xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

77f5a84ee7e8e3eccfcadfcdc022467f83791c14

46

py

Python

s2g/__init__.py

vishalbelsare/s2g

e94a5a99f3bb5a39c574513eb4e343a09f2f6b74

[ "MIT" ]

20

2017-02-14T15:26:47.000Z

2021-05-11T12:44:40.000Z

s2g/__init__.py

vishalbelsare/s2g

e94a5a99f3bb5a39c574513eb4e343a09f2f6b74

[ "MIT" ]

8

2016-12-22T13:01:23.000Z

2021-07-15T09:53:54.000Z

s2g/__init__.py

caesar0301/python-s2g

e94a5a99f3bb5a39c574513eb4e343a09f2f6b74

[ "MIT" ]

6

2017-02-14T15:29:15.000Z

2019-05-03T00:03:27.000Z

from .shapegraph import * from .bonus import *

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py

Python

controllers/reddit/__init__.py

atas98/telegram-reddit-bot

e021533cb8acaa439ed57dc7e20e1b5a04970af8

[ "MIT" ]

4

2021-03-25T09:10:04.000Z

2021-09-25T07:04:30.000Z

controllers/reddit/__init__.py

atas98/telegram-reddit-bot

e021533cb8acaa439ed57dc7e20e1b5a04970af8

[ "MIT" ]

2

2022-01-10T14:12:31.000Z

2022-01-12T22:56:12.000Z

controllers/reddit/__init__.py

atas98/telegram-reddit-bot

e021533cb8acaa439ed57dc7e20e1b5a04970af8

[ "MIT" ]

1

2021-12-18T08:28:34.000Z

from .reddit import Reddit, Post_Types, Sort_Types, Post_Data __all__ = [Post_Types, Sort_Types, Post_Data, Reddit]

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py

Python

benchmark_function/__init__.py

yshimizu12/BenchmarkFunction

66ad568d092f2c325d685e10c02942745ed33b07

[ "MIT" ]

2

2021-07-16T01:10:43.000Z

2021-07-16T04:49:12.000Z

benchmark_function/__init__.py

yshimizu12/BenchmarkFunction

66ad568d092f2c325d685e10c02942745ed33b07

[ "MIT" ]

null

benchmark_function/__init__.py

yshimizu12/BenchmarkFunction

66ad568d092f2c325d685e10c02942745ed33b07

[ "MIT" ]

1

2021-07-16T05:19:20.000Z

from benchmark_function.benchmark_function import BenchmarkFunction

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bb1a7712dcfcd284bd95f5d2e8d558248c33fdc0

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py

Python

server/rest/__init__.py

Nugetzrul3/ApiServer

ee031dd1659331e41f241402afc96cddf9750ced

[ "MIT" ]

1

2021-11-07T10:05:23.000Z

server/rest/__init__.py

equitypay/api-server

6830501351c944c075e4792f941b23c9ff63e029

[ "MIT" ]

3

2021-09-05T18:22:42.000Z

2021-09-26T06:21:39.000Z

server/rest/__init__.py

equitypay/api-server

6830501351c944c075e4792f941b23c9ff63e029

[ "MIT" ]

2

2020-05-19T13:20:00.000Z

2021-09-26T04:58:41.000Z

from .views import rest

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py

Python

am_i_the_asshole/models/__init__.py

mirandrom/am-i-the-asshole

e7e4f00aa193931d45e4012db5cc65d3679faa90

[ "MIT" ]

1

2020-10-05T16:39:18.000Z

am_i_the_asshole/models/__init__.py

amr-amr/am-i-the-asshole

e7e4f00aa193931d45e4012db5cc65d3679faa90

[ "MIT" ]

null

am_i_the_asshole/models/__init__.py

amr-amr/am-i-the-asshole

e7e4f00aa193931d45e4012db5cc65d3679faa90

[ "MIT" ]

null

from am_i_the_asshole.models.regressor import AitaRegressor from am_i_the_asshole.models.bert_sentence_pooler import BertSentencePooler

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24b70b52fbe7f2300946db1df09edfa253165ee4

264

py

Python

novice/python-unit-testing/code/test_addnumbers.py

Southampton-RSG/2019-03-13-southampton-swc

1f07d82c1bd1f237a19fa7a17bb4765e0364dc88

[ "CC-BY-4.0" ]

1

2021-06-20T11:51:37.000Z

novice/python-unit-testing/code/test_addnumbers.py

Southampton-RSG/2019-03-13-southampton-swc

1f07d82c1bd1f237a19fa7a17bb4765e0364dc88

[ "CC-BY-4.0" ]

1

2019-09-30T21:15:32.000Z

novice/python-unit-testing/code/test_addnumbers.py

Southampton-RSG/2019-03-13-southampton-swc

1f07d82c1bd1f237a19fa7a17bb4765e0364dc88

[ "CC-BY-4.0" ]

null

from addnumbers import addnumbers def test_empty(): assert addnumbers([]) == None def test_single_value(): assert addnumbers([1]) == 1 def test_two_values(): assert addnumbers([1, 2]) == 3 def test_three_values(): assert addnumbers([1, 2, 3]) == 6

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py

Python

Task/Fibonacci-sequence/Python/fibonacci-sequence-5.py

djgoku/RosettaCodeData

91df62d46142e921b3eacdb52b0316c39ee236bc

[ "Info-ZIP" ]

null

Task/Fibonacci-sequence/Python/fibonacci-sequence-5.py

djgoku/RosettaCodeData

91df62d46142e921b3eacdb52b0316c39ee236bc

[ "Info-ZIP" ]

null

Task/Fibonacci-sequence/Python/fibonacci-sequence-5.py

djgoku/RosettaCodeData

91df62d46142e921b3eacdb52b0316c39ee236bc

[ "Info-ZIP" ]

null

def fibFastRec(n): def fib(prvprv, prv, c): if c < 1: return prvprv else: return fib(prv, prvprv + prv, c - 1) return fib(0, 1, n)

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py

Python

src/senda/__init__.py

ebanalyse/senda

d40035df455368d3a439055327a7e08b9517d987

[ "MIT" ]

13

2021-04-27T12:48:44.000Z

2021-11-25T15:31:34.000Z

src/senda/__init__.py

ebanalyse/senda

d40035df455368d3a439055327a7e08b9517d987

[ "MIT" ]

null

src/senda/__init__.py

ebanalyse/senda

d40035df455368d3a439055327a7e08b9517d987

[ "MIT" ]

null

from .Model import * from .angry_tweets import *

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705b4928d1637203f85768713a5bda242e89b715

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py

Python

cachupy/__init__.py

patrickbird/cachupy

d1504f3c3129c926bd9897a6660669f146e64c38

[ "MIT" ]

null

cachupy/__init__.py

patrickbird/cachupy

d1504f3c3129c926bd9897a6660669f146e64c38

[ "MIT" ]

null

cachupy/__init__.py

patrickbird/cachupy

d1504f3c3129c926bd9897a6660669f146e64c38

[ "MIT" ]

null

from .cachupy import Cache

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py

Python

double3/double3sdk/events/__init__.py

CLOMING/winter2021_double

9b920baaeb3736a785a6505310b972c49b5b21e9

[ "Apache-2.0" ]

null

double3/double3sdk/events/__init__.py

CLOMING/winter2021_double

9b920baaeb3736a785a6505310b972c49b5b21e9

[ "Apache-2.0" ]

null

double3/double3sdk/events/__init__.py

CLOMING/winter2021_double

9b920baaeb3736a785a6505310b972c49b5b21e9

[ "Apache-2.0" ]

null

from .events import _Events

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3b51ca829363143c002936e4b2a6ee2de0f98d39

117

py

Python

bot/utils/__init__.py

Bluenix2/WinterBot

a7c546ebe881feabf4f2a97ba65354a9244fed31

[ "MIT" ]

1

2021-07-15T09:36:13.000Z

bot/utils/__init__.py

Bluenix2/WinterBot

a7c546ebe881feabf4f2a97ba65354a9244fed31

[ "MIT" ]

null

bot/utils/__init__.py

Bluenix2/WinterBot

a7c546ebe881feabf4f2a97ba65354a9244fed31

[ "MIT" ]

null

from bot.utils.database import ConnectionUtil, Context from bot.utils.dependencies import get_app, get_bot, get_conn

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8e576c36c004928f38b77f813eed553595c2dcb9

69

py

Python

commondtools/network/__init__.py

M0Rph3U56031769/commondtools

ef29716179f80ec38355e871fa67763c5edfc54c

[ "MIT" ]

1

2020-01-27T05:22:48.000Z

commondtools/network/__init__.py

M0Rph3U56031769/commondtools

ef29716179f80ec38355e871fa67763c5edfc54c

[ "MIT" ]

3

2020-03-31T11:05:14.000Z

2020-11-17T08:50:41.000Z

commondtools/network/__init__.py

M0Rph3U56031769/commondtools

ef29716179f80ec38355e871fa67763c5edfc54c

[ "MIT" ]

null

from .ping import * from .portscan import * from .validreqs import *

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8ec7b274385450d7c653df661612650569057787

252

py

Python

mysite/views.py

ardassmedia13/my_first_app

b63479b65e4bf8360cc95e4d8df57388f6fe7e9a

[ "Apache-2.0" ]

null

mysite/views.py

ardassmedia13/my_first_app

b63479b65e4bf8360cc95e4d8df57388f6fe7e9a

[ "Apache-2.0" ]

null

mysite/views.py

ardassmedia13/my_first_app

b63479b65e4bf8360cc95e4d8df57388f6fe7e9a

[ "Apache-2.0" ]

null

from django.shortcuts import render # Create your views here. def index(request): return render(request,'index.html') def elements(request): return render(request,'elements.html') def generic(request): return render(request,'test.html')

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8edaf2fc1464eabbe86c450a01c10a75f0c02274

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py

Python

src/aecg/utils.py

FDA/aecg-python

561aa3881b51af4d7dfdb2b5030f31e970ba9666

[ "CC0-1.0" ]

2

2021-03-04T05:32:19.000Z

2021-12-12T04:24:43.000Z

src/aecg/utils.py

FDA/aecg-python

561aa3881b51af4d7dfdb2b5030f31e970ba9666

[ "CC0-1.0" ]

null

src/aecg/utils.py

FDA/aecg-python

561aa3881b51af4d7dfdb2b5030f31e970ba9666

[ "CC0-1.0" ]

1

2021-06-07T11:07:45.000Z

""" Utility functions for annotated ECG HL7 XML tools This submodule provides utility functions such as basic printing and plotting. See authors, license and disclaimer at the top level directory of this project. """ import logging import pandas as pd import numpy as np import matplotlib.pyplot as plt from enum import Enum from matplotlib import figure # Python logging ============================================================== logger = logging.getLogger(__name__) class ECG_plot_layout(Enum): """Supported plot layouts Args: Enum: Type of plot layout """ #: Leads stacked vertically STACKED = 1 #: Leads organized in 3 x 4 matrix with full lead II at the bottom THREExFOURxRHYTHM = 2 #: Leads superimposed on top of each other (a.k.a., butterfly plot) SUPERIMPOSED = 3 def plot_aecg(rhythm_data: pd.DataFrame, anns_df: pd.DataFrame = None, ecgl_plot_layout: ECG_plot_layout = ECG_plot_layout.STACKED, fig: figure.Figure = None, dpi: int = 300, textsize: int = 6, ecg_linewidth: float = 0.3, plot_grid: bool = True, grid_color: str = "#a88332", v_offset: float = 1.5, xmin: float = 0.0, xmax: float = 10000.0, ymin: float = -1.5, ymax: float = 1.5, x_margin: float = 280, for_gui: bool = True) -> figure.Figure: """Plots the `rhythm_data` waveform and `anns_df` annotations Args: rhythm_data (pd.DataFrame): aECG waveform as returned by :any:`Aecg.rhythm_as_df` or :any:`Aecg.derived_as_df`. anns_df (pd.DataFrame, optional): aECG annotations. For example, as returned by pd.DataFrame(the_aecg.DERIVEDANNS[0].anns) where the_aecg is an :any:`Aecg` object. Defaults to None. ecgl_plot_layout (ECG_plot_layout, optional): Plot layout. Defaults to ECG_plot_layout.STACKED. fig (figure.Figure, optional): Figure containing the plot. Defaults to None. dpi (int, optional): Plot resolution in dots per inch (dpi). Defaults to 300. textsize (int, optional): Default text fontsize. Defaults to 6. ecg_linewidth (float, optional): Line width for the ECG waveform. Defaults to 0.3. plot_grid (bool, optional): Indicates whether to plot the standard ECG grid. Defaults to True. grid_color (str, optional): Color of the ECG grid. Defaults to "#a88332". v_offset (float, optional): Vertical offset between leads in mV. Defaults to 1.5. xmin (float, optional): X axis minimum value in ms. Defaults to 0.0. xmax (float, optional): X axis maximum value in ms. This value may be adjusted automatically when maintaining aspect ratio. Defaults to 10000.0. ymin (float, optional): Y axis minimum value in mV. Defaults to -1.5. ymax (float, optional): Y axis maximum value in mV. This value may be adjusted automatically when maintaining aspect ratio. Defaults to 1.5. x_margin (float, optional): Margin on the X axis in ms. Defaults to 280. for_gui (bool, optional): Indicates whether to plot is generated for a graphical user interface. If true, the figure will be closed before returning the object so a canvas will be needed to render it . Otherwise, the figure will be return immediately. Defaults to True. Returns: figure.Figure: Plot of the aECG waveforms and its annotations """ if ecgl_plot_layout == ECG_plot_layout.STACKED: fig = plot_stdleads_stacked(rhythm_data=rhythm_data, anns_df=anns_df, fig=fig, dpi=dpi, textsize=textsize, ecg_linewidth=ecg_linewidth, plot_grid=plot_grid, grid_color=grid_color, v_offset=v_offset, xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, x_margin=x_margin, for_gui=for_gui) elif ecgl_plot_layout == ECG_plot_layout.THREExFOURxRHYTHM: # Plot 3x4 always up to 10 s only xmax = xmin + 10000.0 fig = plot_stdleads_matrix(rhythm_data=rhythm_data, anns_df=anns_df, fig=fig, dpi=dpi, textsize=textsize, ecg_linewidth=ecg_linewidth, plot_grid=plot_grid, grid_color=grid_color, v_offset=v_offset, xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, x_margin=x_margin, for_gui=for_gui) elif ecgl_plot_layout == ECG_plot_layout.SUPERIMPOSED: fig = plot_stdleads_stacked(rhythm_data=rhythm_data, anns_df=anns_df, fig=fig, dpi=dpi, textsize=textsize, ecg_linewidth=ecg_linewidth, plot_grid=plot_grid, grid_color=grid_color, v_offset=0, xmin=xmin, xmax=xmax, ymin=ymin-1.5, ymax=ymax+1.5, x_margin=x_margin, for_gui=for_gui) return fig def plot_stdleads_stacked(rhythm_data: pd.DataFrame, anns_df: pd.DataFrame = None, fig: figure.Figure = None, dpi: int = 300, textsize: int = 6, ecg_linewidth: float = 0.3, plot_grid: bool = True, grid_color: str = "#a88332", v_offset: float = 1.5, xmin: float = 0.0, xmax: float = 10000.0, ymin: float = -1.5, ymax: float = 1.5, x_margin: float = 280, for_gui: bool = True) -> figure.Figure: """Plots the waveform and annotations in a stacked or superimposed layout Args: rhythm_data (pd.DataFrame): aECG waveform as returned by :any:`Aecg.rhythm_as_df` or :any:`Aecg.derived_as_df`. anns_df (pd.DataFrame, optional): aECG annotations. For example, as returned by pd.DataFrame(the_aecg.DERIVEDANNS[0].anns) where the_aecg is an :any:`Aecg` object. Defaults to None. fig (figure.Figure, optional): Figure containing the plot. Defaults to None. dpi (int, optional): Plot resolution in dots per inch (dpi). Defaults to 300. textsize (int, optional): Default text fontsize. Defaults to 6. ecg_linewidth (float, optional): Line width for the ECG waveform. Defaults to 0.3. plot_grid (bool, optional): Indicates whether to plot the standard ECG grid. Defaults to True. grid_color (str, optional): Color of the ECG grid. Defaults to "#a88332". v_offset (float, optional): Vertical offset between leads in mV. Set to 0 For a superimposed layout. Defaults to 1.5. xmin (float, optional): X axis minimum value in ms. Defaults to 0.0. xmax (float, optional): X axis maximum value in ms. This value may be adjusted automatically when maintaining aspect ratio. Defaults to 10000.0. ymin (float, optional): Y axis minimum value in mV. Defaults to -1.5. ymax (float, optional): Y axis maximum value in mV. This value may be adjusted automatically when maintaining aspect ratio. Defaults to 1.5. x_margin (float, optional): Margin on the X axis in ms. Defaults to 280. for_gui (bool, optional): Indicates whether to plot is generated for a graphical user interface. If true, the figure will be closed before returning the object so a canvas will be needed to render it . Otherwise, the figure will be return immediately. Defaults to True. Returns: figure.Figure: Plot of the aECG waveforms and its annotations """ # Compute maximum height range based on number of leads ecg_ymin = min(ymin, -min(12, (rhythm_data.shape[1]-1))*v_offset) ecg_ymax = max(v_offset, ymax) # Compute image size ecg_width = (xmax - xmin + x_margin)/40.0 # mm (25 mm/s -> 1 mm x 0.04s) ecg_height = (ecg_ymax - ecg_ymin)*10.0 # mm ( 10 mm/mV -> 1 mm x 0.1 mV) ecg_w_in = ecg_width/25.4 # inches ecg_h_in = ecg_height/25.4 # inches # Figure size if fig is None: fig = plt.figure() else: fig.clear() fig.set_size_inches(ecg_w_in, ecg_h_in) fig.set_dpi(dpi) fig.set_facecolor('w') fig.set_edgecolor('k') ax1 = fig.add_axes([0, 0, 1, 1], frameon=False) # ecg grid if plot_grid: grid_major_x = np.arange(xmin, xmax + x_margin, 200.0) grid_minor_x = np.arange(xmin, xmax + x_margin, 40.0) for xc in grid_major_x: ax1.axvline(x=xc, color=grid_color, linewidth=0.5) for xc in grid_minor_x: ax1.axvline(x=xc, color=grid_color, linewidth=0.2) numleads = min(12, len(rhythm_data.columns) - 1) grid_major_y = np.arange(min(ymin, -numleads * v_offset), max(v_offset, ymax), 0.5) grid_minor_y = np.arange(min(ymin, -numleads * v_offset), max(v_offset, ymax), 0.1) for yc in grid_major_y: ax1.axhline(y=yc, color=grid_color, linewidth=0.5) for yc in grid_minor_y: ax1.axhline(y=yc, color=grid_color, linewidth=0.1) # Plot leads stacked with lead I on top and V6 at the bottom idx = 0 lead_zero = 0 ecglibann_voffset = {"RPEAK": 1.0, "PON": 0.7, "QON": 0.4, "QOFF": 0.7, "TOFF": 0.4} for lead in ["I", "II", "III", "aVR", "aVL", "aVF", "V1", "V2", "V3", "V4", "V5", "V6"]: if lead in rhythm_data.columns: lead_zero = - idx * v_offset # ecg calibration pulse ax1.plot([40, 80, 80, 280, 280, 320], [lead_zero, lead_zero, lead_zero + 1, lead_zero + 1, lead_zero, lead_zero], color='black', linewidth=0.5) # lead name ax1.text(x_margin + 80, lead_zero + 0.55, lead, size=textsize) ax1.plot(rhythm_data.TIME[rhythm_data[lead].notna()] + x_margin, rhythm_data[lead][rhythm_data[lead].notna() ].values + lead_zero, color='black', linewidth=ecg_linewidth) lead_start_time = rhythm_data.TIME[ rhythm_data[lead].notna()].values[0] + x_margin # Plot global annotations if anns_df is not None: if anns_df.shape[0] > 0: ann_voffset = 1.0 for j, ann in anns_df[ anns_df["LEADNAM"] == lead].iterrows(): # Annotation type if ann["ECGLIBANNTYPE"] in ecglibann_voffset.keys(): ann_voffset = ecglibann_voffset[ ann["ECGLIBANNTYPE"]] else: ann_voffset = ann_voffset - 0.3 if ann_voffset < 0.0: ann_voffset = 1.0 ax1.text(ann["TIME"] + lead_start_time, lead_zero + ann_voffset, ann["ECGLIBANNTYPE"], size=textsize-1, color="blue") # Annotation vertical line ann_x = ann["TIME"] + lead_start_time, ax1.plot([ann_x, ann_x], [lead_zero-1.0, lead_zero+1.0], color="blue", linewidth=0.5) idx = idx + 1 # Plot global if anns_df is not None: if anns_df.shape[0] > 0: for idx, ann in anns_df[anns_df["LEADNAM"] == "GLOBAL"].iterrows(): # Annotation type ann_voffset = 1.0 if ann["ECGLIBANNTYPE"] in ecglibann_voffset.keys(): ann_voffset = ecglibann_voffset[ann["ECGLIBANNTYPE"]] ax1.text(ann["TIME"] + xmin + x_margin, lead_zero - ann_voffset, ann["ECGLIBANNTYPE"], size=textsize-1, color="red") # Annotation vertical line ax1.axvline(x=ann["TIME"] + x_margin, color="red", linewidth=0.5, linestyle=":") # Turn off tick labels ax1.set_xticks([]) ax1.set_yticks([]) # Set figure width and height ax1.set_xlim(xmin, xmax + x_margin) ax1.set_ylim(ecg_ymin, ecg_ymax) if for_gui: # Close plt plt.close() return fig def plot_stdleads_matrix(rhythm_data: pd.DataFrame, anns_df: pd.DataFrame = None, fig: figure.Figure = None, dpi: int = 300, textsize: int = 6, ecg_linewidth: float = 0.6, plot_grid: bool = True, grid_color: str = "#a88332", v_offset: float = 1.5, xmin: float = 0.0, xmax: float = 10000.0, ymin: float = -1.5, ymax: float = 1.5, x_margin: float = 280, for_gui: bool = True) -> figure.Figure: """Plots the waveform and annotations in a 3x4 + lead II layout Args: rhythm_data (pd.DataFrame): aECG waveform as returned by :any:`Aecg.rhythm_as_df` or :any:`Aecg.derived_as_df`. anns_df (pd.DataFrame, optional): aECG annotations. For example, as returned by pd.DataFrame(the_aecg.DERIVEDANNS[0].anns) where the_aecg is an :any:`Aecg` object. Defaults to None. fig (figure.Figure, optional): Figure containing the plot. Defaults to None. dpi (int, optional): Plot resolution in dots per inch (dpi). Defaults to 300. textsize (int, optional): Default text fontsize. Defaults to 6. ecg_linewidth (float, optional): Line width for the ECG waveform. Defaults to 0.3. plot_grid (bool, optional): Indicates whether to plot the standard ECG grid. Defaults to True. grid_color (str, optional): Color of the ECG grid. Defaults to "#a88332". v_offset (float, optional): Vertical offset between leads in mV. Defaults to 1.5. xmin (float, optional): X axis minimum value in ms. Defaults to 0.0. xmax (float, optional): X axis maximum value in ms. This value may be adjusted automatically when maintaining aspect ratio. Defaults to 10000.0. ymin (float, optional): Y axis minimum value in mV. Defaults to -1.5. ymax (float, optional): Y axis maximum value in mV. This value may be adjusted automatically when maintaining aspect ratio. Defaults to 1.5. x_margin (float, optional): Margin on the X axis in ms. Defaults to 280. for_gui (bool, optional): Indicates whether to plot is generated for a graphical user interface. If true, the figure will be closed before returning the object so a canvas will be needed to render it . Otherwise, the figure will be return immediately. Defaults to True Returns: figure.Figure: Plot of the aECG waveforms and its annotations """ # Add offsets to the leads to match desired 3x4 layout h_offset = 2500 column_padding = 50 # Check if standard leads are present and, if not, populate with np.nan for lead in ["I", "II", "III", "aVR", "aVL", "aVF", "V1", "V2", "V3", "V4", "V5", "V6"]: if lead not in rhythm_data.columns: rhythm_data[lead] = np.nan beat_plot_col1 = rhythm_data[rhythm_data.TIME < ( h_offset - column_padding)][["TIME", "I", "II", "III"]].copy() beat_plot_col2 = rhythm_data[(rhythm_data.TIME >= h_offset) & (rhythm_data.TIME < (2 * h_offset - column_padding))][ ["TIME", "aVR", "aVF", "aVL"]].copy() beat_plot_col3 = rhythm_data[(rhythm_data.TIME >= (2 * h_offset)) & (rhythm_data.TIME < (3 * h_offset - column_padding))][ ["TIME", "V1", "V2", "V3"]].copy() beat_plot_col4 = rhythm_data[(rhythm_data.TIME >= (3 * h_offset)) & (rhythm_data.TIME < (4 * h_offset - column_padding))][ ["TIME", "V4", "V5", "V6"]].copy() beat_plot = pd.concat( [beat_plot_col1, beat_plot_col2, beat_plot_col3, beat_plot_col4]) anns_matrix = None anns_matrix_col1 = None anns_matrix_col2 = None anns_matrix_col3 = None anns_matrix_col4 = None if anns_df is not None: if anns_df.shape[0] > 0: anns_matrix_col1 = anns_df[anns_df.TIME < ( h_offset - column_padding)].copy() anns_matrix_col2 = anns_df[(anns_df.TIME >= h_offset) & (anns_df.TIME < (2 * h_offset - column_padding))].copy() anns_matrix_col3 = anns_df[(anns_df.TIME >= (2 * h_offset)) & (anns_df.TIME < (3 * h_offset - column_padding))].copy() anns_matrix_col4 = anns_df[(anns_df.TIME >= (3 * h_offset)) & (anns_df.TIME < (4 * h_offset - column_padding))].copy() anns_matrix = pd.concat([anns_matrix_col1, anns_matrix_col2, anns_matrix_col3, anns_matrix_col4]) # Compute maximum height range based on number of leads ecg_ymin = min(ymin, -4*v_offset) ecg_ymax = max(v_offset, ymax) # Compute image size ecg_width = (xmax - xmin + x_margin) / 40.0 # mm (25 mm/s -> 1 mm x 0.04s) # mm ( 10 mm/mV -> 1 mm x 0.1 mV) ecg_height = (ecg_ymax - ecg_ymin) * 10.0 ecg_w_in = ecg_width / 25.4 # inches ecg_h_in = ecg_height / 25.4 # inches # Figure size if fig is None: fig = plt.figure(dpi=dpi) else: fig.clear() fig.set_size_inches(ecg_w_in, ecg_h_in) fig.set_dpi(dpi) fig.set_facecolor('w') fig.set_edgecolor('k') ax1 = fig.add_axes([0, 0, 1, 1], frameon=False) # ecg grid if plot_grid: grid_major_x = np.arange(0, xmax + x_margin, 200) grid_minor_x = np.arange(0, xmax + x_margin, 40) for xc in grid_major_x: ax1.axvline(x=xc, color=grid_color, linewidth=0.5) for xc in grid_minor_x: ax1.axvline(x=xc, color=grid_color, linewidth=0.2) grid_major_y = np.arange(-4 * v_offset, v_offset, 0.5) grid_minor_y = np.arange(-4 * v_offset, v_offset, 0.1) for yc in grid_major_y: ax1.axhline(y=yc, color=grid_color, linewidth=0.5) for yc in grid_minor_y: ax1.axhline(y=yc, color=grid_color, linewidth=0.2) ecglibann_voffset = {"RPEAK": 1.0, "PON": 0.7, "QON": 0.4, "QOFF": 0.7, "TOFF": 0.4} # First column # ecg calibration pulse lead_zero = 0.0 ax1.plot([40, 80, 80, 280, 280, 320], [lead_zero, lead_zero, lead_zero + 1, lead_zero + 1, lead_zero, lead_zero], color='black', linewidth=0.5) # Lead I tmp = plt.text(x_margin + 80, 0.55, 'I', size=textsize) if "I" in rhythm_data.columns: ax1.plot(beat_plot.TIME[beat_plot.I.notna()] + x_margin, beat_plot.I[beat_plot.I.notna()], color='black', linewidth=ecg_linewidth) lead_start_time = beat_plot.TIME[ beat_plot.I.notna()].values[0] + x_margin col_end = beat_plot.TIME[ beat_plot.I.notna()].values[-1] + x_margin if anns_matrix_col1 is not None: for idx, ann in anns_matrix_col1[ anns_matrix_col1["LEADNAM"] == "I"].iterrows(): ann_voffset = 1.0 if ann["ECGLIBANNTYPE"] in ecglibann_voffset.keys(): ann_voffset = ecglibann_voffset[ann["ECGLIBANNTYPE"]] ann_x = ann["TIME"] + lead_start_time if ann_x <= col_end: ax1.plot([ann_x, ann_x], [lead_zero-1.0, lead_zero+1.0], color="blue", linewidth=0.5) ax1.text(ann_x, lead_zero + ann_voffset, ann["ECGLIBANNTYPE"], size=textsize-1, color="blue") # ecg calibration pulse lead_zero = - v_offset ax1.plot([40, 80, 80, 280, 280, 320], [lead_zero, lead_zero, lead_zero + 1, lead_zero + 1, lead_zero, lead_zero], color='black', linewidth=0.5) # Lead II ax1.text(x_margin + 80, 0.55 + lead_zero, 'II', size=textsize) if "II" in rhythm_data.columns: beat_plot.II = beat_plot.II + lead_zero ax1.plot(beat_plot.TIME[beat_plot.II.notna()] + x_margin, beat_plot.II[beat_plot.II.notna()], color='black', linewidth=ecg_linewidth) lead_start_time = beat_plot.TIME[ beat_plot.II.notna()].values[0] + x_margin col_end = beat_plot.TIME[ beat_plot.II.notna()].values[-1] + x_margin if anns_matrix_col1 is not None: for idx, ann in anns_matrix_col1[ anns_matrix_col1["LEADNAM"] == "II"].iterrows(): ann_voffset = 1.0 if ann["ECGLIBANNTYPE"] in ecglibann_voffset.keys(): ann_voffset = ecglibann_voffset[ann["ECGLIBANNTYPE"]] ann_x = ann["TIME"] + lead_start_time if ann_x < col_end: ax1.plot([ann_x, ann_x], [lead_zero-1.0, lead_zero+1.0], color="blue", linewidth=0.5) ax1.text(ann_x, lead_zero + ann_voffset, ann["ECGLIBANNTYPE"], size=textsize-1, color="blue") # ecg calibration pulse lead_zero = - 2 * v_offset ax1.plot([40, 80, 80, 280, 280, 320], [lead_zero, lead_zero, lead_zero + 1, lead_zero + 1, lead_zero, lead_zero], color='black', linewidth=0.5) # Lead III ax1.text(x_margin + 80, 0.55 + lead_zero, 'III', size=textsize) if "III" in rhythm_data.columns: beat_plot.III = beat_plot.III + lead_zero ax1.plot(beat_plot.TIME[beat_plot.III.notna()] + x_margin, beat_plot.III[beat_plot.III.notna()], color='black', linewidth=ecg_linewidth) lead_start_time = beat_plot.TIME[ beat_plot.III.notna()].values[0] + x_margin col_end = beat_plot.TIME[ beat_plot.III.notna()].values[-1] + x_margin if anns_matrix_col1 is not None: for idx, ann in anns_matrix_col1[ anns_matrix_col1["LEADNAM"] == "III"].iterrows(): ann_voffset = 1.0 if ann["ECGLIBANNTYPE"] in ecglibann_voffset.keys(): ann_voffset = ecglibann_voffset[ann["ECGLIBANNTYPE"]] ann_x = ann["TIME"] + lead_start_time if ann_x <= col_end: ax1.plot([ann_x, ann_x], [lead_zero-1.0, lead_zero+1.0], color="blue", linewidth=0.5) ax1.text(ann_x, lead_zero + ann_voffset, ann["ECGLIBANNTYPE"], size=textsize-1, color="blue") # Second column # ecg calibration pulse lead_zero = 0 ax1.plot(np.array([40, 80, 80, 280, 280, 320]) + h_offset, [lead_zero, lead_zero, lead_zero + 1, lead_zero + 1, lead_zero, lead_zero], color='black', linewidth=0.5) # Lead aVR ax1.text(h_offset + x_margin + 80, 0.55, 'aVR', size=textsize) if "aVR" in rhythm_data.columns and\ len(beat_plot.TIME[beat_plot.aVR.notna()]) > 0: ax1.plot(beat_plot.TIME[beat_plot.aVR.notna()] + x_margin, beat_plot.aVR[beat_plot.aVR.notna()], color='black', linewidth=ecg_linewidth) lead_start_time = beat_plot.TIME[ beat_plot.aVR.notna()].values[0] + x_margin col_end = beat_plot.TIME[ beat_plot.aVR.notna()].values[-1] + x_margin if anns_matrix_col2 is not None: for idx, ann in anns_matrix_col2[ anns_matrix_col2["LEADNAM"] == "aVR"].iterrows(): ann_voffset = 1.0 if ann["ECGLIBANNTYPE"] in ecglibann_voffset.keys(): ann_voffset = ecglibann_voffset[ann["ECGLIBANNTYPE"]] ann_x = ann["TIME"] + lead_start_time if ann_x <= col_end: ax1.plot([ann_x, ann_x], [lead_zero-1.0, lead_zero+1.0], color="blue", linewidth=0.5) ax1.text(ann_x, lead_zero + ann_voffset, ann["ECGLIBANNTYPE"], size=textsize-1, color="blue") # ecg calibration pulse lead_zero = - v_offset ax1.plot(np.array([40, 80, 80, 280, 280, 320]) + h_offset, [lead_zero, lead_zero, lead_zero + 1, lead_zero + 1, lead_zero, lead_zero], color='black', linewidth=0.5) # Lead aVL ax1.text(h_offset + x_margin + 80, 0.55 + lead_zero, 'aVL', size=textsize) if "aVL" in rhythm_data.columns and\ len(beat_plot.TIME[beat_plot.aVL.notna()]) > 0: beat_plot.aVL = beat_plot.aVL + lead_zero ax1.plot(beat_plot.TIME[beat_plot.aVL.notna()] + x_margin, beat_plot.aVL[beat_plot.aVL.notna()], color='black', linewidth=ecg_linewidth) lead_start_time = beat_plot.TIME[ beat_plot.aVL.notna()].values[0] + x_margin col_end = beat_plot.TIME[ beat_plot.aVL.notna()].values[-1] + x_margin if anns_matrix_col2 is not None: for idx, ann in anns_matrix_col2[ anns_matrix_col2["LEADNAM"] == "aVL"].iterrows(): ann_voffset = 1.0 if ann["ECGLIBANNTYPE"] in ecglibann_voffset.keys(): ann_voffset = ecglibann_voffset[ann["ECGLIBANNTYPE"]] ann_x = ann["TIME"] + lead_start_time if ann_x <= col_end: ax1.plot([ann_x, ann_x], [lead_zero-1.0, lead_zero+1.0], color="blue", linewidth=0.5) ax1.text(ann_x, lead_zero + ann_voffset, ann["ECGLIBANNTYPE"], size=textsize-1, color="blue") lead_zero = - 2 * v_offset ax1.plot(np.array([40, 80, 80, 280, 280, 320]) + h_offset, [lead_zero, lead_zero, lead_zero + 1, lead_zero + 1, lead_zero, lead_zero], color='black', linewidth=0.5) # Lead aVF ax1.text(h_offset + x_margin + 80, 0.55 + lead_zero, 'aVF', size=textsize) if "aVF" in rhythm_data.columns and\ len(beat_plot.TIME[beat_plot.aVF.notna()]) > 0: beat_plot.aVF = beat_plot.aVF + lead_zero ax1.plot(beat_plot.TIME[beat_plot.aVF.notna()] + x_margin, beat_plot.aVF[beat_plot.aVF.notna()], color='black', linewidth=ecg_linewidth) lead_start_time = beat_plot.TIME[ beat_plot.aVF.notna()].values[0] + x_margin col_end = beat_plot.TIME[ beat_plot.aVF.notna()].values[-1] + x_margin if anns_matrix_col2 is not None: for idx, ann in anns_matrix_col2[ anns_matrix_col2["LEADNAM"] == "aVF"].iterrows(): ann_voffset = 1.0 if ann["ECGLIBANNTYPE"] in ecglibann_voffset.keys(): ann_voffset = ecglibann_voffset[ann["ECGLIBANNTYPE"]] ann_x = ann["TIME"] + lead_start_time if ann_x <= col_end: ax1.plot([ann_x, ann_x], [lead_zero-1.0, lead_zero+1.0], color="blue", linewidth=0.5) ax1.text(ann_x, lead_zero + ann_voffset, ann["ECGLIBANNTYPE"], size=textsize-1, color="blue") # Third column # ecg calibration pulse lead_zero = 0 ax1.plot(np.array([40, 80, 80, 280, 280, 320]) + 2*h_offset, [lead_zero, lead_zero, lead_zero + 1, lead_zero + 1, lead_zero, lead_zero], color='black', linewidth=0.5) # Lead V1 ax1.text(2 * h_offset + x_margin + 80, 0.55, 'V1', size=textsize) if "V1" in rhythm_data.columns and\ len(beat_plot.TIME[beat_plot.V1.notna()]) > 0: ax1.plot(beat_plot.TIME[beat_plot.V1.notna()] + x_margin, beat_plot.V1[beat_plot.V1.notna()], color='black', linewidth=ecg_linewidth) lead_start_time = beat_plot.TIME[ beat_plot.V1.notna()].values[0] + x_margin col_end = beat_plot.TIME[ beat_plot.V1.notna()].values[-1] + x_margin if anns_matrix_col3 is not None: for idx, ann in anns_matrix_col3[ anns_matrix_col3["LEADNAM"] == "V1"].iterrows(): ann_voffset = 1.0 if ann["ECGLIBANNTYPE"] in ecglibann_voffset.keys(): ann_voffset = ecglibann_voffset[ann["ECGLIBANNTYPE"]] ann_x = ann["TIME"] + lead_start_time if ann_x <= col_end: ax1.plot([ann_x, ann_x], [lead_zero-1.0, lead_zero+1.0], color="blue", linewidth=0.5) ax1.text(ann_x, lead_zero + ann_voffset, ann["ECGLIBANNTYPE"], size=textsize-1, color="blue") # ecg calibration pulse lead_zero = - v_offset ax1.plot(np.array([40, 80, 80, 280, 280, 320]) + 2*h_offset, [lead_zero, lead_zero, lead_zero + 1, lead_zero + 1, lead_zero, lead_zero], color='black', linewidth=0.5) # Lead V2 ax1.text(2 * h_offset + x_margin + 80, 0.55 + lead_zero, 'V2', size=textsize) if "V2" in rhythm_data.columns and\ len(beat_plot.TIME[beat_plot.V2.notna()]) > 0: beat_plot.V2 = beat_plot.V2 + lead_zero ax1.plot(beat_plot.TIME[beat_plot.V2.notna()] + x_margin, beat_plot.V2[beat_plot.V2.notna()], color='black', linewidth=ecg_linewidth) lead_start_time = beat_plot.TIME[ beat_plot.V2.notna()].values[0] + x_margin col_end = beat_plot.TIME[ beat_plot.V2.notna()].values[-1] + x_margin if anns_matrix_col3 is not None: for idx, ann in anns_matrix_col3[ anns_matrix_col3["LEADNAM"] == "V2"].iterrows(): ann_voffset = 1.0 if ann["ECGLIBANNTYPE"] in ecglibann_voffset.keys(): ann_voffset = ecglibann_voffset[ann["ECGLIBANNTYPE"]] ann_x = ann["TIME"] + lead_start_time if ann_x <= col_end: ax1.plot([ann_x, ann_x], [lead_zero-1.0, lead_zero+1.0], color="blue", linewidth=0.5) ax1.text(ann_x, lead_zero + ann_voffset, ann["ECGLIBANNTYPE"], size=textsize-1, color="blue") # ecg calibration pulse lead_zero = - 2 * v_offset ax1.plot(np.array([40, 80, 80, 280, 280, 320]) + 2*h_offset, [lead_zero, lead_zero, lead_zero + 1, lead_zero + 1, lead_zero, lead_zero], color='black', linewidth=0.5) # Lead V3 ax1.text(2 * h_offset + x_margin + 80, 0.55 + lead_zero, 'V3', size=textsize) if "V3" in rhythm_data.columns and\ len(beat_plot.TIME[beat_plot.V3.notna()]) > 0: beat_plot.V3 = beat_plot.V3 + lead_zero ax1.plot(beat_plot.TIME[beat_plot.V3.notna()] + x_margin, beat_plot.V3[beat_plot.V3.notna()], color='black', linewidth=ecg_linewidth) lead_start_time = beat_plot.TIME[ beat_plot.V3.notna()].values[0] + x_margin col_end = beat_plot.TIME[ beat_plot.V3.notna()].values[-1] + x_margin if anns_matrix_col3 is not None: for idx, ann in anns_matrix_col3[ anns_matrix_col3["LEADNAM"] == "V3"].iterrows(): ann_voffset = 1.0 if ann["ECGLIBANNTYPE"] in ecglibann_voffset.keys(): ann_voffset = ecglibann_voffset[ann["ECGLIBANNTYPE"]] ann_x = ann["TIME"] + lead_start_time if ann_x <= col_end: ax1.plot([ann_x, ann_x], [lead_zero-1.0, lead_zero+1.0], color="blue", linewidth=0.5) ax1.text(ann_x, lead_zero + ann_voffset, ann["ECGLIBANNTYPE"], size=textsize-1, color="blue") # Fourth column # ecg calibration pulse lead_zero = 0 ax1.plot(np.array([40, 80, 80, 280, 280, 320]) + 3*h_offset, [lead_zero, lead_zero, lead_zero + 1, lead_zero + 1, lead_zero, lead_zero], color='black', linewidth=0.5) # Lead V4 ax1.text(3 * h_offset + x_margin + 80, 0.55, 'V4', size=textsize) if "V4" in rhythm_data.columns and\ len(beat_plot.TIME[beat_plot.V4.notna()]) > 0: ax1.plot(beat_plot.TIME[beat_plot.V4.notna()] + x_margin, beat_plot.V4[beat_plot.V4.notna()], color='black', linewidth=ecg_linewidth) lead_start_time = beat_plot.TIME[ beat_plot.V4.notna()].values[0] + x_margin col_end = beat_plot.TIME[ beat_plot.V4.notna()].values[-1] + x_margin if anns_matrix_col4 is not None: for idx, ann in anns_matrix_col4[ anns_matrix_col4["LEADNAM"] == "V4"].iterrows(): ann_voffset = 1.0 if ann["ECGLIBANNTYPE"] in ecglibann_voffset.keys(): ann_voffset = ecglibann_voffset[ann["ECGLIBANNTYPE"]] ann_x = ann["TIME"] + lead_start_time if ann_x <= col_end: ax1.plot([ann_x, ann_x], [lead_zero-1.0, lead_zero+1.0], color="blue", linewidth=0.5) ax1.text(ann_x, lead_zero + ann_voffset, ann["ECGLIBANNTYPE"], size=textsize-1, color="blue") # ecg calibration pulse lead_zero = - v_offset ax1.plot(np.array([40, 80, 80, 280, 280, 320]) + 3*h_offset, [lead_zero, lead_zero, lead_zero + 1, lead_zero + 1, lead_zero, lead_zero], color='black', linewidth=0.5) # Lead V5 ax1.text(3 * h_offset + x_margin + 80, 0.55 + lead_zero, 'V5', size=textsize) if "V5" in rhythm_data.columns and\ len(beat_plot.TIME[beat_plot.V5.notna()]) > 0: beat_plot.V5 = beat_plot.V5 + lead_zero ax1.plot(beat_plot.TIME[beat_plot.V5.notna()] + x_margin, beat_plot.V5[beat_plot.V5.notna()], color='black', linewidth=ecg_linewidth) lead_start_time = beat_plot.TIME[ beat_plot.V5.notna()].values[0] + x_margin col_end = beat_plot.TIME[ beat_plot.V5.notna()].values[-1] + x_margin if anns_matrix_col4 is not None: for idx, ann in anns_matrix_col4[ anns_matrix_col4["LEADNAM"] == "V5"].iterrows(): ann_voffset = 1.0 if ann["ECGLIBANNTYPE"] in ecglibann_voffset.keys(): ann_voffset = ecglibann_voffset[ann["ECGLIBANNTYPE"]] ann_x = ann["TIME"] + lead_start_time if ann_x <= col_end: ax1.plot([ann_x, ann_x], [lead_zero-1.0, lead_zero+1.0], color="blue", linewidth=0.5) ax1.text(ann_x, lead_zero + ann_voffset, ann["ECGLIBANNTYPE"], size=textsize-1, color="blue") # ecg calibration pulse lead_zero = - 2 * v_offset ax1.plot(np.array([40, 80, 80, 280, 280, 320]) + 3*h_offset, [lead_zero, lead_zero, lead_zero + 1, lead_zero + 1, lead_zero, lead_zero], color='black', linewidth=0.5) # Lead V6 ax1.text(3 * h_offset + x_margin + 80, 0.55 + lead_zero, 'V6', size=textsize) if "V6" in rhythm_data.columns and\ len(beat_plot.TIME[beat_plot.V6.notna()]) > 0: beat_plot.V6 = beat_plot.V6 + lead_zero ax1.plot(beat_plot.TIME[beat_plot.V6.notna()] + x_margin, beat_plot.V6[beat_plot.V6.notna()], color='black', linewidth=ecg_linewidth) lead_start_time = beat_plot.TIME[ beat_plot.V6.notna()].values[0] + x_margin col_end = beat_plot.TIME[ beat_plot.V6.notna()].values[-1] + x_margin if anns_matrix_col4 is not None: for idx, ann in anns_matrix_col4[ anns_matrix_col4["LEADNAM"] == "V6"].iterrows(): ann_voffset = 1.0 if ann["ECGLIBANNTYPE"] in ecglibann_voffset.keys(): ann_voffset = ecglibann_voffset[ann["ECGLIBANNTYPE"]] ann_x = ann["TIME"] + lead_start_time if ann_x <= col_end: ax1.plot([ann_x, ann_x], [lead_zero-1.0, lead_zero+1.0], color="blue", linewidth=0.5) ax1.text(ann_x, lead_zero + ann_voffset, ann["ECGLIBANNTYPE"], size=textsize-1, color="blue") # Rhythm strip # ecg calibration pulse lead_zero = - 0.5 - 3 * v_offset ax1.plot(np.array([40, 80, 80, 280, 280, 320]), [lead_zero, lead_zero, lead_zero + 1, lead_zero + 1, lead_zero, lead_zero], color='black', linewidth=0.5) ax1.text(x_margin + 80, lead_zero + 0.55, 'II', size=textsize) if "II" in rhythm_data.columns: ax1.plot(rhythm_data.TIME + x_margin, rhythm_data.II + lead_zero, color='black', linewidth=ecg_linewidth) lead_start_time = rhythm_data.TIME[ rhythm_data.II.notna()].values[0] + x_margin col_end = rhythm_data.TIME[ rhythm_data.II.notna()].values[-1] + x_margin if anns_df is not None: if anns_df.shape[0] > 0: for idx, ann in anns_df[anns_df["LEADNAM"] == "II"].iterrows(): ann_voffset = 1.0 if ann["ECGLIBANNTYPE"] in ecglibann_voffset.keys(): ann_voffset = ecglibann_voffset[ann["ECGLIBANNTYPE"]] ann_x = ann["TIME"] + lead_start_time if ann_x <= col_end: ax1.plot([ann_x, ann_x], [lead_zero-1.0, lead_zero+1.0], color="blue", linewidth=0.5) ax1.text(ann_x, lead_zero + ann_voffset, ann["ECGLIBANNTYPE"], size=textsize-1, color="blue") # Plot global annotations if anns_matrix is not None: if anns_matrix.shape[0] > 0: for idx, ann in anns_matrix[ anns_matrix["LEADNAM"] == "GLOBAL"].iterrows(): ann_voffset = 1.0 if ann["ECGLIBANNTYPE"] in ecglibann_voffset.keys(): ann_voffset = ecglibann_voffset[ann["ECGLIBANNTYPE"]] # Columns ann_x = ann["TIME"] + xmin + x_margin ax1.plot([ann_x, ann_x], [-0.5 - 2 * v_offset, ymax-0.5], color="red", linewidth=0.5, linestyle=":") # Lead II strip at the bottom ax1.plot([ann_x, ann_x], [-1.0 - 3 * v_offset, 1.0 - 0.5 - 3 * v_offset], color="red", linewidth=0.5, linestyle=":") ax1.text(ann_x, - 3 * v_offset - ann_voffset, ann["ECGLIBANNTYPE"], size=textsize-1, color="red") # Turn off tick labels ax1.set_xticks([]) ax1.set_yticks([]) # Set figure width and height ax1.set_xlim(xmin, xmax + x_margin) ax1.set_ylim(ecg_ymin, ecg_ymax) if for_gui: # Close plt tmp = plt.close() return fig def ratio_of_missing_samples(waveform_data: pd.DataFrame) -> float: """Returns the ration of missing samples in a waveform Calculates the total number of samples as well as the number of samples reported as np.nan values (i.e., missing) and returns the ration of missing over the total number of samples. Args: waveform_data (pd.DataFrame): Waveform data like the one returned by :any:`Aecg.rhythm_as_df` Returns: float: ration of number of missing over the total number of samples """ total_samples = (waveform_data.shape[1] - 1) * waveform_data.shape[0] not_nans = waveform_data.drop(columns=["TIME"]).count().sum() num_nans = total_samples - not_nans missing_ratio = num_nans / total_samples return missing_ratio

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43,054

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0.751096

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null

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6

d941d95cc530a79ef9a953e17e71fc87d1ed8b4f

201

py

Python

Code/odooerp/odoo-8.0/openerp/addons/account/tests/__init__.py

zhupangithub/WEBERP

714512082ec5c6db07cbf6af0238ceefe2d2c1a5

[ "MIT" ]

null

Code/odooerp/odoo-8.0/openerp/addons/account/tests/__init__.py

zhupangithub/WEBERP

714512082ec5c6db07cbf6af0238ceefe2d2c1a5

[ "MIT" ]

null

Code/odooerp/odoo-8.0/openerp/addons/account/tests/__init__.py

zhupangithub/WEBERP

714512082ec5c6db07cbf6af0238ceefe2d2c1a5

[ "MIT" ]

3

2020-10-08T14:42:10.000Z

2022-01-28T14:12:29.000Z

from . import test_tax from . import test_search from . import test_reconciliation from . import test_account_move_closed_period from . import test_fiscal_position from . import test_product_id_change

28.714286

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d94a49249ecf348e7e94c0cdb73f38ac2f6973a1

14,277

py

Python

test/hummingbot/connector/derivative/test_perpetual_budget_checker.py

BGTCapital/hummingbot

2c50f50d67cedccf0ef4d8e3f4c8cdce3dc87242

[ "Apache-2.0" ]

2

2022-03-03T10:00:27.000Z

2022-03-08T13:57:56.000Z

test/hummingbot/connector/derivative/test_perpetual_budget_checker.py

BGTCapital/hummingbot

2c50f50d67cedccf0ef4d8e3f4c8cdce3dc87242

[ "Apache-2.0" ]

6

2022-01-31T15:44:54.000Z

2022-03-06T04:27:12.000Z

test/hummingbot/connector/derivative/test_perpetual_budget_checker.py

BGTCapital/hummingbot

2c50f50d67cedccf0ef4d8e3f4c8cdce3dc87242

[ "Apache-2.0" ]

1

2022-02-22T11:03:02.000Z

import unittest from decimal import Decimal from test.mock.mock_perp_connector import MockPerpConnector from hummingbot.connector.derivative.perpetual_budget_checker import PerpetualBudgetChecker from hummingbot.connector.exchange.paper_trade.paper_trade_exchange import QuantizationParams from hummingbot.connector.utils import combine_to_hb_trading_pair from hummingbot.core.data_type.order_candidate import PerpetualOrderCandidate from hummingbot.core.data_type.trade_fee import TradeFeeSchema from hummingbot.core.event.events import OrderType, TradeType class PerpetualBudgetCheckerTest(unittest.TestCase): def setUp(self) -> None: super().setUp() self.base_asset = "COINALPHA" self.quote_asset = "HBOT" self.trading_pair = f"{self.base_asset}-{self.quote_asset}" trade_fee_schema = TradeFeeSchema( maker_percent_fee_decimal=Decimal("0.01"), taker_percent_fee_decimal=Decimal("0.02") ) self.exchange = MockPerpConnector(trade_fee_schema) self.budget_checker = self.exchange.budget_checker def test_populate_collateral_fields_buy_order(self): order_candidate = PerpetualOrderCandidate( trading_pair=self.trading_pair, is_maker=True, order_type=OrderType.LIMIT, order_side=TradeType.BUY, amount=Decimal("10"), price=Decimal("2"), ) populated_candidate = self.budget_checker.populate_collateral_entries(order_candidate) self.assertEqual(self.quote_asset, populated_candidate.order_collateral.token) self.assertEqual(Decimal("20"), populated_candidate.order_collateral.amount) self.assertEqual(self.quote_asset, populated_candidate.percent_fee_collateral.token) self.assertEqual(Decimal("0.2"), populated_candidate.percent_fee_collateral.amount) self.assertEqual(self.quote_asset, populated_candidate.percent_fee_value.token) self.assertEqual(Decimal("0.2"), populated_candidate.percent_fee_value.amount) self.assertEqual(0, len(populated_candidate.fixed_fee_collaterals)) self.assertIsNone(populated_candidate.potential_returns) # order results in position open def test_populate_collateral_fields_taker_buy_order(self): order_candidate = PerpetualOrderCandidate( trading_pair=self.trading_pair, is_maker=False, order_type=OrderType.LIMIT, order_side=TradeType.BUY, amount=Decimal("10"), price=Decimal("2"), ) populated_candidate = self.budget_checker.populate_collateral_entries(order_candidate) self.assertEqual(self.quote_asset, populated_candidate.order_collateral.token) self.assertEqual(Decimal("20"), populated_candidate.order_collateral.amount) self.assertEqual(self.quote_asset, populated_candidate.percent_fee_collateral.token) self.assertEqual(Decimal("0.4"), populated_candidate.percent_fee_collateral.amount) self.assertEqual(self.quote_asset, populated_candidate.percent_fee_value.token) self.assertEqual(Decimal("0.4"), populated_candidate.percent_fee_value.amount) self.assertEqual(0, len(populated_candidate.fixed_fee_collaterals)) self.assertIsNone(populated_candidate.potential_returns) # order results in position open def test_populate_collateral_fields_buy_order_with_leverage(self): order_candidate = PerpetualOrderCandidate( trading_pair=self.trading_pair, is_maker=True, order_type=OrderType.LIMIT, order_side=TradeType.BUY, amount=Decimal("10"), price=Decimal("2"), leverage=Decimal("2") ) populated_candidate = self.budget_checker.populate_collateral_entries(order_candidate) self.assertEqual(self.quote_asset, populated_candidate.order_collateral.token) self.assertEqual(Decimal("10"), populated_candidate.order_collateral.amount) self.assertEqual(self.quote_asset, populated_candidate.percent_fee_collateral.token) self.assertEqual(Decimal("0.2"), populated_candidate.percent_fee_collateral.amount) self.assertEqual(self.quote_asset, populated_candidate.percent_fee_value.token) self.assertEqual(Decimal("0.2"), populated_candidate.percent_fee_value.amount) self.assertEqual(0, len(populated_candidate.fixed_fee_collaterals)) self.assertIsNone(populated_candidate.potential_returns) # order results in position open def test_populate_collateral_fields_sell_order(self): order_candidate = PerpetualOrderCandidate( trading_pair=self.trading_pair, is_maker=True, order_type=OrderType.LIMIT, order_side=TradeType.SELL, amount=Decimal("10"), price=Decimal("2"), ) populated_candidate = self.budget_checker.populate_collateral_entries(order_candidate) self.assertEqual(self.quote_asset, populated_candidate.order_collateral.token) self.assertEqual(Decimal("20"), populated_candidate.order_collateral.amount) self.assertEqual(self.quote_asset, populated_candidate.percent_fee_collateral.token) self.assertEqual(Decimal("0.2"), populated_candidate.percent_fee_collateral.amount) self.assertEqual(self.quote_asset, populated_candidate.percent_fee_value.token) self.assertEqual(Decimal("0.2"), populated_candidate.percent_fee_value.amount) self.assertEqual(0, len(populated_candidate.fixed_fee_collaterals)) self.assertIsNone(populated_candidate.potential_returns) # order results in position open def test_populate_collateral_fields_sell_order_with_leverage(self): order_candidate = PerpetualOrderCandidate( trading_pair=self.trading_pair, is_maker=True, order_type=OrderType.LIMIT, order_side=TradeType.SELL, amount=Decimal("10"), price=Decimal("2"), leverage=Decimal("2"), ) populated_candidate = self.budget_checker.populate_collateral_entries(order_candidate) self.assertEqual(self.quote_asset, populated_candidate.order_collateral.token) self.assertEqual(Decimal("10"), populated_candidate.order_collateral.amount) self.assertEqual(self.quote_asset, populated_candidate.percent_fee_collateral.token) self.assertEqual(Decimal("0.2"), populated_candidate.percent_fee_collateral.amount) self.assertEqual(self.quote_asset, populated_candidate.percent_fee_value.token) self.assertEqual(Decimal("0.2"), populated_candidate.percent_fee_value.amount) self.assertEqual(0, len(populated_candidate.fixed_fee_collaterals)) self.assertIsNone(populated_candidate.potential_returns) # order results in position open def test_populate_collateral_fields_percent_fees_in_third_token(self): pfc_token = "PFC" trade_fee_schema = TradeFeeSchema( percent_fee_token=pfc_token, maker_percent_fee_decimal=Decimal("0.01"), taker_percent_fee_decimal=Decimal("0.01"), ) exchange = MockPerpConnector(trade_fee_schema) pfc_quote_pair = combine_to_hb_trading_pair(self.quote_asset, pfc_token) exchange.set_balanced_order_book( # the quote to pfc price will be 1:2 trading_pair=pfc_quote_pair, mid_price=1.5, min_price=1, max_price=2, price_step_size=1, volume_step_size=1, ) budget_checker: PerpetualBudgetChecker = exchange.budget_checker order_candidate = PerpetualOrderCandidate( trading_pair=self.trading_pair, is_maker=True, order_type=OrderType.LIMIT, order_side=TradeType.BUY, amount=Decimal("10"), price=Decimal("2"), leverage=Decimal("2"), ) populated_candidate = budget_checker.populate_collateral_entries(order_candidate) self.assertEqual(self.quote_asset, populated_candidate.order_collateral.token) self.assertEqual(Decimal("10"), populated_candidate.order_collateral.amount) self.assertEqual(pfc_token, populated_candidate.percent_fee_collateral.token) self.assertEqual(Decimal("0.4"), populated_candidate.percent_fee_collateral.amount) self.assertEqual(pfc_token, populated_candidate.percent_fee_value.token) self.assertEqual(Decimal("0.4"), populated_candidate.percent_fee_value.amount) self.assertEqual(0, len(populated_candidate.fixed_fee_collaterals)) self.assertIsNone(populated_candidate.potential_returns) # order results in position open def test_populate_collateral_for_position_close(self): order_candidate = PerpetualOrderCandidate( trading_pair=self.trading_pair, is_maker=True, order_type=OrderType.LIMIT, order_side=TradeType.SELL, amount=Decimal("10"), price=Decimal("2"), leverage=Decimal("2"), position_close=True, ) populated_candidate = self.budget_checker.populate_collateral_entries(order_candidate) self.assertIsNone(populated_candidate.order_collateral) # the collateral is the contract itself self.assertIsNone(populated_candidate.percent_fee_collateral) self.assertIsNone(populated_candidate.percent_fee_value) self.assertEqual(0, len(populated_candidate.fixed_fee_collaterals)) self.assertEqual(self.quote_asset, populated_candidate.potential_returns.token) self.assertEqual(Decimal("19.8"), populated_candidate.potential_returns.amount) def test_adjust_candidate_sufficient_funds(self): self.exchange.set_balance(self.quote_asset, Decimal("100")) order_candidate = PerpetualOrderCandidate( trading_pair=self.trading_pair, is_maker=True, order_type=OrderType.LIMIT, order_side=TradeType.BUY, amount=Decimal("10"), price=Decimal("2"), ) adjusted_candidate = self.budget_checker.adjust_candidate(order_candidate) self.assertEqual(Decimal("10"), adjusted_candidate.amount) self.assertEqual(self.quote_asset, adjusted_candidate.order_collateral.token) self.assertEqual(Decimal("20"), adjusted_candidate.order_collateral.amount) self.assertEqual(self.quote_asset, adjusted_candidate.percent_fee_collateral.token) self.assertEqual(Decimal("0.2"), adjusted_candidate.percent_fee_collateral.amount) self.assertEqual(self.quote_asset, adjusted_candidate.percent_fee_value.token) self.assertEqual(Decimal("0.2"), adjusted_candidate.percent_fee_value.amount) self.assertEqual(0, len(adjusted_candidate.fixed_fee_collaterals)) self.assertIsNone(adjusted_candidate.potential_returns) # order results in position open def test_adjust_candidate_buy_insufficient_funds_partial_adjustment_allowed(self): q_params = QuantizationParams( trading_pair=self.trading_pair, price_precision=8, price_decimals=2, order_size_precision=8, order_size_decimals=2, ) self.exchange.set_quantization_param(q_params) self.exchange.set_balance(self.quote_asset, Decimal("10")) order_candidate = PerpetualOrderCandidate( trading_pair=self.trading_pair, is_maker=True, order_type=OrderType.LIMIT, order_side=TradeType.BUY, amount=Decimal("10"), price=Decimal("2"), ) adjusted_candidate = self.budget_checker.adjust_candidate(order_candidate, all_or_none=False) self.assertEqual(Decimal("4.95"), adjusted_candidate.amount) # 5 * .99 self.assertEqual(self.quote_asset, adjusted_candidate.order_collateral.token) self.assertEqual(Decimal("9.9"), adjusted_candidate.order_collateral.amount) # 4.95 * 2 self.assertEqual(self.quote_asset, adjusted_candidate.percent_fee_collateral.token) self.assertEqual(Decimal("0.099"), adjusted_candidate.percent_fee_collateral.amount) # 9.9 * 0.01 self.assertEqual(self.quote_asset, adjusted_candidate.percent_fee_value.token) self.assertEqual(Decimal("0.099"), adjusted_candidate.percent_fee_value.amount) # 9.9 * 0.01 self.assertEqual(0, len(adjusted_candidate.fixed_fee_collaterals)) self.assertIsNone(adjusted_candidate.potential_returns) # order results in position open def test_adjust_candidate_sell_insufficient_funds_partial_adjustment_allowed(self): q_params = QuantizationParams( trading_pair=self.trading_pair, price_precision=8, price_decimals=2, order_size_precision=8, order_size_decimals=2, ) self.exchange.set_quantization_param(q_params) self.exchange.set_balance(self.quote_asset, Decimal("10")) order_candidate = PerpetualOrderCandidate( trading_pair=self.trading_pair, is_maker=True, order_type=OrderType.LIMIT, order_side=TradeType.SELL, amount=Decimal("10"), price=Decimal("2"), ) adjusted_candidate = self.budget_checker.adjust_candidate(order_candidate, all_or_none=False) self.assertEqual(Decimal("4.95"), adjusted_candidate.amount) # 5 * .99 self.assertEqual(self.quote_asset, adjusted_candidate.order_collateral.token) self.assertEqual(Decimal("9.9"), adjusted_candidate.order_collateral.amount) # 4.95 * 2 self.assertEqual(self.quote_asset, adjusted_candidate.percent_fee_collateral.token) self.assertEqual(Decimal("0.099"), adjusted_candidate.percent_fee_collateral.amount) # 9.9 * 0.01 self.assertEqual(self.quote_asset, adjusted_candidate.percent_fee_value.token) self.assertEqual(Decimal("0.099"), adjusted_candidate.percent_fee_value.amount) # 9.9 * 0.01 self.assertEqual(0, len(adjusted_candidate.fixed_fee_collaterals)) self.assertIsNone(adjusted_candidate.potential_returns) # order results in position open

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106

0.72193

1,614

14,277

6.070632

0.084263

0.105634

0.073689

0.077159

0.871709

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null

0

1

0

null

0

6

794c140085a1b064a2e760575ac7ddd4aa5577d8

142

py

Python

cmc/python/__init__.py

hschwane/offline_production

e14a6493782f613b8bbe64217559765d5213dc1e

[ "MIT" ]

1

2020-12-24T22:00:01.000Z

cmc/python/__init__.py

hschwane/offline_production

e14a6493782f613b8bbe64217559765d5213dc1e

[ "MIT" ]

null

cmc/python/__init__.py

hschwane/offline_production

e14a6493782f613b8bbe64217559765d5213dc1e

[ "MIT" ]

3

2020-07-17T09:20:29.000Z

2021-03-30T16:44:18.000Z

from icecube import icetray,dataclasses,sim_services from icecube.load_pybindings import load_pybindings load_pybindings(__name__, __path__)

28.4

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0.880282

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142

6.277778

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1

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1

0

6

79a1e2628569761d819316ed465a5bdf7b788119

143

py

Python

oslo/__init__.py

Mehrad0711/oslo

873d771a68bc380903947010da0b66f58f60e496

[ "Apache-2.0" ]

null

oslo/__init__.py

Mehrad0711/oslo

873d771a68bc380903947010da0b66f58f60e496

[ "Apache-2.0" ]

null

oslo/__init__.py

Mehrad0711/oslo

873d771a68bc380903947010da0b66f58f60e496

[ "Apache-2.0" ]

null

28.6

77

0.825175

20

143

5.85

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0.031496

0.111888

143

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1

0

null

0

1

0

1

0

1

0

6

79b8cd00464bc5302b926ee6622a6d08e27cf1c0

87

py

Python

example/articles/tests/perms/__init__.py

Formulka/django-fperms

88b8fa3dd87075a56d8bfeb2b9993c578c22694e

[ "MIT" ]

3

2019-03-29T09:50:45.000Z

2021-05-01T21:11:33.000Z

example/articles/tests/perms/__init__.py

Formulka/django-perms

88b8fa3dd87075a56d8bfeb2b9993c578c22694e

[ "MIT" ]

2

2018-04-12T00:54:05.000Z

2018-04-12T16:32:42.000Z

example/articles/tests/perms/__init__.py

Formulka/django-perms

88b8fa3dd87075a56d8bfeb2b9993c578c22694e

[ "MIT" ]

1

2018-07-13T14:42:07.000Z

from .generic import * from .model import * from .object import * from .field import *

17.4

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87

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0.183908

87

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true

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null

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1

0

1

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6

8dca6c5fe2c9cf1d6a6d311f3f9528b7eb81cfb5

19

py

Python

proc/__init__.py

nomadiq/maunakini

835cf11573129ce5e614382c4d12f3bc9be4c6c5

[ "MIT" ]

1

2022-02-03T16:28:54.000Z

proc/__init__.py

nomadiq/maunakini

835cf11573129ce5e614382c4d12f3bc9be4c6c5

[ "MIT" ]

null

proc/__init__.py

nomadiq/maunakini

835cf11573129ce5e614382c4d12f3bc9be4c6c5

[ "MIT" ]

1

2022-02-03T16:25:59.000Z

from .proc import *

19

0.736842

3

19

4.666667

1

0

0.157895

19

1

19

0.875

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1

0

true

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null

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1

0

1

0

6

8dda7cefccdac6f0a8f8ccf597df7a23e1982766

928

py

Python

10-Making a scatter plot.py

mnabavi84/dcamp-intro-dscience-python

de6dbdf7328e0cdfaab218c01589db269abb100c

[ "MIT" ]

null

10-Making a scatter plot.py

mnabavi84/dcamp-intro-dscience-python

de6dbdf7328e0cdfaab218c01589db269abb100c

[ "MIT" ]

null

10-Making a scatter plot.py

mnabavi84/dcamp-intro-dscience-python

de6dbdf7328e0cdfaab218c01589db269abb100c

[ "MIT" ]

null

# Explore the data print(cellphone.head()) # Create a scatter plot of the data from the DataFrame cellphone plt.scatter(cellphone.x, cellphone.y) # Add labels plt.ylabel('Latitude') plt.xlabel('Longitude') # Display the plot plt.show() # Change the marker color to red plt.scatter(cellphone.x, cellphone.y, color='red') # Add labels plt.ylabel('Latitude') plt.xlabel('Longitude') # Display the plot plt.show() # Change the marker shape to square plt.scatter(cellphone.x, cellphone.y, color='red', marker='s') # Add labels plt.ylabel('Latitude') plt.xlabel('Longitude') # Display the plot plt.show() # Change the transparency to 0.1 plt.scatter(cellphone.x, cellphone.y, color='red', marker='s', alpha=0.1) # Add labels plt.ylabel('Latitude') plt.xlabel('Longitude') # Display the plot plt.show()

17.846154

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124

928

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0.290323

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928

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1

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1

0

null

0

1

0

6

5c05e1e780f57d02f7ad285145df43eafff29192

5,960

py

Python

finnish_media_scrapers/htmltotext.py

hsci-r/finnish-media-scraper

b63b54e3cdc2c55b426eeeeb9656da45a8a9ed4f

[ "MIT" ]

6

2021-06-29T10:31:01.000Z

2022-03-16T16:02:31.000Z

finnish_media_scrapers/htmltotext.py

hsci-r/finnish-media-scraper

b63b54e3cdc2c55b426eeeeb9656da45a8a9ed4f

[ "MIT" ]

6

2021-06-28T15:09:03.000Z

2022-01-10T12:10:05.000Z

finnish_media_scrapers/htmltotext.py

hsci-r/finnish-media-scraper

b63b54e3cdc2c55b426eeeeb9656da45a8a9ed4f

[ "MIT" ]

1

2021-07-17T00:35:09.000Z

"""Functions to extract article plain texts from the YLE/HS/IL/IS HTML articles """ import re from typing import TextIO, Union from bs4 import BeautifulSoup, NavigableString def extract_text_from_svyle_html(html: Union[str, TextIO]) -> str: """Extract article text from Svenska YLE article HTML Args: html (Union[str,TextIO]): a string or a file-like object containing the article HTML Raises: ValueError: The layout of the article was not recognized, or the article parsed as empty Returns: str: article text """ soup = BeautifulSoup(html, 'lxml') elem = soup.select_one('article#main-content') if elem is None: raise ValueError("Article layout not recognized") for elem_to_remove in soup.select('aside#id-article__tags'): elem_to_remove.extract() for elem_to_remove in soup.select('#comments'): elem_to_remove.extract() for elem_to_remove in soup.select('.ydd-share-buttons'): elem_to_remove.extract() for tag in ['h1', 'h2', 'h3', 'h4', 'h5', 'h6', 'h7', 'p', 'div']: for block_elem in elem.find_all(tag): block_elem.insert_after(NavigableString('\n\n')) txt = elem.get_text().strip() if txt == "": raise ValueError("Parsing results in an empty article") return txt def extract_text_from_yle_html(html: Union[str, TextIO]) -> str: """Extract article text from YLE article HTML Args: html (Union[str,TextIO]): a string or a file-like object containing the article HTML Raises: ValueError: The layout of the article was not recognized, or the article parsed as empty Returns: str: article text """ soup = BeautifulSoup(html, 'lxml') elem = soup.select_one('.yle__article') if elem is None: elem = soup.select_one('#yle__section--article') if elem is None: elem = soup.select_one('article.content') if elem is None: raise ValueError("Article layout not recognized") for tag in ['h1', 'h2', 'h3', 'h4', 'h5', 'h6', 'h7', 'p', 'div']: for block_elem in elem.find_all(tag): block_elem.insert_after(NavigableString('\n\n')) txt = elem.get_text().strip() if txt == "": raise ValueError("Parsing results in an empty article") return txt def extract_text_from_is_html(html: Union[str, TextIO]) -> str: """Extract article text from Ilta-Sanomat article HTML Args: html (Union[str,TextIO]): a string or a file-like object containing the article HTML Raises: ValueError: The layout of the article was not recognized, or the article parsed as empty Returns: str: article text """ soup = BeautifulSoup(html, 'lxml') elem = soup.select_one( 'article.single-article,article.article--m,article.article--l,article.article--xl-picture-top,article.article--xl-title-top') if elem is None: raise ValueError("Article layout not recognized") for tag in ['h1', 'h2', 'h3', 'h4', 'h5', 'h6', 'h7', 'p', 'div']: for block_elem in elem.find_all(tag): block_elem.insert_after(NavigableString('\n\n')) txt = elem.get_text().strip() if txt == "": raise ValueError("Parsing results in an empty article") return txt def extract_text_from_il_html(html: Union[str, TextIO]) -> str: """Extract article text from Iltalehti article HTML Args: html (Union[str,TextIO]): a string or a file-like object containing the article HTML Raises: ValueError: The layout of the article was not recognized, or the article parsed as empty Returns: str: article text """ soup = BeautifulSoup(html, 'lxml') soup = soup.select_one('.article-content') if soup is None: raise ValueError("Article layout not recognized") for tag in ['h1', 'h2', 'h3', 'h4', 'h5', 'h6', 'h7', 'p', 'div']: for block_elem in soup.find_all(tag): block_elem.insert_after(NavigableString('\n\n')) txt = soup.get_text().strip() if txt == "": raise ValueError("Parsing results in an empty article") return txt def extract_text_from_hs_html(html: Union[str, TextIO]) -> str: """Extract article text from Helsingin Sanomat article HTML Args: html (Union[str,TextIO]): a string or a file-like object containing the article HTML Raises: ValueError: The layout of the article was not recognized, or the article parsed as empty Returns: str: article text """ soup = BeautifulSoup(html, 'lxml') elem = soup.select_one('#__nuxt,article.article--xxl') if elem is not None: soup = elem else: elem = soup.find('main') if elem is not None: soup = elem elem = soup.select_one('div#page-main-content + article') if elem is not None: soup = elem else: elem = soup.select_one('div#page-main-content,#paid-content') if elem is not None: soup = elem else: raise ValueError("Article layout not recognized") for elem in soup.find_all('aside'): elem.extract() for elem in soup.select('section.article-body + div'): elem.extract() for elem_to_remove in soup.select('div.article-info'): elem_to_remove.extract() for elem_to_remove in soup.select('div.related-articles'): elem_to_remove.extract() for elem_to_remove in soup.select('div.article-actions'): elem_to_remove.extract() for tag in ['h1', 'h2', 'h3', 'h4', 'h5', 'h6', 'h7', 'p', 'div']: for block_elem in soup.find_all(tag): block_elem.insert_after(NavigableString('\n\n')) txt = soup.get_text() txt = txt.replace("\xad", "") txt = re.sub("\n\n+", "\n\n", txt) txt = txt.strip() if txt == "": raise ValueError("Parsing results in an empty article") return txt

35.058824

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0.048847

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0

0.008

0.244966

5,960

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false

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0

0.13

0

null

0

1

0

null

0

6

a50ce6809db33721cc47fbb9073e3f4265f0b647

13,146

py

Python

solver/prolog_attribute_equation_evaluator.py

levilucio/SyVOLT

7526ec794d21565e3efcc925a7b08ae8db27d46a

[ "MIT" ]

3

2017-06-02T19:26:27.000Z

2021-06-14T04:25:45.000Z

solver/prolog_attribute_equation_evaluator.py

levilucio/SyVOLT

7526ec794d21565e3efcc925a7b08ae8db27d46a

[ "MIT" ]

8

2016-08-24T07:04:07.000Z

2017-05-26T16:22:47.000Z

solver/prolog_attribute_equation_evaluator.py

levilucio/SyVOLT

7526ec794d21565e3efcc925a7b08ae8db27d46a

[ "MIT" ]

1

2019-10-31T06:00:23.000Z

''' Created on 2015-01-16 @author: levi ''' from pyswip import Prolog from attribute_equation_solver import AttributeEquationSolver class PrologAttributeEquationEvaluator(AttributeEquationSolver): """ Simple constraint solver based on prolog for string equations in path conditions. Requires pyswip, a bridge between python and prolog to be installed. For information on how to install pyswip see: https://code.google.com/p/pyswip/ """ # to generate fresh var ID names varID = 0 # Keep the variable names in the prolog expression for all attributes of the same # name connected to the same object. This is necessary because an object may have connected # to it more than one attribute with the same name, which means the attribute has multiple # constraints for that object. varNameDatabase = {} def __init__(self, verbosity): self.verbosity = verbosity def newVarID(self): old_varID = self.varID self.varID += 1 return "V" + str(old_varID) # def build_equation_expression(self, node, pathCondition, variablesInExpression, concatsInExpression): # """ # helper for building the attribute equations by recursively going through the operations associated # to the left hand side and to the right hand side of an equation # """ # # # in case it's an attribute, return the object's ID # if pathCondition.vs[node]['mm__'] == 'Attribute': # variablesInExpression.append("X" + str(node)) # return "X" + str(node) # # in case it's a constant, return its value as a list # elif pathCondition.vs[node]['mm__'] == 'Constant': # constant = pathCondition.vs[node]['name'] # print "------> " + constant # constAsList = "[" # for c in range(0,len(constant)): # constAsList += "'" + constant[c] + "'" # if c < len(constant) - 1: # constAsList += "," # constAsList += "]" # return constAsList # # it's a concat operation # else: # # get the arguments of the concat operation # arg1Edge = [i for i in pathCondition.neighbors(node,1) if pathCondition.vs[i]['mm__'] == 'hasArgs'][0] # arg2Edge = [i for i in pathCondition.neighbors(node,1) if pathCondition.vs[i]['mm__'] == 'hasArgs'][0] # arg1 = pathCondition.neighbors(arg1Edge,1)[0] # arg2 = pathCondition.neighbors(arg2Edge,1)[0] # newVar = self.newVarID() # # # add the concat operation to the set of append predicates in the body of the rule # concatsInExpression.append("append(" + self.build_equation_expression(arg1, pathCondition, variablesInExpression, concatsInExpression) + "," + self.build_equation_expression(arg2, pathCondition, variablesInExpression, concatsInExpression) + "," + newVar + ")") # # # return the newly created variable # return newVar # def __call__(self, pathCondition): # """ # Evaluates attribute equations by producing a Prolog predicate out of them and attempting to find a solution for that predicate. # The predicate has as arguments the attributes of the path condition for which a solution needs to exist such that the path condition is possible. # If a solution is found then the evaluator returns true, otherwise false. # """ # # clauseBody = "" # variablesInExpression = [] # concatsInExpression = [] # # # grab all the equation nodes in the path condition # equationNodes = self._find_nodes_with_mm(pathCondition, "Equation") # # now build all the equations # if equationNodes != []: # for equationNode in range(0,len(equationNodes)): # # get the left and the right expressions of the equation # leftExprEdge = [i for i in pathCondition.neighbors(equationNodes[equationNode],1) if pathCondition.vs[i]['mm__'] == 'leftExpr'][0] # rightExprEdge = [i for i in pathCondition.neighbors(equationNodes[equationNode],1) if pathCondition.vs[i]['mm__'] == 'rightExpr'][0] # # leftExprNode = pathCondition.neighbors(leftExprEdge,1)[0] # rightExprNode = pathCondition.neighbors(rightExprEdge,1)[0] # # leftExpr = self.build_equation_expression(leftExprNode, pathCondition, variablesInExpression, concatsInExpression) # rightExpr = self.build_equation_expression(rightExprNode, pathCondition, variablesInExpression, concatsInExpression) # # if equationNode < len(equationNodes)-1: # clauseBody += leftExpr + "=" + rightExpr + "," # else: # clauseBody += leftExpr + "=" + rightExpr # # if concatsInExpression != []: # clauseBody += "," # for concat in concatsInExpression: # clauseBody += concat # # clauseHead = "solve(" # # variablesInExpression = list(set(variablesInExpression)) # for var in range(0,len(variablesInExpression)): # if var < len(variablesInExpression)-1: # clauseHead += variablesInExpression[var] + "," # else: # clauseHead += variablesInExpression[var] # clauseHead += ")" # # prologInput = clauseHead + ":-" + clauseBody # # if self.verbosity >= 2 : # print "\nChecking with Prolog:" # print "----------------" # print prologInput # print "\n" # # p = Prolog() # p.assertz(prologInput) # # l = list(p.query(clauseHead)) # # print "Clause head: " + clauseHead # result = list(p.query(clauseHead)) # print "Prolog result:" # print result # # if result == []: # if self.verbosity >= 2 : print "Prolog check failed!" # return False # else: # if self.verbosity >= 2 : print "Prolog check succeeded!" # return True def build_equation_expression(self, node, pathCondition, variablesInExpression, concatsInExpression, varParentObjects): """ helper for building the attribute equations by recursively going through the operations associated to the left hand side and to the right hand side of an equation """ # in case it's an attribute, return the object's ID if pathCondition.vs[node]['mm__'] == 'Attribute': # get the parent object of the attribute attrEdgeMatch = [i for i in pathCondition.neighbors(node,2) if pathCondition.vs[i]['mm__'] == 'hasAttribute_S'] attrEdgeApply = [i for i in pathCondition.neighbors(node,2) if pathCondition.vs[i]['mm__'] == 'hasAttribute_T'] if attrEdgeMatch != []: parentObject = pathCondition.neighbors(attrEdgeMatch[0],2)[0] else: parentObject = pathCondition.neighbors(attrEdgeApply[0],2)[0] # check if a variable for an attribute having the same name and belonging to the same object has already been created # and in case it has just return it, otherwise create a new variable attrName = pathCondition.vs[node]['name'] varDatabaseKey = str(parentObject) + attrName if not varDatabaseKey in set(self.varNameDatabase.keys()): self.varNameDatabase[varDatabaseKey] = "X" + str(node) variablesInExpression.append(self.varNameDatabase[varDatabaseKey]) return self.varNameDatabase[varDatabaseKey] else: variablesInExpression.append(self.varNameDatabase[varDatabaseKey]) return self.varNameDatabase[varDatabaseKey] # in case it's a constant, return its value as a list elif pathCondition.vs[node]['mm__'] == 'Constant': constant = pathCondition.vs[node]['name'] constAsList = "[" for c in range(0,len(constant)): constAsList += "'" + constant[c] + "'" if c < len(constant) - 1: constAsList += "," constAsList += "]" return constAsList # it's a concat operation else: # get the arguments of the concat operation arg1Edge = [i for i in pathCondition.neighbors(node,1) if pathCondition.vs[i]['mm__'] == 'hasArgs'][0] arg2Edge = [i for i in pathCondition.neighbors(node,1) if pathCondition.vs[i]['mm__'] == 'hasArgs'][1] arg1 = pathCondition.neighbors(arg1Edge,1)[0] arg2 = pathCondition.neighbors(arg2Edge,1)[0] newVar = self.newVarID() # add the concat operation to the set of append predicates in the body of the rule concatsInExpression.append("append(" + self.build_equation_expression(arg1, pathCondition, variablesInExpression, concatsInExpression, varParentObjects) + "," + self.build_equation_expression(arg2, pathCondition, variablesInExpression, concatsInExpression, varParentObjects) + "," + newVar + ")") # return the newly created variable return newVar def __call__(self, pathCondition): """ Evaluates attribute equations by producing a Prolog predicate out of them and attempting to find a solution for that predicate. The predicate has as arguments the attributes of the path condition for which a solution needs to exist such that the path condition is possible. If a solution is found then the evaluator returns true, otherwise false. """ clauseBody = "" variablesInExpression = [] concatsInExpression = [] varParentObjects = [] # grab all the equation nodes in the path condition equationNodes = self._find_nodes_with_mm(pathCondition, "Equation") # now build all the equations if equationNodes != []: for equationNode in range(0,len(equationNodes)): # get the left and the right expressions of the equation leftExprEdge = [i for i in pathCondition.neighbors(equationNodes[equationNode],1) if pathCondition.vs[i]['mm__'] == 'leftExpr'][0] rightExprEdge = [i for i in pathCondition.neighbors(equationNodes[equationNode],1) if pathCondition.vs[i]['mm__'] == 'rightExpr'][0] leftExprNode = pathCondition.neighbors(leftExprEdge,1)[0] rightExprNode = pathCondition.neighbors(rightExprEdge,1)[0] leftExpr = self.build_equation_expression(leftExprNode, pathCondition, variablesInExpression, concatsInExpression, varParentObjects) rightExpr = self.build_equation_expression(rightExprNode, pathCondition, variablesInExpression, concatsInExpression, varParentObjects) if equationNode < len(equationNodes)-1: clauseBody += leftExpr + "=" + rightExpr + "," else: clauseBody += leftExpr + "=" + rightExpr for concat in concatsInExpression: clauseBody += "," clauseBody += concat clauseHead = "solve(" # variablesInExpression = list(set(variablesInExpression)) for var in range(0,len(variablesInExpression)): if var < len(variablesInExpression)-1: clauseHead += variablesInExpression[var] + "," else: clauseHead += variablesInExpression[var] clauseHead += ")" prologInput = clauseHead + ":-" + clauseBody if self.verbosity >= 2 : print "\nChecking with Prolog:" print "----------------" print prologInput print "\n" p = Prolog() p.assertz(prologInput) # l = list(p.query(clauseHead)) if self.verbosity >= 2 : print "Clause head: " + clauseHead result = list(p.query(clauseHead)) print "Prolog result: " + str(result) if result == []: if self.verbosity >= 2 : print "Prolog check failed!" return False else: if self.verbosity >= 2 : print "Prolog check succeeded!" return True def _find_nodes_with_mm(self, graph, mm_names): """ Find all objects of a given type in a rules having theur type name in the mm_names set. TODO: move this method to the himesis_utils file, together with the one from PyRamify """ nodes = [] for node in graph.vs: if node["mm__"] in mm_names: nodes.append(node) return nodes

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eb628ff80c1703f8ca4d5c9fc6c7de5998d632a9

2,212

py

Python

tests/loss/test_nll.py

nlp-greyfoss/metagrad

0f32f177ced1478f0c75ad37bace9a9fc4044ba3

[ "MIT" ]

7

2022-01-27T05:38:02.000Z

2022-03-30T01:48:00.000Z

tests/loss/test_nll.py

nlp-greyfoss/metagrad

0f32f177ced1478f0c75ad37bace9a9fc4044ba3

[ "MIT" ]

null

tests/loss/test_nll.py

nlp-greyfoss/metagrad

0f32f177ced1478f0c75ad37bace9a9fc4044ba3

[ "MIT" ]

2

2022-02-22T07:47:02.000Z

2022-03-22T08:31:59.000Z

import numpy as np import torch import metagrad.functions as F from metagrad.loss import NLLLoss from metagrad.tensor import Tensor def test_simple_nll_loss(): x = np.array([[0, 1, 2, 3], [4, 0, 2, 1]], np.float32) t = np.array([3, 0]).astype(np.int32) mx = Tensor(x, requires_grad=True) mt = Tensor(np.eye(x.shape[-1])[t]) # 需要转换成one-hot向量 tx = torch.tensor(x, dtype=torch.float32, requires_grad=True) tt = torch.tensor(t, dtype=torch.int64) my_loss = NLLLoss() torch_loss = torch.nn.NLLLoss() # 先调用各自的log_softmax转换为对数概率 ml = my_loss(F.log_softmax(mx), mt) tl = torch_loss(torch.log_softmax(tx, dim=-1, dtype=torch.float32), tt) assert np.allclose(ml.item(), tl.item()) ml.backward() tl.backward() assert np.allclose(mx.grad.data, tx.grad.data) def test_nll_loss(): N, CLS_NUM = 100, 10 # 样本数，类别数 x = np.random.randn(N, CLS_NUM) t = np.random.randint(0, CLS_NUM, (N,)) mx = Tensor(x, requires_grad=True) mt = Tensor(np.eye(x.shape[-1])[t]) # 需要转换成one-hot向量 tx = torch.tensor(x, dtype=torch.float32, requires_grad=True) tt = torch.tensor(t, dtype=torch.int64) my_loss = NLLLoss() torch_loss = torch.nn.NLLLoss() # 先调用各自的log_softmax转换为对数概率 ml = my_loss(F.log_softmax(mx), mt) tl = torch_loss(torch.log_softmax(tx, dim=-1, dtype=torch.float32), tt) assert np.allclose(ml.item(), tl.item()) ml.backward() tl.backward() assert np.allclose(mx.grad.data, tx.grad.data) def test_simple_nll_loss_class_indices(): x = np.array([[0, 1, 2, 3], [4, 0, 2, 1]], np.float32) t = np.array([3, 0]) mx = Tensor(x, requires_grad=True) mt = Tensor(t) # 类别索引 tx = torch.tensor(x, dtype=torch.float32, requires_grad=True) tt = torch.tensor(t, dtype=torch.int64) my_loss = NLLLoss() torch_loss = torch.nn.NLLLoss() print(F.softmax(mx)) print(F.log_softmax(mx)) # 先调用各自的log_softmax转换为对数概率 ml = my_loss(F.log_softmax(mx), mt) tl = torch_loss(torch.log_softmax(tx, dim=-1, dtype=torch.float32), tt) assert np.allclose(ml.item(), tl.item()) ml.backward() tl.backward() assert np.allclose(mx.grad.data, tx.grad.data)

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eb85937236d1432d7cf2aa3c8afa6a02e719d805

106

py

Python

test/torchaudio_unittest/sox_io_backend/common.py

adefossez/audio

19fc580da97baf179395bb257647c5c25b993e42

[ "BSD-2-Clause" ]

1

2021-04-20T09:04:24.000Z

test/torchaudio_unittest/sox_io_backend/common.py

adefossez/audio

19fc580da97baf179395bb257647c5c25b993e42

[ "BSD-2-Clause" ]

null

test/torchaudio_unittest/sox_io_backend/common.py

adefossez/audio

19fc580da97baf179395bb257647c5c25b993e42

[ "BSD-2-Clause" ]

1

2019-09-11T08:27:18.000Z

def name_func(func, _, params): return f'{func.__name__}_{"_".join(str(arg) for arg in params.args)}'

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26,257

py

Python

datamodels/tests/test_fluxconversion_models.py

mwregan2/MiriTE

6b65939454db60bf10619d50fcb5769d23598b76

[ "CNRI-Python" ]

null

datamodels/tests/test_fluxconversion_models.py

mwregan2/MiriTE

6b65939454db60bf10619d50fcb5769d23598b76

[ "CNRI-Python" ]

null

datamodels/tests/test_fluxconversion_models.py

mwregan2/MiriTE

6b65939454db60bf10619d50fcb5769d23598b76

[ "CNRI-Python" ]

null

#!/usr/bin/env python # -*- coding:utf-8 -*- """ Module test_fluxconversion_model - Contains the unit tests for the classes in the datamodels.miri_fluxconversion_model module. :History: 16 Jan 2013: Created. 21 Jan 2013: Warning messages controlled with Python warnings module. 05 Feb 2013: File closing problem solved by using "with" context manager. 08 Feb 2013: Replaced 'to_fits' with more generic 'save' method. 17 May 2013: Do not allow a blank table to be created. 22 Aug 2013: columnnames renamed to fieldnames. Check that the field names declared in the class variable match the schema. 02 Sep 2013: Pass the responsibility for creating record arrays to jwst_lib - a solution to the "Types in column 0 do not match" problem suggested by Michael Droettboom at STScI. Compare numpy record arrays in a way that it independent of the byte ordering. 12 Sep 2013: Test that the data product can be copied successfully. 30 Oct 2013: LRS and MRS now use different flux conversion model classes. 09 Jul 2014: field_def changed to dq_def. 29 Aug 2014: Added test_referencefile. 07 Oct 2015: Made exception catching Python 3 compatible. 03 Dec 2015: Added MiriPowerlawColourCorrectionModel. 12 Jul 2017: Replaced "clobber" parameter with "overwrite". 24 Oct 2017: Set the pixel size when testing MiriMrsFluxConversionModel 15 Nov 2018: MRS schema switched to use JWST mirmrs_photom.schema. 3-D versions of the MRS data are no longer accepted. 30 Jan 2019: Test that the REFTYPE and DATAMODL metadata is not altered when the data model is saved to a file. 07 Oct 2019: FIXME: dq_def removed from unit tests until data corruption bug fixed (Bug 589). @author: Steven Beard (UKATC) """ import os import unittest import warnings import numpy as np from miri.datamodels.miri_fluxconversion_models import \ MiriImagingFluxconversionModel, MiriImagingColourCorrectionModel, \ MiriPowerlawColourCorrectionModel, MiriLrsFluxconversionModel, \ MiriMrsFluxconversionModel from miri.datamodels.tests.util import assert_recarray_equal, \ assert_products_equal class TestMiriImagingFluxconversionModel(unittest.TestCase): # Test the MiriImagingFluxconversionModel class. def setUp(self): # Create a typical flux conversion product. self.flux = [('F560W', 1.0, 0.0), ('F770W', 1.1, 0.0), ('F1000W', 1.2, 0.01), ('F1130W', 1.3, 0.0), ('F1280W', 1.4, 0.0), ('F1500W', 1.5, 0.02), ('F1800W', 1.6, 0.0), ('F2100W', 1.7, 0.03), ('F2550W', 1.8, 0.0), ] self.dataproduct = MiriImagingFluxconversionModel( \ flux_table=self.flux ) self.testfile = "MiriImagingFluxconversion_test.fits" def tearDown(self): # Tidy up del self.dataproduct del self.flux # Remove temporary file, if able to. if os.path.isfile(self.testfile): try: os.remove(self.testfile) except Exception as e: strg = "Could not remove temporary file, " + self.testfile + \ "\n " + str(e) warnings.warn(strg) def test_referencefile(self): # Check that the data product contains the standard # reference file metadata. type1 = self.dataproduct.meta.model_type type2 = self.dataproduct.meta.reftype self.assertIsNotNone(type1) self.assertIsNotNone(type2) pedigree = self.dataproduct.meta.pedigree self.assertIsNotNone(pedigree) def test_creation(self): # Check that the field names in the class variable are the same # as the ones declared in the schema. class_names = list(MiriImagingFluxconversionModel.fieldnames) schema_names = list(self.dataproduct.get_field_names('flux_table')) self.assertEqual(class_names, schema_names, "'fieldnames' class variable does not match schema") # It must be possible to create an empty data product and fill # in its contents later. This will generate a warning. with warnings.catch_warnings(): warnings.simplefilter("ignore") nulldp = MiriImagingFluxconversionModel( ) descr1 = str(nulldp) self.assertIsNotNone(descr1) nulldp.flux_table = self.flux self.assertIsNotNone(nulldp.flux_table) descr2 = str(nulldp) self.assertIsNotNone(descr2) del nulldp, descr1, descr2 def test_copy(self): # Test that a copy can be made of the data product. # This will generate a warning. with warnings.catch_warnings(): warnings.simplefilter("ignore") datacopy = self.dataproduct.copy() self.assertIsNotNone(datacopy.flux_table) self.assertEqual( len(self.dataproduct.flux_table), len(datacopy.flux_table) ) table1 = np.asarray(self.dataproduct.flux_table) table2 = np.asarray(datacopy.flux_table) assert_recarray_equal(table1, table2) del datacopy def test_fitsio(self): # Suppress metadata warnings with warnings.catch_warnings(): warnings.simplefilter("ignore") # Check that the data product can be written to a FITS # file and read back again without changing the data. self.dataproduct.save(self.testfile, overwrite=True) with MiriImagingFluxconversionModel(self.testfile) as readback: self.assertEqual(self.dataproduct.meta.reftype, readback.meta.reftype) self.assertEqual(self.dataproduct.meta.model_type, readback.meta.model_type) self.assertIsNotNone(readback.flux_table) self.assertEqual( len(self.dataproduct.flux_table), len(readback.flux_table) ) original = np.asarray(self.dataproduct.flux_table) duplicate = np.asarray(readback.flux_table) assert_recarray_equal(original, duplicate) del readback def test_description(self): # Test that the querying and description functions work. # For the test to pass these need to run without error # and generate non-null strings. descr = str(self.dataproduct) self.assertIsNotNone(descr) del descr descr = repr(self.dataproduct) self.assertIsNotNone(descr) del descr # Attempt to access the flux table through attributes. descr = str(self.dataproduct.flux_table) self.assertIsNotNone(descr) del descr class TestMiriImagingColourCorrectionModel(unittest.TestCase): # Test the MiriImagingColourCorrectionModel class. def setUp(self): # Create a typical imaging flux conversion product. self.flux = [(10.0, 'F560W', 1.0, 0.0), (10.0, 'F770W', 1.1, 0.0), (10.0, 'F1000W', 1.2, 0.01), (10.0, 'F1130W', 1.3, 0.0), (10.0, 'F1280W', 1.4, 0.0), (10.0, 'F1500W', 1.5, 0.02), (10.0, 'F1800W', 1.6, 0.0), (10.0, 'F2100W', 1.7, 0.03), (10.0, 'F2550W', 1.8, 0.0), ] self.dataproduct = MiriImagingColourCorrectionModel( \ flux_table=self.flux ) self.testfile = "MiriImagingColourCorrectionModel_test.fits" def tearDown(self): # Tidy up del self.dataproduct del self.flux # Remove temporary file, if able to. if os.path.isfile(self.testfile): try: os.remove(self.testfile) except Exception as e: strg = "Could not remove temporary file, " + self.testfile + \ "\n " + str(e) warnings.warn(strg) def test_referencefile(self): # Check that the data product contains the standard # reference file metadata. type1 = self.dataproduct.meta.model_type type2 = self.dataproduct.meta.reftype self.assertIsNotNone(type1) self.assertIsNotNone(type2) pedigree = self.dataproduct.meta.pedigree self.assertIsNotNone(pedigree) def test_creation(self): # Check that the field names in the class variable are the same # as the ones declared in the schema. class_names = list(MiriImagingColourCorrectionModel.fieldnames) schema_names = list(self.dataproduct.get_field_names('flux_table')) self.assertEqual(class_names, schema_names, "'fieldnames' class variable does not match schema") # It must be possible to create an empty data product and fill # in its contents later. This will generate a warning. with warnings.catch_warnings(): warnings.simplefilter("ignore") nulldp = MiriImagingColourCorrectionModel( ) descr1 = str(nulldp) self.assertIsNotNone(descr1) nulldp.flux_table = self.flux self.assertIsNotNone(nulldp.flux_table) descr2 = str(nulldp) self.assertIsNotNone(descr2) del nulldp, descr1, descr2 def test_copy(self): # Test that a copy can be made of the data product. # This will generate a warning. with warnings.catch_warnings(): warnings.simplefilter("ignore") datacopy = self.dataproduct.copy() self.assertIsNotNone(datacopy.flux_table) self.assertEqual( len(self.dataproduct.flux_table), len(datacopy.flux_table) ) table1 = np.asarray(self.dataproduct.flux_table) table2 = np.asarray(datacopy.flux_table) assert_recarray_equal(table1, table2) del datacopy def test_fitsio(self): # Suppress metadata warnings with warnings.catch_warnings(): warnings.simplefilter("ignore") # Check that the data product can be written to a FITS # file and read back again without changing the data. self.dataproduct.save(self.testfile, overwrite=True) with MiriImagingColourCorrectionModel(self.testfile) as readback: self.assertEqual(self.dataproduct.meta.reftype, readback.meta.reftype) self.assertEqual(self.dataproduct.meta.model_type, readback.meta.model_type) self.assertIsNotNone(readback.flux_table) self.assertEqual( len(self.dataproduct.flux_table), len(readback.flux_table) ) original = np.asarray(self.dataproduct.flux_table) duplicate = np.asarray(readback.flux_table) assert_recarray_equal(original, duplicate) del readback def test_description(self): # Test that the querying and description functions work. # For the test to pass these need to run without error # and generate non-null strings. descr = str(self.dataproduct) self.assertIsNotNone(descr) del descr descr = repr(self.dataproduct) self.assertIsNotNone(descr) del descr # Attempt to access the flux table through attributes. descr = str(self.dataproduct.flux_table) self.assertIsNotNone(descr) del descr class TestMiriPowerlawColourCorrectionModel(unittest.TestCase): # Test the MiriImagingColourCorrectionModel class. def setUp(self): # Create a typical imaging flux conversion product. self.flux = [(1.1, 'F560W', 1.0, 0.0), (1.1, 'F770W', 1.1, 0.0), (1.2, 'F1000W', 1.2, 0.01), (1.3, 'F1130W', 1.3, 0.0), (1.4, 'F1280W', 1.4, 0.0), (1.5, 'F1500W', 1.5, 0.02), (1.6, 'F1800W', 1.6, 0.0), (1.7, 'F2100W', 1.7, 0.03), (1.8, 'F2550W', 1.8, 0.0), ] self.dataproduct = MiriPowerlawColourCorrectionModel( \ flux_table=self.flux ) self.testfile = "MiriPowerlawColourCorrectionModel_test.fits" def tearDown(self): # Tidy up del self.dataproduct del self.flux # Remove temporary file, if able to. if os.path.isfile(self.testfile): try: os.remove(self.testfile) except Exception as e: strg = "Could not remove temporary file, " + self.testfile + \ "\n " + str(e) warnings.warn(strg) def test_referencefile(self): # Check that the data product contains the standard # reference file metadata. type1 = self.dataproduct.meta.model_type type2 = self.dataproduct.meta.reftype self.assertIsNotNone(type1) self.assertIsNotNone(type2) pedigree = self.dataproduct.meta.pedigree self.assertIsNotNone(pedigree) def test_creation(self): # Check that the field names in the class variable are the same # as the ones declared in the schema. class_names = list(MiriPowerlawColourCorrectionModel.fieldnames) schema_names = list(self.dataproduct.get_field_names('flux_table')) self.assertEqual(class_names, schema_names, "'fieldnames' class variable does not match schema") # It must be possible to create an empty data product and fill # in its contents later. This will generate a warning. with warnings.catch_warnings(): warnings.simplefilter("ignore") nulldp = MiriPowerlawColourCorrectionModel( ) descr1 = str(nulldp) self.assertIsNotNone(descr1) nulldp.flux_table = self.flux self.assertIsNotNone(nulldp.flux_table) descr2 = str(nulldp) self.assertIsNotNone(descr2) del nulldp, descr1, descr2 def test_copy(self): # Test that a copy can be made of the data product. # This will generate a warning. with warnings.catch_warnings(): warnings.simplefilter("ignore") datacopy = self.dataproduct.copy() self.assertIsNotNone(datacopy.flux_table) self.assertEqual( len(self.dataproduct.flux_table), len(datacopy.flux_table) ) table1 = np.asarray(self.dataproduct.flux_table) table2 = np.asarray(datacopy.flux_table) assert_recarray_equal(table1, table2) del datacopy def test_fitsio(self): # Suppress metadata warnings with warnings.catch_warnings(): warnings.simplefilter("ignore") # Check that the data product can be written to a FITS # file and read back again without changing the data. self.dataproduct.save(self.testfile, overwrite=True) with MiriPowerlawColourCorrectionModel(self.testfile) as readback: self.assertEqual(self.dataproduct.meta.reftype, readback.meta.reftype) self.assertEqual(self.dataproduct.meta.model_type, readback.meta.model_type) self.assertIsNotNone(readback.flux_table) self.assertEqual( len(self.dataproduct.flux_table), len(readback.flux_table) ) original = np.asarray(self.dataproduct.flux_table) duplicate = np.asarray(readback.flux_table) assert_recarray_equal(original, duplicate) del readback def test_description(self): # Test that the querying and description functions work. # For the test to pass these need to run without error # and generate non-null strings. descr = str(self.dataproduct) self.assertIsNotNone(descr) del descr descr = repr(self.dataproduct) self.assertIsNotNone(descr) del descr # Attempt to access the flux table through attributes. descr = str(self.dataproduct.flux_table) self.assertIsNotNone(descr) del descr class TestMiriLrsFluxconversionModel(unittest.TestCase): # Test the MiriLrsFluxconversionModel class. def setUp(self): # Create a typical LRS flux conversion product. self.flux = [( 2.0, 1.0, 0.0), ( 4.0, 1.1, 0.0), ( 6.0, 1.2, 0.01), ( 8.0, 1.3, 0.0), (10.0, 1.4, 0.0), (12.0, 1.5, 0.02), (14.0, 1.6, 0.0), (16.0, 1.7, 0.03), (18.0, 1.8, 0.0), ] self.dataproduct = MiriLrsFluxconversionModel( \ flux_table=self.flux ) self.testfile = "MiriLrsFluxconversion_test.fits" def tearDown(self): # Tidy up del self.dataproduct del self.flux # Remove temporary file, if able to. if os.path.isfile(self.testfile): try: os.remove(self.testfile) except Exception as e: strg = "Could not remove temporary file, " + self.testfile + \ "\n " + str(e) warnings.warn(strg) def test_referencefile(self): # Check that the data product contains the standard # reference file metadata. type1 = self.dataproduct.meta.model_type type2 = self.dataproduct.meta.reftype self.assertIsNotNone(type1) self.assertIsNotNone(type2) pedigree = self.dataproduct.meta.pedigree self.assertIsNotNone(pedigree) def test_creation(self): # Check that the field names in the class variable are the same # as the ones declared in the schema. class_names = list(MiriLrsFluxconversionModel.fieldnames) schema_names = list(self.dataproduct.get_field_names('flux_table')) self.assertEqual(class_names, schema_names, "'fieldnames' class variable does not match schema") # It must be possible to create an empty data product and fill # in its contents later. This will generate a warning. with warnings.catch_warnings(): warnings.simplefilter("ignore") nulldp = MiriLrsFluxconversionModel( ) descr1 = str(nulldp) self.assertIsNotNone(descr1) nulldp.flux_table = self.flux self.assertIsNotNone(nulldp.flux_table) descr2 = str(nulldp) self.assertIsNotNone(descr2) del nulldp, descr1, descr2 def test_copy(self): # Test that a copy can be made of the data product. # This will generate a warning. with warnings.catch_warnings(): warnings.simplefilter("ignore") datacopy = self.dataproduct.copy() self.assertIsNotNone(datacopy.flux_table) self.assertEqual( len(self.dataproduct.flux_table), len(datacopy.flux_table) ) table1 = np.asarray(self.dataproduct.flux_table) table2 = np.asarray(datacopy.flux_table) assert_recarray_equal(table1, table2) del datacopy def test_fitsio(self): # Suppress metadata warnings with warnings.catch_warnings(): warnings.simplefilter("ignore") # Check that the data product can be written to a FITS # file and read back again without changing the data. self.dataproduct.save(self.testfile, overwrite=True) with MiriLrsFluxconversionModel(self.testfile) as readback: self.assertEqual(self.dataproduct.meta.reftype, readback.meta.reftype) self.assertEqual(self.dataproduct.meta.model_type, readback.meta.model_type) self.assertIsNotNone(readback.flux_table) self.assertEqual( len(self.dataproduct.flux_table), len(readback.flux_table) ) original = np.asarray(self.dataproduct.flux_table) duplicate = np.asarray(readback.flux_table) assert_recarray_equal(original, duplicate) del readback def test_description(self): # Test that the querying and description functions work. # For the test to pass these need to run without error # and generate non-null strings. descr = str(self.dataproduct) self.assertIsNotNone(descr) del descr descr = repr(self.dataproduct) self.assertIsNotNone(descr) del descr # Attempt to access the flux table through attributes. descr = str(self.dataproduct.flux_table) self.assertIsNotNone(descr) del descr class TestMiriMrsFluxconversionModel(unittest.TestCase): # Test the MiriMrsFluxconversionModel class. def setUp(self): # Create a typical MRS flux conversion product. self.flux = [(1.0, 1.1, 1.2), (1.3, 1.4, 1.5), (1.6, 1.7, 1.8)] self.err = [(0.0, 0.01, 0.0), (0.02, 0.0, 0.03), (0.01, 0.04, 0.0)] self.dq = [(1,0,0), (0,1,0), (1,0,1)] self.pixsiz = [(1.01, 1.02, 1.03), (1.04, 1.05, 1.06), (1.07, 1.08, 1.09)] self.dataproduct = MiriMrsFluxconversionModel( \ data=self.flux, err=self.err, dq=self.dq, pixsiz=self.pixsiz ) self.testfile = "MiriMrsFluxconversion_test.fits" def tearDown(self): # Tidy up del self.dataproduct del self.flux # Remove temporary file, if able to. if os.path.isfile(self.testfile): try: os.remove(self.testfile) except Exception as e: strg = "Could not remove temporary file, " + self.testfile + \ "\n " + str(e) warnings.warn(strg) def test_referencefile(self): # Check that the data product contains the standard # reference file metadata. type1 = self.dataproduct.meta.model_type type2 = self.dataproduct.meta.reftype self.assertIsNotNone(type1) self.assertIsNotNone(type2) pedigree = self.dataproduct.meta.pedigree self.assertIsNotNone(pedigree) def test_creation(self): # It must be possible to create an empty data product and fill # in its contents later. This will generate a warning. with warnings.catch_warnings(): warnings.simplefilter("ignore") nulldp = MiriMrsFluxconversionModel( ) descr1 = str(nulldp) self.assertIsNotNone(descr1) # NOTE: pixsiz must be defined first to prevent a # "Wrong number of dimensions" exception. nulldp.pixsiz = self.pixsiz self.assertIsNotNone(nulldp.pixsiz) nulldp.data = self.flux self.assertIsNotNone(nulldp.data) descr2 = str(nulldp) self.assertIsNotNone(descr2) del nulldp, descr1, descr2 def test_copy(self): # Test that a copy can be made of the data product. # This will generate a warning. with warnings.catch_warnings(): warnings.simplefilter("ignore") datacopy = self.dataproduct.copy() self.assertIsNotNone(datacopy.data) self.assertEqual( len(self.dataproduct.data), len(datacopy.data) ) flux1 = np.asarray(self.dataproduct.data) flux2 = np.asarray(datacopy.data) self.assertTrue( np.allclose(np.nan_to_num(flux1), np.nan_to_num(flux2))) del datacopy def test_fitsio(self): # Suppress metadata warnings with warnings.catch_warnings(): warnings.simplefilter("ignore") # Check that the data product can be written to a FITS # file and read back again without changing the data. self.dataproduct.save(self.testfile, overwrite=True) with MiriMrsFluxconversionModel(self.testfile) as readback: self.assertEqual(self.dataproduct.meta.reftype, readback.meta.reftype) self.assertEqual(self.dataproduct.meta.model_type, readback.meta.model_type) assert_products_equal( self, self.dataproduct, readback, arrays=['data', 'err', 'dq']) # FIXME: removed dq_def until data corruption bug fixed. Bug 589 # tables='dq_def' ) del readback def test_description(self): # Test that the querying and description functions work. # For the test to pass these need to run without error # and generate non-null strings. descr = str(self.dataproduct) self.assertIsNotNone(descr) del descr descr = repr(self.dataproduct) self.assertIsNotNone(descr) del descr # Attempt to access the flux data through attributes. descr = str(self.dataproduct.data) self.assertIsNotNone(descr) del descr # If being run as a main program, run the tests. if __name__ == '__main__': unittest.main()

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Python

acq4/devices/LEDLightSource/__init__.py

ablot/acq4

ba7cd340d9d0282640adb501d3788f8c0837e4c4

[ "MIT" ]

null

acq4/devices/LEDLightSource/__init__.py

ablot/acq4

ba7cd340d9d0282640adb501d3788f8c0837e4c4

[ "MIT" ]

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acq4/devices/LEDLightSource/__init__.py

ablot/acq4

ba7cd340d9d0282640adb501d3788f8c0837e4c4

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from LEDLightSource import *

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Python

anthropos/anthropos/__init__.py

flaxandteal/arches-anthropos-poc

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null

anthropos/anthropos/__init__.py

flaxandteal/arches-anthropos-poc

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anthropos/anthropos/__init__.py

flaxandteal/arches-anthropos-poc

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from .celery_local import app as celery_app

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py

Python

kcfconvoy/__init__.py

maskot1977/kcfconvoy

846602843534ae1a97e16b1eff97d3be32c98119

[ "MIT" ]

null

kcfconvoy/__init__.py

maskot1977/kcfconvoy

846602843534ae1a97e16b1eff97d3be32c98119

[ "MIT" ]

null

kcfconvoy/__init__.py

maskot1977/kcfconvoy

846602843534ae1a97e16b1eff97d3be32c98119

[ "MIT" ]

1

2017-12-22T02:21:52.000Z

# coding: utf-8 from .Compound import Compound from .KCFvec import KCFvec from .KCFmat import KCFmat from .Library import Library from .util import similarity from .util import Classifiers

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Python

app/core/__init__.py

educaware/camera-server

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[ "MIT" ]

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2020-10-06T00:35:57.000Z

2022-03-31T19:56:24.000Z

app/core/__init__.py

educaware/camera-server

80af9f763d0b7299acb2e3851a095f19aaa7e0e5

[ "MIT" ]

8

2022-02-28T19:11:51.000Z

2022-03-31T10:25:42.000Z

app/core/__init__.py

educaware/camera-server

80af9f763d0b7299acb2e3851a095f19aaa7e0e5

[ "MIT" ]

24

2020-11-14T03:04:43.000Z

2022-03-11T15:44:44.000Z

from app.core.config import settings

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me/commands/_utils.py

johnbenjaminlewis/me

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[ "MIT" ]

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2015-11-24T15:10:20.000Z

me/commands/_utils.py

johnbenjaminlewis/me

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[ "MIT" ]

null

me/commands/_utils.py

johnbenjaminlewis/me

974d11ea2dd2a877785351f200e4c8546672c1c4

[ "MIT" ]

null

import click def write(msg): return click.secho(msg, fg='cyan', err=True) def fail(msg): click.secho(msg, fg='red', err=True) raise SystemExit(1)

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c63eff3c90e622ffa260f3d7ea337978f6323b96

99

py

Python

flankers/tagmeapi/secret/keys.py

Mec-iS/chronostriples-backup

79bdd902dc1d4862597469d4ac127f41bb5c1059

[ "Apache-2.0" ]

null

flankers/tagmeapi/secret/keys.py

Mec-iS/chronostriples-backup

79bdd902dc1d4862597469d4ac127f41bb5c1059

[ "Apache-2.0" ]

null

flankers/tagmeapi/secret/keys.py

Mec-iS/chronostriples-backup

79bdd902dc1d4862597469d4ac127f41bb5c1059

[ "Apache-2.0" ]

null

__author__ = ['lorenzo@pramantha.net'] def return_api_key(): return '**********************'

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d6aea201cc93e6410f9e00902f225cd98c631db0

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py

Python

python/testData/refactoring/move/importForMovedElementWithPreferredQualifiedImportStyle/before/src/a.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/refactoring/move/importForMovedElementWithPreferredQualifiedImportStyle/before/src/a.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/refactoring/move/importForMovedElementWithPreferredQualifiedImportStyle/before/src/a.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

def foo(): bar() def bar(): pass

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ba52d9d26cd98a2419f3fed29c562594ecec07ce

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py

Python

gym_collision_avoidance/experiments/src/master_config_deploy.py

meghdeepj/Social-Navigation-Simulator

806d304081bf5ff4fc7a0a58defb050627375865

[ "MIT" ]

null

gym_collision_avoidance/experiments/src/master_config_deploy.py

meghdeepj/Social-Navigation-Simulator

806d304081bf5ff4fc7a0a58defb050627375865

[ "MIT" ]

null

gym_collision_avoidance/experiments/src/master_config_deploy.py

meghdeepj/Social-Navigation-Simulator

806d304081bf5ff4fc7a0a58defb050627375865

[ "MIT" ]

null

number_of_agent = 100 import os from master_scenario_generator import Scenario_Generator, Seeded_Scenario_Generator, Seeded_Population_Scenario_Generator, real_dataset_traj class Master_Config(object): def __init__(self): global_timeout = int(os.environ["global_timeout"]) global_experiment_number = int(os.environ["global_experiment_number"]) global_dataset_name = os.environ["global_dataset_name"] global_population_density = float(os.environ["global_population_density"]) ## print("FROM MASTER") ## print(os.environ["global_timeout"] ) ## print(os.environ["global_experiment_number"]) ## print(os.environ["global_dataset_name"]) ## print(global_population_density) self.exp_setting = None ##################################################################################################################################################### # num mean num std dev vel mean vel std dev x_min x_max y_min y_max plot_size self.ETH = [ 6.312138728 ,4.536521361 ,2.339926573 ,0.7502205478 ,-7.69 ,14.42 ,-3.17 ,13.21 , [[-10, 17], [-6, 16]] ] self.HOTEL = [ 5.598098531 ,3.418910729 ,1.137002131 ,0.6487607538 ,-3.25 ,4.35 ,-10.31 ,4.31 , [[-7,7],[-13,7]] ] self.UNIV = [ 40.83024533 ,6.734736777 ,0.6817478507 ,0.2481828799 ,-0.4619709156 ,15.46918556 ,-0.3183721728 ,13.89190962 , [[-3,17],[-3,15]] ] self.ZARA1 = [ 5.87224158 ,3.213275774 ,1.12739064 ,0.2946279183 ,-0.1395383677 ,15.48055067 ,-0.3746958856 ,12.38644361 , [[-3,17],[-3,15]] ] self.ZARA2 = [ 9.314121037 ,3.926104465 ,1.096467485 ,0.3849301882 ,-0.3577906864 ,15.55842276 ,-0.2737427903 ,13.94274416 , [[-3,17],[-3,15]] ] ######################################################################################################## # num mean num std dev vel mean vel std dev x_min x_max y_min y_max plot_size #self.POPULATION = [ None ,None ,1 ,None ,0 ,10 ,0 ,10 , [[-1,11],[-1,11]] ] self.POPULATION = [ None ,None ,1 ,None ,0 ,5 ,0 ,5 , [[-1,6],[-1,6]] ] #generate random scenario here, write a function to generate and pass to self.scenario if global_experiment_number == 1: #Simulate algorithm using settings from datasets! (e.g. ETH) #print(global_dataset_name) if global_dataset_name == "ETH" : self.exp_setting = self.ETH elif global_dataset_name == "HOTEL" : self.exp_setting = self.HOTEL elif global_dataset_name == "UNIV" : self.exp_setting = self.UNIV elif global_dataset_name == "ZARA1" : self.exp_setting = self.ZARA1 elif global_dataset_name == "ZARA2" : self.exp_setting = self.ZARA2 self.PLT_LIMITS = self.exp_setting[8] ## print("FINALLY") ## print(self.PLT_LIMITS) elif global_experiment_number == 2: #population density evaluation #####for high population density, reduce size, hence less agents required###### #if global_population_density >= 0.5: # self.POPULATION = [ None ,None ,1 ,None ,0 ,5 ,0 ,5 , [[-1,6],[-1,6]] ] self.exp_setting = self.POPULATION self.PLT_LIMITS = self.exp_setting[8] elif global_experiment_number == 3: #touranment 1 vs n-1 self.exp_setting = self.POPULATION self.PLT_LIMITS = self.exp_setting[8] elif global_experiment_number == 4: #touranment 50% vs 50% self.exp_setting = self.POPULATION self.PLT_LIMITS = self.exp_setting[8] ############################################ #EvaluateConfig Level self.EVALUATE_MODE = True self.TRAIN_MODE = False self.DT = 0.1 #0.1 self.MAX_TIME_RATIO = 3. #8. #Formations config Level self.SHOW_EPISODE_PLOTS = False #plot while sim self.SAVE_EPISODE_PLOTS = self.ANIMATE_EPISODES = False #output gif + mp4 self.NEAR_GOAL_THRESHOLD = 0.2 #ETH [[-10, 17], [-6, 16]] #HOTEL [[-7,7],[-13,7]] #UNIV [[-3,17],[-3,15]] #ZARA1 [[-3,17],[-3,15]] #ZARA2 [[-3,17],[-3,15]] #Population [[-1,11],[-1,11]] #Motion prediction [[-10,10],[-10,10]] self.PLT_FIG_SIZE = (10,10) #Actual hidden limit self.PLOT_CIRCLES_ALONG_TRAJ = False self.NUM_AGENTS_TO_TEST = [60] #self.POLICIES_TO_TEST = ['GA3C-CADRL-10'] self.POLICIES_TO_TEST = ['GA3C-CADRL-10']*60 #['RVO']*number_of_agent#['STGCNN']*number_of_agent #['CADRL']*7#['NAVIGAN']*7#['RVO']*7#['GA3C-CADRL-10']*7 self.NUM_TEST_CASES = 2 #correspond to how many letters are there self.MAX_NUM_OTHER_AGENTS_OBSERVED = number_of_agent * 3 self.MAX_NUM_AGENTS_IN_ENVIRONMENT = self.MAX_NUM_OTHER_AGENTS_OBSERVED + 1 self.agent_time_out = global_timeout #180 seconds normal #30 motion prediction class Scenario_Config(object): def __init__(self, experiment_number, algorithm_name, experiment_iteration_num, dataset_name=None, population_density=None): self.exp_setting = None ##################################################################################################################################################### # num mean num std dev vel mean vel std dev x_min x_max y_min y_max plot_size self.ETH = [ 6.312138728 ,4.536521361 ,2.339926573 ,0.7502205478 ,-7.69 ,14.42 ,-3.17 ,13.21 , [[-10, 17], [-6, 16]] ] self.HOTEL = [ 5.598098531 ,3.418910729 ,1.137002131 ,0.6487607538 ,-3.25 ,4.35 ,-10.31 ,4.31 , [[-7,7],[-13,7]] ] self.UNIV = [ 40.83024533 ,6.734736777 ,0.6817478507 ,0.2481828799 ,-0.4619709156 ,15.46918556 ,-0.3183721728 ,13.89190962 , [[-3,17],[-3,15]] ] self.ZARA1 = [ 5.87224158 ,3.213275774 ,1.12739064 ,0.2946279183 ,-0.1395383677 ,15.48055067 ,-0.3746958856 ,12.38644361 , [[-3,17],[-3,15]] ] self.ZARA2 = [ 9.314121037 ,3.926104465 ,1.096467485 ,0.3849301882 ,-0.3577906864 ,15.55842276 ,-0.2737427903 ,13.94274416 , [[-3,17],[-3,15]] ] ######################################################################################################## # num mean num std dev vel mean vel std dev x_min x_max y_min y_max plot_size #self.POPULATION = [ None ,None ,1 ,None ,0 ,10 ,0 ,10 , [[-1,11],[-1,11]] ] self.POPULATION = [ None ,None ,1 ,None ,0 ,5 ,0 ,5 , [[-1,6],[-1,6]] ] #generate random scenario here, write a function to generate and pass to self.scenario if experiment_number == 1: #Simulate algorithm using settings from datasets! (e.g. ETH) #print(dataset_name) if dataset_name == "ETH" : self.exp_setting = self.ETH elif dataset_name == "HOTEL" : self.exp_setting = self.HOTEL elif dataset_name == "UNIV" : self.exp_setting = self.UNIV elif dataset_name == "ZARA1" : self.exp_setting = self.ZARA1 elif dataset_name == "ZARA2" : self.exp_setting = self.ZARA2 self.scenario=[] for i in range(experiment_iteration_num): #set radius from 0.2 to 0.05 to show slstm do better in low radius situation #old approach to gen similar dataset based on speed, num of agents of certain dataset #self.scenario.append( Seeded_Scenario_Generator( self.exp_setting[0], algorithm_name, self.exp_setting[4],self.exp_setting[5], self.exp_setting[6], self.exp_setting[7] , self.exp_setting[2], 0.2 , 0, num_agents_stddev=self.exp_setting[1], pref_speed_stddev=self.exp_setting[3], random_seed=i ).random_square_edge() ) #just use the dataset's real traj self.scenario.append( real_dataset_traj( dataset_name=dataset_name ).pick_start( None, algorithm_name, self.exp_setting[4],self.exp_setting[5], self.exp_setting[6], self.exp_setting[7] , self.exp_setting[2], 0.2 , 0, random_seed=i, num_agents_override= round(self.exp_setting[0]) ) ) elif experiment_number == 2: #population density evaluation #####for high population density, reduce size, hence less agents required###### #if population_density >= 0.5: # self.POPULATION = [ None ,None ,1 ,None ,0 ,5 ,0 ,5 , [[-1,6],[-1,6]] ] self.exp_setting = self.POPULATION self.scenario=[] for i in range(experiment_iteration_num): self.scenario.append( Seeded_Population_Scenario_Generator( population_density, algorithm_name, self.exp_setting[4],self.exp_setting[5], self.exp_setting[6], self.exp_setting[7], self.exp_setting[2], 0.2, 0, random_seed=i ).population_random_square_edge() ) elif experiment_number == 3: #touranment 1 vs n-1 self.exp_setting = self.POPULATION self.scenario=[] print(algorithm_name) algorithm_name = algorithm_name.strip('][').split(',') #make sure it is transformed back to list number_of_agents = int(round(population_density * ( ( self.POPULATION[5] - self.POPULATION[4] ) * ( self.POPULATION[7] - self.POPULATION[6] ) ))) for i in range(experiment_iteration_num): temp_name = [] for j in range(number_of_agents): if j==0: temp_name.append( algorithm_name[0] ) else: temp_name.append( algorithm_name[1] ) algorithm_name = temp_name self.scenario.append( Seeded_Population_Scenario_Generator( population_density, algorithm_name, self.exp_setting[4],self.exp_setting[5], self.exp_setting[6], self.exp_setting[7], self.exp_setting[2], 0.2, 0, random_seed=i ).population_random_square_edge() ) elif experiment_number == 4: #touranment 50% vs 50% self.exp_setting = self.POPULATION self.scenario=[] print(algorithm_name) algorithm_name = algorithm_name.strip('][').split(',') #make sure it is transformed back to list number_of_agents = int(round(population_density * ( ( self.POPULATION[5] - self.POPULATION[4] ) * ( self.POPULATION[7] - self.POPULATION[6] ) ))) for i in range(experiment_iteration_num): temp_name = [] #number of agents from population density, retrieved from master scenario generator for j in range(number_of_agents): if (j%2)==0: temp_name.append( algorithm_name[0] ) else: temp_name.append( algorithm_name[1] ) algorithm_name = temp_name print(algorithm_name) self.scenario.append( Seeded_Population_Scenario_Generator( population_density, algorithm_name, self.exp_setting[4],self.exp_setting[5], self.exp_setting[6], self.exp_setting[7], self.exp_setting[2], 0.2, 0, random_seed=i ).population_random_square_edge() ) elif experiment_number == 5: #mixture of agents, # agents decided by human dataset self.exp_setting = self.POPULATION # number_of_agents = get_number_of_agents(observation) #num agents = population density * area of scenario number_of_agents = int(round(population_density * ( ( self.POPULATION[5] - self.POPULATION[4] ) * ( self.POPULATION[7] - self.POPULATION[6] ) ))) self.scenario=[] for i in range(experiment_iteration_num): temp_name = [] for j in range(number_of_agents): temp_name.append(algorithm_name[0]) # temp_name = get_algorithms(algorithm_name, number_of_agents) #sample from pool of algorithms algorithm_name = temp_name print(algorithm_name) self.scenario.append( Seeded_Population_Scenario_Generator( population_density, algorithm_name, self.exp_setting[4],self.exp_setting[5], self.exp_setting[6], self.exp_setting[7], self.exp_setting[2], 0.2, 0, random_seed=i ).population_random_square_edge() ) ''' self.scenario=[] for i in range(experiment_iteration_num): #100 ####### #random seed # #(ETH) GA3C-CADRL #self.scenario.append( Scenario_Generator( 6.312138728, "GA3C-CADRL-10", -7.69, 14.42, -3.17, 13.21 , 2.339926573, 0.05 , 0, num_agents_stddev=4.536521361, pref_speed_stddev=0.7502205478 ).random_square_edge() ) ###################################FULL traj output 100 0.05 radius ########################### #fixed seed #(ETH) GA3C-CADRL fixed seed #self.scenario.append( Seeded_Scenario_Generator( 6.312138728, "GA3C-CADRL-10", -7.69, 14.42, -3.17, 13.21 , 2.339926573, 0.05 , 0, num_agents_stddev=4.536521361, pref_speed_stddev=0.7502205478, random_seed=i ).random_square_edge() ) #(HOTEL) GA3C-CADRL fixed seed #self.scenario.append( Seeded_Scenario_Generator( 5.598098531, "GA3C-CADRL-10", -3.25, 4.35, -10.31, 4.31 , 1.137002131, 0.05 , 0, num_agents_stddev=3.418910729, pref_speed_stddev=0.6487607538, random_seed=i ).random_square_edge() ) #(UNIV) GA3C-CADRL fixed seed #self.scenario.append( Seeded_Scenario_Generator( 40.83024533, "CADRL", -0.4619709156, 15.46918556, -0.3183721728, 13.89190962 , 0.6817478507, 0.05 , 0, num_agents_stddev=6.734736777, pref_speed_stddev=0.2481828799, random_seed=i ).random_square_edge() ) #(ZARA1) GA3C-CADRL fixed seed #self.scenario.append( Seeded_Scenario_Generator( 5.87224158 , "RVO", -0.1395383677, 15.48055067, -0.3746958856, 12.38644361 , 1.12739064, 0.05 , 0, num_agents_stddev=3.213275774, pref_speed_stddev=0.2946279183, random_seed=i ).random_square_edge() ) #(ZARA2) GA3C-CADRL fixed seed #self.scenario.append( Seeded_Scenario_Generator( 9.314121037, "CADRL", -0.3577906864, 15.55842276,-0.2737427903, 13.94274416 , 1.096467485, 0.05 , 0, num_agents_stddev=3.926104465, pref_speed_stddev=0.3849301882, random_seed=i ).random_square_edge() ) ###################################FULL traj output 20 0.2 radius ########################### #fixed seed #(ETH) GA3C-CADRL fixed seed #self.scenario.append( Seeded_Scenario_Generator( 6.312138728, "RVO", -7.69, 14.42, -3.17, 13.21 , 2.339926573, 0.2 , 0, num_agents_stddev=4.536521361, pref_speed_stddev=0.7502205478, random_seed=i ).random_square_edge() ) #(HOTEL) GA3C-CADRL fixed seed #self.scenario.append( Seeded_Scenario_Generator( 5.598098531, "GA3C-CADRL-10", -3.25, 4.35, -10.31, 4.31 , 1.137002131, 0.2 , 0, num_agents_stddev=3.418910729, pref_speed_stddev=0.6487607538, random_seed=i ).random_square_edge() ) #(UNIV) GA3C-CADRL fixed seed #self.scenario.append( Seeded_Scenario_Generator( 40.83024533, "RVO", -0.4619709156, 15.46918556, -0.3183721728, 13.89190962 , 0.6817478507, 0.2 , 0, num_agents_stddev=6.734736777, pref_speed_stddev=0.2481828799, random_seed=i ).random_square_edge() ) #(ZARA1) GA3C-CADRL fixed seed #self.scenario.append( Seeded_Scenario_Generator( 5.87224158 , "RVO", -0.1395383677, 15.48055067, -0.3746958856, 12.38644361 , 1.12739064, 0.2 , 0, num_agents_stddev=3.213275774, pref_speed_stddev=0.2946279183, random_seed=i ).random_square_edge() ) #(ZARA2) GA3C-CADRL fixed seed #self.scenario.append( Seeded_Scenario_Generator( 9.314121037, "RVO", -0.3577906864, 15.55842276,-0.2737427903, 13.94274416 , 1.096467485, 0.2 , 0, num_agents_stddev=3.926104465, pref_speed_stddev=0.3849301882, random_seed=i ).random_square_edge() ) #(ZARA2) testing with SPEC / STGCNN #self.scenario.append( Seeded_Scenario_Generator( 15, "SLSTM", -5, 5,-5, 5 , 1.096467485, 0.2 , 0, num_agents_stddev=0.001, pref_speed_stddev=0.3849301882, random_seed=i ).random_square_edge() ) #self.scenario.append( Seeded_Scenario_Generator( 15, "SOCIALGAN", -5, 5,-5, 5 , 1.096467485, 0.2 , 0, num_agents_stddev=0.001, pref_speed_stddev=0.3849301882, random_seed=i ).random_square_edge() ) #self.scenario.append( Seeded_Scenario_Generator( 30, "SLSTM", -6, 6,-6, 6 , 1.096467485, 0.2 , 0, num_agents_stddev=0.001, pref_speed_stddev=0.3849301882, random_seed=i ).random_square_edge() ) #self.scenario.append( Seeded_Scenario_Generator( 5, "SOCIALGAN", -3, 3,-3, 3 , 1.096467485, 0.2 , 0, num_agents_stddev=0.001, pref_speed_stddev=0.3849301882, random_seed=i ).random_square_edge() ) self.scenario.append( Seeded_Scenario_Generator( 30, "SPEC", -6, 6,-6, 6 , 1.096467485, 0.2 , 0, num_agents_stddev=0.001, pref_speed_stddev=0.3849301882, random_seed=i ).random_square_edge() ) #self.scenario.append( Seeded_Scenario_Generator( 20, "SPEC", -10, 10,-10, 10 , 1, 0.2 , 0, num_agents_stddev=0.01, pref_speed_stddev=0, random_seed=i ).random_square_edge() ) ################Population density fixed seed fixed speed (1m/s), 0.2m radius gradually increase density, 10x10m ############################# #0.1 #self.scenario.append( Seeded_Population_Scenario_Generator( 0.1, "RVO", 0, 10, 0, 10, 1, 0.2, 0, random_seed=i ).population_random_square_edge() ) #0.15 #self.scenario.append( Seeded_Population_Scenario_Generator( 0.15, "GA3C-CADRL-10", 0, 10, 0, 10, 1, 0.2, 0, random_seed=i ).population_random_square_edge() ) #0.2 #self.scenario.append( Seeded_Population_Scenario_Generator( 0.2, "GA3C-CADRL-10", 0, 10, 0, 10, 1, 0.2, 0, random_seed=i ).population_random_square_edge() ) #0.25 #self.scenario.append( Seeded_Population_Scenario_Generator( 0.25, "GA3C-CADRL-10", 0, 10, 0, 10, 1, 0.2, 0, random_seed=i ).population_random_square_edge() ) #0.3 #self.scenario.append( Seeded_Population_Scenario_Generator( 0.3, "RVO", 0, 10, 0, 10, 1, 0.2, 0, random_seed=i ).population_random_square_edge() ) #0.35 #self.scenario.append( Seeded_Population_Scenario_Generator( 0.35, "RVO", 0, 10, 0, 10, 1, 0.2, 0, random_seed=i ).population_random_square_edge() ) #0.4 #self.scenario.append( Seeded_Population_Scenario_Generator( 0.4, "RVO", 0, 10, 0, 10, 1, 0.2, 0, random_seed=i ).population_random_square_edge() ) #0.45 #self.scenario.append( Seeded_Population_Scenario_Generator( 0.45, "RVO", 0, 10, 0, 10, 1, 0.2, 0, random_seed=i ).population_random_square_edge() ) #0.5 #self.scenario.append( Seeded_Population_Scenario_Generator( 0.5, "RVO", 0, 10, 0, 10, 1, 0.2, 0, random_seed=i ).population_random_square_edge() ) #0.55 #self.scenario.append( Seeded_Population_Scenario_Generator( 0.55, "GA3C-CADRL-10", 0, 10, 0, 10, 1, 0.2, 0, random_seed=i ).population_random_square_edge() ) '''

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6

bab24de2b96a3c5336050dbf200a0de2ba05b53d

23

py

Python

startin/__init__.py

hugoledoux/testmaturin

1309e65a22ace2288300d7f8db3500234c71d542

[ "MIT" ]

null

startin/__init__.py

hugoledoux/testmaturin

1309e65a22ace2288300d7f8db3500234c71d542

[ "MIT" ]

null

startin/__init__.py

hugoledoux/testmaturin

1309e65a22ace2288300d7f8db3500234c71d542

[ "MIT" ]

null

from .startin import *

11.5

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0.73913

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5.666667

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1

0

6

bab7241eb856d660e790dab61dbfd365f8f5e179

293

py

Python

great_expectations/rule_based_profiler/domain_builder/__init__.py

vikramaditya91/great_expectations

4ebcdc0414bec3cf336b43cc54ca63bddb05bac3

[ "Apache-2.0" ]

null

great_expectations/rule_based_profiler/domain_builder/__init__.py

vikramaditya91/great_expectations

4ebcdc0414bec3cf336b43cc54ca63bddb05bac3

[ "Apache-2.0" ]

null

great_expectations/rule_based_profiler/domain_builder/__init__.py

vikramaditya91/great_expectations

4ebcdc0414bec3cf336b43cc54ca63bddb05bac3

[ "Apache-2.0" ]

null

from .column_domain_builder import ColumnDomainBuilder from .inferred_semantic_domain_type import InferredSemanticDomainType from .simple_column_suffix_domain_builder import SimpleColumnSuffixDomainBuilder from .simple_semantic_type_domain_builder import SimpleSemanticTypeColumnDomainBuilder

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0

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6

bafa2dce51f9f59ab4952241dee1fbfa4b8569a9

1,904

py

Python

benchml/models/mod_dscribe.py

rudolfspetrovs/benchml

896673f387a6bb9b185664ddd54f569a1ba54e51

[ "Apache-2.0" ]

3

2021-08-12T13:25:31.000Z

2022-03-21T21:30:22.000Z

benchml/models/mod_dscribe.py

rudolfspetrovs/benchml

896673f387a6bb9b185664ddd54f569a1ba54e51

[ "Apache-2.0" ]

5

2020-12-08T08:59:41.000Z

2022-01-22T06:46:09.000Z

benchml/models/mod_dscribe.py

rudolfspetrovs/benchml

896673f387a6bb9b185664ddd54f569a1ba54e51

[ "Apache-2.0" ]

1

2021-06-25T11:07:32.000Z

import numpy as np import benchml.transforms as btf from benchml.hyper import GridHyper, Hyper def compile_dscribe(**kwargs): return [ btf.Module( tag=DescriptorClass.__name__ + "_ridge", transforms=[ btf.ExtXyzInput(tag="input"), DescriptorClass(tag="descriptor", inputs={"configs": "input.configs"}), btf.ReduceMatrix(tag="reduce", inputs={"X": "descriptor.X"}), btf.Ridge(tag="predictor", inputs={"X": "reduce.X", "y": "input.y"}), ], hyper=GridHyper( Hyper( { "predictor.alpha": np.logspace(-5, +5, 7), } ) ), broadcast={"meta": "input.meta"}, outputs={"y": "predictor.y"}, ) for DescriptorClass in [btf.DscribeCM, btf.DscribeACSF, btf.DscribeMBTR, btf.DscribeLMBTR] ] def compile_dscribe_periodic(**kwargs): return [ btf.Module( tag=DescriptorClass.__name__ + "_ridge", transforms=[ btf.ExtXyzInput(tag="input"), DescriptorClass(tag="descriptor", inputs={"configs": "input.configs"}), btf.ReduceMatrix(tag="reduce", inputs={"X": "descriptor.X"}), btf.Ridge(tag="predictor", inputs={"X": "reduce.X", "y": "input.y"}), ], hyper=GridHyper( Hyper( { "predictor.alpha": np.logspace(-5, +5, 7), } ) ), broadcast={"meta": "input.meta"}, outputs={"y": "predictor.y"}, ) for DescriptorClass in [btf.DscribeSineMatrix] ] def register_all(): return { "dscribe": compile_dscribe, "dscribe_periodic": compile_dscribe_periodic, }

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0

null

0

1

0

null

0

1

0

6

244237b3e9b442b3781e371aceb7ae3d2ea0533d

33

py

Python

src/models/__init__.py

jwoos/web_sticky-note

72c6b7b4b7bf7b7d528ce714dd091e9581b10042

[ "MIT" ]

null

src/models/__init__.py

jwoos/web_sticky-note

72c6b7b4b7bf7b7d528ce714dd091e9581b10042

[ "MIT" ]

3

2017-12-29T04:47:05.000Z

2017-12-29T04:59:22.000Z

src/models/__init__.py

jwoos/web_ephemeral-notes

72c6b7b4b7bf7b7d528ce714dd091e9581b10042

[ "MIT" ]

null

from src.models.note import Note

16.5

32

0.818182

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33

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0.833333

0

0.121212

33

1

33

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0

null

0

1

0

1

0

1

0

6

0364b9fd430536b7bc95e4af8b51fcfd04f625db

18

py

Python

Zoocmd/new_core/version.py

helicontech/zoo

a33ba547f553bcce415f7a54bd89c444f82e48ee

[ "Apache-2.0" ]

2

2017-05-01T07:35:24.000Z

2018-04-12T13:36:03.000Z

Zoocmd/new_core/version.py

helicontech/zoo

a33ba547f553bcce415f7a54bd89c444f82e48ee

[ "Apache-2.0" ]

2

2017-03-23T17:28:37.000Z

2018-06-07T06:38:08.000Z

Zoocmd/new_core/version.py

helicontech/zoo

a33ba547f553bcce415f7a54bd89c444f82e48ee

[ "Apache-2.0" ]

3

2016-06-22T11:11:16.000Z

2019-10-25T15:09:46.000Z

VERSION="1.0.0.0"

9

17

0.611111

5

18

2.2

0.6

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0

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0.055556

18

1

18

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0.388889

0

1

0

false

0

1

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null

1

0

1

0

1

0

1

0

null

0

6

300c64dad9a2edf3f721b3b96a0c044a8213f7ff

48

py

Python

client/py_client/utils/datasources/rest/__init__.py

thefstock/FirstockPy

09b4dcf3470f83de991b43213958d2c6783f997b

[ "MIT" ]

1

2022-03-29T06:56:06.000Z

client/py_client/utils/datasources/rest/__init__.py

thefstock/FirstockPy

09b4dcf3470f83de991b43213958d2c6783f997b

[ "MIT" ]

3

2022-01-17T09:31:21.000Z

2022-03-11T12:12:08.000Z

client/py_client/utils/datasources/rest/__init__.py

thefstock/FirstockPy

09b4dcf3470f83de991b43213958d2c6783f997b

[ "MIT" ]

null

from .datasource import * from .context import *

24

25

0.770833

6

48

6.166667

0.666667

0

0.145833

48

2

26

24

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true

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0

1

0

null

0

1

0

1

0

1

0

6

30187c78d73285d630770d46ffccbd1723da950a

146

py

Python

djstripe/middleware.py

ExtraE113/dj-stripe

1b50be13fc99b624388a005b8aa1e26c57392203

[ "MIT" ]

937

2017-06-04T18:44:20.000Z

2022-03-27T07:28:32.000Z

djstripe/middleware.py

ExtraE113/dj-stripe

1b50be13fc99b624388a005b8aa1e26c57392203

[ "MIT" ]

969

2017-06-05T01:57:20.000Z

2022-03-31T23:42:54.000Z

djstripe/middleware.py

ExtraE113/dj-stripe

1b50be13fc99b624388a005b8aa1e26c57392203

[ "MIT" ]

309

2017-06-12T03:18:10.000Z

2022-03-29T17:05:18.000Z

"""dj-stripe middleware """ from django.utils.deprecation import MiddlewareMixin class SubscriptionPaymentMiddleware(MiddlewareMixin): pass

18.25

53

0.808219

13

146

9.076923

0.923077

0

0.109589

146

7

54

20.857143

0.907692

0.136986

0

1

0

true

0.333333

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null

0

1

0

null

0

1

0

1

0

6

062f41794cd6f4e55101288d94256a3eb2c71d8f

43

py

Python

scripts/dataset_scripts/__init__.py

tedmaksym/leadingtophytoplankton

165a4ccf5b79ef7858be3b13551728fe7873a659

[ "BSD-3-Clause" ]

1

2021-01-09T17:06:40.000Z

scripts/dataset_scripts/__init__.py

tedmaksym/leadingtophytoplankton

165a4ccf5b79ef7858be3b13551728fe7873a659

[ "BSD-3-Clause" ]

null

scripts/dataset_scripts/__init__.py

tedmaksym/leadingtophytoplankton

165a4ccf5b79ef7858be3b13551728fe7873a659

[ "BSD-3-Clause" ]

2

2020-06-30T07:50:30.000Z

2021-02-15T06:24:17.000Z

from .dataset import * from .atl03 import *

21.5

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6

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5.333333

0.666667

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0.055556

0.162791

43

2

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true

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null

0

1

0

1

0

1

0

6

0632a3278ccc3c2914e3089c25b6ae0f281cbc8d

157

py

Python

terrascript/powerdns/r.py

hugovk/python-terrascript

08fe185904a70246822f5cfbdc9e64e9769ec494

[ "BSD-2-Clause" ]

507

2017-07-26T02:58:38.000Z

2022-01-21T12:35:13.000Z

terrascript/powerdns/r.py

hugovk/python-terrascript

08fe185904a70246822f5cfbdc9e64e9769ec494

[ "BSD-2-Clause" ]

135

2017-07-20T12:01:59.000Z

2021-10-04T22:25:40.000Z

terrascript/powerdns/r.py

hugovk/python-terrascript

08fe185904a70246822f5cfbdc9e64e9769ec494

[ "BSD-2-Clause" ]

81

2018-02-20T17:55:28.000Z

2022-01-31T07:08:40.000Z

# terrascript/powerdns/r.py import terrascript class powerdns_zone(terrascript.Resource): pass class powerdns_record(terrascript.Resource): pass

14.272727

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0.380165

0

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null

1

0

null

0

1

0

1

0

6

067ea90b8bd8566019892dc493c8981cad41e2e9

16,085

py

Python

IRM_data/Fig5/Fig5b/Fig5b.py

wangqf1997/Human-injury-based-safety-decision-of-automated-vehicles

b104fdeb3d85e867f6b04c5ae7b5a197e705aeba

[ "CC-BY-4.0" ]

null

IRM_data/Fig5/Fig5b/Fig5b.py

wangqf1997/Human-injury-based-safety-decision-of-automated-vehicles

b104fdeb3d85e867f6b04c5ae7b5a197e705aeba

[ "CC-BY-4.0" ]

null

IRM_data/Fig5/Fig5b/Fig5b.py

wangqf1997/Human-injury-based-safety-decision-of-automated-vehicles

b104fdeb3d85e867f6b04c5ae7b5a197e705aeba

[ "CC-BY-4.0" ]

null

# -*- coding: utf-8 -*- ''' ------------------------------------------------------------------------------------------------- This code accompanies the paper titled "Human injury-based safety decision of automated vehicles" Author: Qingfan Wang, Qing Zhou, Miao Lin, Bingbing Nie Corresponding author: Bingbing Nie (nbb@tsinghua.edu.cn) ------------------------------------------------------------------------------------------------- ''' import cv2 import numpy as np import matplotlib.pyplot as plt import matplotlib.image as mpimg from matplotlib.offsetbox import OffsetImage, AnnotationBbox def resize_rotate(image, angle, l_, w_): ''' resize and rotate the figure. ''' image = cv2.resize(image, (image.shape[1], int(image.shape[0] / (3370 / 8651) * (w_ / l_)))) # grab the dimensions of the image and then determine the center. (h, w) = image.shape[:2] (cX, cY) = (w // 2, h // 2) # grab the rotation matrix and the sine and cosine. M = cv2.getRotationMatrix2D((cX, cY), angle, 1.0) cos = np.abs(M[0, 0]) sin = np.abs(M[0, 1]) # compute the new bounding dimensions of the image. nW = int((h * sin) + (w * cos)) nH = int((h * cos) + (w * sin)) # adjust the rotation matrix to take into account translation. M[0, 2] += (nW / 2) - cX M[1, 2] += (nH / 2) - cY # perform the actual rotation and return the image. return cv2.warpAffine(image, M, (nW, nH), borderValue=(255, 255, 255)) def main(): ''' Plot Fig5b. ''' # Load general data. img_ini_00 = mpimg.imread('../../image/gray__.png') img_ini_0 = mpimg.imread('../../image/gray.png') img_ini_1 = mpimg.imread('../../image/blue.png') img_ini_2 = mpimg.imread('../../image/green.png') img_ini_3 = mpimg.imread('../../image/orange.png') # Load parameters. veh_l_1, veh_l_2, veh_w_1, veh_w_2 = 3.995, 4.07, 1.615, 1.615 color = ['gray', '#3B89F0', '#41B571', '#FFB70A', '#FF5050'] ''' Plot Fig5b_1. ''' # Basic setup. fig, ax = plt.subplots(figsize=(4.5, 4.5)) font1 = {'family': 'Arial', 'size': 15} plt.xlabel("Activation time before the collision [ms]", font1) plt.ylabel('Reduction of OISS [%]', font1, labelpad=-3.5) plt.xticks(np.arange(0, 101, 20), np.arange(0, 101, 20) * 10 - 1000, family='Arial', fontsize=15) plt.yticks(family='Arial', fontsize=15) plt.subplots_adjust(left=0.15, wspace=0.25, hspace=0.25, bottom=0.13, top=0.97, right=0.97) # Load data. data = np.load('data/Fig5b_1.npz') # Plot Fig5b_1. plt.plot(np.arange(0, 101, 10), data['Inj_EB'], color='#3B89F0', marker='o', linestyle='dashed', linewidth=1, markersize=6) plt.plot(np.arange(0, 101, 10), data['Inj_S1'], color='#41B571', marker='v', linestyle='dashed', linewidth=1, markersize=6) plt.plot(np.arange(0, 101, 10), data['Inj_S2'], color='#FFB70A', marker='^', linestyle='dashed', linewidth=1, markersize=6) plt.plot(np.arange(0, 101, 10), data['Inj_S3'], color='#FF5050', marker='s', linestyle='dashed', linewidth=1, markersize=6, clip_on=False) # Show. plt.show() # plt.savefig('Fig5b_1.png', dpi=600) plt.close() ''' Plot Fig5b_2. ''' # Basic setup. fig, ax = plt.subplots(figsize=(2, 2 / 28 * 24)) plt.axis('equal') plt.xlim((-4, 24)) plt.ylim((-11 + 0.5, 13 + 0.5)) plt.xticks([], family='Arial', fontsize=15) plt.yticks([], family='Arial', fontsize=15) plt.subplots_adjust(left=0.02, wspace=0.25, hspace=0.25, bottom=0.02, top=0.98, right=0.98) # Load data. data = np.load('data/Fig5b_2.npz') # Plot road information. x = data['road_x'] y = data['road_y'] plt.plot(x + 3.5, y - 1, color='gray', linestyle='-', linewidth=1.3, alpha=0.7) plt.plot(x[:35] + 3.5, y[:35] - 8.6, color='orange', linestyle='-', linewidth=0.7, alpha=0.5) plt.plot(x[:35] + 3.5, y[:35] - 8.6, color='orange', linestyle='-', linewidth=0.7, alpha=0.5) plt.plot(x[0:36][::-1] + 3.5, y[:36][::-1] - 4.75, color='gray', linestyle=(0, (10, 8)), linewidth=1, alpha=0.35) plt.plot(x[0:33][::-1] + 3.5, y[:33][::-1] - 12.25, color='gray', linestyle=(0, (10, 8)), linewidth=1, alpha=0.35) plt.plot(x[:-65] + 3.5, y[:-65] - 16, color='gray', linestyle='-', linewidth=1.3, alpha=0.7) plt.plot(x[:-65] + 3.5, -y[:-65] + 10.508, color='gray', linestyle='-', linewidth=1.3, alpha=0.7) plt.plot(x[:-68][::-1] - 4, -y[:-68][::-1] + 8.508, color='orange', linestyle=(0, (10, 8)), linewidth=1, alpha=0.5) plt.plot(x[:-10] - 0.5, -y[:-10] + 3.508, color='gray', linestyle='-', linewidth=1.3, alpha=0.7) plt.plot([x[34] + 3.5, x[30] - 0.3], [y[34] - 8.5, y[30] - 2], color='gray', linestyle='-', linewidth=1.3, alpha=0.5) plt.plot(x[70:] + 3.5, y[70:] - 8.7, color='orange', linestyle='-', linewidth=0.7, alpha=0.5) plt.plot(x[70:] + 3.5, y[70:] - 8.9, color='orange', linestyle='-', linewidth=0.7, alpha=0.5) plt.plot(x[70:] + 3.6, y[70:] - 4.95, color='gray', linestyle=(0, (10, 8)), linewidth=1, alpha=0.35) plt.plot([x[70] + 3.5, x[70] + 3.6], [y[70] - 8.8, y[70] - 17], color='gray', linestyle='-', linewidth=1.3, alpha=0.5) plt.plot(x[70:] + 3.5, y[70:] - 12.95, color='gray', linestyle=(0, (10, 8)), linewidth=1, alpha=0.35) # Plot vehicle information. img = resize_rotate(img_ini_00, np.rad2deg(data['traj_t1'][-1]), veh_l_1, veh_w_1) im = OffsetImage(img, zoom=0.003 * veh_l_1, alpha=1) ab = AnnotationBbox(im, xy=(data['traj_x1'][-1], data['traj_y1'][-1]), xycoords='data', pad=0, frameon=False) ax.add_artist(ab) img = resize_rotate(img_ini_00, np.rad2deg(data['traj_t2'][-1]), veh_l_2, veh_w_2) im = OffsetImage(img, zoom=0.003 * veh_l_2, alpha=1) ab = AnnotationBbox(im, xy=(data['traj_x2'][-1], data['traj_y2'][-1]), xycoords='data', pad=0, frameon=False) ax.add_artist(ab) # Plot trajectory information. plt.plot(data['traj_x1'], data['traj_y1'], color=color[0], linestyle='--', linewidth=1.3, alpha=0.5) plt.plot(data['traj_x2'], data['traj_y2'], color=color[0], linestyle='--', linewidth=1.3, alpha=0.5) # Show. plt.show() # plt.savefig('Fig5b_2.png', dpi=600) plt.close() ''' Plot Fig5b_3. ''' # Basic setup. fig, ax = plt.subplots(figsize=(3.5 * 1.1, 3.5 / 9 * 6 * 1.1)) plt.axis('equal') plt.xlim((5.5, 5.5 + 9)) plt.ylim((-3.8, -3.8 + 6)) plt.xticks(np.arange(5.5, 5.5 + 10, 3), np.arange(0, 10, 3), family='Arial', fontsize=14) plt.yticks(np.arange(-3.8, -3.8 + 8, 3), np.arange(0, 8, 3), family='Arial', fontsize=14) plt.subplots_adjust(wspace=0.25, hspace=0.25, left=0.11, bottom=0.11, top=0.96, right=0.96) # Load data. data = np.load('data/Fig5b_3.npz') # Plot vehicle information. img = resize_rotate(img_ini_0, np.rad2deg(data['traj_Re_t1'][-1]), veh_l_1, veh_w_1) im = OffsetImage(img, zoom=0.0155 * veh_l_1, alpha=1) ab = AnnotationBbox(im, xy=(data['traj_Re_x1'][-1], data['traj_Re_y1'][-1]), xycoords='data', pad=0, frameon=False) ax.add_artist(ab) img = resize_rotate(img_ini_0, np.rad2deg(data['traj_Re_t2'][-1]), veh_l_2, veh_w_2) im = OffsetImage(img, zoom=0.0155 * veh_l_2, alpha=1) ab = AnnotationBbox(im, xy=(data['traj_Re_x2'][-1], data['traj_Re_y2'][-1]), xycoords='data', pad=0, frameon=False) ax.add_artist(ab) img = resize_rotate(img_ini_1, np.rad2deg(data['traj_EB_t1'][-1]), veh_l_1, veh_w_1) im = OffsetImage(img, zoom=0.0155 * veh_l_1, alpha=1) ab = AnnotationBbox(im, xy=(data['traj_EB_x1'][-1], data['traj_EB_y1'][-1]), xycoords='data', pad=0, frameon=False) ax.add_artist(ab) img = resize_rotate(img_ini_1, np.rad2deg(data['traj_EB_t2'][-1]), veh_l_2, veh_w_2) im = OffsetImage(img, zoom=0.0155 * veh_l_2, alpha=1) ab = AnnotationBbox(im, xy=(data['traj_EB_x2'][-1], data['traj_EB_y2'][-1]), xycoords='data', pad=0, frameon=False) ax.add_artist(ab) # Plot trajectory information. plt.plot(data['traj_Re_x1'], data['traj_Re_y1'], color=color[0], linestyle='--', linewidth=1.3, alpha=0.5) plt.plot(data['traj_Re_x2'], data['traj_Re_y2'], color=color[0], linestyle='--', linewidth=1.3, alpha=0.5) plt.plot(data['traj_EB_x1'], data['traj_EB_y1'], color=color[1], linestyle='--', linewidth=1.3, alpha=0.5) plt.plot(data['traj_EB_x2'], data['traj_EB_y2'], color=color[1], linestyle='--', linewidth=1.3, alpha=0.5) # Show. plt.show() # plt.savefig('Fig5b_3.png', dpi=600) plt.close() ''' Plot Fig5b_4. ''' # Basic setup. fig, ax = plt.subplots(figsize=(3.5 * 1.1, 3.5 / 9 * 6 * 1.1)) plt.axis('equal') plt.xlim((5.5, 5.5 + 9)) plt.ylim((-3.8, -3.8 + 6)) plt.xticks(np.arange(5.5, 5.5 + 10, 3), np.arange(0, 10, 3), family='Arial', fontsize=14) plt.yticks(np.arange(-3.8, -3.8 + 8, 3), np.arange(0, 8, 3), family='Arial', fontsize=14) plt.subplots_adjust(wspace=0.25, hspace=0.25, left=0.11, bottom=0.11, top=0.96, right=0.96) # Load data. data = np.load('data/Fig5b_4.npz') # Plot vehicle information. img = resize_rotate(img_ini_0, np.rad2deg(data['traj_Re_t1'][-1]), veh_l_1, veh_w_1) im = OffsetImage(img, zoom=0.0155 * veh_l_1, alpha=1) ab = AnnotationBbox(im, xy=(data['traj_Re_x1'][-1], data['traj_Re_y1'][-1]), xycoords='data', pad=0, frameon=False) ax.add_artist(ab) img = resize_rotate(img_ini_0, np.rad2deg(data['traj_Re_t2'][-1]), veh_l_2, veh_w_2) im = OffsetImage(img, zoom=0.0155 * veh_l_2, alpha=1) ab = AnnotationBbox(im, xy=(data['traj_Re_x2'][-1], data['traj_Re_y2'][-1]), xycoords='data', pad=0, frameon=False) ax.add_artist(ab) img = resize_rotate(img_ini_2, np.rad2deg(data['traj_S1_t1'][-1]), veh_l_1, veh_w_1) im = OffsetImage(img, zoom=0.0155 * veh_l_1, alpha=1) ab = AnnotationBbox(im, xy=(data['traj_S1_x1'][-1], data['traj_S1_y1'][-1]), xycoords='data', pad=0, frameon=False) ax.add_artist(ab) img = resize_rotate(img_ini_2, np.rad2deg(data['traj_S1_t2'][-1]), veh_l_2, veh_w_2) im = OffsetImage(img, zoom=0.0155 * veh_l_2, alpha=1) ab = AnnotationBbox(im, xy=(data['traj_S1_x2'][-1], data['traj_S1_y2'][-1]), xycoords='data', pad=0, frameon=False) ax.add_artist(ab) # Plot trajectory information. plt.plot(data['traj_Re_x1'], data['traj_Re_y1'], color=color[0], linestyle='--', linewidth=1.3, alpha=0.5) plt.plot(data['traj_Re_x2'], data['traj_Re_y2'], color=color[0], linestyle='--', linewidth=1.3, alpha=0.5) plt.plot(data['traj_S1_x1'], data['traj_S1_y1'], color=color[2], linestyle='--', linewidth=1.3, alpha=0.5) plt.plot(data['traj_S1_x2'], data['traj_S1_y2'], color=color[2], linestyle='--', linewidth=1.3, alpha=0.5) # Show. plt.show() # plt.savefig('Fig5b_4.png', dpi=600) plt.close() ''' Plot Fig5b_5. ''' # Basic setup. fig, ax = plt.subplots(figsize=(3.5 * 1.1, 3.5 / 9 * 6 * 1.1)) plt.axis('equal') plt.xlim((5.5, 5.5 + 9)) plt.ylim((-3.8, -3.8 + 6)) plt.xticks(np.arange(5.5, 5.5 + 10, 3), np.arange(0, 10, 3), family='Arial', fontsize=14) plt.yticks(np.arange(-3.8, -3.8 + 8, 3), np.arange(0, 8, 3), family='Arial', fontsize=14) plt.subplots_adjust(wspace=0.25, hspace=0.25, left=0.11, bottom=0.11, top=0.96, right=0.96) # Load data. data = np.load('data/Fig5b_5.npz') # Plot vehicle information. img = resize_rotate(img_ini_0, np.rad2deg(data['traj_Re_t1'][-1]), veh_l_1, veh_w_1) im = OffsetImage(img, zoom=0.0155 * veh_l_1, alpha=1) ab = AnnotationBbox(im, xy=(data['traj_Re_x1'][-1], data['traj_Re_y1'][-1]), xycoords='data', pad=0, frameon=False) ax.add_artist(ab) img = resize_rotate(img_ini_0, np.rad2deg(data['traj_Re_t2'][-1]), veh_l_2, veh_w_2) im = OffsetImage(img, zoom=0.0155 * veh_l_2, alpha=1) ab = AnnotationBbox(im, xy=(data['traj_Re_x2'][-1], data['traj_Re_y2'][-1]), xycoords='data', pad=0, frameon=False) ax.add_artist(ab) img = resize_rotate(img_ini_3, np.rad2deg(data['traj_S2_t1'][-1]), veh_l_1, veh_w_1) im = OffsetImage(img, zoom=0.0155 * veh_l_1, alpha=1) ab = AnnotationBbox(im, xy=(data['traj_S2_x1'][-1], data['traj_S2_y1'][-1]), xycoords='data', pad=0, frameon=False) ax.add_artist(ab) img = resize_rotate(img_ini_3, np.rad2deg(data['traj_S2_t2'][-1]), veh_l_2, veh_w_2) im = OffsetImage(img, zoom=0.0155 * veh_l_2, alpha=1) ab = AnnotationBbox(im, xy=(data['traj_S2_x2'][-1], data['traj_S2_y2'][-1]), xycoords='data', pad=0, frameon=False) ax.add_artist(ab) # Plot trajectory information. plt.plot(data['traj_Re_x1'], data['traj_Re_y1'], color=color[0], linestyle='--', linewidth=1.3, alpha=0.5) plt.plot(data['traj_Re_x2'], data['traj_Re_y2'], color=color[0], linestyle='--', linewidth=1.3, alpha=0.5) plt.plot(data['traj_S2_x1'], data['traj_S2_y1'], color=color[3], linestyle='--', linewidth=1.3, alpha=0.5) plt.plot(data['traj_S2_x2'], data['traj_S2_y2'], color=color[3], linestyle='--', linewidth=1.3, alpha=0.5) # Show. plt.show() # plt.savefig('Fig5b_5.png', dpi=600) plt.close() ''' Plot Fig5b_6. ''' # Basic setup. fig, ax = plt.subplots(figsize=(5.15, 1.8)) font1 = {'family': 'Arial', 'size': 14} plt.xlabel("Time [ms]", font1, labelpad=-0.6) plt.ylabel('Velocity [m/s]', font1, labelpad=3) plt.xticks(np.arange(0, 126, 20), np.arange(0, 126, 20) * 10, family='Arial', fontsize=14) plt.yticks(family='Arial', fontsize=14) plt.xlim([-5, 125]) plt.subplots_adjust(left=0.11, wspace=0.25, hspace=0.25, bottom=0.25, top=0.96, right=0.99) # Load data. data = np.load('data/Fig5b_6.npz') # Plot dynamics information. plt.plot(data['traj_Re_V1'], color='lightgray', linestyle='dashed', linewidth=2, zorder=10) plt.plot(data['traj_EB_V1'], color='#3B89F0', linestyle='dashed', linewidth=2, zorder=9) plt.plot(data['traj_S1_V1'], color='#41B571', linestyle='dashed', linewidth=2, zorder=8) plt.plot(data['traj_S2_V1'], color='#FFB70A', linestyle='dashed', linewidth=2, zorder=7) plt.plot(data['traj_Re_V2'], color='lightgray', linestyle='-.', linewidth=2, zorder=5) plt.plot(data['traj_EB_V2'], color='#3B89F0', linestyle='-.', linewidth=2, zorder=4) plt.plot(data['traj_S1_V2'], color='#41B571', linestyle='-.', linewidth=2, zorder=3) plt.plot(data['traj_S2_V2'], color='#FFB70A', linestyle='-.', linewidth=2, zorder=2) # Show. plt.show() # plt.savefig('Fig5b_6.png', dpi=600) plt.close() ''' Plot Fig5b_7. ''' # Basic setup. fig, ax = plt.subplots(figsize=(5.15, 1.8)) font1 = {'family': 'Arial', 'size': 14} plt.xlabel("Time [ms]", font1, labelpad=-0.6) plt.ylabel('Yaw rate [deg/s]', font1, labelpad=1) plt.xticks(np.arange(0, 126, 20), np.arange(0, 126, 20) * 10, family='Arial', fontsize=14) plt.yticks(family='Arial', fontsize=14) plt.xlim([-5, 125]) plt.subplots_adjust(left=0.13, wspace=0.25, hspace=0.25, bottom=0.25, top=0.96, right=0.99) # Load data. data = np.load('data/Fig5b_7.npz') # Plot dynamics information. plt.plot(np.rad2deg(data['traj_Re_W1']), color='lightgray', linestyle='dashed', linewidth=2, zorder=10) plt.plot(np.rad2deg(data['traj_EB_W1']), color='#3B89F0', linestyle='dashed', linewidth=2, zorder=9) plt.plot(np.rad2deg(data['traj_S1_W1']), color='#41B571', linestyle='dashed', linewidth=2, zorder=8) plt.plot(np.rad2deg(data['traj_S2_W1']), color='#FFB70A', linestyle='dashed', linewidth=2, zorder=7) plt.plot(np.rad2deg(data['traj_Re_W2']), color='lightgray', linestyle='-.', linewidth=2, zorder=5) plt.plot(np.rad2deg(data['traj_EB_W2']), color='#3B89F0', linestyle='-.', linewidth=2, zorder=4) plt.plot(np.rad2deg(data['traj_S1_W2']), color='#41B571', linestyle='-.', linewidth=2, zorder=3) plt.plot(np.rad2deg(data['traj_S2_W2']), color='#FFB70A', linestyle='-.', linewidth=2, zorder=2) # Show. plt.show() # plt.savefig('Fig5b_7.png', dpi=600) plt.close() if __name__ == "__main__": main()

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Python

Lib/__np__/__init__.py

Redex-Developers/Nuitka-Python

7c1fc1dd6dfeab4cdafeec1709e6e9c4c8c84227

[ "0BSD" ]

null

Lib/__np__/__init__.py

Redex-Developers/Nuitka-Python

7c1fc1dd6dfeab4cdafeec1709e6e9c4c8c84227

[ "0BSD" ]

null

Lib/__np__/__init__.py

Redex-Developers/Nuitka-Python

7c1fc1dd6dfeab4cdafeec1709e6e9c4c8c84227

[ "0BSD" ]

null

import platform if platform.system() == "Windows": from __np__.windows import * elif platform.system() == "Linux": from __np__.linux import *

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src/backend/marsha/markdown/tests/test_api.py

insad-video/marsha

1e6a708c74527f50c4aa24d811049492e75f47a0

[ "MIT" ]

null

src/backend/marsha/markdown/tests/test_api.py

insad-video/marsha

1e6a708c74527f50c4aa24d811049492e75f47a0

[ "MIT" ]

null

src/backend/marsha/markdown/tests/test_api.py

insad-video/marsha

1e6a708c74527f50c4aa24d811049492e75f47a0

[ "MIT" ]

null

"""Tests for the Markdown application API.""" import json import random from django.test import TestCase, override_settings from rest_framework_simplejwt.tokens import AccessToken from marsha.core import factories as core_factories from ..factories import MarkdownDocumentFactory from ..models import MarkdownDocument # We don't enforce arguments documentation in tests # pylint: disable=unused-argument @override_settings(MARKDOWN_ENABLED=True) class MarkdownAPITest(TestCase): """Test for the Markdown document API.""" maxDiff = None def test_api_document_fetch_anonymous(self): """Anonymous users should not be able to fetch a Markdown document.""" markdown_document = MarkdownDocumentFactory() response = self.client.get(f"/api/markdown-documents/{markdown_document.pk}/") self.assertEqual(response.status_code, 401) content = json.loads(response.content) self.assertEqual( content, {"detail": "Authentication credentials were not provided."} ) def test_api_document_fetch_student(self): """A student should not be allowed to fetch a Markdown document.""" markdown_document = MarkdownDocumentFactory() jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(markdown_document.pk) jwt_token.payload["roles"] = ["student"] jwt_token.payload["permissions"] = {"can_update": True} response = self.client.get( f"/api/markdown-documents/{markdown_document.pk}/", HTTP_AUTHORIZATION=f"Bearer {jwt_token}", ) self.assertEqual(response.status_code, 403) content = json.loads(response.content) self.assertEqual( content, {"detail": "You do not have permission to perform this action."} ) def test_api_document_fetch_instructor(self): """An instructor should be able to fetch a Markdown document.""" markdown_document = MarkdownDocumentFactory( pk="4c51f469-f91e-4998-b438-e31ee3bd3ea6", playlist__pk="6a716ff3-1bfb-4870-906e-fda50293f0ac", playlist__title="foo", playlist__lti_id="course-v1:ufr+mathematics+00001", translations__title="Amazing title", translations__content="# Heading1\nSome content", translations__rendered_content="<h1>Heading1</h1>\n<p>Some content</p>", ) jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(markdown_document.pk) jwt_token.payload["roles"] = [random.choice(["instructor", "administrator"])] jwt_token.payload["permissions"] = {"can_update": True} response = self.client.get( f"/api/markdown-documents/{markdown_document.pk}/", HTTP_AUTHORIZATION=f"Bearer {jwt_token}", ) self.assertEqual(response.status_code, 200) content = json.loads(response.content) self.assertEqual( content, { "id": "4c51f469-f91e-4998-b438-e31ee3bd3ea6", "is_draft": True, "rendering_options": {}, "translations": [ { "language_code": "en", "title": "Amazing title", "content": "# Heading1\nSome content", "rendered_content": "<h1>Heading1</h1>\n<p>Some content</p>", } ], "playlist": { "id": "6a716ff3-1bfb-4870-906e-fda50293f0ac", "title": "foo", "lti_id": "course-v1:ufr+mathematics+00001", }, "position": 0, }, ) def test_api_document_fetch_instructor_read_only(self): """An instructor should not be able to fetch a Markdown document in read_only.""" markdown_document = MarkdownDocumentFactory() jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(markdown_document.pk) jwt_token.payload["roles"] = [random.choice(["instructor", "administrator"])] jwt_token.payload["permissions"] = {"can_update": False} response = self.client.get( f"/api/markdown-documents/{markdown_document.pk}/", HTTP_AUTHORIZATION=f"Bearer {jwt_token}", ) self.assertEqual(response.status_code, 403) content = json.loads(response.content) self.assertEqual( content, {"detail": "You do not have permission to perform this action."} ) def test_api_document_fetch_list_anonymous(self): """An anonymous should not be able to fetch a list of Markdown document.""" response = self.client.get("/api/markdown-documents/") self.assertEqual(response.status_code, 401) def test_api_document_fetch_list_student(self): """A student should not be able to fetch a list of Markdown document.""" markdown_document = MarkdownDocumentFactory() jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(markdown_document.pk) jwt_token.payload["roles"] = ["student"] jwt_token.payload["permissions"] = {"can_update": True} response = self.client.get( "/api/markdown-documents/", HTTP_AUTHORIZATION=f"Bearer {jwt_token}" ) self.assertEqual(response.status_code, 403) def test_api_fetch_list_instructor(self): """An instrustor should not be able to fetch a Markdown document list.""" markdown_document = MarkdownDocumentFactory() jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(markdown_document.pk) jwt_token.payload["roles"] = [random.choice(["instructor", "administrator"])] jwt_token.payload["permissions"] = {"can_update": True} response = self.client.get( "/api/markdown-documents/", HTTP_AUTHORIZATION=f"Bearer {jwt_token}" ) self.assertEqual(response.status_code, 403) def test_api_document_create_anonymous(self): """An anonymous should not be able to create a Markdown document.""" response = self.client.post("/api/markdown-documents/") self.assertEqual(response.status_code, 401) def test_api_document_create_student(self): """A student should not be able to create a Markdown document.""" markdown_document = MarkdownDocumentFactory() jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(markdown_document.pk) jwt_token.payload["roles"] = ["student"] jwt_token.payload["permissions"] = {"can_update": True} response = self.client.post( "/api/markdown-documents/", HTTP_AUTHORIZATION=f"Bearer {jwt_token}" ) self.assertEqual(response.status_code, 403) def test_api_document_create_student_with_playlist_token(self): """A student with a playlist token should not be able to create a Markdown document.""" playlist = core_factories.PlaylistFactory() jwt_token = AccessToken() jwt_token.payload["resource_id"] = "None" jwt_token.payload["roles"] = ["student"] jwt_token.payload["permissions"] = {"can_update": True} jwt_token.payload["playlist_id"] = str(playlist.id) response = self.client.post( "/api/markdown-documents/", HTTP_AUTHORIZATION=f"Bearer {jwt_token}" ) self.assertEqual(response.status_code, 403) def test_api_document_create_instructor(self): """An instrustor should not be able to create a Markdown document.""" markdown_document = MarkdownDocumentFactory() jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(markdown_document.pk) jwt_token.payload["roles"] = [random.choice(["instructor", "administrator"])] jwt_token.payload["permissions"] = {"can_update": True} response = self.client.get( "/api/markdown-documents/", HTTP_AUTHORIZATION=f"Bearer {jwt_token}" ) self.assertEqual(response.status_code, 403) def test_api_document_create_instructor_with_playlist_token(self): """ Create document with playlist token. Used in the context of a lti select request (deep linking). """ playlist = core_factories.PlaylistFactory() jwt_token = AccessToken() jwt_token.payload["resource_id"] = "None" jwt_token.payload["roles"] = [random.choice(["instructor", "administrator"])] jwt_token.payload["permissions"] = {"can_update": True} jwt_token.payload["playlist_id"] = str(playlist.id) self.assertEqual(MarkdownDocument.objects.count(), 0) response = self.client.post( "/api/markdown-documents/", { "lti_id": "document_one", "playlist": str(playlist.id), "title": "Some document", }, HTTP_AUTHORIZATION=f"Bearer {jwt_token}", ) self.assertEqual(MarkdownDocument.objects.count(), 1) self.assertEqual(response.status_code, 201) document = MarkdownDocument.objects.first() self.assertEqual( response.json(), { "id": str(document.id), "is_draft": True, "playlist": { "id": str(playlist.id), "lti_id": playlist.lti_id, "title": playlist.title, }, "position": 0, "rendering_options": {}, "translations": [ { "content": "", "language_code": "en", "rendered_content": "", "title": "Some document", } ], }, ) def test_api_document_delete_anonymous(self): """An anonymous should not be able to delete a Markdown document.""" markdown_document = MarkdownDocumentFactory() response = self.client.delete( f"/api/markdown-documents/{markdown_document.pk}/", ) self.assertEqual(response.status_code, 401) def test_api_document_delete_student(self): """A student should not be able to delete a Markdown document.""" markdown_document = MarkdownDocumentFactory() jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(markdown_document.pk) jwt_token.payload["roles"] = ["student"] jwt_token.payload["permissions"] = {"can_update": True} response = self.client.delete( f"/api/markdown-documents/{markdown_document.pk}/", HTTP_AUTHORIZATION=f"Bearer {jwt_token}", ) self.assertEqual(response.status_code, 403) def test_api_document_delete_instructor(self): """An instructor should not be able to create a Markdown document.""" markdown_document = MarkdownDocumentFactory() jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(markdown_document.pk) jwt_token.payload["roles"] = [random.choice(["instructor", "administrator"])] jwt_token.payload["permissions"] = {"can_update": True} response = self.client.delete( f"/api/markdown-documents/{markdown_document.pk}/", HTTP_AUTHORIZATION=f"Bearer {jwt_token}", ) self.assertEqual(response.status_code, 405) def test_api_document_update_anonymous(self): """An anonymous should not be able to update a Markdown document.""" markdown_document = MarkdownDocumentFactory() response = self.client.put(f"/api/markdown-documents/{markdown_document.pk}/") self.assertEqual(response.status_code, 401) response = self.client.patch( f"/api/markdown-documents/{markdown_document.pk}/save-translations/", content_type="application/json", ) self.assertEqual(response.status_code, 401) response = self.client.post( f"/api/markdown-documents/{markdown_document.pk}/latex-rendering/", content_type="application/json", ) self.assertEqual(response.status_code, 401) def test_api_document_update_student(self): """A student user should not be able to update a Markdown document.""" markdown_document = MarkdownDocumentFactory() jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(markdown_document.pk) jwt_token.payload["roles"] = ["student"] jwt_token.payload["permissions"] = {"can_update": True} data = {"title": "new title"} response = self.client.put( f"/api/markdown-documents/{markdown_document.pk}/", data, HTTP_AUTHORIZATION=f"Bearer {jwt_token}", content_type="application/json", ) self.assertEqual(response.status_code, 403) response = self.client.patch( f"/api/markdown-documents/{markdown_document.pk}/save-translations/", data, # Not important here, wrong data raises 400 HTTP_AUTHORIZATION=f"Bearer {jwt_token}", content_type="application/json", ) self.assertEqual(response.status_code, 403) response = self.client.post( f"/api/markdown-documents/{markdown_document.pk}/latex-rendering/", data, # Not important here, wrong data raises 400 HTTP_AUTHORIZATION=f"Bearer {jwt_token}", content_type="application/json", ) self.assertEqual(response.status_code, 403) def test_api_document_update_instructor_read_only(self): """An instructor should not be able to update a Markdown document in read_only.""" markdown_document = MarkdownDocumentFactory() jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(markdown_document.pk) jwt_token.payload["roles"] = [random.choice(["instructor", "administrator"])] jwt_token.payload["permissions"] = {"can_update": False} data = {"title": "new title"} response = self.client.put( f"/api/markdown-documents/{markdown_document.pk}/", data, HTTP_AUTHORIZATION=f"Bearer {jwt_token}", content_type="application/json", ) self.assertEqual(response.status_code, 403) response = self.client.patch( f"/api/markdown-documents/{markdown_document.pk}/save-translations/", data, # Not important here, wrong data raises 400 HTTP_AUTHORIZATION=f"Bearer {jwt_token}", content_type="application/json", ) self.assertEqual(response.status_code, 403) response = self.client.post( f"/api/markdown-documents/{markdown_document.pk}/latex-rendering/", data, # Not important here, wrong data raises 400 HTTP_AUTHORIZATION=f"Bearer {jwt_token}", content_type="application/json", ) self.assertEqual(response.status_code, 403) def test_api_document_update_instructor(self): """An instructor should be able to update a Markdown document.""" markdown_document = MarkdownDocumentFactory(is_draft=True) jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(markdown_document.pk) jwt_token.payload["roles"] = [random.choice(["instructor", "administrator"])] jwt_token.payload["permissions"] = {"can_update": True} data = {"is_draft": False} response = self.client.put( f"/api/markdown-documents/{markdown_document.pk}/", data, HTTP_AUTHORIZATION=f"Bearer {jwt_token}", content_type="application/json", ) self.assertEqual(response.status_code, 200) markdown_document.refresh_from_db() self.assertEqual(markdown_document.is_draft, False) def test_api_document_translation_update_instructor(self): """An instructor should be able to update a Markdown document translated content.""" markdown_document = MarkdownDocumentFactory(is_draft=True) jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(markdown_document.pk) jwt_token.payload["roles"] = [random.choice(["instructor", "administrator"])] jwt_token.payload["permissions"] = {"can_update": True} data = { "language_code": "en", "title": "A very specific title", "content": "Some interesting content for sure", "rendered_content": "<p>Some interesting content for sure</p>", } response = self.client.patch( f"/api/markdown-documents/{markdown_document.pk}/save-translations/", data, HTTP_AUTHORIZATION=f"Bearer {jwt_token}", content_type="application/json", ) self.assertEqual(response.status_code, 200) markdown_document.refresh_from_db() markdown_document.set_current_language("en") self.assertEqual(markdown_document.title, "A very specific title") self.assertEqual(markdown_document.content, "Some interesting content for sure") self.assertEqual( markdown_document.rendered_content, "<p>Some interesting content for sure</p>", ) def test_api_document_render_latex_instructor(self): """An instructor should be able to render LaTeX content content.""" markdown_document = MarkdownDocumentFactory(is_draft=True) jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(markdown_document.pk) jwt_token.payload["roles"] = [random.choice(["instructor", "administrator"])] jwt_token.payload["permissions"] = {"can_update": True} response = self.client.post( f"/api/markdown-documents/{markdown_document.pk}/latex-rendering/", {"text": r"I = \int \rho R^{2} dV"}, HTTP_AUTHORIZATION=f"Bearer {jwt_token}", content_type="application/json", ) self.assertEqual(response.status_code, 200) content = json.loads(response.content) # Content is already tested elsewhere self.assertIn( "<svg version='1.1' xmlns='http://www.w3.org/2000/svg'", content["latex_image"], ) def test_api_select_instructor_no_document(self): """An instructor should be able to fetch a Markdown document lti select.""" playlist = core_factories.PlaylistFactory() jwt_token = AccessToken() jwt_token.payload["resource_id"] = "None" jwt_token.payload["roles"] = [random.choice(["instructor", "administrator"])] jwt_token.payload["permissions"] = {"can_update": True} jwt_token.payload["playlist_id"] = str(playlist.id) response = self.client.get( "/api/markdown-documents/lti-select/", HTTP_AUTHORIZATION=f"Bearer {jwt_token}", ) self.assertEqual(response.status_code, 200) self.assertDictEqual( { "new_url": "http://testserver/lti/markdown_documents/", "markdown_documents": [], }, response.json(), ) def test_api_select_instructor(self): """An instructor should be able to fetch a Markdown document lti select.""" markdown_document = MarkdownDocumentFactory( translations__title="Amazing title", translations__content="# Heading1\nSome content", translations__rendered_content="<h1>Heading1</h1>\n<p>Some content</p>", ) jwt_token = AccessToken() jwt_token.payload["resource_id"] = "None" jwt_token.payload["roles"] = [random.choice(["instructor", "administrator"])] jwt_token.payload["permissions"] = {"can_update": True} jwt_token.payload["playlist_id"] = str(markdown_document.playlist_id) response = self.client.get( "/api/markdown-documents/lti-select/", HTTP_AUTHORIZATION=f"Bearer {jwt_token}", ) self.assertEqual(response.status_code, 200) self.assertDictEqual( { "new_url": "http://testserver/lti/markdown_documents/", "markdown_documents": [ { "id": str(markdown_document.id), "is_draft": markdown_document.is_draft, "lti_id": str(markdown_document.lti_id), "lti_url": ( f"http://testserver/lti/markdown_documents/{str(markdown_document.id)}" ), "rendering_options": {}, "translations": [ { "language_code": "en", "title": "Amazing title", "content": "# Heading1\nSome content", "rendered_content": "<h1>Heading1</h1>\n<p>Some content</p>", } ], "playlist": { "id": str(markdown_document.playlist_id), "title": markdown_document.playlist.title, "lti_id": markdown_document.playlist.lti_id, }, "position": markdown_document.position, }, ], }, response.json(), )

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py_aco/__init__.py

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py_aco/__init__.py

Joanguitar/ACO

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py_aco/__init__.py

Joanguitar/ACO

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from . import core from . import codebook from . import simulation from . import method

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testpkg/math.py

HurricanKai/PythonTest

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testpkg/math.py

HurricanKai/PythonTest

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null

testpkg/math.py

HurricanKai/PythonTest

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def inc(i): return i + 1 def add(a, b): return a + b def sub(a, b): return a - b

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py

Python

zeus/metrics/mindspore/__init__.py

TianQi-777/xingtian

9b1678ad6ff12f00c2826a7ec7f42d5350b83b31

[ "MIT" ]

240

2020-08-15T15:11:49.000Z

2022-03-28T07:26:23.000Z

zeus/metrics/mindspore/__init__.py

TianQi-777/xingtian

9b1678ad6ff12f00c2826a7ec7f42d5350b83b31

[ "MIT" ]

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2020-08-29T06:18:21.000Z

2022-03-21T04:35:57.000Z

zeus/metrics/mindspore/__init__.py

TianQi-777/xingtian

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[ "MIT" ]

69

2020-08-15T15:41:53.000Z

2022-03-16T08:27:47.000Z

from .metrics import * from .classifier_metric import accuracy from .sr_metric import * from .segmentation_metric import *

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cs229/assignment/problem_set_1/__init__.py

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null

cs229/assignment/problem_set_1/__init__.py

Syhen/stanford-learn

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cs229/assignment/problem_set_1/__init__.py

Syhen/stanford-learn

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[ "MIT" ]

null

# -*- coding: utf-8 -*- """ create on 2021-01-29 20:19 author @66492 """

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Python

src/isecurity_webserver/data_model/domains.py

cybercamp18isecurity/iSecurity

016e1bb7d73864654323e2aac00024483741f8ed

[ "MIT" ]

4

2018-11-30T22:49:52.000Z

2019-06-20T22:36:23.000Z

src/isecurity_webserver/data_model/domains.py

cybercamp18isecurity/iSecurity

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[ "MIT" ]

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2018-11-30T12:06:21.000Z

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src/isecurity_webserver/data_model/domains.py

cybercamp18isecurity/iSecurity

016e1bb7d73864654323e2aac00024483741f8ed

[ "MIT" ]

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2018-12-01T01:19:36.000Z

2019-10-22T05:54:48.000Z

from .abstract_model import AbstractModel class Domains(AbstractModel): def __init__(self, elasticsearch): self.data_type = "domains" AbstractModel.__init__(self, elasticsearch, self.data_type)

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Python

figure_3.py

lullimat/arXiv-2009.12522

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[ "MIT" ]

null

figure_3.py

lullimat/arXiv-2009.12522

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[ "MIT" ]

null

figure_3.py

lullimat/arXiv-2009.12522

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[ "MIT" ]

null

import sys sys.path.append("../../") device_str, lang, _dpi = sys.argv[1], sys.argv[2], int(sys.argv[3]) from sympy import exp as sp_exp from sympy import symbols as sp_symbols from sympy import Rational as sp_Rational from collections import defaultdict import numpy as np from idpy.Utils.ManageData import ManageData from idpy.LBM.LBM import XIStencils from idpy.LBM.SCFStencils import SCFStencils, BasisVectors from idpy.LBM.SCThermo import ShanChanEquilibriumCache from pathlib import Path reproduced_results = Path("reproduced-results") ########################################################################## n = sp_symbols('n') psis = [sp_exp(-1/n), 1 - sp_exp(-n)] psi_codes = {psis[0]: 'exp((NType)(-1./ln))', psis[1]: '1. - exp(-(NType)ln)',} Gs = {psis[0]: [-2.6, -3.1, -3.6], psis[1]: [-1.4, -1.6, -1.75]} Ls = [127, 159, 191, 223, 255, 287, 319, 351] E6_P2F6_sym = sp_symbols("\\boldsymbol{E}^{(6)}_{P2\,F6}") E6_P4F6_sym = sp_symbols("\\boldsymbol{E}^{(6)}_{P4\,F6}") E8_P2F8_sym = sp_symbols("\\boldsymbol{E}^{(8)}_{P2\,F8}") E8_P4F6_sym = sp_symbols("\\boldsymbol{E}^{(8)}_{P4\,F6}") E10_P2F10_sym = sp_symbols("\\boldsymbol{E}^{(10)}_{P2\,F10}") E10_P4F6_sym = sp_symbols("\\boldsymbol{E}^{(10)}_{P4\,F6}") E12_P2F12_sym = sp_symbols("\\boldsymbol{E}^{(12)}_{P2\,F12}") E12_P4F6_sym = sp_symbols("\\boldsymbol{E}^{(12)}_{P4\,F6}") ''' Getting usual weights ''' S5_E6_P2F6 = SCFStencils(E = BasisVectors(x_max = 2), len_2s = [1, 2, 4]) S5_E6_P2F6_W = S5_E6_P2F6.FindWeights() S5_E8_P2F8 = SCFStencils(E = BasisVectors(x_max = 2), len_2s = [1, 2, 4, 5, 8]) S5_E8_P2F8_W = S5_E8_P2F8.FindWeights() S5_E10_P2F10 = SCFStencils(E = BasisVectors(x_max = 3), len_2s = [1, 2, 4, 5, 8, 9, 10]) S5_E10_P2F10_W = S5_E10_P2F10.FindWeights() S5_E12_P2F12 = SCFStencils(E = BasisVectors(x_max = 4), len_2s = [1, 2, 4, 5, 8, 9, 10, 13, 16, 17]) S5_E12_P2F12_W = S5_E12_P2F12.FindWeights() ''' File Names ''' stencil_string = {E6_P2F6_sym: 'E6_P2F6', E6_P4F6_sym: 'E6_P4F6', E8_P2F8_sym: 'E8_P2F8', E8_P4F6_sym: 'E8_P4F6', E10_P2F10_sym: 'E10_P2F10', E10_P4F6_sym: 'E10_P4F6', E12_P2F12_sym: 'E12_P2F12', E12_P4F6_sym: 'E12_P4F6'} stencil_dict = {E6_P2F6_sym: S5_E6_P2F6, E8_P2F8_sym: S5_E8_P2F8, E10_P2F10_sym: S5_E10_P2F10, E12_P2F12_sym: S5_E12_P2F12} stencil_sym_list = [E6_P2F6_sym, E6_P4F6_sym, E8_P2F8_sym, E8_P4F6_sym, E10_P2F10_sym, E10_P4F6_sym, E12_P2F12_sym, E12_P4F6_sym] def FlatFileName(stencil_sym, psi): psi_str = str(psi).replace("/", "_").replace("-", "_") psi_str = psi_str.replace(" ", "_") psi_str = psi_str.replace("(", "").replace(")","") lang_str = str(lang) + "_" + device_str return (lang_str + stencil_string[stencil_sym] + "_" + psi_str + "_flat_profile") def LaplaceFileName(stencil_sym, psi): psi_str = str(psi).replace("/", "_").replace("-", "_") psi_str = psi_str.replace(" ", "_") psi_str = psi_str.replace("(", "").replace(")","") lang_str = str(lang) + "_" + device_str return (lang_str + stencil_string[stencil_sym] + "_" + psi_str + "_laplace") def StencilPsiKey(stencil_sym, psi): return str(stencil_sym) + "_" + str(psi) laplace_files = {} for key in stencil_string: for _psi in psis: laplace_files[StencilPsiKey(key, _psi)] = \ reproduced_results / LaplaceFileName(key, _psi) rho_fields = {} E_sym_dict = {E6_P2F6_sym: 'E6P2', E6_P4F6_sym: 'E6P4'} gibbs_rad = defaultdict( # G lambda: defaultdict( # 'B2F6' lambda: defaultdict( # 'P4Iso=' + YN lambda: defaultdict(dict) # 'droplet' ) ) ) delta_p = defaultdict( # G lambda: defaultdict( # 'B2F6' lambda: defaultdict( # 'P4Iso=' + YN lambda: defaultdict(dict) # 'droplet' ) ) ) E_sym_YN = {E6_P2F6_sym: 'No', E6_P4F6_sym: 'Yes', E8_P2F8_sym: 'No', E8_P4F6_sym: 'Yes', E10_P2F10_sym: 'No', E10_P4F6_sym: 'Yes', E12_P2F12_sym: 'No', E12_P4F6_sym: 'Yes'} for _psi in psis: for _stencil in [E6_P2F6_sym, E6_P4F6_sym, E8_P2F8_sym, E8_P4F6_sym, E10_P2F10_sym, E10_P4F6_sym, E12_P2F12_sym, E12_P4F6_sym]: _data_swap = ManageData(dump_file = laplace_files[StencilPsiKey(_stencil, _psi)]) _is_file_there = _data_swap.Read() if not _is_file_there: raise Exception("Could not find file!", laplace_files[StencilPsiKey(_stencil, _psi)]) for G in Gs[_psi]: _swap_gibbs_rad, _swap_delta_p = [], [] for L in Ls: _data_key = str(G) + "_" + str(L) _swap_gibbs_rad.append(_data_swap.PullData(_data_key)['R_Gibbs']) _swap_delta_p.append(_data_swap.PullData(_data_key)['delta_p']) gibbs_rad['G=' + str(G)][stencil_string[_stencil]]['P4Iso=' + E_sym_YN[_stencil]]['droplet'] = \ np.array(_swap_gibbs_rad) delta_p['G=' + str(G)][stencil_string[_stencil]]['P4Iso=' + E_sym_YN[_stencil]]['droplet'] = \ np.array(_swap_delta_p) sigma_f = defaultdict( # G lambda: defaultdict( # 'B2F6' lambda: defaultdict( # 'P4Iso=' + YN lambda: defaultdict(dict) # 'droplet' ) ) ) for _psi in psis: for _stencil in [E6_P2F6_sym, E8_P2F8_sym, E10_P2F10_sym, E12_P2F12_sym]: for G in Gs[_psi]: _sc_eq_cache = ShanChanEquilibriumCache(stencil = stencil_dict[_stencil], psi_f = _psi, G = G, c2 = XIStencils['D2Q9']['c2']) sigma_f['G=' + str(G)][stencil_string[_stencil]]['P4Iso=' + E_sym_YN[_stencil]]['droplet'] = \ _sc_eq_cache.GetFromCache()['sigma_f'] print("Surface tension (G = ", str(G), ": ", _sc_eq_cache.GetFromCache()['sigma_f'], "), psi = ", _psi) ################################################## ############# END OF DATA PREPARATION ############ ################################################## # https://stackoverflow.com/questions/14737681/fill-the-right-column-of-a-matplotlib-legend-first import numpy as np import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec from mpl_toolkits.axes_grid.inset_locator import (inset_axes, InsetPosition, mark_inset) import matplotlib.ticker as ticker ################################################## #################### FIGURE 1 #################### ################################################## from matplotlib import rc, rcParams ##rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']}) ## for Palatino and other serif fonts use: #rc('font',**{'family':'serif','serif':['Palatino']}) rc('font',**{'family':'STIXGeneral'}) rc('mathtext', **{'fontset': 'stix'}) rc('text', usetex=True) ## To align latex text and symbols!!! ## https://stackoverflow.com/questions/40424249/vertical-alignment-of-matplotlib-legend-labels-with-latex-math rcParams['text.latex.preview'] = True rcParams['text.latex.preamble']=[r"\usepackage{amsmath, sourcesanspro}"] x_lim = 0.042 rm1_axis = np.linspace(0, x_lim, 2**7) _panel_label_pos = (0.02, 0.89) _panel_label_pos = (0.89, 1.05) a = 0.9 b_height = 0.8 legend_size = 10 dashed = {} dashed[-2.6] = '-' dashed[-3.1] = '--' dashed[-3.6] = '-.' dashed[-1.4] = '-' dashed[-1.6] = '--' dashed[-1.75] = '-.' f_s = 14 #################### SIZES #################### fig = plt.figure(figsize=(5.2, 10)) ############################################### #################### PANEL (a) #################### mark_s = 9 ax1 = plt.subplot2grid((4,2), (0,0), colspan=1, rowspan=1) black_lines = [] black_labels = [] G = -2.6 red_p, = ax1.plot(1./gibbs_rad['G=' + str(G)]['E6_P4F6']['P4Iso=' + 'Yes']['droplet'], delta_p['G=' + str(G)]['E6_P4F6']['P4Iso=' + 'Yes']['droplet'], 'x', color = 'red', markersize = mark_s, label = r'$\boldsymbol{E}^{(6)}_{P4,F6}$') blue_p, = ax1.plot(1./gibbs_rad['G=' + str(G)]['E6_P2F6']['P4Iso=' + 'No']['droplet'], delta_p['G=' + str(G)]['E6_P2F6']['P4Iso=' + 'No']['droplet'], '+', color = 'blue', markersize = mark_s, label = r'$\boldsymbol{E}^{(6)}_{P2,F6}$') line_swap, = ax1.plot(rm1_axis, rm1_axis * sigma_f['G=' + str(G)]['E6_P2F6']['P4Iso=' + 'No']['droplet'], label = '$Gc_s^2=' + str(G) + '$', color = 'black') black_lines.append(line_swap) min_y, max_y = 0, 0 for G in [-3.1, -3.6]: if min_y == 0: min_y = np.amin(delta_p['G=' + str(G)]['E6_P4F6']['P4Iso=' + 'Yes']['droplet']) else: min_y = min(min_y, np.amin(delta_p['G=' + str(G)]['E6_P4F6']['P4Iso=' + 'Yes']['droplet'])) if max_y == 0: max_y = np.amax(delta_p['G=' + str(G)]['E6_P4F6']['P4Iso=' + 'Yes']['droplet']) else: max_y = max(max_y, np.amax(delta_p['G=' + str(G)]['E6_P4F6']['P4Iso=' + 'Yes']['droplet'])) ax1.plot(1./gibbs_rad['G=' + str(G)]['E6_P4F6']['P4Iso=' + 'Yes']['droplet'], delta_p['G=' + str(G)]['E6_P4F6']['P4Iso=' + 'Yes']['droplet'], 'x', color = 'red', markersize = mark_s) ax1.plot(1./gibbs_rad['G=' + str(G)]['E6_P2F6']['P4Iso=' + 'No']['droplet'], delta_p['G=' + str(G)]['E6_P2F6']['P4Iso=' + 'No']['droplet'], '+', color = 'blue', markersize = mark_s) line_swap, = ax1.plot(rm1_axis, rm1_axis * sigma_f['G=' + str(G)]['E6_P2F6']['P4Iso=' + 'No']['droplet'], dashed[G], color = 'black', label = '$Gc_s^2=' + str(G) + '$') black_lines.append(line_swap) #(lines, labels) = plt.gca().get_legend_handles_labels() #lines.insert(2, plt.Line2D(rm1_axis, rm1_axis, linestyle='none')) #labels.insert(2,'') # _{\\mbox{\\tiny{Gibbs}}} ax1.ticklabel_format(axis='y', style = 'sci', scilimits=(0,0)) ax1.set_xlabel('$R^{-1}$', fontsize=f_s) ax1.set_ylabel('$\\Delta p$', fontsize=f_s) ax1.set_xlim([0,x_lim]) ### points legend legend_size = 10 if False: lgnd_points = plt.legend(handles = [red_p, blue_p], ncol = 2, handletextpad = 0., columnspacing = 0.75, bbox_to_anchor=(1.55, 1.27), frameon=False) lgnd_points = plt.legend(handles = [red_p, blue_p], loc = 'upper left', frameon=False) lgnd_points.get_texts()[0].set_color("red") lgnd_points.get_texts()[1].set_color("blue") lgnd_points.get_texts()[0].set_size("large") lgnd_points.get_texts()[1].set_size("large") lgnd_points.legendHandles[0]._legmarker.set_markersize(6) lgnd_points.legendHandles[1]._legmarker.set_markersize(6) ### lines legends black_lines.insert(0, plt.Line2D(rm1_axis, rm1_axis, linestyle='none', label = '$\psi = \exp(-1/n)$')) lgnd_lines = plt.legend(handles = black_lines, handlelength = 2, labelspacing=0.2, bbox_to_anchor=(0.925, 1.75), frameon=True) lgnd_lines.get_texts()[0].set_x(-20) lgnd_lines.get_texts()[0].set_size("large") lgnd_lines.get_texts()[1].set_size("large") lgnd_lines.get_texts()[2].set_size("large") lgnd_lines.get_texts()[3].set_size("large") #ml = [method_name for method_name in dir(lgnd_lines.get_texts()[0]) if callable(getattr(lgnd_lines.get_texts()[0], method_name))] #print(ml) ### adding legents to the plot ax1.add_artist(lgnd_points) ax1.add_artist(lgnd_lines) #lgnd1 = ax1.legend(lines,labels,numpoints=1, loc=4,ncol=2) #lgnd1 = ax1.legend(lines, labels, loc='upper center', ncol=2, fancybox=True, # bbox_to_anchor=(0.4, 1.9), frameon=False, handleheight=1., # prop={'size': legend_size}, borderpad=0.8, labelspacing=0.5) # Shrink current axis by 20% b_height = 1 box = ax1.get_position() ax1.set_position([box.x0, box.y0, box.width, box.height * b_height]) ax1.text(_panel_label_pos[0], _panel_label_pos[1], '$(a)$', transform = ax1.transAxes, fontsize=f_s) #################### PANEL (b) #################### mark_s = 9 ax2 = plt.subplot2grid((4,2), (1,0), colspan=1, rowspan=1) black_lines = [] black_labels = [] G = -2.6 red_p, = ax2.plot(1./gibbs_rad['G=' + str(G)]['E8_P4F6']['P4Iso=' + 'Yes']['droplet'], delta_p['G=' + str(G)]['E8_P4F6']['P4Iso=' + 'Yes']['droplet'], 'x', color = 'red', markersize = mark_s, label = r'$\boldsymbol{E}^{(8)}_{P4,F6}$') blue_p, = ax2.plot(1./gibbs_rad['G=' + str(G)]['E8_P2F8']['P4Iso=' + 'No']['droplet'], delta_p['G=' + str(G)]['E8_P2F8']['P4Iso=' + 'No']['droplet'], '+', color = 'blue', markersize = mark_s, label = r'$\boldsymbol{E}^{(8)}_{P2,F8}$') line_swap, = ax2.plot(rm1_axis, rm1_axis * sigma_f['G=' + str(G)]['E8_P2F8']['P4Iso=' + 'No']['droplet'], label = '$Gc_s^2=' + str(G) + '$', color = 'black') black_lines.append(line_swap) min_y, max_y = 0, 0 for G in [-3.1, -3.6]: if min_y == 0: min_y = np.amin(delta_p['G=' + str(G)]['E8_P4F6']['P4Iso=' + 'Yes']['droplet']) else: min_y = min(min_y, np.amin(delta_p['G=' + str(G)]['E8_P4F6']['P4Iso=' + 'Yes']['droplet'])) if max_y == 0: max_y = np.amax(delta_p['G=' + str(G)]['E8_P4F6']['P4Iso=' + 'Yes']['droplet']) else: max_y = max(max_y, np.amax(delta_p['G=' + str(G)]['E8_P4F6']['P4Iso=' + 'Yes']['droplet'])) ax2.plot(1./gibbs_rad['G=' + str(G)]['E8_P4F6']['P4Iso=' + 'Yes']['droplet'], delta_p['G=' + str(G)]['E8_P4F6']['P4Iso=' + 'Yes']['droplet'], 'x', color = 'red', markersize = mark_s) ax2.plot(1./gibbs_rad['G=' + str(G)]['E8_P2F8']['P4Iso=' + 'No']['droplet'], delta_p['G=' + str(G)]['E8_P2F8']['P4Iso=' + 'No']['droplet'], '+', color = 'blue', markersize = mark_s) line_swap, = ax2.plot(rm1_axis, rm1_axis * sigma_f['G=' + str(G)]['E8_P2F8']['P4Iso=' + 'No']['droplet'], dashed[G], color = 'black', label = '$Gc_s^2=' + str(G) + '$') black_lines.append(line_swap) #(lines, labels) = plt.gca().get_legend_handles_labels() #lines.insert(2, plt.Line2D(rm1_axis, rm1_axis, linestyle='none')) #labels.insert(2,'') # _{\\mbox{\\tiny{Gibbs}}} ax2.ticklabel_format(axis='y', style = 'sci', scilimits=(0,0)) ax2.set_xlabel('$R^{-1}$', fontsize=f_s) ax2.set_ylabel('$\\Delta p$', fontsize=f_s) ax2.set_xlim([0,x_lim]) ### points legend legend_size = 10 lgnd_points = plt.legend(handles = [red_p, blue_p], loc = 'upper left', frameon=False) lgnd_points.get_texts()[0].set_color("red") lgnd_points.get_texts()[1].set_color("blue") lgnd_points.get_texts()[0].set_size("large") lgnd_points.get_texts()[1].set_size("large") lgnd_points.legendHandles[0]._legmarker.set_markersize(6) lgnd_points.legendHandles[1]._legmarker.set_markersize(6) #ml = [method_name for method_name in dir(lgnd_lines.get_texts()[0]) if callable(getattr(lgnd_lines.get_texts()[0], method_name))] #print(ml) ### adding legents to the plot ax2.add_artist(lgnd_points) #lgnd1 = ax2.legend(lines,labels,numpoints=1, loc=4,ncol=2) #lgnd1 = ax2.legend(lines, labels, loc='upper center', ncol=2, fancybox=True, # bbox_to_anchor=(0.4, 1.9), frameon=False, handleheight=1., # prop={'size': legend_size}, borderpad=0.8, labelspacing=0.5) # Shrink current axis by 20% b_height = 1 box = ax2.get_position() ax2.set_position([box.x0, box.y0, box.width, box.height * b_height]) ax2.text(_panel_label_pos[0], _panel_label_pos[1], '$(b)$', transform = ax2.transAxes, fontsize=f_s) #################### PANEL (c) #################### mark_s = 9 ax3 = plt.subplot2grid((4,2), (2,0), colspan=1, rowspan=1) black_lines = [] black_labels = [] G = -2.6 red_p, = ax3.plot(1./gibbs_rad['G=' + str(G)]['E8_P4F6']['P4Iso=' + 'Yes']['droplet'], delta_p['G=' + str(G)]['E8_P4F6']['P4Iso=' + 'Yes']['droplet'], 'x', color = 'red', markersize = mark_s, label = r'$\boldsymbol{E}^{(10)}_{P4,F6}$') blue_p, = ax3.plot(1./gibbs_rad['G=' + str(G)]['E8_P2F8']['P4Iso=' + 'No']['droplet'], delta_p['G=' + str(G)]['E8_P2F8']['P4Iso=' + 'No']['droplet'], '+', color = 'blue', markersize = mark_s, label = r'$\boldsymbol{E}^{(10)}_{P2,F10}$') line_swap, = ax3.plot(rm1_axis, rm1_axis * sigma_f['G=' + str(G)]['E8_P2F8']['P4Iso=' + 'No']['droplet'], label = '$Gc_s^2=' + str(G) + '$', color = 'black') black_lines.append(line_swap) min_y, max_y = 0, 0 for G in [-3.1, -3.6]: if min_y == 0: min_y = np.amin(delta_p['G=' + str(G)]['E8_P4F6']['P4Iso=' + 'Yes']['droplet']) else: min_y = min(min_y, np.amin(delta_p['G=' + str(G)]['E8_P4F6']['P4Iso=' + 'Yes']['droplet'])) if max_y == 0: max_y = np.amax(delta_p['G=' + str(G)]['E8_P4F6']['P4Iso=' + 'Yes']['droplet']) else: max_y = max(max_y, np.amax(delta_p['G=' + str(G)]['E8_P4F6']['P4Iso=' + 'Yes']['droplet'])) ax3.plot(1./gibbs_rad['G=' + str(G)]['E8_P4F6']['P4Iso=' + 'Yes']['droplet'], delta_p['G=' + str(G)]['E8_P4F6']['P4Iso=' + 'Yes']['droplet'], 'x', color = 'red', markersize = mark_s) ax3.plot(1./gibbs_rad['G=' + str(G)]['E8_P2F8']['P4Iso=' + 'No']['droplet'], delta_p['G=' + str(G)]['E8_P2F8']['P4Iso=' + 'No']['droplet'], '+', color = 'blue', markersize = mark_s) line_swap, = ax3.plot(rm1_axis, rm1_axis * sigma_f['G=' + str(G)]['E8_P2F8']['P4Iso=' + 'No']['droplet'], dashed[G], color = 'black', label = '$Gc_s^2=' + str(G) + '$') black_lines.append(line_swap) #(lines, labels) = plt.gca().get_legend_handles_labels() #lines.insert(2, plt.Line2D(rm1_axis, rm1_axis, linestyle='none')) #labels.insert(2,'') # _{\\mbox{\\tiny{Gibbs}}} ax3.ticklabel_format(axis='y', style = 'sci', scilimits=(0,0)) ax3.set_xlabel('$R^{-1}$', fontsize=f_s) ax3.set_ylabel('$\\Delta p$', fontsize=f_s) ax3.set_xlim([0,x_lim]) ### points legend legend_size = 10 lgnd_points = plt.legend(handles = [red_p, blue_p], loc = 'upper left', frameon=False) lgnd_points.get_texts()[0].set_color("red") lgnd_points.get_texts()[1].set_color("blue") lgnd_points.get_texts()[0].set_size("large") lgnd_points.get_texts()[1].set_size("large") lgnd_points.legendHandles[0]._legmarker.set_markersize(6) lgnd_points.legendHandles[1]._legmarker.set_markersize(6) #ml = [method_name for method_name in dir(lgnd_lines.get_texts()[0]) if callable(getattr(lgnd_lines.get_texts()[0], method_name))] #print(ml) ### adding legents to the plot ax3.add_artist(lgnd_points) #lgnd1 = ax3.legend(lines,labels,numpoints=1, loc=4,ncol=2) #lgnd1 = ax3.legend(lines, labels, loc='upper center', ncol=2, fancybox=True, # bbox_to_anchor=(0.4, 1.9), frameon=False, handleheight=1., # prop={'size': legend_size}, borderpad=0.8, labelspacing=0.5) # Shrink current axis by 20% b_height = 1 box = ax3.get_position() ax3.set_position([box.x0, box.y0, box.width, box.height * b_height]) ax3.text(_panel_label_pos[0], _panel_label_pos[1], '$(c)$', transform = ax3.transAxes, fontsize=f_s) #################### PANEL (d) #################### mark_s = 9 ax4 = plt.subplot2grid((4,2), (3,0), colspan=1, rowspan=1) black_lines = [] black_labels = [] G = -2.6 red_p, = ax4.plot(1./gibbs_rad['G=' + str(G)]['E12_P4F6']['P4Iso=' + 'Yes']['droplet'], delta_p['G=' + str(G)]['E12_P4F6']['P4Iso=' + 'Yes']['droplet'], 'x', color = 'red', markersize = mark_s, label = r'$\boldsymbol{E}^{(12)}_{P4,F6}$') blue_p, = ax4.plot(1./gibbs_rad['G=' + str(G)]['E12_P2F12']['P4Iso=' + 'No']['droplet'], delta_p['G=' + str(G)]['E12_P2F12']['P4Iso=' + 'No']['droplet'], '+', color = 'blue', markersize = mark_s, label = r'$\boldsymbol{E}^{(12)}_{P2,F12}$') line_swap, = ax4.plot(rm1_axis, rm1_axis * sigma_f['G=' + str(G)]['E12_P2F12']['P4Iso=' + 'No']['droplet'], label = '$Gc_s^2=' + str(G) + '$', color = 'black') black_lines.append(line_swap) min_y, max_y = 0, 0 for G in [-3.1, -3.6]: if min_y == 0: min_y = np.amin(delta_p['G=' + str(G)]['E12_P4F6']['P4Iso=' + 'Yes']['droplet']) else: min_y = min(min_y, np.amin(delta_p['G=' + str(G)]['E12_P4F6']['P4Iso=' + 'Yes']['droplet'])) if max_y == 0: max_y = np.amax(delta_p['G=' + str(G)]['E12_P4F6']['P4Iso=' + 'Yes']['droplet']) else: max_y = max(max_y, np.amax(delta_p['G=' + str(G)]['E12_P4F6']['P4Iso=' + 'Yes']['droplet'])) ax4.plot(1./gibbs_rad['G=' + str(G)]['E12_P4F6']['P4Iso=' + 'Yes']['droplet'], delta_p['G=' + str(G)]['E12_P4F6']['P4Iso=' + 'Yes']['droplet'], 'x', color = 'red', markersize = mark_s) ax4.plot(1./gibbs_rad['G=' + str(G)]['E12_P2F12']['P4Iso=' + 'No']['droplet'], delta_p['G=' + str(G)]['E12_P2F12']['P4Iso=' + 'No']['droplet'], '+', color = 'blue', markersize = mark_s) line_swap, = ax4.plot(rm1_axis, rm1_axis * sigma_f['G=' + str(G)]['E12_P2F12']['P4Iso=' + 'No']['droplet'], dashed[G], color = 'black', label = '$Gc_s^2=' + str(G) + '$') black_lines.append(line_swap) #(lines, labels) = plt.gca().get_legend_handles_labels() #lines.insert(2, plt.Line2D(rm1_axis, rm1_axis, linestyle='none')) #labels.insert(2,'') # _{\\mbox{\\tiny{Gibbs}}} ax4.ticklabel_format(axis='y', style = 'sci', scilimits=(0,0)) ax4.set_xlabel('$R^{-1}$', fontsize=f_s) ax4.set_ylabel('$\\Delta p$', fontsize=f_s) ax4.set_xlim([0,x_lim]) ### points legend legend_size = 10 lgnd_points = plt.legend(handles = [red_p, blue_p], loc = 'upper left', frameon=False) lgnd_points.get_texts()[0].set_color("red") lgnd_points.get_texts()[1].set_color("blue") lgnd_points.get_texts()[0].set_size("large") lgnd_points.get_texts()[1].set_size("large") lgnd_points.legendHandles[0]._legmarker.set_markersize(6) lgnd_points.legendHandles[1]._legmarker.set_markersize(6) #ml = [method_name for method_name in dir(lgnd_lines.get_texts()[0]) if callable(getattr(lgnd_lines.get_texts()[0], method_name))] #print(ml) ### adding legents to the plot ax4.add_artist(lgnd_points) #lgnd1 = ax4.legend(lines,labels,numpoints=1, loc=4,ncol=2) #lgnd1 = ax4.legend(lines, labels, loc='upper center', ncol=2, fancybox=True, # bbox_to_anchor=(0.4, 1.9), frameon=False, handleheight=1., # prop={'size': legend_size}, borderpad=0.8, labelspacing=0.5) # Shrink current axis by 20% b_height = 1 box = ax4.get_position() ax4.set_position([box.x0, box.y0, box.width, box.height * b_height]) ax4.text(_panel_label_pos[0], _panel_label_pos[1], '$(d)$', transform = ax4.transAxes, fontsize=f_s) ################################################### ################## SECOND COLUMN ################## #################### PANEL (e) #################### ax5 = plt.subplot2grid((4,2), (0,1), colspan=1, rowspan=1) black_lines = [] G = -1.4 red_p, = ax5.plot(1./gibbs_rad['G=' + str(G)]['E6_P4F6']['P4Iso=' + 'Yes']['droplet'], delta_p['G=' + str(G)]['E6_P4F6']['P4Iso=' + 'Yes']['droplet'], 'x', color = 'red', markersize = mark_s, label = r'$\boldsymbol{E}^{(6)}_{P4,F6}$') blue_p, = ax5.plot(1./gibbs_rad['G=' + str(G)]['E6_P2F6']['P4Iso=' + 'No']['droplet'], delta_p['G=' + str(G)]['E6_P2F6']['P4Iso=' + 'No']['droplet'], '+', color = 'blue', markersize = mark_s, label = r'$\boldsymbol{E}^{(6)}_{P2,F6}$') line_swap, = ax5.plot(rm1_axis, rm1_axis * sigma_f['G=' + str(G)]['E6_P2F6']['P4Iso=' + 'No']['droplet'], label = '$Gc_s^2=' + str(G) + '$', color = 'black') black_lines.append(line_swap) min_y, max_y = 0, 0 for G in [-1.6, -1.75]: if min_y == 0: min_y = np.amin(delta_p['G=' + str(G)]['E6_P4F6']['P4Iso=' + 'Yes']['droplet']) else: min_y = min(min_y, np.amin(delta_p['G=' + str(G)]['E6_P4F6']['P4Iso=' + 'Yes']['droplet'])) if max_y == 0: max_y = np.amax(delta_p['G=' + str(G)]['E6_P4F6']['P4Iso=' + 'Yes']['droplet']) else: max_y = max(max_y, np.amax(delta_p['G=' + str(G)]['E6_P4F6']['P4Iso=' + 'Yes']['droplet'])) ax5.plot(1./gibbs_rad['G=' + str(G)]['E6_P4F6']['P4Iso=' + 'Yes']['droplet'], delta_p['G=' + str(G)]['E6_P4F6']['P4Iso=' + 'Yes']['droplet'], 'x', color = 'red', markersize = mark_s) ax5.plot(1./gibbs_rad['G=' + str(G)]['E6_P2F6']['P4Iso=' + 'No']['droplet'], delta_p['G=' + str(G)]['E6_P2F6']['P4Iso=' + 'No']['droplet'], '+', color = 'blue', markersize = mark_s) line_swap, = ax5.plot(rm1_axis, rm1_axis * sigma_f['G=' + str(G)]['E6_P2F6']['P4Iso=' + 'No']['droplet'], dashed[G], color = 'black', label = '$Gc_s^2=' + str(G) + '$') black_lines.append(line_swap) ax5.set_xlabel('$R^{-1}$', fontsize=f_s) #ax5.set_title('$\psi = 1 - \\exp(-n)$') ax5.ticklabel_format(axis='y', style = 'sci', scilimits=(0,0)) ax5.set_xlim([0,x_lim]) lgnd_points2 = plt.legend(handles = [red_p, blue_p], loc = 'upper left', frameon=False) lgnd_points2.get_texts()[0].set_color("red") lgnd_points2.get_texts()[1].set_color("blue") lgnd_points2.get_texts()[0].set_size("large") lgnd_points2.get_texts()[1].set_size("large") lgnd_points2.legendHandles[0]._legmarker.set_markersize(6) lgnd_points2.legendHandles[1]._legmarker.set_markersize(6) ### lines legends black_lines.insert(0, plt.Line2D(rm1_axis, rm1_axis, linestyle='none', label = '$\psi = 1 - \exp(-n)$')) lgnd_lines2 = plt.legend(handles = black_lines, handlelength = 2., labelspacing=0.2, bbox_to_anchor=(0.9625, 1.75), frameon=True) lgnd_lines2.get_texts()[0].set_x(-20) lgnd_lines2.get_texts()[0].set_size("large") lgnd_lines2.get_texts()[1].set_size("large") lgnd_lines2.get_texts()[2].set_size("large") lgnd_lines2.get_texts()[3].set_size("large") ### adding legents to the plot ax5.add_artist(lgnd_points2) ax5.add_artist(lgnd_lines2) # Shrink current axis by 20% box = ax5.get_position() ax5.set_position([box.x0, box.y0, box.width, box.height * b_height]) ax5.text(_panel_label_pos[0], _panel_label_pos[1], '$(e)$', transform = ax5.transAxes, fontsize=f_s) #################### PANEL (f) #################### ax6 = plt.subplot2grid((4,2), (1,1), colspan=1, rowspan=1) black_lines = [] G = -1.4 red_p, = ax6.plot(1./gibbs_rad['G=' + str(G)]['E8_P4F6']['P4Iso=' + 'Yes']['droplet'], delta_p['G=' + str(G)]['E8_P4F6']['P4Iso=' + 'Yes']['droplet'], 'x', color = 'red', markersize = mark_s, label = r'$\boldsymbol{E}^{(8)}_{P4,F6}$') blue_p, = ax6.plot(1./gibbs_rad['G=' + str(G)]['E8_P2F8']['P4Iso=' + 'No']['droplet'], delta_p['G=' + str(G)]['E8_P2F8']['P4Iso=' + 'No']['droplet'], '+', color = 'blue', markersize = mark_s, label = r'$\boldsymbol{E}^{(8)}_{P2,F8}$') line_swap, = ax6.plot(rm1_axis, rm1_axis * sigma_f['G=' + str(G)]['E8_P2F8']['P4Iso=' + 'No']['droplet'], label = '$Gc_s^2=' + str(G) + '$', color = 'black') black_lines.append(line_swap) min_y, max_y = 0, 0 for G in [-1.6, -1.75]: if min_y == 0: min_y = np.amin(delta_p['G=' + str(G)]['E8_P4F6']['P4Iso=' + 'Yes']['droplet']) else: min_y = min(min_y, np.amin(delta_p['G=' + str(G)]['E8_P4F6']['P4Iso=' + 'Yes']['droplet'])) if max_y == 0: max_y = np.amax(delta_p['G=' + str(G)]['E8_P4F6']['P4Iso=' + 'Yes']['droplet']) else: max_y = max(max_y, np.amax(delta_p['G=' + str(G)]['E8_P4F6']['P4Iso=' + 'Yes']['droplet'])) ax6.plot(1./gibbs_rad['G=' + str(G)]['E8_P4F6']['P4Iso=' + 'Yes']['droplet'], delta_p['G=' + str(G)]['E8_P4F6']['P4Iso=' + 'Yes']['droplet'], 'x', color = 'red', markersize = mark_s) ax6.plot(1./gibbs_rad['G=' + str(G)]['E8_P2F8']['P4Iso=' + 'No']['droplet'], delta_p['G=' + str(G)]['E8_P2F8']['P4Iso=' + 'No']['droplet'], '+', color = 'blue', markersize = mark_s) line_swap, = ax6.plot(rm1_axis, rm1_axis * sigma_f['G=' + str(G)]['E8_P2F8']['P4Iso=' + 'No']['droplet'], dashed[G], color = 'black', label = '$Gc_s^2=' + str(G) + '$') black_lines.append(line_swap) ax6.set_xlabel('$R^{-1}$', fontsize=f_s) #ax6.set_title('$\psi = 1 - \\exp(-n)$') ax6.ticklabel_format(axis='y', style = 'sci', scilimits=(0,0)) ax6.set_xlim([0,x_lim]) lgnd_points = plt.legend(handles = [red_p, blue_p], loc = 'upper left', frameon=False) lgnd_points.get_texts()[0].set_color("red") lgnd_points.get_texts()[1].set_color("blue") lgnd_points.get_texts()[0].set_size("large") lgnd_points.get_texts()[1].set_size("large") lgnd_points.legendHandles[0]._legmarker.set_markersize(6) lgnd_points.legendHandles[1]._legmarker.set_markersize(6) #ax6.legend(loc='upper center', ncol=1, fancybox=True, # bbox_to_anchor=(0.5, 1.7), frameon=False, prop={'size': legend_size}) # Shrink current axis by 20% box = ax6.get_position() ax6.set_position([box.x0, box.y0, box.width, box.height * b_height]) ax6.text(_panel_label_pos[0], _panel_label_pos[1], '$(f)$', transform = ax6.transAxes, fontsize=f_s) #################### PANEL (g) #################### ax7 = plt.subplot2grid((4,2), (2,1), colspan=1, rowspan=1) black_lines = [] G = -1.4 red_p, = ax7.plot(1./gibbs_rad['G=' + str(G)]['E10_P4F6']['P4Iso=' + 'Yes']['droplet'], delta_p['G=' + str(G)]['E10_P4F6']['P4Iso=' + 'Yes']['droplet'], 'x', color = 'red', markersize = mark_s, label = r'$\boldsymbol{E}^{(10)}_{P4,F6}$') blue_p, = ax7.plot(1./gibbs_rad['G=' + str(G)]['E10_P2F10']['P4Iso=' + 'No']['droplet'], delta_p['G=' + str(G)]['E10_P2F10']['P4Iso=' + 'No']['droplet'], '+', color = 'blue', markersize = mark_s, label = r'$\boldsymbol{E}^{(10)}_{P2,F10}$') line_swap, = ax7.plot(rm1_axis, rm1_axis * sigma_f['G=' + str(G)]['E10_P2F10']['P4Iso=' + 'No']['droplet'], label = '$Gc_s^2=' + str(G) + '$', color = 'black') black_lines.append(line_swap) min_y, max_y = 0, 0 for G in [-1.6, -1.75]: if min_y == 0: min_y = np.amin(delta_p['G=' + str(G)]['E10_P4F6']['P4Iso=' + 'Yes']['droplet']) else: min_y = min(min_y, np.amin(delta_p['G=' + str(G)]['E10_P4F6']['P4Iso=' + 'Yes']['droplet'])) if max_y == 0: max_y = np.amax(delta_p['G=' + str(G)]['E10_P4F6']['P4Iso=' + 'Yes']['droplet']) else: max_y = max(max_y, np.amax(delta_p['G=' + str(G)]['E10_P4F6']['P4Iso=' + 'Yes']['droplet'])) ax7.plot(1./gibbs_rad['G=' + str(G)]['E10_P4F6']['P4Iso=' + 'Yes']['droplet'], delta_p['G=' + str(G)]['E10_P4F6']['P4Iso=' + 'Yes']['droplet'], 'x', color = 'red', markersize = mark_s) ax7.plot(1./gibbs_rad['G=' + str(G)]['E10_P2F10']['P4Iso=' + 'No']['droplet'], delta_p['G=' + str(G)]['E10_P2F10']['P4Iso=' + 'No']['droplet'], '+', color = 'blue', markersize = mark_s) line_swap, = ax7.plot(rm1_axis, rm1_axis * sigma_f['G=' + str(G)]['E10_P2F10']['P4Iso=' + 'No']['droplet'], dashed[G], color = 'black', label = '$Gc_s^2=' + str(G) + '$') black_lines.append(line_swap) ax7.set_xlabel('$R^{-1}$', fontsize=f_s) #ax7.set_title('$\psi = 1 - \\exp(-n)$') ax7.ticklabel_format(axis='y', style = 'sci', scilimits=(0,0)) ax7.set_xlim([0,x_lim]) lgnd_points = plt.legend(handles = [red_p, blue_p], loc = 'upper left', frameon=False) lgnd_points.get_texts()[0].set_color("red") lgnd_points.get_texts()[1].set_color("blue") lgnd_points.get_texts()[0].set_size("large") lgnd_points.get_texts()[1].set_size("large") lgnd_points.legendHandles[0]._legmarker.set_markersize(6) lgnd_points.legendHandles[1]._legmarker.set_markersize(6) #ax7.legend(loc='upper center', ncol=1, fancybox=True, # bbox_to_anchor=(0.5, 1.7), frameon=False, prop={'size': legend_size}) # Shrink current axis by 20% box = ax7.get_position() ax7.set_position([box.x0, box.y0, box.width, box.height * b_height]) ax7.text(_panel_label_pos[0], _panel_label_pos[1], '$(g)$', transform = ax7.transAxes, fontsize=f_s) #################### PANEL (h) #################### ax8 = plt.subplot2grid((4,2), (3,1), colspan=1, rowspan=1) black_lines = [] G = -1.4 red_p, = ax8.plot(1./gibbs_rad['G=' + str(G)]['E12_P4F6']['P4Iso=' + 'Yes']['droplet'], delta_p['G=' + str(G)]['E12_P4F6']['P4Iso=' + 'Yes']['droplet'], 'x', color = 'red', markersize = mark_s, label = r'$\boldsymbol{E}^{(12)}_{P4,F6}$') blue_p, = ax8.plot(1./gibbs_rad['G=' + str(G)]['E12_P2F12']['P4Iso=' + 'No']['droplet'], delta_p['G=' + str(G)]['E12_P2F12']['P4Iso=' + 'No']['droplet'], '+', color = 'blue', markersize = mark_s, label = r'$\boldsymbol{E}^{(12)}_{P2,F12}$') line_swap, = ax8.plot(rm1_axis, rm1_axis * sigma_f['G=' + str(G)]['E12_P2F12']['P4Iso=' + 'No']['droplet'], label = '$Gc_s^2=' + str(G) + '$', color = 'black') black_lines.append(line_swap) min_y, max_y = 0, 0 for G in [-1.6, -1.75]: if min_y == 0: min_y = np.amin(delta_p['G=' + str(G)]['E12_P4F6']['P4Iso=' + 'Yes']['droplet']) else: min_y = min(min_y, np.amin(delta_p['G=' + str(G)]['E12_P4F6']['P4Iso=' + 'Yes']['droplet'])) if max_y == 0: max_y = np.amax(delta_p['G=' + str(G)]['E12_P4F6']['P4Iso=' + 'Yes']['droplet']) else: max_y = max(max_y, np.amax(delta_p['G=' + str(G)]['E12_P4F6']['P4Iso=' + 'Yes']['droplet'])) ax8.plot(1./gibbs_rad['G=' + str(G)]['E12_P4F6']['P4Iso=' + 'Yes']['droplet'], delta_p['G=' + str(G)]['E12_P4F6']['P4Iso=' + 'Yes']['droplet'], 'x', color = 'red', markersize = mark_s) ax8.plot(1./gibbs_rad['G=' + str(G)]['E12_P2F12']['P4Iso=' + 'No']['droplet'], delta_p['G=' + str(G)]['E12_P2F12']['P4Iso=' + 'No']['droplet'], '+', color = 'blue', markersize = mark_s) line_swap, = ax8.plot(rm1_axis, rm1_axis * sigma_f['G=' + str(G)]['E12_P2F12']['P4Iso=' + 'No']['droplet'], dashed[G], color = 'black', label = '$Gc_s^2=' + str(G) + '$') black_lines.append(line_swap) ax8.set_xlabel('$R^{-1}$', fontsize=f_s) #ax8.set_title('$\psi = 1 - \\exp(-n)$') ax8.ticklabel_format(axis='y', style = 'sci', scilimits=(0,0)) ax8.set_xlim([0,x_lim]) lgnd_points = plt.legend(handles = [red_p, blue_p], loc = 'upper left', frameon=False) lgnd_points.get_texts()[0].set_color("red") lgnd_points.get_texts()[1].set_color("blue") lgnd_points.get_texts()[0].set_size("large") lgnd_points.get_texts()[1].set_size("large") lgnd_points.legendHandles[0]._legmarker.set_markersize(6) lgnd_points.legendHandles[1]._legmarker.set_markersize(6) #ax8.legend(loc='upper center', ncol=1, fancybox=True, # bbox_to_anchor=(0.5, 1.7), frameon=False, prop={'size': legend_size}) # Shrink current axis by 20% box = ax8.get_position() ax8.set_position([box.x0, box.y0, box.width, box.height * b_height]) ax8.text(_panel_label_pos[0], _panel_label_pos[1], '$(h)$', transform = ax8.transAxes, fontsize=f_s) #################### SAVING #################### fig.tight_layout() from pathlib import Path reproduced_figures = Path("reproduced-figures") if not reproduced_figures.is_dir(): reproduced_figures.mkdir() plt.savefig(reproduced_figures / 'figure_3.png', bbox_inches = 'tight', dpi = _dpi) plt.close()

39.492424

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3.674971

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0.030016

0.033639

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0.759044

0.72556

0.69482

0

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36,491

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0.001672

0

null

0

1

0

null

0

6

4e325b53dc51c402d92cb47259d5f5b2296a28da

130

py

Python

classwork/10_28_2020.py

Katsute/Baruch-CIS-2300-Assignments

ea374ed1cb229f5e598863ba1777be5f47eaab9d

[ "CC0-1.0" ]

null

classwork/10_28_2020.py

Katsute/Baruch-CIS-2300-Assignments

ea374ed1cb229f5e598863ba1777be5f47eaab9d

[ "CC0-1.0" ]

null

classwork/10_28_2020.py

Katsute/Baruch-CIS-2300-Assignments

ea374ed1cb229f5e598863ba1777be5f47eaab9d

[ "CC0-1.0" ]

1

2022-01-12T18:17:52.000Z

# rand import random random.randint(1, 10) for _ in range(6): print(random.randint(1, 10)) # pandas import pandas as pd

13

32

0.669231

21

130

4.095238

0.666667

0.302326

0.325581

0.372093

0

0.068627

0.215385

130

9

33

14.444444

0.77451

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null

1

0

1

0

null

0

1

0

6

9d91136969dbe70d8ca19b3aaee0329bc8a50cd8

189

py

Python

aulas/aula006b.py

figueiredo-alef/estud-python

f22351ecb966ec84433bb6078d92d4f31d5a0a7e

[ "MIT" ]

null

aulas/aula006b.py

figueiredo-alef/estud-python

f22351ecb966ec84433bb6078d92d4f31d5a0a7e

[ "MIT" ]

null

aulas/aula006b.py

figueiredo-alef/estud-python

f22351ecb966ec84433bb6078d92d4f31d5a0a7e

[ "MIT" ]

null

print('=' * 5, 'AULA_006b', '=' * 5) n0 = float(input('digite um valor: ')) print(n0) n1 = str(input('digite um valor: ')) print(type(n1)) n2 = input('digite algo: ') print(n2.isnumeric())

23.625

38

0.608466

29

189

3.931034

0.551724

0.289474

0.22807

0.315789

0.403509

0

0.067901

0.142857

189

7

39

27

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false

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0.571429

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0

null

1

0

1

0

null

0

1

0

6

9da3cce61a82aaf61028dee623894e05eee20708

8,660

py

Python

tempest/tests/lib/services/identity/v3/test_inherited_roles_client.py

mail2nsrajesh/tempest

1a3b3dc50b418d3a15839830d7d1ff88c8c76cff

[ "Apache-2.0" ]

254

2015-01-05T19:22:52.000Z

2022-03-29T08:14:54.000Z

tempest/tests/lib/services/identity/v3/test_inherited_roles_client.py

mail2nsrajesh/tempest

1a3b3dc50b418d3a15839830d7d1ff88c8c76cff

[ "Apache-2.0" ]

13

2015-03-02T15:53:04.000Z

2022-02-16T02:28:14.000Z

tempest/tests/lib/services/identity/v3/test_inherited_roles_client.py

mail2nsrajesh/tempest

1a3b3dc50b418d3a15839830d7d1ff88c8c76cff

[ "Apache-2.0" ]

367

2015-01-07T15:05:39.000Z

2022-03-04T09:50:35.000Z

# Copyright 2016 NEC Corporation. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. from tempest.lib.services.identity.v3 import inherited_roles_client from tempest.tests.lib import fake_auth_provider from tempest.tests.lib.services import base class TestInheritedRolesClient(base.BaseServiceTest): FAKE_LIST_INHERITED_ROLES = { "roles": [ { "id": "1", "name": "test", "links": "example.com" }, { "id": "2", "name": "test2", "links": "example.com" } ] } def setUp(self): super(TestInheritedRolesClient, self).setUp() fake_auth = fake_auth_provider.FakeAuthProvider() self.client = inherited_roles_client.InheritedRolesClient( fake_auth, 'identity', 'regionOne') def _test_create_inherited_role_on_domains_user(self, bytes_body=False): self.check_service_client_function( self.client.create_inherited_role_on_domains_user, 'tempest.lib.common.rest_client.RestClient.put', {}, bytes_body, domain_id="b344506af7644f6794d9cb316600b020", user_id="123", role_id="1234", status=204) def _test_list_inherited_project_role_for_user_on_domain( self, bytes_body=False): self.check_service_client_function( self.client.list_inherited_project_role_for_user_on_domain, 'tempest.lib.common.rest_client.RestClient.get', self.FAKE_LIST_INHERITED_ROLES, bytes_body, domain_id="b344506af7644f6794d9cb316600b020", user_id="123") def _test_create_inherited_role_on_domains_group(self, bytes_body=False): self.check_service_client_function( self.client.create_inherited_role_on_domains_group, 'tempest.lib.common.rest_client.RestClient.put', {}, bytes_body, domain_id="b344506af7644f6794d9cb316600b020", group_id="123", role_id="1234", status=204) def _test_list_inherited_project_role_for_group_on_domain( self, bytes_body=False): self.check_service_client_function( self.client.list_inherited_project_role_for_group_on_domain, 'tempest.lib.common.rest_client.RestClient.get', self.FAKE_LIST_INHERITED_ROLES, bytes_body, domain_id="b344506af7644f6794d9cb316600b020", group_id="123") def _test_create_inherited_role_on_projects_user(self, bytes_body=False): self.check_service_client_function( self.client.create_inherited_role_on_projects_user, 'tempest.lib.common.rest_client.RestClient.put', {}, bytes_body, project_id="b344506af7644f6794d9cb316600b020", user_id="123", role_id="1234", status=204) def _test_create_inherited_role_on_projects_group(self, bytes_body=False): self.check_service_client_function( self.client.create_inherited_role_on_projects_group, 'tempest.lib.common.rest_client.RestClient.put', {}, bytes_body, project_id="b344506af7644f6794d9cb316600b020", group_id="123", role_id="1234", status=204) def test_create_inherited_role_on_domains_user_with_str_body(self): self._test_create_inherited_role_on_domains_user() def test_create_inherited_role_on_domains_user_with_bytes_body(self): self._test_create_inherited_role_on_domains_user(bytes_body=True) def test_create_inherited_role_on_domains_group_with_str_body(self): self._test_create_inherited_role_on_domains_group() def test_create_inherited_role_on_domains_group_with_bytes_body(self): self._test_create_inherited_role_on_domains_group(bytes_body=True) def test_create_inherited_role_on_projects_user_with_str_body(self): self._test_create_inherited_role_on_projects_user() def test_create_inherited_role_on_projects_group_with_bytes_body(self): self._test_create_inherited_role_on_projects_group(bytes_body=True) def test_list_inherited_project_role_for_user_on_domain_with_str_body( self): self._test_list_inherited_project_role_for_user_on_domain() def test_list_inherited_project_role_for_user_on_domain_with_bytes_body( self): self._test_list_inherited_project_role_for_user_on_domain( bytes_body=True) def test_list_inherited_project_role_for_group_on_domain_with_str_body( self): self._test_list_inherited_project_role_for_group_on_domain() def test_list_inherited_project_role_for_group_on_domain_with_bytes_body( self): self._test_list_inherited_project_role_for_group_on_domain( bytes_body=True) def test_delete_inherited_role_from_user_on_domain(self): self.check_service_client_function( self.client.delete_inherited_role_from_user_on_domain, 'tempest.lib.common.rest_client.RestClient.delete', {}, domain_id="b344506af7644f6794d9cb316600b020", user_id="123", role_id="1234", status=204) def test_check_user_inherited_project_role_on_domain(self): self.check_service_client_function( self.client.check_user_inherited_project_role_on_domain, 'tempest.lib.common.rest_client.RestClient.head', {}, domain_id="b344506af7644f6794d9cb316600b020", user_id="123", role_id="1234", status=204) def test_delete_inherited_role_from_group_on_domain(self): self.check_service_client_function( self.client.delete_inherited_role_from_group_on_domain, 'tempest.lib.common.rest_client.RestClient.delete', {}, domain_id="b344506af7644f6794d9cb316600b020", group_id="123", role_id="1234", status=204) def test_check_group_inherited_project_role_on_domain(self): self.check_service_client_function( self.client.check_group_inherited_project_role_on_domain, 'tempest.lib.common.rest_client.RestClient.head', {}, domain_id="b344506af7644f6794d9cb316600b020", group_id="123", role_id="1234", status=204) def test_delete_inherited_role_from_user_on_project(self): self.check_service_client_function( self.client.delete_inherited_role_from_user_on_project, 'tempest.lib.common.rest_client.RestClient.delete', {}, project_id="b344506af7644f6794d9cb316600b020", user_id="123", role_id="1234", status=204) def test_check_user_has_flag_on_inherited_to_project(self): self.check_service_client_function( self.client.check_user_has_flag_on_inherited_to_project, 'tempest.lib.common.rest_client.RestClient.head', {}, project_id="b344506af7644f6794d9cb316600b020", user_id="123", role_id="1234", status=204) def test_delete_inherited_role_from_group_on_project(self): self.check_service_client_function( self.client.delete_inherited_role_from_group_on_project, 'tempest.lib.common.rest_client.RestClient.delete', {}, project_id="b344506af7644f6794d9cb316600b020", group_id="123", role_id="1234", status=204) def test_check_group_has_flag_on_inherited_to_project(self): self.check_service_client_function( self.client.check_group_has_flag_on_inherited_to_project, 'tempest.lib.common.rest_client.RestClient.head', {}, project_id="b344506af7644f6794d9cb316600b020", group_id="123", role_id="1234", status=204)

39.18552

78

0.677714

1,013

8,660

5.304047

0.128332

0.067746

0.070724

0.078169

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0.830449

0.790434

0.753211

0

0.072644

0.246536

8,660

220

79

39.363636

0.750805

0.066397

0

0.603352

0

0.156238

0.135547

0

1

0.139665

false

0

0.01676

0

0.167598

0

null

0

1

0

null

0

6

9df3cb95726e327177911e561e5a42756d34d54c

33

py

Python

gvision.py

Moulin666/Telegram-Neural-Net-Bot

7da381765a5bc716921f3931339dd06bed8c232e

[ "MIT" ]

null

gvision.py

Moulin666/Telegram-Neural-Net-Bot

7da381765a5bc716921f3931339dd06bed8c232e

[ "MIT" ]

null

gvision.py

Moulin666/Telegram-Neural-Net-Bot

7da381765a5bc716921f3931339dd06bed8c232e

[ "MIT" ]

null

from google.cloud import vision

11

31

0.818182

5

33

5.4

1

0

0.151515

33

2

32

16.5

0.964286

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

9df4f2730016328f3a5f224ef3b5e9145990dc79

98

py

Python

BricksO/bricks/item/__init__.py

Jhonan01/Brick

09d62d8cde3a5503ad8b84eaea54edbd91445479

[ "Apache-2.0" ]

null

BricksO/bricks/item/__init__.py

Jhonan01/Brick

09d62d8cde3a5503ad8b84eaea54edbd91445479

[ "Apache-2.0" ]

null

BricksO/bricks/item/__init__.py

Jhonan01/Brick

09d62d8cde3a5503ad8b84eaea54edbd91445479

[ "Apache-2.0" ]

null

from flask import Blueprint item_bp = Blueprint('item',__name__) from bricks.item import routes

16.333333

36

0.795918

14

98

5.214286

0.642857

0.356164

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0.132653

98

5

37

19.6

0.858824

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0.040816

0

1

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false

0

0.666667

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0.666667

1

0

null

1

0

1

0

null

0

1

0

1

0

6

d1882e0ec192c23f3dbdbd777675d2c106a7b9ea

128

py

Python

bin/apache-hive-3.1.2-bin/lib/py/thrift/reflection/__init__.py

ptrick/hdfs-hive-sql-playground

83f2aaa79f022a3c320939eace1fd2d06583187f

[ "Apache-2.0" ]

null

bin/apache-hive-3.1.2-bin/lib/py/thrift/reflection/__init__.py

ptrick/hdfs-hive-sql-playground

83f2aaa79f022a3c320939eace1fd2d06583187f

[ "Apache-2.0" ]

null

bin/apache-hive-3.1.2-bin/lib/py/thrift/reflection/__init__.py

ptrick/hdfs-hive-sql-playground

83f2aaa79f022a3c320939eace1fd2d06583187f

[ "Apache-2.0" ]

null

version https://git-lfs.github.com/spec/v1 oid sha256:321f6bda5d0842186e6228899745f9973ef88558c1b07a951889f94b47e9499e size 807

32

75

0.882813

13

128

8.692308

1

0

0.45082

0.046875

128

3

76

42.666667

0.47541

0

1

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null

0

null

0

1

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null

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0

1

0

1

0

null

1

0

1

0

6

d1c9cbef22afa42555d886872b440e0476143964

40,387

py

Python

cottonformation/res/msk.py

MacHu-GWU/cottonformation-project

23e28c08cfb5a7cc0db6dbfdb1d7e1585c773f3b

[ "BSD-2-Clause" ]

5

2021-07-22T03:45:59.000Z

2021-12-17T21:07:14.000Z

cottonformation/res/msk.py

MacHu-GWU/cottonformation-project

23e28c08cfb5a7cc0db6dbfdb1d7e1585c773f3b

[ "BSD-2-Clause" ]

1

2021-06-25T18:01:31.000Z

cottonformation/res/msk.py

MacHu-GWU/cottonformation-project

23e28c08cfb5a7cc0db6dbfdb1d7e1585c773f3b

[ "BSD-2-Clause" ]

2

2021-06-27T03:08:21.000Z

2021-06-28T22:15:51.000Z

# -*- coding: utf-8 -*- """ This module """ import attr import typing from ..core.model import ( Property, Resource, Tag, GetAtt, TypeHint, TypeCheck, ) from ..core.constant import AttrMeta #--- Property declaration --- @attr.s class PropClusterS3(Property): """ AWS Object Type = "AWS::MSK::Cluster.S3" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-s3.html Property Document: - ``rp_Enabled``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-s3.html#cfn-msk-cluster-s3-enabled - ``p_Bucket``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-s3.html#cfn-msk-cluster-s3-bucket - ``p_Prefix``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-s3.html#cfn-msk-cluster-s3-prefix """ AWS_OBJECT_TYPE = "AWS::MSK::Cluster.S3" rp_Enabled: bool = attr.ib( default=None, validator=attr.validators.instance_of(bool), metadata={AttrMeta.PROPERTY_NAME: "Enabled"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-s3.html#cfn-msk-cluster-s3-enabled""" p_Bucket: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "Bucket"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-s3.html#cfn-msk-cluster-s3-bucket""" p_Prefix: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "Prefix"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-s3.html#cfn-msk-cluster-s3-prefix""" @attr.s class PropClusterCloudWatchLogs(Property): """ AWS Object Type = "AWS::MSK::Cluster.CloudWatchLogs" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-cloudwatchlogs.html Property Document: - ``rp_Enabled``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-cloudwatchlogs.html#cfn-msk-cluster-cloudwatchlogs-enabled - ``p_LogGroup``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-cloudwatchlogs.html#cfn-msk-cluster-cloudwatchlogs-loggroup """ AWS_OBJECT_TYPE = "AWS::MSK::Cluster.CloudWatchLogs" rp_Enabled: bool = attr.ib( default=None, validator=attr.validators.instance_of(bool), metadata={AttrMeta.PROPERTY_NAME: "Enabled"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-cloudwatchlogs.html#cfn-msk-cluster-cloudwatchlogs-enabled""" p_LogGroup: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "LogGroup"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-cloudwatchlogs.html#cfn-msk-cluster-cloudwatchlogs-loggroup""" @attr.s class PropClusterPublicAccess(Property): """ AWS Object Type = "AWS::MSK::Cluster.PublicAccess" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-publicaccess.html Property Document: - ``p_Type``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-publicaccess.html#cfn-msk-cluster-publicaccess-type """ AWS_OBJECT_TYPE = "AWS::MSK::Cluster.PublicAccess" p_Type: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "Type"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-publicaccess.html#cfn-msk-cluster-publicaccess-type""" @attr.s class PropClusterEncryptionAtRest(Property): """ AWS Object Type = "AWS::MSK::Cluster.EncryptionAtRest" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-encryptionatrest.html Property Document: - ``rp_DataVolumeKMSKeyId``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-encryptionatrest.html#cfn-msk-cluster-encryptionatrest-datavolumekmskeyid """ AWS_OBJECT_TYPE = "AWS::MSK::Cluster.EncryptionAtRest" rp_DataVolumeKMSKeyId: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "DataVolumeKMSKeyId"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-encryptionatrest.html#cfn-msk-cluster-encryptionatrest-datavolumekmskeyid""" @attr.s class PropClusterUnauthenticated(Property): """ AWS Object Type = "AWS::MSK::Cluster.Unauthenticated" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-unauthenticated.html Property Document: - ``rp_Enabled``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-unauthenticated.html#cfn-msk-cluster-unauthenticated-enabled """ AWS_OBJECT_TYPE = "AWS::MSK::Cluster.Unauthenticated" rp_Enabled: bool = attr.ib( default=None, validator=attr.validators.instance_of(bool), metadata={AttrMeta.PROPERTY_NAME: "Enabled"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-unauthenticated.html#cfn-msk-cluster-unauthenticated-enabled""" @attr.s class PropClusterEncryptionInTransit(Property): """ AWS Object Type = "AWS::MSK::Cluster.EncryptionInTransit" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-encryptionintransit.html Property Document: - ``p_ClientBroker``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-encryptionintransit.html#cfn-msk-cluster-encryptionintransit-clientbroker - ``p_InCluster``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-encryptionintransit.html#cfn-msk-cluster-encryptionintransit-incluster """ AWS_OBJECT_TYPE = "AWS::MSK::Cluster.EncryptionInTransit" p_ClientBroker: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "ClientBroker"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-encryptionintransit.html#cfn-msk-cluster-encryptionintransit-clientbroker""" p_InCluster: bool = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(bool)), metadata={AttrMeta.PROPERTY_NAME: "InCluster"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-encryptionintransit.html#cfn-msk-cluster-encryptionintransit-incluster""" @attr.s class PropClusterEncryptionInfo(Property): """ AWS Object Type = "AWS::MSK::Cluster.EncryptionInfo" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-encryptioninfo.html Property Document: - ``p_EncryptionAtRest``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-encryptioninfo.html#cfn-msk-cluster-encryptioninfo-encryptionatrest - ``p_EncryptionInTransit``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-encryptioninfo.html#cfn-msk-cluster-encryptioninfo-encryptionintransit """ AWS_OBJECT_TYPE = "AWS::MSK::Cluster.EncryptionInfo" p_EncryptionAtRest: typing.Union['PropClusterEncryptionAtRest', dict] = attr.ib( default=None, converter=PropClusterEncryptionAtRest.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PropClusterEncryptionAtRest)), metadata={AttrMeta.PROPERTY_NAME: "EncryptionAtRest"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-encryptioninfo.html#cfn-msk-cluster-encryptioninfo-encryptionatrest""" p_EncryptionInTransit: typing.Union['PropClusterEncryptionInTransit', dict] = attr.ib( default=None, converter=PropClusterEncryptionInTransit.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PropClusterEncryptionInTransit)), metadata={AttrMeta.PROPERTY_NAME: "EncryptionInTransit"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-encryptioninfo.html#cfn-msk-cluster-encryptioninfo-encryptionintransit""" @attr.s class PropClusterIam(Property): """ AWS Object Type = "AWS::MSK::Cluster.Iam" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-iam.html Property Document: - ``rp_Enabled``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-iam.html#cfn-msk-cluster-iam-enabled """ AWS_OBJECT_TYPE = "AWS::MSK::Cluster.Iam" rp_Enabled: bool = attr.ib( default=None, validator=attr.validators.instance_of(bool), metadata={AttrMeta.PROPERTY_NAME: "Enabled"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-iam.html#cfn-msk-cluster-iam-enabled""" @attr.s class PropClusterConfigurationInfo(Property): """ AWS Object Type = "AWS::MSK::Cluster.ConfigurationInfo" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-configurationinfo.html Property Document: - ``rp_Arn``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-configurationinfo.html#cfn-msk-cluster-configurationinfo-arn - ``rp_Revision``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-configurationinfo.html#cfn-msk-cluster-configurationinfo-revision """ AWS_OBJECT_TYPE = "AWS::MSK::Cluster.ConfigurationInfo" rp_Arn: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "Arn"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-configurationinfo.html#cfn-msk-cluster-configurationinfo-arn""" rp_Revision: int = attr.ib( default=None, validator=attr.validators.instance_of(int), metadata={AttrMeta.PROPERTY_NAME: "Revision"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-configurationinfo.html#cfn-msk-cluster-configurationinfo-revision""" @attr.s class PropClusterScram(Property): """ AWS Object Type = "AWS::MSK::Cluster.Scram" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-scram.html Property Document: - ``rp_Enabled``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-scram.html#cfn-msk-cluster-scram-enabled """ AWS_OBJECT_TYPE = "AWS::MSK::Cluster.Scram" rp_Enabled: bool = attr.ib( default=None, validator=attr.validators.instance_of(bool), metadata={AttrMeta.PROPERTY_NAME: "Enabled"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-scram.html#cfn-msk-cluster-scram-enabled""" @attr.s class PropClusterJmxExporter(Property): """ AWS Object Type = "AWS::MSK::Cluster.JmxExporter" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-jmxexporter.html Property Document: - ``rp_EnabledInBroker``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-jmxexporter.html#cfn-msk-cluster-jmxexporter-enabledinbroker """ AWS_OBJECT_TYPE = "AWS::MSK::Cluster.JmxExporter" rp_EnabledInBroker: bool = attr.ib( default=None, validator=attr.validators.instance_of(bool), metadata={AttrMeta.PROPERTY_NAME: "EnabledInBroker"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-jmxexporter.html#cfn-msk-cluster-jmxexporter-enabledinbroker""" @attr.s class PropClusterConnectivityInfo(Property): """ AWS Object Type = "AWS::MSK::Cluster.ConnectivityInfo" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-connectivityinfo.html Property Document: - ``p_PublicAccess``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-connectivityinfo.html#cfn-msk-cluster-connectivityinfo-publicaccess """ AWS_OBJECT_TYPE = "AWS::MSK::Cluster.ConnectivityInfo" p_PublicAccess: typing.Union['PropClusterPublicAccess', dict] = attr.ib( default=None, converter=PropClusterPublicAccess.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PropClusterPublicAccess)), metadata={AttrMeta.PROPERTY_NAME: "PublicAccess"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-connectivityinfo.html#cfn-msk-cluster-connectivityinfo-publicaccess""" @attr.s class PropClusterNodeExporter(Property): """ AWS Object Type = "AWS::MSK::Cluster.NodeExporter" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-nodeexporter.html Property Document: - ``rp_EnabledInBroker``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-nodeexporter.html#cfn-msk-cluster-nodeexporter-enabledinbroker """ AWS_OBJECT_TYPE = "AWS::MSK::Cluster.NodeExporter" rp_EnabledInBroker: bool = attr.ib( default=None, validator=attr.validators.instance_of(bool), metadata={AttrMeta.PROPERTY_NAME: "EnabledInBroker"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-nodeexporter.html#cfn-msk-cluster-nodeexporter-enabledinbroker""" @attr.s class PropClusterEBSStorageInfo(Property): """ AWS Object Type = "AWS::MSK::Cluster.EBSStorageInfo" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-ebsstorageinfo.html Property Document: - ``p_VolumeSize``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-ebsstorageinfo.html#cfn-msk-cluster-ebsstorageinfo-volumesize """ AWS_OBJECT_TYPE = "AWS::MSK::Cluster.EBSStorageInfo" p_VolumeSize: int = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(int)), metadata={AttrMeta.PROPERTY_NAME: "VolumeSize"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-ebsstorageinfo.html#cfn-msk-cluster-ebsstorageinfo-volumesize""" @attr.s class PropClusterFirehose(Property): """ AWS Object Type = "AWS::MSK::Cluster.Firehose" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-firehose.html Property Document: - ``rp_Enabled``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-firehose.html#cfn-msk-cluster-firehose-enabled - ``p_DeliveryStream``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-firehose.html#cfn-msk-cluster-firehose-deliverystream """ AWS_OBJECT_TYPE = "AWS::MSK::Cluster.Firehose" rp_Enabled: bool = attr.ib( default=None, validator=attr.validators.instance_of(bool), metadata={AttrMeta.PROPERTY_NAME: "Enabled"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-firehose.html#cfn-msk-cluster-firehose-enabled""" p_DeliveryStream: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "DeliveryStream"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-firehose.html#cfn-msk-cluster-firehose-deliverystream""" @attr.s class PropClusterTls(Property): """ AWS Object Type = "AWS::MSK::Cluster.Tls" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-tls.html Property Document: - ``p_CertificateAuthorityArnList``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-tls.html#cfn-msk-cluster-tls-certificateauthorityarnlist - ``p_Enabled``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-tls.html#cfn-msk-cluster-tls-enabled """ AWS_OBJECT_TYPE = "AWS::MSK::Cluster.Tls" p_CertificateAuthorityArnList: typing.List[TypeHint.intrinsic_str] = attr.ib( default=None, validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), iterable_validator=attr.validators.instance_of(list))), metadata={AttrMeta.PROPERTY_NAME: "CertificateAuthorityArnList"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-tls.html#cfn-msk-cluster-tls-certificateauthorityarnlist""" p_Enabled: bool = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(bool)), metadata={AttrMeta.PROPERTY_NAME: "Enabled"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-tls.html#cfn-msk-cluster-tls-enabled""" @attr.s class PropClusterBrokerLogs(Property): """ AWS Object Type = "AWS::MSK::Cluster.BrokerLogs" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-brokerlogs.html Property Document: - ``p_CloudWatchLogs``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-brokerlogs.html#cfn-msk-cluster-brokerlogs-cloudwatchlogs - ``p_Firehose``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-brokerlogs.html#cfn-msk-cluster-brokerlogs-firehose - ``p_S3``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-brokerlogs.html#cfn-msk-cluster-brokerlogs-s3 """ AWS_OBJECT_TYPE = "AWS::MSK::Cluster.BrokerLogs" p_CloudWatchLogs: typing.Union['PropClusterCloudWatchLogs', dict] = attr.ib( default=None, converter=PropClusterCloudWatchLogs.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PropClusterCloudWatchLogs)), metadata={AttrMeta.PROPERTY_NAME: "CloudWatchLogs"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-brokerlogs.html#cfn-msk-cluster-brokerlogs-cloudwatchlogs""" p_Firehose: typing.Union['PropClusterFirehose', dict] = attr.ib( default=None, converter=PropClusterFirehose.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PropClusterFirehose)), metadata={AttrMeta.PROPERTY_NAME: "Firehose"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-brokerlogs.html#cfn-msk-cluster-brokerlogs-firehose""" p_S3: typing.Union['PropClusterS3', dict] = attr.ib( default=None, converter=PropClusterS3.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PropClusterS3)), metadata={AttrMeta.PROPERTY_NAME: "S3"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-brokerlogs.html#cfn-msk-cluster-brokerlogs-s3""" @attr.s class PropClusterPrometheus(Property): """ AWS Object Type = "AWS::MSK::Cluster.Prometheus" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-prometheus.html Property Document: - ``p_JmxExporter``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-prometheus.html#cfn-msk-cluster-prometheus-jmxexporter - ``p_NodeExporter``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-prometheus.html#cfn-msk-cluster-prometheus-nodeexporter """ AWS_OBJECT_TYPE = "AWS::MSK::Cluster.Prometheus" p_JmxExporter: typing.Union['PropClusterJmxExporter', dict] = attr.ib( default=None, converter=PropClusterJmxExporter.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PropClusterJmxExporter)), metadata={AttrMeta.PROPERTY_NAME: "JmxExporter"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-prometheus.html#cfn-msk-cluster-prometheus-jmxexporter""" p_NodeExporter: typing.Union['PropClusterNodeExporter', dict] = attr.ib( default=None, converter=PropClusterNodeExporter.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PropClusterNodeExporter)), metadata={AttrMeta.PROPERTY_NAME: "NodeExporter"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-prometheus.html#cfn-msk-cluster-prometheus-nodeexporter""" @attr.s class PropClusterLoggingInfo(Property): """ AWS Object Type = "AWS::MSK::Cluster.LoggingInfo" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-logginginfo.html Property Document: - ``rp_BrokerLogs``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-logginginfo.html#cfn-msk-cluster-logginginfo-brokerlogs """ AWS_OBJECT_TYPE = "AWS::MSK::Cluster.LoggingInfo" rp_BrokerLogs: typing.Union['PropClusterBrokerLogs', dict] = attr.ib( default=None, converter=PropClusterBrokerLogs.from_dict, validator=attr.validators.instance_of(PropClusterBrokerLogs), metadata={AttrMeta.PROPERTY_NAME: "BrokerLogs"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-logginginfo.html#cfn-msk-cluster-logginginfo-brokerlogs""" @attr.s class PropClusterSasl(Property): """ AWS Object Type = "AWS::MSK::Cluster.Sasl" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-sasl.html Property Document: - ``p_Iam``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-sasl.html#cfn-msk-cluster-sasl-iam - ``p_Scram``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-sasl.html#cfn-msk-cluster-sasl-scram """ AWS_OBJECT_TYPE = "AWS::MSK::Cluster.Sasl" p_Iam: typing.Union['PropClusterIam', dict] = attr.ib( default=None, converter=PropClusterIam.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PropClusterIam)), metadata={AttrMeta.PROPERTY_NAME: "Iam"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-sasl.html#cfn-msk-cluster-sasl-iam""" p_Scram: typing.Union['PropClusterScram', dict] = attr.ib( default=None, converter=PropClusterScram.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PropClusterScram)), metadata={AttrMeta.PROPERTY_NAME: "Scram"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-sasl.html#cfn-msk-cluster-sasl-scram""" @attr.s class PropClusterStorageInfo(Property): """ AWS Object Type = "AWS::MSK::Cluster.StorageInfo" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-storageinfo.html Property Document: - ``p_EBSStorageInfo``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-storageinfo.html#cfn-msk-cluster-storageinfo-ebsstorageinfo """ AWS_OBJECT_TYPE = "AWS::MSK::Cluster.StorageInfo" p_EBSStorageInfo: typing.Union['PropClusterEBSStorageInfo', dict] = attr.ib( default=None, converter=PropClusterEBSStorageInfo.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PropClusterEBSStorageInfo)), metadata={AttrMeta.PROPERTY_NAME: "EBSStorageInfo"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-storageinfo.html#cfn-msk-cluster-storageinfo-ebsstorageinfo""" @attr.s class PropClusterClientAuthentication(Property): """ AWS Object Type = "AWS::MSK::Cluster.ClientAuthentication" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-clientauthentication.html Property Document: - ``p_Sasl``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-clientauthentication.html#cfn-msk-cluster-clientauthentication-sasl - ``p_Tls``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-clientauthentication.html#cfn-msk-cluster-clientauthentication-tls - ``p_Unauthenticated``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-clientauthentication.html#cfn-msk-cluster-clientauthentication-unauthenticated """ AWS_OBJECT_TYPE = "AWS::MSK::Cluster.ClientAuthentication" p_Sasl: typing.Union['PropClusterSasl', dict] = attr.ib( default=None, converter=PropClusterSasl.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PropClusterSasl)), metadata={AttrMeta.PROPERTY_NAME: "Sasl"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-clientauthentication.html#cfn-msk-cluster-clientauthentication-sasl""" p_Tls: typing.Union['PropClusterTls', dict] = attr.ib( default=None, converter=PropClusterTls.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PropClusterTls)), metadata={AttrMeta.PROPERTY_NAME: "Tls"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-clientauthentication.html#cfn-msk-cluster-clientauthentication-tls""" p_Unauthenticated: typing.Union['PropClusterUnauthenticated', dict] = attr.ib( default=None, converter=PropClusterUnauthenticated.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PropClusterUnauthenticated)), metadata={AttrMeta.PROPERTY_NAME: "Unauthenticated"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-clientauthentication.html#cfn-msk-cluster-clientauthentication-unauthenticated""" @attr.s class PropClusterOpenMonitoring(Property): """ AWS Object Type = "AWS::MSK::Cluster.OpenMonitoring" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-openmonitoring.html Property Document: - ``rp_Prometheus``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-openmonitoring.html#cfn-msk-cluster-openmonitoring-prometheus """ AWS_OBJECT_TYPE = "AWS::MSK::Cluster.OpenMonitoring" rp_Prometheus: typing.Union['PropClusterPrometheus', dict] = attr.ib( default=None, converter=PropClusterPrometheus.from_dict, validator=attr.validators.instance_of(PropClusterPrometheus), metadata={AttrMeta.PROPERTY_NAME: "Prometheus"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-openmonitoring.html#cfn-msk-cluster-openmonitoring-prometheus""" @attr.s class PropClusterBrokerNodeGroupInfo(Property): """ AWS Object Type = "AWS::MSK::Cluster.BrokerNodeGroupInfo" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-brokernodegroupinfo.html Property Document: - ``rp_ClientSubnets``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-brokernodegroupinfo.html#cfn-msk-cluster-brokernodegroupinfo-clientsubnets - ``rp_InstanceType``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-brokernodegroupinfo.html#cfn-msk-cluster-brokernodegroupinfo-instancetype - ``p_BrokerAZDistribution``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-brokernodegroupinfo.html#cfn-msk-cluster-brokernodegroupinfo-brokerazdistribution - ``p_ConnectivityInfo``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-brokernodegroupinfo.html#cfn-msk-cluster-brokernodegroupinfo-connectivityinfo - ``p_SecurityGroups``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-brokernodegroupinfo.html#cfn-msk-cluster-brokernodegroupinfo-securitygroups - ``p_StorageInfo``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-brokernodegroupinfo.html#cfn-msk-cluster-brokernodegroupinfo-storageinfo """ AWS_OBJECT_TYPE = "AWS::MSK::Cluster.BrokerNodeGroupInfo" rp_ClientSubnets: typing.List[TypeHint.intrinsic_str] = attr.ib( default=None, validator=attr.validators.deep_iterable(member_validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), iterable_validator=attr.validators.instance_of(list)), metadata={AttrMeta.PROPERTY_NAME: "ClientSubnets"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-brokernodegroupinfo.html#cfn-msk-cluster-brokernodegroupinfo-clientsubnets""" rp_InstanceType: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "InstanceType"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-brokernodegroupinfo.html#cfn-msk-cluster-brokernodegroupinfo-instancetype""" p_BrokerAZDistribution: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "BrokerAZDistribution"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-brokernodegroupinfo.html#cfn-msk-cluster-brokernodegroupinfo-brokerazdistribution""" p_ConnectivityInfo: typing.Union['PropClusterConnectivityInfo', dict] = attr.ib( default=None, converter=PropClusterConnectivityInfo.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PropClusterConnectivityInfo)), metadata={AttrMeta.PROPERTY_NAME: "ConnectivityInfo"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-brokernodegroupinfo.html#cfn-msk-cluster-brokernodegroupinfo-connectivityinfo""" p_SecurityGroups: typing.List[TypeHint.intrinsic_str] = attr.ib( default=None, validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), iterable_validator=attr.validators.instance_of(list))), metadata={AttrMeta.PROPERTY_NAME: "SecurityGroups"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-brokernodegroupinfo.html#cfn-msk-cluster-brokernodegroupinfo-securitygroups""" p_StorageInfo: typing.Union['PropClusterStorageInfo', dict] = attr.ib( default=None, converter=PropClusterStorageInfo.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PropClusterStorageInfo)), metadata={AttrMeta.PROPERTY_NAME: "StorageInfo"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-msk-cluster-brokernodegroupinfo.html#cfn-msk-cluster-brokernodegroupinfo-storageinfo""" #--- Resource declaration --- @attr.s class Cluster(Resource): """ AWS Object Type = "AWS::MSK::Cluster" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-msk-cluster.html Property Document: - ``rp_BrokerNodeGroupInfo``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-msk-cluster.html#cfn-msk-cluster-brokernodegroupinfo - ``rp_ClusterName``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-msk-cluster.html#cfn-msk-cluster-clustername - ``rp_KafkaVersion``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-msk-cluster.html#cfn-msk-cluster-kafkaversion - ``rp_NumberOfBrokerNodes``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-msk-cluster.html#cfn-msk-cluster-numberofbrokernodes - ``p_ClientAuthentication``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-msk-cluster.html#cfn-msk-cluster-clientauthentication - ``p_ConfigurationInfo``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-msk-cluster.html#cfn-msk-cluster-configurationinfo - ``p_EncryptionInfo``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-msk-cluster.html#cfn-msk-cluster-encryptioninfo - ``p_EnhancedMonitoring``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-msk-cluster.html#cfn-msk-cluster-enhancedmonitoring - ``p_LoggingInfo``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-msk-cluster.html#cfn-msk-cluster-logginginfo - ``p_OpenMonitoring``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-msk-cluster.html#cfn-msk-cluster-openmonitoring - ``p_Tags``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-msk-cluster.html#cfn-msk-cluster-tags """ AWS_OBJECT_TYPE = "AWS::MSK::Cluster" rp_BrokerNodeGroupInfo: typing.Union['PropClusterBrokerNodeGroupInfo', dict] = attr.ib( default=None, converter=PropClusterBrokerNodeGroupInfo.from_dict, validator=attr.validators.instance_of(PropClusterBrokerNodeGroupInfo), metadata={AttrMeta.PROPERTY_NAME: "BrokerNodeGroupInfo"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-msk-cluster.html#cfn-msk-cluster-brokernodegroupinfo""" rp_ClusterName: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "ClusterName"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-msk-cluster.html#cfn-msk-cluster-clustername""" rp_KafkaVersion: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "KafkaVersion"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-msk-cluster.html#cfn-msk-cluster-kafkaversion""" rp_NumberOfBrokerNodes: int = attr.ib( default=None, validator=attr.validators.instance_of(int), metadata={AttrMeta.PROPERTY_NAME: "NumberOfBrokerNodes"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-msk-cluster.html#cfn-msk-cluster-numberofbrokernodes""" p_ClientAuthentication: typing.Union['PropClusterClientAuthentication', dict] = attr.ib( default=None, converter=PropClusterClientAuthentication.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PropClusterClientAuthentication)), metadata={AttrMeta.PROPERTY_NAME: "ClientAuthentication"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-msk-cluster.html#cfn-msk-cluster-clientauthentication""" p_ConfigurationInfo: typing.Union['PropClusterConfigurationInfo', dict] = attr.ib( default=None, converter=PropClusterConfigurationInfo.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PropClusterConfigurationInfo)), metadata={AttrMeta.PROPERTY_NAME: "ConfigurationInfo"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-msk-cluster.html#cfn-msk-cluster-configurationinfo""" p_EncryptionInfo: typing.Union['PropClusterEncryptionInfo', dict] = attr.ib( default=None, converter=PropClusterEncryptionInfo.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PropClusterEncryptionInfo)), metadata={AttrMeta.PROPERTY_NAME: "EncryptionInfo"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-msk-cluster.html#cfn-msk-cluster-encryptioninfo""" p_EnhancedMonitoring: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "EnhancedMonitoring"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-msk-cluster.html#cfn-msk-cluster-enhancedmonitoring""" p_LoggingInfo: typing.Union['PropClusterLoggingInfo', dict] = attr.ib( default=None, converter=PropClusterLoggingInfo.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PropClusterLoggingInfo)), metadata={AttrMeta.PROPERTY_NAME: "LoggingInfo"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-msk-cluster.html#cfn-msk-cluster-logginginfo""" p_OpenMonitoring: typing.Union['PropClusterOpenMonitoring', dict] = attr.ib( default=None, converter=PropClusterOpenMonitoring.from_dict, validator=attr.validators.optional(attr.validators.instance_of(PropClusterOpenMonitoring)), metadata={AttrMeta.PROPERTY_NAME: "OpenMonitoring"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-msk-cluster.html#cfn-msk-cluster-openmonitoring""" p_Tags: dict = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(dict)), metadata={AttrMeta.PROPERTY_NAME: "Tags"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-msk-cluster.html#cfn-msk-cluster-tags"""

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d1d8de48543193073dacbc606b325859efe40767

322

py

Python

lims/models/__init__.py

razorlabs/BRIMS-backend

2c5b7bd126debec459b775e9d11e96fc09975059

[ "MIT" ]

1

2020-03-20T23:00:24.000Z

lims/models/__init__.py

razorlabs/BRIMS-backend

2c5b7bd126debec459b775e9d11e96fc09975059

[ "MIT" ]

null

lims/models/__init__.py

razorlabs/BRIMS-backend

2c5b7bd126debec459b775e9d11e96fc09975059

[ "MIT" ]

1

2020-03-09T09:57:25.000Z

# Initialization file to import models from sub-organized sources into a single "models" import for django injestion from lims.models.user import * from lims.models.schedule import * from lims.models.shipping import * from lims.models.storage import * from lims.models.patient import * from lims.models.specimen import *

35.777778

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1

0

6

061650c680f85973a286c989ab9e6122fccebbe7

18,370

py

Python

rsp1570serial/tests/test_messages.py

pp81381/rsp1570serial

9a31be2578f00905a1df2a78a46f3c87631cd177

[ "MIT" ]

1

2020-03-06T06:04:18.000Z

rsp1570serial/tests/test_messages.py

pp81381/rsp1570serial

9a31be2578f00905a1df2a78a46f3c87631cd177

[ "MIT" ]

null

rsp1570serial/tests/test_messages.py

pp81381/rsp1570serial

9a31be2578f00905a1df2a78a46f3c87631cd177

[ "MIT" ]

1

2020-01-21T18:27:07.000Z

import aiounittest import logging from rsp1570serial.commands import ( encode_command, MSGTYPE_PRIMARY_COMMANDS, MSGTYPE_VOLUME_DIRECT_COMMANDS, ) from rsp1570serial.messages import decode_message_stream, FeedbackMessage from rsp1570serial.protocol import StreamProxy import unittest async def decode_all_messages(ser): messages = [] async for command in decode_message_stream(ser): messages.append(command) return messages async def decode_single_message(ser): messages = await decode_all_messages(ser) assert len(messages) == 1 return messages[0] class AsyncRotelTestMessages(aiounittest.AsyncTestCase): async def test_encode_decode(self): message = encode_command("POWER_TOGGLE") with self.assertLogs(level=logging.INFO) as cm: command = await decode_single_message(StreamProxy(message)) self.assertEqual(command.message_type, MSGTYPE_PRIMARY_COMMANDS) self.assertEqual(command.key, b"\x0a") self.assertEqual( cm.output, ["INFO:rsp1570serial.protocol:Finished reading messages"] ) async def test_encode_decode_with_meta(self): message = encode_command("VOLUME_40") with self.assertLogs(level=logging.INFO) as cm: command = await decode_single_message(StreamProxy(message)) self.assertEqual(command.message_type, MSGTYPE_VOLUME_DIRECT_COMMANDS) self.assertEqual(command.key, b"\x28") self.assertEqual( cm.output, ["INFO:rsp1570serial.protocol:Finished reading messages"] ) async def test_decode_feedback_message(self): with self.assertLogs(level=logging.INFO) as cm: display = await decode_single_message( StreamProxy( b"\xfe1\xa3 FIRE TV VOL 64DOLBY PL\x19 C 48K \x00F\x08\x00\xfc\xf2" ) ) self.assertEqual(display.lines[0], "FIRE TV VOL 64") self.assertEqual(display.lines[1], "DOLBY PL\x19 C 48K ") self.assertCountEqual( display.icons_that_are_on(), [ "II", "HDMI", "Pro Logic", "Standby LED", "SW", "SR", "SL", "FR", "C", "FL", ], ) self.assertEqual( cm.output, ["INFO:rsp1570serial.protocol:Finished reading messages"] ) fields = display.parse_display_lines() self.assertEqual(fields["is_on"], True) self.assertEqual(fields["source_name"], "FIRE TV") self.assertEqual(fields["volume"], 64) self.assertEqual(fields["mute_on"], False) self.assertEqual(fields["party_mode_on"], False) self.assertEqual(fields["info"], "DOLBY PLII C 48K") self.assertEqual(fields["rec_source"], None) self.assertEqual(fields["zone2_source"], None) self.assertEqual(fields["zone2_volume"], None) self.assertEqual(fields["zone3_source"], None) self.assertEqual(fields["zone3_volume"], None) self.assertEqual(fields["zone4_source"], None) self.assertEqual(fields["zone4_volume"], None) async def test_decode_feedback_after_power_off(self): with self.assertLogs(level=logging.INFO) as cm: display = await decode_single_message( StreamProxy( b"\xfe1\xa3 \x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x08\x00\x00\xfc" ) ) self.assertEqual( display.lines[0], "\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00", ) self.assertEqual( display.lines[1], "\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00", ) self.assertCountEqual(display.icons_that_are_on(), ["Standby LED"]) self.assertEqual( cm.output, ["INFO:rsp1570serial.protocol:Finished reading messages"] ) fields = display.parse_display_lines() self.assertEqual(fields["is_on"], False) self.assertEqual(fields["source_name"], None) self.assertEqual(fields["volume"], None) self.assertEqual(fields["mute_on"], None) self.assertEqual(fields["party_mode_on"], None) self.assertEqual(fields["info"], None) self.assertEqual(fields["rec_source"], None) self.assertEqual(fields["zone2_source"], None) self.assertEqual(fields["zone2_volume"], None) self.assertEqual(fields["zone3_source"], None) self.assertEqual(fields["zone3_volume"], None) self.assertEqual(fields["zone4_source"], None) self.assertEqual(fields["zone4_volume"], None) async def test_decode_feedback_message_with_rec_source(self): display = FeedbackMessage( "FIRE TV VOL 64", " REC SOURCE ", b"\x00F\x08\x00\xfc" ) self.assertCountEqual( display.icons_that_are_on(), [ "II", "HDMI", "Pro Logic", "Standby LED", "SW", "SR", "SL", "FR", "C", "FL", ], ) fields = display.parse_display_lines() self.assertEqual(fields["is_on"], True) self.assertEqual(fields["source_name"], "FIRE TV") self.assertEqual(fields["volume"], 64) self.assertEqual(fields["mute_on"], False) self.assertEqual(fields["party_mode_on"], False) self.assertEqual(fields["info"], "REC SOURCE") self.assertEqual(fields["rec_source"], "SOURCE") self.assertEqual(fields["zone2_source"], None) self.assertEqual(fields["zone2_volume"], None) self.assertEqual(fields["zone3_source"], None) self.assertEqual(fields["zone3_volume"], None) self.assertEqual(fields["zone4_source"], None) self.assertEqual(fields["zone4_volume"], None) async def test_decode_feedback_message_with_zone_source(self): display = FeedbackMessage( "FIRE TV VOL 64", " ZONE4 TUNER ", b"\x00F\x08\x00\xfc" ) self.assertCountEqual( display.icons_that_are_on(), [ "II", "HDMI", "Pro Logic", "Standby LED", "SW", "SR", "SL", "FR", "C", "FL", ], ) fields = display.parse_display_lines() self.assertEqual(fields["is_on"], True) self.assertEqual(fields["source_name"], "FIRE TV") self.assertEqual(fields["volume"], 64) self.assertEqual(fields["mute_on"], False) self.assertEqual(fields["party_mode_on"], False) self.assertEqual(fields["info"], "ZONE4 TUNER") self.assertEqual(fields["rec_source"], None) self.assertEqual(fields["zone2_source"], None) self.assertEqual(fields["zone2_volume"], None) self.assertEqual(fields["zone3_source"], None) self.assertEqual(fields["zone3_volume"], None) self.assertEqual(fields["zone4_source"], "TUNER") self.assertEqual(fields["zone4_volume"], None) async def test_decode_feedback_message_with_zone_volume_and_party_mode(self): display = FeedbackMessage( "FIRE TV pty VOL 64", " ZONE3 VOL 55 ", b"\x00F\x08\x00\xfc" ) self.assertCountEqual( display.icons_that_are_on(), [ "II", "HDMI", "Pro Logic", "Standby LED", "SW", "SR", "SL", "FR", "C", "FL", ], ) fields = display.parse_display_lines() self.assertEqual(fields["is_on"], True) self.assertEqual(fields["source_name"], "FIRE TV") self.assertEqual(fields["volume"], 64) self.assertEqual(fields["mute_on"], False) self.assertEqual(fields["party_mode_on"], True) self.assertEqual(fields["info"], "ZONE3 VOL 55") self.assertEqual(fields["rec_source"], None) self.assertEqual(fields["zone2_source"], None) self.assertEqual(fields["zone2_volume"], None) self.assertEqual(fields["zone3_source"], None) self.assertEqual(fields["zone3_volume"], 55) self.assertEqual(fields["zone4_source"], None) self.assertEqual(fields["zone4_volume"], None) async def test_decode_feedback_message_with_meta_escape(self): """ Test feedback message that generates a checksum that needs to be escaped I have verified that the real device does generate this message """ with self.assertLogs(level=logging.INFO) as cm: display = await decode_single_message( StreamProxy( b"\xfe1\xa3 VIRE TV VOL 60DOLBY PL\x19 C 48K \x00F\x08\x00\xfc\xfd\x01" ) ) self.assertEqual(display.lines[0], "VIRE TV VOL 60") self.assertEqual(display.lines[1], "DOLBY PL\x19 C 48K ") self.assertCountEqual( display.icons_that_are_on(), [ "II", "HDMI", "Pro Logic", "Standby LED", "SW", "SR", "SL", "FR", "C", "FL", ], ) self.assertEqual( cm.output, ["INFO:rsp1570serial.protocol:Finished reading messages"] ) async def test_decode_trigger_message(self): with self.assertLogs(level=logging.INFO) as cm: trigger = await decode_single_message( StreamProxy(b"\xfe\x07\xa3\x21\x01\x01\x00\x00\x00\xcd") ) trigger.log() self.assertEqual( cm.output, [ "INFO:rsp1570serial.protocol:Finished reading messages", "INFO:rsp1570serial.messages:[" "['All', ['on', 'off', 'off', 'off', 'off', 'off']], " "['Main', ['on', 'off', 'off', 'off', 'off', 'off']], " "['Zone 2', ['off', 'off', 'off', 'off', 'off', 'off']], " "['Zone 3', ['off', 'off', 'off', 'off', 'off', 'off']], " "['Zone 4', ['off', 'off', 'off', 'off', 'off', 'off']]]", ], ) async def test_decode_stream1(self): ser = StreamProxy( b"\xfe1\xa3 FIRE TV VOL 64DOLBY PL\x19 C 48K \x00F\x08\x00\xfc\xf2\xfe1\xa3 CATV VOL 63DOLBY PL\x19 M 48K \x00F\x08\x00\xfc\x99" ) with self.assertLogs(level=logging.INFO) as cm: feedback_messages = await decode_all_messages(ser) self.assertEqual(len(feedback_messages), 2) self.assertEqual(feedback_messages[0].lines[0], "FIRE TV VOL 64") self.assertEqual(feedback_messages[0].lines[1], "DOLBY PL\x19 C 48K ") self.assertCountEqual( feedback_messages[0].icons_that_are_on(), [ "II", "HDMI", "Pro Logic", "Standby LED", "SW", "SR", "SL", "FR", "C", "FL", ], ) self.assertEqual(feedback_messages[1].lines[0], "CATV VOL 63") self.assertEqual(feedback_messages[1].lines[1], "DOLBY PL\x19 M 48K ") self.assertCountEqual( feedback_messages[1].icons_that_are_on(), [ "II", "HDMI", "Pro Logic", "Standby LED", "SW", "SR", "SL", "FR", "C", "FL", ], ) self.assertEqual( cm.output, ["INFO:rsp1570serial.protocol:Finished reading messages"] ) async def test_decode_stream2(self): """ Deliberately removed the start byte from the first message. Rest of first message will be reported as unexpected bytes. """ ser = StreamProxy( b"1\xa3 FIRE TV VOL 64DOLBY PL\x19 C 48K \x00F\x08\x00\xfc\xf2\xfe1\xa3 CATV VOL 63DOLBY PL\x19 M 48K \x00F\x08\x00\xfc\x99" ) with self.assertLogs(level=logging.INFO) as cm: feedback_messages = await decode_all_messages(ser) self.assertEqual(len(feedback_messages), 1) self.assertEqual(feedback_messages[0].lines[0], "CATV VOL 63") self.assertEqual(feedback_messages[0].lines[1], "DOLBY PL\x19 M 48K ") self.assertCountEqual( feedback_messages[0].icons_that_are_on(), [ "II", "HDMI", "Pro Logic", "Standby LED", "SW", "SR", "SL", "FR", "C", "FL", ], ) self.assertEqual( cm.output, [ "WARNING:rsp1570serial.protocol:51 unexpected bytes encountered while waiting for START_BYTE: bytearray(b'1\\xa3 FIRE TV VOL 64DOLBY PL\\x19 C 48K \\x00F\\x08\\x00\\xfc\\xf2')", "INFO:rsp1570serial.protocol:Finished reading messages", ], ) async def test_decode_stream3(self): """ Deliberately close early. Close method will report discarded payload. """ ser = StreamProxy( b"\xfe1\xa3 FIRE TV VOL 64DOLBY PL\x19 C 48K \x00F\x08\x00\xfc\xf2\xfe1\xa3 CATV VOL 63DOLBY PL\x19 M 48K \x00F\x08\x00\xfc" ) with self.assertLogs(level=logging.INFO) as cm: feedback_messages = await decode_all_messages(ser) self.assertEqual(len(feedback_messages), 1) self.assertEqual(feedback_messages[0].lines[0], "FIRE TV VOL 64") self.assertEqual(feedback_messages[0].lines[1], "DOLBY PL\x19 C 48K ") self.assertCountEqual( feedback_messages[0].icons_that_are_on(), [ "II", "HDMI", "Pro Logic", "Standby LED", "SW", "SR", "SL", "FR", "C", "FL", ], ) self.assertEqual( cm.output, [ "ERROR:rsp1570serial.protocol:Unexpected EOF encountered. Work in progress discarded: bytearray(b'1\\xa3 CATV VOL 63DOLBY PL\\x19 M 48K \\x00F\\x08\\x00\\xfc')", "INFO:rsp1570serial.protocol:Finished reading messages", ], ) async def test_decode_stream4(self): """ Deliberately truncate first message. Partial message will be discarded when unescaped start byte encountered. Next message will be treated as unexpected bytes when EOF encountered. """ ser = StreamProxy( b"\xfe1\xa3 FIRE TV VOL 64DOLBY PL\x19 C 48K \x00F\x08\x00\xfc\xfe1\xa3 CATV VOL 63DOLBY PL\x19 M 48K \x00F\x08\x00\xfc\x99" ) with self.assertLogs(level=logging.INFO) as cm: feedback_messages = await decode_all_messages(ser) self.assertEqual(len(feedback_messages), 0) self.assertEqual( cm.output, [ "ERROR:rsp1570serial.protocol:Invalid byte encountered while processing message content. Work in progress discarded: bytearray(b'1\\xa3 FIRE TV VOL 64DOLBY PL\\x19 C 48K \\x00F\\x08\\x00\\xfc')", "WARNING:rsp1570serial.protocol:51 unexpected bytes discarded when EOF encountered: bytearray(b'1\\xa3 CATV VOL 63DOLBY PL\\x19 M 48K \\x00F\\x08\\x00\\xfc\\x99')", "INFO:rsp1570serial.protocol:Finished reading messages", ], ) async def test_decode_stream5(self): """ Deliberately truncate first message. Partial message will be discarded when unescaped start byte encountered. Next message will be treated as unexpected bytes while waiting for a start byte. Next message should be read normally """ ser = StreamProxy( b"\xfe1\xa3 FIRE TV VOL 64DOLBY PL\x19 C 48K \x00F\x08\x00\xfc\xfe1\xa3 CATV VOL 63DOLBY PL\x19 M 48K \x00F\x08\x00\xfc\x99\xfe1\xa3 FIRE TV VOL 64DOLBY PL\x19 C 48K \x00F\x08\x00\xfc\xf2" ) with self.assertLogs(level=logging.INFO) as cm: feedback_messages = await decode_all_messages(ser) self.assertEqual(len(feedback_messages), 1) self.assertEqual(feedback_messages[0].lines[0], "FIRE TV VOL 64") self.assertEqual(feedback_messages[0].lines[1], "DOLBY PL\x19 C 48K ") self.assertCountEqual( feedback_messages[0].icons_that_are_on(), [ "II", "HDMI", "Pro Logic", "Standby LED", "SW", "SR", "SL", "FR", "C", "FL", ], ) self.assertEqual( cm.output, [ "ERROR:rsp1570serial.protocol:Invalid byte encountered while processing message content. Work in progress discarded: bytearray(b'1\\xa3 FIRE TV VOL 64DOLBY PL\\x19 C 48K \\x00F\\x08\\x00\\xfc')", "WARNING:rsp1570serial.protocol:51 unexpected bytes encountered while waiting for START_BYTE: bytearray(b'1\\xa3 CATV VOL 63DOLBY PL\\x19 M 48K \\x00F\\x08\\x00\\xfc\\x99')", "INFO:rsp1570serial.protocol:Finished reading messages", ], )

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null

0

1

0

null

0

6

ae1f04906b60a3a6eedff92ce26311245c2f9944

21

py

Python

old_blds/EllipsePy/__init__.py

lalithu/EllipsePy

9a1152c8ce60c45388cbb0eab930da0e3fff9cf1

[ "MIT" ]

1

2021-01-24T21:56:12.000Z

old_blds/EllipsePy/__init__.py

lalithu/EllipsePy

9a1152c8ce60c45388cbb0eab930da0e3fff9cf1

[ "MIT" ]

null

old_blds/EllipsePy/__init__.py

lalithu/EllipsePy

9a1152c8ce60c45388cbb0eab930da0e3fff9cf1

[ "MIT" ]

null

# By Lalith U | 2021

10.5

20

0.619048

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21

3.25

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1

0

null

0

1

0

6

ee0d4e67d2b97ee1ed136c8f50bbf0947a795833

104

py

Python

tests/db_test.py

fushinari/wn

be274eda62316622f6bb0e548b5e2a6834dc216a

[ "MIT" ]

null

tests/db_test.py

fushinari/wn

be274eda62316622f6bb0e548b5e2a6834dc216a

[ "MIT" ]

null

tests/db_test.py

fushinari/wn

be274eda62316622f6bb0e548b5e2a6834dc216a

[ "MIT" ]

null

from wn import _db def test_schema_compatibility(): assert _db.is_schema_compatible(create=True)

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1

0

null

0

1

0

1

0

1

0

6

ee6637ae512db06203e60ea8d72a6883fe06f46c

140

py

Python

app/routes.py

kn-xu/stravinsky

88022fc1ea99b91df51b65b9e12bf4a8ef4ac738

[ "MIT" ]

1

2019-09-03T15:18:14.000Z

app/routes.py

kn-xu/stravinsky

88022fc1ea99b91df51b65b9e12bf4a8ef4ac738

[ "MIT" ]

null

app/routes.py

kn-xu/stravinsky

88022fc1ea99b91df51b65b9e12bf4a8ef4ac738

[ "MIT" ]

null

from app import app from flask import render_template @app.route('/') def index(): return render_template('home.html', title='track')

17.5

54

0.721429

20

140

4.95

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0.142857

140

7

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0.2

true

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1

0

null

0

1

0

1

0

6

c9f856d50f910a9ca16b305bf88018c61162d750

38

py

Python

assemblyline/alsvc_characterize/__init__.py

dendisuhubdy/grokmachine

120a21a25c2730ed356739231ec8b99fc0575c8b

[ "BSD-3-Clause" ]

46

2017-05-15T11:15:08.000Z

2018-07-02T03:32:52.000Z

assemblyline/alsvc_characterize/__init__.py

dendisuhubdy/grokmachine

120a21a25c2730ed356739231ec8b99fc0575c8b

[ "BSD-3-Clause" ]

null

assemblyline/alsvc_characterize/__init__.py

dendisuhubdy/grokmachine

120a21a25c2730ed356739231ec8b99fc0575c8b

[ "BSD-3-Clause" ]

24

2017-05-17T03:26:17.000Z

2018-07-09T07:00:50.000Z

from characterize import Characterize

19

37

0.894737

4

38

8.5

0.75

0

0.105263

38

1

38

1

0

1

0

true

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1

0

null

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0

null

0

1

0

1

0

1

0

6

a016b82f70d13dcdc3be8c8eaab710f41d9a7562

22

py

Python

weather/__init__.py

JohnCalhoun/weather-api-demo

f07161dd7a0c3bef5e95c8cfd7a306a62f53e2a0

[ "MIT" ]

null

weather/__init__.py

JohnCalhoun/weather-api-demo

f07161dd7a0c3bef5e95c8cfd7a306a62f53e2a0

[ "MIT" ]

null

weather/__init__.py

JohnCalhoun/weather-api-demo

f07161dd7a0c3bef5e95c8cfd7a306a62f53e2a0

[ "MIT" ]

null

from .code import API

11

21

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null

0

1

0

1

0

1

0

6

4e6a0bca9c00d8944a5b3ea91cd3f4865f037b45

218

py

Python

online_pharmacy/pharmacy/admin.py

geekyJock8/online_pharmacy

892852857786ec17259b71f2a178896cd6d12e60

[ "Apache-2.0" ]

5

2020-09-09T13:59:17.000Z

2021-09-30T07:20:55.000Z

online_pharmacy/pharmacy/admin.py

geekyJock8/online_pharmacy

892852857786ec17259b71f2a178896cd6d12e60

[ "Apache-2.0" ]

10

2017-09-03T06:13:31.000Z

2017-10-10T15:22:30.000Z

online_pharmacy/pharmacy/admin.py

geekyJock8/Online-Pharmacy

892852857786ec17259b71f2a178896cd6d12e60

[ "Apache-2.0" ]

9

2017-09-03T04:59:18.000Z

2019-10-17T11:33:18.000Z

from django.contrib import admin from .models import pharmacy,contact_pharmacy,pharmacy_notifications admin.site.register(pharmacy) admin.site.register(contact_pharmacy) admin.site.register(pharmacy_notifications)

21.8

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0.857798

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218

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0.278689

0.273224

0

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9

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1

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null

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1

0

1

0

6

4e863c957562215f588df27cb2d2b18a2f7e93a3

104

py

Python

Flasky/app/task/__init__.py

LieonShelly/PythonFun

811760d368885109f9359c2663d8ce74886f6ad6

[ "MIT" ]

null

Flasky/app/task/__init__.py

LieonShelly/PythonFun

811760d368885109f9359c2663d8ce74886f6ad6

[ "MIT" ]

null

Flasky/app/task/__init__.py

LieonShelly/PythonFun

811760d368885109f9359c2663d8ce74886f6ad6

[ "MIT" ]

null

from flask import Blueprint task_api = Blueprint('task', __name__) from app.task import TaskUseCelery

20.8

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104

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false

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1

0

1

0

null

0

1

0

1

0

6

4ea9bd8320537dfdc7817f874f73725fef06c230

14,061

py

Python

h0rton/tests/test_trainval_data/test_xy_data.py

jiwoncpark/h0rton

2541885d70d090fdb777339cfb77a3a9f3e7996d

[ "MIT" ]

4

2020-12-02T02:18:08.000Z

2021-11-25T21:56:33.000Z

h0rton/tests/test_trainval_data/test_xy_data.py

jiwoncpark/h0rton

2541885d70d090fdb777339cfb77a3a9f3e7996d

[ "MIT" ]

25

2019-10-17T08:18:38.000Z

2020-12-26T09:38:05.000Z

h0rton/tests/test_trainval_data/test_xy_data.py

jiwoncpark/h0rton

2541885d70d090fdb777339cfb77a3a9f3e7996d

[ "MIT" ]

1

2020-12-03T02:14:12.000Z

import os import shutil import unittest import numpy as np import pandas as pd from addict import Dict from torch.utils.data import DataLoader from h0rton.trainval_data import XYData from baobab.configs import BaobabConfig class TestXYData(unittest.TestCase): """A suite of tests on data preprocessing """ @classmethod def setUpClass(cls): cls.Y_cols = ["lens_mass_center_x", "src_light_center_x","lens_mass_center_y", "src_light_center_y", "external_shear_gamma_ext", "external_shear_psi_ext"] cls.train_Y_mean = np.random.randn(len(cls.Y_cols)) cls.train_Y_std = np.abs(np.random.randn(len(cls.Y_cols))) + 1.0 cls.train_baobab_cfg_path = 'h0rton/tests/test_trainval_data/baobab_train.json' cls.val_baobab_cfg_path = 'h0rton/tests/test_trainval_data/baobab_val.json' cls.train_baobab_cfg = BaobabConfig.from_file(cls.train_baobab_cfg_path) cls.val_baobab_cfg = BaobabConfig.from_file(cls.val_baobab_cfg_path) cls.original_exptime = 5400.0 # value in baobab_[train/val].json ##################### # Generate toy data # ##################### # Training (n_data = 2) os.makedirs(cls.train_baobab_cfg.out_dir, exist_ok=True) cls.train_metadata = pd.DataFrame.from_dict({ "lens_mass_center_x": [1.5, 2.0], "lens_mass_center_y": [1.8, 9.0], "src_light_center_x": [10.1, 12.5], "src_light_center_y": [29.2, 18.0], "external_shear_gamma_ext": [-0.02, 0.02], "external_shear_psi_ext": [-0.5, 0.5], "img_filename": ['X_{0:07d}.npy'.format(i) for i in range(2)], }) cls.train_metadata.to_csv(os.path.join(cls.train_baobab_cfg.out_dir, 'metadata.csv'), index=False) cls.img_0 = np.abs(np.random.randn(9)*2.0).reshape([1, 3, 3]) cls.img_1 = np.abs(np.random.randn(9)*2.0).reshape([1, 3, 3]) np.save(os.path.join(cls.train_baobab_cfg.out_dir, 'X_{0:07d}.npy'.format(0)), cls.img_0) np.save(os.path.join(cls.train_baobab_cfg.out_dir, 'X_{0:07d}.npy'.format(1)), cls.img_1) # Validation (n_data = 3) os.makedirs(cls.val_baobab_cfg.out_dir, exist_ok=True) cls.val_metadata = pd.DataFrame.from_dict({ "lens_mass_center_x": np.random.randn(3), "lens_mass_center_y": np.random.randn(3), "src_light_center_x": np.random.randn(3), "src_light_center_y": np.random.randn(3), "external_shear_gamma_ext": np.random.randn(3), "external_shear_psi_ext": np.random.randn(3), "img_filename": ['X_{0:07d}.npy'.format(i) for i in range(3)], }) cls.img_0_val = np.abs(np.random.randn(9)*2.0).reshape([1, 3, 3]) cls.img_1_val = np.abs(np.random.randn(9)*2.0).reshape([1, 3, 3]) cls.img_2_val = np.abs(np.random.randn(9)*2.0).reshape([1, 3, 3]) np.save(os.path.join(cls.val_baobab_cfg.out_dir, 'X_{0:07d}.npy'.format(0)), cls.img_0_val) np.save(os.path.join(cls.val_baobab_cfg.out_dir, 'X_{0:07d}.npy'.format(1)), cls.img_1_val) np.save(os.path.join(cls.val_baobab_cfg.out_dir, 'X_{0:07d}.npy'.format(2)), cls.img_2_val) cls.val_metadata.to_csv(os.path.join(cls.val_baobab_cfg.out_dir, 'metadata.csv'), index=False) @classmethod def tearDownClass(cls): """Remove the toy data """ shutil.rmtree(cls.train_baobab_cfg.out_dir) shutil.rmtree(cls.val_baobab_cfg.out_dir) def test_X_identity(self): """Test if the input iamge equals the dataset image, when nothing is done to the image at all """ train_data = XYData(True, self.Y_cols, 'FloatTensor', define_src_pos_wrt_lens=True, rescale_pixels=False, log_pixels=False, add_pixel_noise=False, eff_exposure_time={'TDLMC_F160W': self.original_exptime}, train_Y_mean=self.train_Y_mean, train_Y_std=self.train_Y_std, train_baobab_cfg_path=self.train_baobab_cfg_path, val_baobab_cfg_path=self.val_baobab_cfg_path, for_cosmology=False) actual_img, _ = train_data[0] expected_img = self.img_0 np.testing.assert_array_almost_equal(actual_img, expected_img, err_msg='test_X_identity') def test_X_transformation_log(self): """Test if the images transform as expected, with log(1+X) """ train_data = XYData(True, self.Y_cols, 'FloatTensor', define_src_pos_wrt_lens=True, rescale_pixels=False, log_pixels=True, add_pixel_noise=False, eff_exposure_time={'TDLMC_F160W': self.original_exptime}, train_Y_mean=self.train_Y_mean, train_Y_std=self.train_Y_std, train_baobab_cfg_path=self.train_baobab_cfg_path, val_baobab_cfg_path=self.val_baobab_cfg_path, for_cosmology=False) actual_img, _ = train_data[0] expected_img = self.img_0 expected_img = np.log1p(expected_img) np.testing.assert_array_almost_equal(actual_img, expected_img, err_msg='test_X_transformation_log') def test_X_transformation_rescale(self): """Test if the images transform as expected, with whitening """ train_data = XYData(True, self.Y_cols, 'FloatTensor', define_src_pos_wrt_lens=True, rescale_pixels=True, log_pixels=False, add_pixel_noise=False, eff_exposure_time={'TDLMC_F160W': self.original_exptime}, train_Y_mean=self.train_Y_mean, train_Y_std=self.train_Y_std, train_baobab_cfg_path=self.train_baobab_cfg_path, val_baobab_cfg_path=self.val_baobab_cfg_path, for_cosmology=False) actual_img, _ = train_data[0] expected_img = self.img_0 expected_img = (expected_img - np.mean(expected_img))/np.std(expected_img, ddof=1) np.testing.assert_array_almost_equal(actual_img, expected_img, err_msg='test_X_transformation_rescale') def test_X_transformation_log_rescale(self): """Test if the images transform as expected, with log(1+X) and whitening """ # Without exposure time factor train_data = XYData(True, self.Y_cols, 'FloatTensor', define_src_pos_wrt_lens=True, rescale_pixels=True, log_pixels=True, add_pixel_noise=False, eff_exposure_time={'TDLMC_F160W': self.original_exptime}, train_Y_mean=self.train_Y_mean, train_Y_std=self.train_Y_std, train_baobab_cfg_path=self.train_baobab_cfg_path, val_baobab_cfg_path=self.val_baobab_cfg_path, for_cosmology=False) actual_img, _ = train_data[0] expected_img = self.img_0 expected_img = np.log1p(expected_img) # Note torch std takes into account Bessel correction expected_img = (expected_img - np.mean(expected_img))/np.std(expected_img, ddof=1) np.testing.assert_array_almost_equal(actual_img, expected_img, err_msg='test_X_transformation_log_rescale, without exposure time factor') # With exposure time factor train_data = XYData(True, self.Y_cols, 'FloatTensor', define_src_pos_wrt_lens=True, rescale_pixels=True, log_pixels=True, add_pixel_noise=False, eff_exposure_time={'TDLMC_F160W': self.original_exptime*2.0}, train_Y_mean=self.train_Y_mean, train_Y_std=self.train_Y_std, train_baobab_cfg_path=self.train_baobab_cfg_path, val_baobab_cfg_path=self.val_baobab_cfg_path, for_cosmology=False) actual_img, _ = train_data[0] expected_img = self.img_0*2.0 expected_img = np.log1p(expected_img) # Note torch std takes into account Bessel correction expected_img = (expected_img - np.mean(expected_img))/np.std(expected_img, ddof=1) np.testing.assert_array_almost_equal(actual_img, expected_img, err_msg='test_X_transformation_log_rescale, with exposure time factor') def test_X_exposure_time_factor(self): """Test if the images scale by the new effective exposure time correctly """ train_data = XYData(True, self.Y_cols, 'FloatTensor', define_src_pos_wrt_lens=True, rescale_pixels=False, log_pixels=False, add_pixel_noise=False, eff_exposure_time={'TDLMC_F160W': self.original_exptime*2.0}, train_Y_mean=self.train_Y_mean, train_Y_std=self.train_Y_std, train_baobab_cfg_path=self.train_baobab_cfg_path, val_baobab_cfg_path=self.val_baobab_cfg_path, for_cosmology=False) actual_img, _ = train_data[0] expected_img = self.img_0*2.0 np.testing.assert_array_almost_equal(actual_img, expected_img, err_msg='test_X_exposure_time_factor') def test_Y_transformation_(self): """Test if the target Y whitens correctly """ # Training train_data = XYData(True, self.Y_cols, 'FloatTensor', define_src_pos_wrt_lens=True, rescale_pixels=False, log_pixels=False, add_pixel_noise=False, eff_exposure_time={'TDLMC_F160W': self.original_exptime*2.0}, train_Y_mean=None, train_Y_std=None, train_baobab_cfg_path=self.train_baobab_cfg_path, val_baobab_cfg_path=self.val_baobab_cfg_path, for_cosmology=False) _, actual_Y_0 = train_data[0] _, actual_Y_1 = train_data[1] actual_Y = np.stack([actual_Y_0, actual_Y_1], axis=0) Y_df = self.train_metadata[self.Y_cols].copy() Y_df['src_light_center_x'] -= Y_df['lens_mass_center_x'] Y_df['src_light_center_y'] -= Y_df['lens_mass_center_y'] expected_Y = Y_df.values before_whitening_Y = Y_df.values #expected_Y = (expected_Y - self.train_Y_mean.reshape([1, -1]))/self.train_Y_std.reshape([1, -1]) expected_Y[np.argmin(before_whitening_Y, axis=0), np.arange(len(self.Y_cols))] = -1 expected_Y[np.argmax(before_whitening_Y, axis=0), np.arange(len(self.Y_cols))] = 1 np.testing.assert_array_equal(actual_Y_0.shape, [len(self.Y_cols),], err_msg='shape of single example Y for training') np.testing.assert_array_almost_equal(actual_Y, expected_Y, err_msg='transformed Y for training') # Validation val_data = XYData(False, self.Y_cols, 'FloatTensor', define_src_pos_wrt_lens=True, rescale_pixels=False, log_pixels=False, add_pixel_noise=False, eff_exposure_time={'TDLMC_F160W': self.original_exptime*2.0}, train_Y_mean=self.train_Y_mean, train_Y_std=self.train_Y_std, train_baobab_cfg_path=self.train_baobab_cfg_path, val_baobab_cfg_path=self.val_baobab_cfg_path, for_cosmology=False) _, actual_Y_0 = val_data[0] _, actual_Y_1 = val_data[1] _, actual_Y_2 = val_data[2] actual_Y = np.stack([actual_Y_0, actual_Y_1, actual_Y_2], axis=0) expected_Y = self.val_metadata[self.Y_cols].copy() expected_Y['src_light_center_x'] -= expected_Y['lens_mass_center_x'] expected_Y['src_light_center_y'] -= expected_Y['lens_mass_center_y'] expected_Y = expected_Y.values expected_Y = (expected_Y - self.train_Y_mean.reshape([1, -1]))/self.train_Y_std.reshape([1, -1]) np.testing.assert_array_equal(actual_Y_0.shape, [len(self.Y_cols),], err_msg='shape of single example Y for validation for arbitrary train mean and std') np.testing.assert_array_almost_equal(actual_Y, expected_Y, err_msg='transformed Y for validation for arbitrary train mean and std') def test_train_vs_val(self): """Test if the images and metadata are loaded from the correct folder (train/val) """ train_data = XYData(True, self.Y_cols, 'FloatTensor', define_src_pos_wrt_lens=True, rescale_pixels=False, log_pixels=False, add_pixel_noise=False, eff_exposure_time={'TDLMC_F160W': self.original_exptime*2.0}, train_Y_mean=self.train_Y_mean, train_Y_std=self.train_Y_std, train_baobab_cfg_path=self.train_baobab_cfg_path, val_baobab_cfg_path=self.val_baobab_cfg_path, for_cosmology=False) val_data = XYData(False, self.Y_cols, 'FloatTensor', define_src_pos_wrt_lens=True, rescale_pixels=False, log_pixels=False, add_pixel_noise=False, eff_exposure_time={'TDLMC_F160W': self.original_exptime*2.0}, train_Y_mean=self.train_Y_mean, train_Y_std=self.train_Y_std, train_baobab_cfg_path=self.train_baobab_cfg_path, val_baobab_cfg_path=self.val_baobab_cfg_path, for_cosmology=False) np.testing.assert_equal(len(train_data), 2, err_msg='reading from correct folder (train/val)') np.testing.assert_equal(len(val_data), 3, err_msg='reading from correct folder (train/val)') def test_tensor_type(self): """Test if X, Y are of the configured data type """ # DoubleTensor train_data = XYData(True, self.Y_cols, 'DoubleTensor', define_src_pos_wrt_lens=True, rescale_pixels=False, log_pixels=False, add_pixel_noise=False, eff_exposure_time={'TDLMC_F160W': self.original_exptime*2.0}, train_Y_mean=self.train_Y_mean, train_Y_std=self.train_Y_std, train_baobab_cfg_path=self.train_baobab_cfg_path, val_baobab_cfg_path=self.val_baobab_cfg_path, for_cosmology=False) actual_X_0, actual_Y_0 = train_data[0] assert actual_X_0.type() == 'torch.DoubleTensor' assert actual_Y_0.type() == 'torch.DoubleTensor' # FloatTensor train_data = XYData(True, self.Y_cols, 'FloatTensor', define_src_pos_wrt_lens=True, rescale_pixels=False, log_pixels=False, add_pixel_noise=False, eff_exposure_time={'TDLMC_F160W': self.original_exptime*2.0}, train_Y_mean=self.train_Y_mean, train_Y_std=self.train_Y_std, train_baobab_cfg_path=self.train_baobab_cfg_path, val_baobab_cfg_path=self.val_baobab_cfg_path, for_cosmology=False) actual_X_0, actual_Y_0 = train_data[0] assert actual_X_0.type() == 'torch.FloatTensor' assert actual_Y_0.type() == 'torch.FloatTensor' if __name__ == '__main__': unittest.main()

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0

6

4ec088ed3db1723dba14b1bec935f77fa3007a1d

6,517

py

Python

tests/test_vertex_array.py

2dx/moderngl

5f932560a535469626d79d22e4205f400e18f328

[ "MIT" ]

null

tests/test_vertex_array.py

2dx/moderngl

5f932560a535469626d79d22e4205f400e18f328

[ "MIT" ]

null

tests/test_vertex_array.py

2dx/moderngl

5f932560a535469626d79d22e4205f400e18f328

[ "MIT" ]

null

from array import array import unittest import moderngl import numpy as np from common import get_context class TestCase(unittest.TestCase): @classmethod def setUpClass(cls): cls.ctx = get_context() def test_padding(self): prog = self.ctx.program( vertex_shader=""" #version 330 in vec2 pos; in vec2 velocity; out vec2 out_pos; void main() { out_pos = pos + velocity; } """, ) buffer = self.ctx.buffer(array('f', range(16))) self.ctx.vertex_array(prog, [(buffer, '2f 2x4', 'pos')]) self.ctx.vertex_array(prog, [(buffer, '2f 2f', 'pos', 'velocity')]) def test_empty(self): prog = self.ctx.program( vertex_shader=""" #version 330 in vec2 pos; in vec2 velocity; out vec2 out_pos; void main() { out_pos = pos + velocity; } """, ) self.ctx.vertex_array(prog, []) # def test_optional(self): # prog = self.ctx.program( # vertex_shader=""" # #version 330 # in vec2 pos; # in vec2 velocity; # in vec4 color; # out vec2 out_pos; # void main() { # out_pos = pos + velocity; # } # """, # ) # buffer = self.ctx.buffer(array('f', range(16))) # self.ctx.vertex_array(prog, [(buffer, '2f 2f 4f', 'pos', 'velocity', 'color?')]) def test_1(self): prog = self.ctx.program( vertex_shader=''' #version 330 in vec4 in_vert; out vec4 out_vert; void main() { out_vert = in_vert; } ''', varyings=['out_vert'] ) vbo1 = self.ctx.buffer(np.array([4.0, 2.0, 7.5, 1.8], dtype='f4').tobytes()) vbo2 = self.ctx.buffer(reserve=vbo1.size) vao = self.ctx.simple_vertex_array(prog, vbo1, 'in_vert') vao.transform(vbo2, moderngl.POINTS) res = np.frombuffer(vbo2.read(), dtype='f4') np.testing.assert_almost_equal(res, [4.0, 2.0, 7.5, 1.8]) def test_2(self): prog = self.ctx.program( vertex_shader=''' #version 330 in vec4 in_vert; out vec4 out_vert; void main() { out_vert = in_vert; } ''', varyings=['out_vert'] ) vbo1 = self.ctx.buffer(np.array([4.0, 2.0, 7.5, 1.8], dtype='f4').tobytes()) vbo2 = self.ctx.buffer(reserve=16) vao = self.ctx.vertex_array(prog, [(vbo1, '4f', 'in_vert')]) vao.transform(vbo2, moderngl.POINTS, vertices=1) res = np.frombuffer(vbo2.read(), dtype='f4') np.testing.assert_almost_equal(res, [4.0, 2.0, 7.5, 1.8]) vao = self.ctx.vertex_array(prog, [(vbo1, '3f', 'in_vert')]) vao.transform(vbo2, moderngl.POINTS, vertices=1) res = np.frombuffer(vbo2.read(), dtype='f4') np.testing.assert_almost_equal(res, [4.0, 2.0, 7.5, 1.0]) vao = self.ctx.vertex_array(prog, [(vbo1, '2f', 'in_vert')]) vao.transform(vbo2, moderngl.POINTS, vertices=1) res = np.frombuffer(vbo2.read(), dtype='f4') np.testing.assert_almost_equal(res, [4.0, 2.0, 0.0, 1.0]) vao = self.ctx.vertex_array(prog, [(vbo1, '1f', 'in_vert')]) vao.transform(vbo2, moderngl.POINTS, vertices=1) res = np.frombuffer(vbo2.read(), dtype='f4') np.testing.assert_almost_equal(res, [4.0, 0.0, 0.0, 1.0]) def test_3(self): prog = self.ctx.program( vertex_shader=''' #version 330 in mat4 in_mat; out mat4 out_mat; void main() { out_mat = in_mat; } ''', varyings=['out_mat'] ) vbo1 = self.ctx.buffer(np.arange(1, 17, dtype='f4').tobytes()) vbo2 = self.ctx.buffer(reserve=64) vao = self.ctx.vertex_array(prog, [(vbo1, '16f', 'in_mat')]) vao.transform(vbo2, moderngl.POINTS, vertices=1) res = np.frombuffer(vbo2.read(), dtype='f4') np.testing.assert_almost_equal(res, [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, ]) vao = self.ctx.vertex_array(prog, [(vbo1, '12f', 'in_mat')]) vao.transform(vbo2, moderngl.POINTS, vertices=1) res = np.frombuffer(vbo2.read(), dtype='f4') np.testing.assert_almost_equal(res, [ 1, 2, 3, 1, 4, 5, 6, 1, 7, 8, 9, 1, 10, 11, 12, 1, ]) vao = self.ctx.vertex_array(prog, [(vbo1, '8f', 'in_mat')]) vao.transform(vbo2, moderngl.POINTS, vertices=1) res = np.frombuffer(vbo2.read(), dtype='f4') np.testing.assert_almost_equal(res, [ 1, 2, 0, 1, 3, 4, 0, 1, 5, 6, 0, 1, 7, 8, 0, 1, ]) vao = self.ctx.vertex_array(prog, [(vbo1, '4f', 'in_mat')]) vao.transform(vbo2, moderngl.POINTS, vertices=1) res = np.frombuffer(vbo2.read(), dtype='f4') np.testing.assert_almost_equal(res, [ 1, 0, 0, 1, 2, 0, 0, 1, 3, 0, 0, 1, 4, 0, 0, 1, ]) def test_4(self): prog = self.ctx.program( vertex_shader=''' #version 330 in mat2 in_mat; out mat2 out_mat; void main() { out_mat = in_mat; } ''', varyings=['out_mat'] ) vbo1 = self.ctx.buffer(np.array([4.0, 2.0, 7.5, 1.8], dtype='f4').tobytes()) vbo2 = self.ctx.buffer(reserve=16) vao = self.ctx.vertex_array(prog, [(vbo1, '4f', 'in_mat')]) vao.transform(vbo2, moderngl.POINTS, vertices=1) res = np.frombuffer(vbo2.read(), dtype='f4') np.testing.assert_almost_equal(res, [4.0, 2.0, 7.5, 1.8]) vao = self.ctx.vertex_array(prog, [(vbo1, '2f', 'in_mat')]) vao.transform(vbo2, moderngl.POINTS, vertices=1) res = np.frombuffer(vbo2.read(), dtype='f4') np.testing.assert_almost_equal(res, [4.0, 0.0, 2.0, 0.0]) if __name__ == '__main__': unittest.main()

31.181818

90

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0.851625

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false

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0

null

0

1

0

null

0

6

14d1d929af5e2c43fee5138d8cd72ddf00d7f81f

11,861

py

Python

Code/Chenglong/feature_first_last_ngram.py

ChenglongChen/Kaggle_Homedepot

55c1033d0af3b6cf2f033fe4bcf3e1e0ffda3445

[ "MIT" ]

465

2016-04-27T13:17:36.000Z

2020-05-15T11:05:13.000Z

Code/Chenglong/feature_first_last_ngram.py

CharlotteSean/Kaggle_HomeDepot

55c1033d0af3b6cf2f033fe4bcf3e1e0ffda3445

[ "MIT" ]

1

2016-10-15T04:33:54.000Z

Code/Chenglong/feature_first_last_ngram.py

CharlotteSean/Kaggle_HomeDepot

55c1033d0af3b6cf2f033fe4bcf3e1e0ffda3445

[ "MIT" ]

230

2016-04-30T06:35:17.000Z

2019-12-04T08:23:22.000Z

# -*- coding: utf-8 -*- """ @author: Chenglong Chen <c.chenglong@gmail.com> @brief: first and last ngram features @note: in the final submission, we only used intersect count, NOT including intersect position. """ import re import string import numpy as np import pandas as pd import config from utils import dist_utils, ngram_utils, nlp_utils, np_utils, pkl_utils from utils import logging_utils, time_utils from feature_base import BaseEstimator, PairwiseFeatureWrapper from feature_intersect_position import _inter_pos_list, _inter_norm_pos_list # tune the token pattern to get a better correlation with y_train # token_pattern = r"(?u)\b\w\w+\b" # token_pattern = r"\w{1,}" # token_pattern = r"\w+" # token_pattern = r"[\w']+" token_pattern = " " # just split the text into tokens # -------------------------- Count ---------------------------------- class Count_Ngram_BaseEstimator(BaseEstimator): def __init__(self, obs_corpus, target_corpus, ngram, idx, aggregation_mode="", str_match_threshold=config.STR_MATCH_THRESHOLD): super().__init__(obs_corpus, target_corpus, aggregation_mode) self.idx = idx self.ngram = ngram self.ngram_str = ngram_utils._ngram_str_map[self.ngram] self.str_match_threshold = str_match_threshold def _get_match_count(self, obs, target, idx): cnt = 0 if (len(obs) != 0) and (len(target) != 0): for word in target: if dist_utils._is_str_match(word, obs[idx], self.str_match_threshold): cnt += 1 return cnt def transform_one(self, obs, target, id): obs_tokens = nlp_utils._tokenize(obs, token_pattern) target_tokens = nlp_utils._tokenize(target, token_pattern) obs_ngrams = ngram_utils._ngrams(obs_tokens, self.ngram) target_ngrams = ngram_utils._ngrams(target_tokens, self.ngram) return self._get_match_count(obs_ngrams, target_ngrams, self.idx) class FirstIntersectCount_Ngram(Count_Ngram_BaseEstimator): def __init__(self, obs_corpus, target_corpus, ngram, aggregation_mode="", str_match_threshold=config.STR_MATCH_THRESHOLD): super().__init__(obs_corpus, target_corpus, ngram, 0, aggregation_mode, str_match_threshold) def __name__(self): return "FirstIntersectCount_%s"%self.ngram_str class LastIntersectCount_Ngram(Count_Ngram_BaseEstimator): def __init__(self, obs_corpus, target_corpus, ngram, aggregation_mode="", str_match_threshold=config.STR_MATCH_THRESHOLD): super().__init__(obs_corpus, target_corpus, ngram, -1, aggregation_mode, str_match_threshold) def __name__(self): return "LastIntersectCount_%s"%self.ngram_str # ------------------------- Ratio ------------------------------------------- class Ratio_Ngram_BaseEstimator(Count_Ngram_BaseEstimator): def __init__(self, obs_corpus, target_corpus, ngram, idx, aggregation_mode="", str_match_threshold=config.STR_MATCH_THRESHOLD): super().__init__(obs_corpus, target_corpus, ngram, idx, aggregation_mode, str_match_threshold) def transform_one(self, obs, target, id): obs_tokens = nlp_utils._tokenize(obs, token_pattern) target_tokens = nlp_utils._tokenize(target, token_pattern) obs_ngrams = ngram_utils._ngrams(obs_tokens, self.ngram) target_ngrams = ngram_utils._ngrams(target_tokens, self.ngram) return np_utils._try_divide(self._get_match_count(obs_ngrams, target_ngrams, self.idx), len(target_ngrams)) class FirstIntersectRatio_Ngram(Ratio_Ngram_BaseEstimator): def __init__(self, obs_corpus, target_corpus, ngram, aggregation_mode="", str_match_threshold=config.STR_MATCH_THRESHOLD): super().__init__(obs_corpus, target_corpus, ngram, 0, aggregation_mode, str_match_threshold) def __name__(self): return "FirstIntersectRatio_%s"%self.ngram_str class LastIntersectRatio_Ngram(Ratio_Ngram_BaseEstimator): def __init__(self, obs_corpus, target_corpus, ngram, aggregation_mode="", str_match_threshold=config.STR_MATCH_THRESHOLD): super().__init__(obs_corpus, target_corpus, ngram, -1, aggregation_mode, str_match_threshold) def __name__(self): return "LastIntersectRatio_%s"%self.ngram_str # -------------------- Position --------------------- class Position_Ngram_BaseEstimator(BaseEstimator): def __init__(self, obs_corpus, target_corpus, ngram, idx, aggregation_mode=""): super().__init__(obs_corpus, target_corpus, aggregation_mode) self.idx = idx self.ngram = ngram self.ngram_str = ngram_utils._ngram_str_map[self.ngram] def transform_one(self, obs, target, id): obs_tokens = nlp_utils._tokenize(obs, token_pattern) target_tokens = nlp_utils._tokenize(target, token_pattern) obs_ngrams = ngram_utils._ngrams(obs_tokens, self.ngram) target_ngrams = ngram_utils._ngrams(target_tokens, self.ngram) return _inter_pos_list(target_ngrams, [obs_ngrams[self.idx]]) class FirstIntersectPosition_Ngram(Position_Ngram_BaseEstimator): """Single aggregation features""" def __init__(self, obs_corpus, target_corpus, ngram, aggregation_mode=""): super().__init__(obs_corpus, target_corpus, ngram, 0, aggregation_mode) def __name__(self): if isinstance(self.aggregation_mode, str): feat_name = "FirstIntersectPosition_%s_%s"%( self.ngram_str, string.capwords(self.aggregation_mode)) elif isinstance(self.aggregation_mode, list): feat_name = ["FirstIntersectPosition_%s_%s"%( self.ngram_str, string.capwords(m)) for m in self.aggregation_mode] return feat_name class LastIntersectPosition_Ngram(Position_Ngram_BaseEstimator): """Single aggregation features""" def __init__(self, obs_corpus, target_corpus, ngram, aggregation_mode=""): super().__init__(obs_corpus, target_corpus, ngram, -1, aggregation_mode) def __name__(self): if isinstance(self.aggregation_mode, str): feat_name = "LastIntersectPosition_%s_%s"%( self.ngram_str, string.capwords(self.aggregation_mode)) elif isinstance(self.aggregation_mode, list): feat_name = ["LastIntersectPosition_%s_%s"%( self.ngram_str, string.capwords(m)) for m in self.aggregation_mode] return feat_name # -------------------------- Norm Position ---------------------------------- class NormPosition_Ngram_BaseEstimator(BaseEstimator): def __init__(self, obs_corpus, target_corpus, ngram, idx, aggregation_mode=""): super().__init__(obs_corpus, target_corpus, aggregation_mode) self.idx = idx self.ngram = ngram self.ngram_str = ngram_utils._ngram_str_map[self.ngram] def transform_one(self, obs, target, id): obs_tokens = nlp_utils._tokenize(obs, token_pattern) target_tokens = nlp_utils._tokenize(target, token_pattern) obs_ngrams = ngram_utils._ngrams(obs_tokens, self.ngram) target_ngrams = ngram_utils._ngrams(target_tokens, self.ngram) return _inter_norm_pos_list(target_ngrams, [obs_ngrams[self.idx]]) class FirstIntersectNormPosition_Ngram(NormPosition_Ngram_BaseEstimator): """Single aggregation features""" def __init__(self, obs_corpus, target_corpus, ngram, aggregation_mode=""): super().__init__(obs_corpus, target_corpus, ngram, 0, aggregation_mode) def __name__(self): if isinstance(self.aggregation_mode, str): feat_name = "FirstIntersectNormPosition_%s_%s"%( self.ngram_str, string.capwords(self.aggregation_mode)) elif isinstance(self.aggregation_mode, list): feat_name = ["FirstIntersectNormPosition_%s_%s"%( self.ngram_str, string.capwords(m)) for m in self.aggregation_mode] return feat_name class LastIntersectNormPosition_Ngram(NormPosition_Ngram_BaseEstimator): """Single aggregation features""" def __init__(self, obs_corpus, target_corpus, ngram, aggregation_mode=""): super().__init__(obs_corpus, target_corpus, ngram, -1, aggregation_mode) def __name__(self): if isinstance(self.aggregation_mode, str): feat_name = "LastIntersectNormPosition_%s_%s"%( self.ngram_str, string.capwords(self.aggregation_mode)) elif isinstance(self.aggregation_mode, list): feat_name = ["LastIntersectNormPosition_%s_%s"%( self.ngram_str, string.capwords(m)) for m in self.aggregation_mode] return feat_name # ---------------------------- Main -------------------------------------- def run_count(): logname = "generate_feature_first_last_ngram_count_%s.log"%time_utils._timestamp() logger = logging_utils._get_logger(config.LOG_DIR, logname) dfAll = pkl_utils._load(config.ALL_DATA_LEMMATIZED_STEMMED) generators = [ FirstIntersectCount_Ngram, LastIntersectCount_Ngram, FirstIntersectRatio_Ngram, LastIntersectRatio_Ngram, ] obs_fields_list = [] target_fields_list = [] ## query in document obs_fields_list.append( ["search_term", "search_term_product_name", "search_term_alt", "search_term_auto_corrected"][:2] ) target_fields_list.append( ["product_title", "product_title_product_name", "product_description", "product_attribute", "product_brand", "product_color"] ) ## document in query obs_fields_list.append( ["product_title", "product_title_product_name", "product_description", "product_attribute", "product_brand", "product_color"] ) target_fields_list.append( ["search_term", "search_term_product_name", "search_term_alt", "search_term_auto_corrected"][:2] ) ngrams = [1,2,3,12,123][:3] for obs_fields, target_fields in zip(obs_fields_list, target_fields_list): for generator in generators: for ngram in ngrams: param_list = [ngram] pf = PairwiseFeatureWrapper(generator, dfAll, obs_fields, target_fields, param_list, config.FEAT_DIR, logger) pf.go() def run_position(): logname = "generate_feature_first_last_ngram_position_%s.log"%time_utils._timestamp() logger = logging_utils._get_logger(config.LOG_DIR, logname) dfAll = pkl_utils._load(config.ALL_DATA_LEMMATIZED_STEMMED) generators = [ FirstIntersectPosition_Ngram, LastIntersectPosition_Ngram, FirstIntersectNormPosition_Ngram, LastIntersectNormPosition_Ngram, ] obs_fields_list = [] target_fields_list = [] ## query in document obs_fields_list.append( ["search_term", "search_term_product_name", "search_term_alt", "search_term_auto_corrected"][:2] ) target_fields_list.append( ["product_title", "product_title_product_name", "product_description", "product_attribute", "product_brand", "product_color"] ) ## document in query obs_fields_list.append( ["product_title", "product_title_product_name", "product_description", "product_attribute", "product_brand", "product_color"] ) target_fields_list.append( ["search_term", "search_term_product_name", "search_term_alt", "search_term_auto_corrected"][:2] ) ngrams = [1,2,3,12,123][:3] aggregation_mode = ["mean", "std", "max", "min", "median"] for obs_fields, target_fields in zip(obs_fields_list, target_fields_list): for generator in generators: for ngram in ngrams: param_list = [ngram, aggregation_mode] pf = PairwiseFeatureWrapper(generator, dfAll, obs_fields, target_fields, param_list, config.FEAT_DIR, logger) pf.go() if __name__ == "__main__": run_count() # # not used in final submission # run_position()

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py

Python

canary/servicemap.py

bohatiuk/agh-reddit-stash

f0c3cd51f509c81f3df1d48e3867a178b7c84630

[ "Apache-2.0" ]

null

canary/servicemap.py

bohatiuk/agh-reddit-stash

f0c3cd51f509c81f3df1d48e3867a178b7c84630

[ "Apache-2.0" ]

null

canary/servicemap.py

bohatiuk/agh-reddit-stash

f0c3cd51f509c81f3df1d48e3867a178b7c84630

[ "Apache-2.0" ]

null

import json def service_map(): with open('service-map.json') as jsn: return json.load(jsn)

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py

Python

tests/workspace/mod2/mod2_1.py

oraluben/pycds

5c7d11363851e451f07bded967168e493fb16a2f

[ "MIT" ]

8

2022-03-28T02:19:52.000Z

2022-03-29T22:10:14.000Z

tests/workspace/mod2/mod2_1.py

oraluben/pycds

5c7d11363851e451f07bded967168e493fb16a2f

[ "MIT" ]

null

tests/workspace/mod2/mod2_1.py

oraluben/pycds

5c7d11363851e451f07bded967168e493fb16a2f

[ "MIT" ]

1

2022-03-28T07:08:26.000Z

def mod2_1_foo(): pass

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py

Python

psmlprocess/__init__.py

nathan-az/pyspark-ml-processing-utils

1781221ac05b830ccf0d97972204b01b8459119f

[ "Apache-2.0" ]

null

psmlprocess/__init__.py

nathan-az/pyspark-ml-processing-utils

1781221ac05b830ccf0d97972204b01b8459119f

[ "Apache-2.0" ]

null

psmlprocess/__init__.py

nathan-az/pyspark-ml-processing-utils

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from .transformers import *

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Python

tests/test_read_subset.py

GeoscienceAustralia/wagl

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[ "Apache-2.0" ]

22

2018-05-30T23:42:10.000Z

2021-12-25T14:21:46.000Z

tests/test_read_subset.py

sixy6e/wagl

523f574b4a4b62c3aed3a378e13a7548a7f21c0c

[ "Apache-2.0" ]

52

2018-02-20T05:31:55.000Z

2021-11-23T23:38:15.000Z

tests/test_read_subset.py

sixy6e/wagl

523f574b4a4b62c3aed3a378e13a7548a7f21c0c

[ "Apache-2.0" ]

8

2018-02-20T05:08:38.000Z

2021-08-12T23:16:41.000Z

#!/usr/bin/env python from __future__ import absolute_import import os import shutil import tempfile import unittest import numpy import h5py from wagl.data import read_subset from wagl.data import write_img from wagl import unittesting_tools as ut class TestReadSubset(unittest.TestCase): img, geobox = ut.create_test_image((1200, 1500)) img[:] = 1 fid = h5py.File("test-subset.h5", "w", backing_store=False, driver="core") ds = fid.create_dataset("data", data=img) ds.attrs["geotransform"] = geobox.transform.to_gdal() ds.attrs["crs_wkt"] = geobox.crs.ExportToWkt() ds.attrs["fillvalue"] = 0 subs_shape = (200, 300) @unittest.skip("Refactor DSM subsetting logic; TODO update test") def testWestBounds(self): """ Test that a co-ordinate west of the image domain returns an index error. The subset attempts to read a 20 by 20 block with half contained within the image bounds and half contained outside the image bounds. """ img, geobox = ut.create_test_image() # Temporarily write the image to disk temp_dir = tempfile.mkdtemp() fname = os.path.join(temp_dir, "testWestBounds") write_img(img, fname, geobox=geobox) # Create box to read 10 pixels left of the image bounds UL = geobox.convert_coordinates((-9, 0)) UR = geobox.convert_coordinates((9, 0)) LR = geobox.convert_coordinates((9, 10)) LL = geobox.convert_coordinates((-9, 10)) kwds = {"fname": fname, "ul_xy": UL, "ur_xy": UR, "lr_xy": LR, "ll_xy": LL} self.assertRaises(IndexError, read_subset, **kwds) # Cleanup shutil.rmtree(temp_dir) @unittest.skip("Refactor DSM subsetting logic; TODO update test") def testEastBounds(self): """ Test that a co-ordinate east of the image domain returns an index error. The subset attempts to read a 20 by 20 block with half contained within the image bounds and half contained outside the image """ img, geobox = ut.create_test_image() cols, rows = geobox.get_shape_xy() # Temporarily write the image to disk temp_dir = tempfile.mkdtemp() fname = os.path.join(temp_dir, "testEastBounds") write_img(img, fname, geobox=geobox) # Create box to read 10 pixels right of the image bounds UL = geobox.convert_coordinates((cols - 9, 0)) UR = geobox.convert_coordinates((cols + 10, 0)) LR = geobox.convert_coordinates((cols + 10, 10)) LL = geobox.convert_coordinates((cols - 9, 10)) kwds = {"fname": fname, "ul_xy": UL, "ur_xy": UR, "lr_xy": LR, "ll_xy": LL} self.assertRaises(IndexError, read_subset, **kwds) # Cleanup shutil.rmtree(temp_dir) @unittest.skip("Refactor DSM subsetting logic; TODO update test") def testNorthBounds(self): """ Test that a co-ordinate north of the image domain returns an index error. The subset attempts to read a 20 by 20 block with half contained within the image bounds and half contained outside the image """ img, geobox = ut.create_test_image() # Temporarily write the image to disk temp_dir = tempfile.mkdtemp() fname = os.path.join(temp_dir, "testNorthBounds") write_img(img, fname, geobox=geobox) # Create box to read 10 pixels above the image bounds UL = geobox.convert_coordinates((0, -9)) UR = geobox.convert_coordinates((10, -9)) LR = geobox.convert_coordinates((10, 10)) LL = geobox.convert_coordinates((0, 10)) kwds = {"fname": fname, "ul_xy": UL, "ur_xy": UR, "lr_xy": LR, "ll_xy": LL} self.assertRaises(IndexError, read_subset, **kwds) # Cleanup shutil.rmtree(temp_dir) @unittest.skip("Refactor DSM subsetting logic; TODO update test") def testSouthBounds(self): """ Test that a co-ordinate south of the image domain returns an index error. The subset attempts to read a 20 by 20 block with half contained within the image bounds and half contained outside the image """ img, geobox = ut.create_test_image() cols, rows = geobox.get_shape_xy() # Temporarily write the image to disk temp_dir = tempfile.mkdtemp() fname = os.path.join(temp_dir, "testSouthBounds") write_img(img, fname, geobox=geobox) # Create box to read 10 pixels below the image bounds UL = geobox.convert_coordinates((0, rows - 9)) UR = geobox.convert_coordinates((10, rows - 9)) LR = geobox.convert_coordinates((10, rows + 10)) LL = geobox.convert_coordinates((0, rows + 10)) kwds = {"fname": fname, "ul_xy": UL, "ur_xy": UR, "lr_xy": LR, "ll_xy": LL} self.assertRaises(IndexError, read_subset, **kwds) # Cleanup shutil.rmtree(temp_dir) @unittest.skip("Requires refactoring") def test_correct_subset(self): """ Test that the subset is what we expect. Read a 10 by 10 starting at the UL corner. """ img, geobox = ut.create_test_image() cols, rows = geobox.get_shape_xy() # Temporarily write the image to disk temp_dir = tempfile.mkdtemp() fname = os.path.join(temp_dir, "test_image") write_img(img, fname, geobox=geobox) # Create box to read 10 pixels below the image bounds UL = geobox.convert_coordinates((0, 0)) UR = geobox.convert_coordinates((9, 0)) LR = geobox.convert_coordinates((9, 9)) LL = geobox.convert_coordinates((0, 9)) kwds = {"fname": fname, "ul_xy": UL, "ur_xy": UR, "lr_xy": LR, "ll_xy": LL} subs, geobox = read_subset(**kwds) base = img[0:10, 0:10] result = numpy.sum(base - subs) self.assertTrue(result == 0) # Cleanup shutil.rmtree(temp_dir) def test_case_a(self): """ Origin = (-150, -50); `O` O+----+ - - - +-----------+ - - - - +----+ - - - - - - - - - +-----------+ """ # indices based on full array ul = (-150, -50) ur = (ul[0], ul[1] + self.subs_shape[1]) lr = (ul[0] + self.subs_shape[0], ul[1] + self.subs_shape[1]) ll = (ul[0] + self.subs_shape[0], ul[1]) # real world coords (note reversing (y, x) to (x, y) ul_xy_map = self.geobox.transform * ul[::-1] ur_xy_map = self.geobox.transform * ur[::-1] lr_xy_map = self.geobox.transform * lr[::-1] ll_xy_map = self.geobox.transform * ll[::-1] # read subset data, gb = read_subset(self.ds, ul_xy_map, ur_xy_map, lr_xy_map, ll_xy_map) count = 50 * 250 self.assertTrue(data.sum() == count) def test_case_b(self): """ Origin = (-150, 600); `O` O+---+ - - +---- ----+ - - - - - +---+ - - - - - - - - - +-----------+ """ # indices based on full array ul = (-150, 600) ur = (ul[0], ul[1] + self.subs_shape[1]) lr = (ul[0] + self.subs_shape[0], ul[1] + self.subs_shape[1]) ll = (ul[0] + self.subs_shape[0], ul[1]) # real world coords (note reversing (y, x) to (x, y) ul_xy_map = self.geobox.transform * ul[::-1] ur_xy_map = self.geobox.transform * ur[::-1] lr_xy_map = self.geobox.transform * lr[::-1] ll_xy_map = self.geobox.transform * ll[::-1] # read subset data, gb = read_subset(self.ds, ul_xy_map, ur_xy_map, lr_xy_map, ll_xy_map) count = 50 * 300 self.assertTrue(data.sum() == count) def test_case_c(self): """ Origin = (-150, 1400); `O` O+---+ - - +------------+ - - - - - - +---+ - - - - - - - - +------------+ """ # indices based on full array ul = (-150, 1400) ur = (ul[0], ul[1] + self.subs_shape[1]) lr = (ul[0] + self.subs_shape[0], ul[1] + self.subs_shape[1]) ll = (ul[0] + self.subs_shape[0], ul[1]) # real world coords (note reversing (y, x) to (x, y) ul_xy_map = self.geobox.transform * ul[::-1] ur_xy_map = self.geobox.transform * ur[::-1] lr_xy_map = self.geobox.transform * lr[::-1] ll_xy_map = self.geobox.transform * ll[::-1] # read subset data, gb = read_subset(self.ds, ul_xy_map, ur_xy_map, lr_xy_map, ll_xy_map) count = 50 * 100 self.assertTrue(data.sum() == count) def test_case_d(self): """ Origin = (600, -50); `O` +-----------+ - - O+-----+ - - - - - - - - - +-----+ - - - +-----------+ """ # indices based on full array ul = (600, -50) ur = (ul[0], ul[1] + self.subs_shape[1]) lr = (ul[0] + self.subs_shape[0], ul[1] + self.subs_shape[1]) ll = (ul[0] + self.subs_shape[0], ul[1]) # real world coords (note reversing (y, x) to (x, y) ul_xy_map = self.geobox.transform * ul[::-1] ur_xy_map = self.geobox.transform * ur[::-1] lr_xy_map = self.geobox.transform * lr[::-1] ll_xy_map = self.geobox.transform * ll[::-1] # read subset data, gb = read_subset(self.ds, ul_xy_map, ur_xy_map, lr_xy_map, ll_xy_map) count = 200 * 250 self.assertTrue(data.sum() == count) def test_case_e(self): """ Origin = (600, 600); `O` +-----------+ - - - O+---+ - - - - - - - - - - +---+ - - - +-----------+ """ # indices based on full array ul = (600, 600) ur = (ul[0], ul[1] + self.subs_shape[1]) lr = (ul[0] + self.subs_shape[0], ul[1] + self.subs_shape[1]) ll = (ul[0] + self.subs_shape[0], ul[1]) # real world coords (note reversing (y, x) to (x, y) ul_xy_map = self.geobox.transform * ul[::-1] ur_xy_map = self.geobox.transform * ur[::-1] lr_xy_map = self.geobox.transform * lr[::-1] ll_xy_map = self.geobox.transform * ll[::-1] # read subset data, gb = read_subset(self.ds, ul_xy_map, ur_xy_map, lr_xy_map, ll_xy_map) count = 200 * 300 self.assertTrue(data.sum() == count) def test_case_f(self): """ Origin = (600, 1400); `O` +-----------+ - - - O+-----+ - - - - - - - - - +-----+ - - +-----------+ """ # indices based on full array ul = (600, 1400) ur = (ul[0], ul[1] + self.subs_shape[1]) lr = (ul[0] + self.subs_shape[0], ul[1] + self.subs_shape[1]) ll = (ul[0] + self.subs_shape[0], ul[1]) # real world coords (note reversing (y, x) to (x, y) ul_xy_map = self.geobox.transform * ul[::-1] ur_xy_map = self.geobox.transform * ur[::-1] lr_xy_map = self.geobox.transform * lr[::-1] ll_xy_map = self.geobox.transform * ll[::-1] # read subset data, gb = read_subset(self.ds, ul_xy_map, ur_xy_map, lr_xy_map, ll_xy_map) count = 200 * 100 self.assertTrue(data.sum() == count) def test_case_g(self): """ Origin = (1100, -50); `O` +-----------+ - - - - - - - - O+-----+ - - - - - - +-----------+ - - - - +-----+ """ # indices based on full array ul = (1100, -50) ur = (ul[0], ul[1] + self.subs_shape[1]) lr = (ul[0] + self.subs_shape[0], ul[1] + self.subs_shape[1]) ll = (ul[0] + self.subs_shape[0], ul[1]) # real world coords (note reversing (y, x) to (x, y) ul_xy_map = self.geobox.transform * ul[::-1] ur_xy_map = self.geobox.transform * ur[::-1] lr_xy_map = self.geobox.transform * lr[::-1] ll_xy_map = self.geobox.transform * ll[::-1] # read subset data, gb = read_subset(self.ds, ul_xy_map, ur_xy_map, lr_xy_map, ll_xy_map) count = 100 * 250 self.assertTrue(data.sum() == count) def test_case_h(self): """ Origin = (1100, 600); `O` +-----------+ - - - - - - - - - O+----+ - - - - - +-----------+ - - - - +----+ """ # indices based on full array ul = (1100, 600) ur = (ul[0], ul[1] + self.subs_shape[1]) lr = (ul[0] + self.subs_shape[0], ul[1] + self.subs_shape[1]) ll = (ul[0] + self.subs_shape[0], ul[1]) # real world coords (note reversing (y, x) to (x, y) ul_xy_map = self.geobox.transform * ul[::-1] ur_xy_map = self.geobox.transform * ur[::-1] lr_xy_map = self.geobox.transform * lr[::-1] ll_xy_map = self.geobox.transform * ll[::-1] # read subset data, gb = read_subset(self.ds, ul_xy_map, ur_xy_map, lr_xy_map, ll_xy_map) count = 100 * 300 self.assertTrue(data.sum() == count) def test_case_i(self): """ Origin = (1100, 1400); `O` +-----------+ - - - - - - - - - O+-----+ - - - +-----------+--- - - - - +-----+ """ # indices based on full array ul = (1100, 1400) ur = (ul[0], ul[1] + self.subs_shape[1]) lr = (ul[0] + self.subs_shape[0], ul[1] + self.subs_shape[1]) ll = (ul[0] + self.subs_shape[0], ul[1]) # real world coords (note reversing (y, x) to (x, y) ul_xy_map = self.geobox.transform * ul[::-1] ur_xy_map = self.geobox.transform * ur[::-1] lr_xy_map = self.geobox.transform * lr[::-1] ll_xy_map = self.geobox.transform * ll[::-1] # read subset data, gb = read_subset(self.ds, ul_xy_map, ur_xy_map, lr_xy_map, ll_xy_map) count = 100 * 100 self.assertTrue(data.sum() == count) def test_case_j(self): """ Origin = (600, -400) +-----------+ - - O+----+ - - - - - - - - - - +----+ - - - - +-----------+ """ # indices based on full array ul = (600, -400) ur = (ul[0], ul[1] + self.subs_shape[1]) lr = (ul[0] + self.subs_shape[0], ul[1] + self.subs_shape[1]) ll = (ul[0] + self.subs_shape[0], ul[1]) # real world coords (note reversing (y, x) to (x, y) ul_xy_map = self.geobox.transform * ul[::-1] ur_xy_map = self.geobox.transform * ur[::-1] lr_xy_map = self.geobox.transform * lr[::-1] ll_xy_map = self.geobox.transform * ll[::-1] # read subset with self.assertRaises(IndexError): read_subset(self.ds, ul_xy_map, ur_xy_map, lr_xy_map, ll_xy_map) if __name__ == "__main__": unittest.main()

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0.137441

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null

0

1

0

null

0

6

eeba59ebaaeaf5a6a1823df6d22007b608e55a1c

270

py

Python

src/modules/providers/exceptions.py

DmitryBurnaev/podcast-service

53349a3f9aed22a8024d0c83380f9a02464962a3

[ "MIT" ]

5

2021-07-01T16:31:29.000Z

2022-01-29T14:32:13.000Z

src/modules/providers/exceptions.py

DmitryBurnaev/podcast-service

53349a3f9aed22a8024d0c83380f9a02464962a3

[ "MIT" ]

45

2020-10-25T19:41:26.000Z

2022-03-25T06:31:58.000Z

src/modules/providers/exceptions.py

DmitryBurnaev/podcast-service

53349a3f9aed22a8024d0c83380f9a02464962a3

[ "MIT" ]

1

2022-01-27T11:30:07.000Z

from common.exceptions import BaseApplicationError class FFMPegPreparationError(BaseApplicationError): message = "We couldn't prepare file by ffmpeg" class SourceFetchError(BaseApplicationError): message = "We couldn't extract info about requested episode."

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null

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1

0

6

0171cae2e7872622c4cad7a3ca03abf324f9162f

221

py

Python

devices/sensor/smoke.py

volmart/domoticz-zigbee2mqtt-plugin

1b86236cff618b58adceea8a417eff5a92eee548

[ "MIT" ]

null

devices/sensor/smoke.py

volmart/domoticz-zigbee2mqtt-plugin

1b86236cff618b58adceea8a417eff5a92eee548

[ "MIT" ]

null

devices/sensor/smoke.py

volmart/domoticz-zigbee2mqtt-plugin

1b86236cff618b58adceea8a417eff5a92eee548

[ "MIT" ]

null

from devices.boolean_sensor import BooleanSensor class SmokeSensor(BooleanSensor): def __init__(self, devices, alias, value_key): super().__init__(devices, alias, value_key, BooleanSensor.SENSOR_TYPE_SMOKE)

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null

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1

0

1

0

6

6d6a9c3e032f91ce05b1dc8d81e3cffa9ec48378

122

py

Python

tests/conftest.py

xiaofei0722/xfapitest

0050520f872eb2b5c997b7d01d9851048450deff

[ "Apache-2.0" ]

null

tests/conftest.py

xiaofei0722/xfapitest

0050520f872eb2b5c997b7d01d9851048450deff

[ "Apache-2.0" ]

null

tests/conftest.py

xiaofei0722/xfapitest

0050520f872eb2b5c997b7d01d9851048450deff

[ "Apache-2.0" ]

null

import pytest import requests @pytest.fixture(scope="function") def init_session(): return requests.sessions.Session()

24.4

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6.333333

0.733333

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true

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null

0

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null

0

1

0

1

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6

6d809ac7066c900b0a899ed8ae1ec2ad93b5c818

340

py

Python

src/python/unicode_segmentation/_unicode_segmentation__ffi.py

anthrotype/unicode-segmentation-py

7f28f5141660f5f6c7dc5809521c931c335ec640

[ "Apache-2.0" ]

2

2020-03-10T02:22:31.000Z

2020-03-10T07:39:07.000Z

src/python/unicode_segmentation/_unicode_segmentation__ffi.py

anthrotype/unicode-segmentation-py

7f28f5141660f5f6c7dc5809521c931c335ec640

[ "Apache-2.0" ]

null

src/python/unicode_segmentation/_unicode_segmentation__ffi.py

anthrotype/unicode-segmentation-py

7f28f5141660f5f6c7dc5809521c931c335ec640

[ "Apache-2.0" ]

null

# auto-generated file import _cffi_backend ffi = _cffi_backend.FFI('unicode_segmentation._unicode_segmentation__ffi', _version = 0x2601, _types = b'\x00\x00\x05\x0D\x00\x00\x06\x03\x00\x00\x07\x03\x00\x00\x02\x11\x00\x00\x00\x0F\x00\x00\x01\x01\x00\x00\x02\x01\x00\x00\x0A\x01', _globals = (b'\x00\x00\x00\x23graphemes',0,), )

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1

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1

0

1

0

1

null

0

6

6dc57078d2bde188616b562474d7b6fd10929cca

3,923

py

Python

model/stuq.py

YYgroup/STmodel

0e608c7d3fe596a2bf2aad446bbd1e151c56b543

[ "MIT" ]

1

2021-06-18T06:13:09.000Z

model/stuq.py

YYgroup/STmodel

0e608c7d3fe596a2bf2aad446bbd1e151c56b543

[ "MIT" ]

null

model/stuq.py

YYgroup/STmodel

0e608c7d3fe596a2bf2aad446bbd1e151c56b543

[ "MIT" ]

null

import numpy as np from scipy import stats import STmodel.data as std import STmodel.model.st as stm def sample_params(npts, nstd_T=0.1, nstd_C=0.15): # default values A = 0.317 B = 0.033 T = 5.5 C = 1.0 file_path = std.get_file('linear_regression_AB.npz') data = np.load(file_path) A_samples = data['A'][:npts] B_samples = data['B'][:npts] # standard devaition of T and C std_T = T * nstd_T std_C = C * nstd_C T_samples = np.random.normal(T, std_T, npts) C_samples = np.random.normal(C, std_C, npts) return A_samples, B_samples, T_samples, C_samples def fwd_params(ur, lr, unburnt,fuel,oxidizer,chemistry, A_samples, B_samples, T_samples, C_samples): n_samples = len(T_samples) v_samples = np.zeros(n_samples) r = stm.Reactant(unburnt, fuel, oxidizer, chemistry) for i in range(n_samples): m = stm.Model(r, A = A_samples[i], B = B_samples[i], T = T_samples[i], C = C_samples[i]) v_samples[i] = m.ratio_turbulent_burning_velocity(ur, lr) return v_samples def fwd_chem(ur, lr, unburnt, sc0, dl0, Le, sigma, flame_samples): n_samples = flame_samples[::2].shape[0] v_samples = np.zeros(n_samples) for i in range(n_samples): dl = flame_samples[2*i,0] ReF = flame_samples[2*i+1,0] sc = flame_samples[2*i+1,1] I0_table = np.zeros(flame_samples[2*i:2*i+2,1:].shape) I0_table[0] = flame_samples[2*i,1:] * Le I0_table[1] = flame_samples[2*i+1,1:] / sc rsc = sc0/sc rdl = dl0/dl r = stm.Reactant(unburnt, 'progvar', Le=Le, sigma=sigma, ReF=ReF, I0_table=I0_table) m = stm.Model(r) v_samples[i] = m.ratio_turbulent_burning_velocity(ur*rsc, lr*rdl) return v_samples def fwd_chem_para(ur, lr, unburnt, sc0, dl0, Le, sigma, flame_samples, A_samples, B_samples, T_samples, C_samples): n_samples = flame_samples[::2].shape[0] v_samples = np.zeros(n_samples) for i in range(n_samples): dl = flame_samples[2*i,0] ReF = flame_samples[2*i+1,0] sc = flame_samples[2*i+1,1] I0_table = np.zeros(flame_samples[2*i:2*i+2,1:].shape) I0_table[0] = flame_samples[2*i,1:] * Le I0_table[1] = flame_samples[2*i+1,1:] / sc rsc = sc0/sc rdl = dl0/dl r = stm.Reactant(unburnt, 'progvar', Le=Le, sigma=sigma, ReF=ReF, I0_table=I0_table) m = stm.Model(r, A = A_samples[i], B = B_samples[i], T = T_samples[i], C = C_samples[i]) v_samples[i] = m.ratio_turbulent_burning_velocity(ur*rsc, lr*rdl) return v_samples def fwd_lr(ur, lr_samples, unburnt,fuel,oxidizer,chemistry): n_samples = len(lr_samples) v_samples = np.zeros(n_samples) mixture = stm.Mixture(unburnt, fuel, oxidizer, chemistry) for i, lr in enumerate(lr_samples): v_samples[i] = mixture.ratio_turbulent_burning_velocity(ur, lr) return v_samples def fwd_params_lr(ur, lr_samples, unburnt,fuel,oxidizer,chemistry, A_samples, B_samples, T_samples, C_samples): n_samples = len(T_samples) * len(lr_samples) v_samples = np.zeros(n_samples) for i in range(n_samples): mixture = stm.Mixture(unburnt, fuel, oxidizer, chemistry, A = A_samples[i], B = B_samples[i], T = T_samples[i], C = C_samples[i]) for j, lr in enumerate(lr_samples): v_samples[i*len(lr_samples)+j] = mixture.ratio_turbulent_burning_velocity(ur, lr) return v_samples

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null

0

1

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null

0

6

099c4007fecc0a847f245186fdfd5e6c4d0f5c1b

72

py

Python

smartsim/ml/torch/__init__.py

billschereriii/SmartSim

7ef4cffeba23fe19b931bdae819f4de99bb112a3

[ "BSD-2-Clause" ]

1

2022-01-19T21:18:59.000Z

smartsim/ml/torch/__init__.py

billschereriii/SmartSim

7ef4cffeba23fe19b931bdae819f4de99bb112a3

[ "BSD-2-Clause" ]

null

smartsim/ml/torch/__init__.py

billschereriii/SmartSim

7ef4cffeba23fe19b931bdae819f4de99bb112a3

[ "BSD-2-Clause" ]

null

from .data import DataLoader, DynamicDataGenerator, StaticDataGenerator

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1

0

1

0

1

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6

61f30d0969f9508ada86158a1a5334938e26b0fe

126

py

Python

week03/code11.py

byeongal/KMUCP

5bafe02c40aae67fc53d9e6cdcb727929368587e

[ "MIT" ]

null

week03/code11.py

byeongal/KMUCP

5bafe02c40aae67fc53d9e6cdcb727929368587e

[ "MIT" ]

null

week03/code11.py

byeongal/KMUCP

5bafe02c40aae67fc53d9e6cdcb727929368587e

[ "MIT" ]

1

2019-11-27T20:28:19.000Z

pokemon = ["피카츄", "라이츄", "파이리", "꼬부기", "버터플", "야도란", "피죤투", "또가스"] print(pokemon[:3]) print(pokemon[3:]) print(pokemon[::2])

21

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0.371429

0.514286

0.542857

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0.111111

126

5

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false

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0

null

1

0

1

0

null

0

1

0

6

1125326393feb86d200d70d7e850b5c4d73e76e4

38

py

Python

bcolz/run_test.py

nikicc/anaconda-recipes

9c611a5854bf41bbc5e7ed9853dc71c0851a62ef

[ "BSD-3-Clause" ]

130

2015-07-28T03:41:21.000Z

2022-03-16T03:07:41.000Z

bcolz/run_test.py

nikicc/anaconda-recipes

9c611a5854bf41bbc5e7ed9853dc71c0851a62ef

[ "BSD-3-Clause" ]

119

2015-08-01T00:54:06.000Z

2021-01-05T13:00:46.000Z

bcolz/run_test.py

nikicc/anaconda-recipes

9c611a5854bf41bbc5e7ed9853dc71c0851a62ef

[ "BSD-3-Clause" ]

72

2015-07-29T02:35:56.000Z

2022-02-26T14:31:15.000Z

import sys import bcolz bcolz.test()

7.6

12

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6

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4

13

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true

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null

0

1

0

1

0

1

0

6

3a2114257679a30200536e9e5ec88b7ddd09c193

165

py

Python

hknweb/candidate/admin/activities/__init__.py

jyxzhang/hknweb

a01ffd8587859bf63c46213be6a0c8b87164a5c2

[ "MIT" ]

null

hknweb/candidate/admin/activities/__init__.py

jyxzhang/hknweb

a01ffd8587859bf63c46213be6a0c8b87164a5c2

[ "MIT" ]

null

hknweb/candidate/admin/activities/__init__.py

jyxzhang/hknweb

a01ffd8587859bf63c46213be6a0c8b87164a5c2

[ "MIT" ]

null

from hknweb.candidate.admin.activities.officer_challenge import OffChallengeAdmin from hknweb.candidate.admin.activities.bitbyteactivity import BitByteActivityAdmin

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3a42090a810b424b01c30256f2f97572a687ab2d

37

py

Python

python/galois/connected_components.py

bigwater/Galois

03738c883301844cfb15a71647744a59184f43c0

[ "BSD-3-Clause" ]

230

2018-06-20T22:18:31.000Z

2022-03-27T13:09:59.000Z

python/galois/connected_components.py

bigwater/Galois

03738c883301844cfb15a71647744a59184f43c0

[ "BSD-3-Clause" ]

307

2018-06-23T12:45:31.000Z

2022-03-26T01:54:38.000Z

python/galois/connected_components.py

bigwater/Galois

03738c883301844cfb15a71647744a59184f43c0

[ "BSD-3-Clause" ]

110

2018-06-19T04:39:16.000Z

2022-03-29T01:55:47.000Z

from ._connected_components import *

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3a50c8f492b9a769883b1fca6f7e74fa306e4365

46

py

Python

testing/lambdas/echo.py

nmittal18/working_open_lambda

a6b280d107a01ad1366a2ca0ccbb6f4dce736f52

[ "Apache-2.0" ]

null

testing/lambdas/echo.py

nmittal18/working_open_lambda

a6b280d107a01ad1366a2ca0ccbb6f4dce736f52

[ "Apache-2.0" ]

null

testing/lambdas/echo.py

nmittal18/working_open_lambda

a6b280d107a01ad1366a2ca0ccbb6f4dce736f52

[ "Apache-2.0" ]

1

2020-01-08T18:00:04.000Z

def handler(db_conn, event): return event

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6

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29

py

Python

json2dir/__init__.py

Kanahiro/json2dir

d7fc1959bc743df051f9ec43a09b8f01a7caad74

[ "MIT" ]

1

2020-05-08T18:57:00.000Z

json2dir/__init__.py

Kanahiro/json2dir

d7fc1959bc743df051f9ec43a09b8f01a7caad74

[ "MIT" ]

null

json2dir/__init__.py

Kanahiro/json2dir

d7fc1959bc743df051f9ec43a09b8f01a7caad74

[ "MIT" ]

null

from .main import dir_list_of

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1

0

6

e917ab68fda25bcdfc856567ecc1cee1f5cb7041

185

py

Python

configs/dla/dla34_bdd100k.py

XDong18/mmclassification

115c39ed4673d9cdd7b5f543482c1038f0c77ab5

[ "Apache-2.0" ]

null

configs/dla/dla34_bdd100k.py

XDong18/mmclassification

115c39ed4673d9cdd7b5f543482c1038f0c77ab5

[ "Apache-2.0" ]

null

configs/dla/dla34_bdd100k.py

XDong18/mmclassification

115c39ed4673d9cdd7b5f543482c1038f0c77ab5

[ "Apache-2.0" ]

null

_base_ = [ '../_base_/datasets/bdd100k.py', '../_base_/models/dla34_bdd100k.py', '../_base_/schedules/bdd100k.py', '../_base_/default_runtime.py' ] find_unused_parameters = True

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6

3aab1155699ccb69273e6a936478287361f131ab

77

py

Python

tests/test_mysorn.py

lneisenman/mysorn

f81ce6e61594f9f4da32a9323c6f0ee85c27d7af

[ "BSD-2-Clause" ]

null

tests/test_mysorn.py

lneisenman/mysorn

f81ce6e61594f9f4da32a9323c6f0ee85c27d7af

[ "BSD-2-Clause" ]

null

tests/test_mysorn.py

lneisenman/mysorn

f81ce6e61594f9f4da32a9323c6f0ee85c27d7af

[ "BSD-2-Clause" ]

null

import mysorn def test_main(): assert mysorn # use your library here

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6

3ab0aff6362156bad89a898004dc9c57b2abd494

121

py

Python

tests/test_peak_distribution.py

mwang87/SMITER

4af90d20042210e6ea403245dc8f73150fc5d844

[ "MIT" ]

6

2021-03-12T04:23:24.000Z

2022-02-03T19:47:04.000Z

tests/test_peak_distribution.py

mwang87/SMITER

4af90d20042210e6ea403245dc8f73150fc5d844

[ "MIT" ]

103

2021-03-12T00:34:20.000Z

2022-03-31T19:53:05.000Z

tests/test_peak_distribution.py

mwang87/SMITER

4af90d20042210e6ea403245dc8f73150fc5d844

[ "MIT" ]

1

2021-03-12T00:38:57.000Z

"""Summary.""" def test_gauss_dist(): """Summary.""" pass def test_gamma_dist(): """Summary.""" pass

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3ac090235e2e1edaa4bcc0e7120132fbd0aeb302

14,751

py

Python

tests/test_services/test_set_687.py

ucloud/ucloud-sdk-python2

90fb43198df73a78d64bbd98675dc7b302856057

[ "Apache-2.0" ]

19

2019-05-15T13:41:58.000Z

2019-11-13T09:09:37.000Z

tests/test_services/test_set_687.py

ucloud/ucloud-sdk-python2

90fb43198df73a78d64bbd98675dc7b302856057

[ "Apache-2.0" ]

9

2019-07-24T08:31:33.000Z

2020-09-22T04:01:46.000Z

tests/test_services/test_set_687.py

ucloud/ucloud-sdk-python2

90fb43198df73a78d64bbd98675dc7b302856057

[ "Apache-2.0" ]

3

2019-06-18T00:22:07.000Z

2020-04-24T02:28:06.000Z

# -*- coding: utf-8 -*- """ Code is generated by ucloud-model, DO NOT EDIT IT. """ import pytest import logging from ucloud.core import exc from ucloud.testing import env, funcs, op, utest logger = logging.getLogger(__name__) scenario = utest.Scenario(687) @pytest.mark.skipif(env.is_ut(), reason=env.get_skip_reason()) def test_set_687(client, variables): scenario.initial(variables) scenario.variables["VPC_name_1"] = "VPC_api_test_1" scenario.variables["remark"] = "remark_api_test" scenario.variables["tag"] = "tag_api_test" scenario.variables["Subnet_name_1_1"] = "subnet_1_1" scenario.variables["subnet_netmask"] = 24 scenario.variables["project_id"] = "org-achi1o" scenario.run(client) @scenario.step( max_retries=0, retry_interval=0, startup_delay=0, fast_fail=False, validators=lambda variables: [ ("str_eq", "RetCode", 0), ("str_eq", "Action", "GetProjectListResponse"), ], action="GetProjectList", ) def get_project_list_00(client, variables): d = {} try: resp = client.uaccount().get_project_list(d) except exc.RetCodeException as e: resp = e.json() variables["project_list"] = utest.value_at_path(resp, "ProjectSet") return resp @scenario.step( max_retries=0, retry_interval=0, startup_delay=2, fast_fail=False, validators=lambda variables: [("str_eq", "RetCode", 0)], action="CreateVPC", ) def create_vpc_01(client, variables): d = { "Tag": variables.get("tag"), "Remark": variables.get("remark"), "Region": variables.get("Region"), "Network": ["172.16.16.0/20"], "Name": variables.get("VPC_name_1"), } try: resp = client.vpc().create_vpc(d) except exc.RetCodeException as e: resp = e.json() variables["VPCId_1"] = utest.value_at_path(resp, "VPCId") return resp @scenario.step( max_retries=0, retry_interval=0, startup_delay=2, fast_fail=False, validators=lambda variables: [("str_eq", "RetCode", 0)], action="CreateSubnet", ) def create_subnet_02(client, variables): d = { "VPCId": variables.get("VPCId_1"), "Tag": variables.get("tag"), "SubnetName": variables.get("Subnet_name_1_1"), "Subnet": "172.16.17.0", "Remark": variables.get("remark"), "Region": variables.get("Region"), "Netmask": variables.get("subnet_netmask"), } try: resp = client.vpc().create_subnet(d) except exc.RetCodeException as e: resp = e.json() variables["SubnetId_1_1"] = utest.value_at_path(resp, "SubnetId") return resp @scenario.step( max_retries=3, retry_interval=1, startup_delay=2, fast_fail=False, validators=lambda variables: [ ("str_eq", "RetCode", 0), ("str_eq", "Action", "UpdateSubnetAttributeResponse"), ], action="UpdateSubnetAttribute", ) def update_subnet_attribute_03(client, variables): d = { "Tag": "qa", "SubnetId": variables.get("SubnetId_1_1"), "Region": variables.get("Region"), } try: resp = client.vpc().update_subnet_attribute(d) except exc.RetCodeException as e: resp = e.json() return resp @scenario.step( max_retries=0, retry_interval=0, startup_delay=2, fast_fail=False, validators=lambda variables: [("str_eq", "RetCode", 0)], action="DescribeSubnet", ) def describe_subnet_04(client, variables): d = { "SubnetId": variables.get("SubnetId_1_1"), "Region": variables.get("Region"), "Offset": 1, "Limit": 1, } try: resp = client.vpc().describe_subnet(d) except exc.RetCodeException as e: resp = e.json() return resp @scenario.step( max_retries=0, retry_interval=0, startup_delay=0, fast_fail=False, action="CreateVPC", ) def create_vpc_05(client, variables): d = { "Region": variables.get("Region"), "Network": ["192.168.16.0/20"], "Name": "vpc_2", } try: resp = client.vpc().create_vpc(d) except exc.RetCodeException as e: resp = e.json() variables["VPCId_2"] = utest.value_at_path(resp, "VPCId") return resp @scenario.step( max_retries=0, retry_interval=0, startup_delay=2, fast_fail=False, validators=lambda variables: [("str_eq", "RetCode", 0)], action="CreateSubnet", ) def create_subnet_06(client, variables): d = { "VPCId": variables.get("VPCId_2"), "SubnetName": "Subnet_2_1", "Subnet": "192.168.17.0", "Region": variables.get("Region"), "Netmask": variables.get("subnet_netmask"), } try: resp = client.vpc().create_subnet(d) except exc.RetCodeException as e: resp = e.json() variables["SubnetId_2_1"] = utest.value_at_path(resp, "SubnetId") return resp @scenario.step( max_retries=3, retry_interval=1, startup_delay=2, fast_fail=False, validators=lambda variables: [ ("str_eq", "RetCode", 0), ("str_eq", "Action", "CreateSubnetResponse"), ], action="CreateSubnet", ) def create_subnet_07(client, variables): d = { "VPCId": variables.get("VPCId_2"), "Tag": "Subnet_2_2", "SubnetName": "Subnet_2_2", "Subnet": "192.168.18.0", "Region": variables.get("Region"), "Netmask": variables.get("subnet_netmask"), } try: resp = client.vpc().create_subnet(d) except exc.RetCodeException as e: resp = e.json() variables["SubnetId_2_2"] = utest.value_at_path(resp, "SubnetId") return resp @scenario.step( max_retries=0, retry_interval=0, startup_delay=2, fast_fail=False, validators=lambda variables: [ ("str_eq", "RetCode", 0), ("str_eq", "DataSet.0.VPCId", variables.get("VPCId_1")), ("str_eq", "DataSet.0.VPCName", variables.get("VPC_name_1")), ("str_eq", "DataSet.0.SubnetId", variables.get("SubnetId_1_1")), ("str_eq", "DataSet.0.SubnetName", variables.get("Subnet_name_1_1")), ("str_eq", "DataSet.0.Tag", "qa"), ("str_eq", "DataSet.0.Remark", variables.get("remark")), ("str_eq", "DataSet.0.SubnetType", 2), ("str_eq", "DataSet.0.Netmask", 24), ], action="DescribeSubnet", ) def describe_subnet_08(client, variables): d = { "VPCId": variables.get("VPCId_1"), "SubnetId": variables.get("SubnetId_1_1"), "Region": variables.get("Region"), } try: resp = client.vpc().describe_subnet(d) except exc.RetCodeException as e: resp = e.json() return resp @scenario.step( max_retries=0, retry_interval=0, startup_delay=0, fast_fail=False, validators=lambda variables: [("str_eq", "RetCode", 0)], action="AllocateVIP", ) def allocate_vip_09(client, variables): d = { "Zone": variables.get("Zone"), "VPCId": variables.get("VPCId_1"), "SubnetId": variables.get("SubnetId_1_1"), "Remark": "vip_tag1", "Region": variables.get("Region"), "Name": "vip_api_auto", } try: resp = client.unet().allocate_vip(d) except exc.RetCodeException as e: resp = e.json() variables["VIPId_1"] = utest.value_at_path(resp, "VIPSet.0.VIPId") return resp @scenario.step( max_retries=0, retry_interval=0, startup_delay=2, fast_fail=False, validators=lambda variables: [ ("str_eq", "RetCode", 0), ("str_eq", "VIPSet.0.VPCId", variables.get("VPCId_1")), ("str_eq", "VIPSet.0.VIPId", variables.get("VIPId_1")), ("str_eq", "VIPSet.0.SubnetId", variables.get("SubnetId_1_1")), ], action="DescribeVIP", ) def describe_vip_10(client, variables): d = { "Zone": variables.get("Zone"), "VPCId": variables.get("VPCId_1"), "SubnetId": variables.get("SubnetId_1_1"), "Region": variables.get("Region"), } try: resp = client.unet().describe_vip(d) except exc.RetCodeException as e: resp = e.json() variables["VIP_ip_1"] = utest.value_at_path(resp, "DataSet.0") return resp @scenario.step( max_retries=0, retry_interval=0, startup_delay=0, fast_fail=False, validators=lambda variables: [ ("str_eq", "RetCode", 0), ("str_eq", "TotalCount", 1), ("str_eq", "DataSet.0.ResourceId", variables.get("VIPId_1")), ("str_eq", "DataSet.0.IP", variables.get("VIP_ip_1")), ], action="DescribeSubnetResource", ) def describe_subnet_resource_11(client, variables): d = { "SubnetId": variables.get("SubnetId_1_1"), "Region": variables.get("Region"), "Offset": 0, "Limit": 20, } try: resp = client.vpc().describe_subnet_resource(d) except exc.RetCodeException as e: resp = e.json() return resp @scenario.step( max_retries=3, retry_interval=1, startup_delay=0, fast_fail=False, validators=lambda variables: [("str_eq", "RetCode", 0)], action="ReleaseVIP", ) def release_vip_12(client, variables): d = { "Zone": variables.get("Zone"), "VIPId": variables.get("VIPId_1"), "Region": variables.get("Region"), } try: resp = client.unet().release_vip(d) except exc.RetCodeException as e: resp = e.json() return resp @scenario.step( max_retries=3, retry_interval=1, startup_delay=1, fast_fail=False, validators=lambda variables: [("str_eq", "RetCode", 0)], action="DeleteSubnet", ) def delete_subnet_13(client, variables): d = { "SubnetId": variables.get("SubnetId_1_1"), "Region": variables.get("Region"), } try: resp = client.vpc().delete_subnet(d) except exc.RetCodeException as e: resp = e.json() return resp @scenario.step( max_retries=3, retry_interval=1, startup_delay=1, fast_fail=False, validators=lambda variables: [("str_eq", "RetCode", 0)], action="DeleteSubnet", ) def delete_subnet_14(client, variables): d = { "SubnetId": variables.get("SubnetId_2_1"), "Region": variables.get("Region"), } try: resp = client.vpc().delete_subnet(d) except exc.RetCodeException as e: resp = e.json() return resp @scenario.step( max_retries=3, retry_interval=1, startup_delay=1, fast_fail=False, validators=lambda variables: [("str_eq", "RetCode", 0)], action="DeleteSubnet", ) def delete_subnet_15(client, variables): d = { "SubnetId": variables.get("SubnetId_2_2"), "Region": variables.get("Region"), } try: resp = client.vpc().delete_subnet(d) except exc.RetCodeException as e: resp = e.json() return resp @scenario.step( max_retries=3, retry_interval=1, startup_delay=0, fast_fail=False, validators=lambda variables: [ ("str_eq", "RetCode", 0), ("str_eq", "Action", "AddVPCNetworkResponse"), ], action="AddVPCNetwork", ) def add_vpc_network_16(client, variables): d = { "VPCId": variables.get("VPCId_1"), "Region": variables.get("Region"), "Network": ["10.100.96.0/20"], } try: resp = client.vpc().add_vpc_network(d) except exc.RetCodeException as e: resp = e.json() return resp @scenario.step( max_retries=3, retry_interval=1, startup_delay=2, fast_fail=False, validators=lambda variables: [ ("str_eq", "RetCode", 0), ("str_eq", "Action", "DescribeVPCResponse"), ], action="DescribeVPC", ) def describe_vpc_17(client, variables): d = { "VPCIds": [variables.get("VPCId_1")], "Region": variables.get("Region"), } try: resp = client.vpc().describe_vpc(d) except exc.RetCodeException as e: resp = e.json() return resp @scenario.step( max_retries=0, retry_interval=0, startup_delay=0, fast_fail=False, validators=lambda variables: [("str_eq", "RetCode", 0)], action="CreateVPCIntercom", ) def create_vpc_intercom_18(client, variables): d = { "VPCId": variables.get("VPCId_1"), "Region": variables.get("Region"), "DstVPCId": variables.get("VPCId_2"), "DstRegion": variables.get("Region"), "DstProjectId": funcs.search_value( variables.get("project_list"), "IsDefault", True, "ProjectId" ), } try: resp = client.vpc().create_vpc_intercom(d) except exc.RetCodeException as e: resp = e.json() return resp @scenario.step( max_retries=0, retry_interval=0, startup_delay=2, fast_fail=False, validators=lambda variables: [ ("str_eq", "RetCode", 0), ("str_eq", "DataSet.0.VPCId", variables.get("VPCId_2")), ], action="DescribeVPCIntercom", ) def describe_vpc_intercom_19(client, variables): d = {"VPCId": variables.get("VPCId_1"), "Region": variables.get("Region")} try: resp = client.vpc().describe_vpc_intercom(d) except exc.RetCodeException as e: resp = e.json() return resp @scenario.step( max_retries=0, retry_interval=0, startup_delay=2, fast_fail=False, validators=lambda variables: [("str_eq", "RetCode", 0)], action="DeleteVPCIntercom", ) def delete_vpc_intercom_20(client, variables): d = { "VPCId": variables.get("VPCId_1"), "Region": variables.get("Region"), "DstVPCId": variables.get("VPCId_2"), "DstRegion": variables.get("Region"), "DstProjectId": funcs.search_value( variables.get("project_list"), "IsDefault", True, "ProjectId" ), } try: resp = client.vpc().delete_vpc_intercom(d) except exc.RetCodeException as e: resp = e.json() return resp @scenario.step( max_retries=3, retry_interval=1, startup_delay=2, fast_fail=False, validators=lambda variables: [("str_eq", "RetCode", 0)], action="DeleteVPC", ) def delete_vpc_21(client, variables): d = {"VPCId": variables.get("VPCId_1"), "Region": variables.get("Region")} try: resp = client.vpc().delete_vpc(d) except exc.RetCodeException as e: resp = e.json() return resp @scenario.step( max_retries=3, retry_interval=1, startup_delay=2, fast_fail=False, action="DeleteVPC", ) def delete_vpc_22(client, variables): d = {"VPCId": variables.get("VPCId_2"), "Region": variables.get("Region")} try: resp = client.vpc().delete_vpc(d) except exc.RetCodeException as e: resp = e.json() return resp

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0

1

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1

0

1

0

null

0

6

c9457ae8e0f0b711eb26cd64355bddaf8ed2200e

44

py

Python

SecondLife/SimpleBot/Program.py

uoy-research/DED

b03a03ac59a0c3243377ce261d1440cc65731a9e

[ "MIT" ]

null

SecondLife/SimpleBot/Program.py

uoy-research/DED

b03a03ac59a0c3243377ce261d1440cc65731a9e

[ "MIT" ]

null

SecondLife/SimpleBot/Program.py

uoy-research/DED

b03a03ac59a0c3243377ce261d1440cc65731a9e

[ "MIT" ]

null

import bot print 'Starting Iron Python'

6.285714

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0.227273

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6

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7.333333

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null

0

1

0

1

0

1

0

6

c9670e09673239fcdffea0b6bf2f1eb4c63933b4

32

py

Python

src/audio_room/envs/__init__.py

pseeth/otoworld

636ca717c6e571b465ddcd836fa430ccdc53debf

[ "MIT" ]

17

2020-06-16T06:37:03.000Z

2020-10-15T00:25:05.000Z

src/audio_room/envs/__init__.py

pseeth/otoworld

636ca717c6e571b465ddcd836fa430ccdc53debf

[ "MIT" ]

5

2020-10-18T23:50:49.000Z

2021-04-14T02:36:08.000Z

src/audio_room/envs/__init__.py

pseeth/otoworld

636ca717c6e571b465ddcd836fa430ccdc53debf

[ "MIT" ]

2

2020-07-17T11:51:30.000Z

2020-09-21T14:50:56.000Z

from .audio_env import AudioEnv

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a348c1cf107c7e9e719f55a7cef7fbf40fc96452

1,437

py

Python

utils/parameters.py

wcode-wzx/yolov5-for-price-tag-recognition

a488de2b2637753d570343093e2c06f6782927a5

[ "MIT" ]

4

2021-03-24T09:28:26.000Z

2021-04-09T10:15:38.000Z

utils/parameters.py

wcode-wzx/yolov5-for-price-tag-recognition

a488de2b2637753d570343093e2c06f6782927a5

[ "MIT" ]

null

utils/parameters.py

wcode-wzx/yolov5-for-price-tag-recognition

a488de2b2637753d570343093e2c06f6782927a5

[ "MIT" ]

null

import urllib import urllib.request class a_path(): images_path = 'runs/detect/exp/images/' cut_path = '' labels_path = 'runs/detect/exp/labels/' price_path = 'runs/detect/exp/price/' class opt(object): def __init__(self): self.source = 'data/images' self.agnostic_nms = False self.augment = False self.classes = None self.conf_thres=0.25 self.device='0' self.exist_ok=False self.img_size=640 self.iou_thres=0.45 self.name='exp' self.project='runs/detect' self.save_conf=False self.save_txt=True self.view_img=False self.weights='weights/dingwei.pt' def list_all_member(self): for name,value in vars(self).items(): print('%s=%s'%(name,value)) class opt2(object): def __init__(self): self.source = 'runs/detect/exp/images/' self.agnostic_nms = False self.augment = False self.classes = None self.conf_thres=0.25 self.device='0' self.exist_ok=False self.img_size=640 self.iou_thres=0.45 self.name='exp' self.project='runs/detect' self.save_conf=False self.save_txt=True self.view_img=False self.weights='weights/shibie.pt' def list_all_member(self): for name,value in vars(self).items(): print('%s=%s'%(name,value))

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a355fc33833c9a16a716aff867078f035d30a256

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py

Python

crawler/__init__.py

subhendusethi/nytimes-article-crawler

8e74831f76452e3ae2c7155b2361536cc31be3e8

[ "MIT" ]

8

2017-05-08T03:58:07.000Z

2021-04-15T08:42:21.000Z

crawler/__init__.py

subhendusethi/nytimes-article-crawler

8e74831f76452e3ae2c7155b2361536cc31be3e8

[ "MIT" ]

1

2020-01-20T07:55:51.000Z

2020-03-11T12:18:12.000Z

crawler/__init__.py

subhendusethi/nytimes-article-crawler

8e74831f76452e3ae2c7155b2361536cc31be3e8

[ "MIT" ]

5

2018-09-28T13:03:48.000Z

2022-03-02T05:51:02.000Z

from .nytimescrawler import Crawler

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a3893d60597d53e36cc5611617fa5a0317d039f5

96

py

Python

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

GiulianaPola/select_repeats

17a0d053d4f874e42cf654dd142168c2ec8fbd11

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

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

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/53/d2/fd/7a91d37c08f7671c2cc34944c0a1684c1b929cc36e53f4c38d27ffbd97

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1

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null

1

0

1

0

6

a3a020e08283b8ffc790d728c8f8e2d1f5c64e2c

1,290

py

Python

missingnumber_test.py

mutakubwa/python_practice

5fef46659ee162c3accfee44e2675eeb0e9c6e95

[ "Apache-2.0" ]

null

missingnumber_test.py

mutakubwa/python_practice

5fef46659ee162c3accfee44e2675eeb0e9c6e95

[ "Apache-2.0" ]

null

missingnumber_test.py

mutakubwa/python_practice

5fef46659ee162c3accfee44e2675eeb0e9c6e95

[ "Apache-2.0" ]

null

import unittest from missingnumber import missing_number_1, missing_number_2 class MissingNumber_Test(unittest.TestCase): def test_missingnumber1(self): testcase1 = 5 testcase2 = [2,2,3,4,5] expected = 1 self.assertEqual(missing_number_1(testcase1, testcase2), expected) def test_missingnumber2(self): testcase1 = 5 testcase2 = [2,2,5,4,1] expected = 3 self.assertEqual(missing_number_1(testcase1, testcase2), expected) def test_missingnumber3(self): testcase1 = 5 testcase2 = [5,5,3,2,4] expected = 1 self.assertEqual(missing_number_1(testcase1, testcase2), expected) def test_missingnumber4(self): testcase1 = 5 testcase2 = [2,2,3,4,5] expected = 1 self.assertEqual(missing_number_2(testcase1, testcase2), expected) def test_missingnumber5(self): testcase1 = 5 testcase2 = [2,2,5,4,1] expected = 3 self.assertEqual(missing_number_2(testcase1, testcase2), expected) def test_missingnumber6(self): testcase1 = 5 testcase2 = [5,5,3,2,4] expected = 1 self.assertEqual(missing_number_2(testcase1, testcase2), expected) if __name__ == '__main__': unittest.main()

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42e38efbda5c20c695510ee3ddb0d7a82a4e6b8e

144

py

Python

agent/controller/__init__.py

intelligent-control-lab/Composable_Agent_Toolbox

39d71cdc0475ae6901cb30b63d181737bea35889

[ "MIT" ]

4

2020-10-20T14:30:09.000Z

2022-02-19T23:46:04.000Z

agent/controller/__init__.py

intelligent-control-lab/Composable_Agent_Toolbox

39d71cdc0475ae6901cb30b63d181737bea35889

[ "MIT" ]

null

agent/controller/__init__.py

intelligent-control-lab/Composable_Agent_Toolbox

39d71cdc0475ae6901cb30b63d181737bea35889

[ "MIT" ]

1

2022-03-12T10:46:38.000Z

from .controller import Controller from .naive_controller import NaiveController, NaiveJointController from .cbf_controller import CBFController

48

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6

283a67c915a5ff78e753fafe73693a161b6ebbdc

112

py

Python

design_bench/oracles/sklearn/__init__.py

brandontrabucco/design_bench

824516ec59396aded3ca55ec7c1c313626ecaceb

[ "MIT" ]

27

2020-06-30T00:57:12.000Z

2022-03-25T16:24:11.000Z

design_bench/oracles/sklearn/__init__.py

brandontrabucco/design_bench

824516ec59396aded3ca55ec7c1c313626ecaceb

[ "MIT" ]

7

2021-02-16T06:25:02.000Z

2022-03-31T17:21:17.000Z

design_bench/oracles/sklearn/__init__.py

brandontrabucco/design_bench

824516ec59396aded3ca55ec7c1c313626ecaceb

[ "MIT" ]

5

2021-07-19T12:16:32.000Z

2022-03-01T16:56:16.000Z

from .random_forest_oracle import RandomForestOracle from .gaussian_process_oracle import GaussianProcessOracle

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284394bdfa22179834416943d0f83911c4f3a37b

28

py

Python

pigar/tests/imports_example/subfoo/foo.py

yasirroni/pigar

823a3c2478361d53dba408bea75e1766d253f3c0

[ "BSD-3-Clause" ]

959

2016-08-15T10:02:24.000Z

2022-03-31T12:35:39.000Z

pigar/tests/imports_example/subfoo/foo.py

yasirroni/pigar

823a3c2478361d53dba408bea75e1766d253f3c0

[ "BSD-3-Clause" ]

67

2016-10-02T20:48:26.000Z

2022-01-08T16:29:58.000Z

pigar/tests/imports_example/subfoo/foo.py

yasirroni/pigar

823a3c2478361d53dba408bea75e1766d253f3c0

[ "BSD-3-Clause" ]

64

2016-11-30T11:21:36.000Z

2022-02-18T19:33:37.000Z

def foo(): print("FOO")

9.333333

16

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