repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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jorisvandenbossche/geopandas | doc/source/gallery/choro_legends.ipynb | bsd-3-clause | import geopandas
from geopandas import read_file
import mapclassify
mapclassify.__version__
import libpysal
libpysal.__version__
libpysal.examples.available()
_ = libpysal.examples.load_example('South')
pth = libpysal.examples.get_path('south.shp')
df = read_file(pth)
"""
Explanation: Choro legends
End of explana... |
ethen8181/machine-learning | python/class.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... |
ES-DOC/esdoc-jupyterhub | notebooks/csiro-bom/cmip6/models/sandbox-3/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'csiro-bom', 'sandbox-3', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: CSIRO-BOM
Source ID: SANDBOX-3
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, En... |
jurajmajor/ltl3tela | Experiments/Evaluation_FOSSACS19.ipynb | gpl-3.0 | from ltlcross_runner import LtlcrossRunner
from IPython.display import display
import pandas as pd
import spot
import sys
spot.setup(show_default='.a')
pd.options.display.float_format = '{: .0f}'.format
pd.options.display.latex.multicolumn_format = 'c'
"""
Explanation: Experiments for FOSSACS'19
Paper: LTL to Smaller... |
Britefury/deep-learning-tutorial-pydata2016 | INTRO ML 02 - gradient descent for machine learning.ipynb | mit | import numpy as np
import pandas as pd
"""
Explanation: Gradient descent for machine learning - a quick introduction
In this notebook we are going to use gradient descent to estimate the parameters of a model. In this case we are going to compute the parameters to convert temperatures fom Farenheit to Kelvin.
Given th... |
mspieg/dynamical-systems | .ipynb_checkpoints/Bifurcations-checkpoint.ipynb | cc0-1.0 | %matplotlib inline
import numpy
import matplotlib.pyplot as plt
"""
Explanation: <table>
<tr align=left><td><img align=left src="./images/CC-BY.png">
<td>Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Marc Spiegelman</td>
</table>... |
cdawei/digbeta | dchen/tour/data_stats.ipynb | gpl-3.0 | plt.figure(figsize=[15, 5])
ax = plt.subplot()
ax.set_xlabel('#Trajectories')
ax.set_ylabel('#Queries')
ax.set_title('Histogram of #Trajectories')
queries = sorted(dat_obj.TRAJID_GROUP_DICT.keys())
X = [len(dat_obj.TRAJID_GROUP_DICT[q]) for q in queries]
pd.Series(X).hist(ax=ax, bins=20)
"""
Explanation: Plot the hist... |
queirozfcom/python-sandbox | python3/notebooks/number-formatting-post/main.ipynb | mit | '{:.2f}'.format(8.499)
"""
Explanation: View the original blog post at http://queirozf.com/entries/python-number-formatting-examples
round to 2 decimal places
End of explanation
"""
'{:.2f}%'.format(10.12345)
"""
Explanation: format float as percentage
End of explanation
"""
import re
def truncate(num,decimal_pl... |
t-vi/candlegp | notebooks/mcmc.ipynb | apache-2.0 | import sys, os
sys.path.append(os.path.join(os.getcwd(),'..'))
import candlegp
import candlegp.training.hmc
import numpy
import torch
from torch.autograd import Variable
from matplotlib import pyplot
pyplot.style.use('ggplot')
%matplotlib inline
X = Variable(torch.linspace(-3,3,20,out=torch.DoubleTensor()))
Y = Vari... |
ALEXKIRNAS/DataScience | CS231n/assignment2/Dropout.ipynb | mit | # As usual, a bit of setup
from __future__ import print_function
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from cs231n.solv... |
netodeolino/TCC | TCC 02/Resultados/Maio/Maio.ipynb | mit | all_crime_tipos.head(10)
all_crime_tipos_top10 = all_crime_tipos.head(10)
all_crime_tipos_top10.plot(kind='barh', figsize=(12,6), color='#3f3fff')
plt.title('Top 10 crimes por tipo (Mai 2017)')
plt.xlabel('Número de crimes')
plt.ylabel('Crime')
plt.tight_layout()
ax = plt.gca()
ax.xaxis.set_major_formatter(ticker.StrM... |
dsolanno/BarcelonaRentsStatus | airbnb data exploration/host_until/calculate_host_until.ipynb | mit | df1 = pd.read_csv('listings/30042015/30042015.csv', sep = ";")
df2 = pd.read_csv('listings/17072015/17072015.csv', sep = ";")
df3 = pd.read_csv('listings/02102015/02102015.csv', sep = ";")
df4 = pd.read_csv('listings/03012016/03012016.csv', sep = ";")
df5 = pd.read_csv('listings/08122016/08122016.csv', sep = ";")
df6 =... |
samgoodgame/sf_crime | iterations/KK_scripts/W207_Final_Project_logisticRegressionOnly_updated_08_20_1230.ipynb | mit | # Additional Libraries
%matplotlib inline
import matplotlib.pyplot as plt
# Import relevant libraries:
import time
import numpy as np
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from sklearn import preprocessing
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import... |
malogrisard/NTDScourse | algorithms/02_ex_clustering.ipynb | mit | # Load libraries
# Math
import numpy as np
# Visualization
%matplotlib notebook
import matplotlib.pyplot as plt
plt.rcParams.update({'figure.max_open_warning': 0})
from mpl_toolkits.axes_grid1 import make_axes_locatable
from scipy import ndimage
# Print output of LFR code
import subprocess
# Sparse matrix
import ... |
PWhiddy/kbmod | notebooks/kbmod_CNN.ipynb | bsd-2-clause | import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.utils import np_utils
%matplotlib inline
"""
Explanation: Creating a CNN to identify real objects in kbmod data
End of explanation
"""
data = np.genfromtxt('../data/postage_stamp_training.dat')
"""
Explanation: Training Set
Here we are going... |
hailing-li/hailing-li.github.io | HW4/Frequent Itemset.ipynb | mit | import sqlite3
import pandas as pd
from pprint import pprint
from pandas import DataFrame
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import math
import numpy as np
conn = sqlite3.connect('bicycle.db')
c=conn.cursor()
c.execute('SELECT LoTemp, Pre... |
hashiprobr/redes-sociais | encontro04.ipynb | gpl-3.0 | import numpy as np
import socnet as sn
import easyplot as ep
"""
Explanation: Encontro 04: Suporte para Análise Espectral de Grafos
Este guia foi escrito para ajudar você a atingir os seguintes objetivos:
lembrar conceitos básicos de geometria analítica e álgebra linear;
explicar conceitos básicos de matriz de adjacê... |
uwbmrb/BMRB-API | documentation/notebooks/Vicinal Disulfides.ipynb | gpl-3.0 | %%capture
!pip install requests;
import requests
"""
Explanation: Example notebook for using the PDB and BMRB APIs for structural biology data science applications
Introduction
This notebook is designed to walk through some sample queries of both the PDB and BMRB in order to correlate NMR parameters with structure. ... |
Leguark/pynoddy | docs/notebooks/.ipynb_checkpoints/Feature-Sampling-checkpoint.ipynb | gpl-2.0 | from IPython.core.display import HTML
css_file = 'pynoddy.css'
HTML(open(css_file, "r").read())
import sys, os
import matplotlib.pyplot as plt
# adjust some settings for matplotlib
from matplotlib import rcParams
# print rcParams
rcParams['font.size'] = 15
# determine path of repository to set paths corretly below
rep... |
Upward-Spiral-Science/spect-team | Code/Assignment-9/Independent Analysis-3.ipynb | apache-2.0 | # Standard
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import statsmodels.api as sm
# Dimensionality reduction and Clustering
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.cluster import MeanShift, estimate_bandwidth
from sklearn imp... |
caromedellin/Python-notes | python-intro/Untitled1.ipynb | mit | import csv
import requests
"""
Explanation: APIs
There are a few cases when a data set isn't a good enough solution.
An Application Program Interface API is an alternative, it allows you to dinamically query andretrive data
End of explanation
"""
response = requests.get("http://api.open-notify.org/iss-now.json")
res... |
taesiri/noteobooks | old:misc/graph_analysis/check_planarity.ipynb | mit | # generate random graph
G = nx.generators.fast_gnp_random_graph(10, 0.4)
# check planarity and draw the graph
print("The graph is {0} planar".format("" if planarity.is_planar(G) else "not"))
if(planarity.is_planar(G)):
planarity.draw(G)
nx.draw(G)
"""
Explanation: Hello Networkx and Planarity
NetworkX Homepage
Pl... |
maubarsom/biotico-tools | ipython_nb/blast_hits_visualization.ipynb | apache-2.0 | blast_cols = ["query_id","subject_id","pct_id","ali_len","mism","gap_open","q_start","q_end","s_start","s_end","e_value","bitscore"]
pax_hits = pd.read_csv("PAXhs_vs_Pw.tblastn.txt",sep="\t",header=None,names=blast_cols)
print( "Size of dataframe: {}".format(pax_hits.shape ))
pax_hits.head()
"""
Explanation: 1. Read T... |
google-research/google-research | linear_identifiability/identifiability_of_GPT_2_models.ipynb | apache-2.0 | import numpy as np
import torch
import matplotlib.pyplot as plt
from tqdm import tqdm
num_layers = 13
num_sentences = 2000
# Install and import Huggingface Transformer models
!pip install transformers ftfy spacy
from transformers import *
def get_model(model_id):
print('Loading model: ', model_id)
models = {
... |
abatula/MachineLearningIntro | Diabetes_DataSet.ipynb | gpl-2.0 | # Print figures in the notebook
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets # Import datasets from scikit-learn
import matplotlib.cm as cm
from matplotlib.colors import Normalize
"""
Explanation: What is a dataset?
A dataset is a collection of information (or da... |
philiptromans/mapswipe-ml-dataset-generator | 1 - Analysing InceptionV3 results.ipynb | apache-2.0 | from mapswipe_analysis import *
all_projects_solution = Solution(
ground_truth_solutions_file_to_map('../experiment_1/all_projects_dataset/test/solutions.csv'),
predictions_file_to_map('../experiment_1/inception_v3_all_layers.results')
)
all_projects_solution.accuracy
"""
Explanation: Much of the world isn't ... |
Britefury/deep-learning-tutorial-pydata2016 | TUTORIAL 05 - Dogs vs cats with transfer learning and data augmentation.ipynb | mit | %matplotlib inline
"""
Explanation: Dogs vs Cats with Transfer Learning and Data Augmentation
In this Notebook we're going to use transfer learning to attempt to crack the Dogs vs Cats Kaggle competition.
We add data augmentation and assess its effectiveness.
We are going to downsample the images to 64x64; that's pret... |
mne-tools/mne-tools.github.io | 0.18/_downloads/e79896208a72b920b6d32cefb5c9c4b8/plot_point_spread.ipynb | bsd-3-clause | import os.path as op
import numpy as np
from mayavi import mlab
import mne
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, apply_inverse
from mne.simulation import simulate_stc, simulate_evoked
"""
Explanation: Corrupt known signal with point spread
The aim of this tutorial is to... |
GraysonR/titanic-data-analysis | 2015-12-24-titanic-gender-grouping.ipynb | mit | # Import magic
%matplotlib inline
# More imports
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
#Set general plot properties
sns.set_style("white")
sns.set_context({"figure.figsize": (18, 8)})
# Load CSV data
titanic_data = pd.read_csv('titanic_data.csv')
survived = tit... |
fdion/infographics_research | Figure1.6.ipynb | mit | !wget 'http://esa.un.org/unpd/wpp/DVD/Files/1_Indicators%20(Standard)/EXCEL_FILES/2_Fertility/WPP2015_FERT_F04_TOTAL_FERTILITY.XLS'
"""
Explanation: Reproducible visualization
In "The Functional Art: An introduction to information graphics and visualization" by Alberto Cairo, on page 12 we are presented with a visuali... |
rsignell-usgs/notebook | NEXRAD/.ipynb_checkpoints/THREDDS_Radar_Server-checkpoint.ipynb | mit | import matplotlib
import warnings
warnings.filterwarnings("ignore", category=matplotlib.cbook.MatplotlibDeprecationWarning)
%matplotlib inline
"""
Explanation: Using Python to Access NCEI Archived NEXRAD Level 2 Data
This notebook shows how to access the THREDDS Data Server (TDS) instance that is serving up archived N... |
PyDataMadrid2016/Conference-Info | workshops_materials/20160408_1100_Pandas_for_beginners/tutorial/EN - Tutorial 04 - Selecting data.ipynb | mit | # first, the imports
import os
import datetime as dt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import display
np.random.seed(19760812)
%matplotlib inline
# We read the data in the file 'mast.txt'
ipath = os.path.join('Datos', 'mast.txt')
def dateparse(date, time):
... |
danielgoncalvesti/BIGDATA2017 | Atividade03/Lab4a_regressao_linear.ipynb | gpl-3.0 | sc = SparkContext.getOrCreate()
# carregar base de dados
from test_helper import Test
import os.path
baseDir = os.path.join('Data')
inputPath = os.path.join('millionsong.txt')
fileName = os.path.join(baseDir, inputPath)
numPartitions = 2
rawData = sc.textFile(fileName, numPartitions)
# EXERCICIO
numPoints = rawData.... |
deadbeatfour/notebooks | SWE_square_well/swe_square_well.ipynb | mit | delta = 1 # The spacing between neighboring lattice points
L = 1000 # The ends of the lattice
length = 10
momentum = 1
lattice = arange(-L,L,delta)
v = vectorize(potential)
plot(v(lattice))
hamiltonian = np.zeros((lattice.shape[0],lattice.shape[0]))
for row in range(lattice.shape[0]):
for col in range(lattice.shap... |
quantumlib/ReCirq | docs/qaoa/example_problems.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... |
AaronCWong/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):
plt.figure(figsize=(15,2))
x = np.linspace(0,4... |
qutip/qutip-notebooks | examples/piqs_introduction.ipynb | lgpl-3.0 | import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib import cm
from qutip import *
from qutip.piqs import *
from qutip.cy.piqs import j_min
"""
Explanation: Introducing the Permutational Invariant Quantum Solver (PIQS)
Notebook Author: Nathan Shammah (nathan.shammah@gmail.com)
PIQS code: Nathan ... |
DTOcean/dtocean-core | notebooks/DTOcean Mooring and Foundations Example.ipynb | gpl-3.0 | %matplotlib inline
from IPython.display import display, HTML
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (14.0, 8.0)
import numpy as np
from dtocean_core import start_logging
from dtocean_core.core import Core
from dtocean_core.menu import ModuleMenu, ProjectMenu
from dtocean_core.pipeline impo... |
sgrindy/Bayesian-estimation-of-relaxation-spectra | Double_Maxwell_Lognormal_prior.ipynb | mit | def H(tau):
h1 = 1; tau1 = 0.03; sd1 = 0.5;
h2 = 7; tau2 = 10; sd2 = 0.5;
term1 = h1/np.sqrt(2*sd1**2*np.pi) * np.exp(-(np.log10(tau/tau1)**2)/(2*sd1**2))
term2 = h2/np.sqrt(2*sd2**2*np.pi) * np.exp(-(np.log10(tau/tau2)**2)/(2*sd2**2))
return term1 + term2
Nfreq = 50
Nmodes = 30
w = np.logspace(-4,... |
julienchastang/unidata-python-workshop | notebooks/CF Conventions/NetCDF and CF - The Basics.ipynb | mit | # Import some useful Python tools
from datetime import datetime, timedelta
import numpy as np
# Twelve hours of hourly output starting at 22Z today
start = datetime.utcnow().replace(hour=22, minute=0, second=0, microsecond=0)
times = np.array([start + timedelta(hours=h) for h in range(13)])
# 3km spacing in x and y
... |
ES-DOC/esdoc-jupyterhub | notebooks/miroc/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', 'miroc', 'sandbox-1', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: MIROC
Source ID: SANDBOX-1
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy Bal... |
tensorflow/tfx | tfx/examples/airflow_workshop/notebooks/step3.ipynb | apache-2.0 | from __future__ import print_function
!pip install -q papermill
!pip install -q matplotlib
!pip install -q networkx
import os
import tfx_utils
%matplotlib notebook
def _make_default_sqlite_uri(pipeline_name):
return os.path.join(os.environ['HOME'], 'airflow/tfx/metadata', pipeline_name, 'metadata.db')
def get_m... |
dennys-bd/Coursera-Machine-Learning-Specialization | Course 2 - ML, Regression/week-3-polynomial-regression-assignment-blank.ipynb | mit | import graphlab
"""
Explanation: Regression Week 3: Assessing Fit (polynomial regression)
In this notebook you will compare different regression models in order to assess which model fits best. We will be using polynomial regression as a means to examine this topic. In particular you will:
* Write a function to take a... |
y2ee201/Deep-Learning-Nanodegree | autoencoder/Convolutional_Autoencoder_Solution.ipynb | mit | %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... |
opesci/devito | examples/performance/00_overview.ipynb | mit | from examples.performance import unidiff_output, print_kernel
"""
Explanation: Performance optimization overview
The purpose of this tutorial is twofold
Illustrate the performance optimizations applied to the code generated by an Operator.
Describe the options Devito provides to users to steer the optimization proces... |
buruzaemon/svd | PCA.ipynb | bsd-3-clause | corr = X_zscaled.corr()
tmp = pd.np.triu(corr) - np.eye(corr.shape[0])
tmp = tmp.flatten()
tmp = tmp[np.nonzero(tmp)]
tmp = pd.Series(np.abs(tmp))
print('Correlation matrix:\n\n{}\n\n'.format(corr.values))
print('Multicollinearity check using off-diagonal values:\n\n{}'.format(tmp.describe()))
"""
Explanation: Mul... |
google/data-pills | pills/CM/[DATA_PILL]_[CM]_Campaign_Overlap_(ADH)_v1.ipynb | apache-2.0 | # Install additional packages
!pip install -q matplotlib-venn
# Import all necessary libs
import json
import sys
import argparse
import pprint
import random
import datetime
import pandas as pd
from google_auth_oauthlib.flow import InstalledAppFlow
from googleapiclient import discovery
from oauthlib.oauth2.rfc6749.err... |
tcstewar/testing_notebooks | Data extraction from Nengo.ipynb | gpl-2.0 | model = nengo.Network()
with model:
def stim_a_func(t):
return np.sin(t*2*np.pi)
stim_a = nengo.Node(stim_a_func)
a = nengo.Ensemble(n_neurons=50, dimensions=1)
nengo.Connection(stim_a, a)
def stim_b_func(t):
return np.cos(t*np.pi)
stim_b = nengo.Node(stim_b_func)
b = ne... |
GPflow/GPflowOpt | doc/source/notebooks/hyperopt.ipynb | apache-2.0 | %matplotlib inline
import matplotlib.pyplot as plt
# Loading airline data
import numpy as np
data = np.load('airline.npz')
X_train, Y_train = data['X_train'], data['Y_train']
D = Y_train.shape[1];
"""
Explanation: Bayesian Optimization of Hyperparameters
Vincent Dutordoir, Joachim van der Herten
Introduction
Th... |
colonelzentor/occmodel | examples/OCCT_Bottle_Example.ipynb | gpl-2.0 | height = 70.
width = 50.
thickness = 30.
pnt1 = [-width/2., 0., 0.]
pnt2 = [-width/2., -thickness/4., 0.]
pnt3 = [0., -thickness/2., 0.]
pnt4 = [width/2., -thickness/4., 0.]
pnt5 = [width/2., 0., 0.]
edge1 = Edge().createLine(start=pnt1, end=pnt2)
edge2 = Edge().createArc3P(start=pnt2, ... |
QInfer/qinfer-examples | scoremixin_example.ipynb | agpl-3.0 | from __future__ import division, print_function
%matplotlib inline
from qinfer import ScoreMixin, SimplePrecessionModel, RandomizedBenchmarkingModel
import numpy as np
import matplotlib.pyplot as plt
try:
plt.style.use('ggplot')
except:
pass
"""
Explanation: Fisher Score Mixin Example
This notebook demonstr... |
hobgreenson/chicago_employees | ChicagoEmployeeGenderSalary.ipynb | mit | workers = pd.read_csv('Current_Employee_Names__Salaries__and_Position_Titles.csv')
"""
Explanation: Introduction
I downloaded a CSV of City of Chicago employee salary data,
which includes the names, titles, departments and salaries of Chicago employees. I was
interested to see whether men and women earn similar salar... |
ES-DOC/esdoc-jupyterhub | notebooks/ec-earth-consortium/cmip6/models/ec-earth3/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'ec-earth3', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: EC-EARTH-CONSORTIUM
Source ID: EC-EARTH3
Sub-Topics: Radiative ... |
mayank-johri/LearnSeleniumUsingPython | Section 3 - Machine Learning/ThirdParty-scikit-learn-videos-master/07_cross_validation.ipynb | gpl-3.0 | from sklearn.datasets import load_iris
from sklearn.cross_validation import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn import metrics
# read in the iris data
iris = load_iris()
# create X (features) and y (response)
X = iris.data
y = iris.target
# use train/test split with diffe... |
luciansmith/sedml-test-suite | archives/sbml-test-suite/convert-to-combine-arch.ipynb | bsd-3-clause | import pprint, tellurium as te
# level and version of SBML to use
lv_string = 'l3v1'
# run all supported test cases
# cases = te.getSupportedTestCases()
# run just a subset containing all supported cases between 1 and 10
cases = te.getSupportedTestCases(981)
print('Using the following {} cases:'.format(len(cases)))
# ... |
stephank16/enes_graph_use_case | prov_templates/old/PROV-Templates-python-work.ipynb | gpl-3.0 | # Define the variable parts in the template as dictionary keys
# dictionary values are the prov template variable bindings in one case
# and correspond to the variable instance settings in the other case
import prov.model as prov
template_dict = {
'var_author':'var:author',
'var_value':'var:value',
'var_nam... |
shanghai-machine-learning-meetup/presentations | peek_into_keras_backend/Peek into Keras backend.ipynb | apache-2.0 | import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import tensorflow as tf
from keras.layers.embeddings import Embedding
from keras.layers.core import Dense, Dropout, Lambda
from keras.layers import Input, GlobalAveragePooling1D
from keras.layers.convolutional import Conv1D
fro... |
FordyceLab/AcqPack | notebooks/Test20170524.ipynb | mit | import time
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inline
"""
Explanation: SETUP
End of explanation
"""
# config directory must have "__init__.py" file
# from the 'config' directory, import the following classes:
from config import Motor, ASI_Controller, Autosipper
from co... |
kunaltyagi/SDES | notes/python/p_norvig/logic/Mean Misanthrope Density.ipynb | gpl-3.0 | from statistics import mean
def occ(n):
"The expected occupancy for a row of n houses (under misanthrope rules)."
return (0 if n == 0 else
1 if n == 1 else
mean(occ(L) + 1 + occ(R)
for (L, R) in runs(n)))
def runs(n):
"""A list [(L, R), ...] where the i-th tuple co... |
feststelltaste/software-analytics | notebooks/Developers' Habits (Linux Edition).ipynb | gpl-3.0 | import pandas as pd
raw = pd.read_csv(
r'../../linux/git_timestamp_author_email.log',
sep="\t",
encoding="latin-1",
header=None,
names=['unix_timestamp', 'author', 'email'])
# create separate columns for time data
raw[['timestamp', 'timezone']] = raw['unix_timestamp'].str.split(" ", expand=True)
#... |
tensorflow/docs-l10n | site/en-snapshot/addons/tutorials/optimizers_conditionalgradient.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0
# 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 License is d... |
scotthuang1989/Python-3-Module-of-the-Week | developer_tools/Python2ToPython3.ipynb | apache-2.0 | # %load example.py
def greet(name):
print "Hello, {0}!".format(name)
print "What's your name?"
name = raw_input()
greet(name)
# we can convert this file to python3-compliant
!2to3 example.py
"""
Explanation: Automated Python 2 to 3 code translation
2to3 is a Python program that reads Python 2.x source code and a... |
hanleilei/note | training/submit/PythonExercises3rdAnd4th.ipynb | cc0-1.0 | import os
class Dog(object):
def __init__(self):
self.name = "Dog"
def bark(self):
return "woof!"
class Cat(object):
def __init__(self):
self.name = "Cat"
def meow(self):
return "meow!"
class Human(object):
def __init__(self):
self.name = "Human"
... |
mne-tools/mne-tools.github.io | 0.21/_downloads/306dcf0b43a155a02804528d597e4e81/plot_roi_erpimage_by_rt.ipynb | bsd-3-clause | # Authors: Jona Sassenhagen <jona.sassenhagen@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne.event import define_target_events
from mne.channels import make_1020_channel_selections
print(__doc__)
"""
Explanation: Plot single trial activity, grouped by ROI and sorted by RT
This will produce what is someti... |
autumn-lake/Facebook-V-Predicting-Check-Ins | timeIsMin.ipynb | mit | Now, to confirm, let us do a little bit of simple tests.
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import cm as cm
import numpy as np
%matplotlib inline
train=pd.read_csv('../train.csv')
train.describe()
# first take a look at the whole picture of time data:
train['time'].plot(kind='h... |
jrbourbeau/cr-composition | notebooks/legacy/lightheavy/parameter-tuning/RF-parameter-tuning.ipynb | mit | import sys
sys.path.append('/home/jbourbeau/cr-composition')
print('Added to PYTHONPATH')
from __future__ import division, print_function
from collections import defaultdict
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import seaborn.apionly as sns... |
nproctor/phys202-2015-work | assignments/assignment04/MatplotlibExercises.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Visualization 1: Matplotlib Basics Exercises
End of explanation
"""
data = np.random.randn(2, 100)
plt.scatter(data[0], data[1])
plt.xlabel("Random Number 1", fontsize=12, color="#666666")
plt.ylabel("Random Number 2", fontsize=12... |
macks22/gensim | docs/notebooks/doc2vec-IMDB.ipynb | lgpl-2.1 | import locale
import glob
import os.path
import requests
import tarfile
import sys
import codecs
import smart_open
dirname = 'aclImdb'
filename = 'aclImdb_v1.tar.gz'
locale.setlocale(locale.LC_ALL, 'C')
if sys.version > '3':
control_chars = [chr(0x85)]
else:
control_chars = [unichr(0x85)]
# Convert text to l... |
llclave/Springboard-Mini-Projects | data_wrangling_json/json_exercise.ipynb | mit | # Import required packages
import pandas as pd
import json
from pandas.io.json import json_normalize
# Read JSON file as Pandas DataFrame object
world_bank_df = pd.read_json('data/world_bank_projects.json')
world_bank_df
# Check DataFrame info
world_bank_df.info()
# List top 10 countries with the most projects
world... |
dataDogma/Computer-Science | Courses/DAT-208x/DAT208x - Week 3 - Section 2 - Methods.ipynb | gpl-3.0 | """
Instructions:
+ Use the upper() method on room and store the result in room_up.
Use the dot notation.
+ Print out room and room_up. Did both change?
+ Print out the number of o's on the variable room by calling count()
on room and passing the letter "o" as an input to the metho... |
ES-DOC/esdoc-jupyterhub | notebooks/hammoz-consortium/cmip6/models/mpiesm-1-2-ham/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'hammoz-consortium', 'mpiesm-1-2-ham', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: HAMMOZ-CONSORTIUM
Source ID: MPIESM-1-2-HAM
Topic: Aerosol
Sub... |
cathalmccabe/PYNQ | boards/Pynq-Z1/base/notebooks/arduino/arduino_lcd18.ipynb | bsd-3-clause | from pynq.overlays.base import BaseOverlay
base = BaseOverlay("base.bit")
"""
Explanation: Arduino LCD Example using AdaFruit 1.8" LCD Shield
This notebook shows a demo on Adafruit 1.8" LCD shield.
End of explanation
"""
from pynq.lib.arduino import Arduino_LCD18
lcd = Arduino_LCD18(base.ARDUINO)
"""
Explanation: ... |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/prod/n10_dyna_q_with_predictor_full_training.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
import pickle
%matplotlib inline
%pylab inli... |
tritemio/multispot_paper | usALEX - Corrections - Direct excitation fit.ipynb | mit | data_file = 'results/usALEX-5samples-PR-raw-dir_ex_aa-fit-AexAem.csv'
"""
Explanation: Direct ecitation coefficient fit
This notebook estracts the direct excitation coefficient from the set of 5 us-ALEX smFRET measurements.
What it does?
This notebook performs a weighted average of direct excitation coefficient fitt... |
NYUDataBootcamp/Projects | UG_S17/Zhou-Stock Pitch.ipynb | mit | import requests
import sys # system module
import pandas as pd # data package
import pandas_datareader.data as web
import datetime
import matplotlib.pyplot as plt # graphics module
import datetime as dt # date and time module
import numpy as np ... |
chris1610/pbpython | notebooks/Selecting_Columns_in_DataFrame.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
df = pd.read_csv(
'https://data.cityofnewyork.us/api/views/vfnx-vebw/rows.csv?accessType=DOWNLOAD&bom=true&format=true'
)
"""
Explanation: Tips for Selecting Columns in a DataFrame
Notebook to accompany this post.
End of explanation
"""
col_mapping = [f"{c[0]}:{c[1]}" for ... |
akutuzov/gensim | docs/notebooks/topic_coherence_tutorial.ipynb | lgpl-2.1 | import numpy as np
import logging
try:
import pyLDAvis.gensim
except ImportError:
ValueError("SKIP: please install pyLDAvis")
import json
import warnings
warnings.filterwarnings('ignore') # To ignore all warnings that arise here to enhance clarity
from gensim.models.coherencemodel import CoherenceModel
f... |
kazzz24/deep-learning | 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... |
peterdalle/mij | 2 Web scraping and APIs/Web scraping and Exercise.ipynb | gpl-3.0 | !pip install lxml
!pip install BeautifulSoup4
import urllib.request
from lxml import html
from bs4 import BeautifulSoup
"""
Explanation: Web scraping
We will scrape data from:
Internet Movie Database
Washington Post
Wikipedia
Ethics:
Scraping can be done much faster than humans. Pause ~1 second before scraping the... |
VandyAstroML/Vanderbilt_Computational_Bootcamp | notebooks/Week_05/05_Numpy_Matplotlib.ipynb | mit | import numpy as np
"""
Explanation: Week 5 - Numpy & Matplotlib
Today's Agenda
Numpy
Matplotlib
Numpy - Numerical Python
From their website (http://www.numpy.org/):
NumPy is the fundamental package for scientific computing with Python.
* a powerful N-dimensional array object
* sophisticated (broadcasting) function... |
ML4DS/ML4all | P3.Python_datos/Intro3_Working_with_Data_student.ipynb | mit | # Let's import some libraries
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Working with data in Python
Notebook version:
* 1.0 (Sep 3, 2018) - First TMDE version
* 1.1 (Sep 14, 2018) - Minor fixes
Authors: Vanessa Gómez Verdejo (vanessa@tsc.uc3m.es), Óscar García Hinde (oghinde@tsc.uc3m.es),
... |
ledeprogram/algorithms | class7/donow/benzaquen_mercy_donow_7.ipynb | gpl-3.0 | import pandas as pd
%matplotlib inline
import numpy as np
from sklearn.linear_model import LogisticRegression
"""
Explanation: Apply logistic regression to categorize whether a county had high mortality rate due to contamination
1. Import the necessary packages to read in the data, plot, and create a logistic regressi... |
Aggieyixin/cjc2016 | code/04.PythonCrawler_beautifulsoup.ipynb | mit | import urllib2
from bs4 import BeautifulSoup
"""
Explanation: 数据抓取：
Beautifulsoup简介
王成军
wangchengjun@nju.edu.cn
计算传播网 http://computational-communication.com
需要解决的问题
页面解析
获取Javascript隐藏源数据
自动翻页
自动登录
连接API接口
End of explanation
"""
url = 'file:///Users/chengjun/GitHub/cjc2016/data/test.html'
content = urllib2.urlop... |
StudyExchange/Udacity | MachineLearning(Advanced)/p5_image_classification/image_classification_ZH-CN.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'
class DLProgress(tqdm):
last_block = 0
def hoo... |
jbocharov-mids/W207-Machine-Learning | John_Bocharov_p2.ipynb | apache-2.0 | # This tells matplotlib not to try opening a new window for each plot.
%matplotlib inline
# General libraries.
import re
import numpy as np
import matplotlib.pyplot as plt
# SK-learn libraries for learning.
from sklearn.pipeline import Pipeline
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_mo... |
turbomanage/training-data-analyst | courses/machine_learning/tensorflow/c_batched.ipynb | apache-2.0 | import tensorflow as tf
import numpy as np
import shutil
print(tf.__version__)
"""
Explanation: <h1> 2c. Refactoring to add batching and feature-creation </h1>
In this notebook, we continue reading the same small dataset, but refactor our ML pipeline in two small, but significant, ways:
<ol>
<li> Refactor the input t... |
lao-tseu-is-alive/mynotebooks | cgSVGDisplay.ipynb | gpl-2.0 | %config InlineBackend.figure_format = 'svg'
url_svg = 'http://clipartist.net/social/clipartist.net/B/base_tux_g_v_linux.svg'
from IPython.display import SVG, display, HTML
# testing svg inside jupyter next one does not support width parameter at the time of writing
#display(SVG(url=url_svg))
display(HTML('<img src="' +... |
jhillairet/scikit-rf | doc/source/examples/metrology/NanoVNA_V2_4port-splitter.ipynb | bsd-3-clause | import skrf
from skrf.calibration import TwoPortOnePath
# load networks of the raw calibration standard measurements
short_raw = skrf.Network('./data_MiniCircuits_splitter/cal_short_raw.s2p')
open_raw = skrf.Network('./data_MiniCircuits_splitter/cal_open_raw.s2p')
match_raw = skrf.Network('./data_MiniCircuits_splitter... |
pmgbergen/porepy | tutorials/mpsa.ipynb | gpl-3.0 | import numpy as np
import porepy as pp
# Create grid
n = 5
g = pp.CartGrid([n,n])
g.compute_geometry()
"""
Explanation: Multi-point stress approximation (MPSA)
Porepy supports mpsa discretization for linear elasticity problem:
\begin{equation}
\nabla\cdot \sigma = -\vec f,\quad \vec x \in \Omega
\end{equation}
where... |
mabevillar/rmtk | rmtk/plotting/hazard_outputs/plot_hazard_curves.ipynb | agpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
from plot_hazard_outputs import HazardCurve, UniformHazardSpectra
hazard_curve_file = "../sample_outputs/hazard/hazard_curve.xml"
hazard_curves = HazardCurve(hazard_curve_file)
"""
Explanation: Hazard Curves and Uniform Hazard Spectra
This IPython notebook allows the... |
Diyago/Machine-Learning-scripts | time series regression/DL aproach for timeseries/Air Pressure MLP.ipynb | apache-2.0 | from __future__ import print_function
import os
import sys
import pandas as pd
import numpy as np
%matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns
import datetime
#set current working directory
os.chdir('D:/Practical Time Series')
#Read the dataset into a pandas.DataFrame
df = pd.read_csv... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/tensorflow_extended/solutions/Vertex_AI_Training_and_Serving_with_TFX_and_Vertex_Pipelines.ipynb | apache-2.0 | # Use the latest version of pip.
!pip install --upgrade pip
!pip install --upgrade "tfx[kfp]<2"
"""
Explanation: Training and Serving with TFX and Vertex Pipelines
Learning objectives
Prepare example data.
Create a pipeline.
Run the pipeline on Vertex Pipelines.
Test with a prediction request.
Introduction
In this n... |
CodyKochmann/battle_tested | tutorials/hardening_filters.ipynb | mit | def list_of_strings_v1(iterable):
""" converts the iterable input into a list of strings """
# build the output
out = [str(i) for i in iterable]
# validate the output
for i in out:
assert type(i) == str
# return
return out
"""
Explanation: battle_tested was originally created to har... |
celiasmith/syde556 | SYDE 556 Lecture 5 Dynamics.ipynb | gpl-2.0 | %pylab inline
import nengo
model = nengo.Network()
with model:
ensA = nengo.Ensemble(100, dimensions=1)
def feedback(x):
return x+1
conn = nengo.Connection(ensA, ensA, function=feedback, synapse = 0.1)
ensA_p = nengo.Probe(ensA, synapse=.01)
sim = nengo.Simulator(model)
sim.ru... |
amueller/scipy-2016-sklearn | notebooks/03 Data Representation for Machine Learning.ipynb | cc0-1.0 | from sklearn.datasets import load_iris
iris = load_iris()
"""
Explanation: The use of watermark (above) is optional, and we use it to keep track of the changes while developing the tutorial material. (You can install this IPython extension via "pip install watermark". For more information, please see: https://github.c... |
itoledoc/python_coffee | .ipynb_checkpoints/itoledoc_coffee-checkpoint.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
%matplotlib inline
"""
Explanation: Python Coffee, November 5, 2015
Import required libraries
End of explanation
"""
import plotly.tools as tls
import plotly.plotly as py
import cufflinks as cf
import plot... |
5agado/data-science-learning | deep learning/StyleGAN/StyleGAN - Explore Directions.ipynb | apache-2.0 | is_stylegan_v1 = False
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import sys
import os
from datetime import datetime
from tqdm import tqdm
# ffmpeg installation location, for creating videos
plt.rcParams['animation.ffmpeg_path'] = str('/usr/bin/ffmpeg')
import ipywidgets as widgets
fr... |
Danghor/Formal-Languages | Python/Regexp-Tutorial.ipynb | gpl-2.0 | import re
"""
Explanation: Regular Expressions in Python (A Short Tutorial)
This is a tutorial showing how regular expressions are supported in Python.
The assumption is that the reader already has a grasp of the concept of
regular expressions as it is taught in lectures
on formal languages, for example in
Formal L... |
fonnesbeck/scipy2015_tutorial | notebooks/1. Data Preparation.ipynb | cc0-1.0 | counts = pd.Series([632, 1638, 569, 115])
counts
"""
Explanation: Data Preparation using pandas
An initial step in statistical data analysis is the preparation of the data to be used in the analysis. In practice, ~~a little~~ ~~some~~ ~~much~~ the majority of the actual time spent on a statistical modeling project is ... |
biosustain/cameo-notebooks | 02-import-models.ipynb | apache-2.0 | less data/e_coli_core.xml
from cameo import load_model
model = load_model('data/e_coli_core.xml')
model
"""
Explanation: Import models
Import models from files
The function :class:~cameo.io.load_model accepts a number of different input formats.
SBML (Systems Biology Markup Language).
JSON
Pickle (pickled models)
M... |
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