repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
thalesians/tsa | src/jupyter/python/pypeoutgoing.ipynb | apache-2.0 | import os, sys
sys.path.append(os.path.abspath('../../main/python'))
import random
import numpy as np
import thalesians.tsa.pypes as pypes
pype = pypes.Pype(pypes.Direction.OUTGOING, name='EXAMPLE', port=5758); pype
"""
Explanation: This Jupyter notebook should be used in conjunction with pypeincoming.ipynb.
Run thr... |
yongtang/tensorflow | tensorflow/lite/g3doc/examples/super_resolution/overview.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... |
informatics-isi-edu/deriva-py | docs/derivapy-datapath-example-1.ipynb | apache-2.0 | # Import deriva modules
from deriva.core import ErmrestCatalog, get_credential
# Connect with the deriva catalog
protocol = 'https'
hostname = 'www.facebase.org'
catalog_number = 1
credential = get_credential(hostname)
catalog = ErmrestCatalog(protocol, hostname, catalog_number, credential)
"""
Explanation: DataPath ... |
vbarua/PythonWorkshop | Code/Introduction To Python/5 - Functions.ipynb | mit | def plus_one(x):
return x + 1
plus_one(1)
plus_one(2)
"""
Explanation: Functions
Functions in Python (and other languages in general) are a way to package reusable chunks of code. You define a function using the def keyword. They can be simple
End of explanation
"""
def do_math(x):
return (x * 2.)/(x - 1.)... |
IIPBC/Material | Notebooks/Python BootCamp 2017 - Your code pretty and healthy.ipynb | mit | import this
"""
Explanation: Your code pretty and healthy
We all already had (or, at least, will have) the experience of getting to work with someone's piece of code and find it a nightmare to make it run, install, debug, etc. Sometimes this someone is our Past Selves that did not do a good job writing a code thinking... |
GoogleCloudPlatform/mlops-on-gcp | immersion/kubeflow_pipelines/walkthrough/solutions/lab-01_vertex.ipynb | apache-2.0 | !pip freeze | grep google-cloud-aiplatform || pip install google-cloud-aiplatform
import os
import time
from google.cloud import aiplatform
from google.cloud import bigquery
import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import SGDClassifier
from sklearn.pipeline import Pi... |
csdms/coupling | docs/demos/child.ipynb | mit | from __future__ import print_function
# Some magic to make plots appear within the notebook
%matplotlib inline
import numpy as np # In case we need to use numpy
"""
Explanation: CHILD Landscape Evolution Model
Link to this notebook: https://github.com/csdms/pymt/blob/master/docs/demos/child.ipynb
Install command: $... |
IBMDecisionOptimization/tutorials | jupyter/Linear_Programming.ipynb | apache-2.0 | import sys
try:
import cplex
except:
if hasattr(sys, 'real_prefix'):
#we are in a virtual env.
!pip install cplex
else:
!pip install --user cplex
"""
Explanation: Tutorial: Linear Programming, (CPLEX Part 1)
This notebook gives an overview of Linear Programming (or LP). After comple... |
planetlabs/notebooks | jupyter-notebooks/orders/tools_and_toolchains.ipynb | apache-2.0 | import json
import os
import pathlib
import time
import numpy as np
import rasterio
from rasterio.plot import show
import requests
from requests.auth import HTTPBasicAuth
# API Key stored as an env variable
PLANET_API_KEY = os.getenv('PL_API_KEY')
orders_url = 'https://api.planet.com/compute/ops/orders/v2'
# set up... |
jsub10/MLCourse | Notebooks/Learning-Curves.ipynb | mit | import numpy as np
# Simulate a dataset containing one feature (and one target)
# The feature values are contained in X
# The target values are contained in y
def make_data(N=100, err=0.8, rseed=1):
# randomly sample the data
rng = np.random.RandomState(rseed)
X = rng.rand(N, 1) ** 2
y = 10 - 1. / (X.r... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/introduction_to_tensorflow/solutions/quickstart.ipynb | apache-2.0 | # Install required packages.
!pip install -q tensorflow-recommenders
!pip install -q --upgrade tensorflow-datasets
"""
Explanation: Working with TensorFlow Recommenders: Quickstart
Learning Objectives
Read the data and build vocabularies.
Define a model.
Create and train a model.
Introduction
In this tutorial, you b... |
dhiapet/PyMC3 | docs/notebooks/getting_started.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
# Intialize random number generator
np.random.seed(123)
# True parameter values
alpha, sigma = 1, 1
beta = [1, 2.5]
# Size of dataset
size = 100
# Predictor variable
X1 = np.linspace(0, 1, size)
X2 = np.linspace(0,.2, size)
# Simulate outcome variable
Y = alpha + ... |
5agado/data-science-learning | data analysis/Pandas and Seaborn.ipynb | apache-2.0 | # Load conversation messages
msgs = pd.read_csv('test_conv.csv', parse_dates=['datetime'])
# Display first 5 entries of our dataframe
msgs.head()
# Double check of types
msgs.info()
"""
Explanation: Table of Contents
Intro
Data Loading
Function Application
Group-By and Aggregation
Plotting
Multi-Index
Pivoting, St... |
rueedlinger/python-snippets | http/beautifulsoup4.ipynb | mit | html_doc = """
<html><head><title>My new page</title></head>
<body>
<p class="title"><b>Cool my new page</b></p>
<p class="story">I have written the following articles:
<a href="http://foo.bar/A1" class="sister" id="link1">A1</a>,
<a href="http://foo.bar/A2" class="sister" id="link2">A2</a>
<a href="http://foo.bar/A3"... |
oscarmore2/deep-learning-study | sentiment-rnn/Sentiment_RNN_Solution.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[:2000]
"""
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural... |
darcamo/pyphysim | ipython_notebooks/METIS Simple Scenario 2.ipynb | gpl-2.0 | %matplotlib widget
# xxxxxxxxxx Add the parent folder to the python path. xxxxxxxxxxxxxxxxxxxx
import sys
import os
sys.path.append('../')
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
from matplotlib import pyplot as plt
import numpy as np
from ipywidgets.widgets import interact, intera... |
gassantos/ML4Edatics | Relatorio-de-Aprendizado-de-Maquina-Trabalho2A.ipynb | gpl-3.0 | import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import PolynomialFeatures
# Carregando o pacote que contém a função de carregar arquivos Matlab
from scipy.io import loadmat
# Carregando o pacote que contém a função de minimizar uma Função Objetivo
from sc... |
ES-DOC/esdoc-jupyterhub | notebooks/nasa-giss/cmip6/models/giss-e2-1h/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nasa-giss', 'giss-e2-1h', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: NASA-GISS
Source ID: GISS-E2-1H
Topic: Aerosol
Sub-Topics: Transport, Emis... |
feststelltaste/software-analytics | courses/python_pandas_basics/session0/DetectCrimesPresentation.ipynb | gpl-3.0 | import pandas as pd
cdr = pd.read_excel("cdr_data.xlsx")
cdr.head()
"""
Explanation: Solving Crimes with Data Science
<br/>
<br/>
<br/>
<b>Markus Harrer</b>
@feststelltaste
<small>PyDay 1, 19. Dezember 2019</small>
<img src="resources/innoq_logo.jpg" width=20% height="20%" align="right"/>
Facts of the case
a white bu... |
Cyb3rWard0g/ThreatHunter-Playbook | docs/notebooks/windows/02_execution/WIN-190813181020.ipynb | gpl-3.0 | from openhunt.mordorutils import *
spark = get_spark()
"""
Explanation: Service Creation
Metadata
| | |
|:------------------|:---|
| collaborators | ['@Cyb3rWard0g', '@Cyb3rPandaH'] |
| creation date | 2019/08/13 |
| modification date | 2020/09/20 |
| playbook related | [] |
Hypothesis
Ad... |
geoneill12/phys202-2015-work | assignments/assignment05/InteractEx01.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
from IPython.html import widgets
"""
Explanation: Interact Exercise 01
Import
End of explanation
"""
def print_sum(a, b):
c = a + b
pri... |
austinjalexander/sandbox | python/py/TextAnalytics/01_Acquire_Text.ipynb | mit | #Inspect the texts available in the built in gutenberg corpus
from nltk.corpus import gutenberg
fileids = gutenberg.fileids()
print len(fileids), 'files'
print fileids
"""
Explanation: NLTK Built In Corpora
End of explanation
"""
#Inspect the raw text of the Alice in Wonderland text
alice_raw = gutenberg.raw(fileids... |
dsacademybr/PythonFundamentos | Cap04/Notebooks/DSA-Python-Cap04-02-Arquivos-Parte2.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 4</font>
Download: http://github.com/dsacademybr
End of explanation
"""
texto =... |
UWSEDS/LectureNotes | PreFall2018/Visualization-in-Depth/Visualization-in-depth.ipynb | bsd-2-clause | from bokeh.plotting import figure, output_file, show, output_notebook, vplot
import random
import numpy as np
import pandas as pd
output_notebook() # Use so see output in the Jupyter notebook
import bokeh
bokeh.__version__
"""
Explanation: Visualization in Depth With Bokeh
For details on bokeh, see http://bokeh.pyda... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/ml_ops/stage2/get_started_vertex_vizier.ipynb | apache-2.0 | import os
# The Vertex AI Workbench Notebook product has specific requirements
IS_WORKBENCH_NOTEBOOK = os.getenv("DL_ANACONDA_HOME")
IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(
"/opt/deeplearning/metadata/env_version"
)
# Vertex AI Notebook requires dependencies to be installed with '--user'
USER_FLAG = ... |
alfkjartan/nvgimu | notebooks/Get started.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
import nvg.ximu.ximudata as ximudata
%matplotlib notebook
"""
Explanation: Getting started with the analysis of nvg data
This notebook assumes that data exists in a database in the hdf5 format. For instructions how to set up the database with data see [../readme.md].
... |
csaladenes/csaladenes.github.io | present/mcc2/PythonDataScienceHandbook/05.02-Introducing-Scikit-Learn.ipynb | mit | import seaborn as sns
iris = sns.load_dataset('iris')
iris.head()
"""
Explanation: <!--BOOK_INFORMATION-->
<img align="left" style="padding-right:10px;" src="figures/PDSH-cover-small.png">
This notebook contains an excerpt from the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub.
Th... |
cogeorg/black_rhino | examples/solow/Analyse_Solow.ipynb | gpl-3.0 | solow_data = pd.read_csv('measurements/TestMeasurement.csv', index_col=0)
"""
Explanation: Analyse Solow
The notebook can be used to analyse the output of the Solow model.
End of explanation
"""
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10,6))
ax.plot(solow_data.index, solow_data)
ax.set_xlabel('Time', fon... |
mne-tools/mne-tools.github.io | dev/_downloads/7b89f7dac105a44e25d2fbdd898b911f/vector_mne_solution.ipynb | bsd-3-clause | # Author: Marijn van Vliet <w.m.vanvliet@gmail.com>
#
# License: BSD-3-Clause
import numpy as np
import mne
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, apply_inverse
print(__doc__)
data_path = sample.data_path()
subjects_dir = data_path / 'subjects'
smoothing_steps = 7
# Read... |
desihub/desitarget | doc/nb/qso-dr8.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
import numpy as np
import pandas as pd
from pathlib import Path
import astropy.table
import astropy.io.fits as fits
import astropy.coordinates
import astropy.units as u
from sklearn import ensemble, model_selection
import desitarge... |
mne-tools/mne-tools.github.io | stable/_downloads/a179627fc73cce931ace004638e9685c/read_inverse.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
import mne
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator
from mne.viz import set_3d_view
print(__doc__)
data_path = sample.data_path()
subjects_dir = data_path / 'subjects'
meg_path = data_path /... |
ramseylab/networkscompbio | class15_MCODE_python3_template.ipynb | apache-2.0 | edge_list =
"""
Explanation: Run mcode on the adjacency list for your toy graph, with vwp=0.8
Load the Krogan et al. network edge-list data as a Pandas data frame
End of explanation
"""
krogan_graph =
krogan_graph.summary()
"""
Explanation: Make an igraph graph and print its summary
End of explanation
"""
res =... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/feateng/ASL_2.0_bqml_feateng-REVIEW.ipynb | apache-2.0 | %%bash
export PROJECT=$(gcloud config list project --format "value(core.project)")
echo "Your current GCP Project Name is: "$PROJECT
import os
PROJECT = "cloud-training-demos" # REPLACE WITH YOUR PROJECT NAME
REGION = "us-west1-b" # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
# Do not change these
os.environ["P... |
diegocavalca/Studies | deep-learnining-specialization/4. Convolutional Neural Networks/week3/Autonomous+driving+application+-+Car+detection+-+v3.ipynb | cc0-1.0 | import argparse
import os
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow
import scipy.io
import scipy.misc
import numpy as np
import pandas as pd
import PIL
import tensorflow as tf
from keras import backend as K
from keras.layers import Input, Lambda, Conv2D
from keras.models import load_model, Mo... |
MelroLeandro/Matematica-Discreta-para-Hackers | jpynb_source/Chapter2_Calculo_Proposicional/.ipynb_checkpoints/Chapter2_Calculo_Proposicional-checkpoint.ipynb | gpl-2.0 | #
# Tabela da Negação
#
for p in [True,False]:
print('not',p,"=", not p)
"""
Explanation: Chapter 2
Lógica proposicional
"Poder-se-á definir a Lógica como a ciência das regras que legitimam
a utilização da palavra portanto." B. Ruyer in Logique.
Proposição
No caso das instruções if e while, a execução dum bloco... |
rahulkgup/deep-learning-foundation | 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... |
lilleswing/deepchem | examples/tutorials/09_Advanced_Model_Training.ipynb | mit | !curl -Lo conda_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py
import conda_installer
conda_installer.install()
!/root/miniconda/bin/conda info -e
!pip install --pre deepchem
import deepchem
deepchem.__version__
"""
Explanation: Tutorial Part 9: Advanced Model Trainin... |
ES-DOC/esdoc-jupyterhub | notebooks/ncar/cmip6/models/sandbox-3/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncar', 'sandbox-3', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: NCAR
Source ID: SANDBOX-3
Topic: Ocnbgchem
Sub-Topics: Tracers.
Properties:... |
dlsun/symbulate | labs/Lab 2 - Random Variables.ipynb | mit | from symbulate import *
%matplotlib inline
"""
Explanation: Symbulate Lab 2 - Random Variables
This Jupyter notebook provides a template for you to fill in. Read the notebook from start to finish, completing the parts as indicated. To run a cell, make sure the cell is highlighted by clicking on it, then press SHIFT ... |
cmshobe/landlab | notebooks/tutorials/network_sediment_transporter/network_sediment_transporter.ipynb | mit | import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
import numpy as np
from landlab.components import FlowDirectorSteepest, NetworkSedimentTransporter
from landlab.data_record import DataRecord
from landlab.grid.network import NetworkModelGrid
from landlab.plot import graph
from landlab.p... |
scikit-rf/scikit-rf | doc/source/examples/networksets/Sorting Network Sets.ipynb | bsd-3-clause | from time import sleep
import skrf as rf
%matplotlib inline
from pylab import *
rf.stylely()
!rm -rf tmp
!mkdir tmp
wg = rf.wr10 # just a dummy media object to generate data
wg.frequency.npoints = 101
for k in range(10):
# timestamp generated with `rf.now_string()`
ntwk = wg.random(name=rf.now_string()+... |
mne-tools/mne-tools.github.io | 0.20/_downloads/638c39682b0791ce4e430e4d2fcc4c45/plot_tf_dics.ipynb | bsd-3-clause | # Author: Roman Goj <roman.goj@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne.event import make_fixed_length_events
from mne.datasets import sample
from mne.time_frequency import csd_fourier
from mne.beamformer import tf_dics
from mne.viz import plot_source_spectrogram
print(__doc__)
data_path = sample.d... |
ueapy/ueapy.github.io | content/notebooks/2017-09-25-numpy-intro.ipynb | mit | my_collection = [1, 4, 6, 10]
my_collection.append(100000)
my_collection.remove(1)
my_collection[1] = 'abcdef'
my_collection
"""
Explanation: Basic data containers in Python include
high-level number objects: integer, floating point, complex, etc.
containers: lists (costless insertion and append), dictionaries (f... |
sysid/nbs | LP/Introduction-to-linear-programming/Introduction to Linear Programming with Python - Part 2.ipynb | mit | import pulp
"""
Explanation: Introduction to Linear Programming with Python - Part 2
Introduction to PuLP
PuLP is an open source linear programming package for python. PuLP can be installed using pip, instructions here.
In this notebook, we'll explore how to construct and solve the linear programming problem described... |
samgoodgame/sf_crime | iterations/misc/Final_21_2348_AUG_17.ipynb | mit | # Basic libraries
import time
import numpy as np
import pandas as pd
# Modeling libraries and estimators
from sklearn.neighbors import KNeighborsClassifier
from sklearn import preprocessing
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.naive_bayes import B... |
annarev/tensorflow | tensorflow/python/ops/numpy_ops/g3doc/TensorFlow_NumPy_Text_Generation.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... |
mansweet/GaussianLDA | Das Code Check.ipynb | apache-2.0 | import numpy as np
import os
from operator import itemgetter
from collections import Counter
import scipy.stats as stat
from gensim.models import Word2Vec
from nltk import corpus
"""
Explanation: Comparing My Output to Das Java Output
NIPS Dataset with 50 Topics
End of explanation
"""
wvmodel = Word2Vec.load_word2ve... |
cpatrickalves/simprev | notebooks/CalculoEstoqueMetodoProb.ipynb | gpl-3.0 | import numpy as np
n_segurados = 1000
prob = 0.35
# Lista que salva a quantidade de aposentados para cada cenário
lista_nap = []
# Lista de seeds -> 50 cenários
seeds = range(0,50)
# Executa 50 cenários (seeds) diferentes
for seed in seeds:
# Define o seed para geração de números aleatórios
np.random.seed(... |
wmvanvliet/neuroscience_tutorials | mne-intro/expert.ipynb | bsd-2-clause | %matplotlib notebook
# Import the MNE-Python module, which contains all the data analysis routines we need
import mne
print('MNE-Python imported.')
# Configure the graphics engine
from matplotlib import pyplot as plt
plt.rc('figure', max_open_warning=100)
%matplotlib notebook
from mayavi import mlab # Mayavi is used... |
MIREL-UNC/mirel-scripts | yago_scripts/Extended Ontology description and how its populated.ipynb | bsd-3-clause | CATEGORIES = [
"wordnet_association_108049401",
"wordnet_authorization_101138670",
"wordnet_ban_107255401",
"wordnet_case_law_106535035",
"wordnet_civil_liberty_113995662",
"wordnet_code_of_conduct_105668095",
"wordnet_common_law_108453722",
"wordnet_company_108058098",
"wordnet_cont... |
agaveapi/SC17-container-tutorial | content/notebooks/06 - Packaging, publishing, and Portability.ipynb | bsd-3-clause | writefile("funwave-input.tpl","""
!INPUT FILE FOR FUNWAVE_TVD
! NOTE: all input parameter are capital sensitive
! --------------------TITLE-------------------------------------
! title only for log file
TITLE = VESSEL
! -------------------HOT START---------------------------------
HOT_START = F
FileNumber_HOTST... |
pombredanne/https-gitlab.lrde.epita.fr-vcsn-vcsn | doc/notebooks/automaton.has_twins_property.ipynb | gpl-3.0 | import vcsn
q = vcsn.context('lal_char(ab), q')
def std(e):
return q.expression(e).standard()
"""
Explanation: automaton.has_twins_property
Whether the automaton has the twins property.
Sibling states: Two states $p$, $q$ are siblings if there exist two labels $x$ and $y$ such that $p$ and $q$ can be reached from... |
johnnyliu27/openmc | examples/jupyter/post-processing.ipynb | mit | %matplotlib inline
from IPython.display import Image
import numpy as np
import matplotlib.pyplot as plt
import openmc
"""
Explanation: This notebook demonstrates some basic post-processing tasks that can be performed with the Python API, such as plotting a 2D mesh tally and plotting neutron source sites from an eigen... |
hfoffani/deep-learning | 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... |
eigendreams/TensorFlow-Tutorials | 03_PrettyTensor.ipynb | mit | from IPython.display import Image
Image('images/02_network_flowchart.png')
"""
Explanation: TensorFlow Tutorial #03
PrettyTensor
by Magnus Erik Hvass Pedersen
/ GitHub / Videos on YouTube
Introduction
The previous tutorial showed how to implement a Convolutional Neural Network in TensorFlow, which required low-level k... |
mbakker7/ttim | pumpingtest_benchmarks/8_test_of_hardinxveld_recovery.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from ttim import *
import pandas as pd
"""
Explanation: Recovery Test
This test is taken from MLU examples.
End of explanation
"""
H = 27 #aquifer thickness [m]
zt = -10 #upper boundary of aquifer
zb = zt - H
rw = 0.155 #well screen radius [m]
Q =... |
karlstroetmann/Artificial-Intelligence | Python/3 Games/Game-Move-Ordering-Memoization.ipynb | gpl-2.0 | gCache = {}
"""
Explanation: Utilities
The global variable gCache is used as a cache for the function evaluate defined later. Instead of just storing the values for a given State, the cache stores pairs of the form
* ('=', v),
* ('≤', v), or
* ('≥', v).
The first component of these pairs is a flag that specifies wh... |
darkomen/TFG | ipython_notebooks/06_regulador_experto/ensayo3.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... |
srcole/qwm | burrito/.ipynb_checkpoints/Burrito_California-checkpoint.ipynb | mit | %config InlineBackend.figure_format = 'retina'
%matplotlib inline
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
sns.set_style("white")
"""
Explanation: San Diego Burrito Analytics: Data characterization
Scott Cole
21 May 2016
This notebook characteriz... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/structured/labs/3a_bqml_baseline_babyweight.ipynb | apache-2.0 | %%bash
pip freeze | grep google-cloud-bigquery==1.6.1 || \
pip install google-cloud-bigquery==1.6.1
"""
Explanation: LAB 3a: BigQuery ML Model Baseline.
Learning Objectives
Create baseline model with BQML
Evaluate baseline model
Calculate RMSE of baseline model
Introduction
In this notebook, we will create a baseli... |
tjwei/HackNTU_Data_2017 | Week02/04-SVM.ipynb | mit | from PIL import Image
import numpy as np
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
from sklearn import datasets, svm, linear_model
matplotlib.style.use('bmh')
matplotlib.rcParams['figure.figsize']=(10,7)
"""
Explanation: Support Vector Machine
End of explanation
"""
X = np.random.normal(5,... |
ceos-seo/data_cube_notebooks | notebooks/vegetation/SAR/Vegetation_Change_S2.ipynb | apache-2.0 | import sys
import os
sys.path.append(os.environ.get('NOTEBOOK_ROOT'))
import warnings
# Supress Warning
warnings.filterwarnings('ignore')
from datacube.utils.aws import configure_s3_access
configure_s3_access(requester_pays=True)
import utils.data_cube_utilities.data_access_api as dc_api
api = dc_api.DataAccessAp... |
octaflop/django_music | presentation/code_life.ipynb | mit | from IPython.display import HTML
"""
Explanation: How to be a Hacker
End of explanation
"""
print('Hello')
"""
Explanation: Intros
End of explanation
"""
HTML("""
<div style="width:100%;height:0;padding-bottom:56%;position:relative;"><iframe src="https://giphy.com/embed/eCqFYAVjjDksg" width="100%" height="100%" ... |
Naereen/notebooks | Py_Pi_Day_2017.ipynb | mit | print(" pi ~= 3.14 (two first digits).")
print(" pi ~= 22/7 = {} (two first digits).".format(22.0 / 7.0))
print(" pi ~= 355/113 = {} (six first digits).".format(355.0 / 113.0))
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Python-Pi-Day-2017" data-toc-modified-id="Python-Pi-Day-2017-1"><sp... |
mne-tools/mne-tools.github.io | 0.23/_downloads/9253528f5ba1c240d3397da64c84a625/90_phantom_4DBTi.ipynb | bsd-3-clause | # Authors: Alex Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
from mne.datasets import phantom_4dbti
import mne
"""
Explanation: 4D Neuroimaging/BTi phantom dataset tutorial
Here we read 4DBTi epochs data obtained with a spherical phantom
using four differe... |
SheffieldML/notebook | GPy/optimizer-implementation.ipynb | bsd-3-clause | # Get the parameters for Rprop of climin:
climin.Rprop?
class RProp(Optimizer):
# We want the optimizer to know some things in the Optimizer implementation:
def __init__(self, step_shrink=0.5, step_grow=1.2, min_step=1e-06, max_step=1, changes_max=0.1, *args, **kwargs):
super(RProp, self).__init__(*arg... |
ES-DOC/esdoc-jupyterhub | notebooks/cccma/cmip6/models/canesm5/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cccma', 'canesm5', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: CCCMA
Source ID: CANESM5
Topic: Ocnbgchem
Sub-Topics: Tracers.
Properties: 6... |
kidpixo/git_history_visualizer | git_history_test_git.ipynb | mit | import os
path = os.getcwd() # put here the desired git repo path
gt = ghv.git_history(path)
gt.get_history()
# new compact version
gt = ghv.git_history(path, get_history=True)
"""
Explanation: Gather the data
Githistoryvis exposes the class git_history.
inizialization:
python
foo = git_history(PATH)
sets the att... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive2/building_production_ml_systems/solutions/4b_streaming_data_inference.ipynb | apache-2.0 | !pip install --user apache-beam[gcp]
"""
Explanation: Working with Streaming Data
Learning Objectives
1. Learn how to process real-time data for ML models using Cloud Dataflow
2. Learn how to serve online predictions using real-time data
Introduction
It can be useful to leverage real time data in a machine learning ... |
wuafeing/Python3-Tutorial | 01 data structures and algorithms/01.08 calculating with dict.ipynb | gpl-3.0 | prices = {
"ACME" : 45.23,
"AAPL" : 612.78,
"IBM" : 205.55,
"HPQ" : 37.20,
"FB" : 10.75
}
"""
Explanation: Previous
1.8 字典的运算
问题
怎样在数据字典中执行一些计算操作(比如求最小值、最大值、排序等等)？
解决方案
考虑下面的股票名和价格映射字典：
End of explanation
"""
min_price = min(zip(prices.values(), prices.keys()))
print("min_price is:", min_price)
... |
ernestyalumni/CompPhys | moreCUDA/samples02/sinsin2dtex.ipynb | apache-2.0 | %matplotlib inline
import matplotlib.pyplot as plt
import csv
ld = [ 1., 1.]
WIDTH = 640
HEIGHT = 640
print WIDTH*HEIGHT
hd = [ld[0]/(float(WIDTH)), ld[1]/(float(HEIGHT)) ]
with open('sinsin2dtex_result.csv','r') as csvfile_result:
plot_results = csv.reader(csvfile_result, delimiter=',')
result_list = list(... |
ShubhamDebnath/Coursera-Machine-Learning | Course 2/Regularization.ipynb | mit | # import packages
import numpy as np
import matplotlib.pyplot as plt
from reg_utils import sigmoid, relu, plot_decision_boundary, initialize_parameters, load_2D_dataset, predict_dec
from reg_utils import compute_cost, predict, forward_propagation, backward_propagation, update_parameters
import sklearn
import sklearn.da... |
ES-DOC/esdoc-jupyterhub | notebooks/nerc/cmip6/models/sandbox-1/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nerc', 'sandbox-1', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: NERC
Source ID: SANDBOX-1
Topic: Ocnbgchem
Sub-Topics: Tracers.
Properties:... |
ahmadpgh/deepSimDEF | data/protein_protein_interaction_data_prepration.ipynb | mit | import pandas as pd
import numpy as np
import os
import requests
import easydict
import linecache
import pprint
import random
pp = pprint.PrettyPrinter(indent=4)
"""
Explanation: PPI Data Prepration<a id='top'></a>
Sections:<br>
0) Description<br>
1) Importing Modules and Packages<br>
2) Configuration<br>
3) Loading ... |
pastas/pastas | examples/notebooks/08_threshold_non_linear.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pastas as pt
pt.set_log_level('WARNING')
pt.show_versions()
ml = pt.io.load('data_notebook_8/B28H1804_2.pas')
# the model is already solved, but we solve it again to be certain
ml.solve(noise=False, report=False)
# and we plot the results
ml... |
ctroupin/OceanData_NoteBooks | PythonNotebooks/PlatformPlots/plot_positions_latest_global.ipynb | gpl-3.0 | datadir = "~/CMEMS_INSTAC/INSITU_GLO_NRT_OBSERVATIONS_013_030/latest/20151201/"
%matplotlib inline
import matplotlib.pyplot as plt
import glob
import os
import netCDF4
import numpy as np
"""
Explanation: In this exercise we will plot all the data locations available for a given day in the latest directory of the glob... |
JaviMerino/lisa | ipynb/tutorial/00_LisaInANutshell.ipynb | apache-2.0 | import logging
reload(logging)
logging.basicConfig(
format='%(asctime)-9s %(levelname)-8s: %(message)s',
datefmt='%I:%M:%S')
# Enable logging at INFO level
logging.getLogger().setLevel(logging.INFO)
# Execute this cell to enable verbose SSH commands
logging.getLogger('ssh').setLevel(logging.DEBUG)
# Other py... |
SamLau95/nbinteract | packages/nbinteract-core/example-notebooks/ipywidgets_widget_list.ipynb | bsd-3-clause | import ipywidgets as widgets
"""
Explanation: Index - Back - Next
Widget List
End of explanation
"""
widgets.IntSlider(
value=7,
min=0,
max=10,
step=1,
description='Test:',
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True,
readout_format='d'
)
"... |
joommf/tutorial | workshops/Durham/tutorial3_dynamics.ipynb | bsd-3-clause | import oommfc as oc
import discretisedfield as df
%matplotlib inline
# Define macro spin mesh (i.e. one discretisation cell).
p1 = (0, 0, 0) # first point of the mesh domain (m)
p2 = (1e-9, 1e-9, 1e-9) # second point of the mesh domain (m)
cell = (1e-9, 1e-9, 1e-9) # discretisation cell size (m)
mesh = oc.Mesh(p1=p... |
tensorflow/docs | site/en/r1/tutorials/eager/eager_basics.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... |
Caranarq/01_Dmine | Datasets/SNIIV/.ipynb_checkpoints/SNIIV-checkpoint.ipynb | gpl-3.0 | descripciones = {
'P0405': 'Viviendas Verticales',
'P0406': 'Viviendas urbanas en PCU U1 y U2',
'P0411': 'Subsidios CONAVI'
}
# Librerias utilizadas
import pandas as pd
import sys
import urllib
import os
import csv
import zeep
import requests
from lxml import etree
import xmltodict
# Configuracion del sis... |
Bio204-class/bio204-notebooks | Popn-Sample-and-SamplingDistn.ipynb | cc0-1.0 | mu, sigma = 64, 8
popn = np.random.normal(loc=mu,scale=sigma, size=100000)
truemu, truesigma = np.mean(popn), np.std(popn)
s = \
"""For the population of interest, the true mean is {}
and the true standard deviation is {} """
print(s.format(truemu,truesigma))
"""
Explanation: Population distribution
Here is a popula... |
AllenDowney/ProbablyOverthinkingIt | distribution.ipynb | mit | from __future__ import print_function, division
%matplotlib inline
%precision 6
import matplotlib.pyplot as plt
import numpy as np
from numpy.fft import fft, ifft
from inspect import getsourcelines
def show_code(func):
lines, _ = getsourcelines(func)
for line in lines:
print(line, end='')
"""
Expl... |
tlkh/Generating-Inference-from-3D-Printing-Jobs | Data Plots New.ipynb | mit | import numpy as np
import csv
%run 'preprocessor.ipynb' #our own preprocessor functions
"""
Explanation: Simple 2D Plots using Matplotlib
Week 4 onwards (Term 2)
Import dependencies
End of explanation
"""
with open('/Users/timothy/Desktop/Files/data_new/merged.csv', 'r') as f:
reader = csv.reader(f)
data... |
mauriciogtec/PropedeuticoDataScience2017 | Alumnos/DiegoAEstradaRivera/Tarea 2.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
# Recibir el path de la imagen y almacenarla en una variable en escala de grises
imagen = Image.open('C:/Users/Diego/Pictures/Nintendo.png')
imagen = imagen.convert('LA')
# Convertir imagen en una matriz numérica
matriz = np.a... |
sprtkd/OpenHmnD | object/object_detection_tutorial.ipynb | mit | import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
"""
Explanation: Object Detection Demo
Welcome to the object detection ... |
excelsimon/AI | DeepLearning/3-ConvolutionalNeuralNetwork/2-DeepCNN_CaseStudy/KerasTutorial/Keras+-+Tutorial+-+Happy+House+v1.ipynb | mit | import numpy as np
from keras import layers
from keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.models import Model
from keras.preprocessing import im... |
renatocoutinho/ode_examples | Chesson Lesson.ipynb | mit | %matplotlib notebook
from numpy import *
from scipy.integrate import odeint
from matplotlib.pyplot import *
ion()
def consumer_resource1(y, t, r, K, b1, m1, b2, m2):
return array([ y[0] * (r*(1-y[0]/K) - b1*y[1] - b2*y[2]),
y[1] * (b1*y[0] - m1),
y[2] * (b2*y[0] - m2)])
t = a... |
IanHawke/orcomp-training | 03-plotting-data.ipynb | mit | %matplotlib inline
"""
Explanation: Plotting
There are many libraries for plotting in Python. The standard library is matplotlib. Its examples and gallery are particularly useful references.
Matplotlib is most useful if you have data in numpy arrays. We can then plot standard single graphs straightforwardly:
End of ex... |
yvesdubief/UVM-ME249-CFD | .ipynb_checkpoints/ME249-Python-crash-course-checkpoint.ipynb | gpl-2.0 | import math
print("pi = %1.15f" %math.pi)
print("pi = %1.15e" %math.pi)
import numpy as np
import scipy as scp
print(" numpy pi = %1.15f" %np.pi)
print(" scipy pi = %1.15f" %scp.pi)
"""
Explanation: <h1> Crash Course in Python</h1>
Python is a powerful programming language that bears many similarities with matlab, ... |
nwilbert/async-examples | notebook/aio34.ipynb | mit | import asyncio
loop = asyncio.get_event_loop()
"""
Explanation: Simple I/O Loop Examples for Python 3.4
Create an event loop (which automatically becomes the default event loop in the context).
End of explanation
"""
def hello_world():
print('Hello World!')
loop.stop()
loop.call_soon(hello_world)
loop.run_f... |
gregstarr/PyGPS | Examples/Rideout method TEC plots.ipynb | agpl-3.0 | fig = plt.figure(figsize=(14,14))
tlim = 4000
minpoints = 3000
svs_with_pts_in_range = [i for i in data.items if(np.sum(np.logical_and.reduce(
(data[data.labels[:tlim],i,'L1','data']>0,
data[data.labels[:tlim],i,'L2','data']>0,
data[data.labels[:tlim],i,'C1','data']>0,... |
mne-tools/mne-tools.github.io | 0.23/_downloads/e41b6a898e7a75f8a9f1a6c00ca73857/20_visualize_epochs.ipynb | bsd-3-clause | import os
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file, verbose=False).crop(tmax=120)
"""
Explanation: Visualizing epo... |
PMBio/scLVM | tutorials/tcell_demo.ipynb | apache-2.0 | # activiate inline plotting
%pylab inline
# load modules
import sys
import scipy as SP
import pylab as PL
from matplotlib import cm
import h5py
import os
#adjust path
scLVM_BASE = './..'
from scLVM import scLVM
#sys.path.append(scLVM_BASE)
#sys.path.append( scLVM_BASE +'..')
#sys.path.append(scLVM_BASE + 'scLVM/utils... |
musketeer191/job_analytics | filter_dups.ipynb | gpl-3.0 | df = df.sort_values(['employer_name', 'doc'])
print('# posts bf filtering dups: %d' %df.shape[0])
df.head(10)
df = df.drop_duplicates(['employer_name', 'doc'])
print('# posts after filtering dups: %d' %df.shape[0])
df.head(10)
df = df.reset_index()
df.head()
df.to_csv(SKILL_DAT + 'uniq_doc_index.csv', index=False)... |
cxxgtxy/tensorflow | tensorflow/python/ops/numpy_ops/g3doc/TensorFlow_Numpy_Distributed_Image_Classification.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
lo-co/atm-py | examples/instruments_POPS_peak.ipynb | mit | from atmPy.instruments.POPS import peaks,calibration
%matplotlib inline
hm.setRcParams(plt)
from importlib import reload
reload(peaks)
"""
Explanation: Introduction
The main function of this module is to convert recored peak intensities to particle sizes and create a sizedistribution instance.
Imports
End of explana... |
VectorBlox/PYNQ | Pynq-Z1/notebooks/examples/pmod_dac_adc.ipynb | bsd-3-clause | from pynq import Overlay
from pynq.iop import Pmod_ADC, Pmod_DAC
"""
Explanation: DAC-ADC Pmod Examples using Matplotlib and Widget
Contents
Pmod DAC-ADC Feedback
Tracking the IO Error
Error plot with Matplotlib
XKCD Plot
Widget controlled plot
Pmod DAC-ADC Feedback
This example shows how to use the PmodDA4 DAC and ... |
apozas/BIST-Python-Bootcamp | 4_Numpy_Pandas.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
print('NumPy:', np.__version__)
print('Pandas:', pd.__version__)
"""
Explanation: 4 NumPy and Pandas
Introduction
After getting used to how Python works, it is the moment to begin getting our hands dirty with data analysis. We will study two packages: NumPy is the fundamental num... |
vicolab/neural-network-intro | 1-regression/1-linear-regression.ipynb | mit | # Plots will be show inside the notebook
%matplotlib notebook
import matplotlib.pyplot as plt
# NumPy is a package for manipulating N-dimensional array objects
import numpy as np
# Pandas is a data analysis package
import pandas as pd
import problem_unittests as tests
"""
Explanation: Outline
<div class="alert ale... |
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