xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

7faaf6151524c8e82026ebaf789a577660ab08ca

76

py

Python

sep/__init__.py

Fafa87/SEP

cdc8fdad83478d35aeb2992b8382aa4bc1763131

[ "MIT" ]

null

sep/__init__.py

Fafa87/SEP

cdc8fdad83478d35aeb2992b8382aa4bc1763131

[ "MIT" ]

null

sep/__init__.py

Fafa87/SEP

cdc8fdad83478d35aeb2992b8382aa4bc1763131

[ "MIT" ]

null

import sep.evaluate import sep.extract import sep.process import sep.splits

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Python

teste.py

Lausegouras/AulaDevopsTestes

b3221cba77de313168d06fc454340b3ffc7e2898

[ "Apache-2.0" ]

null

teste.py

Lausegouras/AulaDevopsTestes

b3221cba77de313168d06fc454340b3ffc7e2898

[ "Apache-2.0" ]

null

teste.py

Lausegouras/AulaDevopsTestes

b3221cba77de313168d06fc454340b3ffc7e2898

[ "Apache-2.0" ]

null

import pytest from utilitario import mult def teste_mult(): assert mult(3,5)==15

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Python

services/core-api/tests/parties/party_appt/resources/test_mpa_permittee_resource.py

bcgov/mds

6c427a66a5edb4196222607291adef8fd6677038

[ "Apache-2.0" ]

25

2018-07-09T19:04:37.000Z

2022-03-15T17:27:10.000Z

services/core-api/tests/parties/party_appt/resources/test_mpa_permittee_resource.py

areyeslo/mds

e8c38e593e09b78e2a57009c0d003d6c4bfa32e6

[ "Apache-2.0" ]

983

2018-04-25T20:08:07.000Z

2022-03-31T21:45:20.000Z

services/core-api/tests/parties/party_appt/resources/test_mpa_permittee_resource.py

areyeslo/mds

e8c38e593e09b78e2a57009c0d003d6c4bfa32e6

[ "Apache-2.0" ]

58

2018-05-15T22:35:50.000Z

2021-11-29T19:40:52.000Z

import pytest import json import uuid from datetime import datetime from tests.factories import PermitFactory, PartyFactory, MinePartyAppointmentFactory, create_mine_and_permit # GET def test_get_permittee_not_found(test_client, db_session, auth_headers): get_resp = test_client.get( f'/parties/mines/{uuid.uuid4()}', headers=auth_headers['full_auth_header']) get_data = json.loads(get_resp.data.decode()) assert get_resp.status_code == 404, str(get_resp.response) assert get_data['message'] def test_get_permittee(test_client, db_session, auth_headers): appt_guid = MinePartyAppointmentFactory(permittee=True).mine_party_appt_guid get_resp = test_client.get( f'/parties/mines/{appt_guid}', headers=auth_headers['full_auth_header']) get_data = json.loads(get_resp.data.decode()) assert get_resp.status_code == 200, str(get_resp.response) assert get_data['mine_party_appt_guid'] == str(appt_guid) assert get_data['mine_party_appt_type_code'] == 'PMT' #POST def test_post_permittee_no_party(test_client, db_session, auth_headers): mine, permit = create_mine_and_permit() data = { 'mine_guid': str(mine.mine_guid), 'related_guid': str(permit.permit_guid), 'mine_party_appt_type_code': 'PMT', 'effective_date': datetime.today().strftime("%Y-%m-%d") } post_resp = test_client.post( '/parties/mines', data=data, headers=auth_headers['full_auth_header']) assert post_resp.status_code == 404, str(post_resp.response) post_data = json.loads(post_resp.data.decode()) assert post_data['message'] def test_post_permittee_no_permit(test_client, db_session, auth_headers): mine, permit = create_mine_and_permit() party_guid = PartyFactory(company=True).party_guid data = { 'mine_guid': str(mine.mine_guid), 'party_guid': str(party_guid), 'mine_party_appt_type_code': 'PMT', 'effective_date': datetime.today().strftime("%Y-%m-%d") } post_resp = test_client.post( '/parties/mines', data=data, headers=auth_headers['full_auth_header']) post_data = json.loads(post_resp.data.decode()) assert post_resp.status_code == 404, str(post_resp.response) assert post_data['message'] def test_post_permittee(test_client, db_session, auth_headers): mine, permit = create_mine_and_permit() party_guid = PartyFactory(person=True).party_guid data = { 'mine_guid': str(mine.mine_guid), 'party_guid': str(party_guid), 'mine_party_appt_type_code': 'PMT', 'related_guid': str(permit.permit_guid), 'effective_date': datetime.today().strftime("%Y-%m-%d") } post_resp = test_client.post( '/parties/mines', data=data, headers=auth_headers['full_auth_header']) post_data = json.loads(post_resp.data.decode()) assert post_resp.status_code == 200, str(post_resp.response) assert post_data['party_guid'] == str(party_guid) def test_post_permittee_permit_guid_not_found(test_client, db_session, auth_headers): mine, permit = create_mine_and_permit() party_guid = PartyFactory(person=True).party_guid data = { 'mine_guid': str(mine.mine_guid), 'party_guid': str(party_guid), 'mine_party_appt_type_code': 'PMT', 'related_guid': str(uuid.uuid4()), 'effective_date': datetime.today().strftime("%Y-%m-%d") } post_resp = test_client.post( '/parties/mines', data=data, headers=auth_headers['full_auth_header']) post_data = json.loads(post_resp.data.decode()) assert post_resp.status_code == 404, str(post_resp.response) assert post_data['message'] def test_post_permittee_party_guid_not_found(test_client, db_session, auth_headers): mine, permit = create_mine_and_permit() data = { 'mine_guid': str(mine.mine_guid), 'party_guid': str(uuid.uuid4()), 'mine_party_appt_type_code': 'PMT', 'related_guid': str(permit.permit_guid), 'effective_date': datetime.today().strftime("%Y-%m-%d") } post_resp = test_client.post( '/parties/mines', data=data, headers=auth_headers['full_auth_header']) assert post_resp.status_code == 404, str(post_resp.response) post_data = json.loads(post_resp.data.decode()) assert post_data['message']

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f6d2c91c7e27f397a4a85e11f2d128afcfa820e0

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py

Python

iptk/__init__.py

iptk/iptk-py

356e3a4b1acee05b03d25c14f2545a1d12f83787

[ "MIT" ]

null

iptk/__init__.py

iptk/iptk-py

356e3a4b1acee05b03d25c14f2545a1d12f83787

[ "MIT" ]

null

iptk/__init__.py

iptk/iptk-py

356e3a4b1acee05b03d25c14f2545a1d12f83787

[ "MIT" ]

1

2020-05-17T21:45:01.000Z

from .dataset import Dataset from .dataset_store import DatasetStore

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py

Python

Tests/ElementAttribute_test.py

adscheevel/tm1py

8a53c7a63e3c0e2c6198c2cd0c2f57d10a7cfe43

[ "MIT" ]

113

2019-03-12T19:42:39.000Z

2022-03-31T22:40:05.000Z

Tests/ElementAttribute_test.py

adscheevel/tm1py

8a53c7a63e3c0e2c6198c2cd0c2f57d10a7cfe43

[ "MIT" ]

459

2019-01-25T09:32:18.000Z

2022-03-24T21:57:16.000Z

Tests/ElementAttribute_test.py

adscheevel/tm1py

8a53c7a63e3c0e2c6198c2cd0c2f57d10a7cfe43

[ "MIT" ]

107

2019-01-31T15:08:34.000Z

2022-03-16T14:58:38.000Z

import unittest from TM1py import ElementAttribute class TestElementAttribute(unittest.TestCase): def test_eq_happy_case(self): element_attribute1 = ElementAttribute(name="Attribute 1", attribute_type="String") element_attribute2 = ElementAttribute(name="Attribute 1", attribute_type="String") self.assertEqual(element_attribute1, element_attribute2) def test_ne_name(self): element_attribute1 = ElementAttribute(name="Attribute 1", attribute_type="String") element_attribute2 = ElementAttribute(name="Attribute 2", attribute_type="String") self.assertNotEqual(element_attribute1, element_attribute2) def test_ne_type(self): element_attribute1 = ElementAttribute(name="Attribute 1", attribute_type="String") element_attribute2 = ElementAttribute(name="Attribute 1", attribute_type="Numeric") self.assertNotEqual(element_attribute1, element_attribute2) def test_eq_case_space_difference(self): element_attribute1 = ElementAttribute(name="Attribute 1", attribute_type="String") element_attribute2 = ElementAttribute(name="ATTRIBUTE1", attribute_type="String") self.assertEqual(element_attribute1, element_attribute2) def test_hash_happy_case(self): element_attribute1 = ElementAttribute(name="Attribute 1", attribute_type="String") element_attribute2 = ElementAttribute(name="Attribute 1", attribute_type="String") self.assertEqual(hash(element_attribute1), hash(element_attribute2)) def test_construct_body(self): element = ElementAttribute(name="Attribute 1", attribute_type="Numeric") self.assertEqual( element.body_as_dict, {'Name': 'Attribute 1', 'Type': 'Numeric'}) if __name__ == '__main__': unittest.main()

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1272878cddc65b740dcfdbd94cc165038864bd29

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py

Python

survae/utils/loss.py

robert-giaquinto/survae_flows

4d7dc638f77c48ad3c8393b967c33ac9dbad60fe

[ "MIT" ]

2

2021-03-06T19:37:39.000Z

2022-01-09T11:19:45.000Z

survae/utils/loss.py

robert-giaquinto/survae_flows

4d7dc638f77c48ad3c8393b967c33ac9dbad60fe

[ "MIT" ]

null

survae/utils/loss.py

robert-giaquinto/survae_flows

4d7dc638f77c48ad3c8393b967c33ac9dbad60fe

[ "MIT" ]

null

import math import torch def loglik_nats(model, x): """Compute the log-likelihood in nats.""" return - model.log_prob(x).mean() def loglik_bpd(model, x): """Compute the log-likelihood in bits per dim.""" return - model.log_prob(x).sum() / (math.log(2) * x.shape.numel()) def cond_loglik_nats(model, x, context): """Compute the log-likelihood in nats.""" return - model.log_prob(x, context).mean() def cond_loglik_bpd(model, x, context): """Compute the log-likelihood in bits per dim.""" return - model.log_prob(x, context).sum() / (math.log(2) * x.shape.numel()) def elbo_nats(model, x): """ Compute the ELBO in nats. Same as .loglik_nats(), but may improve readability. """ return loglik_nats(model, x) def elbo_bpd(model, x): """ Compute the ELBO in bits per dim. Same as .loglik_bpd(), but may improve readability. """ return loglik_bpd(model, x) def cond_elbo_nats(model, x, context): """ Compute the ELBO in nats for conditional models. Same as .loglik_nats(), but may improve readability. """ return cond_loglik_nats(model, x, context) def cond_elbo_bpd(model, x, context): """ Compute the ELBO in bits per dim for conditional models. Same as .loglik_bpd(), but may improve readability. """ return cond_loglik_bpd(model, x, context) def iwbo(model, x, k): x_stack = torch.cat([x for _ in range(k)], dim=0) ll_stack = model.log_prob(x_stack) ll = torch.stack(torch.chunk(ll_stack, k, dim=0)) return torch.logsumexp(ll, dim=0) - math.log(k) def cond_iwbo(model, x, context, k): x_stack = torch.cat([x for _ in range(k)], dim=0) context_stack = torch.cat([context for _ in range(k)], dim=0) ll_stack = model.log_prob(x_stack, context_stack) ll = torch.stack(torch.chunk(ll_stack, k, dim=0)) return torch.logsumexp(ll, dim=0) - math.log(k) def iwbo_batched(model, x, k, kbs): assert k % kbs == 0 num_passes = k // kbs ll_batched = [] for i in range(num_passes): x_stack = torch.cat([x for _ in range(kbs)], dim=0) ll_stack = model.log_prob(x_stack) ll_batched.append(torch.stack(torch.chunk(ll_stack, kbs, dim=0))) ll = torch.cat(ll_batched, dim=0) return torch.logsumexp(ll, dim=0) - math.log(k) def cond_iwbo_batched(model, x, context, k, kbs): assert k % kbs == 0 num_passes = k // kbs ll_batched = [] for i in range(num_passes): x_stack = torch.cat([x for _ in range(kbs)], dim=0) context_stack = torch.cat([context for _ in range(kbs)], dim=0) ll_stack = model.log_prob(x_stack, context_stack) ll_batched.append(torch.stack(torch.chunk(ll_stack, kbs, dim=0))) ll = torch.cat(ll_batched, dim=0) return torch.logsumexp(ll, dim=0) - math.log(k) def iwbo_nats(model, x, k, kbs=None): """Compute the IWBO in nats.""" if kbs: return - iwbo_batched(model, x, k, kbs).mean() else: return - iwbo(model, x, k).mean() def cond_iwbo_nats(model, x, context, k, kbs=None): """Compute the IWBO in nats.""" if kbs: return - cond_iwbo_batched(model, x, context, k, kbs).mean() else: return - cond_iwbo(model, x, context, k).mean() def iwbo_bpd(model, x, k, kbs=None): """Compute the IWBO in bits per dim.""" if kbs: return - iwbo_batched(model, x, k, kbs).sum() / (x.numel() * math.log(2)) else: return - iwbo(model, x, k).sum() / (x.numel() * math.log(2)) def cond_iwbo_bpd(model, x, context, k, kbs=None): """Compute the IWBO in bits per dim.""" if kbs: return - cond_iwbo_batched(model, x, context, k, kbs).sum() / (x.numel() * math.log(2)) else: return - cond_iwbo(model, x, context, k).sum() / (x.numel() * math.log(2)) def dataset_elbo_nats(model, data_loader, device, double=False, verbose=False): with torch.no_grad(): nats = 0.0 count = 0 for i, x in enumerate(data_loader): if double: x = x.double() x = x.to(device) nats += elbo_nats(model, x).cpu().item() * len(x) count += len(x) if verbose: print('{}/{}'.format(i+1, len(data_loader)), nats/count, end='\r') print(f"Dataset ELBO Nats: {nats/count}") return nats / count def dataset_cond_elbo_nats(model, data_loader, device, double=False, verbose=False): with torch.no_grad(): nats = 0.0 count = 0 for i, x in enumerate(data_loader): if double: x = [x[0].double(), x[1].double()] context = x[1].to(device) x = x[0].to(device) nats += cond_elbo_nats(model, x, context=context).cpu().item() * len(x) count += len(x) if verbose: print('{}/{}'.format(i+1, len(data_loader)), nats/count, end='\r') print(f"Dataset ELBO Nats: {nats/count}") return nats / count def dataset_elbo_bpd(model, data_loader, device, double=False, verbose=False): with torch.no_grad(): bpd = 0.0 count = 0 for i, x in enumerate(data_loader): if double: x = x.double() x = x.to(device) bpd += elbo_bpd(model, x).cpu().item() * len(x) count += len(x) if verbose: print('{}/{}'.format(i+1, len(data_loader)), bpd/count, end='\r') print(f"Dataset ELBO BPD: {bpd/count}") return bpd / count def dataset_cond_elbo_bpd(model, data_loader, device, double=False, verbose=False): with torch.no_grad(): bpd = 0.0 count = 0 for i, x in enumerate(data_loader): if double: x = [x[0].double(), x[1].double()] context = x[1].to(device) x = x[0].to(device) bpd += cond_elbo_bpd(model, x, context=context).cpu().item() * len(x) count += len(x) if verbose: print('{}/{}'.format(i+1, len(data_loader)), bpd/count, end='\r') print(f"Dataset ELBO BPD: {bpd/count}") return bpd / count def dataset_iwbo_nats(model, data_loader, k, device, double=False, kbs=None, verbose=False): with torch.no_grad(): nats = 0.0 count = 0 for i, x in enumerate(data_loader): if double: x = x.double() x = x.to(device) nats += iwbo_nats(model, x, k=k, kbs=kbs).cpu().item() * len(x) count += len(x) if verbose: print('{}/{}'.format(i+1, len(data_loader)), nats/count, end='\r') print(f"Dataset IWBO Nats: {nats/count}") return nats / count def dataset_cond_iwbo_nats(model, data_loader, k, device, double=False, kbs=None, verbose=False): with torch.no_grad(): nats = 0.0 count = 0 for i, x in enumerate(data_loader): if double: x = [x[0].double(), x[1].double()] context = x[1].to(device) x = x[0].to(device) nats += cond_iwbo_nats(model, x, context=context, k=k, kbs=kbs).cpu().item() * len(x) count += len(x) if verbose: print('{}/{}'.format(i+1, len(data_loader)), nats/count, end='\r') print(f"Dataset IWBO Nats: {nats/count}") return nats / count def dataset_iwbo_bpd(model, data_loader, k, device, double=False, kbs=None, verbose=False): with torch.no_grad(): bpd = 0.0 count = 0 for i, x in enumerate(data_loader): if double: x = x.double() x = x.to(device) bpd += iwbo_bpd(model, x, k=k, kbs=kbs).cpu().item() * len(x) count += len(x) if verbose: print('{}/{}'.format(i+1, len(data_loader)), bpd/count, end='\r') print(f"Dataset IWBO BPD: {bpd/count}") return bpd / count def dataset_cond_iwbo_bpd(model, data_loader, k, device, double=False, kbs=None, verbose=False): with torch.no_grad(): bpd = 0.0 count = 0 for i, x in enumerate(data_loader): if double: x = [x[0].double(), x[1].double()] context = x[1].to(device) x = x[0].to(device) bpd += cond_iwbo_bpd(model, x, context=context, k=k, kbs=kbs).cpu().item() * len(x) count += len(x) if verbose: print('{}/{}'.format(i+1, len(data_loader)), bpd/count, end='\r') print(f"Dataset IWBO BPD: {bpd/count}") return bpd / count

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reststub/__init__.py

nharada1/python-rest-stub

a5fb4fc8a6c1e750cac7457716b1ffed796c3a94

[ "MIT" ]

13

2015-11-04T03:34:15.000Z

2017-08-06T15:11:16.000Z

reststub/__init__.py

nharada1/python-rest-stub

a5fb4fc8a6c1e750cac7457716b1ffed796c3a94

[ "MIT" ]

null

reststub/__init__.py

nharada1/python-rest-stub

a5fb4fc8a6c1e750cac7457716b1ffed796c3a94

[ "MIT" ]

null

from .rest_server import rest_stub_app

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Python

tools/Sikuli/ClickContextMenuVisualStudioCode.sikuli/ClickContextMenuVisualStudioCode.py

marmyshev/vanessa-automation

9f87bd6df58b4c205104d3ae8e3643752d67eef7

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296

2018-05-27T08:03:14.000Z

2022-03-19T08:36:11.000Z

tools/Sikuli/ClickContextMenuVisualStudioCode.sikuli/ClickContextMenuVisualStudioCode.py

marmyshev/vanessa-automation

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2018-05-27T18:36:25.000Z

2022-03-31T07:35:11.000Z

tools/Sikuli/ClickContextMenuVisualStudioCode.sikuli/ClickContextMenuVisualStudioCode.py

marmyshev/vanessa-automation

9f87bd6df58b4c205104d3ae8e3643752d67eef7

[ "BSD-3-Clause" ]

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2018-06-18T20:00:56.000Z

2022-03-29T12:29:55.000Z

hover("1532879285882.png") click("1532879285882.png") exit(0)

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Python

riddler/nancy/yahtzee.py

rgc-retired/math_puzzles

0f96fc0f4d53f9ece53fb7af02c037067f710fac

[ "MIT" ]

null

riddler/nancy/yahtzee.py

rgc-retired/math_puzzles

0f96fc0f4d53f9ece53fb7af02c037067f710fac

[ "MIT" ]

null

riddler/nancy/yahtzee.py

rgc-retired/math_puzzles

0f96fc0f4d53f9ece53fb7af02c037067f710fac

[ "MIT" ]

null

import numpy as np # Attempt to get a large straight after # first roll result = 1, 2, 4, 5, X where # X is not a 3. # # What is the optimal strategy to get a large straight? # This is either 1-2-3-4-5 or 2-3-4-5-6. No other result # matters. # # Possibilities: # -------------- # 1. Reroll the X to try for inside straight (e.g. 3)? # 2. Reroll the 1 and X? # 3. Other? # Strategy 1: Reroll the X only def strategy1(): r=np.array([1,2,4,5]) s=np.random.choice(6,1)+1 a=np.r_[r,s] a=np.sort(a) if np.all(a==np.array([1,2,3,4,5])): return(True) s=np.random.choice(6,1)+1 a=np.r_[r,s] a=np.sort(a) if np.all(a==np.array([1,2,3,4,5])): return(True) return(False) # Strategy 3: Reroll the 1,X # Reroll 1 die if you have a 3, 1, or 6 # The priority is to keep the 3 (double ended straight) def strategy3(): r=np.array([2,4,5]) s=np.random.choice(6,2)+1 a=np.r_[r,s] a=np.sort(a) if np.all(a==np.array([1,2,3,4,5])): return(1) elif np.all(a==np.array([2,3,4,5,6])): return(1) # First roll failed -- need to figure out # what to try on the second roll. # If none of the new dice are 1, 3, or 6 then # roll them both again. # elif either of the new dice is 3 then keep it # roll other die - 2 chances to win # elif either of the new dice is 1 then keep it # roll other die - 1 chance to win # elif either of the new dice is 6 then keep it # roll other die - 1 chance to win if 3 in s: # Keep the 3 and roll the other die r=np.array([2,3,4,5]) s=np.random.choice(6,1)+1 rollpath=2 elif 1 in s: # Keep the 1 and roll the other die r=np.array([1,2,4,5]) s=np.random.choice(6,1)+1 rollpath=3 elif 6 in s: # Keep the 6 and roll the other die r=np.array([2,4,5,6]) s=np.random.choice(6,1)+1 rollpath=4 else: # Roll both dice r=np.array([2,4,5]) s=np.random.choice(6,2)+1 rollpath=5 a=np.r_[r,s] a=np.sort(a) if np.all(a==np.array([1,2,3,4,5])): return(rollpath) elif np.all(a==np.array([2,3,4,5,6])): return(rollpath) return(0) # Strategy 2: Reroll the 1,X # Roll 1 die if you have a 3 otherwise roll 2 def strategy2(): r=np.array([2,4,5]) s=np.random.choice(6,2)+1 a=np.r_[r,s] a=np.sort(a) if np.all(a==np.array([1,2,3,4,5])): return(1) elif np.all(a==np.array([2,3,4,5,6])): return(1) # First roll failed -- need to figure out # what to try on the second roll. # If none of the new dice are 1, 3, or 6 then # roll them both again. # elif either of the new dice is 3 then keep it # roll other die - 2 chances to win # elif either of the new dice is 1 then keep it # roll other die - 1 chance to win # elif either of the new dice is 6 then keep it # roll other die - 1 chance to win if 3 in s: # Keep the 3 and roll the other die r=np.array([2,3,4,5]) s=np.random.choice(6,1)+1 rollpath=2 else: # Roll both dice r=np.array([2,4,5]) s=np.random.choice(6,2)+1 rollpath=5 a=np.r_[r,s] a=np.sort(a) if np.all(a==np.array([1,2,3,4,5])): return(rollpath) elif np.all(a==np.array([2,3,4,5,6])): return(rollpath) return(0)

28.396694

57

0.559371

685

3,436

2.79708

0.131387

0.022965

0.021921

0.029228

0.789666

0.764614

0.76357

0.747912

0.724948

0.710856

0

0.076417

0.291618

3,436

120

58

28.633333

0.710764

0.433062

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false

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0.014286

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0.057143

0

null

0

1

0

1

0

null

0

6

d62bcdf8af7be4e285d3a2566f6156bd728f72f3

35

py

Python

nyaggle/testing/__init__.py

harupy/nyaggle

132a93079e364d60b5598de77ab636a603ec06a4

[ "MIT" ]

null

nyaggle/testing/__init__.py

harupy/nyaggle

132a93079e364d60b5598de77ab636a603ec06a4

[ "MIT" ]

null

nyaggle/testing/__init__.py

harupy/nyaggle

132a93079e364d60b5598de77ab636a603ec06a4

[ "MIT" ]

2

2021-03-19T05:57:39.000Z

2021-03-30T04:54:36.000Z

from nyaggle.testing.util import *

17.5

34

0.8

5

35

5.6

1

0

0.114286

35

1

35

0.903226

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

c39554dcec1697b15f55de90a5418a5cbaffe35f

663

py

Python

eloquent/orm/scopes/scope.py

KarthickNamakkalKrishnan/eloquent

0638b688d5fd0c1a46b7471dd465eeb4c2f84666

[ "MIT" ]

47

2015-03-19T02:11:36.000Z

2022-03-29T07:16:42.000Z

eloquent/orm/scopes/scope.py

KarthickNamakkalKrishnan/eloquent

0638b688d5fd0c1a46b7471dd465eeb4c2f84666

[ "MIT" ]

20

2015-03-16T02:56:51.000Z

2015-05-24T17:51:29.000Z

eloquent/orm/scopes/scope.py

sdispater/eloquent

0638b688d5fd0c1a46b7471dd465eeb4c2f84666

[ "MIT" ]

4

2018-08-29T13:42:50.000Z

2021-03-14T11:28:31.000Z

# -*- coding: utf-8 -*- class Scope(object): def apply(self, builder, model): """ Apply the scope to a given query builder. :param builder: The query builder :type builder: eloquent.orm.Builder :param model: The model :type model: eloquent.orm.Model """ raise NotImplementedError def remove(self, builder, model): """ Remove the scope from a given query builder. :param builder: The query builder :type builder: eloquent.orm.Builder :param model: The model :type model: eloquent.orm.Model """ raise NotImplementedError

22.862069

52

0.588235

74

663

5.27027

0.324324

0.123077

0.082051

0.092308

0.723077

0

0.002222

0.321267

663

28

53

23.678571

0.864444

0.550528

0

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0

1

0.4

false

0

0.6

0

null

0

1

0

null

0

1

0

6

c3a34b7edc4dd4f40de782bcb7f8ee256bd72a77

25

py

Python

neuropowertools/apps/__init__.py

jokedurnez/neuropowertools

4e17247867b108f7e928dfb205a62400afba1e34

[ "MIT" ]

null

neuropowertools/apps/__init__.py

jokedurnez/neuropowertools

4e17247867b108f7e928dfb205a62400afba1e34

[ "MIT" ]

null

neuropowertools/apps/__init__.py

jokedurnez/neuropowertools

4e17247867b108f7e928dfb205a62400afba1e34

[ "MIT" ]

null

from . import main, power

25

0.76

4

25

4.75

1

0

0.16

25

1

25

0.904762

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

c3d5fd2d7682d149e476444e4461938d33fe7e58

44

py

Python

eisen/utils/logging/__init__.py

dasturge/eisen-core

09056f1e6aff450ef402b35b10ef96a7d4a3ff87

[ "MIT" ]

null

eisen/utils/logging/__init__.py

dasturge/eisen-core

09056f1e6aff450ef402b35b10ef96a7d4a3ff87

[ "MIT" ]

null

eisen/utils/logging/__init__.py

dasturge/eisen-core

09056f1e6aff450ef402b35b10ef96a7d4a3ff87

[ "MIT" ]

null

from .logs import * from .summaries import *

22

24

0.75

6

44

5.5

0.666667

0

0.159091

44

2

24

22

0.891892

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

7f02664bfcfb1860c752619019d268883ec38f04

129

py

Python

tyxe/__init__.py

TyXe-BDL/TyXe

9b0a0aebb84ddd7eed2f26da967e61ad0cb79039

[ "MIT" ]

34

2021-09-29T16:16:34.000Z

2022-03-13T00:34:29.000Z

tyxe/__init__.py

TyXe-BDL/TyXe

9b0a0aebb84ddd7eed2f26da967e61ad0cb79039

[ "MIT" ]

10

2021-09-20T21:49:55.000Z

2022-03-01T06:25:50.000Z

tyxe/__init__.py

TyXe-BDL/TyXe

9b0a0aebb84ddd7eed2f26da967e61ad0cb79039

[ "MIT" ]

11

2021-06-17T13:31:55.000Z

2022-02-17T05:22:55.000Z

from . import guides from . import likelihoods from . import poutine from . import priors from . import util from .bnn import *

16.125

25

0.751938

18

129

5.388889

0.444444

0.515464

0

0.193798

129

7

26

18.428571

0.932692

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

7f10ed5eb47807ce4b493191fdddd8db468a30dd

37

py

Python

Chapter 07/ch7_1g.py

bpbpublications/TEST-YOUR-SKILLS-IN-PYTHON-LANGUAGE

f6a4194684515495d00aa38347a725dd08f39a0c

[ "MIT" ]

null

Chapter 07/ch7_1g.py

bpbpublications/TEST-YOUR-SKILLS-IN-PYTHON-LANGUAGE

f6a4194684515495d00aa38347a725dd08f39a0c

[ "MIT" ]

null

Chapter 07/ch7_1g.py

bpbpublications/TEST-YOUR-SKILLS-IN-PYTHON-LANGUAGE

f6a4194684515495d00aa38347a725dd08f39a0c

[ "MIT" ]

null

def Pyn(): print("Be Positive! ")

12.333333

24

0.567568

5

37

4.2

1

0

0.216216

37

2

25

18.5

0.724138

0

0.371429

0

1

0.5

true

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

6

615a3cabf456058913fd20a7c63a191fd32ac773

60

py

Python

packages/any-api/wasmer/__init__.py

TheRakeshPurohit/wasmer-python

2375974d9dc50a2caf29fdd9e07d49fd94537e03

[ "MIT" ]

900

2019-04-11T01:52:10.000Z

2020-09-02T11:09:14.000Z

packages/any-api/wasmer/__init__.py

TheRakeshPurohit/wasmer-python

2375974d9dc50a2caf29fdd9e07d49fd94537e03

[ "MIT" ]

172

2019-04-15T18:04:55.000Z

2020-09-01T15:20:06.000Z

packages/any-api/wasmer/__init__.py

TheRakeshPurohit/wasmer-python

2375974d9dc50a2caf29fdd9e07d49fd94537e03

[ "MIT" ]

28

2019-04-11T02:49:04.000Z

2020-08-27T09:47:49.000Z

raise ImportError("Wasmer is not available on this system")

30

59

0.8

9

60

5.333333

1

0

0.133333

60

1

60

0.923077

0

0.633333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

619d1a3c5a829f97da67411ed5d4a228735078c7

2,325

gyp

Python

deps/subversion/ra_serf.gyp

yume-chan/node-svn

47f2eba70b55dcd15bda745b102668223a2b7f20

[ "MIT" ]

null

deps/subversion/ra_serf.gyp

yume-chan/node-svn

47f2eba70b55dcd15bda745b102668223a2b7f20

[ "MIT" ]

5

2018-03-16T06:48:29.000Z

2018-04-17T09:47:15.000Z

deps/subversion/ra_serf.gyp

yume-chan/node-svn

47f2eba70b55dcd15bda745b102668223a2b7f20

[ "MIT" ]

4

2018-04-11T00:06:05.000Z

2019-10-25T01:34:40.000Z

{ "includes": [ "./common.gypi" ], "targets": [ { "target_name": "libsvn_ra_serf", "dependencies": [ "../serf/serf.gyp:serf" ], "sources": [ "subversion/subversion/libsvn_ra_serf/blame.c", "subversion/subversion/libsvn_ra_serf/blncache.c", "subversion/subversion/libsvn_ra_serf/commit.c", "subversion/subversion/libsvn_ra_serf/eagain_bucket.c", "subversion/subversion/libsvn_ra_serf/get_deleted_rev.c", "subversion/subversion/libsvn_ra_serf/get_file.c", "subversion/subversion/libsvn_ra_serf/get_lock.c", "subversion/subversion/libsvn_ra_serf/getdate.c", "subversion/subversion/libsvn_ra_serf/getlocations.c", "subversion/subversion/libsvn_ra_serf/getlocationsegments.c", "subversion/subversion/libsvn_ra_serf/getlocks.c", "subversion/subversion/libsvn_ra_serf/inherited_props.c", "subversion/subversion/libsvn_ra_serf/list.c", "subversion/subversion/libsvn_ra_serf/lock.c", "subversion/subversion/libsvn_ra_serf/log.c", "subversion/subversion/libsvn_ra_serf/merge.c", "subversion/subversion/libsvn_ra_serf/mergeinfo.c", "subversion/subversion/libsvn_ra_serf/multistatus.c", "subversion/subversion/libsvn_ra_serf/options.c", "subversion/subversion/libsvn_ra_serf/property.c", "subversion/subversion/libsvn_ra_serf/replay.c", "subversion/subversion/libsvn_ra_serf/request_body.c", "subversion/subversion/libsvn_ra_serf/sb_bucket.c", "subversion/subversion/libsvn_ra_serf/serf.c", "subversion/subversion/libsvn_ra_serf/stat.c", "subversion/subversion/libsvn_ra_serf/stream_bucket.c", "subversion/subversion/libsvn_ra_serf/update.c", "subversion/subversion/libsvn_ra_serf/util.c", "subversion/subversion/libsvn_ra_serf/util_error.c", "subversion/subversion/libsvn_ra_serf/xml.c" ] } ] }

50.543478

78

0.593548

235

2,325

5.561702

0.2

0.189748

0.284621

0.642693

0.789594

0.765111

0.260138

0

0.297204

2,325

45

79

51.666667

0.799878

0

0.044444

0

0.661842

0.630263

0

1

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true

0

null

0

1

0

1

0

1

null

0

1

0

6

61ad78327315c12c190baa4682e7af00a3a3850a

160

py

Python

src/svgen/__init__.py

pedromxavier/svg-motion

016d95d3302b5519954b89e489f7394bb64eeea9

[ "MIT" ]

null

src/svgen/__init__.py

pedromxavier/svg-motion

016d95d3302b5519954b89e489f7394bb64eeea9

[ "MIT" ]

null

src/svgen/__init__.py

pedromxavier/svg-motion

016d95d3302b5519954b89e489f7394bb64eeea9

[ "MIT" ]

null

from .svgen import SVG, Figure from .svgen import Animation, Frame, Camera from .svglib import Point, Vector from .svglib import Domain, Map, Surface, Transform

40

51

0.79375

23

160

5.521739

0.652174

0.141732

0.23622

0

0.1375

160

4

51

40

0.92029

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

61bfc5849f3285dcce2ee3b38eb927a600c62c69

12,488

py

Python

ztx/views.py

lqs429521992/ztx-srv

1fee866606ce5b00ae29edd526eaad6dc7f9ceff

[ "MIT" ]

null

ztx/views.py

lqs429521992/ztx-srv

1fee866606ce5b00ae29edd526eaad6dc7f9ceff

[ "MIT" ]

null

ztx/views.py

lqs429521992/ztx-srv

1fee866606ce5b00ae29edd526eaad6dc7f9ceff

[ "MIT" ]

null

from rest_framework.viewsets import ModelViewSet from .models import Costomer, Product, Income, Trade from .serializers import CostomerSerializer, ProductSerializer, IncomeSerializer, TradeSerializer from libs import iView from rest_framework.views import APIView from rest_framework.response import Response from decimal import Decimal from django.db.models import Sum from datetime import datetime from datetime import timedelta # Create your views here. class CostomerViewSet(ModelViewSet): """ 角色：增删改查 """ # perms_map = {'get': '*', 'post': 'role_create', # 'put': 'role_update', 'delete': 'role_delete'} permission_classes = [] authentication_classes = [] queryset = Costomer.objects serializer_class = CostomerSerializer # pagination_class = None filter_fields = ('name','introducer') search_fields = ('name','introducer') ordering_fields = ['pk','create_time'] ordering = ['-create_time'] class CostomerRecharge(iView): # 默认认证校验类 authentication_classes = [] # 默认权限校验类 # permission_classes = [] # 权限校验名称 perms_map = { 'get': '*', 'post': '*', 'put': '*', 'delete': '*' } def get(self,request): # 今日昨日本周上周本月上月总计 # 获取今日收入 now = datetime.strptime(datetime.now().strftime('%Y-%m-%d'),'%Y-%m-%d') # for item in range(11): # now = datetime.strptime('2020-' + str(item+1).zfill(2) + '-01','%Y-%m-%d') # 今天 today = now print("今天:"+str(today) + ' ' + str(today.weekday()+1)) # 昨天 yesterday = now - timedelta(days = 1 ) print("昨天:" + str(yesterday) + ' ' + str(yesterday.weekday()+1)) # 本周第一天和最后一天 this_week_start = now - timedelta(days = now.weekday()) this_week_end = now + timedelta(days = 6 - now.weekday()) print("本周:" + str(this_week_start) + '——' + str(this_week_end)) # 上周第一天和最后一天 last_week_start = now - timedelta(days = now.weekday() + 7 ) last_week_end = now - timedelta(days = now.weekday() + 2 ) print("上周:" + str(last_week_start) + '——' + str(last_week_end)) # 本月第一天和最后一天 this_month_start = datetime(now.year, now.month, 1 ) this_month_end = datetime(now.year+now.month//12, (now.month)%12+1, 1 ) # print("本月:" + str(this_month_start) + '——' + str(this_month_end)) # 上月第一天和最后一天 # last_month_end = this_month_start - timedelta(days = 1 ) last_month_start = datetime((this_month_start - timedelta(days = 1 )).year, (this_month_start - timedelta(days = 1 )).month, 1 ) last_month_end = datetime(now.year, now.month, 1 ) # print("上月:" + str(last_month_start) + '——' + str(last_month_end)) # 本季第一天和最后一天 this_quarter_start = datetime(now.year+(now.month+9)//12-1, ((now.month%12//3-1)%4+1)*3, 1 ) this_quarter_end = datetime(now.year+now.month//12, (now.month%12//3+1)*3, 1 ) # print("本季:" + str(this_quarter_start) + '——' + str(this_quarter_end)) # 上季第一天和最后一天 last_quarter_start = datetime(now.year+(now.month+6)//12-1, (((now.month+9)%12//3-1)%4+1)*3 , 1 ) last_quarter_end = this_quarter_start # print("上季:" + str(last_quarter_start) + '——' + str(last_quarter_end)) # 本年第一天和最后一天 this_year_start = datetime(now.year, 1 , 1 ) this_year_end = datetime(now.year + 1 , 1 , 1 ) # print("今年:" + str(this_year_start) + '——' + str(this_year_end)) # 去年第一天和最后一天 last_year_start = datetime((this_year_start - timedelta(days = 1 )).year, 1 , 1 ) last_year_end = this_year_start # print("去年:" + str(last_year_start) + '——' + str(last_year_end)) result = {} result['recharge_today']= Income.objects.filter(create_time__date= today).aggregate(sum=Sum("money"))['sum'] result['recharge_yesterday'] = Income.objects.filter(create_time__date= yesterday).aggregate(sum=Sum("money"))['sum'] result['recharge_this_week'] = Income.objects.filter(create_time__range= (this_week_start,this_week_end)).aggregate(sum=Sum("money"))['sum'] result['recharge_last_week'] = Income.objects.filter(create_time__range= (last_week_start,last_week_end)).aggregate(sum=Sum("money"))['sum'] result['recharge_this_month'] = Income.objects.filter(create_time__range= (this_month_start,this_month_end)).aggregate(sum=Sum("money"))['sum'] result['recharge_last_month'] = Income.objects.filter(create_time__range= (last_month_start,last_month_end)).aggregate(sum=Sum("money"))['sum'] result['recharge_this_quarter'] = Income.objects.filter(create_time__range= (this_quarter_start,this_quarter_end)).aggregate(sum=Sum("money"))['sum'] result['recharge_last_quarter'] = Income.objects.filter(create_time__range= (last_quarter_start,last_quarter_end)).aggregate(sum=Sum("money"))['sum'] result['recharge_this_year'] = Income.objects.filter(create_time__range= (this_year_start,this_year_end)).aggregate(sum=Sum("money"))['sum'] result['recharge_last_year'] = Income.objects.filter(create_time__range= (last_year_start,last_year_end)).aggregate(sum=Sum("money"))['sum'] result['total'] = Income.objects.all().aggregate(sum=Sum("money"))['sum'] # 校验空数据，并将其设置为0 for item in result.keys(): if not result[item]: result[item] = 0 return Response(result) def post(self,request): costomer = Costomer.objects.get(pk=request.data['id']) if request.data['type'] == '理疗卡': costomer.money = costomer.money + Decimal(request.data['money']) costomer.save() Income(type=request.data['type'],money=request.data['money'],costomer= costomer).save() return Response() class CostomerConsume(iView): # 默认认证校验类 authentication_classes = [] # 默认权限校验类 # permission_classes = [] # 权限校验名称 perms_map = { 'get': '*', 'post': '*', 'put': '*', 'delete': '*' } # 获取消费统计信息 def get(self,request): trade = Trade.objects.all().aggregate(sum= Sum("product__price")) # 今日昨日本周上周本月上月总计 # 获取今日消费 now = datetime.strptime(datetime.now().strftime('%Y-%m-%d'),'%Y-%m-%d') # for item in range(11): # now = datetime.strptime('2020-' + str(item+1).zfill(2) + '-01','%Y-%m-%d') # 今天 today = now print("今天:"+str(today) + ' ' + str(today.weekday()+1)) # 昨天 yesterday = now - timedelta(days = 1 ) print("昨天:" + str(yesterday) + ' ' + str(yesterday.weekday()+1)) # 本周第一天和最后一天 this_week_start = now - timedelta(days = now.weekday()) this_week_end = now + timedelta(days = 6 - now.weekday()) print("本周:" + str(this_week_start) + '——' + str(this_week_end)) # 上周第一天和最后一天 last_week_start = now - timedelta(days = now.weekday() + 7 ) last_week_end = now - timedelta(days = now.weekday() + 2 ) print("上周:" + str(last_week_start) + '——' + str(last_week_end)) # 本月第一天和最后一天 this_month_start = datetime(now.year, now.month, 1 ) this_month_end = datetime(now.year+now.month//12, (now.month)%12+1, 1 ) # print("本月:" + str(this_month_start) + '——' + str(this_month_end)) # 上月第一天和最后一天 # last_month_end = this_month_start - timedelta(days = 1 ) last_month_start = datetime((this_month_start - timedelta(days = 1 )).year, (this_month_start - timedelta(days = 1 )).month, 1 ) last_month_end = datetime(now.year, now.month, 1 ) # print("上月:" + str(last_month_start) + '——' + str(last_month_end)) # 本季第一天和最后一天 this_quarter_start = datetime(now.year+(now.month+9)//12-1, ((now.month%12//3-1)%4+1)*3, 1 ) this_quarter_end = datetime(now.year+now.month//12, (now.month%12//3+1)*3, 1 ) # print("本季:" + str(this_quarter_start) + '——' + str(this_quarter_end)) # 上季第一天和最后一天 last_quarter_start = datetime(now.year+(now.month+6)//12-1, (((now.month+9)%12//3-1)%4+1)*3 , 1 ) last_quarter_end = this_quarter_start # print("上季:" + str(last_quarter_start) + '——' + str(last_quarter_end)) # 本年第一天和最后一天 this_year_start = datetime(now.year, 1 , 1 ) this_year_end = datetime(now.year + 1 , 1 , 1 ) # print("今年:" + str(this_year_start) + '——' + str(this_year_end)) # 去年第一天和最后一天 last_year_start = datetime((this_year_start - timedelta(days = 1 )).year, 1 , 1 ) last_year_end = this_year_start # print("去年:" + str(last_year_start) + '——' + str(last_year_end)) # Trade.objects.all().aggregate(sum= Sum("product__price")) result = {} result['consume_today']= Trade.objects.filter(create_time__date= today).aggregate(sum=Sum("product__price"))['sum'] result['consume_yesterday'] = Trade.objects.filter(create_time__date= yesterday).aggregate(sum=Sum("product__price"))['sum'] result['consume_this_week'] = Trade.objects.filter(create_time__range= (this_week_start,this_week_end)).aggregate(sum=Sum("product__price"))['sum'] result['consume_last_week'] = Trade.objects.filter(create_time__range= (last_week_start,last_week_end)).aggregate(sum=Sum("product__price"))['sum'] result['consume_this_month'] = Trade.objects.filter(create_time__range= (this_month_start,this_month_end)).aggregate(sum=Sum("product__price"))['sum'] result['consume_last_month'] = Trade.objects.filter(create_time__range= (last_month_start,last_month_end)).aggregate(sum=Sum("product__price"))['sum'] result['consume_this_quarter'] = Trade.objects.filter(create_time__range= (this_quarter_start,this_quarter_end)).aggregate(sum=Sum("product__price"))['sum'] result['consume_last_quarter'] = Trade.objects.filter(create_time__range= (last_quarter_start,last_quarter_end)).aggregate(sum=Sum("product__price"))['sum'] result['consume_this_year'] = Trade.objects.filter(create_time__range= (this_year_start,this_year_end)).aggregate(sum=Sum("product__price"))['sum'] result['consume_last_year'] = Trade.objects.filter(create_time__range= (last_year_start,last_year_end)).aggregate(sum=Sum("product__price"))['sum'] result['total'] = Trade.objects.filter().aggregate(sum=Sum("product__price"))['sum'] # 校验空数据，并将其设置为0 for item in result.keys(): if not result[item]: result[item] = 0 return Response(result) def post(self,request): # 1. 添加消费记录 # 2. 修改用户余额 costomer = Costomer.objects.get(pk=request.data['id']) product = Product.objects.get(pk=request.data['product_id']) costomer.money = costomer.money - Decimal(request.data['price']) costomer.save() Trade(costomer= costomer, product= product,price= request.data['price'],amount=request.data['amount']).save() return Response() class ProductViewSet(ModelViewSet): """ 角色：增删改查 """ perms_map = {'get': '*', 'post': '*', 'put': '*', 'delete': '*'} permission_classes = [] authentication_classes = [] queryset = Product.objects serializer_class = ProductSerializer # pagination_class = None search_fields = ['name'] ordering_fields = ['pk'] ordering = ['pk'] class IncomeViewSet(ModelViewSet): """ 角色：增删改查 """ # perms_map = {'get': '*', 'post': 'role_create', # 'put': 'role_update', 'delete': 'role_delete'} permission_classes = [] authentication_classes = [] queryset = Income.objects serializer_class = IncomeSerializer # pagination_class = None # search_fields = ['costomer'] ordering_fields = ['pk','create_time'] ordering = ['-create_time'] filterset_fields = ['costomer_id'] class TradeViewSet(ModelViewSet): """ 角色：增删改查 """ # perms_map = {'get': '*', 'post': 'role_create', # 'put': 'role_update', 'delete': 'role_delete'} permission_classes = [] authentication_classes = [] queryset = Trade.objects serializer_class = TradeSerializer # pagination_class = None # search_fields = ['costomer'] ordering_fields = ['pk','create_time'] ordering = ['-create_time'] filterset_fields = ['costomer_id']

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4ee35c211d2a49f4d8ee55fafe01c5f97af44419

25

py

Python

samplings/pk.py

jireh-father/tensorflow-triplet-loss

c8a3b3efbf4c68f63d58ee3bedaa8e42451f6a80

[ "MIT" ]

null

samplings/pk.py

jireh-father/tensorflow-triplet-loss

c8a3b3efbf4c68f63d58ee3bedaa8e42451f6a80

[ "MIT" ]

null

samplings/pk.py

jireh-father/tensorflow-triplet-loss

c8a3b3efbf4c68f63d58ee3bedaa8e42451f6a80

[ "MIT" ]

null

def sampling(): pass

8.333333

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6

4eead47658f3f5e52aa30ea97006b8896542a97f

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py

Python

tests/functions/spin_fields/test_exchange_interaction_field.py

jdalzatec/llg

c0acd728d29a9a821ebadc4f1e17e0327d7e238c

[ "MIT" ]

4

2019-09-02T19:18:55.000Z

2021-05-05T15:04:54.000Z

tests/functions/spin_fields/test_exchange_interaction_field.py

lufvelasquezgo/llg

c0acd728d29a9a821ebadc4f1e17e0327d7e238c

[ "MIT" ]

116

2020-02-09T05:19:52.000Z

2022-03-27T18:47:17.000Z

tests/functions/spin_fields/test_exchange_interaction_field.py

lufvelasquezgo/llg

c0acd728d29a9a821ebadc4f1e17e0327d7e238c

[ "MIT" ]

null

from llg.functions import spin_fields import numpy import pytest def compute_exchange_field( num_sites, state, j_exchange, spin_moments, num_neighbors, neighbors ): total = numpy.zeros(shape=(num_sites, 3)) for i in range(num_sites): sum_nhbs = sum(num_neighbors[:i]) for j in range(num_neighbors[i]): index = j + sum_nhbs j_int = j_exchange[index] nhb = neighbors[index] total[i] += j_int * state[nhb] total /= spin_moments[:, numpy.newaxis] return total @pytest.mark.repeat(10) def test_exchange_interaction_field_null_J_exchange(random_state_spins, build_sample): num_sites, num_interactions, neighbors, num_neighbors = build_sample spin_moments = numpy.ones(shape=num_sites) j_exchange = numpy.zeros(shape=num_interactions) exchanges = j_exchange.reshape(num_sites, 6) neighbors_ = numpy.array(neighbors).reshape(num_sites, 6) expected = numpy.zeros(shape=(num_sites, 3)) total = spin_fields.exchange_interaction_field( random_state_spins, spin_moments, exchanges, neighbors_ ) assert numpy.allclose(expected, total) @pytest.mark.repeat(10) def test_exchange_interaction_field_constant_J_exchange( random_state_spins, build_sample ): num_sites, num_interactions, neighbors, num_neighbors = build_sample spin_moments = numpy.ones(shape=num_sites) j_exchange = numpy.full(num_interactions, numpy.random.uniform(-10, 10)) exchanges = j_exchange.reshape(num_sites, 6) neighbors_ = numpy.array(neighbors).reshape(num_sites, 6) expected = compute_exchange_field( num_sites, random_state_spins, j_exchange, spin_moments, num_neighbors, neighbors, ) assert numpy.allclose( spin_fields.exchange_interaction_field( random_state_spins, spin_moments, exchanges, neighbors_ ), expected, ) @pytest.mark.repeat(10) def test_exchange_interaction_field_random_J_exchange( random_state_spins, build_sample, random_j_exchange ): num_sites, _, neighbors, num_neighbors = build_sample exchanges = random_j_exchange.reshape(num_sites, 6) neighbors_ = numpy.array(neighbors).reshape(num_sites, 6) spin_moments = numpy.ones(shape=num_sites) expected = compute_exchange_field( num_sites, random_state_spins, random_j_exchange, spin_moments, num_neighbors, neighbors, ) assert numpy.allclose( spin_fields.exchange_interaction_field( random_state_spins, spin_moments, exchanges, neighbors_, ), expected, ) @pytest.mark.repeat(10) def test_exchange_interaction_field_random_spin_moments( random_state_spins, build_sample, random_spin_moments, random_j_exchange ): num_sites, _, neighbors, num_neighbors = build_sample exchanges = random_j_exchange.reshape(num_sites, 6) neighbors_ = numpy.array(neighbors).reshape(num_sites, 6) expected = compute_exchange_field( num_sites, random_state_spins, random_j_exchange, random_spin_moments, num_neighbors, neighbors, ) assert numpy.allclose( spin_fields.exchange_interaction_field( random_state_spins, random_spin_moments, exchanges, neighbors_, ), expected, ) @pytest.mark.repeat(10) def test_exchange_interaction_field_null_magnetic_moments( random_state_spins, build_sample, random_j_exchange ): num_sites, _, neighbors, _ = build_sample exchanges = random_j_exchange.reshape(num_sites, 6) neighbors_ = numpy.array(neighbors).reshape(num_sites, 6) null_moments = numpy.array([0.0] * num_sites) expected = numpy.full((num_sites, 3), numpy.inf) total = numpy.abs( spin_fields.exchange_interaction_field( random_state_spins, null_moments, exchanges, neighbors_ ) ) assert numpy.allclose(total, expected)

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4ef490bf509cfc0552e244879896a37730e094ec

46

py

Python

battle_module/__init__.py

alentoghostflame/StupidAlentoBot

c024bfb79a9ecb0d9fda5ddc4e361a0cb878baba

[ "MIT" ]

1

2021-12-12T02:50:20.000Z

battle_module/__init__.py

alentoghostflame/StupidAlentoBot

c024bfb79a9ecb0d9fda5ddc4e361a0cb878baba

[ "MIT" ]

17

2020-02-07T23:40:36.000Z

2020-12-22T16:38:44.000Z

battle_module/__init__.py

alentoghostflame/StupidAlentoBot

c024bfb79a9ecb0d9fda5ddc4e361a0cb878baba

[ "MIT" ]

null

from battle_module.battle import BattleModule

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f644efd8d8a51dbfbc05e92dad3ad61e15278318

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py

Python

apps/gallery/migrations/0006_auto_20190801_1338.py

mrtaalebi/sitigo

cce8b4f5299b58d7365789ead416d4568b443743

[ "Apache-2.0" ]

null

apps/gallery/migrations/0006_auto_20190801_1338.py

mrtaalebi/sitigo

cce8b4f5299b58d7365789ead416d4568b443743

[ "Apache-2.0" ]

8

2020-02-12T01:02:15.000Z

2022-03-11T23:53:39.000Z

apps/gallery/migrations/0006_auto_20190801_1338.py

mrtaalebi/sitigo

cce8b4f5299b58d7365789ead416d4568b443743

[ "Apache-2.0" ]

null

# Generated by Django 2.2.3 on 2019-08-01 09:08 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('gallery', '0005_image_city'), ] operations = [ migrations.AlterField( model_name='city', name='english_name', field=models.CharField(max_length=200), ), migrations.AlterField( model_name='city', name='persian_name', field=models.CharField(max_length=200), ), migrations.AlterField( model_name='country', name='english_name', field=models.CharField(max_length=200), ), migrations.AlterField( model_name='country', name='persian_name', field=models.CharField(max_length=200), ), migrations.AlterField( model_name='image', name='english_caption', field=models.CharField(blank=True, default='default', max_length=200, null=True), ), migrations.AlterField( model_name='image', name='persian_caption', field=models.CharField(blank=True, default='default', max_length=200, null=True), ), ]

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6

9c90710cfd644160403952cb91188d7c52cf2cac

38

py

Python

__init__.py

zahirsalim/kpyprotocol

9435f2e5ead0bb8abc7a89bad261e67e5f1a40a7

[ "MIT" ]

null

__init__.py

zahirsalim/kpyprotocol

9435f2e5ead0bb8abc7a89bad261e67e5f1a40a7

[ "MIT" ]

null

__init__.py

zahirsalim/kpyprotocol

9435f2e5ead0bb8abc7a89bad261e67e5f1a40a7

[ "MIT" ]

null

from KProtocol import KurentoProtocol

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6

9c9a2bf1cd96338828e08958877ef1d7c4e62683

133

py

Python

labAPI/optimization/samplers/__init__.py

robertfasano/labAPI

e671c6af2bb702cde018b6d30582c269965da63c

[ "MIT" ]

null

labAPI/optimization/samplers/__init__.py

robertfasano/labAPI

e671c6af2bb702cde018b6d30582c269965da63c

[ "MIT" ]

null

labAPI/optimization/samplers/__init__.py

robertfasano/labAPI

e671c6af2bb702cde018b6d30582c269965da63c

[ "MIT" ]

null

from .grid_search import GridSearch from .differential_evolution import DifferentialEvolution from .random_search import RandomSearch

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6

141e57536cbe00a3ef68b0f8b3e7370ba6196281

11,758

py

Python

tests/test_enc.py

Roman513/python-xmlsec

4a91bbc352a6eb38f3f2c4dcf35691b985ef9da7

[ "MIT" ]

null

tests/test_enc.py

Roman513/python-xmlsec

4a91bbc352a6eb38f3f2c4dcf35691b985ef9da7

[ "MIT" ]

null

tests/test_enc.py

Roman513/python-xmlsec

4a91bbc352a6eb38f3f2c4dcf35691b985ef9da7

[ "MIT" ]

null

import os import tempfile from lxml import etree import xmlsec from tests import base consts = xmlsec.constants class TestEncryptionContext(base.TestMemoryLeaks): def test_init(self): ctx = xmlsec.EncryptionContext(manager=xmlsec.KeysManager()) del ctx def test_init_no_keys_manager(self): ctx = xmlsec.EncryptionContext() del ctx def test_init_bad_args(self): with self.assertRaisesRegex(TypeError, 'KeysManager required'): xmlsec.EncryptionContext(manager='foo') def test_no_key(self): ctx = xmlsec.EncryptionContext(manager=xmlsec.KeysManager()) self.assertIsNone(ctx.key) def test_get_key(self): ctx = xmlsec.EncryptionContext(manager=xmlsec.KeysManager()) self.assertIsNone(ctx.key) ctx.key = xmlsec.Key.from_file(self.path("rsacert.pem"), format=consts.KeyDataFormatCertPem) self.assertIsNotNone(ctx.key) def test_del_key(self): ctx = xmlsec.EncryptionContext(manager=xmlsec.KeysManager()) ctx.key = xmlsec.Key.from_file(self.path("rsacert.pem"), format=consts.KeyDataFormatCertPem) del ctx.key self.assertIsNone(ctx.key) def test_set_key(self): ctx = xmlsec.EncryptionContext(manager=xmlsec.KeysManager()) ctx.key = xmlsec.Key.from_file(self.path("rsacert.pem"), format=consts.KeyDataFormatCertPem) self.assertIsNotNone(ctx.key) def test_set_key_bad_type(self): ctx = xmlsec.EncryptionContext(manager=xmlsec.KeysManager()) with self.assertRaisesRegex(TypeError, r'instance of \*xmlsec.Key\* expected.'): ctx.key = '' def test_set_invalid_key(self): ctx = xmlsec.EncryptionContext(manager=xmlsec.KeysManager()) with self.assertRaisesRegex(TypeError, 'empty key.'): ctx.key = xmlsec.Key() def test_encrypt_xml(self): root = self.load_xml('enc1-in.xml') enc_data = xmlsec.template.encrypted_data_create(root, consts.TransformAes128Cbc, type=consts.TypeEncElement, ns="xenc") xmlsec.template.encrypted_data_ensure_cipher_value(enc_data) ki = xmlsec.template.encrypted_data_ensure_key_info(enc_data, ns="dsig") ek = xmlsec.template.add_encrypted_key(ki, consts.TransformRsaOaep) xmlsec.template.encrypted_data_ensure_cipher_value(ek) data = root.find('./Data') self.assertIsNotNone(data) manager = xmlsec.KeysManager() manager.add_key(xmlsec.Key.from_file(self.path("rsacert.pem"), format=consts.KeyDataFormatCertPem)) ctx = xmlsec.EncryptionContext(manager) ctx.key = xmlsec.Key.generate(consts.KeyDataAes, 128, consts.KeyDataTypeSession) encrypted = ctx.encrypt_xml(enc_data, data) self.assertIsNotNone(encrypted) enc_method = xmlsec.tree.find_child(enc_data, consts.NodeEncryptionMethod, consts.EncNs) self.assertIsNotNone(enc_method) self.assertEqual("http://www.w3.org/2001/04/xmlenc#aes128-cbc", enc_method.get("Algorithm")) ki = xmlsec.tree.find_child(enc_data, consts.NodeKeyInfo, consts.DSigNs) self.assertIsNotNone(ki) enc_method2 = xmlsec.tree.find_node(ki, consts.NodeEncryptionMethod, consts.EncNs) self.assertIsNotNone(enc_method2) self.assertEqual("http://www.w3.org/2001/04/xmlenc#rsa-oaep-mgf1p", enc_method2.get("Algorithm")) cipher_value = xmlsec.tree.find_node(ki, consts.NodeCipherValue, consts.EncNs) self.assertIsNotNone(cipher_value) def test_encrypt_xml_bad_args(self): ctx = xmlsec.EncryptionContext() with self.assertRaises(TypeError): ctx.encrypt_xml('', 0) def test_encrypt_xml_bad_template(self): ctx = xmlsec.EncryptionContext() with self.assertRaisesRegex(xmlsec.Error, 'unsupported `Type`, it should be `element` or `content`'): ctx.encrypt_xml(etree.Element('root'), etree.Element('node')) def test_encrypt_xml_bad_template_bad_type_attribute(self): ctx = xmlsec.EncryptionContext() with self.assertRaisesRegex(xmlsec.Error, 'unsupported `Type`, it should be `element` or `content`'): root = etree.Element('root') root.attrib['Type'] = 'foo' ctx.encrypt_xml(root, etree.Element('node')) def test_encrypt_xml_fail(self): ctx = xmlsec.EncryptionContext() with self.assertRaisesRegex(xmlsec.Error, 'failed to encrypt xml'): root = etree.Element('root') root.attrib['Type'] = consts.TypeEncElement ctx.encrypt_xml(root, etree.Element('node')) def test_encrypt_binary(self): root = self.load_xml('enc2-in.xml') enc_data = xmlsec.template.encrypted_data_create( root, consts.TransformAes128Cbc, type=consts.TypeEncContent, ns="xenc", mime_type="binary/octet-stream" ) xmlsec.template.encrypted_data_ensure_cipher_value(enc_data) ki = xmlsec.template.encrypted_data_ensure_key_info(enc_data, ns="dsig") ek = xmlsec.template.add_encrypted_key(ki, consts.TransformRsaOaep) xmlsec.template.encrypted_data_ensure_cipher_value(ek) manager = xmlsec.KeysManager() manager.add_key(xmlsec.Key.from_file(self.path("rsacert.pem"), format=consts.KeyDataFormatCertPem)) ctx = xmlsec.EncryptionContext(manager) ctx.key = xmlsec.Key.generate(consts.KeyDataAes, 128, consts.KeyDataTypeSession) encrypted = ctx.encrypt_binary(enc_data, b'test') self.assertIsNotNone(encrypted) self.assertEqual("{%s}%s" % (consts.EncNs, consts.NodeEncryptedData), encrypted.tag) enc_method = xmlsec.tree.find_child(enc_data, consts.NodeEncryptionMethod, consts.EncNs) self.assertIsNotNone(enc_method) self.assertEqual("http://www.w3.org/2001/04/xmlenc#aes128-cbc", enc_method.get("Algorithm")) ki = xmlsec.tree.find_child(enc_data, consts.NodeKeyInfo, consts.DSigNs) self.assertIsNotNone(ki) enc_method2 = xmlsec.tree.find_node(ki, consts.NodeEncryptionMethod, consts.EncNs) self.assertIsNotNone(enc_method2) self.assertEqual("http://www.w3.org/2001/04/xmlenc#rsa-oaep-mgf1p", enc_method2.get("Algorithm")) cipher_value = xmlsec.tree.find_node(ki, consts.NodeCipherValue, consts.EncNs) self.assertIsNotNone(cipher_value) def test_encrypt_binary_bad_args(self): ctx = xmlsec.EncryptionContext() with self.assertRaises(TypeError): ctx.encrypt_binary('', 0) def test_encrypt_binary_bad_template(self): ctx = xmlsec.EncryptionContext() with self.assertRaisesRegex(xmlsec.Error, 'failed to encrypt binary'): ctx.encrypt_binary(etree.Element('root'), b'data') def test_encrypt_uri(self): root = self.load_xml('enc2-in.xml') enc_data = xmlsec.template.encrypted_data_create( root, consts.TransformAes128Cbc, type=consts.TypeEncContent, ns="xenc", mime_type="binary/octet-stream" ) xmlsec.template.encrypted_data_ensure_cipher_value(enc_data) ki = xmlsec.template.encrypted_data_ensure_key_info(enc_data, ns="dsig") ek = xmlsec.template.add_encrypted_key(ki, consts.TransformRsaOaep) xmlsec.template.encrypted_data_ensure_cipher_value(ek) manager = xmlsec.KeysManager() manager.add_key(xmlsec.Key.from_file(self.path("rsacert.pem"), format=consts.KeyDataFormatCertPem)) ctx = xmlsec.EncryptionContext(manager) ctx.key = xmlsec.Key.generate(consts.KeyDataAes, 128, consts.KeyDataTypeSession) with tempfile.NamedTemporaryFile(delete=False) as tmpfile: tmpfile.write(b'test') encrypted = ctx.encrypt_binary(enc_data, 'file://' + tmpfile.name) self.assertIsNotNone(encrypted) self.assertEqual("{%s}%s" % (consts.EncNs, consts.NodeEncryptedData), encrypted.tag) enc_method = xmlsec.tree.find_child(enc_data, consts.NodeEncryptionMethod, consts.EncNs) self.assertIsNotNone(enc_method) self.assertEqual("http://www.w3.org/2001/04/xmlenc#aes128-cbc", enc_method.get("Algorithm")) ki = xmlsec.tree.find_child(enc_data, consts.NodeKeyInfo, consts.DSigNs) self.assertIsNotNone(ki) enc_method2 = xmlsec.tree.find_node(ki, consts.NodeEncryptionMethod, consts.EncNs) self.assertIsNotNone(enc_method2) self.assertEqual("http://www.w3.org/2001/04/xmlenc#rsa-oaep-mgf1p", enc_method2.get("Algorithm")) cipher_value = xmlsec.tree.find_node(ki, consts.NodeCipherValue, consts.EncNs) self.assertIsNotNone(cipher_value) def test_encrypt_uri_bad_args(self): ctx = xmlsec.EncryptionContext() with self.assertRaises(TypeError): ctx.encrypt_uri('', 0) def test_encrypt_uri_fail(self): ctx = xmlsec.EncryptionContext() with self.assertRaisesRegex(xmlsec.InternalError, 'failed to encrypt URI'): ctx.encrypt_uri(etree.Element('root'), '') def test_decrypt1(self): self.check_decrypt(1) def test_decrypt2(self): self.check_decrypt(2) def test_decrypt_key(self): root = self.load_xml('enc3-out.xml') enc_key = xmlsec.tree.find_child(root, consts.NodeEncryptedKey, consts.EncNs) self.assertIsNotNone(enc_key) manager = xmlsec.KeysManager() manager.add_key(xmlsec.Key.from_file(self.path("rsakey.pem"), format=consts.KeyDataFormatPem)) ctx = xmlsec.EncryptionContext(manager) keydata = ctx.decrypt(enc_key) ctx.reset() root.remove(enc_key) ctx.key = xmlsec.Key.from_binary_data(consts.KeyDataAes, keydata) enc_data = xmlsec.tree.find_child(root, consts.NodeEncryptedData, consts.EncNs) self.assertIsNotNone(enc_data) decrypted = ctx.decrypt(enc_data) self.assertIsNotNone(decrypted) self.assertEqual(self.load_xml("enc3-in.xml"), decrypted) def check_decrypt(self, i): root = self.load_xml('enc%d-out.xml' % i) enc_data = xmlsec.tree.find_child(root, consts.NodeEncryptedData, consts.EncNs) self.assertIsNotNone(enc_data) manager = xmlsec.KeysManager() manager.add_key(xmlsec.Key.from_file(self.path("rsakey.pem"), format=consts.KeyDataFormatPem)) ctx = xmlsec.EncryptionContext(manager) decrypted = ctx.decrypt(enc_data) self.assertIsNotNone(decrypted) self.assertEqual(self.load_xml("enc%d-in.xml" % i), root) def test_decrypt_bad_args(self): ctx = xmlsec.EncryptionContext() with self.assertRaises(TypeError): ctx.decrypt('') def check_no_segfault(self): namespaces = {'soap': 'http://schemas.xmlsoap.org/soap/envelope/'} manager = xmlsec.KeysManager() key = xmlsec.Key.from_file(self.path("rsacert.pem"), format=consts.KeyDataFormatCertPem) manager.add_key(key) template = self.load_xml('enc-bad-in.xml') enc_data = xmlsec.template.encrypted_data_create( template, xmlsec.Transform.AES128, type=xmlsec.EncryptionType.CONTENT, ns='xenc' ) xmlsec.template.encrypted_data_ensure_cipher_value(enc_data) key_info = xmlsec.template.encrypted_data_ensure_key_info(enc_data, ns='dsig') enc_key = xmlsec.template.add_encrypted_key(key_info, xmlsec.Transform.RSA_PKCS1) xmlsec.template.encrypted_data_ensure_cipher_value(enc_key) data = template.find('soap:Body', namespaces=namespaces) enc_ctx = xmlsec.EncryptionContext(manager) enc_ctx.key = xmlsec.Key.generate(xmlsec.KeyData.AES, 192, xmlsec.KeyDataType.SESSION) self.assertRaises(Exception, enc_ctx.encrypt_xml(enc_data, data))

45.929688

128

0.696887

1,405

11,758

5.649822

0.120285

0.024691

0.075334

0.064248

0.817964

0.787478

0.761401

0.746032

0.734694

0.720333

0

0.010466

0.187362

11,758

255

129

46.109804

0.820304

0

0.574879

0

0.082072

0

0.236715

1

0.125604

false

0

0.024155

0

0.154589

0

null

0

1

0

null

0

6

1adee8c371f0ae47ceaf49652ee8998ada6ad545

10

py

Python

tests/trigger/samples/sample.py

davidaustinarcher/vulnpy

692703dae701197fd42ae7fc5a9d52f05a501550

[ "MIT" ]

7

2021-03-23T17:40:45.000Z

2022-03-14T16:07:27.000Z

tests/trigger/samples/sample.py

davidaustinarcher/vulnpy

692703dae701197fd42ae7fc5a9d52f05a501550

[ "MIT" ]

27

2020-06-29T13:35:45.000Z

2022-01-21T07:10:55.000Z

tests/trigger/samples/sample.py

davidaustinarcher/vulnpy

692703dae701197fd42ae7fc5a9d52f05a501550

[ "MIT" ]

14

2020-07-26T18:23:16.000Z

2022-03-09T13:44:53.000Z

a = 4 - 2

5

9

0.3

3

10

1

0

0.4

0.5

10

1

10

0.2

0

1

0

false

0

1

null

0

1

0

1

0

1

0

null

0

6

211605b05bcf3dfcce43336376cc719034263dff

24

py

Python

src/Mosaic/main.py

MarcMDE/Mosaic

ecf7628f23dbc0af4ba774d04fbc321b92065af2

[ "CC0-1.0" ]

null

src/Mosaic/main.py

MarcMDE/Mosaic

ecf7628f23dbc0af4ba774d04fbc321b92065af2

[ "CC0-1.0" ]

null

src/Mosaic/main.py

MarcMDE/Mosaic

ecf7628f23dbc0af4ba774d04fbc321b92065af2

[ "CC0-1.0" ]

null

# TODO: Test img resize

12

23

0.708333

4

24

4.25

1

0

0.208333

24

1

24

0.894737

0.875

0

null

0

null

0

null

0

1

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

211757f4325fb8154f1bbb43c1c17f0a565c6dfc

54,173

py

Python

code/recnn/model/data_loader.py

SebastianMacaluso/RecNN_PyTorch

bb67898268aa5d8c1cc432bb747602fb0d93d631

[ "MIT" ]

6

2019-04-01T17:53:04.000Z

2020-05-13T17:00:21.000Z

code/recnn/model/data_loader.py

SebastianMacaluso/RecNN_PyTorch_batch

bb67898268aa5d8c1cc432bb747602fb0d93d631

[ "MIT" ]

1

2020-01-09T17:03:16.000Z

code/recnn/model/data_loader.py

SebastianMacaluso/RecNN_PyTorch_batch

bb67898268aa5d8c1cc432bb747602fb0d93d631

[ "MIT" ]

6

2019-03-27T18:57:37.000Z

2021-06-10T15:42:15.000Z

""" Classes and functions to load the raw data and create the batches """ import random import numpy as np import os import sys import pickle import gzip import subprocess #import matplotlib as mpl import json import itertools import re import random from sklearn.utils import check_random_state import torch from torch.autograd import Variable from sklearn.preprocessing import RobustScaler import logging import sys from model import preprocess #The local dir is the train.py dir ############################################################################################################## #///////////////////// CLASSES //////////////////////////////////////////////////////////////////////// ############################################################################################################## # use GPU if available class torch_params(object): cuda = torch.cuda.is_available() ############################################################################################################## # methods to load the raw data and create the batches class DataLoader(object): """ Handles all aspects of the data. Has the methods to load the raw data and create the batches """ def __init__(self): ''' Empty ''' #----------------------------------------------------------------------------------------------------------- # Make the input tree dictionaries. String should be either qcd or tt def makeTrees(dir_subjets,string,N_jets,label): ''' Function to load the jet events and make the trees. Args: dir_subjets: dir with the event files. Loads all the files in the dir_subjets that satisfy the 'string' label. File format: array, where each entry is a "jet list". Each "jet list" has: tree=np.asarray(event[0]) content=np.asarray(event[1]) mass=np.asarray(event[2]) pt=np.asarray(event[3]) Currently only works for 1 jet per even. Modify for full event studies. string: string that identifies which files to load (signal or background files) N_jets: Number of jet trees to generate. If set to inf, it will load all the jets in the files label: label where 1 is for signal and 0 for background ''' subjetlist = [filename for filename in np.sort(os.listdir(dir_subjets)) if ('tree' in filename and string in filename and filename.endswith('.dat'))] N_analysis=len(subjetlist) logging.info('Number of jet files for '+str(string)+'='+str(N_analysis)) logging.info('Loading '+str(string)+' jet files... ') logging.info(str(string)+' files list = '+str(subjetlist)) Ntotjets=0 final_trees=[] jets=[] ##------------------------------------------------ # loop over the files and the events in each file for ifile in range(N_analysis): for s in open(dir_subjets+'/'+subjetlist[ifile]): if (Ntotjets>=N_jets): return jets else: event=json.loads(s) # print('Full event tree = ',event[0]) Ntotjets+=1 # print('Ntotjets = ', Ntotjets) if Ntotjets%10000==0: logging.info('Ntotjets='+str(Ntotjets)) tree=np.asarray(event[0]) content=np.asarray(event[1]) mass=np.asarray(event[2]) pt=np.asarray(event[3]) charge=np.asarray(event[4]) abs_charge=np.asarray(event[5]) muon=np.asarray(event[6]) tree=np.array([np.asarray(e).reshape(-1,2) for e in tree]) content=np.array([np.asarray(e).reshape(-1,4) for e in content]) # print('tree = ',tree[0]) # print('content = ',content[0]) # print('mass =',mass) # print('pt = ',pt) # # # SANITY CHECK: Below we check that the tree contains the right location of each children subjet # ii = 3; # # print('Content = ',content[0]) # print('Content ',ii,' = ',content[0][ii]) # print('Children location =',tree[0][ii]) # print('Content ',ii,' by adding the 2 children 4-vectors= ',content[0][tree[0][ii,0]] # + content[0][tree[0][ii,1]]) # print('-------------------'*10) ##----------------------------------------------- event=[] # loop over the jets in each event. Currently loads only the 1st jet for i in range(1): #This only works for single jet studies. Modify for full events jet = {} jet["root_id"] = 0 jet["tree"] = tree[i] #Labels for the jet constituents in the tree # jet["content"] = np.reshape(content[i],(-1,4,1)) #Where content[i][0] is the jet 4-momentum, and the other entries are the jets constituents 4 momentum. Use this format if using TensorFlow jet["content"] = np.reshape(content[i],(-1,4)) # Use this format if using Pytorch jet["mass"] = mass[i] jet["pt"] = pt[i] jet["energy"] = content[i][0, 3] jet["charge"]=charge[i] jet["abs_charge"]=abs_charge[i] jet["muon"]=muon[i] px = content[i][0, 0] #The jet is the first entry of content. And then we have (px,py,pz,E) py = content[i][0, 1] pz = content[i][0, 2] p = (content[i][0, 0:3] ** 2).sum() ** 0.5 # jet["Calc energy"]=(p**2+mass[i]**2)**0.5 eta = 0.5 * (np.log(p + pz) - np.log(p - pz)) #pseudorapidity eta phi = np.arctan2(py, px) jet["eta"] = eta jet["phi"] = phi # print('jet contents =', jet.items()) # # #----------------------------- # # Preprocess # # #Ensure that the left sub-jet has always a larger pt than the right # jet= preprocess.permute_by_pt(jet) # # # Change the input variables # jet= preprocess.extract(jet) if label==1: jet["label"]=1 else: jet["label"]=0 # Append each jet dictionary jets.append(jet) # event.append(jet) #Uncomment for full event studies # jets.append(event) #Uncomment for full event studies logging.info('Number of jets ='+ str(len(jets))) logging.info('---'*20) # print('Number of trees =', len(final_trees)) return jets #----------------------------------------------------------------------------------------------------------- # Split the sample into train, cross-validation and test def merge_shuffle_sample(sig, bkg): ''' Function to split the sample into train, cross-validation and test with equal number of sg and bg events. Then shuffle each set. Args: sig: signal sample bkg: background sample train_frac_rel: fraction of data for the train set val_frac_rel: fraction of data for the validation set test_frac_rel: fraction of data for the test set ''' logging.info('---'*20) logging.info('Loading and shuffling the trees ...') rndstate = random.getstate() random.seed(0) size=np.minimum(len(sig),len(bkg)) # print('sg length=',len(sig)) sig_label=np.ones((size),dtype=int) bkg_label=np.zeros((size),dtype=int) ##----------------------------------------------- # Concatenate sg and bg data X=np.concatenate((sig[0:int(size)],bkg[0:int(size)])) Y=np.concatenate((sig_label[0:int(size)],bkg_label[0:int(size)])) ##----------------------------------------------- # Shuffle the sets indices = check_random_state(1).permutation(len(X)) X = X[indices] Y = Y[indices] ##----------------------------------------------- X=np.asarray(X) Y=np.asarray(Y) # Uncomment below if we change the NN output to be of dim=1 # train_y=np.asarray(train_y).reshape((-1,1)) # dev_y=np.asarray(dev_y).reshape((-1,1)) # test_y=np.asarray(test_y).reshape((-1,1)) print('test_y=',Y) print('---'*20) logging.info('X shape='+str(X.shape)) return X, Y #----------------------------------------------------------------------------------------------------------- # def scale_features(jets): """ RobustScaler will remove the median (send then median value to 0) for each feature (each column). Then it divides each value of each feature by 1/2*(distance between 1st and 3rd quartiles). (In a symmetric distribution it would send the 1st and 3rd quartiles to -1 and 1. It uses the 1st and 3rd quartiles by default, but this can be an input to RobustScaler.""" transformer = RobustScaler().fit(np.vstack([jet["content"] for jet in jets])) # remove outliers for jet in jets: jet["content"] = transformer.transform(jet["content"]) # center and scale the data return jets #----------------------------------------------------------------------------------------------------------- # def get_transformer(jets): transformer = RobustScaler().fit(np.vstack([jet["content"] for jet in jets])) # remove outliers return transformer #----------------------------------------------------------------------------------------------------------- # def transform_features(transformer,jets): for jet in jets: jet["content"] = transformer.transform(jet["content"]) # center and scale the data return jets #----------------------------------------------------------------------------------------------------------- # Split the sample into train, cross-validation and test def shuffle_autoencoder(sig, bkg): ''' Function to split the sample into train, cross-validation and test with equal number of sg and bg events. Then shuffle each set. Args: sig: signal sample bkg: background sample train_frac_rel: fraction of data for the train set val_frac_rel: fraction of data for the validation set test_frac_rel: fraction of data for the test set ''' print('---'*20) print('Loading and shuffling the trees ...') rndstate = random.getstate() random.seed(0) size=np.minimum(len(sig),len(bkg)) # print('sg length=',len(sig)) sig_label=np.ones((size),dtype=int) bkg_label=np.zeros((size),dtype=int) ##----------------------------------------------- print('Creating train, val and test datasets ...') # Split data into train, val and test # train_frac=train_frac_rel # val_frac=train_frac+val_frac_rel # test_frac=val_frac+test_frac_rel # # N_train=int(train_frac*size) # Nval=int(val_frac*size) Ntest=int(size) ##----------------------------------------------- # Concatenate sg and bg data # train_x=np.concatenate((sig[0:N_train],bkg[0:N_train])) # train_y=np.concatenate((sig_label[0:N_train],bkg_label[0:N_train])) # # dev_x=np.concatenate((sig[N_train:Nval],bkg[N_train:Nval])) # dev_y=np.concatenate((sig_label[N_train:Nval],bkg_label[N_train:Nval])) test_x=np.concatenate((sig[0:Ntest],bkg[0:Ntest])) test_y=np.concatenate((sig_label[0:Ntest],bkg_label[0:Ntest])) ##----------------------------------------------- # Shuffle the sets # indices_train = check_random_state(1).permutation(len(train_x)) # train_x = train_x[indices_train] # train_y = train_y[indices_train] # # indices_dev = check_random_state(2).permutation(len(dev_x)) # dev_x = dev_x[indices_dev] # dev_y = dev_y[indices_dev] indices_test = check_random_state(3).permutation(len(test_x)) test_x = test_x[indices_test] test_y = test_y[indices_test] ##----------------------------------------------- # train_x=np.asarray(train_x) # dev_x=np.asarray(dev_x) test_x=np.asarray(test_x) # train_y=np.asarray(train_y) # dev_y=np.asarray(dev_y) test_y=np.asarray(test_y) # Uncomment below if we change the NN output to be of dim=1 # train_y=np.asarray(train_y).reshape((-1,1)) # dev_y=np.asarray(dev_y).reshape((-1,1)) # test_y=np.asarray(test_y).reshape((-1,1)) # print('Train shape=',train_x.shape) # print('Val shape=',dev_x.shape) print('Test shape=',test_x.shape) # print('train=',train_x[0]['content']) print('test_y=',test_y) print('---'*20) return test_x, test_y #----------------------------------------------------------------------------------------------------------- # Split the sample into train, cross-validation and test def split_shuffle_sample(sig, bkg, train_frac_rel, val_frac_rel, test_frac_rel): ''' Function to split the sample into train, cross-validation and test with equal number of sg and bg events. Then shuffle each set. Args: sig: signal sample bkg: background sample train_frac_rel: fraction of data for the train set val_frac_rel: fraction of data for the validation set test_frac_rel: fraction of data for the test set ''' print('---'*20) print('Loading and shuffling the trees ...') rndstate = random.getstate() random.seed() size=np.minimum(len(sig),len(bkg)) # print('sg length=',len(sig)) sig_label=np.ones((size),dtype=int) bkg_label=np.zeros((size),dtype=int) ##----------------------------------------------- print('Creating train, val and test datasets ...') # Split data into train, val and test train_frac=train_frac_rel val_frac=train_frac+val_frac_rel test_frac=val_frac+test_frac_rel N_train=int(train_frac*size) Nval=int(val_frac*size) Ntest=int(test_frac*size) # print('len(sg)=',len(sig)) # print('len(bkg)=',len(bkg)) # print('size=',size) # print('N_train=',N_train) # print('Nval=',Nval) # print('Ntest=',Ntest) # print('+-+-'*20) # Shuffle sig and bkg sets independently indices_sig = check_random_state().permutation(len(sig)) sig = sig[indices_sig] indices_bkg = check_random_state().permutation(len(bkg)) bkg = bkg[indices_bkg] ##----------------------------------------------- # Concatenate sg and bg data train_x=np.concatenate((sig[0:N_train],bkg[0:N_train])) train_y=np.concatenate((sig_label[0:N_train],bkg_label[0:N_train])) dev_x=np.concatenate((sig[N_train:Nval],bkg[N_train:Nval])) dev_y=np.concatenate((sig_label[N_train:Nval],bkg_label[N_train:Nval])) test_x=np.concatenate((sig[Nval:Ntest],bkg[Nval:Ntest])) test_y=np.concatenate((sig_label[Nval:Ntest],bkg_label[Nval:Ntest])) # print('train_x=',[x['charge']for x in train_x] ) # print('dev_x=',[x['charge']for x in dev_x]) # print('test_x=',[x['charge']for x in test_x]) ##----------------------------------------------- # Shuffle the sets indices_train = check_random_state().permutation(len(train_x)) print('train_y=',train_y) print('train x shape=',train_x.shape) print('///'*20) train_x = train_x[indices_train] train_y = train_y[indices_train] print('train_y=',train_y) indices_dev = check_random_state().permutation(len(dev_x)) dev_x = dev_x[indices_dev] dev_y = dev_y[indices_dev] indices_test = check_random_state().permutation(len(test_x)) test_x = test_x[indices_test] test_y = test_y[indices_test] ##----------------------------------------------- train_x=np.asarray(train_x) dev_x=np.asarray(dev_x) test_x=np.asarray(test_x) train_y=np.asarray(train_y) dev_y=np.asarray(dev_y) test_y=np.asarray(test_y) # Uncomment below if we change the NN output to be of dim=1 # train_y=np.asarray(train_y).reshape((-1,1)) # dev_y=np.asarray(dev_y).reshape((-1,1)) # test_y=np.asarray(test_y).reshape((-1,1)) print('Train shape=',train_x.shape) print('Val shape=',dev_x.shape) print('Test shape=',test_x.shape) # print('train=',train_x[0]['content']) # print('test_y=',test_y) return train_x, train_y, dev_x, dev_y, test_x, test_y #----------------------------------------------------------------------------------------------------------- # CURRENTLY NOT USED. SKIP TO THE NEXT METHOD. # Batchization of the recursion. #This creates batches without zero padding. def batch_level_no_pad(jets): # Batch the recursive activations across all nodes of a same level # !!! Assume that jets have at least one inner node. # Leads to off-by-one errors otherwise :( # Reindex node IDs over all jets # # jet_children: array of shape [n_nodes, 2] # jet_children[node_id, 0] is the node_id of the left child of node_id # jet_children[node_id, 1] is the node_id of the right child of node_id # # jet_contents: array of shape [n_nodes, n_features] # jet_contents[node_id] is the feature vector of node_id (4-vector in our case) jet_children =np.vstack([jet['tree'] for jet in jets]) # print('jet_children=',jet_children) # jet_children = np.vstack(jet_children) #We concatenate all the jets tree into 1 tree # print('jet_children=',jet_children) jet_contents = np.vstack([jet["content"] for jet in jets]) #We concatenate all the jet['contents'] into 1 array # print('jet_contents=',jet_contents) n_nodes=len(jet_children) #--------------------- # Level-wise traversal level_children = np.zeros((n_nodes, 4), dtype=np.int32) #Array with 4 features per node level_children[:, [0, 2]] -= 1 #We set features 0 and 2 to -1. Features 0 and 2 will be the position of the left and right children of node_i, where node_i is given by "contents[node_i]" and left child is "content[level_children[node,0]]" # # # SANITY CHECK 1: Below we check that the jet_children contains the right location of each children subjet # ii = -28 # print('Content ',ii,' = ',jet_contents[ii]) # print('Children location =',jet_children[ii]) # if jet_children[ii][0]==-1: print('The node is a leaf') # else: print('Content ',ii,' by adding the 2 children 4-vectors= ',jet_contents[jet_children[ii,0]] # + jet_contents[jet_children[ii,1]]) inners = [] # Inner nodes at level i ---- The nodes that are not leaves are in this category (SM) outers = [] # Outer nodes at level i ---- The leaves are in this category (SM) offset = 0 for jet in jets: # We fill the inners and outers array where each row corresponds to 1 level. We have each jet next to each other, so each jet root is a new column at depth 0, the first children add 2 columns at depth 1, .... Then we save in "level_children" the position of the left(right) child in the inners (or outers) array at depth i. So the 4-vector of node_i would be e.g. content[outers[level_children[i,0]] queue = [(jet["root_id"], -1, True, 0)] #(node, parent position, is_left, depth) while len(queue) > 0: node, parent, is_left, depth = queue.pop(0) #We pop the first element (This is expensive because we have to change the position of all the other tuples in the queue) if len(inners) < depth + 1: inners.append([]) #We append an empty list (1 per level) when the first node of a level shows up. if len(outers) < depth + 1: outers.append([]) # Inner node if jet_children[node, 0] != -1:#If node is not a leaf (it has a left child) inners[depth].append(node+offset) #We append the node to the inner list at row=depth because it has children position = len(inners[depth]) - 1 #position on the inners list of the last node we added is_leaf = False queue.append((jet_children[node+offset, 0], node+offset, True, depth + 1)) #Format: (node at the next level, parent node,"left", depth) queue.append((jet_children[node+offset, 1], node+offset, False, depth + 1)) # Outer node else: #If the node is a leaf outers[depth].append(node+offset) # print('outers=',outers) position = len(outers[depth]) - 1 #position on the outers list of the last node we added is_leaf = True # Register node at its parent. We save the position of the left and right children in the inners (or outers) array (at depth=depth_parent+1) if parent >= 0: if is_left: level_children[parent, 0] = position #position of the left child in the inners (or outers) array (at depth=depth_parent+1) level_children[parent, 1] = is_leaf #if True then the left child is a leaf => look in the outers array, else in the inners one else: level_children[parent, 2] = position level_children[parent, 3] = is_leaf offset += len(jet["tree"]) # We need this offset to get the right location in the jet_children array of each jet root node # # # SANITY CHECK 2: Below we check that the level_children contains the right location of each children subjet # ii = 1 #location of the parent in the inner list at level_parent # level_parent=0 # print('Root of jet #',ii+1,' location =',inners[level_parent][ii]) #The root is at level 0 # print('Content jet #',ii+1,'=',jet_contents[inners[level_parent][ii]]) # print('Children location:\n left=',inners[level_parent+1][level_children[inners[level_parent][ii],0]],' right=',inners[level_parent+1][level_children[inners[level_parent][ii],2]]) # if level_children[inners[level_parent][ii],1]==True: print('The node is a leaf') # else: print('Content ',inners[0][ii],' by adding the 2 children 4-vectors= ',jet_contents[inners[level_parent+1][level_children[inners[level_parent][ii],0]]] # + jet_contents[inners[level_parent+1][level_children[inners[level_parent][ii],2]]]) # # print('Is leaf at level ', level_parent,' = ', level_children[inners[level_parent][::],1]) # Reorganize levels[i] so that inner nodes appear first, then outer nodes levels = [] n_inners = [] contents = [] prev_inner = np.array([], dtype=int) print('----'*20) for inner, outer in zip(inners, outers): print('inner=',inner) print('outer=',outer) n_inners.append(len(inner)) # We append the number of inner nodes in each level inner = np.array(inner, dtype=int) outer = np.array(outer, dtype=int) levels.append(np.concatenate((inner, outer))) #Append the inners and outers of each level left = prev_inner[level_children[prev_inner, 1] == 1] # level_children[prev_inner, 1] returns a list with 1 for left children at level prev_inner+1 that are leaves and 0 otherwise. Then prev_inner[level_children[prev_inner, 1] == 1] picks the nodes at level prev_inner whose left children are leaves. So left are all nodes level prev_inner whose left child (at level prev_inner+1) is a leaf. level_children[left, 0] += len(inner) #We apply an offset to "left" because we concatenated inner and outer, with inners coming first. So now we get the right position of the children that are leaves in the levels array. right = prev_inner[level_children[prev_inner, 3] == 1] level_children[right, 2] += len(inner) contents.append(jet_contents[levels[-1]]) # We append the 4-vector given by the nodes in the last row that we added to levels. This way we create a list of contents where each row corresponds to 1 level. # Then, the position of the left and right children in the levels list, will also be the position of them in the contents list, which is given by level_children Note that level_children keeps the old indices arrangement. prev_inner = inner #This will be the inner of the previous level in the next loop # print('level_children[prev_inner, 1] =',level_children[prev_inner, 1] ) # print('left=',left) # print('right=',right) # print('prev_inner=',prev_inner) # print('contents=',contents) # print('length contents=',len(contents)) # print('length levels =',len(levels)) # # # # SANITY CHECK 3: # ii = 1 #location of the parent in the inner list at level_parent # level_parent=3 # print('Final rearrangement of jets in batches') # print('Root of jet #',ii+1,' location =','level',level_parent,' pos:',ii) #The root is at level 0 # print('Content jet #',ii+1,'=',contents[level_parent][ii]) # print('Children location in the contents list','level',level_parent+1,'\n left=',level_children[levels[level_parent][ii],0],' right=',level_children[levels[level_parent][ii],2]) # if level_children[[ii],1]==True: print('The node is a leaf') # else: print('Content ','level',level_parent,' pos:',ii,' by adding the 2 children 4-vectors= ',contents[level_parent+1][level_children[levels[level_parent][ii],0]] # + contents[level_parent+1][level_children[levels[level_parent][ii],2]]) # levels: list of arrays # levels[i][j] is a node id at a level i in one of the trees # inner nodes are positioned within levels[i][:n_inners[i]], while # leaves are positioned within levels[i][n_inners[i]:] # # level_children: array of shape [n_nodes, 4] # level_children[node_id, 0] is the position j in the next level of # the left child of node_id # level_children[node_id, 2] is the position j in the next level of # the right child of node_id # # n_inners: list of shape len(levels) # n_inners[i] is the number of inner nodes at level i, accross all # trees # # contents: array of shape [n_levels, n_nodes, n_features] # contents[sum(len(l) for l in layers[:i]) + j] is the feature vector # or node layers[i][j] print('n_inners[0]=',n_inners[0]) return (levels, level_children[:, [0, 2]], n_inners, contents) #----------------------------------------------------------------------------------------------------------- # Batchization of the recursion (USING G LOUPPE'S CODE). String should be either qcd or tt. Adding zero padding def batch_nyu_pad(jets,features): # Batch the recursive activations across all nodes of a same level # !!! Assume that jets have at least one inner node. # Leads to off-by-one errors otherwise :( # Reindex node IDs over all jets # # jet_children: array of shape [n_nodes, 2] # jet_children[node_id, 0] is the node_id of the left child of node_id # jet_children[node_id, 1] is the node_id of the right child of node_id # # jet_contents: array of shape [n_nodes, n_features] # jet_contents[node_id] is the feature vector of node_id jet_children = [] offset = 0 for jet in jets: tree = np.copy(jet["tree"]) tree[tree != -1] += offset #Everything except the leaves (SM) jet_children.append(tree) offset += len(tree) #I think this is the offset to go to the next jet and be able to train in parallel? (SM) jet_children = np.vstack(jet_children) #To get the tree of each jet one below the other (SM) jet_contents = np.vstack([jet["content"] for jet in jets]) n_nodes = offset # Level-wise traversal level_children = np.zeros((n_nodes, 4), dtype=np.int32) level_children[:, [0, 2]] -= 1 inners = [] # Inner nodes at level i ---- The nodes that are not leaves are in this category (SM) outers = [] # Outer nodes at level i ---- The leaves are in this category (SM) offset = 0 for jet in jets: queue = [(jet["root_id"] + offset, -1, True, 0)] while len(queue) > 0: node, parent, is_left, depth = queue.pop(0) if len(inners) < depth + 1: inners.append([]) if len(outers) < depth + 1: outers.append([]) # Inner node if jet_children[node, 0] != -1:#If left child is not a leaf inners[depth].append(node) #We append the node because it has children position = len(inners[depth]) - 1 is_leaf = False queue.append((jet_children[node, 0], node, True, depth + 1)) #Format: (left children position in contents, parent,"left", depth) queue.append((jet_children[node, 1], node, False, depth + 1)) # Outer node else: outers[depth].append(node) position = len(outers[depth]) - 1 is_leaf = True # Register node at its parent if parent >= 0: if is_left: level_children[parent, 0] = position #position of the left child in the inners (or outers) array level_children[parent, 1] = is_leaf #if True look in the outers array, else in the inners one else: level_children[parent, 2] = position level_children[parent, 3] = is_leaf offset += len(jet["tree"]) # Reorganize levels[i] so that inner nodes appear first, then outer nodes levels = [] n_inners = [] contents = [] n_level=[] prev_inner = np.array([], dtype=int) for inner, outer in zip(inners, outers): n_inners.append(len(inner)) inner = np.array(inner, dtype=int) outer = np.array(outer, dtype=int) levels.append(np.concatenate((inner, outer))) n_level.append(len(levels[-1])) left = prev_inner[level_children[prev_inner, 1] == 1] level_children[left, 0] += len(inner) right = prev_inner[level_children[prev_inner, 3] == 1] level_children[right, 2] += len(inner) contents.append(jet_contents[levels[-1]]) prev_inner = inner # print('----'*20) # print('subjets per level=',n_level) # print('----'*20) # print('----'*20) # print('Number of levels=',len(n_level)) # print('----'*20) ##----------------------------------------------- # Zero padding #We loop over the levels to zero pad the array (only a few levels per jet) n_inners=np.asarray(n_inners) max_n_level=np.max(n_level) # print('max_n_level=',max_n_level) # print('----'*20) for i in range(len(levels)): # print('max_n_level-len(levels[i])=',max_n_level-len(levels[i])) pad_dim=int(max_n_level-len(levels[i])) levels[i]=np.concatenate((levels[i],np.zeros((pad_dim)))) # print('/////'*20) # print('contents[i].shape=',contents[i].shape) contents[i]=np.concatenate((contents[i],np.zeros((pad_dim,int(features))))) ##----------------------------------------------- # levels: list of arrays # levels[i][j] is a node id at a level i in one of the trees # inner nodes are positioned within levels[i][:n_inners[i]], while # leaves are positioned within levels[i][n_inners[i]:] # # level_children: array of shape [n_nodes, 2] # level_children[node_id, 0] is the position j in the next level of # the left child of node_id # level_children[node_id, 1] is the position j in the next level of # the right child of node_id # # level_children is an array with shape = (total n_nodes in the batch,2) where shape[1] contains the left # and right children locations of the node. This location gives the position of the children in the next # level # # n_inners: list of shape len(levels) # n_inners[i] is the number of inner nodes at level i, accross all # trees # # contents: array of shape [n_nodes, n_features] # contents[sum(len(l) for l in layers[:i]) + j] is the feature vector # or node layers[i][j] # return (levels, level_children[:, [0, 2]], n_inners, contents) return (levels, level_children[:, [0, 2]], n_inners, contents, n_level) #----------------------------------------------------------------------------------------------------------- # CURRENTLY NOT USED. SKIP TO THE NEXT METHOD. # Batchization of the recursion with zero padding. def batch_level(jets,features): ''' This methos loads the jet trees, reorganizes the tree by levels, creates a batch of N jets by appending the nodes of each jet to each level and adds zero padding so that all the levels have the same size Args: jets: Number of jets to create the batch ##----------------------------------------------- Batch the recursive activations across all nodes of a same level !!! Assume that jets have at least one inner node. Leads to off-by-one errors otherwise :( Reindex node IDs over all jets jet_children: array of shape [n_nodes, 2] jet_children[node_id, 0] is the node_id of the left child of node_id jet_children[node_id, 1] is the node_id of the right child of node_id jet_contents: array of shape [n_nodes, n_features] jet_contents[node_id] is the feature vector of node_id (4-vector in our case) ''' jet_children =np.vstack([jet['tree'] for jet in jets]) # print('jet_children=',jet_children) # jet_children = np.vstack(jet_children) #concatenate all the jets tree into 1 tree # print('jet_children=',jet_children) jet_contents = np.vstack([jet["content"] for jet in jets]) #concatenate all the jet['contents'] into 1 array # print('jet_contents=',jet_contents) n_nodes=len(jet_children) ##----------------------------------------------- # Level-wise traversal level_children = np.zeros((n_nodes, 4), dtype=np.int32) #Array with 4 features per node level_children[:, [0, 2]] -= 1 #We set features 0 and 2 to -1. Features 0 and 2 will be the position of the left and right children of node_i, where node_i is given by "contents[node_i]" and left child is "content[level_children[node,0]]" ##----------------------------------------------- # # SANITY CHECK 1: Below we check that the jet_children contains the right location of each children subjet # ii = -28 # print('Content ',ii,' = ',jet_contents[ii]) # print('Children location =',jet_children[ii]) # if jet_children[ii][0]==-1: print('The node is a leaf') # else: print('Content ',ii,' by adding the 2 children 4-vectors= ',jet_contents[jet_children[ii,0]] # + jet_contents[jet_children[ii,1]]) ##----------------------------------------------- inners = [] # Inner nodes at level i ---- The nodes that are not leaves are in this category outers = [] # Outer nodes at level i ---- The leaves are in this category offset = 0 # We fill the inners and outers array where each row corresponds to 1 level. We have each jet next to each other, so each jet root is a new column at depth 0, the first children add 2 columns at depth 1, and so on .... Then we save in "level_children" the position of the left(right) child in the inners or outers array at depth i. So the 4-vector of node_i would be e.g. content[outers[level_children[i,0]] for jet in jets: queue = [(jet["root_id"], -1, True, 0)] #(node, parent position, is_left, depth) while len(queue) > 0: node, parent, is_left, depth = queue.pop(0) #We pop the first element (This is expensive because we have to change the position of all the other tuples in the queue) if len(inners) < depth + 1: inners.append([]) #We append an empty list (1 per level) when the first node of a level shows up. if len(outers) < depth + 1: outers.append([]) #----------- # Inner node if jet_children[node, 0] != -1:#If node is not a leaf (it has a left child) inners[depth].append(node+offset) #We append the node to the inner list at row=depth because it has children position = len(inners[depth]) - 1 #position on the inners list of the last node we added is_leaf = False queue.append((jet_children[node+offset, 0], node+offset, True, depth + 1)) #Format: (node at the next level, parent node,"left", depth) queue.append((jet_children[node+offset, 1], node+offset, False, depth + 1)) #----------- # Outer node else: #If the node is a leaf outers[depth].append(node+offset) # print('outers=',outers) position = len(outers[depth]) - 1 #position on the outers list of the last node we added is_leaf = True #----------- # Register node at its parent. We save the position of the left and right children in the inners (or outers) array (at depth=depth_parent+1) if parent >= 0: if is_left: level_children[parent, 0] = position #position of the left child in the inners (or outers) array (at depth=depth_parent+1) level_children[parent, 1] = is_leaf #if True then the left child is a leaf => look in the outers array, else in the inners one else: level_children[parent, 2] = position level_children[parent, 3] = is_leaf offset += len(jet["tree"]) # We need this offset to get the right location in the jet_children array of each jet root node because we concatenate one jet after each other ##----------------------------------------------- # # SANITY CHECK 2: Below we check that the level_children contains the right location of each children subjet # ii = 1 #location of the parent in the inner list at level_parent # level_parent=0 # print('Root of jet #',ii+1,' location =',inners[level_parent][ii]) #The root is at level 0 # print('Content jet #',ii+1,'=',jet_contents[inners[level_parent][ii]]) # print('Children location:\n left=',inners[level_parent+1][level_children[inners[level_parent][ii],0]],' right=',inners[level_parent+1][level_children[inners[level_parent][ii],2]]) # if level_children[inners[level_parent][ii],1]==True: print('The node is a leaf') # else: print('Content ',inners[0][ii],' by adding the 2 children 4-vectors= ',jet_contents[inners[level_parent+1][level_children[inners[level_parent][ii],0]]] # + jet_contents[inners[level_parent+1][level_children[inners[level_parent][ii],2]]]) # # print('Is leaf at level ', level_parent,' = ', level_children[inners[level_parent][::],1]) ##----------------------------------------------- # Reorganize levels[i] so that inner nodes appear first, then outer nodes levels = [] n_inners = [] contents = [] n_level=[] prev_inner = np.array([], dtype=int) for inner, outer in zip(inners, outers): # print('inner=',inner) # print('outer=',outer) n_inners.append(len(inner)) # We append the number of inner nodes in each level inner = np.array(inner, dtype=int) outer = np.array(outer, dtype=int) levels.append(np.concatenate((inner, outer))) #Append the inners and outers of each level n_level.append(len(levels[-1])) left = prev_inner[level_children[prev_inner, 1] == 1] # level_children[prev_inner, 1] returns a list with 1 for left children at level prev_inner+1 that are leaves and 0 otherwise. Then prev_inner[level_children[prev_inner, 1] == 1] picks the nodes at level prev_inner whose left children are leaves. So left are all nodes level prev_inner whose left child (at level prev_inner+1) is a leaf. level_children[left, 0] += len(inner) #We apply an offset to "left" because we concatenated inner and outer, with inners coming first. So now we get the right position of the children that are leaves in the levels array. right = prev_inner[level_children[prev_inner, 3] == 1] level_children[right, 2] += len(inner) contents.append(jet_contents[levels[-1]]) # We append the 4-vector given by the nodes in the last row that we added to levels. This way we create a list of contents where each row corresponds to 1 level. # Then, the position of the left and right children in the levels list, will also be the position of them in the contents list, which is given by level_children Note that level_children keeps the old indices arrangement. prev_inner = inner #This will be the inner of the previous level in the next loop # print('level_children[prev_inner, 1] =',level_children[prev_inner, 1] ) # print('left=',left) # print('right=',right) # print('prev_inner=',prev_inner) # print('contents=',contents) # print('length contents=',len(contents)) # print('length levels =',len(levels)) ##----------------------------------------------- # # # SANITY CHECK 3: # ii = 1 #location of the parent in the inner list at level_parent # level_parent=3 # print('Final rearrangement of jets in batches') # print('Root of jet #',ii+1,' location =','level',level_parent,' pos:',ii) #The root is at level 0 # print('Content jet #',ii+1,'=',contents[level_parent][ii]) # print('Children location in the contents list','level',level_parent+1,'\n left=',level_children[levels[level_parent][ii],0],' right=',level_children[levels[level_parent][ii],2]) # if level_children[[ii],1]==True: print('The node is a leaf') # else: print('Content ','level',level_parent,' pos:',ii,' by adding the 2 children 4-vectors= ',contents[level_parent+1][level_children[levels[level_parent][ii],0]] # + contents[level_parent+1][level_children[levels[level_parent][ii],2]]) # # ##----------------------------------------------- # # Zero padding # #We loop over the levels to zero pad the array (only a few levels per jet) # n_inners=np.asarray(n_inners) # max_n_level=np.max(n_level) # # print('max_n_level=',max_n_level) # # print('----'*20) # # for i in range(len(levels)): # # print('max_n_level-len(levels[i])=',max_n_level-len(levels[i])) # pad_dim=int(max_n_level-len(levels[i])) # levels[i]=np.concatenate((levels[i],np.zeros((pad_dim)))) # contents[i]=np.concatenate((contents[i],np.zeros((pad_dim,int(features))))) # # ##----------------------------------------------- ''' levels: list of arrays levels[i][j] is a node id at a level i in one of the trees inner nodes are positioned within levels[i][:n_inners[i]], while leaves are positioned within levels[i][n_inners[i]:] level_children: array of shape [n_nodes, 4] level_children[node_id, 0] is the position j in the next level of the left child of node_id level_children[node_id, 2] is the position j in the next level of the right child of node_id n_inners: list of shape len(levels) n_inners[i] is the number of inner nodes at level i, accross all trees contents: array of shape [n_levels, n_nodes, n_features] contents[sum(len(l) for l in layers[:i]) + j] is the feature vector or node layers[i][j] n_level: list with the number of nodes in each level ''' # return (levels, level_children[:, [0, 2]], n_inners, contents, n_level) return (levels, level_children[:, [0, 2]], n_inners, contents) #----------------------------------------------------------------------------------------------------------- # Generator function def make_pad_batch_iterator_level_old( batches, batch_size): ''' This method is a generator function that loads the batches, shifts numpy arrays to torch tensors and feeds the training, val pipeline Args: batches: batches of data batch_size: number of jets per batch ''' for i in range(len(batches)): levels = np.asarray(batches[i][0]) children = np.asarray(batches[i][1]) # Children is an array with shape = (total n_nodes in the batch,2) where shape[1] contains the left and right children locations of the node. This location gives the position of the children in the next level n_inners = np.asarray(batches[i][2]) contents = np.asarray(batches[i][3]) n_level = np.asarray(batches[i][4]) labels= np.asarray(batches[i][5]) # print('levels=',levels) # print('----'*20) # print('children=',children) # print('----'*20) # print('n_inners=',n_inners) # print('----'*20) # print('contents=',contents) # print('----'*20) # print('n_level=',n_level) # print('----'*20) # print('labels=',labels) levels=torch.LongTensor(levels) children=torch.LongTensor(children) n_inners=torch.LongTensor(n_inners) contents = torch.FloatTensor(contents) n_level=torch.LongTensor(n_level) labels= torch.LongTensor(labels) ##----------------------------------------------- # shift tensors to GPU if available if torch_params.cuda: levels = levels.cuda() children=children.cuda() n_inners=n_inners.cuda() contents=contents.cuda() n_level= n_level.cuda() labels =labels.cuda() ##----------------------------------------------- # convert them to Variables to record operations in the computational graph levels=Variable(levels) children=Variable(children) n_inners=Variable(n_inners) contents = Variable(contents) n_level=Variable(n_level) labels = Variable(labels) yield levels, children, n_inners, contents, n_level, labels #----------------------------------------------------------------------------------------------------------- # # def batch_array(sample_x,sample_y,batch_size, features): # ''' # Loads the DataLoader class to create the train, val, test datasets # Args: # sample_x: jet trees # sample_y: truth value for the jet labels # batch_size: number of jets in each batch # ''' # # tot_levels=[] # # # # loader=DataLoader # # num_steps=len(sample_x)//batch_size # # batches=[] # # for i in range(num_steps): # # batches.append([]) # levels, children, n_inners, contents, n_level= self.batch_nyu_pad(sample_x,features) # batches[-1].append(levels) # batches[-1].append(children) # batches[-1].append(n_inners) # batches[-1].append(contents) # batches[-1].append(n_level) # batches[-1].append(sample_y[i*batch_size:(i+1)*batch_size]) # if (i+1)%100==0: logging.info('Number of batches created='+str(i+1)) # # # # # #Get average number of levels # # tot_levels.append(n_level) # # # # print('Total jets=',len(tot_levels)) # # print('----'*20) # # print('Average levels per jet=',np.sum([len(level) for level in tot_levels])/len(tot_levels)) # # batches=np.asarray(batches) # # return batches #----------------------------------------------------------------------------------------------------------- # Generator function def make_pad_batch_iterator_level(sample_x,sample_y,batch_size, features,num_steps):# batches, batch_size): ''' This method is a generator function that loads the batches, shifts numpy arrays to torch tensors and feeds the training, val pipeline Args: batches: batches of data batch_size: number of jets per batch ''' # r = np.random.RandomState(seed=None) # If seed is None, then RandomState will try to read data from /dev/urandom (or the Windows analogue) if available or seed from the clock otherwise. # # np.random.seed(0) #If we want to fix the seed number # sample= ##----------------------------------------------- # loader=DataLoader # Shuffle the sets # np.random.seed(0) #If we want to fix the seed number. # indices_train = check_random_state(1).permutation(len(train_x)) # train_x = train_x[indices_train] # train_y = train_y[indices_train] # If seed is None, return the RandomState singleton used by np.random. If seed is an int, return a new RandomState instance seeded with seed. indices = check_random_state(seed=None).permutation(len(sample_x)) # print('sample x=',sample_x) # print('sample_x shape=',sample_x.shape) # print('sample_x[0]=',sample_x[0]) # print('sample_y[0]=',sample_y) sample_x=sample_x[indices] sample_y=sample_y[indices] # print('sample_x[0]=',sample_x[0]) # print('sample_y[0]=',sample_y) # num_steps=len(sample_x)//batch_size for i in range(num_steps): # batches.append([]) levels, children, n_inners, contents, n_level= DataLoader.batch_nyu_pad(sample_x[i*batch_size:(i+1)*batch_size],features) # if (i+1)%1==0: logging.info('Number of batches created='+str(i+1)) # for i in range(len(batches)): # levels = np.asarray(levels) children = np.asarray(children) # Children is an array with shape = (total n_nodes in the batch,2) where shape[1] contains the left and right children locations of the node. This location gives the position of the children in the next level n_inners = np.asarray(n_inners) contents = np.asarray(contents) n_level = np.asarray(n_level) labels= np.asarray(sample_y[i*batch_size:(i+1)*batch_size]) # print('levels=',levels) # print('----'*20) # print('children=',children) # print('----'*20) # print('n_inners=',n_inners) # print('----'*20) # print('contents=',contents) # print('----'*20) # print('n_level=',n_level) # print('----'*20) # print('labels=',labels) levels=torch.LongTensor(levels) children=torch.LongTensor(children) n_inners=torch.LongTensor(n_inners) contents = torch.FloatTensor(contents) n_level=torch.LongTensor(n_level) labels= torch.LongTensor(labels) ##----------------------------------------------- # shift tensors to GPU if available if torch_params.cuda: levels = levels.cuda() children=children.cuda() n_inners=n_inners.cuda() contents=contents.cuda() n_level= n_level.cuda() labels =labels.cuda() ##----------------------------------------------- # convert them to Variables to record operations in the computational graph levels=Variable(levels) children=Variable(children) n_inners=Variable(n_inners) contents = Variable(contents) n_level=Variable(n_level) labels = Variable(labels) yield levels, children, n_inners, contents, n_level, labels ############################################################################################################## #///////////////////// OTHER FUNCTIONS ////////////////////////////////////////////////////////////// ############################################################################################################## # Loads the DataLoader class to create the train, val, test datasets with zero paddings def batch_array(sample_x,sample_y,batch_size, features): ''' Loads the DataLoader class to create the train, val, test datasets Args: sample_x: jet trees sample_y: truth value for the jet labels batch_size: number of jets in each batch ''' tot_levels=[] loader=DataLoader num_steps=len(sample_x)//batch_size batches=[] for i in range(num_steps): batches.append([]) levels, children, n_inners, contents, n_level= loader.batch_nyu_pad(sample_x[i*batch_size:(i+1)*batch_size],features) batches[-1].append(levels) batches[-1].append(children) batches[-1].append(n_inners) batches[-1].append(contents) batches[-1].append(n_level) batches[-1].append(sample_y[i*batch_size:(i+1)*batch_size]) if (i+1)%100==0: logging.info('Number of batches created='+str(i+1)) # # #Get average number of levels # tot_levels.append(n_level) # # print('Total jets=',len(tot_levels)) # print('----'*20) # print('Average levels per jet=',np.sum([len(level) for level in tot_levels])/len(tot_levels)) batches=np.asarray(batches) return batches #------------------------------------------------------------------------------------------------------------- #------------------------------------------------------------------------------------------------------------- ###/////////////////////////////////////////////////////////////////////////////////////////////////////////// #------------------------------------------------------------------------------------------------------------- # if __name__=='__main__': # # myN_jets=10 # batch_size=1 # # load=DataLoader # # sig_tree, sig_list=load.makeTrees(dir_jets_subjets,sg,myN_jets,0) # bkg_tree, bkg_list=load.makeTrees(dir_jets_subjets,bg,myN_jets,0) # # train_data, dev_data, test_data = load.shuffle_split(sig_list, bkg_list, 0.6, 0.2, 0.2) # # data_iterator=load.make_pad_batch_iterator(train_data, batch_size)

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162

py

Python

phyluce/tests/test_imports.py

faircloth-lab/phyluce

ae6801a7e749be2fa38513db9846046241d0fd7a

[ "BSD-3-Clause" ]

63

2015-03-16T15:10:17.000Z

2022-02-16T12:36:23.000Z

phyluce/tests/test_imports.py

faircloth-lab/phyluce

ae6801a7e749be2fa38513db9846046241d0fd7a

[ "BSD-3-Clause" ]

253

2015-01-26T13:03:23.000Z

2022-03-15T19:03:05.000Z

phyluce/tests/test_imports.py

faircloth-lab/phyluce

ae6801a7e749be2fa38513db9846046241d0fd7a

[ "BSD-3-Clause" ]

45

2015-01-26T13:09:50.000Z

2021-05-24T04:20:30.000Z

"""Test various imports from packages""" import phyluce def test_phyluce_version(): """Ensure we can successfully import""" assert phyluce.__version__

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py

Python

src/alert_module.py

fiAnaliz/fiAnaliz

6c617c9ec875b182fa3f26e58701e7ca5aafed2e

[ "MIT" ]

6

2021-05-22T15:12:38.000Z

2021-07-01T13:22:19.000Z

src/alert_module.py

fiAnaliz/fiAnaliz

6c617c9ec875b182fa3f26e58701e7ca5aafed2e

[ "MIT" ]

null

src/alert_module.py

fiAnaliz/fiAnaliz

6c617c9ec875b182fa3f26e58701e7ca5aafed2e

[ "MIT" ]

3

2021-07-01T12:21:43.000Z

2022-01-19T18:59:11.000Z

# -*- coding: utf-8 -*- import pymysql.cursors import random import requests import time import json import datetime """ Database Connection """ class Database: host = "" user = "" password = "" db = "" charset = "utf8mb4" """ Functions """ def divide_chunks(l, n): # looping till length l for i in range(0, len(l), n): yield l[i:i + n] baglanti = "" def connect(): global db global baglanti db = pymysql.connect(host= Database.host, user= Database.user, password= Database.password, db= Database.db, charset= Database.charset, cursorclass=pymysql.cursors.DictCursor) baglanti = db.cursor() """ Alerts Loop """ with open('Config.json', encoding='utf-8') as json_file: dataX = json.load(json_file) connect() while True: try: db.commit() baglanti.execute("SELECT code FROM alerts WHERE completed = 0 AND type = 0 GROUP BY code") alerts = baglanti.fetchall() if (len(alerts) != 0): for coinID in list(divide_chunks(alerts, 10)): coins = "" for i in coinID: coins = coins + i['code'] + "," data = requests.get('https://api.coingecko.com/api/v3/simple/price?ids={}&vs_currencies=usd'.format(coins[:-1])).json() for coinID in coinID: baglanti.execute("SELECT * FROM alerts WHERE code = %s AND completed = 0 AND type = 0", (coinID['code'])) for alert in baglanti.fetchall(): if alert['compare'] == 1 and alert['price'] <= data[coinID['code']]['usd']: baglanti.execute("SELECT * FROM users WHERE uuid = %s", (alert['uuid'])) if alert['platform'] == 0: fromNumber = baglanti.fetchall()[0]['whatsapp'] payload = { "id": alert['id'], "toNumber": alert['toChat'], "fromNumber": fromNumber, "crypto": 1, "message": "*ALARMLARIM* | @%s 🔔🔔🔔\n\n*{}*, *{}* $ hedef fiyatlı *büyük veya eşit olma koşullu* alarmınız gerçekleşmiştir!\n\n*Güncel fiyat:* {} $".format(dataX['COIN_symbols'][dataX['COIN_id'].index(alert['code'])], alert['price'],float(data[coinID['code']]['usd'])) } response = requests.post('http://localhost:9000', json=payload) elif alert['platform'] == 1: fromNumber = baglanti.fetchall()[0]['telegram'] payload = { "id": alert['id'], "toNumber": alert['toChat'], "fromNumber": fromNumber, "crypto": 1, "message": "*ALARMLARIM* | @%s 🔔🔔🔔\n\n*{}*, *{}* $ hedef fiyatlı *büyük veya eşit olma koşullu* alarmınız gerçekleşmiştir!\n\n*Güncel fiyat:* {} $".format(dataX['COIN_symbols'][dataX['COIN_id'].index(alert['code'])], alert['price'],float(data[coinID['code']]['usd'])) } response = requests.post('http://localhost:9001', json=payload) elif alert['platform'] == 2: fromNumber = baglanti.fetchall()[0]['discord'] payload = { "id": alert['id'], "toNumber": alert['toChat'], "fromNumber": fromNumber, "crypto": 1, "message": "**{}**, **{}** $ hedef fiyatlı **büyük veya eşit olma koşullu** alarmınız gerçekleşmiştir!\n\n**Güncel fiyat:** {} $ | %s".format(dataX['COIN_symbols'][dataX['COIN_id'].index(alert['code'])], alert['price'],float(data[coinID['code']]['usd'])) } response = requests.post('http://localhost:9002', json=payload) baglanti.execute('UPDATE alerts SET completed = 1 WHERE id = %s', (alert['id'])) db.commit() elif alert['compare'] == 0 and alert['price'] >= data[coinID['code']]['usd']: baglanti.execute("SELECT * FROM users WHERE uuid = %s", (alert['uuid'])) if alert['platform'] == 0: fromNumber = baglanti.fetchall()[0]['whatsapp'] payload = { "id": alert['id'], "toNumber": alert['toChat'], "fromNumber": fromNumber, "crypto": 1, "message": "*ALARMLARIM* | @%s 🔔🔔🔔\n\n*{}*, *{}* $ hedef fiyatlı *küçük veya eşit olma koşullu* alarmınız gerçekleşmiştir!\n\n*Güncel fiyat:* {} $".format( dataX['COIN_symbols'][dataX['COIN_id'].index(alert['code'])], alert['price'],float(data[coinID['code']]['usd'])) } response = requests.post('http://localhost:9000', json=payload) elif alert['platform'] == 1: fromNumber = baglanti.fetchall()[0]['telegram'] payload = { "id": alert['id'], "toNumber": alert['toChat'], "fromNumber": fromNumber, "crypto": 1, "message": "*ALARMLARIM* | @%s 🔔🔔🔔\n\n*{}*, *{}* $ hedef fiyatlı *küçük veya eşit olma koşullu* alarmınız gerçekleşmiştir!\n\n*Güncel fiyat:* {} $".format( dataX['COIN_symbols'][dataX['COIN_id'].index(alert['code'])], alert['price'],float(data[coinID['code']]['usd'])) } response = requests.post('http://localhost:9001', json=payload) elif alert['platform'] == 2: fromNumber = baglanti.fetchall()[0]['discord'] payload = { "id": alert['id'], "toNumber": alert['toChat'], "fromNumber": fromNumber, "crypto": 1, "message": "**{}**, **{}** $ hedef fiyatlı **küçük veya eşit olma koşullu** alarmınız gerçekleşmiştir!\n\n**Güncel fiyat:** {} $ | %s".format( dataX['COIN_symbols'][dataX['COIN_id'].index(alert['code'])], alert['price'],float(data[coinID['code']]['usd'])) } response = requests.post('http://localhost:9002', json=payload) baglanti.execute('UPDATE alerts SET completed = 1 WHERE id = %s', (alert['id'])) db.commit() now = datetime.datetime.now() if(10 < now.hour < 19 and now.weekday() < 6): baglanti.execute("SELECT code FROM alerts WHERE completed = 0 AND type = 1 GROUP BY code") alerts = baglanti.fetchall() if (len(alerts) != 0): now = datetime.datetime.now() + datetime.timedelta(days=3) yy, mm, dd = str(now.year), str(now.month), str(now.day) if(len(dd)==1): dd = "0" + dd if(len(mm)==1): mm = "0" + mm today = yy+mm+dd now = now - datetime.timedelta(days=10) yy, mm, dd = str(now.year), str(now.month), str(now.day) if(len(dd)==1): dd = "0" + dd if(len(mm)==1): mm = "0" + mm lastday = yy+mm+dd for code in alerts: code = code['code'] data = requests.get("https://web-paragaranti-pubsub.foreks.com/web-services/historical-data?userName=undefined&name={}&exchange=BIST&market=E&group=F&last=300&period=1440&intraPeriod=null&isLast=false&from={}000000&to={}235900".format(code, lastday, today)).json()['dataSet'][-1] baglanti.execute("SELECT * FROM alerts WHERE code = %s AND completed = 0 AND type = 1", (code)) for alert in baglanti.fetchall(): if alert['compare'] == 1 and alert['price'] <= data['close']: baglanti.execute("SELECT * FROM users WHERE uuid = %s", (alert['uuid'])) if alert['platform'] == 0: fromNumber = baglanti.fetchall()[0]['whatsapp'] payload = { "id": alert['id'], "toNumber": alert['toChat'], "fromNumber": fromNumber, "crypto": 1, "message": "*ALARMLARIM* | @%s 🔔🔔🔔\n\n*{}*, *{}* ₺ hedef fiyatlı *büyük veya eşit olma koşullu* alarmınız gerçekleşmiştir!\n\n*Güncel fiyat:* {} ₺".format(alert['code'], alert['price'], float(data['close'])) } response = requests.post('http://localhost:9000', json=payload) elif alert['platform'] == 1: fromNumber = baglanti.fetchall()[0]['telegram'] payload = { "id": alert['id'], "toNumber": alert['toChat'], "fromNumber": fromNumber, "crypto": 1, "message": "*ALARMLARIM* | @%s 🔔🔔🔔\n\n*{}*, *{}* ₺ hedef fiyatlı *büyük veya eşit olma koşullu* alarmınız gerçekleşmiştir!\n\n*Güncel fiyat:* {} ₺".format(alert['code'], alert['price'], float(data['close'])) } response = requests.post('http://localhost:9001', json=payload) elif alert['platform'] == 2: fromNumber = baglanti.fetchall()[0]['discord'] payload = { "id": alert['id'], "toNumber": alert['toChat'], "fromNumber": fromNumber, "crypto": 1, "message": "**{}**, **{}** ₺ hedef fiyatlı *büyük veya eşit olma koşullu* alarmınız gerçekleşmiştir!\n\n*Güncel fiyat:* {} ₺ | %s".format(alert['code'], alert['price'], float(data['close'])) } response = requests.post('http://localhost:9002', json=payload) baglanti.execute('UPDATE alerts SET completed = 1 WHERE id = %s', (alert['id'])) db.commit() elif alert['compare'] == 0 and alert['price'] >= data['close']: baglanti.execute("SELECT whatsapp FROM users WHERE uuid = %s", (alert['uuid'])) if alert['platform'] == 0: fromNumber = baglanti.fetchall()[0]['whatsapp'] payload = { "id": alert['id'], "toNumber": alert['toChat'], "fromNumber": fromNumber, "crypto": 1, "message": "*ALARMLARIM* | @%s 🔔🔔🔔\n\n*{}*, *{}* ₺ hedef fiyatlı *küçük veya eşit olma koşullu* alarmınız gerçekleşmiştir!\n\n*Güncel fiyat:* {} ₺".format(alert['code'] , alert['price'], float(data['close'])) } response = requests.post('http://localhost:9000', json=payload) elif alert['platform'] == 1: fromNumber = baglanti.fetchall()[0]['telegram'] payload = { "id": alert['id'], "toNumber": alert['toChat'], "fromNumber": fromNumber, "crypto": 1, "message": "*ALARMLARIM* | @%s 🔔🔔🔔\n\n*{}*, *{}* ₺ hedef fiyatlı *küçük veya eşit olma koşullu* alarmınız gerçekleşmiştir!\n\n*Güncel fiyat:* {} ₺".format(alert['code'] , alert['price'], float(data['close'])) } response = requests.post('http://localhost:9001', json=payload) elif alert['platform'] == 2: fromNumber = baglanti.fetchall()[0]['discord'] payload = { "id": alert['id'], "toNumber": alert['toChat'], "fromNumber": fromNumber, "crypto": 1, "message": "**{}**, **{}** ₺ hedef fiyatlı *küçük veya eşit olma koşullu* alarmınız gerçekleşmiştir!\n\n*Güncel fiyat:* {} ₺ | %s".format(alert['code'] , alert['price'], float(data['close'])) } response = requests.post('http://localhost:9002', json=payload) baglanti.execute('UPDATE alerts SET completed = 1 WHERE id = %s', (alert['id'])) db.commit() time.sleep(1) except Exception as E: connect() print(E) print('BEKLEMEDE') time.sleep(60)

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dce3819b0f14760a423f8359416c9e8a2160dd5b

26

py

Python

grama/eval/__init__.py

natalia-rubio/py_grama

968c1c0238d7165de3b1b96534791feacc4aa960

[ "MIT" ]

13

2020-02-24T16:51:51.000Z

2022-03-30T18:56:55.000Z

grama/eval/__init__.py

natalia-rubio/py_grama

968c1c0238d7165de3b1b96534791feacc4aa960

[ "MIT" ]

78

2019-12-30T19:13:21.000Z

2022-02-23T18:17:54.000Z

grama/eval/__init__.py

natalia-rubio/py_grama

968c1c0238d7165de3b1b96534791feacc4aa960

[ "MIT" ]

7

2020-10-19T17:49:25.000Z

2021-08-15T20:46:52.000Z

from .eval_pyDOE import *

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py

Python

tests/unit_tests/test_tethys_apps/test_management/test_commands/test_syncstores.py

msouff/tethys

45795d1e6561d5db8fddd838f4d1ae1d91dbb837

[ "BSD-2-Clause" ]

79

2015-10-05T13:13:28.000Z

2022-02-01T12:30:33.000Z

tests/unit_tests/test_tethys_apps/test_management/test_commands/test_syncstores.py

msouff/tethys

45795d1e6561d5db8fddd838f4d1ae1d91dbb837

[ "BSD-2-Clause" ]

542

2015-08-12T22:11:32.000Z

2022-03-29T22:18:08.000Z

tests/unit_tests/test_tethys_apps/test_management/test_commands/test_syncstores.py

msouff/tethys

45795d1e6561d5db8fddd838f4d1ae1d91dbb837

[ "BSD-2-Clause" ]

71

2016-01-16T01:03:41.000Z

2022-03-31T17:55:54.000Z

try: from StringIO import StringIO except ImportError: from io import StringIO import unittest from unittest import mock from argparse import ArgumentParser from tethys_apps.management.commands import syncstores class ManagementCommandsSyncstoresTests(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def test_syncstores_add_arguments(self): parser = ArgumentParser() cmd = syncstores.Command() cmd.add_arguments(parser) self.assertIn('app_name', parser.format_usage()) self.assertIn('[-r]', parser.format_usage()) self.assertIn('[-f]', parser.format_usage()) self.assertIn('[-d DATABASE]', parser.format_usage()) self.assertIn('--refresh', parser.format_help()) self.assertIn('--firsttime', parser.format_help()) self.assertIn('--database DATABASE', parser.format_help()) @mock.patch('tethys_apps.management.commands.syncstores.Command.provision_persistent_stores') def test_handle(self, mock_provision_persistent_stores): # Mock the function, it will be tested elsewhere mock_provision_persistent_stores.return_value = True cmd = syncstores.Command() cmd.handle(app_name='foo') @mock.patch('sys.stdout', new_callable=StringIO) @mock.patch('tethys_apps.models.TethysApp.persistent_store_database_settings') @mock.patch('tethys_apps.models.TethysApp.persistent_store_database_settings') @mock.patch('tethys_apps.models.TethysApp.persistent_store_database_settings') @mock.patch('tethys_apps.models.TethysApp') def test_provision_persistent_stores_all_apps_no_database(self, mock_app, mock_setting1, mock_setting2, mock_setting3, mock_stdout): # Mock arguments mock_app_names = syncstores.ALL_APPS mock_options = {'database': '', 'refresh': True, 'first_time': True} # Mock for ps db settings mock_setting1.name = 'setting1_name' mock_setting1.create_persistent_store_database.return_value = True mock_setting2.name = 'setting2_name' mock_setting2.create_persistent_store_database.return_value = True mock_setting3.name = 'setting3_name' mock_setting3.create_persistent_store_database.return_value = True # Mock for TethysApp (2 apps, 2 settings for first app, 1 setting for second app) mock_app1 = mock.MagicMock() mock_app1.persistent_store_database_settings = [mock_setting1, mock_setting2] mock_app2 = mock.MagicMock() mock_app2.persistent_store_database_settings = [mock_setting3] mock_app.objects.all.return_value = [mock_app1, mock_app2] cmd = syncstores.Command() cmd.provision_persistent_stores(app_names=mock_app_names, options=mock_options) mock_app.objects.all.assert_called_once() mock_setting1.create_persistent_store_database.assert_called_once_with(refresh=True, force_first_time=True) mock_setting2.create_persistent_store_database.assert_called_once_with(refresh=True, force_first_time=True) mock_setting3.create_persistent_store_database.assert_called_once_with(refresh=True, force_first_time=True) self.assertIn('Provisioning Persistent Stores...', mock_stdout.getvalue()) @mock.patch('sys.stdout', new_callable=StringIO) @mock.patch('tethys_apps.models.TethysApp.persistent_store_database_settings') @mock.patch('tethys_apps.models.TethysApp.persistent_store_database_settings') @mock.patch('tethys_apps.models.TethysApp.persistent_store_database_settings') @mock.patch('tethys_apps.models.TethysApp') def test_provision_persistent_stores_all_apps_database_no_match(self, mock_app, mock_setting1, mock_setting2, mock_setting3, mock_stdout): # Mock arguments mock_app_names = syncstores.ALL_APPS mock_options = {'database': '/foo/no_match', 'refresh': True, 'first_time': True} # Mock for ps db settings mock_setting1.name = 'setting1_name' mock_setting1.create_persistent_store_database.return_value = True mock_setting2.name = 'setting2_name' mock_setting2.create_persistent_store_database.return_value = True mock_setting3.name = 'setting3_name' mock_setting3.create_persistent_store_database.return_value = True # Mock for TethysApp (2 apps, 2 settings for first app, 1 setting for second app) mock_app1 = mock.MagicMock() mock_app1.persistent_store_database_settings = [mock_setting1, mock_setting2] mock_app2 = mock.MagicMock() mock_app2.persistent_store_database_settings = [mock_setting3] mock_app.objects.all.return_value = [mock_app1, mock_app2] cmd = syncstores.Command() cmd.provision_persistent_stores(app_names=mock_app_names, options=mock_options) mock_app.objects.all.assert_called_once() mock_setting1.create_persistent_store_database.assert_not_called() mock_setting2.create_persistent_store_database.assert_not_called() mock_setting3.create_persistent_store_database.assert_not_called() self.assertIn('Provisioning Persistent Stores...', mock_stdout.getvalue()) @mock.patch('sys.stdout', new_callable=StringIO) @mock.patch('tethys_apps.models.TethysApp.persistent_store_database_settings') @mock.patch('tethys_apps.models.TethysApp.persistent_store_database_settings') @mock.patch('tethys_apps.models.TethysApp.persistent_store_database_settings') @mock.patch('tethys_apps.models.TethysApp') def test_provision_persistent_stores_all_apps_database_single_match(self, mock_app, mock_setting1, mock_setting2, mock_setting3, mock_stdout): # Mock arguments mock_app_names = syncstores.ALL_APPS mock_options = {'database': '/foo/match', 'refresh': False, 'first_time': False} # Mock for ps db settings mock_setting1.name = 'setting1_name' mock_setting1.create_persistent_store_database.return_value = True mock_setting2.name = '/foo/match' mock_setting2.create_persistent_store_database.return_value = True mock_setting3.name = 'setting3_name' mock_setting3.create_persistent_store_database.return_value = True # Mock for TethysApp (2 apps, 2 settings for first app, 1 setting for second app) mock_app1 = mock.MagicMock() mock_app1.persistent_store_database_settings = [mock_setting1, mock_setting2] mock_app2 = mock.MagicMock() mock_app2.persistent_store_database_settings = [mock_setting3] mock_app.objects.all.return_value = [mock_app1, mock_app2] cmd = syncstores.Command() cmd.provision_persistent_stores(app_names=mock_app_names, options=mock_options) mock_app.objects.all.assert_called_once() mock_setting1.create_persistent_store_database.assert_not_called() mock_setting2.create_persistent_store_database.assert_called_once_with(refresh=False, force_first_time=False) mock_setting3.create_persistent_store_database.assert_not_called() self.assertIn('Provisioning Persistent Stores...', mock_stdout.getvalue()) @mock.patch('sys.stdout', new_callable=StringIO) @mock.patch('tethys_apps.models.TethysApp') def test_provision_persistent_stores_given_apps_not_found(self, mock_app, mock_stdout): # Mock arguments mock_app_names = ['foo_missing'] mock_options = {'database': '', 'refresh': True, 'first_time': True} # Mock for TethysApp (return no apps found) mock_app.objects.filter.return_value = [] cmd = syncstores.Command() cmd.provision_persistent_stores(app_names=mock_app_names, options=mock_options) mock_app.objects.filter.assert_called_once() self.assertIn('The app named "foo_missing" cannot be found.', mock_stdout.getvalue()) self.assertIn('Please make sure it is installed and try again.', mock_stdout.getvalue()) self.assertIn('Provisioning Persistent Stores...', mock_stdout.getvalue())

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null

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6

0d0ea5cc0134919d03aa7c93e90b818c60d7fed2

96

py

Python

venv/lib/python3.8/site-packages/charset_normalizer/constant.py

GiulianaPola/select_repeats

17a0d053d4f874e42cf654dd142168c2ec8fbd11

[ "MIT" ]

1

2022-02-22T04:49:18.000Z

venv/lib/python3.8/site-packages/charset_normalizer/constant.py

GiulianaPola/select_repeats

17a0d053d4f874e42cf654dd142168c2ec8fbd11

[ "MIT" ]

null

venv/lib/python3.8/site-packages/charset_normalizer/constant.py

GiulianaPola/select_repeats

17a0d053d4f874e42cf654dd142168c2ec8fbd11

[ "MIT" ]

null

/home/runner/.cache/pip/pool/5b/8b/b6/29e0be44124fe23c5f4cafeb38750444c9de8e8636d558487853a040ac

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1

0

1

0

null

1

0

1

0

6

0d406fec36b34d8589879d9befbdef5a7dd7579f

41

py

Python

src/qaseio/xcode/__init__.py

qase-tms/qase-xctest

d880cbafa3b69f8535d6ac826aa326c156f4c987

[ "Apache-2.0" ]

null

src/qaseio/xcode/__init__.py

qase-tms/qase-xctest

d880cbafa3b69f8535d6ac826aa326c156f4c987

[ "Apache-2.0" ]

null

src/qaseio/xcode/__init__.py

qase-tms/qase-xctest

d880cbafa3b69f8535d6ac826aa326c156f4c987

[ "Apache-2.0" ]

null

from .qase_exporter import QaseExtractor

20.5

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1

0

1

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6

b4ccb6a6c5f85a1150db9f75b21e97c48356cad9

79

py

Python

wiremock/base/__init__.py

sp1rs/python-wiremock

b570b0ebc60ac0d873812f21f78f2a8a4353792f

[ "Apache-2.0" ]

22

2017-07-01T14:44:04.000Z

2021-09-08T08:45:21.000Z

wiremock/base/__init__.py

sp1rs/python-wiremock

b570b0ebc60ac0d873812f21f78f2a8a4353792f

[ "Apache-2.0" ]

37

2017-04-24T15:28:27.000Z

2021-09-20T08:58:26.000Z

wiremock/base/__init__.py

sp1rs/python-wiremock

b570b0ebc60ac0d873812f21f78f2a8a4353792f

[ "Apache-2.0" ]

22

2017-04-24T14:58:06.000Z

2021-09-09T09:22:31.000Z

from .base_entity import * from .base_resource import BaseResource, RestClient

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b4ed2c3780d9b244b6ed69369f2ac5b827021da3

33

py

Python

alignments/components/__init__.py

Roxot/m-to-m-alignments

f45aaa2132ceb709d948e9db8dc2669678ba5527

[ "MIT" ]

null

alignments/components/__init__.py

Roxot/m-to-m-alignments

f45aaa2132ceb709d948e9db8dc2669678ba5527

[ "MIT" ]

null

alignments/components/__init__.py

Roxot/m-to-m-alignments

f45aaa2132ceb709d948e9db8dc2669678ba5527

[ "MIT" ]

null

from .encoders import RNNEncoder

16.5

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1

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6

3717cec29a070420979983c0cc33f985c3457474

7,842

py

Python

tests/block_patterns_test.py

vcamp314/schemed-parsing

13cfb4a720af533be640afcda2b9731dca2c843a

[ "MIT" ]

null

tests/block_patterns_test.py

vcamp314/schemed-parsing

13cfb4a720af533be640afcda2b9731dca2c843a

[ "MIT" ]

null

tests/block_patterns_test.py

vcamp314/schemed-parsing

13cfb4a720af533be640afcda2b9731dca2c843a

[ "MIT" ]

null

import os import pytest from .context import schemedparsing # the below two lines are for pip installing with test option and when # the tests will open files: CURRENT_DIR = os.path.dirname(os.path.realpath(__file__)) os.chdir(CURRENT_DIR) def test_block_extraction_empty_patterns_returns_empty_list(): txt_gen = (txt for txt in ["import { sampleImportName1, sampleImportName2 } from './sample/path'"]) schemes = [] expected = [] result_names, result_blocklist = schemedparsing.parse(txt_gen, schemes) assert result_names == expected assert result_blocklist == expected @pytest.fixture def single_extraction_pattern(): return [{'query': r'import (\w+)', }, ] def test_extraction_empty_text_returns_empty_list(single_extraction_pattern): txt_gen = (txt for txt in []) expected = [] result_names, result_blocklist = schemedparsing.parse(txt_gen, single_extraction_pattern) assert result_names == expected assert result_blocklist == expected def test_find_flat_blocks(): txt = 'if(isTest == true){ doSomething() }; if(isSpecialTest == true) { doSomethingElse() };' block_schemes = [ { 'block_start_pattern': {'query': '{'}, 'block_end_pattern': {'query': '}'}, 'block_category': 'test_cat', } ] expected = [ { 'block_category': 'test_cat', 'starting_line_no': 1, 'ending_line_no': 1, }, { 'block_category': 'test_cat', 'starting_line_no': 1, 'ending_line_no': 1, } ] result = [] names = [] line_no = 1 schemedparsing.parse_line(txt, block_schemes, result, names, line_no) assert result == expected assert names == [] def test_find_nested_blocks(): txt = 'if(isTest == true){ if(isSpecialTest == true;) { doSomethingElse(); } }' block_schemes = [ { 'block_start_pattern': {'query': '{'}, 'block_end_pattern': {'query': '}'}, 'block_category': 'test_cat', } ] expected = [ { 'block_category': 'test_cat', 'starting_line_no': 1, 'ending_line_no': 1, }, { 'block_category': 'test_cat', 'starting_line_no': 1, 'ending_line_no': 1, 'parent_id': 0, } ] result = [] names = [] line_no = 1 schemedparsing.parse_line(txt, block_schemes, result, names, line_no) assert result == expected assert names == [] def test_find_flat_blocks_and_their_params(): txt = "import { sampleImportName1, sampleImportName2 } from './sample/path'; import { sampleImportName3, " \ "sampleImportName4 } from './sample/path'; " block_schemes = [ { 'block_start_pattern': {'query': '{'}, 'block_end_pattern': {'query': '}'}, 'block_category': 'test_cat', 'extraction_patterns': [ { 'query': r'(\w+)' } ] } ] expected_blocks = [ { 'block_category': 'test_cat', 'starting_line_no': 1, 'ending_line_no': 1, }, { 'block_category': 'test_cat', 'starting_line_no': 1, 'ending_line_no': 1, } ] expected_names = [ { 'name': 'sampleImportName1', 'block_id': 0, }, { 'name': 'sampleImportName2', 'block_id': 0, }, { 'name': 'sampleImportName3', 'block_id': 1, }, { 'name': 'sampleImportName4', 'block_id': 1, }, ] result_blocks = [] result_names = [] line_no = 1 schemedparsing.parse_line(txt, block_schemes, result_blocks, result_names, line_no) assert result_blocks == expected_blocks assert result_names == expected_names def test_find_flat_blocks_with_ending_props_and_params(): txt = "import { sampleImportName1, sampleImportName2 } from './sample/path1'; import { sampleImportName3, " \ "sampleImportName4 } from './sample/path2'; " block_schemes = [ { 'block_start_pattern': {'query': '{'}, 'block_end_pattern': { 'query': '}', 'properties': [ { 'property_name': 'from_path', 'extraction_patterns': [ { 'query': r'from\s*?(?:"|\')(.*)(?:"|\')' } ] } ] }, 'block_category': 'test_cat', 'extraction_patterns': [ { 'query': r'(\w+)' } ] } ] expected_blocks = [ { 'block_category': 'test_cat', 'starting_line_no': 1, 'ending_line_no': 1, 'from_path': './sample/path1', }, { 'block_category': 'test_cat', 'starting_line_no': 1, 'ending_line_no': 1, 'from_path': './sample/path2', } ] expected_names = [ { 'name': 'sampleImportName1', 'block_id': 0, }, { 'name': 'sampleImportName2', 'block_id': 0, }, { 'name': 'sampleImportName3', 'block_id': 1, }, { 'name': 'sampleImportName4', 'block_id': 1, }, ] result_blocks = [] result_names = [] line_no = 1 schemedparsing.parse_line(txt, block_schemes, result_blocks, result_names, line_no) assert result_blocks == expected_blocks assert result_names == expected_names def test_find_flat_blocks_with_starting_props_and_params(): txt = "; import { sampleImportName1, sampleImportName2 } from './sample/path1'; import { sampleImportName3, " \ "sampleImportName4 } from './sample/path2'; " block_schemes = [ { 'block_start_pattern': { 'query': '{', 'properties': [ { 'property_name': 'block_type', 'extraction_patterns': [ { 'query': r'; (\w+)' } ] } ] }, 'block_end_pattern': {'query': '}'}, 'block_category': 'test_cat', 'extraction_patterns': [ { 'query': r'(\w+)' } ] } ] expected_blocks = [ { 'block_category': 'test_cat', 'starting_line_no': 1, 'ending_line_no': 1, 'block_type': 'import', }, { 'block_category': 'test_cat', 'starting_line_no': 1, 'ending_line_no': 1, 'block_type': 'import', } ] expected_names = [ { 'name': 'sampleImportName1', 'block_id': 0, }, { 'name': 'sampleImportName2', 'block_id': 0, }, { 'name': 'sampleImportName3', 'block_id': 1, }, { 'name': 'sampleImportName4', 'block_id': 1, }, ] result_blocks = [] result_names = [] line_no = 1 schemedparsing.parse_line(txt, block_schemes, result_blocks, result_names, line_no) assert result_blocks == expected_blocks assert result_names == expected_names

27.515789

115

0.485973

665

7,842

5.37594

0.138346

0.05035

0.048951

0.083916

0.858741

0.80951

0.780979

0.764476

0.734266

0.701259

0

0.014565

0.387146

7,842

285

116

27.515789

0.729297

0.012114

0

0.505837

0

0.28328

0

0.054475

1

0.031128

false

0

0.097276

0.003891

0.132296

0

null

0

1

0

null

0

6

2ea3142306eb2f8afe86bfbbc9b131c81502e829

3,985

py

Python

基础教程/A2-神经网络基本原理/第8步 - 卷积神经网络/src/ch17-CNNBasic/Level4_Col2Img_Test.py

microsoft/ai-edu

2f59fa4d3cf19f14e0b291e907d89664bcdc8df3

[ "Apache-2.0" ]

11,094

2019-05-07T02:48:50.000Z

2022-03-31T08:49:42.000Z

基础教程/A2-神经网络基本原理/第8步 - 卷积神经网络/src/ch17-CNNBasic/Level4_Col2Img_Test.py

microsoft/ai-edu

2f59fa4d3cf19f14e0b291e907d89664bcdc8df3

[ "Apache-2.0" ]

157

2019-05-13T15:07:19.000Z

2022-03-23T08:52:32.000Z

基础教程/A2-神经网络基本原理/第8步 - 卷积神经网络/src/ch17-CNNBasic/Level4_Col2Img_Test.py

microsoft/ai-edu

2f59fa4d3cf19f14e0b291e907d89664bcdc8df3

[ "Apache-2.0" ]

2,412

2019-05-07T02:55:15.000Z

2022-03-30T06:56:52.000Z

# Copyright (c) Microsoft. All rights reserved. # Licensed under the MIT license. See LICENSE file in the project root for full license information. import numpy import numba import time from MiniFramework.ConvWeightsBias import * from MiniFramework.ConvLayer import * from MiniFramework.HyperParameters_4_2 import * def calculate_output_size(input_h, input_w, filter_h, filter_w, padding, stride=1): output_h = (input_h - filter_h + 2 * padding) // stride + 1 output_w = (input_w - filter_w + 2 * padding) // stride + 1 return (output_h, output_w) def understand_4d_col2img_simple(): batch_size = 1 stride = 1 padding = 0 fh = 2 fw = 2 input_channel = 1 output_channel = 1 iw = 3 ih = 3 (output_height, output_width) = calculate_output_size(ih, iw, fh, fw, padding, stride) wb = ConvWeightsBias(output_channel, input_channel, fh, fw, InitialMethod.MSRA, OptimizerName.SGD, 0.1) wb.Initialize("test", "test", True) wb.W = np.array(range(output_channel * input_channel * fh * fw)).reshape(output_channel, input_channel, fh, fw) wb.B = np.array([0]) x = np.array(range(input_channel * iw * ih * batch_size)).reshape(batch_size, input_channel, ih, iw) print("x=\n", x) col_x = img2col(x, fh, fw, stride, padding) print("col_x=\n", col_x) print("w=\n", wb.W) col_w = wb.W.reshape(output_channel, -1).T print("col_w=\n", col_w) # backward delta_in = np.array(range(batch_size*output_channel*output_height*output_width)).reshape(batch_size, output_channel, output_height, output_width) print("delta_in=\n", delta_in) delta_in_2d = np.transpose(delta_in, axes=(0,2,3,1)).reshape(-1, output_channel) print("delta_in_2d=\n", delta_in_2d) dB = np.sum(delta_in_2d, axis=0, keepdims=True).T / batch_size print("dB=\n", dB) dW = np.dot(col_x.T, delta_in_2d) / batch_size print("dW=\n", dW) dW = np.transpose(dW, axes=(1, 0)).reshape(output_channel, input_channel, fh, fw) print("dW=\n", dW) dcol = np.dot(delta_in_2d, col_w.T) print("dcol=\n", dcol) delta_out = col2img(dcol, x.shape, fh, fw, stride, padding, output_height, output_width) print("delta_out=\n", delta_out) def understand_4d_col2img_complex(): batch_size = 2 stride = 1 padding = 0 fh = 2 fw = 2 input_channel = 3 output_channel = 2 iw = 3 ih = 3 (output_height, output_width) = calculate_output_size(ih, iw, fh, fw, padding, stride) wb = ConvWeightsBias(output_channel, input_channel, fh, fw, InitialMethod.MSRA, OptimizerName.SGD, 0.1) wb.Initialize("test", "test", True) wb.W = np.array(range(output_channel * input_channel * fh * fw)).reshape(output_channel, input_channel, fh, fw) wb.B = np.array([0]) x = np.array(range(input_channel * iw * ih * batch_size)).reshape(batch_size, input_channel, ih, iw) print("x=\n", x) col_x = img2col(x, fh, fw, stride, padding) print("col_x=\n", col_x) print("w=\n", wb.W) col_w = wb.W.reshape(output_channel, -1).T print("col_w=\n", col_w) # backward delta_in = np.array(range(batch_size*output_channel*output_height*output_width)).reshape(batch_size, output_channel, output_height, output_width) print("delta_in=\n", delta_in) delta_in_2d = np.transpose(delta_in, axes=(0,2,3,1)).reshape(-1, output_channel) print("delta_in_2d=\n", delta_in_2d) dB = np.sum(delta_in_2d, axis=0, keepdims=True).T / batch_size print("dB=\n", dB) dW = np.dot(col_x.T, delta_in_2d) / batch_size print("dW=\n", dW) dW = np.transpose(dW, axes=(1, 0)).reshape(output_channel, input_channel, fh, fw) print("dW=\n", dW) dcol = np.dot(delta_in_2d, col_w.T) print("dcol=\n", dcol) delta_out = col2img(dcol, x.shape, fh, fw, stride, padding, output_height, output_width) print("delta_out=\n", delta_out) if __name__ == '__main__': understand_4d_col2img_simple() understand_4d_col2img_complex()

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0.05514

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0.072469

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3,985

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0

null

0

1

0

1

0

null

0

6

2eaede119bad76a2653fb34175a24c2928e809e2

203

py

Python

python/testData/inspections/PyArgumentListInspection/xRange.py

teddywest32/intellij-community

e0268d7a1da1d318b441001448cdd3e8929b2f29

[ "Apache-2.0" ]

null

python/testData/inspections/PyArgumentListInspection/xRange.py

teddywest32/intellij-community

e0268d7a1da1d318b441001448cdd3e8929b2f29

[ "Apache-2.0" ]

11

2017-02-27T22:35:32.000Z

2021-12-24T08:07:40.000Z

python/testData/inspections/PyArgumentListInspection/xRange.py

teddywest32/intellij-community

e0268d7a1da1d318b441001448cdd3e8929b2f29

[ "Apache-2.0" ]

1

2020-11-27T10:36:50.000Z

print(xrange(<warning descr="Parameter 'start' unfilled">)</warning>) print(xrange(1)) print(xrange(1, 2)) print(xrange(1, 2, 3)) print(xrange(1, 2, 3, <warning descr="Unexpected argument">4</warning>))

33.833333

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0.704433

31

203

4.612903

0.419355

0.384615

0.335664

0.272727

0.195804

0

0.053476

0.078818

203

5

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1

0

1

0

null

0

1

0

1

0

6

2ecf9cb187b70dc9ada1b47f66b830606b603579

22

py

Python

pizza.py

gray-adeyi/pizza

659db6e85492903374416295cc3ca3a78584eccb

[ "MIT" ]

null

pizza.py

gray-adeyi/pizza

659db6e85492903374416295cc3ca3a78584eccb

[ "MIT" ]

null

pizza.py

gray-adeyi/pizza

659db6e85492903374416295cc3ca3a78584eccb

[ "MIT" ]

1

2022-03-17T00:54:27.000Z

from pizza import cli

11

21

0.818182

4

22

4.5

1

0

0.181818

22

1

22

1

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

2edf50feb3c2ff65d1609302023b0421c113f1cf

21

py

Python

models/__init__.py

jack-willturner/fbnet_without_training

2dba276121b34cb4e252492f116a21637e75e442

[ "MIT" ]

null

models/__init__.py

jack-willturner/fbnet_without_training

2dba276121b34cb4e252492f116a21637e75e442

[ "MIT" ]

null

models/__init__.py

jack-willturner/fbnet_without_training

2dba276121b34cb4e252492f116a21637e75e442

[ "MIT" ]

null

from .fbnet import *

10.5

20

0.714286

3

21

5

1

0

0.190476

21

1

21

0.882353

0

1

0

true

0

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1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

2c14dc0154155e2ba30c4a79d35447372e773e13

144

py

Python

controller/__init__.py

ivohutasoit/onanplus-service

28ec5efce228b3379d5cada04bf1626b16fc55e0

[ "MIT" ]

null

controller/__init__.py

ivohutasoit/onanplus-service

28ec5efce228b3379d5cada04bf1626b16fc55e0

[ "MIT" ]

null

controller/__init__.py

ivohutasoit/onanplus-service

28ec5efce228b3379d5cada04bf1626b16fc55e0

[ "MIT" ]

null

from .price_controller import price_controller from .product_controller import product_controller from .store_controller import store_controller

48

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0.902778

18

144

6.888889

0.333333

0.387097

0

0.076389

144

3

51

48

0.932331

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1

0

true

0

1

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1

0

1

0

null

1

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1

0

null

0

1

0

1

0

1

0

6

2c2ebe8bee0cbf3a09201aee150aaad32813763f

28,428

py

Python

Boundaries.py

Basistransformoptimusprime/Particle_in_a_Box

61c8587cc449cb0d0d0b6aaa499a524a9133fbca

[ "MIT" ]

1

2021-05-30T19:39:44.000Z

Boundaries.py

Basistransformoptimusprime/Particle_in_a_Box

61c8587cc449cb0d0d0b6aaa499a524a9133fbca

[ "MIT" ]

null

Boundaries.py

Basistransformoptimusprime/Particle_in_a_Box

61c8587cc449cb0d0d0b6aaa499a524a9133fbca

[ "MIT" ]

null

from __future__ import annotations from Backend import * from scipy.optimize import fsolve from scipy.optimize import brentq from scipy.integrate import quad from scipy.misc import derivative import warnings class Symmetric_Boundary(New_Style_Boundary): def __init__(self, L: float, gamma: float, theta: float, l_to_kl_mapper_ref: l_to_kl_mapper) -> None: super().__init__(L, gamma, theta, l_to_kl_mapper_ref) self._pos_energy_even_state_eq = lambda gammaL, kL: gammaL - kL*np.tan(kL/2) self._pos_energy_odd_state_eq = lambda gammaL, kL: gammaL + kL/np.tan(kL/2) self._neg_energy_even_state_eq = lambda gammaL, kappaL: gammaL + kappaL*np.tanh(kappaL/2) self._neg_energy_odd_state_eq = lambda gammaL, kappaL: gammaL + kappaL/np.tanh(kappaL/2) self._eps = np.finfo(np.float32).eps def set_eps(self, new_eps: float) -> None: self._eps = new_eps def get_kn(self, n: int | np.ndarray) -> float | np.ndarray: return n*np.pi/self._L + self._theta/(2*self._L) def get_kl(self, l: int) -> complex: gammaL = self._gamma*self._L eps = self._eps if l == 0: if self._gamma > 0: transc_eq = self._pos_energy_even_state_eq kL_upper_bound = (l+1)*np.pi-eps kL_lower_bound = eps kL_solution = brentq(lambda Kl: transc_eq(gammaL, Kl), kL_lower_bound, kL_upper_bound) return kL_solution/self._L else: transc_eq = self._neg_energy_even_state_eq kL_approx = -gammaL kL_solution = fsolve(lambda Kl: transc_eq(gammaL, Kl), kL_approx)[0] return 1j*kL_solution/self._L elif l == 1: if self._gamma > -2/self._L: transc_eq = self._pos_energy_odd_state_eq kL_upper_bound = (l+1)*np.pi-eps kL_lower_bound = eps kL_solution = brentq(lambda Kl: transc_eq(gammaL, Kl), kL_lower_bound, kL_upper_bound) return kL_solution/self._L else: transc_eq = self._neg_energy_odd_state_eq kL_approx = -gammaL kL_solution = fsolve(lambda Kl: transc_eq(gammaL, Kl), kL_approx)[0] return 1j*kL_solution/self._L else: if l%2 == 0: transc_eq = self._pos_energy_even_state_eq else: transc_eq = self._pos_energy_odd_state_eq kL_upper_bound = (l+1)*np.pi-eps kL_lower_bound = (l-1)*np.pi+eps kL_solution = brentq(lambda Kl: transc_eq(gammaL, Kl), kL_lower_bound, kL_upper_bound) return kL_solution/self._L pass def get_x_space_projection(self, l: int) -> Function_of_n: L = self._L kl = self._l_kl_map.get_kl(l) if l%2 == 1: if np.imag(kl) == 0: return Function_of_n(lambda x: np.sqrt(2/L)*np.power(1-np.sin(kl*L)/(kl*L), -1/2)*np.sin(kl*x)) else: kappal = np.imag(kl) return Function_of_n(lambda x: np.sqrt(2/L)*np.power(-1+np.sinh(kappal*L)/(kappal*L), -1/2)*np.sinh(kappal*x)) else: if np.imag(kl) == 0: return Function_of_n(lambda x: np.sqrt(2/L)*np.power(1+np.sin(kl*L)/(kl*L), -1/2)*np.cos(kl*x)) else: kappal = np.imag(kl) return Function_of_n(lambda x: np.sqrt(2/L)*np.power(1+np.sinh(kappal*L)/(kappal*L), -1/2)*np.cosh(kappal*x)) def get_k_space_projection(self, l: int) -> Function_of_n: #print("computing the k_space_projection using analytic results...") L = self._L kl = self._l_kl_map.get_kl(l) if l%2 == 1: if np.imag(kl) == 0: return Function_of_n(lambda k: 1j*np.sqrt(L/np.pi)/np.sqrt(1 - np.sin(kl*L)/(kl*L))*(np.sin((kl+k)*L/2)/(kl*L+k*L) - np.sin((kl-k)*L/2)/(kl*L-k*L))) else: kappal = np.imag(kl) return Function_of_n(lambda k: (2j)*np.sqrt(L/np.pi)/np.sqrt(-1+np.sinh(kappal*L)/(kappal*L))*(k*L*np.cos(k*L/2)*np.sinh(kappal*L/2) - kappal*L*np.sin(k*L/2)*np.cosh(kappal*L/2))/((kappal*L)**2+(k*L)**2)) else: if np.imag(kl) == 0: return Function_of_n(lambda k: np.sqrt(L/np.pi)/np.sqrt(1 + np.sin(kl*L)/(kl*L))*(np.sin((kl+k)*L/2)/(kl*L+k*L) + np.sin((kl-k)*L/2)/(kl*L-k*L))) else: kappal = np.imag(kl) return Function_of_n(lambda k: (2)*np.sqrt(L/np.pi)/np.sqrt(1+np.sinh(kappal*L)/(kappal*L))*(kappal*L*np.cos(k*L/2)*np.sinh(kappal*L/2) + k*L*np.sin(k*L/2)*np.cosh(kappal*L/2))/((kappal*L)**2+(k*L)**2)) def get_x_matrix_element(self, lhs_state: int, rhs_state: int) -> complex: if lhs_state%2 == rhs_state%2: return 0 if lhs_state%2 == 1: temp = lhs_state lhs_state = rhs_state rhs_state = temp lhs_k = self._l_kl_map.get_kl(lhs_state) rhs_k = self._l_kl_map.get_kl(rhs_state) L = self._L if np.imag(lhs_k) == 0: if np.imag(rhs_k) == 0: cos_expr = np.cos((lhs_k-rhs_k)*L/2)/((lhs_k-rhs_k)*L) - np.cos((lhs_k+rhs_k)*L/2)/((lhs_k+rhs_k)*L) sin_expr = np.sin((lhs_k+rhs_k)*L/2)/(((lhs_k+rhs_k)*L)**2) - np.sin((lhs_k-rhs_k)*L/2)/(((lhs_k-rhs_k)*L)**2) norm_expr = np.sqrt((1+np.sin(lhs_k*L)/(lhs_k*L))*(1-np.sin(rhs_k*L)/(rhs_k*L))) return (cos_expr + 2*sin_expr)/norm_expr*L else: rhs_kappa = np.imag(rhs_k) norm_kappa = rhs_kappa*L/2 norm_k = lhs_k*L/2 cos_cosh_expr = rhs_kappa*np.cos(norm_k)*np.cosh(norm_kappa) sin_sinh_expr = lhs_k*np.sin(norm_k)*np.sinh(norm_kappa) symm_expr = L/(rhs_kappa**2+lhs_k**2)*(sin_sinh_expr + cos_cosh_expr) cos_sinh_expr = (lhs_k**2-rhs_kappa**2)*np.cos(norm_k)*np.sinh(norm_kappa) sin_cosh_expr = 2*rhs_kappa*lhs_k*np.sin(norm_k)*np.cosh(norm_kappa) anti_symm_expr = 2/((rhs_kappa**2+lhs_k**2)**2)*(cos_sinh_expr - sin_cosh_expr) norm_expr = np.sqrt((1+np.sin(lhs_k*L)/(lhs_k*L))*(-1+np.sinh(rhs_kappa*L)/(rhs_kappa*L))) return (2/L)*(symm_expr + anti_symm_expr)/norm_expr else: lhs_kappa = np.imag(lhs_k) if np.imag(rhs_k) == 0: norm_kappa = lhs_kappa*L/2 norm_k = rhs_k*L/2 norm_expr = np.sqrt((1-np.sin(rhs_k*L)/(rhs_k*L))*(1+np.sinh(lhs_kappa*L)/(lhs_kappa*L))) sin_sinh_expr = lhs_kappa*np.sin(norm_k)*np.sinh(norm_kappa) cos_cosh_expr = rhs_k*np.cos(norm_k)*np.cosh(norm_kappa) symm_expr = L/(lhs_kappa**2+rhs_k**2)*(sin_sinh_expr - cos_cosh_expr) cos_sinh_expr = 2*lhs_kappa*rhs_k*np.cos(norm_k)*np.sinh(norm_kappa) sin_cosh_expr = (rhs_k**2-lhs_kappa**2)*np.sin(norm_k)*np.cosh(norm_kappa) anti_symm_expr = 2/((rhs_k**2+lhs_kappa**2)**2)*(sin_cosh_expr + cos_sinh_expr) return (2/L)*(symm_expr + anti_symm_expr)/norm_expr else: rhs_kappa = np.imag(rhs_k) cosh_expr = np.cosh((lhs_kappa+rhs_kappa)*L/2)/((lhs_kappa+rhs_kappa)*L) - np.cosh((lhs_kappa-rhs_kappa)*L/2)/((lhs_kappa-rhs_kappa)*L) sinh_expr = np.sinh((lhs_kappa-rhs_kappa)*L/2)/((lhs_kappa*L-rhs_kappa*L)**2) - np.sinh((lhs_kappa+rhs_kappa)*L/2)/((lhs_kappa*L+rhs_kappa*L)**2) norm_expr = np.sqrt((1+np.sinh(lhs_kappa*L)/(lhs_kappa*L))*(-1+np.sinh(rhs_kappa*L)/(rhs_kappa*L))) return (cosh_expr + 2*sinh_expr)/norm_expr*L def get_pR_matrix_element(self, lhs_state: int, rhs_state: int) -> complex: #print("computing the pR elements using analytic results...") if lhs_state%2 == rhs_state%2: return 0 lhs_k = self._l_kl_map.get_kl(lhs_state) rhs_k = self._l_kl_map.get_kl(rhs_state) L = self._L lhs_sign = (-1)**lhs_state rhs_sign = -lhs_sign if np.imag(lhs_k) == 0: if np.imag(rhs_k) == 0: norm_expr = np.sqrt((1 + lhs_sign*np.sin(lhs_k*L)/(lhs_k*L))*(1 + rhs_sign*np.sin(rhs_k*L)/(rhs_k*L))) sin_expr = np.sin((lhs_k+rhs_k)*L/2)/((lhs_k+rhs_k)*L) + lhs_sign*np.sin((lhs_k-rhs_k)*L/2)/((lhs_k-rhs_k)*L) return (-2j)*rhs_k*sin_expr/norm_expr else: rhs_kappa = np.imag(rhs_k) norm_kappa = rhs_kappa*L/2 norm_k = lhs_k*L/2 norm_expr = np.sqrt((1 + lhs_sign*np.sin(lhs_k*L)/(lhs_k*L))*(rhs_sign + np.sinh(rhs_kappa*L)/(rhs_kappa*L))) if rhs_state%2 == 0: sin_cosh_expr = rhs_kappa*np.sin(norm_k)*np.cosh(norm_kappa) cos_sinh_expr = -lhs_k*np.cos(norm_k)*np.sinh(norm_kappa) else: sin_cosh_expr = lhs_k*np.sin(norm_k)*np.cosh(norm_kappa) cos_sinh_expr = rhs_kappa*np.cos(norm_k)*np.sinh(norm_kappa) anti_symm_expr = 2/(lhs_k**2+rhs_kappa**2)*(sin_cosh_expr + cos_sinh_expr) return (-1j*rhs_kappa)*(2/L)*anti_symm_expr/norm_expr else: lhs_kappa = np.imag(lhs_k) if np.imag(rhs_k) == 0: norm_kappa = lhs_kappa*L/2 norm_k = rhs_k*L/2 norm_expr = np.sqrt((1 + rhs_sign*np.sin(rhs_k*L)/(rhs_k*L))*(lhs_sign + np.sinh(lhs_kappa*L)/(lhs_kappa*L))) if lhs_state%2 == 0: sin_cosh_expr = rhs_k*np.sin(norm_k)*np.cosh(norm_kappa) cos_sinh_expr = lhs_kappa*np.cos(norm_k)*np.sinh(norm_kappa) else: sin_cosh_expr = lhs_kappa*np.sin(norm_k)*np.cosh(norm_kappa) cos_sinh_expr = -rhs_k*np.cos(norm_k)*np.sinh(norm_kappa) anti_symm_expr = 2/(lhs_kappa**2+rhs_k**2)*(sin_cosh_expr + cos_sinh_expr) return (rhs_sign*1j*rhs_k)*(2/L)*anti_symm_expr/norm_expr else: rhs_kappa = np.imag(rhs_k) norm_expr = np.sqrt((lhs_sign + np.sinh(lhs_kappa*L)/(lhs_kappa*L))*(rhs_sign + np.sinh(rhs_kappa*L)/(rhs_kappa*L))) sinh_expr = np.sinh((lhs_kappa+rhs_kappa)*L/2)/((lhs_kappa+rhs_kappa)*L) + lhs_sign*np.sinh((lhs_kappa-rhs_kappa)*L/2)/((lhs_kappa-rhs_kappa)*L) return (-2j)*rhs_kappa*sinh_expr/norm_expr def discrete_momentum_projection_helper(self, l: int, n_array: np.ndarray) -> np.ndarray: kn_array = self.get_kn(n_array) temp_k_space_proj = np.sqrt(np.pi/self._L)*self.get_k_space_projection(l) return temp_k_space_proj(kn_array) def get_new_k_space_projection(self, l: int) -> Function_of_n: return Function_of_n(lambda n: self.discrete_momentum_projection_helper(l, n)) class Neumann_Boudnary(New_Style_Boundary): def __init__(self, L: float, gamma: float, theta: float, l_to_kl_mapper_ref: l_to_kl_mapper) -> None: super().__init__(L, gamma, theta, l_to_kl_mapper_ref) def get_kn(self, n: int | list) -> float | list: return n*np.pi/self._L def get_kl(self, l: int) -> complex: return l*np.pi/self._L def get_x_space_projection(self, l: int) -> Function_of_n: L = self._L if l == 0: return Function_of_n(lambda x: 1/np.sqrt(L)*np.ones(np.shape(x))) else: if l%2 == 0: return Function_of_n(lambda x: np.sqrt(2/L)*np.cos(l*np.pi/L*x)) else: return Function_of_n(lambda x: np.sqrt(2/L)*np.sin(l*np.pi/L*x)) def get_x_matrix_element(self, lhs_state: int, rhs_state: int) -> complex: if lhs_state%2 == rhs_state%2: return 0 if lhs_state%2 == 1: temp_state = lhs_state lhs_state = rhs_state rhs_state = temp_state L = self._L if lhs_state == 0: return (2*np.sqrt(2)*L/(np.pi*rhs_state)**2)*(-1)**((rhs_state-1)/2) else: return (2*L/np.pi**2)*(-1)**((lhs_state+rhs_state-1)/2)*2*(lhs_state**2 + rhs_state**2)/(lhs_state**2 - rhs_state**2)**2 def get_k_space_projection(self, l: int) -> Function_of_n: L = self._L if l == 0: return Function_of_n(lambda k: np.sqrt(2*L/np.pi)*np.sin(k*L/2)/(k*L)) if l%2 == 0: return Function_of_n(lambda k: np.sqrt(L/np.pi)*(np.sin(l*np.pi/2 + k*L/2)/(l*np.pi + k*L) + np.sin(l*np.pi/2 - k*L/2)/(l*np.pi - k*L))) else: return Function_of_n(lambda k: 1j*np.sqrt(L/np.pi)*(np.sin(l*np.pi/2 + k*L/2)/(l*np.pi + k*L) - np.sin(l*np.pi/2 - k*L/2)/(l*np.pi - k*L))) def get_pR_matrix_element(self, lhs_state: int, rhs_state: int) -> complex: L = self._L if lhs_state%2 == rhs_state%2: return 0 if rhs_state == 0: # lhs_state != 0 is already implicitly given # as otherwise lhs_state%2 == rhs_state%2 return 1j*np.sqrt(2)/L*(-1)**((lhs_state-1)/2) elif lhs_state == 0: # rhs_state != 0 is already implicitly given # as otherwise lhs_state%2 == rhs_state%2 return -1j*np.sqrt(2)/L*(-1)**((rhs_state-1)/2) else: # The only case that reamains is when neither rhs_state = 0 nor # lhs_state = 0 and lhs_state%2 != rhs_state%2 return 2j/L*(-1)**((lhs_state+rhs_state-1)/2)*(lhs_state**2 + rhs_state**2)/(lhs_state**2 - rhs_state**2) def discrete_momentum_projection_helper(self, l: int, n_array: np.ndarray) -> np.ndarray: if isinstance(n_array, int): n_array = [n_array] projection_coefficients = [] if l == 0: for n in n_array: if n%2 == 0: coeff_append = 1/np.sqrt(2) if n==0 else 0 else: coeff_append = np.sqrt(2)/(np.pi*n)*(-1)**((n-1)/2) projection_coefficients.append(coeff_append) return np.array(projection_coefficients) if l%2 == 1: for n in n_array: if n%2 == 0: coeff_append = 2j/np.pi*(-1)**((n+l-1)/2)*n/(n**2-l**2) elif n == l: coeff_append = -1j/2 elif n == -l: coeff_append = 1j/2 else: coeff_append = 0 projection_coefficients.append(coeff_append) return np.array(projection_coefficients) elif l%2 == 0: for n in n_array: if n%2 == 1: coeff_append = 2/np.pi*(-1)**((n+l-1)/2)*n/(n**2-l**2) elif abs(n) == abs(l): coeff_append = 1/2 else: coeff_append = 0 projection_coefficients.append(coeff_append) return np.array(projection_coefficients) def get_new_k_space_projection(self, l: int) -> Function_of_n: return Function_of_n(lambda n: self.discrete_momentum_projection_helper(l, n)) def set_theta(self, new_theta: float) -> None: super().set_theta(new_theta) warnings.warn("setting theta has not been implemented for pure Neumann boundaries yet and will thus have no effect") class Dirichlet_Boundary(New_Style_Boundary): def __init__(self, L: float, gamma: float, theta: float, l_to_kl_mapper_ref: l_to_kl_mapper) -> None: super().__init__(L, gamma, theta, l_to_kl_mapper_ref) def get_kn(self, n: int | list) -> float | list: return n*np.pi/self._L def get_kl(self, l: int) -> complex: return (l+1)*np.pi/self._L def get_x_space_projection(self, l: int) -> Function_of_n: L = self._L if l%2 == 0: return Function_of_n(lambda x: np.sqrt(2/L)*np.cos((l+1)*np.pi/L*x)) else: return Function_of_n(lambda x: np.sqrt(2/L)*np.sin((l+1)*np.pi/L*x)) def get_x_matrix_element(self, lhs_state: int, rhs_state: int) -> complex: if lhs_state%2 == rhs_state%2: return 0 else: L = self._L sign_expr = (-1)**((lhs_state+rhs_state-1)/2) return (2*L/np.pi**2)*sign_expr*(4*(lhs_state+1)*(rhs_state+1))/((lhs_state+1)**2 - (rhs_state+1)**2)**2 def get_pR_matrix_element(self, lhs_state: int, rhs_state: int) -> complex: if lhs_state%2 == rhs_state%2: return 0 else: L = self._L sign_expr = (-1)**((lhs_state+rhs_state-1)/2) return 1j/L*sign_expr*(4*(lhs_state+1)*(rhs_state+1))/((lhs_state+1)**2 - (rhs_state+1)**2) def get_k_space_projection(self, l: int) -> Function_of_n: L = self._L i_factor = lambda l: 1j if l%2 == 1 else 1 sign_factor = (-1)**l return Function_of_n(lambda k: i_factor(l)*np.sqrt(L/np.pi)*(np.sin((l+1)*np.pi/2 + k*L/2)/((l+1)*np.pi + k*L) + sign_factor*np.sin((l+1)*np.pi/2 - k*L/2)/((l+1)*np.pi - k*L))) def discrete_momentum_projection_helper(self, l: int, n_array: np.ndarray) -> np.ndarray: if isinstance(n_array, int): n_array = [n_array] projection_coefficients = [] if l%2 == 0: for n in n_array: if n%2 == 0: coeff_append = 2/np.pi*(-1)**((l+n)/2)*(l+1)/((l+1)**2 - n**2) elif abs(n) == abs(l+1): coeff_append = 1/2 else: coeff_append = 0 projection_coefficients.append(coeff_append) return np.array(projection_coefficients) elif l%2 == 1: for n in n_array: if n%2 == 1: coeff_append = 2j/np.pi*(-1)**((l+n)/2)*(l+1)/((l+1)**2 - n**2) elif n == l+1: coeff_append = -1j/2 elif n == -(l+1): coeff_append = 1j/2 else: coeff_append = 0 projection_coefficients.append(coeff_append) return np.array(projection_coefficients) def get_new_k_space_projection(self, l: int) -> Function_of_n: return Function_of_n(lambda n: self.discrete_momentum_projection_helper(l, n)) def set_theta(self, new_theta: float) -> None: super().set_theta(new_theta) warnings.warn("setting theta has not been implemented for pure Dirichlet boundaries yet and will thus have no effect") class Dirichlet_Neumann_Boundary(New_Style_Boundary): def __init__(self, L: float, gamma: float, theta: float, l_to_kl_mapper_ref: l_to_kl_mapper) -> None: super().__init__(L, gamma, theta, l_to_kl_mapper_ref) def get_kn(self, n: int | list) -> float | list: return (n+1/2)*np.pi/self._L def get_kl(self, l: int) -> complex: return (2*l+1)/2*np.pi/self._L def get_x_space_projection(self, l: int) -> Function_of_n: L = self._L kl = self._l_kl_map.get_kl(l) return Function_of_n(lambda x: np.sqrt(2/L)*np.sin(kl*(x+L/2))) def get_k_space_projection(self, l: int) -> Function_of_n: L = self._L kl = self._l_kl_map.get_kl(l) lhs_term = Function_of_n(lambda k: np.sin((kl+k)*L/2)/((kl+k)*L)*np.exp(-1j*(2*l+1)*np.pi/4)) rhs_term = Function_of_n(lambda k: np.sin((kl-k)*L/2)/((kl-k)*L)*np.exp(1j*(2*l+1)*np.pi/4)) return 1j*np.sqrt(L/np.pi)*(lhs_term - rhs_term) def discrete_momentum_projection_helper(self, l: int, n_array: np.ndarray) -> np.ndarray: if isinstance(n_array, int): n_array = [n_array] projection_coefficients = [] for n in n_array: if n == l: coeff_append = 1j/np.pi*(-1)**(l)*np.exp(-1j*(2*l+1)*np.pi/4)/(2*l+1) - 1j/2*np.exp(1j*(2*l+1)*np.pi/4) elif l+n == -1: coeff_append = 1j/2*np.exp(-1j*(2*l+1)*np.pi/4) - 1j/np.pi*(-1)**(l)*np.exp(1j*(2*l+1)*np.pi/4)/(2*l+1) elif (n+l)%2 == 0: coeff_append = 1j/np.pi*(-1)**((l+n)/2)*np.exp(-1j*(2*l+1)*np.pi/4)/(l+n+1) elif (n+l)%2 == 1: coeff_append = -1j/np.pi*(-1)**((l-n-1)/2)*np.exp(1j*(2*l+1)*np.pi/4)/(l-n) else: print("eh?") projection_coefficients.append(coeff_append) return np.array(projection_coefficients) def get_new_k_space_projection(self, l: int) -> Function_of_n: return Function_of_n(lambda n: self.discrete_momentum_projection_helper(l, n)) def get_x_matrix_element(self, lhs_state: int, rhs_state: int) -> complex: L = self._L if lhs_state%2 == rhs_state%2: return (2*L/np.pi**2)/(lhs_state+rhs_state+1)**2 else: return -(2*L/np.pi**2)/(lhs_state-rhs_state)**2 def get_pR_matrix_element(self, lhs_state: int, rhs_state: int) -> complex: L = self._L if lhs_state%2 == rhs_state%2: return 1j/L*(lhs_state-rhs_state)/(lhs_state+rhs_state+1) else: return 1j/L*(lhs_state+rhs_state+1)/(rhs_state-lhs_state) def set_theta(self, new_theta: float) -> None: super().set_theta(new_theta) warnings.warn("setting theta has not been implemented for Dirichlet Neumann boundaries yet and will thus have no effect") class Anti_Symmetric_Boundary(New_Style_Boundary): def __init__(self, L: float, gamma: float, theta: float, l_to_kl_mapper_ref: l_to_kl_mapper) -> None: super().__init__(L, gamma, theta, l_to_kl_mapper_ref) @staticmethod def x_space_projection_for_nummerics(L, gamma, l, kl) -> Function_of_n: phase_factor = lambda l: np.exp(1j*np.arctan((gamma*L)/(np.pi*l))) if l%2 == 0 else np.exp(-1j*np.arctan((np.pi*l)/(gamma*L))) if np.imag(kl) == 0: boundray_expr = ((-1)**l)*(gamma + 1j*kl)/(gamma - 1j*kl) return Function_of_n(lambda x: phase_factor(l)/(np.sqrt(2*L))*(np.exp(1j*kl*x) - boundray_expr*np.exp(-1j*kl*x))) else: return Function_of_n(lambda x: np.sqrt(gamma/np.sinh(gamma*L))*np.exp(-gamma*x)) def get_kn(self, n: int | list) -> float | list: return n*np.pi/self._L + self._theta/(2*self._L) def get_kl(self, l: int) -> complex: if l == 0: return 1j*self._gamma if self._gamma > 0 else -1j*self._gamma else: return l*np.pi/self._L def get_x_space_projection(self, l: int) -> Function_of_n: gamma = self._gamma L = self._L kl = self._l_kl_map.get_kl(l) return self.x_space_projection_for_nummerics(L, gamma, l, kl) def get_x_matrix_element(self, lhs_state: int, rhs_state: int) -> complex: gamma = self._gamma L = self._L lhs_k = self._l_kl_map.get_kl(lhs_state) rhs_k = self._l_kl_map.get_kl(rhs_state) lhs_integrand = self.x_space_projection_for_nummerics(L, gamma, lhs_state, lhs_k) rhs_integrand = self.x_space_projection_for_nummerics(L, gamma, rhs_state, rhs_k) integrand = lambda x: np.conj(lhs_integrand(x))*x*rhs_integrand(x) real = quad(lambda x: np.real(integrand(x)), -L/2, L/2)[0] imag = quad(lambda x: np.imag(integrand(x)), -L/2, L/2)[0] return real + 1j*imag def get_k_space_projection(self, l: int) -> Function_of_n: gamma = self._gamma L = self._L kl = self._l_kl_map.get_kl(l) x_space_proj = self.x_space_projection_for_nummerics(L, gamma, l, kl) def converter(k_range: np.ndarray) -> np.ndarray: if isinstance(k_range, (int, float)): k_range = [k_range] out = [] for k in k_range: integrand = lambda x: x_space_proj(x)*np.exp(-1j*k*x) real = quad(lambda x: np.real(integrand(x)), -L/2, L/2)[0] imag = quad(lambda x: np.imag(integrand(x)), -L/2, L/2)[0] out.append((real + 1j*imag)*1/np.sqrt(2*L)) return np.array(out) return Function_of_n(converter) def discrete_momentum_projection_helper(self, l: int, n_array: np.ndarray) -> np.ndarray: kn_array = self.get_kn(n_array) temp_k_space_proj = np.sqrt(np.pi/self._L)*self.get_k_space_projection(l) return temp_k_space_proj(kn_array) def get_new_k_space_projection(self, l: int) -> Function_of_n: return Function_of_n(lambda n: self.discrete_momentum_projection_helper(l, n)) def get_pR_matrix_element(self, lhs_state: int, rhs_state: int) -> complex: gamma = self._gamma L = self._L lhs_k = self._l_kl_map.get_kl(lhs_state) rhs_k = self._l_kl_map.get_kl(rhs_state) lhs_integrand = self.x_space_projection_for_nummerics(L, gamma, lhs_state, lhs_k) rhs_integrand = self.x_space_projection_for_nummerics(L, gamma, rhs_state, rhs_k) integrand = lambda x: (-1j)*np.conj(lhs_integrand(x))*derivative(rhs_integrand, x, 0.0001) real = quad(lambda x: np.real(integrand(x)), -L/2, L/2)[0] imag = quad(lambda x: np.imag(integrand(x)), -L/2, L/2)[0] return real + 1j*imag class Symmetric_Nummeric(Symmetric_Boundary): def __init__(self, L: float, gamma: float, theta: float, l_to_kl_mapper_ref: l_to_kl_mapper) -> None: super().__init__(L, gamma, theta, l_to_kl_mapper_ref) self._n_range = 100 def get_pR_matrix_element(self, lhs_state: int, rhs_state: int) -> complex: # This implementation of the <get_pR_matrix_element> method determines # the matrix elements <l|pR|l'> by expanding the energy states in the # momentum eigenbasis such that the matrix elements are obtained by # summing over all momentum states. lhs_proj_coeffs = self.get_new_k_space_projection(lhs_state) rhs_proj_coeffs = self.get_new_k_space_projection(rhs_state) # Construction of one or two intervals of momentum quantum numbers that # are taken as samples to approximate the infinite sum given in # <l|pR|l'> if expanded in the momentum basis. n_center = (lhs_state+rhs_state)//2 n_range_adapted = abs(lhs_state-rhs_state)//2+self._n_range n_range_u = n_range_adapted n_range_d = n_range_adapted if n_center > n_range_adapted else n_center n_p = np.arange(n_center-n_range_d+1, n_center+n_range_u+1, 1) n_n = np.arange(-n_center-n_range_u, -n_center+n_range_d+1, 1) n = np.append(n_n, n_p) return np.sum(self.get_kn(n)*np.conj(lhs_proj_coeffs(n))*rhs_proj_coeffs(n)) def set_n_range(self, new_n_range) -> None: self._n_range = new_n_range class Anti_Symmetric_Nummeric(Anti_Symmetric_Boundary): def __init__(self, L: float, gamma: float, theta: float, l_to_kl_mapper_ref: l_to_kl_mapper) -> None: super().__init__(L, gamma, theta, l_to_kl_mapper_ref) self._n_range = 100 def get_pR_matrix_element(self, lhs_state: int, rhs_state: int) -> complex: lhs_proj_coeffs = self.get_new_k_space_projection(lhs_state) rhs_proj_coeffs = self.get_new_k_space_projection(rhs_state) n_center = max(lhs_state, rhs_state) n_range_u = self._n_range n_range_d = self._n_range if n_center > self._n_range else n_center n_p = np.arange(n_center-n_range_d+1, n_center+n_range_u+1, 1) n_n = np.arange(-n_center-n_range_u, -n_center+n_range_d+1, 1) n = np.append(n_n, n_p) return np.sum(self.get_kn(n)*np.conj(lhs_proj_coeffs(n))*rhs_proj_coeffs(n)) def set_n_range(self, new_n_range) -> None: self._n_range = new_n_range

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null

0

1

0

null

0

6

25cd27caf895f841992a27aabb00613836fcc1a2

2,033

py

Python

rt-smart/tools/host.py

dengchow/rt_smart_imx6ull-

4d9879e3d543a4e4ddd4b73ce0d30668127f5c5a

[ "Apache-2.0" ]

null

rt-smart/tools/host.py

dengchow/rt_smart_imx6ull-

4d9879e3d543a4e4ddd4b73ce0d30668127f5c5a

[ "Apache-2.0" ]

null

rt-smart/tools/host.py

dengchow/rt_smart_imx6ull-

4d9879e3d543a4e4ddd4b73ce0d30668127f5c5a

[ "Apache-2.0" ]

2

2021-11-10T12:07:35.000Z

2022-01-17T14:24:56.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # # File : building.py # This file is part of RT-Thread RTOS # COPYRIGHT (C) 2006 - 2019, RT-Thread Development Team # # Change Logs: # Date Author Notes # 2019-05-26 Bernard The first version # import os import sys import string import pdb from SCons.Script import * from building import * def BuildHostApplication(TARGET, SConscriptFile): import platform global Env platform_type = platform.system() if platform_type == 'Windows' or platform_type.find('MINGW') != -1: TARGET = TARGET.replace('.mo', '.exe') HostRtt = os.path.join(os.path.dirname(__file__), 'host', 'rtthread') Env = Environment() if not GetOption('verbose'): # override the default verbose command string Env.Replace( ARCOMSTR = 'AR $TARGET', ASCOMSTR = 'AS $TARGET', ASPPCOMSTR = 'AS $TARGET', CCCOMSTR = 'CC $TARGET', CXXCOMSTR = 'CXX $TARGET', LINKCOMSTR = 'LINK $TARGET' ) objs = SConscript(SConscriptFile) objs += SConscript(HostRtt + '/SConscript') target = Env.Program(TARGET, objs) return target def BuildHostLibrary(TARGET, SConscriptFile): import platform global Env platform_type = platform.system() if platform_type == 'Windows' or platform_type.find('MINGW') != -1: TARGET = TARGET.replace('.mo', '.exe') HostRtt = os.path.join(os.getcwd(), 'tools', 'host', 'rtthread') Env = Environment() if not GetOption('verbose'): # override the default verbose command string Env.Replace( ARCOMSTR = 'AR $TARGET', ASCOMSTR = 'AS $TARGET', ASPPCOMSTR = 'AS $TARGET', CCCOMSTR = 'CC $TARGET', CXXCOMSTR = 'CXX $TARGET', LINKCOMSTR = 'LINK $TARGET' ) objs = SConscript(SConscriptFile) objs += SConscript(HostRtt + '/SConscript') target = Env.Program(TARGET, objs) return target

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0

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0

1

0

null

0

6

25dbe4ce2de2aabfbf836cca40d9a58fb7d3da5d

7,181

py

Python

workspace/module/python-2.7/LxData/datObjects/_datObjData.py

no7hings/Lynxi

43c745198a714c2e5aca86c6d7a014adeeb9abf7

[ "MIT" ]

2

2018-03-06T03:33:55.000Z

2019-03-26T03:25:11.000Z

workspace/module/python-2.7/LxData/datObjects/_datObjData.py

no7hings/lynxi

43c745198a714c2e5aca86c6d7a014adeeb9abf7

[ "MIT" ]

null

workspace/module/python-2.7/LxData/datObjects/_datObjData.py

no7hings/lynxi

43c745198a714c2e5aca86c6d7a014adeeb9abf7

[ "MIT" ]

null

# coding:utf-8 from .. import datCfg, datObjAbs class _Dat_Digit(datObjAbs.Abs_DatData): def __add__(self, other): """ :param other: object of "Data" :return: number """ assert isinstance(other.raw(), self.VAR_dat__raw__rawtype_pattern), u'Argument Error, "arg" Must "VAR_dat__raw__rawtype_pattern".' return self.__class__(self, self.raw() + other.raw()) def __sub__(self, other): """ :param other: object of "Data" :return: number """ assert isinstance(other.raw(), self.VAR_dat__raw__rawtype_pattern), u'Argument Error, "arg" Must "VAR_dat__raw__rawtype_pattern".' return self.__class__(self, self.raw() - other.raw()) def __mul__(self, other): """ :param other: object of "Data" :return: number """ assert isinstance(other.raw(), self.VAR_dat__raw__rawtype_pattern), u'Argument Error, "arg" Must "VAR_dat__raw__rawtype_pattern".' return self.__class__(self, self.raw() * other.raw()) def __div__(self, other): """ :param other: object of "Data" :return: number """ assert isinstance(other.raw(), self.VAR_dat__raw__rawtype_pattern), u'Argument Error, "arg" Must "VAR_dat__raw__rawtype_pattern".' return self.__class__(self, self.raw() / other.raw()) class Dat_Closure(datObjAbs.Abs_DatData): CLS_dat__raw = None VAR_dat__raw__rawtype_pattern = None def __init__(self, *args): """ :param args: 1-1.object of value, raw; 1-2.object of data, raw. """ self._initAbsDatData(*args) def _raw__get_str_(self): return u'' class Dat_Boolean(datObjAbs.Abs_DatData): CLS_dat__raw = bool VAR_dat__raw__rawtype_pattern = bool, int VAR_dat__raw__default = False def __init__(self, *args): """ :param args: 1-1.object of value, raw; 1-2.object of data, raw. """ self._initAbsDatData(*args) def _raw__get_raw_by_str(self, string): _dict = {'false': False, 'true': True} if string in _dict: return _dict[string] else: return False def _raw__get_str_(self): if self.hasRaw(): return [u'false', u'true'][self.raw()] return u'false' class Dat_Integer(_Dat_Digit): CLS_dat__raw = int VAR_dat__raw__rawtype_pattern = int, float VAR_dat__raw__default = 0 def __init__(self, *args): """ :param args: 1-1.object of value, raw; 1-2.object of data, raw. """ self._initAbsDatData(*args) class Dat_IntegerN(datObjAbs.Abs_DatData): CLS_dat__raw = list VAR_dat__raw__rawtype_pattern = list, tuple VAR_dat__raw__default = [] CLS_dat__data__element = Dat_Integer VAR_dat__data__datasep = datCfg.DatUtility.DEF_dat__raw_strsep def __init__(self, *args): """ :param args: 1-1.object of value, raw; 1-2.object of data, raw. """ self._initAbsDatData(*args) class Dat_IntegerNN(datObjAbs.Abs_DatData): CLS_dat__raw = list VAR_dat__raw__rawtype_pattern = list, tuple VAR_dat__raw__default = [] CLS_dat__data__element = Dat_IntegerN VAR_dat__data__datasep = datCfg.DatUtility.DEF_dat__compraw_strsep def __init__(self, *args): """ :param args: 1-1.object of value, raw; 1-2.object of data, raw. """ self._initAbsDatData(*args) class Dat_Float(_Dat_Digit): CLS_dat__raw = float VAR_dat__raw__rawtype_pattern = float, int VAR_dat__raw__default = 0.0 def __init__(self, *args): """ :param args: 1-1.object of value, raw; 1-2.object of data, raw. """ self._initAbsDatData(*args) class Dat_FloatN(datObjAbs.Abs_DatData): CLS_dat__raw = list VAR_dat__raw__rawtype_pattern = list, tuple VAR_dat__raw__default = [] CLS_dat__data__element = Dat_Float VAR_dat__data__datasep = datCfg.DatUtility.DEF_dat__raw_strsep def __init__(self, *args): """ :param args: 1-1.object of value, raw; 1-2.object of data, raw. """ self._initAbsDatData(*args) class Dat_FloatNN(datObjAbs.Abs_DatData): CLS_dat__raw = list VAR_dat__raw__rawtype_pattern = list, tuple VAR_dat__raw__default = [] CLS_dat__data__element = Dat_FloatN VAR_dat__data__datasep = datCfg.DatUtility.DEF_dat__compraw_strsep def __init__(self, *args): """ :param args: 1-1.object of value, raw; 1-2.object of data, raw. """ self._initAbsDatData(*args) class Dat_String(datObjAbs.Abs_DatData): CLS_dat__raw = unicode VAR_dat__raw__rawtype_pattern = unicode, str VAR_dat__raw__default = u'' def __init__(self, *args): """ :param args: 1-1.object of value, raw; 1-2.object of data, raw. """ self._initAbsDatData(*args) class Dat_StringN(datObjAbs.Abs_DatData): CLS_dat__raw = list VAR_dat__raw__rawtype_pattern = list, tuple VAR_dat__raw__default = [] CLS_dat__data__element = Dat_String VAR_dat__data__datasep = datCfg.DatUtility.DEF_dat__raw_strsep def __init__(self, *args): """ :param args: 1-1.object of value, raw; 1-2.object of data, raw. """ self._initAbsDatData(*args) class Dat_Filepath(datObjAbs.Abs_DatData): CLS_dat__raw = unicode VAR_dat__raw__rawtype_pattern = unicode, str VAR_dat__raw__default = u'' def __init__(self, *args): """ :param args: 1-1.object of value, raw; 1-2.object of data, raw. """ self._initAbsDatData(*args) class Dat_FilepathN(datObjAbs.Abs_DatData): CLS_dat__raw = list VAR_dat__raw__rawtype_pattern = list, tuple VAR_dat__raw__default = [] CLS_dat__data__element = Dat_Filepath VAR_dat__data__datasep = datCfg.DatUtility.DEF_dat__raw_strsep def __init__(self, *args): """ :param args: 1-1.object of value, raw; 1-2.object of data, raw. """ self._initAbsDatData(*args) class Dat_Nodename(datObjAbs.Abs_DatData): CLS_dat__raw = unicode VAR_dat__raw__rawtype_pattern = unicode, str VAR_dat__raw__default = u'' def __init__(self, *args): """ :param args: 1-1.object of value, raw; 1-2.object of data, raw. """ self._initAbsDatData(*args) class Dat_NodenameN(datObjAbs.Abs_DatData): CLS_dat__raw = list VAR_dat__raw__rawtype_pattern = list, tuple VAR_dat__raw__default = [] CLS_dat__data__element = Dat_Nodename VAR_dat__data__datasep = datCfg.DatUtility.DEF_dat__raw_strsep def __init__(self, *args): """ :param args: 1-1.object of value, raw; 1-2.object of data, raw. """ self._initAbsDatData(*args)

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null

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6

d333c1deb3ffa5af5f93133be20dd1f2474017f9

774

py

Python

tests/formatters/test_core.py

Arent128/npc

c8a1e227a1d4d7c540c4f4427b611ffc290535ee

[ "MIT" ]

13

2016-02-23T08:15:22.000Z

2021-07-17T20:54:57.000Z

tests/formatters/test_core.py

Arent128/npc

c8a1e227a1d4d7c540c4f4427b611ffc290535ee

[ "MIT" ]

1

2017-03-30T08:11:40.000Z

2017-09-07T15:01:08.000Z

tests/formatters/test_core.py

Arent128/npc

c8a1e227a1d4d7c540c4f4427b611ffc290535ee

[ "MIT" ]

1

2020-02-21T09:44:40.000Z

import npc import pytest def test_listing_formatter(): formatter = npc.formatters.get_listing_formatter('markdown') assert formatter == npc.formatters.markdown.listing formatter = npc.formatters.get_listing_formatter('html') assert formatter == npc.formatters.html.listing formatter = npc.formatters.get_listing_formatter('json') assert formatter == npc.formatters.json.listing def test_report_formatter(): formatter = npc.formatters.get_report_formatter('markdown') assert formatter == npc.formatters.markdown.report formatter = npc.formatters.get_report_formatter('html') assert formatter == npc.formatters.html.report formatter = npc.formatters.get_report_formatter('json') assert formatter == npc.formatters.json.report

40.736842

64

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774

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0.25

0

null

1

0

null

0

6

d36b1b8d7f2d60f43bdee39d6e5cbbca107f625e

5,210

py

Python

tests/test_selenium.py

mkhumtai/6CCS3PRJ

c7d5bedf9529f6e2b7a57e102761716c11f961c8

[ "MIT" ]

null

tests/test_selenium.py

mkhumtai/6CCS3PRJ

c7d5bedf9529f6e2b7a57e102761716c11f961c8

[ "MIT" ]

null

tests/test_selenium.py

mkhumtai/6CCS3PRJ

c7d5bedf9529f6e2b7a57e102761716c11f961c8

[ "MIT" ]

null

from selenium import webdriver import os # Setup selenium driver def test_setup(): PROJECT_ROOT = os.path.abspath(os.path.dirname(__file__)) DRIVER_BIN = os.path.join(PROJECT_ROOT, "chromedriver_mac") global driver #driver = webdriver.Chrome('chromedriver_mac') driver = webdriver.Chrome(executable_path=DRIVER_BIN) driver.implicitly_wait(10) # Check that users who are not logged receives Unauthorized user notice def test_login_required(): driver.get('https://k1763918.herokuapp.com/logout.html') driver.get('https://k1763918.herokuapp.com/allTime.html') assert "Unauthorized" in driver.page_source # Check that users are able to register def test_register(): driver.get('https://k1763918.herokuapp.com/logout.html') driver.get('https://k1763918.herokuapp.com/register.html') driver.find_element_by_id('username').send_keys('testing_user') driver.find_element_by_id('email').send_keys('testing@gmail.com') driver.find_element_by_id('password').send_keys('testing_password') driver.find_element_by_xpath("//button[@class='btn btn-fill btn-primary']").click() # Test that users cannot register with the same information def test_user_exists(): driver.get('https://k1763918.herokuapp.com/logout.html') driver.get('https://k1763918.herokuapp.com/register.html') driver.find_element_by_id('username').send_keys('testing_user') driver.find_element_by_id('email').send_keys('testing@gmail.com') driver.find_element_by_id('password').send_keys('testing_password') driver.find_element_by_xpath("//button[@class='btn btn-fill btn-primary']").click() assert "User exists!" in driver.page_source # Check that users with incorrect login details cannot login def test_unauthenticated_user(): driver.get('https://k1763918.herokuapp.com/login.html') driver.find_element_by_id('username').send_keys('random_user') driver.find_element_by_id('password').send_keys('wrong_password') driver.find_element_by_xpath("//button[@class='btn btn-fill btn-primary']").click() assert "<label>Unknown user</label>" in driver.page_source # Check that users with correct login details can login def test_authenticated_user(): driver.get('https://k1763918.herokuapp.com/login.html') driver.find_element_by_id('username').send_keys('testing_user') driver.find_element_by_id('password').send_keys('testing_password') driver.find_element_by_xpath("//button[@class='btn btn-fill btn-primary']").click() assert "No. of Cases by quarter" in driver.page_source # Check that table visualization can be viewed from /query.html def test_query_table(): driver.get('https://k1763918.herokuapp.com/query.html') sero = driver.find_element_by_xpath("//input[@value='H5N1 HPAI']") region = driver.find_element_by_xpath("//input[@value='Asia']") type = driver.find_element_by_xpath("//input[@value='wild']") sero.click() region.click() type.click() driver.find_element_by_name("from_date").send_keys('01/01/2004') driver.find_element_by_name("to_date").send_keys('01/01/2020') driver.find_element_by_xpath("//input[@value='Table']").click() assert "Data gathered from" in driver.page_source # Check that zero rows are returned when data queried incorrectly def test_query_table(): driver.get('https://k1763918.herokuapp.com/query.html') sero = driver.find_element_by_xpath("//input[@value='H5N1 HPAI']") region = driver.find_element_by_xpath("//input[@value='Asia']") sero.click() region.click() driver.find_element_by_name("from_date").send_keys('01/01/2004') driver.find_element_by_name("to_date").send_keys('01/01/2020') driver.find_element_by_xpath("//input[@value='Table']").click() assert "Total rows: 0" in driver.page_source # Check that marker visualization can be viewed from /query.html def test_query_marker(): driver.get('https://k1763918.herokuapp.com/query.html') sero = driver.find_element_by_xpath("//input[@value='H5N1 HPAI']") region = driver.find_element_by_xpath("//input[@value='Asia']") type = driver.find_element_by_xpath("//input[@value='wild']") sero.click() region.click() type.click() driver.find_element_by_name("from_date").send_keys('01/01/2004') driver.find_element_by_name("to_date").send_keys('01/01/2020') driver.find_element_by_xpath("//input[@value='Markers']").click() assert "Interactive Map" in driver.page_source # Check that heatmap visualization can be viewed from /query.html def test_query_heatmap(): driver.get('https://k1763918.herokuapp.com/query.html') sero = driver.find_element_by_xpath("//input[@value='H5N1 HPAI']") region = driver.find_element_by_xpath("//input[@value='Asia']") type = driver.find_element_by_xpath("//input[@value='wild']") sero.click() region.click() type.click() driver.find_element_by_name("from_date").send_keys('01/01/2004') driver.find_element_by_name("to_date").send_keys('01/01/2020') driver.find_element_by_xpath("//input[@value='Heatmap']").click() assert "Heatmap using Leaflet" in driver.page_source def test_teardown(): driver.close() driver.quit()

42.704918

87

0.732821

745

5,210

4.877852

0.174497

0.101816

0.173088

0.193451

0.759769

0.73005

0.721244

0.701431

0.690149

0

0.034535

0.116315

5,210

121

88

43.057851

0.754778

0.114779

0

0.647727

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0.337174

0.054348

0

0.090909

1

0.125

false

0.045455

0.022727

0

0.147727

0

null

0

1

0

1

0

null

0

6

d3a34210881e07e14dc11529a4498357c5533cea

143

py

Python

ex10_drills.py

shanukk27/learn-python

cb7d76db00101a3ad96858f9a2f9593b5a8c3f93

[ "Apache-2.0" ]

null

ex10_drills.py

shanukk27/learn-python

cb7d76db00101a3ad96858f9a2f9593b5a8c3f93

[ "Apache-2.0" ]

null

ex10_drills.py

shanukk27/learn-python

cb7d76db00101a3ad96858f9a2f9593b5a8c3f93

[ "Apache-2.0" ]

null

family_members = "\nKoyakutty\nFathima\nShinu\nShynu\nArif\nReyah\nShanu\nNasri" print("My family members are: {}".format(family_members))

35.75

81

0.769231

18

143

6

0.777778

0.361111

0

0.083916

143

3

82

47.666667

0.824427

0

0.614286

0.435714

0

1

0

false

0

0.5

1

0

null

1

0

1

0

1

null

0

1

0

6

6ca948e5b1f96d3d024b87ee65402b6be10714d4

1,369

py

Python

models/cog.py

Minigrim0/MACS_VUBot

776c2a6586cf3d54272f72e187e2efc91210cf4e

[ "MIT" ]

null

models/cog.py

Minigrim0/MACS_VUBot

776c2a6586cf3d54272f72e187e2efc91210cf4e

[ "MIT" ]

1

2021-11-14T14:35:53.000Z

2021-11-14T15:32:29.000Z

models/cog.py

Minigrim0/MACS_VUBot

776c2a6586cf3d54272f72e187e2efc91210cf4e

[ "MIT" ]

1

2021-11-14T14:37:07.000Z

from discord.ext import commands from discord.ext.commands import Context from models.client import MaxVUBot class CommandCog(commands.Cog): @commands.command() async def pls_pin(self, ctx: Context): message_reference: Optional[discord.MessageReference] = ctx.message.reference if not message_reference: await ctx.reply("Please use this command while replying on the message you wish to pin") return message_to_pin = message_reference.cached_message or await ctx.channel.fetch_message( message_reference.message_id ) await message_to_pin.pin(reason=f"Pinned by {ctx.author}") await ctx.message.delete(delay=3) # Deletes the request to pin after 3 seconds on command success @commands.command() async def pls_unpin(self, ctx: Context): message_reference: Optional[discord.MessageReference] = ctx.message.reference if not message_reference: await ctx.reply("Please use this command while replying on the message you wish to pin") return message_to_pin = message_reference.cached_message or await ctx.channel.fetch_message( message_reference.message_id ) await message_to_pin.unpin() await ctx.message.delete(delay=3) # Deletes the request to pin after 3 seconds on command success

44.16129

106

0.707816

180

1,369

5.25

0.316667

0.169312

0.050794

0.048677

0.831746

0.77672

0

0.003777

0.226443

1,369

30

107

45.633333

0.888574

0.089847

0

0.615385

0

0.128721

0

1

0

false

0

0.115385

0

0.230769

0

null

0

1

0

null

0

6

9f4ae343e7d053c7246ff591f8a06402f34e5515

276

py

Python

flake8_pie/test_utils.py

sbdchd/flake8-pie

96ae441d92abe64b23e1c37b0eb15778434000cc

[ "BSD-2-Clause" ]

23

2019-01-25T14:58:20.000Z

2022-03-27T02:20:01.000Z

flake8_pie/test_utils.py

sbdchd/flake8-assign-and-return

96ae441d92abe64b23e1c37b0eb15778434000cc

[ "BSD-2-Clause" ]

50

2019-04-17T02:37:01.000Z

2022-03-27T02:19:53.000Z

flake8_pie/test_utils.py

sbdchd/flake8-assign-and-return

96ae441d92abe64b23e1c37b0eb15778434000cc

[ "BSD-2-Clause" ]

5

2019-02-21T07:29:12.000Z

2021-11-06T21:01:26.000Z

from flake8_pie.utils import pairwise def test_pairwise() -> None: assert list(pairwise([1])) == [(1, None)] assert list(pairwise([])) == [] assert list(pairwise([1, 2])) == [(1, 2), (2, None)] assert list(pairwise([1, 2, 3])) == [(1, 2), (2, 3), (3, None)]

30.666667

67

0.550725

40

276

3.75

0.35

0.266667

0.48

0.44

0.446667

0

0.072072

0.195652

276

8

68

34.5

0.603604

0

0.666667

1

0.166667

true

0

0.166667

0

0.333333

0

null

1

0

1

0

null

0

1

0

1

0

6

9f557c14f4347f6753f5f3c3292b5bfda4a23936

4,633

py

Python

batch_test_accuracies.py

Big-Data-Course-Team/Machine-Learning-with-Spark-Streaming

40cbddaa079b7de6c9501951a119ba6fee18ed50

[ "MIT" ]

null

batch_test_accuracies.py

Big-Data-Course-Team/Machine-Learning-with-Spark-Streaming

40cbddaa079b7de6c9501951a119ba6fee18ed50

[ "MIT" ]

null

batch_test_accuracies.py

Big-Data-Course-Team/Machine-Learning-with-Spark-Streaming

40cbddaa079b7de6c9501951a119ba6fee18ed50

[ "MIT" ]

1

2021-12-29T08:56:31.000Z

import pickle import json import importlib import matplotlib.pyplot as plt import numpy as np import os import warnings warnings.filterwarnings("ignore") import argparse plt.rcParams.update({'figure.figsize':(14, 10), 'figure.dpi':100}) plt.rcParams.update({'font.size': 14}) acc_file_1 = open('Batch_1000/test_eval_metrics/lr_3.txt', "r") acc_list_1 = acc_file_1.readlines() acc_list_1 = [float(i) for i in acc_list_1] acc_file_2 = open('Batch_2000/test_eval_metrics/lr_3.txt', "r") acc_list_2 = acc_file_2.readlines() acc_list_2 = [float(i) for i in acc_list_2] acc_file_3 = open('Batch_2500/test_eval_metrics/lr_3.txt', "r") acc_list_3 = acc_file_3.readlines() acc_list_3 = [float(i) for i in acc_list_3] acc_file_4 = open('Batch_3000/test_eval_metrics/lr_3.txt', "r") acc_list_4 = acc_file_4.readlines() acc_list_4 = [float(i) for i in acc_list_4] acc_file_5 = open('Batch_4000/test_eval_metrics/lr_3.txt', "r") acc_list_5 = acc_file_5.readlines() acc_list_5 = [float(i) for i in acc_list_5] acc_file_6 = open('Batch_5000/test_eval_metrics/lr_3.txt', "r") acc_list_6 = acc_file_6.readlines() acc_list_6 = [float(i) for i in acc_list_6] iters=[i for i in range (1,len(acc_list_1)+1)] plt.plot(iters, acc_list_1, label='Batch size - 1000') plt.plot(iters, acc_list_2, label='Batch size - 2000') plt.plot(iters, acc_list_3, label='Batch size - 2500') plt.plot(iters, acc_list_4, label='Batch size - 3000') plt.plot(iters, acc_list_5, label='Batch size - 4000') plt.plot(iters, acc_list_6, label='Batch size - 5000') plt.xlabel('Iteration') plt.ylabel('Accuracy') plt.title("SGD") plt.legend() img_file = open('./batch_accuracy_SGD.eps', "wb+") plt.savefig(img_file, format='eps', bbox_inches='tight') plt.clf() acc_file_1 = open('Batch_1000/test_eval_metrics/mnb_3.txt', "r") acc_list_1 = acc_file_1.readlines() acc_list_1 = [float(i) for i in acc_list_1] acc_file_2 = open('Batch_2000/test_eval_metrics/mnb_3.txt', "r") acc_list_2 = acc_file_2.readlines() acc_list_2 = [float(i) for i in acc_list_2] acc_file_3 = open('Batch_2500/test_eval_metrics/mnb_3.txt', "r") acc_list_3 = acc_file_3.readlines() acc_list_3 = [float(i) for i in acc_list_3] acc_file_4 = open('Batch_3000/test_eval_metrics/mnb_3.txt', "r") acc_list_4 = acc_file_4.readlines() acc_list_4 = [float(i) for i in acc_list_4] acc_file_5 = open('Batch_4000/test_eval_metrics/mnb_3.txt', "r") acc_list_5 = acc_file_5.readlines() acc_list_5 = [float(i) for i in acc_list_5] acc_file_6 = open('Batch_5000/test_eval_metrics/mnb_3.txt', "r") acc_list_6 = acc_file_6.readlines() acc_list_6 = [float(i) for i in acc_list_6] iters=[i for i in range (1,len(acc_list_1)+1)] plt.plot(iters, acc_list_1, label='Batch size - 1000') plt.plot(iters, acc_list_2, label='Batch size - 2000') plt.plot(iters, acc_list_3, label='Batch size - 2500') plt.plot(iters, acc_list_4, label='Batch size - 3000') plt.plot(iters, acc_list_5, label='Batch size - 4000') plt.plot(iters, acc_list_6, label='Batch size - 5000') plt.xlabel('Iteration') plt.ylabel('Accuracy') plt.title("MNB") plt.legend() img_file = open('./batch_accuracy_MNB.eps', "wb+") plt.savefig(img_file, format='eps', bbox_inches='tight') plt.clf() acc_file_1 = open('Batch_1000/test_eval_metrics/pac_3.txt', "r") acc_list_1 = acc_file_1.readlines() acc_list_1 = [float(i) for i in acc_list_1] acc_file_2 = open('Batch_2000/test_eval_metrics/pac_3.txt', "r") acc_list_2 = acc_file_2.readlines() acc_list_2 = [float(i) for i in acc_list_2] acc_file_3 = open('Batch_2500/test_eval_metrics/pac_3.txt', "r") acc_list_3 = acc_file_3.readlines() acc_list_3 = [float(i) for i in acc_list_3] acc_file_4 = open('Batch_3000/test_eval_metrics/pac_3.txt', "r") acc_list_4 = acc_file_4.readlines() acc_list_4 = [float(i) for i in acc_list_4] acc_file_5 = open('Batch_4000/test_eval_metrics/pac_3.txt', "r") acc_list_5 = acc_file_5.readlines() acc_list_5 = [float(i) for i in acc_list_5] acc_file_6 = open('Batch_5000/test_eval_metrics/pac_3.txt', "r") acc_list_6 = acc_file_6.readlines() acc_list_6 = [float(i) for i in acc_list_6] iters=[i for i in range (1,len(acc_list_1)+1)] plt.plot(iters, acc_list_1, label='Batch size - 1000') plt.plot(iters, acc_list_2, label='Batch size - 2000') plt.plot(iters, acc_list_3, label='Batch size - 2500') plt.plot(iters, acc_list_4, label='Batch size - 3000') plt.plot(iters, acc_list_5, label='Batch size - 4000') plt.plot(iters, acc_list_6, label='Batch size - 5000') plt.xlabel('Iteration') plt.ylabel('Accuracy') plt.title("PAC") plt.legend() img_file = open('./batch_accuracy_PAC.eps', "wb+") plt.savefig(img_file, format='eps', bbox_inches='tight') plt.clf()

32.626761

66

0.742715

904

4,633

3.464602

0.084071

0.167625

0.033525

0.046935

0.925287

0.893678

0.866858

0

0.069297

0.102957

4,633

141

67

32.858156

0.684312

0

0.672897

0

0.260475

0.161987

0

1

0

false

0

0.074766

0

0.074766

0

null

0

1

0

null

0

6

9f5d9d483eb8eb7488cb5fe3654317c8666dd3cd

81

py

Python

snippets/numpy/lib/__init__.py

oojBuffalo/micropython-ulab

4407bec88c3a7585ffbdfdd98e72bed12329ff3c

[ "MIT" ]

1

2022-03-07T08:54:35.000Z

snippets/numpy/lib/__init__.py

oojBuffalo/micropython-ulab

4407bec88c3a7585ffbdfdd98e72bed12329ff3c

[ "MIT" ]

null

snippets/numpy/lib/__init__.py

oojBuffalo/micropython-ulab

4407bec88c3a7585ffbdfdd98e72bed12329ff3c

[ "MIT" ]

null

from .function_base import * from .polynomial import * from .type_check import *

20.25

28

0.777778

11

81

5.545455

0.636364

0.327869

0

0.148148

81

4

29

20.25

0.884058

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

9f9e60f0bd92015c665951a32bf941313ff54b3c

46

py

Python

scan_models/modbus/__init__.py

ssdemajia/ids-backend

188af247befa44596f62c660c24b05474d1ba29f

[ "MIT" ]

1

2020-05-22T09:52:33.000Z

scan_models/modbus/__init__.py

ssdemajia/ids-backend

188af247befa44596f62c660c24b05474d1ba29f

[ "MIT" ]

8

2021-03-18T21:22:40.000Z

2022-03-11T23:32:48.000Z

scan_models/modbus/__init__.py

ssdemajia/ids-backend

188af247befa44596f62c660c24b05474d1ba29f

[ "MIT" ]

null

from .scan import modbus_resolve, modbus_scan

23

45

0.847826

7

46

5.285714

0.714286

0

0.108696

46

1

46

0.902439

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

4c8f8ce6ca0385b16b24e39502594550208f0d52

84

py

Python

vk_dialog_backuper/__main__.py

r4rdsn/vk-dialog-backuper

046466f0eaadeeeec4f147062604571ac4666af2

[ "MIT" ]

1

2020-09-07T00:55:13.000Z

vk_dialog_backuper/__main__.py

r4rdsn/vk-dialog-backuper

046466f0eaadeeeec4f147062604571ac4666af2

[ "MIT" ]

null

vk_dialog_backuper/__main__.py

r4rdsn/vk-dialog-backuper

046466f0eaadeeeec4f147062604571ac4666af2

[ "MIT" ]

null

import vk_dialog_backuper if __name__ == '__main__': vk_dialog_backuper.main()

16.8

29

0.761905

11

84

4.727273

0.636364

0.307692

0.615385

0

0.142857

84

4

30

21

0.722222

0

0.095238

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

4c9c287823d32ac61bb3a69fa99170b12f074f58

138

py

Python

dist_zero/cgen/__init__.py

koreiklein/dist_zero

68ef5a0603edc53925daeec1f4bb684025cacbd4

[ "Unlicense" ]

1

2019-03-18T13:27:35.000Z

dist_zero/cgen/__init__.py

koreiklein/dist_zero

68ef5a0603edc53925daeec1f4bb684025cacbd4

[ "Unlicense" ]

null

dist_zero/cgen/__init__.py

koreiklein/dist_zero

68ef5a0603edc53925daeec1f4bb684025cacbd4

[ "Unlicense" ]

null

from .expression import * from .statement import * from .lvalue import * from .program import * from .type import * from .common import *

19.714286

25

0.73913

18

138

5.666667

0.444444

0.490196

0

0.173913

138

6

26

23

0.894737

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

e25ca50c1dfeb91062f7dbb04357e12e791a6ea6

41

py

Python

kuwala/pipelines/google-poi/src/__init__.py

bmahmoudyan/kuwala

7951ed49ac1c31c874a4446bb4661152c4d69c90

[ "Apache-2.0" ]

381

2021-04-08T13:04:57.000Z

2022-03-29T09:49:46.000Z

kuwala/pipelines/google-poi/src/__init__.py

bmahmoudyan/kuwala

7951ed49ac1c31c874a4446bb4661152c4d69c90

[ "Apache-2.0" ]

92

2021-04-20T12:28:40.000Z

2022-03-30T17:55:36.000Z

kuwala/pipelines/google-poi/src/__init__.py

bmahmoudyan/kuwala

7951ed49ac1c31c874a4446bb4661152c4d69c90

[ "Apache-2.0" ]

27

2021-04-26T17:52:32.000Z

2022-03-21T19:36:34.000Z

import nest_asyncio nest_asyncio.apply()

13.666667

20

0.853659

6

41

5.5

0.666667

0

0.073171

41

2

21

20.5

0.868421

0

1

0

true

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

e284e5b1f47084f129d877cd99c5f21af12375e7

208

py

Python

examples/coolColumns.py

jsharf/Hexagons

d9f295c39262e4eaf3f98db3cef872b9ecf37c49

[ "MIT" ]

null

examples/coolColumns.py

jsharf/Hexagons

d9f295c39262e4eaf3f98db3cef872b9ecf37c49

[ "MIT" ]

null

examples/coolColumns.py

jsharf/Hexagons

d9f295c39262e4eaf3f98db3cef872b9ecf37c49

[ "MIT" ]

null

Column(-300, 50) Column(-170, 50) Column(-160, 50) Column(-150, 50) Column(-20, 50) Column(-10, 50) Column(0, 50) Column(10, 50) Column(20, 50) Column(150, 50) Column(160, 50) Column(170, 50) Column(300, 50)

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null

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e2b63ba9c5d56976a7cbe15ed293468a106bb15b

75

py

Python

tests/run_flake8/bytes.py

10sr/flake8-no-implicit-concat

11db2327ffc122d9481c6e03a77cf62b1dc85d25

[ "MIT" ]

15

2020-05-21T19:39:58.000Z

2022-03-22T11:04:12.000Z

tests/run_flake8/bytes.py

10sr/flake8-no-implicit-concat

11db2327ffc122d9481c6e03a77cf62b1dc85d25

[ "MIT" ]

43

2020-05-20T05:19:20.000Z

2021-11-25T05:34:51.000Z

tests/run_flake8/bytes.py

10sr/flake8-no-implicit-concat

11db2327ffc122d9481c6e03a77cf62b1dc85d25

[ "MIT" ]

1

2020-08-25T23:04:08.000Z

a = b"aaa" b"bbb" b = [b"aaa", b"bbb" b"ccc"] c = rb"aaa" b"bbb"

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1

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null

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2c78607fc802cad1bdf3d94a15ab0a3978c6ad29

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py

Python

mystring/__init__.py

lamter/mydealutil

26934e6e61b40327cb9cabc43e41cd01caf5bd2b

[ "MIT" ]

null

mystring/__init__.py

lamter/mydealutil

26934e6e61b40327cb9cabc43e41cd01caf5bd2b

[ "MIT" ]

null

mystring/__init__.py

lamter/mydealutil

26934e6e61b40327cb9cabc43e41cd01caf5bd2b

[ "MIT" ]

null

# coding: utf-8 from .mystring import *

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1

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6

2c820a57f530bbb56c5243e8f2c0b550a2974865

151

py

Python

src/test/anovos/drift/test_distances.py

dattranm/anovos

817378c810b2260e85794ef473c3080efabc34ca

[ "Apache-2.0" ]

null

src/test/anovos/drift/test_distances.py

dattranm/anovos

817378c810b2260e85794ef473c3080efabc34ca

[ "Apache-2.0" ]

3

2022-02-28T18:22:39.000Z

2022-03-28T18:17:46.000Z

src/test/anovos/drift/test_distances.py

dattranm/anovos

817378c810b2260e85794ef473c3080efabc34ca

[ "Apache-2.0" ]

null

import pytest import numpy as np from anovos.drift.distances import hellinger, psi, js_divergence, kl_divergence, ks def test_hellinger(): pass

16.777778

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0.781457

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5.227273

0.818182

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0.15894

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84

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null

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1

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1

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6

3940435e33101f147fa943bc767f608b929fa1ad

110

py

Python

alpaca/utils/__init__.py

kra5h/alpaca

0e014f12bfa6601e5bb2c57c4da083c270560d6c

[ "Apache-2.0" ]

14

2020-03-04T14:16:23.000Z

2021-12-26T17:47:55.000Z

alpaca/utils/__init__.py

kra5h/alpaca

0e014f12bfa6601e5bb2c57c4da083c270560d6c

[ "Apache-2.0" ]

5

2020-07-07T15:27:57.000Z

2020-11-09T14:11:06.000Z

alpaca/utils/__init__.py

kra5h/alpaca

0e014f12bfa6601e5bb2c57c4da083c270560d6c

[ "Apache-2.0" ]

5

2020-03-14T18:27:53.000Z

2021-12-26T17:49:18.000Z

from . import datasets from . import ue_metrics from . import dimension_reduction from . import model_builder

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null

0

1

0

1

0

1

0

6

394f96a5ee3621dd59afdf09917e0c02cee00c42

25

py

Python

bruges/models/__init__.py

hyperiongeo/bruges

6d9a3aae86aaa53107caaa20e9aafa390358b0f8

[ "Apache-2.0" ]

null

bruges/models/__init__.py

hyperiongeo/bruges

6d9a3aae86aaa53107caaa20e9aafa390358b0f8

[ "Apache-2.0" ]

null

bruges/models/__init__.py

hyperiongeo/bruges

6d9a3aae86aaa53107caaa20e9aafa390358b0f8

[ "Apache-2.0" ]

null

from .wedge import wedge

12.5

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5

0.75

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0.16

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1

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0.952381

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null

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1

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1

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6

395249463c197603056a294813a673d6fb0e7f3a

47

py

Python

vigilance/default_suites/__init__.py

wilstoff/vigilance

0947ed5256ae54e941f4b57915395f6fe70ca58a

[ "MIT" ]

1

2019-02-09T01:11:12.000Z

vigilance/default_suites/__init__.py

wilstoff/vigilance

0947ed5256ae54e941f4b57915395f6fe70ca58a

[ "MIT" ]

null

vigilance/default_suites/__init__.py

wilstoff/vigilance

0947ed5256ae54e941f4b57915395f6fe70ca58a

[ "MIT" ]

2

2018-04-21T04:38:43.000Z

2022-03-02T22:34:07.000Z

"""@defgroup default_suites default_suites """

15.666667

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0.085106

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1

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null

0

1

0

6

20066b121ae0abdd0c7da7fabf1b1bda94af7424

185

py

Python

avalanche/benchmarks/utils/__init__.py

lrzpellegrini/avalanche_pre_public

522019a55ce08b92c1ec74b508a8ea6ae8751dfd

[ "MIT" ]

12

2021-04-16T15:49:59.000Z

2022-02-27T18:04:58.000Z

avalanche/benchmarks/utils/__init__.py

lrzpellegrini/avalanche_pre_public

522019a55ce08b92c1ec74b508a8ea6ae8751dfd

[ "MIT" ]

null

avalanche/benchmarks/utils/__init__.py

lrzpellegrini/avalanche_pre_public

522019a55ce08b92c1ec74b508a8ea6ae8751dfd

[ "MIT" ]

2

2021-06-22T04:11:52.000Z

2021-11-12T03:27:18.000Z

from .utils import * from .dataset_utils import IDataset, IDatasetWithTargets from .avalanche_dataset import * from .datasets_from_filelists import * from .torchvision_wrapper import *

30.833333

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1

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1

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6

200676a9830be1f0220554c67dfaa65e76a4a71b

207

py

Python

flask_dance/consumer/__init__.py

timgates42/flask-dance

ebe3ea48d3263136e18ccea37e50292b7c503c67

[ "MIT" ]

836

2015-01-11T23:01:58.000Z

2022-03-28T07:32:52.000Z

flask_dance/consumer/__init__.py

timgates42/flask-dance

ebe3ea48d3263136e18ccea37e50292b7c503c67

[ "MIT" ]

353

2015-02-11T00:32:58.000Z

2022-03-28T14:45:38.000Z

flask_dance/consumer/__init__.py

timgates42/flask-dance

ebe3ea48d3263136e18ccea37e50292b7c503c67

[ "MIT" ]

189

2015-03-10T15:04:29.000Z

2022-03-16T21:49:11.000Z

from .oauth1 import OAuth1ConsumerBlueprint from .oauth2 import OAuth2ConsumerBlueprint from .base import oauth_authorized, oauth_before_login, oauth_error from .requests import OAuth1Session, OAuth2Session

41.4

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null

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6

200f042d8fd57535c0b33ee54e02a3a240d5f8b9

27

py

Python

src/gui/telegrambot/tlgbotcore/sqliteutils/__init__.py

kaefik/wtf

74a4e12e0303fc1341838541a418fee011f2d9a7

[ "MIT" ]

null

src/gui/telegrambot/tlgbotcore/sqliteutils/__init__.py

kaefik/wtf

74a4e12e0303fc1341838541a418fee011f2d9a7

[ "MIT" ]

null

src/gui/telegrambot/tlgbotcore/sqliteutils/__init__.py

kaefik/wtf

74a4e12e0303fc1341838541a418fee011f2d9a7

[ "MIT" ]

null

from .sqliteutils import *

13.5

26

0.777778

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6

645082fdf189a0fc2f989a8aa63d4f1bc824a374

3,876

py

Python

BHP-Code/Chapter9/decryptor.py

yangtze736/Snake

e47f89bec994352562e9e171b2d640d0aa8621b0

[ "MIT" ]

6

2021-12-07T21:02:12.000Z

2022-03-03T12:08:14.000Z

BHP-Code/Chapter9/decryptor.py

yangtze736/Snake

e47f89bec994352562e9e171b2d640d0aa8621b0

[ "MIT" ]

15

2020-01-28T22:25:10.000Z

2022-03-11T23:21:02.000Z

BHP-Code/Chapter9/decryptor.py

yangtze736/Snake

e47f89bec994352562e9e171b2d640d0aa8621b0

[ "MIT" ]

1

2022-01-15T23:57:36.000Z

import zlib import base64 from Crypto.PublicKey import RSA from Crypto.Cipher import PKCS1_OAEP encrypted = """XxfaX7nfQ48K+l0rXM3tQf3ShFcytAQ4sLe6vn8bWdreho4riaJ5Dy5PeijSKbsgWSMoeZLmihxb0YAFgCaIp11AUl4kmIiY+c+8LJonbTTembxv98GePM1SEme5/vMwGORJilw+rTdORSHzwbC56sw5NG8KosgLWwHEGEGbhii2qBkuyQrIc9ydoOKKCe0ofTRnaI2c/lb9Ot3vkEIgxCks94H6qVkAfhO34HS7nClUldn9UN040RYgtEqBgvAFzoEhDuRtfjJu1dzyzaFtRAVhcQ6HdgZMWRfpaxKQOmbhXwYyGRQfwNl/Rwgn1EJBFAhvIaEifHDlCw+hLViNYlae7IdfIb6hWtWPyFrkaNjmkbhhXclNgZe0+iPPDzsZbpHI1IckG0gVlTdlGKGz+nK5Cxyso41icC4gO7tmdXDGgF6bMt/GC1VjMVmL/rYsb8jzJblmuQBAeFNacyhjxrzIH5v60RQ1BxwfD+wLCKfyzn3vQucPak2cnwBs3yTIEShYj0ymP4idU/5Qt5qkqMDyvO4U8DmqB4KT58+o2B3c88+lUZjz7c9ygwKjp2hSNf+Dm9H3YJY2Pn6YlydyT1sYWCy06DZko7z3uae5GYGjez8hnCIFt+mpeLvEelSHeZfyV8wYyHg5Y9eA2NZNX6yNVD8IREhjXGWdbGTn41lVCqEiCetY9SKdWeL1Hp/vJN3SOo4qglbQF7P6oqqg0bofnAcphLVaHw/FOGWtW1CFEQUQdIg9bk+SJqM/s1ozJlisenrRzxv3L5LthEfLflCafK0u3n2gPa4F3ok4tx9i+r+MykRTw+OksMfVu71CAMuJdrFQLMSpyWkQ86Vc/QIXgdoCKkAYx5xr/U8gDXkZ4GvL9biEZv/fb5Wh7Br1Hu6idUgTYpEJVVnMuI13ePGeJLA54Il2S7aDyrgfhb61WQmoMRGvLP7uxCjgLwrxZNjAYJTmXszLvvgmI+lHe5o8rgQw6zSGpl9k27urV4bA0Zt+PsYiLNbEQqqxrJxKcbKqozl8XtfMXanct9pKu4vaq8fH/j9jvZ133UtcaR5iTQ0K7P4J5Qoaxz3uUhGrgplZ1jE9Nr0iyRj722dW82b4m1f/h80K7EuvwEeOfdYZl7iFL8yRi9dfopwATjKbKrWFroGCb/wvpc5ujpzDfwAeWsSU4Nve2qBDo5coVt1GI8rzHUh52TQ007JhcYABIxZGSFeeJ3bFgvqO2kUK/Pc36Au0VlNFds/j+fIuMlmFUuckBLCTpE2W9hYqmVOWBmyeZPJNzVI4gLexFbXbg8+0Eq6Pa4MxZsR3wypgC9LE/dvLbQ3oSn9x7nKMXpdq9r+xK1sjodpeYNz7t/5GpFu1teN0SFbmsoXjVEyOAn3L5Gd4Wxua7y9xOixc1H2/bbyNqJZAjEm34DDmNRTQtrqCwOEXwFGKgRGUzPYGC74wAPDDTaQEBv7Toc7rfkzgRX4ROW0SUaEPmi5tAlXe+CKVdJGtLKXUXYRHLMZ4jTzGsD89dmt2r2Fh6AUUN2e9jzzK2ULMnMhRUnDdcM74jbuDHGtXt56pFxFKJ21FQFS8JK0ZOqYa+0JjLuSzrLN9gSCu/JuTPC60LTxLsLcWZVR7cIHQE+sgDtt40/6O1YE7/8rs6qB9re28gDY1s9R5HFtjowO3ylRWqlaV9MC1OGzM4xHPxG2V+2zuq6ol8Cs=""" private_key = """-----BEGIN RSA PRIVATE KEY----- MIIEpAIBAAKCAQEAyXUTgFoL/2EPKoN31l5Tlak7VxhdusNCWQKDfcN5Jj45GQ1o ZZjsECQ8jK5AaQuCWdmEQkgCEV23L2y71G+Th/zlVPjp0hgC6nOKOuwmlQ1jGvfV vaNZ0YXrs+sX/wg5FT/bTS4yzXeW6920tdls2N7Pu5N1FLRW5PMhk6GW5rzVhwdD vnfaUoSVj7oKaIMLbN/TENvnwhZZKlTZeK79ix4qXwYLe66CrgCHDf4oBJ/nO1oY welxuIXVPhIZnVpkbz3IL6BfEZ3ZDKzGeRs6YLZuR2u5KUbr9uabEzgtrLyOeoK8 UscKmzOvtwxZDcgNijqMJKuqpNZczPHmf9cS1wIDAQABAoIBAAdOiMOKAI9lrNAk 7o7G4w81kSJqjtO8S0bBMZW5Jka90QJYmyW8MyuutMeBdnKY6URrAEILLJAGryM4 NWPSHC69fG/li02Ec26ffC8A67FSR/rtbEIxj4tq6Q6gg0FLwg5EP6b/+vW61a1+ YBSMa0c+ZZhvE7sJg3FQZDJflQKPXFHYxOlS42+UyUP8K07cFznsQCvia9mCHUG6 BDFbV/yjbMyYgKTCVmMeaCS2K0TlbcyGpF0Bz95mVpkrU6pHXY0UAJIv4dyguywe dBZcJlruSRL0OJ+3Gb3CJS7YdsPW807LSyf8gcrHMpgV5z2CdGlaoaLBJyS/nDHi n07PIbECgYEA4Rjlet1xL/Sr9HnHVUH0m1iST0SrLlQCzrMkiw4g5rCOCnhWPNQE dpnRpgUWMhhyZj82SwigkdXC2GpvBP6GDg9pB3Njs8qkwEsGI8GFhUQfKf8Bnnd2 w3GUHiRoJpVxrrE3byh23pUiHBdbp7h2+EaOTrRsc2w3Q4NbNF+FOOkCgYEA5R1Z KvuKn1Sq+0EWpb8fZB+PTwK60qObRENbLdnbmGrVwjNxiBWE4BausHMr0Bz/cQzk tDyohkHx8clp6Qt+hRFd5CXXNidaelkCDLZ7dasddXm1bmIlTIHjWWSsUEsgUTh7 crjVvghU2Sqs/vCLJCW6WYGb9JD2BI5R9pOClb8CgYEAlsOtGBDvebY/4fwaxYDq i43UWSFeIiaExtr30+c/pCOGz35wDEfZQXKfF7p6dk0nelJGVBVQLr1kxrzq5QZw 1UP/Dc18bvSASoc1codwnaTV1rQE6pWLRzZwhYvO8mDQBriNr3cDvutWMEh4zCpi DMJ9GDwCE4DctuxpDvgXa9kCgYEAuxNjo30Qi1iO4+kZnOyZrR833MPV1/hO50Y4 RRAGBkX1lER9ByjK/k6HBPyFYcDLsntcou6EjFt8OnjDSc5g2DZ9+7QKLeWkMxJK Yib+V+4Id8uRIThyTC4ifPN+33D4SllcMyhJHome/lOiPegbNMC5kCwMM33J455x vmxjy/ECgYAOrFR7A9fP4QlPqFCQKDio/FhoQy5ERpl94lGozk4Ma+QDJiRUxA3N GomBPAvYGntvGgPWrsEHrS01ZoOKGBfk5MgubSPFVI00BD6lccmff/0tOxYtb+Pp vOGHt9D9yo3DOhyvJbedpi3u3g13G+FZFw6d1T8Jzm5eZUvG7WeUtg== -----END RSA PRIVATE KEY-----""" rsakey = RSA.importKey(private_key) rsakey = PKCS1_OAEP.new(rsakey) offset = 0 decrypted = "" encrypted = base64.b64decode(encrypted) while offset < len(encrypted): decrypted += rsakey.decrypt(encrypted[offset:offset+256]) offset += 256 # now we decompress to original plaintext = zlib.decompress(decrypted) print plaintext

74.538462

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0

1

0

1

null

1

0

1

0

6

6451a0214449a4edee70920c97ceea448c2cd0bf

2,315

py

Python

mdp/policy_iteration.py

diegocom/ai_implementation

f32227589649a148b002aedf477c2e8803efb7bf

[ "MIT" ]

null

mdp/policy_iteration.py

diegocom/ai_implementation

f32227589649a148b002aedf477c2e8803efb7bf

[ "MIT" ]

null

mdp/policy_iteration.py

diegocom/ai_implementation

f32227589649a148b002aedf477c2e8803efb7bf

[ "MIT" ]

null

import numpy as np import gym import gym_ai_lab import mdps.planning as mdp from timeit import default_timer as timer # Learning parameters delta = 1e-3 gamma = 0.9 pmaxiters = 50 # Max number of policy improvements to perform vmaxiters = 5 # Max number of iterations to perform while evaluating a policy envname = "LavaFloor-v0" print("\n----------------------------------------------------------------") print("\tEnvironment: ", envname) print("----------------------------------------------------------------\n") env = gym.make(envname) env.render() t = timer() policy = mdp.policy_iteration(env, pmaxiters, vmaxiters, gamma, delta) print("\n\nPolicy Iteration:\n----------------------------------------------------------------" "\nExecution time: {0}s\nPolicy:\n{1}".format(round(timer() - t, 4), np.vectorize(env.actions.get)(policy.reshape( env.rows, env.cols)))) envname = "VeryBadLavaFloor-v0" print("\n----------------------------------------------------------------") print("\tEnvironment: ", envname) print("----------------------------------------------------------------\n") env = gym.make(envname) env.render() t = timer() policy = mdp.policy_iteration(env, pmaxiters, vmaxiters, gamma, delta) print("\n\nPolicy Iteration:\n----------------------------------------------------------------" "\nExecution time: {0}s\nPolicy:\n{1}".format(round(timer() - t, 4), np.vectorize(env.actions.get)(policy.reshape( env.rows, env.cols)))) envname = "NiceLavaFloor-v0" print("\n----------------------------------------------------------------") print("\tEnvironment: ", envname) print("----------------------------------------------------------------\n") env = gym.make(envname) env.render() t = timer() policy = mdp.policy_iteration(env, pmaxiters, vmaxiters, gamma, delta) print("\n\nPolicy Iteration:\n----------------------------------------------------------------" "\nExecution time: {0}s\nPolicy:\n{1}".format(round(timer() - t, 4), np.vectorize(env.actions.get)(policy.reshape( env.rows, env.cols))))

36.171875

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0

null

0

1

0

1

0

null

0

6

b38128070f11706580db205e89d4ed3942822e0e

158

py

Python

scripts/models/__init__.py

jmquintana79/utilsDS

1693810b6f10024542b30fdfedbfcd0518f32945

[ "MIT" ]

null

scripts/models/__init__.py

jmquintana79/utilsDS

1693810b6f10024542b30fdfedbfcd0518f32945

[ "MIT" ]

null

scripts/models/__init__.py

jmquintana79/utilsDS

1693810b6f10024542b30fdfedbfcd0518f32945

[ "MIT" ]

null

# -*- coding: utf-8 -*- # @Author: jmquintana79 # @Date: 2018-08-30 23:53:16 # @Last Modified by: jmquintana79 # @Last Modified time: 2018-08-30 23:54:01

26.333333

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0.196078

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null

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6

b39e5b7ebc6df74d5bd401b1545cd3bd530fbb80

1,194

py

Python

tests/Composition/test_Composition__mixture_molar_volume.py

kamilazdybal/multipy

ebdcddb63bfb1cd647ca99bbf9002b04a9b50ed9

[ "MIT" ]

null

tests/Composition/test_Composition__mixture_molar_volume.py

kamilazdybal/multipy

ebdcddb63bfb1cd647ca99bbf9002b04a9b50ed9

[ "MIT" ]

null

tests/Composition/test_Composition__mixture_molar_volume.py

kamilazdybal/multipy

ebdcddb63bfb1cd647ca99bbf9002b04a9b50ed9

[ "MIT" ]

null

import unittest import numpy as np import multipy ################################################################################ ################################################################################ #### #### Class: Composition #### ################################################################################ ################################################################################ class Composition(unittest.TestCase): def test_Composition__mixture_molar_volume__allowed_calls(self): pass ################################################################################ ################################################################################ def test_Composition__mixture_molar_volume__not_allowed_calls(self): pass ################################################################################ ################################################################################ def test_Composition__mixture_molar_volume__computation(self): pass ################################################################################ ################################################################################

34.114286

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0.237856

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1,194

5.909091

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0.080769

0.207692

0.288462

0.569231

0.430769

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0.070352

1,194

34

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null

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0

1

0

1

0

6

b3c2fe038d7d941b137a6310ed7d11ca7ec8040c

30

py

Python

bankreader_demo/demoapp/__init__.py

misli/django-bankreader

c741c9af3f11899b1d9c9f2966da4810b3ade4c2

[ "BSD-3-Clause" ]

1

2018-10-13T22:38:42.000Z

bankreader_demo/demoapp/__init__.py

misli/django-bankreader

c741c9af3f11899b1d9c9f2966da4810b3ade4c2

[ "BSD-3-Clause" ]

null

bankreader_demo/demoapp/__init__.py

misli/django-bankreader

c741c9af3f11899b1d9c9f2966da4810b3ade4c2

[ "BSD-3-Clause" ]

null

from . import readers # noqa

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0

null

0

1

0

1

0

1

0

6

b3c41c959f089fee7d76bb80d33690a1240d77e3

4,464

py

Python

a_rua_dos_cataventos/components/analysis/backup_analysis/iracema_analysis_old.py

DaviRaubach/arua

1e75d449e8f7205cd9522a7f1a1704c89b29023a

[ "MIT" ]

null

a_rua_dos_cataventos/components/analysis/backup_analysis/iracema_analysis_old.py

DaviRaubach/arua

1e75d449e8f7205cd9522a7f1a1704c89b29023a

[ "MIT" ]

null

a_rua_dos_cataventos/components/analysis/backup_analysis/iracema_analysis_old.py

DaviRaubach/arua

1e75d449e8f7205cd9522a7f1a1704c89b29023a

[ "MIT" ]

null

import iracema import matplotlib.pyplot as plt import numpy as np import abjad import muda def IracemaAnalysis(audioin, nharmonics, denominator): audio = iracema.Audio(audioin) # audio.play() # audio.plot() # specifying window and hop sizes window, hop = 2048, 1024 # calculating the FFT fft = iracema.spectral.fft(audio, window, hop) # plotting the spectrogram # iracema.plot.plot_spectrogram(fft) # calculating the RMS # rms = iracema.features.rms(audio, window, hop) # plotting the RMS # rms.plot() # calculating the Peak Envelope # peak = iracema.features.peak_envelope(audio, window, hop) # plotting the Peak Envelope # peak.plot() # extract pitch hps_pitch = iracema.pitch.hps(fft, minf0=1, maxf0=1000) #extract harmonics harmonics = iracema.harmonics.extract(fft, hps_pitch, nharm=nharmonics) # plot the harmonics over the spectrogram # iracema.plot.plot_audio_spectrogram_harmonics( # audio=audio, # rms=rms, # peak_envelope=peak, # fft=fft, # fzero=harmonics['frequency'], # harmonics=harmonics['frequency'], # fftlim=(0,12000) # ) # print(harmonics['frequency'].time) x = harmonics['frequency'].data y = harmonics['frequency'].time # print(x.shape, y.shape) # print(x[1].shape) # for n, data in enumerate(x): # print(n) # plt.plot(y, x[n]) print("fs:", audio.fs) freq_list = [] for n, data in enumerate(x): if n != 0: freq_sub_list = [] samples = data.shape[0] measure = 44.1 * 4 half_second = int(44.1 / denominator) mymod = int(samples / half_second) for i, d in enumerate(data): if i % mymod == 0: freq_sub_list.append(d) freq_list.append(freq_sub_list) freq_list = np.array(freq_list) print(freq_list[0, 2]) print(freq_list.shape) all_pitches = [] container = abjad.Container() for n in range(freq_list.shape[1]): pitches = [] for i, list_ in enumerate(freq_list): pitches.append(abjad.NamedPitch.from_hertz(freq_list[i, n])) chord = abjad.Chord("<e' g' c''>4") chord.written_duration = abjad.Duration(1, denominator) chord.written_pitches = pitches container.append(chord) print(abjad.lilypond(container)) voice = abjad.Voice() voice.append(container) abjad.show(voice) return container analysis = IracemaAnalysis("janela_cut.wav", 12, 8) # audio = iracema.Audio("janela_cut.wav") # # audio.play() # # audio.plot() # # specifying window and hop sizes # window, hop = 2048, 1024 # # calculating the FFT # fft = iracema.spectral.fft(audio, window, hop) # # plotting the spectrogram # # iracema.plot.plot_spectrogram(fft) # # calculating the RMS # rms = iracema.features.rms(audio, window, hop) # # plotting the RMS # # rms.plot() # # calculating the Peak Envelope # peak = iracema.features.peak_envelope(audio, window, hop) # # plotting the Peak Envelope # # peak.plot() # # extract pitch # hps_pitch = iracema.pitch.hps(fft, minf0=1, maxf0=1000) # #extract harmonics # harmonics = iracema.harmonics.extract(fft, hps_pitch, nharm=12) # # plot the harmonics over the spectrogram # # iracema.plot.plot_audio_spectrogram_harmonics( # # audio=audio, # # rms=rms, # # peak_envelope=peak, # # fft=fft, # # fzero=harmonics['frequency'], # # harmonics=harmonics['frequency'], # # fftlim=(0,12000) # # ) # # print(harmonics['frequency'].time) # x = harmonics['frequency'].data # y = harmonics['frequency'].time # print(x.shape, y.shape) # print(x[1].shape) # for n, data in enumerate(x): # print(n) # plt.plot(y, x[n]) # freq_list = [] # for n, data in enumerate(x): # if n != 0: # freq_sub_list = [] # samples = data.shape[0] # half_second = int(0.5 * 44.1) # mymod = int(samples/half_second) # for i, d in enumerate(data): # if i % mymod == 0: # freq_sub_list.append(d) # freq_list.append(freq_sub_list) # freq_list = np.array(freq_list) # print(freq_list[0, 2]) # print(freq_list.shape) # all_pitches = [] # container = abjad.Container() # for n in range(freq_list.shape[1]): # pitches = [] # for i, list_ in enumerate(freq_list): # pitches.append(abjad.NamedPitch.from_hertz(freq_list[i, n])) # chord = abjad.Chord("<e' g' c''>4") # chord.written_duration = abjad.Duration(1, 8) # chord.written_pitches = pitches # container.append(chord) # print(abjad.lilypond(container)) # voice = abjad.Voice(container) # abjad.show(voice)

23.871658

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0.167763

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0.028936

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0.869445

0

0.021589

0.190636

4,464

186

73

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0.602151

0

0.02845

0

1

0.022222

false

0

0.111111

0

0.155556

0.088889

0

null

0

1

0

null

0

6

b3fe24fa1c8f8d599cb66093c9c05f306ae98ca0

44

py

Python

asr_deepspeech/data/__init__.py

shangdibufashi/ASRDeepSpeech

f11134abb79e98062fbc25fab99ca4cf675e538b

[ "MIT" ]

44

2020-03-03T13:05:57.000Z

2022-03-24T03:42:31.000Z

asr_deepspeech/data/__init__.py

shangdibufashi/ASRDeepSpeech

f11134abb79e98062fbc25fab99ca4cf675e538b

[ "MIT" ]

6

2020-12-15T10:58:19.000Z

2021-10-12T01:59:17.000Z

asr_deepspeech/data/__init__.py

shangdibufashi/ASRDeepSpeech

f11134abb79e98062fbc25fab99ca4cf675e538b

[ "MIT" ]

13

2020-05-20T06:42:20.000Z

2022-03-24T03:42:31.000Z

from .noise_injection import NoiseInjection

22

43

0.886364

5

44

7.6

1

0

0.090909

44

1

44

0.95

0

1

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true

0

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0

1

0

null

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1

0

null

0

1

0

1

0

1

0

6

37607a4df1fb814e9da0a9a55dfbc83ae4adcd8d

182

py

Python

neighbor/admin.py

BRIGHTON-ASUMANI/Jirani-hood

7cf8e72e650bc613aa31ef346444a9d727d340da

[ "MIT" ]

1

2019-02-24T21:03:21.000Z

neighbor/admin.py

BRIGHTON-ASUMANI/Jirani-hood

7cf8e72e650bc613aa31ef346444a9d727d340da

[ "MIT" ]

1

2021-06-10T20:56:11.000Z

neighbor/admin.py

BRIGHTON-ASUMANI/Jirani-hood

7cf8e72e650bc613aa31ef346444a9d727d340da

[ "MIT" ]

1

2019-02-24T21:03:22.000Z

from django.contrib import admin from .models import Neighbourhood, Profile, Business admin.site.register(Neighbourhood) admin.site.register(Business) admin.site.register(Profile)

26

53

0.82967

23

182

6.565217

0.478261

0.178808

0.337748

0.331126

0

0.082418

182

6

54

30.333333

0.904192

0

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0

true

0

0.4

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0.4

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1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

3767ce0b076c20f0c9dd26abd935a7a6e6fe8281

169

py

Python

gunicorn.py

skavila/AddressParser

105493146a39096d1258cd18a938bee25f872ac1

[ "MIT" ]

null

gunicorn.py

skavila/AddressParser

105493146a39096d1258cd18a938bee25f872ac1

[ "MIT" ]

null

gunicorn.py

skavila/AddressParser

105493146a39096d1258cd18a938bee25f872ac1

[ "MIT" ]

null

import multiprocessing import os bind = os.getenv('SVC_BIND', '0.0.0.0:3000') workers = int(os.getenv('SVC_CONCURRENCY', 1)) threads = int(os.getenv('SVC_THREADS', 10))

28.166667

46

0.721893

28

169

4.25

0.5

0.201681

0.277311

0.235294

0

0.071895

0.094675

169

6

47

28.166667

0.705882

0

0.270588

0

1

0

false

0

0.4

0

0.4

0

1

0

null

1

0

1

0

null

0

1

0

6

806fde3433acb2e4c179edccfd84f1ebe9c6c743

92

py

Python

Python/tigre/utilities/io/__init__.py

tsadakane/TIGRE

a853cd2d4a6bc9509c01414b85ca75b4448fd700

[ "BSD-3-Clause" ]

326

2016-07-01T10:48:09.000Z

2022-03-20T07:34:52.000Z

Python/tigre/utilities/io/__init__.py

tsadakane/TIGRE

a853cd2d4a6bc9509c01414b85ca75b4448fd700

[ "BSD-3-Clause" ]

311

2016-07-05T16:00:06.000Z

2022-03-30T12:14:55.000Z

Python/tigre/utilities/io/__init__.py

tsadakane/TIGRE

a853cd2d4a6bc9509c01414b85ca75b4448fd700

[ "BSD-3-Clause" ]

157

2016-08-08T12:13:09.000Z

2022-03-17T00:37:45.000Z

from .NikonDataLoader import NikonDataLoader from .BrukerDataLoader import BrukerDataLoader

30.666667

46

0.891304

8

92

10.25

0.5

0

0.086957

92

2

47

46

0.97619

0

1

0

true

0

1

0

1

0

1

0

1

null

0

1

0

null

0

1

0

1

0

1

0

6

80b3e07dd600fe8c4c60ec4e34549ef6791e83c8

31,244

py

Python

dnarandombot.py

saintdanelimbu/Random-Bot

330e557ac609a0dbeba3a126adc7103abef3f08a

[ "MIT" ]

null

dnarandombot.py

saintdanelimbu/Random-Bot

330e557ac609a0dbeba3a126adc7103abef3f08a

[ "MIT" ]

null

dnarandombot.py

saintdanelimbu/Random-Bot

330e557ac609a0dbeba3a126adc7103abef3f08a

[ "MIT" ]

null

import discord import os import time from discord.utils import get from discord.ext import commands, tasks from discord.ext.commands import has_permissions, CheckFailure, check import random from random import randint from dhooks import Webhook,Embed import names client = discord.Client() client = commands.Bot(command_prefix = '-',case_insensitive=True) @client.event async def on_ready(): channel = client.get_channel(903690747197390888) await channel.send('Random Bot is UP!') print("bot online") ##GLOBE## @client.command() async def globe(ctx,howmany:int): def awitized(): numbers=[] for i in range(0,howmany): globe = ['0905','0906','0915','0916','0917','0926','0927','0935','0936','0937','0945','0953','0954','0955','0956','0965','0966','0967','0975','0977','0978','0979','0994','0995','0996','0997'] globenumber = random.choice(globe) randomized = f"{globenumber}{random.randint(1000000,9999999)}" numbers.append(randomized) return '\n'.join(str(e) for e in numbers) hook = Webhook('https://discord.com/api/webhooks/890607746481782815/HHIIWq6PrYTmkfGX-buMS92CGXDfZoek-2JvyfU2kFywge5jW3OcblFar6qMjTNNhD6g') embed = discord.Embed() embed.title='GLOBE' embed.colour = discord.Color.teal() embed.description= awitized() embed.set_footer(text=f'Requested by {ctx.author}',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_thumbnail(url='https://cdn.discordapp.com/attachments/814869462188556339/903697451754590218/globe-removebg-preview.png') embed.set_author(name='DNA Random Bot') await ctx.send(embed=embed) hook.send(embed=embed) ##SMART## @client.command() async def smart(ctx,howmany:int): def awitized(): numbers=[] for i in range(0,howmany): smart = ['0908','0918','0919','0920','0921','0928','0929','0939','0947','0949','0951','0961','0998','0999'] smartnumber = random.choice(smart) randomized = f"{smartnumber}{random.randint(1000000,9999999)}" numbers.append(randomized) return '\n'.join(str(e) for e in numbers) hook = Webhook('https://discord.com/api/webhooks/890607746481782815/HHIIWq6PrYTmkfGX-buMS92CGXDfZoek-2JvyfU2kFywge5jW3OcblFar6qMjTNNhD6g') embed = discord.Embed() embed.title='SMART' embed.colour = discord.Color.teal() embed.description= awitized() embed.set_footer(text=f'Requested by {ctx.author}',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_thumbnail(url='https://download.logo.wine/logo/Smart_Communications/Smart_Communications-Logo.wine.png') embed.set_author(name='DNA Random Bot') await ctx.send(embed=embed) hook.send(embed=embed) ##SUN## @client.command() async def sun(ctx,howmany:int): def awitized(): numbers=[] for i in range(0,howmany): sun = ['0922','0923','0924','0925','0931','0932','0933','0934','0940','0941','0942','0943','0973','0974'] sunnumber = random.choice(sun) randomized = f"{sunnumber}{random.randint(1000000,9999999)}" numbers.append(randomized) return '\n'.join(str(e) for e in numbers) hook = Webhook('https://discord.com/api/webhooks/890607746481782815/HHIIWq6PrYTmkfGX-buMS92CGXDfZoek-2JvyfU2kFywge5jW3OcblFar6qMjTNNhD6g') embed = discord.Embed() embed.title='SUN' embed.colour = discord.Color.teal() embed.description= awitized() embed.set_footer(text=f'Requested by {ctx.author}',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_thumbnail(url='https://static.wikia.nocookie.net/logopedia/images/7/7c/Sun_Cellular_logo.svg/revision/latest/scale-to-width-down/250?cb=20130911111111') embed.set_author(name='DNA Random Bot') await ctx.send(embed=embed) hook.send(embed=embed) ##TNT## @client.command() async def tnt(ctx,howmany:int): def awitized(): numbers=[] for i in range(0,howmany): tnt = ['0907','0909','0910','0912','0930','0938','0946','0948','0950'] tntnumber = random.choice(tnt) randomized = f"{tntnumber}{random.randint(1000000,9999999)}" numbers.append(randomized) return '\n'.join(str(e) for e in numbers) hook = Webhook('https://discord.com/api/webhooks/890607746481782815/HHIIWq6PrYTmkfGX-buMS92CGXDfZoek-2JvyfU2kFywge5jW3OcblFar6qMjTNNhD6g') embed = discord.Embed() embed.title='TNT' embed.colour = discord.Color.teal() embed.description= awitized() embed.set_footer(text=f'Requested by {ctx.author}',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_thumbnail(url='https://upload.wikimedia.org/wikipedia/commons/3/36/TNT_%28cellular_service%29_logo.png') embed.set_author(name='DNA Random Bot') await ctx.send(embed=embed) hook.send(embed=embed) ##ADDRESS## shit = open('words.txt').read().splitlines() ##NCR## @client.command() async def ncr(ctx,howmany:int): def awitized(): address=[] for i in range(0,howmany): ncr = open('ncr.txt').read().splitlines() randomnumber = random.randint(1,999) randomshit = random.choice(shit) randomncr =random.choice(ncr) address.append(f"{str(randomnumber)} {randomshit} Street {randomncr}") return '\n'.join(str(e) for e in address) hook = Webhook('https://discord.com/api/webhooks/890607746481782815/HHIIWq6PrYTmkfGX-buMS92CGXDfZoek-2JvyfU2kFywge5jW3OcblFar6qMjTNNhD6g') embed = discord.Embed() embed.title='NATIONAL CAPITAL REGION (NCR)' embed.colour = discord.Color.teal() embed.description= awitized() embed.set_footer(text=f'Requested by {ctx.author}',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_thumbnail(url='https://wallpaperaccess.com/full/503514.jpg') embed.set_author(name='DNA Random Bot') await ctx.send(embed=embed) hook.send(embed=embed) ## CORDILLERA ADMINISTRATIVE REGION ## @client.command() async def cordillera(ctx,howmany:int): def awitized(): address=[] for i in range(0,howmany): cordillera = open('cordillera.txt').read().splitlines() randomnumber = random.randint(1,999) randomshit = random.choice(shit) randomcordillera =random.choice(cordillera) address.append(f"{str(randomnumber)} {randomshit} Street {randomcordillera}") return '\n'.join(str(e) for e in address) hook = Webhook('https://discord.com/api/webhooks/890607746481782815/HHIIWq6PrYTmkfGX-buMS92CGXDfZoek-2JvyfU2kFywge5jW3OcblFar6qMjTNNhD6g') embed = discord.Embed() embed.title='CORDILLERA ADMINISTRATIVE REGION' embed.colour = discord.Color.teal() embed.description= awitized() embed.set_footer(text=f'Requested by {ctx.author}',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_thumbnail(url='https://wallpaperaccess.com/full/503514.jpg') embed.set_author(name='DNA Random Bot') await ctx.send(embed=embed) hook.send(embed=embed) ## REGION 1 (ILOCOS REGION)## @client.command() async def ilocosregion(ctx,howmany:int): def awitized(): address=[] for i in range(0,howmany): region1 = open('region1.txt').read().splitlines() randomnumber = random.randint(1,999) randomshit = random.choice(shit) randomregion1 =random.choice(region1) address.append(f"{str(randomnumber)} {randomshit} Street {randomregion1}") return '\n'.join(str(e) for e in address) hook = Webhook('https://discord.com/api/webhooks/890607746481782815/HHIIWq6PrYTmkfGX-buMS92CGXDfZoek-2JvyfU2kFywge5jW3OcblFar6qMjTNNhD6g') embed = discord.Embed() embed.title='REGION 1 (ILOCOS REGION)' embed.colour = discord.Color.teal() embed.description= awitized() embed.set_footer(text=f'Requested by {ctx.author}',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_thumbnail(url='https://wallpaperaccess.com/full/503514.jpg') embed.set_author(name='DNA Random Bot') await ctx.send(embed=embed) hook.send(embed=embed) ## REGION 2 (CAGAYAN REGION)## @client.command() async def cagayanregion(ctx,howmany:int): def awitized(): address=[] for i in range(0,howmany): region2 = open('region2.txt').read().splitlines() randomnumber = random.randint(1,999) randomshit = random.choice(shit) randomregion2 =random.choice(region2) address.append(f"{str(randomnumber)} {randomshit} Street {randomregion2}") return '\n'.join(str(e) for e in address) hook = Webhook('https://discord.com/api/webhooks/890607746481782815/HHIIWq6PrYTmkfGX-buMS92CGXDfZoek-2JvyfU2kFywge5jW3OcblFar6qMjTNNhD6g') embed = discord.Embed() embed.title='REGION 2 (CAGAYAN REGION)' embed.colour = discord.Color.teal() embed.description= awitized() embed.set_footer(text=f'Requested by {ctx.author}',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_thumbnail(url='https://wallpaperaccess.com/full/503514.jpg') embed.set_author(name='DNA Random Bot') await ctx.send(embed=embed) hook.send(embed=embed) ## REGION 3 (CENTRAL LUZON)## @client.command() async def centralluzon(ctx,howmany:int): def awitized(): address=[] for i in range(0,howmany): region3 = open('region3.txt').read().splitlines() randomnumber = random.randint(1,999) randomshit = random.choice(shit) randomregion3 =random.choice(region3) address.append(f"{str(randomnumber)} {randomshit} Street {randomregion3}") return '\n'.join(str(e) for e in address) hook = Webhook('https://discord.com/api/webhooks/890607746481782815/HHIIWq6PrYTmkfGX-buMS92CGXDfZoek-2JvyfU2kFywge5jW3OcblFar6qMjTNNhD6g') embed = discord.Embed() embed.title='REGION 3 (CENTRAL LUZON)' embed.colour = discord.Color.teal() embed.description= awitized() embed.set_footer(text=f'Requested by {ctx.author}',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_thumbnail(url='https://wallpaperaccess.com/full/503514.jpg') embed.set_author(name='DNA Random Bot') await ctx.send(embed=embed) hook.send(embed=embed) ## REGION 4 (CALABARZON)## @client.command() async def calabarzon(ctx,howmany:int): def awitized(): address=[] for i in range(0,howmany): calabarzon = open('calabarzon.txt').read().splitlines() randomnumber = random.randint(1,999) randomshit = random.choice(shit) randomcalabarzon =random.choice(calabarzon) address.append(f"{str(randomnumber)} {randomshit} Street {randomcalabarzon}") return '\n'.join(str(e) for e in address) hook = Webhook('https://discord.com/api/webhooks/890607746481782815/HHIIWq6PrYTmkfGX-buMS92CGXDfZoek-2JvyfU2kFywge5jW3OcblFar6qMjTNNhD6g') embed = discord.Embed() embed.title='REGION 4 (CALABARZON)' embed.colour = discord.Color.teal() embed.description= awitized() embed.set_footer(text=f'Requested by {ctx.author}',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_thumbnail(url='https://wallpaperaccess.com/full/503514.jpg') embed.set_author(name='DNA Random Bot') await ctx.send(embed=embed) hook.send(embed=embed) ## REGION 4 (MIMAROPA)## @client.command() async def mimaropa(ctx,howmany:int): def awitized(): address=[] for i in range(0,howmany): mimaropa = open('mimaropa.txt').read().splitlines() randomnumber = random.randint(1,999) randomshit = random.choice(shit) randommimaropa =random.choice(mimaropa) address.append(f"{str(randomnumber)} {randomshit} Street {randommimaropa}") return '\n'.join(str(e) for e in address) hook = Webhook('https://discord.com/api/webhooks/890607746481782815/HHIIWq6PrYTmkfGX-buMS92CGXDfZoek-2JvyfU2kFywge5jW3OcblFar6qMjTNNhD6g') embed = discord.Embed() embed.title='REGION 4 (MIMAROPA)' embed.colour = discord.Color.teal() embed.description= awitized() embed.set_footer(text=f'Requested by {ctx.author}',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_thumbnail(url='https://wallpaperaccess.com/full/503514.jpg') embed.set_author(name='DNA Random Bot') await ctx.send(embed=embed) hook.send(embed=embed) ## REGION 5 (BICOL REGION)## @client.command() async def bicolregion(ctx,howmany:int): def awitized(): address=[] for i in range(0,howmany): region5 = open('region5.txt').read().splitlines() randomnumber = random.randint(1,999) randomshit = random.choice(shit) randomregion5 =random.choice(region5) address.append(f"{str(randomnumber)} {randomshit} Street {randomregion5}") return '\n'.join(str(e) for e in address) hook = Webhook('https://discord.com/api/webhooks/890607746481782815/HHIIWq6PrYTmkfGX-buMS92CGXDfZoek-2JvyfU2kFywge5jW3OcblFar6qMjTNNhD6g') embed = discord.Embed() embed.title='REGION 5 (BICOL REGION)' embed.colour = discord.Color.teal() embed.description= awitized() embed.set_footer(text=f'Requested by {ctx.author}',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_thumbnail(url='https://wallpaperaccess.com/full/503514.jpg') embed.set_author(name='DNA Random Bot') await ctx.send(embed=embed) hook.send(embed=embed) ## REGION 6 (WESTERN VISAYAS)## @client.command() async def westernvisayas(ctx,howmany:int): def awitized(): address=[] for i in range(0,howmany): region6 = open('region6.txt').read().splitlines() randomnumber = random.randint(1,999) randomshit = random.choice(shit) randomregion6 =random.choice(region6) address.append(f"{str(randomnumber)} {randomshit} Street {randomregion6}") return '\n'.join(str(e) for e in address) hook = Webhook('https://discord.com/api/webhooks/890607746481782815/HHIIWq6PrYTmkfGX-buMS92CGXDfZoek-2JvyfU2kFywge5jW3OcblFar6qMjTNNhD6g') embed = discord.Embed() embed.title='REGION 6 (WESTERN VISAYAS)' embed.colour = discord.Color.teal() embed.description= awitized() embed.set_footer(text=f'Requested by {ctx.author}',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_thumbnail(url='https://wallpaperaccess.com/full/503514.jpg') embed.set_author(name='DNA Random Bot') await ctx.send(embed=embed) hook.send(embed=embed) ## REGION 7 (CENTRAL VISAYAS)## @client.command() async def centralvisayas(ctx,howmany:int): def awitized(): address=[] for i in range(0,howmany): region7 = open('region7.txt').read().splitlines() randomnumber = random.randint(1,999) randomshit = random.choice(shit) randomregion7 =random.choice(region7) address.append(f"{str(randomnumber)} {randomshit} Street {randomregion7}") return '\n'.join(str(e) for e in address) hook = Webhook('https://discord.com/api/webhooks/890607746481782815/HHIIWq6PrYTmkfGX-buMS92CGXDfZoek-2JvyfU2kFywge5jW3OcblFar6qMjTNNhD6g') embed = discord.Embed() embed.title='REGION 7 (CENTRAL VISAYAS)' embed.colour = discord.Color.teal() embed.description= awitized() embed.set_footer(text=f'Requested by {ctx.author}',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_thumbnail(url='https://wallpaperaccess.com/full/503514.jpg') embed.set_author(name='DNA Random Bot') await ctx.send(embed=embed) hook.send(embed=embed) ## REGION 8 (EASTERN VISAYAS)## @client.command() async def easternvisayas(ctx,howmany:int): def awitized(): address=[] for i in range(0,howmany): region8 = open('region8.txt').read().splitlines() randomnumber = random.randint(1,999) randomshit = random.choice(shit) randomregion8 =random.choice(region8) address.append(f"{str(randomnumber)} {randomshit} Street {randomregion8}") return '\n'.join(str(e) for e in address) hook = Webhook('https://discord.com/api/webhooks/890607746481782815/HHIIWq6PrYTmkfGX-buMS92CGXDfZoek-2JvyfU2kFywge5jW3OcblFar6qMjTNNhD6g') embed = discord.Embed() embed.title='REGION 8 (EASTERN VISAYAS)' embed.colour = discord.Color.teal() embed.description= awitized() embed.set_footer(text=f'Requested by {ctx.author}',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_thumbnail(url='https://wallpaperaccess.com/full/503514.jpg') embed.set_author(name='DNA Random Bot') await ctx.send(embed=embed) hook.send(embed=embed) ## REGION 9 (ZAMBOANGA PENINSULA)## @client.command() async def zamboanga(ctx,howmany:int): def awitized(): address=[] for i in range(0,howmany): region9 = open('region9.txt').read().splitlines() randomnumber = random.randint(1,999) randomshit = random.choice(shit) randomregion9 =random.choice(region9) address.append(f"{str(randomnumber)} {randomshit} Street {randomregion9}") return '\n'.join(str(e) for e in address) hook = Webhook('https://discord.com/api/webhooks/890607746481782815/HHIIWq6PrYTmkfGX-buMS92CGXDfZoek-2JvyfU2kFywge5jW3OcblFar6qMjTNNhD6g') embed = discord.Embed() embed.title='REGION 9 (ZAMBOANGA PENINSULA)' embed.colour = discord.Color.teal() embed.description= awitized() embed.set_footer(text=f'Requested by {ctx.author}',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_thumbnail(url='https://wallpaperaccess.com/full/503514.jpg') embed.set_author(name='DNA Random Bot') await ctx.send(embed=embed) hook.send(embed=embed) ## REGION 10 (NORTHERN MINDANAO)## @client.command() async def northernmindanao(ctx,howmany:int): def awitized(): address=[] for i in range(0,howmany): region10 = open('region10.txt').read().splitlines() randomnumber = random.randint(1,999) randomshit = random.choice(shit) randomregion10 =random.choice(region10) address.append(f"{str(randomnumber)} {randomshit} Street {randomregion10}") return '\n'.join(str(e) for e in address) hook = Webhook('https://discord.com/api/webhooks/890607746481782815/HHIIWq6PrYTmkfGX-buMS92CGXDfZoek-2JvyfU2kFywge5jW3OcblFar6qMjTNNhD6g') embed = discord.Embed() embed.title='REGION 10 (NORTHERN MINDANAO)' embed.colour = discord.Color.teal() embed.description= awitized() embed.set_footer(text=f'Requested by {ctx.author}',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_thumbnail(url='https://wallpaperaccess.com/full/503514.jpg') embed.set_author(name='DNA Random Bot') await ctx.send(embed=embed) hook.send(embed=embed) ## REGION 11 (DAVAO REGION)## @client.command() async def davaoregion(ctx,howmany:int): def awitized(): address=[] for i in range(0,howmany): region11 = open('region11.txt').read().splitlines() randomnumber = random.randint(1,999) randomshit = random.choice(shit) randomregion11 =random.choice(region11) address.append(f"{str(randomnumber)} {randomshit} Street {randomregion11}") return '\n'.join(str(e) for e in address) hook = Webhook('https://discord.com/api/webhooks/890607746481782815/HHIIWq6PrYTmkfGX-buMS92CGXDfZoek-2JvyfU2kFywge5jW3OcblFar6qMjTNNhD6g') embed = discord.Embed() embed.title='REGION 11 (DAVAO REGION)' embed.colour = discord.Color.teal() embed.description= awitized() embed.set_footer(text=f'Requested by {ctx.author}',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_thumbnail(url='https://wallpaperaccess.com/full/503514.jpg') embed.set_author(name='DNA Random Bot') await ctx.send(embed=embed) hook.send(embed=embed) ## REGION 12 (Soccsksargen)## @client.command() async def region12(ctx,howmany:int): def awitized(): address=[] for i in range(0,howmany): region12 = open('region12.txt').read().splitlines() randomnumber = random.randint(1,999) randomshit = random.choice(shit) randomregion12 =random.choice(region12) address.append(f"{str(randomnumber)} {randomshit} Street {randomregion12}") return '\n'.join(str(e) for e in address) hook = Webhook('https://discord.com/api/webhooks/890607746481782815/HHIIWq6PrYTmkfGX-buMS92CGXDfZoek-2JvyfU2kFywge5jW3OcblFar6qMjTNNhD6g') embed = discord.Embed() embed.title='REGION 12 (Soccsksargen)' embed.colour = discord.Color.teal() embed.description= awitized() embed.set_footer(text=f'Requested by {ctx.author}',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_thumbnail(url='https://wallpaperaccess.com/full/503514.jpg') embed.set_author(name='DNA Random Bot') await ctx.send(embed=embed) hook.send(embed=embed) ## REGION 13 (CARAGA)## @client.command() async def caraga(ctx,howmany:int): def awitized(): address=[] for i in range(0,howmany): region13 = open('region13.txt').read().splitlines() randomnumber = random.randint(1,999) randomshit = random.choice(shit) randomregion13 =random.choice(region13) address.append(f"{str(randomnumber)} {randomshit} Street {randomregion13}") return '\n'.join(str(e) for e in address) hook = Webhook('https://discord.com/api/webhooks/890607746481782815/HHIIWq6PrYTmkfGX-buMS92CGXDfZoek-2JvyfU2kFywge5jW3OcblFar6qMjTNNhD6g') embed = discord.Embed() embed.title='REGION 13 (CARAGA)' embed.colour = discord.Color.teal() embed.description= awitized() embed.set_footer(text=f'Requested by {ctx.author}',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_thumbnail(url='https://wallpaperaccess.com/full/503514.jpg') embed.set_author(name='DNA Random Bot') await ctx.send(embed=embed) hook.send(embed=embed) ##RANDOM NAME## ## FIRSTNAME## @client.command() async def firstname(ctx,howmany:int): def awitized(): name=[] for i in range(0,howmany): awit = names.get_first_name() name.append(awit) return '\n'.join(str(e) for e in name) hook = Webhook('https://discord.com/api/webhooks/890607746481782815/HHIIWq6PrYTmkfGX-buMS92CGXDfZoek-2JvyfU2kFywge5jW3OcblFar6qMjTNNhD6g') embed = discord.Embed() embed.title='FIRST NAME' embed.colour = discord.Color.teal() embed.description= awitized() embed.set_footer(text=f'Requested by {ctx.author}',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_thumbnail(url='https://media.discordapp.net/attachments/814869462188556339/904842661318520882/MALEFEMALE-removebg-preview.png') embed.set_author(name='DNA Random Bot') await ctx.send(embed=embed) hook.send(embed=embed) ## FIRSTNAME MALE## @client.command() async def firstname_male(ctx,howmany:int): def awitized(): name=[] for i in range(0,howmany): awit = names.get_first_name(gender='male') name.append(awit) return '\n'.join(str(e) for e in name) hook = Webhook('https://discord.com/api/webhooks/890607746481782815/HHIIWq6PrYTmkfGX-buMS92CGXDfZoek-2JvyfU2kFywge5jW3OcblFar6qMjTNNhD6g') embed = discord.Embed() embed.title='FIRST NAME (MALE)' embed.colour = discord.Color.teal() embed.description= awitized() embed.set_footer(text=f'Requested by {ctx.author}',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_thumbnail(url='https://upload.wikimedia.org/wikipedia/commons/thumb/4/4f/Mars-male-symbol-pseudo-3D-blue.svg/1200px-Mars-male-symbol-pseudo-3D-blue.svg.png') embed.set_author(name='DNA Random Bot') await ctx.send(embed=embed) hook.send(embed=embed) ## FIRSTNAME FEMALE## @client.command() async def firstname_female(ctx,howmany:int): def awitized(): name=[] for i in range(0,howmany): awit = names.get_first_name(gender='female') name.append(awit) return '\n'.join(str(e) for e in name) hook = Webhook('https://discord.com/api/webhooks/890607746481782815/HHIIWq6PrYTmkfGX-buMS92CGXDfZoek-2JvyfU2kFywge5jW3OcblFar6qMjTNNhD6g') embed = discord.Embed() embed.title='FIRST NAME (FEMALE)' embed.colour = discord.Color.teal() embed.description= awitized() embed.set_footer(text=f'Requested by {ctx.author}',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_thumbnail(url='https://upload.wikimedia.org/wikipedia/commons/thumb/2/24/Venus-female-symbol-pseudo-3D-pink.svg/1200px-Venus-female-symbol-pseudo-3D-pink.svg.png') embed.set_author(name='DNA Random Bot') await ctx.send(embed=embed) hook.send(embed=embed) lastname = open('lastname.txt').read().splitlines() ## FULLNAME## @client.command() async def fullname(ctx,howmany:int): def awitized(): name=[] for i in range(0,howmany): randomlastname = random.choice(lastname) awit = f"{names.get_first_name()} {randomlastname}" name.append(awit) return '\n'.join(str(e) for e in name) hook = Webhook('https://discord.com/api/webhooks/890607746481782815/HHIIWq6PrYTmkfGX-buMS92CGXDfZoek-2JvyfU2kFywge5jW3OcblFar6qMjTNNhD6g') embed = discord.Embed() embed.title='FULL NAME' embed.colour = discord.Color.teal() embed.description= awitized() embed.set_footer(text=f'Requested by {ctx.author}',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_thumbnail(url='https://media.discordapp.net/attachments/814869462188556339/904842661318520882/MALEFEMALE-removebg-preview.png') embed.set_author(name='DNA Random Bot') await ctx.send(embed=embed) hook.send(embed=embed) ## FULLNAME MALE## @client.command() async def fullname_male(ctx,howmany:int): def awitized(): name=[] for i in range(0,howmany): randomlastname = random.choice(lastname) awit = f"{names.get_first_name(gender='male')} {randomlastname}" name.append(awit) return '\n'.join(str(e) for e in name) hook = Webhook('https://discord.com/api/webhooks/890607746481782815/HHIIWq6PrYTmkfGX-buMS92CGXDfZoek-2JvyfU2kFywge5jW3OcblFar6qMjTNNhD6g') embed = discord.Embed() embed.title='FULL NAME (MALE)' embed.colour = discord.Color.teal() embed.description= awitized() embed.set_footer(text=f'Requested by {ctx.author}',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_thumbnail(url='https://upload.wikimedia.org/wikipedia/commons/thumb/4/4f/Mars-male-symbol-pseudo-3D-blue.svg/1200px-Mars-male-symbol-pseudo-3D-blue.svg.png') embed.set_author(name='DNA Random Bot') await ctx.send(embed=embed) hook.send(embed=embed) ## FULLNAME FEMALE## @client.command() async def fullname_female(ctx,howmany:int): def awitized(): name=[] for i in range(0,howmany): randomlastname = random.choice(lastname) awit = f"{names.get_first_name(gender='female')} {randomlastname}" name.append(awit) return '\n'.join(str(e) for e in name) hook = Webhook('https://discord.com/api/webhooks/890607746481782815/HHIIWq6PrYTmkfGX-buMS92CGXDfZoek-2JvyfU2kFywge5jW3OcblFar6qMjTNNhD6g') embed = discord.Embed() embed.title='FULL NAME (FEMALE)' embed.colour = discord.Color.teal() embed.description= awitized() embed.set_footer(text=f'Requested by {ctx.author}',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_thumbnail(url='https://upload.wikimedia.org/wikipedia/commons/thumb/2/24/Venus-female-symbol-pseudo-3D-pink.svg/1200px-Venus-female-symbol-pseudo-3D-pink.svg.png') embed.set_author(name='DNA Random Bot') await ctx.send(embed=embed) hook.send(embed=embed) ##HELP## client.remove_command('help') @client.command(pass_context=True) async def help(ctx): author = ctx.message.author embed = discord.Embed( description="""**RANDOM PHONE NUMBERS:** -globe (value) -smart (value) -sun (value) -tnt (value) **RANDOM ADDRESS:** -ncr (value) -cordillera (value) -ilocosregion (value) -cagayanregion (value) -centralluzon (value) -calabarzon (value) -bicolregion (value) -westernvisayas (value) -centralvisayas (value) -easternvisayas (value) -zamboanga (value) -northernmindanao(value) -davaoregion (value) -region12 (value) -caraga (value) **RANDOM NAME:** -firstname (value) -firstname_male (value) -firstname_female (value) -fullname (value) -fullname_male (value) -fullname_female (value) """ ) embed.colour = discord.Color.teal() embed.title='DNA HELP' embed.set_thumbnail(url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') embed.set_footer(text='Powered by DNA Solutions',icon_url='https://media.discordapp.net/attachments/814869462188556339/862375736416403496/DNA_Logo.png') await author.send(embed=embed) await ctx.send("Look at your DM's!") client.run("TOKEN")

47.628049

201

0.687364

3,653

31,244

5.83274

0.088694

0.030037

0.034824

0.032384

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0.768574

0

0.097144

0.173025

31,244

655

202

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false

0.001704

0.017036

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0.105622

0.001704

0

null

0

1

0

null

0

6

80ce485cd82b771ace6bf484cb0c7c81025c1390

42

py

Python

ttrw/dictionaries/__init__.py

ttomasz/ttrw

ef0418b4f9578ada38efc1d56711ba001e4466af

[ "MIT" ]

null

ttrw/dictionaries/__init__.py

ttomasz/ttrw

ef0418b4f9578ada38efc1d56711ba001e4466af

[ "MIT" ]

null

ttrw/dictionaries/__init__.py

ttomasz/ttrw

ef0418b4f9578ada38efc1d56711ba001e4466af

[ "MIT" ]

null

from .dict_loader import languages, words

21

41

0.833333

6

42

5.666667

1

0

0.119048

42

1

42

0.918919

0

1

0

true

0

1

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1

0

1

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null

0

1

0

null

0

1

0

1

0

1

0

6

80d38f6330b28a2063db82ec476697edd0660c8f

157

py

Python

laspy/__init__.py

Ellon/laspy

ad0a1a43f4e127c2b22a8d4b1e088cad58fd21f3

[ "BSD-2-Clause" ]

1

2020-02-26T20:55:13.000Z

laspy/__init__.py

Ellon/laspy

ad0a1a43f4e127c2b22a8d4b1e088cad58fd21f3

[ "BSD-2-Clause" ]

null

laspy/__init__.py

Ellon/laspy

ad0a1a43f4e127c2b22a8d4b1e088cad58fd21f3

[ "BSD-2-Clause" ]

1

2020-02-26T20:55:19.000Z

from __future__ import absolute_import __version__ = '1.5.0' from laspy import base from laspy import file from laspy import header from laspy import util

17.444444

38

0.808917

25

157

4.72

0.52

0.305085

0.508475

0

0.022727

0.159236

157

8

39

19.625

0.871212

0

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0

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false

0

0.833333

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0.833333

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1

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null

0

1

0

1

0

6

80ed44097a332bdb6c23530973c9d9c1a5844987

1,202

py

Python

conditional_statements_advanced/lab/trade_commissions.py

PetkoAndreev/Python-basics

a376362548380ae50c7c707551cb821547f44402

[ "MIT" ]

null

conditional_statements_advanced/lab/trade_commissions.py

PetkoAndreev/Python-basics

a376362548380ae50c7c707551cb821547f44402

[ "MIT" ]

null

conditional_statements_advanced/lab/trade_commissions.py

PetkoAndreev/Python-basics

a376362548380ae50c7c707551cb821547f44402

[ "MIT" ]

null

city = input() sales_quantity = float(input()) if city == 'Sofia' and 0 <= sales_quantity <= 500: print(f'{0.05*sales_quantity:.2f}') elif city == 'Sofia' and 500 < sales_quantity <= 1000: print(f'{0.07*sales_quantity:.2f}') elif city == 'Sofia' and 1000 < sales_quantity <= 10000: print(f'{0.08*sales_quantity:.2f}') elif city == 'Sofia' and sales_quantity > 10000: print(f'{0.12*sales_quantity:.2f}') elif city == 'Varna' and 0 <= sales_quantity <= 500: print(f'{0.045*sales_quantity:.2f}') elif city == 'Varna' and 500 < sales_quantity <= 1000: print(f'{0.075*sales_quantity:.2f}') elif city == 'Varna' and 1000 < sales_quantity <= 10000: print(f'{0.1*sales_quantity:.2f}') elif city == 'Varna' and sales_quantity > 10000: print(f'{0.13*sales_quantity:.2f}') elif city == 'Plovdiv' and 0 <= sales_quantity <= 500: print(f'{0.055*sales_quantity:.2f}') elif city == 'Plovdiv' and 500 < sales_quantity <= 1000: print(f'{0.08*sales_quantity:.2f}') elif city == 'Plovdiv' and 1000 < sales_quantity <= 10000: print(f'{0.12*sales_quantity:.2f}') elif city == 'Plovdiv' and sales_quantity > 10000: print(f'{0.145*sales_quantity:.2f}') else: print('error')

42.928571

58

0.65807

186

1,202

4.11828

0.16129

0.424282

0.109661

0.272846

0.881201

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0.214099

0.130548

0

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0.15391

1,202

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42.928571

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0

null

1

0

1

0

null

0

1

0

6

03c1e7127a155d14ddbc00cb0cf8d627d6c34ab2

106

py

Python

smart_recruiting_server/conf/environments/staging.py

mohseenrm/smart_recruiting_server

ac73c727b02d8f0c9d630d8bf867ed28a351e671

[ "MIT" ]

null

smart_recruiting_server/conf/environments/staging.py

mohseenrm/smart_recruiting_server

ac73c727b02d8f0c9d630d8bf867ed28a351e671

[ "MIT" ]

81

2019-06-17T20:09:28.000Z

2021-08-02T13:15:38.000Z

smart_recruiting_server/conf/environments/staging.py

mohseenrm/smart_recruiting_server

ac73c727b02d8f0c9d630d8bf867ed28a351e671

[ "MIT" ]

null

from smart_recruiting_server.conf.environments.base import BaseConfig class Config(BaseConfig): pass

21.2

69

0.830189

13

106

6.615385

0.923077

0

0.113208

106

4

70

26.5

0.914894

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true

0.333333

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null

0

1

0

null

0

1

0

1

0

6

03d244e644776ec40444dcc302aab13971a27de8

2,986

py

Python

previous/week7/discreteTimeModels.py

code-lab-org/sys611

3b8c46788dee629a9f2d6b7f84373e041b918ff0

[ "MIT" ]

3

2021-04-07T03:52:07.000Z

2022-03-04T18:16:16.000Z

previous/week7/discreteTimeModels.py

code-lab-org/sys611

3b8c46788dee629a9f2d6b7f84373e041b918ff0

[ "MIT" ]

null

previous/week7/discreteTimeModels.py

code-lab-org/sys611

3b8c46788dee629a9f2d6b7f84373e041b918ff0

[ "MIT" ]

6

2021-02-12T01:57:23.000Z

2022-03-04T18:05:27.000Z

""" SYS-611 Discrete Time Models. @author: Paul T. Grogan, pgrogan@stevens.edu """ # import the python3 behavior for importing, division, and printing in python2 from __future__ import absolute_import, division, print_function # import the matplotlib pyplot package and refer to it as `plt` # see http://matplotlib.org/api/pyplot_api.html for documentation import matplotlib.pyplot as plt #%% delay system example # define the input trajectory x = [1,1,0,0,1,0,0,0,1] # define the state update function def _delta(q, x): return x # define the output function def _lambda(q, x): return x # define the output and state trajectories y = [0,0,0,0,0,0,0,0,0] q = [0,0,0,0,0,0,0,0,0,0] # initialize the simulation t = 0 q[0] = 0 # execute the simulation while t <= 8: # record output value y[t] = _lambda(q[t], x[t]) # record state update q[t+1] = _delta(q[t], x[t]) # advance time t += 1 plt.figure() f, (ax1, ax2, ax3) = plt.subplots(3, sharex=True) ax1.bar(range(9), x, color='k') ax1.set_ylabel('Input ($x$)') ax2.bar(range(9), q[:-1], color='k') ax2.set_ylabel('State ($q$)') ax3.bar(range(9), y, color='k') ax3.set_ylabel('Output ($y$)') plt.xlabel('Time (ticks)') plt.suptitle('Delay System Model') #%% binary counter example # define the input trajectory x = [1,1,0,0,1,0,0,0,1] # define the state update function def _delta(q, x): return q != x # define the output function def _lambda(q, x): return q and x # define the output and state trajectories y = [0,0,0,0,0,0,0,0,0] q = [0,0,0,0,0,0,0,0,0,0] # initialize the simulation t = 0 q[0] = 0 # execute the simulation while t <= 8: # record output value y[t] = _lambda(q[t], x[t]) # record state update q[t+1] = _delta(q[t], x[t]) # advance time t += 1 plt.figure() f, (ax1, ax2, ax3) = plt.subplots(3, sharex=True) ax1.bar(range(9), x, color='k') ax1.set_ylabel('Input ($x$)') ax2.bar(range(9), q[:-1], color='k') ax2.set_ylabel('State ($q$)') ax3.bar(range(9), y, color='k') ax3.set_ylabel('Output ($y$)') plt.xlabel('Time (ticks)') plt.suptitle('Binary Counter Model') #%% delay flip-flop example # define the input trajectory x = [1,1,0,0,1,0,0,0,1] # define the state update function def _delta(q, x): return x # define the output function def _lambda(q): return q # define the output and state trajectories y = [0,0,0,0,0,0,0,0,0] q = [0,0,0,0,0,0,0,0,0,0] # initialize the simulation t = 0 q[0] = 0 # execute the simulation while t <= 8: # record output value y[t] = _lambda(q[t]) # record state update q[t+1] = _delta(q[t], x[t]) # advance time t += 1 plt.figure() f, (ax1, ax2, ax3) = plt.subplots(3, sharex=True) ax1.bar(range(9), x, color='k') ax1.set_ylabel('Input ($x$)') ax2.bar(range(9), q[:-1], color='k') ax2.set_ylabel('State ($q$)') ax3.bar(range(9), y, color='k') ax3.set_ylabel('Output ($y$)') plt.xlabel('Time (ticks)') plt.suptitle('Delay Flip-Flop Model')

22.118519

78

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542

2,986

3.446494

0.171587

0.067452

0.077088

0.083512

0.79015

0

0.061066

0.182853

2,986

135

79

22.118519

0.704508

0.350301

0

0.859155

0

0.108364

0

1

0.084507

false

0

0.028169

0.084507

0.197183

0.014085

0

null

0

1

0

1

0

null

0

6

20a72d76886e1d147bd8695fae0ef21419d26ee9

3,158

py

Python

src/bench/bench_counters.py

JohanSmet/lsim

144d86a68e183436db7d9364d1220580404a53c6

[ "BSD-3-Clause" ]

7

2020-09-17T11:26:47.000Z

2022-03-13T19:20:49.000Z

src/bench/bench_counters.py

JohanSmet/lsim

144d86a68e183436db7d9364d1220580404a53c6

[ "BSD-3-Clause" ]

null

src/bench/bench_counters.py

JohanSmet/lsim

144d86a68e183436db7d9364d1220580404a53c6

[ "BSD-3-Clause" ]

3

2020-09-17T11:26:52.000Z

2022-03-21T19:46:45.000Z

#!/usr/bin/env python3 import lsimpy from bench_utils import * def cycle_clock(sim, circuit): circuit.write_port("Clk", lsimpy.ValueTrue) sim.run_until_stable(2) circuit.write_port("Clk", lsimpy.ValueFalse) sim.run_until_stable(2) def test_bin_counter_4b(lsim): print("*** running BinCounter 4b") sim = lsim.sim() circuit_desc = lsim.user_library().circuit_by_name('BinCounter 4b') pins_D = [circuit_desc.port_by_name(f"D[{i:}]") for i in range(0,4)] pins_Y = [circuit_desc.port_by_name(f"Y[{i:}]") for i in range(0,4)] circuit = circuit_desc.instantiate(sim) sim.init() circuit.write_nibble(pins_D, 0) circuit.write_port("Load", lsimpy.ValueFalse) circuit.write_port("Clk", lsimpy.ValueFalse) circuit.write_port("Res", lsimpy.ValueTrue) circuit.write_port("En", lsimpy.ValueTrue) sim.run_until_stable(2) circuit.write_port("Res", lsimpy.ValueFalse) sim.run_until_stable(2) CHECK(circuit.read_nibble(pins_Y), 0, "reset") for i in range(1, 2**4): cycle_clock(sim, circuit) CHECK(circuit.read_nibble(pins_Y), i, "clock cycle") CHECK(circuit.read_port("RCO"), i == (2**4)-1, "") circuit.write_nibble(pins_D, 5) circuit.write_port("Load", lsimpy.ValueTrue) sim.run_until_stable(2) circuit.write_port("Load", lsimpy.ValueFalse) sim.run_until_stable(2) CHECK(circuit.read_nibble(pins_Y), 5, "after load") cycle_clock(sim, circuit) CHECK(circuit.read_nibble(pins_Y), 6, "increment") def test_bin_counter_8b(lsim): print("*** running BinCounter 8b") sim = lsim.sim() circuit_desc = lsim.user_library().circuit_by_name('BinCounter 8b') pins_D = [circuit_desc.port_by_name(f"D[{i:}]") for i in range(0,8)] pins_Y = [circuit_desc.port_by_name(f"Y[{i:}]") for i in range(0,8)] circuit = circuit_desc.instantiate(sim) sim.init() circuit.write_byte(pins_D, 0) circuit.write_port("Load", lsimpy.ValueFalse) circuit.write_port("Clk", lsimpy.ValueFalse) circuit.write_port("Res", lsimpy.ValueTrue) circuit.write_port("En", lsimpy.ValueTrue) sim.run_until_stable(2) circuit.write_port("Res", lsimpy.ValueFalse) sim.run_until_stable(2) CHECK(circuit.read_byte(pins_Y), 0, "reset") for i in range(1, 2**8): cycle_clock(sim, circuit) CHECK(circuit.read_byte(pins_Y), i, "clock cycle") CHECK(circuit.read_port("RCO"), i == (2**8)-1, "") circuit.write_byte(pins_D, 5) circuit.write_port("Load", lsimpy.ValueTrue) sim.run_until_stable(2) circuit.write_port("Load", lsimpy.ValueFalse) sim.run_until_stable(2) CHECK(circuit.read_byte(pins_Y), 5, "after load") cycle_clock(sim, circuit) CHECK(circuit.read_byte(pins_Y), 6, "increment") def main(): lsim = lsimpy.LSimContext() lsim.add_folder("examples", "../../examples") if (not lsim.load_user_library("examples/cpu_8bit/lib_counter.lsim")): print("Unable to load circuit\n") exit(-1) test_bin_counter_4b(lsim) test_bin_counter_8b(lsim) print_stats() if __name__ == "__main__": main()

32.22449

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0.674478

471

3,158

4.267516

0.165605

0.119403

0.127363

0.084577

0.859204

0.795025

0.768657

0.749751

0.699005

0

0.019563

0.174478

3,158

98

75

32.22449

0.751438

0.00665

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0.010838

0

1

0.052632

false

0

0.026316

0

0.078947

0.052632

0

null

0

1

0

null

0

6

459fe0d93752805ef952c32cdfc1a044a1280650

40

py

Python

eod/historical_prices/stock_price_data_api/__init__.py

gereon/eod-data

4286a03cc08bc8b5dc42ebae0bb8eb22bdfa3230

[ "Apache-2.0" ]

19

2021-09-18T11:31:45.000Z

2022-03-15T20:03:52.000Z

eod/historical_prices/stock_price_data_api/__init__.py

gereon/eod-data

4286a03cc08bc8b5dc42ebae0bb8eb22bdfa3230

[ "Apache-2.0" ]

2

2022-02-18T23:37:48.000Z

2022-03-01T18:14:06.000Z

eod/historical_prices/stock_price_data_api/__init__.py

gereon/eod-data

4286a03cc08bc8b5dc42ebae0bb8eb22bdfa3230

[ "Apache-2.0" ]

8

2021-09-13T16:49:52.000Z

2022-03-31T21:09:44.000Z

from .stock_prices import StockPriceData

40

0.9

5

40

7

1

0

0.075

40

1

40

0.945946

0

1

0

true

0

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1

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0

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0

null

0

1

0

1

0

1

0

6

45b70cb476eb7ce75e5e28f019c0947ad832b1e6

79

py

Python

search/test/test.py

fbennets/HCLC-GDPR-Bot

a26306e6593d8356a7a58dce32090ca21f30ac29

[ "MIT" ]

1

2021-06-04T15:57:11.000Z

search/test/test.py

fbennets/HCLC-GDPR-Bot

a26306e6593d8356a7a58dce32090ca21f30ac29

[ "MIT" ]

175

2020-06-10T23:33:08.000Z

2021-12-26T10:35:51.000Z

search/test/test.py

fbennets/HCLC-GDPR-Bot

a26306e6593d8356a7a58dce32090ca21f30ac29

[ "MIT" ]

2

2020-06-12T15:11:20.000Z

2021-06-13T10:37:35.000Z

from datenanfragen import search_company print(search_company("n62 gbhm", 10))

39.5

41

0.822785

11

79

5.727273

0.818182

0.412698

0

0.055556

0.088608

79

2

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39.5

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0.1

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true

0

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1

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1

0

null

0

1

0

1

0

1

0

6

afd783f4ba77640a6cb1abf30979d80cd56251b1

12,162

py

Python

lib/turkish_nltk/trnltk/morphology/contextless/parser/test/test_rootfinders.py

myasiny/wordembed

d4df516a4ac6eed71d1cc6e085638e895c525de6

[ "MIT" ]

null

lib/turkish_nltk/trnltk/morphology/contextless/parser/test/test_rootfinders.py

myasiny/wordembed

d4df516a4ac6eed71d1cc6e085638e895c525de6

[ "MIT" ]

null

lib/turkish_nltk/trnltk/morphology/contextless/parser/test/test_rootfinders.py

myasiny/wordembed

d4df516a4ac6eed71d1cc6e085638e895c525de6

[ "MIT" ]

null

# coding=utf-8 """ Copyright 2012 Ali Ok (aliokATapacheDOTorg) 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 unittest from hamcrest import * from mock import Mock from trnltk.morphology.model.lexeme import SecondarySyntacticCategory, SyntacticCategory from trnltk.morphology.contextless.parser.rootfinder import DigitNumeralRootFinder, ProperNounFromApostropheRootFinder, ProperNounWithoutApostropheRootFinder, WordRootFinder, TextNumeralRootFinder class WordRootFinderTest(unittest.TestCase): def setUp(self): mock_lexeme1_1 = Mock() mock_lexeme1_2 = Mock() mock_lexeme2_1 = Mock() mock_lexeme2_2 = Mock() mock_lexeme1_1.syntactic_category = SyntacticCategory.NOUN mock_lexeme1_2.syntactic_category = SyntacticCategory.NOUN mock_lexeme2_1.syntactic_category = SyntacticCategory.NOUN mock_lexeme2_2.syntactic_category = SyntacticCategory.NUMERAL self.mock_root1_1 = Mock() self.mock_root1_2 = Mock() self.mock_root2_1 = Mock() self.mock_root2_2 = Mock() self.mock_root1_1.lexeme = mock_lexeme1_1 self.mock_root1_2.lexeme = mock_lexeme1_2 self.mock_root2_1.lexeme = mock_lexeme2_1 self.mock_root2_2.lexeme = mock_lexeme2_2 lexeme_map = {u'root1' : [self.mock_root1_1, self.mock_root1_2], u'root2': [self.mock_root2_1, self.mock_root2_2]} self.root_finder = WordRootFinder(lexeme_map) def test_should_find_roots(self): roots = self.root_finder.find_roots_for_partial_input(u"root1") assert_that(roots, has_length(2)) assert_that(roots, has_items(self.mock_root1_1, self.mock_root1_2)) roots = self.root_finder.find_roots_for_partial_input(u"root2") assert_that(roots, has_length(1)) assert_that(roots, has_items(self.mock_root2_1)) roots = self.root_finder.find_roots_for_partial_input(u"UNDEFINED") assert_that(roots, has_length(0)) class TextNumeralRootFinderTest(unittest.TestCase): def setUp(self): mock_lexeme1_1 = Mock() mock_lexeme1_2 = Mock() mock_lexeme2_1 = Mock() mock_lexeme2_2 = Mock() mock_lexeme1_1.syntactic_category = SyntacticCategory.NUMERAL mock_lexeme1_2.syntactic_category = SyntacticCategory.NUMERAL mock_lexeme2_1.syntactic_category = SyntacticCategory.NUMERAL mock_lexeme2_2.syntactic_category = SyntacticCategory.NOUN self.mock_root1_1 = Mock() self.mock_root1_2 = Mock() self.mock_root2_1 = Mock() self.mock_root2_2 = Mock() self.mock_root1_1.lexeme = mock_lexeme1_1 self.mock_root1_2.lexeme = mock_lexeme1_2 self.mock_root2_1.lexeme = mock_lexeme2_1 self.mock_root2_2.lexeme = mock_lexeme2_2 lexeme_map = {u'root1' : [self.mock_root1_1, self.mock_root1_2], u'root2': [self.mock_root2_1, self.mock_root2_2]} self.root_finder = TextNumeralRootFinder(lexeme_map) def test_should_find_roots(self): roots = self.root_finder.find_roots_for_partial_input(u"root1") assert_that(roots, has_length(2)) assert_that(roots, has_items(self.mock_root1_1, self.mock_root1_2)) roots = self.root_finder.find_roots_for_partial_input(u"root2") assert_that(roots, has_length(1)) assert_that(roots, has_items(self.mock_root2_1)) roots = self.root_finder.find_roots_for_partial_input(u"UNDEFINED") assert_that(roots, has_length(0)) class DigitNumeralRootFinderTest(unittest.TestCase): def setUp(self): self.root_finder = DigitNumeralRootFinder() def test_should_recognize_number_roots(self): roots = self.root_finder.find_roots_for_partial_input(u'3') assert_that(roots[0].str, equal_to(u'3')) roots = self.root_finder.find_roots_for_partial_input(u'0') assert_that(roots[0].str, equal_to(u'0')) roots = self.root_finder.find_roots_for_partial_input(u'-1') assert_that(roots[0].str, equal_to(u'-1')) roots = self.root_finder.find_roots_for_partial_input(u'+3') assert_that(roots[0].str, equal_to(u'+3')) roots = self.root_finder.find_roots_for_partial_input(u'3,5') assert_that(roots[0].str, equal_to(u'3,5')) roots = self.root_finder.find_roots_for_partial_input(u'-999999999999,12345678901') assert_that(roots[0].str, equal_to(u'-999999999999,12345678901')) roots = self.root_finder.find_roots_for_partial_input(u'+2.999.999.999.999,12345678901') assert_that(roots[0].str, equal_to(u'+2.999.999.999.999,12345678901')) class ProperNounFromApostropheRootFinderTest(unittest.TestCase): def setUp(self): self.root_finder = ProperNounFromApostropheRootFinder() def test_should_recognize_abbreviations(self): roots = self.root_finder.find_roots_for_partial_input(u"TR'") assert_that(roots[0].str, equal_to(u'TR')) assert_that(roots[0].lexeme.secondary_syntactic_category, equal_to(SecondarySyntacticCategory.ABBREVIATION)) roots = self.root_finder.find_roots_for_partial_input(u"MB'") assert_that(roots[0].str, equal_to(u'MB')) assert_that(roots[0].lexeme.secondary_syntactic_category, equal_to(SecondarySyntacticCategory.ABBREVIATION)) roots = self.root_finder.find_roots_for_partial_input(u"POL'") assert_that(roots[0].str, equal_to(u'POL')) assert_that(roots[0].lexeme.secondary_syntactic_category, equal_to(SecondarySyntacticCategory.ABBREVIATION)) roots = self.root_finder.find_roots_for_partial_input(u"KAFA1500'") assert_that(roots[0].str, equal_to(u'KAFA1500')) assert_that(roots[0].lexeme.secondary_syntactic_category, equal_to(SecondarySyntacticCategory.ABBREVIATION)) roots = self.root_finder.find_roots_for_partial_input(u"1500KAFA'") assert_that(roots[0].str, equal_to(u'1500KAFA')) assert_that(roots[0].lexeme.secondary_syntactic_category, equal_to(SecondarySyntacticCategory.ABBREVIATION)) roots = self.root_finder.find_roots_for_partial_input(u"İŞÇĞÜÖ'") assert_that(roots[0].str, equal_to(u'İŞÇĞÜÖ')) assert_that(roots[0].lexeme.secondary_syntactic_category, equal_to(SecondarySyntacticCategory.ABBREVIATION)) roots = self.root_finder.find_roots_for_partial_input(u"123'") assert_that(roots, has_length(0)) def test_should_recognize_proper_nouns(self): roots = self.root_finder.find_roots_for_partial_input(u"Ahmet'") assert_that(roots[0].str, equal_to(u'Ahmet')) assert_that(roots[0].lexeme.secondary_syntactic_category, equal_to(SecondarySyntacticCategory.PROPER_NOUN)) roots = self.root_finder.find_roots_for_partial_input(u"Mehmed'") assert_that(roots[0].str, equal_to(u'Mehmed')) assert_that(roots[0].lexeme.secondary_syntactic_category, equal_to(SecondarySyntacticCategory.PROPER_NOUN)) roots = self.root_finder.find_roots_for_partial_input(u"A123a'") assert_that(roots[0].str, equal_to(u'A123a')) assert_that(roots[0].lexeme.secondary_syntactic_category, equal_to(SecondarySyntacticCategory.PROPER_NOUN)) roots = self.root_finder.find_roots_for_partial_input(u"AvA'") assert_that(roots[0].str, equal_to(u'AvA')) assert_that(roots[0].lexeme.secondary_syntactic_category, equal_to(SecondarySyntacticCategory.PROPER_NOUN)) roots = self.root_finder.find_roots_for_partial_input(u"AAxxAA'") assert_that(roots[0].str, equal_to(u'AAxxAA')) assert_that(roots[0].lexeme.secondary_syntactic_category, equal_to(SecondarySyntacticCategory.PROPER_NOUN)) roots = self.root_finder.find_roots_for_partial_input(u"İstanbul'") assert_that(roots[0].str, equal_to(u'İstanbul')) assert_that(roots[0].lexeme.secondary_syntactic_category, equal_to(SecondarySyntacticCategory.PROPER_NOUN)) roots = self.root_finder.find_roots_for_partial_input(u"Çanakkale'") assert_that(roots[0].str, equal_to(u'Çanakkale')) assert_that(roots[0].lexeme.secondary_syntactic_category, equal_to(SecondarySyntacticCategory.PROPER_NOUN)) roots = self.root_finder.find_roots_for_partial_input(u"Ömer'") assert_that(roots[0].str, equal_to(u'Ömer')) assert_that(roots[0].lexeme.secondary_syntactic_category, equal_to(SecondarySyntacticCategory.PROPER_NOUN)) roots = self.root_finder.find_roots_for_partial_input(u"Şaban'") assert_that(roots[0].str, equal_to(u'Şaban')) assert_that(roots[0].lexeme.secondary_syntactic_category, equal_to(SecondarySyntacticCategory.PROPER_NOUN)) roots = self.root_finder.find_roots_for_partial_input(u"Ümmühan'") assert_that(roots[0].str, equal_to(u'Ümmühan')) assert_that(roots[0].lexeme.secondary_syntactic_category, equal_to(SecondarySyntacticCategory.PROPER_NOUN)) roots = self.root_finder.find_roots_for_partial_input(u"aaa'") assert_that(roots, has_length(0)) roots = self.root_finder.find_roots_for_partial_input(u"aAAAA'") assert_that(roots, has_length(0)) roots = self.root_finder.find_roots_for_partial_input(u"1aa'") assert_that(roots, has_length(0)) roots = self.root_finder.find_roots_for_partial_input(u"a111'") assert_that(roots, has_length(0)) roots = self.root_finder.find_roots_for_partial_input(u"şaa'") assert_that(roots, has_length(0)) class ProperNounWithoutApostropheRootFinderTest(unittest.TestCase): def setUp(self): self.root_finder = ProperNounWithoutApostropheRootFinder() def test_should_recognize_proper_nouns(self): roots = self.root_finder.find_roots_for_partial_input(u"A", u"Ali") assert_that(roots[0].str, equal_to(u'A')) assert_that(roots[0].lexeme.secondary_syntactic_category, equal_to(SecondarySyntacticCategory.PROPER_NOUN)) roots = self.root_finder.find_roots_for_partial_input(u"Al", u"Ali") assert_that(roots[0].str, equal_to(u'Al')) assert_that(roots[0].lexeme.secondary_syntactic_category, equal_to(SecondarySyntacticCategory.PROPER_NOUN)) roots = self.root_finder.find_roots_for_partial_input(u"Ali", u"Ali") assert_that(roots[0].str, equal_to(u'Ali')) assert_that(roots[0].lexeme.secondary_syntactic_category, equal_to(SecondarySyntacticCategory.PROPER_NOUN)) roots = self.root_finder.find_roots_for_partial_input(u"Ali8", u"Ali8912") assert_that(roots[0].str, equal_to(u'Ali8')) assert_that(roots[0].lexeme.secondary_syntactic_category, equal_to(SecondarySyntacticCategory.PROPER_NOUN)) def test_should_not_recognize_proper_nouns_when_the_input_is_not(self): roots = self.root_finder.find_roots_for_partial_input(u"A", u"Ali'nin") assert_that(roots, has_length(0)) roots = self.root_finder.find_roots_for_partial_input(u"Al", u"Ali'nin") assert_that(roots, has_length(0)) roots = self.root_finder.find_roots_for_partial_input(u"Ali", u"Ali'nin") assert_that(roots, has_length(0)) roots = self.root_finder.find_roots_for_partial_input(u"Ali8", u"Ali8912'nin") assert_that(roots, has_length(0)) roots = self.root_finder.find_roots_for_partial_input(u"a", u"aa") assert_that(roots, has_length(0)) roots = self.root_finder.find_roots_for_partial_input(u"Ali'nin", u"Ali'nin") assert_that(roots, has_length(0)) roots = self.root_finder.find_roots_for_partial_input(u"123A", u"123A") assert_that(roots, has_length(0)) if __name__ == '__main__': unittest.main()

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null

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b3012d106372b72e9888ee946ff09fafe9394bf1

163

py

Python

terra_sdk/util/converter.py

fabio-nukui/terra.py

adee2e1abf41a05a1c39d52b664bd7cf7c9bc975

[ "MIT" ]

null

terra_sdk/util/converter.py

fabio-nukui/terra.py

adee2e1abf41a05a1c39d52b664bd7cf7c9bc975

[ "MIT" ]

null

terra_sdk/util/converter.py

fabio-nukui/terra.py

adee2e1abf41a05a1c39d52b664bd7cf7c9bc975

[ "MIT" ]

null

from datetime import datetime def to_isoformat(dt: datetime) -> str: return dt.isoformat(timespec="milliseconds").replace("+00:00", "Z").replace("000Z", "Z")

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b318b9b7d5fc786b6110867d33de9ddb143886a2

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py

Python

app/__init__.py

matheusnalmeida/Sistema-de-transporte-de-passageiros

e7c67586af0f814def990690a8389ca90d64fba0

[ "MIT" ]

null

app/__init__.py

matheusnalmeida/Sistema-de-transporte-de-passageiros

e7c67586af0f814def990690a8389ca90d64fba0

[ "MIT" ]

null

app/__init__.py

matheusnalmeida/Sistema-de-transporte-de-passageiros

e7c67586af0f814def990690a8389ca90d64fba0

[ "MIT" ]

null

from app.app import create_app

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2fd8c746105bb3cdbdbf4a26ea7db1d91dd65b0a

27,327

py

Python

messengerext/gallery/tests.py

groupsome/groupsome

4edcf30d66ff458c4df37d3198ef187219a768d7

[ "MIT" ]

6

2016-10-07T13:43:17.000Z

2017-10-07T22:34:44.000Z

messengerext/gallery/tests.py

groupsome/groupsome

4edcf30d66ff458c4df37d3198ef187219a768d7

[ "MIT" ]

null

messengerext/gallery/tests.py

groupsome/groupsome

4edcf30d66ff458c4df37d3198ef187219a768d7

[ "MIT" ]

1

2020-07-15T04:29:31.000Z

from django.test import TestCase, RequestFactory from django.core.urlresolvers import resolve, reverse from django.test.client import Client from django.contrib.auth.models import User from home import models from bot.models import TelegramUser from gallery.models import Album from gallery import queries from groups.tests import create_user, create_group, create_photo, create_album import mock import json class TestAlbumsOverview(TestCase): user = None group = None album = None def create_album_and_photos(self, user): photo = models.Photo.create_and_save(user=self.user, group=self.group, timestamp="2016-05-25 12:59:10", file="1.jpg", thumbnail="TODO") photo_2 = models.Photo.create_and_save(user=self.user, group=self.group, timestamp="2016-05-25 12:59:10", file="2.jpg", thumbnail="TODO") self.album = Album.create_and_save(name='Best of Croatia', description='only the best', group=self.group) self.album.photos.add(photo) self.album.photos.add(photo_2) def create_empty_album(self, user): self.album = Album.create_and_save(name='Best of Croatia', description='only the best', group=self.group) def setUp(self): self.factory = RequestFactory() self.client = Client() self.user = create_user() self.unprivileged_user = create_user("Unprivileged", "unpriv@test.test") self.group = create_group(self.user, is_admin=True, users=[self.unprivileged_user]) def test_gallery_shows_template(self): self.client.force_login(user=self.user) response = self.client.get(reverse('groups:photo_overview', kwargs={"group_id": self.group.id})) self.assertTemplateUsed(response=response, template_name='gallery/group/overview.html') def test_gallery_returns_album(self): self.create_album_and_photos(self.user) self.client.force_login(user=self.user) response = self.client.get(reverse('groups:photo_album', kwargs={"group_id": self.group.id, "album_id": self.album.id})) self.assertEquals(response.context['album'], self.album) def test_gallery_counts_photos_in_an_album(self): self.create_album_and_photos(self.user) self.client.force_login(user=self.user) response = self.client.get(reverse('groups:photo_overview', kwargs={"group_id": self.group.id})) self.assertEquals(response.context['albums'][0]['photo_count'], 2) def test_gallery_serves_media_url_for_title_photo(self): self.create_album_and_photos(self.user) self.client.force_login(user=self.user) response = self.client.get(reverse('groups:photo_overview', kwargs={"group_id": self.group.id})) self.assertTrue(response.context['albums'][0]['photo_file'].find('/media/photo/1') != -1) def test_gallery_uses_placeholder_for_empty_albums(self): self.create_empty_album(self.user) self.client.force_login(user=self.user) response = self.client.get(reverse('groups:photo_overview', kwargs={"group_id": self.group.id})) self.assertEquals(response.context['albums'][0]['photo_file'], '/static/img/add-pictures.jpg') @mock.patch("gallery.models.Album.create_and_save") def test_create_album_redirect(self, create_and_save): self.client.force_login(user=self.user) response = self.client.post( reverse('groups:photo_overview', kwargs={"group_id": self.group.id}), {'new_album': 'true', 'album_group': '1', 'album_name': 'Awesome Album', 'album_description': 'this is a album description'}, follow=True) self.assertRedirects(response, reverse('groups:photo_overview', kwargs={"group_id": self.group.id})) @mock.patch("gallery.models.Album.create_and_save") def test_create_album_unprivileged(self, create_and_save): self.create_album_and_photos(user=self.user) self.client.force_login(user=self.unprivileged_user) response = self.client.post( reverse('groups:photo_overview', kwargs={"group_id": self.group.id}), {'new_album': 'true', 'album_group': self.group.id, 'album_name': 'Awesome Album', 'album_description': 'this is a album description'}, follow=True) self.assertEquals(403, response.status_code) @mock.patch("gallery.models.Album.create_and_save") def test_create_album_works_with_valid_input(self, create_and_save): self.create_album_and_photos(user=self.user) self.client.force_login(user=self.user) response = self.client.post( reverse('groups:photo_overview', kwargs={"group_id": self.group.id}), {'new_album': 'true', 'album_group': self.group.id, 'album_name': 'Awesome Album', 'album_description': 'this is a album description'}, follow=True) create_and_save.assert_called() @mock.patch("gallery.models.Album.create_and_save") def test_no_album_created_with_too_short_name(self, create_and_save): self.client.force_login(user=self.user) response = self.client.post( reverse('groups:photo_overview', kwargs={"group_id": self.group.id}), {'new_album': 'true', 'album_group': '1', 'album_name': 'A', 'album_description': 'this is a album description'}, follow=True) create_and_save.assert_not_called() @mock.patch("gallery.models.Album.create_and_save") def test_no_album_created_without_description(self, create_and_save): self.client.force_login(user=self.user) response = self.client.post( reverse('groups:photo_overview', kwargs={"group_id": self.group.id}), {'new_album': 'true', 'album_group': '1', 'album_name': 'A', 'album_description': ''}, follow=True) create_and_save.assert_not_called() @mock.patch("gallery.models.Album.delete", ) def test_delete_album_unprivileged(self, delete): self.create_album_and_photos(user=self.user) self.client.force_login(user=self.unprivileged_user) response = self.client.post( reverse('groups:photo_overview', kwargs={"group_id": self.group.id}), {'delete_album': 'true', 'album_id': self.album.id}, follow=True) self.assertEquals(403, response.status_code) @mock.patch("gallery.models.Album.delete", ) def test_delete_album_works(self, delete): self.create_album_and_photos(user=self.user) self.client.force_login(user=self.user) response = self.client.post( reverse('groups:photo_overview', kwargs={"group_id": self.group.id}), {'delete_album': 'true', 'album_id': self.album.id}, follow=True) delete.assert_called() @mock.patch("gallery.models.Album.delete") def test_delete_album_is_forbidden_when_album_is_wrong(self, delete): self.client.force_login(user=self.user) response = self.client.post( reverse('groups:photo_overview', kwargs={"group_id": self.group.id}), {'delete_album': 'true', 'album_id': '-1'}, follow=True) delete.assert_not_called() class TestPhotoAlbum(TestCase): group = None group2 = None album = None album2 = None def create_user(self): user = User.objects.create_user('Superuser', 'superuser@super.com', 'Password') user.save() TelegramUser.create_and_save(user=user, telegram_id=1) return user def create_album_and_photos(self, user): self.group = models.Group.create_and_save(name="Croatia 2016", picture="", description="abc", telegram_id=3) self.group.users.add(self.user) self.group2 = models.Group.create_and_save(name="Croatia 2016", picture="", description="abc", telegram_id=4) photo = models.Photo.create_and_save(user=self.user, group=self.group, timestamp="2016-05-25 12:59:10", file="1.jpg", thumbnail="TODO") photo_2 = models.Photo.create_and_save(user=self.user, group=self.group, timestamp="2016-05-25 12:59:10", file="2.jpg", thumbnail="TODO") self.album = Album.create_and_save(name='Best of Croatia', description='only the best', group=self.group) self.album.photos.add(photo) self.album.photos.add(photo_2) user2 = User.objects.create_user('user2', 'user2@super.com', 'Password') user2.save() self.album2 = Album.create_and_save(name='Not this album', description='never', group=self.group2) def setUp(self): self.factory = RequestFactory() self.client = Client() self.user = self.create_user() self.create_album_and_photos(self.user) def test_album_detail_uses_template(self): self.client.force_login(user=self.user) response = self.client.get(reverse('groups:photo_album', kwargs={"group_id": self.group.id, "album_id": self.album.id})) self.assertTemplateUsed(response=response, template_name='gallery/group/album.html') def test_album_from_other_user_is_not_accessible(self): self.client.force_login(user=self.user) response = self.client.get(reverse('groups:photo_album', kwargs={"group_id": self.group2.id, "album_id": self.album2.id})) self.assertEquals(response.status_code, 403) def test_view_returns_pictures(self): self.client.force_login(user=self.user) response = self.client.get(reverse('groups:photo_album', kwargs={"group_id": self.group.id, "album_id": self.album.id})) self.assertEquals(len(response.context['photos']), 2) def test_view_returns_only_other_albums_than_itself(self): self.client.force_login(user=self.user) response = self.client.get(reverse('groups:photo_album', kwargs={"group_id": self.group.id, "album_id": self.album.id})) self.assertEquals(len(response.context['albums']), 0) class TestAddPhotoView(TestCase): group = None album = None photo = None photo_2 = None def create_album_and_photos(self, user): self.group = models.Group.create_and_save(name="Croatia 2016", picture="", description="abc", telegram_id=3, everyone_is_admin=False) self.group.users.add(self.user) self.group.users.add(self.unprivileged_user) self.group.admins.add(self.user) group_2 = models.Group.create_and_save(name="Not allowed to add photo from here", picture="", description="abc", telegram_id=4) self.photo = models.Photo.create_and_save(user=self.user, group=self.group, timestamp="2016-05-25 12:59:10", file="1.jpg", thumbnail="TODO") self.photo_2 = models.Photo.create_and_save(user=self.user, group=group_2, timestamp="2016-05-25 12:59:10", file="2.jpg", thumbnail="TODO") self.album = Album.create_and_save(name='Best of Croatia', description='only the best', group=self.group) def setUp(self): self.factory = RequestFactory() self.client = Client() self.user = create_user() self.unprivileged_user = create_user("Unprivileged", "unpriv@test.test") self.create_album_and_photos(self.user) def test_add_photo_to_album_unprivileged(self): self.client.force_login(user=self.unprivileged_user) response = self.client.post( '/gallery/' + str(self.group.id) + '/' + str(self.album.id) + '/add/' + str(self.photo.id)) self.assertEqual(response.status_code, 403) def test_add_photo_to_album(self): self.client.force_login(user=self.user) response = self.client.post( '/gallery/' + str(self.group.id) + '/' + str(self.album.id) + '/add/' + str(self.photo.id)) self.assertEqual(response.status_code, 200) self.assertEquals( response.content, b'{"message": "Added image successfully"}' ) def test_can_not_add_photo_to_album_from_a_group_user_is_not_in(self): self.client.force_login(user=self.user) response = self.client.post( '/gallery/' + str(self.group.id) + '/' + str(self.album.id) + '/add/' + str(self.photo_2.id)) self.assertEqual(response.status_code, 200) self.assertEquals( response.content, b'{"message": "Something went wrong"}' ) def test_add_photo_to_album(self): self.client.force_login(user=self.user) response = self.client.post( '/gallery/' + str(self.group.id) + '/' + str(self.album.id) + '/add/' + str(self.photo.id)) self.assertEqual(response.status_code, 200) response = self.client.post( '/gallery/' + str(self.group.id) + '/' + str(self.album.id) + '/add/' + str(self.photo.id)) self.assertEquals( response.content, b'{"message": "Already in album"}' ) class TestDeletePhotoFromAlbumView(TestCase): group = None album = None album_2 = None photo = None photo_2 = None def create_album_and_photos(self, user): self.group = models.Group.create_and_save(name="Croatia 2016", picture="", description="abc", telegram_id=3, everyone_is_admin=False) self.group.users.add(self.user) self.group.users.add(self.unprivileged_user) self.group.admins.add(self.user) group_2 = models.Group.create_and_save(name="Not allowed to add photo from here", picture="", description="abc", telegram_id=4) self.photo = models.Photo.create_and_save(user=self.user, group=self.group, timestamp="2016-05-25 12:59:10", file="1.jpg", thumbnail="TODO") self.photo_2 = models.Photo.create_and_save(user=self.user, group=self.group, timestamp="2016-05-25 12:59:11", file="2.jpg", thumbnail="TODO") self.album = Album.create_and_save(name='Best of Croatia', description='only the best', group=self.group) self.album.photos.add(self.photo) self.album.photos.add(self.photo_2) self.album_2 = Album.create_and_save(name='Another album', description='no photo removed from here', group=group_2) self.album_2.photos.add(self.photo_2) def setUp(self): self.factory = RequestFactory() self.client = Client() self.user = create_user() self.unprivileged_user = create_user("Unprivileged", "unpriv@test.test") self.create_album_and_photos(self.user) def test_delete_photo_from_album_unprivileged(self): self.client.force_login(user=self.unprivileged_user) response = self.client.post( '/gallery/' + str(self.group.id) + '/' + str(self.album.id) + '/delete_from_album/' + str(self.photo.id)) self.assertEqual(response.status_code, 403) def test_delete_photo_to_album(self): self.client.force_login(user=self.user) response = self.client.post( '/gallery/' + str(self.group.id) + '/' + str(self.album.id) + '/delete_from_album/' + str(self.photo.id)) self.assertEqual(response.status_code, 200) self.assertEquals( response.content, b'{"message": "Successfully removed from album"}' ) def test_can_not_delete_photo_from_album_of_a_group_the_user_is_not_in(self): self.client.force_login(user=self.user) response = self.client.post( '/gallery/' + str(self.group.id) + '/' + str(self.album_2.id) + '/delete_from_album/' + str( self.photo_2.id)) self.assertEqual(response.status_code, 200) self.assertEquals( response.content, b'{"message": "Something went wrong"}' ) def test_photo_is_removed_from_album(self): self.client.force_login(user=self.user) album_content_count_before = self.album.photos.count() response = self.client.post( '/gallery/' + str(self.group.id) + '/' + str(self.album.id) + '/delete_from_album/' + str(self.photo.id)) album_content_count_after = self.album.photos.count() self.assertEqual(album_content_count_after, album_content_count_before - 1) def test_photo_is_removed_from_only_one_album(self): self.client.force_login(user=self.user) album_content_count_before = self.album_2.photos.count() response = self.client.post( '/gallery/' + str(self.group.id) + '/' + str(self.album.id) + '/delete_from_album/' + str(self.photo.id)) album_content_count_after = self.album_2.photos.count() self.assertEqual(album_content_count_after, album_content_count_before) class TestGroupView(TestCase): def setUp(self): self.factory = RequestFactory() self.client = Client() self.user = create_user() self.group = create_group(self.user) self.photo = create_photo(self.user, self.group) self.album = create_album(self.group, self.photo) def test_overview(self): self.client.force_login(user=self.user) response = self.client.get("/groups/1/photos") self.assertTemplateUsed(response, "gallery/group/overview.html") self.assertIn(self.photo, response.context["photos"]) self.assertEquals(self.album.id, response.context["albums"][0]["id"]) class TestGroupAlbumDetailView(TestCase): def setUp(self): self.factory = RequestFactory() self.client = Client() self.user = create_user() self.group = create_group(self.user) self.photo = create_photo(self.user, self.group) self.album = create_album(self.group, self.photo) self.other_group = create_group(telegram_id=2) self.other_album = create_album(self.other_group) self.uncategorized_photo = create_photo(self.user, self.group) def test_other_album(self): self.client.force_login(user=self.user) response = self.client.get("/groups/1/photos/albums/2") self.assertEquals(response.status_code, 404) def test_album(self): self.client.force_login(user=self.user) response = self.client.get("/groups/1/photos/albums/1") self.assertTemplateUsed(response, "gallery/group/album.html") self.assertEquals(self.album, response.context["album"]) self.assertNotIn(self.album, response.context["albums"]) self.assertEquals(self.photo.media_url, response.context["cover"]) self.assertIn(self.photo, response.context["photos"]) self.assertNotIn(self.uncategorized_photo, response.context["photos"]) class TestSetCoverView(TestCase): def setUp(self): self.factory = RequestFactory() self.client = Client() self.user = create_user() self.unprivileged_user = create_user("Unprivileged", "unpriv@test.test") self.group = create_group(self.user, is_admin=True, users=[self.unprivileged_user]) self.photo = create_photo(self.user, self.group) self.album = create_album(self.group, self.photo) self.other_photo = create_photo(self.user, self.group) self.album.photos.add(self.other_photo) def test_set_cover_unprivileged(self): self.client.force_login(user=self.unprivileged_user) response = self.client.post("/gallery/1/1/cover", follow=True) self.assertEquals(403, response.status_code) def test_set_cover(self): self.client.force_login(user=self.user) response = self.client.post("/gallery/1/1/cover", follow=True) self.assertRedirects(response, reverse('groups:photo_album', kwargs={"group_id": self.group.id, "album_id": self.album.id})) self.album.refresh_from_db() self.assertEquals(self.album.cover, self.photo) def test_default_cover(self): cover = queries.get_album_cover(self.album, self.album.photos) self.assertEquals(cover, self.photo.media_url) def test_explicit_cover(self): self.album.cover = self.other_photo cover = queries.get_album_cover(self.album, self.album.photos) self.assertEquals(cover, self.other_photo.media_url) class TestDeletePhotoView(TestCase): def setUp(self): self.factory = RequestFactory() self.client = Client() self.user = create_user() self.unprivileged_user = create_user("Unprivileged", "unpriv@test.test") self.group = create_group(self.user, is_admin=True, users=[self.unprivileged_user]) self.photo = create_photo(self.user, self.group) def test_delete_photo_unprivileged(self): self.client.force_login(user=self.unprivileged_user) response = self.client.post("/gallery/photos/1/delete") self.assertEquals(response.status_code, 403) def test_delete_photo(self): self.client.force_login(user=self.user) response = self.client.post("/gallery/photos/1/delete") self.assertEquals(response.status_code, 200) self.assertEquals(response.get("Content-Type"), "application/json") data = json.loads(response.content.decode("utf-8")) self.assertEquals(data["message"], "Photo deleted") self.assertEquals(models.Photo.objects.filter(pk=self.photo.id).count(), 0) def test_delete_non_existingphoto(self): self.client.force_login(user=self.user) response = self.client.post("/gallery/photos/2/delete") self.assertEquals(response.status_code, 404) class TestPhotoDetailView(TestCase): def setUp(self): self.factory = RequestFactory() self.client = Client() self.user = create_user() self.group = create_group(self.user) self.photo = create_photo(self.user, self.group) def test_view(self): self.client.force_login(user=self.user) response = self.client.get("/groups/1/photos/1") self.assertTemplateUsed(response, "gallery/group/photo.html") self.assertEquals(self.photo, response.context["photo"]) class TestAlbumPhotoDetailView(TestCase): def setUp(self): self.factory = RequestFactory() self.client = Client() self.user = create_user() self.group = create_group(self.user) self.photo = create_photo(self.user, self.group) self.album = create_album(self.group, self.photo) self.photo2 = create_photo(self.user, self.group) self.photo3 = create_photo(self.user, self.group) self.album.photos.add(self.photo2) self.album.photos.add(self.photo3) def test_view(self): self.client.force_login(user=self.user) response = self.client.get("/groups/1/photos/albums/1/1") self.assertTemplateUsed(response, "gallery/group/album_photo.html") self.assertEquals(self.photo, response.context["photo"]) self.assertEquals(self.album, response.context["album"]) def test_pager(self): self.client.force_login(user=self.user) response = self.client.get("/groups/1/photos/albums/1/2") self.assertTemplateUsed(response, "gallery/group/album_photo.html") self.assertEquals(self.photo, response.context["prev"]) self.assertEquals(self.photo3, response.context["next"]) class TestRotatePhotoLeftView(TestCase): def setUp(self): self.factory = RequestFactory() self.client = Client() self.user = create_user() self.group = create_group(self.user, is_admin=True) self.photo = create_photo(self.user, self.group) @mock.patch("django_rq.enqueue") def test_rotate_photo(self, enqueue): self.client.force_login(user=self.user) response = self.client.post("/gallery/photos/1/rotate/left") self.assertEquals(response.status_code, 200) self.assertEquals(response.get("Content-Type"), "application/json") data = json.loads(response.content.decode("utf-8")) self.assertEquals(data["message"], "Photo rotated") enqueue.assert_called() @mock.patch("django_rq.enqueue") def test_rotate_non_existing_photo(self, enqueue): self.client.force_login(user=self.user) response = self.client.post("/gallery/photos/2/rotate/left") self.assertEquals(response.status_code, 404) enqueue.assert_not_called() class TestRotatePhotoRightView(TestCase): def setUp(self): self.factory = RequestFactory() self.client = Client() self.user = create_user() self.group = create_group(self.user, is_admin=True) self.photo = create_photo(self.user, self.group) @mock.patch("django_rq.enqueue") def test_rotate_photo(self, enqueue): self.client.force_login(user=self.user) response = self.client.post("/gallery/photos/1/rotate/right") self.assertEquals(response.status_code, 200) self.assertEquals(response.get("Content-Type"), "application/json") data = json.loads(response.content.decode("utf-8")) self.assertEquals(data["message"], "Photo rotated") enqueue.assert_called() @mock.patch("django_rq.enqueue") def test_rotate_non_existing_photo(self, enqueue): self.client.force_login(user=self.user) response = self.client.post("/gallery/photos/2/rotate/right") self.assertEquals(response.status_code, 404) enqueue.assert_not_called()

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0.035068

0.049924

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0.813638

0.798782

0.781918

0.766149

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0.202811

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null

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1

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null

0

6

2fe61377eeb431b3fc0fba60524ff0696549b353

104

py

Python

testalign.py

TuxStory/Python3

4c1b2291d1613b32aa36b62b0b881ea40b423cce

[ "MIT" ]

null

testalign.py

TuxStory/Python3

4c1b2291d1613b32aa36b62b0b881ea40b423cce

[ "MIT" ]

null

testalign.py

TuxStory/Python3

4c1b2291d1613b32aa36b62b0b881ea40b423cce

[ "MIT" ]

null

print("Test".ljust(20,".")+"20$") print("Pear".ljust(20,".")+"99$") print("Apple".ljust(20,".")+"120$")

26

35

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104

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6

64093497cb0d8801f31ea89738a3fd86a0f22a9d

41,820

py

Python

src/frr/tests/topotests/bgp_prefix_list_topo1/test_prefix_lists.py

zhouhaifeng/vpe

9c644ffd561988e5740021ed26e0f7739844353d

[ "Apache-2.0" ]

null

src/frr/tests/topotests/bgp_prefix_list_topo1/test_prefix_lists.py

zhouhaifeng/vpe

9c644ffd561988e5740021ed26e0f7739844353d

[ "Apache-2.0" ]

null

src/frr/tests/topotests/bgp_prefix_list_topo1/test_prefix_lists.py

zhouhaifeng/vpe

9c644ffd561988e5740021ed26e0f7739844353d

[ "Apache-2.0" ]

null

#!/usr/bin/python # # Copyright (c) 2019 by VMware, Inc. ("VMware") # Used Copyright (c) 2018 by Network Device Education Foundation, # Inc. ("NetDEF") in this file. # # Permission to use, copy, modify, and/or distribute this software # for any purpose with or without fee is hereby granted, provided # that the above copyright notice and this permission notice appear # in all copies. # # THE SOFTWARE IS PROVIDED "AS IS" AND VMWARE DISCLAIMS ALL WARRANTIES # WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF # MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL VMWARE BE LIABLE FOR # ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY # DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, # WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS # ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE # OF THIS SOFTWARE. # """ Following tests are covered to test prefix-list functionality: Test steps - Create topology (setup module) Creating 4 routers topology, r1, r2, r3 are in IBGP and r3, r4 are in EBGP - Bring up topology - Verify for bgp to converge IP prefix-list tests - Test ip prefix-lists IN permit - Test ip prefix-lists OUT permit - Test ip prefix-lists IN deny and permit any - Test delete ip prefix-lists - Test ip prefix-lists OUT deny and permit any - Test modify ip prefix-lists IN permit to deny - Test modify ip prefix-lists IN deny to permit - Test modify ip prefix-lists OUT permit to deny - Test modify prefix-lists OUT deny to permit - Test ip prefix-lists implicit deny """ import sys import time import os import pytest # Save the Current Working Directory to find configuration files. CWD = os.path.dirname(os.path.realpath(__file__)) sys.path.append(os.path.join(CWD, "../")) # pylint: disable=C0413 # Import topogen and topotest helpers from lib.topogen import Topogen, get_topogen # Import topoJson from lib, to create topology and initial configuration from lib.common_config import ( start_topology, write_test_header, write_test_footer, reset_config_on_routers, verify_rib, create_static_routes, create_prefix_lists, verify_prefix_lists, ) from lib.topolog import logger from lib.bgp import verify_bgp_convergence, create_router_bgp, clear_bgp_and_verify from lib.topojson import build_config_from_json pytestmark = [pytest.mark.bgpd] # Global variables bgp_convergence = False def setup_module(mod): """ Sets up the pytest environment * `mod`: module name """ testsuite_run_time = time.asctime(time.localtime(time.time())) logger.info("Testsuite start time: {}".format(testsuite_run_time)) logger.info("=" * 40) logger.info("Running setup_module to create topology") # This function initiates the topology build with Topogen... json_file = "{}/prefix_lists.json".format(CWD) tgen = Topogen(json_file, mod.__name__) global topo topo = tgen.json_topo # ... and here it calls Mininet initialization functions. # Starting topology, create tmp files which are loaded to routers # to start deamons and then start routers start_topology(tgen) # Creating configuration from JSON build_config_from_json(tgen, topo) # Checking BGP convergence global BGP_CONVERGENCE # Don't run this test if we have any failure. if tgen.routers_have_failure(): pytest.skip(tgen.errors) # Api call verify whether BGP is converged BGP_CONVERGENCE = verify_bgp_convergence(tgen, topo) assert BGP_CONVERGENCE is True, "setup_module :Failed \n Error:" " {}".format( BGP_CONVERGENCE ) logger.info("Running setup_module() done") def teardown_module(mod): """ Teardown the pytest environment * `mod`: module name """ logger.info("Running teardown_module to delete topology") tgen = get_topogen() # Stop toplogy and Remove tmp files tgen.stop_topology() logger.info( "Testsuite end time: {}".format(time.asctime(time.localtime(time.time()))) ) logger.info("=" * 40) ##################################################### # # Tests starting # ##################################################### def test_ip_prefix_lists_in_permit(request): """ Create ip prefix list and test permit prefixes IN direction """ tgen = get_topogen() if BGP_CONVERGENCE is not True: pytest.skip("skipped because of BGP Convergence failure") # test case name tc_name = request.node.name write_test_header(tc_name) # Create Static routes input_dict = { "r1": { "static_routes": [ {"network": "20.0.20.1/32", "no_of_ip": 1, "next_hop": "10.0.0.2"} ] } } result = create_static_routes(tgen, input_dict) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Create ip prefix list input_dict_2 = { "r3": { "prefix_lists": { "ipv4": { "pf_list_1": [{"seqid": 10, "network": "any", "action": "permit"}] } } } } result = create_prefix_lists(tgen, input_dict_2) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Configure bgp neighbor with prefix list input_dict_3 = { "r1": { "bgp": { "address_family": { "ipv4": { "unicast": { "redistribute": [ {"redist_type": "static"}, {"redist_type": "connected"}, ] } } } } }, "r3": { "bgp": { "address_family": { "ipv4": { "unicast": { "neighbor": { "r1": { "dest_link": { "r3": { "prefix_lists": [ {"name": "pf_list_1", "direction": "in"} ] } } } } } } } } }, } result = create_router_bgp(tgen, topo, input_dict_3) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Verifying RIB routes dut = "r3" protocol = "bgp" result = verify_rib(tgen, "ipv4", dut, input_dict, protocol=protocol) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) write_test_footer(tc_name) def test_ip_prefix_lists_out_permit(request): """ Create ip prefix list and test permit prefixes out direction """ tgen = get_topogen() if BGP_CONVERGENCE is not True: pytest.skip("skipped because of BGP Convergence failure") # test case name tc_name = request.node.name write_test_header(tc_name) # Creating configuration from JSON reset_config_on_routers(tgen) # Create Static routes input_dict = { "r1": { "static_routes": [ {"network": "10.0.20.1/32", "no_of_ip": 1, "next_hop": "10.0.0.2"} ] } } result = create_static_routes(tgen, input_dict) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Create Static routes input_dict_1 = { "r1": { "static_routes": [ {"network": "20.0.20.1/32", "no_of_ip": 1, "next_hop": "10.0.0.2"} ] } } result = create_static_routes(tgen, input_dict_1) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) input_dict_5 = { "r3": { "static_routes": [ {"network": "10.0.0.2/30", "no_of_ip": 1, "next_hop": "10.0.0.9"} ] } } result = create_static_routes(tgen, input_dict_5) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Api call to redistribute static routes # Create ip prefix list input_dict_2 = { "r1": { "prefix_lists": { "ipv4": { "pf_list_1": [ {"seqid": 10, "network": "20.0.20.1/32", "action": "permit"} ] } } } } result = create_prefix_lists(tgen, input_dict_2) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Configure prefix list to bgp neighbor # Configure bgp neighbor with prefix list input_dict_3 = { "r1": { "bgp": { "address_family": { "ipv4": { "unicast": { "neighbor": { "r3": { "dest_link": { "r1": { "prefix_lists": [ { "name": "pf_list_1", "direction": "out", } ] } } } }, "redistribute": [ {"redist_type": "static"}, {"redist_type": "connected"}, ], } } } } } } result = create_router_bgp(tgen, topo, input_dict_3) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Verifying RIB routes dut = "r3" protocol = "bgp" result = verify_rib(tgen, "ipv4", dut, input_dict_1, protocol=protocol) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) result = verify_rib( tgen, "ipv4", dut, input_dict, protocol=protocol, expected=False ) assert ( result is not True ), "Testcase {} : Failed \n Error: Routes still" " present in RIB".format(tc_name) write_test_footer(tc_name) def test_ip_prefix_lists_in_deny_and_permit_any(request): """ Create ip prefix list and test permit/deny prefixes IN direction """ tgen = get_topogen() if BGP_CONVERGENCE is not True: pytest.skip("skipped because of BGP Convergence failure") # test case name tc_name = request.node.name write_test_header(tc_name) # Creating configuration from JSON reset_config_on_routers(tgen) # Create Static Routes input_dict = { "r1": { "static_routes": [ {"network": "10.0.20.1/32", "no_of_ip": 1, "next_hop": "10.0.0.2"} ] } } result = create_static_routes(tgen, input_dict) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Api call to redistribute static routes # Create ip prefix list input_dict_2 = { "r1": { "prefix_lists": { "ipv4": { "pf_list_1": [ {"seqid": "10", "network": "10.0.20.1/32", "action": "deny"}, {"seqid": "11", "network": "any", "action": "permit"}, ] } } } } result = create_prefix_lists(tgen, input_dict_2) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Configure bgp neighbor with prefix list input_dict_3 = { "r1": { "bgp": { "address_family": { "ipv4": { "unicast": { "redistribute": [ {"redist_type": "static"}, {"redist_type": "connected"}, ] } } } } }, "r3": { "bgp": { "address_family": { "ipv4": { "unicast": { "neighbor": { "r1": { "dest_link": { "r3": { "prefix_lists": [ {"name": "pf_list_1", "direction": "in"} ] } } } } } } } } }, } # Configure prefix list to bgp neighbor result = create_router_bgp(tgen, topo, input_dict_3) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Verifying RIB routes dut = "r3" protocol = "bgp" result = verify_rib( tgen, "ipv4", dut, input_dict, protocol=protocol, expected=False ) assert ( result is not True ), "Testcase {} : Failed \n Error: Routes still" " present in RIB".format(tc_name) write_test_footer(tc_name) def test_delete_prefix_lists(request): """ Delete ip prefix list """ tgen = get_topogen() if BGP_CONVERGENCE is not True: pytest.skip("skipped because of BGP Convergence failure") # test case name tc_name = request.node.name write_test_header(tc_name) # Creating configuration from JSON reset_config_on_routers(tgen) # Create ip prefix list input_dict_2 = { "r1": { "prefix_lists": { "ipv4": { "pf_list_1": [ {"seqid": "10", "network": "10.0.20.1/32", "action": "deny"} ] } } } } result = create_prefix_lists(tgen, input_dict_2) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) result = verify_prefix_lists(tgen, input_dict_2) assert result is not True, "Testcase {} : Failed \n Error: {}".format( tc_name, result ) # Delete prefix list input_dict_2 = { "r1": { "prefix_lists": { "ipv4": { "pf_list_1": [ { "seqid": "10", "network": "10.0.20.1/32", "action": "deny", "delete": True, } ] } } } } result = create_prefix_lists(tgen, input_dict_2) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) result = verify_prefix_lists(tgen, input_dict_2) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) write_test_footer(tc_name) def test_ip_prefix_lists_out_deny_and_permit_any(request): """ Create ip prefix list and test deny/permit any prefixes OUT direction """ tgen = get_topogen() if BGP_CONVERGENCE is not True: pytest.skip("skipped because of BGP Convergence failure") # test case name tc_name = request.node.name write_test_header(tc_name) # Creating configuration from JSON reset_config_on_routers(tgen) # Create Static Routes input_dict = { "r1": { "static_routes": [ {"network": "10.0.20.1/32", "no_of_ip": 9, "next_hop": "10.0.0.2"} ] } } result = create_static_routes(tgen, input_dict) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Create Static Routes input_dict_1 = { "r2": { "static_routes": [ {"network": "20.0.20.1/32", "no_of_ip": 9, "next_hop": "10.0.0.1"} ] } } result = create_static_routes(tgen, input_dict_1) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Api call to redistribute static routes # Create ip prefix list input_dict_3 = { "r3": { "prefix_lists": { "ipv4": { "pf_list_1": [ { "seqid": "10", "network": "10.0.0.0/8", "le": "32", "action": "deny", }, {"seqid": "11", "network": "any", "action": "permit"}, ] } } } } result = create_prefix_lists(tgen, input_dict_3) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Configure prefix list to bgp neighbor input_dict_4 = { "r1": { "bgp": { "address_family": { "ipv4": { "unicast": { "redistribute": [ {"redist_type": "static"}, {"redist_type": "connected"}, ] } } } } }, "r2": { "bgp": { "address_family": { "ipv4": { "unicast": { "redistribute": [ {"redist_type": "static"}, {"redist_type": "connected"}, ] } } } } }, "r3": { "bgp": { "address_family": { "ipv4": { "unicast": { "neighbor": { "r4": { "dest_link": { "r3": { "prefix_lists": [ { "name": "pf_list_1", "direction": "out", } ] } } } } } } } } }, } result = create_router_bgp(tgen, topo, input_dict_4) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Verifying RIB routes dut = "r4" protocol = "bgp" result = verify_rib(tgen, "ipv4", dut, input_dict_1, protocol=protocol) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Verifying RIB routes dut = "r4" protocol = "bgp" result = verify_rib( tgen, "ipv4", dut, input_dict, protocol=protocol, expected=False ) assert ( result is not True ), "Testcase {} : Failed \n Error: Routes still" " present in RIB".format(tc_name) write_test_footer(tc_name) def test_modify_prefix_lists_in_permit_to_deny(request): """ Modify ip prefix list and test permit to deny prefixes IN direction """ tgen = get_topogen() if BGP_CONVERGENCE is not True: pytest.skip("skipped because of BGP Convergence failure") # test case name tc_name = request.node.name write_test_header(tc_name) # Creating configuration from JSON reset_config_on_routers(tgen) # Create Static Routes input_dict = { "r1": { "static_routes": [ {"network": "10.0.20.1/32", "no_of_ip": 9, "next_hop": "10.0.0.2"} ] } } result = create_static_routes(tgen, input_dict) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Api call to redistribute static routes # Create ip prefix list input_dict_2 = { "r3": { "prefix_lists": { "ipv4": { "pf_list_1": [ { "seqid": "10", "network": "10.0.0.0/8", "le": "32", "action": "permit", } ] } } } } result = create_prefix_lists(tgen, input_dict_2) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Configure prefix list to bgp neighbor input_dict_3 = { "r1": { "bgp": { "address_family": { "ipv4": { "unicast": { "redistribute": [ {"redist_type": "static"}, {"redist_type": "connected"}, ] } } } } }, "r3": { "bgp": { "address_family": { "ipv4": { "unicast": { "neighbor": { "r1": { "dest_link": { "r3": { "prefix_lists": [ {"name": "pf_list_1", "direction": "in"} ] } } } } } } } } }, } result = create_router_bgp(tgen, topo, input_dict_3) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Verifying RIB routes dut = "r3" protocol = "bgp" result = verify_rib(tgen, "ipv4", dut, input_dict, protocol=protocol) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Modify prefix list input_dict_1 = { "r3": { "prefix_lists": { "ipv4": { "pf_list_1": [ { "seqid": "10", "network": "10.0.0.0/8", "le": "32", "action": "deny", }, {"seqid": "11", "network": "any", "action": "permit"}, ] } } } } result = create_prefix_lists(tgen, input_dict_1) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Api call to clear bgp, so config changes would be reflected dut = "r3" result = clear_bgp_and_verify(tgen, topo, dut) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Verifying RIB routes dut = "r3" protocol = "bgp" result = verify_rib( tgen, "ipv4", dut, input_dict, protocol=protocol, expected=False ) assert ( result is not True ), "Testcase {} : Failed \n Error: Routes still" " present in RIB".format(tc_name) write_test_footer(tc_name) def test_modify_prefix_lists_in_deny_to_permit(request): """ Modify ip prefix list and test deny to permit prefixes IN direction """ tgen = get_topogen() if BGP_CONVERGENCE is not True: pytest.skip("skipped because of BGP Convergence failure") # test case name tc_name = request.node.name write_test_header(tc_name) # Creating configuration from JSON reset_config_on_routers(tgen) # Create Static Routes input_dict = { "r1": { "static_routes": [ {"network": "10.0.20.1/32", "no_of_ip": 9, "next_hop": "10.0.0.2"} ] } } result = create_static_routes(tgen, input_dict) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Api call to redistribute static routes # Create ip prefix list input_dict_1 = { "r3": { "prefix_lists": { "ipv4": { "pf_list_1": [ { "seqid": "10", "network": "10.0.0.0/8", "le": "32", "action": "deny", }, {"seqid": "11", "network": "any", "action": "permit"}, ] } } } } result = create_prefix_lists(tgen, input_dict_1) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Configure prefix list to bgp neighbor input_dict_2 = { "r1": { "bgp": { "address_family": { "ipv4": { "unicast": { "redistribute": [ {"redist_type": "static"}, {"redist_type": "connected"}, ] } } } } }, "r3": { "bgp": { "address_family": { "ipv4": { "unicast": { "neighbor": { "r1": { "dest_link": { "r3": { "prefix_lists": [ {"name": "pf_list_1", "direction": "in"} ] } } } } } } } } }, } result = create_router_bgp(tgen, topo, input_dict_2) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Verifying RIB routes dut = "r3" protocol = "bgp" result = verify_rib( tgen, "ipv4", dut, input_dict, protocol=protocol, expected=False ) assert ( result is not True ), "Testcase {} : Failed \n Error: Routes still" " present in RIB".format(tc_name) # Modify ip prefix list input_dict_1 = { "r3": { "prefix_lists": { "ipv4": { "pf_list_1": [ { "seqid": "10", "network": "10.0.0.0/8", "le": "32", "action": "permit", } ] } } } } result = create_prefix_lists(tgen, input_dict_1) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Api call to clear bgp, so config changes would be reflected dut = "r3" result = clear_bgp_and_verify(tgen, topo, dut) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Verifying RIB routes dut = "r3" protocol = "bgp" result = verify_rib(tgen, "ipv4", dut, input_dict, protocol=protocol) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) write_test_footer(tc_name) def test_modify_prefix_lists_out_permit_to_deny(request): """ Modify ip prefix list and test permit to deny prefixes OUT direction """ tgen = get_topogen() if BGP_CONVERGENCE is not True: pytest.skip("skipped because of BGP Convergence failure") # test case name tc_name = request.node.name write_test_header(tc_name) # Creating configuration from JSON reset_config_on_routers(tgen) # Create Static Routes input_dict = { "r1": { "static_routes": [ {"network": "10.0.20.1/32", "no_of_ip": 9, "next_hop": "10.0.0.2"} ] } } result = create_static_routes(tgen, input_dict) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Api call to redistribute static routes # Create ip prefix list input_dict_1 = { "r3": { "prefix_lists": { "ipv4": { "pf_list_1": [ { "seqid": "10", "network": "10.0.0.0/8", "le": "32", "action": "permit", } ] } } } } result = create_prefix_lists(tgen, input_dict_1) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Configure prefix list to bgp neighbor input_dict_2 = { "r1": { "bgp": { "address_family": { "ipv4": { "unicast": { "redistribute": [ {"redist_type": "static"}, {"redist_type": "connected"}, ] } } } } }, "r3": { "bgp": { "address_family": { "ipv4": { "unicast": { "neighbor": { "r4": { "dest_link": { "r3": { "prefix_lists": [ { "name": "pf_list_1", "direction": "out", } ] } } } } } } } } }, } result = create_router_bgp(tgen, topo, input_dict_2) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Verifying RIB routes dut = "r4" protocol = "bgp" result = verify_rib(tgen, "ipv4", dut, input_dict, protocol=protocol) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Modify ip prefix list input_dict_1 = { "r3": { "prefix_lists": { "ipv4": { "pf_list_1": [ { "seqid": "10", "network": "10.0.0.0/8", "le": "32", "action": "deny", }, {"seqid": "11", "network": "any", "action": "permit"}, ] } } } } result = create_prefix_lists(tgen, input_dict_1) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Api call to clear bgp, so config changes would be reflected dut = "r3" result = clear_bgp_and_verify(tgen, topo, dut) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Verifying RIB routes dut = "r4" protocol = "bgp" result = verify_rib( tgen, "ipv4", dut, input_dict, protocol=protocol, expected=False ) assert ( result is not True ), "Testcase {} : Failed \n Error: Routes still" " present in RIB".format(tc_name) write_test_footer(tc_name) def test_modify_prefix_lists_out_deny_to_permit(request): """ Modify ip prefix list and test deny to permit prefixes OUT direction """ tgen = get_topogen() if BGP_CONVERGENCE is not True: pytest.skip("skipped because of BGP Convergence failure") # test case name tc_name = request.node.name write_test_header(tc_name) # Creating configuration from JSON reset_config_on_routers(tgen) # Create Static Routes input_dict = { "r1": { "static_routes": [ {"network": "10.0.20.1/32", "no_of_ip": 9, "next_hop": "10.0.0.2"} ] } } result = create_static_routes(tgen, input_dict) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Api call to redistribute static routes # Create ip prefix list input_dict_1 = { "r3": { "prefix_lists": { "ipv4": { "pf_list_1": [ { "seqid": "10", "network": "10.0.0.0/8", "le": "32", "action": "deny", }, {"seqid": "11", "network": "any", "action": "permit"}, ] } } } } result = create_prefix_lists(tgen, input_dict_1) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Configure prefix list to bgp neighbor input_dict_2 = { "r1": { "bgp": { "address_family": { "ipv4": { "unicast": { "redistribute": [ {"redist_type": "static"}, {"redist_type": "connected"}, ] } } } } }, "r3": { "bgp": { "address_family": { "ipv4": { "unicast": { "neighbor": { "r4": { "dest_link": { "r3": { "prefix_lists": [ { "name": "pf_list_1", "direction": "out", } ] } } } } } } } } }, } result = create_router_bgp(tgen, topo, input_dict_2) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Verifying RIB routes dut = "r4" protocol = "bgp" result = verify_rib( tgen, "ipv4", dut, input_dict, protocol=protocol, expected=False ) assert ( result is not True ), "Testcase {} : Failed \n Error: Routes still" " present in RIB".format(tc_name) # Modify ip prefix list input_dict_1 = { "r3": { "prefix_lists": { "ipv4": { "pf_list_1": [ { "seqid": "10", "network": "10.0.0.0/8", "le": "32", "action": "permit", } ] } } } } result = create_prefix_lists(tgen, input_dict_1) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Api call to clear bgp, so config changes would be reflected dut = "r3" result = clear_bgp_and_verify(tgen, topo, dut) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Verifying RIB routes dut = "r4" protocol = "bgp" result = verify_rib(tgen, "ipv4", dut, input_dict, protocol=protocol) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) write_test_footer(tc_name) def test_ip_prefix_lists_implicit_deny(request): """ Create ip prefix list and test implicit deny """ tgen = get_topogen() if BGP_CONVERGENCE is not True: pytest.skip("skipped because of BGP Convergence failure") # test case name tc_name = request.node.name write_test_header(tc_name) # Creating configuration from JSON reset_config_on_routers(tgen) # Create Static Routes input_dict = { "r1": { "static_routes": [ {"network": "10.0.20.1/32", "no_of_ip": 9, "next_hop": "10.0.0.2"} ] } } result = create_static_routes(tgen, input_dict) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Create Static Routes input_dict_1 = { "r2": { "static_routes": [ {"network": "20.0.20.1/32", "no_of_ip": 9, "next_hop": "10.0.0.1"} ] } } result = create_static_routes(tgen, input_dict) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Api call to redistribute static routes # Create ip prefix list input_dict_3 = { "r3": { "prefix_lists": { "ipv4": { "pf_list_1": [ { "seqid": "10", "network": "10.0.0.0/8", "le": "32", "action": "permit", } ] } } } } result = create_prefix_lists(tgen, input_dict_3) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Configure prefix list to bgp neighbor input_dict_4 = { "r1": { "bgp": { "address_family": { "ipv4": { "unicast": { "redistribute": [ {"redist_type": "static"}, {"redist_type": "connected"}, ] } } } } }, "r2": { "bgp": { "address_family": { "ipv4": { "unicast": { "redistribute": [ {"redist_type": "static"}, {"redist_type": "connected"}, ] } } } } }, "r3": { "bgp": { "address_family": { "ipv4": { "unicast": { "neighbor": { "r4": { "dest_link": { "r3": { "prefix_lists": [ { "name": "pf_list_1", "direction": "out", } ] } } } } } } } } }, } result = create_router_bgp(tgen, topo, input_dict_4) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Verifying RIB routes dut = "r4" protocol = "bgp" result = verify_rib(tgen, "ipv4", dut, input_dict, protocol=protocol) assert result is True, "Testcase {} : Failed \n Error: {}".format(tc_name, result) # Verifying RIB routes dut = "r4" protocol = "bgp" result = verify_rib( tgen, "ipv4", dut, input_dict_1, protocol=protocol, expected=False ) assert ( result is not True ), "Testcase {} : Failed \n Error: Routes still" " present in RIB".format(tc_name) write_test_footer(tc_name) if __name__ == "__main__": args = ["-s"] + sys.argv[1:] sys.exit(pytest.main(args))

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0

null

0

1

0

null

0

6

6432a6ba130dc1aa157faf59393b3506f384d9d8

10,786

py

Python

temporal-difference/TD_Exercise.py

albimc/deep-reinforcement-learning

e11a6c9d4c8991cf229e686b645ae22ec4cff4f5

[ "MIT" ]

null

temporal-difference/TD_Exercise.py

albimc/deep-reinforcement-learning

e11a6c9d4c8991cf229e686b645ae22ec4cff4f5

[ "MIT" ]

null

temporal-difference/TD_Exercise.py

albimc/deep-reinforcement-learning

e11a6c9d4c8991cf229e686b645ae22ec4cff4f5

[ "MIT" ]

null

# TD Exercise # import sys import gym import numpy as np from collections import defaultdict, deque import matplotlib.pyplot as plt import check_test from plot_utils import plot_values # ############# # Environment # # ############# env = gym.make('CliffWalking-v0') # ### print(env.action_space) print(env.observation_space) # ############################## # Optimal state-value function # # ############################## V_opt = np.zeros((4, 12)) print(V_opt) V_opt[0][0:13] = -np.arange(3, 15)[::-1] V_opt[1][0:13] = -np.arange(3, 15)[::-1] + 1 V_opt[2][0:13] = -np.arange(3, 15)[::-1] + 2 V_opt[3][0] = -13 print(V_opt) plot_values(V_opt) plt.show() # ########################### # Part 1: TD Control: Sarsa # # ########################### def update_Q_sarsa(Qsa, Qsa_next, reward, alpha, gamma): """ updates the action-value function estimate using the most recent time step """ return Qsa + (alpha * (reward + (gamma * Qsa_next) - Qsa)) def epsilon_greedy_probs(env, Q_s, epsilon): """ obtains the action probabilities corresponding to epsilon-greedy policy """ policy_s = np.ones(env.nA) * epsilon / env.nA policy_s[np.argmax(Q_s)] = 1 - epsilon + (epsilon / env.nA) return policy_s def sarsa(env, num_episodes, alpha, gamma=1.0, eps_start=1.0, eps_decay=.99999, eps_min=0.05, plot_every=100): Q = defaultdict(lambda: np.zeros(env.nA)) # initialize action-value function (empty dictionary of arrays) epsilon = eps_start # initialize epsilon # initialize performance monitor tmp_scores = deque(maxlen=plot_every) scores = deque(maxlen=num_episodes) # loop over episodes for i_episode in range(1, num_episodes+1): # monitor progress if i_episode % 100 == 0: print("\rEpisode {}/{}".format(i_episode, num_episodes), end="") sys.stdout.flush() # initialize score score = 0 # begin an episode, observe S state = env.reset() # set value of epsilon epsilon = max(epsilon*eps_decay, eps_min) # get epsilon-greedy action probabilities policy_s = epsilon_greedy_probs(env, Q[state], epsilon) # pick action A action = np.random.choice(np.arange(env.nA), p=policy_s) # limit number of time steps per episode # for t_step in np.arange(300): while True: # take action A, observe R, S' next_state, reward, done, info = env.step(action) # add reward to score score += reward if not done: # get epsilon-greedy action probabilities policy_s = epsilon_greedy_probs(env, Q[next_state], epsilon) # pick next action A' next_action = np.random.choice(np.arange(env.nA), p=policy_s) # update TD estimate of Q Q[state][action] = update_Q_sarsa(Q[state][action], Q[next_state][next_action], reward, alpha, gamma) # S <- S' state = next_state # A <- A' action = next_action if done: # update TD estimate of Q Q[state][action] = update_Q_sarsa(Q[state][action], 0, reward, alpha, gamma) # append score tmp_scores.append(score) break if (i_episode % plot_every == 0): scores.append(np.mean(tmp_scores)) # plot performance plt.plot(np.linspace(0, num_episodes, len(scores), endpoint=False), np.asarray(scores)) plt.xlabel('Episode Number') plt.ylabel('Average Reward (Over Next %d Episodes)' % plot_every) plt.show() # print best 100-episode performance print(('Best Average Reward over %d Episodes: ' % plot_every), np.max(scores)) return Q # obtain the estimated optimal policy and corresponding action-value function Q_sarsa = sarsa(env, num_episodes=5000, alpha=0.01, gamma=1.0, eps_start=1.0, eps_decay=0.5, eps_min=1/5000, plot_every=100) # print the estimated optimal policy policy_sarsa = np.array([np.argmax(Q_sarsa[key]) if key in Q_sarsa else -1 for key in np.arange(48)]).reshape(4, 12) check_test.run_check('td_control_check', policy_sarsa) print("\nEstimated Optimal Policy (UP = 0, RIGHT = 1, DOWN = 2, LEFT = 3, N/A = -1):") print(policy_sarsa) # plot the estimated optimal state-value function V_sarsa = ([np.max(Q_sarsa[key]) if key in Q_sarsa else 0 for key in np.arange(48)]) plot_values(V_sarsa) # ######################################### # Part 2: TD Control: Q-learning Sarsamax # # ######################################### def sarsamax(env, num_episodes, alpha, gamma=1.0, eps_start=1.0, eps_decay=.99999, eps_min=0.05, plot_every=100): Q = defaultdict(lambda: np.zeros(env.nA)) # initialize action-value function (empty dictionary of arrays) epsilon = eps_start # initialize epsilon # initialize performance monitor tmp_scores = deque(maxlen=plot_every) scores = deque(maxlen=num_episodes) # loop over episodes for i_episode in range(1, num_episodes+1): # monitor progress if i_episode % 100 == 0: print("\rEpisode {}/{}".format(i_episode, num_episodes), end="") sys.stdout.flush() # initialize score score = 0 # begin an episode, observe S state = env.reset() # set value of epsilon epsilon = max(epsilon*eps_decay, eps_min) while True: # get epsilon-greedy action probabilities policy_s = epsilon_greedy_probs(env, Q[state], epsilon) # pick action A action = np.random.choice(np.arange(env.nA), p=policy_s) # take action A, observe R, S' next_state, reward, done, info = env.step(action) # add reward to score score += reward # pick next best action A' next_best_action = np.argmax(Q[next_state]) # update TD estimate of Q Q[state][action] = update_Q_sarsa(Q[state][action], Q[next_state][next_best_action], reward, alpha, gamma) # S <- S' state = next_state # until S is terminal if done: # update TD estimate of Q Q[state][action] = update_Q_sarsa(Q[state][action], 0, reward, alpha, gamma) # append score tmp_scores.append(score) break if (i_episode % plot_every == 0): scores.append(np.mean(tmp_scores)) # plot performance plt.plot(np.linspace(0, num_episodes, len(scores), endpoint=False), np.asarray(scores)) plt.xlabel('Episode Number') plt.ylabel('Average Reward (Over Next %d Episodes)' % plot_every) plt.show() # print best 100-episode performance print(('Best Average Reward over %d Episodes: ' % plot_every), np.max(scores)) return Q # obtain the estimated optimal policy and corresponding action-value function Q_sarsamax = sarsamax(env, num_episodes=5000, alpha=0.01, gamma=1.0, eps_start=1.0, eps_decay=0.1, eps_min=1/5000, plot_every=100) # print the estimated optimal policy policy_sarsamax = np.array([np.argmax(Q_sarsamax[key]) if key in Q_sarsamax else -1 for key in np.arange(48)]).reshape((4, 12)) check_test.run_check('td_control_check', policy_sarsamax) print("\nEstimated Optimal Policy (UP = 0, RIGHT = 1, DOWN = 2, LEFT = 3, N/A = -1):") print(policy_sarsamax) # plot the estimated optimal state-value function plot_values([np.max(Q_sarsamax[key]) if key in Q_sarsamax else 0 for key in np.arange(48)]) # #################################### # Part 3: TD Control: Expected Sarsa # # #################################### def expsarsa(env, num_episodes, alpha, gamma=1.0, eps_start=1.0, eps_decay=.99999, eps_min=0.05, plot_every=100): Q = defaultdict(lambda: np.zeros(env.nA)) # initialize action-value function (empty dictionary of arrays) epsilon = eps_start # initialize epsilon # initialize performance monitor tmp_scores = deque(maxlen=plot_every) scores = deque(maxlen=num_episodes) # loop over episodes for i_episode in range(1, num_episodes+1): # monitor progress if i_episode % 100 == 0: print("\rEpisode {}/{}".format(i_episode, num_episodes), end="") sys.stdout.flush() # initialize score score = 0 # begin an episode, observe S state = env.reset() # set value of epsilon epsilon = max(epsilon*eps_decay, eps_min) while True: # get epsilon-greedy action probabilities policy_s = epsilon_greedy_probs(env, Q[state], epsilon) # pick action A action = np.random.choice(np.arange(env.nA), p=policy_s) # take action A, observe R, S' next_state, reward, done, info = env.step(action) # add reward to score score += reward # pick next best action A' policy_next_s = epsilon_greedy_probs(env, Q[next_state], epsilon) exp_next_Q = np.dot(Q[next_state], policy_next_s) # update TD estimate of Q Q[state][action] = update_Q_sarsa(Q[state][action], exp_next_Q, reward, alpha, gamma) # S <- S' state = next_state # until S is terminal if done: # update TD estimate of Q Q[state][action] = update_Q_sarsa(Q[state][action], 0, reward, alpha, gamma) # append score tmp_scores.append(score) break if (i_episode % plot_every == 0): scores.append(np.mean(tmp_scores)) # plot performance plt.plot(np.linspace(0, num_episodes, len(scores), endpoint=False), np.asarray(scores)) plt.xlabel('Episode Number') plt.ylabel('Average Reward (Over Next %d Episodes)' % plot_every) plt.show() # print best 100-episode performance print(('Best Average Reward over %d Episodes: ' % plot_every), np.max(scores)) return Q # obtain the estimated optimal policy and corresponding action-value function Q_expsarsa = expsarsa(env, num_episodes=5000, alpha=0.1, gamma=1.0, eps_start=1.0, eps_decay=0.5, eps_min=1/5000, plot_every=100) # print the estimated optimal policy policy_expsarsa = np.array([np.argmax(Q_expsarsa[key]) if key in Q_expsarsa else -1 for key in np.arange(48)]).reshape(4, 12) check_test.run_check('td_control_check', policy_expsarsa) print("\nEstimated Optimal Policy (UP = 0, RIGHT = 1, DOWN = 2, LEFT = 3, N/A = -1):") print(policy_expsarsa) # plot the estimated optimal state-value function plot_values([np.max(Q_expsarsa[key]) if key in Q_expsarsa else 0 for key in np.arange(48)])

41.011407

130

0.613202

1,491

10,786

4.296445

0.120724

0.030909

0.009366

0.019669

0.838589

0.823915

0.820169

0.806744

0.797846

0.756478

0

0.028279

0.249212

10,786

262

131

41.167939

0.762781

0.216762

0

0.645833

0

0.020833

0.075709

0

1

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false

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0.048611

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0.111111

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null

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null

0

6

ff2764f973c6f06d6a5be2f0e783df51cc0e7727

29

py

Python

evaluate/previous_works/svsyn/dataset/__init__.py

Syniez/Joint_360depth

4f28c3b5b7f648173480052e205e898c6c7a5151

[ "MIT" ]

92

2019-09-08T09:55:05.000Z

2022-02-21T21:29:40.000Z

dataset/__init__.py

zjsprit/SphericalViewSynthesis

fcdec95bf3ad109767d27396434b51cf3aad2b4b

[ "BSD-2-Clause" ]

4

2020-05-12T02:29:36.000Z

2021-11-26T07:49:43.000Z

dataset/__init__.py

zjsprit/SphericalViewSynthesis

fcdec95bf3ad109767d27396434b51cf3aad2b4b

[ "BSD-2-Clause" ]

26

2019-09-16T02:26:33.000Z

2021-10-21T03:55:02.000Z

from .dataset_360D import *

14.5

28

0.758621

4

29

5.25

1

0

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0.172414

29

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29

0.75

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true

0

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1

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null

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1

0

1

0

1

0

6

ff2fccd516d5ca3d479310ad6a38bc80a0797bcc

26,098

py

Python

ConvNNet.py

wmorning/IndianaJones

6e69be7a146148a3c1a85f794900f4680d4e7065

[ "MIT" ]

null

ConvNNet.py

wmorning/IndianaJones

6e69be7a146148a3c1a85f794900f4680d4e7065

[ "MIT" ]

4

2015-11-09T05:25:36.000Z

2015-11-23T19:06:25.000Z

ConvNNet.py

wmorning/IndianaJones

6e69be7a146148a3c1a85f794900f4680d4e7065

[ "MIT" ]

null

import numpy as np import inDianajonES as InD import tensorflow as tf import sys ''' ConvNNet implements a convolutional neural network using the TensorFlow framework. It consists of a ConvNNet class, which contains several functions: - Train inputs a list of images and artifacts, builds the design matrix, and implements the neural net (outputting the training) error as it goes - Test runs the neural net on a test data set. Can do whatever we make it do. - Save_model saves the session to the input filename. - Resume_from loads a saved session. Also @Joe if you are wondering why I used a class, its because the class allowed the session to be saved as a global variable without it being a script. ''' # ============================================================ class ConvNNet(object): ''' ConvNNet implements a convolutional neural network using the TensorFlow framework. ''' def __init__(self, nimg, farts, gridsize, cgfactor, mbsize=100, mbpath='/home/jderose/scratch/des/data', batchsize=1, cgafactor=1, Ncategories=29, Nstepspermb=20): self.nimg = nimg self.farts = farts self.gridsize = gridsize self.cgfactor = cgfactor self.cgafactor = cgafactor self.Nmb = (self.nimg+mbsize-1)//mbsize-1 self.Ncategories = Ncategories self.mbpath = mbpath self.mbsize = mbsize self.batchsize = batchsize self.Nstepspermb = Nstepspermb self.savefreq = 1000 if self.Ncategories == 29: self.twoclasses = False elif self.Ncategories == 2: self.twoclasses = True else: print 'You chose the wrong # of classes bro \n' print 'Switching to the default (29) classes \n' self.twoclasses = False self.Ncategories = 29 def convert_labels(self, y, twoclasses): ey = InD.enumerate_labels(y) ey2 = np.zeros([len(ey),self.Ncategories],float) if twoclasses is True: for i in range(len(ey)): ey2[i,ey[i]//29] = 1.0 else: for i in range(len(ey)): ey2[i,ey[i]-1] = 1.0 return ey2 def load_minibatch(self, filepath, nimg, farts, gridsize, cg, num,cg_additional=1,twoclasses=False): """ Load a mini batch of images and their labels. Labels need to be converted to tensorflow format inputs: filepath -- Path where the files are located nimg -- Number of images in the total batch farts -- Fraction of artifacts gridsize -- Number of pixels to a side cg -- Coarsegraining factor num -- The minibatch number cg_additional -- additional coursegraining to perform on the fly """ X = np.load('{0}/X_{1}_{2}_{3}_{4}_mb{5}.npy'.format(filepath, nimg, farts, gridsize, cg, num)) y = np.load('{0}/y_{1}_{2}_{3}_{4}_mb{5}.npy'.format(filepath, nimg, farts, gridsize, cg, num)) X[X==-99] = np.nan if cg_additional!=1: X = np.mean(np.mean(X.reshape([X.shape[0],gridsize//cg,gridsize//cg//cg_additional,cg_additional]),axis=3).T.reshape(gridsize//cg//cg_additional,gridsize//cg//cg_additional,cg_additional,X.shape[0]),axis=2).T.reshape([X.shape[0],(gridsize//cg//cg_additional)**2]) X = 255*(np.arcsinh(X)-np.atleast_2d(np.arcsinh(np.nanmin(X,axis=1))).T)/np.atleast_2d((np.arcsinh(np.nanmax(X,axis=1))-np.arcsinh(np.nanmin(X,axis=1)))).T X[np.isnan(X)] = 0 #X -= np.atleast_2d(np.mean(X,axis=1)).T #print(np.nanmean(X, axis=1)) ey = self.convert_labels(y, twoclasses) return X, ey def Train(self, Nsteps, Nfeatures_conv1=32, Wsize_1=5, Nfeatures_conv2=64, \ Wsize_2=5, Xlen_3=1024, gpu=False): ''' This function creates the design matrix and loads the true clasifications (if they don't already exist). It then runs the neural net to train the optimal predicting scheme. * Currently the neural net is very similar to the one used in the MNIST tutorial from Tensorflow (except modified to use our images etc.). We should modify it further to fit our needs * Function inputs are below: - Nsteps is number of training steps to run - Nfeatures_conv1 is the number of convolution features (images) in the first layer - Wsize_1 is the size of the first convolution filter (assumed to be square) - Nfeatures_conv2 is the number of convolution features (images) in the second layer - Wsize_2 is the size of the second convolution filter (assumed to be square). - Xlen_3 is the length of the densely connected features vector. ''' # start neural net: define x,y placeholders and create session #self.Session = tf.InteractiveSession() # useful if running from notebook print('Allocating placeholders') self.x = tf.placeholder("float",shape=[None,(self.gridsize//(self.cgfactor*self.cgafactor))**2]) self.x_image = tf.reshape(self.x,[-1,(self.gridsize//(self.cgfactor*self.cgafactor)),(self.gridsize//(self.cgfactor*self.cgafactor)),1]) self.y_ = tf.placeholder("float",shape=[None,self.Ncategories]) # create first layer # here we create 32 new images using a convolution with a # 5x5x32 weights filter plus a bias (one for each new image) # This is equivalent to measuring 32 features for each 5x5 # pannel of the original image. We'll likely want many more # features, and to use more pixels. Keep that in mind. #self.W_conv0 = bias_variable([5,5,1,1]) #self.h_conv0 = tf.nn.relu(conv2d(self.x_image,self.W_conv0)) print('Creating first layer') self.W_conv1 = weight_variable([Wsize_1,Wsize_1,1,Nfeatures_conv1]) # play around with altering sizes self.b_conv1 = bias_variable([Nfeatures_conv1])# length should be same as last dimension of W_conv1 self.h_conv1 = tf.nn.relu(conv2d(self.x_image, self.W_conv1)+self.b_conv1) # split each image into 4, and obtain the maximum quadrant self.h_pool1 = max_pool_2x2(self.h_conv1) print('Creating second layer') # create second layer # here each of our 32 intermediate images is convolved with # a 5x5x64 weights filter. We create 64 new images by summing # over all 32 convolutions. Each of the 64 images has its own bias # term. The shape of the result is the shape of the original image # divided by 4 on each axis by 64 (i.e. if you started with a # 2048x2048 image, you now have a 512x512x64 image) self.W_conv2 = weight_variable([Wsize_2,Wsize_2,Nfeatures_conv1,Nfeatures_conv2]) # again, play with altering sizes self.b_conv2 = bias_variable([Nfeatures_conv2]) # of the first two axes self.h_conv2 = tf.nn.relu(conv2d(self.h_pool1, self.W_conv2) + self.b_conv2) # split each image into 4, and obtain the maximum quadrant self.h_pool2 = max_pool_2x2(self.h_conv2) print('Creating densely connected layer') # Densely Connected layer # Here, the 7x7x64 image tensor is flattened, and we get a # 1x1024 vector using the form h_fc1 = h_2 * W + b self.W_fc1 = weight_variable([(self.gridsize//(self.cgfactor*self.cgafactor)//4)**2*Nfeatures_conv2, Xlen_3]) self.b_fc1 = bias_variable([Xlen_3]) self.h_pool2_flat = tf.reshape(self.h_pool2, [-1, \ (self.gridsize//(self.cgfactor*self.cgafactor)//4) \ *(self.gridsize//(self.cgfactor*self.cgafactor)//4)*Nfeatures_conv2]) self.h_fc1 = tf.nn.relu(tf.matmul(self.h_pool2_flat, self.W_fc1)+self.b_fc1) print('Dropout') # avoid overfitting using tensorflows dropout function. # specifically, we keep each component of h_fc1 with # probability keep_prob. self.keep_prob = tf.placeholder("float") self.h_fc1_drop = tf.nn.dropout(self.h_fc1, self.keep_prob) print('Softmax') # finally, a softmax regression to predict the output self.W_fc2 = weight_variable([Xlen_3,self.Ncategories]) self.b_fc2 = bias_variable([self.Ncategories]) print('Setting output format') # output of NN self.y_conv = tf.nn.softmax(tf.matmul(self.h_fc1_drop, self.W_fc2) + self.b_fc2) self.Session = tf.Session() print('Setting optimization parameters') # run the optimization. We'll minimize the cross entropy #self.train_step = tf.train.AdamOptimizer(1e-2, epsilon=0.1).minimize(self.cross_entropy) self.cross_entropy = -tf.reduce_sum(self.y_*tf.log(self.y_conv)) self.train_step = tf.train.AdamOptimizer(1e-5, epsilon=0.1).minimize(self.cross_entropy) #self.nfn = min_false_neg(self.y_conv, self.y_, self.Ncategories, session=self.Session) #self.chisq = tf.reduce_mean(tf.pow(tf.sub(self.y_,self.y_conv),2)+1e-4) #self.train_step = tf.train.GradientDescentOptimizer(1e-4).minimize(self.cross_entropy) self.correct_prediction = tf.equal(tf.argmax(self.y_conv,1), tf.argmax(self.y_,1)) self.accuracy = tf.reduce_mean(tf.cast(self.correct_prediction,"float")) print('Running session') self.Session.run(tf.initialize_all_variables()) # batch gradient descent ticker current_index = 0 testX, testy = self.load_minibatch(self.mbpath, self.nimg, self.farts, self.gridsize, self.cgfactor, self.Nmb, cg_additional=self.cgafactor,twoclasses=self.twoclasses) xentropy = [] testerr = [] for npass in range(Nsteps): for i in range(self.Nmb): #print('Minibatch {0}'.format(i)) self.X, self.y = self.load_minibatch(self.mbpath, self.nimg, self.farts, self.gridsize, self.cgfactor, i, cg_additional=self.cgafactor, twoclasses=self.twoclasses) for j in range(self.Nstepspermb): #print('Batch {0}'.format(j)) # update the parameters using batch gradient descent. # use 50 examples per iteration (can change) next_set = np.arange(current_index,current_index+self.batchsize,1)% self.mbsize x_examples = self.X[next_set,:] y_examples = self.y[next_set,:] current_index = (current_index+self.batchsize) % self.mbsize #for every thousandth step, print the training error. if (i*self.Nstepspermb+j)%1000 ==0: train_accuracy = self.accuracy.eval(feed_dict={self.x:x_examples \ , self.y_: y_examples, self.keep_prob: 1.0},session=self.Session) print "step %d, training accuracy %g"%(i, train_accuracy) self.train_step.run(feed_dict={self.x: x_examples, self.y_: y_examples, self.keep_prob: 0.5},session=self.Session) #debugging step --> dont keep if (npass !=0) or (j != 0): self.W1old = 1*self.W1curr self.W1curr = self.W_conv1.eval(session=self.Session) if (npass!=0) or (j!=0): #print('model evolved by: ', np.sum(abs(self.W1curr-self.W1old))) pass if (i%self.savefreq==0) & (i!=0): if gpu: self.Save_model('Trained_Model_{0}_{1}_{2}_{3}_gpu_mb{4}.tfm'.format(self.nimg, self.farts, self.gridsize, (self.cgfactor*self.cgafactor), i), i*self.Nstepspermb) else: self.Save_model('Trained_Model_{0}_{1}_{2}_{3}_mb{4}.tfm'.format(self.nimg, self.farts, self.gridsize, (self.cgfactor*self.cgafactor), i), i*self.Nstepspermb) testerr.append(self.Test(testX, testy)) xentropy.append(self.cross_entropy.eval(feed_dict = {self.x: testX, self.y_: testy, self.keep_prob:1.0},session=self.Session)) print(xentropy[-1]) return testerr, xentropy def Train2(self, Nsteps, alpha=1e-3, Nfeatures_conv1=32, Wsize_1=5, Nfeatures_conv2=64, \ Wsize_2=5, Xlen_3=1024, gpu=False): ''' This function creates the design matrix and loads the true clasifications (if they don't already exist). It then runs the neural net to train the optimal predicting scheme. * Currently the neural net is very similar to the one used in the MNIST tutorial from Tensorflow (except modified to use our images etc.). We should modify it further to fit our needs * Function inputs are below: - Nsteps is number of training steps to run - Nfeatures_conv1 is the number of convolution features (images) in the first layer - Wsize_1 is the size of the first convolution filter (assumed to be square) - Nfeatures_conv2 is the number of convolution features (images) in the second layer - Wsize_2 is the size of the second convolution filter (assumed to be square). - Xlen_3 is the length of the densely connected features vector. ''' # start neural net: define x,y placeholders and create session #self.Session = tf.InteractiveSession() # useful if running from notebook print('Allocating placeholders') self.x = tf.placeholder("float",shape=[None,(self.gridsize//(self.cgfactor*self.cgafactor))**2]) self.x_image = tf.reshape(self.x,[-1,(self.gridsize//(self.cgfactor*self.cgafactor)),(self.gridsize//(self.cgfactor*self.cgafactor)),1]) self.y_ = tf.placeholder("float",shape=[None,self.Ncategories]) # create first layer # here we create 32 new images using a convolution with a # 5x5x32 weights filter plus a bias (one for each new image) # This is equivalent to measuring 32 features for each 5x5 # pannel of the original image. We'll likely want many more # features, and to use more pixels. Keep that in mind. print('Creating first layer') self.W_conv1 = weight_variable([Wsize_1,Wsize_1,1,Nfeatures_conv1]) # play around with altering sizes self.b_conv1 = bias_variable([Nfeatures_conv1])# length should be same as last dimension of W_conv1 self.h_conv1 = tf.nn.relu(conv2d(self.x_image, self.W_conv1)+self.b_conv1) # split each image into 4, and obtain the maximum quadrant self.h_pool1 = max_pool_2x2(self.h_conv1) print('Creating second layer') # create second layer # here each of our 32 intermediate images is convolved with # a 5x5x64 weights filter. We create 64 new images by summing # over all 32 convolutions. Each of the 64 images has its own bias # term. The shape of the result is the shape of the original image # divided by 4 on each axis by 64 (i.e. if you started with a # 2048x2048 image, you now have a 512x512x64 image) self.W_conv2 = weight_variable([Wsize_2,Wsize_2,Nfeatures_conv1,Nfeatures_conv2]) # again, play with altering sizes self.b_conv2 = bias_variable([Nfeatures_conv2]) # of the first two axes self.h_conv2 = tf.nn.relu(conv2d(self.h_pool1, self.W_conv2) + self.b_conv2) # split each image into 4, and obtain the maximum quadrant self.h_pool2 = max_pool_2x2(self.h_conv2) self.keep_prob1 = tf.placeholder("float") self.h_drop2 = tf.nn.dropout(self.h_pool2, self.keep_prob1) print('Creating densely connected layer') # Densely Connected layer # Here, the 7x7x64 image tensor is flattened, and we get a # 1x1024 vector using the form h_fc1 = h_2 * W + b self.W_fc1 = weight_variable([(self.gridsize//(self.cgfactor*self.cgafactor)//4)**2*Nfeatures_conv2, Xlen_3]) self.b_fc1 = bias_variable([Xlen_3]) self.h_pool2_flat = tf.reshape(self.h_drop2, [-1, \ (self.gridsize//(self.cgfactor*self.cgafactor)//4) \ *(self.gridsize//(self.cgfactor*self.cgafactor)//4)*Nfeatures_conv2]) self.h_fc1 = tf.nn.relu(tf.matmul(self.h_pool2_flat, self.W_fc1)+self.b_fc1) print('Dropout') # avoid overfitting using tensorflows dropout function. # specifically, we keep each component of h_fc1 with # probability keep_prob. self.keep_prob2 = tf.placeholder("float") self.h_fc1_drop = tf.nn.dropout(self.h_fc1, self.keep_prob2) self.W_fc2 = weight_variable([Xlen_3, Xlen_3]) self.b_fc2 = bias_variable([Xlen_3]) self.h_fc2 = tf.nn.relu(tf.matmul(self.h_fc1_drop, self.W_fc2)+self.b_fc2) self.keep_prob3 = tf.placeholder("float") self.h_fc2_drop = tf.nn.dropout(self.h_fc2, self.keep_prob3) print('Softmax') # finally, a softmax regression to predict the output self.W_fc3 = weight_variable([Xlen_3,self.Ncategories]) self.b_fc3 = bias_variable([self.Ncategories]) print('Setting output format') # output of NN self.y_conv = tf.nn.softmax(tf.matmul(self.h_fc2_drop, self.W_fc3) + self.b_fc3) self.Session = tf.Session() print('Setting optimization parameters') # run the optimization. We'll minimize the cross entropy self.cross_entropy = -tf.reduce_sum(self.y_*tf.log(tf.clip_by_value(self.y_conv, 1e-10, 1.0))) self.train_step = tf.train.AdamOptimizer(alpha, epsilon=0.1).minimize(self.cross_entropy) #self.nfn = min_false_neg(self.y_conv, self.y_, self.Ncategories, session=self.Session) #self.chisq = tf.reduce_mean(tf.pow(tf.sub(self.y_,self.y_conv),2)+1e-4) #self.train_step = tf.train.GradientDescentOptimizer(1e-2).minimize(self.cross_entropy) self.correct_prediction = tf.equal(tf.argmax(self.y_conv,1), tf.argmax(self.y_,1)) self.accuracy = tf.reduce_mean(tf.cast(self.correct_prediction,"float")) print('Running session') self.Session.run(tf.initialize_all_variables()) # batch gradient descent ticker current_index = 0 testX, testy = self.load_minibatch(self.mbpath, self.nimg, self.farts, self.gridsize, self.cgfactor, self.Nmb, cg_additional=self.cgafactor,twoclasses=self.twoclasses) xentropy = [] testerr = [] for npass in range(Nsteps): for i in range(self.Nmb): #print('Minibatch {0}'.format(i)) self.X, self.y = self.load_minibatch(self.mbpath, self.nimg, self.farts, self.gridsize, self.cgfactor, i, cg_additional=self.cgafactor, twoclasses=self.twoclasses) for j in range(self.Nstepspermb): #print('Batch {0}'.format(j)) # update the parameters using batch gradient descent. # use 50 examples per iteration (can change) next_set = np.arange(current_index,current_index+self.batchsize,1)% self.mbsize x_examples = self.X[next_set,:] y_examples = self.y[next_set,:] current_index = (current_index+self.batchsize) % self.mbsize #for every thousandth step, print the training error. if (i*self.Nstepspermb+j)%1000 ==0: train_accuracy = self.accuracy.eval(feed_dict={self.x:x_examples \ , self.y_: y_examples, self.keep_prob1: 1.0\ , self.keep_prob2: 1.0, self.keep_prob3: 1.0},session=self.Session) print "step %d, training accuracy %g"%(i, train_accuracy) self.train_step.run(feed_dict={self.x: x_examples, self.y_: y_examples, self.keep_prob1: 0.25, \ self.keep_prob2: 0.5, self.keep_prob3:0.5},session=self.Session) #debugging step --> dont keep if (npass !=0) or (j != 0): self.W1old = 1*self.W1curr self.W1curr = self.W_conv1.eval(session=self.Session) if (npass!=0) or (j!=0): #print('model evolved by: ', np.sum(abs(self.W1curr-self.W1old))) pass if (i%self.savefreq==0) & (i!=0): if gpu: self.Save_model('Trained_Model_{0}_{1}_{2}_{3}_gpu_mb{4}.tfm'.format(self.nimg, self.farts, self.gridsize, (self.cgfactor*self.cgafactor), i), i*self.Nstepspermb) else: self.Save_model('Trained_Model_{0}_{1}_{2}_{3}_mb{4}.tfm'.format(self.nimg, self.farts, self.gridsize, (self.cgfactor*self.cgafactor), i), i*self.Nstepspermb) testerr.append(self.Test(testX, testy,test2=True)) xentropy.append(self.cross_entropy.eval(feed_dict = {self.x: testX, self.y_: testy, self.keep_prob1:1.0, self.keep_prob2:1.0, self.keep_prob3:1.0},session=self.Session)) print(xentropy[-1]) return testerr, xentropy def Test(self,test_data_x,test_data_y,test2=False): ''' Test the current model on an input set of data ''' if test2: test_accuracy = self.accuracy.eval(feed_dict={self.x:test_data_x \ , self.y_: test_data_y, self.keep_prob1: 1.0, self.keep_prob2:1.0, self.keep_prob3:1.0},session=self.Session) else: test_accuracy = self.accuracy.eval(feed_dict={self.x:test_data_x \ , self.y_: test_data_y, self.keep_prob: 1.0},session=self.Session) print('Test Accuracy: ', test_accuracy) #raise Exception('cannot test model yet \n') return test_accuracy def Predict(self, data_x, test2=False): ''' Predict the classes for unseen data :) ''' predictions = tf.arg_max(self.y_conv,1) if test2: return( predictions.eval(feed_dict={self.x:data_x \ , self.keep_prob1: 1.0, self.keep_prob2:1.0, self.keep_prob3:1.0},session=self.Session)) else: return( predictions.eval(feed_dict={self.x:data_x \ , self.keep_prob: 1.0},session=self.Session)) def Save_model(self, filename, Nsteps): ''' Use tensorflow's train.Saver to create checkpoint file. - Nsteps is number of training steps that have already been run. ''' #raise Exception('cannot save model yet \n') saver = tf.train.Saver() saver.save(self.Session, filename, global_step=Nsteps) return def Resume_from(self, filename): ''' Use tensorflow's train.Saver to reload a saved checkpoint, and resume training. ''' raise Exception('cannot resume training yet \n') self.Session = tf.Session() saver = tf.train.Saver() saver.restore(self.Session, filename) return # ------------------------------------------------------------ ''' Neural net functions ''' def weight_variable(shape): ''' Initialize a tensorflow weight variable ''' initial = tf.truncated_normal(shape, stddev=10**-2) return tf.Variable(initial) # note: this won't let us spread across multiple GPUs. def bias_variable(shape): ''' Initialize a tensorflow bias variable ''' #initial = tf.random_normal(shape, stddev=1e-3) initial = tf.constant(0.001,shape=shape) return tf.Variable(initial) def conv2d(x,W): ''' Convolve a 2d image (x) with a filter (W) ''' return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME') def max_pool_2x2(x): ''' Return quadrant of image with max pixel values ''' return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME') def min_false_neg(y, ytrue, nclass, naclass='last', session=None): Lweights = np.ones((nclass, nclass), dtype=np.float32) if naclass=='last': Lweights[-1,:] = 1000 else: Lweights[0,:] = 1000 dL = Lweights.diagonal() dL = 0 Lweights = tf.constant(Lweights) print(Lweights.get_shape()) print(y.get_shape()) print(ytrue.get_shape()) L = tf.matmul(ytrue, tf.matmul(Lweights, tf.transpose(y))) print(L.get_shape()) L = tf.pack([L[i,i] for i in range(nclass)]) return tf.reduce_sum(L) # ------------------------------------------------------------ if __name__=='__main__': if len(sys.argv)>1: gpu = True else: gpu = False cnn = ConvNNet(1000, 1.0, 2048, 2, cgafactor=8) cnn.Train(100, Nfeatures_conv1=16, Nfeatures_conv2=32, Xlen_3=10, gpu=gpu)

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ff55a601271d3caff5c0402f0dcc0e976c2f1049

7,251

py

Python

tests/functional/basic_tests.py

OpertusMundi/clustering-outliers-service

6d3d89eaa8d3c491c5c78d4c12b67aef01391e32

[ "Apache-2.0" ]

null

tests/functional/basic_tests.py

OpertusMundi/clustering-outliers-service

6d3d89eaa8d3c491c5c78d4c12b67aef01391e32

[ "Apache-2.0" ]

null

tests/functional/basic_tests.py

OpertusMundi/clustering-outliers-service

6d3d89eaa8d3c491c5c78d4c12b67aef01391e32

[ "Apache-2.0" ]

null

from os import path, getenv, mkdir import tempfile import logging from clustering_outliers.app import app _tempdir: str = "" def setup_module(): print(f" == Setting up tests for {__name__}") app.config['TESTING'] = True global _tempdir _tempdir = getenv('TEMPDIR') if _tempdir: try: mkdir(_tempdir) except FileExistsError: pass else: _tempdir = tempfile.gettempdir() def teardown_module(): print(f" == Tearing down tests for {__name__}") # Tests dirname = path.dirname(__file__) csv_file = path.join(dirname, '..', 'test_data', 'luxembourg-pois.osm.csv') shp_file = path.join(dirname, '..', 'test_data', 'get_pois_v02_corfu_2100.zip') def test_get_documentation_1(): with app.test_client() as client: res = client.get('/', query_string=dict(), headers=dict()) assert res.status_code == 200 r = res.get_json() assert not (r.get('openapi') is None) def test_get_health_check(): with app.test_client() as client: res = client.get('/_health', query_string=dict(), headers=dict()) assert res.status_code == 200 r = res.get_json() if 'reason' in r: logging.error('The service is unhealthy: %(reason)s\n%(detail)s', r) logging.debug("From /_health: %s" % r) assert r['status'] == 'OK' def test_file_kmeans_csv(): payload = {'resource': (open(csv_file, 'rb'), 'sample.csv'), "resource_type": "csv", "id_column": "ID", "columns-0": "LON", "columns-1": "LAT"} with app.test_client() as client: res = client.post('/kmeans/file', data=payload, content_type='multipart/form-data') assert res.status_code == 200 r = res.get_json() assert set(r.keys()) == {'cluster_centers', 'ids', 'labels'} def test_file_kmeans_shp(): payload = {'resource': (open(shp_file, 'rb'), 'sample.zip'), "resource_type": "shp"} with app.test_client() as client: res = client.post('/kmeans/file', data=payload, content_type='multipart/form-data') assert res.status_code == 200 r = res.get_json() assert set(r.keys()) == {'cluster_centers', 'ids', 'labels'} def test_path_kmeans_csv(): payload = {"resource": csv_file, "resource_type": "csv", "id_column": "ID", "columns-0": "LON", "columns-1": "LAT"} with app.test_client() as client: res = client.post('/kmeans/path', data=payload, content_type='multipart/form-data') assert res.status_code == 200 r = res.get_json() assert set(r.keys()) == {'cluster_centers', 'ids', 'labels'} def test_path_kmeans_shp(): payload = {"resource": shp_file, "resource_type": "shp"} with app.test_client() as client: res = client.post('/kmeans/path', data=payload, content_type='multipart/form-data') assert res.status_code == 200 r = res.get_json() assert set(r.keys()) == {'cluster_centers', 'ids', 'labels'} def test_file_dbscan_csv(): payload = {'resource': (open(csv_file, 'rb'), 'sample.csv'), "resource_type": "csv", "id_column": "ID", "columns-0": "LON", "columns-1": "LAT"} with app.test_client() as client: res = client.post('/dbscan/file', data=payload, content_type='multipart/form-data') assert res.status_code == 200 r = res.get_json() assert set(r.keys()) == {'core_sample_indices', 'components', 'ids', 'labels'} def test_file_dbscan_shp(): payload = {'resource': (open(shp_file, 'rb'), 'sample.zip'), "resource_type": "shp"} with app.test_client() as client: res = client.post('/dbscan/file', data=payload, content_type='multipart/form-data') assert res.status_code == 200 r = res.get_json() assert set(r.keys()) == {'core_sample_indices', 'components', 'ids', 'labels'} def test_file_agglomerative_csv(): payload = {'resource': (open(csv_file, 'rb'), 'sample.csv'), "resource_type": "csv", "id_column": "ID", "columns-0": "LON", "columns-1": "LAT"} with app.test_client() as client: res = client.post('/agglomerative/file', data=payload, content_type='multipart/form-data') assert res.status_code == 200 r = res.get_json() assert set(r.keys()) == {'n_clusters', 'n_leaves', 'n_connected_components', 'children', 'ids', 'labels'} def test_file_agglomerative_shp(): payload = {'resource': (open(shp_file, 'rb'), 'sample.zip'), "resource_type": "shp"} with app.test_client() as client: res = client.post('/agglomerative/file', data=payload, content_type='multipart/form-data') assert res.status_code == 200 r = res.get_json() assert set(r.keys()) == {'n_clusters', 'n_leaves', 'n_connected_components', 'children', 'ids', 'labels'} def test_file_isolation_forest_csv(): payload = {'resource': (open(csv_file, 'rb'), 'sample.csv'), "resource_type": "csv", "id_column": "ID", "columns-0": "LON", "columns-1": "LAT"} with app.test_client() as client: res = client.post('/isolation_forest/file', data=payload, content_type='multipart/form-data') assert res.status_code == 200 r = res.get_json() assert isinstance(r, dict) def test_file_isolation_forest_shp(): payload = {'resource': (open(shp_file, 'rb'), 'sample.zip'), "resource_type": "shp"} with app.test_client() as client: res = client.post('/isolation_forest/file', data=payload, content_type='multipart/form-data') assert res.status_code == 200 r = res.get_json() assert isinstance(r, dict) def test_file_local_outlier_factor_csv(): payload = {'resource': (open(csv_file, 'rb'), 'sample.csv'), "resource_type": "csv", "id_column": "ID", "columns-0": "LON", "columns-1": "LAT"} with app.test_client() as client: res = client.post('/local_outlier_factor/file', data=payload, content_type='multipart/form-data') assert res.status_code == 200 r = res.get_json() assert isinstance(r, dict) def test_file_local_outlier_factor_shp(): payload = {'resource': (open(shp_file, 'rb'), 'sample.zip'), "resource_type": "shp"} with app.test_client() as client: res = client.post('/local_outlier_factor/file', data=payload, content_type='multipart/form-data') assert res.status_code == 200 r = res.get_json() assert isinstance(r, dict) def test_file_one_class_svm_csv(): payload = {'resource': (open(csv_file, 'rb'), 'sample.csv'), "resource_type": "csv", "id_column": "ID", "columns-0": "LON", "columns-1": "LAT"} with app.test_client() as client: res = client.post('/one_class_svm/file', data=payload, content_type='multipart/form-data') assert res.status_code == 200 r = res.get_json() assert isinstance(r, dict) def test_file_one_class_svm_shp(): payload = {'resource': (open(shp_file, 'rb'), 'sample.zip'), "resource_type": "shp"} with app.test_client() as client: res = client.post('/one_class_svm/file', data=payload, content_type='multipart/form-data') assert res.status_code == 200 r = res.get_json() assert isinstance(r, dict)

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null

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null

0

6

440febc339c3c2f7ac3da51fcf2c99c86ca1b5b6

49

py

Python

dask_cuda/__init__.py

paulhendricks/dask-cuda

a8b3d34d00752c57d5ab892b99a5d518dfa4c71d

[ "Apache-2.0" ]

19

2019-01-04T17:50:22.000Z

2019-06-26T02:23:27.000Z

dask_cuda/__init__.py

paulhendricks/dask-cuda

a8b3d34d00752c57d5ab892b99a5d518dfa4c71d

[ "Apache-2.0" ]

4

2019-01-04T17:47:44.000Z

2019-03-29T14:47:07.000Z

dask_cuda/__init__.py

paulhendricks/dask-cuda

a8b3d34d00752c57d5ab892b99a5d518dfa4c71d

[ "Apache-2.0" ]

1

2021-09-20T15:55:35.000Z

from .local_cuda_cluster import LocalCUDACluster

24.5

48

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null

0

1

0

1

0

1

0

6

443ffcd78c071822a07f85f42752b9be2825cffe

84

py

Python

_example/replace.py

flew-software/Dem

20b7eb9bc7c11f1baf23acfe7bfbab359ddd97fb

[ "MIT" ]

1

2021-02-17T08:30:05.000Z

_example/replace.py

flew-software/Dem

20b7eb9bc7c11f1baf23acfe7bfbab359ddd97fb

[ "MIT" ]

null

_example/replace.py

flew-software/Dem

20b7eb9bc7c11f1baf23acfe7bfbab359ddd97fb

[ "MIT" ]

null

import _2D a = [[1, 2, 3][1, 2, 3][1, 2, 3]] print(a) print(_2D.Replace(a, 3, 4))

12

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92371fa6f6cf59f8dd0ad37fce6e01d457af270f

5,013

py

Python

models/base/init_utils.py

sAviOr287/imagenet_ICLR

1ac83d799f5335355161156aa9bba63e0d82a063

[ "MIT" ]

null

models/base/init_utils.py

sAviOr287/imagenet_ICLR

1ac83d799f5335355161156aa9bba63e0d82a063

[ "MIT" ]

null

models/base/init_utils.py

sAviOr287/imagenet_ICLR

1ac83d799f5335355161156aa9bba63e0d82a063

[ "MIT" ]

null

import torch import torch.nn as nn import math def weights_init_kaiming_xavier(m): # print('=> weights init') if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') # nn.init.normal_(m.weight, 0, 0.1) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.Linear): # nn.init.xavier_normal(m.weight) nn.init.normal_(m.weight, 0, 0.01) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): # Note that BN's running_var/mean are # already initialized to 1 and 0 respectively. if m.weight is not None: m.weight.data.fill_(1.0) if m.bias is not None: m.bias.data.zero_() def weights_init_kaiming_relu(m): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out',nonlinearity='relu') # nn.init.normal_(m.weight, 0, 0.1) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.Linear): nn.init.kaiming_normal_(m.weight, nonlinearity='relu') elif isinstance(m, nn.BatchNorm2d): # Note that BN's running_var/mean are # already initialized to 1 and 0 respectively. if m.weight is not None: m.weight.data.fill_(1.0) if m.bias is not None: m.bias.data.zero_() def weights_init_kaiming_tanh(m): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out',nonlinearity='tanh') # nn.init.normal_(m.weight, 0, 0.1) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.Linear): nn.init.kaiming_normal_(m.weight, nonlinearity='tanh') elif isinstance(m, nn.BatchNorm2d): # Note that BN's running_var/mean are # already initialized to 1 and 0 respectively. if m.weight is not None: m.weight.data.fill_(1.0) if m.bias is not None: m.bias.data.zero_() def weights_init_xavier(m): if isinstance(m, nn.Conv2d): nn.init.xavier_normal_(m.weight) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.Linear): nn.init.xavier_normal_(m.weight) elif isinstance(m, nn.BatchNorm2d): # Note that BN's running_var/mean are # already initialized to 1 and 0 respectively. if m.weight is not None: m.weight.data.fill_(1.0) if m.bias is not None: m.bias.data.zero_() def weights_init_EOC(m): if isinstance(m, nn.Conv2d): EOC_weights(m.weight) if m.bias is not None: EOC_bias(m.bias) elif isinstance(m, nn.Linear): EOC_weights(m.weight) if m.bias is not None: EOC_bias(m.bias) elif isinstance(m, nn.BatchNorm2d): # Note that BN's running_var/mean are # already initialized to 1 and 0 respectively. if m.weight is not None: m.weight.data.fill_(1.0) if m.bias is not None: m.bias.data.zero_() def weights_init_ord(m): if isinstance(m, nn.Conv2d): ord_weights(m.weight) if m.bias is not None: ord_bias(m.bias) elif isinstance(m, nn.Linear): ord_weights(m.weight) if m.bias is not None: ord_bias(m.bias) elif isinstance(m, nn.BatchNorm2d): # Note that BN's running_var/mean are # already initialized to 1 and 0 respectively. if m.weight is not None: m.weight.data.fill_(1.0) if m.bias is not None: m.bias.data.zero_() def _calculate_fan_in_and_fan_out(tensor): dimensions = tensor.dim() if dimensions < 2: raise ValueError("Fan in and fan out can not be computed for tensor with fewer than 2 dimensions") if dimensions == 2: # Linear fan_in = tensor.size(1) fan_out = tensor.size(0) else: num_input_fmaps = tensor.size(1) num_output_fmaps = tensor.size(0) receptive_field_size = 1 if tensor.dim() > 2: receptive_field_size = tensor[0][0].numel() fan_in = num_input_fmaps * receptive_field_size fan_out = num_output_fmaps * receptive_field_size return fan_in, fan_out def EOC_weights(tensor, act='relu'): print('#' * 40) print('We are using {} activation on EOC'.format(act)) print('#' * 40) fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) sigma_w2 = 1. if act == 'relu': #print('relu') sigma_w2 = 2. q = 'constant variance' elif act == 'tanh': #print('tanh') sigma_w2 = 1.2981 ** 2 q = 0.49 elif act == 'elu': #print('elu') sigma_w2 = 1.22459 ** 2 q = 1.01 std = math.sqrt(sigma_w2 / float(fan_in)) with torch.no_grad(): return tensor.normal_(0, std) def EOC_bias(tensor, act='relu'): print('#' * 40) print('We are using {} activation on EOC'.format(act)) print('#' * 40) sigma_b2 = 0. if act == 'relu': sigma_b2 = 1e-16 q = 'constant variance' elif act == 'tanh': sigma_b2 = 0.2 ** 2 q = 0.49 elif act == 'elu': sigma_b2 = 0.2 ** 2 q = 1.01 std = math.sqrt(sigma_b2) with torch.no_grad(): return tensor.normal_(0, std) def ord_weights(tensor,sigma_w2): print('#' * 40) print('Ordered phase with {}sigma_w2'.format(sigma_w2)) print('#' * 40) fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) std = math.sqrt(sigma_w2 / float(fan_in)) with torch.no_grad(): return tensor.normal_(0, std) def ord_bias(tensor): sigma_b2 = 1. std = math.sqrt(sigma_b2) with torch.no_grad(): return tensor.normal_(0, std)

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0.689607

876

5,013

3.784247

0.116438

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0.054299

0.048265

0.788839

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0.721267

0.704072

0

0.031559

0.171953

5,013

192

101

26.109375

0.767044

0.137443

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0.708333

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0.066016

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0.076389

false

0

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0

null

0

1

0

1

0

null

0

6

92463d0790fd4f8568e0e8a342f8baf27bd768e9

170

py

Python

zerovl/utils/__init__.py

zerovl/ZeroVL

b48794e74fed0f80adf5fa3010481064411c4182

[ "MIT" ]

14

2022-01-19T08:08:29.000Z

2022-03-10T05:55:36.000Z

zerovl/utils/__init__.py

zerovl/ZeroVL

b48794e74fed0f80adf5fa3010481064411c4182

[ "MIT" ]

2

2022-02-25T14:35:47.000Z

2022-03-01T03:11:13.000Z

zerovl/utils/__init__.py

zerovl/ZeroVL

b48794e74fed0f80adf5fa3010481064411c4182

[ "MIT" ]

3

2022-02-09T01:23:11.000Z

2022-02-15T11:45:30.000Z

from . import logger from .context import * from .dist import * from .misc import * from .registry import * from .checkpoint_utils import * from .interpolate_pe import *

21.25

31

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170

5.521739

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0

0.164706

170

8

32

21.25

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0

1

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true

0

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0

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6