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 |
|---|---|---|---|---|---|
iagapov/ocelot | gui/genesis_plot.py | 1 | 116811 | '''
user interface for viewing genesis simulation results
'''
import sys
import os
import csv
import time
import matplotlib
# check if Xserver is connected
# havedisplay = "DISPLAY" in os.environ
# if not havedisplay:
# # re-check
# exitval = os.system('python -c "import matplotlib.pyplot as plt; plt.figure()"')
# ha... | gpl-3.0 |
MehnaazAsad/ECO_Globular_Clusters | src/visualization/eco_sample.py | 1 | 8054 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 17 04:27:25 2017
@author: asadm2
"""
###DESCRIPTION
#This script plots current sample of objects out of the entire ECO catalog,
#separates this plot into single_halos (group mass of less than 10**14 solar
#masses) and coma_halos (group mass of more... | mit |
giorgiop/scikit-learn | sklearn/utils/tests/test_seq_dataset.py | 79 | 2497 | # Author: Tom Dupre la Tour <tom.dupre-la-tour@m4x.org>
#
# License: BSD 3 clause
import numpy as np
from numpy.testing import assert_array_equal
import scipy.sparse as sp
from sklearn.utils.seq_dataset import ArrayDataset, CSRDataset
from sklearn.datasets import load_iris
from sklearn.utils.testing import assert_eq... | bsd-3-clause |
kinverarity1/pyexperiment | pyexperiment/utils/plot.py | 3 | 5557 | """Provides setup utilities for matplotlib figures.
The `setup_plotting` function will configure basic plot options,
such as font size, line width, etc. Calls after the first call are
ignored unless the override flag is set to True. The `setup_figure`
function will call `setup_plotting` without overriding an existing
... | mit |
JDTimlin/QSO_Clustering | highz_clustering/clustering/Limbers/Limber_MCint_lowz.py | 2 | 9490 | import os
import sys
import numpy as np
from astropy.io import fits as pf
from sklearn.neighbors import KernelDensity as kde
from scipy import integrate
import camb
from camb import model
from scipy.special import j0
from scipy import interpolate
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D a... | mit |
jmmease/pandas | pandas/io/json/normalize.py | 3 | 9206 | # ---------------------------------------------------------------------
# JSON normalization routines
import copy
from collections import defaultdict
import numpy as np
from pandas._libs.lib import convert_json_to_lines
from pandas import compat, DataFrame
def _convert_to_line_delimits(s):
"""Helper function th... | bsd-3-clause |
scikit-learn-contrib/py-earth | examples/plot_classifier_comp.py | 3 | 4753 | """
======================================================
Plotting sckit-learn classifiers comparison with Earth
======================================================
This script recreates the scikit-learn classifier comparison example found at
http://scikit-learn.org/stable/auto_examples/classification/plot_classif... | bsd-3-clause |
architecture-building-systems/CEAforArcGIS | setup.py | 2 | 2557 | """Installation script for the City Energy Analyst"""
import os
from setuptools import setup, find_packages
import cea
__author__ = "Daren Thomas"
__copyright__ = "Copyright 2017, Architecture and Building Systems - ETH Zurich"
__credits__ = ["Daren Thomas"]
__license__ = "MIT"
__version__ = cea.__version__
__mainta... | mit |
ehogan/iris | lib/iris/tests/integration/test_regridding.py | 10 | 3425 | # (C) British Crown Copyright 2013 - 2015, 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... | lgpl-3.0 |
ky822/scikit-learn | sklearn/utils/tests/test_validation.py | 79 | 18547 | """Tests for input validation functions"""
import warnings
from tempfile import NamedTemporaryFile
from itertools import product
import numpy as np
from numpy.testing import assert_array_equal
import scipy.sparse as sp
from nose.tools import assert_raises, assert_true, assert_false, assert_equal
from sklearn.utils.... | bsd-3-clause |
andreabduque/GAFE | ga.py | 1 | 3069 | # import pandas as pd
import numpy as np
import random
from deap import base, creator, tools, algorithms
from functions.FE import FE
from sklearn.preprocessing import MinMaxScaler
#Define GA
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)
class... | mit |
elkingtonmcb/scikit-learn | examples/text/document_clustering.py | 230 | 8356 | """
=======================================
Clustering text documents using k-means
=======================================
This is an example showing how the scikit-learn can be used to cluster
documents by topics using a bag-of-words approach. This example uses
a scipy.sparse matrix to store the features instead of ... | bsd-3-clause |
madjelan/scikit-learn | benchmarks/bench_sample_without_replacement.py | 397 | 8008 | """
Benchmarks for sampling without replacement of integer.
"""
from __future__ import division
from __future__ import print_function
import gc
import sys
import optparse
from datetime import datetime
import operator
import matplotlib.pyplot as plt
import numpy as np
import random
from sklearn.externals.six.moves i... | bsd-3-clause |
JosmanPS/scikit-learn | sklearn/ensemble/partial_dependence.py | 251 | 15097 | """Partial dependence plots for tree ensembles. """
# Authors: Peter Prettenhofer
# License: BSD 3 clause
from itertools import count
import numbers
import numpy as np
from scipy.stats.mstats import mquantiles
from ..utils.extmath import cartesian
from ..externals.joblib import Parallel, delayed
from ..externals im... | bsd-3-clause |
cbmoore/statsmodels | statsmodels/genmod/tests/test_gee.py | 19 | 55589 | """
Test functions for GEE
External comparisons are to R and Stata. The statmodels GEE
implementation should generally agree with the R GEE implementation
for the independence and exchangeable correlation structures. For
other correlation structures, the details of the correlation
estimation differ among implementat... | bsd-3-clause |
apache/arrow | python/pyarrow/tests/test_array.py | 3 | 93104 | # 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 |
lilleswing/deepchem | deepchem/utils/data_utils.py | 1 | 16253 | """
Simple utils to save and load from disk.
"""
import joblib
import gzip
import pickle
import os
import tempfile
import tarfile
import zipfile
import logging
from urllib.request import urlretrieve
from typing import Any, Iterator, List, Optional, Tuple, Union, cast, IO
import pandas as pd
import numpy as np
import ... | mit |
lthurlow/Network-Grapher | proj/external/matplotlib-1.2.1/lib/mpl_examples/pylab_examples/multiple_yaxis_with_spines.py | 6 | 1602 | import matplotlib.pyplot as plt
def make_patch_spines_invisible(ax):
ax.set_frame_on(True)
ax.patch.set_visible(False)
for sp in ax.spines.itervalues():
sp.set_visible(False)
fig = plt.figure()
fig.subplots_adjust(right=0.75)
host = fig.add_subplot(111)
par1 = host.twinx()
par2 = host.twinx()
# ... | mit |
Eniac-Xie/faster-rcnn-resnet | lib/pycocotools/coco.py | 16 | 14881 | __author__ = 'tylin'
__version__ = '1.0.1'
# Interface for accessing the Microsoft COCO dataset.
# Microsoft COCO is a large image dataset designed for object detection,
# segmentation, and caption generation. pycocotools is a Python API that
# assists in loading, parsing and visualizing the annotations in COCO.
# Ple... | mit |
essigmannlab/8oxoG-mutagenicity | analysis/visualize_CtA.py | 1 | 4161 | #!/usr/env/py27
# Visualize C>A portions of mutational spectra in Stratton format
# Runs in py36 too
from figures import spectrum_map
from collections import OrderedDict
import cospec as sa
import matplotlib.pyplot as plt
import scipy.stats as sc
import scipy.cluster.hierarchy as hac
from sklearn.metrics.pairwise impo... | mit |
williamalu/mimo_usrp | scripts/channel_estimator.py | 1 | 3889 | import numpy as np
import numpy as np
import matplotlib.pyplot as plt
import decoder as D
import pll as PLL
j = (0 + 1j)
if __name__ == "__main__":
# Load data files
noise1 = np.fromfile('../data/noise_1.bin', dtype=np.complex64)
noise2 = np.fromfile('../data/noise_2.bin', dtype=np.complex64)
plt.pl... | mit |
totalgood/nlpia | src/nlpia/book/examples/ch02.py | 1 | 1549 | """ NLPIA Chapter 2 Section 2.1 Code Listings and Snippets """
import pandas as pd
sentence = "Thomas Jefferson began building Monticello at the age of twenty-six."
sentence.split()
# ['Thomas', 'Jefferson', 'began', 'building', 'Monticello', 'at', 'the', 'age', 'of', 'twenty-six.']
# As you can see, this simple Pyt... | mit |
hainm/scikit-learn | sklearn/manifold/tests/test_isomap.py | 226 | 3941 | from itertools import product
import numpy as np
from numpy.testing import assert_almost_equal, assert_array_almost_equal
from sklearn import datasets
from sklearn import manifold
from sklearn import neighbors
from sklearn import pipeline
from sklearn import preprocessing
from sklearn.utils.testing import assert_less
... | bsd-3-clause |
ericfourrier/auto-clean | test.py | 1 | 14139 | # -*- coding: utf-8 -*-
"""
@author: efourrier
Purpose : Automated test suites with unittest
run "python -m unittest -v test" in the module directory to run the tests
The clock decorator in utils will measure the run time of the test
"""
#########################################################
# Import Packages an... | mit |
unnikrishnankgs/va | venv/lib/python3.5/site-packages/matplotlib/tests/test_ticker.py | 2 | 19200 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import nose.tools
from nose.tools import assert_equal, assert_raises
from numpy.testing import assert_almost_equal
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.ticker a... | bsd-2-clause |
glouppe/scikit-learn | examples/applications/svm_gui.py | 287 | 11161 | """
==========
Libsvm GUI
==========
A simple graphical frontend for Libsvm mainly intended for didactic
purposes. You can create data points by point and click and visualize
the decision region induced by different kernels and parameter settings.
To create positive examples click the left mouse button; to create
neg... | bsd-3-clause |
datapythonista/pandas | asv_bench/benchmarks/io/excel.py | 4 | 2151 | from io import BytesIO
import numpy as np
from odf.opendocument import OpenDocumentSpreadsheet
from odf.table import (
Table,
TableCell,
TableRow,
)
from odf.text import P
from pandas import (
DataFrame,
ExcelWriter,
date_range,
read_excel,
)
from ..pandas_vb_common import tm
def _gener... | bsd-3-clause |
schets/scikit-learn | examples/ensemble/plot_forest_importances_faces.py | 403 | 1519 | """
=================================================
Pixel importances with a parallel forest of trees
=================================================
This example shows the use of forests of trees to evaluate the importance
of the pixels in an image classification task (faces). The hotter the pixel,
the more impor... | bsd-3-clause |
tttor/csipb-jamu-prj | predictor/connectivity/classifier/selfblm/devel.py | 1 | 5474 | #!/usr/bin/python
import numpy as np
import json
import time
import sys
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.model_selection import KFold
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import average_precisi... | mit |
dancingdan/tensorflow | tensorflow/contrib/learn/python/learn/learn_io/io_test.py | 137 | 5063 | # 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 |
lbishal/scikit-learn | sklearn/manifold/locally_linear.py | 23 | 25123 | """Locally Linear Embedding"""
# Author: Fabian Pedregosa -- <fabian.pedregosa@inria.fr>
# Jake Vanderplas -- <vanderplas@astro.washington.edu>
# License: BSD 3 clause (C) INRIA 2011
import numpy as np
from scipy.linalg import eigh, svd, qr, solve
from scipy.sparse import eye, csr_matrix
from ..base import B... | bsd-3-clause |
dudulianangang/MAE6286-notes | m1t5.py | 1 | 1084 | import numpy as np
import matplotlib.pyplot as plt
# initial parameters
ms = 50.0
mpv = 20.0
g = 9.81
ve = 325.0
rho = 1.091
r = 0.5
A = np.pi*r**2
C_D = 0.15
mp0 = 100.0
h0 = 0.0
v0 = 0.0
# time grid
T = 40.0
dt = 0.01
N = int(T/dt)+1
# numerical scheme function
def f(u):
mp = u[0]
h = u[1]
v = ... | bsd-3-clause |
raghavrv/scikit-learn | examples/cluster/plot_digits_agglomeration.py | 377 | 1694 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Feature agglomeration
=========================================================
These images how similar features are merged together using
feature agglomeration.
"""
print(__doc__)
# Code source: Gaël Varoquaux
#... | bsd-3-clause |
altairpearl/scikit-learn | examples/gaussian_process/plot_gpr_co2.py | 131 | 5705 | """
========================================================
Gaussian process regression (GPR) on Mauna Loa CO2 data.
========================================================
This example is based on Section 5.4.3 of "Gaussian Processes for Machine
Learning" [RW2006]. It illustrates an example of complex kernel engine... | bsd-3-clause |
MingdaZhou/gnuradio | gr-filter/examples/reconstruction.py | 49 | 5015 | #!/usr/bin/env python
#
# Copyright 2010,2012,2013 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 ... | gpl-3.0 |
takuya1981/sms-tools | lectures/06-Harmonic-model/plots-code/sines-partials-harmonics-phase.py | 22 | 1986 | import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import hamming, triang, blackmanharris
import sys, os, functools, time
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
import dftModel as DFT
import utilFunctions as UF
(fs, x) = UF.wavread('../... | agpl-3.0 |
jseabold/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 |
lijinpei/vasper | ViewPos3D.py | 1 | 4846 | #!/usr/bin/python3
import sys
import re
import numpy as np
import matplotlib
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
import matplotlib.cm as cm
n = [3, 3, 1] # number of grids to display
print(sys.argv[1])
f = open(sys.... | gpl-3.0 |
larsmans/scikit-learn | examples/semi_supervised/plot_label_propagation_versus_svm_iris.py | 286 | 2378 | """
=====================================================================
Decision boundary of label propagation versus SVM on the Iris dataset
=====================================================================
Comparison for decision boundary generated on iris dataset
between Label Propagation and SVM.
This demon... | bsd-3-clause |
rachel3834/lcogt-commissioning | scripts/statistics.py | 1 | 10190 | ########################################################################################################
# STATISTICS FUNCTIONS
########################################################################################################
############################
# IMPORT FUNCTIONS
from ... | gpl-3.0 |
natanielruiz/android-yolo | jni-build/jni/include/tensorflow/examples/skflow/multiple_gpu.py | 5 | 1649 | # 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 appl... | apache-2.0 |
MaxInGaussian/ZS-VAFNN | uci-expts/classification/spambase/training.py | 1 | 4012 | # Copyright 2017 Max W. Y. Lam
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, so... | apache-2.0 |
vighneshbirodkar/scikit-image | doc/examples/features_detection/plot_orb.py | 33 | 1807 | """
==========================================
ORB feature detector and binary descriptor
==========================================
This example demonstrates the ORB feature detection and binary description
algorithm. It uses an oriented FAST detection method and the rotated BRIEF
descriptors.
Unlike BRIEF, ORB is c... | bsd-3-clause |
harterj/moose | modules/combined/examples/geochem-porous_flow/geotes_weber_tensleep/scaling.py | 9 | 1503 | #!/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 |
pypot/scikit-learn | sklearn/preprocessing/tests/test_data.py | 113 | 38432 | import warnings
import numpy as np
import numpy.linalg as la
from scipy import sparse
from distutils.version import LooseVersion
from sklearn.utils.testing import assert_almost_equal, clean_warning_registry
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_array_equal... | bsd-3-clause |
cython-testbed/pandas | pandas/io/formats/html.py | 3 | 19924 | # -*- coding: utf-8 -*-
"""
Module for formatting output data in HTML.
"""
from __future__ import print_function
from distutils.version import LooseVersion
from textwrap import dedent
from pandas import compat
from pandas.compat import (lzip, range, map, zip, u,
OrderedDict, unichr)
impor... | bsd-3-clause |
aabadie/scikit-learn | examples/applications/plot_species_distribution_modeling.py | 55 | 7386 | """
=============================
Species distribution modeling
=============================
Modeling species' geographic distributions is an important
problem in conservation biology. In this example we
model the geographic distribution of two south american
mammals given past observations and 14 environmental
varia... | bsd-3-clause |
SunDwarf/Jokusoramame | jokusoramame/plugins/core.py | 1 | 10883 | """
Core plugin.
"""
import sys
import time
from itertools import cycle
import asks
import asyncqlio
import contextlib
import curio
import curious
import git
import matplotlib.pyplot as plt
import numpy as np
import pkg_resources
import platform
import psutil
import tabulate
import traceback
from asks.response_objects... | gpl-3.0 |
aetilley/scikit-learn | sklearn/linear_model/ridge.py | 89 | 39360 | """
Ridge regression
"""
# Author: Mathieu Blondel <mathieu@mblondel.org>
# Reuben Fletcher-Costin <reuben.fletchercostin@gmail.com>
# Fabian Pedregosa <fabian@fseoane.net>
# Michael Eickenberg <michael.eickenberg@nsup.org>
# License: BSD 3 clause
from abc import ABCMeta, abstractmethod
impor... | bsd-3-clause |
louispotok/pandas | pandas/tests/test_common.py | 1 | 7863 | # -*- coding: utf-8 -*-
import pytest
import os
import collections
from functools import partial
import numpy as np
from pandas import Series, DataFrame, Timestamp
from pandas.compat import range, lmap
import pandas.core.common as com
from pandas.core import ops
from pandas.io.common import _get_handle
import pandas... | bsd-3-clause |
gergopokol/renate-od | utility/getdata.py | 1 | 13246 | import os
import urllib.request
import pandas
import h5py
from lxml import etree
from utility import convert
DEFAULT_SETUP = 'getdata_setup.xml'
class GetData:
"""
This class is to access and load data from files. It looks for data in the following order:
1. Common local data path
2. User's local da... | lgpl-3.0 |
simpeg/simpeg | tests/pf/test_sensitivity_PFproblem.py | 1 | 11447 | # from __future__ import print_function
# import unittest
# from SimPEG import *
# from simpegPF import BaseMag
# import matplotlib.pyplot as plt
# import simpegPF as PF
# from scipy.constants import mu_0
# class MagSensProblemTests(unittest.TestCase):
# def setUp(self):
# cs = 25.
# hxind = [(cs... | mit |
rupakc/Kaggle-Compendium | Integer Sequence Learning/integer_sequence_baseline.py | 1 | 1112 | import pandas as pd
from keras.models import Sequential
from keras.layers import Dense,LSTM,GRU,Dropout
from keras.preprocessing.sequence import pad_sequences
import numpy as np
filename = 'train.csv'
train_frame = pd.read_csv(filename)
master_sequence_list = list([])
max_float = 1000000.0
for sequence in list(train_... | mit |
MehnaazAsad/ECO_Globular_Clusters | src/data/main/BUNIT_check.py | 1 | 1704 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 3 16:46:35 2017
@author: asadm2
"""
import pandas as pd
import os
from astropy.io import fits
from glob import glob
import warnings
from astropy.utils.exceptions import AstropyUserWarning
goodObj = '../../../data/interim/goodObj.txt'
#Read goodOb... | mit |
aashish24/seaborn | seaborn/tests/test_axisgrid.py | 1 | 35616 | import numpy as np
import pandas as pd
from scipy import stats
import matplotlib as mpl
import matplotlib.pyplot as plt
from distutils.version import LooseVersion
import nose.tools as nt
import numpy.testing as npt
from numpy.testing.decorators import skipif
import pandas.util.testing as tm
from .. import axisgrid as... | bsd-3-clause |
rajegannathan/grasp-lift-eeg-cat-dog-solution-updated | python-packages/mne-python-0.10/mne/viz/tests/test_misc.py | 17 | 4858 | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Denis Engemann <denis.engemann@gmail.com>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Eric Larson <larson.eric.d@gmail.com>
# Cathy Nangini <cnangini@gmail.com>
# Mainak Jas <mainak@neuro.hut.fi>
#... | bsd-3-clause |
RayMick/scikit-learn | examples/model_selection/plot_precision_recall.py | 249 | 6150 | """
================
Precision-Recall
================
Example of Precision-Recall metric to evaluate classifier output quality.
In information retrieval, precision is a measure of result relevancy, while
recall is a measure of how many truly relevant results are returned. A high
area under the curve represents both ... | bsd-3-clause |
cheind/tf-matplotlib | tfmpl/figure.py | 1 | 5533 | # Copyright 2018 Christoph Heindl.
#
# Licensed under MIT License
# ============================================================
import tensorflow as tf
import traceback
import numpy as np
from functools import wraps
from tfmpl.meta import vararg_decorator, as_list
from tfmpl.meta import PositionalTensorArgs
def fig... | mit |
linebp/pandas | pandas/io/formats/excel.py | 3 | 23353 | """Utilities for conversion to writer-agnostic Excel representation
"""
import re
import warnings
import itertools
import numpy as np
from pandas.compat import reduce
from pandas.io.formats.css import CSSResolver, CSSWarning
from pandas.io.formats.printing import pprint_thing
from pandas.core.dtypes.common import is... | bsd-3-clause |
Djabbz/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 |
waynenilsen/statsmodels | statsmodels/sandbox/tsa/examples/example_var.py | 37 | 1218 | """
Look at some macro plots, then do some VARs and IRFs.
"""
import numpy as np
import statsmodels.api as sm
import scikits.timeseries as ts
import scikits.timeseries.lib.plotlib as tplt
from matplotlib import pyplot as plt
data = sm.datasets.macrodata.load()
data = data.data
### Create Timeseries Representations ... | bsd-3-clause |
appapantula/scikit-learn | examples/ensemble/plot_gradient_boosting_oob.py | 230 | 4762 | """
======================================
Gradient Boosting Out-of-Bag estimates
======================================
Out-of-bag (OOB) estimates can be a useful heuristic to estimate
the "optimal" number of boosting iterations.
OOB estimates are almost identical to cross-validation estimates but
they can be compute... | bsd-3-clause |
jseabold/scikit-learn | sklearn/datasets/tests/test_svmlight_format.py | 228 | 11221 | from bz2 import BZ2File
import gzip
from io import BytesIO
import numpy as np
import os
import shutil
from tempfile import NamedTemporaryFile
from sklearn.externals.six import b
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert... | bsd-3-clause |
kylerbrown/scikit-learn | examples/cluster/plot_kmeans_silhouette_analysis.py | 242 | 5885 | """
===============================================================================
Selecting the number of clusters with silhouette analysis on KMeans clustering
===============================================================================
Silhouette analysis can be used to study the separation distance between the... | bsd-3-clause |
stczhc/neupy | tests/ensemble/test_dan.py | 1 | 2429 | import numpy as np
from sklearn import datasets, preprocessing, cross_validation, metrics
from neupy import algorithms, layers
from neupy.layers import Relu, Sigmoid, Output
from base import BaseTestCase
class DANTestCase(BaseTestCase):
def test_handle_errors(self):
data, target = datasets.make_classifi... | mit |
hasadna/OpenTrain | webserver/opentrain/algorithm/shape_detector.py | 1 | 1286 | from scipy import spatial
import os
import config
import numpy as np
import stops
import shapes
from utils import *
from common.ot_utils import *
from collections import deque
from common import ot_utils
try:
import matplotlib.pyplot as plt
except ImportError:
pass
import datetime
import bssid_tracker
from red... | bsd-3-clause |
calliope-project/calliope | calliope/test/test_backend_pyomo_constraints_conversion_plus.py | 1 | 10791 | import pytest # noqa: F401
from calliope.test.common.util import build_test_model as build_model
from calliope.test.common.util import check_variable_exists
class TestBuildConversionPlusConstraints:
# conversion_plus.py
def test_no_balance_conversion_plus_primary_constraint(self):
"""
sets.l... | apache-2.0 |
GraphProcessor/CommunityDetectionCodes | Prensentation/algorithms/clique_percolation/problem_vis.py | 1 | 1337 | import networkx as nx
import matplotlib.pyplot as plt
from networkx.drawing.nx_agraph import graphviz_layout
def vis_input(graph):
# pos = graphviz_layout(graph)
pos = nx.circular_layout(graph)
nx.draw(graph, with_labels=True, pos=pos, font_size=20, node_size=2000, alpha=0.8, width=4,
edge_col... | gpl-2.0 |
caidongyun/pylearn2 | pylearn2/packaged_dependencies/theano_linear/unshared_conv/test_localdot.py | 44 | 5013 | from __future__ import print_function
import nose
import unittest
import numpy as np
from theano.compat.six.moves import xrange
import theano
from .localdot import LocalDot
from ..test_matrixmul import SymbolicSelfTestMixin
class TestLocalDot32x32(unittest.TestCase, SymbolicSelfTestMixin):
channels = 3
bs... | bsd-3-clause |
cainiaocome/scikit-learn | examples/ensemble/plot_forest_iris.py | 335 | 6271 | """
====================================================================
Plot the decision surfaces of ensembles of trees on the iris dataset
====================================================================
Plot the decision surfaces of forests of randomized trees trained on pairs of
features of the iris dataset.
... | bsd-3-clause |
leesavide/pythonista-docs | Documentation/matplotlib/mpl_examples/mplot3d/mixed_subplots_demo.py | 12 | 1032 | """
Demonstrate the mixing of 2d and 3d subplots
"""
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
def f(t):
s1 = np.cos(2*np.pi*t)
e1 = np.exp(-t)
return np.multiply(s1,e1)
################
# First subplot
################
t1 = np.arange(0.0, 5.0, 0.1)
t2 = n... | apache-2.0 |
mblondel/scikit-learn | sklearn/decomposition/tests/test_kernel_pca.py | 40 | 8143 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import (assert_array_almost_equal, assert_less,
assert_equal, assert_not_equal,
assert_raises)
from sklearn.decomposition import PCA, KernelPCA
from sklearn.datasets import mak... | bsd-3-clause |
deonblaauw/paparazzi | sw/tools/calibration/calibrate_gyro.py | 87 | 4686 | #! /usr/bin/env python
# Copyright (C) 2010 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 your option)
# any later ... | gpl-2.0 |
hlin117/scikit-learn | sklearn/mixture/gmm.py | 6 | 32594 | """
Gaussian Mixture Models.
This implementation corresponds to frequentist (non-Bayesian) formulation
of Gaussian Mixture Models.
"""
# Author: Ron Weiss <ronweiss@gmail.com>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Bertrand Thirion <bertrand.thirion@inria.fr>
# Important note for the deprec... | bsd-3-clause |
harisbal/pandas | pandas/util/_doctools.py | 4 | 7099 | import numpy as np
import pandas.compat as compat
import pandas as pd
class TablePlotter(object):
"""
Layout some DataFrames in vertical/horizontal layout for explanation.
Used in merging.rst
"""
def __init__(self, cell_width=0.37, cell_height=0.25, font_size=7.5):
self.cell_width = cel... | bsd-3-clause |
NicovincX2/Python-3.5 | Physique/Mesure physique/Traitement du signal/Traitement numérique du signal/filtrage_rampe_bruitee.py | 1 | 1290 | # -*- coding: utf-8 -*-
import os
from math import exp, sin, atan, sqrt
import numpy as np
import matplotlib.pyplot as plt
def X(t0, T, h):
return np.arange(t0, t0 + T, h)
def Y(t0, T, h, y0, Phi):
t = X(t0, T, h)
y = np.zeros(len(t))
y[0] = y0
for k in range(len(t) - 1):
y[k + 1] = y[k... | gpl-3.0 |
ThomasMiconi/htmresearch | projects/sequence_classification/run_sequence_classifcation_experiment.py | 11 | 21901 | # ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2016, 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 progra... | agpl-3.0 |
clarkfitzg/xray | xray/test/test_utils.py | 2 | 4438 | import numpy as np
import pandas as pd
from xray.core import ops, utils
from xray.core.pycompat import OrderedDict
from . import TestCase
class TestSafeCastToIndex(TestCase):
def test(self):
dates = pd.date_range('2000-01-01', periods=10)
x = np.arange(5)
td = x * np.timedelta64(1, 'D')
... | apache-2.0 |
FFroehlich/AMICI | python/amici/pandas.py | 1 | 20862 | """
Pandas Wrappers
---------------
This modules contains convenience wrappers that allow for easy interconversion
between C++ objects from :mod:`amici.amici` and pandas DataFrames
"""
import pandas as pd
import numpy as np
import math
import copy
from typing import List, Union, Optional, Dict, SupportsFloat
from .nu... | bsd-2-clause |
EPFL-LQM/gpvmc | tools/vmc_legacy_utils/vmc_utils.py | 2 | 2702 | from scipy.linalg import eigh
from numpy.linalg import matrix_rank
from numpy import dot,conj,amax,argmin,zeros,eye,append,shape,diag,ones
from matplotlib.mlab import find
import copy
import warnings
import code
def argsort(seq):
return sorted(range(len(seq)),key=seq.__getitem__)
def bunch(instat,Nsamp,indices=Fa... | mit |
SoftwareDefinedBuildings/smap | python/doc/en/2.0/resources/plot_oat_tags.py | 6 | 1303 | """Example code plotting one day's worth of outside air time-series,
locating the streams using a metadata query.
@author Stephen Dawson-Haggerty <stevedh@eecs.berkeley.edu>
"""
from smap.archiver.client import SmapClient
from smap.contrib import dtutil
from matplotlib import pyplot
from matplotlib import dates
# m... | bsd-2-clause |
ProkopHapala/SimpleSimulationEngine | python/pySimE/space/exp/pykep/lambert_Fit_2.py | 1 | 1847 |
from pylab import *
import matplotlib.ticker as ticker
from PyKEP import lambert_problem
ax = subplot(111)
ax.xaxis.set_major_locator( ticker.MaxNLocator(nbins=10) )
ax.xaxis.set_minor_locator( ticker.AutoMinorLocator(n=10) )
ax.yaxis.set_major_locator( ticker.MaxNLocator(nbins=10) )
ax.yaxis.set_minor_l... | mit |
wheeler-microfluidics/teensy-minimal-rpc | rename.py | 1 | 2609 | from __future__ import absolute_import
import sys
import pandas as pd
from path_helpers import path
def main(root, old_name, new_name):
names = pd.Series([old_name, new_name], index=['old', 'new'])
underscore_names = names.map(lambda v: v.replace('-', '_'))
camel_names = names.str.split('-').map(lambda x... | gpl-3.0 |
jordancheah/aas | ch11-neuro/fish.py | 16 | 3438 | # coding=utf-8
# Copyright 2015 Sanford Ryza, Uri Laserson, Sean Owen and Joshua Wills
#
# See LICENSE file for further information.
# this code assumes you are working from an interactive Thunder (PySpark) shell
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
plt.ion()
################... | apache-2.0 |
kenshay/ImageScripter | ProgramData/SystemFiles/Python/Lib/site-packages/pandas/io/tests/generate_legacy_storage_files.py | 7 | 9673 | """ self-contained to write legacy storage (pickle/msgpack) files """
from __future__ import print_function
from distutils.version import LooseVersion
from pandas import (Series, DataFrame, Panel,
SparseSeries, SparseDataFrame,
Index, MultiIndex, bdate_range, to_msgpack,
... | gpl-3.0 |
shangwuhencc/scikit-learn | examples/applications/plot_species_distribution_modeling.py | 254 | 7434 | """
=============================
Species distribution modeling
=============================
Modeling species' geographic distributions is an important
problem in conservation biology. In this example we
model the geographic distribution of two south american
mammals given past observations and 14 environmental
varia... | bsd-3-clause |
hellokathy/coursera-compinvesting1-hw | HW3/marketsim.py | 2 | 2840 | ## Computational Investing I
## HW 3 - marketsum.py
##
## Author: alexcpsec
import pandas as pd
import pandas.io.parsers as pd_par
import numpy as np
import math
import copy
import QSTK.qstkutil.qsdateutil as du
import datetime as dt
import QSTK.qstkutil.DataAccess as da
import QSTK.qstkutil.tsutil as tsu
startCash =... | mit |
zooniverse/aggregation | blog/old_weather/faces.py | 1 | 4117 | print(__doc__)
import matplotlib
matplotlib.use('WXAgg')
# Authors: Vlad Niculae, Alexandre Gramfort
# License: BSD 3 clause
import logging
from time import time
from numpy.random import RandomState
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_olivetti_faces
from sklearn.cluster import MiniBatc... | apache-2.0 |
wangyum/spark | python/pyspark/sql/session.py | 4 | 31160 | #
# 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 us... | apache-2.0 |
phildias/dropbox_folder_size_calculator | folder_size_calculator.py | 1 | 3464 | import dropbox
import pandas as pd
# Dropbox access token. Change the string below to your own token.
access_token = 'INSERT_YOUR_TOKEN_HERE'
# Instance of Dropbox class that grants access to the user's Dropbox files.
dbx = dropbox.Dropbox(access_token)
# Global list of all folders.
all_folders = []
# Global list o... | gpl-3.0 |
Lawrence-Liu/scikit-learn | sklearn/utils/tests/test_murmurhash.py | 261 | 2836 | # Author: Olivier Grisel <olivier.grisel@ensta.org>
#
# License: BSD 3 clause
import numpy as np
from sklearn.externals.six import b, u
from sklearn.utils.murmurhash import murmurhash3_32
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal
from nose.tools import assert_equa... | bsd-3-clause |
rohanp/scikit-learn | sklearn/neighbors/graph.py | 26 | 6189 | """Nearest Neighbors graph functions"""
# Author: Jake Vanderplas <vanderplas@astro.washington.edu>
#
# License: BSD 3 clause (C) INRIA, University of Amsterdam
import warnings
from .base import KNeighborsMixin, RadiusNeighborsMixin
from .unsupervised import NearestNeighbors
def _check_params(X, metric, p, metric_... | bsd-3-clause |
fengzhyuan/scikit-learn | examples/linear_model/plot_sgd_iris.py | 286 | 2202 | """
========================================
Plot multi-class SGD on the iris dataset
========================================
Plot decision surface of multi-class SGD on iris dataset.
The hyperplanes corresponding to the three one-versus-all (OVA) classifiers
are represented by the dashed lines.
"""
print(__doc__)
... | bsd-3-clause |
jpautom/scikit-learn | sklearn/metrics/cluster/supervised.py | 22 | 30444 | """Utilities to evaluate the clustering performance of models
Functions named as *_score return a scalar value to maximize: the higher the
better.
"""
# Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Wei LI <kuantkid@gmail.com>
# Diego Molla <dmolla-aliod@gmail.com>
# License: BSD 3 clause
fr... | bsd-3-clause |
dstruck/comet-paper-scripts | synth_variation_pol_rega_scueal/analyze_noise.py | 1 | 8168 | # -*- coding: utf-8 -*-
"""
Created on Tue Jul 23 14:55:49 2013
@author: daniel
"""
from __future__ import division
from collections import Counter, defaultdict
scueal_translation = {
'CRF02':'02_AG','CRF03':'03_AB','CRF04':'04_cpx','CRF05':'05_DF','CRF06':'06_cpx'
,'CRF07':'07_BC','CRF08':'08_BC','CRF09':'09_cpx','C... | gpl-2.0 |
jongyoul/incubator-zeppelin | python/src/main/resources/python/mpl_config.py | 41 | 3653 | # 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 use ... | apache-2.0 |
jlegendary/scikit-learn | examples/model_selection/plot_validation_curve.py | 229 | 1823 | """
==========================
Plotting Validation Curves
==========================
In this plot you can see the training scores and validation scores of an SVM
for different values of the kernel parameter gamma. For very low values of
gamma, you can see that both the training score and the validation score are
low. ... | bsd-3-clause |
bloyl/mne-python | tutorials/raw/40_visualize_raw.py | 5 | 8454 | # -*- coding: utf-8 -*-
"""
.. _tut-visualize-raw:
Built-in plotting methods for Raw objects
=========================================
This tutorial shows how to plot continuous data as a time series, how to plot
the spectral density of continuous data, and how to plot the sensor locations
and projectors stored in `~... | bsd-3-clause |
kenshay/ImageScripter | ProgramData/SystemFiles/Python/Lib/site-packages/pandas/core/panelnd.py | 14 | 4605 | """ Factory methods to create N-D panels """
import warnings
from pandas.compat import zip
import pandas.compat as compat
def create_nd_panel_factory(klass_name, orders, slices, slicer, aliases=None,
stat_axis=2, info_axis=0, ns=None):
""" manufacture a n-d class:
DEPRECATED. Pan... | gpl-3.0 |
vizual54/MissionPlanner | Lib/site-packages/scipy/signal/fir_filter_design.py | 53 | 18572 | """Functions for FIR filter design."""
from math import ceil, log
import numpy as np
from numpy.fft import irfft
from scipy.special import sinc
import sigtools
# Some notes on function parameters:
#
# `cutoff` and `width` are given as a numbers between 0 and 1. These
# are relative frequencies, expressed as a fracti... | gpl-3.0 |
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