repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
tomchor/pymicra | publications/agu2017/pprog/.ipynb_checkpoints/simple_case-checkpoint.ipynb | gpl-3.0 | pm.util.qc_replace(fnames, fconfig,
file_lines=36000,
lower_limits=dict(theta_v=10, mrho_h2o=0, mrho_co2=0),
upper_limits=dict(theta_v=45),
spikes_test=True,
max_replacement_count=360, # replacement count test
chunk_size=1200,
outdir='out1',
replaced_report='rrep.txt')
fnames2 = sorted(... |
htygithub/bokeh | examples/charts/notebook/scatter.ipynb | bsd-3-clause | df2 = df_from_json(data)
df2 = df2.sort('medals.total', ascending=False)
df2 = df2.head(10)
df2 = pd.melt(df2, id_vars=['abbr', 'name'])
scatter5 = Scatter(
df2, x='value', y='name', color='variable', title="x='value', y='name', color='variable'",
xlabel="Medals", ylabel="Top 10 Countries", legend='bottom_righ... |
openfisca/openfisca-france-indirect-taxation | openfisca_france_indirect_taxation/examples/notebooks/quantites_agregats_transports.ipynb | agpl-3.0 | %matplotlib inline
from ipp_macro_series_parser.agregats_transports.transports_cleaner import a6_b, g2_1, g_3a
from openfisca_france_indirect_taxation.examples.utils_example import graph_builder_carburants, \
graph_builder_carburants_no_color
"""
Explanation: Ce script réalise des graphiques à partir des données... |
google/svcca | tutorials/001_Introduction.ipynb | apache-2.0 | import os, sys
from matplotlib import pyplot as plt
%matplotlib inline
import numpy as np
import pickle
import pandas
import gzip
sys.path.append("..")
import cca_core
def _plot_helper(arr, xlabel, ylabel):
plt.plot(arr, lw=2.0)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.grid()
"""
Explanation: <h1>Ta... |
Heroes-Academy/OOP_Spring_2016 | notebooks/giordani/Python_3_OOP_Part_1__Objects_and_types.ipynb | mit | # This is some data
data = (13, 63, 5, 378, 58, 40)
# This is a procedure that computes the average
def avg(d):
return sum(d)/len(d)
avg(data)
"""
Explanation: About this series
Object-oriented programming (OOP) has been the leading programming paradigm for several decades now, starting from the initial atte... |
jgrizou/explauto | notebook/learning_with_environment_context.ipynb | gpl-3.0 | %load_ext autoreload
%autoreload 2
"""
Explanation: Learning a sensorimotor model with a context provided by environment
In this notebook, we will see how to use the Explauto libarary to allow the learning and control of actions that depend on a context provided by the environment. We suppose that the reader is famili... |
aakashsinha19/Aspectus | Image Segmentation/tensorflow_notes-master/fully_convolutional_networks.ipynb | apache-2.0 | %matplotlib inline
from __future__ import division
import os
import sys
import tensorflow as tf
import skimage.io as io
import numpy as np
sys.path.append("/home/aakash-sinha/Documents/Tensorflow/tf-image-segmentation/")
sys.path.append("/home/aakash-sinha/Documents/Tensorflow/models/slim/")
fcn_16s_checkpoint_path... |
softEcon/course | lectures/economic_models/career_choices/lecture.ipynb | mit | %matplotlib inline
"""
Explanation: Modelling Carrer Choices
The model is based on the following research paper:
Derek Neal (1999). The Complexity of Job Mobility among Young Men, Journal of Labor Economics, 17(2), 237-261.
The implementation draws heavily from the material provided on the Quantitative Economics webs... |
ramseylab/networkscompbio | class12_pagerank_python3.ipynb | apache-2.0 | import pandas
import igraph
import numpy
import matplotlib.pyplot
import random
"""
Explanation: CS446/546 - Class Session 12 - Pagerank/Katz/Eigenvector centrality
In this class session we are going to compute the outgoing-edge PageRank centrality of each gene (vertex) in a human gene regulatory network (a directed g... |
eneskemalergin/OldBlog | _oldnotebooks/MemoryManagement_Assignment.ipynb | mit | def bestFit(number): # Function Header
"""
bestFit function takes a number from the job list and find its best fit
from pre-defined partitions
we have 3 different partition sizes:
16
32
64
Then bestFit returns the integer number of the partition size
... |
NuGrid/NuPyCEE | DOC/Teaching/ExtraSources.ipynb | bsd-3-clause | %matplotlib nbagg
import matplotlib.pyplot as plt
import sys
import matplotlib
import numpy as np
from NuPyCEE import sygma as s
from NuPyCEE import omega as o
from NuPyCEE import read_yields as ry
"""
Explanation: How to use different extra sources such as CCSN neutrino-driven winds
Prepared by Christian Ritter
End ... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch2-Problem_2-17.ipynb | unlicense | %pylab notebook
"""
Explanation: Excercises Electric Machinery Fundamentals
Chapter 2
Problem 2-17
End of explanation
"""
Vp = 600 # [V]
Vl = 120 # [V] which is also the load voltage
Vh = 480 # [V]
Sw = 10e3 # [VA]
"""
Explanation: Description
A 10-kVA 480/120-V conventional transformer is to be used to supply ... |
ledeprogram/algorithms | class6/donow/Skinner_Barnaby_DoNow_6.ipynb | gpl-3.0 | import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
import statsmodels.formula.api as smf
"""
Explanation: 1. Import the necessary packages to read in the data, plot, and create a linear regression model
End of explanation
"""
df = pd.read_csv("data/hanford.csv")
df
"""
Explanation: 2. Read in t... |
riceda195/kernel_gateway_demos | scotch_demo/notebooks/scotch_api_python.ipynb | bsd-3-clause | import pandas as pd
import pickle
import requests
import json
"""
Explanation: Got Scotch API?
This notebook is meant to demonstrate the transformation of an annotated notebook into a HTTP API using the Jupyter kernel gateway. The result is a simple scotch recommendation engine.
The original scotch data is from https:... |
denglert/manuals | python/modules/numpy/notebooks/meshgrid.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
x = np.linspace(1.0, 6.0, 5)
y = np.linspace(12.0, 15.0, 3)
X,Y = np.meshgrid(x,y, indexing='xy')
"""
Explanation: np.meshgrid()
Return coordinate matrices from coordinate vectors.
Make N-D coordinate arrays for vectorized evaluations of N-D scala... |
AI-Innovation/cs231n_ass1 | .ipynb_checkpoints/knn-checkpoint.ipynb | mit | # Run some setup code for this notebook.
import random
import numpy as np
from cs231n.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
# This is a bit of magic to make matplotlib figures appear inline in the notebook
# rather than in a new window.
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.... |
aymeric-spiga/eduplanet | TOOLS/atlas.ipynb | gpl-2.0 | filename = 'resultat.nc'
import numpy as np
import matplotlib.pyplot as plt
from pylab import *
import cartopy.crs as ccrs
from netCDF4 import Dataset
%matplotlib inline
data = Dataset(filename)
"""
Explanation: Atlas de l'expérience keyexp
Rappel : pour enregistrer une figure, placer cette ligne après la figure en... |
dtamayo/MachineLearning | Day1/04_model_training.ipynb | gpl-3.0 | # import load_iris function from datasets module
from sklearn.datasets import load_iris
# save "bunch" object containing iris dataset and its attributes
iris = load_iris()
# store feature matrix in "X"
X = iris.data
# store response vector in "y"
y = iris.target
# print the shapes of X and y
print X.shape
print y.s... |
d00d/quantNotebooks | Notebooks/quantopian_research_public/notebooks/lectures/Beta_Hedging/notebook.ipynb | unlicense | # Import libraries
import numpy as np
from statsmodels import regression
import statsmodels.api as sm
import matplotlib.pyplot as plt
import math
# Get data for the specified period and stocks
start = '2014-01-01'
end = '2015-01-01'
asset = get_pricing('TSLA', fields='price', start_date=start, end_date=end)
benchmark ... |
albireox/marvin | docs/sphinx/jupyter/my-first-query.ipynb | bsd-3-clause | # Python 2/3 compatibility
from __future__ import print_function, division, absolute_import
from marvin import config
config.setRelease('MPL-4')
from marvin.tools.query import Query
"""
Explanation: My First Query
One of the most powerful features of Marvin 2.0 is ability to query the newly created DRP and DAP datab... |
leriomaggio/code-coherence-analysis | JFreeChart Versions - Diving Differences.ipynb | bsd-3-clause | # %load preamble_directives.py
"""Some imports and path settings to make notebook code
running smoothly.
"""
# Author: Valerio Maggio <valeriomaggio@gmail.com>
# Copyright (c) 2015 Valerio Maggio <valeriomaggio@gmail.com>
# License: BSD 3 clause
import sys, os
# Extending PYTHONPATH to allow relative import!
sys.path.... |
dcavar/python-tutorial-for-ipython | notebooks/Word2Vec.ipynb | apache-2.0 | import numpy as np
"""
Explanation: Word2Vec Example
(C) 2018 by Damir Cavar
Version: 1.1, November 2018
License: Creative Commons Attribution-ShareAlike 4.0 International License (CA BY-SA 4.0)
This is a tutorial related to the L665 course on Machine Learning for NLP focusing on Deep Learning, Spring and Fall 2018 at... |
BrentDorsey/pipeline | gpu.ml/notebooks/05a_Train_Model_Distributed_GPU.ipynb | apache-2.0 | import tensorflow as tf
cluster = tf.train.ClusterSpec({"local": ["localhost:2222", "localhost:2223"]})
"""
Explanation: Train Model on Distributed Cluster
IMPORTANT: You Must STOP All Kernels and Terminal Session
The GPU is wedged at this point. We need to set it free!!
Define ClusterSpec
End of explanation
"""
... |
unpingco/python_for_prob_stats_ml | chapters/probability/notebooks/moment_generating.ipynb | mit | import sympy as S
from sympy import stats
p,t = S.symbols('p t',positive=True)
x=stats.Binomial('x',10,p)
mgf = stats.E(S.exp(t*x))
"""
Explanation: Python for Probability, Statistics, and Machine Learning
Moment Generating Functions
Generating moments usually involves integrals that are extremely
difficult to compute... |
pombredanne/gensim | docs/notebooks/word2vec.ipynb | lgpl-2.1 | # import modules & set up logging
import gensim, logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
sentences = [['first', 'sentence'], ['second', 'sentence']]
# train word2vec on the two sentences
model = gensim.models.Word2Vec(sentences, min_count=1)
"""
Explanation:... |
axt/cfg-explorer | examples/demo.ipynb | bsd-2-clause | import os
import sys
from pathlib import Path
sys.path.insert(0,str(Path().resolve().parent))
"""
Explanation: Demo for functional usage of CFG-explorer
Now, cfg-explorer can not only be used as a command line tool. We can also call it within a Python program.
Download Spec CPU Benchmark 2006
Save the suite outside o... |
samuxiii/notebooks | houses/House Prices Attempts.ipynb | apache-2.0 | import numpy as np
import pandas as pd
#load the files
train = pd.read_csv('input/train.csv')
test = pd.read_csv('input/test.csv')
data = pd.concat([train, test])
#size of training dataset
train_samples = train.shape[0]
test_samples = test.shape[0]
# remove the Id feature
data.drop(['Id'],1, inplace=True);
#data.de... |
osamoylenko/YSDA_deeplearning17 | Seminar2/Homework2.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import random
from IPython import display
from sklearn import datasets, preprocessing
(X, y) = datasets.make_circles(n_samples=1024, shuffle=True, noise=0.2, factor=0.4)
ind = np.logical_or(y==1, X[:,1] > X[:,0] - 0.5)
X = X[ind,:]
X = preprocessing... |
CELMA-project/CELMA | MES/singleOperators/5-D2DZ2Cylinder/calculations/exactSolutions.ipynb | lgpl-3.0 | %matplotlib notebook
from sympy import init_printing
from sympy import S
from sympy import sin, cos, tanh, exp, pi, sqrt
from boutdata.mms import x, y, z, t
from boutdata.mms import DDZ
import os, sys
# If we add to sys.path, then it must be an absolute path
common_dir = os.path.abspath('./../../../../common')
# Sys... |
yingchi/fastai-notes | deeplearning1/rnn/tf-rnn-modu.ipynb | apache-2.0 | import pickle
import os
import re
def load_text(path):
input_file = os.path.join(path)
with open(input_file, 'r') as f:
text_data = f.read()
return text_data
def preprocess_and_save_data(text, token_lookup, create_lookup_tables):
token_dict = token_lookup()
for key, token in token_dict.ite... |
RittmanResearch/maybrain | docs/02 - The utils Package.ipynb | apache-2.0 | from maybrain import utils
from maybrain import resources as rr
from maybrain import brain as mbt
a = mbt.Brain()
a.import_adj_file(rr.DUMMY_ADJ_FILE_500)
a.import_spatial_info(rr.MNI_SPACE_COORDINATES_500)
a.apply_threshold()
"""
Explanation: The utils Package
As the name says, this package brings some extra functio... |
johnpfay/environ859 | 03_Python/Notebooks/Session2.ipynb | gpl-3.0 | #ForLoopExample.py
# This example uses a for loop to iterate through each item in
# the "fruit" list, updating the value of the "fruit" variable and
# executing whatever lines are indented under the for statement
#Create a list of fruit
fruitList = ("apples","oranges","kiwi","grapes","blueberries")
# Loop through... |
geography-munich/sciprog | material/sub/koldunov/07 - Other modules for geoscientists.ipynb | apache-2.0 | import iris
import iris.quickplot as qplt
temperature = iris.load_cube('air.sig995.2012.nc')
qplt.contourf(temperature[0,:,:])
gca().coastlines()
"""
Explanation: Other modules for geoscientists
Nikolay Koldunov
koldunovn@gmail.com
This is part of Python for Geosciences notes.
=============
Some of the things will n... |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_ems_filtering.ipynb | bsd-3-clause | # Author: Denis Engemann <denis.engemann@gmail.com>
# Jean-Remi King <jeanremi.king@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io, EvokedArray
from mne.datasets import sample
from mne.decoding import EMS, compute_ems
from sklearn.cross_... |
rigetticomputing/pyquil | docs/source/quilt_waveforms.ipynb | apache-2.0 | from pyquil.quilatom import TemplateWaveform
def plot_waveform(wf: TemplateWaveform, sample_rate: float):
""" Plot a template waveform by sampling at the specified sample rate. """
samples = wf.samples(sample_rate)
times = np.arange(len(samples))/sample_rate
print(wf)
plt.plot(times, samples.real)... |
nwfpug/meetings | 2017-04-10/numpy_4_matlab_users.ipynb | gpl-3.0 | import numpy as np
import scipy.linalg as la
"""
Explanation: NumPy for Matlab users
Introduction
MATLAB® and NumPy/SciPy have a lot in common. But there are many differences. NumPy and SciPy were created to do numerical and scientific computing in the most natural way with Python, not to be MATLAB® clones. This page ... |
scotthuang1989/Python-3-Module-of-the-Week | concurrency/threading - Manage Concurrent Operations within a process.ipynb | apache-2.0 | import threading
def worker():
""""thread worker function"""
print("Worker\n")
threads = []
for i in range(5):
t = threading.Thread(target=worker)
threads.append(t)
t.start()
"""
Explanation: Using threads allows a program to run multiple operations concurrently in the same process space
Thread ... |
folivetti/PIPYTHON | Aula02.ipynb | mit | import math
x = float(input("Entre com um valor: "))
y = math.log(x)
print(y)
"""
Explanation: Introdução à Programação em Python
Funções
Na aula anterior utilizamos algumas funções para realizar certas operações além das fundamentais.
End of explanation
"""
x = float(input("Entre com um valor: "))
print(math.cos( x... |
binh-vu/python-tutorial | 3.0_Data_structures.ipynb | mit | pets = ['dog', 'cat', 'pig']
print pets.index('cat')
pets.insert(0, 'rabbit')
print pets
pets.pop(1)
print pets
"""
Explanation: Lists
Some methods of list:
<code>list.append(x)</code>: add <code>x</code> to the end
<code>list.insert(i, x)</code>: insert <code>x</code> at position <code>i</code>
<code>list.index(x)... |
GoogleCloudPlatform/training-data-analyst | quests/serverlessml/07_caip/solution/export_data.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
!pip install tensorflow==2.1 --user
"""
Explanation: Exporting data from BigQuery to Google Cloud Storage
In this notebook, we export BigQuery data to GCS so that we can reuse our Keras model that was developed on CSV data.
End of explanation
"""
%%... |
quantopian/research_public | notebooks/lectures/Random_Variables/questions/notebook.ipynb | apache-2.0 | # Useful Functions
class DiscreteRandomVariable:
def __init__(self, a=0, b=1):
self.variableType = ""
self.low = a
self.high = b
return
def draw(self, numberOfSamples):
samples = np.random.randint(self.low, self.high, numberOfSamples)
return samples
class Bin... |
tsarouch/data_science_references_python | regression/address_business_questions.ipynb | gpl-2.0 | from sklearn.datasets import load_boston
boston = load_boston()
# features
df = pd.DataFrame(boston.data)
df.columns = boston.feature_names
# dependent variable
df['PRICE'] = boston.target
df.head(3)
"""
Explanation: Get Data
End of explanation
"""
# Lets use only one feature
df1 = df[['LSTAT', 'PRICE']]
X = df1['L... |
sassoftware/sas_kernel | notebook/loadSASExtensions.ipynb | apache-2.0 | import notebook
from __future__ import print_function
from jupyter_core.paths import jupyter_data_dir, jupyter_path
print(jupyter_data_dir())
print(jupyter_path())
"""
Explanation: This notebook will help you install the SAS NBExtensions
The process includes a mix of command line and python code and can be done either... |
changhoonhahn/centralMS | centralms/notebooks/notes_SFRmpajhu_uncertainty.ipynb | mit | import numpy as np
import scipy as sp
import env
import util as UT
from ChangTools.fitstables import mrdfits
from pydl.pydlutils.spheregroup import spherematch
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rcParams['text.usetex'] = True
mpl.rcParams['font.family'] = 'serif'
mpl.rcParams['axes.line... |
ivannz/study_notes | year_14_15/spring_2015/netwrok_analysis/notebooks/labs/epidemics_ntwrks.ipynb | mit | import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
from numpy.linalg import eig
from scipy.integrate import odeint
%matplotlib inline
# Let's start from a complete graph
n = 100
G = nx.complete_graph(n)
# G = nx.barabasi_albert_graph(n, 3)
# Get adj. matrix
A = np.array( nx.adjacency_matrix(G).t... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/gapic/automl/showcase_automl_tabular_classification_batch_explain.ipynb | apache-2.0 | import os
import sys
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install -U google-cloud-aiplatform $USER_FLAG
"""
Explanation: Vertex client library: AutoML tabular classification model for batch prediction with explan... |
ElMejorEquipoDeLaSerena/VariableStarsClassification | Final.ipynb | mit | import os.path
import sys
import urllib
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import pylab as pl
from sklearn import svm, cross_validation, metrics
from sklearn.grid_search import GridSearchCV
from sklearn.naive_bayes import GaussianNB
import sc... |
Elucidation/Ngram-Tutorial | NgramTutorial.ipynb | mit | # Find the number links by looking on Project Gutenberg in the address bar for a book.
books = {'Pride and Prejudice': '1342',
'Huckleberry Fin': '76',
'Sherlock Holmes': '1661'}
book = books['Pride and Prejudice']
# Load text from Project Gutenberg URL
import urllib2
url_template = 'https://www.gut... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/text_models/labs/reusable_embeddings.ipynb | apache-2.0 | import os
import pandas as pd
from google.cloud import bigquery
"""
Explanation: Reusable Embeddings
Learning Objectives
1. Learn how to use a pre-trained TF Hub text modules to generate sentence vectors
1. Learn how to incorporate a pre-trained TF-Hub module into a Keras model
1. Learn how to deploy and use a text m... |
balarsen/pymc_learning | Inversion/Inversion2.ipynb | bsd-3-clause | def get_data(x, mag=100, pl=-2.5, xmin=50.0):
C = (-pl - 1)*xmin**(-pl-1)
return mag/0.03*C*x**(pl)
get_data(50)
50**-2.5
100**(-1/2.5) * 50**-2.5
pl = -2.5
xmin = 50
C = (-pl - 1)*xmin**(-pl-1)
get_data(50)
plt.loglog(tb.logspace(50, 5000, 10), get_data(tb.logspace(50, 5000, 10)))
"""
Explanation: ... |
kastnerkyle/kastnerkyle.github.io-nikola | blogsite/posts/polyphase-signal-processing.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
def gen_complex_chirp(fs=44100, pad_frac=.01, time_s=1):
f0= -fs / (2. * (1 + pad_frac))
f1= fs / (2. *(1 + pad_frac))
t1 = time_s
beta = (f1 - f0) / float(t1)
t = np.arange(0, t1, t1/ float(fs))
return np.exp(2j * np.pi * (.... |
tensorflow/docs-l10n | site/ko/tensorboard/dataframe_api.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... |
ES-DOC/esdoc-jupyterhub | notebooks/awi/cmip6/models/sandbox-3/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'awi', 'sandbox-3', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: AWI
Source ID: SANDBOX-3
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Turbulen... |
fsalmoir/PyGeM | tutorials/tutorial-2-iges.ipynb | mit | import pygem as pg
params = pg.params.FFDParameters()
params.read_parameters(filename='../tests/test_datasets/parameters_test_ffd_iges.prm')
"""
Explanation: PyGeM
Tutorial 2: Free Form Deformation on a cylinder in iges file format
In this tutorial we will show the typical workflow. In particular we are going to pars... |
tpin3694/tpin3694.github.io | neural-networks/.ipynb_checkpoints/mnist_nn-checkpoint.ipynb | mit | %matplotlib inline
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import to_categorical
import numpy as np
import matplotlib.pyplot as plt
# Load data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
print(X_train.shape)
print(y_train.shape)... |
JasonSanchez/w261 | week11/MIDS-W261-HW-11-TEMPLATE.ipynb | mit | ## Code goes here
## Drivers & Runners
## Run Scripts, S3 Sync
"""
Explanation: MIDS - w261 Machine Learning At Scale
Course Lead: Dr James G. Shanahan (email Jimi via James.Shanahan AT gmail.com)
Assignment - HW11
Name: Your Name Goes Here
Class: MIDS w261 (Section Your Section Goes Here, e.g., Summer 2016 Grou... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive/09_sequence_keras/labs/reusable-embeddings.ipynb | apache-2.0 | # change these to try this notebook out
BUCKET = 'cloud-training-demos-ml'
PROJECT = 'cloud-training-demos'
REGION = 'us-central1'
"""
Explanation: <h1>Using pre-trained embeddings with TensorFlow Hub</h1>
This notebook illustrates:
<ol>
<li>How to instantiate a TensorFlow Hub module</li>
<li>How to find pre... |
jmschrei/pomegranate | tutorials/A_Overview.ipynb | mit | %matplotlib inline
import time
import pandas
import random
import numpy
import matplotlib.pyplot as plt
import seaborn; seaborn.set_style('whitegrid')
import itertools
from pomegranate import *
random.seed(0)
numpy.random.seed(0)
numpy.set_printoptions(suppress=True)
%load_ext watermark
%watermark -m -n -p numpy,sci... |
G-Node/nix-demo | NWB pvc-6 use-case.ipynb | bsd-3-clause | from nixio import *
from utils.notebook import print_stats
from utils.plotting import Plotter
%matplotlib inline
"""
Explanation: NWB use-case pvc-6
--- Data courtesy of Jim Berg, Allen Institute for Brain Sciences ---
Here we demonstrate how data from the NWB pvc-6 use-case can be stored in a NIX file.
Context:
Who... |
ergosimulation/mpslib | scikit-mps/examples/ex01_mpslib_getting_started.ipynb | lgpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
import mpslib as mps
"""
Explanation: MPSlib: Getting started with MPSlib/scikit-mps in Python
This a small example getting started with MPSlib through an iPython notebook
End of explanation
"""
# Initialize MPSlib using default algortihm, and seetings
O = mps.mpsl... |
synthicity/activitysim | activitysim/examples/example_estimation/notebooks/16_nonmand_tour_scheduling.ipynb | agpl-3.0 | import os
import larch # !conda install larch -c conda-forge # for estimation
import pandas as pd
"""
Explanation: Estimating Non-Mandatory Tour Scheduling
This notebook illustrates how to re-estimate the non-mandatory tour scheduling component for ActivitySim. This process
includes running ActivitySim in estimatio... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/feature_engineering/labs/7_get_started_with_feature_store.ipynb | apache-2.0 | ONCE_ONLY = False
if ONCE_ONLY:
! pip3 install -U tensorflow==2.5 $USER_FLAG
! pip3 install -U tensorflow-data-validation==1.2 $USER_FLAG
! pip3 install -U tensorflow-transform==1.2 $USER_FLAG
! pip3 install -U tensorflow-io==0.18 $USER_FLAG
! pip3 install --upgrade google-cloud-aiplatform[tensorboa... |
navaro1/deep-learning | dcgan-svhn/DCGAN.ipynb | mit | %matplotlib inline
import pickle as pkl
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import loadmat
import tensorflow as tf
!mkdir data
"""
Explanation: Deep Convolutional GANs
In this notebook, you'll build a GAN using convolutional layers in the generator and discriminator. This is called a De... |
sequana/resources | coverage/10-comparison_cnvnator_virus/compare_cnvnator_virus.ipynb | bsd-3-clause | %pylab inline
matplotlib.rcParams['figure.figsize'] = [10,7]
"""
Explanation: Sequana_coverage versus CNVnator (viral genome)
This notebook compares CNVnator, CNOGpro and sequana_coverage behaviour on a viral genome instance (same as in the virus notebook).
Versions used:
- sequana 0.7.0
End of explanation
"""
!wget... |
goodwordalchemy/thinkstats_notes_and_exercises | code/chap13_survival_analysis_notes.ipynb | gpl-3.0 | preg = nsfg.ReadFemPreg()
complete = preg.query('outcome in [1,3,4]').prglngth
cdf = thinkstats2.Cdf(complete, label='cdf')
##note: property is a method that can be invoked as if
##it were a variable.
class SurvivalFunction(object):
def __init__(self, cdf, label=''):
self.cdf = cdf
self.label ... |
eds-uga/csci1360-fa16 | lectures/QA1.ipynb | mit | lloyd = {
"name": "Lloyd",
"homework": [90.0,97.0,75.0,92.0],
"quizzes": [88.0,40.0,94.0],
"tests": [75.0,90.0]
}
alice = {
"name": "Alice",
"homework": [100.0, 92.0, 98.0, 100.0],
"quizzes": [82.0, 83.0, 91.0],
"tests": [89.0, 97.0]
}
tyler = {
"name": "Tyler",
"homework": [0.0, 87.0, 75.0, 22.0],
... |
statsmodels/statsmodels.github.io | v0.12.1/examples/notebooks/generated/statespace_forecasting.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
macrodata = sm.datasets.macrodata.load_pandas().data
macrodata.index = pd.period_range('1959Q1', '2009Q3', freq='Q')
"""
Explanation: Forecasting in statsmodels
This notebook describes forecasting u... |
gear/HPSC | hw/assign1_worksheet.ipynb | gpl-3.0 | x,y,exact = a.exact(128)
fdm = np.zeros([100,128])
for i in range(100):
fdm[i] = a.get_border(a.fdm(32,32,i*10,False))
"""
Explanation: Initialization
Compute exact result with 128 points and create a fdm result array with iteration ranging from 0 to 1000 by step of 10.
End of explanation
"""
a.scatter_plot(x,... |
tensorflow/docs | site/en/install/lang_c.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... |
jonathanmorgan/msu_phd_work | methods/precision_recall/prelim_month-confusion_matrix.ipynb | lgpl-3.0 | import datetime
import math
import pandas
import pandas_ml
import sklearn
import sklearn.metrics
import six
import statsmodels
import statsmodels.api
print( "packages imported at " + str( datetime.datetime.now() ) )
%pwd
"""
Explanation: prelim_month - confusion matrix
original title: 2017.09.20 - work log - prelim... |
BrainIntensive/OnlineBrainIntensive | resources/nipype/nipype_tutorial/notebooks/example_normalize.ipynb | mit | !tree /data/antsdir/sub-0*/
"""
Explanation: Example 3: Normalize data to MNI template
This example covers the normalization of data. Some people prefer to normalize the data during the preprocessing, just before smoothing. I prefer to do the 1st-level analysis completely in subject space and only normalize the contra... |
awadalaa/DataSciencePractice | python_tutorial/Python_Numpy_Tutorial.ipynb | mit | def quicksort(arr):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) / 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(right)
print quicksort([3,6,8,10,1,2,1])
"""
Exp... |
npo-poms/pyapi | demo.ipynb | gpl-3.0 | client = Media(env="test", debug=False).configured_login(create_config_file=True)
"""
Explanation: Talking to the NPO Frontend API with PYTHON
You can instantiate a client like so
End of explanation
"""
client.url
"""
Explanation: The credentials where read from a config file. If that file would not have existed, ... |
mne-tools/mne-tools.github.io | dev/_downloads/b96d98f7c704193a3ede176aaf9433d2/85_brainstorm_phantom_ctf.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD-3-Clause
import os.path as op
import warnings
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import fit_dipole
from mne.datasets.brainstorm import bst_phantom_ctf
from mne.io import read_raw_ctf
print(__doc__)
"""
Explanation... |
lukas/scikit-class | examples/notebooks/Lesson-5-Improving-Text-Classifier.ipynb | gpl-2.0 | import pandas as pd
import numpy as np
from sklearn.model_selection import cross_val_score
df = pd.read_csv('../scikit/tweets.csv')
target = df['is_there_an_emotion_directed_at_a_brand_or_product']
text = df['tweet_text']
# We need to remove the empty rows from the text before we pass into CountVectorizer
fixed_text ... |
IS-ENES-Data/submission_forms | test/tinydb-test.ipynb | apache-2.0 | from tinydb import TinyDB, Query
import glob
import json
from pprint import pprint
from dkrz_forms.config import settings
db = TinyDB("/home/stephan/Forms/db.json")
Form = Query()
# to do: pycodestyle --show-source --show-pep8 dkrz_forms/form_handler.py
json_files = glob.glob(settings.SUBMISSION_REPO+"/test/"+"*.jso... |
taliamo/Final_Project | organ_pitch/Scripts/.ipynb_checkpoints/main_script-checkpoint.ipynb | mit | # I import useful libraries (with functions) so I can visualize my data
# I use Pandas because this dataset has word/string column titles and I like the readability features of commands and finish visual products that Pandas offers
import pandas as pd
import matplotlib.pyplot as plt
import re
import numpy as np
%matp... |
pycam/python-functions-and-modules | python_fm_1.ipynb | unlicense | print('Hello from python!') # to print some text, enclose it between quotation marks - single
print("I'm here today!") # or double
print(34) # print an integer
print(2 + 4) # print the result of an arithmetic operation
print("The answer is", 42) # print multiple expressions, separat... |
fotis007/python_intermediate | Python_2_7.ipynb | gpl-3.0 | import matplotlib.pyplot as plt
x = [10,20,30,40]
y = [5, 3, 7, 4]
plt.plot(x,y)
plt.show()
"""
Explanation: Table of Contents
<p><div class="lev2 toc-item"><a href="#Datenanalyse-III:-Viszualisierung-mit-Matplotlib-und-Seaborn" data-toc-modified-id="Datenanalyse-III:-Viszualisierung-mit-Matplotlib-und-Seaborn-01"><sp... |
xesscorp/pygmyhdl | examples/5_fsm/fsm.ipynb | mit | from pygmyhdl import *
@chunk
def counter(clk_i, cnt_o):
# Here's the counter state variable.
cnt = Bus(len(cnt_o))
# The next state logic is just an adder that adds 1 to the current cnt state variable.
@seq_logic(clk_i.posedge)
def next_state_logic():
cnt.next = cnt + 1
... |
agvergara/DatatonURJC | Day3/Lab_3_regress.ipynb | gpl-3.0 | #read data using pandas
import pandas as pd
import numpy as np
data = pd.read_csv("boston.csv")
#describe
print np.sum(data.isnull()) #Usando isnull nos proporciona un booleano en caso de que tenga algun NaN, por lo que
# si algun numero es distinto de 0 hay algun NaN
data.describe()
"""
Explanation: Métodos de Reg... |
anhaidgroup/py_entitymatching | notebooks/guides/.ipynb_checkpoints/Editing and Generate Features for Blocking Manually-checkpoint.ipynb | bsd-3-clause | # Import py_entitymatching package
import py_entitymatching as em
import os
import pandas as pd
"""
Explanation: Contents
Introduction
Generating Features for Blocking Manually
Introduction
This IPython notebook illustrates how to generate features for blocking manually.
First, we need to import py_entitymatching pa... |
XInterns/IPL-Sparkers | src/Match Outcome Prediction with IPL Data (Soham).ipynb | mit | %matplotlib inline
import numpy as np # imports a fast numerical programming library
import matplotlib.pyplot as plt #sets up plotting under plt
import pandas as pd #lets us handle data as dataframes
#sets up pandas table display
pd.set_option('display.width', 500)
pd.set_option('display.max_columns', 100)
pd.set_opti... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/feature_engineering/solutions/3_keras_basic_feat_eng.ipynb | apache-2.0 | import os
import matplotlib.pyplot as plt
import pandas as pd
import tensorflow
import tensorflow as tf
from sklearn.model_selection import train_test_split
from tensorflow import feature_column as fc
from tensorflow.keras import layers
print("TensorFlow version: ", tf.version.VERSION)
"""
Explanation: Basic Feature... |
SwissTPH/TBRU_serialTB | notebooks/1.6_ATR_Excessive_mutation.ipynb | gpl-3.0 | #Python core packages
from collections import Counter
import string
import pickle
import datetime
import warnings
warnings.filterwarnings("ignore")
#Additional Python packages
import tqdm
#Scientific packages
from scipy import stats as ss
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
fro... |
fdion/stemgraphic | notebooks/performance and usability overview.ipynb | mit | %matplotlib inline
import pandas as pd
import seaborn as sns
from stemgraphic import stem_graphic
texas = pd.read_csv('salaries.csv')
texas.describe(include='all')
"""
Explanation: Performance
Texas state salaries
About 700,000 records. We'll use time to measure execution time. In the pydata video we used %timeit wh... |
chagaz/ma2823_2016 | lab_notebooks/Lab 7 2016-11-25 Support Vector Machines.ipynb | mit | import numpy as np
%pylab inline
# Load the data
# Set up a stratified 10-fold cross-validation
from sklearn import cross_validation
folds = cross_validation.StratifiedKFold(y, 10, shuffle=True)
"""
Explanation: 2016-11-25: Support Vector Machines
In this lab, we will apply support vector classification methods to ... |
planet-os/notebooks | api-examples/cfsr_demo.ipynb | mit | %matplotlib inline
import numpy as np
from dh_py_access import package_api
import dh_py_access.lib.datahub as datahub
import xarray as xr
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from po_data_process import comparison_bar_chart, make_comparison_plot
import warnings
warnings.filterwarning... |
spa-networks/hpa | solver/example_solver.ipynb | mit | state = np.array([0, 1])
birth_rate = 2
growth_rate = 1
for t in range(1, 10):
state = solver.update_preferential_attachment(state, birth_rate, growth_rate, t)
"""
Explanation: Generalized PA equation
This solver implements the generalized PA equation
$$N_k(t+1) = N_k(t) + B\delta_{k,1} + \frac{G}{(B+G)t + K_0} [(... |
lmoresi/UoM-VIEPS-Intro-to-Python | Notebooks/Introduction/4 - Python classes.ipynb | mit | class colour(object):
rgb = (0.0,0.0,0.0)
description = "Black"
c = colour()
d = colour()
print c.description
print c.rgb
print d.description
print d.rgb
d.description = 'Blue'
d.rgb = (0.0,0.0,1.0)
print c.description
print c.rgb
print d.description
print d.rgb
"""
Explanation: Classes and ... |
chengts95/homeworkOfPowerSystem | power_system/调频计算.ipynb | gpl-2.0 | Ka=100
Kb=200
Kc=200
dPla=100
dPgb=50
K=Ka+Kb+Kc
df = lambda dPl,dPg,K: -(dPla-dPgb)/K
df1=df(dPla,dPgb,K)
trans_power=lambda Ka,Kb,Pa,Pb: (Ka*Pb-Kb*Pa)/(Ka+Kb)
Pa=dPla
Pb=dPgb
Pc=0
#BC作为一个系统
Pab=trans_power(Ka,Kb+Kc,Pa,Pb+Pc)
#AB作为一个系统
Pbc=trans_power(Kb+Ka,Kc,Pb+Pa,Pc)
df1,Pab,Pbc
"""
Explanation: 22． 三个电力系统联合运行如图... |
ethen8181/machine-learning | keras/nn_keras_hyperparameter_tuning.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)
# 1. magic to print version
# 2. magic so that the notebo... |
guedou/scapy-appveyor | doc/notebooks/Scapy in 15 minutes.ipynb | gpl-2.0 | send(IP(dst="1.2.3.4")/TCP(dport=502, options=[("MSS", 0)]))
"""
Explanation: Scapy in 15 minutes (or longer)
Guillaume Valadon & Pierre Lalet
Scapy is a powerful Python-based interactive packet manipulation program and library. It can be used to forge or decode packets for a wide number of protocols, send them on the... |
Santana9937/language-translation | .ipynb_checkpoints/dlnd_language_translation-checkpoint.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
"""
Explanation: Language Translation
In this project, you’re going... |
materialsvirtuallab/matgenb | notebooks/2018-11-6-Dopant suggestions using Pymatgen.ipynb | bsd-3-clause | # Imports we need for generating dopant suggestions
from pymatgen.analysis.structure_prediction.dopant_predictor import \
get_dopants_from_shannon_radii, get_dopants_from_substitution_probabilities
from pymatgen.analysis.local_env import CrystalNN
from pymatgen import MPRester
from pprint import pprint
# Establi... |
InsightLab/data-science-cookbook | 2019/09-clustering/cl_JoãoCastelo.ipynb | mit | # import libraries
# linear algebra
import numpy as np
# data processing
import pandas as pd
# data visualization
from matplotlib import pyplot as plt
#coolab upload
from google.colab import files
uploaded = files.upload()
# load the data with pandas
dataset = pd.read_csv('dataset.csv', header=None)
dataset = np.... |
ES-DOC/esdoc-jupyterhub | notebooks/snu/cmip6/models/sandbox-1/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'snu', 'sandbox-1', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: SNU
Source ID: SANDBOX-1
Topic: Aerosol
Sub-Topics: Transport, Emissions, Concent... |
sdpython/ensae_teaching_cs | _doc/notebooks/expose/BJKST.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
%matplotlib inline
"""
Explanation: 2A.algo - Algorithmes de streaming : généralités
Les streams (flux) de données sont aujourd'hui présents dans de nombreux domaines (réseaux sociaux, e-commerce, logs de connexion Internet, etc.). L'analyse rapide et pe... |
OpenAstronomy/workshop_sunpy_astropy | 02-Python1/02-Python-1-Lists_Instructor.ipynb | mit | odds = [1, 3, 5, 7]
print('odds are:', odds)
"""
Explanation: Storing Multiple Values in Lists
<br/>
<section class="objectives panel panel-warning">
<div class="panel-heading">
<h2><span class="fa fa-certificate"></span> Learning Objectives </h2>
</div>
<ul>
<li> Explain what a list is </li>
<li> Create and index lis... |
jaindeepali/jaindeepali.github.com | _includes/jupyter_notebooks/gym_demo.ipynb | mit | n_states = env.observation_space.n # number of states
n_actions = env.action_space.n # number of actions
print("This environment has %s states and %s actions." % (n_states, n_actions))
"""
Explanation: What we see here is a layout of a 'frozen lake'. The agent can move in this world. Some tiles of the lake are walkabl... |
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