repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/tensorflow/c_batched.ipynb | apache-2.0 | import tensorflow.compat.v1 as tf
import numpy as np
import shutil
print(tf.__version__)
"""
Explanation: <h1> 2c. Refactoring to add batching and feature-creation </h1>
In this notebook, we continue reading the same small dataset, but refactor our ML pipeline in two small, but significant, ways:
<ol>
<li> Refactor t... |
ES-DOC/esdoc-jupyterhub | notebooks/thu/cmip6/models/sandbox-2/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'thu', 'sandbox-2', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: THU
Source ID: SANDBOX-2
Topic: Aerosol
Sub-Topics: Transport, Emissions, Concent... |
cavestruz/MLPipeline | notebooks/time_series/sample_time_series.ipynb | mit | import numpy as np
from matplotlib import pyplot as plt
from astroML.time_series import lomb_scargle, generate_damped_RW
from astroML.time_series import ACF_scargle
"""
Explanation: In this first example, we will explore a simulated lightcurve that follows a damped random walk, which is often used to model variabilit... |
SubhankarGhosh/NetworkX | 7. Bipartite Graphs (Instructor).ipynb | mit | G = cf.load_crime_network()
G.edges(data=True)[0:5]
G.nodes(data=True)[0:10]
"""
Explanation: Introduction
Bipartite graphs are graphs that have two (bi-) partitions (-partite) of nodes. Nodes within each partition are not allowed to be connected to one another; rather, they can only be connected to nodes in the othe... |
theandygross/TCGA_differential_expression | Notebooks/Figures/Purgatory/DX_screen_figs.ipynb | mit | import NotebookImport
from Imports import *
import seaborn as sns
sns.set_context('paper',font_scale=1.5)
sns.set_style('white')
"""
Explanation: Differential Analysis
Import everything from the imports notebook. This reads in all of the expression data as well as the functions needed to analyse differential expressi... |
willsa14/ras2las | data/kgs/DownloadLogs_v1.ipynb | mit | elogs = pd.read_csv('temp/ks_elog_scans.txt', parse_dates=True)
lases = pd.read_csv('temp/ks_las_files.txt', parse_dates=True)
elogs_mask = elogs['KID'].isin(lases['KGS_ID']) # Create mask for elogs
both_elog = elogs[elogs_mask] # select items elog that fall in both
both_elog_unique = both_elog.drop_duplicates('KID')... |
ES-DOC/esdoc-jupyterhub | notebooks/cccr-iitm/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', 'cccr-iitm', 'sandbox-3', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: CCCR-IITM
Source ID: SANDBOX-3
Topic: Atmos
Sub-Topics: Dynamical Core, Radiati... |
CNS-OIST/STEPS_Example | user_manual/source/memb_pot.ipynb | gpl-2.0 | from __future__ import print_function # for backward compatibility with Py2
import steps.model as smodel
import steps.geom as sgeom
import steps.rng as srng
import steps.solver as ssolver
import steps.utilities.meshio as meshio
import numpy
import math
import time
from random import *
"""
Explanation: Simulating Membr... |
beyondvalence/biof509_wtl | Wk10-feature_selection_dimension_reduction_clustering/Wk10-dimensionality-reduction-clustering.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
"""
Explanation: Week 10 - Dimensionality reduction and clustering
Learning Objectives
List options available for dimensionality reduction in scikit-learn
Discuss different clustering algorithms
Demonstrate clustering in scikit-learn
End of explan... |
mbeyeler/opencv-machine-learning | notebooks/04.05-Representing-Images.ipynb | mit | import cv2
import matplotlib.pyplot as plt
%matplotlib inline
img_bgr = cv2.imread('data/lena.jpg')
"""
Explanation: <!--BOOK_INFORMATION-->
<a href="https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv" target="_blank"><img align="left" src="data/cover.jpg" style="width: 76px; height: ... |
Upward-Spiral-Science/spect-team | Code/Assignment-3/Exploratory_Kmeans_PCA.ipynb | apache-2.0 | %matplotlib inline
import matplotlib.pyplot as plt
print '\nPlot the distributions of unknown columns (BSC, GSC, LDS):'
print '\nBSC_1 to BSC_101'
bsc = ['BSC_' + str(i) for i in xrange(1, 102)]
plot = df_unknowns[bsc].plot(kind='hist', alpha=0.5, legend=None)
print '\nPlot several random BSC samples:'
fig, axes = ... |
aschaffn/phys202-2015-work | assignments/assignment07/AlgorithmsEx02.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns
import numpy as np
"""
Explanation: Algorithms Exercise 2
Imports
End of explanation
"""
def find_peaks(a):
"""Find the indices of the local maxima in a sequence."""
a = np.array(a)
s = np.sign(np.diff(a))
d = np.diff(s)
... |
qutip/qutip-notebooks | examples/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb | lgpl-3.0 | %pylab inline
import contextlib
import time
import numpy as np
from scipy.optimize import curve_fit
from qutip import *
from qutip.nonmarkov.heom import HEOMSolver, BosonicBath
# Import mpmath functions for evaluation of gamma and zeta functions in the expression for the correlation:
from mpmath import mp
mp.dps... |
augfranco/CienciadosDados | Projeto02.ipynb | mit | %%capture
#Instalando o tweepy
!pip install tweepy
"""
Explanation: Projeto 2 - Classificador Automático de Sentimento - Augusto Franco e Pedro Isidoro
Você foi contratado por uma empresa parar analisar como os clientes estão reagindo a um determinado produto no Twitter. A empresa deseja que você crie um programa que... |
mne-tools/mne-tools.github.io | 0.18/_downloads/ae1f146de31a4665192262a211d6d103/plot_metadata_epochs.ipynb | bsd-3-clause | # Authors: Chris Holdgraf <choldgraf@gmail.com>
# Jona Sassenhagen <jona.sassenhagen@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)
import mne
import numpy as np
import matplotlib.pyplot as plt
# Load the data from the internet
path = mne.datasets.kiloword.data_path() ... |
cbuntain/TutorialSocialMediaCrisis | notebooks/T03 - Parsing Twitter Data.ipynb | apache-2.0 | jsonString = '{"key": "value"}'
# Parse the JSON string
dictFromJson = json.loads(jsonString)
# Python now has a dictionary representing this data
print ("Resulting dictionary object:\n", dictFromJson)
# Will print the value
print ("Data stored in \"key\":\n", dictFromJson["key"])
# This will cause an error!
print ... |
mattilyra/gensim | docs/notebooks/gensim_news_classification.ipynb | lgpl-2.1 | import os
import re
import operator
import matplotlib.pyplot as plt
import warnings
import gensim
import numpy as np
warnings.filterwarnings('ignore') # Let's not pay heed to them right now
import nltk
nltk.download('stopwords') # Let's make sure the 'stopword' package is downloaded & updated
nltk.download('wordnet')... |
blue-yonder/tsfresh | notebooks/advanced/05 Timeseries Forecasting (multiple ids).ipynb | mit | %matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pylab as plt
from tsfresh import extract_features, select_features
from tsfresh.utilities.dataframe_functions import roll_time_series, make_forecasting_frame
from tsfresh.utilities.dataframe_functions import impute
try:
import pandas_dat... |
tata-antares/tagging_LHCb | Analysis-scheme.ipynb | apache-2.0 | from IPython.display import Image
import pandas
"""
Explanation: Inclusive B-tagging
Authors:
Tatiana Likhomanenko (contact)
Alexey Rogozhnikov
Denis Derkach
Data (from working group):
real data $B^{\pm} \to J/\psi K^{\pm}$ (RECO 14), 2012
real data $B_d \to J/\psi K^*$ (RECO 14), 2012 (use EPM for assymetry estima... |
catherinezucker/dustcurve | Old_Runs/tutorial_6slices.ipynb | gpl-3.0 | import emcee
from dustcurve import model
import seaborn as sns
import numpy as np
from dustcurve import pixclass
import matplotlib.pyplot as plt
import pandas as pd
import warnings
from dustcurve import io
from dustcurve import hputils
from dustcurve import kdist
from dustcurve import globalvars as gv
%matplotlib inlin... |
jaekookang/useful_bits | Speech/Extract_Pitch_using_Praat/Extract_Pitch.ipynb | mit | import os
import numpy as np
from subprocess import Popen, PIPE
from sys import platform
import pdb
"""
Explanation: Extract fundamental frequency (F0 or pitch) using Python
<br>
- tested: Python3.6 on Linux and Mac
- 2017-09-24 jk
Logic:
1) Generate Praat script temporarily within Python script
2) Run the Praat scri... |
peterbraden/tensorflow | tensorflow/examples/tutorials/deepdream/deepdream.ipynb | apache-2.0 | # boilerplate code
import os
from cStringIO import StringIO
import numpy as np
from functools import partial
import PIL.Image
from IPython.display import clear_output, Image, display, HTML
import tensorflow as tf
"""
Explanation: DeepDreaming with TensorFlow
Loading and displaying the model graph
Naive feature visua... |
Lstyle1/Deep_learning_projects | autoencoder/Convolutional_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)
img = mnist.train.images[2]
plt.imshow(img.reshape((28, 28)), cmap='Greys_r')
"""
Explanation: C... |
qutip/qutip-notebooks | examples/heom/heom-3-quantum-heat-transport.ipynb | lgpl-3.0 | import numpy as np
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
import qutip as qt
from qutip.nonmarkov.heom import HEOMSolver, DrudeLorentzPadeBath, BathExponent
from ipywidgets import IntProgress
from IPython.display import display
# Qubit parameters
epsilon = 1
# System operators
H1 = e... |
zchq88/MyUdacityDeepLearningProject | 1/dlnd-your-first-neural-network.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code... |
ES-DOC/esdoc-jupyterhub | notebooks/hammoz-consortium/cmip6/models/sandbox-3/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'hammoz-consortium', 'sandbox-3', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: HAMMOZ-CONSORTIUM
Source ID: SANDBOX-3
Topic: Ocean
Sub-Topics: Timeste... |
adlyons/AWOT | examples/awot_utilities_intro.ipynb | gpl-2.0 | # Load the needed packages
import os
import matplotlib.pyplot as plt
import numpy as np
from netCDF4 import Dataset
import awot
%matplotlib inline
"""
Explanation: <h2>Introducing miscellaneous utilities in AWOT.</h2>
<h4>This notebook will grow over time as utilites are added and I have time to update.</h4>
End of ... |
scikit-optimize/scikit-optimize.github.io | 0.8/notebooks/auto_examples/ask-and-tell.ipynb | bsd-3-clause | print(__doc__)
import numpy as np
np.random.seed(1234)
import matplotlib.pyplot as plt
from skopt.plots import plot_gaussian_process
"""
Explanation: Async optimization Loop
Bayesian optimization is used to tune parameters for walking robots or other
experiments that are not a simple (expensive) function call.
Tim He... |
stephensekula/smu-honors-physics | fractals_random/fractal_from_random.ipynb | mit | import pickle,glob
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
%pylab inline
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Generating-Fractal-From-Random-Points---The-Chaos-Game" data-toc-modified-id="Generating-Fractal-From-Random-Points---The-Chaos-Game-1"><sp... |
Shatnerz/rhc | connection.ipynb | mit | import sys
sys.path.append('/opt/rhc')
"""
Explanation: Defining outbound connections
Start by making sure rhc is in python's path,
End of explanation
"""
import rhc.micro as micro
import rhc.async as async
"""
Explanation: and importing a couple of components.
End of explanation
"""
p=micro.load_connection([
... |
tanghaibao/goatools | notebooks/parents_and_ancestors.ipynb | bsd-2-clause | import os
from goatools.obo_parser import GODag
# Load a small test GO DAG and all the optional relationships,
# like 'regulates' and 'part_of'
godag = GODag('../tests/data/i126/viral_gene_silence.obo',
optional_attrs={'relationship'})
"""
Explanation: How to traverse to GO parents and ancestors
Travers... |
utensil/julia-playground | py/CGA-galgebra.ipynb | mit | cga3d = Ga(r'e_1 e_2 e_3 e e_{0}',g='1 0 0 0 0,0 1 0 0 0,0 0 1 0 0,0 0 0 0 -1,0 0 0 -1 0')
cga3d.g
e1,e2,e3,e,e0 = cga3d.mv()
ep = Rational(1,2) * e - e0
ep
en = Rational(1,2) * e + e0
en
ep**2
en**2
ep|en
e0**2
e**2
E = e^e0
E
E == ep^en == ep * en
E**2
E.rev() == -E
E * ep == -en
E * en == -ep
ep * E ... |
lrayle/rental-listings-census | src/Geographically_weight_regression.ipynb | bsd-3-clause | # # TODO: add putty connection too.
# #read SSH connection parameters
# with open('ssh_settings.json') as settings_file:
# settings = json.load(settings_file)
# hostname = settings['hostname']
# username = settings['username']
# password = settings['password']
# local_key_dir = settings['local_key_dir']
# c... |
tjwei/HackNTU_Data_2017 | Week01/01-Numpy.ipynb | mit | # 起手式
import numpy as np
"""
Explanation: Numpy 介紹
End of explanation
"""
np.array([1,2,3,4])
x = _
y = np.array([[1.,2,3],[4,5,6]])
y
"""
Explanation: 建立 ndarray
End of explanation
"""
x.shape
y.shape
x.dtype
y.dtype
"""
Explanation: 看 ndarray 的第一件事情： shape , dtype
End of explanation
"""
# import matplo... |
Who8MyLunch/ipynb_widget_canvas | notebooks/03 - Different Ways to Display an Image.ipynb | mit | from __future__ import print_function, unicode_literals, division, absolute_import
import io
import IPython
from ipywidgets import widgets
import PIL.Image
from widget_canvas import CanvasImage
from widget_canvas.image import read
"""
Explanation: Image Display Examples
End of explanation
"""
data_image = read('i... |
eTomate/ML-TextLearning-Intro | TextLearningSkLearn.ipynb | mit | %%capture
!pip install scikit-learn scipy numpy pandas matplotlib
import pandas as pd
import numpy as np
import math
%matplotlib inline
"""
Explanation: Text Learning with sklearn
This notebook will give you a short overview over text learning with skLearn.
At first we will install and import the required python pac... |
SciTools/courses | course_content/cartopy_course/cartopy_intro.ipynb | gpl-3.0 | import matplotlib.pyplot as plt
import cartopy.crs as ccrs
"""
Explanation: A First Look at Cartopy (for Iris)
Course aims and objectives
The aim of the cartopy course is to introduce the cartopy library and highlight of some of its features.
The learning outcomes of the cartopy course are as follows. By the end of th... |
zhsun/neu-cs5700 | network_basics.ipynb | mit | s = 'Hello world!'
print(s)
print("length is", len(s))
us = 'Hello 世界!'
print(us)
print("length is", len(us))
"""
Explanation: String vs. Bytes
Text in Python 3 is always Unicode and is represented by the str type, and binary data is represented by the bytes type. They cannot be mixed.
Strings can be encoded to bytes... |
royalosyin/Python-Practical-Application-on-Climate-Variability-Studies | ex33-View Northeast Pacifc sea surface temperature based on an ensemble empirical mode decomposition.ipynb | mit | %matplotlib inline
import xarray as xr
from PyEMD import EEMD
import numpy as np
import pylab as plt
plt.rcParams['figure.figsize'] = (9,5)
"""
Explanation: View Northeast Pacific SST based on an Ensemble Empirical Mode Decomposition
The oscillation of sea surface temperature (SST) has substantial impacts on the glo... |
metpy/MetPy | v0.11/_downloads/c93092487c8713b537d47b1774b1c063/unit_tutorial.ipynb | bsd-3-clause | import numpy as np
from metpy.units import units
"""
Explanation: Units Tutorial
Early in our scientific careers we all learn about the importance of paying
attention to units in our calculations. Unit conversions can still get the best
of us and have caused more than one major technical disaster, including the
crash... |
AllenDowney/ProbablyOverthinkingIt | test_scenario_sim.ipynb | mit | from __future__ import print_function, division
from thinkbayes2 import Pmf
from random import random
def flip(p):
return random() < p
def run_single_simulation(func, iters=1000000):
pmf_t = Pmf([0.2, 0.4])
p = 0.1
s = 0.9
outcomes = Pmf()
post_t = Pmf()
for i in range(iters):
t... |
carlosclavero/PySimplex | Documentation/Tutorial librería Simplex.py.ipynb | gpl-3.0 | from PySimplex import Simplex
from PySimplex import rational
import numpy as np
number="2"
print(Simplex.convertStringToRational(number))
number="2/5"
print(Simplex.convertStringToRational(number))
# Si recibe algo que no es un string, devuelve None
number=2
print(Simplex.convertStringToRational(number))
"""
Explan... |
poppy-project/pypot | samples/notebooks/QuickStart playing with a PoppyErgo.ipynb | gpl-3.0 | from pypot.creatures import PoppyErgo
ergo = PoppyErgo()
"""
Explanation: QuickStart: Playing with a Poppy Ergo (or a PoppyErgoJr)
This notebook is still work in progress! Feedbacks are welcomed!
In this tutorial we will show how to get started with your PoppyErgo creature. You can use a PoppyErgoJr instead.
<img src... |
LeeBergstrand/pygenprop | docs/source/_static/tutorial/tutorial.ipynb | apache-2.0 | import requests
from io import StringIO
from pygenprop.results import GenomePropertiesResults, GenomePropertiesResultsWithMatches, \
load_assignment_caches_from_database, load_assignment_caches_from_database_with_matches
from pygenprop.database_file_parser import parse_genome_properties_flat_file
from pygenprop.ass... |
wuafeing/Python3-Tutorial | 01 data structures and algorithms/01.03 keep last n items.ipynb | gpl-3.0 | from collections import deque
q = deque(maxlen = 3)
q.append(1)
q.append(2)
q.append(3)
q
q.append(4)
q
q.append(5)
q
"""
Explanation: Previous
1.3 保留最后N个元素
问题
在迭代操作或者其他操作的时候，怎样只保留最后有限几个元素的历史记录？
解决方案
保留有限历史记录正是 collections.deque 大显身手的时候。比如，下面的代码在多行上面做简单的文本匹配， 并返回匹配所在行的前 N 行：
``` python
from collections import deque... |
ueapy/ueapy.github.io | content/notebooks/2019-02-14-matplotlib-subplots-callum.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Today we'll be using matplotlib's pyplot to make clearer, prettier figures.
First we import packages and generate some data to plot.
End of explanation
"""
plt.rcParams.update({"font.size": 20})
"""
Explanation: Pump up the font sizes on plots. Bet... |
csc-training/python-introduction | notebooks/examples/5.2 Strings.ipynb | mit | ananasakäämä = "höhö 电脑"
print(ananasakäämä)
"""
Explanation: Strings
The most major difference between Python versions 2 and 3 is in string handling.
In Python 3 all strings are by default Unicode strings. The Python interpreter expects Python source files to be UTF-8 encoded Unicode strings.
What Unicode is beyond ... |
sueiras/training | tensorflow/00-Intro_to_tensorflow.ipynb | gpl-3.0 | # Header
# Basic libraries & options
from __future__ import print_function
#Basic libraries
import numpy as np
import tensorflow as tf
print('Tensorflow version: ', tf.__version__)
import time
# Select GPU
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]="1"
#Show images
impo... |
sujitpal/polydlot | src/pytorch/11-shape-generation.ipynb | apache-2.0 | from __future__ import division, print_function
from sklearn.metrics import accuracy_score, confusion_matrix
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import matplotlib as mat
import matplotlib.pyplot as plt
import os
import shutil
%matplot... |
rasbt/algorithms_in_ipython_notebooks | ipython_nbs/essentials/divide-and-conquer-algorithm-intro.ipynb | gpl-3.0 | def linear_search(lst, item):
for i in range(len(lst)):
if lst[i] == item:
return i
return -1
lst = [1, 5, 8, 12, 13]
for k in [8, 1, 23, 11]:
print(linear_search(lst=lst, item=k))
"""
Explanation: Introduction to Divide-and-Conquer Algorithms
The subfamily of Divide-and-Conquer algor... |
dennisobrien/bokeh | examples/howto/Categorical Data.ipynb | bsd-3-clause | from bokeh.io import show, output_notebook
from bokeh.models import CategoricalColorMapper, ColumnDataSource, FactorRange
from bokeh.plotting import figure
output_notebook()
"""
Explanation: Handling Categorical Data with Bokeh
End of explanation
"""
fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'St... |
kwant-project/kwant-tutorial-2016 | 3.3.MagnetoResistance.ipynb | bsd-2-clause | from types import SimpleNamespace
from math import cos, sin, pi
%run matplotlib_setup.ipy
from matplotlib import pyplot
import numpy as np
import scipy.stats as reg
import kwant
lat = kwant.lattice.square()
s_0 = np.identity(2)
s_z = np.array([[1, 0], [0, -1]])
s_x = np.array([[0, 1], [1, 0]])
s_y = np.array([[0, -... |
chainsawriot/pycon2016hk_sklearn | Super_textbook.ipynb | mit | from sklearn.datasets import load_breast_cancer
ourdata = load_breast_cancer()
print ourdata.DESCR
print ourdata.data.shape
ourdata.data
ourdata.target
ourdata.target.shape
ourdata.target_names
"""
Explanation: Textbook examples
Fairy tales kind of situation
The data has been processed, ready to analysis
The lear... |
sbussmann/buda-rank | notebooks/Insight Bayesian Workshop/Insight Bayesian Workshop - Artificial Data.ipynb | mit | import pandas as pd
import os
import numpy as np
import pymc3 as pm
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
true_rating = {
'All Stars': 2.0,
'Average': 0.0,
'Just Having Fun': -1.2,
}
true_index = {
0: 'All Stars',
1: 'Average',
2: 'Just Having Fun',
}
n_tea... |
AllenDowney/ModSimPy | soln/filter_soln.ipynb | mit | # Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'
# import functions from the modsim.py module
from modsim import *
"""
Explanation: Modeling and Simulati... |
geo-fluid-dynamics/phaseflow-fenics | tutorials/FEniCS/00-StefanProblem.ipynb | mit | import fenics
"""
Explanation: Solving the Stefan problem with finite elements
This Jupyter notebook shows how to solve a Stefan problem with finite elements using FEniCS.
Python packages
Import the Python packages for use in this notebook.
We use the finite element method library FEniCS.
End of explanation
"""
impo... |
albahnsen/PracticalMachineLearningClass | exercises/E20-NeuralNetworksKeras.ipynb | mit | import numpy as np
import keras
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: E20- Neural Networks in Keras
Use keras framework to solve the below exercises.
End of explanation
"""
# Import dataset
data = pd.read_csv('https://raw.githubusercontent.com/albahnsen/PracticalMachineLearningClass/... |
esa-as/2016-ml-contest | Facies_classification.ipynb | apache-2.0 | %matplotlib inline
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from mpl_toolkits.axes_grid1 import make_axes_locatable
from pandas import set_option
set_option("display.max_rows", 10)
pd.options.mode.chained_assignment = None
filen... |
napsternxg/GET17_SNA | notebooks/Twitter.ipynb | gpl-3.0 | if not os.path.isfile(TWITTER_CONFIG_FILE):
with open(os.path.join(DATA_DIR, "twitter_config.sample.json")) as fp:
creds = json.load(fp)
for k in sorted(creds.keys()):
v = input("Enter %s:\t" % k)
creds[k] = v
print(creds)
with open(TWITTER_CONFIG_FILE, "w+") as fp:
... |
DistrictDataLabs/ceb-training | notes/BLS Timeseries Data Exploration.ipynb | mit | # Imports
import csv
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from itertools import groupby
from operator import itemgetter
"""
Explanation: BLS Timeseries Data Exploration
In this workbook, I've set up a data frame of Bureau of Labor Statistics time series dat... |
alexandrnikitin/algorithm-sandbox | courses/DAT256x/Module03/03-05-Transformations Eigenvectors and Eigenvalues.ipynb | mit | import numpy as np
v = np.array([1,2])
A = np.array([[2,3],
[5,2]])
t = A@v
print (t)
"""
Explanation: Transformations, Eigenvectors, and Eigenvalues
Matrices and vectors are used together to manipulate spatial dimensions. This has a lot of applications, including the mathematical generation of 3D comp... |
arcyfelix/Courses | 17-08-31-Zero-to-Deep-Learning-with-Python-and-Keras/6 Convolutional Neural Networks.ipynb | apache-2.0 | import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
"""
Explanation: Convolutional Neural Networks
Machine learning on images
End of explanation
"""
from keras.datasets import mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data('/tmp/mnist.npz')
X_train.shape
X_test.... |
tensorflow/docs-l10n | site/en-snapshot/tfx/tutorials/serving/rest_simple.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... |
saullocastro/pyNastran | docs/quick_start/demo/op2_pandas_multi_case.ipynb | lgpl-3.0 | import os
import pandas as pd
import pyNastran
from pyNastran.op2.op2 import read_op2
pkg_path = pyNastran.__path__[0]
model_path = os.path.join(pkg_path, '..', 'models')
"""
Explanation: Static & Transient DataFrames in PyNastran
The iPython notebook for this demo can be found in:
- docs\quick_start\demo\op2_pand... |
glennrfisher/introduction-to-machine-learning | notebook/Teaching a Computer to Diagnose Cancer.ipynb | mit | import pandas as pd
import numpy as np
import sklearn.cross_validation
import sklearn.neighbors
import sklearn.metrics
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import seaborn as sns
# enable matplotlib mode
%matplotlib inline
# configure plot readability
sns.set_style("white")
sns.set_con... |
tlkh/Generating-Inference-from-3D-Printing-Jobs | Simple Data Plots (W1 - W4 data).ipynb | mit | import numpy as np
import csv
%run 'preprocessor.ipynb' #our own preprocessor functions
"""
Explanation: Simple 2D Plots using Matplotlib
These plots are for the data obtained from the cohort classroom printers from Week 1 to Week 4 of Term 2.
Import dependencies
End of explanation
"""
with open('data_w1w4.csv', 'r'... |
rcrehuet/Python_for_Scientists_2017 | notebooks/6_1_linear_regression.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
x = np.arange(10.)
y = 5*x+3
np.random.seed(3)
y+= np.random.normal(scale=10,size=x.size)
plt.scatter(x,y);
def lin_reg(x,y):
"""
Perform a linear regression of x vs y.
x, y are 1 dimensional numpy arrays
returns alpha and beta for ... |
ES-DOC/esdoc-jupyterhub | notebooks/bcc/cmip6/models/sandbox-1/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'bcc', 'sandbox-1', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: BCC
Source ID: SANDBOX-1
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, Radiat... |
NYUDataBootcamp/Materials | Code/notebooks/bootcamp_pandas-summarize.ipynb | mit | import sys # system module
import pandas as pd # data package
import matplotlib.pyplot as plt # graphics module
import datetime as dt # date and time module
import numpy as np # foundation for Pandas
%matplotlib inline ... |
gaufung/Data_Analytics_Learning_Note | Data_Analytics_in_Action/pandasIO.ipynb | mit | import numpy as np
import pandas as pd
csvframe=pd.read_csv('myCSV_01.csv')
csvframe
# 也可以通过read_table来读写数据
pd.read_table('myCSV_01.csv',sep=',')
"""
Explanation: Pandas 数据读写
API
读取 | 写入
--- | ---
read_csv | to_csv
read_excel | to_excel
read_hdf | to_hdf
read_sql | to_sql
read_json | to_json
read_html | to_html
read... |
google-research/computation-thru-dynamics | experimental/notebooks/Contextual Integration RNN Tutorial.ipynb | apache-2.0 | # Numpy, JAX, Matplotlib and h5py should all be correctly installed and on the python path.
from __future__ import print_function, division, absolute_import
import datetime
import h5py
import jax.numpy as np
from jax import random
from jax.experimental import optimizers
import matplotlib.pyplot as plt
import numpy as o... |
QuantConnect/Lean | Tests/Research/RegressionTemplates/BasicTemplateResearchPython.ipynb | apache-2.0 | import warnings
warnings.filterwarnings("ignore")
# Load in our startup script, required to set runtime for PythonNet
%run ./start.py
# Create an instance
qb = QuantBook()
# Select asset data
spy = qb.AddEquity("SPY")
"""
Explanation: Welcome to The QuantConnect Research Page
Refer to this page for documentation ht... |
kthouz/NYC_Green_Taxi | NYC Green Taxi.ipynb | mit | # Download the September 2015 dataset
if os.path.exists('data_september_2015.csv'): # Check if the dataset is present on local disk and load it
data = pd.read_csv('data_september_2015.csv')
else: # Download dataset if not available on disk
url = "https://s3.amazonaws.com/nyc-tlc/trip+data/green_tripdata_2015-09... |
hpparvi/PyTransit | notebooks/roadrunner/roadrunner_model_example_1.ipynb | gpl-2.0 | %pylab inline
rc('figure', figsize=(13,5))
def plot_lc(time, flux, c=None, ylim=(0.9865, 1.0025), ax=None):
if ax is None:
fig, ax = subplots()
else:
fig, ax = None, ax
ax.plot(time, flux, c=c)
ax.autoscale(axis='x', tight=True)
setp(ax, xlabel='Time [d]', ylabel='Flux', xlim=time[[... |
hannorein/rebound | ipython_examples/RadialVelocity.ipynb | gpl-3.0 | import rebound
import emcee # pip install emcee
import corner # pip install corner
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Fitting Radial Velocity Data
This example shows how to fit a dynamical model of a star and two planets to a set of radial velocity observations using the N-body integra... |
xMyrst/BigData | python/howto/010_Importar_Módulos.ipynb | gpl-3.0 | # Importamos solo la función array del modulo numpy
from numpy import array
a = array( [2,3,4] )
a
# Importamos todo el módulo numpy
import numpy
a = numpy.array( [2,3,4] )
a
# Importamos el módulo numpy y le asignamos el alias 'np'
# Cuando queramos importar funciones de dicho módulo lo haremos refiri... |
mdeff/ntds_2016 | toolkit/01_sol_acquisition_exploration.ipynb | mit | # Number of posts / tweets to retrieve.
# Small value for development, then increase to collect final data.
n = 4000 # 20
"""
Explanation: A Python Tour of Data Science: Data Acquisition & Exploration
Michaël Defferrard, PhD student, EPFL LTS2
1 Exercise: problem definition
Theme of the exercise: understand the impac... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/gapic/custom/showcase_custom_image_classification_online.ipynb | apache-2.0 | import os
import sys
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install -U google-cloud-aiplatform $USER_FLAG
"""
Explanation: Vertex client library: Custom training image classification model for online prediction
<ta... |
permamodel/permamodel | notebooks/Ku_2D.ipynb | mit | import os,sys
sys.path.append('../../permamodel/')
from permamodel.components import bmi_Ku_component
from permamodel import examples_directory
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap, addcyclic
import matplotlib as mpl
print examples_directory
... |
hashiprobr/redes-sociais | encontro22/small-world.ipynb | gpl-3.0 | import sys
sys.path.append('..')
import socnet as sn
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Encontro 22: Mundos Pequenos
Importando as bibliotecas:
End of explanation
"""
from random import random
def generate_random_graph(num_nodes, c):
g = sn.generate_empty_graph(num_nodes)
... |
drvinceknight/gt | nbs/solutions/08-Evolutionary-Game-Theory.ipynb | mit | import sympy as sym
x_1 = sym.symbols("x_1")
sym.solveset(3 * x_1 - 2 * (1 - x_1), x_1)
"""
Explanation: Evolutionary game theory - solutions
Assume the frequency dependent selection model for a population with two types of individuals: $x=(x_1, x_2)$ such that $x_1 + x_2 = 1$. Obtain all the stable distribution for ... |
gregcaporaso/short-read-tax-assignment | ipynb/mock-community/taxonomy-assignment-trimmed-dbs.ipynb | bsd-3-clause | from os.path import join, expandvars
from joblib import Parallel, delayed
from glob import glob
from os import system
from tax_credit.framework_functions import (parameter_sweep,
generate_per_method_biom_tables,
move_results_to_rep... |
rrbb014/data_science | fastcampus_dss/2016_05_23/0523_04__누적 분포 함수와 확률 밀도 함수.ipynb | mit | %%tikz
\filldraw [fill=white] (0,0) circle [radius=1cm];
\foreach \angle in {60,30,...,-270} {
\draw[line width=1pt] (\angle:0.9cm) -- (\angle:1cm);
}
\draw (0,0) -- (90:0.8cm);
"""
Explanation: 누적 분포 함수와 확률 밀도 함수
누적 분포 함수(cumulative distribution function)와 확률 밀도 함수(probabiligy density function)는 확률 변수의 분포 즉, 확률 분포를... |
makeyourowntextminingtoolkit/makeyourowntextminingtoolkit | A03_svd_applied_to_slightly_bigger_word_document_matrix.ipynb | gpl-2.0 | #import pandas for conviently labelled arrays
import pandas
# import numpy for SVD function
import numpy
# import matplotlib.pyplot for visualising arrays
import matplotlib.pyplot as plt
"""
Explanation: SVD Applied to a Word-Document Matrix
This notebook applies the SVD to a simple word-document matrix. The aim is to... |
tensorflow/tpu | tools/colab/bert_finetuning_with_cloud_tpus.ipynb | apache-2.0 | # Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
landlab/landlab | notebooks/tutorials/groundwater/groundwater_flow.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
from landlab import RasterModelGrid, imshow_grid
from landlab.components import GroundwaterDupuitPercolator, FlowAccumulator
from landlab.components.uniform_precip import PrecipitationDistribution
"""
Explanation: <a href="http://landlab.github.io"><img style="float:... |
slowvak/MachineLearningForMedicalImages | notebooks/Module 1.ipynb | mit | %matplotlib inline
import os
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import pandas as pd
import csv
from pandas.tools.plotting import scatter_matrix
from sklearn import preprocessing
import nibabel as nib
"""
Explanation: Module 1 - Data Load / Display / Normalizatio... |
egentry/lamat-2016-solutions | day2/randomness.ipynb | mit | poisson_samples = np.random.poisson(lam=1.5, size=20)
print(poisson_samples)
gaussian_samples = np.random.normal(loc=-5.0, scale=2.0, size=20)
print(gaussian_samples)
"""
Explanation: Activity description
See: https://docs.google.com/document/d/1COdCXs4K6kAXLcVvYxG3fqS53l2gzbkDvbbTmm8ZF1U/edit?usp=sharing
Example gen... |
miklevin/pipulate | examples/LESSON08_Selecting-by-Label-with-loc.ipynb | mit | import pandas as pd
pd.set_option('display.max_columns', 500)
def a1_notation(n):
string = ""
while n > 0:
n, remainder = divmod(n - 1, 26)
string = chr(65 + remainder) + string
return string
# First we create a 30 x 30 DataFrame with both row and column labels.
alist = list(range(1, 31))... |
theJollySin/python_for_scientists | classes/12_matplotlib/1_line_plots.ipynb | gpl-3.0 | import numpy
from matplotlib import pyplot
%matplotlib inline
### generate some random data
xdata = numpy.arange(25)
ydata = numpy.random.randn(25)
### initialize the "figure" and "axes" objects
fig, ax = pyplot.subplots()
line_plot = ax.plot(xdata, ydata)
"""
Explanation: Line Plots
Perhaps the most well-known t... |
liganega/Gongsu-DataSci | previous/y2017/W09-numpy-averages/.ipynb_checkpoints/GongSu21_Statistics_Averages-checkpoint.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
from datetime import datetime as dt
from scipy import stats
"""
Explanation: 자료 안내: 여기서 다루는 내용은 아래 사이트의 내용을 참고하여 생성되었음.
https://github.com/rouseguy/intro2stats
안내사항
오늘 다루는 내용은 pandas 모듈의 소개 정도로 이해하고 넘어갈 것을 권장한다.
아래 내용은 엑셀의 스프레드시트지에 담긴 데이터를 분석하여 평균 등을 어떻게 구하는가를 알고 있다면 어렵지 않게 이해할 수... |
gfeiden/Notebook | Daily/20150821_mass_track_compositions.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
cd /Users/grefe950/evolve/dmestar/trk/
"""
Explanation: Diagnostic Checks on Mass Tracks
End of explanation
"""
def loadTrack(filename):
return np.genfromtxt(filename, usecols=(0, 1, 2, 3, 4, 5))
"""
Explanation: Quick mass track loader
End ... |
tensorflow/docs-l10n | site/zh-cn/tensorboard/scalars_and_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... |
pombredanne/https-gitlab.lrde.epita.fr-vcsn-vcsn | doc/notebooks/automaton.is_equivalent.ipynb | gpl-3.0 | import vcsn
"""
Explanation: automaton.is_equivalent(aut)
Whether this automaton is equivalent to aut, i.e., whether they accept the same words with the same weights.
Preconditions:
- The join of the weightsets is either $\mathbb{B}, \mathbb{Z}$, or a field ($\mathbb{F}2, \mathbb{Q}, \mathbb{Q}\text{mp}, \mathbb{R}$).... |
ES-DOC/esdoc-jupyterhub | notebooks/test-institute-3/cmip6/models/sandbox-2/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-3', 'sandbox-2', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: TEST-INSTITUTE-3
Source ID: SANDBOX-2
Topic: Seaice
Sub-Topics: Dynami... |
mne-tools/mne-tools.github.io | dev/_downloads/98d9662291626be9c938eee7a8fcc9bd/sensor_noise_level.ipynb | bsd-3-clause | # Author: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD-3-Clause
import os.path as op
import mne
data_path = mne.datasets.sample.data_path()
raw_erm = mne.io.read_raw_fif(op.join(data_path, 'MEG', 'sample',
'ernoise_raw.fif'), preload=True)
"""
Explanation: Show noise ... |
ES-DOC/esdoc-jupyterhub | notebooks/cnrm-cerfacs/cmip6/models/cnrm-esm2-1-hr/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cnrm-cerfacs', 'cnrm-esm2-1-hr', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: CNRM-CERFACS
Source ID: CNRM-ESM2-1-HR
Topic: Seaice
Sub-Topics: Dyna... |
bryanwweber/PyKED | docs/rcm-example.ipynb | bsd-3-clause | import cantera as ct
import numpy as np
from pyked import ChemKED
"""
Explanation: RCM modeling with varying reactor volume
This example is available as an ipynb (Jupyter Notebook) file in the main GitHub repository at https://github.com/pr-omethe-us/PyKED/blob/master/docs/rcm-example.ipynb
The ChemKED file that will ... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive/05_artandscience/labs/a_handtuning.ipynb | apache-2.0 | import math
import shutil
import numpy as np
import pandas as pd
import tensorflow as tf
print(tf.__version__)
tf.logging.set_verbosity(tf.logging.INFO)
pd.options.display.max_rows = 10
pd.options.display.float_format = '{:.1f}'.format
"""
Explanation: Hand tuning hyperparameters
Learning Objectives:
* Use the Line... |
hetaodie/hetaodie.github.io | assets/media/uda-ml/supervisedlearning/decision/17/Titanic Solutions-zh.ipynb | mit | # Import libraries necessary for this project
import numpy as np
import pandas as pd
from IPython.display import display # Allows the use of display() for DataFrames
# Pretty display for notebooks
%matplotlib inline
# Set a random seed
import random
random.seed(42)
# Load the dataset
in_file = 'titanic_data.csv'
ful... |
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