects.add(request.session.get("project_id")) text.save() context = {} context["form"] = form context["all_texts"] = all_texts context["project_texts"] = project_texts title = request.POST["title"] project_language = get_object_or_404(Project, id=request.session.get("project_id")).spacy_language if request.FILES["file"]: context["message"] = handle_uploaded_file( request, project_language, text, title ) if request.session.get("url", None): context["message"] = handle_url_file(request, project_language, text, title) if request.user.is_staff: return render(request, "data.html", context) else: return render(request, "data_public.html", context) else: form = TextForm() context = {} context["form"] = form context["all_texts"] = all_texts context["project_texts"] = project_texts if request.user.is_staff: return render(request, "data.html", context) else: return render(request, "data_public.html", context)

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chromium/tools/telemetry/telemetry/internal/image_processing/video.py

wedataintelligence/vivaldi-source

925

12138

<gh_stars>100-1000 # Copyright 2014 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. import subprocess from catapult_base import cloud_storage from telemetry.core import platform from telemetry.util import image_util from telemetry.util import rgba_color HIGHLIGHT_ORANGE_FRAME = rgba_color.WEB_PAGE_TEST_ORANGE class BoundingBoxNotFoundException(Exception): pass class Video(object): """Utilities for storing and interacting with the video capture.""" def __init__(self, video_file_obj): assert video_file_obj.delete assert not video_file_obj.close_called self._video_file_obj = video_file_obj self._tab_contents_bounding_box = None def UploadToCloudStorage(self, bucket, target_path): """Uploads video file to cloud storage. Args: target_path: Path indicating where to store the file in cloud storage. """ cloud_storage.Insert(bucket, target_path, self._video_file_obj.name) def GetVideoFrameIter(self): """Returns the iteration for processing the video capture. This looks for the initial color flash in the first frame to establish the tab content boundaries and then omits all frames displaying the flash. Yields: (time_ms, image) tuples representing each video keyframe. Only the first frame is a run of sequential duplicate bitmaps is typically included. time_ms is milliseconds since navigationStart. image may be a telemetry.core.Bitmap, or a numpy array depending on whether numpy is installed. """ frame_generator = self._FramesFromMp4(self._video_file_obj.name) # Flip through frames until we find the initial tab contents flash. content_box = None for _, bmp in frame_generator: content_box = self._FindHighlightBoundingBox( bmp, HIGHLIGHT_ORANGE_FRAME) if content_box: break if not content_box: raise BoundingBoxNotFoundException( 'Failed to identify tab contents in video capture.') # Flip through frames until the flash goes away and emit that as frame 0. timestamp = 0 for timestamp, bmp in frame_generator: if not self._FindHighlightBoundingBox(bmp, HIGHLIGHT_ORANGE_FRAME): yield 0, image_util.Crop(bmp, *content_box) break start_time = timestamp for timestamp, bmp in frame_generator: yield timestamp - start_time, image_util.Crop(bmp, *content_box) def _FindHighlightBoundingBox(self, bmp, color, bounds_tolerance=8, color_tolerance=8): """Returns the bounding box of the content highlight of the given color. Raises: BoundingBoxNotFoundException if the hightlight could not be found. """ content_box, pixel_count = image_util.GetBoundingBox(bmp, color, tolerance=color_tolerance) if not content_box: return None # We assume arbitrarily that tabs are all larger than 200x200. If this # fails it either means that assumption has changed or something is # awry with our bounding box calculation. if content_box[2] < 200 or content_box[3] < 200: raise BoundingBoxNotFoundException('Unexpectedly small tab contents.') # TODO(tonyg): Can this threshold be increased? if pixel_count < 0.9 * content_box[2] * content_box[3]: raise BoundingBoxNotFoundException( 'Low count of pixels in tab contents matching expected color.') # Since we allow some fuzziness in bounding box finding, we want to make # sure that the bounds are always stable across a run. So we cache the # first box, whatever it may be. # # This relies on the assumption that since Telemetry doesn't know how to # resize the window, we should always get the same content box for a tab. # If this assumption changes, this caching needs to be reworked. if not self._tab_contents_bounding_box: self._tab_contents_bounding_box = content_box # Verify that there is only minor variation in the bounding box. If it's # just a few pixels, we can assume it's due to compression artifacts. for x, y in zip(self._tab_contents_bounding_box, content_box): if abs(x - y) > bounds_tolerance: # If this fails, it means either that either the above assumption has # changed or something is awry with our bounding box calculation. raise BoundingBoxNotFoundException( 'Unexpected change in tab contents box.') return self._tab_contents_bounding_box def _FramesFromMp4(self, mp4_file): host_platform = platform.GetHostPlatform() if not host_platform.CanLaunchApplication('avconv'): host_platform.InstallApplication('avconv') def GetDimensions(video): proc = subprocess.Popen(['avconv', '-i', video], stderr=subprocess.PIPE) dimensions = None output = '' for line in proc.stderr.readlines(): output += line if 'Video:' in line: dimensions = line.split(',')[2] dimensions = map(int, dimensions.split()[0].split('x')) break proc.communicate() assert dimensions, ('Failed to determine video dimensions. output=%s' % output) return dimensions def GetFrameTimestampMs(stderr): """Returns the frame timestamp in integer milliseconds from the dump log. The expected line format is: ' dts=1.715 pts=1.715\n' We have to be careful to only read a single timestamp per call to avoid deadlock because avconv interleaves its writes to stdout and stderr. """ while True: line = '' next_char = '' while next_char != '\n': next_char = stderr.read(1) line += next_char if 'pts=' in line: return int(1000 * float(line.split('=')[-1])) dimensions = GetDimensions(mp4_file) frame_length = dimensions[0] * dimensions[1] * 3 frame_data = bytearray(frame_length) # Use rawvideo so that we don't need any external library to parse frames. proc = subprocess.Popen(['avconv', '-i', mp4_file, '-vcodec', 'rawvideo', '-pix_fmt', 'rgb24', '-dump', '-loglevel', 'debug', '-f', 'rawvideo', '-'], stderr=subprocess.PIPE, stdout=subprocess.PIPE) while True: num_read = proc.stdout.readinto(frame_data) if not num_read: raise StopIteration assert num_read == len(frame_data), 'Unexpected frame size: %d' % num_read yield (GetFrameTimestampMs(proc.stderr), image_util.FromRGBPixels(dimensions[0], dimensions[1], frame_data))

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standards/_dev_/regex.py

CambridgeSemiticsLab/nena_data

3

190153

""" version 0.04 beta This module contains standard regex patterns and functions for handling strings in the NENA text corpus. """ import unicodedata def normalize_string(string): """Normalize string for tokenization and matching. Characters can come in composed or decomposed varieties. We normalize to decomposed characters in order to allow fine-grained control of accent patterns. The string is converted to lowercase. Argument: string: str to be normalized Returns: str decomposed with NFD and lowercased NB: normalized text should not replace any display/stored texts because capitalization is stripped and also decomposed strings are unsightly.""" return unicodedata.normalize('NFD', string).lower() def tokenize_string(string): """Split a string up into analyzable characters. Returns a list of individual characters that can then be matched with the regex patterns. Note that all accent characters can be found with the range: \u0300-\u036F. Thus, strings are split by [any_character][any_accent]*. """ norm_string = normalize_string(string) return re.findall('.[\u0300-\u036F]*', norm_string) """ The following regex patterns only work with normalized characters. Use `tokenize_string` to split the string into analyzable characters that can be identified with the regex below. """ # Regex for punctuation (includes spaces) # NB: ellipsis (...) requires a separate pattern # as defined in punctuation.json punctuation = '[ .⁺\-=ˈ,.:—;?!"]' # Regex for foreign letters wrapped with a language tag # e.g. <P>Some foreign letters<P> foreign_letters = '[a-zA-ZðÐɟəƏɛƐʾʿθΘ][\u0300-\u033d]*' # Regex for NENA letters base_chars = '[a-zðɟəɛʾʿθ]' unaccented = base_chars + '(?![\u0300-\u033d])' cons_accented = '[dhlmprstzð]\u0323|[ckpt]\u032d|[csz]\u030c|c[\u0323\u032d]\u030c|g\u0307' vowel_accented = '[aeiouəɛ][\u0300\u0301]|[aeiouɛ]\u0304|[aeiou]\u0304[\u0300\u0301]|[au]\u0306[\u0300\u0301]?' nena_letters = '|'.join([unaccented, cons_accented, vowel_accented]) # i.e. "One Regex to Rule Them All"

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PP4E/Examples/PP4E/Gui/Intro/defaults.py

BeacherHou/Python-_Markdown-

0

1612553

def simple(): spam = 'ni' def action(): print(spam) # name maps to enclosing function return action act = simple() # make and return nested function act() # then call it: prints 'ni' def normal(): def action(): return spam # really, looked up when used spam = 'ni' return action act = normal() print(act()) # also prints 'ni' def weird(): spam = 42 return (lambda: spam * 2) # remembers spam in enclosing scope act = weird() print(act()) # prints 84 def weird(): tmp = (lambda: spam * 2) # remembers spam spam = 42 # even though not set till here return tmp act = weird() print(act()) # prints 84 def weird(): spam = 42 handler = (lambda: spam * 2) # func doesn't save 42 now spam = 50 print(handler()) # prints 100: spam looked up now spam = 60 print(handler()) # prints 120: spam looked up again now weird() def odd(): funcs = [] for c in 'abcdefg': funcs.append((lambda: c)) # c will be looked up later return funcs # does not remember current c for func in odd(): print(func(), end=' ') # print 7 g's, not a,b,c,... ! print() def odd(): funcs = [] for c in 'abcdefg': funcs.append((lambda c=c: c)) # force to remember c now return funcs # defaults eval now for func in odd(): print(func(), end=' ') # OK: now prints a,b,c,... print() funcs = [] # enclosing scope is module for c in 'abcdefg': # force to remember c now funcs.append((lambda c=c: c)) # else prints 7 g's again for func in funcs: print(func(), end=' ') # OK: prints a,b,c,...

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nq_eval.py

Franck-Dernoncourt/natural-questions

0

78415

<reponame>Franck-Dernoncourt/natural-questions<filename>nq_eval.py # coding=utf-8 # Copyright 2018 The Google AI Language Team Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. r"""Official evaluation script for Natural Questions. https://ai.google.com/research/NaturalQuestions ------------------------------------------------------------------------------ Example usage: nq_eval --gold_path=<path-to-gold-files> --predictions_path=<path_to_json> This will compute both the official F1 scores as well as recall@precision tables for both long and short answers. Note that R@P are only meaningful if your model populates the score fields of the prediction JSON format. gold_path should point to the five way annotated dev data in the original download format (gzipped jsonlines). predictions_path should point to a json file containing the predictions in the format given below. ------------------------------------------------------------------------------ Prediction format: {'predictions': [ { 'example_id': -2226525965842375672, 'long_answer': { 'start_byte': 62657, 'end_byte': 64776, 'start_token': 391, 'end_token': 604 }, 'long_answer_score': 13.5, 'short_answers': [ {'start_byte': 64206, 'end_byte': 64280, 'start_token': 555, 'end_token': 560}, ...], 'short_answers_score': 26.4, 'yes_no_answer': 'NONE' }, ... ] } The prediction format mirrors the annotation format in defining each long or short answer span both in terms of byte offsets and token offsets. We do not expect participants to supply both. The order of preference is: if start_byte >= 0 and end_byte >=0, use byte offsets, else if start_token >= 0 and end_token >= 0, use token offsets, else no span is defined (null answer). The short answer metric takes both short answer spans, and the yes/no answer into account. If the 'short_answers' list contains any non/null spans, then 'yes_no_answer' should be set to 'NONE'. ----------------------------------------------------------------------------- Metrics: If >= 2 of the annotators marked a non-null long answer, then the prediction must match any one of the non-null long answers to be considered correct. If >= 2 of the annotators marked a non-null set of short answers, or a yes/no answer, then the short answers prediction must match any one of the non-null sets of short answers *or* the yes/no prediction must match one of the non-null yes/no answer labels. All span comparisons are exact and each individual prediction can be fully correct, or incorrect. Each prediction should be provided with a long answer score, and a short answers score. At evaluation time, the evaluation script will find a score threshold at which F1 is maximized. All predictions with scores below this threshold are ignored (assumed to be null). If the score is not provided, the evaluation script considers all predictions to be valid. The script will also output the maximum recall at precision points of >= 0.5, >= 0.75, and >= 0.9. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from collections import OrderedDict import json import os import pickle from absl import app from absl import flags from absl import logging import eval_utils as util flags.DEFINE_string( 'gold_path', None, 'Path to the gzip JSON data. For ' 'multiple files, should be a glob ' 'pattern (e.g. "/path/to/files-*"') flags.DEFINE_string('predictions_path', None, 'Path to prediction JSON.') flags.DEFINE_bool( 'cache_gold_data', False, 'Whether to cache gold data in Pickle format to speed up ' 'multiple evaluations.') flags.DEFINE_integer('num_threads', 10, 'Number of threads for reading.') flags.DEFINE_bool('pretty_print', False, 'Whether to pretty print output.') FLAGS = flags.FLAGS def safe_divide(x, y): """Compute x / y, but return 0 if y is zero.""" if y == 0: return 0 else: return x / y def score_long_answer(gold_label_list, pred_label): """Scores a long answer as correct or not. 1) First decide if there is a gold long answer with LONG_NO_NULL_THRESHOLD. 2) The prediction will get a match if: a. There is a gold long answer. b. The prediction span match exactly with *one* of the non-null gold long answer span. Args: gold_label_list: A list of NQLabel, could be None. pred_label: A single NQLabel, could be None. Returns: gold_has_answer, pred_has_answer, is_correct, score """ gold_has_answer = util.gold_has_long_answer(gold_label_list) pred_has_answer = pred_label and ( not pred_label.long_answer_span.is_null_span()) is_correct = False score = pred_label.long_score # Both sides are non-null spans. if gold_has_answer and pred_has_answer: for gold_label in gold_label_list: # while the voting results indicate there is an long answer, each # annotator might still say there is no long answer. if gold_label.long_answer_span.is_null_span(): continue if util.nonnull_span_equal(gold_label.long_answer_span, pred_label.long_answer_span): is_correct = True break return gold_has_answer, pred_has_answer, is_correct, score def score_short_answer(gold_label_list, pred_label): """Scores a short answer as correct or not. 1) First decide if there is a gold short answer with SHORT_NO_NULL_THRESHOLD. 2) The prediction will get a match if: a. There is a gold short answer. b. The prediction span *set* match exactly with *one* of the non-null gold short answer span *set*. Args: gold_label_list: A list of NQLabel. pred_label: A single NQLabel. Returns: gold_has_answer, pred_has_answer, is_correct, score """ # There is a gold short answer if gold_label_list not empty and non null # answers is over the threshold (sum over annotators). gold_has_answer = util.gold_has_short_answer(gold_label_list) # There is a pred long answer if pred_label is not empty and short answer # set is not empty. pred_has_answer = pred_label and ( (not util.is_null_span_list(pred_label.short_answer_span_list)) or pred_label.yes_no_answer != 'none') is_correct = False score = pred_label.short_score # Both sides have short answers, which contains yes/no questions. if gold_has_answer and pred_has_answer: if pred_label.yes_no_answer != 'none': # System thinks its y/n questions. for gold_label in gold_label_list: if pred_label.yes_no_answer == gold_label.yes_no_answer: is_correct = True break else: for gold_label in gold_label_list: if util.span_set_equal(gold_label.short_answer_span_list, pred_label.short_answer_span_list): is_correct = True break return gold_has_answer, pred_has_answer, is_correct, score def score_answers(gold_annotation_dict, pred_dict): """Scores all answers for all documents. Args: gold_annotation_dict: a dict from example id to list of NQLabels. pred_dict: a dict from example id to list of NQLabels. Returns: long_answer_stats: List of scores for long answers. short_answer_stats: List of scores for short answers. """ gold_id_set = set(gold_annotation_dict.keys()) pred_id_set = set(pred_dict.keys()) if gold_id_set.symmetric_difference(pred_id_set): raise ValueError('ERROR: the example ids in gold annotations and example ' 'ids in the prediction are not equal.') long_answer_stats = [] short_answer_stats = [] for example_id in gold_id_set: gold = gold_annotation_dict[example_id] pred = pred_dict[example_id] long_answer_stats.append(score_long_answer(gold, pred)) short_answer_stats.append(score_short_answer(gold, pred)) # use the 'score' column, which is last long_answer_stats.sort(key=lambda x: x[-1], reverse=True) short_answer_stats.sort(key=lambda x: x[-1], reverse=True) return long_answer_stats, short_answer_stats def compute_f1(answer_stats, prefix=''): """Computes F1, precision, recall for a list of answer scores. Args: answer_stats: List of per-example scores. prefix (''): Prefix to prepend to score dictionary. Returns: Dictionary mapping string names to scores. """ has_gold, has_pred, is_correct, _ = zip(*answer_stats) precision = safe_divide(sum(is_correct), sum(has_pred)) recall = safe_divide(sum(is_correct), sum(has_gold)) f1 = safe_divide(2 * precision * recall, precision + recall) return OrderedDict({ prefix + 'n': len(answer_stats), prefix + 'f1': f1, prefix + 'precision': precision, prefix + 'recall': recall }) def compute_final_f1(long_answer_stats, short_answer_stats): """Computes overall F1 given long and short answers, ignoring scores. Note: this assumes that the answers have been thresholded. Arguments: long_answer_stats: List of long answer scores. short_answer_stats: List of short answer scores. Returns: Dictionary of name (string) -> score. """ scores = compute_f1(long_answer_stats, prefix='long-answer-') scores.update(compute_f1(short_answer_stats, prefix='short-answer-')) return scores def compute_pr_curves(answer_stats, targets=None): """Computes PR curve and returns R@P for specific targets. The values are computed as follows: find the (precision, recall) point with maximum recall and where precision > target. Arguments: answer_stats: List of statistic tuples from the answer scores. targets (None): List of precision thresholds to target. Returns: List of table with rows: [target, r, p, score]. """ total_correct = 0 total_has_pred = 0 total_has_gold = 0 # Count the number of gold annotations. for has_gold, _, _, _ in answer_stats: total_has_gold += has_gold # Keep track of the point of maximum recall for each target. max_recall = [0 for _ in targets] max_precision = [0 for _ in targets] max_scores = [None for _ in targets] # Only keep track of unique thresholds in this dictionary. scores_to_stats = OrderedDict() # Loop through every possible threshold and compute precision + recall. for has_gold, has_pred, is_correct, score in answer_stats: total_correct += is_correct total_has_pred += has_pred precision = safe_divide(total_correct, total_has_pred) recall = safe_divide(total_correct, total_has_gold) # If there are any ties, this will be updated multiple times until the # ties are all counted. scores_to_stats[score] = [precision, recall] best_f1 = 0.0 best_precision = 0.0 best_recall = 0.0 best_threshold = 0.0 for threshold, (precision, recall) in scores_to_stats.iteritems(): # Match the thresholds to the find the closest precision above some target. for t, target in enumerate(targets): if precision >= target and recall > max_recall[t]: max_recall[t] = recall max_precision[t] = precision max_scores[t] = threshold # Compute optimal threshold. f1 = safe_divide(2 * precision * recall, precision + recall) if f1 > best_f1: best_f1 = f1 best_precision = precision best_recall = recall best_threshold = threshold return ((best_f1, best_precision, best_recall, best_threshold), zip(targets, max_recall, max_precision, max_scores)) def print_r_at_p_table(answer_stats): """Pretty prints the R@P table for default targets.""" opt_result, pr_table = compute_pr_curves( answer_stats, targets=[0.5, 0.75, 0.9]) f1, precision, recall, threshold = opt_result print('Optimal threshold: {:.5}'.format(threshold)) print(' F1 / P / R') print('{: >7.2%} / {: >7.2%} / {: >7.2%}'.format(f1, precision, recall)) for target, recall, precision, row in pr_table: print('R@P={}: {:.2%} (actual p={:.2%}, score threshold={:.4})'.format( target, recall, precision, row)) def get_metrics_as_dict(gold_path, prediction_path, num_threads=10): """Library version of the end-to-end evaluation. Arguments: gold_path: Path to the gzip JSON data. For multiple files, should be a glob pattern (e.g. "/path/to/files-*") prediction_path: Path to the JSON prediction data. num_threads (10): Number of threads to use when parsing multiple files. Returns: metrics: A dictionary mapping string names to metric scores. """ nq_gold_dict = util.read_annotation(gold_path, n_threads=num_threads) nq_pred_dict = util.read_prediction_json(prediction_path) long_answer_stats, short_answer_stats = score_answers(nq_gold_dict, nq_pred_dict) return get_metrics_with_answer_stats(long_answer_stats, short_answer_stats) def get_metrics_with_answer_stats(long_answer_stats, short_answer_stats): """Generate metrics dict using long and short answer stats.""" def _get_metric_dict(answer_stats, prefix=''): """Compute all metrics for a set of answer statistics.""" opt_result, pr_table = compute_pr_curves( answer_stats, targets=[0.5, 0.75, 0.9]) f1, precision, recall, threshold = opt_result metrics = OrderedDict({ 'best-threshold-f1': f1, 'best-threshold-precision': precision, 'best-threshold-recall': recall, 'best-threshold': threshold, }) for target, recall, precision, _ in pr_table: metrics['recall-at-precision>={:.2}'.format(target)] = recall metrics['precision-at-precision>={:.2}'.format(target)] = precision # Add prefix before returning. return dict([(prefix + k, v) for k, v in metrics.iteritems()]) metrics = _get_metric_dict(long_answer_stats, 'long-') metrics.update(_get_metric_dict(short_answer_stats, 'short-')) return metrics def main(_): cache_path = os.path.join(os.path.dirname(FLAGS.gold_path), 'cache') if FLAGS.cache_gold_data and os.path.exists(cache_path): logging.info('Reading from cache: %s', format(cache_path)) nq_gold_dict = pickle.load(open(cache_path, 'r')) else: nq_gold_dict = util.read_annotation( FLAGS.gold_path, n_threads=FLAGS.num_threads) if FLAGS.cache_gold_data: logging.info('Caching gold data for next time to: %s', format(cache_path)) pickle.dump(nq_gold_dict, open(cache_path, 'w')) nq_pred_dict = util.read_prediction_json(FLAGS.predictions_path) long_answer_stats, short_answer_stats = score_answers(nq_gold_dict, nq_pred_dict) if FLAGS.pretty_print: print('*' * 20) print('LONG ANSWER R@P TABLE:') print_r_at_p_table(long_answer_stats) print('*' * 20) print('SHORT ANSWER R@P TABLE:') print_r_at_p_table(short_answer_stats) scores = compute_final_f1(long_answer_stats, short_answer_stats) print('*' * 20) print('METRICS IGNORING SCORES (n={}):'.format(scores['long-answer-n'])) print(' F1 / P / R') print('Long answer {: >7.2%} / {: >7.2%} / {: >7.2%}'.format( scores['long-answer-f1'], scores['long-answer-precision'], scores['long-answer-recall'])) print('Short answer {: >7.2%} / {: >7.2%} / {: >7.2%}'.format( scores['short-answer-f1'], scores['short-answer-precision'], scores['short-answer-recall'])) else: metrics = get_metrics_with_answer_stats(long_answer_stats, short_answer_stats) print(json.dumps(metrics)) if __name__ == '__main__': flags.mark_flag_as_required('gold_path') flags.mark_flag_as_required('predictions_path') app.run(main)

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1495, 13, 29871, 623, 29889, 3389, 29898, 3396, 29897, 13, 2 ]

examples/matmul/matmul_automatic.py

UTexas-PSAAP/Parla.py

0

142453

""" Multi-device matrix multiplication using parla with cupy as the kernel engine. """ import sys import time import numpy as np import cupy as cp from parla import Parla, get_all_devices from parla.array import copy, clone_here from parla.cpu import cpu from parla.cuda import gpu from parla.function_decorators import specialized from parla.ldevice import LDeviceSequenceBlocked from parla.tasks import spawn, TaskSpace, CompletedTaskSpace, reserve_persistent_memory from parla.parray import asarray_batch def main(): @spawn(placement=cpu) async def main_task(): ngpus = cp.cuda.runtime.getDeviceCount() repetitions = int(sys.argv[1]) # set up two n x n arrays to multiply together. # n is chosen so that all three can be # stored within the memory of a single GPU # so that strong scaling numbers make sense. n = 32000 blocks = ngpus block_size = n // ngpus h_ordr = 'C' d_ordr = 'F' print("BlockSize: ", block_size, "GPUS: ", ngpus) # Overdecomposing doesn't actually seem to help in this case # with the current parla runtime. This may be related to # some weirdness within the scheduler though, so # we can leave the code for blocks in-place for further # testing later. np.random.seed(0) a_cpu = np.random.rand(n, n).astype(dtype=np.float32, order=h_ordr) b_cpu = np.random.rand(n, n).astype(dtype=np.float32, order=h_ordr) print("Finished Data Allocation", flush=True) # Partition the two arrays and set up the # partitioned array where the result will be stored. # This could also be done using a parla mapper object. a_part = [] b_part = [] c_part = [] distribute=True reset=True fixed_placement=False verbose=False sync=False time_list = list() # Start all operans from CPU memory. for i in range(blocks): if distribute: with cp.cuda.Device(i): a_part.append(cp.asarray(a_cpu[i * block_size : (i + 1) * block_size], order=d_ordr)) b_part.append(cp.asarray(b_cpu[i * block_size : (i + 1) * block_size], order=d_ordr)) cp.cuda.stream.get_current_stream().synchronize() else: a_part.append(a_cpu[i * block_size : (i + 1) * block_size]) b_part.append(b_cpu[i * block_size : (i + 1) * block_size]) for i in range(blocks): c_part.append(list()) for j in range(blocks): c_part[i].append(np.empty((0, 0), dtype=np.float32, order=h_ordr)) #print(len(c_part), len(c_part[0]), c_part[0][0].shape) # 1. NEW: convert to parray in batch a_part, b_part = asarray_batch(a_part, b_part) c_part = asarray_batch(c_part) #print(len(c_part), len(c_part[0])) for repetition in range(repetitions): #reset cblocks to None for i in range(blocks): for j in range(blocks): c_part[i][j].update(np.empty((0, 0), dtype=np.float32, order=h_ordr)) if reset: #reset coherence to only be in starting locations rspace = TaskSpace("reset") for i in range(blocks): @spawn(rspace[i], placement=gpu(i%ngpus), memory=2*block_size*n, inout=[a_part[i], b_part[i]]) def reset_task(): a_part[i].update(a_part[i].array) b_part[i].update(b_part[i].array) await rspace matmul = TaskSpace("matmul") start = time.perf_counter() for i in range(blocks): for j in range(blocks): a_block = a_part[i] b_block = b_part[j] c_block = c_part[i][j] memsize = (block_size**2)*4 if fixed_placement: loc = gpu(i%ngpus) else: loc = gpu @spawn(matmul[i, j], placement = loc, memory=memsize, input=[a_block, b_block], output=[c_block]) def matmul_task(): a = a_block.array b = b_block.array c = c_block.array stream = cp.cuda.get_current_stream() stream.synchronize() assert(a.device.id == b.device.id) if verbose: print(f"+({i}, {j}): ", a.shape, b.shape, c.shape, " | On Device: ", cp.cuda.runtime.getDevice(), a.device.id, flush=True) local_start = time.perf_counter() c = a @ b.T if sync: stream.synchronize() local_end = time.perf_counter() c_block.update(c) c = c_block.array if verbose: print(f"-({i}, {j}): ", a.shape, b.shape, c.shape, " | Elapsed: ", local_end-local_start, flush=True) await matmul stop = time.perf_counter() print(f"Iteration {repetition} | Time elapsed: ", stop - start, flush=True) time_list.append(stop-start) mean = np.mean(np.array(time_list)) median = np.median(np.array(time_list)) print(f"Execution Time:: Average = {mean} | Median = {median}", flush=True) if __name__ == "__main__": with Parla(): main()

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components/elm/src/external_models/fates/functional_unit_testing/hydro/HydroUTestDriver.py

meng630/GMD_E3SM_SCM

0

113703

<filename>components/elm/src/external_models/fates/functional_unit_testing/hydro/HydroUTestDriver.py # ======================================================================================= # # For usage: $python HydroUTestDriver.py --help # # This script runs unit tests on the hydraulics functions. # # # ======================================================================================= import matplotlib as mpl #mpl.use('Agg') import matplotlib.pyplot as plt from datetime import datetime import argparse #from matplotlib.backends.backend_pdf import PdfPages import platform import numpy as np import os import sys import getopt import code # For development: code.interact(local=dict(globals(), **locals())) import time import imp import ctypes from ctypes import * from operator import add CDLParse = imp.load_source('CDLParse','../shared/py_src/CDLParse.py') F90ParamParse = imp.load_source('F90ParamParse','../shared/py_src/F90ParamParse.py') PyF90Utils = imp.load_source('PyF90Utils','../shared/py_src/PyF90Utils.py') from CDLParse import CDLParseDims, CDLParseParam, cdl_param_type from F90ParamParse import f90_param_type, GetSymbolUsage, GetPFTParmFileSymbols, MakeListUnique from PyF90Utils import c8, ci, cchar, c8_arr, ci_arr # Load the fortran objects via CTYPES f90_unitwrap_obj = ctypes.CDLL('bld/UnitWrapMod.o',mode=ctypes.RTLD_GLOBAL) f90_constants_obj = ctypes.CDLL('bld/FatesConstantsMod.o',mode=ctypes.RTLD_GLOBAL) f90_wftfuncs_obj = ctypes.CDLL('bld/FatesHydroWTFMod.o',mode=ctypes.RTLD_GLOBAL) f90_hydrounitwrap_obj = ctypes.CDLL('bld/HydroUnitWrapMod.o',mode=ctypes.RTLD_GLOBAL) # Alias the F90 functions, specify the return type # ----------------------------------------------------------------------------------- initalloc_wtfs = f90_hydrounitwrap_obj.__hydrounitwrapmod_MOD_initallocwtfs setwrf = f90_hydrounitwrap_obj.__hydrounitwrapmod_MOD_setwrf setwkf = f90_hydrounitwrap_obj.__hydrounitwrapmod_MOD_setwkf th_from_psi = f90_hydrounitwrap_obj.__hydrounitwrapmod_MOD_wrapthfrompsi th_from_psi.restype = c_double psi_from_th = f90_hydrounitwrap_obj.__hydrounitwrapmod_MOD_wrappsifromth psi_from_th.restype = c_double dpsidth_from_th = f90_hydrounitwrap_obj.__hydrounitwrapmod_MOD_wrapdpsidth dpsidth_from_th.restype = c_double ftc_from_psi = f90_hydrounitwrap_obj.__hydrounitwrapmod_MOD_wrapftcfrompsi ftc_from_psi.restype = c_double dftcdpsi_from_psi = f90_hydrounitwrap_obj.__hydrounitwrapmod_MOD_wrapdftcdpsi dftcdpsi_from_psi.restype = c_double # Some constants rwcft = [1.0,0.958,0.958,0.958] rwccap = [1.0,0.947,0.947,0.947] pm_leaf = 1 pm_stem = 2 pm_troot = 3 pm_aroot = 4 pm_rhiz = 5 # These parameters are matched with the indices in FATES-HYDRO vg_type = 1 cch_type = 2 tfs_type = 3 isoil1 = 0 # Top soil layer parameters (@BCI) isoil2 = 1 # Bottom soil layer parameters # Constants for rhizosphere watsat = [0.567, 0.444] sucsat = [159.659, 256.094] bsw = [6.408, 9.27] unconstrained = True # ======================================================================================== # ======================================================================================== # Main # ======================================================================================== # ======================================================================================== class vg_wrf: def __init__(self,index,alpha, psd, th_sat, th_res): self.alpha = alpha self.psd = psd self.th_sat = th_sat self.th_res = th_res init_wrf_args = [self.alpha, self.psd, self.th_sat, self.th_res] iret = setwrf(ci(index),ci(vg_type),ci(len(init_wrf_args)),c8_arr(init_wrf_args)) class cch_wrf: def __init__(self,index,th_sat,psi_sat,beta): self.th_sat = th_sat self.psi_sat = psi_sat self.beta = beta init_wrf_args = [self.th_sat,self.psi_sat,self.beta] iret = setwrf(ci(index),ci(cch_type),ci(len(init_wrf_args)),c8_arr(init_wrf_args)) class vg_wkf: def __init__(self,index,alpha, psd, th_sat, th_res, tort): self.alpha = alpha self.psd = psd self.th_sat = th_sat self.th_res = th_res self.tort = tort init_wkf_args = [self.alpha, self.psd,self.th_sat,self.th_res,self.tort] iret = setwkf(ci(index),ci(vg_type),ci(len(init_wkf_args)),c8_arr(init_wkf_args)) class cch_wkf: def __init__(self,index,th_sat,psi_sat,beta): self.th_sat = th_sat self.psi_sat = psi_sat self.beta = beta init_wkf_args = [self.th_sat,self.psi_sat,self.beta] iret = setwkf(ci(index),ci(cch_type),ci(len(init_wkf_args)),c8_arr(init_wkf_args)) class tfs_wrf: def __init__(self,index,th_sat,th_res,pinot,epsil,rwc_fd,cap_corr,cap_int,cap_slp,pmedia): self.th_sat = th_sat self.th_res = th_res self.pinot = pinot self.epsil = epsil self.rwc_fd = rwc_fd self.cap_corr = cap_corr self.cap_int = cap_int self.cap_slp = cap_slp self.pmedia = pmedia init_wrf_args = [self.th_sat,self.th_res,self.pinot,self.epsil,self.rwc_fd,self.cap_corr,self.cap_int,self.cap_slp,self.pmedia] iret = setwrf(ci(index),ci(tfs_type),ci(len(init_wrf_args)),c8_arr(init_wrf_args)) class tfs_wkf: def __init__(self,index,p50,avuln): self.avuln = avuln self.p50 = p50 init_wkf_args = [self.p50,self.avuln] iret = setwkf(ci(index),ci(tfs_type),ci(len(init_wkf_args)),c8_arr(init_wkf_args)) def main(argv): # First check to make sure python 2.7 is being used version = platform.python_version() verlist = version.split('.') if( not ((verlist[0] == '2') & (verlist[1] == '7') & (int(verlist[2])>=15) ) ): print("The PARTEH driver mus be run with python 2.7") print(" with tertiary version >=15.") print(" your version is {}".format(version)) print(" exiting...") sys.exit(2) # Read in the arguments # ======================================================================================= # parser = argparse.ArgumentParser(description='Parse command line arguments to this script.') # parser.add_argument('--cdl-file', dest='cdlfile', type=str, \ # help="Input CDL filename. Required.", required=True) # args = parser.parse_args() # Set number of analysis points npts = 1000 # min_theta = np.full(shape=(2),dtype=np.float64,fill_value=np.nan) # wrf_type = [vg_type, vg_type, cch_type, cch_type] # wkf_type = [vg_type, tfs_type, cch_type, tfs_type] # th_ress = [0.01, 0.10, -9, -9] # th_sats = [0.55, 0.55, 0.65, 0.65] # alphas = [1.0, 1.0, 1.0, 1.0] # psds = [2.7, 2.7, 2.7, 2.7] # tort = [0.5, 0.5, 0.5, 0.5] # beta = [-9, -9, 6, 9] # avuln = [2.0, 2.0, 2.5, 2.5] # p50 = [-1.5, -1.5, -2.25, -2.25] ncomp= 3 rwc_fd = [1.0,0.958,0.958,0.958] rwccap = [1.0,0.947,0.947,0.947] cap_slp = [] cap_int = [] cap_corr= [] hydr_psi0 = 0.0 hydr_psicap = -0.6 for pm in range(4): if (pm == 0): cap_slp.append(0.0) cap_int.append(0.0) cap_corr.append(1.0) else: cap_slp.append((hydr_psi0 - hydr_psicap )/(1.0 - rwccap[pm])) cap_int.append(-cap_slp[pm] + hydr_psi0) cap_corr.append(-cap_int[pm]/cap_slp[pm]) # Allocate memory to our objective classes iret = initalloc_wtfs(ci(ncomp),ci(ncomp)) print('Allocated') # Define the funcions and their parameters # vg_wrf(1,alpha=1.0,psd=2.7,th_sat=0.55,th_res=0.1) # vg_wkf(1,alpha=1.0,psd=2.7,th_sat=0.55,th_res=0.1,tort=0.5) cch_wrf(1,th_sat=0.55, psi_sat=-1.56e-3, beta=6) cch_wkf(1,th_sat=0.55, psi_sat=-1.56e-3, beta=6) # cch_wrf(3,th_sat=0.55, psi_sat=-1.56e-3, beta=6) # tfs_wkf(3,p50=-2.25, avuln=2.0) names=['Soil','ARoot','Leaf'] # Absorving root tfs_wrf(2,th_sat=0.75,th_res=0.15,pinot=-1.043478, \ epsil=8,rwc_fd=rwc_fd[3],cap_corr=cap_corr[3], \ cap_int=cap_int[3],cap_slp=cap_slp[3],pmedia=4) tfs_wkf(2,p50=-2.25, avuln=2.0) # Leaf tfs_wrf(3,th_sat=0.65,th_res=0.25,pinot=-1.47, \ epsil=12,rwc_fd=rwc_fd[0],cap_corr=cap_corr[0], \ cap_int=cap_int[0],cap_slp=cap_slp[0],pmedia=1) tfs_wkf(3,p50=-2.25, avuln=2.0) print('initialized WRF') theta = np.linspace(0.10, 0.7, num=npts) psi = np.full(shape=(ncomp,len(theta)),dtype=np.float64,fill_value=np.nan) dpsidth = np.full(shape=(ncomp,len(theta)),dtype=np.float64,fill_value=np.nan) cdpsidth = np.full(shape=(ncomp,len(theta)),dtype=np.float64,fill_value=np.nan) for ic in range(ncomp): for i,th in enumerate(theta): psi[ic,i] = psi_from_th(ci(ic+1),c8(th)) # Theta vs psi plots fig0, ax1 = plt.subplots(1,1,figsize=(9,6)) for ic in range(ncomp): ax1.plot(theta,psi[ic,:],label='{}'.format(names[ic])) ax1.set_ylim((-30,5)) ax1.set_ylabel('Matric Potential [MPa]') ax1.set_xlabel('VWC [m3/m3]') ax1.legend(loc='lower right') for ic in range(ncomp): for i in range(1,len(theta)-1): dpsidth[ic,i] = dpsidth_from_th(ci(ic+1),c8(theta[i])) cdpsidth[ic,i] = (psi[ic,i+1]-psi[ic,i-1])/(theta[i+1]-theta[i-1]) # Theta vs dpsi_dth (also checks deriv versus explicit) fig1, ax1 = plt.subplots(1,1,figsize=(9,6)) for ic in range(ncomp): ax1.plot(theta,dpsidth[0,:],label='func') ax1.plot(theta,cdpsidth[0,:],label='check') ax1.set_ylim((0,1000)) ax1.set_ylabel('dPSI/dTh [MPa m3 m-3]') ax1.set_xlabel('VWC [m3/m3]') ax1.legend(loc='upper right') # Push parameters to WKF classes # ------------------------------------------------------------------------- # Generic VGs ftc = np.full(shape=(ncomp,len(theta)),dtype=np.float64,fill_value=np.nan) dftcdpsi = np.full(shape=(ncomp,len(theta)),dtype=np.float64,fill_value=np.nan) cdftcdpsi = np.full(shape=(ncomp,len(theta)),dtype=np.float64,fill_value=np.nan) for ic in range(ncomp): for i in range(0,len(theta)): ftc[ic,i] = ftc_from_psi(ci(ic+1),c8(psi[ic,i])) for ic in range(ncomp): for i in range(1,len(theta)-1): dftcdpsi[ic,i] = dftcdpsi_from_psi(ci(ic+1),c8(psi[ic,i])) cdftcdpsi[ic,i] = (ftc[ic,i+1]-ftc[ic,i-1])/(psi[ic,i+1]-psi[ic,i-1]) # FTC versus Psi fig2, ax1 = plt.subplots(1,1,figsize=(9,6)) for ic in range(ncomp): ax1.plot(psi[ic,:],ftc[ic,:],label='{}'.format(names[ic])) ax1.set_ylabel('FTC') ax1.set_xlabel('Psi [MPa]') ax1.set_xlim([-5,0]) ax1.legend(loc='upper right') # FTC versus theta fig4, ax1 = plt.subplots(1,1,figsize=(9,6)) for ic in range(ncomp): ax1.plot(theta,ftc[ic,:],label='{}'.format(names[ic])) ax1.set_ylabel('FTC') ax1.set_xlabel('Theta [m3/m3]') ax1.legend(loc='lower right') # dFTC/dPSI fig3,ax1 = plt.subplots(1,1,figsize=(9,6)) for ic in range(ncomp): # ax1.plot(psi[ic,:],abs(dftcdpsi[ic,:]-cdftcdpsi[ic,:])/abs(cdftcdpsi[ic,:]),label='{}'.format(ic)) ax1.plot(psi[ic,:],cdftcdpsi[ic,:],label='check') ax1.set_ylabel('dFTC/dPSI') ax1.set_xlabel('Psi [MPa]') # ax1.set_xlim([-30,3]) # ax1.set_ylim([0,10]) ax1.legend(loc='upper right') plt.show() # code.interact(local=dict(globals(), **locals())) # Helper code to plot negative logs def semilogneg(x): y = np.sign(x)*np.log(abs(x)) return(y) def semilog10net(x): y = np.sign(x)*np.log10(abs(x)) return(y) # ======================================================================================= # This is the actual call to main if __name__ == "__main__": main(sys.argv)

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viz/view.py

LynnHongLiu/replayed_distillation

79

138465

import numpy as np import cv2 # This file is a set of commonly used functions by the viz scripts. It # is not meant to be run on its own def unblockshaped(arr, h, w, rgb=False): if rgb: n, nrows, ncols, nchannels = arr.shape return (arr.reshape(h//nrows, -1, nrows, ncols, nchannels) .swapaxes(1,2) .reshape(h, w, 3)) n, nrows, ncols = arr.shape return (arr.reshape(h//nrows, -1, nrows, ncols) .swapaxes(1,2) .reshape(h, w)) def reshape_to_row(arr, side=28, rgb=False): if rgb: grid = np.array([np.reshape(img, (side, side, 3)) for img in arr]) else: grid = np.array([np.reshape(img, (side, side)) for img in arr]) return unblockshaped(grid, int(side), int(side * grid.shape[0]), rgb=rgb) def reshape_to_grid(arr, side=28, rgb=False): if rgb: grid = np.array([np.reshape(img, (side, side, 3)) for img in arr]) else: grid = np.array([np.reshape(img, (side, side)) for img in arr]) size = int(side * np.sqrt(grid.shape[0])) return unblockshaped(grid, size, size, rgb=rgb)

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mplcyberpunk/__init__.py

story645/mplcyberpunk

1

102596

"""mplcyberpunk - A new Python package""" __version__ = '0.1.0' __author__ = '<NAME> <<EMAIL>>' __all__ = [] import matplotlib as mpl import pkg_resources from .core import add_glow_effects, make_lines_glow, add_underglow # register the included stylesheet in the mpl style library data_path = pkg_resources.resource_filename('mplcyberpunk', 'data/') cyberpunk_stylesheets = mpl.style.core.read_style_directory(data_path) mpl.style.core.update_nested_dict(mpl.style.library, cyberpunk_stylesheets)

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example/simple.py

xiaodongxiexie/log2db

1

153393

# -*- coding: utf-8 -*- # @Author: xiaodong # @Date : 2021/4/17 import logging from log2db import init, Proxy, LoggingMQHandler Proxy.DBURL = "sqlite:///{}".format("mylogger.db") _ = init() logger = logging.getLogger("log2db") logger.parent = None handler = LoggingMQHandler(logging.INFO) logger.addHandler(handler) if __name__ == '__main__': import random, time from log2db import Logging session = _.session logger.info("this is a test", extra={"user": "none"}) logger.warning("this is a warn %s", "ohuo") try: 1/0 except: logger.exception("this is a exception", exc_info=True) print(session.query(Logging).count()) for i in range(1000): random.choice([logger.info, logger.warning, logger.exception, logger.debug])(i) print(session.query(Logging).count()) # time.sleep(3) print(session.query(Logging).count())

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scanner3d/metrics/__init__.py

Belval/Scanner3D

1

184192

<reponame>Belval/Scanner3D """ Raise classes to top-level """ from .base_metric import BaseMetric from .fitness import Fitness from .rmse import RMSE

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complex_performance_metrics/solvers/multiclass/coco_qmean_nae.py

hnarasimhan/constrained-classification

9

118432

import numpy as np from copy import copy from complex_performance_metrics.models.plugin import MulticlassPluginClassifier from complex_performance_metrics.utils import nae """ This module implements the COCO algorithm for: minimizing the (multiclass) Q-mean loss subject to (multiclass) Normalized Absolute Error <= epsilon """ def frank_wolfe(x, y, classifier, cpe_model, alpha, num_inner_iter): """ Inner Frank-Wolfe optimization in COCO Perform Lagrangian optimization over confusion matrices for Lagrange multiplier alpha Args: x (array-like, dtype = float, shape = (m,d)): Features y (array-like, dtype = int, shape = (m,)): Labels {0,...,m-1} classifier (RandomizedClassifier): A randomized classifier to which additional base classifiers are to be added cpe_model (sklearn estimator): A model with a predict_proba() function (default: None) alpha (float): Lagrange multiplier num_inner_iter (int): Number of solver iterations Returns: classifier (RandomizedClassifier): Solution classifier for inner maximization """ num_class = classifier.num_class p = np.zeros((num_class,)) for i in range(num_class): p[i] = (y == i).mean() # Intialize gain matrix W = np.eye(num_class, num_class) for i in range(num_class): W[i, i] = 1.0 / p[i] # Create binary plug-in with separate thresholds for protected attribute values plugin = MulticlassPluginClassifier(cpe_model, num_class=num_class) plugin.set_cost_matrix(W, is_cost=False) C = plugin.evaluate_conf(x, y, use_stored_prob=True) # Initialize constants/normalization terms norm_costs_const = 0.5 / (1 - np.min(p)) norm_weights_const = 1.0 # Frank-Wolfe iterations for i in range(num_inner_iter): # Compute gain matrix from objective gradient, # and construct optimal classifier for cost-sensitive learning step W = np.zeros((num_class, num_class)) for j in range(num_class): for k in range(num_class): if j == k: W[j, j] = 2 * (1 - C[j, j] / p[j]) / p[j] / num_class W[j, k] -= alpha * 2 * (C[:,k].sum() - p[k]) * norm_costs_const plugin.set_cost_matrix(W, is_cost=False) # Update confusion matrix iterate C_hat = plugin.evaluate_conf(x, y, use_stored_prob=True) C = (1 - 2.0 / (i + 2)) * C + 2.0 / (i + 2) * C_hat # Append weight and copy of plug-in classifier to randomized classifier if i == 0: classifier.append(1.0, copy(plugin)) else: norm_weights_const *= 1 - 2.0 / (i + 2) classifier.append(2.0 / (i + 2) / norm_weights_const, copy(plugin)) # Normalize classifier weights to sum up to 1 classifier.weights[-num_inner_iter:-1] = [x * norm_weights_const for x in classifier.weights[-num_inner_iter:-1]] classifier.weights[-1] *= norm_weights_const return C, classifier def fit(x, y, classifier, cpe_model, eps, eta, num_outer_iter, num_inner_iter): """ Outer optimization in COCO Run gradient ascent over Lagrange multipliers alpha Args: x (array-like, dtype = float, shape= (m,d)): Features y (array-like, dtype = int, shape = (m,)): Labels {0,...,m-1} classifier (RandomizedClassifier): A randomized classifier to which additional base classifiers are to be added cpe_model (sklearn estimator): A model with a predict_proba() function (default: None) eps (float): Constraint function tolerance eta (float): Step-size for gradient-ascent solver num_outer_iter (int): Number of outer iterations in solver (gradient ascent) num_inner_iter (int): Number of inner iterations in solver (Frank-Wolfe) Returns: classifier (RandomizedClassifier): Final classifier """ # If the problem has no constraints, set alpha to 0 and run outer solver only for one iteration if eps == 1: alpha = 0.0 num_outer_iter = 1 else: # else initialize Lagrange multiplier alpha = 0.01 # Gradient ascent iterations for t in range(num_outer_iter): # Find confusion matrix that maximizes the Lagrangian at alpha C, _ = frank_wolfe(x, y, classifier, cpe_model, alpha, num_inner_iter) er = nae(C) # Gradient update to alpha alpha += eta * 1.0 / np.sqrt(t + 1) * (er - eps) # Projection step if alpha < 0: alpha = 0 # Normalize classifier weights to sum up to 1 classifier.normalize_weights() return classifier

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pip_packages/setup.py

Stonepia/model-gardens

0

82037

## Modified from https://github.com/tensorflow/models/blob/master/official/pip_package/setup.py """Sets up TensorFlow Official Models.""" import datetime import os import sys from setuptools import find_packages from setuptools import setup version = '2.5.0' project_name = 'tf-models-official' long_description = """The TensorFlow official models are a collection of models that use TensorFlow's high-level APIs. They are intended to be well-maintained, tested, and kept up to date with the latest TensorFlow API. They should also be reasonably optimized for fast performance while still being easy to read.""" if '--project_name' in sys.argv: project_name_idx = sys.argv.index('--project_name') project_name = sys.argv[project_name_idx + 1] sys.argv.remove('--project_name') sys.argv.pop(project_name_idx) def _get_requirements(): """Parses requirements.txt file.""" install_requires_tmp = [] dependency_links_tmp = [] with open( os.path.join(os.path.dirname(__file__), '../requirements.txt'), 'r') as f: for line in f: package_name = line.strip() # Skip empty line or comments starting with "#". if not package_name or package_name[0] == '#': continue if package_name.startswith('-e '): dependency_links_tmp.append(package_name[3:].strip()) else: install_requires_tmp.append(package_name) return install_requires_tmp, dependency_links_tmp install_requires, dependency_links = _get_requirements() if project_name == 'tf-models-nightly': version += '.dev' + datetime.datetime.now().strftime('%Y%m%d') install_requires.append('tf-nightly') install_requires.append('tensorflow-text-nightly') else: install_requires.append('tensorflow>=2.4.0') install_requires.append('tensorflow-text>=2.4.0') print('install_requires: ', install_requires) print('dependency_links: ', dependency_links) setup( name=project_name, version=version, description='Model Implementation gardens', long_description=long_description, author='Stonepia', author_email='<EMAIL>', url='https://github.com/Stonepia/model-gardens', license='Apache 2.0', packages=find_packages(exclude=[ 'research*', 'official.pip_package*', 'official.benchmark*', 'official.colab*', ]), exclude_package_data={ '': ['*_test.py',], }, install_requires=install_requires, dependency_links=dependency_links, python_requires='>=3.6', )

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gorillabot/plugins/connection.py

pep7/GorillaBot

21

164254

# Copyright (c) 2013-2016 <NAME> # # 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. from plugins.util import admin @admin() def join(m): """Join a channel.""" #- !join #channel #- #- Joins the specified channel. Only joins one channel at a time. if len(m.line) == 1: m.bot.private_message(m.location, "Please specify a channel to join.") else: chan = m.line[1] if chan[0] != "#": m.bot.private_message(m.location, "Not a valid channel name.") m.bot.join([chan]) m.bot.logger.info("Joining " + chan) @admin("leave") def part(m): """Part from the specified channel.""" #- !part [#channel] [message] #- #- Parts from the specified channel, or the current channel if unspecified. Only parts from #- one channel at a time. If a message is included, this will be used as the part message. part_msg = "" if len(m.line) == 1: m.bot.send("PART " + m.location + " :" + part_msg) m.bot.logger.info("Parting from " + m.location) try: del m.bot.configuration["chans"][m.location] except KeyError: m.bot.logger("Wasn't joined to " + m.location) else: m.bot.update_configuration(m.bot.configuration) return channel = "" if len(m.line) > 2: part_msg = " ".join(m.line[2:]) if m.line[1][0] != "#": m.bot.private_message(m.location, "Not a valid channel name.") return else: channel = m.line[1] m.bot.send("PART " + channel + " :" + part_msg) m.bot.logger.info("Parting from " + channel + ".") try: del m.bot.configuration["chans"][m.location] except KeyError: m.bot.logger.info("Wasn't joined to " + m.location) else: m.bot.update_configuration(m.bot.configuration) @admin("shutdown") def quit(m): """Shut down the bot entirely.""" #- !quit [message] #- #- Quits the bot from the network and shuts down. msg = " ".join(m.line[1:]) if len(m.line) > 1 else "" m.bot.shutdown_message = msg m.bot.reconnect_time = None m.bot.shutdown.set()

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data/databases/create_dataset_from_mongodb_collection.py

james94/driverlessai-recipes

194

129527

"""Create dataset from MonogDB""" # Author: <NAME> # Created: 31/01/2020 # Last Updated: 20/02/2020 import datatable as dt import pandas as pd from h2oaicore.data import CustomData _global_modules_needed_by_name = ["pymongo", "dnspython"] # Please fill before usage # Note that this information is logged in Driverless AI logs. MONGO_CONNECTION_STRING = "mongodb+srv://<username>:<password>@host[/[database][?options]]" MONGO_DB = "sample_mflix" MONGO_COLLECTION = "theaters" DATASET_NAME = "sample_mflix.theaters" class MongoDbData(CustomData): _modules_needed_by_name = ["pymongo", "dnspython"] @staticmethod def create_data(X: dt.Frame = None): from pymongo import MongoClient # Note: adding try clause to help pass tests internally. # can cause unexpected effect of recipe completing successfully but returning an empty dataset. try: # Initialize MongoDB python client client = MongoClient(MONGO_CONNECTION_STRING) # Use MongoDB python client to obtain list of all documents in a specific database + collection db = client.get_database(MONGO_DB) coll = db.get_collection(MONGO_COLLECTION) docs = coll.find() # Convert MongoDB documents cursor to pandas dataframe df = pd.DataFrame.from_dict(docs) # Cast all object columns as string since datatable cannot accept arbitrary objects object_cols = df.select_dtypes(include=['object']).columns df[object_cols] = df[object_cols].astype(str) # return dict where key is name of dataset and value is a datatable Frame of the data. return {DATASET_NAME: dt.Frame(df)} except Exception as e: return []

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records/urls.py

Glucemy/Glucemy-back

0

9680

from rest_framework.routers import DefaultRouter from records.views import RecordViewSet router = DefaultRouter() router.register('', RecordViewSet, basename='records') urlpatterns = router.urls

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src/sage/modules/module_functors.py

bopopescu/sage

1,742

160594

""" Module Functors AUTHORS: - <NAME> (2017-10): Initial implementation of :class:`QuotientModuleFunctor` """ #***************************************************************************** # Copyright (C) 2017 <NAME> <<EMAIL>> # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 2 of the License, or # (at your option) any later version. # http://www.gnu.org/licenses/ #***************************************************************************** ############################################################## # Construction functor for quotient modules ############################################################## from sage.categories.pushout import ConstructionFunctor from sage.categories.modules import Modules class QuotientModuleFunctor(ConstructionFunctor): r""" Construct the quotient of a module by a submodule. INPUT: - ``relations`` -- a module .. NOTE:: This construction functor keeps track of the basis of defining ``relations``. It can only be applied to free modules into which this basis coerces. EXAMPLES:: sage: A = (1/2)*ZZ^2 sage: B = 2*ZZ^2 sage: Q = A / B sage: F = Q.construction()[0] sage: F QuotientModuleFunctor sage: F(A) == Q True The modules are constructed from the cover not the ambient module:: sage: F(B.ambient_module()) == Q False We can construct quotients from different modules:: sage: F((1/2)*ZZ^2) Finitely generated module V/W over Integer Ring with invariants (4, 4) sage: F(ZZ^2) Finitely generated module V/W over Integer Ring with invariants (2, 2) sage: F(2*ZZ^2) Finitely generated module V/W over Integer Ring with invariants () This functor is used for constructing pushouts:: sage: A = ZZ^3 sage: x,y,z = A.basis() sage: A1 = A.submodule([x]) sage: A2 = A.submodule([y, 2*x]) sage: B1 = A.submodule([]) sage: B2 = A.submodule([2*x]) sage: Q1 = A1 / B1 sage: Q2 = A2 / B2 sage: q3 = Q1.an_element() + Q2.an_element() """ rank = 5 # ranking of functor, not rank of module def __init__(self, relations): """ Initialization of ``self``. TESTS:: sage: from sage.modules.module_functors import QuotientModuleFunctor sage: B = (2/3)*ZZ^2 sage: F = QuotientModuleFunctor(B) sage: TestSuite(F).run() """ R = relations.category().base_ring() ConstructionFunctor.__init__(self, Modules(R), Modules(R)) self._relations = relations def relations(self): """ Return the defining relations of ``self``. EXAMPLES:: sage: A = (ZZ**2) / span([[4,0],[0,3]], ZZ) sage: A.construction()[0].relations() Free module of degree 2 and rank 2 over Integer Ring Echelon basis matrix: [4 0] [0 3] """ return self._relations def _apply_functor(self, ambient): """ Apply the functor to an object of ``self``'s domain. TESTS:: sage: A = ZZ^3 sage: B = 2 * A sage: C = 4 * A sage: D = B / C sage: F = D.construction()[0] sage: D == F(D.construction()[1]) True """ return ambient.quotient(self._relations) def __eq__(self, other): """ The quotient functor ``self`` is equal to ``other`` if it is a :class:`QuotientModuleFunctor` and the relations subspace are equal. EXAMPLES:: sage: F1 = ((ZZ^3) / (4*ZZ^3)).construction()[0] sage: F2 = ((2*ZZ^3) / (4*ZZ^3)).construction()[0] sage: F1 == F2 True sage: F3 = ((ZZ^3) / (8*ZZ^3)).construction()[0] sage: F1 == F3 False """ if not isinstance(other, QuotientModuleFunctor): return False return self._relations == other._relations def __ne__(self, other): r""" Check whether ``self`` is not equal to ``other``. EXAMPLES:: sage: F1 = ((ZZ^3) / (4*ZZ^3)).construction()[0] sage: F2 = ((2*ZZ^3) / (4*ZZ^3)).construction()[0] sage: F1 != F2 False sage: F3 = ((ZZ^3) / (8*ZZ^3)).construction()[0] sage: F1 != F3 True """ return not (self == other) def merge(self, other): r""" Merge the construction functors ``self`` and ``other``. EXAMPLES:: sage: A = ZZ^3 sage: x,y,z = A.basis() sage: A1 = A.submodule([x]) sage: A2 = A.submodule([y, 2*x]) sage: B1 = A.submodule([]) sage: B2 = A.submodule([2*x]) sage: Q1 = A1 / B1 sage: Q2 = A2 / B2 sage: F1 = Q1.construction()[0] sage: F2 = Q2.construction()[0] sage: F3 = F1.merge(F2) sage: q3 = Q1.an_element() + Q2.an_element() sage: q3.parent() == F3(A1 + A2) True sage: G = A1.construction()[0]; G SubspaceFunctor sage: F1.merge(G) sage: F2.merge(G) """ if isinstance(other, QuotientModuleFunctor): return QuotientModuleFunctor(self._relations + other._relations)

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SAC2018/SAC.py

Wen2chao/RL-Algorithm-

19

192737

<reponame>Wen2chao/RL-Algorithm- import gym import torch import random import torch.nn as nn import collections import numpy as np import torch.nn.functional as F import torch.optim as optim import matplotlib.pyplot as plt from torch.distributions import Normal class ReplayBeffer(): def __init__(self, buffer_maxlen): self.buffer = collections.deque(maxlen=buffer_maxlen) def push(self, data): self.buffer.append(data) def sample(self, batch_size): state_list = [] action_list = [] reward_list = [] next_state_list = [] done_list = [] batch = random.sample(self.buffer, batch_size) for experience in batch: s, a, r, n_s, d = experience # state, action, reward, next_state, done state_list.append(s) action_list.append(a) reward_list.append(r) next_state_list.append(n_s) done_list.append(d) return torch.FloatTensor(state_list).to(device), \ torch.FloatTensor(action_list).to(device), \ torch.FloatTensor(reward_list).unsqueeze(-1).to(device), \ torch.FloatTensor(next_state_list).to(device), \ torch.FloatTensor(done_list).unsqueeze(-1).to(device) def buffer_len(self): return len(self.buffer) # Value Net class ValueNet(nn.Module): def __init__(self, state_dim, edge=3e-3): super(ValueNet, self).__init__() self.linear1 = nn.Linear(state_dim, 256) self.linear2 = nn.Linear(256, 256) self.linear3 = nn.Linear(256, 1) self.linear3.weight.data.uniform_(-edge, edge) self.linear3.bias.data.uniform_(-edge, edge) def forward(self, state): x = F.relu(self.linear1(state)) x = F.relu(self.linear2(x)) x = self.linear3(x) return x # Soft Q Net class SoftQNet(nn.Module): def __init__(self, state_dim, action_dim, edge=3e-3): super(SoftQNet, self).__init__() self.linear1 = nn.Linear(state_dim + action_dim, 256) self.linear2 = nn.Linear(256, 256) self.linear3 = nn.Linear(256, 1) self.linear3.weight.data.uniform_(-edge, edge) self.linear3.bias.data.uniform_(-edge, edge) def forward(self, state, action): x = torch.cat([state, action], 1) x = F.relu(self.linear1(x)) x = F.relu(self.linear2(x)) x = self.linear3(x) return x # Policy Net class PolicyNet(nn.Module): def __init__(self, state_dim, action_dim, log_std_min=-20, log_std_max=2, edge=3e-3): super(PolicyNet, self).__init__() self.log_std_min = log_std_min self.log_std_max = log_std_max self.linear1 = nn.Linear(state_dim, 256) self.linear2 = nn.Linear(256, 256) self.mean_linear = nn.Linear(256, action_dim) self.mean_linear.weight.data.uniform_(-edge, edge) self.mean_linear.bias.data.uniform_(-edge, edge) self.log_std_linear = nn.Linear(256, action_dim) self.log_std_linear.weight.data.uniform_(-edge, edge) self.log_std_linear.bias.data.uniform_(-edge, edge) def forward(self, state): x = F.relu(self.linear1(state)) x = F.relu(self.linear2(x)) mean = self.mean_linear(x) log_std = self.log_std_linear(x) log_std = torch.clamp(log_std, self.log_std_min, self.log_std_max) return mean, log_std def action(self, state): state = torch.FloatTensor(state).to(device) mean, log_std = self.forward(state) std = log_std.exp() normal = Normal(mean, std) z = normal.sample() action = torch.tanh(z).detach().cpu().numpy() return action # Use re-parameterization tick def evaluate(self, state, epsilon=1e-6): mean, log_std = self.forward(state) std = log_std.exp() normal = Normal(mean, std) noise = Normal(0, 1) z = noise.sample() action = torch.tanh(mean + std * z.to(device)) log_prob = normal.log_prob(mean + std * z.to(device)) - torch.log(1 - action.pow(2) + epsilon) return action, log_prob class SAC: def __init__(self, env, gamma, tau, buffer_maxlen, value_lr, q_lr, policy_lr): self.env = env self.state_dim = env.observation_space.shape[0] self.action_dim = env.action_space.shape[0] # hyperparameters self.gamma = gamma self.tau = tau # initialize networks self.value_net = ValueNet(self.state_dim).to(device) self.target_value_net = ValueNet(self.state_dim).to(device) self.q1_net = SoftQNet(self.state_dim, self.action_dim).to(device) self.q2_net = SoftQNet(self.state_dim, self.action_dim).to(device) self.policy_net = PolicyNet(self.state_dim, self.action_dim).to(device) # Load the target value network parameters for target_param, param in zip(self.target_value_net.parameters(), self.value_net.parameters()): target_param.data.copy_(self.tau * param + (1 - self.tau) * target_param) # Initialize the optimizer self.value_optimizer = optim.Adam(self.value_net.parameters(), lr=value_lr) self.q1_optimizer = optim.Adam(self.q1_net.parameters(), lr=q_lr) self.q2_optimizer = optim.Adam(self.q2_net.parameters(), lr=q_lr) self.policy_optimizer = optim.Adam(self.policy_net.parameters(), lr=policy_lr) # Initialize thebuffer self.buffer = ReplayBeffer(buffer_maxlen) def get_action(self, state): action = self.policy_net.action(state) return action def update(self, batch_size): state, action, reward, next_state, done = self.buffer.sample(batch_size) new_action, log_prob = self.policy_net.evaluate(state) # V value loss value = self.value_net(state) new_q1_value = self.q1_net(state, new_action) new_q2_value = self.q2_net(state, new_action) next_value = torch.min(new_q1_value, new_q2_value) - log_prob value_loss = F.mse_loss(value, next_value.detach()) # Soft q loss q1_value = self.q1_net(state, action) q2_value = self.q2_net(state, action) target_value = self.target_value_net(next_state) target_q_value = reward + done * self.gamma * target_value q1_value_loss = F.mse_loss(q1_value, target_q_value.detach()) q2_value_loss = F.mse_loss(q2_value, target_q_value.detach()) # Policy loss policy_loss = (log_prob - torch.min(new_q1_value, new_q2_value)).mean() # Update Policy self.policy_optimizer.zero_grad() policy_loss.backward() self.policy_optimizer.step() # Update v self.value_optimizer.zero_grad() value_loss.backward() self.value_optimizer.step() # Update Soft q self.q1_optimizer.zero_grad() self.q2_optimizer.zero_grad() q1_value_loss.backward() q2_value_loss.backward() self.q1_optimizer.step() self.q2_optimizer.step() # Update target networks for target_param, param in zip(self.target_value_net.parameters(), self.value_net.parameters()): target_param.data.copy_(self.tau * param + (1 - self.tau) * target_param) def main(env, agent, Episode, batch_size): Return = [] action_range = [env.action_space.low, env.action_space.high] for episode in range(Episode): score = 0 state = env.reset() for i in range(300): action = agent.get_action(state) # action output range[-1,1],expand to allowable range action_in = action * (action_range[1] - action_range[0]) / 2.0 + (action_range[1] + action_range[0]) / 2.0 next_state, reward, done, _ = env.step(action_in) done_mask = 0.0 if done else 1.0 agent.buffer.push((state, action, reward, next_state, done_mask)) state = next_state score += reward if done: break if agent.buffer.buffer_len() > 500: agent.update(batch_size) print("episode:{}, Return:{}, buffer_capacity:{}".format(episode, score, agent.buffer.buffer_len())) Return.append(score) score = 0 env.close() plt.plot(Return) plt.ylabel('Return') plt.xlabel("Episode") plt.grid(True) plt.show() if __name__ == '__main__': env = gym.make("Pendulum-v0") device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu") # Params tau = 0.01 gamma = 0.99 q_lr = 3e-3 value_lr = 3e-3 policy_lr = 3e-3 buffer_maxlen = 50000 Episode = 100 batch_size = 128 agent = SAC(env, gamma, tau, buffer_maxlen, value_lr, q_lr, policy_lr) main(env, agent, Episode, batch_size)

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1678, 363, 12720, 297, 3464, 29898, 29923, 12907, 1125, 30004, 13, 4706, 8158, 353, 29871, 29900, 30004, 13, 4706, 2106, 353, 8829, 29889, 12071, 26471, 13, 4706, 363, 474, 297, 3464, 29898, 29941, 29900, 29900, 1125, 30004, 13, 9651, 3158, 353, 10823, 29889, 657, 29918, 2467, 29898, 3859, 8443, 13, 9651, 396, 3158, 1962, 3464, 14352, 29896, 29892, 29896, 1402, 18837, 304, 2758, 519, 3464, 30004, 13, 9651, 3158, 29918, 262, 353, 29871, 3158, 334, 313, 2467, 29918, 3881, 29961, 29896, 29962, 448, 3158, 29918, 3881, 29961, 29900, 2314, 847, 29871, 29906, 29889, 29900, 718, 29871, 313, 2467, 29918, 3881, 29961, 29896, 29962, 718, 3158, 29918, 3881, 29961, 29900, 2314, 847, 29871, 29906, 29889, 29900, 30004, 13, 30004, 13, 9651, 2446, 29918, 3859, 29892, 20751, 29892, 2309, 29892, 903, 353, 8829, 29889, 10568, 29898, 2467, 29918, 262, 8443, 13, 9651, 2309, 29918, 13168, 353, 29871, 29900, 29889, 29900, 565, 2309, 1683, 29871, 29896, 29889, 29900, 30004, 13, 9651, 10823, 29889, 9040, 29889, 5910, 3552, 3859, 29892, 3158, 29892, 20751, 29892, 2446, 29918, 3859, 29892, 2309, 29918, 13168, 876, 30004, 13, 9651, 2106, 353, 2446, 29918, 3859, 30004, 13, 30004, 13, 9651, 8158, 4619, 20751, 30004, 13, 9651, 565, 2309, 29901, 30004, 13, 18884, 2867, 30004, 13, 9651, 565, 10823, 29889, 9040, 29889, 9040, 29918, 2435, 580, 1405, 29871, 29945, 29900, 29900, 29901, 30004, 13, 18884, 10823, 29889, 5504, 29898, 16175, 29918, 2311, 8443, 13, 30004, 13, 4706, 1596, 703, 1022, 275, 356, 26254, 1118, 7106, 26254, 1118, 6835, 29918, 5030, 5946, 29901, 8875, 1642, 4830, 29898, 1022, 275, 356, 29892, 8158, 29892, 10823, 29889, 9040, 29889, 9040, 29918, 2435, 22130, 30004, 13, 4706, 7106, 29889, 4397, 29898, 13628, 8443, 13, 4706, 8158, 353, 29871, 29900, 30004, 13, 1678, 8829, 29889, 5358, 26471, 13, 1678, 14770, 29889, 5317, 29898, 11609, 8443, 13, 1678, 14770, 29889, 29891, 1643, 877, 11609, 1495, 30004, 13, 1678, 14770, 29889, 29916, 1643, 703, 29923, 12907, 1159, 30004, 13, 1678, 14770, 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hera_notebook_templates/utils_h1c.py

HERA-Team/hera_notebook_templates

0

125478

<filename>hera_notebook_templates/utils_h1c.py # -*- coding: utf-8 -*- # Copyright 2020 the HERA Project # Licensed under the MIT License import matplotlib import matplotlib.pyplot as plt from matplotlib.ticker import FormatStrFormatter import matplotlib.patches as mpatches import matplotlib.gridspec as gridspec import numpy as np from pyuvdata import UVCal, UVData, utils import os import sys import glob import uvtools as uvt from astropy.time import Time from astropy.coordinates import EarthLocation, AltAz, Angle from astropy.coordinates import SkyCoord as sc import pandas import warnings import copy from hera_mc import cm_hookup, geo_sysdef import math from uvtools import dspec import hera_qm from hera_mc import cm_active from matplotlib.lines import Line2D from matplotlib import colors import json from hera_notebook_templates.data import DATA_PATH from astropy.io import fits import csv from astropy import units as u from astropy_healpix import HEALPix from astropy.coordinates import Galactic import healpy import yaml warnings.filterwarnings('ignore') def read_a_priori_ant_flags(a_priori_flags_yaml, ant_indices_only=False, by_ant_pol=False, ant_pols=None): '''Parse an a priori flag YAML file for a priori antenna flags. Parameters ---------- a_priori_flags_yaml : str Path to YAML file with a priori antenna flags ant_indices_only : bool If True, ignore polarizations and flag entire antennas when they appear, e.g. (1, 'Jee') --> 1. by_ant_pol : bool If True, expand all integer antenna indices into per-antpol entries using ant_pols ant_pols : list of str List of antenna polarizations strings e.g. 'Jee'. If not empty, strings in the YAML must be in here or an error is raised. Required if by_ant_pol is True. Returns ------- a_priori_antenna_flags : list List of a priori antenna flags, either integers or ant-pol tuples e.g. (0, 'Jee') ''' if ant_indices_only and by_ant_pol: raise ValueError("ant_indices_only and by_ant_pol can't both be True.") apaf = [] apf = yaml.safe_load(open(a_priori_flags_yaml, 'r')) # Load antenna flags if 'ex_ants' in apf: for ant in apf['ex_ants']: # flag antenna number if type(ant) == int: apaf.append(ant) # flag single antpol elif (type(ant) == list) and (len(ant) == 2) and (type(ant[0]) == int) and (type(ant[1]) == str): # check that antpol string is valid if ant_pols is not empty if (ant_pols is not None) and (ant[1] not in ant_pols): raise ValueError(f'{ant[1]} is not a valid ant_pol in {ant_pols}.') if ant_indices_only: apaf.append(ant[0]) else: apaf += [tuple(ant)] else: raise TypeError(f'ex_ants entires must be integers or a list of one int and one str. {ant} is not.') # Expand all integer antenna flags into antpol pairs if by_ant_pol: if ant_pols is None: raise ValueError('If by_ant_pol is True, then ant_pols must be specified.') apapf = [] for ant in apaf: if type(ant) == int: apapf += [(ant, pol) for pol in ant_pols] else: # then it's already and antpol tuple apapf.append(ant) return sorted(set(apapf)) return list(set(apaf)) def load_data(data_path,JD): HHfiles = sorted(glob.glob("{0}/zen.{1}.*.sum.uvh5".format(data_path,JD))) Nfiles = len(HHfiles) hhfile_bases = map(os.path.basename, HHfiles) sep = '.' x = sep.join(HHfiles[0].split('.')[-4:-2]) y = sep.join(HHfiles[-1].split('.')[-4:-2]) print(f'{len(HHfiles)} sum files found between JDs {x} and {y}') # choose one for single-file plots hhfile1 = HHfiles[len(HHfiles)//2] # Load data uvd_hh = UVData() unread = True while unread is True: try: uvd_hh.read(hhfile1, skip_bad_files=True) except: hhfile += 1 continue unread = False uvd_xx1 = uvd_hh.select(polarizations = -5, inplace = False) uvd_xx1.ants = np.unique(np.concatenate([uvd_xx1.ant_1_array, uvd_xx1.ant_2_array])) # -5: 'xx', -6: 'yy', -7: 'xy', -8: 'yx' uvd_yy1 = uvd_hh.select(polarizations = -6, inplace = False) uvd_yy1.ants = np.unique(np.concatenate([uvd_yy1.ant_1_array, uvd_yy1.ant_2_array])) return HHfiles, uvd_xx1, uvd_yy1 def plot_sky_map(uvd,ra_pad=20,dec_pad=30,clip=True,fwhm=11,nx=300,ny=200,sources=[]): map_path = f'{DATA_PATH}/haslam408_dsds_Remazeilles2014.fits' hdulist = fits.open(map_path) # Set up the HEALPix projection nside = hdulist[1].header['NSIDE'] order = hdulist[1].header['ORDERING'] hp = HEALPix(nside=nside, order=order, frame=Galactic()) #Get RA/DEC coords of observation loc = EarthLocation.from_geocentric(*uvd.telescope_location, unit='m') time_array = uvd.time_array obstime_start = Time(time_array[0],format='jd',location=loc) obstime_end = Time(time_array[-1],format='jd',location=loc) zenith_start = sc(Angle(0, unit='deg'),Angle(90,unit='deg'),frame='altaz',obstime=obstime_start,location=loc) zenith_start = zenith_start.transform_to('icrs') zenith_end = sc(Angle(0, unit='deg'),Angle(90,unit='deg'),frame='altaz',obstime=obstime_end,location=loc) zenith_end = zenith_end.transform_to('icrs') lst_start = obstime_start.sidereal_time('mean').hour lst_end = obstime_end.sidereal_time('mean').hour start_coords = [zenith_start.ra.degree,zenith_start.dec.degree] if start_coords[0] > 180: start_coords[0] = start_coords[0] - 360 end_coords = [zenith_end.ra.degree,zenith_end.dec.degree] if end_coords[0] > 180: end_coords[0] = end_coords[0] - 360 # Sample a 300x200 grid in RA/Dec ra_range = [zenith_start.ra.degree-ra_pad, zenith_end.ra.degree+ra_pad] dec_range = [zenith_start.dec.degree-ra_pad, zenith_end.dec.degree+ra_pad] if clip == True: ra = np.linspace(ra_range[0],ra_range[1], nx) dec = np.linspace(dec_range[0],dec_range[1], ny) else: ra = np.linspace(-180,180,nx) dec = np.linspace(-90,zenith_start.dec.degree+90,ny) ra_grid, dec_grid = np.meshgrid(ra * u.deg, dec * u.deg) #Create alpha grid alphas = np.ones(ra_grid.shape) alphas = np.multiply(alphas,0.5) ra_min = np.argmin(np.abs(np.subtract(ra,start_coords[0]-fwhm/2))) ra_max = np.argmin(np.abs(np.subtract(ra,end_coords[0]+fwhm/2))) dec_min = np.argmin(np.abs(np.subtract(dec,start_coords[1]-fwhm/2))) dec_max = np.argmin(np.abs(np.subtract(dec,end_coords[1]+fwhm/2))) alphas[dec_min:dec_max, ra_min:ra_max] = 1 # Set up Astropy coordinate objects coords = sc(ra_grid.ravel(), dec_grid.ravel(), frame='icrs') # Interpolate values temperature = healpy.read_map(map_path) tmap = hp.interpolate_bilinear_skycoord(coords, temperature) tmap = tmap.reshape((ny, nx)) tmap = np.flip(tmap,axis=1) alphas = np.flip(alphas,axis=1) # Make a plot of the interpolated temperatures plt.figure(figsize=(12, 7)) im = plt.imshow(tmap, extent=[ra[-1], ra[0], dec[0], dec[-1]], cmap=plt.cm.viridis, aspect='auto', vmin=10,vmax=40,alpha=alphas,origin='lower') plt.xlabel('RA (ICRS)') plt.ylabel('DEC (ICRS)') plt.hlines(y=start_coords[1]-fwhm/2,xmin=ra[-1],xmax=ra[0],linestyles='dashed') plt.hlines(y=start_coords[1]+fwhm/2,xmin=ra[-1],xmax=ra[0],linestyles='dashed') plt.vlines(x=start_coords[0],ymin=start_coords[1],ymax=dec[-1],linestyles='dashed') plt.vlines(x=end_coords[0],ymin=start_coords[1],ymax=dec[-1],linestyles='dashed') plt.annotate(np.around(lst_start,2),xy=(start_coords[0],dec[-1]),xytext=(0,8), fontsize=10,xycoords='data',textcoords='offset points',horizontalalignment='center') plt.annotate(np.around(lst_end,2),xy=(end_coords[0],dec[-1]),xytext=(0,8), fontsize=10,xycoords='data',textcoords='offset points',horizontalalignment='center') plt.annotate('LST (hours)',xy=(np.average([start_coords[0],end_coords[0]]),dec[-1]), xytext=(0,22),fontsize=10,xycoords='data',textcoords='offset points',horizontalalignment='center') for s in sources: if s[1] > dec[0] and s[1] < dec[-1]: if s[0] > 180: s = (s[0]-360,s[1],s[2]) if s[2] == 'LMC' or s[2] == 'SMC': plt.annotate(s[2],xy=(s[0],s[1]),xycoords='data',fontsize=8,xytext=(20,-20), textcoords='offset points',arrowprops=dict(facecolor='black', shrink=2,width=1, headwidth=4)) else: plt.scatter(s[0],s[1],c='k',s=6) if len(s[2]) > 0: plt.annotate(s[2],xy=(s[0]+3,s[1]-4),xycoords='data',fontsize=6) plt.show() plt.close() hdulist.close() def plot_inspect_ants(uvd1,jd,badAnts=[],flaggedAnts={},tempAnts={},crossedAnts=[],use_ants='auto'): # status_use = ['RF_ok','digital_ok','calibration_maintenance','calibration_ok','calibration_triage'] if use_ants == 'auto': use_ants = uvd1.get_ants() h = cm_active.ActiveData(at_date=jd) h.load_apriori() inspectAnts = [] for ant in use_ants: # status = h.apriori[f'HH{ant}:A'].status if ant in badAnts or ant in flaggedAnts.keys() or ant in crossedAnts: inspectAnts.append(ant) inspectAnts = np.unique(inspectAnts) inspectTitles = {} for ant in inspectAnts: inspectTitles[ant] = 'Flagged by: ' if ant in badAnts: inspectTitles[ant] = f'{inspectTitles[ant]} correlation matrix,' if ant in flaggedAnts.keys(): inspectTitles[ant] = f'{inspectTitles[ant]} ant_metrics,' if ant in crossedAnts: inspectTitles[ant] = f'{inspectTitles[ant]} cross matrix,' try: for k in tempAnts.keys(): if ant in tempAnts[k]: inspectTitles[ant] = f'{inspectTitles[ant]} template - {k},' except: continue if inspectTitles[ant][-1] == ',': inspectTitles[ant] = inspectTitles[ant][:-1] print('Antennas that require further inspection are:') print(inspectAnts) for ant in inspectAnts: auto_waterfall_lineplot(uvd1,ant,jd,title=inspectTitles[ant]) return inspectAnts def auto_waterfall_lineplot(uv, ant, jd, pols=['xx','yy'], colorbar_min=1e6, colorbar_max=1e8, title=''): freq = uv.freq_array[0]*1e-6 fig = plt.figure(figsize=(12,8)) gs = gridspec.GridSpec(3, 2, height_ratios=[2,0.7,1]) it = 0 pol_dirs = ['NN','EE'] for p,pol in enumerate(pols): waterfall= plt.subplot(gs[it]) jd_ax=plt.gca() times= np.unique(uv.time_array) d = np.abs(uv.get_data((ant,ant, pol))) if len(np.nonzero(d)[0])==0: print('#########################################') print(f'Data for antenna {ant} is entirely zeros') print('#########################################') plt.close() return im = plt.imshow(d,norm=colors.LogNorm(), aspect='auto') waterfall.set_title(f'{pol_dirs[p]} pol') freqs = uv.freq_array[0, :] / 1000000 xticks = np.arange(0, len(freqs), 120) plt.xticks(xticks, labels =np.around(freqs[xticks],2)) if p == 0: jd_ax.set_ylabel('JD') jd_yticks = [int(i) for i in np.linspace(0,len(times)-1,8)] jd_labels = np.around(times[jd_yticks],2) jd_ax.set_yticks(jd_yticks) jd_ax.set_yticklabels(jd_labels) jd_ax.autoscale(False) if p == 1: lst_ax = jd_ax.twinx() lst_ax.set_ylabel('LST (hours)') lsts = uv.lst_array*3.819719 inds = np.unique(lsts,return_index=True)[1] lsts = [lsts[ind] for ind in sorted(inds)] lst_yticks = [int(i) for i in np.linspace(0,len(lsts)-1,8)] lst_labels = np.around([lsts[i] for i in lst_yticks],2) lst_ax.set_yticks(lst_yticks) lst_ax.set_yticklabels(lst_labels) lst_ax.set_ylim(jd_ax.get_ylim()) lst_ax.autoscale(False) jd_ax.set_yticks([]) line= plt.subplot(gs[it+2]) averaged_data= np.abs(np.average(uv.get_data((ant,ant,pol)),0)) plt.plot(freq,averaged_data) line.set_yscale('log') if p == 0: line.set_ylabel('Night Average') else: line.set_yticks([]) line.set_xlim(freq[0],freq[-1]) line.set_xticks([]) line2 = plt.subplot(gs[it+4]) dat = uv.get_data((ant,ant,pol)) dat = np.abs(dat[len(dat)//2,:]) plt.plot(freq,dat) line2.set_yscale('log') line2.set_xlabel('Frequency (MHz)') if p == 0: line2.set_ylabel('Single Slice') else: line2.set_yticks([]) line2.set_xlim(freq[0],freq[-1]) plt.setp(waterfall.get_xticklabels(), visible=False) plt.subplots_adjust(hspace=.0) cbar = plt.colorbar(im, pad= 0.2, orientation = 'horizontal') cbar.set_label('Power') it=1 fig.suptitle(f'{ant}', fontsize=10,y=0.96) plt.annotate(title, xy=(0.5,0.94), ha='center',xycoords='figure fraction') plt.show() plt.close() def plot_autos(uvdx, uvdy): ants = uvdx.get_ants() sorted_ants = sorted(ants) freqs = (uvdx.freq_array[0])*10**(-6) times = uvdx.time_array lsts = uvdx.lst_array Nants = len(ants) Nside = 6 Yside = Nants//6 + 1 t_index = 0 jd = times[t_index] utc = Time(jd, format='jd').datetime xlim = (np.min(freqs), np.max(freqs)) ylim = (0, 20) fig, axes = plt.subplots(Yside, Nside, figsize=(16,Yside*3)) ptitle = 1.92/(Yside*3) fig.suptitle("JD = {0}, time = {1} UTC".format(jd, utc), fontsize=10,y=1+ptitle) fig.tight_layout(rect=(0, 0, 1, 0.95)) fig.subplots_adjust(left=.1, bottom=.1, right=.9, top=1, wspace=0.05, hspace=0.3) k = 0 for n,a in enumerate(ants): j = n%Nside i = n//Nside ax = axes[i,j] ax.set_xlim(xlim) ax.set_ylim(ylim) px, = ax.plot(freqs, 10*np.log10(np.abs(uvdx.get_data((a, a,'xx'))[t_index])), color='r', alpha=0.75, linewidth=1) py, = ax.plot(freqs, 10*np.log10(np.abs(uvdy.get_data((a, a,'yy'))[t_index])), color='b', alpha=0.75, linewidth=1) ax.grid(False, which='both') ax.set_title(f'{a}', fontsize=10) if k == 0: ax.legend([px, py], ['NN', 'EE']) if i == Yside-1: [t.set_fontsize(10) for t in ax.get_xticklabels()] ax.set_xlabel('freq (MHz)', fontsize=10) else: ax.set_xticklabels([]) if j!=0: ax.set_yticklabels([]) else: [t.set_fontsize(10) for t in ax.get_yticklabels()] ax.set_ylabel(r'$10\cdot\log$(amp)', fontsize=10) j += 1 k += 1 for k in range(j,Nside): axes[i,k].axis('off') plt.show() plt.close() def plot_wfs(uvd, pol, mean_sub=False, save=False, jd=''): # amps = np.abs(uvd.data_array[:, :, :, pol].reshape(uvd.Ntimes, uvd.Nants_data, uvd.Nfreqs, 1)) ants = uvd.get_ants() sorted_ants = sorted(ants) freqs = (uvd.freq_array[0])*10**(-6) times = uvd.time_array lsts = uvd.lst_array*3.819719 inds = np.unique(lsts,return_index=True)[1] lsts = [lsts[ind] for ind in sorted(inds)] polnames = ['xx','yy'] Nants = len(ants) Nside = 6 Yside = Nants//6 + 1 t_index = 0 jd = times[t_index] utc = Time(jd, format='jd').datetime ptitle = 1.92/(Yside*3) fig, axes = plt.subplots(Yside, Nside, figsize=(16,Yside*3)) if pol == 0: fig.suptitle("North Polarization", fontsize=14, y=1+ptitle) else: fig.suptitle("East Polarization", fontsize=14, y=1+ptitle) fig.tight_layout(rect=(0, 0, 1, 0.95)) fig.subplots_adjust(left=0, bottom=.1, right=.9, top=1, wspace=0.1, hspace=0.3) vmin = 0 vmax = 2 k = 0 for n,a in enumerate(ants): j = n%Nside i = n//Nside ax = axes[i,j] dat = np.log10(np.abs(uvd.get_data(a,a,polnames[pol]))) if mean_sub == True: ms = np.subtract(dat, np.nanmean(dat,axis=0)) im = ax.imshow(ms, vmin = -0.04, vmax = 0.04, aspect='auto',interpolation='nearest') else: im = ax.imshow(dat, vmin = vmin, vmax = vmax, aspect='auto',interpolation='nearest') ax.set_title(f'{a}', fontsize=10) ax.grid(False, which='both') if i == Yside-1: xticks = [int(i) for i in np.linspace(0,len(freqs)-1,3)] xticklabels = np.around(freqs[xticks],0) ax.set_xticks(xticks) ax.set_xticklabels(xticklabels) ax.set_xlabel('Freq (MHz)', fontsize=10) [t.set_rotation(70) for t in ax.get_xticklabels()] else: ax.set_xticklabels([]) if j!=0: ax.set_yticklabels([]) else: yticks = [int(i) for i in np.linspace(0,len(lsts)-1,6)] yticklabels = [np.around(lsts[ytick],1) for ytick in yticks] [t.set_fontsize(12) for t in ax.get_yticklabels()] ax.set_ylabel('Time(LST)', fontsize=10) ax.set_yticks(yticks) ax.set_yticklabels(yticklabels) ax.set_ylabel('Time(LST)', fontsize=10) if j==Nside-1: pos = ax.get_position() cbar_ax=fig.add_axes([0.91,pos.y0,0.01,pos.height]) cbar = fig.colorbar(im, cax=cbar_ax) cbar.set_label(f'Node {n}',rotation=270, labelpad=15) j += 1 k += 1 for k in range(j,Nside): axes[i,k].axis('off') plt.show() plt.close() # for i,n in enumerate(inclNodes): # ants = nodes[n]['ants'] # j = 0 # for _,a in enumerate(sorted_ants): # if a not in ants: # continue # # status = h.apriori[f'HH{a}:A'].status # # abb = status_abbreviations[status] # ax = axes[i,j] # dat = np.log10(np.abs(uvd.get_data(a,a,polnames[pol]))) # if mean_sub == True: # ms = np.subtract(dat, np.nanmean(dat,axis=0)) # im = ax.imshow(ms, # vmin = -0.07, vmax = 0.07, aspect='auto',interpolation='nearest') # else: # im = ax.imshow(dat, # vmin = vmin, vmax = vmax, aspect='auto',interpolation='nearest') # ax.set_title(f'{a}', fontsize=10) # if i == len(inclNodes)-1: # xticks = [int(i) for i in np.linspace(0,len(freqs)-1,3)] # xticklabels = np.around(freqs[xticks],0) # ax.set_xticks(xticks) # ax.set_xticklabels(xticklabels) # ax.set_xlabel('Freq (MHz)', fontsize=10) # [t.set_rotation(70) for t in ax.get_xticklabels()] # else: # ax.set_xticklabels([]) # if j != 0: # ax.set_yticklabels([]) # else: # yticks = [int(i) for i in np.linspace(0,len(lsts)-1,6)] # yticklabels = [np.around(lsts[ytick],1) for ytick in yticks] # [t.set_fontsize(12) for t in ax.get_yticklabels()] # ax.set_ylabel('Time(LST)', fontsize=10) # ax.set_yticks(yticks) # ax.set_yticklabels(yticklabels) # ax.set_ylabel('Time(LST)', fontsize=10) # j += 1 # for k in range(j,maxants): # axes[i,k].axis('off') # pos = ax.get_position() # cbar_ax=fig.add_axes([0.91,pos.y0,0.01,pos.height]) # cbar = fig.colorbar(im, cax=cbar_ax) # cbar.set_label(f'Node {n}',rotation=270, labelpad=15) # if save is True: # plt.savefig(f'{jd}_mean_subtracted_per_node_{pol}.png',bbox_inches='tight',dpi=300) # plt.show() # plt.close() def plot_mean_subtracted_wfs(uvd, use_ants, jd, pols=['xx','yy']): freqs = (uvd.freq_array[0])*1e-6 times = uvd.time_array lsts = uvd.lst_array*3.819719 inds = np.unique(lsts,return_index=True)[1] lsts = [lsts[ind] for ind in sorted(inds)] ants = sorted(use_ants) Nants = len(ants) pol_labels = ['NN','EE'] # status_colors = { # 'dish_maintenance' : 'salmon', # 'dish_ok' : 'red', # 'RF_maintenance' : 'lightskyblue', # 'RF_ok' : 'royalblue', # 'digital_maintenance' : 'plum', # 'digital_ok' : 'mediumpurple', # 'calibration_maintenance' : 'lightgreen', # 'calibration_ok' : 'green', # 'calibration_triage' : 'lime'} # status_abbreviations = { # 'dish_maintenance' : 'dish-M', # 'dish_ok' : 'dish-OK', # 'RF_maintenance' : 'RF-M', # 'RF_ok' : 'RF-OK', # 'digital_maintenance' : 'dig-M', # 'digital_ok' : 'dig-OK', # 'calibration_maintenance' : 'cal-M', # 'calibration_ok' : 'cal-OK', # 'calibration_triage' : 'cal-Tri'} h = cm_active.ActiveData(at_date=jd) h.load_apriori() fig, axes = plt.subplots(Nants, 2, figsize=(7,Nants*2.2)) fig.suptitle('Mean Subtracted Waterfalls') fig.tight_layout(rect=(0, 0, 1, 0.95)) fig.subplots_adjust(left=.1, bottom=.1, right=.85, top=.975, wspace=0.05, hspace=0.2) for i,ant in enumerate(ants): # status = h.apriori[f'HH{ant}:A'].status # abb = status_abbreviations[status] # color = status_colors[status] for j,pol in enumerate(pols): ax = axes[i,j] dat = np.log10(np.abs(uvd.get_data(ant,ant,pol))) ms = np.subtract(dat, np.nanmean(dat,axis=0)) im = ax.imshow(ms, vmin = -0.07, vmax = 0.07, aspect='auto',interpolation='nearest') ax.set_title(f'{ant} - {pol_labels[j]}', fontsize=10) if j != 0: ax.set_yticklabels([]) else: yticks = [int(i) for i in np.linspace(0,len(lsts)-1,6)] yticklabels = [np.around(lsts[ytick],1) for ytick in yticks] [t.set_fontsize(12) for t in ax.get_yticklabels()] ax.set_ylabel('Time(LST)', fontsize=10) ax.set_yticks(yticks) ax.set_yticklabels(yticklabels) if i != Nants-1: ax.set_xticklabels([]) else: xticks = [int(i) for i in np.linspace(0,len(freqs)-1,8)] xticklabels = np.around(freqs[xticks],0) ax.set_xticks(xticks) ax.set_xticklabels(xticklabels) ax.set_xlabel('Frequency (MHz)', fontsize=10) if j == 1: pos = ax.get_position() cbar_ax=fig.add_axes([0.88,pos.y0,0.02,pos.height]) fig.colorbar(im, cax=cbar_ax) fig.show() def plot_closure(uvd, triad_length, pol): """Plot closure phase for an example triad. Parameters ---------- files : list of strings List of data filenames triad_length : float {14., 29.} Length of the triangle segment length. Must be 14 or 29. pol : str {xx, yy} Polarization to plot. Returns ------- None """ if triad_length == 14.: triad_list = [[0, 11, 12], [0, 1, 12], [1, 12, 13], [1, 2, 13], [2, 13, 14], [11, 23, 24], [11, 12, 24], [12, 24, 25], [12, 13, 25], [13, 25, 26], [13, 14, 26], [14, 26, 27], [23, 36, 37], [23, 24, 37], [24, 37, 38], [24, 25, 38], [25, 38, 39], [25, 26, 39], [26, 39, 40], [26, 27, 40], [27, 40, 41], [36, 37, 51], [37, 51, 52], [37, 38, 52], [38, 52, 53], [38, 39, 53], [39, 53, 54], [39, 40, 54], [40, 54, 55], [40, 41, 55], [51, 66, 67], [51, 52, 67], [53, 54, 69], [54, 69, 70], [54, 55, 70], [55, 70, 71], [65, 66, 82], [66, 82, 83], [66, 67, 83], [67, 83, 84], [70, 71, 87], [120, 121, 140], [121, 140, 141], [121, 122, 141], [122, 141, 142], [122, 123, 142], [123, 142, 143], [123, 124, 143]] else: triad_list = [[0, 23, 25], [0, 2, 25], [1, 24, 26], [2, 25, 27], [11, 36, 38], [11, 13, 38], [12, 37, 39], [12, 14, 39], [13, 38, 40], [14, 39, 41], [23, 25, 52], [24, 51, 53], [24, 26, 53], [25, 52, 54], [25, 27, 54], [26, 53, 55], [36, 65, 67], [36, 38, 67], [38, 67, 69], [38, 40, 69], [39, 41, 70], [40, 69, 71], [51, 82, 84], [51, 53, 84], [52, 83, 85], [52, 54, 85], [54, 85, 87], [83, 85, 120], [85, 120, 122], [85, 87, 122], [87, 122, 124]] # Look for a triad that exists in the data for triad in triad_list: bls = [[triad[0], triad[1]], [triad[1], triad[2]], [triad[2], triad[0]]] triad_in = True for bl in bls: inds = uvd.antpair2ind(bl[0], bl[1], ordered=False) if len(inds) == 0: triad_in = False break if triad_in: break if not triad_in: raise ValueError('Could not find triad in data.') closure_ph = np.angle(uvd.get_data(triad[0], triad[1], pol) * uvd.get_data(triad[1], triad[2], pol) * uvd.get_data(triad[2], triad[0], pol)) plt.imshow(closure_ph, aspect='auto', rasterized=True, interpolation='nearest', cmap = 'twilight') def plotNodeAveragedSummary(uv,HHfiles,jd,use_ants,pols=['xx','yy'],mat_pols=['xx','yy'], baseline_groups=[],removeBadAnts=False,plotRatios=False,plotSummary=True): """ Plots a summary of baseline correlations throughout a night for each baseline group specified, separated into inter-node and intra-node baselines, for each polarization specified. Parameters ---------- uv: UVData object UVData object containing any file from the desired night of observation. HHfiles: List A list of all files to be looked at for the desired night of observation. jd: String The JD of the night of observation pols: List A list containing the desired polarizations to look at. Options are any polarization strings accepted by pyuvdata. baseline_groups: [] A list containing the baseline types to look at, formatted as (length, N-S separation, label (str)). removeBadAnts: Bool Option to flag seemingly dead antennas and remove them from the per-baseline-group averaging. Returns ------- badAnts: List A list specifying the antennas flagged as dead or non-correlating. """ if baseline_groups == []: baseline_groups = [(14,0,'14m E-W'),(14,-11,'14m NW-SE'),(14,11,'14m SW-NE'),(29,0,'29m E-W'),(29,22,'29m SW-NE'), (44,0,'44m E-W'),(58.5,0,'58m E-W'),(73,0,'73m E-W'),(87.6,0,'88m E-W'), (102.3,0,'102m E-W')] nodeMedians,lsts,badAnts=get_correlation_baseline_evolutions(uv,HHfiles,jd,use_ants,pols=pols,mat_pols=mat_pols, bl_type=baseline_groups,removeBadAnts=removeBadAnts, plotRatios=plotRatios) # pols = mat_pols # if plotSummary is False: # return badAnts # if len(lsts)>1: # fig,axs = plt.subplots(len(pols),2,figsize=(16,16)) # maxLength = 0 # cmap = plt.get_cmap('Blues') # for group in baseline_groups: # if group[0] > maxLength: # maxLength = group[0] # for group in baseline_groups: # length = group[0] # data = nodeMedians[group[2]] # colorInd = float(length/maxLength) # if len(data['inter']['xx']) == 0: # continue # for i in range(len(pols)): # pol = pols[i] # axs[i][0].plot(data['inter'][pol], color=cmap(colorInd), label=group[2]) # axs[i][1].plot(data['intra'][pol], color=cmap(colorInd), label=group[2]) # axs[i][0].set_ylabel('Median Correlation Metric') # axs[i][0].set_title('Internode, Polarization %s' % pol) # axs[i][1].set_title('Intranode, Polarization %s' % pol) # xticks = np.arange(0,len(lsts),1) # axs[i][0].set_xticks(xticks) # axs[i][0].set_xticklabels([str(lst) for lst in lsts]) # axs[i][1].set_xticks(xticks) # axs[i][1].set_xticklabels([str(lst) for lst in lsts]) # axs[1][1].legend() # axs[1][0].set_xlabel('LST (hours)') # axs[1][1].set_xlabel('LST (hours)') # fig.tight_layout(pad=2) # else: # print('#############################################################################') # print('Not enough LST coverage to show metric evolution - that plot is being skipped') # print('#############################################################################') # return badAnts def plotVisibilitySpectra(file,jd,use_ants='auto',badAnts=[],pols=['xx','yy']): """ Plots visibility amplitude spectra for a set of redundant baselines, labeled by inter vs. intranode baselines. Parameters --------- file: String File to calculate the spectra from jd: String JD of the night 'file' was observed on badAnts: List A list of antennas not to include in the plot pols: List Polarizations to plot. Can include any polarization strings accepted by pyuvdata. """ pol_labels = ['NS','EW'] plt.subplots_adjust(wspace=0.25) uv = UVData() uv.read_uvh5(file) baseline_groups = get_baseline_groups(uv,use_ants="auto") freqs = uv.freq_array[0]/1000000 loc = EarthLocation.from_geocentric(*uv.telescope_location, unit='m') obstime_start = Time(uv.time_array[0],format='jd',location=loc) startTime = obstime_start.sidereal_time('mean').hour JD = int(obstime_start.jd) j = 0 fig, axs = plt.subplots(len(baseline_groups),2,figsize=(12,4*len(baseline_groups))) for orientation in baseline_groups: bls = baseline_groups[orientation] usable = 0 for i in range(len(bls)): ants = uv.baseline_to_antnums(bls[i]) if ants[0] in badAnts or ants[1] in badAnts: continue if ants[0] in use_ants and ants[1] in use_ants: usable += 1 if usable <=4: use_all = True print(f'Note: not enough baselines of orientation {orientation} - using all available baselines') elif usable <= 10: print(f'Note: only a small number of baselines of orientation {orientation} are available') use_all = False else: use_all = False for p in range(len(pols)): inter=False intra=False pol = pols[p] for i in range(len(bls)): ants = uv.baseline_to_antnums(bls[i]) ant1 = ants[0] ant2 = ants[1] if (ant1 in use_ants and ant2 in use_ants) or use_all == True: # key1 = 'HH%i:A' % (ant1) # n1 = x[key1].get_part_from_type('node')['E<ground'][1:] # key2 = 'HH%i:A' % (ant2) # n2 = x[key2].get_part_from_type('node')['E<ground'][1:] dat = np.mean(np.abs(uv.get_data(ant1,ant2,pol)),0) auto1 = np.mean(np.abs(uv.get_data(ant1,ant1,pol)),0) auto2 = np.mean(np.abs(uv.get_data(ant2,ant2,pol)),0) norm = np.sqrt(np.multiply(auto1,auto2)) dat = np.divide(dat,norm) if ant1 in badAnts or ant2 in badAnts: continue # if n1 == n2: # if intra is False: # axs[j][p].plot(freqs,dat,color='blue',label='intranode') # intra=True # else: # axs[j][p].plot(freqs,dat,color='blue') # else: # if inter is False: # axs[j][p].plot(freqs,dat,color='red',label='internode') # inter=True # else: # axs[j][p].plot(freqs,dat,color='red') axs[j][p].plot(freqs,dat,color='blue') axs[j][p].set_yscale('log') axs[j][p].set_title('%s: %s pol' % (orientation,pol_labels[p])) if j == 0: axs[len(baseline_groups)-1][p].set_xlabel('Frequency (MHz)') if p == 0: axs[j][p].legend() axs[j][0].set_ylabel('log(|Vij|)') axs[j][1].set_yticks([]) j += 1 fig.suptitle('Visibility spectra (JD: %i)' % (JD)) fig.subplots_adjust(top=.94,wspace=0.05) plt.show() plt.close() def plot_antenna_positions(uv, badAnts=[],use_ants='auto'): """ Plots the positions of all antennas that have data, colored by node. Parameters ---------- uv: UVData object Observation to extract antenna numbers and positions from badAnts: List A list of flagged or bad antennas. These will be outlined in black in the plot. flaggedAnts: Dict A dict of antennas flagged by ant_metrics with value corresponding to color in ant_metrics plot """ plt.figure(figsize=(12,10)) if badAnts == None: badAnts = [] all_ants = uv.antenna_numbers # nodes, antDict, inclNodes = generate_nodeDict(uv) # N = len(inclNodes) cmap = plt.get_cmap('tab20') i = 0 # nodePos = geo_sysdef.read_nodes() # antPos = geo_sysdef.read_antennas() # ants = geo_sysdef.read_antennas() # nodes = geo_sysdef.read_nodes() # firstNode = True firstAnt = True for i,a in enumerate(all_ants): width = 0 widthf = 0 if a in badAnts: width = 2 x = uv.antenna_positions[i,0] y = uv.antenna_positions[i,1] if a in use_ants: falpha = 0.5 else: falpha = 0.1 if firstAnt: if a in badAnts: if falpha == 0.1: plt.plot(x,y,marker="h",markersize=40,alpha=falpha,color='b', markeredgecolor='black',markeredgewidth=0) plt.annotate(a, [x-1, y]) continue plt.plot(x,y,marker="h",markersize=40,alpha=falpha,color='b', markeredgecolor='black',markeredgewidth=0) if a in badAnts: plt.plot(x,y,marker="h",markersize=40,color='b', markeredgecolor='black',markeredgewidth=width, markerfacecolor="None") else: if falpha == 0.1: plt.plot(x,y,marker="h",markersize=40,alpha=falpha,color='b', markeredgecolor='black',markeredgewidth=0) plt.annotate(a, [x-1, y]) continue plt.plot(x,y,marker="h",markersize=40,alpha=falpha,color='b', markeredgecolor='black',markeredgewidth=width) firstAnt = False else: plt.plot(x,y,marker="h",markersize=40,alpha=falpha,color='b', markeredgecolor='black',markeredgewidth=0) if a in badAnts and a in use_ants: plt.plot(x,y,marker="h",markersize=40,color='b', markeredgecolor='black',markeredgewidth=width, markerfacecolor="None") plt.annotate(a, [x-1, y]) plt.xlabel('East') plt.ylabel('North') plt.show() plt.close() def plot_lst_coverage(uvd): """ Plots the LST and JD coverage for a particular night. Parameters ---------- uvd: UVData Object Object containing a whole night of data, used to extract the time array. """ lsts = uvd.lst_array*3.819719 jds = np.unique(uvd.time_array) alltimes = np.arange(np.floor(jds[0]),np.ceil(jds[0]),jds[2]-jds[1]) df = jds[2]-jds[1] truetimes = [np.min(np.abs(jds-jd))<=df*0.6 for jd in alltimes] usetimes = np.tile(np.asarray(truetimes),(20,1)) fig = plt.figure(figsize=(20,2)) ax = fig.add_subplot() im = ax.imshow(usetimes, aspect='auto',cmap='RdYlGn',vmin=0,vmax=1,interpolation='nearest') fig.colorbar(im) ax.set_yticklabels([]) ax.set_yticks([]) if len(alltimes) <= 15: xticks = [int(i) for i in np.linspace(0,len(alltimes)-1,len(alltimes))] else: xticks = [int(i) for i in np.linspace(0,len(alltimes)-1,14)] ax.set_xticks(xticks) ax.set_xticklabels(np.around(alltimes[xticks],2)) ax.set_xlabel('JD') ax.set_title('LST (hours)') ax2 = ax.twiny() ax2.set_xticks(xticks) jds = alltimes[xticks] lstlabels = [] loc = EarthLocation.from_geocentric(*uvd.telescope_location, unit='m') for jd in jds: t = Time(jd,format='jd',location=loc) lstlabels.append(t.sidereal_time('mean').hour) ax2.set_xticklabels(np.around(lstlabels,2)) ax2.set_label('LST (hours)') ax2.tick_params(labelsize=12) plt.show() plt.close() def calcEvenOddAmpMatrix(sm,pols=['xx','yy'],nodes='auto', badThresh=0.25, plotRatios=False): """ Calculates a matrix of phase correlations between antennas, where each pixel is calculated as (even/abs(even)) * (conj(odd)/abs(odd)), and then averaged across time and frequency. Paramters: --------- sm: UVData Object Sum observation. df: UVData Object Diff observation. Must be the same time of observation as sm. pols: List Polarizations to plot. Can include any polarization strings accepted by pyuvdata. nodes: String or List Nodes to include in matrix. Default is 'auto', which generates a list of all nodes included in the provided data files. badThresh: Float Threshold correlation metric value to use for flagging bad antennas. Returns: ------- data: Dict Dictionary containing calculated values, formatted as data[polarization][ant1,ant2]. badAnts: List List of antennas that were flagged as bad based on badThresh. """ nants = len(sm.get_ants()) data = {} antnumsAll = sorted(sm.get_ants()) badAnts = [] for p in range(len(pols)): pol = pols[p] data[pol] = np.empty((nants,nants)) for i in range(len(antnumsAll)): thisAnt = [] for j in range(len(antnumsAll)): ant1 = antnumsAll[i] ant2 = antnumsAll[j] dat = sm.get_data(ant1,ant2,pol) even = dat[::2,:] # print('Even') # print(even) odd = dat[1::2,:] # s = sm.get_data(ant1,ant2,pol) # d = df.get_data(ant1,ant2,pol) # even = (s + d)/2 even = np.divide(even,np.abs(even)) # print('Even norm') # print(even) # odd = (s - d)/2 odd = np.divide(odd,np.abs(odd)) product = np.multiply(even,np.conj(odd)) # print('product') # print(product) data[pol][i,j] = np.abs(np.nanmean(product)) thisAnt.append(np.abs(np.mean(product))) pgood = np.count_nonzero(~np.isnan(thisAnt))/len(thisAnt) if (np.nanmedian(thisAnt) < badThresh or pgood<0.2) and antnumsAll[i] not in badAnts: if pol[0]==pol[1]: #Don't assign bad ants based on cross pols badAnts.append(antnumsAll[i]) if plotRatios is True: if len(pols) == 4: data['xx-xy'] = np.subtract(data['xx'],data['xy']) data['xx-yx'] = np.subtract(data['xx'],data['yx']) data['yy-xy'] = np.subtract(data['yy'],data['xy']) data['yy-yx'] = np.subtract(data['yy'],data['yx']) else: print('Can only calculate differences if cross pols were specified') polAnts = {} badAnts = [] subs = ['xx-xy','xx-yx','yy-xy','yy-yx'] for k in subs: for i,ant in enumerate(antnumsAll): dat = data[k][i,:] if np.nanmedian(dat) < 0: if ant in polAnts.keys(): polAnts[ant] = polAnts[ant] + 1 else: polAnts[ant] = 1 if polAnts[ant] == 4: badAnts.append(ant) return data, badAnts def plotCorrMatrix(uv,data,pols=['xx','yy'],vminIn=0,vmaxIn=1,nodes='auto',logScale=False,plotRatios=False): """ Plots a matrix representing the phase correlation of each baseline. Parameters: ---------- uv: UVData Object Observation used for calculating the correlation metric data: Dict Dictionary containing the correlation metric for each baseline and each polarization. Formatted as data[polarization] [ant1,ant2] pols: List Polarizations to plot. Can include any polarization strings accepted by pyuvdata. vminIn: float Lower limit of colorbar. Default is 0. vmaxIn: float Upper limit of colorbar. Default is 1. nodes: Dict Dictionary containing the nodes (and their constituent antennas) to include in the matrix. Formatted as nodes[Node #][Ant List, Snap # List, Snap Location List]. logScale: Bool Option to put colormap on a logarithmic scale. Default is False. """ # if nodes=='auto': # nodeDict, antDict, inclNodes = generate_nodeDict(uv) nantsTotal = len(uv.get_ants()) power = np.empty((nantsTotal,nantsTotal)) fig, axs = plt.subplots(2,2,figsize=(16,16)) dirs = ['NN','EE','NE','EN'] cmap='plasma' if plotRatios is True: pols = ['xx-xy','yy-xy','xx-yx','yy-yx'] dirs=pols vminIn=-1 cmap='seismic' loc = EarthLocation.from_geocentric(*uv.telescope_location, unit='m') jd = uv.time_array[0] t = Time(jd,format='jd',location=loc) lst = round(t.sidereal_time('mean').hour,2) t.format='fits' antnumsAll = sorted(uv.get_ants()) i = 0 for p in range(len(pols)): if p >= 2: i=1 pol = pols[p] nants = len(antnumsAll) if logScale is True: im = axs[i][p%2].imshow(data[pol],cmap=cmap,origin='upper',extent=[0.5,nantsTotal+.5,0.5,nantsTotal+0.5],norm=LogNorm(vmin=vminIn, vmax=vmaxIn)) else: im = axs[i][p%2].imshow(data[pol],cmap=cmap,origin='upper',extent=[0.5,nantsTotal+.5,0.5,nantsTotal+0.5],vmin=vminIn, vmax=vmaxIn) axs[i][p%2].set_xticks(np.arange(0,nantsTotal)+1) axs[i][p%2].set_xticklabels(antnumsAll,rotation=90,fontsize=6) axs[i][p%2].xaxis.set_ticks_position('top') axs[i][p%2].set_title('polarization: ' + dirs[p] + '\n') # n=0 # n=0 # for node in sorted(inclNodes): # n += len(nodeDict[node]['ants']) # axs[0][1].text(nantsTotal+1,nantsTotal-n+len(nodeDict[node]['ants'])/2,node) # axs[1][1].text(nantsTotal+1,nantsTotal-n+len(nodeDict[node]['ants'])/2,node) # axs[0][1].text(1.05,0.4,'Node Number',rotation=270,transform=axs[0][1].transAxes) axs[0][1].set_yticklabels([]) axs[0][1].set_yticks([]) axs[0][0].set_yticks(np.arange(nantsTotal,0,-1)) axs[0][0].set_yticklabels(antnumsAll,fontsize=6) axs[0][0].set_ylabel('Antenna Number') # axs[1][1].text(1.05,0.4,'Node Number',rotation=270,transform=axs[1][1].transAxes) axs[1][1].set_yticklabels([]) axs[1][1].set_yticks([]) axs[1][0].set_yticks(np.arange(nantsTotal,0,-1)) axs[1][0].set_yticklabels(antnumsAll,fontsize=6) axs[1][0].set_ylabel('Antenna Number') cbar_ax = fig.add_axes([0.98,0.18,0.015,0.6]) cbar_ax.set_xlabel('|V|', rotation=0) cbar = fig.colorbar(im, cax=cbar_ax) fig.suptitle('Correlation Matrix - JD: %s, LST: %.0fh' % (str(jd),np.round(lst,0))) fig.subplots_adjust(top=1.28,wspace=0.05,hspace=1.1) fig.tight_layout(pad=2) plt.show() plt.close() def plot_single_matrix(uv,data,vminIn=0,vmaxIn=1,nodes='auto',logScale=False): if nodes=='auto': nodeDict, antDict, inclNodes = generate_nodeDict(uv) nantsTotal = len(uv.get_ants()) power = np.empty((nantsTotal,nantsTotal)) fig, axs = plt.subplots(1,1,figsize=(16,16)) loc = EarthLocation.from_geocentric(*uv.telescope_location, unit='m') jd = uv.time_array[0] t = Time(jd,format='jd',location=loc) lst = round(t.sidereal_time('mean').hour,2) t.format='fits' antnumsAll = sort_antennas(uv) nants = len(antnumsAll) if logScale is True: im = axs[0][0].imshow(data[pol],cmap='plasma',origin='upper', extent=[0.5,nantsTotal+.5,0.5,nantsTotal+0.5],norm=LogNorm(vmin=vminIn, vmax=vmaxIn)) else: im = axs[0][0].imshow(data[pol],cmap='plasma',origin='upper',extent= [0.5,nantsTotal+.5,0.5,nantsTotal+0.5],vmin=vminIn, vmax=vmaxIn) axs[0][0].set_xticks(np.arange(0,nantsTotal)+1) axs[0][0].set_xticklabels(antnumsAll,rotation=90,fontsize=6) axs[0][0].xaxis.set_ticks_position('top') axs[0][0].set_title('polarization: ' + dirs[p] + '\n') n=0 for node in sorted(inclNodes): n += len(nodeDict[node]['ants']) axs[0][0].axhline(len(antnumsAll)-n+.5,lw=4) axs[0][0].axvline(n+.5,lw=4) axs[0][0].text(n-len(nodeDict[node]['ants'])/2,-.5,node) axs[0][0].text(.42,-.05,'Node Number',transform=axs[0][0].transAxes) n=0 for node in sorted(inclNodes): n += len(nodeDict[node]['ants']) axs[0][0].text(nantsTotal+1,nantsTotal-n+len(nodeDict[node]['ants'])/2,node) axs[0][0].text(1.05,0.4,'Node Number',rotation=270,transform=axs[0][0].transAxes) axs[0][0].set_yticks(np.arange(nantsTotal,0,-1)) axs[0][0].set_yticklabels(antnumsAll,fontsize=6) axs[0][0].set_ylabel('Antenna Number') axs[0][0].text(1.05,0.4,'Node Number',rotation=270,transform=axs[0][0].transAxes) cbar_ax = fig.add_axes([0.98,0.18,0.015,0.6]) cbar_ax.set_xlabel('|V|', rotation=0) cbar = fig.colorbar(im, cax=cbar_ax) fig.suptitle('Correlation Matrix - JD: %s, LST: %.0fh' % (str(jd),np.round(lst,0))) fig.subplots_adjust(top=1.28,wspace=0.05,hspace=1.1) fig.tight_layout(pad=2) plt.show() plt.close() def get_hourly_files(uv, HHfiles, jd): """ Generates a list of files spaced one hour apart throughout a night of observation, and the times those files were observed. Parameters: ---------- uv: UVData Object Sample observation from the given night, used only for grabbing the telescope location HHFiles: List List of all files from the desired night of observation jd: String JD of the night of observation Returns: ------- use_files: List List of files separated by one hour use_lsts: List List of LSTs of observations in use_files """ use_lsts = [] use_files = [] use_file_inds = [] loc = EarthLocation.from_geocentric(*uv.telescope_location, unit='m') for i,file in enumerate(HHfiles): try: dat = UVData() dat.read(file, read_data=False) except KeyError: continue jd = dat.time_array[0] t = Time(jd,format='jd',location=loc) lst = round(t.sidereal_time('mean').hour,2) if np.round(lst,0) == 24: continue if np.abs((lst-np.round(lst,0)))<0.05: if len(use_lsts)>0 and np.abs(use_lsts[-1]-lst)<0.5: if np.abs((lst-np.round(lst,0))) < abs((use_lsts[-1]-np.round(lst,0))): use_lsts[-1] = lst use_files[-1] = file use_file_inds[-1] = i else: use_lsts.append(lst) use_files.append(file) use_file_inds.append(i) return use_files, use_lsts, use_file_inds def get_baseline_groups(uv, bl_groups=[(14,0,'14m E-W'),(29,0,'29m E-W'),(14,-11,'14m NW-SE'),(14,11,'14m SW-NE')], use_ants='auto'): """ Generate dictionary containing baseline groups. Parameters: ---------- uv: UVData Object Observation to extract antenna position information from bl_groups: List Desired baseline types to extract, formatted as (length (float), N-S separation (float), label (string)) Returns: -------- bls: Dict Dictionary containing list of lists of redundant baseline numbers, formatted as bls[group label] """ bls={} baseline_groups,vec_bin_centers,lengths = uv.get_redundancies(use_antpos=True,include_autos=False) for i in range(len(baseline_groups)): bl = baseline_groups[i] for group in bl_groups: if np.abs(lengths[i]-group[0])<1: ant1 = uv.baseline_to_antnums(bl[0])[0] ant2 = uv.baseline_to_antnums(bl[0])[1] if use_ants == 'auto' or (ant1 in use_ants and ant2 in use_ants): antPos1 = uv.antenna_positions[np.argwhere(uv.antenna_numbers == ant1)] antPos2 = uv.antenna_positions[np.argwhere(uv.antenna_numbers == ant2)] disp = (antPos2-antPos1)[0][0] if np.abs(disp[2]-group[1])<0.5: bls[group[2]] = bl return bls def get_correlation_baseline_evolutions(uv,HHfiles,jd,use_ants='auto',badThresh=0.35,pols=['xx','yy'],bl_type=(14,0,'14m E-W'), removeBadAnts=False, plotMatrix=True,mat_pols=['xx','yy','xy','yx'],plotRatios=False): """ Calculates the average correlation metric for a set of redundant baseline groups at one hour intervals throughout a night of observation. Parameters: ---------- uv: UVData Object Sample observation from the desired night, used only for getting telescope location information. HHfiles: List List of all files for a night of observation jd: String JD of the given night of observation badThresh: Float Threshold correlation metric value to use for flagging bad antennas. Default is 0.35. pols: List Polarizations to plot. Can include any polarization strings accepted by pyuvdata. bl_type: Tuple Redundant baseline group to calculate correlation metric for. Default is 14m E-W baselines removeBadAnts: Bool Option to exclude antennas marked as bad from calculation. Default is False. plotMatrix: Bool Option to plot the correlation matrix for observations once each hour. Default is True. Returns: ------- result: Dict Per hour correlation metric, formatted as result[baseline type]['inter' or 'intra'][polarization] lsts: List LSTs that metric was calculated for, spaced 1 hour apart. bad_antennas: List Antenna numbers flagged as bad based on badThresh parameter. """ files, lsts, inds = get_hourly_files(uv, HHfiles, jd) if use_ants == 'auto': use_ants = uv.get_ants() if plotRatios is True: files = [files[len(files)//2]] nTimes=1 else: nTimes = len(files) if nTimes > 3: plotTimes = [0,nTimes//2,nTimes-1] else: plotTimes = np.arange(0,nTimes,1) # nodeDict, antDict, inclNodes = generate_nodeDict(uv) JD = math.floor(uv.time_array[0]) bad_antennas = [] pols = mat_pols # corrSummary = generateDataTable(uv,pols=pols) result = {} for f in range(nTimes): file = files[f] ind = inds[f] sm = UVData() df = UVData() try: # print(f'Trying to read {file}') sm.read(file, skip_bad_files=True, antenna_nums=use_ants) # dffile = '%sdiff%s' % (file[0:-8],file[-5:]) # df.read(dffile, skip_bad_files=True, antenna_nums=use_ants) except: i = -5 read = False while i<5 and read==False: try: file = HHfiles[ind+i] # print(f'trying to read {file}') sm.read(file, skip_bad_files=True, antenna_nums=use_ants) # dffile = '%sdiff%s' % (file[0:-8],file[-5:]) # df.read(dffile, skip_bad_files=True, antenna_nums=use_ants) read = True except: i += 1 if read == False: print(f'WARNING: unable to read {file}') continue if f in plotTimes: # print(f) # print(mat_pols) matrix, badAnts = calcEvenOddAmpMatrix(sm,nodes='auto',pols=mat_pols,badThresh=badThresh,plotRatios=plotRatios) # print(badAnts) # print(np.shape(matrix)) # print(np.max(matrix)) # print(matrix) if plotMatrix is True and f in plotTimes: plotCorrMatrix(sm, matrix, pols=mat_pols, nodes='auto',plotRatios=plotRatios) result = None return result,lsts,bad_antennas # def generateDataTable(uv,pols=['xx','yy']): # """ # Simple helper function to generate an empty dictionary of the format desired for get_correlation_baseline_evolutions() # Parameters: # ---------- # uv: UVData Object # Sample observation to extract node and antenna information from. # pols: List # Polarizations to plot. Can include any polarization strings accepted by pyuvdata. Default is ['xx','yy']. # Returns: # ------- # dataObject: Dict # Empty dict formatted as dataObject[node #][polarization]['inter' or 'intra'] # """ # nodeDict, antDict, inclNodes = generate_nodeDict(uv) # dataObject = {} # for node in nodeDict: # dataObject[node] = {} # for pol in pols: # dataObject[node][pol] = { # 'inter' : [], # 'intra' : [] # } # return dataObject def get_baseline_type(uv,bl_type=(14,0,'14m E-W'),use_ants='auto'): """ Parameters: ---------- uv: UVData Object Sample observation to get baseline information from. bl_type: Tuple Redundant baseline group to extract baseline numbers for. Formatted as (length, N-S separation, label). Returns: ------- bl: List List of lists of redundant baseline numbers. Returns None if the provided bl_type is not found. """ baseline_groups,vec_bin_centers,lengths = uv.get_redundancies(use_antpos=True,include_autos=False) for i in range(len(baseline_groups)): bl = baseline_groups[i] if np.abs(lengths[i]-bl_type[0])<1: ant1 = uv.baseline_to_antnums(bl[0])[0] ant2 = uv.baseline_to_antnums(bl[0])[1] if (ant1 in use_ants and ant2 in use_ants) or use_ants == 'auto': antPos1 = uv.antenna_positions[np.argwhere(uv.antenna_numbers == ant1)] antPos2 = uv.antenna_positions[np.argwhere(uv.antenna_numbers == ant2)] disp = (antPos2-antPos1)[0][0] if np.abs(disp[2]-bl_type[1])<0.5: return bl return None def plot_crosses(uvd, ref_ant): ants = uvd.get_ants() freqs = (uvd.freq_array[0])*10**(-6) times = uvd.time_array lsts = uvd.lst_array Nants = len(ants) # Nside = int(np.ceil(np.sqrt(Nants)))*3 Nside = 4 Yside = int(np.ceil(float(Nants)/Nside)) t_index = 0 jd = times[t_index] utc = Time(jd, format='jd').datetime xlim = (np.min(freqs), np.max(freqs)) ylim = (60, 90) fig, axes = plt.subplots(Yside, Nside, figsize=(Yside*2, Nside*60)) fig.suptitle("JD = {0}, time = {1} UTC".format(jd, utc), fontsize=10) fig.tight_layout(rect=(0, 0, 1, 0.95)) fig.subplots_adjust(left=.1, bottom=.1, right=.9, top=.9, wspace=0.05, hspace=0.2) k = 0 for i in range(Yside): for j in range(Nside): ax = axes[i,j] ax.set_xlim(xlim) # ax.set_ylim(ylim) if k < Nants: px, = ax.plot(freqs, 10*np.log10(np.abs(np.mean(uvd.get_data((ants[k], ref_ant, 'xx')),axis=0))), color='red', alpha=0.75, linewidth=1) py, = ax.plot(freqs, 10*np.log10(np.abs(np.mean(uvd.get_data((ants[k], ref_ant, 'yy')),axis=0))), color='darkorange', alpha=0.75, linewidth=1) pxy, = ax.plot(freqs, 10*np.log10(np.abs(np.mean(uvd.get_data((ants[k], ref_ant, 'xy')),axis=0))), color='royalblue', alpha=0.75, linewidth=1) pyx, = ax.plot(freqs, 10*np.log10(np.abs(np.mean(uvd.get_data((ants[k], ref_ant, 'yx')),axis=0))), color='darkviolet', alpha=0.75, linewidth=1) ax.grid(False, which='both') ax.set_title(str(ants[k]), fontsize=14) if k == 0: ax.legend([px, py, pxy, pyx], ['XX', 'YY', 'XY','YX']) else: ax.axis('off') if j != 0: ax.set_yticklabels([]) else: [t.set_fontsize(10) for t in ax.get_yticklabels()] ax.set_ylabel(r'$10\cdot\log_{10}$ amplitude', fontsize=10) if i != Yside-1: ax.set_xticklabels([]) else: [t.set_fontsize(10) for t in ax.get_xticklabels()] ax.set_xlabel('freq (MHz)', fontsize=10) k += 1 fig.show() plt.close() def gather_source_list(): sources = [] sources.append((50.6750,-37.2083,'Fornax A')) sources.append((201.3667,-43.0192,'Cen A')) # sources.append((83.6333,22.0144,'Taurus A')) sources.append((252.7833,4.9925,'Hercules A')) sources.append((139.5250,-12.0947,'Hydra A')) sources.append((79.9583,-45.7789,'Pictor A')) sources.append((187.7042,12.3911,'Virgo A')) sources.append((83.8208,-59.3897,'Orion A')) sources.append((80.8958,-69.7561,'LMC')) sources.append((13.1875,-72.8286,'SMC')) sources.append((201.3667,-43.0192,'Cen A')) sources.append((83.6333,20.0144,'Crab Pulsar')) sources.append((128.8375,-45.1764,'Vela SNR')) cat_path = f'{DATA_PATH}/G4Jy_catalog.tsv' cat = open(cat_path) f = csv.reader(cat,delimiter='\n') for row in f: if len(row)>0 and row[0][0]=='J': s = row[0].split(';') tup = (float(s[1]),float(s[2]),'') sources.append(tup) return sources # def clean_ds(HHfiles, difffiles, bls, area=1000., tol=1e-9, skip_wgts=0.2): # uvd_ds = UVData() # uvd_ds.read(HHfiles[0], bls=bls[0], polarizations=-5, keep_all_metadata=False) # times = np.unique(uvd_ds.time_array) # Nfiles = int(1./((times[-1]-times[0])*24.)) # try: # uvd_ds.read(HHfiles[:Nfiles], bls=bls, polarizations=[-5,-6], keep_all_metadata=False) # uvd_ds.flag_array = np.zeros_like(uvd_ds.flag_array) # uvd_diff = UVData() # uvd_diff.read(difffiles[:Nfiles], bls=bls, polarizations=[-5,-6], keep_all_metadata=False) # except: # uvd_ds.read(HHfiles[len(HHfiles)//2:len(HHfiles)//2+Nfiles], bls=bls, polarizations=[-5,-6], keep_all_metadata=False) # uvd_ds.flag_array = np.zeros_like(uvd_ds.flag_array) # uvd_diff = UVData() # uvd_diff.read(difffiles[len(HHfiles)//2:len(HHfiles)//2+Nfiles], bls=bls, polarizations=[-5,-6], keep_all_metadata=False) # uvf_m, uvf_fws = hera_qm.xrfi.xrfi_h1c_pipe(uvd_ds, sig_adj=1, sig_init=3) # hera_qm.xrfi.flag_apply(uvf_m, uvd_ds) # freqs = uvd_ds.freq_array[0] # FM_idx = np.searchsorted(freqs*1e-6, [85,110]) # flag_FM = np.zeros(freqs.size, dtype=bool) # flag_FM[FM_idx[0]:FM_idx[1]] = True # win = dspec.gen_window('bh7', freqs.size) # pols = ['nn','ee'] # _data_sq_cleaned, data_rs = {}, {} # for bl in bls: # for i, pol in enumerate(pols): # key = (bl[0],bl[1],pol) # data = uvd_ds.get_data(key) # diff = uvd_diff.get_data(key) # wgts = (~uvd_ds.get_flags(key)*~flag_FM[np.newaxis,:]).astype(float) # d_even = (data+diff)*0.5 # d_odd = (data-diff)*0.5 # d_even_cl, d_even_rs, _ = dspec.high_pass_fourier_filter(d_even, wgts, area*1e-9, freqs[1]-freqs[0], # tol=tol, skip_wgt=skip_wgts, window='bh7') # d_odd_cl, d_odd_rs, _ = dspec.high_pass_fourier_filter(d_odd, wgts, area*1e-9, freqs[1]-freqs[0], # tol=tol, skip_wgt=skip_wgts, window='bh7') # idx = np.where(np.mean(np.abs(d_even_cl), axis=1) == 0)[0] # d_even_cl[idx] = np.nan # d_even_rs[idx] = np.nan # idx = np.where(np.mean(np.abs(d_odd_cl), axis=1) == 0)[0] # d_odd_cl[idx] = np.nan # d_odd_rs[idx] = np.nan # _d_even = np.fft.fftshift(np.fft.ifft((d_even_cl+d_even_rs)*win), axes=1) # _d_odd = np.fft.fftshift(np.fft.ifft((d_odd_cl+d_odd_rs)*win), axes=1) # _data_sq_cleaned[key] = _d_odd.conj()*_d_even # data_rs[key] = d_even_rs # return _data_sq_cleaned, data_rs, uvd_ds, uvd_diff # def plot_wfds(uvd, _data_sq, pol): # ants = uvd.get_ants() # freqs = uvd.freq_array[0] # times = uvd.time_array # lsts = uvd.lst_array # taus = np.fft.fftshift(np.fft.fftfreq(freqs.size, np.diff(freqs)[0])) # Nants = len(ants) # Nside = int(np.ceil(np.sqrt(Nants))) # Yside = int(np.ceil(float(Nants)/Nside)) # t_index = 0 # jd = times[t_index] # utc = Time(jd, format='jd').datetime # fig, axes = plt.subplots(Yside, Nside, figsize=(Yside*2,Nside*2)) # if pol == 'ee': # fig.suptitle("delay spectrum (auto) waterfalls from {0} -- {1} East Polarization".format(times[0], times[-1]), fontsize=14) # else: # fig.suptitle("delay spectrum (auto) waterfalls from {0} -- {1} North Polarization".format(times[0], times[-1]), fontsize=14) # fig.tight_layout(rect=(0, 0, 1, 0.95)) # fig.subplots_adjust(left=.1, bottom=.1, right=.9, top=.9, wspace=0.05, hspace=0.2) # k = 0 # for i in range(Yside): # for j in range(Nside): # ax = axes[i,j] # if k < Nants: # key = (ants[k], ants[k], pol) # ds = 10.*np.log10(np.sqrt(np.abs(_data_sq[key])/np.abs(_data_sq[key]).max(axis=1)[:,np.newaxis])) # im = ax.imshow(ds, aspect='auto', rasterized=True, # interpolation='nearest', vmin = -50, vmax = -30, # extent=[taus[0]*1e9, taus[-1]*1e9, np.max(lsts), np.min(lsts)]) # ax.set_title(str(ants[k]), fontsize=10) # else: # ax.axis('off') # if j != 0: # ax.set_yticklabels([]) # else: # [t.set_fontsize(12) for t in ax.get_yticklabels()] # ax.set_ylabel('Time(LST)', fontsize=10) # if i != Yside-1: # ax.set_xticklabels([]) # else: # [t.set_fontsize(10) for t in ax.get_xticklabels()] # [t.set_rotation(25) for t in ax.get_xticklabels()] # ax.xaxis.set_major_formatter(FormatStrFormatter('%.3f')) # ax.set_xlabel('Delay (ns)', fontsize=10) # k += 1 # cbar_ax=fig.add_axes([0.95,0.15,0.02,0.7]) # cb = fig.colorbar(im, cax=cbar_ax) # cb.set_label('dB') # fig.show() # def plot_ds(uvd, uvd_diff, _data_sq_cleaned, data_rs, skip_wgts=0.2): # matplotlib.rcParams.update({'font.size': 8}) # freqs = uvd.freq_array[0] # taus = np.fft.fftshift(np.fft.fftfreq(freqs.size, np.diff(freqs)[0])) # FM_idx = np.searchsorted(freqs*1e-6, [85,110]) # flag_FM = np.zeros(freqs.size, dtype=bool) # flag_FM[FM_idx[0]:FM_idx[1]] = True # ants = np.sort(uvd.get_ants()) # pols = ['nn','ee'] # colors = ['b','r'] # nodes, antDict, inclNodes = generate_nodeDict(uvd) # for i, ant in enumerate(ants): # i2 = i % 2 # if(i2 == 0): # fig = plt.figure(figsize=(10, 3), dpi=110) # grid = plt.GridSpec(5, 4, wspace=0.55, hspace=2) # for j, pol in enumerate(pols): # key = (ant,ant,pol) # ax = fig.add_subplot(grid[:5, 0+i2*2]) # ds_ave = np.sqrt(np.abs(np.nanmean(_data_sq_cleaned[key], axis=0))) # _diff = np.fft.fftshift(np.fft.ifft(uvd_diff.get_data(key)), axes=1) # ns_ave = np.abs(np.nanmean(_diff, axis=0)) # ns_ave_ = np.sqrt(np.abs(np.mean(_diff.conj()*_diff, axis=0))/(2*_diff.shape[0])) # norm = np.max(ds_ave) # plt.plot(taus*1e9, 10*np.log10(ds_ave/norm), color=colors[j], label=pols[j], linewidth=0.7) # plt.plot(taus*1e9, 10*np.log10(ns_ave/norm), color=colors[j], alpha=0.5, linewidth=0.5) # plt.plot(taus*1e9, 10*np.log10(ns_ave_/norm), color=colors[j], ls='--', linewidth=0.5) # plt.axvspan(250, 500, alpha=0.2, facecolor='y') # plt.axvspan(2500, 3000, alpha=0.2, facecolor='g') # plt.axvspan(3500, 4000, alpha=0.2, facecolor='m') # plt.legend(loc='upper right', bbox_to_anchor=(1.0, 0.95), frameon=False) # plt.xlim(0,4500) # plt.ylim(-60,0) # plt.title('ant {}'.format(ant)) # plt.xlabel(r'$\tau$ (ns)') # plt.ylabel(r'$|\tilde{V}(\tau)|$ in dB') # for yl in range(-50,0,10): # plt.axhline(y=yl, color='k', lw=0.5, alpha=0.1) # if(j == 0): # plt.annotate('node {} (snap {})'.format(antDict[ant]['node'],antDict[ant]['snapLocs'][0]), xy=(0.04,0.930), xycoords='axes fraction') # ax2 = fig.add_subplot(grid[:3, 1+i2*2]) # auto = np.abs(uvd.get_data(key)) # auto_ave = np.mean(auto/np.median(auto, axis=1)[:,np.newaxis], axis=0) # wgts = (~uvd.get_flags(key)*~flag_FM[np.newaxis,:]).astype(float) # wgts = np.where(wgts > skip_wgts, wgts, 0) # auto_flagged = auto/wgts # auto_flagged[np.isinf(auto_flagged)] = np.nan # auto_flagged_ave = np.nanmean(auto_flagged/np.nanmedian(auto_flagged, axis=1)[:,np.newaxis], axis=0) # plt.plot(freqs/1e6, np.log10(auto_ave), linewidth=1.0, color=colors[j], label='autocorrelation') # plt.plot(freqs/1e6, np.log10(auto_flagged_ave*0.7), linewidth=1.0, color=colors[j], alpha=0.5) # if(j == 0): # plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.18), frameon=False, handlelength=0, handletextpad=0) # plt.xlim(freqs.min()/1e6, freqs.max()/1e6) # plt.ylabel(r'log$_{10}(|V(\nu)|)$') # plt.ylim(-1, 0.7) # ax3 = fig.add_subplot(grid[3:5, 1+i2*2]) # data_rs_ave = np.nanmean(data_rs[key]/np.nanmedian(auto_flagged, axis=1)[:,np.newaxis], axis=0) # plt.plot(freqs/1e6, np.log10(np.abs(data_rs_ave)), linewidth=1.0, color=colors[j], label='clean residual') # if(j == 0): # plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.31), frameon=False, handlelength=0, handletextpad=0) # plt.xlim(freqs.min()/1e6, freqs.max()/1e6) # plt.ylim(-5,0) # plt.xlabel(r'$\nu$ (MHz)') # plt.ylabel(r'log$_{10}(|V(\nu)|)$') # matplotlib.rcParams.update({'font.size': 10}) # def plot_ds_diagnosis(uvd_diff, _data_sq_cleaned): # freqs = uvd_diff.freq_array[0] # taus = np.fft.fftshift(np.fft.fftfreq(freqs.size, np.diff(freqs)[0])) # ants = np.sort(uvd_diff.get_ants()) # pols = ['nn','ee'] # colors = ['b','r'] # nodes, antDict, inclNodes = generate_nodeDict(uvd_diff) # marker = ['o', 'x', 'P', 's', 'd', 'p', '<', 'h', '+', '>', 'X', '*'] # nodes = [] # ds_ave_250_500_nn, ds_ave_250_500_ee = [], [] # ds_peak_2500_3000_nn, ds_peak_2500_3000_ee = [], [] # ds_ratio_3500_4000_nn, ds_ratio_3500_4000_ee = [], [] # for ant in ants: # nodes.append(int(antDict[ant]['node'])) # for pol in pols: # key = (ant,ant,pol) # ds_ave = np.sqrt(np.abs(np.nanmean(_data_sq_cleaned[key], axis=0))) # _diff = np.fft.fftshift(np.fft.ifft(uvd_diff.get_data(key)), axes=1) # ns_ave = np.sqrt(np.abs(np.mean(_diff.conj()*_diff, axis=0))/(2*_diff.shape[0])) # norm = np.max(ds_ave) # idx_region1 = (np.abs(taus)*1e9 > 250) * (np.abs(taus)*1e9 < 500) # idx_region2 = (np.abs(taus)*1e9 > 2500) * (np.abs(taus)*1e9 < 3000) # idx_region_out = (np.abs(taus)*1e9 > 3000) * (np.abs(taus)*1e9 < 3200) # idx_region3 = (np.abs(taus)*1e9 > 3500) * (np.abs(taus)*1e9 < 4000) # if(pol == 'nn'): # ds_ave_250_500_nn.append(np.nanmean(ds_ave[idx_region1]/norm)) # ds_peak_2500_3000_nn.append(np.std(ds_ave[idx_region2])/np.std(ds_ave[idx_region_out])) # ds_ratio_3500_4000_nn.append(np.nanmean(ds_ave[idx_region3])/np.mean(ns_ave[idx_region3])) # else: # ds_ave_250_500_ee.append(np.nanmean(ds_ave[idx_region1]/norm)) # ds_peak_2500_3000_ee.append(np.std(ds_ave[idx_region2])/np.std(ds_ave[idx_region_out])) # ds_ratio_3500_4000_ee.append(np.nanmean(ds_ave[idx_region3])/np.mean(ns_ave[idx_region3])) # nodes = np.array(nodes) # N_nodes = nodes.size # ds_ave_250_500_nn = np.array(ds_ave_250_500_nn) # ds_ave_250_500_ee = np.array(ds_ave_250_500_ee) # ds_peak_2500_3000_nn = np.array(ds_peak_2500_3000_nn) # ds_peak_2500_3000_ee = np.array(ds_peak_2500_3000_ee) # ds_ratio_3500_4000_nn = np.array(ds_ratio_3500_4000_nn) # ds_ratio_3500_4000_ee = np.array(ds_ratio_3500_4000_ee) # plt.figure(figsize=(15,15)) # plt.subplot(3,1,1) # node_number = np.unique(nodes) # for i, node in enumerate(node_number): # idx_ant = np.where(nodes == node)[0] # plt.plot(ants[idx_ant], 10*np.log10(ds_ave_250_500_nn[idx_ant]), 'bo', label='nn', marker=marker[i%N_nodes]) # plt.plot(ants[idx_ant], 10*np.log10(ds_ave_250_500_ee[idx_ant]), 'ro', label='ee', marker=marker[i%N_nodes]) # if(i == 0): # plt.legend() # for i, ant in enumerate(ants): # plt.annotate('{}'.format(ant), xy=(ants[i], 10*np.log10(ds_ave_250_500_nn[i]))) # plt.xlabel('Antenna number') # plt.ylabel('dB (relative to the peak)') # plt.title('Averaged delay spectrum at 250-500 ns') # plt.subplot(3,1,2) # for i, node in enumerate(node_number): # idx_ant = np.where(nodes == node)[0] # plt.plot(ants[idx_ant], ds_peak_2500_3000_nn[idx_ant], 'bo', label='nn', marker=marker[i%N_nodes]) # plt.plot(ants[idx_ant], ds_peak_2500_3000_ee[idx_ant], 'ro', label='ee', marker=marker[i%N_nodes]) # if(i == 0): # plt.legend() # for i, ant in enumerate(ants): # plt.annotate('{}'.format(ant), xy=(ants[i], ds_peak_2500_3000_nn[i])) # plt.axhline(y=1, ls='--', color='k') # plt.xlabel('Antenna number') # plt.ylabel('$\sigma_{2500-3000}/\sigma_{3000-3200}$') # plt.title('Standard deviation ratio between 2500-3000 ns and 3000-3200 ns') # plt.subplot(3,1,3) # for i, node in enumerate(node_number): # idx_ant = np.where(nodes == node)[0] # plt.plot(ants[idx_ant], 10*np.log10(ds_ratio_3500_4000_nn[idx_ant]), 'bo', label='nn', marker=marker[i%N_nodes]) # plt.plot(ants[idx_ant], 10*np.log10(ds_ratio_3500_4000_ee[idx_ant]), 'ro', label='ee', marker=marker[i%N_nodes]) # if(i == 0): # plt.legend() # for i, ant in enumerate(ants): # plt.annotate('{}'.format(ant), xy=(ants[i], 10*np.log10(ds_ratio_3500_4000_nn[i]))) # plt.axhline(y=0, ls='--', color='k') # plt.xlabel('Antenna number') # plt.ylabel('Deviation from the noise level in dB') # plt.title('Deviation of averaged delay spectrum at 3500-4000 ns from the noise level') # plt.subplots_adjust(hspace=0.4) # def plot_ds_nodes(uvd_diff, _data_sq_cleaned): # freqs = uvd_diff.freq_array[0] # taus = np.fft.fftshift(np.fft.fftfreq(freqs.size, np.diff(freqs)[0])) # ants = np.sort(uvd_diff.get_ants()) # pols = ['nn','ee'] # colors = ['b','r'] # nodes, antDict, inclNodes = generate_nodeDict(uvd_diff) # nodes = [] # ds_ave_nn, ns_ave_nn = [], [] # ds_ave_ee, ns_ave_ee = [], [] # for ant in ants: # nodes.append(int(antDict[ant]['node'])) # for pol in pols: # key = (ant,ant,pol) # ds_ave = np.sqrt(np.abs(np.nanmean(_data_sq_cleaned[key], axis=0))) # _diff = np.fft.fftshift(np.fft.ifft(uvd_diff.get_data(key)), axes=1) # ns_ave = np.abs(np.nanmean(_diff, axis=0)) # norm = np.max(ds_ave) # if(pol == 'nn'): # ds_ave_nn.append(ds_ave/norm) # ns_ave_nn.append(ns_ave/norm) # else: # ds_ave_ee.append(ds_ave/norm) # ns_ave_ee.append(ns_ave/norm) # nodes = np.array(nodes) # ds_ave_nn = np.array(ds_ave_nn).reshape(ants.size,taus.size) # ns_ave_nn = np.array(ns_ave_nn).reshape(ants.size,taus.size) # ds_ave_ee = np.array(ds_ave_ee).reshape(ants.size,taus.size) # ns_ave_ee = np.array(ns_ave_ee).reshape(ants.size,taus.size) # node_number = np.unique(nodes) # for i, node in enumerate(node_number): # if(i % 2 == 0): # plt.figure(figsize=(16,4)) # for j, pol in enumerate(pols): # plt.subplot(1,4,2*(i%2)+j+1) # idx_ant = np.where(nodes == node)[0] # if(pol == 'nn'): # for idx in idx_ant: # plt.plot(taus*1e9, 10*np.log10(ds_ave_nn[idx]), label='({},{})'.format(ants[idx],ants[idx]), linewidth=0.8) # else: # for idx in idx_ant: # plt.plot(taus*1e9, 10*np.log10(ds_ave_ee[idx]), label='({},{})'.format(ants[idx],ants[idx]), linewidth=0.8) # plt.xlim(0,4500) # plt.ylim(-55,0) # plt.title('node {}, {}'.format(node, pol)) # plt.xlabel(r'$\tau$ (ns)') # if(i % 2 == 0 and j == 0): # plt.ylabel(r'$|\tilde{V}(\tau)|$ in dB') # plt.grid(axis='y') # plt.legend(loc='upper left', ncol=2) # def plot_wfds_cr(uvd, _data_sq, pol): # bls = uvd.get_antpairs() # freqs = uvd.freq_array[0] # times = uvd.time_array # lsts = uvd.lst_array # taus = np.fft.fftshift(np.fft.fftfreq(freqs.size, np.diff(freqs)[0])) # idx_mid = np.where(taus == 0)[0] # Nants = len(uvd.get_antpairs()) # Nside = int(np.ceil(np.sqrt(Nants))) # Yside = int(np.ceil(float(Nants)/Nside)) # t_index = 0 # jd = times[t_index] # utc = Time(jd, format='jd').datetime # fig, axes = plt.subplots(Yside, Nside, figsize=(Yside*2,Nside*2), dpi=75) # if pol == 'ee': # fig.suptitle("delay spectrum (cross) waterfalls from {0} -- {1} East Polarization".format(times[0], times[-1]), fontsize=14) # else: # fig.suptitle("delay spectrum (cross) waterfalls from {0} -- {1} North Polarization".format(times[0], times[-1]), fontsize=14) # fig.tight_layout(rect=(0, 0, 1, 0.95)) # fig.subplots_adjust(left=.1, bottom=.1, right=.9, top=.9, wspace=0.05, hspace=0.2) # k = 0 # for i in range(Yside): # for j in range(Nside): # ax = axes[i,j] # if k < Nants: # key = (bls[k][0], bls[k][1], pol) # ds = 10.*np.log10(np.sqrt(np.abs(_data_sq[key])/np.abs(_data_sq[key][:,idx_mid]))) # im = ax.imshow(ds, aspect='auto', rasterized=True, # interpolation='nearest', vmin = -30, vmax = 0, # extent=[taus[0]*1e9, taus[-1]*1e9, np.max(lsts), np.min(lsts)]) # ax.set_title(str(bls[k]), fontsize=10) # else: # ax.axis('off') # if j != 0: # ax.set_yticklabels([]) # else: # [t.set_fontsize(12) for t in ax.get_yticklabels()] # ax.set_ylabel('Time(LST)', fontsize=10) # if i != Yside-1: # ax.set_xticklabels([]) # else: # [t.set_fontsize(10) for t in ax.get_xticklabels()] # [t.set_rotation(25) for t in ax.get_xticklabels()] # ax.xaxis.set_major_formatter(FormatStrFormatter('%.3f')) # ax.set_xlabel('Delay (ns)', fontsize=10) # k += 1 # cbar_ax=fig.add_axes([0.95,0.15,0.02,0.7]) # cb = fig.colorbar(im, cax=cbar_ax) # cb.set_label('dB') # fig.show() # def plot_metric(metrics, ants=None, antpols=None, title='', ylabel='Modified z-Score', xlabel=''): # '''Helper function for quickly plotting an individual antenna metric.''' # if ants is None: # ants = list(set([key[0] for key in metrics.keys()])) # if antpols is None: # antpols = list(set([key[1] for key in metrics.keys()])) # for antpol in antpols: # for i,ant in enumerate(ants): # metric = 0 # if (ant,antpol) in metrics: # metric = metrics[(ant,antpol)] # plt.plot(i,metric,'.') # plt.annotate(str(ant)+antpol,xy=(i,metrics[(ant,antpol)])) # plt.gca().set_prop_cycle(None) # plt.title(title) # plt.ylabel(ylabel) # plt.xlabel(xlabel) # def show_metric(ant_metrics, antmetfiles, ants=None, antpols=None, title='', ylabel='Modified z-Score', xlabel=''): # print("Ant Metrics for {}".format(antmetfiles[1])) # plt.figure() # plot_metric(ant_metrics['final_mod_z_scores']['meanVij'], # title = 'Mean Vij Modified z-Score') # plt.figure() # plot_metric(ant_metrics['final_mod_z_scores']['redCorr'], # title = 'Redundant Visibility Correlation Modified z-Score') # plt.figure() # plot_metric(ant_metrics['final_mod_z_scores']['meanVijXPol'], antpols=['n'], # title = 'Modified z-score of (Vxy+Vyx)/(Vxx+Vyy)') # plt.figure() # plot_metric(ant_metrics['final_mod_z_scores']['redCorrXPol'], antpols=['n'], # title = 'Modified z-Score of Power Correlation Ratio Cross/Same') # plt.figure() # plot_metric(ant_metrics['final_mod_z_scores']['redCorrXPol'], antpols=['e'], # title = 'Modified z-Score of Power Correlation Ratio Cross/Same') # def all_ant_mets(antmetfiles,HHfiles): # file = HHfiles[0] # uvd_hh = UVData() # uvd_hh.read_uvh5(file) # uvdx = uvd_hh.select(polarizations = -5, inplace = False) # uvdx.ants = np.unique(np.concatenate([uvdx.ant_1_array, uvdx.ant_2_array])) # ants = uvdx.get_ants() # times = uvd_hh.time_array # Nants = len(ants) # jd_start = np.floor(times.min()) # antfinfiles = [] # for i,file in enumerate(antmetfiles): # if i%50==0: # antfinfiles.append(antmetfiles[i]) # Nfiles = len(antfinfiles) # Nfiles2 = len(antmetfiles) # xants = np.zeros((Nants*2, Nfiles2)) # dead_ants = np.zeros((Nants*2, Nfiles2)) # cross_ants = np.zeros((Nants*2, Nfiles2)) # badants = [] # pol2ind = {'n':0, 'e':1} # times = [] # for i,file in enumerate(antfinfiles): # time = file[54:60] # times.append(time) # for i,file in enumerate(antmetfiles): # antmets = hera_qm.ant_metrics.load_antenna_metrics(file) # for j in antmets['xants']: # xants[2*np.where(ants==j[0])[0]+pol2ind[j[1]], i] = 1 # badants.extend(map(lambda x: x[0], antmets['xants'])) # for j in antmets['crossed_ants']: # cross_ants[2*np.where(ants==j[0])[0]+pol2ind[j[1]], i] = 1 # for j in antmets['dead_ants']: # dead_ants[2*np.where(ants==j[0])[0]+pol2ind[j[1]], i] = 1 # badants = np.unique(badants) # xants[np.where(xants==1)] *= np.nan # dead_ants[np.where(dead_ants==0)] *= np.nan # cross_ants[np.where(cross_ants==0)] *= np.nan # antslabels = [] # for i in ants: # labeln = str(i) + 'n' # labele = str(i) + 'e' # antslabels.append(labeln) # antslabels.append(labele) # fig, ax = plt.subplots(1, figsize=(16,20)) # # plotting # ax.matshow(xants, aspect='auto', cmap='RdYlGn_r', vmin=-.3, vmax=1.3, # extent=[0, len(times), Nants*2, 0]) # ax.matshow(dead_ants, aspect='auto', cmap='RdYlGn_r', vmin=-.3, vmax=1.3, # extent=[0, len(times), Nants*2, 0]) # ax.matshow(cross_ants, aspect='auto', cmap='RdBu', vmin=-.3, vmax=1.3, # extent=[0, len(times), Nants*2, 0]) # # axes # ax.grid(color='k') # ax.xaxis.set_ticks_position('bottom') # ax.set_xticks(np.arange(len(times))+0.5) # ax.set_yticks(np.arange(Nants*2)+0.5) # ax.tick_params(size=8) # if Nfiles > 20: # ticklabels = times # ax.set_xticklabels(ticklabels) # else: # ax.set_xticklabels(times) # ax.set_yticklabels(antslabels) # [t.set_rotation(30) for t in ax.get_xticklabels()] # [t.set_size(12) for t in ax.get_xticklabels()] # [t.set_rotation(0) for t in ax.get_yticklabels()] # [t.set_size(12) for t in ax.get_yticklabels()] # ax.set_title("Ant Metrics bad ants over observation", fontsize=14) # ax.set_xlabel('decimal of JD = {}'.format(int(jd_start)), fontsize=16) # ax.set_ylabel('antenna number and pol', fontsize=16) # red_ptch = mpatches.Patch(color='red') # grn_ptch = mpatches.Patch(color='green') # blu_ptch = mpatches.Patch(color='blue') # ax.legend([red_ptch, blu_ptch, grn_ptch], ['dead ant', 'cross ant', 'good ant'], fontsize=14)

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my_drawing/my_drawing.py

steven3614/MystanCodeprojects

0

149831

<filename>my_drawing/my_drawing.py<gh_stars>0 """ File: my_drawing Name: 張文銓 ---------------------- TODO: This function is my drawing about the famous show "squid game" including the killer and the invitation card in the story. """ from campy.graphics.gobjects import GOval, GRect, GLabel from campy.graphics.gwindow import GWindow def main(): """ TODO: I drew different items and named them by numbers because there are too many items. the whole picture is about the famous show squid game. """ window = GWindow(width=800, height=790, title='Squid Game') # this is the window of the picture one = GOval(230, 300, x=30, y=130) one.filled = True one.fill_color = 'palevioletred' one.color = 'palevioletred' window.add(one) two = GOval(230, 300, x=280, y=100) two.filled = True two.fill_color = 'palevioletred' two.color = 'palevioletred' window.add(two) three = GOval(230, 300, x=530, y=130) three.filled = True three.fill_color = 'palevioletred' three.color = 'palevioletred' window.add(three) four = GOval(210, 280, x=40, y=140) four.filled = True four.fill_color = 'black' four.color = 'black' window.add(four) five = GOval(210, 280, x=290, y=110) five.filled = True five.fill_color = 'black' five.color = 'black' window.add(five) six = GOval(210, 280, x=540, y=140) six.filled = True six.fill_color = 'black' six.color = 'black' window.add(six) # one to six are the items for the mask of the killer seven = GOval(150, 150, x=72.5, y=205) seven.color = 'white' seven.filled = True seven.fill_color = 'white' window.add(seven) eight = GOval(125, 125, x=85, y=217.5) eight.filled = True eight.fill_color = 'black' eight.color = 'black' window.add(eight) nine = GRect(150, 150, x=320, y=185) nine.filled = True nine.fill_color = 'white' nine.color = 'white' window.add(nine) ten = GRect(125, 125, x=332.5, y=197.5) ten.filled = True ten.fill_color = 'black' ten.color = 'black' window.add(ten) eleven = GLabel('^', x=585, y=490) eleven.color = 'white' eleven.font = '-300' window.add(eleven) twelve = GLabel('-', x=580, y=460) twelve.color = 'white' twelve.font = '-300' window.add(twelve) # seven to twelve are the items on the mask(square, circle and triangle) thirteen = GRect(400, 250, x=200, y=490) thirteen.filled = True thirteen.fill_color = 'khaki' window.add(thirteen) # this is the item for the invitation card fourteen = GLabel('WELCOME TO THE SQUID GAME!', x=210, y=525) fourteen.color = 'red' fourteen.font = '-25' window.add(fourteen) fifteen = GOval(90, 90, x=225, y=570) fifteen.filled = True fifteen.fill_color = 'black' fifteen.color = 'black' window.add(fifteen) sixteen = GRect(90, 90, x=350, y=570) sixteen.filled = True sixteen.fill_color = 'black' sixteen.color = 'black' window.add(sixteen) seventeen = GLabel('^', x=465, y=840) seventeen.font = "-300" seventeen.color = "black" window.add(seventeen) eighteen = GLabel('-', x=460, y=805) eighteen.font = "-300" eighteen.color = "black" window.add(eighteen) nineteen = GOval(70, 70, x=235, y=580) nineteen.filled = True nineteen.fill_color = 'khaki' nineteen.color = 'khaki' window.add(nineteen) twenty = GRect(70, 70, x=360, y=580) twenty.filled = True twenty.fill_color = 'khaki' twenty.color = 'khaki' window.add(twenty) # the rest of the items consist the image on the invitation card if __name__ == '__main__': main()

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src/predectorutils/analyses/localizer.py

ccdmb/predector-utils

0

71913

<filename>src/predectorutils/analyses/localizer.py #!/usr/bin/env python3 import re from typing import Optional from typing import TextIO from typing import Iterator from typing import Tuple from predectorutils.higher import fmap from predectorutils.gff import GFFRecord, GFFAttributes, Strand from predectorutils.analyses.base import Analysis, GFFAble from predectorutils.analyses.base import ( int_or_none, float_or_none, str_or_none ) from predectorutils.parsers import ( FieldParseError, LineParseError, parse_field, raise_it, parse_str, parse_regex ) # This matches strings of the form "Y (0.962| 25-49)" TP_REGEX = re.compile( r"^Y $(?P<prob>\d?\.?\d+)\s*\|\s*(?P<start>\d+)-(?P<end>\d+)$$") NUC_REGEX = re.compile(r"^Y $(?P<sigs>.+)$$") HEADER_REGEX = re.compile( r"^Identifier\s+Chloroplast\s+Mitochondria\s+Nucleus$") def parse_tp_field( field: str, field_name: str, ) -> Tuple[bool, Optional[float], Optional[int], Optional[int]]: field = field.strip() if field == "-": return (False, None, None, None) res = parse_field(parse_regex(TP_REGEX), field_name)(field) if isinstance(res, FieldParseError): raise res # This should be safe because we know the regex matched. return (True, float(res["prob"]), int(res["start"]), int(res["end"])) def parse_nuc_field( field: str, ) -> Tuple[bool, Optional[str]]: field = field.strip() if field == "-": return (False, None) res = parse_field(parse_regex(NUC_REGEX), "nucleus")(field) if isinstance(res, FieldParseError): raise res # This should be safe because we know the regex matched. return (True, res["sigs"]) class LOCALIZER(Analysis, GFFAble): """ """ columns = ["name", "chloroplast_decision", "chloroplast_prob", "chloroplast_start", "chloroplast_end", "mitochondria_decision", "mitochondria_prob", "mitochondria_start", "mitochondria_end", "nucleus_decision", "nucleus_signals"] types = [str, bool, float_or_none, int_or_none, int_or_none, bool, float_or_none, int_or_none, int_or_none, bool, str_or_none] analysis = "localizer" software = "LOCALIZER" def __init__( self, name: str, chloroplast_decision: bool, chloroplast_prob: Optional[float], chloroplast_start: Optional[int], chloroplast_end: Optional[int], mitochondria_decision: bool, mitochondria_prob: Optional[float], mitochondria_start: Optional[int], mitochondria_end: Optional[int], nucleus_decision: bool, nucleus_signals: Optional[str], ) -> None: self.name = name self.chloroplast_decision = chloroplast_decision self.chloroplast_prob = chloroplast_prob self.chloroplast_start = chloroplast_start self.chloroplast_end = chloroplast_end self.mitochondria_decision = mitochondria_decision self.mitochondria_prob = mitochondria_prob self.mitochondria_start = mitochondria_start self.mitochondria_end = mitochondria_end self.nucleus_decision = nucleus_decision self.nucleus_signals = nucleus_signals return @classmethod def from_line(cls, line: str) -> "LOCALIZER": """ Parse an ApoplastP line as an object. """ if line == "": raise LineParseError("The line was empty.") sline = [c.strip() for c in line.strip().split("\t")] if len(sline) != 4: raise LineParseError( "The line had the wrong number of columns. " f"Expected 4 but got {len(sline)}" ) (cp, cp_prob, cp_start, cp_end) = parse_tp_field( sline[1], "chloroplast" ) (mt, mt_prob, mt_start, mt_end) = parse_tp_field( sline[2], "mitochondria" ) (nuc, nuc_sigs) = parse_nuc_field(sline[3]) return cls( raise_it(parse_field(parse_str, "name"))(sline[0]), cp, cp_prob, fmap(lambda x: x - 1 + 20, cp_start), cp_end, mt, mt_prob, fmap(lambda x: x - 1 + 20, mt_start), mt_end, nuc, nuc_sigs ) @classmethod def from_file(cls, handle: TextIO) -> Iterator["LOCALIZER"]: for i, line in enumerate(handle): sline = line.strip() if sline.startswith("#"): continue elif sline == "": continue elif HEADER_REGEX.match(sline) is not None: continue try: yield cls.from_line(sline) except (FieldParseError, LineParseError) as e: raise e.as_parse_error(line=i).add_filename_from_handle(handle) return def as_gff( self, software_version: Optional[str] = None, database_version: Optional[str] = None, keep_all: bool = True, id_index: int = 1, ) -> Iterator[GFFRecord]: source = self.gen_source(software_version, database_version) if self.chloroplast_decision: assert self.chloroplast_start is not None assert self.chloroplast_end is not None attr = GFFAttributes( note=["Putative internal chloroplast localization peptide"], custom={ "prob": str(self.chloroplast_prob), } ) yield GFFRecord( seqid=self.name, source=source, type="peptide_localization_signal", start=self.chloroplast_start, end=self.chloroplast_end, score=self.chloroplast_prob, strand=Strand.PLUS, attributes=attr ) if self.mitochondria_decision: assert self.mitochondria_start is not None assert self.mitochondria_end is not None attr = GFFAttributes( note=["Putative internal mitochondrial localization peptide"], custom={ "prob": str(self.mitochondria_prob), } ) yield GFFRecord( seqid=self.name, source=source, type="mitochondrial_targeting_signal", start=self.mitochondria_start, end=self.mitochondria_end, score=self.mitochondria_prob, strand=Strand.UNSTRANDED, attributes=attr ) return

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tests/deprecated/fastplt.py

davtoh/RRTools

1

151114

# -*- coding: utf-8 -*- from __future__ import division from __future__ import print_function import matplotlib.pyplot as plt import matplotlib.axes import matplotlib.figure from multiprocessing import Process from RRtoolbox.lib.config import FLAG_DEBUG wins = [0] # keeps track of image number through different processes def fastplt(image, cmap = None, title ="visualazor", win = None, block = False, daemon = False): """ Fast plot. :param image: image to show :param cmap: "gray" or None :param title: title of subplot :param win: title of window :param block: if True it wait for window close, else it detaches :param daemon: if True window closes if main thread ends, else windows must be closed to main thread to end :return: plt """ if FLAG_DEBUG: print("fastplt received image type: ",type(image)) def myplot(): if isinstance(image, matplotlib.axes.SubplotBase): f = image.figure elif isinstance(image, matplotlib.figure.Figure): f = image else: f = plt.figure() # Normally this will always be "Figure 1" since it's the first # figure created by this process. So do something about it. plt.imshow(image,cmap) if title: plt.title(title) plt.xticks([]), plt.yticks([]) #plt.colorbar() wins[0]+=1 if win: f.canvas.set_window_title(win) else:f.canvas.set_window_title("Figure {}".format(wins[-1])) if FLAG_DEBUG: print("showing now...") #plt.ion() plt.show() if FLAG_DEBUG: print("showed...") if block: myplot() else: if FLAG_DEBUG: print("multiprocessing...") p = Process(target=myplot) # FIXME i shoud call a miniprogram p.daemon = daemon p.start() if FLAG_DEBUG: print("left fastplt...") if __name__ == "__main__": import argparse from RRtoolbox.lib.serverServices import parseString as _parseString #import sys #if FLAG_DEBUG: print sys.argv parser = argparse.ArgumentParser(description='fast plot of images.') parser.add_argument('image', metavar='N', #action='append', help='path to image or numpy string',nargs="+") parser.add_argument('-m','--cmap', dest='cmap', action='store', help='map to use in matplotlib') parser.add_argument('-t','--title', dest='title', action='store',default="visualazor", help='title of subplot') parser.add_argument('-w','--win', dest='win', action='store', help='title of window') parser.add_argument('-n','--num', dest='num', action='store',type = int, default=0, help='number of Figure') parser.add_argument('-b','--block', dest='block', action='store_true', default=False, help='number of Figure') parser.add_argument('-d','--daemon', dest='daemon', action='store_true', default=False, help='number of Figure') v = vars(parser.parse_args()) images = _parseString(v.pop("image")) wins[-1] = v.pop("num") if FLAG_DEBUG: print("properties:", v) for image in images: fastplt(image,**v) if FLAG_DEBUG: print("leaving fastplt module...")

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website/migrations/0002_auto_20210205_1929.py

TAS-7/KDY-SITE

1

123943

# Generated by Django 3.1.2 on 2021-02-05 19:29 from django.conf import settings from django.db import migrations, models import django.db.models.deletion import django.utils.timezone class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('website', '0001_initial'), ] operations = [ migrations.AlterField( model_name='post', name='date_posted', field=models.DateTimeField(default=django.utils.timezone.now), ), migrations.CreateModel( name='Comment', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('answer', models.TextField()), ('created_date', models.DateTimeField(default=django.utils.timezone.now)), ('approved_comment', models.BooleanField(default=False)), ('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ('question', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='comments', to='website.post')), ], ), ]

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mf2cCommsLib/mf2cComms/channel/msg/__init__.py

cketley/acsl

0

86512

<gh_stars>0 from mf2cComms.channel.msg import message

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pytorch/.circleci/cimodel/data/pytorch_build_definitions.py

raghavnauhria/whatmt

0

85804

#!/usr/bin/env python3 from collections import OrderedDict from cimodel.data.pytorch_build_data import TopLevelNode, CONFIG_TREE_DATA import cimodel.data.dimensions as dimensions import cimodel.lib.conf_tree as conf_tree import cimodel.lib.miniutils as miniutils import cimodel.lib.visualization as visualization from dataclasses import dataclass, field from typing import List, Optional DOCKER_IMAGE_PATH_BASE = "308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/" DOCKER_IMAGE_VERSION = 300 @dataclass class Conf: distro: str parms: List[str] pyver: Optional[str] = None cuda_version: Optional[str] = None # TODO expand this to cover all the USE_* that we want to test for # tesnrorrt, leveldb, lmdb, redis, opencv, mkldnn, ideep, etc. # (from https://github.com/pytorch/pytorch/pull/17323#discussion_r259453608) is_xla: bool = False restrict_phases: Optional[List[str]] = None gpu_resource: Optional[str] = None dependent_tests: List = field(default_factory=list) parent_build: Optional['Conf'] = None is_namedtensor: bool = False is_important: bool = False # TODO: Eliminate the special casing for docker paths # In the short term, we *will* need to support special casing as docker images are merged for caffe2 and pytorch def get_parms(self, for_docker): leading = [] # We just don't run non-important jobs on pull requests; # previously we also named them in a way to make it obvious # if self.is_important and not for_docker: # leading.append("AAA") leading.append("pytorch") if self.is_xla and not for_docker: leading.append("xla") if self.is_namedtensor and not for_docker: leading.append("namedtensor") cuda_parms = [] if self.cuda_version: cuda_parms.extend(["cuda" + self.cuda_version, "cudnn7"]) return leading + ["linux", self.distro] + cuda_parms + self.parms def gen_docker_image_path(self): parms_source = self.parent_build or self base_build_env_name = "-".join(parms_source.get_parms(True)) return miniutils.quote(DOCKER_IMAGE_PATH_BASE + base_build_env_name + ":" + str(DOCKER_IMAGE_VERSION)) def get_build_job_name_pieces(self, build_or_test): return self.get_parms(False) + [build_or_test] def gen_build_name(self, build_or_test): return ("_".join(map(str, self.get_build_job_name_pieces(build_or_test)))).replace(".", "_").replace("-", "_") def get_dependents(self): return self.dependent_tests or [] def gen_yaml_tree(self, build_or_test): build_job_name_pieces = self.get_build_job_name_pieces(build_or_test) build_env_name = "-".join(map(str, build_job_name_pieces)) env_dict = OrderedDict([ ("BUILD_ENVIRONMENT", build_env_name), ("DOCKER_IMAGE", self.gen_docker_image_path()), ]) if self.pyver: env_dict["PYTHON_VERSION"] = miniutils.quote(self.pyver) if build_or_test == "test" and self.gpu_resource: env_dict["USE_CUDA_DOCKER_RUNTIME"] = miniutils.quote("1") d = { "environment": env_dict, "<<": "*" + "_".join(["pytorch", "linux", build_or_test, "defaults"]), } if build_or_test == "test": resource_class = "large" if self.gpu_resource: resource_class = "gpu." + self.gpu_resource if self.gpu_resource == "large": env_dict["MULTI_GPU"] = miniutils.quote("1") d["resource_class"] = resource_class return d def gen_workflow_yaml_item(self, phase): # All jobs require the setup job parameters = OrderedDict({"requires": ["setup"]}) if phase == "test": # TODO When merging the caffe2 and pytorch jobs, it might be convenient for a while to make a # caffe2 test job dependent on a pytorch build job. This way we could quickly dedup the repeated # build of pytorch in the caffe2 build job, and just run the caffe2 tests off of a completed # pytorch build job (from https://github.com/pytorch/pytorch/pull/17323#discussion_r259452641) dependency_build = self.parent_build or self parameters["requires"].append(dependency_build.gen_build_name("build")) if not self.is_important: # If you update this, update # caffe2_build_definitions.py too parameters["filters"] = {"branches": {"only": ["master", r"/ci-all\/.*/"]}} return {self.gen_build_name(phase): parameters} # TODO This is a hack to special case some configs just for the workflow list class HiddenConf(object): def __init__(self, name, parent_build=None): self.name = name self.parent_build = parent_build def gen_workflow_yaml_item(self, phase): return {self.gen_build_name(phase): {"requires": [self.parent_build.gen_build_name("build")]}} def gen_build_name(self, _): return self.name # TODO Convert these to graph nodes def gen_dependent_configs(xenial_parent_config): extra_parms = [ (["multigpu"], "large"), (["NO_AVX2"], "medium"), (["NO_AVX", "NO_AVX2"], "medium"), (["slow"], "medium"), (["nogpu"], None), ] configs = [] for parms, gpu in extra_parms: c = Conf( xenial_parent_config.distro, ["py3"] + parms, pyver="3.6", cuda_version=xenial_parent_config.cuda_version, restrict_phases=["test"], gpu_resource=gpu, parent_build=xenial_parent_config, is_important=xenial_parent_config.is_important, ) configs.append(c) for x in ["pytorch_short_perf_test_gpu", "pytorch_python_doc_push", "pytorch_cpp_doc_push"]: configs.append(HiddenConf(x, parent_build=xenial_parent_config)) return configs def get_root(): return TopLevelNode("PyTorch Builds", CONFIG_TREE_DATA) def gen_tree(): root = get_root() configs_list = conf_tree.dfs(root) return configs_list def instantiate_configs(): config_list = [] root = get_root() found_configs = conf_tree.dfs(root) restrict_phases = None for fc in found_configs: distro_name = fc.find_prop("distro_name") python_version = None if distro_name == "xenial": python_version = fc.find_prop("pyver") parms_list = [fc.find_prop("abbreviated_pyver")] else: parms_list = ["py" + fc.find_prop("pyver")] compiler_name = fc.find_prop("compiler_name") cuda_version = None if compiler_name == "cuda": cuda_version = fc.find_prop("compiler_version") elif compiler_name == "android": android_ndk_version = fc.find_prop("compiler_version") # TODO: do we need clang to compile host binaries like protoc? parms_list.append("clang5") parms_list.append("android-ndk-" + android_ndk_version) restrict_phases = ["build"] elif compiler_name: gcc_version = compiler_name + (fc.find_prop("compiler_version") or "") parms_list.append(gcc_version) # TODO: This is a nasty special case if compiler_name == "clang": parms_list.append("asan") if cuda_version in ["9.2", "10"]: # TODO The gcc version is orthogonal to CUDA version? parms_list.append("gcc7") is_xla = fc.find_prop("is_xla") or False is_namedtensor = fc.find_prop("is_namedtensor") or False is_important = fc.find_prop("is_important") or False gpu_resource = None if cuda_version and cuda_version != "10": gpu_resource = "medium" c = Conf( distro_name, parms_list, python_version, cuda_version, is_xla, restrict_phases, gpu_resource, is_namedtensor=is_namedtensor, is_important=is_important, ) if cuda_version == "9" and python_version == "3.6": c.dependent_tests = gen_dependent_configs(c) config_list.append(c) return config_list def add_build_env_defs(jobs_dict): mydict = OrderedDict() config_list = instantiate_configs() for c in config_list: phases = c.restrict_phases or dimensions.PHASES for phase in phases: # TODO why does this not have a test? if phase == "test" and c.cuda_version == "10": continue d = c.gen_yaml_tree(phase) mydict[c.gen_build_name(phase)] = d if phase == "test": for x in filter(lambda x: type(x) is not HiddenConf, c.get_dependents()): d = x.gen_yaml_tree(phase) mydict[x.gen_build_name(phase)] = d # this is the circleci api version and probably never changes jobs_dict["version"] = 2 jobs_dict["jobs"] = mydict graph = visualization.generate_graph(get_root()) graph.draw("pytorch-config-dimensions.png", prog="twopi") def get_workflow_list(): config_list = instantiate_configs() x = ["setup"] for conf_options in config_list: phases = conf_options.restrict_phases or dimensions.PHASES for phase in phases: # TODO why does this not have a test? if phase == "test" and conf_options.cuda_version == "10": continue x.append(conf_options.gen_workflow_yaml_item(phase)) # TODO convert to recursion for conf in conf_options.get_dependents(): x.append(conf.gen_workflow_yaml_item("test")) return x

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jax_dft/jax_dft/losses_test.py

deepneuralmachine/google-research

23,901

159153

<filename>jax_dft/jax_dft/losses_test.py # coding=utf-8 # Copyright 2021 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Lint as: python3 """Tests for jax_dft.losses.""" from absl.testing import absltest from jax.config import config import jax.numpy as jnp import numpy as np from jax_dft import losses # Set the default dtype as float64 config.update('jax_enable_x64', True) class LossesTest(absltest.TestCase): def test_trajectory_mse_wrong_predict_ndim(self): with self.assertRaisesRegex( ValueError, 'The size of the shape of predict should be ' 'greater or equal to 2, got 1'): losses.trajectory_mse( target=jnp.array([[0.2, 0.2, 0.2, 0.2], [0.6, 0.6, 0.6, 0.6]]), predict=jnp.array([0.6, 0.6, 0.6, 0.6]), discount=1.) def test_trajectory_mse_wrong_predict_target_ndim_difference(self): with self.assertRaisesRegex( ValueError, 'The size of the shape of predict should be greater than ' 'the size of the shape of target by 1, ' r'but got predict $2$ and target $2$'): losses.trajectory_mse( target=jnp.array([[0.2, 0.2, 0.2, 0.2], [0.6, 0.6, 0.6, 0.6]]), predict=jnp.array([[0.2, 0.2, 0.2, 0.2], [0.6, 0.6, 0.6, 0.6]]), discount=1.) def test_density_mse(self): self.assertAlmostEqual( float(losses.mean_square_error( target=jnp.array([[0.2, 0.2, 0.2, 0.2], [0.6, 0.6, 0.6, 0.6]]), predict=jnp.array([[0.4, 0.5, 0.2, 0.3], [0.6, 0.6, 0.6, 0.6]]))), # (( # (0.4 - 0.2) ** 2 + (0.5 - 0.2) ** 2 # + (0.2 - 0.2) ** 2 + (0.3 - 0.2) ** 2 # ) / 4 + 0) / 2 = 0.0175 0.0175) def test_energy_mse(self): self.assertAlmostEqual( float(losses.mean_square_error( target=jnp.array([[0.2, 0.6]]), predict=jnp.array([[0.4, 0.7]]))), # ((0.4 - 0.2) ** 2 + (0.7 - 0.6) ** 2) / 2 = 0.025 0.025) def test_get_discount_coefficients(self): np.testing.assert_allclose( losses._get_discount_coefficients(num_steps=4, discount=0.8), [0.512, 0.64, 0.8, 1.]) def test_trajectory_mse_on_density(self): self.assertAlmostEqual( float(losses.trajectory_mse( target=jnp.array([[0.2, 0.2, 0.2, 0.2], [0.6, 0.6, 0.6, 0.6]]), predict=jnp.array([ [[0.4, 0.5, 0.2, 0.3], [0.3, 0.3, 0.2, 0.2], [0.3, 0.3, 0.3, 0.2]], [[0.6, 0.6, 0.6, 0.6], [0.6, 0.6, 0.6, 0.5], [0.6, 0.6, 0.6, 0.6]]]), discount=0.6)), # First sample in the batch: # ( # (0.4 - 0.2) ** 2 + (0.5 - 0.2) ** 2 # + (0.2 - 0.2) ** 2 + (0.3 - 0.2) ** 2 # ) / 4 * 0.6 * 0.6 # + ( # (0.3 - 0.2) ** 2 + (0.3 - 0.2) ** 2 # + (0.2 - 0.2) ** 2 + (0.2 - 0.2) ** 2 # ) / 4 * 0.6 # + ( # (0.3 - 0.2) ** 2 + (0.3 - 0.2) ** 2 # + (0.3 - 0.2) ** 2 + (0.2 - 0.2) ** 2 # ) / 4 = 0.0231 # Second sample in the batch: # ( # (0.6 - 0.6) ** 2 + (0.6 - 0.6) ** 2 # + (0.6 - 0.6) ** 2 + (0.6 - 0.6) ** 2 # ) / 4 * 0.6 * 0.6 # + ( # (0.6 - 0.6) ** 2 + (0.6 - 0.6) ** 2 # + (0.6 - 0.6) ** 2 + (0.5 - 0.6) ** 2 # ) / 4 * 0.6 # + ( # (0.6 - 0.6) ** 2 + (0.6 - 0.6) ** 2 # + (0.6 - 0.6) ** 2 + (0.6 - 0.6) ** 2 # ) / 4 = 0.0015 # Loss: # (0.0231 + 0.0015) / 2 = 0.0123 0.0123) def test_trajectory_mse_on_energy(self): self.assertAlmostEqual( float(losses.trajectory_mse( target=jnp.array([0.2, 0.6]), predict=jnp.array([[0.4, 0.3, 0.2], [0.7, 0.7, 0.7]]), discount=0.6)), # First sample in the batch: # ((0.4 - 0.2) ** 2 * 0.6 * 0.6 # + (0.3 - 0.2) ** 2 * 0.6 + (0.2 - 0.2) ** 2) = 0.0204 # Second sample in the batch: # ((0.7 - 0.6) ** 2 * 0.6 * 0.6 # + (0.7 - 0.6) ** 2 * 0.6 + (0.7 - 0.6) ** 2) = 0.0196 # Loss: # (0.0204 + 0.0196) / 2 = 0.02 0.02) if __name__ == '__main__': absltest.main()

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setup.py

EncoreTechnologies/py-menandmice

1

48039

# Licensed to the Encore Technologies ("Encore") 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 os.path from setuptools import setup, find_packages from pip.req import parse_requirements from menandmice import __version__ def fetch_requirements(requirements_file_path): """ Return a list of requirements and links by parsing the provided requirements file. """ links = [] reqs = [] for req in parse_requirements(requirements_file_path, session=False): if req.link: links.append(str(req.link)) reqs.append(str(req.req)) return (reqs, links) PACKAGE_NAME = 'menandmice' BASE_DIR = os.path.dirname(os.path.abspath(__file__)) REQUIREMENTS_FILE = os.path.join(BASE_DIR, 'requirements.txt') install_reqs, dep_links = fetch_requirements(REQUIREMENTS_FILE) with open('README.md') as f: readme = f.read() with open('LICENSE') as f: license = f.read() setup( name=PACKAGE_NAME, version=__version__, description='Python bindings for the Men&Mice IPAM REST API ', long_description=readme, author='<NAME>', author_email='<EMAIL>', url='https://github.com/EncoreTechnologies/py-menandmice', license=license, install_requires=install_reqs, dependency_links=dep_links, test_suite="nose.collector", tests_require=["nose", "mock"], packages=find_packages(exclude=['tests']) )

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blocks/bricks/bn.py

janchorowski/blocks

3

199455

<gh_stars>1-10 import collections import numpy from picklable_itertools.extras import equizip import theano from theano import tensor from theano.tensor.nnet import bn from ..graph import add_annotation from ..initialization import Constant from ..roles import (BATCH_NORM_POPULATION_MEAN, BATCH_NORM_POPULATION_STDEV, BATCH_NORM_OFFSET, BATCH_NORM_DIVISOR, BATCH_NORM_MINIBATCH_ESTIMATE, BATCH_NORM_SHIFT_PARAMETER, BATCH_NORM_SCALE_PARAMETER, add_role) from ..utils import (shared_floatx_zeros, shared_floatx, shared_floatx_nans) from .base import lazy, application from .sequences import Sequence, Feedforward, MLP from .interfaces import RNGMixin def _add_batch_axis(var): """Prepend a singleton axis to a TensorVariable and name it.""" new_var = new_var = tensor.shape_padleft(var) new_var.name = 'shape_padleft({})'.format(var.name) return new_var def _add_role_and_annotate(var, role, annotations=()): """Add a role and zero or more annotations to a variable.""" add_role(var, role) for annotation in annotations: add_annotation(var, annotation) class BatchNormalization(RNGMixin, Feedforward): r"""Normalizes activations, parameterizes a scale and shift. Parameters ---------- input_dim : int or tuple Shape of a single input example. It is assumed that a batch axis will be prepended to this. broadcastable : tuple, optional Tuple the same length as `input_dim` which specifies which of the per-example axes should be averaged over to compute means and standard deviations. For example, in order to normalize over all spatial locations in a `(batch_index, channels, height, width)` image, pass `(False, True, True)`. conserve_memory : bool, optional Use an implementation that stores less intermediate state and therefore uses less memory, at the expense of 5-10% speed. Default is `True`. epsilon : float, optional The stabilizing constant for the minibatch standard deviation computation (when the brick is run in training mode). Added to the variance inside the square root, as in the batch normalization paper. scale_init : object, optional Initialization object to use for the learned scaling parameter ($\\gamma$ in [BN]_). By default, uses constant initialization of 1. shift_init : object, optional Initialization object to use for the learned shift parameter ($\\beta$ in [BN]_). By default, uses constant initialization of 0. Notes ----- In order for trained models to behave sensibly immediately upon upon deserialization, by default, this brick runs in *inference* mode, using a population mean and population standard deviation (initialized to zeros and ones respectively) to normalize activations. It is expected that the user will adapt these during training in some fashion, independently of the training objective, e.g. by taking a moving average of minibatch-wise statistics. In order to *train* with batch normalization, one must obtain a training graph by transforming the original inference graph. See :func:`~blocks.graph.apply_batch_normalization` for a routine to transform graphs, and :func:`~blocks.graph.batch_normalization` for a context manager that may enable shorter compile times (every instance of :class:`BatchNormalization` is itself a context manager, entry into which causes applications to be in minibatch "training" mode, however it is usually more convenient to use :func:`~blocks.graph.batch_normalization` to enable this behaviour for all of your graph's :class:`BatchNormalization` bricks at once). Note that training in inference mode should be avoided, as this brick introduces scales and shift parameters (tagged with the `PARAMETER` role) that, in the absence of batch normalization, usually makes things unstable. If you must do this, filter for and remove `BATCH_NORM_SHIFT_PARAMETER` and `BATCH_NORM_SCALE_PARAMETER` from the list of parameters you are training, and this brick should behave as a (somewhat expensive) no-op. This Brick accepts `scale_init` and `shift_init` arguments but is *not* an instance of :class:`~blocks.bricks.Initializable`, and will therefore not receive pushed initialization config from any parent brick. In almost all cases, you will probably want to stick with the defaults (unit scale and zero offset), but you can explicitly pass one or both initializers to override this. This has the necessary properties to be inserted into a :class:`blocks.bricks.conv.ConvolutionalSequence` as-is, in which case the `input_dim` should be omitted at construction, to be inferred from the layer below. """ @lazy(allocation=['input_dim']) def __init__(self, input_dim, broadcastable=None, conserve_memory=True, epsilon=1e-4, scale_init=None, shift_init=None, **kwargs): self.input_dim = input_dim self.broadcastable = broadcastable self.conserve_memory = conserve_memory self.epsilon = epsilon self.scale_init = (Constant(1) if scale_init is None else scale_init) self.shift_init = (Constant(0) if shift_init is None else shift_init) self._training_mode = [] super(BatchNormalization, self).__init__(**kwargs) @application(inputs=['input_'], outputs=['output']) def apply(self, input_, application_call): if self._training_mode: mean, stdev = self._compute_training_statistics(input_) else: mean, stdev = self._prepare_population_statistics() # Useful for filtration of calls that were already made in # training mode when doing graph transformations. # Very important to cast to bool, as self._training_mode is # normally a list (to support nested context managers), which would # otherwise get passed by reference and be remotely mutated. application_call.metadata['training_mode'] = bool(self._training_mode) # Useful for retrieving a list of updates for population # statistics. Ditch the broadcastable first axis, though, to # make it the same dimensions as the population mean and stdev # shared variables. application_call.metadata['offset'] = mean[0] application_call.metadata['divisor'] = stdev[0] # Give these quantities roles in the graph. _add_role_and_annotate(mean, BATCH_NORM_OFFSET, [self, application_call]) _add_role_and_annotate(stdev, BATCH_NORM_DIVISOR, [self, application_call]) scale = _add_batch_axis(self.scale) shift = _add_batch_axis(self.shift) # Heavy lifting is done by the Theano utility function. normalized = bn.batch_normalization(input_, scale, shift, mean, stdev, mode=('low_mem' if self.conserve_memory else 'high_mem')) return normalized def __enter__(self): self._training_mode.append(True) def __exit__(self, *exc_info): self._training_mode.pop() def _compute_training_statistics(self, input_): axes = (0,) + tuple((i + 1) for i, b in enumerate(self.population_mean.broadcastable) if b) mean = input_.mean(axis=axes, keepdims=True) assert mean.broadcastable[1:] == self.population_mean.broadcastable stdev = tensor.sqrt(tensor.var(input_, axis=axes, keepdims=True) + numpy.cast[theano.config.floatX](self.epsilon)) assert stdev.broadcastable[1:] == self.population_stdev.broadcastable add_role(mean, BATCH_NORM_MINIBATCH_ESTIMATE) add_role(stdev, BATCH_NORM_MINIBATCH_ESTIMATE) return mean, stdev def _prepare_population_statistics(self): mean = _add_batch_axis(self.population_mean) stdev = _add_batch_axis(self.population_stdev) return mean, stdev def _allocate(self): input_dim = ((self.input_dim,) if not isinstance(self.input_dim, collections.Sequence) else self.input_dim) broadcastable = (tuple(False for _ in input_dim) if self.broadcastable is None else self.broadcastable) if len(input_dim) != len(broadcastable): raise ValueError("input_dim and broadcastable must be same length") var_dim = tuple(1 if broadcast else dim for dim, broadcast in equizip(input_dim, broadcastable)) broadcastable = broadcastable # "gamma", from the Ioffe & Szegedy manuscript. self.scale = shared_floatx_nans(var_dim, name='batch_norm_scale', broadcastable=broadcastable) # "beta", from the Ioffe & Szegedy manuscript. self.shift = shared_floatx_nans(var_dim, name='batch_norm_shift', broadcastable=broadcastable) add_role(self.scale, BATCH_NORM_SCALE_PARAMETER) add_role(self.shift, BATCH_NORM_SHIFT_PARAMETER) self.parameters.append(self.scale) self.parameters.append(self.shift) # These aren't technically parameters, in that they should not be # learned using the same cost function as other model parameters. self.population_mean = shared_floatx_zeros(var_dim, name='population_mean', broadcastable=broadcastable) self.population_stdev = shared_floatx(numpy.ones(var_dim), name='population_stdev', broadcastable=broadcastable) add_role(self.population_mean, BATCH_NORM_POPULATION_MEAN) add_role(self.population_stdev, BATCH_NORM_POPULATION_STDEV) # Normally these would get annotated by an AnnotatingList, but they # aren't in self.parameters. add_annotation(self.population_mean, self) add_annotation(self.population_stdev, self) def _initialize(self): self.shift_init.initialize(self.shift, self.rng) self.scale_init.initialize(self.scale, self.rng) # Needed for the Feedforward interface. @property def output_dim(self): return self.input_dim # The following properties allow for BatchNormalization bricks # to be used directly inside of a ConvolutionalSequence. @property def image_size(self): return self.input_dim[-2:] @image_size.setter def image_size(self, value): if not isinstance(self.input_dim, collections.Sequence): self.input_dim = (None,) + tuple(value) else: self.input_dim = (self.input_dim[0],) + tuple(value) @property def num_channels(self): return self.input_dim[0] @num_channels.setter def num_channels(self, value): if not isinstance(self.input_dim, collections.Sequence): self.input_dim = (value,) + (None, None) else: self.input_dim = (value,) + self.input_dim[-2:] def get_dim(self, name): if name in ('input', 'output'): return self.input_dim else: raise KeyError @property def num_output_channels(self): return self.num_channels class SpatialBatchNormalization(BatchNormalization): """Convenient subclass for batch normalization across spatial inputs. Parameters ---------- input_dim : int or tuple The input size of a single example. Must be length at least 2. It's assumed that the first axis of this tuple is a "channels" axis, which should not be summed over, and all remaining dimensions are spatial dimensions. Notes ----- See :class:`BatchNormalization` for more details (and additional keyword arguments). """ def _allocate(self): if not isinstance(self.input_dim, collections.Sequence) or len(self.input_dim) < 2: raise ValueError('expected input_dim to be length >= 2 ' 'e.g. (channels, height, width)') self.broadcastable = (False,) + ((True,) * (len(self.input_dim) - 1)) super(SpatialBatchNormalization, self)._allocate() class BatchNormalizedMLP(MLP): """Convenient subclass for building an MLP with batch normalization. Parameters ---------- conserve_memory : bool, optional See :class:`BatchNormalization`. Notes ----- All other parameters are the same as :class:`~blocks.bricks.MLP`. Each activation brick is wrapped in a :class:`~blocks.bricks.Sequence` containing an appropriate :class:`BatchNormalization` brick and the activation that follows it. By default, the contained :class:`~blocks.bricks.Linear` bricks will not contain any biases, as they could be canceled out by the biases in the :class:`BatchNormalization` bricks being added. Pass `use_bias` with a value of `True` if you really want this for some reason. """ @lazy(allocation=['dims']) def __init__(self, activations, dims, *args, **kwargs): conserve_memory = kwargs.pop('conserve_memory', True) activations = [ Sequence([ BatchNormalization(conserve_memory=conserve_memory).apply, act.apply ], name='batch_norm_activation_{}'.format(i)) for i, act in enumerate(activations) ] # Batch normalization bricks incorporate a bias, so there's no # need for our Linear bricks to have them. kwargs.setdefault('use_bias', False) super(BatchNormalizedMLP, self).__init__(activations, dims, *args, **kwargs) @property def conserve_memory(self): return self._conserve_memory @conserve_memory.setter def conserve_memory(self, value): self._conserve_memory = value for act in self.activations: assert isinstance(act.children[0], BatchNormalization) act.children[0].conserve_memory = value def _push_allocation_config(self): super(BatchNormalizedMLP, self)._push_allocation_config() # Do the extra allocation pushing for the BatchNormalization # bricks. They need as their input dimension the output dimension # of each linear transformation. Exclude the first dimension, # which is the input dimension. for act, dim in equizip(self.activations, self.dims[1:]): assert isinstance(act.children[0], BatchNormalization) act.children[0].input_dim = dim

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.tox/scenario/lib/python2.7/site-packages/pycadf/measurement.py

bdrich/neutron-lbaas

0

122430

<reponame>bdrich/neutron-lbaas # Copyright 2013 IBM Corp. # # 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. from pycadf import cadftype from pycadf import identifier from pycadf import metric from pycadf import resource MEASUREMENT_KEYNAME_RESULT = "result" MEASUREMENT_KEYNAME_METRIC = "metric" MEASUREMENT_KEYNAME_METRICID = "metricId" MEASUREMENT_KEYNAME_CALCBY = "calculatedBy" MEASUREMENT_KEYNAMES = [MEASUREMENT_KEYNAME_RESULT, MEASUREMENT_KEYNAME_METRICID, MEASUREMENT_KEYNAME_METRIC, MEASUREMENT_KEYNAME_CALCBY] class Measurement(cadftype.CADFAbstractType): result = cadftype.ValidatorDescriptor(MEASUREMENT_KEYNAME_RESULT) metric = cadftype.ValidatorDescriptor( MEASUREMENT_KEYNAME_METRIC, lambda x: isinstance(x, metric.Metric)) metricId = cadftype.ValidatorDescriptor(MEASUREMENT_KEYNAME_METRICID, lambda x: identifier.is_valid(x)) calculatedBy = cadftype.ValidatorDescriptor( MEASUREMENT_KEYNAME_CALCBY, (lambda x: isinstance(x, resource.Resource) and x.is_valid())) def __init__(self, result=None, metric=None, metricId=None, calculatedBy=None): """Create Measurement data type :param result: value of measurement :param metric: Metric data type of current measurement :param metricId: id of Metric data type of current measurement :param calculatedBy: Resource that calculated measurement """ # Measurement.result if result is not None: setattr(self, MEASUREMENT_KEYNAME_RESULT, result) # Measurement.metricId if metricId is not None: setattr(self, MEASUREMENT_KEYNAME_METRICID, metricId) # Measurement.metric if metric is not None: setattr(self, MEASUREMENT_KEYNAME_METRIC, metric) # Measurement.calculaedBy if calculatedBy is not None: setattr(self, MEASUREMENT_KEYNAME_CALCBY, calculatedBy) # self validate this cadf:Measurement type against schema def is_valid(self): """Validation to ensure Measurement required attributes are set. """ return (self._isset(MEASUREMENT_KEYNAME_RESULT) and (self._isset(MEASUREMENT_KEYNAME_METRIC) ^ self._isset(MEASUREMENT_KEYNAME_METRICID)))

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Problems/5_best_sum.py

v4n5h4j/dynamic-programming-python

0

152091

def bestSum(t, arr, memo=None): """ m: target sum, t n: len(arr) time = O(n^m*m) space = O(m*m) [in each stack frame, I am storing an array, which in worst case would be m] // Memoized complexity time = O(n*m*m) space = O(m*m) """ if memo is None: memo = {} if t in memo: return memo[t] if t == 0: return [] if t < 0: return None shortestCombination = None for ele in arr: r = t - ele rRes = bestSum(r, arr, memo) if rRes is not None: combination = rRes + [ele] # If the combination is shorter than current shortest, update it if (shortestCombination is None or len(combination) < len(shortestCombination)): shortestCombination = combination memo[t] = shortestCombination return memo[t] print(bestSum(7, [5,3,4,7])) print(bestSum(8, [2,3,5])) print(bestSum(8, [1,4,5])) print(bestSum(100, [1,2,5,25]))

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model/VAE.py

y-x-c/electricauth

1

169447

""" PyTorch implementation of VAE found at: https://github.com/pytorch/examples/tree/master/vae. """ import torch from torch import nn import torch.nn.functional as F class VAE(nn.Module): def __init__(self, input_length, n_sensor_channel, n_hidden = 400, n_latent_features=20, no_variational=False ): super(VAE, self).__init__() self.fc1 = nn.Linear(input_length*n_sensor_channel, n_hidden) self.fc21 = nn.Linear(n_hidden, n_latent_features) self.fc22 = nn.Linear(n_hidden, n_latent_features) self.fc3 = nn.Linear(n_latent_features, n_hidden) self.fc4 = nn.Linear(n_hidden, input_length * n_sensor_channel) self.no_variational = no_variational def encode(self, x): h1 = F.relu(self.fc1(x)) return self.fc21(h1), self.fc22(h1) def reparameterize(self, mu, logvar): if self.no_variational: return mu else: std = torch.exp(0.5*logvar) eps = torch.randn_like(std) return eps.mul(std).add_(mu) def decode(self, z): h3 = F.relu(self.fc3(z)) return torch.sigmoid(self.fc4(h3)) def forward(self, x): mu, logvar = self.encode(x) z = self.reparameterize(mu, logvar) return self.decode(z), mu, logvar

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training_scripts/main.py

grantsrb/planet

0

63132

<gh_stars>0 import numpy as np import os import torch import pickle import time from planet.training import * from planet.experience import ExperienceReplay from planet.agents import * from planet.models import * from planet.game_envs import GameEnv import planet.prep_fxns as prep_fxns from planet.utils import try_key import sys import json DEVICE = torch.device("cuda:0") def load_json(file_name): with open(file_name, 'r') as f: return json.load(f) def cuda_if(module): if torch.cuda.is_available(): return module.to(DEVICE) return module if __name__=="__main__": starttime = time.time() # Load hyps if len(sys.argv) < 2: hyps = load_json("hyps.json") else: hyps = load_json(sys.argv[1]) if hyps['exp_name'] == "test": hyps['n_train_loops'] = 2 hyps['n_new_sodes'] = 2 hyps['n_epochs'] = 5 if "float_params" not in hyps and "game_keys" in hyps: keys = hyps['game_keys'] hyps['float_params'] = {k:try_key(hyps,k,0) for k in keys} if "minObjLoc" not in hyps: hyps['float_params']["minObjLoc"] = 0.27 hyps['float_params']["maxObjLoc"] = 0.73 print("Float Params:", hyps['float_params']) hyps['prep_fxn'] = getattr(prep_fxns, hyps['prep_fxn']) # Initiallize saving variables save_folder = hyps['exp_name'] if not os.path.exists(save_folder): os.mkdir(save_folder) _, _, file_names = next(os.walk(save_folder)) save_num = 0 for fname in file_names: if "state_dict" in fname and "log" not in fname: num = int(fname.split("_")[-1].split(".")[0]) if num+1 > save_num: save_num = num+1 file_name = "state_dicts_"+str(save_num)+".pth" save_name = os.path.join(save_folder, file_name) # Prepare game and random agent game = GameEnv(**hyps) if len(game.obs_shape) > 1: depth = hyps['obs_depth']*game.obs_shape[0] obs_shape = (depth, *game.obs_shape[1:]) else: obs_shape = (hyps['obs_depth'], *game.obs_shape) action_repeat = hyps['action_repeat'] discrete = hasattr(game.env.action_space, "n") rand_agent = RandnAgent(obs_shape, hyps['obs_depth'], game.a_size, action_repeat=action_repeat, discrete=discrete) # Collect Initial Data data = game.collect_sodes(rand_agent, hyps['n_sodes'], maxsize=hyps['max_steps']) if hyps['env_name'] == "Pong-v0": rew = data['rews'].sum()/(data['dones'].sum()) else: rew = data['rews'].mean() print("Baseline Rew:", rew) # Make Experience Replay exp_replay = ExperienceReplay(intl_data=data, max_size=hyps['max_steps']) # Make Models h_size = hyps['h_size'] s_size = hyps['s_size'] a_size = data['actions'].shape[-1] min_sigma = hyps['min_sigma'] obs_shape = data['observs'].shape[1:] env_name = hyps['env_name'] bnorm = hyps['bnorm'] dynamics = Dynamics(obs_shape, h_size, s_size, a_size, bnorm=bnorm, env_name=env_name, min_sigma=min_sigma) if env_name=="Pendulum-v0" or env_name == "MountainCarContinuous-v0": decoder = SimpleDecoder(dynamics.encoder.emb_shape, obs_shape, h_size, s_size, bnorm=bnorm) else: decoder = Decoder(dynamics.encoder.emb_shape, obs_shape, h_size, s_size, bnorm=bnorm) rew_model = RewardModel(h_size, s_size, bnorm=bnorm) # Put them up on the gpu dynamics = cuda_if(dynamics) decoder = cuda_if(decoder) rew_model = cuda_if(rew_model) # Make Agent obs_depth = hyps['obs_depth'] agent = DynamicsAgent(obs_shape, obs_depth, a_size, hyps, dynamics, rew_model, discrete=discrete) # Make optimizer params = list(dynamics.parameters()) + list(decoder.parameters()) + list(rew_model.parameters()) optim_type = hyps['optim_type'] lr = hyps['lr'] optimizer = getattr(torch.optim, optim_type)(params, lr=lr) # Potentially restore previous models if "load_file" in hyps and hyps['load_file'] is not None: try: state_dicts = torch.load(hyps['load_file']) dynamics.load_state_dict(state_dicts['dynamics']) decoder.load_state_dict(state_dicts['decoder']) rew_model.load_state_dict(state_dicts['rew_model']) optimizer.load_state_dict(state_dicts['optimizer']) except Exception as e: print(e) print("Failed to load state dicts") # Make Trainer trainer = Trainer(dynamics, decoder, rew_model, optimizer, exp_replay) # Training Loop best_infr_rew = -np.inf n_epochs = hyps['n_epochs'] n_train_loops = hyps['n_train_loops'] steps_seen = len(exp_replay) for epoch in range(n_epochs): log_str = "" s = "\n\nEpoch: {}".format(epoch) print(s) log_str += s + "\n" temptime = time.time() s = "Data steps: {} | Tot running time: {}" s = s.format(steps_seen,temptime-starttime) print(s) log_str += s + "\n" for loop in range(n_train_loops): looptime = temptime losses = trainer.train(hyps) for k in losses.keys(): print(k+":", np.mean(losses[k])) temptime = time.time() print("Execution Time:", temptime-looptime) print("Saving model to:", save_name) state_dicts = { "dynamics": dynamics.state_dict(), "decoder": decoder.state_dict(), "rew_model": rew_model.state_dict(), "optimizer": optimizer.state_dict() } torch.save(state_dicts, save_name) print("\nCollecting new data...", end=" ") for _ in range(hyps['n_new_sodes']): new_sode = game.collect_sode(agent, render=hyps['render']) exp_replay.add_new_data(new_sode) steps_seen += len(new_sode['rews']) print("| Exec Time:", time.time()-temptime) rews = new_sode['rews'] if "raw_rews" not in new_sode else new_sode['raw_rews'] infr_rew = np.mean(rews) if infr_rew > best_infr_rew: best_infr_rew = infr_rew if hyps['env_name'] == "Pong-v0": s = "Inference rews: {}".format(rews.sum()) elif hyps['env_name'] == "Pendulum-v0": quad = max(len(new_sode['rews'])//4, 1) rng = range(0,quad*4,quad) means = [str(np.mean(rews[i:i+quad])) for i in rng] strmeans = " | ".join(means) s="Inference rews by quadrant (early->later): "+strmeans else: s = "Avg Inference Rew: {:.5f} | Best: {}".format(infr_rew, best_infr_rew) print(s) log_str += s + "\n" s = "\nTotal running time: {}".format(time.time()-starttime) print(s) log_str += s + "\n" file_name = "log_"+str(save_num)+".txt" log_path = os.path.join(save_folder, file_name) with open(log_path,'a') as f: f.write(log_str)

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prettyfigure/__init__.py

lue/prettyfigure

2

182459

<gh_stars>1-10 __author__ = 'sasha'

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qube/postprocess/postprocess.py

ClementGeffroy/qube

1

163901

import numpy as np from scipy.signal import savgol_filter from qube.postprocess.dataset import Axis def create_name(name, suffix=None, prefix=None): elements = [] if prefix: elements.append(str(prefix)) elements.append(str(name)) if suffix: elements.append(str(suffix)) name = '_'.join(elements) return name def duplicate_dataset(dataset, suffix=None, prefix=None, custom_name=None): new_ds = dataset.copy(shallow_copy=False) if custom_name: name = custom_name else: name = dataset.name new_ds.name = create_name(name, suffix, prefix) return new_ds def remove_dim_in_axes(axes, dim=None): new_axes = [] if dim is not None: for si in axes: ax = si.copy(shallow_copy=False) if si.dim != dim and si.dim > dim: ax.dim = si.dim - 1 new_axes.append(ax) elif si.dim < dim: new_axes.append(ax) return new_axes def histogram1d(dataset, bins=10, range=None, normed=None, weights=None, density=None): ds = dataset.copy() ds.name = f'{ds.name}_hist1d' hist, bins = np.histogram( ds.value, bins=bins, range=range, normed=normed, weights=weights, density=density, ) bins = bins[:-1] # remove 1 point for ax.plot axis = Axis( name=ds.name, value=bins, unit=ds.unit, dim=0, ) ds.value = hist ds.unit = 'Counts' ds.axes = {axis.name: axis} return ds def take(dataset, indices, axis=None): ds = dataset.copy() ds.name = f'{ds.name}_take' ds.value = np.take(ds.value, indices=indices, axis=axis) old_axes = ds.get_axes(counters=False) ds.clear_axes() if axis is not None: for si in old_axes: ax = si.copy(shallow_copy=False) if si.dim == axis: ax.value = np.take(ax.value, indices=indices) ds.add_axis(ax) elif si.dim < axis or si.dim > axis: ds.add_axis(ax) return ds def mean(dataset, axis=None): ds = dataset.copy() ds.name = f'{ds.name}_mean' ds.value = np.mean(ds.value, axis=axis) old_axes = ds.get_axes(counters=False) ds.clear_axes() new_axes = remove_dim_in_axes(old_axes, axis) for ax in new_axes: ds.add_axis(ax) return ds def nanmean(dataset, axis=None): ds = dataset.copy() ds.name = f'{ds.name}_nanmean' ds.value = np.nanmean(ds.value, axis=axis) old_axes = ds.get_axes(counters=False) ds.clear_axes() new_axes = remove_dim_in_axes(old_axes, axis) for ax in new_axes: ds.add_axis(ax) return ds def subtract(dataset1, dataset2): ds1 = dataset1.copy() ds2 = dataset2 ds1.value = ds1.value - ds2.value ds1.name = f'{ds1.name}-{ds2.name}' return ds1 def gradient(dataset, axis=None, edge_order=1): ds = dataset.copy() ds.name = f'{ds.name}_grad' ds.unit = f'd {ds.unit}' ds.value = np.gradient(ds.value, axis=axis, edge_order=edge_order) old_axes = ds.get_axes(counters=False) ds.clear_axes() if axis is not None: for si in old_axes: ax = si.copy(shallow_copy=False) ds.add_axis(ax) return ds def smooth(dataset, window=5, order=3, axis=-1,**kwargs): ds = dataset.copy() ds.name = f'{ds.name}_smooth' ds.value = savgol_filter(ds.value, window_length=window, polyorder=order, axis=axis,**kwargs) old_axes = ds.get_axes(counters=False) ds.clear_axes() if axis is not None: for si in old_axes: ax = si.copy(shallow_copy=False) ds.add_axis(ax) return ds def fft( dataset, axis=-1, as_period=False, # return "xdata" as period instead of frequency no_dc_offset=True, # take out point at 0 frequency only_positive=True, # get only positive frequencies **kwargs ): ds_amp = dataset.copy() ds_amp.name = f'{ds_amp.name}_fftamp' ds_amp.unit = f'{ds_amp.unit}' ds_pha = dataset.copy() ds_pha.name = f'{ds_pha.name}_fftpha' ds_pha.unit = f'rad' old_axes = dataset.get_axes(counters=False) ds_amp.clear_axes() ds_pha.clear_axes() if as_period: no_dc_offset = True if no_dc_offset: ind0 = 1 else: ind0 = 0 axs = [] for old_axis in old_axes: ax = old_axis.copy(shallow_copy=False) if ax.dim == axis: N = len(ax.value) Nhalf = int(N/2) if only_positive: ind1 = Nhalf else: ind1 = N xdata_freq = np.fft.fftfreq(len(ax.value), np.abs(ax.value[1] - ax.value[0]))[ind0:ind1] if not as_period: ax.name = f'{ax.name}_fftfreq' ax.unit = f'1/{ax.unit}' ax.value = xdata_freq print(ax.unit) else: ax.name = f'{ax.name}_fftper' ax.unit = f'{ax.unit}' ax.value = 1.0/xdata_freq axs.append(ax) data2analyse = np.moveaxis(dataset.value, axis, 0) value_complex = np.fft.fft(data2analyse,axis=0,**kwargs) value_complex = value_complex[ind0:ind1,Ellipsis] value_complex = np.moveaxis(value_complex, 0, axis) ds_amp.value = np.abs(value_complex) ds_pha.value = np.angle(value_complex) for ax in axs: ds_amp.add_axis(ax) ds_pha.add_axis(ax) return ds_amp,ds_pha def value_mask_by_range(dataset, init, final, value, unit=None): ds = dataset.copy() ds.name = f'{ds.name}_vmasked' if init <= final: f1 = np.greater_equal f2 = np.less_equal else: f1 = np.less_equal f2 = np.greater_equal idxs_1 = f1(ds.value, init) idxs_2 = f2(ds.value, final) idxs = np.logical_and(idxs_1, idxs_2) new_value = np.array(ds.value) new_value[idxs] = value ds.value = new_value ds.unit = unit return ds def value_mask_by_bounds(dataset, bounds, values, unit=None): ds = dataset.copy() ds.name = f'{ds.name}_vmasked' " Verify length of bounds and values" bounds = np.array(bounds) values = np.array(values) valid_length = len(values) == len(bounds) + 1 if not valid_length: raise ValueError('len(values) must be len(bounds) + 1)') " Verify that bounds increase or decrease " comparison = [left < right for left, right in zip(bounds[0:-1], bounds[1:])] comparison = np.array(comparison) reduced_comp = list(set(comparison)) valid_slope = len(reduced_comp) == 1 if not valid_slope: raise ValueError('bounds must increase or decrease') " Sort bounds and values to increase " if reduced_comp[0] is False: idxs_sorted = np.argsort(bounds) bounds = np.sort(bounds) values = values[idxs_sorted] " Apply mask " n_bulk = len(values) - 2 new_values = np.zeros_like(ds.value) for i, vi in enumerate(values): if i == 0: idxs = np.less(ds.value, bounds[i]) elif i < n_bulk: idxs_left = np.greater_equal(ds.value, bounds[i - 1]) idxs_right = np.less_equal(ds.value, bounds[i]) idxs = np.logical_and(idxs_left, idxs_right) else: idxs = np.greater(ds.value, bounds[i - 1]) new_values[idxs] = vi ds.value = new_values ds.unit = unit return ds def boolmask(dataset, value, key='=='): ds = dataset.copy() ds.name = f'{ds.name}_bmasked' if key == '==': ds.value = ds.value == value if key == '>=': ds.value = ds.value >= value if key == '<=': ds.value = ds.value <= value if key == '!=': ds.value = ds.value != value if key == '>': ds.value = ds.value > value if key == '<': ds.value = ds.value < value ds.unit = 'boolean' return ds def probability(dataset, value, key='==', axis=None): ds = dataset.copy() ds.name = f'{ds.name}_prob' ds_bool = boolmask(ds, value, key=key) boolv = ds_bool.value ds.value = np.apply_along_axis(_prob, axis=axis, arr=boolv) ds.unit = '%' old_axes = ds.get_axes(counters=False) ds.clear_axes() new_axes = remove_dim_in_axes(old_axes, dim=axis) for ax in new_axes: ds.add_axis(ax) return ds def _prob(arr): total_counts = arr.size nonzero_counts = np.count_nonzero(arr) if total_counts >= 0: prob = 1. * nonzero_counts / total_counts * 100. else: prob = 0 return prob if __name__ == '__main__': pass bonds = [-1, 0, 1, 2] values = [0, 1, 2, 3, 4]

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picoCTF/cryptography/credstuff/examplecode.py

Estheryu991/CaptureTheFlag

0

161337

#this code is just an example from 2018 nur luege / from flask import Flask, request, render_template, abort import os, requests app = Flask(__name__) class user: def __init__(self, username, password): self.username = username self.__password = password self.files = [] def getPass(self): return self.__password users = {} users["admin"] = user("admin", os.environ["FLAG"]) def custom500(error): return str(error), 500 @app.route("/", methods=["GET", "POST"]) def mainpage(): if request.method == "POST": if request.form["action"] == "Login": if request.form["username"] in users: if request.form["password"] == users[request.form["username"]].getPass(): return render_template("index.html", user=users[request.form["username"]]) return "wrong password" return "user does not exist" elif request.form["action"] == "Signup": if request.form["username"] not in users: users[request.form["username"]] = user(request.form["username"], request.form["password"]) return render_template("index.html", user=users[request.form["username"]]) else: return "user already exists" elif request.form["action"] == "Add File": return addfile() return render_template("loggedout.html") #beta feature for viewing info about other users - still testing @app.route("/user/<username>", methods=['POST']) def getInfo(username): val = getattr(users[username], request.form['field'], None) if val != None: return val else: return "error" @app.route("/files/<path:file>", methods=["GET"]) def getFile(file): if "index.py" in file: return "no! bad user! bad!" return open(file, "rb").read() def addfile(): if users[request.form["username"]].getPass() == request.form["password"]: if request.form['url'][-1] == "/": downloadurl = request.form['url'][:-1] else: downloadurl = request.form['url'] if downloadurl.split("/")[-1] in os.listdir("."): return "file already exists" file = requests.get(downloadurl, stream=True) f = open(downloadurl.split("/")[-1], "wb") first = True for chunk in file.iter_content(chunk_size=1024*512): if not first: break f.write(chunk) first = False f.close() users[request.form["username"]].files.append(downloadurl.split("/")[-1]) return render_template("index.html", user=users[request.form["username"]]) return "bad password" if __name__ == "__main__": app.run(host="0.0.0.0")

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example/openpose/pose-paf-master/tf_data_aug.py

boldjoel/tensorlayer

0

75725

import math ####### import random import cv2 import numpy as np import matplotlib.pyplot as plt from tensorpack.dataflow.imgaug.geometry import RotationAndCropValid def crop_meta_image(image,annos,mask): _target_height=368 _target_width =368 if len(np.shape(image))==2: image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB) height,width,_=np.shape(image) # print("the size of original img is:", height, width) if height<=width: ratio=_target_height/height new_width=int(ratio*width) if height==width: new_width=_target_height image,annos,mask=_resize_image(image,annos,mask,new_width,_target_height) for i in annos: if len(i) is not 19: print('Joints of person is not 19 ERROR FROM RESIZE') if new_width>_target_width: crop_range_x=np.random.randint(0, new_width-_target_width) else: crop_range_x=0 image= image[:, crop_range_x:crop_range_x + 368,:] mask = mask[:, crop_range_x:crop_range_x + 368] # joint_list= [] new_joints = [] #annos-pepople-joints (must be 19 or []) for people in annos: # print("number of keypoints is", np.shape(people)) new_keypoints = [] for keypoints in people: if keypoints[0] < -10 or keypoints[1] < -10: new_keypoints.append((-1000, -1000)) continue top=crop_range_x+367 if keypoints[0]>=crop_range_x and keypoints[0]<= top: # pts = (keypoints[0]-crop_range_x, keypoints[1]) pts = (int(keypoints[0] - crop_range_x),int(keypoints[1])) else: pts= (-1000,-1000) new_keypoints.append(pts) new_joints.append(new_keypoints) if len(new_keypoints) != 19: print('1:The Length of joints list should be 0 or 19 but actually:', len(new_keypoints)) annos = new_joints if height>width: ratio = _target_width / width new_height = int(ratio * height) image,annos,mask = _resize_image(image,annos,mask,_target_width, new_height) for i in annos: if len(i) is not 19: print('Joints of person is not 19 ERROR') if new_height > _target_height: crop_range_y = np.random.randint(0, new_height - _target_height) else: crop_range_y = 0 image = image[crop_range_y:crop_range_y + 368, :, :] mask = mask[crop_range_y:crop_range_y + 368, :] new_joints = [] for people in annos: new_keypoints = [] for keypoints in people: # case orginal points are not usable if keypoints[0] < 0 or keypoints[1] < 0: new_keypoints.append((-1000, -1000)) continue # y axis coordinate change bot = crop_range_y + 367 if keypoints[1] >= crop_range_y and keypoints[1] <= bot: # pts = (keypoints[0], keypoints[1]-crop_range_y) pts = (int(keypoints[0]), int (keypoints[1] - crop_range_y)) # if pts[0]>367 or pts[1]>367: # print('Error2') else: pts =(-1000,-1000) new_keypoints.append(pts) new_joints.append(new_keypoints) if len(new_keypoints) != 19: print('2:The Length of joints list should be 0 or 19 but actually:', len(new_keypoints)) annos = new_joints # mask = cv2.resize(mask, (46, 46), interpolation=cv2.INTER_AREA) return image,annos,mask def _resize_image(image,annos,mask,_target_width,_target_height): # _target_height=368 # _target_width =368 #original image y,x,_=np.shape(image) ratio_y= _target_height/y ratio_x= _target_width/x new_joints=[] # update meta # meta.height=_target_height # meta.width =_target_width for people in annos: new_keypoints=[] for keypoints in people: if keypoints[0]<0 or keypoints[1]<0: new_keypoints.append((-1000, -1000)) continue pts = (int(keypoints[0] * ratio_x+0.5), int(keypoints[1] * ratio_y+0.5)) if pts[0] > _target_width-1 or pts[1] > _target_height-1: new_keypoints.append((-1000, -1000)) continue new_keypoints.append(pts) new_joints.append(new_keypoints) annos=new_joints new_image = cv2.resize(image, (_target_width, _target_height), interpolation=cv2.INTER_AREA) new_mask = cv2.resize(mask, (_target_width, _target_height), interpolation=cv2.INTER_AREA) return new_image,annos,new_mask def _rotate_coord(shape, newxy, point, angle): angle = -1 * angle / 180.0 * math.pi ox, oy = shape px, py = point ox /= 2 oy /= 2 qx = math.cos(angle) * (px - ox) - math.sin(angle) * (py - oy) qy = math.sin(angle) * (px - ox) + math.cos(angle) * (py - oy) new_x, new_y = newxy qx += ox - new_x qy += oy - new_y return int(qx + 0.5), int(qy + 0.5) def pose_rotation(image,annos,mask): img_shape=np.shape(image) height = img_shape[0] width = img_shape[1] deg = random.uniform(-15.0, 15.0) img = image center = (img.shape[1] * 0.5, img.shape[0] * 0.5) # x, y rot_m = cv2.getRotationMatrix2D((int(center[0]), int(center[1])), deg, 1) ret = cv2.warpAffine(img, rot_m, img.shape[1::-1], flags=cv2.INTER_AREA, borderMode=cv2.BORDER_CONSTANT) if img.ndim == 3 and ret.ndim == 2: ret = ret[:, :, np.newaxis] neww, newh = RotationAndCropValid.largest_rotated_rect(ret.shape[1], ret.shape[0], deg) neww = min(neww, ret.shape[1]) newh = min(newh, ret.shape[0]) newx = int(center[0] - neww * 0.5) newy = int(center[1] - newh * 0.5) # print(ret.shape, deg, newx, newy, neww, newh) img = ret[newy:newy + newh, newx:newx + neww] # adjust meta data adjust_joint_list = [] for joint in annos: adjust_joint = [] for point in joint: if point[0] < -100 or point[1] < -100: adjust_joint.append((-1000, -1000)) continue # if point[0] <= 0 or point[1] <= 0: # adjust_joint.append((-1, -1)) # continue x, y = _rotate_coord((width, height), (newx, newy), point, deg) # if x > neww or y > newh: # adjust_joint.append((-1000, -1000)) # continue if x>neww-1 or y>newh-1: adjust_joint.append((-1000, -1000)) continue if x < 0 or y < 0: adjust_joint.append((-1000, -1000)) continue adjust_joint.append((x, y)) adjust_joint_list.append(adjust_joint) joint_list = adjust_joint_list msk = mask center = (msk.shape[1] * 0.5, msk.shape[0] * 0.5) # x, y rot_m = cv2.getRotationMatrix2D((int(center[0]), int(center[1])), deg, 1) ret = cv2.warpAffine(msk, rot_m, msk.shape[1::-1], flags=cv2.INTER_AREA, borderMode=cv2.BORDER_CONSTANT) if msk.ndim == 3 and msk.ndim == 2: ret = ret[:, :, np.newaxis] neww, newh = RotationAndCropValid.largest_rotated_rect(ret.shape[1], ret.shape[0], deg) neww = min(neww, ret.shape[1]) newh = min(newh, ret.shape[0]) newx = int(center[0] - neww * 0.5) newy = int(center[1] - newh * 0.5) # print(ret.shape, deg, newx, newy, neww, newh) msk = ret[newy:newy + newh, newx:newx + neww] return img, joint_list, msk def random_flip(image,annos,mask_miss): flip_list=[0, 1, 5, 6, 7, 2, 3, 4, 11, 12, 13, 8, 9, 10, 15, 14, 17, 16, 18] prob = random.uniform(0, 1.0) if prob > 0.5: return image,annos,mask_miss _, width, _ = np.shape(image) image = cv2.flip(image, 1) mask_miss=cv2.flip(mask_miss,1) new_joints = [] for people in annos: new_keypoints = [] for k in flip_list: point=people[k] if point[0] < 0 or point[1] < 0: new_keypoints.append((-1000, -1000)) continue if point[0]>image.shape[1]-1 or point[1]>image.shape[0]-1: new_keypoints.append((-1000, -1000)) continue if (width - point[0])>image.shape[1]-1: new_keypoints.append((-1000, -1000)) continue new_keypoints.append((width - point[0], point[1])) new_joints.append(new_keypoints) annos=new_joints return image, annos, mask_miss def pose_random_scale(image,annos,mask_miss): height=image.shape[0] width =image.shape[1] scalew = np.random.uniform(0.8, 1.2) scaleh = np.random.uniform(0.8, 1.2) # scalew =scaleh=np.random.uniform(0.5, 1.1) # scaleh=0.8934042054560039 # scalew=1.0860957314059887 neww = int(width * scalew) newh = int(height * scaleh) dst = cv2.resize(image, (neww, newh), interpolation=cv2.INTER_AREA) mask_miss=cv2.resize(mask_miss, (neww, newh), interpolation=cv2.INTER_AREA) # adjust meta data adjust_joint_list = [] for joint in annos: adjust_joint = [] for point in joint: if point[0] < -100 or point[1] < -100: adjust_joint.append((-1000, -1000)) continue # if point[0] <= 0 or point[1] <= 0 or int(point[0] * scalew + 0.5) > neww or int( # point[1] * scaleh + 0.5) > newh: # adjust_joint.append((-1, -1)) # continue adjust_joint.append((int(point[0] * scalew + 0.5), int(point[1] * scaleh + 0.5))) adjust_joint_list.append(adjust_joint) return dst,adjust_joint_list,mask_miss def pose_resize_shortestedge_random(image,annos, mask): _target_height = 368 _target_width = 368 if len(np.shape(image))==2: image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB) height,width,_=np.shape(image) ratio_w = _target_width / width ratio_h = _target_height / height ratio = min(ratio_w, ratio_h) target_size = int(min(width * ratio + 0.5, height * ratio + 0.5)) random_target=random.uniform(0.95, 1.6) # random_target=1.1318003767113862 target_size = int(target_size * random_target) # target_size = int(min(_network_w, _network_h) * random.uniform(0.7, 1.5)) return pose_resize_shortestedge(image, annos, mask, target_size) def pose_resize_shortestedge(image, annos,mask,target_size): _target_height = 368 _target_width = 368 img=image height, width, _ = np.shape(image) # adjust image scale = target_size / min(height, width) if height < width: newh, neww = target_size, int(scale * width + 0.5) else: newh, neww = int(scale * height + 0.5), target_size dst = cv2.resize(img, (neww, newh), interpolation=cv2.INTER_AREA) mask = cv2.resize(mask, (neww, newh), interpolation=cv2.INTER_AREA) pw = ph = 0 if neww < _target_width or newh < _target_height: pw = max(0, (_target_width - neww) // 2) ph = max(0, (_target_height - newh) // 2) mw = (_target_width - neww) % 2 mh = (_target_height - newh) % 2 color = np.random.uniform(0.0, 1.0) dst = cv2.copyMakeBorder(dst, ph, ph + mh, pw, pw + mw, cv2.BORDER_CONSTANT, value=(0,0 ,color)) mask = cv2.copyMakeBorder(mask, ph, ph + mh, pw, pw + mw, cv2.BORDER_CONSTANT, value=1) # adjust meta data adjust_joint_list = [] for joint in annos: adjust_joint = [] for point in joint: if point[0] < -100 or point[1] < -100: adjust_joint.append((-1000, -1000)) continue # if point[0] <= 0 or point[1] <= 0 or int(point[0]*scale+0.5) > neww or int(point[1]*scale+0.5) > newh: # adjust_joint.append((-1, -1)) # continue adjust_joint.append((int(point[0] * scale + 0.5) + pw, int(point[1] * scale + 0.5) + ph)) adjust_joint_list.append(adjust_joint) return dst,adjust_joint_list,mask def pose_crop_random(image,annos,mask): _target_height = 368 _target_width = 368 target_size = (_target_width, _target_height) if len(np.shape(image))==2: image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB) height,width,_=np.shape(image) for _ in range(50): x = random.randrange(0, width - target_size[0]) if width > target_size[0] else 0 y = random.randrange(0, height - target_size[1]) if height > target_size[1] else 0 # check whether any face is inside the box to generate a reasonably-balanced datasets for joint in annos: if x <= joint[0][0] < x + target_size[0] and y <= joint[0][1] < y + target_size[1]: break return pose_crop(image,annos,mask, x, y, target_size[0], target_size[1]) def pose_crop(image,annos,mask, x, y, w, h): # adjust image target_size = (w, h) img = image resized = img[y:y+target_size[1], x:x+target_size[0], :] resized_mask = mask[y:y + target_size[1], x:x + target_size[0]] # adjust meta data adjust_joint_list = [] for joint in annos: adjust_joint = [] for point in joint: if point[0] < -10 or point[1] < -10: adjust_joint.append((-1000, -1000)) continue # if point[0] <= 0 or point[1] <= 0: # adjust_joint.append((-1000, -1000)) # continue new_x, new_y = point[0] - x, point[1] - y # if new_x <= 0 or new_y <= 0 or new_x > target_size[0] or new_y > target_size[1]: # adjust_joint.append((-1, -1)) # continue if new_x > 367 or new_y > 367: adjust_joint.append((-1000, -1000)) continue adjust_joint.append((new_x, new_y)) adjust_joint_list.append(adjust_joint) return resized,adjust_joint_list,resized_mask def drawing(image, annos): plt.imshow(image) for j in annos: for i in j: if i[0]>0 and i[1]>0: plt.scatter(i[0],i[1]) plt.savefig('fig/'+str(i)+'.jpg', dpi=100) plt.show()

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website/addons/dataverse/views/widget.py

bdyetton/prettychart

0

140569

<gh_stars>0 import httplib as http from website.addons.dataverse.client import connect_from_settings_or_401, \ get_dataverse, get_dataset from website.project.decorators import must_be_contributor_or_public, \ must_have_addon @must_be_contributor_or_public @must_have_addon('dataverse', 'node') def dataverse_widget(node_addon, **kwargs): node = node_addon.owner widget_url = node.api_url_for('dataverse_get_widget_contents') ret = { 'complete': node_addon.complete, 'widget_url': widget_url, } ret.update(node_addon.config.to_json()) return ret, http.OK @must_be_contributor_or_public @must_have_addon('dataverse', 'node') def dataverse_get_widget_contents(node_addon, **kwargs): data = { 'connected': False, } if not node_addon.complete: return {'data': data}, http.OK doi = node_addon.dataset_doi alias = node_addon.dataverse_alias connection = connect_from_settings_or_401(node_addon) dataverse = get_dataverse(connection, alias) dataset = get_dataset(dataverse, doi) if dataset is None: return {'data': data}, http.BAD_REQUEST dataverse_host = node_addon.external_account.oauth_key dataverse_url = 'http://{0}/dataverse/{1}'.format(dataverse_host, alias) dataset_url = 'http://dx.doi.org/' + doi data.update({ 'connected': True, 'dataverse': node_addon.dataverse, 'dataverseUrl': dataverse_url, 'dataset': node_addon.dataset, 'doi': doi, 'datasetUrl': dataset_url, 'citation': dataset.citation, }) return {'data': data}, http.OK

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_internal/generate.py

damz/gopherjs-feather-icons

0

183393

<reponame>damz/gopherjs-feather-icons import os import glob import lxml.etree import subprocess if __name__ == "__main__": parser = lxml.etree.XMLParser(remove_blank_text=True) def normalize_xml(data): root = lxml.etree.XML(data, parser=parser) root.insert( 0, root.makeelement("path", { "visibility": "hidden", "fill": "none", "stroke": "none", "d": "M0 0 H 24 V 24 H 0 Z", }) ) return lxml.etree.tostring(root) camelize_exceptions = { "cpu": "CPU", } def _camelize(part): try: return camelize_exceptions[part] except KeyError: return part[0].upper() + part[1:] def camelize(name): return "".join( _camelize(part) for part in name.split("_") if part != "" ) with open("icons.go", "wb") as f: f.write("package icons\n\n") f.write("const (\n") for filepath in sorted(glob.glob("feather/icons/*.svg")): name = os.path.basename(filepath)[:-len(".svg")].replace("-", "_") go_name = camelize(name) with open(filepath, "rb") as icon_f: data = icon_f.read() f.write("\t%s = `%s`\n" % ( go_name, normalize_xml(data), )) f.write(")\n") subprocess.check_call([ "gofmt", "-w", "icons.go", ])

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tests/test_conveniences.py

CSC-DPR/eopf-cpm

0

34346

import datetime import os import sys from cmath import inf from typing import Any import hypothesis.extra.numpy as xps import hypothesis.strategies as st import numpy import pytest from hypothesis import assume, given from eopf.product.utils import ( apply_xpath, conv, convert_to_unix_time, is_date, parse_xml, reverse_conv, translate_structure, ) @pytest.fixture def tree(EMBEDED_TEST_DATA_FOLDER: str): snippet_path = os.path.join(EMBEDED_TEST_DATA_FOLDER, "snippet_xfdumanifest.xml") with open(snippet_path) as f: return parse_xml(f) @st.composite def value_with_type(draw, elements=st.integers(), expected_type=int, expected_container_type=None): if isinstance(expected_type, st.SearchStrategy): expected_type = draw(expected_type) if expected_container_type is not None: if isinstance(expected_container_type, st.SearchStrategy): expected_container_type = draw(expected_container_type) return (draw(elements), expected_type, expected_container_type) return (draw(elements), expected_type) @st.composite def numpy_value(draw, dtype_st=xps.scalar_dtypes(), allow_infinity=True, allow_nan=True): return draw(xps.from_dtype(draw(dtype_st), allow_infinity=allow_infinity, allow_nan=allow_nan)) @pytest.mark.unit def test_parse_xml(tree): """Given an input xml, the output of the function must match the expected output""" result = "" display_namespaces = True for element in tree.iter(): tag = element.tag result += f"{tag}\n" if display_namespaces: display_namespaces = False for key, value in element.nsmap.items(): result += f"{key} : {value}\n" attributes = element.attrib for key, value in attributes.items(): result += f"{key} : {value}\n" textual_content = element.text if textual_content and textual_content.strip(): result += textual_content + "\n" file_path = os.path.join(os.path.abspath("tests/data"), "solutions.txt") with open(file_path, "r") as f: expected = f.read() assert result == expected @pytest.mark.unit def test_translate_structure(tree): """Given an input xml, the output of the function must match the expected output""" MAP = { "title": "concat('',metadataSection/metadataObject[@ID='generalProductInformation']/metadataWrap/xmlData/" "sentinel3:generalProductInformation/sentinel3:productName/text())", "Conventions": "'CF-1.9'", } NAMESPACES = { "xfdu": "urn:ccsds:schema:xfdu:1", "gml": "http://www.opengis.net/gml", "sentinel-safe": "http://www.esa.int/safe/sentinel/1.1", "sentinel3": "http://www.esa.int/safe/sentinel/sentinel-3/1.0", "olci": "http://www.esa.int/safe/sentinel/sentinel-3/olci/1.0", } result = translate_structure(MAP, tree, NAMESPACES) assert result == { "title": "S3A_OL_1_EFR____20220116T092821_20220116T093121_20220117T134858_0179_081_036_2160_LN1_O_NT_002.SEN3", "Conventions": "CF-1.9", } @pytest.mark.unit def test_apply_xpath(tree): """Given an input xml, the output of the function must match the expected output""" MAP = { "title": "concat('',metadataSection/metadataObject[@ID='generalProductInformation']/metadataWrap/xmlData/" "sentinel3:generalProductInformation/sentinel3:productName/text())", "Conventions": "'CF-1.9'", } NAMESPACES = { "xfdu": "urn:ccsds:schema:xfdu:1", "gml": "http://www.opengis.net/gml", "sentinel-safe": "http://www.esa.int/safe/sentinel/1.1", "sentinel3": "http://www.esa.int/safe/sentinel/sentinel-3/1.0", "olci": "http://www.esa.int/safe/sentinel/sentinel-3/olci/1.0", } result = {attr: apply_xpath(tree, MAP[attr], NAMESPACES) for attr in MAP} assert result == { "title": "S3A_OL_1_EFR____20220116T092821_20220116T093121_20220117T134858_0179_081_036_2160_LN1_O_NT_002.SEN3", "Conventions": "CF-1.9", } @pytest.mark.unit def test_is_date(): string_date_1 = "2020-03-31T17:19:29.230522Z" # Zulu time string_date_2 = "2020-03-31T17:19:29.230522GMT+3" # GMT+3 Time string_date_3 = "some_random_string" dt_date = datetime.datetime(2020, 3, 31, 17, 19, 29, 230522) assert is_date(string_date_1) assert is_date(string_date_2) assert is_date(str(dt_date)) assert not is_date(string_date_3) @pytest.mark.unit def test_convert_unix_time(): import pytz # Define datetime-like string and verify if conversion match with datetime object and expected unix time. (MS) string_date = "2020-03-31T17:19:29.230522Z" dt_date = datetime.datetime(2020, 3, 31, 17, 19, 29, 230522, pytz.UTC) expected_unix_time = 1585675169230522 assert convert_to_unix_time(string_date) == convert_to_unix_time(dt_date) == expected_unix_time # Define datetime-like string in Zulu Time Zone, and verify that it doesnt match with expected unix time string_date = "2020-03-31T17:19:29.230522GMT-3" assert convert_to_unix_time(string_date) != convert_to_unix_time(dt_date) assert convert_to_unix_time(string_date) != expected_unix_time # try: string_date = "a string that is not a valid date" convert_to_unix_time(string_date) except ValueError: assert True @pytest.mark.unit @given( value_and_types=st.one_of( value_with_type( st.lists(elements=st.floats(allow_infinity=False, allow_nan=False), unique=True, min_size=10), float, list, ), value_with_type(st.lists(elements=st.integers(), unique=True, min_size=10), int, list), value_with_type(st.lists(elements=st.booleans(), unique=True, min_size=2), int, list), value_with_type(st.sets(elements=st.floats(allow_infinity=False, allow_nan=False), min_size=10), float, set), value_with_type(st.sets(elements=st.integers(), min_size=10), int, set), value_with_type(st.sets(elements=st.booleans(), min_size=2), int, set), value_with_type(st.dictionaries(st.text(), st.integers(), min_size=10), int, dict), value_with_type(st.dictionaries(st.text(), st.booleans(), min_size=10), int, dict), value_with_type( st.dictionaries(st.text(), st.floats(allow_infinity=False, allow_nan=False), min_size=10), float, dict, ), value_with_type(xps.arrays(xps.floating_dtypes(), 10, unique=True), float, list), value_with_type(xps.arrays(xps.integer_dtypes(), 10, unique=True), int, list), value_with_type(xps.arrays(xps.boolean_dtypes(), 10, unique=True), int, list), ), ) def test_conv_sequences(value_and_types: tuple[Any, type, type]): values, type_, container_type = value_and_types assume(inf not in values) converted_list = conv(values) assert isinstance(converted_list, container_type) # Check if size of converted value doesn't change assert len(converted_list) == len(values) # Check if type of each item from converted value is correct if isinstance(converted_list, dict): iterator = converted_list.values() original = values.values() else: iterator = converted_list original = values for converted_value, value in zip(sorted(iterator), sorted(original)): assert isinstance(converted_value, type_) conv_value = conv(value) # check if converted values are the same or both are nan assert converted_value == conv_value or (converted_value != converted_value and conv_value != conv_value) @pytest.mark.unit @pytest.mark.parametrize("EPSILON", [0.1]) @given(value=numpy_value(xps.floating_dtypes(), allow_infinity=False, allow_nan=False)) def test_epsilon_on_fp_conv(value, EPSILON): converted_value = conv(value) assert value - converted_value < EPSILON assert converted_value - value < EPSILON @pytest.mark.unit @given( value_and_type=st.one_of( value_with_type( elements=numpy_value(xps.floating_dtypes(), allow_infinity=False, allow_nan=False), expected_type=float, ), value_with_type( elements=numpy_value(xps.integer_dtypes(), allow_infinity=False, allow_nan=False), expected_type=int, ), value_with_type( elements=st.datetimes(), expected_type=int, ), ), ) def test_conv(value_and_type): value, expected_type = value_and_type converted_value = conv(value) assert isinstance(converted_value, expected_type) @pytest.mark.unit @pytest.mark.parametrize( "sysmax, maxint", [ (numpy.int64(sys.maxsize), numpy.int64(9223372036854775807)), ], ) def test_maxint_conv(sysmax, maxint): # Robustness assert conv(sysmax) == maxint @pytest.mark.unit @given( value_and_types=st.one_of( value_with_type( st.integers(min_value=numpy.iinfo("int64").min, max_value=numpy.iinfo("int64").max), int, xps.integer_dtypes(endianness="=", sizes=(64,)), ), value_with_type( st.integers(min_value=numpy.iinfo("int32").min, max_value=numpy.iinfo("int32").max), int, xps.integer_dtypes(endianness="=", sizes=(32,)), ), value_with_type( st.integers(min_value=numpy.iinfo("int16").min, max_value=numpy.iinfo("int16").max), int, xps.integer_dtypes(endianness="=", sizes=(16,)), ), value_with_type( st.integers(min_value=numpy.iinfo("int8").min, max_value=numpy.iinfo("int8").max), int, xps.integer_dtypes(endianness="=", sizes=(8,)), ), value_with_type(st.floats(width=16), float, xps.floating_dtypes(endianness="=", sizes=(16,))), value_with_type(st.floats(width=32), float, xps.floating_dtypes(endianness="=", sizes=(32,))), value_with_type(st.floats(width=64), float, xps.floating_dtypes(endianness="=", sizes=(64,))), ), ) def test_reverse_conv(value_and_types): value, current_type, data_type = value_and_types # verify if the current data type is as expected (int or float) assert isinstance(value, current_type) # convert value to given data type (int64, int32, float64 etc .. ) converted_value = reverse_conv(data_type, value) # check if conversion is performed according to given data (int -> numpy.int64, float -> numpy.float64) assert numpy.issubdtype(type(converted_value), data_type) # check if converted data type is changed and not match with old one assert type(converted_value) != current_type

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bin/import_guest.py

GOMYWAY-NETWORKS-LLC/karesansui

54

111697

<gh_stars>10-100 #!/usr/bin/env python # -*- coding: utf-8 -*- # # This file is part of Karesansui. # # Copyright (C) 2012 HDE, Inc. # # 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 os import os.path import sys import signal import logging from optparse import OptionParser import glob from ksscommand import KssCommand, KssCommandException, KssCommandOptException import __cmd__ try: import karesansui from karesansui import __version__ from karesansui.lib.virt.virt import KaresansuiVirtConnection, KaresansuiVirtException from karesansui.lib.utils import load_locale from karesansui.lib.virt.config_export import ExportConfigParam except ImportError, e: print >>sys.stderr, "[Error] some packages not found. - %s" % e sys.exit(1) _ = load_locale() usage = '%prog [options]' def getopts(): optp = OptionParser(usage=usage, version=__version__) optp.add_option('-e', '--exportuuid', dest='exportuuid', help=_('Export UUID to import')) optp.add_option('-d', '--destuuid', dest='destuuid', help=_('Guest UUID to import')) optp.add_option('-q', '--quiet',dest='verbose', action="store_false", default=True, help=_("don't print status messages")) return optp.parse_args() def chkopts(opts): if not opts.exportuuid: raise KssCommandOptException('ERROR: -e or --exportuuid option is required.') if not opts.destuuid: raise KssCommandOptException('ERROR: -d or --destuuid option is required.') class ImportGuest(KssCommand): def process(self): (opts, args) = getopts() chkopts(opts) self.up_progress(10) conn = KaresansuiVirtConnection(readonly=False) try: progresscb = None if opts.verbose: try: from karesansui.lib.progress import ProgressMeter progresscb = ProgressMeter(command_object=self) except: pass else: try: from karesansui.lib.progress import ProgressMeter progresscb = ProgressMeter(command_object=self,quiet=True) except: pass try: #inactive_pool = conn.list_inactive_storage_pool() inactive_pool = [] active_pool = conn.list_active_storage_pool() pools = inactive_pool + active_pool if not pools: raise KssCommandException("Storage pool does not exist, or has been stopped.") pools.sort() export = [] for pool_name in pools: pool = conn.search_kvn_storage_pools(pool_name) path = pool[0].get_info()["target"]["path"] if os.path.exists(path): for _afile in glob.glob("%s/*/info.dat" % (path,)): e_param = ExportConfigParam() e_param.load_xml_config(_afile) if e_param.get_uuid() != opts.exportuuid: continue export.append({"dir" : os.path.dirname(_afile), "uuid" : opts.exportuuid, }) if len(export) != 1: raise KssCommandException("There are differences in the export data and real data. - uuid=%s" % opts.exportuuid) else: export = export[0] if os.path.isdir(export["dir"]) is False: raise KssCommandException("There is no real data. - dir=%s" % export["dir"]) conn.import_guest(export["dir"], uuid=opts.destuuid, progresscb=progresscb) self.up_progress(40) self.logger.info('Import guest completed. - export=%s, dest=%s' % (opts.exportuuid, opts.destuuid)) print >>sys.stdout, _('Import guest completed. - export=%s, dest=%s' % (opts.exportuuid, opts.destuuid)) return True except KaresansuiVirtException, e: raise KssCommandException('Failed to import guest. - [%s]' \ % (''.join(str(e.args)))) finally: conn.close() if __name__ == "__main__": target = ImportGuest() sys.exit(target.run())

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363, 903, 29874, 1445, 297, 13149, 29889, 23705, 11702, 29879, 29914, 3877, 3888, 29889, 4130, 29908, 1273, 313, 2084, 29892, 22164, 13, 462, 9651, 321, 29918, 3207, 353, 1222, 637, 3991, 4736, 580, 13, 462, 9651, 321, 29918, 3207, 29889, 1359, 29918, 3134, 29918, 2917, 7373, 29874, 1445, 29897, 13, 13, 462, 9651, 565, 321, 29918, 3207, 29889, 657, 29918, 25118, 580, 2804, 29111, 29889, 15843, 25118, 29901, 13, 462, 18884, 6773, 13, 13, 462, 9651, 5609, 29889, 4397, 3319, 29908, 3972, 29908, 584, 2897, 29889, 2084, 29889, 25721, 7373, 29874, 1445, 511, 13, 462, 462, 965, 376, 25118, 29908, 584, 29111, 29889, 15843, 25118, 29892, 13, 462, 462, 965, 5615, 13, 13, 18884, 565, 7431, 29898, 15843, 29897, 2804, 29871, 29896, 29901, 13, 462, 1678, 12020, 476, 893, 6255, 2451, 703, 8439, 526, 12651, 297, 278, 5609, 848, 322, 1855, 848, 29889, 448, 318, 5416, 16328, 29879, 29908, 1273, 29111, 29889, 15843, 25118, 29897, 13, 18884, 1683, 29901, 13, 462, 1678, 5609, 353, 5609, 29961, 29900, 29962, 13, 13, 18884, 565, 2897, 29889, 2084, 29889, 275, 3972, 29898, 15843, 3366, 3972, 20068, 338, 7700, 29901, 13, 462, 1678, 12020, 476, 893, 6255, 2451, 703, 8439, 338, 694, 1855, 848, 29889, 448, 4516, 16328, 29879, 29908, 1273, 5609, 3366, 3972, 20068, 13, 13, 18884, 11009, 29889, 5215, 29918, 2543, 342, 29898, 15843, 3366, 3972, 12436, 318, 5416, 29922, 25707, 29889, 7854, 25118, 29892, 6728, 10702, 29922, 18035, 10702, 29897, 13, 13, 18884, 1583, 29889, 786, 29918, 18035, 29898, 29946, 29900, 29897, 13, 18884, 1583, 29889, 21707, 29889, 3888, 877, 17518, 17838, 8676, 29889, 448, 5609, 16328, 29879, 29892, 2731, 16328, 29879, 29915, 1273, 313, 25707, 29889, 15843, 25118, 29892, 29111, 29889, 7854, 25118, 876, 13, 18884, 1596, 5099, 9675, 29889, 25393, 29892, 903, 877, 17518, 17838, 8676, 29889, 448, 5609, 16328, 29879, 29892, 2731, 16328, 29879, 29915, 1273, 313, 25707, 29889, 15843, 25118, 29892, 29111, 29889, 7854, 25118, 876, 13, 18884, 736, 5852, 13, 13, 9651, 5174, 3467, 267, 550, 1481, 29963, 2728, 2451, 29892, 321, 29901, 13, 18884, 12020, 476, 893, 6255, 2451, 877, 17776, 304, 1053, 17838, 29889, 448, 518, 29995, 29879, 29962, 29915, 320, 13, 462, 462, 418, 1273, 6702, 4286, 7122, 29898, 710, 29898, 29872, 29889, 5085, 13697, 13, 4706, 7146, 29901, 13, 9651, 11009, 29889, 5358, 580, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 3646, 353, 16032, 9485, 342, 580, 13, 1678, 10876, 29889, 13322, 29898, 5182, 29889, 3389, 3101, 13, 2 ]

Outputs_win_app.py

stfbnc/mtsa_py

0

197740

import sys if sys.version_info[0] == 2: import Tkinter as tk from tkFileDialog import askdirectory else: import tkinter as tk from tkinter.filedialog import askdirectory import numpy as np import matplotlib matplotlib.use("TkAgg") import matplotlib.pyplot as plt from matplotlib.figure import Figure from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg import os ### set size for figures x_size = 7 y_size = 5 ############################################################################# ############################## SAVE_FREQ #################################### ############################################################################# def save_freq(arg0,arg2,arg3,arg4): path_file = askdirectory() path_file = os.path.join(path_file,'freq_perc.txt') f = open(path_file,'w') f.write('Frequencies for file %s\n\n' % (arg0)) f.write('Filter_round Frequency Period Percentage\n\n') for i in range(len(arg2)): f.write('%d %.8f %.2f %.2f\n' % (arg2[i],1.0 / arg3[i],arg3[i],arg4[i])) f.close() ############################################################################# ############################################################################# ############################## DETREND_PLOT ################################# ############################################################################# def detrend_plot(main_win,arg0,arg1,arg2): ticks = np.arange(0,len(arg0),len(arg0) / 7,dtype = int) t = np.arange(1,len(arg0) + 1,dtype = int) time = np.array(arg2,dtype = str) ticks_vec = t[ticks] time_label = time[ticks] def save_fig(): path_tot = askdirectory() plt.rc('text',usetex = True) plt.rc('font',family = 'serif') plt.plot(t,arg0 + arg1,'r',label = 'original') if not np.isscalar(arg1): plt.plot(t,arg1,'k',label = 'trend') plt.plot(t,arg0,'b',label = 'detrended') plt.legend(loc = 0) plt.xlim(float(entries[0].get()),float(entries[1].get())) plt.ylim(float(entries[2].get()),float(entries[3].get())) plt.xlabel(entries[4].get()) plt.ylabel(entries[5].get()) plt.title(entries[6].get()) plt.xticks(ticks,time_label) plt.margins(0.2) plt.subplots_adjust(bottom = 0.2) plt.savefig(os.path.join(path_tot,'ts.pdf')) plt.close() def screen_fig(): fig_ts = Figure(figsize = (x_size,y_size)) a = fig_ts.add_subplot(111) a.plot(t,arg0 + arg1,'r',label = 'original') if not np.isscalar(arg1): a.plot(t,arg1,'k',label = 'trend') a.plot(t,arg0,'b',label = 'detrended') a.legend(loc = 0) a.set_xlim(float(entries[0].get()),float(entries[1].get())) a.set_ylim(float(entries[2].get()),float(entries[3].get())) a.set_xlabel(entries[4].get(),fontsize = 15) a.set_ylabel(entries[5].get(),fontsize = 15) a.set_title(entries[6].get(),fontsize = 15) a.set_xticks(ticks_vec) a.set_xticklabels(time_label) fig_ts.tight_layout() canvas = FigureCanvasTkAgg(fig_ts,master = frame_1) canvas.get_tk_widget().grid(row = 0,column = 0) canvas.draw() def reset_fig(): for i in range(len(entries)): entries[i].delete(0,tk.END) entries[i].insert(0,values[i]) screen_fig() top = tk.Toplevel(main_win) top.geometry("%dx%d" % (int(main_win.winfo_screenwidth() * 0.93 * 0.85), int(main_win.winfo_screenheight() * 0.65))) top.wm_title("Time Series") top.resizable(width = False,height = False) frame_1 = tk.Frame(top) frame_1.grid(row = 0,column = 0) frame_2 = tk.Frame(top) frame_2.grid(row = 0,column = 1) names = ["X Limit (left)","X Limit (right)","Y Limit (bottom)","Y Limit (top)","X Label","Y Label","Title"] if not np.isscalar(arg1): values = [t[0],t[-1],np.min([np.min(arg0[~np.isnan(arg0)]),np.min(arg1[~np.isnan(arg1)]), np.min(arg0[~np.isnan(arg0)] + arg1[~np.isnan(arg1)])]) - 1.0, np.max([np.max(arg0[~np.isnan(arg0)]),np.max(arg1[~np.isnan(arg1)]), np.max(arg0[~np.isnan(arg0)] + arg1[~np.isnan(arg1)])]) + 1.0,'t','$X_t$', 'Time Series'] else: values = [t[0],t[-1],np.min(arg0[~np.isnan(arg0)]) - 1.0,np.max(arg0[~np.isnan(arg0)]) + 1.0, 't','$X_t$','Time Series'] entries = [] for i in range(len(names)): tk.Label(frame_2,text = names[i],font = "Verdana 13 bold").grid(row = 2 * i,column = 0, padx = int(main_win.winfo_screenwidth() * 0.01)) entries.append(tk.Entry(frame_2,width = 18)) entries[-1].insert(0,values[i]) entries[-1].grid(row = 2 * i,column = 1) for i in range(len(names)): tk.Label(frame_2,text = "").grid(row = 2 * i + 1,column = 0) screen_fig() tk.Button(frame_2,text = "Replot",font = "Verdana 13 bold",command = screen_fig).grid(row = 2 * len(names),column = 0) tk.Button(frame_2,text = "Save",font = "Verdana 13 bold",command = save_fig).grid(row = 2 * len(names),column = 1) tk.Label(frame_2,text = "").grid(row = 2 * len(names) + 1,column = 0) tk.Button(frame_2,text = "Reset",font = "Verdana 13 bold",command = reset_fig).grid(row = 2 * len(names) + 2,column = 0) ############################################################################# ############################################################################# ############################## SPECTRUM_PLOT ################################ ############################################################################# def spectrum_plot(main_win,arg0,arg1,arg2): def save_fig(): path_tot = askdirectory() plt.rc('text',usetex = True) plt.rc('font',family = 'serif') plt.plot(arg1,arg0,'b') if arg2 != 0: plt.plot((arg1[0],arg1[-1]),(arg2,arg2),'r') plt.xlabel(entries[0].get()) plt.ylabel(entries[1].get()) plt.xlim(float(entries[2].get()),float(entries[3].get())) plt.ylim(float(entries[4].get()),float(entries[5].get())) plt.title(entries[6].get()) plt.savefig(os.path.join(path_tot,'spectrum_in.pdf')) plt.close() def screen_fig(): fig_ts = Figure(figsize = (x_size,y_size)) a = fig_ts.add_subplot(111) a.plot(arg1,arg0,'b') if arg2 != 0: a.plot((arg1[0],arg1[-1]),(arg2,arg2),'r') a.set_xlabel(entries[0].get(),fontsize = 15) a.set_ylabel(entries[1].get(),fontsize = 15) a.set_xlim(float(entries[2].get()),float(entries[3].get())) a.set_ylim(float(entries[4].get()),float(entries[5].get())) a.set_title(entries[6].get(),fontsize = 15) fig_ts.tight_layout() canvas = FigureCanvasTkAgg(fig_ts,master = frame_1) canvas.get_tk_widget().grid(row = 0,column = 0) canvas.draw() def reset_fig(): for i in range(len(entries)): entries[i].delete(0,tk.END) entries[i].insert(0,values[i]) screen_fig() top = tk.Toplevel(main_win) top.geometry("%dx%d" % (int(main_win.winfo_screenwidth() * 0.93 * 0.85), int(main_win.winfo_screenheight() * 0.65))) if arg2 != 0: top.wm_title("Spectrum") else: top.wm_title("Spectrum of residuals") top.resizable(width = False,height = False) frame_1 = tk.Frame(top) frame_1.grid(row = 0,column = 0) frame_2 = tk.Frame(top) frame_2.grid(row = 0,column = 1) names = ["X Label","Y Label","X Limit (left)","X Limit (right)","Y Limit (bottom)","Y Limit (top)","Title"] if arg2 != 0: values = ['$\\nu$','$P(\\nu)$',0,arg1[-1],0,np.max(arg0) + 10.0,'LS spectrum (initial)'] else: values = ['$\\nu$','$P(\\nu)$',0,arg1[-1],0,np.max(arg0) + 10.0,'LS spectrum of residuals'] entries = [] for i in range(len(names)): tk.Label(frame_2,text = names[i],font = "Verdana 13 bold").grid(row = 2 * i,column = 0, padx = int(main_win.winfo_screenwidth() * 0.01)) entries.append(tk.Entry(frame_2,width = 18)) entries[-1].insert(0,values[i]) entries[-1].grid(row = 2 * i,column = 1) for i in range(len(names)): tk.Label(frame_2,text = "").grid(row = 2 * i + 1,column = 0) screen_fig() tk.Button(frame_2,text = "Replot",font = "Verdana 13 bold",command = screen_fig).grid(row = 2 * len(names),column = 0) tk.Button(frame_2,text = "Save",font = "Verdana 13 bold",command = save_fig).grid(row = 2 * len(names),column = 1) tk.Label(frame_2,text = "").grid(row = 2 * len(names) + 1,column = 0) tk.Button(frame_2,text = "Reset",font = "Verdana 13 bold",command = reset_fig).grid(row = 2 * len(names) + 2,column = 0) ############################################################################# ############################################################################# ############################## RES_PLOT ##################################### ############################################################################# def res_plot(main_win,arg0,arg1,arg2,arg3): ticks = np.arange(0,len(arg0),len(arg0) / 7,dtype = int) t = np.arange(1,len(arg0) + 1,dtype = int) time = np.array(arg3,dtype = str) ticks_vec = t[ticks] time_label = time[ticks] pn_norm_notnan = arg2[~np.isnan(arg2)] outlier_lim = 3.0 num_outliers_max = len(pn_norm_notnan[pn_norm_notnan > outlier_lim]) num_outliers_min = len(pn_norm_notnan[pn_norm_notnan < -outlier_lim]) num_outliers = num_outliers_max + num_outliers_min def save_fig(): path_tot = askdirectory() plt.figure(figsize = (12,9)) plt.rc('text',usetex = True) plt.rc('font',family = 'serif') plt.subplot(2,1,1) plt.plot(t,arg0) plt.xlim(int(entries[0].get()),int(entries[1].get())) plt.ylim(float(entries[5].get()),float(entries[6].get())) plt.xticks(ticks,'') plt.ylabel(entries[2].get()) plt.title(entries[4].get()) plt.margins(0.2) plt.subplots_adjust(hspace = 0.0) plt.subplot(2,1,2) sigma = '%.2f' % arg1 if int(matplotlib.__version__.split('.')[0]) == 2: plt.bar(t,arg2,width = 10,label = 'num outl = ' + str(num_outliers)) else: plt.bar(t,arg2,width = 0.1,label = 'num outl = ' + str(num_outliers)) plt.plot((t[0],t[-1]),(outlier_lim,outlier_lim),'r',label = '$\sigma$ = ' + sigma) plt.plot((t[0],t[-1]),(-outlier_lim,-outlier_lim),'r') plt.legend(loc = 0) plt.xlim(int(entries[0].get()),int(entries[1].get())) plt.ylim(float(entries[7].get()),float(entries[8].get())) plt.xticks(ticks,time_label) plt.ylabel(entries[3].get()) plt.margins(0.2) plt.subplots_adjust(hspace = 0.0) plt.savefig(os.path.join(path_tot,'res.pdf')) plt.close() def screen_fig(): fig_ts = Figure(figsize = (x_size,y_size)) a = fig_ts.add_subplot(211) a.plot(t,arg0) a.set_xlim(int(entries[0].get()),int(entries[1].get())) a.set_ylim(float(entries[5].get()),float(entries[6].get())) a.set_xticks(ticks_vec) a.set_xticklabels('') a.set_ylabel(entries[2].get(),fontsize = 15) a.set_title(entries[4].get(),fontsize = 15) b = fig_ts.add_subplot(212) sigma = '%.2f' % arg1 if int(matplotlib.__version__.split('.')[0]) == 2: b.bar(t,arg2,width = 10,label = 'num outl = ' + str(num_outliers)) else: b.bar(t,arg2,width = 0.1,label = 'num outl = ' + str(num_outliers)) b.plot((t[0],t[-1]),(outlier_lim,outlier_lim),'r',label = '$\sigma$ = ' + sigma) b.plot((t[0],t[-1]),(-outlier_lim,-outlier_lim),'r') b.legend(loc = 0) b.set_xlim(int(entries[0].get()),int(entries[1].get())) b.set_ylim(float(entries[7].get()),float(entries[8].get())) b.set_xticks(ticks) b.set_xticklabels(time_label) b.set_ylabel(entries[3].get(),fontsize = 15) fig_ts.tight_layout() canvas = FigureCanvasTkAgg(fig_ts,master = frame_1) canvas.get_tk_widget().grid(row = 0,column = 0) canvas.draw() def reset_fig(): for i in range(len(entries)): entries[i].delete(0,tk.END) entries[i].insert(0,values[i]) screen_fig() top = tk.Toplevel(main_win) top.geometry("%dx%d" % (int(main_win.winfo_screenwidth() * 0.93 * 0.85), int(main_win.winfo_screenheight() * 0.65))) top.wm_title("Residuals") top.resizable(width = False,height = False) frame_1 = tk.Frame(top) frame_1.grid(row = 0,column = 0) frame_2 = tk.Frame(top) frame_2.grid(row = 0,column = 1) names = ["X Limit (left)","X Limit (right)","Y Label (top)","Y Label (bottom)","Title", "Y1 Limit (bottom)","Y1 Limit (top)","Y2 Limit (bottom)","Y2 Limit (top)"] values = [t[0],t[-1],'$N_t$','$N_t^{norm}$','Residuals / Normalised residuals',np.min(arg0[~np.isnan(arg0)]) - 10.0, np.max(arg0[~np.isnan(arg0)]) + 10.0,np.min(arg2[~np.isnan(arg0)]) - 1.0,np.max(arg2[~np.isnan(arg0)]) + 1.0] entries = [] for i in range(len(names)): tk.Label(frame_2,text = names[i],font = "Verdana 13 bold").grid(row = 2 * i,column = 0, padx = int(main_win.winfo_screenwidth() * 0.01)) entries.append(tk.Entry(frame_2,width = 18)) entries[-1].insert(0,values[i]) entries[-1].grid(row = 2 * i,column = 1) for i in range(len(names)): tk.Label(frame_2,text = "").grid(row = 2 * i + 1,column = 0) screen_fig() tk.Button(frame_2,text = "Replot",font = "Verdana 13 bold",command = screen_fig).grid(row = 2 * len(names),column = 0) tk.Button(frame_2,text = "Save",font = "Verdana 13 bold",command = save_fig).grid(row = 2 * len(names),column = 1) tk.Label(frame_2,text = "").grid(row = 2 * len(names) + 1,column = 0) tk.Button(frame_2,text = "Reset",font = "Verdana 13 bold",command = reset_fig).grid(row = 2 * len(names) + 2,column = 0) ############################################################################# ############################################################################# ############################## DFA_PLOT ##################################### ############################################################################# def dfa_plot(main_win,arg0,arg1,arg2,arg3): def save_fig(): path_tot = askdirectory() plt.rc('text',usetex = True) plt.rc('font',family = 'serif') plt.plot(np.log(arg0),np.log(arg1),'o',label = '$H$ = ' + arg3) plt.plot(np.log(arg0),arg2,'r') plt.legend(loc = 0) plt.xlim(float(entries[0].get()),float(entries[1].get())) plt.ylim(float(entries[2].get()),float(entries[3].get())) plt.xlabel(entries[4].get()) plt.ylabel(entries[5].get()) plt.title(entries[6].get()) plt.savefig(os.path.join(path_tot,'dfa.pdf')) plt.close() def screen_fig(): fig_ts = Figure(figsize = (x_size,y_size)) a = fig_ts.add_subplot(111) a.plot(np.log(arg0),np.log(arg1),'o',label = '$H$ = ' + arg3) a.plot(np.log(arg0),arg2,'r') a.legend(loc = 0) a.set_xlim(float(entries[0].get()),float(entries[1].get())) a.set_ylim(float(entries[2].get()),float(entries[3].get())) a.set_xlabel(entries[4].get()) a.set_ylabel(entries[5].get()) a.set_title(entries[6].get()) fig_ts.tight_layout() canvas = FigureCanvasTkAgg(fig_ts,master = frame_1) canvas.get_tk_widget().grid(row = 0,column = 0) canvas.draw() def reset_fig(): for i in range(len(entries)): entries[i].delete(0,tk.END) entries[i].insert(0,values[i]) screen_fig() top = tk.Toplevel(main_win) top.geometry("%dx%d" % (int(main_win.winfo_screenwidth() * 0.93 * 0.85), int(main_win.winfo_screenheight() * 0.65))) top.wm_title("DFA") top.resizable(width = False,height = False) frame_1 = tk.Frame(top) frame_1.grid(row = 0,column = 0) frame_2 = tk.Frame(top) frame_2.grid(row = 0,column = 1) names = ["X Limit (left)","X Limit (right)","Y Limit (bottom)","Y Limit (top)","X Label","Y Label","Title"] values = [np.log(arg0[0]) - 0.3,np.log(arg0[-1]) + 0.3,np.min(np.log(arg1)) - 1.0,np.max(np.log(arg1)) + 1.0, 'log$(F(n))$','log$(n)$','DFA fit'] entries = [] for i in range(len(names)): tk.Label(frame_2,text = names[i],font = "Verdana 13 bold").grid(row = 2 * i,column = 0, padx = int(main_win.winfo_screenwidth() * 0.01)) entries.append(tk.Entry(frame_2,width = 18)) entries[-1].insert(0,values[i]) entries[-1].grid(row = 2 * i,column = 1) for i in range(len(names)): tk.Label(frame_2,text = "").grid(row = 2 * i + 1,column = 0) screen_fig() tk.Button(frame_2,text = "Replot",font = "Verdana 13 bold",command = screen_fig).grid(row = 2 * len(names),column = 0) tk.Button(frame_2,text = "Save",font = "Verdana 13 bold",command = save_fig).grid(row = 2 * len(names),column = 1) tk.Label(frame_2,text = "").grid(row = 2 * len(names) + 1,column = 0) tk.Button(frame_2,text = "Reset",font = "Verdana 13 bold",command = reset_fig).grid(row = 2 * len(names) + 2,column = 0) ############################################################################# ############################################################################# ############################## MDFA_PLOT #################################### ############################################################################# def mdfa_plot(main_win,arg0,arg1,arg2,arg3,arg4,arg5,arg6,arg7): def save_fig(): path_tot = askdirectory() plt.figure(figsize = (11,11)) plt.rc('text',usetex = True) plt.rc('font',family = 'serif') plt.subplot(2,2,1) plt.plot(np.log(arg0),np.log(arg1[0,:]),'b.') plt.plot(np.log(arg0),arg2[:,0],'b',label = 'q = -3') plt.plot(np.log(arg0),np.log(arg1[50,:]),'r.') plt.plot(np.log(arg0),arg2[:,50],'r',label = 'q = 0') plt.plot(np.log(arg0),np.log(arg1[-1,:]),'g.') plt.plot(np.log(arg0),arg2[:,-1],'g',label = 'q = 3') plt.legend(loc = 0) plt.xlim(float(entries[0].get()),float(entries[1].get())) plt.ylim(float(entries[2].get()),float(entries[3].get())) plt.xlabel(entries[4].get()) plt.ylabel(entries[5].get()) plt.title(entries[6].get()) plt.margins(0.2) plt.subplots_adjust(bottom = 0.2) plt.subplot(2,2,2) plt.plot(arg3,arg4,'b',label = 'h(q)') plt.plot((arg3[0],arg3[-1]),(arg5,arg5),'k',label = 'H') plt.legend(loc = 0) plt.xlim(float(entries[7].get()),float(entries[8].get())) plt.ylim(float(entries[9].get()),float(entries[10].get())) plt.xlabel(entries[11].get()) plt.ylabel(entries[12].get()) plt.title(entries[13].get()) plt.margins(0.2) plt.subplots_adjust(bottom = 0.2) plt.subplot(2,2,3) plt.plot(arg6,arg7,'b') plt.xlim(float(entries[14].get()),float(entries[15].get())) plt.ylim(float(entries[16].get()),float(entries[17].get())) plt.xlabel(entries[18].get()) plt.ylabel(entries[19].get()) plt.title(entries[20].get()) plt.margins(0.2) plt.subplots_adjust(bottom = 0.2) plt.savefig(os.path.join(path_tot,'mdfa.pdf')) plt.close() def screen_fig(): fig_ts = Figure(figsize = (x_size,y_size)) a = fig_ts.add_subplot(221) a.plot(np.log(arg0),np.log(arg1[0,:]),'b.') a.plot(np.log(arg0),arg2[:,0],'b',label = 'q = -3') a.plot(np.log(arg0),np.log(arg1[50,:]),'r.') a.plot(np.log(arg0),arg2[:,50],'r',label = 'q = 0') a.plot(np.log(arg0),np.log(arg1[-1,:]),'g.') a.plot(np.log(arg0),arg2[:,-1],'g',label = 'q = 3') a.legend(loc = 0) a.set_xlim(float(entries[0].get()),float(entries[1].get())) a.set_ylim(float(entries[2].get()),float(entries[3].get())) a.set_xlabel(entries[4].get()) a.set_ylabel(entries[5].get()) a.set_title(entries[6].get()) b = fig_ts.add_subplot(222) b.plot(arg3,arg4,'b',label = 'H(q)') b.plot((arg3[0],arg3[-1]),(arg5,arg5),'k',label = 'H') b.legend(loc = 0) b.set_xlim(float(entries[7].get()),float(entries[8].get())) b.set_ylim(float(entries[9].get()),float(entries[10].get())) b.set_xlabel(entries[11].get()) b.set_ylabel(entries[12].get()) b.set_title(entries[13].get()) c = fig_ts.add_subplot(223) c.plot(arg6,arg7,'b') c.set_xlim(float(entries[14].get()),float(entries[15].get())) c.set_ylim(float(entries[16].get()),float(entries[17].get())) c.set_xlabel(entries[18].get()) c.set_ylabel(entries[19].get()) c.set_title(entries[20].get()) fig_ts.tight_layout() canvas = FigureCanvasTkAgg(fig_ts,master = frame_1) canvas.get_tk_widget().grid(row = 0,column = 0) canvas.draw() def reset_fig(): for i in range(len(entries)): entries[i].delete(0,tk.END) entries[i].insert(0,values[i]) screen_fig() top = tk.Toplevel(main_win) top.geometry("%dx%d" % (int(main_win.winfo_screenwidth() * 0.93 * 0.85), int(main_win.winfo_screenheight() * 0.75))) top.wm_title("MFDFA") top.resizable(width = False,height = False) frame_1 = tk.Frame(top) frame_1.grid(row = 0,column = 0) frame_2 = tk.Frame(top) frame_2.grid(row = 0,column = 1) names = ["X1 Limit (left)","X1 Limit (right)","Y1 Limit (bottom)","Y1 Limit (top)","X1 Label","Y1 Label","Title1", "X2 Limit (left)","X2 Limit (right)","Y2 Limit (bottom)","Y2 Limit (top)","X2 Label","Y2 Label","Title2", "X3 Limit (left)","X3 Limit (right)","Y3 Limit (bottom)","Y3 Limit (top)","X3 Label","Y3 Label","Title3"] values = [np.log(arg0[0]),np.log(arg0[-1]),np.min([np.min(np.log(arg1[0,:])),np.min(arg2[:,0]), np.min(np.log(arg1[50,:])),np.min(arg2[:,50]),np.min(np.log(arg1[-1,:])),np.min(arg2[:,-1])]) - 1.0, np.max([np.max(np.log(arg1[0,:])),np.max(arg2[:,0]),np.max(np.log(arg1[50,:])),np.max(arg2[:,50]), np.max(np.log(arg1[-1,:])),np.max(arg2[:,-1])]) + 1.0,'log(n)','log(F(n))','MDFA fit', arg3[0],arg3[-1],np.min(arg4) - 0.1,np.max(arg4) + 0.1,'q','H(q)','Generalised Hurst exponent', np.min(arg6) - 0.2,np.max(arg6) + 0.2,np.min(arg7) - 0.2,1.2,'$\\alpha$','$f(\\alpha)$', 'Singularity spectrum'] entries = [] for i in range(len(names)): tk.Label(frame_2,text = names[i],font = "Verdana 13 bold").grid(row = i,column = 0, padx = int(main_win.winfo_screenwidth() * 0.01)) entries.append(tk.Entry(frame_2,width = 18)) entries[-1].insert(0,values[i]) entries[-1].grid(row = i,column = 1) screen_fig() tk.Label(frame_2,text = "").grid(row = len(names),column = 0) tk.Button(frame_2,text = "Replot",font = "Verdana 13 bold",command = screen_fig).grid(row = len(names) + 1,column = 0) tk.Button(frame_2,text = "Save",font = "Verdana 13 bold",command = save_fig).grid(row = len(names) + 1,column = 1) tk.Button(frame_2,text = "Reset",font = "Verdana 13 bold",command = reset_fig).grid(row = len(names) + 2,column = 0) ############################################################################# ############################################################################# ############################## MFDFA2_PLOT ################################## ############################################################################# def MFDFA2_plot(main_win,arg0,arg1,arg2,arg3,arg4,arg5): def save_fig(): path_tot = askdirectory() plt.figure(figsize = (12,9)) plt.rc('text',usetex = True) plt.rc('font',family = 'serif') plt.subplot(2,1,1) ax = plt.gca() if int(matplotlib.__version__.split('.')[0]) == 2: ax.set_facecolor('black') else: ax.set_axis_bgcolor('black') plt.plot(arg0,'y') plt.plot(0.5 * np.ones((len(arg0),)),'w') plt.plot(np.ones((len(arg0),)),'m') plt.plot(1.5 * np.ones((len(arg0),)),'r') plt.xlim(float(entries[0].get()),float(entries[1].get())) plt.ylim(float(entries[2].get()),float(entries[3].get())) plt.xlabel(entries[4].get()) plt.ylabel(entries[5].get()) plt.title(entries[6].get()) plt.margins(0.2) plt.subplots_adjust(hspace = 0.3) plt.subplot(2,1,2) plt.plot(arg1,arg2,'b',label = '$\mu$ = ' + arg4) plt.plot(arg1,arg3,'r',linewidth = 2.0,label = '$\sigma$ = ' + arg5) plt.legend(loc = 0) plt.xlim(float(entries[7].get()),float(entries[8].get())) plt.ylim(float(entries[9].get()),float(entries[10].get())) plt.ylabel(entries[11].get()) plt.xlabel(entries[12].get()) plt.title(entries[13].get()) plt.margins(0.2) plt.subplots_adjust(hspace = 0.3) plt.savefig(os.path.join(path_tot,'MFDFA2.pdf')) plt.close() def screen_fig(): fig_ts = Figure(figsize = (x_size,y_size)) a = fig_ts.add_subplot(211) ax = fig_ts.gca() if int(matplotlib.__version__.split('.')[0]) == 2: ax.set_facecolor('black') else: ax.set_axis_bgcolor('black') a.plot(arg0,'y') a.plot(0.5 * np.ones((len(arg0),)),'w') a.plot(np.ones((len(arg0),)),'m') a.plot(1.5 * np.ones((len(arg0),)),'r') a.set_xlim(float(entries[0].get()),float(entries[1].get())) a.set_ylim(float(entries[2].get()),float(entries[3].get())) a.set_xlabel(entries[4].get()) a.set_ylabel(entries[5].get()) a.set_title(entries[6].get()) b = fig_ts.add_subplot(212) b.plot(arg1,arg2,'b',label = '$\mu$ = ' + arg4) b.plot(arg1,arg3,'r',linewidth = 2.0,label = '$\sigma$ = ' + arg5) b.legend(loc = 0) b.set_xlim(float(entries[7].get()),float(entries[8].get())) b.set_ylim(float(entries[9].get()),float(entries[10].get())) b.set_ylabel(entries[11].get()) b.set_xlabel(entries[12].get()) b.set_title(entries[13].get()) fig_ts.tight_layout() canvas = FigureCanvasTkAgg(fig_ts,master = frame_1) canvas.get_tk_widget().grid(row = 0,column = 0) canvas.draw() def reset_fig(): for i in range(len(entries)): entries[i].delete(0,tk.END) entries[i].insert(0,values[i]) screen_fig() top = tk.Toplevel(main_win) top.geometry("%dx%d" % (int(main_win.winfo_screenwidth() * 0.93 * 0.85), int(main_win.winfo_screenheight() * 0.65))) top.wm_title("DFA") top.resizable(width = False,height = False) frame_1 = tk.Frame(top) frame_1.grid(row = 0,column = 0) frame_2 = tk.Frame(top) frame_2.grid(row = 0,column = 1) names = ["X1 Limit (left)","X1 Limit (right)","Y1 Limit (bottom)","Y1 Limit (top)","X1 Label (top)", "Y1 Label (top)","Title1 (top)","X2 Limit (left)","X2 Limit (right)","Y2 Limit (bottom)", "Y2 Limit (top)","X2 Label (top)","Y2 Label (bottom)","Title2 (bottom)"] values = [0,len(arg0),0,3,'time','$H_t$','local Hurst exponent',np.min(arg1) - 0.2,np.max(arg1) + 0.2, 0,np.max(arg2) * 11 / 10,'P($H_t$)','$H_t$','Prob distr of $H_t$'] entries = [] for i in range(len(names)): tk.Label(frame_2,text = names[i],font = "Verdana 13 bold").grid(row = i,column = 0, padx = int(main_win.winfo_screenwidth() * 0.01)) entries.append(tk.Entry(frame_2,width = 18)) entries[-1].insert(0,values[i]) entries[-1].grid(row = i,column = 1) screen_fig() tk.Label(frame_2,text = "").grid(row = len(names),column = 0) tk.Button(frame_2,text = "Replot",font = "Verdana 13 bold",command = screen_fig).grid(row = len(names) + 1,column = 0) tk.Button(frame_2,text = "Save",font = "Verdana 13 bold",command = save_fig).grid(row = len(names) + 1,column = 1) tk.Label(frame_2,text = "").grid(row = len(names) + 2,column = 0) tk.Button(frame_2,text = "Reset",font = "Verdana 13 bold",command = reset_fig).grid(row = len(names) + 3,column = 0) #############################################################################

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