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 |
|---|---|---|---|---|---|
stanmoore1/lammps | examples/SPIN/test_problems/validation_damped_precession/llg_precession.py | 9 | 1646 | #!/usr/bin/env python3
import numpy as np , pylab, tkinter
import math
import matplotlib.pyplot as plt
import mpmath as mp
mub=5.78901e-5 # Bohr magneton (eV/T)
hbar=0.658212 # Planck's constant (eV.fs/rad)
g=2.0 # Lande factor (adim)
gyro=g*mub/hbar # gyromag ratio (rad/f... | gpl-2.0 |
johnmgregoire/JCAPGeneratePrintCode | visualize_alloy_platemaps__eg_pm71.py | 1 | 7575 | import numpy, pylab
import numpy as np
import matplotlib.cm as cm
import matplotlib.colors as colors
from readplatemap import readsingleplatemaptxt
from visualize_alloy_platemaps__eg_pm71__userparams import userinputd
path_pm=r'J:\hte_jcap_app_proto\map\0072-04-0100-mp.txt'
#This lets you visualize the printing for ... | bsd-3-clause |
trungnt13/scikit-learn | examples/linear_model/plot_iris_logistic.py | 283 | 1678 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Logistic Regression 3-class Classifier
=========================================================
Show below is a logistic-regression classifiers decision boundaries on the
`iris <http://en.wikipedia.org/wiki/Iris_f... | bsd-3-clause |
chipfranzen/dillinger | dillinger/bandits.py | 1 | 6449 | """Multi-armed bandits"""
# Author: C. Franzen
# License: MIT
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
class SoftMax(object):
'''The softmax bandit algorithm.
Args:
n_arms (int): Number of arms in the bandit
counts (ndarray):
Number of times each... | mit |
google-research/google-research | dvrl/main_dvrl_image_transfer_learning.py | 1 | 9297 | # coding=utf-8
# Copyright 2021 The Google Research Authors.
#
# 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 applicab... | apache-2.0 |
rknLA/sms-tools | lectures/07-Sinusoidal-plus-residual-model/plots-code/stochasticModelFrame.py | 22 | 3298 | import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import hamming, hanning, triang, blackmanharris, resample
import math
import sys, os, time
from scipy.fftpack import fft, ifft
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
import utilFunction... | agpl-3.0 |
probml/pyprobml | scripts/newcomb_plugin_demo.py | 1 | 1631 | # Tests min and variance to check whether Newcomb's speed of light data is Gaussian or not
# Author : Aleyna Kara
# This file is generated from https://github.com/probml/pmtk3/blob/master/demos/newcombPlugin.m
import pyprobml_utils as pml
import numpy as np
import requests
import matplotlib.pyplot as plt
def plot_pos... | mit |
eshook/Forest | forest/primitives/Primitives.py | 1 | 14262 | """
Copyright (c) 2017 Eric Shook. All rights reserved.
Use of this source code is governed by a BSD-style license that can be found in the LICENSE file.
@author: eshook (Eric Shook, eshook@gmail.edu)
@contributors: (Luyi Hunter, chen3461@umn.edu; Xinran Duan, duanx138@umn.edu)
@contributors: <Contribute and add your n... | bsd-3-clause |
OwaJawa/kaggle-galaxies | try_convnet_cc_multirotflip_3x69r45_maxout2048_extradense_dup3.py | 7 | 17439 | import numpy as np
# import pandas as pd
import theano
import theano.tensor as T
import layers
import cc_layers
import custom
import load_data
import realtime_augmentation as ra
import time
import csv
import os
import cPickle as pickle
from datetime import datetime, timedelta
# import matplotlib.pyplot as plt
# plt.i... | bsd-3-clause |
henrykironde/scikit-learn | sklearn/decomposition/tests/test_nmf.py | 130 | 6059 | import numpy as np
from scipy import linalg
from sklearn.decomposition import nmf
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import raises
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_gr... | bsd-3-clause |
ishank08/scikit-learn | examples/cluster/plot_agglomerative_clustering.py | 343 | 2931 | """
Agglomerative clustering with and without structure
===================================================
This example shows the effect of imposing a connectivity graph to capture
local structure in the data. The graph is simply the graph of 20 nearest
neighbors.
Two consequences of imposing a connectivity can be s... | bsd-3-clause |
B3AU/waveTree | sklearn/feature_extraction/dict_vectorizer.py | 7 | 10162 | # Author: Lars Buitinck <L.J.Buitinck@uva.nl>
# License: BSD 3 clause
from array import array
from collections import Mapping
from operator import itemgetter
import numpy as np
import scipy.sparse as sp
from ..base import BaseEstimator, TransformerMixin
from ..externals import six
from ..externals.six.moves import x... | bsd-3-clause |
alephu5/Soundbyte | environment/lib/python3.3/site-packages/matplotlib/tests/test_bbox_tight.py | 1 | 3110 | from matplotlib import rcParams
from matplotlib.testing.decorators import image_comparison
import matplotlib.pyplot as plt
import matplotlib.path as mpath
import matplotlib.patches as mpatches
from matplotlib.ticker import FuncFormatter
import numpy as np
@image_comparison(baseline_images=['bbox_inches_tight'], remove... | gpl-3.0 |
a-doumoulakis/tensorflow | tensorflow/examples/learn/text_classification_character_cnn.py | 29 | 5666 | # 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 |
hargup/sympy | sympy/physics/quantum/state.py | 6 | 29159 | """Dirac notation for states."""
from __future__ import print_function, division
from sympy import (cacheit, conjugate, Expr, Function, integrate, oo, sqrt,
Tuple)
from sympy.core.compatibility import u, range
from sympy.printing.pretty.stringpict import stringPict
from sympy.physics.quantum.qexpr ... | bsd-3-clause |
rohanp/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 |
ltiao/networkx | networkx/drawing/tests/test_pylab.py | 45 | 1137 | """
Unit tests for matplotlib drawing functions.
"""
import os
from nose import SkipTest
import networkx as nx
class TestPylab(object):
@classmethod
def setupClass(cls):
global plt
try:
import matplotlib as mpl
mpl.use('PS',warn=False)
import matplotli... | bsd-3-clause |
fivejjs/pyhsmm | examples/hsmm-geo.py | 4 | 1818 | from __future__ import division
import numpy as np
np.seterr(divide='ignore') # these warnings are usually harmless for this code
from matplotlib import pyplot as plt
import copy, os
import pyhsmm
from pyhsmm.util.text import progprint_xrange
###################
# generate data #
###################
T = 1000
obs_d... | mit |
shangwuhencc/scikit-learn | sklearn/tests/test_learning_curve.py | 225 | 10791 | # Author: Alexander Fabisch <afabisch@informatik.uni-bremen.de>
#
# License: BSD 3 clause
import sys
from sklearn.externals.six.moves import cStringIO as StringIO
import numpy as np
import warnings
from sklearn.base import BaseEstimator
from sklearn.learning_curve import learning_curve, validation_curve
from sklearn.u... | bsd-3-clause |
sclc/NAEF | exp_scripts/worker_exp_160518.py | 1 | 8726 | """
Experiment Diary 2016-05-18
"""
import sys
import math
import matplotlib.pyplot as plt
from scipy import io
import numpy as np
from scipy.sparse.linalg import *
sys.path.append("../src/")
from worker import Worker
from native_conjugate_gradient import NativeConjugateGradient
from native_conjugate_gradient import ... | gpl-3.0 |
ltiao/scikit-learn | sklearn/setup.py | 225 | 2856 | import os
from os.path import join
import warnings
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
from numpy.distutils.system_info import get_info, BlasNotFoundError
import numpy
libraries = []
if os.name == 'posix':
libraries.appe... | bsd-3-clause |
cmap/cmapPy | cmapPy/pandasGEXpress/concat.py | 1 | 22535 | """
concat.py
This function is for concatenating gct(x) files together. You can tell it to
find files using the file_wildcard argument, or you can tell it exactly
which files you want to concatenate using the input_filepaths argument. The
meat of this function are the hstack (i.e. horizontal concatenation of GCToo obj... | bsd-3-clause |
h2oai/h2o-3 | h2o-py/tests/testdir_algos/glm/pyunit_link_functions_binomial_glm.py | 8 | 1710 | from __future__ import division
from __future__ import print_function
from past.utils import old_div
import sys
sys.path.insert(1,"../../../")
import h2o
from tests import pyunit_utils
import pandas as pd
import zipfile
import statsmodels.api as sm
from h2o.estimators.glm import H2OGeneralizedLinearEstimator
def link_... | apache-2.0 |
luizcieslak/AlGDock | Pipeline/align3d.py | 3 | 9287 | # Downloads pdb files
# Uses ProDy to align a set of crystal structures to a reference structures.
# Also performs a principal components analysis.
import os, inspect, shutil, pickle
sequence = ''
ref_pdb_id = ''
ref_chain_id = 'A'
ref_res_id_range = (1,-1)
exclude = []
script_dir = os.path.dirname(os.path.abspath(\... | mit |
khyrulimam/pemrograman-linear-optimasi-gizi-anak-kos | nutrisi.py | 1 | 1544 | import numpy as np
import pulp
import seaborn as sns
from matplotlib import pyplot as plt
from matplotlib.patches import PathPatch
from matplotlib.path import Path
import solver
bayam = 'bayam'
tempe = 'tempe'
problem_name = 'Optimasi Gizi Anak Kos'
# decision variables (variabel keputusan)
x = pulp.LpVariable(bayam... | apache-2.0 |
apoorva-sharma/deep-frame-interpolation | deep_fruc.py | 1 | 7470 | import tensorflow as tf
import numpy as np
import math
import glob
#import msssim
from scipy import misc
import matplotlib.animation as animation
from frame_interpolator import *
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from pylab import *
def normalize_frames(frames, medians):
r... | mit |
dancingdan/tensorflow | tensorflow/contrib/training/python/training/feeding_queue_runner_test.py | 76 | 5052 | # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | apache-2.0 |
miaecle/deepchem | examples/kinase/KINASE_correlations.py | 8 | 1399 | """
Script that computes correlations of KINASE tasks.
"""
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
import os
import numpy as np
import tempfile
import shutil
import deepchem as dc
import pandas as pd
import matplotlib
# Force matplotlib to not use ... | mit |
saiwing-yeung/scikit-learn | sklearn/model_selection/_validation.py | 2 | 37166 | """
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 |
bloyl/mne-python | mne/epochs.py | 1 | 147455 | # -*- coding: utf-8 -*-
"""Tools for working with epoched data."""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Matti Hämäläinen <msh@nmr.mgh.harvard.edu>
# Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
# Denis Engemann <denis.engemann@gmail.com>
# Mainak Jas... | bsd-3-clause |
zhuangjun1981/retinotopic_mapping | retinotopic_mapping/RetinotopicMapping.py | 1 | 111703 | __author__ = 'junz'
import numpy as np
import os
import scipy.ndimage as ni
import scipy.sparse as sparse
import math
import matplotlib.pyplot as plt
from itertools import combinations
from operator import itemgetter
import skimage.morphology as sm
import skimage.transform as tsfm
import cv2
import matplotlib.colors a... | gpl-3.0 |
senthil10/NouGAT | nougat/evaluete.py | 3 | 18678 | from __future__ import absolute_import
from __future__ import print_function
import sys, os, yaml, glob
import subprocess
import pandas as pd
import re
import shutil
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from nougat import common, align
from itertools import groupby
from collections im... | mit |
sinhrks/pyopendata | pyopendata/io/jsdmx.py | 1 | 2560 | # pylint: disable-msg=E1101,W0613,W0603
from __future__ import unicode_literals
import itertools
import os
import requests
import numpy as np
import pandas as pd
import pandas.compat as compat
from pyopendata.io.util import _read_content
def read_jsdmx(path_or_buf):
"""
Convert a SDMX-JSON string to pand... | bsd-2-clause |
glouppe/scikit-learn | examples/model_selection/grid_search_digits.py | 44 | 2672 | """
============================================================
Parameter estimation using grid search with cross-validation
============================================================
This examples shows how a classifier is optimized by cross-validation,
which is done using the :class:`sklearn.model_selection.GridS... | bsd-3-clause |
paolorota/random_scripts | script_ALL.py | 1 | 2131 | from pyflow import reader as rd
from pyflow import export as ex
import os
import numpy as np
from sklearn import mixture
import matplotlib.pyplot as plt
from gmmstuff import plot_distribution, make_ellipses
n_components = 10
mydir = "D:\\autoflow"
patientlist = os.listdir(mydir)
gmmlist = []
for n, patient in enumer... | gpl-3.0 |
victorbergelin/scikit-learn | examples/cluster/plot_kmeans_stability_low_dim_dense.py | 338 | 4324 | """
============================================================
Empirical evaluation of the impact of k-means initialization
============================================================
Evaluate the ability of k-means initializations strategies to make
the algorithm convergence robust as measured by the relative stan... | bsd-3-clause |
lewisc/spark-tk | regression-tests/sparktkregtests/testcases/graph/graph_weight_degree_test.py | 11 | 4742 | # 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 |
jjx02230808/project0223 | benchmarks/bench_random_projections.py | 397 | 8900 | """
===========================
Random projection benchmark
===========================
Benchmarks for random projections.
"""
from __future__ import division
from __future__ import print_function
import gc
import sys
import optparse
from datetime import datetime
import collections
import numpy as np
import scipy.s... | bsd-3-clause |
Tlinne2/Basic-Python-Projects- | Data-Science-Tools/titanic_survival_rates.py | 1 | 1903 | '''
Titanic Survival Factors
A data analysis project using Kaggle Titanic's data set to determine
what factors helped or hurt chances of surviving the Titanic
Coded By: Tyler Linne
Date: 5/1/16
'''
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from pandas import Seri... | mit |
sonnyhu/scikit-learn | sklearn/covariance/robust_covariance.py | 105 | 29653 | """
Robust location and covariance estimators.
Here are implemented estimators that are resistant to outliers.
"""
# Author: Virgile Fritsch <virgile.fritsch@inria.fr>
#
# License: BSD 3 clause
import warnings
import numbers
import numpy as np
from scipy import linalg
from scipy.stats import chi2
from . import empir... | bsd-3-clause |
mmottahedi/neuralnilm_prototype | scripts/e478.py | 2 | 6721 | from __future__ import print_function, division
import matplotlib
import logging
from sys import stdout
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!
from neuralnilm import (Net, RealApplianceSource,
BLSTMLayer, DimshuffleLayer,
Bidirectiona... | mit |
thanhan/seqcrowd-acl17 | util.py | 1 | 31796 | import numpy as np
import os
import hmm
#import re
import matplotlib.pyplot as plt
import pickle
import csv
#import shutil
hmm
class instance:
"""
an instance
"""
def __init__(self, features, label, word = None):
self.features = features
self.label = label
if word != None:
... | mit |
gzd888/artisan | setup-win.py | 9 | 5267 | """
This is a set up script for py2exe
USAGE: python setup-win py2exe
"""
from distutils.core import setup
import matplotlib as mpl
import py2exe
import os
# Remove the build folder, a bit slower but ensures that build contains the latest
import shutil
shutil.rmtree("build", ignore_errors=True)
shu... | gpl-3.0 |
rubikloud/scikit-learn | examples/cluster/plot_kmeans_stability_low_dim_dense.py | 338 | 4324 | """
============================================================
Empirical evaluation of the impact of k-means initialization
============================================================
Evaluate the ability of k-means initializations strategies to make
the algorithm convergence robust as measured by the relative stan... | bsd-3-clause |
AnshulYADAV007/Lean | Algorithm.Python/PythonPackageTestAlgorithm.py | 2 | 8042 | # QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect 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 Lice... | apache-2.0 |
annehutter/grid-model | analysis_tools/size_distribution.py | 1 | 5057 | import sys
import os
import numpy as np
import matplotlib as m
m.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
from statistics import *
import read_parameterfile as rp
import read_fields as rf
def round_down(num):
if num < 0:
return -np.ceil(abs(num))
else:
r... | gpl-2.0 |
dherrebout/pyprojects | map.py | 1 | 1130 | import folium
import pandas as pd
df = pd.read_csv('Volcanoes-USA.txt')
map_carto = folium.Map(
location=[45, -120],
zoom_start=5,
tiles='cartodbpositron')
def color(elev):
if elev in range(0, 1500):
color = 'green'
elif elev in range(1500, 2500):
color = 'brown'
elif elev in... | mit |
timthelion/FreeCAD_sf_master | src/Mod/Plot/InitGui.py | 18 | 2920 | #***************************************************************************
#* *
#* Copyright (c) 2011, 2012 *
#* Jose Luis Cercos Pita <jlcercos@gmail.com> *
#* ... | lgpl-2.1 |
Refefer/pylearn2 | pylearn2/cross_validation/tests/test_cross_validation.py | 49 | 6767 | """
Tests for cross-validation module.
"""
import os
import tempfile
from pylearn2.config import yaml_parse
from pylearn2.testing.skip import skip_if_no_sklearn
def test_train_cv():
"""Test TrainCV class."""
skip_if_no_sklearn()
handle, layer0_filename = tempfile.mkstemp()
handle, layer1_filename = t... | bsd-3-clause |
olivernina/ocropy | ocrolib/psegutils.py | 10 | 7620 | from toplevel import *
from pylab import *
from scipy.ndimage import filters,interpolation
import sl,morph
def B(a):
if a.dtype==dtype('B'): return a
return array(a,'B')
class record:
def __init__(self,**kw): self.__dict__.update(kw)
def blackout_images(image,ticlass):
"""Takes a page image and a tic... | apache-2.0 |
mbayon/TFG-MachineLearning | vbig/lib/python2.7/site-packages/pandas/tests/indexes/period/test_indexing.py | 9 | 12184 | from datetime import datetime
import pytest
import numpy as np
import pandas as pd
from pandas.util import testing as tm
from pandas.compat import lrange
from pandas._libs import tslib
from pandas import (PeriodIndex, Series, DatetimeIndex,
period_range, Period, _np_version_under1p9)
class TestG... | mit |
cauchycui/scikit-learn | doc/tutorial/text_analytics/solutions/exercise_01_language_train_model.py | 254 | 2253 | """Build a language detector model
The goal of this exercise is to train a linear classifier on text features
that represent sequences of up to 3 consecutive characters so as to be
recognize natural languages by using the frequencies of short character
sequences as 'fingerprints'.
"""
# Author: Olivier Grisel <olivie... | bsd-3-clause |
dr-nate/msmbuilder | msmbuilder/project_templates/landmarks/find-landmarks.py | 9 | 1323 | """Cluster based on RMSD between conformations
{{header}}
Meta
----
depends:
- meta.pandas.pickl
- trajs
- top.pdb
"""
import mdtraj as md
from msmbuilder.cluster import MiniBatchKMedoids
from msmbuilder.io import load_meta, itertrajs, save_generic, backup
## Set up parameters
kmed = MiniBatchKMedoids(
n_... | lgpl-2.1 |
nvoron23/scikit-learn | sklearn/cluster/tests/test_k_means.py | 63 | 26190 | """Testing for K-means"""
import sys
import numpy as np
from scipy import sparse as sp
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import SkipTest
from sklearn.utils.testing i... | bsd-3-clause |
mbayon/TFG-MachineLearning | venv/lib/python3.6/site-packages/sklearn/metrics/cluster/__init__.py | 91 | 1468 | """
The :mod:`sklearn.metrics.cluster` submodule contains evaluation metrics for
cluster analysis results. There are two forms of evaluation:
- supervised, which uses a ground truth class values for each sample.
- unsupervised, which does not and measures the 'quality' of the model itself.
"""
from .supervised import ... | mit |
deepesch/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 |
bkendzior/scipy | scipy/signal/fir_filter_design.py | 17 | 36232 | # -*- coding: utf-8 -*-
"""Functions for FIR filter design."""
from __future__ import division, print_function, absolute_import
from math import ceil, log
import warnings
import numpy as np
from numpy.fft import irfft, fft, ifft
from scipy.special import sinc
from scipy.linalg import toeplitz, hankel, pinv
from scipy... | bsd-3-clause |
ephes/scikit-learn | examples/plot_johnson_lindenstrauss_bound.py | 134 | 7452 | """
=====================================================================
The Johnson-Lindenstrauss bound for embedding with random projections
=====================================================================
The `Johnson-Lindenstrauss lemma`_ states that any high dimensional
dataset can be randomly projected in... | bsd-3-clause |
Djabbz/scikit-learn | examples/linear_model/plot_sparse_recovery.py | 27 | 7466 | """
============================================================
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 |
phdowling/scikit-learn | sklearn/metrics/tests/test_regression.py | 272 | 6066 | from __future__ import division, print_function
import numpy as np
from itertools import product
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.... | bsd-3-clause |
espenhgn/nest-simulator | pynest/examples/glif_psc_neuron.py | 5 | 9575 | # -*- coding: utf-8 -*-
#
# glif_psc_neuron.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License... | gpl-2.0 |
lhilt/scipy | scipy/signal/_arraytools.py | 9 | 7561 | """
Functions for acting on a axis of an array.
"""
from __future__ import division, print_function, absolute_import
import numpy as np
def axis_slice(a, start=None, stop=None, step=None, axis=-1):
"""Take a slice along axis 'axis' from 'a'.
Parameters
----------
a : numpy.ndarray
The array ... | bsd-3-clause |
josenavas/qiime | scripts/plot_taxa_summary.py | 15 | 12355 | #!/usr/bin/env python
# File created on 19 Jan 2011
from __future__ import division
__author__ = "Jesse Stombaugh"
__copyright__ = "Copyright 2011, The QIIME project"
__credits__ = ["Jesse Stombaugh", "Julia Goodrich", "Justin Kuczynski",
"John Chase", "Jose Antonio Navas Molina"]
__license__ = "GPL"
__... | gpl-2.0 |
kaichogami/scikit-learn | sklearn/utils/validation.py | 19 | 25724 | """Utilities for input validation"""
# Authors: Olivier Grisel
# Gael Varoquaux
# Andreas Mueller
# Lars Buitinck
# Alexandre Gramfort
# Nicolas Tresegnie
# License: BSD 3 clause
import warnings
import numbers
import numpy as np
import scipy.sparse as sp
from ..externals... | bsd-3-clause |
winklerand/pandas | pandas/tests/test_multilevel.py | 1 | 106143 | # -*- coding: utf-8 -*-
# pylint: disable-msg=W0612,E1101,W0141
from warnings import catch_warnings
import datetime
import itertools
import pytest
import pytz
from numpy.random import randn
import numpy as np
from pandas.core.index import Index, MultiIndex
from pandas import Panel, DataFrame, Series, notna, isna, Tim... | bsd-3-clause |
robbymeals/scikit-learn | sklearn/linear_model/tests/test_perceptron.py | 378 | 1815 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_raises
from sklearn.utils import check_random_state
from sklearn.datasets import load_iris
from sklearn.linear_model import Pe... | bsd-3-clause |
gosox5555/data | pew-religions/Religion-Leah.py | 37 | 3271 | #!/usr/bin/env python
import numpy as np
import pandas as pd
religions = ['Buddhist', 'Catholic', 'Evangel Prot', 'Hindu', 'Hist Black Prot', 'Jehovahs Witness', 'Jewish', 'Mainline Prot', 'Mormon', 'Muslim', 'Orthodox Christian', 'Unaffiliated']
csv = open("current.csv", 'w')
csv.truncate()
def write_row(matrix):
a... | mit |
shanwai1234/Maize_Phenotype_Map | hyperspectral_PCA_visualization.py | 1 | 7348 | import numpy as np
import cv2
from matplotlib import pyplot as plt
import os
import sys
from scipy import linalg as LA
from matplotlib import cm
##############################Hyperspectral Image PCA Visualization##########################################################################################################... | bsd-3-clause |
mayblue9/scikit-learn | examples/text/document_classification_20newsgroups.py | 222 | 10500 | """
======================================================
Classification of text documents using sparse features
======================================================
This is an example showing how scikit-learn can be used to classify documents
by topics using a bag-of-words approach. This example uses a scipy.spars... | bsd-3-clause |
mkukielka/oddt | oddt/scoring/functions/RFScore.py | 1 | 8333 | from __future__ import print_function
import sys
from os.path import dirname, isfile, join as path_join
import numpy as np
from scipy.stats import pearsonr
from sklearn.metrics import r2_score
import warnings
try:
import compiledtrees
except ImportError:
compiledtrees = None
from oddt import random_seed
fro... | bsd-3-clause |
courtarro/gnuradio | gnuradio-runtime/examples/volk_benchmark/volk_plot.py | 78 | 6117 | #!/usr/bin/env python
import sys, math
import argparse
from volk_test_funcs import *
try:
import matplotlib
import matplotlib.pyplot as plt
except ImportError:
sys.stderr.write("Could not import Matplotlib (http://matplotlib.sourceforge.net/)\n")
sys.exit(1)
def main():
desc='Plot Volk performanc... | gpl-3.0 |
vladpopovici/WSItk | WSItk/segm/nuclei.py | 1 | 2446 | from __future__ import (absolute_import, division, print_function, unicode_literals)
__author__ = 'vlad'
__version__ = 0.2
import numpy as np
from scipy import ndimage
# from sklearn.cluster import KMeans
import skimage.morphology as morph
# from skimage.restoration import denoise_tv_bregman
from skimage.feature im... | mit |
schets/scikit-learn | sklearn/metrics/tests/test_common.py | 7 | 44042 | from __future__ import division, print_function
from functools import partial
from itertools import product
import numpy as np
import scipy.sparse as sp
from sklearn.datasets import make_multilabel_classification
from sklearn.preprocessing import LabelBinarizer, MultiLabelBinarizer
from sklearn.utils.multiclass impo... | bsd-3-clause |
aitatanit/filterpy | filterpy/memory/tests/test_fading_memory.py | 4 | 1896 | # -*- coding: utf-8 -*-
"""Copyright 2015 Roger R Labbe Jr.
FilterPy library.
http://github.com/rlabbe/filterpy
Documentation at:
https://filterpy.readthedocs.org
Supporting book at:
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
This is licensed under an MIT license. See the readme.MD file
for mor... | mit |
NixaSoftware/CVis | venv/lib/python2.7/site-packages/pandas/io/gbq.py | 13 | 4006 | """ Google BigQuery support """
def _try_import():
# since pandas is a dependency of pandas-gbq
# we need to import on first use
try:
import pandas_gbq
except ImportError:
# give a nice error message
raise ImportError("Load data from Google BigQuery\n"
... | apache-2.0 |
DynamicGravitySystems/DGP | examples/pyqtgraph_line_selection_plot.py | 1 | 2135 | import os
import sys
import uuid
import logging
import datetime
import traceback
from PyQt5 import QtCore
import PyQt5.QtWidgets as QtWidgets
import PyQt5.Qt as Qt
import numpy as np
from pandas import Series, DatetimeIndex
os.chdir('..')
import dgp.lib.project as project
from dgp.gui.plotting.plotters import PqtLine... | apache-2.0 |
elijah513/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 |
dhruv13J/scikit-learn | sklearn/utils/tests/test_utils.py | 215 | 8100 | import warnings
import numpy as np
import scipy.sparse as sp
from scipy.linalg import pinv2
from itertools import chain
from sklearn.utils.testing import (assert_equal, assert_raises, assert_true,
assert_almost_equal, assert_array_equal,
SkipTest, ... | bsd-3-clause |
abimannans/scikit-learn | examples/cluster/plot_agglomerative_clustering_metrics.py | 402 | 4492 | """
Agglomerative clustering with different metrics
===============================================
Demonstrates the effect of different metrics on the hierarchical clustering.
The example is engineered to show the effect of the choice of different
metrics. It is applied to waveforms, which can be seen as
high-dimens... | bsd-3-clause |
Unidata/MetPy | dev/_downloads/900d76c7356d09e4ca20b90f94a0732e/declarative_tutorial.py | 2 | 21541 | # Copyright (c) 2018 MetPy Developers.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""
MetPy Declarative Syntax Tutorial
=================================
The declarative syntax that is a part of the MetPy packaged is designed to aid in simple
data exploration and... | bsd-3-clause |
yati-sagade/incubator-airflow | airflow/contrib/hooks/bigquery_hook.py | 1 | 49029 | # -*- coding: utf-8 -*-
#
# 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, software
... | apache-2.0 |
jmcnamara/pandas_xlsxwriter_charts | examples/chart_grouped_column.py | 1 | 1616 | ##############################################################################
#
# An example of creating a chart with Pandas and XlsxWriter.
#
# Copyright 2013, John McNamara, jmcnamara@cpan.org
#
import random
import pandas as pd
from vincent.colors import brews
# Some sample data to plot.
cat_4 = ['Metric_' + str(... | bsd-2-clause |
viniciusd/DCO1008---Digital-Signal-Processing | projeto2/question1.py | 1 | 1741 | from pprint import pprint
import matplotlib.pyplot as plt
from common import Fft
def fft_padded_plot(x):
X = Fft(x, sample_rate=8192, padded=True)
plt.figure()
plt.plot(X.hz, X.abs)
plt.xlabel('Frequency (Hz)')
plt.ylabel('|H|')
plt.savefig('q1_fft_padded.png')
def fft_not_padded_plot(x):
... | mit |
amjames/psi4 | psi4/driver/qcdb/util/gph_uno_bipartite.py | 1 | 24398 | """Functions to enumerate all perfect and maximum matchings in bipartite graph.
Implemented following the algorithms in the paper "Algorithms for Enumerating
All Perfect, Maximum and Maximal Matchings in Bipartite Graphs" by Takeaki Uno,
using numpy and networkx modules of python.
NOTICE: optimization needed.
Author... | lgpl-3.0 |
pelson/numpy | numpy/lib/npyio.py | 9 | 65323 | __all__ = ['savetxt', 'loadtxt', 'genfromtxt', 'ndfromtxt', 'mafromtxt',
'recfromtxt', 'recfromcsv', 'load', 'loads', 'save', 'savez',
'savez_compressed', 'packbits', 'unpackbits', 'fromregex', 'DataSource']
import numpy as np
import format
import sys
import os
import re
import sys
import itertoo... | bsd-3-clause |
reflectometry/osrefl | setup.py | 1 | 1759 | #!/usr/bin/env python
import sys
import os
# Find the version number of the application
for line in open("osrefl/__init__.py").readlines():
if line.startswith('__version__'):
exec line
#from distutils.core import Extension
from setuptools import setup, find_packages, Extension
#import fix_setuptools_chmod... | bsd-3-clause |
jlegendary/scikit-learn | examples/semi_supervised/plot_label_propagation_digits.py | 268 | 2723 | """
===================================================
Label Propagation digits: Demonstrating performance
===================================================
This example demonstrates the power of semisupervised learning by
training a Label Spreading model to classify handwritten digits
with sets of very few labels.... | bsd-3-clause |
ioam/holoviews | holoviews/plotting/bokeh/annotation.py | 1 | 11706 | from __future__ import absolute_import, division, unicode_literals
from collections import defaultdict
import param
import numpy as np
from bokeh.models import Span, Arrow, Div as BkDiv
try:
from bokeh.models.arrow_heads import TeeHead, NormalHead
arrow_start = {'<->': NormalHead, '<|-|>': NormalHead}
arr... | bsd-3-clause |
yanlend/scikit-learn | sklearn/mixture/tests/test_gmm.py | 48 | 17414 | import unittest
import copy
import sys
from nose.tools import assert_true
import numpy as np
from numpy.testing import (assert_array_equal, assert_array_almost_equal,
assert_raises)
from scipy import stats
from sklearn import mixture
from sklearn.datasets.samples_generator import make_spd_ma... | bsd-3-clause |
belltailjp/scikit-learn | examples/svm/plot_separating_hyperplane.py | 62 | 1274 | """
=========================================
SVM: Maximum margin separating hyperplane
=========================================
Plot the maximum margin separating hyperplane within a two-class
separable dataset using a Support Vector Machines classifier with
linear kernel.
"""
print(__doc__)
import numpy as np
impo... | bsd-3-clause |
eclee25/flu-SDI-exploratory-age | scripts/create_fluseverity_figs_v3/functions_v3.py | 1 | 76886 | #!/usr/bin/python
##############################################
###Python template
###Author: Elizabeth Lee
###Date: 10/15/14
## Purpose: script of functions for data cleaning and processing to draw flu severity figures; supports figures in create_fluseverity_figs
## v2: swap child:adult OR to adult:child OR
## v3: ... | mit |
equialgo/scikit-learn | sklearn/tests/test_multioutput.py | 3 | 7954 | 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... | bsd-3-clause |
elkingtonmcb/scikit-learn | examples/gaussian_process/gp_diabetes_dataset.py | 223 | 1976 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
========================================================================
Gaussian Processes regression: goodness-of-fit on the 'diabetes' dataset
========================================================================
In this example, we fit a Gaussian Process model onto... | bsd-3-clause |
zobristnicholas/PMM_Readout | pmmcontrol/simulator/detector.py | 1 | 8816 | from pmmcontrol.simulator.hysteresis import Hysteresis
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from scipy.constants import pi
from random import randrange
class Detector():
def __init__(self, rows=9, cols=10):
self.rows = rows
self.cols = cols
... | agpl-3.0 |
UMN-Hydro/GSFLOW_pre-processor | python_scripts/plot_gsflow_csv.py | 1 | 2455 | # -*- coding: utf-8 -*-
"""
Created on Sun Oct 29 15:26:24 2017
@author: gcng
"""
# plot_gsflow_csv.m
#
# List of StatVarNames: see Table 12 of GSFLOW manual and
# create_table_gsflowcsv.m
import sys
import platform
import numpy as np
from matplotlib import pyplot as plt
import pandas as pd
import datetime as dt
... | gpl-3.0 |
samzhang111/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 |
hlin117/scikit-learn | sklearn/svm/setup.py | 83 | 3160 | 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 |
UDST/activitysim | activitysim/abm/models/cdap.py | 2 | 4936 | # ActivitySim
# See full license in LICENSE.txt.
from __future__ import (absolute_import, division, print_function, )
from future.standard_library import install_aliases
install_aliases() # noqa: E402
import logging
import pandas as pd
from activitysim.core import simulate
from activitysim.core import tracing
from... | bsd-3-clause |
rothnic/bokeh | examples/plotting/file/burtin.py | 43 | 4765 | from collections import OrderedDict
from math import log, sqrt
import numpy as np
import pandas as pd
from six.moves import cStringIO as StringIO
from bokeh.plotting import figure, show, output_file
antibiotics = """
bacteria, penicillin, streptomycin, neomycin, gram
Mycobacterium tuberculosis... | bsd-3-clause |
GuessWhoSamFoo/pandas | pandas/tests/indexes/period/test_period.py | 1 | 21238 | import numpy as np
import pytest
from pandas._libs.tslibs.period import IncompatibleFrequency
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
DataFrame, DatetimeIndex, Index, NaT, Period, PeriodIndex, Series,
date_range, offsets, period_range)
from pandas.util import testing... | bsd-3-clause |
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