repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_sensors_decoding.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
import mne
from mne.datasets import sample
from mne.decoding import (SlidingEstimator, GeneralizingEstimator,
... |
FirebaseExtended/codelab-digitclassifier-android | improve_accuracy_and_upload.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... |
pysal/spaghetti | notebooks/network-spatial-autocorrelation.ipynb | bsd-3-clause | %config InlineBackend.figure_format = "retina"
%load_ext watermark
%watermark
import esda
import libpysal
import matplotlib
import matplotlib_scalebar
from matplotlib_scalebar.scalebar import ScaleBar
import numpy
import spaghetti
import splot
%matplotlib inline
%watermark -w
%watermark -iv
"""
Explanation: If any ... |
dalonlobo/GL-Mini-Projects | TweetAnalysis/Final/Q1/Dalon_4_RTD_MiniPro_Tweepy_Q1.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='tweepy.log', level=logging.INFO,
format=logFormat, datefmt="%Y-%m-%d %H:%M:%S")
""... |
johnhw/sqlexperiment | notebooks/ExperimentLog_Doc.ipynb | mit | # import sqlexperiment as sqle
# from sqlexperiment import experimentlog
from explogger import ExperimentLog
# log some JSON data
e = ExperimentLog(":memory:", ntp_sync=False)
e.log("mouse", data={"x":0, "y":0})
e.log("mouse", data={"x":0, "y":1})
e.log("mouse", data={"x":0, "y":2})
e.close()
# from experimentlog imp... |
fastai/course-v3 | nbs/dl2/05b_early_stopping.ipynb | apache-2.0 | x_train,y_train,x_valid,y_valid = get_data()
train_ds,valid_ds = Dataset(x_train, y_train),Dataset(x_valid, y_valid)
nh,bs = 50,512
c = y_train.max().item()+1
loss_func = F.cross_entropy
data = DataBunch(*get_dls(train_ds, valid_ds, bs), c)
#export
class Callback():
_order=0
def set_runner(self, run): self.ru... |
peterdalle/mij | Projects/newsrobot-moviereview.ipynb | gpl-3.0 | # Install libraries. Try pip3 instead if it doesn't work.
!pip install BeautifulSoup4
!pip install lxml
# Import libraries.
from urllib import request
from lxml import html
from bs4 import BeautifulSoup
import ssl
# Function to scrape HTML from web page.
def scrapewebpage(url):
UseSSL = True # If you get SSLErro... |
brunorpinho/ufpel-python | modulos/Modulo 1.ipynb | mit | print 'bem vindos!'
a = 2 + 3
print a
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Módulo-1---O-Básico" data-toc-modified-id="Módulo-1---O-Básico-1"><span class="toc-item-num">1 </span>Módulo 1 - O Básico</a></div><div class="lev2 toc-item"><a href="#Atalhos-do-Jupyter-Notebo... |
ComputationalModeling/spring-2017-danielak | past-semesters/fall_2016/day-by-day/day22-econophysics/Econophysics.ipynb | agpl-3.0 | # Put your code here!
"""
Explanation: Econophysics
Names of group members
// put your names here!
Goals of this assignment
Witness what we call "emergent behavior"; large patterns manifesting from the simple interactions of individual agents
Develop a graphical way to show the dispersion of money across a society... |
Serulab/Py4Bio | notebooks/Chapter 10 - Web Applications.ipynb | mit | !wget https://github.com/Serulab/Py4Bio/archive/master.zip
!unzip master.zip
!mv Py4Bio-master/code/ch10/* ./
!apt-get -y install apache2
!pip install bottle
!wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip
!unzip ngrok-stable-linux-amd64.zip
"""
Explanation: <a href="https://colab.research.g... |
opengeostat/pygslib | pygslib/Ipython_templates/.ipynb_checkpoints/nscore_data_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... |
mabevillar/rmtk | rmtk/plotting/damage_dist/plot_damage_dist.ipynb | agpl-3.0 | %matplotlib inline
import plot_damage_dist as plotdd
"""
Explanation: Plot Damage Distribution
This IPython notebook allows the user to create bar plots of the damage distribution results obtained from a scenario damage analysis, and to export the plots as png files. Damage distribution per taxonomy as well as total d... |
mne-tools/mne-tools.github.io | 0.19/_downloads/0769ca8628a53983ff9f3abc675c424e/plot_head_positions.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
from os import path as op
import mne
print(__doc__)
data_path = op.join(mne.datasets.testing.data_path(verbose=True), 'SSS')
pos = mne.chpi.read_head_pos(op.join(data_path, 'test_move_anon_raw.pos'))
"""
Explanation: Visualize subject he... |
chseifert/tutorials | nlp-ie/Text-Classification.ipynb | apache-2.0 | simple_train = ['call you tonight', 'Call me a cab', 'please call me... PLEASE!']
# import and instantiate CountVectorizer (with the default parameters)
from sklearn.feature_extraction.text import CountVectorizer
vect = CountVectorizer()
# learn the 'vocabulary' of the training data
vect.fit(simple_train)
# examine... |
mholtrop/Phys605 | Python/Signal/Solving_RC_low_pass_with_ODE.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt
#
# Setup an input square wave signal.
#
F = 50. # Frequency of the square wave.
T = .05 # Amount of time to simulate
N = 2**10 # Number of time steps. Choose small to see a diff... |
JasonSanchez/w261 | week1/MIDS-W261-HW-01-Sanchez.ipynb | mit | !wc -w < bio
"""
Explanation: MIDS - w261 Machine Learning At Scale
Course Lead: Dr James G. Shanahan (email Jimi via James.Shanahan AT gmail.com)
Assignment - HW1
Name: Jason Sanchez (26989981)
Class: MIDS w261 (Section Fall 2016 Group 2)
Email: jason.sanchez@iSchool.Berkeley.edu
Week: 1
Due Time: HW is... |
thalesians/tsa | src/jupyter/python/kde.ipynb | apache-2.0 | %matplotlib inline
import os, sys
sys.path.append(os.path.abspath('../../main/python'))
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
import thalesians.tsa.distrs as distrs
import thalesians.tsa.kde as kde
import importlib
importlib.reload(distrs)
importlib.reload(kde)
"""
Explanation:... |
balarsen/pymc_learning | BayesianNetwork/Neural Networks in PyMC3.ipynb | bsd-3-clause | %matplotlib inline
import theano
floatX = theano.config.floatX
import pymc3 as pm
import theano.tensor as T
import sklearn
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('white')
from sklearn import datasets
from sklearn.preprocessing import scale
from sklearn.cross_validation im... |
QuLogic/folium | examples/WidthHeight.ipynb | mit | width, height = 480, 350
fig = Figure(width=width, height=height)
m = folium.Map(
location=location,
tiles=tiles,
width=width,
height=height,
zoom_start=zoom_start
)
fig.add_child(m)
fig.save(os.path.join('results', 'WidthHeight_0.html'))
fig
"""
Explanation: Using same width and height trigge... |
adico-somoto/deep-learning | image-classification/dlnd_image_classification.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
# Use Floyd's cifar-10 dataset if present
floyd_cifar10... |
wanderer2/pymc3 | docs/source/notebooks/discrete_find_MAP.ipynb | apache-2.0 | import pymc3 as mc
"""
Explanation: Using find_MAP on models with discrete variables
Maximum a posterior(MAP) estimation, can be difficult in models which have discrete stochastic variables. Here we demonstrate the problem with a simple model, and present a few possible work arounds.
End of explanation
"""
alpha = 4... |
borja876/Thinkful-DataScience-Borja | Challenge+Preparing+a+dataset+for+modeling+%28Feature+Selection%29.ipynb | mit | df['horsepower'].unique()
df['cylinders'].unique()
df['origin'].unique()
df['modelyear'].unique()
df['mpg'].unique()
"""
Explanation: Getting a grasp of the data set. Which variables are continuous and which ones are categorical
End of explanation
"""
df = df.drop( df[(df.horsepower == '?')].index )
df[["mpg", "... |
ueapy/ueapy.github.io | content/notebooks/2017-11-13-building-classes.ipynb | mit | class MyDescriptiveError(Exception):
pass
"""
Explanation: First, let's refresh the terminology of object-oriented programming.
class
Tell Python to make a new type of thing.
object
Two meanings: the most basic type of thing, and
any instance of some thing.
instance
What you get when you tell Python to create a cl... |
sdpython/ensae_teaching_cs | _doc/notebooks/td1a_dfnp/td1a_cenonce_session_12_plot.ipynb | mit | %matplotlib inline
"""
Explanation: 1A.data - Visualisation des données
Les tableaux et les graphes sont deux outils incontournables des statisticiens. Petite revue des graphes.
End of explanation
"""
import matplotlib
matplotlib.get_backend()
import matplotlib.pyplot as plt
import matplotlib
matplotlib.get_backend... |
kenfeldmann/6_12_16_carpentry_class | titanic_walkthrough.ipynb | mit | df = pd.read_csv("data/train.csv")
df.head()
df.tail()
"""
Explanation: Lets load in the dataset
End of explanation
"""
df.dtypes
df.info()
df.describe()
"""
Explanation: Lets see the types that were imported on our behalf
End of explanation
"""
df.shape
"""
Explanation: See the shape of the dataset
End of ex... |
planetlabs/notebooks | jupyter-notebooks/udm2/udm2_clouds.ipynb | apache-2.0 | from planet import api
import time
import os
import rasterio
from rasterio.plot import show
client = api.ClientV1()
# build a filter for the AOI
filter = api.filters.range_filter("clear_percent", gte=90)
# show the structure of the filter
print(filter)
# we are requesting PlanetScope 4 Band imagery
item_types = ['PS... |
statsmodels/statsmodels.github.io | v0.13.1/examples/notebooks/generated/statespace_forecasting.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
macrodata = sm.datasets.macrodata.load_pandas().data
macrodata.index = pd.period_range('1959Q1', '2009Q3', freq='Q')
"""
Explanation: Forecasting in statsmodels
This notebook describes forecasting u... |
jonbruner/ezgan | EZGAN.ipynb | mpl-2.0 | import tensorflow as tf
import numpy as np
import datetime
import matplotlib.pyplot as plt
%matplotlib inline
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/")
"""
Explanation: This is an illustration of a very simple generative adversarial network, built with... |
jinzekid/codehub | python/day6/.ipynb_checkpoints/2.3 Python语言基础-checkpoint.ipynb | gpl-3.0 | a = 5; b = 6; c = 7
"""
Explanation: 2.3 Python语言基础
1 语言语义（Language Semantics）
缩进，而不是括号
Python使用空格（tabs or spaces)来组织代码结构，而不是像R，C++，Java那样用括号。
建议使用四个空格来作为默认的缩进，设置tab键为四个空格
另外可以用分号隔开多个语句：
End of explanation
"""
result = f(x, y, z)
"""
Explanation: 所有事物都是对象（object）
在python中，number，string，data structure，function，class... |
GoogleCloudPlatform/mlops-on-gcp | continuous_training/kubeflow/labs/multiple_frameworks_lab.ipynb | apache-2.0 | REGION = 'us-central1'
PROJECT_ID = !(gcloud config get-value core/project)
PROJECT_ID = PROJECT_ID[0]
BUCKET = 'gs://' + PROJECT_ID
"""
Explanation: Lab: Continuous Training with TensorFlow, PyTorch, XGBoost, and Scikit-learn Models with KubeFlow and AI Platform Pipelines
In this lab we will create containerized tra... |
oscarmore2/deep-learning-study | transfer-learning/Transfer_Learning_Solution.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... |
ES-DOC/esdoc-jupyterhub | notebooks/ncc/cmip6/models/noresm2-hh/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'noresm2-hh', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: NCC
Source ID: NORESM2-HH
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy Balan... |
mne-tools/mne-tools.github.io | 0.24/_downloads/a786781c3c54739f0be7add5b76a068f/50_ssvep.ipynb | bsd-3-clause | # Authors: Dominik Welke <dominik.welke@web.de>
# Evgenii Kalenkovich <e.kalenkovich@gmail.com>
#
# License: BSD-3-Clause
import matplotlib.pyplot as plt
import mne
import numpy as np
from scipy.stats import ttest_rel
"""
Explanation: Frequency-tagging: Basic analysis of an SSVEP/vSSR dataset
In this tutoria... |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/prod/.ipynb_checkpoints/n08_simple_q_learner-checkpoint.ipynb | mit | # Basic imports
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
import scipy.optimize as spo
import sys
from time import time
from sklearn.metrics import r2_score, median_absolute_error
from multiprocessing import Pool
%matplotlib inline
%pylab inline
pylab.rcPar... |
tensorflow/docs-l10n | site/ja/tfx/tutorials/tfx/template.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... |
fantasycheng/udacity-deep-learning-project | tutorials/reinforcement/Q-learning-cart.ipynb | mit | import gym
import tensorflow as tf
import numpy as np
"""
Explanation: Deep Q-learning
In this notebook, we'll build a neural network that can learn to play games through reinforcement learning. More specifically, we'll use Q-learning to train an agent to play a game called Cart-Pole. In this game, a freely swinging p... |
dnc1994/MachineLearning-UW | ml-regression/blank/week-5-lasso-assignment-1-blank.ipynb | mit | import graphlab
"""
Explanation: Regression Week 5: Feature Selection and LASSO (Interpretation)
In this notebook, you will use LASSO to select features, building on a pre-implemented solver for LASSO (using GraphLab Create, though you can use other solvers). You will:
* Run LASSO with different L1 penalties.
* Choose... |
JamesSample/enviro_mod_notes | notebooks/01_Distributions.ipynb | mit | def gaussian_function(x, mu=0, sigma=1):
""" Simple example function to return the probability density of a Gaussian with mean, mu,
and standard deviation, sigma, evaluated at values in x.
Note that a better function is available in scipy.stats.norm - this version is just
for illu... |
basp/aya | noise_old.ipynb | mit | v0 = 2
v1 = 5
plt.plot([0, 1], [v0, v1], '--')
t = 1.0 / 3
vt = noise.lerp(v0, v1, t)
plt.plot(t, vt, 'ro')
"""
Explanation: linear interpolation
We need a function ${f}$ that, given values ${v_0}$ and ${v_1}$ and some interval ${t}$ where $0 \le {t} \le 1$, returns an interpolated value between ${v_0}$ and ${v_1}$.
T... |
mne-tools/mne-tools.github.io | 0.23/_downloads/cc2f4b498fc65366ac39d017e939eec5/xdawn_denoising.ipynb | bsd-3-clause | # Authors: Alexandre Barachant <alexandre.barachant@gmail.com>
#
# License: BSD (3-clause)
from mne import (io, compute_raw_covariance, read_events, pick_types, Epochs)
from mne.datasets import sample
from mne.preprocessing import Xdawn
from mne.viz import plot_epochs_image
print(__doc__)
data_path = sample.data_pa... |
gaufung/Data_Analytics_Learning_Note | Scikit_Learning/User_Guide/Support_Vector_Machine.ipynb | mit | from sklearn import svm
X = [[0,0], [1,1]]
y = [0, 1]
clf = svm.SVC()
clf.fit(X, y)
clf.predict([[2,2]])
"""
Explanation: Support Vector Machine
SVMs are a set of supervised learning method used for classification, regression and outliers detection
1 Classification
SVC, NuSVC and LinearSVC are classes capable of perf... |
chapmanbe/nlm_clinical_nlp | IntroductionToPyConTextNLP.ipynb | mit | !pip install -U pycontextnlp==0.6.1.1
import pyConTextNLP.pyConTextGraph as pyConText
import pyConTextNLP.itemData as itemData
"""
Explanation: Simple Clinical Natural Language Processing with pyConTextNLP
In this notebook we introduce the basics of pyConTextNLP, a simple Python tool that we have used extensively for... |
Soil-Carbon-Coalition/atlasdata | Length of green season from time-series Landsat NDVI.ipynb | mit | import pandas as pd, numpy as np
from datetime import datetime, timedelta
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Length of green season from cloud-filtered Landsat SR
This takes as input a time-series csv file generated by Google Earth Engine of cloud-filtered Landsat Surface Reflectance N... |
thinkingmachines/deeplearningworkshop | codelab_4_tensorboard.ipynb | mit | # We really like this spiral dataset
import numpy as np
import matplotlib.pyplot as plt
N = 1000 # points per class
D = 2 # dimensionality at 2 so we can eyeball it
K = 3 # number of classes
X = np.zeros((N*K, D)) # generate an empty matrix to hold X features
y = np.zeros(N*K, dtype='int32') # switching this to int3... |
ebegoli/ErrorProne | sandbox/Crude+Error+Insertion.ipynb | apache-2.0 | import pandas as pd
import math
import string
import random
from random import randint
file = pd.read_csv("/Users/michaelbowie/Downloads/INPUTEVENTS_MV.csv")
file = file.iloc[::30, :]
file = file.reset_index(drop=True)
file
"""
Explanation: Crude Error Insertion
For insertion of errors, start with crude errors:
a... |
ronnydw/data-science-projects | class-central-survey-2016-17/Africa vs Rest of the World.ipynb | mit | import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="white")
sns.set_context("talk")
"""
Explanation: Class Central Survey: compare target group 'Africa' with the rest of the sample
End of explanation
"""
df = pd.read_csv('raw/2016-17-ClassCentral-Survey-data-noUserText.csv', deci... |
SheffieldML/notebook | compbio/SingleCellDataWithGPyTutorial.ipynb | bsd-3-clause | import pods, GPy, itertools
%matplotlib inline
from matplotlib import pyplot as plt
"""
Explanation: Blastocyst Development in Mice: Single Cell TaqMan Arrays
presented at the EBI BioPreDyn Course 'The Systems Biology Modelling Cycle'
Max Zwiessele, Oliver Stegle, Neil Lawrence 12th May 2014 University of Sheffield an... |
lionell/laboratories | num_methods/first/lab2+.ipynb | mit | RTOL = 0.
ATOL = 1e-6
"""
Explanation: import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
%matplotlib inline
End of explanation
"""
def normalize(G):
i = 0
n_to_i = {}
i_to_n = G.nodes()
for node in G.nodes():
n_to_i[node] = i
i += 1
return (n_to_i, i_to_n)
... |
keras-team/keras-io | examples/graph/ipynb/mpnn-molecular-graphs.ipynb | apache-2.0 | import os
# Temporary suppress tf logs
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
from rdkit import Chem
from rdkit import RDLogger
from rdkit.C... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/launching_into_ml/labs/supplemental/decision_trees_and_random_Forests_in_Python.ipynb | apache-2.0 | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
%matplotlib inline
"""
Explanation: Decision Trees and Random Forests in Python
Learning Objectives
Explore and analyze data using a Pairplot
Train a single Decision Tree
Predict and evaluate the Decision Tree
Compare the De... |
mne-tools/mne-tools.github.io | 0.21/_downloads/dfd4175ec1a2c7f21de3596573c74301/plot_multidict_reweighted_tfmxne.ipynb | bsd-3-clause | # Author: Mathurin Massias <mathurin.massias@gmail.com>
# Yousra Bekhti <yousra.bekhti@gmail.com>
# Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
import os.path as op
import mne
from mne.datasets import somato... |
ageron/ml-notebooks | 04_training_linear_models.ipynb | apache-2.0 | # To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
# to make this notebook's output stable across runs
np.random.seed(42)
# To plot pretty figures
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot ... |
ryan-leung/PHYS4650_Python_Tutorial | notebooks/Jan2018/python-conditionals-and-loops.ipynb | bsd-3-clause | x = [1,2,3,4,5,6,7,8]
for item in x:
print item
s = ['a','c','b','d','e','g','h',8]
for item in s:
print item
"""
Explanation: Python Conditionals And Loops
Control Flow
Control flow concerns how programs proceed in their execution to arrive a desired outcome.
Programs can make choices depends on our program... |
sclamons/murraylab_tools | examples/Utilities Usage Examples.ipynb | mit | import murraylab_tools.utilities as mt_utils
import time
mt_utils.set_credentials("put_your_username_here", "put_your_password_here")
mt_utils.set_destination("sclamons@gmail.com")
with mt_utils.notify_when_done("A block"):
print("I'm doing a thing.")
print("I'm doing a second thing.")
time.sleep(3)
p... |
dipanjank/ml | text_classification_and_clustering/step_3_classification_of_full_dataset.ipynb | gpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import pandas as pd
import numpy as np
import seaborn as sns
"""
Explanation: <h1 align="center">Level and Group Classification on Train and Test Datasets</h1>
We have two classification tasks:
Predict the level, which ranges from 1-16.
Pre... |
statsmodels/statsmodels.github.io | v0.12.2/examples/notebooks/generated/mediation_survival.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
import statsmodels.api as sm
from statsmodels.stats.mediation import Mediation
"""
Explanation: Mediation analysis with duration data
This notebook demonstrates mediation analysis when the
mediator and outcome are duration variables, modeled
using proportional hazards regression.... |
estepona/PERCEIVE-freddie | TopicFlow_topicSimilarityTest_Zhang_09-14.ipynb | mit | file = 'topicflow/data/test/TopicSimilarity.json'
with open(file, 'r') as ts_file:
ts = ts_file.read()
ts
ts.split('\n')
# 'var sim_data ='
ts_body_pre = ts.split('\n')[1][:14]
# care the last ';'
ts_body = ts.split('\n')[1][14:-1]
ts_body
ts_body_dict = json.loads(ts_body)
ts_body_dict
old_value = np.array(... |
beardeer/prediction-wrapper | run_example.ipynb | mit | import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from binary_classifier_wrappers import KfoldBinaryClassifierWrapper
from metric_wrappers import RSquare, AUC, RMSE
"""
Explanation: 0. Introduction... |
AstroHackWeek/AstroHackWeek2015 | hacks/deep-learning/nolearn_tutorial.ipynb | gpl-2.0 | import os
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
from lasagne.layers import DenseLayer
from lasagne.layers import InputLayer
from lasagne.layers import DropoutLayer
from lasagne.layers import Conv2DLayer
from lasagne.layers import MaxPool2DLayer
from lasagne.nonlinearities import softma... |
HrantDavtyan/Data_Scraping | Week 5/Alternative_approach_(urllib_and_LXML).ipynb | apache-2.0 | import urllib2
from lxml import html
url = "https://careercenter.am/ccidxann.php"
response = urllib2.urlopen(url)
page = response.read()
tree = html.document_fromstring(page)
"""
Explanation: Alternative libraries
Before this, the two main libraries used for scraping a webpage were requests and BeautifulSoup. Howev... |
Kaggle/learntools | notebooks/geospatial/raw/ex2.ipynb | apache-2.0 | import pandas as pd
import geopandas as gpd
from shapely.geometry import LineString
from learntools.core import binder
binder.bind(globals())
from learntools.geospatial.ex2 import *
"""
Explanation: Introduction
You are a bird conservation expert and want to understand migration patterns of purple martins. In your ... |
tiagoantao/biopython-notebook | notebooks/10 - Swiss-Prot and ExPASy.ipynb | mit | import gzip
handle = gzip.open("myswissprotfile.dat.gz")
"""
Explanation: Source of the materials: Biopython cookbook (adapted)
<font color='red'>Status: Draft</font>
Swiss-Prot and ExPASy {#chapter:swiss_prot}
Parsing Swiss-Prot files
Swiss-Prot (http://www.expasy.org/sprot) is a hand-curated database of
protein seq... |
ES-DOC/esdoc-jupyterhub | notebooks/miroc/cmip6/models/nicam16-9d-l78/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'nicam16-9d-l78', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: MIROC
Source ID: NICAM16-9D-L78
Topic: Atmos
Sub-Topics: Dynamical Core, Radia... |
urgedata/pythondata | compare_MLMethods/Comparing Machine Learning Methods.ipynb | mit | import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (20,10)
from sklearn import model_selection
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklear... |
quole/gensim | docs/notebooks/Varembed.ipynb | lgpl-2.1 | from gensim.models.wrappers import varembed
vector_file = '../../gensim/test/test_data/varembed_leecorpus_vectors.pkl'
model = varembed.VarEmbed.load_varembed_format(vectors=vector_file)
"""
Explanation: VarEmbed Tutorial
Varembed is a word embedding model incorporating morphological information, capturing shared sub... |
phoebe-project/phoebe2-docs | 2.2/tutorials/grav_redshift.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.2,<2.3"
"""
Explanation: Gravitational Redshift (rv_grav)
Setup
Let's first make sure we have the latest version of PHOEBE 2.2 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
"""
%matplo... |
sz2472/foundations-homework | data and database/twitter_API_July5.ipynb | mit | response=twitter.search(q="data journalism",result_type="recent",count=20)
first=response['statuses'][0]
first.keys()
first['entities']
for item in first['entities']['urls']:
print(item['expanded_url'])
for item in first['entities']['user_mentions']:
print(item['screen_name'])
cursor = twitter.cursor(twit... |
ericmjl/Network-Analysis-Made-Simple | notebooks/01-introduction/02-networkx-intro.ipynb | mit | from IPython.display import YouTubeVideo
YouTubeVideo(id='sdF0uJo2KdU', width="100%")
"""
Explanation: Introduction
End of explanation
"""
import networkx as nx
from datetime import datetime
import matplotlib.pyplot as plt
import numpy as np
import warnings
from nams import load_data as cf
warnings.filterwarnings(... |
PMEAL/OpenPNM | examples/contrib/predicting_effective_permeability_of_berea.ipynb | mit | import os
import imageio
import scipy as sp
import numpy as np
import openpnm as op
import porespy as ps
import matplotlib.pyplot as plt
np.set_printoptions(precision=4)
np.random.seed(10)
%matplotlib inline
"""
Explanation: Predicting Permeability of Berea
Berea Sandstone Simulation Using PoreSpy and OpenPNM
The exam... |
tensorflow/docs-l10n | site/ja/probability/examples/Bayesian_Gaussian_Mixture_Model.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... |
buntyke/TRo2017 | Experiments/Exp6/experiment2.ipynb | mit | # import the modules
import GPy
import csv
import numpy as np
import cPickle as pickle
import scipy.stats as stats
import sklearn.metrics as metrics
import GPy.plotting.Tango as Tango
from matplotlib import pyplot as plt
%matplotlib notebook
"""
Explanation: Experiment 6: TRo Journal
In this experiment, the generali... |
statsmaths/stat665 | lectures/lec22/.ipynb_checkpoints/notebook21-checkpoint.ipynb | gpl-2.0 | %pylab inline
import copy
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from keras.datasets import imdb, reuters
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.optimizers import SGD, RMSprop
from keras.utils import np_utils
fr... |
Kaggle/learntools | notebooks/nlp/raw/tut3.ipynb | apache-2.0 | import numpy as np
import spacy
# Need to load the large model to get the vectors
nlp = spacy.load('en_core_web_lg')
# Disabling other pipes because we don't need them and it'll speed up this part a bit
text = "These vectors can be used as features for machine learning models."
with nlp.disable_pipes():
vectors =... |
csiu/100daysofcode | misc/day66_clustering-csvconfv3-nonkeynote-talks.ipynb | mit | from bs4 import BeautifulSoup
import requests
import pandas as pd
website_to_parse = "https://csvconf.com/speakers/"
# Save HTML to soup
html_data = requests.get(website_to_parse).text
soup = BeautifulSoup(html_data, "html5lib")
doc = soup.find_all("table", attrs={"class", "speakers"})[1]
names = doc.find_all("span... |
minhhh/charts | pandas/Learn-Pandas-Completely.ipynb | mit | import pandas as pd
from pandas import Series, DataFrame
"""
Explanation: Introduction to Pandas
pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
Our convention for importing pandas:
End of explanation
""... |
atlas-outreach-data-tools/notebooks | november_2017_v-1.0/ATLAS_OpenData_notebook_04.ipynb | gpl-3.0 | import ROOT
"""
Explanation: <CENTER>
<a href="http://opendata.atlas.cern" class="icons"><img src="http://opendata.atlas.cern/DataAndTools/pictures/opendata-top-transblack.png" style="width:40%"></a>
</CENTER>
A more difficult notebook in python
In this notebook you can find a more difficult program that shows fur... |
histogrammar/histogrammar-python | histogrammar/notebooks/histogrammar_tutorial_advanced.ipynb | apache-2.0 | %%capture
# install histogrammar (if not installed yet)
import sys
!"{sys.executable}" -m pip install histogrammar
import histogrammar as hg
import pandas as pd
import numpy as np
import matplotlib
"""
Explanation: Histogrammar advanced tutorial
Histogrammar is a Python package that allows you to make histograms fr... |
kongtiaowang/Loris | docs/notebooks/LORIS_API_Part2_Python-script.ipynb | gpl-3.0 | import getpass # For input prompt hide what is entered
import json # Provides convenient functions to handle json objects
import re # For regular expression
import requests # To handle http requests
import warnings # To ignore warnings
# Because the ssl certificates are unverified, warnings are thrown... |
computational-class/computational-communication-2016 | code/tutorials-scikit-learn-master/2. Robust and calibrated estimators with Scikit-Learn.ipynb | mit | # Global imports and settings
# Matplotlib
%matplotlib inline
from matplotlib import pyplot as plt
plt.rcParams["figure.figsize"] = (8, 8)
plt.rcParams["figure.max_open_warning"] = -1
# Print options
import numpy as np
np.set_printoptions(precision=3)
# Slideshow
from notebook.services.config import ConfigManager
cm... |
EderSantana/seya | examples/NTM.ipynb | bsd-3-clause | %matplotlib inline
from __future__ import absolute_import
from __future__ import print_function
import logging
import numpy as np
np.random.seed(124)
import matplotlib.pyplot as plt
import cPickle
from theano import tensor, function
from keras.datasets import mnist
from keras.models import Graph, Sequential
from kera... |
sdpython/ensae_teaching_cs | _doc/notebooks/td2a_eco/TD2A_eco_debuter_flask.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 2A.eco - Débuter avec Flask
Pour aller vite, Flask est un framework de développement web en Python. Il en existe d'autres, le plus connu d'entre eux est Django. Ce notebook la création d'un site web à usage principalement privé.
En allian... |
sueiras/training | tensorflow_old/05-others/01_TF basic comands.ipynb | gpl-3.0 | from __future__ import print_function
import tensorflow as tf
#Basic interactive session
# Enter an interactive TensorFlow Session.
sess = tf.InteractiveSession()
# Define a var and a constant
x = tf.Variable([1.0, 2.0])
a = tf.constant([3.0, 3.0])
# Initialize the var 'x' using the run() method
x.initializer.run()... |
ethen8181/machine-learning | deep_learning/seq2seq/huggingface_torch_transformer.ipynb | mit | # code for loading the format for the notebook
import os
# path : store the current path to convert back to it later
path = os.getcwd()
os.chdir(os.path.join('..', '..', 'notebook_format'))
from formats import load_style
load_style(css_style='custom2.css', plot_style=False)
os.chdir(path)
# 1. magic for inline plot... |
MTgeophysics/mtpy | examples/notebooks/plot_resistivity_seismic.ipynb | gpl-3.0 | # Add mtpy folder to python path. This may not be necessary
# depending on how mtpy was installed.
import sys
#sys.path.append('/path/to/mtpy')
sys.path.append('/media/data/work/GA/ausLAMP/codes/mtGeoMtpy/')
%%capture
# import modules
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplot... |
EricKightley/sparsekmeans | plots/heuristic/.ipynb_checkpoints/heuristic-checkpoint.ipynb | mit | import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
from scipy.linalg import hadamard
from scipy.fftpack import dct
%matplotlib inline
n = 10 #dimension of data (rows in plot)
K = 3 #number of centroids
m = 4 #subsampling dimension
p = 6 #number of observations (c... |
obscode/bootcamp | IntroToPython.ipynb | mit | import antigravity
"""
Explanation: Carnegie Python Bootcamp
Welcome to the python bootcamp. This thing you're reading is called an ipython notebook and will be your first introduction to the Python programming language. Notebooks are a combination of text markup and code that you can run in real time.
Importing what ... |
satishgoda/learning | python/libs/rxpy/support/Marble Diagrams.ipynb | mit | %run startup.py
"""
Explanation: Marble Diagrams with RxPY
This is a fantastic feature to produce and visualize streams and to verify how various operators work on them.
Have also a look at rxmarbles for interactive visualisations.
ONE DASH IS <font size="40px">100</font> MILLISECONDS!
End of explanation
"""
rst(O.f... |
ceos-seo/data_cube_notebooks | notebooks/vegetation/NDVI_Thresholds.ipynb | apache-2.0 | import sys
import os
sys.path.append(os.environ.get('NOTEBOOK_ROOT'))
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import xarray as xr
from dc_notebook_utilities import generate_metadata_report
from utils.data_cube_utilities.dc_display_map import display_map
from utils.data_cube_utilities.cl... |
RadoslawDryzner/LeRepoDuGuerrier | Homework01/Homework 1.ipynb | mit | DATA_FOLDER = 'Data' # Use the data folder provided in Tutorial 02 - Intro to Pandas.
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import datetime
from dateutil.parser import parse
from os import listdir
from os.path import isfile, join
sns.set_context(... |
aje/POT | docs/source/auto_examples/plot_otda_semi_supervised.ipynb | mit | # Authors: Remi Flamary <remi.flamary@unice.fr>
# Stanislas Chambon <stan.chambon@gmail.com>
#
# License: MIT License
import matplotlib.pylab as pl
import ot
"""
Explanation: OTDA unsupervised vs semi-supervised setting
This example introduces a semi supervised domain adaptation in a 2D setting.
It explicits... |
boffi/boffi.github.io | dati_2015/ha03/01_Impact.ipynb | mit | from math import atan2, cos, pi, sin, sqrt
def format(desc, val, units):
print("%-35s %9g %s"%(desc+":", val, units))
"""
Explanation: Impact
Preliminaries
We have to import the mathematical functions used in the following. Also, we want to define a function to help with the formatting of our results.
End of expla... |
ES-DOC/esdoc-jupyterhub | notebooks/ncc/cmip6/models/sandbox-3/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'sandbox-3', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: NCC
Source ID: SANDBOX-3
Topic: Ocean
Sub-Topics: Timestepping Framework, Advection, ... |
materialsvirtuallab/matgenb | notebooks/2018-01-01-ChemEnv - How to automatically identify coordination environments in a structure.ipynb | bsd-3-clause | from pymatgen.analysis.chemenv.coordination_environments.coordination_geometry_finder import LocalGeometryFinder
import logging
from pymatgen.ext.matproj import MPRester
from pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies import SimplestChemenvStrategy, MultiWeightsChemenvStrategy
from pymatgen.... |
fastai/fastai | nbs/01a_losses.ipynb | apache-2.0 | #|export
class BaseLoss():
"Same as `loss_cls`, but flattens input and target."
activation=decodes=noops
def __init__(self,
loss_cls, # Uninitialized PyTorch-compatible loss
*args,
axis:int=-1, # Class axis
flatten:bool=True, # Flatten `inp` and `targ` before calculating los... |
Ciaran1981/geospatial-learn | example_notebooks/.ipynb_checkpoints/PointCloudClassification-checkpoint.ipynb | gpl-3.0 | from geospatial_learn import learning as ln
incloud = "/path/to/Llandinam.ply"
"""
Explanation: A workflow for classifying a point cloud using point features
The following example will run through the functions to classify a point cloud based on the point neighborhood attributes. This is a very simple example but thi... |
DillmannFrench/Intro-PYTHON | Cours07-DILLMANN-ISEP2016.ipynb | gpl-3.0 | def cube_positif( x ):
if abs( x*x*x >= 0.0):
return x*x*x
print("Erreur")
return
cube_positif(-4)
"""
Explanation: Tant que ce n'est pas bien maîtrisé c'est ...
La Guerre des Fonctions
1) Autes fonctions particulières à corriger
2) Exercices de Simplification du code des fonctions
3) Quelques E... |
goerlitz/text-mining | python/Recipes - Doc2Vec Variations.ipynb | apache-2.0 | import re # Regular Expressions
import os.path # File Operations
import pandas as pd # DataFrames & Manipulation
from gensim.models.doc2vec import LabeledSentence, Doc2Vec # Model ... |
darkomen/TFG | ipython_notebooks/06_regulador_experto/ensayo4.ipynb | cc0-1.0 | #Importamos las librerías utilizadas
import numpy as np
import pandas as pd
import seaborn as sns
#Mostramos las versiones usadas de cada librerías
print ("Numpy v{}".format(np.__version__))
print ("Pandas v{}".format(pd.__version__))
print ("Seaborn v{}".format(sns.__version__))
#Abrimos el fichero csv con los datos... |
rob-dalton/dsi-alumni-lectures | neural_networks/Neural Networking.ipynb | mit | import os
import tensorflow as tf
# import tensorflow.python.platform
from tensorflow.python.platform import gfile
import numpy as np
import pandas as pd
"""
Explanation: TensorFlow Inception-v3 Feature Extraction setup:
Special thanks to KERNIX for their extremely helpful blog
http://www.kernix.com/blog/image-classif... |
BinRoot/TensorFlow-Book | ch09_cnn/Concept02_convolution.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import cifar_tools
import tensorflow as tf
names, data, labels = \
cifar_tools.read_data('./cifar-10-batches-py')
def show_conv_results(data, filename=None):
plt.figure()
rows, cols = 4, 8
for i in range(np.shape(data)[3]):
... |
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