repo_name stringlengths 6 67 | path stringlengths 5 185 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 1.02k 962k | license stringclasses 15
values |
|---|---|---|---|---|---|
CarterBain/AlephNull | tests/risk/answer_key.py | 3 | 11125 | #
# Copyright 2013 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wr... | apache-2.0 |
yvlasov/ConProbIN | try-ml/plot_regression.py | 1 | 1435 | #!/usr/bin/python
"""
============================
Nearest Neighbors regression
============================
Demonstrate the resolution of a regression problem
using a k-Nearest Neighbor and the interpolation of the
target using both barycenter and constant weights.
"""
print(__doc__)
# Author: Alexandre Gramfort <a... | mit |
Aasmi/scikit-learn | examples/cluster/plot_agglomerative_clustering_metrics.py | 402 | 4492 | """
Agglomerative clustering with different metrics
===============================================
Demonstrates the effect of different metrics on the hierarchical clustering.
The example is engineered to show the effect of the choice of different
metrics. It is applied to waveforms, which can be seen as
high-dimens... | bsd-3-clause |
ua-snap/downscale | snap_scripts/baseline_climatologies/cru_cl20_climatology_preprocess_2km.py | 1 | 7559 | # # # PREPROCESS CRU CL20 1961-1990 CLIMATOLOGY DATA (http://www.cru.uea.ac.uk/cru/data/hrg/tmc)
# # author: Michael Lindgren (malindgren@alaska.edu) -- Sept. 2016
# # # #
import numpy as np
def xyz_to_grid( x, y, z, xi, yi, method='linear', output_dtype=np.float32 ):
'''
interpolate points to a grid. simple wrappe... | mit |
rueberger/brio | brio/misc/plotting.py | 1 | 11003 | """
This model contains various plotting utilities
"""
import time
import itertools
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib as mpl
import string
from .sta import auto_sta, factor
from brio.blocks.layer import LIFLayer
plt.ion()
SEAMAP = mpl.colors.ListedColormap(sns.... | gpl-2.0 |
vrv/tensorflow | tensorflow/contrib/learn/python/learn/tests/dataframe/feeding_queue_runner_test.py | 62 | 5053 | # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | apache-2.0 |
ssaeger/scikit-learn | sklearn/utils/multiclass.py | 40 | 12966 |
# Author: Arnaud Joly, Joel Nothman, Hamzeh Alsalhi
#
# License: BSD 3 clause
"""
Multi-class / multi-label utility function
==========================================
"""
from __future__ import division
from collections import Sequence
from itertools import chain
from scipy.sparse import issparse
from scipy.sparse.... | bsd-3-clause |
jdanbrown/pydatalab | google/datalab/bigquery/_schema.py | 5 | 12449 | # Copyright 2015 Google Inc. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... | apache-2.0 |
schreiberx/sweet | benchmarks_sphere/paper_jrn_sl_exp/test_compare_wt_dt_vs_accuracy_galewsky_M512_6hours_l_n_uv/postprocessing_consolidate_prog_vrt.py | 8 | 6177 | #! /usr/bin/env python3
import sys
import math
from mule.plotting.Plotting import *
from mule.postprocessing.JobsData import *
from mule.postprocessing.JobsDataConsolidate import *
sys.path.append('../')
import pretty_plotting as pp
sys.path.pop()
mule_plotting_usetex(False)
groups = ['runtime.timestepping_method'... | mit |
QuLogic/vispy | vispy/testing/__init__.py | 21 | 2415 | # -*- coding: utf-8 -*-
# Copyright (c) 2015, Vispy Development Team.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
"""
Testing
=======
This module provides functions useful for running tests in vispy.
Tests can be run in a few ways:
* From Python, you can import ``vispy`` and do ``vis... | bsd-3-clause |
madjelan/Data-Science-45min-Intros | ml-basis-expansion-101/kernel.py | 347 | 5100 | """
===================================
Simple 1D Kernel Density Estimation
===================================
This example uses the :class:`sklearn.neighbors.KernelDensity` class to
demonstrate the principles of Kernel Density Estimation in one dimension.
The first plot shows one of the problems with using histogram... | unlicense |
jkleve/Optimization-Algorithms | utils/plot_utils.py | 1 | 2558 | import matplotlib.pyplot as plt # plotting
import matplotlib.mlab as mlab
import numpy as np
np.seterr(divide='ignore', invalid='ignore') # TODO FIX!!!
import sys
sys.path.append("../functions")
import rosenbrock_function
class PlotUtils:
def __init__(self, num_dims, bounds, func):
# you can only plot u... | mit |
davidnmurray/iris | lib/iris/tests/unit/plot/test_pcolor.py | 1 | 2423 | # (C) British Crown Copyright 2014 - 2016, Met Office
#
# This file is part of Iris.
#
# Iris is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the
# Free Software Foundation, either version 3 of the License, or
# (at your option) any l... | gpl-3.0 |
brucexiaok/HyperLPR | hyperlpr/segmentation.py | 1 | 9655 | #coding=utf-8
import cv2
import numpy as np
# from matplotlib import pyplot as plt
import scipy.ndimage.filters as f
import scipy
import time
import scipy.signal as l
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPool2D
from k... | apache-2.0 |
CforED/Machine-Learning | examples/model_selection/grid_search_digits.py | 44 | 2672 | """
============================================================
Parameter estimation using grid search with cross-validation
============================================================
This examples shows how a classifier is optimized by cross-validation,
which is done using the :class:`sklearn.model_selection.GridS... | bsd-3-clause |
karthikvadla16/spark-tk | regression-tests/sparktkregtests/testcases/models/pca_test.py | 12 | 12264 | # vim: set encoding=utf-8
# Copyright (c) 2016 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless require... | apache-2.0 |
CCI-Tools/ect-core | test/util/im/test_cmaps.py | 2 | 3391 | from unittest import TestCase
from cate.util.im.cmaps import get_cmaps
class CmapsTest(TestCase):
def test_get_cmaps_returns_singleton(self):
cmaps = get_cmaps()
self.assertIs(cmaps, get_cmaps())
self.assertIs(cmaps, get_cmaps())
def test_get_cmaps_retruns_equal_size_recs(self):
... | mit |
phiedulxp/tweet_search | experiment/ea/make_tweet_tokenized_data_worker.py | 1 | 2519 | import os
import re
import codecs
import random
import multiprocessing
from multiprocessing import Pool
from tqdm import tqdm
import pandas as pd
import spacy
import json
from bson import json_util
import redis
r = redis.StrictRedis(host='localhost',port=6379, db=0)
import pymongo
client = pymongo.MongoClient(... | mit |
rahuldhote/scikit-learn | sklearn/svm/setup.py | 321 | 3157 | import os
from os.path import join
import numpy
from sklearn._build_utils import get_blas_info
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
config = Configuration('svm', parent_package, top_path)
config.add_subpackage('tests')
# Section L... | bsd-3-clause |
cauchycui/scikit-learn | doc/tutorial/text_analytics/solutions/exercise_01_language_train_model.py | 254 | 2253 | """Build a language detector model
The goal of this exercise is to train a linear classifier on text features
that represent sequences of up to 3 consecutive characters so as to be
recognize natural languages by using the frequencies of short character
sequences as 'fingerprints'.
"""
# Author: Olivier Grisel <olivie... | bsd-3-clause |
ryandougherty/mwa-capstone | MWA_Tools/build/matplotlib/lib/mpl_examples/animation/old_animation/animation_blit_tk.py | 4 | 1202 | # For detailed comments on animation and the techniqes used here, see
# the wiki entry http://www.scipy.org/Cookbook/Matplotlib/Animations
import matplotlib
matplotlib.use('TkAgg')
import sys
import pylab as p
import numpy as npy
import time
ax = p.subplot(111)
canvas = ax.figure.canvas
# create the initial line
x... | gpl-2.0 |
giorgiop/scikit-learn | sklearn/calibration.py | 18 | 19402 | """Calibration of predicted probabilities."""
# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Balazs Kegl <balazs.kegl@gmail.com>
# Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# Mathieu Blondel <mathieu@mblondel.org>
#
# License: BSD 3 clause
from __future__ impo... | bsd-3-clause |
paix120/DataScienceLearningClubActivities | Activity06/jenks.py | 1 | 3584 | #original code from: https://gist.github.com/drewda/1299198
#note: danieljlewis site referenced there doesn't appear to exist anymore
def getJenksBreaks( dataList, numClass ):
print("starting getJenksBreaks function...")
dataList.sort()
mat1 = []
for i in range(0,len(dataList)+1):
temp = []
... | gpl-2.0 |
johnson1228/pymatgen | pymatgen/io/abinit/utils.py | 1 | 28136 | # coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
"""Tools and helper functions for abinit calculations"""
from __future__ import unicode_literals, division, print_function
import os
import six
import re
import collections
import shutil
import operator
import ... | mit |
mifumagalli/mypython | ifu/muse_redux_line.py | 1 | 49249 | """
These are sets of procedures optimised for almost empty fields
but with extended line emission
"""
from __future__ import print_function
def individual_resample(listob,refpath='./',nproc=24):
"""
Loop over each OB and re-run scipost using a final coadded cube as
a reference for WCS. This produc... | gpl-2.0 |
jkoelker/python-eoddata | eoddata/datareader.py | 1 | 9079 | # -*- coding: utf-8 -*-
import os
import logging
import pandas as pd
import pytz
from tzlocal import windows_tz
import appdirs
import ws
LOG = logging.getLogger(__name__)
_TYPE_MAP = {'integer': int,
'unicode': str,
'string': str,
'boolean': bool,
'datetime': '... | mit |
shaunstanislaus/pandashells | pandashells/test/lomb_scargle_lib_tests.py | 10 | 3222 | #! /usr/bin/env python
from unittest import TestCase
import pandas as pd
import numpy as np
from pandashells.lib.lomb_scargle_lib import (
_next_power_two,
_compute_pad,
_compute_params,
lomb_scargle,
)
class NextPowerTwoTest(TestCase):
def test_proper_return(self):
past_100 = _next_powe... | bsd-2-clause |
rknLA/sms-tools | lectures/08-Sound-transformations/plots-code/sineModelFreqScale-orchestra.py | 21 | 2666 | import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import hamming, hanning, triang, blackmanharris, resample
import math
import sys, os, functools, time
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
sys.path.append(os.path.join(os.path.dirname... | agpl-3.0 |
ElOceanografo/PyCWT | pycwt/pycwt.py | 1 | 25290 | '''
Python Continuous Wavelet Transform
===================================
This module implements the Continuous Wavelet Transform (CWT).
Mathematics are taken from Torrence and Compo 1998, and this Python
code is a significantly refactored (though (hopefully!) mathematically
identical) version of their Matlab origin... | gpl-2.0 |
hksonngan/pynopticon | src/em/gauss_mix.py | 4 | 26027 | # /usr/bin/python
# Last Change: Tue Jul 17 11:00 PM 2007 J
"""Module implementing GM, a class which represents Gaussian mixtures.
GM instances can be used to create, sample mixtures. They also provide
different plotting facilities, such as isodensity contour for multi dimensional
models, ellipses of confidence."""
... | gpl-3.0 |
manashmndl/scikit-learn | sklearn/linear_model/tests/test_least_angle.py | 44 | 17033 | import tempfile
import shutil
import os.path as op
import warnings
from nose.tools import assert_equal
import numpy as np
from scipy import linalg
from sklearn.cross_validation import train_test_split
from sklearn.externals import joblib
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.t... | bsd-3-clause |
renewables-ninja/gsee | setup.py | 1 | 1793 | #!/usr/bin/env python
import os
from setuptools import setup, find_packages, Extension
# Sets the __version__ variable
with open("gsee/_version.py", "r") as f:
exec(f.read())
with open("README.md", "r") as f:
long_description = f.read()
# Numpy headers are always required for compliation, but to allow
# `py... | bsd-3-clause |
kellyschrock/apm_planner | libs/mavlink/share/pyshared/pymavlink/examples/mavgraph.py | 29 | 5951 | #!/usr/bin/env python
'''
graph a MAVLink log file
Andrew Tridgell August 2011
'''
import sys, struct, time, os, datetime
import math, re
import pylab, pytz, matplotlib
from math import *
# allow import from the parent directory, where mavlink.py is
sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__f... | agpl-3.0 |
vibhorag/scikit-learn | sklearn/metrics/cluster/__init__.py | 312 | 1322 | """
The :mod:`sklearn.metrics.cluster` submodule contains evaluation metrics for
cluster analysis results. There are two forms of evaluation:
- supervised, which uses a ground truth class values for each sample.
- unsupervised, which does not and measures the 'quality' of the model itself.
"""
from .supervised import ... | bsd-3-clause |
BitTiger-MP/DS502-AI-Engineer | DS502-1702/Jason_course/Week4_Codelab2/class1_softmax_regression.py | 1 | 2305 | import numpy as np
from sklearn import linear_model, datasets
import matplotlib.pyplot as plt
def onehot(y):
n = len(np.unique(y))
m = y.shape[0]
b = np.zeros((m, n))
for i in xrange(m):
b[i, y[i]] = 1
return b
def softmax(X):
return (np.exp(X).T / np.sum(np.exp(X), axis=1)).T
def ... | apache-2.0 |
deepmind/deepmind-research | side_effects_penalties/results_summary.py | 1 | 7853 | # Copyright 2019 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... | apache-2.0 |
BerryAI/Acai | OpenMRS/API/user.py | 1 | 2005 | """
user.py
~~~
This module provides all API for users
:auther: Alexander Z Wang
"""
import sklearn
major_version, minor_version = tuple(sklearn.__version__.split('.')[:2])
if int(minor_version) >= 18 or int(major_version) >= 1:
from sklearn.neural_network import MLPClassifier
user_model_type ... | mit |
gef756/statsmodels | statsmodels/sandbox/tools/mctools.py | 33 | 17175 | '''Helper class for Monte Carlo Studies for (currently) statistical tests
Most of it should also be usable for Bootstrap, and for MC for estimators.
Takes the sample generator, dgb, and the statistical results, statistic,
as functions in the argument.
Author: Josef Perktold (josef-pktd)
License: BSD-3
TODOs, Desig... | bsd-3-clause |
simon-pepin/scikit-learn | examples/ensemble/plot_random_forest_embedding.py | 286 | 3531 | """
=========================================================
Hashing feature transformation using Totally Random Trees
=========================================================
RandomTreesEmbedding provides a way to map data to a
very high-dimensional, sparse representation, which might
be beneficial for classificati... | bsd-3-clause |
cojacoo/echoRD_model | echoRD/hydro_tools.py | 1 | 49640 | #hydro_plot - some python functions for hydrological visualisation
#(cc) jackisch@kit.edu
import numpy as np
import pandas as pd
import scipy as sp
import matplotlib.pyplot as plt
import matplotlib
#TOOLS
# a simple progress bar
import sys, time
try:
from IPython.display import clear_output
have_ipython = True... | gpl-3.0 |
rwhitt2049/trouve | trouve/find_events.py | 1 | 3625 | import numpy as np
import pandas as pd
import toolz
from trouve.events import Events
@toolz.curry
def find_events(condition, period, name='events', transformations=None):
"""Find events based off a condition
Find events based off a ``bool`` conditional array and apply a sequence
of transformation functi... | mit |
cauchycui/scikit-learn | sklearn/semi_supervised/label_propagation.py | 128 | 15312 | # coding=utf8
"""
Label propagation in the context of this module refers to a set of
semisupervised classification algorithms. In the high level, these algorithms
work by forming a fully-connected graph between all points given and solving
for the steady-state distribution of labels at each point.
These algorithms per... | bsd-3-clause |
jlegendary/scikit-learn | examples/cluster/plot_affinity_propagation.py | 349 | 2304 | """
=================================================
Demo of affinity propagation clustering algorithm
=================================================
Reference:
Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
Between Data Points", Science Feb. 2007
"""
print(__doc__)
from sklearn.cluster impor... | bsd-3-clause |
pxzhang94/GAN | GAN/mode_regularized_gan/mode_reg_gan_pytorch.py | 1 | 3606 | import torch
import torch.nn
import torch.nn.functional as nn
import torch.autograd as autograd
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import os
from torch.autograd import Variable
from tensorflow.examples.tutorials.mnist import input_data
... | apache-2.0 |
alexeyum/scikit-learn | sklearn/exceptions.py | 35 | 4329 | """
The :mod:`sklearn.exceptions` module includes all custom warnings and error
classes used across scikit-learn.
"""
__all__ = ['NotFittedError',
'ChangedBehaviorWarning',
'ConvergenceWarning',
'DataConversionWarning',
'DataDimensionalityWarning',
'EfficiencyWarn... | bsd-3-clause |
RTHMaK/RPGOne | doc/examples/features_detection/plot_censure.py | 8 | 1167 | """
========================
CENSURE feature detector
========================
The CENSURE feature detector is a scale-invariant center-surround detector
(CENSURE) that claims to outperform other detectors and is capable of real-time
implementation.
"""
from skimage import data
from skimage import transform as tf
fro... | apache-2.0 |
Christoph/tag-connect | hmm_stuff/CRF.py | 1 | 1847 | from sklearn.metrics import classification_report, confusion_matrix
from sklearn.preprocessing import LabelBinarizer
import sklearn
import pycrfsuite
import pandas as pd
import spacy
nlp = spacy.load('en')
data = pd.read_csv("data/train_small.csv")
test = pd.read_csv("data/test_hand_labeled.csv")
docs = [nlp(row) for... | mit |
larsoner/mne-python | mne/decoding/tests/test_base.py | 12 | 15702 | # Author: Jean-Remi King, <jeanremi.king@gmail.com>
# Marijn van Vliet, <w.m.vanvliet@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
from numpy.testing import (assert_array_equal, assert_array_almost_equal,
assert_equal, assert_allclose, assert_array_less)
import pytest
f... | bsd-3-clause |
hsaputra/tensorflow | tensorflow/contrib/gan/python/estimator/python/gan_estimator_test.py | 10 | 12183 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | apache-2.0 |
cainiaocome/scikit-learn | sklearn/covariance/robust_covariance.py | 198 | 29735 | """
Robust location and covariance estimators.
Here are implemented estimators that are resistant to outliers.
"""
# Author: Virgile Fritsch <virgile.fritsch@inria.fr>
#
# License: BSD 3 clause
import warnings
import numbers
import numpy as np
from scipy import linalg
from scipy.stats import chi2
from . import empir... | bsd-3-clause |
sangwook236/sangwook-library | python/test/language_processing/ocropus_data.py | 2 | 10608 | import time, glob
import numpy as np
import cv2
#import sklearn
#import swl.machine_learning.util as swl_ml_util
#import hangeul_util as hg_util
import text_line_data
# REF [site] >> https://github.com/tmbdev/ocropy
class OcropusTextLineDatasetBase(text_line_data.FileBasedTextLineDatasetBase):
def __init__(self, lab... | gpl-2.0 |
mdeger/nest-simulator | examples/neuronview/neuronview.py | 13 | 10676 | # -*- coding: utf-8 -*-
#
# neuronview.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
... | gpl-2.0 |
sniemi/EuclidVisibleInstrument | sandbox/example_digits.py | 1 | 1213 | # load the digits dataset from scikit-learn
# 901 samples, about 180 samples per class
# the digits represented 0,1,2,3,4
from sklearn import datasets
digits = datasets.load_digits(n_class=4)
data = digits.data # matrix where each row is a vector that represent a digit.
num = digits.target # num[i] is the digit repres... | bsd-2-clause |
depet/scikit-learn | sklearn/decomposition/tests/test_pca.py | 1 | 12507 | import warnings
import numpy as np
from scipy.sparse import csr_matrix
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert... | bsd-3-clause |
tyarkoni/pliers | setup.py | 1 | 1553 | from setuptools import setup, find_packages
import os
extra_setuptools_args = dict(
tests_require=['pytest']
)
thispath, _ = os.path.split(__file__)
ver_file = os.path.join(thispath, 'pliers', 'version.py')
with open(ver_file) as fp:
exec(fp.read(), globals(), locals())
setup(
name="pliers",
versio... | bsd-3-clause |
NunoEdgarGub1/scikit-learn | sklearn/metrics/classification.py | 28 | 67703 | """Metrics to assess performance on classification task given classe prediction
Functions named as ``*_score`` return a scalar value to maximize: the higher
the better
Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
the lower the better
"""
# Authors: Alexandre Gramfort <alexandre.gram... | bsd-3-clause |
verilylifesciences/purplequery | purplequery/dataframe_node.py | 1 | 18815 | # Copyright 2019 Verily Life Sciences LLC
#
# Use of this source code is governed by a BSD-style
# license that can be found in the LICENSE file.
'''All subclasses of DataframeNode'''
import itertools
import operator
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union # noqa: F401
import ... | bsd-3-clause |
rseubert/scikit-learn | sklearn/tests/test_dummy.py | 27 | 17468 | from __future__ import division
import warnings
import numpy as np
import scipy.sparse as sp
from sklearn.base import clone
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import a... | bsd-3-clause |
pythonvietnam/scikit-learn | sklearn/externals/joblib/__init__.py | 72 | 4795 | """ Joblib is a set of tools to provide **lightweight pipelining in
Python**. In particular, joblib offers:
1. transparent disk-caching of the output values and lazy re-evaluation
(memoize pattern)
2. easy simple parallel computing
3. logging and tracing of the execution
Joblib is optimized to be **fast*... | bsd-3-clause |
pnedunuri/scipy | scipy/interpolate/fitpack2.py | 39 | 61117 | """
fitpack --- curve and surface fitting with splines
fitpack is based on a collection of Fortran routines DIERCKX
by P. Dierckx (see http://www.netlib.org/dierckx/) transformed
to double routines by Pearu Peterson.
"""
# Created by Pearu Peterson, June,August 2003
from __future__ import division, print_function, abs... | bsd-3-clause |
BennerLab/atg | atg/data/ensembl.py | 1 | 7239 | """
Find species data in Ensembl, recording genome and annotation URLs.
"""
import os
import sys
import pandas
import ftplib
import string
import atg.config
import atg.data.retrieve
ENSEMBL_SPECIES_INFORMATION = 'ftp://ftp.ensemblgenomes.org/pub/release-35/species.txt'
ENSEMBL_DNA_BASE_LOCATION = string.Template('pub... | gpl-3.0 |
kinghadi/word_cloud | doc/sphinxext/gen_rst.py | 17 | 33207 | """
Example generation for the python wordcloud project. Stolen from scikit-learn with modifications from PyStruct.
Generate the rst files for the examples by iterating over the python
example files.
Hacked to plot every example (not only those that start with 'plot').
"""
from time import time
import os
import shuti... | mit |
jamesp/shallowwater | beta_plane/exoplanet.py | 1 | 4070 | import sys
import numpy as np
import xarray as xr
from tqdm import tqdm
from shallowwater import PeriodicLinearShallowWater
from plotting import plot_wind_arrows
nx = 128*4
ny = 129
nd = 25 # number of days to run
DAY = 86400
RADIUS = 6371e3
PLOT = False
SNAP_DAYS = 5
# # Radius of deformation: Rd = sqrt(2 c /... | mit |
Clyde-fare/scikit-learn | examples/mixture/plot_gmm.py | 248 | 2817 | """
=================================
Gaussian Mixture Model Ellipsoids
=================================
Plot the confidence ellipsoids of a mixture of two Gaussians with EM
and variational Dirichlet process.
Both models have access to five components with which to fit the
data. Note that the EM model will necessari... | bsd-3-clause |
ml-lab/neuralnilm | neuralnilm/data/source.py | 4 | 3500 | from __future__ import print_function, division
from copy import copy
import numpy as np
import pandas as pd
from .batch import Batch
import logging
logger = logging.getLogger(__name__)
class Sequence(object):
"""
Attributes
----------
input : np.ndarray
target : np.ndarray
all_appliances : ... | apache-2.0 |
MJuddBooth/pandas | pandas/tests/extension/base/printing.py | 2 | 1231 | import io
import pytest
import pandas as pd
from pandas import compat
from .base import BaseExtensionTests
class BasePrintingTests(BaseExtensionTests):
"""Tests checking the formatting of your EA when printed."""
@pytest.mark.parametrize("size", ["big", "small"])
def test_array_repr(self, data, size):... | bsd-3-clause |
ganeshgore/myremolab | server/src/test/util/stress.py | 2 | 8986 | #!/usr/bin/env python
#-*-*- encoding: utf-8 -*-*-
#
# Copyright (C) 2005 onwards University of Deusto
# All rights reserved.
#
# This software is licensed as described in the file COPYING, which
# you should have received as part of this distribution.
#
# This software consists of contributions made by many individual... | bsd-2-clause |
Sentient07/scikit-learn | benchmarks/bench_plot_approximate_neighbors.py | 244 | 6011 | """
Benchmark for approximate nearest neighbor search using
locality sensitive hashing forest.
There are two types of benchmarks.
First, accuracy of LSHForest queries are measured for various
hyper-parameters and index sizes.
Second, speed up of LSHForest queries compared to brute force
method in exact nearest neigh... | bsd-3-clause |
tempbottle/Nuitka | tests/library/compile_library.py | 1 | 4896 | #!/usr/bin/env python
# Copyright 2015, Kay Hayen, mailto:kay.hayen@gmail.com
#
# Python test originally created or extracted from other peoples work. The
# parts from me are licensed as below. It is at least Free Softwar where
# it's copied from other people. In these cases, that will normally be
# ... | apache-2.0 |
wzbozon/statsmodels | statsmodels/sandbox/tsa/garch.py | 25 | 52178 | '''general non-linear MLE for time series analysis
idea for general version
------------------------
subclass defines geterrors(parameters) besides loglike,...
and covariance matrix of parameter estimates (e.g. from hessian
or outerproduct of jacobian)
update: I don't really need geterrors directly, but get_h the con... | bsd-3-clause |
harterj/moose | modules/tensor_mechanics/test/tests/drucker_prager/small_deform3.py | 24 | 3894 | #!/usr/bin/env python3
#* This file is part of the MOOSE framework
#* https://www.mooseframework.org
#*
#* All rights reserved, see COPYRIGHT for full restrictions
#* https://github.com/idaholab/moose/blob/master/COPYRIGHT
#*
#* Licensed under LGPL 2.1, please see LICENSE for details
#* https://www.gnu.org/licenses/lgp... | lgpl-2.1 |
a113n/bcbio-nextgen | bcbio/rnaseq/featureCounts.py | 1 | 4334 | import os
import shutil
import bcbio.bam as bam
from bcbio.utils import (file_exists, safe_makedir, append_stem)
from bcbio.pipeline import config_utils
from bcbio.bam import is_paired
from bcbio.provenance import do
from bcbio.distributed.transaction import file_transaction
import bcbio.pipeline.datadict as dd
try:
... | mit |
jorgehog/Deux-kMC | scripts/felix_cav/sequential_analyze.py | 1 | 3360 | import sys
import os
import numpy as np
from os.path import join
from matplotlib.pylab import *
sys.path.append(join(os.getcwd(), ".."))
from parse_h5_output import ParseKMCHDF5
from intercombinatorzor import ICZ
def find_front_pos(heights):
return heights.mean()
#9 14 17 39 43 56 59 61 62 64
def main():
... | gpl-3.0 |
jkarnows/scikit-learn | examples/decomposition/plot_incremental_pca.py | 244 | 1878 | """
===============
Incremental PCA
===============
Incremental principal component analysis (IPCA) is typically used as a
replacement for principal component analysis (PCA) when the dataset to be
decomposed is too large to fit in memory. IPCA builds a low-rank approximation
for the input data using an amount of memo... | bsd-3-clause |
moberweger/deep-prior-pp | src/util/handdetector.py | 1 | 39679 | """Provides a basic hand detector in depth images.
HandDetector provides interface for detecting hands in depth image, by using the center of mass.
Copyright 2015 Markus Oberweger, ICG,
Graz University of Technology <oberweger@icg.tugraz.at>
This file is part of DeepPrior.
DeepPrior is free software: you can redist... | gpl-3.0 |
quheng/scikit-learn | sklearn/utils/tests/test_extmath.py | 70 | 16531 | # Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Denis Engemann <d.engemann@fz-juelich.de>
#
# License: BSD 3 clause
import numpy as np
from scipy import sparse
from scipy import linalg
from scipy import stats
from sklearn.utils.testing import assert_eq... | bsd-3-clause |
dch312/numpy | numpy/lib/polynomial.py | 82 | 37957 | """
Functions to operate on polynomials.
"""
from __future__ import division, absolute_import, print_function
__all__ = ['poly', 'roots', 'polyint', 'polyder', 'polyadd',
'polysub', 'polymul', 'polydiv', 'polyval', 'poly1d',
'polyfit', 'RankWarning']
import re
import warnings
import numpy.core.... | bsd-3-clause |
kaichogami/scikit-learn | sklearn/ensemble/tests/test_partial_dependence.py | 365 | 6996 | """
Testing for the partial dependence module.
"""
import numpy as np
from numpy.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import if_matplotlib
from sklearn.ensemble.partial_dependence import partial_dependence
from sklearn.ensemble.partial_dependence... | bsd-3-clause |
tejasckulkarni/hydrology | Plots/MCMC/distribution_plot.py | 1 | 10969 | __author__ = 'kiruba'
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import checkdam.checkdam as cd
from datetime import timedelta
import scipy.stats as stats
import matplotlib
matplotlib.rc('font', **{'family': 'sans-serif', 'serif': ['Computer Modern Roman']})
matplotlib.rc('text', usetex=Tru... | gpl-3.0 |
ahmadia/bokeh | examples/plotting/file/unemployment.py | 46 | 1846 | from collections import OrderedDict
import numpy as np
from bokeh.plotting import ColumnDataSource, figure, show, output_file
from bokeh.models import HoverTool
from bokeh.sampledata.unemployment1948 import data
# Read in the data with pandas. Convert the year column to string
data['Year'] = [str(x) for x in data['Y... | bsd-3-clause |
OpenDrift/opendrift | opendrift/models/oceandrift.py | 1 | 26233 | # This file is part of OpenDrift.
#
# OpenDrift is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, version 2
#
# OpenDrift is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without ev... | gpl-2.0 |
WindfallLabs/dslw | dslw/addons/_gpd.py | 1 | 8015 | # -*- coding: utf-8 -*-
"""
addons/_gpd.py -- geopandas addons for dslw (not implemented)
Copyright (c) 2016 Garin Wally
MIT License; see LICENSE
"""
from __future__ import unicode_literals
import re
from os.path import commonprefix
import sys
import apsw
from pandas import read_sql
from geopandas import GeoSeries,... | mit |
ChanChiChoi/scikit-learn | benchmarks/bench_plot_nmf.py | 206 | 5890 | """
Benchmarks of Non-Negative Matrix Factorization
"""
from __future__ import print_function
from collections import defaultdict
import gc
from time import time
import numpy as np
from scipy.linalg import norm
from sklearn.decomposition.nmf import NMF, _initialize_nmf
from sklearn.datasets.samples_generator import... | bsd-3-clause |
talevy/hetnet | oldsim/simcode/draw.py | 1 | 1414 | from numpy import sqrt, linspace
import numpy as np
import scipy as sp
from scipy.integrate import odeint
from pylab import plot, axis, show, savefig
import matplotlib.pyplot as plt
from math import pow
import random
from igraph import *
import sys
import glob
from urchinsim import *
def plotdiff(x, y, filepath):
... | bsd-2-clause |
PatrickChrist/scikit-learn | examples/mixture/plot_gmm_sin.py | 248 | 2747 | """
=================================
Gaussian Mixture Model Sine Curve
=================================
This example highlights the advantages of the Dirichlet Process:
complexity control and dealing with sparse data. The dataset is formed
by 100 points loosely spaced following a noisy sine curve. The fit by
the GMM... | bsd-3-clause |
Laurae2/LightGBM | tests/python_package_test/test_engine.py | 1 | 23263 | # coding: utf-8
# pylint: skip-file
import copy
import math
import os
import unittest
import lightgbm as lgb
import random
import numpy as np
from sklearn.datasets import (load_boston, load_breast_cancer, load_digits,
load_iris, load_svmlight_file)
from sklearn.metrics import log_loss, me... | mit |
commaai/panda | tests/safety/test_tesla.py | 1 | 6109 | #!/usr/bin/env python3
import unittest
import numpy as np
from panda import Panda
from panda.tests.safety import libpandasafety_py
import panda.tests.safety.common as common
from panda.tests.safety.common import CANPackerPanda
ANGLE_DELTA_BP = [0., 5., 15.]
ANGLE_DELTA_V = [5., .8, .15] # windup limit
ANGLE_DELTA_... | mit |
nchaparr/Sam_Output_Anls | Cont_Stat.py | 1 | 4094 | from netCDF4 import Dataset
import glob,os.path
import numpy as np
from scipy.interpolate import UnivariateSpline
import matplotlib
import matplotlib.pyplot as plt
#import site
#site.addsitedir('/tera/phil/nchaparr/python')
import nchap_fun as nc
from nchap_class import *
"""
For contour plotting statistics output f... | mit |
alkamid/wiktionary | statPlot.py | 1 | 1816 | import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
from matplotlib.ticker import MultipleLocator
# we should have this plot in Polish and English
terms = { 'pages': {'en': 'pages', 'pl': 'strony'},
'entries': {'en': 'entries', 'pl': 'hasła'},
'ylabel': {'en': 'number of pages ... | mit |
jmcorgan/gnuradio | gr-digital/examples/berawgn.py | 32 | 4886 | #!/usr/bin/env python
#
# Copyright 2012,2013 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3, or (at your optio... | gpl-3.0 |
tawsifkhan/scikit-learn | sklearn/utils/multiclass.py | 83 | 12343 |
# Author: Arnaud Joly, Joel Nothman, Hamzeh Alsalhi
#
# License: BSD 3 clause
"""
Multi-class / multi-label utility function
==========================================
"""
from __future__ import division
from collections import Sequence
from itertools import chain
from scipy.sparse import issparse
from scipy.sparse.... | bsd-3-clause |
bootphon/abx_numpy | examples/2D_normal_example.py | 1 | 1680 | """
This example apply the abx evaluation on 2D data sampled from gaussian distributions (diagonal covariance)
"""
from __future__ import print_function
import abx_numpy
import numpy as np
def sample_data(parameters):
data = []
n_samples = []
for klass in parameters:
sample = np.empty((klass['N']... | gpl-3.0 |
endlessm/chromium-browser | third_party/llvm/libc/AOR_v20.02/math/tools/plot.py | 1 | 1404 | #!/usr/bin/python
# ULP error plot tool.
#
# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
import numpy as np
import matplotlib.pyplot as plt
import sys
import re
# ex... | bsd-3-clause |
rbalda/neural_ocr | env/lib/python2.7/site-packages/scipy/optimize/nonlin.py | 34 | 46681 | r"""
Nonlinear solvers
-----------------
.. currentmodule:: scipy.optimize
This is a collection of general-purpose nonlinear multidimensional
solvers. These solvers find *x* for which *F(x) = 0*. Both *x*
and *F* can be multidimensional.
Routines
~~~~~~~~
Large-scale nonlinear solvers:
.. autosummary::
newto... | mit |
jseabold/scipy | scipy/interpolate/fitpack.py | 16 | 46294 | #!/usr/bin/env python
"""
fitpack (dierckx in netlib) --- A Python-C wrapper to FITPACK (by P. Dierckx).
FITPACK is a collection of FORTRAN programs for curve and surface
fitting with splines and tensor product splines.
See
http://www.cs.kuleuven.ac.be/cwis/research/nalag/research/topics/fitpack.html
... | bsd-3-clause |
freeman-lab/dask | dask/dataframe/core.py | 1 | 41050 | from __future__ import division
from itertools import count
from math import ceil, sqrt
from functools import wraps
import bisect
import os
from toolz import (merge, partial, accumulate, unique, first, dissoc, valmap,
first, partition)
import toolz
from operator import getitem, setitem
from datetime import dat... | bsd-3-clause |
RPGroup-PBoC/gist_pboc_2017 | code/project_pt2_measurement.py | 1 | 6926 | # Import the necessary modules.
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# Image processing utilities
import skimage.io
import skimage.filters
import skimage.segmentation
import skimage.measure
# In this script, we will learn some more principles regarding image processing
# and segmen... | mit |
lthurlow/Network-Grapher | proj/external/numpy-1.7.0/doc/source/conf.py | 7 | 10752 | # -*- coding: utf-8 -*-
import sys, os, re
# Check Sphinx version
import sphinx
if sphinx.__version__ < "1.0.1":
raise RuntimeError("Sphinx 1.0.1 or newer required")
needs_sphinx = '1.0'
# -----------------------------------------------------------------------------
# General configuration
# -------------------... | mit |
anguyen8/cnn-vis | cnn_vis.org.py | 1 | 25133 | import sys
import argparse, os, tempfile
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
from scipy.misc import imresize, imsave, imread
from scipy.ndimage.filters import gaussian_filter
import os
os.environ['GLOG_minloglevel'] = '2' # suprress Caffe verbose prints
import sett... | mit |
ephes/scikit-learn | examples/ensemble/plot_gradient_boosting_quantile.py | 392 | 2114 | """
=====================================================
Prediction Intervals for Gradient Boosting Regression
=====================================================
This example shows how quantile regression can be used
to create prediction intervals.
"""
import numpy as np
import matplotlib.pyplot as plt
from skle... | bsd-3-clause |
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