repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
swirlingsand/deep-learning-foundations | gans/batch-norm/Batch_Normalization_Solutions.ipynb | mit | import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True, reshape=False)
"""
Explanation: Batch Normalization – Solutions
Batch normalization is most useful when building deep neural networks. To demonstrate this, we'll create a co... |
pwer21c/pwer21c.github.io | python/pythoncodes/.ipynb_checkpoints/3_preview_for_10022021-checkpoint.ipynb | mit | fruits = ["apple", "banana", "cherry"]
for x in fruits:
print(x)
"""
Explanation: 리스트 공부할때 fruits라는 리스트 이름에 과일을 저장했어요.
이제 하나하나의 과일을 출력해 봅시다.
End of explanation
"""
fruits = ["apple", "banana", "cherry"]
for abc in fruits:
print(x)
"""
Explanation: 사과, 바나나, 체리 순서로 출력이 됩니다.
for 다음에 한칸 띄우고 x라는 이름을 썼어요. 이건 아무거나 써도... |
AllenDowney/ThinkStats2 | code/chap05ex.ipynb | gpl-3.0 | from os.path import basename, exists
def download(url):
filename = basename(url)
if not exists(filename):
from urllib.request import urlretrieve
local, _ = urlretrieve(url, filename)
print("Downloaded " + local)
download("https://github.com/AllenDowney/ThinkStats2/raw/master/code/th... |
astroumd/GradMap | notebooks/Lectures2019/Lecture1/L1_challenge_problem_stars_student.ipynb | gpl-3.0 | # These are your stellar temperatures, you're welcome!
temperatures = [5809, 16589, 4698, 1869, 37809, 8634]
"""
Explanation: Stellar Classification
Background
The
Harvard Spectral Classification system for stars
classifies stars based on their spectral type - where the type of a star is designated as a letter that c... |
AllenDowney/ProbablyOverthinkingIt | ess5.ipynb | mit | from __future__ import print_function, division
import string
import random
import cPickle as pickle
import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
import thinkstats2
import thinkplot
import matplotlib.pyplot as plt
import ess
# colors by colorbrewer2.org
BLUE1 = '#a6cee3'
BLUE2 = '#1... |
Dans-labs/dariah | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | mit | import os,sys,re,collections,json
from os.path import splitext, basename
from functools import reduce
from glob import glob
from lxml import etree
from datetime import datetime
from pymongo import MongoClient
from bson.objectid import ObjectId
"""
Explanation: Importing InKind from FileMaker
We use an XML export of th... |
AllenDowney/ModSim | python/soln/examples/kitten_soln.ipynb | gpl-2.0 | # install Pint if necessary
try:
import pint
except ImportError:
!pip install pint
# download modsim.py if necessary
from os.path import exists
filename = 'modsim.py'
if not exists(filename):
from urllib.request import urlretrieve
url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'
... |
samueljrowell/UVM-ME249-CFD | ME249-Lecture-0.ipynb | gpl-2.0 | %matplotlib inline
# plots graphs within the notebook
%config InlineBackend.figure_format='svg' # not sure what this does, may be default images to svg format
import matplotlib.pyplot as plt #calls the plotting library hereafter referred as to plt
import numpy as np
"""
Explanation: Figure 1. Sketch of a cell (top... |
HumanCompatibleAI/imitation | examples/5_train_preference_comparisons.ipynb | mit | from imitation.algorithms import preference_comparisons
from imitation.rewards.reward_nets import BasicRewardNet
from imitation.util.networks import RunningNorm
from imitation.policies.base import FeedForward32Policy, NormalizeFeaturesExtractor
import seals
import gym
from stable_baselines3.common.vec_env import DummyV... |
dalonlobo/GL-Mini-Projects | TweetAnalysis/Final/Q6/Dalon_4_RTD_MiniPro_Tweepy_Q6.ipynb | mit | import logging # python logging module
# basic format for logging
logFormat = "%(asctime)s - [%(levelname)s] (%(funcName)s:%(lineno)d) %(message)s"
# logs will be stored in tweepy.log
logging.basicConfig(filename='tweepylang.log', level=logging.INFO,
format=logFormat, datefmt="%Y-%m-%d %H:%M:%S")
... |
csyhuang/hn2016_falwa | examples/simple/Example_barotropic.ipynb | mit | from hn2016_falwa.wrapper import barotropic_eqlat_lwa # Module for plotting local wave activity (LWA) plots and
# the corresponding equivalent-latitude profile
from math import pi
from netCDF4 import Dataset
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# --- Parameters... |
yongtang/tensorflow | tensorflow/lite/g3doc/guide/authoring.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... |
flowmatters/veneer-py | doc/examples/nodes/WorkingWithDemandModels.ipynb | isc | v.model.node.water_users.names()
v.model.node.water_users.demands()
v.model.node.water_users.demands(nodes='IrrigationOnlyForestWU')
"""
Explanation: Finding water users and demands
End of explanation
"""
v.model.node.water_users.add_timeseries?
v.model.node.water_users.add_irrigator?
v.model.node.water_users.ad... |
vkuznet/rep | howto/00-intro-ROOT.ipynb | apache-2.0 | %pylab inline
"""
Explanation: Allowing inline plots
End of explanation
"""
import numpy
import root_numpy
# generating random data
data = numpy.random.normal(size=[10000, 2])
# adding names of columns
data = data.view([('first', float), ('second', float)])
#
root_numpy.array2root(data, filename='./toy_datasets/ran... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/structured/solutions/4a_sample_babyweight.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
%%bash
pip freeze | grep google-cloud-bigquery==1.6.1 || \
pip install google-cloud-bigquery==1.6.1
"""
Explanation: LAB 4a: Creating a Sampled Dataset.
Learning Objectives
Setup up the environment.
Sample the natality dataset to create train/eval/t... |
tpin3694/tpin3694.github.io | sql/commenting_sql_code.ipynb | mit | # Ignore
%load_ext sql
%sql sqlite://
%config SqlMagic.feedback = False
"""
Explanation: Title: Commenting SQL Code
Slug: commenting_sql_code
Summary: Commenting code in SQL.
Date: 2017-01-16 12:00
Category: SQL
Tags: Basics
Authors: Chris Albon
Note: This tutorial was written using Catherine Devlin's SQL in Jupyt... |
podondra/bt-spectraldl | notebooks/02-data-to-hdf5.ipynb | gpl-3.0 | %matplotlib inline
import os
import glob
import random
import h5py
import astropy.io.fits
import numpy as np
import matplotlib.pyplot as plt
# find the normalized spectra in data_path directory
# add all filenames to the list fits_paths
FITS_DIR = 'data/ondrejov/'
fits_paths = glob.glob(FITS_DIR + '*.fits')
len(fits_... |
gojomo/gensim | docs/notebooks/online_w2v_tutorial.ipynb | lgpl-2.1 | from gensim.corpora.wikicorpus import WikiCorpus
from gensim.models.word2vec import Word2Vec, LineSentence
from pprint import pprint
from copy import deepcopy
from multiprocessing import cpu_count
from smart_open import smart_open
"""
Explanation: Online word2vec tutorial
So far, word2vec cannot increase the size of v... |
betoesquivel/comment_summarization | .ipynb_checkpoints/Lab1 Text processing with python-checkpoint.ipynb | mit | import sklearn
import numpy as np
import matplotlib.pyplot as plt
data = np.array([[1,2], [2,3], [3,4], [4,5], [5,6]])
x = data[:,0]
y = data[:,1]
data, x, y
"""
Explanation: Basic usage of Sklearn
End of explanation
"""
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(min_d... |
jphall663/GWU_data_mining | 02_analytical_data_prep/src/py_part_2_encoding.ipynb | apache-2.0 | import pandas as pd # pandas for handling mixed data sets
"""
Explanation: License
Copyright (C) 2017 J. Patrick Hall, jphall@gwu.edu
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software... |
arnoldlu/lisa | ipynb/examples/utils/testenv_example.ipynb | apache-2.0 | # One initial cell for imports
import json
import time
import os
import logging
from conf import LisaLogging
LisaLogging.setup()
# For debug information use:
# LisaLogging.setup(level=logging.DEBUG)
"""
Explanation: Test environment API - TestEnv
The test environment is primarily defined by the target configuration (... |
jeffheaton/aifh | math/Untitled.ipynb | apache-2.0 | import numpy as np
i = np.arange(1,11) # 11, because arange is not inclusive
s = np.sum(2*i)
print(s)
More traditional looping (non-Numpy) would perform the summation as follows:
s = 0
for i in range(1,11):
s += 2*i
print(s)
"""
Explanation: Artificial Intelligence for Humans
Introduction to the Math of N... |
WNoxchi/Kaukasos | FADL1/vgg16_lesson1.ipynb | mit | %reload_ext autoreload
%autoreload 2
%matplotlib inline
from fastai.imports import *
from fastai.transforms import *
from fastai.conv_learner import *
from fastai.model import *
from fastai.dataset import *
from fastai.sgdr import *
from fastai.plots import *
PATH = "data/dogscats/"
sz=224
ARCH = vgg16
bs = 16
# Un... |
jquacinella/TutoringSnippets | Histogram.ipynb | gpl-3.0 | %pylab inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Histogram of one column by binning on another continuous
End of explanation
"""
# Class label would be categorical variable derived from binning the continuous column
x = ['Class1']*300 + ['Class2']*400 + ['Class3']... |
sraejones/phys202-2015-work | assignments/assignment04/MatplotlibEx02.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Matplotlib Exercise 2
Imports
End of explanation
"""
!head -n 30 open_exoplanet_catalogue.txt
"""
Explanation: Exoplanet properties
Over the past few decades, astronomers have discovered thousands of extrasolar planets. The follo... |
gabriel-astudillo/jupyter | Rendimiento Computacional.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
import scipy as sp
import plotly.plotly as py
import plotly.figure_factory as ff
import plotly
from plotly.graph_objs import *
plotly.tools.set_credentials_file(username='gastudillo', api_key='OiqcwUGj4Jmtn1KtY6oR')
"""
Explanation: Descripción del software
Diagrama de Estados
<i... |
AaronCWong/phys202-2015-work | assignments/assignment11/OptimizationEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
"""
Explanation: Optimization Exercise 1
Imports
End of explanation
"""
def hat(x,a,b):
v = (-a*(x**2))+(b*(x**4))
return v
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(1.0, 10.0, 1... |
cranium/deep-learning | language-translation/dlnd_language_translation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
"""
Explanation: Language Translation
In this project, you’re going... |
getsmarter/bda | module_4/M4_NB2_PeerNetworkAnalysis.ipynb | mit | # Load the relevant libraries to your notebook.
import pandas as pd # Processing csv files and manipulating the DataFrame.
import networkx as nx # Graph-like object representation and manipulation module.
import matplotlib.pylab as plt # Plotting and data visualization module.
... |
landlab/landlab | notebooks/tutorials/network_sediment_transporter/nst_scaling_profiling.ipynb | mit | import cProfile
import io
import pstats
import time
import warnings
from pstats import SortKey
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import xarray as xr
from landlab.components import FlowDirectorSteepest, NetworkSedimentTransporter
from landlab.data_record import DataRecord
from land... |
ES-DOC/esdoc-jupyterhub | notebooks/ipsl/cmip6/models/ipsl-cm6a-lr/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ipsl', 'ipsl-cm6a-lr', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: IPSL
Source ID: IPSL-CM6A-LR
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, ... |
paulvangentcom/heartrate_analysis_python | examples/4_smartring_data/Analysing_Smart_Ring_Data.ipynb | mit | #Let's import some packages first
import numpy as np
import matplotlib.pyplot as plt
import heartpy as hp
sample_rate = 32
#load the example file
data = hp.get_data('ring_data.csv')
"""
Explanation: Analysing PPG signals from smart rings
There's a range of Smart Rings that recently hit the market. Among other thing... |
martysyuk/PY-3-Learning | homeworks/lesson4-1-docs.ipynb | mit | import pandas as pd
import os.path as path
"""
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
"""
Explanation: Домашнее задание по уроку 4.1
Выполнил Мартысюк Илья.
End of explanation
"""
PATH = '/Users/martysyuk/Documents/Python 3 Coding/Repositorys/PY-3-Learning/homeworks/names/'
names... |
tensorflow/docs-l10n | site/en-snapshot/guide/migrate/tflite.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... |
arcyfelix/Courses | 17-09-17-Python-for-Financial-Analysis-and-Algorithmic-Trading/04-Visualization-Matplotlib-Pandas/04b-Pandas Visualization/01 - Pandas Built-in Data Visualization.ipynb | apache-2.0 | import numpy as np
import pandas as pd
%matplotlib inline
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../../Pierian_Data_Logo.png' /></a>
Pandas Built-in Data Visualization
In this lecture we will learn about pandas built-in capabilities for data visualization! It's built-off of matplotlib, but i... |
alansaul/ods | notebooks/pods/datasets/google_trends.ipynb | bsd-3-clause | import pods
%matplotlib inline
# calling without arguments uses the default query terms
data = pods.datasets.google_trends()
"""
Explanation: Datasets: Downloading Data from Google Trends
28th May 2014
Neil Lawrence
This data set collection was inspired by a ipython notebook from sahuguet which made queries to googl... |
VectorBlox/PYNQ | Pynq-Z1/notebooks/examples/mxp_filters_hdmi.ipynb | bsd-3-clause | from pynq import Overlay
Overlay("vbx.bit").download()
"""
Explanation: OpenCV Filters HDMI
In this notebook, several filters will be applied to HDMI input images.
Those input sources and applied filters will then be displayed either directly in the notebook or on HDMI output.
To run all cells in this notebook a HDMI... |
mne-tools/mne-tools.github.io | 0.17/_downloads/275d6fecfc61c14ad9e91bb36b7359e0/plot_stats_cluster_erp.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import ttest_ind
import mne
from mne.channels import find_ch_connectivity, make_1020_channel_selections
from mne.stats import spatio_temporal_cluster_test
np.random.seed(0)
# Load the data
path = mne.datasets.kiloword.data_path() + '/kword_metadata-... |
kenjisato/intro-macro | doc/python/Optimal Growth (DP).ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Computing the Optimal Growth Model by Dynamic Programming
End of explanation
"""
alpha = 0.3
delta = 0.05
theta = 5.0
rho = alpha * delta * theta - delta
A = 1
def u(c):
"""utility function"""
if theta == 1:
retur... |
joekasp/spectro | DEMO.ipynb | mit | %matplotlib inline
from ipywidgets import *
from IPython.display import display
import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import curve_fit
from util import *
from analysis import *
import fits
from plot import *
from plot3d import *
"""
Explanation: Analysis of 2D-IR spectroscopy
This fir... |
valentina-s/GLM_PythonModules | notebooks/.ipynb_checkpoints/Filters-checkpoint.ipynb | bsd-2-clause | import numpy as np
import scipy as sp
from scipy import linalg
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Filters for Neural Encoding
This notebook discusses the construction of filters for neural encoding via Generalized Linear Models. The basis for the filters consists of raised cosine func... |
Upward-Spiral-Science/grelliam | code/classification_simulation.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
import os
import csv
import igraph as ig
from sklearn import cross_validation
from sklearn.cross_validation import LeaveOneOut
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklea... |
Spaxe/pyconau2017-messy-sensor-data | Messy Sensor Data - A Programmer's Cleaning Guide.ipynb | mit | import pandas as pd
# Open a comma-separated values (CSV) file as a DataFrame
weather_observations = pd.read_csv('observations/Canberra_observations.csv')
# Print the first 5 entries
weather_observations.head()
"""
Explanation: Messy Sensor Data:
A Programmer's Cleaning Guide
@Xavier_Ho, #pyconau
<small>Feel free t... |
timnon/pyschedule | example-notebooks/sports-scheduling.ipynb | apache-2.0 | import sys;sys.path.append('../src')
from pyschedule import Scenario, solvers, plotters, alt
n_teams = 12 # Number of teams
n_fields = int(n_teams/2) # Num of fields
n_rounds = n_teams-1 # Number of rounds
# Create scenario
S = Scenario('sport_scheduling',horizon=n_rounds)
# Game tasks
Games = { (i,j) : S.Task('Game... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/time_series_prediction/solutions/optional_2_feature_engineering.ipynb | apache-2.0 | PROJECT = 'your-gcp-project' # Replace with your project ID.
import pandas as pd
from google.cloud import bigquery
from IPython.core.magic import register_cell_magic
from IPython import get_ipython
bq = bigquery.Client(project = PROJECT)
# Allow you to easily have Python variables in SQL query.
@register_cell_magi... |
ercius/openNCEM | ncempy/notebooks/example_peakFind.ipynb | gpl-3.0 | %matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
# Import these from ncempy.algo
from ncempy.algo import gaussND
from ncempy.algo import peakFind
"""
Explanation: Example of how to find peaks in a synthetic image
Create a set of 2D Gaussians
Find the center of the Guassian to integer accuracy... |
serenejiang/MrOS_VitaminD | notebooks/3.1 PD alpha diversity analysis (Linear Regression).ipynb | gpl-3.0 | import pandas as pd
import numpy as np
import statsmodels.formula.api as smf
from statsmodels.compat import lzip
import statsmodels.stats.api as sms
import statsmodels.api as sm
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
"""
Explanation: output: 'mapping_PDalpha.txt'(mapping file with PD... |
abhi1509/deep-learning | 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... |
Santana9937/Intro_to_recommender_systems | week_4/Week_4_Assign_User-User_Collaborative_Filtering.ipynb | mit | import numpy as np
import pandas as pd
"""
Explanation: Assignment 3: User-User Collaborative Filtering
Importing Libraries
End of explanation
"""
mov_user_data = pd.read_excel('Assign_3_data.xlsx')
"""
Explanation: Loading the Data
Loading the movie data from Excel into a DataFrame.
End of explanation
"""
mov_us... |
eds-uga/csci1360e-su17 | assignments/A5/A5_Q2.ipynb | mit | truth = "This is some text.\nMore text, but on a different line!\nInsert your favorite meme here.\n"
pred = read_file_contents("q1data/file1.txt")
assert truth == pred
retval = -1
try:
retval = read_file_contents("nonexistent/path.txt")
except:
assert False
else:
assert retval is None
"""
Explanation: Q2
... |
ML4DS/ML4all | P5.Data preprocessing/Intro5_DataNormalization_student.ipynb | mit | # Some libraries that will be used along the notebook.
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Data preprocessing methods: Normalization
Notebook version:
* 1.0 (Sep 15, 2020) - First version
* 1.1 (Sep 15, 2021) - Exercises
Authors: Jesús Cid Sueiro (jcid@ing.uc3m.es)
End of explanation
... |
ewulczyn/talk_page_abuse | src/figshare/Wikipedia Talk Data - Getting Started.ipynb | apache-2.0 | import pandas as pd
import urllib
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score
# download annotated comments an... |
masonlab/labdrivers | example_nbs/example_2d_gate_bias_conductance.ipynb | mit | from labdrivers.ni import bnc2110
from labdrivers.keithley import keithley2400
from labdrivers.srs import sr830
"""
Explanation: Importing drivers
End of explanation
"""
daq = bnc2110(device='Dev1')
keithley = keithley2400(GPIBaddr=22)
lockin = sr830(GPIBaddr=8)
"""
Explanation: Object instantiation
End of explanat... |
YuguangTong/AY250-hw | hw_3/homework.ipynb | mit | # you need to install the following package to continue:
# pip3 install SpeechRecognition
# load monty class
from monty import Monty
"""
Explanation: Interaction with the World Homework (#3)
Python Computing for Data Science (c) J Bloom, UC Berkeley, 2016
1) Monty: The Python Siri
Let's make a Siri-like program w... |
Yu-Group/scikit-learn-sandbox | jupyter/backup_deprecated_nbs/07_tree_traversal_function.ipynb | mit | # Setup
%matplotlib inline
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.datasets import load_iris
from sklearn import tree
import ... |
bjlange/csss-nlp-workshop | NLP Workshop (complete).ipynb | mit | csvfile = open('bernie-sanders-announces.csv','r')
reader = csv.reader(csvfile)
data = []
for line in reader:
line[3] = line[3].decode('utf-8')
data.append(line)
len(data)
data[0]
data[1]
comment_text = data[1][-1]
"""
Explanation: Getting data into Python (basic python i/o)
End of explanation
"""
commen... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/explainable_ai/labs/xai_structured_caip.ipynb | apache-2.0 | import os
PROJECT_ID = "dougkelly-sandbox" # TODO: your PROJECT_ID here.
os.environ["PROJECT_ID"] = PROJECT_ID
BUCKET_NAME = "xai-labs" # TODO: your BUCKET_NAME here.
REGION = "us-central1"
os.environ['BUCKET_NAME'] = BUCKET_NAME
os.environ['REGION'] = REGION
"""
Explanation: AI Explanations: Explaining a tabular ... |
airanmehr/bio | notebooks/KGZ/QC.ipynb | mit | %matplotlib inline
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import sys,os
path='/'.join(os.getcwd().split('/')[:-4])
sys.path.insert(1,path)
import Utils.Util as utl
import pandas as pd
pd.options.display.max_rows = 20;
pd.options.display.expand_frame_repr = True
from IPython.display import ... |
bioe-ml-w18/bioe-ml-winter2018 | homeworks/Week3-Fitting.ipynb | mit | % matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy.special import binom
from scipy.optimize import brentq
np.seterr(over='raise')
def StoneMod(Rtot, Kd, v, Kx, L0):
'''
Returns the number of mutlivalent ligand bound to a cell with Rtot
receptors, granted each epitope of the... |
usantamaria/iwi131 | ipynb/19-Diccionarios/Diccionarios.ipynb | cc0-1.0 | d = {"alpha":1, "beta":[1,1,3,5], (0,1):"beta"}
print d # No hay orden!!
"""
Explanation: <header class="w3-container w3-teal">
<img src="images/utfsm.png" alt="" align="left"/>
<img src="images/inf.png" alt="" align="right"/>
</header>
<br/><br/><br/><br/><br/>
IWI131
Programación de Computadores
Sebastián Flores
htt... |
google/flax | examples/sst2/sst2.ipynb | apache-2.0 | example_directory = 'examples/sst2'
editor_relpaths = ('configs/default.py', 'train.py', 'models.py')
# (If you run this code in Jupyter[lab], then you're already in the
# example directory and nothing needs to be done.)
#@markdown **Fetch newest Flax, copy example code**
#@markdown
#@markdown **If you select no** b... |
bloomberg/bqplot | examples/Marks/Object Model/Pie.ipynb | apache-2.0 | data = np.random.rand(3)
pie = Pie(sizes=data, display_labels="outside", labels=list(string.ascii_uppercase))
fig = Figure(marks=[pie], animation_duration=1000)
fig
"""
Explanation: Basic Pie Chart
End of explanation
"""
n = np.random.randint(1, 10)
pie.sizes = np.random.rand(n)
"""
Explanation: Update Data
End of ... |
csaladenes/csaladenes.github.io | test/eis-metadata-validation/Planon metadata validation4-Copy1.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: EIS metadata validation script
Used to validate Planon output with spreadsheet input
1. Data import
End of explanation
"""
planon=pd.read_excel('EIS Assets.xlsx',index_col = 'Code')
master_loggerscontrollers = ... |
vascotenner/holoviews | doc/Tutorials/Pandas_Seaborn.ipynb | bsd-3-clause | import itertools
import numpy as np
import pandas as pd
import seaborn as sb
import holoviews as hv
np.random.seed(9221999)
"""
Explanation: In this notebook we'll look at interfacing between the composability and ability to generate complex visualizations that HoloViews provides, the power of pandas library datafra... |
justinfinkle/pydiffexp | ipynb/example_diffexp.ipynb | gpl-3.0 | import pandas as pd
from pydiffexp import DEAnalysis
"""
Explanation: Pydiffexp
The pydiffexp package is meant to provide an interface between R and Python to do differential expression analysis.
Imports
End of explanation
"""
test_path = "/Users/jfinkle/Documents/Northwestern/MoDyLS/Python/sprouty/data/raw_data/all... |
JaviMerino/lisa | ipynb/utils/testenv_example.ipynb | apache-2.0 | # Setup a target configuration
conf = {
# Platform and board to target
"platform" : "linux",
"board" : "juno",
# Login credentials
"host" : "192.168.0.1",
"username" : "root",
"password" : "",
# Local installation path
"tftp" : {
"folder" : "/var/... |
amitkaps/hackermath | Module_2d_Distributions.ipynb | mit | import pandas as pd
import seaborn as sns
sns.set(color_codes=True)
%matplotlib inline
#Import the data
cars = pd.read_csv("cars_v1.csv", encoding="ISO-8859-1")
#Replace missing values in Mileage with mean
cars.Mileage.fillna(cars.Mileage.mean(), inplace=True)
sns.distplot(cars.Mileage, kde=False)
"""
Explanation: ... |
davidrpugh/pyCollocation | examples/auction-models.ipynb | mit | import functools
class SymmetricIPVPModel(pycollocation.problems.IVP):
def __init__(self, f, F, params):
rhs = self._rhs_factory(f, F)
super(SymmetricIPVPModel, self).__init__(self._initial_condition, 1, 1, params, rhs)
@staticmethod
def _initial_condition(v, sigma, v_lower, ... |
dereneaton/ipyrad | testdocs/analysis/cookbook-tetrad-ipcoal.ipynb | gpl-3.0 | # conda install ipyrad -c conda-forge -c bioconda
# conda install tetrad -c conda-forge
import ipyrad.analysis as ipa
import toytree
import ipcoal
"""
Explanation: <h1><span style="color:gray">ipyrad-analysis toolkit:</span> tetrad</h1>
The tetrad tool is a framework for inferring a species tree topology using quart... |
tensorflow/docs-l10n | site/zh-cn/tutorials/keras/save_and_load.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... |
simkovic/simkovic.github.io | _ipynb/No Way Anova - A theoretical paper reports completely redundant Anova.ipynb | mit | %pylab inline
x=[1,2,5]
y=np.array([[0.41,0.44,0.47],[0.25,0.22,0.21]]).T
plt.errorbar(x,y[:,0],yerr=0.7/9.3,fmt='d-b')
plt.errorbar(x,y[:,1],yerr=0.7/9.3,fmt='o-g')
plt.legend(['focal','non-focal'],loc=7)
plt.grid(False,axis='x')
plt.xlabel('Time pressure');plt.ylabel('choice probability')
plt.title('Figure 1')
plt.xl... |
arokem/seaborn | doc/docstrings/FacetGrid.ipynb | bsd-3-clause | tips = sns.load_dataset("tips")
sns.FacetGrid(tips)
sns.FacetGrid(tips, col="time", row="sex")
g = sns.FacetGrid(tips, col="time", row="sex")
g.map(sns.scatterplot, "total_bill", "tip")
g = sns.FacetGrid(tips, col="time", row="sex")
g.map_dataframe(sns.histplot, x="total_bill")
g = sns.FacetGrid(tips, col="time",... |
amandersillinois/landlab | notebooks/teaching/surface_water_hydrology_exercises/overland_flow_notebooks/hydrograph_class_notebook.ipynb | mit | ## only needed for plotting in a jupyter notebook.
%matplotlib inline
## Code Block 1
import copy
import numpy as np
from matplotlib import pyplot as plt
from landlab import imshow_grid
from landlab.components import OverlandFlow, FlowAccumulator
from landlab.io import read_esri_ascii
"""
Explanation: <a href="htt... |
Naereen/notebooks | Demonstration of numpy.polynomial.Polynomial and nice display with LaTeX and MathJax (python3).ipynb | mit | from numpy.polynomial import Polynomial as P
"""
Explanation: Table of Contents
1. Demonstration of the numpy.polynomial package
1.1 And especially a small hand-made pretty printing function for Polynomial objects
1.2 First goal: pretty print in ASCII text
1.3 Second goal: pretty-print in $\LaTeX{}$ code
1.4 A bonus ... |
snowch/movie-recommender-demo | notebooks/Step 03 - Predict ratings.ipynb | apache-2.0 | from pyspark.mllib.recommendation import Rating
new_user_ID = 0
new_user_ratings = [
Rating(0,260,9), # Star Wars (1977)
Rating(0,1,8), # Toy Story (1995)
Rating(0,16,7), # Casino (1995)
Rating(0,25,8), # Leaving Las Vegas (1995)
Rating(0,32,9), # Twelve Monkeys (a.k.a. 12 Monk... |
ES-DOC/esdoc-jupyterhub | notebooks/test-institute-2/cmip6/models/sandbox-2/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-2', 'sandbox-2', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: TEST-INSTITUTE-2
Source ID: SANDBOX-2
Topic: Ocnbgchem
Sub-Topic... |
johnbachman/emcee | docs/_static/notebooks/autocorr.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
np.random.seed(1234)
# Build the celerite model:
import celerite
from celerite import terms
kernel = terms.RealTerm(log_a=0.0, log_c=-6.0)
kernel += terms.RealTerm(log_a=0.0, log_c=-2.0)
# The true autocorrelation time can be calculated analytically:
true_tau = sum... |
google-research/ott | docs/notebooks/LRSinkhorn.ipynb | apache-2.0 | import jax.numpy as jnp
import jax
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 18})
import ott
def create_points(rng, n, m, d):
rngs = jax.random.split(rng, 4)
x = jax.random.normal(rngs[0], (n,d)) + 1
y = jax.random.uniform(rngs[1], (m,d))
a = jax.random.uniform(rngs[2], (n,))
b = jax... |
letsgoexploring/teaching | winter2017/econ129/python/Econ129_Winter2017_Homework1_Complete.ipynb | mit | # Question 1.1
A = 1
alpha = 0.35
k = np.arange(0,10,0.001)
y = A*k**alpha
plt.plot(k,y,lw=3,alpha = 0.65)
plt.xlabel('capital')
plt.ylabel('output')
plt.title('Cobb-Douglas production function')
plt.grid()
# Question 1.2
def cobbDouglas(A,k,alpha):
return A*k**alpha
A = 1
alpha = 0.35
k = np.arange(0,10,0.001... |
climberwb/pycon-pandas-tutorial | Exercises-3.ipynb | mit | t = titles
t.groupby(t.year // 10 * 10).size().plot(kind='bar')
"""
Explanation: Using groupby(), plot the number of films that have been released each decade in the history of cinema.
End of explanation
"""
t = titles[titles.title == "Hamlet"]
t.groupby(t.year // 10 * 10).size().plot(kind='bar')
"""
Explanation: U... |
CompPhysics/MachineLearning | doc/pub/week34/ipynb/week34.ipynb | cc0-1.0 | import numpy as np
"""
Explanation: <!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)
doconce format html week34.do.txt --no_mako -->
<!-- dom:TITLE: Week 34: Introduction to the course, Logistics and Practicalities -->
Week 34: Introduction to the course, Logistics and ... |
dsquareindia/gensim | docs/notebooks/word2vec.ipynb | lgpl-2.1 | # import modules & set up logging
import gensim, logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
sentences = [['first', 'sentence'], ['second', 'sentence']]
# train word2vec on the two sentences
model = gensim.models.Word2Vec(sentences, min_count=1)
"""
Explanation:... |
phanrahan/magmathon | notebooks/tutorial/icestick/FullAdder.ipynb | mit | import magma as m
m.set_mantle_target('ice40')
import mantle
"""
Explanation: FullAdder - Combinational Circuits
This notebook walks through the implementation of a basic combinational circuit, a full adder. This example introduces many of the features of Magma including circuits, wiring, operators, and the type syste... |
infilect/ml-course1 | keras-notebooks/Transfer-Learning/5.1 HyperParameter Tuning.ipynb | mit | import numpy as np
np.random.seed(1337) # for reproducibility
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.utils import np_utils
from keras.wrappers.scikit_learn import KerasCl... |
tuanvu216/udacity-course | deep_learning/examples/1_notmnist.ipynb | mit | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import os
import tarfile
import urllib
from IPython.display import display, Image
from scipy import ndimage
from sklearn.linear_model import ... |
ishakaur/sandbox | enron_email_analysis/Enron Data Exploration Part 1.ipynb | mit | from IPython.display import display
import pandas as pd
from enrondatahandling import EnronEmailDataset
"""
Explanation: Handling and analysis of the Enron Email Dataset - Part 1
The class definitions
EnronEmailParser class
Parser for the emails included in the Enron Email Dataset.
This particular implementation tre... |
gfeiden/Notebook | Projects/ngc2516_spots/bolometric_corrections.ipynb | mit | # change directory
%cd ../../../Projects/starspot/starspot/
from color import bolcor as bc
"""
Explanation: Bolometric Corrections
Details about the bolometric correction package can be found in the GitHub repository starspot.
End of explanation
"""
bc.utils.log_init('table_limits.log') # initialize bolometric cor... |
paulthulstrup/moose | modules/thermopower_diffusion/thermopower_analysis.ipynb | lgpl-2.1 | # Library import
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Data Import
T_hot = np.arange(0.010,0.5, 0.005)
T_fridge = np.arange(0.005,0.495, 0.005)
df = pd.read_csv("./data/TVar_Thot-0.01-0.495-step0.005_Tcold-0.005-0.49.csv", )
data = df.values
"""
Explanation: The... |
TrinVeerasiri/presta_to_woo_migration | generate_wp_users_and_wp_usermeta.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
"""
Explanation: Customer migration from Prestashop to Woocommerce part 2 : Generate wp_users and wp_usermeta
End of explanation
"""
#Load a raw information
raw_information = pd.read_csv('sql_prestashop/raw_information.csv', index_col='id_customer')
raw_information = raw_inform... |
ianozsvald/ipython_memory_usage | src/ipython_memory_usage/examples/example_usage_np_pd.ipynb | bsd-2-clause | import ipython_memory_usage
help(ipython_memory_usage) # or ipython_memory_usage?
%ipython_memory_usage_start
"""
Explanation: Short demo of using ipython_memory_usage to diagnose numpy and Pandas RAM usage
Author Ian uses this tool in his Higher Performance Python training (https://ianozsvald.com/training/) and it i... |
ES-DOC/esdoc-jupyterhub | notebooks/cmcc/cmip6/models/cmcc-cm2-vhr4/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cmcc', 'cmcc-cm2-vhr4', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: CMCC
Source ID: CMCC-CM2-VHR4
Topic: Ocean
Sub-Topics: Timestepping Framework, A... |
Rotvig/cs231n | Project/RNN-TF.ipynb | mit | import tensorflow as tf
import numpy as np
import random
"""
Explanation: Recurrent Neural Networks for Beginners (in TensorFlow)
This iPython notebook is designed to serve as a walkthrough for beginners on how to implement a simple recurrent neural network using Python and Tensorflow. The code in this notebook is bas... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session09/Day1/ExtractingPeriodicSignals.ipynb | mit | def gen_periodic_data(x, period=1, amplitude=1, phase=0, noise=0):
'''Generate periodic data given the function inputs
y = A*cos(x/p - phase) + noise
Parameters
----------
x : array-like
input values to evaluate the array
period : float (default=1)
period of the pe... |
tensorflow/docs-l10n | site/ja/probability/examples/Gaussian_Copula.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... |
xesscorp/pygmyhdl | examples/3_pwm/pwm.ipynb | mit | from pygmyhdl import *
@chunk
def pwm_simple(clk_i, pwm_o, threshold):
'''
Inputs:
clk_i: PWM changes state on the rising edge of this clock input.
threshold: Bit-length determines counter width and value determines when output goes low.
Outputs:
pwm_o: PWM output starts and stays h... |
CCI-Tools/cate-core | notebooks/heal-cci-sea-level.ipynb | mit | ds = ds0.rename(time='time_step')
ds.time_step
"""
Explanation: We observe two issues here which make it hard to work with this data in the current version of Cate:
Cate can only display dataset variables whose last dimensions are lat and lon, in this order;
there is a dimension and coordinate variable time, which is... |
kkhenriquez/python-for-data-science | Week-4-Pandas/Introduction to Pandas.ipynb | mit | import pandas as pd
"""
Explanation: <p style="font-family: Arial; font-size:3.75em;color:purple; font-style:bold"><br>
Pandas</p>
<br>
pandas is a Python library for data analysis. It offers a number of data exploration, cleaning and transformation operations that are critical in working with data in Python.
pandas ... |
ellisztamas/faps | docs/.ipynb_checkpoints/04 Sibship clustering-checkpoint.ipynb | mit | from faps import *
import numpy as np
np.random.seed(867)
allele_freqs = np.random.uniform(0.3,0.5,50)
adults = make_parents(100, allele_freqs, family_name='a')
"""
Explanation: Sibship clustering
Tom Ellis, March 2017
FAPS uses information in a paternityArray to generate plausible full-sibship configurations. This i... |
avtlearns/automatic_text_summarization | TextRank_Automatic_Summarization_for_Medical_Articles.ipynb | gpl-3.0 | from nltk.tokenize.punkt import PunktSentenceTokenizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
import networkx as nx
import re
import urllib2
from bs4 import BeautifulSoup
import pandas as pd
# -*- coding: utf-8 -*-
"""
Explanation: Autom... |
feststelltaste/software-analytics | demos/20210630_WeAreDevelopersWorldCongress/jQAssistant Demo.ipynb | gpl-3.0 | %load_ext cypher
"""
Explanation: jQAssistant Demo
Clone https://github.com/JavaOnAutobahn/spring-petclinic
Build software
mvn install
Start Neo4j server
mvn jqassistant:server
Open browser
http://localhost:7474/browser/
jQAssistant documentation: https://jqassistant.github.io/jqassistant/doc/1.10.0/manual/index.html... |
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