xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

aef488759816cabfb40bd3b6063dcdfb1b53455d

3,216

py

Python

ane_research/utils/kendall_top_k.py

michaeljneely/sparse-attention-explanation

658b181f67963fe22dd0489bd9b37bdbd05110c1

[ "MIT" ]

2

2020-03-25T22:13:09.000Z

2021-01-06T04:28:03.000Z

ane_research/utils/kendall_top_k.py

michaeljneely/sparse-attention-explanation

658b181f67963fe22dd0489bd9b37bdbd05110c1

[ "MIT" ]

null

ane_research/utils/kendall_top_k.py

michaeljneely/sparse-attention-explanation

658b181f67963fe22dd0489bd9b37bdbd05110c1

[ "MIT" ]

null

'''Top-k kendall-tau distance. This module generalise kendall-tau as defined in [1]. It returns a distance: 0 for identical (in the sense of top-k) lists and 1 if completely different. Example: Simply call kendall_top_k with two same-length arrays of ratings (or also rankings), length of the top elements k (default is the maximum length possible), and p (default is 0, see [1]) as parameters: import kendall a = np.array([1,2,3,4,5]) b = np.array([5,4,3,2,1]) kendall.kendall_top_k(a,b,k=4) Author: Alessandro Checco https://github.com/AlessandroChecco References [1] Fagin, Ronald, Ravi Kumar, and D. Sivakumar. 'Comparing top k lists.' SIAM Journal on Discrete Mathematics 17.1 (2003): 134-160. ''' # pylint: disable=E1101 # pylint incorrectly identifies some types as tuples import math import numpy as np import scipy.stats as stats import scipy.special as special def kendall_top_k(a, b, k=None, kIsNonZero=False, p=0.5): ''' kendall_top_k(np.array,np.array,k,p) This function generalise kendall-tau as defined in [1] Fagin, Ronald, Ravi Kumar, and D. Sivakumar. 'Comparing top k lists.' SIAM Journal on Discrete Mathematics 17.1 (2003): 134-160. It returns a distance: 1 for identical (in the sense of top-k) lists and -1 if completely different. Example: Simply call it with two same-length arrays of ratings (or also rankings), length of the top elements k (default is the maximum length possible), and p (default is 0, see [1]) as parameters: $ a = np.array([1,2,3,4,5]) $ b = np.array([5,4,3,2,1]) $ kendall_top_k(a,b,k=4) If the kIsNonZero option is True, k is set to the amount of non-zero values in a or b, depending on which has least. ''' a = np.array(a) b = np.array(b) if kIsNonZero: anz, bnz = np.count_nonzero(a), np.count_nonzero(b) k = min(np.count_nonzero(a), np.count_nonzero(b)) #print('anz={}, bnz={}, k={}'.format(anz, bnz, k)) elif k is None: k = a.size if a.size != b.size: raise NameError('The two arrays need to have same lengths') k = min(k,a.size) a_top_k = np.argpartition(a,-k)[-k:] b_top_k = np.argpartition(b,-k)[-k:] common_items = np.intersect1d(a_top_k,b_top_k) only_in_a = np.setdiff1d(a_top_k, common_items) only_in_b = np.setdiff1d(b_top_k, common_items) # case 1 kendall = (1 - (stats.kendalltau(a[common_items], b[common_items])[0] / 2 + 0.5)) * common_items.size**2 if np.isnan(kendall): # degenerate case with only one item (not defined by Kendall) #print('DEGENERATE CASE <= 1 in common') kendall = 0 #case 2 (& 3 ?) test = 0 for i in common_items: for j in only_in_a: if a[i] < a[j]: test += 1 for j in only_in_b: if b[i] < b[j]: test += 1 kendall += test # case 4 kendall += 2 * p * special.binom(k-common_items.size, 2) # case 3 kendall /= (only_in_a.size + only_in_b.size + common_items.size)**2 #normalization kendall = -2 * kendall + 1 # change to correct range return (kendall, k)

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3.720149

0.270522

0.032096

0.027583

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0.411234

0.368104

0.338014

0

0.036792

0.247823

3,216

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0.787516

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0

0.166667

0

null

0

null

0

1

0

aef7c4bd6270658e2d5f6a301a21f1fd8ae19292

619

py

Python

test/math/test_matmul.py

ctgk/bayes

96eab9305eaeecc5a5b032cdf92a8285de4f60bf

[ "MIT" ]

21

2019-01-08T05:58:41.000Z

2021-11-26T14:24:11.000Z

test/math/test_matmul.py

ctgk/bayes

96eab9305eaeecc5a5b032cdf92a8285de4f60bf

[ "MIT" ]

null

test/math/test_matmul.py

ctgk/bayes

96eab9305eaeecc5a5b032cdf92a8285de4f60bf

[ "MIT" ]

11

2019-05-04T13:44:19.000Z

2021-08-05T04:26:19.000Z

import unittest import numpy as np import bayesnet as bn class TestMatMul(unittest.TestCase): def test_matmul(self): x = np.random.rand(10, 3) y = np.random.rand(3, 5) g = np.random.rand(10, 5) xp = bn.Parameter(x) z = xp @ y self.assertTrue((z.value == x @ y).all()) z.backward(g) self.assertTrue((xp.grad == g @ y.T).all()) yp = bn.Parameter(y) z = x @ yp self.assertTrue((z.value == x @ y).all()) z.backward(g) self.assertTrue((yp.grad == x.T @ g).all()) if __name__ == '__main__': unittest.main()

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619

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null

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null

0

1

0

aef90fca9fea526b2891e7df58b0f264aee383cd

2,387

py

Python

test.py

VegaSera/SWNDiscordBot2

cb73b9d51591b6af9f2a1a603ea0dd8a7161020c

[ "MIT" ]

2

2020-09-08T18:08:55.000Z

2021-06-22T17:13:32.000Z

test.py

VegaSera/SWNDiscordBot2

cb73b9d51591b6af9f2a1a603ea0dd8a7161020c

[ "MIT" ]

null

test.py

VegaSera/SWNDiscordBot2

cb73b9d51591b6af9f2a1a603ea0dd8a7161020c

[ "MIT" ]

1

2020-06-30T19:12:27.000Z

class char: def __init__(self): self.str = 15 self.dex = 15 self.con = 14 self.wis = 15 self.int = 15 self.cha = 15 def raise_stat(self): stats = [self.str, self.dex, self.con, self.int, self.wis, self.cha] min_stat = min(stats) for index, value in enumerate(stats): if value == min_stat: if index == 0: #self.verbose_log += f"Free 14 - Raised Strength from {self.str} to 14." self.str = 14 break elif index == 1: #self.verbose_log += f"Free 14 - Raised Dexterity from {self.dex} to 14." self.dex = 14 break elif index == 2: #self.verbose_log += f"Free 14 - Raised Constitution from {self.con} to 14." self.con = 14 break elif index == 3: #self.verbose_log += f"Free 14 - Raised Intelligence from {self.int} to 14." self.int = 14 break elif index == 4: #self.verbose_log += f"Free 14 - Raised Wisdom from {self.wis} to 14." self.wis = 14 break elif index == 5: #self.verbose_log += f"Free 14 - Raised Charisma from {self.cha} to 14." self.cha = 14 break print("Prints after for loop") def change_stat(self): self.cha = 15 newchar = char() #newchar.raise_stat() # print(newchar.cha) # newchar.raise_stat() # print(newchar.cha) # # class_type = None # list(class_type) # print(class_type, type(class_type)) # # listthing = [0, 1, 2, 3, 4, 5] # # for i in listthing: # if i == 2: # listthing.append(1) # elif i == 1: # print("I FOUND A ONE! HOPEFULLY I'LL FIND ANOTHER") # elif i == 3: # listthing.remove(i) # print(listthing) # # import random # # featuredict = {1:"Amphibian",2:"Bird",3:"Fish",4:"Insect",5:"Mammal",6:"Reptile",7:"Spider",8:"Exotic"} # print(random.choice(featuredict)) def returns_tuple(): a = 5 b = 6 c = 7 return a, b, c print("Function output", returns_tuple()) x, y, z = returns_tuple() print("x =", x) print("y =", y) print("z =", z)

27.436782

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0.492669

305

2,387

3.780328

0.291803

0.036427

0.072853

0.078057

0.194276

0.140503

0

0.052027

0.379975

2,387

87

106

27.436782

0.727027

0.40553

0

0.222222

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0.032421

0

1

0.088889

false

0

0.133333

0.111111

0

null

0

null

0

1

0

aeffe251e30362d499c33484220e03c6b09531a5

987

py

Python

extracting_information/extract_payments.py

ErikOSorensen/mmrisk_instrument

3a1bf587ec08362a4c24f8a40064142a5307c94c

[ "BSD-3-Clause" ]

null

extracting_information/extract_payments.py

ErikOSorensen/mmrisk_instrument

3a1bf587ec08362a4c24f8a40064142a5307c94c

[ "BSD-3-Clause" ]

null

extracting_information/extract_payments.py

ErikOSorensen/mmrisk_instrument

3a1bf587ec08362a4c24f8a40064142a5307c94c

[ "BSD-3-Clause" ]

null

from mmr2web.models import * import datetime def get_payments_file(nok_per_usd=9.1412): """Default exchange rate taken from Norges Bank, Nov 22, 2019.""" payments_out = open("payments_mmrisk.csv", "w") payments_out.write("amount,message\n") total_payment = 0 for s in Situation.objects.filter(selected=True): if s.choice_risk: amount = DICE[s.die.dienumber]['eyes'][s.draw-1] / nok_per_usd message = "In mmr2 - someone decided to throw a dice on your behalf." if amount==0: amount=0.01 message = "In mmr - someone decided to throw a dice on your behalf and you were unlucky." else: amount = s.safe_amount / nok_per_usd message = "In mmr2 - someone decided for the safe amount on your behalf." payments_out.write("%3.2f,%s\n" % (amount, message)) total_payment += amount payments_out.close() return total_payment get_payments_file()

37.961538

105

0.637285

140

987

4.35

0.528571

0.07225

0.044335

0.052545

0.220033

0.124795

0

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0.261398

987

25

106

39.48

0.805213

0.059777

0

0.265727

0

1

0.047619

false

0

0.095238

0

0.190476

0

null

0

null

0

1

0

4e002a3d2a0b17bea2d95b12a32b8e97ea924162

1,488

py

Python

tests/extmod/uasyncio_threadsafeflag.py

ProofDx/micropython

321d1897c34f16243edf2c94913d7cf877a013d1

[ "MIT" ]

13,648

2015-01-01T01:34:51.000Z

2022-03-31T16:19:53.000Z

tests/extmod/uasyncio_threadsafeflag.py

ProofDx/micropython

321d1897c34f16243edf2c94913d7cf877a013d1

[ "MIT" ]

7,092

2015-01-01T07:59:11.000Z

2022-03-31T23:52:18.000Z

tests/extmod/uasyncio_threadsafeflag.py

ProofDx/micropython

321d1897c34f16243edf2c94913d7cf877a013d1

[ "MIT" ]

4,942

2015-01-02T11:48:50.000Z

2022-03-31T19:57:10.000Z

# Test Event class try: import uasyncio as asyncio except ImportError: print("SKIP") raise SystemExit import micropython try: micropython.schedule except AttributeError: print("SKIP") raise SystemExit try: # Unix port can't select/poll on user-defined types. import uselect as select poller = select.poll() poller.register(asyncio.ThreadSafeFlag()) except TypeError: print("SKIP") raise SystemExit async def task(id, flag): print("task", id) await flag.wait() print("task", id, "done") def set_from_schedule(flag): print("schedule") flag.set() print("schedule done") async def main(): flag = asyncio.ThreadSafeFlag() # Set the flag from within the loop. t = asyncio.create_task(task(1, flag)) print("yield") await asyncio.sleep(0) print("set event") flag.set() print("yield") await asyncio.sleep(0) print("wait task") await t # Set the flag from scheduler context. print("----") t = asyncio.create_task(task(2, flag)) print("yield") await asyncio.sleep(0) print("set event") micropython.schedule(set_from_schedule, flag) print("yield") await asyncio.sleep(0) print("wait task") await t # Flag already set. print("----") print("set event") flag.set() t = asyncio.create_task(task(3, flag)) print("yield") await asyncio.sleep(0) print("wait task") await t asyncio.run(main())

18.6

56

0.633065

192

1,488

4.869792

0.296875

0.057754

0.080214

0.117647

0.396791

0.255615

0

0.007048

0.237231

1,488

79

57

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0.81674

0.105511

0

0.589286

0

0.099623

0

1

0.017857

false

0

0.071429

0

0.089286

0.357143

0

null

0

null

0

1

0

4e039a12924bbf9ee1073f9918fa1b333ccf4193

4,370

py

Python

Python/biopsy/binding_hit.py

JohnReid/biopsy

1eeb714ba5b53f2ecf776d865d32e2078cbc0338

[ "MIT" ]

null

Python/biopsy/binding_hit.py

JohnReid/biopsy

1eeb714ba5b53f2ecf776d865d32e2078cbc0338

[ "MIT" ]

null

Python/biopsy/binding_hit.py

JohnReid/biopsy

1eeb714ba5b53f2ecf776d865d32e2078cbc0338

[ "MIT" ]

null

# # Copyright John Reid 2006 # from _biopsy import * def _hit_str( hit ): return ",".join( [ hit.binder, str( hit.location.position ), str( hit.location.positive_strand ), str( hit.p_binding ) ] ) Hit.__str__ = _hit_str def _location_start( location ): return location.position HitLocation.start = _location_start def _location_end( location ): return location.position + location.length HitLocation.end = _location_end def _location_overlap( location1, location2 ): """Do two hits overlap?""" if location1.position < location2.position: return location1.end() > location2.position else: return location2.end() > location1.position HitLocation.overlap = _location_overlap def _location_separation( location1, location2 ): """The separation between two locations""" if location1.position >= location2.end(): return location1.position - location2.end() else: return location2.position - location1.end() HitLocation.separation = _location_separation def _hits_str( hits ): return '\n'.join( [ str( hit ) for hit in hits ] ) HitVec.__str__ = _hits_str def get_char_for_hit( hit ): return hit.binder def get_score_for_hit( hit ): # return math.log( hit.p_binding ) return hit.p_binding def get_max_p_binding_over_hits( hits ): """Takes a list of hits and returns a dictionary mapping binder names to max( p(binding) ) across all hits""" result = { } for hit in hits: if not result.has_key( hit.binder ) or result[hit.binder] < hit.p_binding: result[hit.binder] = hit.p_binding return result def find_pair_in_analysis( analysis, pair, max_separation = None, separation = None ): """Finds in which analyses a pair of TFs bind analysis: Analysis pair: A tuple ( binder1, binder2, orientation1, orientation2 ) max_separation: If specified determines maximum separation separation: If specified determines exact separation (over-rides max_separation) Returns a list of keys for the analyses """ result = { } for k in analysis.get_keys(): hits = analysis.get_hits_for( k ) found_pairs = find_pair_in_hits( hits, pair, max_separation, separation ) if found_pairs: result[ k ] = found_pairs return result def find_pair_in_hits( hits, pair, max_separation = None, separation = None ): """Finds the locations where a pair of TFs bind in a sequence of hits hits: The hits pair: A tuple ( binder1, binder2, orientation1, orientation2 ) max_separation: If specified determines maximum separation separation: If specified determines exact separation (overrides max_separation) returns a sequence of pairs of hits that satisfy the criteria """ ( binder1, binder2, orientation1, orientation2 ) = pair result = [ ] for h1 in hits: if binder1 != h1.binder: continue for h2 in hits: if binder2 != h2.binder: continue if h1.location.overlap( h2.location ): continue distance = h1.location.separation( h2.location ) if None != separation and separation != distance: continue if None != max_separation and max_separation < distance: continue if h1.location.position < h2.location.position: if ( h1.location.positive_strand != orientation1 or h2.location.positive_strand != orientation2 ): continue else: if ( h1.location.positive_strand == orientation1 or h2.location.positive_strand == orientation2 ): continue result.append( ( h1, h2 ) ) return result def hit_over_threshold_predicate(threshold): "@return: A function that returns True if the hit is over the threshold given." def predicate(hit): "@return: True iff the hit's score is above the threshold." return hit.p_binding >= threshold return predicate def hits_above_threshold(hits, threshold): "@return: Those hits above the threshold." return filter(hit_over_threshold_predicate(threshold), hits)

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0

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0.277117

4,370

141

114

30.992908

0.845837

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false

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0.336957

0

null

0

null

0

1

0

4e0443002a9f7388df8a4ecc7a67f5770910ff51

8,384

py

Python

epithet/epithet.py

mitodl/epithet

4f95054fbdfbae0e9d6db2e3309993d00a8a6867

[ "MIT" ]

null

epithet/epithet.py

mitodl/epithet

4f95054fbdfbae0e9d6db2e3309993d00a8a6867

[ "MIT" ]

null

epithet/epithet.py

mitodl/epithet

4f95054fbdfbae0e9d6db2e3309993d00a8a6867

[ "MIT" ]

null

# -*- coding: utf-8 -*- import click from github import Github from github.GithubException import RateLimitExceededException def main(): cli(obj={}) def get_repos(key, org, repo, url): if url: g = Github(key, base_url=url) else: g = Github(key) if org: g_org = g.get_organization(login=org) else: g_org = g.get_user() if repo: repos = [g_org.get_repo(repo)] else: repos = g_org.get_repos() return repos @click.group() @click.option('--key', envvar='EPITHET_KEY', help="Github OAuth Token") @click.option('--dryrun', is_flag=True, help="Don't actually change or create labels") @click.option('--url', help="API URL - change if GitHub Enterprise") @click.pass_context def cli(ctx, key, dryrun, url): if not key: click.echo("You must provide a GitHub API v3 key") return ctx.obj['dryrun'] = dryrun ctx.obj['url'] = url ctx.obj['key'] = key @cli.command() @click.option('--label', '-l', is_flag=True, help="List labels", default=False) @click.option('--milestone', '-m', is_flag=True, help='List milestones', default=False) @click.option('--org', '-o', help="Organization to get repos from") @click.option('--repo', '-r', help="Optionally select a single repo") @click.pass_context def list(ctx, label, milestone, org, repo): if not label and not milestone: click.echo("--label or --milestone required") return for repo in get_repos(ctx.obj['key'], org, repo, ctx.obj['url']): click.echo("\n * {}:\n".format(repo.name)) if label: for label in repo.get_labels(): click.echo(" - {} ({})".format(label.name, label.color)) if milestone: for milestone in repo.get_milestones(): click.echo(" - {} ({})".format(milestone.title)) @cli.command() @click.option('--label', '-l', is_flag=True, help="Add label", default=False) @click.option('--milestone', '-m', is_flag=True, help='Add milestone', default=False) @click.option('--org', '-o', help="Organization") @click.option('--repo', '-r', help="Optionally select a single repo") @click.option('--name', '-n', help="Name of new label") @click.option('--color', '-c', help="Color of new label") @click.pass_context def add(ctx, label, milestone, org, repo, name, color): if not label and not milestone: click.echo("--label or --milestone required") return for repo in get_repos(ctx.obj['key'], org, repo, ctx.obj['url']): click.echo(" * Checking {}".format(repo.name)) if label: click.echo("Adding a label with name: {} and color: {}".format(name, color)) labels = {label.name: label for label in repo.get_labels()} if name.lower() in [l.lower() for l in labels.keys()]: click.echo( " - Found {} on {} (Dryrun: {})".format( name, repo.name, ctx.obj['dryrun'] ) ) if name not in labels.keys(): for labelname, label in labels.items(): if labelname.lower() == name.lower(): labels[labelname].edit(name=name, color=color) elif labels[name].color != color and not ctx.obj['dryrun'] \ and not repo.archived: labels[name].edit(name=name, color=color) else: click.echo( " - Creating {} on {} (Dryrun: {})".format( name, repo.name, ctx.obj['dryrun'] ) ) if not ctx.obj['dryrun'] and not repo.archived: repo.create_label(name=name, color=color) if milestone: click.echo("Adding a milestone with name: {}".format(name)) milestones = {milestone.title: milestone for milestone in repo.get_milestones()} if name.lower() in [m.lower() for m in milestones.keys()]: click.echo( " - Found {} on {} (Dryrun: {})".format( name, repo.name, ctx.obj['dryrun'] ) ) else: click.echo( " - Creating {} on {} (Dryrun: {})".format( name, repo.name, ctx.obj['dryrun'] ) ) if not ctx.obj['dryrun'] and not repo.archived: repo.create_milestone(title=name) @cli.command() @click.option('--label', '-l', is_flag=True, help="Delete label", default=False) @click.option('--milestone', '-m', is_flag=True, help='Delete milestones', default=False) @click.option('--org', '-o', help="Organization") @click.option('--repo', '-r', help="Optionally select a single repo") @click.option('--name', '-n', help="Name of label or milestone to delete") @click.pass_context def delete(ctx, label, milestone, org, repo, name): if not label and not milestone: click.echo("--label or --milestone required") return for repo in get_repos(ctx.obj['key'], org, repo, ctx.obj['url']): click.echo(" * Checking {}".format(repo.name)) if label: click.echo("Deleting label: {}".format(name)) labels = {} for label in repo.get_labels(): labels[label.name] = label if name in labels: click.echo( " - Found {} on {}, deleting (Dryrun: {})".format( labels[name].name, repo.name, ctx.obj['dryrun'] ) ) if not ctx.obj['dryrun']: labels[name].delete() if milestone: click.echo("Deleting milestone: {}".format(name)) milestones = {} for milestone in repo.get_milestones(): milestones[milestone.title] = milestone if name in milestones: click.echo( " - Found {} on {}, deleting (Dryrun: {})".format( milestones[name].title, repo.name, ctx.obj['dryrun'] ) ) if not ctx.obj['dryrun']: milestones[name].delete() @cli.command() @click.option('--label', '-l', is_flag=True, help="Update label", default=False) @click.option('--milestone', '-m', is_flag=True, help='Update milestone', default=False) @click.option('--org', '-o', help="Organization") @click.option('--repo', '-r', help="Optionally select a single repo") @click.option('--name', '-n', help="Name of the existing label") @click.option('--new-name', help="New name of the label") @click.pass_context def update(ctx, label, milestone, org, repo, name, new_name): if not label and not milestone: click.echo("--label or --milestone required") return for repo in get_repos(ctx.obj['key'], org, repo, ctx.obj['url']): click.echo(" * Checking {}".format(repo.name)) if label: click.echo("Updating label {}".format(name)) labels = {} for label in repo.get_labels(): labels[label.name] = label if name in labels: click.echo( " - Found {} on {}, upating to {} (Dryrun: {})".format( labels[name].name, repo.name, new_name, ctx.obj['dryrun'] ) ) if labels[name].name != new_name and not ctx.obj['dryrun']: labels[name].edit(name=new_name, color=labels[name].color) else: click.echo("{} not found, did you mean 'add'?".format(name)) if milestone: click.echo("Updating milestone with name: {}".format(name)) milestones = {} for milestone in repo.get_milestones(): milestones[milestone.title] = milestone if name in milestones: click.echo( " - Found {} on {}, upating to {} (Dryrun: {})".format( milestones[name].name, repo.name, new_name, ctx.obj['dryrun'] ) ) else: click.echo("{} not found, did you mean 'add'?".format(name)) if __name__ == "__main__": main(obj={})

40.699029

89

0.532085

980

8,384

4.49898

0.109184

0.055115

0.040826

0.028578

0.687231

0.646859

0.608528

0.564981

0.54797

0.517804

0

0.000347

0.311904

8,384

205

90

40.897561

0.763911

0.002505

0

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0

0.1928

0

1

0.037234

false

0.026596

0.015957

0

0.085106

0

null

0

null

0

1

0

4e04cfd6696b1d79b63702e52778fdde33cbdd79

1,876

py

Python

Tarea1/utilities.py

aleluman/CC5114

aae4ea9faf0a7cb3eb3bf53f8eecaf209aebf4d6

[ "MIT" ]

null

Tarea1/utilities.py

aleluman/CC5114

aae4ea9faf0a7cb3eb3bf53f8eecaf209aebf4d6

[ "MIT" ]

null

Tarea1/utilities.py

aleluman/CC5114

aae4ea9faf0a7cb3eb3bf53f8eecaf209aebf4d6

[ "MIT" ]

null

import numpy as np def normalize(matrix, nh=1, nl=0): """Normalizes each column in a matrix by calculating its maximum and minimum values, the parameters nh and nl specify the final range of the normalized values""" return (matrix - matrix.min(0)) * ((nh - nl) / matrix.ptp(0)) + nl def one_hot_encoding(array): """Encodes each unique label in 'array' in a vector of the same length as the number of unique labels. This vector is filled with zeros and a 1 representing the position assigned to the label""" labels = np.unique(array) number_of_labels = labels.size encoded = {} for i in range(number_of_labels): encoding = np.zeros(number_of_labels) encoding[i] = 1 encoded[labels[i]] = encoding return encoded def encode(array, encoding): """Encodes 'array' with the encoding specified in encoding. This value must be a dictionary""" encoded = [] for i in array: encoded.append(encoding[i]) return encoded def load_data_wrapper(name, input_cols, output_col, output_type="float", delimiter=None): """Wrapper to load the desired data in an easier way. It returns the normalized and encoded data, alongside with the size of the values in the inputs and outputs to initialize the neural network correctly""" data_x = np.loadtxt(name, usecols=input_cols, delimiter=delimiter) data_x = normalize(data_x) data_y = np.loadtxt(name, usecols=output_col, delimiter=delimiter, dtype=output_type) encoding = one_hot_encoding(data_y) data_y = encode(data_y, encoding) # x_len will be the number of input neurons, and y_len the number of output neurons x_len = np.shape(data_x)[1] y_len = np.shape(data_y)[1] data = [[np.reshape(x, (x_len, 1)), np.reshape(y, (y_len, 1))] for x, y in zip(data_x, data_y)] return data, x_len, y_len

39.083333

99

0.695096

297

1,876

4.265993

0.340067

0.037885

0.026046

0.020521

0

0.006757

0.211087

1,876

47

100

39.914894

0.849324

0.382729

0

0.074074

0

0.004509

0

1

0.148148

false

0

0.037037

0

0.333333

0

null

0

null

0

1

0

4e051ec8fbfa4fdbb801b562f9028e2cec2f9219

1,304

py

Python

tests/test_searcher.py

jrdelmar/cbis

6cce46680555d622ecea88f2ee2721209810abbe

[ "MIT" ]

1

2019-03-19T14:10:19.000Z

tests/test_searcher.py

jrdelmar/cbis

6cce46680555d622ecea88f2ee2721209810abbe

[ "MIT" ]

14

2020-01-28T22:38:54.000Z

2022-03-11T23:43:34.000Z

tests/test_searcher.py

jrdelmar/cbis

6cce46680555d622ecea88f2ee2721209810abbe

[ "MIT" ]

null

from pyimagesearch.searcher import Searcher from pyimagesearch.utils import * import pytest indexPath = "D:/APP/cbis/" verbose = True #test Search class @pytest.fixture def searcher(): return Searcher(indexPath, verbose) pred_file = "D://APP//cbis//tests//out//predictions_test.csv" top_k = 20 def test_search_gun( searcher ): threshold = 0.50 image_list = searcher.search_gun(pred_file, top_k, threshold) assert len(image_list) == 1 assert image_list[0][3] == 'gun' def test_search_not_gun(searcher): threshold = 0.70 search_list = ['wooden_spoon'] image_list = searcher.search_list(pred_file,search_list, top_k, threshold) assert len(image_list) == 2 assert image_list[0][3] == 'wooden_spoon' def test_search_not_gun1(searcher): threshold = 0.80 search_list = ['wooden_spoon', 'revolver'] image_list = searcher.search_list(pred_file,search_list, top_k, threshold) assert len(image_list) == 1 assert image_list[0][3] == 'revolver' def test_search_not_gun2(searcher): threshold = 0.70 search_list = ['wooden_spoon', 'revolver'] image_list = searcher.search_list(pred_file,search_list, top_k, threshold) assert len(image_list) == 3 assert image_list[0][3] == 'wooden_spoon' assert image_list[2][3] == 'revolver'

29.636364

78

0.713957

188

1,304

4.68617

0.244681

0.132804

0.085131

0.104427

0.533485

0.469921

0.400681

0

0.027498

0.163344

1,304

43

79

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0.013037

0

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0

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1

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false

0

0.088235

0.029412

0.264706

0

null

0

null

0

1

0

4e08b9785d412b27c9f6fb1800aa24f2a6fc367a

9,484

py

Python

ntfs.py

kartone/INDXRipper

88e663115b8705b1bb153b28fd74f943c515b9ca

[ "MIT" ]

null

ntfs.py

kartone/INDXRipper

88e663115b8705b1bb153b28fd74f943c515b9ca

[ "MIT" ]

null

ntfs.py

kartone/INDXRipper

88e663115b8705b1bb153b28fd74f943c515b9ca

[ "MIT" ]

null

""" Provides functions for working with NTFS volumes Author: Harel Segev 05/16/2020 """ from construct import Struct, Padding, Computed, IfThenElse, BytesInteger, Const, Enum, Array, FlagsEnum, Switch, Tell from construct import PaddedString, Pointer, Seek, Optional, StopIf, RepeatUntil, Padded from construct import Int8ul, Int16ul, Int32ul, Int64ul, Int8sl from dataruns import get_dataruns, NonResidentStream from sys import exit as sys_exit class EmptyNonResidentAttributeError(ValueError): pass BOOT_SECTOR = Struct( "OffsetInImage" / Tell, Padding(3), "Magic" / Optional(Const(b'NTFS')), StopIf(lambda this: this.Magic is None), Padding(4), "BytsPerSec" / Int16ul, "SecPerClus" / Int8ul, "BytsPerClus" / Computed(lambda this: this.BytsPerSec * this.SecPerClus), Padding(34), "MftClusNumber" / Int64ul, Padding(8), "BytsOrClusPerRec" / Int8sl, "BytsPerRec" / IfThenElse( lambda this: this.BytsOrClusPerRec > 0, Computed(lambda this: this.BytsOrClusPerRec * this.BytsPerClus), Computed(lambda this: 2 ** abs(this.BytsOrClusPerRec)), ), Padding(3), "BytsOrClusPerIndx" / Int8sl, "BytsPerIndx" / IfThenElse( lambda this: this.BytsOrClusPerIndx > 0, Computed(lambda this: this.BytsOrClusPerIndx * this.BytsPerClus), Computed(lambda this: 2 ** abs(this.BytsOrClusPerIndx)), ), "BytsPerMftChunk" / IfThenElse( lambda this: this.BytsPerClus > this.BytsPerRec, Computed(lambda this: this.BytsPerClus), Computed(lambda this: this.BytsPerRec) ), ) FILE_REFERENCE = Struct( "FileRecordNumber" / BytesInteger(6, swapped=True, signed=False), "SequenceNumber" / Int16ul ) FILE_RECORD_HEADER = Struct( "OffsetInChunk" / Tell, "Magic" / Optional(Const(b'FILE')), StopIf(lambda this: this.Magic is None), "UpdateSequenceOffset" / Int16ul, "UpdateSequenceSize" / Int16ul, Padding(8), "SequenceNumber" / Int16ul, Padding(2), "FirstAttributeOffset" / Int16ul, "Flags" / FlagsEnum(Int16ul, IN_USE=1, DIRECTORY=2), Padding(8), "BaseRecordReference" / FILE_REFERENCE, Seek(lambda this: this.UpdateSequenceOffset + this.OffsetInChunk), "UpdateSequenceNumber" / Int16ul, "UpdateSequenceArray" / Array(lambda this: this.UpdateSequenceSize - 1, Int16ul) ) FILE_RECORD_HEADERS = Struct( "RecordHeaders" / Array( lambda this: this._.records_per_chunk, Padded(lambda this: this._.bytes_per_record, FILE_RECORD_HEADER) ) ) ATTRIBUTE_HEADER = Struct( "EndOfRecordSignature" / Optional(Const(b'\xFF\xFF\xFF\xFF')), StopIf(lambda this: this.EndOfRecordSignature is not None), "OffsetInChunk" / Tell, "Type" / Enum(Int32ul, FILE_NAME=0x30, INDEX_ALLOCATION=0xA0, DATA=0x80), "Length" / Int32ul, "Residence" / Enum(Int8ul, RESIDENT=0x00, NON_RESIDENT=0x01), "NameLength" / Int8ul, "NameOffset" / Int16ul, "AttributeName" / Pointer(lambda this: this.NameOffset + this.OffsetInChunk, PaddedString(lambda this: 2 * this.NameLength, "utf16")), Padding(4), "Metadata" / Switch( lambda this: this.Residence, { "RESIDENT": Struct( "AttributeLength" / Int32ul, "AttributeOffset" / Int16ul, ), "NON_RESIDENT": Struct( Padding(16), "DataRunsOffset" / Int16ul, Padding(6), "AllocatedSize" / Int64ul, "RealSize" / Int64ul, ) } ), Seek(lambda this: this.Length + this.OffsetInChunk) ) ATTRIBUTE_HEADERS = Struct( Seek(lambda this: this._.offset), "AttributeHeaders" / RepeatUntil(lambda obj, lst, ctx: obj.EndOfRecordSignature is not None, ATTRIBUTE_HEADER) ) FILENAME_ATTRIBUTE = Struct( "ParentDirectoryReference" / FILE_REFERENCE, Padding(56), "FilenameLengthInCharacters" / Int8ul, "FilenameNamespace" / Enum(Int8ul, POSIX=0, WIN32=1, DOS=2, WIN32_DOS=3), "FilenameInUnicode" / PaddedString(lambda this: this.FilenameLengthInCharacters * 2, "utf16") ) def get_boot_sector(raw_image, partition_offset): raw_image.seek(partition_offset) return BOOT_SECTOR.parse_stream(raw_image) def panic_on_invalid_boot_sector(vbr): if vbr["Magic"] is None: sys_exit("INDXRipper: error: invalid volume boot record") def get_mft_offset(vbr): return vbr["MftClusNumber"] * vbr["BytsPerClus"] + vbr["OffsetInImage"] def get_first_mft_chunk(vbr, raw_image): raw_image.seek(get_mft_offset(vbr)) return bytearray(raw_image.read(vbr["BytsPerMftChunk"])) def get_record_headers(mft_chunk, vbr): return FILE_RECORD_HEADERS.parse( mft_chunk, bytes_per_record=vbr["BytsPerRec"], records_per_chunk=vbr["BytsPerMftChunk"] // vbr["BytsPerRec"] )["RecordHeaders"] def is_valid_record_signature(record_header): return record_header["Magic"] is not None def apply_record_fixup(mft_chunk, record_header, vbr): usn = record_header["UpdateSequenceNumber"] first_fixup_offset = record_header["OffsetInChunk"] + vbr["BytsPerSec"] - 2 end_of_record_offset = record_header["OffsetInChunk"] + vbr["BytsPerRec"] for i, usn_offset in enumerate(range(first_fixup_offset, end_of_record_offset, vbr["BytsPerSec"])): if Int16ul.parse(mft_chunk[usn_offset:usn_offset + 2]) != usn: return False mft_chunk[usn_offset:usn_offset + 2] = Int16ul.build(record_header["UpdateSequenceArray"][i]) return True def apply_fixup(mft_chunk, record_headers, vbr): for record_header in record_headers: if is_valid_record_signature(record_header): record_header["IsValidFixup"] = apply_record_fixup(mft_chunk, record_header, vbr) def is_valid_fixup(record_header): return record_header["IsValidFixup"] def is_used(record_header): return record_header["Flags"]["IN_USE"] def is_directory(record_header): return record_header["Flags"]["DIRECTORY"] def get_sequence_number(record_header): if is_used(record_header): return record_header["SequenceNumber"] else: return record_header["SequenceNumber"] - 1 def is_base_record(record_header): return record_header["BaseRecordReference"]["FileRecordNumber"] == 0 def get_base_record_reference(record_header): base_reference = record_header["BaseRecordReference"] return base_reference["FileRecordNumber"], base_reference["SequenceNumber"] def get_attribute_headers(mft_chunk, record_header): first_attribute_offset = record_header["FirstAttributeOffset"] + record_header["OffsetInChunk"] res = ATTRIBUTE_HEADERS.parse(mft_chunk, offset=first_attribute_offset) return res["AttributeHeaders"][:-1] def get_resident_attribute(mft_chunk, attribute_header): offset = attribute_header["OffsetInChunk"] + attribute_header["Metadata"]["AttributeOffset"] return mft_chunk[offset: offset + attribute_header["Metadata"]["AttributeLength"]] def get_attribute_type(attribute_header): return attribute_header["Type"] def get_attribute_name(attribute_header): return attribute_header["AttributeName"] def is_resident(attribute_header): return attribute_header["Residence"]["RESIDENT"] def get_attribute_header(attribute_headers, attribute_type): for attribute_header in attribute_headers: if attribute_header["Type"] == attribute_type: yield attribute_header def parse_filename_attribute(filename_attribute): return FILENAME_ATTRIBUTE.parse(filename_attribute) def get_non_resident_attribute(vbr, raw_image, mft_chunk, attribute_header, is_allocated): dataruns_offset_in_chunk = attribute_header["OffsetInChunk"] + attribute_header["Metadata"]["DataRunsOffset"] dataruns = get_dataruns(mft_chunk, dataruns_offset_in_chunk) if not dataruns: raise EmptyNonResidentAttributeError return NonResidentStream(vbr["BytsPerClus"], vbr["OffsetInImage"], raw_image, dataruns, is_allocated) def panic_on_invalid_first_record(record_header): if not is_valid_record_signature(record_header): sys_exit(f"INDXRipper: error: invalid 'FILE' signature in first file record") if not is_valid_fixup(record_header): sys_exit(f"INDXRipper: error: fixup validation failed for first file record") def get_mft_data_attribute(vbr, raw_image): panic_on_invalid_boot_sector(vbr) mft_chunk = get_first_mft_chunk(vbr, raw_image) record_headers = get_record_headers(mft_chunk, vbr) apply_fixup(mft_chunk, record_headers, vbr) panic_on_invalid_first_record(record_headers[0]) attribute_headers = get_attribute_headers(mft_chunk, record_headers[0]) mft_data_attribute_header = next(get_attribute_header(attribute_headers, "DATA")) return get_non_resident_attribute(vbr, raw_image, mft_chunk, mft_data_attribute_header, True) def get_mft_chunks(vbr, mft_data_attribute_stream): while current_chunk := mft_data_attribute_stream.read(vbr["BytsPerMftChunk"]): yield current_chunk

33.75089

119

0.690953

1,034

9,484

6.077369

0.185687

0.063017

0.044558

0.022915

0.253978

0.173775

0.0993

0.038829

0.013367

0

0.016247

0.208245

9,484

280

120

33.871429

0.820615

0.00833

0

0.090909

0

0.153981

0.005491

0

0.002197

0

1

0.126263

false

0.005051

0.025253

0.055556

0.267677

0

null

0

null

0

1

0

4e0c94378cede26866700f316056f4a9b045008f

486

py

Python

writer.py

ZitRos/edu-text-analysis

a03f22f9c6e72e4cac4d38b9e963d1554cae35d0

[ "MIT" ]

9

2017-11-28T22:42:06.000Z

2021-01-27T05:05:52.000Z

writer.py

ZitRos/edu-text-analysis

a03f22f9c6e72e4cac4d38b9e963d1554cae35d0

[ "MIT" ]

null

writer.py

ZitRos/edu-text-analysis

a03f22f9c6e72e4cac4d38b9e963d1554cae35d0

[ "MIT" ]

1

2022-02-08T21:55:29.000Z

import xlsxwriter from slugify import slugify import os def write_to_xlsx(filename, title="Worksheet", data=None): directory = os.path.dirname(filename) if not os.path.exists(directory): os.makedirs(directory) workbook = xlsxwriter.Workbook(filename) worksheet = workbook.add_worksheet(slugify(title)[:28]) row_count = 0 for row in data: cell_count = 0 for cell in row: worksheet.write(row_count, cell_count, cell) cell_count += 1 row_count += 1 workbook.close()

24.3

58

0.746914

71

486

4.985915

0.450704

0.067797

0.050847

0

0.014493

0.148148

486

19

59

25.578947

0.84058

0

0.018519

0

1

0.058824

false

0

0.176471

0

0.235294

0

null

0

null

0

1

0

4e0ca604df69608c9b3245228eab46db3a285865

4,251

py

Python

src/4. Ajuste de curvas/Metodos/MC_multilineal.py

thonyblaz/Numerical-Methods

fdeccb9e2eba4a1eb7892ab3a55bd6169c430502

[ "MIT" ]

1

2021-04-24T20:47:26.000Z

src/4. Ajuste de curvas/Metodos/MC_multilineal.py

Desarrollador2021/Numerical-Methods

fdeccb9e2eba4a1eb7892ab3a55bd6169c430502

[ "MIT" ]

null

src/4. Ajuste de curvas/Metodos/MC_multilineal.py

Desarrollador2021/Numerical-Methods

fdeccb9e2eba4a1eb7892ab3a55bd6169c430502

[ "MIT" ]

1

2021-04-24T20:47:03.000Z

import numpy as np def sisEcua(mat_A, mat_B): a_inv = np.linalg.inv(mat_A) C = a_inv.dot(mat_B.T) return C def matrices(sm, smm, smy, smn, datos, cant_datos): dimension = datos+1 s = (dimension, dimension) mat_A = np.zeros(s) mat_B = np.matrix(smy) # contadores n = len(smn) fin = datos-1 con_master = fin-1 ini = 0 fil = 1 col = 1 # primer numero ubicado mat_A[0][0] = cant_datos for i in range(0, datos): mat_A[i+1][i+1] = smm[i] mat_A[0][i+1] = sm[i] mat_A[i+1][0] = sm[i] # ubicacion de la variables multiplicadas por otras variables for i in range(1, datos): for j in range(ini, fin): mat_A[i][col+1] = smn[j] mat_A[col+1][i] = smn[j] col += 1 fil += 1 col = col-con_master ini = fin fin = fin+con_master con_master -= 1 #para visualizar las matrices # print(mat_A) # print(mat_B) return sisEcua(mat_A, mat_B) def multilineal(var_dependiente, var_independiente, nombre_variables): variables = len(nombre_variables) sis_ecuaciones = len(nombre_variables)+1 cant_datos = len(var_dependiente) # vectores auxiliares var_al_cuadrado = [] var_por_y = [] var_multiplicadas = [] # vectores de las sumas suma_var_al_cuadrado = [] suma_var = [] suma_por_y = [] suma_de_var_por_var = [] # variable dependiente y = np.array(var_dependiente) sum_y = np.sum(y) suma_por_y.append(sum_y) # multiplicaciones de m*n, m*p y n*p k = 1 # operaciones for var_i in range(variables): m = np.array(var_independiente[var_i]) y_por_m = y*m m_cuadrado = m*m # anade las m**2 y los m*y var_al_cuadrado.append(m_cuadrado) var_por_y.append(y_por_m) # sumas suma_mm = np.sum(m_cuadrado) suma_var_al_cuadrado.append(suma_mm) suma_m = np.sum(m) suma_var.append(suma_m) suma_my = np.sum(y_por_m) suma_por_y.append(suma_my) # multiplicaciones cor cada variable for i in range(k, variables): n = np.array(var_independiente[i]) multipl = m*n var_multiplicadas.append(multipl) # suma de las multiplicaciones suma_mn = np.sum(multipl) suma_de_var_por_var.append(suma_mn) k += 1 """ #para visualizar las sumatorias print(var_al_cuadrado) print(var_por_y) print(var_multiplicadas) print(suma_var) print(suma_var_al_cuadrado) print(suma_por_y) print(suma_de_var_por_var) """ resultado=matrices(suma_var, suma_var_al_cuadrado, suma_por_y, suma_de_var_por_var, variables, cant_datos) #resultados finales ecuacion_final='y = ' print('\n COEFICIENTES DEL AJUSTE LINEAL MULTIPLE\n') for i in range(0,variables+1): solucion=float(resultado[i]) sol_redondeada="{0:.7f}".format(solucion) print(f' a{i} = {sol_redondeada} ') if i>0: ec=' + '+str(sol_redondeada)+'*'+str(nombre_variables[i-1]) else: ec=str(sol_redondeada) ecuacion_final=ecuacion_final+ec print('\n La ecuacion de ajuste es:\n') print(f' {ecuacion_final}') print('\nNota: y = Var. Dependiente') # datos de prueba #set 1 """ agua = [27.5, 28, 28.8, 29.1, 30, 31, 32] cal = [2, 3.5, 4.5, 2.5, 8.5, 10.5, 13.5] puzo = [18, 16.5, 10.5, 2.5, 9, 4.5, 1.5] dr = [5, 2, 3, 4, 1, 2, 3] gh = [7, 2, 1, 1, 1, 6, 7] puzos = [15, 15.5, 11.5, 5, 5, 3, 1] variables_data = [cal, puzo] variable = ['u', 'v'] variables_data = [cal, puzo, dr, gh, puzos] variable = ['u', 'v', 'w', 'z', 's'] """ #set 2 """ u=[0.02,0.02,0.02,0.02,0.1,0.1,0.1,0.1,0.18,0.18,0.18,0.18] v=[1000,1100,1200,1300,1000,1100,1200,1300,1000,1100,1200,1300] fuv=[78.9,65.1,55.2,56.4,80.9,69.7,57.4,55.4,85.3,71.8,60.7,58.9] variables_data = [u,v] variable = ['u', 'v'] """ """ agua = [27.5, 28, 28.8, 29.1, 30, 31, 32] cal = [2, 3.5, 4.5, 2.5, 8.5, 10.5, 13.5] puzo = [18, 16.5, 10.5, 2.5, 9, 4.5, 1.5] variables_data = [cal, puzo] variable = ['u', 'v'] multilineal(agua, variables_data, variable) """

28.152318

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0.584098

713

4,251

3.302945

0.225806

0.018684

0.038641

0.018684

0.180042

0.128238

0.123567

0.089172

0.049257

0

0.086329

0.266996

4,251

150

72

28.34

0.669448

0.092919

0

0.066834

0

1

0.036585

false

0

0.012195

0

0.073171

0.060976

0

null

0

null

0

1

0

4e14a820dce8b0c05972db39e72bc127d5d06743

3,550

py

Python

vcf_reader.py

ZhiGroup/ROH-DICE

5a2edfd04e285fe1f40bb199117c03a33b176984

[ "MIT" ]

1

2021-09-01T15:46:26.000Z

vcf_reader.py

ZhiGroup/ROH-DICE

5a2edfd04e285fe1f40bb199117c03a33b176984

[ "MIT" ]

1

2021-05-21T13:13:55.000Z

2021-05-25T17:56:06.000Z

vcf_reader.py

ZhiGroup/ROH-DICE

5a2edfd04e285fe1f40bb199117c03a33b176984

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # ============================================================================= # Created By : Ardalan Naseri # Created Date: Mon September 21 2020 # ============================================================================= """The module is a VCF reader to parse input VCF file.""" import gzip import random def eff_split(string_input, array_vals, delimeter='\t'): counter = 0 start_pos = 0 end_pos = start_pos while start_pos < len(string_input) - 1: end_pos = start_pos + 1 while end_pos < len(string_input) and string_input[end_pos] != delimeter and start_pos != end_pos: end_pos += 1 array_vals[counter] = string_input[start_pos:end_pos] start_pos = end_pos + 1 counter = counter + 1 class VCFReader: def __init__(self, vcf_input_compressed): self.vcf_file = gzip.open(vcf_input_compressed) self.samples = [] self.done = False self.vals = [] self.genome_pos = [] self.valid = True self.entries_started = False self.inter_vals = None self._line = None def set_samples(self): done = False while not done: line = self.vcf_file.readline() if not line: done = True self.done = True continue if '#CHROM' in line: self.entries_started = True i = 9 _values = line.replace("\n", "").split() while i < len(_values): self.samples.append(_values[i]) i += 1 self.vals = [0] * len(self.samples) self.inter_vals = ['0|1'] * (len(self.samples) + 9) done = True def read_next_site(self): site_counter = 0 line = self.vcf_file.readline().replace("\n", "") self._line = line self.valid = True if not line: self.done = True self.vcf_file.close() return False if self.entries_started: eff_split(line, self.inter_vals, '\t') _pos = self.inter_vals[1] alt = self.inter_vals[4] if len(alt.split(',')) > 1: self.valid = False return True i = 2 while i < len(self.inter_vals) and self.inter_vals[i] != 'GT': i += 1 i += 1 if i >= len(self.inter_vals): self.valid = False return True tags = self.inter_vals[7] if len(self.inter_vals[3]) > 1 or len(self.inter_vals[4]) > 1: self.valid = False return True i = 9 site_values = '' j = 0 while i < len(self.inter_vals): site_values = self.inter_vals[i].replace("\n", '').split("|") if site_values[0] == '.' or len(site_values) < 2 or (len(site_values) > 1 and site_values[1] == '.'): self.valid = False return True al1 = int(site_values[0]) al2 = int(site_values[1]) if al1 == al2: self.vals[j] = al1 else: self.vals[j] = random.randint(0, 1) j = j + 1 i += 1 self.genome_pos.append(self.inter_vals[1]) site_counter = site_counter + 1 return True

31.415929

117

0.468169

411

3,550

3.861314

0.231144

0.079395

0.114682

0.05041

0.131065

0.076244

0.032766

0

0.023842

0.385634

3,550

112

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0

null

0

null

0

1

0

4e15597d3a91189d8d9a4e8575fb172c9d0972ad

2,865

py

Python

neighbor/tests.py

Elianehbmna/Neighborhood

3e684fe813904f10fca7f3ea8c71adb1f2bc6a3d

[ "MIT" ]

null

neighbor/tests.py

Elianehbmna/Neighborhood

3e684fe813904f10fca7f3ea8c71adb1f2bc6a3d

[ "MIT" ]

5

2020-02-12T03:17:58.000Z

2021-09-08T01:23:33.000Z

neighbor/tests.py

Elianehbmna/Neighbourhood

3e684fe813904f10fca7f3ea8c71adb1f2bc6a3d

[ "MIT" ]

null

from django.test import TestCase from django.contrib.auth.models import User from .models import Profile, Neighbourhood, Post, Business # Create your tests here. class ProfileTestClass(TestCase): ''' Test case for the Profile class ''' def setUp(self): ''' Method that creates an instance of Profile class ''' # Create instance of Profile class self.new_profile = Profile(bio="I am superwoman") def test_instance(self): ''' Test case to check if self.new_profile in an instance of Profile class ''' self.assertTrue(isinstance(self.new_profile, Profile)) def test_get_other_profiles(self): ''' Test case to check if all profiles are gotten from the database ''' self.eliane = User(username="elly") self.eliane.save() self.eliane = User(username="habibi") self.eliane.save() self.test_profile = Profile(user=self.eliane, bio="Another Profile") gotten_profiles = Profile.get_other_profiles(self.eliane.id) profiles = Profile.objects.all() class Neighbourhood(TestCase): ''' Test case for the Neighbourhood class ''' def setUp(self): ''' Method that creates an instance of Profile class ''' # Create a Image instance self.new_Image = Image( caption='hey') def test_instance(self): ''' Test case to check if self.new_Image in an instance of Image class ''' self.assertTrue(isinstance(self.new_Image, Image)) class Post(TestCase): ''' Test case for the Comment class ''' def setUp(self): ''' Method that creates an instance of Comment class ''' # Create a Comment instance self.new_comment = Comment( comment_content='hey') def test_instance(self): ''' Test case to check if self.new_comment in an instance of Comment class ''' self.assertTrue(isinstance(self.new_comment, Comment)) def test_get_Image_comments(self): ''' Test case to check if get Image comments is getting comments for a specific Image ''' self.eliane = User(username="eli") self.eliane.save() self.eliane = User(username="habibi") self.eliane.save() self.test_profile = Profile(user=self.eliane, bio="Another Profile") self.test_Image = Image(user=self.eliane, caption="Another Profile") self.test_comment = Comment( Image=self.test_Image, comment_content="Wow") gotten_comments = Comment.get_Image_comments(self.test_Image.id) comments = Comment.objects.all() # No comments were saved so expect True self.assertTrue(len(gotten_comments) == len(comments))

26.045455

89

0.622339

345

2,865

5.072464

0.22029

0.068571

0.041143

0.04

0.490857

0.386857

0.301143

0

0.282373

2,865

109

90

26.284404

0.851167

0.261431

0

0.35

0

0.046809

0

0.1

1

0.2

false

0

0.075

0

0.35

0

null

0

null

0

1

0

4e1a4e1f3d76e5fdbb618878f0f9c68ef36c94ef

13,944

py

Python

src/flintfiller/dataframe_to_frame_parser.py

discipl/flintfiller

15d220c980a962ac2c4b7ac232f091666ab24e66

[ "Apache-2.0" ]

null

src/flintfiller/dataframe_to_frame_parser.py

discipl/flintfiller

15d220c980a962ac2c4b7ac232f091666ab24e66

[ "Apache-2.0" ]

null

src/flintfiller/dataframe_to_frame_parser.py

discipl/flintfiller

15d220c980a962ac2c4b7ac232f091666ab24e66

[ "Apache-2.0" ]

null

""" Copyright (C) 2020 Nederlandse Organisatie voor Toegepast Natuur- wetenschappelijk Onderzoek TNO / TNO, Netherlands Organisation for applied scientific research Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. @author: Maaike de Boer, Roos Bakker @contact: maaike.deboer@tno.nl, roos.bakker@tno.nl """ import ast # This script transforms POStagged text to a FLINT frame. import json from typing import Tuple import pandas as pd action_verbs = ['aanbrengen', 'aanwijzen', 'achterwege blijven', 'afnemen', 'afwijken', 'afwijzen', 'ambtshalve verlenen', 'ambtshalve verlengen', 'annuleren', 'behandelen', 'beheren', 'bepalen', 'beperken', 'betreden', 'beveiligen', 'bevelen', 'bevorderen', 'bieden gelegenheid', 'bijhouden', 'buiten behandeling stellen', 'buiten werking stellen', 'doorzoeken', 'erop wijzen', 'gebruiken maken van', 'gedwongen ontruimen', 'geven', 'heffen', 'in bewaring stellen', 'in de gelegenheid stellen zich te doen horen', 'in kennis stellen', 'in werking doen treden', 'in werking stellen', 'indienen', 'innemen', 'instellen', 'intrekken', 'invorderen', 'inwilligen', 'maken', 'naar voren brengen', 'nemen', 'niet in behandeling nemen', 'niet-ontvankelijk verklaren', 'nogmaals verlengen', 'om niet vervoeren', 'onderwerpen', 'onderzoeken', 'ongewenstverklaren', 'onmiddellijk bepalen', 'onmiddellijk verlaten', 'ontnemen', 'ontvangen', 'opheffen', 'opleggen', 'oproepen', 'overbrengen', 'overdragen', 'plaatsen', 'schorsen', 'schriftelijk in kennis stellen', 'schriftelijk laten weten', 'schriftelijk mededelen', 'schriftelijk naar voren brengen', 'signaleren', 'sluiten', 'staande houden', 'stellen', 'straffen', 'ter hand stellen', 'teruggeven', 'tijdelijk in bewaring nemen', 'toetsen', 'toezenden', 'uitstellen', 'uitvaardigen', 'uitzetten', 'van rechtswege verkrijgen', 'vaststellen', 'vergelijken', 'verhalen', 'verhogen', 'verklaren', 'verkorten', 'verkrijgen', 'verlaten', 'verlenen', 'verlengen', 'verplichten', 'verschaffen', 'verstrekken', 'verzoeken', 'voegen', 'vorderen', 'vragen', 'willigen', 'weigeren', 'wijzigen'] set_propernouns = ["PRP", "PRP$", "NNP", "NNPS"] list_act = [] list_fact = [] global facts_list def read_csv_to_df(csv_file): datafrm = pd.read_csv(csv_file) print("csv loaded from " + csv_file) return datafrm def write_df_to_csv(df, fle): df.to_csv(fle) print("df written to " + fle) def get_empty_flint_frame_format() -> dict: flint_frame = { "acts": [], "facts": [], "duties": [] } return flint_frame def get_empty_act_frame() -> dict: act_frame = { "act": "", "actor": "", "action": "", "object": "", "recipient": "", "preconditions": { "expression": "LITERAL", "operand": True }, "create": [], "terminate": [], "sources": [], # with validFrom, validTo, citation juriconnect and text "explanation": "" } return act_frame def get_empty_fact_frame() -> dict: fact_frame = { "fact": "", "function": [], "sources": [], # with validFrom, validTo, citation juriconnect and text "explanation": "" } return fact_frame def get_source_dict(row, text, name_law) -> dict: source_dict = {"validFrom": row["Versie"]} try: source_dict["citation"] = "art. " + row['jci 1.3'].split("artikel=")[1].split('&')[0] + "lid " + \ row['jci 1.3'].split("lid=")[1].split('&')[0] + ", " + name_law except: # if split("lid=")[1] is not filled in, do not add this part source_dict["citation"] = "art. " + row['jci 1.3'].split("artikel=")[1].split('&')[0] + ", " + name_law source_dict['text'] = text.replace('\n', '').replace('\r', '').replace("\t", " ") source_dict['juriconnect'] = row['jci 1.3'] return source_dict def create_fact_or_act_function(list_text: list) -> dict: fact_function = {"expression": "AND"} fact_function_operands = [] for fct in list_text: try: if 'Onderdeel' not in fct and 'Lid' not in fct and len(fct) > 3: fact_function_operands.append( "[" + fct.replace('\n', '').replace('\r', '').split(";")[0].replace("\t", "")[1:] + "]") # . except: # if the fact is empty or has length of 0, [1:] does not work 'do nothing' # get rid of the empty list at the beginning if len(fact_function_operands) > 1: fact_function_operands.pop(0) fact_function["operands"] = fact_function_operands else: fact_function = { "expression": "LITERAL", "operand": True } return fact_function def get_object_and_actor(orig, tags) -> Tuple[str, str]: vp_found = False obj = "" actor_num = -1 # check the index of the verb for i in range(0, len(tags)): try: # find the VP if tags[i][0] == "VP" and (tags[i][len(tags[i][0])][0] == orig): vp_found = True for num in range(1, len(tags[i])): # get the first NP; this is the object # TODO: version 2: create better code using dependencies to determine the object and actor if not vp_found: # bug fix: no lower, because the link to the actor is gone then obj += " " + (str(tags[i][num][0])) # only add NPs if they are in the same sentence as the VP of the act if "$" in str(tags[i][num][0]) and not vp_found: obj = "" # try to find the actor and recipient # Hack: make a list of characters and check whether the first is uppercased (capitalized) if tags[i][num][1] in set_propernouns and list(tags[i][num][0])[0].isupper() and actor_num < 0: list_non_actors = ['Onderdeel', 'Lid', 'Indien', 'Tenzij', 'Onverminderd', 'Nadat'] if not (any(non_actor in tags[i][num][0] for non_actor in list_non_actors)): # print(tags[i][num]) actor_num = i except: # if tags[i][len(tags[i][0])][0] or tags[i][0] does not exist, we have an error 'do nothing' # the actor is the NP of the actor_num (number in the tags) actor = "" # fixed bug: bigger than -1 if the word occurs as the first word if actor_num > -1: # range starts with 1, because 0 is the type NP for nr in range(1, len(tags[actor_num])): actor += " " + tags[actor_num][nr][0] # hacks to get a better object if len(actor) > 1 and actor in obj: obj = obj.replace(actor, "") if "kan" in obj: obj = obj.replace("kan", "") return actor, obj def check_infinitive(inf, row) -> bool: return inf in action_verbs and not \ ("het " + inf) in row['Brontekst'] or \ ("de " + inf) in row['Brontekst'] or \ ("een " + inf) in row['Brontekst'] # This is a first version! def get_acts(row, verbs, tags, flint_frames, name_law) -> dict: # for each verb (if one verb this also works) for infinitive, original in verbs.items(): # if the verb is in the first part (before the :) (could be more verbs) parts = row['Brontekst'].split(":") # print("verbs found " + infinitive + " " + original) # addition to wrong parsing: # acts are not those that have a determiner before it; Dutch determiners are 'de', # 'het' and 'een' # acts are not those that have 'indien' as a form of 'indienen' if check_infinitive(infinitive, row) and not original == 'indien': act_frame = get_empty_act_frame() # print("act found: " + original) list_act.append([original, row['Brontekst']]) # print(original + "\t" + row['text:']) act_frame['action'] = "[" + infinitive + "]" # if we know that there should be preconditions, add them if ":" in row['Brontekst'] and original in parts[0]: act_function = create_fact_or_act_function(''.join(parts[1:]).split("$")) act_frame['preconditions'] = act_function # print(''.join(parts[1:]).split("$$")) # print(act_function) # TODO in version 2: make a fact of the pre-condition # get_empty_fact_frame() actor, obj = get_object_and_actor(original, tags) # hack: first character is a space; use from second on act_frame['actor'] = "[" + actor[1:] + "]" act_frame['act'] = "<<" + infinitive + obj.lower() + ">>" act_frame['object'] = "[" + obj[1:].lower() + "]" # TODO in version 2: make code better; now only vreemdeling as recipient if "vreemdeling" in row['Brontekst']: act_frame['recipient'] = "[vreemdeling]" source_dict_act = get_source_dict(row, row['Brontekst'], name_law) act_frame['sources'].append(source_dict_act) flint_frames['acts'].append(act_frame) return flint_frames def get_facts(row, part, name_law) -> dict: global facts_list fact_frame = get_empty_fact_frame() source_dict = get_source_dict(row, part, name_law) fact_frame['sources'].append(source_dict) # The facts has to be in between brackets fact_frame['fact'] = "[" + part.split(":")[0][1:] + "]" facts_list.append(part.split(":")[0][1:]) # create the function. In case of Artikel 1 this is the (one) definition that is after the : list_defs = [part.split(":")[1]] fact_function = create_fact_or_act_function(list_defs) fact_frame['function'] = fact_function return fact_frame def create_flint_frames(df, name_law) -> dict: flint_frames = get_empty_flint_frame_format() global facts_list facts_list = [] # loop through the rows and create acts and facts as we go for index, row in df.iterrows(): # we start with Facts that are present in the First Article # try: # Bug Fix: able to handle all prefixes before Artikel1 if str(row['Nummer'].split("/")[len(row['Nummer'].split("/")) - 1]) == 'Artikel1' and type( row['Brontekst']) != float: for part in row['Brontekst'].split("$"): if ":" in part and not "Onderdeel" in part: if part.split(":")[0][1:] not in facts_list and len(part.split(":")[1]) > 2: # Facts list_fact.append([part.split(":")[0][1:], part.split(":")[1].split(";")[0]]) # print(part.split(":")[0][1:] + "\t" + part.split(":")[1].split(";")[0]) fact_frame = get_facts(row, part, name_law) flint_frames['facts'].append(fact_frame) # Acts: only if we have verbs if not "[]" == row['verbs']: # hack: make it a dict / list again is we load in a dataframe from another format verbs = ast.literal_eval(row['verbs']) tags = ast.literal_eval(row['tags']) # because more than one act_frame could be created, go on the level of the flint_frames flint_frames = get_acts(row, verbs, tags, flint_frames, name_law) else: 'no acts' return flint_frames def write_flint_frames_to_json(flint_frames, flint_file): with open(str(flint_file), 'w') as f: json.dump(flint_frames, f) print("flint frames written to " + str(flint_file)) def dataframe_to_frame_parser(csv_file, output_file): name_law = csv_file.split("_")[len(csv_file.split("_")) - 1].split(".")[0] if name_law == 'postagged': name_law = csv_file.split("_")[len(csv_file.split("_")) - 2].split(".")[0] pos_tagged_df = read_csv_to_df(str(csv_file)) # print(name_law) flint_frames = create_flint_frames(pos_tagged_df, name_law) write_flint_frames_to_json(flint_frames, output_file) # if __name__ == '__main__': # method = "TOGS" # base = 'C:\\Users\\boermhtd\\PycharmProjects\\calculemus\\nlp\\data\\csv_files\\postagged\\' # if method == "TOGS": # csv_file = base + 'BWBR0043324_2020-03-31_0_TOGS_postagged.csv' # elif method == "TOZO": # csv_file = base + 'BWBR0043402_2020-04-22_0_TOZO_postagged.csv' # elif method == "AWB": # csv_file = base + 'BWBR0005537_2020-04-15_0_AWB_postagged.csv' # # #'BWBR0011823_2019-02-27_Vreemdelingenwet_postagged.csv' # # output_file = method + '_new.json' # dataframe_to_frame_parser(csv_file, output_file) # # act_file = "acts_" + method + ".csv" # df_act = pd.DataFrame(list_act, columns = ['action', 'sentence']) # df_act.to_csv(act_file, index=False) # # fact_file = "facts_" + method + ".csv" # df_fact = pd.DataFrame(list_fact, columns = ['fact', 'definition']) # df_fact.to_csv(fact_file, index=False) # # df = read_csv_to_df(str(csv_file)) # flint_frames = create_flint_frames(df) # write_flint_frames_to_json(flint_frames)

41.748503

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0.015957

0

null

0

null

0

1

0

4e1b7e1efb40a138e872299167e3dc139051bf3e

4,677

py

Python

tools/webcam/webcam_apis/nodes/mmdet_node.py

pallgeuer/mmpose

d3c17d5e6bdb9dbaca19f3bf53aa2802105355fd

[ "Apache-2.0" ]

null

tools/webcam/webcam_apis/nodes/mmdet_node.py

pallgeuer/mmpose

d3c17d5e6bdb9dbaca19f3bf53aa2802105355fd

[ "Apache-2.0" ]

null

tools/webcam/webcam_apis/nodes/mmdet_node.py

pallgeuer/mmpose

d3c17d5e6bdb9dbaca19f3bf53aa2802105355fd

[ "Apache-2.0" ]

null

# Copyright (c) OpenMMLab. All rights reserved. from typing import List, Optional, Union import numpy as np from .builder import NODES from .node import MultiInputNode, Node try: from mmdet.apis import inference_detector, init_detector has_mmdet = True except (ImportError, ModuleNotFoundError): has_mmdet = False @NODES.register_module() class DetectorNode(Node): def __init__(self, name: str, model_config: str, model_checkpoint: str, input_buffer: str, output_buffer: Union[str, List[str]], enable_key: Optional[Union[str, int]] = None, enable: bool = True, device: str = 'cuda:0'): # Check mmdetection is installed assert has_mmdet, \ f'MMDetection is required for {self.__class__.__name__}.' super().__init__(name=name, enable_key=enable_key, enable=enable) self.model_config = model_config self.model_checkpoint = model_checkpoint self.device = device.lower() # Init model self.model = init_detector( self.model_config, self.model_checkpoint, device=self.device) # Register buffers self.register_input_buffer(input_buffer, 'input', trigger=True) self.register_output_buffer(output_buffer) def bypass(self, input_msgs): return input_msgs['input'] def process(self, input_msgs): input_msg = input_msgs['input'] img = input_msg.get_image() preds = inference_detector(self.model, img) det_result = self._post_process(preds) input_msg.add_detection_result(det_result, tag=self.name) return input_msg def _post_process(self, preds): if isinstance(preds, tuple): dets = preds[0] segms = preds[1] else: dets = preds segms = [None] * len(dets) det_model_classes = self.model.CLASSES if isinstance(det_model_classes, str): det_model_classes = (det_model_classes, ) assert len(dets) == len(det_model_classes) assert len(segms) == len(det_model_classes) result = {'preds': [], 'model_cfg': self.model.cfg.copy()} for i, (cls_name, bboxes, masks) in enumerate(zip(det_model_classes, dets, segms)): if masks is None: masks = [None] * len(bboxes) else: assert len(masks) == len(bboxes) preds_i = [{ 'cls_id': i, 'label': cls_name, 'bbox': bbox, 'mask': mask, } for (bbox, mask) in zip(bboxes, masks)] result['preds'].extend(preds_i) return result @NODES.register_module() class MultiFrameDetectorNode(DetectorNode, MultiInputNode): """Detect hand with one frame in a video clip. The length of clip is decided on the frame rate and the inference speed of detector. Parameters: inference_frame (str): indicate the frame selected in a clip to run detect hand. Can be set to ('begin', 'mid', 'last'). Default: 'mid'. """ def __init__(self, name: str, model_config: str, model_checkpoint: str, input_buffer: str, output_buffer: Union[str, List[str]], inference_frame: str = 'mid', enable_key: Optional[Union[str, int]] = None, device: str = 'cuda:0'): DetectorNode.__init__( self, name, model_config, model_checkpoint, input_buffer, output_buffer, enable_key, device=device) self.inference_frame = inference_frame def process(self, input_msgs): """Select frame and detect hand.""" input_msg = input_msgs['input'] if self.inference_frame == 'last': key_frame = input_msg[-1] elif self.inference_frame == 'mid': key_frame = input_msg[len(input_msg) // 2] elif self.inference_frame == 'begin': key_frame = input_msg[0] else: raise ValueError(f'Invalid inference_frame {self.inference_frame}') img = key_frame.get_image() preds = inference_detector(self.model, img) det_result = self._post_process(preds) imgs = [frame.get_image() for frame in input_msg] key_frame.set_image(np.stack(imgs, axis=0)) key_frame.add_detection_result(det_result, tag=self.name) return key_frame

32.034247

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0.584135

537

4,677

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0.26257

0.030781

0.0404

0.018469

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0.320505

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145

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0

0.278846

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0

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0.173077

0

null

0

null

0

1

0

4e1e6490f04076ef930623904d9e0fdabc66c26f

1,325

py

Python

gryphon/fsm/machine.py

vittorfp/labskit_cli

28e109b4a9f36a03d499eb953e04a4fb787632fe

[ "MIT" ]

null

gryphon/fsm/machine.py

vittorfp/labskit_cli

28e109b4a9f36a03d499eb953e04a4fb787632fe

[ "MIT" ]

1

2022-03-08T14:54:26.000Z

2022-03-08T15:02:52.000Z

gryphon/fsm/machine.py

vittorfp/labskit_cli

28e109b4a9f36a03d499eb953e04a4fb787632fe

[ "MIT" ]

null

class HaltSignal(Exception): def __init__(self): super().__init__() class Machine: def __init__(self, initial_state, possible_states): self.history = [initial_state.name] self.possible_states = possible_states self.current_state = initial_state def find_state_by_name(self, name): filtered = list(filter(lambda x: name == x.name, self.possible_states)) if len(filtered): return filtered[0] else: names = [ p.name for p in self.possible_states ] raise RuntimeError(f"State '{name}' not found in possible states: {names}") def run_interaction(self, context: dict): context = self.current_state.on_start(context) transition = self.current_state.check_transitions(context) self.current_state = self.find_state_by_name(transition.next_state) if transition is None: raise HaltSignal() context = transition.callback(context) self.history.append(self.current_state.name) return context def run(self): context = {} while self.current_state and not self.current_state.is_final_state(): context = self.run_interaction(context) self.current_state.on_start(context)

29.444444

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0.636226

154

1,325

5.201299

0.344156

0.109863

0.1598

0.086142

0.092385

0

0.001042

0.275472

1,325

44

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0.15625

false

0

0.28125

0

null

0

null

0

1

0

4e1ef7bc29a97c874523d2f21ef24ab69fc641da

708

py

Python

cursos_complementarios/estructuras_datos_lineales_python/modulo_II_arrays/utils/cube.py

EdinsonRequena/articicial-inteligence-and-data-science

953566220e64cbd8f732c2667b818da807bb54c0

[ "MIT" ]

30

2020-06-19T16:21:04.000Z

2022-02-19T01:48:39.000Z

cursos_complementarios/estructuras_datos_lineales_python/modulo_II_arrays/utils/cube.py

Samsuesca/articicial-inteligence-and-data-science

953566220e64cbd8f732c2667b818da807bb54c0

[ "MIT" ]

87

2021-02-12T04:42:13.000Z

2021-09-20T04:25:29.000Z

cursos_complementarios/estructuras_datos_lineales_python/modulo_II_arrays/utils/cube.py

Samsuesca/articicial-inteligence-and-data-science

953566220e64cbd8f732c2667b818da807bb54c0

[ "MIT" ]

11

2020-08-13T04:04:01.000Z

2022-01-20T20:10:43.000Z

from .array import Array from .grid import Grid class Cube(object): """three-dimensional array""" def __init__(self, nrows, ncols, deep, value=None) -> None: """Initializes the Cube with nrows, ncols, deep and optional value""" self.data = Array(deep) for i in range(deep): self.data[i] = Grid(nrows, ncols, value) def __getdeep__(self) -> int: """Return the whole cube""" return len(self.data) def __str__(self) -> str: """Return the cube as a string""" result = "" for array in range(self.__getdeep__()): result += self.data[array].__str__() result += "\n" return str(result)

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0.574859

88

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4.397727

0.431818

0.082687

0.072351

0

0.292373

708

25

78

28.32

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0.193503

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0

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0.2

false

0

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0

0.533333

0

null

0

null

0

1

0

4e26c8f3d5348e863a10d16b62007dbfcaa204c5

1,126

py

Python

setup.py

TimSusa/aptly-api-cli

011ba8e7f464726b336b53f6b2cbdc4490b5180c

[ "MIT" ]

17

2016-03-15T10:07:27.000Z

2022-03-07T17:55:01.000Z

setup.py

TimSusa/aptly-api-cli

011ba8e7f464726b336b53f6b2cbdc4490b5180c

[ "MIT" ]

2

2016-03-15T12:50:58.000Z

2018-04-17T03:45:17.000Z

setup.py

TimSusa/aptly-api-cli

011ba8e7f464726b336b53f6b2cbdc4490b5180c

[ "MIT" ]

5

2017-05-07T20:01:49.000Z

2018-06-06T13:43:02.000Z

try: from setuptools import setup, find_packages from pkg_resources import Requirement, resource_filename except ImportError: from distutils.core import setup, find_packages setup( name='Aptly-Api-Cli', version='0.1', url='https://github.com/TimSusa/aptly_api_cli', license='MIT', keywords="aptly aptly-server debian", author='Tim Susa', author_email='timsusa@gmx.de', description='This cli executes remote calls to the Aptly server, without blocking the Aptly database.', long_description=__doc__, packages=find_packages(), package_dir={'aptly_cli': 'aptly_cli'}, # packages=['aptly_cli', 'aptly_cli.api', 'aptly_cli.cli', 'aptly_cli.util'], # py_modules=['aptly_cli.api.api', 'cli'], entry_points={ 'console_scripts': [ 'aptly-cli=aptly_cli.cli.cli:main' ] }, # data_files=[ # ('configs', ['configs/aptly-cli.conf']), # ], # package_data={'configs': ['aptly_cli/configs/aptly-cli.conf']}, platforms='any' ) filename = resource_filename(Requirement.parse("Aptly-Api-Cli"), "configs/aptly-cli.conf")

33.117647

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0.667851

141

1,126

5.12766

0.48227

0.143845

0.060858

0.06639

0.060858

0

0.002167

0.180284

1,126

33

108

34.121212

0.781148

0.214032

0

0.338269

0.061503

0

1

0

false

0

0.16

0

0.16

0

null

0

null

0

1

0

4e27d12ca0167eeef14eeab8dc9bfe483d5dc2db

417

py

Python

2018-04/2018-04-11.py

shangpf1/python_study

6730519ce7b5cf4612e1c778ae5876cfbb748a4f

[ "MIT" ]

null

2018-04/2018-04-11.py

shangpf1/python_study

6730519ce7b5cf4612e1c778ae5876cfbb748a4f

[ "MIT" ]

null

2018-04/2018-04-11.py

shangpf1/python_study

6730519ce7b5cf4612e1c778ae5876cfbb748a4f

[ "MIT" ]

null

class Employee: def __init__(self,first,last,pay): self.first = first self.last = last self.email = first+last+'@123.com' self.pay = pay def fullname(self): return('{} {}'.format(self.first,self.last)) emp_1 = Employee('hello','world',1900) emp_2 = Employee('test','world',2000) print(emp_1) print(emp_2) print(emp_1.fullname()) print(emp_2.fullname())

18.130435

52

0.606715

58

417

4.189655

0.396552

0.131687

0.106996

0

0.052632

0.22542

417

22

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18.954545

0.69969

0

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0

1

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false

0

0.071429

0.214286

0.285714

0

null

0

null

0

1

0

4e298d0ad6de43261aab1a6d6e7529e6494b22c8

658

py

Python

src/heatmap.py

JsPatenaude/INF8808_projet

601a7505188f379365a32594b484cee3d924a52a

[ "MIT" ]

null

src/heatmap.py

JsPatenaude/INF8808_projet

601a7505188f379365a32594b484cee3d924a52a

[ "MIT" ]

null

src/heatmap.py

JsPatenaude/INF8808_projet

601a7505188f379365a32594b484cee3d924a52a

[ "MIT" ]

null

import plotly.express as px from preprocess import PreprocessHeatmap def get_figure(df): pp = PreprocessHeatmap() heatmap_df = pp.preprocess_heatmap(df) hover_template = \ ''' <b style="font-size: 20px;>%{x}, %{y}h00</b> <br> <span style="font-size: 16px;>%{z:.0f} likes générés</span> <extra></extra> ''' fig = px.imshow(heatmap_df) fig.update_layout( xaxis_title='Jour de la semaine', yaxis_title='Heure de la journée', yaxis_nticks=24, paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)' ) fig.update_traces(hovertemplate=hover_template) return fig

26.32

63

0.62766

90

658

4.444444

0.611111

0.03

0.065

0.075

0

0.033399

0.226444

658

25

64

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false

0

0.125

0

0.25

0

null

0

null

0

1

0

4e2a78cc73dc66dd46aa1290d150d2b082861993

13,985

py

Python

client/forumgame.py

codingforhelp/fbserv

b09cc2ce20eaa3714e80d23e0f5741f144d2eed2

[ "MIT" ]

5

2019-01-31T08:09:53.000Z

2020-04-13T22:48:25.000Z

client/forumgame.py

codingforhelp/fbserv

b09cc2ce20eaa3714e80d23e0f5741f144d2eed2

[ "MIT" ]

2

2021-04-30T21:04:37.000Z

2021-06-01T23:42:18.000Z

client/forumgame.py

codingforhelp/fbserv

b09cc2ce20eaa3714e80d23e0f5741f144d2eed2

[ "MIT" ]

3

2019-08-04T07:51:58.000Z

2022-02-25T13:39:30.000Z

from dom import e, Div, TextInput, Button, TextArea from basicboard import BasicBoard from connection import getconn from utils import queryparams, random, setseed mainseed = 80 class Forumnode(e): def __init__(self, root, args = {}): super().__init__("div") self.root = root self.move = args["move"] self.uci = args["uci"] self.comment = args["comment"] if not self.comment: self.comment = "" self.owner = args["owner"] self.fen = args["fen"] self.parent = args["parent"] self.isadmin = args["isadmin"] self.halfmoveno = args["halfmoveno"] if not self.halfmoveno: self.halfmoveno = -1 self.childs = [] self.build() def toobj(self): moveobjs = {} for child in self.childs: moveobjs[child.move] = child.toobj() return { "uci": self.uci, "comment": self.comment, "owner": self.owner, "fen": self.fen, "moves": moveobjs } def appendchild(self, node): node.halfmoveno = self.halfmoveno + 1 node.build() self.childs.append(node) self.containerdiv.a(node) if len(self.childs) > 1: rgb = "rgb({},{},{})".format(int(random()*128 + 127),int(random()*128 + 127),int(random()*128 + 127)) self.containerdiv.bc(rgb).bds("solid").bdw(10).bdr(20).bdc(rgb) def addnode(self): input = window.prompt("Move:uci:owner:fen", "") if input: self.root.shift() parts = input.split(":") self.appendchild(Forumnode(self.root, { "move": parts[0], "uci": None, "comment": "", "uci": parts[0], "owner": parts[2], "fen": parts[2], "parent": self, "isadmin": self.isadmin })) self.root.parse() def edituci(self): input = window.prompt("Uci", "") if input: self.uci = input self.setboard() self.ucidiv.html(self.uci) self.root.parse() def editfen(self): input = window.prompt("Fen", "") if input: self.fen = input self.setboard() self.root.parse() def setmovelabel(self): if self.halfmoveno < 0: moveno = "" elif ( self.halfmoveno % 2 ) == 0: moveno = ( ( self.halfmoveno + 2 ) / 2 ) + ". " else: moveno = ( ( self.halfmoveno + 1 ) / 2 ) + ".. " self.movelabeldiv.html("{}{}".format(moveno, self.move)) def editsan(self): input = window.prompt("San", "") if input: self.move = input self.setmovelabel() self.root.parse() def editcomment(self): input = window.prompt("Comment", self.comment) if input: self.comment = input self.commentdiv.html(self.comment) self.root.parse() def editowner(self): input = window.prompt("Owner", "") if input: self.owner = input self.ownerdiv.html(self.owner) self.root.parse() def movecallback(self, variantkey, fen, uci): if self.reqfenunderway: print("a fen request is in progress, cannot start a new one") return self.root.shift() self.root.reqfenunderway = True self.root.reqnode = self getconn().sioreq({ "kind": "forumgamemove", "owner": "forumgame", "moveuci": uci, "variantkey": variantkey, "fen": fen }) def bbdragstart(self, ev): ev.stopPropagation() def setboard(self): initobj = { "fen": self.fen, "squaresize": 20, "showfen": False, "movecallback": self.movecallback, "variantkey": "atomic" } if self.uci: initobj["positioninfo"] = { "genmove": { "uci": self.uci } } b = BasicBoard(initobj) b.cp().ae("dragstart", self.bbdragstart) self.boarddiv.x().a(b) def analyzelocal(self): try: self.root.mainboard.variantchanged("atomic", self.fen) self.root.parenttabpane.selectbykey("board") except: pass def analyzelichess(self): window.open("https://lichess.org/analysis/atomic/" + self.fen, "_blank") def delchilds(self): self.childs = [] self.root.rebuild(mainseed) def delme(self): parent = self.parent if parent: newchilds = [] for child in parent.childs: print("child", child.move, child.uci) if not ( child == self ): newchilds.append(child) parent.childs = newchilds self.root.rebuild(mainseed) def serializefunc(self): self.root.rebuild(mainseed + 1) self.root.store() def serialize(self): self.infohook.html("serializing") setTimeout(self.serializefunc, 100) def copysrc(self): self.root.copysrc() def copylink(self): ti = TextInput() self.linktexthook.a(ti) ti.setText("https://fbserv.herokuapp.com/analysis/atomic/" + self.fen.replace(" ", "%20")) ti.e.select() document.execCommand("copy") self.linktexthook.x() def build(self): self.movediv = Div().disp("flex").fd("row").ai("center") self.movedescdiv = Div().bc("#eee").w(110).maw(110).pad(3) self.movelabeldiv = Div().fw("bold").pad(3).ff("monospace") self.setmovelabel() self.ownerdiv = Div().html(self.owner).ff("monospace").fs("10").c("#007") self.ucidiv = Div().ff("monospace").fs("12").pad(3) self.commentdiv = Div().fs("12").pad(5).html(self.comment) if self.uci: self.ucidiv.html(self.uci) self.movedescdiv.a([self.movelabeldiv, self.ownerdiv, self.commentdiv]) self.movedescdiv.a(Button("Analyze local", self.analyzelocal).mar(2)) self.movedescdiv.a(Button("Analyze lichess", self.analyzelichess).mar(2)) self.infohook = Div().ff("monospace").pad(3).c("#007").fw("bold").html("built") if self.isadmin: self.movedescdiv.a(self.infohook) self.linktexthook = Div() self.movedescdiv.a(self.ucidiv) self.movedescdiv.a(Button("+", self.addnode).pad(5)) self.movedescdiv.a(Button("san", self.editsan).pad(5)) self.movedescdiv.a(Button("uci", self.edituci).pad(5)) self.movedescdiv.a(Button("fen", self.editfen).pad(5)) self.movedescdiv.a(Button("comment", self.editcomment).pad(5)) self.movedescdiv.a(Button("owner", self.editowner).pad(5)) self.movedescdiv.a(Button("serialize", self.serialize).pad(5).bc("#ffa")) self.movedescdiv.a(Button("copy", self.copysrc).pad(5).bc("#afa")) self.movedescdiv.a(self.linktexthook) self.movedescdiv.a(Button("link", self.copylink).pad(5).bc("#aff")) self.movedescdiv.a(Button("delchilds", self.delchilds).pad(5).bc("#faa")) self.movedescdiv.a(Button("delme", self.delme).pad(5).bc("#faa")) self.boarddiv = Div().pad(2) self.movecontainerdiv = Div().disp("flex").fd("row").ai("center") self.movecontainerdiv.a([self.movedescdiv, self.boarddiv]) self.containerdiv = Div().disp("flex").fd("column").ai("flex-start") self.movediv.a([self.movecontainerdiv, self.containerdiv]) self.setboard() self.x().a(self.movediv) self.mw(600) class Forumgame(e): def __init__(self): super().__init__("div") self.messagediv = Div().disp("inline-block").pad(3).ff("monospace") self.contentdiv = Div() self.a([self.messagediv, self.contentdiv]) self.reqfenunderway = False self.reqnode = None self.requestforumgame() self.ae("mousemove", self.mousemove) self.ae("mouseup", self.mouseup) self.ae("mouseleave", self.mouseleave) def copysrc(self): self.textarea.e.select() document.execCommand("copy") window.alert("Copied source to clipboard, {} characters.".format(len(self.textarea.getText()))) def mousemove(self, ev): if self.dragunderway: dx = ev.clientX - self.dragstartx dy = ev.clientY - self.dragstarty self.parenttabpane.contentdiv.e.scrollTop = self.scrolltop + 20 * dy self.parenttabpane.contentdiv.e.scrollLeft = self.scrollleft + 20 * dx def mouseup(self, ev): self.dragunderway = False def mouseleave(self, ev): self.dragunderway = False def parse(self): obj = self.rootnode.toobj() text = JSON.stringify(obj, None, 2) self.textarea.setText(text) return text def store(self): self.parenttabpane.contentdiv.bc("#faa") self.messagediv.html("Parsing JSON") try: obj = JSON.parse(self.textarea.getText()) self.messagediv.html("Storing JSON") getconn().sioreq({ "kind": "setforumgame", "owner": "forumgame", "forumgame": obj }) except: self.messagediv.html("Error: could not parse JSON") return def requestforumgame(self): getconn().sioreq({ "kind": "getforumgame", "owner": "forumgame" }) def buildrec(self, parentnode, tree): __pragma__("jsiter") if not tree["moves"]: return for move in tree["moves"]: moveobj = tree["moves"][move] node = Forumnode(self, { "move": move, "uci": moveobj["uci"], "comment": moveobj["comment"], "owner": moveobj["owner"], "fen": moveobj["fen"], "parent": parentnode, "isadmin": self.isadmin }) parentnode.appendchild(node) self.buildrec(node, moveobj) __pragma__("nojsiter") def build(self, text, seed): setseed(seed) self.contentdiv.x().pad(3) self.textarea = TextArea().w(1000).h(200) self.textarea.setText(text) self.controlpanel = Div() self.controlpanel.a(Button("Store", self.store)) if self.isadmin: self.contentdiv.a(self.textarea) self.contentdiv.a(self.controlpanel) self.rootnode = Forumnode(self, { "move": "startpos", "uci": None, "owner": "Wolfram_EP", "comment": "Forum game", "fen": "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1", "parent": None, "isadmin": self.isadmin }) self.contentdiv.a(self.rootnode) self.buildrec(self.rootnode, self.forumgame) #self.rootnode.e.scrollIntoView(True) self.parenttabpane.setscroll() self.contentdiv.sa("draggable", True).cm().ae("dragstart", self.dragstart) def dragstart(self, ev): ev.preventDefault() self.dragstartx = ev.clientX self.dragstarty = ev.clientY self.scrolltop = self.parenttabpane.contentdiv.e.scrollTop self.scrollleft = self.parenttabpane.contentdiv.e.scrollLeft self.dragunderway = True def rebuild(self, seed): text = self.parse() self.forumgame = JSON.parse(text) self.build(text, seed) def shift(self): sl = self.parenttabpane.contentdiv.e.scrollLeft self.parenttabpane.contentdiv.e.scrollLeft = sl + 300 def siores(self, response): if response["kind"] == "setforumgame": self.forumgame = response["forumgame"] self.messagediv.html("Forumgame loaded") self.isadmin = response["isadmin"] if queryparams.get("noadmin", "false") == "true": self.isadmin = False self.build(JSON.stringify(self.forumgame, None, 2), mainseed) self.parenttabpane.contentdiv.bc("#def") if response["kind"] == "setforumgamedone": self.messagediv.html("Stored, refreshing") self.requestforumgame() if response["kind"] == "setforumgamefen": posinfo = response["positioninfo"] fen = response["fen"] san = posinfo["genmove"]["san"] uci = posinfo["genmove"]["uci"] rp = self.reqnode.parent owner = None if rp: owner = rp.owner if not owner: owner = window.prompt("Owner", "?") if not owner: owner = "?" self.reqnode.appendchild(Forumnode(self, { "move": san, "uci": uci, "comment": "", "owner": owner, "fen": fen, "parent": self.reqnode, "isadmin": self.isadmin })) self.parse()

36.609948

125

0.512835

1,388

13,985

5.148415

0.198847

0.02239

0.038063

0.040022

0.133361

0.095018

0.014134

0

0.012072

0.348445

13,985

382

126

36.609948

0.772169

0.002574

0

0.209302

0

0.096119

0.003084

0

1

0.104651

false

0.002907

0.011628

0

0.136628

0.005814

0

null

0

null

0

1

0

4e2ce6d71349214a1161e5b470a89bc7da49773f

6,513

py

Python

tests/mathbot_tests.py

RubyMarsden/Crayfish

33bbb1248beec2fc40eee59e462711dd8cbc33da

[ "MIT" ]

null

tests/mathbot_tests.py

RubyMarsden/Crayfish

33bbb1248beec2fc40eee59e462711dd8cbc33da

[ "MIT" ]

8

2021-03-19T06:35:48.000Z

2021-03-31T14:23:24.000Z

tests/mathbot_tests.py

RubyMarsden/Crayfish

33bbb1248beec2fc40eee59e462711dd8cbc33da

[ "MIT" ]

null

import unittest from models import settings from models.mathbot import * from models.settings import U238_DECAY_CONSTANT, U238_DECAY_CONSTANT_ERROR, TH232_DECAY_CONSTANT, \ TH232_DECAY_CONSTANT_ERROR class MathbotTests(unittest.TestCase): ######################################## ### Outlier resistant mean and stdev ### ######################################## def test_outlier_resistant_mean_no_outliers_allowed(self): test_data = [1, 1, 2, 1, 4, 1, 2, 3, 9, 2] mean, st_dev = calculate_outlier_resistant_mean_and_st_dev(test_data, 0) self.assertEqual(np.mean(test_data), mean) self.assertEqual(np.std(test_data), st_dev) def test_outlier_resistant_mean_zeros(self): test_data = [0] * 10 self.assertEqual((0, 0), calculate_outlier_resistant_mean_and_st_dev(test_data, 2)) def test_outlier_resistant_mean_empty_set(self): self.assertRaises(IndexError, calculate_outlier_resistant_mean_and_st_dev, [], 2) def test_outlier_resistant_mean_one_higher_outlier(self): test_data = [1, 1, 1, 1, 1, 1, 1, 1, 1, 40] mean, st_dev = calculate_outlier_resistant_mean_and_st_dev(test_data, 1) mean_2, st_dev_2 = calculate_outlier_resistant_mean_and_st_dev(test_data, 2) self.assertEqual(1, mean) self.assertEqual(0, st_dev) self.assertEqual(1, mean_2) self.assertEqual(0, st_dev_2) def test_outlier_resistant_mean_one_lower_outlier(self): test_data = [1, 40, 40, 40, 40, 40, 40, 40, 40, 40] mean, st_dev = calculate_outlier_resistant_mean_and_st_dev(test_data, 1) mean_2, st_dev_2 = calculate_outlier_resistant_mean_and_st_dev(test_data, 2) self.assertEqual(40, mean) self.assertEqual(0, st_dev) self.assertEqual(40, mean_2) self.assertEqual(0, st_dev_2) def test_outlier_resistant_mean_two_outliers(self): test_data = [1, 40, 40, 40, 40, 40, 40, 40, 40, 400] mean, st_dev = calculate_outlier_resistant_mean_and_st_dev(test_data, 1) mean_2, st_dev_2 = calculate_outlier_resistant_mean_and_st_dev(test_data, 2) self.assertEqual(np.mean(test_data), mean) self.assertEqual(np.std(test_data), st_dev) self.assertEqual(40, mean_2) self.assertEqual(0, st_dev_2) ####################### ### Relative errors ### ####################### def test_relative_errors_zero_case(self): self.assertEqual(0, relative_error(0, 4)) def test_relative_errors_general(self): self.assertEqual(0.1, relative_error(10, 1)) ############################ ### Errors in quadrature ### ############################ def test_errors_in_quadrature_single_error(self): self.assertEqual(1, errors_in_quadrature([1])) def test_errors_in_quadrature_general(self): self.assertEqual(13, errors_in_quadrature([5, 12])) def test_errors_in_quadrature_negative(self): self.assertEqual(13, errors_in_quadrature([-5, 12])) def test_errors_in_quadrature_decimals(self): self.assertEqual(0.2, errors_in_quadrature([0.1, 0.1, 0.1, 0.1])) ######################################## ### Interpolate to exponential curve ### ######################################## def test_interpolate_to_exponential(self): a, b, y_est_rounded, y_est_rounded_uncertainty = interpolate_to_exponential((0, 10), 3, (1, 5), 2, 0.5) self.assertEqual(10, a) self.assertAlmostEqual(-0.693147180559945, b, 14) self.assertAlmostEqual(7.07106781186548, y_est_rounded, 14) self.assertAlmostEqual(1.76776695296637, y_est_rounded_uncertainty, 14) def test_interpolate_to_exponential_invalid_points(self): self.assertRaises(AssertionError, interpolate_to_exponential, (0, 0), 0, (0, 0), 0, 0) self.assertRaises(AssertionError, interpolate_to_exponential, (0, 10), 0, (1, 5), 0, 2) ###################### ### Activity ratio ### ###################### def test_activity_ratio_general(self): ratio, ratio_uncertainty = activity_ratio( cps_mass_1=10, cps_mass_1_uncertainty=1, decay_constant_1=2, decay_constant_1_uncertainty=0.2, cps_mass_2=20, cps_mass_2_uncertainty=2, decay_constant_2=5, decay_constant_2_uncertainty=0.5 ) self.assertEqual(0.2, ratio) self.assertAlmostEqual(0.04, ratio_uncertainty, 16) def test_activity_ratio_data_values(self): # using data from Heidelberg University 05/2020 ratio, ratio_uncertainty = activity_ratio( cps_mass_1=12.0540007, cps_mass_1_uncertainty=0.01, decay_constant_1=U238_DECAY_CONSTANT, decay_constant_1_uncertainty=U238_DECAY_CONSTANT_ERROR, cps_mass_2=10, cps_mass_2_uncertainty=0.01, decay_constant_2=TH232_DECAY_CONSTANT, decay_constant_2_uncertainty=TH232_DECAY_CONSTANT_ERROR ) self.assertAlmostEqual(3.77943781422436, ratio, 14) self.assertAlmostEqual(0.00531355971346501, ratio_uncertainty, 14) def test_activity_ratio_invalid_input(self): self.assertRaises(AssertionError, activity_ratio, cps_mass_1=-1, cps_mass_1_uncertainty=0.01, decay_constant_1=U238_DECAY_CONSTANT, decay_constant_1_uncertainty=U238_DECAY_CONSTANT_ERROR, cps_mass_2=10, cps_mass_2_uncertainty=0.01, decay_constant_2=TH232_DECAY_CONSTANT, decay_constant_2_uncertainty=TH232_DECAY_CONSTANT_ERROR ) ######################### ### Age from gradient ### ######################### def test_age_from_gradient_zero_uncertainty(self): age, uncertainty = calculate_age_from_values(0.5, 0, 1, 0, 0, 0) self.assertEqual(-math.log(0.5) / settings.TH230_DECAY_CONSTANT, age) self.assertEqual(uncertainty, 0) def test_age_from_gradient_more_realistic(self): age, uncertainty = calculate_age_from_values(3.02, 0.05, 6.33, 0.16, 0.32, 0.01) self.assertEqual(-math.log(1 - (3.02 - 0.32)/(6.33 - 0.32)) / settings.TH230_DECAY_CONSTANT, age) self.assertAlmostEqual(2459.439109, uncertainty, 6) if __name__ == '__main__': unittest.main()

40.453416

111

0.634423

831

6,513

4.583634

0.139591

0.088737

0.084012

0.023103

0.580992

0.502232

0.47414

0.415857

0.375689

0.36545

0

0.081621

0.223092

6,513

160

112

40.70625

0.671146

0.028558

0

0.305556

0

0.001349

0

0.342593

1

0.175926

false

0

0.037037

0

0.222222

0

null

0

null

0

1

0

4e2d4927d418a10f01fca137a00d8c7a207d49a7

2,748

py

Python

flask_modular_auth/manager.py

fabian-rump/flask_modular_auth

509def7b2cb366cba5d0d18187d99932c8ca00ef

[ "MIT" ]

null

flask_modular_auth/manager.py

fabian-rump/flask_modular_auth

509def7b2cb366cba5d0d18187d99932c8ca00ef

[ "MIT" ]

null

flask_modular_auth/manager.py

fabian-rump/flask_modular_auth

509def7b2cb366cba5d0d18187d99932c8ca00ef

[ "MIT" ]

null

from .abstract import AbstractAuthProvider, AbstractUnauthenticatedEntity from .utils import _context_processor from flask import _request_ctx_stack, has_request_context class AuthManager: def __init__(self, app=None, unauthorized_callback=None, unauthenticated_entity_class=None): self._auth_providers = [] if unauthenticated_entity_class: self._unauthenticated_entity_class = unauthenticated_entity_class else: self._unauthenticated_entity_class = AbstractUnauthenticatedEntity self._unauthorized_callback = unauthorized_callback if app is not None: self.init_app(app) def init_app(self, app): app.auth_manager = self app.context_processor(_context_processor) def set_unauthenticated_entity_class(self, unauthenticated_entity_class): self._unauthenticated_entity_class = unauthenticated_entity_class def unauthorized(self): if has_request_context() and hasattr(_request_ctx_stack.top, 'unauthorized_callback'): return _request_ctx_stack.top.unauthorized_callback() elif self._unauthorized_callback: return self._unauthorized_callback() else: return 'Not authorized', 403 def get_auth_providers(self): """ Get a list of all registered authentication providers. :return:List of authentication providers """ return self._auth_providers def register_auth_provider(self, auth_provider): """ Register an authentication provider with the manager. :param auth_provider: A valid authentication provider (i.e. an instance of a subclass of AbstractAuthenticationProvider) """ if auth_provider.__class__ == AbstractAuthProvider: raise RuntimeError('Tried to add AbstractAuthProvider. Please add an implementing subclass object instead.') elif not isinstance(auth_provider, AbstractAuthProvider): raise ValueError('Tried to add an object which is no valid AuthProvider. Object should be instantiated from a subclass of AbstractAuthProvider.') else: self._auth_providers.append(auth_provider) def _load_authenticated_entity(self): ctx = _request_ctx_stack.top if not self._auth_providers: raise RuntimeError('Please register at least one authentication provider to get authenticated entities.') for auth_provider in self._auth_providers: entity = auth_provider.get_authenticated_entity() if entity: ctx.authenticated_entity = entity return True ctx.authenticated_entity = self._unauthenticated_entity_class() return False

42.9375

157

0.71361

294

2,748

6.346939

0.292517

0.11254

0.139335

0.080386

0.144695

0.103966

0.087889

0

0.00142

0.231441

2,748

63

158

43.619048

0.882102

0.098617

0

0.111111

0

0.022222

0.136288

0.026098

0

1

0.155556

false

0

0.066667

0

0.377778

0

null

0

null

0

1

0

4e30d02b5676aa65a9e86f44cc1848fd4a7d7bb2

13,400

py

Python

models/iscnet/modules/relation_model.py

blakeyy/Relational-RfDNet

72f4e35601e963c91515f40707174c0d79cb5403

[ "MIT" ]

1

2022-03-31T13:00:15.000Z

models/iscnet/modules/relation_model.py

blakeyy/Relational-RfDNet

72f4e35601e963c91515f40707174c0d79cb5403

[ "MIT" ]

null

models/iscnet/modules/relation_model.py

blakeyy/Relational-RfDNet

72f4e35601e963c91515f40707174c0d79cb5403

[ "MIT" ]

null

import torch import torch.nn as nn import torch.nn.functional as F import numpy as np from net_utils.nn_distance import nn_distance from net_utils.relation_tool import PositionalEmbedding from models.registers import MODULES from models.iscnet.modules.proposal_module import decode_scores from configs.scannet_config import ScannetConfig #param2obb @MODULES.register_module class RelationalProposalModule(nn.Module): def __init__(self, cfg, optim_spec = None): ''' Relation-based Proposal Module to enhance detected proposals. :param config: configuration file. :param optim_spec: optimizer parameters. ''' super(RelationalProposalModule, self).__init__() '''Optimizer parameters used in training''' self.optim_spec = optim_spec self.cfg = cfg '''Parameters''' self.num_class = cfg.dataset_config.num_class self.num_heading_bin = cfg.dataset_config.num_heading_bin self.num_size_cluster = cfg.dataset_config.num_size_cluster appearance_feature_dim = cfg.config['model']['relation_module']['appearance_feature_dim'] key_feature_dim = cfg.config['model']['relation_module']['key_feature_dim'] geo_feature_dim = cfg.config['model']['relation_module']['geo_feature_dim'] self.isDuplication = cfg.config['model']['relation_module']['isDuplication'] self.Nr = cfg.config['model']['relation_module']['n_relations'] self.dim_g = geo_feature_dim '''Modules''' self.gamma = nn.Parameter(torch.ones(1)) # requires_grad is True by default for Parameter nn.init.constant_(self.gamma, 0.0) #if self.cfg.config['model']['relation_module']['use_learned_pos_embed']: # self.pos_embedding = PositionEmbeddingLearned(6, geo_feature_dim) self.relation = nn.ModuleList() for N in range(self.Nr): self.relation.append(RelationUnit(appearance_feature_dim, key_feature_dim=key_feature_dim, geo_feature_dim=geo_feature_dim)) ##### Adding concat to f_a self.feature_transform1 = nn.Sequential(nn.Conv1d(128,128,1), \ nn.BatchNorm1d(128), \ nn.ReLU(), \ nn.Conv1d(128, appearance_feature_dim, 1)) self.feature_transform2 = nn.Sequential(nn.Conv1d(appearance_feature_dim, 128, 1), \ nn.BatchNorm1d(128), \ nn.ReLU(), \ nn.Conv1d(128, 128, 1)) self.proposal_generation = nn.Sequential(nn.Conv1d(128,128,1), \ nn.BatchNorm1d(128), \ nn.ReLU(), \ nn.Conv1d(128,128,1), \ nn.BatchNorm1d(128), \ nn.ReLU(), \ nn.Conv1d(128,5 + self.num_heading_bin*2 + self.num_size_cluster*4 + self.num_class,1)) ##### Concatenate concat to f_a #self.feature_transform2 = nn.Sequential(nn.Conv1d(appearance_feature_dim + self.dim_g*self.Nr, 128, 1), \ # nn.BatchNorm1d(128), \ # nn.ReLU(), \ # nn.Conv1d(128, 128, 1)) #self.proposal_generation = nn.Sequential(nn.Conv1d(128,128,1), \ # nn.BatchNorm1d(128), \ # nn.ReLU(), \ # nn.Conv1d(128,128,1), \ # nn.BatchNorm1d(128), \ # nn.ReLU(), \ # nn.Conv1d(128, 5 + self.num_heading_bin*2 + self.num_size_cluster*4 + self.num_class, 1)) #self.init_weights() #self.bn_momentum = cfg.config['bnscheduler']['bn_momentum_init'] #self.init_bn_momentum() #self.relation.apply(init_weights) #self.feature_transform1.apply(init_weights) #self.feature_transform2.apply(init_weights) #self.proposal_generation.apply(init_weights) def forward(self, proposal_features, end_points, data, mode='train'): if self.cfg.config['model']['relation_module']['compute_two_losses']: prefix = 'proposal_' else: prefix = '' center = end_points[f'{prefix}center'] # (B, K, 3) if not self.cfg.config['model']['relation_module']['use_gt_boxsize'] or mode == 'test': ### Compute predicted box size config_dict = self.cfg.eval_config pred_size_class = torch.argmax(end_points[f'{prefix}size_scores'], -1) # B,num_proposal size_residuals = end_points[f'{prefix}size_residuals_normalized'] * torch.from_numpy( config_dict['dataset_config'].mean_size_arr.astype(np.float32)).cuda().unsqueeze(0).unsqueeze(0) pred_size_residual = torch.gather(size_residuals, 2, pred_size_class.unsqueeze(-1).unsqueeze(-1).repeat(1, 1, 1, 3)) # B,num_proposal,1,3 pred_size_residual.squeeze_(2) mean_size_arr = torch.from_numpy(config_dict['dataset_config'].mean_size_arr.astype(np.float32)).cuda() pred_size_class = torch.squeeze(pred_size_class.type(torch.cuda.LongTensor)) ## Problem if batch_size==1 -> change where to squeeze temp = mean_size_arr[pred_size_class, :] box_size = temp + pred_size_residual else: ### Compute GT box size # choose the cluster for each proposal based on GT class of that proposal. GT class of each proposal is the closest GT box to each predicted proposal aggregated_vote_xyz = end_points['aggregated_vote_xyz'] #(B,K,3) gt_center = data['center_label'] #(B,K2,3) _, ind1, _, _ = nn_distance(aggregated_vote_xyz, gt_center) object_assignment = ind1 # (B,K) with values in 0,1,...,K2-1 size_class_label = torch.gather(data['size_class_label'], 1, object_assignment) # select (B,K) from (B,K2), object_assignment: (B,K) with values in 0,1,...,K2-1 size_residual_label = torch.gather(data['size_residual_label'], 1, object_assignment.unsqueeze(-1).repeat(1,1,3)) # select (B,K,3) from (B,K2,3) mean_size_label = torch.from_numpy(self.cfg.dataset_config.mean_size_arr.astype(np.float32)).to('cuda')[size_class_label] # (B,K,3) box_size = size_residual_label + mean_size_label # (B,K,3) # get geometric feature and feed it into PositionalEmbedding geometric_feature = torch.cat([center, box_size], dim=-1) # (B, K, 6) #if not self.cfg.config['model']['relation_module']['use_learned_pos_embed']: # position_embedding = PositionalEmbedding(geometric_feature, dim_g=self.dim_g) # (B,K,K, dim_g) #else: # position_embedding = self.pos_embedding(geometric_feature) # #position_embedding = self.feature_transform_pos(proposal_features) # # position_embedding = position_embedding.transpose(1, 2).contiguous() # position_embedding = PositionalEmbedding(geometric_feature, dim_g=self.dim_g) # (B,K,K, dim_g) #transform proposal_features from 128-dim to appearance_feature_dim proposal_features = self.feature_transform1(proposal_features) #(B,appearance_feature_dim, K) proposal_features = proposal_features.transpose(1, 2).contiguous() # (B, K, appearance_feature_dim) # proposal_features: (B,K,appearance_feature_dim) # positional_embedding: (B,K,K,dim_g) if(self.isDuplication): f_a, embedding_f_a, position_embedding = (proposal_features, position_embedding) else: f_a, position_embedding = (proposal_features, position_embedding) #input_data # f_a: (B,K,appearance_feature_dim), position_embedding: (B,K,K,dim_g) isFirst=True for N in range(self.Nr): if(isFirst): if(self.isDuplication): concat = self.relation[N](embedding_f_a,position_embedding) #(B,K,dim_k) else: concat = self.relation[N](f_a,position_embedding) isFirst=False else: if(self.isDuplication): concat = torch.cat((concat, self.relation[N](embedding_f_a, position_embedding)), -1) else: concat = torch.cat((concat, self.relation[N](f_a, position_embedding)), -1) proposal_features = self.gamma * concat + f_a # proposal_features: (B,K, appearance_feature_dim) #proposal_features = concat #proposal_features = f_a + concat #proposal_features = torch.cat((f_a, concat), -1) proposal_features = proposal_features.transpose(1,2).contiguous() #(B,appearance_feature_dim, K) proposal_features = self.feature_transform2(proposal_features) # (B,128,K) net = self.proposal_generation(proposal_features) # # (B, 2+3+num_heading_bin*2+num_size_cluster*4 + num_class, K) if self.cfg.config['model']['relation_module']['compute_two_losses']: prefix = 'last_' else: prefix = '' end_points = decode_scores(net, end_points, self.num_heading_bin, self.num_size_cluster, prefix=prefix) return end_points, proposal_features def init_weights(self): # initialize transformer #for m in self.relation.parameters(): # if m.dim() > 1: # nn.init.xavier_uniform_(m) for m in self.feature_transform1.parameters(): if m.dim() > 1: nn.init.xavier_uniform_(m) for m in self.feature_transform2.parameters(): if m.dim() > 1: nn.init.xavier_uniform_(m) for m in self.proposal_generation.parameters(): if m.dim() > 1: nn.init.xavier_uniform_(m) #for m in self.prediction_heads.parameters(): # if m.dim() > 1: # nn.init.xavier_uniform_(m) def init_bn_momentum(self): for m in self.modules(): if isinstance(m, (nn.BatchNorm2d, nn.BatchNorm1d)): m.momentum = self.bn_momentum class RelationUnit(nn.Module): def __init__(self, appearance_feature_dim=768,key_feature_dim = 96, geo_feature_dim = 96): super(RelationUnit, self).__init__() self.dim_g = geo_feature_dim self.dim_k = key_feature_dim self.WG = nn.Linear(geo_feature_dim, 1, bias=True) self.WK = nn.Linear(appearance_feature_dim, key_feature_dim, bias=True) self.WQ = nn.Linear(appearance_feature_dim, key_feature_dim, bias=True) self.WV = nn.Linear(appearance_feature_dim, key_feature_dim, bias=True) self.relu = nn.ReLU(inplace=True) def forward(self, f_a, position_embedding):#f_a: (B,K,appearance_feature_dim), position_embedding: (B,K,K,dim_g) B,K,_ = f_a.size() w_g = self.relu(self.WG(position_embedding)) # (B,K,K,1) w_k = self.WK(f_a) # (B,K,dim_k) w_k = w_k.view(B,K,1,self.dim_k) w_q = self.WQ(f_a) # (B,K,dim_k) w_q = w_q.view(B,1,K,self.dim_k) scaled_dot = torch.sum((w_k*w_q),-1 ) # (B,K,K). Note that 1st K is key, 2nd K is query scaled_dot = scaled_dot / np.sqrt(self.dim_k) w_g = w_g.view(B,K,K) # Note that 1st K is key, 2nd K is query w_a = scaled_dot.view(B,K,K) w_mn = torch.log(torch.clamp(w_g, min = 1e-6)) + w_a # (B,K,K) w_mn = torch.nn.Softmax(dim=1)(w_mn) # compute softmax along key dimension w_v = self.WV(f_a) # (B,K,dim_k) w_mn = w_mn.view(B,K,K,1) # (B,K,K,1) w_v = w_v.view(B,K,1,-1) # (B,K,1,dim_k) output = w_mn*w_v # (B,K,K, dim_k) output = torch.sum(output,1) # (B,K,dim_k) return output class PositionEmbeddingLearned(nn.Module): """ Absolute pos embedding, learned. """ def __init__(self, input_channel, num_pos_feats=128): super().__init__() self.position_embedding_head = nn.Sequential( nn.Conv1d(input_channel, num_pos_feats, kernel_size=1), nn.BatchNorm1d(num_pos_feats), nn.ReLU(inplace=True), nn.Conv1d(num_pos_feats, num_pos_feats, kernel_size=1)) def forward(self, xyz): xyz = xyz.transpose(1, 2).contiguous() position_embedding = self.position_embedding_head(xyz) return position_embedding #def init_weights(m): # if type(m) == nn.Linear or type(m) == nn.Conv1d: # gain = nn.init.calculate_gain('relu') # nn.init.xavier_uniform_(m.weight, gain=gain) # m.bias.data.fill_(0.01) #gain = nn.init.calculate_gain('relu') #nn.init.xavier_uniform_(m.weight, gain=gain) #m.bias.data.fill_(0.01)

47.51773

172

0.592313

1,702

13,400

4.39953

0.143361

0.050748

0.048077

0.02938

0.461939

0.409322

0.363114

0.309028

0.279781

0.246795

0

0.025814

0.291716

13,400

282

173

47.517731

0.763144

0.278209

0

0.21519

0

0.050111

0.005827

0

1

0.050633

false

0

0.056962

0

0.14557

0

null

0

null

0

1

0

4e3154ae1d10762e4681a612915a4720d50696c7

1,760

py

Python

ipware/descriptor.py

phi1010/django-ipware

9d4e5f3b17e8669757ea9590e3e02580bd310634

[ "MIT" ]

null

ipware/descriptor.py

phi1010/django-ipware

9d4e5f3b17e8669757ea9590e3e02580bd310634

[ "MIT" ]

null

ipware/descriptor.py

phi1010/django-ipware

9d4e5f3b17e8669757ea9590e3e02580bd310634

[ "MIT" ]

null

from enum import Enum, auto from typing import List, Union, Callable from ipaddress import IPv4Address, IPv4Network, IPv6Address, IPv6Network, ip_network, ip_address from warnings import warn class Order(Enum): HEADER_APPENDED = auto() HEADER_PREPENDED = auto() class Header: def __init__(self, name: str, order:Order=Order.HEADER_APPENDED, custom_parser: Callable[[str], Union[IPv4Address, IPv6Address]] = None ): self.custom_parser = custom_parser self.order = order # header field names are case insensitive # https://datatracker.ietf.org/doc/html/rfc7230#section-3.2 # we convert them to uppercase now to avoid different parts of code matching differenly; # if this breaks anything, the remainder of the code is broken. self.uppercase_name = name.upper() class ReverseProxy: def __init__(self, header_added: Header, *ip_addresses: Union[str, IPv4Address, IPv4Network, IPv6Address, IPv6Network], ): """ :param ip_addresses: You can use anything that ipaddress.ip_network accepts, e.g. `127.0.0.0/8` or `::1` :param headers_added: Specify here which header this host adds. We only support one header per reverse proxy """ if not ip_addresses: warn("A reverse proxy configuration without IP addresses will be ignored.") self.header_added = header_added ip_networks = [] for address in ip_addresses: # Addresses will be converted to /32 resp. /128 networks, matching exactly one IP ip_networks.append(ip_network(address)) self.ip_networks = ip_networks

38.26087

116

0.651136

215

1,760

5.186047

0.52093

0.049327

0.059193

0.078924

0

0.022727

0.275

1,760

45

117

39.111111

0.851097

0.306818

0

0.142857

0

0.056636

0

1

0.071429

false

0

0.142857

0

0.392857

0

null

0

null

0

1

0

4e31ecc86ddefaf67265db380dc7eba40617c43e

2,333

py

Python

locs/models/anisotropic_filter.py

mkofinas/locs

4cb0ab9e989ebfee42d1d2850bdf3360336b5c1c

[ "MIT" ]

16

2021-11-04T07:57:58.000Z

2022-03-01T17:45:32.000Z

locs/models/anisotropic_filter.py

mkofinas/locs

4cb0ab9e989ebfee42d1d2850bdf3360336b5c1c

[ "MIT" ]

null

locs/models/anisotropic_filter.py

mkofinas/locs

4cb0ab9e989ebfee42d1d2850bdf3360336b5c1c

[ "MIT" ]

null

from torch import nn import torch.nn.functional as F from locs.models.activations import ACTIVATIONS class AnisotropicEdgeFilter(nn.Module): def __init__(self, in_size, pos_size, hidden_size, dummy_size, out_size, act='elu', **kwargs): super().__init__() self.num_relative_features = in_size self.out_size = out_size self._act = act self.edge_filter = nn.Sequential( nn.Linear(pos_size, hidden_size), ACTIVATIONS[act](), nn.Linear(hidden_size, self.num_relative_features * out_size), ) self.init_weights() def init_weights(self): if self._act == 'elu': gain = nn.init.calculate_gain('relu') else: gain = nn.init.calculate_gain(self._act) nn.init.orthogonal_(self.edge_filter[0].weight, gain=gain) nn.init.orthogonal_(self.edge_filter[2].weight) def forward(self, edge_attr, edge_pos): edge_weight = self.edge_filter(edge_pos) edge_weight = edge_weight.reshape( edge_weight.shape[:-1] + tuple([self.num_relative_features, -1])) edge_attr = (edge_attr.unsqueeze(-2) @ edge_weight).squeeze(-2) return edge_attr class MLPEdgeFilter(nn.Module): """2-layer MLP, follows same template as AnisotropicEdgeFilter""" def __init__(self, in_size, pos_size, hidden_size, bottleneck_size, out_size, do_prob=0.0): super().__init__() self.num_relative_features = in_size self.out_size = out_size self.hidden_size = bottleneck_size self.lin1 = nn.Linear(self.num_relative_features, bottleneck_size) self.drop1 = nn.Dropout(p=do_prob) self.lin2 = nn.Linear(bottleneck_size, out_size) self.init_weights() def init_weights(self): for m in self.modules(): if isinstance(m, nn.Linear): nn.init.xavier_normal_(m.weight.data) m.bias.data.fill_(0.1) elif isinstance(m, nn.BatchNorm1d): m.weight.data.fill_(1) m.bias.data.zero_() def forward(self, edge_attr, edge_pos): edge_attr = F.relu(self.lin1(edge_attr)) edge_attr = self.drop1(edge_attr) edge_attr = F.relu(self.lin2(edge_attr)) return edge_attr

35.348485

77

0.629233

308

2,333

4.464286

0.253247

0.069818

0.04

0.083636

0.341818

0.286545

0.242909

0.194909

0.087273

0

0.011021

0.261037

2,333

65

78

35.892308

0.786543

0.025289

0

0.264151

0

0.004409

0

1

0.113208

false

0

0.056604

0

0.245283

0

null

0

null

0

1

0

4e32180523c62ff4dfee0a5445151998ee1a7804

1,798

py

Python

src/data_files/sample_data.py

gorried/hexgraph

b179e2fe0f8afc465ce92eac02f3cc2c4d1ac38e

[ "MIT" ]

null

src/data_files/sample_data.py

gorried/hexgraph

b179e2fe0f8afc465ce92eac02f3cc2c4d1ac38e

[ "MIT" ]

null

src/data_files/sample_data.py

gorried/hexgraph

b179e2fe0f8afc465ce92eac02f3cc2c4d1ac38e

[ "MIT" ]

null

#! /usr/bin/env python """ Daniel Gorrie Large dataset sampler """ import random import os from os import listdir from os.path import isfile, join # Constants INPUT_FILE = 'train.features' INPUT_FILE_SIZE = 8352136 OUTPUT_FILE = 'train_small.features' SAMPLE_SIZE = 110000 INPUT_LABEL_DIR = 'labels/' OUTPUT_LABEL_DIR = 'labels_small/' def main(): random.seed() # Generate array of SAMPLE_SIZE random integers in range [0, INPUT_FILE.length) # Iterate over the input file grabbing the indices = dict.fromkeys([random.randint(0, INPUT_FILE_SIZE) for _ in xrange(SAMPLE_SIZE)]) while len(indices) < SAMPLE_SIZE: indices[random.randint(0, INPUT_FILE_SIZE)] = 0 # Grab the proper training data with open(OUTPUT_FILE, 'w') as out: with open(INPUT_FILE, 'r') as f: line_count = 0 for line in f: if line_count in indices: # append the line to the output file out.write(line) line_count += 1 # Grab the label files label_files = [ f for f in listdir(INPUT_LABEL_DIR) if isfile(join(INPUT_LABEL_DIR,f)) ] # make a new directory d = os.path.dirname(OUTPUT_LABEL_DIR) if not os.path.exists(d): os.makedirs(d) # put versions of all the label files in the output directory for label_file in label_files: with open(INPUT_LABEL_DIR + label_file, 'r') as f: with open (OUTPUT_LABEL_DIR + label_file, 'w') as out: line_count = 0 for line in f: if line_count in indices: # append the line to the output file out.write(line) line_count += 1 if __name__ == '__main__': main()

24.630137

94

0.613459

251

1,798

4.191235

0.342629

0.059886

0.04943

0.036122

0.222433

0.171103

0

0.016813

0.305339

1,798

72

95

24.972222

0.82546

0.215795

0

0.277778

0

0.047482

0

1

0.027778

false

0

0.111111

0

0.138889

0

null

0

null

0

1

0

4e323ee929773b5d99e66e15ebdc6631d0480bf5

1,581

py

Python

utils/uniprot.py

glycosciences/covid-19-Annotations-on-Structures

3337bc5aec0ba79287ab0fd8c4763b15a4783378

[ "MIT" ]

2

2020-04-06T18:12:47.000Z

2021-08-01T20:17:59.000Z

utils/uniprot.py

glycosciences/covid-19-Annotations-on-Structures

3337bc5aec0ba79287ab0fd8c4763b15a4783378

[ "MIT" ]

20

2020-04-02T18:02:14.000Z

2020-08-10T12:29:46.000Z

utils/uniprot.py

glycosciences/covid-19-Annotations-on-Structures

3337bc5aec0ba79287ab0fd8c4763b15a4783378

[ "MIT" ]

9

2020-04-06T12:39:02.000Z

2021-08-01T20:18:00.000Z

import re import urllib.request """ Collection of handy functions related to uniprot. Potential reimplementations of code that would be available in various packages with the goal of keeping dependencies at a minimum. """ def valid_uniprot_ac_pattern(uniprot_ac): """ Checks whether Uniprot AC is formally correct according to https://www.uniprot.org/help/accession_numbers This is no check whether it actually exists. :param uniprot_ac: Accession code to be checked """ ac_pat = "[OPQ][0-9][A-Z0-9]{3}[0-9]|[A-NR-Z][0-9]([A-Z][A-Z0-9]{2}[0-9]){1,2}" if re.match(ac_pat, uniprot_ac): return True else: return False def seq_from_ac(uniprot_ac): """ Fetches raw sequence string for given uniprot accession code :param uniprot_ac: Accession code for which you want the sequence """ if not valid_uniprot_ac_pattern(uniprot_ac): raise RuntimeError("Uniprot AC does not look valid") data = None try: # that's the default uniprot access url = "https://www.uniprot.org/uniprot/%s.fasta" % uniprot_ac with urllib.request.urlopen(url) as response: data = response.readlines() except: # this is only temporary, as SARS-CoV2 is not yet in uniprot url = ( "https://www.ebi.ac.uk/uniprot/api/covid-19/uniprotkb/accession/%s.fasta" % (uniprot_ac) ) with urllib.request.urlopen(url) as response: data = response.readlines() return "".join(line.decode().strip() for line in data[1:])

29.830189

85

0.655281

229

1,581

4.441048

0.49345

0.106195

0.00885

0.041298

0.255654

0.202557

0.143559

0

0.016584

0.237192

1,581

52

86

30.403846

0.8267

0.268185

0

0.166667

0

0.083333

0.227421

0.073993

0

1

0.083333

false

0

0.083333

0

0.291667

0

null

0

null

0

1

0

4e3b40be7c29c65a9fd22f72903754a1e504955c

5,643

py

Python

structures/solution/bar.py

EladSharony/Mechanics

078f97bea84114fc1db6fe9700b92b96b18a0d5e

[ "MIT" ]

24

2021-02-23T13:53:14.000Z

2022-03-29T16:40:56.000Z

structures/solution/bar.py

EladSharony/Mechanics

078f97bea84114fc1db6fe9700b92b96b18a0d5e

[ "MIT" ]

2

2021-04-23T12:30:32.000Z

2022-03-31T10:51:12.000Z

structures/solution/bar.py

EladSharony/Mechanics

078f97bea84114fc1db6fe9700b92b96b18a0d5e

[ "MIT" ]

12

2021-04-11T20:44:03.000Z

2022-03-30T19:23:58.000Z

from geom2d import Segment, make_vector_between from structures.model.bar import StrBar from .node import StrNodeSolution class StrBarSolution: """ A truss structure bar with the solution values included. This class is a decorator of the original `StrBar` class that's linked to the solution nodes, that include their displacement vectors. It's thanks to the solution displaced nodes that we can obtain the stress and strain values for the bar. """ def __init__( self, original_bar: StrBar, start_node: StrNodeSolution, end_node: StrNodeSolution ): if original_bar.start_node.id != start_node.id: raise ValueError('Wrong start node') if original_bar.end_node.id != end_node.id: raise ValueError('Wrong end node') self.__original_bar = original_bar self.start_node = start_node self.end_node = end_node @property def id(self): """ The original bar's identifier. :return: id for the bar """ return self.__original_bar.id @property def cross_section(self): """ The original bar's cross section area value. :return: the cross section """ return self.__original_bar.cross_section @property def young_mod(self): """ The original bar's Young modulus (or elasticity modulus). :return: the Young modulus """ return self.__original_bar.young_mod @property def original_geometry(self): """ The original bar's geometry described by a line segment. :return: the bar's geometry """ return self.__original_bar.geometry @property def final_geometry(self): """ The bar's geometry, described by a line segment, after the computed displacements are applied. :return: the solution bar's geometry """ return Segment( self.start_node.displaced_pos, self.end_node.displaced_pos ) @property def original_length(self): """ The original bar's length. This is, the distance between its nodes. :return: the bar's length """ return self.original_geometry.length @property def final_length(self): """ The bar's length after the computed displacements are applied. This is the distance between the solution nodes. :return: the solution bar's length """ return self.final_geometry.length @property def elongation(self): """ The difference between the solution bar's length and the original bar's length. A positive elongation means the bar has elongated (due to a tensile stress) and a negative elongation means the bar has shortened (due to a compressive stress). :return: the bar's elongation """ return self.final_length - self.original_length @property def strain(self): """ The bar's elongation per unit of length. This is a unit-less quantity. :return: the bar's strain """ return self.elongation / self.original_length @property def stress(self): """ The bar's axial force per unit of cross section area. Using Hooke's law, the stress can be computed as the product of the bar's strain and Young modulus. :return: """ return self.young_mod * self.strain @property def internal_force_value(self): """ The bar's internal force. :return: the bar's internal force """ return self.stress * self.cross_section def force_in_node(self, node: StrNodeSolution): """ Returns the force this bar exerts on of of its to nodes. The passed in node needs to be one or the bar's end nodes, otherwise, this method will throw a `ValueError`. :param node: one of the bar's end nodes :return: force exerted by the bar on the given node """ if node is self.start_node: return make_vector_between( self.end_node.displaced_pos, self.start_node.displaced_pos ).with_length( self.internal_force_value ) elif node is self.end_node: return make_vector_between( self.start_node.displaced_pos, self.end_node.displaced_pos ).with_length( self.internal_force_value ) raise ValueError( f'Bar {self.id} does not know about node {node.id}' ) def has_node(self, node: StrNodeSolution): """ Tests whether the given `node` is one of this bar's end nodes. :param node: structure node :return: is the node connected with this bar? """ return node is self.start_node or node is self.end_node def final_geometry_scaling_displacement(self, scale: float): """ Computes the geometry of the bar after the displacements of its nodes have been applied with a given scale factor. This scaled geometry can be used for drawing the solution diagram. :param scale: used to scale the displacements :return: the solution bar's final geometry scaled """ return Segment( self.start_node.displaced_pos_scaled(scale), self.end_node.displaced_pos_scaled(scale) )

28.356784

67

0.608187

703

5,643

4.753912

0.200569

0.028725

0.027229

0.02693

0.327947

0.145123

0.088869

0.073609

0.052663

0.028725

0

0.000263

0.325359

5,643

198

68

28.5

0.877594

0.39837

0

0.294872

0

0.028088

0

1

0.192308

false

0

0.038462

0

0.435897

0

null

0

null

0

1

0

4e3df3a417c99ed4ce96f722ac39d7ce01ef8e82

219

py

Python

baekjoon/1436/nth_666.py

ucyang/AlgoEx

465c88f04b9449c06ee5c9a684ded5aba8ccf399

[ "MIT" ]

null

baekjoon/1436/nth_666.py

ucyang/AlgoEx

465c88f04b9449c06ee5c9a684ded5aba8ccf399

[ "MIT" ]

null

baekjoon/1436/nth_666.py

ucyang/AlgoEx

465c88f04b9449c06ee5c9a684ded5aba8ccf399

[ "MIT" ]

null

import sys input = lambda: sys.stdin.readline().rstrip() n = int(input()) i = 666 c = 0 while True: if str(i).find("666") != -1: c += 1 if c == n: print(i) break i += 1

14.6

45

0.452055

33

219

3

0.636364

0

0.073529

0.378995

219

14

46

15.642857

0.654412

0

0.013699

0

1

0

false

0

0.083333

0

0.083333

0

null

0

null

0

1

0

9d62368843928d090cd812f1e7a939bf13155d3f

988

py

Python

tests/mock_urllib.py

cedricduriau/PackagerBuddy

3eda40cd1b72f030e4f02e38af452e6377b20148

[ "MIT" ]

1

2019-01-10T11:15:40.000Z

tests/mock_urllib.py

cedricduriau/PackagerBuddy

3eda40cd1b72f030e4f02e38af452e6377b20148

[ "MIT" ]

6

2019-01-06T16:56:22.000Z

2019-01-07T01:43:54.000Z

tests/mock_urllib.py

cedricduriau/PackagerBuddy

3eda40cd1b72f030e4f02e38af452e6377b20148

[ "MIT" ]

null

# stdlib modules try: from urllib.response import addinfourl from urllib.error import HTTPError from urllib.request import HTTPHandler from io import StringIO except ImportError: from urllib2 import addinfourl, HTTPError, HTTPHandler from StringIO import StringIO def mock_response(req): url = req.get_full_url() if url.startswith("http://valid"): resp = addinfourl(StringIO("valid"), "valid", url) resp.code = 200 resp.msg = "OK" resp.headers = {"content-disposition": "filename=valid.tar"} return resp elif url.startswith("http://filename"): resp = addinfourl(StringIO("filename"), "filename", url) resp.code = 200 resp.msg = "OK" resp.headers = {} return resp elif url.startswith("http://invalid"): raise HTTPError(url, 404, "invalid", "", StringIO()) class MockHTTPHandler(HTTPHandler): def http_open(self, req): return mock_response(req)

29.058824

68

0.648785

113

988

5.628319

0.40708

0.04717

0.080189

0.044025

0.204403

0.106918

0

0.013263

0.236842

988

33

69

29.939394

0.830239

0.01417

0

0.222222

0

0.118313

0

1

0.074074

false

0

0.259259

0.037037

0.481481

0

null

0

null

0

1

0

9d6381be8993257224fb80b97034c3a236987a13

2,192

py

Python

slickbird/web/hcollection.py

lpenz/slickbird

1ad6c615be7edbc0c8c5abd97373058abea3d794

[ "Apache-2.0" ]

null

slickbird/web/hcollection.py

lpenz/slickbird

1ad6c615be7edbc0c8c5abd97373058abea3d794

[ "Apache-2.0" ]

null

slickbird/web/hcollection.py

lpenz/slickbird

1ad6c615be7edbc0c8c5abd97373058abea3d794

[ "Apache-2.0" ]

null

'''Slickbird collection handler''' import logging import json from tornado.web import URLSpec import tornado.web from slickbird import datparse import slickbird.orm as orm import slickbird from slickbird.web import hbase def _log(): if not _log.logger: _log.logger = logging.getLogger(__name__) return _log.logger _log.logger = None # Add handler: ############################################################### class CollectionAddHandler(hbase.PageHandler): name = 'collection_add' @tornado.gen.coroutine def collectionadd(self, cadder, dat): for gn, gd in dat['games'].items(): cadder.game_add(gn, gd) yield tornado.gen.moment cadder.done() self.settings['session'].commit() @tornado.gen.coroutine def post(self): name = self.get_argument('name') directory = self.get_argument('directory') filename = self.request.files['datfile'][0]['filename'] dat = datparse.parse( datstr=self.request.files['datfile'][0]['body'].decode('utf-8')) cadder = slickbird.CollectionAdder( self.settings['session'], self.settings['home'], name, directory, filename, dat) self.redirect(self.reverse_url('game_lst', cadder.name)) tornado.ioloop.IOLoop.current() \ .spawn_callback(self.collectionadd, cadder, dat) # API: ####################################################################### class CollectionListDataHandler(tornado.web.RequestHandler): def get(self): self.write(json.dumps([c.as_dict() for c in self.settings['session'].query(orm.Collection)])) # Install: ################################################################### def install(app): app.add_handlers('.*', [ URLSpec(r'/collection/add', CollectionAddHandler, name='collection_add'), URLSpec(r'/collection/list', hbase.genPageHandler('collection_lst'), name='collection_lst'), URLSpec(r'/api/collection_lst.json', CollectionListDataHandler, name='api_collection_lst'), ])

28.842105

78

0.570255

217

2,192

5.64977

0.391705

0.029364

0.046493

0.029364

0.039152

0

0.001766

0.224909

2,192

75

79

29.226667

0.719835

0.02646

0

0.039216

0

0.110707

0.012474

0

1

0.098039

false

0

0.156863

0

0.333333

0

null

0

null

0

1

0

9d664e109ebe34ba1e2952a24047d4157da5bc86

715

py

Python

connected_devices.py

savlakaran/bluetooth-profile-manager

a485560cecd6668241539d7d7fa96756a1a8dc9f

[ "MIT" ]

null

connected_devices.py

savlakaran/bluetooth-profile-manager

a485560cecd6668241539d7d7fa96756a1a8dc9f

[ "MIT" ]

null

connected_devices.py

savlakaran/bluetooth-profile-manager

a485560cecd6668241539d7d7fa96756a1a8dc9f

[ "MIT" ]

null

import pydbus bus = pydbus.SystemBus() adapter = bus.get('org.bluez', '/org/bluez/hci0') mngr = bus.get('org.bluez', '/') def list_connected_devices(): connected = [] mngd_objs = mngr.GetManagedObjects() for path in mngd_objs: con_state = mngd_objs[path].get('org.bluez.Device1', {}).get('Connected', False) if con_state: addr = mngd_objs[path].get('org.bluez.Device1', {}).get('Address') name = mngd_objs[path].get('org.bluez.Device1', {}).get('Name') connected.append({'name': name, 'address': addr}) return connected if __name__ == '__main__': connected = list_connected_devices() for item in connected: print(item['name'])

31.086957

88

0.625175

89

715

4.808989

0.370787

0.11215

0.128505

0.10514

0.231308

0

0.00703

0.204196

715

23

89

31.086957

0.745167

0

0.178771

0

1

0.055556

false

0

0.055556

0

0.166667

0.055556

0

null

0

null

0

1

0

9d66606d079a0a649bc4ef6dda1629c7be67e773

5,079

py

Python

etl_base/etl_base/dags/acme/operators/file_operators.py

buckylee2019/sqlg-airflow

37610a23b99bea8d9fdc8b066a01736ff2ff0c9d

[ "Apache-2.0" ]

null

etl_base/etl_base/dags/acme/operators/file_operators.py

buckylee2019/sqlg-airflow

37610a23b99bea8d9fdc8b066a01736ff2ff0c9d

[ "Apache-2.0" ]

null

etl_base/etl_base/dags/acme/operators/file_operators.py

buckylee2019/sqlg-airflow

37610a23b99bea8d9fdc8b066a01736ff2ff0c9d

[ "Apache-2.0" ]

1

2022-03-10T03:47:35.000Z

# -*- coding: utf-8 -*- # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os, fnmatch import logging from shutil import copyfile from airflow.contrib.hooks.fs_hook import FSHook from airflow.models import BaseOperator from airflow.utils.decorators import apply_defaults from datetime import datetime # You can also make this format a parameter in the Operator, for example # if you expect that you work with different intervals than "@daily". # Then you can introduce time components to have a finer grain for file storage. DATE_FORMAT = '%Y%m%d' class FileToPredictableLocationOperator(BaseOperator): """ Picks up a file from somewhere and lands this in a predictable location elsewhere """ template_fields = ('file_mask',) @apply_defaults def __init__(self, src_conn_id, dst_conn_id, file_mask, *args, **kwargs): """ :param src_conn_id: Hook with a conn id that points to the source directory. :type src_conn_id: string :param dst_conn_id: Hook with a conn id that points to the destination directory. :type dst_conn_id: string """ super(FileToPredictableLocationOperator, self).__init__(*args, **kwargs) self.src_conn_id = src_conn_id self.dst_conn_id = dst_conn_id self.file_mask = file_mask def execute(self, context): """ Picks up all files from a source directory and dumps them into a root directory system, organized by dagid, taskid and execution_date """ execution_date = context['execution_date'].strftime(DATE_FORMAT) src_hook = FSHook(conn_id=self.src_conn_id) source_dir = src_hook.get_path() dest_hook = FSHook(conn_id=self.dst_conn_id) dest_root_dir = dest_hook.get_path() dag_id = self.dag.dag_id task_id = self.task_id logging.info("Now searching for files like {0} in {1}".format(self.file_mask, source_dir)) file_names = fnmatch.filter(os.listdir(source_dir), self.file_mask) for file_name in file_names: full_path = os.path.join(source_dir, file_name) dest_dir = os.path.join(dest_root_dir, dag_id, task_id, execution_date) logging.info("Now creating path structure {0}".format(dest_dir)) os.makedirs(dest_dir) dest_file_name = os.path.join(dest_dir, os.path.basename(file_name)) logging.info("Now moving {0} to {1}".format(full_path, dest_file_name)) copyfile(full_path, dest_file_name) class PredictableLocationToFinalLocationOperator(BaseOperator): """ Picks up a file from predictable location storage and loads/transfers the results to a target system (in this case another directory, but it could be anywhere). """ @apply_defaults def __init__(self, src_conn_id, dst_conn_id, src_task_id, *args, **kwargs): """ :param src_conn_id: Hook with a conn id that points to the source directory. :type src_conn_id: string :param dst_conn_id: Hook with a conn id that points to the destination directory. :type dst_conn_id: string :param src_task_id: Source task that produced the file of interest :type src_task_id: string """ super(PredictableLocationToFinalLocationOperator, self).__init__(*args, **kwargs) self.src_conn_id = src_conn_id self.dst_conn_id = dst_conn_id self.src_task_id = src_task_id def execute(self, context): """ Picks up all files from a source directory and dumps them into a root directory system, organized by dagid, taskid and execution_date """ execution_date = context['execution_date'].strftime(DATE_FORMAT) src_hook = FSHook(conn_id=self.src_conn_id) dest_hook = FSHook(conn_id=self.dst_conn_id) dest_dir = dest_hook.get_path() dag_id = self.dag.dag_id source_dir = os.path.join(src_hook.get_path(), dag_id, self.src_task_id, execution_date) if os.path.exists(source_dir): for file_name in os.listdir(source_dir): full_path = os.path.join(source_dir, file_name) dest_file_name = os.path.join(dest_hook.get_path(), file_name) logging.info("Now moving {0} to final destination {1}".format(full_path, dest_file_name)) copyfile(full_path, dest_file_name)

40.632

105

0.669817

713

5,079

4.535764

0.26648

0.059369

0.033395

0.024119

0.470006

0.46444

0.44094

0.426098

0.406926

0

0.003159

0.252018

5,079

124

106

40.959677

0.848118

0.351644

0

0.46875

0

0.056333

0

1

0.0625

false

0

0.109375

0

0.21875

0

null

0

null

0

1

0

9d675985cd1e3fa2d6a896298711a9c21776ae26

7,052

py

Python

pyllusion/image/utilities.py

RebeccaHirst/Pyllusion

9944076e38bced0eabb49c607482b71809150bdb

[ "MIT" ]

null

pyllusion/image/utilities.py

RebeccaHirst/Pyllusion

9944076e38bced0eabb49c607482b71809150bdb

[ "MIT" ]

null

pyllusion/image/utilities.py

RebeccaHirst/Pyllusion

9944076e38bced0eabb49c607482b71809150bdb

[ "MIT" ]

null

import numpy as np import PIL.ImageColor, PIL.ImageFont from .rescale import rescale def _rgb(x): """Convert 0-1 values to RGB 0-255 values. """ return rescale(x, to=[0, 255], scale=[0, 1]) def _color(color="black", alpha=1, mode="RGB"): """Sanitize color to RGB(A) format. """ if isinstance(color, str): if color == "transparent": return (0, 0, 0, 0) color = PIL.ImageColor.getrgb(color) elif isinstance(color, (int, np.integer)): color = tuple([color] * 3) elif isinstance(color, (list, np.ndarray)): color = tuple(color) # Add transparency if mode == "RGBA": if len(color) == 3: color = color + tuple([np.int(_rgb(alpha))]) return color def _coord_circle(image, diameter=0.1, x=0, y=0, unit="grid", method="pil"): """Get circle coordinates Examples -------- >>> import pyllusion as ill >>> import PIL.Image, PIL.ImageDraw >>> >>> image = PIL.Image.new('RGB', (500, 400), color = "white") >>> draw = PIL.ImageDraw.Draw(image, 'RGBA') >>> >>> coord = _coord_circle(image, diameter=1, x=0, y=0) >>> draw.ellipse(coord, fill="red", width=0) >>> draw.ellipse(_coord_circle(image, diameter=1.5, x=0, y=0), outline="blue") >>> image #doctest: +ELLIPSIS <PIL.Image.Image ...> """ if unit == "grid": # Get coordinates in pixels width, height = image.size x = np.int(rescale(x, to=[0, width], scale=[-1, 1])) if method == "pil": y = np.int(rescale(-y, to=[0, height], scale=[-1, 1])) elif method == "psychopy": y = np.int(rescale(y, to=[0, height], scale=[-1, 1])) # Convert diameter based on height diameter = np.int(rescale(diameter, to=[0, height], scale=[0, 2])) diameter = 2 if diameter < 2 else diameter radius = diameter / 2 # Choose diameter and centre coord = [(x - radius, y - radius), (x + radius, y + radius)] if method == "pil": return coord elif method == "psychopy": return radius, x, y def _coord_text( image, text="hello", size="auto", x=0, y=0, font="arial.ttf", unit="grid", method="pil" ): """Get text coordinates Examples -------- >>> import pyllusion as ill >>> import PIL.Image, PIL.ImageDraw >>> >>> image = PIL.Image.new('RGB', (500, 500), color = "white") >>> draw = PIL.ImageDraw.Draw(image, 'RGB') >>> >>> coord, font = _coord_text(image, size="auto", x=-0.5, y=0.5) #doctest: +SKIP >>> draw.text(coord, text="hello", fill="black", font=font) #doctest: +SKIP >>> image #doctest: +SKIP """ if unit == "grid": # Get coordinates in pixels width, height = image.size x = np.int(rescale(x, to=[0, width], scale=[-1, 1])) if method == "pil": y = np.int(rescale(-y, to=[0, height], scale=[-1, 1])) elif method == "psychopy": y = np.int(rescale(y, to=[0, height], scale=[-1, 1])) if size == "auto": # Initialize values size, top_left_x, top_left_y, right_x, bottom_y = 0, width, height, 0, 0 # Loop until max size is reached while ( top_left_x > 0.01 * width and right_x < 0.99 * width and top_left_y > 0.01 * height and bottom_y < 0.99 * height ): loaded_font = PIL.ImageFont.truetype(font, size) text_width, text_height = loaded_font.getsize(text) top_left_x = x - (text_width / 2) top_left_y = y - (text_height / 2) right_x = top_left_x + text_width bottom_y = top_left_y + text_height size += 1 # Increment text size else: loaded_font = PIL.ImageFont.truetype(font, size) text_width, text_height = loaded_font.getsize(text) top_left_x = x - (text_width / 2) top_left_y = y - (text_height / 2) coord = top_left_x, top_left_y return coord, loaded_font, x, y def _coord_line( image=None, x=0, y=0, x1=None, y1=None, x2=None, y2=None, length=None, angle=None, adjust_width=False, adjust_height=False, method="pil", ): """ """ # Center to None if x1 entered x = None if x1 is not None else x y = None if y1 is not None else y # Get missing parameters if x is None and y is None: if x2 is None and y2 is None: x2, y2 = _coord_line_x2y2(x1, y1, length, angle) if length is None and angle is None: length, angle = _coord_line_lengthangle(x1, y1, x2, y2) else: if x2 is None and y2 is None: x2, y2 = _coord_line_x2y2(x, y, length / 2, angle) if length is None and angle is None: length, angle = _coord_line_lengthangle(x, y, x2, y2) length = length * 2 x1, y1 = _coord_line_x2y2(x2, y2, length, 180 + angle) # Get coordinates in pixels if image is not None: width, height = image.size if adjust_width is True: x1, x2 = x1 * (height / width), x2 * (height / width) if adjust_height is True: y1, y2 = y1 * (width / height), y2 * (width / height) x1 = np.int(rescale(x1, to=[0, width], scale=[-1, 1])) x2 = np.int(rescale(x2, to=[0, width], scale=[-1, 1])) if method == "pil": y1 = np.int(rescale(-y1, to=[0, height], scale=[-1, 1])) y2 = np.int(rescale(-y2, to=[0, height], scale=[-1, 1])) elif method == "psychopy": y1 = np.int(rescale(y1, to=[0, height], scale=[-1, 1])) y2 = np.int(rescale(y2, to=[0, height], scale=[-1, 1])) length = np.int(rescale(length, to=[0, height], scale=[0, 2])) return (x1, y1, x2, y2), length, angle def _coord_line_x2y2(x1=None, y1=None, length=None, angle=None): x2 = x1 + np.sin(np.deg2rad(angle)) * length y2 = y1 + np.cos(np.deg2rad(angle)) * length return x2, y2 def _coord_line_lengthangle(x1=None, y1=None, x2=None, y2=None): length = np.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2) angle = np.rad2deg(np.arccos(np.abs(x1 - x2) / length)) return length, angle def _coord_rectangle(image=None, x=0, y=0, size_width=1, size_height=1, method="pil"): """ """ x1 = x - (size_width / 2) y1 = y + (size_height / 2) x2 = x + (size_width / 2) y2 = y - (size_height / 2) # Get coordinates in pixels if image is not None: width, height = image.size x1 = np.int(rescale(x1, to=[0, width], scale=[-1, 1])) x2 = np.int(rescale(x2, to=[0, width], scale=[-1, 1])) if method == "pil": y1 = np.int(rescale(-y1, to=[0, height], scale=[-1, 1])) y2 = np.int(rescale(-y2, to=[0, height], scale=[-1, 1])) elif method == "psychopy": y1 = np.int(rescale(y1, to=[0, height], scale=[-1, 1])) y2 = np.int(rescale(y2, to=[0, height], scale=[-1, 1])) return (x1, y1, x2, y2)

32.648148

86

0.548497

1,021

7,052

3.697356

0.132223

0.016689

0.063576

0.051921

0.500397

0.457219

0.433377

0.414834

0.39894

0

0.04974

0.29013

7,052

215

87

32.8

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0

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0

null

0

null

0

1

0

9d67bc8055c64e00d851f4955360ca97f28db935

6,971

py

Python

pyfluka/pyfluka_merge.py

morgenst/pyfluka

6dd3aa8cc29cfce0b2f084fb6b08bdebd2233298

[ "MIT" ]

null

pyfluka/pyfluka_merge.py

morgenst/pyfluka

6dd3aa8cc29cfce0b2f084fb6b08bdebd2233298

[ "MIT" ]

null

pyfluka/pyfluka_merge.py

morgenst/pyfluka

6dd3aa8cc29cfce0b2f084fb6b08bdebd2233298

[ "MIT" ]

null

import sys import argparse import fnmatch import os import re import shutil import glob import logging import multiprocessing from copy_reg import pickle from types import MethodType _logger = logging.getLogger('default') _logger.addHandler(logging.StreamHandler()) _logger.setLevel(logging.CRITICAL) def _pickle_method(method): func_name = method.im_func.__name__ obj = method.im_self cls = method.im_class return _unpickle_method, (func_name, obj, cls) def _unpickle_method(func_name, obj, cls): for cls in cls.mro(): try: func = cls.__dict__[func_name] except KeyError: pass else: break return func.__get__(obj, cls) class InputParser: def __init__(self, path): self.path = path self.parsedInfo = {'resnuc': [], 'usrbin': []} def _get_bins(self): r = re.compile("([0-9]{2})$") self.bins = set([int(re.search(r, fN).group(1)) for fN in glob.glob(self.path + '/*fort*')]) def _drop_bin(self, bin): try: self.bins.remove(bin) return True except: return False def __parse_scoring_cards(self): re_resnuc = re.compile("^RESNUC") re_usrbin = re.compile("^(USRBIN)\s+\d+.?\d?\s+\w+.*") try: input_file = glob.glob(self.path + '/*.inp')[0] except IndexError: _logger.critical("Unable to locate .inp file required for parsing scoring card information. Either provide " "it in the input directory or specify card and bins explicitly.") sys.exit(1) for line in open(input_file).readlines(): if len(self.bins) == 0: return if re.match(re_resnuc, line): index = abs(int(line.split()[2].rstrip('.'))) add_bin = self._drop_bin(index) if add_bin: self.parsedInfo['resnuc'].append(index) elif re.match(re_usrbin, line): index = abs(int(line.split()[3].rstrip('.'))) add_bin = self._drop_bin(index) if add_bin: self.parsedInfo['usrbin'].append(index) def parse(self): self._get_bins() self.__parse_scoring_cards() return self.parsedInfo class Merger(object): def __init__(self, path, out_path): self.curdir = os.getcwd() self.path = path self.bins = [] self.filelist = [] self.cycle = [] self.parse_dir() self.mergingCodeLookup = {'resnuc': 'usrsuw', 'usrbin': 'usbsuw'} self.out_path = out_path self.__class__.check_fluka_loaded() self.check_out_path() @staticmethod def check_fluka_loaded(): try: os.environ['FLUPRO'] except KeyError: _logger.critical('FLUPRO environment not setup. Please export FLUPRO pointing to your FLUKA \ installation directory.') sys.exit(1) def check_out_path(self): if self.out_path is not None: self.out_path = os.path.abspath(self.out_path) if not os.path.exists(self.out_path): os.makedirs(self.out_path) def parse_dir(self): for file_name in os.listdir(self.path): if fnmatch.fnmatch(file_name, '*???_fort.??*'): self.geom = file_name[:-11] c = int(file_name[-11:-8]) b = int(file_name[-2:]) self.filelist.append(file_name) if b not in self.bins: self.bins.append(b) if c not in self.cycle: self.cycle.append(c) def merge(self, cards): pickle(MethodType, _pickle_method, _unpickle_method) jobs = [(k,v) for k, values in cards.items() for v in values] pool = multiprocessing.Pool(processes=min(len(jobs), multiprocessing.cpu_count())) pool.map(self._merge_impl, jobs) def _merge_impl(self, *args): card = args[0][0] b = args[0][1] _logger.debug("Merge " + card + " for bin " + str(b)) os.chdir(self.path) list_name = 'list_' + str(b) + '_' + card os.system('ls -1 *_fort.'+str(b)+'* > ' + list_name) os.system('echo "" >> ' + list_name) os.system('echo "' + self.geom + '_' + card + '_'+str(b)+'" >> ' + list_name) os.system('%s/flutil/%s < %s ' % (os.environ['FLUPRO'], self.mergingCodeLookup[card], list_name)) if self.out_path is not None: self.move(card, b) if card == 'usrbin': self.convert_to_ascii(card, b) def move(self, card, index): for fName in glob.glob(r'%s/%s_%s_%s*' % (self.path, self.geom, card, index)): shutil.move(fName, os.path.join(self.out_path, fName.split('/')[-1])) def convert_to_ascii(self, card, bin): os.chdir(self.out_path) tmp_file_name = 'asciiconversion_%s_%i.txt' % (card, bin) for file_name in glob.glob(r'%s/%s_%s_%s*' % (self.out_path, self.geom, card, bin)): if file_name.endswith('.ascii'): continue file_name = os.path.split(file_name)[1] tmp_file = open(os.path.join(self.curdir, tmp_file_name), 'w+') print >> tmp_file, file_name print >> tmp_file, file_name + '.ascii' tmp_file.close() os.system('%s/flutil/usbrea < %s > /dev/null' % (os.environ['FLUPRO'], os.path.join(self.curdir, tmp_file_name))) os.remove(os.path.join(self.curdir, tmp_file_name)) os.chdir(self.curdir) def main(argv): parser = argparse.ArgumentParser(description='Script for merging fluka bin data') parser.add_argument('path', help='input path') parser.add_argument('--card', '-c', required=False, default=None, help='card') parser.add_argument('--bins', '-b', required=False, default=None, type=int, nargs='+', help='bins') parser.add_argument('--output', '-o', default=None, help='output directory') parser.add_argument('--debug', '-d', action='store_true', default=False, help='Switch on debug messages') args = parser.parse_args() if args.debug: _logger.setLevel(logging.DEBUG) path = os.path.abspath(args.path) if not args.card and not args.bins: parser = InputParser(path) scoring_cards = parser.parse() else: scoring_cards = {args.card : args.bins} merger = Merger(path, args.output) merger.merge(scoring_cards) if __name__ == '__main__': main(sys.argv[1:])

36.307292

125

0.551427

853

6,971

4.310668

0.237984

0.034811

0.029916

0.01523

0.134621

0.117215

0.078053

0.05548

0.027196

0

0.005038

0.316597

6,971

191

126

36.497382

0.766793

0

0.13253

0

0.090374

0.007603

0

1

0.096386

false

0.006024

0.066265

0

0.210843

0.012048

0

null

0

null

0

1

0

9d6864036da06d6197930101a35bf7b6e92aebea

1,325

py

Python

calculation.py

n-a-iliev/NBA-PER-Calculator

590c617cc8c47009224a33f60fc4cba75f4b26bd

[ "MIT" ]

null

calculation.py

n-a-iliev/NBA-PER-Calculator

590c617cc8c47009224a33f60fc4cba75f4b26bd

[ "MIT" ]

null

calculation.py

n-a-iliev/NBA-PER-Calculator

590c617cc8c47009224a33f60fc4cba75f4b26bd

[ "MIT" ]

null

from balldontlie import balldontlie, player, stats from matplotlib import pyplot as plt '''This function gets more information about the player by inputting their name and dataset to search''' def getplayer(firstname, lastname, datalist): for players in datalist: for info in players.data: if info['first_name'] == firstname and info['last_name'] == lastname: return player(info['first_name'], info['last_name'], info['id']) def main(): totalpages = range(1, 34) kobeyears = range(1996,2016) kobestatlist = [] kobeperlist = [] datalist = [] for page in totalpages: datalist.append(balldontlie('https://www.balldontlie.io/api/v1/players?page=' + str(page))) kobe = getplayer('Kobe', 'Bryant', datalist) for year in kobeyears: kobestatlist.append(kobe.getstats(kobe,year)) for stat in kobestatlist: kobeperlist.append(stat.calculate_PER(stat)) plt.plot(kobeyears, kobeperlist, label= "Kobe Bryant's Player Efficiency Rating", color='yellow') plt.xlabel('Season') plt.xticks(kobeyears) plt.ylabel('Player Efficiency Rating') plt.title('Change in PER Over Time') ax = plt.gca() ax.set_facecolor('purple') plt.legend() plt.show() if __name__ == "__main__": main()

33.974359

99

0.659623

163

1,325

5.276074

0.527607

0.051163

0.030233

0

0.011572

0.217358

1,325

38

100

34.868421

0.817743

0

0.170632

0

1

0.0625

false

0

0.0625

0

0.15625

0

null

0

null

0

1

0

9d6b7d2817a9a11d4f368ca09bd16da81be04b5f

1,496

py

Python

rides/forms.py

andrenbrandao/pirauber

d7c5647ec6df698fa3d7397907ff629c74cc76b9

[ "MIT" ]

null

rides/forms.py

andrenbrandao/pirauber

d7c5647ec6df698fa3d7397907ff629c74cc76b9

[ "MIT" ]

6

2020-06-05T23:27:38.000Z

2022-02-10T08:14:16.000Z

rides/forms.py

andrenbrandao/pirauber

d7c5647ec6df698fa3d7397907ff629c74cc76b9

[ "MIT" ]

null

from django import forms from crispy_forms.helper import FormHelper from crispy_forms.layout import Submit from django.utils.translation import ugettext_lazy as _ from .models import Ride class RideForm(forms.ModelForm): date = forms.DateField( label=_('Date'), widget=forms.DateInput(format=('%Y-%m-%d'),attrs={ 'class': 'form-control input-group-alternative', 'type': 'date' }) ) time = forms.TimeField( label=_('Time'), required=False, input_formats=['%H:%M'], widget=forms.TimeInput(format=('%H:%M'), attrs={ 'class': 'form-control input-group-alternative', 'type': 'time' }) ) description = forms.CharField( label=_('Description'), required=False, help_text=_('Write here any additional information.'), widget=forms.Textarea(attrs={ 'class': 'form-control input-group-alternative', }) ) class Meta: model = Ride fields = ('date', 'time', 'origin', 'destination', 'seats', 'price', 'description') def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.helper = FormHelper() self.helper.form_method = 'post' self.helper.add_input( Submit('submit', _('Save Ride'), css_class='btn-block')) for visible in self.visible_fields(): visible.field.widget.attrs['class'] = 'input-group-alternative'

31.166667

91

0.592914

158

1,496

5.468354

0.481013

0.046296

0.097222

0.072917

0.155093

0.106481

0

0.25869

1,496

47

92

31.829787

0.77908

0

0.195122

0

0.21123

0.061497

0

1

0.02439

false

0

0.121951

0

0.268293

0

null

0

null

0

1

0

9d6dfe9a0fb4cf150a1dbedc9b781a51974ddeed

843

py

Python

tests/testdata/models.py

dtpryce/MLServer

02744b3c770141b0b1d9dad2a0256d243051de61

[ "Apache-2.0" ]

null

tests/testdata/models.py

dtpryce/MLServer

02744b3c770141b0b1d9dad2a0256d243051de61

[ "Apache-2.0" ]

null

tests/testdata/models.py

dtpryce/MLServer

02744b3c770141b0b1d9dad2a0256d243051de61

[ "Apache-2.0" ]

null

import asyncio from mlserver import MLModel from mlserver.codecs import NumpyCodec from mlserver.types import InferenceRequest, InferenceResponse class SumModel(MLModel): async def predict(self, payload: InferenceRequest) -> InferenceResponse: decoded = self.decode(payload.inputs[0]) total = decoded.sum(axis=1, keepdims=True) output = NumpyCodec().encode(name="total", payload=total) return InferenceResponse(id=payload.id, model_name=self.name, outputs=[output]) class SlowModel(MLModel): async def load(self) -> bool: await asyncio.sleep(10) self.ready = True return self.ready async def infer(self, payload: InferenceRequest) -> InferenceResponse: await asyncio.sleep(10) return InferenceResponse(id=payload.id, model_name=self.name, outputs=[])

31.222222

87

0.71293

97

843

6.175258

0.43299

0.0601

0.050083

0.146912

0.193656

0

0.008759

0.187426

843

26

88

32.423077

0.865693

0

0.111111

0

0.005931

0

1

0

false

0

0.222222

0

0.5

0

null

0

null

0

1

0

9d6f477bb8496ccbe8298b0d502cfaf9b42c5d1c

10,459

py

Python

PERFORMER.py

ShivamRajSharma/Transformer-Architecure_From_Scratch

f7f24cb5146c09e6cf38a41e5e5ef721389803c1

[ "MIT" ]

17

2020-09-13T07:53:41.000Z

2022-03-17T09:58:23.000Z

PERFORMER.py

ShivamRajSharma/Transformer-Architecure_From_Scratch

f7f24cb5146c09e6cf38a41e5e5ef721389803c1

[ "MIT" ]

null

PERFORMER.py

ShivamRajSharma/Transformer-Architecure_From_Scratch

f7f24cb5146c09e6cf38a41e5e5ef721389803c1

[ "MIT" ]

3

2020-12-15T14:20:47.000Z

2022-01-24T02:26:04.000Z

from time import time import torch import torch.nn as nn class FastAttention(nn.Module): def __init__(self, input_shape, head, n_features): super(FastAttention, self).__init__() self.head = head self.input_shape = input_shape self.depth = int(input_shape // head) self.n_features = n_features self.key_ORF = self.OrthogonalRandomFeature() self.query_ORF = self.OrthogonalRandomFeature() self.query = nn.Linear(self.depth, self.depth) self.key = nn.Linear(self.depth, self.depth) self.value = nn.Linear(self.depth, self.depth) self.fc = nn.Linear(self.depth*head, input_shape) def kernel_function(self, x, flag): ORF = self.query_ORF if flag == 'query' else self.key_ORF normalization_factor = 1/ORF.shape[-1]**0.25 x *= normalization_factor out = torch.einsum('nhsd, fd -> nhsf', x, ORF) kernel_fn = nn.ReLU()(out) + 1e-3 return kernel_fn def OrthogonalRandomFeature(self): n = self.n_features//self.depth remainder = self.n_features%self.depth orthogonal_features = [] for _ in range(n): normal_feature = torch.rand(self.depth, self.depth) orthogonal_feature, _ = torch.qr(normal_feature) orthogonal_features.append(orthogonal_feature) if remainder > 0 : normal_feature = torch.rand(self.depth, self.depth) orthogonal_feature, _ = torch.qr(normal_feature) orthogonal_features.append(orthogonal_feature[0: remainder]) orthogonal_features = torch.cat(orthogonal_features) mutilplier = torch.randn(self.n_features, self.depth).norm(dim=1) final_features = torch.matmul(torch.diag(mutilplier), orthogonal_features) return final_features def causal_attention(self, q, k, v): denominator = 1/torch.einsum('nhqf, nhkf -> nhqf', q, k.cumsum(dim=-2)) x = torch.einsum('nhkf, nhkd -> nhkfd', k, v) x = x.cumsum(dim=-3) out = torch.einsum('nhqfd, nhqf, nhqf -> nhqd', x, q, denominator) return out def bidirectional_attention(self, q, k, v): kt_i = torch.einsum('nhkf -> nhf', k) normalization_factor = 1/(torch.einsum('nhqf, nhf -> nhq', q, kt_i)) k_v = torch.einsum('nhkf, nhkd -> nhfd', k, v) attention = torch.einsum('nhfd, nhqf, nhq-> nhqd', k_v, q, normalization_factor) return attention def forward(self, query, key, value, mask=None, casual_mask=False): batch = query.shape[0] query_len, key_len, value_len = query.shape[1], key.shape[1], value.shape[1] query = query.reshape(batch, query_len, self.head, self.depth) key = key.reshape(batch, key_len, self.head, self.depth) value = value.reshape(batch, value_len, self.head, self.depth) query = query.permute(0, 2, 1, 3) key = key.permute(0, 2, 1, 3) value = value.permute(0, 2, 1, 3) query = self.query(query) key = self.key(key) value = self.value(value) if mask is not None: key.masked_fill(mask == 0, float("-1e20")) query = self.kernel_function(query, 'query') key = self.kernel_function(key, 'key') if casual_mask: out = self.causal_attention(query, key, value) else: out = self.bidirectional_attention(query, key, value) out = out.permute(0, 2, 1, 3) out = out.reshape(batch, query_len, self.head*self.depth) out = self.fc(out) return out class PerformerBlock(nn.Module): def __init__(self, input_shape, head, n_features, dropout, forward_expansion): super(PerformerBlock, self).__init__() self.attention = FastAttention(input_shape, head, n_features) self.feed_forward = nn.Sequential( nn.Linear(input_shape, input_shape*forward_expansion), nn.GELU(), nn.Linear(input_shape*forward_expansion, input_shape) ) self.layernorm1 = nn.LayerNorm(input_shape) self.layernorm2 = nn.LayerNorm(input_shape) self.dropout = nn.Dropout(dropout) def forward(self, query, key, value, mask): attention = self.attention(query, key, value, mask) add = attention + query regulazation = self.dropout(self.layernorm1(add)) forward = self.feed_forward(regulazation) out = self.dropout(self.layernorm2(forward + regulazation)) return out class Encoder(nn.Module): def __init__( self, vocab_size, embedding_out, num_layers, heads, n_features, forward_expansion, dropout, max_len ): super(Encoder, self).__init__() self.word_embedding = nn.Embedding(vocab_size, embedding_out) self.postional_embedding = nn.Parameter(torch.zeros(1, max_len, embedding_out)) self.dropout = nn.Dropout(dropout) self.layers = nn.Sequential( *[ PerformerBlock( embedding_out, heads, n_features, dropout, forward_expansion ) for _ in range(num_layers) ] ) def forward(self, x, mask): word_embedding = self.word_embedding(x) postional_embedding = self.postional_embedding[:, :x.shape[1], :] out = self.dropout(word_embedding + postional_embedding) for layer in self.layers: out = layer(out, out, out, mask) return out class DecoderBlock(nn.Module): def __init__( self, embedding_out, head, n_features, forward_expansion, dropout ): super(DecoderBlock, self).__init__() self.attention = FastAttention(embedding_out, head, n_features) self.Performer_block = PerformerBlock( embedding_out, head, n_features, dropout, forward_expansion ) self.dropout = nn.Dropout(dropout) self.norm = nn.LayerNorm(embedding_out) def forward(self, query, key, value, src_mask): attention = self.attention(query, query, query, src_mask, True) query = self.dropout(self.norm(attention + query)) out = self.Performer_block(query, key, value, src_mask) return out class Decoder(nn.Module): def __init__( self, vocab_size, embedding_out, num_layers, head, n_features, forward_expansion, dropout, max_len ): super(Decoder, self).__init__() self.word_embedding = nn.Embedding(vocab_size, embedding_out) self.positional_embedding = nn.Parameter(torch.zeros(1, max_len, embedding_out)) self.layers = nn.Sequential( *[ DecoderBlock( embedding_out, head, n_features, forward_expansion, dropout ) for _ in range(num_layers) ] ) self.fc = nn.Linear(embedding_out, vocab_size) self.dropout = nn.Dropout(dropout) def forward(self, x, encoder_output, src_mask): x = self.dropout(self.word_embedding(x) + self.positional_embedding[:, :x.shape[1], :]) for layer in self.layers: x = layer( x, encoder_output, encoder_output, src_mask ) out = self.fc(x) return out class Performers(nn.Module): def __init__( self, input_vocab_size, output_vocab_size, pad_idx, embedding_out, num_layers, forward_expansion, head, n_features, dropout, max_len ): super(Performers, self).__init__() self.encoder = Encoder( input_vocab_size, embedding_out, num_layers, head, n_features, forward_expansion, dropout, max_len ) self.decoder = Decoder( output_vocab_size, embedding_out, num_layers, head, n_features, forward_expansion, dropout, max_len ) self.pad_idx = pad_idx self.apply(self._init_weights) #From @HuggingFace def _init_weights(self, module): if isinstance(module, (nn.Linear, nn.Embedding)): module.weight.data.normal_(mean=0.0, std=0.02) elif isinstance(module, nn.LayerNorm): module.weight.data.fill_(1.0) if isinstance(module, nn.Linear) and module.bias is not None: module.bias.data.zero_() def input_pad_mask(self, inputs): pad_mask = (inputs != self.pad_idx).unsqueeze(1).unsqueeze(3) return pad_mask def output_pad_mask(self, targets): pad_mask = (targets != self.pad_idx).unsqueeze(1).unsqueeze(3) def forward(self, inputs, target): input_pad_mask = self.input_pad_mask(inputs) output_pad_mask = self.output_pad_mask(targets) encoder_output = self.encoder(inputs, input_pad_mask) decoder_out = self.decoder(target, encoder_output, output_pad_mask) return decoder_out if __name__ == "__main__": #Depends on the Tokenizer input_vocab_size = 100 output_vocab_size = 200 #DEFAULT PerFORMERS PARAMETERS:- pad_idx = 0 embedding_out = 512 num_layers = 6 forward_expansion = 4 head = 8 n_features = 256 dropout = 0.1 max_len = 512 inputs = torch.randint(0, 100, (32, 200)) targets = torch.randint(0, 100, (32,100)) model = Performers( input_vocab_size, output_vocab_size, pad_idx, embedding_out, num_layers, forward_expansion, head, n_features, dropout, max_len ) start = time() y = model(inputs, targets) print(f'INFERENCE TIME = {time() - start}sec') x = sum(p.numel() for p in model.parameters() if p.requires_grad) print(f'NUMBER OF PARAMETERS ARE = {x}')

30.852507

95

0.581222

1,207

10,459

4.809445

0.153273

0.031008

0.026873

0.015504

0.418432

0.292334

0.264255

0.230146

0.178467

0

0.013485

0.319342

10,459

339

96

30.852507

0.801938

0.006884

0

0.394265

0

0.022918

0

1

0.0681

false

0

0.010753

0

0.139785

0.007168

0

null

0

null

0

1

0

9d6fa2ce7adb3f0d8fb6ff64a2befb7535e72eca

28,970

py

Python

nogo/gtp_connection.py

douglasrebstock/alpha-zero-general

2237522be5a1bbfebbc2fc1b2a8e8a6bcb6d5aab

[ "MIT" ]

null

nogo/gtp_connection.py

douglasrebstock/alpha-zero-general

2237522be5a1bbfebbc2fc1b2a8e8a6bcb6d5aab

[ "MIT" ]

null

nogo/gtp_connection.py

douglasrebstock/alpha-zero-general

2237522be5a1bbfebbc2fc1b2a8e8a6bcb6d5aab

[ "MIT" ]

null

""" gtp_connection.py Module for playing games of Go using GoTextProtocol Parts of this code were originally based on the gtp module in the Deep-Go project by Isaac Henrion and Amos Storkey at the University of Edinburgh. """ import signal, os import traceback from sys import stdin, stdout, stderr from board_util import GoBoardUtil, BLACK, WHITE, EMPTY, BORDER, PASS, \ MAXSIZE, coord_to_point import numpy as np import re import time import random class GtpConnection(): def __init__(self, go_engine, board, debug_mode = False): """ Manage a GTP connection for a Go-playing engine Parameters ---------- go_engine: a program that can reply to a set of GTP commandsbelow board: Represents the current board state. """ self.totalTime = 0 self.count = 0 self.nodeExp = 0 self.timeLimit = 1 self.to_play = BLACK #H table is a dictionary that stores (state,value) pairs #value = Black win -> 1, White win -1 self.H_table = {} self._winner = '' self._optimal_move = '' self._debug_mode = debug_mode self.go_engine = go_engine self.board = board self.commands = { "protocol_version": self.protocol_version_cmd, "quit": self.quit_cmd, "name": self.name_cmd, "boardsize": self.boardsize_cmd, "showboard": self.showboard_cmd, "clear_board": self.clear_board_cmd, "komi": self.komi_cmd, "version": self.version_cmd, "known_command": self.known_command_cmd, "genmove": self.genmove_cmd, "list_commands": self.list_commands_cmd, "play": self.play_cmd, "legal_moves": self.legal_moves_cmd, "gogui-rules_game_id": self.gogui_rules_game_id_cmd, "gogui-rules_board_size": self.gogui_rules_board_size_cmd, "gogui-rules_legal_moves": self.gogui_rules_legal_moves_cmd, "gogui-rules_side_to_move": self.gogui_rules_side_to_move_cmd, "gogui-rules_board": self.gogui_rules_board_cmd, "gogui-rules_final_result": self.gogui_rules_final_result_cmd, "gogui-analyze_commands": self.gogui_analyze_cmd, "timelimit": self.timelimit_cmd, "solve":self.solve_cmd } # used for argument checking # values: (required number of arguments, # error message on argnum failure) self.argmap = { "boardsize": (1, 'Usage: boardsize INT'), "komi": (1, 'Usage: komi FLOAT'), "known_command": (1, 'Usage: known_command CMD_NAME'), "genmove": (1, 'Usage: genmove {w,b}'), "play": (2, 'Usage: play {b,w} MOVE'), "legal_moves": (1, 'Usage: legal_moves {w,b}'), "timelimit": (1, 'Usage: timelimit INT, 1 <= INT <= 100'), } def write(self, data): stdout.write(data) def flush(self): stdout.flush() def start_connection(self): """ Start a GTP connection. This function continuously monitors standard input for commands. """ line = stdin.readline() while line: self.get_cmd(line) line = stdin.readline() def get_cmd(self, command): """ Parse command string and execute it """ if len(command.strip(' \r\t')) == 0: return if command[0] == '#': return # Strip leading numbers from regression tests if command[0].isdigit(): command = re.sub("^\d+", "", command).lstrip() elements = command.split() if not elements: return command_name = elements[0]; args = elements[1:] if self.has_arg_error(command_name, len(args)): return if command_name in self.commands: try: self.commands[command_name](args) except Exception as e: self.debug_msg("Error executing command {}\n".format(str(e))) self.debug_msg("Stack Trace:\n{}\n". format(traceback.format_exc())) raise e else: self.debug_msg("Unknown command: {}\n".format(command_name)) self.error('Unknown command') stdout.flush() def has_arg_error(self, cmd, argnum): """ Verify the number of arguments of cmd. argnum is the number of parsed arguments """ if cmd in self.argmap and self.argmap[cmd][0] != argnum: self.error(self.argmap[cmd][1]) return True return False def debug_msg(self, msg): """ Write msg to the debug stream """ if self._debug_mode: stderr.write(msg) stderr.flush() def error(self, error_msg): """ Send error msg to stdout """ stdout.write('? {}\n\n'.format(error_msg)) stdout.flush() def respond(self, response=''): """ Send response to stdout """ stdout.write('= {}\n\n'.format(response)) stdout.flush() def reset(self, size): """ Reset the board to empty board of given size """ self.board.reset(size) def board2d(self): return str(GoBoardUtil.get_twoD_board(self.board)) def protocol_version_cmd(self, args): """ Return the GTP protocol version being used (always 2) """ self.respond('2') def quit_cmd(self, args): """ Quit game and exit the GTP interface """ self.respond() exit() def name_cmd(self, args): """ Return the name of the Go engine """ self.respond(self.go_engine.name) def version_cmd(self, args): """ Return the version of the Go engine """ self.respond(self.go_engine.version) def clear_board_cmd(self, args): """ clear the board """ self.reset(self.board.size) self.respond() def boardsize_cmd(self, args): """ Reset the game with new boardsize args[0] """ self.reset(int(args[0])) self.respond() #newly added def timelimit_cmd(self, args): """ Reset the game with new timelimit args[0] """ self.timeLimit = int(args[0]) self.respond() def showboard_cmd(self, args): self.respond('\n' + self.board2d()) def komi_cmd(self, args): """ Set the engine's komi to args[0] """ self.go_engine.komi = float(args[0]) self.respond() def known_command_cmd(self, args): """ Check if command args[0] is known to the GTP interface """ if args[0] in self.commands: self.respond("true") else: self.respond("false") def list_commands_cmd(self, args): """ list all supported GTP commands """ self.respond(' '.join(list(self.commands.keys()))) def legal_moves_cmd(self, args): """ List legal moves for color args[0] in {'b','w'} """ board_color = args[0].lower() color = color_to_int(board_color) moves = GoBoardUtil.generate_legal_moves(self.board, color) gtp_moves = [] for move in moves: coords = point_to_coord(move, self.board.size) gtp_moves.append(format_point(coords)) sorted_moves = ' '.join(sorted(gtp_moves)) self.respond(sorted_moves) def play_cmd(self, args): """ play a move args[1] for given color args[0] in {'b','w'} """ try: board_color = args[0].lower() board_move = args[1] if board_color != "b" and board_color !="w": self.respond("illegal move: \"{}\" wrong color".format(board_color)) return color = color_to_int(board_color) #change turn to the other player self.to_play = GoBoardUtil.opponent(color) if args[1].lower() == 'pass': self.respond("illegal move: \"{} {}\" wrong coordinate".format(args[0], args[1])) return coord = move_to_coord(args[1], self.board.size) if coord: move = coord_to_point(coord[0],coord[1], self.board.size) else: self.error("Error executing move {} converted from {}" .format(move, args[1])) return if not self.board.play_move(move, color): self.respond("illegal move: \"{} {}\" ".format(args[0], board_move)) return else: self.debug_msg("Move: {}\nBoard:\n{}\n". format(board_move, self.board2d())) self.respond() except Exception as e: self.respond('illegal move: \"{} {}\" {}'.format(args[0], args[1], str(e))) def solve_helper(self): winner = 'unknown' #the copy of board can be viewed as a state cp_board = self.board.copy() start = time.time() signal.signal(signal.SIGALRM, handler) signal.alarm(self.timeLimit) try: value,move = self.advanced_search(cp_board,81,-1,1) except Exception as e: value,move = 0,None #print("nodeExp",self.nodeExp) #print("count",self.count) signal.alarm(0) end = time.time() print("time: ",end - start) #print("partial time: ",self.totalTime) if value == 1: winner = 'b' elif value == -1: winner = 'w' if (winner == 'b' and self.to_play !=BLACK) or (winner == 'w' and self.to_play !=WHITE): move = None return winner,move #newly added def solve_cmd(self,args): moveStr = '' winner,move = self.solve_helper() if move: moveStr = ' '+ coord_to_move(move,self.board.size) self.respond(winner+moveStr) #alpha beta pruning, referencing from wikipedia: https://en.wikipedia.org/wiki/Alpha%E2%80%93beta_pruning #color is the player. black is max player, white is min player def ab_search(self, color, copy_of_board, depth, alpha, beta): _alpha = alpha _beta = beta bestMove = None #base case, no more legal move #print(GoBoardUtil.generate_legal_moves(copy_of_board, color)) if depth == 0 or (GoBoardUtil.generate_legal_moves(copy_of_board, color) == []): #depth should always be >0 #since NOGO cannot capture nor suiside, if last move is by WHITE/BLACK, it must be a BLACK/WHITE win. if color == WHITE: return 1,None #color == BLACK else: return -1,None #color is black; max player if color == BLACK: value = -1000000 #make a copy of current state allmoves = GoBoardUtil.generate_legal_moves(copy_of_board, color) #print("allmoves:") #print(allmoves) for move in allmoves: child = copy_of_board.copy() child.play_move(move, color) childValue,_ = self.ab_search(WHITE,child,depth-1,_alpha,_beta) value = max(value,childValue) _alpha = max(_alpha,value) bestMove = move #beta cut-off if _alpha >= _beta: break return value,bestMove #color is white; min player else: value = 1000000 allmoves = GoBoardUtil.generate_legal_moves(copy_of_board, color) #print("allmoves:") #print(allmoves) for move in allmoves: child = copy_of_board.copy() child.play_move(move, color) childValue,_ = self.ab_search(BLACK,child,depth-1,_alpha,_beta) value = min(value,childValue) _beta = min(_beta,value) bestMove = move #alpha cut-off if _alpha >= _beta: break return value,bestMove def advanced_search(self,copy_of_board,depth,alpha,beta): _alpha = alpha _beta = beta bestMove = None self.nodeExp += 1 #base case, depth 0 if depth == 0: return 0,None #Start = time.time() allmoves = GoBoardUtil.generate_legal_moves(copy_of_board, copy_of_board.current_player) #End =time.time() #self.totalTime += End-Start #base case, no more legal move if allmoves == []: #since NOGO cannot capture nor suiside, if last move is by WHITE/BLACK, it must be a BLACK/WHITE win. if copy_of_board.current_player == WHITE: self.H_table[self.tuple_to_str(self.matrix_to_tuple(GoBoardUtil.get_twoD_board(copy_of_board),copy_of_board.size))] = 1 return 1,None #color == BLACK else: self.H_table[self.tuple_to_str(self.matrix_to_tuple(GoBoardUtil.get_twoD_board(copy_of_board),copy_of_board.size))] = -1 return -1,None searchedMoves = [] unsearchedMoves = [] unsearched = {} searchedValue = {} isoSet = set() singleMoveIsoSet = set() for move in allmoves: singleMoveIsoSet.clear() child = copy_of_board.copy() child.play_move(move, copy_of_board.current_player) #get all isomorphics of the board, in order to prunning as many as redundent states possible isomorphics = self.get_all_isomorphic(GoBoardUtil.get_twoD_board(child),child.size) found = False for iso in isomorphics: if self.tuple_to_str(iso) in self.H_table: found = True searchedMoves.append(move) searchedValue[move] = self.H_table[self.tuple_to_str(iso)] break if iso in isoSet: found = True break else: isoSet.add(iso) singleMoveIsoSet.add(iso) if not found: ''' the following is the heuristic I created for ordering the moves: (1) eye-filling is the last thing we want to do; (2) the few the number of player's stones with MD 1, the better; (3) the more the number of opponent's stones with MD 1, the better; (4) the more the number of player's stones with MD 2, the better; ''' num_same = 49 dis1 = [move+1,move-1,move+child.size+1,move-child.size-1] dis2 = [move+2,move-2,move+2*(child.size+1),move-2*(child.size+1),move+child.size+2,move-child.size-2,move+child.size,move-child.size] valid1 = [] for point in dis1: x = point%(child.size+1) y = point//(child.size+1) if 1<=x<=child.size and 1<=y<=child.size: valid1.append(point) valid2 = [] for point in dis2: x = point%(child.size+1) y = point//(child.size+1) if 1<=x<=child.size and 1<=y<=child.size: valid2.append(point) if copy_of_board.is_eye(move,copy_of_board.current_player): num_same += 1000 for point in valid1: if child.get_color(point)==copy_of_board.current_player: num_same += 100 if child.get_color(point)== BLACK+WHITE-copy_of_board.current_player: num_same -= 10 for point in valid2: if child.get_color(point)==copy_of_board.current_player: num_same -= 1 unsearched[move] = num_same #print("dic:",unsearched) #print("searched:",searchedMoves) #sorting unsearched moves by the heuristic value sorted_x = sorted(unsearched.items(), key=lambda kv: kv[1]) for item in sorted_x: unsearchedMoves.append(item[0]) orderedMoves = searchedMoves + unsearchedMoves self.count += len(allmoves) - len(orderedMoves) state = self.tuple_to_str(self.matrix_to_tuple(GoBoardUtil.get_twoD_board(copy_of_board),copy_of_board.size)) #below is normal alpha-beta search #color is black; max player if copy_of_board.current_player == BLACK: value = -1000000 #make a copy of current state for move in orderedMoves: if move in searchedMoves: childValue = searchedValue[move] else: child = copy_of_board.copy() child.play_move(move, copy_of_board.current_player) childValue,_ = self.advanced_search(child,depth-1,_alpha,_beta) #childValue,_ = self.advanced_search(copy_of_board,depth-1,_alpha,_beta) value = max(value,childValue) _alpha = max(_alpha,value) bestMove = move #beta cut-off if _alpha >= _beta: break self.H_table[state] = value return value,bestMove #color is white; min player else: value = 1000000 for move in orderedMoves: if move in searchedMoves: childValue = searchedValue[move] else: child = copy_of_board.copy() child.play_move(move, copy_of_board.current_player) #childValue,_ = self.advanced_search(copy_of_board,depth-1,_alpha,_beta) childValue,_ = self.advanced_search(child,depth-1,_alpha,_beta) value = min(value,childValue) _beta = min(_beta,value) bestMove = move #alpha cut-off if _alpha >= _beta: break self.H_table[state] = value return value,bestMove def get_all_isomorphic(self, board_2d,size): """ input: matrix of a board output: a set of tuples """ isomorphics = set() #original #print("mat to tuple:") #print(self.matrix_to_tuple(board_2d,size)) isomorphics.add(self.matrix_to_tuple(board_2d,size)) #return isomorphics tmp_board = [] #reflectional sym, 2 cases #swap rows cp_board_2dx = board_2d.copy() for i in range(size//2): tmp = cp_board_2dx[i,:].copy() cp_board_2dx[i,:] = cp_board_2dx[size-1-i,:] cp_board_2dx[size-1-i,:]=tmp isomorphics.add(self.matrix_to_tuple(cp_board_2dx,size)) #swap columns cp_board_2dy = board_2d.copy() for j in range(size//2): for i in range(size): tmp = cp_board_2dy[i,j] cp_board_2dy[i,j] = cp_board_2dy[i,size-1-j] cp_board_2dy[i,size-1-j] = tmp isomorphics.add(self.matrix_to_tuple(cp_board_2dy,size)) #rotational sym, 3 cases board_90 = np.rot90(board_2d) #board_90 = self.rotateMatrix(board_2d,size) isomorphics.add(self.matrix_to_tuple(board_90,size)) #reflectional sym of 90 degree, 2 cases #swap rows cp_board_90x = board_90.copy() for i in range(size//2): tmp = cp_board_90x[i,:].copy() cp_board_90x[i,:] = cp_board_90x[size-1-i,:] cp_board_90x[size-1-i,:] = tmp isomorphics.add(self.matrix_to_tuple(cp_board_90x,size)) #swap columns cp_board_90y = board_90.copy() for j in range(size//2): for i in range(size): tmp = cp_board_90y[i,j] cp_board_90y[i,j] = cp_board_90y[i,size-1-j] cp_board_90y[i,size-1-j] = tmp isomorphics.add(self.matrix_to_tuple(cp_board_90y,size)) #print("90",board_90) board_180 = np.rot90(board_90) #print("180",board_180) isomorphics.add(self.matrix_to_tuple(board_180,size)) board_270 = np.rot90(board_180) #print("270",board_270) isomorphics.add(self.matrix_to_tuple(board_270,size)) #board_180 = self.rotateMatrix(board_90,size) #isomorphics.add(self.matrix_to_tuple(board_180,size)) #board_270 = self.rotateMatrix(board_180,size) #isomorphics.add(self.matrix_to_tuple(board_270,size)) return isomorphics def matrix_to_tuple(self,matrix,dim): board1d = np.zeros((dim* dim), dtype = np.int32) for i in range(dim): board1d[i*dim:i*dim+dim] = matrix[i,:] return tuple(board1d) def get_oneD_board(self,goboard): """ Return: numpy array a 1-d numpy array with the stones as the goboard. Does not pad with BORDER Rows 1..size of goboard are copied into rows 0..size - 1 of board2d """ size = goboard.size board1d = np.zeros((size* size), dtype = np.int32) for row in range(size): start = goboard.row_start(row + 1) board1d[row*size:row*size+size] = goboard.board[start : start + size] return board1d def tuple_to_str(self,tup): res = '' for i in tup: res += str(int(i)) return res #genemove overrided def genmove_cmd(self, args): """ Generate a move for the color args[0] in {'b', 'w'}, for the game of gomoku. """ board_color = args[0].lower() color = color_to_int(board_color) self.to_play = color winnerStr,optMove = self.solve_helper() winner = EMPTY if winnerStr=='b': winner = BLACK elif winnerStr =='w': winner = WHITE #if current player is winner, we will take bestmove; otherwise we should take a random move if board_color == winner: move = optMove else: move = GoBoardUtil.generate_random_move(self.board, color,False) move_coord = point_to_coord(move, self.board.size) move_as_string = format_point(move_coord) if self.board.is_legal(move, color): self.board.play_move(move, color) self.respond(move_as_string) else: self.respond("resign") def gogui_rules_game_id_cmd(self, args): self.respond("NoGo") def gogui_rules_board_size_cmd(self, args): self.respond(str(self.board.size)) def legal_moves_cmd(self, args): """ List legal moves for color args[0] in {'b','w'} """ board_color = args[0].lower() color = color_to_int(board_color) moves = GoBoardUtil.generate_legal_moves(self.board, color) gtp_moves = [] for move in moves: coords = point_to_coord(move, self.board.size) gtp_moves.append(format_point(coords)) sorted_moves = ' '.join(sorted(gtp_moves)) self.respond(sorted_moves) def gogui_rules_legal_moves_cmd(self, args): empties = self.board.get_empty_points() color = self.board.current_player legal_moves = [] for move in empties: if self.board.is_legal(move, color): legal_moves.append(move) gtp_moves = [] for move in legal_moves: coords = point_to_coord(move, self.board.size) gtp_moves.append(format_point(coords)) sorted_moves = ' '.join(sorted(gtp_moves)) self.respond(sorted_moves) def gogui_rules_side_to_move_cmd(self, args): color = "black" if self.board.current_player == BLACK else "white" self.respond(color) def gogui_rules_board_cmd(self, args): size = self.board.size str = '' for row in range(size-1, -1, -1): start = self.board.row_start(row + 1) for i in range(size): point = self.board.board[start + i] if point == BLACK: str += 'X' elif point == WHITE: str += 'O' elif point == EMPTY: str += '.' else: assert False str += '\n' self.respond(str) def gogui_rules_final_result_cmd(self, args): empties = self.board.get_empty_points() color = self.board.current_player legal_moves = [] for move in empties: if self.board.is_legal(move, color): legal_moves.append(move) if not legal_moves: result = "black" if self.board.current_player == WHITE else "white" else: result = "unknown" self.respond(result) def gogui_analyze_cmd(self, args): self.respond("pstring/Legal Moves For ToPlay/gogui-rules_legal_moves\n" "pstring/Side to Play/gogui-rules_side_to_move\n" "pstring/Final Result/gogui-rules_final_result\n" "pstring/Board Size/gogui-rules_board_size\n" "pstring/Rules GameID/gogui-rules_game_id\n" "pstring/Show Board/gogui-rules_board\n" ) def point_to_coord(point, boardsize): """ Transform point given as board array index to (row, col) coordinate representation. Special case: PASS is not transformed """ if point == PASS: return PASS else: NS = boardsize + 1 return divmod(point, NS) def format_point(move): """ Return move coordinates as a string such as 'a1', or 'pass'. """ column_letters = "ABCDEFGHJKLMNOPQRSTUVWXYZ" #column_letters = "abcdefghjklmnopqrstuvwxyz" if move == PASS: return "pass" row, col = move if not 0 <= row < MAXSIZE or not 0 <= col < MAXSIZE: raise ValueError return column_letters[col - 1]+ str(row) def move_to_coord(point_str, board_size): """ Convert a string point_str representing a point, as specified by GTP, to a pair of coordinates (row, col) in range 1 .. board_size. Raises ValueError if point_str is invalid """ if not 2 <= board_size <= MAXSIZE: raise ValueError("board_size out of range") s = point_str.lower() if s == "pass": return PASS try: col_c = s[0] if (not "a" <= col_c <= "z") or col_c == "i": raise ValueError col = ord(col_c) - ord("a") if col_c < "i": col += 1 row = int(s[1:]) if row < 1: raise ValueError except (IndexError, ValueError): # e.g. "a0" raise ValueError("wrong coordinate") if not (col <= board_size and row <= board_size): # e.g. "a20" raise ValueError("wrong coordinate") return row, col def coord_to_move(move, board_size): """ Convert a string point_str representing a point, as specified by GTP, to a pair of coordinates (row, col) in range 1 .. board_size. Raises ValueError if point_str is invalid """ if not 2 <= board_size <= MAXSIZE: raise ValueError("board_size out of range") #s = point_str.lower() x = move%(board_size+1) y = move//(board_size+1) col = chr(x-1 + ord("a")) #col = col.upper() return col+str(y) def color_to_int(c): """convert character to the appropriate integer code""" color_to_int = {"b": BLACK , "w": WHITE, "e": EMPTY, "BORDER": BORDER} return color_to_int[c] def handler(signum, frame): print('Signal handler called with signal', signum) raise Exception("Timeout!")

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0.023265

0.01573

0.429941

0.379577

0.346861

0.320291

0.295704

0.271844

0

0.017755

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28,970

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0.791379

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false

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0.167293

0.003759

0

null

0

null

0

1

0

9d71192a0442b7eef7acad0763b92e91ecac841f

965

py

Python

plugins/help.py

A0vanc01/Frisky

d4d7f9892858b5412755c9dee594e5b60b6d2b94

[ "MIT" ]

5

2020-01-22T18:16:59.000Z

2021-06-14T13:23:57.000Z

plugins/help.py

A0vanc01/Frisky

d4d7f9892858b5412755c9dee594e5b60b6d2b94

[ "MIT" ]

104

2020-02-12T00:36:14.000Z

2022-02-10T08:18:28.000Z

plugins/help.py

A0vanc01/Frisky

d4d7f9892858b5412755c9dee594e5b60b6d2b94

[ "MIT" ]

4

2020-01-30T15:44:04.000Z

2020-08-27T19:22:57.000Z

from frisky.events import MessageEvent from frisky.plugin import FriskyPlugin, PluginRepositoryMixin from frisky.responses import FriskyResponse class HelpPlugin(FriskyPlugin, PluginRepositoryMixin): commands = ['help'] def command_help(self, message: MessageEvent) -> FriskyResponse: if len(message.args) == 1: plugin_name = message.args[0] if plugin_name == 'help': return 'Usage: `?help` or `?help <plugin_name>`' plugin = self.get_plugin_by_name(plugin_name) if plugin is None: return f'No such plugin: `{plugin_name}`, try `?help` to list installed plugins' if (help_text := plugin.help_text()) is None: return f'Plugin `{plugin_name}` does not provide help text.' return help_text plugins = self.get_plugin_names() joined_string = ', '.join(plugins) return f'Available plugins: {joined_string}'

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0.260104

965

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0.052632

false

0

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0

0.578947

0

null

0

null

0

1

0

9d71751143901cbe72d8513a42c3b74da3d29bf0

998

py

Python

composer/models/ssd/ssd_hparams.py

anisehsani/composer

42599682d50409b4a4eb7c91fad85d67418cee13

[ "Apache-2.0" ]

null

composer/models/ssd/ssd_hparams.py

anisehsani/composer

42599682d50409b4a4eb7c91fad85d67418cee13

[ "Apache-2.0" ]

null

composer/models/ssd/ssd_hparams.py

anisehsani/composer

42599682d50409b4a4eb7c91fad85d67418cee13

[ "Apache-2.0" ]

null

# Copyright 2022 MosaicML. All Rights Reserved. from dataclasses import dataclass import yahp as hp from composer.models.model_hparams import ModelHparams @dataclass class SSDHparams(ModelHparams): input_size: int = hp.optional( doc="input size", default=300, ) num_classes: int = hp.optional( doc="num_classes", default=80, ) overlap_threshold: float = hp.optional( doc="threshold", default=0.5, ) nms_max_detections: int = hp.optional( doc="nms max dets", default=200, ) data: str = hp.optional( doc="data", default="/localdisk/coco", ) def initialize_object(self): from composer.models.ssd.ssd import SSD return SSD( input_size=self.input_size, overlap_threshold=self.overlap_threshold, nms_max_detections=self.nms_max_detections, num_classes=self.num_classes, data=self.data, )

22.681818

55

0.617234

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998

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0.108696

0.080268

0

0.019774

0.290581

998

43

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0

0.117647

0

0.352941

0

null

0

null

0

1

0

9d73808fab2e4c633d3b7d43187bc4821f1bfb77

1,303

py

Python

src/lib/base_dataset.py

CvHadesSun/Camera-Calibration

5c054672749aa0b3be1bdff8b8f4f3d2fcf3ee85

[ "MIT" ]

null

src/lib/base_dataset.py

CvHadesSun/Camera-Calibration

5c054672749aa0b3be1bdff8b8f4f3d2fcf3ee85

[ "MIT" ]

null

src/lib/base_dataset.py

CvHadesSun/Camera-Calibration

5c054672749aa0b3be1bdff8b8f4f3d2fcf3ee85

[ "MIT" ]

null

from os.path import join from utils import getFileList class ImageFolder: def __init__(self, path, sub=None, annot='annot') -> None: self.root = path self.image = 'images' self.annot = annot self.image_root = join(path, self.image) self.annot_root = join(path, self.annot) self.annot_root_tmp = join(path, self.annot + '_tmp') if sub is None: self.imgnames = getFileList(self.image_root, ext='.jpg') self.annnames = getFileList(self.annot_root, ext='.json') else: self.imgnames = getFileList(join(self.image_root, sub), ext='.jpg') self.annnames = getFileList(join(self.annot_root, sub), ext='.json') self.imgnames = [join(sub, name) for name in self.imgnames] self.annnames = [join(sub, name) for name in self.annnames] self.isTmp = True assert len(self.imgnames) == len(self.annnames) def __getitem__(self, index): imgname = join(self.image_root, self.imgnames[index]) if self.isTmp: annname = join(self.annot_root_tmp, self.annnames[index]) else: annname = join(self.annot_root, self.annnames[index]) return imgname, annname def __len__(self): return len(self.imgnames)

40.71875

80

0.61934

166

1,303

4.710843

0.23494

0.103581

0.099744

0.065217

0.196931

0.061381

0

0.261704

1,303

32

81

40.71875

0.81289

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0.025307

0

0.034483

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0.103448

false

0

0.068966

0.034483

0.275862

0

null

0

null

0

1

0

9d73d6f049758b5497d67b41cd027577eaf0250d

1,704

py

Python

main.py

sunkr1995/genetic-drawing

6e5cc755a55c1994770c3f18fb14f1cc651bb700

[ "MIT" ]

null

main.py

sunkr1995/genetic-drawing

6e5cc755a55c1994770c3f18fb14f1cc651bb700

[ "MIT" ]

null

main.py

sunkr1995/genetic-drawing

6e5cc755a55c1994770c3f18fb14f1cc651bb700

[ "MIT" ]

null

''' Author: your name Date: 2021-06-18 10:13:00 LastEditTime: 2021-07-08 14:13:07 LastEditors: Please set LastEditors Description: In User Settings Edit FilePath: /genetic-drawing/main.py ''' import cv2 import os import time from IPython.display import clear_output from genetic_drawing import * gen = GeneticDrawing('03.jpg', seed=time.time()) out = gen.generate(400, 50) brushesRange = np.array([[0.1, 0.3], [0.3, 0.7]]) for i in range(len(gen.imgBuffer)): cv2.imwrite(os.path.join("out", f"{i:06d}.png"), gen.imgBuffer[i]) try: for i in range(5): brushesRange_tmp = brushesRange/(2**(i+1)) gen.brushesRange = brushesRange_tmp.tolist() maskname = "masks-03/mask-{}.jpg".format(i) gen.sampling_mask = cv2.cvtColor(cv2.imread(maskname), cv2.COLOR_BGR2GRAY) #keep drawing on top of our previous result out = gen.generate(100, 30) for i in range(len(gen.imgBuffer)): cv2.imwrite(os.path.join("out", f"{i:06d}.png"), gen.imgBuffer[i]) except: if not os.path.exists('out'): os.mkdir("out") for i in range(len(gen.imgBuffer)): cv2.imwrite(os.path.join("out", f"{i:06d}.png"), gen.imgBuffer[i]) #brushesRange_tmp = brushesRange/100 #gen.brushesRange = brushesRange_tmp.tolist() ##gen.brushesRange = [[0.005, 0.015],[0.015, 0.035]] #gen.sampling_mask = cv2.cvtColor(cv2.imread("masks/mask-end.jpg"), cv2.COLOR_BGR2GRAY) # ##keep drawing on top of our previous result #out = gen.generate(50, 30) #save all the images from the image buffer if not os.path.exists('out'): os.mkdir("out") for i in range(len(gen.imgBuffer)): cv2.imwrite(os.path.join("out", f"{i:06d}.png"), gen.imgBuffer[i])

34.08

87

0.669601

271

1,704

4.173432

0.372694

0.084881

0.026525

0.04863

0.541114

0.477454

0.415561

0

0.067975

0.162559

1,704

50

88

34.08

0.724597

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1

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0

0.178571

0

0.178571

0

null

0

null

0

1

0

9d740fa3ec721433e495424e2743d9af67d910eb

10,991

py

Python

flair/models/sandbox/simple_sequence_tagger_model.py

bratao/flair

67b53cc2a615a2e2a4e552d6f787c2efa708a939

[ "MIT" ]

null

flair/models/sandbox/simple_sequence_tagger_model.py

bratao/flair

67b53cc2a615a2e2a4e552d6f787c2efa708a939

[ "MIT" ]

null

flair/models/sandbox/simple_sequence_tagger_model.py

bratao/flair

67b53cc2a615a2e2a4e552d6f787c2efa708a939

[ "MIT" ]

null

import logging from typing import List, Union, Optional import torch import torch.nn import torch.nn.functional as F from tqdm import tqdm import flair.nn from flair.data import Dictionary, Sentence, Label from flair.datasets import SentenceDataset, DataLoader from flair.embeddings import TokenEmbeddings from flair.training_utils import store_embeddings log = logging.getLogger("flair") class SimpleSequenceTagger(flair.nn.Classifier): """ This class is a simple version of the SequenceTagger class. The purpose of this class is to demonstrate the basic hierarchy of a sequence tagger (this could be helpful for new developers). It only uses the given embeddings and maps them with a linear layer to the tag_dictionary dimension. Thus, this class misses following functionalities from the SequenceTagger: - CRF, - RNN, - Reprojection. As a result, only poor results can be expected. """ def __init__( self, embeddings: TokenEmbeddings, tag_dictionary: Dictionary, tag_type: str, ): """ Initializes a SimpleSequenceTagger :param embeddings: word embeddings used in tagger :param tag_dictionary: dictionary of tags you want to predict :param tag_type: string identifier for tag type :param beta: Parameter for F-beta score for evaluation and training annealing """ super(SimpleSequenceTagger, self).__init__() # embeddings self.embeddings = embeddings # dictionaries self.tag_dictionary: Dictionary = tag_dictionary self.tag_type: str = tag_type self.tagset_size: int = len(tag_dictionary) # linear layer self.linear = torch.nn.Linear(self.embeddings.embedding_length, len(tag_dictionary)) # all parameters will be pushed internally to the specified device self.to(flair.device) def forward_loss( self, data_points: Union[List[Sentence], Sentence], sort=True ) -> torch.tensor: features = self.forward(data_points) return self._calculate_loss(features, data_points) def _get_state_dict(self): model_state = { "state_dict": self.state_dict(), "embeddings": self.embeddings, "tag_dictionary": self.tag_dictionary, "tag_type": self.tag_type, } return model_state @staticmethod def _init_model_with_state_dict(state): model = SimpleSequenceTagger( embeddings=state["embeddings"], tag_dictionary=state["tag_dictionary"], tag_type=state["tag_type"], ) model.load_state_dict(state["state_dict"]) return model def predict( self, sentences: Union[List[Sentence], Sentence], mini_batch_size=32, all_tag_prob: bool = False, verbose: bool = False, label_name: Optional[str] = None, return_loss=False, embedding_storage_mode="none", ): """ Predict sequence tags for Named Entity Recognition task :param sentences: a Sentence or a List of Sentence :param mini_batch_size: size of the minibatch, usually bigger is more rapid but consume more memory, up to a point when it has no more effect. :param all_tag_prob: True to compute the score for each tag on each token, otherwise only the score of the best tag is returned :param verbose: set to True to display a progress bar :param return_loss: set to True to return loss :param label_name: set this to change the name of the label type that is predicted :param embedding_storage_mode: default is 'none' which is always best. Only set to 'cpu' or 'gpu' if you wish to not only predict, but also keep the generated embeddings in CPU or GPU memory respectively. 'gpu' to store embeddings in GPU memory. """ if label_name is None: label_name = self.tag_type with torch.no_grad(): if not sentences: return sentences if isinstance(sentences, Sentence): sentences = [sentences] # reverse sort all sequences by their length rev_order_len_index = sorted( range(len(sentences)), key=lambda k: len(sentences[k]), reverse=True ) reordered_sentences: List[Union[Sentence, str]] = [ sentences[index] for index in rev_order_len_index ] dataloader = DataLoader( dataset=SentenceDataset(reordered_sentences), batch_size=mini_batch_size ) # progress bar for verbosity if verbose: dataloader = tqdm(dataloader) overall_loss = 0 batch_no = 0 for batch in dataloader: batch_no += 1 if verbose: dataloader.set_description(f"Inferencing on batch {batch_no}") batch = self._filter_empty_sentences(batch) # stop if all sentences are empty if not batch: continue feature = self.forward(batch) if return_loss: overall_loss += self._calculate_loss(feature, batch) tags, all_tags = self._obtain_labels( feature=feature, batch_sentences=batch, get_all_tags=all_tag_prob, ) for (sentence, sent_tags) in zip(batch, tags): for (token, tag) in zip(sentence.tokens, sent_tags): token.add_tag_label(label_name, tag) # all_tags will be empty if all_tag_prob is set to False, so the for loop will be avoided for (sentence, sent_all_tags) in zip(batch, all_tags): for (token, token_all_tags) in zip(sentence.tokens, sent_all_tags): token.add_tags_proba_dist(label_name, token_all_tags) # clearing token embeddings to save memory store_embeddings(batch, storage_mode=embedding_storage_mode) if return_loss: return overall_loss / batch_no def forward(self, sentences: List[Sentence]): self.embeddings.embed(sentences) names = self.embeddings.get_names() lengths: List[int] = [len(sentence.tokens) for sentence in sentences] longest_token_sequence_in_batch: int = max(lengths) pre_allocated_zero_tensor = torch.zeros( self.embeddings.embedding_length * longest_token_sequence_in_batch, dtype=torch.float, device=flair.device, ) all_embs = list() for sentence in sentences: all_embs += [ emb for token in sentence for emb in token.get_each_embedding(names) ] nb_padding_tokens = longest_token_sequence_in_batch - len(sentence) if nb_padding_tokens > 0: t = pre_allocated_zero_tensor[ : self.embeddings.embedding_length * nb_padding_tokens ] all_embs.append(t) sentence_tensor = torch.cat(all_embs).view( [ len(sentences), longest_token_sequence_in_batch, self.embeddings.embedding_length, ] ) features = self.linear(sentence_tensor) return features def _calculate_loss( self, features: torch.tensor, sentences: List[Sentence] ) -> float: lengths: List[int] = [len(sentence.tokens) for sentence in sentences] tag_list: List = [] for s_id, sentence in enumerate(sentences): # get the tags in this sentence tag_idx: List[int] = [ self.tag_dictionary.get_idx_for_item(token.get_tag(self.tag_type).value) for token in sentence ] # add tags as tensor tag = torch.tensor(tag_idx, device=flair.device) tag_list.append(tag) score = 0 for sentence_feats, sentence_tags, sentence_length in zip( features, tag_list, lengths ): sentence_feats = sentence_feats[:sentence_length] score += torch.nn.functional.cross_entropy( sentence_feats, sentence_tags ) score /= len(features) return score def _obtain_labels( self, feature: torch.Tensor, batch_sentences: List[Sentence], get_all_tags: bool, ) -> (List[List[Label]], List[List[List[Label]]]): """ Returns a tuple of two lists: - The first list corresponds to the most likely `Label` per token in each sentence. - The second list contains a probability distribution over all `Labels` for each token in a sentence for all sentences. """ lengths: List[int] = [len(sentence.tokens) for sentence in batch_sentences] tags = [] all_tags = [] feature = feature.cpu() for index, length in enumerate(lengths): feature[index, length:] = 0 softmax_batch = F.softmax(feature, dim=2).cpu() scores_batch, prediction_batch = torch.max(softmax_batch, dim=2) feature = zip(softmax_batch, scores_batch, prediction_batch) for feats, length in zip(feature, lengths): softmax, score, prediction = feats confidences = score[:length].tolist() tag_seq = prediction[:length].tolist() scores = softmax[:length].tolist() tags.append( [ Label(self.tag_dictionary.get_item_for_index(tag), conf) for conf, tag in zip(confidences, tag_seq) ] ) if get_all_tags: all_tags.append( [ [ Label( self.tag_dictionary.get_item_for_index(score_id), score ) for score_id, score in enumerate(score_dist) ] for score_dist in scores ] ) return tags, all_tags @staticmethod def _filter_empty_sentences(sentences: List[Sentence]) -> List[Sentence]: filtered_sentences = [sentence for sentence in sentences if sentence.tokens] if len(sentences) != len(filtered_sentences): log.warning( f"Ignore {len(sentences) - len(filtered_sentences)} sentence(s) with no tokens." ) return filtered_sentences @property def label_type(self): return self.tag_type

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0.031842

0

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0.337185

10,991

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0.004902

0.156863

0

null

0

null

0

1

0

9d7508b796c963b53ae0eb9f9680e4518db45e86

1,708

py

Python

exercise/xiaohuar/spider-xiaohuar.com.py

PorYoung/bigData-camp-8d

8fa31b48065da27fd1c4f8432232342cede6f56c

[ "MIT" ]

1

2019-12-27T06:34:06.000Z

exercise/xiaohuar/spider-xiaohuar.com.py

PorYoung/bigData-camp-8d

8fa31b48065da27fd1c4f8432232342cede6f56c

[ "MIT" ]

1

2021-12-14T20:40:06.000Z

exercise/xiaohuar/spider-xiaohuar.com.py

PorYoung/bigData-camp-8d

8fa31b48065da27fd1c4f8432232342cede6f56c

[ "MIT" ]

null

import requests from bs4 import BeautifulSoup def spider_xiaohuar_content(url, headers): response = requests.get(url=url, headers=headers) print(response.status_code) if response.status_code == 200: response.encoding = 'utf-8' html = response.content # 参数：网页内容，解析器 soup = BeautifulSoup(html, 'html5lib') div_list = soup.find_all('div', attrs={'class': 'all_lanmu'}) text = '' file = open('爬虫校花.md', 'w', encoding='utf-8') for div in div_list: title_div = div.find('div', attrs={'class': 'title1000'}) title = title_div.find('a').string text += '<style>img[src*="headimg-style"]{width:100px;height:100px}</style>\n\n## 标题：'+title+'\n\n' ul = div.find('ul') li_list = ul.find_all('li') for li in li_list: img_src = li.find('img').attrs['lazysrc'] a_href = li.find('a').attrs['href'] img_title = li.find('span').string school = li.find('b', attrs={'class': 'b1'}).string fav = li.find('b', attrs={'class': 'b2'}).string if url not in img_src: img_src = url+img_src text += '> ' + img_title+'\n\n' text += '!['+img_title+']('+img_src+'#headimg-style)'+'\n\n' text += '- 学校：'+school+'\n\n' text += '- 点赞人数:'+fav+'\n\n' file.write(text) file.close url = 'http://xiaohuar.com/' headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.100 Safari/537.36'} spider_xiaohuar_content(url, headers)

38.818182

136

0.538056

223

1,708

4.013453

0.41704

0.040223

0.020112

0.053631

0.107263

0

0.039409

0.286885

1,708

44

137

38.818182

0.695402

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0

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0.220519

0.042453

0.027778

0

1

0.027778

false

0

0.055556

0

0.083333

0.027778

0

null

0

null

0

1

0

9d75c627939ebcaa3bf24644789f819936e04c59

749

py

Python

v1.1/auc_csv_merge.py

lz-pku-1997/so-many-tricks-for-Image-classification

3df7a0672f88219f893b0fa23c31ae6b30d01264

[ "MIT" ]

2

2020-04-21T06:06:28.000Z

2020-12-27T12:35:57.000Z

v1.1/auc_csv_merge.py

lz-pku-1997/so-many-tricks-for-Image-classification

3df7a0672f88219f893b0fa23c31ae6b30d01264

[ "MIT" ]

null

v1.1/auc_csv_merge.py

lz-pku-1997/so-many-tricks-for-Image-classification

3df7a0672f88219f893b0fa23c31ae6b30d01264

[ "MIT" ]

null

#尝试直接读取文件夹内所有csv，记得看看列表，是不是读对了 import glob import pandas as pd import numpy as np io = glob.glob(r"*.csv") len_io=len(io) print('总共输入表的数量为：',len_io) prob_list=[] for i in range(len_io): sub_1 = pd.read_csv(io[i]) denominator=len(sub_1) for my_classes in ['healthy','multiple_diseases','rust','scab']: sub_label_1 = sub_1.loc[:, my_classes].values sort_1=np.argsort(sub_label_1) for i,temp_sort in enumerate(sort_1): sub_label_1[temp_sort]=i/denominator sub_1.loc[:,my_classes]=sub_label_1 prob_list.append(sub_1.loc[:,'healthy':].values) sub_1.loc[:,'healthy':] = np.mean(prob_list,axis =0) sub_1.to_csv('out/submission.csv', index=False) print(sub_1.head())

31.208333

69

0.663551

124

749

3.75

0.403226

0.068817

0.077419

0.03871

0.068817

0

0.02455

0.184246

749

24

70

31.208333

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0.038718

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false

0

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0

0.15

0.1

0

null

0

null

0

1

0

9d76b727796967801234a59f7efe009b01c9e636

10,468

py

Python

masakari-7.0.0/masakari/objects/base.py

scottwedge/OpenStack-Stein

7077d1f602031dace92916f14e36b124f474de15

[ "Apache-2.0" ]

null

masakari-7.0.0/masakari/objects/base.py

scottwedge/OpenStack-Stein

7077d1f602031dace92916f14e36b124f474de15

[ "Apache-2.0" ]

5

2019-08-14T06:46:03.000Z

2021-12-13T20:01:25.000Z

masakari-7.0.0/masakari/objects/base.py

scottwedge/OpenStack-Stein

7077d1f602031dace92916f14e36b124f474de15

[ "Apache-2.0" ]

2

2020-03-15T01:24:15.000Z

2020-07-22T20:34:26.000Z

# Copyright 2016 NTT Data. # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. """Masakari common internal object model""" import datetime from oslo_utils import versionutils from oslo_versionedobjects import base as ovoo_base from oslo_versionedobjects import fields as obj_fields from masakari import objects def get_attrname(name): """Return the mangled name of the attribute's underlying storage.""" return '_obj_' + name class MasakariObjectRegistry(ovoo_base.VersionedObjectRegistry): notification_classes = [] def registration_hook(self, cls, index): # NOTE(Dinesh_Bhor): This is called when an object is registered, # and is responsible for maintaining masakari.objects.$OBJECT # as the highest-versioned implementation of a given object. version = versionutils.convert_version_to_tuple(cls.VERSION) if not hasattr(objects, cls.obj_name()): setattr(objects, cls.obj_name(), cls) else: cur_version = versionutils.convert_version_to_tuple( getattr(objects, cls.obj_name()).VERSION) if version >= cur_version: setattr(objects, cls.obj_name(), cls) @classmethod def register_notification(cls, notification_cls): """Register a class as notification. Use only to register concrete notification or payload classes, do not register base classes intended for inheritance only. """ cls.register_if(False)(notification_cls) cls.notification_classes.append(notification_cls) return notification_cls @classmethod def register_notification_objects(cls): """Register previously decorated notification as normal ovos. This is not intended for production use but only for testing and document generation purposes. """ for notification_cls in cls.notification_classes: cls.register(notification_cls) remotable_classmethod = ovoo_base.remotable_classmethod remotable = ovoo_base.remotable class MasakariObject(ovoo_base.VersionedObject): """Base class and object factory. This forms the base of all objects that can be remoted or instantiated via RPC. Simply defining a class that inherits from this base class will make it remotely instantiatable. Objects should implement the necessary "get" classmethod routines as well as "save" object methods as appropriate. """ OBJ_SERIAL_NAMESPACE = 'masakari_object' OBJ_PROJECT_NAMESPACE = 'masakari' def masakari_obj_get_changes(self): """Returns a dict of changed fields with tz unaware datetimes. Any timezone aware datetime field will be converted to UTC timezone and returned as timezone unaware datetime. This will allow us to pass these fields directly to a db update method as they can't have timezone information. """ # Get dirtied/changed fields changes = self.obj_get_changes() # Look for datetime objects that contain timezone information for k, v in changes.items(): if isinstance(v, datetime.datetime) and v.tzinfo: # Remove timezone information and adjust the time according to # the timezone information's offset. changes[k] = v.replace(tzinfo=None) - v.utcoffset() # Return modified dict return changes def obj_reset_changes(self, fields=None, recursive=False): """Reset the list of fields that have been changed. .. note:: - This is NOT "revert to previous values" - Specifying fields on recursive resets will only be honored at the top level. Everything below the top will reset all. :param fields: List of fields to reset, or "all" if None. :param recursive: Call obj_reset_changes(recursive=True) on any sub-objects within the list of fields being reset. """ if recursive: for field in self.obj_get_changes(): # Ignore fields not in requested set (if applicable) if fields and field not in fields: continue # Skip any fields that are unset if not self.obj_attr_is_set(field): continue value = getattr(self, field) # Don't reset nulled fields if value is None: continue # Reset straight Object and ListOfObjects fields if isinstance(self.fields[field], obj_fields.ObjectField): value.obj_reset_changes(recursive=True) elif isinstance(self.fields[field], obj_fields.ListOfObjectsField): for thing in value: thing.obj_reset_changes(recursive=True) if fields: self._changed_fields -= set(fields) else: self._changed_fields.clear() class MasakariObjectDictCompat(ovoo_base.VersionedObjectDictCompat): def __iter__(self): for name in self.obj_fields: if (self.obj_attr_is_set(name) or name in self.obj_extra_fields): yield name def keys(self): return list(self) class MasakariTimestampObject(object): """Mixin class for db backed objects with timestamp fields. Sqlalchemy models that inherit from the oslo_db TimestampMixin will include these fields and the corresponding objects will benefit from this mixin. """ fields = { 'created_at': obj_fields.DateTimeField(nullable=True), 'updated_at': obj_fields.DateTimeField(nullable=True), } class MasakariPersistentObject(object): """Mixin class for Persistent objects. This adds the fields that we use in common for most persistent objects. """ fields = { 'created_at': obj_fields.DateTimeField(nullable=True), 'updated_at': obj_fields.DateTimeField(nullable=True), 'deleted_at': obj_fields.DateTimeField(nullable=True), 'deleted': obj_fields.BooleanField(default=False), } class ObjectListBase(ovoo_base.ObjectListBase): @classmethod def _obj_primitive_key(cls, field): return 'masakari_object.%s' % field @classmethod def _obj_primitive_field(cls, primitive, field, default=obj_fields.UnspecifiedDefault): key = cls._obj_primitive_key(field) if default == obj_fields.UnspecifiedDefault: return primitive[key] else: return primitive.get(key, default) class MasakariObjectSerializer(ovoo_base.VersionedObjectSerializer): """A Masakari Object Serializer. This implements the Oslo Serializer interface and provides the ability to serialize and deserialize MasakariObject entities. Any service that needs to accept or return MasakariObjects as arguments or result values should pass this to its RPCClient and RPCServer objects. """ OBJ_BASE_CLASS = MasakariObject def __init__(self): super(MasakariObjectSerializer, self).__init__() def obj_make_list(context, list_obj, item_cls, db_list, **extra_args): """Construct an object list from a list of primitives. This calls item_cls._from_db_object() on each item of db_list, and adds the resulting object to list_obj. :param:context: Request context :param:list_obj: An ObjectListBase object :param:item_cls: The MasakariObject class of the objects within the list :param:db_list: The list of primitives to convert to objects :param:extra_args: Extra arguments to pass to _from_db_object() :returns: list_obj """ list_obj.objects = [] for db_item in db_list: item = item_cls._from_db_object(context, item_cls(), db_item, **extra_args) list_obj.objects.append(item) list_obj._context = context list_obj.obj_reset_changes() return list_obj def obj_to_primitive(obj): """Recursively turn an object into a python primitive. A MasakariObject becomes a dict, and anything that implements ObjectListBase becomes a list. """ if isinstance(obj, ObjectListBase): return [obj_to_primitive(x) for x in obj] elif isinstance(obj, MasakariObject): result = {} for key in obj.obj_fields: if obj.obj_attr_is_set(key) or key in obj.obj_extra_fields: result[key] = obj_to_primitive(getattr(obj, key)) return result else: return obj def obj_equal_prims(obj_1, obj_2, ignore=None): """Compare two primitives for equivalence ignoring some keys. This operation tests the primitives of two objects for equivalence. Object primitives may contain a list identifying fields that have been changed - this is ignored in the comparison. The ignore parameter lists any other keys to be ignored. :param:obj1: The first object in the comparison :param:obj2: The second object in the comparison :param:ignore: A list of fields to ignore :returns: True if the primitives are equal ignoring changes and specified fields, otherwise False. """ def _strip(prim, keys): if isinstance(prim, dict): for k in keys: prim.pop(k, None) for v in prim.values(): _strip(v, keys) if isinstance(prim, list): for v in prim: _strip(v, keys) return prim if ignore is not None: keys = ['masakari_object.changes'] + ignore else: keys = ['masakari_object.changes'] prim_1 = _strip(obj_1.obj_to_primitive(), keys) prim_2 = _strip(obj_2.obj_to_primitive(), keys) return prim_1 == prim_2

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null

0

null

0

1

0

9d7ad5477f4bf8f12192323e1ee2103954aa57db

3,925

py

Python

twitter_bot/MyBot.py

diem-ai/datascience-projects

deef93217bd3b0cfc2ca7802933142d1dad7fcba

[ "MIT" ]

null

twitter_bot/MyBot.py

diem-ai/datascience-projects

deef93217bd3b0cfc2ca7802933142d1dad7fcba

[ "MIT" ]

null

twitter_bot/MyBot.py

diem-ai/datascience-projects

deef93217bd3b0cfc2ca7802933142d1dad7fcba

[ "MIT" ]

null

""" Class SaleBot It is initialised by nlp model (bag-of-word, tf-idf, word2vec) It returns response with a question as the input """ from gensim.corpora import Dictionary #from gensim.models import FastText from gensim.models import Word2Vec , WordEmbeddingSimilarityIndex from gensim.similarities import SoftCosineSimilarity, SparseTermSimilarityMatrix from gensim.models import TfidfModel from multiprocessing import cpu_count from nlp_helper import preprocessing class AskeBayBot: """ - Using tf-idf and word2vec to build vector matrix from the corpus - Using soft-cosine similarity to calculate the similarity between query and matrix """ """ References: - https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/soft_cosine_tutorial.ipynb """ def __init__(self, questions, responses, model_type="word2vec"): self.questions = questions self.responses = responses self.model_type = model_type self.docsim_index = [] self.dictionary = [] self.tfidf = [] self.compute_sim_matrix() def compute_sim_matrix(self): ''' if(self.model_type.lower() == "fasttext"): model = FastText(self.questions) else: model = Word2Vec(self.questions) ''' self.dictionary = Dictionary(self.questions) self.tfidf = TfidfModel(dictionary = self.dictionary) word2vec_model = Word2Vec(self.questions , workers=cpu_count() , min_count=5 , size=300 , seed=12345) sim_index = WordEmbeddingSimilarityIndex(word2vec_model.wv) sim_matrix = SparseTermSimilarityMatrix(sim_index , self.dictionary , self.tfidf , nonzero_limit=100) bow_corpus = [self.dictionary.doc2bow(document) for document in self.questions] tfidf_corpus = [self.tfidf[bow] for bow in bow_corpus] self.docsim_index = SoftCosineSimilarity(tfidf_corpus, sim_matrix, num_best=10) def get_similarities(self, question): ''' @return indices of anwsers whose questions are similar to the input question ''' vectorizer = self.dictionary.doc2bow(preprocessing(question)) tfidf_vectorizer = self.tfidf[vectorizer] similarities = self.docsim_index[tfidf_vectorizer] return similarities def get_response(self, question): similarities = self.get_similarities(question) return self.get_sim(similarities, 1) def get_all_responses(self, question): similarities = self.get_similarities(question) return self.get_sim(similarities, 10) def get_sim(self, similarities, n_top=1): """ @return a tuple of similar question and best response in similarity matrix """ sim_questions = [] sim_responses = [] sim_scores = [] if (len(similarities) > 0): for (idx, score) in similarities: if (idx < len(self.responses)): sim_questions.append(self.questions[idx]) sim_responses.append(self.responses[idx]) sim_scores.append(score) # return self.questions[idx], self.responses[idx], score else: return "Just a moment, someone will contact you" if (n_top == 1): return sim_questions[0], sim_responses[0], sim_scores[0] else: return sim_questions, sim_responses, sim_scores if __name__ == "__main__": print("I'm a bot")

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0

9d8165f8ce202fddd44b2d3bc70e29ad7d9245a2

1,482

py

Python

hail_scripts/v01/convert_tsv_to_vds.py

NLSVTN/hail-elasticsearch-pipelines

8b895a2e46a33d347dd2a1024101a6d515027a03

[ "MIT" ]

null

hail_scripts/v01/convert_tsv_to_vds.py

NLSVTN/hail-elasticsearch-pipelines

8b895a2e46a33d347dd2a1024101a6d515027a03

[ "MIT" ]

null

hail_scripts/v01/convert_tsv_to_vds.py

NLSVTN/hail-elasticsearch-pipelines

8b895a2e46a33d347dd2a1024101a6d515027a03

[ "MIT" ]

null

import argparse as ap import hail from pprint import pprint import time from hail_scripts.v01.utils.vds_utils import write_vds p = ap.ArgumentParser(description="Convert a tsv table to a .vds") p.add_argument("-c", "--chrom-column", required=True) p.add_argument("-p", "--pos-column", required=True) p.add_argument("-r", "--ref-column", required=True) p.add_argument("-a", "--alt-column", required=True) p.add_argument("table_path", nargs="+") args = p.parse_args() print(", ".join(args.vcf_path)) hc = hail.HailContext(log="./hail_{}.log".format(time.strftime("%y%m%d_%H%M%S"))) for table_path in args.table_path: print("\n") print("==> import_table: %s" % table_path) output_path = table_path.replace(".tsv", "").replace(".gz", "").replace(".bgz", "") + ".vds" print("==> output: %s" % output_path) kt = hc.import_table(table_path, impute=True, no_header=args.no_header, delimiter=args.delimiter, missing=args.missing_value, min_partitions=1000) #kt = kt.drop(columns_to_drop) #kt = kt.rename(rename_columns) kt = kt.filter("%(ref_column)s == %(alt_column)s" % args.__dict__, keep=False) kt = kt.annotate("variant=Variant(%(chrom_column)s, %(pos_column)s, %(ref_column)s, %(alt_column)s)" % args.__dict__) kt = kt.key_by('variant') kt = kt.drop([args.chrom_column, args.pos_column, args.ref_column, args.alt_column]) vds = hail.VariantDataset.from_table(kt) pprint(vds.variant_schema) write_vds(vds, output_path)

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0

null

0

null

0

1

0

9d81808e7a83247fd981f349fc73abe0b9de1e1e

4,649

py

Python

scripts/Old/fixSequenceIDs.py

paepcke/json_to_relation

acfa58d540f8f51d1d913d0c173ee3ded1b6c2a9

[ "BSD-3-Clause" ]

4

2015-10-10T19:09:49.000Z

2021-09-02T00:58:06.000Z

scripts/Old/fixSequenceIDs.py

paepcke/json_to_relation

acfa58d540f8f51d1d913d0c173ee3ded1b6c2a9

[ "BSD-3-Clause" ]

null

scripts/Old/fixSequenceIDs.py

paepcke/json_to_relation

acfa58d540f8f51d1d913d0c173ee3ded1b6c2a9

[ "BSD-3-Clause" ]

8

2015-05-16T14:33:33.000Z

2019-10-24T08:56:25.000Z

#!/usr/bin/env python # Copyright (c) 2014, Stanford University # All rights reserved. # # Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: # 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. # 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. # 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ''' Created on Dec 22, 2013 @author: paepcke ''' import os import re import sys from edxTrackLogJSONParser import EdXTrackLogJSONParser from modulestoreImporter import ModulestoreImporter from unidecode import unidecode idExtractPat = re.compile(r'^"([^"]*)') seqIDExtractPat = re.compile(r'","([^"]*)') hashLookup = ModulestoreImporter(os.path.join(os.path.dirname(__file__),'data/modulestore_latest.json'), useCache=True) def makeInsertSafe(unsafeStr): ''' Makes the given string safe for use as a value in a MySQL INSERT statement. Looks for embedded CR or LFs, and turns them into semicolons. Escapes commas and single quotes. Backslash is replaced by double backslash. This is needed for unicode, like \0245 (invented example) @param unsafeStr: string that possibly contains unsafe chars @type unsafeStr: String @return: same string, with unsafe chars properly replaced or escaped @rtype: String ''' #return unsafeStr.replace("'", "\\'").replace('\n', "; ").replace('\r', "; ").replace(',', "\\,").replace('\\', '\\\\') if unsafeStr is None or not isinstance(unsafeStr, basestring) or len(unsafeStr) == 0: return '' # Check for chars > 128 (illegal for standard ASCII): for oneChar in unsafeStr: if ord(oneChar) > 128: # unidecode() replaces unicode with approximations. # I tried all sorts of escapes, and nothing worked # for all cases, except this: unsafeStr = unidecode(unicode(unsafeStr)) break return unsafeStr.replace('\n', "; ").replace('\r', "; ").replace('\\', '').replace("'", r"\'") def fixSequencIDs(): counter = 0 with open('/home/paepcke/tmp/sequenceIDs.sql','w') as outfd: outfd.write("USE Edx;\nINSERT INTO EdxTrackEvent(_id,resource_display_name)\n") with open('/home/paepcke/tmp/sequenceIDs.csv','r') as fd: for idSeqID in fd: sqlid = idExtractPat.search(idSeqID).group(1) seqID = seqIDExtractPat.search(idSeqID).group(1) resourceNameMatch = EdXTrackLogJSONParser.findHashPattern.search(seqID) if resourceNameMatch is not None: resourceName = makeInsertSafe(hashLookup.getDisplayName(resourceNameMatch.group(1))) if counter == 0: outfd.write('("%s","%s")' % (sqlid,resourceName)) else: outfd.write(',\n("%s","%s")' % (sqlid,resourceName)) else: continue counter += 1 #if counter > 10: # break outfd.write("\nON DUPLICATE KEY UPDATE resource_display_name = VALUES(resource_display_name);\n") print("Created %d corrections." % counter) if __name__ == '__main__': fixSequencIDs() #INSERT INTO EdxTrackEvent (_id,long_answer) VALUES ('fbcefe06_fb7c_48aa_a12e_d85e6988dbda','first answer'),('bbd3ddf3_8ed0_4eee_8ff7_f5791b9e4a7e','second answer') ON DUPLICATE KEY UPDATE long_answer=VALUES(long_answer);

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0

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0.025

0

null

0

null

0

1

0

9d818b86a7daa5558c49d73a26208235e0d52b89

8,433

py

Python

tests/test_logger_device.py

ska-telescope/lmc-base-classes

e3ac46a731aca4d49d53747b4352ec4be089ff5d

[ "BSD-3-Clause" ]

3

2019-04-18T20:46:02.000Z

2019-07-30T17:47:40.000Z

tests/test_logger_device.py

ska-telescope/lmc-base-classes

e3ac46a731aca4d49d53747b4352ec4be089ff5d

[ "BSD-3-Clause" ]

26

2018-10-30T07:50:50.000Z

2020-07-13T12:50:36.000Z

tests/test_logger_device.py

ska-telescope/lmc-base-classes

e3ac46a731aca4d49d53747b4352ec4be089ff5d

[ "BSD-3-Clause" ]

4

2019-01-16T07:47:59.000Z

2021-06-01T11:17:32.000Z

######################################################################################### # -*- coding: utf-8 -*- # # This file is part of the SKALogger project # # # ######################################################################################### """Contain the tests for the SKALogger.""" import re import pytest from tango import DevState from tango.test_context import MultiDeviceTestContext from ska_tango_base.base import ReferenceBaseComponentManager from ska_tango_base.logger_device import SKALogger from ska_tango_base.subarray import SKASubarray import tango # PROTECTED REGION ID(SKALogger.test_additional_imports) ENABLED START # from ska_tango_base.control_model import ( AdminMode, ControlMode, HealthState, LoggingLevel, SimulationMode, TestMode, ) # PROTECTED REGION END # // SKALogger.test_additional_imports # PROTECTED REGION ID(SKALogger.test_SKALogger_decorators) ENABLED START # @pytest.mark.usefixtures("tango_context", "initialize_device") # PROTECTED REGION END # // SKALogger.test_SKALogger_decorators class TestSKALogger(object): """ Test class for tests of the SKALogger device class. """ @pytest.fixture(scope="class") def device_test_config(self, device_properties): """ Fixture that specifies the device to be tested, along with its properties and memorized attributes. """ return { "device": SKALogger, "component_manager_patch": lambda self: ReferenceBaseComponentManager( self.op_state_model, logger=self.logger ), "properties": device_properties, "memorized": {"adminMode": str(AdminMode.ONLINE.value)}, } @pytest.mark.skip("Not implemented") def test_properties(self, tango_context): # test the properties # PROTECTED REGION ID(SKALogger.test_properties) ENABLED START # # PROTECTED REGION END # // SKALogger.test_properties pass # PROTECTED REGION ID(SKALogger.test_State_decorators) ENABLED START # # PROTECTED REGION END # // SKALogger.test_State_decorators def test_State(self, tango_context): """Test for State""" # PROTECTED REGION ID(SKALogger.test_State) ENABLED START # assert tango_context.device.State() == DevState.OFF # PROTECTED REGION END # // SKALogger.test_State # PROTECTED REGION ID(SKALogger.test_Status_decorators) ENABLED START # # PROTECTED REGION END # // SKALogger.test_Status_decorators def test_Status(self, tango_context): """Test for Status""" # PROTECTED REGION ID(SKALogger.test_Status) ENABLED START # assert tango_context.device.Status() == "The device is in OFF state." # PROTECTED REGION END # // SKALogger.test_Status # PROTECTED REGION ID(SKALogger.test_GetVersionInfo_decorators) ENABLED START # # PROTECTED REGION END # // SKALogger.test_GetVersionInfo_decorators def test_GetVersionInfo(self, tango_context): """Test for GetVersionInfo""" # PROTECTED REGION ID(SKALogger.test_GetVersionInfo) ENABLED START # versionPattern = re.compile( f"{tango_context.device.info().dev_class}, ska_tango_base, [0-9]+.[0-9]+.[0-9]+, " "A set of generic base devices for SKA Telescope." ) versionInfo = tango_context.device.GetVersionInfo() assert (re.match(versionPattern, versionInfo[0])) is not None # PROTECTED REGION END # // SKALogger.test_GetVersionInfo # PROTECTED REGION ID(SKALogger.test_buildState_decorators) ENABLED START # # PROTECTED REGION END # // SKALogger.test_buildState_decorators def test_buildState(self, tango_context): """Test for buildState""" # PROTECTED REGION ID(SKALogger.test_buildState) ENABLED START # buildPattern = re.compile( r"ska_tango_base, [0-9]+.[0-9]+.[0-9]+, " r"A set of generic base devices for SKA Telescope" ) assert (re.match(buildPattern, tango_context.device.buildState)) is not None # PROTECTED REGION END # // SKALogger.test_buildState # PROTECTED REGION ID(SKALogger.test_versionId_decorators) ENABLED START # # PROTECTED REGION END # // SKALogger.test_versionId_decorators def test_versionId(self, tango_context): """Test for versionId""" # PROTECTED REGION ID(SKALogger.test_versionId) ENABLED START # versionIdPattern = re.compile(r"[0-9]+.[0-9]+.[0-9]+") assert (re.match(versionIdPattern, tango_context.device.versionId)) is not None # PROTECTED REGION END # // SKALogger.test_versionId # PROTECTED REGION ID(SKALogger.test_loggingLevel_decorators) ENABLED START # # PROTECTED REGION END # // SKALogger.test_loggingLevel_decorators def test_loggingLevel(self, tango_context): """Test for loggingLevel""" # PROTECTED REGION ID(SKALogger.test_loggingLevel) ENABLED START # assert tango_context.device.loggingLevel == LoggingLevel.INFO # PROTECTED REGION END # // SKALogger.test_loggingLevel # PROTECTED REGION ID(SKALogger.test_healthState_decorators) ENABLED START # # PROTECTED REGION END # // SKALogger.test_healthState_decorators def test_healthState(self, tango_context): """Test for healthState""" # PROTECTED REGION ID(SKALogger.test_healthState) ENABLED START # assert tango_context.device.healthState == HealthState.OK # PROTECTED REGION END # // SKALogger.test_healthState # PROTECTED REGION ID(SKALogger.test_adminMode_decorators) ENABLED START # # PROTECTED REGION END # // SKALogger.test_adminMode_decorators def test_adminMode(self, tango_context): """Test for adminMode""" # PROTECTED REGION ID(SKALogger.test_adminMode) ENABLED START # assert tango_context.device.adminMode == AdminMode.ONLINE # PROTECTED REGION END # // SKALogger.test_adminMode # PROTECTED REGION ID(SKALogger.test_controlMode_decorators) ENABLED START # # PROTECTED REGION END # // SKALogger.test_controlMode_decorators def test_controlMode(self, tango_context): """Test for controlMode""" # PROTECTED REGION ID(SKALogger.test_controlMode) ENABLED START # assert tango_context.device.controlMode == ControlMode.REMOTE # PROTECTED REGION END # // SKALogger.test_controlMode # PROTECTED REGION ID(SKALogger.test_simulationMode_decorators) ENABLED START # # PROTECTED REGION END # // SKALogger.test_simulationMode_decorators def test_simulationMode(self, tango_context): """Test for simulationMode""" # PROTECTED REGION ID(SKALogger.test_simulationMode) ENABLED START # assert tango_context.device.simulationMode == SimulationMode.FALSE # PROTECTED REGION END # // SKALogger.test_simulationMode # PROTECTED REGION ID(SKALogger.test_testMode_decorators) ENABLED START # # PROTECTED REGION END # // SKALogger.test_testMode_decorators def test_testMode(self, tango_context): """Test for testMode""" # PROTECTED REGION ID(SKALogger.test_testMode) ENABLED START # assert tango_context.device.testMode == TestMode.NONE # PROTECTED REGION END # // SKALogger.test_testMode @pytest.mark.forked def test_SetLoggingLevel(): """Test for SetLoggingLevel""" logging_level = int(tango.LogLevel.LOG_ERROR) logging_target = "logger/target/1" logger_device = "logger/device/1" devices_info = ( {"class": SKALogger, "devices": [{"name": logger_device}]}, {"class": SKASubarray, "devices": [{"name": logging_target}]}, ) with MultiDeviceTestContext(devices_info, process=False) as multi_context: dev_proxy = multi_context.get_device(logging_target) dev_proxy.Init() dev_proxy.loggingLevel = int(tango.LogLevel.LOG_FATAL) assert dev_proxy.loggingLevel != logging_level levels = [] levels.append(logging_level) targets = [] targets.append(multi_context.get_device_access(logging_target)) device_details = [] device_details.append(levels) device_details.append(targets) multi_context.get_device(logger_device).SetLoggingLevel(device_details) assert dev_proxy.loggingLevel == logging_level

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9d83b4f58893d59845ef72aeb0870f92b39fa121

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py

Python

baseline/find_pairs.py

parallelcrawl/DataCollection

4308473e6b53779159a15c1416bff3f2291dd1f2

[ "Apache-2.0" ]

8

2018-02-08T16:03:00.000Z

2022-01-19T11:41:38.000Z

baseline/find_pairs.py

christianbuck/CorpusMining

f9248c3528a415a1e5af2c5a54a60c16cd79ff1d

[ "Apache-2.0" ]

3

2017-08-08T10:53:29.000Z

2017-08-08T10:58:51.000Z

baseline/find_pairs.py

parallelcrawl/DataCollection

4308473e6b53779159a15c1416bff3f2291dd1f2

[ "Apache-2.0" ]

4

2018-06-09T21:53:09.000Z

2022-01-19T11:41:48.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- import sys import re import urlparse def process_buffer(buffer): if not buffer or len(buffer) < 2: return buffer = [line.decode('utf-8', 'ignore') for line in buffer] split_buffer = [line.strip().lower().split("\t") for line in buffer] if list(set(map(len, split_buffer))) != [4]: for line in buffer: sys.stderr.write(line.encode('utf-8')) return original_urls = [] stripped_languages = [] detected_languages = [] for stripped_url, \ original_url, \ stripped_language, \ detected_language in split_buffer: original_urls.append(original_url) stripped_languages.append(stripped_language) detected_languages.append(detected_language) if len(set(original_urls)) < 2: # print "not enough urls" return if len(set(stripped_languages)) < 2: # print "not enough stripped languages", languages_stripped return if len(set(detected_languages)) < 2: # print "not enough detected_languages", detected_languages return for language in stripped_languages: for detected_language in detected_languages: # print "looking for ", language, " in ", detected_languages if language in detected_language.replace("chineset", "chinese") \ .split('/'): for line in buffer: sys.stdout.write(line.encode("utf-8")) return if __name__ == "__main__": import argparse parser = argparse.ArgumentParser() buffer = [] buffer_url = None for line in sys.stdin: # line = line.decode("utf-8", "ignore") url = line.split("\t", 1)[0] if url != buffer_url: process_buffer(buffer) buffer = [line] buffer_url = url else: buffer.append(line) # print url != buffer_url process_buffer(buffer)

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0

9d87c99f7edc4a51975ce4aad83b2a68eca0165b

4,931

py

Python

utils.py

nea23/greek_alphabets_tf-idf

94094dd6d7383400e0f0a9d4a1b05744dd2f3ba9

[ "MIT" ]

null

utils.py

nea23/greek_alphabets_tf-idf

94094dd6d7383400e0f0a9d4a1b05744dd2f3ba9

[ "MIT" ]

null

utils.py

nea23/greek_alphabets_tf-idf

94094dd6d7383400e0f0a9d4a1b05744dd2f3ba9

[ "MIT" ]

null

import matplotlib import matplotlib.pyplot as plt import numpy as np import pandas as pd """ The following functions are used to create an annotated heatmap and they were copied from: https://matplotlib.org/stable/gallery/images_contours_and_fields/image_annotated_heatmap.html#using-the-helper-function-code-style """ def heatmap(data, row_labels, col_labels, ax=None, **kwargs): """ Create a heatmap from a numpy array and two lists of labels. Parameters ---------- data A 2D numpy array of shape (N, M). row_labels A list or array of length N with the labels for the rows. col_labels A list or array of length M with the labels for the columns. ax A `matplotlib.axes.Axes` instance to which the heatmap is plotted. If not provided, use current axes or create a new one. Optional. cbar_kw A dictionary with arguments to `matplotlib.Figure.colorbar`. Optional. cbarlabel The label for the colorbar. Optional. **kwargs All other arguments are forwarded to `imshow`. """ if not ax: ax = plt.gca() # Plot the heatmap im = ax.imshow(data, **kwargs) # We want to show all ticks... ax.set_xticks(np.arange(data.shape[1])) ax.set_yticks(np.arange(data.shape[0])) # ... and label them with the respective list entries. ax.set_xticklabels(col_labels) ax.set_yticklabels(row_labels) # Let the horizontal axes labeling appear on top. ax.tick_params(top=True, bottom=False, labeltop=True, labelbottom=False) # Rotate the tick labels and set their alignment. plt.setp(ax.get_xticklabels(), rotation=-30, ha="right", rotation_mode="anchor") # Turn spines off and create white grid. # ax.spines[:].set_visible(False) ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True) ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True) ax.grid(which="minor", color="w", linestyle='-', linewidth=3) ax.tick_params(which="minor", bottom=False, left=False) return im def annotate_heatmap(im, data=None, valfmt="{x:.2f}", textcolors=("black", "white"), threshold=None, **textkw): """ A function to annotate a heatmap. Parameters ---------- im The AxesImage to be labeled. data Data used to annotate. If None, the image's data is used. Optional. valfmt The format of the annotations inside the heatmap. This should either use the string format method, e.g. "$ {x:.2f}", or be a `matplotlib.ticker.Formatter`. Optional. textcolors A pair of colors. The first is used for values below a threshold, the second for those above. Optional. threshold Value in data units according to which the colors from textcolors are applied. If None (the default) uses the middle of the colormap as separation. Optional. **kwargs All other arguments are forwarded to each call to `text` used to create the text labels. """ if not isinstance(data, (list, np.ndarray)): data = im.get_array() # Normalize the threshold to the images color range. if threshold is not None: threshold = im.norm(threshold) else: threshold = im.norm(data.max())/2. # Set default alignment to center, but allow it to be # overwritten by textkw. kw = dict(horizontalalignment="center", verticalalignment="center") kw.update(textkw) # Get the formatter in case a string is supplied if isinstance(valfmt, str): valfmt = matplotlib.ticker.StrMethodFormatter(valfmt) # Loop over the data and create a `Text` for each "pixel". # Change the text's color depending on the data. texts = [] for i in range(data.shape[0]): for j in range(data.shape[1]): kw.update(color=textcolors[int(im.norm(data[i, j]) > threshold)]) text = im.axes.text(j, i, valfmt(data[i, j], None), **kw) texts.append(text) return texts """ The following functions are used to get the top pairs from a correlation matrix and they were copied from: https://stackoverflow.com/a/41453817 """ def get_redundant_pairs(df): '''Get diagonal and lower triangular pairs of correlation matrix''' pairs_to_drop = set() cols = df.columns for i in range(0, df.shape[1]): for j in range(0, i+1): pairs_to_drop.add((cols[i], cols[j])) return pairs_to_drop def get_top_abs_correlations(df, min_val=0.6): au_corr = df.corr().abs().unstack() labels_to_drop = get_redundant_pairs(df) au_corr = au_corr.drop(labels=labels_to_drop).sort_values(ascending=False) au_corr_df = pd.DataFrame(au_corr, columns=['Score']) return au_corr_df.where(au_corr_df['Score'] >= min_val, np.nan).dropna()

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null

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9d87fe4b4c7aa76322c36b84c9220f5fee728c3d

6,675

py

Python

built-in/MindSpore/Official/cv/detection/CenterFace_for_MindSpore/src/launch.py

Huawei-Ascend/modelzoo

df51ed9c1d6dbde1deef63f2a037a369f8554406

[ "Apache-2.0" ]

12

2020-12-13T08:34:24.000Z

2022-03-20T15:17:17.000Z

built-in/MindSpore/Official/cv/detection/CenterFace_for_MindSpore/src/launch.py

Huawei-Ascend/modelzoo

df51ed9c1d6dbde1deef63f2a037a369f8554406

[ "Apache-2.0" ]

3

2021-03-31T20:15:40.000Z

2022-02-09T23:50:46.000Z

built-in/MindSpore/Official/cv/detection/CenterFace_for_MindSpore/src/launch.py

Huawei-Ascend/modelzoo

df51ed9c1d6dbde1deef63f2a037a369f8554406

[ "Apache-2.0" ]

2

2021-07-10T12:40:46.000Z

2021-12-17T07:55:15.000Z

# Copyright 2020 Huawei Technologies Co., Ltd # # 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. # ============================================================================ """auto generate rank table and export envs""" import sys import subprocess import os import socket import json from argparse import ArgumentParser, REMAINDER def parse_args(): parser = ArgumentParser(description="mindspore distributed training launch " "helper utilty that will spawn up " "multiple distributed processes") parser.add_argument("--nproc_per_node", type=int, default=1, help="The number of processes to launch on each node, " "for D training, this is recommended to be set " "to the number of D in your system so that " "each process can be bound to a single D.") parser.add_argument("--visible_devices", type=str, default="0,1,2,3,4,5,6,7", help="will use the visible devices sequentially") parser.add_argument("--env_sh", type=str, default="", help="env for 1p") parser.add_argument("--server_id", type=str, default="", help="server ip") # positional parser.add_argument("training_script", type=str, help="The full path to the single D training " "program/script to be launched in parallel, " "followed by all the arguments for the " "training script") # device mode parser.add_argument("--device", type=str, default="A+K") # task_set, to impove cpu utilization for multi-npu(e.g., 8P) training parser.add_argument("--task_set", type=bool, default=False) parser.add_argument("--task_set_core", type=int, default=24) # ranktable file parser.add_argument("--table_fn", type=str, default="", help="The ranktable file path, if not set, " "we will auto-generate a ranktable for user") # rest from the training program parser.add_argument('training_script_args', nargs=REMAINDER) return parser.parse_args() def main(): args = parse_args() print('args:{}'.format(args)) visible_devices = args.visible_devices.split(',') assert len(visible_devices) >= args.nproc_per_node print('visible_devices:{}'.format(visible_devices)) if(args.server_id == ''): print('pleaser input server ip!!!') exit(0) print('server_id:{}'.format(args.server_id)) hccn_configs = open('/etc/hccn.conf', 'r').readlines() device_ips = {} for hccn_item in hccn_configs: hccn_item = hccn_item.strip() if hccn_item.startswith('address_'): device_id, device_ip = hccn_item.split('=') device_id = device_id.split('_')[1] device_ips[device_id] = device_ip print('device_id:{}, device_ip:{}'.format(device_id, device_ip)) hccn_table = {} if args.device == 'A+K': hccn_table['board_id'] = '0x002f' else: hccn_table['board_id'] = '0x0000' hccn_table['chip_info'] = '910' hccn_table['deploy_mode'] = 'lab' hccn_table['group_count'] = '1' hccn_table['group_list'] = [] instance_list = [] usable_dev = '' for instance_id in range(args.nproc_per_node): instance = {} instance['devices'] = [] device_id = visible_devices[instance_id] device_ip = device_ips[device_id] usable_dev += str(device_id) instance['devices'].append({ 'device_id': device_id, 'device_ip': device_ip, }) instance['rank_id'] = str(instance_id) instance['server_id'] = args.server_id instance_list.append(instance) hccn_table['group_list'].append({ 'device_num': str(args.nproc_per_node), 'server_num': '1', 'group_name': '', 'instance_count': str(args.nproc_per_node), 'instance_list': instance_list, }) hccn_table['para_plane_nic_location'] = 'device' hccn_table['para_plane_nic_name'] = [] for instance_id in range(args.nproc_per_node): eth_id = visible_devices[instance_id] hccn_table['para_plane_nic_name'].append('eth{}'.format(eth_id)) hccn_table['para_plane_nic_num'] = str(args.nproc_per_node) hccn_table['status'] = 'completed' if args.table_fn is "": table_fn = os.path.join(os.getcwd(), 'rank_table_{}p_{}_{}.json'.format(args.nproc_per_node, usable_dev, args.server_id)) with open(table_fn, 'w') as table_fp: json.dump(hccn_table, table_fp, indent=4) else: table_fn = args.table_fn # world size in terms of number of processes dist_group_size = args.nproc_per_node for rank in range(0, args.nproc_per_node): rank_id = rank device_id = visible_devices[rank] device_root_fn = os.path.join(os.getcwd(), 'device{}'.format(device_id)) #format(rank_id)) rank_process = '' if args.nproc_per_node > 1: rank_process += 'export RANK_TABLE_FILE={} && '.format(table_fn) if args.task_set: left = int(device_id) * args.task_set_core right = left + args.task_set_core - 1 rank_process += 'export RANK_SIZE={} && source {} && export RANK_ID={} && export DEVICE_ID={} && rm -rf {} && mkdir {} && cd {} && taskset -c {}-{} python {} '.format(args.nproc_per_node, args.env_sh, rank_id, device_id, device_root_fn, device_root_fn, device_root_fn, left, right, args.training_script) else: rank_process += 'export RANK_SIZE={} && source {} && export RANK_ID={} && export DEVICE_ID={} && rm -rf {} && mkdir {} && cd {} && python {} '.format(args.nproc_per_node, args.env_sh, rank_id, device_id, device_root_fn, device_root_fn, device_root_fn, args.training_script) rank_process += ' '.join(args.training_script_args) + ' >log{}.log 2>&1 &'.format(rank_id) os.system(rank_process) if __name__ == "__main__": main()

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null

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null

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9d886ff7c8fb1d674ed9db521c7c448a657e5fe1

3,799

py

Python

Incident-Response/Tools/cyphon/cyphon/responder/actions/tests/test_models.py

sn0b4ll/Incident-Playbook

cf519f58fcd4255674662b3620ea97c1091c1efb

[ "MIT" ]

1

2021-07-24T17:22:50.000Z

Incident-Response/Tools/cyphon/cyphon/responder/actions/tests/test_models.py

sn0b4ll/Incident-Playbook

cf519f58fcd4255674662b3620ea97c1091c1efb

[ "MIT" ]

2

2022-02-28T03:40:31.000Z

2022-02-28T03:40:52.000Z

Incident-Response/Tools/cyphon/cyphon/responder/actions/tests/test_models.py

sn0b4ll/Incident-Playbook

cf519f58fcd4255674662b3620ea97c1091c1efb

[ "MIT" ]

2

2022-02-25T08:34:51.000Z

2022-03-16T17:29:44.000Z

# -*- coding: utf-8 -*- # Copyright 2017-2019 ControlScan, Inc. # # This file is part of Cyphon Engine. # # Cyphon Engine 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, version 3 of the License. # # Cyphon Engine is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with Cyphon Engine. If not, see <http://www.gnu.org/licenses/>. """ """ # standard library try: from unittest.mock import Mock, patch except ImportError: from mock import Mock, patch # third party from django.test import TestCase # local import platforms.jira.handlers as jira_module from responder.actions.models import Action from tests.fixture_manager import get_fixtures class ActionsBaseTestCase(TestCase): """ Base class for testing Actions. """ fixtures = get_fixtures(['actions', 'dispatches']) def setUp(self): self.action = Action.objects.get(pk=1) class ActionTestCase(ActionsBaseTestCase): """ Tests the Action class. """ def test_str(self): """ Tests the string representation of a Pipe. """ self.assertEqual(str(self.action), 'Jira IssueAPI') def test_get_module(self): """ Tests the _get_module method for getting the module for an Action's Destination. """ self.assertEqual(self.action._get_module(), jira_module) def test_create_request_handler(self): """ Tests the create_request_handler method for getting a request handler for an Action. """ mock_user = Mock() mock_handler = Mock() with patch('platforms.jira.handlers.IssueAPI', return_value=mock_handler) as mock_api: kwargs = { 'user': mock_user, } result = self.action.create_request_handler(**kwargs) mock_api.assert_called_once_with(endpoint=self.action, user=mock_user) self.assertEqual(result, mock_handler) def test_save_w_no_descr(self): """ Test the save method of an Action with the Action has no description. """ self.assertEqual(self.action.description, None) self.action.save() self.assertEqual(self.action.description, 'Jira IssueAPI') def test_save_w_descr(self): """ Test the save method of an Action with the Action has a description. """ self.action.description = 'Create a JIRA Issue' self.action.save() self.assertEqual(self.action.description, 'Create a JIRA Issue') def test_get_dispatch(self): """ Test the get_dispatch method of an Action. """ mock_alert = Mock() mock_user = Mock() mock_record = Mock() mock_handler = Mock() mock_handler.run = Mock(return_value=mock_record) mock_handler.record = mock_record with patch('platforms.jira.handlers.IssueAPI', return_value=mock_handler) as mock_api: kwargs = { 'alert': mock_alert, 'user': mock_user, } result = self.action.get_dispatch(**kwargs) mock_api.assert_called_once_with(endpoint=self.action, user=mock_user) mock_handler.run.assert_called_once_with(mock_alert) self.assertEqual(result, mock_record)

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0.309091

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null

0

null

0

1

0

9d8881a2641e3115485a61059c62987f2d27bf5d

4,805

py

Python

predictions/lambda/handler.py

aaronshim/alexa-github-today

4f3e7adffa9bb9f3d63cfc1f4a79f396078c787c

[ "MIT" ]

null

predictions/lambda/handler.py

aaronshim/alexa-github-today

4f3e7adffa9bb9f3d63cfc1f4a79f396078c787c

[ "MIT" ]

null

predictions/lambda/handler.py

aaronshim/alexa-github-today

4f3e7adffa9bb9f3d63cfc1f4a79f396078c787c

[ "MIT" ]

null

import json import requests from collections import defaultdict from fuzzywuzzy import process from random import sample # Constants """ Constants for default responses that do not need any further computation. """ DEFAULT_STOP_RESPONSE = 'All right. See you next time!' DEFAULT_ERROR_MESSAGE = "I'm sorry. I don't know how to do that yet." DEFAULT_HELP_MESSAGE = "Try asking me about prediction markets. Ask me to look up midterm elections." PREDEFINED_RESPONSES = { 'AMAZON.FallbackIntent': "I couldn't understand what you were asking. Why don't you ask me about elections?", 'AMAZON.CancelIntent': DEFAULT_STOP_RESPONSE, 'AMAZON.HelpIntent': DEFAULT_HELP_MESSAGE, 'AMAZON.StopIntent': DEFAULT_STOP_RESPONSE, 'AMAZON.NavigateHomeIntent': DEFAULT_STOP_RESPONSE, } """ To be considered as a match, any other title would have to be within this percentage of the score of the best match. """ PERCENTAGE_THRESHOLD = 0.1 # API Helpers def get_all_markets(): """ Query the PredictIt API to get all available markets in a dictionary that maps from the name of the market to its ID. """ all_markets = requests.request( 'GET', 'https://www.predictit.org/api/marketdata/all/') all_markets = json.loads(all_markets.content) return dict((market['name'], market['id']) for market in all_markets['markets']) def get_market(id): """ Query the PredictIt API to get the details of a particular market given the market's ID. """ market = requests.request( 'GET', "https://www.predictit.org/api/marketdata/markets/%d" % id) return json.loads(market.content) # "UI" Helpers def market_message(market): """ Given the response from `get_market`, generates a message that conveys the relevant information of the particular market. """ if len(market['contracts']) > 1: return "%s is too complicated." % market['name'] return "%s is trading at %d percent." % \ (market['name'], market['contracts'][0]['lastTradePrice'] * 100) def response_from_message(message): """ Helper to wrap a message string into the minimum acceptable Alexa response JSON. """ return { 'version': '1.0', 'response': { 'outputSpeech': { 'type': 'PlainText', 'text': message, } } } def can_fulfill(intent): if intent['name'] == 'Query' and intent['slots'] and \ intent['slots']['Market'] and intent['slots']['Market']['value']: return { 'version': '1.0', 'response': { 'canFulfillIntent': { 'canFulfill': 'YES', 'slots': { 'Market': { 'canUnderstand': 'YES', 'canFulfill': 'YES' }, } } } } return { 'version': '1.0', 'response': { 'canFulfillIntent': { 'canFulfill': 'NO', } } } # Main function def main(event, context): """ Entry point for the Alexa action. """ request_type = event['request']['type'] if request_type != 'IntentRequest': if request_type == 'LaunchRequest': return response_from_message(DEFAULT_HELP_MESSAGE) elif request_type == 'CanFulfillIntentRequest': return can_fulfill(event['request']['intent']) elif request_type == 'SessionEndedRequest': return intent = event['request']['intent'] intent_type = intent['name'] # Get the canned responses out of the way before we do any heavy lifting # with external API calls. if intent_type in PREDEFINED_RESPONSES: return response_from_message(PREDEFINED_RESPONSES[intent_type]) # Sanity check. if intent_type != 'Query' or 'Market' not in intent['slots']: return response_from_message(DEFAULT_ERROR_MESSAGE) keyword = intent['slots']['Market']['value'] markets = get_all_markets() # Only take the ones that are within percentage threshold of the first # result. Bucket them by score. likely_markets = process.extract(keyword, markets.keys(), limit=100) (_, best_score) = likely_markets[0] result_markets = defaultdict(list) # Multimap score -> id's for (name, score) in likely_markets: if best_score - score <= PERCENTAGE_THRESHOLD * best_score: result_markets[score].append(markets[name]) # List of market JSON response's. result_markets = [get_market(id) for id in sum( [sample(ids, 1) for (_, ids) in result_markets.items()], [])] return response_from_message(' '.join(market_message(market) for market in result_markets))

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4,805

5.189381

0.346903

0.010232

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0.034789

0

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0.262227

4,805

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0

null

0

null

0

1

0

9d88973447a6fc9a97038839f4db33428c51196b

12,649

py

Python

Train.py

prattcmp/speakerembedding

5ed051261e69aaf7a1306c390b36cedb8da3f095

[ "MIT" ]

null

Train.py

prattcmp/speakerembedding

5ed051261e69aaf7a1306c390b36cedb8da3f095

[ "MIT" ]

null

Train.py

prattcmp/speakerembedding

5ed051261e69aaf7a1306c390b36cedb8da3f095

[ "MIT" ]

null

import torch import numpy as np import logging, yaml, os, sys, argparse, time from tqdm import tqdm from collections import defaultdict from Logger import Logger import matplotlib matplotlib.use('agg') matplotlib.rcParams['agg.path.chunksize'] = 10000 import matplotlib.pyplot as plt from scipy.io import wavfile from random import sample from sklearn.manifold import TSNE from Modules import GE2E, GE2E_Loss from Datasets import Dataset, Collater, Inference_Collater from Noam_Scheduler import Modified_Noam_Scheduler from Radam import RAdam from Arg_Parser import Recursive_Parse hp = Recursive_Parse(yaml.load( open('Hyper_Parameters.yaml', encoding='utf-8'), Loader=yaml.Loader )) if not hp.Device is None: os.environ['CUDA_VISIBLE_DEVICES']= str(hp.Device) if not torch.cuda.is_available(): device = torch.device('cpu') else: device = torch.device('cuda:0') torch.backends.cudnn.benchmark = True torch.cuda.set_device(0) logging.basicConfig( level=logging.INFO, stream=sys.stdout, format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s" ) if hp.Use_Mixed_Precision: try: from apex import amp except: logging.warn('There is no apex modules in the environment. Mixed precision does not work.') hp.Use_Mixed_Precision = False class Trainer: def __init__(self, steps= 0): self.steps = steps self.epochs = 0 self.Datset_Generate() self.Model_Generate() self.scalar_Dict = { 'Train': defaultdict(float), 'Evaluation': defaultdict(float), } self.writer_Dict = { 'Train': Logger(os.path.join(hp.Log_Path, 'Train')), 'Evaluation': Logger(os.path.join(hp.Log_Path, 'Evaluation')), } self.Load_Checkpoint() def Datset_Generate(self): train_Dataset = Dataset( pattern_path= hp.Train.Train_Pattern.Path, metadata_file= hp.Train.Train_Pattern.Metadata_File, pattern_per_speaker= hp.Train.Batch.Train.Pattern_per_Speaker, use_cache= hp.Train.Use_Pattern_Cache ) dev_Dataset = Dataset( pattern_path= hp.Train.Eval_Pattern.Path, metadata_file= hp.Train.Eval_Pattern.Metadata_File, pattern_per_speaker= hp.Train.Batch.Eval.Pattern_per_Speaker, use_cache= hp.Train.Use_Pattern_Cache ) inference_Dataset = Dataset( pattern_path= hp.Train.Eval_Pattern.Path, metadata_file= hp.Train.Eval_Pattern.Metadata_File, pattern_per_speaker= hp.Train.Batch.Eval.Pattern_per_Speaker, num_speakers= 50, #Maximum number by tensorboard. use_cache= hp.Train.Use_Pattern_Cache ) logging.info('The number of train speakers = {}.'.format(len(train_Dataset))) logging.info('The number of development speakers = {}.'.format(len(dev_Dataset))) collater = Collater( min_frame_length= hp.Train.Frame_Length.Min, max_frame_length= hp.Train.Frame_Length.Max ) inference_Collater = Inference_Collater( samples= hp.Train.Inference.Samples, frame_length= hp.Train.Inference.Frame_Length, overlap_length= hp.Train.Inference.Overlap_Length ) self.dataLoader_Dict = {} self.dataLoader_Dict['Train'] = torch.utils.data.DataLoader( dataset= train_Dataset, shuffle= True, collate_fn= collater, batch_size= hp.Train.Batch.Train.Speaker, num_workers= hp.Train.Num_Workers, pin_memory= True ) self.dataLoader_Dict['Dev'] = torch.utils.data.DataLoader( dataset= dev_Dataset, shuffle= True, collate_fn= collater, batch_size= hp.Train.Batch.Eval.Speaker, num_workers= hp.Train.Num_Workers, pin_memory= True ) self.dataLoader_Dict['Inference'] = torch.utils.data.DataLoader( dataset= inference_Dataset, shuffle= True, collate_fn= inference_Collater, batch_size= hp.Train.Batch.Eval.Speaker, num_workers= hp.Train.Num_Workers, pin_memory= True ) def Model_Generate(self): self.model = GE2E( mel_dims= hp.Sound.Mel_Dim, lstm_size= hp.GE2E.LSTM.Sizes, lstm_stacks= hp.GE2E.LSTM.Stacks, embedding_size= hp.GE2E.Embedding_Size, ).to(device) self.criterion = GE2E_Loss().to(device) self.optimizer = RAdam( params= self.model.parameters(), lr= hp.Train.Learning_Rate.Initial, betas= (hp.Train.ADAM.Beta1, hp.Train.ADAM.Beta2), eps= hp.Train.ADAM.Epsilon, weight_decay= hp.Train.Weight_Decay ) self.scheduler = Modified_Noam_Scheduler( optimizer= self.optimizer, base= hp.Train.Learning_Rate.Base, ) if hp.Use_Mixed_Precision: self.model, self.optimizer = amp.initialize( models= self.model, optimizers=self.optimizer ) logging.info(self.model) def Train_Step(self, mels): loss_Dict = {} mels = mels.to(device, non_blocking=True) embeddings = self.model(mels) loss_Dict['Embedding'] = self.criterion(embeddings, hp.Train.Batch.Train.Pattern_per_Speaker) self.optimizer.zero_grad() if hp.Use_Mixed_Precision: with amp.scale_loss(loss_Dict['Embedding'], self.optimizer) as scaled_loss: scaled_loss.backward() torch.nn.utils.clip_grad_norm_( parameters= amp.master_params(self.optimizer), max_norm= hp.Train.Gradient_Norm ) else: loss_Dict['Embedding'].backward() torch.nn.utils.clip_grad_norm_( parameters= self.model.parameters(), max_norm= hp.Train.Gradient_Norm ) self.optimizer.step() self.scheduler.step() self.steps += 1 self.tqdm.update(1) for tag, loss in loss_Dict.items(): self.scalar_Dict['Train']['Loss/{}'.format(tag)] += loss_Dict['Embedding'] def Train_Epoch(self): for mels in self.dataLoader_Dict['Train']: self.Train_Step(mels) if self.steps % hp.Train.Checkpoint_Save_Interval == 0: self.Save_Checkpoint() if self.steps % hp.Train.Logging_Interval == 0: self.scalar_Dict['Train'] = { tag: loss / hp.Train.Logging_Interval for tag, loss in self.scalar_Dict['Train'].items() } self.scalar_Dict['Train']['Learning_Rate'] = self.scheduler.get_last_lr() self.writer_Dict['Train'].add_scalar_dict(self.scalar_Dict['Train'], self.steps) self.scalar_Dict['Train'] = defaultdict(float) if self.steps % hp.Train.Evaluation_Interval == 0: self.Evaluation_Epoch() if self.steps % hp.Train.Inference_Interval == 0: self.Inference_Epoch() if self.steps >= hp.Train.Max_Step: return self.epochs += 1 @torch.no_grad() def Evaluation_Step(self, mels): loss_Dict = {} mels = mels.to(device, non_blocking=True) embeddings = self.model(mels) loss_Dict['Embedding'] = self.criterion(embeddings, hp.Train.Batch.Eval.Pattern_per_Speaker) for tag, loss in loss_Dict.items(): self.scalar_Dict['Evaluation']['Loss/{}'.format(tag)] += loss def Evaluation_Epoch(self): logging.info('(Steps: {}) Start evaluation.'.format(self.steps)) self.model.eval() for step, mels in tqdm(enumerate(self.dataLoader_Dict['Dev'], 1), desc='[Evaluation]'): self.Evaluation_Step(mels) self.scalar_Dict['Evaluation'] = { tag: loss / step for tag, loss in self.scalar_Dict['Evaluation'].items() } self.writer_Dict['Evaluation'].add_scalar_dict(self.scalar_Dict['Evaluation'], self.steps) self.writer_Dict['Evaluation'].add_histogram_model(self.model, self.steps, delete_keywords=['layer_Dict', 'layer']) self.scalar_Dict['Evaluation'] = defaultdict(float) self.model.train() @torch.no_grad() def Inference_Step(self, mels): return self.model( mels= mels.to(device, non_blocking=True), samples= hp.Train.Inference.Samples ) def Inference_Epoch(self): logging.info('(Steps: {}) Start inference.'.format(self.steps)) self.model.eval() embeddings, speakers = zip(*[ (self.Inference_Step(mels), speakers) for mels, speakers in tqdm(self.dataLoader_Dict['Inference'], desc='[Inference]') ]) embeddings = torch.cat(embeddings, dim= 0).cpu().numpy() speakers = [speaker for speaker_List in speakers for speaker in speaker_List] self.writer_Dict['Evaluation'].add_embedding( embeddings, metadata= speakers, global_step= self.steps, tag= 'Embeddings' ) self.model.train() def Load_Checkpoint(self): if self.steps == 0: paths = [ os.path.join(root, file).replace('\\', '/') for root, _, files in os.walk(hp.Checkpoint_Path) for file in files if os.path.splitext(file)[1] == '.pt' ] if len(paths) > 0: path = max(paths, key = os.path.getctime) else: return # Initial training else: path = os.path.join(path, 'S_{}.pt'.format(self.steps).replace('\\', '/')) state_Dict = torch.load(os.path.join(path), map_location= 'cpu') self.model.load_state_dict(state_Dict['Model']) self.optimizer.load_state_dict(state_Dict['Optimizer']) self.scheduler.load_state_dict(state_Dict['Scheduler']) self.steps = state_Dict['Steps'] self.epochs = state_Dict['Epochs'] if hp.Use_Mixed_Precision: if not 'AMP' in state_Dict.keys(): logging.warn('No AMP state dict is in the checkpoint. Model regards this checkpoint is trained without mixed precision.') else: amp.load_state_dict(state_Dict['AMP']) logging.info('Checkpoint loaded at {} steps.'.format(self.steps)) def Save_Checkpoint(self): os.makedirs(hp.Checkpoint_Path, exist_ok= True) state_Dict = { 'Model': self.model.state_dict(), 'Optimizer': self.optimizer.state_dict(), 'Scheduler': self.scheduler.state_dict(), 'Steps': self.steps, 'Epochs': self.epochs, } if hp.Use_Mixed_Precision: state_Dict['AMP'] = amp.state_dict() torch.save( state_Dict, os.path.join(hp.Checkpoint_Path, 'S_{}.pt'.format(self.steps).replace('\\', '/')) ) logging.info('Checkpoint saved at {} steps.'.format(self.steps)) def Train(self): hp_Path = os.path.join(hp.Checkpoint_Path, 'Hyper_Parameters.yaml').replace('\\', '/') if not os.path.exists(hp_Path): from shutil import copyfile os.makedirs(hp.Checkpoint_Path, exist_ok= True) copyfile('Hyper_Parameters.yaml', hp_Path) if self.steps == 0: self.Evaluation_Epoch() if hp.Train.Initial_Inference: self.Inference_Epoch() self.tqdm = tqdm( initial= self.steps, total= hp.Train.Max_Step, desc='[Training]' ) while self.steps < hp.Train.Max_Step: try: self.Train_Epoch() except KeyboardInterrupt: self.Save_Checkpoint() exit(1) self.tqdm.close() logging.info('Finished training.') if __name__ == '__main__': argParser = argparse.ArgumentParser() argParser.add_argument('-s', '--steps', default= 0, type= int) args = argParser.parse_args() new_Trainer = Trainer(steps= args.steps) new_Trainer.Train()

35.233983

137

0.591035

1,446

12,649

4.981328

0.185339

0.041788

0.023324

0.018465

0.374705

0.295155

0.206303

0.168957

0.147022

0.140358

0

0.004048

0.296861

12,649

359

138

35.233983

0.805824

0.003716

0

0.216949

0

0.00339

0.080787

0.006825

0

1

0.040678

false

0

0.061017

0.00339

0.115254

0

null

0

null

0

1

0

9d8c97671a23367d026ea52b147ffe064cc2939a

881

py

Python

ga/gen_graph.py

k4t0mono/exercicios-ia

06f76db20f519b8d7e9b5ee2cf5c7a72b21e188c

[ "BSD-3-Clause" ]

1

2018-09-23T15:38:04.000Z

ga/gen_graph.py

k4t0mono/exercicios-ia

06f76db20f519b8d7e9b5ee2cf5c7a72b21e188c

[ "BSD-3-Clause" ]

null

ga/gen_graph.py

k4t0mono/exercicios-ia

06f76db20f519b8d7e9b5ee2cf5c7a72b21e188c

[ "BSD-3-Clause" ]

null

import sys import numpy as np import matplotlib.pyplot as plt f = open(sys.argv[1], 'r') lines = f.readlines() f.close() pop_size = int(lines.pop(0)) pops = [] for l in lines: if l[0] == '[': pops.append(l.strip()) for j in range(len(pops)): p = [] for n in pops[j][1:-1].split(','): p.append(int(n)) d = {} for i in range(-16, 16): d[i] = 0 for i in p: d[i] += 1 x = [] y = [] for k in d: x.append(k) y.append(d[k]) axes = plt.gca() axes.set_xlim([-17, 16]) axes.set_ylim([0, pop_size+1]) # plt.scatter(x, y, s=5, c=[(0,0,0)], alpha=0.5) plt.bar(x, y, 1, color='blue') plt.title('Population {:03d}'.format(j)) plt.xlabel('x') plt.ylabel('qnt') name = 'pop{:03d}.png'.format(j) plt.savefig(name) print('saving {}'.format(name)) plt.clf()

17.979592

52

0.506243

151

881

2.927152

0.437086

0.013575

0.027149

0

0.044094

0.279228

881

48

53

18.354167

0.651969

0.052213

0

0.060024

0

1

0

false

0

0.083333

0

0.083333

0.027778

0

null

0

null

0

1

0

9d8f0a7d44e8c877c0f58c7e9fe5bd054fd5c40a

7,486

py

Python

src/analyses/analyses.py

zahariaa/disentangled-dynamics

2dbdf9884f6f90ff67073f571191227e7abce81d

[ "MIT" ]

null

src/analyses/analyses.py

zahariaa/disentangled-dynamics

2dbdf9884f6f90ff67073f571191227e7abce81d

[ "MIT" ]

null

src/analyses/analyses.py

zahariaa/disentangled-dynamics

2dbdf9884f6f90ff67073f571191227e7abce81d

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ analyses for bVAE entanglement, etc """ import torch import sys sys.path.append("..") # Adds higher directory to python modules path. import matplotlib.pyplot as plt import numpy as np from data.dspritesb import dSpriteBackgroundDataset from torchvision import transforms ds = dSpriteBackgroundDataset(transform=transforms.Resize((32,32)),shapetype = 'circle') # Build sweeps through model ... def sweepCircleLatents(model,latents=np.linspace(0,1,16),def_latents=None): """sweepCircleLatents(model,latents,def_latents): generates input images that sweep through each latent variable, and evaluates them on given model model = loaded model, e.g., vae = staticVAE32(n_latent = 4) latents = latents to sweep through. defaults to np.linspace(0,1,16) def_latents = 'default latents': defines the non-swept latents. defaults to [0.5,0.5,0.5,0.5] if None ---e.g.,--- yhat, x = sweepCircleLatents(vae) """ # Initialization nsweep = len(latents) if type(model).__name__ == 'encoderBVAE_like': n_latent = model.fc.out_features encoder = model else: n_latent = model.n_latent encoder = model.encode if def_latents is None: def_latents = 0.5*np.ones(n_latent) # Generate stimulus sweeps x = torch.zeros((n_latent,nsweep,1,32,32)) for i in np.arange(0,nsweep): x[0,i,:,:,:] = ds.arbitraryCircle(latents[i],def_latents[1],def_latents[2],def_latents[3]) x[1,i,:,:,:] = ds.arbitraryCircle(def_latents[0],latents[i],def_latents[2],def_latents[3]) x[2,i,:,:,:] = ds.arbitraryCircle(def_latents[0],def_latents[1],latents[i],def_latents[3]) x[3,i,:,:,:] = ds.arbitraryCircle(def_latents[0],def_latents[1],def_latents[2],latents[i]) # ... and evaulate them all at once yhat = encoder(x) if not (type(model).__name__ == 'encoderBVAE_like' or type(model).__name__ == 'dynamicAE32'): yhat = yhat[0] return yhat,x # Plot sweeps through model def plotCircleSweep(x=None,nimgs=5): """plotCircleSweep(yhat,x): plots a subset of stimuli, generated from sweepCircleLatents() ---e.g.,--- yhat, x = sweepCircleLatents(vae) plotCircleSweep(x) alternatively, plotCircleSweep(sweepCircleLatents(vae)) """ # Initialization if x is None and type(nimgs) is tuple: x = yhat[1] # Start a-plottin' fig, ax = plt.subplots(nimgs,4,figsize=(9, 15), dpi= 80, facecolor='w', edgecolor='k') for latentdim in range(4): cnt = -1 for img in np.linspace(0,15,nimgs).astype(int): cnt+=1 plt.sca(ax[cnt,latentdim]) plt.set_cmap('gray') ax[cnt,latentdim].imshow( x[latentdim*16+img,:,:,:].squeeze(), vmin=0, vmax=1) plt.axis('off') return fig, ax def plotLatentsSweep(yhat,nmodels=1): """plotLatentsSweep(yhat): plots model latents and a subset of the corresponding stimuli, generated from sweepCircleLatents() ---e.g.,--- yhat, x = sweepCircleLatents(vae) plotCircleSweep(yhat,x) alternatively, plotLatentsSweep(sweepCircleLatents(vae)) """ # Initialization if type(yhat) is tuple: yhat = yhat[0] # Start a-plottin' fig, ax = plt.subplots(nmodels,4,figsize=(9, 15), dpi= 80, facecolor='w', edgecolor='k', sharey='row',sharex='col') for latentdim in range(4): if nmodels > 1: for imodel in range(nmodels): plt.sca(ax[imodel,latentdim]) plt.plot(yhat[imodel][latentdim*16+np.arange(0,16),:].detach().numpy()) # ax[imodel,latentdim].set_aspect(1./ax[imodel,latentdim].get_data_ratio()) ax[imodel,latentdim].spines['top'].set_visible(False) ax[imodel,latentdim].spines['right'].set_visible(False) if latentdim>0: ax[imodel,latentdim].spines['left'].set_visible(False) # ax[imodel,latentdim].set_yticklabels([]) ax[imodel,latentdim].tick_params(axis='y', length=0) # if imodel<nmodels-1 or latentdim>0: ax[imodel,latentdim].spines['bottom'].set_visible(False) ax[imodel,latentdim].set_xticklabels([]) ax[imodel,latentdim].tick_params(axis='x', length=0) else: imodel=0 plt.sca(ax[latentdim]) plt.plot(yhat[latentdim*16+np.arange(0,16),:].detach().numpy()) ax[latentdim].set_aspect(1./ax[latentdim].get_data_ratio()) ax[latentdim].spines['top'].set_visible(False) ax[latentdim].spines['right'].set_visible(False) if latentdim>0: ax[latentdim].spines['left'].set_visible(False) ax[latentdim].tick_params(axis='y', length=0) # if imodel<nmodels-1 or latentdim>0: ax[latentdim].spines['bottom'].set_visible(False) ax[latentdim].set_xticklabels([]) ax[latentdim].tick_params(axis='x', length=0) return fig, ax def colorAxisNormalize(colorbar): """colorAxisNormalize(colorbar): normalizes a color axis so it is centered on zero. useful for diverging colormaps (e.g., cmap='bwr': blue=negative, red=positive, white=0) input is already initialized colorbar object from a plot ---e.g.,--- corr_vae = np.corrcoef(yhat_vae.detach().numpy().T) plt.set_cmap('bwr') plt.imshow(corr_vae) cb = plt.colorbar() colorAxisNormalize(cb) ---or--- colorAxisNormalize(plt.colorbar()) """ cm = np.max(np.abs(colorbar.get_clim())) colorbar.set_clim(-cm,cm) def showReconstructionsAndErrors(model): """showReconstructionsAndErrors(model): generates random inputs, runs them through a specified model to generate their reconstructions. plots the inputs, reconstructions, and their difference ---e.g.--- from staticvae.models import staticVAE32 vae = staticVAE32(n_latent = 4) vae.eval() checkpoint = torch.load('../staticvae/trained/staticvae32_dsprites_circle_last_500K',map_location='cpu') vae.load_state_dict(checkpoint['model_states']['net']) showReconstructionsAndErrors(model) """ fig=plt.figure(figsize=(18, 16), dpi= 80, facecolor='w', edgecolor='k') cnt = 0 for ii in range(12): x,label = ds[np.random.randint(1000)] x = x[np.newaxis, :, :] mu,logvar = model.encode(x.float()) recon = model.decode(mu).detach() diff = x - recon cnt += 1 ax = plt.subplot(6,6,cnt) plt.set_cmap('gray') ax.imshow(x.squeeze(), vmin=0, vmax=1) plt.title('true') plt.axis('off') cnt += 1 ax = plt.subplot(6,6,cnt) ax.imshow(recon.squeeze(), vmin=0, vmax=1) plt.title('recon') plt.axis('off') cnt += 1 ax = plt.subplot(6,6,cnt) plt.set_cmap('bwr') img = ax.imshow(diff.numpy().squeeze()) colorAxisNormalize(fig.colorbar(img)) plt.title('diff') plt.axis('off')

36.339806

119

0.593909

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null

0

null

0

1

0

9d9030a3ab27bda98f5076efe7e1d4f4d61c1b31

2,684

py

Python

Chapter_BestPractices/Centering_Scaling.py

ML-PSE/Machine_Learning_for_PSE

b53578d7cc0e0eca4907527b188a60de06d6710e

[ "Apache-2.0" ]

2

2022-02-20T18:57:46.000Z

2022-03-03T07:07:12.000Z

Chapter_BestPractices/Centering_Scaling.py

ML-PSE/Machine_Learning_for_PSE

b53578d7cc0e0eca4907527b188a60de06d6710e

[ "Apache-2.0" ]

null

Chapter_BestPractices/Centering_Scaling.py

ML-PSE/Machine_Learning_for_PSE

b53578d7cc0e0eca4907527b188a60de06d6710e

[ "Apache-2.0" ]

null

##%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ## Centering & Scaling ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #%% Standard scaling import numpy as np from sklearn.preprocessing import StandardScaler X = np.array([[ 1000, 0.01, 300], [ 1200, 0.06, 350], [ 1500, 0.1, 320]]) scaler = StandardScaler().fit(X) # computes mean & std column-wise X_scaled = scaler.transform(X) # transform using computed mean and std # check mean = 0 and variance = 1 for every variable/column after scaling print(X_scaled.mean(axis=0)) # return 1D array of size(3,1) print(X_scaled.std(axis=0)) # return 1D array of size(3,1) # access mean and variance via object properties print(scaler.mean_) # return 1D array of size(3,1) print(scaler.var_) # return 1D array of size(3,1) #%% Normalization from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler() # create object X_scaled = scaler.fit_transform(X) # fit & transform # check min = 0 and max = 1 for every variable/column after scaling print(X_scaled.min(axis=0)) print(X_scaled.max(axis=0)) # access min and max via object properties print(scaler.data_min_) print(scaler.data_max_) ##%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ## Robust Centering & Scaling ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #%% Generate oulier-infested data X = np.random.normal(40, 1, (1500,1)) X[200:300] = X[200:300] +8; X[1000:1150] = X[1000:1150] + 8 # plot import matplotlib.pyplot as plt plt.plot(X, '.-') plt.xlabel('sample #'), plt.ylabel('variable measurement') plt.title('Raw measurements') #%% Transform via standard scaling scaler = StandardScaler().fit(X) X_scaled = scaler.transform(X) # mean and std print('Estimated mean = ', scaler.mean_[0]) print('Estimated standard deviation = ', np.sqrt(scaler.var_[0])) # plot plt.figure() plt.plot(X_scaled, '.-') plt.xlabel('sample #'), plt.ylabel('scaled variable measurement') plt.xlim((0,1500)) plt.title('Standard scaling') #%% Transform via robust MAD scaling # compute median and MAD from scipy import stats median = np.median(X) MAD = stats.median_absolute_deviation(X) # scale X_scaled = (X - median)/MAD[0] # median and MAD print('Estimated robust location = ', median) print('Estimated robust spread = ', MAD) # plot plt.figure() plt.plot(X_scaled, '.-') plt.xlabel('sample #'), plt.ylabel('scaled variable measurement') plt.xlim((0,1500)) plt.title('Robust MAD scaling')

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0.272727

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null

0

null

0

1

0

9d91be2759fba448a3db8257c92c32db569fc6fc

2,244

py

Python

web/addons/mass_mailing/models/mass_mailing_report.py

diogocs1/comps

63df07f6cf21c41e4527c06e2d0499f23f4322e7

[ "Apache-2.0" ]

1

2019-12-29T11:53:56.000Z

odoo/addons/mass_mailing/models/mass_mailing_report.py

tuanquanghpvn/odoo8-tutorial

52d25f1ca5f233c431cb9d3b24b79c3b4fb5127e

[ "MIT" ]

null

odoo/addons/mass_mailing/models/mass_mailing_report.py

tuanquanghpvn/odoo8-tutorial

52d25f1ca5f233c431cb9d3b24b79c3b4fb5127e

[ "MIT" ]

3

2020-10-08T14:42:10.000Z

2022-01-28T14:12:29.000Z

# -*- coding: utf-8 -*- from openerp.osv import fields, osv from openerp import tools class MassMailingReport(osv.Model): _name = 'mail.statistics.report' _auto = False _description = 'Mass Mailing Statistics' _columns = { 'scheduled_date': fields.datetime('Scheduled Date', readonly=True), 'name': fields.char('Mass Mail', readonly=True), 'campaign': fields.char('Mass Mail Campaign', readonly=True), 'sent': fields.integer('Sent', readonly=True), 'delivered': fields.integer('Delivered', readonly=True), 'opened': fields.integer('Opened', readonly=True), 'bounced': fields.integer('Bounced', readonly=True), 'replied': fields.integer('Replied', readonly=True), 'state': fields.selection( [('draft', 'Draft'), ('test', 'Tested'), ('done', 'Sent')], string='Status', readonly=True, ), 'email_from': fields.char('From', readonly=True), } def init(self, cr): """Mass Mail Statistical Report: based on mail.mail.statistics that models the various statistics collected for each mailing, and mail.mass_mailing model that models the various mailing performed. """ tools.drop_view_if_exists(cr, 'mail_statistics_report') cr.execute(""" CREATE OR REPLACE VIEW mail_statistics_report AS ( SELECT min(ms.id) as id, ms.scheduled as scheduled_date, mm.name as name, mc.name as campaign, count(ms.bounced) as bounced, count(ms.sent) as sent, (count(ms.sent) - count(ms.bounced)) as delivered, count(ms.opened) as opened, count(ms.replied) as replied, mm.state, mm.email_from FROM mail_mail_statistics as ms left join mail_mass_mailing as mm ON (ms.mass_mailing_id=mm.id) left join mail_mass_mailing_campaign as mc ON (ms.mass_mailing_campaign_id=mc.id) GROUP BY ms.scheduled, mm.name, mc.name, mm.state, mm.email_from )""")

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null

0

null

0

1

0

9d934505c9a5de277afc3e1a3c4cc83a509daf62

2,750

py

Python

modules/springerlink.py

Christoph-D/paperget

9887936039ecc9fafe4dcce7988e75e964a05bcd

[ "MIT" ]

3

2016-06-17T15:52:02.000Z

2017-12-21T02:44:49.000Z

modules/springerlink.py

Christoph-D/paperget

9887936039ecc9fafe4dcce7988e75e964a05bcd

[ "MIT" ]

null

modules/springerlink.py

Christoph-D/paperget

9887936039ecc9fafe4dcce7988e75e964a05bcd

[ "MIT" ]

1

2021-02-16T21:10:33.000Z

import urllib, re class FakeUseragentURLopener(urllib.FancyURLopener): version = "Mozilla/5.0 (Ubuntu; X11; Linux i686; rv:9.0.1) Gecko/20100101 Firefox/9.0.1" urllib._urlopener = FakeUseragentURLopener() download_pdf_regex = re.compile('.*<li class="pdf"><a class="sprite pdf-resource-sprite" href="([^"]*)" title="Download PDF.*') viewstate_regex = re.compile('.*<input type="hidden" name="__VIEWSTATE" id="__VIEWSTATE" value="([^"]*)" />.*') eventvalidation_regex = re.compile('.*<input type="hidden" name="__EVENTVALIDATION" id="__EVENTVALIDATION" value="([^"]*)" />.*') def download_pdf(url, filename): page = urllib.urlopen(url).read() result = download_pdf_regex.search(page) if result is None: return False fulltext_url = "http://www.springerlink.com" + result.group(1) return urllib.urlretrieve(fulltext_url, filename) is not None def download_bib(url, filename): url += 'export-citation/' form = urllib.urlopen(url).read() viewstate = viewstate_regex.search(form) eventvalidation = eventvalidation_regex.search(form) if viewstate is None or eventvalidation is None: return False viewstate = viewstate.group(1) eventvalidation = eventvalidation.group(1) data = urllib.urlencode([ ('__VIEWSTATE', viewstate), ('ctl00$ctl14$cultureList', 'en-us'), ('ctl00$ctl14$SearchControl$BasicSearchForTextBox', ''), ('ctl00$ctl14$SearchControl$BasicAuthorOrEditorTextBox', ''), ('ctl00$ctl14$SearchControl$BasicPublicationTextBox', ''), ('ctl00$ctl14$SearchControl$BasicVolumeTextBox', ''), ('ctl00$ctl14$SearchControl$BasicIssueTextBox', ''), ('ctl00$ctl14$SearchControl$BasicPageTextBox', ''), ('ctl00$ContentPrimary$ctl00$ctl00$Export', 'CitationOnlyRadioButton'), ('ctl00$ContentPrimary$ctl00$ctl00$CitationManagerDropDownList', 'BibTex'), ('ctl00$ContentPrimary$ctl00$ctl00$ExportCitationButton', 'Export+Citation'), ('__EVENTVALIDATION', eventvalidation)]) return urllib.urlretrieve(url, filename, data=data) is not None def download_pdf_chapter(url, filename): return urllib.urlretrieve(url.replace('/chapter/', '/content/pdf/', 1) + '.pdf', filename) is not None import base base.register_module('http://www\.springerlink\.com/content/.*', {'name': 'springerlink', 'download_pdf': download_pdf, 'download_bib': download_bib, }) base.register_module('http://link\.springer\.com/chapter/.*', {'name': 'springerlink_chapter', 'download_pdf': download_pdf_chapter, })

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null

0

null

0

1

0

9d9caa03a4ae2fbdbadf5bfc3fd2600ade753a1b

3,460

py

Python

modules/colors.py

trybefore/discordbot

1ffce8149cde586e8c5883e8200b02937c5a15f6

[ "MIT" ]

3

2020-09-15T23:19:18.000Z

2021-02-17T10:24:54.000Z

modules/colors.py

trybefore/discordbot

1ffce8149cde586e8c5883e8200b02937c5a15f6

[ "MIT" ]

3

2021-06-22T10:57:14.000Z

2021-06-22T10:57:15.000Z

modules/colors.py

trybefore/discordbot

1ffce8149cde586e8c5883e8200b02937c5a15f6

[ "MIT" ]

2

2020-05-03T20:54:57.000Z

2020-09-12T18:49:13.000Z

from threading import Lock import discord from discord.ext import commands from loguru import logger from local_types import Snowflake from modules import is_bot_admin class Colors(commands.Cog): bot: discord.ext.commands.Bot colorRoles = {} mutex = Lock() def __init__(self, bot): self.bot = bot self.reload() def reload(self): self.mutex.acquire() for g in self.bot.guilds: try: self.colorRoles[g.id].clear() except Exception: pass # Ignore error d = {} for r in g.roles: if r.name.lower().startswith("color- "): color_name = r.name.lower().split("color- ")[1] d[color_name] = Snowflake(r.id) # logger.debug(f"color roles: {d}") self.colorRoles[g.id] = d self.mutex.release() @commands.command(name='reload_colors', hidden=True) @commands.check_any(is_bot_admin(), commands.has_permissions(manage_roles=True), commands.is_owner()) @commands.max_concurrency(1, wait=True) @commands.guild_only() async def reload_colors(self, ctx): await self.reload() async def print_colors(self, ctx: discord.ext.commands.Context): g: discord.Guild = ctx.guild d: dict = self.colorRoles[g.id] roles = [] for r in d.keys(): roles.append(r) await ctx.send(f"```{', '.join(roles)}```") # do not use outside of color command function async def remove_roles(self, ctx: discord.ext.commands.Context): g: discord.Guild = ctx.guild member: discord.member.Member = g.get_member(ctx.author.id) d: dict = self.colorRoles[g.id] to_remove = [] for r in d.values(): for mr in member.roles: if r.id == mr.id: to_remove.append(r) await member.remove_roles(*to_remove, reason="Color Command", atomic=True) @commands.command(name='color', help="Choose your name color") @commands.cooldown(type=commands.BucketType.user, rate=1, per=3) @commands.guild_only() async def color(self, ctx: discord.ext.commands.Context, color: str): self.mutex.acquire() g: discord.Guild = ctx.guild member: discord.member.Member = g.get_member(ctx.author.id) color = color.lower() if color == "list": await self.print_colors(ctx) else: d: dict = self.colorRoles[g.id] if d is None: await ctx.send(f"{ctx.author.mention} could not find any color roles in this server!") else: try: r = d[color] await self.remove_roles(ctx) await member.add_roles(r) await ctx.send(f"{ctx.author.mention} successfully changed your color to {color}") except KeyError: await ctx.send( f"{ctx.author.mention} could not find any such color!\n ```{self.bot.command_prefix}{ctx.command.name} list``` to view available colors") self.mutex.release() @color.error async def color_error(self, ctx, error): if isinstance(error, discord.ext.commands.errors.CommandOnCooldown): await ctx.send(f"{ctx.author.mention} {error}") else: logger.error(f"color error: {error}")

31.454545

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0.025

0

null

0

null

0

1

0

9d9e064b6bf0f12b09cc360b5115a0ae4d5fbeff

1,645

py

Python

examples/basic_dsp_example.py

Camotubi/basic_dsp

38a380439cc8936c64febbc12227df78d95fce7f

[ "Apache-2.0", "MIT" ]

40

2015-11-23T02:23:35.000Z

2022-03-18T11:19:11.000Z

examples/basic_dsp_example.py

Camotubi/basic_dsp

38a380439cc8936c64febbc12227df78d95fce7f

[ "Apache-2.0", "MIT" ]

47

2015-11-23T01:58:38.000Z

2021-01-11T07:53:37.000Z

examples/basic_dsp_example.py

Camotubi/basic_dsp

38a380439cc8936c64febbc12227df78d95fce7f

[ "Apache-2.0", "MIT" ]

9

2018-05-19T07:25:26.000Z

2022-01-09T20:51:40.000Z

import ctypes import struct import time # # A small example how to use basic_dsp in a different language. # class VecResult(ctypes.Structure): _fields_ = [("resultCode", ctypes.c_int), ("result", ctypes.c_void_p)] lib = ctypes.WinDLL('basic_dsp.dll') new64Proto = ctypes.WINFUNCTYPE ( ctypes.c_void_p, # Return type. ctypes.c_int, ctypes.c_int, ctypes.c_double, ctypes.c_ulong, ctypes.c_double) new64 = new64Proto (("new64", lib)) getValue64Proto = ctypes.WINFUNCTYPE ( ctypes.c_double, # Return type. ctypes.c_void_p, ctypes.c_ulong) getValue64 = getValue64Proto (("get_value64", lib)) offset64Proto = ctypes.WINFUNCTYPE ( VecResult, # Return type. ctypes.c_void_p, ctypes.c_double) offset64 = offset64Proto (("real_offset64", lib)) vec = new64( ctypes.c_int(0), ctypes.c_int(0), ctypes.c_double(0.0), ctypes.c_ulong(100000), ctypes.c_double(1.0)) val = getValue64(vec, ctypes.c_ulong(0)) print('At the start: vec[0] = {}'.format(val)) start = time.clock() iterations = 100000 toNs = 1e9 / iterations increment = 5.0 for x in range(0, iterations): vecRes = offset64(vec, ctypes.c_double(increment)) vec = vecRes.result end = time.clock() print('{} ns per iteration, each iteration has {} samples'.format((end - start) * toNs, iterations)) print('Result code: {} (0 means no error)'.format(vecRes.resultCode)) vecRes = offset64(vec, ctypes.c_double(5.0)) vec = vecRes.result val = getValue64(vec, ctypes.c_ulong(0)) print('After {} iterations of increment by {}: vec[0] = {}'.format(iterations + 1, increment, val))

26.967213

100

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0

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1,645

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27.416667

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0.085106

0

null

0

null

0

1

0

9da1a92cdcf88a9e292d7bdc3fb0eeb027139777

2,305

py

Python

chemex/experiments/cpmg/fast/liouvillian.py

marcuscangussu/chemex_bouvignies

ce9ec20a42604eb5995abb0f8a84094b29747651

[ "BSD-3-Clause" ]

null

chemex/experiments/cpmg/fast/liouvillian.py

marcuscangussu/chemex_bouvignies

ce9ec20a42604eb5995abb0f8a84094b29747651

[ "BSD-3-Clause" ]

null

chemex/experiments/cpmg/fast/liouvillian.py

marcuscangussu/chemex_bouvignies

ce9ec20a42604eb5995abb0f8a84094b29747651

[ "BSD-3-Clause" ]

null

""" Created on Sep 1, 2011 @author: guillaume """ from scipy import zeros from chemex.bases.two_states.fast import R_IXY, DR_IXY, DW, KAB, KBA def compute_liouvillians(pb=0.0, kex=0.0, dw=0.0, r_ixy=5.0, dr_ixy=0.0): """ Compute the exchange matrix (Liouvillian) The function assumes a 2-site (A <-> B) exchanging system. The matrix is written in 6x6 cartesian basis, that is {Nx, Ny, Nz}{a,b}. Here the thermal equilibrium is assumed to be 0. This is justified because of the +/- phase cycling of the first 90 degree pulse at the beginning of the cpmg block. Parameters ---------- pb : float Fractional population of state B. 0.0 for 0%, 1.0 for 100%. kex : float Exchange rate between state A and B in /s. dw : float Chemical shift difference between states A and B in rad/s. r_nz : float Longitudinal relaxation rate of state {a,b} in /s. r_nxy : float Transverse relaxation rate of state a in /s. dr_nxy : float Transverse relaxation rate difference between states a and b in /s. cs_offset : float Offset from the carrier in rad/s. Returns ------- out: numpy.matrix Liouvillian describing free precession of one isolated spin in presence of two-site exchange. """ kab = kex * pb kba = kex - kab l_free = R_IXY * r_ixy l_free += DR_IXY * dr_ixy l_free += DW * dw l_free += KAB * kab l_free += KBA * kba return l_free def compute_iy_eq(pb): """ Returns the equilibrium magnetization vector. Parameters ---------- pb : float Fractional population of state B. 0.0 for 0%, 1.0 for 100%. Returns ------- out: numpy.matrix Magnetization vector at equilibrium. """ mag_eq = zeros((4, 1)) mag_eq[1, 0] += (1.0 - pb) mag_eq[3, 0] += pb return mag_eq def get_iy(mag): """ Returns the amount of magnetization along z. Parameters ---------- mag : ndarray Magnetization vector. Returns ------- magy_a, magy_b : float Amount of magnetization in state a and b along z. """ magy_a = mag[1, 0] magy_b = mag[3, 0] return magy_a, magy_b

21.745283

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0

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0.306291

2,305

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0.805503

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0

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0

0.380952

0

null

0

null

0

1

0

9da26db5109dcd203a39bfcab1fbaa5c755f0368

33,787

py

Python

Software/python/config_dialog.py

edavalosanaya/SKORE

72e742611ba96b0df542781ded0685f525bea82b

[ "MIT" ]

1

2020-09-20T19:00:17.000Z

Software/python/config_dialog.py

MrCodingRobot/SKORE

72e742611ba96b0df542781ded0685f525bea82b

[ "MIT" ]

null

Software/python/config_dialog.py

MrCodingRobot/SKORE

72e742611ba96b0df542781ded0685f525bea82b

[ "MIT" ]

null

# General Utility Libraries import sys import os import warnings # PyQt5, GUI Library from PyQt5 import QtCore, QtGui, QtWidgets # Serial and Midi Port Library import rtmidi import serial import serial.tools.list_ports # SKORE Library from lib_skore import read_config, update_config import globals #------------------------------------------------------------------------------- # Classes class ArduinoComboBox(QtWidgets.QComboBox): """ This class allows the combobox to recognize arduinos connected as soon as the user clicks the combobox. """ def avaliable_arduino_com(self): """ This fuction returns all the available COM ports in a list of strings. """ ports = serial.tools.list_ports.comports(include_links=False) results = [] for port in ports: results.append(str(port.device)) return results def showPopup(self): """ This function appends to the original showPopup function from the QComboBox by adding the avaliable arduino com ports. """ avaliable_arduino_ports = self.avaliable_arduino_com() self.clear() for avaliable_port in avaliable_arduino_ports: self.addItem(avaliable_port) super(ArduinoComboBox, self).showPopup() return None class PianoComboBox(QtWidgets.QComboBox): """ This class allows the combobox to recognize piano connected as soon as the user clicks the combobox. """ def avaliable_piano_port(self): """ This function returns all the available MIDI ports in a list of string. """ temp_midi_in = [] temp_midi_in = rtmidi.MidiIn() avaliable_ports = temp_midi_in.get_ports() results = [] for port_name in avaliable_ports: results.append(str(port_name)) return results def showPopup(self): """ This function appends to the showPopup function of the QComboBox by adding the avaliable MIDI ports to the listed items in the QComboBox. """ avaliable_piano_ports = self.avaliable_piano_port() self.clear() for avaliable_piano_port_connected in avaliable_piano_ports: self.addItem(avaliable_piano_port_connected) super(PianoComboBox, self).showPopup() return None class ConfigDialog(QtWidgets.QDialog): """ This class is the settings dialog that provides the user the capability of changing the settings of the SKORE application. """ finish_apply_signal = QtCore.pyqtSignal() def __init__(self): """ This function sets the settings dialog by changing the title, size, icon, and placing the widgets. """ super(QtWidgets.QDialog, self).__init__() self.setObjectName("Dialog") self.resize(530 * globals.S_W_R, 679 * globals.S_H_R) self.setWindowTitle("SKORE - General Configuration") self.setWindowIcon(QtGui.QIcon('.\images\skore_icon.png')) self.setup_ui() self.setup_func() self.read_all_settings() self.update_settings() return None def setup_ui(self): """ This function places all the widgets in the settings dialog. """ self.apply_close_buttonBox = QtWidgets.QDialogButtonBox(self) self.apply_close_buttonBox.setGeometry(QtCore.QRect(310 * globals.S_W_R, 640 * globals.S_H_R, 201 * globals.S_W_R, 32 * globals.S_H_R)) self.apply_close_buttonBox.setLayoutDirection(QtCore.Qt.RightToLeft) self.apply_close_buttonBox.setOrientation(QtCore.Qt.Horizontal) self.apply_close_buttonBox.setStandardButtons(QtWidgets.QDialogButtonBox.Apply|QtWidgets.QDialogButtonBox.Close) self.apply_close_buttonBox.setObjectName("apply_cancel_buttonBox") #----------------------------------------------------------------------- # Tab Widget self.tabWidget = QtWidgets.QTabWidget(self) self.tabWidget.setGeometry(QtCore.QRect(10 * globals.S_W_R, 10 * globals.S_H_R, 511 * globals.S_W_R, 621 * globals.S_H_R)) self.tabWidget.setLayoutDirection(QtCore.Qt.LeftToRight) self.tabWidget.setObjectName("tabWidget") #-----------------------------------------------------------------------# # Tab Widget -> path_and_comm_tab self.path_and_comm_tab = QtWidgets.QWidget() self.path_and_comm_tab.setObjectName("path_and_comm_tab") #----------------------------------------------------------------------- # Tab Widget -> path_and_comm_tab -> path section self.configure_path_label = QtWidgets.QLabel(self.path_and_comm_tab) self.configure_path_label.setGeometry(QtCore.QRect(10 * globals.S_W_R, 5 * globals.S_H_R, 231 * globals.S_W_R, 16 * globals.S_H_R)) self.configure_path_label.setObjectName("configure_path_label") self.path_line = QtWidgets.QFrame(self.path_and_comm_tab) self.path_line.setGeometry(QtCore.QRect(10 * globals.S_W_R, 20 * globals.S_H_R, 481 * globals.S_W_R, 20 * globals.S_H_R)) self.path_line.setFrameShape(QtWidgets.QFrame.HLine) self.path_line.setFrameShadow(QtWidgets.QFrame.Sunken) self.path_line.setObjectName("path_line") self.audiveris_pushButton = QtWidgets.QPushButton(self.path_and_comm_tab) self.audiveris_pushButton.setGeometry(QtCore.QRect(400 * globals.S_W_R, 60 * globals.S_H_R, 93 * globals.S_W_R, 31 * globals.S_H_R)) self.audiveris_pushButton.setObjectName("audiveris_pushButton") self.audiveris_label = QtWidgets.QLabel(self.path_and_comm_tab) self.audiveris_label.setGeometry(QtCore.QRect(10 * globals.S_W_R, 40 * globals.S_H_R, 101 * globals.S_W_R, 16 * globals.S_H_R)) self.audiveris_label.setObjectName("audiveris_label") self.audiveris_lineEdit = QtWidgets.QLineEdit(self.path_and_comm_tab) self.audiveris_lineEdit.setGeometry(QtCore.QRect(10 * globals.S_W_R, 60 * globals.S_H_R, 381 * globals.S_W_R, 31 * globals.S_H_R)) self.audiveris_lineEdit.setObjectName("audiveris_lineEdit") self.amazingmidi_lineEdit = QtWidgets.QLineEdit(self.path_and_comm_tab) self.amazingmidi_lineEdit.setGeometry(QtCore.QRect(10 * globals.S_W_R, 120 * globals.S_H_R, 381 * globals.S_W_R, 31 * globals.S_H_R)) self.amazingmidi_lineEdit.setObjectName("amazingmidi_lineEdit") self.amazingmidi_label = QtWidgets.QLabel(self.path_and_comm_tab) self.amazingmidi_label.setGeometry(QtCore.QRect(10 * globals.S_W_R, 100 * globals.S_H_R, 121 * globals.S_W_R, 16 * globals.S_H_R)) self.amazingmidi_label.setObjectName("amazingmidi_label") self.amazingmidi_pushButton = QtWidgets.QPushButton(self.path_and_comm_tab) self.amazingmidi_pushButton.setGeometry(QtCore.QRect(400 * globals.S_W_R, 120 * globals.S_H_R, 93 * globals.S_W_R, 31 * globals.S_H_R)) self.amazingmidi_pushButton.setObjectName("amazingmidi_pushButton") self.anthemscore_pushButton = QtWidgets.QPushButton(self.path_and_comm_tab) self.anthemscore_pushButton.setGeometry(QtCore.QRect(400 * globals.S_W_R, 180 * globals.S_H_R, 93 * globals.S_W_R, 31 * globals.S_H_R)) self.anthemscore_pushButton.setObjectName("anthemscore_pushButton") self.anthemscore_lineEdit = QtWidgets.QLineEdit(self.path_and_comm_tab) self.anthemscore_lineEdit.setGeometry(QtCore.QRect(10 * globals.S_W_R, 180 * globals.S_H_R, 381 * globals.S_W_R, 31 * globals.S_H_R)) self.anthemscore_lineEdit.setObjectName("anthemscore_lineEdit") self.anthemscore_label = QtWidgets.QLabel(self.path_and_comm_tab) self.anthemscore_label.setGeometry(QtCore.QRect(10 * globals.S_W_R, 160 * globals.S_H_R, 191 * globals.S_W_R, 16 * globals.S_H_R)) self.anthemscore_label.setObjectName("anthemscore_label") self.muse_score_pushButton = QtWidgets.QPushButton(self.path_and_comm_tab) self.muse_score_pushButton.setGeometry(QtCore.QRect(400 * globals.S_W_R, 240 * globals.S_H_R, 93 * globals.S_W_R, 31 * globals.S_H_R)) self.muse_score_pushButton.setObjectName("muse_score_pushButton") self.muse_score_lineEdit = QtWidgets.QLineEdit(self.path_and_comm_tab) self.muse_score_lineEdit.setGeometry(QtCore.QRect(10 * globals.S_W_R, 240 * globals.S_H_R, 381 * globals.S_W_R, 31 * globals.S_H_R)) self.muse_score_lineEdit.setObjectName("muse_score_linedEdit") self.muse_score_label = QtWidgets.QLabel(self.path_and_comm_tab) self.muse_score_label.setGeometry(QtCore.QRect(10 * globals.S_W_R, 220 * globals.S_H_R, 191 * globals.S_W_R, 16 * globals.S_H_R)) self.muse_score_label.setObjectName("muse_score_label") self.mp3_to_midi_converter_label = QtWidgets.QLabel(self.path_and_comm_tab) self.mp3_to_midi_converter_label.setGeometry(QtCore.QRect(10 * globals.S_W_R, 280 * globals.S_H_R, 141 * globals.S_W_R, 16 * globals.S_H_R)) self.mp3_to_midi_converter_label.setObjectName("mp3_to_midi_converter_label") self.open_source_radioButton = QtWidgets.QRadioButton(self.path_and_comm_tab) self.open_source_radioButton.setGeometry(QtCore.QRect(240 * globals.S_W_R, 280 * globals.S_H_R, 111 * globals.S_W_R, 20 * globals.S_H_R)) self.open_source_radioButton.setObjectName("open_source_radioButton") self.close_source_radioButton = QtWidgets.QRadioButton(self.path_and_comm_tab) self.close_source_radioButton.setGeometry(QtCore.QRect(380 * globals.S_W_R, 280 * globals.S_H_R, 111 * globals.S_W_R, 20 * globals.S_H_R)) self.close_source_radioButton.setObjectName("close_source_radioButton") #----------------------------------------------------------------------- # Tab Widget -> path_and_comm_tab -> comm section self.comm_line = QtWidgets.QFrame(self.path_and_comm_tab) self.comm_line.setGeometry(QtCore.QRect(10 * globals.S_W_R, 300 * globals.S_H_R, 481 * globals.S_W_R, 20 * globals.S_H_R)) self.comm_line.setFrameShape(QtWidgets.QFrame.HLine) self.comm_line.setFrameShadow(QtWidgets.QFrame.Sunken) self.comm_line.setObjectName("comm_line") self.portsettings_label = QtWidgets.QLabel(self.path_and_comm_tab) self.portsettings_label.setGeometry(QtCore.QRect(210 * globals.S_W_R, 320 * globals.S_H_R, 81* globals.S_W_R, 20 * globals.S_H_R)) self.portsettings_label.setObjectName("portsettings_label") self.piano_port_label = QtWidgets.QLabel(self.path_and_comm_tab) self.piano_port_label.setGeometry(QtCore.QRect(10 * globals.S_W_R, 340 * globals.S_H_R, 71 * globals.S_W_R, 16 * globals.S_H_R)) self.piano_port_label.setObjectName("pianoport_label") self.piano_port_comboBox = PianoComboBox(self.path_and_comm_tab) self.piano_port_comboBox.setGeometry(QtCore.QRect(10 * globals.S_W_R, 360 * globals.S_H_R, 481 * globals.S_W_R, 31 * globals.S_H_R)) self.piano_port_comboBox.setObjectName("pianoport_comboBox") self.piano_size_label = QtWidgets.QLabel(self.path_and_comm_tab) self.piano_size_label.setGeometry(QtCore.QRect(10 * globals.S_W_R, 400 * globals.S_H_R, 71* globals.S_W_R, 16* globals.S_H_R)) self.piano_size_label.setObjectName("pianosize_label") self.piano_size_comboBox = QtWidgets.QComboBox(self.path_and_comm_tab) self.piano_size_comboBox.setGeometry(QtCore.QRect(10 * globals.S_W_R, 420 * globals.S_H_R, 481 * globals.S_W_R, 31 * globals.S_H_R)) self.piano_size_comboBox.setObjectName("pianosize_comboBox") self.arduinoport_label = QtWidgets.QLabel(self.path_and_comm_tab) self.arduinoport_label.setGeometry(QtCore.QRect(10 * globals.S_W_R, 460 * globals.S_H_R, 81 * globals.S_W_R, 16* globals.S_H_R)) self.arduinoport_label.setObjectName("arduinoport_label") self.arduino_port_comboBox = ArduinoComboBox(self.path_and_comm_tab) self.arduino_port_comboBox.setGeometry(QtCore.QRect(10 * globals.S_W_R, 480 * globals.S_H_R, 481 * globals.S_W_R, 31 * globals.S_H_R)) self.arduino_port_comboBox.setObjectName("arduinoport_comboBox") self.arduino_baud_rate_label = QtWidgets.QLabel(self.path_and_comm_tab) self.arduino_baud_rate_label.setGeometry(QtCore.QRect(10 * globals.S_W_R, 520 * globals.S_H_R, 200 * globals.S_W_R, 20* globals.S_H_R)) self.arduino_baud_rate_label.setText("Arduino Baud Rate") self.arduino_baud_rate_comboBox = QtWidgets.QComboBox(self.path_and_comm_tab) self.arduino_baud_rate_comboBox.setGeometry(QtCore.QRect(10 * globals.S_W_R, 540 * globals.S_H_R, 481* globals.S_W_R, 31 * globals.S_H_R)) self.tabWidget.addTab(self.path_and_comm_tab, "") #----------------------------------------------------------------------- # Tab Widget -> Lighting and Color Tab self.color_tab = QtWidgets.QWidget() self.color_tab.setObjectName("color_tab") #----------------------------------------------------------------------- # Tab Widget -> Tutoring Tab -> Timing Section self.timingsettings_label = QtWidgets.QLabel(self.color_tab) self.timingsettings_label.setGeometry(QtCore.QRect(200 * globals.S_W_R, 10 * globals.S_H_R, 151 * globals.S_W_R, 20 * globals.S_H_R)) self.timingsettings_label.setObjectName("timingsettings_label") self.chord_tick_tolerance_label = QtWidgets.QLabel(self.color_tab) self.chord_tick_tolerance_label.setGeometry(QtCore.QRect(20 * globals.S_W_R, 40* globals.S_H_R, 200 * globals.S_W_R, 20 * globals.S_H_R)) self.chord_tick_tolerance_label.setText("Chord Tick Tolerance:") self.chord_tick_tolerance_lineEdit = QtWidgets.QLineEdit(self.color_tab) self.chord_tick_tolerance_lineEdit.setGeometry(QtCore.QRect(200 * globals.S_W_R, 40 * globals.S_H_R, 280 * globals.S_W_R, 20 * globals.S_H_R)) self.chord_sum_tolerance_label = QtWidgets.QLabel(self.color_tab) self.chord_sum_tolerance_label.setGeometry(QtCore.QRect(20 * globals.S_W_R, 80 * globals.S_H_R, 200 * globals.S_W_R, 20 * globals.S_H_R)) self.chord_sum_tolerance_label.setText("Chord Sum Tolerance:") self.chord_sum_tolerance_lineEdit = QtWidgets.QLineEdit(self.color_tab) self.chord_sum_tolerance_lineEdit.setGeometry(QtCore.QRect(200 * globals.S_W_R, 80 * globals.S_H_R, 280 * globals.S_W_R, 20 * globals.S_H_R)) self.record_chord_tolerance_label = QtWidgets.QLabel(self.color_tab) self.record_chord_tolerance_label.setGeometry(QtCore.QRect(20* globals.S_W_R, 120 * globals.S_H_R, 200* globals.S_W_R, 20 * globals.S_H_R)) self.record_chord_tolerance_label.setText("Record Chord Tolerance:") self.record_chord_tolerance_lineEdit = QtWidgets.QLineEdit(self.color_tab) self.record_chord_tolerance_lineEdit.setGeometry(QtCore.QRect(200* globals.S_W_R, 120 * globals.S_H_R, 280 * globals.S_W_R, 20 * globals.S_H_R)) self.arduino_handshake_timeout_label = QtWidgets.QLabel(self.color_tab) self.arduino_handshake_timeout_label.setGeometry(QtCore.QRect(20 * globals.S_W_R, 160* globals.S_H_R, 200 * globals.S_W_R, 20 * globals.S_H_R)) self.arduino_handshake_timeout_label.setText("Arduino Handshake Timeout:") self.arduino_handshake_timeout_lineEdit = QtWidgets.QLineEdit(self.color_tab) self.arduino_handshake_timeout_lineEdit.setGeometry(QtCore.QRect(200 * globals.S_W_R, 160 * globals.S_H_R, 280 * globals.S_W_R, 20 * globals.S_H_R)) self.line = QtWidgets.QFrame(self.color_tab) self.line.setGeometry(QtCore.QRect(10 * globals.S_W_R, 230 * globals.S_H_R, 481 * globals.S_W_R, 16 * globals.S_H_R)) self.line.setFrameShape(QtWidgets.QFrame.HLine) self.line.setFrameShadow(QtWidgets.QFrame.Sunken) self.line.setObjectName("line") #----------------------------------------------------------------------- # Tab Widget -> Tutoring Tab -> Color Section self.colorsettings_label = QtWidgets.QLabel(self.color_tab) self.colorsettings_label.setGeometry(QtCore.QRect(210 * globals.S_W_R, 250 * globals.S_H_R, 81 * globals.S_W_R, 20 * globals.S_H_R)) self.colorsettings_label.setObjectName("colorsettings_label_2") bw_y = ( 250 + 40 ) * globals.S_H_R space = 20 * globals.S_H_R self.black_key_label = QtWidgets.QLabel(self.color_tab) self.black_key_label.setGeometry(QtCore.QRect(80 * globals.S_W_R, bw_y, 61 * globals.S_W_R, 16 * globals.S_H_R)) self.black_key_label.setObjectName("black_key_label") self.black_key_pushButton = QtWidgets.QPushButton(self.color_tab) self.black_key_pushButton.setGeometry(QtCore.QRect(40 * globals.S_W_R, bw_y + space, 141 * globals.S_W_R, 61 * globals.S_H_R)) self.black_key_pushButton.setText("") self.black_key_pushButton.setObjectName("black_key_pushButton") self.white_key_label = QtWidgets.QLabel(self.color_tab) self.white_key_label.setGeometry(QtCore.QRect(360 * globals.S_W_R, bw_y, 71 * globals.S_W_R, 16 * globals.S_H_R)) self.white_key_label.setObjectName("white_key_label") self.white_key_pushButton = QtWidgets.QPushButton(self.color_tab) self.white_key_pushButton.setGeometry(QtCore.QRect(320 * globals.S_W_R, bw_y + space, 141 * globals.S_W_R, 61 * globals.S_W_R)) self.white_key_pushButton.setText("") self.white_key_pushButton.setObjectName("white_key_pushButton") wu_y = ( 390 + 40 ) * globals.S_H_R self.wrong_label = QtWidgets.QLabel(self.color_tab) self.wrong_label.setGeometry(QtCore.QRect(75 * globals.S_W_R, wu_y, 71 * globals.S_W_R, 16 * globals.S_H_R)) self.wrong_label.setObjectName("wrong_label") self.wrong_pushButton = QtWidgets.QPushButton(self.color_tab) self.wrong_pushButton.setGeometry(QtCore.QRect(40 * globals.S_W_R, wu_y + space, 141 * globals.S_W_R, 61 * globals.S_H_R)) self.wrong_pushButton.setText("") self.wrong_pushButton.setObjectName("wrong_pushButton") self.upcoming_label = QtWidgets.QLabel(self.color_tab) self.upcoming_label.setGeometry(QtCore.QRect(350 * globals.S_W_R, wu_y, 91 * globals.S_W_R, 16 * globals.S_H_R)) self.upcoming_label.setObjectName("upcoming_label") self.upcoming_pushButton = QtWidgets.QPushButton(self.color_tab) self.upcoming_pushButton.setGeometry(QtCore.QRect(320 * globals.S_W_R, wu_y + space, 141 * globals.S_W_R, 61 * globals.S_H_R)) self.upcoming_pushButton.setText("") self.upcoming_pushButton.setObjectName("upcoming_pushButton") self.tabWidget.addTab(self.color_tab, "") self.retranslate_ui() self.tabWidget.setCurrentIndex(0) self.apply_close_buttonBox.accepted.connect(self.accept) self.apply_close_buttonBox.rejected.connect(self.close) QtCore.QMetaObject.connectSlotsByName(self) def setup_func(self): """ This function places all the slot and signals for the widgets of the settings dialog. """ self.browse_button_group = QtWidgets.QButtonGroup() self.browse_button_group.addButton(self.audiveris_pushButton) self.browse_button_group.addButton(self.amazingmidi_pushButton) self.browse_button_group.addButton(self.anthemscore_pushButton) self.browse_button_group.addButton(self.muse_score_pushButton) self.browse_button_group.buttonClicked.connect(self.upload_exe_file) self.browse_button_dict = {self.audiveris_pushButton: ['', self.audiveris_lineEdit, 'audiveris'], self.amazingmidi_pushButton: ['',self.amazingmidi_lineEdit, 'amazing_midi'], self.anthemscore_pushButton: ['', self.anthemscore_lineEdit,'anthemscore'], self.muse_score_pushButton: ['', self.muse_score_lineEdit, 'muse_score']} self.port_dict = {self.piano_port_comboBox: ['','piano'], self.piano_size_comboBox: ['','piano_size'], self.arduino_port_comboBox: ['','arduino'], self.arduino_baud_rate_comboBox: ['', 'arduino baud rate']} self.piano_size_comboBox.addItem('76 Key Piano') self.piano_size_comboBox.addItem('88 Key Piano') self.arduino_baud_rate_comboBox.addItem('300') self.arduino_baud_rate_comboBox.addItem('600') self.arduino_baud_rate_comboBox.addItem('1200') self.arduino_baud_rate_comboBox.addItem('4800') self.arduino_baud_rate_comboBox.addItem('9600') self.arduino_baud_rate_comboBox.addItem('14400') self.arduino_baud_rate_comboBox.addItem('19200') self.arduino_baud_rate_comboBox.addItem('28800') self.arduino_baud_rate_comboBox.addItem('38400') self.arduino_baud_rate_comboBox.addItem('57600') self.arduino_baud_rate_comboBox.addItem('115200') self.arduino_baud_rate_comboBox.addItem('230400') self.timing_button_dict = {self.chord_tick_tolerance_lineEdit: ['', 'chord tick tolerance'], self.chord_sum_tolerance_lineEdit: ['','chord sum tolerance'], self.record_chord_tolerance_lineEdit: ['', 'record chord tolerance'], self.arduino_handshake_timeout_lineEdit: ['', 'count timeout'] } self.color_button_group = QtWidgets.QButtonGroup() self.color_button_group.addButton(self.black_key_pushButton) self.color_button_group.addButton(self.white_key_pushButton) self.color_button_group.addButton(self.wrong_pushButton) self.color_button_group.addButton(self.upcoming_pushButton) self.color_button_group.buttonClicked.connect(self.color_picker) self.color_button_dict = {self.black_key_pushButton: ['','black'], self.white_key_pushButton: ['','white'], self.wrong_pushButton: ['','wrong'], self.upcoming_pushButton: ['','upcoming'] } self.apply_close_buttonBox.button(QtWidgets.QDialogButtonBox.Apply).clicked.connect(self.apply_changes) return None #--------------------------------------------------------------------------- # Path Section Functions def open_file_name_dialog_exe_file(self): """ This file dialog is used to obtain the file location of the .exe file. """ options = QtWidgets.QFileDialog.Options() options |= QtWidgets.QFileDialog.DontUseNativeDialog fileName, _ = QtWidgets.QFileDialog.getOpenFileName(self, "Select .exe/.bat File", "", "Executiable Files (*.exe);; Batch Files (*.bat)", options=options) if fileName: file_dialog_output = str(fileName) else: return "" file_dialog_output = file_dialog_output.replace('/' , '\\' ) return file_dialog_output def open_directory_name_dialog_exe_path(self): """ This file dialog is used to obtain the folder directory of the desired exe folder location. """ options = QtWidgets.QFileDialog.Options() options |= QtWidgets.QFileDialog.ShowDirsOnly options |= QtWidgets.QFileDialog.DontUseNativeDialog directory = QtWidgets.QFileDialog.getExistingDirectory(self, caption = 'Select a folder', options = options) if directory: file_dialog_output = str(directory) else: return "" file_dialog_output = file_dialog_output.replace('/' , '\\' ) return file_dialog_output def upload_exe_file(self, button): """ This function decides wether to use the exe file or exe path function. If the pushButton is for audiveris, utlize the exe path. Else, use the standard exe file function. """ upload_exe_path = self.open_file_name_dialog_exe_file() if upload_exe_path != '': self.browse_button_dict[button][0] = upload_exe_path self.update_settings() return None #--------------------------------------------------------------------------- # Color def color_picker(self, button): """ This function creates a QColorDialog when the user clicks the color wheel color. Once the user selects a color, it will display the RGB colors in the lineedits. """ color = QtWidgets.QColorDialog.getColor() if color.isValid(): # Converting Hexadecimal to RGB values value = color.name() value = value.lstrip('#') rgb = tuple(int(value[i:i+2], 16) for i in (0, 2, 4)) rgb = str(rgb)[1:-1].replace(" ","") self.color_button_dict[button][0] = rgb button.setStyleSheet('background-color:rgb({})'.format(rgb)) return None #--------------------------------------------------------------------------- # Reading Settings def read_all_settings(self): """ This function reads all the settings in the config.yml and stores them in dictionaries that correlate the settings to the widgets. """ cfg = read_config() # Path Settings for key in self.browse_button_dict.keys(): self.browse_button_dict[key][0] = cfg['app_path'][self.browse_button_dict[key][2]] # Mp3 to midi Settings self.mp3_to_midi_setting = cfg['app_path']['open_close_source'] # Port Settings for key in self.port_dict.keys(): self.port_dict[key][0] = cfg['port'][self.port_dict[key][1]] # Timing Settings for key in self.timing_button_dict.keys(): self.timing_button_dict[key][0] = cfg['timing'][self.timing_button_dict[key][1]] # Color Settings for key in self.color_button_dict.keys(): self.color_button_dict[key][0] = cfg['color'][self.color_button_dict[key][1]] return None def update_settings(self): """ This function places the information of the settings into the widgets, such as placing the value or color to the widget. """ # Path Settings for button in self.browse_button_dict: self.browse_button_dict[button][1].setText(self.browse_button_dict[button][0]) # Mp3 to midi Settings if self.mp3_to_midi_setting == 'open_source': self.open_source_radioButton.setChecked(True) self.close_source_radioButton.setChecked(False) elif self.mp3_to_midi_setting == 'close_source': self.close_source_radioButton.setChecked(True) self.open_source_radioButton.setChecked(False) # Port Settings for key in self.port_dict.keys(): if self.port_dict[key][1] == 'piano_size': key.setCurrentText(str(self.port_dict[key][0]) + ' Key Piano') elif key == self.arduino_baud_rate_comboBox: key.setCurrentText(str(self.port_dict[key][0])) else: key.addItem(str(self.port_dict[key][0])) key.setCurrentText(str(self.port_dict[key][0])) # Timing Settings for key in self.timing_button_dict.keys(): key.setText(str(self.timing_button_dict[key][0])) # Color Settings for key in self.color_button_dict.keys(): rgb = self.color_button_dict[key][0] key.setStyleSheet('background-color:rgb({})'.format(rgb)) return None def apply_changes(self): """ This fuction applies any of the changes done by the user to the settings. This changes are recorded in the config.yml file. """ cfg = read_config() # Apply Path for button in self.browse_button_dict: text = self.browse_button_dict[button][1].text() cfg['app_path'][self.browse_button_dict[button][2]] = text # Mp3 to midi Settings if self.open_source_radioButton.isChecked(): cfg['app_path']['open_close_source'] = 'open_source' elif self.close_source_radioButton.isChecked(): cfg['app_path']['open_close_source'] = 'close_source' # Color Settings for key in self.color_button_dict.keys(): rgb = self.color_button_dict[key][0] cfg['color'][self.color_button_dict[key][1]] = rgb for key in self.timing_button_dict.keys(): cfg['timing'][self.timing_button_dict[key][1]] = int(key.text()) # Port Settings for key in self.port_dict.keys(): index = key.currentIndex() if index == -1: continue if key == self.piano_port_comboBox or key == self.arduino_port_comboBox: cfg['port'][self.port_dict[key][1]] = key.currentText() elif key == self.piano_size_comboBox: cfg['port'][self.port_dict[key][1]] = key.currentText()[:2] elif key == self.arduino_baud_rate_comboBox: cfg['port'][self.port_dict[key][1]] = int(key.currentText()) update_config(cfg) print("Applied Changes") self.finish_apply_signal.emit() return None #--------------------------------------------------------------------------- # Misc Functions def retranslate_ui(self): """ This function places all the text content in the configuration dialog widgets. """ _translate = QtCore.QCoreApplication.translate self.anthemscore_pushButton.setText(_translate("Dialog", "Browse")) self.anthemscore_label.setText(_translate("Dialog", "AnthemScore [.exe] (Optional)")) self.audiveris_pushButton.setText(_translate("Dialog", "Browse")) self.audiveris_label.setText(_translate("Dialog", "Audiveris [folder]")) self.amazingmidi_pushButton.setText(_translate("Dialog", "Browse")) self.amazingmidi_label.setText(_translate("Dialog", "AmazingMIDI [.exe]")) self.muse_score_label.setText(_translate("Dialog", "MuseScore [.exe]")) self.muse_score_pushButton.setText(_translate("Dialog", "Browse")) self.configure_path_label.setText(_translate("Dialog", "Configure the path for each program.")) self.mp3_to_midi_converter_label.setText(_translate("Dialog", "MP3 to MIDI Converter:")) self.open_source_radioButton.setText(_translate("Dialog", "Open-Source")) self.close_source_radioButton.setText(_translate("Dialog", "Close-Source")) self.piano_port_label.setText(_translate("Dialog", "Piano Port")) self.piano_size_label.setText(_translate("Dialog", "Piano Size")) self.portsettings_label.setText(_translate("Dialog", "Port Settings")) self.arduinoport_label.setText(_translate("Dialog", "Arduino Port")) self.tabWidget.setTabText(self.tabWidget.indexOf(self.path_and_comm_tab), _translate("Dialog", "Path and Communication Settings")) self.timingsettings_label.setText(_translate("Dialog", "Timing Settings")) self.colorsettings_label.setText(_translate("Dialog", "Color Settings")) self.black_key_label.setText(_translate("Dialog", "Black Keys")) self.white_key_label.setText(_translate("Dialog", "White Keys")) self.wrong_label.setText(_translate("Dialog", "Wrong Note")) self.upcoming_label.setText(_translate("Dialog", "Upcoming Note")) self.tabWidget.setTabText(self.tabWidget.indexOf(self.color_tab), _translate("Dialog", "Tutoring Settings")) #----------------------------------------------------------------------- # Text Scaling font = self.anthemscore_label.font() font.setPixelSize(13) print("Prescaling Font Pixel Size: ", font.pixelSize()) font.setPixelSize(font.pixelSize() * globals.S_W_R) print("Postscaling Font Pixel Size: ", font.pixelSize()) text_group = [self.anthemscore_pushButton, self.anthemscore_label, self.anthemscore_lineEdit, self.audiveris_pushButton, self.audiveris_label, self.audiveris_lineEdit, self.amazingmidi_pushButton, self.amazingmidi_label, self.amazingmidi_lineEdit, self.muse_score_pushButton, self.muse_score_label, self.muse_score_lineEdit, self.configure_path_label, self. mp3_to_midi_converter_label, self.piano_port_label, self.piano_size_label, self.piano_size_comboBox, self.portsettings_label, self.arduinoport_label, self.piano_port_comboBox, self.arduino_port_comboBox, self.timingsettings_label, self.colorsettings_label, self.black_key_label, self.white_key_label, self.wrong_label, self.upcoming_label, self.arduino_baud_rate_comboBox, self.open_source_radioButton, self.close_source_radioButton, self.chord_tick_tolerance_label, self.chord_tick_tolerance_lineEdit, self.chord_sum_tolerance_label, self.chord_sum_tolerance_lineEdit, self.record_chord_tolerance_label, self.record_chord_tolerance_lineEdit, self.arduino_handshake_timeout_label, self.arduino_handshake_timeout_lineEdit, self.apply_close_buttonBox, self.tabWidget] for element in text_group: element.setFont(font) #------------------------------------------------------------------------------- # Main Code if __name__ == "__main__": app = QtWidgets.QApplication(sys.argv) config_dialog = ConfigDialog() config_dialog.show() sys.exit(app.exec_())

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184

0.671797

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33,787

4.96676

0.095769

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0.0417

0.046333

0.600178

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0.296298

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null

0

null

0

1

0

9da270a879210ead826c86bdc8c185c7e2c0effa

1,814

py

Python

valorant/caller.py

frissyn/valorant.py

49abceab5cc1f3af016ce0b1d253d10089aeb0b4

[ "MIT" ]

56

2021-01-22T01:48:23.000Z

2022-03-31T20:44:23.000Z

valorant/caller.py

Tominous/valorant.py

b462441ab4ab403123ad245cab30f3abbd891a66

[ "MIT" ]

20

2021-02-03T10:40:37.000Z

2022-03-24T11:23:57.000Z

valorant/caller.py

Tominous/valorant.py

b462441ab4ab403123ad245cab30f3abbd891a66

[ "MIT" ]

15

2021-03-24T01:17:58.000Z

2022-02-01T02:10:27.000Z

import requests from .values import ROUTES from .values import LOCALES from .values import REGIONS from .values import ENDPOINTS def value_check(*args): KEYS = ROUTES + LOCALES + REGIONS for arg in args: if arg not in KEYS: raise ValueError else: return True class WebCaller(object): def __init__(self, token: str, locale: str, region: str, route: str): self.base = "https://{root}.api.riotgames.com/" self.eps = ENDPOINTS["web"] self.sess = requests.Session() self.sess.params.update({"locale": locale}) self.sess.headers.update( { "Accept-Charset": "application/x-www-form-urlencoded; charset=UTF-8", "User-Agent": "Mozilla/5.0", "X-Riot-Token": token, } ) if value_check(locale, region, route): self.locale = locale self.region = region self.route = route def call(self, m: str, ep: str, params=None, route=False, **kw): if ep not in list(self.eps.keys()): raise ValueError else: pass prefix = self.base.format(root=self.route if route else self.region) url = prefix + self.eps[ep].format(**kw) r = self.sess.request(m, url, params=params) r.raise_for_status() return r.json() class ClientCaller(object): def __init__(self, token: str): self.base = "https://pd.{code}.a.pvp.net/" self.token = token self.sess = requests.Session() self.sess.headers.update( { "Authorization": f"Bearer {token}", "Content-Type": "application/json", "X-Riot-Entitlements-JWT": "riot_entitlement", } )

27.484848

85

0.555678

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1,814

4.693396

0.424528

0.048241

0.064322

0.046231

0.112563

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0.002435

0.320838

1,814

65

86

27.907692

0.805195

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0.156863

0

0.142778

0.031422

0

1

0.078431

false

0.019608

0.098039

0

0.254902

0

null

0

null

0

1

0

9da846794dabe811239a290251111e03ccfb593a

1,256

py

Python

test_LearnSubtitles.py

heitor31415/LearnSubtitles

153178ea11d700a49a1f3692de39e8fc81e3cc4e

[ "MIT" ]

8

2020-02-13T03:08:25.000Z

2021-01-11T20:28:39.000Z

test_LearnSubtitles.py

heitor31415/LearnSubtitles

153178ea11d700a49a1f3692de39e8fc81e3cc4e

[ "MIT" ]

1

2020-04-28T19:48:16.000Z

2020-04-29T12:28:15.000Z

test_LearnSubtitles.py

heitor31415/LearnSubtitles

153178ea11d700a49a1f3692de39e8fc81e3cc4e

[ "MIT" ]

1

2020-03-14T00:46:36.000Z

import os import pytest from typing import Any, Callable, Dict, List import LearnSubtitles as ls def prepare(language: str) -> List: """ Create LearnSubtitles objects for every subtitle in folder 'language' """ test_dir = "testfiles/" + language subs = [ ls.LearnSubtitles(os.path.abspath(os.path.join(test_dir, x)), language) for x in os.listdir(test_dir) ] return subs languages = ["de", "en", "pt"] # supported languages def test_LearnSubtitles_parsing(): for language in languages: subs = prepare(language) for sub in subs: assert len(sub.text) != 0 def test_LearnSubtitles_bad_file(): with pytest.raises(FileNotFoundError): ls.LearnSubtitles(os.path.abspath("testfiles/fail/fail.srt"), "en") with pytest.raises(ls.LearnSubtitlesError): ls.LearnSubtitles(os.path.abspath("testfiles/fail/bad_file.srt"), "en") def test_LearnSubtitles_level(): levels = ["A1", "A2", "B1"] subs = [ ls.LearnSubtitles( "testfiles/de/Nicos Weg – " + level + " – Ganzer Film - German.srt", "de" ) for level in levels ] assert subs[0].film_level > subs[1].film_level assert subs[1].film_level > subs[2].film_level

26.723404

85

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4.975155

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0.141074

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0

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1,256

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null

0

null

0

1

0

9dacec32c244293fcf0c09720725cd6c562e10da

4,888

py

Python

fast_downloader_mt/main.py

Kirozen/fast-downloader

febdcc8b6a6ad3b8d263a8923b8f24e8402df618

[ "MIT" ]

null

fast_downloader_mt/main.py

Kirozen/fast-downloader

febdcc8b6a6ad3b8d263a8923b8f24e8402df618

[ "MIT" ]

null

fast_downloader_mt/main.py

Kirozen/fast-downloader

febdcc8b6a6ad3b8d263a8923b8f24e8402df618

[ "MIT" ]

null

from __future__ import annotations import multiprocessing import os import re import sys from concurrent.futures import ThreadPoolExecutor from dataclasses import dataclass, field from itertools import chain from pathlib import Path from urllib.parse import urlparse import click import requests from requests.models import HTTPError from rich.progress import ( BarColumn, DownloadColumn, Progress, TextColumn, TimeRemainingColumn, TransferSpeedColumn, ) @dataclass class DownloadFile: urls: list[str] dest: Path = Path.cwd() filename: str = field(init=False) def __post_init__(self): self.filename = Path(self.urls[0]).name @property def filepath(self): return self.dest / self.filename BUFFER_SIZE = 32768 progress = Progress( TextColumn("[bold blue]{task.fields[filename]}", justify="right"), BarColumn(bar_width=None), "[progress.percentage]{task.percentage:>3.1f}%", "•", DownloadColumn(), "•", TransferSpeedColumn(), "•", TimeRemainingColumn(), ) def parse_aria2(data: list[str], destination: Path): files = [] out_re = re.compile(r"^\s+out=(?P<out>.*)$") for line in data: if line.startswith("#") or not line: continue if line.startswith("http"): files.append(DownloadFile(line.split("\t"), destination)) else: match_out = out_re.match(line) if match_out: files[-1].filename = match_out.groupdict()["out"] return files def get_inputs(inputs: list[str], destination: Path, aria2_compatibility: bool): paths = [] for input in inputs: lines = Path(input).read_text().splitlines(keepends=False) if aria2_compatibility: paths.extend(parse_aria2(lines, destination)) else: paths.extend( DownloadFile([url], destination) for url in lines if url.startswith("http") ) return paths def downloader(downloadfile: DownloadFile, buffer_size: int, quiet: bool): if not quiet: task_id = progress.add_task( "download", filename=downloadfile.filename, ) iterator = iter(downloadfile.urls) response = None try: while not response: url = next(iterator) try: response = requests.get(url, allow_redirects=True, stream=True) response.raise_for_status() except HTTPError: response = None if not quiet: size = int(response.headers.get("content-length")) progress.update(task_id, total=size) with open(downloadfile.filepath, "wb") as handler: if not quiet: progress.start_task(task_id) for data in response.iter_content(chunk_size=buffer_size): handler.write(data) if not quiet: progress.update(task_id, advance=len(data)) except StopIteration: print("Urls are not available") def executor(threads, downloadfiles, buffer_size, quiet): with ThreadPoolExecutor(max_workers=threads) as pool: for downloadfile in sorted( downloadfiles, key=lambda df: len(df.filename), reverse=True ): try: for url in downloadfile.urls: urlparse(url) except ValueError: print(f"An url in {downloadfile.urls} is not valid!", file=sys.stderr) continue pool.submit(downloader, downloadfile, buffer_size, quiet) @click.command() @click.option( "-t", "--threads", default=lambda: multiprocessing.cpu_count(), type=click.IntRange(min=1, max=1000, clamp=True), help="thread number", ) @click.option( "-i", "--input", "inputs", multiple=True, type=click.Path(exists=True, file_okay=True), help="input file", ) @click.option("-q", "--quiet", is_flag=True) @click.option( "-d", "--destination", type=click.Path(dir_okay=True, allow_dash=True), default=Path(os.getcwd()), ) @click.option("--aria2-compatibility", is_flag=True) @click.option( "--buffer-size", type=click.IntRange(min=1, clamp=True), default=BUFFER_SIZE ) @click.argument("urls", nargs=-1, type=click.Path()) def fast_downloader( threads, inputs, quiet, destination, buffer_size, aria2_compatibility, urls ): download_urls = (DownloadFile([url], Path(destination)) for url in urls) download_files = list( chain(download_urls, get_inputs(inputs, Path(destination), aria2_compatibility)) ) if quiet: executor(threads, download_files, buffer_size, quiet) else: with progress: executor(threads, download_files, buffer_size, quiet) if __name__ == "__main__": fast_downloader()

28.091954

88

0.625818

550

4,888

5.447273

0.341818

0.033378

0.013351

0.014686

0.056742

0.028705

0

0.006354

0.259411

4,888

173

89

28.254335

0.820442

0

0.165563

0

0.067308

0.019231

0

1

0.046358

false

0

0.092715

0.006623

0.18543

0.013245

0

null

0

null

0

1

0

9dad8057a50b53867020fcecaeb0676d2cfff102

4,362

py

Python

sitch/sitchlib/geo_correlator.py

codecuisine/sensor

06fb0908178af1ab673b95e7f435b873cc62e61b

[ "ECL-2.0", "Apache-2.0", "BSD-2-Clause" ]

68

2016-08-08T17:28:59.000Z

2021-11-26T09:31:52.000Z

sitch/sitchlib/geo_correlator.py

codecuisine/sensor

06fb0908178af1ab673b95e7f435b873cc62e61b

[ "ECL-2.0", "Apache-2.0", "BSD-2-Clause" ]

61

2016-08-20T21:01:01.000Z

2020-07-22T06:10:45.000Z

sitch/sitchlib/geo_correlator.py

codecuisine/sensor

06fb0908178af1ab673b95e7f435b873cc62e61b

[ "ECL-2.0", "Apache-2.0", "BSD-2-Clause" ]

40

2017-01-28T23:06:22.000Z

2021-08-13T15:09:43.000Z

"""Correlate based on geograpgic information.""" from alert_manager import AlertManager from utility import Utility class GeoCorrelator(object): """Geographic correlator.""" def __init__(self, device_id): """Initialize the Geographic Correlator.""" self.geo_anchor = {} self.threshold = 100 self.time_threshold = 10 self.device_id = device_id def correlate(self, scan_bolus): """Correlate one geo event. The first time we get a geo event, we set the state and print a message to stdout to that effect. Every subsequent message is compared against the geo_anchor. Once the anchor is set, it does not change for the life of the instance. Correlation of subsequent events causes the distance beween the anchor and current event to be determined and if the threshold of 10km is exceeded, an alert is returned. Args: scan_bolus (tuple): Two-item tuple. Position 0 contains the scan type, which is not checked. We should only ever have geo events coming through this method. Position 1 is expected to contain geo json. Returns: list: List of alerts. If no alerts are fired, the list returned is zero-length. """ scan_body = scan_bolus[1] if self.geo_anchor == {}: self.geo_anchor = scan_body print("GeoCorrelator: Setting anchor to %s" % str(scan_body)) alerts = [] else: alerts = GeoCorrelator.geo_drift_check(self.geo_anchor, scan_body, self.threshold, self.device_id) for alert in GeoCorrelator.time_drift_check(scan_body, self.time_threshold, self.device_id): alerts.append(alert) for alert in alerts: alert[1]["site_name"] = scan_body["site_name"] alert[1]["sensor_name"] = scan_body["sensor_name"] alert[1]["sensor_id"] = scan_body["sensor_id"] return alerts @classmethod def geo_drift_check(cls, geo_anchor, gps_scan, threshold, device_id): """Fire alarm if distance between points exceeds threshold. Args: geo_anchor (dict): Geographic anchor point, usually stored in an instance variable and passed in via the `correlate()` method. gps_scan (dict): Same format as geo_anchor, expects the same format as `geo_anchor`. threshold (int): Alerting threshold in km. Returns: list: list of alerts (usually just one) or an empty list of there are no alerts. """ lat_1 = geo_anchor["location"]["coordinates"][1] lon_1 = geo_anchor["location"]["coordinates"][0] lat_2 = gps_scan["location"]["coordinates"][1] lon_2 = gps_scan["location"]["coordinates"][0] current_distance = Utility.calculate_distance(lon_1, lat_1, lon_2, lat_2) if current_distance < threshold: return [] else: message = "Possible GPS spoofing attack! %d delta from anchor at %s / %s %s !" % (current_distance, gps_scan["site_name"], gps_scan["sensor_name"], Utility.create_gmaps_link(lat_1, lon_1)) # NOQA alert = AlertManager(device_id).build_alert(300, message, gps_scan["location"]) return[alert] @classmethod def time_drift_check(cls, gps_scan, threshold_mins, device_id): """Checks drift value, alarms if beyond threshold.""" current_delta = gps_scan["time_drift"] if current_delta < threshold_mins: return [] else: message = "Possible GPS time spoofing attack! %d delta from system at %s / %s" % (current_delta, gps_scan["site_name"], gps_scan["sensor_name"]) # NOQA alert = AlertManager(device_id).build_alert(310, message, gps_scan["location"]) return[alert]

44.969072

208

0.570381

505

4,362

4.744554

0.330693

0.035058

0.020033

0.01419

0.231219

0.086811

0.059265

0.026711

0

0.01162

0.348923

4,362

96

209

45.4375

0.832042

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0

0.211538

0

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0

1

0.076923

false

0

0.038462

0

0.192308

0.019231

0

null

0

null

0

1

0

9dadf1bb28dc34ec81f4c906780d3dcd3137e862

1,697

py

Python

grid_search_results_v1/get_vals_heatmap.py

malfarasplux/pnet2019

ae34d5c84fb4d3985634b237a14dfb69e98b8339

[ "BSD-3-Clause" ]

1

2020-11-29T12:42:30.000Z

grid_search_results_v1/get_vals_heatmap.py

malfarasplux/pnet2019

ae34d5c84fb4d3985634b237a14dfb69e98b8339

[ "BSD-3-Clause" ]

null

grid_search_results_v1/get_vals_heatmap.py

malfarasplux/pnet2019

ae34d5c84fb4d3985634b237a14dfb69e98b8339

[ "BSD-3-Clause" ]

null

import numpy as np import matplotlib.pyplot as plt N =[20,40,50,75,100,150,200] scale = [0.0001, 0.001, 0.005, 0.01, 0.1, 1, 10] mem = [0.001, 0.01, 0.1, 0.13, 0.25, 0.5, 1] sigexp = [0.01, 0.1, 0.5, 1, 2, 5, 10] val_key = {} with open("./grid_search_results_v1/F1_report.txt") as f: for i, line in enumerate(f): lineval = line.split()[0] print ("line {0} = {1}".format(i, lineval)) val_key[lineval.split(".txt:")[0][7:]] = float(lineval.split(".txt:")[1]) F1_matrix = np.zeros((len(scale),len(mem)),dtype=np.float) N_i = str(200) sigexp_i = str(0.1) for i in range(len(scale)): scale_i = str(scale[i]) for j in range(len(mem)): mem_i = str(mem[j]) key_i = N_i + "_" + scale_i + "_" + mem_i + "_" + sigexp_i F1_matrix[i,j] = val_key[key_i] fig, ax = plt.subplots() im = ax.imshow(F1_matrix) ax.set_title("Grid search F1 opt") ax.set_xticks(np.arange(len(mem))) ax.set_yticks(np.arange(len(scale))) ax.set_xticklabels(mem) ax.set_yticklabels(scale) ax.set_xlabel('mem') ax.set_ylabel('scale') cbar = ax.figure.colorbar(im, ax=ax) # Loop over data dimensions and create text annotations. for i in range(len(scale)): for j in range(len(mem)): text = ax.text(j, i, F1_matrix[i, j], ha="center", va="center", color="w")

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1,697

3.171429

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0.05148

0.019305

0.136422

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0

0.080675

0.371833

1,697

43

161

39.465116

0.648218

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0.063376

0.023157

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1

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false

0

0.057143

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0.057143

0.028571

0

null

0

null

0

1

0

9dafa0a196d3c478e9ef8c55c4f9dd2dd56b60ad

1,457

py

Python

_snippets/scrape_RAND_pdfs.py

vashu1/data_snippets

b0ae5230d60c2054c7b9278093533b7f71f3758b

[ "MIT" ]

1

2021-02-10T20:33:43.000Z

_snippets/scrape_RAND_pdfs.py

vashu1/data_snippets

b0ae5230d60c2054c7b9278093533b7f71f3758b

[ "MIT" ]

null

_snippets/scrape_RAND_pdfs.py

vashu1/data_snippets

b0ae5230d60c2054c7b9278093533b7f71f3758b

[ "MIT" ]

null

# scrape articles from RAND site, see https://vashu11.livejournal.com/20523.html import re import requests from bs4 import BeautifulSoup import os content = ['https://www.rand.org/pubs/papers.html'] + ['https://www.rand.org/pubs/papers.{}.html'.format(i) for i in range(2, 108)] def get_articles(page): page = requests.get(page) soup = BeautifulSoup(page.content, 'html.parser') return [('https://www.rand.org' + link.get('href')) for link in soup.findAll('a', attrs={'href': re.compile("/pubs/papers/.*")})] def get_pdfs(link): page = requests.get(link) soup = BeautifulSoup(page.content, 'html.parser') name = soup.findAll('h1', attrs={'id': 'RANDTitleHeadingId'})[0].text return set([(name, ('https://www.rand.org' if not 'http' in link.get('href') else '') + link.get('href')) for link in soup.findAll('a', attrs={'href': re.compile(".*\.pdf")})]) os.mkdir('pdfs') for page in content[11:]: print('PAGE', page) articles = get_articles(page) for article in articles: print('ARTICLE', article) c = 0 for d in get_pdfs(article): name, link = d if c > 0: name += '_{}'.format(c) print('NAME', name) r = requests.get(link) l = len(r.content) print('LEN', l) with open('./pdfs/' + re.sub('[^\w\-_\. ]', '_', name) + '.pdf', 'wb') as f: f.write(r.content) c += 1

38.342105

180

0.577213

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1,457

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0.057554

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0

0.016949

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1,457

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0.125

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null

0

null

0

1

0

9db66809b3f7cfe04fff2e0d4fd9725d23130f54

2,422

py

Python

inputs/fino2_dats.py

a2edap/WE-Validate

6e4be8228c9b4f66fb1a056f7566030b79441f2e

[ "BSD-3-Clause" ]

1

2022-01-21T08:09:03.000Z

inputs/fino2_dats.py

a2edap/WE-Validate

6e4be8228c9b4f66fb1a056f7566030b79441f2e

[ "BSD-3-Clause" ]

null

inputs/fino2_dats.py

a2edap/WE-Validate

6e4be8228c9b4f66fb1a056f7566030b79441f2e

[ "BSD-3-Clause" ]

1

2021-06-14T09:32:36.000Z

# A parser for multiple FINO2 .dat files in a directory. import os import pathlib import pandas as pd import numpy as np import glob import sys class fino2_dats: """FINO2 data class """ def __init__(self, info, conf): self.path = os.path.join( (pathlib.Path(os.getcwd()).parent), str(info['path']) ) self.var = info['var'] # self.lev = conf['levels']['height_agl'] self.target_var = info['target_var'] def get_ts(self, lev): """The directory can contain multiple FINO2 files, and each file contains data at one height level. The function only read in one data file at one height level. """ file_list = glob.glob(os.path.join(self.path, '*.dat')) for file in file_list: if str(lev)+'m' in file: df_all = pd.read_csv(file) # Get variable name and column names var_name = df_all.iloc[0][0].split(': ', 1)[1] col_names = df_all.iloc[3][0].split('\t')[1:] df = pd.read_csv(file, skiprows=6, sep='\s+') # Turn column names into 1st row df = pd.DataFrame(np.vstack([df.columns, df])) # Combine 2 time columns, hard coded df['t'] = df[0].map(str)+' '+df[1] # Drop duplicating columns df.pop(0) df.pop(1) # Reassign column names for i in range(len(col_names)): df[col_names[i]] = df[i+2] df.pop(i+2) df = df.set_index('t').sort_index() df.index = pd.to_datetime(df.index) # FINO data are averages centered at each 10-minute period # Data between 10:30 and 10:40 are averaged and labelled as # 10:35 # Apply correction to label data at the end of each period # Hence data between 10:30 and 10:40 are averaged and labelled # as 10:40 df.index = df.index+pd.Timedelta('5minutes') # Extract only 1 column of data out_df = df.loc[:, [self.var]] out_df.rename( columns={self.var: self.target_var}, inplace=True ) out_df = out_df.astype(float) return out_df

31.051282

78

0.514038

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3.776398

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0.020559

0.016447

0.026316

0.078947

0

0.031936

0.379438

2,422

77

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0

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false

0

0.162162

0

0.27027

0

null

0

null

0

1

0

9db67e536e2a5337dee11670942d6aa03db5b908

2,481

py

Python

bin/ess/dependencies.py

clu3bot/cora

de4d1af983c135184ebaf557271fa14c7c0e1849

[ "MIT" ]

null

bin/ess/dependencies.py

clu3bot/cora

de4d1af983c135184ebaf557271fa14c7c0e1849

[ "MIT" ]

null

bin/ess/dependencies.py

clu3bot/cora

de4d1af983c135184ebaf557271fa14c7c0e1849

[ "MIT" ]

null

import subprocess as sp import os import time import platform from os.path import exists #colar vars class color: lightblue='\033[1;34m' #light blue lightred='\033[1;31m' #light red lightgreen='\033[1;32m' #lightgreen red='\033[0;31m' #red yellow='\033[1;33m' #yellow none='\033[0m' #no color purple='\033[1;35m' #purple cyan='\033[0;36m' #cyan green='\033[0;32m' #green def permissions(): #checks for root permissions if not os.environ.get("SUDO_UID") and os.geteuid() != 0: print(color.lightred + "You need to run this script with sudo or as root.") time.sleep(0.3) quit() permissions() def getos(): osys=platform.system() if osys != "Linux": print(color.lightred + "This program only runs on Linux operating systems.") time.sleep(2) quit() getos() def check_file(): file = exists("tmp/flag.txt") if file == 'True': os.system("rm -rf tmp/flag.txt") else: time.sleep(0.5) check_file() #dependencies class dependencies: dependencie1 = 'mdk3' dependencie2 = 'aircrack-ng' dependencie3 = 'xterm' dependencie4 = 'macchanger' def check_mdk3(): check_d1 = sp.getoutput("bash etc/dpkg-check/dpkg-check-mdk3.sh") if check_d1 == '0': mdk3 = 'null' else: mdk3 = 'inst' return mdk3 def check_aircrack(): check_d2 = sp.getoutput("bash etc/dpkg-check/dpkg-check-aircrack-ng.sh") if check_d2 == '0': aircrack = 'null' else: aircrack = 'inst' return aircrack def check_xterm(): check_d3 = sp.getoutput("bash etc/dpkg-check/dpkg-check-xterm.sh") if check_d3 == '0': xterm = 'null' else: xterm = 'inst' return xterm def check_macchanger(): check_d4 = sp.getoutput("bash etc/dpkg-check/dpkg-check-macchanger.sh") if check_d4 == '0': macchanger = 'null' else: macchanger = 'inst' return macchanger def export(): mdk3 = check_mdk3() aircrack = check_aircrack() xterm = check_xterm() macchanger = check_macchanger() if mdk3 == 'null': flag = "null" elif aircrack == 'null': flag = "null" elif xterm == 'null': flag = "null" elif macchanger == "null": flag = "null" else: time.sleep(1) if flag == 'null': os.system("echo "+flag+" > tmp/flag.txt") else: check_file()

20.675

84

0.584442

320

2,481

4.46875

0.334375

0.05035

0.041958

0.05035

0.100699

0

0.046745

0.275695

2,481

119

85

20.84874

0.749026

0.044337

0

0.172414

0

0.221374

0.061917

0

1

0.091954

false

0

0.057471

0

0.367816

0.022989

0

null

0

null

0

1

0

9db72ff4ce32323ddaf8107b708ab0ac40987bfc

2,748

py

Python

src/bfh.py

Pella86/Snake4d

cdf3773b42efc888affa33dd22ebe56a48f6d979

[ "MIT" ]

79

2018-05-23T09:39:00.000Z

2021-11-29T02:26:07.000Z

src/bfh.py

Pella86/Snake4d

cdf3773b42efc888affa33dd22ebe56a48f6d979

[ "MIT" ]

1

2020-06-13T17:57:14.000Z

2020-06-16T15:53:40.000Z

src/bfh.py

Pella86/Snake4d

cdf3773b42efc888affa33dd22ebe56a48f6d979

[ "MIT" ]

6

2018-06-28T13:03:38.000Z

2021-03-06T14:24:32.000Z

# -*- coding: utf-8 -*- """ Created on Wed Jun 27 17:24:58 2018 @author: Mauro """ #============================================================================== # Imports #============================================================================== import struct #============================================================================== # Helpers #============================================================================== def as_bytes(dtype, data): return struct.pack(dtype, data) #============================================================================== # Constants #============================================================================== # little conversion table for the supported files type_to_size = {} type_to_size['I'] = 4 type_to_size['d'] = 8 type_to_size['c'] = 1 #============================================================================== # Binary file class #============================================================================== class BinaryFile: ''' reads the bytes from a file object with custom cumulative offset''' def __init__(self, fobj, co = 0): ''' self.file is a file object, self.co is the cumulative offset where to start the procedure ''' self.file = fobj self.co = co def write(self, dtype, data): ''' writes a data packet and moves the offset''' self.file.seek(self.co) b = as_bytes(dtype, data) self.file.write(b) self.co += len(b) def read(self, dtype): ''' reads a data packet and moves the offset, returns the data packet in the specified format ''' self.file.seek(self.co) size_read = type_to_size[dtype] b = self.file.read(size_read) self.co += size_read return struct.unpack(dtype, b)[0] def write_string(self, string): ''' Writess a string saving the length first and then the caracters encoded with UTF-8 ''' self.file.seek(self.co) strlen = len(string) #write str len self.write("I", strlen) fmt = 'c'*strlen data = [] for c in string: data.append(bytes(c, "utf-8")) b = struct.pack(fmt, *data) self.file.write(b) self.co += len(b) def read_string(self): ''' readst the string from a binary file... in ascii? mmh... ''' self.file.seek(self.co) # read the length strlen = self.read("I") b = self.file.read(strlen) s = str(b, "ascii") self.co += strlen return s

26.941176

79

0.409025

279

2,748

3.953405

0.329749

0.072529

0.045331

0.058024

0.17951

0.114234

0.063463

0

0.01003

0.274381

2,748

101

80

27.207921

0.543129

0.456696

0

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0.011791

0

1

0.15

false

0

0.025

0.275

0

null

0

null

0

1

0

9db736834f35ad283117ff978c76815cc0ba771c

8,726

py

Python

bin/read_analysis.py

louperelo/longmetarg

026b66c3621a4bcc71f5bc8a73955faf57978985

[ "MIT" ]

null

bin/read_analysis.py

louperelo/longmetarg

026b66c3621a4bcc71f5bc8a73955faf57978985

[ "MIT" ]

null

bin/read_analysis.py

louperelo/longmetarg

026b66c3621a4bcc71f5bc8a73955faf57978985

[ "MIT" ]

null

#!/usr/bin/env python import pandas as pd from scipy import stats import numpy as np #import seaborn as sns #import matplotlib.pyplot as plt import math from Bio import SeqIO import io import re import pysam from functools import reduce import argparse import os parser = argparse.ArgumentParser() parser.add_argument("--bam_file", metavar="<BAM>", dest="bam", help="enter the path to the alignment.bam file. By default 'aln_F4.bam' will be used", type=str, default="aln_F4.bam") parser.add_argument("--reads_fasta", metavar="<FASTA>", dest="fasta", help="enter the path to the original fasta file being analysed. By default 'reads.fasta' will be used", type=str, default="reads.fasta") parser.add_argument("--ident", metavar="<IDENT>", dest="ident", help="enter the int value for minimum identity. By default 80 will be used", type=int, default= 80) parser.add_argument("--cov_length", metavar="<COV>", dest="cov", help="enter the int value for minimum coverage length. By default 95 will be used", type=int, default= 95) parser.add_argument("--folder_out", metavar="<OUT>", dest="out", help="enter name for output files. By default 'arg_results' will be used", type=str, default="../out_dir/") parser.add_argument("--aro_idx", metavar="<IDX>", dest="idx", help="enter the path to the aro_index.csv file. By default 'aro_index.tsv' will be used", type=str, default="aro_index.tsv") # print help message for user parser.print_help() # get command line arguments args = parser.parse_args() # read files from path bam = args.bam fasta = args.fasta ident = args.ident covlen = args.cov folder = args.out idx = args.idx #read list of cigar tuples and get number of matches (0), insertions (1) or deletions (2) #auxiliary function in parse_bam() def read_cigar(lof_tup, idnum): x = 0 for t in lof_tup: if(t[0]==idnum): x += t[1] return x #Joins information from BAM file in pandas dataframe #query sequence: query_name, query_length #reference sequence: reference_name (gives one string, is split into ARO, ID, gene name and NCBI reference id), reference_start, reference_length #alignment: query_alignment_length, number of mismatches and gaps (tag 'NM) #calculates sequence identity % (identity(A,B)=100*(identical nucleotides / min(length(A),length(B)))), with identical nucleotides = query_alignment_length - NM #calculates cover length % (query_alignment_length*100 / reference_length) pd.options.mode.chained_assignment = None def parse_bam(bam_path): aln_file = pysam.AlignmentFile(bam_path, "rb") lst = [] # loop over alignments, get values per contig and store in list of lists (lst) for index, aln in enumerate(aln_file.fetch(until_eof = True)): #index = int(0 ... n), aln = all information on read substr = [aln.query_name, aln.query_length, aln.query_alignment_length, aln.get_tag('NM'), aln.reference_length, aln.reference_start, aln.cigartuples] #divide information in reference_name string = str(aln.reference_name) start=[] stop=[] for i, c in enumerate(string): if ((c==':')): start.append(i+1) elif (c=='|'): stop.append(i) else: continue stop.append(len(string)) for i in range(0, len(start)): #substr = [] substr.append(string[start[i]:stop[i]]) lst.append(substr) #print(lst[0:10]) df = pd.DataFrame(lst, columns=('contig_name', 'contig_length', 'aln_length', 'aln_nm', 'ref_length', 'ref_start', 'c_tuples', 'ref_ARO', 'ref_ID', 'ref_genename', 'ref_NCBI')) #get number of matches from cigar tuples df['matches'] = df['c_tuples'].apply(lambda x: read_cigar(x, 0)) df['insertions'] = df['c_tuples'].apply(lambda x: read_cigar(x, 1)) df['deletions'] = df['c_tuples'].apply(lambda x: read_cigar(x, 2)) #infer contig_length in repetitions of same contig_name (otherwise the value is 0) for i in range(1, df.shape[0]-1): if (df['contig_name'].iloc[i+1]==df['contig_name'].iloc[i]): df['contig_length'].iloc[i+1] = df['contig_length'].iloc[i] #calculate coverage length df['cov_length'] = df['aln_length']*100/df['ref_length'] #Sequence identity is the amount of characters which match exactly between two different sequences. #identity(A,B)=100% (num identical nucleotides / min(length(A),length(B))) df['cov_identity'] = 100*df['matches']/(df.loc[:,['aln_length','ref_length']].min(axis=1)) return df #Filter df for highest identity and coverlength rates def filter_best(df, ident, cov_l): return df[(df['cov_identity']>=ident) & (df['cov_length']>=cov_l)] #Filter assembly fasta for contigs of interest (data) and save to out_name.fasta #for taxonomic analysis def arg_contigs(data, fasta, out_name): #filter contigs with antibiotic resistance genes arg_contigs = data['contig_name'].drop_duplicates().to_list() # filter contig sequence information from original fasta file #filter fasta for contigs with antibiotic resistance genes (arg) for taxonomic analysis fasta_sequences = SeqIO.parse(open(fasta),'fasta') with open(out_name, 'w') as out_file: for fasta in fasta_sequences: #name, sequence = fasta.id, fasta.seq.tostring() #tostring() should be replaced by str(fasta.seq), but is not working on my computer name, sequence = fasta.id, str(fasta.seq) for c in arg_contigs: if (name==c): out_file.write('>'+ name + '\n' + sequence + '\n') #check for and eliminate less significant (lower cover identity) overlaps #generate list of index numbers of non-overlapping hits from df sorted by coverage identity (highest first) #in case of overlaps, keep the hit with the highest coverage identity def overlaps(df_in): df = df_in.reset_index() #list of contig_names reads = df['contig_name'].unique() #list of indices to keep keep = [] #check overlaps for one contig_name at a time for read in reads: #create dataframe for each contig_name, sorted by cov_identity, highest value first readdf = df[df['contig_name']==read].sort_values(by='cov_identity', ascending=False) #list of indices to keep for each read k=[] #iterate over each enty for one read for i in range(0, readdf.shape[0]-1): #append first entry of sorted readdf (highest cov_identity) to list of indices to keep for this contig_name k.append(readdf['index'].iloc[0]) #list for indices of contigs not overlapping with first entry lst=[] #compare first entry with all other entries for j in range (i+1, readdf.shape[0]): #get start s and end e position of two resistance gene hits s1, e1 = readdf['ref_start'].iloc[i], readdf['ref_start'].iloc[i] + readdf['ref_length'].iloc[i] s2, e2 = readdf['ref_start'].iloc[j], readdf['ref_start'].iloc[j] + readdf['ref_length'].iloc[j] #if there is no overlap, add the entry index to lst if (e1<s2 or e2<s1): lst.append(readdf['index'].iloc[j]) #update readdf, only keep entries with index in lst readdf = readdf[readdf['index'].isin(lst)] #if updated readdf only contains one entry, add index to k and pass on to next read if (readdf.shape[0]==1): k.append(readdf['index'].iloc[0]) break #if updated readdf is empty, pass on to next read if(readdf.shape[0]==0): break #append indices for each read to lst keep keep.append(k) #flatten list of lists (keep) keep = reduce(lambda x,y: x+y,keep) return(df[df['index'].isin(keep)]) if __name__ == "__main__": #extract data of interest from bam file, filter best hits and eliminate overlaps result_df = overlaps(filter_best(parse_bam(bam), ident, covlen)) #add corresponding drug class from CARD aro_index.tsv to result_df rgdrug_dict = pd.read_csv(idx, sep='\t').set_index('ARO Name').to_dict()['Drug Class'] result_df['drug_class'] = result_df['ref_genename'].map(rgdrug_dict) #save result_df as tsv result_df.to_csv("argHitsDf.tsv", sep='\t') #save reads/contigs of hits in result_df in 'result.fasta' for further analysis with PlasFlow or Blast/Diamond arg_contigs(result_df, fasta, "argHits.fasta")

47.68306

180

0.655168

1,280

8,726

4.352344

0.244531

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0

0.009771

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0

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0.009174

0

null

0

null

0

1

0

9db737d0aa2bbc9904ff5f6209cdc235a2493a9c

6,315

py

Python

parkinglot/admin.py

YangWanjun/areaparking

b08bc9b8f8d5f602d823115263b9d040edb9f245

[ "Apache-2.0" ]

1

2018-08-02T04:00:44.000Z

parkinglot/admin.py

YangWanjun/areaparking

b08bc9b8f8d5f602d823115263b9d040edb9f245

[ "Apache-2.0" ]

null

parkinglot/admin.py

YangWanjun/areaparking

b08bc9b8f8d5f602d823115263b9d040edb9f245

[ "Apache-2.0" ]

null

import datetime from django.contrib import admin from django.core.exceptions import ObjectDoesNotExist from django.db.models import Max from . import models, forms from address.biz import geocode from utils import common from utils.django_base import BaseAdmin # Register your models here. class ParkingPositionInline(admin.TabularInline): model = models.ParkingPosition extra = 0 class ParkingLotDocInline(admin.TabularInline): model = models.ParkingLotDoc form = forms.ParkingLotDocForm extra = 0 class ParkingLotImageInline(admin.TabularInline): model = models.ParkingLotImage extra = 0 class ParkingLotCommentInline(admin.TabularInline): model = models.ParkingLotComment extra = 0 class ParkingLotKeyInline(admin.TabularInline): model = models.ParkingLotKey extra = 0 class ParkingLotStaffHistoryInline(admin.TabularInline): model = models.ParkingLotStaffHistory extra = 0 def has_add_permission(self, request): return False # def has_delete_permission(self, request, obj=None): # return False class ParkingPositionKeyInline(admin.TabularInline): model = models.ParkingPositionKey extra = 0 class ManagementCompanyStaffInline(admin.TabularInline): model = models.ManagementCompanyStaff extra = 0 @admin.register(models.ParkingLotType) class ParkingLotTypeAdmin(BaseAdmin): list_display = ('code', 'name') list_display_links = ('code', 'name') # @admin.register(models.LeaseManagementCompany) # class LeaseManagementCompanyAdmin(BaseAdmin): # list_display = ('name', 'department', 'position', 'staff', 'address', 'tel', 'email') # # # @admin.register(models.BuildingManagementCompany) # class BuildingManagementCompanyAdmin(BaseAdmin): # list_display = ('name', 'department', 'position', 'staff', 'address', 'tel', 'email') @admin.register(models.ManagementCompany) class ManagementCompanyAdmin(BaseAdmin): list_display = ('name', 'address', 'tel', 'email') inlines = (ManagementCompanyStaffInline,) @admin.register(models.TryPuttingOperator) class TryPuttingOperatorAdmin(BaseAdmin): pass @admin.register(models.ParkingLot) class ParkingLotAdmin(BaseAdmin): form = forms.ParkingLotForm icon = '<i class="material-icons">local_parking</i>' list_display = ('code', 'name', 'category', 'address', 'subscription_list_send_type') search_fields = ('code', 'name',) inlines = (ParkingLotCommentInline, ParkingLotStaffHistoryInline, ParkingLotDocInline, ParkingLotImageInline, ParkingLotKeyInline) def save_model(self, request, obj, form, change): if change is False or ( 'pref_name' in form.changed_data or 'city_name' in form.changed_data or 'town_name' in form.changed_data or 'aza_name' in form.changed_data or 'other_name' in form.changed_data ): # 新規の場合、または住所変更した場合、座標を取得しなおします。 coordinate = geocode(obj.address) if coordinate.get('lng', None): obj.lng = coordinate.get('lng', None) if coordinate.get('lat', None): obj.lat = coordinate.get('lat', None) if coordinate.get('post_code', None): obj.post_code = coordinate.get('post_code', None) # 担当者変更時、駐車場担当者履歴追加 if change and 'staff' in form.changed_data: queryset = models.ParkingLotStaffHistory.objects.public_filter(parking_lot=obj) try: last_staff = models.ParkingLot.objects.get(pk=obj.pk).staff last_start_date = models.ParkingLot.objects.get(pk=obj.pk).staff_start_date history_end_date = queryset.aggregate(Max('end_date')).get('end_date__max', None) if (history_end_date is None or history_end_date < obj.staff_start_date) and last_start_date != obj.staff_start_date: models.ParkingLotStaffHistory.objects.create( parking_lot=obj, member=last_staff, start_date=last_start_date, end_date=(obj.staff_start_date + datetime.timedelta(days=-1)) ) except ObjectDoesNotExist: pass super(ParkingLotAdmin, self).save_model(request, obj, form, change) @admin.register(models.ParkingPosition) class ParkingPosition(BaseAdmin): form = forms.ParkingPositionForm list_display = ('parking_lot', 'name', 'length', 'width', 'height', 'weight') list_display_links = ('parking_lot', 'name',) search_fields = ('parking_lot__code', 'parking_lot__name') fieldsets = ( (None, { 'fields': ( 'parking_lot', 'name', 'category', 'cost', ) }), ("賃料", { 'classes': ('collapse',), 'fields': ( ('price_recruitment_no_tax', 'price_recruitment'), ('price_homepage_no_tax', 'price_homepage'), ('price_handbill_no_tax', 'price_handbill'), ) }), ("サイズ", { 'classes': ('collapse',), 'fields': ( ('length', 'width', 'height', 'weight'), ('tyre_width', 'tyre_width_ap', 'min_height', 'min_height_ap'), ('f_value', 'r_value',), ) }), ('備考', { 'fields': ( 'comment', ) }), ) inlines = (ParkingPositionKeyInline,) save_as = True def save_model(self, request, obj, form, change): continued_positions = common.get_continued_positions(obj.name) if continued_positions: split_positions = [] else: split_positions = [s for s in obj.name.split(',') if s] continued_positions.extend(split_positions) if not change and continued_positions: # 複数の車室を追加の場合 for name in continued_positions: if models.ParkingPosition.objects.public_filter(parking_lot=obj.parking_lot, name=name).count() == 0: obj.pk = None obj.name = name obj.save() else: super(ParkingPosition, self).save_model(request, obj, form, change)

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0.047619

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0.041925

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0.002142

0.260808

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0.423358

0

null

0

null

0

1

0

9db76eb5840b9b7ac5d4ffae358c55f69c7c5da4

965

py

Python

graficas.py

dianuchitop/el26

e84bb35ca9d6a603d515a624a85dae27cd4d10f2

[ "MIT" ]

null

graficas.py

dianuchitop/el26

e84bb35ca9d6a603d515a624a85dae27cd4d10f2

[ "MIT" ]

null

graficas.py

dianuchitop/el26

e84bb35ca9d6a603d515a624a85dae27cd4d10f2

[ "MIT" ]

null

import matplotlib import matplotlib.pyplot as plt import numpy as np filenames=["euler.dat","rk4.dat","leapfrog.dat"] fig, axs = plt.subplots(nrows=3, ncols=3) ax=axs[0][0] ax.set_title('Euler') ax=axs[0][1] ax.set_title('RK4') ax=axs[0][2] ax.set_title('Leap_frog') for i in range(3): f=open(filenames[i],"r") s=list(map(float,f.readline().split())) s1=list(map(float,f.readline().split())) time=list(map(float,f.readline().split())) ax=axs[0][i] ax.set_xlabel("time") ax.set_ylabel("posistion") ax.plot(time,s ) ax.set_ylim(-1.5,1.5) ax.set_xlim(0,15) ax=axs[1][i] ax.plot(time, s1) ax.set_ylim(-1.5,1.5) ax.set_xlim(0,15) ax.set_xlabel("time") ax.set_ylabel("velocity") ax=axs[2][i] ax.plot(s, s1) ax.set_ylim(-2.0,2.0) ax.set_xlim(-2.0,2.0) ax.set_xlabel("position") ax.set_ylabel("velocity") fig.subplots_adjust(hspace=1, wspace=1) plt.savefig('graficas.png') plt.show()

24.74359

48

0.635233

181

965

3.292818

0.320442

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0.040268

0.065436

0.33557

0.174497

0.090604

0

0.049939

0.149223

965

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0

0.081081

0

null

0

null

0

1

0

9dbc6591cdea251b119f8bcead36767b18ac8b75

4,654

py

Python

mailpile/plugins/contacts.py

k0nsl/Mailpile

556f5f9040c4e01b005b4d633f3213668a474936

[ "Apache-2.0" ]

null

mailpile/plugins/contacts.py

k0nsl/Mailpile

556f5f9040c4e01b005b4d633f3213668a474936

[ "Apache-2.0" ]

null

mailpile/plugins/contacts.py

k0nsl/Mailpile

556f5f9040c4e01b005b4d633f3213668a474936

[ "Apache-2.0" ]

null

import mailpile.plugins from mailpile.commands import Command from mailpile.mailutils import Email, ExtractEmails from mailpile.util import * class VCard(Command): """Add/remove/list/edit vcards""" ORDER = ('Internals', 6) KIND = '' SYNOPSIS = '<nickname>' def command(self, save=True): session, config = self.session, self.session.config vcards = [] for email in self.args: vcard = config.get_vcard(email) if vcard: vcards.append(vcard) else: session.ui.warning('No such contact: %s' % email) return vcards def _fparse(self, fromdata): email = ExtractEmails(fromdata)[0] name = fromdata.replace(email, '').replace('<>', '').strip() return email, (name or email) def _prepare_new_vcard(self, vcard): pass def _valid_vcard_handle(self, vc_handle): return (vc_handle and '@' in vc_handle[1:]) def _add_from_messages(self): pairs, idx = [], self._idx() for email in [Email(idx, i) for i in self._choose_messages(self.args)]: pairs.append(self._fparse(email.get_msg_info(idx.MSG_FROM))) return pairs def _pre_delete_vcard(self, vcard): pass def add_vcards(self): session, config, idx = self.session, self.session.config, self._idx() if (len(self.args) > 2 and self.args[1] == '=' and self._valid_vcard_handle(self.args[0])): pairs = [(self.args[0], ' '.join(self.args[2:]))] elif self.data: if self.data.has_key("@contactname") and self.data.has_key("@contactemail"): pairs = [(self.data["@contactemail"], self.data["@contactname"])] elif self.data.has_key("contactnames") and self.data.has_key("contactemails"): pairs = zip(self.data["contactemails"], self.data["contactnames"]) else: pairs = self._add_from_messages() if pairs: vcards = [] for handle, name in pairs: if handle.lower() not in config.vcards: vcard = config.add_vcard(handle, name, self.KIND) self._prepare_new_vcard(vcard) vcards.append(vcard) else: session.ui.warning('Already exists: %s' % handle) else: return self._error('Nothing to do!') return {"contacts": [x.as_mpCard() for x in vcards]} def _format_values(self, key, vals): if key.upper() in ('MEMBER', ): return [['mailto:%s' % e, []] for e in vals] else: return [[e, []] for e in vals] def set_vcard(self): session, config = self.session, self.session.config handle, var = self.args[0], self.args[1] if self.args[2] == '=': val = ' '.join(self.args[3:]) else: val = ' '.join(self.args[2:]) try: vcard = config.get_vcard(handle) if not vcard: return self._error('Contact not found') config.deindex_vcard(vcard) if val: if ',' in val: vcard[var] = self._format_values(var, val.split(',')) else: vcard[var] = val else: del vcard[var] vcard.save() config.index_vcard(vcard) session.ui.display_vcard(vcard, compact=False) return True except: self._ignore_exception() return self._error('Error setting %s = %s' % (var, val)) def rm_vcards(self): session, config = self.session, self.session.config for handle in self.args: vcard = config.get_vcard(handle) if vcard: self._pre_delete_vcard(vcard) config.del_vcard(handle) else: session.ui.error('No such contact: %s' % handle) return True def find_vcards(self): session, config = self.session, self.session.config if self.args and self.args[0] == '--full': self.args.pop(0) compact = False else: compact = True kinds = self.KIND and [self.KIND] or [] vcards = config.find_vcards(self.args, kinds=kinds) #for vcard in vcards: # session.ui.display_vcard(vcard, compact=compact) ctx = {} ctx["contacts"] = [x.as_mpCard() for x in vcards] ctx["query"] = " ".join(self.args) ctx["total"] = len(vcards) ctx["start"] = 1 ctx["end"] = len(vcards) ctx["count"] = len(vcards) return ctx SUBCOMMANDS = { 'add': (add_vcards, '<msgs>|<email> = <name>'), 'set': (set_vcard, '<email> <attr> <value>'), 'list': (find_vcards, '[--full] [<terms>]'), 'delete': (rm_vcards, '<email>'), } class Contact(VCard): """Add/remove/list/edit contacts""" KIND = 'individual' ORDER = ('Tagging', 3) SYNOPSIS = '<email>' TEMPLATE_IDS = ['contact'] mailpile.plugins.register_command('C:', 'contact=', Contact) mailpile.plugins.register_command('_vcard', 'vcard=', VCard)

30.220779

84

0.613666

608

4,654

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0.225329

0.051743

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0

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false

0.015504

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0.007752

0.294574

0

null

0

null

0

1

0

9dbe26545533c7c7d397d2847ba2a1eeca8ad8ef

1,663

py

Python

hw2/codes/plot.py

Trinkle23897/Artificial-Neural-Network-THU-2018

3326ed131298caaaf3fd0b6af80de37fd1ff9526

[ "MIT" ]

38

2019-01-23T07:14:19.000Z

2022-03-07T06:03:21.000Z

hw2/codes/plot.py

ywythu/Artificial-Neural-Network-THU-2018

3326ed131298caaaf3fd0b6af80de37fd1ff9526

[ "MIT" ]

null

hw2/codes/plot.py

ywythu/Artificial-Neural-Network-THU-2018

3326ed131298caaaf3fd0b6af80de37fd1ff9526

[ "MIT" ]

17

2019-03-30T06:33:06.000Z

2021-12-24T10:42:39.000Z

import numpy as np from pylab import * D = 10 acc1 = np.load('res/small/acc.npy').reshape(D, -1).mean(axis=0) loss1 = np.load('res/small/loss.npy').reshape(D, -1).mean(axis=0) acc2 = np.load('res/large/acc.npy').reshape(D, -1).mean(axis=0) loss2 = np.load('res/large/loss.npy').reshape(D, -1).mean(axis=0) cut = int(acc1.shape[0] / 10 * 4) print(' 1: %.2f %.6f'%(100*acc1[:cut].max(), loss1[:cut].min())) print(' 2: %.2f %.6f'%(100*acc2[:cut].max(), loss2[:cut].min())) iter_ = np.arange(acc1.shape[0]) * D print(acc1.shape, iter_.shape[0]) figure() p = subplot(111) p.plot(iter_[:cut], loss1[:cut], '-', label='Original CNN') p.plot(iter_[:cut], loss2[:cut], '-', label='Designed CNN') p.set_ylim((0, .4)) p.set_xlabel(r'# of Iterations') p.set_ylabel(r'Loss') p.legend(loc='upper right') tight_layout() savefig("loss.pdf") figure() p = subplot(111) p.plot(iter_[:cut], acc1[:cut], '-', label='Original CNN') p.plot(iter_[:cut], acc2[:cut], '-', label='Designed CNN') p.set_ylim((.9, 1)) p.set_xlabel(r'# of Iterations') p.set_ylabel(r'Accuracy') p.legend(loc='lower right') tight_layout() savefig("acc.pdf") # 1: 23:24:44.414 Testing, total mean loss 0.019417, total acc 0.863300 - 23:24:33.131 # 2s: 20:20:39.807 Testing, total mean loss 0.003224, total acc 0.967700 - 20:18:21.597 # 2r: 20:48:01.448 Testing, total mean loss 0.002306, total acc 0.981300 - 20:45:16.709 #-2r: 20:38:47.940 Testing, total mean loss 0.002271, total acc 0.981500 - 20:35:59.910 # 3s: 00:38:10.865 Testing, total mean loss 0.001759, total acc 0.980098 - 00:33:01.622 # 3r: 21:24:04.253 Testing, total mean loss 0.001675, total acc 0.980588 - 21:19:28.262

41.575

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0.654841

313

1,663

3.434505

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0.022326

0.089302

0.111628

0.433488

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0.266047

0.063256

0

0.1718

0.121467

1,663

40

92

41.575

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0.323512

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0

0.0625

0

0.0625

0.09375

0

null

0

null

0

1

0

9dbe2a0458905fed950a4384ff34ad0dc77f394d

696

py

Python

app/helpers/__init__.py

jaywonder20/Flask_Api_Starter

d3cf69f4742923737e826261f5e737f00d1c6270

[ "MIT" ]

1

2020-07-28T13:28:42.000Z

app/helpers/__init__.py

jaywonder20/Flask_Api_Starter

d3cf69f4742923737e826261f5e737f00d1c6270

[ "MIT" ]

null

app/helpers/__init__.py

jaywonder20/Flask_Api_Starter

d3cf69f4742923737e826261f5e737f00d1c6270

[ "MIT" ]

null

from flask_restful import reqparse def send_api_response(response_code, response_message, http_status, response_data={}): if http_status not in [200, 201]: return {'responseCode': response_code, 'responseMessage': response_message }, int(http_status), \ {"Access-Control-Allow-Origin": "*"} else: return {'responseCode': response_code, 'responseMessage': response_message, 'data': response_data }, int(http_status), \ {"Access-Control-Allow-Origin": "*"} parser = reqparse.RequestParser() parser.add_argument('email_address', help='field cannot be blank.')

33.142857

86

0.616379

69

696

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0.565217

0.097087

0.126214

0.145631

0.470874

0

0.011742

0.265805

696

20

87

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0.794521

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0.21408

0.077586

0

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0.066667

false

0

0.066667

0

0.266667

0

null

0

null

0

1

0

9dc60e93e26c2a9f12204a366a70cced0bf9b339

4,081

py

Python

chapter_3_featurization/text_features.py

fancyerii/voicebook

def82da8577086d0361643a05fec2463006533a9

[ "Apache-2.0" ]

1

2020-03-05T01:19:17.000Z

chapter_3_featurization/text_features.py

fancyerii/voicebook

def82da8577086d0361643a05fec2463006533a9

[ "Apache-2.0" ]

null

chapter_3_featurization/text_features.py

fancyerii/voicebook

def82da8577086d0361643a05fec2463006533a9

[ "Apache-2.0" ]

null

''' ================================================ ## VOICEBOOK REPOSITORY ## ================================================ repository name: voicebook repository version: 1.0 repository link: https://github.com/jim-schwoebel/voicebook author: Jim Schwoebel author contact: js@neurolex.co description: a book and repo to get you started programming voice applications in Python - 10 chapters and 200+ scripts. license category: opensource license: Apache 2.0 license organization name: NeuroLex Laboratories, Inc. location: Seattle, WA website: https://neurolex.ai release date: 2018-09-28 This code (voicebook) is hereby released under a Apache 2.0 license license. For more information, check out the license terms below. ================================================ ## LICENSE TERMS ## ================================================ Copyright 2018 NeuroLex Laboratories, Inc. 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. ================================================ ## SERVICE STATEMENT ## ================================================ If you are using the code written for a larger project, we are happy to consult with you and help you with deployment. Our team has >10 world experts in Kafka distributed architectures, microservices built on top of Node.js / Python / Docker, and applying machine learning to model speech and text data. We have helped a wide variety of enterprises - small businesses, researchers, enterprises, and/or independent developers. If you would like to work with us let us know @ js@neurolex.co. ================================================ ## TEXT_FEATURES.PY ## ================================================ extract all text features: nltk_features() spacy_features() gensim_features() ''' import transcribe as ts import sounddevice as sd import soundfile as sf import nltk_features as nf import spacy_features as spf import gensim_features as gf import numpy as np import os, json def sync_record(filename, duration, fs, channels): print('recording') myrecording = sd.rec(int(duration * fs), samplerate=fs, channels=channels) sd.wait() sf.write(filename, myrecording, fs) print('done recording') def text_featurize(filename,jsondump): # transcribe with sphinx transcript=ts.transcribe_sphinx('test.wav') # now put transcript through various feature engines nltk_featureset, nltk_labels=nf.nltk_featurize(transcript) spacy_featureset, spacy_labels=spf.spacy_featurize(transcript) # make gensim embedding on alice and wonderland text # (or any text corpus you'd like) modelname='alice.pickle' if modelname not in os.listdir(): text=open('alice.txt').read() gf.w2v_train(text,100,modelname) gensim_featureset=gf.sentence_embedding(transcript,100,modelname) data={ 'transcript':transcript, 'transcript type':'sphinx', 'nltk':np.array(nltk_featureset).tolist(), 'spacy':np.array(spacy_featureset).tolist(), 'gensim':np.array(gensim_featureset).tolist(), } if jsondump == True: jsonfilename=filename[0:-4]+'.json' jsonfile=open(jsonfilename,'w') json.dump(data,jsonfile) jsonfile.close() return data # # record and get transcript # if 'test.wav' not in os.listdir(): # sync_record('test.wav', 10, 44100, 2) # # now extract all text features # data=text_featurize('test.wav', True)

34.584746

121

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5.201207

0.480885

0.027079

0.009284

0.011605

0

0.013956

0.192355

4,081

117

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0.669199

0

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0

1

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false

0

0.222222

0

0.305556

0.055556

0

null

0

null

0

1

0

9dcad228e81ec6b0f9a3bb86c1710900d1f1972c

1,755

py

Python

3. Python Advanced (September 2021)/3.2 Python OOP (October 2021)/24. Exam Preparation/22.08.2020/project/everland.py

kzborisov/SoftUni

ccb2b8850adc79bfb2652a45124c3ff11183412e

[ "MIT" ]

1

2021-02-07T07:51:12.000Z

3. Python Advanced (September 2021)/3.2 Python OOP (October 2021)/24. Exam Preparation/22.08.2020/project/everland.py

kzborisov/softuni

9c5b45c74fa7d9748e9b3ea65a5ae4e15c142751

[ "MIT" ]

null

3. Python Advanced (September 2021)/3.2 Python OOP (October 2021)/24. Exam Preparation/22.08.2020/project/everland.py

kzborisov/softuni

9c5b45c74fa7d9748e9b3ea65a5ae4e15c142751

[ "MIT" ]

null

class Everland: def __init__(self): self.rooms = [] def add_room(self, room): self.rooms.append(room) def get_monthly_consumptions(self): total_consumption = 0 for room in self.rooms: total_consumption += room.expenses + room.room_cost return f"Monthly consumption: {total_consumption:.2f}$." def pay(self): result = [] for room in self.rooms: total_cost = room.expenses + room.room_cost if room.budget >= total_cost: room.budget -= total_cost result.append(f"{room.family_name} paid {total_cost:.2f}$ and" f" have {room.budget:.2f}$ left.") else: self.rooms.remove(room) result.append(f"{room.family_name} does not have enough" f" budget and must leave the hotel.") return "\n".join(result) def status(self): result = "" result += f"Total population: {sum([r.members_count for r in self.rooms])}\n" for r in self.rooms: result += f"{r.family_name} with {r.members_count} members. Budget: {r.budget:.2f}$, " \ f"Expenses: {r.expenses:.2f}$\n" if r.children: counter = 0 for c in r.children: counter += 1 result += f"--- Child {counter} monthly cost: {c.cost * 30:.2f}$\n" if hasattr(r, "appliances"): total_expenses = 0 for a in r.appliances: total_expenses += a.get_monthly_expense() result += f"--- Appliances monthly cost: {total_expenses:.2f}$\n" return result

39

100

0.520228

206

1,755

4.305825

0.291262

0.071026

0.049605

0.029312

0.200676

0.11274

0

0.011638

0.363533

1,755

44

101

39.886364

0.782453

0

0.05

0

0.025

0.271795

0.039316

0

1

0.125

false

0

0.225

0

null

0

null

0

1

0

9dcd01c7a81f81cad912ec87f997c4e5ba58f9bb

2,448

py

Python

minifold/log.py

nokia/minifold

3687d32ab6119dc8293ae370c8c4ba9bbbb47deb

[ "BSD-3-Clause" ]

15

2018-09-03T09:40:59.000Z

2021-07-16T16:14:46.000Z

src/log.py

Infinite-Blue-1042/minifold

cd0aa9207f9e1819ed2ecbb24373cdcfe27abd16

[ "BSD-3-Clause" ]

null

src/log.py

Infinite-Blue-1042/minifold

cd0aa9207f9e1819ed2ecbb24373cdcfe27abd16

[ "BSD-3-Clause" ]

8

2019-01-25T07:18:59.000Z

2021-04-07T17:54:54.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # # This file is part of the minifold project. # https://github.com/nokia/minifold __author__ = "Marc-Olivier Buob" __maintainer__ = "Marc-Olivier Buob" __email__ = "marc-olivier.buob@nokia-bell-labs.com" __copyright__ = "Copyright (C) 2018, Nokia" __license__ = "BSD-3" import sys from pprint import pformat DEBUG = 0 INFO = 1 WARNING = 2 ERROR = 3 # Shell colors DEFAULT = 0 RED = 1 GREEN = 2 YELLOW = 3 BLUE = 4 PINK = 5 CYAN = 6 GRAY = 7 # Shell style DEFAULT = 0 BOLD = 1 UNDERLINED = 4 BLINKING = 5 HIGHLIGHTED = 7 class Log: enable_print = False # TODO: The following static paramaters should be load from ~/.minifoldrc # TODO: dark / light colors with_color = True log_level = 0 message_header = { DEBUG : "DEBUG", INFO : "INFO", WARNING : "WARNING", ERROR : "ERROR", } message_color = { DEBUG : CYAN, INFO : GREEN, WARNING : YELLOW, ERROR : RED, } @staticmethod def start_style( fg_color :int = None, bg_color :int = None, styles :list = list() ) -> str: styling = list() if fg_color != None: styling.append("3%d" % fg_color) if bg_color != None: styling.append("4%d" % bg_color) if styles: styling += styles return "\033[%sm" % ";".join(styling) if styling else "" @staticmethod def default_style() -> str: return "\033[0m" @classmethod def print(cls, message_type :int, message :str, file = sys.stderr): if cls.enable_print and message_type >= cls.log_level: color = cls.message_color[message_type] header = cls.message_header[message_type] print( "%(start_style)s%(message)s%(end_style)s" % { "start_style" : cls.start_style(fg_color = color), "message" : " ".join([header, message if isinstance(message, str) else pformat(message)]), "end_style" : cls.default_style() }, file = file ) @classmethod def debug(cls, s): cls.print(DEBUG, s) @classmethod def info(cls, s): cls.print(INFO, s) @classmethod def warning(cls, s): cls.print(WARNING, s) @classmethod def error(cls, s): cls.print(ERROR, s)

24

114

0.562908

296

2,448

4.493243

0.381757

0.052632

0.021053

0.03609

0

0.02042

0.319853

2,448

101

115

24.237624

0.778378

0.099265

0

0.12

0

0.096128

0.034624

0

0.009901

0

1

0.093333

false

0

0.026667

0.013333

0.226667

0.12

0

null

0

null

0

1

0

9dce2d32fa35d3b007796ab403b5019d5baeeffb

2,820

py

Python

data_collection/omscs_website/omscs_cleaner.py

yashchitalia/jack-holmes

1ce3c65c1477390fb15d99a14f608f62745548b1

[ "Apache-2.0" ]

1

2017-03-30T02:25:18.000Z

data_collection/omscs_website/omscs_cleaner.py

yashchitalia/jack-holmes

1ce3c65c1477390fb15d99a14f608f62745548b1

[ "Apache-2.0" ]

null

data_collection/omscs_website/omscs_cleaner.py

yashchitalia/jack-holmes

1ce3c65c1477390fb15d99a14f608f62745548b1

[ "Apache-2.0" ]

null

from bs4 import BeautifulSoup import re import urllib import pickle as pkl def cleanhtml(raw_html): cleanr = re.compile('<.*?>') cleantext = re.sub(cleanr, '', raw_html) cleanr_still = re.compile('\\xa0') cleanertext = re.sub(cleanr_still, '', cleantext) cleanr_even = re.compile('\\u2019s') cleanesttext= re.sub(cleanr_even, '', cleanertext) cleanr_more = re.compile('\\u2019ll') cleanest_even = re.sub(cleanr_more, ' ', cleanesttext) cleanest_even_more = cleanest_even.replace('\\xa0', ' ') cleanest_even_more = cleanest_even_more.replace('\\u2014', ' ') cleanest_even_more = cleanest_even_more.replace('\\u201c', ' ') cleanest_even_more = cleanest_even_more.replace('\\u201d', ' ') cleanest_even_more = cleanest_even_more.replace('\\u2013', ' ') return cleanest_even_more unclean_dat = pkl.load(open('omscs_website_data.p', 'rb')) clean_dat = {} for course_number in unclean_dat.keys(): curr_unclean_dat = unclean_dat[course_number] curr_clean_dat = {} for attribute in curr_unclean_dat.keys(): if attribute == 'Instructor': try: instructor_name = str(curr_unclean_dat[attribute][0]) except: continue curr_clean_dat[attribute] = instructor_name elif attribute == 'Name': try: class_name = str(curr_unclean_dat[attribute]) except: continue curr_clean_dat[attribute] = class_name elif attribute in ['Overview', 'Prerequisites', 'Grading', 'Technical', 'Reading']: final_string= '' unclean_list = curr_unclean_dat[attribute] unclean_list.pop(0) for item in unclean_list: try: if str(type(item)) == "<class 'bs4.element.NavigableString'>": item = item.encode('ascii', errors='backslashreplace') if str(item) == '\n': continue final_string = final_string+ ' ' + str(item) elif str(type(item)) == "<class 'bs4.element.Tag'>": if item.next == '\n': continue final_string = final_string+ ' '+ str(item.next) except UnicodeEncodeError: item = item.encode('ascii', errors='backslashreplace') if str(item) == '\n': continue final_string = final_string+ ' ' + str(item) html_cleaned_string = cleanhtml(final_string) curr_clean_dat[attribute] = html_cleaned_string continue clean_dat[course_number] = curr_clean_dat pkl.dump(clean_dat, open('omscs_cleaned_data.p', 'wb'))

40.285714

91

0.575887

298

2,820

5.174497

0.278523

0.093385

0.103761

0.077821

0.411154

0.392996

0.239948

0.138781

0.114137

0

0.014834

0.306738

2,820

69

92

40.869565

0.773913

0

0.274194

0

0.099681

0.010642

0

1

0.016129

false

0

0.064516

0

0.096774

0

null

0

null

0

1

0

9dce34cc1f5685467f230a6aaddab0a3ca10dd09

1,116

py

Python

testinfra/test_hypervisor-runc.py

devbox-tools/sfc

0a5a9c3db165b35506f84d4c2dbfc1dace3fcea1

[ "Apache-2.0" ]

1

2019-02-26T13:25:17.000Z

testinfra/test_hypervisor-runc.py

devbox-tools/sfc

0a5a9c3db165b35506f84d4c2dbfc1dace3fcea1

[ "Apache-2.0" ]

null

testinfra/test_hypervisor-runc.py

devbox-tools/sfc

0a5a9c3db165b35506f84d4c2dbfc1dace3fcea1

[ "Apache-2.0" ]

null

# Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import utils import yaml class TestHypervisorRunC(utils.Base): def test_slaves_are_running(self, host): assert host.check_output("runc list -q") def test_slaves_are_isolated(self, host): group_vars = yaml.safe_load(open( "/var/lib/software-factory/ansible/group_vars/all.yaml")) if group_vars.get("enable_insecure_slaves") is not True: # Make sure managesf internal url access fails assert host.run("curl --connect-timeout 3 %s" % group_vars[ "managesf_internal_url"]).rc in (7, 28)

39.857143

75

0.713262

164

1,116

4.756098

0.670732

0.076923

0.033333

0.041026

0

0.008989

0.202509

1,116

27

76

41.333333

0.867416

0.508065

0

0.251866

0.179104

0

0.181818

1

0.181818

false

0

0.181818

0

0.454545

0

null

0

null

0

1

0

9dcee3a8fc687322519c4ee6dd19ea787ec8d273

280

py

Python

Frameworks/urls.py

MiniJez/TP_Django

e7540f3178d44efeab69a8c8bea14a70fdaa9b4e

[ "MIT" ]

null

Frameworks/urls.py

MiniJez/TP_Django

e7540f3178d44efeab69a8c8bea14a70fdaa9b4e

[ "MIT" ]

null

Frameworks/urls.py

MiniJez/TP_Django

e7540f3178d44efeab69a8c8bea14a70fdaa9b4e

[ "MIT" ]

null

from django.urls import path from .views import index, create, delete, update urlpatterns = [ path('', index, name='index'), path('create/', create, name='create'), path('delete/<int:pk>', delete, name='delete'), path('update/<int:pk>', update, name='update'), ]

28

51

0.639286

36

280

4.972222

0.388889

0.055866

0

0.157143

280

10

52

28

0.758475

0

0.213523

0

1

0

false

0

0.25

0

0.25

0

null

0

null

0

1

0

9dd02fb84f2d21edf2c3f482fb528f7ff864783d

1,831

py

Python

scrape.py

valvoda/holjplus

6a214911b477adf1253b43e46f7f5afc3076a86a

[ "MIT" ]

null

scrape.py

valvoda/holjplus

6a214911b477adf1253b43e46f7f5afc3076a86a

[ "MIT" ]

null

scrape.py

valvoda/holjplus

6a214911b477adf1253b43e46f7f5afc3076a86a

[ "MIT" ]

null

""" Adapted from https://realpython.com/python-web-scraping-practical-introduction/ for the purpose of scraping https://publications.parliament.uk/pa/ld/ldjudgmt.HTML to create an expanded HOLJ+ corpus """ import requests from requests import get from requests.exceptions import RequestException from contextlib import closing class Scrape: def simple_get(self, url): """ Attempts to get the content at `url` by making an HTTP GET request. If the content-type of response is some kind of HTML/XML, return the text content, otherwise return None """ try: with closing(get(url, stream=True)) as resp: if self.is_good_response(resp): return resp.content else: return None except RequestException as e: self.log_error('Error during requests to {0} : {1}'.format(url, str(e))) return None def is_good_response(self, resp): """ Returns true if the response seems to be HTML, false otherwise """ content_type = resp.headers['Content-Type'].lower() return (resp.status_code == 200 and content_type is not None and content_type.find('html') > -1) def log_error(self, e): """ It is always a good idea to log errors. This function just prints them, but you can make it do anything. """ print(e) if __name__ == "__main__": sc = Scrape() print("Testing the scaper:") raw_html = sc.simple_get('https://realpython.com/blog/') assert (len(raw_html) > 0), "Error, does not get" no_html = sc.simple_get("https://doesnotexist.com/thereshouldbenothing/") assert (no_html == None), "Error, does get" print("Working")

30.516667

84

0.616057

237

1,831

4.654008

0.49789

0.049864

0.032638

0.027199

0.036265

0

0.005344

0.284544

1,831

59

85

31.033898

0.836641

0.293829

0

0.066667

0

0.162299

0

0.066667

1

0.1

false

0

0.133333

0

0.4

0.1

0

null

0

null

0

1

0

9dd06c5c9ed12f49b25dc9756a8a419ae3530b18

1,881

py

Python

emotional_ai/model.py

fuluny/Emotional-AI

1372933ec410f72cd500513ea560f43167382e34

[ "MIT" ]

null

emotional_ai/model.py

fuluny/Emotional-AI

1372933ec410f72cd500513ea560f43167382e34

[ "MIT" ]

null

emotional_ai/model.py

fuluny/Emotional-AI

1372933ec410f72cd500513ea560f43167382e34

[ "MIT" ]

null

# #!/usr/bin/python import os import numpy as np import pandas as pd from keras.models import load_model from keras.models import Sequential from keras.utils import np_utils from keras.layers.core import Dense, Activation, Dropout from keras import optimizers from matplotlib import pyplot as plt print('Loading data...') data = pd.read_csv('fer2013.csv') #data = pd.read_csv('testdata.csv') im = data['pixels'] im_list = [] print('Pre-processing data...') for i in range(len(im)): im_list.append(list(map(int,im[i].split()))) X_train = np.asarray(im_list).astype('float32') y_train = np_utils.to_categorical(np.asarray(data['emotion'])) X_train *= 2.0/255 X_train -= 1 input_dim = X_train.shape[1] nb_classes = y_train.shape[1] # Parameters were chosen from most commonly used and sometimes at random # Further development of the model may be needed print('Making model') model = Sequential() # Dense define number of nodes model.add(Dense(1000, input_dim=input_dim)) # Activation defines the output model.add(Activation('relu')) # Dropout to avoid overfitting. model.add(Dropout(0.15)) model.add(Dense(500)) model.add(Activation('relu')) model.add(Dropout(0.15)) model.add(Dense(100)) model.add(Activation('relu')) model.add(Dropout(0.15)) model.add(Dense(50)) model.add(Activation('relu')) model.add(Dropout(0.15)) model.add(Dense(10)) model.add(Activation('relu')) model.add(Dropout(0.15)) model.add(Dense(nb_classes)) model.add(Activation('softmax')) print(model.summary()) sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss='categorical_crossentropy',optimizer=sgd,metrics=['accuracy']) print("Training...") model.fit(X_train, y_train, epochs=100, validation_split=0.1, verbose=2) scores = model.evaluate(X_train, y_train, verbose=0) print(scores) # save model to HDF5 model.save('model.h5') print("Saved model to disk")

25.767123

81

0.747475

306

1,881

4.509804

0.418301

0.098551

0.056522

0.07971

0.176087

0.153623

0

0.034767

0.09782

1,881

72

82

26.125

0.778433

0.14673

0

0.2

0

0.111041

0.015056

0

1

0

false

0

0.18

0

0.18

0.14

0

null

0

null

0

1

0

9dd27bec72ba1ef4b5afcb916eaaa9109718bd5c

2,487

py

Python

detect_port_services.py

amir78729/penetration-test-project

c85376303ce0451e2e3a3150617484d5e6837168

[ "MIT" ]

1

2022-02-04T19:29:18.000Z

detect_port_services.py

amir78729/penetration-test-project

c85376303ce0451e2e3a3150617484d5e6837168

[ "MIT" ]

null

detect_port_services.py

amir78729/penetration-test-project

c85376303ce0451e2e3a3150617484d5e6837168

[ "MIT" ]

null

from socket import socket, gaierror, getservbyport, AF_INET, SOCK_STREAM, setdefaulttimeout from tqdm import tqdm from datetime import datetime def detect_port_services(ip, range_start, range_end): port_services = {} port_detecting_progress = tqdm(range(range_start, range_end + 1)) try: for port in port_detecting_progress: port_detecting_progress.set_description('checking port {}'.upper().format(port)) setdefaulttimeout(2) s = socket(AF_INET, SOCK_STREAM) result = s.connect_ex((ip, port)) # trying to get more information about port service try: message = b'WhoAreYou' s.send(message) banner = s.recv(100) s.close() except IOError: banner = b'' if result == 0: service_name = getservbyport(port) port_services.update({port: (service_name, banner.replace(b'\r\n', b'').decode('utf-8'))}) s.close() log_port_services(ip, range_start, range_end, port_services) except KeyboardInterrupt: print("\ncanceled...".upper()) except gaierror: print("\nHostname Could Not Be Resolved".upper()) return port_services def log_port_services(ip, range_start, range_end, port_services): try: with open("results/result_port_services.txt", "a") as file: file.write('@ {}'.upper().format(datetime.now())) file.write('\nhost {} open ports\' services from {} to {}:'.upper().format(ip, range_start, range_end)) [file.write('\n {}:\t{} {}' .format(port, port_services[port][0].upper(), '' if not port_services[port][1] else '\n\t\t({})\n' .format(port_services[port][1])) ) for port in port_services.keys()] if not port_services.keys(): file.write('\n× no open ports was founded!'.upper()) file.write('\n----------------------------------------------------\n') except FileNotFoundError: print('PLEASE CREATE \"/results/result_detect_open_ports.txt\" AND TRY AGAIN.') if __name__ == '__main__': detect_port_services( ip=input('TARGET IP ADDRESS: '), range_start=int(input('START OF RANGE : ')), range_end=int(input('END OF RANGE : ')), )

38.859375

115

0.556494

281

2,487

4.725979

0.362989

0.135542

0.056476

0.067771

0.118976

0.103916

0.070783

0

0.005744

0.29996

2,487

63

116

39.47619

0.756462

0.019702

0

0.098039

0

0.157225

0.052956

0

1

0.039216

false

0

0.058824

0

0.117647

0.058824

0

null

0

null

0

1

0

9dd2a344fe4c04f0564d9da26c93b7f70200954e

14,829

py

Python

zvdata/apps/data_app.py

freedom6xiaobai/zvt

f4ba510a30f1014cc0e48b85370b0d3936bd851a

[ "MIT" ]

1

2019-10-28T08:03:26.000Z

zvdata/apps/data_app.py

freedom6xiaobai/zvt

f4ba510a30f1014cc0e48b85370b0d3936bd851a

[ "MIT" ]

null

zvdata/apps/data_app.py

freedom6xiaobai/zvt

f4ba510a30f1014cc0e48b85370b0d3936bd851a

[ "MIT" ]

null

# -*- coding: utf-8 -*- import json from collections import OrderedDict from typing import List import dash_core_components as dcc import dash_html_components as html import dash_table import pandas as pd from dash import dash from dash.dependencies import Input, Output, State from zvdata import IntervalLevel from zvdata.app import app from zvdata.chart import Drawer from zvdata.domain import global_providers, get_schemas, get_schema_by_name, get_schema_columns from zvdata.normal_data import NormalData, IntentType from zvdata.reader import DataReader from zvdata.utils.pd_utils import df_is_not_null from zvdata.utils.time_utils import now_pd_timestamp, TIME_FORMAT_DAY current_df = None layout = html.Div( [ html.Div( [ # provider selector dcc.Dropdown( id='provider-selector', placeholder='select provider', options=[{'label': provider, 'value': provider} for provider in global_providers]), # schema selector dcc.Dropdown(id='schema-selector', placeholder='select schema'), # level selector dcc.Dropdown(id='level-selector', placeholder='select level', options=[{'label': level.value, 'value': level.value} for level in IntervalLevel], value=IntervalLevel.LEVEL_1DAY.value), # column selector html.Div(id='schema-column-selector-container', children=None), dcc.Dropdown( id='properties-selector', options=[ {'label': 'undefined', 'value': 'undefined'} ], value='undefined', multi=True ), # codes filter dcc.Input(id='input-code-filter', type='text', placeholder='input codes', style={'width': '400px'}), # time range filter dcc.DatePickerRange( id='date-picker-range', start_date='2009-01-01', end_date=now_pd_timestamp(), display_format=TIME_FORMAT_DAY ), # load data for table html.Button('load data', id='btn-load-data', n_clicks_timestamp=0), # table container html.Div(id='data-table-container', children=None), # selected properties html.Label('setting y_axis and chart type for the columns:'), # col setting container html.Div(id='col-setting-container', children=dash_table.DataTable( id='col-setting-table', columns=[ {'id': 'property', 'name': 'property', 'editable': False}, {'id': 'y_axis', 'name': 'y_axis', 'presentation': 'dropdown'}, {'id': 'chart', 'name': 'chart', 'presentation': 'dropdown'} ], dropdown={ 'y_axis': { 'options': [ {'label': i, 'value': i} for i in ['y1', 'y2', 'y3', 'y4', 'y5'] ] }, 'chart': { 'options': [ {'label': chart_type.value, 'value': chart_type.value} for chart_type in NormalData.get_charts_by_intent(IntentType.compare_self) ] } }, editable=True ), ), html.Div(id='table-type-label', children=None), html.Div( [ html.Div([dcc.Dropdown(id='intent-selector')], style={'width': '50%', 'display': 'inline-block'}), html.Div([dcc.Dropdown(id='chart-selector')], style={'width': '50%', 'display': 'inline-block'}) ] ), html.Div(id='chart-container', children=None) ]) ] ) @app.callback( Output('schema-selector', 'options'), [Input('provider-selector', 'value')]) def update_schema_selector(provider): if provider: return [{'label': schema.__name__, 'value': schema.__name__} for schema in get_schemas(provider=provider)] raise dash.exceptions.PreventUpdate() @app.callback( Output('schema-column-selector-container', 'children'), [Input('schema-selector', 'value')], state=[State('provider-selector', 'value')]) def update_column_selector(schema_name, provider): if provider and schema_name: schema = get_schema_by_name(name=schema_name) cols = get_schema_columns(schema=schema) return dcc.Dropdown( id='schema-column-selector', options=[ {'label': col, 'value': col} for col in cols ], value=get_schema_by_name(name=schema_name).important_cols(), multi=True ) raise dash.exceptions.PreventUpdate() @app.callback( [Output('properties-selector', 'options'), Output('properties-selector', 'value')], [Input('schema-column-selector', 'value')], state=[State('provider-selector', 'value'), State('schema-selector', 'value'), State('properties-selector', 'options'), State('properties-selector', 'value')]) def update_selected_properties(selected_cols, provider, schema_name, options, value): if selected_cols and provider and schema_name: current_options = options current_value = value added_labels = [] added_values = [] for col in selected_cols: added_labels.append(col) added_values.append( json.dumps({ 'provider': provider, 'schema': schema_name, 'column': col })) added_options = [{'label': col, 'value': added_values[i]} for i, col in enumerate(added_labels)] if 'undefined' in value: current_options = [] current_value = [] current_options += added_options current_value += added_values return current_options, current_value raise dash.exceptions.PreventUpdate() def properties_to_readers(properties, level, codes, start_date, end_date) -> List[DataReader]: provider_schema_map_cols = {} for prop in properties: provider = prop['provider'] schema_name = prop['schema'] key = (provider, schema_name) if key not in provider_schema_map_cols: provider_schema_map_cols[key] = [] provider_schema_map_cols[key].append(prop['column']) readers = [] for item, columns in provider_schema_map_cols.items(): provider = item[0] schema_name = item[1] schema = get_schema_by_name(schema_name) readers.append(DataReader(data_schema=schema, provider=provider, codes=codes, level=level, columns=columns, start_timestamp=start_date, end_timestamp=end_date, time_field=schema.time_field())) return readers @app.callback( [Output('data-table-container', 'children'), Output('col-setting-table', 'data'), Output('table-type-label', 'children'), Output('intent-selector', 'options'), Output('intent-selector', 'value')], [Input('btn-load-data', 'n_clicks')], state=[State('properties-selector', 'value'), State('level-selector', 'value'), State('input-code-filter', 'value'), State('date-picker-range', 'start_date'), State('date-picker-range', 'end_date')]) def update_data_table(n_clicks, properties, level, codes: str, start_date, end_date): if n_clicks and properties: props = [] for prop in properties: props.append(json.loads(prop)) readers = properties_to_readers(properties=props, level=level, codes=codes, start_date=start_date, end_date=end_date) if readers: data_df = readers[0].data_df for reader in readers[1:]: if df_is_not_null(reader.data_df): data_df = data_df.join(reader.data_df, how='outer') global current_df current_df = data_df if not df_is_not_null(current_df): return 'no data,please reselect!', [], '', [ {'label': 'compare_self', 'value': 'compare_self'}], 'compare_self' normal_data = NormalData(current_df) data_table = Drawer(data=normal_data).draw_data_table(id='data-table-content') # generate col setting table properties = normal_data.data_df.columns.to_list() df = pd.DataFrame(OrderedDict([ ('property', properties), ('y_axis', ['y1'] * len(properties)), ('chart', ['line'] * len(properties)) ])) # generate intents intents = normal_data.get_intents() intent_options = [ {'label': intent.value, 'value': intent.value} for intent in intents ] intent_value = intents[0].value return data_table, df.to_dict('records'), normal_data.get_table_type(), intent_options, intent_value else: return 'no data,please reselect!', [], '', [ {'label': 'compare_self', 'value': 'compare_self'}], 'compare_self' raise dash.exceptions.PreventUpdate() @app.callback( [Output('chart-selector', 'options'), Output('chart-selector', 'value')], [Input('intent-selector', 'value')]) def update_chart_selector(intent): if intent: charts = NormalData.get_charts_by_intent(intent=intent) options = [ {'label': chart.value, 'value': chart.value} for chart in charts ] value = charts[0].value return options, value raise dash.exceptions.PreventUpdate() operators_df = [['ge ', '>='], ['le ', '<='], ['lt ', '<'], ['gt ', '>'], ['ne ', '!='], ['eq ', '='], ['contains '], ['datestartswith ']] operators_sql = [['>= ', '>='], ['<= ', '<='], ['< ', '<'], ['> ', '>'], ['!= ', '!='], ['== ', '='], ['contains '], ['datestartswith ']] def split_filter_part(filter_part, operators=operators_df): for operator_type in operators: for operator in operator_type: if operator in filter_part: name_part, value_part = filter_part.split(operator, 1) name = name_part[name_part.find('{') + 1: name_part.rfind('}')] value_part = value_part.strip() v0 = value_part[0] if (v0 == value_part[-1] and v0 in ("'", '"', '`')): value = value_part[1: -1].replace('\\' + v0, v0) else: try: value = float(value_part) except ValueError: value = value_part # word operators need spaces after them in the filter string, # but we don't want these later return name, operator_type[0].strip(), value return [None] * 3 @app.callback( [Output('data-table-content', "data"), Output('chart-container', "children")], [Input('data-table-content', "page_current"), Input('data-table-content', "page_size"), Input('data-table-content', "sort_by"), Input('data-table-content', "filter_query"), Input('intent-selector', "value"), Input('chart-selector', "value"), Input('col-setting-table', 'data'), Input('col-setting-table', 'columns')]) def update_table_and_graph(page_current, page_size, sort_by, filter, intent, chart, rows, columns): if chart: property_map = {} for row in rows: property_map[row['property']] = { 'y_axis': row['y_axis'], 'chart': row['chart'] } dff = current_df if filter: filtering_expressions = filter.split(' && ') for filter_part in filtering_expressions: col_name, operator, filter_value = split_filter_part(filter_part) if operator in ('eq', 'ne', 'lt', 'le', 'gt', 'ge'): # these operators match pandas series operator method names dff = dff.loc[getattr(dff[col_name], operator)(filter_value)] elif operator == 'contains': dff = dff.loc[dff[col_name].str.contains(filter_value)] elif operator == 'datestartswith': # this is a simplification of the front-end filtering logic, # only works with complete fields in standard format dff = dff.loc[dff[col_name].str.startswith(filter_value)] # if sort_by: # dff = dff.sort_values( # [col['entity_id'] for col in sort_by], # ascending=[ # col['direction'] == 'asc' # for col in sort_by # ], # inplace=False # ) if intent in (IntentType.compare_self.value, IntentType.compare_to_other.value): graph_data, graph_layout = Drawer(NormalData(dff)).draw_compare(chart=chart, property_map=property_map, render=None, keep_ui_state=False) else: graph_data, graph_layout = Drawer(NormalData(dff)).draw(chart=chart, property_map=property_map, render=None, keep_ui_state=False) table_data = dff.iloc[page_current * page_size: (page_current + 1) * page_size ].to_dict('records') return table_data, \ dcc.Graph( id='chart-content', figure={ 'data': graph_data, 'layout': graph_layout } ) raise dash.exceptions.PreventUpdate()

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120

0.52458

1,470

14,829

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0.167347

0.025986

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null

0

null

0

1

0

9dd3506fa61a6efdbedcfd729d5128ff929686bf

4,333

py

Python

src/hmmmr/non_batched_functions.py

carojasq/HMMMR

f94846d8f02fe8993a0e5fb55e936dd1c1596187

[ "MIT" ]

null

src/hmmmr/non_batched_functions.py

carojasq/HMMMR

f94846d8f02fe8993a0e5fb55e936dd1c1596187

[ "MIT" ]

1

2019-11-01T08:32:04.000Z

src/hmmmr/non_batched_functions.py

carojasq/HMMMR

f94846d8f02fe8993a0e5fb55e936dd1c1596187

[ "MIT" ]

1

2019-04-05T00:06:31.000Z

from common_libs import * from cublas_functions import * linalg.init() def cublas_calculate_transpose_non_batched(h, a_gpu): cublas_transpose = get_single_transpose_function(a_gpu) m, k = a_gpu.shape at_gpu = gpuarray.empty((k, m), a_gpu.dtype) k, n = at_gpu.shape # Calculate transpose transa = transb = 't' cublas_transpose(h, transa, transb, m, k, 1.0, a_gpu.gpudata, k, 0.0, a_gpu.gpudata, k, at_gpu.gpudata, m) return at_gpu # Matrix product, there is a batch equivalent for this function too # Make sure it has 2 dimensions (use reshape in the case is 1d) def cublas_matrix_product_gemm_non_batched(handle, a_gpu, b_gpu): """ :param handle: :param a_gpu: Be carefull to pass X here :param b_gpu: Xt should be here :return: """ cublas_dot = get_single_dot_function(b_gpu) if len(a_gpu.shape)!=2 or len(a_gpu.shape)!=2: raise ValueError('Make sure the arrays are 2 dimensional') n, l = a_gpu.shape k, m = b_gpu.shape c_gpu = gpuarray.empty((n, m), b_gpu.dtype) lda = max(1, a_gpu.strides[0] // a_gpu.dtype.itemsize) ldb = max(1, b_gpu.strides[0] // b_gpu.dtype.itemsize) ldc = max(1, c_gpu.strides[0] // c_gpu.dtype.itemsize) alpha = np.float32(1.0) beta = np.float32(0.0) transa = transb = 'n' cublas_dot(handle, transb, transa, m, n, k, alpha, b_gpu.gpudata, ldb, a_gpu.gpudata, lda, beta, c_gpu.gpudata, ldc) return c_gpu def cublas_matrix_product_gemm_batched(handle, as_gpu, bs_gpu): cublas_dot = get_batched_dot_function(as_gpu) if len(a_gpu.shape) != 2 or len(a_gpu.shape) != 2: raise ValueError('Make sure the arrays are 2 dimensional') # n, z, l n, l = as_gpu.shape k, m = bs_gpu.shape c_gpu = gpuarray.empty((n, m), b_gpu.dtype) lda = max(1, a_gpu.strides[0] // a_gpu.dtype.itemsize) ldb = max(1, b_gpu.strides[0] // b_gpu.dtype.itemsize) ldc = max(1, c_gpu.strides[0] // c_gpu.dtype.itemsize) alpha = np.float32(1.0) beta = np.float32(0.0) transa = transb = 'n' cublas_dot(handle, transb, transa, m, n, k, alpha, b_gpu.gpudata, ldb, a_gpu.gpudata, lda, beta, c_gpu.gpudata, ldc) return c_gpu "TODO: Fix this function, like linalg.inv" def cublas_single_matrix_inversion_non_batched(h, a_gpu, overwrite=False, ipiv_gpu=None): (cublas_getrf, bufsize, cublas_getrs) = get_single_inverse_function(a_gpu) data_type = a_gpu.dtype n = a_gpu.shape[0] if ipiv_gpu is None: ipiv_gpu = gpuarray.empty((n, 1), np.int32) try: in_gpu = a_gpu if overwrite else a_gpu.copy() Lwork = bufsize(h, n, n, in_gpu.gpudata, n) Work = gpuarray.empty(Lwork, data_type) devInfo = gpuarray.empty(1, np.int32) cublas_getrf(h, n, n, in_gpu.gpudata, n, Work.gpudata, ipiv_gpu.gpudata, devInfo.gpudata) except cusolver.CUSOLVER_ERROR as e: raise ValueError("Error while generating inverse of the matrix") d = devInfo.get()[0] if d != 0: raise ValueError("Singular matrix or wrong params") try: b_gpu = linalg.eye(n, data_type) cublas_getrs(h, cublas._CUBLAS_OP['n'], n, n, in_gpu.gpudata, n, ipiv_gpu.gpudata, b_gpu.gpudata, n, devInfo.gpudata) # Since CUSOLVER's getrs functions save their output in b_gpu, we # need to copy it back to the input matrix if overwrite is requested: if overwrite: a_gpu.set(b_gpu) return a_gpu else: return b_gpu except cusolver.CUSOLVER_ERROR as e: raise "Error with cusolver {}".format(e.message) return h def calculate_regression_coeffs_non_batched(handle, x_gpu, y_gpu): xt_gpu = cublas_calculate_transpose_non_batched(handle, x_gpu) xtx_gpu = cublas_matrix_product_gemm_non_batched(handle, xt_gpu, x_gpu) xty_gpu = cublas_matrix_product_gemm_non_batched(handle, xt_gpu, y_gpu) # xtx_inv_gpu = cublas_single_matrix_inversion(handle, xtx_gpu) xtx_inv_gpu = linalg.inv(xtx_gpu, lib="cusolver") b_coefficients = cublas_matrix_product_gemm_non_batched(handle, xtx_inv_gpu, xty_gpu) return b_coefficients def calculate_predictions_from_model_non_batched(handle, x_gpu, b_coefficients_gpu): return cublas_matrix_product_gemm_non_batched(handle, x_gpu, b_coefficients_gpu)

41.663462

120

0.686591

710

4,333

3.930986

0.209859

0.038696

0.045862

0.049445

0.460408

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0

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0.206554

4,333

104

121

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0.797557

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false

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0.012346

0.197531

0

null

0

null

0

1

0

9dd862d583434b6ed73a9e6519551c5f6c54561e

1,575

py

Python

examples/run_fieldtrip_IF.py

annapasca/ephypype

6dbacdd6913234a28b690b401862ff062accecc7

[ "BSD-3-Clause" ]

18

2018-04-18T12:14:52.000Z

2022-02-25T19:31:44.000Z

examples/run_fieldtrip_IF.py

annapasca/ephypype

6dbacdd6913234a28b690b401862ff062accecc7

[ "BSD-3-Clause" ]

106

2017-12-09T13:34:30.000Z

2022-03-12T01:02:17.000Z

examples/run_fieldtrip_IF.py

annapasca/ephypype

6dbacdd6913234a28b690b401862ff062accecc7

[ "BSD-3-Clause" ]

13

2017-05-28T20:38:56.000Z

2022-03-06T15:58:02.000Z

""" .. _ft_seeg_example: ========================================= Apply bipolar montage to depth electrodes ========================================= This scripts shows a very simple example on how to create an Interface wrapping a desired function of a Matlab toolbox (|FieldTrip|). .. |FieldTrip| raw:: html <a href="http://www.fieldtriptoolbox.org/" target="_blank">FieldTrip</a> The **input** data should be a **.mat** file containing a FieldTrip data struct """ # Authors: Annalisa Pascarella <a.pascarella@iac.cnr.it> # License: BSD (3-clause) import os.path as op import ephypype from ephypype.nodes.FT_tools import Reference from ephypype.datasets import fetch_ieeg_dataset ############################################################################### # Let us fetch the data first. It is around 675 MB download. base_path = op.join(op.dirname(ephypype.__file__), '..', 'examples') data_path = fetch_ieeg_dataset(base_path) ft_path = '/usr/local/MATLAB/R2018a/toolbox/MEEG/fieldtrip-20200327/' refmethod = 'bipolar' channels_name = '{\'RAM*\', \'RHH*\', \'RTH*\', \'ROC*\', \'LAM*\',\'LHH*\', \'LTH*\'}' # noqa # Now we call the interface Reference to apply a bipolar montage to sEEG data reference_if = Reference() reference_if.inputs.data_file = op.join(data_path, 'SubjectUCI29_data.mat') reference_if.inputs.channels = channels_name reference_if.inputs.ft_path = ft_path reference_if.inputs.refmethod = refmethod reference_if.inputs.script = '' out = reference_if.run() print('Rereferenced data saved at {}'.format(out.outputs.data_output))

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95

0.665397

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4.956098

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0.083661

0

0.012866

0.111746

1,575

47

96

33.510638

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0.43619

0

0.180678

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0

1

0

false

0

0.235294

0

0.235294

0.058824

0

null

0

null

0

1

0

9dda3faed30d9ee945694fcad8f057ec177bc507

6,568

py

Python

rak_net/protocol/handler.py

L0RD-ZER0/aio-rak-net

0ec0b6ac4daf6a4b146ac94ac2d0313c13975363

[ "MIT" ]

1

2021-12-02T04:37:08.000Z

rak_net/protocol/handler.py

L0RD-ZER0/aio-rak-net

0ec0b6ac4daf6a4b146ac94ac2d0313c13975363

[ "MIT" ]

null

rak_net/protocol/handler.py

L0RD-ZER0/aio-rak-net

0ec0b6ac4daf6a4b146ac94ac2d0313c13975363

[ "MIT" ]

null

from __future__ import annotations from typing import TYPE_CHECKING from .packet import ( ConnectionRequest, ConnectionRequestAccepted, NewIncomingConnection, OfflinePing, OfflinePong, OnlinePing, OnlinePong, OpenConnectionRequest1, OpenConnectionReply1, OpenConnectionRequest2, OpenConnectionReply2, IncompatibleProtocolVersion, ) from .protocol_info import ProtocolInfo from ..utils import InternetAddress if TYPE_CHECKING: from ..server import Server __all__ = 'Handler', class Handler: """ Class containing various handler methods to handle packets :param server: Server for which handler is intended """ __slots__ = 'server', def __init__(self, server: Server): self.server = server async def handle_connection_request(self, data: bytes, address: InternetAddress, *, server: Server = None) -> bytes: """ Handler to handle `Connection-Request` :param data: data of the packet :param address: :class:`InternetAddress` of the packet :param server: Optional server to use the handler with, defaults to ``self.handler`` :return: returns the processed data """ server = server or self.server packet: ConnectionRequest = ConnectionRequest(data) packet.decode() new_packet: ConnectionRequestAccepted = ConnectionRequestAccepted() new_packet.client_address = address new_packet.system_index = 0 new_packet.server_guid = server.guid new_packet.system_addresses = [InternetAddress("255.255.255.255", 19132)] * 20 new_packet.request_timestamp = server.get_time_ms() new_packet.encode() return new_packet.data async def handle_connection_request_accepted(self, data: bytes, address: InternetAddress, *, server: Server = None) -> bytes: """ Handler to handle `Connection-Request-Accepted` :param data: data of the packet :param address: :class:`InternetAddress` of the packet :param server: Optional server to use the handler with, defaults to ``self.handler`` :return: returns the processed data """ server = server or self.server packet: ConnectionRequestAccepted = ConnectionRequestAccepted(data) packet.decode() new_packet: NewIncomingConnection = NewIncomingConnection() new_packet.server_address = address new_packet.system_addresses = packet.system_addresses new_packet.request_timestamp = packet.accepted_timestamp new_packet.accepted_timestamp = server.get_time_ms() new_packet.encode() return new_packet.data async def handle_offline_ping(self, data: bytes, address: InternetAddress = None, *, server: Server = None) -> bytes: """ Handler to handle `Offline-Ping` :param data: data of the packet :param address: :class:`InternetAddress` of the packet :param server: Optional server to use the handler with, defaults to ``self.handler`` :return: returns the processed data """ server = server or self.server packet: OfflinePing = OfflinePing(data) packet.decode() new_packet: OfflinePong = OfflinePong() new_packet.client_timestamp = packet.client_timestamp new_packet.server_guid = server.guid new_packet.magic = ProtocolInfo.MAGIC new_packet.server_name = server.name if hasattr(server, "name") else "" new_packet.encode() return new_packet.data async def handle_online_ping(self, data: bytes, address: InternetAddress = None, *, server: Server = None) -> bytes: """ Handler to handle `Online-Ping` :param data: data of the packet :param address: :class:`InternetAddress` of the packet :param server: Optional server to use the handler with, defaults to ``self.handler`` :return: returns the processed data """ server = server or self.server packet: OnlinePing = OnlinePing(data) packet.decode() new_packet: OnlinePong = OnlinePong() new_packet.client_timestamp = packet.client_timestamp new_packet.server_timestamp = server.get_time_ms() new_packet.encode() return new_packet.data async def handle_open_connection_request_1(self, data: bytes, address: InternetAddress = None, *, server: Server = None) -> bytes: """ Handler to handle `Open-Connection-Request-1` :param data: data of the packet :param address: :class:`InternetAddress` of the packet :param server: Optional server to use the handler with, defaults to ``self.handler`` :return: returns the processed data """ server = server or self.server packet: OpenConnectionRequest1 = OpenConnectionRequest1(data) packet.decode() if packet.protocol_version == server.protocol_version: new_packet: OpenConnectionReply1 = OpenConnectionReply1() new_packet.magic = ProtocolInfo.MAGIC new_packet.server_guid = server.guid new_packet.use_security = False new_packet.mtu_size = packet.mtu_size else: new_packet: IncompatibleProtocolVersion = IncompatibleProtocolVersion() new_packet.protocol_version = server.protocol_version new_packet.magic = ProtocolInfo.MAGIC new_packet.server_guid = server.guid new_packet.encode() return new_packet.data async def handle_open_connection_request_2(self, data: bytes, address: InternetAddress = None, *, server: Server = None) -> bytes: """ Handler to handle `Open-Connection-Request-2` :param data: data of the packet :param address: :class:`InternetAddress` of the packet :param server: Optional server to use the handler with, defaults to ``self.handler`` :return: returns the processed data """ server = server or self.server packet: OpenConnectionRequest2 = OpenConnectionRequest2(data) packet.decode() new_packet: OpenConnectionReply2 = OpenConnectionReply2() new_packet.magic = ProtocolInfo.MAGIC new_packet.server_guid = server.guid new_packet.client_address = address new_packet.mtu_size = packet.mtu_size new_packet.use_encryption = False new_packet.encode() await server.add_connection(address, packet.mtu_size) return new_packet.data

40.294479

134

0.676309

711

6,568

6.075949

0.136428

0.095833

0.030556

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0

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6,568

162

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1

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false

0

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0

0.153061

0

null

0

null

0

1

0

9ddc3d1e0254e6926c024e8ba5ff8037971f9673

5,434

py

Python

software/pynguin/pynguin/testcase/execution/monkeytypeexecutor.py

se2p/artifact-pynguin-ssbse2020

32b5f4d27ef1b81e5c541471e98fa6e50f5ce8a6

[ "CC-BY-4.0" ]

3

2020-08-20T10:27:13.000Z

2021-11-02T20:28:16.000Z

software/pynguin/pynguin/testcase/execution/monkeytypeexecutor.py

se2p/artifact-pynguin-ssbse2020

32b5f4d27ef1b81e5c541471e98fa6e50f5ce8a6

[ "CC-BY-4.0" ]

null

software/pynguin/pynguin/testcase/execution/monkeytypeexecutor.py

se2p/artifact-pynguin-ssbse2020

32b5f4d27ef1b81e5c541471e98fa6e50f5ce8a6

[ "CC-BY-4.0" ]

null

# This file is part of Pynguin. # # Pynguin is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # Pynguin is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with Pynguin. If not, see <https://www.gnu.org/licenses/>. """An executor that executes a test under the inspection of the MonkeyType tool.""" import contextlib import logging import os import sys from typing import Any, Dict, Iterable, List, Optional import astor from monkeytype.config import DefaultConfig from monkeytype.db.base import CallTraceStore, CallTraceThunk from monkeytype.encoding import CallTraceRow, serialize_traces from monkeytype.tracing import CallTrace, CallTraceLogger, CallTracer import pynguin.configuration as config import pynguin.testcase.execution.executioncontext as ctx import pynguin.testcase.testcase as tc class _MonkeyTypeCallTraceStore(CallTraceStore): def __init__(self): self._values: Dict[str, Any] = {} def add(self, traces: Iterable[CallTrace]) -> None: for row in serialize_traces(traces): self._values[row.module] = ( row.qualname, row.arg_types, row.return_type, row.yield_type, ) def filter( self, module: str, qualname_prefix: Optional[str] = None, limit: int = 2000 ) -> List[CallTraceThunk]: result: List[CallTraceThunk] = [] for stored_module, row in self._values.items(): is_qualname = qualname_prefix is not None and qualname_prefix in row[0] if stored_module == module or is_qualname: result.append( CallTraceRow( module=module, qualname=row[0], arg_types=row[1], return_type=row[2], yield_type=row[3], ) ) return result if len(result) < limit else result[:limit] @classmethod def make_store(cls, connection_string: str) -> "CallTraceStore": return cls() def list_modules(self) -> List[str]: return [k for k, _ in self._values.items()] class _MonkeyTypeCallTraceLogger(CallTraceLogger): def __init__(self) -> None: self._traces: List[CallTrace] = [] def log(self, trace: CallTrace) -> None: self._traces.append(trace) @property def traces(self) -> List[CallTrace]: """Provides the collected traces""" return self._traces class _MonkeyTypeConfig(DefaultConfig): def trace_store(self) -> CallTraceStore: return _MonkeyTypeCallTraceStore() def trace_logger(self) -> CallTraceLogger: return _MonkeyTypeCallTraceLogger() # pylint:disable=too-few-public-methods class MonkeyTypeExecutor: """An executor that executes a test under the inspection of the MonkeyType tool.""" _logger = logging.getLogger(__name__) def __init__(self): """""" self._config = _MonkeyTypeConfig() self._tracer = CallTracer( logger=self._config.trace_logger(), code_filter=self._config.code_filter(), sample_rate=self._config.sample_rate(), ) self._call_traces: List[CallTrace] = [] def execute(self, test_cases: List[tc.TestCase]) -> List[CallTrace]: """Execute the given test cases.""" with open(os.devnull, mode="w") as null_file: with contextlib.redirect_stdout(null_file): for test_case in test_cases: exec_ctx = ctx.ExecutionContext(test_case) self._execute_ast_nodes(exec_ctx) self._filter_and_append_call_traces() return self._call_traces def _execute_ast_nodes(self, exec_ctx: ctx.ExecutionContext): for node in exec_ctx.executable_nodes(): try: if self._logger.isEnabledFor(logging.DEBUG): self._logger.debug("Executing %s", astor.to_source(node)) code = compile(node, "<ast>", "exec") sys.setprofile(self._tracer) # pylint: disable=exec-used exec(code, exec_ctx.global_namespace, exec_ctx.local_namespace) # nosec except BaseException as err: # pylint: disable=broad-except failed_stmt = astor.to_source(node) self._logger.info( "Fatal! Failed to execute statement with MonkeyType\n%s%s", failed_stmt, err.args, ) break finally: sys.setprofile(None) def _filter_and_append_call_traces(self) -> None: assert isinstance(self._tracer.logger, _MonkeyTypeCallTraceLogger) module_name = config.INSTANCE.module_name for trace in self._tracer.logger.traces: func_name = trace.funcname if func_name.startswith(module_name): self._call_traces.append(trace)

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null

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null

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1

0

9ddca262545e263f1aa26d015f1d96948d664c84

7,778

py

Python

testproject/testapp/tests/test_history_entries.py

innovationinit/django-wicked-historian

bef0011639791e2275c6bf2272b57542174b4cf0

[ "BSD-2-Clause" ]

null

testproject/testapp/tests/test_history_entries.py

innovationinit/django-wicked-historian

bef0011639791e2275c6bf2272b57542174b4cf0

[ "BSD-2-Clause" ]

null

testproject/testapp/tests/test_history_entries.py

innovationinit/django-wicked-historian

bef0011639791e2275c6bf2272b57542174b4cf0

[ "BSD-2-Clause" ]

1

2022-03-15T07:29:58.000Z

"Test history entries for migrated, obsolete fields" from datetime import ( time, timedelta, ) from decimal import Decimal from typing import ( Any, Dict, ) from django.contrib.auth.models import User from django.db import models from wicked_historian.usersmuggler import usersmuggler from wicked_historian.utils import FieldDescription from testapp.factories import BookFactory from testapp.models import ( Author, Book, BookEditHistory, Language, OBSOLETE_BOOK_FIELD_CHOICES, ) from .base import FreezeTimeTestCase class GettingHistoryEntriesForChangedFieldsTestCase(FreezeTimeTestCase): UNKNOWN_FIELD_ID = 'unknown_field_id' def setUp(self): super().setUp() # test languages self.languages = { 'english': Language.objects.create(name='english'), 'polish': Language.objects.create(name='polish'), } # test authors self.authors = { 'william_shakespeare': Author.objects.create(name='William Shakespeare'), 'john_paul_ii': Author.objects.create(name='John Paul II'), 'nostradamus': Author.objects.create(name='Nostradamus'), } self.user = User.objects.create(username='john.smith') with usersmuggler.set_user(self.user): self.book = BookFactory( # type: Book title='Macbeth', issue_year=1603, language=self.languages['english'], has_pictures=False, literary_period=2, date_of_publication=(self.frozen_time + timedelta(days=1)).date(), moment_of_appearance_on_torrents=self.frozen_time + timedelta(hours=1), ebook_length=timedelta(days=1, hours=3, minutes=12, seconds=7), number_of_downloads_on_torrents=1223372036854775808, encrypted_book=b'some_data', cash_lost_because_of_piracy=Decimal('666666666.66'), plain_text='foo', first_download_hour=time(hour=1), ) self.book.authors.set([self.authors['william_shakespeare']]) self.book = Book.objects.get(pk=self.book.pk) # just to reset any instance attributes used for creating history self.field_choices_by_name = {description.name: description for description in BookEditHistory.FIELDS_DESCRIPTIONS} self.obsolete_field_by_name = {description.name: description for description in OBSOLETE_BOOK_FIELD_CHOICES} BookEditHistory.objects.all().delete() def test_unknown_field(self): self.create_fake_history_entry( self.UNKNOWN_FIELD_ID, old_value=1603, new_value=2018, ) with self.assertRaises(BookEditHistory.UnknownFieldException): BookEditHistory.get_for(self.book) def test_deleted_field_with_choices(self): self.create_fake_history_entry( self.obsolete_field_by_name['age'].id, old_value=1, new_value=2, ) history_entry = self.get_last_history_entry(self.book) self.assertDictEqual(history_entry, { 'change_date': self.frozen_time, 'user': self.user, 'field_verbose_name': 'age', 'old_value': 'XV', 'new_value': 'XIX', }) def test_deleted_char_field(self): self.create_fake_history_entry( self.obsolete_field_by_name['description'].id, old_value='abc', new_value='xyz', ) history_entry = self.get_last_history_entry(self.book) self.assertDictEqual(history_entry, { 'change_date': self.frozen_time, 'user': self.user, 'field_verbose_name': 'description', 'old_value': 'abc', 'new_value': 'xyz', }) def test_deleted_foreign_key_field(self): william_shakespeare = {'pk': self.authors['william_shakespeare'].pk, 'str': str(self.authors['william_shakespeare'])} john_paul_ii = {'pk': self.authors['john_paul_ii'].pk, 'str': str(self.authors['john_paul_ii'])} self.create_fake_history_entry( self.obsolete_field_by_name['author'].id, old_value=william_shakespeare, new_value=john_paul_ii, ) history_entry = self.get_last_history_entry(self.book) self.assertDictEqual(history_entry, { 'change_date': self.frozen_time, 'user': self.user, 'field_verbose_name': 'author', 'old_value': william_shakespeare, 'new_value': john_paul_ii, }) def test_deleted_many_to_many_field(self): english = {'pk': self.languages['english'].pk, 'str': str(self.languages['english'])} polish = {'pk': self.languages['polish'].pk, 'str': str(self.languages['polish'])} self.create_fake_history_entry( self.obsolete_field_by_name['languages'].id, old_value=[english], new_value=[english, polish] ) history_entry = self.get_last_history_entry(self.book) self.assertDictEqual(history_entry, { 'change_date': self.frozen_time, 'user': self.user, 'field_verbose_name': 'languages', 'old_value': [english], 'new_value': [english, polish] }) def test_different_id_for_different_type_with_the_same_name(self): first = FieldDescription('description', models.TextField()) second = FieldDescription('description', models.CharField()) third = FieldDescription('description', models.CharField(max_length=50)) self.assertNotEqual(first.id, second.id) self.assertEqual(second.id, third.id) def test_changed_from_string_to_int(self): self.create_fake_history_entry( self.field_choices_by_name['issue_year'].id, old_value='MDCIII', new_value='MMXVIII' ) history_entry = self.get_last_history_entry(self.book) self.assertDictEqual(history_entry, { 'change_date': self.frozen_time, 'user': self.user, 'field_verbose_name': 'issue year', 'old_value': 'MDCIII', 'new_value': 'MMXVIII' }) def test_presence_of_field_names_on_fields_descriptions_list(self): field_names = {description.name for description in BookEditHistory.FIELDS_DESCRIPTIONS} self.assertEqual(field_names, { 'age', 'author', 'authors', 'book_shelf_slot', 'cash_lost_because_of_piracy', 'date_of_publication', 'description', 'ebook_length', 'encrypted_book', 'first_download_hour', 'has_pictures', 'id', 'issue_number', 'issue_year', 'language', 'languages', 'literary_period', 'moment_of_appearance_on_torrents', 'number_of_downloads_on_torrents', 'number_of_pages', 'plain_text', 'text_as_pdf', 'title', 'pirates', 'printers', 'chapter_set', }) @staticmethod def get_last_history_entry(book: Book) -> Dict[str, Any]: return BookEditHistory.get_for(book)[0] def create_fake_history_entry(self, field: str, old_value: Any, new_value: Any) -> BookEditHistory: return BookEditHistory.objects.create(**{ 'model': self.book, 'user': self.user, 'change_date': self.frozen_time, 'field': field, 'old_value': old_value, 'new_value': new_value })

36.862559

125

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null

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null

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0

9ddcebb4e8c7a0186684b52cc9c2d36af16dce87

12,639

py

Python

mmdetection/third_party/text_perceptron/mmdet/models/seg_heads/tp_head.py

chengzhanzhan/DAVAR-Lab-OCR

79776915c616731698d452d935e7b599b1ce46f0

[ "Apache-2.0" ]

4

2021-07-08T03:08:16.000Z

2022-03-20T02:53:29.000Z

mmdetection/third_party/text_perceptron/mmdet/models/seg_heads/tp_head.py

chengzhanzhan/DAVAR-Lab-OCR

79776915c616731698d452d935e7b599b1ce46f0

[ "Apache-2.0" ]

null

mmdetection/third_party/text_perceptron/mmdet/models/seg_heads/tp_head.py

chengzhanzhan/DAVAR-Lab-OCR

79776915c616731698d452d935e7b599b1ce46f0

[ "Apache-2.0" ]

null

""" #################################################################################################### # Copyright Info : Copyright (c) Davar Lab @ Hikvision Research Institute. All rights reserved. # Filename : tp_head.py # Abstract : Text Perceptron head structure, mainly including losses for segmentation part and regression part # Current Version: 1.0.0 # Author : Liang Qiao # Date : 2020-05-31 # Modified Date : 2020-11-26 # Modified by : inusheng # Comments : Code and comment standardized ###################################################################################################### """ import numpy as np import torch import torch.nn as nn from mmdet.models.builder import build_loss from mmdet.models.registry import HEADS from mmdet.ops import ConvModule from mmdet.core import force_fp32, auto_fp16 def make_one_hot(input_tensor, num_classes): """ Description: convert a feature map of shape [N, 1, H, W] into its one-hot encoding version of shape [N, C, H, W], where C is the number of classes. Arguments: input_tensor: input tensor, [N, 1, *] num_classes : the number of classes of feature maps Returns: one-hot encoding of input tensor, [N, num_classes, *] """ input_tensor = input_tensor[:, np.newaxis, ::] shape = np.array(input_tensor.shape) shape[1] = num_classes shape = tuple(shape) result = torch.zeros(shape) result = result.scatter_(1, input_tensor.cpu(), 1).to(input_tensor.device) return result @HEADS.register_module class TPHead(nn.Module): """ Description: Text Perceptron head structure, this head is used for further feature extraction and generate loss wrt ground-truth labels. Arguments: in_channels : the number of channels of input feature maps conv_out_channels: the number of channels of output feature maps conv_cfg : configuration of conv filters norm_cfg ： configuration of normalization loss_seg : segmentation loss loss_reg_head : regression loss of head area loss_reg_tail : regression loss of tail area loss_reg_bond : regression loss of center area """ def __init__(self, in_channels=256, conv_out_channels=256, conv_cfg=None, norm_cfg=None, loss_seg=None, loss_reg_head=None, loss_reg_bond=None, loss_reg_tail=None, ): super().__init__() self.in_channels = in_channels self.conv_out_channels = conv_out_channels self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg assert loss_seg is not None self.loss_seg = build_loss(loss_seg) self.loss_reg_head = loss_reg_head self.loss_reg_bond = loss_reg_bond self.loss_reg_tail = loss_reg_tail if loss_reg_head is not None: self.loss_reg_head = build_loss(loss_reg_head) if loss_reg_tail is not None: self.loss_reg_tail = build_loss(loss_reg_tail) if loss_reg_bond is not None: self.loss_reg_bond = build_loss(loss_reg_bond) # define extra conv filters for long text feature extraction self.P4_conv = ConvModule(self.in_channels, self.conv_out_channels, kernel_size=3, stride=1, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg) self.P4_1x7_conv = ConvModule(self.conv_out_channels, self.conv_out_channels, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg) self.channel4_1x7_conv = ConvModule(self.in_channels, self.conv_out_channels, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg) self.rpn4 = ConvModule(self.conv_out_channels, self.conv_out_channels, 3, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg) self.seg_branch_conv = ConvModule(self.conv_out_channels, self.conv_out_channels, 3, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg) self.reg_branch_conv = ConvModule(self.conv_out_channels, self.conv_out_channels, 3, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg) self.conv_logits_text = nn.Conv2d(self.conv_out_channels, 1, 1) self.conv_logits_head = nn.Conv2d(self.conv_out_channels, 1, 1) self.conv_logits_tail = nn.Conv2d(self.conv_out_channels, 1, 1) self.conv_logits_bond = nn.Conv2d(self.conv_out_channels, 1, 1) self.conv_regress_head = nn.Conv2d(self.conv_out_channels, 4, 1) self.conv_regress_tail = nn.Conv2d(self.conv_out_channels, 4, 1) self.conv_regress_bond = nn.Conv2d(self.conv_out_channels, 4, 1) self.relu = nn.ReLU(inplace=True) def init_weights(self): """ Description: network parameters initialization """ for module in [self.conv_logits_text, self.conv_logits_head, self.conv_logits_tail, self.conv_logits_bond, self.conv_regress_bond,self.conv_regress_tail, self.conv_regress_head]: if module is None: continue nn.init.xavier_normal_(module.weight) nn.init.constant_(module.bias, 0) @auto_fp16() def forward(self, x): """ Description: network forward pass """ # compute loss from 4x feature maps only # you can add other supervisions on feature maps in terms of your compute resources x_4 = x[0] # extract long text feature x_p4 = self.P4_conv(x_4) x_4_1x7 = self.channel4_1x7_conv(x_4) x_p4_1x7 = self.P4_1x7_conv(x_p4) x_4 = x_p4_1x7 + x_p4 + x_4_1x7 x_4 = self.rpn4(x_4) # generate predicted segmentation map x_4_seg = self.seg_branch_conv(x_4) score_text_pred = self.conv_logits_text(x_4_seg) # segmentation map for center area [N, 1, H, W] score_head_pred = self.conv_logits_head(x_4_seg) # segmentation map for head area [N, 1, H, W] score_tail_pred = self.conv_logits_tail(x_4_seg) # segmentation map for tail area [N, 1, H, W] score_bond_pred = self.conv_logits_bond(x_4_seg) # segmentation map for top and bottom boundaries area [N, 1, H, W] # generate predicted regression map x4_reg = self.seg_branch_conv(x_4) reg_head_pred = self.conv_regress_head(x4_reg) # predicted regression map for head corner points [N, 4, H, W] reg_tail_pred = self.conv_regress_tail(x4_reg) # predicted regression map for tail corner points [N, 4, H, W] reg_bond_pred = self.conv_regress_bond(x4_reg) # predicted regression map for center area [N, 4, H, W] return score_text_pred, score_head_pred, score_tail_pred, score_bond_pred, reg_head_pred, reg_tail_pred, reg_bond_pred def get_target(self, gt_masks): """ Description: generate ground-truth labels Arguments: gt_masks : input ground-truth labels gt_mask:[:,0] : gt_score_map gt_mask:[:,1] : gt_score_map_mask, 1 Care / 0 Not Care gt_mask:[:,2:6] : gt_geo_map_head gt_mask:[:,6:10] : gt_geo_map_head_weight gt_mask:[:,10:14]: gt_geo_map_tail gt_mask:[:,14:18]: gt_geo_map_tail_weight gt_mask:[:,18:22]: gt_geo_map_bond gt_mask:[:,22:26]: gt_geo_map_bond_weight Returns: score_text_target : one-hot encoding of segmentation map ground-truth of center area of shape [N, 1, H, W] score_head_target : one-hot encoding of segmentation map ground-truth of head area of shape [N, 1, H, W] score_tail_target : one-hot encoding of segmentation map ground-truth of tail area of shape [N, 1, H, W] score_bond_target : one-hot encoding of segmentation map ground-truth of top and bottom boundaries, [N, 1, H, W] score_map_masks_target : mask of segmentation map ground-truth, [N, 1, H, W] geo_head_target : ground-truth of head corner points regression, [N, 4, H, W] geo_head_weights_target: weights of ground-truth of head regression, [N, 4, H, W] geo_tail_target : gound-truth of tail corner points regression, [N, 4, H, W] geo_tail_weights_target: weights of ground-truth of tail regression, [N, 4, H, W] geo_bond_target : ground-truth of top and bottom boundaries regression, [N, 4, H, W] geo_bond_weights_target: weights of ground-truth of top and bottom boundaries regression, [N, 4, H, W] """ assert len(gt_masks[0]) == 26 score_map_target = gt_masks[:, 0, :, :].long() score_map_masks_target = gt_masks[:, 1, :, :].float() geo_head_target = gt_masks[:, 2:6, :, :] geo_head_weights_target = gt_masks[:, 6:10, :, :] geo_tail_target = gt_masks[:, 10:14, :, :] geo_tail_weights_target = gt_masks[:, 14:18, :, :] geo_bond_target = gt_masks[:, 18:22, :, :] geo_bond_weights_target = gt_masks[:, 22:, :, :] # convert into one-hot encodings score_map_one_hot = make_one_hot(score_map_target, 5).float() score_text_target = score_map_one_hot[:, 1: 2, :, :] score_head_target = score_map_one_hot[:, 2: 3, :, :] score_tail_target = score_map_one_hot[:, 3: 4, :, :] score_bond_target = score_map_one_hot[:, 4: 5, :, :] return score_text_target, score_head_target, score_tail_target, score_bond_target, score_map_masks_target,\ geo_head_target, geo_head_weights_target, geo_tail_target, geo_tail_weights_target, geo_bond_target,\ geo_bond_weights_target @force_fp32(apply_to=('mask_pred',)) def loss(self, mask_pred, mask_targets): score_text_pred, score_head_pred, score_tail_pred, score_bond_pred, reg_head_pred, reg_tail_pred, reg_bond_pred = mask_pred score_text_target, score_head_target, score_tail_target, score_bond_target, score_map_masks_target, \ geo_head_target, geo_head_weights_target, geo_tail_target, geo_tail_weights_target, geo_bond_target, \ geo_bond_weights_target = mask_targets loss = dict() # compute segmentation loss loss["loss_seg_text"] = self.loss_seg(score_text_pred, score_text_target, weight=score_map_masks_target) loss["loss_seg_head"] = self.loss_seg(score_head_pred, score_head_target, weight=score_map_masks_target) loss["loss_seg_tail"] = self.loss_seg(score_tail_pred, score_tail_target, weight=score_map_masks_target) loss["loss_seg_bond"] = self.loss_seg(score_bond_pred, score_bond_target, weight=score_map_masks_target) # compute regression loss if self.loss_reg_head is not None: loss_reg_head = self.loss_reg_head(reg_head_pred, geo_head_target, weight=geo_head_weights_target) loss["loss_reg_head"] = loss_reg_head if self.loss_reg_tail is not None: loss_reg_tail = self.loss_reg_tail(reg_tail_pred, geo_tail_target, weight=geo_tail_weights_target) loss["loss_reg_tail"] = loss_reg_tail if self.loss_reg_bond is not None: loss_reg_bond = self.loss_reg_bond(reg_bond_pred, geo_bond_target, weight=geo_bond_weights_target) loss["loss_reg_bond"] = loss_reg_bond return loss

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