repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
DSSG-paratransit/main_repo | Access_Analysis_Project/notebooks/topPercenters.ipynb | agpl-3.0 | import pandas as pd
schedule = pd.read_csv('../data/UW_Trip_Data_4mo_QC_capacity.csv')
dh_data = pd.read_csv('../data/4mo_deadhead_results.csv')
"""
Explanation: This file will have more deadheading analysis
At least at the beginning I'll be looking at busruns that are 50% plus deadheading
End of explanation
"""
fif... |
ktaneishi/deepchem | examples/notebooks/Deepchem_NumpyDataset_tutorial.ipynb | mit | import deepchem as dc
import numpy as np
import random
"""
Explanation: Using Deepchem Datasets
In this tutorial we will have a look at various deepchem dataset methods present in deepchem.datasets.
End of explanation
"""
# data is your dataset in numpy array of size : 20x20.
data = np.random.random((4, 4))
labels ... |
whitead/numerical_stats | unit_10/hw_2020/homework_10_key.ipynb | gpl-3.0 | import scipy.stats as ss
#Poisson
1 - ss.poisson.cdf(10, 8)
"""
Explanation: Homework 10 Key
CHE 116: Numerical Methods and Statistics
4/9/2020
Problem 1
State which hypothesis best matches the scenario and justify your answer
You have the historic mean and standard deviation of temperature for April and want to kno... |
kmorel/kmorel.github.io | images/better-plots/XY_Trend.ipynb | mit | import pandas
import numpy
import toyplot
import toyplot.pdf
import toyplot.png
import toyplot.svg
print('Pandas version: ', pandas.__version__)
print('Numpy version: ', numpy.__version__)
print('Toyplot version: ', toyplot.__version__)
"""
Explanation: When analyzing data, I usually use the following three module... |
ProjectQ-Framework/ProjectQ | examples/simulator_tutorial.ipynb | apache-2.0 | import projectq
eng = projectq.MainEngine() # This loads the simulator as it is the default backend
"""
Explanation: ProjectQ Simulator Tutorial
The aim of this tutorial is to introduce some of the basic and more advanced features of the ProjectQ simulator. Please note that all the simulator features can be found in o... |
SKA-ScienceDataProcessor/crocodile | examples/notebooks/wtowers-predict.ipynb | apache-2.0 | %matplotlib inline
import sys
sys.path.append('../..')
from matplotlib import pylab as plt
from ipywidgets import interact
import itertools
import numpy
import numpy.linalg
import scipy
import scipy.special
import time
from crocodile.synthesis import *
from crocodile.simulate import *
from util.visualize import *
f... |
JAmarel/Phys202 | Interact/InteractEx02.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
"""
Explanation: Interact Exercise 2
Imports
End of explanation
"""
def plot_sine1(a,b):
labels = ['0','$\pi$.','$2\pi$.','$3\pi$.','$4\pi$... |
royalosyin/Python-Practical-Application-on-Climate-Variability-Studies | ex32-North Atlantic Winter Weather Regimes from a Self-Organizing Map Perspective.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import xarray as xr
import cartopy.crs as ccrs
from sompy.sompy import SOMFactory
"""
Explanation: North Atlantic Winter Weather Regimes from a Self-Organizing Map Perspective
The four weather regimes typically found over the North Atlantic in wint... |
mayank-johri/LearnSeleniumUsingPython | Section 1 - Core Python/Chapter 05 - Data Types/5.0.1 Answers - Data_Type.ipynb | gpl-3.0 | a=[1,2,3,4,5,6,7,8,9]
print(a[::2])
a=[1,2,3,4,5,6,7,8,9]
a[::2]=10,20,30,40,50,60 # a[0], a[2],... = 10,20,30
print(a)
a=[1,2,3,4,5,6,7,8,9]
a[::2]=10,20,30,40,50
print(a)
a=[1,2,3,4,5]
a[3:1:-1]
a=[1,2,3,4,5]
print(a[3:0:-1])
arr = [[1, 2, 3, 4],
[4, 5, 6, 7],
[8, 9, 10, 11],
[12, 13, 14, 15... |
tensorflow/docs-l10n | site/ko/tutorials/structured_data/feature_columns.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... |
tensorflow/tfx | docs/tutorials/tfx/components.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... |
amueller/scipy-2017-sklearn | notebooks/17.In_Depth-Linear_Models.ipynb | cc0-1.0 | from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
X, y, true_coefficient = make_regression(n_samples=200, n_features=30, n_informative=10, noise=100, coef=True, random_state=5)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=5, train_size=60, test_... |
hammerlab/isovar | notebook/Naive Strategy.ipynb | apache-2.0 | import pysam
import numpy as np
import pandas as pd
def contexify(samfile, chromosome, location, allele, radius):
# This will be our score board
counts = np.zeros(shape=((radius * 2) + 1, 5)) # 5 slots for each of the bases
d = pd.DataFrame(counts,
index=range(location - radius, locati... |
bbengfort/mosaic | notebooks/usage-visualization.ipynb | mit | %matplotlib inline
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from datetime import datetime
from mosaic.usage import FileUsage
from mosaic.utils import humanize_bytes
# Set the Seaborn style and context
sns.set_style('darkgrid')
sns.set_context('poster')
sns.set_palette('Set1')
# Set ... |
jamesHuffman/pyciss | docs/examples.ipynb | isc | from pyciss import io
"""
Explanation: Usage examples
End of explanation
"""
io.config
io.get_db_root()
"""
Explanation: The io module manages where data is stored and
This is my database root path, where all downloaded images are automatically stored.
Check if this is automatically set for you at a reasonable lo... |
konstantinstadler/pymrio | doc/source/notebooks/load_save_export.ipynb | gpl-3.0 | import pymrio
import os
io = pymrio.load_test().calc_all()
"""
Explanation: Loading, saving and exporting data
Pymrio includes several functions for data reading and storing. This section presents the methods to use for saving and loading data already in a pymrio compatible format. For parsing raw MRIO data see the di... |
frol/python-tutorials | notebooks/Intro.ipynb | mit | # you can mix text and code in one place and
# run code from a Web browser
"""
Explanation: This is an iPython Notebook!
End of explanation
"""
a = 10
a
"""
Explanation: Basics
All you need to know about Python is here:
You don't need to specify type of a variable
End of explanation
"""
a, b = 1, 2
a, b
b, a = ... |
cranmer/look-elsewhere-2d | two-experiment-lee-testing.ipynb | mit | %pylab inline --no-import-all
#plt.rc('text', usetex=True)
plt.rcParams['figure.figsize'] = (6.0, 6.0)
#plt.rcParams['savefig.dpi'] = 60
import george
from george.kernels import ExpSquaredKernel, My2ExpLEEKernel, MySignificanceKernel
from scipy.stats import chi2, norm
length_scale_of_correaltion=3.
ratio_of_length_sc... |
AntonelliLab/seqcap_processor | docs/notebook/subdocs/align_paralogs.ipynb | mit | %%bash
head -n 10 ../../data/processed/target_contigs_paralogs/1061/info_paralogous_loci.txt
"""
Explanation: Align paralogous contigs to reference
If you applied the --keep-paralogs flag in the SECAPR find_target_contigs function, the function will print a text file with paralogous information into the subfolder of e... |
tensorflow/privacy | tensorflow_privacy/privacy/privacy_tests/membership_inference_attack/codelabs/membership_probability_codelab.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... |
eds-uga/csci1360-fa16 | lectures/L6.ipynb | mit | squares = []
for element in range(10):
squares.append(element ** 2)
print(squares)
"""
Explanation: Lecture 6: Advanced Data Structures
CSCI 1360: Foundations for Informatics and Analytics
Overview and Objectives
We've covered list, tuples, sets, and dictionaries. These are the foundational data structures in Pyth... |
ES-DOC/esdoc-jupyterhub | notebooks/mpi-m/cmip6/models/sandbox-1/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mpi-m', 'sandbox-1', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: MPI-M
Source ID: SANDBOX-1
Topic: Ocnbgchem
Sub-Topics: Tracers.
Propertie... |
TomAugspurger/PracticalPandas | Practical Pandas 02 - EDA.ipynb | mit | %matplotlib inline
import os
import datetime
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_hdf(os.path.join('data', 'cycle_store.h5'), key='merged')
df.head()
"""
Explanation: Practical Pandas 02: Exporatory Data Analysis
Welcome back. As a reminder, we've got a dataset with... |
Vvkmnn/books | ThinkBayes/12_Evidence.ipynb | gpl-3.0 | import thinkbayes
class TopLevel(thinkbayes.Suite):
def Update(self, data):
a_sat, b_sat = data
a_like = thinkbayes.PmfProbGreater(a_sat, b_sat)
b_like = thinkbayes.PmfProbLess(a_sat, b_sat)
c_like = thinkbayes.PmfProbEqual(a_sat, b_sat)
a_like += c_like / 2
b_lik... |
mbakker7/timml | notebooks/circareasink_example.ipynb | mit | N = 0.001
R = 100
ml = ModelMaq(kaq=5, z=[10, 0])
ca = CircAreaSink(ml, xc=0, yc=0, R=100, N=0.001)
ml.solve()
x = np.linspace(-200, 200, 100)
h = ml.headalongline(x, 0)
plt.plot(x, h[0]);
qx = np.zeros_like(x)
for i in range(len(x)):
qx[i], qy = ml.disvec(x[i], 1e-6)
plt.plot(x, qx)
qxb = N * np.pi * R ** 2 / (2 ... |
NEONScience/NEON-Data-Skills | tutorials-in-development/Python/neon_api/neon_api_04_locations_py.ipynb | agpl-3.0 | import requests
import json
import pandas as pd
#Define API call componenets
SERVER = 'http://data.neonscience.org/api/v0/'
SITECODE = 'TEAK'
PRODUCTCODE = 'DP1.10003.001'
"""
Explanation: syncID:
title: "Querying Location Data with NEON API and Python"
description: "Querying the 'locations/' NEON API endpoint with ... |
Pittsburgh-NEH-Institute/Institute-Materials-2017 | schedule/week_2/collation/4_collate-outside-the-notebook.ipynb | gpl-3.0 | from collatex import *
collation = Collation()
collation.add_plain_witness( "A", "The quick brown fox jumped over the lazy dog.")
collation.add_plain_witness( "B", "The brown fox jumped over the dog." )
collation.add_plain_witness( "C", "The bad fox jumped over the lazy dog.")
table = collate(collation)
print(table)
"... |
jorgemauricio/INIFAP_Course | algoritmos/Validacion_App_Movil_climMAPcore_Son_BW.ipynb | mit | # librerias
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.formula.api as sm
%matplotlib inline
plt.style.use('grayscale')
# leer archivo
data = pd.read_csv('../data/dataFromSonoraClimmapcore.csv')
# verificar su contenido
data.head()
# diferencia entre valores de precipita... |
linuxlewis/django-diffs | examples/Tutorial.ipynb | mit | # Setup django
import os
os.environ['DJANGO_SETTINGS_MODULE'] = 'example.settings'
import django
django.setup()
"""
Explanation: django-diffs tutorial
This is a walkthrough tutorial demonstrating the features of django diffs
End of explanation
"""
from django.conf import settings
settings.DIFFS_SETTINGS
from diffs... |
ES-DOC/esdoc-jupyterhub | notebooks/bnu/cmip6/models/sandbox-1/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'bnu', 'sandbox-1', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: BNU
Source ID: SANDBOX-1
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy Balance... |
Unidata/unidata-python-workshop | notebooks/AWIPS/Watch_and_Warning_Polygons.ipynb | mit | from awips.dataaccess import DataAccessLayer
from awips.tables import vtec
from datetime import datetime
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
from cartopy.feature import ... |
FavioVazquez/MexicanNumericalSimulationSchool | school/projects/HabibProject/Solutions/PkEmu/.ipynb_checkpoints/plotPrueba-checkpoint.ipynb | gpl-3.0 | import matplotlib
matplotlib.use('nbagg')
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib import rc
rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
## for Palatino and other serif fonts use:
#rc('font',**{'family':'serif','serif':['Palatino']})
rc('text', usetex=True)
#LaTeX
plt.rc... |
wasat/JupyTEPIDE | notebooks/grass/bash/terrain_modeling_ascii.ipynb | apache-2.0 | # Obtain sample data and set new Grass mapset
import urllib
from zipfile import ZipFile
import os.path
zip_path = "/home/jovyan/work/tmp/nc_spm_08_grass7.zip"
mapset_path = "/home/jovyan/grassdata"
if not os.path.exists(zip_path):
urllib.urlretrieve("https://grass.osgeo.org/sampledata/north_carolina/nc_spm_08_gras... |
radhikapc/foundation-homework | homework07/Homework07-BuildingPandas-Radhika.ipynb | mit | import pandas as pd
"""
Explanation: 1.Import pandas with the right name:
End of explanation
"""
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: 2. Set all graphics from matplotlib to display inline
End of explanation
"""
df = pd.read_csv("07-hw-animals.csv")
df
"""
Explanation: 3. Read the... |
AllenDowney/ModSimPy | notebooks/filter.ipynb | mit | # Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'
# import functions from the modsim.py module
from modsim import *
"""
Explanation: Modeling and Simulati... |
jepegit/cellpy | dev_utils/new_external_libs/UserNumba001.ipynb | mit | my_data = cellreader.CellpyData()
# only for my MacBook
filename = "/Users/jepe/scripting/cellpy/dev_data/out/20190204_FC_snx012_01_cc_01.h5"
assert os.path.isfile(filename)
my_data.load(filename)
"""
Explanation: Setting things up
End of explanation
"""
%%timeit
my_data.make_summary()
%%timeit
my_data.make_step_ta... |
eggie5/ipython-notebooks | housing/Home Value Regression Exercise - Alex Egg.ipynb | mit | import pandas as pd
import numpy as np
import scipy.stats
%pylab inline
csv = pd.read_csv("single_family_home_values.csv", parse_dates=["last_sale_date"])
print csv.shape
csv.head()
#scale the data
from sklearn import preprocessing
from scipy import stats
"""
Explanation: Estimating Home Prices
Estimating home valu... |
georgetown-analytics/yelp-classification | Yelp_web_scrapper/Business_Scrapper.ipynb | mit | from bs4 import BeautifulSoup
import requests
import re
import json
import scrapping_functions as sf
reload(sf)
from selenium import webdriver
#Start = signifies the listing to start it, increases in increments of 10 per page
#End = 990
target_url = 'https://www.yelp.com/search?find_desc=Restaurants&find_loc=Washington... |
edwardd1/phys202-2015-work | assignments/assignment03/NumpyEx01.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import antipackage
import github.ellisonbg.misc.vizarray as va
"""
Explanation: Numpy Exercise 1
Imports
End of explanation
"""
def checkerboard(size):
"""Return a 2d checkboard of 0.0 and 1.0 as a NumPy array"""
che... |
nvergos/DAT-ATX-1_Project | Notebooks/1. Data Preparation & Exploratory Analysis.ipynb | mit | import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
from scipy import stats # For correlation coefficient calculation
"""
Explanation: DAT-ATX-1 Capstone Project
Nikolaos Vergos, February 2016
nv&#... |
quanta413/Population-Evolution-Project-Source-Code | Plots related to the traveling wave regime-Revisions.ipynb | bsd-2-clause | fbvary_results = []
for file in glob.glob('runs/evolved_mu_f_b_vary?replicate?datetime.datetime(2019, 5, *).hdf5'):
try:
fbvary_results.append(popev.PopulationReader(file))
except OSError:
pass
favary_results = []
for file in glob.glob('runs/evolved_mu_f_a_vary?replicate?datetime.datetime(2019,... |
asimshankar/tensorflow | tensorflow/contrib/eager/python/examples/generative_examples/dcgan.ipynb | apache-2.0 | # Install imgeio in order to generate an animated gif showing the image generating process
!pip install imageio
"""
Explanation: Copyright 2018 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License").
Generating Handwritten Digits with DCGAN
<table class="tfo-notebook-buttons" align="lef... |
befelix/lyapunov-learning | 1d_example.ipynb | mit | # Discretization constant
tau = 0.001
# x_min, x_max, discretization
grid_param = [-1., 1., tau]
extent = np.array(grid_param[:2])
# Create a grid
grid = np.arange(*grid_param)[:, None]
num_samples = len(grid)
print('Grid size: {0}'.format(len(grid)))
"""
Explanation: We start by defining a discretization of the s... |
ctroupin/CMEMS_INSTAC_Training | PythonNotebooks/IndexFilePlots/IndexFile_Folium_Visalization.ipynb | mit | indexfile = "../PlatformPlots/datafiles/index_latest.txt"
"""
Explanation: Index file visualization
This notebook shows an easy way to represent the In Situ data positions using the index files.<br>
For this visualization of a sample <i>index_latest.txt</i> dataset of the Copernicus Marine Environment Monitoring Servi... |
yingchi/fastai-notes | deeplearning1/nbs/lesson6_yingchi.ipynb | apache-2.0 | from theano.sandbox import cuda
cuda.use('gpu1')
%matplotlib inline
import utils;
from utils import *
from keras.layers import TimeDistributed, Activation
from numpy.random import choice
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Setup" data-toc-modified-id="Setup-1"><span class="toc-i... |
mne-tools/mne-tools.github.io | 0.23/_downloads/33d5dd5786fed13908838e94d55ac785/90_compute_covariance.ipynb | bsd-3-clause | import os.path as op
import mne
from mne.datasets import sample
"""
Explanation: Computing a covariance matrix
Many methods in MNE, including source estimation and some classification
algorithms, require covariance estimations from the recordings.
In this tutorial we cover the basics of sensor covariance computations... |
KECB/learn | BAMM.101x/datetime_objects.ipynb | mit | d1 = "10/24/2017"
d2 = "11/24/2016"
max(d1,d2)
"""
Explanation: <h1>datetime library</h1>
<li>Time is linear
<li>progresses as a straightline trajectory from the big bag
<li>to now and into the future
<li>日期库官方说明 https://docs.python.org/3.5/library/datetime.html
<h3>Reasoning about time is important in data analysis... |
akchinSTC/systemml | samples/jupyter-notebooks/Linear_Regression_Algorithms_Demo.ipynb | apache-2.0 | !pip show systemml
"""
Explanation: Linear Regression Algorithms using Apache SystemML
This notebook shows:
- Install SystemML Python package and jar file
- pip
- SystemML 'Hello World'
- Example 1: Matrix Multiplication
- SystemML script to generate a random matrix, perform matrix multiplication, and compute th... |
Pantkowsky/electricitymap | datascience/exchange.ipynb | gpl-3.0 | from utils import *
# Enable inline plotting
%matplotlib inline
from ggplot import *
"""
Explanation: Analysis of Electricity Exchange
This notebook shows how to use utils function. In particular we show how to pull data of electricity exchange.
We first import utils function. This lets you access a set of handy fu... |
mayankjohri/LetsExplorePython | Section 1 - Core Python/Chapter 16 - Standard library/Reference, Shallow and deep copy.ipynb | gpl-3.0 | x = 10
y = x
print(id(x), id(y))
x = [10, 3]
y = x
print(x , y)
x[1] = "This is a test message"
print(x, y)
x = 10
print(id(x))
x +=1
y = x
print(id(x), id(y))
x = 10
print(id(x))
y = x
x = "d"
print(id(x), id(y))
print(y, x)
x = "10"
y = x + "1"
print(id(x), id(y))
print(x , y)
x = 10
y = x
print(id(x), id(y))
... |
uber-common/deck.gl | bindings/pydeck/examples/02 - Scatterplots.ipynb | mit | import pandas as pd
import pydeck as pdk
# First, let's use Pandas to download our data
URL = 'https://raw.githubusercontent.com/ajduberstein/data_sets/master/beijing_subway_station.csv'
df = pd.read_csv(URL)
df.head()
"""
Explanation: Scatterplots in pydeck: A case study using Beijing subway stops
Below we'll plot t... |
tyarkoni/transitions | examples/Frequently asked questions.ipynb | mit | from transitions import Machine
import json
class Model:
def say_hello(self, name):
print(f"Hello {name}!")
# import json
json_config = """
{
"name": "MyMachine",
"states": [
"A",
"B",
{ "name": "C", "on_enter": "say_hello" }
],
"transitions": [
["go", "A", "B"],
{"trigger":... |
eds-uga/csci1360e-su16 | lectures/L18.ipynb | mit | import matplotlib as mpl
import matplotlib.pyplot as plt
"""
Explanation: Lecture 18: Data Visualization
CSCI 1360E: Foundations for Informatics and Analytics
Overview and Objectives
Data visualization is one of, if not the, most important method of communicating data science results. It's analogous to writing: if you... |
farr/kepler-selection | kephackwk/BinnedOccurrence.ipynb | mit | hw_data_directory = '/Users/farr/Documents/Research/KepHackWeek/data'
occur_dir = '/Users/farr/Google Drive/Kepler ExoPop Hack 2015/end2end_occ_calc'
eff_dir = '/Volumes/KepHacWkWMF/Kepler_HW2015/Dp4_DetectionCountours/v0'
rbins = array([1.5**(i-1) for i in range(9)])
pbins = array([10*2**i for i in range(6)])
print r... |
reychil/project-alpha-1 | code/utils/misc/.ipynb_checkpoints/BART_Data_Beginning-checkpoint.ipynb | bsd-3-clause | from __future__ import absolute_import, division, print_function
import numpy as np
import numpy.linalg as npl
import matplotlib.pyplot as plt
import nibabel as nib
import pandas as pd # new
import os # new
# the last one is a major thing for ipython notebook, don't include in regular python code
%matplotlib inline
... |
JustasB/MitralSuite | SimpleNeuronCellTests.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as g
from neuronunit.neuron.models import *
from neuronunit.tests import *
import neuronunit.neuroelectro
from quantities import nA, pA, s, ms, mV
from neuron import h
# DEBUG TESTING
#from importlib import *
#import neuronunit
#reload(neuronunit.neuron.models)
#from neuro... |
sbussmann/sleep-bit | notebooks/sbussmann_get-fitbit-data.ipynb | mit | %load_ext pypath_magic
%pypath -a /Users/rbussman/Projects/sleep-bit
from src.data import get_fitbit
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set_context('poster')
import pandas as pd
import time
daterange = pd.date_range('2017-03-30', '2017-08-10')
"""
Explanation: Summary
Use... |
fantasycheng/udacity-deep-learning-project | tutorials/gan_mnist/Intro_to_GANs_Solution.ipynb | mit | %matplotlib inline
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
"""
Explanation: Generative Adversarial Network
In this notebook, we'll be building a generativ... |
mohanprasath/Course-Work | numpy/numpy_exercises_from_kyubyong/Statistics_solutions.ipynb | gpl-3.0 | __author__ = "kyubyong. kbpark.linguist@gmail.com"
import numpy as np
np.__version__
"""
Explanation: Statistics
End of explanation
"""
x = np.arange(4).reshape((2, 2))
print("x=\n", x)
print("ans=\n", np.amin(x, 1))
"""
Explanation: Order statistics
Q1. Return the minimum value of x along the second axis.
End of... |
ffyu/Build_Model_from_Scratch | 5_Anomaly_Detection.ipynb | mit | import math
import numpy as np
import scipy
class AnomalyDetection():
def __init__(self, multi_variate=False):
# if multi_variate is True, we will use multivariate Gaussian distribution
# to estimate the probabilities
self.multi_variate = multi_variate
self.mu = None
self.... |
IS-ENES-Data/submission_forms | test/prov/old/prov-submission-Copy1.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
%load_ext autoreload
%autoreload 2
import sys
sys.path.append('/home/stephan/Repos/ENES-EUDAT/submission_forms')
from dkrz_forms import form_handler
from dkrz_forms import checks
from dkrz_forms.config import test_config
from dkrz_forms.config import workflow_steps
#print test_con... |
GeoffreyBessardon/end_of_day_two | DefensiveProgramming_3.ipynb | mit | def test_range_overlap():
assert range_overlap([(-3.0, 5.0), (0.0, 4.5), (-1.5, 2.0)]) == (0.0, 2.0)
assert range_overlap([ (2.0, 3.0), (2.0, 4.0) ]) == (2.0, 3.0)
assert range_overlap([ (0.0, 1.0), (0.0, 2.0), (-1.0, 1.0) ]) == (0.0, 1.0)
"""
Explanation: # Defensive programming (2)
We have seen the ba... |
zhaojijet/UdacityDeepLearningProject | examples/Convolutional_Autoencoder_Solution.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', validation_size=0)
img = mnist.train.images[2]
plt.imshow(img.reshape((28, 28)), cmap='Greys_r')
"""
Explanation: C... |
xlbaojun/Note-jupyter | 05其他/pandas文档-zh-master/.ipynb_checkpoints/数据结构-checkpoint.ipynb | gpl-2.0 | import numpy as np
import pandas as pd
"""
Explanation: 数据结构
这一节介绍pandas中的数据结构。首先，导入numpy和pandas:
End of explanation
"""
s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])
s
s.index
pd.Series(np.random.randn(5))
"""
Explanation: 我们先对数据结构进行简短的介绍, 然后再详细说明各个数据结构内置的方法。
Series
Series是一个一维带label的数组，元素可以... |
sourabhrohilla/ds-masterclass-hands-on | session-2/python/TFIDF_NewsRecommender.ipynb | mit | PATH_NEWS_ARTICLES="/home/phoenix/Documents/HandsOn/Final/news_articles.csv"
ARTICLES_READ=[2,7]
NUM_RECOMMENDED_ARTICLES=5
try:
import numpy
import pandas as pd
import pickle as pk
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
... |
rigetticomputing/pyquil | docs/source/quilt_raw_capture.ipynb | apache-2.0 | from pyquil import Program, get_qc
qc = get_qc('Aspen-8')
cals = qc.compiler.calibration_program
"""
Explanation: RAW-CAPTURE on Aspen-8
In this we are going to show how to access "raw" measurement data with Quilt.
End of explanation
"""
from pyquil.quilatom import Qubit, Frame
from pyquil.quilbase import Pulse, C... |
Olsthoorn/TransientGroundwaterFlow | Assignment/VScode/AssJan2017.ipynb | gpl-3.0 | # import the necessary fucntionality
import numpy as np
import matplotlib.pyplot as plt
from scipy.special import exp1 as W # Theis well function
def newfig(title='?', xlabel='?', ylabel='?', xlim=None, ylim=None, xscale=None, yscale=None, figsize=(10, 8),
fontsize=16):
sizes = ['xx-small', 'x-small', ... |
dssg/diogenes | doc/notebooks/.ipynb_checkpoints/display-checkpoint.ipynb | mit | %matplotlib inline
import diogenes
import numpy as np
wine_data = diogenes.read.open_csv_url('http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv',
delimiter=';')
"""
Explanation: The Display Module
The :mod:diogenes.display module provides tools for summarizing/exploring d... |
willingc/jupyter-data-seeker | Repos for an Organization.ipynb | gpl-2.0 | import github3
import os
# Set ORGANIZATION
ORGANIZATION = 'jupyter'
GH_NAME= os.environ.get('GH_NAME')
GH_PASSWD = os.environ.get('GH_PASSWORD')
GH_TOKEN = os.environ.get('GH_TOKEN')
# Authenticate and get a github object for accessing API without rate limits
gh = github3.login(GH_NAME, GH_PASSWD)
"""
Explanation:... |
aliakbars/uai-ai | scripts/tugas1.ipynb | mit | from __future__ import print_function, division # Gunakan print(...) dan bukan print ...
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import random
import keras
from keras.models import Sequential, load_model
from keras.layers import Dense, Dropout, Flatten
from keras.la... |
ES-DOC/esdoc-jupyterhub | notebooks/test-institute-2/cmip6/models/sandbox-2/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-2', 'sandbox-2', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: TEST-INSTITUTE-2
Source ID: SANDBOX-2
Topic: Ocean
Sub-Topics: Timestepp... |
fonnesbeck/scientific-python-workshop | notebooks/Data Wrangling with Pandas.ipynb | cc0-1.0 | %matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Data Wrangling with Pandas
Now that we have been exposed to the basic functionality of Pandas, lets explore some more advanced features that will be useful when addressing more complex data management tasks.
As m... |
nyoungb2/CLdb | doc/examples/Ecoli/spacers_shared.ipynb | gpl-2.0 | # directory where you want the spacer blasting to be done
## CHANGE THIS!
workDir = "/home/nyoungb2/t/CLdb_Ecoli/spacers_shared/"
"""
Explanation: Description:
This notebook goes through the assessment of spacers shared across CRISPR loci
Before running this notebook:
run the Setup notebook
User-defined variables
... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session11/Day1/IntroductionToBasicStellarPhotometry.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
%matplotlib notebook
"""
Explanation: Introduction to Basic Stellar Photometry
Measuring Flux in 1D
Version 0.1
In this notebook we will introduce some basic concepts related to measuring the flux of a point source. As this is an introduction, several challenges asso... |
mne-tools/mne-tools.github.io | 0.24/_downloads/cfbef36033f8d33f28c4fe2cfa35314a/30_cluster_ftest_spatiotemporal.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Eric Larson <larson.eric.d@gmail.com>
# License: BSD-3-Clause
import os.path as op
import numpy as np
from scipy import stats as stats
import mne
from mne import spatial_src_adjacency
from mne.stats import spatio_temporal_cluster_test, summarize_... |
buruzaemon/natto-py | notebooks/05_制約付き解析.ipynb | bsd-2-clause | from natto import MeCab
"""
Explanation: 制約付き解析
End of explanation
"""
text1 = """\
証券コードは４桁の銘柄識別コードです。
たとえば、7777 です。
あるいは 7777 JP や 7777.Tというのもあります。
また「７７７７ＪＰ」のような全角文字を使う表し方もあるかも知れません。
\
"""
# 簡単な証券コードの正規表現
patt = "[0-9\uFF10-\uFF19]{4}((\s|\.)+[a-zA-Z]{1,2}|[\uFF21-\uFF3A]{2})"
with MeCab(r"-F%m\t%f[0]\t%s") as... |
pschragger/big-data-python-class | Lectures/Week 2 - Python and Jupyter for Big-Data/Lecture-2-Introduction-to-Python-Programming.ipynb | mit | ls ..\..\Scripts\hello-world*.py
"""
Explanation: Introduction to Python programming
This crash course on python is take from two souces:
http://github.com/jrjohansson/scientific-python-lectures.
and
Chapter 2 of the Datascience from scratch: First principles with python
Code from https://github.com/joelgrus/data-scie... |
HumanCompatibleAI/imitation | examples/4_train_airl.ipynb | mit | from stable_baselines3 import PPO
from stable_baselines3.ppo import MlpPolicy
import gym
import seals
env = gym.make("seals/CartPole-v0")
expert = PPO(
policy=MlpPolicy,
env=env,
seed=0,
batch_size=64,
ent_coef=0.0,
learning_rate=0.0003,
n_epochs=10,
n_steps=64,
)
expert.learn(1000) # ... |
chetan51/nupic.research | projects/dynamic_sparse/notebooks/ExperimentAnalysis-MNISTSparser.ipynb | gpl-3.0 | %load_ext autoreload
%autoreload 2
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import glob
import tabulate
import pprint
import click
import numpy as np
import pandas as pd
from ray.tune.commands import *
from nupic.research.frameworks.dynamic... |
wholmgren/pvlib-python | docs/tutorials/tmy_and_diffuse_irrad_models.ipynb | bsd-3-clause | # built-in python modules
import os
import inspect
# scientific python add-ons
import numpy as np
import pandas as pd
# plotting stuff
# first line makes the plots appear in the notebook
%matplotlib inline
import matplotlib.pyplot as plt
# finally, we import the pvlib library
import pvlib
# Find the absolute file ... |
tensorflow/docs-l10n | site/ja/guide/keras/sequential_model.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... |
franklinsales/udacity-data-analyst-nanodegree | project0/Data_Analyst_ND_Project0.ipynb | mit | import pandas as pd
# pandas é uma biblioteca para manipulação e anáise de dados
# geralmente usamos nomes mais curtos para as bibliotecas.Pabdas é geralmente abreviada para pd
# tecle shift + enter para rodar esta célula ou bloco de código
path = r'~/Downloads/chopstick-effectiveness.csv'
# Mude o caminho para o loc... |
VVard0g/ThreatHunter-Playbook | docs/notebooks/windows/05_defense_evasion/WIN-201012183248.ipynb | mit | from openhunt.mordorutils import *
spark = get_spark()
"""
Explanation: Wuauclt CreateRemoteThread Execution
Metadata
| Metadata | Value |
|:------------------|:---|
| collaborators | ['@Cyb3rWard0g'] |
| creation date | 2020/10/12 |
| modification date | 2020/10/12 |
| playbook related | [] |
Hypo... |
betatim/studyGroupJupyter | python.ipynb | mit | import math
print("The square root of 3 is:", math.sqrt(3))
print("π is:", math.pi)
print("The sin of 90 degrees is:", math.sin(math.radians(90)))
"""
Explanation: Topics
We will cover:
Basic math
Exploring modules and getting help
Rich displaying
Inline plots
Interactive inline plots
Interactive elements
Basic mat... |
ramabrahma/data-sci-int-capstone | .ipynb_checkpoints/data-exploration-life-insurance-checkpoint.ipynb | gpl-3.0 | # Importing libraries
%pylab inline
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
from sklearn import preprocessing
import numpy as np
# Convert variable data into categorical, continuous, discrete,
# and dummy variable lists the following into a dictio... |
kit-cel/wt | wt/vorlesung/ch7_9/generating_distributions.ipynb | gpl-2.0 | # importing
import numpy as np
from scipy import stats, special
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: Con... |
AllenDowney/ModSim | soln/chap15.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/'
... |
Energya/cma-es-configuration-data-mining | module_analysis.ipynb | mit | # Imports + definitions
%matplotlib inline
from __future__ import division, print_function, unicode_literals
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import networkx as nx
import numpy as np
import os
import pydot
import scipy.io.arff as arff
from collections import Counter, defaultdict
from cy... |
danresende/deep-learning | sentiment_network/Sentiment Classification - Project 1 Solution.ipynb | mit | def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].upper(),g.readlines()))
g.close()... |
DiXiT-eu/collatex-tutorial | unit6/Tokenization.ipynb | gpl-3.0 | from collatex import *
collation = Collation()
collation.add_plain_witness("A", "Peter's cat.")
collation.add_plain_witness("B", "Peter's dog.")
table = collate(collation, segmentation=False)
print(table)
"""
Explanation: Tokenization
Default tokenization
Tokenization (the first of the five parts of the Gothenburg mod... |
deepmind/graph_nets | graph_nets/demos_tf2/graph_nets_basics.ipynb | apache-2.0 | #@title ### Install the Graph Nets library on this Colaboratory runtime { form-width: "60%", run: "auto"}
#@markdown <br>1. Connect to a local or hosted Colaboratory runtime by clicking the **Connect** button at the top-right.<br>2. Choose "Yes" below to install the Graph Nets library on the runtime machine with the c... |
felixcheung/spark-ml-streaming | ipython_notebook/Streaming k-means.ipynb | apache-2.0 | from IPython.display import IFrame
IFrame('https://lightning-docs.herokuapp.com/visualizations/4/iframe/', 1155, 673)
"""
Explanation: Visualizing Streaming k-means on IPython + Lightning
<img src="http://lightning-viz.org/images/logo.png" align="left"><br><h1>Lightning</h1>DATA VISUALIZATION SERVER
<br>
<br>
Lightnin... |
agile-geoscience/notebooks | Programming_a_seismic_program.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
from shapely.geometry import Point, LineString
import geopandas as gpd
import pandas as pd
from fiona.crs import from_epsg
%matplotlib inline
"""
Explanation: Programming a seismic program
This notebook goes with the article
Bianco, E and M Hall (2015). Programming ... |
LorenzoBi/courses | CQD/.ipynb_checkpoints/exercise1-checkpoint.ipynb | mit | import numpy as np
from numpy import linalg as LA
import matplotlib.pyplot as plt
from math import factorial
from itertools import combinations_with_replacement
from scipy.integrate import quad
%matplotlib inline
def generate_random_hermitian(N):
A = np.random.normal(size=(N, N))
H = (np.tril(A) + np.triu(A... |
clarecorthell/nlp_workshop | nlp_basics_workshop.ipynb | mit | %pwd
# make sure we're running our script from the right place;
# imports like "filename" are relative to where we're running ipython
"""
Explanation: NLP Workshop
Author: Clare Corthell, Luminant Data
Conference: Talking Machines, Manila
Date: 18 February 2016
Description: Much of human knowledge is “locked up” in ... |
tensorflow/gan | tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb | apache-2.0 | # Check that imports for the rest of the file work.
import tensorflow.compat.v1 as tf
!pip install tensorflow-gan
import tensorflow_gan as tfgan
import tensorflow_datasets as tfds
import matplotlib.pyplot as plt
import numpy as np
# Allow matplotlib images to render immediately.
%matplotlib inline
tf.logging.set_verbos... |
ML4DS/ML4all | P5.Data preprocessing/Intro5_DataNormalization_professor.ipynb | mit | # Some libraries that will be used along the notebook.
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Data preprocessing methods: Normalization
Notebook version:
* 1.0 (Sep 15, 2020) - First version
* 1.1 (Sep 15, 2021) - Exercises
Authors: Jesús Cid Sueiro (jcid@ing.uc3m.es)
End of explanation
... |
patrickfuller/imolecule | examples/ipython.ipynb | mit | import imolecule
imolecule.draw("CC1(C(N2C(S1)C(C2=O)NC(=O)CC3=CC=CC=C3)C(=O)O)C")
"""
Explanation: imolecule in the IPython notebook
I created imolecule to fix a deficiency in my workflow. While my chemical simulations were entirely in notebooks, I had to use external programs like mercury to visually debug chemical ... |
otavio-r-filho/AIND-Deep_Learning_Notebooks | gan_mnist/Intro_to_GANs_Exercises.ipynb | mit | %matplotlib inline
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
"""
Explanation: Generative Adversarial Network
In this notebook, we'll be building a generativ... |
ES-DOC/esdoc-jupyterhub | notebooks/mpi-m/cmip6/models/icon-esm-lr/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mpi-m', 'icon-esm-lr', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: MPI-M
Source ID: ICON-ESM-LR
Topic: Ocnbgchem
Sub-Topics: Tracers.
Prope... |
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