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 |
|---|---|---|---|---|---|
LuciusV/ORCA-pyPDOS | PlotPartialDOS.py | 1 | 2508 | from functions import Gaussian, dump
import numpy as np
import matplotlib.pyplot as plt
def PlotPartialDOS(args, fig, ax, Type, SpeciesToPlot, PlotParameters, Eigenvalues, Occupations, ContributionMatrix, **kwargs):
Domain = PlotParameters['Domain']
MoInEnergyRange = PlotParameters['MoInEnergyRange']
MoIn... | gpl-3.0 |
NiclasEriksen/py-towerwars | src/numpy/doc/creation.py | 54 | 5503 | """
==============
Array Creation
==============
Introduction
============
There are 5 general mechanisms for creating arrays:
1) Conversion from other Python structures (e.g., lists, tuples)
2) Intrinsic numpy array array creation objects (e.g., arange, ones, zeros,
etc.)
3) Reading arrays from disk, either from... | cc0-1.0 |
ssaeger/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 |
mbayon/TFG-MachineLearning | venv/lib/python3.6/site-packages/pandas/core/computation/expressions.py | 7 | 7864 | """
Expressions
-----------
Offer fast expression evaluation through numexpr
"""
import warnings
import numpy as np
from pandas.core.common import _values_from_object
from pandas.core.computation import _NUMEXPR_INSTALLED
from pandas.core.config import get_option
if _NUMEXPR_INSTALLED:
import numexpr as ne
_TE... | mit |
ratschlab/RGAN | RVAE/sine_generation_RVAE_concatenated_input.py | 1 | 18775 | # Targets not used, not possible to condition generated sequences
import data_utils_2
import pandas as pd
import numpy as np
import tensorflow as tf
import math, random, itertools
import pickle
import time
import json
import os
import math
from data_utils_2 import get_data
import tensorflow as tf
import numpy as np
... | mit |
SheffieldML/GPy | GPy/testing/plotting_tests.py | 1 | 21759 | #===============================================================================
# Copyright (c) 2015, Max Zwiessele
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source... | bsd-3-clause |
marionleborgne/nupic.research | projects/nlp/run_tm_learning.py | 11 | 5601 | #!/usr/bin/env python
# ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2015, Numenta, Inc. Unless you have purchased from
# Numenta, Inc. a separate commercial license for this software code, the
# following terms and conditio... | agpl-3.0 |
ilyes14/scikit-learn | sklearn/neighbors/base.py | 71 | 31147 | """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 |
tawsifkhan/scikit-learn | benchmarks/bench_plot_nmf.py | 206 | 5890 | """
Benchmarks of Non-Negative Matrix Factorization
"""
from __future__ import print_function
from collections import defaultdict
import gc
from time import time
import numpy as np
from scipy.linalg import norm
from sklearn.decomposition.nmf import NMF, _initialize_nmf
from sklearn.datasets.samples_generator import... | bsd-3-clause |
jdavidrcamacho/Tests_GP | 04 - Code tests/Tests.py | 1 | 6809 | # -*- coding: utf-8 -*-
import Gedi as gedi
import numpy as np
import matplotlib.pylab as pl
pl.close()
##### INITIAL DATA ###########################################################
#np.random.seed(12345)
x = 10 * np.sort(np.random.rand(101))
yerr = 0.2 * np.ones_like(x)
y = np.sin(x) + yerr * np.random.randn(len(... | mit |
GauravBh1010tt/DeepLearn | fake news challenge (FNC-1)/cnn_stop.py | 1 | 12257 | # -*- coding: utf-8 -*-
import utility
import warnings
import numpy as np
import gensim as gen
from keras.preprocessing import sequence
from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Activation, Input, Merge
from keras.layers import Embedding
from keras.layers import Conv... | mit |
JsNoNo/scikit-learn | doc/tutorial/text_analytics/solutions/exercise_01_language_train_model.py | 254 | 2253 | """Build a language detector model
The goal of this exercise is to train a linear classifier on text features
that represent sequences of up to 3 consecutive characters so as to be
recognize natural languages by using the frequencies of short character
sequences as 'fingerprints'.
"""
# Author: Olivier Grisel <olivie... | bsd-3-clause |
APPIAN-PET/APPIAN | src/qc.py | 1 | 42641 | # vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4 mouse=a
import matplotlib
matplotlib.rcParams['figure.facecolor'] = '1.'
matplotlib.use('Agg')
import ants
import numpy as np
import pandas as pd
import os
import imageio
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as niu
import nibabel as... | mit |
muendelezaji/workload-automation | wlauto/utils/doc.py | 4 | 10459 | # Copyright 2014-2015 ARM Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in w... | apache-2.0 |
nlaanait/pyxrim | pyxrim/PlotLib.py | 1 | 6186 | # -*- coding: utf-8 -*-
"""
Created on Fri Oct 9 10:23:05 2015
@author: nouamanelaanait
"""
import numpy as np
from matplotlib import pyplot as plt
def imageGallery(n_col, n_row, Title , images, **kwargs):
''' Plots a bunch of images.
num := figure num
n_col := # of columns
n_rows := ...
titleLi... | mit |
jkarnows/scikit-learn | examples/covariance/plot_lw_vs_oas.py | 248 | 2903 | """
=============================
Ledoit-Wolf vs OAS estimation
=============================
The usual covariance maximum likelihood estimate can be regularized
using shrinkage. Ledoit and Wolf proposed a close formula to compute
the asymptotically optimal shrinkage parameter (minimizing a MSE
criterion), yielding th... | bsd-3-clause |
cggh/scikit-allel | allel/stats/selection.py | 1 | 39470 | # -*- coding: utf-8 -*-
import multiprocessing
from multiprocessing.pool import ThreadPool
import numpy as np
from allel.compat import memoryview_safe
from allel.util import asarray_ndim, check_dim0_aligned, check_integer_dtype
from allel.model.ndarray import HaplotypeArray, AlleleCountsArray
from allel.stats.windo... | mit |
hdmetor/scikit-learn | sklearn/feature_extraction/hashing.py | 15 | 5727 | # Author: Lars Buitinck <L.J.Buitinck@uva.nl>
# License: BSD 3 clause
import numbers
import numpy as np
import scipy.sparse as sp
from . import _hashing
from ..base import BaseEstimator, TransformerMixin
def _iteritems(d):
"""Like d.iteritems, but accepts any collections.Mapping."""
return d.iteritems() if... | bsd-3-clause |
yyjiang/scikit-learn | sklearn/ensemble/weight_boosting.py | 97 | 40773 | """Weight Boosting
This module contains weight boosting estimators for both classification and
regression.
The module structure is the following:
- The ``BaseWeightBoosting`` base class implements a common ``fit`` method
for all the estimators in the module. Regression and classification
only differ from each ot... | bsd-3-clause |
pythonvietnam/scikit-learn | examples/linear_model/plot_ols_3d.py | 350 | 2040 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Sparsity Example: Fitting only features 1 and 2
=========================================================
Features 1 and 2 of the diabetes-dataset are fitted and
plotted below. It illustrates that although feature... | bsd-3-clause |
tongwang01/tensorflow | tensorflow/contrib/learn/python/learn/tests/dataframe/feeding_functions_test.py | 30 | 4777 | # 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 |
m4rx9/rna-pdb-tools | rna_tools/tools/pdbs_measure_atom_dists/pdbs_measure_atom_dists.py | 2 | 5992 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""This is a quick and dirty method of comparison two RNA structures (stored in pdb files).
It measures the distance between the relevan atoms (C4') for nucleotides defined as "x" in the
sequence alignment.
author: F. Stefaniak, modified by A. Zyla, supervision of mmagnu... | mit |
ActiveState/code | recipes/Python/578834_Hierarchical_Clustering_Heatmap/recipe-578834.py | 1 | 11406 | """
This code was adapted from the following recipe:
* http://altanalyze.blogspot.se/2012/06/hierarchical-clustering-heatmaps-in.html
* http://code.activestate.com/recipes/578175/
Which was in turn inspired by many other posts:
* http://stackoverflow.com/questions/7664826
* http://stackoverflow.com/quest... | mit |
darylsew/potato | caffe/examples/finetune_flickr_style/assemble_data.py | 38 | 3636 | #!/usr/bin/env python
"""
Form a subset of the Flickr Style data, download images to dirname, and write
Caffe ImagesDataLayer training file.
"""
import os
import urllib
import hashlib
import argparse
import numpy as np
import pandas as pd
from skimage import io
import multiprocessing
# Flickr returns a special image i... | apache-2.0 |
hugobowne/scikit-learn | sklearn/linear_model/bayes.py | 50 | 16145 | """
Various bayesian regression
"""
from __future__ import print_function
# Authors: V. Michel, F. Pedregosa, A. Gramfort
# License: BSD 3 clause
from math import log
import numpy as np
from scipy import linalg
from .base import LinearModel
from ..base import RegressorMixin
from ..utils.extmath import fast_logdet, p... | bsd-3-clause |
francesco-mannella/dmp-esn | parametric/parametric_dmp/bin/data/results/plot.py | 18 | 1043 | #!/usr/bin/env python
import glob
import numpy as np
import matplotlib.pyplot as plt
import os
import sys
pathname = os.path.dirname(sys.argv[0])
if pathname:
os.chdir(pathname)
n_dim = None
trains = []
for fname in glob.glob("tl*"):
t = np.loadtxt(fname)
trains.append(t)
tests = []
for fname in glob... | gpl-2.0 |
rew4332/tensorflow | tensorflow/examples/skflow/resnet.py | 12 | 5640 | # 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 |
hugobowne/scikit-learn | sklearn/linear_model/tests/test_sparse_coordinate_descent.py | 34 | 9987 | 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 |
TravisCouture/GtManipulator | gtmanipulator/gtmanipulator.py | 1 | 14359 | import pandas as pd
import numpy as np
import operator
class GtManipulator:
"""Class for handling Illumina iScan microarray data.
Args:
file (str): Filepath for file to be loaded as a DataFrame object.
(default None)
data (DataFrame object): Alternative to loading in data from... | mit |
corburn/scikit-bio | skbio/stats/power.py | 7 | 51714 | r"""
Empirical Power Estimation (:mod:`skbio.stats.power`)
=====================================================
.. currentmodule:: skbio.stats.power
The purpose of this module is to provide empirical, post-hoc power estimation
of normally and non-normally distributed data. It also provides support to
subsample data ... | bsd-3-clause |
AlirezaShahabi/zipline | tests/risk/answer_key.py | 39 | 11989 | #
# 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 |
ddsc/ddsc-worker | ddsc_worker/import_auth.py | 1 | 4685 | # (c) Fugro Geoservices. MIT licensed, see LICENSE.rst.
from __future__ import absolute_import
from uuid import UUID
import logging
from django.contrib.auth.models import User
import pandas
from ddsc_core.auth import PERMISSION_CHANGE
from ddsc_core.models import Folder
from ddsc_core.models import IPAddr... | mit |
ikki407/stacking | examples/Santander/ikki_feat_ver1.py | 1 | 20266 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import os
#os.chdir('/Users/IkkiTanaka/Documents/kaggle/Santander/')
#各種PATH
from stacking.base import FOLDER_NAME, PATH, INPUT_PATH, OUTPUT_PATH, ORIGINAL_TRAIN_FORMAT, SUBMIT_FORMAT
#import bloscpack
from datetime import date
fr... | mit |
zorojean/tushare | tushare/stock/reference.py | 27 | 25190 | # -*- coding:utf-8 -*-
"""
投资参考数据接口
Created on 2015/03/21
@author: Jimmy Liu
@group : waditu
@contact: jimmysoa@sina.cn
"""
from __future__ import division
from tushare.stock import cons as ct
from tushare.stock import ref_vars as rv
from tushare.util import dateu as dt
import pandas as pd
import time
i... | bsd-3-clause |
jrversteegh/softsailor | deps/scipy-0.10.0b2/scipy/interpolate/ndgriddata.py | 3 | 6229 | """
Convenience interface to N-D interpolation
.. versionadded:: 0.9
"""
import numpy as np
from interpnd import LinearNDInterpolator, NDInterpolatorBase, \
CloughTocher2DInterpolator, _ndim_coords_from_arrays
from scipy.spatial import cKDTree
__all__ = ['griddata', 'NearestNDInterpolator', 'LinearNDInterpolat... | gpl-3.0 |
hlin117/statsmodels | statsmodels/sandbox/examples/try_multiols.py | 33 | 1243 | # -*- coding: utf-8 -*-
"""
Created on Sun May 26 13:23:40 2013
Author: Josef Perktold, based on Enrico Giampieri's multiOLS
"""
#import numpy as np
import pandas as pd
import statsmodels.api as sm
from statsmodels.sandbox.multilinear import multiOLS, multigroup
data = sm.datasets.longley.load_pandas()
df = data.e... | bsd-3-clause |
sinhrks/scikit-learn | examples/calibration/plot_calibration_multiclass.py | 272 | 6972 | """
==================================================
Probability Calibration for 3-class classification
==================================================
This example illustrates how sigmoid calibration changes predicted
probabilities for a 3-class classification problem. Illustrated is the
standard 2-simplex, wher... | bsd-3-clause |
pombredanne/metamorphosys-desktop | metamorphosys/META/analysis_tools/PYTHON_RICARDO/output_ergonomics/scripts/ergonomics.py | 1 | 55890 | """Perform ergonomics tests on assembly """
import logging, operator, os, sys, _winreg
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.tri import Triangulation
from matplotlib.patches import Wedge
from matplotlib.ticker import NullFormatter
def query_analysis_tools():
analysis_too... | mit |
cpcloud/dask | dask/dataframe/tests/test_format.py | 1 | 12438 | # coding: utf-8
import pandas as pd
import dask.dataframe as dd
def test_repr():
df = pd.DataFrame({'x': list(range(100))})
ddf = dd.from_pandas(df, 3)
for x in [ddf, ddf.index, ddf.x]:
assert type(x).__name__ in repr(x)
assert str(x.npartitions) in repr(x)
def test_repr_meta_mutation(... | bsd-3-clause |
NunoEdgarGub1/scikit-learn | sklearn/covariance/graph_lasso_.py | 127 | 25626 | """GraphLasso: sparse inverse covariance estimation with an l1-penalized
estimator.
"""
# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
# License: BSD 3 clause
# Copyright: INRIA
import warnings
import operator
import sys
import time
import numpy as np
from scipy import linalg
from .empirical_covariance_ im... | bsd-3-clause |
deepakantony/sms-tools | lectures/09-Sound-description/plots-code/spectralFlux-onsetFunction.py | 25 | 1330 | import numpy as np
import matplotlib.pyplot as plt
import essentia.standard as ess
M = 1024
N = 1024
H = 512
fs = 44100
spectrum = ess.Spectrum(size=N)
window = ess.Windowing(size=M, type='hann')
flux = ess.Flux()
onsetDetection = ess.OnsetDetection(method='hfc')
x = ess.MonoLoader(filename = '../../../sounds/speech-m... | agpl-3.0 |
SKA-ScienceDataProcessor/algorithm-reference-library | deprecated_code/workflows/mpi/imaging-pipelines-predict-mpi.py | 1 | 9477 |
# coding: utf-8
# # Pipeline processing using serial workflows.
#
# This is a serial unrolled version of the predict step
# In[1]:
#get_ipython().run_line_magic('matplotlib', 'inline')
import os
import sys
sys.path.append(os.path.join('..', '..'))
from data_models.parameters import arl_path
from mpi4py import ... | apache-2.0 |
alberlab/alabtools | docs/conf.py | 1 | 5369 | # -*- coding: utf-8 -*-
#
# alabtools documentation build configuration file, created by
# sphinx-quickstart on Thu Mar 23 14:06:15 2017.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
#... | gpl-3.0 |
treycausey/scikit-learn | sklearn/decomposition/__init__.py | 11 | 1307 | """
The :mod:`sklearn.decomposition` module includes matrix decomposition
algorithms, including among others PCA, NMF or ICA. Most of the algorithms of
this module can be regarded as dimensionality reduction techniques.
"""
from .nmf import NMF, ProjectedGradientNMF
from .pca import PCA, RandomizedPCA, ProbabilisticPC... | bsd-3-clause |
18padx08/PPTex | PPTexEnv_x86_64/lib/python2.7/site-packages/mpl_toolkits/axes_grid1/anchored_artists.py | 8 | 8116 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from matplotlib.patches import Rectangle, Ellipse
import numpy as np
from matplotlib.offsetbox import AnchoredOffsetbox, AuxTransformBox, VPacker,\
TextArea, AnchoredText, DrawingArea, Annota... | mit |
anewmark/galaxy_dark_matter | def_age_plots.py | 1 | 2969 | import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
import math
outdir='/Users/amandanewmark/repositories/galaxy_dark_matter/lumprofplots/clumps/'
def age_plot(x,y, start, end, tag=[]):
#print('x spacing: ', xspace)
print('x_center values: ', x)
print('y values= ', y)
start=np.arr... | mit |
shennjia/weblte | matplot/test_subframe.py | 1 | 6044 | __author__ = 'shenojia'
import pprint
import re
import sys
import unittest
import weakref
import numpy as np
from matplotlib.path import Path
from matplotlib.patches import PathPatch
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
sys.path.insert(0, '..')
from R12_36211.RE import RE
from R12_362... | mit |
lmd-in-learning/MachineLearning | RegressionTree/treeExplore.py | 1 | 2311 | #treeExplore.py
from numpy import *
from Tkinter import *
import regTrees
import matplotlib
matplotlib.use('TkAgg')
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.figure import Figure
def reDraw(tolS, tolN):
reDraw.f.clf()
reDraw.a = reDraw.f.add_subplot(111)
if chkBtnVar.g... | gpl-3.0 |
pratapvardhan/scikit-learn | examples/svm/plot_svm_nonlinear.py | 268 | 1091 | """
==============
Non-linear SVM
==============
Perform binary classification using non-linear SVC
with RBF kernel. The target to predict is a XOR of the
inputs.
The color map illustrates the decision function learned by the SVC.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn imp... | bsd-3-clause |
mavlyutovrus/interval_index | python/graphs_2_time_individ_query_len.py | 1 | 8416 | # -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import matplotlib
from pylab import *
import numpy
from copy_reg import remove_extension
from heapq import heappush
from mpl_toolkits.axes_grid1 import host_subplot
import mpl_toolkits.axisartist as AA
algo2index = []
def load_data(filename):
all_results = ... | apache-2.0 |
AvengersPy/MyPairs | zipline/examples/pairtrade.py | 11 | 4925 | #!/usr/bin/env python
#
# Copyright 2013 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | apache-2.0 |
YihaoLu/statsmodels | statsmodels/sandbox/rls.py | 33 | 5179 | """Restricted least squares
from pandas
License: Simplified BSD
"""
from __future__ import print_function
import numpy as np
from statsmodels.regression.linear_model import WLS, GLS, RegressionResults
class RLS(GLS):
"""
Restricted general least squares model that handles linear constraints
Parameters
... | bsd-3-clause |
bhargav/scikit-learn | sklearn/linear_model/tests/test_sgd.py | 14 | 44270 | 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 |
procoder317/scikit-learn | sklearn/decomposition/dict_learning.py | 104 | 44632 | """ Dictionary learning
"""
from __future__ import print_function
# Author: Vlad Niculae, Gael Varoquaux, Alexandre Gramfort
# License: BSD 3 clause
import time
import sys
import itertools
from math import sqrt, ceil
import numpy as np
from scipy import linalg
from numpy.lib.stride_tricks import as_strided
from ..b... | bsd-3-clause |
warith-harchaoui/TensorflowWrappers4NeuralNetworks | conv_example.py | 1 | 6853 | """
Tensorflow Personal Toolbox for Neural Networks
Convolutional Neural Network for MNIST Demo
--
Warith HARCHAOUI, Astrid MERCKLING
2017
"""
print(" === Convolutional Neural Network for MNIST Demo === ")
import tensorflow as tf
import numpy as np
import math
from utils import *
from matplotlib import pyplot as p... | bsd-3-clause |
irockafe/revo_healthcare | src/data/data_exploration.py | 1 | 6439 | import numpy as np
import pandas as pd
import seaborn as sns
import os
import matplotlib.pyplot as plt
# Code to explore raw data
# and get a feel for it
def plot_feature_sparsity(data, class_dict=None, fxn=None, **kwargs):
# data is in (samples x features)
# sum all nans in each sample (true zeros)
# di... | mit |
tapomayukh/projects_in_python | sandbox_tapo/src/skin_related/Stiffness/calc_stiffness_all_taxels.py | 1 | 6127 | #!/usr/bin/env python
import math, numpy as np
#from enthought.mayavi import mlab
import matplotlib.pyplot as pp
import matplotlib.cm as cm
import scipy.ndimage as ni
import roslib; roslib.load_manifest('sandbox_tapo_darpa_m3')
import rospy
import tf
#import hrl_lib.mayavi2_util as mu
import hrl_lib.viz as hv
import... | mit |
jlegendary/scikit-learn | examples/cluster/plot_dict_face_patches.py | 337 | 2747 | """
Online learning of a dictionary of parts of faces
==================================================
This example uses a large dataset of faces to learn a set of 20 x 20
images patches that constitute faces.
From the programming standpoint, it is interesting because it shows how
to use the online API of the sciki... | bsd-3-clause |
arjoly/scikit-learn | sklearn/utils/tests/test_class_weight.py | 90 | 12846 | import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_blobs
from sklearn.utils.class_weight import compute_class_weight
from sklearn.utils.class_weight import compute_sample_weight
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testin... | bsd-3-clause |
anupam-mitra/PySpikeSort | spikesort/cluster/__init__.py | 1 | 2703 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# ${FILENAME}
#
# Copyright 2015 Anupam Mitra <anupam.mitra@gmail.com>
#
# 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... | gpl-3.0 |
sserrot/champion_relationships | venv/share/doc/networkx-2.4/examples/graph/plot_football.py | 1 | 1470 | #!/usr/bin/env python
"""
========
Football
========
Load football network in GML format and compute some network statistcs.
Shows how to download GML graph in a zipped file, unpack it, and load
into a NetworkX graph.
Requires Internet connection to download the URL
http://www-personal.umich.edu/~mejn/netdata/footba... | mit |
kylerbrown/scikit-learn | sklearn/cluster/tests/test_affinity_propagation.py | 341 | 2620 | """
Testing for Clustering methods
"""
import numpy as np
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.cluster.affinity_propagation_ import AffinityPropagation
from sklearn.cluster.affinity_propagatio... | bsd-3-clause |
kyleabeauchamp/HMCNotes | code/correctness/old/test_hmc_correctness_ljbox_converger.py | 1 | 2651 | import lb_loader
import pandas as pd
import simtk.openmm.app as app
import numpy as np
import simtk.openmm as mm
from simtk import unit as u
from openmmtools import hmc_integrators, testsystems
pd.set_option('display.width', 1000)
collision_rate = 10000.0 / u.picoseconds
sysname = "ljbox"
system, positions, groups, ... | gpl-2.0 |
berkeley-stat222/mousestyles | doc/source/report/plots/plot_kernel_smoothing.py | 3 | 1288 | import matplotlib.pyplot as plt
import numpy as np
from mousestyles.distribution import (powerlaw_pdf, exp_pdf,
powerlaw_inverse_cdf, exp_inverse_cdf)
from mousestyles.est_power_param import (fit_powerlaw, fit_exponential,
getdistance)
from ... | bsd-2-clause |
thesuperzapper/tensorflow | tensorflow/examples/learn/hdf5_classification.py | 60 | 2190 | # 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 |
legacysurvey/legacypipe | validationtests/DESIccdManera.py | 2 | 38903 | #
import numpy as np
import healpy as hp
import astropy.io.fits as pyfits
from multiprocessing import Pool
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
from quicksipManera import *
import fitsio
### ------------ A couple of useful conversions -----------------------
def zeropointToScale(z... | bsd-3-clause |
tosolveit/scikit-learn | examples/ensemble/plot_voting_decision_regions.py | 230 | 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 |
mxjl620/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 |
tomlof/scikit-learn | sklearn/tests/test_dummy.py | 186 | 17778 | from __future__ import division
import numpy as np
import scipy.sparse as sp
from sklearn.base import clone
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_eq... | bsd-3-clause |
fidelram/deepTools | deeptools/plotCorrelation.py | 1 | 10795 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import argparse
import numpy as np
import matplotlib
matplotlib.use('Agg')
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['svg.fonttype'] = 'none'
import matplotlib.pyplot as plt
from deeptools.correlation import Correlation
from deeptools.parserC... | gpl-3.0 |
andaag/scikit-learn | sklearn/covariance/tests/test_robust_covariance.py | 213 | 3359 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Virgile Fritsch <virgile.fritsch@inria.fr>
#
# License: BSD 3 clause
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_alm... | bsd-3-clause |
TimKreienkamp/TextMiningProject | Code/deeplearning.py | 1 | 5657 | """
Created on Tue Jun 9 10:24:53 2015
@author: timkreienkamp
"""
################################################################
# 0. Load Data and Libraries
################################################################
import pandas as pd
import numpy as np
from nltk.stem import *
from h2o import *
from sklea... | cc0-1.0 |
aabadie/scikit-learn | examples/neighbors/plot_digits_kde_sampling.py | 108 | 2026 | """
=========================
Kernel Density Estimation
=========================
This example shows how kernel density estimation (KDE), a powerful
non-parametric density estimation technique, can be used to learn
a generative model for a dataset. With this generative model in place,
new samples can be drawn. These... | bsd-3-clause |
q1ang/seaborn | examples/structured_heatmap.py | 24 | 1304 | """
Discovering structure in heatmap data
=====================================
_thumb: .4, .2
"""
import pandas as pd
import seaborn as sns
sns.set(font="monospace")
# Load the brain networks example dataset
df = sns.load_dataset("brain_networks", header=[0, 1, 2], index_col=0)
# Select a subset of the networks
use... | bsd-3-clause |
mrgloom/h2o-3 | h2o-py/tests/testdir_algos/glm/pyunit_link_functions_poissonGLM.py | 3 | 2252 | import sys
sys.path.insert(1, "../../../")
import h2o
import pandas as pd
import zipfile
import statsmodels.api as sm
def link_functions_poisson(ip,port):
print("Read in prostate data.")
h2o_data = h2o.import_file(path=h2o.locate("smalldata/prostate/prostate_complete.csv.zip"))
sm_data = pd.rea... | apache-2.0 |
NickiRom/SetList | scripts/Beats2EchoNest.py | 1 | 2202 | # -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>
# <codecell>
# search for EchoNest ids based on track name and duration
import requests
import json
from json import load
from pprint import pprint
import urllib2
from urllib2 import urlopen
import pandas as pd
from pandas import *
import numpy as np
import urllib
... | mit |
intfloat/weibo-emotion-analyzer | scripts/process_data.py | 1 | 3689 | # -*- coding: utf-8 -*-
import argparse
import numpy as np
import cPickle
from collections import defaultdict
import sys, re
import pandas as pd
def build_data_cv(data_folder):
"""
Loads data
"""
revs = []
vocab = defaultdict(float)
# idx = 0 corresponds to training data,
# idx = 1 corr... | gpl-2.0 |
yavalvas/yav_com | build/matplotlib/examples/statistics/histogram_demo_multihist.py | 7 | 1032 | """
Demo of the histogram (hist) function with multiple data sets.
Plot histogram with multiple sample sets and demonstrate:
* Use of legend with multiple sample sets
* Stacked bars
* Step curve with a color fill
* Data sets of different sample sizes
"""
import numpy as np
import matplotlib.pyplot as ... | mit |
phobson/statsmodels | statsmodels/examples/ex_generic_mle_t.py | 29 | 10826 | # -*- coding: utf-8 -*-
"""
Created on Wed Jul 28 08:28:04 2010
Author: josef-pktd
"""
from __future__ import print_function
import numpy as np
from scipy import stats, special
import statsmodels.api as sm
from statsmodels.base.model import GenericLikelihoodModel
#redefine some shortcuts
np_log = np.log
np_pi = np... | bsd-3-clause |
kgullikson88/TS23-Scripts | PlotFits.py | 1 | 2665 | import sys
import itertools
import matplotlib.pyplot as plt
import numpy as np
import FitsUtils
if __name__ == "__main__":
fileList = []
tellurics = False
normalize = False
byorder = False # Plots one order at a time
pixelscale = False
oneplot = False
for arg in sys.argv[1:]:
if... | gpl-3.0 |
gyanderson/SimColumn | simulation.py | 1 | 16850 | #!/usr/bin/python -tt
""" Simulate a distillation """
import sys
from scipy.integrate import odeint
import numpy as np
import matplotlib.pyplot as plt
eps = sys.float_info.epsilon
def Antoine(T, species):
""" Find individual vapor pressure as a function of temperature.
Each species has characteristic cons... | gpl-3.0 |
BRML/climin | climin/util.py | 1 | 13906 | # -*- coding: utf-8 -*-
from __future__ import absolute_import
import inspect
import itertools
import random
import warnings
import numpy as np
from .gd import GradientDescent
from .bfgs import Lbfgs
from .cg import NonlinearConjugateGradient
from .rprop import Rprop
from .rmsprop import RmsProp
from .adadelta impo... | bsd-3-clause |
KristianJensen/cameo | cameo/config.py | 1 | 1676 | # Copyright 2014 Novo Nordisk Foundation Center for Biosustainability, DTU.
#
# 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 requi... | apache-2.0 |
gertingold/scipy | scipy/stats/_multivariate.py | 5 | 121436 | #
# Author: Joris Vankerschaver 2013
#
from __future__ import division, print_function, absolute_import
import math
import numpy as np
from numpy import asarray_chkfinite, asarray
import scipy.linalg
from scipy._lib import doccer
from scipy.special import gammaln, psi, multigammaln, xlogy, entr
from scipy._lib._util i... | bsd-3-clause |
untom/scikit-learn | sklearn/covariance/__init__.py | 389 | 1157 | """
The :mod:`sklearn.covariance` module includes methods and algorithms to
robustly estimate the covariance of features given a set of points. The
precision matrix defined as the inverse of the covariance is also estimated.
Covariance estimation is closely related to the theory of Gaussian Graphical
Models.
"""
from ... | bsd-3-clause |
ssaeger/scikit-learn | examples/linear_model/plot_logistic.py | 312 | 1426 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Logit function
=========================================================
Show in the plot is how the logistic regression would, in this
synthetic dataset, classify values as either 0 or 1,
i.e. class one or two, u... | bsd-3-clause |
rcrowder/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/texmanager.py | 69 | 16818 | """
This module supports embedded TeX expressions in matplotlib via dvipng
and dvips for the raster and postscript backends. The tex and
dvipng/dvips information is cached in ~/.matplotlib/tex.cache for reuse between
sessions
Requirements:
* latex
* \*Agg backends: dvipng
* PS backend: latex w/ psfrag, dvips, and Gh... | agpl-3.0 |
mfjb/scikit-learn | sklearn/datasets/lfw.py | 141 | 19372 | """Loader for the Labeled Faces in the Wild (LFW) dataset
This dataset is a collection of JPEG pictures of famous people collected
over the internet, all details are available on the official website:
http://vis-www.cs.umass.edu/lfw/
Each picture is centered on a single face. The typical task is called
Face Veri... | bsd-3-clause |
jorge2703/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 |
pranavtbhat/EE219 | project2/a.py | 2 | 1417 | from sklearn.datasets import fetch_20newsgroups
import matplotlib.pyplot as plt
from collections import Counter
def fetch_all_categories():
return [
"comp.graphics",
"comp.os.ms-windows.misc",
"comp.sys.ibm.pc.hardware",
"comp.sys.mac.hardware"
] + [
"rec.autos",
"rec... | unlicense |
openstack/openstack-health | openstack_health/run_aggregator.py | 1 | 4510 | # Copyright 2015 Hewlett-Packard Development Company, L.P.
#
# 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... | apache-2.0 |
vdrhtc/Measurement-automation | resonator_tools/resonator_tools/calibration.py | 1 | 4324 |
import numpy as np
from scipy import sparse
from scipy.interpolate import interp1d
class calibration(object):
'''
some useful tools for manual calibration
'''
def normalize_zdata(self,z_data,cal_z_data):
return z_data/cal_z_data
def normalize_amplitude(self,z_data,cal_ampdata):
return z_data/cal_ampdata
... | gpl-3.0 |
karstenw/nodebox-pyobjc | examples/Extended Application/sklearn/examples/covariance/plot_sparse_cov.py | 1 | 5889 | """
======================================
Sparse inverse covariance estimation
======================================
Using the GraphLasso estimator to learn a covariance and sparse precision
from a small number of samples.
To estimate a probabilistic model (e.g. a Gaussian model), estimating the
precision matrix, t... | mit |
jaeilepp/mne-python | mne/viz/tests/test_ica.py | 1 | 10966 | # Authors: Denis Engemann <denis.engemann@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: Simplified BSD
import os.path as op
import warnings
from numpy.testing import assert_raises, assert_equal, assert_array_equal
from nose.tools import assert_true
from mne import re... | bsd-3-clause |
wesm/statsmodels | scikits/statsmodels/sandbox/examples/ex_cusum.py | 1 | 3231 | # -*- coding: utf-8 -*-
"""
Created on Fri Apr 02 11:41:25 2010
Author: josef-pktd
"""
import numpy as np
from scipy import stats
from numpy.testing import assert_almost_equal
import scikits.statsmodels.api as sm
from scikits.statsmodels.sandbox.regression.onewaygls import OneWayLS
from scikits.statsmodels.sandbox.s... | bsd-3-clause |
pklaus/brother_ql | setup.py | 1 | 2263 | # -*- coding: utf-8 -*-
try:
from setuptools import setup
except ImportError:
from distutils.core import setup
try:
import pypandoc
LDESC = open('README.md', 'r').read()
LDESC = pypandoc.convert(LDESC, 'rst', format='md')
except (ImportError, IOError, RuntimeError) as e:
print("Could not creat... | gpl-3.0 |
enricopal/STEM | src/scorer/scorer.py | 1 | 5690 | import numpy as np
import pandas as pd
import optparse
#INPUTS: gold_standard and output file
class Scorer:
def __init__(self,file_name,gs):
self.file_name = file_name
self.gs = gs
def scorer(self):
file_input = open(self.file_name,'rU') #output of the algorithm
gold... | apache-2.0 |
jlund3/ankura | ankura/validate.py | 1 | 9962 | """Functionality for evaluating topic models"""
import collections
import itertools
import scipy.stats
import numpy as np
from sklearn.linear_model import LogisticRegression
class Contingency(object):
"""Contingency is a table which gives the multivariate frequency
distribution across known (or gold) label... | gpl-3.0 |
fzalkow/scikit-learn | sklearn/datasets/species_distributions.py | 198 | 7923 | """
=============================
Species distribution dataset
=============================
This dataset represents the geographic distribution of species.
The dataset is provided by Phillips et. al. (2006).
The two species are:
- `"Bradypus variegatus"
<http://www.iucnredlist.org/apps/redlist/details/3038/0>`_... | bsd-3-clause |
mdeff/ntds_2017 | projects/reports/crunchbase_startups/helpers.py | 1 | 13876 | """
Helper functions
"""
from graph_tool.all import *
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy
from pygsp import graphs, filters, plotting
import configparser
import requests
from mpl_toolkits.basemap import Basemap
from tqdm import tqdm
credentials = configparser.ConfigPars... | mit |
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