repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
saudijack/unfpyboot | Day_01/00_Lists/01_ListDictionaryTuples-Solutions.ipynb | mit | oldList = [[1,2,3],[4,5,6]]
"""
Explanation: List - Dictionary - Tuples: Solutions
<font color='red'>List</font>
Problem 1
Consider the list:
End of explanation
"""
a = oldList[0]
print a
a.remove(3)
b = oldList[1]
print b
b.remove(5)
newList = []
newList.append(a)
print newList
newList = newList.append(b)
print new... |
tpin3694/tpin3694.github.io | python/pandas_missing_data.ipynb | mit | import pandas as pd
import numpy as np
"""
Explanation: Title: Missing Data In Pandas Dataframes
Slug: pandas_missing_data
Summary: Missing Data In Pandas Dataframes
Date: 2016-05-01 12:00
Category: Python
Tags: Data Wrangling
Authors: Chris Albon
import modules
End of explanation
"""
raw_data = {'first_name': ['J... |
jacobdein/alpine-soundscapes | figures/Figure 3 - noise removal.ipynb | mit | from matplotlib import pyplot
%matplotlib inline
from matplotlib.patches import Rectangle
from matplotlib.lines import Line2D
import numpy
from scipy.io import wavfile
from os import path
from datetime import timedelta
from django.db import connection
from database.models import Sound
from database.models import Sit... |
ggf84/python-tutorial | Intro_to_Matplotlib.ipynb | mit | # Importing the libraries
import matplotlib.pyplot as plt
import numpy as np
plt.rcParams["figure.figsize"] = (10, 8) # Set the figure size for plots
# Creating the x-axis array (from 0 to 2pi, step of 0.1)
x = np.arange(0, 2*np.pi, 0.1)
# Creating the y-axis array
y = np.sin(x)
# Making the Plot
plt.plot(x, y)
plt... |
folivetti/PIPYTHON | Aula04.ipynb | mit | def DivResto(num, base):
"""
Retorna o quociente e resto da divisão de num por base
"""
return num//base, num%base
dec = 14
bin = "" # string vazia
div, resto = DivResto(dec,2)
dec = div
bin = str(resto) + bin
print(bin)
# repete mais uma vez, pois temos outro dígito
div, resto = DivResto(dec,2)
dec... |
liumengjun/cn-deep-learning | tutorials/intro-to-tflearn/TFLearn_Sentiment_Analysis.ipynb | mit | import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
"""
Explanation: Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's work by building a network for sentiment analysis on the movie review data. Instead of a network w... |
nick-youngblut/SIPSim | ipynb/bac_genome/fullCyc/.ipynb_checkpoints/Day1_rep10_noPCR-checkpoint.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
Extension of Day1_rep10 simulations... |
mjuenema/ipython-notebooks | iscpy.ipynb | bsd-2-clause | import iscpy
with open('named.conf') as fp:
s = fp.read()
config = iscpy.ParseISCString(s)
type(config)
"""
Explanation: ISCpy
ISCpy a robust ISC config file parser. It has virtually unlimited
possibilities for depth and quantity of ISC config files. ISC config files include BIND and DHCP config files among a f... |
AllenDowney/ModSimPy | examples/throwingaxe.ipynb | mit | # 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/'
... |
GoogleCloudPlatform/practical-ml-vision-book | 07_training/07a_ingest.ipynb | apache-2.0 | import tensorflow as tf
print('TensorFlow version' + tf.version.VERSION)
print('Built with GPU support? ' + ('Yes!' if tf.test.is_built_with_cuda() else 'Noooo!'))
print('There are {} GPUs'.format(len(tf.config.experimental.list_physical_devices("GPU"))))
device_name = tf.test.gpu_device_name()
if device_name != '/devi... |
Startupsci/data-science-notebooks | python-data-files.ipynb | mit | # For reading/writing CSV files
import csv
# For listing system file folders
from subprocess import check_output
# Use with open to ensure file is closed when block ends
# The wb flag opens file for writing
with open('data/fileops/vehicles.csv', 'wb') as csv_file:
# Prepare csv writer
wtr = csv.writer(csv_file... |
lsanomaly/lsanomaly | lsanomaly/notebooks/static_dynamic.ipynb | mit | import os
import numpy as np
from pathlib import Path
cwd = os.getcwd()
os.chdir(Path(cwd).parents[1])
from lsanomaly import LSAnomaly
import lsanomaly.notebooks.dynamic as demo
"""
Explanation: Detection of Cardiac Arrhythmia from ECG Sequence
In the posted 1.2 version several demonstrations were included in the di... |
jasonding1354/pyDataScienceToolkits_Base | Scikit-learn/.ipynb_checkpoints/(2)choose_a_ml_model-checkpoint.ipynb | mit | from sklearn.datasets import load_iris
iris = load_iris()
# create X(features) and y(response)
X = iris.data
y = iris.target
"""
Explanation: 内容概要
这一节我们介绍以下几个内容：
- 我们该怎样选择模型用于监督学习任务？
- 我们该如何选择调整得到最好的模型参数？
- 我们该如何对测试数据进行预测估计？
1. 使用整个数据集进行训练和测试
这里我们使用手中的整个数据集来训练模型
使用同样的数据集来测试模型，然后评估预测的结果和真实结果的差别
End of explanation
"""... |
chapinb/pythonforensics | jupyter-notebooks/Registry Parsing.ipynb | gpl-2.0 | from Registry import Registry
from Registry.RegistryParse import ParseException
path_to_reg_hive = '../data/system' # The included SYSTEM hive file
hive = Registry.Registry(path_to_reg_hive)
print(type(hive))
print("Hive Name: ", hive.hive_name())
print("Hive Type: ", hive.hive_type())
# We can also open the hive d... |
ZiqianXY/MLEN | src/p0_titanic_survival_exploration/Titanic_Survival_Exploration.ipynb | mit | import numpy as np
import pandas as pd
# RMS Titanic data visualization code
from titanic_visualizations import survival_stats
from IPython.display import display
%matplotlib inline
# Load the dataset
in_file = 'titanic_data.csv'
full_data = pd.read_csv(in_file)
# Print the first few entries of the RMS Titanic data... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session03/Day4/ANTARES/miniAntares_parallel.ipynb | mit | # first we need to construct a client that will interface with our cluster
from ipyparallel import Client, require
worker = Client()
# once we create a client, we can decide how to allocate tasks across the cluster
# we've got however many 'engines' you started in the cluster
# lets just use all of them
lview = worke... |
ajul/zerosum | python/examples/wh40k.ipynb | bsd-3-clause | import _initpath
import numpy
import dataset.csv
import dataset.wh40k
import zerosum.balance
from pandas import DataFrame
balance_7 = zerosum.balance.MultiplicativeBalance(dataset.wh40k.wh40k_7_to_wound).optimize()
balance_8 = zerosum.balance.MultiplicativeBalance(dataset.wh40k.wh40k_8_to_wound).optimize()
labels = ... |
dsnair/GCP | datalab/nyc_taxi.ipynb | gpl-3.0 | %sql -d standard
SELECT
*
FROM
`nyc-tlc.yellow.trips`
LIMIT
5
"""
Explanation: Datalab Tutorial
In this tutorial, we'll do some exploratory data analysis in BigQuery using Datalab.
Requirements
If you haven't already, you may sign-up for the free GCP trial credit. Before you begin, give this project any name y... |
myfunprograms/deep_learning | project1/files/dlnd-your-first-neural-network-instruction.ipynb | gpl-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... |
Alex-Ian-Hamilton/solarbextrapolation | docs/auto_examples/plot_gaussian_example_data.ipynb | mit | # Module imports
from solarbextrapolation.example_data_generator import generate_example_data, dummyDataToMap
"""
Explanation: Generating Example Gaussian Boundary Data
In this example you will be generating some example data and extrapolate this
using the basic potential extrapolator.
You can start by importing the n... |
opengeostat/pygslib | pygslib/Ipython_templates/.ipynb_checkpoints/draw_raw-checkpoint.ipynb | mit | #general imports
import matplotlib.pyplot as plt
import pygslib
import numpy as np
import pandas as pd
#make the plots inline
%matplotlib inline
"""
Explanation: PyGSLIB
Draw
The GSLIb equivalent parameter file is
```
Parameters for DRAW
***
START OF PARAMETERS:
data/cluster... |
delsner/dl-exploration | notebooks/00 - Logistic Regression tensorflow - MNIST.ipynb | mit | # import necessary packages
import tensorflow as tf
import numpy as np
"""
Explanation: Import packages
End of explanation
"""
# test tensorflow
a = tf.constant(2)
b = tf.constant(2)
mult = tf.multiply(a,b)
sess = tf.Session()
sess.run(mult)
# initialize weights for feed-forward nn, 300x200 matrice with normal dist... |
ddcampayo/ddcampayo.github.io | cursos_previos/Curso_CFD_OS_2019/notebooks/conveccion_1d.ipynb | gpl-3.0 | %matplotlib inline
import scipy as np
from matplotlib import pyplot as plt
"""
Explanation: Convección en una dimensión
End of explanation
"""
L = 1.0 # longitud del sistema 1D
nx = 42 # nodos espaciales
dx = L / (nx-2) # sí, quitamos dos nodos ...
x = np.linspace( 0 , L , num=nx )
T= 0.1 ... |
alephcero/adsProject | olds/testeoModelos.ipynb | gpl-3.0 | #get data
getEPHdbf('t310')
data1 = pd.read_csv('data/cleanDatat310.csv')
data2 = categorize.categorize(data1)
data3 = schoolYears.schoolYears(data2)
data = make_dummy.make_dummy(data3)
dataModel = functionsForModels.prepareDataForModel(data)
dataModel.head()
"""
Explanation: NOTAS
CALCULAR EN BASE AL MODELO DE C... |
DB2-Samples/db2jupyter | Db2 Statistical Functions.ipynb | apache-2.0 | %run db2.ipynb
"""
Explanation: <a id='top'></a>
Db2 Statistical Functions
Db2 already has a variety of Statistical functions built in. In Db2 11.1, a number of new
functions have been added including:
COVARIANCE_SAMP - The COVARIANCE_SAMP function returns the sample covariance of a set of number pairs
STDDEV_SAMP - ... |
besser82/shogun | doc/ipython-notebooks/computer_vision/Scene_classification.ipynb | bsd-3-clause | #import Opencv library
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
try:
import cv2
except ImportError:
print "You must have OpenCV installed"
exit(1)
#check the OpenCV version
try:
v=cv2.__version__
assert (tuple(map(int,v.split(".")))>(2,4,2))
except (AssertionError, Va... |
pmgbergen/porepy | tutorials/incompressible_flow_model.ipynb | gpl-3.0 | import porepy as pp
import numpy as np
import inspect
"""
Explanation: Model classes
Setting up a simulation in PorePy requires a certain knowledge of the code - both what objects need to be defined and how to define them. To simplify this task, PorePy exploits encapsulation through Model classes for some common probl... |
JAmarel/Phys202 | Interact/.ipynb_checkpoints/MatplotlibEx03-checkpoint.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Matplotlib Exercise 3
Imports
End of explanation
"""
def well2d(x, y, nx, ny, L=1.0):
"""Compute the 2d quantum well wave function."""
return (2/L)*np.sin(nx * np.pi * x/L)*np.sin(ny * np.pi * y/L)
psi = well2d(np.linspac... |
anthonyng2/FX-Trading-with-Python-and-Oanda | Oanda v1 REST-oandapy/09.00 Bonus Materials I.ipynb | mit | import pandas as pd
%matplotlib inline
import seaborn as sns
import oandapy
import configparser
config = configparser.ConfigParser()
config.read('../config/config_v1.ini')
account_id = config['oanda']['account_id']
api_key = config['oanda']['api_key']
oanda = oandapy.API(environment="practice",
a... |
tpin3694/tpin3694.github.io | python/matplotlib_scatterplot_from_pandas.ipynb | mit | %matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Title: Making A Matplotlib Scatterplot From A Pandas Dataframe
Slug: matplotlib_scatterplot_from_pandas
Summary: Making A Matplotlib Scatterplot From A Pandas Dataframe
Date: 2016-05-01 12:00
Category: Python
Tag... |
cjcardinale/climlab | docs/source/courseware/Latitude-dependent_grey_radiation.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import climlab
from climlab import constants as const
model = climlab.GreyRadiationModel(name='Grey Radiation', num_lev=30, num_lat=90)
print(model)
model.to_xarray()
insolation = climlab.radiation.DailyInsolation(domains=model.Ts.domain)
model.a... |
resendislab/corda | docs/index.ipynb | mit | from corda import reaction_confidence
gene_conf = {"gene1": 1, "gene2": 3, "gene4": -1} # missing entries are automatically assigned zeroes
rule = "gene1 and gene2 or (gene3 and gene4)"
reaction_confidence(rule, gene_conf)
"""
Explanation: CORDA tutorial
Reactions, Genes and confidences
In order to start a reconstru... |
pmgbergen/porepy | tutorials/grid_structure.ipynb | gpl-3.0 | import numpy as np
import porepy as pp
nx = np.array([3, 2])
g = pp.CartGrid(nx)
"""
Explanation: The porepy grid structure
In this tutorial we investigate the PorePy grid structure, and explain how to access information stored in the grid.
Basic grid construction
The simplest grids are Cartesian. PorePy can create ... |
gdsfactory/gdsfactory | docs/notebooks/plugins/simphony/02_components_SiPANN.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import gdsfactory as gf
import gdsfactory.simulation.sipann as gs
def pltAttr(x, y, title=None, legend="upper right", save=None):
if legend is not None:
plt.legend(loc=legend)
plt.xlabel(x)
plt.ylabel(y)
if title is not None:
plt.title... |
google/starthinker | colabs/segmentology_dv360.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: 1. Install Dependencies
First install the libraries needed to execute recipes, this only needs to be done once, then click play.
End of explanation
"""
CLOUD_PROJECT = 'PASTE PROJECT ID HERE'
print("Cloud Project Set To: %s" % CLOUD_PROJECT)
... |
GoogleCloudPlatform/analytics-componentized-patterns | retail/recommendation-system/bqml-scann/03_create_embedding_lookup_model.ipynb | apache-2.0 | !pip install -q -U pip
!pip install -q tensorflow==2.2.0
!pip install -q -U google-auth google-api-python-client google-api-core
"""
Explanation: Part 3: Create a model to serve the item embedding data
This notebook is the third of five notebooks that guide you through running the Real-time Item-to-item Recommendation... |
BeyondTheClouds/enoslib | docs/apidoc/examples/dask.ipynb | gpl-3.0 | from enoslib import *
import logging
# get some logs
logging.basicConfig(level=logging.INFO)
"""
Explanation: Dask deployment on Grid'5000
This notebook will deploy a Dask cluster on Grid'5000 and launch a simpe computation.
Requirements:
- A conda[[1]] environment setup on the Grid'5000 frontend with dask install... |
ViralLeadership/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | Chapter4_TheGreatestTheoremNeverTold/Chapter4.ipynb | mit | %matplotlib inline
import numpy as np
from IPython.core.pylabtools import figsize
import matplotlib.pyplot as plt
figsize(12.5, 5)
import pymc as pm
sample_size = 100000
expected_value = lambda_ = 4.5
poi = pm.rpoisson
N_samples = range(1, sample_size, 100)
for k in range(3):
samples = poi(lambda_, size=sample_... |
google/learned_optimization | docs/notebooks/Part3_Truncation_TruncatedStep.ipynb | apache-2.0 | !pip install git+https://github.com/google/learned_optimization.git
import numpy as np
import jax.numpy as jnp
import jax
import functools
from matplotlib import pylab as plt
from learned_optimization.outer_trainers import full_es
from learned_optimization.outer_trainers import truncated_pes
from learned_optimization... |
anntzer/seaborn | doc/docstrings/kdeplot.ipynb | bsd-3-clause | import seaborn as sns; sns.set()
tips = sns.load_dataset("tips")
sns.kdeplot(data=tips, x="total_bill")
"""
Explanation: Plot a univariate distribution along the x axis:
End of explanation
"""
sns.kdeplot(data=tips, y="total_bill")
"""
Explanation: Flip the plot by assigning the data variable to the y axis:
End of ... |
lizoyu/kaggle-DigitRecognizer | jupyter/Traditional methods - opencv+scikit_learn.ipynb | gpl-3.0 | import cv2
import numpy as np
from skimage.feature import hog
from sklearn.decomposition import PCA
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from lib.data_utils import get_MNIST_data... |
ES-DOC/esdoc-jupyterhub | notebooks/nasa-giss/cmip6/models/giss-e2-1g/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nasa-giss', 'giss-e2-1g', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: NASA-GISS
Source ID: GISS-E2-1G
Topic: Atmos
Sub-Topics: Dynamical Core, Radia... |
tensorflow/docs | site/en/guide/migrate/early_stopping.ipynb | apache-2.0 | #@title 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 agreed to in writing, software
# distributed under... |
csdms/dakota | examples/hydrotrend-polynomial-chaos-study.ipynb | mit | from dakotathon import Dakota
"""
Explanation: <img src="http://csdms.colorado.edu/mediawiki/images/CSDMS_high_res_weblogo.jpg">
HydroTrend Study with the Polynomial Chaos Method
HydroTrend is a numerical model that creates synthetic river discharge and sediment load time series as a function of climate trends and bas... |
ES-DOC/esdoc-jupyterhub | notebooks/cmcc/cmip6/models/cmcc-cm2-hr4/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cmcc', 'cmcc-cm2-hr4', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: CMCC
Source ID: CMCC-CM2-HR4
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics... |
scientific-visualization-2016/ClassMaterials | Week-02/02_python_overview.ipynb | cc0-1.0 | import math
r = float("4.2")
s = math.sin(r)
print('hello world! The sin({0}) = {1:0.2f}'.format(r,s))
"""
Explanation: <img src='https://www.rc.colorado.edu/sites/all/themes/research/logo.png' style="height:75px">
Python Overview
Objectives
Review python
Introduction to functional concepts
Data analysis and visualiz... |
willrogers/pytac | jupyter/tutorial.ipynb | apache-2.0 | import sys, os
# Make the pytac package available from this subdirectory
sys.path.append(os.path.join(os.getcwd(), '..'))
import pytac
"""
Explanation: In this tutorial we will go through some of the most common ways of using pytac. The aim is to give you an understanding of the interface and how to find out what is a... |
sfegan/calin | examples/iact_data/read and dump calin and ACTL header and event from zfits file.ipynb | gpl-2.0 | %pylab inline
import calin.iact_data.raw_actl_event_data_source
import calin.iact_data.telescope_data_source
import json
import struct
import base64
"""
Explanation: Read and dump calin and ACTL header and event
calin/examples/iact_data/read and dump calin and ACTL raw header and event from zfits file.ipynb - Stephen ... |
rastala/mmlspark | notebooks/samples/303 - Transfer Learning by DNN Featurization - Airplane or Automobile.ipynb | mit | from mmlspark import CNTKModel, ModelDownloader
import numpy as np, pandas as pd
import os, urllib, tarfile, pickle, array
from os.path import abspath
from pyspark.sql.functions import col, udf
from pyspark.sql.types import *
cdnURL = "https://mmlspark.azureedge.net/datasets"
# Please note that this is a copy of the ... |
fja05680/pinkfish | examples/200.momentum-gem-portfolio/strategy.ipynb | mit | import pandas as pd
import matplotlib.pyplot as plt
import datetime
import pinkfish as pf
import strategy
# Format price data.
pd.options.display.float_format = '{:0.2f}'.format
%matplotlib inline
pd. set_option('display.max_rows', None)
# Set size of inline plots.
'''note: rcParams can't be in same cell as import... |
agile-geoscience/xlines | notebooks/11_Gridding_map_data.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
"""
Explanation: X lines of Python
Gridding map data
This notebook goes with the Agile Scientific blog post from 8 March 2019.
I'm using a small dataset originally from Geoff Bohling at the Kansas Geological Survey. I can no l... |
5agado/conversation-analyzer | src/test/textStatsTool.ipynb | apache-2.0 | def load_text(filepaths):
"""
Load text you want to analyse.
:param filepaths: list of paths to text files to load
:return: single string representing all retrieved text
"""
text = ""
for path in filepaths:
with open(path, 'r', encoding='UTF-8') as f:
text += "\n"+f.read(... |
rxa254/MoodCube | simData.ipynb | bsd-3-clause | %matplotlib inline
from __future__ import division
import matplotlib.cm as cm
from matplotlib import rcParams
import matplotlib.pyplot as plt
import numpy as np
#import os
#import scipy.constants as scc
from scipy.io import savemat
from scipy.stats import rayleigh
#import scipy.signal as sig
#import sys
from timeit im... |
atulsingh0/MachineLearning | MasteringML_wSkLearn/02a_Classification.ipynb | gpl-3.0 | # import
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
%matplotlib inline
X = [[6], [8], [10], [14], [18]]
y = [[7], [9], [13], [17.5], [18]]
plt.figure()
plt.title('Pizza price plotted against diameter')
plt... |
opesci/devito | examples/compiler/04_iet-B.ipynb | mit | from devito import SpaceDimension, TimeDimension
dims = {'i': SpaceDimension(name='i'),
'j': SpaceDimension(name='j'),
'k': SpaceDimension(name='k'),
't0': TimeDimension(name='t0'),
't1': TimeDimension(name='t1')}
dims
"""
Explanation: In this tutorial we will learn how to build, comp... |
parcicn/Introduction-python | Intro-Python-ICN-2017.ipynb | gpl-3.0 | # ceci est un commentaire, l'interpréteur ne le lit même pas
# les commentaires sont destinés au lecteur humain du code source
code = 'sesame' #affectation de variable
rep = input('Entrez le code : ') #affectation et instruction d'entrée
if rep == code: # test et début d'u... |
mathause/regionmask | docs/notebooks/create_own_regions.ipynb | mit | import cartopy.crs as ccrs
import numpy as np
import matplotlib.pyplot as plt
import regionmask
regionmask.__version__
"""
Explanation: Create your own region
Creating own regions is straightforward. Import regionmask and check the version:
End of explanation
"""
US1 = np.array([[-100.0, 30], [-100, 40], [-120, 35... |
voytekresearch/nsaba | notebooks/demos/Nsaba_Short_Demo.ipynb | mit | #local paths to data
ns_path = "/Users/Torben/Documents/ABI analysis/current_data_new/"
aba_path = '/Users/Torben/Documents/ABI analysis/normalized_microarray_donor9861/'
#initialization methods
nsaba.Nsaba.ns_load(ns_path)
nsaba.Nsaba.aba_load(aba_path)
#Creating Nsaba object and loading previously estimated gene ac... |
zzsza/TIL | pytorch/Transfer-Learning.ipynb | mit | data_transforms = {
'train': transforms.Compose([
transforms.RandomSizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'val': transforms.Compose([
transforms.Scale(256),
... |
jskDr/keraspp | tf2/ex2_1/PyTorch_nb_ex2_1_ann_mnist_cl.ipynb | mit | # %load /home/sjkim/.jupyter/head.py
%matplotlib inline
%load_ext autoreload
%autoreload 2
from importlib import reload
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import os
#os.environ["CUDA_VISIBLE_DEVICES"]="0"
# seaborn
#import seaborn as sns
#sns.set( style = 'white', font_scale = 1.7... |
QuantCrimAtLeeds/PredictCode | examples/Chicago/Check large datasets agree.ipynb | artistic-2.0 | import sys, os, csv, lzma
import open_cp.sources.chicago as chicago
filename = os.path.join("..", "..", "open_cp", "sources", "chicago.csv")
filename1 = os.path.join("..", "..", "open_cp", "sources", "chicago1.csv")
filename_all = os.path.join("..", "..", "open_cp", "sources", "chicago_all.csv.xz")
filename_all1 = os.... |
fran6w/CoderLaVille | CoderLaVille.ipynb | mit | import pandas as pnd
import matplotlib.pylab as plt
import matplotlib.patches as mpatches
from IPython.display import HTML
%matplotlib inline
img = plt.imread("rueildigital.jpg")
plt.axis('off')
plt.imshow(img);
"""
Explanation: Hackathon de Nanterre
17 octobre 2015
Coder la ville...
... en Python
End of explanation
... |
tensorflow/quantum | docs/tutorials/barren_plateaus.ipynb | apache-2.0 | #@title 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 agreed to in writing, software
# distributed under... |
AtmaMani/pyChakras | udemy_ml_bootcamp/Python-for-Data-Visualization/Seaborn/Style and Color.ipynb | mit | import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
tips = sns.load_dataset('tips')
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
Style and Color
We've shown a few times how to control figure aesthetics in seaborn, but let's now go over it forma... |
JQGoh/jqlearning | posts/Pair-Distances.ipynb | gpl-3.0 | # Import functions
import sys
sys.path.append("../")
import pandas as pd
df1 = pd.read_csv("stations1.csv")
df2 = pd.read_csv("stations2.csv")
df1.head(10)
df2.head(10)
"""
Explanation: Pair Distances
It is a common practice that we try to make use of the available data and combine data sets from different sources ... |
bcorlett/Notebooks_Modelling | Corlett_Assign2.ipynb | mit | # Import clusters
from scipy import sparse #Allows me to create sparse matrices (i.e. not store all of the zeros in the 'A' matrix)
import scipy.sparse.linalg as spla #To solve each time iteration, as my SOR code (below) grows exponentially when attempting to solve with negative values on the right-hand side.
from nump... |
GoogleCloudPlatform/tensorflow-without-a-phd | tensorflow-mnist-tutorial/keras_05_mnist_batch_norm.ipynb | apache-2.0 | BATCH_SIZE = 64
EPOCHS = 10
training_images_file = 'gs://mnist-public/train-images-idx3-ubyte'
training_labels_file = 'gs://mnist-public/train-labels-idx1-ubyte'
validation_images_file = 'gs://mnist-public/t10k-images-idx3-ubyte'
validation_labels_file = 'gs://mnist-public/t10k-labels-idx1-ubyte'
"""
Explanation:... |
yedivanseven/LPDE | LegPolyDE-2D_1.ipynb | gpl-3.0 | import matplotlib.pyplot as plt
import seaborn as sbn
import numpy as np
import numpy.polynomial.legendre as npl
from scipy.optimize import minimize
"""
Explanation: ORTHOGONAL POLYNOMIAL DENSITY ESTIMATION
Preliminaries
Imports
End of explanation
"""
%matplotlib notebook
sbn.set_style('white')
legend_params = {'l... |
osunderdog/PythonLearning | PythonZipExploration.ipynb | gpl-2.0 | exlst = ["This", "That", "these", "those", "wit"]
exlsv = [8,10,9,4,20]
"""
Explanation: Studying "starred expressions" in Python
Author: Bruce W. Lowther
licensed under the GNU General Public License v2.0
Starting with list of strings and a list of numbers. Both equal length.
End of explanation
"""
A,B,C,D,E = ex... |
cloudera/ibis | docs/source/user_guide/extending/extending_reduce_expr.ipynb | apache-2.0 | import ibis.expr.datatypes as dt
import ibis.expr.rules as rlz
from ibis.expr.operations import Reduction
from ibis.expr.signature import Argument as Arg
class LastDate(Reduction):
arg = Arg(rlz.column(rlz.date))
where = Arg(rlz.boolean, default=None)
output_type = rlz.scalar_like('arg')
"""
Explanation... |
browtayl/cs446-net-comp-bio | homework1/homework1.ipynb | gpl-3.0 | import pandas
import igraph
edge_list = pandas.read_csv('hsmetnet.txt', sep='\t', header=None).values.tolist()
graph = igraph.Graph.TupleList(edge_list, directed=True)
igraph.summary(graph)
"""
Explanation: Homework 1
End of explanation
"""
flatten = lambda l: sum(l, [])
vertex_set = set(flatten(edge_list))
isreac... |
InsightLab/data-science-cookbook | 2020/05-geographic-information-system/Notebook_Geometric_Objects.ipynb | mit | # Import necessary geometric objects from shapely module
from shapely.geometry import Point, LineString, Polygon
# Create Point geometric object(s) with coordinates
point1 = Point(2.2, 4.2)
point2 = Point(7.2, -25.1)
point3 = Point(9.26, -2.456)
point3D = Point(9.26, -2.456, 0.57)
"""
Explanation: Biblioteca Shapely ... |
tritemio/multispot_paper | LCOS_pattern/LCOS Pattern.ipynb | mit | import matplotlib.pyplot as plt
import matplotlib as mpl
%matplotlib inline
#%config InlineBackend.figure_format='retina' # for hi-dpi displays
%run patternlib
"""
Explanation: LCOS Multi-Spot Phase Pattern
This notebook shows how to call the function compute_pattern() to
generate a multispot LCOS pattern with arbi... |
DataPilot/notebook-miner | summary_of_work/6. Probabilistic Model of Nodes.ipynb | apache-2.0 | # Necessary imports
import os
import time
from nbminer.notebook_miner import NotebookMiner
from nbminer.cells.cells import Cell
from nbminer.features.ast_features import ASTFeatures
from nbminer.stats.summary import Summary
from nbminer.stats.multiple_summary import MultipleSummary
#Loading in the notebooks
people = ... |
CRPropa/CRPropa3 | doc/pages/example_notebooks/galactic_backtracking/galactic_backtracking.v4.ipynb | gpl-3.0 | from crpropa import *
# magnetic field setup
B = JF12Field()
#seed = 691342
#B.randomStriated(seed)
#B.randomTurbulent(seed)
# simulation setup
sim = ModuleList()
sim.add(PropagationCK(B, 1e-4, 0.1 * parsec, 100 * parsec))
obs = Observer()
obs.add(ObserverSurface( Sphere(Vector3d(0), 20 * kpc) ))
# obs.onDetection(Te... |
huajianmao/learning | coursera/deep-learning/5.nlp-sequence-models/week1/Neural machine translation with attention - v2.ipynb | mit | from keras.layers import Bidirectional, Concatenate, Permute, Dot, Input, LSTM, Multiply
from keras.layers import RepeatVector, Dense, Activation, Lambda
from keras.optimizers import Adam
from keras.utils import to_categorical
from keras.models import load_model, Model
import keras.backend as K
import numpy as np
from... |
ES-DOC/esdoc-jupyterhub | notebooks/ec-earth-consortium/cmip6/models/sandbox-3/landice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'sandbox-3', 'landice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: EC-EARTH-CONSORTIUM
Source ID: SANDBOX-3
Topic: Landice
Sub-Topic... |
tpin3694/tpin3694.github.io | machine-learning/imbalanced_classes_in_svm.ipynb | mit | # Load libraries
from sklearn.svm import SVC
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
import numpy as np
"""
Explanation: Title: Imbalanced Classes In SVM
Slug: imbalanced_classes_in_svm
Summary: How to handle imbalanced classes in support vector machines in Scikit-Learn
Date: ... |
Hexiang-Hu/mmds | week3/Quiz-Week3.ipynb | mit | ## Solution 1.
import numpy as np
A = np.array([[0, 0, 1, 0, 0, 1, 0, 0], #A
[0, 0, 0, 0, 1, 0, 0, 1], #B
[1, 0, 0, 1, 0, 1, 0, 0], #C
[0, 0, 1, 0, 1, 0, 1, 0], #D
[0, 1, 0, 1, 0, 0, 0, 1], #E
[1, 0, 1, 0, 0, 0, 1, 0], #F
[0, 0, 0, 1, ... |
Mynti207/cs207project | docs/presentation.ipynb | mit | # database parameters
ts_length = 100
data_dir = '../db_files'
db_name = 'default'
dir_path = data_dir + '/' + db_name + '/'
# clear file system for testing
if not os.path.exists(dir_path):
os.makedirs(dir_path)
filelist = [dir_path + f for f in os.listdir(dir_path)]
for f in filelist:
os.remove(f)
"""
Explan... |
ktmud/deep-learning | intro-to-rnns/Anna_KaRNNa_Solution.ipynb | mit | import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
"""
Explanation: Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network is base... |
DCPROGS/HJCFIT | exploration/CB.ipynb | gpl-3.0 | %matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
from dcprogs.likelihood import QMatrix
tau = 0.2
qmatrix = QMatrix([ [-2, 1, 1, 0],
[ 1, -101, 0, 100],
[50, 0, -50, 0],
[ 0, 5.6, 0, -5.6]], 1)
"""
Explanation: ... |
MATH497project/MATH497-DiabeticRetinopathy | diagnosis_changing_exploratory/diagnosis_changing_exploratory.ipynb | mit | from collections import Counter
Counter(df[['mNPDR', 'MNPDR', 'SNPDR', 'PDR']].sum(axis=1))
"""
Explanation: There are encounters have more than one diagnosis of DR.
End of explanation
"""
tmp={k:v[['mNPDR', 'MNPDR', 'SNPDR', 'PDR']].sum(axis=0) for k,v in df.groupby('Person_Nbr')}
tmp1 = {k:len(v[~v.isin([0])].ind... |
ML4DS/ML4all | R7.Regression_Overview/regression_overview.ipynb | mit | # Import some libraries that will be necessary for working with data and displaying plots
# To visualize plots in the notebook
%matplotlib inline
import numpy as np
import scipy.io # To read matlab files
import pandas as pd # To read data tables from csv files
# For plots and graphical results
import matplo... |
ypeleg/Deep-Learning-Keras-Tensorflow-PyCon-Israel-2017 | 3.2 RNN and LSTM.ipynb | mit | %matplotlib inline
import numpy as np
import pandas as pd
import theano
import theano.tensor as T
import keras
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
# -- ... |
khalido/nd101 | earthquake.ipynb | gpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
df = pd.read_csv("data/earthquake-database.csv")
print(df.shape)
df.head()
"""
Explanation: build a neural network to predict the magnitude of an Earthquake given the date, time, Latitude, and Longitude as features. This is the ... |
apryor6/apryor6.github.io | visualizations/seaborn/notebooks/colors.ipynb | mit | %matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['figure.figsize'] = (20.0, 10.0)
df = pd.read_csv('../../../datasets/movie_metadata.csv')
df.head()
"""
Explanation: seaborn.countplot
Bar graphs are useful for displaying relationships between categorical data... |
LorenzoBi/courses | TSAADS/tutorial 1/.ipynb_checkpoints/assigment1-checkpoint.ipynb | mit | import numpy as np
import scipy.io as sio
import matplotlib.pyplot as plt
from numpy.fft import fft, ifft
%matplotlib inline
def estimated_autocorrelation(x):
n = len(x)
mu, sigma2 = np.mean(x), np.var(x)
r = np.correlate(x - mu, x - mu, mode = 'full')[-n:]
result = r/(sigma2 * (np.arange(n, 0, -1)))
... |
scikit-multilearn/scikit-multilearn | docs/source/modelselection.ipynb | bsd-2-clause | from skmultilearn.dataset import load_dataset
X_train, y_train, feature_names, label_names = load_dataset('emotions', 'train')
X_test, y_test, _, _ =load_dataset('emotions', 'test')
"""
Explanation: How to select a classifier
This document will guide you through the process of selecting a classifier for your problem.... |
eds-uga/csci1360-fa16 | lectures/L19.ipynb | mit | book = None
try: # Good coding practices!
f = open("alice.txt", "r")
book = f.read()
except FileNotFoundError:
print("Could not find alice.txt.")
else:
f.close()
print(book[:71]) # Print the first 71 characters.
"""
Explanation: Lecture 19: Natural Language Processing I
CSCI 1360: Foundations for... |
dwhswenson/openpathsampling | examples/gromacs/AD_tps_2a_run_flex.ipynb | mit | from __future__ import print_function
%matplotlib inline
import openpathsampling as paths
from openpathsampling.engines import gromacs as ops_gmx
"""
Explanation: This is file runs the main calculation for the flexible length TPS simulation. It requires the file alanine_dipeptide_tps_equil.nc, which is written in the... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/managed_notebooks/recommender_systems/recommender-system-on-retail-data.ipynb | apache-2.0 | PROJECT_ID = ""
# Get your Google Cloud project ID from gcloud
if not os.getenv("IS_TESTING"):
shell_output=!gcloud config list --format 'value(core.project)' 2>/dev/null
PROJECT_ID = shell_output[0]
print("Project ID: ", PROJECT_ID)
"""
Explanation: Build a recommender system with retail data on Vertex A... |
lrayle/rental-listings-census | src/sfbay_modeling.ipynb | bsd-3-clause | import numpy as np
import pandas as pd
import os
import math
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
DATA_DIR = os.path.join('..','data','urbansim')
"""
Explanation: Modeling rent prices for the SF Bay Area
This notebook will develop a predictive model for rent prices in the Bay Area... |
ES-DOC/esdoc-jupyterhub | notebooks/inm/cmip6/models/inm-cm5-0/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'inm', 'inm-cm5-0', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: INM
Source ID: INM-CM5-0
Sub-Topics: Radiative Forcings.
Properties: 85 (42 re... |
makeyourowntextminingtoolkit/makeyourowntextminingtoolkit | A02_svd_applied_to_word_document_matrix.ipynb | gpl-2.0 | #import pandas for conviently labelled arrays
import pandas
# import numpy for SVD function
import numpy
# import matplotlib.pyplot for visualising arrays
import matplotlib.pyplot as plt
"""
Explanation: SVD Applied to a Word-Document Matrix
This notebook applies the SVD to a simple word-document matrix. The aim is to... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/tfx_pipelines/cicd/solutions/tfx_cicd_vertex.ipynb | apache-2.0 | REGION = "us-central1"
PROJECT_ID = !(gcloud config get-value core/project)
PROJECT_ID = PROJECT_ID[0]
ARTIFACT_STORE = f"gs://{PROJECT_ID}"
"""
Explanation: CI/CD for TFX pipelines
Learning Objectives
Develop a CI/CD workflow with Cloud Build to build and deploy TFX pipeline code.
Integrate with Github to automatica... |
whitead/numerical_stats | unit_9/hw_2018/Homework_9_Key.ipynb | gpl-3.0 | import numpy as np
x = np.array([1, 4, 7, 11, 34, -3, 5, 7, 5, 2, 3, 13])
###-------###
x[::2]
"""
Explanation: Homework 9 Key
CHE 116: Numerical Methods and Statistics
3/29/2018
Homework Requirements:
Write all equations in $\LaTeX$
Simplify all expressions
Put comments in your Python code
Explain or show your work... |
great-expectations/great_expectations | examples/notebooks/Crop_Expectations_With_Reshape.ipynb | apache-2.0 | import pandas as pd
import great_expectations as ge
df = pd.read_csv('../data/FAO-Rice-Production-Asia.csv')
"""
Explanation: Great Expectations Basics
This example will walk through using basic expectations with crop data from the Food and Agricultural Organization of the United States.
Data are available here: htt... |
bspalding/research_public | lectures/Arbitrage Pricing Theory.ipynb | apache-2.0 | import numpy as np
import pandas as pd
from statsmodels import regression
import matplotlib.pyplot as plt
"""
Explanation: Arbitrage Pricing Theory
Arbitrage pricing theory is a major asset pricing theory that relies on expressing the returns using a linear factor model:
$$R_i = a_i + b_{i1} F_1 + b_{i2} F_2 + \ldots ... |
Xilinx/meta-petalinux | recipes-examples/openamp/openamp-demo-notebooks/apu-start-rpu.ipynb | mit | ! date; uname -a; id; pwd; ls -l /lib/firmware/i* /usr/bin/{echo_test,mat_mul_demo,proxy_app}
"""
Explanation: OpenAMP examples
Introduction
Implementation details
Setup
Load Kernel modules
Add firmware name
Demo: Echo Test
Demo: Matrix Multiplication
References
Introduction <a name="introduction"></a>
<img src="p... |
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