repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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xboard/xboard.github.io | ipynb/IDH-Education.ipynb | mpl-2.0 | %matplotlib inline
import pandas as pd
import requests as req
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import ttest_ind, ttest_rel
from scipy.stats import gaussian_kde
from statsmodels.formula.api import ols, mixedlm, gee
from statsmodels.stats.outliers_influence import ... |
ctools/ctools | doc/source/users/tutorials/cta/howto/howto_likelihood_profiles.ipynb | gpl-3.0 | # Load the modules
import gammalib
import ctools
import cscripts
# Define the ctools install directory
import os
ct_dir = os.environ['CTOOLS']
os.environ['CALDB'] = ct_dir + '/share/caldb/'
# Configure some preliminary variables
inmodel = ct_dir + '/share/models/crab.xml'
caldb = 'prod2'
irf = 'North_5h'
emin ... |
jacksongomesbr/academia-md | probabilidade/probabilidade.ipynb | cc0-1.0 | from fractions import Fraction
def P(evento, espaco):
"A probabilidade de um evento, dado o espaco amostral"
return Fraction(len(evento & espaco), len(espaco))
"""
Explanation: Probabilidade
Definições iniciais
Experimento: uma ocorrência com um resultado incerto. Também pode ser chamado "processo". Exemplo:... |
QuantScientist/Deep-Learning-Boot-Camp | day03/2.2.3 CNN HandsON - CIFAR10.ipynb | mit | from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Flatten, Activation
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.optimizers import Adadelta
input_shape = (3, 32, 32)
nb_classes = 10
## [conv@32x3x3+relu]x2 --> MaxPool@2x2 --> DropOut@0.25 -->
## [conv... |
martinjrobins/hobo | examples/optimisation/spotting-unidentifiable-parameters.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import numpy as np
import pints
import pints.plot
class BadLogisticModel(pints.ForwardModel):
"""
Logistic model of population growth with unidentifiable parameters.
"""
def __init__(self):
super(BadLogisticModel, self).__init__()
# Initial pop... |
tylere/docker-tmpnb-ee | notebooks/1 - IPython Notebook Examples/IPython Project Examples/Interactive Widgets/Export As (nbconvert).ipynb | apache-2.0 | notebook_name = widgets.Text()
"""
Explanation: This notebook shows a really roundabout way to get the name of the notebook file using widgets. The true purpose of this demo is to demonstrate how Javascript and Python widget models are related by id.
Create a text Widget without displaying it. The widget will be use... |
ianctse/pvlib-python | docs/tutorials/pvsystem.ipynb | bsd-3-clause | # built-in python modules
import os
import inspect
import datetime
# scientific python add-ons
import numpy as np
import pandas as pd
# plotting stuff
# first line makes the plots appear in the notebook
%matplotlib inline
import matplotlib.pyplot as plt
# seaborn makes your plots look better
try:
import seaborn ... |
dpshelio/2015-EuroScipy-pandas-tutorial | 02 - Data structures.ipynb | bsd-2-clause | s = pd.Series([0.1, 0.2, 0.3, 0.4])
s
"""
Explanation: Data structures
Pandas does this through two fundamental object types, both built upon NumPy arrays: the Series object, and the DataFrame object.
Series
A Series is a basic holder for one-dimensional labeled data. It can be created much as a NumPy array is created... |
junhwanjang/DataSchool | Lecture/07. 미적분과 최적화/1) SymPy를 사용한 함수 미분.ipynb | mit | def f(x):
return 2*x
x = 10
y = f(x)
print(x, y)
"""
Explanation: SymPy를 사용한 함수 미분
데이터 분석에서 미분의 필요성
그다지 관련이 없어 보이지만 사실 데이터 분석에도 미분(differentiation)이 필요하다. 데이터 분석의 목표 중 하나는 확률 모형의 모수(parameter)나 상태 변수(state)를 추정(estimation)하는 작업이다. 이러한 작업은 근본적으로 함수의 최소점 혹은 최대점을 찾는 최적화(optimization) 작업이며 미분 혹은 편미분을 사용한 도함수를 필요로 한다... |
HrantDavtyan/Data_Scraping | Week 3/HW3_solutions.ipynb | apache-2.0 | import pandas as pd
urls = ["http://rate.am/am/armenian-dram-exchange-rates/banks/non-cash/2017/06/01/20-15",
"http://rate.am/am/armenian-dram-exchange-rates/banks/non-cash/2017/06/02/20-15",
"http://rate.am/am/armenian-dram-exchange-rates/banks/non-cash/2017/06/03/20-15",
"http://rate.am/am/arm... |
proinsias/gilbert-shannon-reeds | Gilbert-Shannon-Reeds.ipynb | mit | def get_random_number_for_right_deck(n: int, seed: int=None, ) -> int:
"""
Return the number of cards to split into the right sub-deck.
:param n: one above the highest number that could be returned by this
function.
:param seed: optional seed for the random number generator to enable
... |
mne-tools/mne-tools.github.io | 0.22/_downloads/8f7e6dfc30a66795f2d4e4ae5ca6d23e/plot_40_artifact_correction_ica.ipynb | bsd-3-clause | import os
import mne
from mne.preprocessing import (ICA, create_eog_epochs, create_ecg_epochs,
corrmap)
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif... |
facemelters/data-science | Atlas/Erik vs the rest of our content.ipynb | gpl-2.0 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
df_all = pd.read_csv('All content.csv')
df_erik = pd.read_csv('Erik content.csv')
"""
Explanation: TL;DR: Erik's articles perform poorly when compared to the rest of our articles published in the same time period.
While he i... |
enbanuel/phys202-2015-work | assignments/assignment03/NumpyEx01.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import antipackage
import github.ellisonbg.misc.vizarray as va
"""
Explanation: Numpy Exercise 1
Imports
End of explanation
"""
def checkerboard(size): #I couldn't figure this out on my own. My partner helped me during pa... |
mne-tools/mne-tools.github.io | 0.23/_downloads/31239620dd9631320a99b07ac4a81074/interpolate_bad_channels.ipynb | bsd-3-clause | # Authors: Denis A. Engemann <denis.engemann@gmail.com>
# Mainak Jas <mainak.jas@telecom-paristech.fr>
#
# License: BSD (3-clause)
import mne
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
fname = data_path + '/MEG/sample/sample_audvis-ave.fif'
evoked = mne.read_evokeds(fnam... |
phoebe-project/phoebe2-docs | 2.1/tutorials/reflection_heating.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.1,<2.2"
"""
Explanation: Reflection and Heating
For a comparison between "Horvat" and "Wilson" methods in the "irad_method" parameter, see the tutorial on Lambert Scattering.
Setup
Let's first make sure we have the latest version of PHOEBE 2.1 installed. (You can comment out this line if you... |
lcharleux/numerical_analysis | doc/Interpolation/1D_interpolation.ipynb | gpl-2.0 | # Setup
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
params = {'font.size' : 14,
'figure.figsize':(15.0, 8.0),
'lines.linewidth': 2.,
'lines.markersize': 15,}
matplotlib.rcParams.update(params)
"""
Explanation: 1D interpolation
Scope
Finite... |
fgnt/nara_wpe | examples/NTT_wrapper_offline.ipynb | mit | def aquire_audio_data():
D, T = 4, 10000
y = np.random.normal(size=(D, T))
return y
y = aquire_audio_data()
start = time.perf_counter()
x = wpe(y)
end = time.perf_counter()
print(f"Time: {end-start}")
"""
Explanation: Minimal example with random data
End of explanation
"""
channels = 8
sampling_rate =... |
mayankjohri/LetsExplorePython | Section 1 - Core Python/Chapter 09 - Classes & OOPS/02_03_Multiple_Inheritance.ipynb | gpl-3.0 | class Base1:
pass
class Base2:
pass
class MultiDerived(Base1, Base2):
pass
"""
Explanation: Multiple Inheritance
In multiple inheritance, a class can be derived from more than one base classes. The syntax for multiple inheritance is similar to single inheritance except we list all the base classes in se... |
SystemsBiologyUniandes/Cytometry | HOW TO USE THE LIBRARY.ipynb | mit | import cytometry_lib as cy
"""
Explanation: Within the library the required packages for the code to run correctly are imported.
If there's an import error check the following things:
Check pandas is isntalled.
Check fcs parser is installed.
Check OS is installed.
Once you've checked this run this code in 4 simple s... |
mne-tools/mne-tools.github.io | 0.19/_downloads/03db2d983950efa77a26beb0ac22b422/plot_20_rejecting_bad_data.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_filt-0-40_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file, verbose=False)
events_file = os.path.join(sample_data... |
ajgpitch/qutip-notebooks | examples/measures-trajectories-cats-kerr.ipynb | lgpl-3.0 | import matplotlib.pyplot as plt
import numpy as np
from qutip import *
from IPython.display import display, Math, Latex
"""
Explanation: Cat vs coherent states in a Kerr resonator, and the role of measurement
$\newcommand{\ket}[1]{| #1 \rangle}$
$\newcommand{\bra}[1]{\langle #1 |}$
$\newcommand{\braket}[1]{\langle #1 ... |
openfisca/combine-calculators | scripts/legislation_refactoring.ipynb | gpl-3.0 | from utils import show_histogram, percent_diff, scatter_plot, multi_scatter
from econometrics import gini, draw_ginis
import matplotlib.pyplot as plt
# import bqplot.pyplot as plt
import pandas as pd
import numpy as np
import random
import qgrid
import copy
from reformators import Excalibur
qgrid.nbinstall(overwrit... |
thomasantony/CarND-Projects | Exercises/Term1/transfer-learning-lab/Transfer_Learning_Lab.ipynb | mit | from keras.layers import Dense, Flatten, Input, Dropout
from keras.models import Sequential
import pickle
def load_bottleneck_data(network, dataset):
"""
Arguments:
network - String, one of 'resnet', 'vgg', 'inception'
dataset - String, one of 'cifar10', 'traffic'
"""
train_file = '{}_{... |
asazo/ANN | tarea1/tarea1.ipynb | mit | # Importar Librerías
import numpy as np
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
# Guardar semilla para numeros aleatorios
seed = 21
np.random.seed(seed)
def generate_data(n):
# Lista para gua... |
JAmarel/Phys202 | Numpy/NumpyEx03.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import antipackage
import github.ellisonbg.misc.vizarray as va
"""
Explanation: Numpy Exercise 3
Imports
End of explanation
"""
def brownian(maxt, n):
"""Return one realization of a Brownian (Wiener) process with n steps... |
darcamo/pyphysim | ipython_notebooks/Partially Connected.ipynb | gpl-2.0 | %matplotlib inline
from ipywidgets.widgets import Image
from IPython.display import display, display_svg, display_png
# xxxxxxxxxx Add the parent folder to the python path. xxxxxxxxxxxxxxxxxxxx
import sys
parent_dir = "../"
sys.path.append(parent_dir)
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx... |
ES-DOC/esdoc-jupyterhub | notebooks/ncc/cmip6/models/noresm2-hh/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'noresm2-hh', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: NCC
Source ID: NORESM2-HH
Topic: Aerosol
Sub-Topics: Transport, Emissions, Conce... |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/dev/n03_feature_extraction.ipynb | mit | def generate_train_intervals(data_df, train_time, base_time, step, days_ahead, today):
pass
"""
Explanation: The first objective of this notebook is to implement the next function (to extract sample intervals from the total period).
End of explanation
"""
# I will try to keep the convention to name with the "day... |
wbbeyourself/cn-deep-learning | tutorials/intro-to-tflearn/TFLearn_Sentiment_Analysis_Solution.ipynb | mit | import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
"""
Explanation: Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's work by building a network for sentiment analysis on the movie review data. Instead of a network w... |
ueapy/ueapy.github.io | content/notebooks/2017-06-02-matplotlib-contourf-subplots.ipynb | mit | import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Today we are going to learn some tricks about plotting two dimensional data with matplotlib contourf function.
End of explanation
"""
# Array 1
delta1 = 0.025
x1 = np.arange(-3.0, 3.0, delta1)
y1 = n... |
ebachelet/pyLIMA | examples/pyLIMA_example_3.ipynb | gpl-3.0 | ## First import the required libraries
%matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
import os, sys
from pyLIMA import microlsimulator
"""
Explanation: Welcome to pyLIMA tutorial!
Let's simulate, and fit back :), some events.
Do not forget, if not made already. To obtain working link to the... |
qinwf-nuan/keras-js | notebooks/layers/normalization/BatchNormalization.ipynb | mit | data_in_shape = (4, 3)
norm = BatchNormalization(epsilon=1e-05, axis=-1, center=True, scale=True)
layer_0 = Input(shape=data_in_shape)
layer_1 = norm(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
weights = []
for i, w in enumerate(model.get_weights()):
... |
deme-rs/notebooks | BMI-vs-Distance.ipynb | mit | import numpy
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
pd.set_option('display.mpl_style', 'default')
plt.rcParams['figure.figsize'] = (15, 5)
plt.rcParams['font.family'] = 'sans-serif'
import re
"""
Explanation: The Relationship between Athlete Shape and Running Distance
I'm naively assum... |
danijel3/ASRDemos | notebooks/TIMITDataPrep.ipynb | apache-2.0 | import sys
sys.path.append('../python')
from timit import *
"""
Explanation: Preparing the TIMIT database
If you are lucky enough to own the TIMIT database, or are willing to buy it from here, you can use this simple script to prepare the HDF5 files, similarly to how it was done with the Voxforge dataset. For a deta... |
GoogleCloudPlatform/training-data-analyst | blogs/sme_academy/tfx/02_transform.ipynb | apache-2.0 | import apache_beam as beam
import tensorflow as tf
import tensorflow_data_validation as tfdv
import tensorflow_transform as tft
print('TF version: {}'.format(tf.__version__))
print('TFT version: {}'.format(tft.__version__))
print('TFDV version: {}'.format(tfdv.__version__))
print('Apache Beam version: {}'.format(beam.... |
ucbrise/clipper | examples/image_query/keras_on_docker_example.ipynb | apache-2.0 | from keras.applications.resnet50 import ResNet50
# https://keras.io/applications/#classify-imagenet-classes-with-resnet50
model = ResNet50(weights='imagenet')
"""
Explanation: Prerequisites
pip install clipper_admin==0.4.1 \
keras==2.2.4 \
tensorflow==1.13.1 \
numpy==1.16.4 \
... |
info-370/classification | knn/INFO370-KNN_Exercise_Solutions.ipynb | mit | from sklearn.datasets import load_boston
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import scale
from sklearn.neighbors import KNeighborsRegressor
from sklearn.metrics import mean_squared_error
from sklearn.cross_validation import KFold
import matplotlib.pyplot as plt
import numpy ... |
tensorflow/docs-l10n | site/en-snapshot/addons/tutorials/layers_normalizations.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... |
marcus-nystrom/python_course | Week6_lecture.ipynb | gpl-3.0 | # Create a string
my_str = 'This is my string'
# Split it...
my_str_split = my_str.split()
print(my_str_split)
# ... and restore (join) it again
my_str_joined = ' '.join(my_str_split) # ' ' means join with space
print(my_str_joined)
# Find first occurence of word containing 'str'
print(my_str.find('str'))
# Replac... |
masterfish2015/my_project | python/demo1/scipy-advanced-tutorial-master/Part2/Widget Events.ipynb | mit | from __future__ import print_function
"""
Explanation: Index - Back - Next
Widget Events
Special events
End of explanation
"""
from IPython.html import widgets
print(widgets.Button.on_click.__doc__)
"""
Explanation: The Button is not used to represent a data type. Instead the button widget is used to handle mouse ... |
srcole/qwm | misc/COGS108/clustering.ipynb | mit | %config InlineBackend.figure_format = 'retina'
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: NOTES
* jupyter nbconvert --to slides clustering.ipynb
End of explanation
"""
np.random.seed(0)
x1 = np.random.randn(30)
y1 = 4*x1 + 10 + np.random.randn(len(x1))
x2 = x1*10
y2 = (x... |
idc9/law-net | vertex_metrics_experiment/procedural_v_substantive_federal.ipynb | mit | %time d_pagerank = G.pagerank()
%time u_pagerank = G.as_undirected().pagerank()
%time d_betweenness = G.betweenness(directed=True)
# %time u_betweenness = G.as_undirected().betweenness(directed=False)
#COMPUTING D BETWEEN right now started at 6:35 pm
%time d_closeness = G.closeness(mode="IN", normalized=True)
%t... |
Chipe1/aima-python | search.ipynb | mit | from search import *
from notebook import psource, heatmap, gaussian_kernel, show_map, final_path_colors, display_visual, plot_NQueens
# Needed to hide warnings in the matplotlib sections
import warnings
warnings.filterwarnings("ignore")
"""
Explanation: Solving problems by Searching
This notebook serves as supportin... |
maurodoglio/taar | analysis/TAARExperimentV2Analysis.ipynb | mpl-2.0 | import pyspark.sql.functions as F
from pyspark.sql import Row
import pyspark.sql.types as T
import pandas as pd
import re
import matplotlib.pyplot as plt
import seaborn as sns
import datetime as dt
from collections import Counter
from IPython.display import Markdown, display
from moztelemetry import Dataset
from pyspar... |
JAmarel/Phys202 | Algorithms/AlgorithmsEx01.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
"""
Explanation: Algorithms Exercise 1
Imports
End of explanation
"""
def tokenize(s, stop_words=None, punctuation='`~!@#$%^&*()_-+={[}]|\:;"<,>.?/}\t'):
"""Split a string into a list of words, removing punctuation and stop words."""
... |
deepmind/educational | colabs/introductory/protein_folding.ipynb | apache-2.0 | #@title Look how complex this structure is!
from IPython.display import YouTubeVideo
YouTubeVideo('GM9buhWJjlA', width=600, height=400)
"""
Explanation: <p><small><small>Copyright 2020 DeepMind Technologies Limited.</p>
<p><small><small> Licensed under the Apache License, Version 2.0 (the "License"); you may not use t... |
NelisW/ComputationalRadiometry | 06-Diverse-pyradi-utilities.ipynb | mpl-2.0 | from IPython.display import display
from IPython.display import Image
from IPython.display import HTML
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: 6 Diverse pyradi utilities
This notebook forms part of a series on computational optical radiometry. The notebooks can be down... |
karolaya/PDI | PS-05/problem_set_5.ipynb | mit | '''This is a definition script, so we do not have to rewrite code'''
import numpy as np
import os
import cv2
import matplotlib.pyplot as mplt
import random
import json
# set matplotlib to print inline (Jupyter)
%matplotlib inline
# path prefix
pth = '../data/'
# files to be used as samples
# list *files* holds the... |
gtesei/python-for-finance-notes | 2__Optimizing_Portfolios.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy.optimize as spo
def parab(X):
## X = 2 is the min
Y = (X - 2)**2 + 1.5
return Y
initial_guess = 3
opt_methods_no_Jacobian = ['Nelder-Mead','Powell','CG','BFGS','L-BFGS-B','TNC','COBYLA','SLSQP']
mins = [spo.minimize(pa... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_ecog.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
# Chris Holdgraf <choldgraf@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
from scipy.io import loadmat
from mayavi import mlab
import mne
from mne.viz import plot_trans, snapshot_brain_montage
print(__doc__)
"""
Ex... |
archman/phantasy | docs/source/src/notebooks/phantasy_cavity.ipynb | bsd-3-clause | import os
import phantasy
import scan
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Cavity Tuning
Import modules/packages
End of explanation
"""
machine = "FRIB_FLAME"
mp = phantasy.MachinePortal(machine=machine, segment='LINAC')
"""
Explanation: Model Machine
End of explanation
"""
all_cavi... |
py-chemist/Deep_Learning_with_Keras | classification.ipynb | gpl-3.0 | from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import StratifiedKFold, cross_val_score
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
import time
# 1. Number of times pregna... |
xqding/StatisticsExamples | Nested_Sampling/Nested_Sampling.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import math
import matplotlib.pyplot as plt
"""
Explanation: Nested Sampling
Nested sampling is used for calculating the normalization constant in Bayesian inference. The normalization constant is also called evidence.
1. Analytical Results of A Simple Gaussian Problem
Considering... |
rahulremanan/python_tutorial | Beginners/notebook/Pickle_tutorial.ipynb | mit | import gc
import pickle
import psutil
import numpy as np
import pandas as pd
"""
Explanation: Using Pickle to manage memory in Python
Author: Dr. Rahul Remanan, CEO, Moad Computer
Run this notebook in Google Colab
Import dependencies
End of explanation
"""
def memory_utilization():
print('Current memory utili... |
magwenelab/mini-term-2016 | ode-modeling2.ipynb | cc0-1.0 | # import statements to make numeric and plotting functions available
%matplotlib inline
from numpy import *
from matplotlib.pyplot import *
## We'll specify the behavior of X as a series of pulse of different length
## so we'll define a function to generate pulses
def pulse(ontime, offtime, ntimes, onval=1):
if ... |
GoogleCloudPlatform/mlops-on-gcp | immersion/guided_projects/guided_project_1.ipynb | apache-2.0 | import os
"""
Explanation: Guided Project 1
Learning Objectives:
Learn how to generate a standard TFX template pipeline using tfx template
Learn how to modify and run a templated TFX pipeline
Note: This guided project is adapted from Create a TFX pipeline using templates).
End of explanation
"""
PATH=%env PATH
%e... |
adrn/tutorials | notebooks/gaia-galactic-orbits/gaia-galactic-orbits.ipynb | cc0-1.0 | # astropy imports
import astropy.coordinates as coord
from astropy.table import QTable
import astropy.units as u
from astroquery.gaia import Gaia
# Third-party imports
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
# gala imports
import gala.coordinates as gc
import gal... |
josephcslater/mousai | docs/tutorial/demos/Duffing_state_space.ipynb | bsd-3-clause | %matplotlib inline
%load_ext autoreload
%autoreload 2
import scipy as sp
import numpy as np
import matplotlib.pyplot as plt
import mousai as ms
from scipy import pi, sin
# Test that all is working.
# f_tol adjusts accuracy. This is smaller than reasonable, but illustrative of usage.
t, x, e, amps, phases = ms.hb_tim... |
juanshishido/visualization-with-python | visualization-with-python_answers.ipynb | bsd-2-clause | import math
import random
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
"""
Explanation: Theory of Data Visualization
Visualization is meant to convey information.
The power of a graph is its ability to enable one to take in the quantitative information, organize it,... |
QuantEcon/QuantEcon.notebooks | markov_chain_ex01_py.ipynb | bsd-3-clause | %matplotlib inline
from __future__ import division, print_function
import numpy as np
import matplotlib.pyplot as plt
from quantecon.markov import MarkovChain
"""
Explanation: Finite Markov Chains: Examples
Daisuke Oyama
Faculty of Economics, University of Tokyo
This notebook demonstrates how to analyze finite-state ... |
jegibbs/phys202-2015-work | assignments/assignment10/ODEsEx02.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy.integrate import odeint
from IPython.html.widgets import interact, fixed
"""
Explanation: Ordinary Differential Equations Exercise 1
Imports
End of explanation
"""
def lorentz_derivs(yvec, t, sigma, rho, beta):
x = yvec[0]
y = y... |
trangel/Data-Science | deep_learning_ai/Convolution+model+-+Step+by+Step+-+v2.ipynb | gpl-3.0 | import numpy as np
import h5py
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
%load_ext autoreload
%autoreload 2
np.random.seed(1)
"""
Explanation: Convolut... |
cvxgrp/cvxpylayers | examples/tf/ReLU Layers.ipynb | apache-2.0 | from cvxpylayers.tensorflow import CvxpyLayer
import cvxpy as cp
import tensorflow as tf
from tensorflow.keras import layers
tf.keras.backend.set_floatx('float64')
class ReluLayer(layers.Layer):
def __init__(self, input_dim, output_dim):
super(ReluLayer, self).__init__()
self.W = tf.Variable(1e-3 ... |
khalido/algorithims | monty_hall.ipynb | gpl-3.0 | import random
import numpy as np
# for plots, cause visuals
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
"""
Explanation: The famous Monty Hall brain teaser:
Suppose you're on a game show, and you're given the choice of three doors: Behind one door is a car; behind the others, goats. You... |
Seeliglab/2017---Deep-learning-yeast-UTRs | Jupyter_notebooks/Notebook_3_CNN_Predictions_of_Native_UTR_HIS3_data.ipynb | mit | plt.rcParams["patch.force_edgecolor"] = True
sns.set_style('whitegrid',
{'axes.grid': True,
'grid.linestyle': u'--',
'axes.edgecolor': '0.1',
'axes.labelcolor': '0',
'axes.labelsize': 15,
'axes.titlesize': 15,
'legen... |
plipp/informatica-pfr-2017 | nbs/4/2-2-OPTIONAL-Classification-Random-Forests-w-One-Hot-Encoding-Exercise.ipynb | mit | import pandas as pd
import numpy as np
"""
Explanation: Predicting Earnings from Census Data with Random Forests
taken from The Analytics Edge
The Task
The United States government periodically collects demographic information by conducting a census.
In this problem, we are going to use census information about an ind... |
Z0m6ie/Zombie_Code | Data_Science_Course/Michigan Data Analysis Course/1 Applied Plotting, Charting & Data Representation/Week 2/Week2.ipynb | mit | %matplotlib notebook
import matplotlib as mpl
mpl.get_backend()
import matplotlib.pyplot as plt
plt.plot?
# because the default is the line style '-',
# nothing will be shown if we only pass in one point (3,2)
plt.plot(3, 2)
# we can pass in '.' to plt.plot to indicate that we want
# the point (3,2) to be indicate... |
opesci/notebooks | AcousticFWI/Acoustic-wave-equation.ipynb | bsd-3-clause | p=Function('p')
m,s,h = symbols('m s h')
m=M(x,y,z)
q=Q(x,y,t)
d=D(x,y,t)
e=E(x,y)
"""
Explanation: PDE
The acoustic wave equation for the square slowness m and a source q is given in 3D by :
\begin{cases}
&m \frac{d^2 u(x,t)}{dt^2} - \nabla^2 u(x,t) =q \
&u(.,0) = 0 \
&\frac{d u(x,t)}{dt}|_{t=0} = 0
\end{cases}... |
Mashimo/datascience | 00-tidyData/CleanDataHAR.ipynb | apache-2.0 | import pandas as pd
"""
Explanation: Read and clean data using Python and Pandas
End of explanation
"""
cat UCI\ HAR\ Dataset/activity_labels.txt
act = pd.read_table('UCI HAR Dataset/activity_labels.txt', header=None, sep=' ', names=('ID','Activity'))
act
type(act)
act.columns
"""
Explanation: Read the files an... |
tensorflow/docs-l10n | site/ja/probability/examples/Eight_Schools.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... |
jfrygeo/Read-Headers-CSV-python | View-Headers-Pandas.ipynb | apache-2.0 | import pandas as pd
print (pd.__version__)
response = input("Please Enter Location of Input CSV (""Example: C:\Test\TestCSV.csv)"": ")
"""
Explanation: View-Headers-And-Edit-CSV
If a file is too large to be opened by Excel or Notepad, this script will read the csv file and allow a user to output a clean csv
End of ex... |
kellerberrin/OSM-QSAR | Notebooks/EC50_Results/EC50 Prelim Results.ipynb | mit | from IPython.display import display
import pandas as pd
print("EC50_1000 Results")
ec50_1000_results = pd.read_csv("./EC50_1000_results.csv")
display(ec50_1000_results)
print("EC50_500 Results")
ec50_500_results = pd.read_csv("./EC50_500_results.csv")
display(ec50_500_results)
print("EC50_200 Results")
ec50_200_results... |
dlab-berkeley/geocoding-workshop | intro-geocoding.ipynb | mit | import json
import requests
import pandas as pd
from pprint import pprint
"""
Explanation: Introductory Geocoding and Mapping
Juan Shishido
School of Information
GSR, D-Lab
Imports
End of explanation
"""
def single_address(address, api='dstk'):
'''
Individual address lookup with
either DSTK or Photon
... |
renekm/CD-atualizado- | MiniProjeto2/Mini-Projeto-2.ipynb | gpl-3.0 | %matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import os
from scipy import stats
lambp=10
dist= stats.poisson(lambp)
x= stats.poisson.rvs(mu=lambp, loc=0, size=1000000)
media= np.mean(x)
var= np.var(x)
media2=[]
for i in range (0,10001):
valores = np.random.choice(x,2)
... |
evanmiltenburg/python-for-text-analysis | Chapters-colab/Chapter_01_Getting_Started_with_Variables_and_Values.ipynb | apache-2.0 | %%capture
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/Data.zip
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/images.zip
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/Extra_Material.zip
!unzip Data.zip -d ../
!unzip images.zip -d ./
!unzip Ext... |
ChadFulton/statsmodels | examples/notebooks/mixed_lm_example.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import statsmodels.api as sm
import statsmodels.formula.api as smf
%load_ext rpy2.ipython
%R library(lme4)
"""
Explanation: Linear Mixed Effects Models
End of explanation
"""
data = sm.datasets.get_rdataset('dietox', 'geepack').data
md = smf.mixedlm("Weight ~ Time", data, gro... |
sangheestyle/ml2015project | howto/model09_refactoring_more_for _feat_avg_pos.ipynb | mit | import gzip
import pickle
from os import path
from collections import defaultdict
from numpy import sign
"""
Load buzz data as a dictionary.
You can give parameter for data so that you will get what you need only.
"""
def load_buzz(root='../data', data=['train', 'test', 'questions'], format='pklz'):
buzz_data = {... |
nick-youngblut/SIPSim | ipynb/theory/.ipynb_checkpoints/qPCR_error_propagation-checkpoint.ipynb | mit | %load_ext rpy2.ipython
%%R
library(ggplot2)
library(dplyr)
library(tidyr)
def neg_binom_err(m, r, negs=False):
"""Adding negative binomial distribuiton error, where variance
scales more with the mean than a poisson distribution if (r < inf).
Parameters
----------
m : float
Mean value
... |
tensorflow/docs-l10n | site/ko/tutorials/keras/overfit_and_underfit.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... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/05_artandscience/labs/a_handtuning.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
"""
Explanation: Hand tuning hyperparameters
Learning Objectives:
* Use the LinearRegressor class in TensorFlow to predict median housing price, at the granularity of city blocks, based on one input feature
* Evaluate the accuracy of a model's pred... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/03_tensorflow/d_traineval.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.5
from google.cloud import bigquery
import tensorflow as tf
import numpy as np
import shutil
print(tf.__version__)
"""
Explanation: <h1> 2d. Distributed training ... |
CartoDB/cartoframes | docs/examples/data_observatory/do_geography_notebook_template.ipynb | bsd-3-clause | !pip install -U cartoframes
# Note: a kernel restart is required after installing the library
import cartoframes
cartoframes.__version__
"""
Explanation: CARTO Data Observatory
This is a basic template notebook to start exploring your new Geography from CARTO's Data Observatory via the Python library CARTOframes.
Y... |
coolharsh55/advent-of-code | 2016/python3/Day09.ipynb | mit | with open('../inputs/day09.txt', 'r') as f:
data = ''.join([''.join(line.split()) for line in f.readlines()])
# TEST DATA
# data = ''.join([
# 'ADVENT',
# 'A(1x5)BC',
# '(3x3)XYZ',
# 'A(2x2)BCD(2x2)EFG',
# '(6x1)(1x3)A',
# 'X(8x2)(3x3)ABCY'
# ])
"""
Expla... |
ES-DOC/esdoc-jupyterhub | notebooks/cccma/cmip6/models/sandbox-2/landice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cccma', 'sandbox-2', 'landice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: CCCMA
Source ID: SANDBOX-2
Topic: Landice
Sub-Topics: Glaciers, Ice.
Propertie... |
akshayrangasai/akshayrangasai.github.io | Blog Post Content/Airport Waiting Time.ipynb | mit | %matplotlib inline
#Imports for solution
import numpy as np
import scipy.stats as sp
from matplotlib.pyplot import *
#Setting Distribution variables
##All rates are in per Minute.
"""
Explanation: Airport Wait Time Simulation
End of explanation
"""
#Everything will me modeled as a Poisson Process
SIM_TIME = 180
Q... |
mediagit2016/workcamp-maschinelles-lernen-grundlagen | 20-02-20-ml-workcamp/aufgaben/arbeiten-mit-listen.ipynb | gpl-3.0 | #Haben Sie schon gemerkt dass # einen Kommentar einletet
#Für diese einfache Berechnungen muss keine Bibliothek zugeladen werden
x = [4,2,6,3] #Erzeugt eine Liste mit Integer Werten
x1 = [4,2,6,3] #Erzeugt eine Liste mit den gleichen Werten
x2 = [1.0,2.0,3.0,4.0] #Erzeugt eine Liste mit float Werten
y = list() # Erzeu... |
google/compass | packages/propensity/02.ml_data_preparation.ipynb | apache-2.0 | # Uncomment to install required python modules
# !sh ../utils/setup.sh
# Add custom utils module to Python environment
import os
import sys
sys.path.append(os.path.abspath(os.pardir))
import inspect
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
from gps_building_blocks.cloud.ut... |
Hash--/documents | notebooks/TP Master Fusion/IC-Hands-on_solutions.ipynb | mit | a = 140e-3 /2 # inner conductor radius
b = 230e-3 /2 # inner conductor radius
def coax_electric_field(rho, V, a, b):
"""
Returns the electric field in a coaxial line.
"""
return 1/rho*V/log(b/a)
rho = linspace(a, b, 101)
V = 1 # V
plot(rho/a, coax_electric_field(rho, V, a, b)/V)
xlabel('rho/a')
ylabel... |
Xyllan/boun_cmpe548_project | Paper_Implementation.ipynb | apache-2.0 | import numpy as np
m = 6040 # users
n = 3952 # movies
Rnan = np.full((m, n), np.nan) # Ratings matrix with nans instead of 0s
"""
Explanation: 1. Introduction
We will reimplement the methodology of the paper in Python.
2. Preliminary Concepts
Initially, we will recreate the basic variables defined in the paper. To ma... |
kit-cel/wt | wt/vorlesung/ch7_9/queue.ipynb | gpl-2.0 | # importing
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
# showing figures inline
%matplotlib inline
# plotting options
font = {'size' : 20}
plt.rc('font', **font)
plt.rc('text', usetex=True)
matplotlib.rc('figure', figsize=(18, 6) )
"""
Explanation: Content and Objective
Using Poisson ... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/end-to-end-structured/solutions/5b_deploy_keras_ai_platform_babyweight_vertex.ipynb | apache-2.0 | import os
from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
"""
Explanation: LAB 5b: Deploy and predict with Keras model on Vertex AI
Learning Objectives
Setup up the environment
Deploy trained Keras model to an endpoint for online prediction on... |
tpin3694/tpin3694.github.io | python/matplotlib_grouped_bar_plot.ipynb | mit | %matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Title: Group Bar Plot In MatPlotLib
Slug: matplotlib_grouped_bar_plot
Summary: Group Bar Plot In MatPlotLib
Date: 2016-05-01 12:00
Category: Python
Tags: Data Visualization
Authors: Chris Albon
Based on: Sebast... |
stlk/socialist | jupyter/photo_recomendation.ipynb | mit | from IPython.display import display, HTML
for item in media:
display(HTML(('<img src="%s"/>' % item.get_standard_resolution_url())))
from functools import reduce
from collections import Counter
def retreive_tags(result, tags):
result += list(map(lambda tag: tag.name, tags))
return result
tags_... |
Cyb3rWard0g/ThreatHunter-Playbook | docs/notebooks/windows/09_collection/WIN-200609225055.ipynb | gpl-3.0 | from openhunt.mordorutils import *
spark = get_spark()
"""
Explanation: Access to Microphone Device
Metadata
| | |
|:------------------|:---|
| collaborators | ['@Cyb3rWard0g', '@Cyb3rPandaH'] |
| creation date | 2020/06/09 |
| modification date | 2020/09/20 |
| playbook related | [] |
Hy... |
davidbrough1/pymks | notebooks/localization_cahn_hilliard_Legendre_2D.ipynb | mit | import pymks
%matplotlib inline
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Cahn-Hilliard with Primtive and Legendre Bases
This example uses a Cahn-Hilliard model to compare two different bases representations to discretize the microstructure. One basis rep... |
nick-youngblut/SIPSim | ipynb/bac_genome/n1147/atomIncorp_taxaIncorp_MW-HR-SIP_postSparsity.ipynb | mit | import os
import glob
import itertools
import nestly
%load_ext rpy2.ipython
%load_ext pushnote
%%R
library(ggplot2)
library(dplyr)
library(tidyr)
library(gridExtra)
"""
Explanation: Goal
Follow-up to: atomIncorp_taxaIncorp
Determining the effect of 'heavy' BD window (number of windows & window sizes) on HR-SIP accu... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_forward.ipynb | bsd-3-clause | import mne
from mne.datasets import sample
data_path = sample.data_path()
# the raw file containing the channel location + types
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
# The paths to Freesurfer reconstructions
subjects_dir = data_path + '/subjects'
subject = 'sample'
"""
Explanation: Head model a... |
dnc1994/MachineLearning-UW | ml-foundations/backup/wiki-retrieval/Document retrieval.ipynb | mit | import graphlab
"""
Explanation: Document retrieval from wikipedia data
Fire up GraphLab Create
End of explanation
"""
people = graphlab.SFrame('people_wiki.gl/')
"""
Explanation: Load some text data - from wikipedia, pages on people
End of explanation
"""
people.head()
len(people)
"""
Explanation: Data contain... |
cdiener/micom | docs/source/community.ipynb | apache-2.0 | from micom.data import test_taxonomy
taxonomy = test_taxonomy()
taxonomy
"""
Explanation: Building communities
micom will construct communities from a specification via a Pandas DataFrame. Here, the DataFrame needs at least two columns: "id" and "file" which specify the ID of the organism/tissue and a file containing... |
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