repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
sgkang/simpegAIP | notebook/StoltzaAndMacnae1998_simple_stepon.ipynb | mit | def AofT(time,T, ai, taui):
return ai*np.exp(-time/taui)/(1.+np.exp(-T/(2*taui)))
from SimPEG import *
import sys
sys.path.append("./DoubleLog/")
from plotting import mapDat
class LinearSurvey(Survey.BaseSurvey):
nD = None
def __init__(self, time, **kwargs):
self.time = time
self.nD = tim... |
albahnsen/PracticalMachineLearningClass | exercises/P2-MovieGenrePrediction.ipynb | mit | import pandas as pd
import os
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.multiclass import OneVsRestClassifier
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from sklearn.metrics import r2_... |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_source_label_time_frequency.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, source_induced_power
print(__doc__)
"""
Explanation... |
robertoalotufo/ia898 | master/Revisao_NumerosComplexos.ipynb | mit | import numpy as np
C = np.complex(3,4)
print('C=', C)
print(type(C))
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#IA898-Revisão-em-números-complexos" data-toc-modified-id="IA898-Revisão-em-números-complexos-1"><span class="toc-item-num">1 </span>IA898 Revisão em números comple... |
vitojph/2016progpln | notebooks/2-Python-control-flujo.ipynb | mit | # asignamos unos cuantos valores a variables
numero1 = 2
numero2 = 34
print(numero1 == numero2)
print(numero1 != numero2)
print(numero1 == numero1)
print(numero2 <= 10)
print(19 >= (10 * numero1))
print("------------------")
print(10 == (5*2))
print(MiVariable != 10)
"""
Explanation: Control de flujo
Hasta ahora ... |
aaschroeder/Titanic_example | Final_ensemble.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
titanic=pd.read_csv('./titanic_clean_data.csv')
"""
Explanation: In this section, we're going to
Setup the titanic data for ensembling
Call the five individual models
Ensemble the models (We use another Gradient Booster
Output for submission
End of explanation
"""
cols_to_nor... |
gboeing/urban-data-science | modules/08-urban-networks-ii/lecture.ipynb | mit | import geopandas as gpd
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import osmnx as ox
import pandana
import pandas as pd
from shapely.geometry import Point
# consistent randomization
np.random.seed(0)
# configure OSMnx
cache_folder = '../../data/cache2'
ox.config(log_console=True, use_ca... |
khrapovs/metrix | notebooks/computing_ols.ipynb | mit | import numpy as np
import matplotlib.pylab as plt
import seaborn as sns
from numpy.linalg import inv, lstsq
sns.set_context('notebook')
"""
Explanation: Different ways to compute OLS
Import all necessary modules
End of explanation
"""
%matplotlib inline
"""
Explanation: If you want to embed plots inside IPython n... |
dennys-bd/Coursera-Machine-Learning-Specialization | Course 2 - ML, Regression/week-2-multiple-regression-assignment-1-blank.ipynb | mit | import graphlab
"""
Explanation: Regression Week 2: Multiple Regression (Interpretation)
The goal of this first notebook is to explore multiple regression and feature engineering with existing graphlab functions.
In this notebook you will use data on house sales in King County to predict prices using multiple regressi... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive2/image_classification/solutions/4_tpu_training.ipynb | apache-2.0 | import os
os.environ["BUCKET"] = "your-bucket-here"
"""
Explanation: Transfer Learning on TPUs
In the <a href="3_tf_hub_transfer_learning.ipynb">previous notebook</a>, we learned how to do transfer learning with TensorFlow Hub. In this notebook, we're going to kick up our training speed with TPUs.
Learning Objectives
... |
Kyubyong/numpy_exercises | 2_Array_manipulation_routines_Solutions.ipynb | mit | import numpy as np
np.__version__
"""
Explanation: Array manipulation routines
End of explanation
"""
x = np.ones([10, 10, 3])
out = np.reshape(x, [-1, 150])
print out
assert np.allclose(out, np.ones([10, 10, 3]).reshape([-1, 150]))
"""
Explanation: Q1. Let x be a ndarray [10, 10, 3] with all elements set to one. ... |
tpin3694/tpin3694.github.io | machine-learning/replace_characters.ipynb | mit | # Import library
import re
"""
Explanation: Title: Replace Characters
Slug: replace_characters
Summary: How to remove characters to clean unstructured text data for machine learning in Python.
Date: 2016-09-06 12:00
Category: Machine Learning
Tags: Preprocessing Text
Authors: Chris Albon
Preliminaries
End of explanat... |
pglauner/misc | src/cs730/1_notmnist.ipynb | gpl-2.0 | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import tarfile
from IPython.display import display, Image
from scipy import ndimage
from sklearn.line... |
tpin3694/tpin3694.github.io | sql/select_rows_that_contain_an_item_in_a_list.ipynb | mit | # Ignore
%load_ext sql
%sql sqlite://
%config SqlMagic.feedback = False
"""
Explanation: Title: Select Rows That Contain An Item In A List
Slug: select_rows_that_contain_an_item_in_a_list
Summary: Select rows that contain an item in a list.
Date: 2016-05-01 12:00
Category: SQL
Tags: Basics
Authors: Chris Albon
Note... |
destrys/euler | python/notebooks/Problem1.ipynb | mit | %psource p1.natural_3and5_brute
from destryseuler import p1
p1.natural_3and5_brute??
%pfile ../destryseuler/p1.py
def natural3and5Brute(upper=1000):
output = 0
for i in range(1,upper):
if i % 3 == 0:
output += i
continue
if i % 5 == 0:
output += i
... |
quantopian/research_public | notebooks/lectures/Introduction_to_Research/notebook.ipynb | apache-2.0 | 2 + 2
"""
Explanation: Introduction to the Research Environment
The research environment is powered by IPython notebooks, which allow one to perform a great deal of data analysis and statistical validation. We'll demonstrate a few simple techniques here.
Code Cells vs. Text Cells
As you can see, each cell can be eithe... |
adrianstaniec/deep-learning | 03_intro-to-tflearn/TFLearn_Sentiment_Analysis.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... |
jhconning/Dev-II | notebooks/Vollrath.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
from ipywidgets import interact, fixed
from scipy.optimize import fsolve, brentq, minimize
def F(la, A, alpha):
return A * la**alpha
def y(la= 0.5, Aa = 1, Am = 1, alpA = 0.5, alpM = 0.5, p = 1):
'''GDP per capita'''
return F(la, Aa, alpA) + p * F(1-la... |
hamogu/Cepheids | CompanionPSF.ipynb | mit | import numpy as np
import astropy.units as u
from astropy.coordinates import SkyCoord
from astroquery.gaia import Gaia
from astropy import table
from astropy.table import Table
from astropy.wcs import WCS
from astropy.io import fits
from astropy.nddata import NDData
from photutils.psf import extract_stars
import she... |
laserkelvin/Molecule-Tools | Coulomb Matrix.ipynb | gpl-3.0 | Atoms = ["C", "B", "H"]
"""
Explanation: Atoms
Atoms are defined as a list of strings here.
End of explanation
"""
Coordinates = []
for AtomNumber in range(len(Atoms)):
Coordinates.append(np.random.rand(3))
"""
Explanation: Generate some random coordinates
Making some random $x,y,z$ coordinates up
End of explan... |
ALEXKIRNAS/DataScience | Coursera/Machine-learning-data-analysis/Course 2/Week_04/grad_boosting.ipynb | mit | import sklearn.datasets as datasets
import numpy as np
from matplotlib import pyplot as plt
import pandas as pd
%matplotlib inline
data = datasets.load_boston()
target = data['target']
data = data['data']
data.shape
x_train, x_test, y_train, y_test = data[:380], data[380:], target[:380], target[380:]
"""
Explanati... |
landlab/landlab | notebooks/teaching/geomorphology_exercises/hillslope_notebooks/north_carolina_piedmont_hillslope_class_notebook.ipynb | mit | # Code Block 1
import numpy as np
from landlab.io import read_esri_ascii
from landlab.plot.imshow import imshow_grid
import matplotlib.pyplot as plt
# below is to make plots show up in the notebook
%matplotlib inline
"""
Explanation: <a href="http://landlab.github.io"><img style="float: left" src="../../../landlab_h... |
cxxgtxy/tensorflow | tensorflow/lite/g3doc/tutorials/model_maker_image_classification.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... |
huilyu2/DataVisualization | Assignment+3_Hui+Lyu.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import csv
"""
Explanation: Assignment: Week 04
This week, we will be putting to use some of the skills we have developed over the last couple weeks.
You will be tasked with examining a new dataset, constructing a class that operates on it, and plo... |
y2ee201/Deep-Learning-Nanodegree | transfer-learning/Transfer_Learning.ipynb | mit | from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
vgg_dir = 'tensorflow_vgg/'
# Make sure vgg exists
if not isdir(vgg_dir):
raise Exception("VGG directory doesn't exist!")
class DLProgress(tqdm):
last_block = 0
def hook(self, block_num=1, block_size=1, total_s... |
quantopian/research_public | notebooks/data/eventvestor.earnings_releases/notebook.ipynb | apache-2.0 | # import the dataset
from quantopian.interactive.data.eventvestor import earnings_releases
# or if you want to import the free dataset, use:
# from quantopian.interactivedata.eventvestor import earnings_releases_free
# import data operations
from odo import odo
# import other libraries we will use
import pandas as pd
... |
henriquepgomide/caRtola | src/python/desafio_valorizacao/Descobrindo o algoritmo de valorização do Cartola FC - Parte II.ipynb | mit | # Importar bibliotecas
import pprint
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import lightgbm as lgb
from sklearn import linear_model
from sklearn.metrics import (mean_squared_error, r2_score)
from sklearn import ensemble
from sklearn.model_selection import train_test... |
mne-tools/mne-tools.github.io | 0.22/_downloads/542dc1082b4eb8bba1ec06e757cdf52d/plot_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... |
connordurkin/CPSC_458 | final_project.ipynb | mit | %matplotlib inline
import yahoo_finance
from yahoo_finance import Share
import numpy as np
import pandas
import matplotlib.pyplot as plt
import datetime
import cvxopt as opt
from cvxopt import blas, solvers
# We will do a lot of optimizations,
# and don't want to see each step.
solvers.options['show_progress'] = False... |
beangoben/HistoriaDatos_Higgs | Dia1/1_Computacion_con_Jupyter.ipynb | gpl-2.0 | !pwd
print("hola bolivia")
"""
Explanation: 1 Introduccion a IPython notebooks/ Jupyter
Que es exactamente?
Una libreta IPython/Jupyter es un ambiente interactivo para escribir y correr codigo de python. Es un historial completo y auto-contenido de un calculo y puede ser convertido a otros formatos para compartir ... |
EmuKit/emukit | notebooks/Emukit-tutorial-basic-use-of-the-library.ipynb | apache-2.0 | # General imports and parameters of figures should be loaded at the beginning of the overview
import numpy as np
"""
Explanation: Basic use of Emukit
Overview
End of explanation
"""
from emukit.test_functions import branin_function
from emukit.core import ParameterSpace, ContinuousParameter
from emukit.core.initia... |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_ems_filtering.ipynb | bsd-3-clause | # Author: Denis Engemann <denis.engemann@gmail.com>
# Jean-Remi King <jeanremi.king@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io, EvokedArray
from mne.datasets import sample
from mne.decoding import EMS, compute_ems
from sklearn.model_... |
ES-DOC/esdoc-jupyterhub | notebooks/mohc/cmip6/models/hadgem3-gc31-mm/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'hadgem3-gc31-mm', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: MOHC
Source ID: HADGEM3-GC31-MM
Topic: Ocean
Sub-Topics: Timestepping Framewor... |
asharel/ml | LAB3/src/LAB3.ipynb | gpl-3.0 | print ('benign=>', (all_classes<0.5).sum())
print ('malignant=>', (all_classes>0.5).sum())
"""
Explanation: Datos de partida:
¿Cuántas clases hay? ¿Cuántos ejemplos de cada?
Hay dos clases en nuestro grupo de datos, muestras malignas y muestras benignas, que se representan el las imagenes de la celda 4.
Tenemos 6691 m... |
gtrichards/PHYS_T480 | NonlinearDimensionReduction.ipynb | mit | import numpy as np
from sklearn.manifold import LocallyLinearEmbedding
X = np.random.normal(size=(1000,2)) # 1000 points in 2D
R = np.random.random((2,10)) # projection matrix
X = np.dot(X,R) # now a 2D linear manifold in 10D space
k = 5 # Number of neighbors to use in fit
n = 2 # Number of dimensions to fit
lle = Loca... |
junhwanjang/DataSchool | Lecture/18. 문서 전처리/2) NLTK 자연어 처리 패키지 소개.ipynb | mit | nltk.download('averaged_perceptron_tagger')
nltk.download("gutenberg")
nltk.download('punkt')
nltk.download('reuters')
nltk.download("stopwords")
nltk.download("taggers")
nltk.download("webtext")
nltk.download("wordnet")
nltk.corpus.gutenberg.fileids()
emma_raw = nltk.corpus.gutenberg.raw("austen-emma.txt")
print(emm... |
IST256/learn-python | content/lessons/13-Visualization/WMC1-Matplotlib.ipynb | mit | # Jupyter Directive
%matplotlib inline
# imports
import matplotlib
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
matplotlib.rcParams['figure.figsize'] = (20.0, 10.0) # larger figure size
"""
Explanation: Watch Me Code 1: Matplotlib
We will demonstrate Pythons data visualization library Matp... |
balarsen/pymc_learning | Counting/Counting multi detector Dead TIme.ipynb | bsd-3-clause | %matplotlib inline
from pprint import pprint
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymc3 as mc
import spacepy.toolbox as tb
import spacepy.plot as spp
import tqdm
from scipy import stats
import seaborn as sns
sns.set()
matplotlib.pyplot.rc('figure', figsize=(1... |
prasants/pyds | .ipynb_checkpoints/01.Unix_and_Shell_Command_Basics-checkpoint.ipynb | mit | !pwd
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Overview" data-toc-modified-id="Overview-1"><span class="toc-item-num">1 </span>Overview</a></div><div class="lev2 toc-item"><a href="#pwd---Print-Working-Directory" data-toc-modified-id="pwd---Print-Working-Directory-11"><span ... |
M4573R/BerkeleyX-CS190.1x-Scalable-Machine-Learning | Week 1 - Course Overview and Machine Learning Basics/ML_lab1_review_student.ipynb | mit | labVersion = 'cs190_week1_v_1_2'
"""
Explanation: Math and Python review and CTR data download
This notebook reviews vector and matrix math, the NumPy Python package, and Python lambda expressions. It also covers downloading the data required for Lab 4, where you will analyze website click-through rates. Part 1 cove... |
waltervh/BornAgain-tutorial | talks/day_3/complex_samples_2_M/diffuse_scattering.ipynb | gpl-3.0 | # %load density_grad.py
import numpy as np
import bornagain as ba
from bornagain import deg, angstrom, nm
# define used SLDs
sld_D2O = 6.34e-06
sld_polymer = 4.0e-06
sld_Si = 2.07e-06
h = 100.0*nm # thickness of the non-uniform polymer layer
nslices = 100 # number of slices to slice the polymer layer
def get_sl... |
cjbrasher/LipidFinder | docs/LipidFinder2_manual.ipynb | mit | config_params.py -m peakfilter
"""
Explanation: Welcome to LipidFinder 2
LipidFinder is an open-source Python workflow designed to facilitate further targeted lipidomics analysis. LipidFinder categorises and removes noise and artifacts from liquid chromatography/mass spectrometry (LC/MS) datasets, searches the outcome... |
AtmaMani/pyChakras | udemy_ml_bootcamp/Python-for-Data-Visualization/Matplotlib/Advanced Matplotlib Concepts.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
x = np.linspace(0, 5, 11)
y = x**2
fig, axes = plt.subplots(1, 2, figsize=(10,4))
axes[0].plot(x, x**2, x, np.exp(x))
axes[0].set_title("Normal scale")
axes[1].plot(x, x**2, x, np.exp(x))
axes[1].set_yscale("log")
axes[1].set_title("Logarit... |
trolldbois/python-haystack | docs/Haystack_basic_usage.ipynb | gpl-3.0 | !cat ../test/src/test-ctypes3.c
"""
Explanation: Let's look at a basic usage example for Haystack.
First we have a program which allocates structures on the HEAP:
End of explanation
"""
!clang2py ../test/src/test-ctypes3.c #-o ../test/src/clang3_gen64.py
"""
Explanation: Then, using ctypeslib, we have generated pyt... |
keras-team/keras-io | examples/nlp/ipynb/lstm_seq2seq.ipynb | apache-2.0 | import numpy as np
import tensorflow as tf
from tensorflow import keras
"""
Explanation: Character-level recurrent sequence-to-sequence model
Author: fchollet<br>
Date created: 2017/09/29<br>
Last modified: 2020/04/26<br>
Description: Character-level recurrent sequence-to-sequence model.
Introduction
This example dem... |
Kaggle/learntools | notebooks/ml_intermediate/raw/tut2.ipynb | apache-2.0 | #$HIDE$
import pandas as pd
from sklearn.model_selection import train_test_split
# Load the data
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
# Select target
y = data.Price
# To keep things simple, we'll use only numerical predictors
melb_predictors = data.drop(['Price'], axis=1)
X = melb_... |
cwhite1026/Py2PAC | examples/Calculating_correlation_functions.ipynb | bsd-3-clause | import AngularCatalog_class as ac
import numpy.random as rand
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (10, 6)
"""
Explanation: Calculating correlation functions
This document walks through using Py2PAC to calculate correlation functions with or without error estimates. We... |
molgor/spystats | notebooks/Sandboxes/TensorFlow/BiospytialGaussianModels.ipynb | bsd-2-clause | run ../../../../traversals/tests.py
"""
Explanation: In this notebook I´ll create functions for easing the development of geostatistical models using the GPFlow (James H, et.al )the library for modelling gaussian processes in Tensor Flow (Google) (Great Library, btw).
Requirements
Inputs
Design Matrix X composed of c... |
googledatalab/notebooks | tutorials/BigQuery/BigQuery APIs.ipynb | apache-2.0 | import google.datalab.bigquery as bq
"""
Explanation: BigQuery APIs
Google Cloud Datalab provides an integrated environment for working with Google BigQuery for both ad-hoc, exploratory work as well as pipeline development. You've already seen the use of %%bq in the Hello BigQuery notebook, and various commands in the... |
zacwentzell/BIA-660-C-Spring2017 | In-class Lectures/March 09/Lecture_March_9.ipynb | mit | import matplotlib
import matplotlib.pyplot as plt
matplotlib.style.use('ggplot')
# Pandas has a ton of built-in visualizations
# Play and Learn
# http://pandas.pydata.org/pandas-docs/stable/visualization.html
df.plot.scatter(x='Start_Date', y='Price')
df['Price'].plot.box()
color = dict(boxes='DarkGreen', whiskers=... |
paolorivas/homeworkfoundations | classes and excersises/class9/.ipynb_checkpoints/09 - Functions-checkpoint.ipynb | mit | len
"""
Explanation: Class 9: Functions
A painful analogy
What do you do when you wake up in the morning?
I don't know about you, but I get ready.
"Obviously," you say, a little too snidely for my liking. You're particular, very detail-oriented, and need more information out of me.
Fine, then. Since you're going to be... |
keras-team/keras-io | examples/generative/ipynb/cyclegan.ipynb | apache-2.0 | import os
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import tensorflow_addons as tfa
import tensorflow_datasets as tfds
tfds.disable_progress_bar()
autotune = tf.data.AUTOTUNE
"""
Explanation: CycleGAN
Author: A_K_Nain... |
mbakker7/timml | notebooks/timml_xsection.ipynb | mit | from pylab import *
%matplotlib inline
from timml import *
"""
Explanation: TimML Notebook
Cross-sectional model
End of explanation
"""
ml = ModelMaq(kaq=[1, 2], z=[4, 3, 2, 1, 0], c=[1000, 1000], \
topboundary='semi', hstar=5)
ls = HeadLineSink1D(ml, xls=0, hls=2, layers=0)
ml.solve()
x = linspace(-2... |
SKA-ScienceDataProcessor/crocodile | examples/notebooks/predict.ipynb | apache-2.0 | %matplotlib inline
import sys
sys.path.append('../..')
from matplotlib import pylab
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
pylab.rcParams['figure.figsize'] = 12, 10
import functools
import numpy
import scipy
import scipy.special
from crocodile.clean import *
from crocodile.synthesi... |
adolfoguimaraes/machinelearning | SupervisedLearning/Tutorial03_ArvoreDeDecisao.ipynb | mit | from sklearn import tree
import pandas as pd
data = pd.read_csv('http://www.data2learning.com/machinelearning/datasets/exemplo_jogar.csv')
data
"""
Explanation: Aprendizado Supervisionado
Árvore de Decisão
Na primeira parte do curso apresentamos dois algoritmos clássicos de aprendizagem de máquian: o KNN e o algorit... |
mathinmse/mathinmse.github.io | Lecture-10A-Taylors-Series.ipynb | mit | %matplotlib inline
import numpy as np
import sympy as sp
import matplotlib.pyplot as plt
# You can change the default figure size to be a bit larger if you want,
# uncomment the next line for that:
plt.rc('figure', figsize=(10, 6))
"""
Explanation: In-Class Demonstration and Visualization
End of explanation
"""
def... |
opengeostat/pygslib | pygslib/Ipython_templates/.ipynb_checkpoints/nscore_ttable_raw-checkpoint.ipynb | mit | #general imports
import matplotlib.pyplot as plt
import pygslib
from matplotlib.patches import Ellipse
import numpy as np
import pandas as pd
#make the plots inline
%matplotlib inline
"""
Explanation: Testing the nscore transformation table
End of explanation
"""
#get the data in gslib format into a pandas ... |
csdms/coupling | docs/demos/cem_and_waves.ipynb | mit | %matplotlib inline
import numpy as np
"""
Explanation: <img src="../_static/pymt-logo-header-text.png">
Coastline Evolution Model + Waves
Link to this notebook: https://github.com/csdms/pymt/blob/master/docs/demos/cem_and_waves.ipynb
Install command: $ conda install notebook pymt_cem
This example explores how to use... |
HaoMood/cs231n | assignment1/assignment1sol/features/main.ipynb | gpl-3.0 | # Run some setup code
import numpy as np
import matplotlib.pyplot as plt
# This is a bit of magic to make matplotlib figures appear inline in the notebook
# rather than in a new window.
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'ne... |
peterdalle/mij | 1 Introduction to Python/Introduction.ipynb | gpl-3.0 | print("Hello World!")
# We can write comments like this.
print("Hello World!") # Or here. You can write anything you want.
# It's a good way of describing what your code does.
# Note: we can't use upper case. We will get a "NameError".
PRINT("Hello World!")
"""
Explanation: Introduction to Python and Jupyter Notebo... |
weleen/mxnet | example/notebooks/recommendation_systems/cdl/collaborative-dl.ipynb | apache-2.0 | import mxnet as mx
import numpy as np
import logging
import data
from math import sqrt
from autoencoder import AutoEncoderModel
import os
"""
Explanation: MXNet for Collaborative Deep Learning in Recommender Systems
In this tutorial, we build on MXNet to implement the Collaborative Deep Learning (CDL) [1] model for re... |
JoseGuzman/myIPythonNotebooks | MachineLearning/PCA_new.ipynb | gpl-2.0 | %pylab inline
%config InlineBackend.figure_format = 'retina'
from matplotlib import style
style.use('https://raw.githubusercontent.com/JoseGuzman/minibrain/master/minibrain/paper.mplstyle')
from scipy.stats import norm
"""
Explanation: <h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class... |
IS-ENES-Data/submission_forms | dkrz_forms/Templates/CORDEX_submission_form.ipynb | apache-2.0 | # Evaluate this cell to identifiy your form
from dkrz_forms import form_widgets, form_handler, checks
form_infos = form_widgets.show_selection()
# Evaluate this cell to generate your personal form instance
form_info = form_infos[form_widgets.FORMS.value]
sf = form_handler.init_form(form_info)
form = sf.sub.entity_o... |
spulido99/Programacion | Margarita/.ipynb_checkpoints/Taller 2 - Archivos y Bases de Datos-checkpoint.ipynb | mit | import pandas as pd
df = pd.read_csv('datoss.tsv', sep='\t')
df [1:1]
"""
Explanation: Archivos y Bases de datos
La idea de este taller es manipular archivos (leerlos, parsearlos y escribirlos) y hacer lo mismo con bases de datos estructuradas.
Ejercicio 1
Baje el archivo de "All associations with added ontology anno... |
pysal/pysal | notebooks/viz/splot/libpysal_non_planar_joins_viz.ipynb | bsd-3-clause | from pysal.lib.weights.contiguity import Queen
import pysal.lib
from pysal.lib import examples
import matplotlib.pyplot as plt
import geopandas as gpd
%matplotlib inline
from pysal.viz.splot.pysal.lib import plot_spatial_weights
"""
Explanation: splot.pysal.lib: assessing neighbors & spatial weights
In spatial analys... |
rasilab/ferrin_elife_2017 | scripts/run_simulations_based_on_experiment_fits.ipynb | gpl-3.0 | %%writefile simulation_run_3.py
#!/usr/bin/env python
#SBATCH --mem=8000
import subprocess as sp
import os
import sys
jobindex = int(sys.argv[1])
currentindex = -1
mrnafiles = filter(lambda x: x.startswith('yfp'), os.listdir('../annotations/simulations/run3/'))
mrnafiles = ['../annotations/simulations/run3/' + File ... |
mne-tools/mne-tools.github.io | 0.20/_downloads/90c71f0d36d740bc290fd9fa30bddd8c/plot_compute_covariance.ipynb | bsd-3-clause | import os.path as op
import mne
from mne.datasets import sample
"""
Explanation: Computing a covariance matrix
Many methods in MNE, including source estimation and some classification
algorithms, require covariance estimations from the recordings.
In this tutorial we cover the basics of sensor covariance computations... |
PyDataMadrid2016/Conference-Info | talks_materials/20160409_1815_Lightning_talks/02_poliastro/Going to Mars with Python in 5 minutes.ipynb | mit | %matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import astropy.units as u
from astropy import time
from poliastro import iod
from poliastro.plotting import plot
from poliastro.bodies import Sun, Earth
from poliastro.twobody import State
from poliastro im... |
antoniomezzacapo/qiskit-tutorial | qiskit/aqua/artificial_intelligence/qsvm_kernel_classification.ipynb | apache-2.0 | from qsvm_datasets import *
from qiskit_aqua.utils import split_dataset_to_data_and_labels, map_label_to_class_name
from qiskit_aqua.input import get_input_instance
from qiskit_aqua import run_algorithm
# setup aqua logging
import logging
from qiskit_aqua._logging import set_logging_config, build_logging_config
# set_... |
eaton-lab/eaton-lab.github.io | slides/ipyrad-analysis/ipyrad-analysis.ipynb | mit | # conda install -c ipyrad ipyrad structure clumpp bpp
# conda install -c eaton-lab toytree toyplot
# conda install -c bioconda raxml
"""
Explanation: The ipyrad.analysis tool kit
Deren Eaton
Install software
All required software for this walkthrough is available on conda.
End of explanation
"""
# ipcluster start... |
bwgref/nustar_pysolar | notebooks/20210114/Planning 20210114.ipynb | mit | fname = io.download_occultation_times(outdir='../data/')
print(fname)
"""
Explanation: Download the list of occultation periods from the MOC at Berkeley.
Note that the occultation periods typically only are stored at Berkeley for the future and not for the past. So this is only really useful for observation planning.
... |
bio-guoda/guoda-examples | 02_iDigBio_and_GBIF_Specimens_By_Continent_Over_Time.ipynb | mit | # col() selects columns from a data frame, year() works on dates, and udf() creates user
# defined functions
from pyspark.sql.functions import col, year, udf
# Plotting library and configuration to show graphs in the notebook
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Continuing Graphs of Spe... |
bengranett/minimask | demos/mosaic.ipynb | mit | %matplotlib notebook
import os
import numpy as np
import tempfile
import matplotlib.pyplot as pyplot
import logging
logging.basicConfig(level=logging.INFO)
import minimask.mask as mask
import minimask.healpix_projection as hp
import minimask.io.mosaic as mosaic
"""
Explanation: minimask mosaic example
Construct a m... |
zerothi/sisl | docs/visualization/viz_module/basic-tutorials/Demo.ipynb | mpl-2.0 | import sisl
# We define the root directory where our files are
siesta_files = sisl._environ.get_environ_variable("SISL_FILES_TESTS") / "sisl" / "io" / "siesta"
"""
Explanation: First steps
End of explanation
"""
import sisl.viz
from sisl.viz import Plot
"""
Explanation: Activating the viz framework
The first thing ... |
diegocavalca/Studies | deep-learnining-specialization/1. neural nets and deep learning/resources/Building your Deep Neural Network - Step by Step v3.ipynb | cc0-1.0 | import numpy as np
import h5py
import matplotlib.pyplot as plt
from testCases_v2 import *
from dnn_utils_v2 import sigmoid, sigmoid_backward, relu, relu_backward
%matplotlib inline
plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['imag... |
tensorflow/docs | site/en/r1/guide/keras.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/structured/solutions/4b_keras_dnn_babyweight.ipynb | apache-2.0 | import datetime
import os
import shutil
import matplotlib.pyplot as plt
import tensorflow as tf
print(tf.__version__)
"""
Explanation: LAB 4b: Create Keras DNN model.
Learning Objectives
Set CSV Columns, label column, and column defaults
Make dataset of features and label from CSV files
Create input layers for raw f... |
d-k-b/udacity-deep-learning | batch-norm/Batch_Normalization_Exercises.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 – Practice
Batch normalization is most useful when building deep neural networks. To demonstrate this, we'll create a con... |
noammor/coursera-machinelearning-python | ex7/ml-ex7-pca.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import scipy.io
import itertools
%matplotlib inline
"""
Explanation: # Exercise 7 | Principle Component Analysis
End of explanation
"""
ex7data1 = scipy.io.loadmat('ex7data1.mat')
X = ex7data1['X']
def plot_data(X, ax):
ax.plot(X[:,0], X[:,1], 'bo')
fig, ... |
vk3105/Data-Intensive-Programming-CSE-587 | Lab5/Lab5-Task1.ipynb | apache-2.0 | import pyspark
import string
import re
import time
import os
from pylab import *
from itertools import *
from pyspark.sql import SQLContext, Row
"""
Explanation: LAB 5 : TASK 1 - GENERATING THE 2-GRAM & 3-GRAM
Load the libraries
End of explanation
"""
regex = re.compile('[%s]' % re.escape(string.punctuation))
"""
E... |
Linusp/panic-notebook | ipynb/stdlib.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('ggplot')
"""
Explanation: Python 标准库
End of explanation
"""
import collections
"""
Explanation: collections
End of explanation
"""
d = {'id': 1, 'content': 'hello world'}
d['author']
"""
Explanation: collections 中实现了一些高性能、易用的容器类型
defaultdict 继承... |
benwaugh/NuffieldProject2016 | notebooks/SimpleMassHistogram.ipynb | mit | import pylab
import matplotlib.pyplot as plt
%matplotlib inline
pylab.rcParams['figure.figsize'] = 12,8
"""
Explanation: These commands give us access to some tools for plotting histograms and other graphs:
End of explanation
"""
data_file = open('Invariant_Masses.txt')
masses = [] # Create an ... |
eriksalt/jupyter | Python Quick Reference/Functions.ipynb | mit | def print_text():
print('this is text')
# call the function
print_text()
"""
Explanation: Python Function Quick Reference
Table of contents
<a href="#1.-Declaring-Functions">Declaring Functions</a>
<a href="#2.-Return-Values">Return Values</a>
<a href="#3.-Parameters">Parameters</a>
<a href="#4.-DocStrings">DocS... |
enbanuel/phys202-2015-work | assignments/assignment12/FittingModelsEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
"""
Explanation: Fitting Models Exercise 1
Imports
End of explanation
"""
a_true = 0.5
b_true = 2.0
c_true = -4.0
"""
Explanation: Fitting a quadratic curve
For this problem we are going to work with the following mod... |
marcino239/notebooks | nlp_bag_of_words.ipynb | gpl-2.0 | %matplotlib inline
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from bs4 import BeautifulSoup
from matplotlib import pyplot as plt
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.feature_selection import SelectKBest, chi2
from sklearn.metrics ... |
mrGeen/metaseq | doc/source/example_session_2.ipynb | mit | # Enable in-line plots for this IPython Notebook
%matplotlib inline
"""
Explanation: Example 2: Differential expression scatterplots
This example looks more closely at using the results table part of :mod:metaseq, and highlights the flexibility in plotting afforded by :mod:metaseq.
End of explanation
"""
import meta... |
jshudzina/keras-tutorial | notebooks/02-Yellowstone-visitors-part2.ipynb | apache-2.0 | from pandas import DataFrame
from pandas import Series
from pandas import concat
from pandas import read_csv
from pandas import datetime
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense
from keras.layers impo... |
gasabr/AtoD | examples/hero_example.ipynb | mit | am = Hero(1)
# You can use attributes to get some hero properties which depends on lvl, examples:
print('Anti-Mage stats on lvl {}'.format(am.lvl))
print('\tstrength = {}'.format(am.str))
print('\tagility = {}'.format(am.agi))
print('\tintellect = {}'.format(am.int))
print('\t...')
"""
Explanation: Hero creation... |
ernestyalumni/Propulsion | Physique/Physique_jupyter.ipynb | gpl-2.0 | import os, sys
"""
Explanation: Physique
Mini Table of Contents
Using Physique from a working directory not containing Physique itself
NIST Fundamental Constants
NIST Official Conversions (to metric)
Webscraping example: JPL Solar System Dynamics (JPL SSD) - Planets and Pluto
Using Physique from a working directory... |
probprog/pyprob | examples/gaussian_unknown_mean_marsaglia.ipynb | bsd-2-clause | class GaussianUnknownMean(Model):
def __init__(self):
super().__init__(name='Gaussian with unknown mean (Marsaglia)') # give the model a name
self.prior_mean = 1
self.prior_std = math.sqrt(5)
self.likelihood_std = math.sqrt(2)
def marsaglia(self, mean, stddev):
u... |
agconti/kaggle-titanic | Titanic.ipynb | apache-2.0 | import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
from statsmodels.nonparametric.kde import KDEUnivariate
from statsmodels.nonparametric import smoothers_lowess
from pandas import Series, DataFrame
from patsy import dmatrices
from sklearn import datas... |
GoogleCloudPlatform/vertex-ai-samples | community-content/tf_keras_image_classification_distributed_multi_worker_with_vertex_sdk/multi_worker_vertex_training_on_gpu_with_custom_container.ipynb | apache-2.0 | PROJECT_ID = "YOUR PROJECT ID"
BUCKET_NAME = "gs://YOUR BUCKET NAME"
REGION = "YOUR REGION"
SERVICE_ACCOUNT = "YOUR SERVICE ACCOUNT"
content_name = "tf-keras-img-cls-dist-multi-worker-gpu-cust-cont"
"""
Explanation: TF-Keras Image Classification Distributed Multi-Worker Training on GPU using Vertex Training with Cust... |
AstroHackWeek/AstroHackWeek2017 | day1/notebooks/demo-SDSS.ipynb | mit | from os import path
from astropy.table import Table
import h5py
import matplotlib.pyplot as plt
plt.style.use('notebook.mplstyle')
%matplotlib inline
import numpy as np
data_path = '../data/'
"""
Explanation: SDSS/BOSS spectroscopic data
The SDSS dataset contains 100,000 spectra and catalog data for the same sources... |
msampathkumar/data_science_sessions | Session-1-Introduction_to_Data_Sciences/Data Science Presentation.ipynb | mit | %%sh
# ls -l ~/Downloads/G20*csv
# mv ~/Downloads/G20*csv G20.csv
"""
Explanation: INDIA, G-20 AND THE WORLD - Statistical Year Book India 2016
Navigation Path: Home > Statistical Year Book India 2016 > INDIA, G-20 AND THE WORLD
The G20 (or G-20 or Group of Twenty) is an international forum for the governments and... |
stallmanifold/cs229-machine-learning-stanford-fall-2016 | src/homework2/q3/homework2_3.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import read_matrix as rm
import nb_train
import nb_test
import svm_train
import svm_test
"""
Explanation: Homework 2 Problem 3
End of explanation
"""
df_train = rm.read_data('spam_data/MATRIX.TRAIN')
nb_model = nb_train.train(df_train)
"""
Explanation: Part 3.a
The first mach... |
ramseylab/networkscompbio | class18_motifs_python3_template.ipynb | apache-2.0 | import igraph
import random
import collections
"""
Explanation: Example implementation of the ESU subgraph enumeration algorithm in python
Load the packages that we will need
End of explanation
"""
def exclusive_neighborhood(graph, v, Vp):
assert type(graph)==igraph.Graph
assert type(v)==int
assert type(... |
keras-team/keras-io | examples/nlp/ipynb/text_classification_from_scratch.ipynb | apache-2.0 | import tensorflow as tf
import numpy as np
"""
Explanation: Text classification from scratch
Authors: Mark Omernick, Francois Chollet<br>
Date created: 2019/11/06<br>
Last modified: 2020/05/17<br>
Description: Text sentiment classification starting from raw text files.
Introduction
This example shows how to do text cl... |
mtpain/metacorps | notebooks/Results Plots Walkthrough.ipynb | bsd-3-clause | metaphors_url = 'http://metacorps.io/static/viomet-snapshot-project-df.csv'
project_df = get_project_data_frame(metaphors_url)
print(project_df.columns)
"""
Explanation: Publically-available .csv for reproducibility
I generate two files currently named viomet-snapshot-project-df.csv and viomet-2012-snapshot-project-d... |
openstreams/wflow | notebooks/wflow-test-sbm.ipynb | gpl-3.0 | %pylab inline
# First import the model. Here we use the HBV version
from wflow.wflow_sbm import *
import IPython
from IPython.display import display, clear_output
#clear_output = IPython.core.display.clear_output
"""
Explanation: Use of the wflow OpenStreams framework API
This ipython notebook demonstrates how to lo... |
hubenjm/scrapediscogs | discogs_client_example.ipynb | gpl-3.0 | import discogs_client
token = open('./token.txt').readline().strip()
print token
d = discogs_client.Client('MusicDBCreater/0.1', user_token=token)
"""
Explanation: discogs_client brief overview:
End of explanation
"""
results = d.search('Bob Dylan - Time Out of Mind', type = 'release')
type(results)
len(results... |
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