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 |
|---|---|---|---|---|---|
davidwaroquiers/pymatgen | pymatgen/io/lammps/tests/test_inputs.py | 5 | 4354 | # coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
import filecmp
import os
import re
import shutil
import unittest
import pandas as pd
from pymatgen.core.lattice import Lattice
from pymatgen.core.structure import Structure
from pymatgen.io.lammps.data import ... | mit |
JeanKossaifi/scikit-learn | benchmarks/bench_multilabel_metrics.py | 276 | 7138 | #!/usr/bin/env python
"""
A comparison of multilabel target formats and metrics over them
"""
from __future__ import division
from __future__ import print_function
from timeit import timeit
from functools import partial
import itertools
import argparse
import sys
import matplotlib.pyplot as plt
import scipy.sparse as... | bsd-3-clause |
klocey/hydrobide | tools/SADfits/SAD-Models.py | 9 | 12909 | from __future__ import division
import os, collections, math
from scipy import stats, optimize
import statsmodels.formula.api as smf
import statsmodels.api as sm
from scipy.stats import nbinom
import numpy as np
import pandas as pd
from macroeco_distributions import pln, pln_solver, negbin_solver, trunc_geom
from scipy... | mit |
haaspt/panopti | scraper.py | 1 | 5320 | from __future__ import print_function
import time
import praw
import pandas as pd
import numpy as np
def get_new_authors(reddit_post_generator, author_series=None):
"""Takes a reddit post generator object and an optional pandas series.
Iterates through the generator and adds praw user objects to the series
... | mit |
maheshakya/scikit-learn | sklearn/utils/tests/test_utils.py | 23 | 6045 | import warnings
import numpy as np
import scipy.sparse as sp
from scipy.linalg import pinv2
from sklearn.utils.testing import (assert_equal, assert_raises, assert_true,
assert_almost_equal, assert_array_equal,
SkipTest)
from sklearn.utils import c... | bsd-3-clause |
sargas/scipy | scipy/signal/spectral.py | 3 | 13369 | """Tools for spectral analysis.
"""
from __future__ import division, print_function, absolute_import
import numpy as np
from scipy import fftpack
from . import signaltools
from .windows import get_window
from ._spectral import lombscargle
import warnings
from scipy.lib.six import string_types
__all__ = ['periodogra... | bsd-3-clause |
andaag/scikit-learn | sklearn/neighbors/tests/test_nearest_centroid.py | 305 | 4121 | """
Testing for the nearest centroid module.
"""
import numpy as np
from scipy import sparse as sp
from numpy.testing import assert_array_equal
from numpy.testing import assert_equal
from sklearn.neighbors import NearestCentroid
from sklearn import datasets
from sklearn.metrics.pairwise import pairwise_distances
# t... | bsd-3-clause |
ch3ll0v3k/scikit-learn | sklearn/utils/arpack.py | 265 | 64837 | """
This contains a copy of the future version of
scipy.sparse.linalg.eigen.arpack.eigsh
It's an upgraded wrapper of the ARPACK library which
allows the use of shift-invert mode for symmetric matrices.
Find a few eigenvectors and eigenvalues of a matrix.
Uses ARPACK: http://www.caam.rice.edu/software/ARPACK/
"""
#... | bsd-3-clause |
mayblue9/scikit-learn | sklearn/linear_model/tests/test_sgd.py | 68 | 43439 | import pickle
import unittest
import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing ... | bsd-3-clause |
gaomeng1900/SQIP-py | sqip/api/api.py | 1 | 11570 | #!/usr/bin/env python
#-*-coding:utf-8-*-
#
# @author Meng G.
# 2016-03-28 restructed
import sys
reload(sys)
sys.setdefaultencoding("utf-8")
from sqip.config import *
from sqip.libs import *
# @TODO
# 为什么没有后三行就一直提示
# 'module' object has no attribute 'stu'
import models
from models import project, meta
from sqip.b... | cc0-1.0 |
untom/scikit-learn | examples/bicluster/bicluster_newsgroups.py | 162 | 7103 | """
================================================================
Biclustering documents with the Spectral Co-clustering algorithm
================================================================
This example demonstrates the Spectral Co-clustering algorithm on the
twenty newsgroups dataset. The 'comp.os.ms-windows... | bsd-3-clause |
louisLouL/pair_trading | capstone_env/lib/python3.6/site-packages/matplotlib/backends/backend_gtkagg.py | 2 | 3347 | """
Render to gtk from agg
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import os
import matplotlib
from matplotlib.figure import Figure
from matplotlib.backends.backend_agg import FigureCanvasAgg
from matplotlib.backends.backend_gtk impo... | mit |
WafaaT/spark-tk | regression-tests/sparktkregtests/testcases/scoretests/scoring_pipeline_test.py | 9 | 2856 | # 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 |
bhargav/scikit-learn | sklearn/metrics/cluster/tests/test_unsupervised.py | 26 | 3305 | import numpy as np
from scipy.sparse import csr_matrix
from sklearn import datasets
from sklearn.metrics.cluster.unsupervised import silhouette_score
from sklearn.metrics import pairwise_distances
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.te... | bsd-3-clause |
khkaminska/scikit-learn | sklearn/tests/test_calibration.py | 213 | 12219 | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# License: BSD 3 clause
import numpy as np
from scipy import sparse
from sklearn.utils.testing import (assert_array_almost_equal, assert_equal,
assert_greater, assert_almost_equal,
... | bsd-3-clause |
sunopy/amaterasu | python/import_data.py | 1 | 1650 | from __future__ import division
import numpy
import matplotlib.pyplot as plt
ace = numpy.genfromtxt('../data/data.csv', dtype=None,names = ['year', 'day', 'hr', 'min', 'sec', 'fp_year', 'fp_day', 'ACEepoch', 'proton_density', 'proton_temp', 'He4toprotons', 'proton_speed', 'x_dot_RTN', 'y_dot_RTN', 'z_dot_RTN', 'x_dot_... | apache-2.0 |
JasonKessler/scattertext | demo_sklearn.py | 1 | 2580 | from lightning.classification import CDClassifier
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.metrics import f1_score
import scattertext as st
newsgroups_train = fetch_20newsgroups(subset='train', remove=('headers', 'footers'... | apache-2.0 |
ningchi/scikit-learn | sklearn/utils/arpack.py | 265 | 64837 | """
This contains a copy of the future version of
scipy.sparse.linalg.eigen.arpack.eigsh
It's an upgraded wrapper of the ARPACK library which
allows the use of shift-invert mode for symmetric matrices.
Find a few eigenvectors and eigenvalues of a matrix.
Uses ARPACK: http://www.caam.rice.edu/software/ARPACK/
"""
#... | bsd-3-clause |
blab/nextstrain-augur | base/fitness_predictors.py | 1 | 19731 | import Bio
import time
import numpy as np
import pandas as pd
from scipy.stats import linregress
import sys
try:
import itertools.izip as zip
except ImportError:
pass
from .scores import calculate_LBI, select_nodes_in_season
from .titer_model import SubstitutionModel, TiterCollection, TreeModel
# all fitness... | agpl-3.0 |
plotly/plotly.py | packages/python/plotly/plotly/graph_objs/_scatterternary.py | 1 | 86927 | from plotly.basedatatypes import BaseTraceType as _BaseTraceType
import copy as _copy
class Scatterternary(_BaseTraceType):
# class properties
# --------------------
_parent_path_str = ""
_path_str = "scatterternary"
_valid_props = {
"a",
"asrc",
"b",
"bsrc",
... | mit |
natasasdj/OpenWPM | analysis_parallel/01b_responseDomains_sqlite.py | 1 | 1463 | import sys
import sqlite3
import os
import pandas as pd
from urlparse import urlparse
from timeit import default_timer as timer
data_dir = sys.argv[1]
db = os.path.join(data_dir,'crawl-data.sqlite')
print db
conn = sqlite3.connect(db)
res_dir = sys.argv[2]
db = os.path.join(res_dir,'domains.sqlite')
print db
conn1 ... | gpl-3.0 |
mmoiozo/IROS | sw/misc/attitude_reference/test_att_ref.py | 49 | 3485 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2014 Antoine Drouin
#
# This file is part of paparazzi.
#
# paparazzi 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, or (at y... | gpl-2.0 |
woozzu/pylearn2 | pylearn2/train_extensions/roc_auc.py | 30 | 4854 | """
TrainExtension subclass for calculating ROC AUC scores on monitoring
dataset(s), reported via monitor channels.
"""
__author__ = "Steven Kearnes"
__copyright__ = "Copyright 2014, Stanford University"
__license__ = "3-clause BSD"
import numpy as np
try:
from sklearn.metrics import roc_auc_score
except ImportEr... | bsd-3-clause |
ahnitz/pycbc | setup.py | 1 | 11507 | #!/usr/bin/env python
# Copyright (C) 2012 Alex Nitz, Duncan Brown, Andrew Miller, Josh Willis
#
# This program 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 (at your
# op... | gpl-3.0 |
ltiao/scikit-learn | sklearn/cluster/bicluster.py | 211 | 19443 | """Spectral biclustering algorithms.
Authors : Kemal Eren
License: BSD 3 clause
"""
from abc import ABCMeta, abstractmethod
import numpy as np
from scipy.sparse import dia_matrix
from scipy.sparse import issparse
from . import KMeans, MiniBatchKMeans
from ..base import BaseEstimator, BiclusterMixin
from ..external... | bsd-3-clause |
hsk81/calaganne | 2016-03-01/The Probability of Co-Prime Integers/plot.py | 1 | 1128 | #!/usr/bin/env python
###############################################################################
import numpy as np
from math import gcd
from matplotlib import pyplot as pp
###############################################################################
def NEXT(n):
return np.random.random_integers(2**n)
def CO... | isc |
mlperf/training_results_v0.6 | Fujitsu/benchmarks/resnet/implementations/mxnet/example/rcnn/symdata/loader.py | 11 | 8759 | # 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 |
Crobisaur/KMeans_MNIST | CpE520_HW5.py | 1 | 2749 | print(__doc__)
from time import time
import numpy as np
import matplotlib.pyplot as plt
import h5py
from sklearn import metrics
from sklearn.cluster import KMeans
from sklearn.datasets import load_digits
from sklearn import decomposition
from sklearn.preprocessing import scale
from skimage.transform import rescale
i... | mit |
Arn-O/kadenze-deep-creative-apps | session-3/libs/gif.py | 4 | 1797 | """Utility for creating a GIF.
Creative Applications of Deep Learning w/ Tensorflow.
Kadenze, Inc.
Copyright Parag K. Mital, June 2016.
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
def build_gif(imgs, interval=0.1, dpi=72,
save_gif=True, saveto='animat... | apache-2.0 |
wiki2014/Learning-Summary | alps/cts/apps/CameraITS/tests/scene1/test_param_shading_mode.py | 1 | 4654 | # Copyright 2015 The Android Open Source Project
#
# 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 a... | gpl-3.0 |
schets/scikit-learn | examples/covariance/plot_outlier_detection.py | 235 | 3891 | """
==========================================
Outlier detection with several methods.
==========================================
When the amount of contamination is known, this example illustrates two
different ways of performing :ref:`outlier_detection`:
- based on a robust estimator of covariance, which is assumin... | bsd-3-clause |
kylerbrown/scikit-learn | examples/applications/plot_out_of_core_classification.py | 255 | 13919 | """
======================================================
Out-of-core classification of text documents
======================================================
This is an example showing how scikit-learn can be used for classification
using an out-of-core approach: learning from data that doesn't fit into main
memory. ... | bsd-3-clause |
asnorkin/sentiment_analysis | site/lib/python2.7/site-packages/sklearn/tests/test_multioutput.py | 39 | 6609 | import numpy as np
import scipy.sparse as sp
from sklearn.utils import shuffle
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_raises_regex
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing impor... | mit |
changsiyao/mousestyles | mousestyles/path_diversity/tests/test_dist_speed.py | 3 | 1642 | from __future__ import (absolute_import, division,
print_function, unicode_literals)
import pandas as pd
import pytest
from mousestyles import data
from mousestyles import path_diversity
def test_dist_speed_input():
movement = data.load_movement(0, 0, 0)
# Check if function raises t... | bsd-2-clause |
tobegit3hub/deep_cnn | java_predict_client/src/main/proto/tensorflow/contrib/learn/python/learn/learn_io/data_feeder_test.py | 24 | 8691 | # 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 |
jnez71/demos | geometry/quaternion_exponential.py | 1 | 3360 | #!/usr/bin/env python3
"""
Computational demo of the exponential map for the quaternion representation of SO3.
Quaternions here are stored as arrays [w, i, j, k]. (NOT the ROS TF convention).
"""
import numpy as np
from matplotlib import pyplot
from mpl_toolkits.mplot3d import Axes3D
npl = np.linalg
PI = np.pi
#####... | mit |
francesco-mannella/dmp-esn | DMP/stulp/src/functionapproximators/tests/testFunctionApproximatorTraining.py | 2 | 2473 | from mpl_toolkits.mplot3d.axes3d import Axes3D
import numpy
import matplotlib.pyplot as plt
import os, sys, subprocess
lib_path = os.path.abspath('../plotting')
sys.path.append(lib_path)
from plotData impo... | gpl-2.0 |
tom-f-oconnell/multi_tracker | nodes/delta_video_simplebuffer.py | 1 | 25393 | #!/usr/bin/env python
from __future__ import division
import copy
import threading
from subprocess import Popen
import time
import os
import sys
import numpy as np
import cv2
from cv_bridge import CvBridge, CvBridgeError
import matplotlib.pyplot as plt
import rospy
import rosparam
from sensor_msgs.msg import Image
f... | mit |
zehpunktbarron/iOSMAnalyzer | scripts/c2_actuality_point.py | 1 | 5651 | # -*- coding: utf-8 -*-
#!/usr/bin/python2.7
#description :This file creates a plot: Calculate the actuality of all points
#author :Christopher Barron @ http://giscience.uni-hd.de/
#date :19.01.2013
#version :0.1
#usage :python pyscript.py
#====================================... | gpl-3.0 |
terkkila/scikit-learn | examples/linear_model/plot_sparse_recovery.py | 243 | 7461 | """
============================================================
Sparse recovery: feature selection for sparse linear models
============================================================
Given a small number of observations, we want to recover which features
of X are relevant to explain y. For this :ref:`sparse linear ... | bsd-3-clause |
lscheinkman/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/__init__.py | 69 | 28184 | """
This is an object-orient plotting library.
A procedural interface is provided by the companion pylab module,
which may be imported directly, e.g::
from pylab import *
or using ipython::
ipython -pylab
For the most part, direct use of the object-oriented library is
encouraged when programming rather tha... | agpl-3.0 |
krez13/scikit-learn | sklearn/model_selection/_validation.py | 14 | 35648 | """
The :mod:`sklearn.model_selection._validation` module includes classes and
functions to validate the model.
"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
# Gael Varoquaux <gael.varoquaux@normalesup.org>,
# Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
from __... | bsd-3-clause |
scivision/piradar | PlotSpectrum.py | 1 | 3699 | #!/usr/bin/env python
"""
Plot time & frequency spectrum of a GNU Radio received file.
Also attempts to playback sound from file (optionally, write .wav file)
CW Example (file with Fs=100kHz, Fc=10kHz, taking 4 sec. time steps from 30 to 60 sec., 10x zero-padding)
./PlotSpectrum.py ~/Dropbox/piradar/data/MH_exercise.b... | agpl-3.0 |
MostafaGazar/tensorflow | tensorflow/contrib/learn/python/learn/estimators/rnn_test.py | 8 | 5977 | # 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 |
ryfeus/lambda-packs | Tensorflow_LightGBM_Scipy_nightly/source/scipy/interpolate/_fitpack_impl.py | 10 | 46541 | """
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
or
http://www.netlib.... | mit |
akrherz/idep | scripts/ucs/huc12results.py | 2 | 1275 | from geopandas import read_postgis
from pandas.io.sql import read_sql
from pyiem.util import get_dbconn
years = 8.0
pgconn = get_dbconn("idep")
# Get the initial geometries
df = read_postgis(
"""
SELECT huc_12, geom from huc12 WHERE states ~* 'IA' and scenario = 0
""",
pgconn,
index_col="huc_12",
... | mit |
hughperkins/gpu-experiments | gpuexperiments/globalwrite_gridsize_graphs.py | 1 | 1977 | from __future__ import print_function, division
import argparse
import string
import numpy as np
import os
from collections import defaultdict
import array
import csv
import matplotlib.pyplot as plt
plt.rcdefaults()
import matplotlib.pyplot as plt
from os.path import join
import lib_clgpuexp
parser = argparse.Argumen... | bsd-2-clause |
VDBWRAIR/bio_pieces | bio_bits/beast_checkpoint.py | 3 | 5209 | #!/usr/bin/env python
# Designed to allow checkpointing of BEAST output files
# Input: beast_analysis.xml output_file_1.log output_file_2.log ... tree_file.trees
# Expect at least 3 files
# This matches column names in log files to parameter names in the original XML to set initial conditions
from __future__ import p... | gpl-2.0 |
cogeorg/BlackRhino | networkx/convert_matrix.py | 3 | 33714 | """Functions to convert NetworkX graphs to and from numpy/scipy matrices.
The preferred way of converting data to a NetworkX graph is through the
graph constuctor. The constructor calls the to_networkx_graph() function
which attempts to guess the input type and convert it automatically.
Examples
--------
Create a 10... | gpl-3.0 |
avolkov1/keras_experiments | examples/variational_autoencoder/variational_autoencoder_deconv_mgpu.py | 1 | 5976 | '''This script demonstrates how to build a variational autoencoder
with Keras and deconvolution layers.
Reference: "Auto-Encoding Variational Bayes" https://arxiv.org/abs/1312.6114
Multigpu modifications running asynchronous training.
original implementation:
https://github.com/fchollet/keras/blob/master/examples/va... | unlicense |
pythonvietnam/scikit-learn | examples/mixture/plot_gmm_pdf.py | 284 | 1528 | """
=============================================
Density Estimation for a mixture of Gaussians
=============================================
Plot the density estimation of a mixture of two Gaussians. Data is
generated from two Gaussians with different centers and covariance
matrices.
"""
import numpy as np
import ma... | bsd-3-clause |
macks22/gensim | gensim/sklearn_api/tfidf.py | 1 | 1952 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2011 Radim Rehurek <radimrehurek@seznam.cz>
# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
"""
Scikit learn interface for gensim for easy use of gensim with scikit-learn
Follows scikit-learn API conventions
"""
from sklearn.ba... | lgpl-2.1 |
geopy/geopy | geopy/extra/rate_limiter.py | 1 | 14731 | """:class:`.RateLimiter` and :class:`.AsyncRateLimiter` allow to perform bulk
operations while gracefully handling error responses and adding delays
when needed.
In the example below a delay of 1 second (``min_delay_seconds=1``)
will be added between each pair of ``geolocator.geocode`` calls; all
:class:`geopy.exc.Geo... | mit |
Manolo94/manolo94.github.io | MLpython/HW5.py | 1 | 7050 | import pandas as pd
import numpy as np
import sys
import random
import copy
# Task 1
task1_data = {'Wins_2016': [3, 3, 2, 2, 6, 6, 7, 7, 8, 7], 'Wins_2017': [5, 4, 8, 3, 2, 4, 3, 4, 5, 6]}
task1_pd = pd.DataFrame(data=task1_data)
iris_df = pd.read_csv('./iris_input/iris.data', names=['sepal_length', 'sepal_width', 'p... | apache-2.0 |
cjforman/pele | pele/potentials/_sutton_chen.py | 5 | 3354 | import numpy as np
from pele.potentials import BasePotential
from pele.potentials.fortran import scdiff_periodic as fortran_sc
class SuttonChen(BasePotential):
"""The sutton chen potential
for modelling the surfaces of metal crystals
:
First calculate the potential energy. Choosing SIG=... | gpl-3.0 |
tahoemph/polar_roses | python/polar_matplotlib_animate.py | 1 | 1575 | """
Started from a Matplotlib example by
Jake Vanderplas (vanderplas@astro.washington.edu)
"""
import math
from matplotlib import pyplot as plt
from matplotlib import animation
import numpy as np
# First set up the figure, the axis, and the plot element we want to animate
fig = plt.figure()
ax = plt.axes(xlim=(-1.25,... | mit |
Bhare8972/LOFAR-LIM | LIM_scripts/stationTimings/timingFitter_4_polt.py | 1 | 24482 | #!/usr/bin/env python3
#python
import time
from os import mkdir, listdir
from os.path import isdir, isfile
from itertools import chain
#from pickle import load
#external
import numpy as np
np.set_printoptions(precision=10, threshold=np.inf)
from scipy.optimize import least_squares
from matplotlib import pyplot as plt... | mit |
jlegendary/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/bezier.py | 70 | 14387 | """
A module providing some utility functions regarding bezier path manipulation.
"""
import numpy as np
from math import sqrt
from matplotlib.path import Path
from operator import xor
# some functions
def get_intersection(cx1, cy1, cos_t1, sin_t1,
cx2, cy2, cos_t2, sin_t2):
""" return a... | gpl-3.0 |
larlequin/toad | tasks/05-correction.py | 2 | 23544 | # -*- coding: utf-8 -*-
import os
import math
import matplotlib
from core.toad.generictask import GenericTask
from lib.images import Images
from lib import util, mriutil
__author__ = "Mathieu Desrosiers"
__copyright__ = "Copyright (C) 2014, TOAD"
__credits__ = ["Mathieu Desrosiers", "Basile Pinsard"]
matplotlib.u... | gpl-2.0 |
hejunbok/paparazzi | sw/airborne/test/stabilization/compare_ref_quat.py | 38 | 1206 | #! /usr/bin/env python
from __future__ import division, print_function, absolute_import
import numpy as np
import matplotlib.pyplot as plt
import ref_quat_float
import ref_quat_int
steps = 512 * 2
ref_eul_res = np.zeros((steps, 3))
ref_quat_res = np.zeros((steps, 3))
ref_quat_float.init()
ref_quat_int.init()
# re... | gpl-2.0 |
bearing/dosenet-analysis | Programming Lesson Modules/Module 5- Other Forms of Visualization.py | 1 | 4056 | """
# Module 4- Example Plot of Weather Data
#### author: Radley Rigonan
In this module, I will be desmonstrating a few other graphical capabilities in Python.
I will be using the following link to create a table and pi chart:
https://radwatch.berkeley.edu/sites/default/files/pictures/rooftop_tmp/weather.csv
""... | mit |
kenshay/ImageScript | ProgramData/SystemFiles/Python/Lib/site-packages/mpl_toolkits/axisartist/floating_axes.py | 18 | 22796 | """
An experimental support for curvilinear grid.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from six.moves import zip
# TODO :
# *. see if tick_iterator method can be simplified by reusing the parent method.
from itertools import chai... | gpl-3.0 |
ryfeus/lambda-packs | Pandas_numpy/source/pandas/io/sas/sas7bdat.py | 5 | 27243 | """
Read SAS7BDAT files
Based on code written by Jared Hobbs:
https://bitbucket.org/jaredhobbs/sas7bdat
See also:
https://github.com/BioStatMatt/sas7bdat
Partial documentation of the file format:
https://cran.r-project.org/web/packages/sas7bdat/vignettes/sas7bdat.pdf
Reference for binary data compression:
h... | mit |
dhruv13J/scikit-learn | sklearn/ensemble/tests/test_forest.py | 2 | 34969 | """
Testing for the forest module (sklearn.ensemble.forest).
"""
# Authors: Gilles Louppe,
# Brian Holt,
# Andreas Mueller,
# Arnaud Joly
# License: BSD 3 clause
import pickle
from collections import defaultdict
from itertools import product
import numpy as np
from scipy.sparse import csr_... | bsd-3-clause |
Djabbz/scikit-learn | examples/applications/wikipedia_principal_eigenvector.py | 233 | 7819 | """
===============================
Wikipedia principal eigenvector
===============================
A classical way to assert the relative importance of vertices in a
graph is to compute the principal eigenvector of the adjacency matrix
so as to assign to each vertex the values of the components of the first
eigenvect... | bsd-3-clause |
seckcoder/lang-learn | python/sklearn/examples/linear_model/plot_lasso_and_elasticnet.py | 3 | 1765 | """
========================================
Lasso and Elastic Net for Sparse Signals
========================================
"""
print __doc__
import numpy as np
import pylab as pl
from sklearn.metrics import r2_score
###############################################################################
# generate some ... | unlicense |
jorik041/scikit-learn | examples/linear_model/plot_logistic_l1_l2_sparsity.py | 384 | 2601 | """
==============================================
L1 Penalty and Sparsity in Logistic Regression
==============================================
Comparison of the sparsity (percentage of zero coefficients) of solutions when
L1 and L2 penalty are used for different values of C. We can see that large
values of C give mo... | bsd-3-clause |
weixuanfu2016/tpot | tests/stacking_estimator_tests.py | 2 | 4622 | # -*- coding: utf-8 -*-
"""This file is part of the TPOT library.
TPOT was primarily developed at the University of Pennsylvania by:
- Randal S. Olson (rso@randalolson.com)
- Weixuan Fu (weixuanf@upenn.edu)
- Daniel Angell (dpa34@drexel.edu)
- and many more generous open source contributors
TPOT is f... | lgpl-3.0 |
huongttlan/statsmodels | statsmodels/graphics/dotplots.py | 31 | 18190 | import numpy as np
from statsmodels.compat import range
from . import utils
def dot_plot(points, intervals=None, lines=None, sections=None,
styles=None, marker_props=None, line_props=None,
split_names=None, section_order=None, line_order=None,
stacked=False, styles_order=None, s... | bsd-3-clause |
gautamkmr/incubator-mxnet | example/dec/dec.py | 24 | 7846 | # 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 |
JanKalin/zcutils | miningrate.py | 1 | 7729 | #!/usr/bin/env python
###########################################################################
# author: JanKalin
#
# Calculates an estimate of mining rate on this computer
###########################################################################
import argparse
import datetime
import matplotlib.pyplot as plt
im... | mit |
msdogan/pyvin | calvin/plots.py | 1 | 2114 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import csv
import matplotlib.cm as cm
import matplotlib
matplotlib.style.use('ggplot')
def plot_clustered_stacked(dfall, labels=None, title="Water Supply Portfolio", H="/", **kwargs):
n_df = len(dfall)
n_col = len(dfall[0].columns)
... | mit |
nbfigueroa/daft | examples/classic.py | 7 | 1057 | """
The Quintessential PGM
======================
This is a demonstration of a very common structure found in graphical models.
It has been rendered using Daft's default settings for all the parameters
and it shows off how much beauty is baked in by default.
"""
from matplotlib import rc
rc("font", family="serif", s... | mit |
tboyle1/DissertationCode | DeltaHedge.py | 1 | 3679 | import numpy as np
import pandas as pd
from pandas import DataFrame as df
pd.set_option('display.width', 320)
pd.set_option('display.max_rows', 100)
pd.options.display.float_format = '{:,.2f}'.format
from scipy.stats import norm
import matplotlib.pyplot as plt
def BlackScholes(tau, S, K, sigma):
d1=np.log(S/K)/si... | unlicense |
michaelbramwell/sms-tools | software/transformations_interface/sineTransformations_function.py | 25 | 5018 | # function call to the transformation functions of relevance for the sineModel
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import get_window
import sys, os
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../models/'))
sys.path.append(os.path.join(os.path.dirname(os.p... | agpl-3.0 |
vigilv/scikit-learn | sklearn/linear_model/tests/test_sparse_coordinate_descent.py | 244 | 9986 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_true
from sklearn.utils.t... | bsd-3-clause |
pombredanne/bokeh | examples/charts/server/interactive_excel.py | 6 | 3202 | import xlwings as xw
import pandas as pd
from pandas.util.testing import assert_frame_equal
from bokeh.client import push_session
from bokeh.charts import Line, Bar
from bokeh.charts.operations import blend
from bokeh.models import Paragraph
from bokeh.io import curdoc, hplot, vplot
wb = xw.Workbook() # Creates a co... | bsd-3-clause |
stephenliu1989/HK_DataMiner | hkdataminer/cluster/dbscan_.py | 1 | 15860 | # -*- coding: utf-8 -*-
"""
DBSCAN: Density-Based Spatial Clustering of Applications with Noise
"""
# Author: Robert Layton <robertlayton@gmail.com>
# Joel Nothman <joel.nothman@gmail.com>
# Lars Buitinck
#
# License: BSD 3 clause
import numpy as np
import warnings
from scipy import sparse
from sklea... | apache-2.0 |
ch3ll0v3k/scikit-learn | sklearn/manifold/t_sne.py | 106 | 20057 | # Author: Alexander Fabisch -- <afabisch@informatik.uni-bremen.de>
# License: BSD 3 clause (C) 2014
# This is the standard t-SNE implementation. There are faster modifications of
# the algorithm:
# * Barnes-Hut-SNE: reduces the complexity of the gradient computation from
# N^2 to N log N (http://arxiv.org/abs/1301.... | bsd-3-clause |
numenta/NAB | nab/detectors/htmjava/nab/util.py | 9 | 9025 | # ----------------------------------------------------------------------
# Copyright (C) 2014-2015, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions apply:
#
# This program is free software: you can redistribute it and/... | agpl-3.0 |
jkarnows/scikit-learn | examples/decomposition/plot_pca_vs_fa_model_selection.py | 78 | 4510 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
===============================================================
Model selection with Probabilistic PCA and Factor Analysis (FA)
===============================================================
Probabilistic PCA and Factor Analysis are probabilistic models.
The consequence ... | bsd-3-clause |
CI-WATER/gsshapy | gsshapy/grid/grid_to_gssha.py | 1 | 55286 | # -*- coding: utf-8 -*-
#
# grid_to_gssha.py
# GSSHApy
#
# Created by Alan D Snow, 2016.
# License BSD 3-Clause
from builtins import range
from datetime import datetime
from io import open as io_open
import logging
import numpy as np
from os import mkdir, path, remove, rename
import pangaea as pa
import pandas as ... | bsd-3-clause |
AIML/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 |
johnmgregoire/NanoCalorimetry | fixdramreaderror_1kHz_fV.py | 1 | 6643 | import numpy, h5py, pylab, copy
from PnSC_h5io import *
import scipy.optimize
from matplotlib.ticker import FuncFormatter
class fitfcns: #datatuples are x1,x2,...,y
#.finalparams .sigmas .parnames useful, returns fitfcn(x)
def genfit(self, fcn, initparams, datatuple, markstr='unspecified', parnames=[], interac... | bsd-3-clause |
ianctse/pvlib-python | pvlib/test/test_tmy.py | 2 | 1174 | import inspect
import os
from pandas.util.testing import network
test_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
tmy3_testfile = os.path.join(test_dir, '../data/703165TY.csv')
tmy2_testfile = os.path.join(test_dir, '../data/12839.tm2')
from pvlib import tmy
def test_readtmy3():... | bsd-3-clause |
jhprinz/openpathsampling | openpathsampling/pathsimulator.py | 1 | 41587 | import time
import sys
import logging
import numpy as np
import pandas as pd
from openpathsampling.netcdfplus import StorableNamedObject, StorableObject
import openpathsampling as paths
import openpathsampling.tools
import collections
from openpathsampling.pathmover import SubPathMover
from .ops_logging import init... | lgpl-2.1 |
larsoner/mne-python | examples/decoding/plot_receptive_field_mtrf.py | 15 | 11238 | """
.. _ex-receptive-field-mtrf:
=========================================
Receptive Field Estimation and Prediction
=========================================
This example reproduces figures from Lalor et al.'s mTRF toolbox in
MATLAB :footcite:`CrosseEtAl2016`. We will show how the
:class:`mne.decoding.ReceptiveField... | bsd-3-clause |
Odingod/mne-python | examples/realtime/ftclient_rt_compute_psd.py | 17 | 2460 | """
==============================================================
Compute real-time power spectrum density with FieldTrip client
==============================================================
Please refer to `ftclient_rt_average.py` for instructions on
how to get the FieldTrip connector working in MNE-Python.
This e... | bsd-3-clause |
moutai/scikit-learn | sklearn/neighbors/tests/test_dist_metrics.py | 38 | 6118 | import itertools
import pickle
import numpy as np
from numpy.testing import assert_array_almost_equal
import scipy
from scipy.spatial.distance import cdist
from sklearn.neighbors.dist_metrics import DistanceMetric
from nose import SkipTest
def dist_func(x1, x2, p):
return np.sum((x1 - x2) ** p) ** (1. / p)
de... | bsd-3-clause |
msto/svplot | svplot/venn.py | 2 | 8650 | # -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright © 2016 msto <mstone5@mgh.harvard.edu>
#
# Distributed under terms of the MIT license.
"""
Simple venn diagrams.
"""
import matplotlib.pyplot as plt
import matplotlib.patches as patches
def venn4(subsets,
set_labels=('A', 'B', 'C', 'D'),
# s... | mit |
pkruskal/scikit-learn | benchmarks/bench_plot_parallel_pairwise.py | 297 | 1247 | # Author: Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause
import time
import pylab as pl
from sklearn.utils import check_random_state
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.metrics.pairwise import pairwise_kernels
def plot(func):
random_state = check_random_state(0)
... | bsd-3-clause |
aglne/lenskit | lenskit-integration-tests/src/it/eval/item-item-identical/verify.py | 5 | 2214 | # LensKit, an open source recommender systems toolkit.
# Copyright 2010-2014 Regents of the University of Minnesota and contributors
# Work on LensKit has been funded by the National Science Foundation under
# grants IIS 05-34939, 08-08692, 08-12148, and 10-17697.
#
# This program is free software; you can redistribute... | lgpl-2.1 |
NonVolatileComputing/arrow | python/pyarrow/tests/pandas_examples.py | 1 | 3889 | # -*- coding: utf-8 -*-
# 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
# "... | apache-2.0 |
spectralDNS/spectralDNS | sandbox/cheb_helmholtz_neumann_sft.py | 2 | 7391 | from numpy.polynomial import chebyshev as n_cheb
from sympy import chebyshevt, Symbol, sin, cos, pi, lambdify, sqrt as Sqrt
import numpy as np
import matplotlib.pyplot as plt
from scipy.linalg import solve_banded
from scipy.sparse import diags
import scipy.sparse.linalg as la
from spectralDNS.shen.shentransform import ... | lgpl-3.0 |
wathen/PhD | MHD/FEniCS/MHD/Stabilised/SaddlePointForm/Test/SplitMatrix/CoupleTest/MHDmatrixSetup.py | 1 | 6202 |
import petsc4py
import sys
petsc4py.init(sys.argv)
from petsc4py import PETSc
from dolfin import *
# from MatrixOperations import *
import numpy as np
#import matplotlib.pylab as plt
from scipy.sparse import coo_matrix, csr_matrix, spdiags, bmat
import os, inspect
from HiptmairSetup import BoundaryEdge
import matpl... | mit |
bthirion/scikit-learn | sklearn/tests/test_discriminant_analysis.py | 37 | 11979 | import numpy as np
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 assert_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert... | bsd-3-clause |
musically-ut/statsmodels | statsmodels/tsa/tests/test_seasonal.py | 27 | 9216 | import numpy as np
from numpy.testing import assert_almost_equal, assert_equal, assert_raises
from statsmodels.tsa.seasonal import seasonal_decompose
from pandas import DataFrame, DatetimeIndex
class TestDecompose:
@classmethod
def setupClass(cls):
# even
data = [-50, 175, 149, 214, 247, 237, ... | bsd-3-clause |
B3AU/waveTree | examples/manifold/plot_compare_methods.py | 8 | 3592 | """
=========================================
Comparison of Manifold Learning methods
=========================================
An illustration of dimensionality reduction on the S-curve dataset
with various manifold learning methods.
For a discussion and comparison of these algorithms, see the
:ref:`manifold module... | bsd-3-clause |
willzfarmer/datagraph | bin/graph.py | 1 | 6342 | #!/usr/bin/env python
IMPORT_FLAG = False
try:
import csv
import subprocess
import argparse
import sys
import os
import pydot
import ast
import networkx as nx
import matplotlib.pyplot as plt
except ImportError:
IMPORT_FLAG = True
def main():
args = get_args()
if args.... | bsd-3-clause |
NelisVerhoef/scikit-learn | examples/decomposition/plot_ica_vs_pca.py | 306 | 3329 | """
==========================
FastICA on 2D point clouds
==========================
This example illustrates visually in the feature space a comparison by
results using two different component analysis techniques.
:ref:`ICA` vs :ref:`PCA`.
Representing ICA in the feature space gives the view of 'geometric ICA':
ICA... | bsd-3-clause |
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