xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

24e1ac4f666b8d35294b6791390859f3bd239115

96

py

Python

venv/lib/python3.8/site-packages/past/utils/__init__.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/past/utils/__init__.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/past/utils/__init__.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/7b/c9/75/b0e7dd8832777647240562cd5af0b56a10b9057e616870b64c674d800f

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py

Python

dockerfiles/jdk17/s2ipf_stub_l2_dockerfile/scripts/WrapperCore/JobOrderInventoryReader.py

CSC-PS-S2/s2-workflow

4efadfb164e14f4a142d9e1d011f7a881d39250f

[ "Apache-2.0" ]

7

2021-07-29T09:24:52.000Z

2021-12-15T17:23:58.000Z

dockerfiles/jdk17/s2ipf_stub_l2_dockerfile/scripts/WrapperCore/JobOrderInventoryReader.py

CSC-PS-S2/s2-workflow

4efadfb164e14f4a142d9e1d011f7a881d39250f

[ "Apache-2.0" ]

34

2021-09-28T07:38:32.000Z

2022-01-25T13:59:03.000Z

dockerfiles/jdk17/s2ipf_stub_l2_dockerfile/scripts/WrapperCore/JobOrderInventoryReader.py

CSC-PS-S2/s2-workflow

4efadfb164e14f4a142d9e1d011f7a881d39250f

[ "Apache-2.0" ]

null

# coding=utf-8 import copy from lxml import etree as et class JobOrderInventoryReader(object): def __init__(self, filename): self.tree_hdr = et.parse(filename) self.root_hdr = self.tree_hdr.getroot() self.filename = filename def set_acquisition_station(self,station): result = self.root_hdr.xpath( '//Ipf_Job_Order/Ipf_Conf/Acquisition_Station') result[0].text = station def set_processing_station(self,station): result = self.root_hdr.xpath( '//Ipf_Job_Order/Ipf_Conf/Processing_Station') result[0].text = station def set_l2_ds_input(self, filename): result = self.root_hdr.xpath( '//Ipf_Job_Order/List_of_Ipf_Procs[1]/Ipf_Proc/List_of_Inputs/Input[File_Type/text()=\'DS\']' '/List_of_File_Names[1]/File_Name') result[0].text = filename def set_l1_ds_input(self, filename): result = self.root_hdr.xpath( '//Ipf_Job_Order/List_of_Ipf_Procs[1]/Ipf_Proc/List_of_Inputs/Input[File_Type/text()=\'L1C_DS\']' '/List_of_File_Names[1]/File_Name') result[0].text = filename def set_l2_tl_input(self, filenames): base_xpath_filenames = '//Ipf_Job_Order/List_of_Ipf_Procs[1]/Ipf_Proc/List_of_Inputs/' \ 'Input[File_Type/text()=\'TL\']/List_of_File_Names' list_of_filenames = self.root_hdr.xpath(base_xpath_filenames) filename_node_tpl = self.root_hdr.xpath(base_xpath_filenames + '[1]/File_Name') for g in filenames: new_node = copy.deepcopy(filename_node_tpl[0]) new_node.text = g list_of_filenames[0].append(new_node) list_of_filenames[0].remove(filename_node_tpl[0]) list_of_filenames[0].attrib['count'] = str(len(filenames)) def set_l1_tl_input(self, filenames): base_xpath_filenames = '//Ipf_Job_Order/List_of_Ipf_Procs[1]/Ipf_Proc/List_of_Inputs/' \ 'Input[File_Type/text()=\'TL_L1C\']/List_of_File_Names' list_of_filenames = self.root_hdr.xpath(base_xpath_filenames) filename_node_tpl = self.root_hdr.xpath(base_xpath_filenames + '[1]/File_Name') for g in filenames: new_node = copy.deepcopy(filename_node_tpl[0]) new_node.text = g list_of_filenames[0].append(new_node) list_of_filenames[0].remove(filename_node_tpl[0]) list_of_filenames[0].attrib['count'] = str(len(filenames)) def set_working_input(self, filename): result = self.root_hdr.xpath( '//Ipf_Job_Order/List_of_Ipf_Procs[1]/Ipf_Proc/List_of_Inputs/Input[File_Type/' 'text()=\'WORKING\']/List_of_File_Names[1]/File_Name') result[0].text = filename def set_l2_tl_sensing_start(self, sensing_start): result = self.root_hdr.xpath('//Ipf_Job_Order/Ipf_Conf/Dynamic_Processing_Parameters/Processing_Parameter[Name/text()=\'SENSING_START\']/Value') result[0].text = sensing_start def set_l2_tl_sensing_stop(self, sensing_stop): result = self.root_hdr.xpath('//Ipf_Job_Order/Ipf_Conf/Dynamic_Processing_Parameters/Processing_Parameter[Name/text()=\'SENSING_STOP\']/Value') result[0].text = sensing_stop def write_to_file(self, filename): self.tree_hdr.write(filename, encoding="UTF-8")

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70877165d6eb7419d67348e97e49fd69895add2f

263

py

Python

desktop/core/ext-py/nose-1.3.7/functional_tests/support/ltfn/test_pak1/__init__.py

kokosing/hue

2307f5379a35aae9be871e836432e6f45138b3d9

[ "Apache-2.0" ]

5,079

2015-01-01T03:39:46.000Z

2022-03-31T07:38:22.000Z

desktop/core/ext-py/nose-1.3.7/functional_tests/support/ltfn/test_pak1/__init__.py

zks888/hue

93a8c370713e70b216c428caa2f75185ef809deb

[ "Apache-2.0" ]

1,623

2015-01-01T08:06:24.000Z

2022-03-30T19:48:52.000Z

desktop/core/ext-py/nose-1.3.7/functional_tests/support/ltfn/test_pak1/__init__.py

zks888/hue

93a8c370713e70b216c428caa2f75185ef809deb

[ "Apache-2.0" ]

2,033

2015-01-04T07:18:02.000Z

2022-03-28T19:55:47.000Z

from state import called def setup(): called.append('test_pak1.setup') def teardown(): called.append('test_pak1.teardown') def test_one_one(): called.append('test_pak1.test_one_one') def test_one_two(): called.append('test_pak1.test_one_two')

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6

7094832b7cb4be99f15cc22b4a9f8ac37513298e

229

py

Python

fdbk/data_tools/functions/__init__.py

kangasta/fdbk

426a04131869ceefd3bd2c80d327b60a3a8e2d7b

[ "MIT" ]

1

2019-05-04T09:18:48.000Z

fdbk/data_tools/functions/__init__.py

kangasta/fdbk

426a04131869ceefd3bd2c80d327b60a3a8e2d7b

[ "MIT" ]

36

2018-10-25T13:29:12.000Z

2021-09-23T22:30:07.000Z

fdbk/data_tools/functions/__init__.py

kangasta/fdbk

426a04131869ceefd3bd2c80d327b60a3a8e2d7b

[ "MIT" ]

null

from ._chart_funcs import * from ._collection_funcs import * from ._status_funcs import * from ._value_funcs import * # pylint: disable=invalid-name functions = {**CHART_FUNCS, **COLLECTION_FUNCS, **STATUS_FUNCS, **VALUE_FUNCS}

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py

Python

app/apigateway/tests/test_api.py

rodrigoalveslima/buzzblog

2b301ee363fbbebbe0ee31b1bf9538811d97b293

[ "Apache-2.0" ]

null

app/apigateway/tests/test_api.py

rodrigoalveslima/buzzblog

2b301ee363fbbebbe0ee31b1bf9538811d97b293

[ "Apache-2.0" ]

null

app/apigateway/tests/test_api.py

rodrigoalveslima/buzzblog

2b301ee363fbbebbe0ee31b1bf9538811d97b293

[ "Apache-2.0" ]

null

# Copyright (C) 2020 Georgia Tech Center for Experimental Research in Computer # Systems import random import string import time import unittest import requests from requests.auth import HTTPBasicAuth # Constants SERVER_HOSTNAME = "localhost" SERVER_PORT = 8080 URL = "{hostname}:{port}".format(hostname=SERVER_HOSTNAME, port=SERVER_PORT) def random_id(size=16, chars=string.ascii_letters + string.digits): return ''.join(random.choice(chars) for _ in range(size)) class TestService(unittest.TestCase): def __init__(self, *args, **kwargs): super(TestService, self).__init__(*args, **kwargs) # Create test accounts. self._accounts = [ { "username": random_id(), "password": "passwd", "first_name": "George", "last_name": "Burdell" } for i in range(4) ] for account in self._accounts: r = requests.post("http://{url}/account".format(url=URL), params={"request_id": random_id()}, json={ "username": account["username"], "password": account["password"], "first_name": account["first_name"], "last_name": account["last_name"] } ) response = r.json() account["id"] = response["id"] # Create test follow. self._follow = { "follower": self._accounts[0], "followee": self._accounts[1] } r = requests.post("http://{url}/follow".format(url=URL), auth=HTTPBasicAuth(self._follow["follower"]["username"], self._follow["follower"]["password"]), params={"request_id": random_id()}, json={"account_id": self._follow["followee"]["id"]} ) response = r.json() self._follow["id"] = response["id"] # Create test posts. self._posts = [ { "text": "Lorem ipsum", "author": self._accounts[0] } for i in range(2) ] for post in self._posts: r = requests.post("http://{url}/post".format(url=URL), auth=HTTPBasicAuth(post["author"]["username"], post["author"]["password"]), params={"request_id": random_id()}, json={"text": post["text"]} ) response = r.json() post["id"] = response["id"] # Create test like. self._like = { "account": self._accounts[1], "post": self._posts[0] } r = requests.post("http://{url}/like".format(url=URL), auth=HTTPBasicAuth(self._like["account"]["username"], self._like["account"]["password"]), params={"request_id": random_id()}, json={"post_id": self._like["post"]["id"]} ) response = r.json() self._like["id"] = response["id"] def test_create_account_200(self): r = requests.post("http://{url}/account".format(url=URL), params={"request_id": random_id()}, json={ "username": "jane.doe", "password": "passwd", "first_name": "Jane", "last_name": "Doe" } ) self.assertEqual(200, r.status_code) response = r.json() self.assertEqual("account", response["object"]) self.assertEqual("standard", response["mode"]) self.assertIsInstance(response["id"], int) self.assertAlmostEqual(time.time(), response["created_at"], delta=60) self.assertTrue(response["active"]) self.assertEqual("jane.doe", response["username"]) self.assertEqual("Jane", response["first_name"]) self.assertEqual("Doe", response["last_name"]) def test_retrieve_account_200(self): r = requests.get("http://{url}/account/{account_id}".format(url=URL, account_id=self._accounts[3]["id"]), auth=HTTPBasicAuth(self._accounts[0]["username"], self._accounts[0]["password"]), params={"request_id": random_id()} ) self.assertEqual(200, r.status_code) response = r.json() self.assertEqual("account", response["object"]) self.assertEqual("expanded", response["mode"]) self.assertEqual(self._accounts[3]["id"], response["id"]) self.assertAlmostEqual(time.time(), response["created_at"], delta=60) self.assertTrue(response["active"]) self.assertEqual(self._accounts[3]["username"], response["username"]) self.assertEqual(self._accounts[3]["first_name"], response["first_name"]) self.assertEqual(self._accounts[3]["last_name"], response["last_name"]) self.assertFalse(response["follows_you"]) self.assertFalse(response["followed_by_you"]) self.assertEqual(0, response["n_followers"]) self.assertEqual(0, response["n_following"]) self.assertEqual(0, response["n_posts"]) self.assertEqual(0, response["n_likes"]) def test_update_account_200(self): self._accounts[0]["last_name"] = "P. Burdell" r = requests.put("http://{url}/account/{account_id}".format(url=URL, account_id=self._accounts[0]["id"]), auth=HTTPBasicAuth(self._accounts[0]["username"], self._accounts[0]["password"]), params={"request_id": random_id()}, json={ "password": self._accounts[0]["password"], "first_name": self._accounts[0]["first_name"], "last_name": self._accounts[0]["last_name"] } ) self.assertEqual(200, r.status_code) response = r.json() self.assertEqual("account", response["object"]) self.assertEqual("standard", response["mode"]) self.assertIsInstance(response["id"], int) self.assertAlmostEqual(time.time(), response["created_at"], delta=60) self.assertTrue(response["active"]) self.assertEqual(self._accounts[0]["username"], response["username"]) self.assertEqual(self._accounts[0]["first_name"], response["first_name"]) self.assertEqual(self._accounts[0]["last_name"], response["last_name"]) def test_delete_account_200(self): # Create an account. r = requests.post("http://{url}/account".format(url=URL), params={"request_id": random_id()}, json={ "username": random_id(), "password": "passwd", "first_name": "George", "last_name": "Burdell" } ) response = r.json() # Delete that account. r = requests.delete("http://{url}/account/{account_id}".format(url=URL, account_id=response["id"]), auth=HTTPBasicAuth(response["username"], "passwd"), params={"request_id": random_id()} ) self.assertEqual(200, r.status_code) def test_list_accounts_200(self): r = requests.get("http://{url}/account".format(url=URL), auth=HTTPBasicAuth(self._accounts[0]["username"], self._accounts[0]["password"]), params={ "request_id": random_id(), "username": random_id() }, json={"limit": 10, "offset": 0} ) self.assertEqual(200, r.status_code) response = r.json() self.assertEqual(0, len(response)) def test_follow_account_200(self): r = requests.post("http://{url}/follow".format(url=URL), auth=HTTPBasicAuth(self._accounts[1]["username"], self._accounts[1]["password"]), params={"request_id": random_id()}, json={"account_id": self._accounts[0]["id"]} ) self.assertEqual(200, r.status_code) response = r.json() self.assertEqual("follow", response["object"]) self.assertEqual("standard", response["mode"]) self.assertIsInstance(response["id"], int) self.assertAlmostEqual(time.time(), response["created_at"], delta=60) self.assertEqual(self._accounts[1]["id"], response["follower_id"]) self.assertEqual(self._accounts[0]["id"], response["followee_id"]) def test_retrieve_follow_200(self): r = requests.get("http://{url}/follow/{follow_id}".format(url=URL, follow_id=self._follow["id"]), auth=HTTPBasicAuth(self._accounts[0]["username"], self._accounts[0]["password"]), params={"request_id": random_id()} ) self.assertEqual(200, r.status_code) response = r.json() self.assertEqual("follow", response["object"]) self.assertEqual("expanded", response["mode"]) self.assertIsInstance(response["id"], int) self.assertAlmostEqual(time.time(), response["created_at"], delta=60) self.assertEqual(self._follow["follower"]["id"], response["follower_id"]) self.assertEqual(self._follow["followee"]["id"], response["followee_id"]) self.assertEqual(self._follow["follower"]["id"], response["follower"]["id"]) self.assertEqual(self._follow["followee"]["id"], response["followee"]["id"]) def test_delete_follow_200(self): # Follow an account. r = requests.post("http://{url}/follow".format(url=URL), auth=HTTPBasicAuth(self._accounts[0]["username"], self._accounts[0]["password"]), params={"request_id": random_id()}, json={"account_id": self._accounts[2]["id"]} ) response = r.json() # Delete that follow. r = requests.delete("http://{url}/follow/{follow_id}".format(url=URL, follow_id=response["id"]), auth=HTTPBasicAuth(self._accounts[0]["username"], self._accounts[0]["password"]), params={"request_id": random_id()} ) self.assertEqual(200, r.status_code) def test_list_follows_200(self): r = requests.get("http://{url}/follow".format(url=URL), auth=HTTPBasicAuth(self._accounts[0]["username"], self._accounts[0]["password"]), params={ "request_id": random_id(), "follower_id": self._accounts[3]["id"] }, json={"limit": 10, "offset": 0} ) self.assertEqual(200, r.status_code) response = r.json() self.assertEqual(0, len(response)) def test_create_post_200(self): r = requests.post("http://{url}/post".format(url=URL), auth=HTTPBasicAuth(self._accounts[0]["username"], self._accounts[0]["password"]), params={"request_id": random_id()}, json={"text": "Lorem ipsum"} ) self.assertEqual(200, r.status_code) response = r.json() self.assertEqual("post", response["object"]) self.assertEqual("standard", response["mode"]) self.assertIsInstance(response["id"], int) self.assertAlmostEqual(time.time(), response["created_at"], delta=60) self.assertTrue(response["active"]) self.assertEqual("Lorem ipsum", response["text"]) self.assertEqual(self._accounts[0]["id"], response["author_id"]) def test_retrieve_post_200(self): r = requests.get("http://{url}/post/{post_id}".format(url=URL, post_id=self._posts[0]["id"]), auth=HTTPBasicAuth(self._accounts[0]["username"], self._accounts[0]["password"]), params={"request_id": random_id()} ) self.assertEqual(200, r.status_code) response = r.json() self.assertEqual("post", response["object"]) self.assertEqual("expanded", response["mode"]) self.assertIsInstance(response["id"], int) self.assertAlmostEqual(time.time(), response["created_at"], delta=60) self.assertTrue(response["active"]) self.assertEqual(self._posts[0]["text"], response["text"]) self.assertEqual(self._posts[0]["author"]["id"], response["author_id"]) self.assertEqual(self._posts[0]["author"]["id"], response["author"]["id"]) def test_delete_post_200(self): # Create a post. r = requests.post("http://{url}/post".format(url=URL), auth=HTTPBasicAuth(self._accounts[0]["username"], self._accounts[0]["password"]), params={"request_id": random_id()}, json={"text": "Lorem ipsum"} ) response = r.json() # Delete that post. r = requests.delete("http://{url}/post/{post_id}".format(url=URL, post_id=response["id"]), auth=HTTPBasicAuth(self._accounts[0]["username"], self._accounts[0]["password"]), params={"request_id": random_id()} ) self.assertEqual(200, r.status_code) def test_list_posts_200(self): r = requests.get("http://{url}/post".format(url=URL), auth=HTTPBasicAuth(self._accounts[0]["username"], self._accounts[0]["password"]), params={ "request_id": random_id(), "author_id": self._accounts[3]["id"] }, json={"limit": 10, "offset": 0} ) self.assertEqual(200, r.status_code) response = r.json() self.assertEqual(0, len(response)) def test_like_post_200(self): r = requests.post("http://{url}/like".format(url=URL), auth=HTTPBasicAuth(self._accounts[0]["username"], self._accounts[0]["password"]), params={"request_id": random_id()}, json={"post_id": self._posts[1]["id"]} ) self.assertEqual(200, r.status_code) response = r.json() self.assertEqual("like", response["object"]) self.assertEqual("standard", response["mode"]) self.assertIsInstance(response["id"], int) self.assertAlmostEqual(time.time(), response["created_at"], delta=60) self.assertEqual(self._accounts[0]["id"], response["account_id"]) self.assertEqual(self._posts[1]["id"], response["post_id"]) def test_retrieve_like_200(self): r = requests.get("http://{url}/like/{like_id}".format(url=URL, like_id=self._like["id"]), auth=HTTPBasicAuth(self._accounts[0]["username"], self._accounts[0]["password"]), params={"request_id": random_id()} ) self.assertEqual(200, r.status_code) response = r.json() self.assertEqual("like", response["object"]) self.assertEqual("expanded", response["mode"]) self.assertIsInstance(response["id"], int) self.assertAlmostEqual(time.time(), response["created_at"], delta=60) self.assertEqual(self._like["account"]["id"], response["account_id"]) self.assertEqual(self._like["post"]["id"], response["post_id"]) self.assertEqual(self._like["account"]["id"], response["account"]["id"]) self.assertEqual(self._like["post"]["id"], response["post"]["id"]) def test_delete_like_200(self): # Like a post. r = requests.post("http://{url}/like".format(url=URL), auth=HTTPBasicAuth(self._accounts[0]["username"], self._accounts[0]["password"]), params={"request_id": random_id()}, json={"post_id": self._posts[0]["id"]} ) response = r.json() # Delete that like. r = requests.delete("http://{url}/like/{like_id}".format(url=URL, like_id=response["id"]), auth=HTTPBasicAuth(self._accounts[0]["username"], self._accounts[0]["password"]), params={"request_id": random_id()} ) self.assertEqual(200, r.status_code) def test_list_likes_200(self): r = requests.get("http://{url}/like".format(url=URL), auth=HTTPBasicAuth(self._accounts[0]["username"], self._accounts[0]["password"]), params={ "request_id": random_id(), "account_id": self._accounts[3]["id"] }, json={"limit": 10, "offset": 0} ) self.assertEqual(200, r.status_code) response = r.json() self.assertEqual(0, len(response)) if __name__ == "__main__": unittest.main()

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563fa93b815aaed27fa895274bd437dc9564b383

368

py

Python

sudoku/example.py

mflood/whimsical

e39865193b232cc8fc280f371a76f7ac7d07d782

[ "Apache-2.0" ]

null

sudoku/example.py

mflood/whimsical

e39865193b232cc8fc280f371a76f7ac7d07d782

[ "Apache-2.0" ]

null

sudoku/example.py

mflood/whimsical

e39865193b232cc8fc280f371a76f7ac7d07d782

[ "Apache-2.0" ]

null

from sudoku import solve puzzle = [[0,0,0,0,0,3,9,0,0], [5,0,0,0,0,0,4,1,0], [0,0,8,7,5,0,0,0,0], [0,0,7,0,0,0,5,9,1], [0,4,0,0,2,0,0,6,0], [6,8,5,0,0,0,7,0,0], [0,0,0,0,4,2,1,0,0], [0,7,4,0,0,0,0,0,2], [0,0,2,6,0,0,0,0,0]] solution = solve(puzzle) for row in solution: print(row)

21.647059

30

0.410326

95

368

1.589474

0.210526

0.503311

0.476821

0.370861

0.344371

0.192053

0.092715

0

0.313953

0.298913

368

16

31

23

0.271318

0

1

0

false

0

0.076923

0

0.076923

0

1

null

1

0

1

0

1

0

null

0

6

567c0bdca0347b593c0ebc0e01dc47164dea5643

32,419

py

Python

Synthesis/post/plot/scatter.py

pablorutschmann/3DPopSynthesis

6c2206bef0cf0b0dc21aeb6cbda6386a525ffbc7

[ "MIT" ]

null

Synthesis/post/plot/scatter.py

pablorutschmann/3DPopSynthesis

6c2206bef0cf0b0dc21aeb6cbda6386a525ffbc7

[ "MIT" ]

null

Synthesis/post/plot/scatter.py

pablorutschmann/3DPopSynthesis

6c2206bef0cf0b0dc21aeb6cbda6386a525ffbc7

[ "MIT" ]

null

import matplotlib.pyplot as plt import numpy as np from matplotlib import cm from matplotlib import colors from matplotlib import patches import os.path as path from Synthesis.units import * from tqdm import tqdm from scipy.integrate import quad def Power_Law(x, a, b): return a * np.power(x, b) def scatter_parameters(pop): TotalMasses = [] SigmaCoeffs = [] Reference = [] for sim in pop.SIMS.values(): TotalMasses.append(sim.Total_Mass) SigmaCoeffs.append(sim.Sigma_Exponent) print(sim.Sigma_Exponent) print(sim.Sigma_Norm * (R_S / au)**sim.Sigma_Exponent / denstos) Reference.append(sim.Sigma_Norm / (R_S / au)**sim.Sigma_Exponent / denstos * pow(au/R_S, sim.Sigma_Exponent) / denstos) plt.rcParams.update({'figure.autolayout': True}) plt.style.use('seaborn-paper') plt.rcParams.update({'font.size': pop.fontsize}) cmap = pop.cmap_standart cmin = min(Reference) cmax = max(Reference) norm = colors.LogNorm(cmin, cmax) fig, ax = plt.subplots(figsize=pop.figsize) ax.scatter(SigmaCoeffs, TotalMasses, c=Reference, cmap=cmap, norm=norm, s=12) x_labels = ax.get_xticklabels() plt.setp(x_labels, horizontalalignment='center') ax.set(xlabel='Surface Density Power Law Exponent', ylabel=r'Total Mass [$M_{\odot}$]', xticks=SigmaCoeffs) ax2 = ax.twinx() mn, mx = ax.get_ylim() ax2.set_ylim(M_S / M_J * mn, M_S / M_J * mx) ax2.set_ylabel('Total Disk Mass [$M_{J}$]') fig.colorbar(cm.ScalarMappable(cmap=cmap, norm=norm), orientation='vertical', label=r'Reference Value at $1 \mathrm{au}$ [$\mathrm{g}\mathrm{cm}^{-2}$]', ax=ax2, pad=0.12) # ax.set_yscale('log') if pop.plot_config == 'presentation': ax.set(title=r'Synthesis Parameters') fig.savefig(path.join(pop.PLOT, 'scatter_parameters.png'), transparent=False, dpi=pop.dpi, bbox_inches="tight") plt.close(fig) def scatter_parameters_numbers(pop, m_low_lim=0, a_up_lim=30): TotalMasses = [] SigmaCoeffs = [] Reference = [] Masses = [] Orb_Dist = [] Numbers = [] Means = [] Systems = [] for id,sim in pop.SIMS.items(): TotalMasses.append(sim.Total_Mass) SigmaCoeffs.append(sim.Sigma_Exponent) Masses = list(sim.snaps[sim.N_snaps - 1].satellites['M'].values * M_S / M_E) Orb_Dist = list(sim.snaps[sim.N_snaps - 1].satellites['a'].values * R_S / au) system = zip(Masses, Orb_Dist) filtered = [item for item in system if item[0] >= m_low_lim and item[1] <= a_up_lim] # mean = np.max([item[0] for item in filtered])/np.sum([item[0] for item in filtered]) # Means.append(mean) Numbers.append(len(filtered)) #Means = np.array(Means) / np.sum(Means) print(Numbers) Numbers = np.array(Numbers) plt.rcParams.update({'figure.autolayout': True}) plt.style.use('seaborn-paper') plt.rcParams.update({'font.size': pop.fontsize}) cmap = pop.cmap_standart cmin = min(Numbers) cmax = max(Numbers) norm = colors.Normalize(cmin, cmax) fig, ax = plt.subplots(figsize=pop.figsize) ax.scatter(SigmaCoeffs, TotalMasses, c=Numbers, cmap=cmap, norm=norm, s=12) x_labels = ax.get_xticklabels() plt.setp(x_labels, horizontalalignment='center') ax.set(xlabel='Surface Density Power Law Exponent', ylabel=r'Total Disk Mass [$M_{\odot}$]', xticks=SigmaCoeffs) ax2 = ax.twinx() mn, mx = ax.get_ylim() ax2.set_ylim(M_S / M_J * mn, M_S / M_J * mx) ax2.set_ylabel('Total Disk Mass [$M_{J}$]') fig.colorbar(cm.ScalarMappable(cmap=cmap, norm=norm), orientation='vertical', label=r'Number of Planets', ax=ax2, pad=0.12) # ax.set_yscale('log') if pop.plot_config == 'presentation': ax.set(title=r'Synthesis Parameters') fig.savefig(path.join(pop.PLOT, 'scatter_parameters_numbers.png'), transparent=False, dpi=pop.dpi, bbox_inches="tight") plt.close(fig) def scatter_parameters_lost_mass(pop, m_low_lim=0, a_up_lim=30): TotalMasses = [] SigmaCoeffs = [] lost_mass = [] numbers = [] Reference = [] TM = [] for id, sim in pop.SIMS.items(): TotalMasses.append(sim.Total_Mass) SigmaCoeffs.append(sim.Sigma_Exponent) TM.append(np.sum([item for item in list(sim.snaps[sim.N_snaps - 1].satellites['M'].values * M_S / M_E)])) Reference.append(sim.Sigma_Norm / (R_S / au)**sim.Sigma_Exponent / denstos * pow(au/R_S, sim.Sigma_Exponent) / denstos) masses = sim.lost_satellites['mass'].values * M_S / M_J cols = sim.lost_satellites['collision'].values filter_null = cols == 0.0 filtered = masses[filter_null] summed = np.sum(filtered) numbers.append(len(filtered)) # summed = np.sum(filtered) lost_mass.append(summed) lost_mass = np.array(lost_mass) #Means = np.array(Means) / np.sum(Means) # print(Numbers / np.array(Means)) Numbers = np.array(SigmaCoeffs) plt.rcParams.update({'figure.autolayout': True}) plt.style.use('seaborn-paper') plt.rcParams.update({'font.size': pop.fontsize}) # arr = np.unique(SigmaCoeffs) cmap = plt.get_cmap(pop.cmap_standart,len(SigmaCoeffs)) norm = colors.BoundaryNorm(np.linspace(-1.625, -0.375, len(np.unique(SigmaCoeffs))+1, endpoint=True), cmap.N) fig, ax = plt.subplots(figsize=pop.figsize) ax.scatter(Reference, lost_mass, c=SigmaCoeffs, cmap=cmap, norm=norm, s=12) x_labels = ax.get_xticklabels() plt.setp(x_labels, horizontalalignment='center') ax.set(ylabel='Total Lost Mass [$\mathrm{M_J}$]', xlabel=r'Reference Value at 1 $\mathrm{au}$ [$\mathrm{g cm^{-2}}$]') # ax2 = ax.twinx() # mn, mx = ax.get_ylim() # ax2.set_ylim(M_S / M_J * mn, M_S / M_J * mx) # ax2.set_ylabel('Total Disk Mass [$M_{J}$]') fig.colorbar(cm.ScalarMappable(cmap=cmap,norm=norm), orientation='vertical', label=r'Power-Law Exponent', ax=ax, ticks=np.unique(SigmaCoeffs)) ax.set_yscale('log') ax.set_xscale('log') if pop.plot_config == 'presentation': ax.set(title=r'Synthesis Parameters') fig.savefig(path.join(pop.PLOT, 'scatter_reference_lost_mass.png'), transparent=False, dpi=pop.dpi, bbox_inches="tight") plt.close(fig) def scatter_parameters_AMD(pop, m_low_lim=0, a_up_lim=30): TotalMasses = [] SigmaCoeffs = [] Reference = [] Masses = [] Orb_Dist = [] Numbers = [] Means = [] Systems = [] AMDS = [] for sim in tqdm(pop.SIMS.values()): TotalMasses.append(sim.Total_Mass) SigmaCoeffs.append(sim.Sigma_Exponent) AMD, N = sim.get_AMD(m_low_lim, a_up_lim) AMDS.append(AMD) Means = np.array(Means) / np.sum(Means) print(Numbers * Means) Numbers = np.array(AMDS) plt.rcParams.update({'figure.autolayout': True}) plt.style.use('seaborn-paper') plt.rcParams.update({'font.size': pop.fontsize}) cmap = pop.cmap_standart cmin = min(Numbers) cmax = max(Numbers) norm = colors.LogNorm(cmin, cmax) fig, ax = plt.subplots(figsize=pop.figsize) ax.scatter(SigmaCoeffs, TotalMasses, c=Numbers, cmap=cmap, norm=norm, s=12) x_labels = ax.get_xticklabels() plt.setp(x_labels, horizontalalignment='center') ax.set(xlabel='Surface Density Power Law Exponent', ylabel=r'Total Disk Mass [$M_{\odot}$]', xticks=SigmaCoeffs) ax2 = ax.twinx() mn, mx = ax.get_ylim() ax2.set_ylim(M_S / M_J * mn, M_S / M_J * mx) ax2.set_ylabel('Total Disk Mass [$M_{J}$]') fig.colorbar(cm.ScalarMappable(cmap=cmap, norm=norm), orientation='vertical', label=r'AMD', ax=ax2, pad=0.12) # ax.set_yscale('log') if pop.plot_config == 'presentation': ax.set(title=r'Synthesis Parameters') fig.savefig(path.join(pop.PLOT, 'scatter_parameters_amd.png'), transparent=False, dpi=pop.dpi, bbox_inches="tight") plt.close(fig) def scatter_parameters_RMC(pop, m_low_lim=0, a_up_lim=30): TotalMasses = [] SigmaCoeffs = [] Reference = [] Masses = [] Orb_Dist = [] Numbers = [] Means = [] Systems = [] RMCS = [] for sim in tqdm(pop.SIMS.values()): TotalMasses.append(sim.Total_Mass) SigmaCoeffs.append(sim.Sigma_Exponent) RMC, N = sim.get_RMC(m_low_lim, a_up_lim) RMCS.append(RMC) Means = np.array(Means) / np.sum(Means) print(Numbers * Means) Numbers = np.array(RMCS) plt.rcParams.update({'figure.autolayout': True}) plt.style.use('seaborn-paper') plt.rcParams.update({'font.size': pop.fontsize}) cmap = pop.cmap_standart cmin = min(RMCS) cmax = max(RMCS) norm = colors.Normalize(cmin, cmax) fig, ax = plt.subplots(figsize=pop.figsize) ax.scatter(SigmaCoeffs, TotalMasses, c=RMCS, cmap=cmap, norm=norm, s=12) x_labels = ax.get_xticklabels() plt.setp(x_labels, horizontalalignment='center') ax.set(xlabel='Surface Density Power Law Exponent', ylabel=r'Total Disk Mass [$M_{\odot}$]', xticks=SigmaCoeffs) ax2 = ax.twinx() mn, mx = ax.get_ylim() ax2.set_ylim(M_S / M_J * mn, M_S / M_J * mx) ax2.set_ylabel('Total Disk Mass [$M_{J}$]') fig.colorbar(cm.ScalarMappable(cmap=cmap, norm=norm), orientation='vertical', label=r'RMC', ax=ax2, pad=0.12) # ax.set_yscale('log') if pop.plot_config == 'presentation': ax.set(title=r'Synthesis Parameters') fig.savefig(path.join(pop.PLOT, 'scatter_parameters_rmc_nonlog.png'), transparent=False, dpi=pop.dpi, bbox_inches="tight") plt.close(fig) def scatter_collision_number(pop, m_low_lim=0, a_up_lim=30): TotalMasses = [] SigmaCoeffs = [] times = [] for sim in tqdm(pop.SIMS.values()): TotalMasses.append(sim.Total_Mass) SigmaCoeffs.append(sim.Sigma_Exponent) times.append(len(sim.collisions.index)) plt.rcParams.update({'figure.autolayout': True}) plt.style.use('seaborn-paper') plt.rcParams.update({'font.size': pop.fontsize}) cmap = pop.cmap_standart cmin = min(times) cmax = max(times) norm = colors.Normalize(cmin, cmax) fig, ax = plt.subplots(figsize=pop.figsize) ax.scatter(SigmaCoeffs, TotalMasses, c=times, cmap=cmap, norm=norm, s=12) x_labels = ax.get_xticklabels() plt.setp(x_labels, horizontalalignment='center') ax.set(xlabel='Surface Density Power Law Exponent', ylabel=r'Total Disk Mass [$M_{\odot}$]', xticks=SigmaCoeffs) ax2 = ax.twinx() mn, mx = ax.get_ylim() ax2.set_ylim(M_S / M_J * mn, M_S / M_J * mx) ax2.set_ylabel('Total Disk Mass [$M_{J}$]') fig.colorbar(cm.ScalarMappable(cmap=cmap, norm=norm), orientation='vertical', label=r'Number of Collisions', ax=ax2, pad=0.12) # ax.set_yscale('log') if pop.plot_config == 'presentation': ax.set(title=r'Synthesis Parameters') fig.savefig(path.join(pop.PLOT, 'scatter_parameters_collision_number.png'), transparent=False, dpi=pop.dpi, bbox_inches="tight") plt.close(fig) def scatter_ecc_inc(pop, m_low_lim=0, a_up_lim=30): Masses = [] Orb_Dist = [] Ecc = [] Inc = [] Types = [] for sim in pop.SIMS.values(): Masses += list(sim.snaps[sim.N_snaps - 1].satellites['M'].values * M_S / M_E) Orb_Dist += list(sim.snaps[sim.N_snaps - 1].satellites['a'].values * R_S / au) Ecc += list(sim.snaps[sim.N_snaps - 1].satellites['e'].values) Inc += list(sim.snaps[sim.N_snaps - 1].satellites['i'].values) Types += list(sim.snaps[sim.N_snaps - 1].satellites['Type'].values) data = zip(Masses, Orb_Dist, Ecc, Inc, Types) data = [item for item in data if item[0] >= m_low_lim and item[1] <= a_up_lim] Masses, Orb_Dist, Ecc, Inc, Types = zip(*data) number_of_no_accretion = len([item for item in data if np.abs(0.01-item[0])/item[0] < 0.01 and item[-1] == 1]) print(f'Number of Object: {len(Masses)}') print(f'Number of Embryos with no significant accretion: {number_of_no_accretion}, {number_of_no_accretion/len(Masses)}') plt.rcParams.update({'figure.autolayout': True}) plt.style.use('seaborn-paper') plt.rcParams.update({'font.size': pop.fontsize}) plt.rcParams.update({"legend.title_fontsize": pop.legend_fontsize}) cmap = pop.cmap_standart cmin = min(Orb_Dist) cmax = max(Orb_Dist) norm = colors.LogNorm(cmin, cmax) fig, ax = plt.subplots(figsize=pop.figsize) ax.scatter(Ecc, np.sin(np.array(Inc)/360 * 2 * np.pi), c=Orb_Dist, cmap=cmap, norm=norm, s=3) # ax.scatter(Ecc, np.sin(np.array(Inc)), c=Orb_Dist, cmap=cmap, norm=norm, s=3) x_labels = ax.get_xticklabels() plt.setp(x_labels, horizontalalignment='center') ax.set(xlabel='Eccentricity', ylabel=r'$\sin(\mathrm{inclination})$') fig.colorbar(cm.ScalarMappable(cmap=cmap, norm=norm), orientation='vertical', label=r'Orbital Distance [$\mathrm{au}$]', ax=ax) ax.set_xscale('log') ax.set_yscale('log') if pop.plot_config == 'presentation': ax.set(title=r'Eccentricity and Inclination') save_name = 'scatter_ecc_inc' if a_up_lim < 30 and m_low_lim > 0: save_name += '_lim' fig.savefig(path.join(pop.PLOT, save_name + '.png'), transparent=False, dpi=pop.dpi, bbox_inches="tight") plt.close(fig) def scatter_a_mass(pop, m_low_lim=0, a_up_lim=30): Masses = [] Orb_Dist = [] WM = [] SWM = [] for sim in pop.SIMS.values(): Masses += list(sim.snaps[sim.N_snaps - 1].satellites['M'].values * M_S / M_E) Orb_Dist += list(sim.snaps[sim.N_snaps - 1].satellites['a'].values * R_S / au) WM += list(sim.snaps[sim.N_snaps - 1].satellites['WM'].values * M_S / M_E) SWM += list(sim.snaps[sim.N_snaps - 1].satellites['SWM'].values * M_S / M_E) data = zip(Masses, Orb_Dist, WM, SWM) data = [(m, a, wm / m, swm / m) for (m, a, wm, swm) in data if m >= m_low_lim and a <= a_up_lim] Masses, Orb_Dist, WMF, SWMF = zip(*data) TWMF = np.array(WMF) + np.array(SWMF) print(f'Number of Object: {len(Masses)}') plt.rcParams.update({'figure.autolayout': True}) plt.style.use('seaborn-paper') plt.rcParams.update({'font.size': pop.fontsize}) plt.rcParams.update({"legend.title_fontsize": pop.legend_fontsize}) cmap = pop.cmap_standart cmin = min(TWMF) cmax = max(TWMF) norm = colors.Normalize(cmin, cmax) fig, ax = plt.subplots(figsize=pop.figsize) ax.scatter(Orb_Dist, Masses, c=TWMF, cmap=cmap, norm=norm, s=2, alpha=1) x_labels = ax.get_xticklabels() plt.setp(x_labels, horizontalalignment='center') ax.set(xlabel=r'Orbital Distance [$\mathrm{au}$]', ylabel=r'Mass [$\mathrm{M_{\oplus}}$]') fig.colorbar(cm.ScalarMappable(cmap=cmap, norm=norm), orientation='vertical', label=r'Total WMF', ax=ax) ax.set_xscale('log') ax.set_yscale('log') if pop.plot_config == 'presentation': ax.set(title=r'Eccentricity and Inclination') save_name = 'scatter_a_mass' if a_up_lim < 30 and m_low_lim > 0: save_name += '_lim' fig.savefig(path.join(pop.PLOT, save_name + '.png'), transparent=False, dpi=pop.dpi, bbox_inches="tight") plt.close(fig) def scatter_radial_twmf(pop, m_low_lim=0, a_up_lim=30): Masses = [] Orb_Dist = [] WM = [] SWM = [] Ecc = [] System = [] for key, sim in pop.SIMS.items(): Masses += list(sim.snaps[sim.N_snaps - 1].satellites['M'].values * M_S / M_E) Ecc += list(sim.snaps[sim.N_snaps - 1].satellites['e'].values * M_S / M_E) Orb_Dist += list(sim.snaps[sim.N_snaps - 1].satellites['a'].values * R_S / au) WM += list(sim.snaps[sim.N_snaps - 1].satellites['WM'].values * M_S / M_E) SWM += list(sim.snaps[sim.N_snaps - 1].satellites['SWM'].values * M_S / M_E) System += [key for i in sim.snaps[sim.N_snaps - 1].satellites['M'].values] WMF = np.array(WM) / np.array(Masses) SWMF = np.array(SWM) / np.array(Masses) TWMF = WMF + SWMF total_number = len(Masses) print(f'Total Number of planets: {total_number}') data = zip(Masses, Orb_Dist, WMF, SWMF, TWMF, Ecc, System) data = [item for item in data if item[0] >= 0.3 and item[0] <= 3] mass_lim_number = len(data) print(f'Number of planets in mass limit: {mass_lim_number}, {mass_lim_number/total_number}') data_copy = data.copy() data_wmf = [item for item in data_copy if item[2] > 0.0] Masses, Orb_Dist, WMF, SWMF, TWMF, Ecc, System = zip(*data_wmf) n_ea_ml_nz_wmf = len(Masses) print(f'Number of planets in mass limit with nonzero liquid watermass fraction: {n_ea_ml_nz_wmf}, {n_ea_ml_nz_wmf/mass_lim_number}') plt.rcParams.update({'figure.autolayout': True}) plt.style.use('seaborn-paper') plt.rcParams.update({'font.size': pop.fontsize}) N_bins = 15 bins = 10 ** np.linspace(np.log10(min(WMF)), np.log10(max(WMF)), N_bins) fig, ax = plt.subplots(figsize=pop.figsize) # ax.hist(Masses, bins=bins) # values, base, _ = plt.hist(Orb_Dist, bins=bins, rwidth=0.95) ax.hist(WMF, bins=bins, rwidth=0.95) ax.axvline(OE/M_E, color='red', linewidth=1) ax.set(xlabel=r'Mass Fraction', ylabel=r'Counts') ax.set_xscale('log') # ax.set_yscale('log') if pop.plot_config == 'presentation': ax.set(title=r'Histrogram of Terrestrial Planets Orbital Distances') save_name = 'histogram_earth_analogs_wmf' if a_up_lim < 30 and m_low_lim > 0: save_name += '_lim' fig.savefig(path.join(pop.PLOT, save_name + '.png'), transparent=False, dpi=pop.dpi, bbox_inches="tight") plt.close(fig) data_copy = data.copy() data_wmf_lim = [item for item in data_copy if item[2] > 0.0 and item[3] > 0.00075] Masses, Orb_Dist, WMF, SWMF, TWMF, Ecc, System = zip(*data_wmf_lim) # n_ea_ml_nz_wmf = len(Masses) # print(f'Number of planets in mass limit with nonzero liquid watermass fraction: {n_ea_ml_nz_wmf}, {n_ea_ml_nz_wmf/mass_lim_number}') plt.rcParams.update({'figure.autolayout': True}) plt.style.use('seaborn-paper') plt.rcParams.update({'font.size': pop.fontsize}) N_bins = 15 bins = 10 ** np.linspace(np.log10(min(WMF)), np.log10(max(WMF)), N_bins) fig, ax = plt.subplots(figsize=pop.figsize) # ax.hist(Masses, bins=bins) # values, base, _ = plt.hist(Orb_Dist, bins=bins, rwidth=0.95) ax.hist(WMF, bins=bins, rwidth=0.95) ax.axvline(OE/M_E, color='red', linewidth=1) ax.set(xlabel=r'Mass Fraction', ylabel=r'Counts') ax.set_xscale('log') # ax.set_yscale('log') if pop.plot_config == 'presentation': ax.set(title=r'Histrogram of Terrestrial Planets Orbital Distances') save_name = 'histogram_earth_analogs_wmf_lim' if a_up_lim < 30 and m_low_lim > 0: save_name += '_lim' fig.savefig(path.join(pop.PLOT, save_name + '.png'), transparent=False, dpi=pop.dpi, bbox_inches="tight") plt.close(fig) data_copy = data.copy() data_swmf_lim = [item for item in data_copy if item[3] > 0.0 and item[2] > 0.00025] Masses, Orb_Dist, WMF, SWMF, TWMF, Ecc, System = zip(*data_swmf_lim) # n_ea_ml_nz_wmf = len(Masses) # print(f'Number of planets in mass limit with nonzero liquid watermass fraction: {n_ea_ml_nz_wmf}, {n_ea_ml_nz_wmf/mass_lim_number}') plt.rcParams.update({'figure.autolayout': True}) plt.style.use('seaborn-paper') plt.rcParams.update({'font.size': pop.fontsize}) N_bins = 15 bins = 10 ** np.linspace(np.log10(min(WMF)), np.log10(max(WMF)), N_bins) fig, ax = plt.subplots(figsize=pop.figsize) # ax.hist(Masses, bins=bins) # values, base, _ = plt.hist(Orb_Dist, bins=bins, rwidth=0.95) ax.hist(WMF, bins=bins, rwidth=0.95) ax.axvline(OE/M_E, color='red', linewidth=1) ax.set(xlabel=r'Mass Fraction', ylabel=r'Counts') ax.set_xscale('log') # ax.set_yscale('log') if pop.plot_config == 'presentation': ax.set(title=r'Histrogram of Terrestrial Planets Orbital Distances') save_name = 'histogram_earth_analogs_swmf_lim' if a_up_lim < 30 and m_low_lim > 0: save_name += '_lim' fig.savefig(path.join(pop.PLOT, save_name + '.png'), transparent=False, dpi=pop.dpi, bbox_inches="tight") plt.close(fig) data_copy = data.copy() data_swmf = [item for item in data_copy if item[3] > 0.0] Masses, Orb_Dist, WMF, SWMF, TWMF, Ecc, System = zip(*data_swmf) n_ea_ml_nz_swmf = len(Masses) print(f'Number of planets in mass limit with nonzero hydrated solids watermass fraction: {n_ea_ml_nz_swmf}, {n_ea_ml_nz_swmf/mass_lim_number}') plt.rcParams.update({'figure.autolayout': True}) plt.style.use('seaborn-paper') plt.rcParams.update({'font.size': pop.fontsize}) N_bins = 15 bins = 10 ** np.linspace(np.log10(min(SWMF)), np.log10(max(SWMF)), N_bins) fig, ax = plt.subplots(figsize=pop.figsize) # ax.hist(Masses, bins=bins) # values, base, _ = plt.hist(Orb_Dist, bins=bins, rwidth=0.95) ax.hist(WMF, bins=bins, rwidth=0.95) ax.axvline(3 * OE/M_E, color='red', linewidth=1) ax.set(xlabel=r'Mass Fraction', ylabel=r'Counts') ax.set_xscale('log') # ax.set_yscale('log') if pop.plot_config == 'presentation': ax.set(title=r'Histrogram of Terrestrial Planets Orbital Distances') save_name = 'histogram_earth_analogs_swmf' if a_up_lim < 30 and m_low_lim > 0: save_name += '_lim' fig.savefig(path.join(pop.PLOT, save_name + '.png'), transparent=False, dpi=pop.dpi, bbox_inches="tight") plt.close(fig) data_copy = data.copy() data_twmf = [item for item in data_copy if item[2] > 0.0 and item[3] > 0.0] Masses, Orb_Dist, WMF, SWMF, TWMF, Ecc, System = zip(*data_twmf) n_ea_ml_nz_twmf = len(Masses) print(f'Number of planets in mass limit with nonzero wmf and swmf: {n_ea_ml_nz_twmf}, {n_ea_ml_nz_twmf/mass_lim_number}') plt.rcParams.update({'figure.autolayout': True}) plt.style.use('seaborn-paper') plt.rcParams.update({'font.size': pop.fontsize}) ratios = np.array(WMF)/np.array(SWMF) N_bins = 15 bins = 10 ** np.linspace(np.log10(min(ratios)), np.log10(max(ratios)), N_bins) fig, ax = plt.subplots(figsize=pop.figsize) # ax.hist(Masses, bins=bins) # values, base, _ = plt.hist(Orb_Dist, bins=bins, rwidth=0.95) ax.hist(ratios, bins=bins, rwidth=0.95) ax.axvline(1/3, color='red', linewidth=1) ax.set(xlabel=r'Ratio', ylabel=r'Counts') ax.set_xscale('log') ax.set_yscale('log') if pop.plot_config == 'presentation': ax.set(title=r'Histrogram of Terrestrial Planets Orbital Distances') save_name = 'histogram_earth_analogs_twmf' if a_up_lim < 30 and m_low_lim > 0: save_name += '_lim' fig.savefig(path.join(pop.PLOT, save_name + '.png'), transparent=False, dpi=pop.dpi, bbox_inches="tight") plt.close(fig) data = [item for item in data if item[4] > 0] non_zero_wm = data.copy() non_zero_wmf_number = len(data) print(f'Number of planets in mass limit with non zero TWMF: {non_zero_wmf_number}, {non_zero_wmf_number/mass_lim_number} ({non_zero_wmf_number/total_number})') earth_analogs = [item for item in data if item[0] >= 0.101 and item[1] <= a_up_lim and item[2] > 0.001] #print(earth_analogs) Masses, Orb_Dist, WMF, SWMF, TWMF, Ecc, System = zip(*data) plt.rcParams.update({'figure.autolayout': True}) plt.style.use('seaborn-paper') plt.rcParams.update({'font.size': pop.fontsize}) plt.rcParams.update({"legend.title_fontsize": pop.legend_fontsize}) cmap = pop.cmap_standart TWMF = TWMF cmin = min(TWMF) cmax = max(TWMF) norm = colors.LogNorm(cmin, cmax) fig, ax = plt.subplots(figsize=pop.figsize) ax.scatter(Orb_Dist, Masses, c=TWMF, cmap=cmap, norm=norm, s=7, alpha=1) # ax.scatter(obs, ms, c=twmf, cmap=cmap, norm=norm, s=10) ax.axvline(1, color='black', linewidth=0.7, linestyle='--') ax.axhline(1, color='black', linewidth=0.7, linestyle='--') x_labels = ax.get_xticklabels() plt.setp(x_labels, horizontalalignment='center', ) ax.set(xlabel='Orbital Distance [$\mathrm{au}$]', ylabel=r'Mass [$\mathrm{M_{\oplus}}$]') fig.colorbar(cm.ScalarMappable(cmap=cmap, norm=norm), orientation='vertical', label=r'Total WMF', ax=ax) ax.set_xscale('log') ax.set_yscale('log') if pop.plot_config == 'presentation': ax.set(title=r'Total WMF Radial Distribution') save_name = 'scatter_radial_twmf' if a_up_lim < 30 and m_low_lim > 0: save_name += '_lim' fig.savefig(path.join(pop.PLOT, save_name + '.png'), transparent=False, dpi=pop.dpi, bbox_inches="tight") plt.close(fig) def scatter_pie(earth_analogs): plt.rcParams.update({'figure.autolayout': True}) plt.style.use('seaborn-paper') plt.rcParams.update({'font.size': pop.fontsize}) plt.rcParams.update({"legend.title_fontsize": pop.legend_fontsize}) fig, ax = plt.subplots(figsize=pop.figsize) colors = ['red', 'blue'] labels = ['Hydrated Silica', 'Water/Ice'] red_patch = patches.Patch(color='red', label='Hydrated Silica') blue_patch = patches.Patch(color='blue', label='Water/Ice') handles = [red_patch, blue_patch] Masses, Orb_Dist, WMF, SWMF, TWMF, Ecc, System = zip(*earth_analogs) mean_mass = np.min(Masses) mass_scaling = mean_mass / 90000 mass_scaling = 0.000000001 def pie_1d(r1, r2): # calculate the points of the first pie marker # these are just the origin (0, 0) + some (cos, sin) points on a circle x1 = np.cos(2 * np.pi * np.linspace(0, r1)) y1 = np.sin(2 * np.pi * np.linspace(0, r1)) xy1 = np.row_stack([[0, 0], np.column_stack([x1, y1])]) s1 = np.abs(xy1).max() x2 = np.cos(2 * np.pi * np.linspace(r1, 1)) y2 = np.sin(2 * np.pi * np.linspace(r1, 1)) xy2 = np.row_stack([[0, 0], np.column_stack([x2, y2])]) s2 = np.abs(xy2).max() # x3 = np.cos(2 * np.pi * np.linspace(r2, 1)) # y3 = np.sin(2 * np.pi * np.linspace(r2, 1)) # xy3 = np.row_stack([[0, 0], np.column_stack([x3, y3])]) # s3 = np.abs(xy3).max() return xy1, s1, xy2, s2#, xy3, s3 # cale the masses to the marker sizes # def NormalizeData(m): # return (np.log10(m) - np.log10(np.min(TWMF))) / (np.log10(np.max(TWMF)) - np.log10(np.min(TWMF))) def NormalizeData(m): return (np.log10(m) - np.log10(np.min(Masses))) / (np.log10(np.max(Masses)) - np.log10(np.min(Masses))) # def NormalizeData(m): # return (m - (np.min(TWMF))) / ((np.max(TWMF)) - (np.min(TWMF))) # def NormalizeData(m): # return (m - (np.min(Masses))) / ((np.max(Masses)) - (np.min(Masses))) earth_point = (1,1,0.00025,0.00075,0.001,0,0) def plot_one(row,earth=False): WMF_ratio = row[2]/row[4] SWMF_Ratio = 1 #xy1, s1, xy2, s2, xy3, s3 = pie_1d(WMF_ratio, SWMF_ratio) xy1, s1, xy2, s2 = pie_1d(WMF_ratio, 1) scale = NormalizeData(row[0]) * 50 if earth == True: ax.scatter(row[1], row[4], s=s2 * scale * 2, facecolor='green') ax.scatter(row[1], row[4], marker=xy1, s=s1 * scale , facecolor='blue') ax.scatter(row[1], row[4], marker=xy2, s=s2 * scale, facecolor='red') #ax.scatter(row[1], row[6], marker=xy3, s=s3 * scale , facecolor='red') for index, row in enumerate(earth_analogs): plot_one(row) plot_one(earth_point,True) #ax.set_ylim(-1 * min(self.satellites['e']), 1.1 * max(self.satellites['e'])) ax.set_xlabel(r'Orbital Distance [$\mathrm{au}$]') ax.set_ylabel('Total Water Mass Fractions') ax.set_xscale('log') ax.set_yscale('log') ax.legend(handles=handles, title='Components') fig.savefig(path.join(pop.PLOT, 'scatter_ratios.png'), transparent=False, dpi=pop.dpi, bbox_inches="tight") plt.close(fig) #filter twmf close to earth data = [item for item in data if item[2] >= 0.00025 and item[3] >= 0.00075] systems_id = [sys[-1] for sys in data] print(f'Number of systems with Earth Candidate {len(np.unique(systems_id))}, {len(np.unique(systems_id))/pop.NSIMS} ') scatter_pie(non_zero_wm) earth_analogs2 = data.copy() wmf_sim_number = len(data) #print(data) print(f'Number of planets in mass limit and WMF above 0.00025 and SWMF above 0.00075: {len(data)}, {wmf_sim_number/mass_lim_number} ({wmf_sim_number/total_number})') # for earth in data: # print(f'System: {earth[-1]}') # print(f'Mass: {earth[0]}') # print(f'Orb Dist: {earth[1]}') # print(f'WMF: {earth[2]}') # print(f'SWMF: {earth[2]}') # print(f'TWMF: {earth[2]}') # print(f'Exponent: {pop.SIMS[earth[-1]].Sigma_Exponent}') # print(f'Disk Mass: {pop.SIMS[earth[-1]].Total_Mass * M_S / M_J}') # print(" ") ms, obs, wmf, swmf, twmf, ecc, system = zip(*earth_analogs2) plt.rcParams.update({'figure.autolayout': True}) plt.style.use('seaborn-paper') plt.rcParams.update({'font.size': pop.fontsize}) plt.rcParams.update({"legend.title_fontsize": pop.legend_fontsize}) cmap = pop.cmap_standart cmin = min(twmf) cmax = max(twmf) norm = colors.Normalize(cmin, cmax) fig, ax = plt.subplots(figsize=pop.figsize) ax.scatter(wmf, swmf, c=twmf, cmap=cmap, norm=norm, s=2, alpha=1) x_labels = ax.get_xticklabels() plt.setp(x_labels, horizontalalignment='center') ax.set(xlabel=r'Water Mass Fraction]', ylabel=r'Solids Water Mass Fraction') ax.axvline(0.00025, color='black', linewidth=0.7, linestyle='--') ax.axhline(0.00075, color='black', linewidth=0.7, linestyle='--') fig.colorbar(cm.ScalarMappable(cmap=cmap, norm=norm), orientation='vertical', label=r'Total WMF', ax=ax) ax.set_xscale('log') ax.set_yscale('log') if pop.plot_config == 'presentation': ax.set(title=r'Eccentricity and Inclination') save_name = 'scatter_wmf_swmf' if a_up_lim < 30 and m_low_lim > 0: save_name += '_lim' fig.savefig(path.join(pop.PLOT, save_name + '.png'), transparent=False, dpi=pop.dpi, bbox_inches="tight") plt.close(fig) #filter roughly earth mass already roughly in the right positions data = [item for item in data if item[1] <= 2] earth_like_number = len(data) print(f'Number of planets in mass limit and WMF above 0.00025 and SWMF above 0.00075 at correct positions: {earth_like_number}, {earth_like_number/wmf_sim_number} ({earth_like_number/total_number})') SE = [] TM = [] RE = [] for id in np.unique([sys[-1] for sys in earth_analogs2]): SE.append(pop.SIMS[id].Sigma_Exponent) TM.append(pop.SIMS[id].Total_Mass) RE.append(pop.SIMS[id].Sigma_Norm / (R_S / au)**pop.SIMS[id].Sigma_Exponent / denstos * pow(au/R_S, pop.SIMS[id].Sigma_Exponent) / denstos) SE = np.array(SE) TM = np.array(TM) cmap = pop.cmap_standart cmin = min(RE) cmax = max(RE) norm = colors.LogNorm(cmin, cmax) fig, ax = plt.subplots(figsize=pop.figsize) ax.scatter(SE, TM, c=RE, cmap=cmap, norm=norm, s=12) x_labels = ax.get_xticklabels() plt.setp(x_labels, horizontalalignment='center') ax.set(xlabel='Surface Density Power Law Exponent', ylabel=r'Total Disk Mass [$M_{\odot}$]', xticks=SE) ax2 = ax.twinx() mn, mx = ax.get_ylim() ax2.set_ylim(M_S / M_J * mn, M_S / M_J * mx) ax2.set_ylabel('Total Disk Mass [$M_{J}$]') fig.colorbar(cm.ScalarMappable(cmap=cmap, norm=norm), orientation='vertical', label=r'Reference Value at $1 \mathrm{au}$ [$\mathrm{g}\mathrm{cm}^{-2}$]', ax=ax2, pad=0.12) # ax.set_yscale('log') if pop.plot_config == 'presentation': ax.set(title=r'Synthesis Parameters') fig.savefig(path.join(pop.PLOT, 'scatter_parameters_earth_analogs.png'), transparent=False, dpi=pop.dpi, bbox_inches="tight") plt.close(fig)

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py

Python

didcomm/core/__init__.py

alex-polosky/didcomm-python

955866024c9f6191df9c5a898cc77e1979781eae

[ "Apache-2.0" ]

8

2021-09-04T19:28:18.000Z

2021-12-22T16:00:18.000Z

didcomm/core/__init__.py

alex-polosky/didcomm-python

955866024c9f6191df9c5a898cc77e1979781eae

[ "Apache-2.0" ]

4

2021-07-27T23:44:33.000Z

2021-10-13T13:29:39.000Z

didcomm/core/__init__.py

alex-polosky/didcomm-python

955866024c9f6191df9c5a898cc77e1979781eae

[ "Apache-2.0" ]

7

2021-07-22T08:19:13.000Z

2022-01-04T14:46:38.000Z

from authlib.jose import JsonWebEncryption from authlib.jose.drafts import register_jwe_draft register_jwe_draft(JsonWebEncryption)

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py

Python

venv/lib/python3.8/site-packages/clikit/io/__init__.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/clikit/io/__init__.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/clikit/io/__init__.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/08/57/cc/d62b02ab43b1de37202b2fed8b7e8b8c420a6fe582a902d5e0493984fe

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8ede3657a816cc37e5324bdb902a8687cc5cee73

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py

Python

NCM/visualization/visualization.py

az10az/example_package

d21998069e947e41a71d2847db15d45b74a12e38

[ "MIT" ]

null

NCM/visualization/visualization.py

az10az/example_package

d21998069e947e41a71d2847db15d45b74a12e38

[ "MIT" ]

null

NCM/visualization/visualization.py

az10az/example_package

d21998069e947e41a71d2847db15d45b74a12e38

[ "MIT" ]

null

def visualization(): """Extract final adjust process values from BHDS tables. Args: pri_df {pyspark.sql.dataframe.DataFrame} -- Primary BHDS data Returns: [pyspark.sql.dataframe.DataFrame] -- Dataframe with features. """ print('visualizing data....')

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py

Python

terrascript/mysql/__init__.py

hugovk/python-terrascript

08fe185904a70246822f5cfbdc9e64e9769ec494

[ "BSD-2-Clause" ]

507

2017-07-26T02:58:38.000Z

2022-01-21T12:35:13.000Z

terrascript/mysql/__init__.py

hugovk/python-terrascript

08fe185904a70246822f5cfbdc9e64e9769ec494

[ "BSD-2-Clause" ]

135

2017-07-20T12:01:59.000Z

2021-10-04T22:25:40.000Z

terrascript/mysql/__init__.py

hugovk/python-terrascript

08fe185904a70246822f5cfbdc9e64e9769ec494

[ "BSD-2-Clause" ]

81

2018-02-20T17:55:28.000Z

2022-01-31T07:08:40.000Z

# terrascript/mysql/__init__.py import terrascript class mysql(terrascript.Provider): pass

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d906806bd6c9b301631f68150d3e607d3168058d

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py

Python

jstc/test_compiler.py

canaryhealth/jstc

d4be1f213e041b80708e8a7e40edfe2ae308b637

[ "MIT" ]

null

jstc/test_compiler.py

canaryhealth/jstc

d4be1f213e041b80708e8a7e40edfe2ae308b637

[ "MIT" ]

null

jstc/test_compiler.py

canaryhealth/jstc

d4be1f213e041b80708e8a7e40edfe2ae308b637

[ "MIT" ]

null

# -*- coding: utf-8 -*- #------------------------------------------------------------------------------ # file: $Id$ # auth: Philip J Grabner <phil@canary.md> # date: 2016/09/15 # copy: (C) Copyright 2016-EOT Canary Health, Inc., All Rights Reserved. #------------------------------------------------------------------------------ import unittest import os import textwrap from aadict import aadict import fso #------------------------------------------------------------------------------ class TestCompiler(unittest.TestCase): maxDiff = None #---------------------------------------------------------------------------- def test_fragments(self): import jstc.compiler compiler = jstc.compiler.Compiler() hooks = aadict(name_transform=compiler._name_transform) self.assertEqual( list(compiler.fragments('foo/bar.jst', '', 'i am a template.', hooks)), [('i am a template.', aadict(name='foo/bar', type='.jst'))]) self.assertEqual( list(compiler.fragments('foo/bar.jst', '', '''\ ##! zig i am the zig template. ##! __here__ i am the root template. ''', hooks)), [ (' i am the zig template.\n', aadict(name='foo/bar/zig', type='.jst')), (' i am the root template.\n', aadict(name='foo/bar', type='.jst')), ]) #---------------------------------------------------------------------------- def test_attributes(self): import jstc.compiler compiler = jstc.compiler.Compiler() hooks = aadict(name_transform=compiler._name_transform) self.assertEqual( list(compiler.fragments('foo/bar.jst', '', '''\ ##! zig; channels: "public,protected" i am the zig template. ##! __here__; public; protected i am the root template. ##! zag; type: text/jst; !public; !protected i am the zag template. ''', hooks)), [ (' i am the zig template.\n', aadict(name='foo/bar/zig', type='.jst', channels='public,protected')), (' i am the root template.\n', aadict(name='foo/bar', type='.jst', public=True, protected=True)), (' i am the zag template.\n', aadict(name='foo/bar/zag', type='text/jst', public=False, protected=False)), ]) #---------------------------------------------------------------------------- def writecontent(self, files, dedent=True): for name, content in files.items(): path = os.path.join(os.path.dirname(__file__), name) pdir = os.path.dirname(path) if not os.path.isdir(pdir): os.makedirs(pdir) with open(path, 'wb') as fp: fp.write(textwrap.dedent(content)) #---------------------------------------------------------------------------- def test_render_simple(self): import jstc.compiler compiler = jstc.compiler.Compiler( overrides=dict(inline=True, precompile=False)) with fso.push() as overlay: self.writecontent({ 'test/common/hello.hbs': '''\ ##! __here__ Hello, world! ##! name Hello, {{name}}! ''' }) self.assertEqual( compiler.render_assets('jstc:test/common/hello.hbs', 'test'), '''\ <script type="text/x-handlebars" data-template-name="common/hello">Hello, world!</script>\ <script type="text/x-handlebars" data-template-name="common/hello/name">Hello, {{name}}!</script>\ ''') #---------------------------------------------------------------------------- def test_render_trim_deprecated(self): import jstc.compiler compiler = jstc.compiler.Compiler( overrides=dict(inline=True, precompile=False)) with fso.push() as overlay: self.writecontent({ 'test.hbs': '''\ ##! 0-default <span> text </span> ##! 1-trim; trim <span> text </span> ##! 2-notrim; !trim <span> text </span> ''' }) self.assertEqual( compiler.render_assets('jstc:test.hbs'), '''\ <script type="text/x-handlebars" data-template-name="test/0-default"><span> text </span></script>\ <script type="text/x-handlebars" data-template-name="test/1-trim"><span> text </span></script>\ <script type="text/x-handlebars" data-template-name="test/2-notrim"> <span> text </span> </script>\ ''') #---------------------------------------------------------------------------- def test_render_space_default(self): import jstc.compiler compiler = jstc.compiler.Compiler( overrides=dict(inline=True, precompile=False)) with fso.push() as overlay: self.writecontent({ 'test.hbs': '''\ ##! default {{#if value}} <span> {{value}} </span> {{else}} <span>default</span> {{/if}} ''' }) self.assertEqual( compiler.render_assets('jstc:test.hbs'), '''\ <script type="text/x-handlebars" data-template-name="test/default">{{#if value}} <span> {{value}} </span> {{else}} <span>default</span> {{/if}}</script>\ ''') #---------------------------------------------------------------------------- def test_render_space_preserve(self): import jstc.compiler compiler = jstc.compiler.Compiler( overrides=dict(inline=True, precompile=False)) with fso.push() as overlay: self.writecontent({ 'test.hbs': '''\ ##! preserve; space: preserve {{#if value}} <span> {{value}} </span> {{else}} <span>default</span> {{/if}} ''' }) self.assertEqual( compiler.render_assets('jstc:test.hbs'), '''\ <script type="text/x-handlebars" data-template-name="test/preserve"> {{#if value}} <span> {{value}} </span> {{else}} <span>default</span> {{/if}} </script>\ ''') #---------------------------------------------------------------------------- def test_render_space_trim(self): import jstc.compiler compiler = jstc.compiler.Compiler( overrides=dict(inline=True, precompile=False)) with fso.push() as overlay: self.writecontent({ 'test.hbs': '''\ ##! trim; space: trim {{#if value}} <span> {{value}} </span> {{else}} <span>default</span> {{/if}} ''' }) self.assertEqual( compiler.render_assets('jstc:test.hbs'), '''\ <script type="text/x-handlebars" data-template-name="test/trim">{{#if value}} <span> {{value}} </span> {{else}} <span>default</span> {{/if}}</script>\ ''') #---------------------------------------------------------------------------- def test_render_space_dedent(self): import jstc.compiler compiler = jstc.compiler.Compiler( overrides=dict(inline=True, precompile=False)) with fso.push() as overlay: self.writecontent({ 'test.hbs': '''\ ##! dedent; space: dedent {{#if value}} <span> {{value}} </span> {{else}} <span>default</span> {{/if}} ''' }) self.assertEqual( compiler.render_assets('jstc:test.hbs'), '''\ <script type="text/x-handlebars" data-template-name="test/dedent">{{#if value}} <span> {{value}} </span> {{else}} <span>default</span> {{/if}}</script>\ ''') #---------------------------------------------------------------------------- def test_render_space_collapse_complete(self): import jstc.compiler compiler = jstc.compiler.Compiler( overrides=dict(inline=True, precompile=False)) with fso.push() as overlay: self.writecontent({ 'test.hbs': '''\ ##! collapse/complete; space: collapse {{#if value}} <span> {{value}} </span> {{else}} <span>default</span> {{/if}} ''' }) self.assertEqual( compiler.render_assets('jstc:test.hbs'), '''\ <script type="text/x-handlebars" data-template-name="test/collapse/complete">{{#if value}}<span>{{value}}</span>{{else}}<span>default</span>{{/if}}</script>\ ''') #---------------------------------------------------------------------------- def test_render_space_collapse_htmlSpace(self): import jstc.compiler compiler = jstc.compiler.Compiler( overrides=dict(inline=True, precompile=False)) with fso.push() as overlay: self.writecontent({ 'test.hbs': '''\ ##! collapse/htmlspace; space: collapse {{#if value}} <span > {{value}} </span > {{else}} <span>default</span > {{/if}} ''' }) self.assertEqual( compiler.render_assets('jstc:test.hbs'), '''\ <script type="text/x-handlebars" data-template-name="test/collapse/htmlspace">{{#if value}}<span> {{value}}</span> {{else}}<span>default</span> {{/if}}</script>\ ''') #---------------------------------------------------------------------------- def test_render_space_collapse_hbsSpace(self): import jstc.compiler compiler = jstc.compiler.Compiler( overrides=dict(inline=True, precompile=False)) with fso.push() as overlay: self.writecontent({ 'test.hbs': '''\ ##! collapse/hbsspace; space: collapse {{#if value }} <span> {{value }} </span> {{else }} <span>default</span> {{/if }} ''' }) self.assertEqual( compiler.render_assets('jstc:test.hbs'), '''\ <script type="text/x-handlebars" data-template-name="test/collapse/hbsspace">{{#if value}} <span>{{value}} </span>{{else}} <span>default</span>{{/if}} </script>\ ''') #---------------------------------------------------------------------------- def test_comments(self): import jstc.compiler compiler = jstc.compiler.Compiler( overrides=dict(inline=True, precompile=False)) with fso.push() as overlay: self.writecontent({ 'test/application.hbs': '''\ <div> ## TODO: super-secret comment! Nothing to see here. </div> ''' }) self.assertEqual( compiler.render_assets('jstc:test/application.hbs', 'test'), '''\ <script type="text/x-handlebars" data-template-name="application"><div> Nothing to see here. </div>\ </script>\ ''') #---------------------------------------------------------------------------- def test_root(self): import jstc.compiler compiler = jstc.compiler.Compiler( overrides=dict(inline=True, precompile=False)) with fso.push() as overlay: self.writecontent({ 'test/one/template.hbs': 'template "one".', 'test/two/template.hbs': 'template "two".', }) self.assertEqual( compiler.render_assets('jstc:test/one/template.hbs', 'test/one'), '''\ <script type="text/x-handlebars" data-template-name="template">template "one".</script>\ ''') self.assertEqual( compiler.render_assets('jstc:test/two/template.hbs', 'test/two'), '''\ <script type="text/x-handlebars" data-template-name="template">template "two".</script>\ ''') self.assertEqual( compiler.render_assets( ['jstc:test/one/template.hbs', 'jstc:test/two/template.hbs'], 'test'), '''\ <script type="text/x-handlebars" data-template-name="one/template">template "one".</script>\ <script type="text/x-handlebars" data-template-name="two/template">template "two".</script>\ ''') #---------------------------------------------------------------------------- def test_collision_error(self): import jstc.compiler compiler = jstc.compiler.Compiler( overrides=dict(inline=True, precompile=False)) with fso.push() as overlay: self.writecontent({ 'test/one/template.hbs': 'template "one".', 'test/two/template.hbs': 'template "two".', }) with self.assertRaises(jstc.TemplateCollision) as cm: compiler.render_assets( ['jstc:test/one/template.hbs', 'jstc:test/two/template.hbs'], ['test/one', 'test/two']) self.assertEqual( str(cm.exception), ''''text/x-handlebars' template 'template' is already defined''') #---------------------------------------------------------------------------- def test_collision_ignore(self): import jstc.compiler compiler = jstc.compiler.Compiler( defaults=dict(collision='ignore'), overrides=dict(inline=True, precompile=False), ) with fso.push() as overlay: self.writecontent({ 'test/one/template.hbs': 'template "one".', 'test/two/template.hbs': 'template "two".', }) self.assertEqual( compiler.render_assets( ['jstc:test/one/template.hbs', 'jstc:test/two/template.hbs'], ['test/one', 'test/two']), '''\ <script type="text/x-handlebars" data-template-name="template">template "one".</script>\ ''') #---------------------------------------------------------------------------- def test_collision_override(self): import jstc.compiler compiler = jstc.compiler.Compiler( defaults=dict(collision='override'), overrides=dict(inline=True, precompile=False), ) with fso.push() as overlay: self.writecontent({ 'test/one/template.hbs': 'template "one".', 'test/two/template.hbs': 'template "two".', }) self.assertEqual( compiler.render_assets( ['jstc:test/one/template.hbs', 'jstc:test/two/template.hbs'], ['test/one', 'test/two']), '''\ <script type="text/x-handlebars" data-template-name="template">template "two".</script>\ ''') #---------------------------------------------------------------------------- def test_collision_pertemplate(self): import jstc.compiler compiler = jstc.compiler.Compiler( defaults=dict(collision='ignore'), overrides=dict(inline=True, precompile=False), ) with fso.push() as overlay: self.writecontent({ 'test/one/template.hbs': '''\ ##! a template "one/a". ##! b template "one/b". ''', 'test/two/template.hbs': '''\ ##! a; collision: ignore template "two/a". ##! b; collision: override template "two/b". ''', }) self.assertEqual( compiler.render_assets( ['jstc:test/one/template.hbs', 'jstc:test/two/template.hbs'], ['test/one', 'test/two']), '''\ <script type="text/x-handlebars" data-template-name="template/a">template "one/a".</script>\ <script type="text/x-handlebars" data-template-name="template/b">template "two/b".</script>\ ''') #---------------------------------------------------------------------------- def test_precompile(self): import jstc with fso.push() as overlay: self.writecontent({ 'test/hello.hbs': 'hello, world!', 'test/hello/name.hbs': 'hello, {{name}}!', }) compiled = jstc.render_assets('jstc:test/**.hbs', force_inline=True, force_precompile=True) if 'text/x-handlebars' in compiled: raise unittest.SkipTest( 'handlebars executable not available (use "npm install handlebars")') self.assertMultiLineEqual( compiled, '''\ <script type="text/javascript" >(function(){var t=Handlebars.template,ts=Handlebars.templates=Handlebars.templates||{};ts["hello"]=t({"compiler":[7,">= 4.0.0"],"main":function(container,depth0,helpers,partials,data) { return "hello, world!"; },"useData":true});ts["hello/name"]=t({"compiler":[7,">= 4.0.0"],"main":function(container,depth0,helpers,partials,data) { var helper; return "hello, " + container.escapeExpression(((helper = (helper = helpers.name || (depth0 != null ? depth0.name : depth0)) != null ? helper : helpers.helperMissing),(typeof helper === "function" ? helper.call(depth0 != null ? depth0 : {},{"name":"name","hash":{},"data":data}) : helper))) + "!"; },"useData":true});})();</script>''') #---------------------------------------------------------------------------- def test_asset_filter(self): import jstc with fso.push() as overlay: self.writecontent({ 'test/hello.hbs': 'hello!', 'test/goodbye.hbs': 'so long!', }) self.assertEqual( jstc.render_assets('jstc:test/**.hbs', force_inline=True, force_precompile=False), '''\ <script type="text/x-handlebars" data-template-name="goodbye">so long!</script>\ <script type="text/x-handlebars" data-template-name="hello">hello!</script>\ ''') self.assertEqual( jstc.render_assets( 'jstc:test/**.hbs', force_inline=True, force_precompile=False, asset_filter=lambda name: name == 'test/hello.hbs'), '''\ <script type="text/x-handlebars" data-template-name="hello">hello!</script>\ ''') self.assertEqual( jstc.render_assets('jstc:test/**.hbs', force_inline=True, force_precompile=False, asset_filter=lambda name: name != 'test/hello.hbs'), '''\ <script type="text/x-handlebars" data-template-name="goodbye">so long!</script>\ ''') #---------------------------------------------------------------------------- def test_name_transform(self): import jstc with fso.push() as overlay: self.writecontent({ 'test/hello.hbs': 'hello!', 'test/goodbye.hbs': 'so long!', }) def mynt(name, root): return (name[2:].replace('d', 'd-').split('.')[0], 'text/x-mustache') self.assertEqual( jstc.render_assets('jstc:test/**.hbs', force_inline=True, force_precompile=False, name_transform=mynt), '''\ <script type="text/x-mustache" data-template-name="st/good-bye">so long!</script>\ <script type="text/x-mustache" data-template-name="st/hello">hello!</script>\ ''') #---------------------------------------------------------------------------- def test_template_transform(self): import jstc with fso.push() as overlay: self.writecontent({ 'test/hello.hbs': 'hello!', 'test/goodbye.hbs': 'so long!', }) def mytt(text, attrs): if attrs.name == 'hello': text = 'hello, world!' attrs.id = 'HW' else: attrs.type = 'template/jst' return (text, attrs) self.assertEqual( jstc.render_assets('jstc:test/**.hbs', force_inline=True, force_precompile=False, template_transform=mytt), '''\ <script type="template/jst" data-template-name="goodbye">so long!</script>\ <script type="text/x-handlebars" data-template-name="hello" id="HW">hello, world!</script>\ ''') #---------------------------------------------------------------------------- def test_template_filter(self): import jstc with fso.push() as overlay: self.writecontent({ 'test/hello.hbs': 'hello!', 'test/goodbye.hbs': '''\ ##! __here__ so long! ##! friend ciao! ''' }) self.assertEqual( jstc.render_assets('jstc:test/**.hbs', force_inline=True, force_precompile=False), '''\ <script type="text/x-handlebars" data-template-name="goodbye">so long!</script>\ <script type="text/x-handlebars" data-template-name="goodbye/friend">ciao!</script>\ <script type="text/x-handlebars" data-template-name="hello">hello!</script>\ ''') self.assertEqual( jstc.render_assets('jstc:test/**.hbs', force_inline=True, force_precompile=False, template_filter=lambda text, attrs: 'ciao' not in text), '''\ <script type="text/x-handlebars" data-template-name="goodbye">so long!</script>\ <script type="text/x-handlebars" data-template-name="hello">hello!</script>\ ''') #---------------------------------------------------------------------------- def test_script_wrapper(self): import jstc with fso.push() as overlay: self.writecontent({ 'test/hello.hbs': 'hello, world!', 'test/hello/name.hbs': 'hello, {{name}}!', }) compiled = jstc.render_assets( 'jstc:test/**.hbs', force_inline=True, force_precompile=True, script_wrapper = lambda script, *args, **kw: '<SCRIPT>' + script + '</SCRIPT>') if 'text/x-handlebars' in compiled: raise unittest.SkipTest( 'handlebars executable not available (use "npm install handlebars")') self.assertMultiLineEqual( compiled, '''\ <SCRIPT>(function(){var t=Handlebars.template,ts=Handlebars.templates=Handlebars.templates||{};ts["hello"]=t({"compiler":[7,">= 4.0.0"],"main":function(container,depth0,helpers,partials,data) { return "hello, world!"; },"useData":true});ts["hello/name"]=t({"compiler":[7,">= 4.0.0"],"main":function(container,depth0,helpers,partials,data) { var helper; return "hello, " + container.escapeExpression(((helper = (helper = helpers.name || (depth0 != null ? depth0.name : depth0)) != null ? helper : helpers.helperMissing),(typeof helper === "function" ? helper.call(depth0 != null ? depth0 : {},{"name":"name","hash":{},"data":data}) : helper))) + "!"; },"useData":true});})();</SCRIPT>''') #------------------------------------------------------------------------------ # end of $Id$ # $ChangeLog$ #------------------------------------------------------------------------------

34.990446

276

0.51388

2,175

21,974

5.132874

0.095172

0.016571

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0.044339

0.844231

0.826585

0.814493

0.79631

0.792458

0.770244

0

0.002993

0.224538

21,974

627

277

35.046252

0.652171

0.109129

0

0.649275

0

0.197872

0.050553

0

0.001595

0.086957

1

0.075362

false

0

0.081159

0.002899

0.168116

0

null

0

1

0

null

0

6

d92ecb84da9cb8b48f3870622cd21c4d6d2bd473

211

py

Python

payjp/account.py

wozozo/payjp-python

98c07d402c89bfba73009bd1de574ca17dc1f6e2

[ "MIT" ]

null

payjp/account.py

wozozo/payjp-python

98c07d402c89bfba73009bd1de574ca17dc1f6e2

[ "MIT" ]

null

payjp/account.py

wozozo/payjp-python

98c07d402c89bfba73009bd1de574ca17dc1f6e2

[ "MIT" ]

null

class Account: resource = 'accounts' def __init__(self, requestor): self.requestor = requestor def retrieve(self): return self.requestor.request('GET', '{}'.format(self.resource))

21.1

72

0.64455

22

211

6

0.590909

0.295455

0

0.222749

211

9

73

23.444444

0.804878

0

0.061611

0

1

0.333333

false

0

0.166667

0.833333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

d977c8ed87a80746c717a179a589834e7156fccd

69

py

Python

attax/__init__.py

jonasrauber/attax

952f2500c7ed675ba0f547570053f7163e572566

[ "MIT" ]

2

2020-02-10T23:24:26.000Z

2021-09-27T01:45:04.000Z

attax/__init__.py

jonasrauber/attax

952f2500c7ed675ba0f547570053f7163e572566

[ "MIT" ]

null

attax/__init__.py

jonasrauber/attax

952f2500c7ed675ba0f547570053f7163e572566

[ "MIT" ]

null

from . import utils # noqa: F401 from .pgd import pgd # noqa: F401

23

34

0.681159

11

69

4.272727

0.545455

0.340426

0

0.113208

0.231884

69

2

35

34.5

0.773585

0.304348

0

1

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true

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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

79c4f164992ac85743973dd21a74e08e5fc6f055

8,485

py

Python

tests/test_hashes.py

methane/pip

ee44e13716cb8dad3b52f0ab222eb2c7ce107e48

[ "MIT" ]

2

2015-07-17T06:45:10.000Z

2015-11-08T11:42:37.000Z

tests/test_hashes.py

methane/pip

ee44e13716cb8dad3b52f0ab222eb2c7ce107e48

[ "MIT" ]

null

tests/test_hashes.py

methane/pip

ee44e13716cb8dad3b52f0ab222eb2c7ce107e48

[ "MIT" ]

null

import os from nose.tools import assert_raises from pip.download import _get_hash_from_file, _check_hash from pip.exceptions import InstallationError from pip.index import Link def test_get_hash_from_file_md5(): file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "packages", "gmpy-1.15.tar.gz") file_link = Link("http://testserver/gmpy-1.15.tar.gz#md5=d41d8cd98f00b204e9800998ecf8427e") download_hash = _get_hash_from_file(file_path, file_link) assert download_hash.digest_size == 16 assert download_hash.hexdigest() == "d41d8cd98f00b204e9800998ecf8427e" def test_get_hash_from_file_sha1(): file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "packages", "gmpy-1.15.tar.gz") file_link = Link("http://testserver/gmpy-1.15.tar.gz#sha1=da39a3ee5e6b4b0d3255bfef95601890afd80709") download_hash = _get_hash_from_file(file_path, file_link) assert download_hash.digest_size == 20 assert download_hash.hexdigest() == "da39a3ee5e6b4b0d3255bfef95601890afd80709" def test_get_hash_from_file_sha224(): file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "packages", "gmpy-1.15.tar.gz") file_link = Link("http://testserver/gmpy-1.15.tar.gz#sha224=d14a028c2a3a2bc9476102bb288234c415a2b01f828ea62ac5b3e42f") download_hash = _get_hash_from_file(file_path, file_link) assert download_hash.digest_size == 28 assert download_hash.hexdigest() == "d14a028c2a3a2bc9476102bb288234c415a2b01f828ea62ac5b3e42f" def test_get_hash_from_file_sha384(): file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "packages", "gmpy-1.15.tar.gz") file_link = Link("http://testserver/gmpy-1.15.tar.gz#sha384=38b060a751ac96384cd9327eb1b1e36a21fdb71114be07434c0cc7bf63f6e1da274edebfe76f65fbd51ad2f14898b95b") download_hash = _get_hash_from_file(file_path, file_link) assert download_hash.digest_size == 48 assert download_hash.hexdigest() == "38b060a751ac96384cd9327eb1b1e36a21fdb71114be07434c0cc7bf63f6e1da274edebfe76f65fbd51ad2f14898b95b" def test_get_hash_from_file_sha256(): file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "packages", "gmpy-1.15.tar.gz") file_link = Link("http://testserver/gmpy-1.15.tar.gz#sha256=e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855") download_hash = _get_hash_from_file(file_path, file_link) assert download_hash.digest_size == 32 assert download_hash.hexdigest() == "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855" def test_get_hash_from_file_sha512(): file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "packages", "gmpy-1.15.tar.gz") file_link = Link("http://testserver/gmpy-1.15.tar.gz#sha512=cf83e1357eefb8bdf1542850d66d8007d620e4050b5715dc83f4a921d36ce9ce47d0d13c5d85f2b0ff8318d2877eec2f63b931bd47417a81a538327af927da3e") download_hash = _get_hash_from_file(file_path, file_link) assert download_hash.digest_size == 64 assert download_hash.hexdigest() == "cf83e1357eefb8bdf1542850d66d8007d620e4050b5715dc83f4a921d36ce9ce47d0d13c5d85f2b0ff8318d2877eec2f63b931bd47417a81a538327af927da3e" def test_get_hash_from_file_unknown(): file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "packages", "gmpy-1.15.tar.gz") file_link = Link("http://testserver/gmpy-1.15.tar.gz#unknown_hash=d41d8cd98f00b204e9800998ecf8427e") download_hash = _get_hash_from_file(file_path, file_link) assert download_hash is None def test_check_hash_md5_valid(): file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "packages", "gmpy-1.15.tar.gz") file_link = Link("http://testserver/gmpy-1.15.tar.gz#md5=d41d8cd98f00b204e9800998ecf8427e") download_hash = _get_hash_from_file(file_path, file_link) _check_hash(download_hash, file_link) def test_check_hash_md5_invalid(): file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "packages", "gmpy-1.15.tar.gz") file_link = Link("http://testserver/gmpy-1.15.tar.gz#md5=deadbeef") download_hash = _get_hash_from_file(file_path, file_link) assert_raises(InstallationError, _check_hash, download_hash, file_link) def test_check_hash_sha1_valid(): file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "packages", "gmpy-1.15.tar.gz") file_link = Link("http://testserver/gmpy-1.15.tar.gz#sha1=da39a3ee5e6b4b0d3255bfef95601890afd80709") download_hash = _get_hash_from_file(file_path, file_link) _check_hash(download_hash, file_link) def test_check_hash_sha1_invalid(): file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "packages", "gmpy-1.15.tar.gz") file_link = Link("http://testserver/gmpy-1.15.tar.gz#sha1=deadbeef") download_hash = _get_hash_from_file(file_path, file_link) assert_raises(InstallationError, _check_hash, download_hash, file_link) def test_check_hash_sha224_valid(): file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "packages", "gmpy-1.15.tar.gz") file_link = Link("http://testserver/gmpy-1.15.tar.gz#sha224=d14a028c2a3a2bc9476102bb288234c415a2b01f828ea62ac5b3e42f'") download_hash = _get_hash_from_file(file_path, file_link) _check_hash(download_hash, file_link) def test_check_hash_sha224_invalid(): file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "packages", "gmpy-1.15.tar.gz") file_link = Link("http://testserver/gmpy-1.15.tar.gz#sha224=deadbeef") download_hash = _get_hash_from_file(file_path, file_link) assert_raises(InstallationError, _check_hash, download_hash, file_link) def test_check_hash_sha384_valid(): file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "packages", "gmpy-1.15.tar.gz") file_link = Link("http://testserver/gmpy-1.15.tar.gz#sha384=38b060a751ac96384cd9327eb1b1e36a21fdb71114be07434c0cc7bf63f6e1da274edebfe76f65fbd51ad2f14898b95b") download_hash = _get_hash_from_file(file_path, file_link) _check_hash(download_hash, file_link) def test_check_hash_sha384_invalid(): file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "packages", "gmpy-1.15.tar.gz") file_link = Link("http://testserver/gmpy-1.15.tar.gz#sha384=deadbeef") download_hash = _get_hash_from_file(file_path, file_link) assert_raises(InstallationError, _check_hash, download_hash, file_link) def test_check_hash_sha256_valid(): file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "packages", "gmpy-1.15.tar.gz") file_link = Link("http://testserver/gmpy-1.15.tar.gz#sha256=e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855") download_hash = _get_hash_from_file(file_path, file_link) _check_hash(download_hash, file_link) def test_check_hash_sha256_invalid(): file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "packages", "gmpy-1.15.tar.gz") file_link = Link("http://testserver/gmpy-1.15.tar.gz#sha256=deadbeef") download_hash = _get_hash_from_file(file_path, file_link) assert_raises(InstallationError, _check_hash, download_hash, file_link) def test_check_hash_sha512_valid(): file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "packages", "gmpy-1.15.tar.gz") file_link = Link("http://testserver/gmpy-1.15.tar.gz#sha512=cf83e1357eefb8bdf1542850d66d8007d620e4050b5715dc83f4a921d36ce9ce47d0d13c5d85f2b0ff8318d2877eec2f63b931bd47417a81a538327af927da3e") download_hash = _get_hash_from_file(file_path, file_link) _check_hash(download_hash, file_link) def test_check_hash_sha512_invalid(): file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "packages", "gmpy-1.15.tar.gz") file_link = Link("http://testserver/gmpy-1.15.tar.gz#sha512=deadbeef") download_hash = _get_hash_from_file(file_path, file_link) assert_raises(InstallationError, _check_hash, download_hash, file_link) def test_check_hasher_mismsatch(): file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "packages", "gmpy-1.15.tar.gz") file_link = Link("http://testserver/gmpy-1.15.tar.gz#md5=d41d8cd98f00b204e9800998ecf8427e") other_link = Link("http://testserver/gmpy-1.15.tar.gz#sha256=e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855") download_hash = _get_hash_from_file(file_path, file_link) assert_raises(InstallationError, _check_hash, download_hash, other_link)

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kujenga/__init__.py

macd/kujenga

27906d274565966a8da5918bed04ece8d3b9d70e

[ "MIT" ]

3

2017-08-06T19:16:57.000Z

2017-08-17T15:50:35.000Z

kujenga/__init__.py

macd/kujenga

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kujenga/__init__.py

macd/kujenga

27906d274565966a8da5918bed04ece8d3b9d70e

[ "MIT" ]

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from kujenga.kujenga import create_image

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att_app/models.py

tunir27/django-Attendance

4075c93bce56f02b06de126349bcc63294e07f0b

[ "MIT" ]

3

2019-07-05T16:03:39.000Z

2019-11-06T07:20:29.000Z

att_app/models.py

tunir27/django-Attendance

4075c93bce56f02b06de126349bcc63294e07f0b

[ "MIT" ]

6

2020-06-05T17:53:31.000Z

2021-09-07T23:50:09.000Z

att_app/models.py

tunir27/django-Attendance

4075c93bce56f02b06de126349bcc63294e07f0b

[ "MIT" ]

3

2018-04-30T15:09:04.000Z

2018-12-15T12:45:14.000Z

from django.db import models import login from django.conf import settings # Create your models here. class Student_Details(models.Model): st_id = models.ForeignKey(settings.AUTH_USER_MODEL,on_delete=models.CASCADE,limit_choices_to={'is_staff': False}) first_name=models.CharField(max_length=50, help_text="Enter the First-Name",verbose_name="First Name",null=True) last_name=models.CharField(max_length=50, help_text="Enter the Last Name",verbose_name="Last Name",null=True) dob=models.DateField(max_length=8,help_text="Enter Date of Birth",verbose_name="Date of Birth",null=True) address=models.CharField(max_length=50, help_text="Enter the Address",verbose_name="Address",null=True) g_name=models.CharField(max_length=50, help_text="Enter the Student Guardian Name",verbose_name="Guardian Name",null=True) phone=models.CharField(max_length=15, help_text="Enter Guardian Number",verbose_name="Guardian Phone",null=True) s_class=models.CharField(max_length=1, help_text="Enter Student Class",verbose_name="Student Class",null=True) sec=models.CharField(max_length=1, help_text="Enter Student Section",verbose_name="Student Section",null=True) gender=models.CharField(max_length=1, help_text="Enter Student Gender(M/F/T)",verbose_name="Student Gender",null=True,blank=True) email = models.EmailField(max_length=70,help_text="Enter Email",verbose_name="Email",blank=True,null=True,unique=True) def __str__(self): return str(self.st_id) class Teacher_Details(models.Model): t_id=models.ForeignKey(settings.AUTH_USER_MODEL,on_delete=models.CASCADE,limit_choices_to={'is_staff': True}) first_name=models.CharField(max_length=50, help_text="Enter the First-Name",verbose_name="First Name",null=True) last_name=models.CharField(max_length=50, help_text="Enter the Last Name",verbose_name="Last Name",null=True) dob=models.DateField(max_length=8,help_text="Enter Date of Birth",verbose_name="Date of Birth",null=True) address=models.CharField(max_length=50, help_text="Enter the Address",verbose_name="Address",null=True) phone=models.CharField(max_length=15, help_text="Enter Phone Number",verbose_name="Phone No",null=True) gender=models.CharField(max_length=1, help_text="Enter Teacher Gender(M/F/T)",verbose_name="Teacher Gender",null=True,blank=True) email = models.EmailField(max_length=70,help_text="Enter Email",verbose_name="Email",blank=True,null=True,unique=True) def __str__(self): return str(self.t_id) class Student_Attendance(models.Model): st_id = models.ForeignKey(settings.AUTH_USER_MODEL,on_delete=models.CASCADE,limit_choices_to={'is_staff': False}) date=models.CharField(max_length=15, help_text="Enter the Date",verbose_name="Date",null=True) in_time= models.CharField(max_length=15, help_text="Enter the IN Time",verbose_name="IN Time",null=True,blank=True) out_time=models.CharField(max_length=15, help_text="Enter the OUT Time",verbose_name="OUT Time",null=True,blank=True) duration=models.CharField(max_length=15, help_text="Enter the Duration",verbose_name="Duration",null=True,blank=True) status = models.CharField(max_length=1, help_text="Enter the Status",verbose_name="Student Status",null=True) def __str__(self): return (str(self.st_id)+' '+str(self.date)) class Token(models.Model): uid = models.ForeignKey(settings.AUTH_USER_MODEL,on_delete=models.CASCADE) token= models.CharField(max_length=300,help_text="Enter the token",verbose_name="Token",null=True) def __str__(self): return str(self.uid)

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Python

python-client/swagger_client/__init__.py

tamalsaha/kube-openapi-generator

6607d1e208965e3a09a0ee6d1f2de7e462939150

[ "Apache-2.0" ]

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2018-04-23T09:07:04.000Z

2019-09-27T10:25:29.000Z

python-client/swagger_client/__init__.py

tamalsaha/kube-openapi-generator

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[ "Apache-2.0" ]

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python-client/swagger_client/__init__.py

tamalsaha/kube-openapi-generator

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# coding: utf-8 # flake8: noqa """ stash-server No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501 OpenAPI spec version: v0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import # import apis into sdk package from swagger_client.api.apis_api import ApisApi from swagger_client.api.repositories_stash_appscode_com_api import RepositoriesStashAppscodeComApi from swagger_client.api.repositories_stash_appscode_com_v1alpha1_api import RepositoriesStashAppscodeComV1alpha1Api from swagger_client.api.stash_appscode_com_api import StashAppscodeComApi from swagger_client.api.stash_appscode_com_v1alpha1_api import StashAppscodeComV1alpha1Api # import ApiClient from swagger_client.api_client import ApiClient from swagger_client.configuration import Configuration # import models into sdk package from swagger_client.models.com_github_appscode_stash_apis_repositories_v1alpha1_snapshot import ComGithubAppscodeStashApisRepositoriesV1alpha1Snapshot from swagger_client.models.com_github_appscode_stash_apis_repositories_v1alpha1_snapshot_list import ComGithubAppscodeStashApisRepositoriesV1alpha1SnapshotList from swagger_client.models.com_github_appscode_stash_apis_repositories_v1alpha1_snapshot_status import ComGithubAppscodeStashApisRepositoriesV1alpha1SnapshotStatus from swagger_client.models.com_github_appscode_stash_apis_stash_v1alpha1_azure_spec import ComGithubAppscodeStashApisStashV1alpha1AzureSpec from swagger_client.models.com_github_appscode_stash_apis_stash_v1alpha1_b2_spec import ComGithubAppscodeStashApisStashV1alpha1B2Spec from swagger_client.models.com_github_appscode_stash_apis_stash_v1alpha1_backend import ComGithubAppscodeStashApisStashV1alpha1Backend from swagger_client.models.com_github_appscode_stash_apis_stash_v1alpha1_file_group import ComGithubAppscodeStashApisStashV1alpha1FileGroup from swagger_client.models.com_github_appscode_stash_apis_stash_v1alpha1_gcs_spec import ComGithubAppscodeStashApisStashV1alpha1GCSSpec from swagger_client.models.com_github_appscode_stash_apis_stash_v1alpha1_local_spec import ComGithubAppscodeStashApisStashV1alpha1LocalSpec from swagger_client.models.com_github_appscode_stash_apis_stash_v1alpha1_local_typed_reference import ComGithubAppscodeStashApisStashV1alpha1LocalTypedReference from swagger_client.models.com_github_appscode_stash_apis_stash_v1alpha1_recovery import ComGithubAppscodeStashApisStashV1alpha1Recovery from swagger_client.models.com_github_appscode_stash_apis_stash_v1alpha1_recovery_list import ComGithubAppscodeStashApisStashV1alpha1RecoveryList from swagger_client.models.com_github_appscode_stash_apis_stash_v1alpha1_recovery_spec import ComGithubAppscodeStashApisStashV1alpha1RecoverySpec from swagger_client.models.com_github_appscode_stash_apis_stash_v1alpha1_recovery_status import ComGithubAppscodeStashApisStashV1alpha1RecoveryStatus from swagger_client.models.com_github_appscode_stash_apis_stash_v1alpha1_repository import ComGithubAppscodeStashApisStashV1alpha1Repository from swagger_client.models.com_github_appscode_stash_apis_stash_v1alpha1_repository_list import ComGithubAppscodeStashApisStashV1alpha1RepositoryList from swagger_client.models.com_github_appscode_stash_apis_stash_v1alpha1_repository_spec import ComGithubAppscodeStashApisStashV1alpha1RepositorySpec from swagger_client.models.com_github_appscode_stash_apis_stash_v1alpha1_repository_status import ComGithubAppscodeStashApisStashV1alpha1RepositoryStatus from swagger_client.models.com_github_appscode_stash_apis_stash_v1alpha1_restic import ComGithubAppscodeStashApisStashV1alpha1Restic from swagger_client.models.com_github_appscode_stash_apis_stash_v1alpha1_restic_list import ComGithubAppscodeStashApisStashV1alpha1ResticList from swagger_client.models.com_github_appscode_stash_apis_stash_v1alpha1_restic_spec import ComGithubAppscodeStashApisStashV1alpha1ResticSpec from swagger_client.models.com_github_appscode_stash_apis_stash_v1alpha1_restore_stats import ComGithubAppscodeStashApisStashV1alpha1RestoreStats from swagger_client.models.com_github_appscode_stash_apis_stash_v1alpha1_retention_policy import ComGithubAppscodeStashApisStashV1alpha1RetentionPolicy from swagger_client.models.com_github_appscode_stash_apis_stash_v1alpha1_s3_spec import ComGithubAppscodeStashApisStashV1alpha1S3Spec from swagger_client.models.com_github_appscode_stash_apis_stash_v1alpha1_swift_spec import ComGithubAppscodeStashApisStashV1alpha1SwiftSpec from swagger_client.models.io_k8s_api_core_v1_aws_elastic_block_store_volume_source import IoK8sApiCoreV1AWSElasticBlockStoreVolumeSource from swagger_client.models.io_k8s_api_core_v1_azure_disk_volume_source import IoK8sApiCoreV1AzureDiskVolumeSource from swagger_client.models.io_k8s_api_core_v1_azure_file_volume_source import IoK8sApiCoreV1AzureFileVolumeSource from swagger_client.models.io_k8s_api_core_v1_ceph_fs_volume_source import IoK8sApiCoreV1CephFSVolumeSource from swagger_client.models.io_k8s_api_core_v1_cinder_volume_source import IoK8sApiCoreV1CinderVolumeSource from swagger_client.models.io_k8s_api_core_v1_config_map_projection import IoK8sApiCoreV1ConfigMapProjection from swagger_client.models.io_k8s_api_core_v1_config_map_volume_source import IoK8sApiCoreV1ConfigMapVolumeSource from swagger_client.models.io_k8s_api_core_v1_downward_api_projection import IoK8sApiCoreV1DownwardAPIProjection from swagger_client.models.io_k8s_api_core_v1_downward_api_volume_file import IoK8sApiCoreV1DownwardAPIVolumeFile from swagger_client.models.io_k8s_api_core_v1_downward_api_volume_source import IoK8sApiCoreV1DownwardAPIVolumeSource from swagger_client.models.io_k8s_api_core_v1_empty_dir_volume_source import IoK8sApiCoreV1EmptyDirVolumeSource from swagger_client.models.io_k8s_api_core_v1_fc_volume_source import IoK8sApiCoreV1FCVolumeSource from swagger_client.models.io_k8s_api_core_v1_flex_volume_source import IoK8sApiCoreV1FlexVolumeSource from swagger_client.models.io_k8s_api_core_v1_flocker_volume_source import IoK8sApiCoreV1FlockerVolumeSource from swagger_client.models.io_k8s_api_core_v1_gce_persistent_disk_volume_source import IoK8sApiCoreV1GCEPersistentDiskVolumeSource from swagger_client.models.io_k8s_api_core_v1_git_repo_volume_source import IoK8sApiCoreV1GitRepoVolumeSource from swagger_client.models.io_k8s_api_core_v1_glusterfs_volume_source import IoK8sApiCoreV1GlusterfsVolumeSource from swagger_client.models.io_k8s_api_core_v1_host_path_volume_source import IoK8sApiCoreV1HostPathVolumeSource from swagger_client.models.io_k8s_api_core_v1_iscsi_volume_source import IoK8sApiCoreV1ISCSIVolumeSource from swagger_client.models.io_k8s_api_core_v1_key_to_path import IoK8sApiCoreV1KeyToPath from swagger_client.models.io_k8s_api_core_v1_local_object_reference import IoK8sApiCoreV1LocalObjectReference from swagger_client.models.io_k8s_api_core_v1_nfs_volume_source import IoK8sApiCoreV1NFSVolumeSource from swagger_client.models.io_k8s_api_core_v1_object_field_selector import IoK8sApiCoreV1ObjectFieldSelector from swagger_client.models.io_k8s_api_core_v1_persistent_volume_claim_volume_source import IoK8sApiCoreV1PersistentVolumeClaimVolumeSource from swagger_client.models.io_k8s_api_core_v1_photon_persistent_disk_volume_source import IoK8sApiCoreV1PhotonPersistentDiskVolumeSource from swagger_client.models.io_k8s_api_core_v1_portworx_volume_source import IoK8sApiCoreV1PortworxVolumeSource from swagger_client.models.io_k8s_api_core_v1_projected_volume_source import IoK8sApiCoreV1ProjectedVolumeSource from swagger_client.models.io_k8s_api_core_v1_quobyte_volume_source import IoK8sApiCoreV1QuobyteVolumeSource from swagger_client.models.io_k8s_api_core_v1_rbd_volume_source import IoK8sApiCoreV1RBDVolumeSource from swagger_client.models.io_k8s_api_core_v1_resource_field_selector import IoK8sApiCoreV1ResourceFieldSelector from swagger_client.models.io_k8s_api_core_v1_resource_requirements import IoK8sApiCoreV1ResourceRequirements from swagger_client.models.io_k8s_api_core_v1_scale_io_volume_source import IoK8sApiCoreV1ScaleIOVolumeSource from swagger_client.models.io_k8s_api_core_v1_secret_projection import IoK8sApiCoreV1SecretProjection from swagger_client.models.io_k8s_api_core_v1_secret_volume_source import IoK8sApiCoreV1SecretVolumeSource from swagger_client.models.io_k8s_api_core_v1_storage_os_volume_source import IoK8sApiCoreV1StorageOSVolumeSource from swagger_client.models.io_k8s_api_core_v1_volume_mount import IoK8sApiCoreV1VolumeMount from swagger_client.models.io_k8s_api_core_v1_volume_projection import IoK8sApiCoreV1VolumeProjection from swagger_client.models.io_k8s_api_core_v1_vsphere_virtual_disk_volume_source import IoK8sApiCoreV1VsphereVirtualDiskVolumeSource from swagger_client.models.io_k8s_apimachinery_pkg_api_resource_quantity import IoK8sApimachineryPkgApiResourceQuantity from swagger_client.models.io_k8s_apimachinery_pkg_apis_meta_v1_api_group import IoK8sApimachineryPkgApisMetaV1APIGroup from swagger_client.models.io_k8s_apimachinery_pkg_apis_meta_v1_api_group_list import IoK8sApimachineryPkgApisMetaV1APIGroupList from swagger_client.models.io_k8s_apimachinery_pkg_apis_meta_v1_api_resource import IoK8sApimachineryPkgApisMetaV1APIResource from swagger_client.models.io_k8s_apimachinery_pkg_apis_meta_v1_api_resource_list import IoK8sApimachineryPkgApisMetaV1APIResourceList from swagger_client.models.io_k8s_apimachinery_pkg_apis_meta_v1_delete_options import IoK8sApimachineryPkgApisMetaV1DeleteOptions from swagger_client.models.io_k8s_apimachinery_pkg_apis_meta_v1_group_version_for_discovery import IoK8sApimachineryPkgApisMetaV1GroupVersionForDiscovery from swagger_client.models.io_k8s_apimachinery_pkg_apis_meta_v1_initializer import IoK8sApimachineryPkgApisMetaV1Initializer from swagger_client.models.io_k8s_apimachinery_pkg_apis_meta_v1_initializers import IoK8sApimachineryPkgApisMetaV1Initializers from swagger_client.models.io_k8s_apimachinery_pkg_apis_meta_v1_label_selector import IoK8sApimachineryPkgApisMetaV1LabelSelector from swagger_client.models.io_k8s_apimachinery_pkg_apis_meta_v1_label_selector_requirement import IoK8sApimachineryPkgApisMetaV1LabelSelectorRequirement from swagger_client.models.io_k8s_apimachinery_pkg_apis_meta_v1_list_meta import IoK8sApimachineryPkgApisMetaV1ListMeta from swagger_client.models.io_k8s_apimachinery_pkg_apis_meta_v1_object_meta import IoK8sApimachineryPkgApisMetaV1ObjectMeta from swagger_client.models.io_k8s_apimachinery_pkg_apis_meta_v1_owner_reference import IoK8sApimachineryPkgApisMetaV1OwnerReference from swagger_client.models.io_k8s_apimachinery_pkg_apis_meta_v1_patch import IoK8sApimachineryPkgApisMetaV1Patch from swagger_client.models.io_k8s_apimachinery_pkg_apis_meta_v1_preconditions import IoK8sApimachineryPkgApisMetaV1Preconditions from swagger_client.models.io_k8s_apimachinery_pkg_apis_meta_v1_server_address_by_client_cidr import IoK8sApimachineryPkgApisMetaV1ServerAddressByClientCIDR from swagger_client.models.io_k8s_apimachinery_pkg_apis_meta_v1_status import IoK8sApimachineryPkgApisMetaV1Status from swagger_client.models.io_k8s_apimachinery_pkg_apis_meta_v1_status_cause import IoK8sApimachineryPkgApisMetaV1StatusCause from swagger_client.models.io_k8s_apimachinery_pkg_apis_meta_v1_status_details import IoK8sApimachineryPkgApisMetaV1StatusDetails from swagger_client.models.io_k8s_apimachinery_pkg_apis_meta_v1_time import IoK8sApimachineryPkgApisMetaV1Time from swagger_client.models.io_k8s_apimachinery_pkg_apis_meta_v1_watch_event import IoK8sApimachineryPkgApisMetaV1WatchEvent from swagger_client.models.io_k8s_apimachinery_pkg_runtime_raw_extension import IoK8sApimachineryPkgRuntimeRawExtension

102.26087

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0.153317

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null

0

1

0

1

0

1

0

6

30942aabf8c2e1f864b86deca63301214b973e1b

33

py

Python

test_29.py

ccie8030/pynet

84be459c6cb50a025a801e3d4b9bd237c698776a

[ "Apache-2.0" ]

1

2016-01-30T03:36:15.000Z

test_29.py

ccie8030/pynet

84be459c6cb50a025a801e3d4b9bd237c698776a

[ "Apache-2.0" ]

null

test_29.py

ccie8030/pynet

84be459c6cb50a025a801e3d4b9bd237c698776a

[ "Apache-2.0" ]

null

print 'this is a new test file'

16.5

32

0.69697

7

33

3.285714

1

0

0.242424

33

1

33

0.92

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0.69697

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null

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0

1

0

null

0

1

0

1

0

6

30c35727de92a7a31e043a0f7fbb03425e3ba42d

6,292

py

Python

tests/testGlobalEM1D_FD_jac_layers.py

igotchalk/simpegEM1D

8f2233fc86bf26f14fe9c45f28c6b22ff54fafdc

[ "MIT" ]

11

2015-04-11T03:35:45.000Z

2022-02-26T02:04:18.000Z

tests/testGlobalEM1D_FD_jac_layers.py

igotchalk/simpegEM1D

8f2233fc86bf26f14fe9c45f28c6b22ff54fafdc

[ "MIT" ]

38

2018-04-21T23:07:29.000Z

2022-01-11T07:22:27.000Z

tests/testGlobalEM1D_FD_jac_layers.py

igotchalk/simpegEM1D

8f2233fc86bf26f14fe9c45f28c6b22ff54fafdc

[ "MIT" ]

13

2015-07-15T21:54:33.000Z

2021-11-30T09:18:54.000Z

from __future__ import print_function import unittest import numpy as np from simpegEM1D import ( GlobalEM1DProblemFD, GlobalEM1DSurveyFD, get_vertical_discretization_frequency ) from SimPEG import ( regularization, Inversion, InvProblem, DataMisfit, Utils, Mesh, Maps, Optimization, Tests ) np.random.seed(41) class GlobalEM1DFD(unittest.TestCase): def setUp(self, parallel=True): frequency = np.array([900, 7200, 56000], dtype=float) hz = get_vertical_discretization_frequency( frequency, sigma_background=1./10. ) n_sounding = 10 dx = 20. hx = np.ones(n_sounding) * dx mesh = Mesh.TensorMesh([hx, hz], x0='00') inds = mesh.gridCC[:, 1] < 25 inds_1 = mesh.gridCC[:, 1] < 50 sigma = np.ones(mesh.nC) * 1./100. sigma[inds_1] = 1./10. sigma[inds] = 1./50. sigma_em1d = sigma.reshape(mesh.vnC, order='F').flatten() mSynth = np.log(sigma_em1d) x = mesh.vectorCCx y = np.zeros_like(x) z = np.ones_like(x) * 30. rx_locations = np.c_[x, y, z] src_locations = np.c_[x, y, z] topo = np.c_[x, y, z-30.].astype(float) mapping = Maps.ExpMap(mesh) survey = GlobalEM1DSurveyFD( rx_locations=rx_locations, src_locations=src_locations, frequency=frequency, offset=np.ones_like(frequency) * 8., src_type="VMD", rx_type="Hz", field_type='secondary', topo=topo ) problem = GlobalEM1DProblemFD( [], sigmaMap=mapping, hz=hz, parallel=parallel, n_cpu=2 ) problem.pair(survey) survey.makeSyntheticData(mSynth) # Now set up the problem to do some minimization dmis = DataMisfit.l2_DataMisfit(survey) reg = regularization.Tikhonov(mesh) opt = Optimization.InexactGaussNewton( maxIterLS=20, maxIter=10, tolF=1e-6, tolX=1e-6, tolG=1e-6, maxIterCG=6 ) invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta=0.) inv = Inversion.BaseInversion(invProb) self.inv = inv self.reg = reg self.p = problem self.mesh = mesh self.m0 = mSynth self.survey = survey self.dmis = dmis def test_misfit(self): passed = Tests.checkDerivative( lambda m: ( self.survey.dpred(m), lambda mx: self.p.Jvec(self.m0, mx) ), self.m0, plotIt=False, num=3 ) self.assertTrue(passed) def test_adjoint(self): # Adjoint Test # u = np.random.rand(self.mesh.nC * self.survey.nSrc) v = np.random.rand(self.mesh.nC) w = np.random.rand(self.survey.dobs.shape[0]) wtJv = w.dot(self.p.Jvec(self.m0, v)) vtJtw = v.dot(self.p.Jtvec(self.m0, w)) passed = np.abs(wtJv - vtJtw) < 1e-10 print('Adjoint Test', np.abs(wtJv - vtJtw), passed) self.assertTrue(passed) def test_dataObj(self): passed = Tests.checkDerivative( lambda m: [self.dmis(m), self.dmis.deriv(m)], self.m0, plotIt=False, num=3 ) self.assertTrue(passed) class GlobalEM1DFD_Height(unittest.TestCase): def setUp(self, parallel=True): frequency = np.array([900, 7200, 56000], dtype=float) hz = np.r_[1.] n_sounding = 10 dx = 20. hx = np.ones(n_sounding) * dx e = np.ones(n_sounding) mSynth = np.r_[e*np.log(1./100.), e*20] x = np.arange(n_sounding) y = np.zeros_like(x) z = np.ones_like(x) * 30. rx_locations = np.c_[x, y, z] src_locations = np.c_[x, y, z] topo = np.c_[x, y, z-30.].astype(float) wires = Maps.Wires(('sigma', n_sounding),('h', n_sounding)) expmap = Maps.ExpMap(nP=n_sounding) sigmaMap = expmap * wires.sigma survey = GlobalEM1DSurveyFD( rx_locations=rx_locations, src_locations=src_locations, frequency=frequency, offset=np.ones_like(frequency) * 8., src_type="VMD", rx_type="ppm", field_type='secondary', topo=topo, half_switch=True ) problem = GlobalEM1DProblemFD( [], sigmaMap=sigmaMap, hMap=wires.h, hz=hz, parallel=parallel, n_cpu=2 ) problem.pair(survey) survey.makeSyntheticData(mSynth) # Now set up the problem to do some minimization mesh = Mesh.TensorMesh([int(n_sounding * 2)]) dmis = DataMisfit.l2_DataMisfit(survey) reg = regularization.Tikhonov(mesh) opt = Optimization.InexactGaussNewton( maxIterLS=20, maxIter=10, tolF=1e-6, tolX=1e-6, tolG=1e-6, maxIterCG=6 ) invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta=0.) inv = Inversion.BaseInversion(invProb) self.inv = inv self.reg = reg self.p = problem self.mesh = mesh self.m0 = mSynth * 1.2 self.survey = survey self.dmis = dmis def test_misfit(self): passed = Tests.checkDerivative( lambda m: ( self.survey.dpred(m), lambda mx: self.p.Jvec(self.m0, mx) ), self.m0, plotIt=False, num=3 ) self.assertTrue(passed) def test_adjoint(self): # Adjoint Test # u = np.random.rand(self.mesh.nC * self.survey.nSrc) v = np.random.rand(self.mesh.nC) w = np.random.rand(self.survey.dobs.shape[0]) wtJv = w.dot(self.p.Jvec(self.m0, v)) vtJtw = v.dot(self.p.Jtvec(self.m0, w)) passed = np.abs(wtJv - vtJtw) < 1e-10 print('Adjoint Test', np.abs(wtJv - vtJtw), passed) self.assertTrue(passed) def test_dataObj(self): passed = Tests.checkDerivative( lambda m: [self.dmis(m), self.dmis.deriv(m)], self.m0, plotIt=False, num=3 ) self.assertTrue(passed) if __name__ == '__main__': unittest.main()

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0.045714

false

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0

null

0

1

0

null

0

1

0

6

30d0ccf5eb015ef02caaf40aebf3152051cb2f81

108

py

Python

model_definitions/cnns/inceptions/inception_encoder.py

yyx1994/pytorch.repmet

847a2b71fa751e6d381c233df0107a53592d8ce5

[ "MIT" ]

4

2019-09-29T08:57:09.000Z

2021-04-20T09:36:56.000Z

model_definitions/cnns/inceptions/inception_encoder.py

xtynwfn/pytorch.repmet

847a2b71fa751e6d381c233df0107a53592d8ce5

[ "MIT" ]

null

model_definitions/cnns/inceptions/inception_encoder.py

xtynwfn/pytorch.repmet

847a2b71fa751e6d381c233df0107a53592d8ce5

[ "MIT" ]

1

2021-04-12T06:58:06.000Z

import torch.nn as nn import torch.nn.functional as F from torchvision.models.inception import inception_v3

27

53

0.842593

18

108

5

0.611111

0.244444

0.288889

0

0.010417

0.111111

108

4

53

27

0.927083

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

30d4b8a19a6b78ac07f60bbbb4050d7854b92e3e

53

py

Python

test/test_step.py

codesaurus97/fruit

21f284cdf6ffd0b4484dc8133ef90b06d530b060

[ "MIT" ]

null

test/test_step.py

codesaurus97/fruit

21f284cdf6ffd0b4484dc8133ef90b06d530b060

[ "MIT" ]

null

test/test_step.py

codesaurus97/fruit

21f284cdf6ffd0b4484dc8133ef90b06d530b060

[ "MIT" ]

null

from fruit.modules.step import Step import unittest

13.25

35

0.830189

8

53

5.5

0.75

0.454545

0

0.132075

53

3

36

17.666667

0.956522

0

1

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true

0

1

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1

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1

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null

1

0

1

0

null

0

1

0

1

0

1

0

6

a50210b57e44eba3016e054032697c13ec5d4d58

43

py

Python

fancypages/assets/forms/__init__.py

ashiazed/django-fancypages

7587bac8f61ed8567f27ffee78c5dbedf039f345

[ "BSD-3-Clause" ]

1

2018-05-28T09:50:13.000Z

fancypages/assets/forms/__init__.py

ashiazed/django-fancypages

7587bac8f61ed8567f27ffee78c5dbedf039f345

[ "BSD-3-Clause" ]

null

fancypages/assets/forms/__init__.py

ashiazed/django-fancypages

7587bac8f61ed8567f27ffee78c5dbedf039f345

[ "BSD-3-Clause" ]

null

from .forms import * from .fields import *

14.333333

21

0.72093

6

43

5.166667

0.666667

0

0.186047

43

2

22

21.5

0.885714

0

1

0

true

0

1

0

1

0

1

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null

0

1

0

null

0

1

0

1

0

1

0

6

eb75f24917ce4a4b667bc3a397b1c676c2316202

125

py

Python

node/interface/iblockchainmanager.py

tinker-coin/tinker-coin

3d599f642f4f49d30ba9bc58316a502e8a325e85

[ "MIT" ]

null

node/interface/iblockchainmanager.py

tinker-coin/tinker-coin

3d599f642f4f49d30ba9bc58316a502e8a325e85

[ "MIT" ]

null

node/interface/iblockchainmanager.py

tinker-coin/tinker-coin

3d599f642f4f49d30ba9bc58316a502e8a325e85

[ "MIT" ]

null

import abc from interface.irunnable import IRunnable class IBlockchainManager(IRunnable, metaclass=abc.ABCMeta): pass

15.625

59

0.808

14

125

7.214286

0.714286

0

0.136

125

7

60

17.857143

0.935185

0

1

0

true

0.25

0.5

0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

6

ccf30f2a4f17173b44b65d05417b4749811c97cb

3,577

py

Python

python/test/graph/directed_graph.py

Kinnoo/cs404.1-1

0fd2e2fbf02953eb1b2192945ab4107034399a68

[ "MIT" ]

null

python/test/graph/directed_graph.py

Kinnoo/cs404.1-1

0fd2e2fbf02953eb1b2192945ab4107034399a68

[ "MIT" ]

null

python/test/graph/directed_graph.py

Kinnoo/cs404.1-1

0fd2e2fbf02953eb1b2192945ab4107034399a68

[ "MIT" ]

null

import unittest from python.test.util.utilities import Utilities class DirectedGraphTest(unittest.TestCase): def setUp(self): self.graph = Utilities.small_directed_graph() def test_single_edge_direction(self): self.assertTrue(6 in self.graph.adjacent_vertices(0)) self.assertTrue(2 in self.graph.adjacent_vertices(0)) self.assertTrue(1 in self.graph.adjacent_vertices(0)) self.assertTrue(5 in self.graph.adjacent_vertices(0)) self.assertTrue(5 in self.graph.adjacent_vertices(3)) self.assertTrue(4 in self.graph.adjacent_vertices(3)) self.assertTrue(5 in self.graph.adjacent_vertices(4)) self.assertTrue(6 in self.graph.adjacent_vertices(4)) self.assertTrue(8 in self.graph.adjacent_vertices(7)) self.assertTrue(10 in self.graph.adjacent_vertices(9)) self.assertTrue(11 in self.graph.adjacent_vertices(9)) self.assertFalse(0 in self.graph.adjacent_vertices(6)) self.assertFalse(0 in self.graph.adjacent_vertices(2)) self.assertFalse(0 in self.graph.adjacent_vertices(1)) self.assertFalse(0 in self.graph.adjacent_vertices(5)) self.assertFalse(3 in self.graph.adjacent_vertices(5)) self.assertFalse(3 in self.graph.adjacent_vertices(4)) self.assertFalse(4 in self.graph.adjacent_vertices(5)) self.assertFalse(4 in self.graph.adjacent_vertices(6)) self.assertFalse(7 in self.graph.adjacent_vertices(8)) self.assertFalse(9 in self.graph.adjacent_vertices(10)) self.assertFalse(9 in self.graph.adjacent_vertices(11)) def test_outdegree(self): expected = [4, 0, 0, 2, 2, 0, 0, 1, 0, 2, 0, 0] actual = [0] * len(expected) for i in range(self.graph.num_vertices()): actual[i] = self.graph.outdegree(i) self.assertEqual(expected, actual) def test_indegree(self): expected = [0, 1, 1, 0, 1, 3, 2, 0, 1, 0, 1, 1] actual = [0] * len(expected) for i in range(self.graph.num_vertices()): actual[i] = self.graph.indegree(i) self.assertEqual(expected, actual) def test_reversal(self): reverse = self.graph.reverse() self.assertTrue(0 in reverse.adjacent_vertices(6)) self.assertTrue(0 in reverse.adjacent_vertices(2)) self.assertTrue(0 in reverse.adjacent_vertices(1)) self.assertTrue(0 in reverse.adjacent_vertices(5)) self.assertTrue(3 in reverse.adjacent_vertices(5)) self.assertTrue(3 in reverse.adjacent_vertices(4)) self.assertTrue(4 in reverse.adjacent_vertices(5)) self.assertTrue(4 in reverse.adjacent_vertices(6)) self.assertTrue(7 in reverse.adjacent_vertices(8)) self.assertTrue(9 in reverse.adjacent_vertices(10)) self.assertTrue(9 in reverse.adjacent_vertices(11)) self.assertFalse(6 in reverse.adjacent_vertices(0)) self.assertFalse(2 in reverse.adjacent_vertices(0)) self.assertFalse(1 in reverse.adjacent_vertices(0)) self.assertFalse(5 in reverse.adjacent_vertices(0)) self.assertFalse(5 in reverse.adjacent_vertices(3)) self.assertFalse(4 in reverse.adjacent_vertices(3)) self.assertFalse(5 in reverse.adjacent_vertices(4)) self.assertFalse(6 in reverse.adjacent_vertices(4)) self.assertFalse(8 in reverse.adjacent_vertices(7)) self.assertFalse(10 in reverse.adjacent_vertices(9)) self.assertFalse(10 in reverse.adjacent_vertices(9)) if __name__ == '__main__': unittest.main()

43.096386

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3,577

4.868154

0.103448

0.293333

0.100833

0.174167

0.825

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3,577

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0

1

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6

691b6b61246e6607753f1df65657bfd7ce8e2efa

13,026

py

Python

tests/helpers/test_perturb_func.py

sebastian-lapuschkin/Quantus

c3b8a9fb2018f34bd89ba38efa2b2b8c38128b3f

[ "MIT" ]

null

tests/helpers/test_perturb_func.py

sebastian-lapuschkin/Quantus

c3b8a9fb2018f34bd89ba38efa2b2b8c38128b3f

[ "MIT" ]

null

tests/helpers/test_perturb_func.py

sebastian-lapuschkin/Quantus

c3b8a9fb2018f34bd89ba38efa2b2b8c38128b3f

[ "MIT" ]

null

from typing import Union import numpy as np import pytest from pytest_lazyfixture import lazy_fixture from ..fixtures import * from ...quantus.helpers import * from ...quantus.helpers import utils @pytest.fixture def input_zeros_1d_1ch(): return np.zeros(shape=(1, 224)) @pytest.fixture def input_zeros_1d_3ch(): return np.zeros(shape=(3, 224)) @pytest.fixture def input_zeros_2d_1ch(): return np.zeros(shape=(1, 224, 224)) @pytest.fixture def input_zeros_2d_3ch(): return np.zeros(shape=(3, 224, 224)) @pytest.fixture def input_zeros_2d_3ch_flattened(): return np.zeros(shape=(3, 224, 224)).flatten() @pytest.fixture def input_uniform_2d_3ch_flattened(): return np.random.uniform(0, 0.1, size=(3, 224, 224)).flatten() @pytest.fixture def input_ones_mnist(): return np.ones(shape=(1, 28, 28)) @pytest.fixture def input_ones_mnist_flattened(): return np.ones(shape=(1, 28, 28)).flatten() @pytest.fixture def input_zeros_mnist_flattened(): return np.zeros(shape=(1, 28, 28)).flatten() @pytest.fixture def input_uniform_1d_3ch(): return np.random.uniform(0, 0.1, size=(3, 224)) @pytest.fixture def input_uniform_2d_3ch(): return np.random.uniform(0, 0.1, size=(3, 224, 224)) @pytest.fixture def input_uniform_2d_3ch_flattened(): return np.random.uniform(0, 0.1, size=(3, 224, 224)).flatten() @pytest.fixture def input_uniform_3d_3ch(): return np.random.uniform(0, 0.1, size=(3, 224, 224, 224)) @pytest.fixture def input_uniform_mnist(): return np.random.uniform(0, 0.1, size=(1, 28, 28)) @pytest.mark.perturb_func @pytest.mark.parametrize( "data,params,expected", [ ( lazy_fixture("input_uniform_1d_3ch"), {}, True, ), ( lazy_fixture("input_uniform_2d_3ch"), {}, True, ), ( lazy_fixture("input_uniform_2d_3ch_flattened"), {}, True, ), ], ) def test_gaussian_noise( data: np.ndarray, params: dict, expected: Union[float, dict, bool] ): out = gaussian_noise(arr=data, **params) assert any(out.flatten()[0] != out.flatten()), "Test failed." assert any(out.flatten() != data.flatten()) == expected, "Test failed." @pytest.mark.fixed @pytest.mark.parametrize( "data,params,expected", [ ( lazy_fixture("input_zeros_2d_3ch"), { "indices": [0, 2], "fixed_values": 1.0, }, 1, ), ( lazy_fixture("input_zeros_2d_3ch_flattened"), { "indices": [0, 2], "fixed_values": 1.0, }, 1, ), ( lazy_fixture("input_ones_mnist"), { "indices": np.arange(0, 784), "input_shift": -1.0, }, 0, # TODO: verify expected ), ( lazy_fixture("input_ones_mnist_flattened"), { "indices": np.arange(0, 784), "input_shift": -1.0, }, 0, ), ( lazy_fixture("input_zeros_1d_1ch"), { "indices": [0, 2, 112, 113, 128, 223], "fixed_values": 1.0, }, 1, ), ( lazy_fixture("input_zeros_1d_3ch"), { "indices": [0, 2, 112, 113, 128, 223], "fixed_values": 1.0, }, 1, ), ( lazy_fixture("input_zeros_2d_1ch"), { "indices": [0, 2, 224, 226, 448, 450], "fixed_values": 1.0, }, 1, ), ( lazy_fixture("input_zeros_2d_3ch"), { "indices": [0, 2, 224, 226, 448, 450], "fixed_values": 1.0, }, 1, ), ( lazy_fixture("input_ones_mnist"), { "indices": np.arange(0, 784), "input_shift": -1.0, "nr_channels": 1, }, 0, ), ( lazy_fixture("input_zeros_mnist_flattened"), { "indices": np.arange(0, 784), "input_shift": -1.0, "nr_channels": 1, }, -1, ), ( lazy_fixture("input_ones_mnist_flattened"), { "indices": np.arange(0, 784), "input_shift": 1.0, "nr_channels": 1, }, 2, ), ], ) def test_baseline_replacement_by_indices( data: np.ndarray, params: dict, expected: Union[float, dict, bool] ): out = baseline_replacement_by_indices(arr=data, **params) indices = np.unravel_index(params["indices"], data.shape) if isinstance(expected, (int, float)): assert np.all([i == expected for i in out[indices]]), f"Test failed.{out}" @pytest.mark.perturb_func @pytest.mark.parametrize( "data,params,expected", [ ( lazy_fixture("input_zeros_1d_1ch"), { "perturb_baseline": 1.0, "patch_size": 4, "coords": (0,), }, {}, ), ( lazy_fixture("input_zeros_1d_3ch"), { "perturb_baseline": 1.0, "patch_size": 4, "coords": (0,), }, {}, ), ( lazy_fixture("input_zeros_2d_1ch"), { "perturb_baseline": 1.0, "patch_size": 4, "coords": (0, 0), }, {}, ), ( lazy_fixture("input_zeros_2d_3ch"), { "perturb_baseline": 1.0, "patch_size": 4, "coords": (0, 0), }, {}, ), ( lazy_fixture("input_zeros_2d_3ch"), { "perturb_baseline": 1.0, "patch_size": 10, "coords": (0, 0), }, {}, ), ( lazy_fixture("input_zeros_2d_3ch"), { "perturb_baseline": 1.0, "patch_size": 4, "coords": (11, 22), }, {}, ), ( lazy_fixture("input_zeros_2d_3ch"), { "perturb_baseline": 1.0, "patch_size": 4, "coords": (11, ), }, {"exception": ValueError}, ), ( lazy_fixture("input_zeros_2d_3ch"), { "perturb_baseline": 1.0, "patch_size": 4, "coords": (11, 11, 11, ), }, {"exception": ValueError}, ), ( lazy_fixture("input_zeros_1d_3ch"), { "perturb_baseline": 1.0, "patch_size": 4, "coords": (11, 11, ), }, {"exception": ValueError}, ), ], ) def test_baseline_replacement_by_patch( data: np.ndarray, params: dict, expected: dict ): print(params["patch_size"], params["coords"]) patch_slice = utils.create_patch_slice( patch_size=params["patch_size"], coords=params["coords"], expand_first_dim=True, ) print(patch_slice) if "exception" in expected: with pytest.raises(expected["exception"]): out = baseline_replacement_by_patch( arr=data, patch_slice=patch_slice, perturb_baseline=params["perturb_baseline"], ) return out = baseline_replacement_by_patch( arr=data, patch_slice=patch_slice, perturb_baseline=params["perturb_baseline"], ) patch_mask = np.zeros(data.shape, dtype=bool) patch_mask[patch_slice] = True assert np.all(out[patch_mask] != data[patch_mask]), "Test failed." assert np.all(out[~patch_mask] == data[~patch_mask]), "Test failed." @pytest.mark.perturb_func @pytest.mark.parametrize( "data,params,expected", [ ( lazy_fixture("input_uniform_1d_3ch"), {"perturb_radius": 0.02}, True, ), ( lazy_fixture("input_uniform_2d_3ch"), {"perturb_radius": 0.02}, True, ), ( lazy_fixture("input_uniform_2d_3ch_flattened"), {"perturb_radius": 0.02}, True, ), ], ) def test_uniform_sampling( data: np.ndarray, params: dict, expected: Union[float, dict, bool] ): out = uniform_sampling(arr=data, **params) assert any(out.flatten()[0] != out.flatten()), "Test failed." assert any(out.flatten() != data.flatten()) == expected, "Test failed." @pytest.mark.perturb_func @pytest.mark.parametrize( "data,params,expected", [ ( lazy_fixture("input_uniform_2d_3ch"), {"perturb_angle": 30}, True, ), ], ) def test_rotation(data: dict, params: dict, expected: Union[float, dict, bool]): out = rotation(arr=data, **params) assert np.any(out != data) == expected, "Test failed." @pytest.mark.perturb_func @pytest.mark.parametrize( "data,params,expected", [ ( lazy_fixture("input_uniform_2d_3ch"), {"perturb_dx": 20, "perturb_baseline": "black"}, True, ) ], ) def test_translation_x_direction( data: np.ndarray, params: dict, expected: Union[float, dict, bool] ): out = translation_x_direction(arr=data, **params) assert np.any(out != data) == expected, "Test failed." @pytest.mark.perturb_func @pytest.mark.parametrize( "data,params,expected", [ ( lazy_fixture("input_uniform_2d_3ch"), {"perturb_dx": 20, "perturb_baseline": "black"}, True, ) ], ) def test_translation_y_direction( data: np.ndarray, params: dict, expected: Union[float, dict, bool] ): out = translation_y_direction(arr=data, **params) assert np.any(out != data) == expected, "Test failed." @pytest.mark.perturb_func @pytest.mark.parametrize( "data,params,expected", [ ( lazy_fixture("input_uniform_2d_3ch"), {"perturb_dx": 20}, True, ), ], ) def test_no_perturbation( data: np.ndarray, params: dict, expected: Union[float, dict, bool] ): out = no_perturbation(arr=data, **params) assert (out == data).all() == expected, "Test failed." @pytest.mark.perturb_func @pytest.mark.parametrize( "data,params,expected", [ ( lazy_fixture("input_uniform_2d_3ch"), { "blur_kernel_size": 15, "patch_size": 4, "coords": (0, 0), }, {}, ), ( lazy_fixture("input_uniform_2d_3ch"), { "blur_kernel_size": 7, "patch_size": 4, "coords": (0, 0), }, {}, ), ( lazy_fixture("input_uniform_mnist"), { "blur_kernel_size": 15, "patch_size": 4, "coords": (0, 0), }, {}, ), ( lazy_fixture("input_uniform_1d_3ch"), { "blur_kernel_size": 15, "patch_size": 4, "coords": (0, ), }, {"exception": NotImplementedError}, ), ( lazy_fixture("input_uniform_3d_3ch"), { "blur_kernel_size": 15, "patch_size": 4, "coords": (0, 0, 0), }, {"exception": ValueError}, ), ], ) def test_baseline_replacement_by_blur( data: np.ndarray, params: dict, expected: Union[float, dict, bool] ): patch_slice = utils.create_patch_slice( patch_size=params["patch_size"], coords=params["coords"], expand_first_dim=True, ) if "exception" in expected: with pytest.raises(expected["exception"]): out = baseline_replacement_by_blur( arr=data, patch_slice=patch_slice, blur_kernel_size=params["blur_kernel_size"], ) return out = baseline_replacement_by_blur( arr=data, patch_slice=patch_slice, blur_kernel_size=params["blur_kernel_size"], ) patch_mask = np.zeros(data.shape, dtype=bool) patch_mask[patch_slice] = True assert out.shape == data.shape, "Test failed." assert np.all(out[patch_mask] != data[patch_mask]), "Test failed." assert np.all(out[~patch_mask] == data[~patch_mask]), "Test failed."

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null

0

1

0

null

0

6

6934411f1e4c15deb25b9b4f795dd5f539cddac9

303

py

Python

tools/Canvas/Canvas/data/__init__.py

Oshlack/Slinker

725d2c0861156034ef4d16293e2a3b74ac23c9e7

[ "MIT" ]

15

2021-08-23T14:36:35.000Z

2022-03-17T06:56:17.000Z

tools/Canvas/Canvas/data/__init__.py

Oshlack/Slinker

725d2c0861156034ef4d16293e2a3b74ac23c9e7

[ "MIT" ]

2

2021-08-17T03:00:23.000Z

2022-02-08T23:24:16.000Z

tools/Canvas/Canvas/data/__init__.py

Oshlack/Slinker

725d2c0861156034ef4d16293e2a3b74ac23c9e7

[ "MIT" ]

null

#=#======================================================================================================================= # # CANVAS # Author: Breon Schmidt # License: MIT # #=======================================================================================================================

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1

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1

0

null

1

0

1

0

6

694139cf29b43ed0da35c60d491d5071f6a4580e

124

py

Python

blisk/run_blisk_py.py

suleymanmuti/CalculiX-Examples

3f5bc0247de90cfc312bf13a1d0e93b39da4b5e7

[ "MIT" ]

null

blisk/run_blisk_py.py

suleymanmuti/CalculiX-Examples

3f5bc0247de90cfc312bf13a1d0e93b39da4b5e7

[ "MIT" ]

null

blisk/run_blisk_py.py

suleymanmuti/CalculiX-Examples

3f5bc0247de90cfc312bf13a1d0e93b39da4b5e7

[ "MIT" ]

1

2021-02-22T10:56:47.000Z

#!/usr/bin/python import os os.system("cgx -bg blisk_pre.fbd") os.system("ccx blisk") os.system("cgx -bg blisk_post.fbd")

15.5

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124

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null

1

0

1

0

null

0

1

0

6

15f9391d10aa798635201237c3b36d66afa91b10

2,279

py

Python

django/kisaan/master/models.py

AkshitOstwal/cfthacks2019

7260ff1b4c0ce8ee288bd3dc445e0465845410d2

[ "MIT" ]

2

2019-08-24T16:50:37.000Z

2020-09-05T08:39:49.000Z

django/kisaan/master/models.py

AkshitOstwal/cfthacks2019

7260ff1b4c0ce8ee288bd3dc445e0465845410d2

[ "MIT" ]

null

django/kisaan/master/models.py

AkshitOstwal/cfthacks2019

7260ff1b4c0ce8ee288bd3dc445e0465845410d2

[ "MIT" ]

null

from django.db import models # Create your models here. class State(models.Model): ACTIVE_CHOICES = ( (0,'Inactive'), (1, 'Active'), ) statecode = models.CharField(max_length=2, unique=True, primary_key=True) name = models.CharField(max_length=50) active = models.IntegerField(choices=ACTIVE_CHOICES) is_deleted = models.BooleanField(default=False) def __str__(self): # Built-in attribute of django.contrib.auth.models.User ! return self.name class Meta: db_table = "master_state" class Zone(models.Model): ACTIVE_CHOICES = ( (0,'Inactive'), (1, 'Active'), ) zonecode = models.CharField(max_length=4, unique=True, primary_key=True) statecode = models.ForeignKey(State, related_name="zone_belongs_to_state", on_delete=models.CASCADE) name = models.CharField(max_length=50) active = models.IntegerField(choices=ACTIVE_CHOICES) is_deleted = models.BooleanField(default=False) def __str__(self): # Built-in attribute of django.contrib.auth.models.User ! return self.name class Meta: db_table = "master_zone" class District(models.Model): ACTIVE_CHOICES = ( (0,'Inactive'), (1, 'Active'), ) districtcode = models.CharField(max_length=4, unique=True, primary_key=True) statecode = models.ForeignKey(State, related_name="district_belongs_to_state", on_delete=models.CASCADE) zonecode = models.ForeignKey(Zone, related_name="district_belongs_to_zone", on_delete=models.CASCADE) name = models.CharField(max_length=50) active = models.IntegerField(choices=ACTIVE_CHOICES) is_deleted = models.BooleanField(default=False) def __str__(self): # Built-in attribute of django.contrib.auth.models.User ! return self.name class Meta: db_table = "master_district" class Language(models.Model): id = models.CharField(max_length=2, unique=True, primary_key=True) name = models.CharField(max_length=5) timecreated = models.IntegerField() timemodified = models.IntegerField() def __str__(self): # Built-in attribute of django.contrib.auth.models.User ! return self.name class Meta: db_table = "languages"

28.848101

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0.009377

0.204476

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0

null

0

1

0

1

0

null

0

1

0

6

15fa7ca859f1461554f88764f618035b3eca2aee

138

py

Python

geneal/genetic_algorithms/__init__.py

NeveIsa/geneal

064b0409912088886bf56fe9a729d74dac92a235

[ "MIT" ]

47

2020-07-10T14:28:52.000Z

2022-03-25T17:20:52.000Z

geneal/genetic_algorithms/__init__.py

NeveIsa/geneal

064b0409912088886bf56fe9a729d74dac92a235

[ "MIT" ]

10

2020-08-08T16:35:40.000Z

2022-03-08T00:07:19.000Z

geneal/genetic_algorithms/__init__.py

NeveIsa/geneal

064b0409912088886bf56fe9a729d74dac92a235

[ "MIT" ]

14

2020-08-07T20:49:18.000Z

2022-03-31T17:55:47.000Z

from geneal.genetic_algorithms._binary import BinaryGenAlgSolver from geneal.genetic_algorithms._continuous import ContinuousGenAlgSolver

46

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0.442623

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1

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null

0

1

0

1

0

6

c61d5d2cb8347bcae20c2bea737bd15f8e6ca93c

215

py

Python

api/admin.py

SergioLeguizamon/prueba_tecnica_quick

1d09afa6ba4ed60221a88f7f2bd0811482860733

[ "MIT" ]

null

api/admin.py

SergioLeguizamon/prueba_tecnica_quick

1d09afa6ba4ed60221a88f7f2bd0811482860733

[ "MIT" ]

null

api/admin.py

SergioLeguizamon/prueba_tecnica_quick

1d09afa6ba4ed60221a88f7f2bd0811482860733

[ "MIT" ]

null

from django.contrib import admin from .models import Clients, Products, Bills, BillsProducts admin.site.register(Clients) admin.site.register(Products) admin.site.register(Bills) admin.site.register(BillsProducts)

26.875

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215

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26.875

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true

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1

0

1

0

null

0

1

0

1

0

6

c64fa50b57778b2cbf189cd31620c08ccf546a72

58,861

py

Python

Processing.py

Christoper-Harvey/ecg-file-processing

85859b9892f242c3a07ce05364839cf3a174e039

[ "MIT" ]

null

Processing.py

Christoper-Harvey/ecg-file-processing

85859b9892f242c3a07ce05364839cf3a174e039

[ "MIT" ]

null

Processing.py

Christoper-Harvey/ecg-file-processing

85859b9892f242c3a07ce05364839cf3a174e039

[ "MIT" ]

null

import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import base64 import glob from scipy.signal import medfilt from scipy.integrate import trapz import xml.etree.ElementTree as et from datetime import date today = date.today() np.warnings.filterwarnings('ignore') sns.set(style="darkgrid") roots = [] root_names = [] for n in glob.glob('*.xml'): roots.append(et.parse(n).getroot()) root_names.append(n) def modified_z_score(intensity): median_int = np.median(intensity) mad_int = np.median([np.abs(intensity - median_int)]) if mad_int == 0: mad_int = 1 modified_z_scores = 0.6745 * (intensity - median_int) / mad_int return modified_z_scores def df_fixer(y,n): threshold = 0 x = 0 while threshold == 0: if np.nanquantile(abs(np.array(modified_z_score(np.diff(y)))), 1) > 150: if abs(np.array(modified_z_score(np.diff(y))))[int(data.Qonset[n*12])+x:int(data.Qoffset[n*12])+30].max() < np.nanquantile(abs(np.array(modified_z_score(np.diff(y)))), .98)+55: threshold = abs(np.array(modified_z_score(np.diff(y))))[int(data.Qonset[n*12])+x:int(data.Qoffset[n*12])+30].max() + 1 elif abs(np.array(modified_z_score(np.diff(y))))[int(data.Qonset[n*12])+x:int(data.Qoffset[n*12])+30].max() > np.nanquantile(abs(np.array(modified_z_score(np.diff(y)))), .98)+55: x += 5 elif np.nanquantile(abs(np.array(modified_z_score(np.diff(y)))), 1) <= 150: if abs(np.array(modified_z_score(np.diff(y))))[int(data.Qonset[n*12])+x:int(data.Qoffset[n*12])+30].max() < np.nanquantile(abs(np.array(modified_z_score(np.diff(y)))), .992)+55: threshold = abs(np.array(modified_z_score(np.diff(y))))[int(data.Qonset[n*12])+x:int(data.Qoffset[n*12])+30].max() + 1 elif abs(np.array(modified_z_score(np.diff(y))))[int(data.Qonset[n*12])+x:int(data.Qoffset[n*12])+30].max() > np.nanquantile(abs(np.array(modified_z_score(np.diff(y)))), .992)+55: x += 5 spikes = abs(np.array(modified_z_score(np.diff(y)))) > threshold y_out = y.copy() for i in np.arange(len(spikes)): if spikes[i] != 0: y_out[i+y_out.index[0]] = None return y_out def half_df_fixer(y,n): threshold = 0 x = 0 while threshold == 0: if np.nanquantile(abs(np.array(modified_z_score(np.diff(y)))), 1) > 150: if abs(np.array(modified_z_score(np.diff(y))))[int(half_data.Qonset[n*12])+x:int(half_data.Qoffset[n*12])+30].max() < np.nanquantile(abs(np.array(modified_z_score(np.diff(y)))), .98)+60: threshold = abs(np.array(modified_z_score(np.diff(y))))[int(half_data.Qonset[n*12])+x:int(half_data.Qoffset[n*12])+30].max() + 1 elif abs(np.array(modified_z_score(np.diff(y))))[int(half_data.Qonset[n*12])+x:int(half_data.Qoffset[n*12])+30].max() > np.nanquantile(abs(np.array(modified_z_score(np.diff(y)))), .98)+60: x += 2 elif np.nanquantile(abs(np.array(modified_z_score(np.diff(y)))), 1) <= 150: if abs(np.array(modified_z_score(np.diff(y))))[int(half_data.Qonset[n*12])+x:int(half_data.Qoffset[n*12])+30].max() < np.nanquantile(abs(np.array(modified_z_score(np.diff(y)))), .992)+60: threshold = abs(np.array(modified_z_score(np.diff(y))))[int(half_data.Qonset[n*12])+x:int(half_data.Qoffset[n*12])+30].max() + 1 elif abs(np.array(modified_z_score(np.diff(y))))[int(half_data.Qonset[n*12])+x:int(half_data.Qoffset[n*12])+30].max() > np.nanquantile(abs(np.array(modified_z_score(np.diff(y)))), .992)+60: x += 2 spikes = abs(np.array(modified_z_score(np.diff(y)))) > threshold y_out = y.copy() for i in np.arange(len(spikes)): if spikes[i] != 0: y_out[i+y_out.index[0]] = None return y_out def hanging_line(point1, point2): a = (point2[1] - point1[1])/(np.cosh(point2[0] % 600) - np.cosh(point1[0] % 600)) b = point1[1] - a*np.cosh(point1[0] % 600) x = np.linspace(point1[0], point2[0], (point2[0] - point1[0])+1) y = a*np.cosh(x % 600) + b return (x,y) Tags = {'tags':[]} tags = {'tags':[]} for root in roots: if len(root.find('{http://www3.medical.philips.com}waveforms').getchildren()) == 2: if int(root.find('{http://www3.medical.philips.com}waveforms')[1].attrib['samplespersec']) == 1000: for elem in root.find('{http://www3.medical.philips.com}waveforms')[1]: tag = {} tag['Lead'] = elem.attrib['leadname'] if (root[6][1][0][14].text == 'Invalid' or elem[0].text == 'Invalid') and root[6].tag == '{http://www3.medical.philips.com}internalmeasurements': if root[6][1][0][14].text == None or root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text == 'Invalid' or root[6][1][0][14].text == '\n ' or root[6][1][0][14].text == 'Failed': tag['Ponset'] = 0 tag['Pdur'] = 0 tag['Print'] = 0 tag['Poffset'] = 0 else: tag['Ponset'] = int(root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text) - int(root[6][1][0][14].text) tag['Pdur'] = 0 tag['Print'] = int(root[6][1][0][14].text) tag['Poffset'] = (int(root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text) - int(root[6][1][0][14].text)) + 0 elif root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text == 'Invalid' or root[6][1][0][14].text == None or root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text == 'Failed' or root[6][1][0][14].text == 'Failed' or (root[6][1][0][14].text == 'Invalid' or elem[0].text == 'Invalid'): tag['Ponset'] = 0 tag['Pdur'] = 0 tag['Print'] = 0 tag['Poffset'] = 0 else: tag['Ponset'] = int(root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text) - int(root[6][1][0][14].text) tag['Pdur'] = int(elem[0].text) tag['Print'] = int(root[6][1][0][14].text) tag['Poffset'] = (int(root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text) - int(root[6][1][0][14].text)) + int(elem[0].text) if (root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text == 'Invalid' or root[6][0][29].text == 'Invalid' or elem[4].text == 'Invalid' or root[6][1][0][18].text == 'Invalid'): tag['Qonset'] = np.nan tag['Qrsdur'] = np.nan tag['Qoffset'] = np.nan tag['Tonset'] = np.nan tag['Qtint'] = np.nan tag['Toffset'] = np.nan tag['Tdur'] = np.nan else: tag['Qonset'] = int(root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text) tag['Qrsdur'] = int(root[6][0][29].text) tag['Qoffset'] = tag['Qonset'] + tag['Qrsdur'] tag['Tonset'] = int(elem[4].text) tag['Qtint'] = int(root[6][1][0][18].text) tag['Toffset'] = tag['Qonset'] + tag['Qtint'] tag['Tdur'] = tag['Qoffset'] - tag['Qonset'] if root[7].tag == '{http://www3.medical.philips.com}interpretations' and root[6].tag == '{http://www3.medical.philips.com}internalmeasurements': if root[7][0][1][0].text != None and (root[7][0][1][0].text).isdigit(): tag['HeartRate'] = int(root[7][0][1][0].text) if root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[1].text != None: tag['RRint'] = int(root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[1].text) if root[6][1][0][9].text != None: tag['AtrialRate'] = int(root[6][1][0][9].text) if root[6][0][15].text != None and root[6][0][15].text != 'Indeterminate': tag['QRSFrontAxis'] = int(root[6][0][15].text) if root[6][0][31].text != None and root[6][0][31].text != 'Failed': tag['QTC'] = int(root[6][0][31].text) tag['Target'] = [] for n in range(len(root[7][0][root[7][0].getchildren().index(root[7][0].find('{http://www3.medical.philips.com}statement')):])): tag['Target'].append(root[7][0][root[7][0].getchildren().index(root[7][0].find('{http://www3.medical.philips.com}statement')):][n][0].text) else: tag['HeartRate'] = np.nan tag['RRint'] = np.nan tag['AtrialRate'] = np.nan tag['QRSFrontAxis'] = np.nan tag['QTC'] = np.nan tag['Target'] = [] if root[3].tag == '{http://www3.medical.philips.com}reportinfo' and root[5].tag == '{http://www3.medical.philips.com}patient': time = root[3].attrib tag['Date'] = time['date'] tag['Time'] = time['time'] tag['Sex'] = root[5][0][6].text tag['ID'] = root[5][0][0].text tag['Name'] = root[5][0].find('{http://www3.medical.philips.com}name')[0].text + ', ' + root[5][0].find('{http://www3.medical.philips.com}name')[1].text if root[5][0].find('{http://www3.medical.philips.com}age')[0].tag == '{http://www3.medical.philips.com}dateofbirth': tag['Age'] = int(today.strftime("%Y")) - int(root[5][0].find('{http://www3.medical.philips.com}age')[0].text[0:4]) if root[5][0].find('{http://www3.medical.philips.com}age')[0].tag == '{http://www3.medical.philips.com}years': tag['Age'] = int(root[5][0].find('{http://www3.medical.philips.com}age')[0].text) tag['Waveform'] = elem[6].text # tag['LongWaveform'] = root[8][0].text tags['tags'].append(tag) else: for elem in root.find('{http://www3.medical.philips.com}waveforms')[1]: Tag = {} Tag['Lead'] = elem.attrib['leadname'] if (root[6][1][0][14].text == 'Invalid' or elem[0].text == 'Invalid') and root[6].tag == '{http://www3.medical.philips.com}internalmeasurements': if root[6][1][0][14].text == None or root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text == 'Invalid' or root[6][1][0][14].text == '\n ' or root[6][1][0][14].text == 'Failed': Tag['Ponset'] = 0 Tag['Pdur'] = 0 Tag['Print'] = 0 Tag['Poffset'] = 0 else: Tag['Ponset'] = float(root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text) - int(root[6][1][0][14].text) Tag['Pdur'] = 0 Tag['Print'] = int(root[6][1][0][14].text) Tag['Poffset'] = (int(root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text) - int(root[6][1][0][14].text)) + 0 elif root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text == 'Invalid' or root[6][1][0][14].text == None or root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text == None or root[6][1][0][14].text == 'Invalid' or elem[0].text == 'Invalid' and root[6].tag == '{http://www3.medical.philips.com}internalmeasurements': Tag['Ponset'] = 0 Tag['Pdur'] = 0 Tag['Print'] = 0 Tag['Poffset'] = 0 else: Tag['Ponset'] = int(root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text) - int(root[6][1][0][14].text) Tag['Pdur'] = int(elem[0].text) Tag['Print'] = int(root[6][1][0][14].text) Tag['Poffset'] = (int(root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text) - int(root[6][1][0][14].text)) + int(elem[0].text) if (root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text == 'Invalid' or root[6][1][0][18].text == None or root[6][0][29].text == 'Invalid' or elem[4].text == 'Invalid' or root[6][1][0][18].text == 'Invalid'): Tag['Qonset'] = np.nan Tag['Qrsdur'] = np.nan Tag['Qoffset'] = np.nan Tag['Tonset'] = np.nan Tag['Qtint'] = np.nan Tag['Toffset'] = np.nan Tag['Tdur'] = np.nan else: Tag['Qonset'] = int(root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[5].text) Tag['Qrsdur'] = int(root[6][0][29].text) Tag['Qoffset'] = Tag['Qonset'] + Tag['Qrsdur'] Tag['Tonset'] = int(elem[4].text) Tag['Qtint'] = int(root[6][1][0][18].text) Tag['Toffset'] = Tag['Qonset'] + Tag['Qtint'] Tag['Tdur'] = Tag['Qoffset'] - Tag['Qonset'] if root[7].tag == '{http://www3.medical.philips.com}interpretations' and root[6].tag == '{http://www3.medical.philips.com}internalmeasurements': if root[7][0][1][0].text != None and (root[7][0][1][0].text).isdigit(): Tag['HeartRate'] = int(root[7][0][1][0].text) if root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[1].text != None: Tag['RRint'] = int(root[7][0].find('{http://www3.medical.philips.com}globalmeasurements')[1].text) if root[6][1][0][9].text != None: Tag['AtrialRate'] = int(root[6][1][0][9].text) if root[6][0][15].text != None and root[6][0][15].text != 'Indeterminate': Tag['QRSFrontAxis'] = int(root[6][0][15].text) if root[6][0][31].text != None: Tag['QTC'] = int(root[6][0][31].text) Tag['Target'] = [] for n in range(len(root[7][0][root[7][0].getchildren().index(root[7][0].find('{http://www3.medical.philips.com}statement')):])): Tag['Target'].append(root[7][0][root[7][0].getchildren().index(root[7][0].find('{http://www3.medical.philips.com}statement')):][n][0].text) else: Tag['HeartRate'] = np.nan Tag['RRint'] = np.nan Tag['AtrialRate'] = np.nan Tag['QRSFrontAxis'] = np.nan Tag['QTC'] = np.nan Tag['Target'] = [] if root[3].tag == '{http://www3.medical.philips.com}reportinfo' and root[5].tag == '{http://www3.medical.philips.com}patient': time = root[3].attrib Tag['Date'] = time['date'] Tag['Time'] = time['time'] Tag['Sex'] = root[5][0][6].text Tag['ID'] = root[5][0][0].text Tag['Name'] = root[5][0].find('{http://www3.medical.philips.com}name')[0].text + ', ' + root[5][0].find('{http://www3.medical.philips.com}name')[1].text if len(root[5][0].find('{http://www3.medical.philips.com}age')) > 0: if root[5][0].find('{http://www3.medical.philips.com}age')[0].tag == '{http://www3.medical.philips.com}dateofbirth': Tag['Age'] = int(today.strftime("%Y")) - int(root[5][0].find('{http://www3.medical.philips.com}age')[0].text[0:4]) if root[5][0].find('{http://www3.medical.philips.com}age')[0].tag == '{http://www3.medical.philips.com}years': Tag['Age'] = int(root[5][0].find('{http://www3.medical.philips.com}age')[0].text) Tag['Waveform'] = elem[6].text # Tag['LongWaveform'] = root[8][0].text Tags['tags'].append(Tag) half_data = pd.DataFrame(Tags['tags']) data = pd.DataFrame(tags['tags']) del roots del root del elem count1000 = int(len(data)/12) count500 = int(len(half_data)/12) count = count1000 + count500 if len(data) > 0: array = np.unique(data[data.isnull().any(axis=1)][['ID', 'Date', 'Time']]) missing_data = data.loc[data['ID'].isin(array) & data['Date'].isin(array) & data['Time'].isin(array)] data.drop(missing_data.index, axis=0,inplace=True) missing_data = missing_data.reset_index(drop=True) del tag del tags data = data.reset_index(drop=True) for n in range(count1000): data.Tonset[n*12:(n+1)*12] = np.repeat(int(data.Tonset[n*12:(n+1)*12].sum()/12), 12) data.Pdur[n*12:(n+1)*12] = np.repeat(int(data.Pdur[n*12:(n+1)*12].sum()/12), 12) x = 0 p = [] for x in range(len(data.Waveform)): t = base64.b64decode(data.Waveform[x]) p.append(np.asarray(t)) x+=1 p = np.asarray(p) a = [] for i in p: o = [] for x in i: o.append(x) a.append(o) df = pd.DataFrame(a) df.insert(0, 'Lead', data['Lead']) blank = [] for n in range(count1000): blank.append(pd.pivot_table(df[(n*12):(n+1)*12], columns=df.Lead)) test = pd.concat(blank) new = [] array = [] for n in range(13): for index, num in zip(test.iloc[:, n-1][::2], test.iloc[:, n-1][1::2]): if num > 128: new.append(index - (256 * (256 - num))) elif num < 128: new.append(index + (256 * num)) elif num == 0: new.append(index) else: new.append(index) new = [] array.append(new) array = np.asarray([array[0], array[1], array[2], array[3], array[4], array[5], array[6], array[7], array[8], array[9], array[10], array[11]]) df = pd.DataFrame(array) df = pd.pivot_table(df, columns=test.columns) df = df.fillna(0) del a del p del o del t del blank del new del array for n in range(count1000): for x in range(12): if (data.Toffset[n*12]-data.RRint[n*12]) >= data.Ponset[n*12] or (data.Ponset[n*12] + data.RRint[n*12]) - data.Toffset[n*12] == 1: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - (df.iloc[:,x][n*1200:int(data.Qonset[n*12])+(n*1200)].mean() + df.iloc[:,x][int(data.Qoffset[n*12])+(n*1200):(n+1)*1200].mean()) / 2 else: rrint = data.RRint[n*12] if (rrint + data.Ponset[n*12]) > 1200 and (data.Toffset[n*12]-rrint) < 0: temp = df.iloc[:,x][int(n*1200):int(data.Ponset[n*12]+(n*1200))] test = df.iloc[:,x][int(data.Toffset[n*12]+(n*1200)):int((n+1)*1200)] if test.empty == False and temp.empty == False: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - ((temp[len(temp)//3:len(temp)*2//3].mean() + test[len(test)//3:len(test)*2//3].mean()) / 2) elif temp.empty: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - test[len(test)//3:len(test)*2//3].mean() elif test.empty: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - temp[len(temp)//3:len(temp)*2//3].mean() elif test.empty and temp.empty: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - (df.iloc[:,x][n*1200:int(data.Qonset[n*12])+(n*1200)].mean() + df.iloc[:,x][int(data.Qoffset[n*12])+(n*1200):(n+1)*1200].mean()) / 2 elif (rrint + data.Ponset[n*12]) > 1200 and (data.Toffset[n*12]-rrint) > 0: temp = df.iloc[:,x][int(data.Toffset[n*12]+(n*1200)-rrint):int(data.Ponset[n*12]+(n*1200))] test = df.iloc[:,x][int(data.Toffset[n*12]+(n*1200)):int((n+1)*1200)] if test.empty == False and temp.empty == False: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - ((temp[len(temp)//3:len(temp)*2//3].mean() + test[len(test)//3:len(test)*2//3].mean()) / 2) elif temp.empty: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - test[len(test)//3:len(test)*2//3].mean() elif test.empty: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - temp[len(temp)//3:len(temp)*2//3].mean() elif test.empty and temp.empty: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - (df.iloc[:,x][n*1200:int(data.Qonset[n*12])+(n*1200)].mean() + df.iloc[:,x][int(data.Qoffset[n*12])+(n*1200):(n+1)*1200].mean()) / 2 elif rrint + data.Ponset[n*12] < 1200 and (data.Toffset[n*12]-rrint) < 0: temp = df.iloc[:,x][int(n*1200):int(data.Ponset[n*12]+(n*1200))] test = df.iloc[:,x][int(data.Toffset[n*12]+(n*1200)):int(rrint + data.Ponset[n*12]+(n*1200))] if test.empty == False and temp.empty == False: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - ((temp[len(temp)//3:len(temp)*2//3].mean() + test[len(test)//3:len(test)*2//3].mean()) / 2) elif temp.empty: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - test[len(test)//3:len(test)*2//3].mean() elif test.empty: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - temp[len(temp)//3:len(temp)*2//3].mean() elif test.empty and temp.empty: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - (df.iloc[:,x][n*1200:int(data.Qonset[n*12])+(n*1200)].mean() + df.iloc[:,x][int(data.Qoffset[n*12])+(n*1200):(n+1)*1200].mean()) / 2 else: temp = df.iloc[:,x][int(data.Toffset[n*12]+(n*1200)-rrint):int(data.Ponset[n*12]+(n*1200))] test = df.iloc[:,x][int(data.Toffset[n*12]+(n*1200)):int(rrint + data.Ponset[n*12]+(n*1200))] if test.empty == False and temp.empty == False: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - ((temp[len(temp)//3:len(temp)*2//3].mean() + test[len(test)//3:len(test)*2//3].mean()) / 2) elif temp.empty: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - test[len(test)//3:len(test)*2//3].mean() elif test.empty: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - temp[len(temp)//3:len(temp)*2//3].mean() elif test.empty and temp.empty: df.iloc[:,x][n*1200:1200*(n+1)] = df.iloc[:,x][n*1200:1200*(n+1)] - (df.iloc[:,x][n*1200:int(data.Qonset[n*12])+(n*1200)].mean() + df.iloc[:,x][int(data.Qoffset[n*12])+(n*1200):(n+1)*1200].mean()) / 2 unfiltered_leads = df.copy() for n in range(count1000): for inx in range(12): test = df_fixer(df.iloc[:,inx][n*1200:(n+1)*1200], n) gaps = [] lstOfNs = [] gap = [] for num in test[test.isna() == True].index: lstOfNs.append(num) if len(lstOfNs) == 1: gap.append(lstOfNs[0]) if len(lstOfNs) > 1: if lstOfNs[-1] - lstOfNs[-2] < 5: gap.append(num) elif lstOfNs[-1] - lstOfNs[-2] > 5: gaps.append(gap) gap = [] gap.append(num) gaps.append(gap) if gaps != [[]]: x = [] y = [] for g in gaps: if len(g) == 1: x.append([g[-1]+1]) y.append(test[g[-1]+1]) if np.isnan(test.iloc[0]): point1 = [g[0], test[g[-1]+1]] point2 = [g[-1]+1, test[g[-1]+1]] x_temp,y_temp = hanging_line(point1, point2) x.append(x_temp) y.append(y_temp) else: point1 = [g[0]-1, test[g[0]-1]] point2 = [g[-1]+1, test[g[-1]+1]] x_temp,y_temp = hanging_line(point1, point2) x.append(x_temp) y.append(y_temp) for i in range(len(x)): test[x[i]] = y[i] if (trapz(abs(test[int(data.Qonset[n*12]):int(data.Qoffset[n*12])]))/trapz(abs(df.iloc[:,inx][int(data.Qonset[12*n]+(1200*n)):int(data.Qoffset[12*n]+(1200*n))]))) < .60: test = df.iloc[:,inx][n*1200:(n+1)*1200] test = medfilt(test, kernel_size=9) df.iloc[:,inx][n*1200:(n+1)*1200] = test del gaps del lstOfNs del gap del test VTI_leads = df[['III', 'aVF', 'aVL', 'aVR']] df = df[['I', 'II', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6']] Unfiltered_VTI_leads = unfiltered_leads[['III', 'aVF', 'aVL', 'aVR']] unfiltered_leads = unfiltered_leads[['I', 'II', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6']] matrix = [[.38, -.07, -.13, .05, -.01, .14, .06, .54], [-.07, .93, .06, -.02, -.05, .06, -.17, .13], [.11, -.23, -.43, -.06, -.14, -.20, -.11, .31]] x = matrix[0] y = matrix[1] z = matrix[2] n = 0 xtemp = [] ytemp = [] ztemp = [] for i in range(len(df)): xtemp.append((df.iloc[n].values * x).sum()) ytemp.append((df.iloc[n].values * y).sum()) ztemp.append((df.iloc[n].values * z).sum()) n+=1 df['x'] = xtemp df['y'] = ytemp df['z'] = ztemp n = 0 xtemp = [] ytemp = [] ztemp = [] for i in range(len(unfiltered_leads)): xtemp.append((unfiltered_leads.iloc[n].values * x).sum()) ytemp.append((unfiltered_leads.iloc[n].values * y).sum()) ztemp.append((unfiltered_leads.iloc[n].values * z).sum()) n+=1 df['Unfiltered_x'] = xtemp df['Unfiltered_y'] = ytemp df['Unfiltered_z'] = ztemp del xtemp del ytemp del ztemp df['Date'] = data['Date'] df['ID'] = data['ID'] df['Time'] = data['Time'] df['Print'] = data['Print'] df['Ponset'] = data['Ponset'] df['Pdur'] = data['Pdur'] df['Poffset'] = data['Poffset'] df['Qonset'] = data['Qonset'] df['Qrsdur'] = data['Qrsdur'] df['Qtint'] = data['Qtint'] df['Qoffset'] = data['Qoffset'] df['Tonset'] = data['Tonset'] df['Tdur'] = data['Tdur'] df['Toffset'] = data['Toffset'] df['HeartRate'] = data['HeartRate'] df['QRSFrontAxis'] = data['QRSFrontAxis'] df['Sex'] = data['Sex'] df['QTC'] = data['QTC'] df['Age'] = data['Age'] df['Name'] = data['Name'] for n in range(count1000): df['Ponset'][(n*1200):(n+1)*1200] = data['Ponset'][n*12] df['Print'][(n*1200):(n+1)*1200] = data['Print'][n*12] df['Pdur'][(n*1200):(n+1)*1200] = data['Pdur'][n*12] df['Poffset'][(n*1200):(n+1)*1200] = data['Poffset'][n*12] df['Qonset'][(n*1200):(n+1)*1200] = data['Qonset'][n*12] df['Qrsdur'][(n*1200):(n+1)*1200] = data['Qrsdur'][n*12] df['Qtint'][(n*1200):(n+1)*1200] = data['Qtint'][n*12] df['Qoffset'][(n*1200):(n+1)*1200] = data['Qoffset'][n*12] df['Tonset'][(n*1200):(n+1)*1200] = data['Tonset'][n*12] df['Tdur'][(n*1200):(n+1)*1200] = data['Tdur'][n*12] df['Toffset'][(n*1200):(n+1)*1200] = data['Toffset'][n*12] df['HeartRate'][(n*1200):(n+1)*1200] = data['HeartRate'][n*12] df['QRSFrontAxis'][(n*1200):(n+1)*1200] = data['QRSFrontAxis'][n*12] df['Sex'][(n*1200):(n+1)*1200] = data['Sex'][n*12] df['QTC'][(n*1200):(n+1)*1200] = data['QTC'][n*12] df['Age'][(n*1200):(n+1)*1200] = data['Age'][n*12] df['Date'][(n*1200):(n+1)*1200] = data['Date'][12*n] df['Time'][(n*1200):(n+1)*1200] = data['Time'][12*n] df['ID'][(n*1200):(n+1)*1200] = data['ID'][12*n] df['Name'][(n*1200):(n+1)*1200] = data['Name'][12*n] df[['III', 'aVF', 'aVL', 'aVR']] = VTI_leads unfiltered_leads[['III', 'aVF', 'aVL', 'aVR']] = Unfiltered_VTI_leads df[['Unfiltered_I', 'Unfiltered_II', 'Unfiltered_III', 'Unfiltered_V1', 'Unfiltered_V2', 'Unfiltered_V3', 'Unfiltered_V4', 'Unfiltered_V5', 'Unfiltered_V6', 'Unfiltered_aVF', 'Unfiltered_aVL', 'Unfiltered_aVR']] = unfiltered_leads[['I', 'II', 'III', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'aVF', 'aVL', 'aVR']] del unfiltered_leads del VTI_leads if len(half_data) > 0: array = np.unique(half_data[half_data.isnull().any(axis=1)][['ID', 'Date', 'Time']]) missing_half_data = half_data.loc[half_data['ID'].isin(array) & half_data['Date'].isin(array) & half_data['Time'].isin(array)] half_data.drop(missing_half_data.index, axis=0,inplace=True) missing_half_data = missing_half_data.reset_index(drop=True) del Tag del Tags half_data = half_data.reset_index(drop=True) for n in range(count500): half_data.Tonset[n*12:(n+1)*12] = np.repeat(int(half_data.Tonset[n*12:(n+1)*12].sum()/12), 12) half_data.Pdur[n*12:(n+1)*12] = np.repeat(int(half_data.Pdur[n*12:(n+1)*12].sum()/12), 12) x = 0 p = [] for x in range(len(half_data.Waveform)): t = base64.b64decode(half_data.Waveform[x]) p.append(np.asarray(t)) x+=1 p = np.asarray(p) a = [] for i in p: o = [] for x in i: o.append(x) a.append(o) half_df = pd.DataFrame(a) half_df.insert(0, 'Lead', half_data['Lead']) blank = [] for n in range(count500): blank.append(pd.pivot_table(half_df[(n*12):(n+1)*12], columns=half_df.Lead)) test = pd.concat(blank) new = [] array = [] for n in range(13): for index, num in zip(test.iloc[:, n-1][::2], test.iloc[:, n-1][1::2]): if num > 128: new.append(index - (256 * (256 - num))) elif num < 128: new.append(index + (256 * num)) elif num == 0: new.append(index) else: new.append(index) new = [] array.append(new) array = np.asarray([array[0], array[1], array[2], array[3], array[4], array[5], array[6], array[7], array[8], array[9], array[10], array[11]]) half_df = pd.DataFrame(array) half_df = pd.pivot_table(half_df, columns=test.columns) half_df = half_df.fillna(0) blank = [] for n in range(count500): blank.append(half_df[(n*1200):((n+1)*1200)-600]) test = pd.concat(blank) half_df = test half_df = half_df.reset_index(drop=True) half_df = pd.pivot_table(half_df, columns=half_df.index) array = [] for i in range(count500): for x in range(12): temp = [] new = [] for n in half_df.iloc[x,i*600:(i+1)*600]: temp.append(n) if len(temp) > 1: new.append(temp[-2]) if len(temp) < 601 and len(temp) > 1: new.append((temp[-1]+temp[-2])/2) if len(temp) == 600: new.append(temp[-1]) new.append(temp[-1]) array.append(new) I = (np.asarray(array[::12])).reshape(count500*1200) II = (np.asarray(array[1::12])).reshape(count500*1200) III = (np.asarray(array[2::12])).reshape(count500*1200) V1 = (np.asarray(array[3::12])).reshape(count500*1200) V2 = (np.asarray(array[4::12])).reshape(count500*1200) V3 = (np.asarray(array[5::12])).reshape(count500*1200) V4 = (np.asarray(array[6::12])).reshape(count500*1200) V5 = (np.asarray(array[7::12])).reshape(count500*1200) V6 = (np.asarray(array[8::12])).reshape(count500*1200) aVF = (np.asarray(array[9::12])).reshape(count500*1200) aVL = (np.asarray(array[10::12])).reshape(count500*1200) aVR = (np.asarray(array[11::12])).reshape(count500*1200) half_df = pd.pivot_table(pd.DataFrame([I, II, III, V1, V2, V3, V4, V5, V6, aVF, aVL, aVR]), columns=test.columns) half_df = half_df.fillna(0) del I del II del III del V1 del V2 del V3 del V4 del V5 del V6 del aVF del aVL del aVR del a del p del o del t del blank del new del array del temp for n in range(count500): for x in range(12): if ((half_data.Toffset[n*12]-half_data.RRint[n*12]) >= half_data.Ponset[n*12]) or ((half_data.Ponset[n*12] + half_data.RRint[n*12]) - half_data.Toffset[n*12] == 1): half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - (half_df.iloc[:,x][n*1200:int(half_data.Qonset[n*12])+(n*1200)].mean() + half_df.iloc[:,x][int(half_data.Qoffset[n*12])+(n*1200):(n+1)*1200].mean()) / 2 else: rrint = half_data.RRint[n*12] if (rrint + half_data.Ponset[n*12]) > 1200 and (half_data.Toffset[n*12]-rrint) < 0: temp = half_df.iloc[:,x][int(n*1200):int(half_data.Ponset[n*12]+(n*1200))] test = half_df.iloc[:,x][int(half_data.Toffset[n*12]+(n*1200)):int((n+1)*1200)] if test.empty == False and temp.empty == False: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - ((temp[len(temp)//3:len(temp)*2//3].mean() + test[len(test)//3:len(test)*2//3].mean()) / 2) elif temp.empty: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - test[len(test)//3:len(test)*2//3].mean() elif test.empty: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - temp[len(temp)//3:len(temp)*2//3].mean() elif test.empty and temp.empty: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - (half_df.iloc[:,x][n*1200:int(half_data.Qonset[n*12])+(n*1200)].mean() + half_df.iloc[:,x][int(half_data.Qoffset[n*12])+(n*1200):(n+1)*1200].mean()) / 2 elif (rrint + half_data.Ponset[n*12]) > 1200 and (half_data.Toffset[n*12]-rrint) > 0: temp = half_df.iloc[:,x][int(half_data.Toffset[n*12]+(n*1200)-rrint):int(half_data.Ponset[n*12]+(n*1200))] test = half_df.iloc[:,x][int(half_data.Toffset[n*12]+(n*1200)):int((n+1)*1200)] if test.empty == False and temp.empty == False: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - ((temp[len(temp)//3:len(temp)*2//3].mean() + test[len(test)//3:len(test)*2//3].mean()) / 2) elif temp.empty: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - test[len(test)//3:len(test)*2//3].mean() elif test.empty: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - temp[len(temp)//3:len(temp)*2//3].mean() elif test.empty and temp.empty: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - (half_df.iloc[:,x][n*1200:int(half_data.Qonset[n*12])+(n*1200)].mean() + half_df.iloc[:,x][int(half_data.Qoffset[n*12])+(n*1200):(n+1)*1200].mean()) / 2 elif rrint + half_data.Ponset[n*12] < 1200 and (half_data.Toffset[n*12]-rrint) < 0: temp = half_df.iloc[:,x][int(n*1200):int(half_data.Ponset[n*12]+(n*1200))] test = half_df.iloc[:,x][int(half_data.Toffset[n*12]+(n*1200)):int(rrint + half_data.Ponset[n*12]+(n*1200))] if test.empty == False and temp.empty == False: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - ((temp[len(temp)//3:len(temp)*2//3].mean() + test[len(test)//3:len(test)*2//3].mean()) / 2) elif temp.empty: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - test[len(test)//3:len(test)*2//3].mean() elif test.empty: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - temp[len(temp)//3:len(temp)*2//3].mean() elif test.empty and temp.empty: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - (half_df.iloc[:,x][n*1200:int(half_data.Qonset[n*12])+(n*1200)].mean() + half_df.iloc[:,x][int(half_data.Qoffset[n*12])+(n*1200):(n+1)*1200].mean()) / 2 else: temp = half_df.iloc[:,x][int(half_data.Toffset[n*12]+(n*1200)-rrint):int(half_data.Ponset[n*12]+(n*1200))] test = half_df.iloc[:,x][int(half_data.Toffset[n*12]+(n*1200)):int(rrint + half_data.Ponset[n*12]+(n*1200))] if test.empty == False and temp.empty == False: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - ((temp[len(temp)//3:len(temp)*2//3].mean() + test[len(test)//3:len(test)*2//3].mean()) / 2) elif temp.empty: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - test[len(test)//3:len(test)*2//3].mean() elif test.empty: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - temp[len(temp)//3:len(temp)*2//3].mean() elif test.empty and temp.empty: half_df.iloc[:,x][n*1200:1200*(n+1)] = half_df.iloc[:,x][n*1200:1200*(n+1)] - (half_df.iloc[:,x][n*1200:int(half_data.Qonset[n*12])+(n*1200)].mean() + half_df.iloc[:,x][int(half_data.Qoffset[n*12])+(n*1200):(n+1)*1200].mean()) / 2 for x in range(12): half_df.iloc[:,x] = half_df.iloc[:,x]*2.5 unfiltered_half_leads = half_df.copy() for n in range(count500): for inx in range(12): test = half_df_fixer(half_df.iloc[:,inx][n*1200:(n+1)*1200], n) gaps = [] lstOfNs = [] gap = [] for num in test[test.isna() == True].index: lstOfNs.append(num) if len(lstOfNs) == 1: gap.append(lstOfNs[0]) if len(lstOfNs) > 1: if lstOfNs[-1] - lstOfNs[-2] < 5: gap.append(num) elif lstOfNs[-1] - lstOfNs[-2] > 5: gaps.append(gap) gap = [] gap.append(num) gaps.append(gap) if gaps != [[]]: x = [] y = [] for g in gaps: if len(g) == 1: x.append([g[-1]+1]) y.append(test[g[-1]+1]) if np.isnan(test.iloc[0]): point1 = [g[0], test[g[-1]+1]] point2 = [g[-1]+1, test[g[-1]+1]] x_temp,y_temp = hanging_line(point1, point2) x.append(x_temp) y.append(y_temp) else: point1 = [g[0]-1, test[g[0]-1]] point2 = [g[-1]+1, test[g[-1]+1]] x_temp,y_temp = hanging_line(point1, point2) x.append(x_temp) y.append(y_temp) for i in range(len(x)): test[x[i]] = y[i] if (trapz(abs(test[int(half_data.Qonset[n*12]):int(half_data.Qoffset[n*12])]))/trapz(abs(half_df.iloc[:,inx][int(half_data.Qonset[12*n]+(1200*n)):int(half_data.Qoffset[12*n]+(1200*n))]))) < .60: test = half_df.iloc[:,inx][n*1200:(n+1)*1200] test = medfilt(test, kernel_size=9) half_df.iloc[:,inx][n*1200:(n+1)*1200] = test del gaps del lstOfNs del gap del test half_VTI_leads = half_df[['III', 'aVF', 'aVL', 'aVR']] half_df = half_df[['I', 'II', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6']] Unfiltered_half_VTI_leads = unfiltered_half_leads[['III', 'aVF', 'aVL', 'aVR']] unfiltered_half_leads = unfiltered_half_leads[['I', 'II', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6']] matrix = [[.38, -.07, -.13, .05, -.01, .14, .06, .54], [-.07, .93, .06, -.02, -.05, .06, -.17, .13], [.11, -.23, -.43, -.06, -.14, -.20, -.11, .31]] x = matrix[0] y = matrix[1] z = matrix[2] n = 0 xtemp = [] ytemp = [] ztemp = [] for i in range(len(half_df)): xtemp.append((half_df.iloc[n].values * x).sum()) ytemp.append((half_df.iloc[n].values * y).sum()) ztemp.append((half_df.iloc[n].values * z).sum()) n+=1 half_df['x'] = xtemp half_df['y'] = ytemp half_df['z'] = ztemp x = matrix[0] y = matrix[1] z = matrix[2] n = 0 xtemp = [] ytemp = [] ztemp = [] for i in range(len(unfiltered_half_leads)): xtemp.append((unfiltered_half_leads.iloc[n].values * x).sum()) ytemp.append((unfiltered_half_leads.iloc[n].values * y).sum()) ztemp.append((unfiltered_half_leads.iloc[n].values * z).sum()) n+=1 half_df['Unfiltered_x'] = xtemp half_df['Unfiltered_y'] = ytemp half_df['Unfiltered_z'] = ztemp del xtemp del ytemp del ztemp half_df['Date'] = half_data['Date'] half_df['ID'] = half_data['ID'] half_df['Time'] = half_data['Time'] half_df['Ponset'] = half_data['Ponset'] half_df['Print'] = half_data['Print'] half_df['Pdur'] = half_data['Pdur'] half_df['Poffset'] = half_data['Poffset'] half_df['Qonset'] = half_data['Qonset'] half_df['Qrsdur'] = half_data['Qrsdur'] half_df['Qtint'] = half_data['Qtint'] half_df['Qoffset'] = half_data['Qoffset'] half_df['Tonset'] = half_data['Tonset'] half_df['Tdur'] = half_data['Tdur'] half_df['Toffset'] = half_data['Toffset'] half_df['HeartRate'] = half_data['HeartRate'] half_df['QRSFrontAxis'] = half_data['QRSFrontAxis'] half_df['Sex'] = half_data['Sex'] half_df['QTC'] = half_data['QTC'] half_df['Age'] = half_data['Age'] half_df['Name'] = half_data['Name'] for n in range(count500): half_df['Ponset'][(n*1200):(n+1)*1200] = half_data['Ponset'][n*12] half_df['Print'][(n*1200):(n+1)*1200] = half_data['Print'][n*12] half_df['Pdur'][(n*1200):(n+1)*1200] = half_data['Pdur'][n*12] half_df['Poffset'][(n*1200):(n+1)*1200] = half_data['Poffset'][n*12] half_df['Qonset'][(n*1200):(n+1)*1200] = half_data['Qonset'][n*12] half_df['Qrsdur'][(n*1200):(n+1)*1200] = half_data['Qrsdur'][n*12] half_df['Qtint'][(n*1200):(n+1)*1200] = half_data['Qtint'][n*12] half_df['Qoffset'][(n*1200):(n+1)*1200] = half_data['Qoffset'][n*12] half_df['Tonset'][(n*1200):(n+1)*1200] = half_data['Tonset'][n*12] half_df['Tdur'][(n*1200):(n+1)*1200] = half_data['Tdur'][n*12] half_df['Toffset'][(n*1200):(n+1)*1200] = half_data['Toffset'][n*12] half_df['HeartRate'][(n*1200):(n+1)*1200] = half_data['HeartRate'][n*12] half_df['QRSFrontAxis'][(n*1200):(n+1)*1200] = half_data['QRSFrontAxis'][n*12] half_df['Sex'][(n*1200):(n+1)*1200] = half_data['Sex'][n*12] half_df['QTC'][(n*1200):(n+1)*1200] = half_data['QTC'][n*12] half_df['Name'][(n*1200):(n+1)*1200] = half_data['Name'][12*n] half_df['Age'][(n*1200):(n+1)*1200] = half_data['Age'][12*n] half_df['ID'][(n*1200):(n+1)*1200] = half_data['ID'][12*n] half_df['Date'][(n*1200):(n+1)*1200] = half_data['Date'][12*n] half_df['Time'][(n*1200):(n+1)*1200] = half_data['Time'][12*n] half_df[['III', 'aVF', 'aVL', 'aVR']] = half_VTI_leads unfiltered_half_leads[['III', 'aVF', 'aVL', 'aVR']] = Unfiltered_half_VTI_leads half_df[['Unfiltered_I', 'Unfiltered_II', 'Unfiltered_III', 'Unfiltered_V1', 'Unfiltered_V2', 'Unfiltered_V3', 'Unfiltered_V4', 'Unfiltered_V5', 'Unfiltered_V6', 'Unfiltered_aVF', 'Unfiltered_aVL', 'Unfiltered_aVR']] = unfiltered_half_leads[['I', 'II', 'III', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'aVF', 'aVL', 'aVR']] del unfiltered_half_leads del half_VTI_leads if (len(half_data) > 0) and (len(data) > 0): df = pd.concat([df, half_df]) df = df.reset_index(drop=True) del half_data del data del half_df if (len(half_data) > 0) and (len(data) == 0): df = half_df del half_df del half_data if (len(half_data) == 0) and (len(data) > 0): df = df del data df['total_xyz'] = ((df.x)**2 + (df.y)**2 + (df.z)**2)**0.5 QRSVTI = [] for n in range(count): QRSVTI.append(trapz(df.total_xyz[int(df.Qonset[1200*n]+(1200*n)):int(df.Qoffset[1200*n]+(1200*n))])) QRSVTI = np.repeat(QRSVTI, 1200) df['QRSVTI'] = QRSVTI del QRSVTI QRStVTI = [] for n in range(count): QRStVTI.append(trapz(df.total_xyz[int(df.Qonset[1200*n]+(1200*n)):int(df.Toffset[1200*n]+(1200*n))])) QRStVTI = np.repeat(QRStVTI, 1200) df['QRStVTI'] = QRStVTI del QRStVTI XVTI = [] for n in range(count): XVTI.append(trapz(abs(df.x[int(df.Qonset[1200*n]+(1200*n)):int(df.Qoffset[1200*n]+(1200*n))]))) XVTI = np.repeat(XVTI, 1200) df['XVTI'] = XVTI del XVTI YVTI = [] for n in range(count): YVTI.append(trapz(abs(df.y[int(df.Qonset[1200*n]+(1200*n)):int(df.Qoffset[1200*n]+(1200*n))]))) YVTI = np.repeat(YVTI, 1200) df['YVTI'] = YVTI del YVTI ZVTI = [] for n in range(count): ZVTI.append(trapz(abs(df.z[int(df.Qonset[1200*n]+(1200*n)):int(df.Qoffset[1200*n]+(1200*n))]))) ZVTI = np.repeat(ZVTI, 1200) df['ZVTI'] = ZVTI del ZVTI df['QRS3DArea'] = ((df.XVTI)**2 + (df.YVTI)**2 + (df.ZVTI)**2)**0.5 XtVTI = [] for n in range(count): XtVTI.append(trapz(abs(df.x[int(df.Qonset[1200*n]+(1200*n)):int(df.Toffset[1200*n]+(1200*n))]))) XtVTI = np.repeat(XtVTI, 1200) df['XtVTI'] = XtVTI del XtVTI YtVTI = [] for n in range(count): YtVTI.append(trapz(abs(df.y[int(df.Qonset[1200*n]+(1200*n)):int(df.Toffset[1200*n]+(1200*n))]))) YtVTI = np.repeat(YtVTI, 1200) df['YtVTI'] = YtVTI del YtVTI ZtVTI = [] for n in range(count): ZtVTI.append(trapz(abs(df.z[int(df.Qonset[1200*n]+(1200*n)):int(df.Toffset[1200*n]+(1200*n))]))) ZtVTI = np.repeat(ZtVTI, 1200) df['ZtVTI'] = ZtVTI del ZtVTI df['QRSt3DArea'] = ((df.XtVTI)**2 + (df.YtVTI)**2 + (df.ZtVTI)**2)**0.5 XVTI = [] for n in range(count): XVTI.append(trapz((df.x[int(df.Qonset[1200*n]+(1200*n)):int(df.Qoffset[1200*n]+(1200*n))]))) XVTI = np.repeat(XVTI, 1200) df['XVector_VTI'] = XVTI del XVTI YVTI = [] for n in range(count): YVTI.append(trapz((df.y[int(df.Qonset[1200*n]+(1200*n)):int(df.Qoffset[1200*n]+(1200*n))]))) YVTI = np.repeat(YVTI, 1200) df['YVector_VTI'] = YVTI del YVTI ZVTI = [] for n in range(count): ZVTI.append(trapz((df.z[int(df.Qonset[1200*n]+(1200*n)):int(df.Qoffset[1200*n]+(1200*n))]))) ZVTI = np.repeat(ZVTI, 1200) df['ZVector_VTI'] = ZVTI del ZVTI df['QRS3DVector_Area'] = ((df.XVector_VTI)**2 + (df.YVector_VTI)**2 + (df.ZVector_VTI)**2)**0.5 XtVTI = [] for n in range(count): XtVTI.append(trapz((df.x[int(df.Qonset[1200*n]+(1200*n)):int(df.Toffset[1200*n]+(1200*n))]))) XtVTI = np.repeat(XtVTI, 1200) df['XtVector_VTI'] = XtVTI del XtVTI YtVTI = [] for n in range(count): YtVTI.append(trapz((df.y[int(df.Qonset[1200*n]+(1200*n)):int(df.Toffset[1200*n]+(1200*n))]))) YtVTI = np.repeat(YtVTI, 1200) df['YtVector_VTI'] = YtVTI del YtVTI ZtVTI = [] for n in range(count): ZtVTI.append(trapz((df.z[int(df.Qonset[1200*n]+(1200*n)):int(df.Toffset[1200*n]+(1200*n))]))) ZtVTI = np.repeat(ZtVTI, 1200) df['ZtVector_VTI'] = ZtVTI del ZtVTI df['QRSt3DVector_Area'] = ((df.XtVector_VTI)**2 + (df.YtVector_VTI)**2 + (df.ZtVector_VTI)**2)**0.5 Tamp = [] XTamp = [] YTamp = [] ZTamp = [] TpTe = [] XTpTe = [] YTpTe = [] ZTpTe = [] QpQe = [] for x in range(count): if int(df.Tonset[1200*x]+(1200*x)) > int(df.Toffset[1200*x]+(1200*x)): XTamp.append(np.nan) XTpTe.append(np.nan) YTamp.append(np.nan) YTpTe.append(np.nan) ZTamp.append(np.nan) ZTpTe.append(np.nan) Tamp.append(np.nan) TpTe.append(np.nan) Qa = [abs(n) for n in df.total_xyz[int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]].index(max([abs(n) for n in df.total_xyz[int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]])) QpQe.append(len([abs(n) for n in df.total_xyz[int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]][Qa:])) elif df.Tonset[1200*x] == df.Toffset[1200*x]: XTamp.append(max([abs(n) for n in df.x[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]])) Ta = [abs(n) for n in df.x[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]].index(max([abs(n) for n in df.x[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]])) XTpTe.append(len([abs(n) for n in df.x[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]][Ta:])) YTamp.append(max([abs(n) for n in df.y[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]])) Ta = [abs(n) for n in df.y[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]].index(max([abs(n) for n in df.y[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]])) YTpTe.append(len([abs(n) for n in df.y[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]][Ta:])) ZTamp.append(max([abs(n) for n in df.z[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]])) Ta = [abs(n) for n in df.z[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]].index(max([abs(n) for n in df.z[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]])) ZTpTe.append(len([abs(n) for n in df.z[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]][Ta:])) Tamp.append(max([abs(n) for n in df.total_xyz[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]])) Ta = [abs(n) for n in df.total_xyz[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]].index(max([abs(n) for n in df.total_xyz[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]])) TpTe.append(len([abs(n) for n in df.total_xyz[int(df.Tonset[1200*x]+(1200*x))-10:int(df.Toffset[1200*x]+(1200*x))+10]][Ta:])) Qa = [abs(n) for n in df.total_xyz[int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]].index(max([abs(n) for n in df.total_xyz[int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]])) QpQe.append(len([abs(n) for n in df.total_xyz[int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]][Qa:])) else: XTamp.append(max([abs(n) for n in df.x[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]])) Ta = [abs(n) for n in df.x[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]].index(max([abs(n) for n in df.x[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]])) XTpTe.append(len([abs(n) for n in df.x[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]][Ta:])) YTamp.append(max([abs(n) for n in df.y[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]])) Ta = [abs(n) for n in df.y[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]].index(max([abs(n) for n in df.y[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]])) YTpTe.append(len([abs(n) for n in df.y[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]][Ta:])) ZTamp.append(max([abs(n) for n in df.z[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]])) Ta = [abs(n) for n in df.z[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]].index(max([abs(n) for n in df.z[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]])) ZTpTe.append(len([abs(n) for n in df.z[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]][Ta:])) Tamp.append(max([abs(n) for n in df.total_xyz[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]])) Ta = [abs(n) for n in df.total_xyz[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]].index(max([abs(n) for n in df.total_xyz[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]])) TpTe.append(len([abs(n) for n in df.total_xyz[int(df.Tonset[1200*x]+(1200*x)):int(df.Toffset[1200*x]+(1200*x))]][Ta:])) Qa = [abs(n) for n in df.total_xyz[int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]].index(max([abs(n) for n in df.total_xyz[int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]])) QpQe.append(len([abs(n) for n in df.total_xyz[int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]][Qa:])) QpQe = np.repeat(QpQe, 1200) df['QpQe'] = QpQe Tamp = np.repeat(Tamp, 1200) df['Tamp'] = Tamp XTamp = np.repeat(XTamp, 1200) df['XTamp'] = XTamp YTamp = np.repeat(YTamp, 1200) df['YTamp'] = YTamp ZTamp = np.repeat(ZTamp, 1200) df['ZTamp'] = ZTamp XTpTe = np.repeat(XTpTe, 1200) df['XTpTe'] = XTpTe YTpTe = np.repeat(YTpTe, 1200) df['YTpTe'] = YTpTe ZTpTe = np.repeat(ZTpTe, 1200) df['ZTpTe'] = ZTpTe TpTe = np.repeat(TpTe, 1200) df['TpTe'] = TpTe del Tamp del XTamp del YTamp del ZTamp del XTpTe del YTpTe del ZTpTe del TpTe del QpQe temp = df[['I', 'II', 'III', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'aVR', 'aVL', 'aVF', 'x', 'y', 'z', 'total_xyz']] Qamp = [] for x in range(count): for i in range(16): if min(temp.iloc[:,i][int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]) < 0 and max(temp.iloc[:,i][int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]) > 0: Qamp.append(max(temp.iloc[:,i][int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]) - min(temp.iloc[:,i][int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))])) elif min(temp.iloc[:,i][int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]) > 0 and max(temp.iloc[:,i][int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]) < 0: Qamp.append(max(temp.iloc[:,i][int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]) - min(temp.iloc[:,i][int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))])) elif min(temp.iloc[:,i][int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]) < 0 and max(temp.iloc[:,i][int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))]) < 0: Qamp.append(min(temp.iloc[:,i][int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))])) else: Qamp.append(max(temp.iloc[:,i][int(df.Qonset[1200*x]+(1200*x)):int(df.Qoffset[1200*x]+(1200*x))])) del temp XQamp = Qamp[12::16] XQamp = np.repeat(XQamp, 1200) YQamp = Qamp[13::16] YQamp = np.repeat(YQamp, 1200) ZQamp = Qamp[14::16] ZQamp = np.repeat(ZQamp, 1200) Qamp = Qamp[15::16] Qamp = np.repeat(Qamp, 1200) df['XQamp'] = XQamp df['YQamp'] = YQamp df['ZQamp'] = ZQamp df['Qamp'] = Qamp del XQamp del YQamp del ZQamp del Qamp text_df = df[['ID', 'Name', 'Age', 'Sex', 'Date', 'Time','HeartRate','Pdur','Print','Qrsdur','Qtint','QTC','TpTe','QRSFrontAxis', 'QRSVTI','XVector_VTI', 'YVector_VTI','ZVector_VTI', 'QRStVTI', 'XtVTI','YtVTI', 'ZtVTI', 'Qamp','XQamp','YQamp', 'ZQamp','Tamp','XTamp', 'YTamp','ZTamp']] text_df = text_df[::1200] # text_df.to_csv('Entresto_Final_Data.csv', index=False) # signal_df.to_pickle('Entresto_Final_ML.pkl') text_df.to_csv('data.csv', index=False) for n in range(count): # pd.DataFrame(text_df.iloc[n,:]).T.to_csv('{}.csv'.format(root_names[n][:-4]), index=False) x = df.x[n*1200:(n+1)*1200] y = df.y[n*1200:(n+1)*1200] z = df.z[n*1200:(n+1)*1200] rms = df.total_xyz[n*1200:(n+1)*1200] fig, ((ax, ax1), (ax2, ax3)) = plt.subplots(2, 2, figsize=(15, 8)) ax.plot(x) ax.set_title('Lead X') ax1.plot(y) ax1.set_title('Lead Y') ax2.plot(z) ax2.set_title('Lead Z') ax3.plot(rms) ax3.set_title('XYZ RMS') fig.subplots_adjust(hspace=.3) fig.subplots_adjust(wspace=.1) fig.savefig('{}.png'.format(root_names[n][:-4]), dpi=1800, format='png') del df

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null

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6

d6adff9d311c47f0b9f78e43e24aa7eb48cd8acf

30

py

Python

src/export/__init__.py

ethan-ou/speech-edit

d35b58f36c2f24423cf62013d54149da93deb245

[ "MIT" ]

2

2021-04-15T15:47:33.000Z

2021-09-07T23:15:34.000Z

src/export/__init__.py

ethan-ou/speech-edit

d35b58f36c2f24423cf62013d54149da93deb245

[ "MIT" ]

null

src/export/__init__.py

ethan-ou/speech-edit

d35b58f36c2f24423cf62013d54149da93deb245

[ "MIT" ]

1

2020-09-28T01:48:09.000Z

from .timeline import Timeline

30

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6.5

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6

d6b2bde72f4c90235159cdd45776a32bac7e359a

27

py

Python

examples/sentences/__init__.py

nprezant/GAlgorithm

5259281fb7ed0efe1effcdc39ae1850c0a47b9a5

[ "MIT" ]

1

2021-12-18T23:25:12.000Z

examples/sentences/__init__.py

nprezant/GAlgorithm

5259281fb7ed0efe1effcdc39ae1850c0a47b9a5

[ "MIT" ]

1

2022-03-12T01:04:13.000Z

examples/sentences/__init__.py

nprezant/GAlgorithm

5259281fb7ed0efe1effcdc39ae1850c0a47b9a5

[ "MIT" ]

null

from .sentences import run

13.5

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py

Python

discodo/server/__init__.py

eunwoo1104/discodo

699250d4fb62d970acd2573a5d967966872b7403

[ "MIT" ]

105

2020-06-21T23:37:20.000Z

2022-02-11T14:27:07.000Z

discodo/server/__init__.py

eunwoo1104/discodo

699250d4fb62d970acd2573a5d967966872b7403

[ "MIT" ]

116

2020-07-12T03:55:24.000Z

2022-03-31T23:02:54.000Z

discodo/server/__init__.py

eunwoo1104/discodo

699250d4fb62d970acd2573a5d967966872b7403

[ "MIT" ]

32

2020-07-12T03:38:35.000Z

2022-02-02T23:03:29.000Z

from .server import app as server

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py

Python

dpy_cooldowns/psql/__init__.py

TheGabDooSan/dpy-psql-cooldowns

413d1dc536c70c256722d8649e4ced94debb8b30

[ "MIT" ]

1

2021-04-05T16:29:32.000Z

dpy_cooldowns/psql/__init__.py

gabriel-dahan/dpy-cooldowns

413d1dc536c70c256722d8649e4ced94debb8b30

[ "MIT" ]

null

dpy_cooldowns/psql/__init__.py

gabriel-dahan/dpy-cooldowns

413d1dc536c70c256722d8649e4ced94debb8b30

[ "MIT" ]

null

from .cooldowns import *

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6

24042e88e3f05abb52b25897f4087c1761558f65

40

py

Python

w2_pscr/phw.py

polde-live/python-mich-2

f5890ca366451bde93b58e3b5ee167ee68f0aa6f

[ "Unlicense" ]

null

w2_pscr/phw.py

polde-live/python-mich-2

f5890ca366451bde93b58e3b5ee167ee68f0aa6f

[ "Unlicense" ]

null

w2_pscr/phw.py

polde-live/python-mich-2

f5890ca366451bde93b58e3b5ee167ee68f0aa6f

[ "Unlicense" ]

null

print "Hello world from Linux shell :)"

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null

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1

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1

0

6

79e8d11fa27da8d18cdfc4d15beba9e4eb65501e

39

py

Python

learning/encoders/__init__.py

jesse-michael-han/oracle

654c8e3aa27ab48a0b3533b102536d2a33cd701a

[ "Apache-2.0" ]

null

learning/encoders/__init__.py

jesse-michael-han/oracle

654c8e3aa27ab48a0b3533b102536d2a33cd701a

[ "Apache-2.0" ]

null

learning/encoders/__init__.py

jesse-michael-han/oracle

654c8e3aa27ab48a0b3533b102536d2a33cd701a

[ "Apache-2.0" ]

null

from .text_encoders import TextEncoder

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6

79f1c52c4aa435759bd824ef1901192cadf183c4

29

py

Python

school/schema/__init__.py

iPalmTech/django-ariadne-starter

5930b6ca13c9d2a726d3889ce899f49fb6d5301c

[ "MIT" ]

null

school/schema/__init__.py

iPalmTech/django-ariadne-starter

5930b6ca13c9d2a726d3889ce899f49fb6d5301c

[ "MIT" ]

null

school/schema/__init__.py

iPalmTech/django-ariadne-starter

5930b6ca13c9d2a726d3889ce899f49fb6d5301c

[ "MIT" ]

null

from .school import type_defs

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0

1

0

6

0306ab6bcea018b13da0950e665833ecd5252f64

160

py

Python

fixtures/python/random-names/tests/function.py

guiloga/scalade

fd59b239fb35e8a7028baea3ed6d4b23282c200d

[ "MIT" ]

4

2021-12-22T18:07:10.000Z

2021-12-29T09:22:44.000Z

fixtures/python/random-names/tests/function.py

guiloga/scalade

fd59b239fb35e8a7028baea3ed6d4b23282c200d

[ "MIT" ]

null

fixtures/python/random-names/tests/function.py

guiloga/scalade

fd59b239fb35e8a7028baea3ed6d4b23282c200d

[ "MIT" ]

null

from src.function import generate_random_names def test_generate_random_names(): names = generate_random_names(12) assert len(names.split(",")) == 12

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null

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6

0323316ced880dd2e480e12e3f966e612afedddc

43

py

Python

smartblinds_client/__init__.py

zhangquan0126/smartblinds-client

940ae7a1d99d7e0172686b277c9195bd1bba76c1

[ "MIT" ]

null

smartblinds_client/__init__.py

zhangquan0126/smartblinds-client

940ae7a1d99d7e0172686b277c9195bd1bba76c1

[ "MIT" ]

null

smartblinds_client/__init__.py

zhangquan0126/smartblinds-client

940ae7a1d99d7e0172686b277c9195bd1bba76c1

[ "MIT" ]

null

from .smartblinds import SmartBlindsClient

21.5

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1

0

1

0

6

034a8d4b81d044394349125761ab8c5bc639bf80

29

py

Python

nn/transformer/__init__.py

ollmer/clickbait

3dd54e6b6c804b97d9d955c2f4bea7bfbcadbfc7

[ "MIT" ]

22

2018-07-27T13:50:34.000Z

2021-01-05T08:46:34.000Z

nn/transformer/__init__.py

ollmer/clickbait

3dd54e6b6c804b97d9d955c2f4bea7bfbcadbfc7

[ "MIT" ]

1

2020-06-07T23:06:10.000Z

nn/transformer/__init__.py

ollmer/clickbait

3dd54e6b6c804b97d9d955c2f4bea7bfbcadbfc7

[ "MIT" ]

7

2018-08-06T23:12:35.000Z

2020-05-09T08:46:33.000Z

from .decoder import Decoder

14.5

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1

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1

0

6

034f059f0fa3e01996a1f4415b3d6cfea37a0d0a

243

py

Python

pymtl3/dsl/Placeholder.py

kevinyuan/pymtl3

5949e6a4acc625c0ccbbb25be3af1d0db683df3c

[ "BSD-3-Clause" ]

152

2020-06-03T02:34:11.000Z

2022-03-30T04:16:45.000Z

pymtl3/dsl/Placeholder.py

kevinyuan/pymtl3

5949e6a4acc625c0ccbbb25be3af1d0db683df3c

[ "BSD-3-Clause" ]

139

2019-05-29T00:37:09.000Z

2020-05-17T16:49:26.000Z

pymtl3/dsl/Placeholder.py

kevinyuan/pymtl3

5949e6a4acc625c0ccbbb25be3af1d0db683df3c

[ "BSD-3-Clause" ]

22

2020-05-18T13:42:05.000Z

2022-03-11T08:37:51.000Z

""" ======================================================================== Placeholder.py ======================================================================== Author : Shunning Jiang Date : June 1, 2019 """ class Placeholder: pass

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null

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6

035bc141ad5b616760dea191363435d4fc89d497

138

py

Python

pythainlp/tokenize/ssg.py

Subarna578/pythainlp

9650a40396719284add17bb09f50e948dea41053

[ "Apache-2.0" ]

null

pythainlp/tokenize/ssg.py

Subarna578/pythainlp

9650a40396719284add17bb09f50e948dea41053

[ "Apache-2.0" ]

null

pythainlp/tokenize/ssg.py

Subarna578/pythainlp

9650a40396719284add17bb09f50e948dea41053

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from typing import List import ssg def segment(text: str) -> List[str]: return ssg.syllable_tokenize(text)

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1

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6

cee5dda684f675e865540b98daeb3dfbcf846de7

19,332

py

Python

autolens/data/array/util/grid_util.py

AshKelly/PyAutoLens

043795966338a655339e61782253ad67cc3c14e6

[ "MIT" ]

null

autolens/data/array/util/grid_util.py

AshKelly/PyAutoLens

043795966338a655339e61782253ad67cc3c14e6

[ "MIT" ]

null

autolens/data/array/util/grid_util.py

AshKelly/PyAutoLens

043795966338a655339e61782253ad67cc3c14e6

[ "MIT" ]

null

from autolens import decorator_util import numpy as np from autolens.data.array.util import mask_util @decorator_util.jit() def centres_from_shape_pixel_scales_and_origin(shape, pixel_scales, origin): """Determine the (y,x) arc-second central coordinates of an array from its shape, pixel-scales and origin. The coordinate system is defined such that the positive y axis is up and positive x axis is right. Parameters ---------- shape : (int, int) The (y,x) shape of the 2D array the arc-second centre is computed for. pixel_scales : (float, float) The (y,x) arc-second to pixel scales of the 2D array. origin : (float, flloat) The (y,x) origin of the 2D array, which the centre is shifted to. Returns -------- tuple (float, float) The (y,x) arc-second central coordinates of the input array. Examples -------- centres_arc_seconds = centres_from_shape_pixel_scales_and_origin(shape=(5,5), pixel_scales=(0.5, 0.5), origin=(0.0, 0.0)) """ y_centre_arcsec = float(shape[0] - 1) / 2 + (origin[0] / pixel_scales[0]) x_centre_arcsec = float(shape[1] - 1) / 2 - (origin[1] / pixel_scales[1]) return (y_centre_arcsec, x_centre_arcsec) @decorator_util.jit() def regular_grid_2d_from_shape_pixel_scales_and_origin(shape, pixel_scales, origin=(0.0, 0.0)): """Compute the (y,x) arc second coordinates at the centre of every pixel of an array of shape (rows, columns). Coordinates are defined from the top-left corner, such that the first pixel at location [0, 0] has negative x \ and y values in arc seconds. The regular grid is returned on an array of shape (total_pixels, total_pixels, 2) where coordinate indexes match \ those of the original 2D array. y coordinates are stored in the 0 index of the third dimension, x coordinates in \ the 1 index. Parameters ---------- shape : (int, int) The (y,x) shape of the 2D array the regular grid of coordinates is computed for. pixel_scales : (float, float) The (y,x) arc-second to pixel scales of the 2D array. origin : (float, flloat) The (y,x) origin of the 2D array, which the regular grid is shifted around. Returns -------- ndarray A regular grid of (y,x) arc-second coordinates at the centre of every pixel on a 2D array. The regular grid \ array has dimensions (total_pixels, total_pixels, 2). Examples -------- regular_grid_1d = regular_grid_2d_from_shape_pixel_scales_and_origin(shape=(5,5), pixel_scales=(0.5, 0.5), \ origin=(0.0, 0.0)) """ grid_2d = np.zeros((shape[0], shape[1], 2)) centres_arc_seconds = centres_from_shape_pixel_scales_and_origin(shape=shape, pixel_scales=pixel_scales, origin=origin) for y in range(shape[0]): for x in range(shape[1]): grid_2d[y, x, 0] = -(y - centres_arc_seconds[0]) * pixel_scales[0] grid_2d[y, x, 1] = (x - centres_arc_seconds[1]) * pixel_scales[1] return grid_2d @decorator_util.jit() def regular_grid_1d_from_shape_pixel_scales_and_origin(shape, pixel_scales, origin=(0.0, 0.0)): """Compute the (y,x) arc second coordinates at the centre of every pixel of an array of shape (rows, columns). Coordinates are defined from the top-left corner, such that the first pixel at location [0, 0] has negative x \ and y values in arc seconds. The regular grid is returned on an array of shape (total_pixels**2, 2) where the 2D dimension of the original 2D \ array are reduced to one dimension. y coordinates are stored in the 0 index of the second dimension, x coordinates in the 1 index. Parameters ---------- shape : (int, int) The (y,x) shape of the 2D array the regular grid of coordinates is computed for. pixel_scales : (float, float) The (y,x) arc-second to pixel scales of the 2D array. origin : (float, flloat) The (y,x) origin of the 2D array, which the regular grid is shifted around. Returns -------- ndarray A regular grid of (y,x) arc-second coordinates at the centre of every pixel on a 2D array. The regular grid array has dimensions (total_pixels**2, 2). Examples -------- regular_grid_1d = regular_grid_1d_from_shape_pixel_scales_and_origin(shape=(5,5), pixel_scales=(0.5, 0.5), \ origin=(0.0, 0.0)) """ grid_1d = np.zeros((shape[0]*shape[1], 2)) centres_arc_seconds = centres_from_shape_pixel_scales_and_origin(shape=shape, pixel_scales=pixel_scales, origin=origin) i=0 for y in range(shape[0]): for x in range(shape[1]): grid_1d[i, 0] = -(y - centres_arc_seconds[0]) * pixel_scales[0] grid_1d[i, 1] = (x - centres_arc_seconds[1]) * pixel_scales[1] i += 1 return grid_1d @decorator_util.jit() def regular_grid_1d_masked_from_mask_pixel_scales_and_origin(mask, pixel_scales, origin=(0.0, 0.0)): """Compute the (y,x) arc second coordinates at the centre of every pixel of a 2D mask array of shape (rows, columns). Coordinates are defined from the top-left corner, where the first unmasked pixel corresponds to index 0. The pixel \ at the top-left of the array has negative x and y values in arc seconds. The regular grid is returned on an array of shape (total_unmasked_pixels, 2). y coordinates are stored in the 0 \ index of the second dimension, x coordinates in the 1 index. Parameters ---------- mask : ndarray A 2D array of bools, where *False* values mean unmasked and are therefore included as part of the calculated \ regular grid. pixel_scales : (float, float) The (y,x) arc-second to pixel scales of the 2D mask array. origin : (float, flloat) The (y,x) origin of the 2D array, which the regular grid is shifted around. Returns -------- ndarray A regular grid of (y,x) arc-second coordinates at the centre of every pixel unmasked pixel on the 2D mask \ array. The regular grid array has dimensions (total_unmasked_pixels, 2). Examples -------- mask = np.array([[True, False, True], [False, False, False] [True, False, True]]) regular_grid_1d = regular_grid_1d_masked_from_mask_pixel_scales_and_origin(mask=mask, pixel_scales=(0.5, 0.5), origin=(0.0, 0.0)) """ grid_2d = regular_grid_2d_from_shape_pixel_scales_and_origin(mask.shape, pixel_scales, origin) total_regular_pixels = mask_util.total_regular_pixels_from_mask(mask) regular_grid = np.zeros(shape=(total_regular_pixels, 2)) pixel_count = 0 for y in range(mask.shape[0]): for x in range(mask.shape[1]): if not mask[y, x]: regular_grid[pixel_count, :] = grid_2d[y, x] pixel_count += 1 return regular_grid @decorator_util.jit() def sub_grid_1d_masked_from_mask_pixel_scales_and_sub_grid_size(mask, pixel_scales, sub_grid_size, origin=(0.0, 0.0)): """ For the sub-grid, every unmasked pixel of a 2D mask array of shape (rows, columns) is divided into a finer \ uniform grid of shape (sub_grid_size, sub_grid_size). This routine computes the (y,x) arc second coordinates at \ the centre of every sub-pixel defined by this grid. Coordinates are defined from the top-left corner, where the first unmasked sub-pixel corresponds to index 0. \ Sub-pixels that are part of the same mask array pixel are indexed next to one another, such that the second \ sub-pixel in the first pixel has index 1, its next sub-pixel has index 2, and so forth. The sub-grid is returned on an array of shape (total_unmasked_pixels*sub_grid_size**2, 2). y coordinates are \ stored in the 0 index of the second dimension, x coordinates in the 1 index. Parameters ---------- mask : ndarray A 2D array of bools, where *False* values mean unmasked and are therefore included as part of the calculated \ regular grid. pixel_scales : (float, float) The (y,x) arc-second to pixel scales of the 2D mask array. sub_grid_size : int The size of the sub-grid that each pixel of the 2D mask array is divided into. origin : (float, flloat) The (y,x) origin of the 2D array, which the sub-grid is shifted around. Returns -------- ndarray A sub grid of (y,x) arc-second coordinates at the centre of every pixel unmasked pixel on the 2D mask \ array. The sub grid array has dimensions (total_unmasked_pixels*sub_grid_size**2, 2). Examples -------- mask = np.array([[True, False, True], [False, False, False] [True, False, True]]) sub_grid_1d = sub_grid_1d_from_mask_pixel_scales_and_origin(mask=mask, pixel_scales=(0.5, 0.5), origin=(0.0, 0.0)) """ total_sub_pixels = mask_util.total_sub_pixels_from_mask_and_sub_grid_size(mask, sub_grid_size) sub_grid = np.zeros(shape=(total_sub_pixels, 2)) centres_arc_seconds = centres_from_shape_pixel_scales_and_origin(shape=mask.shape, pixel_scales=pixel_scales, origin=origin) sub_index = 0 y_sub_half = pixel_scales[0] / 2 y_sub_step = pixel_scales[0] / (sub_grid_size + 1) x_sub_half = pixel_scales[1] / 2 x_sub_step = pixel_scales[1] / (sub_grid_size + 1) for y in range(mask.shape[0]): for x in range(mask.shape[1]): if not mask[y, x]: y_arcsec = (y - centres_arc_seconds[0]) * pixel_scales[0] x_arcsec = (x - centres_arc_seconds[1]) * pixel_scales[1] for y1 in range(sub_grid_size): for x1 in range(sub_grid_size): sub_grid[sub_index, 0] = -(y_arcsec - y_sub_half + (y1 + 1) * y_sub_step) sub_grid[sub_index, 1] = x_arcsec - x_sub_half + (x1 + 1) * x_sub_step sub_index += 1 return sub_grid @decorator_util.jit() def grid_arc_seconds_1d_to_grid_pixels_1d(grid_arc_seconds_1d, shape, pixel_scales, origin=(0.0, 0.0)): """ Convert a grid of (y,x) arc second coordinates to a grid of (y,x) pixel coordinate values. Pixel coordinates \ are returned as floats such that they include the decimal offset from each pixel's top-left corner relative to \ the input arc-second coordinate. The pixel coordinate origin is at the top left corner of the grid, such that the pixel [0,0] corresponds to \ the highest y arc-second coordinate and lowest x arc-second coordinate on the gird. The arc-second grid is defined by an origin and coordinates are shifted to this origin before computing their \ 1D grid pixel coordinate values. The input and output grids are both of shape (total_pixels, 2). Parameters ---------- grid_arc_seconds_1d: ndarray The grid of (y,x) coordinates in arc seconds which is converted to pixel value coordinates. shape : (int, int) The (y,x) shape of the original 2D array the arc-second coordinates were computed on. pixel_scales : (float, float) The (y,x) arc-second to pixel scales of the original 2D array. origin : (float, flloat) The (y,x) origin of the grid, which the arc-second grid is shifted to. Returns -------- ndarray A grid of (y,x) pixel-value coordinates with dimensions (total_pixels, 2). Examples -------- grid_arc_seconds_1d = np.array([[1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [4.0, 4.0]]) grid_pixels_1d = grid_arc_seconds_1d_to_grid_pixels_1d(grid_arc_seconds_1d=grid_arc_seconds_1d, shape=(2,2), pixel_scales=(0.5, 0.5), origin=(0.0, 0.0)) """ grid_pixels = np.zeros((grid_arc_seconds_1d.shape[0], 2)) centres_arc_seconds = centres_from_shape_pixel_scales_and_origin(shape=shape, pixel_scales=pixel_scales, origin=origin) for i in range(grid_arc_seconds_1d.shape[0]): grid_pixels[i, 0] = (-grid_arc_seconds_1d[i, 0] / pixel_scales[0]) + centres_arc_seconds[0] + 0.5 grid_pixels[i, 1] = (grid_arc_seconds_1d[i, 1] / pixel_scales[1]) + centres_arc_seconds[1] + 0.5 return grid_pixels @decorator_util.jit() def grid_arc_seconds_1d_to_grid_pixel_centres_1d(grid_arc_seconds_1d, shape, pixel_scales, origin=(0.0, 0.0)): """ Convert a grid of (y,x) arc second coordinates to a grid of (y,x) pixel values. Pixel coordinates are \ returned as integers such that they map directly to the pixel they are contained within. The pixel coordinate origin is at the top left corner of the grid, such that the pixel [0,0] corresponds to \ higher y arc-second coordinate value and lowest x arc-second coordinate. The arc-second coordinate grid is defined by the class attribute origin, and coordinates are shifted to this \ origin before computing their 1D grid pixel indexes. The input and output grids are both of shape (total_pixels, 2). Parameters ---------- grid_arc_seconds_1d: ndarray The grid of (y,x) coordinates in arc seconds which is converted to pixel indexes. shape : (int, int) The (y,x) shape of the original 2D array the arc-second coordinates were computed on. pixel_scales : (float, float) The (y,x) arc-second to pixel scales of the original 2D array. origin : (float, flloat) The (y,x) origin of the grid, which the arc-second grid is shifted Returns -------- ndarray A grid of (y,x) pixel indexes with dimensions (total_pixels, 2). Examples -------- grid_arc_seconds_1d = np.array([[1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [4.0, 4.0]]) grid_pixels_1d = grid_arc_seconds_1d_to_grid_pixel_centres_1d(grid_arc_seconds_1d=grid_arc_seconds_1d, shape=(2,2), pixel_scales=(0.5, 0.5), origin=(0.0, 0.0)) """ grid_pixels = np.zeros((grid_arc_seconds_1d.shape[0], 2)) centres_arc_seconds = centres_from_shape_pixel_scales_and_origin(shape=shape, pixel_scales=pixel_scales, origin=origin) for i in range(grid_arc_seconds_1d.shape[0]): grid_pixels[i, 0] = int((-grid_arc_seconds_1d[i, 0] / pixel_scales[0]) + centres_arc_seconds[0] + 0.5) grid_pixels[i, 1] = int((grid_arc_seconds_1d[i, 1] / pixel_scales[1]) + centres_arc_seconds[1] + 0.5) return grid_pixels @decorator_util.jit() def grid_arc_seconds_1d_to_grid_pixel_indexes_1d(grid_arc_seconds_1d, shape, pixel_scales, origin=(0.0, 0.0)): """ Convert a grid of (y,x) arc second coordinates to a grid of (y,x) pixel 1D indexes. Pixel coordinates are \ returned as integers such that they are the pixel from the top-left of the 2D grid going rights and then \ downwards. For example: The pixel at the top-left, whose 2D index is [0,0], corresponds to 1D index 0. The fifth pixel on the top row, whose 2D index is [0,5], corresponds to 1D index 4. The first pixel on the second row, whose 2D index is [0,1], has 1D index 10 if a row has 10 pixels. The arc-second coordinate grid is defined by the class attribute origin, and coordinates are shifted to this \ origin before computing their 1D grid pixel indexes. The input and output grids are both of shape (total_pixels, 2). Parameters ---------- grid_arc_seconds_1d: ndarray The grid of (y,x) coordinates in arc seconds which is converted to 1D pixel indexes. shape : (int, int) The (y,x) shape of the original 2D array the arc-second coordinates were computed on. pixel_scales : (float, float) The (y,x) arc-second to pixel scales of the original 2D array. origin : (float, flloat) The (y,x) origin of the grid, which the arc-second grid is shifted. Returns -------- ndarray A grid of 1d pixel indexes with dimensions (total_pixels, 2). Examples -------- grid_arc_seconds_1d = np.array([[1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [4.0, 4.0]]) grid_pixels_1d = grid_arc_seconds_1d_to_grid_pixel_indexes_1d(grid_arc_seconds_1d=grid_arc_seconds_1d, shape=(2,2), pixel_scales=(0.5, 0.5), origin=(0.0, 0.0)) """ grid_pixels = grid_arc_seconds_1d_to_grid_pixel_centres_1d(grid_arc_seconds_1d=grid_arc_seconds_1d, shape=shape, pixel_scales=pixel_scales, origin=origin) grid_pixel_indexes = np.zeros(grid_pixels.shape[0]) for i in range(grid_pixels.shape[0]): grid_pixel_indexes[i] = int(grid_pixels[i,0] * shape[1] + grid_pixels[i,1]) return grid_pixel_indexes @decorator_util.jit() def grid_pixels_1d_to_grid_arc_seconds_1d(grid_pixels_1d, shape, pixel_scales, origin=(0.0, 0.0)): """ Convert a grid of (y,x) pixel coordinates to a grid of (y,x) arc second values. The pixel coordinate origin is at the top left corner of the grid, such that the pixel [0,0] corresponds to \ higher y arc-second coordinate value and lowest x arc-second coordinate. The arc-second coordinate origin is defined by the class attribute origin, and coordinates are shifted to this \ origin after computing their values from the 1D grid pixel indexes. The input and output grids are both of shape (total_pixels, 2). Parameters ---------- grid_pixels_1d: ndarray The grid of (y,x) coordinates in pixel values which is converted to arc-second coordinates. shape : (int, int) The (y,x) shape of the original 2D array the arc-second coordinates were computed on. pixel_scales : (float, float) The (y,x) arc-second to pixel scales of the original 2D array. origin : (float, flloat) The (y,x) origin of the grid, which the arc-second grid is shifted. Returns -------- ndarray A grid of 1d arc-second coordinates with dimensions (total_pixels, 2). Examples -------- grid_pixels_1d = np.array([[0,0], [0,1], [1,0], [1,1]) grid_pixels_1d = grid_pixels_1d_to_grid_arc_seconds_1d(grid_pixels_1d=grid_pixels_1d, shape=(2,2), pixel_scales=(0.5, 0.5), origin=(0.0, 0.0)) """ grid_arc_seconds = np.zeros((grid_pixels_1d.shape[0], 2)) centres_arc_seconds = centres_from_shape_pixel_scales_and_origin(shape=shape, pixel_scales=pixel_scales, origin=origin) for i in range(grid_arc_seconds.shape[0]): grid_arc_seconds[i, 0] = -(grid_pixels_1d[i, 0] - centres_arc_seconds[0] - 0.5) * pixel_scales[0] grid_arc_seconds[i, 1] = (grid_pixels_1d[i, 1] - centres_arc_seconds[1] - 0.5) * pixel_scales[1] return grid_arc_seconds

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3061fe14ede5d5973d4e49b21690e046e878a7c7

141

py

Python

users/models.py

oangervuori/namubufferi

b9353b1d1a32e18e93cb1e9bd2b591950d54269a

[ "MIT" ]

2

2016-12-05T03:31:47.000Z

2017-02-13T20:10:39.000Z

users/models.py

oangervuori/namubufferi

b9353b1d1a32e18e93cb1e9bd2b591950d54269a

[ "MIT" ]

1

2016-12-14T10:53:15.000Z

2016-12-17T18:52:25.000Z

users/models.py

oangervuori/namubufferi

b9353b1d1a32e18e93cb1e9bd2b591950d54269a

[ "MIT" ]

1

2017-01-14T10:56:28.000Z

from django.contrib.auth.models import AbstractUser from uuidmodels.models import UUIDModel class User(AbstractUser, UUIDModel): pass

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37

py

Python

clustergram/__init__.py

mwielondek/clustergram

9906b433493f965c0168477bbd3d83c7c4ec03bf

[ "MIT" ]

1

2020-09-05T10:58:45.000Z

clustergram/__init__.py

mwielondek/clustergram

9906b433493f965c0168477bbd3d83c7c4ec03bf

[ "MIT" ]

null

clustergram/__init__.py

mwielondek/clustergram

9906b433493f965c0168477bbd3d83c7c4ec03bf

[ "MIT" ]

null

from .clustergram import Clustergram

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067ba05f307e084995250f1f32cb3c3a656b97e6

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py

Python

ray_tracer/tests/test_checker_board.py

jjason/RayTracerChallenge

ab3cea8968407426bddfa9e11319664fc0b595f6

[ "MIT" ]

1

2020-05-13T03:54:00.000Z

ray_tracer/tests/test_checker_board.py

jjason/RayTracerChallenge

ab3cea8968407426bddfa9e11319664fc0b595f6

[ "MIT" ]

null

ray_tracer/tests/test_checker_board.py

jjason/RayTracerChallenge

ab3cea8968407426bddfa9e11319664fc0b595f6

[ "MIT" ]

null

import unittest from color import Color from point import Point from patterns.checker_board import CheckerBoard class TestCheckerBoard(unittest.TestCase): @classmethod def setUpClass(cls): cls.white = Color(red=1, green=1, blue=1) cls.black = Color(red=0, green=0, blue=0) def test_color_should_repeat_in_x(self): c = CheckerBoard(color_a=self.__class__.white, color_b=self.__class__.black) self.assertEqual(c.color_at(position=Point(x=0, y=0, z=0)), self.__class__.white) self.assertEqual(c.color_at(position=Point(x=0.99, y=0, z=0)), self.__class__.white) self.assertEqual(c.color_at(position=Point(x=1.01, y=0, z=0)), self.__class__.black) def test_color_should_repeat_in_y(self): c = CheckerBoard(color_a=self.__class__.white, color_b=self.__class__.black) self.assertEqual(c.color_at(position=Point(x=0, y=0, z=0)), self.__class__.white) self.assertEqual(c.color_at(position=Point(x=0, y=0.99, z=0)), self.__class__.white) self.assertEqual(c.color_at(position=Point(x=0, y=1.01, z=0)), self.__class__.black) def test_color_should_repeat_in_z(self): c = CheckerBoard(color_a=self.__class__.white, color_b=self.__class__.black) self.assertEqual(c.color_at(position=Point(x=0, y=0, z=0)), self.__class__.white) self.assertEqual(c.color_at(position=Point(x=0, y=0, z=0.99)), self.__class__.white) self.assertEqual(c.color_at(position=Point(x=0, y=0, z=1.01)), self.__class__.black) if __name__ == '__main__': unittest.main()

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null

0

6

0691e6b397f33e306d5327b51135e85cfb385e40

11,944

py

Python

app/forms.py

CS-Hunt/Get-Placed

f79f79f2dd37510405a24578b3a91acea00f9244

[ "MIT" ]

14

2021-08-28T04:05:55.000Z

2022-02-20T07:03:16.000Z

app/forms.py

CS-Hunt/Get-Placed

f79f79f2dd37510405a24578b3a91acea00f9244

[ "MIT" ]

null

app/forms.py

CS-Hunt/Get-Placed

f79f79f2dd37510405a24578b3a91acea00f9244

[ "MIT" ]

9

2021-08-28T04:06:03.000Z

2021-09-26T16:45:28.000Z

from django import forms from .models import Placement_Company_Detail,Profile,StudentBlogModel,ResorcesModel from django.contrib.auth.forms import UserCreationForm,AuthenticationForm from django.utils.translation import gettext,gettext_lazy as _ from django.contrib.auth.models import User from django.contrib.auth.forms import UserCreationForm,UserChangeForm,PasswordChangeForm from allauth.account.forms import LoginForm from django.contrib.auth.forms import ReadOnlyPasswordHashField class Job_Post_Form(forms.ModelForm): class Meta: model = Placement_Company_Detail fields = ('title','snippet','author','Company_image','Job_Description','apply_link','job_type') widgets = { 'title' : forms.TextInput(attrs={'class':'form-control','placeholder':'Title of the Job Post'}), 'apply_link' : forms.TextInput(attrs={'class':'form-control','placeholder':'link of apply button'}), 'author' : forms.TextInput(attrs={'class':'form-control','value':'','id':'elder','type':'hidden'}), # 'author' : forms.Select(attrs={'class':'form-control','placeholder':"author's name"}), 'job_type' : forms.Select(attrs={'class':'form-control','placeholder':"Job Type"}), 'Job_Description' : forms.Textarea(attrs={'class':'form-control','placeholder':'Body of the Blog'}), 'snippet' : forms.Textarea(attrs={'class':'form-control','placeholder':'Add short detail of job'}), } class Edit_Post_Form(forms.ModelForm): class Meta: model = Placement_Company_Detail fields = ('title','snippet','Company_image','Job_Description','apply_link','job_type') widgets = { 'title' : forms.TextInput(attrs={'class':'form-control','placeholder':'Title of the Job Post'}), 'apply_link' : forms.TextInput(attrs={'class':'form-control','placeholder':'link of apply button'}), # 'author' : forms.Select(attrs={'class':'form-control','placeholder':"author's name"}), 'job_type' : forms.Select(attrs={'class':'form-control','placeholder':"Job Type"}), 'Job_Description' : forms.Textarea(attrs={'class':'form-control','placeholder':'Body of the Blog'}), 'snippet' : forms.Textarea(attrs={'class':'form-control','placeholder':'Add short detail of job'}), } class Blog_Post_Form(forms.ModelForm): class Meta: model = StudentBlogModel fields = ('title','author','body','snippet') widgets = { 'title' : forms.TextInput(attrs={'class':'form-control','placeholder':'Title of the Blog Post'}), 'author' : forms.TextInput(attrs={'class':'form-control','value':'','id':'elder','type':'hidden'}), # 'author' : forms.Select(attrs={'class':'form-control','placeholder':"author's name"}), 'body' : forms.Textarea(attrs={'class':'form-control','placeholder':'Body of the Blog'}), 'snippet' : forms.Textarea(attrs={'class':'form-control','placeholder':'Add snippet of Blog'}), } class ResorcesModelForm(forms.ModelForm): class Meta: model = ResorcesModel fields = ('title','docs','author','course1_title','course1_Img','course1_link','course2_title','course2_Img','course2_link','course3_title','course3_Img','course3_link','course4_title','course4_Img','course4_link','course5_title','course5_Img','course5_link', 'channel1_title','channel1_Img','channel1_link','channel2_title','channel2_Img','channel2_link','channel3_title','channel3_Img','channel3_link','channel4_title','channel4_Img','channel4_link','channel5_title','channel5_Img','channel5_link', 'Website1_title','Website1_Img','Website1_link','Website2_title','Website2_Img','Website2_link','Website3_title','Website3_Img','Website3_link','Website4_title','Website4_Img','Website4_link','Website5_title','Website5_Img','Website5_link',) widgets = { 'title' : forms.TextInput(attrs={'class':'form-control','placeholder':'Title of the Blog Post'}), 'author' : forms.TextInput(attrs={'class':'form-control','value':'','id':'elder','type':'hidden'}), # 'author' : forms.Select(attrs={'class':'form-control','placeholder':"author's name"}), 'docs' : forms.TextInput(attrs={'class':'form-control','placeholder':'Link of documentation'}), 'course1_title' : forms.TextInput(attrs={'class':'form-control','placeholder':'Title of course 1'}), 'course1_link' : forms.TextInput(attrs={'class':'form-control','placeholder':'link of course 1'}), 'course2_title' : forms.TextInput(attrs={'class':'form-control','placeholder':'Title of course 2'}), 'course2_link' : forms.TextInput(attrs={'class':'form-control','placeholder':'link of course 2'}), 'course3_title' : forms.TextInput(attrs={'class':'form-control','placeholder':'Title of course 3'}), 'course3_link' : forms.TextInput(attrs={'class':'form-control','placeholder':'link of course 3'}), 'course4_title' : forms.TextInput(attrs={'class':'form-control','placeholder':'Title of course 3'}), 'course4_link' : forms.TextInput(attrs={'class':'form-control','placeholder':'link of course 3'}), 'course5_title' : forms.TextInput(attrs={'class':'form-control','placeholder':'Title of course 3'}), 'course5_link' : forms.TextInput(attrs={'class':'form-control','placeholder':'link of course 3'}), 'channel1_title' : forms.TextInput(attrs={'class':'form-control','placeholder':'Title of channel 1'}), 'channel1_link' : forms.TextInput(attrs={'class':'form-control','placeholder':'link of channel 1'}), 'channel2_title' : forms.TextInput(attrs={'class':'form-control','placeholder':'Title of channel 2'}), 'channel2_link' : forms.TextInput(attrs={'class':'form-control','placeholder':'link of channel 2'}), 'channel3_title' : forms.TextInput(attrs={'class':'form-control','placeholder':'Title of channel 3'}), 'channel3_link' : forms.TextInput(attrs={'class':'form-control','placeholder':'link of channel 3'}), 'channel4_title' : forms.TextInput(attrs={'class':'form-control','placeholder':'Title of channel 3'}), 'channel4_link' : forms.TextInput(attrs={'class':'form-control','placeholder':'link of channel 3'}), 'channel5_title' : forms.TextInput(attrs={'class':'form-control','placeholder':'Title of channel 3'}), 'channel5_link' : forms.TextInput(attrs={'class':'form-control','placeholder':'link of channel 3'}), 'Website1_title' : forms.TextInput(attrs={'class':'form-control','placeholder':'Title of Website 1'}), 'Website1_link' : forms.TextInput(attrs={'class':'form-control','placeholder':'link of Website 1'}), 'Website2_title' : forms.TextInput(attrs={'class':'form-control','placeholder':'Title of Website 2'}), 'Website2_link' : forms.TextInput(attrs={'class':'form-control','placeholder':'link of Website 2'}), 'Website3_title' : forms.TextInput(attrs={'class':'form-control','placeholder':'Title of Website 3'}), 'Website3_link' : forms.TextInput(attrs={'class':'form-control','placeholder':'link of Website 3'}), 'Website4_title' : forms.TextInput(attrs={'class':'form-control','placeholder':'Title of Website 3'}), 'Website4_link' : forms.TextInput(attrs={'class':'form-control','placeholder':'link of Website 3'}), 'Website5_title' : forms.TextInput(attrs={'class':'form-control','placeholder':'Title of Website 3'}), 'Website5_link' : forms.TextInput(attrs={'class':'form-control','placeholder':'link of Website 3'}), } class Edit_Blog_Post_Form(forms.ModelForm): class Meta: model = StudentBlogModel fields = ('title','snippet','body') widgets = { 'title' : forms.TextInput(attrs={'class':'form-control','placeholder':'Title of the Blog Post'}), # 'author' : forms.TextInput(attrs={'class':'form-control','value':'','id':'elder','type':'hidden'}), # 'author' : forms.Select(attrs={'class':'form-control','placeholder':"author's name"}), 'body' : forms.Textarea(attrs={'class':'form-control','placeholder':'Body of the Blog'}), 'snippet' : forms.Textarea(attrs={'class':'form-control','placeholder':'Add snippet of Blog'}), } class UserLoginForm(LoginForm): username=forms.CharField(widget=forms.TextInput(attrs={'autofocus':True,'class':'form-control'})) password=forms.CharField(label=_('Password'),strip=False,widget=forms.PasswordInput(attrs={'autocomplete':'current-password','autofocus':True,'class':'form-control'})) class ProfilePageView(forms.ModelForm): class Meta: model = Profile fields = ('bio','Gender','Mobile_Number','city','state','profile_pic','twitter_url','instagram_url','linkdin_url','github_url') widgets = { 'bio': forms.Textarea(attrs={'class':'form-control','placeholder':'Write a summary about you...'}), # 'profile_pic': forms.ImageField(), 'Gender': forms.Select(attrs={'class':'form-control'}), 'Mobile_Number': forms.TextInput(attrs={'class':'form-control','placeholder':'Enter your Mobile number'}), 'city': forms.TextInput(attrs={'class':'form-control'}), 'state': forms.Select(attrs={'class':'form-control'}), 'twitter_url': forms.TextInput(attrs={'class':'form-control'}), 'instagram_url': forms.TextInput(attrs={'class':'form-control'}), 'linkdin_url': forms.TextInput(attrs={'class':'form-control'}), 'github_url': forms.TextInput(attrs={'class':'form-control'}), } class EditProfileFormPage(forms.ModelForm): class Meta: model = Profile fields = ('bio','Gender','Mobile_Number','city','state','profile_pic','twitter_url','instagram_url','linkdin_url','github_url') widgets = { 'bio': forms.Textarea(attrs={'class':'form-control','placeholder':'Write a summary about you...'}), # 'profile_pic': forms.ImageField(), 'Gender': forms.Select(attrs={'class':'form-control'}), 'Mobile_Number': forms.TextInput(attrs={'class':'form-control'}), 'city': forms.TextInput(attrs={'class':'form-control'}), 'state': forms.Select(attrs={'class':'form-control'}), 'twitter_url': forms.TextInput(attrs={'class':'form-control'}), 'instagram_url': forms.TextInput(attrs={'class':'form-control'}), 'linkdin_url': forms.TextInput(attrs={'class':'form-control'}), 'github_url': forms.TextInput(attrs={'class':'form-control'}), } class EditProfileForm(UserChangeForm): date_joined = forms.CharField(max_length=100,disabled=True) password = ReadOnlyPasswordHashField(label=("Password"), help_text=("Raw passwords are not stored, so there is no way to see " "this user's password, but you can change the password " "using <a href=\"../accounts/password/change/\">this form</a>.")) class Meta: model =User fields = ['username','first_name','last_name','email','date_joined'] labels={ 'first_name' : 'First Name', 'last_name':'Last Name', 'email': 'Email', } widgets = { 'username': forms.TextInput(attrs={'class':'form-control'}), 'first_name': forms.TextInput(attrs={'class':'form-control'}), 'last_name': forms.TextInput(attrs={'class':'form-control'}), 'email': forms.EmailInput(attrs={'class':'form-control'}), 'date_joined': forms.TextInput(attrs={'class':'form-control'}), }

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null

0

1

0

1

0

null

0

6

06a0368a88d8af76595a8af881a8e66f694de51e

4,221

py

Python

hail_scripts/v02/utils/computed_fields/test_flags.py

NLSVTN/hail-elasticsearch-pipelines

8b895a2e46a33d347dd2a1024101a6d515027a03

[ "MIT" ]

15

2017-11-22T14:48:04.000Z

2020-10-05T18:22:24.000Z

hail_scripts/v02/utils/computed_fields/test_flags.py

NLSVTN/hail-elasticsearch-pipelines

8b895a2e46a33d347dd2a1024101a6d515027a03

[ "MIT" ]

94

2020-10-21T17:37:57.000Z

2022-03-29T14:59:46.000Z

hail_scripts/v02/utils/computed_fields/test_flags.py

NLSVTN/hail-elasticsearch-pipelines

8b895a2e46a33d347dd2a1024101a6d515027a03

[ "MIT" ]

7

2019-01-29T09:08:10.000Z

2020-02-25T16:22:57.000Z

import unittest import hail as hl from .flags import ( get_expr_for_consequence_lc_lof_flag, get_expr_for_variant_lc_lof_flag, get_expr_for_genes_with_lc_lof_flag, get_expr_for_consequence_loftee_flag_flag, get_expr_for_variant_loftee_flag_flag, get_expr_for_genes_with_loftee_flag_flag, ) class TestFlags(unittest.TestCase): def setUp(self): self.all_lc_lof = hl.literal( [ hl.struct(gene_id="foo", lof="LC", lof_flags="", lof_info=""), hl.struct(gene_id="foo", lof="NC", lof_flags="", lof_info=""), hl.struct(gene_id="bar", lof="LC", lof_flags="", lof_info=""), hl.struct(gene_id="baz", lof="", lof_flags="", lof_info=""), hl.struct(gene_id="baz", lof="LC", lof_flags="", lof_info=""), ] ) self.some_lc_lof = hl.literal( [ hl.struct(gene_id="foo", lof="LC", lof_flags="", lof_info=""), hl.struct(gene_id="foo", lof="", lof_flags="", lof_info=""), hl.struct(gene_id="bar", lof="LC", lof_flags="", lof_info=""), hl.struct(gene_id="baz", lof="HC", lof_flags="", lof_info=""), hl.struct(gene_id="baz", lof="LC", lof_flags="", lof_info=""), ] ) self.all_loftee_flags = hl.literal( [ hl.struct(gene_id="foo", lof="HC", lof_flags="flag1", lof_info=""), hl.struct(gene_id="foo", lof="HC", lof_flags="flag2", lof_info=""), hl.struct(gene_id="bar", lof="LC", lof_flags="flag1", lof_info=""), hl.struct(gene_id="baz", lof="HC", lof_flags="flag2", lof_info=""), hl.struct(gene_id="baz", lof="", lof_flags="", lof_info=""), ] ) self.some_loftee_flags = hl.literal( [ hl.struct(gene_id="foo", lof="HC", lof_flags="flag1", lof_info=""), hl.struct(gene_id="foo", lof="HC", lof_flags="", lof_info=""), hl.struct(gene_id="bar", lof="", lof_flags="flag1", lof_info=""), hl.struct(gene_id="bar", lof="LC", lof_flags="flag1", lof_info=""), hl.struct(gene_id="baz", lof="HC", lof_flags="flag2", lof_info=""), hl.struct(gene_id="baz", lof="HC", lof_flags="flag3", lof_info=""), ] ) def test_consequence_lc_lof_flag(self): self.assertTrue(hl.eval(get_expr_for_consequence_lc_lof_flag(hl.struct(lof="LC")))) self.assertFalse(hl.eval(get_expr_for_consequence_lc_lof_flag(hl.struct(lof="HC")))) self.assertFalse(hl.eval(get_expr_for_consequence_lc_lof_flag(hl.struct(lof="")))) def test_variant_lc_lof_flag(self): self.assertTrue(hl.eval(get_expr_for_variant_lc_lof_flag(self.all_lc_lof))) self.assertFalse(hl.eval(get_expr_for_variant_lc_lof_flag(self.some_lc_lof))) def test_genes_with_lc_lof_flag(self): self.assertSetEqual(hl.eval(get_expr_for_genes_with_lc_lof_flag(self.all_lc_lof)), set(["foo", "bar", "baz"])) self.assertSetEqual(hl.eval(get_expr_for_genes_with_lc_lof_flag(self.some_lc_lof)), set(["foo", "bar"])) def test_consequence_loftee_flag_flag(self): self.assertTrue(hl.eval(get_expr_for_consequence_loftee_flag_flag(hl.struct(lof="HC", lof_flags="foo")))) self.assertFalse(hl.eval(get_expr_for_consequence_loftee_flag_flag(hl.struct(lof="", lof_flags="")))) self.assertFalse(hl.eval(get_expr_for_consequence_loftee_flag_flag(hl.struct(lof="", lof_flags="bar")))) def test_variant_loftee_flag_flag(self): self.assertTrue(hl.eval(get_expr_for_variant_loftee_flag_flag(self.all_loftee_flags))) self.assertFalse(hl.eval(get_expr_for_variant_loftee_flag_flag(self.some_loftee_flags))) def test_genes_with_loftee_flag_flag(self): self.assertSetEqual( hl.eval(get_expr_for_genes_with_loftee_flag_flag(self.all_loftee_flags)), set(["foo", "bar", "baz"]) ) self.assertSetEqual( hl.eval(get_expr_for_genes_with_loftee_flag_flag(self.some_loftee_flags)), set(["bar", "baz"]) ) if __name__ == "__main__": unittest.main()

46.384615

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4,221

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0.105351

0.12291

0.908027

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0.790552

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119

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0.213333

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0

0.146667

0

null

0

1

0

null

0

6

2332081121773228b60742f99365108ae2a26db4

273

py

Python

Chapter_9/backAndRename.py

TravisLeeWolf/ATBS

9075eabaa0a788f58435eff9e0df488806a0770b

[ "Apache-2.0" ]

null

Chapter_9/backAndRename.py

TravisLeeWolf/ATBS

9075eabaa0a788f58435eff9e0df488806a0770b

[ "Apache-2.0" ]

null

Chapter_9/backAndRename.py

TravisLeeWolf/ATBS

9075eabaa0a788f58435eff9e0df488806a0770b

[ "Apache-2.0" ]

null

#! python3 # - backAndRename.py - Learning the copytree and move functions of shutil import shutil, os os.chdir('S:\\Documents\\GitHub') # Copies all folder content to new folder shutil.copytree('S:\\Documents\\GitHub\\ATBS\\Chapter_9', 'S:\\Documents\\GitHub\\ATBS_C9')

30.333333

91

0.736264

39

273

5.102564

0.692308

0.150754

0.241206

0.201005

0

0.012346

0.10989

273

8

92

34.125

0.806584

0.443223

0

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1

0

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0

1

0

1

null

0

1

0

1

0

6

235bee18a3ffbdb1005be299c155afbe6b6c73b1

347

py

Python

utils/config.py

Elishanto/HarryBotter

e1977dbade44840288145f08aef60746ac66982b

[ "MIT" ]

3

2016-06-12T19:37:05.000Z

2016-06-12T20:23:33.000Z

utils/config.py

Elishanto/HarryBotter

e1977dbade44840288145f08aef60746ac66982b

[ "MIT" ]

null

utils/config.py

Elishanto/HarryBotter

e1977dbade44840288145f08aef60746ac66982b

[ "MIT" ]

null

import yaml class Config: def __init__(self, config_file='config.yml'): self.config_file = config_file self.config = yaml.load(open(config_file)) def __getitem__(self, item): return self.config[item] def keys(self): return self.config.keys() def items(self): return self.config.items()

20.411765

50

0.636888

45

347

4.644444

0.355556

0.287081

0.229665

0.191388

0

0.25072

347

16

51

21.6875

0.803846

0

0.028818

0

1

0.363636

false

0

0.090909

0.272727

0.818182

0

null

1

0

null

0

1

0

1

0

6

2362ac8054549506bb5d7927a58398f0274a4c52

29

py

Python

testes e exercícios/sistemas/teste01.py

LightSnow17/exercicios-Python

3ac016ce284860f45d71cfb396d33a73ec06c25d

[ "MIT" ]

null

testes e exercícios/sistemas/teste01.py

LightSnow17/exercicios-Python

3ac016ce284860f45d71cfb396d33a73ec06c25d

[ "MIT" ]

null

testes e exercícios/sistemas/teste01.py

LightSnow17/exercicios-Python

3ac016ce284860f45d71cfb396d33a73ec06c25d

[ "MIT" ]

null

print('teste do sistema 01')

14.5

28

0.724138

5

29

4.2

1

0

0.08

0.137931

29

1

29

0.76

0

0.655172

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

2383507e8bb580884ed53df332f90f74bca68ed3

38

py

Python

python/packages/isce3/antenna/__init__.py

isce3-testing/isce3-circleci-poc

ec1dfb6019bcdc7afb7beee7be0fa0ce3f3b87b3

[ "Apache-2.0" ]

null

python/packages/isce3/antenna/__init__.py

isce3-testing/isce3-circleci-poc

ec1dfb6019bcdc7afb7beee7be0fa0ce3f3b87b3

[ "Apache-2.0" ]

1

2021-12-23T00:00:31.000Z

python/packages/isce3/antenna/__init__.py

isce3-testing/isce3-circleci-poc

ec1dfb6019bcdc7afb7beee7be0fa0ce3f3b87b3

[ "Apache-2.0" ]

1

2021-12-02T21:10:11.000Z

from isce3.ext.isce3.antenna import *

19

37

0.789474

6

38

5

0.833333

0

0.058824

0.105263

38

1

38

0.823529

0

1

0

true

0

1

0

1

0

1

0

null

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1

0

null

0

1

0

1

0

1

0

6

cc6bc9a0939dd857f5efb48bd5b22d3556c069b0

134

py

Python

camera.py

Zarchan/IoT_lockbox

2c39e8caaf35f176b15c496fb18c64add3b91eff

[ "MIT" ]

null

camera.py

Zarchan/IoT_lockbox

2c39e8caaf35f176b15c496fb18c64add3b91eff

[ "MIT" ]

null

camera.py

Zarchan/IoT_lockbox

2c39e8caaf35f176b15c496fb18c64add3b91eff

[ "MIT" ]

null

import picamera # https://picamera.readthedocs.io/en/release-1.10/recipes1.html provides recipes # for using the raspberry pi camera

26.8

80

0.798507

20

134

5.35

0.95

0

0.033333

0.104478

134

5

81

26.8

0.858333

0.835821

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

ccc48042b50a24725d97810cdd07772b3ca59030

604

py

Python

sdk/python/pulumi_aws/chime/__init__.py

chivandikwa/pulumi-aws

19c08bf9dcb90544450ffa4eec7bf6751058fde2

[ "ECL-2.0", "Apache-2.0" ]

260

2018-06-18T14:57:00.000Z

2022-03-29T11:41:03.000Z

sdk/python/pulumi_aws/chime/__init__.py

chivandikwa/pulumi-aws

19c08bf9dcb90544450ffa4eec7bf6751058fde2

[ "ECL-2.0", "Apache-2.0" ]

1,154

2018-06-19T20:38:20.000Z

2022-03-31T19:48:16.000Z

sdk/python/pulumi_aws/chime/__init__.py

chivandikwa/pulumi-aws

19c08bf9dcb90544450ffa4eec7bf6751058fde2

[ "ECL-2.0", "Apache-2.0" ]

115

2018-06-28T03:20:27.000Z

2022-03-29T11:41:06.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** from .. import _utilities import typing # Export this package's modules as members: from .voice_connector import * from .voice_connector_group import * from .voice_connector_logging import * from .voice_connector_organization import * from .voice_connector_streaming import * from .voice_connector_termination import * from .voice_connector_termination_credentials import * from ._inputs import * from . import outputs

35.529412

87

0.786424

83

604

5.53012

0.566265

0.174292

0.27451

0.313725

0.152505

0

0.001923

0.139073

604

16

88

37.75

0.880769

0.362583

0

1

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

4e3648723131b1401c5d3d2978c53737452d7cb3

89

py

Python

gensim/untitled.py

Abas-Khan/thesis

b733bd4382371203cc4992571890619a2e314047

[ "MIT" ]

null

gensim/untitled.py

Abas-Khan/thesis

b733bd4382371203cc4992571890619a2e314047

[ "MIT" ]

null

gensim/untitled.py

Abas-Khan/thesis

b733bd4382371203cc4992571890619a2e314047

[ "MIT" ]

null

from gensim.models.word2vec_inner import train_batch_sg, train_batch_cbow, FAST_VERSION

29.666667

87

0.876404

14

89

5.142857

0.857143

0.277778

0

0.012195

0.078652

89

2

88

44.5

0.865854

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

9db217e56d9eca5152affca1ee18a4f7deba1e35

2,730

py

Python

unit/api/supervised/test_supervised_api.py

ONSdigital/dp-fastText

f21949e6c499e08b24423dfe75600bde96b055be

[ "MIT" ]

null

unit/api/supervised/test_supervised_api.py

ONSdigital/dp-fastText

f21949e6c499e08b24423dfe75600bde96b055be

[ "MIT" ]

null

unit/api/supervised/test_supervised_api.py

ONSdigital/dp-fastText

f21949e6c499e08b24423dfe75600bde96b055be

[ "MIT" ]

2

2021-04-11T08:01:20.000Z

2021-12-20T12:52:16.000Z

""" Tests all routes on the /supervised route """ from json import dumps from unit.utils.test_app import FastTextTestApp class TestSupervisedApi(FastTextTestApp): def test_get_sentence_vector(self): """ Tests the /supervised/sentence/vector API :return: """ # Set request params data = { "query": "rpi" } # Set the target target = '/supervised/vector' # Assert 200 response request, response = self.post(target, 200, data=dumps(data)) # Check if response JSON is valid self.assertTrue(hasattr(response, 'json'), "response should contain JSON") json = response.json self.assertIsInstance(json, dict, "JSON should be instanceof dict") expected_keys = ["query", "vector"] for key in expected_keys: self.assertIn(key, json, "JSON should contain key '{0}'".format(key)) self.assertIsNotNone(json.get(key), "value for key '{0}' should not be None") def test_get_sentence_vector_bad_request(self): """ Tests the /supervised/sentence/vector API returns a 400 for an invalid request :return: """ # Set empty request params data = {} # Set the target target = '/supervised/vector' # Assert 200 response request, response = self.post(target, 400, data=dumps(data)) def test_predict(self): """ Tests the /supervised/sentence/vector API :return: """ # Set request params data = { "query": "rpi", "num_labels": 5, "threshold": 0.0 } # Set the target target = '/supervised/predict' # Assert 200 response request, response = self.post(target, 200, data=dumps(data)) # Check if response JSON is valid self.assertTrue(hasattr(response, 'json'), "response should contain JSON") json = response.json self.assertIsInstance(json, dict, "JSON should be instanceof dict") expected_keys = ["labels", "probabilities"] for key in expected_keys: self.assertIn(key, json, "JSON should contain key '{0}'".format(key)) self.assertIsNotNone(json.get(key), "value for key '{0}' should not be None") def test_predict_bad_request(self): """ Tests the /supervised/sentence/vector API returns a 400 for an invalid request :return: """ # Set empty request params data = {} # Set the target target = '/supervised/predict' # Assert 200 response request, response = self.post(target, 400, data=dumps(data))

29.354839

89

0.589744

307

2,730

5.185668

0.237785

0.052764

0.030151

0.055276

0.872487

0.846734

0

0.019525

0.305861

2,730

92

90

29.673913

0.82058

0.224176

0

0.615385

0

0.201216

0

0.205128

1

0.102564

false

0

0.051282

0

0.179487

0

null

0

1

0

null

0

6

9dfb9a3f304731c4c7222dd34cf09486ba442f27

27

py

Python

covalent_api/__init__.py

pradeeptadas/uniswap-v3-project

8f938dc5602fdb6e58b2cf42393a01994f48682d

[ "MIT" ]

null

covalent_api/__init__.py

pradeeptadas/uniswap-v3-project

8f938dc5602fdb6e58b2cf42393a01994f48682d

[ "MIT" ]

null

covalent_api/__init__.py

pradeeptadas/uniswap-v3-project

8f938dc5602fdb6e58b2cf42393a01994f48682d

[ "MIT" ]

null

from .covalent_api import *

27

0.814815

4

27

5.25

1

0

0.111111

27

1

27

0.875

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

d179c9c36ec554d566ae39407fcdfe54c05cb00c

123

py

Python

src/routes/setup.py

BlackSugarMilkyTea/tasks-list

51b2fa642de04d202b591d767815679e74b35a21

[ "MIT" ]

null

src/routes/setup.py

BlackSugarMilkyTea/tasks-list

51b2fa642de04d202b591d767815679e74b35a21

[ "MIT" ]

null

src/routes/setup.py

BlackSugarMilkyTea/tasks-list

51b2fa642de04d202b591d767815679e74b35a21

[ "MIT" ]

null

from app import app from .conf import tasks_api from . import tasks # initialize routes of tasks tasks_api.init_app(app)

20.5

49

0.788618

21

123

4.47619

0.47619

0.234043

0

0.162602

123

5

50

24.6

0.912621

0.211382

0

1

0

true

0

0.75

0

0.75

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

d17f850450525d03b5929ae15016e3ebef12d92f

15,617

py

Python

env/lib/python3.6/site-packages/torch/nn/_functions/thnn/upsampling.py

bopopescu/smart_contracts7

40a487cb3843e86ab5e4cb50b1aafa2095f648cd

[ "Apache-2.0" ]

null

env/lib/python3.6/site-packages/torch/nn/_functions/thnn/upsampling.py

bopopescu/smart_contracts7

40a487cb3843e86ab5e4cb50b1aafa2095f648cd

[ "Apache-2.0" ]

null

env/lib/python3.6/site-packages/torch/nn/_functions/thnn/upsampling.py

bopopescu/smart_contracts7

40a487cb3843e86ab5e4cb50b1aafa2095f648cd

[ "Apache-2.0" ]

1

2020-07-24T17:53:25.000Z

from numbers import Integral import torch from torch.autograd.function import Function from torch._thnn import type2backend from . import _all_functions from ...modules.utils import _single, _pair, _triple import warnings def _check_size_scale_factor(size, scale_factor): if size is None and scale_factor is None: raise ValueError('either size or scale_factor should be defined') if scale_factor is not None and not isinstance(scale_factor, (Integral, tuple)): raise ValueError('scale_factor must be of integer type or a tuple of integer types') def _check_linear_scale_factor(scale_factor, dim=2): if dim == 1: scale_factor = _single(scale_factor) elif dim == 2: scale_factor = _pair(scale_factor) elif dim == 3: scale_factor = _triple(scale_factor) else: raise ValueError("dim has to be 1, 2 or 3") try: assert len(scale_factor) == 1 or len(scale_factor) == 2 or len(scale_factor) == 3 assert all(isinstance(s, Integral) and s >= 1 for s in scale_factor) except AssertionError as e: raise ValueError('scale_factor must be a non-negative integer, ' 'or a tuple of non-negative integers for linear, bilinear and trilinear upsampling, but got: ' '{}'.format(scale_factor)) return scale_factor class UpsamplingNearest1d(Function): @staticmethod def forward(ctx, input, size=None, scale_factor=None): assert input.dim() == 3 _check_size_scale_factor(size, scale_factor) ctx.size = size ctx.scale_factor = scale_factor if ctx.scale_factor is not None and not isinstance(ctx.scale_factor, Integral): raise ValueError('scale_factor must be a single Integer value for nearest neighbor sampling') if ctx.scale_factor is None: if (ctx.size[0] % input.size(2) != 0): raise RuntimeError("output size specified in UpsamplingNearest " "({}) has to be divisible by the input size, but got: " "{}".format('x'.join(map(str, ctx.size)), 'x'.join(map(str, input.size())))) ctx.scale_factor = ctx.size[0] // input.size(2) output = input.new() backend = type2backend[type(input)] ctx.save_for_backward(input) backend.TemporalUpSamplingNearest_updateOutput( backend.library_state, input, output, ctx.scale_factor ) return output @staticmethod def backward(ctx, grad_output): input, = ctx.saved_variables grad_input = UpsamplingNearest1dBackward.apply(input, grad_output, ctx.scale_factor) return grad_input, None, None class UpsamplingNearest1dBackward(Function): @staticmethod def forward(ctx, input, grad_output, scale_factor): assert grad_output.dim() == 3 ctx.scale_factor = scale_factor grad_input = grad_output.new() backend = type2backend[type(input)] backend.TemporalUpSamplingNearest_updateGradInput( backend.library_state, input, grad_output, grad_input, ctx.scale_factor ) return grad_input @staticmethod def backward(ctx, ggI): gI = None ggO = UpsamplingNearest1d.apply(ggI, None, ctx.scale_factor) return gI, ggO, None class UpsamplingLinear1d(Function): @staticmethod def forward(ctx, input, size=None, scale_factor=None): assert input.dim() == 3 ctx.size = size ctx.scale_factor = scale_factor if ctx.scale_factor is not None: ctx.scale_factor = _check_linear_scale_factor(ctx.scale_factor, dim=1) if ctx.scale_factor is not None: ctx.output_size = ( input.size(2) * ctx.scale_factor[0], ) else: ctx.output_size = ctx.size ctx.input_size = input.size() output = input.new() backend = type2backend[type(input)] backend.TemporalUpSamplingLinear_updateOutput( backend.library_state, input, output, ctx.output_size[0] ) return output @staticmethod def backward(ctx, grad_output): grad_input = UpsamplingLinear1dBackward.apply(grad_output, ctx.input_size, ctx.output_size) return grad_input, None, None class UpsamplingLinear1dBackward(Function): @staticmethod def forward(ctx, grad_output, input_size, output_size): assert grad_output.dim() == 3 ctx.input_size = input_size ctx.output_size = output_size grad_output = grad_output.contiguous() grad_input = grad_output.new() backend = type2backend[type(grad_output)] backend.TemporalUpSamplingLinear_updateGradInput( backend.library_state, grad_output, grad_input, ctx.input_size[0], ctx.input_size[1], ctx.input_size[2], ctx.output_size[0], ) return grad_input @staticmethod def backward(ctx, ggI): ggO = UpsamplingLinear1d.apply(ggI, ctx.output_size, None) return ggO, None, None class UpsamplingNearest2d(Function): @staticmethod def symbolic(g, input, size=None, scale_factor=None): if scale_factor is None: scale_factor = 1.0 if size is not None and set(size) != set([None]): warnings.warn("ONNX export failed on UpsamplingNearest2d because size is not supported") return g.op("ResizeNearest", input, width_scale_f=scale_factor, height_scale_f=scale_factor) @staticmethod def forward(ctx, input, size=None, scale_factor=None): assert input.dim() == 4 _check_size_scale_factor(size, scale_factor) ctx.size = size ctx.scale_factor = scale_factor if ctx.scale_factor is not None and not isinstance(ctx.scale_factor, Integral): raise ValueError('scale_factor must be a single Integer value for nearest neighbor sampling') if ctx.scale_factor is None: if (ctx.size[0] % input.size(2) != 0 or ctx.size[1] % input.size(3) != 0): raise RuntimeError("output size specified in UpsamplingNearest " "({}) has to be divisible by the input size, but got: " "{}".format('x'.join(map(str, ctx.size)), 'x'.join(map(str, input.size())))) ctx.scale_factor = ctx.size[0] // input.size(2) if ctx.scale_factor != ctx.size[1] // input.size(3): raise RuntimeError("input aspect ratio doesn't match the " "output ratio") output = input.new() backend = type2backend[type(input)] ctx.save_for_backward(input) backend.SpatialUpSamplingNearest_updateOutput( backend.library_state, input, output, ctx.scale_factor ) return output @staticmethod def backward(ctx, grad_output): input, = ctx.saved_variables grad_input = UpsamplingNearest2dBackward.apply(input, grad_output, ctx.scale_factor) return grad_input, None, None class UpsamplingNearest2dBackward(Function): @staticmethod def forward(ctx, input, grad_output, scale_factor): assert grad_output.dim() == 4 ctx.scale_factor = scale_factor grad_input = grad_output.new() backend = type2backend[type(input)] backend.SpatialUpSamplingNearest_updateGradInput( backend.library_state, input, grad_output, grad_input, ctx.scale_factor ) return grad_input @staticmethod def backward(ctx, ggI): gI = None ggO = UpsamplingNearest2d.apply(ggI, None, ctx.scale_factor) return gI, ggO, None class UpsamplingBilinear2d(Function): @staticmethod def forward(ctx, input, size=None, scale_factor=None): assert input.dim() == 4 ctx.size = size ctx.scale_factor = scale_factor if ctx.scale_factor is not None: ctx.scale_factor = _check_linear_scale_factor(ctx.scale_factor, dim=2) if ctx.scale_factor is not None: ctx.output_size = ( input.size(2) * ctx.scale_factor[0], input.size(3) * ctx.scale_factor[1], ) else: ctx.output_size = ctx.size ctx.input_size = input.size() output = input.new() backend = type2backend[type(input)] backend.SpatialUpSamplingBilinear_updateOutput( backend.library_state, input, output, ctx.output_size[0], ctx.output_size[1], ) return output @staticmethod def backward(ctx, grad_output): grad_input = UpsamplingBilinear2dBackward.apply(grad_output, ctx.input_size, ctx.output_size) return grad_input, None, None class UpsamplingBilinear2dBackward(Function): @staticmethod def forward(ctx, grad_output, input_size, output_size): assert grad_output.dim() == 4 ctx.input_size = input_size ctx.output_size = output_size grad_output = grad_output.contiguous() grad_input = grad_output.new() backend = type2backend[type(grad_output)] backend.SpatialUpSamplingBilinear_updateGradInput( backend.library_state, grad_output, grad_input, ctx.input_size[0], ctx.input_size[1], ctx.input_size[2], ctx.input_size[3], ctx.output_size[0], ctx.output_size[1], ) return grad_input @staticmethod def backward(ctx, ggI): ggO = UpsamplingBilinear2d.apply(ggI, ctx.output_size, None) return ggO, None, None class UpsamplingNearest3d(Function): @staticmethod def forward(ctx, input, size=None, scale_factor=None): assert input.dim() == 5 ctx.size = size ctx.scale_factor = scale_factor if ctx.scale_factor is not None and not isinstance(ctx.scale_factor, Integral): raise ValueError('scale_factor must be a single Integer value for nearest neighbor sampling') if ctx.scale_factor is None: if (ctx.size[0] % input.size(2) != 0 or ctx.size[1] % input.size(3) != 0 or ctx.size[2] % input.size(4) != 0): raise RuntimeError("output size specified in UpSamplingNearest " "({}) has to be divisible by the input size, but got: " "{}".format('x'.join(map(str, ctx.size)), 'x'.join(map(str, input.size())))) ctx.scale_factor = ctx.size[0] // input.size(2) if (ctx.scale_factor != ctx.size[1] // input.size(3) or ctx.scale_factor != ctx.size[2] // input.size(4)): raise RuntimeError("input aspect ratio doesn't match the " "output ratio") output = input.new() backend = type2backend[type(input)] ctx.save_for_backward(input) backend.VolumetricUpSamplingNearest_updateOutput(backend.library_state, input, output, ctx.scale_factor) return output @staticmethod def backward(ctx, grad_output): input, = ctx.saved_variables grad_input = UpsamplingNearest3dBackward.apply(input, grad_output, ctx.scale_factor) return grad_input, None, None class UpsamplingNearest3dBackward(Function): @staticmethod def forward(ctx, input, grad_output, scale_factor): assert grad_output.dim() == 5 ctx.scale_factor = scale_factor grad_input = grad_output.new() backend = type2backend[type(input)] backend.VolumetricUpSamplingNearest_updateGradInput(backend.library_state, input, grad_output, grad_input, ctx.scale_factor) return grad_input @staticmethod def backward(ctx, ggI): gI = None ggO = UpsamplingNearest3d.apply(ggI, None, ctx.scale_factor) return gI, ggO, None class UpsamplingTrilinear3d(Function): @staticmethod def forward(ctx, input, size=None, scale_factor=None): assert input.dim() == 5 ctx.size = size ctx.scale_factor = scale_factor if ctx.scale_factor is not None: ctx.scale_factor = _check_linear_scale_factor(ctx.scale_factor, dim=3) if ctx.scale_factor is not None: ctx.output_size = ( input.size(2) * ctx.scale_factor[0], input.size(3) * ctx.scale_factor[1], input.size(4) * ctx.scale_factor[2], ) else: ctx.output_size = ctx.size ctx.input_size = input.size() output = input.new() backend = type2backend[type(input)] backend.VolumetricUpSamplingTrilinear_updateOutput( backend.library_state, input, output, ctx.output_size[0], ctx.output_size[1], ctx.output_size[2] ) return output @staticmethod def backward(ctx, grad_output): grad_input = UpsamplingTrilinear3dBackward.apply(grad_output, ctx.input_size, ctx.output_size) return grad_input, None, None class UpsamplingTrilinear3dBackward(Function): @staticmethod def forward(ctx, grad_output, input_size, output_size): assert grad_output.dim() == 5 ctx.input_size = input_size ctx.output_size = output_size grad_output = grad_output.contiguous() grad_input = grad_output.new() backend = type2backend[type(grad_output)] backend.VolumetricUpSamplingTrilinear_updateGradInput( backend.library_state, grad_output, grad_input, ctx.input_size[0], ctx.input_size[1], ctx.input_size[2], ctx.input_size[3], ctx.input_size[4], ctx.output_size[0], ctx.output_size[1], ctx.output_size[2] ) return grad_input @staticmethod def backward(ctx, ggI): ggO = UpsamplingTrilinear3d.apply(ggI, ctx.output_size, None) return ggO, None, None _all_functions.append(UpsamplingNearest1d) _all_functions.append(UpsamplingNearest1dBackward) _all_functions.append(UpsamplingLinear1d) _all_functions.append(UpsamplingLinear1dBackward) _all_functions.append(UpsamplingNearest2d) _all_functions.append(UpsamplingNearest2dBackward) _all_functions.append(UpsamplingBilinear2d) _all_functions.append(UpsamplingBilinear2dBackward) _all_functions.append(UpsamplingNearest3d) _all_functions.append(UpsamplingNearest3dBackward) _all_functions.append(UpsamplingTrilinear3d) _all_functions.append(UpsamplingTrilinear3dBackward)

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d199323ada9485947bbfa69536ab9ce4873ea68b

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py

Python

hcl_translator/__init__.py

clearcare/cc_hcl_translator

2515356fc75fe6adfa6ac0b1ceb51f588e0ee2a8

[ "Apache-2.0" ]

null

hcl_translator/__init__.py

clearcare/cc_hcl_translator

2515356fc75fe6adfa6ac0b1ceb51f588e0ee2a8

[ "Apache-2.0" ]

1

2018-12-06T15:34:12.000Z

2018-12-06T15:34:13.000Z

hcl_translator/__init__.py

clearcare/cc_hcl_translator

2515356fc75fe6adfa6ac0b1ceb51f588e0ee2a8

[ "Apache-2.0" ]

null

from .dynamodb2 import dynamodb2_translator # NOQA from .dynamodb3 import dynamodb3_translator # NOQA

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0.826923

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6

d1b9b4d92f386b67008f851afd77e2d8d5eef4a8

40

py

Python

shorttext/metrics/embedfuzzy/__init__.py

vishalbelsare/PyShortTextCategorization

4fa46a148a3eeb923885a7d70c789e988554f758

[ "MIT" ]

481

2016-10-07T16:48:40.000Z

2022-03-16T12:44:12.000Z

shorttext/metrics/embedfuzzy/__init__.py

vishalbelsare/PyShortTextCategorization

4fa46a148a3eeb923885a7d70c789e988554f758

[ "MIT" ]

56

2017-02-02T17:50:14.000Z

2021-12-15T05:14:28.000Z

shorttext/metrics/embedfuzzy/__init__.py

vishalbelsare/PyShortTextCategorization

4fa46a148a3eeb923885a7d70c789e988554f758

[ "MIT" ]

70

2017-01-28T15:20:46.000Z

2021-09-30T15:08:41.000Z

from .jaccard import jaccardscore_sents

20

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0.875

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6

d1c647b99897254969f20de4b1a8d08a0ab0ebcf

141

py

Python

gentelella/app/forms.py

horoyoii/admin_dashboard_edgex

9aea5e43eeb3da17d9e9c65c3ed0337fe7694cb8

[ "MIT" ]

2

2020-05-24T20:34:41.000Z

2021-08-28T07:27:45.000Z

dashboard/app/forms.py

horoyoii/graduation_piece

4f907a10636e3862d09e950c6eb5f12e95b1a8e5

[ "MIT" ]

5

2021-03-19T09:14:10.000Z

2021-06-10T19:54:28.000Z

dashboard/app/forms.py

horoyoii/graduation_piece

4f907a10636e3862d09e950c6eb5f12e95b1a8e5

[ "MIT" ]

1

2021-08-28T07:27:48.000Z

from django import forms class DeviceForm(forms.Form): name = forms.CharField(max_length=30) you = forms.CharField(max_length=30)

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1

0

null

0

1

0

6

d1cc1eb43e5bf17d8024714b3dd40552826ea81d

43

py

Python

scripts/Scripts.py

joelcarlson/OpenKE

256b360b7920808911358fd06b33b1b77ae60cb4

[ "MIT" ]

null

scripts/Scripts.py

joelcarlson/OpenKE

256b360b7920808911358fd06b33b1b77ae60cb4

[ "MIT" ]

null

scripts/Scripts.py

joelcarlson/OpenKE

256b360b7920808911358fd06b33b1b77ae60cb4

[ "MIT" ]

null

def convert_ecad_to_required_files(): pass

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null

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null

0

1

0

6

d1ec32c7260d071f6cc9bfd6b5426f1d15f761a9

39

py

Python

datasets/__init__.py

bozliu/E2E-Keyword-Spotting

64fc6fe414370a12a22fdf8ca5c8379d2c60b64e

[ "MIT" ]

2

2021-04-19T06:42:04.000Z

2021-05-05T04:07:12.000Z

datasets/__init__.py

bozliu/E2E-Keyword-Spotting

64fc6fe414370a12a22fdf8ca5c8379d2c60b64e

[ "MIT" ]

null

datasets/__init__.py

bozliu/E2E-Keyword-Spotting

64fc6fe414370a12a22fdf8ca5c8379d2c60b64e

[ "MIT" ]

null

from .speech_commands_dataset import *

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1

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6

060e2635ebab08fe6512eb8f1f4cdba098026d81

109

py

Python

gsfarc/gptool/parameter/templates/ulong64.py

geospatial-services-framework/gsfpyarc

5ef69299fbc0b763ad4c1857ceac3ff087c0dc14

[ "MIT" ]

1

2021-11-06T18:36:28.000Z

gsfarc/gptool/parameter/templates/ulong64.py

geospatial-services-framework/gsfpyarc

5ef69299fbc0b763ad4c1857ceac3ff087c0dc14

[ "MIT" ]

null

gsfarc/gptool/parameter/templates/ulong64.py

geospatial-services-framework/gsfpyarc

5ef69299fbc0b763ad4c1857ceac3ff087c0dc14

[ "MIT" ]

null

""" """ from .basic import BASIC class ULONG64(BASIC): pass def template(): return ULONG64('GPLong')

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1

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6

ae0fe3d826e27a3d2fa9cde0308fc3ce2411d52b

37

py

Python

yo_extensions/alg/__init__.py

okulovsky/yo_ds

9e1fa2e7a1b9746c3982afc152c024169fec45ca

[ "MIT" ]

16

2019-09-26T09:05:42.000Z

2021-02-04T01:39:09.000Z

yo_extensions/alg/__init__.py

okulovsky/yo_ds

9e1fa2e7a1b9746c3982afc152c024169fec45ca

[ "MIT" ]

2

2019-10-23T19:01:23.000Z

2020-06-11T09:08:45.000Z

yo_extensions/alg/__init__.py

okulovsky/yo_ds

9e1fa2e7a1b9746c3982afc152c024169fec45ca

[ "MIT" ]

2

2019-09-26T09:05:50.000Z

2019-10-23T18:46:11.000Z

from yo_fluq_ds._fluq.pandas import *

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null

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1

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1

0

1

0

6

ae4fbf22afde19d6073678ffaa41cf42837e69cf

20

py

Python

MMO/external/__init__.py

laloc2496/cdn_configuration_optimization

58cf2278456d0ef8796570f12f1d00fd68aec686

[ "MIT" ]

41

2020-10-21T01:17:45.000Z

2022-02-07T01:42:44.000Z

MMO/external/__init__.py

laloc2496/cdn_configuration_optimization

58cf2278456d0ef8796570f12f1d00fd68aec686

[ "MIT" ]

2

2020-11-06T19:28:22.000Z

2021-03-11T15:19:45.000Z

MMO/external/__init__.py

laloc2496/cdn_configuration_optimization

58cf2278456d0ef8796570f12f1d00fd68aec686

[ "MIT" ]

9

2020-11-16T05:24:49.000Z

2022-01-21T08:19:17.000Z

from .lhs import lhs

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true

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1

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1

0

6

ae5b9c39157ec21f374799d98144d2c63bff5dc5

36

py

Python

src/pydev_conda/__init__.py

terasakisatoshi/pydev_conda

dc26fed9d329a06151354e692c6d18ac342cf08c

[ "MIT" ]

null

src/pydev_conda/__init__.py

terasakisatoshi/pydev_conda

dc26fed9d329a06151354e692c6d18ac342cf08c

[ "MIT" ]

null

src/pydev_conda/__init__.py

terasakisatoshi/pydev_conda

dc26fed9d329a06151354e692c6d18ac342cf08c

[ "MIT" ]

null

from pydev_conda.greet import greet

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null

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1

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1

0

1

0

6

8828085770cdce55775689dcc45185fa7221941b

77

py

Python

common/trainers/trecqa_trainer.py

karkaroff/castor

881673f3dadb4f757fdfdf5d2ab9031e08512406

[ "Apache-2.0" ]

132

2017-04-02T12:31:55.000Z

2019-03-09T07:53:29.000Z

common/trainers/trecqa_trainer.py

sudipta90/castor

fa2f59535c71a0fb4586afbe543b81ba812c8630

[ "Apache-2.0" ]

111

2017-04-01T23:00:24.000Z

2019-03-10T08:29:20.000Z

common/trainers/trecqa_trainer.py

karkaroff/Castor

881673f3dadb4f757fdfdf5d2ab9031e08512406

[ "Apache-2.0" ]

53

2017-04-06T01:17:18.000Z

2019-02-27T03:10:35.000Z

from .qa_trainer import QATrainer class TRECQATrainer(QATrainer): pass

12.833333

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true

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null

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6

88a711508576854d420d1777216c31f9c52db2cf

46

py

Python

2021/examples-in-class-2021-09-24/helloworld1.py

ati-ozgur/course-python

38237d120043c07230658b56dc3aeb01c3364933

[ "Apache-2.0" ]

1

2021-02-04T16:59:11.000Z

2021/examples-in-class-2021-09-24/helloworld1.py

ati-ozgur/course-python

38237d120043c07230658b56dc3aeb01c3364933

[ "Apache-2.0" ]

null

2021/examples-in-class-2021-09-24/helloworld1.py

ati-ozgur/course-python

38237d120043c07230658b56dc3aeb01c3364933

[ "Apache-2.0" ]

1

2019-10-30T14:37:48.000Z

print("Hello, I am Atilla, I am from Turkey")

23

45

0.695652

9

46

3.555556

0.777778

0.1875

0

0.173913

46

1

46

0.842105

0

0.782609

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

31f24458b47ca5609afdc3b297db01ad63d221bb

86

py

Python

customers/managers.py

moshthepitt/probsc

9b8cab206bb1c41238e36bd77f5e0573df4d8e2d

[ "MIT" ]

null

customers/managers.py

moshthepitt/probsc

9b8cab206bb1c41238e36bd77f5e0573df4d8e2d

[ "MIT" ]

null

customers/managers.py

moshthepitt/probsc

9b8cab206bb1c41238e36bd77f5e0573df4d8e2d

[ "MIT" ]

null

from core.managers import CoreManager class CustomerManager(CoreManager): pass

12.285714

37

0.790698

9

86

7.555556

0.888889

0

0.162791

86

6

38

14.333333

0.944444

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

ee07e5dd05abddd6a9c5bd9b0d0870dbb6657f22

148

py

Python

python/lib/davis/measures/__init__.py

flyingleafe/davis

4f90f1095761a062ab4f22781864a035fec568e7

[ "BSD-3-Clause" ]

null

python/lib/davis/measures/__init__.py

flyingleafe/davis

4f90f1095761a062ab4f22781864a035fec568e7

[ "BSD-3-Clause" ]

null

python/lib/davis/measures/__init__.py

flyingleafe/davis

4f90f1095761a062ab4f22781864a035fec568e7

[ "BSD-3-Clause" ]

null

from .jaccard import db_eval_iou # from .t_stability import db_eval_t_stab // tstab does not work from .f_boundary import db_eval_boundary

24.666667

66

0.777027

25

148

4.24

0.6

0.226415

0.339623

0

0.182432

148

5

67

29.6

0.876033

0.432432

0

1

0

true

0

1

0

1

0

1

0

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1

0

1

0

null

0

1

0

1

0

6

ee4941bf4c2024ed4b6ea863c72d581555d2c36d

174

py

Python

helpers/custom_exceptions.py

LavinaVRovine/hazard

e0408374dc0b76f8b9a0107f5f12cca2d4c033ef

[ "MIT" ]

1

2020-10-05T14:19:35.000Z

helpers/custom_exceptions.py

LavinaVRovine/hazard

e0408374dc0b76f8b9a0107f5f12cca2d4c033ef

[ "MIT" ]

null

helpers/custom_exceptions.py

LavinaVRovine/hazard

e0408374dc0b76f8b9a0107f5f12cca2d4c033ef

[ "MIT" ]

null

class TeamNotFound(Exception): """Raise when the team is not found in the database""" class NoMatchData(Exception): """Raise when data for match cant be created"""

24.857143

58

0.712644

24

174

5.166667

0.791667

0.225806

0.290323

0

0.183908

174

6

59

29

0.873239

0.517241

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

ee5fb8bfdb0575fadea6249e37d89844ced705d6

136

py

Python

schemas/user_schema.py

JhonArroyo/fastapi-python

b6ddb250cd9d34534a576bb8948ce9f63458a73f

[ "MIT" ]

null

schemas/user_schema.py

JhonArroyo/fastapi-python

b6ddb250cd9d34534a576bb8948ce9f63458a73f

[ "MIT" ]

null

schemas/user_schema.py

JhonArroyo/fastapi-python

b6ddb250cd9d34534a576bb8948ce9f63458a73f

[ "MIT" ]

null

from pydantic import BaseModel from typing import Optional class User(BaseModel): id: Optional[str] name: str password: str

19.428571

30

0.735294

18

136

5.555556

0.666667

0

0.205882

136

7

31

19.428571

0.925926

0

1

0

true

0.166667

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

ee6e5b387a0cf283520091b6af9d7a1acc1e012a

42

py

Python

tests/roots/test-ext-autodoc/target/imported_members.py

samdoran/sphinx

4c91c038b220d07bbdfe0c1680af42fe897f342c

[ "BSD-2-Clause" ]

4,973

2015-01-03T15:44:00.000Z

2022-03-31T03:11:51.000Z

tests/roots/test-ext-autodoc/target/imported_members.py

samdoran/sphinx

4c91c038b220d07bbdfe0c1680af42fe897f342c

[ "BSD-2-Clause" ]

7,850

2015-01-02T08:09:25.000Z

2022-03-31T18:57:40.000Z

tests/roots/test-ext-autodoc/target/imported_members.py

samdoran/sphinx

4c91c038b220d07bbdfe0c1680af42fe897f342c

[ "BSD-2-Clause" ]

2,179

2015-01-03T15:26:53.000Z

2022-03-31T12:22:44.000Z

from .partialfunction import func2, func3

21

41

0.833333

5

42

7

1

0

0.054054

0.119048

42

1

42

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

ee7b839672690424818093c14803c5cad1d05102

173

py

Python

diofant/printing/pretty/__init__.py

rajkk1/diofant

6b361334569e4ec2e8c7d30dc324387a4ad417c2

[ "BSD-3-Clause" ]

57

2016-09-13T23:16:26.000Z

2022-03-29T06:45:51.000Z

diofant/printing/pretty/__init__.py

rajkk1/diofant

6b361334569e4ec2e8c7d30dc324387a4ad417c2

[ "BSD-3-Clause" ]

402

2016-05-11T11:11:47.000Z

2022-03-31T14:27:02.000Z

diofant/printing/pretty/__init__.py

rajkk1/diofant

6b361334569e4ec2e8c7d30dc324387a4ad417c2

[ "BSD-3-Clause" ]

20

2016-05-11T08:17:37.000Z

2021-09-10T09:15:51.000Z

"""ASCII-ART 2D pretty-printer""" from .pretty import pprint, pprint_use_unicode, pretty, pretty_print __all__ = 'pprint', 'pprint_use_unicode', 'pretty', 'pretty_print'

24.714286

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0.751445

23

173

5.217391

0.521739

0.2

0.25

0.366667

0.65

0

0.006494

0.109827

173

6

69

28.833333

0.772727

0.156069

0

0.3

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false

0

0.5

0

0.5

1

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null

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1

0

1

0

null

0

1

0

1

0

6

c990488bd2d79a3cb156178cf80764f9537d9b83

23

py

Python

engine/physics/__init__.py

TWoolhouse/Libraries

26079ed387cb800cb97f20980720ae094008c7bf

[ "MIT" ]

1

2020-10-11T15:34:56.000Z

engine/physics/__init__.py

TWoolhouse/Libraries

26079ed387cb800cb97f20980720ae094008c7bf

[ "MIT" ]

null

engine/physics/__init__.py

TWoolhouse/Libraries

26079ed387cb800cb97f20980720ae094008c7bf

[ "MIT" ]

null

from . import collider

11.5

22

0.782609

3

23

6

1

0

0.173913

23

1

23

0.947368

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

c9b476d5ddaea068ff57cfa0cfa5517133c802a2

133

py

Python

dblinea/__init__.py

linea-it/lineadb

42e782e73691c5378a7a1182c70e2134b4409552

[ "MIT" ]

null

dblinea/__init__.py

linea-it/lineadb

42e782e73691c5378a7a1182c70e2134b4409552

[ "MIT" ]

9

2022-02-07T21:59:08.000Z

2022-03-18T18:15:45.000Z

dblinea/__init__.py

linea-it/lineadb

42e782e73691c5378a7a1182c70e2134b4409552

[ "MIT" ]

null

from dblinea.dblinea import DBBase from dblinea.db_postgresql import DBPostgresql from dblinea.scienceserver import ScienceServerApi

33.25

50

0.887218

16

133

7.3125

0.5625

0.282051

0

0.090226

133

3

51

44.333333

0.966942

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

c9f33fb35eeafba4862326eaa1c7906d9e9a1c0a

119

py

Python

utils/__init__.py

SeaWakeYT/SeaWake-Bot-FIXED-

995d8a9ad9a045d42aca8fec78e04946f442db32

[ "MIT" ]

null

utils/__init__.py

SeaWakeYT/SeaWake-Bot-FIXED-

995d8a9ad9a045d42aca8fec78e04946f442db32

[ "MIT" ]

null

utils/__init__.py

SeaWakeYT/SeaWake-Bot-FIXED-

995d8a9ad9a045d42aca8fec78e04946f442db32

[ "MIT" ]

null

from .buttons import * from .extra import * #this imports everything from buttons as it's the top of the buttons class.

39.666667

75

0.773109

20

119

4.6

0.7

0.23913

0

0.168067

119

3

75

39.666667

0.929293

0.621849

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

a01ea5bb2f14b4882d96813aaf5c358c76d09c2c

49

py

Python

DutchDraw/__init__.py

joris-pries/DutchDraw

7bc81fc5fb456a27bc977dc201c75c9caa2c6996

[ "MIT" ]

null

DutchDraw/__init__.py

joris-pries/DutchDraw

7bc81fc5fb456a27bc977dc201c75c9caa2c6996

[ "MIT" ]

null

DutchDraw/__init__.py

joris-pries/DutchDraw

7bc81fc5fb456a27bc977dc201c75c9caa2c6996

[ "MIT" ]

null

from . import DutchDraw from .DutchDraw import *

16.333333

24

0.77551

6

49

6.333333

0.5

0

0.163265

49

2

25

24.5

0.926829

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

4e590ef1a5d6f87821e1561b8cb6bba36cc0f6ea

43

py

Python

src/ctc/db/schemas/block_gas_stats/__init__.py

fei-protocol/checkthechain

ec838f3d0d44af228f45394d9ba8d8eb7f677520

[ "MIT" ]

94

2022-02-15T19:34:49.000Z

2022-03-26T19:26:22.000Z

src/ctc/db/schemas/block_gas_stats/__init__.py

fei-protocol/checkthechain

ec838f3d0d44af228f45394d9ba8d8eb7f677520

[ "MIT" ]

7

2022-03-03T02:58:47.000Z

2022-03-11T18:41:05.000Z

src/ctc/db/schemas/block_gas_stats/__init__.py

fei-protocol/checkthechain

ec838f3d0d44af228f45394d9ba8d8eb7f677520

[ "MIT" ]

7

2022-02-15T17:53:07.000Z

2022-03-17T19:14:17.000Z

from .block_gas_stats_schema_defs import *

21.5

42

0.860465

7

43

4.714286

1

0

0.093023

43

1

43

0.846154

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

4e6b2a614b1c3d092e97ac12ea0c1f8d1fb2f3ce

1,766

py

Python

test_flood.py

TK594/IA-flood-risk-project

17902a255d1e76b4085760d5ab655aa5dce762dc

[ "MIT" ]

null

test_flood.py

TK594/IA-flood-risk-project

17902a255d1e76b4085760d5ab655aa5dce762dc

[ "MIT" ]

null

test_flood.py

TK594/IA-flood-risk-project

17902a255d1e76b4085760d5ab655aa5dce762dc

[ "MIT" ]

null

from floodsystem.station import * from floodsystem.station import MonitoringStation from floodsystem.flood import stations_level_over_threshold, stations_highest_rel_level def test_stations_level_over_thershold(): station_A = MonitoringStation('ID A', 'Measurement ID A', 'Name A', (0,1), (1,10), 'river 1', 'Town 1') station_B = MonitoringStation('ID B', 'Measurement ID B', 'Name B', (5,5), (1,3), 'river 2', 'Town 2') station_C = MonitoringStation('ID C', 'Measurement ID C', 'Name C', (2,5), (4,7), 'river 3', 'Town 3') station_D = MonitoringStation('ID D', 'Measurement ID D', 'Name D', (4,9), (2,8), 'river 4', 'Town 4') station_A.latest_level = 5.8 station_B.latest_level = 1.7 station_C.latest_level = 6 station_D.latest_level = 6.7 list = stations_level_over_threshold((station_A, station_B, station_C, station_D), 0.4) A, B, C, D = 8/15, 0.35, 2/3, 47/60 assert list == [(station_D, D), (station_C, C), (station_A, A)] def test_stations_highest_rel_level(): station_A = MonitoringStation('ID A', 'Measurement ID A', 'Name A', (0,1), (1,10), 'river 1', 'Town 1') station_B = MonitoringStation('ID B', 'Measurement ID B', 'Name B', (5,5), (1,3), 'river 2', 'Town 2') station_C = MonitoringStation('ID C', 'Measurement ID C', 'Name C', (2,5), (4,7), 'river 3', 'Town 3') station_D = MonitoringStation('ID D', 'Measurement ID D', 'Name D', (4,9), (2,8), 'river 4', 'Town 4') station_A.latest_level = 5.8 station_B.latest_level = 1.7 station_C.latest_level = 6 station_D.latest_level = 6.7 list = stations_highest_rel_level((station_A, station_B, station_C, station_D), 2) A, B, C, D = 8/15, 0.35, 2/3, 47/60 assert list == [(station_D, D), (station_C, C)]

50.457143

107

0.652888

295

1,766

3.715254

0.142373

0.138686

0.043796

0.062956

0.801095

0.766423

0.708029

0

0.062629

0.177237

1,766

35

108

50.457143

0.691672

0

0.666667

0

0.17657

0

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1

0.074074

false

0

0.111111

0

0.185185

0

null

0

1

0

null

0

6

14c8b337b1a866be138ce87f8287153a7f5720b0

13,092

py

Python

Scikit_Extensions_For_Stocks/time_series_scaler/_TimeSeriesScaler.py

uNRealCoder/Scikit-Extensions-Stock

0968f2319373b91218589119cb06c561aa315e58

[ "MIT" ]

null

Scikit_Extensions_For_Stocks/time_series_scaler/_TimeSeriesScaler.py

uNRealCoder/Scikit-Extensions-Stock

0968f2319373b91218589119cb06c561aa315e58

[ "MIT" ]

null

Scikit_Extensions_For_Stocks/time_series_scaler/_TimeSeriesScaler.py

uNRealCoder/Scikit-Extensions-Stock

0968f2319373b91218589119cb06c561aa315e58

[ "MIT" ]

null

import numpy from sklearn.base import BaseEstimator,TransformerMixin from copy import deepcopy class LinearAutoRegressiveScaler(TransformerMixin,BaseEstimator): """ Transformers Time Series/Linear Data by calculating AR(1) and dividing by max delta. """ def __init__(self): self._initialValue = None self.MaxDiff = None def __reset_state(self): self._initialValue = None self.MaxDiff = None def __isInitialized(self): if(self._initialValue is None or self.MaxDiff is None): return False else: return True def __partial_fit(self,X,y=None,n=1,prepend=True,forceReshape=False,**fit_params): """Fit the Linear Time Series Transformer on X. Expects a 1D numerical Array. y will be ignored. Args: X {1D nd.array like} of shape (n,): Data for transformer to fit y Will be ignored. n (int, optional): NOT SUPPORTED . nth difference. Defaults to 1. prepend (bool, optional): Prepend a 0 to data. Defaults to False. forceReshape (bool, optional): Tries to force the shape of the array to 1D. Defaults to False. """ assert n==1, "Not Supported" X = numpy.array(deepcopy(X)).astype(float) #Paranoia if(forceReshape==True): X = numpy.ravel(X) #OMG ravel has flattened them. Will someone stop the match already?! assert X.ndim == 1, "Array should be 1D" if(self.__isInitialized()==False): self._initialValue = deepcopy(X[0]) DiffArray = numpy.diff(X, int(n)) if(prepend==True): DiffArray = numpy.insert(DiffArray,[0],0,axis=None) if(self.__isInitialized()==False): self.MaxDiff = numpy.max(numpy.abs(DiffArray)) if(int(self.MaxDiff)==0): #0 protection return DiffArray DiffArray = DiffArray/self.MaxDiff return DiffArray def fit(self,X,y=None, **fit_params): """Fit the Linear Time Series Transformer on X. Expects a 1D numerical Array. y will be ignored. Args: X {1D nd.array like} of shape (n,): Data for transformer to fit y Will be ignored. n (int, optional): NOT SUPPORTED . nth difference. Defaults to 1. prepend (bool, optional): Prepend a 0 to data. Defaults to False. forceReshape (bool, optional): Tries to force the shape of the array to 1D. Defaults to False. """ self.__reset_state() self.__partial_fit(X, y, **fit_params) pass def transform(self,X,prepend = True,forceReshape=False): """ Transform 1D array X. Args: X {1D nd.array like} of shape (n,): Data for transformer to transform forceReshape (bool, optional): [description]. Defaults to False. """ return self.__partial_fit(X,y=None,n=1,prepend=prepend, forceReshape=forceReshape) def fit_transform(self, X, y=None,prepend = True,forceReshape=False, **fit_params): if y is None: self.fit(X, **fit_params) return self.transform(X,prepend=prepend,forceReshape=forceReshape) else: self.fit(X, y, **fit_params) return self.transform(X,prepend=prepend,forceReshape=forceReshape) def inverse_transform(self,X,prepend=False): arr = deepcopy(X) arr = arr*self.MaxDiff if(prepend==False): arr = numpy.insert(arr,[0],0,axis=None) else: arr[0] = self._initialValue arr = numpy.cumsum(arr) return arr class Linear2DAutoRegressiveScaler(TransformerMixin,BaseEstimator): def __init__(self): self.Transformers = [] def fit(self, X, y=None,axis=-1,**fit_params): """Fit the Linear Auto Regressive Transformer on X. Expects a 1D numerical Array. y will be ignored. Args: X {1D nd.array like} of shape (n,): Data for transformer to fit y Will be ignored. n (int, optional): NOT SUPPORTED . nth difference. Defaults to 1. prepend (bool, optional): Prepend a 0 to data. Defaults to False. forceReshape (bool, optional): Tries to force the shape of the array to 1D. Defaults to False. """ X = numpy.array(deepcopy(X)) for val in numpy.rollaxis(X,axis): LAR = LinearAutoRegressiveScaler() LAR.fit(val) self.Transformers.append(LAR) def transform(self,X,y=None,axis=-1,prepend = True,forceReshape=False): """[summary] Args: X ([type]): [description] y ([type], optional): [description]. Defaults to None. """ X = numpy.array(deepcopy(X)) Res = [] assert len(self.Transformers) == X.shape[axis] n = X.shape[axis] for ind,val in zip(range(0,n),numpy.rollaxis(X,axis)): Res.append(self.Transformers[ind].transform(val,prepend=prepend,forceReshape=forceReshape)) return numpy.array(Res) def fit_transform(self, X, y=None,axis=-1,prepend = True,forceReshape=False, **fit_params): X = numpy.array(deepcopy(X)) n = X.shape[axis] Res = [] for val in numpy.rollaxis(X,axis): LAR = LinearAutoRegressiveScaler() Res.append(LAR.fit_transform(val,y,prepend=prepend,forceReshape=forceReshape,**fit_params)) self.Transformers.append(LAR) return numpy.array(Res) class Linear1DGainScaler(TransformerMixin,BaseEstimator): """ Transformers Time Series/Linear Data by calculating AR(1) and dividing by previous value. """ def __partial_fit(self,X,y=None,forceReshape=False,**fit_params): """Fit the Linear Time Series Transformer on X. Expects a 1D numerical Array. y will be ignored. Args: X {1D nd.array like} of shape (n,): Data for transformer to fit y Will be ignored. n (int, optional): NOT SUPPORTED . nth difference. Defaults to 1. forceReshape (bool, optional): Tries to force the shape of the array to 1D. Defaults to False. """ X = numpy.array(deepcopy(X)).astype(float) #Paranoia if(forceReshape==True): X = numpy.ravel(X) #OMG ravel has flattened them. Will someone stop the match already?! assert X.ndim == 1, "Array should be 1D" if(self.__isInitialized()==False): self._initialValue = deepcopy(X[0]) DiffArray = numpy.diff(X, 1) DiffArray = numpy.insert(DiffArray,[0],0,axis=None) DiffArray[1:] = numpy.divide(DiffArray[1:],X[:-1]) return DiffArray def inverse_transform(self,X,prepend=True): f = [] X = numpy.array(deepcopy(X)).astype(float) assert X.ndim == 1, "Array should be 1D" if(prepend==False): X = numpy.insert(X,[0],0) assert int(X[0]) == 0, "First element should be zero, pass prepend=True" f.append(self._initialValue) for i,v in zip(range(1,len(X[1:])+1),X[1:]): val = v*f[i-1] val += f[i-1] f.append(val) return numpy.array(deepcopy(f)).astype(float) class Linear2DGainScaler(TransformerMixin,BaseEstimator): def __init__(self): self.Scalers = [] def fit(self, X, y=None,axis=-1,**fit_params): """Fit the Linear Auto Regressive Transformer on X. Expects a 2D numerical Array. y will be ignored. Args: X {1D nd.array like} of shape (n,): Data for transformer to fit y Will be ignored. n (int, optional): NOT SUPPORTED . nth difference. Defaults to 1. prepend (bool, optional): Prepend a 0 to data. Defaults to False. forceReshape (bool, optional): Tries to force the shape of the array to 1D. Defaults to False. """ X = numpy.array(deepcopy(X)) for val in numpy.rollaxis(X,axis): L1DG = Linear1DGainScaler() L1DG.fit(val) self.Transformers.append(L1DG) def transform(self,X,y=None,axis=-1,prepend = True,forceReshape=False): """[summary] Args: X ([type]): [description] y ([type], optional): [description]. Defaults to None. """ X = numpy.array(deepcopy(X)) Res = [] assert len(self.Transformers) == X.shape[axis] n = X.shape[axis] for ind,val in zip(range(0,n),numpy.rollaxis(X,axis)): Res.append(self.Transformers[ind].transform(val,prepend=prepend,forceReshape=forceReshape)) return numpy.array(Res) def fit_transform(self, X, y=None,axis=-1,prepend = True,forceReshape=False, **fit_params): self.fit(X=X,y=y,axis=axis,**fit_params) return self.transform(X=X,y=y,axis=axis,prepend=prepend,forceReshape=forceReshape,**fit_params) class Linear1DLogGainScaler(TransformerMixin,BaseEstimator): def __init__(self): self._initialValue = None def __reset_state(self): self._initialValue = None def __isInitialized(self): if(self._initialValue is None): return False else: return True def __partial_fit(self, X, y=None, forceReshape=False,logbase=10 ,**fit_params): """ """ X = numpy.array(deepcopy(X)).astype(float) #Paranoia if(forceReshape==True): X = numpy.ravel(X) #OMG ravel has flattened them. Will someone stop the match already?! assert X.ndim == 1, "Array should be 1D" self._initialValue = deepcopy(X[0]) Xshift = numpy.roll(X,1) Xshift[0] = Xshift[1] return deepcopy(numpy.log10(X/Xshift)) def inverse_transform(self,X): f = [] X = numpy.array(deepcopy(X)).astype(float) assert X.ndim == 1, "Array should be 1D" return deepcopy(numpy.cumprod(10**X)*self._initialValue) def fit(self,X,y=None, **fit_params): """Fit the Linear Time Series Transformer on X. Expects a 1D numerical Array. y will be ignored. Args: X {1D nd.array like} of shape (n,): Data for transformer to fit y Will be ignored. n (int, optional): NOT SUPPORTED . nth difference. Defaults to 1. forceReshape (bool, optional): Tries to force the shape of the array to 1D. Defaults to False. """ self.__partial_fit(X, y, **fit_params) pass def transform(self,X,**fit_params): """ Transform 1D array X. Args: X {1D nd.array like} of shape (n,): Data for transformer to transform forceReshape (bool, optional): [description]. Defaults to False. """ if(not self.__isInitialized()): raise Exception("Not initialized") return self.__partial_fit(X,y=None, **fit_params) def fit_transform(self, X, y=None, **fit_params): if y is None: self.fit(X, **fit_params) return self.transform(X,**fit_params) else: self.fit(X, y, **fit_params) return self.transform(X,**fit_params) class Linear2DLogGainScaler(TransformerMixin,BaseEstimator): def __init__(self): self.LogScalers = [] def fit(self, X, y=None,axis=-1,**fit_params): """Fit the Linear Auto Regressive Transformer on X. Expects a 2D numerical Array. y will be ignored. Args: X {1D nd.array like} of shape (n,): Data for transformer to fit y Will be ignored. n (int, optional): NOT SUPPORTED . nth difference. Defaults to 1. prepend (bool, optional): Prepend a 0 to data. Defaults to False. forceReshape (bool, optional): Tries to force the shape of the array to 1D. Defaults to False. """ X = numpy.array(deepcopy(X)) for val in numpy.rollaxis(X,axis): L1DG = Linear1DLogGainScaler() L1DG.fit(val) self.LogScalers.append(L1DG) def transform(self,X,y=None,axis=-1): """[summary] Args: X ([type]): [description] y ([type], optional): [description]. Defaults to None. """ X = numpy.array(deepcopy(X)) Res = [] assert len(self.LogScalers) == X.shape[axis] n = X.shape[axis] for ind,val in zip(range(0,n),numpy.rollaxis(X,axis)): Res.append(self.LogScalers[ind].transform(val)) return deepcopy(numpy.array(Res).T) def fit_transform(self, X, y=None,axis=-1,**fit_params): self.fit(X=X,y=y,axis=axis,**fit_params) return self.transform(X=X,y=y,axis=axis,**fit_params) def inverse_transform(self,X,y=None,axis=-1,**fit_params): X = numpy.array(deepcopy(X)) Res = [] assert len(self.LogScalers) == X.shape[axis] n = X.shape[axis] for ind,val in zip(range(0,n),numpy.rollaxis(X,axis)): Res.append(self.LogScalers[ind].inverse_transform(val)) return deepcopy(numpy.array(Res).T)

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14dc685cda7f743f589218c9ae90117f5c9bd950

220

py

Python

plumbum/path/__init__.py

weka-io/plumbum

2d244c02f38498cacfb3519bdebe42e4c5dc72b3

[ "MIT" ]

1

2019-06-12T19:42:55.000Z

src/plumbum/path/__init__.py

ownport/playbook

6d3196ddf68f2c3c3efc4a52e26719c3e5596dca

[ "MIT" ]

null

src/plumbum/path/__init__.py

ownport/playbook

6d3196ddf68f2c3c3efc4a52e26719c3e5596dca

[ "MIT" ]

null

from plumbum.path.local import LocalPath, LocalWorkdir from plumbum.path.remote import RemotePath, RemoteWorkdir from plumbum.path.base import Path, FSUser, RelativePath from plumbum.path.utils import copy, move, delete

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0942d6913393d350496dce103a858261d5a38ac2

76,180

py

Python

System/EnergySystem.py

fnbillimoria/OPEN

b63fb2e7bc5e43cc32034ed5f8b7df715b435461

[ "Apache-2.0" ]

1

2020-05-14T01:56:23.000Z

System/EnergySystem.py

fnbillimoria/OPEN

b63fb2e7bc5e43cc32034ed5f8b7df715b435461

[ "Apache-2.0" ]

null

System/EnergySystem.py

fnbillimoria/OPEN

b63fb2e7bc5e43cc32034ed5f8b7df715b435461

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ OPEN Energy System Module. The EnergySystem Class has two types of methods i) energy management system (EMS) methods which implement algorithms to calculate Asset control references, and ii) simulation methods which call an EMS method to obtain control references for Asset objects, update the state of Asset objects by calling their updatecontrol method and update the state of the Network by calling its power flow method. An EnergySystem has two separate time series, one for the EMS, and the other for simulation. OPEN includes two EMS methods for controllable Asset objects: (i) one for multi-period optimisation with a simple ‘copper plate’ network model, and (ii) one for multi-period optimisation with a linear multi-phase distribution network model which includes voltage and current flow constraints. Open has simulation methods for: (i) open-loop optimisation, where the EMS method is run ahead of operation to obtain controllable Asset references over the EMS time-series; and (ii) for MPC, where the EMS method is implemented with a receding horizon so that the flexible Asset references are updated at each step of the EMS time series. """ #import modules import copy import pandas as pd import pandapower as pp import pandapower.networks as pn import numpy as np import picos as pic import matplotlib.pyplot as plt from System.Network_3ph_pf import Network_3ph import cvxopt __version__ = "1.0.2" class EnergySystem: """ Base Energy Sysem Class Parameters ---------- storage_assets : list of objects Containing details of each storage asset building_assets : list of objects Containsing details of each building asset nondispatch_assets : list of objects Containsing details of each nondispatchable asset network : object Object containing information about the network market : object Object containing information about the market dt_ems : float EMS time interval duration (hours) T_ems : int Number of EMS time intervals dt : float time interval duration (hours) T : int number of time intervals Returns ------- EnergySystem """ def __init__(self, storage_assets, nondispatch_assets, network, market, dt, T, dt_ems, T_ems, building_assets=[]): self.storage_assets = storage_assets self.building_assets = building_assets self.nondispatch_assets = nondispatch_assets self.network = network self.market = market self.dt_ems = dt_ems self.T_ems = T_ems self.dt = dt self.T = T ####################################### ### Open Loop Control Methods ####################################### def EMS_copper_plate(self): """ Energy management system optimization assuming all assets connected to a single node. Parameters ---------- self : EnergySystem object Object containing information on assets, market, network and time resolution. Returns ------- Output : dictionary The following numpy.ndarrays are present depending upon asset mix: P_ES_val : Charge/discharge power for storage assets (kW) P_BLDG_val :Builfing power consumption (kW) P_import_val :Power imported from central grid (kW) P_export_val :Power exported to central grid (kW) P_demand_val :System power demand at energy management time resolution """ #setup and run a basic energy optimisation #(single copper plate network model) ####################################### ### STEP 0: setup variables ####################################### prob = pic.Problem() N_ES = len(self.storage_assets) N_BLDG = len(self.building_assets) N_INDEPENDENT = N_ES + N_BLDG N_nondispatch = len(self.nondispatch_assets) P_demand_actual = np.zeros(self.T) P_demand = np.zeros(self.T_ems) for i in range(N_nondispatch): P_demand_actual += self.nondispatch_assets[i].Pnet #convert P_demand_actual to EMS time series scale for t_ems in range(self.T_ems): t_indexes = (t_ems*self.dt_ems/self.dt\ + np.arange(0,self.dt_ems/self.dt)).astype(int) P_demand[t_ems] = np.mean(P_demand_actual[t_indexes]) ####################################### ### STEP 1: set up decision variables ####################################### #controllable asset input powers P_ctrl_asset = prob.add_variable('P_ctrl_asset',(self.T_ems,\ N_INDEPENDENT),\ vtype='continuous') if N_BLDG > 0: # cooling power P_cooling = prob.add_variable('P_cooling',(self.T_ems,N_BLDG),\ vtype='continuous') # heating power P_heating = prob.add_variable('P_heating',(self.T_ems,N_BLDG),\ vtype='continuous') # internal temperature T_bldg = prob.add_variable('T_bldg',(self.T_ems,N_BLDG),\ vtype='continuous') #(positive) net power imports P_import = prob.add_variable('P_import',(self.T_ems,1),\ vtype='continuous') #(positive) net power exports P_export = prob.add_variable('P_export',(self.T_ems,1),\ vtype='continuous') #(positive) maximum demand dummy variable P_max_demand = prob.add_variable('P_max_demand',1,\ vtype='continuous') ####################################### ### STEP 2: set up constraints ####################################### Asum_np = np.tril(np.ones([self.T_ems,self.T_ems])).astype('double') #lower triangle matrix summing powers Asum = pic.new_param('Asum',Asum_np) #lbuilding thermal model constraints for i in range(N_BLDG): #maximum heating constraint prob.add_constraint(P_heating[:,i] <= self.building_assets[i].Hmax) #maximum cooling constraint prob.add_constraint(P_cooling[:,i] <= self.building_assets[i].Cmax) #minimum heating constraint prob.add_constraint(P_heating[:,i] >= 0) #minimum cooling constraint prob.add_constraint(P_cooling[:,i] >= 0) #maximum temperature constraint prob.add_constraint(T_bldg[:,i] <= self.building_assets[i].Tmax) #minimum temperature constraint prob.add_constraint(T_bldg[:,i] >= self.building_assets[i].Tmin) #power consumption is the sum of heating and cooling prob.add_constraint(P_ctrl_asset[:,i] == P_cooling[:,i]\ + P_heating[:,i]) for t in range(self.T_ems): if t == 0: # initial temperature constraint prob.add_constraint(T_bldg[t,i] ==\ self.building_assets[i].T0) else: # Inside temperature is a function of heating/cooling and # outside temperature. Alpha, beta and gamma are parameters # derived from the R and C values of the building. # Relation between alpha, beta, gamma, R and C can be found # in the BuildingAsset class in the Assets.py file prob.add_constraint(T_bldg[t,i] ==\ self.building_assets[i].\ alpha*T_bldg[t-1,i] \ - self.building_assets[i].\ beta*P_cooling[t-1,i] \ + self.building_assets[i].\ beta*self.building_assets[i].\ CoP*P_heating[t-1,i] \ + self.building_assets[i].\ gamma*self.building_assets[i].\ Ta[t-1]) #linear battery model constraints for i in range(N_ES): #maximum power constraint prob.add_constraint(P_ctrl_asset[:,N_BLDG+i] <=\ self.storage_assets[i].Pmax) #minimum power constraint prob.add_constraint(P_ctrl_asset[:,N_BLDG+i] >=\ self.storage_assets[i].Pmin) #maximum energy constraint prob.add_constraint(self.dt_ems*Asum*P_ctrl_asset[:,N_BLDG+i] <=\ self.storage_assets[i].Emax\ -self.storage_assets[i].E0) #minimum energy constraint prob.add_constraint(self.dt_ems*Asum*P_ctrl_asset[:,N_BLDG+i] >=\ self.storage_assets[i].Emin\ -self.storage_assets[i].E0) #final energy constraint prob.add_constraint(self.dt_ems*Asum[self.T_ems-1,:]\ *P_ctrl_asset[:,N_BLDG+i] ==\ self.storage_assets[i].ET\ -self.storage_assets[i].E0) #import/export constraints for t in range(self.T_ems): # power balance prob.add_constraint(sum(P_ctrl_asset[t,:]) + P_demand[t] ==\ P_import[t]-P_export[t]) #maximum import constraint prob.add_constraint(P_import[t] <= self.market.Pmax[t]) #maximum import constraint prob.add_constraint(P_import[t] >= 0) #maximum import constraint prob.add_constraint(P_export[t] <= -self.market.Pmin[t]) #maximum import constraint prob.add_constraint(P_export[t] >= 0) #maximum demand dummy variable constraint prob.add_constraint(P_max_demand >= P_import[t]-P_export[t]) if self.market.FR_window is not None: FR_window = self.market.FR_window FR_SoC_max = self.market.FR_SOC_max FR_SoC_min = self.market.FR_SOC_min for t in range(self.T_ems): if FR_window[t] ==1: for i in range(N_ES): # final energy constraint prob.add_constraint(self.dt_ems * Asum[t,:] * P_ctrl_asset[:,N_BLDG+i] <= (FR_SoC_max * self.storage_assets[i].Emax) - self.storage_assets[i].E0) # final energy constraint prob.add_constraint(self.dt_ems * Asum[t,:] * P_ctrl_asset[:,N_BLDG+i] >= (FR_SoC_min * self.storage_assets[i].Emax) - self.storage_assets[i].E0) ####################################### ### STEP 3: set up objective ####################################### prob.set_objective('min',self.market.demand_charge*P_max_demand+\ sum(self.market.prices_import[t]*P_import[t]+\ -self.market.prices_export[t]*P_export[t]\ for t in range(self.T_ems))) ####################################### ### STEP 3: solve the optimisation ####################################### print('*** SOLVING THE OPTIMISATION PROBLEM ***') prob.solve(verbose = 0) print('*** OPTIMISATION COMPLETE ***') P_ctrl_asset_val = P_ctrl_asset.value P_import_val = P_import.value P_export_val = P_export.value P_demand_val = P_demand if N_BLDG > 0: #Store internal temperature inside object T_bldg_val = T_bldg.value for b in range(N_BLDG): self.building_assets[b].T_int = T_bldg_val[:,b] if N_ES > 0 and N_BLDG > 0: output = {'P_BLDG_val':P_ctrl_asset_val[:,:N_BLDG],\ 'P_ES_val':P_ctrl_asset_val[:,N_BLDG:N_ES+N_BLDG],\ 'P_import_val':P_import_val,\ 'P_export_val':P_export_val,\ 'P_demand_val':P_demand_val} elif N_ES == 0 and N_BLDG > 0: output = {'P_BLDG_val':P_ctrl_asset_val[:,:N_BLDG],\ 'P_import_val':P_import_val,\ 'P_export_val':P_export_val,\ 'P_demand_val':P_demand_val} elif N_ES > 0 and N_BLDG == 0: output = {'P_ES_val':P_ctrl_asset_val[:,:N_ES],\ 'P_import_val':P_import_val,\ 'P_export_val':P_export_val,\ 'P_demand_val':P_demand_val} else: raise ValueError('No dispatchable assets.') return output def simulate_network(self): """ Run the Energy Management System in open loop and simulate a pandapower network. Parameters ---------- self : EnergySystem object Object containing information on assets, market, network and time resolution. Returns ------- Output : dictionary The following numpy.ndarrays are present depending upon asset mix: buses_Vpu : Voltage magnitude at bus (V) buses_Vang : Voltage angle at bus (rad) buses_Pnet : Real power at bus (kW) buses_Qnet : Reactive power at bus (kVAR) Pnet_market : Real power seen by the market (kW) Qnet_market : Reactive power seen by the market (kVAR) P_ES_ems : Charge/discharge power for storage assets at energy management time resolution (kW) P_BLDG_ems :Builfing power consumption at energy management time resolution (kW) P_import_ems :Power imported from central grid at energy management time resolution (kW) P_export_ems :Power exported to central grid at energy management time resolution(kW) P_demand_ems :System power demand at energy management time resolution (kW) """ ####################################### ### STEP 1: solve the optimisation ####################################### t0 = 0 output_ems = self.EMS_copper_plate() N_ESs = len(self.storage_assets) #number of EVs N_BLDGs = len(self.building_assets) #number of buildings N_nondispatch = len(self.nondispatch_assets) #number of EVs P_import_ems = output_ems['P_import_val'] P_export_ems = output_ems['P_export_val'] if N_ESs > 0: P_ES_ems = output_ems['P_ES_val'] if N_BLDGs > 0: P_BLDG_ems = output_ems['P_BLDG_val'] P_demand_ems = output_ems['P_demand_val'] #convert P_ES and P_BLDG signals to system time-series scale if N_ESs > 0: P_ESs = np.zeros([self.T,N_ESs]) for t in range(self.T): t_ems = int(t/(self.dt_ems/self.dt)) P_ESs[t,:] = P_ES_ems[t_ems,:] if N_BLDGs > 0: P_BLDGs = np.zeros([self.T,N_BLDGs]) for t in range(self.T): t_ems = int(t/(self.dt_ems/self.dt)) P_BLDGs[t,:] = P_BLDG_ems[t_ems,:] ####################################### ### STEP 2: update the controllable assets ####################################### if N_ESs > 0: for i in range(N_ESs): self.storage_assets[i].update_control(P_ESs[:,i]) if N_BLDGs > 0: for i in range(N_BLDGs): self.building_assets[i].update_control(P_BLDGs[:,i]) ####################################### ### STEP 3: simulate the network ####################################### N_buses = self.network.bus['name'].size P_demand_buses = np.zeros([self.T,N_buses]) Q_demand_buses = np.zeros([self.T,N_buses]) if N_ESs > 0: #calculate the total real and reactive power demand at each bus for i in range(N_ESs): bus_id = self.storage_assets[i].bus_id P_demand_buses[:,bus_id] += self.storage_assets[i].Pnet Q_demand_buses[:,bus_id] += self.storage_assets[i].Qnet if N_BLDGs > 0: #calculate the total real and reactive power demand at each bus for i in range(N_BLDGs): bus_id = self.building_assets[i].bus_id P_demand_buses[:,bus_id] += self.building_assets[i].Pnet Q_demand_buses[:,bus_id] += self.building_assets[i].Qnet for i in range(N_nondispatch): bus_id = self.nondispatch_assets[i].bus_id P_demand_buses[:,bus_id] += self.nondispatch_assets[i].Pnet Q_demand_buses[:,bus_id] += self.nondispatch_assets[i].Qnet buses_Vpu = np.zeros([self.T,N_buses]) buses_Vang = np.zeros([self.T,N_buses]) buses_Pnet = np.zeros([self.T,N_buses]) buses_Qnet = np.zeros([self.T,N_buses]) Pnet_market = np.zeros(self.T) Qnet_market = np.zeros(self.T) #print(P_demand_buses) print('*** SIMULATING THE NETWORK ***') for t in range(self.T): #for each time interval: #set up a copy of the network for simulation interval t network_t = copy.deepcopy(self.network) for bus_id in range(N_buses): P_t = P_demand_buses[t,bus_id] Q_t = Q_demand_buses[t,bus_id] #add P,Q loads to the network copy pp.create_load(network_t,bus_id,P_t/1e3,Q_t/1e3) #run the power flow simulation pp.runpp(network_t,max_iteration=100) # or “nr” if t % 100 == 0: print('network sim complete for t = '\ + str(t) + ' of ' + str(self.T)) Pnet_market[t] = network_t.res_ext_grid['p_mw'][0]*1e3 Qnet_market[t] = network_t.res_ext_grid['q_mvar'][0]*1e3 for bus_i in range(N_buses): buses_Vpu[t,bus_i] = network_t.res_bus['vm_pu'][bus_i] buses_Vang[t,bus_i] = network_t.res_bus['va_degree'][bus_i] buses_Pnet[t,bus_i] = network_t.res_bus['p_mw'][bus_i]*1e3 buses_Qnet[t,bus_i] = network_t.res_bus['q_mvar'][bus_i]*1e3 print('*** NETWORK SIMULATION COMPLETE ***') if N_ESs > 0 and N_BLDGs > 0: output = {'buses_Vpu':buses_Vpu,\ 'buses_Vang':buses_Vang,\ 'buses_Pnet':buses_Pnet,\ 'buses_Qnet':buses_Qnet,\ 'Pnet_market':Pnet_market,\ 'Qnet_market':Qnet_market,\ 'P_ES_ems':P_ES_ems,\ 'P_BLDG_ems':P_BLDG_ems,\ 'P_import_ems':P_import_ems,\ 'P_export_ems':P_export_ems,\ 'P_demand_ems':P_demand_ems} elif N_ESs == 0 and N_BLDGs > 0: output = {'buses_Vpu':buses_Vpu,\ 'buses_Vang':buses_Vang,\ 'buses_Pnet':buses_Pnet,\ 'buses_Qnet':buses_Qnet,\ 'Pnet_market':Pnet_market,\ 'Qnet_market':Qnet_market,\ 'P_BLDG_ems':P_BLDG_ems,\ 'P_import_ems':P_import_ems,\ 'P_export_ems':P_export_ems,\ 'P_demand_ems':P_demand_ems} elif N_ESs > 0 and N_BLDGs == 0: output = {'buses_Vpu':buses_Vpu,\ 'buses_Vang':buses_Vang,\ 'buses_Pnet':buses_Pnet,\ 'buses_Qnet':buses_Qnet,\ 'Pnet_market':Pnet_market,\ 'Qnet_market':Qnet_market,\ 'P_ES_ems':P_ES_ems,\ 'P_import_ems':P_import_ems,\ 'P_export_ems':P_export_ems,\ 'P_demand_ems':P_demand_ems} else: raise ValueError('No dispatchable assets.') return output # NEEDED FOR OXEMF EV CASE STUDY def simulate_network_3phPF(self, ems_type = '3ph', i_unconstrained_lines=[], v_unconstrained_buses = []): """ Run the Energy Management System in open loop and simulate an IEEE 13 bus network either copper plate or 3ph Parameters ---------- self : EnergySystem object Object containing information on assets, market, network and time resolution. ems_type : string Identifies whether the system is copper plate or 3ph. Default 3ph i_unconstrained_lines : list List of network lines which have unconstrained current v_unconstrained_buses : list List of buses at which the voltage is not constrained Returns ------- Output : dictionary PF_network_res : Network power flow results stored as a list of objects P_ES_ems : Charge/discharge power for storage assets at energy management time resolution (kW) P_import_ems :Power imported from central grid at energy management time resolution (kW) P_export_ems :Power exported to central grid at energy management time resolution(kW) P_demand_ems :System power demand at energy management time resolution (kW) """ ####################################### ### STEP 1: solve the optimisation ####################################### t0 = 0 if ems_type == 'copper_plate': output_ems = self.EMS_copper_plate_t0(t0) else: output_ems = self.EMS_3ph_linear_t0(t0, i_unconstrained_lines, v_unconstrained_buses) P_import_ems = output_ems['P_import_val'] P_export_ems = output_ems['P_export_val'] P_ES_ems = output_ems['P_ES_val'] P_demand_ems = output_ems['P_demand_val'] #convert P_EV signals to system time-series scale N_ESs = len(self.storage_assets) N_nondispatch = len(self.nondispatch_assets) P_ESs = np.zeros([self.T,N_ESs]) for t in range(self.T): t_ems = int(t/(self.dt_ems/self.dt)) P_ESs[t,:] = P_ES_ems[t_ems,:] ####################################### ### STEP 2: update the controllable assets ####################################### for i in range(N_ESs): self.storage_assets[i].update_control(P_ESs[:,i]) ####################################### ### STEP 3: simulate the network ####################################### N_buses = self.network.N_buses N_phases = self.network.N_phases P_demand_buses = np.zeros([self.T,N_buses,N_phases]) Q_demand_buses = np.zeros([self.T,N_buses,N_phases]) #calculate the total real and reactive power demand at each bus phase for i in range(N_ESs): bus_id = self.storage_assets[i].bus_id phases_i = self.storage_assets[i].phases N_phases_i = np.size(phases_i) for ph_i in np.nditer(phases_i): P_demand_buses[:,bus_id,ph_i] +=\ self.storage_assets[i].Pnet/N_phases_i Q_demand_buses[:,bus_id,ph_i] +=\ self.storage_assets[i].Qnet/N_phases_i for i in range(N_nondispatch): bus_id = self.nondispatch_assets[i].bus_id phases_i = self.nondispatch_assets[i].phases N_phases_i = np.size(phases_i) for ph_i in np.nditer(phases_i): P_demand_buses[:,bus_id,ph_i] +=\ self.nondispatch_assets[i].Pnet/N_phases_i Q_demand_buses[:,bus_id,ph_i] +=\ self.nondispatch_assets[i].Qnet/N_phases_i #Store power flow results as a list of network objects PF_network_res = [] print('*** SIMULATING THE NETWORK ***') for t in range(self.T): #for each time interval: #set up a copy of the network for simulation interval t network_t = copy.deepcopy(self.network) network_t.clear_loads() for bus_id in range(N_buses): for ph_i in range(N_phases): Pph_t = P_demand_buses[t,bus_id,ph_i] Qph_t = Q_demand_buses[t,bus_id,ph_i] #add P,Q loads to the network copy network_t.set_load(bus_id,ph_i,Pph_t,Qph_t) #run the power flow simulation network_t.zbus_pf() PF_network_res.append(network_t) print('*** NETWORK SIMULATION COMPLETE ***') return {'PF_network_res' :PF_network_res,\ 'P_ES_ems':P_ES_ems,\ 'P_import_ems':P_import_ems,\ 'P_export_ems':P_export_ems,\ 'P_demand_ems':P_demand_ems} ####################################### ### Model Predictive Control Methods ####################################### def EMS_copper_plate_t0(self, t0): """ Setup and run a basic energy optimisation (single copper plate network model) for MPC interval t0 """ ####################################### ### STEP 0: setup variables ####################################### t0_dt = int(t0*self.dt_ems/self.dt) T_mpc = self.T_ems-t0 T_range = np.arange(t0,self.T_ems) prob = pic.Problem() N_ES = len(self.storage_assets) N_nondispatch = len(self.nondispatch_assets) P_demand_actual = np.zeros(self.T) P_demand_pred = np.zeros(self.T) P_demand = np.zeros(T_mpc) for i in range(N_nondispatch): P_demand_actual += self.nondispatch_assets[i].Pnet P_demand_pred += self.nondispatch_assets[i].Pnet_pred # Assemble P_demand out of P actual and P predicted and convert to EMS # time series scale for t_ems in T_range: t_indexes = ((t_ems * self.dt_ems / self.dt + np.arange(0, self.dt_ems / self.dt)).astype(int)) if t_ems == t0: P_demand[t_ems-t0] = np.mean(P_demand_actual[t_indexes]) else: P_demand[t_ems-t0] = np.mean(P_demand_pred[t_indexes]) # get total ES system demand (before optimisation) Pnet_ES_sum = np.zeros(self.T) for i in range(N_ES): Pnet_ES_sum += self.storage_assets[i].Pnet #get the maximum (historical) demand before t0 if t0 > 0: P_max_demand_pre_t0 = np.max(P_demand_actual[0:t0_dt]\ + Pnet_ES_sum[0:t0_dt]) else: P_max_demand_pre_t0 = 0 ####################################### ### STEP 1: set up decision variables ####################################### # energy storage system input powers P_ES = prob.add_variable('P_ES', (T_mpc,N_ES), vtype='continuous') # energy storage system input powers P_ES_ch = prob.add_variable('P_ES_ch', (T_mpc,N_ES), vtype='continuous') # energy storage system output powers P_ES_dis = prob.add_variable('P_ES_dis', (T_mpc,N_ES), vtype='continuous') # (positive) net power imports P_import = prob.add_variable('P_import', (T_mpc,1), vtype='continuous') # (positive) net power exports P_export = prob.add_variable('P_export', (T_mpc,1), vtype='continuous') # (positive) maximum demand dummy variable P_max_demand = prob.add_variable('P_max_demand', 1, vtype='continuous') # (positive) minimum terminal energy dummy variable E_T_min = prob.add_variable('E_T_min', 1, vtype='continuous') ####################################### ### STEP 2: set up constraints ####################################### #lower triangle matrix summing powers Asum = pic.new_param('Asum',np.tril(np.ones([T_mpc,T_mpc]))) eff_opt = self.storage_assets[i].eff_opt # linear battery model constraints for i in range(N_ES): # maximum power constraint prob.add_constraint((P_ES_ch[:,i] - P_ES_dis[:,i])\ <= self.storage_assets[i].Pmax[T_range]) # minimum power constraint prob.add_constraint((P_ES_ch[:,i] - P_ES_dis[:,i])\ >= self.storage_assets[i].Pmin[T_range]) # maximum energy constraint prob.add_constraint((self.dt_ems * Asum * (P_ES_ch[:,i] - P_ES_dis[:,i]))\ <= (self.storage_assets[i].Emax[T_range] - self.storage_assets[i].E[t0_dt])) # minimum energy constraint prob.add_constraint((self.dt_ems * Asum * (P_ES_ch[:,i] - P_ES_dis[:,i]))\ >= (self.storage_assets[i].Emin[T_range] - self.storage_assets[i].E[t0_dt])) # final energy constraint prob.add_constraint((self.dt_ems * Asum[T_mpc-1,:] * (P_ES_ch[:,i] - P_ES_dis[:,i]) + E_T_min)\ >= (self.storage_assets[i].ET - self.storage_assets[i].E[t0_dt])) eff_opt = self.storage_assets[i].eff_opt # P_ES_ch & P_ES_dis dummy variables for t in range(T_mpc): prob.add_constraint(P_ES[t, i] == (P_ES_ch[t, i] / eff_opt - P_ES_dis[t, i] * eff_opt)) prob.add_constraint(P_ES_ch[t, i] >= 0) prob.add_constraint(P_ES_dis[t, i] >= 0) # import/export constraints for t in range(T_mpc): # net import variables prob.add_constraint((sum(P_ES[t, :]) + P_demand[t])\ == (P_import[t] - P_export[t])) # maximum import constraint prob.add_constraint(P_import[t] <= self.market.Pmax[t0+t]) # maximum import constraint prob.add_constraint(P_import[t] >= 0) # maximum import constraint prob.add_constraint(P_export[t] <= -self.market.Pmin[t0+t]) # maximum import constraint prob.add_constraint(P_export[t] >= 0) #maximum demand dummy variable constraint prob.add_constraint((P_max_demand + P_max_demand_pre_t0)\ >= (P_import[t] - P_export[t]) ) # maximum demand dummy variable constraint prob.add_constraint(P_max_demand >= 0) if self.market.FR_window is not None: FR_window = self.market.FR_window FR_SoC_max = self.market.FR_SOC_max FR_SoC_min = self.market.FR_SOC_min for t in range(t0,self.T_ems): if FR_window[t] ==1: for i in range(N_ES): # final energy constraint prob.add_constraint((self.dt_ems * Asum[t, :] * (P_ES_ch[:, i] - P_ES_dis[:, i]))\ <= (FR_SoC_max * self.storage_assets[i].Emax) - self.storage_assets[i].E[t0_dt]) # final energy constraint prob.add_constraint((self.dt_ems * Asum[t, :] * (P_ES_ch[:,i] - P_ES_dis[:,i]))\ >= (FR_SoC_min * self.storage_assets[i].Emax) - self.storage_assets[i].E[t0_dt]) # minimum terminal energy dummy variable constraint prob.add_constraint(E_T_min >= 0) ####################################### ### STEP 3: set up objective ####################################### prices_import = pic.new_param('prices_import', self.market.prices_import) prices_export = pic.new_param('prices_export', self.market.prices_export) terminal_const = 1e12 # coeff for objective terminal soft constraint prob.set_objective('min', (self.market.demand_charge * P_max_demand +\ sum(sum(self.dt_ems * self.storage_assets[i].c_deg_lin * (P_ES_ch[t, i] + P_ES_dis[t,i])\ for i in range(N_ES)) + self.dt_ems * prices_import[t0 + t] * P_import[t] - self.dt_ems * prices_export[t0 + t] * P_export[t]\ for t in range(T_mpc)) + terminal_const * E_T_min)) ####################################### ### STEP 3: solve the optimisation ####################################### print('*** SOLVING THE OPTIMISATION PROBLEM ***') prob.solve(verbose = 0) print('*** OPTIMISATION COMPLETE ***') P_ES_val = np.array(P_ES.value) P_import_val = np.array(P_import.value) P_export_val = np.array(P_export.value) P_demand_val = np.array(P_demand) E_T_min_val = np.array(E_T_min.value) return {'P_ES_val':P_ES_val,\ 'P_import_val':P_import_val,\ 'P_export_val':P_export_val,\ 'P_demand_val':P_demand_val,\ 'E_T_min_val':E_T_min_val} def EMS_copper_plate_t0_c1deg(self, t0): """ setup and run a basic energy optimisation (single copper plate network model) for MPC interval t0 """ ####################################### ### STEP 0: setup variables ####################################### t0_dt = int(t0 * self.dt_ems / self.dt) T_mpc = self.T_ems - t0 T_range = np.arange(t0,self.T_ems) prob = pic.Problem() N_ES = len(self.storage_assets) N_nondispatch = len(self.nondispatch_assets) P_demand_actual = np.zeros(self.T) P_demand_pred = np.zeros(self.T) P_demand = np.zeros(T_mpc) for i in range(N_nondispatch): P_demand_actual += self.nondispatch_assets[i].Pnet P_demand_pred += self.nondispatch_assets[i].Pnet_pred # Assemble P_demand out of P actual and P predicted and convert to # EMS time series scale for t_ems in T_range: t_indexes = (t_ems * self.dt_ems / self.dt + np.arange(0, self.dt_ems / self.dt)).astype(int) if t_ems == t0: P_demand[t_ems-t0] = np.mean(P_demand_actual[t_indexes]) else: P_demand[t_ems-t0] = np.mean(P_demand_pred[t_indexes]) #get total ES system demand (before optimisation) Pnet_ES_sum = np.zeros(self.T) for i in range(N_ES): Pnet_ES_sum += self.storage_assets[i].Pnet #get the maximum (historical) demand before t0 if t0 > 0: P_max_demand_pre_t0 = (np.max(P_demand_actual[0:t0_dt] + Pnet_ES_sum[0: t0_dt])) else: P_max_demand_pre_t0 = 0 ####################################### ### STEP 1: set up decision variables ####################################### # energy storage system input powers P_ES = prob.add_variable('P_ES', (T_mpc,N_ES), vtype='continuous') # energy storage system input powers P_ES_ch = prob.add_variable('P_ES_ch', (T_mpc,N_ES), vtype='continuous') # energy storage system output powers P_ES_dis = prob.add_variable('P_ES_dis', (T_mpc,N_ES), vtype='continuous') # (positive) net power imports P_import = prob.add_variable('P_import', (T_mpc,1), vtype='continuous') # (positive) net power exports P_export = prob.add_variable('P_export', (T_mpc,1), vtype='continuous') # (positive) maximum demand dummy variable P_max_demand = prob.add_variable('P_max_demand', 1, vtype='continuous') # (positive) minimum terminal energy dummy variable E_T_min = prob.add_variable('E_T_min', 1, vtype='continuous') ####################################### ### STEP 2: set up constraints ####################################### # lower triangle matrix summing powers Asum = pic.new_param('Asum', np.tril(np.ones([T_mpc,T_mpc]))) # Asum = cvxopt.matrix(np.tril(np.ones([T_mpc,T_mpc])), (T_mpc,T_mpc), # 'd') # linear battery model constraints for i in range(N_ES): # maximum power constraint prob.add_constraint((P_ES_ch[:, i] - P_ES_dis[:, i])\ <= self.storage_assets[i].Pmax[T_range]) # minimum power constraint prob.add_constraint((P_ES_ch[:, i] - P_ES_dis[:, i])\ >= self.storage_assets[i].Pmin[T_range]) # maximum energy constraint prob.add_constraint((self.dt_ems * Asum * (P_ES_ch[:,i] - P_ES_dis[:,i]))\ <= (self.storage_assets[i].Emax[T_range] - self.storage_assets[i].E[t0_dt])) # minimum energy constraint prob.add_constraint((self.dt_ems * Asum * (P_ES_ch[:,i] - P_ES_dis[:,i]))\ >= (self.storage_assets[i].Emin[T_range] - self.storage_assets[i].E[t0_dt])) # final energy constraint prob.add_constraint((self.dt_ems * Asum[T_mpc-1, :] * (P_ES_ch[:, i] - P_ES_dis[:,i]) + E_T_min)\ >= (self.storage_assets[i].ET - self.storage_assets[i].E[t0_dt])) eff_opt = self.storage_assets[i].eff_opt #P_ES_ch & P_ES_dis dummy variables for t in range(T_mpc): prob.add_constraint(P_ES[t, i] == (P_ES_ch[t, i] / eff_opt - P_ES_dis[t, i] * eff_opt)) prob.add_constraint(P_ES_ch[t, i] >= 0) prob.add_constraint(P_ES_dis[t, i] >= 0) #import/export constraints for t in range(T_mpc): # net import variables prob.add_constraint(sum(P_ES[t, :]) + P_demand[t]\ == P_import[t] - P_export[t]) # maximum import constraint prob.add_constraint(P_import[t] <= self.market.Pmax[t0+t]) # maximum import constraint prob.add_constraint(P_import[t] >= 0) # maximum import constraint prob.add_constraint(P_export[t] <= -self.market.Pmin[t0 + t]) # maximum import constraint prob.add_constraint(P_export[t] >= 0) # maximum demand dummy variable constraint prob.add_constraint(P_max_demand + P_max_demand_pre_t0\ >= P_import[t] - P_export[t]) # maximum demand dummy variable constraint prob.add_constraint(P_max_demand >= 0) # minimum terminal energy dummy variable constraint prob.add_constraint(E_T_min[:] >= 0) #if FFR energy constraints if self.market.FR_window is not None: FR_window = self.market.FR_window FR_SoC_max = self.market.FR_SOC_max FR_SoC_min = self.market.FR_SOC_min for t in range(len(T_mpc)): if FR_window[t] == 1: for i in range(N_ES): # final energy constraint prob.add_constraint((self.dt_ems * Asum[t, :] * P_ES[:, i])\ <= ((FR_SoC_max * self.storage_assets[i].Emax) - self.storage_assets[i].E[t0_dt])) # final energy constraint prob.add_constraint((self.dt_ems * Asum[t, :] * P_ES[:, i])\ >= ((FR_SoC_min * self.storage_assets[i].Emax) - self.storage_assets[i].E[t0_dt])) ####################################### ### STEP 3: set up objective ####################################### prices_import = pic.new_param('prices_import', self.market.prices_import) prices_export = pic.new_param('prices_export', self.market.prices_export) terminal_const = 1e12 #coeff for objective terminal soft constraint prob.set_objective('min', (self.market.demand_charge * P_max_demand + sum(sum(self.dt_ems * self.storage_assets[i].c_deg_lin * (P_ES_ch[t,i] + P_ES_dis[t,i])\ for i in range(N_ES)) + self.dt_ems * prices_import[t0 + t] * P_import[t] + -self.dt_ems * prices_export[t0 + t] * P_export[t]\ for t in range(T_mpc)) + terminal_const * E_T_min)) ####################################### ### STEP 3: solve the optimisation ####################################### print('*** SOLVING THE OPTIMISATION PROBLEM ***') #prob.solve(verbose = 0,solver='cvxopt') prob.solve(verbose = 0) print('*** OPTIMISATION COMPLETE ***') P_ES_val = np.array(P_ES.value) P_import_val = np.array(P_import.value) P_export_val = np.array(P_export.value) P_demand_val = np.array(P_demand) return {'opt_prob':prob,\ 'P_ES_val':P_ES_val,\ 'P_import_val':P_import_val,\ 'P_export_val':P_export_val,\ 'P_demand_val':P_demand_val} # NEEDED FOR OXEMF EV CASE def EMS_3ph_linear_t0(self, t0, i_unconstrained_lines=[], v_unconstrained_buses = []): """ Energy management system optimization assuming 3 phase linear network model for Model Predictive Control interval t0 Parameters ---------- self : EnergySystem object Object containing information on assets, market, network and time resolution. t0 : int Interval in Model Predictive Control. If open loop, t0 = 0 i_unconstrained_lines : list List of network lines which have unconstrained current v_unconstrained_buses : list List of buses at which the voltage is not constrained Returns ------- Output : dictionary The following numpy.ndarrays are present depending upon asset mix: P_ES_val : Charge/discharge power for storage assets (kW) P_import_val : Power imported from central grid (kW) P_export_val : Power exported to central grid (kW) P_demand_val : System power demand at energy management time resolution (kW) PF_networks_lin : Network 3ph list of objects, one for each optimisation interval, storing the linear power flow model used to formulate netowrk constraints """ ####################################### ### STEP 0: setup variables ####################################### prob = pic.Problem() t0_dt = int(t0*self.dt_ems/self.dt) T_mpc = self.T_ems-t0 T_range = np.arange(t0,self.T_ems) N_buses = self.network.N_buses N_phases = self.network.N_phases N_ES = len(self.storage_assets) N_nondispatch = len(self.nondispatch_assets) P_demand_actual = np.zeros([self.T,N_nondispatch]) P_demand_pred = np.zeros([self.T,N_nondispatch]) P_demand = np.zeros([T_mpc,N_nondispatch]) Q_demand_actual = np.zeros([self.T,N_nondispatch]) Q_demand_pred = np.zeros([self.T,N_nondispatch]) Q_demand = np.zeros([T_mpc,N_nondispatch]) for i in range(N_nondispatch): P_demand_actual[:,i] = self.nondispatch_assets[i].Pnet P_demand_pred[:,i] = self.nondispatch_assets[i].Pnet_pred Q_demand_actual[:,i] = self.nondispatch_assets[i].Qnet Q_demand_pred[:,i] = self.nondispatch_assets[i].Qnet_pred #Assemble P_demand out of P actual and P predicted and convert to EMS #time series scale for i in range(N_nondispatch): for t_ems in T_range: t_indexes = (t_ems*self.dt_ems/self.dt + np.arange(0,self.dt_ems/self.dt)).astype(int) if t_ems == t0: P_demand[t_ems-t0,i] =\ np.mean(P_demand_actual[t_indexes,i]) Q_demand[t_ems-t0,i] = \ np.mean(Q_demand_actual[t_indexes,i]) else: P_demand[t_ems-t0,i] = np.mean(P_demand_pred[t_indexes,i]) Q_demand[t_ems-t0,i] = np.mean(Q_demand_pred[t_indexes,i]) #get total ES system demand (before optimisation) Pnet_ES_sum = np.zeros(self.T) for i in range(N_ES): Pnet_ES_sum += self.storage_assets[i].Pnet #get the maximum (historical) demand before t0 if t0 == 0: P_max_demand_pre_t0 = 0 else: if N_nondispatch == 0: P_max_demand_pre_t0 = Pnet_ES_sum[0:t0_dt] else: P_demand_act_sum = sum(P_demand_actual[0:t0_dt,i] \ for i in range(N_nondispatch)) P_max_demand_pre_t0 = np.max(P_demand_act_sum + Pnet_ES_sum[0:t0_dt]) #Set up Matrix linking nondispatchable assets to their bus and phase G_wye_nondispatch = np.zeros([3*(N_buses-1),N_nondispatch]) G_del_nondispatch = np.zeros([3*(N_buses-1),N_nondispatch]) for i in range(N_nondispatch): asset_N_phases = self.nondispatch_assets[i].phases.size bus_id = self.nondispatch_assets[i].bus_id # check if Wye connected wye_flag = self.network.bus_df[self.\ network.bus_df['number']==\ bus_id]['connect'].values[0]=='Y' for ph in np.nditer(self.nondispatch_assets[i].phases): bus_ph_index = 3*(bus_id-1) + ph if wye_flag is True: G_wye_nondispatch[bus_ph_index,i] = 1/asset_N_phases else: G_del_nondispatch[bus_ph_index,i] = 1/asset_N_phases #Set up Matrix linking energy storage assets to their bus and phase G_wye_ES = np.zeros([3*(N_buses-1),N_ES]) G_del_ES = np.zeros([3*(N_buses-1),N_ES]) for i in range(N_ES): asset_N_phases = self.storage_assets[i].phases.size bus_id = self.storage_assets[i].bus_id # check if Wye connected wye_flag = self.network.bus_df[self.\ network.bus_df['number']==\ bus_id]['connect'].values[0]=='Y' for ph in np.nditer(self.storage_assets[i].phases): bus_ph_index = 3*(bus_id-1) + ph if wye_flag is True: G_wye_ES[bus_ph_index,i] = 1/asset_N_phases else: G_del_ES[bus_ph_index,i] = 1/asset_N_phases G_wye_nondispatch_PQ = np.concatenate((G_wye_nondispatch, G_wye_nondispatch),axis=0) G_del_nondispatch_PQ = np.concatenate((G_del_nondispatch, G_del_nondispatch),axis=0) G_wye_ES_PQ = np.concatenate((G_wye_ES,G_wye_ES),axis=0) G_del_ES_PQ = np.concatenate((G_del_ES,G_del_ES),axis=0) ####################################### ### STEP 1: set up decision variables ####################################### # energy storage system input powers P_ES = prob.add_variable('P_ES', (T_mpc,N_ES), vtype='continuous') # energy storage system input powers P_ES_ch = prob.add_variable('P_ES_ch', (T_mpc,N_ES), vtype='continuous') # energy storage system output powers P_ES_dis = prob.add_variable('P_ES_dis', (T_mpc,N_ES), vtype='continuous') # (positive) net power imports P_import = prob.add_variable('P_import', (T_mpc,1), vtype='continuous') # (positive) net power exports P_export = prob.add_variable('P_export', (T_mpc,1), vtype='continuous') # (positive) maximum demand dummy variable P_max_demand = prob.add_variable('P_max_demand', 1, vtype='continuous') # (positive) minimum terminal energy dummy variable E_T_min = prob.add_variable('E_T_min', N_ES, vtype='continuous') ####################################### ### STEP 2: set up linear power flow models ####################################### PF_networks_lin = [] P_lin_buses = np.zeros([T_mpc,N_buses,N_phases]) Q_lin_buses = np.zeros([T_mpc,N_buses,N_phases]) for t in range(T_mpc): #Setup linear power flow model: for i in range(N_nondispatch): bus_id = self.nondispatch_assets[i].bus_id phases_i = self.nondispatch_assets[i].phases for ph_i in np.nditer(phases_i): bus_ph_index = 3*(bus_id-1) + ph_i P_lin_buses[t,bus_id,ph_i] +=\ (G_wye_nondispatch[bus_ph_index,i]+\ G_del_nondispatch[bus_ph_index,i])*P_demand[t,i] Q_lin_buses[t,bus_id,ph_i] +=\ (G_wye_nondispatch[bus_ph_index,i]+\ G_del_nondispatch[bus_ph_index,i])*Q_demand[t,i] #set up a copy of the network for MPC interval t network_t = copy.deepcopy(self.network) network_t.clear_loads() for bus_id in range(N_buses): for ph_i in range(N_phases): Pph_t = P_lin_buses[t,bus_id,ph_i] Qph_t = Q_lin_buses[t,bus_id,ph_i] #add P,Q loads to the network copy network_t.set_load(bus_id,ph_i,Pph_t,Qph_t) network_t.zbus_pf() v_lin0 = network_t.v_net_res S_wye_lin0 = network_t.S_PQloads_wye_res S_del_lin0 = network_t.S_PQloads_del_res network_t.linear_model_setup(v_lin0,S_wye_lin0,S_del_lin0) # note that phases need to be 120degrees out for good results network_t.linear_pf() PF_networks_lin.append(network_t) ####################################### ### STEP 3: set up constraints ####################################### # lower triangle matrix summing powers Asum = pic.new_param('Asum',np.tril(np.ones([T_mpc,T_mpc]))) # energy storage asset constraints for i in range(N_ES): # maximum power constraint prob.add_constraint(P_ES[:,i] <= self.storage_assets[i].Pmax[T_range]) # minimum power constraint prob.add_constraint(P_ES[:,i] >= self.storage_assets[i].Pmin[T_range]) # maximum energy constraint prob.add_constraint(self.dt_ems * Asum * (P_ES_ch[:,i] - P_ES_dis[:,i]) <= self.storage_assets[i].Emax[T_range] - self.storage_assets[i].E[t0_dt]) # minimum energy constraint prob.add_constraint(self.dt_ems * Asum * (P_ES_ch[:,i] - P_ES_dis[:,i]) >= self.storage_assets[i].Emin[T_range] - self.storage_assets[i].E[t0_dt]) # final energy constraint prob.add_constraint(self.dt_ems * Asum[T_mpc-1,:] * (P_ES_ch[:,i] - P_ES_dis[:,i]) + E_T_min[i] >= self.storage_assets[i].ET - self.storage_assets[i].E[t0_dt]) eff_opt = self.storage_assets[i].eff_opt #P_ES_ch & P_ES_dis dummy variables for t in range(T_mpc): prob.add_constraint(P_ES[t,i] == P_ES_ch[t,i]/eff_opt - P_ES_dis[t,i] * eff_opt) prob.add_constraint(P_ES_ch[t,i] >= 0) prob.add_constraint(P_ES_dis[t,i] >= 0) #import/export constraints for t in range(T_mpc): # maximum import constraint prob.add_constraint(P_import[t] <= self.market.Pmax[t0 + t]) # maximum import constraint prob.add_constraint(P_import[t] >= 0) # maximum import constraint prob.add_constraint(P_export[t] <= -self.market.Pmin[t0 + t]) # maximum import constraint prob.add_constraint(P_export[t] >= 0) # maximum demand dummy variable constraint prob.add_constraint(P_max_demand + P_max_demand_pre_t0 >= P_import[t]-P_export[t]) # maximum demand dummy variable constraint prob.add_constraint(P_max_demand >= 0) # Network constraints for t in range(T_mpc): network_t = PF_networks_lin[t] # Note that linear power flow matricies are in units of W (not kW) PQ0_wye = np.concatenate((np.real(network_t.S_PQloads_wye_res),\ np.imag(network_t.S_PQloads_wye_res)))\ *1e3 PQ0_del = np.concatenate((np.real(network_t.S_PQloads_del_res),\ np.imag(network_t.S_PQloads_del_res)))\ *1e3 A_Pslack = (np.matmul\ (np.real(np.matmul\ (network_t.vs.T,\ np.matmul(np.conj(network_t.Ysn),\ np.conj(network_t.M_wye)))),\ G_wye_ES_PQ)\ + np.matmul\ (np.real(np.matmul\ (network_t.vs.T,\ np.matmul(np.conj(network_t.Ysn),\ np.conj(network_t.M_del)))),\ G_del_ES_PQ)) b_Pslack = np.real(np.matmul\ (network_t.vs.T,\ np.matmul(np.conj\ (network_t.Ysn),\ np.matmul(np.conj\ (network_t.M_wye),\ PQ0_wye))))\ +np.real(np.matmul\ (network_t.vs.T,\ np.matmul(np.conj\ (network_t.Ysn),\ np.matmul(np.conj\ (network_t.M_del), PQ0_del))))\ +np.real(np.matmul\ (network_t.vs.T,\ (np.matmul(np.conj\ (network_t.Yss),\ np.conj(network_t.vs))\ + np.matmul(np.conj\ (network_t.Ysn),\ np.conj(network_t.M0))))) # net import variables prob.add_constraint(P_import[t]-P_export[t] ==\ (np.sum(A_Pslack[i]*P_ES[t,i]\ *1e3 for i in range(N_ES))\ + b_Pslack)/1e3) # Voltage magnitude constraints A_vlim = np.matmul(network_t.K_wye,G_wye_ES_PQ)\ + np.matmul(network_t.K_del,G_del_ES_PQ) b_vlim = network_t.v_lin_abs_res #get max/min bus voltages, removing slack and reshaping in a column v_abs_max_vec = network_t.v_abs_max[1:,:].reshape(-1,1) v_abs_min_vec = network_t.v_abs_min[1:,:].reshape(-1,1) for bus_ph_index in range(0,N_phases*(N_buses-1)): if int(bus_ph_index/3) not in (np.array\ (v_unconstrained_buses)-1): prob.add_constraint(sum(A_vlim[bus_ph_index,i]\ *(P_ES[t,i])\ *1e3 for i in range(N_ES))\ + b_vlim[bus_ph_index] <=\ v_abs_max_vec[bus_ph_index]) prob.add_constraint(sum(A_vlim[bus_ph_index,i]\ *(P_ES[t,i])\ *1e3 for i in range(N_ES))\ + b_vlim[bus_ph_index] >=\ v_abs_min_vec[bus_ph_index]) # Line current magnitude constraints: for line_ij in range(network_t.N_lines): if line_ij not in i_unconstrained_lines: iabs_max_line_ij = network_t.i_abs_max[line_ij,:] #3 phases # maximum current magnitude constraint A_line = np.matmul(network_t.Jabs_dPQwye_list[line_ij],\ G_wye_ES_PQ)\ + np.matmul(network_t.\ Jabs_dPQdel_list[line_ij],\ G_del_ES_PQ) for ph in range(N_phases): prob.add_constraint(sum(A_line[ph,i]\ * P_ES[t,i]\ * 1e3 for i in range(N_ES))\ + network_t.\ Jabs_I0_list[line_ij][ph] <=\ iabs_max_line_ij[ph]) #if FFR energy constraints if self.market.FR_window is not None: FR_window = self.market.FR_window FR_SoC_max = self.market.FR_SOC_max FR_SoC_min = self.market.FR_SOC_min for t in range(len(T_mpc)): if FR_window[t] ==1: for i in range(N_ES): # final energy constraint prob.add_constraint((self.dt_ems * Asum[t, :] * P_ES[:,i])\ <= ((FR_SoC_max * self.storage_assets[i].Emax) - self.storage_assets[i].E[t0_dt])) # final energy constraint prob.add_constraint((self.dt_ems * Asum[t,:] * P_ES[:,i])\ >= ((FR_SoC_min * self.storage_assets[i].Emax) - self.storage_assets[i].E[t0_dt])) ####################################### ### STEP 4: set up objective ####################################### # minimum terminal energy dummy variable constraint prob.add_constraint(E_T_min[i] >= 0) #coeff for objective terminal soft constraint terminal_const = 1e12 prices_import = pic.new_param('prices_import', self.market.prices_import) prices_export = pic.new_param('prices_export', self.market.prices_export) prob.set_objective('min', self.market.demand_charge*\ (P_max_demand+P_max_demand_pre_t0) + sum(sum(self.dt_ems*self.storage_assets[i].\ c_deg_lin*(P_ES_ch[t,i]+ P_ES_dis[t,i])\ for i in range(N_ES))\ + self.dt_ems*prices_import[t0+t]*P_import[t]\ - self.dt_ems*prices_export[t0+t]*P_export[t] for t in range(T_mpc))\ + sum(terminal_const*E_T_min[i]\ for i in range(N_ES))) ####################################### ### STEP 5: solve the optimisation ####################################### print('*** SOLVING THE OPTIMISATION PROBLEM ***') prob.solve(verbose = 0) print('*** OPTIMISATION COMPLETE ***') P_ES_val = np.array(P_ES.value) P_import_val = np.array(P_import.value) P_export_val = np.array(P_export.value) P_demand_val = np.array(P_demand) return {'P_ES_val':P_ES_val, 'P_import_val':P_import_val, 'P_export_val':P_export_val, 'P_demand_val':P_demand_val, 'PF_networks_lin':PF_networks_lin} # NEEDED FOR OXEMF EV CASE def simulate_network_mpc_3phPF(self, ems_type = '3ph', i_unconstrained_lines=[], v_unconstrained_buses = []): """ Run the Energy Management System using Model Predictive Control (MPC) and simulate an IEEE 13 bus network either copper plate or 3ph Parameters ---------- self : EnergySystem object Object containing information on assets, market, network and time resolution. ems_type : string Identifies whether the system is copper plate or 3ph. Default 3ph i_unconstrained_lines : list List of network lines which have unconstrained current v_unconstrained_buses : list List of buses at which the voltage is not constrained Returns ------- Output : dictionary PF_network_res : Network power flow results stored as a list of objects P_ES_ems : Charge/discharge power for storage assets at energy management time resolution (kW) P_import_ems :Power imported from central grid at energy management time resolution (kW) P_export_ems :Power exported to central grid at energy management time resolution(kW) P_demand_ems :System power demand at energy management time resolution (kW) """ ####################################### ### STEP 0: setup variables ####################################### N_ESs = len(self.storage_assets) #number of EVs N_nondispatch = len(self.nondispatch_assets) #number of EVs P_import_ems = np.zeros(self.T_ems) P_export_ems = np.zeros(self.T_ems) P_ES_ems = np.zeros([self.T_ems,N_ESs]) if ems_type == 'copper_plate': P_demand_ems = np.zeros(self.T_ems) else: P_demand_ems = np.zeros([self.T_ems,N_nondispatch]) N_buses = self.network.N_buses N_phases = self.network.N_phases P_demand_buses = np.zeros([self.T,N_buses,N_phases]) Q_demand_buses = np.zeros([self.T,N_buses,N_phases]) PF_network_res = [] ####################################### ### STEP 1: MPC Loop ####################################### print('*** MPC SIMULATION START ***') for t_mpc in range(self.T_ems): print('************************') print('MPC Interval '+ str(t_mpc)+ ' of '+ str(self.T_ems)) print('************************') ####################################### ### STEP 1.1: Optimisation ####################################### if ems_type == 'copper_plate': output_ems = self.EMS_copper_plate_t0_c1deg(t_mpc) P_demand_ems[t_mpc] = output_ems['P_demand_val'][0] else: output_ems = self.EMS_3ph_linear_t0(t_mpc, i_unconstrained_lines, v_unconstrained_buses) P_demand_ems[t_mpc,:] = output_ems['P_demand_val'][0,:] P_import_ems[t_mpc] = output_ems['P_import_val'][0] P_export_ems[t_mpc] = output_ems['P_export_val'][0] P_ES_ems[t_mpc,:] = output_ems['P_ES_val'][0,:] # convert P_EV signals to system time-series scale T_interval = int(self.dt_ems/self.dt) P_ESs = np.zeros([T_interval,N_ESs]) for t in range(T_interval): P_ESs[t,:] = P_ES_ems[t_mpc,:] ####################################### ### STEP 1.2: update the controllable assets ####################################### t0 = int(t_mpc*(self.dt_ems/self.dt)) # get the simulation time intervals within each EMS time interval # and implement the ES system control for them t_range = np.arange(t0,t0+T_interval) for i in range(N_ESs): for t_index in range(T_interval): t = t_range[t_index] self.storage_assets[i].update_control_t(P_ESs[t_index,i],t) ####################################### ### STEP 1.3: simulate the network ####################################### # total real and reactive power demand at each bus phase for t_index in range(T_interval): t = t_range[t_index] for i in range(N_ESs): bus_id = self.storage_assets[i].bus_id phases_i = self.storage_assets[i].phases N_phases_i = np.size(phases_i) for ph_i in phases_i: P_demand_buses[t,bus_id,ph_i] +=\ self.storage_assets[i].Pnet[t]/N_phases_i Q_demand_buses[t,bus_id,ph_i] +=\ self.storage_assets[i].Qnet[t]/N_phases_i for i in range(N_nondispatch): bus_id = self.nondispatch_assets[i].bus_id phases_i = self.nondispatch_assets[i].phases N_phases_i = np.size(phases_i) for ph_i in np.nditer(phases_i): P_demand_buses[t,bus_id,ph_i] +=\ self.nondispatch_assets[i].Pnet[t]/N_phases_i Q_demand_buses[t,bus_id,ph_i] +=\ self.nondispatch_assets[i].Qnet[t]/N_phases_i # set up a copy of the network for simulation interval t network_t = copy.deepcopy(self.network) network_t.clear_loads() for bus_id in range(N_buses): for ph_i in range(N_phases): Pph_t = P_demand_buses[t,bus_id,ph_i] Qph_t = Q_demand_buses[t,bus_id,ph_i] #add P,Q loads to the network copy network_t.set_load(bus_id,ph_i,Pph_t,Qph_t) # run the power flow simulation network_t.zbus_pf() # store power flow results as a list of network objects PF_network_res.append(network_t) print('*** MPC SIMULATION COMPLETE ***') return {'PF_network_res' :PF_network_res,\ 'P_ES_ems':P_ES_ems,\ 'P_import_ems':P_import_ems,\ 'P_export_ems':P_export_ems,\ 'P_demand_ems':P_demand_ems} def simulate_network_3phPF_lean(self, ems_type = '3ph'): """ run the EMS in open loop and simulate a 3-phase AC network """ ####################################### ### STEP 1: solve the optimisation ####################################### t0 = 0 if ems_type == 'copper_plate': # self.EMS_copper_plate() output_ems = self.EMS_copper_plate_t0_c1deg(t0) else: # self.EMS_copper_plate() output_ems = self.EMS_3ph_linear_t0(t0) #output_ems = self.EMS_copper_plate P_import_ems = output_ems['P_import_val'] P_export_ems = output_ems['P_export_val'] P_ES_ems = output_ems['P_ES_val'] P_demand_ems = output_ems['P_demand_val'] #convert P_EV signals to system time-series scale N_ESs = len(self.storage_assets) #number of EVs N_nondispatch = len(self.nondispatch_assets) #number of EVs P_ESs = np.zeros([self.T,N_ESs]) for t in range(self.T): t_ems = int(t/(self.dt_ems/self.dt)) P_ESs[t,:] = P_ES_ems[t_ems,:] ####################################### ### STEP 2: update the controllable assets ####################################### for i in range(N_ESs): self.storage_assets[i].update_control(P_ESs[:,i]) ####################################### ### STEP 3: simulate the network ####################################### N_buses = self.network.N_buses N_phases = self.network.N_phases P_demand_buses = np.zeros([self.T,N_buses,N_phases]) Q_demand_buses = np.zeros([self.T,N_buses,N_phases]) #calculate the total real and reactive power demand at each bus phase for i in range(N_ESs): bus_id = self.storage_assets[i].bus_id phases_i = self.storage_assets[i].phases N_phases_i = np.size(phases_i) for ph_i in np.nditer(phases_i): P_demand_buses[:,bus_id,ph_i] += (self.storage_assets[i].Pnet / N_phases_i) Q_demand_buses[:,bus_id,ph_i] += (self.storage_assets[i].Qnet / N_phases_i) for i in range(N_nondispatch): bus_id = self.nondispatch_assets[i].bus_id phases_i = self.nondispatch_assets[i].phases N_phases_i = np.size(phases_i) for ph_i in np.nditer(phases_i): P_demand_buses[:, bus_id, ph_i]\ += (self.nondispatch_assets[i].Pnet / N_phases_i) Q_demand_buses[:, bus_id, ph_i]\ += (self.nondispatch_assets[i].Qnet / N_phases_i) #Store power flow results as a list of network objects PF_network_res = [] print('*** SIMULATING THE NETWORK ***') for t in range(self.T): #for each time interval: #set up a copy of the network for simulation interval t network_t = copy.deepcopy(self.network) network_t.clear_loads() for bus_id in range(N_buses): for ph_i in range(N_phases): Pph_t = P_demand_buses[t,bus_id,ph_i] Qph_t = Q_demand_buses[t,bus_id,ph_i] #add P,Q loads to the network copy network_t.set_load(bus_id,ph_i,Pph_t,Qph_t) #run the power flow simulation network_t.zbus_pf() if t % 1 == 0: print('network sim complete for t = ' + str(t) + ' of ' + str(self.T)) PF_network_res.append(network_t.res_bus_df) print('*** NETWORK SIMULATION COMPLETE ***') return {'PF_network_res' :PF_network_res,\ 'P_ES_ems':P_ES_ems,\ 'P_import_ems':P_import_ems,\ 'P_export_ems':P_export_ems,\ 'P_demand_ems':P_demand_ems}

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Python

Python/Tests/TestData/EditorTests/BackslashCompletion.py

nanshuiyu/pytools

9f9271fe8cf564b4f94e9456d400f4306ea77c23

[ "Apache-2.0" ]

null

Python/Tests/TestData/EditorTests/BackslashCompletion.py

nanshuiyu/pytools

9f9271fe8cf564b4f94e9456d400f4306ea77c23

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null

Python/Tests/TestData/EditorTests/BackslashCompletion.py

nanshuiyu/pytools

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[ "Apache-2.0" ]

null

x = 42 x\

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0.333333

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12

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null

0

1

0

1

0

1

0

null

0

6

11af74f8b58d6ea6fe15337fd57dce4618151dd1

48

py

Python

pyxb/bundles/opengis/misc/xAL.py

eLBati/pyxb

14737c23a125fd12c954823ad64fc4497816fae3

[ "Apache-2.0" ]

123

2015-01-12T06:43:22.000Z

2022-03-20T18:06:46.000Z

pyxb/bundles/opengis/misc/xAL.py

eLBati/pyxb

14737c23a125fd12c954823ad64fc4497816fae3

[ "Apache-2.0" ]

103

2015-01-08T18:35:57.000Z

2022-01-18T01:44:14.000Z

pyxb/bundles/opengis/misc/xAL.py

eLBati/pyxb

14737c23a125fd12c954823ad64fc4497816fae3

[ "Apache-2.0" ]

54

2015-02-15T17:12:00.000Z

2022-03-07T23:02:32.000Z

from pyxb.bundles.opengis.misc.raw.xAL import *

24

47

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48

4.75

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1

48

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null

0

1

0

1

0

1

0

6

11c2d2a035761b9b372e50166d8ee83d855a877b

117

py

Python

springust/command/entity_config.py

VEINHORN/springust

b53ac5b877824359b9d75a81a02cb4ddd987a1a8

[ "MIT" ]

null

springust/command/entity_config.py

VEINHORN/springust

b53ac5b877824359b9d75a81a02cb4ddd987a1a8

[ "MIT" ]

null

springust/command/entity_config.py

VEINHORN/springust

b53ac5b877824359b9d75a81a02cb4ddd987a1a8

[ "MIT" ]

null

class EntityConfig: def __init__(self, templates_folder = None): self.templates_folder = templates_folder

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117

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null

0

1

0

1

0

6

11c75dec7f6822eb9fa730525b156e644265e4f1

98

py

Python

packages/routines/about_to_Quit.py

robmanganelly/PyJournal

dcf0e6e69a62ad5c6019b099104ae64880825814

[ "MIT" ]

1

2021-02-02T03:58:56.000Z

packages/routines/about_to_Quit.py

rlothbrock/PyJournal

e44bca524c46364a6931375d8ac3ab8b90f71ad2

[ "MIT" ]

null

packages/routines/about_to_Quit.py

rlothbrock/PyJournal

e44bca524c46364a6931375d8ac3ab8b90f71ad2

[ "MIT" ]

null

from packages.modules.app_clock import kill_clock def about_to_quit_routine(): kill_clock()

16.333333

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0.795918

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0

1

0

null

0

1

0

1

0

6

11d8236fd82771a2cb954a723d80a984ac390aab

136

py

Python

setup.py

ervitis/vswitch

95e6ec173b3028e0f379d9b8834fbee4108a8ce0

[ "MIT" ]

null

setup.py

ervitis/vswitch

95e6ec173b3028e0f379d9b8834fbee4108a8ce0

[ "MIT" ]

null

setup.py

ervitis/vswitch

95e6ec173b3028e0f379d9b8834fbee4108a8ce0

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- from setuptools import setup from vswitch import install_params setup(**install_params)

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136

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0

null

0

1

0

1

0

1

0

6

e10a42e8615985c893f944d091e6f4371b9a17fc

122

py

Python

apps/ask_codex/history/admin.py

joy-void-joy/jarvis-codex-server

10b3d5dfbc958361ea5cbb085079456ee2b502ca

[ "MIT" ]

null

apps/ask_codex/history/admin.py

joy-void-joy/jarvis-codex-server

10b3d5dfbc958361ea5cbb085079456ee2b502ca

[ "MIT" ]

null

apps/ask_codex/history/admin.py

joy-void-joy/jarvis-codex-server

10b3d5dfbc958361ea5cbb085079456ee2b502ca

[ "MIT" ]

null

from django.contrib import admin from .models import Log @admin.register(Log) class LogAdmin(admin.ModelAdmin): pass

17.428571

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0.778689

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122

5.588235

0.705882

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0.139344

122

6

34

20.333333

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true

0.2

0.4

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1

0

null

0

1

0

1

0

6

01507b7d940698d7cc13a3c71dd5a4dff80efc5f

147

py

Python

lesson-01/01/hello_world.py

minimum-hsu/tutorial-python

667692e7cd13a8a4d061a4da530dc2dfe25ac1de

[ "MIT" ]

null

lesson-01/01/hello_world.py

minimum-hsu/tutorial-python

667692e7cd13a8a4d061a4da530dc2dfe25ac1de

[ "MIT" ]

null

lesson-01/01/hello_world.py

minimum-hsu/tutorial-python

667692e7cd13a8a4d061a4da530dc2dfe25ac1de

[ "MIT" ]

null

#!/usr/bin/env python3 print('hello world') print('hello', 'world') ## use "sep" parameter to change output print('hello', 'world', sep = '_')

14.7

39

0.639456

20

147

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147

9

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16.333333

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true

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0

1

0

null

0

1

0

1

0

6

6d6e317f1decc36fb28039fecf19e241c3a2e975

1,101

py

Python

cryptodataaccess/Transactions/TransactionStore.py

athanikos/cryptodataaccess

6189a44c65a9b03c02822a534e865740ab488809

[ "MIT" ]

null

cryptodataaccess/Transactions/TransactionStore.py

athanikos/cryptodataaccess

6189a44c65a9b03c02822a534e865740ab488809

[ "MIT" ]

null

cryptodataaccess/Transactions/TransactionStore.py

athanikos/cryptodataaccess

6189a44c65a9b03c02822a534e865740ab488809

[ "MIT" ]

null

from abc import ABCMeta, abstractmethod class TransactionStore(metaclass=ABCMeta): @abstractmethod def fetch_distinct_user_ids(self): pass @abstractmethod def fetch_distinct_user_ids(self): pass @abstractmethod def fetch_transaction(self, id): pass @abstractmethod def fetch_transactions(self, user_id): pass @abstractmethod def fetch_transactions(self, user_id): pass @abstractmethod def fetch_transactions_before_date(self, user_id, date): pass @abstractmethod def insert_transaction(self, user_id, volume, symbol, value, price, currency, date, source, source_id, operation): pass @abstractmethod def update_transaction(self, id, user_id, volume, symbol, value, price, currency, date, source, source_id, operation): pass @abstractmethod def delete_transaction(self, id, throw_if_does_not_exist): pass @abstractmethod def delete_transaction_by_source_id(self, source_id, throw_if_does_not_exist): pass

24.466667

118

0.682107

124

1,101

5.774194

0.290323

0.23743

0.263966

0.181564

0.722067

0.650838

0.581006

0

0.249773

1,101

44

119

25.022727

0.866828

0

0.727273

0

1

0.30303

false

0.30303

0.030303

0

0.363636

0

null

1

0

1

0

null

0

1

0

1

0

6

6d7726ef07ea613510b793192de6063e6e113415

7,799

py

Python

ngraph/frontends/caffe2/tests/test_ops_constant.py

NervanaSystems/ngraph-python

ac032c83c7152b615a9ad129d54d350f9d6a2986

[ "Apache-2.0" ]

18

2018-03-19T04:16:49.000Z

2021-02-08T14:44:58.000Z

ngraph/frontends/caffe2/tests/test_ops_constant.py

rsumner31/ngraph

5e5c9bb9f24d95aee190b914dd2d44122fc3be53

[ "Apache-2.0" ]

2

2019-04-16T06:41:49.000Z

2019-05-06T14:08:13.000Z

ngraph/frontends/caffe2/tests/test_ops_constant.py

rsumner31/ngraph

5e5c9bb9f24d95aee190b914dd2d44122fc3be53

[ "Apache-2.0" ]

11

2018-06-16T15:59:08.000Z

2021-03-06T00:45:30.000Z

# ****************************************************************************** # Copyright 2017-2018 Intel Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ****************************************************************************** from __future__ import print_function from __future__ import division from caffe2.python import core, workspace from ngraph.frontends.caffe2.c2_importer.importer import C2Importer from ngraph.testing import ExecutorFactory import numpy as np import random as random def test_constant(): workspace.ResetWorkspace() shape = [10, 10] val = random.random() net = core.Net("net") net.ConstantFill([], ["Y"], shape=shape, value=val, run_once=0, name="Y") # Execute via Caffe2 workspace.RunNetOnce(net) # Import caffe2 network into ngraph importer = C2Importer() importer.parse_net_def(net.Proto(), verbose=False) # Get handle f_ng = importer.get_op_handle("Y") # Execute with ExecutorFactory() as ex: f_result = ex.executor(f_ng)() # compare Caffe2 and ngraph results assert(np.ma.allequal(f_result, workspace.FetchBlob("Y"))) assert(np.isclose(f_result[0][0], val, atol=1e-6, rtol=0)) def test_gaussianfill(): workspace.ResetWorkspace() # Size of test matrix N = 100 shape = [N, N] net = core.Net("net") net.GaussianFill([], ["Y"], shape=shape, mean=0.0, std=1.0, name="Y") # Execute via Caffe2 workspace.RunNetOnce(net) # Import caffe2 network into ngraph importer = C2Importer() importer.parse_net_def(net.Proto(), verbose=False) # Get handle f_ng = importer.get_op_handle("Y") # Execute with ExecutorFactory() as ex: f_result = ex.executor(f_ng)() # get caffe result caffe_res = workspace.FetchBlob("Y") # Elementwise difference of the two random matrixes difference_res = caffe_res - f_result # standard deviation of Difference Matrix diffe_res_std = difference_res.std() # testing can only be approximate (so in rare cases may fail!!) # if fails once try to re-run a couple of times to make sure there is a problem) # the difference must be still gaussian and P(|m'-m|)<3*std = 99.73%, and # std(m) = std/N, having N*N elements assert(np.isclose(difference_res.mean(), 0, atol=3 * diffe_res_std / N, rtol=0)) def test_uniformfill(): workspace.ResetWorkspace() # Size of test matrix N = 100 shape = [N, N] net = core.Net("net") net.UniformFill([], ["Y"], shape=shape, min=-2., max=2., name="Y") # Execute via Caffe2 workspace.RunNetOnce(net) # Import caffe2 network into ngraph importer = C2Importer() importer.parse_net_def(net.Proto(), verbose=False) # Get handle f_ng = importer.get_op_handle("Y") # Execute with ExecutorFactory() as ex: f_result = ex.executor(f_ng)() # get caffe result caffe_res = workspace.FetchBlob("Y") # Elementwise difference of the two random matrixes difference_res = caffe_res - f_result # standard deviation of Difference Matrix diffe_res_std = difference_res.std() # testing can only be approximated, so sometimes can fail!! # approach mimicking gaussian test, and this time the multiplier is set to 5 # to account for distorsion from gaussian # if fails once try to re-run a couple of times to make sure there is a problem) assert(np.isclose(difference_res.mean(), 0, atol=5 * diffe_res_std / N, rtol=0)) def test_uniformintfill(): workspace.ResetWorkspace() N = 100 shape = [N, N] net = core.Net("net") net.UniformIntFill([], ["Y"], shape=shape, min=-2, max=2, name="Y") # Execute via Caffe2 workspace.RunNetOnce(net) # Import caffe2 network into ngraph importer = C2Importer() importer.parse_net_def(net.Proto(), verbose=False) # Get handle f_ng = importer.get_op_handle("Y") # Execute with ExecutorFactory() as ex: f_result = ex.executor(f_ng)() # get caffe result caffe_res = workspace.FetchBlob("Y") # Elementwise difference of the two random matrixes difference_res = caffe_res - f_result # standard deviation of Difference Matrix diffe_res_std = difference_res.std() # testing can only be approximated, so sometimes can fail!! # approach mimicking gaussian test, and this time the multiplier is set # to 8 to account for distorsion from gaussian # if fails once try to re-run a couple of times to make sure there is a problem) assert(np.isclose(difference_res.mean(), 0, atol=8 * diffe_res_std / N, rtol=0)) def test_xavierfill(): workspace.ResetWorkspace() N = 100 shape = [N, N] net = core.Net("net") net.XavierFill([], ["Y"], shape=shape, name="Y") # Execute via Caffe2 workspace.RunNetOnce(net) # Import caffe2 network into ngraph importer = C2Importer() importer.parse_net_def(net.Proto(), verbose=False) # Get handle f_ng = importer.get_op_handle("Y") # Execute with ExecutorFactory() as ex: f_result = ex.executor(f_ng)() # get caffe result caffe_res = workspace.FetchBlob("Y") # Elementwise difference of the two random matrixes difference_res = caffe_res - f_result # standard deviation of Difference Matrix diffe_res_std = difference_res.std() # testing can only be approximated, so sometimes can fail!! # approach mimicking gaussian test # if fails once try to re-run a couple of times to make sure there is a problem) assert(np.isclose(difference_res.mean(), 0, atol=3 * diffe_res_std / N, rtol=0)) def test_giventensorfill(): workspace.ResetWorkspace() shape = [10, 10] data1 = np.random.random(shape) net = core.Net("net") net.GivenTensorFill([], ["Y"], shape=shape, values=data1, name="Y") # Execute via Caffe2 workspace.RunNetOnce(net) # Import caffe2 network into ngraph importer = C2Importer() importer.parse_net_def(net.Proto(), verbose=False) # Get handle f_ng = importer.get_op_handle("Y") # Execute with ExecutorFactory() as ex: f_result = ex.executor(f_ng)() # compare Caffe2 and ngraph results assert(np.ma.allequal(f_result, workspace.FetchBlob("Y"))) assert(np.ma.allclose(f_result, data1, atol=1e-6, rtol=0)) def test_giventensorintfill(): workspace.ResetWorkspace() shape = [10, 10] data1 = np.random.random_integers(-100, 100, shape) net = core.Net("net") net.GivenTensorIntFill([], ["Y"], shape=shape, values=data1, name="Y") # Execute via Caffe2 workspace.RunNetOnce(net) # Import caffe2 network into ngraph importer = C2Importer() importer.parse_net_def(net.Proto(), verbose=False) # Get handle f_ng = importer.get_op_handle("Y") # Execute with ExecutorFactory() as ex: f_result = ex.executor(f_ng)() # compare Caffe2 and ngraph results assert(np.ma.allequal(f_result, workspace.FetchBlob("Y"))) assert(np.ma.allequal(f_result, data1))

29.881226

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0

null

0

1

0

1

0

null

0

6

6dda9dca07ec1377d3bd38e50bdabaae79a0dd40

46

py

Python

src/scrawl/moves/core.py

astromancer/graphical

2d72407c53967714953485dd52ad72e34e549ef5

[ "MIT" ]

null

src/scrawl/moves/core.py

astromancer/graphical

2d72407c53967714953485dd52ad72e34e549ef5

[ "MIT" ]

null

src/scrawl/moves/core.py

astromancer/graphical

2d72407c53967714953485dd52ad72e34e549ef5

[ "MIT" ]

null

from matplotlib.offsetbox import DraggableBase

46

0.913043

5

46

8.4

1

0

0.065217

46

1

46

0.976744

0

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true

0

1

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1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

61e1c5a955a2ebe49ed9824bbd8ba7695260bf14

48,542

py

Python

stv/generators/ispl/bridge.py

wp777/stv-compute

313b574c43ef87b629e70c25c38dbb7b24d1f130

[ "MIT" ]

2

2021-07-11T09:52:59.000Z

2022-02-13T17:34:59.000Z

stv/generators/ispl/bridge.py

wp777/stv-compute

313b574c43ef87b629e70c25c38dbb7b24d1f130

[ "MIT" ]

3

2020-07-26T13:49:59.000Z

2021-01-19T18:04:10.000Z

stv/generators/ispl/bridge.py

wp777/stv-compute

313b574c43ef87b629e70c25c38dbb7b24d1f130

[ "MIT" ]

null

from stv.generators.ispl.ispl_generator import IsplGenerator import itertools import random class BridgeModelIsplGenerator(IsplGenerator): @property def card_names(self) -> [str]: return ["Ace", "King", "Queen", "Jack", "ten", "nine", "eight", "seven", "six", "five", "four", "three", "two"] @property def card_colors(self) -> [str]: return ["Spade", "Heart", "Diamond", "Club"] @property def player_names(self) -> [str]: return ["SPlayer", "WPlayer", "NPlayer", "EPlayer"] def __init__(self, number_of_cards, number_of_cards_in_hand, card_ordering=None): super().__init__() if card_ordering is None: card_ordering = self.generate_random_card_array(4 * number_of_cards_in_hand) self._number_of_cards = number_of_cards self._number_of_cards_in_hand = number_of_cards_in_hand self._card_ordering = card_ordering self._cards = [] self._available_cards = [] self._cards_values = {} self._cards_colors = {} self._create_cards_array() self._create_available_cards_array() self._assign_cards_values() self._assign_cards_colors() def _create_cards_array(self) -> None: self._cards = [] for card_name in self.card_names: for card_color in self.card_colors: self._cards.append(card_name + card_color) def _create_available_cards_array(self) -> None: self._available_cards = [] for j in range(0, 4 * self._number_of_cards): self._available_cards.append(self._cards[j]) def _assign_cards_values(self) -> None: i = 0 for card_value in range(0, 13): for j in range(0, 4): self._cards_values[self._cards[i]] = 13 - card_value i += 1 def _assign_cards_colors(self) -> None: i = 0 for _ in range(0, 13): for color in self.card_colors: self._cards_colors[self._cards[i]] = color i += 1 def _create_agents(self) -> str: agents = "" players_ids = [0, 1, 3] for player_id in players_ids: agents += self._create_player(player_id) return agents def _define_semantics(self) -> str: semantics = "Semantics=SingleAssignment;\n\n" return semantics def _create_environment_obsvars(self) -> str: obsvars = f"\tObsvars:\n" \ f"\t\tfirstTeamScore: 0..{self._number_of_cards_in_hand};\n" \ f"\t\tsecondTeamScore: 0..{self._number_of_cards_in_hand};\n" \ f"\t\tbeginningPlayer: 0..3;\n" \ f"\t\tcurrentPlayer: 0..4;\n" \ f"\t\tclock: 0..4;\n" obsvars += self._create_env_player_cards_obsvars() obsvars += self._create_env_n_cards_obsvars() obsvars += self._create_env_history_cards_obsvars() obsvars += "\t\tsuit: {Spade, Heart, Diamond, Club, None};\n" obsvars += self._create_env_has_color_obsvars() obsvars += "\tend Obsvars\n" return obsvars def _create_env_player_cards_obsvars(self) -> str: obsvars = "" for player in self.player_names: obsvars += f"\t\t{player}Card: {{" for j in range(0, 4 * self._number_of_cards): obsvars += f"{self._cards[j]}, " obsvars += "None};\n" return obsvars def _create_env_n_cards_obsvars(self) -> str: obsvars = "" for i in range(1, self._number_of_cards_in_hand + 1): obsvars += f"\t\tcardN{i}: {{" for j in range(0, 4 * self._number_of_cards): obsvars += f"N{self._cards[j]}, " obsvars += "None};\n" return obsvars def _create_env_history_cards_obsvars(self) -> str: obsvars = "" for i in range(0, self._number_of_cards * 4): obsvars += f"\t\t{self._cards[i]}H: boolean;\n" return obsvars def _create_env_has_color_obsvars(self) -> str: obsvars = "" for color in self.card_colors: obsvars += f"\t\thas{color}: 0..{self._number_of_cards_in_hand};\n" return obsvars def _create_environment_vars(self) -> str: vars = "\tVars:\n" \ "\t\tsmc: 0..1;\n" \ "\tend Vars\n" return vars def _create_environment_actions(self) -> str: actions = "\tActions = {none};\n" return actions def _create_environment_protocol(self) -> str: protocol = "\tProtocol:\n" \ "\t\tOther:{none};\n" \ "\tend Protocol\n" return protocol def _create_environment_evolution(self) -> str: evolution = "\tEvolution:\n" evolution += self._create_env_first_team_score_evolution() evolution += self._create_env_second_team_score_evolution() evolution += self._create_env_beginning_player_evolution() evolution += self._create_env_current_player_evolution() evolution += self._create_env_clock_evolution() evolution += self._create_env_player_cards_evolution() evolution += self._create_env_suit_evolution() evolution += self._create_env_history_evolution() evolution += self._create_env_n_cards_evolution() evolution += self._create_env_has_color_evolution() evolution += "\tend Evolution\n" return evolution def _create_env_first_team_score_evolution(self) -> str: evolution = "\t\tfirstTeamScore=firstTeamScore+1 if\n" for combination in itertools.permutations(self._available_cards, 4): for beginning_player in range(0, 4): winning_player_number = beginning_player for i in range(0, 4): if i == beginning_player: continue if self._cards_colors[combination[i]] == self._cards_colors[ combination[winning_player_number]]: if self._cards_values[combination[i]] > self._cards_values[ combination[winning_player_number]]: winning_player_number = i if not (winning_player_number == 0 or winning_player_number == 2): continue evolution += "\t\t\t(\n" for player in range(0, 4): evolution += f"\t\t\t\t{self.player_names[player]}Card={combination[player]} and\n" evolution += f"\t\t\t\tbeginningPlayer={beginning_player}) or\n" evolution = evolution.rstrip("\nro ") evolution += ";\n" return evolution def _create_env_second_team_score_evolution(self) -> str: evolution = "\t\tsecondTeamScore=secondTeamScore+1 if\n" for combination in itertools.permutations(self._available_cards, 4): for beginning_player in range(0, 4): winning_player_number = beginning_player for i in range(0, 4): if i == beginning_player: continue if self._cards_colors[combination[i]] == self._cards_colors[ combination[winning_player_number]]: if self._cards_values[combination[i]] > self._cards_values[ combination[winning_player_number]]: winning_player_number = i if not (winning_player_number == 1 or winning_player_number == 3): continue evolution += "\t\t\t(\n" for player in range(0, 4): evolution += f"\t\t\t\t{self.player_names[player]}Card={combination[player]} and\n" evolution += f"\t\t\t\tbeginningPlayer={beginning_player}) or\n" evolution = evolution.rstrip("\nro ") evolution += ";\n" return evolution def _create_env_beginning_player_evolution(self) -> str: evolution = "" for winning_player in range(0, 4): evolution += f"\t\tbeginningPlayer={winning_player} if\n" for combination in itertools.permutations(self._available_cards, 4): for beginning_player in range(0, 4): winning_player_number = beginning_player for i in range(0, 4): if i == beginning_player: continue if self._cards_colors[combination[i]] == self._cards_colors[ combination[winning_player_number]]: if self._cards_values[combination[i]] > self._cards_values[ combination[winning_player_number]]: winning_player_number = i if not (winning_player_number == winning_player): continue evolution += "\t\t\t(\n" for player in range(0, 4): evolution += f"\t\t\t\t{self.player_names[player]}Card={combination[player]} and\n" evolution += f"\t\t\t\tbeginningPlayer={beginning_player}) or\n" evolution = evolution.rstrip("\nro ") evolution += ";\n" return evolution def _create_env_current_player_evolution(self) -> str: evolution = "" for winning_player in range(0, 4): evolution += f"\t\tcurrentPlayer={winning_player} if\n" for combination in itertools.permutations(self._available_cards, 4): for beginning_player in range(0, 4): winning_player_number = beginning_player for i in range(0, 4): if i == beginning_player: continue if self._cards_colors[combination[i]] == self._cards_colors[ combination[winning_player_number]]: if self._cards_values[combination[i]] > self._cards_values[ combination[winning_player_number]]: winning_player_number = i if not (winning_player_number == winning_player): continue evolution += "\t\t\t(\n" for player in range(0, 4): evolution += f"\t\t\t\t{self.player_names[player]}Card={combination[player]} and\n" evolution += f"\t\t\t\tbeginningPlayer={beginning_player}) or\n" previous_player = winning_player - 1 if previous_player == -1: previous_player = 3 evolution += f"\t\t\t(currentPlayer={previous_player} and clock<4);\n" return evolution def _create_env_clock_evolution(self) -> str: evolution = "\t\tsuit=None if clock=4;\n" \ "\t\tclock=0 if clock=4;\n" for clock in range(1, 5): evolution += f"\t\tclock={clock} if clock={clock - 1};\n" for player in self.player_names: evolution += f"\t\t{player}Card=None if clock=4;\n" return evolution def _create_env_player_cards_evolution(self) -> str: evolution = "" for i in range(0, self._number_of_cards * 4): card = self._cards[i] for player_number in range(0, 4): player = self.player_names[player_number] if player == self.player_names[2]: evolution += f"\t\t{player}Card={card} if {self.player_names[0]}.Action=Play{card} " \ f"and currentPlayer=2;\n" else: evolution += f"\t\t{player}Card={card} if {player}.Action=Play{card} and " \ f"currentPlayer={player_number};\n" return evolution def _create_env_suit_evolution(self) -> str: evolution = "" for color in self.card_colors: evolution += f"\t\tsuit={color} if clock=0 and (\n" for i in range(0, self._number_of_cards * 4): card = self._cards[i] if self._cards_colors[card] != color: continue for player in self.player_names: if player == self.player_names[2]: continue evolution += f"\t\t\t{player}.Action=Play{card} or\n" evolution = evolution.rstrip("\nro ") evolution += ");\n" return evolution def _create_env_history_evolution(self) -> str: evolution = "" for i in range(0, self._number_of_cards * 4): card = self._cards[i] evolution += f"\t\t{card}H=true if\n" for player in self.player_names: if player == self.player_names[2]: continue evolution += f"\t\t\t{player}.Action=Play{card} or\n" evolution = evolution.rstrip("\nro ") evolution += ";\n" return evolution def _create_env_n_cards_evolution(self) -> str: evolution = "" for i in range(0, self._number_of_cards * 4): card = self._cards[i] for j in range(1, self._number_of_cards_in_hand + 1): evolution += f"\t\tcardN{j}=None if {self.player_names[0]}.Action=Play{card} and cardN{j}=N{card};\n" return evolution def _create_env_has_color_evolution(self) -> str: evolution = "" for color in self.card_colors: evolution += f"\t\thas{color}=has{color}+-1 if (\n" for i in range(0, self._number_of_cards * 4): card = self._cards[i] if self._cards_colors[card] != color: continue evolution += f"\t\t\t({self.player_names[0]}.Action=Play{card} and (" for j in range(1, self._number_of_cards_in_hand + 1): evolution += f"cardN{j}=N{card} or " evolution = evolution.rstrip(" ro ") evolution += ")) or\n" evolution = evolution.rstrip("\nro ") evolution += ");\n" return evolution def _create_player(self, player_number) -> str: player = f"Agent {self.player_names[player_number]}\n" # if player_name != "ThirdPlayer": # player += self.__create_player_lobsvars() player += self._create_player_vars(player_number) player += self._create_player_actions() player += self._create_player_protocol(player_number) player += self._create_player_evolution(player_number) player += "end Agent\n\n" return player def _create_player_lobsvars(self) -> str: lobsvars = "\tLobsvars = {" for i in range(1, self._number_of_cards_in_hand + 1): lobsvars += f"ThirdPlayer.card{i}" if i != self._number_of_cards_in_hand: lobsvars += ", " lobsvars += "};\n" return lobsvars def _create_player_vars(self, player_number) -> str: vars = "\tVars:\n" for i in range(1, self._number_of_cards_in_hand + 1): vars += f"\t\t{self.player_names[player_number][0]}card{i}: {{" for j in range(0, 4 * self._number_of_cards): vars += f"{self.player_names[player_number][0]}{self._cards[j]}, " vars += "None};\n" for color in self.card_colors: vars += f"\t\thas{color}: 0..{self._number_of_cards_in_hand};\n" vars += "\tend Vars\n" return vars def _create_player_actions(self) -> str: actions = "\tActions = {" for i in range(0, 4 * self._number_of_cards): actions += f"Play{self._cards[i]}, " actions += "Wait};\n" return actions def _create_player_protocol(self, player_number) -> str: protocol = "\tProtocol:\n" for i in range(1, self._number_of_cards_in_hand + 1): for j in range(0, 4 * self._number_of_cards): protocol += f"\t\t{self.player_names[player_number][0]}card{i}=" \ f"{self.player_names[player_number][0]}{self._cards[j]} and " \ f"Environment.currentPlayer={player_number} and Environment.clock<4 and " \ f"(Environment.suit=None or Environment.suit={self._cards_colors[self._cards[j]]} or " \ f"((hasSpade<=0 and Environment.suit=Spade) or (hasClub<=0 and " \ f"Environment.suit=Club) or (hasDiamond<=0 and Environment.suit=Diamond) or " \ f"(hasHeart<=0 and Environment.suit=Heart))): {{Play" + \ self._cards[j] + "};\n" if player_number == 0: for i in range(1, self._number_of_cards_in_hand + 1): for j in range(0, 4 * self._number_of_cards): protocol += "\t\tEnvironment.cardN" + str(i) + "=N" protocol += f"{self._cards[j]} and Environment.currentPlayer=2 and " \ "Environment.clock<4 and " \ "(Environment.suit=None or Environment.suit=" \ f"{self._cards_colors[self._cards[j]]} or " \ "((Environment.hasSpade<=0 and Environment.suit=Spade) or " \ "(Environment.hasClub<=0 and Environment.suit=Club) or " \ "(Environment.hasDiamond<=0 and Environment.suit=Diamond) or " \ "(Environment.hasHeart<=0 and Environment.suit=Heart))): " \ f"{{Play{self._cards[j]}}};\n" if player_number != 0: protocol += f"\t\t!(Environment.currentPlayer={player_number}) or Environment.clock=4: " \ f"{{Wait}};\n" else: protocol += f"\t\t(!(Environment.currentPlayer={player_number}) and " \ f"!(Environment.currentPlayer=2)) or Environment.clock=4: {{Wait}};\n" protocol += "\tend Protocol\n" return protocol def _create_player_evolution(self, player_number) -> str: evolution = "\tEvolution:\n" for i in range(1, self._number_of_cards_in_hand + 1): evolution += f"\t\t{self.player_names[player_number][0]}card{i}=None if\n" for j in range(0, 4 * self._number_of_cards): card = self._cards[j] evolution += f"\t\t\t({self.player_names[player_number][0]}card{i}=" \ f"{self.player_names[player_number][0]}{card} and Action=Play{card}) or\n" evolution = evolution.rstrip("\nro ") evolution += ";\n" for color in self.card_colors: evolution += f"\t\thas{color}=has{color}+-1 if\n" for i in range(1, self._number_of_cards_in_hand + 1): for j in range(0, 4 * self._number_of_cards): card = self._cards[j] if self._cards_colors[card] != color: continue evolution += f"\t\t\t({self.player_names[player_number][0]}card{i}=" \ f"{self.player_names[player_number][0]}{card} and Action=Play{card}) or\n" evolution = evolution.rstrip("\nro ") evolution += ";\n" evolution += "\tend Evolution\n" return evolution def _create_evaluation(self) -> str: evaulation = "Evaluation\n" \ "\tFirstTeamWin if Environment.firstTeamScore>Environment.secondTeamScore and " \ f"Environment.firstTeamScore+Environment.secondTeamScore={self._number_of_cards_in_hand};\n" \ "\tSecondTeamWin if Environment.firstTeamScore<Environment.secondTeamScore and " \ "Environment.firstTeamScore+Environment.secondTeamScore={self.__number_of_cards_in_hand};\n" \ "end Evaluation\n\n" return evaulation def _create_init_states(self) -> str: init_states = "InitStates\n" oponents_cards = [] for k in range(self._number_of_cards_in_hand, self._number_of_cards_in_hand * 2): oponents_cards.append(self._card_ordering[k]) for k in range(self._number_of_cards_in_hand * 3, self._number_of_cards_in_hand * 4): oponents_cards.append(self._card_ordering[k]) oponents_cards.sort() number_of_beginning_states = 0 for combination in itertools.combinations(oponents_cards, self._number_of_cards_in_hand): second_player_cards = combination fourth_player_cards = oponents_cards[:] for card in second_player_cards: fourth_player_cards.remove(card) new_card_ordering = self._card_ordering[:] i = 0 for k in range(self._number_of_cards_in_hand, self._number_of_cards_in_hand * 2): new_card_ordering[k] = second_player_cards[i] i += 1 i = 0 for k in range(self._number_of_cards_in_hand * 3, self._number_of_cards_in_hand * 4): new_card_ordering[k] = fourth_player_cards[i] i += 1 init_states += "\t(Environment.smc=0 and Environment.firstTeamScore=0 and " \ "Environment.secondTeamScore=0 and Environment.beginningPlayer=0 and " \ "Environment.currentPlayer=0 and Environment.clock=0 and " \ "Environment.SPlayerCard=None and Environment.WPlayerCard=None and " \ "Environment.NPlayerCard=None and Environment.EPlayerCard=None and " \ "Environment.suit=None" colors_count = {} i = 0 for player in self.player_names: colors_count[player] = {} for color in self.card_colors: colors_count[player][color] = 0 for j in range(1, self._number_of_cards_in_hand + 1): colors_count[player][self._cards_colors[self._cards[new_card_ordering[i]]]] += 1 i += 1 i = 0 for player in self.player_names: for color in self.card_colors: if player == "NPlayer": init_states += f" and Environment.has{color}={colors_count[player][color]}" else: init_states += f" and {player}.has{color}={colors_count[player][color]}" for player in self.player_names: for j in range(1, self._number_of_cards_in_hand + 1): if player == "NPlayer": init_states += f" and Environment.cardN{j}=N{self._cards[new_card_ordering[i]]}" else: init_states += f" and {player}.{player[0]}card{j}=" \ f"{player[0]}{self._cards[new_card_ordering[i]]}" i += 1 for j in range(0, self._number_of_cards * 4): init_states += f" and Environment.{self._cards[j]}H=false" init_states += ") or\n" number_of_beginning_states += 1 print(f"Number of beginning states: {number_of_beginning_states}") init_states = init_states.rstrip("\nro ") init_states += ";\nend InitStates\n\n" return init_states def _create_groups(self) -> str: groups = "Groups\n" \ "\tg1={SPlayer};\n" \ "end Groups\n\n" return groups def _create_formulae(self) -> str: formulae = "Formulae\n" \ "\t<g1>F FirstTeamWin;\n" \ "end Formulae\n\n" return formulae @staticmethod def generate_random_card_array(length: int) -> [int]: array = [] used = [] for i in range(0, length): used.append(False) for i in range(0, length): number = random.randrange(length) while used[number]: number = random.randrange(length) array.append(number) used[number] = True return array class AbsentMindedBridgeModelIsplGenerator(BridgeModelIsplGenerator): def __init__(self, number_of_cards, number_of_cards_in_hand, card_ordering=None): super().__init__(number_of_cards, number_of_cards_in_hand, card_ordering) def _define_semantics(self) -> str: semantics = "Semantics=MultiAssignment;\n\n" return semantics def _create_environment_obsvars(self): obsvars = "\tObsvars:\n" \ f"\t\tfirstTeamScore: 0..{self._number_of_cards_in_hand};\n" \ f"\t\tsecondTeamScore: 0..{self._number_of_cards_in_hand};\n" \ "\t\tbeginningPlayer: 0..3;\n" \ "\t\tcurrentPlayer: 0..4;\n" \ "\t\tclock: 0..5;\n" obsvars += self._create_env_player_cards_obsvars() obsvars += self._create_env_n_cards_obsvars() obsvars += self._create_env_history_cards_obsvars() obsvars += "\tend Obsvars\n" return obsvars def _create_env_player_cards_obsvars(self) -> str: obsvars = f"\t\t{self.player_names[0]}Card: {{" for j in range(0, 4 * self._number_of_cards): obsvars += f"{self._cards[j]}, " obsvars += "None};\n" return obsvars def _create_env_n_cards_obsvars(self) -> str: obsvars = "" for i in range(1, self._number_of_cards_in_hand + 1): obsvars += f"\t\tcardN{i}: {{" for j in range(0, 4 * self._number_of_cards): obsvars += f"{self._cards[j]}, " obsvars += "None};\n" return obsvars def _create_environment_vars(self): vars = "\tVars:\n" vars += self._create_env_card_vars() vars += self._create_env_n_card_vars() vars += "\t\tsuit: {Spade, Heart, Diamond, Club, None};\n" \ "\tend Vars\n" return vars def _create_env_card_vars(self) -> str: vars = "" for player in self.player_names: if player == self.player_names[0]: continue vars += f"\t\t{player}Card: {{" for j in range(0, 4 * self._number_of_cards): vars += f"{self._cards[j]}, " vars += "None};\n" return vars def _create_env_n_card_vars(self) -> str: vars = "" for i in range(1, self._number_of_cards_in_hand + 1): vars += f"\t\tcurrentCardN{i}: {{" for j in range(0, 4 * self._number_of_cards): vars += f"{self._cards[j]}, " vars += "None};\n" return vars def _create_environment_evolution(self) -> str: evolution = "\tEvolution:\n" for winning_player in range(0, 4): if winning_player % 2 == 0: evolution += "\t\tfirstTeamScore=firstTeamScore+1" else: evolution += "\t\tsecondTeamScore=secondTeamScore+1" evolution += f" and beginningPlayer={winning_player}" \ f" and clock=0 and suit=None and currentPlayer={winning_player}" for player in self.player_names: evolution += f" and {player}Card=None" for j in range(1, self._number_of_cards_in_hand + 1): evolution += f" and cardN{j}=currentCardN{j}" evolution += " if\n" add_or = False for combination in itertools.permutations(self._available_cards, 4): for beginning_player in range(0, 4): winning_player_number = beginning_player for i in range(0, 4): if i == beginning_player: continue if self._cards_colors[combination[i]] == self._cards_colors[combination[winning_player_number]]: if self._cards_values[combination[i]] > self._cards_values[ combination[winning_player_number]]: winning_player_number = i if not (winning_player_number == winning_player): continue if add_or: evolution += " or\n" else: add_or = True evolution += "\t\t\t(" for player in range(0, 4): evolution += f"{self.player_names[player]}Card={combination[player]} and " evolution += f"beginningPlayer={beginning_player} and clock>=4)" evolution += ";\n" for i in range(0, self._number_of_cards * 4): card = self._cards[i] # Player S plays evolution += f"\t\tcurrentPlayer=1 and clock=clock+1 and SPlayerCard={card} and {card}H=true if\n" \ f"\t\t\tcurrentPlayer=0 and clock<4 and clock>0 and SPlayer.Action=Play{card};\n" \ f"\t\tcurrentPlayer=1 and clock=clock+1 and SPlayerCard={card} and {card}H=true" \ f" and suit={self._cards_colors[card]} if\n" \ f"\t\t\tcurrentPlayer=0 and clock<4 and clock=0 and SPlayer.Action=Play{card};\n" # Player S should play, but play Player N card for j in range(1, self._number_of_cards_in_hand + 1): evolution += f"\t\tNPlayerCard={card} and {card}H=true and currentCardN{j}=None if\n" \ f"\t\t\tcurrentPlayer=0 and clock<4 and SPlayer.Action=PlayN{card} and " \ f"currentCardN{j}={card} and NPlayerCard=None;\n" # Player W plays, Player S Wait evolution += f"\t\tcurrentPlayer=2 and clock=clock+1 and WPlayerCard={card} and {card}H=true if\n" \ f"\t\t\tcurrentPlayer=1 and clock>0 and WPlayer.Action=Play{card} and SPlayer.Action=Wait and NPlayerCard=None;\n" \ f"\t\tcurrentPlayer=2 and clock=clock+1 and WPlayerCard={card} and {card}H=true" \ f" and suit={self._cards_colors[card]} if\n" \ f"\t\t\tcurrentPlayer=1 and clock=0 and WPlayer.Action=Play{card} and SPlayer.Action=Wait and NPlayerCard=None;\n" \ f"\t\tcurrentPlayer=3 and clock=clock+2 and WPlayerCard={card} and {card}H=true if\n" \ f"\t\t\tcurrentPlayer=1 and clock>0 and WPlayer.Action=Play{card} and SPlayer.Action=Wait and !(NPlayerCard=None);\n" \ f"\t\tcurrentPlayer=3 and clock=clock+2 and WPlayerCard={card} and {card}H=true" \ f" and suit={self._cards_colors[card]} if\n" \ f"\t\t\tcurrentPlayer=1 and clock=0 and WPlayer.Action=Play{card} and SPlayer.Action=Wait and !(NPlayerCard=None);\n" # Player W plays, Player S Play his card for i2 in range(0, self._number_of_cards * 4): card2 = self._cards[i2] if card == card2: continue evolution += f"\t\tcurrentPlayer=2 and clock=clock+1 and WPlayerCard={card} and {card}H=true" \ f" and SPlayerCard={card2} and {card2}H=true" \ f" and suit={self._cards_colors[card]} if\n" \ f"\t\t\tcurrentPlayer=1 and clock=0 and WPlayer.Action=Play{card} and SPlayer.Action=Play{card2} and NPlayerCard=None;\n" \ f"\t\tcurrentPlayer=3 and clock=clock+2 and WPlayerCard={card} and {card}H=true" \ f" and SPlayerCard={card2} and {card2}H=true" \ f" and suit={self._cards_colors[card]} if\n" \ f"\t\t\tcurrentPlayer=1 and clock=0 and WPlayer.Action=Play{card} and SPlayer.Action=Play{card2} and !(NPlayerCard=None);\n" # Player W plays, Player S Play N card for i2 in range(0, self._number_of_cards * 4): card2 = self._cards[i2] if card == card2: continue for j in range(1, self._number_of_cards_in_hand + 1): evolution += f"\t\tcurrentPlayer=3 and clock=clock+2 and WPlayerCard={card} and {card}H=true" \ f" and NPlayerCard={card2} and {card2}H=true" \ f" and currentCardN{j}=None if\n" \ f"\t\t\tcurrentPlayer=1 and clock>0 and WPlayer.Action=Play{card} " \ f"and SPlayer.Action=PlayN{card2} and NPlayerCard=None" \ f" and currentCardN{j}={card2};\n" \ f"\t\tcurrentPlayer=3 and clock=clock+2 and WPlayerCard={card} and {card}H=true" \ f" and NPlayerCard={card2} and {card2}H=true" \ f" and currentCardN{j}=None" \ f" and suit={self._cards_colors[card]} if\n" \ f"\t\t\tcurrentPlayer=1 and clock=0 and WPlayer.Action=Play{card} and " \ f"SPlayer.Action=PlayN{card2} and NPlayerCard=None" \ f" and currentCardN{j}={card2};\n" evolution += f"\t\tcurrentPlayer=3 and clock=clock+2 and WPlayerCard={card} and {card}H=true if\n" \ f"\t\t\tcurrentPlayer=1 and clock>0 and WPlayer.Action=Play{card} and SPlayer.Action=PlayN{card2} " \ f"and !(NPlayerCard=None);\n" \ f"\t\tcurrentPlayer=3 and clock=clock+2 and WPlayerCard={card} and {card}H=true" \ f" and suit={self._cards_colors[card]} if\n" \ f"\t\t\tcurrentPlayer=1 and clock=0 and WPlayer.Action=Play{card} and SPlayer.Action=PlayN{card2} " \ f"and !(NPlayerCard=None);\n" # Player N Plays for j in range(1, self._number_of_cards_in_hand + 1): evolution += f"\t\tcurrentPlayer=3 and clock=clock+1 and NPlayerCard={card} and {card}H=true " \ f"and currentCardN{j}=None if\n" \ f"\t\t\tcurrentPlayer=2 and clock>0 and clock<4 and SPlayer.Action=PlayN{card} " \ f"and currentCardN{j}={card} and NPlayerCard=None;\n" \ f"\t\tcurrentPlayer=3 and clock=clock+1 and NPlayerCard={card} and {card}H=true and " \ f"currentCardN{j}=None and suit={self._cards_colors[card]} if\n" \ f"\t\t\tcurrentPlayer=2 and clock=0 and SPlayer.Action=PlayN{card} and " \ f"currentCardN{j}={card} and NPlayerCard=None;\n" # Player N should Play, Player S play his own card evolution += f"\t\tSPlayerCard={card} and {card}H=true if\n" \ f"\t\t\tcurrentPlayer=2 and clock<4 and SPlayer.Action=Play{card};\n" # Player E Plays, Player S Wait evolution += f"\t\tcurrentPlayer=0 and clock=clock+1 and EPlayerCard={card} and {card}H=true if\n" \ f"\t\t\tcurrentPlayer=3 and clock>0 and EPlayer.Action=Play{card} and SPlayer.Action=Wait " \ f"and SPlayerCard=None;\n" \ f"\t\tcurrentPlayer=0 and clock=clock+1 and EPlayerCard={card} and {card}H=true" \ f" and suit={self._cards_colors[card]} if\n" \ f"\t\t\tcurrentPlayer=3 and clock=0 and EPlayer.Action=Play{card} and SPlayer.Action=Wait " \ f"and SPlayerCard=None;\n" \ f"\t\tcurrentPlayer=1 and clock=clock+2 and EPlayerCard={card} and {card}H=true if\n" \ f"\t\t\tcurrentPlayer=3 and clock>0 and EPlayer.Action=Play{card} and SPlayer.Action=Wait " \ f"and !(SPlayerCard=None);\n" \ f"\t\tcurrentPlayer=1 and clock=clock+2 and EPlayerCard={card} and {card}H=true" \ f" and suit={self._cards_colors[card]} if\n" \ f"\t\t\tcurrentPlayer=3 and clock=0 and EPlayer.Action=Play{card} and SPlayer.Action=Wait and !(SPlayerCard=None);\n" # Player E Plays, Player S Play his card for i2 in range(0, self._number_of_cards * 4): card2 = self._cards[i2] if card == card2: continue evolution += f"\t\tcurrentPlayer=1 and clock=clock+2 and EPlayerCard={card} and {card}H=true" \ f" and SPlayerCard={card2} and {card2}H=true" \ f" and suit={self._cards_colors[card]} if\n" \ f"\t\t\tcurrentPlayer=3 and clock=0 and EPlayer.Action=Play{card} and SPlayer.Action=Play{card2};\n" \ f"\t\tcurrentPlayer=1 and clock=clock+2 and EPlayerCard={card} and {card}H=true" \ f" and SPlayerCard={card2} and {card2}H=true if\n" \ f"\t\t\tcurrentPlayer=3 and clock>0 and EPlayer.Action=Play{card} and SPlayer.Action=Play{card2};\n" # Player E Plays, Player S Play N card for i2 in range(0, self._number_of_cards * 4): card2 = self._cards[i2] if card == card2: continue for j in range(1, self._number_of_cards_in_hand + 1): evolution += f"\t\tcurrentPlayer=0 and clock=clock+1 and EPlayerCard={card} and {card}H=true " \ f"and NPlayerCard={card2} and {card2}H=true and currentCardN{j}=None " \ f"and suit={self._cards_colors[card]} if\n" \ f"\t\t\tcurrentPlayer=3 and clock=0 and EPlayer.Action=Play{card} " \ f"and SPlayer.Action=PlayN{card2} and NPlayerCard=None " \ f"and currentCardN{j}={card2} and SPlayerCard=None;\n" \ f"\t\tcurrentPlayer=1 and clock=clock+2 and EPlayerCard={card} and {card}H=true " \ f"and NPlayerCard={card2} and {card2}H=true " \ f"and currentCardN{j}=None and suit={self._cards_colors[card]} if\n" \ f"\t\t\tcurrentPlayer=3 and clock=0 and EPlayer.Action=Play{card} " \ f"and SPlayer.Action=PlayN{card2} and NPlayerCard=None and currentCardN{j}={card2} " \ f"and !(SPlayerCard=None);\n" evolution += f"\t\tcurrentPlayer=0 and clock=clock+1 and EPlayerCard={card} and {card}H=true if\n" \ f"\t\t\tcurrentPlayer=3 and clock>0 and EPlayer.Action=Play{card} and SPlayer.Action=PlayN{card2} " \ f"and !(NPlayerCard=None) and SPlayerCard=None;\n" \ f"\t\tcurrentPlayer=1 and clock=clock+2 and EPlayerCard={card} and {card}H=true if\n" \ f"\t\t\tcurrentPlayer=3 and clock>0 and EPlayer.Action=Play{card} and SPlayer.Action=PlayN{card2} " \ f"and !(NPlayerCard=None) and !(SPlayerCard=None);\n" evolution += "\tend Evolution\n" return evolution def _create_player(self, player_number) -> str: player = f"Agent {self.player_names[player_number]}\n" if player_number != 0: player += self._create_player_lobsvars() player += self._create_player_vars(player_number) player += self._create_player_actions(player_number) player += self._create_player_protocol(player_number) player += self._create_player_evolution(player_number) player += "end Agent\n\n" return player def _create_player_lobsvars(self) -> str: lobsvars = "\tLobsvars = {" for player in self.player_names: if player == self.player_names[0]: continue lobsvars += f"{player}Card, " for i in range(1, self._number_of_cards_in_hand + 1): lobsvars += f"currentCardN{i}, " lobsvars += "suit};\n" return lobsvars def _create_player_vars(self, player_number: int) -> str: vars = "\tVars:\n" for i in range(1, self._number_of_cards_in_hand + 1): vars += f"\t\tcard{i}: {{" for j in range(0, 4 * self._number_of_cards): vars += f"{self._cards[j]}, " vars += "None};\n" if player_number != 0: for color in self.card_colors: vars += f"\t\thas{color}: 0..{self._number_of_cards_in_hand};\n" vars += "\tend Vars\n" return vars def _create_player_actions(self, player_number: int) -> str: actions = "\tActions = {" for i in range(0, 4 * self._number_of_cards): actions += f"Play{self._cards[i]}, " if player_number == 0: for i in range(0, 4 * self._number_of_cards): actions += f"PlayN{self._cards[i]}, " actions += "Wait};\n" return actions def _create_player_protocol(self, player_number: int) -> str: protocol = "\tProtocol:\n" for i in range(1, self._number_of_cards_in_hand + 1): for j in range(0, 4 * self._number_of_cards): protocol += f"\t\tcard{i}={self._cards[j]}" if player_number != 0: protocol += f" and Environment.currentPlayer={player_number} and Environment.clock<4 and " \ f"(Environment.suit=None or Environment.suit={self._cards_colors[self._cards[j]]} " \ f"or ((hasSpade<=0 and Environment.suit=Spade) or " \ f"(hasClub<=0 and Environment.suit=Club) or (hasDiamond<=0 and " \ f"Environment.suit=Diamond) or (hasHeart<=0 and Environment.suit=Heart))):" else: protocol += " and Environment.SPlayerCard=None:" protocol += f" {{Play{self._cards[j]}" if player_number == 0: protocol += ", Wait" protocol += "};\n" if player_number == 0: for i in range(1, self._number_of_cards_in_hand + 1): for j in range(0, 4 * self._number_of_cards): protocol += f"\t\tEnvironment.cardN{i}={self._cards[j]}: {{PlayN{self._cards[j]}, Wait}};\n" protocol += "\t\tOther: {Wait};\n" \ "\tend Protocol\n" return protocol def _create_player_evolution(self, player_number: int) -> str: evolution = "\tEvolution:\n" for i in range(1, self._number_of_cards_in_hand + 1): for j in range(0, 4 * self._number_of_cards): evolution += f"\t\tcard{i}=None" if player_number != 0: evolution += f" and has{self._cards_colors[self._cards[j]]}=" \ f"has{self._cards_colors[self._cards[j]]}-1" evolution += f" if card{i}={self._cards[j]} and Action=Play{self._cards[j]}" if player_number == 0: evolution += " and Environment.SPlayerCard=None;\n" else: evolution += f" and Environment.currentPlayer={player_number};\n" evolution += "\tend Evolution\n" return evolution def _create_evaluation(self) -> str: evaulation = f"Evaluation\n" \ f"\tFirstTeamWin if Environment.firstTeamScore>Environment.secondTeamScore " \ f"and Environment.firstTeamScore+Environment.secondTeamScore={self._number_of_cards_in_hand};\n" \ f"\tSecondTeamWin if Environment.firstTeamScore<Environment.secondTeamScore and " \ f"Environment.firstTeamScore+Environment.secondTeamScore={self._number_of_cards_in_hand};\n" \ f"end Evaluation\n\n" return evaulation def _create_init_states(self) -> str: init_states = "InitStates\n" oponents_cards = [] for k in range(self._number_of_cards_in_hand, self._number_of_cards_in_hand * 2): oponents_cards.append(self._card_ordering[k]) for k in range(self._number_of_cards_in_hand * 3, self._number_of_cards_in_hand * 4): oponents_cards.append(self._card_ordering[k]) oponents_cards.sort() number_of_beginning_states = 0 for combination in itertools.combinations(oponents_cards, self._number_of_cards_in_hand): second_player_cards = combination fourth_player_cards = oponents_cards[:] for card in second_player_cards: fourth_player_cards.remove(card) new_card_ordering = self._card_ordering[:] i = 0 for k in range(self._number_of_cards_in_hand, self._number_of_cards_in_hand * 2): new_card_ordering[k] = second_player_cards[i] i += 1 i = 0 for k in range(self._number_of_cards_in_hand * 3, self._number_of_cards_in_hand * 4): new_card_ordering[k] = fourth_player_cards[i] i += 1 init_states += "\t(Environment.firstTeamScore=0 and Environment.secondTeamScore=0 and " \ "Environment.beginningPlayer=0 and Environment.currentPlayer=0 and " \ "Environment.clock=0 and Environment.SPlayerCard=None and " \ "Environment.WPlayerCard=None and Environment.NPlayerCard=None and " \ "Environment.EPlayerCard=None and Environment.suit=None" colors_count = {} i = 0 for player in self.player_names: colors_count[player] = {} for color in self.card_colors: colors_count[player][color] = 0 for j in range(1, self._number_of_cards_in_hand + 1): colors_count[player][self._cards_colors[self._cards[new_card_ordering[i]]]] += 1 i += 1 i = 0 for player in self.player_names: for color in self.card_colors: if player != "NPlayer" and player != 'SPlayer': init_states += f" and {player}.has{color}={colors_count[player][color]}" for player in self.player_names: for j in range(1, self._number_of_cards_in_hand + 1): if player == "NPlayer": init_states += f" and Environment.cardN{j}={self._cards[new_card_ordering[i]]}" \ f" and Environment.currentCardN{j}={self._cards[new_card_ordering[i]]}" else: init_states += f" and {player}.card{j}={self._cards[new_card_ordering[i]]}" i += 1 for j in range(0, self._number_of_cards * 4): init_states += f" and Environment.{self._cards[j]}H=false" init_states += ") or\n" number_of_beginning_states += 1 init_states = init_states.rstrip("\nro ") init_states += ";\nend InitStates\n\n" return init_states def _create_groups(self) -> str: groups = "Groups\n" groups += "\tg1={SPlayer};\n" groups += "end Groups\n\n" return groups def _create_formulae(self) -> str: formulae = "Formulae\n" formulae += "\t<g1>F FirstTeamWin;\n" formulae += "end Formulae\n\n" return formulae if __name__ == "__main__": n = 2 ispl_generator = BridgeModelIsplGenerator(n, n) model_txt = ispl_generator.create_model() file = open("bridge.ispl", "w") file.write(model_txt) file.close()

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61fa2a85412e3dc12da42668a285f9756a7abb1a

845

py

Python

tests/test_hello_svc_acceptance_scenarios.py

pppillai/armageddon

6c9780a3f3e2a7ea50bc1d62e9140dd7067efb7f

[ "MIT" ]

null

tests/test_hello_svc_acceptance_scenarios.py

pppillai/armageddon

6c9780a3f3e2a7ea50bc1d62e9140dd7067efb7f

[ "MIT" ]

null

tests/test_hello_svc_acceptance_scenarios.py

pppillai/armageddon

6c9780a3f3e2a7ea50bc1d62e9140dd7067efb7f

[ "MIT" ]

null

def test_hello_svc_without_param(hello_svc_client): """ Given: hello svc running on cluster And: I have cluster ip address And: I have service port When: I do a get call Then: I should get back 200 Ok And: I should get back string Hi there, !" """ status, response = hello_svc_client.get() assert status == 200 assert response == f"Hi there, !" def test_hello_svc_with_param(hello_svc_client): """ Given: hello svc running on cluster And: I have cluster ip address And: I have service port When: I do a get call with string parameter Then: I should get back 200 Ok And: I should get back string Hi there, <param>!" """ name = "pradeep" status, response = hello_svc_client.get(name=name) assert status == 200 assert response == f"Hi there, {name}!"

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61fbb837045dd9d06e93d9588bbbc7840e374e34

277

py

Python

ann/fme/fme_renderer.py

yt7589/iching

6673da38f4c80e7fd297c86fedc5616aee8ac09b

[ "Apache-2.0" ]

32

2020-04-14T08:32:18.000Z

2022-02-09T07:05:08.000Z

ann/fme/fme_renderer.py

trinh-hoang-hiep/iching

e1feae5741c3cbde535d7a275b01d4f0cf9e21ed

[ "Apache-2.0" ]

1

2020-04-08T10:42:15.000Z

2020-04-15T01:38:03.000Z

ann/fme/fme_renderer.py

trinh-hoang-hiep/iching

e1feae5741c3cbde535d7a275b01d4f0cf9e21ed

[ "Apache-2.0" ]

4

2020-08-25T03:56:46.000Z

2021-05-11T05:55:51.000Z

# class FmeRenderer(object): RENDER_MODE_CONSOLE = 1 RENDER_MODE_GRAPH = 2 def __init__(self, render_mode=RENDER_MODE_CONSOLE): self.name = 'apps.fme.FmeRender' self.render_mode = render_mode def render_obs(self, obs): pass

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