repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
xR86/ml-stuff | kaggle/iris/Simple analysis of Iris dataset.ipynb | mit | # analytics libraries installed listed in the kaggle/python docker image: https://github.com/kaggle/docker-python
# Input data files are available in the "../input/" directory.
#from subprocess import check_output
#print(check_output(["ls", "../input"]).decode("utf8"))
# Any results you write to the current directory ... |
tpin3694/tpin3694.github.io | regex/match_text_between_html_tags.ipynb | mit | # Load regex package
import re
"""
Explanation: Title: Match Text Between HTML Tags
Slug: match_text_between_html_tags
Summary: Match Text Between HTML Tags
Date: 2016-05-01 12:00
Category: Regex
Tags: Basics
Authors: Chris Albon
Based on: Regular Expressions Cookbook
Preliminaries
End of explanation
"""
# Create ... |
danielgoncalvesti/BIGDATA2017 | Atividade02/Lab/Lab1_Rev_Python_Numpy_AlgeLin.ipynb | gpl-3.0 | x = 10 # x é um inteiro
print type(x)
x = 1.3 # x é um ponto flutuante
print type(x)
x = "Ola" # x é uma string
print type(x)
x = [1, 5, 10] # x é uma lista
print type(x)
"""
Explanation: Lab 1: Revisão de Conceitos Básicos
Nesse notebook revisaremos os conceitos básicos da linguagem de prog... |
Mashimo/datascience | 01-Regression/prob.ipynb | apache-2.0 | import random
def genEven():
'''
Returns a random even number x, where 0 <= x < 100
'''
return random.randrange(0,100,2)
genEven()
def stochasticNumber():
'''
Stochastically generates and returns a uniformly distributed even
number between 9 and 21
'''
return random.randrange(10,... |
gboeing/urban-data-science | modules/11-supervised-learning/lecture.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression, Perceptron
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes impor... |
ES-DOC/esdoc-jupyterhub | notebooks/ec-earth-consortium/cmip6/models/ec-earth3-lr/land.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-lr', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: EC-EARTH-CONSORTIUM
Source ID: EC-EARTH3-LR
Topic: Land
Sub-Topics: ... |
LCAV/pyroomacoustics | notebooks/norm_music_demo.ipynb | mit | # imports
import pickle
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.signal import stft
from random import uniform, sample
from pyroomacoustics import doa, Room, ShoeBox
"""
Explanation: NormMUSIC Demo
The goal of this notebook is to demonstrate the effect of ... |
cliburn/sta-663-2017 | notebook/18A_Dask.ipynb | mit | ! pip install dask
import dask
import dask.array as da
import dask.bag as db
import dask.dataframe as dd
from dask import delayed
"""
Explanation: Working with large data sets
Small-scale distributed programming
Using dask
For data sets that are not too big (say up to 1 TB), it is typically sufficient to process on a... |
Neuroglycerin/neukrill-net-work | notebooks/tutorials/Training a Maxout CIFAR10-based model.ipynb | mit | !obj:pylearn2.train.Train {
dataset: &train !obj:neukrill_net.dense_dataset.DensePNGDataset {
settings_path: %(settings_path)s,
run_settings: %(run_settings_path)s,
training_set_mode: "train"
},
model: !obj:pylearn2.models.mlp.MLP {
batch_size: &batch_size 128,
... |
eds-uga/csci1360e-su17 | lectures/L15.ipynb | mit | import matplotlib as mpl
import matplotlib.pyplot as plt
"""
Explanation: Lecture 15: Data Visualization
CSCI 1360E: Foundations for Informatics and Analytics
Overview and Objectives
Data visualization is one of, if not the, most important method of communicating data science results. It's analogous to writing: if you... |
gidden/aneris | doc/source/convenience.ipynb | apache-2.0 | import matplotlib.pyplot as plt
import pandas as pd
import aneris.tutorial
import aneris.convenience
plt.rcParams["figure.figsize"] = (12, 8)
"""
Explanation: Convenience
This is an example of aneris' convenience module. This module doesn't have anywhere near the error checking of aneris' other features, but it does... |
timothyb0912/pylogit | examples/notebooks/.ipynb_checkpoints/Main PyLogit Example-checkpoint.ipynb | bsd-3-clause | from collections import OrderedDict # For recording the model specification
import pandas as pd # For file input/output
import numpy as np # For vectorized math operations
import pylogit as pl # For MNL model estimation and
... |
mkarakoc/aim | examples/04_AIMpy_spiked_harmonic_oscillator_potentials.ipynb | gpl-3.0 | # Python program to use AIM tools
from asymptotic import *
"""
Explanation: ENERGY EIGENVALUES OF SPIKED HARMONIC <br> OSCILLATOR POTENTIALS USING AIMpy CODE
M. Karakoc
Presented at https://technoscience2020.weebly.com/live-hall.html
Reference:
ASYMPTOTIC ITERATION METHOD FOR SINGULAR POTENTIALS
B. Champion, R. L. Hal... |
kit-cel/wt | wt/vorlesung/ch7_9/annulus.ipynb | gpl-2.0 | # importing
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
# showing figures inline
%matplotlib inline
# plotting options
font = {'size' : 20}
plt.rc('font', **font)
plt.rc('text', usetex=1)
matplotlib.rc('figure', figsize=(18, 6) )
"""
Explanation: Content and Objective
Show points that ... |
dsiufl/2015-Fall-Hadoop | instructor-notes/4-pyspark-example.ipynb | mit | import findspark
import os
findspark.init('/home/ubuntu/shortcourse/spark-1.5.1-bin-hadoop2.6')
from pyspark import SparkContext, SparkConf
conf = SparkConf().setAppName("pyspark-example").setMaster("local[2]")
sc = SparkContext(conf=conf)
"""
Explanation: Example: Use pyspark to process GDELT event data
GDELT: Globa... |
martinjrobins/hobo | examples/toy/model-stochastic-degradation.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import numpy as np
import pints
import pints.toy
"""
Explanation: Stochastic degradation model
This example shows how the stochastic degradation model can be used.
This model describes the stochastic process of a single chemical reaction, in which the concentration of a substance degrad... |
datacommonsorg/api-python | notebooks/intro_data_science/Classification_and_Model_Evaluation.ipynb | apache-2.0 | """
Install Data Commons API
We need to install the Data Commons API, since they don't ship natively with
most python installations.
In Colab, we'll be installing the Data Commons python and pandas APIs
through pip.
"""
!pip install datacommons --upgrade --quiet
!pip install datacommons_pandas --upgrade --quiet
""... |
keras-team/keras-io | examples/vision/ipynb/pointnet_segmentation.ipynb | apache-2.0 | import os
import json
import random
import numpy as np
import pandas as pd
from tqdm import tqdm
from glob import glob
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import matplotlib.pyplot as plt
"""
Explanation: Point cloud segmentation with PointNet
Author: Soumik Rakshi... |
setiQuest/ML4SETI | tutorials/Step_5c_Convert_TS_to_unit8Dataset_PowerAI.ipynb | apache-2.0 | import requests
import json
#!pip install ibmseti
import ibmseti
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import tensorflow as tf
import os
from PIL import Image
#!pip install pandas
#import pandas as pd
import findspark
findspark.init()
import pyspark
import random
sc = pyspark.SparkCo... |
fcollonval/coursera_data_visualization | RandomForest.ipynb | mit | # Magic command to insert the graph directly in the notebook
%matplotlib inline
# Load a useful Python libraries for handling data
import pandas as pd
import numpy as np
import statsmodels.formula.api as smf
import seaborn as sns
import matplotlib.pyplot as plt
from IPython.display import Markdown, display, Image
from... |
jnobre/lxmls-toolkit-2017 | lxmls/laboratories/day2/Lxmls_Day2.ipynb | mit | import sys
sys.path.append('../../../')
import lxmls.readers.simple_sequence as ssr
simple = ssr.SimpleSequence()
print simple.train
print simple.test
for sequence in simple.train.seq_list:
print sequence
for sequence in simple.train.seq_list:
print sequence.x, sequence.y
"""
Explanation: LXMLS 2017 - Day 2... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/feature_engineering/labs/4_keras_adv_feat_eng.ipynb | apache-2.0 | import datetime
import logging
import os
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow import feature_column as fc
from tensorflow.keras import layers, models
# set TF error log verbosity
logging.getLogger("tensorflow").setLevel(logging.ERROR)
print(tf.version.VERSION)
"... |
Hiteshsaai/BikeShare_DataAnalysis | Bike_Share_Analysis.ipynb | mit | ## import all necessary packages and functions.
import csv # read and write csv files
from datetime import datetime # operations to parse dates
from datetime import time
from datetime import date
import pprint # use to print data structures like dictionaries in
# a nicer way than the base pri... |
anilcs13m/MachineLearning_Mastering | week-4-ridge-regression-assignment-1-blank.ipynb | gpl-2.0 | import graphlab
"""
Explanation: Regression Week 4: Ridge Regression (interpretation)
In this notebook, we will run ridge regression multiple times with different L2 penalties to see which one produces the best fit. We will revisit the example of polynomial regression as a means to see the effect of L2 regularization.... |
jo-c-2017/DS_Projects | JC_inferential_statistics_ex2.ipynb | apache-2.0 | import pandas as pd
import numpy as np
import math
from scipy import stats
import matplotlib.pyplot as plt
data = pd.io.stata.read_stata('data/us_job_market_discrimination.dta')
data.head(5)
"""
Explanation: Examining Racial Discrimination in the US Job Market
Background
Racial discrimination continues to be pervasiv... |
djfan/yelp-challenge | descriptive_stats/descriptive_statistics.ipynb | mit | {
"business_id":"encrypted business id",
"name":"business name",
"neighborhood":"hood name",
"address":"full address",
"city":"city",
"state":"state -- if applicable --",
"postal code":"postal code",
"latitude":latitude,
"longitude":longitude,
"stars":star rating, ***rounded to h... |
probml/pyprobml | notebooks/misc/numpyro_intro.ipynb | mit | import numpy as np
np.set_printoptions(precision=3)
import matplotlib.pyplot as plt
import math
# When running in colab pro (high RAM mode), you get 4 CPUs.
# But we need to force XLA to use all 4 CPUs
# This is generally faster than running in GPU mode
import os
os.environ["XLA_FLAGS"] = "--xla_force_host_platfor... |
yongtang/tensorflow | tensorflow/lite/g3doc/guide/model_analyzer.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... |
SKA-ScienceDataProcessor/crocodile | examples/notebooks/awtowers.ipynb | apache-2.0 | %matplotlib inline
import sys
sys.path.append('../..')
from matplotlib import pylab as plt
from ipywidgets import interact
import itertools
import numpy
import numpy.linalg
import scipy
import scipy.special
import time
import astropy
from crocodile.synthesis import *
from crocodile.simulate import *
from util.visua... |
kubeflow/examples | mnist/mnist_azure.ipynb | apache-2.0 | import os
# This is the Kubernetes namespace into which you installed Kubeflow
os.environ['TARGET_NAMESPACE'] = '<your-kubeflow-namespace-name>'
# The credentials you obtained from the newly created Service Principal
os.environ['AZ_CLIENT_ID'] = '<your-service-principal-client-id>'
os.environ['AZ_CLIENT_SECRET'] = '<... |
natronics/rust-fc | analysis/results.ipynb | gpl-3.0 | import psas_packet
from psas_packet.io import BinFile
import csv
import matplotlib.pyplot as plt
from matplotlib import gridspec
%matplotlib inline
FPS2M = 0.3048
LBF2N = 4.44822
LBS2KG = 0.453592
# Extend PSAS Packet to include our state message
psas_packet.messages.MESSAGES["STAT"] = psas_packet.messages.Message({
... |
Rotvig/cs231n | Project/Deep Network Comparison.ipynb | mit | #Load necessary libraries
import tensorflow as tf
import numpy as np
import tensorflow.contrib.slim as slim
import input_data
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Deep Neural Network Comparison
End of explanation
"""
def unpickle(file):
import cPickle
fo = open(file, 'rb')
... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_head_positions.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
from os import path as op
import mne
print(__doc__)
data_path = op.join(mne.datasets.testing.data_path(verbose=True), 'SSS')
pos = mne.chpi.read_head_pos(op.join(data_path, 'test_move_anon_raw.pos'))
"""
Explanation: Visualize subject he... |
metpy/MetPy | v0.10/_downloads/395a4944c820661485ef078b05c2140c/Combined_plotting.ipynb | bsd-3-clause | import xarray as xr
from metpy.cbook import get_test_data
from metpy.plots import ContourPlot, ImagePlot, MapPanel, PanelContainer
from metpy.units import units
# Use sample NARR data for plotting
narr = xr.open_dataset(get_test_data('narr_example.nc', as_file_obj=False))
"""
Explanation: Combined Plotting
Demonstra... |
CLEpy/CLEpy-MotM | Boltons/Boltons.ipynb | mit | # Some imports used in examples
import time
from datetime import datetime
from pprint import pprint
"""
Explanation: Boltons
Like builtins, but boltons. Constructs/recipes/snippets that would be handy in the standard library. Nothing like Michael Bolton.
https://boltons.readthedocs.org
Boltons is a set of pure-Python ... |
phoebe-project/phoebe2-docs | 2.0/tutorials/building_a_system.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.0,<2.1"
"""
Explanation: Building a System
Setup
Let's first make sure we have the latest version of PHOEBE 2.0 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
"""
import phoebe
from pho... |
metpy/MetPy | v1.0/_downloads/e1a6a28aa03f7e0f88631b525fc7c40d/Hodograph_Inset.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import pandas as pd
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.plots import add_metpy_logo, Hodograph, SkewT
from metpy.units import units
"""
Explanation: Hodograph Inset
Layout a Skew-T plo... |
EricChiquitoG/Simulacion2017 | Modulo1/Clase5_OsciladorAmortiguado.ipynb | mit | # Primero importamos todas las librerias, paquetes y/o funciones que vamos a utlizar
import matplotlib.pyplot as plt # ¿Para qué sirve?
import numpy as np # ¿Para qué sirve?
from scipy.integrate import odeint # Función para integrar EDO vectoriales de primer orden
%matplotlib inline ... |
dsacademybr/PythonFundamentos | Cap03/Notebooks/DSA-Python-Cap03-02-For.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 3</font>
Download: http://github.com/dsacademybr
End of explanation
"""
# Crian... |
google/starthinker | colabs/sa_report.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: SA360 Report
Move SA360 report to BigQuery.
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 a copy of the License a... |
ceos-seo/data_cube_notebooks | notebooks/training/ardc_training/Training_TaskF_DataExport.ipynb | apache-2.0 | import datacube
import utils.data_cube_utilities.data_access_api as dc_api
from datacube.utils.aws import configure_s3_access
configure_s3_access(requester_pays=True)
api = dc_api.DataAccessApi()
dc = datacube.Datacube(app = 'ardc_task_f')
api.dc = dc
"""
Explanation: ARDC Training: Python Notebooks
Task-F: Data E... |
tpin3694/tpin3694.github.io | python/chain_together_lists.ipynb | mit | from itertools import chain
"""
Explanation: Title: Chain Together Lists
Slug: chain_together_lists
Summary: Chain Together Lists Using Python.
Date: 2017-02-02 12:00
Category: Python
Tags: Basics
Authors: Chris Albon
Preliminaries
End of explanation
"""
# Create a list of allies
allies = ['Spain', 'Germany', 'Nam... |
batrlatom/udacityP2 | LeNet-Lab-Solution.ipynb | gpl-3.0 | from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", reshape=False)
X_train, y_train = mnist.train.images, mnist.train.labels
X_validation, y_validation = mnist.validation.images, mnist.validation.labels
X_test, y_test = mnist.test.images, mn... |
HarllanAndrye/nilmtk | docs/manual/user_guide/elecmeter_and_metergroup.ipynb | apache-2.0 | from matplotlib import rcParams
import matplotlib.pyplot as plt
%matplotlib inline
rcParams['figure.figsize'] = (13, 6)
plt.style.use('ggplot')
from nilmtk import DataSet
redd = DataSet('/data/REDD/redd.h5')
elec = redd.buildings[1].elec
elec
"""
Explanation: MeterGroup, ElecMeter, selection and basic statistics
All... |
iRipVanWinkle/ml | Data Science UA - September 2017/Lecture 04 - Overview of Linear Algebra and Matrix Computations/Linear_Equations.ipynb | mit | import numpy as np
import scipy.linalg as la
import sys
"""
Explanation: Systems of Linear Equations
In this notebook we leran various functions for solving and manipulating systems of linear equations.
Initialization
End of explanation
"""
la?
"""
Explanation: The main linear algebra routines are contained in scip... |
ondrolexa/sg2 | 14_Simultaneous_deformation.ipynb | mit | %pylab inline
from sg2lib import *
"""
Explanation: Simultaneous deformation
End of explanation
"""
gamma = 1
Sx = 2
Fs = array([[1, gamma], [0, 1]])
Fp = array([[Sx, 0], [0, 1/Sx]])
"""
Explanation: Naive concept of simultaneous deformation
Here we try to split simple shear and pure shear to several incremental s... |
ES-DOC/esdoc-jupyterhub | notebooks/nasa-giss/cmip6/models/sandbox-3/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nasa-giss', 'sandbox-3', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: NASA-GISS
Source ID: SANDBOX-3
Topic: Atmos
Sub-Topics: Dynamical Core, Radiati... |
cliburn/sta-663-2017 | homework/02_Working_With_Text_Solutions.ipynb | mit | def encode(s, k):
"""Caesar cipher encoding with offset k for string s"""
import string
t = {c: chr(ord('A') + (ord(c) - ord('A') + k) % 26)
for c in string.ascii_uppercase}
t1 = {c: chr(ord('a') + (ord(c) - ord('a') + k) % 26)
for c in string.ascii_lowercase}
t.update(t1)
... |
phoebe-project/phoebe2-docs | 2.3/tutorials/compute_times_phases.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.3,<2.4"
"""
Explanation: Advanced: compute_times & compute_phases
Setup
Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if running in an online notebook session such as colab).
End of explanation
"""
import phoebe
from phoebe import u # units
... |
bajorekp/ForexPredictor | GradientBoostingRegressor.ipynb | mit | import pandas as pd
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import urllib.request # for HTTP requests (web scraping, APIs)
from datetime import datetime
import os
from sklearn.ensemble import GradientBoostingRegressor
"""
Explanation: Import t... |
Neuroglycerin/neukrill-net-work | notebooks/model_run_and_result_analyses/Analyse alexnet_based_extra_convlayer model.ipynb | mit | import pylearn2.utils
import pylearn2.config
import theano
import neukrill_net.dense_dataset
import neukrill_net.utils
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import holoviews as hl
%load_ext holoviews.ipython
import sklearn.metrics
m = pylearn2.utils.serial.load(
"/disk/scratch/neuro... |
metpy/MetPy | v0.10/_downloads/951207f2bc1e1100e2784e6132c75710/GINI_Water_Vapor.ipynb | bsd-3-clause | import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import xarray as xr
from metpy.cbook import get_test_data
from metpy.io import GiniFile
from metpy.plots import add_metpy_logo, add_timestamp, colortables
# Open the GINI file from the test data
f = GiniFile(get_test_data('WEST-CONUS_4km_WV_20151208_2... |
necromuralist/machine_learning_studies | machine_learning/udacity/project_2/student_intervention.ipynb | mit | # Import libraries
import numpy
import pandas as pd
# my imports
import matplotlib.pyplot as plot
import seaborn
from sklearn.cross_validation import train_test_split
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import f1_score, make_scorer
%matplotlib inline
# Read student data
student_data = p... |
rainyear/pytips | Tips/2016-03-30-Date-and-Time.ipynb | mit | from calendar import TextCalendar, HTMLCalendar
tc = TextCalendar(firstweekday=6)
tc.prmonth(2016, 3)
"""
Explanation: Python 时间与日期
时间和日期可能涉及到不同的时区、格式，同时又经常需要作为时间戳保存，有时候还需要进行一些加减操作，因此处理起来通常会因为方法太多而无从下手。Python 中与时间和日期相关的标准库有3个：time、datetime 和 calendar。其中 time 模块更偏向于系统相关的时间数据，最常用的可能就是获取当前时间的秒数 time.time()，此外该模块中的很多方法都是... |
schaber/deep-learning | image-classification/dlnd_image_classification.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
# Use Floyd's cifar-10 dataset if present
floyd_cifar10... |
fazzolini/fast_ai | deeplearning1/nbs/lesson1_old.ipynb | apache-2.0 | %matplotlib inline
"""
Explanation: Using Convolutional Neural Networks
Welcome to the first week of the first deep learning certificate! We're going to use convolutional neural networks (CNNs) to allow our computer to see - something that is only possible thanks to deep learning.
Introduction to this week's task: 'Do... |
ES-DOC/esdoc-jupyterhub | notebooks/dwd/cmip6/models/sandbox-3/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'dwd', 'sandbox-3', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: DWD
Source ID: SANDBOX-3
Sub-Topics: Radiative Forcings.
Properties: 85 (42 re... |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/dev/n01_first_look_at_the_data.ipynb | mit | import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
import scipy.optimize as spo
import sys
%matplotlib inline
%pylab inline
pylab.rcParams['figure.figsize'] = (20.0, 10.0)
%load_ext autoreload
%autoreload 2
sys.path.append('../../')
data_df = pd.read_pickle('../.... |
poldrack/fmri-analysis-vm | analysis/firstlevel/FirstLevelModeling.ipynb | mit | import nipype.algorithms.modelgen as model # model generation
from nipype.interfaces import fsl, ants
from nipype.interfaces.base import Bunch
import os,json,glob,sys
import numpy
import nibabel
import nilearn.plotting
sys.path.append('../utils/')
from compute_fd_dvars import compute_fd,compute_dvars
from mak... |
gogartom/caffe-textmaps | examples/hdf5_classification.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
# Make sure that caffe is on the python path:
caffe_root = '../' # this file is expected to be in {caffe_root}/examples
import sys
sys.path.insert(0, caffe_root + 'python')
import caffe
import os
import h5py
import shutil
impo... |
sujitpal/polydlot | src/keras/04-mnist-rnn.ipynb | apache-2.0 | from __future__ import division, print_function
from keras.callbacks import ModelCheckpoint
from keras.layers.recurrent import LSTM
from keras.layers.core import Dense, Dropout
from keras.models import Model, Sequential, load_model
from keras.utils import np_utils
from sklearn.metrics import accuracy_score, confusion_m... |
ES-DOC/esdoc-jupyterhub | notebooks/cnrm-cerfacs/cmip6/models/sandbox-1/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cnrm-cerfacs', 'sandbox-1', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: CNRM-CERFACS
Source ID: SANDBOX-1
Topic: Atmoschem
Sub-Topics: Trans... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive/09_sequence/reusable-embeddings.ipynb | apache-2.0 | !sudo pip3 install -q tensorflow-hub==0.4.0
!sudo pip3 install -q --upgrade tensorflow==1.15.0
"""
Explanation: <h1>Using pre-trained embeddings with TensorFlow Hub</h1>
This notebook illustrates:
<ol>
<li>How to instantiate a TensorFlow Hub module</li>
<li>How to find pre-trained TensorFlow Hub modules for a... |
akchinSTC/systemml | scripts/nn/examples/Example - MNIST LeNet.ipynb | apache-2.0 | # Create a SystemML MLContext object
from systemml import MLContext, dml
ml = MLContext(sc)
"""
Explanation: Quick Setup
End of explanation
"""
%%sh
mkdir -p data/mnist/
cd data/mnist/
curl -O https://pjreddie.com/media/files/mnist_train.csv
curl -O https://pjreddie.com/media/files/mnist_test.csv
"""
Explanation: D... |
jorisvandenbossche/DS-python-data-analysis | notebooks/pandas_05_groupby_operations.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
"""
Explanation: <p><font size="6"><b>06 - Pandas: "Group by" operations</b></font></p>
© 2021, Joris Van den Bossche and Stijn Van Hoey (jorisvandenb&#... |
geodocker/geodocker-jupyter-geopyspark | notebooks/Park citing.ipynb | apache-2.0 | import geopyspark as gps
import fiona
from pyspark import SparkContext, StorageLevel
from shapely.geometry import MultiPoint, MultiPolygon, shape
import folium
"""
Explanation: Finding the Optimal Location for a New Park
This example notebook will show how to find the next potential location for a new park in San Fr... |
IS-ENES-Data/submission_forms | dkrz_forms/Templates/Forms/Doc/Workflow_Form_Overview.ipynb | apache-2.0 | ## the following libraries are needed to interact with
## json based form submissions
from dkrz_forms import form_handler, utils, checks,wflow_handler
from datetime import datetime
## info_file = "path to json file"
info_file = "../Forms/../xxx.json"
# load json file and convert to Form object for simple updating
m... |
metpy/MetPy | v0.7/_downloads/Point_Interpolation.ipynb | bsd-3-clause | import cartopy
import cartopy.crs as ccrs
from matplotlib.colors import BoundaryNorm
import matplotlib.pyplot as plt
import numpy as np
from metpy.cbook import get_test_data
from metpy.gridding.gridding_functions import (interpolate, remove_nan_observations,
remove_repeat... |
KshitijT/fundamentals_of_interferometry | 3_Positional_Astronomy/3_4_direction_cosine_coordinates.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
"""
Explanation: Outline
Glossary
Positional Astronomy
Previous: 3.3 Horizontal Coordinates (ALT/AZ)
Next: 3.x Further Reading and References
Import standard mo... |
ES-DOC/esdoc-jupyterhub | notebooks/csir-csiro/cmip6/models/sandbox-3/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'csir-csiro', 'sandbox-3', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: CSIR-CSIRO
Source ID: SANDBOX-3
Sub-Topics: Radiative Forcings.
Propert... |
gtzan/mir_book | ConditionalProbabilities.ipynb | cc0-1.0 | %matplotlib inline
import matplotlib.pyplot as plt
from scipy import stats
import numpy as np
class Random_Variable:
def __init__(self, name, values, probability_distribution):
self.name = name
self.values = values
self.probability_distribution = probability_distribution
... |
PmagPy/2017_MagIC_Workshop_PmagPy_Tutorial | Jupyter_notebook_introduction.ipynb | bsd-3-clause | print('hello, world.')
"""
Explanation: Jupyter notebook tutorial introduction
The goal of this short tutorial is to introduce some of the basics of using a Jupyter notebook. Looking at the official Jupyter documentation can also be helpful.
Some materials in this tutorial were modified from materials of the Caltech c... |
amitkaps/full-stack-data-science | credit-risk/notebooks/1-ML-Model.ipynb | mit | #Load the libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#Default Variables
%matplotlib inline
plt.rcParams['figure.figsize'] = (8,6)
plt.style.use('ggplot')
pd.set_option('display.float_format', lambda x: '%.2f' % x)
#Load the training dataset
df = pd.read_csv("../data/historical_lo... |
tensorflow/docs | site/en/r1/tutorials/non-ml/pdes.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... |
ledatelescope/bifrost | BifrostDemo.ipynb | bsd-3-clause | # @title Install C++ deps
%%shell
sudo apt-get -qq install exuberant-ctags libopenblas-dev software-properties-common build-essential
# @title Install python deps
%%shell
pip install -q contextlib2 pint simplejson ctypesgen==1.0.2
# @title Build and Install Bifrost
%%shell
cd "${HOME}"
if [ -d "${HOME}/bifrost_repo... |
sraejones/phys202-2015-work | assignments/assignment07/AlgorithmsEx01.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
"""
Explanation: Algorithms Exercise 1
Imports
End of explanation
"""
def tokenize(s, stop_words= None, punctuation='`~!@#$%^&*()_-+={[}]|\:;"<,>.?/}\t'):
"""Split a string into a list of words, removing punctuation and stop words."""
... |
ireapps/cfj-2017 | exercises/13. Web scraping (Part 3)-working.ipynb | mit | # use the `get()` method to fetch a copy of the IRE home page
# feed the text of the web page to a BeautifulSoup object
"""
Explanation: Let's scrape the IRE homepage
Our goal: Print out the headlines from the IRE home page.
requests is a handy third-party library for making HTTP requests. It does the same thing yo... |
ajgpitch/qutip-notebooks | examples/qip-processor-DJ-algorithm.ipynb | lgpl-3.0 | import numpy as np
from qutip.qip.device import OptPulseProcessor, LinearSpinChain
from qutip.qip.circuit import QubitCircuit
from qutip.operators import sigmaz, sigmax, identity
from qutip.tensor import tensor
from qutip.states import basis
from qutip.qobj import ptrace
basis00 = tensor([basis(2,0), basis(2,0)])
qc =... |
alasdairtran/mclearn | projects/david/lab/test_accpm_toy.ipynb | bsd-3-clause | import numpy as np
import pandas as pd
import accpm
%load_ext autoreload
%autoreload 1
%aimport accpm
"""
Explanation: Computation of cutting planes: example 1
The set-up
End of explanation
"""
def funcobj(x):
return (x[0]-5)**2 + (x[1]-5)**2
def func0(x):
return x[0] - 20
def func1(x):
return -x[0... |
AllenDowney/ThinkBayes2 | examples/sister.ipynb | mit | import pandas as pd
qs = [(2, 0),
(1, 1),
(0, 2),
(3, 0),
(2, 1),
(1, 2),
(0, 3),
(4, 0),
(3, 1),
(2, 2),
(1, 3),
(0, 4),
]
index = pd.MultiIndex.from_tuples(qs, names=['Boys', 'Girls'])
"""
Explanation: The Left Handed Sister Problem
Think Bayes, ... |
tensorflow/docs-l10n | site/en-snapshot/io/tutorials/dicom.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... |
jay-johnson/sci-pype | red10/Red10-IRIS-Predictions.ipynb | apache-2.0 | from __future__ import print_function
import sys, os, requests, json, datetime
# Load the environment and login the user
from src.common.load_redten_ipython_env import user_token, user_login, csv_file, run_job, core, api_urls, ppj, rt_url, rt_user, rt_pass, rt_email, lg, good, boom, anmt, mark, ppj, uni_key, rest_logi... |
ocarneiro/twitter-reach | TwitterReach.ipynb | apache-2.0 | import tweepy
"""
Explanation: The Reach of a Tweet
What are we doing?
Have you ever guessed how many people could see a tweet that has been retweeted by a lot of people? Could you find it out using python and Twitter API? Let's do it!
Which tweet are we looking at?
My latest tweet received a ton more attention than a... |
pastas/pasta | examples/notebooks/03_diagnostic_checking.ipynb | mit | import numpy as np
import pandas as pd
import pastas as ps
from scipy import stats
import matplotlib.pyplot as plt
ps.set_log_level("ERROR")
ps.show_versions(numba=True)
"""
Explanation: Model Diagnostic Checking
R.A. Collenteur, University of Graz, July 2020.
This notebook provides an overview of the different metho... |
tensorflow/docs-l10n | site/ko/model_optimization/guide/pruning/pruning_with_keras.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... |
phoebe-project/phoebe2-docs | 2.1/examples/legacy.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.1,<2.2"
"""
Explanation: Comparing PHOEBE 2 vs PHOEBE Legacy
NOTE: PHOEBE 1.0 legacy is an alternate backend and is not installed with PHOEBE 2.0. In order to run this backend, you'll need to have PHOEBE 1.0 installed and manually build the python bindings in the phoebe-py directory.
Setup
... |
google-research/neural-structural-optimization | notebooks/optimization-examples.ipynb | apache-2.0 | # Install dependencies with pip. Only run this once.
! pip install -q tf-nightly git+https://github.com/google-research/neural-structural-optimization.git
# Copyright 2019 Google LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
... |
lmoresi/UoM-VIEPS-Intro-to-Python | Notebooks/SphericalMeshing/CartesianTriangulations/Ex1-Cartesian-Triangulations.ipynb | mit | import stripy as stripy
import numpy as np
# Vertices of a square mesh
vertices_xy = np.array([[0.0, 0.0],
[1.0, 0.0],
[0.0, 1.0],
[1.0, 1.0]])
vertices_x = vertices_xy.T[0]
vertices_y = vertices_xy.T[1]
triangulation = stripy.Triangu... |
AndreySheka/dl_ekb | hw3/HW3_main.ipynb | mit | %matplotlib inline
from time import time, sleep
import numpy as np
import matplotlib.pyplot as plt
from IPython import display
"""
Explanation: Home work 3: Basic Artificial Neural Networks
The goal of this homework is simple, yet an actual implementation may take some time :). We are going to write an Artificial Neur... |
tensorflow/docs | site/en/tutorials/customization/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... |
keras-team/keras-io | examples/generative/ipynb/deep_dream.ipynb | apache-2.0 | import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.applications import inception_v3
base_image_path = keras.utils.get_file("sky.jpg", "https://i.imgur.com/aGBdQyK.jpg")
result_prefix = "sky_dream"
# These are the names of the layers
# for which we try to maximize activation,... |
matthiaskoenig/sbmlutils | docs/presentations/harmony2017/sbmlutils-HARMONY2017-flash-talk.ipynb | lgpl-3.0 | from sbmlutils.report import sbmlreport
sbmlreport.create_sbml_report('./examples/glucose/Hepatic_glucose_3.xml',
out_dir='./examples/glucose', validate=True)
"""
Explanation: <img alt="sbmlutils logo" src="./images/sbmlutils-logo-small.png" style="height: 60px;" />
sbmlutils: Python uti... |
ES-DOC/esdoc-jupyterhub | notebooks/mpi-m/cmip6/models/icon-esm-lr/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mpi-m', 'icon-esm-lr', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: MPI-M
Source ID: ICON-ESM-LR
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics... |
metpy/MetPy | v0.8/_downloads/Find_Natural_Neighbors_Verification.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import numpy as np
from scipy.spatial import Delaunay
from metpy.gridding.triangles import find_natural_neighbors
# Create test observations, test points, and plot the triangulation and points.
gx, gy = np.meshgrid(np.arange(0, 20, 4), np.arange(0, 20, 4))
pts = np.vstack([gx.ravel(), ... |
phobson/statsmodels | examples/notebooks/statespace_cycles.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 pandas.io.data import DataReader
endog = DataReader('UNRATE', 'fred', start='1954-01-01')
"""
Explanation: Trends and cycles in unemployment
Here we consider three methods for separating a tren... |
hungiyang/StatisticalMethods | notes/LMreview4.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (8.0, 8.0)
# the model parameters
a = np.pi
b = 1.6818
# my arbitrary constants
mu_x = np.exp(1.0) # see definitions above
tau_x = 1.0
s = 1.0
N = 50 # number of data points
# get some x's and y's
x = mu_x + tau_x... |
quantopian/research_public | notebooks/data/zacks.broker_ratings/notebook.ipynb | apache-2.0 | # import the free sample of the dataset
from quantopian.interactive.data.zacks import broker_ratings as dataset
# import data operations
from odo import odo
# import other libraries we will use
import pandas as pd
import matplotlib.pyplot as plt
# Let's use blaze to understand the data a bit using Blaze dshape()
data... |
sdss/marvin | docs/sphinx/jupyter/dap_maps.ipynb | bsd-3-clause | # import the maps
from marvin.tools.maps import Maps
# Load a MPL-5 map
mapfile = '/Users/Brian/Work/Manga/analysis/v2_0_1/2.0.2/SPX-GAU-MILESHC/8485/1901/manga-8485-1901-MAPS-SPX-GAU-MILESHC.fits.gz'
# Let's get a default map of
maps = Maps(filename=mapfile)
print(maps)
"""
Explanation: Marvin Maps
Marvin Maps is h... |
jdsanch1/SimRC | 02. Parte 2/15. Clase 15/.ipynb_checkpoints/11Class NB-checkpoint.ipynb | mit | #importar los paquetes que se van a usar
import pandas as pd
import numpy as np
import datetime
from datetime import datetime
import scipy.stats as stats
import scipy as sp
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn.covariance as skcov
import cvxopt as opt
from cvxopt import blas, solvers
solv... |
transcranial/keras-js | notebooks/layers/recurrent/LSTM.ipynb | mit | data_in_shape = (3, 6)
rnn = LSTM(4, activation='tanh', recurrent_activation='hard_sigmoid')
layer_0 = Input(shape=data_in_shape)
layer_1 = rnn(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
weights = []
for i, w in enumerate(model.get_weights()):
np... |
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