repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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ES-DOC/esdoc-jupyterhub | notebooks/fio-ronm/cmip6/models/sandbox-2/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'fio-ronm', 'sandbox-2', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: FIO-RONM
Source ID: SANDBOX-2
Topic: Ocean
Sub-Topics: Timestepping Framework, A... |
quantopian/research_public | notebooks/lectures/PCA/notebook.ipynb | apache-2.0 | from numpy import linalg as LA
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Principal Component Analysis
by Rene Zhang and Max Margenot
Part of the Quantopian Lecture Series:
www.quantopian.com/lectures
https://github.com/quantopian/research_public
Applications in many fie... |
GoogleCloudPlatform/training-data-analyst | courses/ai-for-finance/solution/arima_model.ipynb | apache-2.0 | !pip install --user statsmodels
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime
%config InlineBackend.figure_format = 'retina'
"""
Explanation: Building an ARIMA Model for a Financial Dataset
In this notebook, you will build an ARIMA model for AAPL stock clo... |
NYUDataBootcamp/Projects | MBA_S16/Gurmen-Manos-Shaikh-College.ipynb | mit | import pandas as pd # data package
import matplotlib.pyplot as plt # graphics
import sys # system module, used to get Python version
import os # operating system tools (check files)
import datetime as dt # date tools, used to note current date
import ... |
thom056/ada-parliament-ML | 01-Scraping/05-FormatPersonInfo.ipynb | gpl-2.0 | import pandas as pd
import numpy as np
import glob
import os
# There's a lot of columns in the DF.
# Therefore, we add this option so that we can see more columns
pd.options.display.max_columns = 100
"""
Explanation: 05 - Format Personal Information
From the scraped data from MemberCouncil, we want to format a file c... |
blakeflei/IntroScientificPythonWithJupyter | 09 - Ordinary Differential Equation (ODE) Examples.ipynb | bsd-3-clause | # Python imports
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from scipy.misc import derivative
from scipy import integrate
"""
Explanation: Integration and derivatives:
Many systems are represented dynamically and present the need for some simple calculus. Derivatives and integrals are impor... |
andsor/pypercolate | docs/hpc-performance/pypercolate-hpc-performance.ipynb | isc | # configure plotting
%config InlineBackend.rc = {'figure.dpi': 300, 'savefig.dpi': 300, \
'figure.figsize': (6, 3), 'font.size': 12, \
'figure.facecolor': (1, 1, 1, 0)}
%matplotlib inline
import timeit
import matplotlib.pyplot as plt
import memory_profiler
impor... |
mne-tools/mne-tools.github.io | 0.19/_downloads/5f84ce88b4773e5ca1f9b3502aef334a/plot_eeg_erp.ipynb | bsd-3-clause | import mne
from mne.datasets import sample
"""
Explanation: EEG processing and Event Related Potentials (ERPs)
For a generic introduction to the computation of ERP and ERF
see tut_epoching_and_averaging.
:depth: 1
End of explanation
"""
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_au... |
mathnathan/notebooks | JSD_Example.ipynb | mit | from PIL import Image
import numpy as np
luke = Image.open("/home/nathan/Downloads/Luke_Van_Poppering.jpeg")
luke.thumbnail((300,300)) # Thanks for this, John....
john = Image.open("/home/nathan/Downloads/John_Abascal.jpg")
john.thumbnail((300,300))
luke
john
"""
Explanation: Calculate Jensen-Shannon Divergence Bet... |
DBWangGroupUNSW/COMP6714 | GradientDescend.ipynb | mit | import torch
torch.manual_seed(42) # fix the seed; easy to debug
"""
Explanation: Gradient Descend
We will construct a simple linear model and see how Gradient Descend algorithm helps us to find the best parameters.
End of explanation
"""
u = torch.randint(10, (5, 1))
v = torch.randint(10, (5, 1))
print(u)
print(v)... |
zczapran/datascienceintensive | naive_bayes/Mini_Project_Naive_Bayes.ipynb | mit | %matplotlib inline
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from six.moves import range
# Setup Pandas
pd.set_option('display.width', 500)
pd.set_option('display.max_columns', 100)
pd.set_option('... |
mercye/foundations-homework | 05/Spotify_graded.ipynb | mit | import requests
# query = 'lil' type = 'artist' number of responses(limit) = '50' market = 'US'
response = requests.get('https://api.spotify.com/v1/search?q=lil&type=artist&limit=50&market=US')
# data is a string, use json
data = response.json()
# print(type(data))
# print(data.keys())
# print(type(data['artists']))... |
GoogleCloudPlatform/training-data-analyst | courses/dataflow/demos/beam_notebooks/beam_notebooks_demo.ipynb | apache-2.0 | # Python's regular expression library
import re
# Beam and interactive Beam imports
import apache_beam as beam
from apache_beam.runners.interactive.interactive_runner import InteractiveRunner
import apache_beam.runners.interactive.interactive_beam as ib
# Dataframe API imports
from apache_beam.dataframe.convert impor... |
Vvkmnn/books | AutomateTheBoringStuffWithPython/lesson17.ipynb | gpl-3.0 | eggs = {
'name': 'Zophie',
'species': 'cat',
'age': 8
}
ham = {
'species': 'cat',
'name': 'Zophie',
'age': 8
}
print(eggs)
print(ham)
"""
Explanation: Lesson 17:
The Dictionary Data Type
Switched to the Jupyter Notebook for REPL convenience.
Dictionaries use key pairs to store values... |
amercer1/titanic_data_exploration | titanic_walkthrough.ipynb | bsd-3-clause | df = pd.read_csv("data/train.csv")
df.head()
df.tail()
"""
Explanation: Lets load in the dataset
End of explanation
"""
df.dtypes
df.info()
df.describe()
"""
Explanation: Lets see the types that were imported on our behalf
End of explanation
"""
df.shape
"""
Explanation: See the shape of the dataset
End of ex... |
kingb12/languagemodelRNN | old_comparisons/noing10_LSTM_v_BOW.ipynb | mit | report_files = ["/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing10_200_512_04drb/encdec_noing10_200_512_04drb.json", "/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing10_bow_200_512_04drb/encdec_noing10_bow_200_512_04drb.json"]
log_files = ["/Users/bking/IdeaProjects/... |
jamesfolberth/NGC_STEM_camp_AWS | notebooks/machineLearning_notebooks/01_Naive_Bayes/Day 3 - Probability and Statistics - Morning.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import numpy as np
fig = plt.figure()
ax = plt.axes()
x = np.array((5,3,7,4,2))
ax.scatter(x,np.zeros(len(x)), s=75, label = 'Values')
ax.scatter(4.2,0, s=75, label = 'Mean')
ax.legend()
"""
Explanation: Introduction to Probability and Statistics!
In... |
gloriakang/vax-sentiment | to_do/vax_temp/test.ipynb | mit | import pandas as pd
import numpy as np
import networkx as nx
from copy import deepcopy
import matplotlib.pyplot as plt
%matplotlib inline
from matplotlib.backends.backend_pdf import PdfPages
from glob import glob
fileName = 'article0'
def getFiles(fileName):
matches = glob('*'+fileName+'*')
bigFile = matches... |
PDBeurope/PDBe_Programming | search_interface/notebooks/Timeline.ipynb | apache-2.0 | import pandas
PDBE_SOLR_URL = "http://www.ebi.ac.uk/pdbe/search/pdb"
# or https://www.ebi.ac.uk/pdbe/search/pdb/select?rows=0&q=status:REL&wt=json
from mysolr import Solr
solr = Solr(PDBE_SOLR_URL, version=4)
response = solr.search(**{
"rows" : 0, # "fl" : "deposition_year",
"q" : '... |
jgrizou/explauto | notebook/Learning the IK of the arm of a simulated Poppy using Explauto.ipynb | gpl-3.0 | from poppy.creatures import PoppyHumanoid
poppy = PoppyHumanoid(simulator='vrep')
"""
Explanation: Learning the IK of the arm of a Poppy robot using Explauto
Explauto comes with a PoppyEnvironment which links a Poppy robot to the explauto framework.
Those robots, also developed in the Flowers team, were designed as ... |
IanHawke/maths-with-python | 06-numpy-plotting.ipynb | mit | x = [1, 2, 3]
y = [4, 9, 16]
print(x+y)
"""
Explanation: Scientific Python
A lot of computational algorithms are expressed using Linear Algebra terminology - vectors and matrices. This is thanks to the wide range of methods within Linear Algebra for solving the sort of problems that computers are good at solving!
With... |
Naereen/notebooks | Extending_lolviz_with_a_unified_display_function_that_chooses_the_best_one_based_on_the_datatype.ipynb | mit | %load_ext watermark
%watermark -v -m -p lolviz
import lolviz
"""
Explanation: <h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Extending-lolviz-with-a-unified-display-function-that-chooses-the-best-one-based-on-the-datatype" data-toc-modified-id="Extend... |
NYUDataBootcamp/Projects | UG_S17/Madhok-Rinkacs-Yao-USFlightDelays.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
import seaborn as sns
%matplotlib inline
sns.set_style('whitegr... |
jsub10/MLCourse | Notebooks/Ensemble-Prediction-Techniques.ipynb | mit | import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
%matplotlib inline
def clf_preds(accuracy, truth_vector):
# accuracy of classifier
# truth_vector is the actual value of the target
preds = []
for i in range(len(truth)):
pred = np.random.choice([truth[i], 1-truth[i]], p... |
RittmanResearch/maybrain | docs/06 - The algorithms Package.ipynb | apache-2.0 | %matplotlib inline
import networkx as nx
import matplotlib.pyplot as plt
from maybrain import utils
from maybrain import resources as rr
from maybrain import brain as mbt
from maybrain import algorithms as mba
from maybrain import constants as ct
from maybrain.plotting import histograms as hh
a = mbt.Brain()
a.import... |
jld23/saspy | sas_tabulate_example.ipynb | apache-2.0 | import saspy
sas = saspy.SASsession(cfgname='default')
cars = sas.sasdata('cars', 'sashelp')
cars.head()
"""
Explanation: SASPy Tabulation for Descriptive Statistics
This notebook demonstrates the usage of a powerful set of tools for descriptive statistics and nesting data in SASPy, powered by the TABULATE procedure.... |
metpy/MetPy | v0.11/_downloads/591c50ddf519b58966833b985f7ca28b/Parse_Angles.ipynb | bsd-3-clause | import metpy.calc as mpcalc
"""
Explanation: Parse angles
Demonstrate how to convert direction strings to angles.
The code below shows how to parse directional text into angles.
It also demonstrates the function's flexibility
in handling various string formatting.
End of explanation
"""
dir_str = 'SOUTH SOUTH EAST'... |
shirtsgroup/physical-validation | doc/examples/ensemble_check.ipynb | lgpl-2.1 | # enable plotting in notebook
%matplotlib notebook
"""
Explanation: Ensemble check
Note: This notebook can be run locally by cloning the
Github repository.
The notebook is located in doc/examples/ensemble_check.ipynb. Be aware
that probabilistic quantities such as error estimates based on bootstrapping
will differ whe... |
jegibbs/phys202-2015-work | assignments/assignment06/DisplayEx01.ipynb | mit | %matplotlib inline
import numpy as np
from matplotlib import pyplot as plt
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
from IPython.html import widgets
from IPython.display import Image
assert True # leave this to grade the import statements
"""
Explanation: Disp... |
kwant-project/kwant-tutorial-2016 | 3.1.Aharonov-Bohm.ipynb | bsd-2-clause | from math import pi
%run matplotlib_setup.ipy
from matplotlib import pyplot
import numpy as np
import kwant
lat=kwant.lattice.square()
L,W=30,16
def myshape(R): return (
(R[0]**2 + R[1]**2) > (L-W/2)**2 and
(R[0]**2 + R[1]**2) < (L+W/2)**2)
H=kwant.Builder()
H[lat.shape(myshape,(L,0)... |
karlstroetmann/Artificial-Intelligence | Python/3 Games/Connect-Four-Frame.ipynb | gpl-2.0 | gPlayers = [ 'X', 'O' ]
"""
Explanation: Connect Four
This notebook defines the game Connect Four.
Connect Four is played on a board of dimension $6 \times 7$, i.e. there are $6$ rows $7$ columns. Instead of Red and Yellow we call the players X and O. Player X starts. Player X and O take turns to choose columns tha... |
nyirock/mg_blast_wrapper | wps2_blast_pp_metagenome_v1.4.ipynb | mit | from Bio import SeqIO
import time
import os
import shutil
import pandas
#parameters
version = 'v1.4'
project_name = 'wps2_bl_metagenome'
e_value = 1e-20
iden = 95.0
metric = 50.0
!cat ../ss_genomes/AP_WPS-2_bacterium* > ../ss_genomes/all_AP_WPS-2_bacterium.fna
!makeblastdb -in ../ss_genomes/all_AP_WPS-2_bacterium.fn... |
mne-tools/mne-tools.github.io | 0.23/_downloads/1f2e88712289c5315357ca3b8efc1bbb/sensor_connectivity.ipynb | bsd-3-clause | # Author: Martin Luessi <mluessi@nmr.mgh.harvard.edu>
#
# License: BSD (3-clause)
import mne
from mne.connectivity import spectral_connectivity
from mne.datasets import sample
from mne.viz import plot_sensors_connectivity
print(__doc__)
"""
Explanation: Compute all-to-all connectivity in sensor space
Computes the Ph... |
feststelltaste/software-analytics | prototypes/Reading a Git log file output with Pandas SCRATCHBOOK.ipynb | gpl-3.0 | import pandas as pd
from io import StringIO
commits_raw = pd.read_csv(StringIO(log),
sep="#",
header=None,
names=['file_stats','sha', 'date', 'author'])
commits_raw.head()
"""
Explanation: After this, we have to tell Git which information we want. We can do this via the <tt>pretty-format</tt> option.... |
UIUC-iSchool-DataViz/spring2017 | week07/examples_week07.ipynb | mit | %matplotlib inline
"""
Explanation: Examples: Week 7
This week, we will apply some of our discussions around filtering, splitting and so on to build out comparisons between different variables within the World Bank Economic Indicators dataset.
This dataset, which covers 1960-2016, 264 countries and 1452 variables, can... |
julienchastang/unidata-python-workshop | notebooks/Primer/Primer.ipynb | mit | # Convention for import to get shortened namespace
import numpy as np
# Create an array for testing
a = np.arange(12).reshape(3, 4)
a
"""
Explanation: <div style="width:1000 px">
<div style="float:right; width:98 px; height:98px;">
<img src="https://raw.githubusercontent.com/Unidata/MetPy/master/metpy/plots/_static... |
takanory/python-machine-learning | Chapter13.ipynb | mit | import theano
from theano import tensor as T
# 初期化: scalar メソッドではスカラー(単純な配列)を生成
x1 = T.scalar()
w1 = T.scalar()
w0 = T.scalar()
z1 = w1 * x1 + w0
# コンパイル
net_input = theano.function(inputs=[w1, x1, w0], outputs=z1)
# 実行
net_input(2.0, 1.0, 0.5)
"""
Explanation: 第13章 ニューラルネットワーク
著者オリジナル: https://github.com/rasbt/py... |
rmhyman/DataScience | Intro To Data Science Lesson 2.ipynb | mit | import numpy as np
import pandas as pd
df = pd.read_csv('Master.csv')
df.head(5)
"""
Explanation: Intro to Data Wrangling
Data Wrangling is the concept of arranging your dataset into a workable format for analysis. When retreiving data from various sources, it is not always in a format that is ready to be analyzed. ... |
smorton2/think-stats | code/chap05ex.ipynb | gpl-3.0 | from __future__ import print_function, division
%matplotlib inline
import numpy as np
import nsfg
import first
import analytic
import thinkstats2
import thinkplot
"""
Explanation: Examples and Exercises from Think Stats, 2nd Edition
http://thinkstats2.com
Copyright 2016 Allen B. Downey
MIT License: https://opensou... |
icrtiou/coursera-ML | ex5-bias vs variance/4- polynomial regression.ipynb | mit | lr.poly_features(X, power=3)
"""
Explanation: create polynomial features
End of explanation
"""
X_poly, Xval_poly, Xtest_poly= lr.prepare_poly_data(X, Xval, Xtest, power=8)
X_poly[:3, :]
"""
Explanation: prepare polynomial regression data
expand feature with power = 8, or any power you want to use
Apply normaliza... |
stanmoore1/lammps | python/examples/pylammps/interface_usage_bonds.ipynb | gpl-2.0 | from lammps import IPyLammps
L = IPyLammps()
# 2d circle of particles inside a box with LJ walls
import math
b = 0
x = 50
y = 20
d = 20
# careful not to slam into wall too hard
v = 0.3
w = 0.08
L.units("lj")
L.dimension(2)
L.atom_style("bond")
L.boundary("f f p")
L.lattice("hex", 0.85)
L.region("... |
graphistry/pygraphistry | demos/demos_databases_apis/neo4j/official/graphistry_bolt_tutorial_public.ipynb | bsd-3-clause | #!pip install --user pandas
#!pip install --user graphistry[bolt]
### ADVANCED:
### If you already have the neo4j python driver, you can leave out '[bolt]':
### !pip install --user graphistry
### If you already have graphistry but not neo4j, you can reuse your existing graphistry:
### !pip install --user neo4j
"""
Ex... |
danresende/deep-learning | reinforcement/Q-learning-cart.ipynb | mit | import gym
import tensorflow as tf
import numpy as np
"""
Explanation: Deep Q-learning
In this notebook, we'll build a neural network that can learn to play games through reinforcement learning. More specifically, we'll use Q-learning to train an agent to play a game called Cart-Pole. In this game, a freely swinging p... |
laowantong/mocodo | doc/fr_refman.ipynb | mit | %%mocodo --mld --colors ocean --shapes copperplate --relations diagram markdown_data_dict
DF, 11 Élève, 1N Classe
Classe: Num. classe, Num. salle
Faire Cours, 1N Classe, 1N Prof: Vol. horaire
Catégorie: Code catégorie, Nom catégorie
Élève: Num. élève, Nom élève
Noter, 1N Élève, 0N Prof, 0N Matière, 1N Date: Note
Prof:... |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_forward.ipynb | bsd-3-clause | import mne
from mne.datasets import sample
data_path = sample.data_path()
# the raw file containing the channel location + types
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
# The paths to freesurfer reconstructions
subjects_dir = data_path + '/subjects'
subject = 'sample'
"""
Explanation: Head model a... |
pligor/predicting-future-product-prices | 04_time_series_prediction/37_price_history_clustering.ipynb | agpl-3.0 | factors(689)
max_seq_len = 682
data_path = '../../../../Dropbox/data'
phae_path = data_path + '/price_hist_autoencoder'
npz_dates = phae_path + '/price_history_full_seqs_dates.npz'
assert path.isfile(npz_dates)
npz_train = phae_path + '/price_history_seqs_dates_normed_train.npz'
assert path.isfile(npz_train)
npz_... |
jorisvandenbossche/2015-EuroScipy-pandas-tutorial | 02 - Data structures.ipynb | bsd-2-clause | df = pd.read_csv("data/titanic.csv")
df.head()
"""
Explanation: Tabular data
End of explanation
"""
df['Age'].hist()
"""
Explanation: Starting from reading this dataset, to answering questions about this data in a few lines of code:
What is the age distribution of the passengers?
End of explanation
"""
df.groupb... |
folivetti/BIGDATA | Spark/Lab03.ipynb | mit | # Data for manual OHE
# Note: the first data point does not include any value for the optional third feature
sampleOne = [(0, 'mouse'), (1, 'black')]
sampleTwo = [(0, 'cat'), (1, 'tabby'), (2, 'mouse')]
sampleThree = [(0, 'bear'), (1, 'black'), (2, 'salmon')]
sampleDataRDD = sc.parallelize([sampleOne, sampleTwo, sampl... |
HeyIamJames/bikeshare | BikeShareStep2.ipynb | mit | import pandas as pd
import numpy as np
from pandas import Series, DataFrame
weather = pd.read_table('daily_weather.tsv')
weather.groupby('season_desc').agg({'temp': np.mean})
fix = weather.replace("Fall", "Summer_").replace("Summer", "Spring_").replace("Winter", "Fall_").replace("Spring", "Winter_")
weather.grou... |
tpin3694/tpin3694.github.io | statistics/variance_and_standard_deviation.ipynb | mit | # Import data
import math
"""
Explanation: Title: Variance And Standard Deviation
Slug: variance_and_standard_deviation
Summary: Calculating Variance And Standard Deviation in Python.
Date: 2016-02-08 12:00
Category: Statistics
Tags: Basics
Authors: Chris Albon
Preliminary
End of explanation
"""
# Create list o... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive2/structured/solutions/5a_train_keras_ai_platform_babyweight.ipynb | apache-2.0 | import os
"""
Explanation: LAB 5a: Training Keras model on Cloud AI Platform.
Learning Objectives
Setup up the environment
Create trainer module's task.py to hold hyperparameter argparsing code
Create trainer module's model.py to hold Keras model code
Run trainer module package locally
Submit training job to Cloud A... |
google/starthinker | colabs/bigquery_census_normalize.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: Census Data Normalized
Convert given census table to percent of population, normalizing it.
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... |
jonathf/chaospy | docs/user_guide/advanced_topics/seir_model.ipynb | mit | def ode_seir(variables, coordinates, parameters):
var_s, var_e, var_i, var_r = variables
alpha, beta, gamma = parameters
delta_s = -beta*var_i*var_s
delta_e = beta*var_i*var_s-alpha*var_e
delta_i = -gamma*var_i+alpha*var_e
delta_r = gamma*var_i
return delta_s, delta_e, delta_i, del... |
diging/tethne-notebooks | 1. Working with data from the Web of Science.ipynb | gpl-3.0 | print "This is a code cell!"
"""
Explanation: Introduction to Tethne: Working with data from the Web of Science
In this notebook we will take our first steps with the Tethne Python package. We'll parse some bibliographic records from the ISI Web of Science, and take a look at the Corpus class and its various features.... |
metpy/MetPy | dev/_downloads/3f80eaf969d597174a3c39544b2f3037/Mesonet_Stationplot.ipynb | bsd-3-clause | import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import pandas as pd
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.plots import add_metpy_logo, StationPlot
from metpy.units import units
"""
Explanation: Mesonet Station Plot
Make a surface sta... |
DJCordhose/ai | notebooks/workshops/d2d/cnn-prediction.ipynb | mit | import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
%pylab inline
import matplotlib.pylab as plt
import numpy as np
from distutils.version import StrictVersion
import sklearn
print(sklearn.__version__)
assert StrictVersion(sklearn.__version__ ) >= StrictVersion('0.18.1')
import tensorflow as tf
t... |
ES-DOC/esdoc-jupyterhub | notebooks/bnu/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', 'bnu', 'sandbox-2', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: BNU
Source ID: SANDBOX-2
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, Radiat... |
pyro-ppl/numpyro | notebooks/source/truncated_distributions.ipynb | apache-2.0 | !pip install -q git+https://github.com/pyro-ppl/numpyro.git
import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
import numpyro
import numpyro.distributions as dist
from jax import lax, random
from jax.scipy.special import ndtr, ndtri
from jax.scipy.stats import poisson, norm
from nump... |
ALEXKIRNAS/DataScience | CS231n/assignment2/PyTorch.ipynb | mit | import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torch.utils.data import sampler
import torchvision.datasets as dset
import torchvision.transforms as T
import numpy as np
import timeit
"""
Explanation: Training a ConvNet ... |
ioos/notebooks_demos | notebooks/2017-11-01-Creating-Archives-Using-Bagit.ipynb | mit | import os
import pandas as pd
fname = os.path.join("data", "dsg", "timeseriesProfile.csv")
df = pd.read_csv(fname, parse_dates=["time"])
df.head()
"""
Explanation: Using BagIt to tag oceanographic data
BagIt is a packaging format that supports storage of arbitrary digital content. The "bag" consists of arbitrary co... |
dit/dit | examples/Time Series Analysis.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import dit
from dit.inference import binned, dist_from_timeseries
from dit.multivariate import total_correlation as I, intrinsic_total_correlation as IMI
dit.ditParams['repr.print'] = True
"""
Explanation: Time Series Analysis
One important area o... |
Diyago/Machine-Learning-scripts | DEEP LEARNING/Pytorch from scratch/TODO/Autoencoders/linear-autoencoder/Simple_Autoencoder_Solution.ipynb | apache-2.0 | import torch
import numpy as np
from torchvision import datasets
import torchvision.transforms as transforms
# convert data to torch.FloatTensor
transform = transforms.ToTensor()
# load the training and test datasets
train_data = datasets.MNIST(root='data', train=True,
download=True... |
tritemio/multispot_paper | out_notebooks/usALEX-5samples-PR-raw-dir_ex_aa-fit-out-all-ph-17d.ipynb | mit | ph_sel_name = "all-ph"
data_id = "17d"
# ph_sel_name = "all-ph"
# data_id = "7d"
"""
Explanation: Executed: Mon Mar 27 11:37:24 2017
Duration: 10 seconds.
usALEX-5samples - Template
This notebook is executed through 8-spots paper analysis.
For a direct execution, uncomment the cell below.
End of explanation
"""
f... |
eford/rebound | ipython_examples/Resonances_of_Jupiters_moons.ipynb | gpl-3.0 | import rebound
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
sim = rebound.Simulation()
sim.units = ('AU', 'days', 'Msun')
# We can add Jupiter and four of its moons by name, since REBOUND is linked to the HORIZONS database.
labels = ["Jupiter", "Io", "Europa","Ganymede","Callisto"]
sim.add(l... |
whitead/numerical_stats | unit_10/hw_2017/problem_set_3.ipynb | gpl-3.0 | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv('november.csv')
ind = data['center_diff_valid']
plt.plot(data['time'][ind] / (60 * 60 * 24), data['temp'][ind], '.')
plt.ylabel('Temperature [F]')
plt.xlabel('Days')
plt.show()
"""
Explanation: Introduction
We have two datasets ... |
diminishedprime/.org | programmey_stuff/kaggle/titanic/titanic.ipynb | mit | %matplotlib inline
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import scipy.stats as stats
matplotlib.style.use('ggplot')
"""
Explanation: Titanic: this time with cross-validation
And also a Neural Net
End of explanation
"""
import math
def scrub(filePath):
data = p... |
minxuancao/shogun | doc/ipython-notebooks/classification/Classification.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
from modshogun import *
#Needed lists for the final plot
classifiers_linear = []*10
classifiers_non_linear = []*10
classifiers_names = []*10
fadings = []*10
"""
Explanation: Vi... |
ClementPhil/deep-learning | transfer-learning/Transfer_Learning.ipynb | mit | from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
vgg_dir = 'tensorflow_vgg/'
# Make sure vgg exists
if not isdir(vgg_dir):
raise Exception("VGG directory doesn't exist!")
class DLProgress(tqdm):
last_block = 0
def hook(self, block_num=1, block_size=1, total_s... |
maartenbreddels/vaex | examples/science_pal5_tidal_tails.ipynb | mit | import vaex as vx
import numpy as np
import matplotlib.pylab as plt
import scipy.ndimage.filters
from astropy.coordinates import SkyCoord
import numpy as np
import xarray as xr
%matplotlib inline
# alias for gaussian filter
gf = scipy.ndimage.filters.gaussian_filter
"""
Explanation: Finding Palomar 5's tidal tail in... |
Hyperparticle/deep-learning-foundation | lessons/intro-to-tflearn/intro_to_tensorflow_solution.ipynb | mit | # Problem 1 - Implement Min-Max scaling for grayscale image data
def normalize_grayscale(image_data):
"""
Normalize the image data with Min-Max scaling to a range of [0.1, 0.9]
:param image_data: The image data to be normalized
:return: Normalized image data
"""
a = 0.1
b = 0.9
grayscale... |
probml/pyprobml | notebooks/book2/14/linreg_height_weight_numpyro.ipynb | mit | !pip install -q numpyro@git+https://github.com/pyro-ppl/numpyro
!pip install -q arviz
import numpy as np
np.set_printoptions(precision=3)
import matplotlib.pyplot as plt
import math
import os
import warnings
import pandas as pd
import jax
print("jax version {}".format(jax.__version__))
print("jax backend {}".format(... |
ud3sh/coursework | deeplearning.ai/coursera-improving-neural-networks/week1/assignment2/Regularization+-+v2.ipynb | unlicense | # import packages
import numpy as np
import matplotlib.pyplot as plt
from reg_utils import sigmoid, relu, plot_decision_boundary, initialize_parameters, load_2D_dataset, predict_dec
from reg_utils import compute_cost, predict, forward_propagation, backward_propagation, update_parameters
import sklearn
import sklearn.da... |
harmsm/pythonic-science | chapters/00_inductive-python/09_pandas.ipynb | unlicense | some_dict = {"x":{"a":1,"b":2,"c":3},
"y":{"a":4,"b":5,"c":6}}
"""
Explanation: Pandas
Sometimes you want a spreadsheet.
Starting point
End of explanation
"""
# Answer
some_dict["y"]["b"]
"""
Explanation: Write a piece of code that prints the number 5, taken from some_dict.
End of explanation
"""
i... |
eugeniopacceli/ComputerVision | quiz1/Quiz1-Calibration.ipynb | mit | import os
import numpy as np
import cv2
%matplotlib inline
from matplotlib import pyplot as plt
REALSQUARE = 23.500 # Size of a square
BOARDDIM = (6,8) # Dimensions of the given board
NUMIMG = 9 # Number of images to open
imagesList = list()
# Opens each image and adds to the image list
PATH = "quiz1-data/"
for fileN... |
juditacs/snippets | feature_extraction/train_valid_test.ipynb | lgpl-3.0 | import numpy as np
# matrix dimensions
N = 100
M = 20
# train-valid-test ratio, let's use 80-10-10
train_ratio = 0.8
valid_ratio = 0.1
test_ratio = 1.0 - train_ratio - valid_ratio # this is never used
# array indices
train_split = int(train_ratio * N)
valid_split = int(valid_ratio * N)
# create a random matrix
X =... |
km-Poonacha/python4phd | Session 1/ipython/Lesson 2 - Functions and Modules.ipynb | gpl-3.0 | # FUNCTION DEFINITION
def check_if_5(user_number):
"""This function just checks if the number passed to it is equal
to 5. It returns 1 if the number is 5 and returns 0 if the number is not 5 """
if user_number == 5:
return 1
else:
return 0
#FUNCTION CALL
return_val = check_if_5(5)
if r... |
sdpython/ensae_teaching_cs | _doc/notebooks/td2a_eco/td2a_eco_sql_correction.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 2A.eco - Python et la logique SQL - correction
Correction d'exercices sur SQL.
End of explanation
"""
import sqlite3
# on va se connecter à une base de données SQL vide
# SQLite stocke la BDD dans un simple fichier
filepath = "./DataBas... |
statsmodels/statsmodels.github.io | v0.13.1/examples/notebooks/generated/deterministics.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
plt.rc("figure", figsize=(16, 9))
plt.rc("font", size=16)
"""
Explanation: Deterministic Terms in Time Series Models
End of explanation
"""
from statsmodels.tsa.deterministic import DeterministicProcess
index = pd.RangeIndex(0, 100)
det_proc = ... |
GoogleCloudPlatform/datalab-samples | basemap/earthquakes.ipynb | apache-2.0 | %bash
echo 'Y' | apt-get install python-mpltoolkits.basemap
"""
Explanation: <h1> Geographic data in Datalab </h1>
This notebook demonstrates how to use Datalab to display the earthquakes that have happened over the past 7 days. The data come from USGS, and we will use the Python module basemap to do the plotting.
<h... |
ES-DOC/esdoc-jupyterhub | notebooks/mohc/cmip6/models/hadgem3-gc31-lm/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'hadgem3-gc31-lm', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: MOHC
Source ID: HADGEM3-GC31-LM
Topic: Aerosol
Sub-Topics: Transport, Emis... |
rvuduc/cse6040-ipynbs | 18--matrix-storage.ipynb | bsd-3-clause | import numpy as np
"""
Explanation: CSE 6040, Fall 2015 [18]: Matrix storage
Today's lab continues Lab 16, which is a survey of techniques using Numpy, a Python module that provides fast primitives for multidimensional arrays.
By the way, a partial solution set for Lab 16 is also available here.
Downloads. For today's... |
ES-DOC/esdoc-jupyterhub | notebooks/nasa-giss/cmip6/models/giss-e2-1g/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nasa-giss', 'giss-e2-1g', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: NASA-GISS
Source ID: GISS-E2-1G
Topic: Seaice
Sub-Topics: Dynamics, Thermody... |
GoogleCloudPlatform/bigquery-notebooks | notebooks/community/analytics-componetized-patterns/retail/recommendation-system/bqml-scann/02_export_bqml_mf_embeddings.ipynb | apache-2.0 | !pip install -U -q apache-beam[gcp]
"""
Explanation: Part 2: Process the item embedding data in BigQuery and export it to Cloud Storage
This notebook is the second of five notebooks that guide you through running the Real-time Item-to-item Recommendation with BigQuery ML Matrix Factorization and ScaNN solution.
Use th... |
dnc1994/MachineLearning-UW | ml-regression/blank/week-1-simple-regression-assignment-blank.ipynb | mit | import graphlab
"""
Explanation: Regression Week 1: Simple Linear Regression
In this notebook we will use data on house sales in King County to predict house prices using simple (one input) linear regression. You will:
* Use graphlab SArray and SFrame functions to compute important summary statistics
* Write a functio... |
4dsolutions/Python5 | Pi Day Fun.ipynb | mit | from IPython.display import YouTubeVideo
YouTubeVideo("HrRMnzANHHs")
"""
Explanation: Pi Day Fun
March 14, 2016
Updated since.
End of explanation
"""
from fractions import Fraction
from itertools import count, islice
from decimal import Decimal, localcontext
def convert(f):
"""get a Decimal from a Fraction (and... |
fullmetalfelix/ML-CSC-tutorial | NeuralNetwork - Intro.ipynb | gpl-3.0 | # --- INITIAL DEFINITIONS ---
from sklearn.neural_network import MLPRegressor
import numpy, math, random
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# This function takes a 2-element vector p=[x,y] and computes z
def Model(p):
result = math.sin(0.4*p[0] - 0.3*p[1] + 0.2*p[0]*p[1])
... |
SSDS-Croatia/SSDS-2017 | Day-5/Day 5 - Vanilla GAN Solved.ipynb | mit | %matplotlib inline
%load_ext autoreload
%autoreload 2
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import os, util
"""
Explanation: Data Science Summer School - Split '17
5. Generating images of digits with Generative Adversarial Networks
End of explanation
"""
data_folder = 'data'; da... |
ES-DOC/esdoc-jupyterhub | notebooks/csir-csiro/cmip6/models/vresm-1-0/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'csir-csiro', 'vresm-1-0', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: CSIR-CSIRO
Source ID: VRESM-1-0
Sub-Topics: Radiative Forcings.
Propert... |
cherepaha/PyDLV | demos/dlv_tutorial.ipynb | gpl-3.0 | import os
from pydlv import data_reader, derivative_calculator
dr = data_reader.DataReader()
dc = derivative_calculator.DerivativeCalculator()
data = dr.read_data(path='../../../../data/scirep_locdyn')
# rewards_sum defines the experimental conditions to be analysed (see data description on OSF for details)
# reward ... |
r31415smith/intro_python | Crash Course v0.62.ipynb | lgpl-3.0 | import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
"""
Explanation: A Crash Course in Python for Scientists
Rick Muller, Sandia National Laboratories
version 0.62, Updated Dec 15, 2016 by Ryan Smith, Cal State East Bay
Using Python 3.5.2 | Anaconda 4.1.1
This work is licensed under a Creative Commo... |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_receptive_field1.ipynb | bsd-3-clause | # Authors: Chris Holdgraf <choldgraf@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
# sphinx_gallery_thumbnail_number = 7
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.decoding import ReceptiveField, TimeDelayingRidge
from scipy.stats import multivar... |
jonnydyer/pypropep | ipython_doc/BasicUsage.ipynb | gpl-3.0 | help(ppp.init)
ppp.init()
"""
Explanation: Initalization
First we must initalize the library to load the thermo and propellant files. Note that they can be overriden in the init function.
End of explanation
"""
print ppp.PROPELLANT_FILE
print ppp.THERMO_FILE
"""
Explanation: Custom Data files
The propellant and th... |
idekerlab/vizbi-2015 | tutorials/Lesson_1_Introduction_to_cyREST.ipynb | mit | # HTTP Client for Python
import requests
# Standard JSON library
import json
# Basic Setup
PORT_NUMBER = 1234 # This is the default port number of CyREST
"""
Explanation: VIZBI Tutorial Session
Part 2: Cytoscape, IPython, Docker, and reproducible network data visualization workflows
Tuesday, 3/24/2015
Lesson 1: Intr... |
myuuuuun/ichimura_seminar | inagaki/inagaki_ch11.ipynb | mit | %matplotlib inline
import math
import numpy as np
import scipy.stats as stats
import matplotlib as mpl
import matplotlib.pyplot as plt
plt.rcParams['font.size'] = 13
# 日本語対応
mpl.rcParams['font.family'] = 'Osaka'
"""
Explanation: 稲垣数理統計 Chapter11 問題
End of explanation
"""
data = np.array([
[167, 59],
... |
mne-tools/mne-tools.github.io | 0.24/_downloads/1a105d401683707ed0696f30397d6253/40_artifact_correction_ica.ipynb | bsd-3-clause | import os
import mne
from mne.preprocessing import (ICA, create_eog_epochs, create_ecg_epochs,
corrmap)
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_filt-0-... |
simonschmoll/ActivityRecognition | P22 Activity Recognition.ipynb | apache-2.0 | # General Imports for more than one file
import pandas as pd
import numpy as np
# For reading the CSV
from pandas import read_csv
# Imports for the classification
from sklearn.metrics import confusion_matrix
from sklearn.metrics import f1_score
from sklearn.model_selection import cross_validate
from sklearn.ensemble... |
bekbote/project_repository | 0420_PytorchIntro_1555587538086.ipynb | apache-2.0 | import torch
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: <a href="https://colab.research.google.com/github/bekbote/project_repository/blob/master/0420_PytorchIntro_1555587538086.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>... |
fsilva/deputado-histogramado | notebooks/Deputado-Histogramado-3.ipynb | gpl-3.0 | %matplotlib inline
import pylab
import matplotlib
import pandas
import numpy
dateparse = lambda x: pandas.datetime.strptime(x, '%Y-%m-%d')
sessoes = pandas.read_csv('sessoes_democratica_org.csv',index_col=0,parse_dates=['data'], date_parser=dateparse)
"""
Explanation: Deputado Histogramado
expressao.xyz/deputado/
Co... |
pombredanne/gensim | docs/notebooks/WMD_tutorial.ipynb | lgpl-2.1 | from time import time
start_nb = time()
# Initialize logging.
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s')
sentence_obama = 'Obama speaks to the media in Illinois'
sentence_president = 'The president greets the press in Chicago'
sentence_obama = sentence_obama.lower().split()... |
rgbkrk/commuter | packages/server/examples/display-updates.ipynb | bsd-3-clause | print("typical output")
"""
Explanation: Outputs you can update by name
This notebook demonstrates the new name-based display functionality in the notebook. Previously, notebooks could only attach output to the cell that was currently being executed:
End of explanation
"""
# a temporary patch of display for demonstr... |
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