repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
k326xh/SingleCellRNA-seq | Notebook/Smushing.ipynb | mit | # Import required modules
# Python plotting library
import matplotlib.pyplot as plt
# Numerical python library
import numpy as np
# Dataframes in Python
import pandas as pd
# Statistical plotting library we'll use
import seaborn as sns
# Import packages for dimension reduction
from sklearn.decomposition import PCA, Fas... |
SKA-ScienceDataProcessor/algorithm-reference-library | deprecated_code/workflows/mpi/imaging_mpi.ipynb | apache-2.0 | %lsmagic
from mpi4py import MPI
print(MPI.COMM_WORLD.size)
%matplotlib inline
import os
import sys
sys.path.append(os.path.join('..', '..'))
from data_models.parameters import arl_path
results_dir = arl_path('test_results')
from matplotlib import pylab
pylab.rcParams['figure.figsize'] = (8.0, 8.0)
pylab.rc... |
MehtapIsik/assaytools | examples/direct-fluorescence-assay/1 Simulating Experimental Fluorescence Binding Data.ipynb | lgpl-2.1 | import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from IPython.display import display, Math, Latex #Do we even need this anymore?
%pylab inline
"""
Explanation: In this notebook we will explore plotting complex concentration as a function of Kd.
We will simulate expected fluorescence results for... |
Hash--/documents | notebooks/pywed_xray/PyWED dataframe and xray.ipynb | mit | %pylab inline
import xray
"""
Explanation: This notebook explores the possibility to use the Python package xray in order to ease the data processing from the TS/WEST database.
xray is an open source project and Python package that aims to bring the labeled data power of pandas to the physical sciences, by providing N... |
maxis42/ML-DA-Coursera-Yandex-MIPT | 4 Stats for data analysis/Lectures notebooks/7 student tests/stat.student_tests.ipynb | mit | import numpy as np
import pandas as pd
import scipy
from statsmodels.stats.weightstats import *
%pylab inline
"""
Explanation: Критерии Стьюдента
End of explanation
"""
data = pd.read_csv('ADHD.txt', sep = ' ', header = 0)
data.columns = ['Placebo', 'Methylphenidate']
data.plot.scatter('Placebo', 'Methylphenidate... |
nonabelian/tda_dionysus | TADA_Dionysus.ipynb | gpl-3.0 | import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# Local companion package
from topology.data import coffee_mug
from topology.data import pail
from topology.plotting import plot_mug_3D
from topology.plotting import plot_pail_3D
from topology.plotting import plot_circle_2D
%matplotlib notebook
... |
arnoldlu/lisa | ipynb/examples/energy_meter/EnergyMeter_ACME.ipynb | apache-2.0 | import logging
from conf import LisaLogging
LisaLogging.setup()
"""
Explanation: Energy Meter Examples
BayLibre's ACME Cape and IIOCapture
More information can be found at https://github.com/ARM-software/lisa/wiki/Energy-Meters-Requirements#iiocapture---baylibre-acme-cape.
End of explanation
"""
# Generate plots inl... |
keras-team/keras-io | examples/structured_data/ipynb/classification_with_tfdf.ipynb | apache-2.0 | import math
import urllib
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import tensorflow_decision_forests as tfdf
"""
Explanation: Classification with TensorFlow Decision Forests
Author: Khalid Salama<br>
Date created: 2022/01/25<br>
La... |
andalexo/cryoblm | jupyter-notebooks/CryoBLM_BLEDP.ipynb | mit | import numpy as np
import logging
from time import time
from datetime import datetime
from os.path import join
from os import walk
%matplotlib notebook
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from itertools import cycle
"""
Explanation: CryoBLM BLEDP
2017-03-26
Display, extraction and analys... |
ysh329/Homework | Python for Genomic Data Science/Lecture 7 Quiz.ipynb | mit | f = open('b.py', 'r')
"""
Explanation: Lecture 7 Quiz
Question 1
A student wants to write into a file called myfile, without deleting its existing content. Which one of the following functions should he or she use?
* f = open('myfile', 'w')
* f = open('myfile', 'a')
* f = open('myfile', 'r')
* f = open('myfile', 'w+b'... |
ernestyalumni/MLgrabbag | coursera_Ng/KNN.ipynb | mit | %matplotlib inline
# sanity check for python setup
import sys
print(sys.executable)
print(sys.path)
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn.cluster import KMeans
from sklearn import datasets
np.random.seed(5)
centers=[[1,1],[-1,-1],[1,-1]]
iris... |
ES-DOC/esdoc-jupyterhub | notebooks/ncc/cmip6/models/sandbox-2/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'sandbox-2', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: NCC
Source ID: SANDBOX-2
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, Radiat... |
mrcslws/nupic.research | projects/meta_cl/compute_sparsity_stats_per_layer.ipynb | agpl-3.0 | BATCH_SIZE = 16
PROJECT_DIR = os.path.expanduser("~/nta/nupic.research/projects/gsc")
EXPERIMENT_NAME = "default_sparse_cnn"
CHECKPOINT_FILE = "/home/ec2-user/nta/results/experiments/gsc/default_sparse_cnn/RemoteProcessTrainable_0_2021-01-07_15-54-473wnu2slv/checkpoint_30/checkpoint"
USE_CUDA = torch.cuda.is_available(... |
desihub/desitarget | doc/nb/elg-properties-dr7.1.ipynb | bsd-3-clause | import os, warnings, pdb
import numpy as np
import fitsio
from astropy.table import Table
import matplotlib.pyplot as plt
import seaborn as sns
rc = {'font.family': 'serif'}#, 'text.usetex': True}
sns.set(style='ticks', font_scale=1.5, palette='Set2', rc=rc)
%matplotlib inline
"""
Explanation: Properties of ELGs in ... |
gertingold/lit2017 | lit2017.ipynb | mit | %matplotlib inline
import numpy as np
from scipy import misc, ndimage
import matplotlib as mpl
import matplotlib.pyplot as plt
from skimage import (color, data, draw, exposure, filters, measure,
morphology, transform)
"""
Explanation: Bildverarbeitung mit Python
Gert-Ludwig Ingold
<div style="marg... |
OXPHOS/shogun | doc/ipython-notebooks/multiclass/Tree/DecisionTrees.ipynb | gpl-3.0 | import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../../data')
# training data
train_income=['Low','Medium','Low','High','Low','High','Medium','Medium','High','Low','Medium',
'Medium','High','Low','Medium']
train_age = ['Old','Young','Old','Young','Old','Young','Young','Old','Old','Old','Young','Old',
'Old... |
hzzyyy/pymcef | examples/2. Black Scholes revisit, delta hedging as an efficient portfolio.ipynb | bsd-3-clause | from scipy.stats import norm
import numpy as np
def bs_put(df, fwd, t, vol, k):
log_fwd_moneyness = np.log(fwd/k)
d1 = (log_fwd_moneyness + vol**2 /2.0 * t) / vol / np.sqrt(t)
d2 = d1 - vol * np.sqrt(t)
return df * (-norm.cdf(-d1) * fwd + norm.cdf(-d2) * k), norm.cdf(-d1)
"""
Explanation: Introduction... |
soracom/handson | cloud/gcp/src/datalab/datalab_basic.ipynb | apache-2.0 | # このセルにカーソルを当ててCtrl+Enter もしくは Shift+Enterを押すと'hello world'と出力することができます
print('hello world')
# 各種制御構文、クラスや関数なども含めて通常のプログラミングと同じように動作させることができます
for i in range(10):
print(i)
# 変数の定義はセル内だけでなく、ノートブック全体がスコープとなり、通常のPythonと同じような扱いとなります
x = 10
# xは10なので、10+20となります
y = x + 20
print(y)
"""
Explanation: Pythonのコードをセル内に書いて... |
statsmodels/statsmodels.github.io | v0.12.1/examples/notebooks/generated/ets.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inline
from statsmodels.tsa.exponential_smoothing.ets import ETSModel
plt.rcParams['figure.figsize'] = (12, 8)
"""
Explanation: ETS models
The ETS models are a family of time series models with an underlying state space model consistin... |
sjsrey/pysal | notebooks/explore/segregation/inference_wrappers_example.ipynb | bsd-3-clause | %matplotlib inline
import geopandas as gpd
from pysal.explore import segregation
import pysal.lib
import pandas as pd
import numpy as np
from pysal.explore.segregation.inference import SingleValueTest, TwoValueTest
"""
Explanation: Inference Wrappers use cases
This is an example of the PySAL segregation framework to... |
CentroGeo/Analisis-Espacial | muap/MUAP.ipynb | gpl-2.0 | from geopandas import GeoDataFrame
estatal = GeoDataFrame.from_file('data/des_rezago_estado.shp')
municipal = GeoDataFrame.from_file('data/muns_geo_des.shp')
"""
Explanation: Unidad de Area Modificable
En esta práctica vamos a explorar la forma en la agregación delos datos influye en los resultados de un análisis.
Par... |
berlemontkevin/Jupyter_Notebook | Algorithm_Physic/M2_PTSC_17/.ipynb_checkpoints/Sampling_pi-checkpoint.ipynb | apache-2.0 | import random #Library of random numbers
import matplotlib.pyplot as plt #Ploting Library
%matplotlib inline # In order to plot in the notebook
n_trials = 4000 #Number of thrown peebles
num_inside = 0
data_inside_x = []
data_inside_y = []
data_outside_x = []
data_o... |
acehanks/projects | machine_learning_hello_world/Iris_dataset_ML_getting_started.ipynb | mit | iris['data'][:5]
print(iris['DESCR'] + "\n")
iris['data'].shape
iris['target'].shape
X= iris['data']
y= iris['target']
"""
Explanation: data from first 5 columns
End of explanation
"""
from sklearn.neighbors import KNeighborsClassifier
knn= KNeighborsClassifier(n_neighbors=1)
"""
Explanation: instantiate KNN ... |
dsacademybr/PythonFundamentos | Cap02/Notebooks/DSA-Python-Cap02-Exercicios.ipynb | gpl-3.0 | # Versão da Linguagem Python
from platform import python_version
print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())
"""
Explanation: <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 2</font>
Download: http://github.com/dsacademybr
End of explanation
"""
# Exerc... |
tpin3694/tpin3694.github.io | machine-learning/.ipynb_checkpoints/loading_scikit-learns_digits-dataset-checkpoint.ipynb | mit | # Load libraries
from sklearn import datasets
import matplotlib.pyplot as plt
"""
Explanation: Title: Loading Scikit-Learn's Digits Dataset
Slug: loading_scikit-learns_digits-dataset
Summary: Loading the built-in digits datasets of Scikit-Learn.
Date: 2016-08-31 12:00
Category: Machine Learning
Tags: Basics
Authors:... |
sassoftware/sas-viya-programming | python/network-analysis/Hartford Drug User Social Network Analysis using CAS HyperGroup.ipynb | apache-2.0 | import swat
import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cmx
# Also import networkx used for rendering a network
import networkx as nx
%matplotlib inline
"""
Explanation: A Simple Pipeline using Hypergroup to Perform Com... |
eds-uga/csci4360-fa17 | workshops/w0/W0--Getting Started with Anaconda for Data Science in Python.ipynb | mit | # Step 1: right-click the "download" link on the left
# Step 2: select "copy link address"
# Step 3: paste the link into the following bash command, after "wget"
!wget https://repo.continuum.io/archive/Anaconda3-4.4.0-Linux-x86_64.sh
"""
Explanation: Preliminaries
Assuming either
Linux (Ubuntu)
macOS
though Windo... |
yevheniyc/Projects | 1j_NLP_Python/ex08.ipynb | mit | from datasketch import MinHash # greate library for Probabilistic Data Structures
def mh_digest (data):
mh = MinHash(num_perm=512)
for d in data:
mh.update(d.encode('utf8')) # hashing and encoding with utf8
return mh
"""
Explanation: Exercise 08: semantic similarity
First, let's create a function to calcu... |
zonemercy/Kaggle | quora/solution/Importance of a Linguistic Pre-processing .ipynb | mit | from nltk.stem.wordnet import WordNetLemmatizer
from nltk.stem import LancasterStemmer
stemmer = LancasterStemmer()
lemmer = WordNetLemmatizer()
"""
Explanation: I've seen a couple of nice kernels here, but no one explained the importance of a morphological pre-processing of the data. So I decided to compare two appr... |
darrenxyli/deeplearning | lessons/sentimentanalysis/sentiment-analysis/Sentiment analysis with TFLearn.ipynb | apache-2.0 | 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... |
Naereen/notebooks | Obfuscated_code_or_piece_of_art.ipynb | mit | %%time
b_ = (
255,
lambda
V ,B,c
:c and Y(V*V+B,B, c
-1)if(abs(V)<6)else
( ... |
anandha2017/udacity | nd101 Deep Learning Nanodegree Foundation/DockerImages/projects/01-first-neural-network/notebooks/Your_first_neural_network_v0.00.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code... |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_objects_from_arrays.ipynb | bsd-3-clause | # Author: Jaakko Leppakangas <jaeilepp@student.jyu.fi>
#
# License: BSD (3-clause)
import numpy as np
import neo
import mne
print(__doc__)
"""
Explanation: Creating MNE objects from data arrays
In this simple example, the creation of MNE objects from
numpy arrays is demonstrated. In the last example case, a
NEO fil... |
y2ee201/Deep-Learning-Nanodegree | autoencoder/Simple_Autoencoder.ipynb | mit | %matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', validation_size=0)
"""
Explanation: A Simple Autoencoder
We'll start off by building a simple autoencoder to compres... |
VectorBlox/PYNQ | Pynq-Z1/notebooks/examples/arduino_grove_ledbar.ipynb | bsd-3-clause | # Make sure the base overlay is loaded
from pynq import Overlay
Overlay("base.bit").download()
"""
Explanation: Grove LED Bar Example
This example shows how to use the Grove LED Bar on the Pynq-Z1 board. The LED bar has 10 LEDs: 8 green LEDs, 1 orange LED, and 1 red LED. The brightness for each LED can be set indepen... |
achave11/bioapi-examples | python_notebooks/1kg.ipynb | apache-2.0 | import ga4gh.client as client
c = client.HttpClient("http://1kgenomes.ga4gh.org")
"""
Explanation: GA4GH 1000 Genome Examples
Variant data from the Thousand Genomes project is being served by the GA4GH reference server. In this example we show how to use the GA4GH client to access the data.
Initialize the client
In t... |
google/starthinker | colabs/sa360_web_query.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: SA360 Web Query Report
Download SA360 reports into a Google Sheet.
License
Copyright 2020 Google LLC,
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 ... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/launching_into_ml/solutions/supplemental/basic_intro_logistic_regression.ipynb | apache-2.0 | import os
import matplotlib.pyplot as plt
import tensorflow as tf
print(f"TensorFlow version: {tf.__version__}")
print(f"Eager execution: {tf.executing_eagerly()}")
"""
Explanation: Introduction to Logistic Regression Using TF 2.0
Learning Objectives
Build a model,
Train this model on example data, and
Use the mod... |
nicococo/tilitools | lectures/optimization_exercise.ipynb | mit | from functools import partial
from scipy.optimize import check_grad, minimize
import numpy as np
import cvxopt as cvx
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Exercise: Optimization
We will implement various optimization algorithms and examine their performance for various tasks.
First-o... |
mne-tools/mne-tools.github.io | 0.20/_downloads/78dfec6019dc9e7214e1efd97200f1c4/plot_10_overview.ipynb | bsd-3-clause | import os
import numpy as np
import mne
"""
Explanation: Overview of MEG/EEG analysis with MNE-Python
This tutorial covers the basic EEG/MEG pipeline for event-related analysis:
loading data, epoching, averaging, plotting, and estimating cortical activity
from sensor data. It introduces the core MNE-Python data struct... |
lukechen526/kaggle-competitions | house-prices-advanced-regression-techniques/comprehensive_exploration.ipynb | mit | #invite people for the Kaggle party
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy.stats import norm
from sklearn.preprocessing import StandardScaler
from scipy import stats
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
# Download the file... |
angelmtenor/deep-learning | tv-script-generation/AMT_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
Student: Angel Martinez-Tenor <br>
Deep Learning Nanodegree... |
greenelab/GCB535 | 32_ML-V/ML5.ipynb | bsd-3-clause | # numpy provides python tools to easily load comma separated files.
import numpy as np
# use numpy to load disease #1 data
d1 = np.loadtxt(open("../31_Data_ML-IV/D1.csv", "rb"), delimiter=",")
# features are all rows for columns before 200
# The canonical way to name this is that X is our matrix of
# examples by feat... |
tensorflow/docs-l10n | site/ja/addons/tutorials/layers_normalizations.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
tensorflow/docs-l10n | site/ja/guide/eager.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... |
fastai/course-v3 | nbs/dl1/rossman_data_clean.ipynb | apache-2.0 | PATH=Config().data_path()/Path('rossmann/')
table_names = ['train', 'store', 'store_states', 'state_names', 'googletrend', 'weather', 'test']
tables = [pd.read_csv(PATH/f'{fname}.csv', low_memory=False) for fname in table_names]
train, store, store_states, state_names, googletrend, weather, test = tables
len(train),len... |
anilcs13m/MachineLearning_Mastering | movie review sentiment analysis/Movie Review Sentiment Analysis.ipynb | gpl-2.0 | import csv
from sklearn.linear_model import LogisticRegression as LR
from sklearn import svm
from sklearn.naive_bayes import BernoulliNB
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn import cross_validation
from sklearn.metrics impo... |
quantopian/research_public | notebooks/lectures/Introduction_to_Pairs_Trading/notebook.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import statsmodels
import statsmodels.api as sm
from statsmodels.tsa.stattools import coint
# just set the seed for the random number generator
np.random.seed(107)
import matplotlib.pyplot as plt
"""
Explanation: Introduction to Pairs Trading
By Delaney Mackenzie and Maxwell Ma... |
tensorflow/docs-l10n | site/en-snapshot/lite/guide/authoring.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
dataplumber/nexus | esip-workshop/student-material/workshop1/4 - Student Exercise.ipynb | apache-2.0 | # TODO: Make sure you run this cell before continuing!
%matplotlib inline
import matplotlib.pyplot as plt
def show_plot(x_data, y_data, x_label, y_label):
"""
Display a simple line plot.
:param x_data: Numpy array containing data for the X axis
:param y_data: Numpy array containing data for the Y... |
amsehili/audio-segmentation-by-classification-tutorial | multiclass_audio_segmentation.ipynb | gpl-3.0 | import wave
import pickle
import numpy as np
from sklearn.mixture import GMM
import librosa
from auditok import DataValidator, ADSFactory, DataSource, StreamTokenizer, BufferAudioSource, player_for
"""
Explanation: Multiclass audio segmentation using auditok and GMMs
What is audio segmentation?
Audio segmentation mean... |
rashikaranpuria/Machine-Learning-Specialization | Classification/Week 1/module-2-linear-classifier-assignment-blank.ipynb | mit | from __future__ import division
import graphlab
import math
import string
"""
Explanation: Predicting sentiment from product reviews
The goal of this first notebook is to explore logistic regression and feature engineering with existing GraphLab functions.
In this notebook you will use product review data from Amazon.... |
AllenDowney/ThinkBayes2 | soln/chap08.ipynb | mit | # If we're running on Colab, install empiricaldist
# https://pypi.org/project/empiricaldist/
import sys
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
!pip install empiricaldist
# Get utils.py
from os.path import basename, exists
def download(url):
filename = basename(url)
if not exists(filename... |
scottquiring/Udacity_Deeplearning | sentiment-rnn/Sentiment_RNN.ipynb | mit | import numpy as np
import tensorflow as tf
with open('../sentiment-network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment-network/labels.txt', 'r') as f:
labels_orig = f.read()
reviews[:2000]
"""
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent n... |
jarrodmcc/OpenFermion | examples/jordan_wigner_and_bravyi_kitaev_transforms.ipynb | apache-2.0 | from openfermion import *
# Create some ladder operators
annihilate_2 = FermionOperator('2')
create_2 = FermionOperator('2^')
annihilate_5 = FermionOperator('5')
create_5 = FermionOperator('5^')
# Construct occupation number operators
num_2 = create_2 * annihilate_2
num_5 = create_5 * annihilate_5
# Map FermionOpera... |
HrWangChengdu/CS231n | assignment1/two_layer_net.ipynb | mit | # A bit of setup
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.neural_net import TwoLayerNet
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
# for auto-reloadi... |
aayushmudgal/Reducing-Manufacturing-Failures | scripts/visualizer.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import collections
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
# import seaborn as sns
# sns.set_style("whitegrid", {'axes.grid' : False})
train_categorical_iter=pd.read_csv("../data/train_categorical.csv",chunksize=100000, dtype=str,usecols=list(range... |
oemof/feedinlib | examples/run_pvlib_model.ipynb | mit | from feedinlib import Photovoltaic
# suppress warnings
import warnings
warnings.filterwarnings("ignore")
"""
Explanation: Example for using the Pvlib model
The Pvlib model can be used to determine the feed-in of a photovoltaic module using the pvlib.
The pvlib is a python library for simulating the performance of pho... |
tensorflow/docs-l10n | site/ja/hub/tutorials/cropnet_cassava.ipynb | apache-2.0 | !pip install matplotlib==3.2.2
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow_datasets as tfds
import tensorflow_hub as hub
#@title Helper function for displaying examples
def plot(examples, predictions=None):
# Get the images, labels, and optionally predictions
imag... |
zegnus/self-driving-car-machine-learning | p01-finding-lane-lines/P1.ipynb | mit | #importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline
#reading in an image
image = mpimg.imread('test_images/solidWhiteRight.jpg')
#printing out some stats and plotting
print('This image is:', type(image), 'with dimesions:', im... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_visualize_evoked.ipynb | bsd-3-clause | import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
# sphinx_gallery_thumbnail_number = 9
"""
Explanation: Visualize Evoked data
In this tutorial we focus on the plotting functions of :class:mne.Evoked.
End of explanation
"""
data_path = mne.datasets.sample.data_path()
fname = op.joi... |
sueiras/training | sklearn/02 - Regression models - SOLVED.ipynb | gpl-3.0 | from sklearn import datasets
all_data = datasets.california_housing.fetch_california_housing()
# Describe dataset
print(all_data.DESCR)
print(all_data.feature_names)
# Print some data lines
print(all_data.data[:10])
print(all_data.target)
#Randomize, normalize and separate train & test
from sklearn.utils import sh... |
google-aai/tf-serving-k8s-tutorial | jupyter/estimator_training_to_serving.ipynb | apache-2.0 | import numpy as np
import os
import tensorflow as tf
import urllib.request
# Define a constant indicating the number of layers in our loaded model. We're loading a
# resnet-50 model.
RESNET_SIZE = 50
# Model and serving directories
MODEL_DIR="resnet_model_checkpoints"
SERVING_DIR="estimator_servable"
SAMPLE_DIR=".... |
paolorivas/homeworkfoundations | 08/Homework_8_Paolo_Rivas.ipynb | mit | df['Count'].plot.box()
"""
Explanation: 0. How is this data distributed?
The presence of extrame values is a common pattern in this dataframe. We need to be carefull while using cetered measures.
End of explanation
"""
df.groupby('Year')['Count'].sum().sort_values(ascending=False)
df.groupby('Year')['Count'].sum()... |
materialsvirtuallab/megnet | notebooks/molecule_example.ipynb | bsd-3-clause | import tensorflow as tf
import pandas as pd
data = pd.read_json('molecules.json').sample(1000)
"""
Explanation: Example using MEGNet with Molecules
This example shows how to use MEGNet with molecular data. It will require that you have installed MEGNet with the extra depedencies for molecules, as described in the doc... |
richlewis42/scikit-chem | docs/tutorial/filters.ipynb | bsd-3-clause | skchem.filters.__all__
"""
Explanation: Filtering
Operations looking to remove compounds from a collection are implemented as Filters in scikit-chem. These are implemented in the skchem.filters packages:
End of explanation
"""
of = skchem.filters.OrganicFilter()
benzene = skchem.Mol.from_smiles('c1ccccc1', name='b... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session09/Day1/ConditionalEntropy.ipynb | mit | def gen_periodic_data(x, period=1, amplitude=1, phase=0, noise=0):
'''Generate periodic data given the function inputs
y = A*cos(x/p - phase) + noise
Parameters
----------
x : array-like
input values to evaluate the array
period : float (default=1)
period of the pe... |
statsmodels/statsmodels.github.io | v0.13.1/examples/notebooks/generated/statespace_arma_0.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
from scipy import stats
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.graphics.api import qqplot
"""
Explanation: Autoregressive Moving Average (ARMA): Sunspots data
This notebook replicates the existing ARMA notebook using th... |
datamicroscopes/release | examples/gauss2d.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pylab as plt
import numpy as np
import time
import seaborn as sns
import pandas as pd
rc = plt.rc("figure", figsize=(8,8))
sns.set_style('darkgrid', rc)
sns.set_context('talk')
sns.set_palette("Set2", 30)
"""
Explanation: Inferring Gaussians with the Dirichlet Process Mixture Model... |
atlury/deep-opencl | DL0110EN/2.2.1linear_regression_one_parameter_v2.ipynb | lgpl-3.0 | # These are the libraries will be used for this lab.
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: <a href="http://cocl.us/pytorch_link_top">
<img src="https://cocl.us/Pytorch_top" width="750" alt="IBM 10TB Storage" />
</a>
<img src="https://ibm.box.com/shared/static/ugcqz6ohbvff804xp84y4kq... |
hjelmj/electrochem | notebooks/EIS_2D_interactive.ipynb | mit | #python dependencies
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from ipywidgets import interact, FloatSlider, RadioButtons
#change the default font set (matplotlib.rc)
mpl.rc('mathtext', fontset='stixsans', default='regular')
#increase text size somewhat
mpl.rcParams.update({'axes.labe... |
xodus7/tensorflow | tensorflow/contrib/lite/tutorials/post_training_quant.ipynb | apache-2.0 | ! pip uninstall -y tensorflow
! pip install -U tf-nightly
import tensorflow as tf
tf.enable_eager_execution()
! git clone --depth 1 https://github.com/tensorflow/models
import sys
import os
if sys.version_info.major >= 3:
import pathlib
else:
import pathlib2 as pathlib
# Add `models` to the python path.
mo... |
dsacademybr/PythonFundamentos | Cap02/Notebooks/DSA-Python-Cap02-Exercicios-Solucao.ipynb | gpl-3.0 | # Versão da Linguagem Python
from platform import python_version
print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())
"""
Explanation: <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 2</font>
Download: http://github.com/dsacademybr
End of explanation
"""
# Exerc... |
efoley/deep-learning | embeddings/Skip-Gram word2vec.ipynb | mit | import time
import numpy as np
import tensorflow as tf
import utils
"""
Explanation: Skip-gram word2vec
In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in natural language p... |
bmcinnes/VCU-VIP-Nanoinformatics | NERD/DecisionTree/Decision Tree NER Model Results.ipynb | gpl-3.0 | #python3 arff_translator.py [filename]
"""
Explanation: Experimental Results from a Decision Tree based NER model
Decisions Trees, as opposed to other machine learning techniques such as SVM's and Neural Networks, provide a human-interpretable classification model. We will exploit this to both generate pretty pictures... |
karlstroetmann/Formal-Languages | ANTLR4-Python/Calculator/Calculator.ipynb | gpl-2.0 | !cat -n Program.g4
"""
Explanation: Embedded Actions in <span style="font-variant:small-caps;">Antlr</span> Grammars
The pure grammar is stored in the file Grammar.g4. On Unix-like operating
systems, the following command displays the file Program.g4.
End of explanation
"""
!type Program.g4
"""
Explanation: On a W... |
gaufung/PythonStandardLibrary | DataPersistence/sqllite.ipynb | mit | import os
import sqlite3
db_filename = 'todo.db'
db_is_new = not os.path.exists(db_filename)
conn = sqlite3.connect(db_filename)
if db_is_new:
print('Need to create schema')
else:
print('Database exists, assume schme does, too.')
conn.close()
%ls *.db
%rm -rf todo.db
import os
import sqlite3
db_filename= 'tod... |
lucasbrsa/MLANN-1 | src/ann_guide/architecture.ipynb | gpl-3.0 | import numpy as np
from scipy import optimize
# training data
x_train = np.array(([3,5],[5,1],[10,2],[6,1.5]), dtype=float)
y_train = np.array(([75],[82],[93],[70]), dtype=float)
# testing data
x_test = np.array(([4, 5.5],[4.5, 1],[9,2.5],[6,2]), dtype=float)
y_test = np.array(([70],[89],[85],[75]), dtype=float)
"""... |
ksooklall/deep_learning_foundation | sentiment-rnn/Sentiment_RNN.ipynb | mit | import numpy as np
import tensorflow as tf
with open('../sentiment-network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment-network/labels.txt', 'r') as f:
labels = f.read()
reviews[:200]
"""
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural ... |
pvalienteverde/ElCuadernillo | ElCuadernillo/20160725_SistemasDeRecomendacionContentBased/Content-Based paso a paso.ipynb | mit | import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.neighbors import NearestNeighbor... |
kimkipyo/dss_git_kkp | 통계, 머신러닝 복습/160627월_21일차_PCA Principal Component Analysis/3.PCA.ipynb | mit | X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
plt.scatter(X[:,0], X[:,1], s=100)
plt.xlim(-4,4)
plt.ylim(-3,3)
plt.title("original data")
plt.show()
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
pca.fit(X)
Z = pca.transform(X)
Z
w, V = np.linalg.eig(pca.get_covariance())
V.T... |
hbjornoy/DataAnalysis | Backup (not final delivery)/Homework 1.ipynb | apache-2.0 | # Imports
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import glob
import csv
import calendar
import webbrowser
from datetime import datetime
# Constants
DATA_FOLDER = 'Data/'
"""
Explanation: Table of Contents
<p><div class="lev1"><a href="#Task-1.-Compiling-Ebola-Data">... |
exe0cdc/PyscesToolbox | documentation/notebooks/RateChar.ipynb | bsd-3-clause | mod = pysces.model('lin4_fb.psc')
rc = psctb.RateChar(mod)
"""
Explanation: RateChar
RateChar is a tool for performing generalised supply-demand analysis (GSDA) [2,3]. This entails the generation data needed to draw rate characteristic plots for all the variable species of metabolic model through parameter scans and t... |
maxkleiner/maXbox4 | MNISTSinglePredict2Test.ipynb | gpl-3.0 | #sign:max: MAXBOX8: 13/03/2021 07:46:37
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import tree
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn import datasets
from sklearn.metrics import accuracy_score
from sklearn.model_selection im... |
dusenberrymw/incubator-systemml | samples/jupyter-notebooks/Linear Regression Algorithms Demo.ipynb | apache-2.0 | !pip uninstall systemml --y
!pip install --user https://repository.apache.org/content/groups/snapshots/org/apache/systemml/systemml/1.0.0-SNAPSHOT/systemml-1.0.0-20171201.070207-23-python.tar.gz
!pip show systemml
"""
Explanation: Linear Regression Algorithms using Apache SystemML
This notebook shows:
- Install Syste... |
rsheftel/raccoon | examples/usage_series.ipynb | mit | # remove comment to use latest development version
import sys; sys.path.insert(0, '../')
# import libraries
import raccoon as rc
"""
Explanation: Example Usage for Series
End of explanation
"""
# empty DataFrame
srs = rc.Series()
srs
# with indexes but no data
srs = rc.Series(index=[1, 2, 3])
srs
# with data
srs ... |
cloudedbats/cloudedbats_dsp | notebooks/experimental/librosa_detect_bat_pulses_in_time_domain.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (15, 4)
import numpy as np
import librosa
"""
Explanation: Librosa - detect bat pulses in time domain
This is an early test to check if librosa can be used to detect bat pulses and sequences in time domain.
Bat sound is normally "spa... |
Leguark/GeMpy | Prototype Notebook/Sandstone Project.ipynb | mit | # Setting extend, grid and compile
# Setting the extent
sandstone = GeMpy_core.GeMpy()
# Create Data class with raw data
sandstone.import_data( 696000,747000,6863000,6950000,-20000, 2000,
path_f = os.pardir+"/input_data/a_Foliations.csv",
path_i... |
matthewzimmer/carnd-behavioral-cloning | basic_training.ipynb | mit | class RecordingMeasurement:
"""
A representation of a vehicle's state at a point in time while driving
around a track during recording.
Features available are:
left_camera_view - An image taken by the LEFT camera.
center_camera_view - An image taken by the CENTER camera.
... |
jorgehatccrma/pyGrFNN | notebooks/utils/.ipynb_checkpoints/Resonance Sequences-checkpoint.ipynb | bsd-3-clause | !date
%matplotlib inline
from __future__ import division
import math
def fareySequence(N, k=1):
"""
Generate Farey sequence of order N, less than 1/k
"""
# assert type(N) == int, "Order (N) must be an integer"
a, b = 0, 1
c, d = 1, N
seq = [(a,b)]
while c/d <= 1/k:
seq.append((... |
pvchaumier/ml_by_example | src/PCA.ipynb | isc | # to display interactive plots within the notebook
%matplotlib notebook
# to define the size of the plotted images
from pylab import rcParams
rcParams['figure.figsize'] = (10, 8)
import matplotlib.pyplot as plt
import numpy as np
from fct import generate_multivariate, normalize, plot_3d, plot_2d, Arrow3D
"""
Explana... |
ComputationalModeling/spring-2017-danielak | past-semesters/fall_2016/day-by-day/day20-monte-carlo-integration/MonteCarlo_Integration_SOLUTIONS.ipynb | agpl-3.0 | # Put your code here!
import random as rand
import math
def f(x):
return 2.0*(x**2) + 3.0
# x min, max: -2, 4 (delta_x = 6)
# y min, max: 0, 35
Area = (35-0)*(4+2)
real_area = 66.0
samples = []
errors = []
for i in range(1,7):
N_samples = 10**i
N_below = 0
for j in range(N_samples):... |
btel/2015_eitn_swc_pandas | 01 - Introduction.ipynb | bsd-2-clause | %matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.options.display.max_rows = 8
"""
Explanation: <h1>Pandas Tutorial</h1>
<h3>Software Carpentry, EITN, Paris, November 20th, 2015</h3>
<h2>Bartosz Teleńczuk</h2>
forked from the tutorial at EuroScipy 2015 by Joris Van den Boss... |
tpin3694/tpin3694.github.io | sql/select_entire_table.ipynb | mit | # Ignore
%load_ext sql
%sql sqlite://
"""
Explanation: Title: Select An Entire Table
Slug: select_entire_table
Summary: Select an entire table in SQL.
Date: 2016-05-01 12:00
Category: SQL
Tags: Basics
Authors: Chris Albon
Note: This tutorial was written using Catherine Devlin's SQL in Jupyter Notebooks library. If ... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/building_production_ml_systems/labs/4b_streaming_data_inference_vertex.ipynb | apache-2.0 | import numpy as np
import os
import shutil
import tensorflow as tf
from google.cloud import aiplatform
from google.cloud import bigquery
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
from matplotlib import pyplot as plt
from tensorflow import keras
from tensorflow.keras.callbacks... |
feststelltaste/software-analytics | prototypes/Quantifying Graph Data.ipynb | gpl-3.0 | import py2neo
import pandas as pd
graph = py2neo.Graph()
graph.dbms.kernel_version
"""
Explanation: Introduction
This is a simple helper notebook to quickly get some numbers about your graphs out of a Neo4j database. You just need to start your Neo4j database locally with the default values before running this noteb... |
alorenzo175/pvlib-python | docs/tutorials/tmy_to_power.ipynb | bsd-3-clause | # built-in python modules
import os
import inspect
# scientific python add-ons
import numpy as np
import pandas as pd
# plotting stuff
# first line makes the plots appear in the notebook
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib as mpl
# finally, we import the pvlib library
import pvlib
... |
intel-analytics/analytics-zoo | pyzoo/zoo/chronos/use-case/fsi/stock_prediction_prophet.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import os
FILE_NAME = 'all_stocks_5yr.csv'
filepath = os.path.join('data', FILE_NAME)
print(filepath)
# read data
data = pd.read_csv(filepath)
print(data[:10])
# change input column name
data = data[data['Name']=='MMM'].rename(columns={"date":"ds", "close":"y"})
data.head()
... |
opengeostat/pygslib | pygslib/Ipython_templates/.ipynb_checkpoints/bicalib_raw-checkpoint.ipynb | mit | #general imports
import matplotlib.pyplot as plt
import pygslib
import numpy as np
import pandas as pd
#make the plots inline
%matplotlib inline
"""
Explanation: PyGSLIB
Bicalib
The GSLIb equivalent parameter file is
```
Parameters for BICALIB
****
START OF PARAMETERS:
... |
ES-DOC/esdoc-jupyterhub | notebooks/nims-kma/cmip6/models/sandbox-2/landice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nims-kma', 'sandbox-2', 'landice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: NIMS-KMA
Source ID: SANDBOX-2
Topic: Landice
Sub-Topics: Glaciers, Ice.
Pro... |
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