repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
phoebe-project/phoebe2-docs | 2.1/tutorials/general_concepts.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.1,<2.2"
"""
Explanation: General Concepts
HOW TO RUN THIS FILE: if you're running this in a Jupyter notebook or Google Colab session, you can click on a cell and then shift+Enter to run the cell and automatically select the next cell. Alt+Enter will run a cell and create a new cell below it... |
VUInformationRetrieval/IR2016_2017 | make_dataset.ipynb | gpl-2.0 | from Bio import Entrez
# NCBI requires you to set your email address to make use of NCBI's E-utilities
Entrez.email = "Your.Name.Here@example.org"
"""
Explanation: Building the dataset of research papers
The Entrez module, a part of the Biopython library, will be used to interface with PubMed.<br>
You can download Bi... |
jaduimstra/nilmtk | notebooks/experimental/mle.ipynb | apache-2.0 | import numpy as np
import pandas as pd
from os.path import join
from pylab import rcParams
import matplotlib.pyplot as plt
%matplotlib inline
rcParams['figure.figsize'] = (13, 6)
#plt.style.use('ggplot')
from datetime import datetime as datetime2
from datetime import timedelta
import nilmtk
from nilmtk.disaggregate.ma... |
rashikaranpuria/Machine-Learning-Specialization | Clustering_&_Retrieval/Week6/.ipynb_checkpoints/6_hierarchical_clustering_blank-checkpoint.ipynb | mit | import graphlab
import matplotlib.pyplot as plt
import numpy as np
import sys
import os
import time
from scipy.sparse import csr_matrix
from sklearn.cluster import KMeans
from sklearn.metrics import pairwise_distances
%matplotlib inline
"""
Explanation: Hierarchical Clustering
Hierarchical clustering refers to a class... |
gregstarr/PyGPS | Examples/.ipynb_checkpoints/Combining Pseudorange and Phase-checkpoint.ipynb | agpl-3.0 | files = glob("/home/greg/Documents/Summer Research/rinex files/ma*")
poop=rinexobs(files[6])
plt.figure(figsize=(14,14))
ax1 = plt.subplot(211)
ax1.xaxis.set_major_formatter(fmt)
plt.plot(2.85*(poop[:,23,'P2','data']*1.0E9/3.0E8-poop[:,23,'C1','data']*1.0E9/3.0E8)[10:],
'.',markersize=3,label='pr tec')
plt.p... |
tiagoft/curso_audio | afinacao.ipynb | mit | referencia_inicial = 440.0 # Hz
frequencias = [] # Esta lista recebera todas as frequencias de uma escala
f = referencia_inicial
while len(frequencias) < 12:
if f > (referencia_inicial * 2):
f /= 2.
frequencias.append(f)
f *= (3/2.)
frequencias.sort()
print frequencias
print f
"""
Explanation: A... |
suresh/notes | python/In Depth - Kernel Density Estimation.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
import numpy as np
import pandas as pd
"""
Explanation: Gaussian Mixture Models (GMM) are a kind of hybrid between a clustering estimator and a density estimator. Density estimator is an algorithm which takes a D-dimensional dataset an... |
DawesLab/LabNotebooks | Double Slit Correlation Model.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as sp
from numpy import pi, sin, cos, linspace, exp, real, imag, abs, conj, meshgrid, log, log10, angle, zeros, complex128, random
from numpy.fft import fft, fftshift, ifft
from mpl_toolkits.mplot3d import axes3d
import BeamOptics as bopt
%matplot... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_lcmv_beamformer.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import numpy as np
import mne
from mne.datasets import sample
from mne.beamformer import lcmv
print(__doc__)
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_a... |
sdpython/actuariat_python | _doc/notebooks/sessions/seance5_approche_fonctionnelle_enonce.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import pyensae
from pyquickhelper.helpgen import NbImage
from jyquickhelper import add_notebook_menu
add_notebook_menu()
from actuariat_python.data import table_mortalite_euro_stat
table_mortalite_euro_stat()
import pandas
df = pandas.read_csv... |
domino14/macondo | notebooks/preendgame_heuristics/preendgame_heuristics.ipynb | gpl-3.0 | from copy import deepcopy
import csv
from datetime import date
import numpy as np
import pandas as pd
import seaborn as sns
import time
log_folder = '../logs/'
log_file = log_folder + 'log_20200515_preendgames.csv'
todays_date = date.today().strftime("%Y%m%d")
final_spread_dict = {}
out_first_dict = {}
win_dict = {}... |
gwu-libraries/notebooks | 20170720-building-social-network-graphs-CSV.ipynb | mit | import sys
import json
import re
import numpy as np
from datetime import datetime
import pandas as pd
tweetfile = '/home/soominpark/sfmproject/Work/Network Graphs/food_security.csv'
tweets = pd.read_csv(tweetfile)
"""
Explanation: Exports nodes and edges from tweets (Retweets, Mentions, or Replies) [CSV]
Exports n... |
KUrushi/knocks | chapter2/UNIX command.ipynb | mit | with open("hightemp.txt") as f:
count = len(f.readlines())
print(count)
"""
Explanation: 10. 行数のカウント
行数をカウントせよ．確認にはwcコマンドを用いよ
End of explanation
"""
%%bash
wc -l hightemp.txt
"""
Explanation: wc
ファイル内のバイト数, 単語数, 及び行数を集計し表示する.
また, 空白で区切られたものを単語として扱う.
表示: 行数 単語数 バイト数
wc [-clw] [--bytes] [--chars] [--lines] [--wor... |
mne-tools/mne-tools.github.io | 0.22/_downloads/f094864c4eeae2b4353a90789dd18b2b/plot_mixed_source_space_inverse.ipynb | bsd-3-clause | # Author: Annalisa Pascarella <a.pascarella@iac.cnr.it>
#
# License: BSD (3-clause)
import os.path as op
import matplotlib.pyplot as plt
from nilearn import plotting
import mne
from mne.minimum_norm import make_inverse_operator, apply_inverse
# Set dir
data_path = mne.datasets.sample.data_path()
subject = 'sample'
... |
planet-os/notebooks | api-examples/Metno_wind_demo.ipynb | mit | %matplotlib notebook
import urllib.request
import numpy as np
import simplejson as json
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import warnings
import datetime
import dateutil.parser
import matplotlib.cbook
warnings.filterwarnings("ignore",category=matplotlib.cbook.mplDeprecation)
import reque... |
tommyogden/maxwellbloch | docs/examples/mbs-lambda-weak-pulse-cloud-atoms-with-coupling-store.ipynb | mit | mb_solve_json = """
{
"atom": {
"fields": [
{
"coupled_levels": [[0, 1]],
"detuning": 0.0,
"label": "probe",
"rabi_freq": 1.0e-3,
"rabi_freq_t_args":
{
"ampl": 1.0,
"centre": 0.0,
"fwhm": 1.0
},
"rabi... |
InsightSoftwareConsortium/SimpleITK-Notebooks | Python/00_Setup.ipynb | apache-2.0 | import importlib
from distutils.version import LooseVersion
# check that all packages are installed (see requirements.txt file)
required_packages = {
"jupyter",
"numpy",
"matplotlib",
"ipywidgets",
"scipy",
"pandas",
"numba",
"multiprocess",
"SimpleITK",
}
problem_packages = list()... |
jhamrick/original-nbgrader | examples/grade_assignment/Submitted Assignment.ipynb | mit | NAME = "Jane Doe"
COLLABORATORS = "n/a"
"""
Explanation: Example Assignment
<a href="#Problem-1">Problem 1</a>
<a href="#Problem-2">Problem 2</a>
<a href="#Part-A">Part A</a>
<a href="#Part-B">Part B</a>
<a href="#Part-C">Part C</a>
Before you turn this problem in, make sure everything runs as expected. First, re... |
ES-DOC/esdoc-jupyterhub | notebooks/ipsl/cmip6/models/sandbox-2/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ipsl', 'sandbox-2', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: IPSL
Source ID: SANDBOX-2
Topic: Aerosol
Sub-Topics: Transport, Emissions, Conce... |
mne-tools/mne-tools.github.io | 0.24/_downloads/686e03eb7a01e30e026e3dd11e64df18/30_filtering_resampling.ipynb | bsd-3-clause | import os
import numpy as np
import matplotlib.pyplot as plt
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file)
# use just 6... |
AlDanial/cloc | tests/inputs/Trapezoid_Rule.ipynb | gpl-2.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
def f(x):
return (x-3)*(x-5)*(x-7)+85
x = np.linspace(0, 10, 200)
y = f(x)
"""
Explanation: Basic Numerical Integration: the Trapezoid Rule
A simple illustration of the trapezoid rule for definite integration:
$$
\int_{a}^{b} f(x)\, dx \approx... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_source_label_time_frequency.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, source_induced_power
print(__doc__)
"""
Explanation... |
aje/POT | docs/source/auto_examples/plot_otda_d2.ipynb | mit | # Authors: Remi Flamary <remi.flamary@unice.fr>
# Stanislas Chambon <stan.chambon@gmail.com>
#
# License: MIT License
import matplotlib.pylab as pl
import ot
import ot.plot
"""
Explanation: OT for domain adaptation on empirical distributions
This example introduces a domain adaptation in a 2D setting. It exp... |
ES-DOC/esdoc-jupyterhub | notebooks/miroc/cmip6/models/miroc-es2l/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'miroc-es2l', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: MIROC
Source ID: MIROC-ES2L
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy B... |
dblyon/PandasIntro | Pandas_Introduction.ipynb | mit | %%javascript
$.getScript('misc/kmahelona_ipython_notebook_toc.js')
"""
Explanation: <h1 id="tocheading">Table of Contents</h1>
<div id="toc"></div>
End of explanation
"""
from IPython.core.display import HTML
HTML("<iframe src=https://jupyter.readthedocs.io/en/latest/_images/notebook_components.png width=800 height=... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_stats_cluster_1samp_test_time_frequency.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.time_frequency import tfr_morlet
from mne.stats import permutation_cluster_1samp_test
from mne.datasets import sample
print(__doc__)
"""
Explanat... |
dalya/WeirdestGalaxies | outlier_detection_RF_demo.ipynb | mit | %pylab inline
import numpy
import sklearn
from sklearn.preprocessing import Imputer
import matplotlib.pyplot as plt
"""
Explanation: Outlier Detection Algorithm with unsupervised Random Forest (RF)
This notebook shows the basic steps for using the outlier detection algorithm we developped, based on unsupervised RF, an... |
tuanavu/coursera-university-of-washington | machine_learning/1_machine_learning_foundations/assignment/week3/Analyzing product sentiment.ipynb | mit | import graphlab
"""
Explanation: Predicting sentiment from product reviews
Fire up GraphLab Create
End of explanation
"""
products = graphlab.SFrame('amazon_baby.gl/')
"""
Explanation: Read some product review data
Loading reviews for a set of baby products.
End of explanation
"""
products.head()
"""
Explanation... |
mne-tools/mne-tools.github.io | 0.17/_downloads/9460321824116e4964fbe6d88d27462e/plot_cluster_stats_evoked.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.stats import permutation_cluster_test
from mne.datasets import sample
print(__doc__)
"""
Explanation: Permutation F-test on sensor data with 1D c... |
julienchastang/unidata-python-workshop | failing_notebooks/casestudy.ipynb | mit | import xarray
ds = xarray.open_dataset('https://motherlode.ucar.edu/repository/opendap/41f2b38a-4e70-4135-8ff8-dbf3d1dcbfc1/entry.das',
decode_times=False)
print(ds)
"""
Explanation: netCDF File Visualization Case Study
I was asked by a colleague to visualize data contained within this netCDF f... |
mikarubi/notebooks | worker/notebooks/bolt/tutorials/basic-usage.ipynb | mit | from bolt import ones
a = ones((2,3,4))
a.shape
"""
Explanation: Basic usage
The primary object in Bolt is the Bolt array. We can construct these arrays using familiar operators (like zeros and ones), or from an existing array, and manipulate them like ndarrays whether in local or distributed settings. This notebook ... |
xpharry/Udacity-DLFoudation | tutorials/intro-to-tensorflow/intro_to_tensorflow_solution.ipynb | mit | # Problem 1 - Implement Min-Max scaling for grayscale image data
def normalize_grayscale(image_data):
"""
Normalize the image data with Min-Max scaling to a range of [0.1, 0.9]
:param image_data: The image data to be normalized
:return: Normalized image data
"""
a = 0.1
b = 0.9
grayscale... |
chengsoonong/mclass-sky | projects/alasdair/notebooks/04_learning_curves.ipynb | bsd-3-clause | # remove after testing
%load_ext autoreload
%autoreload 2
import pickle
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from itertools import product
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection impo... |
automl/SpySMAC | examples/autopytorch/apt_notebook.ipynb | bsd-3-clause | # Remove the old example output
import os
import logging
import tempfile
import shutil
log_dir = "logs/apt-cave-notebook/"
rerun_apt = False
logging.basicConfig(level=logging.DEBUG)
from autoPyTorch import AutoNetClassification
import os as os
import openml
import json
from ConfigSpace.read_and_write import json as p... |
JBed/Pandas_Analysis_Worksheet | Pandas_Worksheet_Solutions.ipynb | apache-2.0 | import pandas as pd
%matplotlib inline
"""
Explanation: Pandas Worksheet Solutions
End of explanation
"""
df = pd.read_csv('nba-shot-logs.zip')
"""
Explanation: The goal of this worksheet is to provide practical examples of aggregating (with group by), plotting, and pivoting data with the Pandas Python package.
Thi... |
computational-class/cjc2016 | code/04.PythonCrawler_beautifulsoup.ipynb | mit | import requests
from bs4 import BeautifulSoup
help(requests.get)
url = 'http://computational-class.github.io/bigdata/data/test.html'
content = requests.get(url)
help(content)
print(content.text)
content.encoding
"""
Explanation: 数据抓取：
Requests、Beautifulsoup、Xpath简介
王成军
wangchengjun@nju.edu.cn
计算传播网 http://comp... |
TomAugspurger/PracticalPandas | Practical Pandas 01 - Reading the Data.ipynb | mit | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from IPython import display
"""
Explanation: Practical Pandas: Cycling (Part 1)
This is the first post in a series where I'll show how I use pandas on real-world datasets.
For this post, we'll look at data I collected with Cy... |
zerothi/sids | docs/visualization/viz_module/showcase/PdosPlot.ipynb | lgpl-3.0 | import sisl
import sisl.viz
# This is just for convenience to retreive files
siesta_files = sisl._environ.get_environ_variable("SISL_FILES_TESTS") / "sisl" / "io" / "siesta"
"""
Explanation: PdosPlot
End of explanation
"""
plot = sisl.get_sile(siesta_files / "SrTiO3.PDOS").plot(Erange=[-10,10])
"""
Explanation: We ... |
google/physics-math-tutorials | colabs/statistics1.ipynb | apache-2.0 | import numpy as np
def normalize(x):
return x / np.sum(x)
def posterior_covid(observed, prevalence=None, sensitivity=None):
# observed = 0 for negative test, 1 for positive test
# hidden state = 0 if no-covid, 1 if have-covid
if sensitivity is None:
sensitivity = 0.875
specificity = 0.975
TPR = sensit... |
hankcs/HanLP | plugins/hanlp_demo/hanlp_demo/zh/tok_restful.ipynb | apache-2.0 | pip install hanlp_restful -U
"""
Explanation: <h2 align="center">点击下列图标在线运行HanLP</h2>
<div align="center">
<a href="https://colab.research.google.com/github/hankcs/HanLP/blob/doc-zh/plugins/hanlp_demo/hanlp_demo/zh/tok_restful.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.sv... |
pinga-lab/magnetic-ellipsoid | code/lambda_oblate_ellipsoids.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: $\lambda$ variable for oblate ellipsoids
End of explanation
"""
a = 11.
b = 20.
x = 21.
y = 23.
z = 30.
"""
Explanation: Here, we follow the reasoning presented by Webster (1904) for analyzing the ellipsoidal coordinate $\lambd... |
mne-tools/mne-tools.github.io | 0.20/_downloads/3292f41d8bad9e2a2bc48714f7f39668/plot_30_epochs_metadata.ipynb | bsd-3-clause | import os
import numpy as np
import pandas as pd
import mne
kiloword_data_folder = mne.datasets.kiloword.data_path()
kiloword_data_file = os.path.join(kiloword_data_folder,
'kword_metadata-epo.fif')
epochs = mne.read_epochs(kiloword_data_file)
"""
Explanation: Working with Epoch meta... |
juanshishido/text-classification | notebooks/kaggle-writeup.ipynb | mit | %matplotlib inline
import re
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import regexp_tokenize
from nltk.stem.porter import PorterStemmer
from sklearn import cross_validation
from sklearn.feature_extraction.text import TfidfV... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/recommendation_systems/labs/3_als_bqml_hybrid.ipynb | apache-2.0 | PROJECT = !(gcloud config get-value core/project)
PROJECT = PROJECT[0]
%env PROJECT=$PROJECT
"""
Explanation: Hybrid Recommendations with the Movie Lens Dataset
Note: It is recommended that you complete the companion als_bqml.ipynb notebook before continuing with this als_bqml_hybrid.ipynb notebook. This is, however,... |
feststelltaste/software-analytics | prototypes/Reading Git logs with Pandas 2.0 with bonus.ipynb | gpl-3.0 | import git
GIT_REPO_PATH = r'../../spring-petclinic/'
repo = git.Repo(GIT_REPO_PATH)
git_bin = repo.git
git_bin
"""
Explanation: Introduction
Introduction
There are multiple reasons for analyzing a version control system like your Git repository. See for example Adam Tornhill's book "Your Code as a Crime Scene" or h... |
arruda/bgarena_analysis | notebooks/gametables_finished_games_by_elo_rating.ipynb | mit | %pylab inline
import os
from pandas import read_csv
csv_files_location = "../bgarena_gatherer/db_backup/"
game_tables_df = read_csv(os.path.join(csv_files_location, 'gametables.csv'))
game_tables_df = game_tables_df.set_index("id")
# remove non existing tables
non_error_game_table_df = game_tables_df[game_tables_df[... |
sys-bio/tellurium | examples/notebooks/core/tellurium_utility.ipynb | apache-2.0 | %matplotlib inline
from __future__ import print_function
import tellurium as te
# to get the tellurium version use
print('te.__version__')
print(te.__version__)
# or
print('te.getTelluriumVersion()')
print(te.getTelluriumVersion())
# to print the full version info use
print('-' * 80)
te.printVersionInfo()
print('-' *... |
abulbasar/machine-learning | Spacy.ipynb | apache-2.0 | sentence = """European authorities fined Google a record $5.1 billion on Wednesday for
abusing its power in the mobile phone market and ordered the company to alter its practices"""
"""
Install spacy
$ pip install spacy
Download en_core_web_sm module
$ python -m spacy download en_core_web_sm
"""
import spacy
from ... |
pvalienteverde/ElCuadernillo | ElCuadernillo/20160214_TensorFlowTutorialBasico/TensorFlow_Interactivo_IPython.ipynb | mit | import tensorflow as tf
"""
Explanation: Ejemplo de como correr interactivamente la libreria TensorFlow en IPython
End of explanation
"""
sess = tf.InteractiveSession()
x = tf.Variable([[2.0, 3.0],[4.0, 12.0]])
"""
Explanation: De esta manera lanzar una sesion interactiva, util cuando queremos probar metodos
End o... |
robertoalotufo/ia898 | src/h2stats.ipynb | mit | def h2stats(h):
import numpy as np
import ia898.src as ia
hn = 1.0*h/h.sum() # compute the normalized image histogram
v = np.zeros(11) # number of statistics
# compute statistics
n = len(h) # number of gray values
v[0] = np.sum((np.arange(n)*hn)) # mean
v[1] = np.sum(np.power((np.ara... |
phoebe-project/phoebe2-docs | 2.3/tutorials/general_concepts.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.3,<2.4"
"""
Explanation: General Concepts: The PHOEBE Bundle
HOW TO RUN THIS FILE: if you're running this in a Jupyter notebook or Google Colab session, you can click on a cell and then shift+Enter to run the cell and automatically select the next cell. Alt+Enter will run a cell and create... |
mne-tools/mne-tools.github.io | 0.17/_downloads/f12d4c974f9dc78c910e072ebccba291/plot_rereference_eeg.ipynb | bsd-3-clause | # Authors: Marijn van Vliet <w.m.vanvliet@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import mne
from mne.datasets import sample
from matplotlib import pyplot as plt
print(__doc__)
# Setup for reading the raw data
data_path = sample.data_path()
raw_f... |
fivetentaylor/rpyca | TGA_Testing.ipynb | mit | %matplotlib inline
"""
Explanation: Robust PCA Example
Robust PCA is an awesome relatively new method for factoring a matrix into a low rank component and a sparse component. This enables really neat applications for outlier detection, or models that are robust to outliers.
End of explanation
"""
import matplotlib.... |
google/making_with_ml | instafashion/scripts/getMatches.ipynb | apache-2.0 | # For each fashion inspiration pic, check to make sure that it's
# a "fashion" picture. Ignore all other pics
storage_client = storage.Client()
blobs = list(storage_client.list_blobs(INSPO_BUCKET, prefix=INSPO_SUBFOLDER))
uris = [os.path.join("gs://", blobs[0].bucket.name, x.name)
for x in blobs if '.jpg' in x... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/plots_boxplots.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.api as sm
"""
Explanation: Box Plots
The following illustrates some options for the boxplot in statsmodels. These include violin_plot and bean_plot.
End of explanation
"""
data = sm.datasets.anes96.load_pandas()
party_ID = np.a... |
olgaliak/cntk-cyclegan | simpleGan/CNTK_206B_DCGAN.ipynb | mit | import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import os
import cntk as C
from cntk import Trainer
from cntk.layers import default_options
from cntk.device import set_default_device, gpu, cpu
from cntk.initializer import normal
from cntk.io import (MinibatchSource, CTFDeserializer, StreamD... |
DistrictDataLabs/tribe | notebooks/Introduction to Networkx.ipynb | mit | %matplotlib inline
import os
import random
import community
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
from tribe.utils import *
from tribe.stats import *
from operator import itemgetter
## Some Helper constants
FIXTURES = os.path.join(os.getcwd(), "fixtures")
# GRAPHML = os.path.join... |
saketkc/notebooks | python/CrossValidated-222556.ipynb | bsd-2-clause | %pylab inline
from __future__ import division
import scipy as sp
import seaborn as sns
sns.set_style('whitegrid')
sns.set_context('paper', font_scale=2)
np.random.seed(42)
def calculate_shannon_entropy(p):
'''
Parameters
----------
p: list
list of probability values such that sum(p) = 1
Re... |
pombredanne/https-gitlab.lrde.epita.fr-vcsn-vcsn | doc/notebooks/automaton.is_accessible.ipynb | gpl-3.0 | import vcsn
"""
Explanation: automaton.is_accessible
Whether all its states are accessible, i.e., all its states can be reached from an initial state.
Preconditions:
- None
See also:
- automaton.accessible
- automaton.is_coaccessible
- automaton.trim
Examples
End of explanation
"""
%%automaton a
context = "lal_char(... |
geektoni/shogun | doc/ipython-notebooks/multiclass/Tree/DecisionTrees.ipynb | bsd-3-clause | import os
import numpy as np
import shogun as sg
import matplotlib.pyplot as plt
%matplotlib inline
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../../data')
# training data
train_income=['Low','Medium','Low','High','Low','High','Medium','Medium','High','Low','Medium',
'Medium','High','Low','Medium']
train_age ... |
ls-cwi/eXamine | doc/tutorial/eXamineNotebook/eXamineTutorial.ipynb | gpl-2.0 | # HTTP Client for Python
import requests
# Cytoscape port number
PORT_NUMBER = 1234
BASE_URL = "https://raw.githubusercontent.com/ls-cwi/eXamine/master/data/"
# The Base path for the CyRest API
BASE = 'http://localhost:' + str(PORT_NUMBER) + '/v1/'
#Helper command to call a command via HTTP POST
def executeRestComm... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session04/Day1/LSSTC-DSFP4-Juric-FrequentistAndBayes-01-Probability.ipynb | mit | # Generating some simple photon count data
import numpy as np
from scipy import stats
np.random.seed(1) # for repeatability
F_true = 1000 # true flux, say number of photons measured in 1 second
N = 50 # number of measurements
F = stats.poisson(F_true).rvs(N) # N measurements of the flux
e = np.sqrt(F) # errors on ... |
sdpython/ensae_teaching_cs | _doc/notebooks/td2a_ml/ml_b_imbalanced.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
%matplotlib inline
"""
Explanation: 2A.ml - Imbalanced dataset
Un jeu de données imbalanced signifie qu'une classe est sous représentée dans un problème de classification. Lire 8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset.
End ... |
luiscruz/udacity_data_analyst | P05/src/Data exploration.ipynb | mit | del data_dict['TOTAL']
df = pandas.DataFrame.from_dict(data_dict, orient='index')
df.head()
print "Dataset size: %d rows x %d columns"%df.shape
df.dtypes
print "Feature | Missing values"
print "---|---"
for column in df.columns:
if column != 'poi':
print "%s | %d"%(column,(df[column] == 'NaN').sum())
... |
paris-saclay-cds/python-workshop | Day_2_Software_engineering_best_practices/solutions/01_function_factorization.ipynb | bsd-3-clause | def read_spectra(path_csv):
s = pandas.read_csv(path_csv)
c = s['concentration']
m = s['molecule']
s = s['spectra']
x = []
for spec in s:
x.append(numpy.fromstring(spec[1:-1], sep=','))
s = pandas.DataFrame(x)
return s, c, m
f = pandas.read_csv('data/freq.csv')
filenames =... |
tensorflow/probability | tensorflow_probability/examples/jupyter_notebooks/HLM_TFP_R_Stan.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# 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 agreed to in writing, sof... |
cvxgrp/cvxpylayers | examples/jax/tutorial.ipynb | apache-2.0 | import cvxpy as cp
import numpy as np
import jax
import jax.numpy as jnp
from cvxpylayers.jax import CvxpyLayer
import matplotlib.pyplot as plt
plt.style.use('bmh')
np.set_printoptions(precision=2, suppress=True)
"""
Explanation: Differentiable Convex Optimization Layers: JAX Tutorial
End of explanation
"""
# Imp... |
amkatrutsa/MIPT-Opt | Spring2017-2019/18-LinProgPrimalInterior/Seminar18en.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import scipy.optimize as scopt
import scipy.linalg as sclin
USE_COLAB = False
if not USE_COLAB:
plt.rc("text", usetex=True)
def NewtonLinConstraintsFeasible(f, gradf, hessf, A, x0, line_search, linsys_solver, args=(),
... |
regata/dbda2e_py | chapters/10.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import seaborn as sns
import pystan as ps
import numpy as np
model_code = """
data {
int N;
int y[N];
real omega;
real kappa;
}
parameters {
real<lower=0,upper=1> theta;
}
model {
real alpha;
real beta;
... |
lily-tian/fanfictionstatistics | jupyter_notebooks/profile_analysis.ipynb | mit | # opens raw data
with open ('../data/clean_data/df_profile', 'rb') as fp:
df = pickle.load(fp)
# creates subset of data of active users
df_active = df.loc[df.status != 'inactive', ].copy()
# sets current year
cyear = datetime.datetime.now().year
# sets stop word list for text parsing
stop_word_list = stopwor... |
justiceamoh/ENGS108 | vagrant/notebooks/Keras Introduction.ipynb | apache-2.0 | %matplotlib inline
# Standard Libraries
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='ticks')
"""
Explanation: An Introduction to Keras
In this notebook, I will provide a brief introduction to Keras, a high-level neural networks python library; it is a wrapper ontop of eithe... |
svdwulp/da-programming-1 | week_07_oefeningen-uitwerkingen.ipynb | gpl-2.0 | # 1a
pi_times_xi = []
for d1 in range(1, 7):
pi_times_xi.append(d1 / 6)
expected_value = sum(pi_times_xi)
print("Expected value:", expected_value)
# 1b
pi_times_xi = []
for d1 in range(1, 7):
for d2 in range(1, 7):
for d3 in range(1, 7):
pi_times_xi.append((d1 + d2 + d3) / (6**3))
expected_... |
jonathanmorgan/msu_phd_work | analysis/step-1-sourcenet-to-context.ipynb | lgpl-3.0 | me = "sourcenet-to-context"
"""
Explanation: <h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#notes-and-questions" data-toc-modified-id="notes-and-questions-1"><span class="toc-item-num">1 </span>notes and questions</a></span></li><li><span><a... |
leyhline/vix-term-structure | comparison1-4.ipynb | mit | argmin_epchs_basic = np.array([v[3] for v in experiment1["val_loss"].loc[:,:,False].groupby(("depth", "width", "datetime")).idxmin()])
argmin_epchs_normal = np.array([v[3] for v in experiment1["val_loss"].loc[:,:,True].groupby(("depth", "width", "datetime")).idxmin()])
print(argmin_epchs_basic.mean(), argmin_epchs_basi... |
yuhao0531/dmc | notebooks/week-4/01-tensorflow ANN for regression.ipynb | apache-2.0 | %matplotlib inline
import math
import random
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import load_boston
import numpy as np
import tensorflow as tf
sns.set(style="ticks", color_codes=True)
"""
Explanation: Lab 4 - Tensorflow ANN for regression
In this lab we wi... |
cloudmesh/book | notebooks/opencv/opencv.ipynb | apache-2.0 | import os, sys
from os.path import expanduser
os.path
home = expanduser("~")
sys.path.append('/usr/local/Cellar/opencv/3.3.1_1/lib/python3.6/site-packages/')
sys.path.append(home + '/.pyenv/versions/OPENCV/lib/python3.6/site-packages/')
import cv2
cv2.__version__
"""
Explanation: Open CV
OpenCV (Open Source Computer ... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/gapic/automl/showcase_automl_image_segmentation_online.ipynb | apache-2.0 | import os
import sys
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install -U google-cloud-aiplatform $USER_FLAG
"""
Explanation: Vertex client library: AutoML image segmentation model for online prediction
<table align="... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_decoding_sensors.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Jean-Remi King <jeanremi.king@gmail.com>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
from mne.decoding import TimeDecoding
print(__doc__)
data_path = sampl... |
amueller/scipy-2017-sklearn | notebooks/22.Unsupervised_learning-anomaly_detection.ipynb | cc0-1.0 | %matplotlib inline
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
"""
Explanation: Anomaly detection
Anomaly detection is a machine learning task that consists in spotting so-called outliers.
“An outlier is an observation in a data set which app... |
matthiaskoenig/sbmlutils | src/sbmlutils/examples/van_der_pol/van_der_pol.ipynb | lgpl-3.0 | # print settings for notebook
%matplotlib inline
import matplotlib
matplotlib.rcParams; # available global parameters
matplotlib.rcParams['figure.figsize'] = (9.0, 6.0)
matplotlib.rcParams['axes.labelsize'] = 'medium'
font = {'family' : 'sans-serif',
'weight' : 'normal', # bold
'size' : 14}
matplo... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_time_frequency_global_field_power.ipynb | bsd-3-clause | # Authors: Denis A. Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import somato
from mne.baseline import rescale
from mne.stats import _bootstrap_ci
"""
Explanation: Explore event-related dynamics for specific frequency... |
msampathkumar/kaggle-quora-tensorflow | preprocessor.ipynb | apache-2.0 | df_train = pd.read_csv(TRAIN_CSV)
df_test = pd.read_csv(TEST_CSV)
# Train Data
train_feature_1_string = pd.Series(df_train['question1'].tolist()).astype(str)
train_feature_2_string = pd.Series(df_train['question2'].tolist()).astype(str)
target = pd.Series(df_train['is_duplicate'].tolist())
all_train_qs = train_featu... |
Kusdhill/bioapi-examples | python_notebooks/1kg.ipynb | apache-2.0 | import ga4gh.client as client
c = client.HttpClient("http://1kgenomes.ga4gh.org/")
"""
Explanation: GA4GH 1000 Genome Examples
Variant data from the Thousand Genomes project is being served by the GA4GH reference server. In this example we show how to use the GA4GH client to access the data.
Initialize the client
In t... |
minh5/cpsc | reports/api data.ipynb | mit | import pickle
import operator
import numpy as np
import pandas as pd
import gensim.models
data = pickle.load(open('/home/datauser/cpsc/data/processed/cleaned_api_data', 'rb'))
data.head()
"""
Explanation: Introduction
This notebook serves as a reporting tool for the CPSC. In this notebook, I laid out the questions ... |
andre-martini/advanced-comp-2017 | 02-trees/splitting-criteria.ipynb | gpl-3.0 | X = np.array([[0,0,0], [0,0,1], [0,1,0], [0,1,1], [0,1,1], [1,0,0],
[1,0,0], [1,0,0], [1,0,0], [1,1,1]])
# two class problem with classes 0 and 1
y = [0,0,1,1,1,1,1,1,1,1]
def accuracy(a, b):
N = a + b
return 1 - max(a/N, b/N)
def entropy(a, b):
p = b/(a + b) # fraction or probability for c... |
rasilab/ferrin_elife_2017 | scripts/fit_simulation_to_experiment.ipynb | gpl-3.0 | import pandas as pd
import re
import os
import numpy as np
import simulation_utils
from scipy.interpolate import interp1d
"""
Explanation: Fit simulation results to experiment
<div id="toc-wrapper"><h3> Table of Contents </h3><div id="toc" style="max-height: 787px;"><ol class="toc-item"><li><a href="#Globals">Globals<... |
kit-cel/wt | wt/vorlesung/ch1_3/relative_frequency.ipynb | gpl-2.0 | # importing
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
# showing figures inline
%matplotlib inline
# plotting options
font = {'size' : 20}
plt.rc('font', **font)
plt.rc('text', usetex=True)
matplotlib.rc('figure', figsize=(18, 6) )
"""
Explanation: Content and Objective
Plotting relat... |
barjacks/pythonrecherche | Kursteilnehmer/Dominique Strebel/06/Python Functions, 10 Übungen.ipynb | mit | def test(element):
element = element * 2
return element
"""
Explanation: 03 Python Functions, 10 Übungen
Hier nochmals zur Erinnerung, wie Funktionen geschrieben werden.
End of explanation
"""
test(5)
"""
Explanation: Multipliziert Integers oder Floats mit 2
End of explanation
"""
lst = [3,7,14,222,6]
lst... |
sassoftware/sas-viya-programming | recommend/bx_recommender.ipynb | apache-2.0 | import html
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import swat
from IPython.core.display import display, HTML, Markdown
from io import StringIO
%matplotlib inline
"""
Explanation: Build a Recommender System for Books
This notebook demonstrates the use of man... |
ES-DOC/esdoc-jupyterhub | notebooks/bnu/cmip6/models/sandbox-3/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'bnu', 'sandbox-3', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: BNU
Source ID: SANDBOX-3
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy Balance... |
SHDShim/pytheos | examples/11_pvt-eos_fit-el_anh.ipynb | apache-2.0 | %config InlineBackend.figure_format = 'retina'
"""
Explanation: For high dpi displays.
End of explanation
"""
import numpy as np
import matplotlib.pyplot as plt
import uncertainties as uct
from uncertainties import unumpy as unp
import pytheos as eos
"""
Explanation: 0. General note
This notebook shows an example... |
exe0cdc/PyscesToolbox | example_notebooks/RateChar.ipynb | bsd-3-clause | mod = pysces.model('lin4_fb.psc')
rc = psctb.RateChar(mod)
"""
Explanation: RateChar
RateChar is a tool for performing generalised supply-demand analysis (GSDA) [2,3]. This entails the generation data needed to draw rate characteristic plots for all the variable species of metabolic model through parameter scans and t... |
qinwf-nuan/keras-js | notebooks/layers/wrappers/TimeDistributed.ipynb | mit | data_in_shape = (3, 6)
layer_0 = Input(shape=data_in_shape)
layer_1 = TimeDistributed(Dense(4))(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
weights = []
for i, w in enumerate(model.get_weights()):
np.random.seed(4000 + i)
weights.append(2 * np... |
martin-hunt/hublib | hublib/rappture/test/CompositeLaminate.ipynb | mit | import os, sys
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
sys.path.insert(0, os.path.abspath('../../..'))
import hublib.rappture as rappture
io = rappture.Tool("complam")
io.inputs
"""
Explanation: Composite laminate
Starting from a list of properties for the matrix and fibers we use rules ... |
tanghaibao/goatools | notebooks/cell_cycle.ipynb | bsd-2-clause | # Get http://geneontology.org/ontology/go-basic.obo
from goatools.base import download_go_basic_obo
obo_fname = download_go_basic_obo()
"""
Explanation: Cell Cycle genes
Using Gene Ontologies (GO), create an up-to-date list of all human protein-coding genes that are know to be associated with cell cycle.
1. Download O... |
ES-DOC/esdoc-jupyterhub | notebooks/cccr-iitm/cmip6/models/sandbox-1/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cccr-iitm', 'sandbox-1', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: CCCR-IITM
Source ID: SANDBOX-1
Topic: Seaice
Sub-Topics: Dynamics, Thermodyna... |
mne-tools/mne-tools.github.io | 0.19/_downloads/9256698a6dc1e67585812dacb4e9f960/plot_elekta_epochs.ipynb | bsd-3-clause | # Author: Jussi Nurminen (jnu@iki.fi)
#
# License: BSD (3-clause)
import mne
import os
from mne.datasets import multimodal
fname_raw = os.path.join(multimodal.data_path(), 'multimodal_raw.fif')
print(__doc__)
"""
Explanation: ======================================
Getting averaging info from .fif files
==========... |
visualfabriq/bquery | bquery/benchmarks/bench_groupby.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import itertools as itt
import time
import shutil
import os
import contextlib
import pandas as pd
import blaze as blz
import bquery
import cytoolz
from cytoolz.curried import pluck as cytoolz_pluck
from collections import OrderedDict
import copy
fro... |
csaladenes/csaladenes.github.io | present/mcc2/sklearn_tutorial/04.1-Dimensionality-PCA.ipynb | mit | from __future__ import print_function, division
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
plt.style.use('seaborn')
"""
Explanation: Dénes Csala
MCC, 2022
Based on Elements of Data Science (Allen B. Downey, 2021) and Python Data Science Handbook (Jake VanderPlas, ... |
dariox2/CADL | session-5/.ipynb_checkpoints/lecture-5-checkpoint.ipynb | apache-2.0 | import tensorflow as tf
from libs.datasets import CELEB
files = CELEB()
"""
Explanation: Session 5: Generative Models
<p class="lead">
<a href="https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow/info">Creative Applications of Deep Learning with Google's Tensorflow</a><br />
<a href=... |
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