xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

ecdc5db1bf4e292ca2494bbac36ea901279643fc

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py

Python

src/apps/reservation/models/__init__.py

lizaveta-stasevich/booking

acebd0d9d3035e802cdf6e719a142fe5f74ec2c1

[ "Apache-2.0" ]

null

src/apps/reservation/models/__init__.py

lizaveta-stasevich/booking

acebd0d9d3035e802cdf6e719a142fe5f74ec2c1

[ "Apache-2.0" ]

6

2020-06-06T00:11:43.000Z

2022-02-10T09:33:51.000Z

src/apps/reservation/models/__init__.py

lizaveta-stasevich/booking

acebd0d9d3035e802cdf6e719a142fe5f74ec2c1

[ "Apache-2.0" ]

null

from .city import City from .train import Train from .comfort import Comfort

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py

Python

tests/conftest.py

charmed-kubernetes/layer-docker

f47ea965f598f25aaf1d02e457b54660afa8303a

[ "Apache-2.0" ]

11

2015-09-02T16:32:33.000Z

2020-08-19T02:10:35.000Z

tests/conftest.py

charmed-kubernetes/layer-docker

f47ea965f598f25aaf1d02e457b54660afa8303a

[ "Apache-2.0" ]

130

2015-09-14T17:28:45.000Z

2020-03-02T15:47:40.000Z

tests/conftest.py

charmed-kubernetes/layer-docker

f47ea965f598f25aaf1d02e457b54660afa8303a

[ "Apache-2.0" ]

22

2015-09-26T23:34:53.000Z

2021-03-03T06:30:48.000Z

import sys from unittest.mock import MagicMock # mock dependencies which we don't care about covering in our tests sys.modules['charms.docker'] = MagicMock() sys.modules['charms.reactive'] = MagicMock()

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py

Python

objdict/__init__.py

StepaTa/ModularBotForUser

c82691aa970ae936336de1981708abb40f0c5ac9

[ "MIT" ]

null

objdict/__init__.py

StepaTa/ModularBotForUser

c82691aa970ae936336de1981708abb40f0c5ac9

[ "MIT" ]

null

objdict/__init__.py

StepaTa/ModularBotForUser

c82691aa970ae936336de1981708abb40f0c5ac9

[ "MIT" ]

null

from objdict.objDict import dumps, loads, from_json, to_json from objdict.objDict import ClassRegistry, JsonEncoder, ObjDict from objdict.objDict import JsonDecodeError from objdict.dualUrlJson import combiParse, unParse from objdict import objDict as objDicter from objdict.pytestcode import pytester from objdict.struct import Struct, DictStruct from objdict.inputs import inputs from objdict.enums import OEnum #extra line

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py

Python

generi/commands/__init__.py

nick-lehmann/Generi

5f13860eb91973670fe378479731f95feab1e380

[ "MIT" ]

1

2019-12-21T22:04:07.000Z

generi/commands/__init__.py

nick-lehmann/Generi

5f13860eb91973670fe378479731f95feab1e380

[ "MIT" ]

null

generi/commands/__init__.py

nick-lehmann/Generi

5f13860eb91973670fe378479731f95feab1e380

[ "MIT" ]

null

from .build import build from .push import push from .write import write

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1721fdf33b91dc8187371ebef485db814baa3ad3

76

py

Python

tk02.py

visalpop/tkinter_sample

15474250431727e2b24b6f6aebc654c36ccf8d87

[ "MIT" ]

null

tk02.py

visalpop/tkinter_sample

15474250431727e2b24b6f6aebc654c36ccf8d87

[ "MIT" ]

null

tk02.py

visalpop/tkinter_sample

15474250431727e2b24b6f6aebc654c36ccf8d87

[ "MIT" ]

null

# -*- coding: utf-8 -*- # tk02.pyw import tkinter as tk print(tk.TkVersion)

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py

Python

tortilla/api.py

camomile-project/tortilla

e749a7be49cc272bd1149a3acfe0d352f87f372b

[ "MIT" ]

261

2015-01-02T02:18:44.000Z

2018-04-02T07:33:53.000Z

tortilla/api.py

camomile-project/tortilla

e749a7be49cc272bd1149a3acfe0d352f87f372b

[ "MIT" ]

31

2015-01-26T15:25:32.000Z

2018-03-30T15:13:01.000Z

tortilla/api.py

camomile-project/tortilla

e749a7be49cc272bd1149a3acfe0d352f87f372b

[ "MIT" ]

29

2015-01-05T19:21:43.000Z

2017-11-07T14:52:42.000Z

# -*- coding: utf-8 -*- from . import wrappers def wrap(url, **options): """Syntax sugar for creating service wrappers.""" return wrappers.Wrap(part=url, **options)

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py

Python

trackdecoder/__init__.py

PredaaA/aikaterna-cogs

d34732def7bf8d0be0adc2fa7bd63e57596ff88f

[ "Apache-2.0" ]

null

trackdecoder/__init__.py

PredaaA/aikaterna-cogs

d34732def7bf8d0be0adc2fa7bd63e57596ff88f

[ "Apache-2.0" ]

null

trackdecoder/__init__.py

PredaaA/aikaterna-cogs

d34732def7bf8d0be0adc2fa7bd63e57596ff88f

[ "Apache-2.0" ]

null

from .trackdecoder import TrackDecoder def setup(bot): bot.add_cog(TrackDecoder(bot))

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175ac9439e03546b2bbebb60f278dde2447e2fbc

227

py

Python

src/plotman/_tests/archive_test.py

pieterhelsen/plotman

1b7dfba139ed03d3c32a2f04f6011e03bfb1f442

[ "Apache-2.0" ]

1,016

2020-11-24T10:27:08.000Z

2022-03-20T23:46:45.000Z

src/plotman/_tests/archive_test.py

pieterhelsen/plotman

1b7dfba139ed03d3c32a2f04f6011e03bfb1f442

[ "Apache-2.0" ]

436

2021-01-23T23:28:54.000Z

2022-03-30T00:33:29.000Z

src/plotman/_tests/archive_test.py

pieterhelsen/plotman

1b7dfba139ed03d3c32a2f04f6011e03bfb1f442

[ "Apache-2.0" ]

332

2021-02-02T03:42:25.000Z

2022-03-31T09:03:38.000Z

from plotman import archive, job def test_compute_priority() -> None: assert archive.compute_priority( job.Phase(major=3, minor=1), 1000, 10 ) > archive.compute_priority(job.Phase(major=3, minor=6), 1000, 10)

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1772d990f11381101ceb9d8d1bc4176f4d25c788

45

py

Python

rdf_io/views/__init__.py

GlauberMC/django-rdf-io

5deaec40264407574351dd20f758b17b89b495a9

[ "CC0-1.0" ]

null

rdf_io/views/__init__.py

GlauberMC/django-rdf-io

5deaec40264407574351dd20f758b17b89b495a9

[ "CC0-1.0" ]

null

rdf_io/views/__init__.py

GlauberMC/django-rdf-io

5deaec40264407574351dd20f758b17b89b495a9

[ "CC0-1.0" ]

null

from serialize import * from manage import *

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5

bd77b2a831bc46979ce050cab6205e2a0557cecb

161

py

Python

Day 2/BMI_Calculator.py

hamzaoda/100-Days-of-Code---The-Complete-Python-Pro-Bootcamp-for-2021

5340007d8405df2e29643b47d3ff9fa4f7af9e10

[ "Unlicense" ]

null

Day 2/BMI_Calculator.py

hamzaoda/100-Days-of-Code---The-Complete-Python-Pro-Bootcamp-for-2021

5340007d8405df2e29643b47d3ff9fa4f7af9e10

[ "Unlicense" ]

null

Day 2/BMI_Calculator.py

hamzaoda/100-Days-of-Code---The-Complete-Python-Pro-Bootcamp-for-2021

5340007d8405df2e29643b47d3ff9fa4f7af9e10

[ "Unlicense" ]

null

height= float(input("Please Enter your height : ")) weight= int(input("Please Enter your weight : ")) BMI=weight/height ** 2 print("your BMI is : " + str(BMI))

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bd8200c45ae0a7535198fbd7fa17cbf7ec1616aa

43

py

Python

src/models/rotate/__init__.py

wang-yuhao/Practical-Big-Data-Science-ADL-AI

0bf63bf210f506e287f8492e716bb3394137d74b

[ "MIT" ]

null

src/models/rotate/__init__.py

wang-yuhao/Practical-Big-Data-Science-ADL-AI

0bf63bf210f506e287f8492e716bb3394137d74b

[ "MIT" ]

null

src/models/rotate/__init__.py

wang-yuhao/Practical-Big-Data-Science-ADL-AI

0bf63bf210f506e287f8492e716bb3394137d74b

[ "MIT" ]

1

2021-12-24T00:26:26.000Z

from .rotate_evaluation_model import RotatE

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bdc8e14eb3536e316c7d637d8c43082a1cd80ba8

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py

Python

hypnospy/__init__.py

HypnosPy/HypnosPy

28b17d07ee78f7714bbbbd66f6253764addf9d94

[ "MIT" ]

4

2022-01-02T18:40:57.000Z

2022-02-17T12:59:57.000Z

hypnospy/__init__.py

ippozuelo/HypnosPy

28b17d07ee78f7714bbbbd66f6253764addf9d94

[ "MIT" ]

2

2020-11-11T07:13:56.000Z

2020-11-11T07:38:54.000Z

hypnospy/__init__.py

ippozuelo/HypnosPy

28b17d07ee78f7714bbbbd66f6253764addf9d94

[ "MIT" ]

2

2020-11-24T22:46:31.000Z

2021-02-05T16:43:12.000Z

#__all__ = ["Wearable", "data"] from .diary import Diary from .wearable import Wearable from .experiment import Experiment from .demographics import Demographics from .cgm import CGM

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bdee20a5ee98079d4824390ed4132e8fbf4a5462

97

py

Python

thg/toplevel.py

thgdevelopers/thg_lib

d9fc28096c8e05267a22f6899890ae5f429b5b19

[ "BSD-3-Clause" ]

null

thg/toplevel.py

thgdevelopers/thg_lib

d9fc28096c8e05267a22f6899890ae5f429b5b19

[ "BSD-3-Clause" ]

4

2020-04-22T02:24:27.000Z

2020-04-22T02:28:57.000Z

thg/toplevel.py

thgdevelopers/thg_lib

d9fc28096c8e05267a22f6899890ae5f429b5b19

[ "BSD-3-Clause" ]

null

from thglibs import * try: import cPickle as pickle except ImportError: import pickle

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bdf68382c03ed2c427ad74794bc6451649a2128b

179

py

Python

utils/__init__.py

feelool007/Lotto1224

fd360fad7260fd9022c8f71d6f48a79f59266b72

[ "MIT" ]

null

utils/__init__.py

feelool007/Lotto1224

fd360fad7260fd9022c8f71d6f48a79f59266b72

[ "MIT" ]

null

utils/__init__.py

feelool007/Lotto1224

fd360fad7260fd9022c8f71d6f48a79f59266b72

[ "MIT" ]

null

from .analysis import analysis from .oddAndEven import oddAndEven from .smallAndLarge import smallAndLarge from .parseInt import parseInt from .winNumCounter import winNumCounter

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da0b3fb05b93ec711bbba7ea7a9d3b300a60a1ae

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py

Python

tests/test_model_evaluators.py

superphy/acheron

cd9838f000085409e306a5f66b04276a1e4eb5f5

[ "Apache-2.0" ]

1

2022-01-07T17:23:14.000Z

tests/test_model_evaluators.py

superphy/acheron

cd9838f000085409e306a5f66b04276a1e4eb5f5

[ "Apache-2.0" ]

null

tests/test_model_evaluators.py

superphy/acheron

cd9838f000085409e306a5f66b04276a1e4eb5f5

[ "Apache-2.0" ]

1

2021-06-18T17:36:08.000Z

from acheron.helpers import model_evaluators def test_to_float(): mics = [32, 12, '>32', '>32.0000', '>16'] floats = [float(i) for i in [32, 12, 32, 32, 16]] for i in range(len(mics)): assert(model_evaluators.to_float(mics[i]) == floats[i]) def test_is_equiv(): mics = [32, 16, '>32', '>32.0000', '>16'] floats = [model_evaluators.to_float(i) for i in [32, 12, 32, 32, 16]] for i in range(len(mics)): mic_float = model_evaluators.to_float(mics[i]) if i == 1: assert(not model_evaluators.is_equiv(mic_float,floats[i])) else: assert(model_evaluators.is_equiv(mic_float,floats[i])) assert model_evaluators.is_equiv(0.12, 0.125) def test_to_resistance_type(): bps = {} bps['AMP'] = [8,[16],32] bps['AZM'] = [16,[],32] bps['CIP'] = [0.06,[0.12,0.25,0.5],1] assert model_evaluators.to_resistance_type(1, bps['AMP']) == 'S' assert model_evaluators.to_resistance_type(8, bps['AMP']) == 'S' assert model_evaluators.to_resistance_type(16, bps['AMP']) == 'I' assert model_evaluators.to_resistance_type(32, bps['AMP']) == 'R' assert model_evaluators.to_resistance_type(64, bps['AMP']) == 'R' assert model_evaluators.to_resistance_type(8, bps['AZM']) == 'S' assert model_evaluators.to_resistance_type(16, bps['AZM']) == 'S' assert model_evaluators.to_resistance_type(32, bps['AZM']) == 'R' assert model_evaluators.to_resistance_type(0.06, bps['CIP']) == 'S' assert model_evaluators.to_resistance_type(0.12, bps['CIP']) == 'I' assert model_evaluators.to_resistance_type(0.25, bps['CIP']) == 'I' assert model_evaluators.to_resistance_type(1, bps['CIP']) == 'R' def test_find_error_type(): #find_error_type(predicted, actual, abx) assert model_evaluators.find_error_type(1, '32', "AMP") == "Very Major Error" assert model_evaluators.find_error_type('1', 32, "AMP") == "Very Major Error" assert model_evaluators.find_error_type(16, 32, "AMP") == "Non Major Error" assert model_evaluators.find_error_type(64, 1, "AMP") == "Major Error" assert model_evaluators.find_error_type(0.06, 1, "CIP") == "Very Major Error" assert model_evaluators.find_error_type(1, 0.06, "CIP") == "Major Error" assert model_evaluators.find_error_type(0.12, '0.125', "CIP") =="Correct"

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py

Python

teragested/cli.py

demosdemon/furry-octo-fiesta

e72d95cb73a02a25dfb34ca327aea7cc9eb1391f

[ "MIT" ]

null

teragested/cli.py

demosdemon/furry-octo-fiesta

e72d95cb73a02a25dfb34ca327aea7cc9eb1391f

[ "MIT" ]

1

2021-06-01T23:04:04.000Z

teragested/cli.py

demosdemon/teragested

e72d95cb73a02a25dfb34ca327aea7cc9eb1391f

[ "MIT" ]

null

# -*- coding: utf-8 -*- """The teragested command line interface.""" import click @click.command() def main(): """Interact with the teragested parser and a shell script.""" pass

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py

Python

ds18b20S/__init__.py

danielbair/ds18b20S

4531b70ed5788e3f551aee2deb0f1effad2b9d61

[ "MIT" ]

null

ds18b20S/__init__.py

danielbair/ds18b20S

4531b70ed5788e3f551aee2deb0f1effad2b9d61

[ "MIT" ]

null

ds18b20S/__init__.py

danielbair/ds18b20S

4531b70ed5788e3f551aee2deb0f1effad2b9d61

[ "MIT" ]

null

#Code by Sahak Sahakyan #Library for ds18b20 temperature sensor #Contact` #Email: sahak.sahakyan2017@gmail.com import sys import glob import os class DsbS(): def __init__(self, initial=False): if initial: os.system('modprobe w1-gpio') os.system('modprobe w1-therm') def getSensorIds(slef): return [i.split("/")[5] for i in glob.glob("/sys/bus/w1/devices/28-0*/w1_slave")] def getTemps(self, Ttype="C"): temps = list() if len([i.split("/")[5] for i in glob.glob("/sys/bus/w1/devices/28-0*/w1_slave")]) < 1: print("NO DEVICES FOUND"); return "Null" for sensor in glob.glob("/sys/bus/w1/devices/28-0*/w1_slave"): id = sensor.split("/")[5] try: f = open(sensor, "r") data = f.read() f.close() if "YES" in data: (discard, sep, reading) = data.partition(' t=') if Ttype.lower() == "c": t = float(reading) / 1000.0 temps.append(t) elif Ttype.lower() == "f": t = (float(reading) / 1000.0 * 9 / 5) + 32 temps.append(t) elif Ttype.lower() == "k": t = (float(reading) / 1000.0) + 273.15 temps.append(t) else: t = float(reading) / 1000.0 temps.append(t) print("WARNING: UNKOWN TEMPERATURE TYPE") else: print("EROR WHILE READING TEMPERATURE") except: print("EROR WHILE READING TEMPERATURE1") return temps def getIdTemp(self, Ttype="c"): temps = dict() if len([i.split("/")[5] for i in glob.glob("/sys/bus/w1/devices/28-0*/w1_slave")]) < 1: print("NO DEVICES FOUND"); return "Null" for sensor in glob.glob("/sys/bus/w1/devices/28-0*/w1_slave"): id = sensor.split("/")[5] try: f = open(sensor, "r") data = f.read() f.close() if "YES" in data: (discard, sep, reading) = data.partition(' t=') if Ttype.lower() == "c": t = float(reading) / 1000.0 temps[sensor] = t elif Ttype.lower() == "f": t = (float(reading) / 1000.0 * 9 / 5) + 32 temps[sensor] = t elif Ttype.lower() == "k": t = (float(reading) / 1000.0) + 273.15 temps[sensor] = t else: t = float(reading) / 1000.0 temps[sensor] = t print("WARNING: UNKOWN TEMPERATURE TYPE") else: print("EROR WHILE READING TEMPERATURE") except: print("EROR WHILE READING TEMPERATURE") return temps

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da8ed0e7e7abc4fc5c1f608294795bed2be4d831

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py

Python

src/__init__.py

uliebal/batchslopes

a68c4e85a836cc1f378f8ae91f0d6fa38d66b4e3

[ "MIT" ]

null

src/__init__.py

uliebal/batchslopes

a68c4e85a836cc1f378f8ae91f0d6fa38d66b4e3

[ "MIT" ]

null

src/__init__.py

uliebal/batchslopes

a68c4e85a836cc1f378f8ae91f0d6fa38d66b4e3

[ "MIT" ]

null

from .batchslopes import *

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da9008c4e7fa1addf1f9f1db21e7fe01a2ba36f2

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py

Python

src/extract/compute_predictions.py

atseng95/fourier_attribution_priors

53f668e315621e4f64f9e11a403f2ea80529eb29

[ "MIT" ]

8

2020-11-19T02:58:55.000Z

2021-09-10T14:11:29.000Z

src/extract/compute_predictions.py

amtseng/fourier_attribution_priors

53f668e315621e4f64f9e11a403f2ea80529eb29

[ "MIT" ]

null

src/extract/compute_predictions.py

amtseng/fourier_attribution_priors

53f668e315621e4f64f9e11a403f2ea80529eb29

[ "MIT" ]

1

2020-09-26T00:49:25.000Z

import model.util as model_util import model.profile_models as profile_models import model.binary_models as binary_models import extract.data_loading as data_loading import numpy as np import torch import tqdm def _get_profile_model_predictions_batch( model, coords, num_tasks, input_func, controls=None, fourier_att_prior_freq_limit=200, fourier_att_prior_freq_limit_softness=0.2, att_prior_grad_smooth_sigma=3, return_losses=False, return_gradients=False ): """ Fetches the necessary data from the given coordinates or bin indices and runs it through a profile or binary model. This will perform computation in a single batch. Arguments: `model`: a trained `ProfilePredictorWithMatchedControls`, `ProfilePredictorWithSharedControls`, or `ProfilePredictorWithoutControls` `coords`: a B x 3 array of coordinates to compute outputs for `num_tasks`: number of tasks for the model `input_func`: a function that takes in `coords` and returns the B x I x 4 array of one-hot sequences and the B x (T or T + 1 or 2T) x O x S array of profiles (perhaps with controls) `controls`: the type of control profiles (if any) used in model; can be "matched" (each task has a matched control), "shared" (all tasks share a control), or None (no controls); must match the model class `fourier_att_prior_freq_limit`: limit for frequencies in Fourier prior loss `fourier_att_prior_freq_limit_softness`: degree of softness for limit `att_prior_grad_smooth_sigma`: width of smoothing kernel for gradients `return_losses`: if True, compute/return the loss values `return_gradients`: if True, compute/return the input gradients and sequences Returns a dictionary of the following structure: true_profs: true profile raw counts (B x T x O x S) log_pred_profs: predicted profile log probabilities (B x T x O x S) true_counts: true total counts (B x T x S) log_pred_counts: predicted log counts (B x T x S) prof_losses: profile NLL losses (B-array), if `return_losses` is True count_losses: counts MSE losses (B-array) if `return_losses` is True att_losses: prior losses (B-array), if `return_losses` is True input_seqs: one-hot input sequences (B x I x 4), if `return_gradients` is true input_grads: "hypothetical" input gradients (B x I x 4), if `return_gradients` is true """ result = {} input_seqs, profiles = input_func(coords) if return_gradients: input_seqs_np = input_seqs model.zero_grad() # Zero out weights because we are computing gradients input_seqs = model_util.place_tensor(torch.tensor(input_seqs)).float() profiles = model_util.place_tensor(torch.tensor(profiles)).float() if controls is not None: tf_profs = profiles[:, :num_tasks, :, :] cont_profs = profiles[:, num_tasks:, :, :] # Last half or just one else: tf_profs, cont_profs = profiles, None if return_losses or return_gradients: input_seqs.requires_grad = True # Set gradient required logit_pred_profs, log_pred_counts = model(input_seqs, cont_profs) # Subtract mean along output profile dimension; this wouldn't change # softmax probabilities, but normalizes the magnitude of gradients norm_logit_pred_profs = logit_pred_profs - \ torch.mean(logit_pred_profs, dim=2, keepdim=True) # Weight by post-softmax probabilities, but do not take the # gradients of these probabilities; this upweights important regions # exponentially pred_prof_probs = profile_models.profile_logits_to_log_probs( logit_pred_profs ).detach() weighted_norm_logits = norm_logit_pred_profs * pred_prof_probs input_grads, = torch.autograd.grad( weighted_norm_logits, input_seqs, grad_outputs=model_util.place_tensor( torch.ones(weighted_norm_logits.size()) ), retain_graph=True, create_graph=True # We'll be operating on the gradient itself, so we need to # create the graph # Gradients are summed across strands and tasks ) input_grads_np = input_grads.detach().cpu().numpy() input_seqs.requires_grad = False # Reset gradient required else: logit_pred_profs, log_pred_counts = model(input_seqs, cont_profs) result["true_profs"] = tf_profs.detach().cpu().numpy() result["true_counts"] = np.sum(result["true_profs"], axis=2) logit_pred_profs_np = logit_pred_profs.detach().cpu().numpy() result["log_pred_profs"] = profile_models.profile_logits_to_log_probs( logit_pred_profs_np ) result["log_pred_counts"] = log_pred_counts.detach().cpu().numpy() if return_losses: log_pred_profs = profile_models.profile_logits_to_log_probs( logit_pred_profs ) num_samples = log_pred_profs.size(0) result["prof_losses"] = np.empty(num_samples) result["count_losses"] = np.empty(num_samples) result["att_losses"] = np.empty(num_samples) # Compute losses separately for each example for i in range(num_samples): _, prof_loss, count_loss = model.correctness_loss( tf_profs[i:i+1], log_pred_profs[i:i+1], log_pred_counts[i:i+1], 1, return_separate_losses=True ) att_loss = model.fourier_att_prior_loss( model_util.place_tensor(torch.ones(1)), input_grads[i:i+1], fourier_att_prior_freq_limit, fourier_att_prior_freq_limit_softness, att_prior_grad_smooth_sigma ) result["prof_losses"][i] = prof_loss result["count_losses"][i] = count_loss result["att_losses"][i] = att_loss if return_gradients: result["input_seqs"] = input_seqs_np result["input_grads"] = input_grads_np return result def _get_binary_model_predictions_batch( model, bins, input_func, fourier_att_prior_freq_limit=150, fourier_att_prior_freq_limit_softness=0.2, att_prior_grad_smooth_sigma=3, return_losses=False, return_gradients=False ): """ Arguments: `model`: a trained `BinaryPredictor`, `bins`: an N-array of bin indices to compute outputs for `input_func`: a function that takes in `bins` and returns the B x I x 4 array of one-hot sequences, the B x T array of output values, and B x 3 array of underlying coordinates for the input sequence `fourier_att_prior_freq_limit`: limit for frequencies in Fourier prior loss `fourier_att_prior_freq_limit_softness`: degree of softness for limit `att_prior_grad_smooth_sigma`: width of smoothing kernel for gradients `return_losses`: if True, compute/return the loss values `return_gradients`: if True, compute/return the input gradients and sequences Returns a dictionary of the following structure: true_vals: true binary values (B x T) pred_vals: predicted probabilities (B x T) coords: coordinates used for prediction (B x 3 object array) corr_losses: correctness losses (B-array) if `return_losses` is True att_losses: prior losses (B-array), if `return_losses` is True input_seqs: one-hot input sequences (B x I x 4), if `return_gradients` is True input_grads: "hypothetical" input gradients (B x I x 4), if `return_gradients` is true """ result = {} input_seqs, output_vals, coords = input_func(bins) output_vals_np = output_vals if return_gradients: input_seqs_np = input_seqs model.zero_grad() input_seqs = model_util.place_tensor(torch.tensor(input_seqs)).float() output_vals = model_util.place_tensor(torch.tensor(output_vals)).float() if return_losses or return_gradients: input_seqs.requires_grad = True # Set gradient required logit_pred_vals = model(input_seqs) # Compute the gradients of the output with respect to the input input_grads, = torch.autograd.grad( logit_pred_vals, input_seqs, grad_outputs=model_util.place_tensor( torch.ones(logit_pred_vals.size()) ), retain_graph=True, create_graph=True # We'll be operating on the gradient itself, so we need to # create the graph # Gradients are summed across tasks ) input_grads_np = input_grads.detach().cpu().numpy() input_seqs.requires_grad = False # Reset gradient required else: logit_pred_vals = model(input_seqs) status, input_grads = None, None result["true_vals"] = output_vals_np logit_pred_vals_np = logit_pred_vals.detach().cpu().numpy() result["pred_vals"] = binary_models.binary_logits_to_probs( logit_pred_vals_np ) result["coords"] = coords if return_losses: num_samples = logit_pred_vals.size(0) result["corr_losses"] = np.empty(num_samples) result["att_losses"] = np.empty(num_samples) # Compute losses separately for each example for i in range(num_samples): corr_loss = model.correctness_loss( output_vals[i:i+1], logit_pred_vals[i:i+1], True ) att_loss = model.fourier_att_prior_loss( model_util.place_tensor(torch.ones(1)), input_grads[i:i+1], fourier_att_prior_freq_limit, fourier_att_prior_freq_limit_softness, att_prior_grad_smooth_sigma ) result["corr_losses"][i] = corr_loss result["att_losses"][i] = att_loss if return_gradients: result["input_seqs"] = input_seqs_np result["input_grads"] = input_grads_np return result def get_profile_model_predictions( model, coords, num_tasks, input_func, controls=None, fourier_att_prior_freq_limit=200, fourier_att_prior_freq_limit_softness=0.2, att_prior_grad_smooth_sigma=3, return_losses=False, return_gradients=False, batch_size=128, show_progress=False ): """ Fetches the necessary data from the given coordinates and runs it through a profile model. Arguments: `model`: a trained `ProfilePredictorWithMatchedControls`, `ProfilePredictorWithSharedControls`, or `ProfilePredictorWithoutControls` `coords`: a N x 3 array of coordinates to compute outputs for `num_tasks`: number of tasks for the model `input_func`: a function that takes in `coords` and returns the N x I x 4 array of one-hot sequences and the N x (T or T + 1 or 2T) x O x S array of profiles (perhaps with controls) `controls`: the type of control profiles (if any) used in model; can be "matched" (each task has a matched control), "shared" (all tasks share a control), or None (no controls); must match the model class `fourier_att_prior_freq_limit`: limit for frequencies in Fourier prior loss `fourier_att_prior_freq_limit_softness`: degree of softness for limit `att_prior_grad_smooth_sigma`: width of smoothing kernel for gradients `return_losses`: if True, compute/return the loss values `return_gradients`: if True, compute/return the input gradients and sequences `batch_size`: batch size to use for prediction `show_progress`: whether or not to show progress bar over batches Returns a dictionary of the following structure: true_profs: true profile raw counts (N x T x O x S) log_pred_profs: predicted profile log probabilities (N x T x O x S) true_counts: true total counts (N x T x S) log_pred_counts: predicted log counts (N x T x S) prof_losses: profile NLL losses (N-array), if `return_losses` is True count_losses: counts MSE losses (N-array) if `return_losses` is True att_loss: prior losses (N-array), if `return_losses` is True input_seqs: one-hot input sequences (N x I x 4), if `return_gradients` is true input_grads: "hypothetical" input gradients (N x I x 4), if `return_gradients` is true """ result = {} num_examples = len(coords) num_batches = int(np.ceil(num_examples / batch_size)) t_iter = tqdm.trange(num_batches) if show_progress else range(num_batches) first_batch = True for i in t_iter: batch_slice = slice(i * batch_size, (i + 1) * batch_size) coords_batch = coords[batch_slice] batch_result = _get_profile_model_predictions_batch( model, coords_batch, num_tasks, input_func, controls=controls, fourier_att_prior_freq_limit=fourier_att_prior_freq_limit, fourier_att_prior_freq_limit_softness=fourier_att_prior_freq_limit_softness, att_prior_grad_smooth_sigma=att_prior_grad_smooth_sigma, return_losses=return_losses, return_gradients=return_gradients ) if first_batch: # Allocate arrays of the same size, but holding all examples result["true_profs"] = np.empty( (num_examples,) + batch_result["true_profs"].shape[1:] ) result["log_pred_profs"] = np.empty( (num_examples,) + batch_result["log_pred_profs"].shape[1:] ) result["true_counts"] = np.empty( (num_examples,) + batch_result["true_counts"].shape[1:] ) result["log_pred_counts"] = np.empty( (num_examples,) + batch_result["log_pred_counts"].shape[1:] ) if return_losses: result["prof_losses"] = np.empty(num_examples) result["count_losses"] = np.empty(num_examples) result["att_losses"] = np.empty(num_examples) if return_gradients: result["input_seqs"] = np.empty( (num_examples,) + batch_result["input_seqs"].shape[1:] ) result["input_grads"] = np.empty( (num_examples,) + batch_result["input_grads"].shape[1:] ) first_batch = False result["true_profs"][batch_slice] = batch_result["true_profs"] result["log_pred_profs"][batch_slice] = batch_result["log_pred_profs"] result["true_counts"][batch_slice] = batch_result["true_counts"] result["log_pred_counts"][batch_slice] = batch_result["log_pred_counts"] if return_losses: result["prof_losses"][batch_slice] = batch_result["prof_losses"] result["count_losses"][batch_slice] = batch_result["count_losses"] result["att_losses"][batch_slice] = batch_result["att_losses"] if return_gradients: result["input_seqs"][batch_slice] = batch_result["input_seqs"] result["input_grads"][batch_slice] = batch_result["input_grads"] return result def get_binary_model_predictions( model, bins, input_func, fourier_att_prior_freq_limit=150, fourier_att_prior_freq_limit_softness=0.2, att_prior_grad_smooth_sigma=3, return_losses=False, return_gradients=False, batch_size=128, show_progress=False ): """ Fetches the necessary data from the given bin indices and runs it through a binary model. Arguments: `model`: a trained `BinaryPredictor`, `bins`: an N-array of bin indices to compute outputs for `input_func`: a function that takes in `bins` and returns the B x I x 4 array of one-hot sequences, the B x T array of output values, and B x 3 array of underlying coordinates for the input sequence `fourier_att_prior_freq_limit`: limit for frequencies in Fourier prior loss `fourier_att_prior_freq_limit_softness`: degree of softness for limit `att_prior_grad_smooth_sigma`: width of smoothing kernel for gradients `return_losses`: if True, compute/return the loss values `return_gradients`: if True, compute/return the input gradients and sequences `batch_size`: batch size to use for prediction `show_progress`: whether or not to show progress bar over batches Returns a dictionary of the following structure: true_vals: true binary values (N x T) pred_vals: predicted probabilities (N x T) coords: coordinates used for prediction (N x 3 object array) corr_losses: correctness losses (N-array) if `return_losses` is True att_losses: prior losses (N-array), if `return_losses` is True input_seqs: one-hot input sequences (N x I x 4), if `return_gradients` is true input_grads: "hypothetical" input gradients (N x I x 4), if `return_gradients` is true """ result = {} num_examples = len(bins) num_batches = int(np.ceil(num_examples / batch_size)) t_iter = tqdm.trange(num_batches) if show_progress else range(num_batches) first_batch = True for i in t_iter: batch_slice = slice(i * batch_size, (i + 1) * batch_size) bins_batch = bins[batch_slice] batch_result = _get_binary_model_predictions_batch( model, bins_batch, input_func, fourier_att_prior_freq_limit=fourier_att_prior_freq_limit, fourier_att_prior_freq_limit_softness=fourier_att_prior_freq_limit_softness, att_prior_grad_smooth_sigma=att_prior_grad_smooth_sigma, return_losses=return_losses, return_gradients=return_gradients ) if first_batch: # Allocate arrays of the same size, but holding all examples result["true_vals"] = np.empty( (num_examples,) + batch_result["true_vals"].shape[1:] ) result["pred_vals"] = np.empty( (num_examples,) + batch_result["pred_vals"].shape[1:] ) result["coords"] = np.empty((num_examples, 3), dtype=object) if return_losses: result["corr_losses"] = np.empty(num_examples) result["att_losses"] = np.empty(num_examples) if return_gradients: result["input_seqs"] = np.empty( (num_examples,) + batch_result["input_seqs"].shape[1:] ) result["input_grads"] = np.empty( (num_examples,) + batch_result["input_grads"].shape[1:] ) first_batch = False result["true_vals"][batch_slice] = batch_result["true_vals"] result["pred_vals"][batch_slice] = batch_result["pred_vals"] result["coords"][batch_slice] = batch_result["coords"] if return_losses: result["corr_losses"][batch_slice] = batch_result["corr_losses"] result["att_losses"][batch_slice] = batch_result["att_losses"] if return_gradients: result["input_seqs"][batch_slice] = batch_result["input_seqs"] result["input_grads"][batch_slice] = batch_result["input_grads"] return result if __name__ == "__main__": reference_fasta = "/users/amtseng/genomes/hg38.fasta" chrom_set = ["chr21"] print("Testing profile model") input_length = 1346 profile_length = 1000 controls = "matched" num_tasks = 4 files_spec_path = "/users/amtseng/att_priors/data/processed/ENCODE_TFChIP/profile/config/SPI1/SPI1_training_paths.json" model_class = profile_models.ProfilePredictorWithMatchedControls model_path = "/users/amtseng/att_priors/models/trained_models/profile/SPI1/1/model_ckpt_epoch_1.pt" input_func = data_loading.get_profile_input_func( files_spec_path, input_length, profile_length, reference_fasta, ) pos_coords = data_loading.get_positive_profile_coords( files_spec_path, chrom_set=chrom_set ) print("Loading model...") torch.set_grad_enabled(True) device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model = model_util.restore_model(model_class, model_path) model.eval() model = model.to(device) print("Running predictions...") x = get_profile_model_predictions( model, pos_coords, num_tasks, input_func, controls=controls, return_losses=True, return_gradients=True, show_progress=True ) print("") print("Testing binary model") input_length = 1000 files_spec_path = "/users/amtseng/att_priors/data/processed/ENCODE_TFChIP/binary/config/SPI1/SPI1_training_paths.json" model_class = binary_models.BinaryPredictor model_path = "/users/amtseng/att_priors/models/trained_models/binary/SPI1/1/model_ckpt_epoch_1.pt" input_func = data_loading.get_binary_input_func( files_spec_path, input_length, reference_fasta ) pos_bins = data_loading.get_positive_binary_bins( files_spec_path, chrom_set=chrom_set ) print("Loading model...") torch.set_grad_enabled(True) device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model = model_util.restore_model(model_class, model_path) model.eval() model = model.to(device) print("Running predictions...") x = get_binary_model_predictions( model, pos_bins, input_func, return_losses=True, return_gradients=True, show_progress=True )

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e53e5afbe7fe1556a0bd1e7522da7fbebd4148d8

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py

Python

test/test_main.py

jenshnielsen/versioningit

b575e300ae2ea78e283254537cffd30135ae7fe6

[ "MIT" ]

17

2021-07-05T23:41:36.000Z

2022-03-10T14:55:24.000Z

test/test_main.py

jenshnielsen/versioningit

b575e300ae2ea78e283254537cffd30135ae7fe6

[ "MIT" ]

20

2021-07-05T23:56:09.000Z

2022-03-14T13:04:09.000Z

test/test_main.py

jenshnielsen/versioningit

b575e300ae2ea78e283254537cffd30135ae7fe6

[ "MIT" ]

4

2021-09-04T13:24:49.000Z

2022-03-25T19:44:19.000Z

import logging import os from pathlib import Path import subprocess import sys from _pytest.capture import CaptureFixture import pytest from pytest_mock import MockerFixture from versioningit.__main__ import main from versioningit.errors import Error def test_command( capsys: CaptureFixture[str], mocker: MockerFixture, monkeypatch: pytest.MonkeyPatch ) -> None: monkeypatch.setattr(sys, "argv", ["versioningit"]) m = mocker.patch("versioningit.__main__.get_version", return_value="THE VERSION") spy = mocker.spy(logging, "basicConfig") main() m.assert_called_once_with(os.curdir, write=False, fallback=True) spy.assert_called_once_with( format="[%(levelname)-8s] %(name)s: %(message)s", level=logging.WARNING, ) out, err = capsys.readouterr() assert out == "THE VERSION\n" assert err == "" def test_command_arg( capsys: CaptureFixture[str], mocker: MockerFixture, tmp_path: Path ) -> None: m = mocker.patch("versioningit.__main__.get_version", return_value="THE VERSION") main([str(tmp_path)]) m.assert_called_once_with(str(tmp_path), write=False, fallback=True) out, err = capsys.readouterr() assert out == "THE VERSION\n" assert err == "" def test_command_write(capsys: CaptureFixture[str], mocker: MockerFixture) -> None: m = mocker.patch("versioningit.__main__.get_version", return_value="THE VERSION") main(["--write"]) m.assert_called_once_with(os.curdir, write=True, fallback=True) out, err = capsys.readouterr() assert out == "THE VERSION\n" assert err == "" def test_command_next_version( capsys: CaptureFixture[str], mocker: MockerFixture ) -> None: m = mocker.patch( "versioningit.__main__.get_next_version", return_value="THE NEXT VERSION" ) main(["--next-version"]) m.assert_called_once_with(os.curdir) out, err = capsys.readouterr() assert out == "THE NEXT VERSION\n" assert err == "" def test_command_next_version_arg( capsys: CaptureFixture[str], mocker: MockerFixture, tmp_path: Path ) -> None: m = mocker.patch( "versioningit.__main__.get_next_version", return_value="THE NEXT VERSION" ) main(["-n", str(tmp_path)]) m.assert_called_once_with(str(tmp_path)) out, err = capsys.readouterr() assert out == "THE NEXT VERSION\n" assert err == "" @pytest.mark.parametrize( "arg,log_level", [ ("-v", logging.INFO), ("-vv", logging.DEBUG), ("-vvv", logging.DEBUG), ], ) def test_command_verbose( capsys: CaptureFixture[str], mocker: MockerFixture, arg: str, log_level: int ) -> None: m = mocker.patch("versioningit.__main__.get_version", return_value="THE VERSION") spy = mocker.spy(logging, "basicConfig") main([arg]) m.assert_called_once_with(os.curdir, write=False, fallback=True) spy.assert_called_once_with( format="[%(levelname)-8s] %(name)s: %(message)s", level=log_level, ) out, err = capsys.readouterr() assert out == "THE VERSION\n" assert err == "" def test_command_error( capsys: CaptureFixture[str], mocker: MockerFixture, monkeypatch: pytest.MonkeyPatch ) -> None: monkeypatch.setattr(sys, "argv", ["versioningit"]) m = mocker.patch( "versioningit.__main__.get_version", side_effect=Error("Something broke") ) with pytest.raises(SystemExit) as excinfo: main() assert excinfo.value.args == (1,) m.assert_called_once_with(os.curdir, write=False, fallback=True) out, err = capsys.readouterr() assert out == "" assert err == "versioningit: Error: Something broke\n" def test_command_subprocess_error( caplog: pytest.LogCaptureFixture, capsys: CaptureFixture[str], mocker: MockerFixture, monkeypatch: pytest.MonkeyPatch, ) -> None: monkeypatch.setattr(sys, "argv", ["versioningit"]) m = mocker.patch( "versioningit.__main__.get_version", side_effect=subprocess.CalledProcessError( returncode=42, cmd=["git", "-C", ".", "get details"], output=b"", stderr=b"" ), ) with pytest.raises(SystemExit) as excinfo: main() assert excinfo.value.args == (42,) m.assert_called_once_with(os.curdir, write=False, fallback=True) out, err = capsys.readouterr() assert out == "" assert err == "" assert caplog.record_tuples == [ ("versioningit", logging.ERROR, "git -C . 'get details': command returned 42") ]

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0

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e5404c05d2371c5c4f196346c816c08400d2dfec

55

py

Python

src/ionotomo/tests/test_geometry.py

Joshuaalbert/IonoTomo

9f50fbac698d43a824dd098d76dce93504c7b879

[ "Apache-2.0" ]

7

2017-06-22T08:47:07.000Z

2021-07-01T12:33:02.000Z

src/ionotomo/tests/test_geometry.py

Joshuaalbert/IonoTomo

9f50fbac698d43a824dd098d76dce93504c7b879

[ "Apache-2.0" ]

1

2019-04-03T15:21:19.000Z

2019-04-03T15:48:31.000Z

src/ionotomo/tests/test_geometry.py

Joshuaalbert/IonoTomo

9f50fbac698d43a824dd098d76dce93504c7b879

[ "Apache-2.0" ]

2

2020-03-01T16:20:00.000Z

2020-07-07T15:09:02.000Z

import numpy as np from ionotomo import * import os

7.857143

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5

008b713d67efa7f6a689a868bbfcf7920ce7c668

28

py

Python

python/ql/test/library-tests/PointsTo/new/code/package/module.py

vadi2/codeql

a806a4f08696d241ab295a286999251b56a6860c

[ "MIT" ]

4,036

2020-04-29T00:09:57.000Z

2022-03-31T14:16:38.000Z

python/ql/test/library-tests/PointsTo/new/code/package/module.py

vadi2/codeql

a806a4f08696d241ab295a286999251b56a6860c

[ "MIT" ]

2,970

2020-04-28T17:24:18.000Z

2022-03-31T22:40:46.000Z

python/ql/test/library-tests/PointsTo/new/code/package/module.py

ScriptBox99/github-codeql

2ecf0d3264db8fb4904b2056964da469372a235c

[ "MIT" ]

794

2020-04-29T00:28:25.000Z

2022-03-30T08:21:46.000Z

def module(args): pass

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null

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5

008e25d18360eef4eadfd656279caa05e2e43534

113

py

Python

tests/conftest.py

elaspic/elaspic2-rest-api

460315387c5a7ea5a96bece8b6888e0b97af0580

[ "MIT" ]

null

tests/conftest.py

elaspic/elaspic2-rest-api

460315387c5a7ea5a96bece8b6888e0b97af0580

[ "MIT" ]

null

tests/conftest.py

elaspic/elaspic2-rest-api

460315387c5a7ea5a96bece8b6888e0b97af0580

[ "MIT" ]

null

import os from dotenv import load_dotenv load_dotenv(dotenv_path=os.getenv("ENV_FILE", ".env"), override=True)

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5

00e8116f1110d01caf75ccb9eeaf96ec7d7ddc0b

9,719

py

Python

tests/test_multipass_vm.py

jebeckford/cloudmesh-multipass

c8bc14c5093ab599c184b2bf5f934b8a4f2b0791

[ "Apache-2.0" ]

null

tests/test_multipass_vm.py

jebeckford/cloudmesh-multipass

c8bc14c5093ab599c184b2bf5f934b8a4f2b0791

[ "Apache-2.0" ]

5

2020-02-12T09:10:46.000Z

2020-03-04T22:16:35.000Z

tests/test_multipass_vm.py

jebeckford/cloudmesh-multipass

c8bc14c5093ab599c184b2bf5f934b8a4f2b0791

[ "Apache-2.0" ]

14

2020-01-29T23:15:48.000Z

2020-03-23T03:04:38.000Z

############################################################### # pytest -v --capture=no tests # pytest -v --capture=no tests/test_multipass_general.py # pytest -v tests/test_multipass_general.py # pytest -v --capture=no tests/test_multipass_general.py::Test_Multipass::<METHODNAME> ############################################################### import pytest from cloudmesh.common.Shell import Shell from cloudmesh.common.debug import VERBOSE from cloudmesh.common.util import HEADING from cloudmesh.common.Benchmark import Benchmark from cloudmesh.multipass.Provider import Provider Benchmark.debug() cloud= "local" instance="cloudmesh-test" @pytest.mark.incremental class TestMultipass: vm_name_prefix = "cloudmeshvm" #Note: multipass does not allow - or _ in vm name. def test_cms_vm(self): HEADING() self.provider = Provider() Benchmark.Start() result = Shell.execute("cms multipass vm", shell=True) Benchmark.Stop() VERBOSE(result) result = str(result) assert "18.04" in result Benchmark.Status(True) def test_provider_vm(self): HEADING() self.provider = Provider() Benchmark.Start() result = self.provider.vm() Benchmark.Stop() VERBOSE(result) result = str(result) assert "18.04" in result Benchmark.Status(True) def test_cms_shell(self): HEADING() Benchmark.Start() Shell.execute(f"cms multipass launch --name={instance}", shell=True) result = Shell.execute(f"cms multipass shell {instance}", shell=True) Shell.execute(f"cms multipass delete {instance}",shell=True) Shell.execute(f"cms multipass purge",shell=True) Benchmark.Stop() VERBOSE(result) # assertion missing Benchmark.Status(True) def test_provider_shell(self): HEADING() Benchmark.Start() Shell.execute(f"cms multipass launch --name={instance}", shell=True) result = self.provider.shell(name=instance) Shell.execute(f"cms multipass delete {instance}",shell=True) Shell.execute(f"cms multipass purge",shell=True) Benchmark.Stop() VERBOSE(result) # assertion missing Benchmark.Status(True) def test_info(self): HEADING() Benchmark.Start() result = Shell.execute("cms multipass info", shell=True) Benchmark.Stop() VERBOSE(result) assert result != None, "result cannot be null" Benchmark.Status(True) def test_create(self): HEADING() vm_name = f"{self.vm_name_prefix}1" Benchmark.Start() result = Shell.execute(f"cms multipass create {vm_name}", shell=True) Benchmark.Stop() VERBOSE(result) assert f'Launched: {vm_name}' in result, "Error creating instance" Benchmark.Status(True) def test_provider_create(self): HEADING() self.provider = Provider() vm_name = f"{self.vm_name_prefix}2" provider = Provider(vm_name) Benchmark.Start() result = provider.create(vm_name) Benchmark.Stop() VERBOSE(result) assert 'Running' in result['status'], "Error creating instance" Benchmark.Status(True) def test_create_with_options(self): HEADING() vm_name = f"{self.vm_name_prefix}3" Benchmark.Start() result = Shell.execute(f"cms multipass create {vm_name} --cpus=2 --size=3G --image=bionic --mem=1G", shell=True) Benchmark.Stop() VERBOSE(result) assert f'Launched: {vm_name}' in result, "Error creating instance" Benchmark.Status(True) def test_stop(self): HEADING() #Using 2 VMs to test_created usingn test_create* methods. vm_names = f"{self.vm_name_prefix}1,{self.vm_name_prefix}3" Benchmark.Start() result = Shell.execute(f"cms multipass stop {vm_names}", shell=True) Benchmark.Stop() VERBOSE(result) assert 'Stopped' in result, "Error stopping instance" Benchmark.Status(True) def test_provider_stop(self): HEADING() self.provider = Provider() vm_name = f"{self.vm_name_prefix}2" provider = Provider(vm_name) Benchmark.Start() result = provider.stop(vm_name) Benchmark.Stop() VERBOSE(result) assert 'Stopped' in result['status'], "Error stopping instance" Benchmark.Status(True) def test_start(self): HEADING() #Using 2 VMs to test_created usingn test_create* methods. vm_names = f"{self.vm_name_prefix}1,{self.vm_name_prefix}3" Benchmark.Start() result = Shell.execute(f"cms multipass start {vm_names}", shell=True) Benchmark.Stop() VERBOSE(result) assert 'Running' in result, "Error starting instance" Benchmark.Status(True) def test_provider_start(self): HEADING() self.provider = Provider() vm_name = f"{self.vm_name_prefix}2" provider = Provider(vm_name) Benchmark.Start() result = provider.start(vm_name) Benchmark.Stop() VERBOSE(result) assert 'Running' in result['status'], "Error starting instance" Benchmark.Status(True) def test_suspend(self): HEADING() #Using 2 VMs to test_created usingn test_create* methods. vm_names = f"{self.vm_name_prefix}1,{self.vm_name_prefix}3" Benchmark.Start() result = Shell.execute(f"cms multipass suspend {vm_names}", shell=True) Benchmark.Stop() VERBOSE(result) assert 'Suspended' in result, "Error suspending instance" Benchmark.Status(True) def test_provider_suspend(self): HEADING() vm_name = f"{self.vm_name_prefix}2" provider = Provider(vm_name) Benchmark.Start() result = provider.suspend(vm_name) Benchmark.Stop() VERBOSE(result) assert 'Suspend' in result['status'], "Error suspending instance" Benchmark.Status(True) def test_resume(self): HEADING() #Using 2 VMs to test_created usingn test_create* methods. vm_names = f"{self.vm_name_prefix}1,{self.vm_name_prefix}3" Shell.execute(f"cms multipass suspend {vm_names}", shell=True) Benchmark.Start() result = Shell.execute(f"cms multipass resume {vm_names}", shell=True) Benchmark.Stop() VERBOSE(result) assert 'Resumed' in result, "Error resuming instance" Benchmark.Status(True) def test_provider_resume(self): HEADING() vm_name = f"{self.vm_name_prefix}2" provider = Provider(vm_name) Provider.suspend(vm_name) Benchmark.Start() result = provider.resume(vm_name) Benchmark.Stop() VERBOSE(result) assert 'Resume' in result['status'], "Error resuming instance" Benchmark.Status(True) def test_reboot(self): HEADING() self.provider = Provider() #Using 2 VMs to test_created usingn test_create* methods. vm_names = f"{self.vm_name_prefix}1,{self.vm_name_prefix}3" Benchmark.Start() result = Shell.execute(f"cms multipass reboot {vm_names}", shell=True) Benchmark.Stop() VERBOSE(result) assert 'Running' in result, "Error rebooting instance" def test_provider_reboot(self): HEADING() self.provider = Provider() vm_name = f"{self.vm_name_prefix}2" provider = Provider(vm_name) Benchmark.Start() result = provider.reboot(vm_name) Benchmark.Stop() VERBOSE(result) assert 'Running' in result['status'], "Error rebooting instance" Benchmark.Status(True) def test_delete(self): HEADING() #Using 2 VMs to test_created usingn test_create* methods. vm_names = f"{self.vm_name_prefix}1,{self.vm_name_prefix}3" Benchmark.Start() result = Shell.execute(f"cms multipass delete {vm_names}", shell=True) Benchmark.Stop() VERBOSE(result) assert 'deleted' in result, "Error deleting instance" Benchmark.Status(True) def test_provider_delete(self): HEADING() self.provider = Provider() vm_name = f"{self.vm_name_prefix}2" provider = Provider(vm_name) Benchmark.Start() result = provider.delete(vm_name) Benchmark.Stop() VERBOSE(result) assert 'deleted' in result['status'], "Error deleting instance" Benchmark.Status(True) def test_destroy(self): HEADING() #Using 2 VMs to test_created usingn test_create* methods. vm_names = f"{self.vm_name_prefix}1,{self.vm_name_prefix}3" Benchmark.Start() result = Shell.execute(f"cms multipass destroy {vm_names}", shell=True) Benchmark.Stop() VERBOSE(result) assert 'destroyed' in result, "Error destroying instance" Benchmark.Status(True) def test_provider_destroy(self): HEADING() self.provider = Provider() vm_name = f"{self.vm_name_prefix}2" provider = Provider(vm_name) Benchmark.Start() result = provider.destroy(vm_name) Benchmark.Stop() VERBOSE(result) assert 'destroyed' in result['status'], "Error destroying instance" Benchmark.Status(True) # # NOTHING BELOW THIS LINE # def test_benchmark(self): HEADING() Benchmark.print(csv=True, tag=cloud)

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null

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null

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5

da9a2b19cb2fbe247f93d12fbf85877a0ee27508

171

py

Python

python/djoro_regulation_server.py

damienlaine/djoro-bcvtb

84e5a4b46d554c20ac2ffa22383f386b19af8bc5

[ "MIT" ]

2

2019-12-15T13:45:54.000Z

2021-12-26T00:26:26.000Z

python/djoro_regulation_server.py

damienlaine/djoro-bcvtb

84e5a4b46d554c20ac2ffa22383f386b19af8bc5

[ "MIT" ]

null

python/djoro_regulation_server.py

damienlaine/djoro-bcvtb

84e5a4b46d554c20ac2ffa22383f386b19af8bc5

[ "MIT" ]

1

2021-12-26T00:26:41.000Z

# -*- coding: utf-8 -*- from djoro_regulation_server.djoro_regulation_server import DjoroRegulationServer djoro = DjoroRegulationServer() djoro.start("localhost", 9100)

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5

daa9314caa642a03d9397c8a05b4f6456c29d69f

1,472

py

Python

generateGUID/tests/test_GUID_Bash.py

INT-NIT-calcul/generateGUID

446aa5e6d4d37a6c41732b63995c9e39e3ce9b1b

[ "MIT" ]

null

generateGUID/tests/test_GUID_Bash.py

INT-NIT-calcul/generateGUID

446aa5e6d4d37a6c41732b63995c9e39e3ce9b1b

[ "MIT" ]

1

2020-01-07T13:51:59.000Z

2020-01-07T15:58:22.000Z

generateGUID/tests/test_GUID_Bash.py

INT-NIT-calcul/generateGUID

446aa5e6d4d37a6c41732b63995c9e39e3ce9b1b

[ "MIT" ]

3

2019-10-04T09:03:34.000Z

2019-10-28T15:22:45.000Z

# coding: utf-8 from guid_core.generate_GUID import generate_GUID import unittest GUID = generate_GUID("Jean-Michel"+"Frégnac"+"22/03/1949"+"M") class TestUM(unittest.TestCase): def setUp(self): pass def test_guid(self): key = "Jean-Michel"+"Frégnac"+"22/03/1949"+"M" self.assertEqual(generate_GUID(key), GUID) def test_guid1(self): key = "Jean-michel"+"Frégnac"+"22/03/1949"+"M" self.assertEqual(generate_GUID(key), GUID) def test_guid2(self): key = "Jean-michel"+"Fregnac"+"22/03/1949"+"M" self.assertEqual(generate_GUID(key), GUID) def test_guid3(self): key = "Jean michel"+"Frégnac"+"22/03/1949"+"M" self.assertEqual(generate_GUID(key), GUID) def test_guid4(self): key = "Jean-Michel"+"Frégnac"+r"22\03\1949"+"M" self.assertEqual(generate_GUID(key), GUID) def test_guid5(self): key = "Jean-michel"+"Fregnac"+"22-03-1949"+"M" self.assertEqual(generate_GUID(key), GUID) def test_guid6(self): key = "Jean michel"+"Frégnac"+"22 03 1949"+"M" self.assertEqual(generate_GUID(key), GUID) def test_guid7(self): key = "jean-michel"+"frégnac"+"22/03/1949"+"M" self.assertEqual(generate_GUID(key), GUID) def test_guid8(self): key = "Jean michel"+"Frégnac"+"22031949"+"M" self.assertEqual(generate_GUID(key), GUID) if __name__ == '__main__': unittest.main()

23

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1

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1

0

null

0

1

0

5

dad0ed184dabd9b1844b79fe42abb7b917c7763b

3,814

py

Python

code/main.py

jiaxx/temporal_learning_paper

abffd5bfb36aaad7139485a9b8bd29f3858389e8

[ "MIT" ]

null

code/main.py

jiaxx/temporal_learning_paper

abffd5bfb36aaad7139485a9b8bd29f3858389e8

[ "MIT" ]

null

code/main.py

jiaxx/temporal_learning_paper

abffd5bfb36aaad7139485a9b8bd29f3858389e8

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Thu Oct 4 17:55:20 2020 @author: Xiaoxuan Jia """ import numpy as np import scipy from scipy.stats import norm import numpy.random as npr import random import utils as ut import learningutil as lt def d_prime(CF): d = [] for i in range(len(CF[1])): H = CF[i, i]/sum(CF[:,i]) # H = target diagnal/target column tempCF = scipy.delete(CF, i, 1) # delete the target column F = sum(tempCF[i,:])/sum(tempCF) d.append(norm.ppf(H)-norm.ppf(F)) return d def sample_with_replacement(list): l = len(list) # the sample needs to be as long as list r = xrange(l) _random = random.random return [list[int(_random()*l)] for i in r] # using def compute_CM(neuron, meta, obj, s, train, test): metric_kwargs = {'model_type': 'MCC2'} # multi-class classifier eval_config = { 'train_q': {'obj': [obj[0], obj[1]]}, # train on all sizes 'test_q': {'obj': [obj[0], obj[1]], 's': [s]}, #'size_range': [1.3], 'npc_train': train, #smaller than total number of samples in each split_by object 'npc_test': test, 'npc_validate': 0, 'num_splits': 100, 'split_by': 'obj', 'metric_screen': 'classifier', # use correlation matrix as classifier 'labelfunc': 'obj', 'metric_kwargs': metric_kwargs, } result = ut.compute_metric_base(neuron, meta, eval_config) # sum of the CMs is equal to npc_test*number of objs CMs = [] for i in range(eval_config['num_splits']): temp = np.array(result['result_summary']['cms'])[:,:,i] CMs.append(lt.normalize_CM(temp)) d = ut.dprime(np.mean(CMs,axis=0))[0] return CMs, d def compute_CM_samesize(neuron, meta, obj, s, train, test): metric_kwargs = {'model_type': 'MCC2'} # multi-class classifier eval_config = { 'train_q': {'obj': [obj[0], obj[1]], 's': [s]}, # train on particular size 'test_q': {'obj': [obj[0], obj[1]], 's': [s]}, #test on particular size 'npc_train': train, #smaller than total number of samples in each split_by object 'npc_test': test, 'npc_validate': 0, 'num_splits': 100, 'split_by': 'obj', 'metric_screen': 'classifier', # use correlation matrix as classifier 'labelfunc': 'obj', 'metric_kwargs': metric_kwargs, } result = ut.compute_metric_base(neuron, meta, eval_config) # sum of the CMs is equal to npc_test*number of objs CMs = [] for i in range(eval_config['num_splits']): temp = np.array(result['result_summary']['cms'])[:,:,i] CMs.append(lt.normalize_CM(temp)) d = ut.dprime(np.mean(CMs,axis=0))[0] return CMs, d def compute_CM_fixed_classifier(neuron, meta, obj, s, train, test): metric_kwargs = {'model_type': 'MCC2'} # multi-class classifier eval_config = { 'train_q': {'obj': [obj[0], obj[1]], 'test_phase':['Pre']}, # train on all sizes 'test_q': {'obj': [obj[0], obj[1]], 's': [s], 'test_phase':['Post']}, #'size_range': [1.3], 'npc_train': train, #smaller than total number of samples in each split_by object 'npc_test': test, 'npc_validate': 0, 'num_splits': 100, 'split_by': 'obj', 'metric_screen': 'classifier', # use correlation matrix as classifier 'labelfunc': 'obj', 'metric_kwargs': metric_kwargs, } result = ut.compute_metric_base(neuron, meta, eval_config) # sum of the CMs is equal to npc_test*number of objs CMs = [] for i in range(eval_config['num_splits']): temp = np.array(result['result_summary']['cms'])[:,:,i] CMs.append(lt.normalize_CM(temp)) d = ut.dprime(np.mean(CMs,axis=0))[0] return CMs, d

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5

9742ca228af8af48e54daa5ae0d6558ce4105225

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py

Python

src/mode_manager.py

brulzki/pianopad

3e67d9edaf6786876c22917926ef4736691d1653

[ "MIT" ]

3

2018-01-04T10:43:49.000Z

2021-02-26T23:23:51.000Z

src/mode_manager.py

brulzki/pianopad

3e67d9edaf6786876c22917926ef4736691d1653

[ "MIT" ]

8

2017-08-25T02:24:39.000Z

2021-10-10T03:49:42.000Z

src/mode_manager.py

brulzki/pianopad

3e67d9edaf6786876c22917926ef4736691d1653

[ "MIT" ]

null

import os from mode import Mode def load_modes(): """ """ all_modes = [] for directory in os.listdir(r'modes'): full_dir = 'modes' + os.sep + directory if os.path.isdir(full_dir): all_modes.append(Mode(full_dir)) return all_modes modes = load_modes() current_mode_position = 0 current_mode = modes[current_mode_position] favorites = [None, None] def cycle_mode(midiout): global current_mode_position global current_mode current_mode_position += 1 if current_mode_position >= len(modes): current_mode_position = 0 modes[current_mode_position].refresh_background(midiout) current_mode = modes[current_mode_position] def next_mode(midiout): global current_mode_position global current_mode if current_mode_position < len(modes)-1: current_mode_position += 1 modes[current_mode_position].refresh_background(midiout) current_mode = modes[current_mode_position] def previous_mode(midiout): global current_mode_position global current_mode if current_mode_position > 0: current_mode_position -= 1 modes[current_mode_position].refresh_background(midiout) current_mode = modes[current_mode_position] def set_mode(midiout, mode): global current_mode_position global current_mode current_mode_position = mode modes[current_mode_position].refresh_background(midiout) current_mode = modes[current_mode_position]

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py

Python

socialml/__init__.py

erees1/socialml

76ec276395e7819c6c834f4819ccdf8989aa6cab

[ "MIT" ]

null

socialml/__init__.py

erees1/socialml

76ec276395e7819c6c834f4819ccdf8989aa6cab

[ "MIT" ]

null

socialml/__init__.py

erees1/socialml

76ec276395e7819c6c834f4819ccdf8989aa6cab

[ "MIT" ]

null

from socialml.extractors import FbMessenger, IMessage from socialml.make_dataset import make_training_examples from socialml.filter_array import filter_array

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979c981a445f7850b183db2350040d8d6d5ca0b6

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py

Python

tests/test_components_eta_letters.py

Robpol86/etaprogress

224e8a248c2bf820bad218763281914ad3983fff

[ "MIT" ]

13

2015-08-25T05:54:21.000Z

2021-03-23T15:56:58.000Z

tests/test_components_eta_letters.py

Robpol86/etaprogress

224e8a248c2bf820bad218763281914ad3983fff

[ "MIT" ]

5

2015-03-14T16:31:38.000Z

2019-01-13T20:46:25.000Z

tests/test_components_eta_letters.py

Robpol86/etaprogress

224e8a248c2bf820bad218763281914ad3983fff

[ "MIT" ]

5

2015-05-31T14:16:50.000Z

2021-02-06T11:23:43.000Z

from etaprogress.components.eta_conversions import eta_letters def test(): assert '0s' == eta_letters(0) assert '9s' == eta_letters(9) assert '59s' == eta_letters(59) assert '1m 0s' == eta_letters(60) assert '1m 1s' == eta_letters(61) assert '59m 59s' == eta_letters(3599) assert '1h 0m 0s' == eta_letters(3600) assert '1h 0m 1s' == eta_letters(3601) assert '1h 1m 1s' == eta_letters(3661) assert '6d 23h 59m 59s' == eta_letters(604799) assert '1w 0d 0h 0m 0s' == eta_letters(604800) assert '1w 0d 0h 0m 1s' == eta_letters(604801) def test_leading_zero(): assert '00s' == eta_letters(0, leading_zero=True) assert '09s' == eta_letters(9, leading_zero=True) assert '59s' == eta_letters(59, leading_zero=True) assert '01m 00s' == eta_letters(60, leading_zero=True) assert '01m 01s' == eta_letters(61, leading_zero=True) assert '59m 59s' == eta_letters(3599, leading_zero=True) assert '1h 00m 00s' == eta_letters(3600, leading_zero=True) assert '1h 00m 01s' == eta_letters(3601, leading_zero=True) assert '1h 01m 01s' == eta_letters(3661, leading_zero=True) assert '6d 23h 59m 59s' == eta_letters(604799, leading_zero=True) assert '1w 0d 0h 00m 00s' == eta_letters(604800, leading_zero=True) assert '1w 0d 0h 00m 01s' == eta_letters(604801, leading_zero=True) def test_shortest(): assert '0s' == eta_letters(0, shortest=True) assert '9s' == eta_letters(9, shortest=True) assert '59s' == eta_letters(59, shortest=True) assert '1m' == eta_letters(60, shortest=True) assert '1m' == eta_letters(61, shortest=True) assert '59m' == eta_letters(3599, shortest=True) assert '1h' == eta_letters(3600, shortest=True) assert '1h' == eta_letters(3601, shortest=True) assert '1h' == eta_letters(3661, shortest=True) assert '6d' == eta_letters(604799, shortest=True) assert '1w' == eta_letters(604800, shortest=True) assert '1w' == eta_letters(604801, shortest=True) def test_shortest_and_leading_zero(): assert '00s' == eta_letters(0, shortest=True, leading_zero=True) assert '09s' == eta_letters(9, shortest=True, leading_zero=True) assert '59s' == eta_letters(59, shortest=True, leading_zero=True) assert '01m' == eta_letters(60, shortest=True, leading_zero=True) assert '01m' == eta_letters(61, shortest=True, leading_zero=True) assert '59m' == eta_letters(3599, shortest=True, leading_zero=True) assert '1h' == eta_letters(3600, shortest=True, leading_zero=True) assert '1h' == eta_letters(3601, shortest=True, leading_zero=True) assert '1h' == eta_letters(3661, shortest=True, leading_zero=True) assert '6d' == eta_letters(604799, shortest=True, leading_zero=True) assert '1w' == eta_letters(604800, shortest=True, leading_zero=True) assert '1w' == eta_letters(604801, shortest=True, leading_zero=True)

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97cae70ef775586d562727daf919a47339bd2ec9

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py

Python

project/settings/common.py

teracyhq-incubator/django-boilerplate

827ace7d3a89caab9c3bba4da7c31f3daef58e2f

[ "BSD-3-Clause" ]

1

2018-01-11T14:20:56.000Z

project/settings/common.py

teracyhq-incubator/django-boilerplate

827ace7d3a89caab9c3bba4da7c31f3daef58e2f

[ "BSD-3-Clause" ]

null

project/settings/common.py

teracyhq-incubator/django-boilerplate

827ace7d3a89caab9c3bba4da7c31f3daef58e2f

[ "BSD-3-Clause" ]

2

2018-09-29T05:28:20.000Z

2019-07-10T17:47:45.000Z

""" common specific project settings """ from settings.common import * # noqa

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1

0

1

0

5

8ae2b34490cd980519864c061431753907a158bc

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py

Python

nautobot_chatops_ipfabric/api/__init__.py

justinjeffery-ipf/nautobot-plugin-chatops-ipfabric

67e58e3d251b41227808cabd6120d78411193863

[ "Apache-2.0" ]

6

2021-11-26T15:50:21.000Z

2022-01-25T18:36:44.000Z

nautobot_chatops_ipfabric/api/__init__.py

justinjeffery-ipf/nautobot-plugin-chatops-ipfabric

67e58e3d251b41227808cabd6120d78411193863

[ "Apache-2.0" ]

21

2021-11-30T02:31:40.000Z

2022-02-17T04:17:36.000Z

nautobot_chatops_ipfabric/api/__init__.py

justinjeffery-ipf/nautobot-plugin-chatops-ipfabric

67e58e3d251b41227808cabd6120d78411193863

[ "Apache-2.0" ]

2

2022-01-18T17:53:29.000Z

2022-02-16T16:06:15.000Z

"""REST API module for ipfabric plugin."""

21.5

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1

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null

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1

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5

c11cd35941eddb0fe50f15c9a5f57ed3519a55de

118

bzl

Python

pesto/nil/sycl/platform.bzl

quantapix/semtools

dce8840adc86e6a9672447aace969d37e236f922

[ "MIT" ]

null

pesto/nil/sycl/platform.bzl

quantapix/semtools

dce8840adc86e6a9672447aace969d37e236f922

[ "MIT" ]

null

pesto/nil/sycl/platform.bzl

quantapix/semtools

dce8840adc86e6a9672447aace969d37e236f922

[ "MIT" ]

null

def sycl_library_path(name): return "lib/lib{}.so".format(name) def readlink_command(): return "readlink"

13.111111

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0

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0

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null

0

1

0

null

0

1

0

1

0

5

c12477fb1b7e78f0ce243f4efb53297ce9171583

56

py

Python

api/__init__.py

rsoorajs/stream-cloud

e285f9d4cb3f12dc8e22584fc8948a02f5f035dd

[ "MIT" ]

43

2021-10-30T08:18:11.000Z

2022-03-22T07:33:04.000Z

api/__init__.py

Artinfee/stream-cloud

c3469d43542bb97261e4884297cffe87c4d68e7a

[ "MIT" ]

4

2021-11-15T14:24:48.000Z

2022-03-19T21:24:03.000Z

api/__init__.py

Artinfee/stream-cloud

c3469d43542bb97261e4884297cffe87c4d68e7a

[ "MIT" ]

113

2021-10-30T06:45:59.000Z

2022-03-31T15:52:53.000Z

from .router import Router from .telegram import Client

18.666667

28

0.821429

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56

5.75

0.625

0

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28

18.666667

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0

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true

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1

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1

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5

c13289691b24ba81cea60dd38f57c281bc585030

41

py

Python

tests/__init__.py

ojengwa/interledger

ccc24970a2ec7bd075d99efe0a18cf9922556605

[ "MIT" ]

null

tests/__init__.py

ojengwa/interledger

ccc24970a2ec7bd075d99efe0a18cf9922556605

[ "MIT" ]

null

tests/__init__.py

ojengwa/interledger

ccc24970a2ec7bd075d99efe0a18cf9922556605

[ "MIT" ]

null

"""Unit test package for interledger."""

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null

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5

c17a4b8f7ae47f60b652c7b8406ebb869e8e9de8

127

py

Python

python/testData/intentions/googleNoReturnSectionForInit_after.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/intentions/googleNoReturnSectionForInit_after.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/intentions/googleNoReturnSectionForInit_after.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

class C: def __init__(self, x, y): """ Args: x: y: """ return None

14.111111

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9

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14.111111

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0

1

0.333333

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0

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null

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1

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1

0

null

0

1

0

1

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5

c1ad744702da335ac7f00cac8f066b714404ae29

160

py

Python

src/cookbook/ingredients/admin.py

miguelzetina/python-graphene-initial

e6823ee4a7b2f72ebf592478966cd25339861019

[ "MIT" ]

null

src/cookbook/ingredients/admin.py

miguelzetina/python-graphene-initial

e6823ee4a7b2f72ebf592478966cd25339861019

[ "MIT" ]

2

2020-06-05T19:17:52.000Z

2021-06-10T20:55:11.000Z

src/cookbook/ingredients/admin.py

miguelzetina/python-graphene-initial

e6823ee4a7b2f72ebf592478966cd25339861019

[ "MIT" ]

null

from django.contrib import admin from cookbook.ingredients.models import Category, Ingredient admin.site.register(Category) admin.site.register(Ingredient)

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1

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5

c1c8130de3f0e9007575af26bb261e2e6b5a4473

98

py

Python

EduData/__init__.py

BAOOOOOM/EduData

affa465779cb94db00ed19291f8411229d342c0f

[ "Apache-2.0" ]

98

2019-07-05T03:27:36.000Z

2022-03-30T08:38:09.000Z

EduData/__init__.py

BAOOOOOM/EduData

affa465779cb94db00ed19291f8411229d342c0f

[ "Apache-2.0" ]

45

2020-12-25T03:49:43.000Z

2021-11-26T09:45:42.000Z

EduData/__init__.py

BAOOOOOM/EduData

affa465779cb94db00ed19291f8411229d342c0f

[ "Apache-2.0" ]

50

2019-08-17T05:11:15.000Z

2022-03-29T07:54:13.000Z

# coding: utf-8 # create by tongshiwei on 2019/7/2 from .DataSet import get_data, list_resources

19.6

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c1c838280a76d5cdcedce820d891666a71f259f6

49

py

Python

tests/__init__.py

mlf-core/system-intelligence

f1241dbb9783f6277c7f22327a532486b9876263

[ "Apache-2.0" ]

6

2020-06-23T10:41:17.000Z

2021-08-09T07:02:50.000Z

tests/__init__.py

mlf-core/system-intelligence

f1241dbb9783f6277c7f22327a532486b9876263

[ "Apache-2.0" ]

161

2020-06-12T14:53:37.000Z

2022-03-31T21:02:07.000Z

tests/__init__.py

mlf-core/system-intelligence

f1241dbb9783f6277c7f22327a532486b9876263

[ "Apache-2.0" ]

null

"""Unit test package for system_intelligence."""

24.5

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null

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1

0

5

a9b40805073bb7b510cea719d103112923f88822

548

py

Python

main.py

kewlamogh/theamogh

e1caaa808a6a2fbc4eb2be9dd0fc1433a7f59691

[ "MIT" ]

null

main.py

kewlamogh/theamogh

e1caaa808a6a2fbc4eb2be9dd0fc1433a7f59691

[ "MIT" ]

null

main.py

kewlamogh/theamogh

e1caaa808a6a2fbc4eb2be9dd0fc1433a7f59691

[ "MIT" ]

null

from flask import Flask, render_template from os import system as sys app = Flask('app', template_folder = "pages", static_folder = 'static') @app.route('/') def home(): return render_template("not-article/home.html") @app.route('/halo5tips') def htips(): return render_template("articles/halo5tips.html") @app.route('/humpty') def humpty(): return render_template("articles/humptydumpty.html") @app.route('/pytutorial') def pytut(): return render_template("articles/python-tutorial.html") app.run(host='0.0.0.0', port=8080) sys('clear')

27.4

71

0.729927

77

548

5.103896

0.454545

0.178117

0.203562

0.21374

0

0.020202

0.096715

548

20

72

27.4

0.773737

0

0.28051

0.180328

0

1

0.235294

false

0

0.117647

0.235294

0.588235

0

null

0

1

0

null

0

1

0

1

0

5

a9b560cacaa3451173cc2b804c8098f2521c9235

14

py

Python

test1.py

Arik5050/PyDjangoApi

b2e5781ecdfab18c7da520020734f1f9da5ad0b1

[ "MIT" ]

null

test1.py

Arik5050/PyDjangoApi

b2e5781ecdfab18c7da520020734f1f9da5ad0b1

[ "MIT" ]

null

test1.py

Arik5050/PyDjangoApi

b2e5781ecdfab18c7da520020734f1f9da5ad0b1

[ "MIT" ]

null

print("ffff")

7

13

0.642857

2

14

4.5

1

0

0.071429

14

1

14

0.692308

0

0.285714

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

e71affbfecf71180074bcb762037b7513dacf9f9

118

py

Python

testing/test_gcd.py

pcordemans/algorithms_examples

ce49fc1c1fc9ad02c8bd169051a22dd5b98a7ab4

[ "Apache-2.0" ]

null

testing/test_gcd.py

pcordemans/algorithms_examples

ce49fc1c1fc9ad02c8bd169051a22dd5b98a7ab4

[ "Apache-2.0" ]

null

testing/test_gcd.py

pcordemans/algorithms_examples

ce49fc1c1fc9ad02c8bd169051a22dd5b98a7ab4

[ "Apache-2.0" ]

null

from gcd import gcd def test_gcd(): assert gcd(3,6) == 3 assert gcd(1,2) == 1 assert gcd(12,18) == 6

19.666667

31

0.550847

22

118

2.909091

0.545455

0.421875

0

0.13253

0.29661

118

6

31

19.666667

0.638554

0

0.6

1

0.2

true

0

0.2

0

0.4

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

e721ab4aa94c9d49792a8e9ca2de72e193c24b56

338

py

Python

Treasuregram/main_app/models.py

Khalil71/Treasurers-

a22ee48480b18789e78e69cbc61eea386c7cea85

[ "MIT" ]

null

Treasuregram/main_app/models.py

Khalil71/Treasurers-

a22ee48480b18789e78e69cbc61eea386c7cea85

[ "MIT" ]

null

Treasuregram/main_app/models.py

Khalil71/Treasurers-

a22ee48480b18789e78e69cbc61eea386c7cea85

[ "MIT" ]

null

from django.db import models class Treasure(models.Model): name = models.CharField(max_length=100) value = models.DecimalField(max_digits=10, decimal_places=2) material = models.CharField(max_length=100) location = models.CharField(max_length=100) img_url = models.CharField(max_length=100) def __str__(self): return self.name

28.166667

61

0.784024

49

338

5.183673

0.571429

0.23622

0.283465

0.377953

0.425197

0

0.049834

0.109467

338

11

62

30.727273

0.79402

0

1

0.111111

false

0

0.111111

1

0

null

1

0

null

0

1

0

5

e73f383404b47e76efcb466c9786fbe85f67697e

195

py

Python

pcd_loader.py

ruc98/3D_PointClouds2

bf1ed31d895be6616992b9a35697ce762d102aec

[ "MIT" ]

4

2019-08-20T09:38:50.000Z

2021-02-24T14:54:11.000Z

pcd_loader.py

ruc98/3D_PointClouds2

bf1ed31d895be6616992b9a35697ce762d102aec

[ "MIT" ]

1

2021-02-08T10:17:25.000Z

2021-03-29T03:10:36.000Z

pcd_loader.py

ruc98/3D_PointClouds2

bf1ed31d895be6616992b9a35697ce762d102aec

[ "MIT" ]

7

2019-03-29T21:05:05.000Z

2021-03-12T01:48:59.000Z

from pypcd import pypcd pc = pypcd.PointCloud.from_path('/home/rahulchakwate/My_tensorflow/3D_Object_Segmentation/PointNet_Implementation/Edge_Extraction-master/ArtificialPointClouds/bunny.pcd')

65

170

0.876923

24

195

6.875

0.875

0

0.005319

0.035897

195

2

171

97.5

0.87234

0

0.692308

0

1

0

false

0

0.5

0

0.5

0

1

0

null

0

1

0

1

null

0

1

0

5

e740e20a26c5e948135fef39c05049d05cc7ff8e

211

py

Python

tests/test_asingleton.py

guallo/asingleton

03421633e2f17586584dbdf2a010449ffed7f675

[ "MIT" ]

null

tests/test_asingleton.py

guallo/asingleton

03421633e2f17586584dbdf2a010449ffed7f675

[ "MIT" ]

null

tests/test_asingleton.py

guallo/asingleton

03421633e2f17586584dbdf2a010449ffed7f675

[ "MIT" ]

null

import unittest import doctest import asingleton.asingleton def load_tests(loader, standard_tests, pattern): standard_tests.addTests(doctest.DocTestSuite(asingleton.asingleton)) return standard_tests

21.1

72

0.824645

24

211

7.083333

0.541667

0.229412

0

0.113744

211

9

73

23.444444

0.909091

0

1

0.166667

false

0

0.5

0

0.833333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

e79f471e20d608a2f436afca9cf9c16c22f636c0

113

py

Python

Chess/controller.py

surajsinghbisht054/GameOfChess

47edfa3fd4cc33fce9f2b9bc62e04e9ff93e5489

[ "Apache-2.0" ]

null

Chess/controller.py

surajsinghbisht054/GameOfChess

47edfa3fd4cc33fce9f2b9bc62e04e9ff93e5489

[ "Apache-2.0" ]

null

Chess/controller.py

surajsinghbisht054/GameOfChess

47edfa3fd4cc33fce9f2b9bc62e04e9ff93e5489

[ "Apache-2.0" ]

1

2019-08-30T13:51:18.000Z

import model class Controller(): def __init__(self): pass def init_model(self): self.model=model.Model()

12.555556

26

0.716814

16

113

4.75

0.5

0.184211

0

0.159292

113

8

27

14.125

0.8

0

1

0.333333

false

0.166667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

e7c9eb1c3dd2ee80d516670e2775e2dccb5e03de

10,747

py

Python

tests/geo/test_quat.py

bigblindbais/pytk

7e21604ba9b4fc869949d3d7da845d98c480d872

[ "MIT" ]

null

tests/geo/test_quat.py

bigblindbais/pytk

7e21604ba9b4fc869949d3d7da845d98c480d872

[ "MIT" ]

null

tests/geo/test_quat.py

bigblindbais/pytk

7e21604ba9b4fc869949d3d7da845d98c480d872

[ "MIT" ]

null

import unittest import pytk.geo as geo class GeoQuatTest(unittest.TestCase): def test_shape(self): self.assertRaises(geo.GeoException, geo.quat, []) self.assertRaises(geo.GeoException, geo.quat, [0]) self.assertRaises(geo.GeoException, geo.quat, [0, 1]) self.assertRaises(geo.GeoException, geo.quat, [0, 1, 2]) self.assertRaises(geo.GeoException, geo.quat, [0, 1, 2, 3, 4]) self.assertRaises(geo.GeoException, geo.quat, [0, 1, 2, 3, 4, 5]) self.assertRaises(geo.GeoException, geo.quat, [0, 1, 2, 3, 4, 5, 6]) self.assertRaises(geo.GeoException, geo.quat, [0, 1, 2, 3, 4, 5, 6, 7]) def test_equality(self): w = geo.quat([1, 0, 0, 0]) x = geo.quat([0, 1, 0, 0]) y = geo.quat([0, 0, 1, 0]) z = geo.quat([0, 0, 0, 1]) self.assertEqual(w, w) self.assertEqual(x, x) self.assertEqual(y, y) self.assertEqual(z, z) self.assertNotEqual(w, x) self.assertNotEqual(w, y) self.assertNotEqual(w, z) self.assertNotEqual(x, y) self.assertNotEqual(x, z) self.assertNotEqual(y, z) def test_add_sub(self): w = geo.quat([1, 0, 0, 0]) x = geo.quat([0, 1, 0, 0]) y = geo.quat([0, 0, 1, 0]) z = geo.quat([0, 0, 0, 1]) self.assertEqual( w + x + y , geo.quat([1, 1, 1, 0])) self.assertEqual( w + x + z, geo.quat([1, 1, 0, 1])) self.assertEqual( w + y + z, geo.quat([1, 0, 1, 1])) self.assertEqual( x + y + z, geo.quat([0, 1, 1, 1])) self.assertEqual( w - x + y , geo.quat([ 1, -1, 1, 0])) self.assertEqual( w - x + z, geo.quat([ 1, -1, 0, 1])) self.assertEqual( w - y + z, geo.quat([ 1, 0, -1, 1])) self.assertEqual( x - y + z, geo.quat([ 0, 1, -1, 1])) self.assertEqual(- w + x - y , geo.quat([-1, 1, -1, 0])) self.assertEqual(- w + x - z, geo.quat([-1, 1, 0, -1])) self.assertEqual(- w + y - z, geo.quat([-1, 0, 1, -1])) self.assertEqual( - x + y - z, geo.quat([ 0, -1, 1, -1])) def test_neg(self): w = geo.quat([1, 0, 0, 0]) x = geo.quat([0, 1, 0, 0]) y = geo.quat([0, 0, 1, 0]) z = geo.quat([0, 0, 0, 1]) self.assertEqual(w - x, w + (-x)) self.assertEqual(w - y, w + (-y)) self.assertEqual(w - z, w + (-z)) self.assertEqual(x - y, x + (-y)) self.assertEqual(x - z, x + (-z)) self.assertEqual(y - z, y + (-z)) def test_pow(self): w = geo.quat([1, 0, 0, 0]) x = geo.quat([0, 1, 0, 0]) y = geo.quat([0, 0, 1, 0]) z = geo.quat([0, 0, 0, 1]) self.assertAlmostEqual(w ** 2, 1) self.assertAlmostEqual(x ** 2, 1) self.assertAlmostEqual(y ** 2, 1) self.assertAlmostEqual(z ** 2, 1) self.assertAlmostEqual(( w - x + y ) ** 2, 3) self.assertAlmostEqual(( w - x + z) ** 2, 3) self.assertAlmostEqual(( w - y + z) ** 2, 3) self.assertAlmostEqual(( x - y + z) ** 2, 3) self.assertAlmostEqual((- w + x - y ) ** 2, 3) self.assertAlmostEqual((- w + x - z) ** 2, 3) self.assertAlmostEqual((- w + y - z) ** 2, 3) self.assertAlmostEqual(( - x + y - z) ** 2, 3) def test_conj(self): w = geo.quat([1, 0, 0, 0]) x = geo.quat([0, 1, 0, 0]) y = geo.quat([0, 0, 1, 0]) z = geo.quat([0, 0, 0, 1]) self.assertEqual(w + w.conj, geo.quat([2, 0, 0, 0])) self.assertEqual(x + x.conj, geo.quat([0, 0, 0, 0])) self.assertEqual(y + y.conj, geo.quat([0, 0, 0, 0])) self.assertEqual(z + z.conj, geo.quat([0, 0, 0, 0])) def test_inv(self): w = geo.quat([1, 0, 0, 0]) x = geo.quat([0, 1, 0, 0]) y = geo.quat([0, 0, 1, 0]) z = geo.quat([0, 0, 0, 1]) self.assertEqual(w * w.inv, geo.quat([1, 0, 0, 0])) self.assertEqual(w.inv * w, geo.quat([1, 0, 0, 0])) self.assertEqual(x * x.inv, geo.quat([1, 0, 0, 0])) self.assertEqual(x.inv * x, geo.quat([1, 0, 0, 0])) self.assertEqual(y * y.inv, geo.quat([1, 0, 0, 0])) self.assertEqual(y.inv * y, geo.quat([1, 0, 0, 0])) self.assertEqual(z * z.inv, geo.quat([1, 0, 0, 0])) self.assertEqual(z.inv * z, geo.quat([1, 0, 0, 0])) def test_normal(self): w = geo.quat([1, 0, 0, 0]) x = geo.quat([0, 1, 0, 0]) y = geo.quat([0, 0, 1, 0]) z = geo.quat([0, 0, 0, 1]) self.assertAlmostEqual((w ).normal ** 2, 1) self.assertAlmostEqual(( x ).normal ** 2, 1) self.assertAlmostEqual(( y ).normal ** 2, 1) self.assertAlmostEqual(( z).normal ** 2, 1) self.assertAlmostEqual((w + x ).normal ** 2, 1) self.assertAlmostEqual((w + y ).normal ** 2, 1) self.assertAlmostEqual((w + z).normal ** 2, 1) self.assertAlmostEqual(( x + y ).normal ** 2, 1) self.assertAlmostEqual(( x + z).normal ** 2, 1) self.assertAlmostEqual(( y + z).normal ** 2, 1) self.assertAlmostEqual((w + x + y ).normal ** 2, 1) self.assertAlmostEqual((w + x + z).normal ** 2, 1) self.assertAlmostEqual((w + y + z).normal ** 2, 1) self.assertAlmostEqual(( x + y + z).normal ** 2, 1) self.assertAlmostEqual((w + x + y + z).normal ** 2, 1) def test_as_rquat(self): w = geo.quat([1, 0, 0, 0]) x = geo.quat([0, 1, 0, 0]) y = geo.quat([0, 0, 1, 0]) z = geo.quat([0, 0, 0, 1]) self.assertEqual((w ).as_rquat, rquat([1, 0, 0, 0])) self.assertEqual(( x ).as_rquat, rquat([0, 1, 0, 0])) self.assertEqual(( y ).as_rquat, rquat([0, 0, 1, 0])) self.assertEqual(( z).as_rquat, rquat([0, 0, 0, 1])) self.assertEqual((w + x ).as_rquat, rquat([1, 1, 0, 0])) self.assertEqual((w + y ).as_rquat, rquat([1, 0, 1, 0])) self.assertEqual((w + z).as_rquat, rquat([1, 0, 0, 1])) self.assertEqual(( x + y ).as_rquat, rquat([0, 1, 1, 0])) self.assertEqual(( x + z).as_rquat, rquat([0, 1, 0, 1])) self.assertEqual(( y + z).as_rquat, rquat([0, 0, 1, 1])) self.assertEqual((w + x + y ).as_rquat, rquat([1, 1, 1, 0])) self.assertEqual((w + x + z).as_rquat, rquat([1, 1, 0, 1])) self.assertEqual((w + y + z).as_rquat, rquat([1, 0, 1, 1])) self.assertEqual(( x + y + z).as_rquat, rquat([0, 1, 1, 1])) self.assertEqual((w + x + y + z).as_rquat, rquat([1, 1, 1, 1])) def test_as_vect(self): w = geo.quat([1, 0, 0, 0]) x = geo.quat([0, 1, 0, 0]) y = geo.quat([0, 0, 1, 0]) z = geo.quat([0, 0, 0, 1]) self.assertEqual((w ).as_vect, vect([0, 0, 0])) self.assertEqual(( x ).as_vect, vect([1, 0, 0])) self.assertEqual(( y ).as_vect, vect([0, 1, 0])) self.assertEqual(( z).as_vect, vect([0, 0, 1])) self.assertEqual((w + x ).as_vect, vect([1, 0, 0])) self.assertEqual((w + y ).as_vect, vect([0, 1, 0])) self.assertEqual((w + z).as_vect, vect([0, 0, 1])) self.assertEqual(( x + y ).as_vect, vect([1, 1, 0])) self.assertEqual(( x + z).as_vect, vect([1, 0, 1])) self.assertEqual(( y + z).as_vect, vect([0, 1, 1])) self.assertEqual((w + x + y ).as_vect, vect([1, 1, 0])) self.assertEqual((w + x + z).as_vect, vect([1, 0, 1])) self.assertEqual((w + y + z).as_vect, vect([0, 1, 1])) self.assertEqual(( x + y + z).as_vect, vect([1, 1, 1])) self.assertEqual((w + x + y + z).as_vect, vect([1, 1, 1])) def test_mul(self): w = geo.quat([1, 0, 0, 0]) x = geo.quat([0, 1, 0, 0]) y = geo.quat([0, 0, 1, 0]) z = geo.quat([0, 0, 0, 1]) self.assertEqual( w * x , geo.quat([ 0, 1, 0, 0])) self.assertEqual( w * y , geo.quat([ 0, 0, 1, 0])) self.assertEqual( w * z , geo.quat([ 0, 0, 0, 1])) self.assertEqual( x * y , geo.quat([ 0, 0, 0, 1])) self.assertEqual( x * z , geo.quat([ 0, 0, -1, 0])) self.assertEqual( y * z , geo.quat([ 0, 1, 0, 0])) self.assertEqual( w * x * y , geo.quat([ 0, 0, 0, 1])) self.assertEqual( w * x * z , geo.quat([ 0, 0, -1, 0])) self.assertEqual( w * y * z , geo.quat([ 0, 1, 0, 0])) self.assertEqual( x * y * z , geo.quat([-1, 0, 0, 0])) self.assertEqual( w * x * y * z , geo.quat([-1, 0, 0, 0])) self.assertEqual( (w * x) * y * z , geo.quat([-1, 0, 0, 0])) self.assertEqual( w * (x * y) * z , geo.quat([-1, 0, 0, 0])) self.assertEqual( w * x * (y * z) , geo.quat([-1, 0, 0, 0])) self.assertEqual((w * x * y) * (z), geo.quat([-1, 0, 0, 0])) self.assertEqual((w * x) * (y * z), geo.quat([-1, 0, 0, 0])) self.assertEqual((w) * (x * y * z), geo.quat([-1, 0, 0, 0])) self.assertNotEqual(x * y, y * x) self.assertNotEqual(x * z, z * x) self.assertNotEqual(y * z, z * y) def test_rotate(self): v = geo.vect([0, 1, 2]) w = geo.quat([1, 0, 0, 0]) x = geo.quat([0, 1, 0, 0]) y = geo.quat([0, 0, 1, 0]) z = geo.quat([0, 0, 0, 1]) self.assertEqual( w * v , geo.vect([0, 1, 2])) self.assertEqual( x * v , geo.vect([0, -1, -2])) self.assertEqual( y * v , geo.vect([0, 1, -2])) self.assertEqual( z * v , geo.vect([0, -1, 2])) self.assertEqual( w * x * v , geo.vect([0, -1, -2])) self.assertEqual( w * y * v , geo.vect([0, 1, -2])) self.assertEqual( w * z * v , geo.vect([0, -1, 2])) self.assertEqual( x * y * v , geo.vect([0, -1, 2])) self.assertEqual( x * z * v , geo.vect([0, 1, -2])) self.assertEqual( y * z * v , geo.vect([0, -1, -2])) self.assertEqual( w * x * y * v , geo.vect([0, -1, 2])) self.assertEqual( w * x * z * v , geo.vect([0, 1, -2])) self.assertEqual( w * y * z * v , geo.vect([0, -1, -2])) self.assertEqual( x * y * z * v , geo.vect([0, 1, 2])) self.assertEqual( w * x * y * z * v , geo.vect([0, 1, 2]))

46.124464

79

0.456313

1,656

10,747

2.934179

0.025966

0.055979

0.090554

0.057419

0.889278

0.865404

0.802017

0.72731

0.671537

0.512245

0

0.081676

0.340374

10,747

232

80

46.323276

0.603752

0

0.22

0

0.7

1

0.06

false

0

0.01

0

0.075

0

null

0

1

0

null

0

1

0

5

99ccaa99fbe6412e99b1532758fc9507644a067b

191

py

Python

src/yellowdog_client/model/double_range.py

yellowdog/yellowdog-sdk-python-public

da69a7d6e45c92933e34fefcaef8b5d98dcd6036

[ "Apache-2.0" ]

null

src/yellowdog_client/model/double_range.py

yellowdog/yellowdog-sdk-python-public

da69a7d6e45c92933e34fefcaef8b5d98dcd6036

[ "Apache-2.0" ]

null

src/yellowdog_client/model/double_range.py

yellowdog/yellowdog-sdk-python-public

da69a7d6e45c92933e34fefcaef8b5d98dcd6036

[ "Apache-2.0" ]

null

from dataclasses import dataclass from typing import Optional from .range import Range @dataclass class DoubleRange(Range): min: Optional[float] = None max: Optional[float] = None

17.363636

33

0.753927

24

191

6

0.541667

0.180556

0.236111

0

0.17801

191

10

34

19.1

0.917197

0

1

0

true

0

0.428571

0

0.857143

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

99d99aa30023ecc85d6d727ce08aaecdfb54f6e4

187

py

Python

indonesian_dot/agents/dfs_agent.py

Ra-Ni/Indonesian-Dot-Solver

2baf507d23816b686f046f89d4c833728b25f2dc

[ "MIT" ]

null

indonesian_dot/agents/dfs_agent.py

Ra-Ni/Indonesian-Dot-Solver

2baf507d23816b686f046f89d4c833728b25f2dc

[ "MIT" ]

null

indonesian_dot/agents/dfs_agent.py

Ra-Ni/Indonesian-Dot-Solver

2baf507d23816b686f046f89d4c833728b25f2dc

[ "MIT" ]

1

2020-03-18T15:23:24.000Z

from . import Agent class DFSAgent(Agent): def g(self, n) -> int: return 0 def h(self, n) -> int: return 0 def __str__(self) -> str: return 'dfs'

13.357143

29

0.518717

26

187

3.576923

0.576923

0.107527

0.172043

0.301075

0.387097

0

0.016667

0.358289

187

13

30

14.384615

0.758333

0

0.25

0

0.016043

0

1

0.375

false

0

0.125

0.375

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

99dde7b3f5fcb0483ace26f84365fe2ed11d4cb8

24

py

Python

src/clusto/drivers/locations/racks/__init__.py

thekad/clusto

c141ea3ef4931c6a21fdf42845c6e9de5ee08caa

[ "BSD-3-Clause" ]

216

2015-01-10T17:03:25.000Z

2022-03-24T07:23:41.000Z

src/clusto/drivers/locations/racks/__init__.py

thekad/clusto

c141ea3ef4931c6a21fdf42845c6e9de5ee08caa

[ "BSD-3-Clause" ]

23

2015-01-08T16:51:22.000Z

2021-03-13T12:56:04.000Z

src/clusto/drivers/locations/racks/__init__.py

thekad/clusto

c141ea3ef4931c6a21fdf42845c6e9de5ee08caa

[ "BSD-3-Clause" ]

49

2015-01-08T00:13:17.000Z

2021-09-22T02:01:20.000Z

from basicrack import *

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null

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5

99ff041d1b8830dbc1ce3b632b548fc922a8713f

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py

Python

qiwi_handler/qiwi_handler/samples/__init__.py

bezumnui/qiwi_handler

9562b1a8c8fcc1910dbc722278cb6f5af313fa02

[ "MIT" ]

null

qiwi_handler/qiwi_handler/samples/__init__.py

bezumnui/qiwi_handler

9562b1a8c8fcc1910dbc722278cb6f5af313fa02

[ "MIT" ]

null

qiwi_handler/qiwi_handler/samples/__init__.py

bezumnui/qiwi_handler

9562b1a8c8fcc1910dbc722278cb6f5af313fa02

[ "MIT" ]

null

from qiwi_handler.samples.checkPay import *

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5

82061fd674b4b4f92d6437e0a3b7b4fdc08d22e9

1,031

py

Python

tests/test_propensity.py

yuxinchenNU/causalml

b0c280d99c0d5ab70e3b3d1a86c6f9d4170d53b1

[ "Apache-2.0" ]

1

2020-08-20T13:58:25.000Z

tests/test_propensity.py

yuxinchenNU/causalml

b0c280d99c0d5ab70e3b3d1a86c6f9d4170d53b1

[ "Apache-2.0" ]

null

tests/test_propensity.py

yuxinchenNU/causalml

b0c280d99c0d5ab70e3b3d1a86c6f9d4170d53b1

[ "Apache-2.0" ]

null

from causalml.propensity import ElasticNetPropensityModel, GradientBoostedPropensityModel from causalml.metrics import roc_auc_score from .const import RANDOM_SEED def test_elasticnet_propensity_model(generate_regression_data): y, X, treatment, tau, b, e = generate_regression_data() pm = ElasticNetPropensityModel(random_state=RANDOM_SEED) ps = pm.fit_predict(X, treatment) assert roc_auc_score(treatment, ps) > .5 def test_gradientboosted_propensity_model(generate_regression_data): y, X, treatment, tau, b, e = generate_regression_data() pm = GradientBoostedPropensityModel(random_state=RANDOM_SEED) ps = pm.fit_predict(X, treatment) assert roc_auc_score(treatment, ps) > .5 def test_gradientboosted_propensity_model_earlystopping(generate_regression_data): y, X, treatment, tau, b, e = generate_regression_data() pm = GradientBoostedPropensityModel(random_state=RANDOM_SEED, early_stop=True) ps = pm.fit_predict(X, treatment) assert roc_auc_score(treatment, ps) > .5

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82211888cd67f8fad5037e098394c479e87b5ab3

6,796

py

Python

source/remediation_runbooks/scripts/test/test_enableautoscalinggroupelbhealthcheck.py

j-erickson/aws-security-hub-automated-response-and-remediation

f1722c00817e1358a1d80272b67fc226f1105965

[ "Apache-2.0" ]

129

2020-08-11T18:18:50.000Z

2021-10-04T20:00:35.000Z

source/remediation_runbooks/scripts/test/test_enableautoscalinggroupelbhealthcheck.py

j-erickson/aws-security-hub-automated-response-and-remediation

f1722c00817e1358a1d80272b67fc226f1105965

[ "Apache-2.0" ]

39

2020-08-11T18:07:58.000Z

2021-10-15T16:26:24.000Z

source/remediation_runbooks/scripts/test/test_enableautoscalinggroupelbhealthcheck.py

j-erickson/aws-security-hub-automated-response-and-remediation

f1722c00817e1358a1d80272b67fc226f1105965

[ "Apache-2.0" ]

35

2020-08-15T04:57:27.000Z

2021-09-21T06:23:17.000Z

#!/usr/bin/python ############################################################################### # Copyright Amazon.com, Inc. or its affiliates. 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. A copy of the License # # is located at # # # # http://www.apache.org/licenses/LICENSE-2.0/ # # # # or in the "license" file accompanying this file. This file is distributed # # on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, express # # or implied. See the License for the specific language governing permis- # # sions and limitations under the License. # ############################################################################### import boto3 import json import botocore.session from botocore.stub import Stubber from botocore.config import Config import pytest from pytest_mock import mocker import EnableAutoScalingGroupELBHealthCheck_validate as validate my_session = boto3.session.Session() my_region = my_session.region_name #===================================================================================== # EnableAutoScalingGroupELBHealthCheck_remediation SUCCESS #===================================================================================== def test_validation_success(mocker): event = { 'SolutionId': 'SO0000', 'SolutionVersion': '1.2.3', 'AsgName': 'my_asg', 'region': my_region } good_response = { "AutoScalingGroups": [ { "AutoScalingGroupName": "sharr-test-autoscaling-1", "AutoScalingGroupARN": "arn:aws:autoscaling:us-east-1:111111111111:autoScalingGroup:785d81e1-cd66-435d-96de-d6ed5416defd:autoScalingGroupName/sharr-test-autoscaling-1", "LaunchTemplate": { "LaunchTemplateId": "lt-05ad2fca4f4ea7d2f", "LaunchTemplateName": "sharrtest", "Version": "$Default" }, "MinSize": 0, "MaxSize": 1, "DesiredCapacity": 0, "DefaultCooldown": 300, "AvailabilityZones": [ "us-east-1b" ], "LoadBalancerNames": [], "TargetGroupARNs": [ "arn:aws:elasticloadbalancing:us-east-1:111111111111:targetgroup/WebDemoTarget/fc9a82512b92af62" ], "HealthCheckType": "ELB", "HealthCheckGracePeriod": 300, "Instances": [], "CreatedTime": "2021-01-27T14:08:16.949000+00:00", "SuspendedProcesses": [], "VPCZoneIdentifier": "subnet-86a594ab", "EnabledMetrics": [], "Tags": [], "TerminationPolicies": [ "Default" ], "NewInstancesProtectedFromScaleIn": False, "ServiceLinkedRoleARN": "arn:aws:iam::111111111111:role/aws-service-role/autoscaling.amazonaws.com/AWSServiceRoleForAutoScaling" } ] } BOTO_CONFIG = Config( retries ={ 'mode': 'standard' }, region_name=my_region ) asg_client = botocore.session.get_session().create_client('autoscaling', config=BOTO_CONFIG) asg_stubber = Stubber(asg_client) asg_stubber.add_response( 'describe_auto_scaling_groups', good_response ) asg_stubber.activate() mocker.patch('EnableAutoScalingGroupELBHealthCheck_validate.connect_to_autoscaling', return_value=asg_client) assert validate.verify(event, {}) == { "response": { "message": "Autoscaling Group health check type updated to ELB", "status": "Success" } } asg_stubber.deactivate() def test_validation_failed(mocker): event = { 'SolutionId': 'SO0000', 'SolutionVersion': '1.2.3', 'AsgName': 'my_asg', 'region': my_region } bad_response = { "AutoScalingGroups": [ { "AutoScalingGroupName": "sharr-test-autoscaling-1", "AutoScalingGroupARN": "arn:aws:autoscaling:us-east-1:111111111111:autoScalingGroup:785d81e1-cd66-435d-96de-d6ed5416defd:autoScalingGroupName/sharr-test-autoscaling-1", "LaunchTemplate": { "LaunchTemplateId": "lt-05ad2fca4f4ea7d2f", "LaunchTemplateName": "sharrtest", "Version": "$Default" }, "MinSize": 0, "MaxSize": 1, "DesiredCapacity": 0, "DefaultCooldown": 300, "AvailabilityZones": [ "us-east-1b" ], "LoadBalancerNames": [], "TargetGroupARNs": [ "arn:aws:elasticloadbalancing:us-east-1:111111111111:targetgroup/WebDemoTarget/fc9a82512b92af62" ], "HealthCheckType": "EC2", "HealthCheckGracePeriod": 300, "Instances": [], "CreatedTime": "2021-01-27T14:08:16.949000+00:00", "SuspendedProcesses": [], "VPCZoneIdentifier": "subnet-86a594ab", "EnabledMetrics": [], "Tags": [], "TerminationPolicies": [ "Default" ], "NewInstancesProtectedFromScaleIn": False, "ServiceLinkedRoleARN": "arn:aws:iam::111111111111:role/aws-service-role/autoscaling.amazonaws.com/AWSServiceRoleForAutoScaling" } ] } BOTO_CONFIG = Config( retries ={ 'mode': 'standard' }, region_name=my_region ) asg_client = botocore.session.get_session().create_client('autoscaling', config=BOTO_CONFIG) asg_stubber = Stubber(asg_client) asg_stubber.add_response( 'describe_auto_scaling_groups', bad_response ) asg_stubber.activate() mocker.patch('EnableAutoScalingGroupELBHealthCheck_validate.connect_to_autoscaling', return_value=asg_client) assert validate.verify(event, {}) == { "response": { "message": "Autoscaling Group health check type is not ELB", "status": "Failed" } } asg_stubber.deactivate()

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0

5

822d0bbd584abe23edb59c54b12576de7bb8ef74

58,734

py

Python

crawler.py

bernardhu/gzlianjia

a8fa3c237123079c12b8890cbece099b813bfc4c

[ "MIT" ]

2

2017-05-11T09:41:13.000Z

2017-07-24T11:46:59.000Z

crawler.py

bernardhu/gzlianjia

a8fa3c237123079c12b8890cbece099b813bfc4c

[ "MIT" ]

null

crawler.py

bernardhu/gzlianjia

a8fa3c237123079c12b8890cbece099b813bfc4c

[ "MIT" ]

null

# -*- coding: utf-8 -*- import pickle import math import os.path import shutil import datetime import time import random import json import re import chardet import string import base64 import requests from bs4 import BeautifulSoup from model import TradedHouse, DistricHouse, BidHouse, RentHouse, create_table, clear_table grabedPool = {} gz_district = ['tianhe', 'yuexiu', 'liwan', 'haizhu', 'panyu', 'baiyun', 'huangpugz', 'conghua', 'zengcheng', 'huadou', 'luogang', 'nansha'] gz_district_name = {"tianhe":u"天河", "yuexiu":u"越秀", "liwan":u"荔湾", "haizhu":u"海珠", "panyu":u"番禺", "baiyun":u"白云", "huangpugz":u"黄埔", "conghua": u"从化", "zengcheng": u"增城", "huadou":u"花都", "luogang": u"萝岗","nansha":u"南沙"} global start_offset start_offset = 1 user_agent_list = [ "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/22.0.1207.1 Safari/537.1", "Mozilla/5.0 (X11; CrOS i686 2268.111.0) AppleWebKit/536.11 (KHTML, like Gecko) Chrome/20.0.1132.57 Safari/536.11", "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.6 (KHTML, like Gecko) Chrome/20.0.1092.0 Safari/536.6", "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.6 (KHTML, like Gecko) Chrome/20.0.1090.0 Safari/536.6", "Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/19.77.34.5 Safari/537.1", "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/536.5 (KHTML, like Gecko) Chrome/19.0.1084.9 Safari/536.5", "Mozilla/5.0 (Windows NT 6.0) AppleWebKit/536.5 (KHTML, like Gecko) Chrome/19.0.1084.36 Safari/536.5", "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3", "Mozilla/5.0 (Windows NT 5.1) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3", "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1062.0 Safari/536.3", "Mozilla/5.0 (Windows NT 6.1; WOW64) 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CriOS/57.0.2987.100 Mobile/13G36 Safari/601.1.46", "Mozilla/5.0 (iPad; CPU OS 9_2_1 like Mac OS X) AppleWebKit/601.1.46 (KHTML, like Gecko) FxiOS/6.1 Mobile/13D15 Safari/601.1.46", "Mozilla/5.0 (iPad; CPU OS 9_2 like Mac OS X) AppleWebKit/601.1.46 (KHTML, like Gecko) Version/9.0 Mobile/13BC75 Safari/601.1", "Mozilla/5.0 (iPad; CPU OS 9_3_3 like Mac OS X) AppleWebKit/601.1 (KHTML, like Gecko) CriOS/56.0.2924.79 Mobile/13G34 Safari/601.1.46", "Mozilla/5.0 (iPad; CPU OS 9_3_5 like Mac OS X) AppleWebKit/601.1 (KHTML, like Gecko) CriOS/57.0.2987.137 Mobile/13G36 Safari/601.1.46", "Mozilla/5.0 (iPad; CPU OS 9_3_5 like Mac OS X) AppleWebKit/601.1.46(KHTML, like Gecko) FxiOS/6.1 Mobile/13G36 Safari/601.1.46", "Mozilla/6.0 (iPhone; CPU iPhone OS 8_0 like Mac OS X) AppleWebKit/536.26 (KHTML, like Gecko) Version/8.0 Mobile/10A5376e Safari/8536.25", "Mozilla/5.0 (iPad; CPU OS 9_0 like Mac OS X) AppleWebKit/600.1.4 (KHTML, like Gecko) Version/9.0 Mobile/13A340 Safari/600.1.4", "Mozilla/5.0 (iPad; CPU OS 9_3_5 like Mac OS X) AppleWebKit/537.51.2 (KHTML, like Gecko) CriOS/36.0.1985.49 Mobile/13G36 Safari/9537.53", "Mozilla/5.0 (iPad; CPU OS 9_3_5 like Mac OS X) AppleWebKit/601.1 (KHTML, like Gecko) CriOS/59.0.3071.102 Mobile/13G36 Safari/601.1.46" ] def get_header(): i = random.randint(0,len(user_agent_list)-1) headers = { 'User-Agent': user_agent_list[i], 'x-forearded-for': "1.2.3.4" } return headers def get_multipart_formdata(data, bondary): post_data = [] for key, value in data.iteritems(): if value is None: continue post_data.append('--' + bondary ) post_data.append('Content-Disposition: form-data; name="{0}"'.format(key)) post_data.append('') if isinstance(value, int): value = str(value) post_data.append(value) post_data.append('--' + bondary + '--') post_data.append('') body = '\r\n'.join(post_data) return body.encode('utf-8') def verify_captcha(): url = "http://captcha.lianjia.com" r = requests.get(url, headers= get_header(), timeout= 30) soup = BeautifulSoup(r.content, "lxml") pages = soup.find("form", class_="human").find_all("input") print pages[2]['value'], pages[2]['name'] csrf = pages[2]['value'] time.sleep(1) url = "http://captcha.lianjia.com/human" r = requests.get(url, headers= get_header(), timeout= 30) cookie = r.headers['Set-Cookie'] soup = BeautifulSoup(r.content, "lxml") images = json.loads(r.content)['images'] uuid = json.loads(r.content)['uuid'] #print images for idx in xrange(0, len(images)): fh = open("%d.jpg"%idx, "wb") data = images['%d'%idx].split(',', 1) fh.write(base64.b64decode(data[1])) fh.close() step = 0 mask = 0 while 1: if step == 0: val = raw_input("check 0.jpg reverse,(y/n):\t") if val == 'y' or val == 'Y': mask = mask + 1 step = 1 elif step == 1: val = raw_input("check 1.jpg reverse,(y/n):\t") if val == 'y' or val == 'Y': mask = mask + 2 step = 2 elif step == 2: val = raw_input("check 2.jpg reverse,(y/n):\t") if val == 'y' or val == 'Y': mask = mask + 4 step = 3 elif step == 3: val = raw_input("check 3.jpg reverse,(y/n):\t") if val == 'y' or val == 'Y': mask = mask + 8 break print mask boundary='----WebKitFormBoundary7MA4YWxkTrZu0gW' headers = get_header() headers['content-type'] = "multipart/form-data; boundary={0}".format(boundary) headers['Cookie'] = cookie print get_multipart_formdata({'uuid':uuid, 'bitvalue': mask, '_csrf': csrf}, boundary) print headers r = requests.post(url, headers=headers, data=get_multipart_formdata({'uuid':uuid, 'bitvalue': mask, '_csrf': csrf}, boundary)) print r.request print r.content def get_distric_rent_cnt(distric): print "try to grab %s community rent cnt "%distric url = "http://gz.lianjia.com/zufang/%s/"%distric r = requests.get(url, headers= get_header(), timeout= 30) #print r.text.encode("utf-8") soup = BeautifulSoup(r.content, "lxml") pages = soup.find("div", class_="page-box house-lst-page-box") time.sleep(random.randint(5,10)) try: pageStr = pages["page-data"] except Exception, e: print e,r.content os._exit(0) jo = json.loads(pageStr) return jo['totalPage'] def get_distric_community_cnt(distric): print "try to grab %s community cnt "%distric url = "http://gz.lianjia.com/xiaoqu/%s/"%distric r = requests.get(url, headers= get_header(), timeout= 30) #print r.text.encode("utf-8") soup = BeautifulSoup(r.content, "lxml") pages = soup.find("div", class_="page-box house-lst-page-box") time.sleep(random.randint(5,10)) try: pageStr = pages["page-data"] except Exception, e: print e,r.content,r.text os._exit(0) jo = json.loads(pageStr) return jo['totalPage'] def grab_distric(url): print "try to grab distric page ", url r = requests.get(url, headers= get_header(), timeout= 30) soup = BeautifulSoup(r.content, "lxml") try: districList = soup.find("ul", class_="listContent").find_all('li') except Exception, e: print e,r.content os._exit(0) if not districList: return for item in districList: # 房屋详情链接，唯一标识符 distUrl = item.a["href"] or '' #if distUrl in grabedPool["data"]: # print distUrl, "already exits，skip" # continue print "start to crawl" , distUrl # 抓取历史成交 title = item.find("div", class_="title").a.string.encode("utf-8").rstrip() historyList = item.find("div", class_="houseInfo").find_all('a') history = historyList[0].string.encode("utf-8") m = re.match(r"(\d+)天成交(\d+)套", history) print m, history historyRange = 0 historySell = 0 if m: historyRange = m.group(1) historySell = m.group(2) print title, history, historyRange, historySell # 抓取区&商圈 pos = item.find("div", class_="positionInfo").find_all('a') dis = pos[0].string.encode("utf-8") bizcircle = pos[1].string.encode("utf-8") print dis, bizcircle #抓取成交均价噢 avgStr = item.find("div", class_="totalPrice").span.string.encode("utf-8") m = re.match(r"(\d+)", avgStr) if m: avg = int(avgStr) else: avg = 0 print avg #抓取在售 onSell = int(item.find("div", class_="xiaoquListItemSellCount").a.span.string) print onSell # 通过 ORM 存储到 sqlite distItem = DistricHouse( name = title, district = dis, bizcircle = bizcircle, historyRange = historyRange, historySell = historySell, ref = distUrl, avgpx = avg, onsell = onSell, ) distItem.save() # 添加到已经抓取的池 #grabedPool["data"].add(distUrl) # 抓取完成后，休息几秒钟，避免给对方服务器造成大负担 time.sleep(random.randint(1,3)) def get_distric_chengjiao_cnt(distric, proxy): print "try to grab %s chengjiao cnt "%distric url = "http://gz.lianjia.com/chengjiao/%s/"%distric r = requests.get(url, headers= get_header(), timeout= 30) #print r.text.encode("utf-8") soup = BeautifulSoup(r.content, "lxml") try: pages = soup.find("div", class_="page-box house-lst-page-box") time.sleep(random.randint(5,10)) pageStr = pages["page-data"] jo = json.loads(pageStr) return jo['totalPage'] except Exception, e: print e,r.content os._exit(0) def get_distric_bid_cnt(distric, proxy): print "try to grab %s bid cnt "%distric url = "http://gz.lianjia.com/ershoufang/%s/"%distric r = requests.get(url, headers= get_header(), timeout= 30) #print r.text.encode("utf-8") soup = BeautifulSoup(r.content, "lxml") try: pages = soup.find("div", class_="page-box house-lst-page-box") time.sleep(random.randint(5,10)) pageStr = pages["page-data"] jo = json.loads(pageStr) return jo['totalPage'] except Exception, e: print e,r.content os._exit(0) #i = random.randint(0,len(proxy)-1) #proxies = { # "http": proxy[i] # } #print "try proxy", proxy[i] #r = requests.get(url, headers= get_header(), proxies=proxies, timeout= 30) #soup = BeautifulSoup(r.content, "lxml") #pages = soup.find("div", class_="page-box house-lst-page-box") #time.sleep(random.randint(5,10)) #pageStr = pages["page-data"] #jo = json.loads(pageStr) #return jo['totalPage'] def get_xici_proxy(url, proxys): print "get proxy", url r = requests.get(url, headers= get_header(), timeout= 10) soup = BeautifulSoup(r.content, "lxml") pages = soup.find_all("tr", class_="odd") for page in pages: items = page.find_all("td") proxy ="http://%s:%s"%(items[1].string, items[2].string) url = "http://gz.lianjia.com/chengjiao/tianhe/" proxies = { "http": proxy } try: r = requests.get(url, headers= get_header(), proxies=proxies, timeout= 3) soup = BeautifulSoup(r.content, "lxml") tradedHoustList = soup.find("ul", class_="listContent") if not tradedHoustList: continue proxys.append(proxy) print proxy, proxys except Exception, e: #print Exception,":",e continue def get_kuaidaili_proxy(url, proxys): print "get proxy", url r = requests.get(url, headers= get_header(), timeout= 10) soup = BeautifulSoup(r.content, "lxml") pages = soup.find("tbody").find_all("tr") for page in pages: items = page.find_all("td") proxy ="http://%s:%s"%(items[0].string, items[1].string) print proxy url = "http://gz.lianjia.com/chengjiao/tianhe/" proxies = { "http": proxy } try: r = requests.get(url, headers= get_header(), proxies=proxies, timeout= 3) soup = BeautifulSoup(r.content, "lxml") tradedHoustList = soup.find("ul", class_="listContent") if not tradedHoustList: continue proxys.append(proxy) print proxy, proxys except Exception, e: #print Exception,":",e continue def get_youdaili_proxy(url, proxys): print "get proxy", url r = requests.get(url, headers= get_header(), timeout= 10) soup = BeautifulSoup(r.content, "lxml") pages = soup.find("div", class_="chunlist").find_all("a") page = pages[0] u = page["href"] html = requests.get(u, headers= get_header(), timeout= 3).content proxy_list = re.findall(r'\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}:\d{1,5}', html) for proxy in proxy_list: url = "http://gz.lianjia.com/chengjiao/tianhe/" proxies = { "http": proxy } try: r = requests.get(url, headers= get_header(), proxies=proxies, timeout= 3) soup = BeautifulSoup(r.content, "lxml") tradedHoustList = soup.find("ul", class_="listContent") if not tradedHoustList: continue proxys.append(proxy) print proxy, proxys except Exception, e: #print Exception,":",e continue def build_proxy(): proxys = [] #get_xici_proxy("http://www.xicidaili.com/nn/1", proxys) #get_xici_proxy("http://www.xicidaili.com/nn/2", proxys) #get_kuaidaili_proxy("http://www.kuaidaili.com/proxylist/1", proxys) #get_kuaidaili_proxy("http://www.kuaidaili.com/proxylist/2", proxys) #get_kuaidaili_proxy("http://www.kuaidaili.com/proxylist/3", proxys) #get_kuaidaili_proxy("http://www.kuaidaili.com/proxylist/4", proxys) #get_youdaili_proxy("http://www.youdaili.net/Daili/http", proxys) r = requests.get("http://127.0.0.1:5000/get_all/", headers= get_header(), timeout= 10) print r.content proxys= json.loads(r.content) print proxys return proxys def grabRent(url, proxy, disName, priceDic, bizDic): print "try to grab page ", url r = requests.get(url, headers= get_header(), timeout= 30) soup = BeautifulSoup(r.content, "lxml") try: bidHoustList = soup.find("ul", class_="house-lst").find_all('li') except Exception, e: print e,r.content os._exit(0) if not bidHoustList: return storge = [] for item in bidHoustList: # 房屋详情链接，唯一标识符 houseUrl = item.a["href"] or '' #if houseUrl in grabedPool["data"]: # print houseUrl, "already exit, skip" # continue print 'start to crawl' , houseUrl # 抓取小区，户型，面积朝向，装修，电梯 xiaoqu = item.find("div", class_="where").a.string.rstrip().encode("utf-8") houseType = item.find("span", class_="zone").span.string.rstrip().encode("utf-8") squareStr = item.find("span", class_="meters").string.rstrip().encode("utf-8") orientation = item.find("div", class_="where").findAll("span")[4].string.encode("utf-8").rstrip() print xiaoqu, houseType, squareStr, orientation m = re.match(r"\b[0-9]+(\.[0-9]+)?", squareStr) square = 0 if m: square = string.atof(m.group(0)) print squareStr, square #楼层，楼龄 posInfo = item.find("div", class_="con").contents[2] m = re.match(ur"(.*)楼层$共(\d+)层$", posInfo) floorLevel = 'Nav' floorTotal = -1 if m: floorLevel = m.group(1) floorTotal = m.group(2) print m.group(1).encode("utf-8"), m.group(2) print floorLevel.encode("utf-8"), floorTotal #挂牌价 priceInfo = item.find("div", class_="price").span if priceInfo: price = string.atof(priceInfo.string) else : price = 0 print price pricePre = item.find("div", class_="price-pre").string priceUpdate, misc = ([x.strip() for x in pricePre.split(" ")]) print priceUpdate #关注，带看，放盘 seenStr = item.find("div", class_="square").find("span", class_="num").string seen = 0 if m: seen = string.atoi(seenStr) print seen try: avg = priceDic[xiaoqu] except Exception, e: print e avg = 0 print "avg", avg try: biz = bizDic[xiaoqu] except Exception, e: print e biz = "" print "biz", biz loan = 0 loan = square*avg -1500000 loanRet = 0 yearRate = 0.049 monthRate = 0.049/12 loanYear = 30 loanMonth = loanYear*12 if loan < 0 : loan = 0 loanRet = 0 else: loanRet = loan*monthRate*((1+monthRate)**loanMonth)/(((1+monthRate)**loanMonth)-1) loan = round(loan/10000) print loan, loanRet # 通过 ORM 存储到 sqlite BidItem = RentHouse( xiaoqu = xiaoqu, houseType = houseType, square = square, houseUrl = houseUrl, orientation = orientation, floorLevel = floorLevel, floorTotal = floorTotal, price = price, avg = avg, loan = loan, loanRet = loanRet, seen = seen, bizcircle = biz, district = disName, ) storge.append(BidItem) for s in storge: s.save() # 添加到已经抓取的池 #grabedPool["data"].add(s.houseUrl) # 抓取完成后，休息几秒钟，避免给对方服务器造成大负担 time.sleep(random.randint(1,3)) def grabBid(url, proxy, disName, priceDic): print "try to grabbid page ", url r = requests.get(url, headers= get_header(), timeout= 30) soup = BeautifulSoup(r.content, "lxml") try: bidHoustList = soup.find("ul", class_="sellListContent").find_all('li') except Exception, e: print e,r.content os._exit(0) i = random.randint(0,len(proxy)-1) proxies = { "http": proxy[i] } print "try proxy", proxy[i] r = requests.get(url, headers= get_header(), proxies=proxies, timeout= 30) soup = BeautifulSoup(r.content, "lxml") bidHoustList = soup.find("ul", class_="sellListContent").find_all('li') if not bidHoustList: return storge = [] for item in bidHoustList: # 房屋详情链接，唯一标识符 houseUrl = item.a["href"] or '' #if houseUrl in grabedPool["data"]: # print houseUrl, "already exit, skip" # continue print 'start to crawl' , houseUrl # 抓取小区，户型，面积朝向，装修，电梯 houseInfo = item.find("div", class_="houseInfo").contents[2] xiaoqu = item.find("div", class_="houseInfo").a.string.encode("utf-8").rstrip() if houseInfo: if len(houseInfo.split("|")) == 5: null, houseType, squareStr, orientation, decoration = ([x.strip() for x in houseInfo.split("|")]) elevator = 'Nav' if len(houseInfo.split("|")) == 6: null, houseType, squareStr, orientation, decoration, elevator = ([x.strip() for x in houseInfo.split("|")]) print xiaoqu, houseType.encode("utf-8"), orientation.encode("utf-8"), decoration.encode("utf-8"), elevator.encode("utf-8") m = re.match(ur"\b[0-9]+(\.[0-9]+)?", squareStr) square = 0 if m: square = string.atof(m.group(0)) print squareStr.encode("utf-8"), square #楼层，楼龄 posInfo = item.find("div", class_="positionInfo").contents[1] print posInfo.encode("utf-8") m = re.match(ur"(.*)楼层$共(\d+)层$(\d+)年建", posInfo) floorLevel = 'Nav' floorTotal = -1 build = -1 if m: floorLevel = m.group(1) floorTotal = m.group(2) build = int(m.group(3)) print m.group(1).encode("utf-8"), m.group(2), m.group(3) print floorLevel.encode("utf-8"), floorTotal, build biz = item.find("div", class_="positionInfo").a.string print biz #挂牌价 priceInfo = item.find("div", class_="totalPrice").span if priceInfo: bid = string.atof(priceInfo.string) else : bid = 0 print bid #均价 priceInfo = item.find("div", class_="unitPrice").span priceStr = "" if priceInfo: priceStr = priceInfo.string m = re.match(ur"单价(\d+)元", priceStr) price = 0 if m: price = m.group(1) print price, priceStr.encode("utf-8") #关注，带看，放盘 followInfo = item.find("div", class_="followInfo").contents[1] if followInfo: watchStr, seenStr, releaseStr = ([x.strip() for x in followInfo.split("/")]) print watchStr.encode("utf-8"), seenStr.encode("utf-8"), releaseStr.encode("utf-8") m = re.match(ur"(\d+)人", watchStr) watch = 0 if m: watch = m.group(1) m = re.match(ur"共(\d+)次", seenStr) seen = 0 if m: seen = m.group(1) m = re.match(ur"(\d+)天", releaseStr) release = 0 if m: release = int(m.group(1)) else: m = re.match(ur"(\d+)个月", releaseStr) if m: release = int(m.group(1))*30 else: m = re.match(ur"(.*)年", releaseStr) if m: release = m.group(1) if release == u"一": release = 365 try: avg = priceDic[xiaoqu] except Exception, e: avg = 0 print watch, seen, release, avg # 通过 ORM 存储到 sqlite BidItem = BidHouse( xiaoqu = xiaoqu, houseType = houseType, square = square, houseUrl = houseUrl, orientation = orientation, decoration = decoration, elevator = elevator, floorLevel = floorLevel, floorTotal = floorTotal, build = build, price = price, avg = avg, bid = bid, watch = watch, seen = seen, release = release, bizcircle = biz, district = disName, ) storge.append(BidItem) for s in storge: s.save() # 添加到已经抓取的池 #grabedPool["data"].add(s.houseUrl) # 抓取完成后，休息几秒钟，避免给对方服务器造成大负担 time.sleep(random.randint(1,3)) def grab(url, proxy, disName, bizDic, lastMarkTrade): print "try to grab page ", url r = requests.get(url, headers= get_header(), timeout= 30) soup = BeautifulSoup(r.content, "lxml") try: tradedHoustList = soup.find("ul", class_="listContent").find_all('li') except Exception, e: print e,r.content #os._exit(0) tradedHoustList = soup.find("li", class_="pictext") if not tradedHoustList: tradedHoustList = soup.find("ul", class_="listContent").find_all('li') else: i = random.randint(0,len(proxy)-1) proxies = { "http": proxy[i] } print "try proxy", proxy[i] r = requests.get(url, headers= get_header(), proxies=proxies, timeout= 30) soup = BeautifulSoup(r.content, "lxml") tradedHoustList = soup.find("ul", class_="listContent").find_all('li') if not tradedHoustList: return storge = [] stop = False for item in tradedHoustList: # 房屋详情链接，唯一标识符 houseUrl = item.a["href"] or '' #if houseUrl in grabedPool["data"]: # print houseUrl, "already exit, skip" # continue print 'start to crawl' , houseUrl # 抓取小区，户型，面积 title = item.find("div", class_="title") if title: print title xiaoqu, houseType, square = (title.string.replace(" ", " ").split(" ")) m = re.match(ur"\b[0-9]+(\.[0-9]+)?", square) if m: square = string.atof(m.group(0)) else: xiaoqu, houseType, square = ('Nav', 'Nav', 0) xiaoqu = xiaoqu.encode("utf-8").rstrip() houseType = houseType.encode("utf-8") print xiaoqu, houseType, square dealInfo = item.find("div", class_="totalPrice").span try: deal = string.atof(dealInfo.string.encode("utf-8")) except Exception, e: deal = -1 print deal # 朝向，装修，电梯 houseInfo = item.find("div", class_="houseInfo").contents[1] if houseInfo: if len(houseInfo.split("|")) == 2: orientation, decoration = ([x.strip() for x in houseInfo.split("|")]) elevator = 'Nav' if len(houseInfo.split("|")) == 3: orientation, decoration, elevator = ([x.strip() for x in houseInfo.split("|")]) print orientation.encode("utf-8"), decoration.encode("utf-8"), elevator.encode("utf-8") #成交日期 dealDate = item.find("div", class_="dealDate") if dealDate: tradeDate = datetime.datetime.strptime(dealDate.string, '%Y.%m.%d') or datetime.datetime(1990, 1, 1) print tradeDate if lastMarkTrade >= tradeDate: print 'break for time' stop = True break #楼层，楼龄 posInfo = item.find("div", class_="positionInfo").contents[1] if posInfo: floor, buildStr = ([x.strip() for x in posInfo.split(" ")]) print floor.encode("utf-8"), buildStr.encode("utf-8") m = re.match(ur"(.*)楼层$共(\d+)层$", floor) floorLevel = 'Nav' floorTotal = -1 if m: floorLevel = m.group(1) floorTotal = m.group(2) print m.group(1).encode("utf-8"), m.group(2) m = re.match(ur"(\d+)年建", buildStr) build = -1 if m: build = m.group(1) print floorLevel.encode("utf-8"), floorTotal, build #均价 priceInfo = item.find("div", class_="unitPrice").span if priceInfo: price = int(priceInfo.string) else : price = 0 print price #挂牌价，成交周期 dealCycle = item.find("span", class_="dealCycleTxt").find_all('span') bid = -1 cycle = -1 if dealCycle: if len(dealCycle) == 1: bidStr = dealCycle[0].string cycleStr = "" if len(dealCycle) == 2: bidStr = dealCycle[0].string cycleStr = dealCycle[1].string print bidStr.encode("utf-8"), cycleStr.encode("utf-8") m = re.match(ur"挂牌(\d+)万", bidStr) if m: bid = m.group(1) m = re.match(ur"成交周期(\d+)天", cycleStr) if m: cycle = m.group(1) try: biz = bizDic[xiaoqu] except Exception, e: biz = "unknown" #print bid, cycle, disName, biz # 通过 ORM 存储到 sqlite tradeItem = TradedHouse( xiaoqu = xiaoqu, houseType = houseType, square = square, houseUrl = houseUrl, orientation = orientation, decoration = decoration, elevator = elevator, floorLevel = floorLevel, floorTotal = floorTotal, build = build, price = price, tradeDate = tradeDate, bid = bid, deal = deal, cycle = cycle, district = disName, bizcircle = biz, ) storge.append(tradeItem) for s in storge: s.save() # 添加到已经抓取的池 #grabedPool["data"].add(s.houseUrl) # 抓取完成后，休息几秒钟，避免给对方服务器造成大负担 time.sleep(random.randint(1,3)) return stop step_context = {"phase":0, "cnt":0, "offset":0, "pgoffset":1, "date":"20170705"} def save_context(): global step_context print "save", step_context, type(step_context) json.dump(step_context, open('context','w')) def load_context(): global step_context step_context = json.load(open('context','r')) print "load", step_context, type(step_context) def crawl_district(): global step_context for dis_offset in xrange(step_context['offset'], len(gz_district)): dis = gz_district[dis_offset] step_context['offset'] = dis_offset save_context() cnt = step_context['cnt'] if cnt == 0: cnt = get_distric_community_cnt(dis) print "get_distric_info", dis, cnt step_context['cnt'] = cnt save_context() for i in xrange(step_context['pgoffset'], cnt+1): step_context['pgoffset'] = i save_context() url = "http://gz.lianjia.com/xiaoqu/%s/pg%s/"%(dis, format(str(i))) grab_distric(url) step_context['pgoffset'] = 1 step_context['cnt'] = 0 save_context() def crawl_district_chengjiao(): global step_context for dis_offset in xrange(step_context['offset'], len(gz_district)): dis = gz_district[dis_offset] step_context['offset'] = dis_offset save_context() distric = DistricHouse.select(DistricHouse.name, DistricHouse.bizcircle, DistricHouse.avgpx).where(DistricHouse.district == gz_district_name[dis]) print distric bizDic = {} priceDic = {} for item in distric: name = item.name.rstrip().encode("utf-8") biz = item.bizcircle.encode("utf-8") bizDic[name] = biz price = item.avgpx priceDic[name] = price #print name cnt = step_context['cnt'] if cnt == 0: cnt = get_distric_chengjiao_cnt(dis, []) step_context['cnt'] = cnt save_context() ts = TradedHouse.select(TradedHouse.tradeDate).where(TradedHouse.district == gz_district_name[dis]).order_by(TradedHouse.tradeDate.desc()).limit(1) print ts for item in ts: print item.tradeDate, type(item.tradeDate) lastMarkTrade = item.tradeDate for i in xrange(step_context['pgoffset'], cnt+1): step_context['pgoffset'] = i save_context() page = "http://gz.lianjia.com/chengjiao/%s/pg%s/"%(dis, format(str(i))) stop = grab(page, [], gz_district_name[dis], bizDic, lastMarkTrade) if stop == True: break step_context['pgoffset'] = 1 step_context['cnt'] = 0 save_context() def crawl_district_bid(): global step_context #proxy = build_proxy() for dis_offset in xrange(step_context['offset'], len(gz_district)): dis = gz_district[dis_offset] distric = DistricHouse.select(DistricHouse.name, DistricHouse.bizcircle, DistricHouse.avgpx).where(DistricHouse.district == gz_district_name[dis]) print distric bizDic = {} priceDic = {} for item in distric: name = item.name.rstrip().encode("utf-8") biz = item.bizcircle.encode("utf-8") bizDic[name] = biz price = item.avgpx priceDic[name] = price #print name step_context['offset'] = dis_offset save_context() cnt = step_context['cnt'] if cnt == 0: cnt = get_distric_bid_cnt(dis, []) step_context['cnt'] = cnt save_context() for i in xrange(step_context['pgoffset'], cnt+1): step_context['pgoffset'] = i save_context() page = "http://gz.lianjia.com/ershoufang/%s/pg%s/"%(dis, format(str(i))) grabBid(page, [], gz_district_name[dis], priceDic) step_context['pgoffset'] = 1 step_context['cnt'] = 0 save_context() def crawl_district_rent(): global step_context for dis_offset in xrange(step_context['offset'], len(gz_district)): dis = gz_district[dis_offset] distric = DistricHouse.select(DistricHouse.name, DistricHouse.bizcircle, DistricHouse.avgpx).where(DistricHouse.district == gz_district_name[dis]) print distric bizDic = {} priceDic = {} for item in distric: name = item.name.rstrip().encode("utf-8") biz = item.bizcircle.encode("utf-8") bizDic[name] = biz price = item.avgpx priceDic[name] = price #print name step_context['offset'] = dis_offset save_context() cnt = step_context['cnt'] if cnt == 0: cnt = get_distric_rent_cnt(dis) step_context['cnt'] = cnt save_context() for i in xrange(step_context['pgoffset'], cnt+1): step_context['pgoffset'] = i save_context() page = "http://gz.lianjia.com/zufang/%s/pg%s/"%(dis, format(str(i))) grabRent(page, [], gz_district_name[dis], priceDic, bizDic) step_context['pgoffset'] = 1 step_context['cnt'] = 0 save_context() def process_context(): #global step_context print step_context['phase'] if step_context['phase'] == 0: crawl_district() step_context['phase'] = 1 step_context['cnt'] = 0 step_context['offset'] = 0 step_context['pgoffset'] = 1 step_context['date'] = time.strftime("%Y%m%d", time.localtime()) save_context() elif step_context['phase'] == 1: crawl_district_chengjiao() step_context['phase'] = 2 step_context['cnt'] = 0 step_context['offset'] = 0 step_context['pgoffset'] = 1 save_context() elif step_context['phase'] == 2: crawl_district_bid() step_context['phase'] = 3 step_context['cnt'] = 0 step_context['offset'] = 0 step_context['pgoffset'] = 1 save_context() elif step_context['phase'] == 3: crawl_district_rent() step_context['phase'] = -1 step_context['cnt'] = 0 step_context['offset'] = 0 step_context['pgoffset'] = 1 save_context() elif step_context['phase'] == -1: #shutil.copy('houseprice.db', time.strftime("houseprice_%Y%m%d.db", time.localtime())) clear_table() step_context['phase'] = 1 if __name__== "__main__": #save_context() load_context() #verify_captcha() if step_context['phase'] == -1: process_context() while step_context['phase'] != -1: process_context()

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412db9c7cef044c2683619df8e0ed4cea588f793

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py

Python

polls/admin.py

harshit-j/VotePlex-Django-Project

3ceae098ae29be79f27f15f0ef50bb3dca6a2f31

[ "Unlicense", "MIT" ]

null

polls/admin.py

harshit-j/VotePlex-Django-Project

3ceae098ae29be79f27f15f0ef50bb3dca6a2f31

[ "Unlicense", "MIT" ]

null

polls/admin.py

harshit-j/VotePlex-Django-Project

3ceae098ae29be79f27f15f0ef50bb3dca6a2f31

[ "Unlicense", "MIT" ]

null

from django.contrib import admin from .models import Poll,Choice # Register your models here. admin.site.register(Poll) admin.site.register(Choice)

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4137ff96de7ba46bd83b1d001d68f27777635219

187

py

Python

geometry/__init__.py

FRidh/python-geometry

62cb6210bcad3b1e4c1a7e0516ca17138793c1b3

[ "BSD-3-Clause" ]

8

2015-12-16T04:39:12.000Z

2021-04-08T15:49:23.000Z

geometry/__init__.py

FRidh/python-geometry

62cb6210bcad3b1e4c1a7e0516ca17138793c1b3

[ "BSD-3-Clause" ]

1

2015-08-07T15:03:02.000Z

geometry/__init__.py

FRidh/python-geometry

62cb6210bcad3b1e4c1a7e0516ca17138793c1b3

[ "BSD-3-Clause" ]

2

2015-03-23T02:03:04.000Z

2020-01-09T05:01:50.000Z

from .quat import Quat from .point import Point from .vector import Vector from .plane import Plane from .polygon import Polygon from .edge import Edge from .pointlist import PointList

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41547329fa8a963ce10488a8647273da8ecf5058

184

py

Python

vedaseg/utils/__init__.py

E18301194/vedaseg

c62c8ea46dbba12f03262452dd7bed22969cfe4e

[ "Apache-2.0" ]

2

2020-07-15T02:36:46.000Z

2021-03-08T03:18:26.000Z

vedaseg/utils/__init__.py

E18301194/vedaseg

c62c8ea46dbba12f03262452dd7bed22969cfe4e

[ "Apache-2.0" ]

null

vedaseg/utils/__init__.py

E18301194/vedaseg

c62c8ea46dbba12f03262452dd7bed22969cfe4e

[ "Apache-2.0" ]

1

2021-09-16T09:40:12.000Z

from .config import ConfigDict, Config from .common import build_from_cfg, get_root_logger, set_random_seed from .registry import Registry from .metrics import MetricMeter, dice_score

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417ac447cea1375c78db7c41bdda9318c895f13c

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py

Python

projects/DistillReID/kdreid/modeling/__init__.py

asvk/fast-reid

cf246e9bee5b5e5d154de98ba0395b7a5d0d0ab7

[ "Apache-2.0" ]

71

2021-03-12T07:43:43.000Z

2022-03-30T03:28:16.000Z

projects/DistillReID/kdreid/modeling/__init__.py

asvk/fast-reid

cf246e9bee5b5e5d154de98ba0395b7a5d0d0ab7

[ "Apache-2.0" ]

8

2021-04-06T03:02:58.000Z

2022-02-16T14:05:47.000Z

projects/DistillReID/kdreid/modeling/__init__.py

asvk/fast-reid

cf246e9bee5b5e5d154de98ba0395b7a5d0d0ab7

[ "Apache-2.0" ]

7

2021-04-19T02:55:58.000Z

2021-11-11T12:39:09.000Z

from .backbones import build_shufflenetv2_backbone

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1

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5

68d81d1d60b2e941cd671a96decb29d75ce45d87

159

py

Python

web_services/admin.py

berv-uni-project/audio-watermarik-web-services

0eb445b4fbd35ee564b910f90419c67cc8380604

[ "MIT" ]

1

2021-12-13T01:32:02.000Z

web_services/admin.py

berv-uni-project/audio-watermarik-web-services

0eb445b4fbd35ee564b910f90419c67cc8380604

[ "MIT" ]

4

2021-12-13T23:14:27.000Z

2022-01-11T11:40:04.000Z

web_services/admin.py

berv-uni-project/audio-watermark-web-services

997fac664e1838210eaad64fe8951bb458fdfb63

[ "MIT" ]

null

from django.contrib import admin from .models import Embed,Extract # Register your models here. admin.site.register(Embed) admin.site.register(Extract)

22.714286

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null

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5

ec115da1b0f8b7f3f9bbd10c81b0458af3536f04

200

py

Python

micromagneticmodel/dynamics/__init__.py

ubermag/micromagneticmodel

91ad92d26cdbec369a5a41f7b90a17ca5328cd07

[ "BSD-3-Clause" ]

5

2019-10-21T01:12:16.000Z

2021-09-24T03:52:30.000Z

micromagneticmodel/dynamics/__init__.py

ubermag/micromagneticmodel

91ad92d26cdbec369a5a41f7b90a17ca5328cd07

[ "BSD-3-Clause" ]

11

2019-08-12T22:38:17.000Z

2022-03-15T00:08:47.000Z

micromagneticmodel/dynamics/__init__.py

ubermag/micromagneticmodel

91ad92d26cdbec369a5a41f7b90a17ca5328cd07

[ "BSD-3-Clause" ]

4

2020-06-27T15:36:28.000Z

2021-12-06T15:08:04.000Z

from .dynamicsterm import DynamicsTerm from .precession import Precession from .damping import Damping from .zhangli import ZhangLi from .slonczewski import Slonczewski from .dynamics import Dynamics

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5

6b74209eb5c6de464ddb2c029dcfaf0f83a380eb

5,692

py

Python

src/quantrt/models/order.py

ardieb/quantrt

4a65b97218711ce8ebc7ae90588546cbee72e646

[ "MIT" ]

null

src/quantrt/models/order.py

ardieb/quantrt

4a65b97218711ce8ebc7ae90588546cbee72e646

[ "MIT" ]

null

src/quantrt/models/order.py

ardieb/quantrt

4a65b97218711ce8ebc7ae90588546cbee72e646

[ "MIT" ]

null

import asyncio import asyncpg import quantrt.common.config import quantrt.common.log import quantrt.util.database import quantrt.util.time from dataclasses import dataclass from datetime import datetime from decimal import Decimal from enum import Enum from typing import Optional, Iterable __all__ = ["OrderStatus", "Order", "save", "save_batch", "fetch", "fetch_batch", "fetch_open"] class OrderStatus(Enum): Open = "open" Maker = "maker" Taker = "taker" Canceled = "canceled" @dataclass class Order: # order id from coinbase order_id: str # Product ticker. product: str # Timestamp of the trade. tstamp: datetime # Order status status: OrderStatus # Which side? side: str # What was the trade size? amount: Decimal # Price. price: Decimal async def save(order: Order, pool: Optional[asyncpg.Pool] = None): if not pool: pool = quantrt.common.config.db_conn_pool if not pool: quantrt.common.log.QuantrtLog.exception("No connection pool has been configured.") raise EnvironmentError("No connection pool has been configured.") async with pool.acquire() as conn: sql = """ INSERT INTO order (id, product, tstamp, status, side, amount, price) VALUES ($1, $2, $3, $4, $5, $6, $7) ON CONFLICT (product, order_id) DO UPDATE SET tstamp = EXCLUDED.tstamp, status = EXCLUDED.status, side = EXCLUDED.side, amount = EXCLUDED.amount, price = EXCLUDED.price """ statement = await quantrt.util.database.prepare_sql(sql, conn) await statement.executemany(( order.order_id, order.product, order.tstamp, order.status.name, order.side, order.amount, order.price)) async def save_batch(orders: Iterable[Order], pool: Optional[asyncpg.Pool] = None): if not pool: pool = quantrt.common.config.db_conn_pool if not pool: quantrt.common.log.QuantrtLog.exception("No connection pool has been configured.") raise EnvironmentError("No connection pool has been configured.") async with pool.acquire() as conn: sql = """ INSERT INTO order (id, product, tstamp, status, side, amount, price) VALUES ($1, $2, $3, $4, $5, $6, $7) ON CONFLICT (product, order_id) DO UPDATE SET tstamp = EXCLUDED.tstamp, status = EXCLUDED.status, side = EXCLUDED.side, amount = EXCLUDED.amount, price = EXCLUDED.price """ statement = await quantrt.util.database.prepare_sql(sql, conn) await statement.executemany([( order.order_id, order.product, order.tstamp, order.status.name, order.side, order.amount, order.price) for order in orders]) async def fetch(id: str, pool: Optional[asyncpg.Pool] = None) -> Order: if not pool: pool = quantrt.common.config.db_conn_pool if not pool: quantrt.common.log.QuantrtLog.exception("No connection pool has been configured.") raise EnvironmentError("No connection pool has been configured.") async with pool.acquire() as conn: sql = """ SELECT * FROM order WHERE order_id = $1 """ statement = await quantrt.util.database.prepare_sql(sql, conn) row = await statement.fetch(id) return Order( order_id=row[0]["order_id"], product=row[0]["product"], tstamp=row[0]["tstamp"], status=row[0]["status"], side=row[0]["side"], amount=row[0]["amount"], price=row[0]["amount"] ) async def fetch_batch(ids: Iterable[str], pool: Optional[asyncpg.Pool] = None) -> Iterable[Order]: if not pool: pool = quantrt.common.config.db_conn_pool if not pool: quantrt.common.log.QuantrtLog.exception("No connection pool has been configured.") raise EnvironmentError("No connection pool has been configured.") async with pool.acquire() as conn: sql = """ SELECT * FROM order WHERE order_id = $1 """ statement = await quantrt.util.database.prepare_sql(sql, conn) rows = [await statement.fetchrow(id) for order_id in ids] return [Order( order_id=row["order_id"], product=row["product"], tstamp=row["tstamp"], status=row["status"], side=row["side"], amount=row["amount"], price=row["amount"] ) for row in rows] async def fetch_open(product_id: str, pool: Optional[asyncpg.Pool] = None) -> Iterable[Order]: if not pool: pool = quantrt.common.config.db_conn_pool if not pool: quantrt.common.log.QuantrtLog.exception("No connection pool has been configured.") raise EnvironmentError("No connection pool has been configured.") async with pool.acquire() as conn: sql = """ SELECT * FROM order WHERE product = $1 AND status = "open" """ statement = await quantrt.util.database.prepare_sql(sql, conn) rows = await statement.fetch(product_id) return [Order( order_id=row["order_id"], product=row["product"], tstamp=row["tstamp"], status=row["status"], side=row["side"], amount=row["amount"], price=row["amount"] ) for row in rows]

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0

5

6bf9811632f4fe69277437cdd9d4d69bba02f031

1,296

py

Python

Conteudo das Aulas/009/Balada2-vR.py

cerberus707/lab-python

ebba3c9cde873d70d4bb61084f79ce30b7f9e047

[ "Apache-2.0" ]

null

Conteudo das Aulas/009/Balada2-vR.py

cerberus707/lab-python

ebba3c9cde873d70d4bb61084f79ce30b7f9e047

[ "Apache-2.0" ]

null

Conteudo das Aulas/009/Balada2-vR.py

cerberus707/lab-python

ebba3c9cde873d70d4bb61084f79ce30b7f9e047

[ "Apache-2.0" ]

null

""" Estruturas de decisao: IF e Else (se nao ou caso contrario) ':' no python, substitui o then a condicao if so depende de resposta True ou Fale, exeplo case 2 """ #Case 1 idade = int(input('Quantos Anos voce tem?')) resp = idade >= 18 if resp == True: print('Voce pode beber a vontade') if resp == False: print ('Voce so pode beber refrigerante') print (resp) #Case 2 idade = int(input('Quantos Anos voce tem?')) resp = idade >= 18 if resp: print('Voce pode beber a vontade') if resp != True: print ('Voce so pode beber refrigerante') print (resp) #Case 3 idade = int(input('Quantos Anos voce tem?')) if idade >= 18: print('Voce pode beber a vontade') if idade < 18: print ('Voce so pode beber refrigerante') #Case 4 idade = int(input('Quantos Anos voce tem?')) if idade >= 18: print('Voce pode beber a vontade') if idade >= 21: print ('Voce é cliente VIP') if idade < 18: print ('Voce so pode beber refrigerante') #Adocao do ELSE #Case 1 idade = int(input('Quantos Anos voce tem?')) if idade >= 18: print('Voce pode beber a vontade') if idade >= 21: print ('Voce é cliente VIP') else: print ('Voce so pode beber refrigerante') #Case 2 if 1 > 1: Print('Sim') else: print('nao')

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5

d407fa67f9b4da2e9a43ce405caf45b9fba0e258

203

py

Python

app/models/__init__.py

ZanMax/wrs

b62bcb50f305a83b7fe08f83f5e2d9f1c2cf1ec5

[ "MIT" ]

null

app/models/__init__.py

ZanMax/wrs

b62bcb50f305a83b7fe08f83f5e2d9f1c2cf1ec5

[ "MIT" ]

null

app/models/__init__.py

ZanMax/wrs

b62bcb50f305a83b7fe08f83f5e2d9f1c2cf1ec5

[ "MIT" ]

null

from .users import Users from .tokens import Tokens from .groups import Groups from .openedlogs import OpenedLogs from .reports import Reports from .worktime import WorkTime from .version import Version

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d40a7a607ea0772b28f5e4494ede8d2c08c8f234

317

py

Python

thonny/plugins/microbit/api_stubs/radio.py

shreyas202/thonny

ef894c359200b0591cf98451907243395b817c63

[ "MIT" ]

2

2020-02-13T06:41:07.000Z

2022-02-14T09:28:02.000Z

Thonny/Lib/site-packages/thonny/plugins/microbit/api_stubs/radio.py

Pydiderot/pydiderotIDE

a42fcde3ea837ae40c957469f5d87427e8ce46d3

[ "MIT" ]

30

2019-01-04T10:14:56.000Z

2020-10-12T14:00:31.000Z

Thonny/Lib/site-packages/thonny/plugins/microbit/api_stubs/radio.py

Pydiderot/pydiderotIDE

a42fcde3ea837ae40c957469f5d87427e8ce46d3

[ "MIT" ]

3

2018-11-24T14:00:30.000Z

2019-07-02T02:32:26.000Z

RATE_1MBIT = 0 RATE_250KBIT = 2 RATE_2MBIT = 1 def config(): pass def off(): pass def on(): pass def receive(): pass def receive_bytes(): pass def receive_bytes_into(): pass def receive_full(): pass def reset(): pass def send(): pass def send_bytes(): pass

7.204545

25

0.583596

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317

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0.409091

0.355932

0.316384

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0

0.036697

0.312303

317

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7.372093

0.775229

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0.434783

false

0.434783

0

0.434783

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null

1

0

null

0

1

0

1

0

5

d42f63a9dc78db8f405b571060d5d20d5fe2ac9e

123

py

Python

__init__.py

maiconandsilva/receitas

924aa3acbd5b24286fb2a0527c2a2e133f904937

[ "MIT" ]

null

__init__.py

maiconandsilva/receitas

924aa3acbd5b24286fb2a0527c2a2e133f904937

[ "MIT" ]

null

__init__.py

maiconandsilva/receitas

924aa3acbd5b24286fb2a0527c2a2e133f904937

[ "MIT" ]

null

from sqlalchemy_utils.listeners import force_auto_coercion # Chama antes da definicao das entidades force_auto_coercion()

24.6

58

0.861789

17

123

5.941176

0.823529

0.178218

0.336634

0

0.105691

123

4

59

30.75

0.918182

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null

0

1

0

1

0

5

d4331dde745665d41097acdf474f921c6a622045

73

py

Python

8_kyu/Reversed_Words.py

JoaoVitorLeite/CodeWars

156feda7273b37fdc90d007e1f638cf0dc73959f

[ "MIT" ]

null

8_kyu/Reversed_Words.py

JoaoVitorLeite/CodeWars

156feda7273b37fdc90d007e1f638cf0dc73959f

[ "MIT" ]

null

8_kyu/Reversed_Words.py

JoaoVitorLeite/CodeWars

156feda7273b37fdc90d007e1f638cf0dc73959f

[ "MIT" ]

null

# 8 kyu def reverse_words(s): return " ".join(reversed(s.split()))

12.166667

40

0.616438

11

73

4

0.909091

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0.016949

0.191781

73

5

41

14.6

0.728814

0.068493

0

0.015385

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0

0.5

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0

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null

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1

0

null

0

1

0

1

0

5

2e04be4c31c4273fd8a956bbb4b7b56890f8872b

233

py

Python

src/postprocess/base_processor.py

jwpttcg66/ExcelToTransfer

3afc0cf088f4c991bbf4dc2d6d1f395a71cbc3c7

[ "Apache-2.0" ]

47

2017-06-23T07:47:50.000Z

2022-03-07T22:36:19.000Z

xl2code/postprocess/base_processor.py

twjitm/ExcelToCode

d160c75b9b7a305f4b3367d85ee0550572869d3e

[ "MIT" ]

1

2019-03-12T06:12:50.000Z

2019-04-03T00:50:01.000Z

xl2code/postprocess/base_processor.py

twjitm/ExcelToCode

d160c75b9b7a305f4b3367d85ee0550572869d3e

[ "MIT" ]

23

2017-05-12T07:46:07.000Z

2022-01-22T03:19:50.000Z

# -*- coding: utf-8 -*- class BaseProcessor(object): def __init__(self, exporter, generator_info): super(BaseProcessor, self).__init__() self.exporter = exporter self.generator_info = generator_info def run(self): pass

17.923077

46

0.716738

28

233

5.571429

0.535714

0.25

0.205128

0

0.005076

0.154506

233

12

47

19.416667

0.786802

0.090129

0

1

0.285714

false

0.142857

0

0.428571

0

1

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null

1

0

null

0

1

0

1

0

5

2e77d1a9d6aa8a2b551fbd613f310f1de5e10ebf

215

py

Python

stitch/__init__.py

pystitch/stitch

09a16da2f2af2be6a960e2338de488c8de2c2271

[ "MIT" ]

468

2016-08-31T19:17:17.000Z

2022-03-07T13:51:53.000Z

stitch/__init__.py

hschuett/stitch

09a16da2f2af2be6a960e2338de488c8de2c2271

[ "MIT" ]

40

2016-08-29T20:34:47.000Z

2020-09-21T03:25:49.000Z

stitch/__init__.py

TomAugspurger/stitch

09a16da2f2af2be6a960e2338de488c8de2c2271

[ "MIT" ]

29

2016-08-31T19:44:19.000Z

2019-05-16T14:37:44.000Z

from .stitch import ( # noqa convert, convert_file, kernel_factory, run_code, Stitch ) from .cli import cli # noqa from ._version import get_versions __version__ = get_versions()['version'] del get_versions

21.5

59

0.753488

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215

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0.218543

0.238411

0

0.162791

215

9

60

23.888889

0.838889

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false

0

0.428571

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0.428571

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1

0

null

1

0

1

0

null

0

1

0

5

cf126e7af080062504718d41e07a5ea043576365

21,441

py

Python

survos/lib/io.py

paskino/SuRVoS

e01e784442e2e9f724826cdb70f3a50c034c6455

[ "Apache-2.0" ]

22

2016-09-30T08:04:42.000Z

2022-03-05T07:24:18.000Z

survos/lib/io.py

paskino/SuRVoS

e01e784442e2e9f724826cdb70f3a50c034c6455

[ "Apache-2.0" ]

81

2016-11-21T15:32:14.000Z

2022-02-20T00:22:27.000Z

survos/lib/io.py

paskino/SuRVoS

e01e784442e2e9f724826cdb70f3a50c034c6455

[ "Apache-2.0" ]

6

2018-11-22T10:19:59.000Z

2022-02-04T06:15:48.000Z

import os import numpy as np import six import mrcfile as mrc rhd = [ ("nx", "i4"), # Number of columns ("ny", "i4"), # Number of rows ("nz", "i4"), ("mode", "i4"), # Types of pixels in the image. Values used by IMOD: # 0 = unsigned or signed bytes depending on flag in imodFlags # 1 = signed short integers (16 bits) # 2 = float (32 bits) # 3 = short * 2, (used for complex data) # 4 = float * 2, (used for complex data) # 6 = unsigned 16-bit integers (non-standard) # 16 = unsigned char * 3 (for rgb data, non-standard) ("nxstart", "i4"), # Starting point of sub-image (not used in IMOD) ("nystart", "i4"), ("nzstart", "i4"), ("mx", "i4"), # Grid size in X, Y and Z ("my", "i4"), ("mz", "i4"), ("xlen", "f4"), # Cell size; pixel spacing = xlen/mx, ylen/my, zlen/mz ("ylen", "f4"), ("zlen", "f4"), ("alpha", "f4"), # Cell angles - ignored by IMOD ("beta", "f4"), ("gamma", "f4"), # These need to be set to 1, 2, and 3 for pixel spacing to be interpreted correctly ("mapc", "i4"), # map column 1=x,2=y,3=z. ("mapr", "i4"), # map row 1=x,2=y,3=z. ("maps", "i4"), # map section 1=x,2=y,3=z. # These need to be set for proper scaling of data ("amin", "f4"), # Minimum pixel value ("amax", "f4"), # Maximum pixel value ("amean", "f4"), # Mean pixel value ("ispg", "i4"), # space group number (ignored by IMOD) ("next", "i4"), # number of bytes in extended header (called nsymbt in MRC standard) ("creatid", "i2"), # used to be an ID number, is 0 as of IMOD 4.2.23 ("extra_data", "V30"), # (not used, first two bytes should be 0) # These two values specify the structure of data in the extended header; their meaning depend on whether the # extended header has the Agard format, a series of 4-byte integers then real numbers, or has data # produced by SerialEM, a series of short integers. SerialEM stores a float as two shorts, s1 and s2, by: # value = (sign of s1)*(|s1|*256 + (|s2| modulo 256)) * 2**((sign of s2) * (|s2|/256)) ("nint", "i2"), # Number of integers per section (Agard format) or number of bytes per section (SerialEM format) ("nreal", "i2"), # Number of reals per section (Agard format) or bit # Number of reals per section (Agard format) or bit # flags for which types of short data (SerialEM format): # 1 = tilt angle * 100 (2 bytes) # 2 = piece coordinates for montage (6 bytes) # 4 = Stage position * 25 (4 bytes) # 8 = Magnification / 100 (2 bytes) # 16 = Intensity * 25000 (2 bytes) # 32 = Exposure dose in e-/A2, a float in 4 bytes # 128, 512: Reserved for 4-byte items # 64, 256, 1024: Reserved for 2-byte items # If the number of bytes implied by these flags does # not add up to the value in nint, then nint and nreal # are interpreted as ints and reals per section ("extra_data2", "V20"), # extra data (not used) ("imodStamp", "i4"), # 1146047817 indicates that file was created by IMOD ("imodFlags", "i4"), # Bit flags: 1 = bytes are stored as signed # Explanation of type of data ("idtype", "i2"), # ( 0 = mono, 1 = tilt, 2 = tilts, 3 = lina, 4 = lins) ("lens", "i2"), ("nd1", "i2"), # for idtype = 1, nd1 = axis (1, 2, or 3) ("nd2", "i2"), ("vd1", "i2"), # vd1 = 100. * tilt increment ("vd2", "i2"), # vd2 = 100. * starting angle # Current angles are used to rotate a model to match a new rotated image. The three values in each set are # rotations about X, Y, and Z axes, applied in the order Z, Y, X. ("triangles", "f4", 6), # 0,1,2 = original: 3,4,5 = current ("xorg", "f4"), # Origin of image ("yorg", "f4"), ("zorg", "f4"), ("cmap", "S4"), # Contains "MAP " #("stamp", "U4", 4), # First two bytes have 17 and 17 for big-endian or 68 and 65 for little-endian ("stamp", 'f4', 4), ("rms", "f4"), # RMS deviation of densities from mean density ("nlabl", "i4"), # Number of labels with useful data ] class MRC(object): def __init__(self, X, stats=None): self.header = self.header_dict = self.data = None self.yz_swapped = False if isinstance(X, six.string_types): self.read(X) else: # assuming X to be a numpy array self.parse(X, stats=stats) def __getitem__(self, item): if self.header_dict is not None and \ item in self.header_dict: return self.header_dict[item] return None def keys(self): if self.header_dict is not None: return self.header_dict.keys() def values(self): if self.header_dict is not None: return self.header_dict.values() def items(self): if self.header_dict is not None: return self.header_dict.items() def parse(self, X, stats=None): self.data = np.ascontiguousarray(X) return self def read(self, filename): print("Reading MRC file {}".format(filename)) with mrc.open(filename, mode='r+') as mrc_file: print("Read data of len: {}".format(len(mrc_file.data))) self.header = mrc_file.header self.data = mrc_file.data self.header_dict = {} # FIX: empty dictionary def save(self, filename): print("Saving MRC file: {}".format(filename)) new_mrcfile = mrc.new(filename) new_mrcfile.set_data(self.data) new_mrcfile.update_header_from_data() new_mrcfile.update_header_stats() new_mrcfile.close() # The previous MRC export class broke with python/numpy version changes # current bug: the numpy structured array is giving # "underlying view is not C-contiguous" array writing error class MRC_old(object): def __init__(self, X, stats=None): self.header = self.header_dict = self.data = None self.yz_swapped = False if isinstance(X, six.string_types): self.read(X) else: # assuming X to be a numpy array self.parse(X, stats=stats) def __getitem__(self, item): if self.header_dict is not None and \ item in self.header_dict: return self.header_dict[item] return None def keys(self): if self.header_dict is not None: return self.header_dict.keys() def values(self): if self.header_dict is not None: return self.header_dict.values() def items(self): if self.header_dict is not None: return self.header_dict.items() def parse(self, X, stats=None): if stats is not None: amin, amax, amean = stats else: amin = X.min(); amax = X.max(); amean = X.mean(); dt = np.dtype(rhd) imodFlags = 0 if X.dtype in [np.uint8, np.int8]: mode = 0 imodFlags = (X.dtype == np.int8) elif X.dtype == np.int16: mode = 1 elif X.dtype == np.float32: mode = 2 elif X.dtype == np.complex64: mode = 4 elif X.dtype == np.uint16: mode = 6 else: mode = 16 values = ( X.shape[2], # Number of columns X.shape[1], # Number of rows X.shape[0], # Number of sections mode, # Types of pixels in the image. Values used by IMOD: # 0 = unsigned or signed bytes depending on flag in imodFlags # 1 = signed short integers (16 bits) # 2 = float (32 bits) # 3 = short * 2, (used for complex data) # 4 = float * 2, (used for complex data) # 6 = unsigned 16-bit integers (non-standard) # 16 = unsigned char * 3 (for rgb data, non-standard) 0, # Starting point of sub-image (not used in IMOD) 0, 0, X.shape[2], # Grid size in X, Y and Z X.shape[1], X.shape[0], X.shape[2], # Cell size; pixel spacing = xlen/mx, ylen/my, zlen/mz X.shape[1], X.shape[0], 90.0, # Cell angles - ignored by IMOD 90.0, 90.0, # These need to be set to 1, 2, and 3 for pixel spacing to be interpreted correctly 1, # map column 1=x,2=y,3=z. 2, # map row 1=x,2=y,3=z. 3, # map section 1=x,2=y,3=z. # These need to be set for proper scaling of data amin, # Minimum pixel value amax, # Maximum pixel value amean, # Mean pixel value 1, # space group number (ignored by IMOD) 0, # number of bytes in extended header (called nsymbt in MRC standard) 0, # used to be an ID number, is 0 as of IMOD 4.2.23 "", # (not used, first two bytes should be 0) # These two values specify the structure of data in the extended header; their meaning depend on whether the # extended header has the Agard format, a series of 4-byte integers then real numbers, or has data # produced by SerialEM, a series of short integers. SerialEM stores a float as two shorts, s1 and s2, by: # value = (sign of s1)*(|s1|*256 + (|s2| modulo 256)) * 2**((sign of s2) * (|s2|/256)) 0, # Number of integers per section (Agard format) or number of bytes per section (SerialEM format) 0, # Number of reals per section (Agard format) or bit # Number of reals per section (Agard format) or bit # flags for which types of short data (SerialEM format): # 1 = tilt angle * 100 (2 bytes) # 2 = piece coordinates for montage (6 bytes) # 4 = Stage position * 25 (4 bytes) # 8 = Magnification / 100 (2 bytes) # 16 = Intensity * 25000 (2 bytes) # 32 = Exposure dose in e-/A2, a float in 4 bytes # 128, 512: Reserved for 4-byte items # 64, 256, 1024: Reserved for 2-byte items # If the number of bytes implied by these flags does # not add up to the value in nint, then nint and nreal # are interpreted as ints and reals per section "", # extra data (not used) 0, # 1146047817 indicates that file was created by IMOD imodFlags, # Bit flags: 1 = bytes are stored as signed # Explanation of type of data 0, # ( 0 = mono, 1 = tilt, 2 = tilts, 3 = lina, 4 = lins) 0, 0, # for idtype = 1, nd1 = axis (1, 2, or 3) 0, 0, # vd1 = 100. * tilt increment 0, # vd2 = 100. * starting angle # Current angles are used to rotate a model to match a new rotated image. The three values in each set are # rotations about X, Y, and Z axes, applied in the order Z, Y, X. [ 0., 0., 0., 90., 0., 0.], # 0,1,2 = original: 3,4,5 = current 0., # Origin of image X.shape[1] / 2., 0., 'MAP ', # Contains "MAP " [68, 65, 0, 0], # First two bytes have 17 and 17 for big-endian or 68 and 65 for little-endian 0.0, # RMS deviation of densities from mean density 6, # Number of labels with useful data [ 'tif2mrc: Converted to mrc format. 21-Oct-15 13:02:25 ', 'CCDERASER: Bad points replaced with interpolated values 21-Oct-15 13:03:42 ', 'NEWSTACK: Images copied, transformed 21-Oct-15 13:21:42 ', 'TILT: Tomographic reconstruction 30-Nov-15 12:29:39 ', 'NEWSTACK: Images copied , densities scaled 30-Nov-15 13:59:05 ', 'clip: flipyz 30-Nov-15 13:59:10 ', '', '', '', ''] # 10 labels of 80 charactors ) values = ( X.shape[2], # Number of columns X.shape[1], # Number of rows X.shape[0], mode, # Types of pixels in the image. Values used by IMOD: # 0 = unsigned or signed bytes depending on flag in imodFlags # 1 = signed short integers (16 bits) # 2 = float (32 bits) # 3 = short * 2, (used for complex data) # 4 = float * 2, (used for complex data) # 6 = unsigned 16-bit integers (non-standard) # 16 = unsigned char * 3 (for rgb data, non-standard) 0, # Starting point of sub-image (not used in IMOD) 0, 0, X.shape[2], # Grid size in X, Y and Z X.shape[1], X.shape[0], X.shape[2], # Cell size; pixel spacing = xlen/mx, ylen/my, zlen/mz X.shape[1], X.shape[0], 90.0, # Cell angles - ignored by IMOD 90.0, 90.0, # These need to be set to 1, 2, and 3 for pixel spacing to be interpreted correctly 1, # map column 1=x,2=y,3=z. 2, # map row 1=x,2=y,3=z. 3, # map section 1=x,2=y,3=z. # These need to be set for proper scaling of data amin, # Minimum pixel value amax, # Maximum pixel value amean, # Mean pixel value 1, # space group number (ignored by IMOD) 0, # number of bytes in extended header (called nsymbt in MRC standard) 0, # used to be an ID number, is 0 as of IMOD 4.2.23 bytes([0x00] * 30), # (not used, first two bytes should be 0) # These two values specify the structure of data in the extended header; their meaning depend on whether the # extended header has the Agard format, a series of 4-byte integers then real numbers, or has data # produced by SerialEM, a series of short integers. SerialEM stores a float as two shorts, s1 and s2, by: # value = (sign of s1)*(|s1|*256 + (|s2| modulo 256)) * 2**((sign of s2) * (|s2|/256)) 0, # Number of integers per section (Agard format) or number of bytes per section (SerialEM format) 0, # Number of reals per section (Agard format) or bit # Number of reals per section (Agard format) or bit # flags for which types of short data (SerialEM format): # 1 = tilt angle * 100 (2 bytes) # 2 = piece coordinates for montage (6 bytes) # 4 = Stage position * 25 (4 bytes) # 8 = Magnification / 100 (2 bytes) # 16 = Intensity * 25000 (2 bytes) # 32 = Exposure dose in e-/A2, a float in 4 bytes # 128, 512: Reserved for 4-byte items # 64, 256, 1024: Reserved for 2-byte items # If the number of bytes implied by these flags does # not add up to the value in nint, then nint and nreal # are interpreted as ints and reals per section bytes([0x00] * 20), #"", # extra data (not used) 0, # 1146047817 indicates that file was created by IMOD imodFlags, # Bit flags: 1 = bytes are stored as signed # Explanation of type of data 0, # ( 0 = mono, 1 = tilt, 2 = tilts, 3 = lina, 4 = lins) 0, 0, # for idtype = 1, nd1 = axis (1, 2, or 3) 0, 0, # vd1 = 100. * tilt increment 0, # vd2 = 100. * starting angle # Current angles are used to rotate a model to match a new rotated image. The three values in each set are # rotations about X, Y, and Z axes, applied in the order Z, Y, X. ( 0., 0., 0., 90., 0., 0.), # 0,1,2 = original: 3,4,5 = current 0., # Origin of image X.shape[1] / 2., 0., 'MAP ', # Contains "MAP " (68.0, 65.0, 0.0, 0.0), # First two bytes have 17 and 17 for big-endian or 68 and 65 for little-endian 0.0, # RMS deviation of densities from mean density 6, # Number of labels with useful data ) header = np.array(values, dtype=dt) dt= np.dtype(rhd) header_dict = {} for name in dt.names: header_dict[name] = header[name] self.header = header self.data = np.ascontiguousarray(X) self.header_dict = header_dict return self def read(self, filename): rec_header_dtype = np.dtype(rhd) assert rec_header_dtype.itemsize == 1024 fd = open(filename, 'rb') stats = os.stat(filename) header = np.fromfile(fd, dtype=rhd, count=1) # Seek header #fd.seek(header.itemsize) if header['next'] > 0: fd.seek(header['next']) # ignore extended header mode = int(header['mode']) bo = "<" if header['stamp'][0, 0] == 68 and header['stamp'][0, 1] == 65 else "<" # BitOrder: little or big endian sign = "i1" if header['imodFlags'] == 1 else "u1" # signed or unsigned # 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 dtype = [sign, "i2", "f", "c4", "c8", None, "u2", None, None, None, None, None, None, None, None, None, "u1"][mode] dsize = [ 1, 2, 4, 4, 8, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3][mode] # data dimensions nx, ny, nz = int(header['nx']), int(header['ny']), int(header['nz']) img_size = nx * ny * nz * dsize img_str = fd.read(img_size) dtype = bo + dtype # Make sure that we have readed the whole file assert not fd.read(), "Error loading the file" #assert stats.st_size == header.itemsize + img_size fd.close() if mode == 16: data = np.ndarray(shape=(nx, ny, nz, 3), dtype=dtype, buffer=img_str, order='F') else: data = np.ndarray(shape=(nx, ny, nz), dtype=dtype, buffer=img_str, order='F') data = np.swapaxes(data, 0, 2) header_dict = {} for name in header.dtype.names: header_dict[name] = header[name][0] if len(header[name]) == 1 else header[name] self.header = header self.data = np.ascontiguousarray(data) self.header_dict = header_dict return self def save(self, filename): fd = open(filename, 'wb') fd.write(self.header.data) data = self.data data = np.swapaxes(data, 0, 2) if self.yz_swapped: data = np.swapaxes(data, 1, 2) data = np.asfortranarray(data) fd.write(data.data) fd.close()

46.109677

127

0.47773

2,685

21,441

3.785475

0.146369

0.006297

0.03168

0.003542

0.774203

0.75364

0.734947

0.719402

0.702971

0.68172

0

0.061811

0.424281

21,441

464

128

46.209052

0.761585

0.418777

0

0.54417

0

0.076032

0

0.000653

0

0.007067

1

0.056537

false

0

0.014134

0

0.123675

0.010601

0

null

0

1

0

null

0

5

cf468a8f614c8a4f2f5bc7bca1cfa9750a4aa03f

58

py

Python

quiz_bot/cli/group.py

livestreamx/quiz-bot

e08e9161d908ce9cb851cd6c689f04703db1928f

[ "MIT" ]

1

2022-03-05T13:42:08.000Z

quiz_bot/cli/group.py

livestreamx/quiz-bot

e08e9161d908ce9cb851cd6c689f04703db1928f

[ "MIT" ]

null

quiz_bot/cli/group.py

livestreamx/quiz-bot

e08e9161d908ce9cb851cd6c689f04703db1928f

[ "MIT" ]

2

2021-06-20T10:40:25.000Z

2022-02-15T04:26:58.000Z

import click @click.group() def app() -> None: pass

8.285714

18

0.603448

8

58

4.375

0.875

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5

cf70795be65d6665d53daaacbef418d5b5a98561

53

py

Python

ExactHistogramSpecification/__init__.py

jkschluesener/ExactHistogramSpecification

ee95ee7e8a672e510aac0e67b6780722503e4b40

[ "Apache-2.0" ]

null

ExactHistogramSpecification/__init__.py

jkschluesener/ExactHistogramSpecification

ee95ee7e8a672e510aac0e67b6780722503e4b40

[ "Apache-2.0" ]

null

ExactHistogramSpecification/__init__.py

jkschluesener/ExactHistogramSpecification

ee95ee7e8a672e510aac0e67b6780722503e4b40

[ "Apache-2.0" ]

null

from .histogram_matching import ExactHistogramMatcher

53

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53

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1

0

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1

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5

cf7a55688cee308add364424d082e86290dfb780

6,551

py

Python

recipes/site_listers/__init__.py

cfeenstra67/recipes

a2d296500b4ff70e11ff3177a1092a033498f82a

[ "MIT" ]

null

recipes/site_listers/__init__.py

cfeenstra67/recipes

a2d296500b4ff70e11ff3177a1092a033498f82a

[ "MIT" ]

null

recipes/site_listers/__init__.py

cfeenstra67/recipes

a2d296500b4ff70e11ff3177a1092a033498f82a

[ "MIT" ]

null

from recipes.site_listers.acouplecooks import ACoupleCooksLister from recipes.site_listers.allrecipes import AllRecipesLister from recipes.site_listers.ambitiouskitchen import AmbitiousKitchenLister from recipes.site_listers.archanaskitchen import ArchanasKitchenLister from recipes.site_listers.averiecooks import AverieCooksLister from recipes.site_listers.bakingmischief import BakingMischiefLister from recipes.site_listers.bakingsense import BakingSenseLister from recipes.site_listers.bbc import BBCLister from recipes.site_listers.bbcgoodfood import BBCGoodFoodLister from recipes.site_listers.bettycrocker import BettyCrockerLister from recipes.site_listers.bonappetit import BonAppetitLister from recipes.site_listers.bowlofdelicious import BowlOfDeliciousLister from recipes.site_listers.budgetbytes import BudgetBytesLister from recipes.site_listers.castironketo import CastIronKetoLister from recipes.site_listers.closetcooking import ClosetCookingLister from recipes.site_listers.cookeatshare import CookEatShareLister from recipes.site_listers.cookieandkate import CookieAndKateLister from recipes.site_listers.cookstr import CookStrLister from recipes.site_listers.eatingbirdfood import EatingBirdFoodLister from recipes.site_listers.eatsmarter import EatSmarterLister from recipes.site_listers.eatwhattonight import EatWhatTonightLister from recipes.site_listers.epicurious import EpicuriousLister from recipes.site_listers.food import FoodLister from recipes.site_listers.foodnetwork import FoodNetworkLister from recipes.site_listers.foodrepublic import FoodRepublicLister from recipes.site_listers.forksoverknives import ForksOverKnivesLister from recipes.site_listers.gimmesomeoven import GimmeSomeOvenLister from recipes.site_listers.gonnawantseconds import GonnaWantSecondsLister from recipes.site_listers.greatbritishchefs import GreatBritishChefsLister from recipes.site_listers.halfbakedharvest import HalfBakedHarvestLister from recipes.site_listers.headbangerskitchen import HeadBangersKitchenLister from recipes.site_listers.hellofresh import HelloFreshLister from recipes.site_listers.hostthetoast import HostTheToastLister from recipes.site_listers.indianhealthyrecipes import IndianHealthyRecipesLister from recipes.site_listers.innit import InnitLister from recipes.site_listers.jamieoliver import JamieOliverLister from recipes.site_listers.jimcooksgoodfood import JimCooksGoodFoodLister from recipes.site_listers.joyfoodsunshine import JoyFoodSunshineLister from recipes.site_listers.justataste import JustATasteLister from recipes.site_listers.justbento import JustBentoLister from recipes.site_listers.kennymcgovern import KennyMcgovernLister from recipes.site_listers.kingarthurbaking import KingArthurBakingLister from recipes.site_listers.lecremedelacrumb import LeCremeDeLaCrumbLister from recipes.site_listers.littlespicejar import LittleSpiceJarLister from recipes.site_listers.livelytable import LivelyTableLister from recipes.site_listers.lovingitvegan import LovingItVeganLister from recipes.site_listers.marthastewart import MarthaStewartLister from recipes.site_listers.melskitchencafe import MelsKitchenCafeLister from recipes.site_listers.minimalistbaker import MinimalistBakerLister from recipes.site_listers.momswithcrockpots import MomsWithCrockpotsLister from recipes.site_listers.mybakingaddiction import MyBakingAddictionLister from recipes.site_listers.myrecipes import MyRecipesLister from recipes.site_listers.nourishedbynutrition import NourishedByNutritionLister from recipes.site_listers.nutritionbynathalie import NutritionByNathalieLister from recipes.site_listers._101cookbooks import _101CookbooksLister from recipes.site_listers.paleorunningmomma import PaleoRunningMommaLister from recipes.site_listers.paninihappy import PaniniHappyLister from recipes.site_listers.practicalselfreliance import PracticalSelfRelianceLister from recipes.site_listers.primaledgehealth import PrimalEdgeHealthLister from recipes.site_listers.purelypope import PurelyPopeLister from recipes.site_listers.purplecarrot import PurpleCarrotLister from recipes.site_listers.rachlmansfield import RachlmansFieldLister from recipes.site_listers.rainbowplantlife import RainbowPlantLifeLister from recipes.site_listers.realsimple import RealSimpleLister from recipes.site_listers.recipetineats import RecipeTinEatsLister from recipes.site_listers.redhousespice import RedHouseSpiceLister from recipes.site_listers.sallysbakingaddiction import SallysBakingAddictionLister from recipes.site_listers.saveur import SaveurLister from recipes.site_listers.seriouseats import SeriousEatsLister from recipes.site_listers.simplyquinoa import SimplyQuinoaLister from recipes.site_listers.simplyrecipes import SimplyRecipesLister from recipes.site_listers.simplywhisked import SimplyWhiskedLister from recipes.site_listers.skinnytaste import SkinnyTasteLister from recipes.site_listers.southernliving import SouthernLivingLister from recipes.site_listers.spendwithpennies import SpendWithPenniesLister from recipes.site_listers.steamykitchen import SteamyKitchenLister from recipes.site_listers.sunbasket import SunBasketLister from recipes.site_listers.sweetcsdesigns import SweetCsDesignsLister from recipes.site_listers.sweetpeasandsaffron import SweetPeasAndSaffronLister from recipes.site_listers.tasteofhome import TasteOfHomeLister from recipes.site_listers.tastesoflizzyt import TastesOfLizzyTLister from recipes.site_listers.tastykitchen import TastyKitchenLister from recipes.site_listers.theclevercarrot import TheCleverCarrotLister from recipes.site_listers.thehappyfoodie import TheHappyFoodieLister from recipes.site_listers.thekitchenmagpie import TheKitchenMagpieLister from recipes.site_listers.thekitchn import TheKitchnLister from recipes.site_listers.thenutritiouskitchen import TheNutritiousKitchenLister from recipes.site_listers.thespruceeats import TheSpruceEatsLister from recipes.site_listers.thevintagemixer import TheVintageMixerLister from recipes.site_listers.thewoksoflife import TheWoksOfLifeLister from recipes.site_listers.twopeasandtheirpod import TwoPeasAndTheirPodLister from recipes.site_listers.vanillaandbean import VanillaAndBeanLister from recipes.site_listers.vegrecipesofindia import VegRecipesOfIndiaLister from recipes.site_listers.watchwhatueat import WatchWhatUEatLister from recipes.site_listers.whatsgabycooking import WhatsGabyCookingLister from recipes.site_listers.wholefoodsmarket import WholeFoodsMarketLister from recipes.site_listers.wikibooks import WikibooksLister

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1

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5

cf7c17f66c443150478272cca7ffcef07ee11f88

328

py

Python

lab3_SAT/sat/satisfiability.py

j-adamczyk/ADPTO_templates

e0a4e77ba8de21fe966388ccee66ef62224a2d99

[ "MIT" ]

null

lab3_SAT/sat/satisfiability.py

j-adamczyk/ADPTO_templates

e0a4e77ba8de21fe966388ccee66ef62224a2d99

[ "MIT" ]

null

lab3_SAT/sat/satisfiability.py

j-adamczyk/ADPTO_templates

e0a4e77ba8de21fe966388ccee66ef62224a2d99

[ "MIT" ]

1

2022-03-25T07:25:26.000Z

import matplotlib.pyplot as plt import numpy as np import pycosat def calculate_SAT_probabilities_and_plot() -> None: """ Calculate probability of random formula being satisfiable based on it's size n (number of variables). The result is plotted and shown. Conclusion: TODO write conclusion """ pass

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5

d8554fc184769407a0a282c94adfb4300bba0cfe

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py

Python

bindings/python/py_src/yotsuba/version.py

yutayamazaki/yotsuba

5fac8da1a9e0c9e2929a1041a51e15f104cfe1fc

[ "MIT" ]

null

bindings/python/py_src/yotsuba/version.py

yutayamazaki/yotsuba

5fac8da1a9e0c9e2929a1041a51e15f104cfe1fc

[ "MIT" ]

3

2020-08-28T18:19:07.000Z

2020-09-02T15:16:15.000Z

bindings/python/py_src/yotsuba/version.py

yutayamazaki/yotsuba

5fac8da1a9e0c9e2929a1041a51e15f104cfe1fc

[ "MIT" ]

null

__version__: str = '0.1.3'

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5

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0

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true

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0

null

0

1

0

1

0

null

0

1

0

5

d85ab9c51aed780355de019fe468a59f3e852eab

113

py

Python

lycheepy/configuration/configuration/validators/__init__.py

gabrielbazan/lycheepy

f314d3f591f4a449b37ead9baf26b9f5d58d9f0d

[ "MIT" ]

17

2018-08-14T02:42:43.000Z

2022-02-25T00:38:47.000Z

lycheepy/configuration/configuration/validators/__init__.py

gabrielbazan/lycheepy

f314d3f591f4a449b37ead9baf26b9f5d58d9f0d

[ "MIT" ]

1

2018-11-01T02:55:01.000Z

lycheepy/configuration/configuration/validators/__init__.py

gabrielbazan/lycheepy

f314d3f591f4a449b37ead9baf26b9f5d58d9f0d

[ "MIT" ]

4

2018-10-30T16:01:49.000Z

2021-06-08T20:21:07.000Z

from process import ProcessValidator from chain import ChainValidator from repository import RepositoryValidator

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1

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1

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1

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5

d861d0a05a0179a645980e9a0c937a7ec5cb1b3f

48

py

Python

data_layer/postgresql/__init__.py

xyla-io/data_layer

8d10aab6cae7f63eacf4e139e09576dd14a87354

[ "MIT" ]

null

data_layer/postgresql/__init__.py

xyla-io/data_layer

8d10aab6cae7f63eacf4e139e09576dd14a87354

[ "MIT" ]

null

data_layer/postgresql/__init__.py

xyla-io/data_layer

8d10aab6cae7f63eacf4e139e09576dd14a87354

[ "MIT" ]

null

from .postgresql import PostgreSQLLayer as Layer

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null

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1

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1

0

5

d8ad805b2c80809157544a4be44a6d6431a27cb3

252

py

Python

tests/test_torchreinforce.py

Lucien-MG/torchreinforce

5ba852bb255c14140d7bc300a44e60e7b4b572ff

[ "MIT" ]

null

tests/test_torchreinforce.py

Lucien-MG/torchreinforce

5ba852bb255c14140d7bc300a44e60e7b4b572ff

[ "MIT" ]

null

tests/test_torchreinforce.py

Lucien-MG/torchreinforce

5ba852bb255c14140d7bc300a44e60e7b4b572ff

[ "MIT" ]

null

import torchreinforce from torchreinforce import __version__ def test_version(): assert __version__ == '0.1.0' def test_import_agents(): assert 'agents' in dir(torchreinforce) def test_import_io(): assert 'agents' in dir(torchreinforce)

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252

11

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1

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1

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5

d8b42e4688f2756d39ce1179491402338af0c08c

96

py

Python

venv/lib/python3.8/site-packages/future/backports/email/quoprimime.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/future/backports/email/quoprimime.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/future/backports/email/quoprimime.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/c3/dd/d6/e5781d169c86683a83049ae75c5feffe7a47e6bdb45ae0319ab033c908

96

0.895833

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0

1

0

1

0

null

1

0

1

0

5

d8ed4b4621107ed0e18965ebc96c9179f69878ce

44

py

Python

tests/addition.py

manniepmkam/PvZ

e76aff52791b47b8e6c81c8efe409920df64e000

[ "MIT" ]

1

2021-05-20T02:31:33.000Z

tests/addition.py

manniepmkam/PvZ

e76aff52791b47b8e6c81c8efe409920df64e000

[ "MIT" ]

null

tests/addition.py

manniepmkam/PvZ

e76aff52791b47b8e6c81c8efe409920df64e000

[ "MIT" ]

1

2019-11-03T15:14:09.000Z

def addition(X,Y): Z = X+Y return Z

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18

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0

1

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1

0

5

2b3bfecd47a1cd22102736c69daa62a8f28fe45d

147

py

Python

scheduleapp/admin.py

MiSokol/ScheduleApp

01b7eec406c64de36a0fa70eee1d2c400c792ec3

[ "MIT" ]

null

scheduleapp/admin.py

MiSokol/ScheduleApp

01b7eec406c64de36a0fa70eee1d2c400c792ec3

[ "MIT" ]

1

2018-05-03T17:10:16.000Z

scheduleapp/admin.py

MiSokol/ScheduleApp

01b7eec406c64de36a0fa70eee1d2c400c792ec3

[ "MIT" ]

null

from django.contrib import admin # Register your models here. from .models import User, Task admin.site.register(User) admin.site.register(Task)

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null

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1

0

5

2b5bbf3d58384a6e5dd4537c33623dc93592053b

22

py

Python

python/testData/completion/moduleFromNamespacePackage/a.after.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/completion/moduleFromNamespacePackage/a.after.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/completion/moduleFromNamespacePackage/a.after.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

from pkg import m7 m7

7.333333

18

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5

22

3.4

0.8

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19

7.333333

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true

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null

0

1

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null

0

1

0

1

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5

2b62dbc2f534750ff6d637433002dd293c01ac8d

150

py

Python

components/layer/__init__.py

huzexi/SADenseNet

95ab1b6b29f6dff973f3770ecd8b23cca37c9f0b

[ "BSD-3-Clause" ]

8

2021-12-10T12:50:06.000Z

2022-03-11T21:50:02.000Z

components/layer/__init__.py

huzexi/SADenseNet

95ab1b6b29f6dff973f3770ecd8b23cca37c9f0b

[ "BSD-3-Clause" ]

null

components/layer/__init__.py

huzexi/SADenseNet

95ab1b6b29f6dff973f3770ecd8b23cca37c9f0b

[ "BSD-3-Clause" ]

null

from .Reorder import Reorder from .AngularConv import AngularConv from .SpatialConv import SpatialConv from .CorrelationBlock import CorrelationBlock

30

46

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150

8.125

0.375

0

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4

47

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0.970149

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true

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null

0

1

0

1

0

1

0

5

99498dc92245bb6f0a919d8ce41b9eeac94beb92

138

py

Python

venv/Lib/site-packages/nipype/interfaces/cat12/__init__.py

richung99/digitizePlots

6b408c820660a415a289726e3223e8f558d3e18b

[ "MIT" ]

null

venv/Lib/site-packages/nipype/interfaces/cat12/__init__.py

richung99/digitizePlots

6b408c820660a415a289726e3223e8f558d3e18b

[ "MIT" ]

null

venv/Lib/site-packages/nipype/interfaces/cat12/__init__.py

richung99/digitizePlots

6b408c820660a415a289726e3223e8f558d3e18b

[ "MIT" ]

null

from .preprocess import CAT12Segment from .surface import ( ExtractAdditionalSurfaceParameters, ExtractROIBasedSurfaceMeasures, )

23

39

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138

12.555556

0.777778

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138

5

40

27.6

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0

1

0

true

0

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0

0.4

0

1

0

1

null

0

1

0

null

0

1

0

1

0

5

99680458f1ed4f9db08434e7d0ecfbc5bbab3b4a

209

py

Python

django_actionlog/handler/null.py

randlet/django-actionlog

1dee6c6d18c18312da4b34c84efe76ba01f42f69

[ "BSD-3-Clause" ]

3

2016-12-03T05:35:26.000Z

2017-04-30T05:28:28.000Z

django_actionlog/handler/null.py

randlet/django-actionlog

1dee6c6d18c18312da4b34c84efe76ba01f42f69

[ "BSD-3-Clause" ]

6

2016-12-29T01:00:29.000Z

2018-01-25T10:01:39.000Z

django_actionlog/handler/null.py

randlet/django-actionlog

1dee6c6d18c18312da4b34c84efe76ba01f42f69

[ "BSD-3-Clause" ]

3

2016-12-28T14:23:15.000Z

2019-05-16T20:57:30.000Z

# -*- coding: utf-8 -*- from . import handler_manager class Nullout(object): def __init__(self, config): pass def emit(self, data): pass handler_manager.register('null', Nullout)

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209

5

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209

14

42

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0

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0

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1

0

null

0

1

0

1

0

1

0

5

9973c0b5e197d5068f67f29078cdcee2138dee95

265,227

py

Python

prisim/delay_spectrum.py

LBJ-Wade/PRISim

04f790523dadca0a7215e3cb18e7d0f39881a96c

[ "MIT" ]

11

2016-02-08T21:54:45.000Z

2021-07-15T04:36:53.000Z

prisim/delay_spectrum.py

LBJ-Wade/PRISim

04f790523dadca0a7215e3cb18e7d0f39881a96c

[ "MIT" ]

38

2016-11-11T00:42:06.000Z

2020-06-13T03:02:23.000Z

prisim/delay_spectrum.py

LBJ-Wade/PRISim

04f790523dadca0a7215e3cb18e7d0f39881a96c

[ "MIT" ]

7

2016-10-31T19:45:29.000Z

2020-05-29T00:01:25.000Z

from __future__ import division import numpy as NP import multiprocessing as MP import itertools as IT import progressbar as PGB # import aipy as AP import astropy from astropy.io import fits import astropy.cosmology as CP import scipy.constants as FCNST import healpy as HP from distutils.version import LooseVersion import yaml, h5py from astroutils import writer_module as WM from astroutils import constants as CNST from astroutils import DSP_modules as DSP from astroutils import mathops as OPS from astroutils import geometry as GEOM from astroutils import lookup_operations as LKP import prisim from prisim import primary_beams as PB from prisim import interferometry as RI from prisim import baseline_delay_horizon as DLY try: from pyuvdata import UVBeam except ImportError: uvbeam_module_found = False else: uvbeam_module_found = True prisim_path = prisim.__path__[0]+'/' # cosmo100 = CP.FlatLambdaCDM(H0=100.0, Om0=0.27) # Using H0 = 100 km/s/Mpc cosmoPlanck15 = CP.Planck15 # Planck 2015 cosmology cosmo100 = cosmoPlanck15.clone(name='Modified Planck 2015 cosmology with h=1.0', H0=100.0) # Modified Planck 2015 cosmology with h=1.0, H= 100 km/s/Mpc ################################################################################# def _astropy_columns(cols, tabtype='BinTableHDU'): """ ---------------------------------------------------------------------------- !!! FOR INTERNAL USE ONLY !!! This internal routine checks for Astropy version and produces the FITS columns based on the version Inputs: cols [list of Astropy FITS columns] These are a list of Astropy FITS columns tabtype [string] specifies table type - 'BinTableHDU' (default) for binary tables and 'TableHDU' for ASCII tables Outputs: columns [Astropy FITS column data] ---------------------------------------------------------------------------- """ try: cols except NameError: raise NameError('Input cols not specified') if tabtype not in ['BinTableHDU', 'TableHDU']: raise ValueError('tabtype specified is invalid.') use_ascii = False if tabtype == 'TableHDU': use_ascii = True if astropy.__version__ == '0.4': columns = fits.ColDefs(cols, tbtype=tabtype) elif LooseVersion(astropy.__version__)>=LooseVersion('0.4.2'): columns = fits.ColDefs(cols, ascii=use_ascii) return columns ################################################################################ # def _gentle_clean(dd, _w, tol=1e-1, area=None, stop_if_div=True, maxiter=100, # verbose=False, autoscale=True): # if verbose: # print("Performing gentle clean...") # scale_factor = 1.0 # if autoscale: # scale_factor = NP.nanmax(NP.abs(_w)) # dd /= scale_factor # _w /= scale_factor # cc, info = AP.deconv.clean(dd, _w, tol=tol, area=area, stop_if_div=False, # maxiter=maxiter, verbose=verbose) # #dd = info['res'] # cc = NP.zeros_like(dd) # inside_res = NP.std(dd[area!=0]) # outside_res = NP.std(dd[area==0]) # initial_res = inside_res # #print(inside_res,'->',) # ncycle=0 # if verbose: # print("inside_res outside_res") # print(inside_res, outside_res) # inside_res = 2*outside_res #just artifically bump up the inside res so the loop runs at least once # while(inside_res>outside_res and maxiter>0): # if verbose: print('.',) # _d_cl, info = AP.deconv.clean(dd, _w, tol=tol, area=area, stop_if_div=stop_if_div, maxiter=maxiter, verbose=verbose, pos_def=True) # res = info['res'] # inside_res = NP.std(res[area!=0]) # outside_res = NP.std(res[area==0]) # dd = info['res'] # cc += _d_cl # ncycle += 1 # if verbose: print(inside_res*scale_factor, outside_res*scale_factor) # if ncycle>1000: break # info['ncycle'] = ncycle-1 # dd *= scale_factor # _w *= scale_factor # cc *= scale_factor # info['initial_residual'] = initial_res * scale_factor # info['final_residual'] = inside_res * scale_factor # return cc, info ################################################################################# def complex1dClean_arg_splitter(args, **kwargs): return complex1dClean(*args, **kwargs) def complex1dClean(inp, kernel, cbox=None, gain=0.1, maxiter=10000, threshold=5e-3, threshold_type='relative', verbose=False, progressbar=False, pid=None, progressbar_yloc=0): """ ---------------------------------------------------------------------------- Hogbom CLEAN algorithm applicable to 1D complex array Inputs: inp [numpy vector] input 1D array to be cleaned. Can be complex. kernel [numpy vector] 1D array that acts as the deconvolving kernel. Can be complex. Must be of same size as inp cbox [boolean array] 1D boolean array that acts as a mask for pixels which should be cleaned. Same size as inp. Only pixels with values True are to be searched for maxima in residuals for cleaning and the rest are not searched for. Default=None (means all pixels are to be searched for maxima while cleaning) gain [scalar] gain factor to be applied while subtracting clean component from residuals. This is the fraction of the maximum in the residuals that will be subtracted. Must lie between 0 and 1. A lower value will have a smoother convergence but take a longer time to converge. Default=0.1 maxiter [scalar] maximum number of iterations for cleaning process. Will terminate if the number of iterations exceed maxiter. Default=10000 threshold [scalar] represents the cleaning depth either as a fraction of the maximum in the input (when thershold_type is set to 'relative') or the absolute value (when threshold_type is set to 'absolute') in same units of input down to which inp should be cleaned. Value must always be positive. When threshold_type is set to 'relative', threshold mu st lie between 0 and 1. Default=5e-3 (found to work well and converge fast) assuming threshold_type is set to 'relative' threshold_type [string] represents the type of threshold specified by value in input threshold. Accepted values are 'relative' and 'absolute'. If set to 'relative' the threshold value is the fraction (between 0 and 1) of maximum in input down to which it should be cleaned. If set to 'asbolute' it is the actual value down to which inp should be cleaned. Default='relative' verbose [boolean] If set to True (default), print diagnostic and progress messages. If set to False, no such messages are printed. progressbar [boolean] If set to False (default), no progress bar is displayed pid [string or integer] process identifier (optional) relevant only in case of parallel processing and if progressbar is set to True. If pid is not specified, it defaults to the Pool process id progressbar_yloc [integer] row number where the progressbar is displayed on the terminal. Default=0 Output: outdict [dictionary] It consists of the following keys and values at termination: 'termination' [dictionary] consists of information on the conditions for termination with the following keys and values: 'threshold' [boolean] If True, the cleaning process terminated because the threshold was reached 'maxiter' [boolean] If True, the cleaning process terminated because the number of iterations reached maxiter 'inrms<outrms' [boolean] If True, the cleaning process terminated because the rms inside the clean box is below the rms outside of it 'iter' [scalar] number of iterations performed before termination 'rms' [numpy vector] rms of the residuals as a function of iteration 'inrms' [numpy vector] rms of the residuals inside the clean box as a function of iteration 'outrms' [numpy vector] rms of the residuals outside the clean box as a function of iteration 'res' [numpy array] uncleaned residuals at the end of the cleaning process. Complex valued and same size as inp 'cc' [numpy array] clean components at the end of the cleaning process. Complex valued and same size as inp ---------------------------------------------------------------------------- """ try: inp, kernel except NameError: raise NameError('Inputs inp and kernel not specified') if not isinstance(inp, NP.ndarray): raise TypeError('inp must be a numpy array') if not isinstance(kernel, NP.ndarray): raise TypeError('kernel must be a numpy array') if threshold_type not in ['relative', 'absolute']: raise ValueError('invalid specification for threshold_type') if not isinstance(threshold, (int,float)): raise TypeError('input threshold must be a scalar') else: threshold = float(threshold) if threshold <= 0.0: raise ValueError('input threshold must be positive') inp = inp.flatten() kernel = kernel.flatten() kernel /= NP.abs(kernel).max() kmaxind = NP.argmax(NP.abs(kernel)) if inp.size != kernel.size: raise ValueError('inp and kernel must have same size') if cbox is None: cbox = NP.ones(inp.size, dtype=NP.bool) elif isinstance(cbox, NP.ndarray): cbox = cbox.flatten() if cbox.size != inp.size: raise ValueError('Clean box must be of same size as input') cbox = NP.where(cbox > 0.0, True, False) # cbox = cbox.astype(NP.int) else: raise TypeError('cbox must be a numpy array') cbox = cbox.astype(NP.bool) if threshold_type == 'relative': lolim = threshold else: lolim = threshold / NP.abs(inp).max() if lolim >= 1.0: raise ValueError('incompatible value specified for threshold') # inrms = [NP.std(inp[cbox])] inrms = [NP.median(NP.abs(inp[cbox] - NP.median(inp[cbox])))] if inp.size - NP.sum(cbox) <= 2: outrms = None else: # outrms = [NP.std(inp[NP.invert(cbox)])] outrms = [NP.median(NP.abs(inp[NP.invert(cbox)] - NP.median(inp[NP.invert(cbox)])))] if not isinstance(gain, float): raise TypeError('gain must be a floating point number') else: if (gain <= 0.0) or (gain >= 1.0): raise TypeError('gain must lie between 0 and 1') if not isinstance(maxiter, int): raise TypeError('maxiter must be an integer') else: if maxiter <= 0: raise ValueError('maxiter must be positive') cc = NP.zeros_like(inp) res = NP.copy(inp) cond4 = False # prevrms = NP.std(res) # currentrms = [NP.std(res)] prevrms = NP.median(NP.abs(res - NP.median(res))) currentrms = [NP.median(NP.abs(res - NP.median(res)))] itr = 0 terminate = False if progressbar: if pid is None: pid = MP.current_process().name else: pid = '{0:0d}'.format(pid) progressbar_loc = (0, progressbar_yloc) writer=WM.Writer(progressbar_loc) progress = PGB.ProgressBar(widgets=[pid+' ', PGB.Percentage(), PGB.Bar(marker='-', left=' |', right='| '), PGB.Counter(), '/{0:0d} Iterations '.format(maxiter), PGB.ETA()], maxval=maxiter, fd=writer).start() while not terminate: itr += 1 indmaxres = NP.argmax(NP.abs(res*cbox)) maxres = res[indmaxres] ccval = gain * maxres cc[indmaxres] += ccval res = res - ccval * NP.roll(kernel, indmaxres-kmaxind) prevrms = NP.copy(currentrms[-1]) # currentrms += [NP.std(res)] currentrms += [NP.median(NP.abs(res - NP.median(res)))] # inrms += [NP.std(res[cbox])] inrms += [NP.median(NP.abs(res[cbox] - NP.median(res[cbox])))] # cond1 = NP.abs(maxres) <= inrms[-1] cond1 = NP.abs(maxres) <= lolim * NP.abs(inp).max() cond2 = itr >= maxiter terminate = cond1 or cond2 if outrms is not None: # outrms += [NP.std(res[NP.invert(cbox)])] outrms += [NP.median(NP.abs(res[NP.invert(cbox)] - NP.median(res[NP.invert(cbox)])))] cond3 = inrms[-1] <= outrms[-1] terminate = terminate or cond3 if progressbar: progress.update(itr) if progressbar: progress.finish() inrms = NP.asarray(inrms) currentrms = NP.asarray(currentrms) if outrms is not None: outrms = NP.asarray(outrms) outdict = {'termination':{'threshold': cond1, 'maxiter': cond2, 'inrms<outrms': cond3}, 'iter': itr, 'rms': currentrms, 'inrms': inrms, 'outrms': outrms, 'cc': cc, 'res': res} return outdict ################################################################################ def dkprll_deta(redshift, cosmo=cosmo100): """ ---------------------------------------------------------------------------- Compute jacobian to transform delays (eta or tau) to line-of-sight wavenumbers (h/Mpc) corresponding to specified redshift(s) and cosmology corresponding to the HI 21 cm line Inputs: redshift [scalar, list or numpy array] redshift(s). Must be a scalar, list or numpy array cosmo [instance of cosmology class from astropy] An instance of class FLRW or default_cosmology of astropy cosmology module. Default uses Flat lambda CDM cosmology with Omega_m=0.27, H0=100 km/s/Mpc Outputs: Jacobian to convert eta (lags) to k_parallel. Same size as redshift ---------------------------------------------------------------------------- """ if not isinstance(redshift, (int, float, list, NP.ndarray)): raise TypeError('redshift must be a scalar, list or numpy array') redshift = NP.asarray(redshift) if NP.any(redshift < 0.0): raise ValueError('redshift(s) must be non-negative') if not isinstance(cosmo, (CP.FLRW, CP.default_cosmology)): raise TypeError('Input cosmology must be a cosmology class defined in Astropy') jacobian = 2 * NP.pi * cosmo.H0.value * CNST.rest_freq_HI * cosmo.efunc(redshift) / FCNST.c / (1+redshift)**2 * 1e3 return jacobian ################################################################################ def beam3Dvol(beam, freqs, freq_wts=None, hemisphere=True): """ ---------------------------------------------------------------------------- Compute 3D volume relevant for power spectrum given an antenna power pattern. It is estimated by summing square of the beam in angular and frequency coordinates and in units of "Sr Hz". Inputs: beam [numpy array] Antenna power pattern with peak normalized to unity. It can be of shape (npix x nchan) or (npix x 1) or (npix,). npix must be a HEALPix compatible value. nchan is the number of frequency channels, same as the size of input freqs. If it is of shape (npix x 1) or (npix,), the beam will be assumed to be identical for all frequency channels. freqs [list or numpy array] Frequency channels (in Hz) of size nchan freq_wts [numpy array] Frequency weights to be applied to the beam. Must be of shape (nchan,) or (nwin, nchan) Keyword Inputs: hemisphere [boolean] If set to True (default), the 3D volume will be estimated using the upper hemisphere. If False, the full sphere is used. Output: The product Omega x bandwdith (in Sr Hz) computed using the integral of squared power pattern. It is of shape (nwin,) ---------------------------------------------------------------------------- """ try: beam, freqs except NameError: raise NameError('Both inputs beam and freqs must be specified') if not isinstance(beam, NP.ndarray): raise TypeError('Input beam must be a numpy array') if not isinstance(freqs, (list, NP.ndarray)): raise TypeError('Input freqs must be a list or numpy array') freqs = NP.asarray(freqs).astype(NP.float).reshape(-1) if freqs.size < 2: raise ValueError('Input freqs does not have enough elements to determine frequency resolution') if beam.ndim > 2: raise ValueError('Invalid dimensions for beam') elif beam.ndim == 2: if beam.shape[1] != 1: if beam.shape[1] != freqs.size: raise ValueError('Dimensions of beam do not match the number of frequency channels') elif beam.ndim == 1: beam = beam.reshape(-1,1) else: raise ValueError('Invalid dimensions for beam') if freq_wts is not None: if not isinstance(freq_wts, NP.ndarray): raise TypeError('Input freq_wts must be a numpy array') if freq_wts.ndim == 1: freq_wts = freq_wts.reshape(1,-1) elif freq_wts.ndim > 2: raise ValueError('Input freq_wts must be of shape nwin x nchan') freq_wts = NP.asarray(freq_wts).astype(NP.float).reshape(-1,freqs.size) if freq_wts.shape[1] != freqs.size: raise ValueError('Input freq_wts does not have shape compatible with freqs') else: freq_wts = NP.ones(freqs.size, dtype=NP.float).reshape(1,-1) eps = 1e-10 if beam.max() > 1.0+eps: raise ValueError('Input beam maximum exceeds unity. Input beam should be normalized to peak of unity') nside = HP.npix2nside(beam.shape[0]) domega = HP.nside2pixarea(nside, degrees=False) df = freqs[1] - freqs[0] bw = df * freqs.size weighted_beam = beam[:,NP.newaxis,:] * freq_wts[NP.newaxis,:,:] theta, phi = HP.pix2ang(nside, NP.arange(beam.shape[0])) if hemisphere: ind, = NP.where(theta <= NP.pi/2) # Select upper hemisphere else: ind = NP.arange(beam.shape[0]) omega_bw = domega * df * NP.nansum(weighted_beam[ind,:,:]**2, axis=(0,2)) if NP.any(omega_bw > 4*NP.pi*bw): raise ValueError('3D volume estimated from beam exceeds the upper limit. Check normalization of the input beam') return omega_bw ################################################################################ class DelaySpectrum(object): """ ---------------------------------------------------------------------------- Class to manage delay spectrum information on a multi-element interferometer array. Attributes: ia [instance of class InterferometerArray] An instance of class InterferometerArray that contains the results of the simulated interferometer visibilities bp [numpy array] Bandpass weights of size n_baselines x nchan x n_acc, where n_acc is the number of accumulations in the observation, nchan is the number of frequency channels, and n_baselines is the number of baselines bp_wts [numpy array] Additional weighting to be applied to the bandpass shapes during the application of the member function delay_transform(). Same size as attribute bp. f [list or numpy vector] frequency channels in Hz cc_freq [list or numpy vector] frequency channels in Hz associated with clean components of delay spectrum. Same size as cc_lags. This computed inside member function delayClean() df [scalar] Frequency resolution (in Hz) lags [numpy vector] Time axis obtained when the frequency axis is inverted using a FFT. Same size as channels. This is computed in member function delay_transform(). cc_lags [numpy vector] Time axis obtained when the frequency axis is inverted using a FFT. Same size as cc_freq. This is computed in member function delayClean(). lag_kernel [numpy array] Inverse Fourier Transform of the frequency bandpass shape. In other words, it is the impulse response corresponding to frequency bandpass. Same size as attributes bp and bp_wts. It is initialized in __init__() member function but effectively computed in member functions delay_transform() and delayClean() cc_lag_kernel [numpy array] Inverse Fourier Transform of the frequency bandpass shape. In other words, it is the impulse response corresponding to frequency bandpass shape used in complex delay clean routine. It is initialized in __init__() member function but effectively computed in member function delayClean() n_acc [scalar] Number of accumulations horizon_delay_limits [numpy array] NxMx2 numpy array denoting the neagtive and positive horizon delay limits where N is the number of timestamps, M is the number of baselines. The 0 index in the third dimenstion denotes the negative horizon delay limit while the 1 index denotes the positive horizon delay limit skyvis_lag [numpy array] Complex visibility due to sky emission (in Jy Hz or K Hz) along the delay axis for each interferometer obtained by FFT of skyvis_freq along frequency axis. Same size as vis_freq. Created in the member function delay_transform(). Read its docstring for more details. Same dimensions as skyvis_freq vis_lag [numpy array] The simulated complex visibility (in Jy Hz or K Hz) along delay axis for each interferometer obtained by FFT of vis_freq along frequency axis. Same size as vis_noise_lag and skyis_lag. It is evaluated in member function delay_transform(). vis_noise_lag [numpy array] Complex visibility noise (in Jy Hz or K Hz) along delay axis for each interferometer generated using an FFT of vis_noise_freq along frequency axis. Same size as vis_noise_freq. Created in the member function delay_transform(). Read its docstring for more details. cc_skyvis_lag [numpy array] Complex cleaned visibility delay spectra (in Jy Hz or K Hz) of noiseless simulated sky visibilities for each baseline at each LST. Size is nbl x nlags x nlst cc_skyvis_res_lag [numpy array] Complex residuals from cleaned visibility delay spectra (in Jy Hz or K Hz) of noiseless simulated sky visibilities for each baseline at each LST. Size is nbl x nlags x nlst cc_skyvis_net_lag [numpy array] Sum of complex cleaned visibility delay spectra and residuals (in Jy Hz or K Hz) of noiseless simulated sky visibilities for each baseline at each LST. Size is nbl x nlags x nlst. cc_skyvis_net_lag = cc_skyvis_lag + cc_skyvis_res_lag cc_vis_lag [numpy array] Complex cleaned visibility delay spectra (in Jy Hz or K Hz) of noisy simulated sky visibilities for each baseline at each LST. Size is nbl x nlags x nlst cc_vis_res_lag [numpy array] Complex residuals from cleaned visibility delay spectra (in Jy Hz or K Hz) of noisy simulated sky visibilities for each baseline at each LST. Size is nbl x nlags x nlst cc_vis_net_lag [numpy array] Sum of complex cleaned visibility delay spectra and residuals (in Jy Hz or K Hz) of noisy simulated sky visibilities for each baseline at each LST. Size is nbl x nlags x nlst. cc_vis_net_lag = cc_vis_lag + cc_vis_res_lag cc_skyvis_freq [numpy array] Complex cleaned visibility delay spectra transformed to frequency domain (in Jy or K.Sr) obtained from noiseless simulated sky visibilities for each baseline at each LST. Size is nbl x nlags x nlst cc_skyvis_res_freq [numpy array] Complex residuals from cleaned visibility delay spectra transformed to frequency domain (in Jy or K.Sr) obtained from noiseless simulated sky visibilities for each baseline at each LST. Size is nbl x nlags x nlst cc_skyvis_net_freq [numpy array] Sum of complex cleaned visibility delay spectra and residuals transformed to frequency domain (in Jy or K.Sr) obtained from noiseless simulated sky visibilities for each baseline at each LST. Size is nbl x nlags x nlst. cc_skyvis_net_freq = cc_skyvis_freq + cc_skyvis_res_freq cc_vis_freq [numpy array] Complex cleaned visibility delay spectra transformed to frequency domain (in Jy or K.Sr) obtained from noisy simulated sky visibilities for each baseline at each LST. Size is nbl x nlags x nlst cc_vis_res_freq [numpy array] Complex residuals from cleaned visibility delay spectra transformed to frequency domain (in Jy or K.Sr) of noisy simulated sky visibilities for each baseline at each LST. Size is nbl x nlags x nlst cc_vis_net_freq [numpy array] Sum of complex cleaned visibility delay spectra and residuals transformed to frequency domain (in Jy or K.Sr) obtained from noisy simulated sky visibilities for each baseline at each LST. Size is nbl x nlags x nlst. cc_vis_net_freq = cc_vis_freq + cc_vis_res_freq clean_window_buffer [scalar] number of inverse bandwidths to extend beyond the horizon delay limit to include in the CLEAN deconvolution. pad [scalar] Non-negative scalar indicating padding fraction relative to the number of frequency channels. For e.g., a pad of 1.0 pads the frequency axis with zeros of the same width as the number of channels. After the delay transform, the transformed visibilities are downsampled by a factor of 1+pad. If a negative value is specified, delay transform will be performed with no padding subband_delay_spectra [dictionary] contains two top level keys, namely, 'cc' and 'sim' denoting information about CLEAN and simulated visibilities respectively. Under each of these keys is information about delay spectra of different frequency sub-bands (n_win in number) in the form of a dictionary under the following keys: 'freq_center' [numpy array] contains the center frequencies (in Hz) of the frequency subbands of the subband delay spectra. It is of size n_win. It is roughly equivalent to redshift(s) 'freq_wts' [numpy array] Contains frequency weights applied on each frequency sub-band during the subband delay transform. It is of size n_win x nchan. 'bw_eff' [numpy array] contains the effective bandwidths (in Hz) of the subbands being delay transformed. It is of size n_win. It is roughly equivalent to width in redshift or along line-of-sight 'shape' [string] shape of the window function applied. Accepted values are 'rect' (rectangular), 'bhw' (Blackman-Harris), 'bnw' (Blackman-Nuttall). 'bpcorrect' [boolean] If True (default), correct for frequency weights that were applied during the original delay transform using which the delay CLEAN was done. This would flatten the bandpass after delay CLEAN. If False, do not apply the correction, namely, inverse of bandpass weights. This applies only CLEAned visibilities under the 'cc' key and hence is present only if the top level key is 'cc' and absent for key 'sim' 'npad' [scalar] Numbber of zero-padded channels before performing the subband delay transform. 'lags' [numpy array] lags of the subband delay spectra after padding in frequency during the transform. It is of size nchan+npad where npad is the number of frequency channels padded specified under the key 'npad'. It roughly corresponds to k_parallel. 'lag_kernel' [numpy array] delay transform of the frequency weights under the key 'freq_wts'. It is of size n_bl x n_win x (nchan+npad) x n_t. 'lag_corr_length' [numpy array] It is the correlation timescale (in pixels) of the subband delay spectra. It is proportional to inverse of effective bandwidth. It is of size n_win. The unit size of a pixel is determined by the difference between adjacent pixels in lags under key 'lags' which in turn is effectively inverse of the total bandwidth (nchan x df) simulated. 'skyvis_lag' [numpy array] subband delay spectra of simulated or CLEANed noiseless visibilities, depending on whether the top level key is 'cc' or 'sim' respectively, after applying the frequency weights under the key 'freq_wts'. It is of size n_bl x n_win x (nchan+npad) x n_t. 'vis_lag' [numpy array] subband delay spectra of simulated or CLEANed noisy visibilities, depending on whether the top level key is 'cc' or 'sim' respectively, after applying the frequency weights under the key 'freq_wts'. It is of size n_bl x n_win x (nchan+npad) x n_t. 'vis_noise_lag' [numpy array] subband delay spectra of simulated noise after applying the frequency weights under the key 'freq_wts'. Only present if top level key is 'sim' and absent for 'cc'. It is of size n_bl x n_win x (nchan+npad) x n_t. 'skyvis_res_lag' [numpy array] subband delay spectra of residuals after delay CLEAN of simulated noiseless visibilities obtained after applying frequency weights specified under key 'freq_wts'. Only present for top level key 'cc' and absent for 'sim'. It is of size n_bl x n_win x (nchan+npad) x n_t 'vis_res_lag' [numpy array] subband delay spectra of residuals after delay CLEAN of simulated noisy visibilities obtained after applying frequency weights specified under key 'freq_wts'. Only present for top level key 'cc' and absent for 'sim'. It is of size n_bl x n_win x (nchan+npad) x n_t 'skyvis_net_lag' [numpy array] subband delay spectra of sum of residuals and clean components after delay CLEAN of simulated noiseless visibilities obtained after applying frequency weights specified under key 'freq_wts'. Only present for top level key 'cc' and absent for 'sim'. It is of size n_bl x n_win x (nchan+npad) x n_t 'vis_res_lag' [numpy array] subband delay spectra of sum of residuals and clean components after delay CLEAN of simulated noisy visibilities obtained after applying frequency weights specified under key 'freq_wts'. Only present for top level key 'cc' and absent for 'sim'. It is of size n_bl x n_win x (nchan+npad) x n_t subband_delay_spectra_resampled [dictionary] Very similar to the attribute subband_delay_spectra except now it has been resampled along delay axis to contain usually only independent delay bins. It contains two top level keys, namely, 'cc' and 'sim' denoting information about CLEAN and simulated visibilities respectively. Under each of these keys is information about delay spectra of different frequency sub-bands (n_win in number) after resampling to independent number of delay bins in the form of a dictionary under the following keys: 'freq_center' [numpy array] contains the center frequencies (in Hz) of the frequency subbands of the subband delay spectra. It is of size n_win. It is roughly equivalent to redshift(s) 'bw_eff' [numpy array] contains the effective bandwidths (in Hz) of the subbands being delay transformed. It is of size n_win. It is roughly equivalent to width in redshift or along line-of-sight 'lags' [numpy array] lags of the subband delay spectra after padding in frequency during the transform. It is of size nlags where nlags is the number of independent delay bins. It roughly corresponds to k_parallel. 'lag_kernel' [numpy array] delay transform of the frequency weights under the key 'freq_wts'. It is of size n_bl x n_win x nlags x n_t. 'lag_corr_length' [numpy array] It is the correlation timescale (in pixels) of the resampled subband delay spectra. It is proportional to inverse of effective bandwidth. It is of size n_win. The unit size of a pixel is determined by the difference between adjacent pixels in lags under key 'lags' which in turn is usually approximately inverse of the effective bandwidth of the subband 'skyvis_lag' [numpy array] subband delay spectra of simulated or CLEANed noiseless visibilities, depending on whether the top level key is 'cc' or 'sim' respectively, after applying the frequency weights under the key 'freq_wts'. It is of size n_bl x n_win x nlags x n_t. 'vis_lag' [numpy array] subband delay spectra of simulated or CLEANed noisy visibilities, depending on whether the top level key is 'cc' or 'sim' respectively, after applying the frequency weights under the key 'freq_wts'. It is of size n_bl x n_win x nlags x n_t. 'vis_noise_lag' [numpy array] subband delay spectra of simulated noise after applying the frequency weights under the key 'freq_wts'. Only present if top level key is 'sim' and absent for 'cc'. It is of size n_bl x n_win x nlags x n_t. 'skyvis_res_lag' [numpy array] subband delay spectra of residuals after delay CLEAN of simulated noiseless visibilities obtained after applying frequency weights specified under key 'freq_wts'. Only present for top level key 'cc' and absent for 'sim'. It is of size n_bl x n_win x nlags x n_t 'vis_res_lag' [numpy array] subband delay spectra of residuals after delay CLEAN of simulated noisy visibilities obtained after applying frequency weights specified under key 'freq_wts'. Only present for top level key 'cc' and absent for 'sim'. It is of size n_bl x n_win x nlags x n_t 'skyvis_net_lag' [numpy array] subband delay spectra of sum of residuals and clean components after delay CLEAN of simulated noiseless visibilities obtained after applying frequency weights specified under key 'freq_wts'. Only present for top level key 'cc' and absent for 'sim'. It is of size n_bl x n_win x nlags x n_t 'vis_res_lag' [numpy array] subband delay spectra of sum of residuals and clean components after delay CLEAN of simulated noisy visibilities obtained after applying frequency weights specified under key 'freq_wts'. Only present for top level key 'cc' and absent for 'sim'. It is of size n_bl x n_win x nlags x n_t Member functions: __init__() Initializes an instance of class DelaySpectrum delay_transform() Transforms the visibilities from frequency axis onto delay (time) axis using an IFFT. This is performed for noiseless sky visibilities, thermal noise in visibilities, and observed visibilities. delay_transform_allruns() Transforms the visibilities of multiple runs from frequency axis onto delay (time) axis using an IFFT. clean() Transforms the visibilities from frequency axis onto delay (time) axis using an IFFT and deconvolves the delay transform quantities along the delay axis. This is performed for noiseless sky visibilities, thermal noise in visibilities, and observed visibilities. delayClean() Transforms the visibilities from frequency axis onto delay (time) axis using an IFFT and deconvolves the delay transform quantities along the delay axis. This is performed for noiseless sky visibilities, thermal noise in visibilities, and observed visibilities. This calls an in-house module complex1dClean instead of the clean routine in AIPY module. It can utilize parallelization subband_delay_transform() Computes delay transform on multiple frequency sub-bands with specified weights subband_delay_transform_allruns() Computes delay transform on multiple frequency sub-bands with specified weights for multiple realizations of visibilities subband_delay_transform_closure_phase() Computes delay transform of closure phases on antenna triplets on multiple frequency sub-bands with specified weights get_horizon_delay_limits() Estimates the delay envelope determined by the sky horizon for the baseline(s) for the phase centers set_horizon_delay_limits() Estimates the delay envelope determined by the sky horizon for the baseline(s) for the phase centers of the DelaySpectrum instance. No output is returned. Uses the member function get_horizon_delay_limits() save() Saves the interferometer array delay spectrum information to disk. ---------------------------------------------------------------------------- """ def __init__(self, interferometer_array=None, init_file=None): """ ------------------------------------------------------------------------ Intialize the DelaySpectrum class which manages information on delay spectrum of a multi-element interferometer. Class attributes initialized are: f, bp, bp_wts, df, lags, skyvis_lag, vis_lag, n_acc, vis_noise_lag, ia, pad, lag_kernel, horizon_delay_limits, cc_skyvis_lag, cc_skyvis_res_lag, cc_skyvis_net_lag, cc_vis_lag, cc_vis_res_lag, cc_vis_net_lag, cc_skyvis_freq, cc_skyvis_res_freq, cc_sktvis_net_freq, cc_vis_freq, cc_vis_res_freq, cc_vis_net_freq, clean_window_buffer, cc_freq, cc_lags, cc_lag_kernel, subband_delay_spectra, subband_delay_spectra_resampled Read docstring of class DelaySpectrum for details on these attributes. Input(s): interferometer_array [instance of class InterferometerArray] An instance of class InterferometerArray from which certain attributes will be obtained and used init_file [string] full path to filename in FITS format containing delay spectrum information of interferometer array Other input parameters have their usual meanings. Read the docstring of class DelaySpectrum for details on these inputs. ------------------------------------------------------------------------ """ argument_init = False init_file_success = False if init_file is not None: try: hdulist = fits.open(init_file) except IOError: argument_init = True print('\tinit_file provided but could not open the initialization file. Attempting to initialize with input parameters...') extnames = [hdulist[i].header['EXTNAME'] for i in xrange(1,len(hdulist))] try: self.df = hdulist[0].header['freq_resolution'] except KeyError: hdulist.close() raise KeyError('Keyword "freq_resolution" not found in header') try: self.n_acc = hdulist[0].header['N_ACC'] except KeyError: hdulist.close() raise KeyError('Keyword "N_ACC" not found in header') try: self.pad = hdulist[0].header['PAD'] except KeyError: hdulist.close() raise KeyError('Keyword "PAD" not found in header') try: self.clean_window_buffer = hdulist[0].header['DBUFFER'] except KeyError: hdulist.close() raise KeyError('Keyword "DBUFFER" not found in header') try: iarray_init_file = hdulist[0].header['IARRAY'] except KeyError: hdulist.close() raise KeyError('Keyword "IARRAY" not found in header') self.ia = RI.InterferometerArray(None, None, None, init_file=iarray_init_file) # if 'SPECTRAL INFO' not in extnames: # raise KeyError('No extension table found containing spectral information.') # else: # self.f = hdulist['SPECTRAL INFO'].data['frequency'] # try: # self.lags = hdulist['SPECTRAL INFO'].data['lag'] # except KeyError: # self.lags = None try: self.f = hdulist['FREQUENCIES'].data except KeyError: hdulist.close() raise KeyError('Extension "FREQUENCIES" not found in header') self.lags = None if 'LAGS' in extnames: self.lags = hdulist['LAGS'].data self.cc_lags = None if 'CLEAN LAGS' in extnames: self.cc_lags = hdulist['CLEAN LAGS'].data self.cc_freq = None if 'CLEAN FREQUENCIES' in extnames: self.cc_freq = hdulist['CLEAN FREQUENCIES'].data if 'BANDPASS' in extnames: self.bp = hdulist['BANDPASS'].data else: raise KeyError('Extension named "BANDPASS" not found in init_file.') if 'BANDPASS WEIGHTS' in extnames: self.bp_wts = hdulist['BANDPASS WEIGHTS'].data else: self.bp_wts = NP.ones_like(self.bp) if 'HORIZON LIMITS' in extnames: self.horizon_delay_limits = hdulist['HORIZON LIMITS'].data else: self.set_horizon_delay_limits() self.lag_kernel = None if 'LAG KERNEL REAL' in extnames: self.lag_kernel = hdulist['LAG KERNEL REAL'].data if 'LAG KERNEL IMAG' in extnames: self.lag_kernel = self.lag_kernel.astype(NP.complex) self.lag_kernel += 1j * hdulist['LAG KERNEL IMAG'].data self.cc_lag_kernel = None if 'CLEAN LAG KERNEL REAL' in extnames: self.cc_lag_kernel = hdulist['CLEAN LAG KERNEL REAL'].data if 'CLEAN LAG KERNEL IMAG' in extnames: self.cc_lag_kernel = self.cc_lag_kernel.astype(NP.complex) self.cc_lag_kernel += 1j * hdulist['CLEAN LAG KERNEL IMAG'].data self.skyvis_lag = None if 'NOISELESS DELAY SPECTRA REAL' in extnames: self.skyvis_lag = hdulist['NOISELESS DELAY SPECTRA REAL'].data if 'NOISELESS DELAY SPECTRA IMAG' in extnames: self.skyvis_lag = self.skyvis_lag.astype(NP.complex) self.skyvis_lag += 1j * hdulist['NOISELESS DELAY SPECTRA IMAG'].data self.vis_lag = None if 'NOISY DELAY SPECTRA REAL' in extnames: self.vis_lag = hdulist['NOISY DELAY SPECTRA REAL'].data if 'NOISY DELAY SPECTRA IMAG' in extnames: self.vis_lag = self.vis_lag.astype(NP.complex) self.vis_lag += 1j * hdulist['NOISY DELAY SPECTRA IMAG'].data self.vis_noise_lag = None if 'DELAY SPECTRA NOISE REAL' in extnames: self.vis_noise_lag = hdulist['DELAY SPECTRA NOISE REAL'].data if 'DELAY SPECTRA NOISE IMAG' in extnames: self.vis_noise_lag = self.vis_noise_lag.astype(NP.complex) self.vis_noise_lag += 1j * hdulist['DELAY SPECTRA NOISE IMAG'].data self.cc_skyvis_lag = None if 'CLEAN NOISELESS DELAY SPECTRA REAL' in extnames: self.cc_skyvis_lag = hdulist['CLEAN NOISELESS DELAY SPECTRA REAL'].data if 'CLEAN NOISELESS DELAY SPECTRA IMAG' in extnames: self.cc_skyvis_lag = self.cc_skyvis_lag.astype(NP.complex) self.cc_skyvis_lag += 1j * hdulist['CLEAN NOISELESS DELAY SPECTRA IMAG'].data self.cc_vis_lag = None if 'CLEAN NOISY DELAY SPECTRA REAL' in extnames: self.cc_vis_lag = hdulist['CLEAN NOISY DELAY SPECTRA REAL'].data if 'CLEAN NOISY DELAY SPECTRA IMAG' in extnames: self.cc_vis_lag = self.cc_vis_lag.astype(NP.complex) self.cc_vis_lag += 1j * hdulist['CLEAN NOISY DELAY SPECTRA IMAG'].data self.cc_skyvis_res_lag = None if 'CLEAN NOISELESS DELAY SPECTRA RESIDUALS REAL' in extnames: self.cc_skyvis_res_lag = hdulist['CLEAN NOISELESS DELAY SPECTRA RESIDUALS REAL'].data if 'CLEAN NOISELESS DELAY SPECTRA RESIDUALS IMAG' in extnames: self.cc_skyvis_res_lag = self.cc_skyvis_res_lag.astype(NP.complex) self.cc_skyvis_res_lag += 1j * hdulist['CLEAN NOISELESS DELAY SPECTRA RESIDUALS IMAG'].data self.cc_vis_res_lag = None if 'CLEAN NOISY DELAY SPECTRA RESIDUALS REAL' in extnames: self.cc_vis_res_lag = hdulist['CLEAN NOISY DELAY SPECTRA RESIDUALS REAL'].data if 'CLEAN NOISY DELAY SPECTRA RESIDUALS IMAG' in extnames: self.cc_vis_res_lag = self.cc_vis_res_lag.astype(NP.complex) self.cc_vis_res_lag += 1j * hdulist['CLEAN NOISY DELAY SPECTRA RESIDUALS IMAG'].data self.cc_skyvis_freq = None if 'CLEAN NOISELESS VISIBILITIES REAL' in extnames: self.cc_skyvis_freq = hdulist['CLEAN NOISELESS VISIBILITIES REAL'].data if 'CLEAN NOISELESS VISIBILITIES IMAG' in extnames: self.cc_skyvis_freq = self.cc_skyvis_freq.astype(NP.complex) self.cc_skyvis_freq += 1j * hdulist['CLEAN NOISELESS VISIBILITIES IMAG'].data self.cc_vis_freq = None if 'CLEAN NOISY VISIBILITIES REAL' in extnames: self.cc_vis_freq = hdulist['CLEAN NOISY VISIBILITIES REAL'].data if 'CLEAN NOISY VISIBILITIES IMAG' in extnames: self.cc_vis_freq = self.cc_vis_freq.astype(NP.complex) self.cc_vis_freq += 1j * hdulist['CLEAN NOISY VISIBILITIES IMAG'].data self.cc_skyvis_res_freq = None if 'CLEAN NOISELESS VISIBILITIES RESIDUALS REAL' in extnames: self.cc_skyvis_res_freq = hdulist['CLEAN NOISELESS VISIBILITIES RESIDUALS REAL'].data if 'CLEAN NOISELESS VISIBILITIES RESIDUALS IMAG' in extnames: self.cc_skyvis_res_freq = self.cc_skyvis_res_freq.astype(NP.complex) self.cc_skyvis_res_freq += 1j * hdulist['CLEAN NOISELESS VISIBILITIES RESIDUALS IMAG'].data self.cc_vis_res_freq = None if 'CLEAN NOISY VISIBILITIES RESIDUALS REAL' in extnames: self.cc_vis_res_freq = hdulist['CLEAN NOISY VISIBILITIES RESIDUALS REAL'].data if 'CLEAN NOISY VISIBILITIES RESIDUALS IMAG' in extnames: self.cc_vis_res_freq = self.cc_vis_res_freq.astype(NP.complex) self.cc_vis_res_freq += 1j * hdulist['CLEAN NOISY VISIBILITIES RESIDUALS IMAG'].data self.cc_skyvis_net_lag = None if (self.cc_skyvis_lag is not None) and (self.cc_skyvis_res_lag is not None): self.cc_skyvis_net_lag = self.cc_skyvis_lag + self.cc_skyvis_res_lag self.cc_vis_net_lag = None if (self.cc_vis_lag is not None) and (self.cc_vis_res_lag is not None): self.cc_vis_net_lag = self.cc_vis_lag + self.cc_vis_res_lag self.cc_skyvis_net_freq = None if (self.cc_skyvis_freq is not None) and (self.cc_skyvis_res_freq is not None): self.cc_skyvis_net_freq = self.cc_skyvis_freq + self.cc_skyvis_res_freq self.cc_vis_net_freq = None if (self.cc_vis_freq is not None) and (self.cc_vis_res_freq is not None): self.cc_vis_net_freq = self.cc_vis_freq + self.cc_vis_res_freq self.subband_delay_spectra = {} self.subband_delay_spectra_resampled = {} if 'SBDS' in hdulist[0].header: for key in ['cc', 'sim']: if '{0}-SBDS'.format(key) in hdulist[0].header: self.subband_delay_spectra[key] = {} self.subband_delay_spectra[key]['shape'] = hdulist[0].header['{0}-SBDS-WSHAPE'.format(key)] if key == 'cc': self.subband_delay_spectra[key]['bpcorrect'] = bool(hdulist[0].header['{0}-SBDS-BPCORR'.format(key)]) self.subband_delay_spectra[key]['npad'] = hdulist[0].header['{0}-SBDS-NPAD'.format(key)] self.subband_delay_spectra[key]['freq_center'] = hdulist['{0}-SBDS-F0'.format(key)].data self.subband_delay_spectra[key]['freq_wts'] = hdulist['{0}-SBDS-FWTS'.format(key)].data self.subband_delay_spectra[key]['bw_eff'] = hdulist['{0}-SBDS-BWEFF'.format(key)].data self.subband_delay_spectra[key]['lags'] = hdulist['{0}-SBDS-LAGS'.format(key)].data self.subband_delay_spectra[key]['lag_kernel'] = hdulist['{0}-SBDS-LAGKERN-REAL'.format(key)].data + 1j * hdulist['{0}-SBDS-LAGKERN-IMAG'.format(key)].data self.subband_delay_spectra[key]['lag_corr_length'] = hdulist['{0}-SBDS-LAGCORR'.format(key)].data self.subband_delay_spectra[key]['skyvis_lag'] = hdulist['{0}-SBDS-SKYVISLAG-REAL'.format(key)].data + 1j * hdulist['{0}-SBDS-SKYVISLAG-IMAG'.format(key)].data self.subband_delay_spectra[key]['vis_lag'] = hdulist['{0}-SBDS-VISLAG-REAL'.format(key)].data + 1j * hdulist['{0}-SBDS-VISLAG-IMAG'.format(key)].data if key == 'sim': self.subband_delay_spectra[key]['vis_noise_lag'] = hdulist['{0}-SBDS-NOISELAG-REAL'.format(key)].data + 1j * hdulist['{0}-SBDS-NOISELAG-IMAG'.format(key)].data if key == 'cc': self.subband_delay_spectra[key]['skyvis_res_lag'] = hdulist['{0}-SBDS-SKYVISRESLAG-REAL'.format(key)].data + 1j * hdulist['{0}-SBDS-SKYVISRESLAG-IMAG'.format(key)].data self.subband_delay_spectra[key]['vis_res_lag'] = hdulist['{0}-SBDS-VISRESLAG-REAL'.format(key)].data + 1j * hdulist['{0}-SBDS-VISRESLAG-IMAG'.format(key)].data self.subband_delay_spectra[key]['skyvis_net_lag'] = self.subband_delay_spectra[key]['skyvis_lag'] + self.subband_delay_spectra[key]['skyvis_res_lag'] self.subband_delay_spectra[key]['vis_net_lag'] = self.subband_delay_spectra[key]['vis_lag'] + self.subband_delay_spectra[key]['vis_res_lag'] if 'SBDS-RS' in hdulist[0].header: for key in ['cc', 'sim']: if '{0}-SBDS-RS'.format(key) in hdulist[0].header: self.subband_delay_spectra_resampled[key] = {} self.subband_delay_spectra_resampled[key]['freq_center'] = hdulist['{0}-SBDSRS-F0'.format(key)].data self.subband_delay_spectra_resampled[key]['bw_eff'] = hdulist['{0}-SBDSRS-BWEFF'.format(key)].data self.subband_delay_spectra_resampled[key]['lags'] = hdulist['{0}-SBDSRS-LAGS'.format(key)].data self.subband_delay_spectra_resampled[key]['lag_kernel'] = hdulist['{0}-SBDSRS-LAGKERN-REAL'.format(key)].data + 1j * hdulist['{0}-SBDSRS-LAGKERN-IMAG'.format(key)].data self.subband_delay_spectra_resampled[key]['lag_corr_length'] = hdulist['{0}-SBDSRS-LAGCORR'.format(key)].data self.subband_delay_spectra_resampled[key]['skyvis_lag'] = hdulist['{0}-SBDSRS-SKYVISLAG-REAL'.format(key)].data + 1j * hdulist['{0}-SBDSRS-SKYVISLAG-IMAG'.format(key)].data self.subband_delay_spectra_resampled[key]['vis_lag'] = hdulist['{0}-SBDSRS-VISLAG-REAL'.format(key)].data + 1j * hdulist['{0}-SBDSRS-VISLAG-IMAG'.format(key)].data if key == 'sim': self.subband_delay_spectra_resampled[key]['vis_noise_lag'] = hdulist['{0}-SBDSRS-NOISELAG-REAL'.format(key)].data + 1j * hdulist['{0}-SBDSRS-NOISELAG-IMAG'.format(key)].data if key == 'cc': self.subband_delay_spectra_resampled[key]['skyvis_res_lag'] = hdulist['{0}-SBDSRS-SKYVISRESLAG-REAL'.format(key)].data + 1j * hdulist['{0}-SBDSRS-SKYVISRESLAG-IMAG'.format(key)].data self.subband_delay_spectra_resampled[key]['vis_res_lag'] = hdulist['{0}-SBDSRS-VISRESLAG-REAL'.format(key)].data + 1j * hdulist['{0}-SBDSRS-VISRESLAG-IMAG'.format(key)].data self.subband_delay_spectra_resampled[key]['skyvis_net_lag'] = self.subband_delay_spectra_resampled[key]['skyvis_lag'] + self.subband_delay_spectra_resampled[key]['skyvis_res_lag'] self.subband_delay_spectra_resampled[key]['vis_net_lag'] = self.subband_delay_spectra_resampled[key]['vis_lag'] + self.subband_delay_spectra_resampled[key]['vis_res_lag'] hdulist.close() init_file_success = True return else: argument_init = True if (not argument_init) and (not init_file_success): raise ValueError('Initialization failed with the use of init_file.') if not isinstance(interferometer_array, RI.InterferometerArray): raise TypeError('Input interferometer_array must be an instance of class InterferometerArray') self.ia = interferometer_array self.f = interferometer_array.channels self.df = interferometer_array.freq_resolution self.n_acc = interferometer_array.n_acc self.horizon_delay_limits = self.get_horizon_delay_limits() self.bp = interferometer_array.bp # Inherent bandpass shape self.bp_wts = interferometer_array.bp_wts # Additional bandpass weights self.pad = 0.0 self.lags = DSP.spectral_axis(self.f.size, delx=self.df, use_real=False, shift=True) self.lag_kernel = None self.skyvis_lag = None self.vis_lag = None self.vis_noise_lag = None self.clean_window_buffer = 1.0 self.cc_lags = None self.cc_freq = None self.cc_lag_kernel = None self.cc_skyvis_lag = None self.cc_skyvis_res_lag = None self.cc_vis_lag = None self.cc_vis_res_lag = None self.cc_skyvis_net_lag = None self.cc_vis_net_lag = None self.cc_skyvis_freq = None self.cc_skyvis_res_freq = None self.cc_vis_freq = None self.cc_vis_res_freq = None self.cc_skyvis_net_freq = None self.cc_vis_net_freq = None self.subband_delay_spectra = {} self.subband_delay_spectra_resampled = {} ############################################################################# def delay_transform(self, pad=1.0, freq_wts=None, downsample=True, action=None, verbose=True): """ ------------------------------------------------------------------------ Transforms the visibilities from frequency axis onto delay (time) axis using an IFFT. This is performed for noiseless sky visibilities, thermal noise in visibilities, and observed visibilities. Inputs: pad [scalar] Non-negative scalar indicating padding fraction relative to the number of frequency channels. For e.g., a pad of 1.0 pads the frequency axis with zeros of the same width as the number of channels. After the delay transform, the transformed visibilities are downsampled by a factor of 1+pad. If a negative value is specified, delay transform will be performed with no padding freq_wts [numpy vector or array] window shaping to be applied before computing delay transform. It can either be a vector or size equal to the number of channels (which will be applied to all time instances for all baselines), or a nchan x n_snapshots numpy array which will be applied to all baselines, or a n_baselines x nchan numpy array which will be applied to all timestamps, or a n_baselines x nchan x n_snapshots numpy array. Default (None) will not apply windowing and only the inherent bandpass will be used. downsample [boolean] If set to True (default), the delay transform quantities will be downsampled by exactly the same factor that was used in padding. For instance, if pad is set to 1.0, the downsampling will be by a factor of 2. If set to False, no downsampling will be done even if the original quantities were padded action [boolean] If set to None (default), just return the delay- transformed quantities. If set to 'store', these quantities will be stored as internal attributes verbose [boolean] If set to True (default), print diagnostic and progress messages. If set to False, no such messages are printed. ------------------------------------------------------------------------ """ if verbose: print('Preparing to compute delay transform...\n\tChecking input parameters for compatibility...') if not isinstance(pad, (int, float)): raise TypeError('pad fraction must be a scalar value.') if pad < 0.0: pad = 0.0 if verbose: print('\tPad fraction found to be negative. Resetting to 0.0 (no padding will be applied).') if freq_wts is not None: if freq_wts.size == self.f.size: freq_wts = NP.repeat(NP.expand_dims(NP.repeat(freq_wts.reshape(1,-1), self.ia.baselines.shape[0], axis=0), axis=2), self.n_acc, axis=2) elif freq_wts.size == self.f.size * self.n_acc: freq_wts = NP.repeat(NP.expand_dims(freq_wts.reshape(self.f.size, -1), axis=0), self.ia.baselines.shape[0], axis=0) elif freq_wts.size == self.f.size * self.ia.baselines.shape[0]: freq_wts = NP.repeat(NP.expand_dims(freq_wts.reshape(-1, self.f.size), axis=2), self.n_acc, axis=2) elif freq_wts.size == self.f.size * self.ia.baselines.shape[0] * self.n_acc: freq_wts = freq_wts.reshape(self.ia.baselines.shape[0], self.f.size, self.n_acc) else: raise ValueError('window shape dimensions incompatible with number of channels and/or number of tiemstamps.') else: freq_wts = self.bp_wts if verbose: print('\tFrequency window weights assigned.') if not isinstance(downsample, bool): raise TypeError('Input downsample must be of boolean type') if verbose: print('\tInput parameters have been verified to be compatible.\n\tProceeding to compute delay transform.') result = {} result['freq_wts'] = freq_wts result['pad'] = pad result['lags'] = DSP.spectral_axis(int(self.f.size*(1+pad)), delx=self.df, use_real=False, shift=True) if pad == 0.0: result['vis_lag'] = DSP.FT1D(self.ia.vis_freq * self.bp * freq_wts, ax=1, inverse=True, use_real=False, shift=True) * self.f.size * self.df result['skyvis_lag'] = DSP.FT1D(self.ia.skyvis_freq * self.bp * freq_wts, ax=1, inverse=True, use_real=False, shift=True) * self.f.size * self.df result['vis_noise_lag'] = DSP.FT1D(self.ia.vis_noise_freq * self.bp * freq_wts, ax=1, inverse=True, use_real=False, shift=True) * self.f.size * self.df result['lag_kernel'] = DSP.FT1D(self.bp * freq_wts, ax=1, inverse=True, use_real=False, shift=True) * self.f.size * self.df if verbose: print('\tDelay transform computed without padding.') else: npad = int(self.f.size * pad) result['vis_lag'] = DSP.FT1D(NP.pad(self.ia.vis_freq * self.bp * freq_wts, ((0,0),(0,npad),(0,0)), mode='constant'), ax=1, inverse=True, use_real=False, shift=True) * (npad + self.f.size) * self.df result['skyvis_lag'] = DSP.FT1D(NP.pad(self.ia.skyvis_freq * self.bp * freq_wts, ((0,0),(0,npad),(0,0)), mode='constant'), ax=1, inverse=True, use_real=False, shift=True) * (npad + self.f.size) * self.df result['vis_noise_lag'] = DSP.FT1D(NP.pad(self.ia.vis_noise_freq * self.bp * freq_wts, ((0,0),(0,npad),(0,0)), mode='constant'), ax=1, inverse=True, use_real=False, shift=True) * (npad + self.f.size) * self.df result['lag_kernel'] = DSP.FT1D(NP.pad(self.bp * freq_wts, ((0,0),(0,npad),(0,0)), mode='constant'), ax=1, inverse=True, use_real=False, shift=True) * (npad + self.f.size) * self.df if verbose: print('\tDelay transform computed with padding fraction {0:.1f}'.format(pad)) if downsample: result['vis_lag'] = DSP.downsampler(result['vis_lag'], 1+pad, axis=1) result['skyvis_lag'] = DSP.downsampler(result['skyvis_lag'], 1+pad, axis=1) result['vis_noise_lag'] = DSP.downsampler(result['vis_noise_lag'], 1+pad, axis=1) result['lag_kernel'] = DSP.downsampler(result['lag_kernel'], 1+pad, axis=1) result['lags'] = DSP.downsampler(result['lags'], 1+pad) result['lags'] = result['lags'].flatten() if verbose: print('\tDelay transform products downsampled by factor of {0:.1f}'.format(1+pad)) print('delay_transform() completed successfully.') if action == 'store': self.pad = pad self.lags = result['lags'] self.bp_wts = freq_wts self.vis_lag = result['vis_lag'] self.skyvis_lag = result['skyvis_lag'] self.vis_noise_lag = result['vis_noise_lag'] self.lag_kernel = result['lag_kernel'] return result ############################################################################# # def clean(self, pad=1.0, freq_wts=None, clean_window_buffer=1.0, # verbose=True): # """ # ------------------------------------------------------------------------ # TO BE DEPRECATED!!! USE MEMBER FUNCTION delayClean() # Transforms the visibilities from frequency axis onto delay (time) axis # using an IFFT and deconvolves the delay transform quantities along the # delay axis. This is performed for noiseless sky visibilities, thermal # noise in visibilities, and observed visibilities. # Inputs: # pad [scalar] Non-negative scalar indicating padding fraction # relative to the number of frequency channels. For e.g., a # pad of 1.0 pads the frequency axis with zeros of the same # width as the number of channels. If a negative value is # specified, delay transform will be performed with no padding # freq_wts [numpy vector or array] window shaping to be applied before # computing delay transform. It can either be a vector or size # equal to the number of channels (which will be applied to all # time instances for all baselines), or a nchan x n_snapshots # numpy array which will be applied to all baselines, or a # n_baselines x nchan numpy array which will be applied to all # timestamps, or a n_baselines x nchan x n_snapshots numpy # array. Default (None) will not apply windowing and only the # inherent bandpass will be used. # verbose [boolean] If set to True (default), print diagnostic and # progress messages. If set to False, no such messages are # printed. # ------------------------------------------------------------------------ # """ # if not isinstance(pad, (int, float)): # raise TypeError('pad fraction must be a scalar value.') # if pad < 0.0: # pad = 0.0 # if verbose: # print('\tPad fraction found to be negative. Resetting to 0.0 (no padding will be applied).') # if freq_wts is not None: # if freq_wts.size == self.f.size: # freq_wts = NP.repeat(NP.expand_dims(NP.repeat(freq_wts.reshape(1,-1), self.ia.baselines.shape[0], axis=0), axis=2), self.n_acc, axis=2) # elif freq_wts.size == self.f.size * self.n_acc: # freq_wts = NP.repeat(NP.expand_dims(freq_wts.reshape(self.f.size, -1), axis=0), self.ia.baselines.shape[0], axis=0) # elif freq_wts.size == self.f.size * self.ia.baselines.shape[0]: # freq_wts = NP.repeat(NP.expand_dims(freq_wts.reshape(-1, self.f.size), axis=2), self.n_acc, axis=2) # elif freq_wts.size == self.f.size * self.ia.baselines.shape[0] * self.n_acc: # freq_wts = freq_wts.reshape(self.ia.baselines.shape[0], self.f.size, self.n_acc) # else: # raise ValueError('window shape dimensions incompatible with number of channels and/or number of tiemstamps.') # self.bp_wts = freq_wts # if verbose: # print('\tFrequency window weights assigned.') # bw = self.df * self.f.size # pc = self.ia.phase_center # pc_coords = self.ia.phase_center_coords # if pc_coords == 'hadec': # pc_altaz = GEOM.hadec2altaz(pc, self.ia.latitude, units='degrees') # pc_dircos = GEOM.altaz2dircos(pc_altaz, units='degrees') # elif pc_coords == 'altaz': # pc_dircos = GEOM.altaz2dircos(pc, units='degrees') # npad = int(self.f.size * pad) # lags = DSP.spectral_axis(self.f.size + npad, delx=self.df, use_real=False, shift=False) # dlag = lags[1] - lags[0] # clean_area = NP.zeros(self.f.size + npad, dtype=int) # skyvis_lag = (npad + self.f.size) * self.df * DSP.FT1D(NP.pad(self.ia.skyvis_freq*self.bp*self.bp_wts, ((0,0),(0,npad),(0,0)), mode='constant'), ax=1, inverse=True, use_real=False, shift=False) # vis_lag = (npad + self.f.size) * self.df * DSP.FT1D(NP.pad(self.ia.vis_freq*self.bp*self.bp_wts, ((0,0),(0,npad),(0,0)), mode='constant'), ax=1, inverse=True, use_real=False, shift=False) # lag_kernel = (npad + self.f.size) * self.df * DSP.FT1D(NP.pad(self.bp, ((0,0),(0,npad),(0,0)), mode='constant'), ax=1, inverse=True, use_real=False, shift=False) # ccomponents_noiseless = NP.zeros_like(skyvis_lag) # ccres_noiseless = NP.zeros_like(skyvis_lag) # ccomponents_noisy = NP.zeros_like(vis_lag) # ccres_noisy = NP.zeros_like(vis_lag) # for snap_iter in xrange(self.n_acc): # progress = PGB.ProgressBar(widgets=[PGB.Percentage(), PGB.Bar(marker='-', left=' |', right='| '), PGB.Counter(), '/{0:0d} Baselines '.format(self.ia.baselines.shape[0]), PGB.ETA()], maxval=self.ia.baselines.shape[0]).start() # for bl_iter in xrange(self.ia.baselines.shape[0]): # clean_area[NP.logical_and(lags <= self.horizon_delay_limits[snap_iter,bl_iter,1]+clean_window_buffer/bw, lags >= self.horizon_delay_limits[snap_iter,bl_iter,0]-clean_window_buffer/bw)] = 1 # cc_noiseless, info_noiseless = _gentle_clean(skyvis_lag[bl_iter,:,snap_iter], lag_kernel[bl_iter,:,snap_iter], area=clean_area, stop_if_div=False, verbose=False, autoscale=True) # ccomponents_noiseless[bl_iter,:,snap_iter] = cc_noiseless # ccres_noiseless[bl_iter,:,snap_iter] = info_noiseless['res'] # cc_noisy, info_noisy = _gentle_clean(vis_lag[bl_iter,:,snap_iter], lag_kernel[bl_iter,:,snap_iter], area=clean_area, stop_if_div=False, verbose=False, autoscale=True) # ccomponents_noisy[bl_iter,:,snap_iter] = cc_noisy # ccres_noisy[bl_iter,:,snap_iter] = info_noisy['res'] # progress.update(bl_iter+1) # progress.finish() # deta = lags[1] - lags[0] # cc_skyvis = NP.fft.fft(ccomponents_noiseless, axis=1) * deta # cc_skyvis_res = NP.fft.fft(ccres_noiseless, axis=1) * deta # cc_vis = NP.fft.fft(ccomponents_noisy, axis=1) * deta # cc_vis_res = NP.fft.fft(ccres_noisy, axis=1) * deta # self.skyvis_lag = NP.fft.fftshift(skyvis_lag, axes=1) # self.vis_lag = NP.fft.fftshift(vis_lag, axes=1) # self.lag_kernel = NP.fft.fftshift(lag_kernel, axes=1) # self.cc_skyvis_lag = NP.fft.fftshift(ccomponents_noiseless, axes=1) # self.cc_skyvis_res_lag = NP.fft.fftshift(ccres_noiseless, axes=1) # self.cc_vis_lag = NP.fft.fftshift(ccomponents_noisy, axes=1) # self.cc_vis_res_lag = NP.fft.fftshift(ccres_noisy, axes=1) # self.cc_skyvis_net_lag = self.cc_skyvis_lag + self.cc_skyvis_res_lag # self.cc_vis_net_lag = self.cc_vis_lag + self.cc_vis_res_lag # self.lags = NP.fft.fftshift(lags) # self.cc_skyvis_freq = cc_skyvis # self.cc_skyvis_res_freq = cc_skyvis_res # self.cc_vis_freq = cc_vis # self.cc_vis_res_freq = cc_vis_res # self.cc_skyvis_net_freq = cc_skyvis + cc_skyvis_res # self.cc_vis_net_freq = cc_vis + cc_vis_res # self.clean_window_buffer = clean_window_buffer ############################################################################# def delay_transform_allruns(self, vis, pad=1.0, freq_wts=None, downsample=True, verbose=True): """ ------------------------------------------------------------------------ Transforms the visibilities of multiple runs from frequency axis onto delay (time) axis using an IFFT. Inputs: vis [numpy array] Visibilities which will be delay transformed. It must be of shape (...,nbl,nchan,ntimes) pad [scalar] Non-negative scalar indicating padding fraction relative to the number of frequency channels. For e.g., a pad of 1.0 pads the frequency axis with zeros of the same width as the number of channels. After the delay transform, the transformed visibilities are downsampled by a factor of 1+pad. If a negative value is specified, delay transform will be performed with no padding freq_wts [numpy vector or array] window shaping to be applied before computing delay transform. It can either be a vector or size equal to the number of channels (which will be applied to all time instances for all baselines), or a nchan x n_snapshots numpy array which will be applied to all baselines, or a n_baselines x nchan numpy array which will be applied to all timestamps, or a n_baselines x nchan x n_snapshots numpy array or have shape identical to input vis. Default (None) will not apply windowing and only the inherent bandpass will be used. downsample [boolean] If set to True (default), the delay transform quantities will be downsampled by exactly the same factor that was used in padding. For instance, if pad is set to 1.0, the downsampling will be by a factor of 2. If set to False, no downsampling will be done even if the original quantities were padded verbose [boolean] If set to True (default), print diagnostic and progress messages. If set to False, no such messages are printed. Output: Dictionary containing delay spectrum information. It contains the following keys and values: 'lags' [numpy array] lags of the subband delay spectra with or without resampling. If not resampled it is of size nlags=nchan+npad where npad is the number of frequency channels padded specified under the key 'npad'. If resampled, it is of shape nlags where nlags is the number of independent delay bins 'lag_kernel' [numpy array] The delay kernel which is the result of the bandpass shape and the spectral window used in determining the delay spectrum. It is of shape n_bl x n_win x nlags x n_t. 'vis_lag' [numpy array] delay spectra of visibilities, after applying the frequency weights under the key 'freq_wts'. It is of size n_win x (n1xn2x... n_runs dims) x n_bl x nlags x x n_t. ------------------------------------------------------------------------ """ if verbose: print('Preparing to compute delay transform...\n\tChecking input parameters for compatibility...') try: vis except NameError: raise NameError('Input vis must be provided') if not isinstance(vis, NP.ndarray): raise TypeError('Input vis must be a numpy array') elif vis.ndim < 3: raise ValueError('Input vis must be at least 3-dimensional') elif vis.shape[-3:] == (self.ia.baselines.shape[0],self.f.size,self.n_acc): if vis.ndim == 3: shp = (1,) + vis.shape else: shp = vis.shape vis = vis.reshape(shp) else: raise ValueError('Input vis does not have compatible shape') if not isinstance(pad, (int, float)): raise TypeError('pad fraction must be a scalar value.') if pad < 0.0: pad = 0.0 if verbose: print('\tPad fraction found to be negative. Resetting to 0.0 (no padding will be applied).') if freq_wts is not None: if freq_wts.shape == self.f.shape: freq_wts = freq_wts.reshape(tuple(NP.ones(len(vis.shape[:-3]),dtype=NP.int))+(1,-1,1)) elif freq_wts.shape == (self.f.size, self.n_acc): freq_wts = freq_wts.reshape(tuple(NP.ones(len(vis.shape[:-3]),dtype=NP.int))+(1,self.f.size,self.n_acc)) elif freq_wts.shape == (self.ia.baselines.shape[0], self.f.size): freq_wts = freq_wts.reshape(tuple(NP.ones(len(vis.shape[:-3]),dtype=NP.int))+(self.ia.baselines.shape[0],self.f.size,1)) elif freq_wts.shape == (self.ia.baselines.shape[0], self.f.size, self.n_acc): freq_wts = freq_wts.reshape(tuple(NP.ones(len(vis.shape[:-3]),dtype=NP.int))+(self.ia.baselines.shape[0],self.f.size,self.n_acc)) elif not freq_wts.shape != vis.shape: raise ValueError('window shape dimensions incompatible with number of channels and/or number of tiemstamps.') else: freq_wts = self.bp_wts.reshape(tuple(NP.ones(len(vis.shape[:-3]),dtype=NP.int))+self.bp_wts.shape) bp = self.bp.reshape(tuple(NP.ones(len(vis.shape[:-3]),dtype=NP.int))+self.bp.shape) if verbose: print('\tFrequency window weights assigned.') if not isinstance(downsample, bool): raise TypeError('Input downsample must be of boolean type') if verbose: print('\tInput parameters have been verified to be compatible.\n\tProceeding to compute delay transform.') result = {} result['freq_wts'] = freq_wts result['pad'] = pad result['lags'] = DSP.spectral_axis(int(self.f.size*(1+pad)), delx=self.df, use_real=False, shift=True) if pad == 0.0: result['vis_lag'] = DSP.FT1D(vis * bp * freq_wts, ax=-2, inverse=True, use_real=False, shift=True) * self.f.size * self.df result['lag_kernel'] = DSP.FT1D(bp * freq_wts, ax=-2, inverse=True, use_real=False, shift=True) * self.f.size * self.df if verbose: print('\tDelay transform computed without padding.') else: npad = int(self.f.size * pad) pad_shape = NP.zeros((len(vis.shape[:-3]),2), dtype=NP.int).tolist() pad_shape += [[0,0], [0,npad], [0,0]] result['vis_lag'] = DSP.FT1D(NP.pad(vis * bp * freq_wts, pad_shape, mode='constant'), ax=-2, inverse=True, use_real=False, shift=True) * (npad + self.f.size) * self.df result['lag_kernel'] = DSP.FT1D(NP.pad(bp * freq_wts, pad_shape, mode='constant'), ax=-2, inverse=True, use_real=False, shift=True) * (npad + self.f.size) * self.df if verbose: print('\tDelay transform computed with padding fraction {0:.1f}'.format(pad)) if downsample: result['vis_lag'] = DSP.downsampler(result['vis_lag'], 1+pad, axis=-2) result['lag_kernel'] = DSP.downsampler(result['lag_kernel'], 1+pad, axis=-2) result['lags'] = DSP.downsampler(result['lags'], 1+pad) result['lags'] = result['lags'].flatten() if verbose: print('\tDelay transform products downsampled by factor of {0:.1f}'.format(1+pad)) print('delay_transform() completed successfully.') return result ############################################################################# def delayClean(self, pad=1.0, freq_wts=None, clean_window_buffer=1.0, gain=0.1, maxiter=10000, threshold=5e-3, threshold_type='relative', parallel=False, nproc=None, verbose=True): """ ------------------------------------------------------------------------ Transforms the visibilities from frequency axis onto delay (time) axis using an IFFT and deconvolves the delay transform quantities along the delay axis. This is performed for noiseless sky visibilities, thermal noise in visibilities, and observed visibilities. This calls an in-house module complex1dClean instead of the clean routine in AIPY module. It can utilize parallelization Inputs: pad [scalar] Non-negative scalar indicating padding fraction relative to the number of frequency channels. For e.g., a pad of 1.0 pads the frequency axis with zeros of the same width as the number of channels. If a negative value is specified, delay transform will be performed with no padding freq_wts [numpy vector or array] window shaping to be applied before computing delay transform. It can either be a vector or size equal to the number of channels (which will be applied to all time instances for all baselines), or a nchan x n_snapshots numpy array which will be applied to all baselines, or a n_baselines x nchan numpy array which will be applied to all timestamps, or a n_baselines x nchan x n_snapshots numpy array. Default (None) will not apply windowing and only the inherent bandpass will be used. gain [scalar] gain factor to be applied while subtracting clean component from residuals. This is the fraction of the maximum in the residuals that will be subtracted. Must lie between 0 and 1. A lower value will have a smoother convergence but take a longer time to converge. Default=0.1 maxiter [scalar] maximum number of iterations for cleaning process. Will terminate if the number of iterations exceed maxiter. Default=10000 threshold [scalar] represents the cleaning depth either as a fraction of the maximum in the input (when thershold_type is set to 'relative') or the absolute value (when threshold_type is set to 'absolute') in same units of input down to which inp should be cleaned. Value must always be positive. When threshold_type is set to 'relative', threshold mu st lie between 0 and 1. Default=5e-3 (found to work well and converge fast) assuming threshold_type is set to 'relative' threshold_type [string] represents the type of threshold specified by value in input threshold. Accepted values are 'relative' and 'absolute'. If set to 'relative' the threshold value is the fraction (between 0 and 1) of maximum in input down to which it should be cleaned. If set to 'asbolute' it is the actual value down to which inp should be cleaned. Default='relative' parallel [boolean] specifies if parallelization is to be invoked. False (default) means only serial processing nproc [integer] specifies number of independent processes to spawn. Default = None, means automatically determines the number of process cores in the system and use one less than that to avoid locking the system for other processes. Applies only if input parameter 'parallel' (see above) is set to True. If nproc is set to a value more than the number of process cores in the system, it will be reset to number of process cores in the system minus one to avoid locking the system out for other processes verbose [boolean] If set to True (default), print diagnostic and progress messages. If set to False, no such messages are printed. ------------------------------------------------------------------------ """ if not isinstance(pad, (int, float)): raise TypeError('pad fraction must be a scalar value.') if pad < 0.0: pad = 0.0 if verbose: print('\tPad fraction found to be negative. Resetting to 0.0 (no padding will be applied).') if freq_wts is not None: if freq_wts.size == self.f.size: freq_wts = NP.repeat(NP.expand_dims(NP.repeat(freq_wts.reshape(1,-1), self.ia.baselines.shape[0], axis=0), axis=2), self.n_acc, axis=2) elif freq_wts.size == self.f.size * self.n_acc: freq_wts = NP.repeat(NP.expand_dims(freq_wts.reshape(self.f.size, -1), axis=0), self.ia.baselines.shape[0], axis=0) elif freq_wts.size == self.f.size * self.ia.baselines.shape[0]: freq_wts = NP.repeat(NP.expand_dims(freq_wts.reshape(-1, self.f.size), axis=2), self.n_acc, axis=2) elif freq_wts.size == self.f.size * self.ia.baselines.shape[0] * self.n_acc: freq_wts = freq_wts.reshape(self.ia.baselines.shape[0], self.f.size, self.n_acc) else: raise ValueError('window shape dimensions incompatible with number of channels and/or number of tiemstamps.') self.bp_wts = freq_wts if verbose: print('\tFrequency window weights assigned.') bw = self.df * self.f.size pc = self.ia.phase_center pc_coords = self.ia.phase_center_coords if pc_coords == 'hadec': pc_altaz = GEOM.hadec2altaz(pc, self.ia.latitude, units='degrees') pc_dircos = GEOM.altaz2dircos(pc_altaz, units='degrees') elif pc_coords == 'altaz': pc_dircos = GEOM.altaz2dircos(pc, units='degrees') npad = int(self.f.size * pad) lags = DSP.spectral_axis(self.f.size + npad, delx=self.df, use_real=False, shift=False) dlag = lags[1] - lags[0] clean_area = NP.zeros(self.f.size + npad, dtype=int) skyvis_lag = (npad + self.f.size) * self.df * DSP.FT1D(NP.pad(self.ia.skyvis_freq*self.bp*self.bp_wts, ((0,0),(0,npad),(0,0)), mode='constant'), ax=1, inverse=True, use_real=False, shift=False) vis_lag = (npad + self.f.size) * self.df * DSP.FT1D(NP.pad(self.ia.vis_freq*self.bp*self.bp_wts, ((0,0),(0,npad),(0,0)), mode='constant'), ax=1, inverse=True, use_real=False, shift=False) lag_kernel = (npad + self.f.size) * self.df * DSP.FT1D(NP.pad(self.bp*self.bp_wts, ((0,0),(0,npad),(0,0)), mode='constant'), ax=1, inverse=True, use_real=False, shift=False) ccomponents_noiseless = NP.zeros_like(skyvis_lag) ccres_noiseless = NP.zeros_like(skyvis_lag) ccomponents_noisy = NP.zeros_like(vis_lag) ccres_noisy = NP.zeros_like(vis_lag) if parallel: if nproc is None: nproc = min(max(MP.cpu_count()-1, 1), self.ia.baselines.shape[0]*self.n_acc) else: nproc = min(max(MP.cpu_count()-1, 1), self.ia.baselines.shape[0]*self.n_acc, nproc) list_of_skyvis_lag = [] list_of_vis_lag = [] list_of_dkern = [] list_of_cboxes = [] for bli in xrange(self.ia.baselines.shape[0]): for ti in xrange(self.n_acc): list_of_skyvis_lag += [skyvis_lag[bli,:,ti]] list_of_vis_lag += [vis_lag[bli,:,ti]] list_of_dkern += [lag_kernel[bli,:,ti]] clean_area = NP.zeros(self.f.size + npad, dtype=int) clean_area[NP.logical_and(lags <= self.horizon_delay_limits[ti,bli,1]+clean_window_buffer/bw, lags >= self.horizon_delay_limits[ti,bli,0]-clean_window_buffer/bw)] = 1 list_of_cboxes += [clean_area] list_of_gains = [gain] * self.ia.baselines.shape[0]*self.n_acc list_of_maxiter = [maxiter] * self.ia.baselines.shape[0]*self.n_acc list_of_thresholds = [threshold] * self.ia.baselines.shape[0]*self.n_acc list_of_threshold_types = [threshold_type] * self.ia.baselines.shape[0]*self.n_acc list_of_verbosity = [verbose] * self.ia.baselines.shape[0]*self.n_acc list_of_pid = range(self.ia.baselines.shape[0]*self.n_acc) # list_of_pid = [None] * self.ia.baselines.shape[0]*self.n_acc list_of_progressbars = [True] * self.ia.baselines.shape[0]*self.n_acc list_of_progressbar_ylocs = NP.arange(self.ia.baselines.shape[0]*self.n_acc) % min(nproc, WM.term.height) list_of_progressbar_ylocs = list_of_progressbar_ylocs.tolist() pool = MP.Pool(processes=nproc) list_of_noiseless_cleanstates = pool.map(complex1dClean_arg_splitter, IT.izip(list_of_skyvis_lag, list_of_dkern, list_of_cboxes, list_of_gains, list_of_maxiter, list_of_thresholds, list_of_threshold_types, list_of_verbosity, list_of_progressbars, list_of_pid, list_of_progressbar_ylocs)) list_of_noisy_cleanstates = pool.map(complex1dClean_arg_splitter, IT.izip(list_of_vis_lag, list_of_dkern, list_of_cboxes, list_of_gains, list_of_maxiter, list_of_thresholds, list_of_threshold_types, list_of_verbosity, list_of_progressbars, list_of_pid, list_of_progressbar_ylocs)) for bli in xrange(self.ia.baselines.shape[0]): for ti in xrange(self.n_acc): ind = bli * self.n_acc + ti noiseless_cleanstate = list_of_noiseless_cleanstates[ind] ccomponents_noiseless[bli,:,ti] = noiseless_cleanstate['cc'] ccres_noiseless[bli,:,ti] = noiseless_cleanstate['res'] noisy_cleanstate = list_of_noisy_cleanstates[ind] ccomponents_noisy[bli,:,ti] = noisy_cleanstate['cc'] ccres_noisy[bli,:,ti] = noisy_cleanstate['res'] else: for snap_iter in xrange(self.n_acc): progress = PGB.ProgressBar(widgets=[PGB.Percentage(), PGB.Bar(marker='-', left=' |', right='| '), PGB.Counter(), '/{0:0d} Baselines '.format(self.ia.baselines.shape[0]), PGB.ETA()], maxval=self.ia.baselines.shape[0]).start() for bl_iter in xrange(self.ia.baselines.shape[0]): clean_area[NP.logical_and(lags <= self.horizon_delay_limits[snap_iter,bl_iter,1]+clean_window_buffer/bw, lags >= self.horizon_delay_limits[snap_iter,bl_iter,0]-clean_window_buffer/bw)] = 1 cleanstate = complex1dClean(skyvis_lag[bl_iter,:,snap_iter], lag_kernel[bl_iter,:,snap_iter], cbox=clean_area, gain=gain, maxiter=maxiter, threshold=threshold, threshold_type=threshold_type, verbose=verbose) ccomponents_noiseless[bl_iter,:,snap_iter] = cleanstate['cc'] ccres_noiseless[bl_iter,:,snap_iter] = cleanstate['res'] cleanstate = complex1dClean(vis_lag[bl_iter,:,snap_iter], lag_kernel[bl_iter,:,snap_iter], cbox=clean_area, gain=gain, maxiter=maxiter, threshold=threshold, threshold_type=threshold_type, verbose=verbose) ccomponents_noisy[bl_iter,:,snap_iter] = cleanstate['cc'] ccres_noisy[bl_iter,:,snap_iter] = cleanstate['res'] progress.update(bl_iter+1) progress.finish() deta = lags[1] - lags[0] pad_factor = (1.0 + 1.0*npad/self.f.size) # to make sure visibilities after CLEANing are at the same amplitude level as before CLEANing cc_skyvis = NP.fft.fft(ccomponents_noiseless, axis=1) * deta * pad_factor cc_skyvis_res = NP.fft.fft(ccres_noiseless, axis=1) * deta * pad_factor cc_vis = NP.fft.fft(ccomponents_noisy, axis=1) * deta * pad_factor cc_vis_res = NP.fft.fft(ccres_noisy, axis=1) * deta * pad_factor self.lags = lags self.skyvis_lag = NP.fft.fftshift(skyvis_lag, axes=1) self.vis_lag = NP.fft.fftshift(vis_lag, axes=1) self.lag_kernel = NP.fft.fftshift(lag_kernel, axes=1) self.cc_lag_kernel = NP.fft.fftshift(lag_kernel, axes=1) self.cc_skyvis_lag = NP.fft.fftshift(ccomponents_noiseless, axes=1) self.cc_skyvis_res_lag = NP.fft.fftshift(ccres_noiseless, axes=1) self.cc_vis_lag = NP.fft.fftshift(ccomponents_noisy, axes=1) self.cc_vis_res_lag = NP.fft.fftshift(ccres_noisy, axes=1) self.cc_skyvis_net_lag = self.cc_skyvis_lag + self.cc_skyvis_res_lag self.cc_vis_net_lag = self.cc_vis_lag + self.cc_vis_res_lag self.cc_lags = NP.fft.fftshift(lags) self.cc_skyvis_freq = cc_skyvis self.cc_skyvis_res_freq = cc_skyvis_res self.cc_vis_freq = cc_vis self.cc_vis_res_freq = cc_vis_res self.cc_skyvis_net_freq = cc_skyvis + cc_skyvis_res self.cc_vis_net_freq = cc_vis + cc_vis_res self.clean_window_buffer = clean_window_buffer ############################################################################# def subband_delay_transform(self, bw_eff, freq_center=None, shape=None, fftpow=None, pad=None, bpcorrect=False, action=None, verbose=True): """ ------------------------------------------------------------------------ Computes delay transform on multiple frequency sub-bands with specified weights Inputs: bw_eff [dictionary] dictionary with two keys 'cc' and 'sim' to specify effective bandwidths (in Hz) on the selected frequency windows for subband delay transform of CLEANed and simulated visibilities respectively. The values under these keys can be a scalar, list or numpy array and are independent of each other. If a scalar value is provided, the same will be applied to all frequency windows under that key freq_center [dictionary] dictionary with two keys 'cc' and 'sim' to specify frequency centers (in Hz) of the selected frequency windows for subband delay transform of CLEANed and simulated visibilities respectively. The values under these keys can be a scalar, list or numpy array and are independent of each other. If a scalar is provided, the same will be applied to all frequency windows. Default=None uses the center frequency from the class attribute named channels for both keys 'cc' and 'sim' shape [dictionary] dictionary with two keys 'cc' and 'sim' to specify frequency window shape for subband delay transform of CLEANed and simulated visibilities respectively. Values held by the keys must be a string. Accepted values for the string are 'rect' or 'RECT' (for rectangular), 'bnw' and 'BNW' (for Blackman-Nuttall), and 'bhw' or 'BHW' (for Blackman-Harris). Default=None sets it to 'rect' (rectangular window) for both keys fftpow [dictionary] dictionary with two keys 'cc' and 'sim' to specify the power to which the FFT of the window will be raised. The values under these keys must be a positive scalar. Default = 1.0 for each key pad [dictionary] dictionary with two keys 'cc' and 'sim' to specify padding fraction relative to the number of frequency channels for CLEANed and simualted visibilities respectively. Values held by the keys must be a non-negative scalar. For e.g., a pad of 1.0 pads the frequency axis with zeros of the same width as the number of channels. After the delay transform, the transformed visibilities are downsampled by a factor of 1+pad. If a negative value is specified, delay transform will be performed with no padding. Default=None sets to padding factor to 1.0 under both keys. bpcorrect [boolean] Only applicable on delay CLEANed visibilities. If True, correct for frequency weights that were applied during the original delay transform using which the delay CLEAN was done. This would flatten the bandpass after delay CLEAN. If False (default), do not apply the correction, namely, inverse of bandpass weights action [string or None] If set to None (default) just updates the attribute. If set to 'return_oversampled' it returns the output dictionary corresponding to oversampled delay space quantities and updates its attribute subband_delay_spectra with full resolution in delay space. If set to 'return_resampled' it returns the output dictionary corresponding to resampled/downsampled delay space quantities and updates the attribute. verbose [boolean] If set to True (default), print diagnostic and progress messages. If set to False, no such messages are printed. Output: If keyword input action is set to None (default), the output is internally stored in the class attributes subband_delay_spectra and subband_delay_spectra_resampled. If action is set to 'return_oversampled', the following output is returned. The output is a dictionary that contains two top level keys, namely, 'cc' and 'sim' denoting information about CLEAN and simulated visibilities respectively. Under each of these keys is information about delay spectra of different frequency sub-bands (n_win in number) in the form of a dictionary under the following keys: 'freq_center' [numpy array] contains the center frequencies (in Hz) of the frequency subbands of the subband delay spectra. It is of size n_win. It is roughly equivalent to redshift(s) 'freq_wts' [numpy array] Contains frequency weights applied on each frequency sub-band during the subband delay transform. It is of size n_win x nchan. 'bw_eff' [numpy array] contains the effective bandwidths (in Hz) of the subbands being delay transformed. It is of size n_win. It is roughly equivalent to width in redshift or along line-of-sight 'shape' [string] shape of the window function applied. Accepted values are 'rect' (rectangular), 'bhw' (Blackman-Harris), 'bnw' (Blackman-Nuttall). 'bpcorrect' [boolean] If True (default), correct for frequency weights that were applied during the original delay transform using which the delay CLEAN was done. This would flatten the bandpass after delay CLEAN. If False, do not apply the correction, namely, inverse of bandpass weights. This applies only CLEAned visibilities under the 'cc' key and hence is present only if the top level key is 'cc' and absent for key 'sim' 'npad' [scalar] Numbber of zero-padded channels before performing the subband delay transform. 'lags' [numpy array] lags of the subband delay spectra after padding in frequency during the transform. It is of size nchan+npad where npad is the number of frequency channels padded specified under the key 'npad' 'lag_kernel' [numpy array] delay transform of the frequency weights under the key 'freq_wts'. It is of size n_bl x n_win x (nchan+npad) x n_t. 'lag_corr_length' [numpy array] It is the correlation timescale (in pixels) of the subband delay spectra. It is proportional to inverse of effective bandwidth. It is of size n_win. The unit size of a pixel is determined by the difference between adjacent pixels in lags under key 'lags' which in turn is effectively inverse of the total bandwidth (nchan x df) simulated. 'skyvis_lag' [numpy array] subband delay spectra of simulated or CLEANed noiseless visibilities, depending on whether the top level key is 'cc' or 'sim' respectively, after applying the frequency weights under the key 'freq_wts'. It is of size n_bl x n_win x (nchan+npad) x n_t. 'vis_lag' [numpy array] subband delay spectra of simulated or CLEANed noisy visibilities, depending on whether the top level key is 'cc' or 'sim' respectively, after applying the frequency weights under the key 'freq_wts'. It is of size n_bl x n_win x (nchan+npad) x n_t. 'vis_noise_lag' [numpy array] subband delay spectra of simulated noise after applying the frequency weights under the key 'freq_wts'. Only present if top level key is 'sim' and absent for 'cc'. It is of size n_bl x n_win x (nchan+npad) x n_t. 'skyvis_res_lag' [numpy array] subband delay spectra of residuals after delay CLEAN of simualted noiseless visibilities obtained after applying frequency weights specified under key 'freq_wts'. Only present for top level key 'cc' and absent for 'sim'. It is of size n_bl x n_win x (nchan+npad) x n_t 'vis_res_lag' [numpy array] subband delay spectra of residuals after delay CLEAN of simualted noisy visibilities obtained after applying frequency weights specified under key 'freq_wts'. Only present for top level key 'cc' and absent for 'sim'. It is of size n_bl x n_win x (nchan+npad) x n_t If action is set to 'return_resampled', the following output is returned. The output is a dictionary that contains two top level keys, namely, 'cc' and 'sim' denoting information about CLEAN and simulated visibilities respectively. Under each of these keys is information about delay spectra of different frequency sub-bands (n_win in number) in the form of a dictionary under the following keys: 'freq_center' [numpy array] contains the center frequencies (in Hz) of the frequency subbands of the subband delay spectra. It is of size n_win. It is roughly equivalent to redshift(s) 'bw_eff' [numpy array] contains the effective bandwidths (in Hz) of the subbands being delay transformed. It is of size n_win. It is roughly equivalent to width in redshift or along line-of-sight 'lags' [numpy array] lags of the resampled subband delay spectra after padding in frequency during the transform. It is of size nlags where nlags is the number of independent delay bins 'lag_kernel' [numpy array] delay transform of the frequency weights under the key 'freq_wts'. It is of size n_bl x n_win x nlags x n_t. 'lag_corr_length' [numpy array] It is the correlation timescale (in pixels) of the resampled subband delay spectra. It is proportional to inverse of effective bandwidth. It is of size n_win. The unit size of a pixel is determined by the difference between adjacent pixels in lags under key 'lags' which in turn is effectively inverse of the effective bandwidth 'skyvis_lag' [numpy array] resampled subband delay spectra of simulated or CLEANed noiseless visibilities, depending on whether the top level key is 'cc' or 'sim' respectively, after applying the frequency weights under the key 'freq_wts'. It is of size n_bl x n_win x nlags x n_t. 'vis_lag' [numpy array] resampled subband delay spectra of simulated or CLEANed noisy visibilities, depending on whether the top level key is 'cc' or 'sim' respectively, after applying the frequency weights under the key 'freq_wts'. It is of size n_bl x n_win x nlags x n_t. 'vis_noise_lag' [numpy array] resampled subband delay spectra of simulated noise after applying the frequency weights under the key 'freq_wts'. Only present if top level key is 'sim' and absent for 'cc'. It is of size n_bl x n_win x nlags x n_t. 'skyvis_res_lag' [numpy array] resampled subband delay spectra of residuals after delay CLEAN of simualted noiseless visibilities obtained after applying frequency weights specified under key 'freq_wts'. Only present for top level key 'cc' and absent for 'sim'. It is of size n_bl x n_win x nlags x n_t 'vis_res_lag' [numpy array] resampled subband delay spectra of residuals after delay CLEAN of simualted noisy visibilities obtained after applying frequency weights specified under key 'freq_wts'. Only present for top level key 'cc' and absent for 'sim'. It is of size n_bl x n_win x nlags x n_t ------------------------------------------------------------------------ """ try: bw_eff except NameError: raise NameError('Effective bandwidth must be specified') else: if not isinstance(bw_eff, dict): raise TypeError('Effective bandwidth must be specified as a dictionary') for key in ['cc','sim']: if key in bw_eff: if not isinstance(bw_eff[key], (int, float, list, NP.ndarray)): raise TypeError('Value of effective bandwidth must be a scalar, list or numpy array') bw_eff[key] = NP.asarray(bw_eff[key]).reshape(-1) if NP.any(bw_eff[key] <= 0.0): raise ValueError('All values in effective bandwidth must be strictly positive') if freq_center is None: freq_center = {key: NP.asarray(self.f[self.f.size/2]).reshape(-1) for key in ['cc', 'sim']} # freq_center = NP.asarray(self.f[self.f.size/2]).reshape(-1) elif isinstance(freq_center, dict): for key in ['cc', 'sim']: if isinstance(freq_center[key], (int, float, list, NP.ndarray)): freq_center[key] = NP.asarray(freq_center[key]).reshape(-1) if NP.any((freq_center[key] <= self.f.min()) | (freq_center[key] >= self.f.max())): raise ValueError('Value(s) of frequency center(s) must lie strictly inside the observing band') else: raise TypeError('Values(s) of frequency center must be scalar, list or numpy array') else: raise TypeError('Input frequency center must be specified as a dictionary') for key in ['cc', 'sim']: if (bw_eff[key].size == 1) and (freq_center[key].size > 1): bw_eff[key] = NP.repeat(bw_eff[key], freq_center[key].size) elif (bw_eff[key].size > 1) and (freq_center[key].size == 1): freq_center[key] = NP.repeat(freq_center[key], bw_eff[key].size) elif bw_eff[key].size != freq_center[key].size: raise ValueError('Effective bandwidth(s) and frequency center(s) must have same number of elements') if shape is not None: if not isinstance(shape, dict): raise TypeError('Window shape must be specified as a dictionary') for key in ['cc', 'sim']: if not isinstance(shape[key], str): raise TypeError('Window shape must be a string') if shape[key] not in ['rect', 'bhw', 'bnw', 'RECT', 'BHW', 'BNW']: raise ValueError('Invalid value for window shape specified.') else: shape = {key: 'rect' for key in ['cc', 'sim']} # shape = 'rect' if fftpow is None: fftpow = {key: 1.0 for key in ['cc', 'sim']} else: if not isinstance(fftpow, dict): raise TypeError('Power to raise FFT of window by must be specified as a dictionary') for key in ['cc', 'sim']: if not isinstance(fftpow[key], (int, float)): raise TypeError('Power to raise window FFT by must be a scalar value.') if fftpow[key] < 0.0: raise ValueError('Power for raising FFT of window by must be positive.') if pad is None: pad = {key: 1.0 for key in ['cc', 'sim']} else: if not isinstance(pad, dict): raise TypeError('Padding for delay transform must be specified as a dictionary') for key in ['cc', 'sim']: if not isinstance(pad[key], (int, float)): raise TypeError('pad fraction must be a scalar value.') if pad[key] < 0.0: pad[key] = 0.0 if verbose: print('\tPad fraction found to be negative. Resetting to 0.0 (no padding will be applied).') if not isinstance(bpcorrect, bool): raise TypeError('Input keyword bpcorrect must be of boolean type') vis_noise_freq = NP.copy(self.ia.vis_noise_freq) result = {} for key in ['cc', 'sim']: if (key == 'sim') or ((key == 'cc') and (self.cc_lags is not None)): freq_wts = NP.empty((bw_eff[key].size, self.f.size), dtype=NP.float_) frac_width = DSP.window_N2width(n_window=None, shape=shape[key], fftpow=fftpow[key], area_normalize=False, power_normalize=True) window_loss_factor = 1 / frac_width n_window = NP.round(window_loss_factor * bw_eff[key] / self.df).astype(NP.int) ind_freq_center, ind_channels, dfrequency = LKP.find_1NN(self.f.reshape(-1,1), freq_center[key].reshape(-1,1), distance_ULIM=0.5*self.df, remove_oob=True) sortind = NP.argsort(ind_channels) ind_freq_center = ind_freq_center[sortind] ind_channels = ind_channels[sortind] dfrequency = dfrequency[sortind] n_window = n_window[sortind] for i,ind_chan in enumerate(ind_channels): window = NP.sqrt(frac_width * n_window[i]) * DSP.window_fftpow(n_window[i], shape=shape[key], fftpow=fftpow[key], centering=True, peak=None, area_normalize=False, power_normalize=True) # window = NP.sqrt(frac_width * n_window[i]) * DSP.windowing(n_window[i], shape=shape[key], centering=True, peak=None, area_normalize=False, power_normalize=True) window_chans = self.f[ind_chan] + self.df * (NP.arange(n_window[i]) - int(n_window[i]/2)) ind_window_chans, ind_chans, dfreq = LKP.find_1NN(self.f.reshape(-1,1), window_chans.reshape(-1,1), distance_ULIM=0.5*self.df, remove_oob=True) sind = NP.argsort(ind_window_chans) ind_window_chans = ind_window_chans[sind] ind_chans = ind_chans[sind] dfreq = dfreq[sind] window = window[ind_window_chans] window = NP.pad(window, ((ind_chans.min(), self.f.size-1-ind_chans.max())), mode='constant', constant_values=((0.0,0.0))) freq_wts[i,:] = window bpcorrection_factor = 1.0 npad = int(self.f.size * pad[key]) lags = DSP.spectral_axis(self.f.size + npad, delx=self.df, use_real=False, shift=True) if key == 'cc': skyvis_freq = self.cc_skyvis_freq[:,:self.f.size,:] vis_freq = self.cc_vis_freq[:,:self.f.size,:] skyvis_res_freq = self.cc_skyvis_res_freq[:,:self.f.size,:] vis_res_freq = self.cc_vis_res_freq[:,:self.f.size,:] skyvis_net_freq = self.cc_skyvis_net_freq[:,:self.f.size,:] vis_net_freq = self.cc_vis_net_freq[:,:self.f.size,:] if bpcorrect: bpcorrection_factor = NP.where(NP.abs(self.bp_wts)>0.0, 1/self.bp_wts, 0.0) bpcorrection_factor = bpcorrection_factor[:,NP.newaxis,:,:] else: skyvis_freq = NP.copy(self.ia.skyvis_freq) vis_freq = NP.copy(self.ia.vis_freq) skyvis_lag = DSP.FT1D(NP.pad(skyvis_freq[:,NP.newaxis,:,:] * self.bp[:,NP.newaxis,:,:] * freq_wts[NP.newaxis,:,:,NP.newaxis], ((0,0),(0,0),(0,npad),(0,0)), mode='constant'), ax=2, inverse=True, use_real=False, shift=True) * (npad + self.f.size) * self.df vis_lag = DSP.FT1D(NP.pad(vis_freq[:,NP.newaxis,:,:] * self.bp[:,NP.newaxis,:,:] * freq_wts[NP.newaxis,:,:,NP.newaxis], ((0,0),(0,0),(0,npad),(0,0)), mode='constant'), ax=2, inverse=True, use_real=False, shift=True) * (npad + self.f.size) * self.df vis_noise_lag = DSP.FT1D(NP.pad(vis_noise_freq[:,NP.newaxis,:,:] * self.bp[:,NP.newaxis,:,:] * freq_wts[NP.newaxis,:,:,NP.newaxis], ((0,0),(0,0),(0,npad),(0,0)), mode='constant'), ax=2, inverse=True, use_real=False, shift=True) * (npad + self.f.size) * self.df lag_kernel = DSP.FT1D(NP.pad(self.bp[:,NP.newaxis,:,:] * freq_wts[NP.newaxis,:,:,NP.newaxis], ((0,0),(0,0),(0,npad),(0,0)), mode='constant'), ax=2, inverse=True, use_real=False, shift=True) * (npad + self.f.size) * self.df result[key] = {'freq_center': freq_center[key], 'shape': shape[key], 'freq_wts': freq_wts, 'bw_eff': bw_eff[key], 'npad': npad, 'lags': lags, 'skyvis_lag': skyvis_lag, 'vis_lag': vis_lag, 'lag_kernel': lag_kernel, 'lag_corr_length': self.f.size / NP.sum(freq_wts, axis=1)} if key == 'cc': skyvis_res_lag = DSP.FT1D(NP.pad(skyvis_res_freq[:,NP.newaxis,:,:] * self.bp[:,NP.newaxis,:,:] * freq_wts[NP.newaxis,:,:,NP.newaxis], ((0,0),(0,0),(0,npad),(0,0)), mode='constant'), ax=2, inverse=True, use_real=False, shift=True) * (npad + self.f.size) * self.df vis_res_lag = DSP.FT1D(NP.pad(vis_res_freq[:,NP.newaxis,:,:] * self.bp[:,NP.newaxis,:,:] * freq_wts[NP.newaxis,:,:,NP.newaxis], ((0,0),(0,0),(0,npad),(0,0)), mode='constant'), ax=2, inverse=True, use_real=False, shift=True) * (npad + self.f.size) * self.df skyvis_net_lag = DSP.FT1D(NP.pad(skyvis_net_freq[:,NP.newaxis,:,:] * self.bp[:,NP.newaxis,:,:] * freq_wts[NP.newaxis,:,:,NP.newaxis], ((0,0),(0,0),(0,npad),(0,0)), mode='constant'), ax=2, inverse=True, use_real=False, shift=True) * (npad + self.f.size) * self.df vis_net_lag = DSP.FT1D(NP.pad(vis_net_freq[:,NP.newaxis,:,:] * self.bp[:,NP.newaxis,:,:] * freq_wts[NP.newaxis,:,:,NP.newaxis], ((0,0),(0,0),(0,npad),(0,0)), mode='constant'), ax=2, inverse=True, use_real=False, shift=True) * (npad + self.f.size) * self.df result[key]['vis_res_lag'] = vis_res_lag result[key]['skyvis_res_lag'] = skyvis_res_lag result[key]['vis_net_lag'] = vis_net_lag result[key]['skyvis_net_lag'] = skyvis_net_lag result[key]['bpcorrect'] = bpcorrect else: result[key]['vis_noise_lag'] = vis_noise_lag if verbose: print('\tSub-band(s) delay transform computed') self.subband_delay_spectra = result result_resampled = {} for key in ['cc', 'sim']: if key in result: result_resampled[key] = {} result_resampled[key]['freq_center'] = result[key]['freq_center'] result_resampled[key]['bw_eff'] = result[key]['bw_eff'] downsample_factor = NP.min((self.f.size + npad) * self.df / result_resampled[key]['bw_eff']) result_resampled[key]['lags'] = DSP.downsampler(result[key]['lags'], downsample_factor, axis=-1, method='interp', kind='linear') result_resampled[key]['lag_kernel'] = DSP.downsampler(result[key]['lag_kernel'], downsample_factor, axis=2, method='interp', kind='linear') result_resampled[key]['skyvis_lag'] = DSP.downsampler(result[key]['skyvis_lag'], downsample_factor, axis=2, method='FFT') result_resampled[key]['vis_lag'] = DSP.downsampler(result[key]['vis_lag'], downsample_factor, axis=2, method='FFT') dlag = result_resampled[key]['lags'][1] - result_resampled[key]['lags'][0] result_resampled[key]['lag_corr_length'] = (1/result[key]['bw_eff']) / dlag if key == 'cc': result_resampled[key]['skyvis_res_lag'] = DSP.downsampler(result[key]['skyvis_res_lag'], downsample_factor, axis=2, method='FFT') result_resampled[key]['vis_res_lag'] = DSP.downsampler(result[key]['vis_res_lag'], downsample_factor, axis=2, method='FFT') result_resampled[key]['skyvis_net_lag'] = DSP.downsampler(result[key]['skyvis_net_lag'], downsample_factor, axis=2, method='FFT') result_resampled[key]['vis_net_lag'] = DSP.downsampler(result[key]['vis_net_lag'], downsample_factor, axis=2, method='FFT') else: result_resampled[key]['vis_noise_lag'] = DSP.downsampler(result[key]['vis_noise_lag'], downsample_factor, axis=2, method='FFT') if verbose: print('\tDownsampled Sub-band(s) delay transform computed') self.subband_delay_spectra_resampled = result_resampled if action is not None: if action == 'return_oversampled': return result if action == 'return_resampled': return result_resampled ############################################################################# def subband_delay_transform_allruns(self, vis, bw_eff, freq_center=None, shape=None, fftpow=None, pad=None, bpcorrect=False, action=None, verbose=True): """ ------------------------------------------------------------------------ Computes delay transform on multiple frequency sub-bands with specified weights for multiple realizations of visibilities Inputs: vis [numpy array] Visibilities which will be delay transformed. It must be of shape (...,nbl,nchan,ntimes) bw_eff [scalar, list or numpy array] effective bandwidths (in Hz) on the selected frequency windows for subband delay transform of visibilities. The values can be a scalar, list or numpy array. If a scalar value is provided, the same will be applied to all frequency windows. freq_center [scalar, list or numpy array] frequency centers (in Hz) of the selected frequency windows for subband delay transform of visibilities. The values can be a scalar, list or numpy array. If a scalar is provided, the same will be applied to all frequency windows. Default=None uses the center frequency from the class attribute shape [string] frequency window shape for subband delay transform of visibilities. It must be a string. Accepted values for the string are 'rect' or 'RECT' (for rectangular), 'bnw' and 'BNW' (for Blackman-Nuttall), and 'bhw' or 'BHW' (for Blackman-Harris). Default=None sets it to 'rect' (rectangular window) fftpow [scalar] the power to which the FFT of the window will be raised. The value must be a positive scalar. Default = 1.0 pad [scalar] padding fraction relative to the number of frequency channels. Value must be a non-negative scalar. For e.g., a pad of 1.0 pads the frequency axis with zeros of the same width as the number of channels. After the delay transform, the transformed visibilities are downsampled by a factor of 1+pad. If a negative value is specified, delay transform will be performed with no padding. Default=None sets to padding factor to 1.0 action [string or None] If set to 'return_oversampled' it returns the output dictionary corresponding to oversampled delay space quantities with full resolution in delay space. If set to None (default) or 'return_resampled' it returns the output dictionary corresponding to resampled/downsampled delay space quantities. verbose [boolean] If set to True (default), print diagnostic and progress messages. If set to False, no such messages are printed. Output: The output is a dictionary that contains information about delay spectra of different frequency sub-bands (n_win in number). If action is set to 'return_resampled', it contains the following keys and values: 'freq_center' [numpy array] contains the center frequencies (in Hz) of the frequency subbands of the subband delay spectra. It is of size n_win. It is roughly equivalent to redshift(s) 'freq_wts' [numpy array] Contains frequency weights applied on each frequency sub-band during the subband delay transform. It is of size n_win x nchan. 'bw_eff' [numpy array] contains the effective bandwidths (in Hz) of the subbands being delay transformed. It is of size n_win. It is roughly equivalent to width in redshift or along line-of-sight 'shape' [string] shape of the window function applied. Accepted values are 'rect' (rectangular), 'bhw' (Blackman-Harris), 'bnw' (Blackman-Nuttall). 'npad' [scalar] Numbber of zero-padded channels before performing the subband delay transform. 'lags' [numpy array] lags of the subband delay spectra after padding in frequency during the transform. It is of size nchan+npad where npad is the number of frequency channels padded specified under the key 'npad' 'lag_kernel' [numpy array] delay transform of the frequency weights under the key 'freq_wts'. It is of size n_win x (1 x 1 x ... nruns times) x n_bl x (nchan+npad) x n_t. 'lag_corr_length' [numpy array] It is the correlation timescale (in pixels) of the subband delay spectra. It is proportional to inverse of effective bandwidth. It is of size n_win. The unit size of a pixel is determined by the difference between adjacent pixels in lags under key 'lags' which in turn is effectively inverse of the total bandwidth (nchan x df) simulated. It is of size n_win 'vis_lag' [numpy array] subband delay spectra of visibilities, after applying the frequency weights under the key 'freq_wts'. It is of size n_win x (n1xn2x... n_runs dims) x n_bl x (nchan+npad) x x n_t. If action is set to 'return_resampled', the following output is returned. The output is a dictionary that contains information about delay spectra of different frequency sub-bands (n_win in number) with the following keys and values: 'freq_center' [numpy array] contains the center frequencies (in Hz) of the frequency subbands of the subband delay spectra. It is of size n_win. It is roughly equivalent to redshift(s) 'bw_eff' [numpy array] contains the effective bandwidths (in Hz) of the subbands being delay transformed. It is of size n_win. It is roughly equivalent to width in redshift or along line-of-sight 'lags' [numpy array] lags of the resampled subband delay spectra after padding in frequency during the transform. It is of size nlags where nlags is the number of independent delay bins 'lag_kernel' [numpy array] delay transform of the frequency weights under the key 'freq_wts'. It is of size n_win x (1 x 1 x ... nruns times) x n_bl x nlags x n_t 'lag_corr_length' [numpy array] It is the correlation timescale (in pixels) of the subband delay spectra. It is proportional to inverse of effective bandwidth. It is of size n_win. The unit size of a pixel is determined by the difference between adjacent pixels in lags under key 'lags' which in turn is effectively inverse of the total bandwidth (nchan x df) simulated. It is of size n_win 'vis_lag' [numpy array] subband delay spectra of visibilities, after applying the frequency weights under the key 'freq_wts'. It is of size n_win x (n1xn2x... n_runs dims) x n_bl x nlags x n_t ------------------------------------------------------------------------ """ try: vis, bw_eff except NameError: raise NameError('Input visibilities and effective bandwidth must be specified') else: if not isinstance(vis, NP.ndarray): raise TypeError('Input vis must be a numpy array') elif vis.ndim < 3: raise ValueError('Input vis must be at least 3-dimensional') elif vis.shape[-3:] == (self.ia.baselines.shape[0],self.f.size,self.n_acc): if vis.ndim == 3: shp = (1,) + vis.shape else: shp = vis.shape vis = vis.reshape(shp) else: raise ValueError('Input vis does not have compatible shape') if not isinstance(bw_eff, (int, float, list, NP.ndarray)): raise TypeError('Value of effective bandwidth must be a scalar, list or numpy array') bw_eff = NP.asarray(bw_eff).reshape(-1) if NP.any(bw_eff <= 0.0): raise ValueError('All values in effective bandwidth must be strictly positive') if freq_center is None: freq_center = NP.asarray(self.f[self.f.size/2]).reshape(-1) elif isinstance(freq_center, (int, float, list, NP.ndarray)): freq_center = NP.asarray(freq_center).reshape(-1) if NP.any((freq_center <= self.f.min()) | (freq_center >= self.f.max())): raise ValueError('Value(s) of frequency center(s) must lie strictly inside the observing band') else: raise TypeError('Values(s) of frequency center must be scalar, list or numpy array') if (bw_eff.size == 1) and (freq_center.size > 1): bw_eff = NP.repeat(bw_eff, freq_center.size) elif (bw_eff.size > 1) and (freq_center.size == 1): freq_center = NP.repeat(freq_center, bw_eff.size) elif bw_eff.size != freq_center.size: raise ValueError('Effective bandwidth(s) and frequency center(s) must have same number of elements') if shape is not None: if not isinstance(shape, str): raise TypeError('Window shape must be a string') if shape.lower() not in ['rect', 'bhw', 'bnw']: raise ValueError('Invalid value for window shape specified.') else: shape = 'rect' if fftpow is None: fftpow = 1.0 else: if not isinstance(fftpow, (int, float)): raise TypeError('Power to raise window FFT by must be a scalar value.') if fftpow < 0.0: raise ValueError('Power for raising FFT of window by must be positive.') if pad is None: pad = 1.0 else: if not isinstance(pad, (int, float)): raise TypeError('pad fraction must be a scalar value.') if pad < 0.0: pad = 0.0 if verbose: print('\tPad fraction found to be negative. Resetting to 0.0 (no padding will be applied).') result = {} freq_wts = NP.empty((bw_eff.size, self.f.size), dtype=NP.float_) frac_width = DSP.window_N2width(n_window=None, shape=shape, fftpow=fftpow, area_normalize=False, power_normalize=True) window_loss_factor = 1 / frac_width n_window = NP.round(window_loss_factor * bw_eff / self.df).astype(NP.int) ind_freq_center, ind_channels, dfrequency = LKP.find_1NN(self.f.reshape(-1,1), freq_center.reshape(-1,1), distance_ULIM=0.5*self.df, remove_oob=True) sortind = NP.argsort(ind_channels) ind_freq_center = ind_freq_center[sortind] ind_channels = ind_channels[sortind] dfrequency = dfrequency[sortind] n_window = n_window[sortind] for i,ind_chan in enumerate(ind_channels): window = NP.sqrt(frac_width * n_window[i]) * DSP.window_fftpow(n_window[i], shape=shape, fftpow=fftpow, centering=True, peak=None, area_normalize=False, power_normalize=True) window_chans = self.f[ind_chan] + self.df * (NP.arange(n_window[i]) - int(n_window[i]/2)) ind_window_chans, ind_chans, dfreq = LKP.find_1NN(self.f.reshape(-1,1), window_chans.reshape(-1,1), distance_ULIM=0.5*self.df, remove_oob=True) sind = NP.argsort(ind_window_chans) ind_window_chans = ind_window_chans[sind] ind_chans = ind_chans[sind] dfreq = dfreq[sind] window = window[ind_window_chans] window = NP.pad(window, ((ind_chans.min(), self.f.size-1-ind_chans.max())), mode='constant', constant_values=((0.0,0.0))) freq_wts[i,:] = window freq_wts = freq_wts.reshape((bw_eff.size,)+tuple(NP.ones(len(vis.shape[:-3]),dtype=NP.int))+(1,self.f.size,1)) bp = self.bp.reshape(tuple(NP.ones(len(vis.shape[:-3]),dtype=NP.int))+self.bp.shape) npad = int(self.f.size * pad) lags = DSP.spectral_axis(self.f.size + npad, delx=self.df, use_real=False, shift=True) pad_shape = [[0,0]] + NP.zeros((len(vis.shape[:-3]),2), dtype=NP.int).tolist() pad_shape += [[0,0], [0,npad], [0,0]] vis_lag = DSP.FT1D(NP.pad(vis[NP.newaxis,...] * bp[NP.newaxis,...] * freq_wts, pad_shape, mode='constant'), ax=-2, inverse=True, use_real=False, shift=True) * (npad + self.f.size) * self.df lag_kernel = DSP.FT1D(NP.pad(bp[NP.newaxis,...] * freq_wts, pad_shape, mode='constant'), ax=-2, inverse=True, use_real=False, shift=True) * (npad + self.f.size) * self.df result = {'freq_center': freq_center, 'shape': shape, 'freq_wts': freq_wts, 'bw_eff': bw_eff, 'npad': npad, 'lags': lags, 'vis_lag': vis_lag, 'lag_kernel': lag_kernel, 'lag_corr_length': self.f.size / NP.squeeze(NP.sum(freq_wts, axis=-2))} if verbose: print('\tSub-band(s) delay transform computed') if action is not None: action = 'return_resampled' if action == 'return_oversampled': return result elif action == 'return_resampled': downsample_factor = NP.min((self.f.size + npad) * self.df / result['bw_eff']) result['lags'] = DSP.downsampler(result['lags'], downsample_factor, axis=-1, method='interp', kind='linear') result['lag_kernel'] = DSP.downsampler(result['lag_kernel'], downsample_factor, axis=-2, method='interp', kind='linear') result['vis_lag'] = DSP.downsampler(result['vis_lag'], downsample_factor, axis=-2, method='FFT') dlag = result['lags'][1] - result['lags'][0] result['lag_corr_length'] = (1/result['bw_eff']) / dlag return result else: raise ValueError('Invalid value specified for keyword input action') if verbose: print('\tDownsampled Sub-band(s) delay transform computed') ############################################################################# def subband_delay_transform_closure_phase(self, bw_eff, cpinfo=None, antenna_triplets=None, specsmooth_info=None, delay_filter_info=None, spectral_window_info=None, freq_center=None, shape=None, fftpow=None, pad=None, action=None, verbose=True): """ ------------------------------------------------------------------------ Computes delay transform of closure phases on antenna triplets on multiple frequency sub-bands with specified weights. It will have units of Hz Inputs: bw_eff [scalar or numpy array] effective bandwidths (in Hz) on the selected frequency windows for subband delay transform of closure phases. If a scalar value is provided, the same will be applied to all frequency windows cpinfo [dictionary] If set to None, it will be determined based on other inputs. Otherwise, it will be used directly. The dictionary will contain the following keys and values: 'closure_phase_skyvis' [numpy array] [optional] Closure phases (in radians) for the given antenna triplets from the noiseless visibilities. It is of shape ntriplets x ... x nchan x ntimes 'closure_phase_vis' [numpy array] [optional] Closure phases (in radians) for the given antenna triplets for noisy visibilities. It is of shape ntriplets x ... x nchan x ntimes 'closure_phase_noise' [numpy array] [optional] Closure phases (in radians) for the given antenna triplets for thermal noise in visibilities. It is of shape ntriplets x ... x nchan x ntimes 'antenna_triplets' [list of tuples] List of three-element tuples of antenna IDs for which the closure phases are calculated. 'baseline_triplets' [numpy array] List of 3x3 numpy arrays. Each 3x3 unit in the list represents triplets of baseline vectors where the three rows denote the three baselines in the triplet and the three columns define the x-, y- and z-components of the triplet. The number of 3x3 unit elements in the list will equal the number of elements in the list under key 'antenna_triplets'. antenna_triplets [list of tuples] List of antenna ID triplets where each triplet is given as a tuple. If set to None (default), all the unique triplets based on the antenna layout attribute in class InterferometerArray specsmooth_info [NoneType or dictionary] Spectral smoothing window to be applied prior to the delay transform. If set to None, no smoothing is done. This is usually set if spectral smoothing is to be done such as in the case of RFI. The smoothing window parameters are specified using the following keys and values: 'op_type' [string] Smoothing operation type. Default='median' (currently accepts only 'median' or 'interp'). 'window_size' [integer] Size of smoothing window (in pixels) along frequency axis. Applies only if op_type is set to 'median' 'maskchans' [NoneType or numpy array] Numpy boolean array of size nchan. False entries imply those channels are not masked and will be used in in interpolation while True implies they are masked and will not be used in determining the interpolation function. If set to None, all channels are assumed to be unmasked (False). 'evalchans' [NoneType or numpy array] Channel numbers at which visibilities are to be evaluated. Will be useful for filling in RFI flagged channels. If set to None, all channels will be evaluated 'noiseRMS' [NoneType or scalar or numpy array] If set to None (default), the rest of the parameters are used in determining the RMS of thermal noise. If specified as scalar, all other parameters will be ignored in estimating noiseRMS and this value will be used instead. If specified as a numpy array, it must be of shape broadcastable to (nbl,nchan,ntimes). So accpeted shapes can be (1,1,1), (1,1,ntimes), (1,nchan,1), (nbl,1,1), (1,nchan,ntimes), (nbl,nchan,1), (nbl,1,ntimes), or (nbl,nchan,ntimes). delay_filter_info [NoneType or dictionary] Info containing delay filter parameters. If set to None (default), no delay filtering is performed. Otherwise, delay filter is applied on each of the visibilities in the triplet before computing the closure phases. The delay filter parameters are specified in a dictionary as follows: 'type' [string] 'horizon' (default) or 'regular'. If set to 'horizon', the horizon delay limits are estimated from the respective baseline lengths in the triplet. If set to 'regular', the extent of the filter is determined by the 'min' and 'width' keys (see below). 'min' [scalar] Non-negative number (in seconds) that specifies the minimum delay in the filter span. If not specified, it is assumed to be 0. If 'type' is set to 'horizon', the 'min' is ignored and set to 0. 'width' [scalar] Non-negative number (in numbers of inverse bandwidths). If 'type' is set to 'horizon', the width represents the delay buffer beyond the horizon. If 'type' is set to 'regular', this number has to be positive and determines the span of the filter starting from the minimum delay in key 'min'. 'mode' [string] 'discard' (default) or 'retain'. If set to 'discard', the span defining the filter is discarded and the rest retained. If set to 'retain', the span defining the filter is retained and the rest discarded. For example, if 'type' is set to 'horizon' and 'mode' is set to 'discard', the horizon-to-horizon is filtered out (discarded). spectral_window_info [NoneType or dictionary] Spectral window parameters to determine the spectral weights and apply to the visibilities in the frequency domain before filtering in the delay domain. THESE PARAMETERS ARE APPLIED ON THE INDIVIDUAL VISIBILITIES THAT GO INTO THE CLOSURE PHASE. THESE ARE NOT TO BE CONFUSED WITH THE PARAMETERS THAT WILL BE USED IN THE ACTUAL DELAY TRANSFORM OF CLOSURE PHASE SPECTRA WHICH ARE SPECIFIED SEPARATELY FURTHER BELOW. If set to None (default), unity spectral weights are applied. If spectral weights are to be applied, it must be a provided as a dictionary with the following keys and values: bw_eff [scalar] effective bandwidths (in Hz) for the spectral window freq_center [scalar] frequency center (in Hz) for the spectral window shape [string] frequency window shape for the spectral window. Accepted values are 'rect' or 'RECT' (for rectangular), 'bnw' and 'BNW' (for Blackman-Nuttall), and 'bhw' or 'BHW' (for Blackman-Harris). Default=None sets it to 'rect' fftpow [scalar] power to which the FFT of the window will be raised. The value must be a positive scalar. freq_center [scalar, list or numpy array] frequency centers (in Hz) of the selected frequency windows for subband delay transform of closure phases. The value can be a scalar, list or numpy array. If a scalar is provided, the same will be applied to all frequency windows. Default=None uses the center frequency from the class attribute named channels shape [string] frequency window shape for subband delay transform of closure phases. Accepted values for the string are 'rect' or 'RECT' (for rectangular), 'bnw' and 'BNW' (for Blackman-Nuttall), and 'bhw' or 'BHW' (for Blackman-Harris). Default=None sets it to 'rect' (rectangular window) fftpow [scalar] the power to which the FFT of the window will be raised. The value must be a positive scalar. Default = 1.0 pad [scalar] padding fraction relative to the number of frequency channels for closure phases. Value must be a non-negative scalar. For e.g., a pad of 1.0 pads the frequency axis with zeros of the same width as the number of channels. After the delay transform, the transformed closure phases are downsampled by a factor of 1+pad. If a negative value is specified, delay transform will be performed with no padding. Default=None sets to padding factor to 1.0 action [string or None] If set to None (default) just updates the attribute. If set to 'return_oversampled' it returns the output dictionary corresponding to oversampled delay space quantities with full resolution in delay space. If set to None (default) or 'return_resampled', it returns the output dictionary corresponding to resampled or downsampled delay space quantities. verbose [boolean] If set to True (default), print diagnostic and progress messages. If set to False, no such messages are printed. Output: If keyword input action is set to 'return_oversampled', the following output is returned. The output is a dictionary that contains information about delay spectra of different frequency sub-bands (n_win in number) under the following keys: 'antenna_triplets' [list of tuples] List of antenna ID triplets where each triplet is given as a tuple. Closure phase delay spectra in subbands is computed for each of these antenna triplets 'baseline_triplets' [numpy array] List of 3x3 numpy arrays. Each 3x3 unit in the list represents triplets of baseline vectors where the three rows denote the three baselines in the triplet and the three columns define the x-, y- and z-components of the triplet. The number of 3x3 unit elements in the list will equal the number of elements in the list under key 'antenna_triplets'. Closure phase delay spectra in subbands is computed for each of these baseline triplets which correspond to the antenna triplets 'freq_center' [numpy array] contains the center frequencies (in Hz) of the frequency subbands of the subband delay spectra. It is of size n_win. It is roughly equivalent to redshift(s) 'freq_wts' [numpy array] Contains frequency weights applied on each frequency sub-band during the subband delay transform. It is of size n_win x nchan. 'bw_eff' [numpy array] contains the effective bandwidths (in Hz) of the subbands being delay transformed. It is of size n_win. It is roughly equivalent to width in redshift or along line-of-sight 'shape' [string] shape of the window function applied. Accepted values are 'rect' (rectangular), 'bhw' (Blackman-Harris), 'bnw' (Blackman-Nuttall). 'npad' [scalar] Numbber of zero-padded channels before performing the subband delay transform. 'lags' [numpy array] lags of the subband delay spectra after padding in frequency during the transform. It is of size nchan+npad where npad is the number of frequency channels padded specified under the key 'npad' 'lag_kernel' [numpy array] delay transform of the frequency weights under the key 'freq_wts'. It is of size n_triplets x ... x n_win x (nchan+npad) x n_t. 'lag_corr_length' [numpy array] It is the correlation timescale (in pixels) of the subband delay spectra. It is proportional to inverse of effective bandwidth. It is of size n_win. The unit size of a pixel is determined by the difference between adjacent pixels in lags under key 'lags' which in turn is effectively inverse of the total bandwidth (nchan x df) simulated. 'closure_phase_skyvis' [numpy array] subband delay spectra of closure phases of noiseless sky visiblities from the specified antenna triplets. It is of size n_triplets x ... n_win x nlags x n_t. It is in units of Hz 'closure_phase_vis' [numpy array] subband delay spectra of closure phases of noisy sky visiblities from the specified antenna triplets. It is of size n_triplets x ... x n_win x nlags x n_t. It is in units of Hz 'closure_phase_noise' [numpy array] subband delay spectra of closure phases of noise visiblities from the specified antenna triplets. It is of size n_triplets x ... x n_win x nlags x n_t. It is in units of Hz If action is set to 'return_resampled', the following output is returned. The output is a dictionary that contains information about closure phases. Under each of these keys is information about delay spectra of different frequency sub-bands (n_win in number) under the following keys: 'antenna_triplets' [list of tuples] List of antenna ID triplets where each triplet is given as a tuple. Closure phase delay spectra in subbands is computed for each of these antenna triplets 'baseline_triplets' [numpy array] List of 3x3 numpy arrays. Each 3x3 unit in the list represents triplets of baseline vectors where the three rows denote the three baselines in the triplet and the three columns define the x-, y- and z-components of the triplet. The number of 3x3 unit elements in the list will equal the number of elements in the list under key 'antenna_triplets'. Closure phase delay spectra in subbands is computed for each of these baseline triplets which correspond to the antenna triplets 'freq_center' [numpy array] contains the center frequencies (in Hz) of the frequency subbands of the subband delay spectra. It is of size n_win. It is roughly equivalent to redshift(s) 'bw_eff' [numpy array] contains the effective bandwidths (in Hz) of the subbands being delay transformed. It is of size n_win. It is roughly equivalent to width in redshift or along line-of-sight 'lags' [numpy array] lags of the resampled subband delay spectra after padding in frequency during the transform. It is of size nlags where nlags is the number of independent delay bins 'lag_kernel' [numpy array] delay transform of the frequency weights under the key 'freq_wts'. It is of size n_triplets x ... x n_win x nlags x n_t. 'lag_corr_length' [numpy array] It is the correlation timescale (in pixels) of the resampled subband delay spectra. It is proportional to inverse of effective bandwidth. It is of size n_win. The unit size of a pixel is determined by the difference between adjacent pixels in lags under key 'lags' which in turn is effectively inverse of the effective bandwidth 'closure_phase_skyvis' [numpy array] subband delay spectra of closure phases of noiseless sky visiblities from the specified antenna triplets. It is of size n_triplets x ... x n_win x nlags x n_t. It is in units of Hz 'closure_phase_vis' [numpy array] subband delay spectra of closure phases of noisy sky visiblities from the specified antenna triplets. It is of size n_triplets x ... x n_win x nlags x n_t. It is in units of Hz 'closure_phase_noise' [numpy array] subband delay spectra of closure phases of noise visiblities from the specified antenna triplets. It is of size n_triplets x ... x n_win x nlags x n_t. It is in units of Hz ------------------------------------------------------------------------ """ try: bw_eff except NameError: raise NameError('Effective bandwidth must be specified') else: if not isinstance(bw_eff, (int, float, list, NP.ndarray)): raise TypeError('Value of effective bandwidth must be a scalar, list or numpy array') bw_eff = NP.asarray(bw_eff).reshape(-1) if NP.any(bw_eff <= 0.0): raise ValueError('All values in effective bandwidth must be strictly positive') if freq_center is None: freq_center = NP.asarray(self.f[self.f.size/2]).reshape(-1) elif isinstance(freq_center, (int, float, list, NP.ndarray)): freq_center = NP.asarray(freq_center).reshape(-1) if NP.any((freq_center <= self.f.min()) | (freq_center >= self.f.max())): raise ValueError('Value(s) of frequency center(s) must lie strictly inside the observing band') else: raise TypeError('Values(s) of frequency center must be scalar, list or numpy array') if (bw_eff.size == 1) and (freq_center.size > 1): bw_eff = NP.repeat(bw_eff, freq_center.size) elif (bw_eff.size > 1) and (freq_center.size == 1): freq_center = NP.repeat(freq_center, bw_eff.size) elif bw_eff.size != freq_center.size: raise ValueError('Effective bandwidth(s) and frequency center(s) must have same number of elements') if shape is not None: if not isinstance(shape, str): raise TypeError('Window shape must be a string') if shape not in ['rect', 'bhw', 'bnw', 'RECT', 'BHW', 'BNW']: raise ValueError('Invalid value for window shape specified.') else: shape = 'rect' if fftpow is None: fftpow = 1.0 else: if not isinstance(fftpow, (int, float)): raise TypeError('Power to raise window FFT by must be a scalar value.') if fftpow < 0.0: raise ValueError('Power for raising FFT of window by must be positive.') if pad is None: pad = 1.0 else: if not isinstance(pad, (int, float)): raise TypeError('pad fraction must be a scalar value.') if pad < 0.0: pad = 0.0 if verbose: print('\tPad fraction found to be negative. Resetting to 0.0 (no padding will be applied).') if cpinfo is not None: if not isinstance(cpinfo, dict): raise TypeError('Input cpinfo must be a dictionary') else: cpinfo = self.ia.getClosurePhase(antenna_triplets=antenna_triplets, specsmooth_info=specsmooth_info, delay_filter_info=delay_filter_info, spectral_window_info=spectral_window_info) result = {'antenna_triplets': cpinfo['antenna_triplets'], 'baseline_triplets': cpinfo['baseline_triplets']} freq_wts = NP.empty((bw_eff.size, self.f.size), dtype=NP.float_) frac_width = DSP.window_N2width(n_window=None, shape=shape, fftpow=fftpow, area_normalize=False, power_normalize=True) window_loss_factor = 1 / frac_width n_window = NP.round(window_loss_factor * bw_eff / self.df).astype(NP.int) ind_freq_center, ind_channels, dfrequency = LKP.find_1NN(self.f.reshape(-1,1), freq_center.reshape(-1,1), distance_ULIM=0.5*self.df, remove_oob=True) sortind = NP.argsort(ind_channels) ind_freq_center = ind_freq_center[sortind] ind_channels = ind_channels[sortind] dfrequency = dfrequency[sortind] n_window = n_window[sortind] for i,ind_chan in enumerate(ind_channels): window = NP.sqrt(frac_width * n_window[i]) * DSP.window_fftpow(n_window[i], shape=shape, fftpow=fftpow, centering=True, peak=None, area_normalize=False, power_normalize=True) window_chans = self.f[ind_chan] + self.df * (NP.arange(n_window[i]) - int(n_window[i]/2)) ind_window_chans, ind_chans, dfreq = LKP.find_1NN(self.f.reshape(-1,1), window_chans.reshape(-1,1), distance_ULIM=0.5*self.df, remove_oob=True) sind = NP.argsort(ind_window_chans) ind_window_chans = ind_window_chans[sind] ind_chans = ind_chans[sind] dfreq = dfreq[sind] window = window[ind_window_chans] window = NP.pad(window, ((ind_chans.min(), self.f.size-1-ind_chans.max())), mode='constant', constant_values=((0.0,0.0))) freq_wts[i,:] = window npad = int(self.f.size * pad) lags = DSP.spectral_axis(self.f.size + npad, delx=self.df, use_real=False, shift=True) # lag_kernel = DSP.FT1D(NP.pad(self.bp[:,NP.newaxis,:,:] * freq_wts[NP.newaxis,:,:,NP.newaxis], ((0,0),(0,0),(0,npad),(0,0)), mode='constant'), ax=2, inverse=True, use_real=False, shift=True) * (npad + self.f.size) * self.df # lag_kernel = DSP.FT1D(NP.pad(freq_wts[NP.newaxis,:,:,NP.newaxis], ((0,0),(0,0),(0,npad),(0,0)), mode='constant'), ax=-2, inverse=True, use_real=False, shift=True) * (npad + self.f.size) * self.df result = {'freq_center': freq_center, 'shape': shape, 'freq_wts': freq_wts, 'bw_eff': bw_eff, 'npad': npad, 'lags': lags, 'lag_corr_length': self.f.size / NP.sum(freq_wts, axis=-1)} for key in cpinfo: if key in ['closure_phase_skyvis', 'closure_phase_vis', 'closure_phase_noise']: available_CP_key = key ndim_padtuple = [(0,0) for i in range(1+len(cpinfo[key].shape[:-2]))] + [(0,npad), (0,0)] result[key] = DSP.FT1D(NP.pad(NP.exp(-1j*cpinfo[key].reshape(cpinfo[key].shape[:-2]+(1,)+cpinfo[key].shape[-2:])) * freq_wts.reshape(tuple(NP.ones(len(cpinfo[key].shape[:-2])).astype(int))+freq_wts.shape+(1,)), ndim_padtuple, mode='constant'), ax=-2, inverse=True, use_real=False, shift=True) * (npad + self.f.size) * self.df # result[key] = DSP.FT1D(NP.pad(NP.exp(-1j*cpinfo[key][:,NP.newaxis,:,:]) * freq_wts[NP.newaxis,:,:,NP.newaxis], ((0,0),(0,0),(0,npad),(0,0)), mode='constant'), ax=-2, inverse=True, use_real=False, shift=True) * (npad + self.f.size) * self.df lag_kernel = DSP.FT1D(NP.pad(freq_wts.reshape(tuple(NP.ones(len(cpinfo[available_CP_key].shape[:-2])).astype(int))+freq_wts.shape+(1,)), ndim_padtuple, mode='constant'), ax=-2, inverse=True, use_real=False, shift=True) * (npad + self.f.size) * self.df result['lag_kernel'] = lag_kernel if verbose: print('\tSub-band(s) delay transform computed') result_resampled = {'antenna_triplets': cpinfo['antenna_triplets'], 'baseline_triplets': cpinfo['baseline_triplets']} result_resampled['freq_center'] = result['freq_center'] result_resampled['bw_eff'] = result['bw_eff'] result_resampled['freq_wts'] = result['freq_wts'] downsample_factor = NP.min((self.f.size + npad) * self.df / result_resampled['bw_eff']) result_resampled['lags'] = DSP.downsampler(result['lags'], downsample_factor, axis=-1, method='interp', kind='linear') result_resampled['lag_kernel'] = DSP.downsampler(result['lag_kernel'], downsample_factor, axis=-2, method='interp', kind='linear') dlag = result_resampled['lags'][1] - result_resampled['lags'][0] result_resampled['lag_corr_length'] = (1/result['bw_eff']) / dlag for key in ['closure_phase_skyvis', 'closure_phase_vis', 'closure_phase_noise']: if key in result: result_resampled[key] = DSP.downsampler(result[key], downsample_factor, axis=-2, method='FFT') if verbose: print('\tDownsampled Sub-band(s) delay transform computed') if (action is None) or (action.lower() == 'return_resampled'): return result_resampled elif action.lower() == 'return_oversampled': return result else: raise ValueError('Invalid action specified') ################################################################################ def get_horizon_delay_limits(self, phase_center=None, phase_center_coords=None): """ ------------------------------------------------------------------------- Estimates the delay envelope determined by the sky horizon for the baseline(s) for the phase centers Inputs: phase_center [numpy array] Phase center of the observation as 2-column or 3-column numpy array. Two columns are used when it is specified in 'hadec' or 'altaz' coordinates as indicated by the input phase_center_coords or by three columns when 'dircos' coordinates are used. This is where the telescopes will be phased up to as reference. Coordinate system for the phase_center is specified by another input phase_center_coords. Default=None implies the corresponding attribute from the DelaySpectrum instance is used. This is a Nx2 or Nx3 array phase_center_coords [string] Coordinate system for array phase center. Accepted values are 'hadec' (HA-Dec), 'altaz' (Altitude-Azimuth) or 'dircos' (direction cosines). Default=None implies the corresponding attribute from the DelaySpectrum instance is used. Outputs: horizon_envelope: NxMx2 matrix where M is the number of baselines and N is the number of phase centers. horizon_envelope[:,:,0] contains the minimum delay after accounting for (any) non-zenith phase center. horizon_envelope[:,:,1] contains the maximum delay after accounting for (any) non-zenith phase center(s). ------------------------------------------------------------------------- """ if phase_center is None: phase_center = self.ia.phase_center phase_center_coords = self.ia.phase_center_coords if phase_center_coords not in ['hadec', 'altaz', 'dircos']: raise ValueError('Phase center coordinates must be "altaz", "hadec" or "dircos"') if phase_center_coords == 'hadec': pc_altaz = GEOM.hadec2altaz(phase_center, self.ia.latitude, units='degrees') pc_dircos = GEOM.altaz2dircos(pc_altaz, units='degrees') elif phase_center_coords == 'altaz': pc_dircos = GEOM.altaz2dircos(phase_center, units='degrees') elif phase_center_coords == 'dircos': pc_dircos = phase_center horizon_envelope = DLY.horizon_delay_limits(self.ia.baselines, pc_dircos, units='mks') return horizon_envelope ############################################################################# def set_horizon_delay_limits(self): """ ------------------------------------------------------------------------- Estimates the delay envelope determined by the sky horizon for the baseline(s) for the phase centers of the DelaySpectrum instance. No output is returned. Uses the member function get_horizon_delay_limits() ------------------------------------------------------------------------- """ self.horizon_delay_limits = self.get_horizon_delay_limits() ############################################################################# def save(self, ds_outfile, ia_outfile, tabtype='BinTabelHDU', overwrite=False, verbose=True): """ ------------------------------------------------------------------------- Saves the interferometer array delay spectrum information to disk. Inputs: outfile [string] Filename with full path for for delay spectrum data to be saved to. Will be appended with '.ds.fits' ia_outfile [string] Filename with full path for interferometer array data to be saved to. Will be appended with '.fits' extension Keyword Input(s): tabtype [string] indicates table type for one of the extensions in the FITS file. Allowed values are 'BinTableHDU' and 'TableHDU' for binary and ascii tables respectively. Default is 'BinTableHDU'. overwrite [boolean] True indicates overwrite even if a file already exists. Default = False (does not overwrite) verbose [boolean] If True (default), prints diagnostic and progress messages. If False, suppress printing such messages. ------------------------------------------------------------------------- """ try: ds_outfile, ia_outfile except NameError: raise NameError('Both delay spectrum and interferometer array output filenames must be specified. Aborting DelaySpectrum.save()...') if verbose: print('\nSaving information about interferometer array...') self.ia.save(ia_outfile, tabtype=tabtype, overwrite=overwrite, verbose=verbose) if verbose: print('\nSaving information about delay spectra...') hdulist = [] hdulist += [fits.PrimaryHDU()] hdulist[0].header['EXTNAME'] = 'PRIMARY' hdulist[0].header['NCHAN'] = (self.f.size, 'Number of frequency channels') hdulist[0].header['NLAGS'] = (self.lags.size, 'Number of lags') hdulist[0].header['freq_resolution'] = (self.df, 'Frequency resolution (Hz)') hdulist[0].header['N_ACC'] = (self.n_acc, 'Number of accumulations') hdulist[0].header['PAD'] = (self.pad, 'Padding factor') hdulist[0].header['DBUFFER'] = (self.clean_window_buffer, 'CLEAN window buffer (1/bandwidth)') hdulist[0].header['IARRAY'] = (ia_outfile+'.fits', 'Location of InterferometerArray simulated visibilities') if verbose: print('\tCreated a primary HDU.') # cols = [] # cols += [fits.Column(name='frequency', format='D', array=self.f)] # cols += [fits.Column(name='lag', format='D', array=self.lags)] # columns = _astropy_columns(cols, tabtype=tabtype) # tbhdu = fits.new_table(columns) # tbhdu.header.set('EXTNAME', 'SPECTRAL INFO') # hdulist += [tbhdu] # if verbose: # print('\tCreated an extension for spectral information.') hdulist += [fits.ImageHDU(self.f, name='FREQUENCIES')] hdulist += [fits.ImageHDU(self.lags, name='LAGS')] if verbose: print('\tCreated an extension for spectral information.') hdulist += [fits.ImageHDU(self.horizon_delay_limits, name='HORIZON LIMITS')] if verbose: print('\tCreated an extension for horizon delay limits of size {0[0]} x {0[1]} x {0[2]} as a function of snapshot instance, baseline, and (min,max) limits'.format(self.horizon_delay_limits.shape)) hdulist += [fits.ImageHDU(self.bp, name='BANDPASS')] if verbose: print('\tCreated an extension for bandpass functions of size {0[0]} x {0[1]} x {0[2]} as a function of baseline, frequency, and snapshot instance'.format(self.bp.shape)) hdulist += [fits.ImageHDU(self.bp_wts, name='BANDPASS WEIGHTS')] if verbose: print('\tCreated an extension for bandpass weights of size {0[0]} x {0[1]} x {0[2]} as a function of baseline, frequency, and snapshot instance'.format(self.bp_wts.shape)) if self.lag_kernel is not None: hdulist += [fits.ImageHDU(self.lag_kernel.real, name='LAG KERNEL REAL')] hdulist += [fits.ImageHDU(self.lag_kernel.imag, name='LAG KERNEL IMAG')] if verbose: print('\tCreated an extension for convolving lag kernel of size {0[0]} x {0[1]} x {0[2]} as a function of baseline, lags, and snapshot instance'.format(self.lag_kernel.shape)) if self.skyvis_lag is not None: hdulist += [fits.ImageHDU(self.skyvis_lag.real, name='NOISELESS DELAY SPECTRA REAL')] hdulist += [fits.ImageHDU(self.skyvis_lag.imag, name='NOISELESS DELAY SPECTRA IMAG')] if self.vis_lag is not None: hdulist += [fits.ImageHDU(self.vis_lag.real, name='NOISY DELAY SPECTRA REAL')] hdulist += [fits.ImageHDU(self.vis_lag.imag, name='NOISY DELAY SPECTRA IMAG')] if self.vis_noise_lag is not None: hdulist += [fits.ImageHDU(self.vis_noise_lag.real, name='DELAY SPECTRA NOISE REAL')] hdulist += [fits.ImageHDU(self.vis_noise_lag.imag, name='DELAY SPECTRA NOISE IMAG')] if self.cc_freq is not None: hdulist += [fits.ImageHDU(self.cc_freq, name='CLEAN FREQUENCIES')] if self.cc_lags is not None: hdulist += [fits.ImageHDU(self.cc_lags, name='CLEAN LAGS')] if verbose: print('\tCreated an extension for spectral axes of clean components') if self.cc_lag_kernel is not None: hdulist += [fits.ImageHDU(self.cc_lag_kernel.real, name='CLEAN LAG KERNEL REAL')] hdulist += [fits.ImageHDU(self.cc_lag_kernel.imag, name='CLEAN LAG KERNEL IMAG')] if verbose: print('\tCreated an extension for deconvolving lag kernel of size {0[0]} x {0[1]} x {0[2]} as a function of baseline, lags, and snapshot instance'.format(self.cc_lag_kernel.shape)) if self.cc_skyvis_lag is not None: hdulist += [fits.ImageHDU(self.cc_skyvis_lag.real, name='CLEAN NOISELESS DELAY SPECTRA REAL')] hdulist += [fits.ImageHDU(self.cc_skyvis_lag.imag, name='CLEAN NOISELESS DELAY SPECTRA IMAG')] if self.cc_skyvis_res_lag is not None: hdulist += [fits.ImageHDU(self.cc_skyvis_res_lag.real, name='CLEAN NOISELESS DELAY SPECTRA RESIDUALS REAL')] hdulist += [fits.ImageHDU(self.cc_skyvis_res_lag.imag, name='CLEAN NOISELESS DELAY SPECTRA RESIDUALS IMAG')] if self.cc_skyvis_freq is not None: hdulist += [fits.ImageHDU(self.cc_skyvis_freq.real, name='CLEAN NOISELESS VISIBILITIES REAL')] hdulist += [fits.ImageHDU(self.cc_skyvis_freq.imag, name='CLEAN NOISELESS VISIBILITIES IMAG')] if self.cc_skyvis_res_freq is not None: hdulist += [fits.ImageHDU(self.cc_skyvis_res_freq.real, name='CLEAN NOISELESS VISIBILITIES RESIDUALS REAL')] hdulist += [fits.ImageHDU(self.cc_skyvis_res_freq.imag, name='CLEAN NOISELESS VISIBILITIES RESIDUALS IMAG')] if self.cc_vis_lag is not None: hdulist += [fits.ImageHDU(self.cc_vis_lag.real, name='CLEAN NOISY DELAY SPECTRA REAL')] hdulist += [fits.ImageHDU(self.cc_vis_lag.imag, name='CLEAN NOISY DELAY SPECTRA IMAG')] if self.cc_vis_res_lag is not None: hdulist += [fits.ImageHDU(self.cc_vis_res_lag.real, name='CLEAN NOISY DELAY SPECTRA RESIDUALS REAL')] hdulist += [fits.ImageHDU(self.cc_vis_res_lag.imag, name='CLEAN NOISY DELAY SPECTRA RESIDUALS IMAG')] if self.cc_vis_freq is not None: hdulist += [fits.ImageHDU(self.cc_vis_freq.real, name='CLEAN NOISY VISIBILITIES REAL')] hdulist += [fits.ImageHDU(self.cc_vis_freq.imag, name='CLEAN NOISY VISIBILITIES IMAG')] if self.cc_vis_res_freq is not None: hdulist += [fits.ImageHDU(self.cc_vis_res_freq.real, name='CLEAN NOISY VISIBILITIES RESIDUALS REAL')] hdulist += [fits.ImageHDU(self.cc_vis_res_freq.imag, name='CLEAN NOISY VISIBILITIES RESIDUALS IMAG')] if verbose: print('\tCreated extensions for clean components of noiseless, noisy and residuals of visibilities in frequency and delay coordinates of size {0[0]} x {0[1]} x {0[2]} as a function of baselines, lags/frequency and snapshot instance'.format(self.lag_kernel.shape)) if self.subband_delay_spectra: hdulist[0].header['SBDS'] = (1, 'Presence of Subband Delay Spectra') for key in self.subband_delay_spectra: hdulist[0].header['{0}-SBDS'.format(key)] = (1, 'Presence of {0} Subband Delay Spectra'.format(key)) hdulist[0].header['{0}-SBDS-WSHAPE'.format(key)] = (self.subband_delay_spectra[key]['shape'], 'Shape of {0} subband frequency weights'.format(key)) if key == 'cc': hdulist[0].header['{0}-SBDS-BPCORR'.format(key)] = (int(self.subband_delay_spectra[key]['bpcorrect']), 'Truth value for {0} subband delay spectrum bandpass windows weights correction'.format(key)) hdulist[0].header['{0}-SBDS-NPAD'.format(key)] = (self.subband_delay_spectra[key]['npad'], 'Number of zero-padded channels for subband delay spectra'.format(key)) hdulist += [fits.ImageHDU(self.subband_delay_spectra[key]['freq_center'], name='{0}-SBDS-F0'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra[key]['freq_wts'], name='{0}-SBDS-FWTS'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra[key]['bw_eff'], name='{0}-SBDS-BWEFF'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra[key]['lags'], name='{0}-SBDS-LAGS'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra[key]['lag_kernel'].real, name='{0}-SBDS-LAGKERN-REAL'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra[key]['lag_kernel'].imag, name='{0}-SBDS-LAGKERN-IMAG'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra[key]['lag_corr_length'], name='{0}-SBDS-LAGCORR'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra[key]['skyvis_lag'].real, name='{0}-SBDS-SKYVISLAG-REAL'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra[key]['skyvis_lag'].imag, name='{0}-SBDS-SKYVISLAG-IMAG'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra[key]['vis_lag'].real, name='{0}-SBDS-VISLAG-REAL'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra[key]['vis_lag'].imag, name='{0}-SBDS-VISLAG-IMAG'.format(key))] if key == 'sim': hdulist += [fits.ImageHDU(self.subband_delay_spectra[key]['vis_noise_lag'].real, name='{0}-SBDS-NOISELAG-REAL'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra[key]['vis_noise_lag'].imag, name='{0}-SBDS-NOISELAG-IMAG'.format(key))] if key == 'cc': hdulist += [fits.ImageHDU(self.subband_delay_spectra[key]['skyvis_res_lag'].real, name='{0}-SBDS-SKYVISRESLAG-REAL'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra[key]['skyvis_res_lag'].imag, name='{0}-SBDS-SKYVISRESLAG-IMAG'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra[key]['vis_res_lag'].real, name='{0}-SBDS-VISRESLAG-REAL'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra[key]['vis_res_lag'].imag, name='{0}-SBDS-VISRESLAG-IMAG'.format(key))] if verbose: print('\tCreated extensions for information on subband delay spectra for simulated and clean components of visibilities as a function of baselines, lags/frequency and snapshot instance') if self.subband_delay_spectra_resampled: hdulist[0].header['SBDS-RS'] = (1, 'Presence of Resampled Subband Delay Spectra') for key in self.subband_delay_spectra_resampled: hdulist[0].header['{0}-SBDS-RS'.format(key)] = (1, 'Presence of {0} Reampled Subband Delay Spectra'.format(key)) hdulist += [fits.ImageHDU(self.subband_delay_spectra_resampled[key]['freq_center'], name='{0}-SBDSRS-F0'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra_resampled[key]['bw_eff'], name='{0}-SBDSRS-BWEFF'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra_resampled[key]['lags'], name='{0}-SBDSRS-LAGS'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra_resampled[key]['lag_kernel'].real, name='{0}-SBDSRS-LAGKERN-REAL'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra_resampled[key]['lag_kernel'].imag, name='{0}-SBDSRS-LAGKERN-IMAG'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra_resampled[key]['lag_corr_length'], name='{0}-SBDSRS-LAGCORR'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra_resampled[key]['skyvis_lag'].real, name='{0}-SBDSRS-SKYVISLAG-REAL'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra_resampled[key]['skyvis_lag'].imag, name='{0}-SBDSRS-SKYVISLAG-IMAG'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra_resampled[key]['vis_lag'].real, name='{0}-SBDSRS-VISLAG-REAL'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra_resampled[key]['vis_lag'].imag, name='{0}-SBDSRS-VISLAG-IMAG'.format(key))] if key == 'sim': hdulist += [fits.ImageHDU(self.subband_delay_spectra_resampled[key]['vis_noise_lag'].real, name='{0}-SBDSRS-NOISELAG-REAL'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra_resampled[key]['vis_noise_lag'].imag, name='{0}-SBDSRS-NOISELAG-IMAG'.format(key))] if key == 'cc': hdulist += [fits.ImageHDU(self.subband_delay_spectra_resampled[key]['skyvis_res_lag'].real, name='{0}-SBDSRS-SKYVISRESLAG-REAL'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra_resampled[key]['skyvis_res_lag'].imag, name='{0}-SBDSRS-SKYVISRESLAG-IMAG'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra_resampled[key]['vis_res_lag'].real, name='{0}-SBDSRS-VISRESLAG-REAL'.format(key))] hdulist += [fits.ImageHDU(self.subband_delay_spectra_resampled[key]['vis_res_lag'].imag, name='{0}-SBDSRS-VISRESLAG-IMAG'.format(key))] if verbose: print('\tCreated extensions for information on resampled subband delay spectra for simulated and clean components of visibilities as a function of baselines, lags/frequency and snapshot instance') hdu = fits.HDUList(hdulist) hdu.writeto(ds_outfile+'.ds.fits', clobber=overwrite) ################################################################################ class DelayPowerSpectrum(object): """ ---------------------------------------------------------------------------- Class to manage delay power spectrum from visibility measurements of a multi-element interferometer array. Attributes: cosmo [instance of cosmology class from astropy] An instance of class FLRW or default_cosmology of astropy cosmology module. ds [instance of class DelaySpectrum] An instance of class DelaySpectrum that contains the information on delay spectra of simulated visibilities f [list or numpy vector] frequency channels in Hz lags [numpy vector] Time axis obtained when the frequency axis is inverted using a FFT. Same size as channels. This is computed in member function delay_transform(). cc_lags [numpy vector] Time axis obtained when the frequency axis is inverted using a FFT. Same size as cc_freq. This is computed in member function delayClean(). df [scalar] Frequency resolution (in Hz) bl [M x 3 Numpy array] The baseline vectors associated with the M interferometers in SI units bl_length [M-element numpy array] Lengths of the baseline in SI units f0 [scalar] Central frequency (in Hz) wl0 [scalar] Central wavelength (in m) z [scalar] redshift bw [scalar] (effective) bandwidth (in Hz) kprll [numpy array] line-of-sight wavenumbers (in h/Mpc) corresponding to delays in the delay spectrum kperp [numpy array] transverse wavenumbers (in h/Mpc) corresponding to baseline lengths horizon_kprll_limits [numpy array] limits on k_parallel corresponding to limits on horizon delays. It is of size NxMx2 denoting the neagtive and positive horizon delay limits where N is the number of timestamps, M is the number of baselines. The 0 index in the third dimenstion denotes the negative horizon limit while the 1 index denotes the positive horizon limit drz_los [scalar] comoving line-of-sight depth (Mpc/h) corresponding to specified redshift and bandwidth for redshifted 21 cm line rz_transverse [scalar] comoving transverse distance (Mpc/h) corresponding to specified redshift for redshifted 21 cm line rz_los [scalar] comoving line-of-sight distance (Mpc/h) corresponding to specified redshift for redshifted 21 cm line jacobian1 [scalar] first jacobian in conversion of delay spectrum to power spectrum. It is equal to A_eff / wl**2 / bw jacobian2 [scalar] second jacobian in conversion of delay spectrum to power spectrum. It is equal to rz_los**2 * drz_los / bw Jy2K [scalar] factor to convert Jy/Sr to K. It is equal to wl**2 * Jy / (2k) K2Jy [scalar] factor to convert K to Jy/Sr. It is equal to 1/Jy2K dps [dictionary of numpy arrays] contains numpy arrays containing delay power spectrum in units of K^2 (Mpc/h)^3 under the following keys: 'skyvis' [numpy array] delay power spectrum of noiseless delay spectra 'vis' [numpy array] delay power spectrum of noisy delay spectra 'noise' [numpy array] delay power spectrum of thermal noise delay spectra 'cc_skyvis' [numpy array] delay power spectrum of clean components of noiseless delay spectra 'cc_vis' [numpy array] delay power spectrum of clean components of noisy delay spectra 'cc_skyvis_res' [numpy array] delay power spectrum of residuals after delay cleaning of noiseless delay spectra 'cc_vis_res' [numpy array] delay power spectrum of residuals after delay cleaning of noisy delay spectra 'cc_skyvis_net' [numpy array] delay power spectrum of sum of residuals and clean components after delay cleaning of noiseless delay spectra 'cc_vis_net' [numpy array] delay power spectrum of sum of residuals and clean components after delay cleaning of noisy delay spectra subband_delay_power_spectra [dictionary] contains two top level keys, namely, 'cc' and 'sim' denoting information about CLEAN and simulated visibilities respectively. Essentially this is the power spectrum equivalent of the attribute suuband_delay_spectra under class DelaySpectrum. Under each of these keys is information about delay power spectra of different frequency sub-bands (n_win in number) in the form of a dictionary under the following keys: 'z' [numpy array] contains the redshifts corresponding to center frequencies (in Hz) of the frequency subbands of the subband delay spectra. It is of size n_win. 'dz' [numpy array] contains the width in redshifts corresponding to the effective bandwidths (in Hz) of the subbands being delay transformed. It is of size n_win. 'kprll' [numpy array] line-of-sight k-modes (in h/Mpc) corresponding to lags of the subband delay spectra. It is of size n_win x (nchan+npad) 'kperp' [numpy array] transverse k-modes (in h/Mpc) corresponding to the baseline lengths and the center frequencies. It is of size n_win x n_bl horizon_kprll_limits [numpy array] limits on k_parallel corresponding to limits on horizon delays for each subband. It is of size N x n_win x M x 2 denoting the neagtive and positive horizon delay limits where N is the number of timestamps, n_win is the number of subbands, M is the number of baselines. The 0 index in the fourth dimenstion denotes the negative horizon limit while the 1 index denotes the positive horizon limit 'rz_los' [numpy array] Comoving distance along LOS (in Mpc/h) corresponding to the different redshifts under key 'z'. It is of size n_win 'rz_transverse' [numpy array] transverse comoving distance (in Mpc/h) corresponding to the different redshifts under key 'z'. It is of size n_win 'drz_los' [numpy array] line-of-sight comoving depth (in Mpc/h) corresponding to the redshift widths under key 'dz' and redshifts under key 'z'. It is of size n_win 'jacobian1' [numpy array] first jacobian in conversion of delay spectrum to power spectrum. It is equal to A_eff / wl**2 / bw. It is of size n_win 'jacobian2' [numpy array] second jacobian in conversion of delay spectrum to power spectrum. It is equal to rz_los**2 * drz_los / bw. It is of size n_win 'Jy2K' [numpy array] factor to convert Jy/Sr to K. It is equal to wl**2 * Jy / (2k). It is of size n_win 'factor' [numpy array] conversion factor to convert delay spectrum (in Jy Hz) to delay power spectrum (in K^2 (Mpc/h)^3). It is equal to jacobian1 * jacobian2 * Jy2K**2. It is of size n_win 'skyvis_lag' [numpy array] delay power spectrum (in K^2 (Mpc/h)^3) corresponding to noiseless simulated (under top level key 'sim') or CLEANed (under top level key 'cc') delay spectrum under key 'skyvis_lag' in attribute subband_delay_spectra under instance of class DelaySpectrum. It is of size n_bl x n_win x (nchan+npad) x n_t 'vis_lag' [numpy array] delay power spectrum (in K^2 (Mpc/h)^3) corresponding to noisy simulated (under top level key 'sim') or CLEANed (under top level key 'cc') delay spectrum under key 'vis_lag' in attribute subband_delay_spectra under instance of class DelaySpectrum. It is of size n_bl x n_win x (nchan+npad) x n_t 'vis_noise_lag' [numpy array] delay power spectrum (in K^2 (Mpc/h)^3) corresponding to thermal noise simulated (under top level key 'sim') delay spectrum under key 'vis_noise_lag' in attribute subband_delay_spectra under instance of class DelaySpectrum. It is of size n_bl x n_win x (nchan+npad) x n_t 'skyvis_res_lag' [numpy array] delay power spectrum (in K^2 (Mpc/h)^3) corresponding to CLEAN residuals (under top level key 'cc') from noiseless simulated delay spectrum under key 'skyvis_res_lag' in attribute subband_delay_spectra under instance of class DelaySpectrum. It is of size n_bl x n_win x (nchan+npad) x n_t 'vis_res_lag' [numpy array] delay power spectrum (in K^2 (Mpc/h)^3) corresponding to CLEAN residuals (under top level key 'cc') from noisy delay spectrum under key 'vis_res_lag' in attribute subband_delay_spectra under instance of class DelaySpectrum. It is of size n_bl x n_win x (nchan+npad) x n_t 'skyvis_net_lag' [numpy array] delay power spectrum (in K^2 (Mpc/h)^3) corresponding to sum of CLEAN components and residuals (under top level key 'cc') from noiseless simulated delay spectrum under key 'skyvis_net_lag' in attribute subband_delay_spectra under instance of class DelaySpectrum. It is of size n_bl x n_win x (nchan+npad) x n_t 'vis_net_lag' [numpy array] delay power spectrum (in K^2 (Mpc/h)^3) corresponding to sum of CLEAN components and residuals (under top level key 'cc') from noisy delay spectrum under key 'vis_net_lag' in attribute subband_delay_spectra under instance of class DelaySpectrum. It is of size n_bl x n_win x (nchan+npad) x n_t subband_delay_power_spectra_resampled [dictionary] contains two top level keys, namely, 'cc' and 'sim' denoting information about CLEAN and simulated visibilities respectively. Essentially this is the power spectrum equivalent of the attribute suuband_delay_spectra_resampled under class DelaySpectrum. Under each of these keys is information about delay power spectra of different frequency sub-bands (n_win in number) in the form of a dictionary under the following keys: 'kprll' [numpy array] line-of-sight k-modes (in h/Mpc) corresponding to lags of the subband delay spectra. It is of size n_win x nlags, where nlags is the resampeld number of delay bins 'kperp' [numpy array] transverse k-modes (in h/Mpc) corresponding to the baseline lengths and the center frequencies. It is of size n_win x n_bl 'horizon_kprll_limits' [numpy array] limits on k_parallel corresponding to limits on horizon delays for each subband. It is of size N x n_win x M x 2 denoting the negative and positive horizon delay limits where N is the number of timestamps, n_win is the number of subbands, M is the number of baselines. The 0 index in the fourth dimenstion denotes the negative horizon limit while the 1 index denotes the positive horizon limit 'skyvis_lag' [numpy array] delay power spectrum (in K^2 (Mpc/h)^3) corresponding to noiseless simulated (under top level key 'sim') or CLEANed (under top level key 'cc') delay spectrum under key 'skyvis_lag' in attribute subband_delay_spectra_resampled under instance of class DelaySpectrum. It is of size n_bl x n_win x nlags x n_t 'vis_lag' [numpy array] delay power spectrum (in K^2 (Mpc/h)^3) corresponding to noisy simulated (under top level key 'sim') or CLEANed (under top level key 'cc') delay spectrum under key 'vis_lag' in attribute subband_delay_spectra_resampled under instance of class DelaySpectrum. It is of size n_bl x n_win x nlags x n_t 'vis_noise_lag' [numpy array] delay power spectrum (in K^2 (Mpc/h)^3) corresponding to thermal noise simulated (under top level key 'sim') delay spectrum under key 'vis_noise_lag' in attribute subband_delay_spectra_resampled under instance of class DelaySpectrum. It is of size n_bl x n_win x nlags x n_t 'skyvis_res_lag' [numpy array] delay power spectrum (in K^2 (Mpc/h)^3) corresponding to CLEAN residuals (under top level key 'cc') from noiseless simulated delay spectrum under key 'skyvis_res_lag' in attribute subband_delay_spectra_resampled under instance of class DelaySpectrum. It is of size n_bl x n_win x nlags x n_t 'vis_res_lag' [numpy array] delay power spectrum (in K^2 (Mpc/h)^3) corresponding to CLEAN residuals (under top level key 'cc') from noisy delay spectrum under key 'vis_res_lag' in attribute subband_delay_spectra_resampled under instance of class DelaySpectrum. It is of size n_bl x n_win x nlags x n_t 'skyvis_net_lag' [numpy array] delay power spectrum (in K^2 (Mpc/h)^3) corresponding to sum of CLEAN components and residuals (under top level key 'cc') from noiseless simulated delay spectrum under key 'skyvis_net_lag' in attribute subband_delay_spectra_resampled under instance of class DelaySpectrum. It is of size n_bl x n_win x nlags x n_t 'vis_net_lag' [numpy array] delay power spectrum (in K^2 (Mpc/h)^3) corresponding to sum of CLEAN components and residuals (under top level key 'cc') from noisy delay spectrum under key 'vis_net_lag' in attribute subband_delay_spectra_resampled under instance of class DelaySpectrum. It is of size n_bl x n_win x nlags x n_t Member functions: __init__() Initialize an instance of class DelayPowerSpectrum comoving_los_depth() Compute comoving line-of-sight depth (Mpc/h) corresponding to specified redshift and bandwidth for redshifted 21 cm line comoving_transverse_distance() Compute comoving transverse distance (Mpc/h) corresponding to specified redshift for redshifted 21 cm line comoving_los_distance() Compute comoving line-of-sight distance (Mpc/h) corresponding to specified redshift for redshifted 21 cm line k_parallel() Compute line-of-sight wavenumbers (h/Mpc) corresponding to specified delays and redshift for redshifted 21 cm line k_perp() Compute transverse wavenumbers (h/Mpc) corresponding to specified baseline lengths and redshift for redshifted 21 cm line assuming a mean wavelength (in m) for the relationship between baseline lengths and spatial frequencies (u and v) compute_power_spectrum() Compute delay power spectrum in units of K^2 (Mpc/h)^3 from the delay spectrum in units of Jy Hz compute_power_spectrum_allruns() Compute delay power spectrum in units of K^2 (Mpc/h)^3 from the delay spectrum in units of Jy Hz from multiple runs of visibilities compute_individual_closure_phase_power_spectrum() Compute delay power spectrum of closure phase in units of K^2 (Mpc/h)^3 from the delay spectrum in units of Jy Hz where the original visibility amplitudes of closure phase complex exponents are assumed to be 1 Jy across the band compute_averaged_closure_phase_power_spectrum() Compute delay power spectrum of closure phase in units of K^2 (Mpc/h)^3 from the delay spectrum in units of Jy Hz and average over 'auto' and 'cross' modes, where the original visibility amplitudes of closure phase complex exponents are assumed to be 1 Jy across the band ---------------------------------------------------------------------------- """ def __init__(self, dspec, cosmo=cosmo100): """ ------------------------------------------------------------------------ Initialize an instance of class DelayPowerSpectrum. Attributes initialized are: ds, cosmo, f, df, f0, z, bw, drz_los, rz_transverse, rz_los, kprll, kperp, jacobian1, jacobian2, subband_delay_power_spectra, subband_delay_power_spectra_resampled Inputs: dspec [instance of class DelaySpectrum] An instance of class DelaySpectrum that contains the information on delay spectra of simulated visibilities cosmo [instance of a cosmology class in Astropy] An instance of class FLRW or default_cosmology of astropy cosmology module. Default value is set using concurrent cosmology but keep H0=100 km/s/Mpc ------------------------------------------------------------------------ """ try: dspec except NameError: raise NameError('No delay spectrum instance supplied for initialization') if not isinstance(dspec, DelaySpectrum): raise TypeError('Input dspec must be an instance of class DelaySpectrum') if not isinstance(cosmo, (CP.FLRW, CP.default_cosmology)): raise TypeError('Input cosmology must be a cosmology class defined in Astropy') self.cosmo = cosmo self.ds = dspec self.f = self.ds.f self.lags = self.ds.lags self.cc_lags = self.ds.cc_lags self.bl = self.ds.ia.baselines self.bl_length = self.ds.ia.baseline_lengths self.df = self.ds.df self.f0 = self.f[int(self.f.size/2)] self.wl0 = FCNST.c / self.f0 self.z = CNST.rest_freq_HI / self.f0 - 1 self.bw = self.df * self.f.size self.kprll = self.k_parallel(self.lags, redshift=self.z, action='return') # in h/Mpc self.kperp = self.k_perp(self.bl_length, redshift=self.z, action='return') # in h/Mpc self.horizon_kprll_limits = self.k_parallel(self.ds.horizon_delay_limits, redshift=self.z, action='return') # in h/Mpc self.drz_los = self.comoving_los_depth(self.bw, self.z, action='return') # in Mpc/h self.rz_transverse = self.comoving_transverse_distance(self.z, action='return') # in Mpc/h self.rz_los = self.comoving_los_distance(self.z, action='return') # in Mpc/h # self.jacobian1 = NP.mean(self.ds.ia.A_eff) / self.wl0**2 / self.bw omega_bw = self.beam3Dvol(freq_wts=self.ds.bp_wts[0,:,0]) self.jacobian1 = 1 / omega_bw # self.jacobian2 = self.rz_transverse**2 * self.drz_los / self.bw self.jacobian2 = self.rz_los**2 * self.drz_los / self.bw self.Jy2K = self.wl0**2 * CNST.Jy / (2*FCNST.k) self.K2Jy = 1 / self.Jy2K self.dps = {} self.dps['skyvis'] = None self.dps['vis'] = None self.dps['noise'] = None self.dps['cc_skyvis'] = None self.dps['cc_vis'] = None self.dps['cc_skyvis_res'] = None self.dps['cc_vis_res'] = None self.dps['cc_skyvis_net'] = None self.dps['cc_vis_net'] = None self.subband_delay_power_spectra = {} self.subband_delay_power_spectra_resampled = {} ############################################################################ def comoving_los_depth(self, bw, redshift, action=None): """ ------------------------------------------------------------------------ Compute comoving line-of-sight depth (Mpc/h) corresponding to specified redshift and bandwidth for redshifted 21 cm line Inputs: bw [scalar] bandwidth in Hz redshift [scalar] redshift action [string] If set to None (default), the comoving depth along the line of sight (Mpc/h) and specified reshift are stored internally as attributes of the instance of class DelayPowerSpectrum. If set to 'return', the comoving depth along line of sight (Mpc/h) computed is returned Outputs: If keyword input action is set to 'return', the comoving depth along line of sight (Mpc/h) computed is returned ------------------------------------------------------------------------ """ drz_los = (FCNST.c/1e3) * bw * (1+redshift)**2 / CNST.rest_freq_HI / self.cosmo.H0.value / self.cosmo.efunc(redshift) # in Mpc/h if action is None: self.z = redshift self.drz_los = drz_los return else: return drz_los ############################################################################ def comoving_transverse_distance(self, redshift, action=None): """ ------------------------------------------------------------------------ Compute comoving transverse distance (Mpc/h) corresponding to specified redshift for redshifted 21 cm line Inputs: redshift [scalar] redshift action [string] If set to None (default), the comoving transverse distance (Mpc/h) and specified reshift are stored internally as attributes of the instance of class DelayPowerSpectrum. If set to 'return', the comoving transverse distance (Mpc/h) computed is returned Outputs: If keyword input action is set to 'return', the comoving transverse distance (Mpc/h) computed is returned ------------------------------------------------------------------------ """ rz_transverse = self.cosmo.comoving_transverse_distance(redshift).to('Mpc').value # in Mpc/h if action is None: self.z = redshift self.rz_transverse = rz_transverse return else: return rz_transverse ############################################################################ def comoving_los_distance(self, redshift, action=None): """ ------------------------------------------------------------------------ Compute comoving line-of-sight distance (Mpc/h) corresponding to specified redshift for redshifted 21 cm line Inputs: redshift [scalar] redshift action [string] If set to None (default), the comoving line-of-sight distance (Mpc/h) and specified reshift are stored internally as attributes of the instance of class DelayPowerSpectrum. If set to 'return', the comoving line-of-sight distance (Mpc/h) computed is returned Outputs: If keyword input action is set to 'return', the comoving line-of-sight distance (Mpc/h) computed is returned ------------------------------------------------------------------------ """ rz_los = self.cosmo.comoving_distance(redshift).to('Mpc').value # in Mpc/h if action is None: self.z = redshift self.rz_los = rz_los return else: return rz_los ############################################################################ def k_parallel(self, lags, redshift, action=None): """ ------------------------------------------------------------------------ Compute line-of-sight wavenumbers (h/Mpc) corresponding to specified delays and redshift for redshifted 21 cm line Inputs: lags [numpy array] geometric delays (in seconds) obtained as Fourier conjugate variable of frequencies in the bandpass redshift [scalar] redshift action [string] If set to None (default), the line-of-sight wavenumbers (h/Mpc) and specified reshift are stored internally as attributes of the instance of class DelayPowerSpectrum. If set to 'return', the line-of-sight wavenumbers (h/Mpc) computed is returned Outputs: If keyword input action is set to 'return', the line-of-sight wavenumbers (h/Mpc) computed is returned. It is of same size as input lags ------------------------------------------------------------------------ """ eta2kprll = dkprll_deta(redshift, cosmo=self.cosmo) kprll = eta2kprll * lags if action is None: self.z = redshift self.kprll = kprll return else: return kprll ############################################################################ def k_perp(self, baseline_length, redshift, action=None): """ ------------------------------------------------------------------------ Compute transverse wavenumbers (h/Mpc) corresponding to specified baseline lengths and redshift for redshifted 21 cm line assuming a mean wavelength (in m) for the relationship between baseline lengths and spatial frequencies (u and v) Inputs: baseline_length [numpy array] baseline lengths (in m) redshift [scalar] redshift action [string] If set to None (default), the transverse wavenumbers (h/Mpc) and specified reshift are stored internally as attributes of the instance of class DelayPowerSpectrum. If set to 'return', the transverse wavenumbers (h/Mpc) computed is returned Outputs: If keyword input action is set to 'return', the transverse wavenumbers (h/Mpc) computed is returned ------------------------------------------------------------------------ """ kperp = 2 * NP.pi * (baseline_length/self.wl0) / self.comoving_transverse_distance(redshift, action='return') if action is None: self.z = redshift self.kperp = kperp return else: return kperp ############################################################################ def beam3Dvol(self, freq_wts=None, nside=32): """ ------------------------------------------------------------------------ Compute three-dimensional (transverse-LOS) volume of the beam in units of "Sr Hz". freq_wts [numpy array] Frequency weights centered on different spectral windows or redshifts. Its shape is (nwin,nchan). nchan should match the number of spectral channels in the class attribute for frequency channels 'nside' [integer] NSIDE parameter for determining and interpolating the beam. If not set, it will be set to 64 (default). Output: omega_bw [numpy array] Integral of the square of the power pattern over transverse and spectral axes. Its shape is (nwin,) ------------------------------------------------------------------------ """ if self.ds.ia.simparms_file is not None: parms_file = open(self.ds.ia.simparms_file, 'r') parms = yaml.safe_load(parms_file) parms_file.close() # sky_nside = parms['fgparm']['nside'] beam_info = parms['beam'] use_external_beam = beam_info['use_external'] beam_chromaticity = beam_info['chromatic'] select_beam_freq = beam_info['select_freq'] if select_beam_freq is None: select_beam_freq = self.f0 theta, phi = HP.pix2ang(nside, NP.arange(HP.nside2npix(nside))) theta_phi = NP.hstack((theta.reshape(-1,1), phi.reshape(-1,1))) if use_external_beam: beam_file = beam_info['file'] if beam_info['filefmt'].lower() in ['hdf5', 'fits', 'uvbeam']: beam_filefmt = beam_info['filefmt'].lower() else: raise ValueError('Invalid beam file format specified') if beam_info['filepathtype'] == 'default': beam_file = prisim_path+'data/beams/' + beam_file beam_pol = beam_info['pol'] beam_id = beam_info['identifier'] pbeam_spec_interp_method = beam_info['spec_interp'] if beam_filefmt == 'fits': extbeam = fits.getdata(beam_file, extname='BEAM_{0}'.format(beam_pol)) beam_freqs = fits.getdata(beam_file, extname='FREQS_{0}'.format(beam_pol)) extbeam = extbeam.reshape(-1,beam_freqs.size) # npix x nfreqs prihdr = fits.getheader(beam_file, 0) beamunit = prihdr['GAINUNIT'] elif beam_filefmt.lower() == 'hdf5': with h5py.File(beam_file, 'r') as fileobj: extbeam = fileobj['gain_info'][beam_pol].value extbeam = extbeam.T beam_freqs = fileobj['spectral_info']['freqs'].value beamunit = fileobj['header']['gainunit'].value elif beam_filefmt == 'uvbeam': if uvbeam_module_found: uvbm = UVBeam() uvbm.read_beamfits(beam_file) axis_vec_ind = 0 # for power beam spw_ind = 0 # spectral window index if beam_pol.lower() in ['x', 'e']: beam_pol_ind = 0 else: beam_pol_ind = 1 extbeam = uvbm.data_array[axis_vec_ind,spw_ind,beam_pol_ind,:,:].T # npix x nfreqs beam_freqs = uvbm.freq_array.ravel() # nfreqs (in Hz) else: raise ImportError('uvbeam module not installed/found') if NP.abs(NP.abs(extbeam).max() - 1.0) > 1e-10: extbeam /= NP.abs(extbeam).max() beamunit = '' else: raise ValueError('Specified external beam file format not currently supported') if beamunit.lower() == 'db': extbeam = 10**(extbeam/10.0) beam_nside = HP.npix2nside(extbeam.shape[0]) if beam_nside < nside: nside = beam_nside if beam_chromaticity: if pbeam_spec_interp_method == 'fft': extbeam = extbeam[:,:-1] beam_freqs = beam_freqs[:-1] interp_logbeam = OPS.healpix_interp_along_axis(NP.log10(extbeam), theta_phi=theta_phi, inloc_axis=beam_freqs, outloc_axis=self.f, axis=1, kind=pbeam_spec_interp_method, assume_sorted=True) else: nearest_freq_ind = NP.argmin(NP.abs(beam_freqs - select_beam_freq)) interp_logbeam = OPS.healpix_interp_along_axis(NP.log10(NP.repeat(extbeam[:,nearest_freq_ind].reshape(-1,1), self.f.size, axis=1)), theta_phi=theta_phi, inloc_axis=self.f, outloc_axis=self.f, axis=1, assume_sorted=True) interp_logbeam_max = NP.nanmax(interp_logbeam, axis=0) interp_logbeam_max[interp_logbeam_max <= 0.0] = 0.0 interp_logbeam_max = interp_logbeam_max.reshape(1,-1) interp_logbeam = interp_logbeam - interp_logbeam_max beam = 10**interp_logbeam else: alt = 90.0 - NP.degrees(theta) az = NP.degrees(phi) altaz = NP.hstack((alt.reshape(-1,1), az.reshape(-1,1))) if beam_chromaticity: beam = PB.primary_beam_generator(altaz, self.f, self.ds.ia.telescope, freq_scale='Hz', skyunits='altaz', east2ax1=0.0, pointing_info=None, pointing_center=None) else: beam = PB.primary_beam_generator(altaz, select_beam_freq, self.ds.ia.telescope, skyunits='altaz', pointing_info=None, pointing_center=None, freq_scale='Hz', east2ax1=0.0) beam = beam.reshape(-1,1) * NP.ones(self.f.size).reshape(1,-1) else: theta, phi = HP.pix2ang(nside, NP.arange(HP.nside2npix(nside))) alt = 90.0 - NP.degrees(theta) az = NP.degrees(phi) altaz = NP.hstack((alt.reshape(-1,1), az.reshape(-1,1))) beam = PB.primary_beam_generator(altaz, self.f, self.ds.ia.telescope, freq_scale='Hz', skyunits='altaz', east2ax1=0.0, pointing_info=None, pointing_center=None) omega_bw = beam3Dvol(beam, self.f, freq_wts=freq_wts, hemisphere=True) return omega_bw ############################################################################ def compute_power_spectrum(self): """ ------------------------------------------------------------------------ Compute delay power spectrum in units of K^2 (Mpc/h)^3 from the delay spectrum in units of Jy Hz. ------------------------------------------------------------------------ """ self.dps = {} factor = self.jacobian1 * self.jacobian2 * self.Jy2K**2 if self.ds.skyvis_lag is not None: self.dps['skyvis'] = NP.abs(self.ds.skyvis_lag)**2 * factor if self.ds.vis_lag is not None: self.dps['vis'] = NP.abs(self.ds.vis_lag)**2 * factor if self.ds.vis_noise_lag is not None: self.dps['noise'] = NP.abs(self.ds.vis_noise_lag)**2 * factor if self.ds.cc_lags is not None: if self.ds.cc_skyvis_lag is not None: self.dps['cc_skyvis'] = NP.abs(self.ds.cc_skyvis_lag)**2 * factor if self.ds.cc_vis_lag is not None: self.dps['cc_vis'] = NP.abs(self.ds.cc_vis_lag)**2 * factor if self.ds.cc_skyvis_res_lag is not None: self.dps['cc_skyvis_res'] = NP.abs(self.ds.cc_skyvis_res_lag)**2 * factor if self.ds.cc_vis_res_lag is not None: self.dps['cc_vis_res'] = NP.abs(self.ds.cc_vis_res_lag)**2 * factor if self.ds.cc_skyvis_net_lag is not None: self.dps['cc_skyvis_net'] = NP.abs(self.ds.cc_skyvis_net_lag)**2 * factor if self.ds.cc_vis_net_lag is not None: self.dps['cc_vis_net'] = NP.abs(self.ds.cc_vis_net_lag)**2 * factor if self.ds.subband_delay_spectra: for key in self.ds.subband_delay_spectra: self.subband_delay_power_spectra[key] = {} wl = FCNST.c / self.ds.subband_delay_spectra[key]['freq_center'] self.subband_delay_power_spectra[key]['z'] = CNST.rest_freq_HI / self.ds.subband_delay_spectra[key]['freq_center'] - 1 self.subband_delay_power_spectra[key]['dz'] = CNST.rest_freq_HI / self.ds.subband_delay_spectra[key]['freq_center']**2 * self.ds.subband_delay_spectra[key]['bw_eff'] kprll = NP.empty((self.ds.subband_delay_spectra[key]['freq_center'].size, self.ds.subband_delay_spectra[key]['lags'].size)) kperp = NP.empty((self.ds.subband_delay_spectra[key]['freq_center'].size, self.bl_length.size)) horizon_kprll_limits = NP.empty((self.ds.n_acc, self.ds.subband_delay_spectra[key]['freq_center'].size, self.bl_length.size, 2)) for zind,z in enumerate(self.subband_delay_power_spectra[key]['z']): kprll[zind,:] = self.k_parallel(self.ds.subband_delay_spectra[key]['lags'], z, action='return') kperp[zind,:] = self.k_perp(self.bl_length, z, action='return') horizon_kprll_limits[:,zind,:,:] = self.k_parallel(self.ds.horizon_delay_limits, z, action='return') self.subband_delay_power_spectra[key]['kprll'] = kprll self.subband_delay_power_spectra[key]['kperp'] = kperp self.subband_delay_power_spectra[key]['horizon_kprll_limits'] = horizon_kprll_limits self.subband_delay_power_spectra[key]['rz_los'] = self.cosmo.comoving_distance(self.subband_delay_power_spectra[key]['z']).to('Mpc').value # in Mpc/h self.subband_delay_power_spectra[key]['rz_transverse'] = self.comoving_transverse_distance(self.subband_delay_power_spectra[key]['z'], action='return') # in Mpc/h self.subband_delay_power_spectra[key]['drz_los'] = self.comoving_los_depth(self.ds.subband_delay_spectra[key]['bw_eff'], self.subband_delay_power_spectra[key]['z'], action='return') # self.subband_delay_power_spectra[key]['jacobian1'] = NP.mean(self.ds.ia.A_eff) / wl**2 / self.ds.subband_delay_spectra[key]['bw_eff'] omega_bw = self.beam3Dvol(freq_wts=self.ds.subband_delay_spectra[key]['freq_wts']) self.subband_delay_power_spectra[key]['jacobian1'] = 1 / omega_bw # self.subband_delay_power_spectra[key]['jacobian2'] = self.subband_delay_power_spectra[key]['rz_transverse']**2 * self.subband_delay_power_spectra[key]['drz_los'] / self.ds.subband_delay_spectra[key]['bw_eff'] self.subband_delay_power_spectra[key]['jacobian2'] = self.subband_delay_power_spectra[key]['rz_los']**2 * self.subband_delay_power_spectra[key]['drz_los'] / self.ds.subband_delay_spectra[key]['bw_eff'] self.subband_delay_power_spectra[key]['Jy2K'] = wl**2 * CNST.Jy / (2*FCNST.k) self.subband_delay_power_spectra[key]['factor'] = self.subband_delay_power_spectra[key]['jacobian1'] * self.subband_delay_power_spectra[key]['jacobian2'] * self.subband_delay_power_spectra[key]['Jy2K']**2 conversion_factor = self.subband_delay_power_spectra[key]['factor'].reshape(1,-1,1,1) self.subband_delay_power_spectra[key]['skyvis_lag'] = NP.abs(self.ds.subband_delay_spectra[key]['skyvis_lag'])**2 * conversion_factor self.subband_delay_power_spectra[key]['vis_lag'] = NP.abs(self.ds.subband_delay_spectra[key]['vis_lag'])**2 * conversion_factor if key == 'cc': self.subband_delay_power_spectra[key]['skyvis_res_lag'] = NP.abs(self.ds.subband_delay_spectra[key]['skyvis_res_lag'])**2 * conversion_factor self.subband_delay_power_spectra[key]['vis_res_lag'] = NP.abs(self.ds.subband_delay_spectra[key]['vis_res_lag'])**2 * conversion_factor self.subband_delay_power_spectra[key]['skyvis_net_lag'] = NP.abs(self.ds.subband_delay_spectra[key]['skyvis_net_lag'])**2 * conversion_factor self.subband_delay_power_spectra[key]['vis_net_lag'] = NP.abs(self.ds.subband_delay_spectra[key]['vis_net_lag'])**2 * conversion_factor else: self.subband_delay_power_spectra[key]['vis_noise_lag'] = NP.abs(self.ds.subband_delay_spectra[key]['vis_noise_lag'])**2 * conversion_factor if self.ds.subband_delay_spectra_resampled: for key in self.ds.subband_delay_spectra_resampled: self.subband_delay_power_spectra_resampled[key] = {} kprll = NP.empty((self.ds.subband_delay_spectra_resampled[key]['freq_center'].size, self.ds.subband_delay_spectra_resampled[key]['lags'].size)) kperp = NP.empty((self.ds.subband_delay_spectra_resampled[key]['freq_center'].size, self.bl_length.size)) horizon_kprll_limits = NP.empty((self.ds.n_acc, self.ds.subband_delay_spectra_resampled[key]['freq_center'].size, self.bl_length.size, 2)) for zind,z in enumerate(self.subband_delay_power_spectra[key]['z']): kprll[zind,:] = self.k_parallel(self.ds.subband_delay_spectra_resampled[key]['lags'], z, action='return') kperp[zind,:] = self.k_perp(self.bl_length, z, action='return') horizon_kprll_limits[:,zind,:,:] = self.k_parallel(self.ds.horizon_delay_limits, z, action='return') self.subband_delay_power_spectra_resampled[key]['kprll'] = kprll self.subband_delay_power_spectra_resampled[key]['kperp'] = kperp self.subband_delay_power_spectra_resampled[key]['horizon_kprll_limits'] = horizon_kprll_limits conversion_factor = self.subband_delay_power_spectra[key]['factor'].reshape(1,-1,1,1) self.subband_delay_power_spectra_resampled[key]['skyvis_lag'] = NP.abs(self.ds.subband_delay_spectra_resampled[key]['skyvis_lag'])**2 * conversion_factor self.subband_delay_power_spectra_resampled[key]['vis_lag'] = NP.abs(self.ds.subband_delay_spectra_resampled[key]['vis_lag'])**2 * conversion_factor if key == 'cc': self.subband_delay_power_spectra_resampled[key]['skyvis_res_lag'] = NP.abs(self.ds.subband_delay_spectra_resampled[key]['skyvis_res_lag'])**2 * conversion_factor self.subband_delay_power_spectra_resampled[key]['vis_res_lag'] = NP.abs(self.ds.subband_delay_spectra_resampled[key]['vis_res_lag'])**2 * conversion_factor self.subband_delay_power_spectra_resampled[key]['skyvis_net_lag'] = NP.abs(self.ds.subband_delay_spectra_resampled[key]['skyvis_net_lag'])**2 * conversion_factor self.subband_delay_power_spectra_resampled[key]['vis_net_lag'] = NP.abs(self.ds.subband_delay_spectra_resampled[key]['vis_net_lag'])**2 * conversion_factor else: self.subband_delay_power_spectra_resampled[key]['vis_noise_lag'] = NP.abs(self.ds.subband_delay_spectra_resampled[key]['vis_noise_lag'])**2 * conversion_factor ############################################################################ def compute_power_spectrum_allruns(self, dspec, subband=False): """ ------------------------------------------------------------------------ Compute delay power spectrum in units of K^2 (Mpc/h)^3 from the delay spectrum in units of Jy Hz from multiple runs of visibilities Inputs: dspec [dictionary] Delay spectrum information. If subband is set to False, it contains the keys 'vislag1' and maybe 'vislag2' (optional). If subband is set to True, it must contain these keys as well - 'lags', 'freq_center', 'bw_eff', 'freq_wts' as well. The value under these keys are described below: 'vislag1' [numpy array] subband delay spectra of first set of visibilities. It is of size n_win x (n1xn2x... n_runs dims) x n_bl x nlags x n_t if subband is set to True or of shape (n1xn2x... n_runs dims) x n_bl x nlags x n_t if subband is set to False It must be specified independent of subband value 'vislag2' [numpy array] subband delay spectra of second set of visibilities (optional). If not specified, value under key 'vislag1' is copied under this key and auto-delay spectrum is computed. If explicitly specified, it must be of same shape as value under 'vislag1' and cross-delay spectrum will be computed. It is of size n_win x (n1xn2x... n_runs dims) x n_bl x nlags x n_t if subband is set to True or of shape (n1xn2x... n_runs dims) x n_bl x nlags x n_t if subband is set to False. It is applicable independent of value of input subband 'lags' [numpy array] Contains the lags in the delay spectrum. Applicable only if subband is set to True. It is of size nlags 'freq_center' [numpy array] frequency centers (in Hz) of the selected frequency windows for subband delay transform of visibilities. The values can be a scalar, list or numpy array. Applicable only if subband is set to True. It is of size n_win 'bw_eff' [scalar, list or numpy array] effective bandwidths (in Hz) on the selected frequency windows for subband delay transform of visibilities. The values can be a scalar, list or numpy array. Applicable only if subband is set to True. It is of size n_win 'freq_wts' [numpy array] Contains frequency weights applied on each frequency sub-band during the subband delay transform. It is of size n_win x nchan. Applicable only if subband is set to True. subband [boolean] If set to False (default), the entire band is used in determining the delay power spectrum and only value under key 'vislag1' and optional key 'vislag2' in input dspec is required. If set to True, delay pwoer spectrum in specified subbands is determined. In addition to key 'vislag1' and optional key 'vislag2', following keys are also required in input dictionary dspec, namely, 'lags', 'freq_center', 'bw_eff', 'freq_wts' Output: Dictionary containing delay power spectrum (in units of K^2 (Mpc/h)^3) of shape (n1xn2x... n_runs dims) x n_bl x nlags x n_t under key 'fullband' if subband is set to False or of shape n_win x (n1xn2x... n_runs dims) x n_bl x nlags x n_t under key 'subband' if subband is set to True. ------------------------------------------------------------------------ """ try: dspec except NameError: raise NameError('Input dspec must be specified') if not isinstance(dspec, dict): raise TypeError('Input dspec must be a dictionary') else: mode = 'auto' if 'vislag1' not in dspec: raise KeyError('Key "vislag1" not found in input dspec') if not isinstance(dspec['vislag1'], NP.ndarray): raise TypeError('Value under key "vislag1" must be a numpy array') if 'vislag2' not in dspec: dspec['vislag2'] = dspec['vislag1'] else: mode = 'cross' if not isinstance(dspec['vislag2'], NP.ndarray): raise TypeError('Value under key "vislag2" must be a numpy array') if dspec['vislag1'].shape != dspec['vislag2'].shape: raise ValueError('Value under keys "vislag1" and "vislag2" must have same shape') if not isinstance(subband, bool): raise TypeError('Input subband must be boolean') dps = {} if not subband: factor = self.jacobian1 * self.jacobian2 * self.Jy2K**2 # scalar factor = factor.reshape(tuple(NP.ones(dspec['vislag1'].ndim, dtype=NP.int))) key = 'fullband' else: dspec['freq_center'] = NP.asarray(dspec['freq_center']).ravel() # n_win dspec['bw_eff'] = NP.asarray(dspec['bw_eff']).ravel() # n_win wl = FCNST.c / dspec['freq_center'] # n_win redshift = CNST.rest_freq_HI / dspec['freq_center'] - 1 # n_win dz = CNST.rest_freq_HI / dspec['freq_center']**2 * dspec['bw_eff'] # n_win kprll = NP.empty((dspec['freq_center'].size, dspec['lags'].size)) # n_win x nlags kperp = NP.empty((dspec['freq_center'].size, self.bl_length.size)) # n_win x nbl for zind,z in enumerate(redshift): kprll[zind,:] = self.k_parallel(dspec['lags'], z, action='return') kperp[zind,:] = self.k_perp(self.bl_length, z, action='return') rz_los = self.cosmo.comoving_distance(redshift).to('Mpc').value rz_transverse = self.comoving_transverse_distance(redshift, action='return') # n_win drz_los = self.comoving_los_depth(dspec['bw_eff'], redshift, action='return') # n_win omega_bw = self.beam3Dvol(freq_wts=NP.squeeze(dspec['freq_wts'])) jacobian1 = 1 / omega_bw # n_win # jacobian2 = rz_transverse**2 * drz_los / dspec['bw_eff'] # n_win jacobian2 = rz_los**2 * drz_los / dspec['bw_eff'] # n_win Jy2K = wl**2 * CNST.Jy / (2*FCNST.k) # n_win factor = jacobian1 * jacobian2 * Jy2K**2 # n_win factor = factor.reshape((-1,)+tuple(NP.ones(dspec['vislag1'].ndim-1, dtype=NP.int))) key = 'subband' dps[key] = dspec['vislag1'] * dspec['vislag2'].conj() * factor dps[key] = dps[key].real if mode == 'cross': dps[key] *= 2 return dps ############################################################################ def compute_individual_closure_phase_power_spectrum(self, closure_phase_delay_spectra): """ ------------------------------------------------------------------------ Compute delay power spectrum of closure phase in units of Mpc/h from the delay spectrum in units of Hz Inputs: closure_phase_delay_spectra [dictionary] contains information about closure phase delay spectra of different frequency sub-bands (n_win in number) under the following keys: 'antenna_triplets' [list of tuples] List of antenna ID triplets where each triplet is given as a tuple. Closure phase delay spectra in subbands is computed for each of these antenna triplets 'baseline_triplets' [numpy array] List of 3x3 numpy arrays. Each 3x3 unit in the list represents triplets of baseline vectors where the three rows denote the three baselines in the triplet and the three columns define the x-, y- and z-components of the triplet. The number of 3x3 unit elements in the list will equal the number of elements in the list under key 'antenna_triplets'. Closure phase delay spectra in subbands is computed for each of these baseline triplets which correspond to the antenna triplets 'freq_center' [numpy array] contains the center frequencies (in Hz) of the frequency subbands of the subband delay spectra. It is of size n_win. It is roughly equivalent to redshift(s) 'bw_eff' [numpy array] contains the effective bandwidths (in Hz) of the subbands being delay transformed. It is of size n_win. It is roughly equivalent to width in redshift or along line-of-sight 'lags' [numpy array] lags of the resampled subband delay spectra after padding in frequency during the transform. It is of size nlags where nlags is the number of independent delay bins 'lag_kernel' [numpy array] delay transform of the frequency weights under the key 'freq_wts'. It is of size n_bl x n_win x nlags x n_t. 'lag_corr_length' [numpy array] It is the correlation timescale (in pixels) of the resampled subband delay spectra. It is proportional to inverse of effective bandwidth. It is of size n_win. The unit size of a pixel is determined by the difference between adjacent pixels in lags under key 'lags' which in turn is effectively inverse of the effective bandwidth 'closure_phase_skyvis' (optional) [numpy array] subband delay spectra of closure phases of noiseless sky visiblities from the specified antenna triplets. It is of size n_triplets x n_win x nlags x n_t. It must be in units of Hz. 'closure_phase_vis' (optional) [numpy array] subband delay spectra of closure phases of noisy sky visiblities from the specified antenna triplets. It is of size n_triplets x n_win x nlags x n_t. It must be in units of Hz. 'closure_phase_noise' (optional) [numpy array] subband delay spectra of closure phases of noise visiblities from the specified antenna triplets. It is of size n_triplets x n_win x nlags x n_t. It must be in units of Hz. Output: Dictionary with closure phase delay power spectra containing the following keys and values: 'z' [numpy array] Redshifts corresponding to the centers of the frequency subbands. Same size as number of values under key 'freq_center' which is n_win 'kprll' [numpy array] k_parallel (h/Mpc) for different subbands and various delays. It is of size n_win x nlags 'kperp' [numpy array] k_perp (h/Mpc) for different subbands and the antenna/baseline triplets. It is of size n_win x n_triplets x 3 x 3 where the 3 x 3 refers to 3 different baselines and 3 components of the baseline vector respectively 'horizon_kprll_limits' [numpy array] limits on k_parallel corresponding to limits on horizon delays for each of the baseline triplets and subbands. It is of shape n_t x n_win x n_triplets x 3 x 2, where 3 is for the three baselines involved in the triplet, 2 limits (upper and lower). It has units of h/Mpc 'closure_phase_skyvis' [numpy array] subband delay power spectra of closure phases of noiseless sky visiblities from the specified antenna triplets. It is of size n_triplets x n_win x nlags x n_t. It is in units of Mpc/h. This is returned if this key is present in the input closure_phase_delay_spectra 'closure_phase_vis' [numpy array] subband delay power spectra of closure phases of noisy sky visiblities from the specified antenna triplets. It is of size n_triplets x n_win x nlags x n_t. It is in units of Mpc/h. This is returned if this key is present in the input closure_phase_delay_spectra 'closure_phase_noise' [numpy array] subband delay power spectra of closure phases of noise visiblities from the specified antenna triplets. It is of size n_triplets x n_win x nlags x n_t. It is in units of Mpc/h. This is returned if this key is present in the input closure_phase_delay_spectra ------------------------------------------------------------------------ """ try: closure_phase_delay_spectra except NameError: raise NameError('Input closure_phase_delay_spectra must be provided') closure_phase_delay_power_spectra = {} wl = FCNST.c / closure_phase_delay_spectra['freq_center'] z = CNST.rest_freq_HI / closure_phase_delay_spectra['freq_center'] - 1 dz = CNST.rest_freq_HI / closure_phase_delay_spectra['freq_center']**2 * closure_phase_delay_spectra['bw_eff'] kprll = NP.empty((closure_phase_delay_spectra['freq_center'].size, closure_phase_delay_spectra['lags'].size)) kperp = NP.empty((closure_phase_delay_spectra['freq_center'].size, len(closure_phase_delay_spectra['antenna_triplets']), 3)) # n_win x n_triplets x 3, where 3 is for the three baselines involved horizon_kprll_limits = NP.empty((self.ds.n_acc, closure_phase_delay_spectra['freq_center'].size, len(closure_phase_delay_spectra['antenna_triplets']), 3, 2)) # n_t x n_win x n_triplets x 3 x 2, where 3 is for the three baselines involved for zind,redshift in enumerate(z): kprll[zind,:] = self.k_parallel(closure_phase_delay_spectra['lags'], redshift, action='return') for triplet_ind, ant_triplet in enumerate(closure_phase_delay_spectra['antenna_triplets']): bl_lengths = NP.sqrt(NP.sum(closure_phase_delay_spectra['baseline_triplets'][triplet_ind]**2, axis=1)) kperp[zind,triplet_ind,:] = self.k_perp(bl_lengths, redshift, action='return') horizon_delay_limits = bl_lengths.reshape(1,-1,1) / FCNST.c # 1x3x1, where 1 phase center, 3 is for the three baselines involved in the triplet, 1 upper limit horizon_delay_limits = NP.concatenate((horizon_delay_limits, -horizon_delay_limits), axis=2) # 1x3x2, where 1 phase center, 3 is for the three baselines involved in the triplet, 2 limits (upper and lower) horizon_kprll_limits[:,zind,triplet_ind,:,:] = self.k_parallel(horizon_delay_limits, redshift, action='return') # 1 x n_win x n_triplets x 3 x 2, where 1 phase center, 3 is for the three baselines involved in the triplet, 2 limits (upper and lower) closure_phase_delay_power_spectra['z'] = z closure_phase_delay_power_spectra['kprll'] = kprll closure_phase_delay_power_spectra['kperp'] = kperp closure_phase_delay_power_spectra['horizon_kprll_limits'] = horizon_kprll_limits # rz_transverse = self.comoving_transverse_distance(closure_phase_delay_power_spectra['z'], action='return') drz_los = self.comoving_los_depth(closure_phase_delay_spectra['bw_eff'], closure_phase_delay_power_spectra['z'], action='return') # omega_bw = self.beam3Dvol(freq_wts=closure_phase_delay_spectra['freq_wts']) # jacobian1 = 1 / omega_bw # jacobian2 = rz_transverse**2 * drz_los / closure_phase_delay_spectra['bw_eff'] # Jy2K = wl**2 * CNST.Jy / (2*FCNST.k) jacobian1 = 1 / closure_phase_delay_spectra['bw_eff'] jacobian2 = drz_los / closure_phase_delay_spectra['bw_eff'] factor = jacobian1 * jacobian2 conversion_factor = factor.reshape(1,-1,1,1) for key in ['closure_phase_skyvis', 'closure_phase_vis', 'closure_phase_noise']: if key in closure_phase_delay_spectra: closure_phase_delay_power_spectra[key] = NP.abs(closure_phase_delay_spectra[key])**2 * conversion_factor return closure_phase_delay_power_spectra ############################################################################ def compute_averaged_closure_phase_power_spectrum(self, closure_phase_delay_spectra): """ ------------------------------------------------------------------------ Compute delay power spectrum of closure phase in units of Mpc/h from the delay spectrum in units of Jy Hz and average over 'auto' and 'cross' modes Inputs: closure_phase_delay_spectra [dictionary] contains information about closure phase delay spectra of different frequency sub-bands (n_win in number) under the following keys: 'antenna_triplets' [list of tuples] List of antenna ID triplets where each triplet is given as a tuple. Closure phase delay spectra in subbands is computed for each of these antenna triplets 'baseline_triplets' [numpy array] List of 3x3 numpy arrays. Each 3x3 unit in the list represents triplets of baseline vectors where the three rows denote the three baselines in the triplet and the three columns define the x-, y- and z-components of the triplet. The number of 3x3 unit elements in the list will equal the number of elements in the list under key 'antenna_triplets'. Closure phase delay spectra in subbands is computed for each of these baseline triplets which correspond to the antenna triplets 'freq_center' [numpy array] contains the center frequencies (in Hz) of the frequency subbands of the subband delay spectra. It is of size n_win. It is roughly equivalent to redshift(s) 'bw_eff' [numpy array] contains the effective bandwidths (in Hz) of the subbands being delay transformed. It is of size n_win. It is roughly equivalent to width in redshift or along line-of-sight 'lags' [numpy array] lags of the resampled subband delay spectra after padding in frequency during the transform. It is of size nlags where nlags is the number of independent delay bins 'lag_kernel' [numpy array] delay transform of the frequency weights under the key 'freq_wts'. It is of size n_bl x n_win x nlags x n_t. 'lag_corr_length' [numpy array] It is the correlation timescale (in pixels) of the resampled subband delay spectra. It is proportional to inverse of effective bandwidth. It is of size n_win. The unit size of a pixel is determined by the difference between adjacent pixels in lags under key 'lags' which in turn is effectively inverse of the effective bandwidth 'closure_phase_skyvis' (optional) [numpy array] subband delay spectra of closure phases of noiseless sky visiblities from the specified antenna triplets. It is of size n_triplets x n_win x nlags x n_t. It must be in units of Hz. 'closure_phase_vis' (optional) [numpy array] subband delay spectra of closure phases of noisy sky visiblities from the specified antenna triplets. It is of size n_triplets x n_win x nlags x n_t. It must be in units of Hz. 'closure_phase_noise' (optional) [numpy array] subband delay spectra of closure phases of noise visiblities from the specified antenna triplets. It is of size n_triplets x n_win x nlags x n_t. It must be in units of Hz. Output: Dictionary with closure phase delay power spectra containing the following keys and values: 'z' [numpy array] Redshifts corresponding to the centers of the frequency subbands. Same size as number of values under key 'freq_center' which is n_win 'kprll' [numpy array] k_parallel (h/Mpc) for different subbands and various delays. It is of size n_win x nlags 'kperp' [numpy array] k_perp (h/Mpc) for different subbands and the antenna/baseline triplets. It is of size n_win x n_triplets x 3 x 3 where the 3 x 3 refers to 3 different baselines and 3 components of the baseline vector respectively 'horizon_kprll_limits' [numpy array] limits on k_parallel corresponding to limits on horizon delays for each of the baseline triplets and subbands. It is of shape n_t x n_win x n_triplets x 3 x 2, where 3 is for the three baselines involved in the triplet, 2 limits (upper and lower). It has units of h/Mpc 'auto' [dictionary] average of diagonal terms in the power spectrum matrix with possibly the following keys and values: 'closure_phase_skyvis' [numpy array] subband delay power spectra of closure phases of noiseless sky visiblities from the specified antenna triplets. It is of size n_triplets x n_win x nlags x n_t. It is in units of Mpc/h. This is returned if this key is present in the input closure_phase_delay_spectra 'closure_phase_vis' [numpy array] subband delay power spectra of closure phases of noisy sky visiblities from the specified antenna triplets. It is of size 1 x n_win x nlags x n_t. It is in units of Mpc/h. This is returned if this key is present in the input closure_phase_delay_spectra 'closure_phase_noise' [numpy array] subband delay power spectra of closure phases of noise visiblities from the specified antenna triplets. It is of size 1 x n_win x nlags x n_t. It is in units of Mpc/h. This is returned if this key is present in the input closure_phase_delay_spectra 'cross' [dictionary] average of off-diagonal terms in the power spectrum matrix with possibly the following keys and values: 'closure_phase_skyvis' [numpy array] subband delay power spectra of closure phases of noiseless sky visiblities from the specified antenna triplets. It is of size n_triplets x n_win x nlags x n_t. It is in units of Mpc/h. This is returned if this key is present in the input closure_phase_delay_spectra 'closure_phase_vis' [numpy array] subband delay power spectra of closure phases of noisy sky visiblities from the specified antenna triplets. It is of size 1 x n_win x nlags x n_t. It is in units of Mpc/h. This is returned if this key is present in the input closure_phase_delay_spectra 'closure_phase_noise' [numpy array] subband delay power spectra of closure phases of noise visiblities from the specified antenna triplets. It is of size 1 x n_win x nlags x n_t. It is in units of Mpc/h. This is returned if this key is present in the input closure_phase_delay_spectra ------------------------------------------------------------------------ """ try: closure_phase_delay_spectra except NameError: raise NameError('Input closure_phase_delay_spectra must be provided') closure_phase_delay_power_spectra = {} wl = FCNST.c / closure_phase_delay_spectra['freq_center'] z = CNST.rest_freq_HI / closure_phase_delay_spectra['freq_center'] - 1 dz = CNST.rest_freq_HI / closure_phase_delay_spectra['freq_center']**2 * closure_phase_delay_spectra['bw_eff'] kprll = NP.empty((closure_phase_delay_spectra['freq_center'].size, closure_phase_delay_spectra['lags'].size)) kperp = NP.empty((closure_phase_delay_spectra['freq_center'].size, len(closure_phase_delay_spectra['antenna_triplets']), 3)) # n_win x n_triplets x 3, where 3 is for the three baselines involved horizon_kprll_limits = NP.empty((self.ds.n_acc, closure_phase_delay_spectra['freq_center'].size, len(closure_phase_delay_spectra['antenna_triplets']), 3, 2)) # n_t x n_win x n_triplets x 3 x 2, where 3 is for the three baselines involved for zind,redshift in enumerate(z): kprll[zind,:] = self.k_parallel(closure_phase_delay_spectra['lags'], redshift, action='return') for triplet_ind, ant_triplet in enumerate(closure_phase_delay_spectra['antenna_triplets']): bl_lengths = NP.sqrt(NP.sum(closure_phase_delay_spectra['baseline_triplets'][triplet_ind]**2, axis=1)) kperp[zind,triplet_ind,:] = self.k_perp(bl_lengths, redshift, action='return') horizon_delay_limits = bl_lengths.reshape(1,-1,1) / FCNST.c # 1x3x1, where 1 phase center, 3 is for the three baselines involved in the triplet, 1 upper limit horizon_delay_limits = NP.concatenate((horizon_delay_limits, -horizon_delay_limits), axis=2) # 1x3x2, where 1 phase center, 3 is for the three baselines involved in the triplet, 2 limits (upper and lower) horizon_kprll_limits[:,zind,triplet_ind,:,:] = self.k_parallel(horizon_delay_limits, redshift, action='return') # 1 x n_win x n_triplets x 3 x 2, where 1 phase center, 3 is for the three baselines involved in the triplet, 2 limits (upper and lower) closure_phase_delay_power_spectra['z'] = z closure_phase_delay_power_spectra['kprll'] = kprll closure_phase_delay_power_spectra['kperp'] = kperp closure_phase_delay_power_spectra['horizon_kprll_limits'] = horizon_kprll_limits # rz_transverse = self.comoving_transverse_distance(closure_phase_delay_power_spectra['z'], action='return') drz_los = self.comoving_los_depth(closure_phase_delay_spectra['bw_eff'], closure_phase_delay_power_spectra['z'], action='return') # omega_bw = self.beam3Dvol(freq_wts=closure_phase_delay_spectra['freq_wts']) # jacobian1 = 1 / omega_bw # jacobian2 = rz_transverse**2 * drz_los / closure_phase_delay_spectra['bw_eff'] # Jy2K = wl**2 * CNST.Jy / (2*FCNST.k) jacobian1 = 1 / closure_phase_delay_spectra['bw_eff'] jacobian2 = drz_los / closure_phase_delay_spectra['bw_eff'] factor = jacobian1 * jacobian2 for key in ['closure_phase_skyvis', 'closure_phase_vis', 'closure_phase_noise']: if key in closure_phase_delay_spectra: ndim_shape = NP.ones(closure_phase_delay_spectra[key].ndim, dtype=int) ndim_shape[-3] = -1 ndim_shape = tuple(ndim_shape) conversion_factor = factor.reshape(ndim_shape) for mode in ['auto', 'cross']: closure_phase_delay_power_spectra[mode] = {} for key in ['closure_phase_skyvis', 'closure_phase_vis', 'closure_phase_noise']: if key in closure_phase_delay_spectra: nruns = closure_phase_delay_spectra[key].shape[0] if mode == 'auto': closure_phase_delay_power_spectra[mode][key] = NP.mean(NP.abs(closure_phase_delay_spectra[key])**2, axis=0, keepdims=True) * conversion_factor else: closure_phase_delay_power_spectra[mode][key] = 1.0 / (nruns*(nruns-1)) * (conversion_factor * NP.abs(NP.sum(closure_phase_delay_spectra[key], axis=0, keepdims=True))**2 - nruns * closure_phase_delay_power_spectra['auto'][key]) return closure_phase_delay_power_spectra ############################################################################

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null

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5

41d68dfe8dbeed9c7637dbf333fcd1bbebbd7e7b

121

py

Python

rusty/asgi/app.py

justanotherbyte/Rusty

0132c44a99ebc36f31c70482b19161196f41bc5e

[ "MIT" ]

1

2021-09-03T13:03:16.000Z

rusty/asgi/app.py

justanotherbyte/Rusty

0132c44a99ebc36f31c70482b19161196f41bc5e

[ "MIT" ]

null

rusty/asgi/app.py

justanotherbyte/Rusty

0132c44a99ebc36f31c70482b19161196f41bc5e

[ "MIT" ]

1

2021-12-24T12:33:09.000Z

from starlette.applications import Starlette class RustyMain(Starlette): """ Inheritance for asgi spec. """

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41db9cbfbd544f0784150a7a2453e75cfdec8d7e

40

py

Python

tests/__init__.py

Alviner/async_lock

ffa42cb845c4567c997a2a654cda1e31b28097d7

[ "MIT" ]

null

tests/__init__.py

Alviner/async_lock

ffa42cb845c4567c997a2a654cda1e31b28097d7

[ "MIT" ]

null

tests/__init__.py

Alviner/async_lock

ffa42cb845c4567c997a2a654cda1e31b28097d7

[ "MIT" ]

1

2021-09-30T18:31:16.000Z

"""Unit test package for async_lock."""

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5

41f14f8dd9e49c71f29b0a0c4fbd22fe713f65d8

194

py

Python

reactEx/jobs/admin.py

IsDon/isdon-JobShifts

f4cbed32b6f24754153a77f7d47baa8895dbf3a3

[ "MIT" ]

2

2017-04-25T10:44:55.000Z

2020-08-06T12:48:22.000Z

reactEx/jobs/admin.py

IsDon/isdon-JobShifts

f4cbed32b6f24754153a77f7d47baa8895dbf3a3

[ "MIT" ]

null

reactEx/jobs/admin.py

IsDon/isdon-JobShifts

f4cbed32b6f24754153a77f7d47baa8895dbf3a3

[ "MIT" ]

null

from django.contrib import admin from .models import Job, WorkShift, Position # Register your models here. admin.site.register(Job) admin.site.register(WorkShift) admin.site.register(Position)

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41fe5df4e3e91688763c979a535d64bdd5e27c6c

9,448

py

Python

test/test_card.py

furgerf/pyjass

a4270d2a93ec65fc5101e4595c3c0a1361c5ffbe

[ "Apache-2.0" ]

null

test/test_card.py

furgerf/pyjass

a4270d2a93ec65fc5101e4595c3c0a1361c5ffbe

[ "Apache-2.0" ]

6

2020-01-28T22:35:11.000Z

2022-02-10T00:06:37.000Z

test/test_card.py

furgerf/pyjass

a4270d2a93ec65fc5101e4595c3c0a1361c5ffbe

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- from config import Config from unittest import TestCase from card import Card from encoding import Encoding from game_type import GameType from parameterized import parameterized class CardTest(TestCase): # pylint: disable=line-too-long def setUp(self): self.cards_suit_1 = [Card(0, 1), Card(0, 2)] self.cards_suit_2 = [Card(1, 1), Card(1, 2)] @parameterized.expand([ [GameType.OBENABE], [GameType.TRUMP_HEARTS], [GameType.TRUMP_CLUBS], [GameType.TRUMP_DIAMONDS], [GameType.TRUMP_SPADES], ]) def test_has_worse_value_than_obenabe_trump(self, game_type): for card in self.cards_suit_1 + self.cards_suit_2: card.set_game_type(game_type) self.assertFalse(self.cards_suit_1[0].has_worse_value_than(self.cards_suit_1[0])) self.assertTrue(self.cards_suit_1[0].has_worse_value_than(self.cards_suit_1[1])) self.assertFalse(self.cards_suit_1[0].has_worse_value_than(self.cards_suit_2[0])) self.assertTrue(self.cards_suit_1[0].has_worse_value_than(self.cards_suit_2[1])) self.assertFalse(self.cards_suit_1[1].has_worse_value_than(self.cards_suit_1[0])) self.assertFalse(self.cards_suit_1[1].has_worse_value_than(self.cards_suit_1[1])) self.assertFalse(self.cards_suit_1[1].has_worse_value_than(self.cards_suit_2[0])) self.assertFalse(self.cards_suit_1[1].has_worse_value_than(self.cards_suit_2[1])) def test_has_worse_value_than_unnenufe(self): for card in self.cards_suit_1 + self.cards_suit_2: card.set_game_type(GameType.UNNENUFE) self.assertFalse(self.cards_suit_1[0].has_worse_value_than(self.cards_suit_1[0])) self.assertFalse(self.cards_suit_1[0].has_worse_value_than(self.cards_suit_1[1])) self.assertFalse(self.cards_suit_1[0].has_worse_value_than(self.cards_suit_2[0])) self.assertFalse(self.cards_suit_1[0].has_worse_value_than(self.cards_suit_2[1])) self.assertTrue(self.cards_suit_1[1].has_worse_value_than(self.cards_suit_1[0])) self.assertFalse(self.cards_suit_1[1].has_worse_value_than(self.cards_suit_1[1])) self.assertTrue(self.cards_suit_1[1].has_worse_value_than(self.cards_suit_2[0])) self.assertFalse(self.cards_suit_1[1].has_worse_value_than(self.cards_suit_2[1])) def test_is_beaten_by_obenabe(self): for card in self.cards_suit_1 + self.cards_suit_2: card.set_game_type(GameType.OBENABE) self.assertFalse(self.cards_suit_1[0].is_beaten_by(self.cards_suit_1[0])) self.assertTrue(self.cards_suit_1[0].is_beaten_by(self.cards_suit_1[1])) self.assertFalse(self.cards_suit_1[0].is_beaten_by(self.cards_suit_2[0])) self.assertFalse(self.cards_suit_1[0].is_beaten_by(self.cards_suit_2[1])) self.assertFalse(self.cards_suit_1[1].is_beaten_by(self.cards_suit_1[0])) self.assertFalse(self.cards_suit_1[1].is_beaten_by(self.cards_suit_1[1])) self.assertFalse(self.cards_suit_1[1].is_beaten_by(self.cards_suit_2[0])) self.assertFalse(self.cards_suit_1[1].is_beaten_by(self.cards_suit_2[1])) def test_is_beaten_by_unnenufe(self): for card in self.cards_suit_1 + self.cards_suit_2: card.set_game_type(GameType.UNNENUFE) self.assertFalse(self.cards_suit_1[0].is_beaten_by(self.cards_suit_1[0])) self.assertFalse(self.cards_suit_1[0].is_beaten_by(self.cards_suit_1[1])) self.assertFalse(self.cards_suit_1[0].is_beaten_by(self.cards_suit_2[0])) self.assertFalse(self.cards_suit_1[0].is_beaten_by(self.cards_suit_2[1])) self.assertTrue(self.cards_suit_1[1].is_beaten_by(self.cards_suit_1[0])) self.assertFalse(self.cards_suit_1[1].is_beaten_by(self.cards_suit_1[1])) self.assertFalse(self.cards_suit_1[1].is_beaten_by(self.cards_suit_2[0])) self.assertFalse(self.cards_suit_1[1].is_beaten_by(self.cards_suit_2[1])) def test_is_beaten_by_trump(self): non_trump_1 = Card(Card.SPADES, 1) non_trump_2 = Card(Card.CLUBS, 2) normal_trump_1 = Card(Card.HEARTS, 0) normal_trump_2 = Card(Card.HEARTS, 8) nell = Card(Card.HEARTS, 3) buur = Card(Card.HEARTS, 5) cards = [non_trump_1, non_trump_2, normal_trump_1, normal_trump_2, nell, buur] for card in cards: card.set_game_type(GameType.TRUMP_HEARTS) self.assertFalse(non_trump_1.is_trump) self.assertFalse(non_trump_2.is_trump) self.assertTrue(normal_trump_1.is_trump) self.assertTrue(normal_trump_2.is_trump) self.assertTrue(nell.is_trump) self.assertTrue(nell.is_nell) self.assertTrue(buur.is_trump) self.assertTrue(buur.is_buur) # buur beats everyone self.assertFalse(buur.is_beaten_by(non_trump_1)) self.assertFalse(buur.is_beaten_by(normal_trump_1)) self.assertFalse(buur.is_beaten_by(nell)) self.assertTrue(non_trump_1.is_beaten_by(buur)) self.assertTrue(normal_trump_1.is_beaten_by(buur)) self.assertTrue(nell.is_beaten_by(buur)) # nell beats all non-buurs self.assertFalse(nell.is_beaten_by(non_trump_1)) self.assertFalse(nell.is_beaten_by(normal_trump_1)) self.assertTrue(nell.is_beaten_by(buur)) self.assertTrue(non_trump_1.is_beaten_by(nell)) self.assertTrue(normal_trump_1.is_beaten_by(nell)) self.assertFalse(buur.is_beaten_by(nell)) # normal trump beats smaller trump and non-trumps self.assertFalse(normal_trump_2.is_beaten_by(non_trump_1)) self.assertFalse(normal_trump_2.is_beaten_by(normal_trump_1)) self.assertTrue(normal_trump_2.is_beaten_by(nell)) self.assertTrue(normal_trump_2.is_beaten_by(buur)) self.assertTrue(non_trump_1.is_beaten_by(normal_trump_2)) self.assertTrue(normal_trump_1.is_beaten_by(normal_trump_2)) self.assertFalse(nell.is_beaten_by(normal_trump_2)) self.assertFalse(buur.is_beaten_by(normal_trump_2)) # non-trumps are normal and always lose against trumps self.assertFalse(non_trump_1.is_beaten_by(non_trump_2)) self.assertFalse(non_trump_2.is_beaten_by(non_trump_1)) self.assertTrue(non_trump_1.is_beaten_by(normal_trump_1)) self.assertTrue(non_trump_1.is_beaten_by(nell)) self.assertTrue(non_trump_1.is_beaten_by(buur)) def test_card_index_by_suit(self): Config.ENCODING = Encoding("better", [1, 2, 3, 4], 5, 10, 50, 0, 0, order_value=True, card_index_by_suit=True) cards = [ Card(Card.SPADES, 0), Card(Card.SPADES, Card.VALUE_NELL), Card(Card.SPADES, Card.VALUE_BUUR), Card(Card.SPADES, 8), Card(Card.HEARTS, 0), Card(Card.HEARTS, Card.VALUE_NELL), Card(Card.HEARTS, Card.VALUE_BUUR), Card(Card.HEARTS, 8), Card(Card.DIAMONDS, 0), Card(Card.DIAMONDS, Card.VALUE_NELL), Card(Card.DIAMONDS, Card.VALUE_BUUR), Card(Card.DIAMONDS, 8), Card(Card.CLUBS, 0), Card(Card.CLUBS, Card.VALUE_NELL), Card(Card.CLUBS, Card.VALUE_BUUR), Card(Card.CLUBS, 8) ] for card in cards: card.set_game_type(GameType.TRUMP_DIAMONDS) # SPADES: not trump, doesn't get reshuffled self.assertEqual(0, cards[0].card_index) self.assertEqual(3, cards[1].card_index) self.assertEqual(5, cards[2].card_index) self.assertEqual(8, cards[3].card_index) # HEARTS: not trump, doesn't get reshuffled self.assertEqual(0+9, cards[4].card_index) self.assertEqual(3+9, cards[5].card_index) self.assertEqual(5+9, cards[6].card_index) self.assertEqual(8+9, cards[7].card_index) # DIAMONDS: trump, gets reshuffled self.assertEqual(0+18, cards[8].card_index) self.assertEqual(7+18, cards[9].card_index) self.assertEqual(8+18, cards[10].card_index) self.assertEqual(6+18, cards[11].card_index) # CLUBS: not trump, doesn't get reshuffled self.assertEqual(0+27, cards[12].card_index) self.assertEqual(3+27, cards[13].card_index) self.assertEqual(5+27, cards[14].card_index) self.assertEqual(8+27, cards[15].card_index) def test_card_index_by_value(self): Config.ENCODING = Encoding("better", [1, 2, 3, 4], 5, 10, 50, 0, 0, order_value=True, card_index_by_suit=False) cards = [ Card(Card.SPADES, 0), Card(Card.SPADES, Card.VALUE_NELL), Card(Card.SPADES, Card.VALUE_BUUR), Card(Card.SPADES, 8), Card(Card.HEARTS, 0), Card(Card.HEARTS, Card.VALUE_NELL), Card(Card.HEARTS, Card.VALUE_BUUR), Card(Card.HEARTS, 8), Card(Card.DIAMONDS, 0), Card(Card.DIAMONDS, Card.VALUE_NELL), Card(Card.DIAMONDS, Card.VALUE_BUUR), Card(Card.DIAMONDS, 8), Card(Card.CLUBS, 0), Card(Card.CLUBS, Card.VALUE_NELL), Card(Card.CLUBS, Card.VALUE_BUUR), Card(Card.CLUBS, 8) ] for card in cards: card.set_game_type(GameType.TRUMP_DIAMONDS) # SPADES: not trump, doesn't get reshuffled self.assertEqual(0, cards[0].card_index) self.assertEqual(12, cards[1].card_index) self.assertEqual(20, cards[2].card_index) self.assertEqual(32, cards[3].card_index) # HEARTS: not trump, doesn't get reshuffled self.assertEqual(1, cards[4].card_index) self.assertEqual(13, cards[5].card_index) self.assertEqual(21, cards[6].card_index) self.assertEqual(33, cards[7].card_index) # DIAMONDS: trump, gets reshuffled self.assertEqual(2, cards[8].card_index) self.assertEqual(14+16, cards[9].card_index) self.assertEqual(22+12, cards[10].card_index) self.assertEqual(34-8, cards[11].card_index) # CLUBS: not trump, doesn't get reshuffled self.assertEqual(3, cards[12].card_index) self.assertEqual(15, cards[13].card_index) self.assertEqual(23, cards[14].card_index) self.assertEqual(35, cards[15].card_index)

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null

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1

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5

5104fb8d997531a0318e39179723387b2f922d0f

157

py

Python

LABWORK1/api/admin.py

maxovic/summerpractice2019

0b61ca6302f74618a62bad60615c47f29fa531cb

[ "MIT" ]

null

LABWORK1/api/admin.py

maxovic/summerpractice2019

0b61ca6302f74618a62bad60615c47f29fa531cb

[ "MIT" ]

null

LABWORK1/api/admin.py

maxovic/summerpractice2019

0b61ca6302f74618a62bad60615c47f29fa531cb

[ "MIT" ]

null

from django.contrib import admin from .models import TaskList, Task # Register your models here. admin.site.register(TaskList) admin.site.register(Task)

15.7

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157

5.636364

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5

51370945cde16a4afa413cf4a793854fbac1fb3a

60

py

Python

api/models/__init__.py

biancarosa/neural-networks-api

2066a24c7f5af818d14a8cab5cb243ac84a9b3f5

[ "CC-BY-4.0" ]

1

2019-04-26T17:56:57.000Z

api/models/__init__.py

biancarosa/neural-networks-api

2066a24c7f5af818d14a8cab5cb243ac84a9b3f5

[ "CC-BY-4.0" ]

null

api/models/__init__.py

biancarosa/neural-networks-api

2066a24c7f5af818d14a8cab5cb243ac84a9b3f5

[ "CC-BY-4.0" ]

1

2019-04-26T17:57:00.000Z

from api.models.classifier_network import ClassifierNetwork

30

59

0.9

7

60

7.571429

1

0

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60

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60

0.946429

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1

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1

0

1

0

5

5139d41609e1e1347a992528a31aa63d70a3c0c4

419

py

Python

console.py

sjbecque/tetris-python

76d43f332f4299b9acc676dbc13e1e05ea0939ad

[ "MIT" ]

null

console.py

sjbecque/tetris-python

76d43f332f4299b9acc676dbc13e1e05ea0939ad

[ "MIT" ]

null

console.py

sjbecque/tetris-python

76d43f332f4299b9acc676dbc13e1e05ea0939ad

[ "MIT" ]

null

# simple console test environment from tetris.src.engine import Engine from tetris.src.game import Game from tetris.src.tetromino_factory import TetrominoFactory from tetris.src.cube import Cube from tetris.src.cube_sets.cube_set import CubeSet from tetris.src.cube_sets.stones import Stones from tetris.src.cube_sets.tetromino import Tetromino e = Engine(True, True) g = Game() f = TetrominoFactory() t = f.produce()

29.928571

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0.811456

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419

5.153846

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0.208955

0.271642

0.202985

0.18806

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14

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29.928571

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null

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1

0

1

0

5

514a5f5ebb7b85377e2b35edc36c224183b541ed

176

py

Python

scripts/item/consume_2433183.py

Snewmy/swordie

ae01ed4ec0eb20a18730e8cd209eea0b84a8dd17

[ "MIT" ]

null

scripts/item/consume_2433183.py

Snewmy/swordie

ae01ed4ec0eb20a18730e8cd209eea0b84a8dd17

[ "MIT" ]

null

scripts/item/consume_2433183.py

Snewmy/swordie

ae01ed4ec0eb20a18730e8cd209eea0b84a8dd17

[ "MIT" ]

null

# Super Spooky Damage Skin success = sm.addDamageSkin(2433183) if success: sm.chat("The Super Spooky Damage Skin has been added to your account's damage skin collection.")

35.2

100

0.767045

27

176

5

0.703704

0.222222

0.251852

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0

0.047297

0.159091

176

4

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false

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1

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null

1

0

1

0

null

0

5

514b68f62058220ab48544f1a1635fbff41092bc

45

py

Python

examples/hf_transformers/custom/__init__.py

AhmedHussKhalifa/torchdistill

071089765f95aa09da9573039ac2bd54f47cea1e

[ "MIT" ]

576

2020-11-26T03:20:50.000Z

2022-03-31T16:42:49.000Z

examples/hf_transformers/custom/__init__.py

AhmedHussKhalifa/torchdistill

071089765f95aa09da9573039ac2bd54f47cea1e

[ "MIT" ]

24

2020-12-02T12:16:44.000Z

2022-02-17T16:14:49.000Z

examples/hf_transformers/custom/__init__.py

AhmedHussKhalifa/torchdistill

071089765f95aa09da9573039ac2bd54f47cea1e

[ "MIT" ]

60

2020-11-26T03:27:04.000Z

2022-03-30T09:49:00.000Z

from custom import forward_proc, loss, optim

22.5

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5.142857

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0

0.133333

45

1

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0.923077

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1

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null

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1

0

1

0

5

8506c3b6b6c9cd6c3d942e50789fdc2e1d1bcca3

92

py

Python

myProject/tests/testMotors.py

thatguy1234510/NuvuDuckyBotCodebase

1a2d46d990bee7495ca60dff58039bb1c4374357

[ "MIT" ]

2

2019-07-23T20:05:57.000Z

2019-07-23T20:18:38.000Z

myProject/tests/testMotors.py

theloni-monk/NuvuDuckyBotCodebase

1a2d46d990bee7495ca60dff58039bb1c4374357

[ "MIT" ]

null

myProject/tests/testMotors.py

theloni-monk/NuvuDuckyBotCodebase

1a2d46d990bee7495ca60dff58039bb1c4374357

[ "MIT" ]

null

import sys sys.path.append("..") import CORE.motor #WRITEME: just have it drive in a square

18.4

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0

0.141304

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true

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1

0

null

0

1

0

1

0

1

0

5

517e993506eea7ff6d8b92b937db4303a7139397

163

py

Python

Super_users/admin.py

amirhRahimi1993/info

29e3e356e37d37415c4fb708185c9448f36d33ca

[ "Apache-2.0" ]

null

Super_users/admin.py

amirhRahimi1993/info

29e3e356e37d37415c4fb708185c9448f36d33ca

[ "Apache-2.0" ]

null

Super_users/admin.py

amirhRahimi1993/info

29e3e356e37d37415c4fb708185c9448f36d33ca

[ "Apache-2.0" ]

null

from django.contrib import admin from .models import collector , Dr_info admin.site.register(Dr_info) admin.site.register(collector) # Register your models here.

23.285714

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163

6

40

27.166667

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0

1

0

true

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1

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1

0

null

0

1

0

1

0

5

518873d3c92b94ec5fbfcd4935f34e15dd42d818

199

py

Python

source/__init__.py

jameswind/neuralCT

969829459570c808cceafec665931eb411fff5e2

[ "Apache-2.0" ]

27

2019-10-01T22:59:26.000Z

2020-12-10T14:07:33.000Z

source/__init__.py

jameswind/neuralCT

969829459570c808cceafec665931eb411fff5e2

[ "Apache-2.0" ]

1

2020-03-08T12:11:35.000Z

2020-03-09T08:58:30.000Z

source/__init__.py

jameswind/neuralCT

969829459570c808cceafec665931eb411fff5e2

[ "Apache-2.0" ]

4

2019-10-02T08:13:39.000Z

2021-04-02T14:50:26.000Z

from .gaussian import Gaussian from .multivariateGaussian import MultivariateGaussian from .ringLike import Ring2d,Ring2dNoMomentum from .harmonicChain import HarmonicChain from .source import Source

39.8

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1

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1

0

1

0

5

519b56c3e75ca144edfa8cde14b23897845869f6

641

py

Python

src/data_augmentation/scripts/move.py

duccl/cardiomyopathy-monograph

89056ece724dea3443d01552eb6dd1c0d59b949b

[ "MIT" ]

3

2021-08-18T22:54:10.000Z

2021-09-29T12:47:58.000Z

src/data_augmentation/scripts/move.py

duccl/cardiomyopathy-monograph

89056ece724dea3443d01552eb6dd1c0d59b949b

[ "MIT" ]

null

src/data_augmentation/scripts/move.py

duccl/cardiomyopathy-monograph

89056ece724dea3443d01552eb6dd1c0d59b949b

[ "MIT" ]

null

import cv2 import numpy as np def vertically(image: np.ndarray, value: int) -> np.ndarray: print('Moving image vertically') (height, width) = image.shape[:2] translation_matrix = np.float32([[1, 0, 1], [0, 1, value]]) translated_image = cv2.warpAffine(image, translation_matrix, (width, height)) return translated_image def horizontally(image: np.ndarray, value: int) -> np.ndarray: print('Moving image horizontally') (height, width) = image.shape[:2] translation_matrix = np.float32([[1, 0, value], [0, 1, 1]]) translated_image = cv2.warpAffine(image, translation_matrix, (width, height)) return translated_image

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null

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null

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5

51a907f90f09cdc014342f4d552aa48efc25499e

62

py

Python

cluster/__init__.py

jevandezande/cluster

6207a2622f2d36558853d50f079ef916d84a3e18

[ "MIT" ]

null

cluster/__init__.py

jevandezande/cluster

6207a2622f2d36558853d50f079ef916d84a3e18

[ "MIT" ]

null

cluster/__init__.py

jevandezande/cluster

6207a2622f2d36558853d50f079ef916d84a3e18

[ "MIT" ]

null

from .cluster import Cluster from .cmolecule import CMolecule

20.666667

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0.83871

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62

6.5

0.5

0

0.129032

62

2

33

31

0.962963

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true

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51cf569c7e2d7c6246b6f94d1011fa19c95f5de2

38

py

Python

pyc64/__main__.py

jepebe/c64

89d884b03f4e05019143f1be4b46fd9b7e890ad2

[ "BSD-2-Clause" ]

1

2020-12-11T14:20:20.000Z

pyc64/__main__.py

jepebe/c64

89d884b03f4e05019143f1be4b46fd9b7e890ad2

[ "BSD-2-Clause" ]

null

pyc64/__main__.py

jepebe/c64

89d884b03f4e05019143f1be4b46fd9b7e890ad2

[ "BSD-2-Clause" ]

null

import c64 if __name__ == '__main__':

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38

3.8

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2

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19

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0

0.210526

0

null

0

0.5

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null

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