repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
nbokulich/short-read-tax-assignment | ipynb/mock-community/taxonomy-assignment-qiime1.ipynb | bsd-3-clause | from os.path import join, expandvars
from joblib import Parallel, delayed
from glob import glob
from os import system
from tax_credit.framework_functions import (parameter_sweep,
generate_per_method_biom_tables,
move_results_to_rep... |
tanyaschlusser/stats-via-python | notebooks/input_output.ipynb | mit | # The library for handling tabular data is called 'pandas'
# Everyone shortens this to 'pd' for convenience.
import pandas as pd
"""
Explanation: Reading Excel files
This notebook demonstrates how to read and manipulate data from
Excel using Pandas:
Input / Output
summaries
plotting
First, import the Pandas library:... |
ereodeereigeo/dataTritiumWS22 | histograma de velocidades.ipynb | gpl-2.0 | import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Número de datos obtenidos y perdidos
Importamos las librerías necesarias
End of explanation
"""
import ext_datos as ext
import procesar as pro
import time_plot as tplt
"""
Explanation: Importamos las librerías creadas para tra... |
google/starthinker | colabs/lineitem_read_to_bigquery_via_value.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)
... |
drakero/Electron_Spectrometer | Read MCNP Output.ipynb | mit | #Imports
from math import *
import cmath
import numpy as np
import scipy as sp
import scipy.special
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import IPython.display as IPdisplay
import sys
import os
#Import custom modules
from physics import *
sns.set(font_scale=2.0)
sns.set_style... |
mne-tools/mne-tools.github.io | 0.23/_downloads/98d9662291626be9c938eee7a8fcc9bd/sensor_noise_level.ipynb | bsd-3-clause | # Author: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import mne
data_path = mne.datasets.sample.data_path()
raw_erm = mne.io.read_raw_fif(op.join(data_path, 'MEG', 'sample',
'ernoise_raw.fif'), preload=True)
"""
Explanation: Show nois... |
xtr33me/deep-learning | embeddings/Skip-Grams-Solution.ipynb | mit | import time
import numpy as np
import tensorflow as tf
import utils
"""
Explanation: Skip-gram word2vec
In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in natural language p... |
sourabhrohilla/ds-masterclass-hands-on | session-1/python/intrusion_detection_binary.ipynb | mit | # read raw data
raw_data = pd.read_csv('/home/phoenix/Documents/session_1_data_train.csv')
test_data = pd.read_csv('/home/phoenix/Documents/session_1_data_test.csv')
test_data.columns = raw_data.columns
raw_data.head()
raw_data.label.value_counts().keys()
test_data.label.value_counts().keys()
# remove ". " from la... |
andim/evolimmune | figSIopt/figure-SIopt.ipynb | mit | import sys
sys.path.append('../lib/')
import numpy as np
import matplotlib.text
import matplotlib.pyplot as plt
from matplotlib import cm
%matplotlib inline
import shapely.ops
import plotting
import evolimmune
from plotting import *
import analysis
%load_ext autoreload
%autoreload 2
plt.style.use(['paper'])
eps = 1e-8
... |
bennyrowland/suspect | docs/notebooks/plot_1D_signals_tutorial.ipynb | mit | import suspect
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline
"""
Explanation: Plotting 1D MRS data using Suspect's plot_1D_signals module
Suspect's plot_1D_signals module provides a set of functions for plotting MRS data. It is built on matplotlib's library of plotting functio... |
adrianstaniec/deep-learning | 01_first-neural-network/Your_first_neural_network.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: My 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, ... |
satishgoda/learning | python/jupyter/usecases/py3_docs_tutorial_controlflow.ipynb | mit | x = int(input("Please enter an integer: "))
x
if x < 0:
x = 0
print('Negative changed to zero')
elif x == 0:
print('Zero')
elif x == 1:
print('Single')
else:
print('More')
"""
Explanation: https://docs.python.org/3.7/tutorial/controlflow.html
if Statements
End of explanation
"""
# Measure some ... |
jacobdein/alpine-soundscapes | Compute naturalness areas.ipynb | mit | naturalness = 'naturalness'
naturalness_value_field = 'value'
n_types = 7
sample_points = 'sample_points_field'
radius = 500
use_viewshed = False
"""
Explanation: Compute naturalness areas
This notebook computes the naturalness areas for each point in a 'sample_points' vector map based on a 'naturalness' vector m... |
JShadowMan/package | python/course/ch02-syntax-and-container/常用容器.ipynb | mit | # 空元祖可以直接使用 `()` 进行声明, 也可以使用tuple()进行声明
(), type(()), tuple(), type(tuple())
# 单个元素的元组使用`()`的方式声明必须在元素之后增加`,`
print( (1), (1,) )
t1 = (1, 2.33, ("tuple",), ["hello"])
"""
Explanation: Python中的常用容器
Python中的容器类型包括序列类型, 集合类型, 映射类型
序列类型
Python中有以下三种基本序列类型, 所谓序列(Sequence)即表示容器内的元素是有顺序的.
* tuple(元组): 元组是不可变序列，通常用于储存异构数据的... |
jserenson/Python_Bootcamp | Numbers.ipynb | gpl-3.0 | # Addition
2+1
# Subtraction
2-1
# Multiplication
2*2
# Division
3/2
"""
Explanation: Numbers and more in Python!
In this lecture, we will learn about numbers in Python and how to use them.
We'll learn about the following topics:
1.) Types of Numbers in Python
2.) Basic Arithmetic
3.) Differences between Python 2 v... |
ES-DOC/esdoc-jupyterhub | notebooks/dwd/cmip6/models/sandbox-3/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'dwd', 'sandbox-3', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: DWD
Source ID: SANDBOX-3
Topic: Ocean
Sub-Topics: Timestepping Framework, Advection, ... |
luisriera/Udacity_Finding_Lane_Lines | P1-luis.riera.ipynb | gpl-3.0 | #importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline
"""
Explanation: Self-Driving Car Engineer Nanodegree
Project: Finding Lane Lines on the Road
In this project, you will use the tools you learned about in the lesson to ide... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/recommendation_systems/labs/dcn.ipynb | apache-2.0 | # Installing the necessary libraries.
!pip install -q tensorflow-recommenders
!pip install -q --upgrade tensorflow-datasets
# Importing the necessary modules.
import pprint
%matplotlib inline
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np
import tensorflow ... |
JamesSample/icpw | toc_report_feb_2019_part2.ipynb | mit | # Select projects
prj_grid = nivapy.da.select_resa_projects(eng)
prj_grid
prj_df = prj_grid.get_selected_df()
print (len(prj_df))
prj_df
# Get stations
stn_df = nivapy.da.select_resa_project_stations(prj_df, eng)
print(len(stn_df))
stn_df.head()
# Map
nivapy.spatial.quickmap(stn_df, popup='station_code')
"""
Explan... |
h-mayorquin/time_series_basic | presentations/2016-01-22(Wall-Street-Letter-Prediction-Ndata).ipynb | bsd-3-clause | import numpy as np
import h5py
from sklearn import svm, cross_validation
from sklearn.naive_bayes import MultinomialNB
"""
Explanation: Prediction of letters as a function of data used
In this notebook we do the same tasks of predicting next letter using Nexa. However, we test here how does the prediction become with ... |
mne-tools/mne-tools.github.io | 0.23/_downloads/f5853db1ea98f82173310d147f23289c/compute_mne_inverse_epochs_in_label.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.minimum_norm import apply_inverse_epochs, read_inverse_operator
from mne.minimum_norm import apply_inverse
print(__doc__)
data... |
TheOregonian/long-term-care-db | notebooks/analysis/.ipynb_checkpoints/facilities_analysis-checkpoint.ipynb | mit | import pandas as pd
import numpy as np
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
df = pd.read_csv('/Users/fzarkhin/OneDrive - Advance Central Services, Inc/fproj/github/database-story/data/processed/facilities.csv')
"""
Explanation: This is a ... |
ES-DOC/esdoc-jupyterhub | notebooks/miroc/cmip6/models/miroc-es2h/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'miroc-es2h', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: MIROC
Source ID: MIROC-ES2H
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy B... |
vinitsamel/udacitydeeplearning | 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... |
griffinfoster/fundamentals_of_interferometry | 6_Deconvolution/6_5_source_finding.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
import matplotlib
from scipy import optimize
import astropy.io.fits
matplotlib.rcParams.update({'font.size': 18})
matplotlib.rcParams.update({'figure.figsize': [12,8]}... |
pastas/pasta | examples/notebooks/19_snowmodel.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.signal import fftconvolve
import pastas as ps
ps.set_log_level("ERROR")
ps.show_versions(numba=True)
"""
Explanation: Snow model
R.A. Collenteur, University of Graz / Eawag, November 2021
In this notebook it is shown how to account for... |
ES-DOC/esdoc-jupyterhub | notebooks/miroc/cmip6/models/miroc-es2l/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'miroc-es2l', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: MIROC
Source ID: MIROC-ES2L
Sub-Topics: Radiative Forcings.
Properties: 85 ... |
BDannowitz/polymath-progression-blog | jlab-ml-lunch-2/notebooks/01-Recommender-System.ipynb | gpl-2.0 | import numpy as np
import pandas as pd
from scipy.sparse.linalg import svds
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
from jlab import load_test_data
X_train = pd.read_csv('MLchallenge2_training.csv')
X_test = load_test_data('test_in.csv')
X = (pd.concat([X_test, X_train], axis=0)... |
paulcon/active_subspaces | tutorials/test_functions/wing_weight/wing_example.ipynb | mit | import active_subspaces as ac
import numpy as np
%matplotlib inline
# The wing_functions.py file contains two functions: the wing function (wing(xx))
# and its gradient (wing_grad(xx)). Each takes an Mx10 matrix (M is the number of data
# points) with rows being normalized inputs; wing returns a column vector of funct... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session05/Day4/stackdiff_Narayan/01_Registration/Register_images_solutions.ipynb | mit | !ls *fits
"""
Explanation: Image Registration Exercise
Written by Gautham Narayan (gnarayan@stsci.edu) for LSST DSFP #5
In this directory, you should be able to find two fits file from one of the projects I worked on
End of explanation
"""
import astropy.io.fits as afits
from astropy.wcs import WCS
from astropy.visu... |
neurodata/ndmg | tutorials/Tutorial_of_QA_for_FAST.ipynb | apache-2.0 | import matplotlib.pyplot as plt
import numpy as np
import nibabel as nb
from scipy import ndimage
from matplotlib.colors import LinearSegmentedColormap
from m2g.utils.qa_utils import pad_im
from m2g.stats.qa_fast import qa_fast_png
"""
Explanation: Tutorial of QA_FAST(FMRIB's Automated Segmentation Tool)
FAST (FMRIB's... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/docker_and_kubernetes/solutions/3_k8s_hello_node.ipynb | apache-2.0 | !cat ./src/server.js
"""
Explanation: Hello Node Kubernetes
Learning Objectives
* Create a Node.js server
* Create a Docker container image
* Create a container cluster and a Kubernetes pod
* Scale up your services
Overview
The goal of this hands-on lab is for you to turn code that you have developed into a replic... |
NathanYee/ThinkBayes2 | bayesianLinearRegression/Final Report-V2.ipynb | gpl-2.0 | from __future__ import print_function, division
% matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import math
import numpy as np
from thinkbayes2 import Pmf, Cdf, Suite, Joint, EvalNormalPdf
import thinkplot
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Bayesian Linear Reg... |
empet/Plotly-plots | Strips-on-sphere.ipynb | gpl-3.0 | import numpy as np
"""
Explanation: Strips on sphere
End of explanation
"""
theta=np.linspace(0, 5*np.pi/36, 30) #5pi/36=25 degrees
phi=np.linspace(-np.pi/2, np.pi/2, 100)
theta, phi=np.meshgrid(theta, phi)
x=np.cos(theta)*np.cos(phi)
y=np.sin(theta)*np.cos(phi)
z=np.sin(phi)
"""
Explanation: We generate a sphere ... |
ebegoli/SynthNotes | extra/clustering.ipynb | mit | # do the reading
templates = pd.read_parquet('data/processed_dfs/templates.parquet' )
sentences = pd.read_parquet('data/processed_dfs/sentences.parquet')
mentions = pd.read_parquet('data/processed_dfs/mentions.parquet')
umls = pd.read_parquet('data/processed_dfs/umls.parquet')
sentences.head()
mentions.head()
templ... |
heprom/pymicro | examples/SampleDataUserGuide/4_SampleData_Mesh_groups.ipynb | mit | # Import the Sample Data class
from pymicro.core.samples import SampleData as SD
from config import PYMICRO_EXAMPLES_DATA_DIR # import file directory path
import os
dataset_file = os.path.join(PYMICRO_EXAMPLES_DATA_DIR, 'test_sampledata_ref') # test dataset file path
data = SD(filename=dataset_file)
"""
Explanation: ... |
idekerlab/py2cytoscape | examples/view_api_samples.ipynb | mit | from py2cytoscape.data.cynetwork import CyNetwork
from py2cytoscape.data.cyrest_client import CyRestClient
import pandas as pd
import json
# Create REST client for Cytoscape
cy = CyRestClient()
# Reset current session for fresh start
cy.session.delete()
# Load a sample network
network1 = cy.network.create_from('htt... |
google-research/rlds | rlds/examples/rlds_performance.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... |
nick-youngblut/SIPSim | ipynb/bac_genome/fullCyc/trimDataset/.ipynb_checkpoints/dataset_info-checkpoint.ipynb | mit | %load_ext rpy2.ipython
%%R
workDir = '/home/nick/notebook/SIPSim/dev/fullCyc/'
physeqDir = '/home/nick/notebook/SIPSim/dev/fullCyc_trim/'
physeqBulkCore = 'bulk-core_trm'
physeqSIP = 'SIP-core_unk_trm'
ampFragFile = '/home/nick/notebook/SIPSim/dev/bac_genome1147/validation/ampFrags_kde.pkl'
"""
Explanation: General... |
jvitria/DeepLearningBBVA2016 | 3.0 Basic Concepts.ipynb | mit | # import some python packages
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn
"""
Explanation: Start with TensorFlow
End of explanation
"""
import numpy as np
a = np.zeros((2,2)); b = np.ones((2,2))
print np.sum(b,axis=1)
print a.shape
print np.reshape(a,(1,4))
"""
Explanation: What is TensorF... |
rsterbentz/phys202-2015-work | days/day20/Cython.ipynb | mit | import numpy as np
%load_ext Cython
"""
Explanation: Short Tour of Cython
In general, Python is slower than other programming languages such as C, C++ and Java. Much of this comes from the fact that Python is dynamically typed. This means that when Python is compiling code to be run by the interpreter, it doesn't ma... |
garibaldu/multicauseRBM | Max/Evaluating-using-Dreams-from-the-orbm.ipynb | mit | pp.image(expit(two_dream_phi).reshape(28,28), color_range=(0,1))
pp.image(expit(three_dream_phi).reshape(28,28), color_range=(0,1))
"""
Explanation: Lets take a quick look at the images if instead of combining they had just done their own thing
End of explanation
"""
pp.image(np.maximum(two_ds[0], three_ds[0]), cmap... |
sz2472/foundations-homework | data and database/Homework_5.ipynb | mit | from bs4 import BeautifulSoup
from urllib.request import urlopen
html = urlopen("http://static.decontextualize.com/cats.html").read()
document = BeautifulSoup(html, "html.parser")
"""
Explanation: Homework #5
This homework presents a sophisticated scenario in which you must design a SQL schema, insert data into it, an... |
adamamiller/PS1_star_galaxy | gaia/Gaia_PS1_compare.ipynb | mit | gaia_dir = "/Users/adamamiller/Desktop/PS1_fits/gaia_stars/"
ps1_files = glob.glob("/Users/adamamiller/Desktop/PS1_fits/hdf5/*h5")
"""
Explanation: Merge the PS1 RF catalogs and the Gaia obvi stars catalog
End of explanation
"""
gaia_df = pd.read_hdf(gaia_dir + "parallax_ps1_gaia_mag_pm_plx.h5")
gaia_df.set_index("o... |
RealPolitiX/mpes | examples/Tutorial_04_Distortion Correction.ipynb | mit | fpath = r'../data/data_114_4axis_100x100x200x50.h5'
fbinned = fp.readBinnedhdf5(fpath)
V = fbinned['V']
V.shape
Eslice = V[15:20, :, :, 35:41].sum(axis=(0,3))
plt.imshow(Eslice, origin='lower', cmap='terrain_r')
mc = aly.MomentumCorrector(V[15:20, :, :, :].sum(axis=0), rotsym=6)
"""
Explanation: 3.1 Correction of s... |
TeachingDataScience/python-data-080815 | Admin Notes.ipynb | mit | # Copy the file
!curl https://raw.githubusercontent.com/TeachingDataScience/datasets/master/nyt1.csv > nyt1.csv
# %load nytimes_counter.py
# Import required libraries
import csv
# Start a counter and store the textfile in memory
gender_counter = {}
age_counter = {}
lines = csv.reader(open('nyt1.csv'))
lines.next(... |
mne-tools/mne-tools.github.io | 0.17/_downloads/66fec418bceb5ce89704fb8b44930330/plot_3d_to_2d.ipynb | bsd-3-clause | # Authors: Christopher Holdgraf <choldgraf@berkeley.edu>
#
# License: BSD (3-clause)
from scipy.io import loadmat
import numpy as np
from mayavi import mlab
from matplotlib import pyplot as plt
from os import path as op
import mne
from mne.viz import ClickableImage # noqa
from mne.viz import plot_alignment, snapshot_... |
synthicity/activitysim | activitysim/examples/example_mtc/notebooks/change_in_auto_ownership.ipynb | agpl-3.0 | !activitysim create -e example_mtc -d example_base_auto_own
!activitysim create -e example_mtc -d example_base_auto_own_alternative
"""
Explanation: Testing a Change in the Auto Owernship Model
Create two auto ownership examples to illustrate running two scenarios and analyzing results. This notebook assumes users a... |
ethen8181/machine-learning | deep_learning/softmax_tensorflow.ipynb | mit | # code for loading the format for the notebook
import os
# path : store the current path to convert back to it later
path = os.getcwd()
os.chdir(os.path.join('..', 'notebook_format'))
from formats import load_style
load_style(plot_style = False)
os.chdir(path)
import numpy as np
import matplotlib.pyplot as plt
# 1.... |
google/applied-machine-learning-intensive | content/00_prerequisites/00_introduction_to_python/colab.ipynb | apache-2.0 | # 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 the L... |
IanHawke/ET-NumericalMethods-2016 | solutions/01-finite-differencing.ipynb | mit | def backward_differencing(f, x_i, dx):
"""
Backward differencing of f at x_i with grid spacing dx.
"""
f_i = f(x_i)
f_i_minus_1 = f(x_i - dx)
return (f_i - f_i_minus_1) / dx
def forward_differencing(f, x_i, dx):
"""
Forward differencing of f at x_i with grid spacing dx.
"""
... |
hankcs/HanLP | plugins/hanlp_demo/hanlp_demo/zh/tok_mtl.ipynb | apache-2.0 | !pip install hanlp -U
"""
Explanation: <h2 align="center">点击下列图标在线运行HanLP</h2>
<div align="center">
<a href="https://colab.research.google.com/github/hankcs/HanLP/blob/doc-zh/plugins/hanlp_demo/hanlp_demo/zh/tok_mtl.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Ope... |
sorig/shogun | doc/ipython-notebooks/ica/bss_image.ipynb | bsd-3-clause | # change to the shogun-data directory
import os
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
os.chdir(os.path.join(SHOGUN_DATA_DIR, 'ica'))
import Image
import numpy as np
# Load Images as grayscale images and convert to numpy arrays
s1 = np.asarray(Image.open("lena.jpg").convert('L'))
s2 =... |
statsmodels/statsmodels | examples/notebooks/statespace_sarimax_faq.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
rng = np.random.default_rng(20210819)
eta = rng.standard_normal(5200)
rho = 0.8
beta = 10
epsilon = eta.copy()
for i in range(1, eta.shape[0]):
epsilon[i] = rho * epsilon[i - 1] + eta[i]
y = beta + epsilon
y = y[200:]
from statsmodels.tsa.api import SARIM... |
telescopeuser/workshop_blog | setup_cloud.ipynb | mit | !python --version
!pip install -U html
!pip install -U pyqrcode
!pip install -U config
!pip install -U backports.tempfile
!mv docs org_docs
"""
Explanation: Workshop Installation Guide
如何使用和开发微信聊天机器人的系列教程
A workshop to develop & use an intelligent and interactive chat-bot in WeChat
WeChat is a popular social medi... |
rishuatgithub/MLPy | classification/.ipynb_checkpoints/iris_ds-checkpoint.ipynb | apache-2.0 | # Import the necessary ML Libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import neighbors, datasets
from sklearn.model_selection import train_test_split
# Read the Iris data set from pre-build scikit learn library
iris=datasets.load_iris()
iris.keys()
# printing the fea... |
ppham27/MLaPP-solutions | chap01/1.ipynb | mit | %matplotlib inline
import numpy as np
import idx2numpy
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import matplotlib.colors as mpcolors
import mnist
from sklearn import neighbors
train_image_labels = idx2numpy.convert_from_file('train-labels-idx1-ubyte')
train_images = idx2numpy.convert_from_file... |
hasadna/knesset-data-pipelines | jupyter-notebooks/running kns_committeesession pipeline.ipynb | mit | !{'cd /pipelines; KNESSET_LOAD_FROM_URL=1 dpp run ./committees/kns_documentcommitteesession >/dev/null'}
!{'cd /pipelines; KNESSET_LOAD_FROM_URL=1 dpp run ./committees/kns_committee >/dev/null'}
!{'cd /pipelines; KNESSET_LOAD_FROM_URL=1 dpp run ./committees/kns_cmtsessionitem >/dev/null'}
!{'cd /pipelines; KNESSET_L... |
rasbt/pattern_classification | parameter_estimation_techniques/maximum_likelihood_estimate.ipynb | gpl-3.0 | import numpy as np
np.random.seed(123456)
# Generate 100 random patterns for class1
mu_vec1 = np.array([[0],[0]])
cov_mat1 = np.array([[3,0],[0,3]])
x1_samples = np.random.multivariate_normal(mu_vec1.ravel(), cov_mat1, 100)
# Generate 100 random patterns for class2
mu_vec2 = np.array([[9],[0]])
cov_mat2 = np.array([... |
AutuanLiu/Python | fastai_notes/LinearAlgebra/speech05.ipynb | mit | import numpy as np
n = 5
A = np.random.rand(n,n)
npQ, npR = np.linalg.qr(A)
"""
Explanation: fastai 线性代数
8. Implementing QR Factorization
QR 分解
for any matrix $A$, $A = QR$ where $Q$ is orthogonal and $R$ is upper-triangular.
End of explanation
"""
np.allclose(np.eye(n), npQ @ npQ.T), np.allclose(np.eye(n), npQ.T... |
rigetticomputing/grove | examples/IsingSolver.ipynb | apache-2.0 | from mock import patch, Mock
from pyquil.api import WavefunctionSimulator
from grove.ising.ising_qaoa import ising
"""
Explanation: This notebook is a short example of how to use the Ising solver implemented using the QAOA algorithm. We start by declaring the import of the ising function.
End of explanation
"""
wit... |
fluffy-hamster/A-Beginners-Guide-to-Python | A Beginners Guide to Python/Final Project (Minesweeper)/07. Lets Play!.ipynb | mit | import sys
sys.path.append("..\misc") # Adding to sys.path allows us to find "minsweeper.py"
import minesweeper
minesweeper.main()
"""
Explanation: Lets play Minesweeper
This lecture is a chill one. All I am going to do is play my implementation of minesweeper. I'll show you the code in the next lecture.
End of exp... |
nkarast/notebooks | Matplotlib_tutorial.ipynb | mit | from matplotlib import pyplot as plt
%matplotlib inline
plt.plot([5,6,7,8],[7,4,8,4])
## Adding labels
plt.title("Random Plot")
plt.xlabel("$1/2\cdot x$")
plt.ylabel("Dispersion $[m]$")
"""
Explanation: Matplotlib Tutorial
Usually what we use is the pyplot module of matplotlib.
Let's inline plt and make a simple plo... |
gaufung/Data_Analytics_Learning_Note | DesignPattern/ComponentPattern.ipynb | mit | class Company:
name = ''
def __init__(self, name):
self.name = name
def add(self, company):
pass
def remove(self, company):
pass
def display(self, depth):
pass
def listDuty(self):
pass
class ConcreteCompany(Company):
childrenCompany = None
def __i... |
feststelltaste/software-analytics | courses/20191014_ML-Summit/Parsing and Analysing vmstat Data the Easy Way (Demo Notebook).ipynb | gpl-3.0 | %less ../datasets/vmstat_loadtest.log
"""
Explanation: Idea
Using the vmstat command line utility to quickly determine the root cause of performance problems.
End of explanation
"""
from ozapfdis.linux import vmstat
stats = vmstat.read_logfile("../datasets/vmstat_loadtest.log")
stats.head()
"""
Explanation: Data I... |
echohenry2006/tvb-library | tvb/simulator/demos/region_stochastic.ipynb | gpl-2.0 | from tvb.simulator.lab import *
"""
Explanation: Demonstrate using the simulator at the region level, stochastic integration.
Run time: approximately 2 seconds (workstation circa 2010)
Memory requirement: < 1GB
End of explanation
"""
LOG.info("Configuring...")
#Initialise a Model, Coupling, and Connectivity.
oscill... |
deepmind/educational | colabs/summer_schools/intro_to_graph_nets_tutorial_with_jraph.ipynb | apache-2.0 | !pip install git+https://github.com/deepmind/jraph.git
!pip install flax
!pip install dm-haiku
# Imports
%matplotlib inline
import functools
import matplotlib.pyplot as plt
import jax
import jax.numpy as jnp
import jax.tree_util as tree
import jraph
import flax
import haiku as hk
import optax
import pickle
import nump... |
InsightSoftwareConsortium/SimpleITK-Notebooks | Python/35_Segmentation_Shape_Analysis.ipynb | apache-2.0 | import SimpleITK as sitk
import pandas as pd
%matplotlib notebook
import matplotlib.pyplot as plt
import gui
from math import ceil
%run update_path_to_download_script
from downloaddata import fetch_data as fdata
"""
Explanation: Focused Ion Beam Scanning Electron Microscopy Image Segmentation
Summary:
1. SimpleITK ... |
pligor/predicting-future-product-prices | 04_time_series_prediction/27_price_history_generate_train_test_and_baseline.ipynb | agpl-3.0 | bltest = MyBaseline(npz_path=npz_test)
bltest.getMSE()
bltest.renderMSEs()
plt.show()
bltest.getHuberLoss()
bltest.renderHuberLosses()
plt.show()
%%time
bltest.get_dtw()
bltest.renderRandomTargetVsPrediction()
plt.show()
"""
Explanation: Baseline is static, a straight line for each input - Test
End of explanatio... |
Kappa-Dev/ReGraph | examples/Tutorial_hierarchy_audit.ipynb | mit | from regraph import NXGraph, NXHierarchy
from regraph.audit import VersionedHierarchy
from regraph.rules import Rule
from regraph import print_graph, plot_rule, plot_graph
"""
Explanation: Audit trails for hierarchy objects in ReGraph (aka versioning)
ReGraph implements a framework for the version control (VC) of grap... |
brettavedisian/Liquid-Crystals-Summer-2015 | .ipynb_checkpoints/Annulus_Simple_Matplotlib-checkpoint.ipynb | mit | # commands for plotting, "plot" works with matplotlib
def mesh2triang(mesh):
xy = mesh.coordinates()
return tri.Triangulation(xy[:, 0], xy[:, 1], mesh.cells())
def mplot_cellfunction(cellfn):
C = cellfn.array()
tri = mesh2triang(cellfn.mesh())
return plt.tripcolor(tri, facecolors=C)
def mplot_fun... |
pmorissette/bt | examples/buy_and_hold.ipynb | mit | names = ['foo','bar','rf']
dates = pd.date_range(start='2017-01-01',end='2017-12-31', freq=pd.tseries.offsets.BDay())
n = len(dates)
rdf = pd.DataFrame(
np.zeros((n, len(names))),
index = dates,
columns = names
)
np.random.seed(1)
rdf['foo'] = np.random.normal(loc = 0.1/n,scale=0.2/np.sqrt(n),size=n)
rdf['... |
solowPy/solowPy | examples/0 Motivation.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import sympy as sym
import quantecon as qe
import solowpy
"""
Explanation: <div align='center' ><img src='https://raw.githubusercontent.com/davidrpugh/numerical-methods/master/images/sgpe-logo.jpg' width="1200" height="100"><... |
KitwareMedical/TubeTK | docs/examples/ConvertTubesToImages/ConvertTubesToImages.ipynb | apache-2.0 | import os
import sys
import numpy
# Path for TubeTK libs and bin
#Values takend from TubeTK launcher
sys.path.append("C:/src/TubeTK_Python_ITK/TubeTK-build/lib/")
sys.path.append("C:/src/TubeTK_Python_ITK/TubeTK-build/lib/Release")
# Setting TubeTK Build Directory
TubeTK_BUILD_DIR=None
if 'TubeTK_BUILD_DIR' in os.e... |
xMyrst/BigData | python/howto/011_Módulo_NumPy_Arrays.ipynb | gpl-3.0 | import numpy as np
"""
Explanation: MÓDULO NumPy
Los módulos NumPy (Numerical Python) y SciPy proporcionan funciones y rutinas matemáticas para la manipulación de arrays y matrices de datos numéricos de una forma eficiente.
El módulo SciPy extiende la funcionalidad de NumPy con una colección de algoritmos matemáticos... |
beangoben/HistoriaDatos_Higgs | Dia3/.ipynb_checkpoints/6_Datos_Iris-checkpoint.ipynb | gpl-2.0 | import pandas as pd
import numpy as np # modulo de computo numerico
import matplotlib.pyplot as plt # modulo de graficas
# esta linea hace que las graficas salgan en el notebook
import seaborn as sns
%matplotlib inline
"""
Explanation: Classificando Iris
Ahora vamos a ver un conjunto de datos muy famosos, los datos ir... |
wise-r/R-Topics | 7.Crawler/Presentation/爬虫工具.ipynb | mit | # 载入rpy2.ipython
# rpy2提供了Python和R之间的交互环境
%load_ext rpy2.ipython
"""
Explanation: rvest & urllib2 简介
Fibears
2016.03.29
End of explanation
"""
%%R
library(rvest)
# vignette("selectorgadget")
lego_movie <- read_html("http://www.imdb.com/title/tt1490017/")
%%R
lego_movie
%%R
rating <- lego_movie %>%
html_nodes(... |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/prod/.ipynb_checkpoints/n08_simple_q_learner_1000_states_4_actions_full_training-checkpoint.ipynb | mit | # Basic imports
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
import scipy.optimize as spo
import sys
from time import time
from sklearn.metrics import r2_score, median_absolute_error
from multiprocessing import Pool
%matplotlib inline
%pylab inline
pylab.rcPar... |
nbokulich/short-read-tax-assignment | ipynb/simulated-community/dataset-generation.ipynb | bsd-3-clause | from os.path import join, expandvars
from tax_credit.simulated_communities import generate_simulated_communities
# Project directory
project_dir = expandvars("$HOME/Desktop/projects/short-read-tax-assignment/")
# Directory containing reference sequence databases
reference_database_dir = join(project_dir, 'data', 'ref... |
yhilpisch/dx | 06_dx_portfolio_parallel.ipynb | agpl-3.0 | from dx import *
import time
from pylab import plt
plt.style.use('seaborn')
%matplotlib inline
"""
Explanation: <img src="http://hilpisch.com/tpq_logo.png" alt="The Python Quants" width="45%" align="right" border="4">
Parallel Valuation of Large Portfolios
Derivatives (portfolio) valuation by Monte Carlo simulation is... |
kevntao/ThinkStats2 | code/.ipynb_checkpoints/chap03ex-checkpoint.ipynb | gpl-3.0 | kids = resp['numkdhh']
kids
"""
Explanation: Make a PMF of <tt>numkdhh</tt>, the number of children under 18 in the respondent's household.
End of explanation
"""
pmf = thinkstats2.Pmf(kids)
thinkplot.Pmf(pmf, label='PMF')
thinkplot.Show(xlabel='# of Children', ylabel='PMF')
"""
Explanation: Display the PMF.
End of... |
eigendreams/TensorFlow-Tutorials | 01_Simple_Linear_Model.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
from sklearn.metrics import confusion_matrix
"""
Explanation: TensorFlow Tutorial #01
Simple Linear Model
by Magnus Erik Hvass Pedersen
/ GitHub / Videos on YouTube
Introduction
This tutorial demonstrates the basic workflow o... |
xR86/ml-stuff | labs-python/PythonLab-7.ipynb | mit | import time
import random
#import sys
#a = int(sys.argv[1])
#b = int(sys.argv[2])
def wait(x):
time.sleep(x)
def time_cron(a,b):
time_interval = random.uniform(a,b)
# while(1):
# measure process time
t0 = time.clock()
wait(time_interval)
print time.clock() - t0, "seconds process time"
... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch2-Problem_2-07.ipynb | unlicense | %pylab notebook
%precision 4
"""
Explanation: Excercises Electric Machinery Fundamentals
Chapter 2
Problem 2-7
End of explanation
"""
Zeq = 20.0 + 100.0j # [Ohm]
Rc = 100.0e3 # [Ohm]
Xm = 20.0e3 # [Ohm]
Zload = 2.0 + 0.7j # [Ohm]
Xload = -3.0j # [Ohm]
Vp = 7967.0 # [V... |
jamesfolberth/NGC_STEM_camp_AWS | notebooks/data8_notebooks/project2/project2.ipynb | bsd-3-clause | # Run this cell to set up the notebook, but please don't change it.
import numpy as np
from datascience import *
# These lines do some fancy plotting magic.
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
import warnings
warnings.simplefilter('ignore', FutureWarnin... |
marcelomiky/PythonCodes | scikit-learn/scikit-learn-book/Chapter 3 - Unsupervised Learning - Principal Component Analysis.ipynb | mit | %pylab inline
import IPython
import sklearn as sk
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
print 'IPython version:', IPython.__version__
print 'numpy version:', np.__version__
print 'scikit-learn version:', sk.__version__
print 'matplotlib version:', matplotlib.__version__
"""
Explanation:... |
ceos-seo/data_cube_notebooks | notebooks/water/coastline/Coastline_Classifier.ipynb | apache-2.0 | import scipy.ndimage.filters as conv
import numpy as np
def _coastline_classification(dataset, water_band='wofs'):
kern = np.array([[1, 1, 1], [1, 0.001, 1], [1, 1, 1]])
convolved = conv.convolve(dataset[water_band], kern, mode='constant') // 1
ds = dataset.where(convolved > 0)
ds = ds.where(convolved... |
MBARIMike/biofloat | notebooks/calibrate_all_oxygen_floats.ipynb | mit | from biofloat import ArgoData
from os.path import join, expanduser
ad = ArgoData(cache_file = join(expanduser('~'),
'biofloat_fixed_cache_age365_variablesDOXY_ADJUSTED-PSAL_ADJUSTED-TEMP_ADJUSTED.hdf'))
ocdf = ad.get_cache_file_oxy_count_df()
print ocdf.groupby('wmo').sum().sum()
print 'Float having DOXY_ADJUSTE... |
Kaggle/learntools | notebooks/ethics/raw/ex2.ipynb | apache-2.0 | # Set up feedback system
from learntools.core import binder
binder.bind(globals())
from learntools.ethics.ex2 import *
print("Setup Complete")
"""
Explanation: In the tutorial, you learned about human-centered design (HCD) and became familiar with six general steps to apply it to AI systems. In this exercise, you will... |
maxkleiner/maXbox4 | ARIMA_Predictor2.ipynb | gpl-3.0 | #sign:max: MAXBOX8: 03/02/2021 18:34:41
# optimal moving average OMA for market index signals ARIMA study- Max Kleiner
# v2 shell argument forecast days - 4 lines compare - ^GDAXI for DAX
# pip install pandas-datareader
# C:\maXbox\mX46210\DataScience\princeton\AB_NYC_2019.csv AB_NYC_2019.csv
#https://medium.co... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/supplemental_gradient_boosting/labs/c_boosted_trees_model_understanding.ipynb | apache-2.0 | import time
# We will use some np and pandas for dealing with input data.
import numpy as np
import pandas as pd
# And of course, we need tensorflow.
import tensorflow as tf
from matplotlib import pyplot as plt
from IPython.display import clear_output
tf.__version__
"""
Explanation: Model understanding and interpre... |
rhyswhitley/fire_limitation | notebooks/prepare_data.ipynb | bsd-3-clause | # data munging and analytical libraries
import re
import os
import numpy as np
import pandas as pd
from netCDF4 import Dataset
# graphical libraries
import matplotlib.pyplot as plt
%matplotlib inline
# set paths
outPath = "../data/globfire.csv"
"""
Explanation: <table style="width: 100%; border-collapse: collapse;... |
chseifert/tutorials | PythonTutorial/PythonTutorial.ipynb | apache-2.0 | # variable assignments
myInt = 1
myFloat = 12.2
myString = "Hallo"
myBoolean = True
"""
Explanation: # Introduction to Python!
Python Files and Modules
Python Files
Structure of the Code file
Code files end with ".py":
myprogram.py
Every line is a Python statement (or part thereof).
* comment line start with `#... |
satishkt/sudo_make_me_sandwich | hackathon/Intro_to_Pandas.ipynb | unlicense | # imports a library 'pandas', names it as 'pd'
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
# enables inline plots, without it plots don't show up in the notebook
%matplotlib inline
"""
Explanation: Set up
Use conda to install seaborn by running in a terminal
conda ins... |
nproctor/phys202-2015-work | assignments/assignment05/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):
#style graph
plt.figure(figsize=(10,5))
pl... |
ES-DOC/esdoc-jupyterhub | notebooks/bcc/cmip6/models/bcc-csm2-mr/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'bcc', 'bcc-csm2-mr', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: BCC
Source ID: BCC-CSM2-MR
Topic: Ocean
Sub-Topics: Timestepping Framework, Advecti... |
planetlabs/notebooks | jupyter-notebooks/in-class-exercises/mosaicing-and-masking/mosaicing-and-masking-key.ipynb | apache-2.0 | # Load our 3 images using rasterio
import rasterio
img1 = rasterio.open('data/20170721_175836_103c/20170721_175836_103c_3B_Visual.tif')
img2 = rasterio.open('data/20170721_175837_103c/20170721_175837_103c_3B_Visual.tif')
img3 = rasterio.open('data/20170721_175838_103c/20170721_175838_103c_3B_Visual.tif')
"""
Explana... |
agrc/Presentations | UGIC/2022/SpatiallyEnabledDataFrames/alpha.ipynb | mit | import pandas as pd
medians_df = pd.read_csv('assets/median_age.csv')
medians_df.head()
"""
Explanation: Ditch the Cursor
Editing Feature Classes with Spatialy-Enabled DataFrames
ArcPy Is Great, But...
Problem one: row[0]
```python
def update_year_built(layer, year_fields):
with arcpy.da.UpdateCursor(layer, yea... |
feststelltaste/software-analytics | demos/20191119_rheinJUG_Duesseldorf/Analyzing the community activity for version control systems (Slides Edition).ipynb | gpl-3.0 | import pandas as pd
vcs_data = pd.read_csv('../dataset/stackoverflow_vcs_data_subset.gz')
vcs_data.head()
"""
Explanation: Analyzing the community activity for version control systems
Context
You are a new team member in a software company
The developers there are using CVS (Concurrent Versions System)
You propose G... |
mdezube/sms-analysis | sms_analysis.ipynb | mit | from __future__ import print_function
from __future__ import division
import copy
import json
import re
import string
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import seaborn # To improve the chart styling.
import wordtree
from IPython.display import display
from IPython.display import H... |
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