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 |
|---|---|---|---|---|---|
yebrahim/pydatalab | datalab/utils/commands/_csv.py | 6 | 2397 | # Copyright 2016 Google Inc. 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 applicable law or agreed ... | apache-2.0 |
MohammedWasim/scikit-learn | benchmarks/bench_covertype.py | 120 | 7381 | """
===========================
Covertype dataset benchmark
===========================
Benchmark stochastic gradient descent (SGD), Liblinear, and Naive Bayes, CART
(decision tree), RandomForest and Extra-Trees on the forest covertype dataset
of Blackard, Jock, and Dean [1]. The dataset comprises 581,012 samples. It ... | bsd-3-clause |
wathen/PhD | MHD/FEniCS/MHD/Stabilised/SaddlePointForm/Test/SplitMatrix/ScottTest/Newton/CavityDriven3D.py | 1 | 13114 | #!/usr/bin/python
import petsc4py
import sys
petsc4py.init(sys.argv)
from petsc4py import PETSc
from dolfin import *
# from MatrixOperations import *
import numpy as np
import PETScIO as IO
import common
import scipy
import scipy.io
import time
import scipy.sparse as sp
import BiLinear as forms
import IterOperations... | mit |
lukeiwanski/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 |
olologin/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 |
AliShug/RoboVis | robovis/ik.py | 1 | 13796 | import numpy as np
import cv2
# from matplotlib import pyplot as plt
from robovis import RVConfig
def runIK(config, stamp):
return (RVIK(config), stamp)
class RVSolver(object):
def __init__(self, pool, config=None):
self.subscribers = {
'ready': []
}
self.outline = None
... | mit |
yousrabk/mne-python | tutorials/plot_cluster_stats_time_frequency_repeated_measures_anova.py | 15 | 9323 | """
.. _tut_stats_cluster_sensor_rANOVA_tfr
====================================================================
Mass-univariate twoway repeated measures ANOVA on single trial power
====================================================================
This script shows how to conduct a mass-univariate repeated measure... | bsd-3-clause |
bjlittle/iris | lib/iris/tests/unit/plot/test_points.py | 1 | 2384 | # Copyright Iris contributors
#
# This file is part of Iris and is released under the LGPL license.
# See COPYING and COPYING.LESSER in the root of the repository for full
# licensing details.
"""Unit tests for the `iris.plot.points` function."""
# Import iris.tests first so that some things can be initialised before
... | lgpl-3.0 |
dcherian/pyroms | examples/Yellow_Sea/Inputs/Boundary/remap_bdry.py | 1 | 9404 | import numpy as np
import os
try:
import netCDF4 as netCDF
except:
import netCDF3 as netCDF
import matplotlib.pyplot as plt
import time
from datetime import datetime
from matplotlib.dates import date2num, num2date
import pyroms
import pyroms_toolbox
import _remapping
class nctime(object):
pass
def remap_bdry... | bsd-3-clause |
crazyhottommy/some-unorganized-old-scripts | python_scripts/metaPlot_by_metaseq.py | 1 | 1858 | # From http://www.biostars.org/p/83800/:
# "What I want to do is to plot reads of my histone marks (in bam file)
# around TSS with CpG and TSS without CpG (Essentially a coverage profile)."
# to install metaseq and dependencies:
# 1. get the metaseq source:
#
# git clone https://github.com/daler/metaseq.git
#... | mit |
fredhusser/scikit-learn | sklearn/linear_model/tests/test_sparse_coordinate_descent.py | 244 | 9986 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_true
from sklearn.utils.t... | bsd-3-clause |
AndySchuetz/homemade_pi | serverImageProcRuntime.py | 1 | 3441 | #
#
# This module provides server runtime image classiciation functions
#
#
import time
import math
import numpy as np
# import Image
from PIL import Image
import sklearn
from sklearn.externals import joblib
def runtimeImageProcessByWindow(modelPath,imagePath,
windowSize=(40,80),
... | mit |
caiorss/m2py | scratchpad/rplot2.py | 1 | 2766 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#!/usr/bin/env python
# Plot a graph of Data which is comming in on the fly
# uses pylab
# Author: Norbert Feurle
# Date: 12.1.2012
# License: if you get any profit from this then please share it with me and only use it for good
import pylab
from pylab import *
import tki... | bsd-3-clause |
wlamond/scikit-learn | sklearn/cross_decomposition/cca_.py | 151 | 3192 | from .pls_ import _PLS
__all__ = ['CCA']
class CCA(_PLS):
"""CCA Canonical Correlation Analysis.
CCA inherits from PLS with mode="B" and deflation_mode="canonical".
Read more in the :ref:`User Guide <cross_decomposition>`.
Parameters
----------
n_components : int, (default 2).
numb... | bsd-3-clause |
mwidner/WebArchiveTextTools | src/subcorpus.py | 1 | 4350 | '''
The Corpus Slicer
Read in an CSV file of metadata describing corpus and file locations
Organize and combine the text files for analysis and further processing
Mike Widner <mikewidner@stanford.edu>
'''
import os
import re
import sys
import argparse
import pandas as pd
from string import punctuation
def get_optio... | gpl-2.0 |
joshloyal/scikit-learn | sklearn/utils/tests/test_estimator_checks.py | 26 | 7393 | import scipy.sparse as sp
import numpy as np
import sys
from sklearn.externals.six.moves import cStringIO as StringIO
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils.testing import assert_raises_regex, assert_true
from sklearn.utils.estimator_checks import check_estimator
from sklearn.utils.... | bsd-3-clause |
rsivapr/scikit-learn | examples/applications/wikipedia_principal_eigenvector.py | 41 | 7742 | """
===============================
Wikipedia principal eigenvector
===============================
A classical way to assert the relative importance of vertices in a
graph is to compute the principal eigenvector of the adjacency matrix
so as to assign to each vertex the values of the components of the first
eigenvect... | bsd-3-clause |
shangwuhencc/scikit-learn | sklearn/tests/test_discriminant_analysis.py | 35 | 11709 | try:
# Python 2 compat
reload
except NameError:
# Regular Python 3+ import
from importlib import reload
import numpy as np
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.t... | bsd-3-clause |
GoogleCloudPlatform/ai-platform-samples | training/pytorch/structured/python_package/trainer/inputs.py | 1 | 5303 | # Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the \"License\");
# you may not use this file except in compliance with the License.\n",
# 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 writ... | apache-2.0 |
calico/basenji | bin/basenji_bench_phylop_folds.py | 1 | 14867 | #!/usr/bin/env python
# Copyright 2019 Calico LLC
# 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
# https://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agr... | apache-2.0 |
agentfog/qiime | scripts/compare_trajectories.py | 15 | 9046 | #!/usr/bin/env python
from __future__ import division
__author__ = "Jose Antonio Navas Molina"
__copyright__ = "Copyright 2011, The QIIME Project"
__credits__ = ["Jose Antonio Navas Molina", "Antonio Gonzalez Pena",
"Yoshiki Vazquez Baeza"]
__license__ = "GPL"
__version__ = "1.9.1-dev"
__maintainer__ = ... | gpl-2.0 |
cjbrasher/LipidFinder | LipidFinder/PeakFilter/FalseDiscoveryRate.py | 1 | 4580 | # Copyright (c) 2019 J. Alvarez-Jarreta and C.J. Brasher
#
# This file is part of the LipidFinder software tool and governed by the
# 'MIT License'. Please see the LICENSE file that should have been
# included as part of this software.
"""Set of methods aimed to calculate the False Discovery Rate:
> get_fdr():
... | mit |
preete-dixit-ck/incubator-airflow | airflow/contrib/hooks/salesforce_hook.py | 30 | 12110 | # -*- 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 |
Xeralux/tensorflow | tensorflow/examples/learn/hdf5_classification.py | 75 | 2899 | # 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 |
wmvanvliet/mne-python | examples/connectivity/plot_mne_inverse_label_connectivity.py | 13 | 7516 | """
=========================================================================
Compute source space connectivity and visualize it using a circular graph
=========================================================================
This example computes the all-to-all connectivity between 68 regions in
source space based on... | bsd-3-clause |
fabioticconi/scikit-learn | sklearn/utils/tests/test_sparsefuncs.py | 78 | 17611 | import numpy as np
import scipy.sparse as sp
from scipy import linalg
from numpy.testing import (assert_array_almost_equal,
assert_array_equal,
assert_equal)
from numpy.random import RandomState
from sklearn.datasets import make_classification
from sklearn.utils.s... | bsd-3-clause |
wmvanvliet/mne-python | examples/time_frequency/plot_compute_csd.py | 20 | 3996 | """
==================================================
Compute a cross-spectral density (CSD) matrix
==================================================
A cross-spectral density (CSD) matrix is similar to a covariance matrix, but in
the time-frequency domain. It is the first step towards computing
sensor-to-sensor cohe... | bsd-3-clause |
Insight-book/data-science-from-scratch | scratch/recommender_systems.py | 2 | 12803 | users_interests = [
["Hadoop", "Big Data", "HBase", "Java", "Spark", "Storm", "Cassandra"],
["NoSQL", "MongoDB", "Cassandra", "HBase", "Postgres"],
["Python", "scikit-learn", "scipy", "numpy", "statsmodels", "pandas"],
["R", "Python", "statistics", "regression", "probability"],
["machine learning", ... | unlicense |
thientu/scikit-learn | sklearn/feature_extraction/tests/test_feature_hasher.py | 258 | 2861 | from __future__ import unicode_literals
import numpy as np
from sklearn.feature_extraction import FeatureHasher
from nose.tools import assert_raises, assert_true
from numpy.testing import assert_array_equal, assert_equal
def test_feature_hasher_dicts():
h = FeatureHasher(n_features=16)
assert_equal("dict",... | bsd-3-clause |
jrabenoit/fizzy | estimators.py | 1 | 8082 | #!/usr/bin/env python3
import numpy as np
import pandas as pd
import copy, pickle
from sklearn import svm, naive_bayes, neighbors, ensemble, linear_model, tree, neural_network
#Quick note: feature_importances_ can be used with random forest etc. to generate feature importance lists
def InnerFolds():
with open('... | gpl-3.0 |
yavalvas/yav_com | build/matplotlib/doc/mpl_examples/units/bar_demo2.py | 9 | 1062 | """
plot using a variety of cm vs inches conversions. The example shows
how default unit instrospection works (ax1), how various keywords can
be used to set the x and y units to override the defaults (ax2, ax3,
ax4) and how one can set the xlimits using scalars (ax3, current units
assumed) or units (conversions applie... | mit |
deepesch/scikit-learn | sklearn/cross_validation.py | 5 | 61899 | """
The :mod:`sklearn.cross_validation` module includes utilities for cross-
validation and performance evaluation.
"""
# 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 |
chizhizhen/DNT | caffe/examples/finetune_flickr_style/assemble_data.py | 18 | 3323 | #!/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
import multiprocessing
# Flickr returns a special image if the request is unavai... | mit |
lyijin/email_analysis | plot.aranda.swarmplot.py | 1 | 2624 | #!/usr/bin/env python3
"""
> plot.aranda.swarmplot.py <
Based on the parsed inbox file, plot a swarmplot to provide chronological
context to email volume from lab members.
"""
import csv
import datetime
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
KAUST_START_UNIX_TIME = 1367712000
LAB_... | gpl-3.0 |
Nyker510/scikit-learn | examples/cluster/plot_kmeans_digits.py | 230 | 4524 | """
===========================================================
A demo of K-Means clustering on the handwritten digits data
===========================================================
In this example we compare the various initialization strategies for
K-means in terms of runtime and quality of the results.
As the gr... | bsd-3-clause |
deprofundis/deprofundis | utils/utils.py | 1 | 3403 | from matplotlib import pyplot as plt
import numpy as np
from types import FunctionType
class HashableDict(dict):
# http://code.activestate.com/recipes/414283-frozen-dictionaries/
def __hash__(self):
return hash(tuple(sorted(self.items())))
def grab_minibatch(patterns, n_in_minibatch):
assert len... | mit |
scikit-multilearn/scikit-multilearn | skmultilearn/adapt/mltsvm.py | 1 | 6351 | # Authors: Grzegorz Kulakowski <grzegorz7w@gmail.com>
# License: BSD 3 clause
from skmultilearn.base import MLClassifierBase
import numpy as np
import scipy.sparse as sp
from scipy.linalg import norm
from scipy.sparse.linalg import inv as inv_sparse
from scipy.linalg import inv as inv_dense
class MLTSVM(MLClassifier... | bsd-2-clause |
google/NeuroNER-CSPMC | neuroner/evaluate.py | 1 | 15107 | import json
import os
import pkg_resources
import time
import numpy as np
import matplotlib.pyplot as plt
import sklearn.metrics
from neuroner import utils_plots
from neuroner import utils_nlp
def assess_model(y_pred, y_true, labels, target_names, labels_with_o, target_names_with_o, dataset_type, stats_graph_folder,... | mit |
wlamond/scikit-learn | examples/semi_supervised/plot_label_propagation_digits_active_learning.py | 36 | 4076 | """
========================================
Label Propagation digits active learning
========================================
Demonstrates an active learning technique to learn handwritten digits
using label propagation.
We start by training a label propagation model with only 10 labeled points,
then we select the t... | bsd-3-clause |
fy2462/apollo | modules/tools/routing/util.py | 1 | 3821 | #!/usr/bin/env python
###############################################################################
# Copyright 2017 The Apollo 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 ... | apache-2.0 |
sinhrks/seaborn | examples/many_facets.py | 26 | 1062 | """
Plotting on a large number of facets
====================================
"""
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="ticks")
# Create a dataset with many short random walks
rs = np.random.RandomState(4)
pos = rs.randint(-1, 2, (20, 5)).cumsum(ax... | bsd-3-clause |
Haunter17/MIR_SU17 | exp2/exp2l.py | 1 | 8332 | import numpy as np
import tensorflow as tf
import h5py
import time
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import sys
# Functions for initializing neural nets parameters
def init_weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1, dtype=tf.float32)
return tf.Var... | mit |
bsipocz/statsmodels | statsmodels/discrete/tests/test_discrete.py | 19 | 55886 | """
Tests for discrete models
Notes
-----
DECIMAL_3 is used because it seems that there is a loss of precision
in the Stata *.dta -> *.csv output, NOT the estimator for the Poisson
tests.
"""
# pylint: disable-msg=E1101
from statsmodels.compat.python import range
import os
import numpy as np
from numpy.testing import ... | bsd-3-clause |
plotly/python-api | packages/python/plotly/plotly/graph_objs/_pie.py | 1 | 78165 | from plotly.basedatatypes import BaseTraceType as _BaseTraceType
import copy as _copy
class Pie(_BaseTraceType):
# class properties
# --------------------
_parent_path_str = ""
_path_str = "pie"
_valid_props = {
"automargin",
"customdata",
"customdatasrc",
"directi... | mit |
shikhardb/scikit-learn | examples/model_selection/grid_search_digits.py | 227 | 2665 | """
============================================================
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.grid_search.GridSearc... | bsd-3-clause |
facebookresearch/ParlAI | parlai/crowdsourcing/tasks/model_chat/analysis/compile_results.py | 1 | 21420 | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import json
import os
import re
from datetime import datetime
from typing import Any, Dict
import numpy as np
import pa... | mit |
GuessWhoSamFoo/pandas | pandas/tseries/offsets.py | 2 | 83512 | # -*- coding: utf-8 -*-
from datetime import date, datetime, timedelta
import functools
import operator
from dateutil.easter import easter
import numpy as np
from pandas._libs.tslibs import (
NaT, OutOfBoundsDatetime, Timedelta, Timestamp, ccalendar, conversion,
delta_to_nanoseconds, frequencies as libfrequen... | bsd-3-clause |
mylxiaoyi/rgbdslam_v2 | test/figures.py | 8 | 24389 | #!/usr/bin/python
import argparse
import random
import matplotlib
from matplotlib.pyplot import figure, text, show, xticks, xlabel, ylabel, gcf, close
from numpy import arange, pi, cos, sin, pi, asarray, median
#If new parameter found, append a list for its values to the error and duration collections
def check_for_n... | gpl-3.0 |
debugger87/spark | python/pyspark/sql/udf.py | 16 | 17796 | #
# 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 |
bearishtrader/trading-with-python | lib/interactivebrokers.py | 77 | 18140 | """
Copyright: Jev Kuznetsov
Licence: BSD
Interface to interactive brokers together with gui widgets
"""
import sys
# import os
from time import sleep
from PyQt4.QtCore import (SIGNAL, SLOT)
from PyQt4.QtGui import (QApplication, QFileDialog, QDialog, QVBoxLayout, QHBoxLayout, QDialogButtonBox,
... | bsd-3-clause |
duncanwp/cis_plugins | gassp.py | 1 | 5417 | from cis.data_io.products.NCAR_NetCDF_RAF import NCAR_NetCDF_RAF, NCAR_NetCDF_RAF_variable_name_selector
class GASSP_variable_name_selector(NCAR_NetCDF_RAF_variable_name_selector):
# Static air pressure value for faam rack measurements
CORRECTED_PRESSURE_VAR_NAME = 'PS_RVSM'
# Standard static air pressure... | lgpl-3.0 |
hvanhovell/spark | python/pyspark/worker.py | 3 | 27527 | #
# 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 |
wzbozon/statsmodels | examples/python/generic_mle.py | 33 | 7532 |
## Maximum Likelihood Estimation (Generic models)
# This tutorial explains how to quickly implement new maximum likelihood models in `statsmodels`. We give two examples:
#
# 1. Probit model for binary dependent variables
# 2. Negative binomial model for count data
#
# The `GenericLikelihoodModel` class eases the p... | bsd-3-clause |
jatraug/Dataclass | Module4/assignment3.py | 1 | 4575 | import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import assignment2_helper as helper
import numpy as np
# Look pretty...
# matplotlib.style.use('ggplot')
plt.style.use('ggplot')
# Do * NOT * alter this line, until instructed!
scaleFeatures = True
##False
# TODO: Load up the dataset and remov... | mit |
paulocoding/DataScienceMachineLearning | Decision_Trees/decision_trees_regression.py | 2 | 1216 | # -*- coding: utf-8 -*-
"""
Created on Tue Jul 07 20:42:01 2015
@author: Allen Thomas Varghese
"""
import numpy as np
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
# Create a random dataset
rng = np.random.RandomState(1)
X = np.sort(5 * rng.rand(80, 1), axis=0)
y = np.sin(X).ravel()
y... | mit |
icdishb/scikit-learn | sklearn/datasets/twenty_newsgroups.py | 4 | 13427 | """Caching loader for the 20 newsgroups text classification dataset
The description of the dataset is available on the official website at:
http://people.csail.mit.edu/jrennie/20Newsgroups/
Quoting the introduction:
The 20 Newsgroups data set is a collection of approximately 20,000
newsgroup documents,... | bsd-3-clause |
escherba/annx | setup.py | 1 | 10154 | import os
import sys
import re
import platform
import numpy
import warnings
import logging
import itertools
import subprocess
import distutils.sysconfig
from glob import glob
from setuptools import Command, setup, Extension
from setuptools.dist import Distribution
from setuptools.command.build_ext import build_ext as _... | mit |
kanchenxi04/vnpy-app | vn.trader/ctaAlgo/strategy_MACD_v01_1.py | 1 | 25181 | # encoding: UTF-8
# 首先写系统内置模块
from datetime import datetime, timedelta, date
from time import sleep
# 其次，导入vnpy的基础模块
import sys
# sys.path.append('C:\\vnpy_1.5\\vnpy-master\\vn.trader')
sys.path.append('../')
from vtConstant import EMPTY_STRING, EMPTY_INT, DIRECTION_LONG, DIRECTION_SHORT, OFFSET_OPEN, STA... | mit |
shawnwanderson/466T7Music | feature_plots_w_wo_instr/get_charts_instr_occurr.py | 1 | 1078 | import csv
import numpy as np
import pylab as pylab
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
# vocals row[0]
# drums row[1]
# claps row[2]
# zero crossing row[3]
# centroid row[4]
# roll off row[5]
# flux row[6]
with open('zcrf_features.csv') as f:
reader = csv.reader(f)
featurex_instr... | mit |
tskisner/healpix-autotools | src/healpy/healpy/newvisufunc.py | 1 | 7036 | __all__ = ['mollview', 'projplot']
import numpy as np
from .pixelfunc import ang2pix, npix2nside
from .rotator import Rotator
from matplotlib.projections.geo import GeoAxes
###### WARNING #################
# this module is work in progress, the aim is to reimplement the healpy
# plot functions using the new features ... | gpl-2.0 |
ssaeger/scikit-learn | sklearn/cluster/birch.py | 18 | 22732 | # Authors: Manoj Kumar <manojkumarsivaraj334@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Joel Nothman <joel.nothman@gmail.com>
# License: BSD 3 clause
from __future__ import division
import warnings
import numpy as np
from scipy import sparse
from math import sqrt
fro... | bsd-3-clause |
lthurlow/Network-Grapher | proj/external/matplotlib-1.2.1/examples/user_interfaces/gtk_spreadsheet.py | 13 | 2463 | #!/usr/bin/env python
"""
Example of embedding matplotlib in an application and interacting with
a treeview to store data. Double click on an entry to update plot
data
"""
import pygtk
pygtk.require('2.0')
import gtk
from gtk import gdk
import matplotlib
matplotlib.use('GTKAgg') # or 'GTK'
from matplotlib.backends.... | mit |
bblais/Classy | classy/mlp.py | 1 | 6918 | import numpy as np
import warnings
from itertools import cycle
from sklearn.utils import gen_even_slices
from sklearn.utils import shuffle
from sklearn.base import BaseEstimator
from sklearn.base import ClassifierMixin
from sklearn.preprocessing import LabelBinarizer
def _softmax(x):
np.exp(x, x)
x /= np.sum... | mit |
Achuth17/scikit-learn | examples/svm/plot_oneclass.py | 249 | 2302 | """
==========================================
One-class SVM with non-linear kernel (RBF)
==========================================
An example using a one-class SVM for novelty detection.
:ref:`One-class SVM <svm_outlier_detection>` is an unsupervised
algorithm that learns a decision function for novelty detection:
... | bsd-3-clause |
beepee14/scikit-learn | sklearn/feature_extraction/hashing.py | 183 | 6155 | # 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 |
benjaminaschultz/shakespeare | shakespeare/__init__.py | 1 | 6194 | import sys,os
import re,glob
import numpy as np
import scipy.sparse
from sklearn.naive_bayes import MultinomialNB
import cPickle as pickle
import pkg_resources
from . import content_sources
#remove punctuation and prepositions from a string
def find_keywords(text):
keywords=re.sub('[{}:?!@#$%^&*\(\)_.\\/,\'\"]','... | mit |
rodluger/planetplanet | scripts/spitzer_example.py | 1 | 5615 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
spitzer_example.py |github|
---------------------------
Sample observations of TRAPPIST-1 PPOs with Spitzer.
.. plot::
:align: center
from scripts import spitzer_example
spitzer_example._test()
.. role:: raw-html(raw)
:format: html
... | gpl-3.0 |
kmike/scikit-learn | sklearn/linear_model/ridge.py | 2 | 31990 | """
Ridge regression
"""
# Author: Mathieu Blondel <mathieu@mblondel.org>
# Reuben Fletcher-Costin <reuben.fletchercostin@gmail.com>
# Fabian Pedregosa <fabian@fseoane.net>
# License: Simplified BSD
from abc import ABCMeta, abstractmethod
import warnings
import numpy as np
from scipy import linalg
f... | bsd-3-clause |
dsullivan7/scikit-learn | sklearn/ensemble/tests/test_base.py | 284 | 1328 | """
Testing for the base module (sklearn.ensemble.base).
"""
# Authors: Gilles Louppe
# License: BSD 3 clause
from numpy.testing import assert_equal
from nose.tools import assert_true
from sklearn.utils.testing import assert_raise_message
from sklearn.datasets import load_iris
from sklearn.ensemble import BaggingCla... | bsd-3-clause |
idlead/scikit-learn | examples/applications/plot_prediction_latency.py | 234 | 11277 | """
==================
Prediction Latency
==================
This is an example showing the prediction latency of various scikit-learn
estimators.
The goal is to measure the latency one can expect when doing predictions
either in bulk or atomic (i.e. one by one) mode.
The plots represent the distribution of the pred... | bsd-3-clause |
guozixiang/vnpy | vn.datayes/api.py | 19 | 45371 | #encoding: UTF-8
import os
import json
import time
import requests
import pymongo
import pandas as pd
from datetime import datetime, timedelta
from Queue import Queue, Empty
from threading import Thread, Timer
from pymongo import MongoClient
from requests.exceptions import ConnectionError
from errors im... | mit |
alexis-jacq/shape_learning | scripts/shape_model_gui.py | 2 | 3648 | #!/usr/bin/env python
# coding: utf-8
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button
from shape_learning.shape_modeler import ShapeModeler
import argparse
parser = argparse.ArgumentParser(description='Displays the effect of the parameters in a shape model');
parser.ad... | isc |
procoder317/scikit-learn | sklearn/semi_supervised/label_propagation.py | 71 | 15342 | # coding=utf8
"""
Label propagation in the context of this module refers to a set of
semisupervised classification algorithms. In the high level, these algorithms
work by forming a fully-connected graph between all points given and solving
for the steady-state distribution of labels at each point.
These algorithms per... | bsd-3-clause |
diegocavalca/Studies | phd-thesis/nilmtk/nilmtk/tests/test_datastore.py | 1 | 6061 | #!/usr/bin/python
from __future__ import print_function, division
import unittest
from os.path import join
import pandas as pd
from datetime import timedelta
from .testingtools import data_dir
from nilmtk.datastore import HDFDataStore, CSVDataStore
from nilmtk import TimeFrame
# class name can't begin with test
class... | cc0-1.0 |
mariusvniekerk/ibis | ibis/sql/alchemy.py | 1 | 23791 | # Copyright 2015 Cloudera 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 writing, so... | apache-2.0 |
changbindu/rufeng-finance | src/tushare/test/storing_test.py | 40 | 1729 | # -*- coding:utf-8 -*-
import os
from sqlalchemy import create_engine
from pandas.io.pytables import HDFStore
import tushare as ts
def csv():
df = ts.get_hist_data('000875')
df.to_csv('c:/day/000875.csv',columns=['open','high','low','close'])
def xls():
df = ts.get_hist_data('000875')
#直接保存
df.t... | lgpl-3.0 |
danielballan/mpld3 | mpld3/tests/test_elements.py | 16 | 5658 | """
Test creation of basic plot elements
"""
import numpy as np
import matplotlib.pyplot as plt
from .. import fig_to_dict, fig_to_html
from numpy.testing import assert_equal
def test_line():
fig, ax = plt.subplots()
ax.plot(np.arange(10), np.random.random(10),
'--k', alpha=0.3, zorder=10, lw=2)
... | bsd-3-clause |
shadmanj/college-code | BME301-Bioelectricity/hodgkin-huxley-solver-PYTHON/2nd-order-DE-solver.py | 1 | 3308 | #Shadman Jubaer
#BME 301
#HW 2
#These functions will numerically solve a second order differential
#equation using either Euler or Runge-Kutta numerical solving methods.
#------------------------------------------------------------
import math as m
import matplotlib.pyplot as plt
#Generates interval over wh... | mit |
schoolie/bokeh | bokeh/core/compat/bokeh_renderer.py | 6 | 21121 | "Supporting objects and functions to convert Matplotlib objects into Bokeh."
#-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2014, Continuum Analytics, Inc. All rights reserved.
#
# Powered by the Bokeh Development Team.
#
# The full license is in the file LICENSE.t... | bsd-3-clause |
bzero/statsmodels | statsmodels/tsa/filters/hp_filter.py | 27 | 3507 | from __future__ import absolute_import
from scipy import sparse
from scipy.sparse import dia_matrix, eye as speye
from scipy.sparse.linalg import spsolve
import numpy as np
from ._utils import _maybe_get_pandas_wrapper
def hpfilter(X, lamb=1600):
"""
Hodrick-Prescott filter
Parameters
----------
... | bsd-3-clause |
Eric89GXL/scipy | scipy/stats/_distn_infrastructure.py | 2 | 120310 | #
# Author: Travis Oliphant 2002-2011 with contributions from
# SciPy Developers 2004-2011
#
from __future__ import division, print_function, absolute_import
from scipy._lib.six import string_types, exec_, PY3
from scipy._lib._util import getargspec_no_self as _getargspec
import sys
import keyword
import r... | bsd-3-clause |
dbohn/openlab | appli/iotlab_examples/robot_broadcaster/robot_broadcaster.py | 4 | 7396 | """
live_imu.py
IoT-LAB M3 Live Sensors
Display data from an IoT-LAB M3 node running the robot_listener firmware.
The application uses input from the serial_aggregator.py.
Please see REAME.md
Avalaible sensors are :
- Gyroscop and Accerometer (from IMU)
- Light
- Temperature (from pression sensor)
Author: Guilla... | gpl-3.0 |
ZenDevelopmentSystems/scikit-learn | sklearn/neighbors/approximate.py | 71 | 22357 | """Approximate nearest neighbor search"""
# Author: Maheshakya Wijewardena <maheshakya.10@cse.mrt.ac.lk>
# Joel Nothman <joel.nothman@gmail.com>
import numpy as np
import warnings
from scipy import sparse
from .base import KNeighborsMixin, RadiusNeighborsMixin
from ..base import BaseEstimator
from ..utils.va... | bsd-3-clause |
3manuek/scikit-learn | sklearn/linear_model/tests/test_coordinate_descent.py | 44 | 22866 | # Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
from sys import version_info
import numpy as np
from scipy import interpolate, sparse
from copy import deepcopy
from sklearn.datasets import load_boston
from sklearn.utils.testing ... | bsd-3-clause |
illume/numpy3k | numpy/core/code_generators/ufunc_docstrings.py | 2 | 75295 | # Docstrings for generated ufuncs
docdict = {}
def get(name):
return docdict.get(name)
def add_newdoc(place, name, doc):
docdict['.'.join((place, name))] = doc
add_newdoc('numpy.core.umath', 'absolute',
"""
Calculate the absolute value element-wise.
Parameters
----------
x : array_like... | bsd-3-clause |
DSLituiev/scikit-learn | doc/conf.py | 15 | 8446 | # -*- coding: utf-8 -*-
#
# scikit-learn documentation build configuration file, created by
# sphinx-quickstart on Fri Jan 8 09:13:42 2010.
#
# 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.
... | bsd-3-clause |
0todd0000/spm1d | spm1d/rft1d/examples/paper/fig05_EC_broken.py | 1 | 3709 |
from math import pi,log,sqrt,exp
import numpy as np
from scipy import stats
from matplotlib import pyplot
from spm1d import rft1d
### EPS production preliminaries:
fig_width_mm = 100
fig_height_mm = 80
mm2in = 1/25.4
fig_width = fig_width_mm*mm2in # width in inches
fig_height = fig_height_mm*mm2in # height... | gpl-3.0 |
argriffing/numpy | numpy/lib/recfunctions.py | 148 | 35012 | """
Collection of utilities to manipulate structured arrays.
Most of these functions were initially implemented by John Hunter for
matplotlib. They have been rewritten and extended for convenience.
"""
from __future__ import division, absolute_import, print_function
import sys
import itertools
import numpy as np
im... | bsd-3-clause |
trungnt13/scikit-learn | sklearn/manifold/tests/test_mds.py | 324 | 1862 | import numpy as np
from numpy.testing import assert_array_almost_equal
from nose.tools import assert_raises
from sklearn.manifold import mds
def test_smacof():
# test metric smacof using the data of "Modern Multidimensional Scaling",
# Borg & Groenen, p 154
sim = np.array([[0, 5, 3, 4],
... | bsd-3-clause |
jeremiedecock/snippets | python/matplotlib/contours/label_on_contours_and_choose_contour_levels.py | 1 | 1448 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright (c) 2015 Jérémie DECOCK (http://www.jdhp.org)
# See http://matplotlib.org/api/axes_api.html?highlight=contour#matplotlib.axes.Axes.contour
import math
import numpy as np
import matplotlib
import matplotlib.cm as cm
import matplotlib.mlab as mlab
import matp... | mit |
warren-oneill/powerline | tests/test_history.py | 2 | 15600 | from unittest import TestCase
import numpy as np
import pandas as pd
from zipline.history.history import HistorySpec
from zipline.protocol import BarData
from zipline.finance.trading import TradingEnvironment
from powerline.history.history_container import EpexHistoryContainer
__author__ = 'Max'
class TestHistory(... | apache-2.0 |
muxspace/facial_expressions | python/JN721.py | 2 | 5371 | # Augmenting Face Image Database via Transformations (Flip, Rotate, Crop & Scale)
# Jay Narhan
# UserID: JN721
#
# This class is designed to apply a series of image transformations to a set of images. A transformation is simply a
# function. It is a function that maps the image to another version of the image.
#
# G(x... | apache-2.0 |
jaidevd/scikit-learn | sklearn/tests/test_kernel_approximation.py | 78 | 7586 | import numpy as np
from scipy.sparse import csr_matrix
from sklearn.utils.testing import assert_array_equal, assert_equal, assert_true
from sklearn.utils.testing import assert_not_equal
from sklearn.utils.testing import assert_array_almost_equal, assert_raises
from sklearn.utils.testing import assert_less_equal
from ... | bsd-3-clause |
SuLab/scheduled-bots | scheduled_bots/phenotypes/download_mitodb.py | 1 | 4192 | # http://mitodb.com/symptoms.php?oid=302060&symptoms=Show
import subprocess
from itertools import chain
from tqdm import tqdm
from bs4 import BeautifulSoup
import requests
import pandas as pd
def download_disease_list():
## get all diseases
url = "http://mitodb.com/"
bs = BeautifulSoup(requests.get(url).t... | mit |
eayoungs/DeltaMtrSvs | test_2_amsaves.py | 1 | 10564 | #!/usr/bin/env python
__author__ = "Eric Allen Youngson"
__email__ = "eric@successionecological.com"
__copyright__ = "Copyright 2015, Succession Ecological Services"
__license__ = "GNU Affero (GPLv3)"
""" This module provides functions for requesting results from the DeltaMeter
Services API * deltameterservices.c... | agpl-3.0 |
seberg/numpy | doc/source/conf.py | 3 | 14190 | # -*- coding: utf-8 -*-
import os
import re
import sys
# Minimum version, enforced by sphinx
needs_sphinx = '3.2.0'
# This is a nasty hack to use platform-agnostic names for types in the
# documentation.
# must be kept alive to hold the patched names
_name_cache = {}
def replace_scalar_type_names():
""" Rename... | bsd-3-clause |
jeremyjbowers/nba_py | setup.py | 1 | 3686 | """A setuptools based setup module.
See:
https://packaging.python.org/en/latest/distributing.html
https://github.com/pypa/sampleproject
"""
# Always prefer setuptools over distutils
from setuptools import setup, find_packages
# To use a consistent encoding
from codecs import open
from os import path
here = path.absp... | bsd-3-clause |
nitish-tripathi/Simplery | Datasets/UCI_Wine/LDA_WineData.py | 1 | 2501 |
import pandas as pd
import numpy as np
from sklearn.lda import LDA
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
from matplotlib.colors... | mit |
brianlorenz/COSMOS_IMACS_Redshifts | PlotCodes/Plot_Avfinal_dist.py | 1 | 3816 | #Plots the Av magnitude due to the balmer decerment
import numpy as np
import matplotlib.pyplot as plt
from astropy.io import ascii
import sys, os, string
import pandas as pd
from astropy.io import fits
import collections
#Folder to save the figures
figout = '/Users/blorenz/COSMOS/Reports/2018/Images/'
#The location ... | mit |
WaveBlocks/WaveBlocks | src/scripts_advanced/PlotQRNodesSupportEvolution.py | 1 | 3340 | """The WaveBlocks Project
Script to plot the support of the transformed quadrature
nodes during the time evolution.
@author: R. Bourquin
@copyright: Copyright (C) 2012 R. Bourquin
@license: Modified BSD License
"""
import sys
from numpy import unique, array, squeeze
from matplotlib.pyplot import *
from WaveBlocks i... | bsd-3-clause |
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