repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
google/starthinker | colabs/dv360_api_insert_from_bigquery.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: 1. Install Dependencies
First install the libraries needed to execute recipes, this only needs to be done once, then click play.
End of explanation
"""
CLOUD_PROJECT = 'PASTE PROJECT ID HERE'
print("Cloud Project Set To: %s" % CLOUD_PROJECT)
... |
tensorflow/docs-l10n | site/en-snapshot/io/tutorials/colorspace.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... |
google/starthinker | colabs/iam.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: Project IAM
Sets project permissions for an email.
License
Copyright 2020 Google LLC,
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 Li... |
chetan51/nupic.research | projects/dynamic_sparse/notebooks/ExperimentAnalysis-Neurips-debug-hebbianANDmagnitude-opposite.ipynb | gpl-3.0 | %load_ext autoreload
%autoreload 2
import sys
sys.path.append("../../")
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import glob
import tabulate
import pprint
import click
import numpy as np
import pandas as pd
from ray.tune.commands import *
... |
choderalab/MSMs | initial_ipynbs/Abl_longsim_initial_MSM.ipynb | gpl-2.0 | #Import libraries
import matplotlib.pyplot as plt
import mdtraj as md
import glob
import numpy as np
from msmbuilder.dataset import dataset
%pylab inline
#Import longest trajectory.
t = md.load("run0-clone35.h5")
"""
Explanation: Analysis of large set of Abl simulations on Folding@home (project 10468), one starting... |
SteveDiamond/cvxpy | examples/notebooks/WWW/sparse_solution.ipynb | gpl-3.0 | import cvxpy as cp
import numpy as np
# Fix random number generator so we can repeat the experiment.
np.random.seed(1)
# The threshold value below which we consider an element to be zero.
delta = 1e-8
# Problem dimensions (m inequalities in n-dimensional space).
m = 100
n = 50
# Construct a feasible set of inequali... |
tedunderwood/fiction | bert/logistic_regression_baselines.ipynb | mit | # Things that will come in handy
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score, f1_score
from collections import Counter
from scipy.stats import pearsonr
import random, glob, csv
""... |
samgoodgame/sf_crime | iterations/Error Analysis/W207_Final_Project_errorAnalysis_updated_08_21_1930.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... |
kaushikpavani/neural_networks_in_python | src/linear_regression/linear_regression.ipynb | mit | def generate_random_points_along_a_line (slope, intercept, num_points, abs_value, abs_noise):
# randomly select x
x = np.random.uniform(-abs_value, abs_value, num_points)
# y = mx + b + noise
y = slope*x + intercept + np.random.uniform(-abs_noise, abs_noise, num_points)
return x, y
def plot_points(... |
adamamiller/NUREU17 | LSST/VariableStarClassification/First_Sources.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from astropy.table import Table as tab
"""
Explanation: Inital Sources
Using the sources at 007.20321 +14.87119 and RA = 20:50:00.91, dec = -00:42:23.8 taken from the NASA/IPAC Infrared Science Archieve on 6/22/17.
End of explanation
"""
source_1 ... |
royalosyin/Python-Practical-Application-on-Climate-Variability-Studies | ex34-Correlations between SOI and SLP, Temperature and Precipitation.ipynb | mit | %matplotlib inline
import numpy as np
import xarray as xr
import pandas as pd
from numba import jit
from functools import partial
from scipy.stats import pearsonr
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
# Set som... |
projectmesa/mesa-examples | examples/ForestFire/Forest Fire Model.ipynb | apache-2.0 | import random
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from mesa import Model, Agent
from mesa.time import RandomActivation
from mesa.space import Grid
from mesa.datacollection import DataCollector
from mesa.batchrunner import BatchRunner
"""
Explanation: The Forest Fire Model
A rapid i... |
amueller/scipy-2017-sklearn | notebooks/15.Pipelining_Estimators.ipynb | cc0-1.0 | import os
with open(os.path.join("datasets", "smsspam", "SMSSpamCollection")) as f:
lines = [line.strip().split("\t") for line in f.readlines()]
text = [x[1] for x in lines]
y = [x[0] == "ham" for x in lines]
from sklearn.model_selection import train_test_split
text_train, text_test, y_train, y_test = train_test... |
philippbayer/cats_dogs_redux | Statefarm.ipynb | mit | %%bash
cut -f 1 -d ',' driver_imgs_list.csv | grep -v subject | uniq -c
lines=$(expr `wc -l driver_imgs_list.csv | cut -f 1 -d ' '` - 1)
echo "Got ${lines} pics"
"""
Explanation: First, make the validation set with different drivers
End of explanation
"""
import csv
import os
to_get = set(['p081','p075', 'p072', 'p0... |
drvinceknight/gt | assets/assessment/2021-2022/ind/assignment.ipynb | mit | import nashpy as nash
import numpy as np
np.random.seed(0)
repetitions = 2000
### BEGIN SOLUTION
### END SOLUTION
"""
Explanation: Game Theory - 2021-2022 individual coursework
Important Do not delete the cells containing:
```
BEGIN SOLUTION
END SOLUTION
```
write your solution attempts in those cells.
To submit t... |
PythonFreeCourse/Notebooks | week06/2_Functional_Behavior.ipynb | mit | def square(x):
return x ** 2
"""
Explanation: <img src="images/logo.jpg" style="display: block; margin-left: auto; margin-right: auto;" alt="לוגו של מיזם לימוד הפייתון. נחש מצויר בצבעי צהוב וכחול, הנע בין האותיות של שם הקורס: לומדים פייתון. הסלוגן המופיע מעל לשם הקורס הוא מיזם חינמי ללימוד תכנות בעברית.">
<span st... |
mne-tools/mne-tools.github.io | dev/_downloads/a96f6d7ea0f7ccafcacc578a25e1f8c5/ica_comparison.ipynb | bsd-3-clause | # Authors: Pierre Ablin <pierreablin@gmail.com>
#
# License: BSD-3-Clause
from time import time
import mne
from mne.preprocessing import ICA
from mne.datasets import sample
print(__doc__)
"""
Explanation: Compare the different ICA algorithms in MNE
Different ICA algorithms are fit to raw MEG data, and the correspo... |
theideasmith/theideasmith.github.io | _notebooks/.ipynb_checkpoints/ODE N-Dimensional Test 1-checkpoint.ipynb | mit | import numpy as np
import numpy.ma as ma
from scipy.integrate import odeint
mag = lambda r: np.sqrt(np.sum(np.power(r, 2)))
def g(y, t, q, m, n,d, k):
"""
n: the number of particles
d: the number of dimensions
(for fun's sake I want this
to work for k-dimensional systems)
y: an (n*2,d) di... |
pycam/python-basic | python_basic_2_2.ipynb | unlicense | codeList = ['NA06984', 'NA06985', 'NA06986', 'NA06989', 'NA06991']
for code in codeList:
print(code)
"""
Explanation: An introduction to solving biological problems with Python
Session 2.2: Loops
The <tt>for</tt> loop
Exercises 2.2.1
The <tt>while</tt> loop
Exercises 2.2.2
Skipping and breaking loops
More loopin... |
VVard0g/ThreatHunter-Playbook | docs/notebooks/windows/07_discovery/WIN-190625024610.ipynb | mit | from openhunt.mordorutils import *
spark = get_spark()
"""
Explanation: SysKey Registry Keys Access
Metadata
| Metadata | Value |
|:------------------|:---|
| collaborators | ['@Cyb3rWard0g', '@Cyb3rPandaH'] |
| creation date | 2019/06/25 |
| modification date | 2020/09/20 |
| playbook related | []... |
PythonSanSebastian/ep-tools | notebooks/programme_grid.ipynb | mit | %%javascript
IPython.OutputArea.auto_scroll_threshold = 99999;
//increase max size of output area
import json
import datetime as dt
from random import choice, randrange, shuffle
from copy import deepcopy
from collections import OrderedDict, defaultdict
from itertools import product
from functools import partial
from ... |
ES-DOC/esdoc-jupyterhub | notebooks/bcc/cmip6/models/sandbox-3/landice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'bcc', 'sandbox-3', 'landice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: BCC
Source ID: SANDBOX-3
Topic: Landice
Sub-Topics: Glaciers, Ice.
Properties: 3... |
scottquiring/Udacity_Deeplearning | intro-to-tflearn/TFLearn_Digit_Recognition.ipynb | mit | # Import Numpy, TensorFlow, TFLearn, and MNIST data
import numpy as np
import tensorflow as tf
import tflearn
import tflearn.datasets.mnist as mnist
"""
Explanation: Handwritten Number Recognition with TFLearn and MNIST
In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-9.
This... |
DS-100/sp17-materials | sp17/hw/hw4/hw4.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import sqlalchemy
!pip install -U okpy
from client.api.notebook import Notebook
ok = Notebook('hw4.ok')
"""
Explanation: Homework 4: SQL, FEC Data, and Small Donors
Due: 11:59pm Tuesday, March 14
Note: The ... |
conversationai/unintended-ml-bias-analysis | archive/unintended_ml_bias/fat-star-bias-measurement-tutorial.ipynb | apache-2.0 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import pandas as pd
import numpy as np
import pkg_resources
import matplotlib.pyplot as plt
import seaborn as sns
import time
import scipy.stats as stats
from sklearn import metrics
from keras.prepr... |
johnbachman/indra | models/indra_statements_demo.ipynb | bsd-2-clause | %pylab inline
import json
from indra.sources import trips
from indra.statements import draw_stmt_graph, stmts_to_json
"""
Explanation: Inspecting INDRA Statements and assembled models
In this example we look at how intermediate results of the assembly process from word models to executable models can be inspected. We ... |
stuser/temp | pneumoniamnist_CNN.ipynb | mit | # import package
import matplotlib.pyplot as plt
import numpy as np
import os
import tensorflow as tf
import tensorflow.keras as keras
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import (Input, Dense, Dropout, Activation, GlobalAverag... |
c-north/hdbscan | notebooks/Benchmarking scalability of clustering implementations.ipynb | bsd-3-clause | import hdbscan
import debacl
import fastcluster
import sklearn.cluster
import scipy.cluster
import sklearn.datasets
import numpy as np
import pandas as pd
import time
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
sns.set_context('poster')
sns.set_palette('Paired', 10)
sns.set_color_codes()
"... |
xgrg/alfa | notebooks/Ages distributions.ipynb | mit | %matplotlib inline
import pandas as pd
from scipy import stats
from matplotlib import pyplot as plt
data = pd.read_excel('/home/grg/spm/data/covariates.xls')
for i in xrange(5):
x = data[data['apo'] == i]['age'].values
plt.hist(x, bins=20)
print i, 'W:%.4f p:%.4f -'%stats.shapiro(x), len(x), 'subjects bet... |
newlawrence/poliastro | docs/source/examples/Analyzing the Parker Solar Probe flybys.ipynb | mit | from astropy import units as u
T_ref = 150 * u.day
T_ref
from poliastro.bodies import Earth, Sun, Venus
k = Sun.k
k
import numpy as np
"""
Explanation: Analyzing the Parker Solar Probe flybys
1. Modulus of the exit velocity, some features of Orbit #2
First, using the data available in the reports, we try to comput... |
fastai/course-v3 | nbs/dl1/lesson4-collab.ipynb | apache-2.0 | user,item,title = 'userId','movieId','title'
path = untar_data(URLs.ML_SAMPLE)
path
ratings = pd.read_csv(path/'ratings.csv')
ratings.head()
"""
Explanation: Collaborative filtering example
collab models use data in a DataFrame of user, items, and ratings.
End of explanation
"""
data = CollabDataBunch.from_df(rati... |
julienchastang/unidata-python-workshop | notebooks/Primer/Numpy and Matplotlib Basics.ipynb | mit | # Convention for import to get shortened namespace
import numpy as np
# Create a simple array from a list of integers
a = np.array([1, 2, 3])
a
# See how many dimensions the array has
a.ndim
# Print out the shape attribute
a.shape
# Print out the data type attribute
a.dtype
# This time use a nested list of floats
... |
radhikapc/foundation-homework | homework11/Homework11-Radhika.ipynb | mit | import dateutils
import dateutil.parser
import pandas as pd
parking_df = pd.read_csv("small-violations.csv")
parking_df
parking_df.dtypes
import datetime
parking_df.head()['Issue Date'].astype(datetime.datetime)
import pandas as pd
parking_df = pd.read_csv("small-violations.csv")
parking_df
"""
Explanation: I w... |
ziweiwu/ziweiwu.github.io | notebook/Titanic_Investigation.ipynb | mit | #load the libraries that I might need to use
%matplotlib inline
import pandas as pd
import numpy as np
import csv
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
#read the csv file into a pandas dataframe
titanic_original = pd.DataFrame.from_csv('titanic-data.csv', index_col=None)
titanic_orig... |
jgarciab/wwd2017 | class8/class8_impute.ipynb | gpl-3.0 | ##Some code to run at the beginning of the file, to be able to show images in the notebook
##Don't worry about this cell
#Print the plots in this screen
%matplotlib inline
#Be able to plot images saved in the hard drive
from IPython.display import Image
#Make the notebook wider
from IPython.core.display import dis... |
transcranial/keras-js | notebooks/layers/pooling/GlobalAveragePooling3D.ipynb | mit | data_in_shape = (6, 6, 3, 4)
L = GlobalAveragePooling3D(data_format='channels_last')
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(270)
data_in = 2 * np.random.random(data_in_shape) - 1
res... |
CAChemE/stochastic-optimization | PSO/1D/1D-Python-PSO-algorithm-viz.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
# import scipy as sp
# import time
%matplotlib inline
plt.style.use('bmh')
"""
Explanation: Particle Swarm Optimization Algorithm (in Python!)
[SPOILER] We will be using the Particle Swarm Optimization algorithm to obtain the minumum of a customed objective function... |
goerlitz/ds-notebooks | jupyter/kaggle_sf-crime/SF Crime - Convert To DataFrame.ipynb | apache-2.0 | import csv
import pyspark
from pyspark.sql import SQLContext
from pyspark.sql.types import *
from StringIO import StringIO
from datetime import *
from dateutil.parser import parse
"""
Explanation: San Francisco Crime Dataset Conversion
Challenge
Spark does not support out-of-the box data frame creation from CSV files... |
tensorflow/docs-l10n | site/zh-cn/hub/tutorials/cropnet_cassava.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow_datasets as tfds
import tensorflow_hub as hub
#@title Helper function for displaying examples
def plot(examples, predictions=None):
# Get the images, labels, and optionally predictions
images = examples['image']
labels ... |
dolittle007/dolittle007.github.io | notebooks/GLM-robust-with-outlier-detection.ipynb | gpl-3.0 | %matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import optimize
import pymc3 as pm
import theano as thno
import theano.tensor as T
# configure some basic options
sns.set(style="darkgrid", pa... |
renecnielsen/twitter-diy | ipynb/02 Parse Twitter Data.ipynb | mit | from IPython.core.display import HTML
styles = open("../css/custom.css", "r").read()
HTML(styles)
"""
Explanation: Parse Twitter Data
Import retrieved tweets (from JSON file, pickle or similar)
Read in individual tweets
Create TSV file (and drop unwanted data)
Jupyter Notebook Style
Let's make this thing look nice.
... |
uwkejia/Clean-Energy-Outlook | examples/Demo.ipynb | mit | from ceo import data_cleaning
from ceo import missing_data
from ceo import svr_prediction
from ceo import ridge_prediction
"""
Explanation: Examples
Importing libraries
End of explanation
"""
data_cleaning.clean_all_data()
"""
Explanation: datacleaning
The datacleaning module is used to clean and organize the data... |
vierth/chinese_stylometry | Stanford DH Asia Stylometry.ipynb | gpl-3.0 | %pylab inline
pylab.rcParams['figure.figsize']=(12,8)
"""
Explanation: Digital Humanities Asia Workshop
Stylometerics and Genre Research in Imperial Chinese Studies
Coding for Stylometric Analysis
Paul Vierthaler, Boston College
@pvierth, vierthal@bc.edu
Texts encodings
It is important to know the encodings of the fil... |
gon1213/SDC | find_lane_lines/CarND_LaneLines_P1/P1.ipynb | gpl-3.0 | #importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline
#reading in an image
image = mpimg.imread('test_images/solidWhiteRight.jpg')
#printing out some stats and plotting
print('This image is:', type(image), 'with dimesions:', im... |
lukasmerten/CRPropa3 | doc/pages/example_notebooks/galactic_lensing/lensing_cr.v4.ipynb | gpl-3.0 | import crpropa
import numpy as np
# read data from CRPropa output into container.
# The data is weighted with the source energy ~E**-1
M = crpropa.ParticleMapsContainer()
crdata = np.genfromtxt("crpropa_output.txt")
Id = np.array([int(x) for x in crdata[:,0]])
E = crdata[:,3] * crpropa.EeV
E0 = crdata[:,4] * crpropa.E... |
phoebe-project/phoebe2-docs | 2.1/examples/detached_rotstar.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.1,<2.2"
%matplotlib inline
"""
Explanation: Detached Binary: Roche vs Rotstar
Setup
Let's first make sure we have the latest version of PHOEBE 2.1 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of expl... |
PMEAL/OpenPNM-Examples | Tutorials/intermediate_usage.ipynb | mit | import openpnm as op
import scipy as sp
"""
Explanation: Tutorial 2 of 3: Digging Deeper into OpenPNM
This tutorial will follow the same outline as Getting Started, but will dig a little bit deeper at each step to reveal the important features of OpenPNM that were glossed over previously.
Learning Objectives
Explore ... |
marburg-open-courseware/gmoc | docs/mpg-if_error_continue/worksheets/w-02-2_conditionals.ipynb | mit | import pandas as pd
url = "http://www.cpc.ncep.noaa.gov/data/indices/oni.ascii.txt"
# help(pd.read_fwf)
oni = pd.read_fwf(url, widths = [5, 5, 7, 7])
oni.head()
## Your solution goes here:
"""
Explanation: W02-2.1: Count the number of occurrences of each warm ENSO category
Using the ONI data set from the previous w... |
lisitsyn/shogun | doc/ipython-notebooks/ica/ecg_sep.ipynb | bsd-3-clause | # change to the shogun-data directory
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
os.chdir(os.path.join(SHOGUN_DATA_DIR, 'ica'))
import numpy as np
# load data
# Data originally from:
# http://perso.telecom-paristech.fr/~cardoso/icacentral/base_single.html
data = np.loadtxt('foetal_ecg.dat... |
xmnlab/pywim | notebooks/StorageRawData.ipynb | mit | from IPython.display import display
from datetime import datetime
from matplotlib import pyplot as plt
from scipy import misc
import h5py
import json
import numpy as np
import os
import pandas as pd
import sys
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#1.-Weigh-in-Motion-Storage-Raw-Da... |
KshitijT/fundamentals_of_interferometry | 6_Deconvolution/6_1_sky_models.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
6. Deconvolution in Imaging
Previous: 6. Introduction
Next: 6.2 Interative Deconvolution with Point Sources (CLEAN)
Import sta... |
bosscha/alma-calibrator | notebooks/selecting_source/alma_database_selection11.ipynb | gpl-2.0 | from collections import Counter
filename = "report_8_nonALMACAL_priority.txt"
with open(filename, 'r') as ifile:
wordcount = Counter(ifile.read().split())
"""
Explanation: find a word and count them
End of explanation
"""
current = ['3c454.3', 'J0006-0623', 'J0137+3309', 'J0211+1051', 'J0237+2848',
'J0241-0... |
EvanBianco/Practical_Programming_for_Geoscientists | Part2b__Synthetic_seismogram.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: EXERCISE — Simple synthetic
This notebook looks at the convolutional model of a seismic trace.
For a fuller example, see Bianco, E (2004) in The Leading Edge.
First, the usual preliminaries.
End of explanation
"""
from welly impor... |
adamamiller/PS1_star_galaxy | gaia/pmStarsForZTFdatabase.ipynb | mit | gaia_dir = "/Users/adamamiller/Desktop/PS1_fits/gaia_stars/"
gaia_df = pd.read_hdf(gaia_dir + "parallax_ps1_gaia_mag_pm_plx.h5")
pxl_not_pm = np.where((gaia_df["parallax_over_error"] >= 8) &
(gaia_df["pm_over_error"] < 7.5))
gaia_df.iloc[pxl_not_pm]
"""
Explanation: First - test to see if there... |
vadim-ivlev/STUDY | handson-data-science-python/DataScience-Python3/MultivariateRegression.ipynb | mit | import pandas as pd
df = pd.read_excel('http://cdn.sundog-soft.com/Udemy/DataScience/cars.xls')
df.head()
"""
Explanation: Multivariate Regression
Let's grab a small little data set of Blue Book car values:
End of explanation
"""
import statsmodels.api as sm
from sklearn.preprocessing import StandardScaler
scale ... |
atavory/ibex | examples/digits_confidence_intervals.ipynb | bsd-3-clause | import multiprocessing
import pandas as pd
import numpy as np
from sklearn import datasets
import seaborn as sns
sns.set_style('whitegrid')
from sklearn.externals import joblib
from ibex.sklearn import decomposition as pd_decomposition
from ibex.sklearn import linear_model as pd_linear_model
from ibex.sklearn import... |
Dioptas/pymatgen | examples/Plotting a Pourbaix Diagram.ipynb | mit | from pymatgen.matproj.rest import MPRester
from pymatgen.core.ion import Ion
from pymatgen import Element
from pymatgen.phasediagram.pdmaker import PhaseDiagram
from pymatgen.analysis.pourbaix.entry import PourbaixEntry, IonEntry
from pymatgen.analysis.pourbaix.maker import PourbaixDiagram
from pymatgen.analysis.pourb... |
KiranArun/A-Level_Maths | Matrices/Matrices.ipynb | mit | # we will be using numpy to create the arrays
# the code isn't so important in this notebook, just the arrays are
import numpy as np
"""
Explanation: A-Level: Matrices
End of explanation
"""
# array conaining 12 consecutive values in shape 3 by 4
a = np.arange(12).reshape([3,4])
print(a)
"""
Explanation: Matrices a... |
Aniruddha-Tapas/Applied-Machine-Learning | Classification/Classifiying Ionosphere structure using K nearest neigbours algorithm.ipynb | mit | import csv
import numpy as np
# Size taken from the dataset and is known
X = np.zeros((351, 34), dtype='float')
y = np.zeros((351,), dtype='bool')
with open("data/Ionosphere/ionosphere.data", 'r') as input_file:
reader = csv.reader(input_file)
for i, row in enumerate(reader):
# Get the data, convertin... |
mne-tools/mne-tools.github.io | 0.23/_downloads/b36af73820a7a52a4df3c42b66aef8a5/source_power_spectrum_opm.ipynb | bsd-3-clause | # Authors: Denis Engemann <denis.engemann@gmail.com>
# Luke Bloy <luke.bloy@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
from mne.filter import next_fast_len
import mne
print(__doc__)
data_path = mne.datasets.opm.data_path()
subject = 'OPM_s... |
chi-hung/SementicProj | webCrawler/amzProd.ipynb | mit | %watermark
"""
Explanation: This notebook is written by Yishin and Chi-Hung.
End of explanation
"""
def getVacuumTypeUrl(vacuumType,pageNum=1):
vcleaners={"central":11333709011,"canister":510108,"handheld":510114,"robotic":3743561,"stick":510112,"upright":510110,"wetdry":553022}
url_type_base="https://www.am... |
cfjhallgren/shogun | doc/ipython-notebooks/statistical_testing/mmd_two_sample_testing.ipynb | gpl-3.0 | %pylab inline
%matplotlib inline
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
import shogun as sg
import numpy as np
"""
Explanation: Kernel hypothesis testing in Shogun
Heiko Strathmann - heiko.strathmann@gmail.com - http://github.com/karlnapf - http://herrstrathmann.de
Soumyajit De - soumy... |
phaustin/pyman | Book/chap4/chap4_io.ipynb | cc0-1.0 | strname = input("prompt to user ")
"""
Explanation: Input and Output
A good relationship depends on good communication. In this chapter you
learn how to communicate with Python. Of course, communicating is a
two-way street: input and output. Generally, when you have Python
perform some task, you need to feed it inform... |
feffenberger/StatisticalMethods | examples/SDSScatalog/GalaxySizes.ipynb | gpl-2.0 | %load_ext autoreload
%autoreload 2
from __future__ import print_function
import numpy as np
import SDSS
import pandas as pd
import matplotlib
%matplotlib inline
galaxies = "SELECT top 1000 \
petroR50_i AS size, \
petroR50Err_i AS err \
FROM PhotoObjAll \
WHERE \
(type = '3' AND petroR50Err_i > 0)"
print (galaxies)
#... |
UltronAI/Deep-Learning | 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... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/08_image/labs/flowers_fromscratch.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
import os
PROJECT = "cloud-training-demos" # REPLACE WITH YOUR PROJECT ID
BUCKET = "cloud-training-demos-ml" # REPLACE WITH YOUR BUCKET NAME
REGION = "us-central1" # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
# do not change these
os.environ["PR... |
alexandrnikitin/algorithm-sandbox | courses/DAT256x/Module02/02 - 02 - Limits.ipynb | mit | %matplotlib inline
# Here's the function
def f(x):
return x**2 + x
from matplotlib import pyplot as plt
# Create an array of x values from 0 to 10 to plot
x = list(range(0, 11))
# Get the corresponding y values from the function
y = [f(i) for i in x]
# Set up the graph
plt.xlabel('x')
plt.ylabel('f(x)')
plt.g... |
arcyfelix/Courses | 17-09-17-Python-for-Financial-Analysis-and-Algorithmic-Trading/02-NumPy/.ipynb_checkpoints/Numpy Exercises-checkpoint.ipynb | apache-2.0 | # CODE HERE
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
<center>Copyright Pierian Data 2017</center>
<center>For more information, visit us at www.pieriandata.com</center>
NumPy Exercises
Now that we've learned about NumPy let's test your knowledge. We'll start of... |
ALEXKIRNAS/DataScience | CS231n/assignment2/BatchNormalization.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... |
UltronAI/Deep-Learning | CS231n/assignment2/.ipynb_checkpoints/PyTorch-checkpoint.ipynb | mit | import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torch.utils.data import sampler
import torchvision.datasets as dset
import torchvision.transforms as T
import numpy as np
import timeit
"""
Explanation: Training a ConvNet ... |
diego0020/va_course_2015 | text_analysis/Text_Analysis_Tutorial.ipynb | mit | %cd C:/temp/
import pandas as pd
train = pd.read_csv("labeledTrainData.tsv", header=0, delimiter="\t", quoting=3)
"""
Explanation: Bag-of-Words
The bag-of-words model is a simplifying representation used in natural language processing and information retrieval (IR). In this model, a text (such as a sentence or... |
ES-DOC/esdoc-jupyterhub | notebooks/cccma/cmip6/models/sandbox-3/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cccma', 'sandbox-3', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: CCCMA
Source ID: SANDBOX-3
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, Ra... |
atlury/deep-opencl | DL0110EN/3.3.1_softmax_in_one_dimension_v2.ipynb | lgpl-3.0 | # Import the libraries we need for this lab
import torch.nn as nn
import torch
import matplotlib.pyplot as plt
import numpy as np
from torch.utils.data import Dataset, DataLoader
"""
Explanation: <a href="http://cocl.us/pytorch_link_top">
<img src="https://cocl.us/Pytorch_top" width="750" alt="IBM 10TB Storage" ... |
Kaggle/learntools | notebooks/data_viz_to_coder/raw/ex4.ipynb | apache-2.0 | import pandas as pd
pd.plotting.register_matplotlib_converters()
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
print("Setup Complete")
"""
Explanation: In this exercise, you will use your new knowledge to propose a solution to a real-world scenario. To succeed, you will need to import data ... |
PrincetonACM/princetonacm.github.io | events/code-at-night/archive/python_talk/intro_to_python.ipynb | mit | # When a line begins with a '#' character, it designates a comment. This means that it's not actually a line of code
# Can you print 'hello world', as is customary for those new to a language?
# Can you make Python print the staircase below:
#
# ========
# | |
# ... |
Nixonite/Handwritten-Digit-Classification-Using-SVD | SVD Classification of Handwritten Digits.ipynb | gpl-2.0 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
data = pd.read_csv("train.csv")
data.head()
"""
Explanation: For this project, I will attempt to classify handwritten digits using SVD's left-singular vectors as fundamental subspaces for each digit.
The approach will be to gene... |
noppanit/machine-learning | parking-signs-nyc/.ipynb_checkpoints/Parking Signs-checkpoint.ipynb | mit | row = 'NO PARKING (SANITATION BROOM SYMBOL) 7AM-7:30AM EXCEPT SUNDAY'
assert from_time(row) == '07:00AM'
assert to_time(row) == '07:30AM'
special_case1 = 'NO PARKING (SANITATION BROOM SYMBOL) 11:30AM TO 1PM THURS'
assert from_time(special_case1) == '11:30AM'
assert to_time(special_case1) == '01:00PM'
special_case2 = ... |
nafitzgerald/allennlp | tutorials/notebooks/data_pipeline.ipynb | apache-2.0 | # This cell just makes sure the library paths are correct.
# You need to run this cell before you run the rest of this
# tutorial, but you can ignore the contents!
import os
import sys
module_path = os.path.abspath(os.path.join('../..'))
if module_path not in sys.path:
sys.path.append(module_path)
"""
Explanation... |
tata-antares/jet_tagging_LHCb | jet-tagging-stacking.ipynb | apache-2.0 | treename = 'tag'
data_b = pandas.DataFrame(root_numpy.root2array('datasets/type=5.root', treename=treename)).dropna()
data_b = data_b[::40]
data_c = pandas.DataFrame(root_numpy.root2array('datasets/type=4.root', treename=treename)).dropna()
data_light = pandas.DataFrame(root_numpy.root2array('datasets/type=0.root', tr... |
googlecodelabs/odml-pathways | object-detection/codelab2/python/Train_a_salad_detector_with_TFLite_Model_Maker.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... |
psi4/DatenQM | docs/qcfractal/source/quickstart.ipynb | bsd-3-clause | from qcfractal import FractalSnowflakeHandler
import qcfractal.interface as ptl
"""
Explanation: Example
This tutorial will go over general QCFractal usage to give a feel for the ecosystem.
In this tutorial, we employ Snowflake, a simple QCFractal stack which runs on a local machine
for demonstration and exploration... |
IST256/learn-python | content/lessons/05-Functions/SmallGroup-Functions.ipynb | mit | #ORIGINAL CODE
import random
choices = ['rock', 'paper', 'scissors']
wins = 0
losses = 0
ties = 0
computer = random.choice(choices)
you = 'rock' #Always rock strategy
if (you == 'rock' and computer == 'scissors'):
outcome = "win"
elif (you == 'scissors' and computer =='rock'):
outcome = "lose"
elif (you == ... |
anandha2017/udacity | nd101 Deep Learning Nanodegree Foundation/DockerImages/projects/03-tv-script-generation/notebooks/dlnd_tv_script_generation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
"""
Explanation: TV Script Generation
In this project, you'll generate your own Simpsons TV scri... |
isendel/machine-learning | ml-regression/week3-4/.ipynb_checkpoints/week-4-ridge-regression-assignment-1-checkpoint.ipynb | apache-2.0 | import pandas as pd
import numpy as np
from sklearn import linear_model
dtype_dict = {'bathrooms':float, 'waterfront':int, 'sqft_above':int, 'sqft_living15':float, 'grade':int, 'yr_renovated':int, 'price':float, 'bedrooms':float, 'zipcode':str, 'long':float, 'sqft_lot15':float, 'sqft_living':float, 'floors':float, 'co... |
freedomtan/tensorflow | tensorflow/lite/examples/experimental_new_converter/Keras_LSTM_fusion_Codelab.ipynb | apache-2.0 | !pip install tf-nightly
"""
Explanation: Overview
This CodeLab demonstrates how to build a fused TFLite LSTM model for MNIST recognition using Keras, and how to convert it to TensorFlow Lite.
The CodeLab is very similar to the Keras LSTM CodeLab. However, we're creating fused LSTM ops rather than the unfused versoin.
... |
QuantStack/quantstack-talks | 2018-11-14-PyParis-widgets/notebooks/1.ipywidgets.ipynb | bsd-3-clause | from ipywidgets import IntSlider
slider = IntSlider()
slider
slider.value
slider.value = 20
slider
"""
Explanation: <center><img src="src/ipywidgets.svg" width="50%"></center>
Repository: https://github.com/jupyter-widgets/ipywidgets
Installation:
conda install -c conda-forge ipywidgets
Simple slider for driving ... |
thehackerwithin/berkeley | code_examples/python_mayavi/mayavi_basic.ipynb | bsd-3-clause | # try one example, figure is created by default
mlab.test_molecule()
"""
Explanation: Overview
Mayavi is a high level plotting library built on tvtk.
Mayavi has a uses mlab for it's higher level plotting functions. The list of plotting functions can be found here. Functions exist for ploting lines, surfaces, 3d contou... |
geoffbacon/semrep | semrep/evaluate/koehn/koehn.ipynb | mit | %matplotlib inline
import os
import csv
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.metrics import roc_c... |
dipanjank/ml | data_analysis/digit_recognition/feature_extractor.ipynb | gpl-3.0 | %pylab inline
pylab.style.use('ggplot')
import numpy as np
import pandas as pd
import cv2
import os
image_dir = os.path.join(os.getcwd(), 'font_images')
if not os.path.isdir(image_dir) or len(os.listdir(image_dir)) == 0:
print('no images found in {}'.format(image_dir))
"""
Explanation: In this notebook, we
loa... |
h2oai/h2o-3 | h2o-py/demos/uplift_drf_demo.ipynb | apache-2.0 | import h2o
from h2o.estimators.uplift_random_forest import H2OUpliftRandomForestEstimator
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.style as style
import pandas as pd
h2o.init(strict_version_check=False) # max_mem_size=10
"""
Explanation: H2O Uplift Distributed Random Forest
Author:... |
DOV-Vlaanderen/pydov | docs/notebooks/search_grondwatermonsters.ipynb | mit | %matplotlib inline
import inspect, sys
# check pydov path
import pydov
"""
Explanation: Example of DOV search methods for groundwater samples (grondwatermonsters)
Use cases:
Get groundwater samples in a bounding box
Get groundwater samples with specific properties
Get the coordinates of all groundwater samples in G... |
Quantiacs/quantiacs-python | sampleSystems/svm_momentum_tutorial.ipynb | mit | import quantiacsToolbox
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn import svm
%matplotlib inline
%%html
<style>
table {float:left}
</style>
"""
Explanation: Quantiacs Toolbox Sample: Support Vector Machine(Momentum)
This tutorial will show you how to use svm and momentum to pr... |
liganega/Gongsu-DataSci | notebooks/GongSu11_List_Comprehension.ipynb | gpl-3.0 | odd_20 = [1, 3, 5, 7, 9, 11, 13, 15, 17, 19]
"""
Explanation: 리스트 조건제시법(List Comprehension)
주요 내용
주어진 리스트를 이용하여 특정 성질을 만족하는 새로운 리스트를 생성하고자 할 때
리스트 조건제시법을 활용하면 매우 효율적인 코딩을 할 수 있다.
리스트 조건제시법은 집합을 정의할 때 사용하는 조건제시법과 매우 유사하다.
예를 들어,0부터 1억 사이에 있는 홀수들을 원소로 갖는 집합을 정의하려면
두 가지 방법을 활용할 수 있다.
원소나열법
{1, 3, 5, 7, 9, 11, ..., ... |
akritichadda/K-AND | daniel/.ipynb_checkpoints/DB_project_oculomotor_v2-checkpoint.ipynb | mit | fid_VIS_SCm, fpd_VIS_SCm=get_connectivity('VIS','SCm')
fid_SCm_PRNc, fpd_SCm_PRNc=get_connectivity('SCm','PRNc')
fid_SCm_PRNr, fpd_SCm_PRNr=get_connectivity('SCm','PRNr')
fid_PRNc_III, fpd_PRNc_III=get_connectivity('PRNc','III')
fid_PRNc_VI, fpd_PRNc_VI=get_connectivity('PRNc','VI')
fid_PRNr_VI, fpd_PRNr_VI=get_co... |
ajgpitch/qutip-notebooks | examples/qip-noisy-device-simulator.ipynb | lgpl-3.0 | import copy
import numpy as np
import matplotlib.pyplot as plt
pi = np.pi
from qutip.qip.device import Processor
from qutip.operators import sigmaz, sigmay, sigmax, destroy
from qutip.states import basis
from qutip.metrics import fidelity
from qutip.qip.operations import rx, ry, rz, hadamard_transform
"""
Explanation... |
ozorich/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):
x=np.linspace(0,4*np.pi,100)
y=np.sin(a*x+b)
... |
mangeshjoshi819/ml-learn-python3 | BasicString _and_Csv.ipynb | mit | print(3+"mangesh")
print(str(3)+"mangesh")
record={"name":"mangeesh","price":34,"country":"Brazil"}
"""
Explanation: Basic Python Strings
Python3 has representation of String using unicode.Unicode is by default in python.
Python has dynamic typing e.g. print(3+"mangesh") will not work need to convert 3 to str(3)
En... |
mne-tools/mne-tools.github.io | 0.12/_downloads/plot_read_evoked.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
from mne import read_evokeds
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
fname = data_path + '/MEG/sample/sample_audvis-ave.fif'
# Reading
condition = 'Left Auditory'
evoked = read_e... |
googleinterns/multimodal-long-transformer-2021 | preprocessing/create_fashion_gen_metadata.ipynb | apache-2.0 | import pandas as pd
import tensorflow as tf
# i2t: image-to-text.
i2t_path = '/bigstore/mmt/raw_data/fashion_gen/fashion_gen_i2t_test_pairs.csv'
# t2i: text-to-image.
t2i_path = '/bigstore/mmt/raw_data/fashion_gen/fashion_gen_t2i_test_pairs.csv'
t2i_output_path = '/bigstore/mmt/fashion_gen/metadata/fashion_bert_t2i_t... |
poppy-project/community-notebooks | debug/poppy-torso_poppy-humanoid_poppy-ergo__motor_scan.ipynb | lgpl-3.0 | import pypot.dynamixel
ports = pypot.dynamixel.get_available_ports()
if not ports:
raise IOError('no port found!')
print 'ports found', ports
"""
Explanation: Motors scan
Scan all ports to find the connected Dynamixel motors
End of explanation
"""
using_XL320 = False
my_baudrate = 1000000
"""
Explanation: Prot... |
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