repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
DJMedhaug/code_guild | wk0/notebooks/challenges/primes/.ipynb_checkpoints/primes_challenge-checkpoint.ipynb | mit | def list_primes(n):
# TODO: Implement me
pass
"""
Explanation: <small><i>This notebook was prepared by Thunder Shiviah. Source and license info is on GitHub.</i></small>
Challenge Notebook
Problem: Implement list_primes(n), which returns a list of primes up to n (inclusive).
Constraints
Test Cases
Algorithm
C... |
cfobel/colonists | colonists/notebooks/Colonists map data structures.ipynb | gpl-3.0 | # ## Create hex grid ##
hex_grid = HexGrid(8, 17, .165, 1.75)
np.random.seed(2)
# ## Set up board on grid ##
# - Assign region (land, port, sea) and terrain type (clay, sheep, ore, wheat, wood,
# desert, clay port, sheep port, ore port, wheat port, wood port, 3:1 port, sea)
# to each hex.
df_hexes = get_hexes(... |
moagstar/puzzles | Array/Pascal's Triangle.ipynb | mit | import sys; sys.path.append('../..')
from puzzles import leet_puzzle
leet_puzzle('pascals-triangle')
"""
Explanation: Pascal's Triangle
End of explanation
"""
def pascals_triangle(k):
prev_row = None
for r in xrange(k+1):
row = [None] * r
for c in xrange(r):
if c == 0 or c == r-1:... |
SylvainCorlay/bqplot | examples/Interactions/Mark Interactions.ipynb | apache-2.0 | x_sc = LinearScale()
y_sc = LinearScale()
x_data = np.arange(20)
y_data = np.random.randn(20)
scatter_chart = Scatter(x=x_data, y=y_data, scales= {'x': x_sc, 'y': y_sc}, colors=['dodgerblue'],
interactions={'click': 'select'},
selected_style={'opacity': 1.0, 'fill': 'Dar... |
tbenthompson/tectosaur | examples/notebooks/fullspace_qd_run.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import tectosaur.mesh.mesh_gen
import tectosaur as tct
import tectosaur.qd as qd
qd.configure(
gpu_idx = 0, # Which GPU to use if there are multiple. Best to leave as 0.
fast_plot = True, # Let's make fast, inexpensive figures. Set to false for higher resolut... |
tensorflow/tfx | docs/tutorials/model_analysis/tfma_basic.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... |
samuelsinayoko/kaggle-housing-prices | prepare_data.ipynb | mit | from scipy.stats.mstats import mode
import pandas as pd
import numpy as np
import time
from sklearn.preprocessing import LabelEncoder
"""
Read Data
"""
train = pd.read_csv('data/train.csv')
test = pd.read_csv('data/test.csv')
target = train['SalePrice']
train = train.drop(['SalePrice'],axis=1)
trainlen = train.shape[0... |
rsignell-usgs/notebook | WMS/wms_sample.ipynb | mit | %matplotlib inline
from owslib.wms import WebMapService
#We just need a WMS url from one TDS dataset...
serverurl ='http://thredds.ucar.edu/thredds/wms/grib/NCEP/NAM/CONUS_12km/best'
wms = WebMapService( serverurl, version='1.1.1')
"""
Explanation: Exploring Web Map Service (WMS)
WMS and OWSLib
Getting some informati... |
karlstroetmann/Formal-Languages | Python/Top-Down-Parser.ipynb | gpl-2.0 | import re
"""
Explanation: A Recursive Parser for Arithmetic Expressions
In this notebook we implement a simple recursive descend parser for arithmetic expressions.
This parser will implement the following grammar:
$$
\begin{eqnarray}
\mathrm{expr} & \rightarrow & \mathrm{product}\;\;\mathrm{exprRest} ... |
parkerzf/kaggle-expedia | notebooks/time_based_anlaysis.ipynb | bsd-3-clause | daily_stats[['count_click', 'count_booking_train', 'count_booking_test']].sum()/1000
print 'booking ratio for train set: ', daily_stats.count_booking_train.sum() * 1.0 \
/ (daily_stats.count_click.sum() + daily_stats.count_booking_train.sum())
print 'daily booking in train set: ', daily_stats.count_booking_train.su... |
xesscorp/myhdlpeek | examples/peeker_options.ipynb | mit | from myhdl import *
from myhdlpeek import Peeker
def adder_bit(a, b, c_in, sum_, c_out):
'''Single bit adder.'''
@always_comb
def adder_logic():
sum_.next = a ^ b ^ c_in
c_out.next = (a & b) | (a & c_in) | (b & c_in)
# Add some peekers to monitor the inputs and outputs.
Peeker(... |
halflings/bio-data-workshop | notebook.ipynb | apache-2.0 | # The dataset doesn't contain a header containing column names
# so we generate them ourselves.
feature_columns = ['feature_{}'.format(i) for i in range(1, 31)]
columns = ['id', 'diagnosis'] + feature_columns
# Reading data from a
#DATA_PATH = 'https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-w... |
santosjorge/cufflinks | Cufflinks Tutorial - Colors.ipynb | mit | import cufflinks as cf
"""
Explanation: Cufflinks Colors
Cufflinks also provides a wide set of tools for color managements; including color conversion across multiple spectrums and color table generation.
End of explanation
"""
# The colors module includes a pre-defined set of commonly used colors
cf.colors.cnames
... |
agussman/aws_name_similarity | aws_name_similarity.ipynb | mit | from itertools import combinations
import jellyfish
from scipy.cluster import hierarchy
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Setup
$ mkvirtualenv aws_name_similarity
$ pip install --upgrade pip
$ pip install jellyfish jupyter scipy matplotlib
$ jupyter notebook
End of explanation
"""
#... |
nslatysheva/data_science_blogging | expanding_ML_toolkit/expanding_toolkit.ipynb | gpl-3.0 | import wget
import pandas as pd
import numpy as np
from sklearn.cross_validation import train_test_split
# Import the dataset
data_url = 'https://raw.githubusercontent.com/nslatysheva/data_science_blogging/master/datasets/wine/winequality-red.csv'
dataset = wget.download(data_url)
dataset = pd.read_csv(dataset, sep=";... |
egentry/dwarf_photo-z | dwarfz/catalog_only_classifier/classifier_comparison.ipynb | mit | # give access to importing dwarfz
import os, sys
dwarfz_package_dir = os.getcwd().split("dwarfz")[0]
if dwarfz_package_dir not in sys.path:
sys.path.insert(0, dwarfz_package_dir)
import dwarfz
# back to regular import statements
%matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns
s... |
nwfpug/python-primer | notebooks/05-looping.ipynb | gpl-3.0 | for num in range(10,20): #to iterate between 10 to 20
for i in range(2,num): #to iterate on the factors of the number
if num%i == 0: #to determine the first factor
j=num/i #to calculate the second factor
print '%d equals %d * %d' % (num,i,j)
break ... |
ini-python-course/ss15 | notebooks/List Comprehensions.ipynb | mit | V = [2**i for i in range(13)]
print V
S = set([x**2 for x in range(10)])
print S
M = set([x for x in S if x % 2 == 0])
print M
"""
Explanation: List comprehensions
In Python there is a special way to initialize lists (and dictionaries) called list comprehensions. For many lists that we are going to create, list comp... |
lknelson/text-analysis-2017 | 03-Pandas_and_DTM/01-DTM_DistinctiveWords.ipynb | bsd-3-clause | import pandas
#create a dataframe called "df"
df = pandas.read_csv("../Data/BDHSI2016_music_reviews.csv", sep = '\t', encoding = 'utf-8')
#view the dataframe
#The column "body" contains our text of interest.
df
#print the first review from the column 'body'
df.loc[0,'body']
"""
Explanation: The Document Term Matrix... |
ihmeuw/dismod_mr | examples/cross_walks.ipynb | agpl-3.0 | import numpy as np, pandas as pd, dismod_mr
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
# set a random seed to ensure reproducible simulation results
np.random.seed(123456)
# simulate data
n = 20
data = dict(age=np.random.randint(0, 10, size=n)*10,
year=np.random.randint(199... |
lmcinnes/hdbscan | notebooks/How HDBSCAN Works.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn.datasets as data
%matplotlib inline
sns.set_context('poster')
sns.set_style('white')
sns.set_color_codes()
plot_kwds = {'alpha' : 0.5, 's' : 80, 'linewidths':0}
"""
Explanation: How HDBSCAN Works
HDBSCAN is a clustering algorithm d... |
antongrin/EasyMig | EasyMig_v3.ipynb | apache-2.0 | # -*- coding: utf-8 -*-
"""
Created on Fri Feb 12 13:21:45 2016
@author: GrinevskiyAS
"""
from __future__ import division
import numpy as np
from numpy import sin,cos,tan,pi,sqrt
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
%matplotlib inline
font = {'family': 'Arial', 'weigh... |
bert9bert/statsmodels | examples/notebooks/statespace_arma_0.ipynb | bsd-3-clause | %matplotlib inline
from __future__ import print_function
import numpy as np
from scipy import stats
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.graphics.api import qqplot
"""
Explanation: Autoregressive Moving Average (ARMA): Sunspots data
This notebook replicat... |
BrentDorsey/pipeline | gpu.ml/notebooks/03a_Train_Model_GPU.ipynb | apache-2.0 | import tensorflow as tf
from tensorflow.python.client import timeline
import pylab
import numpy as np
import os
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
tf.logging.set_verbosity(tf.logging.INFO)
"""
Explanation: Train Model with GPU (and CPU*)
CPU is still used to store variables that we are... |
machinelearningnanodegree/stanford-cs231 | solutions/vijendra/assignment1/knn.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.... |
bouhlelma/smt | tutorial/SMT_MixedInteger_application.ipynb | bsd-3-clause | %matplotlib inline
from math import exp
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors
from mpl_toolkits.mplot3d import Axes3D
from scipy.stats import norm
from scipy.optimize import minimize
import scipy
import six
from smt.applications import EGO
from smt.surrogate_models import K... |
laurentperrinet/Khoei_2017_PLoSCB | notebooks/figure_3_FLE.ipynb | mit | %%writefile experiment_fle.py
import MotionParticlesFLE as mp
gen_dot = mp.generate_dot
import numpy as np
import os
from default_param import *
image = {}
experiment = 'FLE'
do_sim = False
do_sim = True
for stimulus_tag, im_arg in zip(stim_labels, stim_args):
# generating the movie
image[stimulus_tag] = {}
... |
Sebbenbear/notebooks | Natural Language Processing.ipynb | apache-2.0 | text6.concordance("swallow")
text6.similar("Soldier")
text6.common_contexts(["oh", "very"])
text6.dispersion_plot(["swallow", "European", "it", "oh", "very"])
len(text6)
sorted(set(text6))
"""
Explanation: Search text with context
End of explanation
"""
len(set(text6)) / len(text6)
text6.count("Allo")
sentenc... |
seg/2016-ml-contest | JLOWE/JLowe_NN.ipynb | apache-2.0 | import numpy as np
np.random.seed(1000)
import warnings
warnings.filterwarnings("ignore")
import time as tm
import pandas as pd
from scipy.signal import medfilt
from keras.models import Sequential
from keras.constraints import maxnorm
from keras.layers import Dense, Dropout
from keras.utils import np_utils
from skl... |
surprisoh/crowdfunding_prediction | 4. Before Funding.ipynb | mit | from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import cross_val_score
from sklearn.cross_validation import KFold
from sklearn.cross_validation import StratifiedKFold
from sklearn.neighbors im... |
hungiyang/StatisticalMethods | examples/XrayImage/Modeling.ipynb | gpl-2.0 | from __future__ import print_function
import astropy.io.fits as pyfits
import astropy.visualization as viz
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 10.0)
"""
Explanation: Forward Modeling the X-ray Image data
In this notebook, we'll take a closer loo... |
wenduowang/git_home | python/MSBA/intro/HW3/HW3_WenduoWang.ipynb | gpl-3.0 | gold = pd.read_table("gold.txt", names=["url", "category"]).dropna()
labels = pd.read_table("labels.txt", names=["turk", "url", "category"]).dropna()
"""
Explanation: Question 1: Read in data
Read in the data from "gold.txt" and "labels.txt".
Since there are no headers in the files, names parameter should be set expli... |
adelavega/neurosynth-mfc | other/Create MFC mask.ipynb | mit | cortex = nib.load('cerbcort.nii.gz')
# Binarize
cortex = nib.Nifti1Image((cortex.get_data() > 0).astype('int'), cortex.get_header().get_best_affine())
niplt.plot_roi(cortex)
"""
Explanation: Here, I'm going to create the mask that defined MFC for further analysis.
First, I load a cerebral cortex probabilty map, from ... |
akseshina/dl_course | seminar_12/homework/homework.ipynb | gpl-3.0 | import numpy as np
import tensorflow as tf
import tensorflow.contrib.slim as slim
from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets
import matplotlib.pyplot as plt
%matplotlib inline
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('fashion-mni... |
TomTranter/OpenPNM | examples/percolation/Part B - Invasion Percolation.ipynb | mit | import sys
import openpnm as op
import numpy as np
np.random.seed(10)
import matplotlib.pyplot as plt
import porespy as ps
from ipywidgets import interact, IntSlider
from openpnm.topotools import trim
%matplotlib inline
ws = op.Workspace()
ws.settings["loglevel"] = 40
"""
Explanation: Part B: Invasion Percolation
The ... |
CivicKnowledge/metatab-packages | census.gov/census.gov-pums-20165/notebooks/Extract.ipynb | mit | rac1p_map = {
1: 'white',
2: 'black',
3: 'amind',
4: 'alaskanat',
5: 'aian',
6: 'asian',
7: 'nhopi',
8: 'other',
9: 'many'
}
pop['race'] = pop.rac1p.astype('category')
pop['race'] = pop.race.cat.rename_categories(rac1p_map)
# The raceeth variable is the race varaiable, but with 'wh... |
ALEXKIRNAS/DataScience | Coursera/Machine-learning-data-analysis/Course 2/Week_01/PA_linreg_stochastic_grad_descent.ipynb | mit | def write_answer_to_file(answer, filename):
with open(filename, 'w') as f_out:
f_out.write(str(round(answer, 3)))
"""
Explanation: Линейная регрессия и стохастический градиентный спуск
Задание основано на материалах лекций по линейной регрессии и градиентному спуску. Вы будете прогнозировать выручку компан... |
harsh6292/machine-learning-nd | projects/customer_segments/customer_segments.ipynb | mit | # Import libraries necessary for this project
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from IPython.display import display # Allows the use of display() for DataFrames
# Import supplementary visualizations code visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib in... |
arborh/tensorflow | tensorflow/lite/experimental/micro/examples/hello_world/create_sine_model.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
csyhuang/hn2016_falwa | examples/nh2018_science/demo_script_for_nh2018.ipynb | mit | import numpy as np
from numpy import dtype
from math import pi
from netCDF4 import Dataset
import matplotlib.pyplot as plt
import datetime as dt
%matplotlib inline
from hn2016_falwa.oopinterface import QGField
import hn2016_falwa.utilities as utilities
import datetime as dt
"""
Explanation: Last updated on Apr 9, 2020... |
xiongzhenggang/xiongzhenggang.github.io | data-science/27-错误可视化.ipynb | gpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import numpy as np
x = np.linspace(0, 10, 50)
dy = 0.8
y = np.sin(x) + dy * np.random.randn(50)
# yerr表示y的误差
plt.errorbar(x, y, yerr=dy, fmt='.k');
"""
Explanation: 错误可视化
对于任何科学的度量，准确地计算错误几乎和准确报告数字本身一样重要，甚至更为重要。例如，假设我正在使用一些天体观测来估计哈... |
Schiphol-Hub/schiphol-geo-notebooks | Creating_schiphol_map.ipynb | gpl-3.0 | from arcgis.gis import *
from arcgis.viz import MapView
from IPython.display import display
"""
Explanation: Create a Schiphol map using Arcgis online and Jupyter notebook
Documentation for the beta Esri Arcgis Python API can be found here:
http://esri.github.io/arcgis-python-api/apidoc/html/index.html
End of explana... |
goyalsid/phageParser | demos/Spacer Length Analysis.ipynb | mit | %matplotlib inline
#Import packages
import requests
import json
import numpy as np
import random
import matplotlib.pyplot as plt
from matplotlib import mlab
import seaborn as sns
import pandas as pd
from scipy.stats import poisson
sns.set_palette("husl")
#Url of the phageParser API
apiurl = 'https://phageparser.herok... |
amueller/scipy-2017-sklearn | notebooks/10.Case_Study-Titanic_Survival.ipynb | cc0-1.0 | from sklearn.datasets import load_iris
iris = load_iris()
print(iris.data.shape)
"""
Explanation: Case Study - Titanic Survival
Feature Extraction
Here we will talk about an important piece of machine learning: the extraction of
quantitative features from data. By the end of this section you will
Know how features... |
TUW-GEO/pygeogrids | docs/examples/creating_and_working_with_grid_objects.ipynb | mit | import pygeogrids.grids as grids
import numpy as np
"""
Explanation: Basics
End of explanation
"""
# create the longitudes
lons = np.arange(-180 + 5, 180, 10)
print(lons)
lats = np.arange(90 - 5, -90, -10)
print(lats)
"""
Explanation: Let's create a simple regular 10x10 degree grid with grid points at the center of... |
amcdawes/QMlabs | Lab 3 - Operators.ipynb | mit | import matplotlib.pyplot as plt
from numpy import sqrt,cos,sin,arange,pi
from qutip import *
%matplotlib inline
H = Qobj([[1],[0]])
V = Qobj([[0],[1]])
P45 = Qobj([[1/sqrt(2)],[1/sqrt(2)]])
M45 = Qobj([[1/sqrt(2)],[-1/sqrt(2)]])
R = Qobj([[1/sqrt(2)],[-1j/sqrt(2)]])
L = Qobj([[1/sqrt(2)],[1j/sqrt(2)]])
"""
Explanatio... |
ray-project/ray | doc/source/tune/examples/tune-wandb.ipynb | apache-2.0 | import numpy as np
import wandb
from ray import tune
from ray.tune import Trainable
from ray.tune.integration.wandb import (
WandbLoggerCallback,
WandbTrainableMixin,
wandb_mixin,
)
"""
Explanation: Using Weights & Biases with Tune
(tune-wandb-ref)=
Weights & Biases (Wandb) is a tool for experiment
tracki... |
tensorflow/docs-l10n | site/zh-cn/hub/tutorials/text_classification_with_tf_hub.ipynb | apache-2.0 | # Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
gprakhar/janCC | Janacare_User-Segmentation_dataset_Aug2014-Apr2016.ipynb | bsd-3-clause | # This to clear all variable values
%reset
# Import the required modules
import pandas as pd
import numpy as np
#import scipy as sp
# simple function to read in the user data file.
# the argument parse_dates takes in a list of colums, which are to be parsed as date format
user_data_raw = pd.read_csv("janacare_user-en... |
wikistat/Ateliers-Big-Data | CatsVSDogs/Atelier-keras-CatsVSDogs.ipynb | mit | # Utils
import sys
import os
import shutil
import time
import pickle
import numpy as np
# Deep Learning Librairies
import tensorflow as tf
import keras.preprocessing.image as kpi
import keras.layers as kl
import keras.optimizers as ko
import keras.backend as k
import keras.models as km
import keras.applications as ka
... |
eblur/AstroHackWeek2015 | day3-machine-learning/09.1 - Linear models.ipynb | gpl-2.0 | from sklearn.datasets import make_regression
from sklearn.cross_validation import train_test_split
X, y, true_coefficient = make_regression(n_samples=80, n_features=30, n_informative=10, noise=100, coef=True, random_state=5)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=5)
print(X_train.shape)... |
tedunderwood/horizon | chapter3/notebooks/chapter3table3.ipynb | mit | # some standard modules
import csv, os, sys
from collections import Counter
import numpy as np
from scipy.stats import pearsonr
# now a module that I wrote myself, located
# a few directories up, in the software
# library for this repository
sys.path.append('../../lib')
import FileCabinet as filecab
"""
Explanatio... |
landmanbester/fundamentals_of_interferometry | 7_Observing_Systems/7_8_rfi.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
"""
Explanation: Outline
Glossary
7. Observing Systems
Previous: 7.7 Propagation Effects
Next: 7.x Further Reading and References
Import standard modules:
End of ... |
shngli/Data-Mining-Python | Mining massive datasets/association.ipynb | gpl-3.0 | from __future__ import division
import itertools
import operator
from sys import argv
support = 99
mappings = []
itemCounts = []
transactions = 0
"""
Explanation: Association Rules
Use the online browsing behavior dataset "browsing.txt". Each line represents a browsing session of a customer. On each line, each s... |
datascience-course/datascience-course.github.io | 2016/assets/slides/03-hypothesis-testing-1.ipynb | mit | import scipy as sc
from scipy.stats import bernoulli
from scipy.stats import binom
from scipy.stats import norm
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (10, 6)
"""
Explanation: Introduction to Data Science, CS 5963 / Math 3900
Lecture 3: Hypothesis Testing I
In this lectur... |
maigimenez/trolls | Notebooks/0. Gather data.ipynb | mit | config = ConfigParser()
config.read(join(pardir,'src','credentials.ini'))
APP_KEY = config['twitter']['app_key']
APP_SECRET = config['twitter']['app_secret']
OAUTH_TOKEN = config['twitter']['oauth_token']
OAUTH_TOKEN_SECRET = config['twitter']['oauth_token_secret']
from twitter import oauth, Twitter, TwitterHTTPErr... |
kaka0525/Process-Bike-Share-data-with-Pandas | bikeshare.ipynb | mit | import pandas as pd
import numpy as np
weather = pd.read_table("daily_weather.tsv")
usage = pd.read_table("usage_2012.tsv")
station = pd.read_table("stations.tsv")
"""
Explanation: <strong>Process Bike-Share data with Pandas</strong>
End of explanation
"""
weather
mean = weather.groupby('season_desc')['temp'].m... |
NLeSC/noodles | notebooks/An interactive introduction.ipynb | apache-2.0 | from noodles import schedule
@schedule
def add(x, y):
return x + y
@schedule
def mul(x,y):
return x * y
"""
Explanation: An interactive introduction to Noodles: translating Poetry
Noodles is there to make your life easier, in parallel! The reason why Noodles can be easy and do parallel Python at the same tim... |
dsacademybr/PythonFundamentos | Cap07/DesafioDSA/Missao2/missao2.ipynb | gpl-3.0 | # Versão da Linguagem Python
from platform import python_version
print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())
"""
Explanation: <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 7</font>
Download: http://github.com/dsacademybr
End of explanation
"""
import ... |
YuriyGuts/kaggle-quora-question-pairs | notebooks/preproc-extract-unique-questions.ipynb | mit | from pygoose import *
import nltk
"""
Explanation: Preprocessing: Unique Question Corpus
Based on the training and test sets, extract a list of unique documents.
Imports
This utility package imports numpy, pandas, matplotlib and a helper kg module into the root namespace.
End of explanation
"""
project = kg.Project... |
phoebe-project/phoebe2-docs | 2.3/tutorials/meshes.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.3,<2.4"
import phoebe
logger = phoebe.logger()
b = phoebe.default_binary()
"""
Explanation: Advanced: Accessing and Plotting Meshes
Setup
Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if running in an online notebook session such as colab).... |
tclaudioe/Scientific-Computing | SC1/10_GMRes.ipynb | bsd-3-clause | import numpy as np
import scipy as sp
from scipy import linalg as la
import matplotlib.pyplot as plt
import scipy.sparse.linalg
%matplotlib inline
#%load_ext memory_profiler
import matplotlib as mpl
mpl.rcParams['font.size'] = 14
mpl.rcParams['axes.labelsize'] = 20
mpl.rcParams['xtick.labelsize'] = 14
mpl.rcParams['yti... |
megbedell/wobble | notebooks/espresso.ipynb | mit | data = wobble.Data()
filenames = glob.glob('/Users/mbedell/python/wobble/data/toi/TOI-*_CCF_A.fits')
for filename in tqdm(filenames):
try:
sp = wobble.Spectrum()
sp.from_ESPRESSO(filename, process=True)
data.append(sp)
except Exception as e:
print("File {0} failed; error: {1}".f... |
spectralDNS/shenfun | binder/stokes.ipynb | bsd-2-clause | import os
import sys
import numpy as np
from sympy import symbols, sin, cos
from shenfun import *
"""
Explanation: <!-- dom:TITLE: Demo - Stokes equations -->
Demo - Stokes equations
<!-- dom:AUTHOR: Mikael Mortensen Email:mikaem@math.uio.no at Department of Mathematics, University of Oslo. -->
<!-- Author: -->
Mikael... |
pcm-ca/pcm-ca.github.io | pages/informatication/extra-files/codes/notebooks/Ajustes.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
medidas = np.random.normal(0, 1, size=100)
plt.figure()
plt.plot(medidas, '.')
plt.axhline(y=0, ls='--', c='k')
plt.show()
medidas = np.random.normal(0, 0.1, size=100)
plt.figure()
plt.plot(medidas, '.')
plt.axhline(y=0, ls='--', c='k')
plt.show(... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_label_from_stc.ipynb | bsd-3-clause | # Author: Luke Bloy <luke.bloy@gmail.com>
# Alex Gramfort <alexandre.gramfort@telecom-paristech.fr>
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.minimum_norm import read_inverse_operator, apply_inverse
from mne.datasets import sample
print(__doc__)
data_pa... |
atulsingh0/MachineLearning | HandsOnML/code/10_introduction_to_artificial_neural_networks.ipynb | gpl-3.0 | # To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
# to make this notebook's output stable across runs
def reset_graph(seed=42):
tf.reset_default_graph()
tf.set_random_seed(seed)
np.random.seed(seed)
# To... |
jGaboardi/Facility_Location | Gurobi_v_Cplex__Set_Cover.ipynb | lgpl-3.0 | import pysal as ps
import numpy as np
import networkx as nx
import shapefile as shp
import gurobipy as gbp
import cplex as cp
import datetime as dt
import time
from collections import OrderedDict
import IPython.display as IPd
%pylab inline
from mpl_toolkits.basemap import Basemap
"""
Explanation: <font size='5' face='... |
kdmurray91/kwip-experiments | writeups/coalescent/50reps_2016-05-18/sqrt-dist.ipynb | mit | expts = list(map(lambda fp: path.basename(fp.rstrip('/')), glob('data/*/')))
print("Number of replicate experiments:", len(expts))
def process_expt(expt):
expt_results = []
def extract_info(filename):
return re.search(r'kwip/(\d\.?\d*)x-(0\.\d+)-(wip|ip).dist', filename).groups()
def r_sqrt(tr... |
mne-tools/mne-tools.github.io | 0.22/_downloads/243172b1ef6a2d804d3245b8c0a927ef/plot_60_maxwell_filtering_sss.ipynb | bsd-3-clause | import os
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import mne
from mne.preprocessing import find_bad_channels_maxwell
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
... |
boffi/boffi.github.io | dati_2016/08/Subspace2.ipynb | mit | def redeigh(K, M, phi):
"""Solves the reduced eigenproblem in subspace iteration method.
Input: phi, a 2-d array containing the current subspace;
output: 1. 1-d array of eigenvalues estimates;
2. 2-d array of eigenvector estimates in Ritz coordinates."""
# compute the reduced matrices
... |
AjinkyaBhave/CarND_P1_FindLanes | P1.ipynb | agpl-3.0 | #importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
import os
import math
from scipy import misc
%matplotlib inline
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import H... |
ernestyalumni/MLgrabbag | sklearn_ML.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import sklearn
from sklearn import datasets
import os, sys
os.getcwd()
os.listdir( os.getcwd() ) ;
import numpy as np
import scipy
import pandas as pd
"""
Explanation: Using sci-kit learn, i.e. sklearn for Machine Learning (ML); in combination with numpy,scipy, an... |
CentreForResearchInAppliedLinguistics/clic | docs/notebooks/Cheshire/.ipynb_checkpoints/Cheshire objects and methods-checkpoint.ipynb | mit | # coding: utf-8
import os
from cheshire3.baseObjects import Session
from cheshire3.document import StringDocument
from cheshire3.internal import cheshire3Root
from cheshire3.server import SimpleServer
session = Session()
session.database = 'db_dickens'
serv = SimpleServer(session, os.path.join(cheshire3Root, 'con... |
meta-mind/workspace | kaggle/Titanic: Machine Learning from Disaster/scripts/Titanic Machine Learning from Disaster.ipynb | mit | import pandas as pd
"""
Explanation: Titanic: Machine Learning from Disaster
Get the Data with Pandas
Import the Pandas library
End of explanation
"""
train_url = "http://s3.amazonaws.com/assets.datacamp.com/course/Kaggle/train.csv"
train = pd.read_csv(train_url)
test_url = "http://s3.amazonaws.com/assets.datacamp.... |
jinntrance/MOOC | coursera/ml-foundations/week5/Song recommender.ipynb | cc0-1.0 | import graphlab
"""
Explanation: Building a song recommender
Fire up GraphLab Create
End of explanation
"""
song_data = graphlab.SFrame('song_data.gl/')
"""
Explanation: Load music data
End of explanation
"""
song_data.head()
"""
Explanation: Explore data
Music data shows how many times a user listened to a song... |
tangsttw/python_tips_and_notes | pandas/pandas.ipynb | mit | import pandas as pd
import numpy as np
"""
Explanation: pandas
THis notebook records some tips for the pandas module
End of explanation
"""
df = pd.DataFrame(np.random.randint(0,100,size=(10, 4)), columns=list('ABCD'))
df
"""
Explanation: Create dataframe
Create a dataframe of random integers
End of explanation
... |
prasants/pyds | 11.Introduction_to_Numpy.ipynb | mit | import numpy as np
# Create an array with the statement np.array
a = np.array([1,2,3,4])
print('a is of type:', type(a))
print('dimension of a:', a.ndim) # To find the dimension of 'a'
arr1 = np.array([1,2,3,4])
arr1.ndim
arr2 = np.array([[1,2],[2,3],[3,4],[4,5]])
arr2.ndim
# Doesn't make a difference to a computer... |
dryadb11781/machine-learning-python | Classification/ipython_notebook/EX2.ipynb | bsd-3-clause | %matplotlib inline
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
n_train = 20 # samples for training
n_test = 200 # samples for testing
n_averages = 50 # how often to re... |
GoogleCloudPlatform/mlops-on-gcp | model_serving/caip-load-testing/02-perf-testing.ipynb | apache-2.0 | %pip install -q -U locust google-cloud-monitoring google-cloud-logging google-cloud-monitoring-dashboards
# Automatically restart kernel after installs
import IPython
app = IPython.Application.instance()
app.kernel.do_shutdown(True)
"""
Explanation: AI Platform Prediction Load Testing using Locust
This Notebook dem... |
LaubachLab/Spikes-and-Fields | Working with NEx files using oct2py.ipynb | gpl-3.0 | import numpy as np
from scipy.io import loadmat
%load_ext oct2py.ipython
%cd ~/Desktop/Spikes-and-Fields/NEx-demo
"""
Explanation: This post demonstrates how oct2py can be used to run legacy Matlab/Octave code to load data saved in NeuroExplorer files into Python. As will be illustrated in a forthcoming post, this sa... |
xiongzhenggang/xiongzhenggang.github.io | AI/ML/week4反向传播实现.ipynb | gpl-3.0 |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
from scipy.io import loadmat
from sklearn.preprocessing import OneHotEncoder
data = loadmat('../data/andrew_ml_ex33507/ex3data1.mat')
data
X = data['X']
y = data['y']
X.shape, y.shape#看下维度
# 目前考虑输入是图片的像素值，20*20像素的图片有400个输入层单元，... |
quantumlib/OpenFermion-FQE | docs/tutorials/hamiltonian_time_evolution_and_expectation_estimation.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... |
kubeflow/pipelines | components/gcp/dataproc/create_cluster/sample.ipynb | apache-2.0 | %%capture --no-stderr
!pip3 install kfp --upgrade
"""
Explanation: Name
Data processing by creating a cluster in Cloud Dataproc
Label
Cloud Dataproc, cluster, GCP, Cloud Storage, KubeFlow, Pipeline
Summary
A Kubeflow Pipeline component to create a cluster in Cloud Dataproc.
Details
Intended use
Use this component at ... |
rajul/tvb-library | tvb/simulator/demos/display_region_connectivity.ipynb | gpl-2.0 | from tvb.simulator.lab import *
"""
Explanation: Plot regions and connection edges.
Xmas balls scaled is in the range [0 - 1], representing
the cumulative input to each region.
End of explanation
"""
white_matter = connectivity.Connectivity(load_default=True)
#Compute cumulative input for each region
node_data = wh... |
JAmarel/LiquidCrystals | ElectroOptics/CurveFitAttempt.ipynb | mit | import numpy as np
from scipy.integrate import quad, dblquad
%matplotlib inline
import matplotlib.pyplot as plt
import scipy.optimize as opt
"""
Explanation: TO DO:
Need to be able to scatter plot measured values of Psi on top of the current Psi plot.
Alpha and rho LaTeX not working in plots.
Legend needs to be move i... |
qinwf-nuan/keras-js | notebooks/layers/convolutional/ZeroPadding1D.ipynb | mit | data_in_shape = (3, 5)
L = ZeroPadding1D(padding=1)
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
np.random.seed(240)
data_in = 2 * np.random.random(data_in_shape) - 1
result = model.predict(np.array([dat... |
tensorflow/docs-l10n | site/en-snapshot/probability/examples/TFP_Release_Notebook_0_12_1.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# 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, sof... |
mne-tools/mne-tools.github.io | 0.12/_downloads/plot_clickable_image.ipynb | bsd-3-clause | # Authors: Christopher Holdgraf <choldgraf@berkeley.edu>
#
# License: BSD (3-clause)
from scipy.ndimage import imread
import numpy as np
from matplotlib import pyplot as plt
from os import path as op
import mne
from mne.viz import ClickableImage, add_background_image # noqa
from mne.channels import generate_2d_layout ... |
ktmud/deep-learning | first-neural-network/Your_first_neural_network.solution.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... |
cokelaer/colormap | notebooks/colormap package demonstration.ipynb | bsd-3-clause | %pylab inline
from colormap import Colormap
c = Colormap()
cmap = c.cmap('cool')
# let us see what it looks like
c.test_colormap(cmap)
#Would be nice to plot a bunch of colormap to pick up one interesting
c.plot_colormap('diverging')
c.plot_colormap(c.misc)
c.plot_colormap(c.qualitative)
c.plot_colormap(c.sequen... |
kimkipyo/dss_git_kkp | 통계, 머신러닝 복습/160502월_1일차_분석 환경, 소개/16.Pandas 데이터 입출력.ipynb | mit | %cd /home/dockeruser/data/pydata-book-master/
"""
Explanation: Pandas 데이터 입출력
이 노트북의 예제를 실행하기 위해서는 datascienceschool/rpython 도커 이미지의 다음 디렉토리로 이동해야 한다.
End of explanation
"""
!cat ../../pydata-book-master/ch06/ex1.csv
!cat ch06/ex1.csv
df = pd.read_csv('../../pydata-book-master/ch06/ex1.csv')
df
"""
Explanation: p... |
piyushbhattacharya/machine-learning | python/Carvan script.ipynb | gpl-3.0 | ld_train, ld_test = train_test_split(cd_train, test_size=0.2, random_state=2)
x80_train = ld_train.drop(['V86'],1)
y80_train = ld_train['V86']
x20_test = ld_test.drop(['V86'],1)
y20_test = ld_test['V86']
"""
Explanation: Optimizing model...
Run train_test splits on the train data
End of explanation
"""
model_logr1... |
tonyfast/tidy-harness | README.ipynb | bsd-3-clause | import harness
from harness import Harness
from pandas import Categorical
from sklearn import datasets, discriminant_analysis
iris = datasets.load_iris()
# Harness is just a dataframe
df = Harness(
data=iris['data'], index=Categorical(iris['target']),
estimator=discriminant_analysis.LinearDiscriminantAnalysis... |
massie/notebooks | Physio.ipynb | apache-2.0 | from math import log
# RT/F = 26.73 at room temperature
rt_div_f = 26.73
nernst = lambda xO, xI, z: rt_div_f/z * log(1.0 * xO / xI)
Na_Eq = nernst(145, 15, 1)
K_Eq = nernst(4.5, 120, 1)
Cl_Eq = nernst(116, 20, -1)
print "Na+ equilibrium potential is %.2f mV" % (Na_Eq)
print "K+ equilibrium potential is %.2f mV" % (K... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/art_and_science_of_ml/labs/neural_network.ipynb | apache-2.0 | import os, json, math
import numpy as np
import shutil
import tensorflow as tf
print("TensorFlow version: ",tf.version.VERSION)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # SET TF ERROR LOG VERBOSITY
"""
Explanation: Build a DNN using the Keras Functional API
Learning objectives
Review how to read in CSV file data usi... |
tsarouch/python_minutes | core/Hypothesis_Testing.ipynb | gpl-2.0 | import random
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Common Use Cases when dealing with Hypothesis Testing
End of explanation
"""
# se have:
n_h = 140
n_t = 110
observations = (n_h, n_t)
n_observations = n_h + n_t
print observations, n_observations,
# We define the ... |
materialsvirtuallab/matgenb | notebooks/2021-5-12-Explanation of Corrections.ipynb | bsd-3-clause | from pymatgen.entries.computed_entries import ComputedEntry
from pymatgen.entries.compatibility import MaterialsProjectCompatibility, \
MaterialsProject2020Compatibility
from pymatgen.ext.matproj import MPRester
"""
Explanation: Demonstration of Materials Project Energy Corre... |
simulkade/peteng | python/.ipynb_checkpoints/two_phase_1D_fipy-checkpoint.ipynb | mit | from fipy import *
# relperm parameters
swc = 0.1
sor = 0.1
krw0 = 0.3
kro0 = 1.0
nw = 2.0
no = 2.0
# domain and boundaries
k = 1e-12 # m^2
phi = 0.4
u = 1.e-5
p0 = 100e5 # Pa
Lx = 100.
Ly = 10.
nx = 100
ny = 10
dx = Lx/nx
dy = Ly/ny
# fluid properties
muo = 0.002
muw = 0.001
# define the fractional flow functions
... |
nadvamir/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... |
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