repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
FluVigilanciaBR/fludashboard | Notebooks/Season_color_new_specs.ipynb | gpl-3.0 | import pandas as pd
from fludashboard.libs.flu_data import FluDB
fluDB = FluDB()
territory_name = 'Rio Grande do Sul'
year=2016
territory_id = fluDB.get_territory_id_from_name(territory_name)
dataset_from_id = {
1: 'srag',
2: 'sragflu',
3: 'obitoflu'
}
def contingency_alert(dataset_id: int, year: int, ter... |
dsiufl/2015-Fall-Hadoop | notes/4-pyspark-example.ipynb | mit | import findspark
import os
findspark.init('/home/ubuntu/shortcourse/spark-1.5.1-bin-hadoop2.6')
from pyspark import SparkContext, SparkConf
conf = SparkConf().setAppName("pyspark-example").setMaster("local[2]")
sc = SparkContext(conf=conf)
"""
Explanation: Example: Use pyspark to process GDELT event data
GDELT: Globa... |
zhmz90/CS231N | assign/assignment1/.ipynb_checkpoints/svm-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
#lt.rcParams['figure.figsize'] = (10.... |
ajhenrikson/phys202-2015-work | assignments/assignment04/TheoryAndPracticeEx01.ipynb | mit | from IPython.display import Image
"""
Explanation: Theory and Practice of Visualization Exercise 1
Imports
End of explanation
"""
# Add your filename and uncomment the following line:
Image(filename='main.0 (1).png')
"""
Explanation: Graphical excellence and integrity
Find a data-focused visualization on one of the... |
markdewing/qmc_algorithms | Variational/Variational_Helium.ipynb | mit | r1 = Symbol('r1') # magnitude of vector r1 (electron 1 to nucleus distance)
r2 = Symbol('r2') # magnitude of vector r2 (electron 2 to nucleus distance)
r12 = Symbol('r12') # |r2-r1| (magnitude of vector r2-r1)
"""
Explanation: Energy of a Helium atom
The Hamiltonian for this system is
$$-\frac{1}{2} (\nabla_1^2... |
loldja/loldja.github.io | assets/code/blog/mta_cleaning.ipynb | mit | import pandas as pd
import numpy as np
import os
%matplotlib inline
import matplotlib.pyplot as plt
# Data source: http://web.mta.info/developers/turnstile.html
#Download all desired weeks from website, store them in a folder called ../data/mta_turnstiles
# This will load combined CSV files that are in this folder in... |
ES-DOC/esdoc-jupyterhub | notebooks/dwd/cmip6/models/sandbox-2/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'dwd', 'sandbox-2', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: DWD
Source ID: SANDBOX-2
Topic: Ocnbgchem
Sub-Topics: Tracers.
Properties: 6... |
gregversteeg/corex_topic | corextopic/example/corex_topic_example.ipynb | apache-2.0 | import numpy as np
import scipy.sparse as ss
import matplotlib.pyplot as plt
import corextopic.corextopic as ct
import corextopic.vis_topic as vt # jupyter notebooks will complain matplotlib is being loaded twice
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectoriz... |
mne-tools/mne-tools.github.io | 0.20/_downloads/bc5044f9d3ef1d29067dd6b7d83ceed2/plot_20_visualize_epochs.ipynb | bsd-3-clause | import os
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file, verbose=False).crop(tmax=120)
"""
Explanation: Visualizing epo... |
gatmeh/Udacity-deep-learning | 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: Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code... |
Kulbear/deep-learning-nano-foundation | DLND-face-generation/dlnd_face_generation.ipynb | mit | data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
"""
Explanation: Face Generation
In this project, you'll use generative adv... |
phungkh/phys202-2015-work | assignments/assignment03/NumpyEx02.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
"""
Explanation: Numpy Exercise 2
Imports
End of explanation
"""
def np_fact(n):
"""Compute n! = n*(n-1)*...*1 using Numpy."""
if n==0:
return 1
else:
a=np.arange(1.0, (n+1), 1.0)
b= a.cump... |
brandoncgay/deep-learning | 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... |
tsarouch/data_science_references_python | regression/prediction_efficiency_baseline_study.ipynb | gpl-2.0 | import pandas as pd
import numpy as np
def get_features_values_combinations(list_a, list_b):
""" Returns a list of combinations of the values of
e.g. from the lists
list_a = ['L', 'M', 'W']
list_b = ['F', 'I', 'S']
we get the combinations:
[('L', 'F'), ('L', 'I'), ('L', 'S'), ('M', 'F'), ('M'... |
rileyrustad/pdxapartmentfinder | analysis/Second_Analysis.ipynb | mit | # start with imports
import numpy as np
import pandas as pd
from pandas import DataFrame, Series
import json
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
"""
Explanation: This is my second attempt at creating a model using sklearn alogithms
This time we'll be breaki... |
alorozco53/RPYAApublic | Regression.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from semana2_datos import *
"""
Explanation: Regresión en Scikit-Learn
End of explanation
"""
X_1 = np.a... |
cesarcontre/Simulacion2017 | Modulo3/Clase23_Repaso1(Mod.3).ipynb | mit | # Librería de cálculo simbólico
import sympy as sym
# Para imprimir en formato TeX
from sympy import init_printing; init_printing(use_latex='mathjax')
"""
Explanation: Repaso (Módulo 3)
El tema principal en este módulo fue optimización. Al finalizar este módulo, se espera que ustedes tengan las siguientes competencia... |
rusty1s/embedded_gcnn | notebooks/speed_comparison.ipynb | mit | import sys
sys.path.insert(0, '..')
import time
import matplotlib.pyplot as plt
%matplotlib inline
def plot_durations(t_current, t_other):
print('Currently used version: {:.5f}s'.format(t_current))
print('Version of other package: {:.5f}s'.format(t_other))
print('=> {:.2f}%'.format(100 * (t_other - ... |
SSQ/Coursera-UW-Machine-Learning-Classification | Week 5 PA 2/module-8-boosting-assignment-2.ipynb | mit | import numpy as np
import pandas as pd
import json
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Boosting a decision stump
The goal of this notebook is to implement your own boosting module.
Brace yourselves! This is going to be a fun and challenging assignment.
Use SFrames to do some feature en... |
PyLadiesCZ/pyladies.cz | original/v1/s005-modules/ostrava/Feedback k domácím projektům.ipynb | mit | soubor = open("soubor.txt", mode="r", encoding='utf-8')
novy_soubor = open("soubor2.txt", mode="w", encoding='utf-8')
for text in soubor:
novy_soubor.write(text)
print ("Soubor zkopírován.")
soubor.close()
novy_soubor.close()
with open("soubor.txt", mode="r", encoding='utf-8') as soubor:
with open("soubor2... |
sraejones/phys202-2015-work | days/day11/Interpolation.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
"""
Explanation: Interpolation
Learning Objective: Learn to interpolate 1d and 2d datasets of structured and unstructured points using SciPy.
End of explanation
"""
x = np.linspace(0,4*np.pi,10)
x
"""
Explanation: Overview
W... |
maestrotf/pymepps | examples/example_plot_stationnc.ipynb | gpl-3.0 | import pymepps
import matplotlib.pyplot as plt
"""
Explanation: Load station data based on NetCDF files
In this example we show how to load station data based on NetCDF files.
The data is loaded with the pymepps package. Thanks to Ingo Lange we could use original data from the Wettermast for this example. In the follo... |
ES-DOC/esdoc-jupyterhub | notebooks/mpi-m/cmip6/models/mpi-esm-1-2-hr/landice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mpi-m', 'mpi-esm-1-2-hr', 'landice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: MPI-M
Source ID: MPI-ESM-1-2-HR
Topic: Landice
Sub-Topics: Glaciers, Ice. ... |
ntung/ramp-elnino-Saigon | edaElNino-Saigon.ipynb | gpl-2.0 | %matplotlib inline
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import xray # should be installed with pip
import pyresample # should be installed with pip
from sklearn.cross_validation import cross_val_score
"""
Explanation: <a href="http://www.datas... |
newlawrence/poliastro | docs/source/examples/Propagation using Cowell's formulation.ipynb | mit | # Temporary hack, see https://github.com/poliastro/poliastro/issues/281
from IPython.display import HTML
HTML('<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>')
import numpy as np
from astropy import units as u
from matplotlib import pyplot as plt... |
agile-geoscience/gio | docs/userguide/Read_Surfer_grids.ipynb | apache-2.0 | !head ../data/Surfer/surfer-6-ascii-tiny.grd
import gio
da = gio.read_surfer('../data/Surfer/surfer-6-ascii-tiny.grd')
da
da.max()
"""
Explanation: Read surfer grids
There are 3 varieties:
Surfer 6 binary
Surfer 6 ASCII
Surfer 7 binary
In theory we can read all of these, but I don't have a Surfer 6 binary file to... |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_python_intro.ipynb | bsd-3-clause | a = 3
print(type(a))
b = [1, 2.5, 'This is a string']
print(type(b))
c = 'Hello world!'
print(type(c))
"""
Explanation: Introduction to Python
Python is a modern, general-purpose, object-oriented, high-level programming
language. First make sure you have a working python environment and
dependencies (see install_pytho... |
phungkh/phys202-2015-work | assignments/assignment08/InterpolationEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy.interpolate import interp1d
"""
Explanation: Interpolation Exercise 1
End of explanation
"""
"""with np.load('trajectory.npz') as data: #http://docs.scipy.org/doc/numpy/reference/generated/numpy.load.html
t =... |
JorisBolsens/PYNQ | Pynq-Z1/notebooks/examples/pmod_tmp2.ipynb | bsd-3-clause | from pynq import Overlay
Overlay("base.bit").download()
from pynq.iop import Pmod_TMP2
from pynq.iop import PMODB
mytmp = Pmod_TMP2(PMODB)
temperature = mytmp.read()
print(str(temperature) + " C")
"""
Explanation: PmodTMP2 Sensor example
In this example, the Pmod temperature sensor is initialized and set to log a r... |
tuwien-musicir/rp_extract | RP_extract_Tutorial.v4.ipynb | gpl-3.0 | # to install iPython notebook on your computer, use this in Terminal
sudo pip install "ipython[notebook]"
"""
Explanation: <center><h1>Rhythm and Timbre Analysis from Music</h1></center>
<center><h2>Rhythm Pattern Music Features</h2></center>
<center><h2>Extraction and Application Tutorial</h2></center>
<br>
<center><... |
sony/nnabla | tutorial/vae_unsupervised_learning.ipynb | apache-2.0 | !pip install nnabla-ext-cuda100
!git clone https://github.com/sony/nnabla-examples.git
%cd nnabla-examples
"""
Explanation: In this tutorial, we will show you how to implement variational auto-encoder (VAE) in NNabla, which can be a powerful model for unsupervised learning by estimating variational lower bound. VAE ca... |
JohanSchott/Pade_approximants | python/Pade.ipynb | mit | %matplotlib notebook
# import modules
import numpy as np
from scipy.optimize import leastsq
from scipy.optimize import curve_fit
import sys
import matplotlib.pylab as plt
def padeThiele(z,f,zut):
'''
Thiele's reciprocal difference method.
Input variables:
z - complex. points in the complex plan... |
cliburn/sta-663-2017 | homework/04_Data_Munging.ipynb | mit | # Your answer here
"""
Explanation: Data Science Stack
The following exercises give practice in the use of the Python data science stack, especially pandas for data manipulation and seaborn for quick statistical plotting.
1. (40 points) Read in the CSV file pokemon.csv in the local directory (Source: Kaggle). Do ... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_tf_dics.ipynb | bsd-3-clause | # Author: Roman Goj <roman.goj@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne.event import make_fixed_length_events
from mne.datasets import sample
from mne.time_frequency import csd_epochs
from mne.beamformer import tf_dics
from mne.viz import plot_source_spectrogram
print(__doc__)
data_path = sample.da... |
jaidevd/inmantec_fdp | notebooks/day4/01_intro_to_derivatives.ipynb | mit | import numpy as np
from theano import tensor as T
from theano import function
from theano.gradient import jacobian
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rcParams['figure.figsize'] = 8, 6
plt.style.use('ggplot')
%matplotlib inline
"""
Explanation: Derivatives in Theano
End of explanation
"""
xx... |
Diyago/Machine-Learning-scripts | DEEP LEARNING/Pytorch from scratch/TODO/GAN/gan-mnist/MNIST_GAN_Exercise.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
import torch
import matplotlib.pyplot as plt
from torchvision import datasets
import torchvision.transforms as transforms
# number of subprocesses to use for data loading
num_workers = 0
# how many samples per batch to load
batch_size = 64
# convert data to torch.FloatTensor
tr... |
guyk1971/deep-learning | seq2seq/sequence_to_sequence_implementation.ipynb | mit | import numpy as np
import time
import helper
source_path = 'data/letters_source.txt'
target_path = 'data/letters_target.txt'
source_sentences = helper.load_data(source_path)
target_sentences = helper.load_data(target_path)
"""
Explanation: Character Sequence to Sequence
In this notebook, we'll build a model that ta... |
harmsm/pythonic-science | chapters/03_dealing-with-files/01_parsing-a-pdb.ipynb | unlicense | #record atom_name chain x y z occupancy atom_type
# | | | | | | | |
#ATOM 1086 CG LYS A 141 -4.812 9.683 2.584 1.00 26.78 N0
# | | | |
# atom_... |
kubeflow/testing | scripts/Flakiness_report.ipynb | apache-2.0 | %load_ext google.cloud.bigquery
"""
Explanation: Kubeflow test flakiness report
This notebook reports kubeflow test flakiness. We leverqe kettle which uploads test metadata into bigquery upon publishing in GCS buckets via Prow. We also customize flakiness queries used in k8s infra-test metrics to compute daily flakine... |
Santana9937/Classification_ML_Specialization | Week_2_Learning_Linear_Classifiers/week_2_assign_1_lin_class_scratch.ipynb | mit | import os
import zipfile
import string
import numpy as np
import pandas as pd
from sklearn import linear_model
from sklearn.feature_extraction.text import CountVectorizer
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
%matplotlib inline
"""
Explanation: Impleme... |
mne-tools/mne-tools.github.io | 0.23/_downloads/d76fc6b994433139acc22a226164eadd/receptive_field_mtrf.ipynb | bsd-3-clause | # Authors: Chris Holdgraf <choldgraf@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
# Nicolas Barascud <nicolas.barascud@ens.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
from scipy.io import loadmat
from os.path import join
import mne
from mne.decoding impor... |
ceos-seo/Data_Cube_v2 | agdc-v2/contrib/notebooks/Catapult Project - L-Band Gamma0 SAR example.ipynb | apache-2.0 | from pprint import pprint
%matplotlib inline
from matplotlib import pyplot as plt
import xarray
import datacube.api
dc = datacube.api.API()
alos2 = dc.get_dataset(product='gamma0', platform='ALOS_2',
y=(-42.55,-42.57), x=(147.55,147.57),
variables=['hh_gamma0', 'hv_gamma0... |
thmp/qmc | Variational Monte Carlo.ipynb | mit | import numpy as np
import numpy.random as random
N = 10 # number of walkers
delta = 0.1 # step size
"""
Explanation: Quantum Monte Carlo for Optimization
Quantum Monte Carlo methods were originally developed to solve the Schrödinger equation for many-body systems, which cannot be exactly solved by numerical means. Wh... |
madsenmj/ml-introduction-course | Class11/Class11.ipynb | apache-2.0 | import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
irisdf = pd.read_csv('../Class01/Class01_iris_data.csv')
irisdf.columns=['sepalLen','sepalWid','petalLen','petalWid','target']
irisdf.head()
# Plot the data to see what we are working with
sns.pairplot(irisdf, hue='target')
"""
Explanation: Cl... |
ManuSetty/wishbone | notebooks/Wishbone_for_single_cell_RNAseq.ipynb | gpl-2.0 | import wishbone
# Plotting and miscellaneous imports
import os
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: <h3>Wishbone for single cell RNA-seq</h3>
<h4>Table of contents</h4>
<br/>
<a href='#intro'>Introduction</a>
<br/>
<a href='#data'>Loading data</a>
<br/>
<a href='#pre... |
kkamb/miniprojects | Yelp_NLP.ipynb | mit | class HandleGZippedJSON:
def __init__(self, url):
self.url = url
self.json_data = None
def run(self):
request = urllib2.Request(self.url)
request.add_header('Accept-encoding', 'gzip')
opener = urllib2.build_opener()
f = opener.open(request)
c_data = f.re... |
chainsawriot/pycon2016hk_sklearn | Super_readl.ipynb | mit | import csv
import re
subsetData = open("SAsubset.csv", "r")
for row in csv.DictReader(subsetData):
print row['Sentiment'], row['SentimentText']
subsetData.close()
"""
Explanation: Data Preprocessing
Use a small subset of data to experiment with the data preprocessing and feature extraction. Testing the CSV module ... |
phanrahan/magmathon | notebooks/tutorial/coreir/Counter.ipynb | mit | import magma as m
from mantle import Register
"""
Explanation: Counter
To implement a counter we can use the Register primitive and the + operator. How can we make a more general counter of arbitray width?
End of explanation
"""
class Counter(m.Generator):
@staticmethod
def generate(width: int):
clas... |
sidazhang/udacity-dlnd | 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: Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code... |
armgilles/presentation | EPSI/I5/Qualite des donnees/EP1/notebook/Pokemon.ipynb | mit | pokemon = data[data.Generation == 1]
"""
Explanation: On souhaite regarder uniquement les pokémons de 1ère génération. Il va donc falloir filtrer nos données
On va donc crée une nouvelle variable qui aura uniquement les pokémons de "Generation" = 1
End of explanation
"""
Image(url="http://i.giphy.com/yidUzHnBk32Um9a... |
LucaCanali/Miscellaneous | Oracle_Jupyter/Oracle_IPython_sqlplus.ipynb | apache-2.0 | %%bash
sqlplus -s test/test@dbserver:1521/orcl.mydomain.com <<EOF
set serveroutput on
begin
dbms_output.put_line('Hello world!');
end;
/
select sysdate from dual;
EOF
"""
Explanation: Oracle SQL*Plus in IPython/Jupyter notebooks
A simple example of how to query Oracle from a Jupyter notebook
It runs sqlplus (ora... |
sapohl/data-pipeline | reports/update-orphaning/Update orphaning analysis using longitudinal dataset.ipynb | mpl-2.0 | import datetime as dt
import urllib2
import ujson as json
from os import environ
%pylab inline
"""
Explanation: Update orphaning
End of explanation
"""
starttime = dt.datetime.now()
starttime
"""
Explanation: Get the time when this job was started (for debugging purposes).
End of explanation
"""
channel_to_proce... |
brentp/gemini | inheritance.ipynb | mit | import gemini.tests
from gemini.tests.test_inheritance import TestFamily, family
Sample = family.Sample
HOM_REF, HET, UNKNOWN, HOM_ALT = range(4)
fam = TestFamily("""
#family_id sample_id paternal_id maternal_id sex phenotype
1 dad 0 0 1 1
1 mom grandpa grandma 2 1
1 kid dad mom 1 2
... |
tensorflow/docs-l10n | site/ja/r1/tutorials/images/hub_with_keras.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... |
vvishwa/deep-learning | face_generation/dlnd_face_generation.ipynb | mit | data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
"""
Explanation: Face Generation
In this project, you'll use generative adv... |
cgivre/oreilly-sec-ds-fundamentals | Notebooks/Supervised/Supervised Example.ipynb | apache-2.0 | import pandas as pd
import itertools
import matplotlib.pyplot as plt
import numpy as np
from sklearn import svm
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.metrics import confusion_matrix
from ... |
dblyon/PandasIntro | Exercises_part_A.ipynb | mit | %%javascript
$.getScript('misc/kmahelona_ipython_notebook_toc.js')
"""
Explanation: <h1 id="tocheading">Table of Contents</h1>
<div id="toc"></div>
End of explanation
"""
data = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'],
'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.n... |
georgetown-analytics/yelp-classification | .ipynb_checkpoints/Mongo_Connect-checkpoint.ipynb | mit | from pymongo import MongoClient
from datetime import datetime
import json
"""
Explanation: __| __|_ )
_| ( / Amazon Linux AMI
___|\___|___|
End of explanation
"""
ip = '54.236.23.221'
"""
Explanation: You have to change this variable each time the EC2 server stops or restarts. Please email/text me to ... |
scotthuang1989/Python-3-Module-of-the-Week | data_structure/namedtuple — Tuple Subclass with Named Fields.ipynb | apache-2.0 | import collections
Person = collections.namedtuple('Person', 'name age')
bob = Person(name='Bob', age=30)
print('\nRepresentation:', bob)
jane = Person(name='Jane', age=29)
print('\nField by name:', jane.name)
print('\nFields by index:')
for p in [bob, jane]:
print('{} is {} years old'.format(*p))
"""
Explanat... |
phoebe-project/phoebe2-docs | 2.3/examples/diher_misaligned.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.3,<2.4"
"""
Explanation: DI Her: Misaligned Binary
In this example, we'll reproduce Figure 8 in the misalignment release paper (Horvat et al. 2018).
<img src="horvat+18_fig8.png" alt="Figure 8" width="400px"/>
Setup
Let's first make sure we have the latest version of PHOEBE 2.3 installed (u... |
pyrdr/charlas | intro_text_mining/notebooks/Text Mining.ipynb | mit | # Funcion para quitar todo el texto que este entre parentesis,
# lo que no sea letras y sustituir series de espacios en blanco por uno solo
def cleanup_str(raw):
rs = re.sub("\\(.*?\\)|[^a-zA-Z\\s]"," ",raw)
rs = re.sub("\\s+"," ",rs).strip().lower()
return rs
my_str = """
Some people, when confronted wit... |
parrt/msan501 | notes/gradient-descent.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
def f(x): return (x - 2)**2 + 1
graphx = np.arange(0, 4, 0.01)
graphy = f(graphx) # apply f to all of graphx (BROADCAST)
plt.plot(graphx, graphy)
plt.show()
def foo(x):
print(type(x))
return (x - 2)**2 + 1
foo(2), foo(10)
X = np.array([1,2,3])
foo(X)
"""
... |
mne-tools/mne-tools.github.io | dev/_downloads/88563c785f9a977b7ce2000e660aeacf/30_annotate_raw.ipynb | bsd-3-clause | import os
from datetime import timedelta
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file, verbose=False)
raw.crop(tmax=60)... |
dcavar/python-tutorial-for-ipython | notebooks/Python Feature Extraction for Timbl - Class session.ipynb | apache-2.0 | from nltk import word_tokenize
"""
Explanation: Python Feature Extraction for Timbl
(C) 2017-2019 by Damir Cavar
Download: This and various other Jupyter notebooks are available from my GitHub repo.
License: Creative Commons Attribution-ShareAlike 4.0 International License (CA BY-SA 4.0)
This is a tutorial related to ... |
ES-DOC/esdoc-jupyterhub | notebooks/cas/cmip6/models/sandbox-2/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cas', 'sandbox-2', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: CAS
Source ID: SANDBOX-2
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, Radiat... |
ivukotic/ML_platform_tests | tutorial/DL with Keras/IntroToDLwithKeras.ipynb | gpl-3.0 | import random
x=random.random()
print ("The Value of x is", x)
"""
Explanation: An Introduction to Deep Learning with Keras
This tutorial is meant to teach a beginning HEP under-graduate or graduate student who may be unfamiliar with python or data science in python to train Deep Learning models using Keras. Usually, ... |
kingb12/languagemodelRNN | model_comparisons/noing15_LSTM_v_BOW.ipynb | mit | report_files = ["/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing15_200_512_04drb/encdec_noing15_200_512_04drb.json", "/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing15_bow_200_512_04drb/encdec_noing15_bow_200_512_04drb.json"]
log_files = ["/Users/bking/IdeaProjects/... |
d-li14/CS231n-Assignments | assignment2/ConvolutionalNetworks.ipynb | gpl-3.0 | # As usual, a bit of setup
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.cnn import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient
from cs231n.layers import *
fro... |
KIPAC/StatisticalMethods | tutorials/probability_transformations.ipynb | gpl-2.0 | exec(open('tbc.py').read()) # define TBC and TBC_above
import numpy as np
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Tutorial: Probability Transformations
In this notebook, we will work through an application of the change of variables formula for prob... |
CUFCTL/DLBD | Fall2017/DataLoading.ipynb | mit | import sys, os
import pickle
def unpickle(fname):
with open(fname, 'rb') as f:
Dict = pickle.load(f, encoding='bytes')
return Dict
"""
Explanation: Data Loading Tutorial
Loading data is one crucial step in the deep learning pipeline. PyTorch makes it easy to write custom data loaders for your partic... |
mne-tools/mne-tools.github.io | 0.24/_downloads/758680cba517820dcb0b486577bea58f/70_fnirs_processing.ipynb | bsd-3-clause | import os.path as op
import numpy as np
import matplotlib.pyplot as plt
from itertools import compress
import mne
fnirs_data_folder = mne.datasets.fnirs_motor.data_path()
fnirs_cw_amplitude_dir = op.join(fnirs_data_folder, 'Participant-1')
raw_intensity = mne.io.read_raw_nirx(fnirs_cw_amplitude_dir, verbose=True)
ra... |
rongchuhe2/workshop_data_analysis_python | Overview_Scientific_Packages_for_Python.ipynb | mit | import math
math.factorial(5) # functions in math package
math.e # variables in math package
"""
Explanation: Overview Scientific Packages for Python
Package can be seen as a container of variables and functions provided by others to help us accomplish our tasks
Import Packages into our enviroment
To use a pack... |
Kappa-Dev/ReGraph | examples/Tutorial_graph_audit.ipynb | mit | from regraph import NXGraph
from regraph.audit import VersionedGraph
from regraph.rules import Rule
from regraph import print_graph, plot_rule, plot_graph
"""
Explanation: Audit trails for graph objects in ReGraph (aka versioning)
ReGraph implements a framework for the version control (VC) of graph transformations
The... |
davidgutierrez/HeartRatePatterns | Jupyter/MimicIII/01 Fill Database WaveForm Headers.ipynb | gpl-3.0 | import urllib.request
import wfdb
import psycopg2
from psycopg2.extensions import AsIs
"""
Explanation: Fill Database WaveForm Headers
1) Import de las librerias que utilizaremos
End of explanation
"""
target_url = "https://physionet.org/physiobank/database/mimic3wdb/matched/RECORDS-waveforms"
data = urllib.request.... |
arcyfelix/Courses | 17-09-17-Python-for-Financial-Analysis-and-Algorithmic-Trading/03-General Pandas/02-Series.ipynb | apache-2.0 | import numpy as np
import pandas as pd
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
Series
The first main data type we will learn about for pandas is the Series data type. Let's import Pandas and explore the Series object.
A Series is very similar to a NumPy array ... |
kylepolich/dataskeptic | shownotes/multiple-regression.ipynb | cc0-1.0 | from IPython.display import Image
Image(filename='redfin.png', width=300)
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import statsmodels.api as sm
import math
"""
Explanation: Multiple Regression
This episode is a discussion of multiple regression: the use of observations... |
xtr33me/deep-learning | intro-to-tensorflow/.ipynb_checkpoints/intro_to_tensorflow-checkpoint.ipynb | mit | import hashlib
import os
import pickle
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils import resample
from tqdm import tqdm
from zipfile import ZipFile
print('All m... |
rringham/deep-learning-notebooks | udacity/5_word2vec.ipynb | mit | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import collections
import math
import numpy as np
import os
import random
import tensorflow as tf
import zipfile
from matplotlib import pylab
from six.moves import range
fro... |
m7thon/tom | doc/example.ipynb | mit | import tom
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
rand = tom.Random(1234567)
"""
Explanation: Usage example
To showcase the use of this toolkit, we first create a simple learning task, and then learn an OOM model using spectral learning.
We start by importing the toolkit and initializing... |
podondra/bt-spectraldl | notebooks/01-data-download.ipynb | gpl-3.0 | %matplotlib inline
import urllib.request
import urllib.parse
import io
import os
import csv
import glob
from functools import partial
from itertools import count
import numpy as np
from astropy.io import fits
import matplotlib.pyplot as plt
LABELS_FILE = 'data/ondrejov-dataset.csv'
!head $LABELS_FILE
"""
Explanation... |
wbbeyourself/cn-deep-learning | 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: 你的第一个神经网络
在此项目中，你将构建你的第一个神经网络，并用该网络预测每日自行车租客人数。我们提供了一些代码，但是需要你来实现神经网络（大部分内容）。提交此项目后，欢迎进一步探索该数据和模型。
End of explanation
"""
data_path = 'Bike-Sharing-Dataset/hour.c... |
synthicity/activitysim | activitysim/examples/example_estimation/notebooks/10_joint_tour_freq.ipynb | agpl-3.0 | import os
import larch # !conda install larch -c conda-forge # for estimation
import pandas as pd
"""
Explanation: Estimating Joint Tour Frequency
This notebook illustrates how to re-estimate a single model component for ActivitySim. This process
includes running ActivitySim in estimation mode to read household tra... |
ComputationalModeling/spring-2017-danielak | past-semesters/spring_2016/day-by-day/day07-modeling-viral-load/day07-in_class_activity_SOLUTIONS.ipynb | agpl-3.0 | # Make plots inline
%matplotlib inline
# Make inline plots vector graphics instead of raster graphics
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('pdf', 'svg')
# import modules for plotting and data analysis
import matplotlib.pyplot as plt
import numpy as np
import pandas
"""
Explanatio... |
justacec/bokeh | examples/howto/notebook_comms/Numba Image Example.ipynb | bsd-3-clause | from __future__ import print_function, division
from timeit import default_timer as timer
from bokeh.plotting import figure, show, output_notebook
from bokeh.models import GlyphRenderer, LinearColorMapper
from bokeh.io import push_notebook
from numba import jit, njit
from ipywidgets import interact
import numpy as n... |
harishkrao/DSE200x | Week5-Visualization/05a_Matplotlib_Notebook.ipynb | mit | import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
data = pd.read_csv(r'C:\Users\hrao\Documents\Personal\HK\Python\world-development-indicators\Indicators.csv')
data.shape
"""
Explanation: <br><p style="font-family: Arial; font-size:3.75em;color:purple; font-style:bold">
Matplotlib: ... |
Alexoner/mooc | cs231n/assignment3/q1.ipynb | apache-2.0 | # A bit of setup
import numpy as np
import matplotlib.pyplot as plt
from time import time
from cs231n.layers import *
from cs231n.fast_layers import *
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] ... |
adrianstaniec/deep-learning | 13_seq2seq/sequence_to_sequence_implementation.ipynb | mit | import numpy as np
import time
import helper
source_path = 'data/letters_source.txt'
target_path = 'data/letters_target.txt'
source_sentences = helper.load_data(source_path)
target_sentences = helper.load_data(target_path)
"""
Explanation: Character Sequence to Sequence
In this notebook, we'll build a model that ta... |
mas-dse-greina/bayes | Bayes AB Hypothesis Testing.ipynb | unlicense | import numpy as np
from matplotlib import pyplot as plt
from scipy import stats
%matplotlib inline
"""
Explanation: Bayes A/B Hypothesis Testing
This notebook describes how to implement Bayes A/B Testing in Python. I created this based on Bogdan Kulynych's excellent presentation at PyConPl14.
Bayes A/B is a replace... |
gsvijayraajaa/workshop | 5.DecisionTree.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('fivethirtyeight')
df = pd.read_csv("data/creditRisk.csv")
df.head()
df.dtypes
"""
Explanation: Learning from Data
Decision Trees are a non-parametric supervised learning method used for classification and regres... |
deepankarsharma/udacity_traffic_signs | Traffic_Sign_Classifier.ipynb | mit | # Load pickled data
import pickle
import tensorflow as tf
import numpy as np
# TODO: Fill this in based on where you saved the training and testing data
training_file = 'traffic-signs-data/train.p'
validation_file= 'traffic-signs-data/valid.p'
testing_file = 'traffic-signs-data/test.p'
with open(training_file, mode=... |
chengjun/iching | .ipynb_checkpoints/iching_intro-checkpoint.ipynb | mit | from iching import iching
from datetime import date
today = date.today()
today = str(today).replace('-', '')
birthtoday = int('19850526' + today)
iching.ichingDate(birthtoday)
fixPred, changePred = iching.getPredict()
print iching.ichingName(fixPred, changePred )， iching.ichingText(fixPred, iching)
iching.ichingD... |
ES-DOC/esdoc-jupyterhub | notebooks/mohc/cmip6/models/sandbox-2/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'sandbox-2', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: MOHC
Source ID: SANDBOX-2
Topic: Ocnbgchem
Sub-Topics: Tracers.
Properties:... |
intellimath/pyaxon | examples/axon_tutorial.ipynb | mit | from __future__ import unicode_literals, print_function, division
from pprint import pprint
import axon
import json
import xml.etree as etree
from IPython.display import HTML, display, display_html
"""
Explanation: AXON: Tutorial
Let's import inventory for playing with AXON with python.
End of explanation
"""
!cat b... |
CAChemE/curso-python-datos | notebooks_vacios/050-Pandas-Intro.ipynb | bsd-3-clause | # Importamos pandas
"""
Explanation: Carga y manipulación de datos con pandas
_ pandas es una biblioteca de análisis de datos en Python que nos provee de las estructuras de datos y herramientas para realizar análisis de manera rápida. Se articula sobre la biblioteca NumPy y nos permite enfrentarnos a situaciones en la... |
wenduowang/git_home | python/MSBA/intro/HW2/.ipynb_checkpoints/HW2_wenduowang-checkpoint.ipynb | gpl-3.0 | restaurants = pd.read_csv("NYC_Restaurants.csv", dtype=unicode)
for index, item in enumerate(restaurants.columns.values):
print index, item
"""
Explanation: NYC Restaurants
Read in data from csv, and check column names to keep in mind.
End of explanation
"""
#use .apply() method to combine the 4 columns to get t... |
spulido99/Programacion | Margarita/Taller 3 y 4.ipynb | mit | #Librerias requeridas para el ejercicio
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inline
from sklearn import datasets, linear_model
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.ensemble... |
rsterbentz/phys202-2015-work | assignments/assignment06/ProjectEuler17.ipynb | mit | def number_to_words(n):
"""Given a number n between 1-1000 inclusive return a list of words for the number."""
n=str(n)
key = {1:'one', 2:'two', 3:'three', 4:'four', 5:'five', 6:'six', 7:'seven', 8:'eight', 9:'nine', 10:'ten', 11:'eleven', 12:'twelve', 13:'thirteen', 14:'fourteen', 15:'fifteen', 16:'sixteen... |
mathusuthan/autodidacti | DSE220x-MLFundamentals/Week-1/NN_MNIST/Nearest_neighbor_MNIST.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import time
## Load the training set
train_data = np.load('MNIST/train_data.npy')
train_labels = np.load('MNIST/train_labels.npy')
## Load the testing set
test_data = np.load('MNIST/test_data.npy')
test_labels = np.load('MNIST/test_labels.npy')
#... |
geoscixyz/computation | notebooks/fundamentals/tools.ipynb | mit | # python has /types
print(type(1) == int)
print(type(1.) == float)
print(type(1j) == complex)
type(None)
# What happens if you add values of different types?
print(1 + 1.)
"""
Explanation: Tools
Purpose: To introduce and provide resources for the tools used to build and work with <a href="http://simpeg.xyz">SimPEG</... |
davofis/computational_seismology | 08_finite_volumes/fv_hetero_1d.ipynb | gpl-3.0 | # Import all necessary libraries, this is a configuration step for the exercise.
# Please run it before the simulation code!
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
# Show the plots in the Notebook.
plt.switch_backend("nbagg")
"""
Explanation: <div style='background... |
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