repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
atreyv/atom-phys | patterns - ipython example tutorial.ipynb | gpl-3.0 | %matplotlib inline
from libpatternsworkflow import *
from __future__ import division
mpl.rc('font', size=14)
mpl.rcParams['figure.figsize'] = (16.0, 8.0)
"""
Explanation: Import all necessary libraries. Libpatterns imports libphys as well
End of explanation
"""
dsave = '/home/pedro/Dropbox/PhD at Strathclyde/Thesis/... |
Zhenxingzhang/AnalyticsVidhya | Articles/Ridge_Lasso_Regression/Ridge_Lasso_Regression.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 12, 10
import random
"""
Explanation: Ridge & Lasso Regression Tutorial
Ridge and Lasso regression are techniques used for preventing overfitting. Before going i... |
peterwittek/open_science_tutorial | Symbolic calculations and functional programming.ipynb | gpl-3.0 | %quickref
"""
Explanation: The notebook interface
The IPython -- being rebranded as Jupyter -- notebook interface is becoming a standard for a number of languages other than Python: Julia, Scala, R, Haskell, bash are all getting their kernels in IPython. Since Python allows you to call MATLAB anyway, you can also use ... |
BerryAI/Acai | examples/tutorial.ipynb | mit | try:
import OpenMRS as om
except:
# At this point, you probably haven't installed OpenMRS. You can install it by:
# sudo pip install git+https://github.com/BerryAI/Acai
# Now we are going to import OpenMRS from the source.
# Note: This assumes you are currently in the 'examples/' folder running th... |
jpwhite3/python-analytics-demo | Part_1.ipynb | cc0-1.0 | from __future__ import division, unicode_literals
import pandas as pd
import numpy as np
import glob
import warnings
import calendar
warnings.filterwarnings("ignore")
"""
Explanation: 1.) Import the modules we will need
End of explanation
"""
glob.glob('./input/sales-*.xlsx')
"""
Explanation: 2.) Take a look at the... |
PWhiddy/kbmod | notebooks/kbmod_demo-Copy1.ipynb | bsd-2-clause | import numpy as np
import matplotlib.pyplot as plt
import subprocess
%matplotlib inline
%load_ext autoreload
%autoreload 2
"""
Explanation: KBMOD Demo
The purpose of this demo is to showcase how KBMOD can be used to search through images for moving objects. The images used here are from the Subaru telescope and were p... |
ComputationalModeling/spring-2017-danielak | past-semesters/spring_2016/day-by-day/day16-analyzing-tweets-with-string-processing/In-Class-Strings-SOLUTION.ipynb | agpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
from string import punctuation
"""
Explanation: Day 16 In-class assignment: Data analysis and Modeling in Social Sciences
Part 3
The first part of this notebook is a copy of a blog post tutorial written by Dr. Neal Caren (University of North Carolina, Chapel Hill). Th... |
xpmethod/middlemarch-critical-histories | old/e1/e1b-analysis.ipynb | gpl-3.0 | import pandas as pd
%matplotlib inline
from ast import literal_eval
import numpy as np
import re
import json
from nltk.corpus import names
from collections import Counter
from matplotlib import pyplot as plt
plt.rcParams["figure.figsize"] = [16, 6]
plt.style.use('ggplot')
with open('../middlemarch.txt') as f:
mm ... |
michigraber/neuralyzer | notebooks/doc/DataHandlingUtilities.ipynb | mit | %%bash
build_tiff_stack.py --help
"""
Explanation: Data Handling Utilities
tiff file directory to tiff stack conversion
A utility script that can be executed from the command line to convert tif files in a directory into a tif stack:
End of explanation
"""
%%bash
extract_channels_from_raw.py --help
"""
Explanation:... |
emjotde/UMZ | Cwiczenia/01/Uczenie Maszynowe - Ćwiczenia 1.3 - NumPy, algebra liniowa.ipynb | cc0-1.0 | import numpy as np
x = np.array([[1,2,3]]).T
xt = x.T
x.shape
xt.shape
"""
Explanation: 1.3 NumPy - Algebra liniowa
NumPy jest pakietem szczególnie przydatnym do obliczeń w dziedzinie algebry liniowej. W uczeniu maszynowym algebra liniowa będzie miała duże znaczenie.
Wektor o wymiarach $1 \times N$
$$
X =
\... |
alexandonian/lightning | Basic-Usage.ipynb | apache-2.0 | imcontroller = ImageController(demo.image_info)
demo.image_info.items()
"""
Explanation: Let's see the ImageController in action:
Since we don't have a database up and running, we will pass the ImageController the information it needs manually. As soon as the database is set up, the Provider will make queries to the d... |
kjlawlor/intro-numerical-methods | 1_intro_to_python.ipynb | mit | 2 + 2
32 - (4 + 2)**2
1 / 2
"""
Explanation: Discussion 1: Introduction to Python
So you want to code in Python? We will do some basic manipulations and demonstrate some of the basics of the notebook interface that we will be using extensively throughout the course.
Topics:
- Math
- Variables
- Lists
- Control... |
jeffzhengye/pylearn | google_cloud/.ipynb_checkpoints/google_cloud-checkpoint.ipynb | unlicense | # check firewall
!rm index.html*
!wget www.google.com
import uuid
from google.cloud import dialogflow
# session format: 'projects/*/locations/*/agent/environments/*/users/*/sessions/*'.
def get_session(project_id, session_id, env=None):
"""
Using the same `session_id` between requests allows continuation
... |
wuafeing/Python3-Tutorial | 02 strings and text/02.02 match text at start end.ipynb | gpl-3.0 | filename = "spam.txt"
filename.endswith(".txt")
filename.startswith("file:")
url = "http://www.python.org"
url.startswith("http:")
"""
Explanation: Previous
2.2 字符串开头或结尾匹配
问题
你需要通过指定的文本模式去检查字符串的开头或者结尾，比如文件名后缀，URL Scheme 等等。
解决方案
检查字符串开头或结尾的一个简单方法是使用 str.startswith() 或者是 str.endswith() 方法。比如：
End of explanation
"""
... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive2/time_series_prediction/solutions/4_modeling_keras.ipynb | apache-2.0 | import os
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
from google.cloud import bigquery
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import (Dense, DenseFeatures... |
materials-commons/materials-commons.github.io | materials-commons-cli/html/examples/MaterialsCommons-Project-Shell-Example.ipynb | mit | import os
import pathlib
import shutil
parent_path = pathlib.Path.home() / "mc_projects"
os.makedirs(parent_path, exist_ok=True)
# Project name
name = "ExampleProjectFromJupyter"
project_path = parent_path / name
# Projct summary - short description to show in tables
summary = "Example project created via Jupyter no... |
amkatrutsa/MIPT-Opt | Spring2021/newton_quasi.ipynb | mit | import numpy as np
import liboptpy.unconstr_solvers as methods
import liboptpy.step_size as ss
import jax
import jax.numpy as jnp
from jax.config import config
config.update("jax_enable_x64", True)
import sklearn.datasets as skldata
n = 300
m = 2000
X, y = skldata.make_classification(n_classes=2, n_features=n, n_sa... |
techforspace/sentinel | SNAP_Python_Tutorial_3/SNAP-Python_Tutorial_3.ipynb | mit | from snappy import ProductIO
from snappy import jpy
from snappy import GPF
file_path = 'C:\Program Files\snap\S2A_MSIL1C_20170202T090201_N0204_R007_T35SNA_20170202T090155.SAFE\MTD_MSIL1C.xml'
product = ProductIO.readProduct(file_path)
HashMap = jpy.get_type('java.util.HashMap')
parameters = HashMap()
parameters.put... |
liufuyang/coursera-Applied-Machine-Learning-in-Python | Assignment 2.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
np.random.seed(0)
n = 15
x = np.linspace(0,10,n) + np.random.randn(n)/5
y = np.sin(x)+x/6 + np.random.randn(n)/10
X_train, X_test, y_train, y_test = train_test_split(x, y, random_state=0)
# Y... |
CUBoulder-ASTR2600/lectures | lecture_17_monte_carlo.ipynb | isc | from IPython.display import Image
Image(url='http://upload.wikimedia.org/wikipedia/commons/thumb/b/b4/The_Sun_by_the_Atmospheric_Imaging_Assembly_of_NASA%27s_Solar_Dynamics_Observatory_-_20100819.jpg/251px-The_Sun_by_the_Atmospheric_Imaging_Assembly_of_NASA%27s_Solar_Dynamics_Observatory_-_20100819.jpg')
"""
Explanat... |
SylvainCorlay/bqplot | examples/Scales/Color Scales.ipynb | apache-2.0 | import numpy as np
import bqplot.pyplot as plt
from bqplot import ColorScale, DateColorScale, OrdinalColorScale, ColorAxis
# setup data for plotting
np.random.seed(0)
n = 100
x_data = range(n)
y_data = np.cumsum(np.random.randn(n) * 100.0)
def create_fig(color_scale, color_data, fig_margin=None):
# allow some ma... |
feststelltaste/software-analytics | demos/20190425_JUGH_Kassel/DatenanalysenProblemeEntwicklung.ipynb | gpl-3.0 | import pandas as pd
log = pd.read_csv("../dataset/linux_blame_log.csv.gz")
log.head()
"""
Explanation: Mit Datenanalysen Probleme in der Entwicklung aufzeigen
<small>Java User Group Hessen, Kassel, 25.04.2019</small>
<b>Markus Harrer</b>, Software Development Analyst
Twitter: @feststelltaste
Blog: feststelltaste.de
<i... |
jni/useful-histories | hydronic-heat-pump-payback-period.ipynb | bsd-3-clause | import pint
u = pint.UnitRegistry()
u.define('dollar = [currency]')
u.define('cent = 0.01 * dollar')
gas_price = 1.78 * u('cent / MJ') # based on current prices 2022-05-23
elec_price = 20.35 * u('cent / kWh') # based on current prices
"""
Explanation: Payback period of electric heat pumps
Hydronic heating works by ... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_gamma_map_inverse.ipynb | bsd-3-clause | # Author: Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne.datasets import sample
from mne.inverse_sparse import gamma_map, make_stc_from_dipoles
from mne.viz import (plot_sparse_source_estimate... |
jaidevd/inmantec_fdp | notebooks/day1/02_fourier_analysis.ipynb | mit | Fs = 32768
duration = 0.25
t = np.linspace(0, duration, duration * Fs)
f1, f2 = 697, 1336
y1 = np.sin(2 * np.pi * f1 * t);
y2 = np.sin(2 * np.pi * f2 * t);
y = (y1 + y2) / 2
plt.plot(t, y)
from IPython.display import Audio
Audio(y, rate=44100)
"""
Explanation: DTMF: Linear combination of two sinusoids
End of explanat... |
tiagoft/curso_audio | estatisticas_de_timbre.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
# Demonstrando propriedades de vetores
# Ideia: coloque mais dimensoes nos vetores e veja o que acontece!
x = np.array([4, 3])
y = np.array([3, 4])
print x
print y
print x + y # Soma de vetores
print 10 * x # Multiplicacao por escalar
print np.lina... |
ShinjiKatoA16/UCSY-sw-eng | NumberOfDivisor.ipynb | mit | # Simple but not efficient answer
def num_div0(n):
'''
n: Integer (bigger than 0)
output: Number of Divisor (Including 1 and n)
'''
count_div = 0
for div in range(1, n+1): # 1 - n
if n % div == 0:
count_div += 1
return count_div
print (5, num_div0(5)... |
dasnah/TitanicDataSet | Project 2 Titanic Data Final.ipynb | unlicense | ##import everything
import numpy as np
import pandas as pd
import scipy as sp
import matplotlib.pyplot as plt
import seaborn as sea
%matplotlib inline
sea.set(style="whitegrid")
titanic_ds = pd.read_csv('titanic-data.csv')
"""
Explanation: Questions:
1: What sex has a higher probability of surviving?
2: What was the... |
gwtsa/gwtsa | examples/notebooks/11_WellModel.ipynb | mit | import numpy as np
import pandas as pd
import pastas as ps
from pastas.stressmodels import WellModel
"""
Explanation: WellModel (many wells with one response function)
This notebook shows how a WellModel can be used to fit multiple wells with one response function. The influence of the individual wells is scaled by th... |
calebmadrigal/radio-hacking-scripts | fsk_modem_research.ipynb | mit | samp_rate = 1000
len_in_sec = 1
t = np.linspace(0, 1, samp_rate * len_in_sec)
hz_4 = 1*np.sin(4 * 2 * np.pi * t)
hz_8 = hz_4 * (2 * np.cos(4 * 2 * np.pi * t))
plt.plot(t, hz_4)
plt.show()
plt.plot(t, hz_8)
plt.show()
"""
Explanation: FSK Modulation
Now that we've got some ideas for demodulating fsk, let's do some frea... |
jjehl/poppy_education | poppy-4dof-arm-mini/poppy-4dof-arm-mini_couple_vertical.ipynb | gpl-2.0 | from poppy.creatures import Poppy4dofArmMini
mini_dof = Poppy4dofArmMini(simulator='vrep')
import time
%pylab inline
"""
Explanation: Corriger une position en fonction du couple mesuré sur un moteur
Compétences visées par cette activité :
Mettre en place un asservissement PID lié au couple mesuré sur un moteur. En ... |
JamesSample/icpw | toc_trends_oct_2018_part3.ipynb | mit | # Read station data
stn_path = r'../../update_autumn_2018/toc_trends_oct18_stations.xlsx'
stn_df = pd.read_excel(stn_path, sheet_name='Data')
## Update stations table
#with eng.begin() as conn:
# for idx, row in stn_df.iterrows():
# # Add new vals to dict
# var_dict = {'elev':row['elevation'],
# ... |
sspickle/sci-comp-notebooks | P03-TaylorSeries.ipynb | mit | import sympy as sp
sp.init_printing()
Um,x,x0,alpha=sp.symbols('Um x x_0 alpha', real=True)
"""
Explanation: Taylor Series
Suppose you have some function that may be expensive or difficult to evaluate and so you’d like to find an easy approximation for that function in some limited domain. One particularly nice way t... |
phenology/infrastructure | applications/notebooks/stable/plot_kmeans_clusters.ipynb | apache-2.0 | import sys
sys.path.append("/usr/lib/spark/python")
sys.path.append("/usr/lib/spark/python/lib/py4j-0.10.4-src.zip")
sys.path.append("/usr/lib/python3/dist-packages")
import os
os.environ["HADOOP_CONF_DIR"] = "/etc/hadoop/conf"
import os
os.environ["PYSPARK_PYTHON"] = "python3"
os.environ["PYSPARK_DRIVER_PYTHON"] = "... |
mttaggart/codeforteachers | drag-race/drag-race.ipynb | mit | class Car:
"""Our Car class"""
def __init__(self,
year,
make,
model,
top_speed,
acceleration
):
"""Car Constructor function"""
self.year = year
self.make = make
self.model = mode... |
TariqAHassan/BioVida | tutorials/2_cancer_imaging_archive.ipynb | bsd-3-clause | from biovida.images import CancerImageInterface
"""
Explanation: BioVida: The Cancer Imaging Archive
The Cancer Imaging Archive is a large repository of medical images of various forms of cancer. Programmatic web access is granted through a RESTful web API. However, this service requires an API-key to use, which you ... |
GoogleCloudPlatform/ai-platform-samples | notebooks/samples/tables/result_slicing/slicing_eval_results.ipynb | apache-2.0 | ! pip install --upgrade --quiet --user sklearn
! pip install --upgrade --quiet --user witwidget
! pip install --upgrade --quiet --user tensorflow==1.15
! pip install --upgrade --quiet --user tensorflow_model_analysis
! pip install --upgrade --quiet --user pandas-gbq
"""
Explanation: Slicing AutoML Tables Evaluation Re... |
jbliss1234/ML | t81_558_class1_intro_python.ipynb | apache-2.0 | # What version of Python do you have?
import sys
import tensorflow as tf
import sklearn as sk
import pandas as pd
print("Python {}".format(sys.version))
print('TensorFlow {}'.format(tf.__version__))
print('Pandas {}'.format(pd.__version__))
print('Scikit-Learn {}'.format(sk.__version__))
"""
Explanation: T81-558: Ap... |
julienchastang/unidata-python-workshop | notebooks/Skew_T/SkewT_and_Hodograph.ipynb | mit | # Create a datetime for our request - notice the times are from laregest (year) to smallest (hour)
from datetime import datetime
request_time = datetime(1999, 5, 3, 12)
# Store the station name in a variable for flexibility and clarity
station = 'OUN'
# Import the Wyoming simple web service and request the data
# Don... |
pandas-dev/pandas | doc/source/user_guide/style.ipynb | bsd-3-clause | import matplotlib.pyplot
# We have this here to trigger matplotlib's font cache stuff.
# This cell is hidden from the output
import pandas as pd
import numpy as np
import matplotlib as mpl
df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],
index=pd.Index(['Tumour (P... |
nick-youngblut/SIPSim | ipynb/bac_genome/fullCyc/Day1_fullDataset/xRich.ipynb | mit | import os
import glob
import re
import nestly
%load_ext rpy2.ipython
%load_ext pushnote
%%R
library(ggplot2)
library(dplyr)
library(tidyr)
library(gridExtra)
library(phyloseq)
## BD for G+C of 0 or 100
BD.GCp0 = 0 * 0.098 + 1.66
BD.GCp100 = 1 * 0.098 + 1.66
"""
Explanation: Goal
Simulating fullCyc Day1 control grad... |
DBWangGroupUNSW/COMP9318 | L8 - Hierarchical Clustering.ipynb | mit | from matplotlib import pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage, fcluster
import numpy as np
%matplotlib inline
np.set_printoptions(precision=5, suppress=True)
"""
Explanation: Clustering-2: Hierarchical Clustering
import Modules
End of explanation
"""
np.random.seed(42)
a = np.random.m... |
statsmodels/statsmodels.github.io | v0.13.2/examples/notebooks/generated/glm.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import statsmodels.api as sm
from scipy import stats
from matplotlib import pyplot as plt
plt.rc("figure", figsize=(16,8))
plt.rc("font", size=14)
"""
Explanation: Generalized Linear Models
End of explanation
"""
print(sm.datasets.star98.NOTE)
"""
Explanation: GLM: Binomial r... |
LeviBarnes/PythonSecrets | SecretCodes.ipynb | mit | print ("Hello my name is Levi.")
"""
Explanation: Sending Secret Messages with Python
This notebook will teach you how to send secret messages to your friends using a computer language called "Python." Python is used by thousands of programmers around the world to create websites and video games, to do science and mat... |
mraty/applied-data-science | course-2_applied_plotting/Assignment2.ipynb | mit | import matplotlib.pyplot as plt
import mplleaflet
import pandas as pd
import numpy as np
def leaflet_plot_stations(binsize, hashid):
df = pd.read_csv('BinSize_d{}.csv'.format(binsize))
station_locations_by_hash = df[df['hash'] == hashid]
lons = station_locations_by_hash['LONGITUDE'].tolist()
lats = ... |
chrlttv/Teaching | Session1/2.Perceptron.ipynb | mit | import random, numpy as np, matplotlib.pyplot as plt, time
%matplotlib inline
# Training data for the first question
training_data = [
(np.array([0,0,1]), 0),
(np.array([0,1,1]), 1),
(np.array([1,0,1]), 1),
(np.array([1,1,1]), 1),
]
def unit_step(value):
if value < 0:
return 0
else: ... |
muratcemkose/cy-rest-python | basic/CytoscapeREST_Basic1.ipynb | mit | import sys
print ('My Python Version = ' + sys.version)
"""
Explanation: Basic Workflow 1: Introduction to cyREST API
by Keiichiro Ono
Introduction
This is an introduction to cyREST and its API. You will learn how to access Cytoscape via RESTful API.
Prerequisites
Basic knowledge of RESTful API
This is a good intr... |
jhconning/Dev-II | notebooks/Beta_Delta.ipynb | bsd-3-clause | %reload_ext watermark
%watermark -u -n -t
"""
Explanation: Breakable Commitments...
Code to generate figures
Karna Basu and Jonathan Conning
Department of Economics, Hunter College and The Graduate Center, City University of New York
End of explanation
"""
%matplotlib inline
import numpy as np
import matplotlib.pyp... |
telescopeuser/uat_shl | rnd03/shl_sm_NoOCR_v012 2017-09.ipynb | mit | import pandas as pd
"""
Explanation: SHL Project
simulation module: shl_sm
shl_sm required data feeds:
live bidding price, per second, time series
prediction module parameters/csv
parm_si.csv (seasonality index per second)
parm_month.csv (parameter like alpha, beta, gamma, etc. per month)
SHL Simulation Modu... |
myinxd/agn-ae | code-sdss/SDSS_Analysis-KS-Chi2-tests.ipynb | mit | lumo_fr1_typical = lumo[idx2_same] * 10**-22
lumo_fr2_typical = lumo[idx3_same] * 10**-22
mag_fr1_typical = mag_abs[idx2_same]
mag_fr2_typical = mag_abs[idx3_same]
lumo_fr1_like = lumo[idx_fr1] * 10**-22
lumo_fr2_like = lumo[idx_fr2] * 10**-22
mag_fr1_like = mag_abs[idx_fr1]
mag_fr2_like = mag_abs[idx_fr2]
mag_fr1 ... |
ES-DOC/esdoc-jupyterhub | notebooks/ncc/cmip6/models/noresm2-hh/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'noresm2-hh', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: NCC
Source ID: NORESM2-HH
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Turbul... |
jstac/quantecon_nyu_2016 | lecture14/pre_RuixueGong.ipynb | bsd-3-clause | from IPython.display import Image
Image(filename='scikit-learn-flow-chart.jpg') #source: web
"""
Explanation: Statsmodels v.s. Scikit-learn
Ruixue Gong, NYU
This notebook helps economists better understand the differences and similarities between machine learning package Scikit-learn and traditional statistical pack... |
graphistry/pygraphistry | demos/data/benchmarking/SparseDatasets.ipynb | bsd-3-clause | import random
import graphistry as g
import pandas as pd
"""
Explanation: Sparse Datasets
This notebook is used for benchmarking and debugging sparse datasets
Import the necessary libaries
End of explanation
"""
g.__version__
# To specify Graphistry account & server, use:
# graphistry.register(api=3, username='..... |
datactive/bigbang | examples/name-and-gender/Analyze Senders - Name and Gender.ipynb | mit | %matplotlib inline
"""
Explanation: Experimenting with estimating the gender of mailing list participants.
End of explanation
"""
import bigbang.ingress.mailman as mailman
import bigbang.analysis.graph as graph
import bigbang.analysis.process as process
from bigbang.parse import get_date
from bigbang.archive import ... |
weixuanfu/tpot | tutorials/Portuguese Bank Marketing/Portuguese Bank Marketing Strategy.ipynb | lgpl-3.0 | # Import required libraries
from tpot import TPOTClassifier
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
#Load the data
Marketing=pd.read_csv('Data_FinalProject.csv')
Marketing.head(5)
"""
Explanation: Portuguese Bank Marketing Strategy- TPOT Tutorial
The data is relate... |
tensorflow/hub | examples/colab/bert_experts.ipynb | apache-2.0 | #@title Copyright 2020 The TensorFlow Hub 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 ... |
dongwooc/StatisticalMethods | notes/InferenceSandbox.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
import scipy.stats
%matplotlib inline
plt.rcParams['figure.figsize'] = (5.0, 5.0)
# the model parameters
a = np.pi
b = 1.6818
# my arbitrary constants
mu_x = np.exp(1.0) # see definitions above
tau_x = 1.0
s = 1.0
N = 50 # number of data points
# get some x's and y... |
Yu-Group/scikit-learn-sandbox | jupyter/backup_deprecated_nbs/11_Create_Binary_Tree.ipynb | mit | # Step by Step version
def search(aList, target):
for v in aList:
if target == v:
return True
return False
# Recursive approach
def searchRecursive(aList, target):
if len(aList) == 0:
return False
if aList[0] == target:
return True
return searchRecursive(aList[1:... |
dnc1994/MachineLearning-UW | ml-regression/blank/week-2-multiple-regression-assignment-2-blank.ipynb | mit | import graphlab
"""
Explanation: Regression Week 2: Multiple Regression (gradient descent)
In the first notebook we explored multiple regression using graphlab create. Now we will use graphlab along with numpy to solve for the regression weights with gradient descent.
In this notebook we will cover estimating multiple... |
shikhar413/openmc | examples/jupyter/pincell_depletion.ipynb | mit | %matplotlib inline
import math
import openmc
"""
Explanation: Pincell Depletion
This notebook is intended to introduce the reader to the depletion interface contained in OpenMC. It is recommended that you are moderately familiar with building models using the OpenMC Python API. The earlier examples are excellent start... |
jrmontag/Data-Science-45min-Intros | pandas-201/functional_ish_pandas.ipynb | unlicense | import os
import zipfile
import requests
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
"""
Explanation: This will lean heavily on Tom Augspurger's excellent series on Modern Pandas.
Quote:
Method chaining, where you call methods on an object one after another, is in vogu... |
cfcdavidchan/Deep-Learning-Foundation-Nanodegree | image-classification/dlnd_image_classification.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
# Use Floyd's cifar-10 dataset if present
floyd_cifar10... |
Bismarrck/deep-learning | batch-norm/Batch_Normalization_Exercises.ipynb | mit | import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True, reshape=False)
"""
Explanation: Batch Normalization – Practice
Batch normalization is most useful when building deep neural networks. To demonstrate this, we'll create a con... |
grokkaine/biopycourse | day2/scicomp_scipy.ipynb | cc0-1.0 | from sklearn.datasets import load_iris
iris = load_iris()
print(iris.feature_names, iris.target_names)
print(iris.data.shape)
#print(iris.DESCR)
from scipy import linalg
# perform SVD
A = iris.data
U, s, V = linalg.svd(A)
print("U.shape, V.shape, s.shape: ", U.shape, V.shape, s.shape)
print("Singular values:", s)
#c... |
M-R-Houghton/euroscipy_2015 | scikit_image/lectures/solutions/3_morphological_operations.ipynb | mit | import numpy as np
from matplotlib import pyplot as plt, cm
import skdemo
plt.rcParams['image.cmap'] = 'cubehelix'
plt.rcParams['image.interpolation'] = 'none'
image = np.array([[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 0,... |
mcc-petrinets/formulas | spot/tests/python/decompose.ipynb | mit | aut = spot.translate('(Ga -> Gb) W c')
aut
"""
Explanation: This notebook demonstrates how to use the decompose_scc() function to split an automaton in up to three automata capturing different behaviors. This is based on the paper Strength-based decomposition of the property Büchi automaton for faster model checking... |
AllenDowney/ModSim | python/soln/chap12.ipynb | gpl-2.0 | # install Pint if necessary
try:
import pint
except ImportError:
!pip install pint
# download modsim.py if necessary
from os.path import exists
filename = 'modsim.py'
if not exists(filename):
from urllib.request import urlretrieve
url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'
... |
basp/aya | noise.ipynb | mit | img = np.random.ranf((128,128))
plt.imshow(img, cmap=plt.cm.ocean)
"""
Explanation: plotting images
We can easily plot images by using the imshow function. Conveniently, an image can just be a 2-dimensional numpy array of floats in the range of 0 to 1. We can easily create such an array with the ranf function. Below w... |
moranconnorj/code_guild | wk0/notebooks/challenges/primes/.ipynb_checkpoints/primes_challenge-checkpoint.ipynb | mit | def list_primes(n):
primes = []
for i in range(0, n + 1):
for j in range(0, i):
if i % j == 0:
break
else:
primes.append(i)
return primes
"""
Explanation: <small><i>This notebook was prepared by Thunder Shiviah. Source and license info is on GitHub.... |
nimagh/MachineLearning | BayesianOptimization/BayesianOptimization.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import minimize
from scipy.linalg import det
from scipy.linalg import pinv2 as inv #pinv uses linalg.lstsq algorithm while pinv2 uses SVD
from scipy.stats import norm
%matplotlib inline
%load_ext autoreload
%autoreload 2
%autosave 0
"""
Explana... |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_read_epochs.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Matti Hamalainen <msh@nmr.mgh.harvard.edu>
#
# License: BSD (3-clause)
import mne
from mne import io
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
"""
Explanation: Reading epochs from a raw FIF file
Th... |
mne-tools/mne-tools.github.io | dev/_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... |
mne-tools/mne-tools.github.io | 0.18/_downloads/3c22b754d3ee35b041302de37d5f9515/plot_decoding_spatio_temporal_source.ipynb | bsd-3-clause | # sphinx_gallery_thumbnail_number = 2
# Author: Denis A. Engemann <denis.engemann@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Jean-Remi King <jeanremi.king@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import m... |
pikepdf/pikepdf | docs/_notebooks/pages.ipynb | mpl-2.0 | from pikepdf import Pdf
pdf = Pdf.open('../../tests/resources/fourpages.pdf')
"""
Explanation: Manipulating pages
pikepdf presents the pages in a PDF through the Pdf.pages property, which
follows the list protocol. As such page numbers begin at 0.
Let's look at a simple PDF that contains four pages.
End of explanation... |
axant/notebooks | notebooks/MongoDB.ipynb | mit | from pymongo import MongoClient
client = MongoClient('mongodb://localhost:27017/')
db = client.phonebook
print db.collection_names()
"""
Explanation: MongoDB
Schema Free
Document Based
Supports Indexing
Not Transactional
Does not support relations (no JOIN)
Supports Autosharding
Automatic Replication and Failover
Re... |
simonvh/gimmemotifs | docs/api_examples.ipynb | mit | with open("MA0099.3.jaspar") as f:
motifs = read_motifs(f, fmt="jaspar")
print(motifs[0])
"""
Explanation: Read motifs from files in other formats.
End of explanation
"""
with open("example.pfm") as f:
motifs = read_motifs(f)
# pwm
print(motifs[0].to_pwm())
# pfm
print(motifs[0].to_pfm())
# consensus sequ... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/end-to-end-structured/solutions/5a_train_keras_ai_platform_babyweight.ipynb | apache-2.0 | import os
"""
Explanation: LAB 5a: Training Keras model on Cloud AI Platform.
Learning Objectives
Setup up the environment
Create trainer module's task.py to hold hyperparameter argparsing code
Create trainer module's model.py to hold Keras model code
Run trainer module package locally
Submit training job to Cloud A... |
KIPAC/StatisticalMethods | tutorials/probability_essentials.ipynb | gpl-2.0 | exec(open('tbc.py').read()) # define TBC and TBC_above
import numpy as np
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Tutorial: Probability Essentials
Analytic and numerical manipulations of probability distributions
In this notebook we will work throug... |
jamesfolberth/jupyterhub_AWS_deployment | notebooks/data8_notebooks/lab02/lab02.ipynb | bsd-3-clause | from datascience import *
from client.api.assignment import load_assignment
tests = load_assignment('lab02.ok')
"""
Explanation: Lab 2: Data Types
Welcome to lab 2!
Last time, we had our first look at Python and Jupyter notebooks. So far, we've only used Python to manipulate numbers. There's a lot more to life tha... |
smeingast/PNICER | notebooks/pnicer.ipynb | gpl-3.0 | import sys
from pnicer import ApparentMagnitudes
from pnicer.utils.auxiliary import get_resource_path
%matplotlib inline
"""
Explanation: <h1 align="center">PNICER demonstration notebook</h1>
Preparations
The main dependencies of PNICER are astropy, numpy, scipy, matplotlib, and scikit-learn. Here we only import th... |
aimalz/qp | docs/notebooks/kld.ipynb | mit | import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
%matplotlib inline
import qp
import numpy as np
import scipy.stats as sps
P = qp.PDF(funcform=sps.norm(loc=0.0, scale=1.0))
x, sigma = 2.0, 1.0
Q = qp.PDF(funcform=sps.norm(loc=x, scale=sigma))
infinity = 100.0
D = qp.metrics.calculate_kld(P... |
sastels/Onboarding | 1 - Introduction.ipynb | mit | a = 6 ## set a variable in this interpreter session
a ## entering an expression prints its value
a + 2
a = 'hi' ## 'a' can hold a string just as well
a
len(a) ## call the len() function on a string
a + len(a) ## try something that doesn't work
a + str(len(a)) ## probably what you really w... |
DfAC/MiningMassiveDatasets | week02.ipynb | gpl-2.0 | from itertools import combinations as Combinations
#https://github.com/ztane/python-Levenshtein
from Levenshtein import distance as EditDistance
from Levenshtein import editops as EditOps
from collections import Counter
inputWords = ['he', 'she', 'his', 'hers']
distanceGroups = []
for wordA,wordB in list(Combinations... |
openfisca/senegal | notebooks/Fake-data-Senegal.ipynb | agpl-3.0 | import matplotlib.pyplot as plt # For graphics
%matplotlib inline
import numpy as np # linear algebra and math
import pandas as pd # data frames
from openfisca_core.model_api import *
from openfisca_senegal import SenegalTaxBenefitSystem # The Senegalese tax-benefits system
from openfisca_senegal.survey_scenario... |
lzctony/data-512-a1 | hcds-a1-data-curation.ipynb | mit | def get_data(url, access, file_name):
"""
This function takes an url, parameter for the key
'access'/'access-site' depends on getting pageviews
or pagecounts dataset. Then save the data as json
file with the name as given file_name to your directory.
Args:
param1 (str): an url for ... |
HNoorazar/PyOpinionGame | One_Topic_Driver_Example.ipynb | gpl-3.0 | import numpy as np
from numpy.random import randn
import pandas as pd
from pandas import Series, DataFrame
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import matplotlib.image as mpimg
from matplotlib import rcParams
import seaborn as sb
"""
Explanation: Opinion Game - One Topic in the net... |
quantopian/research_public | notebooks/data/quandl.cboe_rvx/notebook.ipynb | apache-2.0 | # For use in Quantopian Research, exploring interactively
from quantopian.interactive.data.quandl import cboe_rvx as dataset
# import data operations
from odo import odo
# import other libraries we will use
import pandas as pd
# Let's use blaze to understand the data a bit using Blaze dshape()
dataset.dshape
# And h... |
bronesto/firstNeuralNetwork | Your_first_neural_network (1).ipynb | agpl-3.0 | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code... |
j-coll/opencga | opencga-client/src/main/python/notebooks/pyopencga_basic_notebook_001.ipynb | apache-2.0 | # Initialize PYTHONPATH for pyopencga
import sys
import os
from pprint import pprint
"""
Explanation: pyOpenCGA Basic User Usage
[NOTE] The server methods used by pyopencga client are defined in the following swagger URL:
- http://bioinfo.hpc.cam.ac.uk/opencga-demo/webservices
For tutorials and more info about acces... |
blua/deep-learning | tv-script-generation/olds_ipnbs/old1_dlnd_tv_script_generation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
"""
Explanation: TV Script Generation
In this project, you'll generate your own Simpsons TV scrip... |
napsternxg/gensim | docs/notebooks/topic_coherence-movies.ipynb | gpl-3.0 | from __future__ import print_function
import re
import os
from scipy.stats import pearsonr
from datetime import datetime
from gensim.models import CoherenceModel
from gensim.corpora.dictionary import Dictionary
from smart_open import smart_open
"""
Explanation: Benchmark testing of coherence pipeline on Movies data... |
Olsthoorn/TransientGroundwaterFlow | Assignment/.ipynb_checkpoints/Inclass020200129-checkpoint.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from scipy.special import exp1 as W
"""
Explanation: Assingment in class, Jan 29, 2004
The questions and explanation of the assignement goes here.
I expect you to return the assignment notebook, well documented by yourself. I don't want to read my ... |
phoebe-project/phoebe2-docs | 2.3/tutorials/plotting_advanced.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.3,<2.4"
"""
Explanation: Advanced: Plotting Options
For basic plotting usage, see the plotting tutorial
PHOEBE 2.3 uses autofig 1.1 as an intermediate layer for highend functionality to matplotlib.
Setup
Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment thi... |
dssg/diogenes | doc/notebooks/grid_search.ipynb | mit | %matplotlib inline
import diogenes
import numpy as np
data = diogenes.read.open_csv_url(
'http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv',
delimiter=';')
labels = data['quality']
labels = labels < np.average(labels)
M = diogenes.modify.remove_cols(data, 'quality')
"... |
deehzee/cs231n | assignment2/KerasNeonFullyConnected.ipynb | mit | input_dim = 3 * 32 * 32
hidden_dim = 50
#std = np.sqrt(2.0 / num_train)
#std = np.sqrt(2.0 / input_dim)
std = 0.01
num_iters = 1000
batch_size = 200
lr = 0.00179573608347
#decay = 0.960001695353
decay = 1
reg = 0.316227766017
net = TwoLayerNet(input_dim, hidden_dim, num_classes, std)
stats = net.train(X_train, y_trai... |
stevenydc/2015lab1 | Lab1-pythonpandas_original.ipynb | mit | # The %... is an iPython thing, and is not part of the Python language.
# In this case we're just telling the plotting library to draw things on
# the notebook, instead of on a separate window.
%matplotlib inline
#this line above prepares IPython notebook for working with matplotlib
# See all the "as ..." contructs? ... |
daniel-severo/dask-ml | docs/source/examples/xgboost.ipynb | bsd-3-clause | %matplotlib inline
"""
Explanation: Dask and XGBoost
<img src="http://dask.readthedocs.io/en/latest/_images/dask_horizontal.svg" align="left" width="30%" alt="Dask logo">
<img src="https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo-m/xgboost.png" align="left" width="25%" alt="Dask logo">
End of expl... |
davicsilva/dsintensive | notebooks/miniprojects/data_wrangling_json/.ipynb_checkpoints/sliderule_dsi_xml_exercise-checkpoint.ipynb | apache-2.0 | from xml.etree import ElementTree as ET
"""
Explanation: XML example and exercise
study examples of accessing nodes in XML tree structure
work on exercise to be completed and submitted
reference: https://docs.python.org/2.7/library/xml.etree.elementtree.html
data source: http://www.dbis.informatik.uni-goettinge... |
tatjanus/cianparser | cian_ml.ipynb | bsd-2-clause | data.drop(['Bal_na', 'Distr_N', 'Brick_na'], axis = 1, inplace = True)
"""
Explanation: Готовим данные к линейной модели. Уберем n-ные столбцы после one-hot encoding, чтоб не образовывалась линейная зависимость
End of explanation
"""
data_sq = data.copy()
squared_columns = ['Distance', 'Kitsp', 'Livsp', 'Totsp', 'M... |
shirtsgroup/physical-validation | doc/examples/openmm_replica_exchange.ipynb | lgpl-2.1 | # enable plotting in notebook
%matplotlib notebook
"""
Explanation: Check ensemble of OpenMM temperature replica exchange simulations
Note: This notebook can be run locally by cloning the
Github repository.
The notebook is located in doc/examples/openmm_replica_exchange.ipynb. The input and output files of the simulat... |
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