repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
vascotenner/holoviews | doc/Tutorials/Bokeh_Backend.ipynb | bsd-3-clause | import numpy as np
import pandas as pd
import holoviews as hv
hv.notebook_extension('bokeh')
"""
Explanation: <div class="alert alert-info" role="alert">
This tutorial contains a lot of bokeh plots, which may take a little while to load and render.
</div>
One of the major design principles of HoloViews is that t... |
SylvainCorlay/bqplot | examples/Tutorials/Linking Plots With Widgets.ipynb | apache-2.0 | import numpy as np
import ipywidgets as widgets
import bqplot.pyplot as plt
"""
Explanation: Building interactive plots using bqplot and ipywidgets
bqplot is built on top of the ipywidgets framework
ipwidgets and bqplot widgets can be seamlessly integrated to build interactive plots
bqplot figure widgets can be stac... |
spm2164/foundations-homework | 14/14 - TF-IDF Homework.ipynb | artistic-2.0 | # If you'd like to download it through the command line...
!curl -O http://www.cs.cornell.edu/home/llee/data/convote/convote_v1.1.tar.gz
# And then extract it through the command line...
!tar -zxf convote_v1.1.tar.gz
"""
Explanation: Homework 14 (or so): TF-IDF text analysis and clustering
Hooray, we kind of figured ... |
mayank-johri/LearnSeleniumUsingPython | Section 2 - Advance Python/Chapter S2.05 - REST API - Server & Clients/Web%20scraping%20with%20Python.ipynb | gpl-3.0 | import urllib2
response = urllib2.urlopen("http://example.com")
print response.read()
"""
Explanation: Web scraping with Python
This is an introduction to web scraping using Python, where our task is to extract information from web pages.
Prerequisites (knowledge):
basic Python (its data structures, string manipulati... |
giacomov/3ML | examples/Joint fitting XRT and GBM data with XSPEC models.ipynb | bsd-3-clause | %matplotlib inline
%matplotlib notebook
from threeML import *
import os
"""
Explanation: Joint fitting XRT and GBM data with XSPEC models
Goals
3ML is designed to properly joint fit data from different instruments with thier instrument dependent likelihoods.
We demostrate this with joint fitting data from GBM and XRT ... |
drphilmarshall/OM10 | notebooks/tutorial.ipynb | mit | from __future__ import division, print_function
import os, numpy as np
import matplotlib
matplotlib.use('TkAgg')
%matplotlib inline
import om10
%load_ext autoreload
%autoreload 2
"""
Explanation: OM10 Tutorial
In this notebook we demonstrate the basic functionality of the om10 package, including how to:
Make some ... |
terrydolan/lfctransfers | lfctransfers.ipynb | mit | import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import sys
import collections
from datetime import datetime
from __future__ import division
# enable inline plotting
%matplotlib inline
"""
Explanation: LFC Data Analysis: The Transfers
See Terry's blog Inspiring Transfers... |
CUFCTL/DLBD | Fall2017/Module1.ipynb | mit | import sys, os
import pickle
import torch
import torch.utils.data as data
import glob
from PIL import Image
import numpy as np
def unpickle(fname):
with open(fname, 'rb') as f:
Dict = pickle.load(f, encoding='bytes')
return Dict
def load_data(batch):
print ("Loading batch:{}".format(batch))
... |
Saytiras/StalkerML | Calculate Opinion with Base.ipynb | gpl-2.0 | # import logging
# logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s')
# logging.root.level = logging.INFO
from os import path
from random import shuffle
from corputil import FileCorpus, ListCorpus
from corputil.utils import load_stopwords
from gensim.models.word2vec import LineSentence, Word2Vec
... |
robertoalotufo/ia898 | master/tutorial_numpy_1_10.ipynb | mit | import numpy as np
a = np.array([11,1,2,3,4,5,12,-3,-4,7,4])
print('a = ',a)
print('np.clip(a,0,10) = ', np.clip(a,0,10))
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Clip" data-toc-modified-id="Clip-1"><span class="toc-item-num">1 </span>Clip</a></div><div class="lev2 toc-ite... |
mne-tools/mne-tools.github.io | 0.20/_downloads/3927e2933ae8d1b19effcbd5c5341bd0/plot_20_visualize_evoked.ipynb | bsd-3-clause | import os
import numpy as np
import mne
"""
Explanation: Visualizing Evoked data
This tutorial shows the different visualization methods for
:class:~mne.Evoked objects.
:depth: 2
As usual we'll start by importing the modules we need:
End of explanation
"""
sample_data_folder = mne.datasets.sample.data_path()
samp... |
pysal/pysal | notebooks/explore/pointpats/pointpattern.ipynb | bsd-3-clause | import pysal.lib as ps
import numpy as np
from pysal.explore.pointpats import PointPattern
"""
Explanation: Planar Point Patterns in PySAL
Author: Serge Rey sjsrey@gmail.com and Wei Kang weikang900... |
reetawwsum/Jupyter-Notebooks | Data analysis in Python with pandas.ipynb | mit | import pandas as pd
"""
Explanation: Data analysis in Python with pandas
What is pandas?
pandas: Open source library in Python for data analysis, data manipulation, and data visualisation.
Pros:
1. Tons of functionality
2. Well supported by community
3. Active development
4. Lot of documentation
5. Plays well with oth... |
tyamamot/h29iro | codes/4_Link_Analysis.ipynb | mit | import numpy as np
import numpy.linalg as lg
import networkx as nx
import matplotlib.pyplot as plt
%matplotlib inline
%precision 2
"""
Explanation: 第4回 リンク解析
この演習では，PageRankアルゴリズムの実装例を通して、アルゴリズムの理解を深めることおよび，既存のライブラリを用いてグラフの描画およびPageRankの計算ができることを目的とします．
この演習では以下のライブラリを使用します．
- NetworkX
- グラフの生成，分析，描画などグラフに対する各種操作の... |
stephank16/enes_graph_use_case | .ipynb_checkpoints/ENES1-checkpoint.ipynb | gpl-3.0 | import ENESNeoTools
from py2neo import Graph, Node, Relationship, authenticate
authenticate("localhost:7474", ENESNeoTools.user_name, ENESNeoTools.pass_word)
# connect to authenticated graph database
graph = Graph("http://localhost:7474/db/data/")
"""
Explanation: ENES use case graph example
A graph is generated re... |
GoogleCloudPlatform/mlops-on-gcp | immersion/kubeflow_pipelines/walkthrough/labs/lab-01.ipynb | apache-2.0 | import json
import os
import numpy as np
import pandas as pd
import pickle
import uuid
import time
import tempfile
from googleapiclient import discovery
from googleapiclient import errors
from google.cloud import bigquery
from jinja2 import Template
from kfp.components import func_to_container_op
from typing import N... |
shareactorIO/pipeline | source.ml/jupyterhub.ml/notebooks/spark/Deploy_SparkML_Census_DecisionTree.ipynb | apache-2.0 | # You may need to Reconnect (more than Restart) the Kernel to pick up changes to these sett
import os
master = '--master spark://127.0.0.1:47077'
conf = '--conf spark.cores.max=1 --conf spark.executor.memory=512m'
packages = '--packages com.amazonaws:aws-java-sdk:1.7.4,org.apache.hadoop:hadoop-aws:2.7.1'
jars = '--jar... |
chris1610/pbpython | notebooks/Combining-Multiple-Excel-File-with-Pandas.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
"""
Explanation: Introduction
One of the most common tasks for pandas and python is to automate the process to aggregate data from multiple spreadsheets and files.
This article will walk through the basic flow required to parse multiple excel files, combine some data, clean it up... |
JannesKlaas/MLiFC | Week 4/Ch. 17 - NLP and Word Embeddings.ipynb | mit | import os
imdb_dir = './aclImdb' # Data directory
train_dir = os.path.join(imdb_dir, 'train') # Get the path of the train set
# Setup empty lists to fill
labels = []
texts = []
# First go through the negatives, then through the positives
for label_type in ['neg', 'pos']:
# Get the sub path
dir_name = os.path... |
ozorich/phys202-2015-work | assignments/assignment07/AlgorithmsEx02.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns
import numpy as np
"""
Explanation: Algorithms Exercise 2
Imports
End of explanation
"""
a=[1,2,3,4,5,3]
for x in a:
print(x)
def find_peaks(a):
"""Find the indices of the local maxima in a sequence."""
a=list(a)
index=[]... |
tomlyscan/Ordenacao | Notas Ordenacao.ipynb | gpl-3.0 | # Encontrando o maximo e minimo valor em uma lista:
a = [1, -2, 2, 0, 3, 4, 5, 10, -3, -1]
print('Maior valor da lista: ', max(a))
print('Menor valor da lista: ', min(a))
# Criar uma lista de tamanho fixo inicializado com 0:
contador = max(a) + abs(min(a)) + 1
pos_zero = abs(min(a))
lista_contador = [0]*contador
prin... |
aldian/tensorflow | tensorflow/lite/examples/experimental_new_converter/keras_lstm.ipynb | apache-2.0 | !pip install tf-nightly --upgrade
"""
Explanation: Overview
This CodeLab demonstrates how to build a LSTM model for MNIST recognition using Keras, and how to convert it to TensorFlow Lite.
The CodeLab is very similar to the tf.lite.experimental.nn.TFLiteLSTMCell
CodeLab. However, with the control flow support in the e... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session07/Day0/TooBriefVizSolutions.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Introduction to Visualization:
Density Estimation and Data Exploration
Version 0.1
There are many flavors of data analysis that fall under the "visualization" umbrella in astronomy. Today, by way of example, we will focus on 2 basic... |
jvines/Metodos-Numericos | Catedras/Catedra_04.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Catedra 04
End of explanation
"""
def bisection(f, a, b, eps=1e-5):
'''
Bisection busca la raiz de la funcion f a traves del metodo de la biseccion.
Parameters
----------
f : function
Funcion a ev... |
krosaen/ml-study | python-ml-book/ch12/ch12.ipynb | mit | import os
import struct
import numpy as np
def load_mnist(path, kind='train'):
"""Load MNIST data from `path`"""
labels_path = os.path.join(path,
'%s-labels-idx1-ubyte' % kind)
images_path = os.path.join(path,
'%s-images-idx3-ubyte' % kind)
... |
jansoe/FUImaging | examples/Chaining/CompareMFInitialisation.ipynb | mit | import sys
import os
import pickle
import matplotlib.pyplot as plt
import numpy as np
from collections import defaultdict
from scipy.spatial.distance import pdist
from scipy.stats import gaussian_kde
pythonpath_for_regnmf = os.path.realpath(os.path.join(os.path.pardir, os.path.pardir))
sys.path.append(pythonpath_for_r... |
raybuhr/pyfolio | pyfolio/examples/bayesian.ipynb | apache-2.0 | %matplotlib inline
import pyfolio as pf
"""
Explanation: Bayesian performance analysis example in pyfolio
There are also a few more advanced (and still experimental) analysis methods in pyfolio based on Bayesian statistics.
The main benefit of these methods is uncertainty quantification. All the values you saw above,... |
ES-DOC/esdoc-jupyterhub | notebooks/awi/cmip6/models/sandbox-1/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'awi', 'sandbox-1', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: AWI
Source ID: SANDBOX-1
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy Balance... |
CSB-book/CSB | scientific/solutions/Lord_of_the_flies_solution.ipynb | gpl-3.0 | from Bio import Entrez
import re
"""
Explanation: Solution of 6.6.1, Lord of the Fruit Flies
Identify the number of papers in PubMed that has Drosophila virilis in the title or abstract
End of explanation
"""
# Always tell NCBI who you are (edit the e-mail below!)
Entrez.email = "your_name@yourmailhost.com"
handle =... |
cancilla/streamsx.health | samples/HealthcareJupyterDemo/notebooks/experimental/HealthcareDemo-AnalyticsService.ipynb | apache-2.0 | !pip install --user --upgrade streamsx
!pip install --user --upgrade "git+https://github.com/IBMStreams/streamsx.health.git#egg=healthdemo&subdirectory=samples/HealthcareJupyterDemo/package"
"""
Explanation: Healthcare Python Streaming Application Demo
This application demonstrates how users can develop Python Streami... |
htwangtw/Patterns-of-Thought | notebooks/2.0-FC-vs-NYCQ-nestedKFold-Yeo7nodes.ipynb | mit | import copy
import os, sys
import numpy as np
import pandas as pd
import joblib
os.chdir('../')
# loa my modules
from src.utils import load_pkl
from src.file_io import save_output
from src.models import nested_kfold_cv_scca, clean_confound, permutate_scca
from src.visualise import set_text_size, show_results, write_... |
monaen/CellClassification | shape/analysis_shape_classification.ipynb | mit | import numpy as np
import os, sys
import matplotlib.pyplot as plt
from pylab import *
import glob
import collections
import random
import math
from PIL import Image, ImageDraw
%matplotlib inline
caffe_root = '../../../'
import caffe
from caffe import layers as L, params as P
## define workspace paramsworkspace
works... |
kwant-project/kwant-tutorial-2016 | 3.4.graphene_qshe.ipynb | bsd-2-clause | # We'll have 3D plotting and 2D band structure, so we need a handful of helper functions.
%run matplotlib_setup.ipy
from types import SimpleNamespace
from ipywidgets import interact
import matplotlib
from matplotlib import pyplot
from mpl_toolkits import mplot3d
import numpy as np
import kwant
from wraparound impor... |
tpin3694/tpin3694.github.io | python/pandas_lowercase_column_names.ipynb | mit | # Import modules
import pandas as pd
# Set ipython's max row display
pd.set_option('display.max_row', 1000)
# Set iPython's max column width to 50
pd.set_option('display.max_columns', 50)
"""
Explanation: Title: Lower Case Column Names In Pandas Dataframe
Slug: pandas_lowercase_column_names
Summary: Lower Case Colum... |
csiu/100daysofcode | misc/day44_querying_database.ipynb | mit | dbname="kick"
tblname="info"
engine = create_engine(
'postgresql://localhost:5432/{dbname}'.format(dbname=dbname))
# Connect to database
conn = psycopg2.connect(dbname=dbname)
cur = conn.cursor()
"""
Explanation: Questions to answer
What kind of projects are popular on Kickstarter?
How much are people askin... |
plopd/music-mining-massive-datasets | Duplicate Detection with LSH Cosine Similarity.ipynb | mit | data_path = os.path.join('MillionSongSubset', 'AdditionalFiles', 'subset_msd_summary_file.h5')
features = ['duration', 'end_of_fade_in','key', 'loudness', 'mode', 'start_of_fade_out', 'tempo', 'time_signature']
verbose = False
"""
Explanation: Reading the data
data has to be a .h5 data file.
data_path should contain ... |
nicolas998/wmf | Examples/Calibracion_Barbosa_NSGAII.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import pylab as pl
from wmf import wmf
import pandas as pnd
# Herramientas para DEAP
from deap import base, creator
import random
from deap import tools
"""
Explanation: Calibracion BARBOSA NSGAII
Este es un ensayo de como se puede implementar el algortimo NSGAII para la cali... |
junghao/fdsn | examples/GeoNet_FDSN_demo_station.ipynb | mit | from obspy import UTCDateTime
from obspy.clients.fdsn import Client as FDSN_Client
from obspy import read_inventory
"""
Explanation: GeoNet FDSN webservice with Obspy demo - Station Service
This demo introduces some simple code that requests data using GeoNet's FDSN webservices and the obspy module in python. This not... |
ledeprogram/algorithms | class7/donow/Kandrach_Sasha_7_donow.ipynb | gpl-3.0 | import pandas as pd
%matplotlib inline
import numpy as np
from sklearn.linear_model import LogisticRegression
"""
Explanation: Apply logistic regression to categorize whether a county had high mortality rate due to contamination
1. Import the necessary packages to read in the data, plot, and create a logistic regressi... |
bollwyvl/ipymd | examples/ex2.notebook.ipynb | bsd-3-clause | # some code in python
def f(x):
y = x * x
return y
"""
Explanation: Test notebook
This is a text notebook. Here are some rich text, code, $\pi\simeq 3.1415$ equations.
Another equation:
$$\sum_{i=1}^n x_i$$
Python code:
End of explanation
"""
import IPython
print("Hello world!")
2*2
def decorator(f):
r... |
vadim-ivlev/STUDY | coding/.ipynb_checkpoints/hacker rank-checkpoint.ipynb | mit | # Это единственный комментарий который имеет смысл
# I s
def find_index(m,a):
try:
return a.index(m)
except :
return -1
def find_two_sum(a, s):
'''
>>> (3, 5) == find_two_sum([1, 3, 5, 7, 9], 12)
True
'''
if len(a)<2:
return (-1,-1)
idx = dict( (v,i) f... |
jinntrance/MOOC | coursera/deep-neural-network/quiz and assignments/week 5/Gradient+Checking+v1.ipynb | cc0-1.0 | # Packages
import numpy as np
from testCases import *
from gc_utils import sigmoid, relu, dictionary_to_vector, vector_to_dictionary, gradients_to_vector
"""
Explanation: Gradient Checking
Welcome to the final assignment for this week! In this assignment you will learn to implement and use gradient checking.
You are ... |
james-prior/cohpy | 20160523-cohpy-speed-of-searching-sets-and-lists-simplified.ipynb | mit | def make_list(n):
if True:
return list(range(n))
else:
return list(str(i) for i in range(n))
n = int(25e6)
# n = 5
m = (0, n // 2, n-1, n)
a_list = make_list(n)
a_set = set(a_list)
n, m
# Finding something that is in a set is fast.
# The key one is looking for has little effect on the speed.
... |
mne-tools/mne-tools.github.io | 0.20/_downloads/d5764d6befb13ad52368247a508e45f6/plot_3d_to_2d.ipynb | bsd-3-clause | # Authors: Christopher Holdgraf <choldgraf@berkeley.edu>
#
# License: BSD (3-clause)
from scipy.io import loadmat
import numpy as np
from matplotlib import pyplot as plt
from os import path as op
import mne
from mne.viz import ClickableImage # noqa
from mne.viz import (plot_alignment, snapshot_brain_montage,
... |
privong/pythonclub | sessions/06-mcmc/MCMC with emcee.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import emcee
import corner
"""
Explanation: MCMC Demonstration
Markov Chain Monte Carlo is a useful technique for fitting models to data and obtaining estimates for the uncertainties of the model parameters.
There are a slew of python modules and in... |
mattgiguere/EPRV | code/make_missings.ipynb | mit | import re
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import matplotlib
#%matplotlib inline
"""
Explanation: manipulate_regonline_output
This notebook reads the RegOnline output into a pandas DataFrame and reworks it to have each row contain the attendee, th... |
dchandan/rebound | ipython_examples/OrbitPlot.ipynb | gpl-3.0 | import rebound
sim = rebound.Simulation()
sim.add(m=1)
sim.add(m=0.1, e=0.041, a=0.4, inc=0.2, f=0.43, Omega=0.82, omega=2.98)
sim.add(m=1e-3, e=0.24, a=1.0, pomega=2.14)
sim.add(m=1e-3, e=0.24, a=1.5, omega=1.14, l=2.1)
sim.add(a=-2.7, e=1.4, f=-1.5,omega=-0.7) # hyperbolic orbit
"""
Explanation: Orbit Plot
REBOUND c... |
phoebe-project/phoebe2-docs | development/tutorials/pblum.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.4,<2.5"
import phoebe
from phoebe import u # units
import numpy as np
logger = phoebe.logger()
b = phoebe.default_binary()
"""
Explanation: Passband Luminosity
Setup
Let's first make sure we have the latest version of PHOEBE 2.4 installed (uncomment this line if running in an online note... |
EnergyID/opengrid | scripts/TimeSeries.ipynb | gpl-2.0 | import os, sys
import inspect
import numpy as np
import datetime as dt
import time
import pytz
import pandas as pd
import pdb
script_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
# add the path to opengrid to sys.path
sys.path.append(os.path.join(script_dir, os.pardir, os.pardir))
f... |
pombredanne/https-gitlab.lrde.epita.fr-vcsn-vcsn | doc/notebooks/automaton.infiltration.ipynb | gpl-3.0 | import vcsn
c = vcsn.context('lal_char, seriesset<lal_char, z>')
std = lambda exp: c.expression(exp).standard()
c
"""
Explanation: automaton.infiltration
Create the (accessible part of the) infiltration product of two automata. In a way the infiltration product combines the conjunction (synchronized) and the shuffle ... |
vlad17/vlad17.github.io | assets/2020-11-01-lbfgs-vs-gd.ipynb | apache-2.0 | from numpy_ringbuffer import RingBuffer
import numpy as np
from scipy.stats import special_ortho_group
from scipy import linalg as sla
%matplotlib inline
from matplotlib import pyplot as plt
from scipy.optimize import line_search
class LBFGS:
def __init__(self, m, d, x0, g0):
self.s = RingBuffer(capacity=m... |
ldiary/marigoso | notebooks/an_example_of_using_jupyter_for_documenting_and_automating_bdd_style_tests.ipynb | mit | from marigoso import Test
browser = Test().launch_browser("Firefox")
browser.get_url("https://www.blogger.com/")
header = browser.get_element("tag=h2")
assert header.text == "Sign in to continue to Blogger"
"""
Explanation: An example of using Jupyter for Documenting and Automating BDD Style Tests
This is a sample doc... |
CalPolyPat/phys202-2015-work | days/day12/Integration.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
"""
Explanation: Numerical Integration
Learning Objectives: Learn how to numerically integrate 1d and 2d functions that are represented as Python functions or numerical arrays of data using scipy.integrate.
This lesson was orgi... |
Cairo4/pythonkurs | 02 jupyter notebook, python/02 Jupyter Notebook & Python Intro.ipynb | mit | #Mit einem Hashtag vor einer Zeile können wir Code kommentieren, auch das ist sehr wichtig.
#Immer, wirklich, immer den eigenen Code zu kommentieren. Vor allem am Anfang.
print('hello world')
#Der Printbefehl druckt einfach alles aus. Nicht wirklich wahnsinnig toll.
#Doch er ist später sehr nützlich. Vorallem wenn ... |
EvanBianco/striplog | tutorial/Basic_objects.ipynb | apache-2.0 | import striplog
striplog.__version__
"""
Explanation: Basic objects
A striplog depends on a hierarchy of objects. This notebook shows the objects and their basic functionality.
Lexicon: A dictionary containing the words and word categories to use for rock descriptions.
Component: A set of attributes.
Interval: One e... |
maviator/Kaggle_home_price_prediction | Script/SKlearn models.ipynb | mit | # Adding needed libraries and reading data
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import ElasticNet, Lasso, BayesianRidge, LassoLarsIC
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
from sklearn.kernel_rid... |
amozie/amozie | testzie/keras_logistic_regression.ipynb | apache-2.0 | from keras.layers import *
from keras.models import *
from keras.optimizers import *
from keras.callbacks import *
import keras
from keras import backend as K
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import itertools
%matplotlib inline
"""
Explanation: <h1>Table of Contents<span class="t... |
jcmgray/xyzpy | docs/examples/visualize matrix.ipynb | mit | import xyzpy as xyz
import numpy as np
import scipy.linalg as sla
"""
Explanation: Visualizing Linear Algebra Decompositions
In this notebook we just demonstrate the utility function xyzpy.visualize_matrix on
various linear algebra decompositions taken from scipy. This function plots matrices
with the values of numbe... |
miykael/nipype_tutorial | notebooks/introduction_quickstart.ipynb | bsd-3-clause | import os
from os.path import abspath
from nipype import Workflow, Node, MapNode, Function
from nipype.interfaces.fsl import BET, IsotropicSmooth, ApplyMask
from nilearn.plotting import plot_anat
%matplotlib inline
import matplotlib.pyplot as plt
"""
Explanation: Nipype Quickstart
Existing documentation
Visuali... |
gfeiden/MagneticUpperSco | notes/convective_structure.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import interp1d
"""
Explanation: Radiative Cores & Convective Envelopes
Analysis of how magnetic fields influence the extent of radiative cores and convective envelopes in young, pre-main-sequence stars.
Begin with some prelim... |
AaronCWong/phys202-2015-work | assignments/assignment05/MatplotlibEx03.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Matplotlib Exercise 3
Imports
End of explanation
"""
def well2d(x, y, nx, ny, L=1.0):
firstsine = (nx*np.pi*x)/L
secondsine = ((ny*np.pi*y)/L)
psi = np.array(2/L*((np.sin(firstsine)*(np.sin(secondsine)))))
return p... |
MLIME/12aMostra | src/Tensorflow Tutorial.ipynb | gpl-3.0 | import numpy as np
import tensorflow as tf
import pandas as pd
import util
%matplotlib inline
"""
Explanation: Tutorial em Tensorflow: Regressão Linear
Nesse tutorial vamos montar um modelo de regressão linear usando a biblioteca Tensorflow.
End of explanation
"""
# Podemos olhar o começo dessa tabela
df = pd.read_e... |
mne-tools/mne-tools.github.io | 0.23/_downloads/da9f4c4e77e7268fbe1384cfc1b249a5/70_eeg_mri_coords.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD Style.
import os.path as op
import nibabel
from nilearn.plotting import plot_glass_brain
import numpy as np
import mne
from mne.channels import compute_native_head_t, read_custom_montage
from mne.viz import plot_alignment
"""
Explanation: EEG source ... |
blackjax-devs/blackjax | examples/TemperedSMC.ipynb | apache-2.0 | import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
from jax.scipy.stats import multivariate_normal
jax.config.update("jax_platform_name", "cpu")
import blackjax
import blackjax.smc.resampling as resampling
"""
Explanation: Use Tempered SMC to improve exploration of MCMC methods.
Mu... |
GlobalFishingWatch/vessel-scoring | notebooks/Model-Sensitivity-to-Seed.ipynb | apache-2.0 | %matplotlib inline
from vessel_scoring import data, utils
from vessel_scoring.models import train_model_on_data
from vessel_scoring.evaluate_model import evaluate_model, compare_models
from IPython.core.display import display, HTML, Markdown
import numpy as np
import sys
from sklearn import metrics
from vessel_scoring... |
cloudera/ibis | docs/source/user_guide/geospatial_analysis.ipynb | apache-2.0 | # Launch the postgis container.
# This may take a bit of time if it needs to download the image.
!docker run -d -p 5432:5432 --name postgis-db -e POSTGRES_PASSWORD=supersecret mdillon/postgis:9.6-alpine
"""
Explanation: Ibis and Geospatial Operations
One of the most popular extensions to PostgreSQL is PostGIS,
which a... |
mercybenzaquen/foundations-homework | databases_hw/db04/Homework_4-graded.ipynb | mit | numbers_str = '496,258,332,550,506,699,7,985,171,581,436,804,736,528,65,855,68,279,721,120'
"""
Explanation: Graded = 10/11
Homework #4
These problem sets focus on list comprehensions, string operations and regular expressions.
Problem set #1: List slices and list comprehensions
Let's start with some data. The followi... |
root-mirror/training | NCPSchool2021/RDataFrame/04-rdataframe-advanced.ipynb | gpl-2.0 | import numpy
import ROOT
np_dict = {colname: numpy.random.rand(100) for colname in ["a","b","c"]}
df = ROOT.RDF.MakeNumpyDataFrame(np_dict)
print(f"Columns in the RDataFrame: {df.GetColumnNames()}")
co = df.Count()
m_a = df.Mean("a")
fil1 = df.Filter("c < 0.7")
def1 = fil1.Define("d", "a+b+c")
h = def1.Histo1D("d"... |
rsignell-usgs/notebook | NEXRAD/THREDDS_NEXRAD.ipynb | mit | import matplotlib
import warnings
warnings.filterwarnings("ignore", category=matplotlib.cbook.MatplotlibDeprecationWarning)
%matplotlib inline
"""
Explanation: Using Python to Access NEXRAD Level 2 Data from Unidata THREDDS Server
This is a modified version of Ryan May's notebook here:
http://nbviewer.jupyter.org/gist... |
bhargavvader/pycobra | docs/notebooks/voronoi_clustering.ipynb | mit | %matplotlib inline
import numpy as np
from pycobra.cobra import Cobra
from pycobra.visualisation import Visualisation
from pycobra.diagnostics import Diagnostics
import matplotlib.pyplot as plt
from sklearn import cluster
"""
Explanation: Visualising Clustering with Voronoi Tesselations
When experimenting with using t... |
fisicatyc/Cuantica_Jupyter | estados_ligados.ipynb | mit | from tecnicas_numericas import *
import tecnicas_numericas
print(dir(tecnicas_numericas))
"""
Explanation: <div class="alert alert-success">
Este notebook de ipython depende de los modulos:
<li> `tecnicas_numericas`, ilustrado en el notebook [Técnicas numéricas](tecnicas_numericas.ipynb).
<li> `vis_int`, ilustrado... |
iurilarosa/thesis | codici/Archiviati/numpy/.ipynb_checkpoints/Prove numpy-checkpoint.ipynb | gpl-3.0 | unimatr = numpy.ones((10,10))
#unimatr
duimatr = unimatr*2
#duimatr
uniarray = numpy.ones((10,1))
#uniarray
triarray = uniarray*3
scalarray = numpy.arange(10)
scalarray = scalarray.reshape(10,1)
#NB fare il reshape da orizzontale a verticale è come se aggiungesse
#una dimensione all'array facendolo diventare un nda... |
csyhuang/hn2016_falwa | examples/.ipynb_checkpoints/example_barotropic-checkpoint.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... |
ES-DOC/esdoc-jupyterhub | notebooks/inpe/cmip6/models/besm-2-7/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'inpe', 'besm-2-7', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: INPE
Source ID: BESM-2-7
Sub-Topics: Radiative Forcings.
Properties: 85 (42 re... |
seifip/udacity-deep-learning-nanodegree | !P3 - TV Script Generation/dlnd_tv_script_generation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
"""
Explanation: TV Script Generation
In this project, you'll generate your own Simpsons TV scrip... |
eneskemalergin/OldBlog | _oldnotebooks/Basic_Sequence_Analysis.ipynb | mit | from Bio import Entrez, SeqIO
# Using my email
Entrez.email = "eneskemalergin@gmail.com"
# Get the FASTA file
hdl = Entrez.efetch(db='nucleotide', id=['NM_002299'],rettype='fasta') # Lactase gene
# Read it and store it in seq
seq = SeqIO.read(hdl, 'fasta')
print "First 10 and last 10: " + seq.seq[:10] + "..." + seq.se... |
tudarmstadt-lt/sensegram | QuickStart.ipynb | apache-2.0 | import sensegram
# see README for model download information
sense_vectors_fpath = "model/dewiki.txt.clusters.minsize5-1000-sum-score-20.sense_vectors"
sv = sensegram.SenseGram.load_word2vec_format(sense_vectors_fpath, binary=False)
"""
Explanation: Demonstrating various stages of word sense disambiguation
The exampl... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/official/explainable_ai/sdk_automl_tabular_classification_online_explain.ipynb | apache-2.0 | import os
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG
"""
Explanation: Vertex SDK: AutoML training tabular classification model for online explanation
<table align="l... |
nealjean/predicting-poverty | figures/Figure 4.ipynb | mit | from fig_utils import *
import matplotlib.pyplot as plt
import time
%matplotlib inline
"""
Explanation: Figure 4: Evaluation of model performance
This notebook generates individual panels of Figure 4 in "Combining satellite imagery and machine learning to predict poverty".
End of explanation
"""
country_path = '../... |
evanmiltenburg/python-for-text-analysis | Chapters/Chapter 10 - Dictionaries.ipynb | apache-2.0 | student_grades = ['Frank', 8, 'Susan', 7, 'Guido', 10]
student = 'Frank'
index_of_student = student_grades.index(student) # we use the index method (list.index)
print('grade of', student, 'is', student_grades[index_of_student + 1])
"""
Explanation: Chapter 10 - Dictionaries
This notebook uses code snippets and explan... |
sdpython/ensae_teaching_cs | _doc/notebooks/td2a_ml/td2a_tree_selection_correction.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
%matplotlib inline
"""
Explanation: 2A.ml - Réduction d'une forêt aléatoire - correction
Le modèle Lasso permet de sélectionner des variables, une forêt aléatoire produit une prédiction comme étant la moyenne d'arbres de régression. Et si on mélangeait l... |
naifrec/cnn-dropout | cnn-scyfer-project.ipynb | mit | import cPickle
import gzip
import os
import sys
import timeit
import numpy
import theano
import theano.tensor as T
from theano.tensor.signal import downsample
from theano.tensor.nnet import conv
rng = numpy.random.RandomState(23455)
# instantiate 4D tensor for input
input = T.tensor4(name='input')
w_shp = (2, 3, 9... |
sdpython/pyquickhelper | _unittests/ut_ipythonhelper/data/example_corrplot.ipynb | mit | %pylab inline
import pyensae
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import pandas
import numpy
letters = "ABCDEFGHIJKLM"[0:10]
df = pandas.DataFrame(dict(( (k, numpy.random.random(10)+ord(k)-65) for k in letters)))
df.head()
from pyensae.graph_helper import Corrplot
c = Corrplot(df)
c.plot(figsize=(... |
anhaidgroup/py_entitymatching | notebooks/guides/step_wise_em_guides/Performing Matching with a Rule-Based Matcher.ipynb | bsd-3-clause | # Import py_entitymatching package
import py_entitymatching as em
import os
import pandas as pd
"""
Explanation: Introduction
This IPython notebook illustrates how to perform matching using the rule-based matcher.
First, we need to import py_entitymatching package and other libraries as follows:
End of explanation
"""... |
mne-tools/mne-tools.github.io | 0.24/_downloads/93b9388c9b54989a6ee795fd5dedd153/otp.ipynb | bsd-3-clause | # Author: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD-3-Clause
import os.path as op
import mne
import numpy as np
from mne import find_events, fit_dipole
from mne.datasets.brainstorm import bst_phantom_elekta
from mne.io import read_raw_fif
print(__doc__)
"""
Explanation: Plot sensor denoising using over... |
samuxiii/prototypes | learning/stock/stock.ipynb | mit | from sklearn.linear_model import RidgeCV
from sklearn.model_selection import train_test_split
from sklearn.externals import joblib
import numpy as np
import matplotlib.pyplot as plt
import os
data = np.loadtxt(fname = 'data.txt', delimiter = ',')
X, y = data[:,:5], data[:,5]
print("Features sample: {}".format(X[1]))
... |
gaufung/Data_Analytics_Learning_Note | DesignPattern/AdapterPattern.ipynb | mit | class ACpnStaff(object):
name=""
id=""
phone=""
def __init__(self,id):
self.id=id
def getName(self):
print ("A protocol getName method...id:%s"%self.id)
return self.name
def setName(self,name):
print ("A protocol setName method...id:%s"%self.id)
self.name=... |
ual/hedonic-models | sales-hedonic-output.ipynb | bsd-3-clause | # Startup steps
import pandas as pd, numpy as np, statsmodels.api as sm
import matplotlib.pyplot as plt, matplotlib.cm as cm, matplotlib.font_manager as fm
import matplotlib.mlab as mlab
from scipy.stats import pearsonr, ttest_rel
%matplotlib inline
"""
Explanation: Tutorial on Hedonic Regression
This material uses Py... |
robblack007/clase-cinematica-robot | Practicas/practica4/Practica.ipynb | mit | # Esta libreria tiene las funciones principales que utilizaremos
from sympy import var, Matrix, Function, sin, cos, pi, trigsimp
# Esta libreria contiene una funcion que la va a dar un formato "bonito" a nuestras ecuaciones
from sympy.physics.mechanics import mechanics_printing
mechanics_printing()
τ = 2*pi
"""
Explan... |
GEMScienceTools/rmtk | notebooks/vulnerability/derivation_fragility/NLTHA_on_SDOF/MSA_on_SDOF.ipynb | agpl-3.0 | import numpy as np
from rmtk.vulnerability.common import utils
from rmtk.vulnerability.derivation_fragility.NLTHA_on_SDOF import MSA_on_SDOF
from rmtk.vulnerability.derivation_fragility.NLTHA_on_SDOF import MSA_utils
from rmtk.vulnerability.derivation_fragility.NLTHA_on_SDOF.read_pinching_parameters import read_paramet... |
PyDataMadrid2016/Conference-Info | workshops_materials/20160408_1100_Pandas_for_beginners/tutorial/EN - Tutorial 03 - Basic operations with pandas data structures.ipynb | mit | # first, the imports
import os
import datetime as dt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(19760812)
%matplotlib inline
# we read data from file 'mast.txt'
ipath = os.path.join('Datos', 'mast.txt')
# Now, we define a function to parse the dates
def dateparse(date, tim... |
MIT-LCP/mimic-workshop | intro_to_mimic/01-example-patient-heart-failure.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sqlite3
%matplotlib inline
"""
Explanation: Exploring the trajectory of a single patient
Import Python libraries
We first need to import some tools for working with data in Python.
- NumPy is for working with numbers
- Pandas is for analysi... |
MBARIMike/stoqs | stoqs/loaders/CANON/toNetCDF/notebooks/lrauv_nav_adjust.ipynb | gpl-3.0 | from netCDF4 import Dataset
import numpy as np
# 1. Initial daphne file from the https://stoqs.mbari.org/stoqs_canon_may2018 campaign, wget'ted from:
# http://dods.mbari.org/data/lrauv/daphne/missionlogs/2018/20180603_20180611/20180608T003220/201806080032_201806090421.nc4
#df = '/vagrant/dev/stoqsgit/201806080032_2018... |
VUInformationRetrieval/IR2016_2017 | 02_building.ipynb | gpl-2.0 | Summaries_file = 'data/malaria__Summaries.pkl.bz2'
Abstracts_file = 'data/malaria__Abstracts.pkl.bz2'
import pickle, bz2
from collections import namedtuple
Summaries = pickle.load( bz2.BZ2File( Summaries_file, 'rb' ) )
paper = namedtuple( 'paper', ['title', 'authors', 'year', 'doi'] )
for (id, paper_info) in Summar... |
miaecle/deepchem | examples/tutorials/15_Synthetic_Feasibility_Scoring.ipynb | mit | %tensorflow_version 1.x
!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py
import deepchem_installer
%time deepchem_installer.install(version='2.3.0')
import deepchem as dc
# Lets get some molecules to play with
from deepchem.molnet.load_function import... |
chbrandt/pynotes | SS82_filtering/.ipynb_checkpoints/Untitled-checkpoint.ipynb | gpl-2.0 | from IPython.display import HTML
HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide();
} else {
$('div.input').show();
}
code_show = !code_show
}
$( document ).ready(code_toggle);
</script>
<form action="javascript:code_toggle()"><input type="submit" value="Click here... |
AhmetHamzaEmra/Deep-Learning-Specialization-Coursera | Sequence Models/Operations+on+word+vectors+-+v1.ipynb | mit | import numpy as np
from w2v_utils import *
"""
Explanation: Operations on word vectors
Welcome to your first assignment of this week!
Because word embeddings are very computionally expensive to train, most ML practitioners will load a pre-trained set of embeddings.
After this assignment you will be able to:
Load pr... |
iglpdc/comp-phys | 01_01_euler.ipynb | mit | t0 = 10. # initial temperature
ts = 83. # temp. of the environment
r = 0.1 # cooling rate
dt = 0.05 # time step
tmax = 60. # maximum time
nsteps = int(tmax/dt) # number of steps
t = t0
for i in range(1,nsteps+1):
new_t = t - r*(t-ts)*dt
t = new_t
print i,i*dt, t
# we can also do t = t - r*(t-t... |
gonzmg88/cnn_basic_course | transfer_learning.ipynb | gpl-3.0 | import dogs_vs_cats as dvc
all_files = dvc.image_files()
"""
Explanation: Pretrained CNN: transfer learning
Nature article: Dermatologist-level classification of skin cancer with deep neural networks
End of explanation
"""
from keras.applications.nasnet import NASNetMobile
from keras.preprocessing import image
from ... |
kmunve/APS | aps/notebooks/meps_det_pp_1km.ipynb | mit | # ensure loading of APS modules
import sys, os
sys.path.append(r'C:\Users\kmu\PycharmProjects\APS')
print(sys.path)
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import matplotlib
matplotlib.style.use('seaborn-notebook')
import matplotlib.patches as patches
plt.rcParams['figure.figsize'] = (14, 6)
%matplotli... |
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