repo_name stringlengths 6 67 | path stringlengths 5 185 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 1.02k 962k | license stringclasses 15
values |
|---|---|---|---|---|---|
pypyrus/pypyrus | jupyter/config/jupyter_notebook_config.py | 1 | 19505 | #--- nbextensions configuration ---
from jupyter_core.paths import jupyter_config_dir, jupyter_data_dir
import os
import sys
# nbextensions #
#data_dir = jupyter_data_dir()
data_dir = os.path.join(os.getcwd(), 'jupyter', 'data')
sys.path.append(os.path.join(data_dir, 'extensions'))
c = get_config()
c.NotebookApp.ser... | gpl-2.0 |
etsrepo/currentcostgui | currentcostgraphs.py | 9 | 7933 | # -*- coding: utf-8 -*-
#
# CurrentCost GUI
#
# Copyright (C) 2008 Dale Lane
#
# 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 3 of the License, or
# (... | gpl-3.0 |
alvations/Sensible-SemEval | xgboost_ensemble.py | 2 | 2022 | import pandas as pd
import numpy as np
from sklearn.cross_validation import train_test_split
import xgboost as xgb
import operator
types = {'m1': np.dtype(float), 'm2': np.dtype(float), 'm3': np.dtype(float), 'm4': np.dtype(float),
'm5': np.dtype(float), 'target': np.dtype(float)}
train_valid = pd.read_csv(... | mit |
kdmurray91/scikit-bio | skbio/stats/ordination/tests/test_ordination_results.py | 1 | 12214 | # ----------------------------------------------------------------------------
# Copyright (c) 2013--, scikit-bio development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
# --------------------------------------------... | bsd-3-clause |
synergetics/nest | examples/nest/plot_tsodyks_depr_fac.py | 13 | 1130 | # -*- coding: utf-8 -*-
#
# plot_tsodyks_depr_fac.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 L... | gpl-2.0 |
mdboom/astropy-helpers | astropy_helpers/sphinx/conf.py | 1 | 10879 | # -*- coding: utf-8 -*-
# Licensed under a 3-clause BSD style license - see LICENSE.rst
#
# Astropy shared Sphinx settings. These settings are shared between
# astropy itself and affiliated packages.
#
# Note that not all possible configuration values are present in this file.
#
# All configuration values have a defau... | bsd-3-clause |
effa/flocs | analysis/taskInstance/flow_on_time.py | 3 | 1278 | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# TODO: Create infrastructure for analysis and desribe it on our wiki.
def show_flow_on_time_plot(name, show, store):
data = pd.read_csv('data/{name}.csv'.format(name=name))
plot_practice_session(data)
if store:
plt.savefig('p... | gpl-2.0 |
Jimmy-Morzaria/scikit-learn | examples/svm/plot_custom_kernel.py | 115 | 1546 | """
======================
SVM with custom kernel
======================
Simple usage of Support Vector Machines to classify a sample. It will
plot the decision surface and the support vectors.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
# import some data... | bsd-3-clause |
GuillaumeArruda/INF4705 | TP2/Python/Plot/Plot/Plot.py | 1 | 1476 | import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import functools
import csv
import scipy.optimize
import numpy
def main():
fxys = []
xs = []
ys = []
with open('d.csv', newline='') as file:
reader = csv.reader(file, delimiter=',')
for x, y, fxy in reader:
... | bsd-3-clause |
robin-lai/scikit-learn | sklearn/decomposition/__init__.py | 147 | 1421 | """
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
from .incrementa... | bsd-3-clause |
TNT-Samuel/Coding-Projects | DNS Server/Source - Copy/Lib/site-packages/dask/dataframe/io/json.py | 5 | 6650 | from __future__ import absolute_import
import io
import pandas as pd
from dask.bytes import open_files, read_bytes
import dask
def to_json(df, url_path, orient='records', lines=None, storage_options=None,
compute=True, encoding='utf-8', errors='strict',
compression=None, **kwargs):
"""Wri... | gpl-3.0 |
kemerelab/NeuroHMM | helpers/hc3.py | 1 | 7529 | # hc3.py
# helper functions to load data from CRCNS hc-3 repository
import os.path
import pandas as pd
import numpy as np
import re
from mymap import Map
def get_num_electrodes(sessiondir):
numelec = 0
files = [f for f in os.listdir(sessiondir) if (os.path.isfile(os.path.join(sessiondir, f)))]
for ff in ... | mit |
thientu/scikit-learn | examples/linear_model/plot_ols.py | 220 | 1940 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Linear Regression Example
=========================================================
This example uses the only the first feature of the `diabetes` dataset, in
order to illustrate a two-dimensional plot of this regre... | bsd-3-clause |
Lawrence-Liu/scikit-learn | examples/neighbors/plot_classification.py | 287 | 1790 | """
================================
Nearest Neighbors Classification
================================
Sample usage of Nearest Neighbors classification.
It will plot the decision boundaries for each class.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColorm... | bsd-3-clause |
mattjj/pyhawkes | test/test_sbm_mf.py | 2 | 3644 | import copy
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import adjusted_mutual_info_score, adjusted_rand_score
from pyhawkes.models import DiscreteTimeNetworkHawkesModelSpikeAndSlab, \
DiscreteTimeNetworkHawkesModelGammaMixture
from pyhawkes.plotting.plotting i... | mit |
jonathanunderwood/numpy | numpy/fft/fftpack.py | 4 | 45580 | """
Discrete Fourier Transforms
Routines in this module:
fft(a, n=None, axis=-1)
ifft(a, n=None, axis=-1)
rfft(a, n=None, axis=-1)
irfft(a, n=None, axis=-1)
hfft(a, n=None, axis=-1)
ihfft(a, n=None, axis=-1)
fftn(a, s=None, axes=None)
ifftn(a, s=None, axes=None)
rfftn(a, s=None, axes=None)
irfftn(a, s=None, axes=None... | bsd-3-clause |
sanjayankur31/nest-simulator | extras/ConnPlotter/tcd_nest.py | 20 | 6959 | # -*- coding: utf-8 -*-
#
# tcd_nest.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, or
# ... | gpl-2.0 |
DiamondLightSource/auto_tomo_calibration-experimental | old_code_scripts/Segmentation.py | 1 | 7058 | import pylab as pl
import numpy as np
import matplotlib.pyplot as plt
from scipy import ndimage as ndi
from scipy.ndimage import measurements
from scipy import optimize
import EqnLine as line
from skimage import io
from skimage import measure, color
from skimage.morphology import watershed
from skimage.feature import ... | apache-2.0 |
SeldonIO/seldon-server | python/seldon/anomaly/AnomalyDetection.py | 2 | 10901 | import numpy as np
import pandas as pd
import scipy.spatial.distance as ssd
from sklearn.utils import check_array
import logging
from time import time
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - ... | apache-2.0 |
projectchrono/chrono | src/demos/python/irrlicht/demo_IRR_crank_plot.py | 4 | 5790 | #------------------------------------------------------------------------------
# Name: pychrono example
# Purpose:
#
# Author: Alessandro Tasora
#
# Created: 1/01/2019
# Copyright: (c) ProjectChrono 2019
#------------------------------------------------------------------------------
import pychrono... | bsd-3-clause |
costypetrisor/scikit-learn | examples/neural_networks/plot_rbm_logistic_classification.py | 258 | 4609 | """
==============================================================
Restricted Boltzmann Machine features for digit classification
==============================================================
For greyscale image data where pixel values can be interpreted as degrees of
blackness on a white background, like handwritten... | bsd-3-clause |
gotomypc/scikit-learn | examples/neighbors/plot_species_kde.py | 282 | 4059 | """
================================================
Kernel Density Estimate of Species Distributions
================================================
This shows an example of a neighbors-based query (in particular a kernel
density estimate) on geospatial data, using a Ball Tree built upon the
Haversine distance metric... | bsd-3-clause |
wathen/PhD | MHD/FEniCS/MHD/Stabilised/SaddlePointForm/Test/GeneralisedEigen/MHDallatonce.py | 4 | 9242 | import petsc4py
import sys
petsc4py.init(sys.argv)
from petsc4py import PETSc
import numpy as np
from dolfin import tic, toc
import HiptmairSetup
import PETScIO as IO
import scipy.sparse as sp
import matplotlib.pylab as plt
import MatrixOperations as MO
class BaseMyPC(object):
def setup(self, pc):
pass
... | mit |
MattWellie/PAGE_MPO | tsv_gene_names_grab.py | 1 | 3555 | import csv, cPickle
import numpy as np
import matplotlib.pyplot as plt
"""
Something quick to get a set of genes from a csv file
"""
file_in = 'batch_query_no_infertile.tsv'
field = 'human_gene_symbol'
ddg2p = 'DDG2P.csv'
annotations = 'annotations.cPickle'
all_output = 'tsv_names_summary_out.txt'
gene_set = set()
ge... | apache-2.0 |
asurve/arvind-sysml2 | scripts/perftest/python/google_docs/update.py | 15 | 4666 | #!/usr/bin/env python3
# -------------------------------------------------------------
#
# 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 ... | apache-2.0 |
pratapvardhan/pandas | pandas/tests/series/test_constructors.py | 2 | 43384 | # coding=utf-8
# pylint: disable-msg=E1101,W0612
import pytest
from datetime import datetime, timedelta
from collections import OrderedDict
from numpy import nan
import numpy as np
import numpy.ma as ma
import pandas as pd
from pandas.api.types import CategoricalDtype
from pandas.core.dtypes.common import (
is_... | bsd-3-clause |
PatrickChrist/scikit-learn | examples/ensemble/plot_voting_probas.py | 316 | 2824 | """
===========================================================
Plot class probabilities calculated by the VotingClassifier
===========================================================
Plot the class probabilities of the first sample in a toy dataset
predicted by three different classifiers and averaged by the
`VotingC... | bsd-3-clause |
mattweirick/mattweirick.github.io | markdown_generator/talks.py | 199 | 4000 |
# coding: utf-8
# # Talks markdown generator for academicpages
#
# Takes a TSV of talks with metadata and converts them for use with [academicpages.github.io](academicpages.github.io). This is an interactive Jupyter notebook ([see more info here](http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_i... | mit |
apache/arrow | dev/archery/setup.py | 3 | 1985 | #!/usr/bin/env python
# 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
# "Li... | apache-2.0 |
ycasg/PyNLO | src/validation/Old and Partial Tests/ppln_generate_stepped_apodized_design.py | 2 | 3351 | # -*- coding: utf-8 -*-
"""
Created on Thu Oct 23 15:54:36 2014
This file is part of pyNLO.
pyNLO 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 of the License, or
(at your optio... | gpl-3.0 |
shangwuhencc/scikit-learn | sklearn/metrics/metrics.py | 233 | 1262 | import warnings
warnings.warn("sklearn.metrics.metrics is deprecated and will be removed in "
"0.18. Please import from sklearn.metrics",
DeprecationWarning)
from .ranking import auc
from .ranking import average_precision_score
from .ranking import label_ranking_average_precision_score
fro... | bsd-3-clause |
stevenbergner/stevenbergner.github.io | Teaching/cmpt767/lab2/code/elevation_grid.py | 1 | 5397 | #!/usr/bin/env python3
"""
Efficient, local elevation lookup using intermediate tile representation
of world-wide SRTM elevation data.
Examples:
import elevation_grid as eg
import numpy as np
el = eg.get_elevation(50, -123.1)
print("A place near Whistler, BC is {} m above sea level".format(el))
import matplotlib... | mit |
olafhauk/mne-python | examples/time_frequency/plot_source_power_spectrum.py | 19 | 1959 | """
======================================================
Compute source power spectral density (PSD) in a label
======================================================
Returns an STC file containing the PSD (in dB) of each of the sources
within a label.
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
... | bsd-3-clause |
eickenberg/scikit-learn | sklearn/preprocessing/__init__.py | 3 | 1041 | """
The :mod:`sklearn.preprocessing` module includes scaling, centering,
normalization, binarization and imputation methods.
"""
from .data import Binarizer
from .data import KernelCenterer
from .data import MinMaxScaler
from .data import Normalizer
from .data import StandardScaler
from .data import add_dummy_feature
... | bsd-3-clause |
ntung/ramp | gaussian_process_no_normalization_of_inputs.py | 1 | 34661 | # -*- coding: utf-8 -*-
# Author: Vincent Dubourg <vincent.dubourg@gmail.com>
# (mostly translation, see implementation details)
# Licence: BSD 3 clause
from __future__ import print_function
import numpy as np
from scipy import linalg, optimize
from sklearn.base import BaseEstimator, RegressorMixin
from skl... | bsd-3-clause |
pastas/pastas | pastas/rfunc.py | 1 | 30984 | # coding=utf-8
"""This module contains all the response functions available in Pastas.
"""
import numpy as np
from pandas import DataFrame
from scipy.integrate import quad
from scipy.special import (gammainc, gammaincinv, k0, k1,
exp1, erfc, lambertw, erfcinv)
__all__ = ["Gamma", "Exponent... | mit |
ammarkhann/FinalSeniorCode | lib/python2.7/site-packages/pandas/core/indexes/category.py | 4 | 24566 | import numpy as np
from pandas._libs import index as libindex
from pandas import compat
from pandas.compat.numpy import function as nv
from pandas.core.dtypes.generic import ABCCategorical, ABCSeries
from pandas.core.dtypes.common import (
is_categorical_dtype,
_ensure_platform_int,
is_list_like,
is_in... | mit |
Jokiva/Computational-Physics | lecture 9/Problem 1.py | 1 | 1279 | from fitting import *
import numpy as np
import matplotlib.pyplot as plt
# linear function
def f(x, coeffs):
if len(coeffs) != 2:
raise ValueError('the length of coefficient array should be two')
return coeffs[0] + coeffs[1] * x
# data 1
x_1 = np.array([10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5])
y_1 = n... | gpl-3.0 |
mmottahedi/neuralnilm_prototype | scripts/e430.py | 2 | 7309 | 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,
Bidirectio... | mit |
murali-munna/scikit-learn | sklearn/neighbors/base.py | 115 | 29783 | """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 |
KT12/hands_on_machine_learning | time_series_rnn_without_wrapper.py | 1 | 3226 | # Predict time series w/o using OutputProjectWrapper
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
# Create time series
t_min, t_max = 0, 30
resolution = 0.1
def time_series(t):
return t * np.sin(t) / 3 + 2 * np.sin(t * 5)
def next_batch(batch_size, n_steps):
t0 = np.random.rand... | mit |
MartinSavc/scikit-learn | benchmarks/bench_sgd_regression.py | 283 | 5569 | """
Benchmark for SGD regression
Compares SGD regression against coordinate descent and Ridge
on synthetic data.
"""
print(__doc__)
# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
# License: BSD 3 clause
import numpy as np
import pylab as pl
import gc
from time import time
from sklearn.linear_model i... | bsd-3-clause |
chetan51/nupic | examples/opf/clients/cpu/cpu.py | 17 | 3151 | #!/usr/bin/env python
# ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2013, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions ... | gpl-3.0 |
matthiasplappert/motion_classification | src/evaluate_features.py | 1 | 7928 | # coding=utf8
from collections import namedtuple
from argparse import ArgumentParser
import timeit
import os
import logging
from itertools import chain, combinations
import csv
import numpy as np
from sklearn.cross_validation import ShuffleSplit
from sklearn.preprocessing import MinMaxScaler
from toolkit.hmm.impl_hmm... | mit |
oduwa/Wheat-Count | PicNumero/build_classifier.py | 2 | 8319 | import Display
import Helper
from skimage.color import rgb2gray
import numpy as np
from scipy import misc
from sklearn import svm, grid_search, metrics
from sklearn.neural_network import MLPClassifier
from skimage.feature import greycomatrix, greycoprops
from skimage import img_as_ubyte, io
from sklearn import decompos... | mit |
eickenberg/scikit-learn | benchmarks/bench_glm.py | 297 | 1493 | """
A comparison of different methods in GLM
Data comes from a random square matrix.
"""
from datetime import datetime
import numpy as np
from sklearn import linear_model
from sklearn.utils.bench import total_seconds
if __name__ == '__main__':
import pylab as pl
n_iter = 40
time_ridge = np.empty(n_it... | bsd-3-clause |
MJuddBooth/pandas | pandas/core/groupby/ops.py | 1 | 29237 | """
Provide classes to perform the groupby aggregate operations.
These are not exposed to the user and provide implementations of the grouping
operations, primarily in cython. These classes (BaseGrouper and BinGrouper)
are contained *in* the SeriesGroupBy and DataFrameGroupBy objects.
"""
import collections
import n... | bsd-3-clause |
dsquareindia/scikit-learn | examples/ensemble/plot_random_forest_regression_multioutput.py | 46 | 2640 | """
============================================================
Comparing random forests and the multi-output meta estimator
============================================================
An example to compare multi-output regression with random forest and
the :ref:`multioutput.MultiOutputRegressor <multiclass>` meta-e... | bsd-3-clause |
wangyum/tensorflow | tensorflow/contrib/learn/python/learn/tests/dataframe/dataframe_test.py | 62 | 3753 | # 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 |
luckyharryji/smoking-modeling | smoking/map/test_map.py | 1 | 1030 | from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
import json
class Map(object):
def __init__(self):
self.m = Basemap(llcrnrlon=-119,llcrnrlat=22,urcrnrlon=-64,urcrnrlat=49,projection='lcc',lat_1=33,lat_2=45,lon_0=-95,resolution='c')
self.min_marker_size = ... | mit |
aymen82/kaggler-competitions-scripts | dev/rossman/rossmann.py | 1 | 11046 | # -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>
# <codecell>
%matplotlib inline
import pandas as pd
import argparse
import os
import matplotlib.pyplot as plt
import numpy as np
# <codecell>
def rmspe(tru, pred):
if tru==0.0 or isinstance(tru, str) or np.isnan(tru) or np.isnan(pred):
return 0.0
re... | bsd-3-clause |
AIML/scikit-learn | examples/linear_model/plot_logistic_path.py | 349 | 1195 | #!/usr/bin/env python
"""
=================================
Path with L1- Logistic Regression
=================================
Computes path on IRIS dataset.
"""
print(__doc__)
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
from datetime import datetime
import numpy as np
import... | bsd-3-clause |
nmayorov/scikit-learn | examples/cluster/plot_digits_linkage.py | 369 | 2959 | """
=============================================================================
Various Agglomerative Clustering on a 2D embedding of digits
=============================================================================
An illustration of various linkage option for agglomerative clustering on
a 2D embedding of the di... | bsd-3-clause |
alephu5/Soundbyte | environment/lib/python3.3/site-packages/pandas/tools/tests/test_tile.py | 1 | 7578 | import os
import nose
import numpy as np
from pandas.compat import zip
from pandas import DataFrame, Series, unique
import pandas.util.testing as tm
from pandas.util.testing import assertRaisesRegexp
import pandas.core.common as com
from pandas.core.algorithms import quantile
from pandas.tools.tile import cut, qcut
... | gpl-3.0 |
Dhivyap/ansible | hacking/aws_config/build_iam_policy_framework.py | 25 | 11861 | # Requires pandas, bs4, html5lib, and lxml
#
# Call script with the output from aws_resource_actions callback, e.g.
# python build_iam_policy_framework.py ['ec2:AuthorizeSecurityGroupEgress', 'ec2:AuthorizeSecurityGroupIngress', 'sts:GetCallerIdentity']
#
# The sample output:
# {
# "Version": "2012-10-17",
# "S... | gpl-3.0 |
arahuja/scikit-learn | sklearn/neighbors/tests/test_dist_metrics.py | 3 | 5300 | import itertools
import pickle
import numpy as np
from numpy.testing import assert_array_almost_equal
import scipy
from scipy.spatial.distance import cdist
from sklearn.neighbors.dist_metrics import DistanceMetric
from nose import SkipTest
def dist_func(x1, x2, p):
return np.sum((x1 - x2) ** p) ** (1. / p)
de... | bsd-3-clause |
Tong-Chen/scikit-learn | sklearn/tree/tree.py | 1 | 29287 | """
This module gathers tree-based methods, including decision, regression and
randomized trees. Single and multi-output problems are both handled.
"""
# Authors: Gilles Louppe <g.louppe@gmail.com>
# Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Brian Holt <bdholt1@gmail.com>
# Noel Da... | bsd-3-clause |
Adai0808/scikit-learn | examples/classification/plot_classification_probability.py | 242 | 2624 | """
===============================
Plot classification probability
===============================
Plot the classification probability for different classifiers. We use a 3
class dataset, and we classify it with a Support Vector classifier, L1
and L2 penalized logistic regression with either a One-Vs-Rest or multinom... | bsd-3-clause |
wcalvert/LPC11U_LPC13U_CodeBase | src/drivers/filters/iir/python/iir_f_noisysine_test.py | 2 | 2628 | #-------------------------------------------------------------------------------
# Name: iir_f_tester
#
# Purpose: Displays IIR output of a sine wave with optional random noise
#
# Author: K. Townsend (microBuilder.eu)
#
# Created: 05/05/2013
# Copyright: (c) K. Townsend 2013
# Licence: BSD
#
... | bsd-3-clause |
jjx02230808/project0223 | examples/tree/plot_iris.py | 271 | 2186 | """
================================================================
Plot the decision surface of a decision tree on the iris dataset
================================================================
Plot the decision surface of a decision tree trained on pairs
of features of the iris dataset.
See :ref:`decision tree ... | bsd-3-clause |
kenshay/ImageScripter | ProgramData/SystemFiles/Python/Lib/site-packages/ipykernel/pylab/config.py | 10 | 4485 | """Configurable for configuring the IPython inline backend
This module does not import anything from matplotlib.
"""
#-----------------------------------------------------------------------------
# Copyright (C) 2011 The IPython Development Team
#
# Distributed under the terms of the BSD License. The full lice... | gpl-3.0 |
binhqnguyen/ln | src/flow-monitor/examples/wifi-olsr-flowmon.py | 108 | 7439 | # -*- Mode: Python; -*-
# Copyright (c) 2009 INESC Porto
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License version 2 as
# published by the Free Software Foundation;
#
# This program is distributed in the hope that it will be useful,
#... | gpl-2.0 |
graphistry/pygraphistry | docs/source/conf.py | 1 | 7678 | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | bsd-3-clause |
VikParuchuri/simpsons-scripts | tasks/train.py | 1 | 16847 | from __future__ import division
from itertools import chain
from sklearn.feature_extraction.text import CountVectorizer
import numpy as np
import pandas as pd
from fisher import pvalue
import re
import collections
from nltk.stem.porter import PorterStemmer
import math
from percept.tasks.base import Task
from percept.fi... | apache-2.0 |
nrhine1/scikit-learn | examples/covariance/plot_sparse_cov.py | 300 | 5078 | """
======================================
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... | bsd-3-clause |
m4734/mysql_pio | boost_1_59_0/libs/numeric/odeint/performance/plot_result.py | 43 | 2225 | """
Copyright 2011-2014 Mario Mulansky
Copyright 2011-2014 Karsten Ahnert
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE_1_0.txt or
copy at http://www.boost.org/LICENSE_1_0.txt)
"""
import numpy as np
from matplotlib import pyplot as plt
plt.rc("font", size=16)
def g... | gpl-2.0 |
hadim/spindle_tracker | spindle_tracker/tracking/begin_mitosis_tracker.py | 1 | 7324 | import gc
import logging
import numpy as np
import pandas as pd
import scipy
from skimage import measure
from ..trajectories import Trajectories
from ..tracker.solver import ByFrameSolver
from ..io import TiffFile
from ..tracking import Tracker
log = logging.getLogger(__name__)
class BeginMitosisTracker(Tracker):... | bsd-3-clause |
datapythonista/pandas | pandas/tests/indexes/timedeltas/test_searchsorted.py | 4 | 1040 | import numpy as np
import pytest
from pandas import (
Series,
TimedeltaIndex,
Timestamp,
array,
)
import pandas._testing as tm
class TestSearchSorted:
@pytest.mark.parametrize("klass", [list, np.array, array, Series])
def test_searchsorted_different_argument_classes(self, klass):
idx ... | bsd-3-clause |
binghongcha08/pyQMD | GWP/2D/1.0.2/plt.py | 14 | 1041 | ##!/usr/bin/python
import numpy as np
import pylab as plt
import seaborn as sns
sns.set_context('poster')
#with open("traj.dat") as f:
# data = f.read()
#
# data = data.split('\n')
#
# x = [row.split(' ')[0] for row in data]
# y = [row.split(' ')[1] for row in data]
#
# fig = plt.figure()
#
# ax1 ... | gpl-3.0 |
MikeDMorgan/gwas_pipeline | scripts/snpPriority.py | 1 | 11661 | '''
snpPriority.py - score SNPs based on their LD score and SE weighted effect sizes
===============================================================================
:Author: Mike Morgan
:Release: $Id$
:Date: |today|
:Tags: Python
Purpose
-------
.. Score SNPs based on their LD score and SE weighted effect sizes from... | mit |
stefanodoni/mtperf | main.py | 2 | 18296 | #!/usr/bin/python3
import os
import argparse
import csv
import sqlite3
import sqlalchemy as sqlal
import pandas as pd
import numpy as np
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
from database import DBConstants
from datasets.BenchmarkDataset import BenchmarkDataset
from graph_plotters.HTM... | gpl-2.0 |
Delphine-L/tools-iuc | tools/repmatch_gff3/repmatch_gff3_util.py | 22 | 17958 | import bisect
import csv
import os
import shutil
import sys
import tempfile
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot # noqa: I202,E402
# Graph settings
Y_LABEL = 'Counts'
X_LABEL = 'Number of matched replicates'
TICK_WIDTH = 3
# Amount to shift the graph to make labels fit, [left, right,... | mit |
cloud-fan/spark | python/pyspark/pandas/tests/test_numpy_compat.py | 15 | 8672 | #
# 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 |
phdowling/scikit-learn | sklearn/tests/test_multiclass.py | 136 | 23649 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.testing ... | bsd-3-clause |
btabibian/scikit-learn | sklearn/tests/test_base.py | 15 | 14534 | # Author: Gael Varoquaux
# License: BSD 3 clause
import numpy as np
import scipy.sparse as sp
import sklearn
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_equal
from sklearn.uti... | bsd-3-clause |
microsoft/Azure-Kinect-Sensor-SDK | src/python/k4a/examples/image_transformations.py | 1 | 4449 | '''
image_transformations.py
A simple program that transforms images from one camera coordinate to another.
Requirements:
Users should install the following python packages before using this module:
matplotlib
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
Kinect For Azu... | mit |
k2kobayashi/sprocket | sprocket/model/tests/test_ms.py | 1 | 1786 | import unittest
import numpy as np
from sprocket.model import MS
from sprocket.util import low_pass_filter
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
saveflag = True
dim = 4
class MSTest(unittest.TestCase):
def test_MSstatistics(self):
ms = MS()
datalist = []
... | mit |
Djabbz/scikit-learn | examples/gaussian_process/plot_gpr_co2.py | 9 | 5718 | """
========================================================
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 |
CGATOxford/CGATPipelines | CGATPipelines/pipeline_rnaseqqc.py | 1 | 57789 |
"""
====================
RNASeqQC pipeline
====================
Overview
========
This pipeline should be run as the first step in your RNA seq analysis
work flow. It will help detect error and biases within your raw
data. The output of the pipeline can be used to filter out problematic
cells in a standard RNA seq... | mit |
kevin-intel/scikit-learn | sklearn/model_selection/tests/test_successive_halving.py | 3 | 24213 | from math import ceil
import pytest
from scipy.stats import norm, randint
import numpy as np
from sklearn.datasets import make_classification
from sklearn.dummy import DummyClassifier
from sklearn.experimental import enable_halving_search_cv # noqa
from sklearn.model_selection import StratifiedKFold
from sklearn.mod... | bsd-3-clause |
steinnymir/RegAscope2017 | test_scripts/GUI_test/qt_mpl_dataplot.py | 1 | 8289 | """
Series of data are loaded from a .csv file, and their names are
displayed in a checkable list view. The user can select the series
it wants from the list and plot them on a matplotlib canvas.
Use the sample .csv file that comes with the script for an example
of data series.
Eli Bendersky (eliben@gmail.com)
L... | mit |
Bismarrck/tensorflow | tensorflow/contrib/losses/python/metric_learning/metric_loss_ops_test.py | 24 | 20551 | # Copyright 2017 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 |
bitemyapp/ggplot | ggplot/geoms/geom_boxplot.py | 12 | 1218 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook
from .geom import geom
from ggplot.utils import is_string
from ggplot.utils import is_categorical
class g... | bsd-2-clause |
QJonny/CyNest | extras/ConnPlotter/ConnPlotter.py | 4 | 80722 | # ConnPlotter --- A Tool to Generate Connectivity Pattern Matrices
#
# This file is part of ConnPlotter.
#
# Copyright (C) 2009 Hans Ekkehard Plesser/UMB
#
# ConnPlotter 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 Fou... | gpl-2.0 |
sandiegodata/age-friendly-communities | users/david/RCFE_Capacity.py | 1 | 2036 | """
@author: David Albrecht
anaconda 4.2.13
python 3.5.2
pandas 0.19.1
Dataset Name:
CDSS RCFE List - https://secure.dss.ca.gov/CareFacilitySearch/Home/DownloadData
Purpose:
Answer the first two bullet points within the first question block, "RCFE Capacity":
1) What is the number of RCFEs in a given c... | mit |
IndraVikas/scikit-learn | sklearn/utils/fixes.py | 133 | 12882 | """Compatibility fixes for older version of python, numpy and scipy
If you add content to this file, please give the version of the package
at which the fixe is no longer needed.
"""
# Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# ... | bsd-3-clause |
davidam/python-examples | scikit/plot_voting_regressor.py | 6 | 1745 | """
=================================================
Plot individual and voting regression predictions
=================================================
.. currentmodule:: sklearn
Plot individual and averaged regression predictions for Boston dataset.
First, three exemplary regressors are initialized
(:class:`~ense... | gpl-3.0 |
Windy-Ground/scikit-learn | examples/cluster/plot_lena_ward_segmentation.py | 271 | 1998 | """
===============================================================
A demo of structured Ward hierarchical clustering on Lena image
===============================================================
Compute the segmentation of a 2D image with Ward hierarchical
clustering. The clustering is spatially constrained in order
... | bsd-3-clause |
bptripp/grasp-convnet | py/perspective.py | 1 | 30382 | __author__ = 'bptripp'
from os import listdir
from os.path import isfile, join
import time
import numpy as np
import matplotlib.pyplot as plt
import cPickle
from PIL import Image
from scipy.optimize import bisect
from quaternion import angle_between_quaterions, to_quaternion
def get_random_points(n, radius, surface=... | mit |
rosspalmer/DataTools | depr/0.2.5/dtools/holder.py | 1 | 2726 |
from .formatting import format
from .source import data_source
import pandas as pd
class data_holder(object):
def __init__(self):
self.ds = {}
self.x = None
self.y = None
self.current_ds = None
self.current_index = []
self.remain_index = []
self.subs = {}... | mit |
rbalda/neural_ocr | env/lib/python2.7/site-packages/scipy/stats/_discrete_distns.py | 6 | 21463 | #
# Author: Travis Oliphant 2002-2011 with contributions from
# SciPy Developers 2004-2011
#
from __future__ import division, print_function, absolute_import
from scipy import special
from scipy.special import entr, gammaln as gamln
from scipy.misc import logsumexp
from numpy import floor, ceil, log, exp, ... | mit |
saiphcita/crowdsource-platform | fixtures/createJson.py | 16 | 2463 | __author__ = 'Megha'
# Script to transfer csv containing data about various models to json
# Input csv file constituting of the model data
# Output json file representing the csv data as json object
# Assumes model name to be first line
# Field names of the model on the second line
# Data seperated by __DELIM__
# Examp... | mit |
xuewei4d/scikit-learn | sklearn/linear_model/tests/test_sag.py | 8 | 32096 | # Authors: Danny Sullivan <dbsullivan23@gmail.com>
# Tom Dupre la Tour <tom.dupre-la-tour@m4x.org>
#
# License: BSD 3 clause
import math
import pytest
import numpy as np
import scipy.sparse as sp
from scipy.special import logsumexp
from sklearn.linear_model._sag import get_auto_step_size
from sklearn.linear_... | bsd-3-clause |
HarryRybacki/SensorDataResearchReproduction | workbook.py | 1 | 3412 | import matplotlib.pyplot as pyplot
import helpers
"""
Begin phase one - Read Input
Note: This phase will need slight tweaking for each data source as they do not follow a truly standard data format.
As a result, interfaces will likely need to be written for each source akin to read_ibrl_data()
Note: While I had or... | apache-2.0 |
rseubert/scikit-learn | examples/linear_model/plot_lasso_coordinate_descent_path.py | 254 | 2639 | """
=====================
Lasso and Elastic Net
=====================
Lasso and elastic net (L1 and L2 penalisation) implemented using a
coordinate descent.
The coefficients can be forced to be positive.
"""
print(__doc__)
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
import num... | bsd-3-clause |
logston/plottags | setup.py | 1 | 1160 | from setuptools import setup
import plottags
with open("LICENSE") as fd:
LICENSE = fd.read()
with open("README.rst") as fd:
README = fd.read()
setup(
name='plottags',
version=plottags.__version__,
description='A package for plotting the tag history of repositories',
license=LICENSE,
lon... | bsd-3-clause |
monkeybutter/AeroMetTree | datasets/dataset_generator.py | 1 | 2702 | import numpy as np
import pandas as pd
from random import random, randint
from datetime import datetime
def randomizer(i0, i1, prob):
if random() < prob and i0 < i1-1:
new_i0 = randint(i0, i1-1)
new_i1 = randint(new_i0+1, i1)
return (new_i0, new_i1)
else:
return None
def splitt... | mit |
MLWave/kepler-mapper | setup.py | 1 | 2366 | #!/usr/bin/env python
from setuptools import setup
import re
VERSIONFILE="kmapper/_version.py"
verstrline = open(VERSIONFILE, "rt").read()
VSRE = r"^__version__ = ['\"]([^'\"]*)['\"]"
mo = re.search(VSRE, verstrline, re.M)
if mo:
verstr = mo.group(1)
else:
raise RuntimeError("Unable to find version string in ... | mit |
masasin/spirit | src/analysis/ttest_analysis.py | 1 | 2526 | from collections import namedtuple
from pathlib import Path
import numpy as np
import pandas as pd
from scipy import stats
from .csv_analysis import analyze_data, load_surveys
from ..data.survey_utils import ExperimentType
TTEST_DIR = Path(__file__).parent.joinpath("../../models")
ColResult = namedtuple("ColResult... | mit |
dp7-PU/QCLAS_public | src/qclasGUI.py | 1 | 30542 | """
Make a GUI for qclas. This program uses HAPI to generate absorption profiles.
The program comes in without HITRAN data files. User can use the program to
download lines they need.
GUI of the program is based on PyQt4.
Da Pan, v-alpha, started on 02/13/2016
"""
import hapi
import numpy as np
from Py... | mit |
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