repo_name stringlengths 7 90 | path stringlengths 5 191 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 976 581k | license stringclasses 15
values |
|---|---|---|---|---|---|
henryiii/rootpy | setup.py | 1 | 4868 | #!/usr/bin/env python
# Copyright 2012 the rootpy developers
# distributed under the terms of the GNU General Public License
import sys
# check Python version
if sys.version_info < (2, 6):
sys.exit("rootpy only supports python 2.6 and above")
# check that ROOT can be imported
try:
import ROOT
except ImportEr... | gpl-3.0 |
devs1991/test_edx_docmode | venv/lib/python2.7/site-packages/sklearn/cluster/tests/test_spectral.py | 2 | 4661 | """Testing for Spectral Clustering methods"""
from cPickle import dumps, loads
import nose
import numpy as np
from numpy.testing import assert_equal
from nose.tools import assert_raises
from scipy import sparse
from sklearn.datasets.samples_generator import make_blobs
from sklearn.utils.testing import assert_greater
... | agpl-3.0 |
cqychen/quants | quants/loaddata/skyeye_ods_tra_day_k.py | 1 | 2243 | #coding=utf8
import tushare as ts;
import pymysql;
import time as dt
from datashape.coretypes import string
from pandas.io.sql import SQLDatabase
import sqlalchemy
import datetime
from sqlalchemy import create_engine
from pandas.io import sql
import threading
import pandas as pd;
import sys
sys.path.append('../') #添加配... | epl-1.0 |
all-umass/metric-learn | metric_learn/mlkr.py | 1 | 5278 | """
Metric Learning for Kernel Regression (MLKR), Weinberger et al.,
MLKR is an algorithm for supervised metric learning, which learns a distance
function by directly minimising the leave-one-out regression error. This
algorithm can also be viewed as a supervised variation of PCA and can be used
for dimensionality red... | mit |
466152112/scikit-learn | sklearn/kernel_ridge.py | 44 | 6504 | """Module :mod:`sklearn.kernel_ridge` implements kernel ridge regression."""
# Authors: Mathieu Blondel <mathieu@mblondel.org>
# Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# License: BSD 3 clause
import numpy as np
from .base import BaseEstimator, RegressorMixin
from .metrics.pairwise import pairwise... | bsd-3-clause |
Vimos/scikit-learn | examples/calibration/plot_compare_calibration.py | 82 | 5012 | """
========================================
Comparison of Calibration of Classifiers
========================================
Well calibrated classifiers are probabilistic classifiers for which the output
of the predict_proba method can be directly interpreted as a confidence level.
For instance a well calibrated (bi... | bsd-3-clause |
clemkoa/scikit-learn | examples/model_selection/plot_precision_recall.py | 7 | 10356 | """
================
Precision-Recall
================
Example of Precision-Recall metric to evaluate classifier output quality.
Precision-Recall is a useful measure of success of prediction when the
classes are very imbalanced. In information retrieval, precision is a
measure of result relevancy, while recall is a m... | bsd-3-clause |
Titan-C/scikit-learn | examples/plot_kernel_ridge_regression.py | 4 | 6351 | """
=============================================
Comparison of kernel ridge regression and SVR
=============================================
Both kernel ridge regression (KRR) and SVR learn a non-linear function by
employing the kernel trick, i.e., they learn a linear function in the space
induced by the respective k... | bsd-3-clause |
prheenan/Research | Personal/EventDetection/OtherMethods/Roduit2012_OpenFovea/main_minimal_working_example.py | 1 | 1950 | # force floating point division. Can still use integer with //
from __future__ import division
# This file is used for importing the common utilities classes.
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.append("../../../../../")
from Research.Personal.EventDetection.OtherMethods import metho... | gpl-3.0 |
jmschrei/scikit-learn | examples/model_selection/plot_train_error_vs_test_error.py | 349 | 2577 | """
=========================
Train error vs Test error
=========================
Illustration of how the performance of an estimator on unseen data (test data)
is not the same as the performance on training data. As the regularization
increases the performance on train decreases while the performance on test
is optim... | bsd-3-clause |
dancingdan/tensorflow | tensorflow/contrib/metrics/python/kernel_tests/histogram_ops_test.py | 24 | 9587 | # Copyright 2016 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 |
jajcayn/pyclits | examples/8-empirical_model.py | 1 | 5518 | """
Examples for pyCliTS -- https://github.com/jajcayn/pyclits
"""
# now, we'll build similar model as in Kondrashov et al., J. Climate, 18, 2005. that is multi-level model based on idea od
# LIM - linear inverse model, so it will be data-based
# import modules
import pyclits as clt
from datetime import date
import... | mit |
Orcuslc/ECSGCC-Data | scripts/data_prep/get_max_daily_load.py | 1 | 1709 | import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import dates as mdt
import datetime as dt
def get_max_load(data_path, date_index_path, max_load_path):
max_load_list = []
data = pd.read_csv(data_path, encoding='gbk')
date_index = pd.read_csv(date_index_path)
for... | gpl-3.0 |
dcolombo/FilFinder | examples/paper_figures/patch_vs_thresh_figure.py | 3 | 1863 | # Licensed under an MIT open source license - see LICENSE
from fil_finder import fil_finder_2D
from astropy.io.fits import getdata
import matplotlib.pyplot as p
img, hdr = getdata("filaments_updatedhdr.fits", header=True)
# Add some noise
import numpy as np
np.random.seed(500)
threshs = [75, 95, 99]
patches = [7, 1... | mit |
lin-credible/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 |
natanaelfneto/kNN-example | src/apps/kNN/views.py | 1 | 1799 | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.core import serializers
from django.views.generic.base import TemplateView
from django.shortcuts import render
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_sp... | mit |
TheCoSMoCompany/biopredyn | Prototype/python/biopredyn/output.py | 1 | 14158 | #!/usr/bin/env python
# coding=utf-8
## @package biopredyn
## Copyright: [2012-2019] Cosmo Tech, All Rights Reserved
## License: BSD 3-Clause
import io, csv
from random import gauss
import signals
import libsbml
import libsedml, libnuml
from matplotlib import pyplot as plt
import colorsys
## Base class for encoding ... | bsd-3-clause |
OrkoHunter/networkx | examples/drawing/atlas.py | 54 | 2609 | #!/usr/bin/env python
"""
Atlas of all graphs of 6 nodes or less.
"""
__author__ = """Aric Hagberg (hagberg@lanl.gov)"""
# Copyright (C) 2004 by
# Aric Hagberg <hagberg@lanl.gov>
# Dan Schult <dschult@colgate.edu>
# Pieter Swart <swart@lanl.gov>
# All rights reserved.
# BSD license.
import networkx... | bsd-3-clause |
karstenw/nodebox-pyobjc | examples/Extended Application/matplotlib/examples/units/units_scatter.py | 1 | 1573 | """
=============
Unit handling
=============
The example below shows support for unit conversions over masked
arrays.
.. only:: builder_html
This example requires :download:`basic_units.py <basic_units.py>`
"""
import numpy as np
import matplotlib.pyplot as plt
from basic_units import secs, hertz, minutes
# no... | mit |
apoorvingle/info-ret | parseW.py | 1 | 5137 | import os
import glob
import multiprocessing
from nltk.util import ngrams
from nltk.stem.porter import PorterStemmer
import ast
import sys
from matplotlib import pyplot
from math import log
#Stemmer to stem the words
ps = PorterStemmer()
def digestData(rawData):
"""generates the ngrams of the given text
|... | gpl-3.0 |
iproduct/course-social-robotics | 11-dnn-keras/venv/Lib/site-packages/pandas/tests/frame/methods/test_rename_axis.py | 4 | 4074 | import numpy as np
import pytest
from pandas import DataFrame, Index, MultiIndex
import pandas._testing as tm
class TestDataFrameRenameAxis:
def test_rename_axis_inplace(self, float_frame):
# GH#15704
expected = float_frame.rename_axis("foo")
result = float_frame.copy()
return_val... | gpl-2.0 |
kiliakis/BLonD-minimal-cpp | python/plot_parameters.py | 2 | 1267 |
# Copyright 2016 CERN. This software is distributed under the
# terms of the GNU General Public Licence version 3 (GPL Version 3),
# copied verbatim in the file LICENCE.md.
# In applying this licence, CERN does not waive the privileges and immunities
# granted to it by virtue of its status as an Intergovernmental Orga... | gpl-3.0 |
mhallsmoore/qstrader | tests/unit/broker/portfolio/test_portfolio.py | 1 | 12720 | import pandas as pd
import pytz
import pytest
from qstrader.broker.portfolio.portfolio import Portfolio
from qstrader.broker.portfolio.portfolio_event import PortfolioEvent
from qstrader.broker.transaction.transaction import Transaction
def test_initial_settings_for_default_portfolio():
"""
Test that the ini... | mit |
louispotok/pandas | pandas/tests/test_panel.py | 1 | 105261 | # -*- coding: utf-8 -*-
# pylint: disable=W0612,E1101
from warnings import catch_warnings
from datetime import datetime
import operator
import pytest
import numpy as np
from pandas.core.dtypes.common import is_float_dtype
from pandas import (Series, DataFrame, Index, date_range, isna, notna,
pivo... | bsd-3-clause |
drpngx/tensorflow | tensorflow/contrib/learn/python/learn/preprocessing/tests/categorical_test.py | 137 | 2219 | # encoding: utf-8
# Copyright 2016 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 r... | apache-2.0 |
gautamkmr/incubator-mxnet | example/ssd/detect/detector.py | 7 | 7047 | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | apache-2.0 |
Kate-Willett/HadISDH_Build | gridbox_sampling_uncertainty.py | 2 | 34644 | # python 3
#
# Author: Kate Willett
# Created: 18 January 2019
# Last update: 24 January 2019
# Location: /data/local/hadkw/HADCRUH2/MARINE/EUSTACEMDS/EUSTACE_SST_MAT/
# GitHub: https://github.com/Kate-Willett/HadISDH_Marine_Build
# -----------------------
# CODE PURPOSE AND OUTPUT
# -----------------------
# Th... | cc0-1.0 |
jreback/pandas | pandas/tests/frame/methods/test_set_axis.py | 2 | 3005 | import numpy as np
import pytest
from pandas import DataFrame, Series
import pandas._testing as tm
class SharedSetAxisTests:
@pytest.fixture
def obj(self):
raise NotImplementedError("Implemented by subclasses")
def test_set_axis(self, obj):
# GH14636; this tests setting index for both Se... | bsd-3-clause |
kenshay/ImageScripter | ProgramData/SystemFiles/Python/Lib/site-packages/scipy/optimize/nonlin.py | 4 | 46969 | 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... | gpl-3.0 |
khkaminska/scikit-learn | sklearn/linear_model/logistic.py | 57 | 65098 | """
Logistic Regression
"""
# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
# Fabian Pedregosa <f@bianp.net>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Manoj Kumar <manojkumarsivaraj334@gmail.com>
# Lars Buitinck
# Simon Wu <s8wu@uwaterloo.ca>
imp... | bsd-3-clause |
jm-begon/scikit-learn | sklearn/neighbors/approximate.py | 128 | 22351 | """Approximate nearest neighbor search"""
# Author: Maheshakya Wijewardena <maheshakya.10@cse.mrt.ac.lk>
# Joel Nothman <joel.nothman@gmail.com>
import numpy as np
import warnings
from scipy import sparse
from .base import KNeighborsMixin, RadiusNeighborsMixin
from ..base import BaseEstimator
from ..utils.va... | bsd-3-clause |
BlueBrain/deap | examples/es/cma_plotting.py | 12 | 4326 | # This file is part of DEAP.
#
# DEAP 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 later version.
#
# DEAP is distributed ... | lgpl-3.0 |
murali-munna/scikit-learn | sklearn/datasets/twenty_newsgroups.py | 126 | 13591 | """Caching loader for the 20 newsgroups text classification dataset
The description of the dataset is available on the official website at:
http://people.csail.mit.edu/jrennie/20Newsgroups/
Quoting the introduction:
The 20 Newsgroups data set is a collection of approximately 20,000
newsgroup documents,... | bsd-3-clause |
autocorr/besl | besl/ppv_group.py | 1 | 31445 | """
===========================
PPV Grouping and Clustering
===========================
Functions and routines to perform clustering analysis on the BGPS HCO+/N2H+
molecular line survey.
"""
from __future__ import division
import numpy as np
import pandas as pd
import cPickle as pickle
from collections import deque
... | gpl-3.0 |
RuthAngus/granola | granola/seismology/GProtation.py | 1 | 5438 | # This script contains the prior, lhf and logprob functions, plus plotting
# routines.
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os
import george
from george.kernels import ExpSine2Kernel, ExpSquaredKernel, WhiteKernel
import emcee3
import corn... | mit |
zorojean/scikit-learn | sklearn/cluster/setup.py | 263 | 1449 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
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
cblas_libs, blas_info = ... | bsd-3-clause |
abhisg/scikit-learn | sklearn/linear_model/tests/test_omp.py | 272 | 7752 | # Author: Vlad Niculae
# Licence: BSD 3 clause
import numpy as np
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equa... | bsd-3-clause |
plotly/python-api | packages/python/plotly/plotly/graph_objs/table/_header.py | 1 | 17792 | from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Header(_BaseTraceHierarchyType):
# class properties
# --------------------
_parent_path_str = "table"
_path_str = "table.header"
_valid_props = {
"align",
"alignsrc",
... | mit |
sonnyhu/scikit-learn | examples/ensemble/plot_voting_decision_regions.py | 86 | 2386 | """
==================================================
Plot the decision boundaries of a VotingClassifier
==================================================
Plot the decision boundaries of a `VotingClassifier` for
two features of the Iris dataset.
Plot the class probabilities of the first sample in a toy dataset
pred... | bsd-3-clause |
aaronzink/tensorflow-visual-inspection | models/autoencoder/AutoencoderRunner.py | 12 | 1660 | import numpy as np
import sklearn.preprocessing as prep
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from autoencoder_models.Autoencoder import Autoencoder
mnist = input_data.read_data_sets('MNIST_data', one_hot = True)
def standard_scale(X_train, X_test):
preprocessor = pr... | apache-2.0 |
abonil91/ncanda-data-integration | scripts/redcap/scoring/fh_drug/__init__.py | 1 | 6973 | #!/usr/bin/env python
##
## Copyright 2016 SRI International
## See COPYING file distributed along with the package for the copyright and license terms.
##
import pandas
import string
import time
import datetime
import numpy
input_fields = { 'youthreport1' : [ 'youthreport1_ydi6', # number of full sib... | bsd-3-clause |
wathen/PhD | MHD/FEniCS/MHD/CG/PicardIter_Direct/DecoupleTest/KappaChange/tests/CDmu.py | 1 | 12388 | #!/usr/bin/python
# interpolate scalar gradient onto nedelec space
from dolfin import *
import petsc4py
import sys
petsc4py.init(sys.argv)
from petsc4py import PETSc
Print = PETSc.Sys.Print
# from MatrixOperations import *
import numpy as np
#import matplotlib.pylab as plt
import PETScIO as IO
import common
import ... | mit |
alexis-roche/nipy | nipy/algorithms/clustering/hierarchical_clustering.py | 1 | 30021 | # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
These routines perform some hierrachical agglomerative clustering
of some input data. The following alternatives are proposed:
- Distance based average-link
- Similarity-based average-link
- Distance ba... | bsd-3-clause |
OzFlux/PyFluxPro | utilities/portal_audit.py | 1 | 4554 | # standard modules
from collections import OrderedDict
import datetime
import glob
import os
import pickle
import sys
# 3rd party modules
import dateutil
import matplotlib.pyplot as plt
import numpy
import pylab
import xlwt
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
# PF... | bsd-3-clause |
nshaud/content-recommendation | distribute/python/python/detect.py | 23 | 5743 | #!/usr/bin/env python
"""
detector.py is an out-of-the-box windowed detector
callable from the command line.
By default it configures and runs the Caffe reference ImageNet model.
Note that this model was trained for image classification and not detection,
and finetuning for detection can be expected to improve results... | mit |
aminert/scikit-learn | examples/linear_model/plot_sgd_penalties.py | 249 | 1563 | """
==============
SGD: Penalties
==============
Plot the contours of the three penalties.
All of the above are supported by
:class:`sklearn.linear_model.stochastic_gradient`.
"""
from __future__ import division
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
def l1(xs):
return np.array([np.... | bsd-3-clause |
GingerNinja23/alkane-lysis | application.py | 2 | 1700 | from flask import Flask, render_template,request
import csv
from sklearn import linear_model
from scipy.optimize import curve_fit
import scipy
app = Flask(__name__)
def compute_coeffs():
csv_file = open('dop_c2.csv','r')
dim1 = []
dim2 = []
lines = csv_file.readlines()
for line in lines:
row = line.strip('\r\n'... | mit |
beepee14/scikit-learn | examples/model_selection/plot_learning_curve.py | 250 | 4171 | """
========================
Plotting Learning Curves
========================
On the left side the learning curve of a naive Bayes classifier is shown for
the digits dataset. Note that the training score and the cross-validation score
are both not very good at the end. However, the shape of the curve can be found
in ... | bsd-3-clause |
nju-websoft/JAPE | code/ent2vec_sparse.py | 1 | 8130 | import numpy as np
import time
import sys
from scipy import io
from sklearn import preprocessing
import scipy as sp
from data_utils import *
SPLIT = '\t'
beishu = 10
def read_ents_props(props_file):
ents = dict()
file = open(props_file, 'r', encoding='utf8')
for line in file.readlines():
params ... | mit |
jayhetee/BDA_py_demos | demos_ch2/demo2_1.py | 19 | 1659 | """Bayesian Data Analysis, 3rd ed
Chapter 2, demo 1
437 girls and 543 boys have been observed. Calculate and plot the posterior
distribution of the proportion of girls $\theta$, using uniform prior on
$\theta$.
"""
import numpy as np
from scipy.stats import beta
import matplotlib.pyplot as plt
# Edit default plo... | gpl-3.0 |
ammarkhann/FinalSeniorCode | lib/python2.7/site-packages/scipy/interpolate/tests/test_rbf.py | 14 | 4604 | # Created by John Travers, Robert Hetland, 2007
""" Test functions for rbf module """
from __future__ import division, print_function, absolute_import
import numpy as np
from numpy.testing import (assert_, assert_array_almost_equal,
assert_almost_equal, run_module_suite)
from numpy import l... | mit |
nguyentu1602/statsmodels | statsmodels/datasets/star98/data.py | 25 | 3880 | """Star98 Educational Testing dataset."""
__docformat__ = 'restructuredtext'
COPYRIGHT = """Used with express permission from the original author,
who retains all rights."""
TITLE = "Star98 Educational Dataset"
SOURCE = """
Jeff Gill's `Generalized Linear Models: A Unified Approach`
http://jgill.wustl.e... | bsd-3-clause |
ilo10/scikit-learn | benchmarks/bench_lasso.py | 297 | 3305 | """
Benchmarks of Lasso vs LassoLars
First, we fix a training set and increase the number of
samples. Then we plot the computation time as function of
the number of samples.
In the second benchmark, we increase the number of dimensions of the
training set. Then we plot the computation time as function of
the number o... | bsd-3-clause |
akuefler/fovea | examples/HH_neuron/HH_detailed_demo.py | 1 | 29849 | """
This is the main run script for the detailed demo involving
Hodgkin-Huxley analysis.
"""
from PyDSTool.Toolbox.dssrt import *
from PyDSTool.Toolbox.phaseplane import *
import PyDSTool as dst
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import sys
from fovea.graphics import gui
from model... | bsd-3-clause |
moosemaniam/learning | ud120-projects/final_project/poi_id.py | 2 | 1799 | #!/usr/bin/python
import sys
import pickle
sys.path.append("../tools/")
from feature_format import featureFormat, targetFeatureSplit
from tester import test_classifier, dump_classifier_and_data
### Task 1: Select what features you'll use.
### features_list is a list of strings, each of which is a feature name.
### T... | cc0-1.0 |
ephes/scikit-learn | sklearn/linear_model/tests/test_passive_aggressive.py | 121 | 6117 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_array_almost_equal, assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.base import ClassifierMixin
from skle... | bsd-3-clause |
cpcloud/seaborn | seaborn/tests/test_linearmodels.py | 1 | 30319 | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import nose.tools as nt
import numpy.testing as npt
import pandas.util.testing as pdt
from numpy.testing.decorators import skipif
try:
import statsmodels.api as sm
_no_statsmodels = False
except ImportError:
_no_statsmodels = True
fro... | bsd-3-clause |
lmallin/coverage_test | python_venv/lib/python2.7/site-packages/pandas/core/reshape/util.py | 20 | 1915 | import numpy as np
from pandas.core.dtypes.common import is_list_like
from pandas.compat import reduce
from pandas.core.index import Index
from pandas.core import common as com
def match(needles, haystack):
haystack = Index(haystack)
needles = Index(needles)
return haystack.get_indexer(needles)
def ca... | mit |
nens/python-subgrid | setup.py | 1 | 2299 | from setuptools import setup
import sys
version = '0.25.dev0'
long_description = '\n\n'.join([
open('README.rst').read(),
open('CREDITS.rst').read(),
open('CHANGES.rst').read(),
])
install_requires = [
'setuptools',
'numpy',
'pandas',
'webob',
'netCDF4',
'mmi',
'scipy',
... | gpl-3.0 |
jostep/tensorflow | tensorflow/contrib/learn/python/learn/estimators/estimator.py | 3 | 59364 | # Copyright 2016 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 |
apahl/cellpainting | cellpainting/processing.py | 1 | 55494 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
##########
Processing
##########
*Created on Thu Jun 1 14:15 2017 by A. Pahl*
Processing results from the CellPainting Assay in the Jupyter notebook.
This module provides the DataSet class and its methods.
Additional functions in this module act on pandas DataFrames... | mit |
verdverm/pypge | pypge/benchmarks/explicit.py | 1 | 10968 | import sympy
import numpy as np
import pandas as pd
np.random.seed(23)
import pprint
pp = pprint.PrettyPrinter(indent=4)
x = sympy.Symbol('x')
y = sympy.Symbol('y')
z = sympy.Symbol('z')
v = sympy.Symbol('v')
w = sympy.Symbol('w')
def gen(prob_params, **kwargs):
prob_params = prep_params(prob_params, **kwargs)
p... | mit |
FarnazH/horton | horton/meanfield/scf_diis.py | 4 | 18496 | # -*- coding: utf-8 -*-
# HORTON: Helpful Open-source Research TOol for N-fermion systems.
# Copyright (C) 2011-2017 The HORTON Development Team
#
# This file is part of HORTON.
#
# HORTON is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by th... | gpl-3.0 |
RobertABT/heightmap | build/matplotlib/lib/matplotlib/spines.py | 4 | 18883 | from __future__ import division, print_function
import matplotlib
rcParams = matplotlib.rcParams
import matplotlib.artist as martist
from matplotlib.artist import allow_rasterization
from matplotlib import docstring
import matplotlib.transforms as mtransforms
import matplotlib.lines as mlines
import matplotlib.patche... | mit |
btabibian/scikit-learn | sklearn/linear_model/tests/test_least_angle.py | 20 | 26139 | import warnings
import numpy as np
from scipy import linalg
from sklearn.model_selection import train_test_split
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from ... | bsd-3-clause |
kdebrab/pandas | pandas/tests/sparse/series/test_series.py | 2 | 55371 | # pylint: disable-msg=E1101,W0612
import operator
from datetime import datetime
import pytest
from numpy import nan
import numpy as np
import pandas as pd
from pandas import (Series, DataFrame, bdate_range,
isna, compat, _np_version_under1p12)
from pandas.tseries.offsets import BDay
import panda... | bsd-3-clause |
Unidata/MetPy | v0.12/startingguide-1.py | 4 | 1432 | import matplotlib.pyplot as plt
import numpy as np
import metpy.calc as mpcalc
from metpy.plots import SkewT
from metpy.units import units
fig = plt.figure(figsize=(9, 9))
skew = SkewT(fig)
# Create arrays of pressure, temperature, dewpoint, and wind components
p = [902, 897, 893, 889, 883, 874, 866, 857, 849, 841, 8... | bsd-3-clause |
jmmease/pandas | pandas/tests/io/parser/common.py | 3 | 60098 | # -*- coding: utf-8 -*-
import csv
import os
import platform
import codecs
import re
import sys
from datetime import datetime
import pytest
import numpy as np
from pandas._libs.lib import Timestamp
import pandas as pd
import pandas.util.testing as tm
from pandas import DataFrame, Series, Index, MultiIndex
from pand... | bsd-3-clause |
jart/tensorflow | tensorflow/examples/get_started/regression/imports85.py | 41 | 6589 | # Copyright 2016 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 |
victorbergelin/scikit-learn | examples/cluster/plot_lena_segmentation.py | 271 | 2444 | """
=========================================
Segmenting the picture of Lena in regions
=========================================
This example uses :ref:`spectral_clustering` on a graph created from
voxel-to-voxel difference on an image to break this image into multiple
partly-homogeneous regions.
This procedure (spe... | bsd-3-clause |
potash/scikit-learn | examples/preprocessing/plot_function_transformer.py | 158 | 1993 | """
=========================================================
Using FunctionTransformer to select columns
=========================================================
Shows how to use a function transformer in a pipeline. If you know your
dataset's first principle component is irrelevant for a classification task,
you ca... | bsd-3-clause |
huobaowangxi/scikit-learn | sklearn/decomposition/tests/test_pca.py | 199 | 10949 | import numpy as np
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_greater
from sklearn.utils.testing import assert_rai... | bsd-3-clause |
CartoDB/bigmetadata | tasks/fr/insee.py | 1 | 10692 | # -*- coding: utf-8 -*-
from tasks.base_tasks import (ColumnsTask, TableTask, TagsTask, RepoFileUnzipTask, CSV2TempTableTask, MetaWrapper,
RepoFile)
from tasks.util import classpath, copyfile
from tasks.meta import current_session, GEOM_REF
from collections import OrderedDict
from luigi i... | bsd-3-clause |
glennq/scikit-learn | sklearn/decomposition/nmf.py | 15 | 47073 | """ Non-negative matrix factorization
"""
# Author: Vlad Niculae
# Lars Buitinck
# Mathieu Blondel <mathieu@mblondel.org>
# Tom Dupre la Tour
# Author: Chih-Jen Lin, National Taiwan University (original projected gradient
# NMF implementation)
# ... | bsd-3-clause |
ablifedev/ABLIRC | ABLIRC/bin/public/Extract_Region_sequence.py | 1 | 12799 | #!/usr/bin/env python2.7
# -*- coding: utf-8 -*-
####################################################################################
### Copyright (C) 2015-2019 by ABLIFE
####################################################################################
#########################################################... | mit |
yejingxin/kaggle-ndsb | train_convnet.py | 6 | 9280 | import numpy as np
import theano
import theano.tensor as T
import lasagne as nn
import time
import os
import sys
import importlib
import cPickle as pickle
from datetime import datetime, timedelta
import string
from itertools import izip
import matplotlib
matplotlib.use('agg')
import pylab as plt
import data
import ... | mit |
WarrenWeckesser/scikits-image | doc/examples/plot_marked_watershed.py | 8 | 1999 | """
===============================
Markers for watershed transform
===============================
The watershed is a classical algorithm used for **segmentation**, that
is, for separating different objects in an image.
Here a marker image is built from the region of low gradient inside the image.
In a gradient imag... | bsd-3-clause |
valexandersaulys/airbnb_kaggle_contest | venv/lib/python3.4/site-packages/scipy/spatial/_plotutils.py | 53 | 4034 | from __future__ import division, print_function, absolute_import
import numpy as np
from scipy._lib.decorator import decorator as _decorator
__all__ = ['delaunay_plot_2d', 'convex_hull_plot_2d', 'voronoi_plot_2d']
@_decorator
def _held_figure(func, obj, ax=None, **kw):
import matplotlib.pyplot as plt
if ax... | gpl-2.0 |
StructuralNeurobiologyLab/SyConnFS | syconnfs/representations/skel_based_classifier.py | 1 | 22871 | import cPickle as pkl
import glob
import numpy as np
import os
import re
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, AdaBoostClassifier
from sklearn.externals import joblib
from sklearn.metrics import precision_recall_fscore_support, precision_recall_curve
import matplotlib
matplotlib.us... | gpl-2.0 |
baseband-geek/singlepulse-visualizer | interactive/interactive_sp_plot.py | 1 | 10768 | #!/usr/bin/python
# DM Sigma Time (s) Sample Downfact
import numpy as np
import matplotlib as mpl
import matplotlib.patches as patches
import matplotlib.pyplot as plt
from pulsar_tools import disp_delay
import math
import sys
import pandas as pd
import bokeh.io
from bokeh.io import output_file, show... | mit |
brenthuisman/phd_tools | analysis.lyso4.falloff.py | 1 | 6376 | #!/usr/bin/env python
import numpy as np,plot,auger,subprocess,tableio,dump
#OPT: quickly get sorted rundirs
# zb autogen | sort -k1.13 -r
#OPT: fix seed
#np.random.seed(65983247)
np.random.seed(983452324)
addnoise=False
precolli=False #gaan we niet meer doen
pgexit = True #if so, then pgprod_ratio must be set.
pr... | lgpl-3.0 |
bavardage/statsmodels | statsmodels/sandbox/tsa/examples/ex_mle_arma.py | 4 | 4490 | # -*- coding: utf-8 -*-
"""
TODO: broken because of changes to arguments and import paths
fixing this needs a closer look
Created on Thu Feb 11 23:41:53 2010
Author: josef-pktd
copyright: Simplified BSD see license.txt
"""
import numpy as np
from numpy.testing import assert_almost_equal
import matplotlib.pyplot as p... | bsd-3-clause |
marioharper182/Patterns | PatternRecognition/ProgrammingProject1/MaxLiklihood.py | 1 | 2560 | __author__ = 'Mario'
__author__ = 'Mario'
import numpy as np
from scipy.stats import multivariate_normal as norm
import pandas as pd
import matplotlib.pyplot as plt
dataTraining = pd.read_table('./Data/iris_training.txt',delim_whitespace=True, header=None)
dataTest = pd.read_table('./Data/iris_test.txt',delim_whites... | apache-2.0 |
ML-KULeuven/socceraction | socceraction/spadl/opta.py | 1 | 60410 | # -*- coding: utf-8 -*-
"""Opta event stream data to SPADL converter."""
import copy
import glob
import json # type: ignore
import os
import re
import warnings
from abc import ABC
from datetime import datetime, timedelta
from typing import Any, Dict, List, Mapping, Optional, Tuple, Type
import pandas as pd # type: i... | mit |
JFriel/honours_project | networkx/networkx/tests/test_convert_pandas.py | 43 | 2177 | from nose import SkipTest
from nose.tools import assert_true
import networkx as nx
class TestConvertPandas(object):
numpy=1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
@classmethod
def setupClass(cls):
try:
import pandas as pd
except ImportError:
... | gpl-3.0 |
phaustin/pyman | Book/chap9/Supporting Materials/specFuncPlots.py | 3 | 2545 | import numpy as np
import scipy.special
import matplotlib.pyplot as plt
# create a figure window
fig = plt.figure(1, figsize=(9,8))
# create arrays for a few Bessel functions and plot them
x = np.linspace(0, 20, 256)
j0 = scipy.special.jn(0, x)
j1 = scipy.special.jn(1, x)
y0 = scipy.special.yn(0, x)
y1 = scipy.specia... | cc0-1.0 |
jmschrei/scikit-learn | sklearn/neighbors/base.py | 30 | 30586 | """Base and mixin classes for nearest neighbors"""
# Authors: Jake Vanderplas <vanderplas@astro.washington.edu>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Sparseness support by Lars Buitinck <L.J.Buitinck@uva.nl>
# Multi-output... | bsd-3-clause |
cactusbin/nyt | matplotlib/lib/matplotlib/backends/backend_webagg.py | 1 | 21417 | """
Displays Agg images in the browser, with interactivity
"""
from __future__ import division, print_function
import datetime
import errno
import io
import json
import os
import random
import socket
import numpy as np
try:
import tornado
except ImportError:
raise RuntimeError("The WebAgg backend requires To... | unlicense |
BhallaLab/moose-full | moose-examples/neuroml/LIF/twoLIFxml_firing.py | 3 | 3082 | # -*- coding: utf-8 -*-
## all SI units
########################################################################################
## Plot the membrane potential for a leaky integrate and fire neuron with current injection
## Author: Aditya Gilra
## Creation Date: 2012-06-08
## Modification Date: 2012-06-08
#############... | gpl-2.0 |
alongwithyou/auto-sklearn | autosklearn/data/split_data.py | 5 | 3748 | import numpy as np
import sklearn.cross_validation
import autosklearn.util.logging_
logger = autosklearn.util.logging_.get_logger(__name__)
def split_data(X, Y, classification=None):
num_data_points = X.shape[0]
num_labels = Y.shape[1] if len(Y.shape) > 1 else 1
X_train, X_valid, Y_train, Y_valid = None... | bsd-3-clause |
YuepengGuo/zipline | zipline/utils/tradingcalendar_tse.py | 17 | 10125 | #
# Copyright 2014 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 |
jkarnows/scikit-learn | sklearn/preprocessing/data.py | 113 | 56747 | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Mathieu Blondel <mathieu@mblondel.org>
# Olivier Grisel <olivier.grisel@ensta.org>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# Eric Martin <eric@ericmart.in>
# License: BSD 3 clause
from itertools import chain, combina... | bsd-3-clause |
MichielCottaar/pymc3 | pymc3/examples/lasso_missing.py | 10 | 1958 | from pymc3 import *
import numpy as np
import pandas as pd
from numpy.ma import masked_values
# Import data, filling missing values with sentinels (-999)
test_scores = pd.read_csv(get_data_file('pymc3.examples', 'data/test_scores.csv')).fillna(-999)
# Extract variables: test score, gender, number of siblings, previou... | apache-2.0 |
iismd17/scikit-learn | examples/svm/plot_svm_margin.py | 318 | 2328 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
SVM Margins Example
=========================================================
The plots below illustrate the effect the parameter `C` has
on the separation line. A large value of `C` basically tells
our model that w... | bsd-3-clause |
andaag/scikit-learn | benchmarks/bench_isotonic.py | 268 | 3046 | """
Benchmarks of isotonic regression performance.
We generate a synthetic dataset of size 10^n, for n in [min, max], and
examine the time taken to run isotonic regression over the dataset.
The timings are then output to stdout, or visualized on a log-log scale
with matplotlib.
This alows the scaling of the algorith... | bsd-3-clause |
jseabold/statsmodels | statsmodels/distributions/empirical_distribution.py | 5 | 5236 | """
Empirical CDF Functions
"""
import numpy as np
from scipy.interpolate import interp1d
def _conf_set(F, alpha=.05):
r"""
Constructs a Dvoretzky-Kiefer-Wolfowitz confidence band for the eCDF.
Parameters
----------
F : array_like
The empirical distributions
alpha : float
Set a... | bsd-3-clause |
power-system-simulation-toolbox/psst | psst/case/__init__.py | 1 | 7705 | import os
import logging
import pandas as pd
from .descriptors import (
Name, Version, BaseMVA, BusName, Bus, Branch, BranchName,
Gen, GenName, GenCost, Load, Period, _Attributes
)
from . import matpower
from .utils import convert_to_model_one
logger = logging.getLogger(__name__)
pd.options.display.max_row... | mit |
henrykironde/scikit-learn | examples/plot_multioutput_face_completion.py | 330 | 3019 | """
==============================================
Face completion with a multi-output estimators
==============================================
This example shows the use of multi-output estimator to complete images.
The goal is to predict the lower half of a face given its upper half.
The first column of images sho... | bsd-3-clause |
jmmease/pandas | pandas/plotting/_misc.py | 5 | 18194 | # being a bit too dynamic
# pylint: disable=E1101
from __future__ import division
import numpy as np
from pandas.util._decorators import deprecate_kwarg
from pandas.core.dtypes.missing import notna
from pandas.compat import range, lrange, lmap, zip
from pandas.io.formats.printing import pprint_thing
from pandas.plo... | bsd-3-clause |
DelonShen/Model-Builder-ProtonML | getData.py | 1 | 3423 | import os
datadir = "/asd"
asdf = False
# while(os.path.isdir(datadir)==False):
# datadir = input("Enter full data directory (e.g. /home/bob/Desktop/data) \nNote that this is case sensitive:\n")
# if(os.path.isdir(datadir)==False):
# print("That is not a valid directory")
import argparse
parser = argp... | mit |
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