repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
MTG/essentia | src/examples/python/tutorial_io_audio.ipynb | agpl-3.0 | import essentia.standard as es
filename = 'audio/dubstep.flac'
# Load the whole file in mono
audio = es.MonoLoader(filename=filename)()
print(audio.shape)
# Load the whole file in stereo
audio, _, _, _, _, _ = es.AudioLoader(filename=filename)()
print(audio.shape)
# Load and resample to 16000 Hz
audio = es.MonoLoad... |
DigNeurosurgeon/seeg | notebooks/3 seeg_predict_implantation_accuracy-turicreate.ipynb | gpl-3.0 | # import libraries
import turicreate as tc
import h5py
import numpy as np
import pandas as pd
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
plt.style.use('ggplot')
%matplotlib inline
import warnings; warnings.simplefilter('ignore')
#%xmode plain; # shorter error messages
p... |
root-mirror/training | OldSummerStudentsCourse/2017/examples/notebooks/TTreeAccess_Example_py.ipynb | gpl-2.0 | import ROOT
"""
Explanation: Access TTree in Python using PyROOT
<hr style="border-top-width: 4px; border-top-color: #34609b;">
End of explanation
"""
f = ROOT.TFile.Open("https://root.cern.ch/files/summer_student_tutorial_tracks.root")
"""
Explanation: Open a file which is located on the web. No type is to be spec... |
tpin3694/tpin3694.github.io | machine-learning/break_up_dates_and_times_into_multiple_features.ipynb | mit | # Load library
import pandas as pd
"""
Explanation: Title: Break Up Dates And Times Into Multiple Features
Slug: break_up_dates_and_times_into_multiple_features
Summary: How to break up dates and times into multiple features for machine learning in Python.
Date: 2017-09-11 12:00
Category: Machine Learning
Tags: Pre... |
sylvchev/coursIntroPython | cours/4-ApprendrePython-Modules.ipynb | gpl-3.0 | # Module nombres de Fibonacci
def fib(n): # écrit la série de Fibonacci jusqu’à n
a, b = 0, 1
while b < n:
print (b, end=' ')
a, b = b, a+b
def fib2(n): # retourne la série de Fibonacci jusqu’à n
result = []
a, b = 0, 1
while b < n:
result.append(b)
a... |
GoogleCloudPlatform/python-docs-samples | notebooks/tutorials/bigquery/Visualizing BigQuery public data.ipynb | apache-2.0 | %%bigquery
SELECT
source_year AS year,
COUNT(is_male) AS birth_count
FROM `bigquery-public-data.samples.natality`
GROUP BY year
ORDER BY year DESC
LIMIT 15
"""
Explanation: Vizualizing BigQuery data in a Jupyter notebook
BigQuery is a petabyte-scale analytics data warehouse that you can use to run SQL queries ... |
tpin3694/tpin3694.github.io | machine-learning/discretize_features.ipynb | mit | # Load libraries
from sklearn.preprocessing import Binarizer
import numpy as np
"""
Explanation: Title: Discretize Features
Slug: discretize_features
Summary: How to discretize features for machine learning in Python.
Date: 2016-09-06 12:00
Category: Machine Learning
Tags: Preprocessing Structured Data
Authors: Ch... |
mne-tools/mne-tools.github.io | 0.18/_downloads/82dd66e6bdf7150b8691eaa46b63bcf9/plot_read_events.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Chris Holdgraf <choldgraf@berkeley.edu>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
fname = data_path + '/MEG/sample/sample_audvi... |
NEONScience/NEON-Data-Skills | tutorials/Python/NEON-API-python/neon_api_01_introduction_requests_py/neon_api_01_introduction_requests_py.ipynb | agpl-3.0 | import requests
import json
#Every request begins with the server's URL
SERVER = 'http://data.neonscience.org/api/v0/'
"""
Explanation: syncID: f059914f7cf74327908228e63e204d60
title: "Introduction to NEON API in Python"
description: "Use the NEON API in Python, via requests package and json package."
dateCreated: 20... |
domijin/ml-fit | gcpg_test.ipynb | mit | %pylab inline
import numpy as np
from datetime import datetime
import random
import pandas as pd
import os
"""
Explanation: Outline
GC per Galaxy
Harris Data Inspection
clean data
add iMType
exclude 0: Milky Way Galaxy & 356: A1689-BCG with NaN VMag
remove duplicate NGC4417(228=NaN), select better result for VCC-1386... |
sdpython/ensae_teaching_cs | _doc/notebooks/td2a_eco/td2a_eco_exercices_de_manipulation_de_donnees_correction_b.ipynb | mit | %matplotlib inline
from jyquickhelper import add_notebook_menu
add_notebook_menu()
from pyensae.datasource import download_data
files = download_data("td2a_eco_exercices_de_manipulation_de_donnees.zip",
url="https://github.com/sdpython/ensae_teaching_cs/raw/master/_doc/notebooks/td2a_eco/data/")... |
flothesof/SongCreator | IPython notebooks/Explore XML file names in wikifonia dump.ipynb | mit | import glob
fnames = glob.glob("../MusicXML_files/wikifonia20100503/*.xml")
fnames[:10]
"""
Explanation: Let's explore the song names in the files that are in the Wikifonia dump from 2010.
The folder wikifonia20100503 comes from a dump of the wikifonia database found here:
https://github.com/jganseman/musq
First, let... |
Honestpuck/charming | Notebooks/Importing Notebooks.ipynb | artistic-2.0 | import io, os, sys, types
from IPython import get_ipython
from IPython.nbformat import current
from IPython.core.interactiveshell import InteractiveShell
"""
Explanation: Importing IPython Notebooks as Modules
It is a common problem that people want to import code from IPython Notebooks.
This is made difficult by the... |
briennakh/BIOF509 | Wk12/Wk12-machine-learning-workflow.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
%matplotlib inline
"""
Explanation: Week 12 - The Machine Learning Workflow
End of explanation
"""
# http://scikit-learn.org/stable/auto_examples/plot_digits_pipe.html#example-plot-digits-pipe-py
import numpy as np
import matplotlib.pyplot as p... |
NREL/bifacial_radiance | docs/tutorials/6 - Advanced topics - Understanding trackerdict structure.ipynb | bsd-3-clause | import bifacial_radiance
from pathlib import Path
import os
testfolder = str(Path().resolve().parent.parent / 'bifacial_radiance' / 'Tutorial_06')
if not os.path.exists(testfolder):
os.makedirs(testfolder)
simulationName = 'tutorial_6'
moduletype = 'test-module'
albedo = "litesoil" # this is... |
jepegit/cellpy | dev_utils/lookup/cellpy_check_hdf5_queries.ipynb | mit | my_data.make_step_table()
filename2 = Path("/Users/jepe/Arbeid/Data/celldata/20171120_nb034_11_cc.nh5")
my_data.save(filename2)
print(f"size: {filename2.stat().st_size/1_048_576} MB")
my_data2 = cellreader.CellpyData()
my_data2.load(filename2)
dataset2 = my_data2.dataset
print(dataset2.steps.columns)
del my_data2
de... |
eford/rebound | ipython_examples/Units.ipynb | gpl-3.0 | import rebound
import math
sim = rebound.Simulation()
sim.G = 6.674e-11
"""
Explanation: Unit convenience functions
For convenience, REBOUND offers simple functionality for converting units. One implicitly sets the units for the simulation through the values used for the initial conditions, but one has to set the app... |
PyLCARS/PythonUberHDL | myHDL_ComputerFundamentals/Counters/CountersInMyHDL.ipynb | bsd-3-clause | from myhdl import *
from myhdlpeek import Peeker
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from sympy import *
init_printing()
import random
#https://github.com/jrjohansson/version_information
%load_ext version_information
%version_information myhdl, myhdlpeek, numpy, ... |
malogrisard/NTDScourse | toolkit/02_ex_exploitation.ipynb | mit | import pandas as pd
import numpy as np
from IPython.display import display
import os.path
folder = os.path.join('..', 'data', 'social_media')
# Your code here.
fb = pd.read_sql('facebook', 'sqlite:///' + os.path.join(folder, 'facebook.sqlite'))
tw = pd.read_sql('twitter', 'sqlite:///' + os.path.join(folder, 'twitter.... |
ML4DS/ML4all | R1.Intro_Regression/.ipynb_checkpoints/regression_intro_student-checkpoint.ipynb | mit | # Import some libraries that will be necessary for working with data and displaying plots
# To visualize plots in the notebook
%matplotlib inline
import numpy as np
import scipy.io # To read matlab files
import pandas as pd # To read data tables from csv files
# For plots and graphical results
import matplo... |
wheeler-microfluidics/teensy-minimal-rpc | teensy_minimal_rpc/notebooks/dma-examples/Example - [BROKEN] Periodic multi-channel ADC multiple samples using DMA and PIT.ipynb | gpl-3.0 | import pandas as pd
def get_pdb_divide_params(frequency, F_BUS=int(48e6)):
mult_factor = np.array([1, 10, 20, 40])
prescaler = np.arange(8)
clock_divide = (pd.DataFrame([[i, m, p, m * (1 << p)]
for i, m in enumerate(mult_factor) for p in prescaler],
... |
bearing/dosenet-analysis | Programming Lesson Modules/Module 4- Example Plot of Weather Data.ipynb | mit | %matplotlib inline
import csv
import io
import urllib.request
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
# another matplotlib convention; this extension facilitates dates as
# axes labels.
from datetime import datetime
# we will use the datetime extension so we ca... |
phoebe-project/phoebe2-docs | 2.2/tutorials/ltte.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.2,<2.3"
"""
Explanation: Rømer and Light Travel Time Effects (ltte)
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
"""... |
woobe/h2o_tutorials | introduction_to_machine_learning/py_03a_regression_basics.ipynb | mit | # Start and connect to a local H2O cluster
import h2o
h2o.init(nthreads = -1)
"""
Explanation: Machine Learning with H2O - Tutorial 3a: Regression Models (Basics)
<hr>
Objective:
This tutorial explains how to build regression models with four different H2O algorithms.
<hr>
Wine Quality Dataset:
Source: https://ar... |
ES-DOC/esdoc-jupyterhub | notebooks/cccr-iitm/cmip6/models/iitm-esm/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cccr-iitm', 'iitm-esm', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: CCCR-IITM
Source ID: IITM-ESM
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation... |
widdowquinn/Notebooks-Bioinformatics | Biopython_NCBI_Entrez_downloads.ipynb | mit | # This line imports the Bio.Entrez module, and makes it available
# as 'Entrez'.
from Bio import Entrez
# The line below imports the Bio.SeqIO module, which allows reading
# and writing of common bioinformatics sequence formats.
from Bio import SeqIO
# Create a new directory (if needed) for output/downloads
import os... |
iRipVanWinkle/ml | mlcourse_open[solutions]/homeworks/hw3_session2_decision_trees.ipynb | mit | import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
%matplotlib inline
from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier, export_graphviz
"""
Explanation: <center>
<img s... |
widdowquinn/notebooks | sampling_fnr_fpr.ipynb | mit | %pylab inline
from scipy import stats
from ipywidgets import interact, fixed
def sample_distributions(mu_neg, mu_pos, sd_neg, sd_pos,
n_neg, n_pos, fnr, fpr,
clip_low, clip_high):
"""Returns subsamples and observations from two normal
distributions.
-... |
ES-DOC/esdoc-jupyterhub | notebooks/hammoz-consortium/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', 'hammoz-consortium', 'sandbox-2', 'landice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: HAMMOZ-CONSORTIUM
Source ID: SANDBOX-2
Topic: Landice
Sub-Topics: G... |
ljwolf/pysal | pysal/contrib/viz/mapping_guide.ipynb | bsd-3-clause | shp_link = ps.examples.get_path('columbus.shp')
shp = ps.open(shp_link)
some = [bool(random.getrandbits(1)) for i in ps.open(shp_link)]
fig = plt.figure()
base = maps.map_poly_shp(shp)
base.set_facecolor('none')
base.set_linewidth(0.75)
base.set_edgecolor('0.8')
some = maps.map_poly_shp(shp, which=some)
some.set_alph... |
anandha2017/udacity | nd101 Deep Learning Nanodegree Foundation/DockerImages/19_Autoencoders/notebooks/autoencoder/Simple_Autoencoder_Solution.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... |
GoogleCloudPlatform/ai-platform-samples | notebooks/samples/tables/census_income_prediction/getting_started_notebook.ipynb | apache-2.0 | # Use the latest major GA version of the framework.
! pip install --upgrade --quiet --user --user google-cloud-automl
"""
Explanation: Getting Started with AutoML Tables
<table align="left">
<td>
<a href="https://colab.sandbox.google.com/github/GoogleCloudPlatform/ai-platform-samples/blob/main/notebooks/samples/... |
rvuduc/cse6040-ipynbs | 14--pagerank-partial-solns2.ipynb | bsd-3-clause | import sqlite3 as db
import pandas as pd
def get_table_names (conn):
assert type (conn) == db.Connection # Only works for sqlite3 DBs
query = "SELECT name FROM sqlite_master WHERE type='table'"
return pd.read_sql_query (query, conn)
def print_schemas (conn, table_names=None, limit=0):
assert type (con... |
jacobdein/alpine-soundscapes | utilities/Set weather data datetime.ipynb | mit | weather_filepath = ""
"""
Explanation: Set weather data datetime
This notebook formats a date and a time column for weather data measurements with a unix timestamp. Each measurement is then inserted into a pumilio database.
Required packages
<a href="https://github.com/pydata/pandas">pandas</a> <br />
<a href="https:/... |
CQuIC/pysme | notebooks/mollow-triplets/mollow-triplets-2.ipynb | mit | from functools import partial
import pdb
import pickle
import numpy as np
from scipy.optimize import minimize
from scipy.fftpack import fft, fftshift, fftfreq
from scipy.integrate import quad
from scipy.special import factorial, sinc
import matplotlib.pyplot as plt
import pysme.integrate as integ
import pysme.hierarc... |
phoebe-project/phoebe2-docs | development/tutorials/distance.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.4,<2.5"
"""
Explanation: Distance
Setup
Let's first make sure we have the latest version of PHOEBE 2.4 installed (uncomment this line if running in an online notebook session such as colab).
End of explanation
"""
import phoebe
from phoebe import u # units
import numpy as np
import matplo... |
GoogleCloudPlatform/cloudml-samples | notebooks/scikit-learn/TrainingWithScikitLearnInCMLE.ipynb | apache-2.0 | # 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 the L... |
gaufung/ISL | training-materials/Stasmodels-training/OLS.ipynb | mit | import numpy as np
import statsmodels.api as sm
import matplotlib.pyplot as plt
from statsmodels.sandbox.regression.predstd import wls_prediction_std
%matplotlib inline
"""
Explanation: Ordinary Least Squares
End of explanation
"""
# artificial data
nsample = 100
x = np.linspace(0, 10, nsample)
X = np.column_stack((... |
iurilarosa/thesis | codici/Archiviati/numpy/.ipynb_checkpoints/Hough Numpy-checkpoint.ipynb | gpl-3.0 | import scipy.io
import pandas
import numpy
import os
from matplotlib import pyplot
from scipy import sparse
import multiprocessing
%matplotlib inline
#carico file dati
percorsoFile = "/home/protoss/Documenti/TESI/DATI/peakmap1.mat.mat"
#print(picchi.shape)
#picchi[0]
#nb: picchi ha 0-tempi
# 1-frequenz... |
ES-DOC/esdoc-jupyterhub | notebooks/ec-earth-consortium/cmip6/models/ec-earth3-gris/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'ec-earth3-gris', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: EC-EARTH-CONSORTIUM
Source ID: EC-EARTH3-GRIS
Topic: Ocean
Sub-T... |
sdpython/ensae_teaching_cs | _doc/notebooks/td1a_home/2020_covid.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
%matplotlib inline
"""
Explanation: Algo - simulation COVID
Ou comment utiliser les mathématiques pour comprendre la propagation de l'épidémie.
End of explanation
"""
from pandas import read_csv, to_datetime
url = "https://www.data.gouv.fr/en/datasets/... |
rashikaranpuria/Machine-Learning-Specialization | Clustering_&_Retrieval/Week4/Assignment1/.ipynb_checkpoints/3_em-for-gmm_blank-checkpoint.ipynb | mit | import graphlab as gl
import numpy as np
import matplotlib.pyplot as plt
import copy
from scipy.stats import multivariate_normal
%matplotlib inline
"""
Explanation: Fitting Gaussian Mixture Models with EM
In this assignment you will
* implement the EM algorithm for a Gaussian mixture model
* apply your implementatio... |
guiquanz/msaf | examples/Run MSAF.ipynb | mit | from __future__ import print_function
import msaf
import librosa
import seaborn as sns
# and IPython.display for audio output
import IPython.display
# Setup nice plots
sns.set(style="dark")
%matplotlib inline
"""
Explanation: Running MSAF
The main MSAF functionality is demonstrated here.
End of explanation
"""
# C... |
edosedgar/xs-pkg | machine_learning/hw3/HW3/ML2019HW03-part1.ipynb | gpl-2.0 | import numpy as np
import pandas as pd
import torch
%matplotlib inline
import matplotlib.pyplot as plt
"""
Explanation: Home Assignment No. 3: Part 1
In this part of the homework you are to solve several problems related to machine learning algorithms.
* For every separate problem you can get only 0 points or maxima... |
sailuh/perceive | Notebooks/Dataset_Comparision/dataset_comparision.ipynb | gpl-2.0 | #import packages
import pandas as pd
import glob
import csv
from xml.etree.ElementTree import ElementTree
import re
"""
Explanation: Dataset Comparision
End of explanation
"""
#function to load a csv file
#accepts folderpath and headerlist as parameter to load the data files
def file_csv(folderpath,addheader,headerl... |
roatienza/Deep-Learning-Experiments | versions/2022/mlp/python/mlp_pytorch_demo.ipynb | mit | import torch
import torchvision
import wandb
import math
from torch import nn
from einops import rearrange
from argparse import ArgumentParser
from pytorch_lightning import LightningModule, Trainer, Callback
from pytorch_lightning.loggers import WandbLogger
from torchmetrics.functional import accuracy
from torch.optim ... |
mne-tools/mne-tools.github.io | stable/_downloads/48e14d460d6470997b890b156746a671/30_strf.ipynb | bsd-3-clause | # Authors: Chris Holdgraf <choldgraf@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD-3-Clause
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.decoding import ReceptiveField, TimeDelayingRidge
from scipy.stats import multivariate_normal
from scipy.io import loadmat
... |
Capepy/scipy_2015_sklearn_tutorial | notebooks/03.6 Case Study - Titanic Survival.ipynb | cc0-1.0 | from sklearn.datasets import load_iris
iris = load_iris()
print(iris.data.shape)
"""
Explanation: Feature Extraction
Here we will talk about an important piece of machine learning: the extraction of
quantitative features from data. By the end of this section you will
Know how features are extracted from real-world d... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/building_production_ml_systems/solutions/3_kubeflow_pipelines.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
pip freeze | grep kfp || pip install kfp
from os import path
import kfp
import kfp.compiler as compiler
import kfp.components as comp
import kfp.dsl as dsl
import kfp.gcp as gcp
import kfp.notebook
"""
Explanation: Kubeflow pipelines
Learning Object... |
garth-wells/IA-maths-Jupyter | Lecture02.ipynb | mit | from sympy import *
# This initialises pretty printing
init_printing()
from IPython.display import display
# This command makes plots appear inside the browser window
%matplotlib inline
"""
Explanation: Lecture 2: second-order ordinary differential equations
We now look at solving second-order ordinary differential ... |
linamnt/studyGroup | lessons/misc/quantum-computing/grovers-algorthim-2-qubits.ipynb | apache-2.0 | import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
"""
Explanation: Simulating Grover's Search Algorithm with 2 Qubits
End of explanation
"""
zero = np.matrix([[1],[0]]);
one = np.matrix([[0],[1]]);
psi = np.kron(zero,zero);
print(psi)
"""
Explanation: Define the zero and one vectors
Define... |
alshedivat/tensorflow | tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb | apache-2.0 | # Install TensorFlow; note that Colab notebooks run remotely, on virtual
# instances provided by Google.
!pip install -U -q tf-nightly
import os
import time
import tensorflow as tf
from tensorflow.contrib import autograph
import matplotlib.pyplot as plt
import numpy as np
import six
from google.colab import widgets... |
benwaugh/NuffieldProject2016 | notebooks/ROOTDataAccessExample.ipynb | mit | import pylab
import matplotlib.pyplot as plt
%matplotlib inline
pylab.rcParams['figure.figsize'] = 12,8
"""
Explanation: Simple test of using ROOT in a Python notebook
Trying to read and process some data from a ROOT file over the network. Using material from
* Example of a Z Analysis ROOT C++ kernel
* ROOT reference ... |
tensorflow/docs-l10n | site/ja/guide/keras/custom_callback.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... |
jjehl/poppy_education | poppy-4dof-arm-mini/poppy_4dof_arm_mini_test.ipynb | gpl-2.0 | import pypot.dynamixel
import time
"""
Explanation: Some tests to check if your setup is running correctly - Using dynamixel XL320 motor
End of explanation
"""
print(pypot.dynamixel.get_available_ports())
"""
Explanation: Low level test
Find the available usb port. The port where USB2AX or USBDynamixel is plug.
End... |
CristinaFoltea/pythonD3 | IPythonD3.ipynb | bsd-2-clause | # import requirments
from IPython.display import Image
from IPython.display import display
from IPython.display import HTML
from datetime import *
import json
from copy import *
from pprint import *
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import json
from ggplot import *
import networkx ... |
d00d/quantNotebooks | Notebooks/quantopian_research_public/notebooks/lectures/Introduction_to_Python/notebook.ipynb | unlicense | # This is a comment
# These lines of code will not change any values
# Anything following the first # is not run as code
"""
Explanation: Introduction to Python
by Maxwell Margenot
Part of the Quantopian Lecture Series:
www.quantopian.com/lectures
github.com/quantopian/research_public
Notebook released under the Cre... |
jrg365/gpytorch | examples/04_Variational_and_Approximate_GPs/Modifying_the_variational_strategy_and_distribution.ipynb | mit | import urllib.request
import os
from scipy.io import loadmat
from math import floor
# this is for running the notebook in our testing framework
smoke_test = ('CI' in os.environ)
if not smoke_test and not os.path.isfile('../elevators.mat'):
print('Downloading \'elevators\' UCI dataset...')
urllib.request.url... |
jseabold/statsmodels | examples/notebooks/statespace_structural_harvey_jaeger.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
from IPython.display import display, Latex
"""
Explanation: Detrending, Stylized Facts and the Business Cycle
In an influential article, Harvey and Jaeger (1993) described the use of unobserved comp... |
empet/Math | hypocycloid-online.ipynb | bsd-3-clause | from IPython.display import Image
Image(filename='generate-hypocycloid.png')
"""
Explanation: Hypocycloid definition and animation
Deriving the parametric equations of a hypocycloid
On May 11 @fermatslibrary posted a gif file, https://twitter.com/fermatslibrary/status/862659602776805379, illustrating the motion of eig... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session14/Day2/BuildingPerceptronsForClassification.ipynb | mit | def walk_dog( # complete
'''Perceptron to calculate whether we should walk the dog
Parameters
----------
questions : array-like, size = 3
weights : array-lik, optional (default = np.array([-2, -1, 5]))
threshold : float, optional (default = 2.5)
decision threshold for whether to wal... |
dinrker/PredictiveModeling | Session 3 - Classification.ipynb | mit | from IPython.display import Image
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import time
%matplotlib inline
"""
Explanation: Goals of this Lesson
Extend the regression framework to support classification
Logistic Regression
Training with Gradient Descent
Training with Newton's Method
... |
transcranial/keras-js | notebooks/layers/pooling/GlobalAveragePooling2D.ipynb | mit | data_in_shape = (6, 6, 3)
L = GlobalAveragePooling2D(data_format='channels_last')
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
np.random.seed(270)
data_in = 2 * np.random.random(data_in_shape) - 1
result... |
jmhsi/justin_tinker | data_science/courses/temp/courses/dl1/embedding_refactoring_unit_tests.ipynb | apache-2.0 | embed = torch.nn.Embedding(10,3)
words = torch.autograd.Variable(torch.LongTensor([[1,2,4,5] ,[4,3,2,9]]))
"""
Explanation: Test 1
Initialize embedding matrix and input
End of explanation
"""
torch.manual_seed(88123)
dropout_out_old = embedded_dropout(embed, words, dropout=0.40)
dropout_out_old
"""
Explanation: pro... |
martinjrobins/hobo | examples/toy/distribution-neals-funnel.ipynb | bsd-3-clause | import pints
import pints.toy
import numpy as np
import matplotlib.pyplot as plt
# Create log pdf
log_pdf = pints.toy.NealsFunnelLogPDF()
# Plot marginal density
levels = np.linspace(-7, -1, 20)
x = np.linspace(-10, 10, 100)
y = np.linspace(-10, 10, 100)
X, Y = np.meshgrid(x, y)
Z = [[log_pdf.marginal_log_pdf(i, j) f... |
ampl/amplpy | notebooks/colab_bash.ipynb | bsd-3-clause | !pip install -q amplpy
"""
Explanation: AMPLPY: Google Colab Template
Documentation: http://amplpy.readthedocs.io
GitHub Repository: https://github.com/ampl/amplpy
PyPI Repository: https://pypi.python.org/pypi/amplpy
Jupyter Notebooks: https://github.com/ampl/amplpy/tree/master/notebooks
Setup
End of explanation
"""
... |
ES-DOC/esdoc-jupyterhub | notebooks/inpe/cmip6/models/sandbox-2/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'inpe', 'sandbox-2', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: INPE
Source ID: SANDBOX-2
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Turbul... |
YeEmrick/learning | cs231/assignment/assignment1/two_layer_net.ipynb | apache-2.0 | # A bit of setup
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.neural_net import TwoLayerNet
from __future__ import print_function
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['im... |
bjshaw/phys202-2015-work | days/day11/Interpolation.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
"""
Explanation: Interpolation
Learning Objective: Learn to interpolate 1d and 2d datasets of structured and unstructured points using SciPy.
End of explanation
"""
x = np.linspace(0,4*np.pi,10)
x
"""
Explanation: Overview
W... |
mattmcd/PyBayes | scripts/amm_math_20210308.ipynb | apache-2.0 | from IPython.display import HTML
# Hide code cells https://gist.github.com/uolter/970adfedf44962b47d32347d262fe9be
def hide_code():
return HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$("div.input").hide();
} else {
$("div.input").show();
... |
steinam/teacher | jup_notebooks/data-science-ipython-notebooks-master/deep-learning/tensor-flow-exercises/2_fullyconnected.ipynb | mit | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
import cPickle as pickle
import numpy as np
import tensorflow as tf
"""
Explanation: Deep Learning with TensorFlow
Credits: Forked from TensorFlow by Google
Setup
Refer to the setup instructions.
Exercise 2
Pre... |
gdsfactory/gdsfactory | docs/notebooks/04_components_hierarchy.ipynb | mit | import gdsfactory as gf
# gf.CONF.plotter = 'holoviews'
@gf.cell
def bend_with_straight(
bend=gf.components.bend_euler,
straight=gf.components.straight,
) -> gf.Component:
c = gf.Component()
b = bend()
s = straight()
bref = c << b
sref = c << s
sref.connect("o2", bref.ports["o2"])
... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/image_classification/labs/1_mnist_linear.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
import os
import shutil
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard
from tensorflow.keras.layers import Dense, F... |
noammor/coursera-machinelearning-python | ex4/ml-ex4.ipynb | mit | import numpy as np
import scipy.io
import scipy.optimize
import matplotlib.pyplot as plt
%matplotlib inline
# uncomment for console - useful for debugging
# %qtconsole
ex3data1 = scipy.io.loadmat("./ex4data1.mat")
X = ex3data1['X']
y = ex3data1['y'][:,0]
m, n = X.shape
m, n
input_layer_size = n # 20x20 Input Image... |
flutter/codelabs | tfrs-flutter/step5/backend/ranking/ranking.ipynb | bsd-3-clause | #@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... |
quantumlib/OpenFermion-Cirq | openfermioncirq/experiments/hfvqe/quickstart.ipynb | apache-2.0 | # Import library functions and define a helper function
import numpy as np
import cirq
from openfermioncirq.experiments.hfvqe.gradient_hf import rhf_func_generator
from openfermioncirq.experiments.hfvqe.opdm_functionals import OpdmFunctional
from openfermioncirq.experiments.hfvqe.analysis import (compute_opdm,
... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/production_ml/labs/samples/core/ai_platform/ai_platform.ipynb | apache-2.0 | %%capture
# Install the SDK (Uncomment the code if the SDK is not installed before)
!python3 -m pip install 'kfp>=0.1.31' --quiet
!python3 -m pip install pandas --upgrade -q
"""
Explanation: Chicago Crime Prediction Pipeline
An example notebook that demonstrates how to:
* Download data from BigQuery
* Create a Kubef... |
ScottFreeLLC/AlphaPy | alphapy/examples/Trading Model/A Trading Model.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
import pandas as pd
pwd
cd output
ls
"""
Explanation: This notebook analyzes the predictions of the trading model. <br/>At different thresholds, how effective is the model at predicting<br/> larger-than-average range days?
End of explanation
"""
ranking_frame = pd.read_csv('... |
rochefort-lab/fissa | examples/Basic usage.ipynb | gpl-3.0 | # Import the FISSA toolbox
import fissa
"""
Explanation: Object-oriented FISSA interface
This notebook contains a step-by-step example of how to use the object-oriented (class-based) interface to the FISSA toolbox.
The object-oriented interface, which involves creating a fissa.Experiment instance, allows more flexibli... |
javierfdr/credit-scoring-analysis | src/credit_notebook.ipynb | mit | %matplotlib inline
from classifiers import *
from dim_red import *
"""
Explanation: Fitting Linear and Non-Linear Models to solve the German credit risk scoring classification problem
Let's import the support libraries developed manually for this project and load the original dataset
End of explanation
"""
[X,y] = ... |
makcedward/nlpaug | example/flow.ipynb | mit | import os
os.environ["MODEL_DIR"] = '../model'
"""
Explanation: Example of Flow Usage<a class="anchor" id="home"></a>:
Flow
Sequential
Sometimes
End of explanation
"""
import nlpaug.augmenter.char as nac
import nlpaug.augmenter.word as naw
import nlpaug.augmenter.sentence as nas
import nlpaug.flow as naf
from nlpa... |
ozak/geopandas | examples/choropleths.ipynb | bsd-3-clause | %matplotlib inline
import geopandas as gpd
import matplotlib.pyplot as plt
# We use a PySAL example shapefile
import pysal as ps
pth = ps.examples.get_path("columbus.shp")
tracts = gpd.GeoDataFrame.from_file(pth)
print('Observations, Attributes:',tracts.shape)
tracts.head()
"""
Explanation: Choropleth classification... |
mohsinhaider/pythonbootcampacm | Objects and Data Structures/List Comprehensions.ipynb | mit | # Store even numbers from 0 to 20
even_lst = [num for num in range(21) if num % 2 == 0]
print(even_lst)
"""
Explanation: List Comprehensions and Generators
Python comes with more than just a programming language, it also includes a way to write elegant code. Pythonic code is syntax that wishes to emulate natural const... |
DavidDobr/icef_thesis | data/.ipynb_checkpoints/dobrinskiy_thesis_v2_october-Copy1-checkpoint.ipynb | gpl-3.0 | # You should be running python3
import sys
print(sys.version)
import pandas as pd # http://pandas.pydata.org/
import numpy as np # http://numpy.org/
import statsmodels.api as sm # http://statsmodels.sourceforge.net/stable/index.html
import statsmodels.formula.api as smf
import statsmodels
print("Pandas Version:... |
dmolina/es_intro_python | 02-Basic-Python-Syntax.ipynb | gpl-3.0 | # set the midpoint
midpoint = 5
# make two empty lists
lower = []; upper = []
# split the numbers into lower and upper
for i in range(10):
if (i < midpoint):
lower.append(i)
else:
upper.append(i)
print("lower:", lower)
print("upper:", upper)
"""
Explanation: <!--BOOK_INFORMATION-->
<... |
ES-DOC/esdoc-jupyterhub | notebooks/test-institute-2/cmip6/models/sandbox-2/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-2', 'sandbox-2', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: TEST-INSTITUTE-2
Source ID: SANDBOX-2
Topic: Atmos
Sub-Topics: Dynamical... |
sz2472/foundations-homework | data and database/2016-06-21 NOTES.ipynb | mit | x= ["duck","aardvark","crocodile", "emu", "bee"]
x.sort()
x
### sorted by descending order
sorted(x,reverse=True)
### sorted by second letter:
#sorted(x, key=??)
def get_second_letter(s):
return s[1]
get_second_letter("cheese")
sorted(x,key=get_second_letter) #key is a parameter, value is a function:get_seco... |
cwhanse/pvlib-python | docs/tutorials/forecast.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
# built in python modules
import datetime
import os
# python add-ons
import numpy as np
import pandas as pd
# for accessing UNIDATA THREDD servers
from siphon.catalog import TDSCatalog
from siphon.ncss import NCSS
import pvlib
from pvlib.forecast import GFS, HRRR_E... |
calebmadrigal/radio-hacking-scripts | audio_signal_generation.ipynb | mit | # Imports and boilerplate to make graphs look better
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy
import wave
import random
from IPython.display import Audio
def setup_graph(title='', x_label='', y_label='', fig_size=None):
fig = plt.figure()
if fig_size != None:
f... |
the-deep-learners/nyc-ds-academy | notebooks/intro_to_tensorflow_times_a_million.ipynb | mit | import numpy as np
np.random.seed(42)
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import tensorflow as tf
tf.set_random_seed(42)
xs = np.linspace(0., 8., 8000000) # eight million points spaced evenly over the interval zero to eight
ys = 0.3*xs-0.8+np.random.normal(scale=0.25, size=len(xs)) #... |
scruwys/and-the-award-goes-to | notebooks/prepare_data.ipynb | mit | import re
import pandas as pd
import numpy as np
pd.set_option('display.float_format', lambda x: '%.3f' % x)
nominations = pd.read_csv('../data/nominations.csv')
# clean out some obvious mistakes...
nominations = nominations[~nominations['film'].isin(['2001: A Space Odyssey', 'Oliver!', 'Closely Observed Train'])]
n... |
svdwulp/da-programming-1 | week_01_oefeningen_uitwerkingen.ipynb | gpl-2.0 | ## Opgave 1 - uitwerking
for A in [False, True]:
for B in [False, True]:
print(A, B, not(A or B))
"""
Explanation: Data Analysis - Programming
Week 1
Oefeningen met uitwerkingen
Opageve 1. Schrijf een Python programma dat de waarheidstabel van de volgende expressie produceert:
$\neg{(A \lor B)}$ (Quine's D... |
jinzishuai/learn2deeplearn | deeplearning.ai/C5.SequenceModel/Week1_RNN/assignment/Dinosaur Island -- Character-level language model/Dinosaurus Island -- Character level language model final - v1.ipynb | gpl-3.0 | import numpy as np
from utils import *
import random
from random import shuffle
"""
Explanation: Character level language model - Dinosaurus land
Welcome to Dinosaurus Island! 65 million years ago, dinosaurs existed, and in this assignment they are back. You are in charge of a special task. Leading biology researchers... |
SylvainCorlay/bqplot | examples/Tutorials/Object Model.ipynb | apache-2.0 | from bqplot import (LinearScale, Axis, Figure, OrdinalScale,
LinearScale, Bars, Lines, Scatter)
# first, let's create two vectors x and y to plot using a Lines mark
import numpy as np
x = np.linspace(-10, 10, 100)
y = np.sin(x)
# 1. Create the scales
xs = LinearScale()
ys = LinearScale()
# 2. Cr... |
eneskemalergin/OldBlog | _oldnotebooks/Inferential_Statistics.ipynb | mit | # Calling the binom module from scipy stats package
from scipy.stats import binom
# Plotting Function
import matplotlib.pyplot as plt
%matplotlib inline
x = list(range(7))
n, p = 6, 0.5
rv = binom(n, p)
plt.vlines(x, 0, rv.pmf(x), colors='r', linestyles='-', lw=1, label='Probability')
plt.legend(loc='best', frameon=... |
gaoshuming/udacity | 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... |
suryaavala/stockprediction | Crypto/btc/PrepareData - Technical Indicators.ipynb | mit | def MACD(df,period1,period2,periodSignal):
EMA1 = pd.DataFrame.ewm(df,span=period1).mean()
EMA2 = pd.DataFrame.ewm(df,span=period2).mean()
MACD = EMA1-EMA2
Signal = pd.DataFrame.ewm(MACD,periodSignal).mean()
Histogram = MACD-Signal
return Histogram
def stochastics_oscillator(df,p... |
danresende/deep-learning | sentiment_network/.ipynb_checkpoints/Sentiment Classification - Mini Project 5-checkpoint.ipynb | mit | def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].upper(),g.readlines()))
g.close()... |
vvishwa/deep-learning | batch-norm/Batch_Normalization_Lesson.ipynb | mit | # Import necessary packages
import tensorflow as tf
import tqdm
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Import MNIST data so we have something for our experiments
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
"... |
MartyWeissman/Python-for-number-theory | PwNT Notebook 1.ipynb | gpl-3.0 | 2 + 3
2 * 3
5 - 11
5 / 11
"""
Explanation: Part 1. Computing with Python.
What is the difference between Python and a calculator? We begin this first lesson by showing how Python can be used as a calculator, and we move into some of the basic programming language constructs: data types, variables, lists, and loo... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.