repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
loujine/musicbrainz-dataviz | 0-introduction.ipynb | mit | %load_ext watermark
%watermark --python -r
%watermark --date --updated
"""
Explanation: Visualizing MusicBrainz data with Python/JS, an introduction
This introductory notebook will explain how I get database from MusicBrainz and how I transform it to Python format for display in tables or plots.
A static HTML version... |
phoebe-project/phoebe2-docs | 2.0/examples/minimal_synthetic.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.0,<2.1"
%matplotlib inline
"""
Explanation: Minimal Example to Produce a Synthetic Light Curve
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 rel... |
h-mayorquin/time_series_basic | presentations/.ipynb_checkpoints/2015-august-checkpoint.ipynb | bsd-3-clause | # Scientific Python libraries
import numpy as np
import matplotlib.pyplot as plt
import mpld3
import seaborn as sn
mpld3.enable_notebook()
import sys
sys.path.append("../")
# Nexa in-house libraries
from signals.time_series_class import MixAr
from signals.aux_functions import sidekick
from input.sensors import Perceptu... |
ebridge2/FNGS_website | sic/sic_ndmg.ipynb | apache-2.0 | %%bash
ndmg_demo-func
"""
Explanation: SIC for NDMG Pipeline
The NDMG pipeline estimates connectomes from M3r (multi-modal MRI) scans. The NDMG pipeline is designed to operate with:
NDMG-d
+ 1xdiffusion-weighted image (DWI) for a particular subject.
+ 1xbval file giving the magnitude of diffuion vectors in the DWI.
+... |
cypherai/PySyft | notebooks/Syft - Testing - Benchmark Tests.ipynb | apache-2.0 | from syft.test.benchmark import Benchmark
Benchmark(str)
"""
Explanation: Testing: Benchmark Tests
One goal of the OpenMined project is to efficiently train Deep Learning models in a homomorphically encrypted state. Therefore it is very important to benchmark new and existing features in order to achieve better and f... |
resendislab/cobrame-docker | getting_started.ipynb | apache-2.0 | import pickle
with open("me_models/iLE1678.pickle", "rb") as model_file:
ecoli = pickle.load(model_file)
"""
Explanation: Building and solving the E. coli ME model
The image includes the COBRAme and ECOLIme Python packages to get you started quickly. The docker image includes a prebuild version of the E. coli ME ... |
Ragnamus/sci-comp | notebooks/matthew.truscott.ipynb | mit | from prettytable import PrettyTable
import numpy as np
def root_finder():
a = 1
b = 1
crray = np.geomspace(0.1, (10 ** (-200)), num=200, endpoint=False)
t = PrettyTable(['root1', 'root2', 'root3', 'root4'])
for c in crray:
root = np.sqrt((b * b) - (4 * a * c))
#print(root)
r... |
BrainIntensive/OnlineBrainIntensive | resources/matplotlib/AnatomyOfMatPlotLib/AnatomyOfMatplotlib-Part3-HowToSpeakMPL.ipynb | mit | %load exercises/3.1-colors.py
t = np.arange(0.0, 5.0, 0.2)
plt.plot(t, t, t, t**2, t, t**3)
plt.show()
"""
Explanation: How to speak "MPL"
In the previous parts, you learned how Matplotlib organizes plot-making by figures and axes. We broke down the components of a basic figure and learned how to create them. You als... |
landlab/landlab | notebooks/tutorials/overland_flow/linear_diffusion_overland_flow/linear_diffusion_overland_flow_router.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
from landlab import RasterModelGrid, imshow_grid
from landlab.components.overland_flow import LinearDiffusionOverlandFlowRouter
from landlab.io.esri_ascii import read_esri_ascii
"""
Explanation: <a href="http://landlab.github.io"><img style="float: left" src="../../..... |
ryan-leung/PHYS4650_Python_Tutorial | notebooks/06-Python-Matplotlib.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
"""
Explanation: Matplotlib
<img src="images/matplotlib.svg" alt="matplotlib" style="width: 600px;"/>
<a href="https://colab.research.google.com/github/ryan-leung/PHYS4650_Python_Tutorial/blob/master/notebooks/06-Python-Matplotlib.ipynb"><img align... |
ekaakurniawan/iPyMacLern | ML-W1/Linear Regression With One Variable.ipynb | gpl-3.0 | import sys
print("Python %d.%d.%d" % (sys.version_info.major, \
sys.version_info.minor, \
sys.version_info.micro))
import numpy as np
print("NumPy %s" % np.__version__)
import matplotlib
import matplotlib.pyplot as plt
print("matplotlib %s" % matplotlib.__version_... |
turi-code/tutorials | strata-sj-2016/time-series/anomaly_detection.ipynb | apache-2.0 | import graphlab as gl
okla_daily = gl.load_timeseries('working_data/ok_daily_stats.ts')
print "Number of rows:", len(okla_daily)
print "Start:", okla_daily.min_time
print "End:", okla_daily.max_time
okla_daily.print_rows(3)
import matplotlib.pyplot as plt
%matplotlib notebook
plt.style.use('ggplot')
fig, ax = plt.s... |
tritemio/multispot_paper | Multi-spot Gamma Fitting.ipynb | mit | from fretbursts import fretmath
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from cycler import cycler
import seaborn as sns
%matplotlib inline
%config InlineBackend.figure_format='retina' # for hi-dpi displays
import matplotlib as mpl
from cycler import cycler
bma... |
jamesfolberth/NGC_STEM_camp_AWS | notebooks/data8_notebooks/project3/project3.ipynb | bsd-3-clause | # Run this cell to set up the notebook, but please don't change it.
import numpy as np
import math
from datascience import *
# These lines set up the plotting functionality and formatting.
import matplotlib
matplotlib.use('Agg', warn=False)
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('fivethirtye... |
tensorflow/model-remediation | docs/min_diff/guide/customizing_min_diff_model.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... |
CentroGeo/Analisis-Espacial | muap/MUAP II.ipynb | gpl-2.0 | from geopandas import GeoDataFrame
datos = GeoDataFrame.from_file('data/distritos_variables.shp')
datos.head()
"""
Explanation: Parte II: efecto de agregación
En la primera parte de la práctica vimos cómo la escala de análisis tiene influencia sobre los resultados del mismo. En esta segunda parte vamos a ver como dife... |
metpy/MetPy | v1.0/_downloads/cdca3e0cb8a2930cccab0e29b97ef52a/upperair_soundings.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 Hodograph, SkewT
from metpy.units import units
"""
Explanation: Upper Air Sounding Tutorial
Upper air analysis is a... |
kyleabeauchamp/mdtraj | examples/rmsd-benchmark.ipynb | lgpl-2.1 | t = md.Trajectory(xyz=np.random.randn(1000, 100, 3), topology=None)
print(t)
"""
Explanation: To benchmark the speed of the RMSD calculation, it's not really
necessary to use 'real' coordinates, so let's just generate
some random numbers from a normal distribution for the cartesian
coordinates.
End of explanation
"""
... |
tensorflow/docs-l10n | site/ja/guide/gpu.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... |
AbeHandler/minhash_tutorial | start.ipynb | mit | import itertools
import string
import functools
letters = string.ascii_lowercase
vocab = list(map(''.join, itertools.product(letters, repeat=2)))
from random import choices
def zipf_pdf(k):
return 1/k**1.07
def exponential_pdf(k, base):
return base**k
def new_document(n_words, pdf):
return set(
... |
ajgpitch/qutip-notebooks | examples/photon_birth_death.ipynb | lgpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from qutip import *
"""
Explanation: QuTiP Example: Birth and Death of Photons in a Cavity
J.R. Johansson and P.D. Nation
For more information about QuTiP see http://qutip.org
End of explanation
"""
N=5
a=destroy(N)
H=a.dag()*a # Simple os... |
lileiting/goatools | notebooks/report_depth_level.ipynb | bsd-2-clause | # Get http://geneontology.org/ontology/go-basic.obo
from goatools.base import download_go_basic_obo
obo_fname = download_go_basic_obo()
"""
Explanation: Report counts of GO terms at various levels and depths
Reports the number of GO terms at each level and depth.
Level refers to the length of the shortest path fro... |
lepmik/nest-simulator | doc/model_details/aeif_models_implementation.ipynb | gpl-2.0 | import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (15, 6)
"""
Explanation: NEST implementation of the aeif models
Hans Ekkehard Plesser and Tanguy Fardet, 2016-09-09
This notebook provides a reference solution for the Adaptive Expo... |
nimagh/CNN_Implementations | Notebooks/VAE.ipynb | gpl-3.0 | %load_ext autoreload
%autoreload 2
import os, sys
sys.path.append('../')
sys.path.append('../common')
sys.path.append('../GenerativeModels')
from tools_general import tf, np
from IPython.display import Image
from tools_train import get_train_params, OneHot, vis_square
from tools_config import data_dir
from tools_trai... |
jsharpna/DavisSML | lectures/lecture8/lecture8.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
## open wine data
wine = pd.read_csv('../../data/winequality-red.csv',delimiter=';')
Y = wine.values[:,-1]
X = wine.values[:,:-1]
n,p = X.shape
X = n**0.5 * (X - X.mean(axis=0)) / X.std(axis=0)
## Look at LROnline.py
from LROnline import *
lear... |
openearth/notebooks | unstrucgridplot.ipynb | gpl-3.0 | # Create split locations
if not hasattr(netelemnode, 'mask'):
netelemnode = np.ma.masked_array(netelemnode, mask=False)
splitidx = np.cumsum(np.r_[(~netelemnode.mask).sum(1)][:-1])
# Convert to 1d filled idx
idx = netelemnode[(~netelemnode.mask)]-1
xpoly = np.split(X[idx],splitidx) # x vector per poly
ypoly = np.sp... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/08_image_keras/labs/mnist_models.ipynb | apache-2.0 | import os
PROJECT = "cloud-training-demos" # REPLACE WITH YOUR PROJECT ID
BUCKET = "cloud-training-demos-ml" # REPLACE WITH YOUR BUCKET NAME
REGION = "us-central1" # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
MODEL_TYPE = "linear" # "linear", "dnn", "dnn_dropout", or "cnn"
# do not change these
os.environ["PROJ... |
noammor/coursera-machinelearning-python | ex1/ml-ex1-multivariate.ipynb | mit | import pandas
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Exercise 1: Linear regression with multiple variables
End of explanation
"""
data = pandas.read_csv('ex1data2.txt', header=None, names=['x1', 'x2', 'y'])
data.head()
data.shape
X = data[['x1', 'x2']].values
Y = da... |
IS-ENES-Data/submission_forms | test/forms/CMIP6/.ipynb_checkpoints/CMIP6_ki_123-checkpoint.ipynb | apache-2.0 | # initialize your CORDEX submission form template
from dkrz_forms import form_handler
from dkrz_forms import checks
"""
Explanation: DKRZ CMIP6 submission form for ESGF data publication
General Information
Data to be submitted for ESGF data publication must follow the rules outlined in the CMIP6 Archive Design Docume... |
ericmjl/Network-Analysis-Made-Simple | notebooks/03-practical/01-io.ipynb | mit | from IPython.display import YouTubeVideo
YouTubeVideo(id="3sJnTpeFXZ4", width="100%")
"""
Explanation: Introduction
End of explanation
"""
from pyprojroot import here
"""
Explanation: In order to get you familiar with graph ideas,
I have deliberately chosen to steer away from
the more pedantic matters
of loading g... |
coolharsh55/advent-of-code | 2016/python3/Day05.ipynb | mit | with open('../inputs/day05.txt', 'r') as f:
door_id = f.readline().strip()
"""
Explanation: Day 5: How About a Nice Game of Chess?
author: Harshvardhan Pandit
license: MIT
link to problem statement
You are faced with a security door designed by Easter Bunny engineers that seem to have acquired most of their securi... |
xgrg/alfa | notebooks/Miscellaneous/Box-Cox transformation.ipynb | mit | # Generate data
x = stats.loggamma.rvs(5, size=500) + 5
# Plot it
fig = plt.figure(figsize=(6,9))
ax1 = fig.add_subplot(211)
prob = stats.probplot(x, dist=stats.norm, plot=ax1)
ax1.set_title('Probplot against normal distribution')
# Plot an histogram
ax2 = fig.add_subplot(212)
ax2.hist(x)
ax2.set_title('Histogram')
... |
xebia-france/luigi-airflow | Luigi_airflow_004.ipynb | apache-2.0 | raw_dataset = pd.read_csv(source_path + "Speed_Dating_Data.csv",encoding = "ISO-8859-1")
"""
Explanation: Import data
End of explanation
"""
raw_dataset.head(3)
raw_dataset_copy = raw_dataset
check1 = raw_dataset_copy[raw_dataset_copy["iid"] == 1]
check1_sel = check1[["iid", "pid", "match","gender","date","go_out"... |
buntyke/DataAnalysis | startup.ipynb | mit | # Hit shift + enter or use the run button to run this cell and see the results
print 'hello world'
# The last line of every code cell will be displayed by default,
# even if you don't print it. Run this cell to see how this works.
2 + 2 # The result of this line will not be displayed
3 + 3 # The result of this line... |
feststelltaste/software-analytics | cheatbooks/groupby.ipynb | gpl-3.0 | import pandas as pd
df = pd.DataFrame({
"file" : ['hello.java', 'tutorial.md', 'controller.java', "build.sh", "deploy.sh"],
"dir" : ["src", "docs", "src", "src", "src"],
"bytes" : [54, 124, 36, 78, 62]
})
df
"""
Explanation: groupby
With groupby, you can group data in a DataFrame and apply calculations... |
dolittle007/dolittle007.github.io | notebooks/bayesian_neural_network_advi.ipynb | gpl-3.0 | %matplotlib inline
import theano
floatX = theano.config.floatX
import pymc3 as pm
import theano.tensor as T
import sklearn
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('white')
from sklearn import datasets
from sklearn.preprocessing import scale
from sklearn.cross_validation im... |
RyRose/College-Projects | lab4/Lab 4.ipynb | mit | ## Imports!
%matplotlib inline
import os
import re
import string
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib.mlab import PCA
from scipy.cluster.vq import kmeans, vq
"""
Explanation: Lab 4
Ryan Rose
Scientific Computing
9/21/2016
End of explanation
"""
os.chdir("/home/ryan/... |
jpcofr/svgpathtools | README.ipynb | mit | from __future__ import division, print_function
# Coordinates are given as points in the complex plane
from svgpathtools import Path, Line, QuadraticBezier, CubicBezier, Arc
seg1 = CubicBezier(300+100j, 100+100j, 200+200j, 200+300j) # A cubic beginning at (300, 100) and ending at (200, 300)
seg2 = Line(200+300j, 250+... |
jjehl/poppy_education | python-divers/python_language_objet.ipynb | gpl-2.0 | objet1 = 'bol'
"""
Explanation: Séquences 1 - Découverte de la programmation objet et du language Python
Activité 1 - Manipuler les objets Python
Compétences visées par cette activité :
Savoir créer des variables de types chaîne de caractères et liste. Utiliser une méthode liée à un objet par la syntaxe objet.méthode... |
idc9/law-net | vertex_metrics_experiment/data_pipline_scotus.ipynb | mit | setup_data_dir(data_dir)
make_subnetwork_directory(data_dir, network_name)
"""
Explanation: set up the data directory
End of explanation
"""
download_op_and_cl_files(data_dir, network_name)
"""
Explanation: data download
get opinion and cluster files from CourtListener
opinions/cluster files are saved in data_dir/... |
dnc1994/MachineLearning-UW | ml-regression/blank/week-4-ridge-regression-assignment-1-blank.ipynb | mit | 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.... |
OpenDSA/Analysis | Yusuf/Test_Clustering_Results_22.ipynb | mit | clusters_df_22 = pd.read_csv("Clustered_Sessions_FCM.csv")
clusters_df_22.columns
framesets_credit_seek = clusters_df_22[clusters_df_22['cluster']=='Credit Seeking']['curr_frameset_name'].unique()
framesets_normal = clusters_df_22[clusters_df_22['cluster']=='Normal'] ['curr_frameset_name'].unique()
for framename in f... |
OceanPARCELS/parcels | parcels/examples/parcels_tutorial.ipynb | mit | %matplotlib inline
from parcels import FieldSet, ParticleSet, Variable, JITParticle, AdvectionRK4, plotTrajectoriesFile
import numpy as np
import math
from datetime import timedelta
from operator import attrgetter
"""
Explanation: Parcels Tutorial
Welcome to a quick tutorial on Parcels. This is meant to get you starte... |
GoogleCloudPlatform/mlops-on-gcp | model_serving/bqml-caip/02-bqml-to-caip-pipeline.ipynb | apache-2.0 | import kfp
import kfp.components as comp
import random
import os
#Common Parameters
PROJECT_ID=[] #enter your project name
KFPHOST=[] #enter your KFP hostname
#Parameters for BQML
DATASET=[] #name of dataset to create or use if exists
VIEW= [] #name of view to be created for BQML create model
MODEL=[] ... |
Esri/gis-stat-analysis-py-tutor | notebooks/PythonFeatureIO.ipynb | apache-2.0 | import arcpy as ARCPY
import numpy as NUM
import SSDataObject as SSDO
"""
Explanation: Using the Spatial Statistics Data Object (SSDataObject) Makes Feature IO Simple
SSDataObject does the read/write and accounting of feature/attribute and NumPy Array order
Write/Utilize methods that take NumPy Arrays
Using NumPy as... |
marco-olimpio/ufrn | IMD0104 - PROGRAMAÇÃO ORIENTADA A OBJETOS E MAPEAMENTO OBJETO-RELACIONAL/assignments/3/all-that-you-need-to-know-about-the-android-market.ipynb | gpl-3.0 | print('Number of apps in the dataset : ' , len(df))
df.sample(7)
"""
Explanation: Sneak peek at the dataset
df = pd.read_csv('../input/googleplaystore.csv')
print(df.dtypes)
df.loc[df.App=='Tiny Scanner - PDF Scanner App']
df[df.duplicated(keep='first')]
df.drop_duplicates(subset='App', inplace=True)
df = df[df['Andro... |
taliamo/Final_Project | organ_pitch/.ipynb_checkpoints/upload_pitch_data-checkpoint.ipynb | mit | # I import useful libraries (with functions) so I can visualize my data
# I use Pandas because this dataset has word/string column titles and I like the readability features of commands and finish visual products that Pandas offers
import pandas as pd
import matplotlib.pyplot as plt
import re
import numpy as np
%matp... |
phoebe-project/phoebe2-docs | development/tutorials/ETV.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.4,<2.5"
"""
Explanation: ETV Datasets and Options
Setup
Let's first make sure we have the latest version of PHOEBE 2.4 installed (uncomment this line if running in an online notebook session such as colab).
End of explanation
"""
import phoebe
from phoebe import u # units
import numpy as ... |
thinkingmachines/deeplearningworkshop | codelab_3_tensorflow_nn.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
N = 100 # points per class
D = 2 # dimensionality at 2 so we can eyeball it
K = 3 # number of classes
X = np.zeros((N*K, D)) # generate an empty matrix to hold X features
y = np.zeros(N*K, dtype='int32') # switching this to int32
# for 3 classes, evenly generates s... |
LimeeZ/phys292-2015-work | assignments/assignment08/InterpolationEx02.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
sns.set_style('white')
from scipy.interpolate import griddata
"""
Explanation: Interpolation Exercise 2
End of explanation
"""
x = np.linspace(-5.0,5.0 , 10)
y = np.linspace(-5.0, 5.0, 10)
f = (x,y)
np.hstack?
"""
Explanat... |
drphilmarshall/StatisticalMethods | lessons/templates/RISE_example.ipynb | gpl-2.0 | # This is a code block within slide #2.
b = 1
# Obviously, its the same python instance under the hood.
b
"""
Explanation: This is slide #1.
Space and ctrl-space (or the clickable arrows) are used to move between slides.
Up/down arrows (or mouse) are used to move between cells.
Shift-enter executes a cell, as usual.... |
dtamayo/rebound | ipython_examples/VariationalEquationsWithChainRule.ipynb | gpl-3.0 | import rebound
import numpy as np
"""
Explanation: Using Variational Equations With the Chain Rule
For a complete introduction to variational equations, please read the paper by Rein and Tamayo (2016).
Variational equations can be used to calculate derivatives in an $N$-body simulation. More specifically, given a set ... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_mixed_norm_inverse.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import mne
from mne.datasets import sample
from mne.inverse_sparse import mixed_norm
from mne.minimum_norm import make_inverse_operator, apply_inverse
from mne.viz import plot_sparse_source_estimates
print(__doc__)
dat... |
belteki/alarms | Alarms_GitHub.ipynb | gpl-3.0 | import IPython
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import os
import sys
import pickle
import scipy as sp
from scipy import stats
from pandas import Series, DataFrame
from datetime import datetime, timedelta
%matplotlib inline
matplotlib.style.use('classic')
matplot... |
European-XFEL/h5tools-py | docs/apply_geometry.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import h5py
from karabo_data import RunDirectory, stack_detector_data
from karabo_data.geometry2 import LPD_1MGeometry
run = RunDirectory('/gpfs/exfel/exp/FXE/201830/p900020/proc/r0221/')
run.info()
# Find a train with some data in
empty = np.asar... |
timkpaine/lantern | experimental/ipysheet.ipynb | apache-2.0 | import ipysheet
sheet = ipysheet.sheet()
sheet
"""
Explanation: Spreadsheet widget for the Jupyter Notebook
Installation
To install use pip:
$ pip install ipysheet
To make it work for Jupyter lab:
$ jupyter labextension ipysheet
If you have notebook 5.2 or below, you also need to execute:
$ jupyter nbextension enable ... |
julienchastang/unidata-python-workshop | notebooks/Declarative_Plotting/Satellite_Declarative.ipynb | mit | from siphon.catalog import TDSCatalog
from datetime import datetime
# Create variables for URL generation
image_date = datetime.utcnow().date()
region = 'Mesoscale-1'
channel = 8
# Create the URL to provide to siphon
data_url = ('https://thredds.ucar.edu/thredds/catalog/satellite/goes/east/products/'
f'C... |
snowicecat/umich-eecs445-f16 | handsOn_lecture06_MLE-MAP-Coding/HandsOn06_MAP-coding.ipynb | mit | # all the packages you need
import numpy as np
import matplotlib.pyplot as plt
import scipy.io
from numpy.linalg import inv
# load data from .mat
mat = scipy.io.loadmat('mnist_49_3000.mat')
print (mat.keys())
x = mat['x'].T
y = mat['y'].T
print (x.shape, y.shape)
# show example image
plt.imshow (x[4, :].reshape(28, ... |
alepoydes/introduction-to-numerical-simulation | practice/covid/COVID-19.ipynb | mit | # Устанавливаем библиотеки, если это не было сделано ранее.
# ! pip3 install seaborn matplotlib numpy pandas
# Импорт библиотек
import numpy as np
import matplotlib.pyplot as plt
import urllib.request
import pandas as pd
import seaborn as sns
# Используем настройки seaborn по-умолчанию, устанавливаем только размер ри... |
mitdbg/modeldb | demos/webinar-2020-5-6/02-mdb_versioned/01-train/02 Positive Data NLP.ipynb | mit | from __future__ import unicode_literals, print_function
import boto3
import json
import numpy as np
import pandas as pd
import spacy
"""
Explanation: Versioning Example (Part 2/3)
In part 1, we trained and logged a tweet sentiment classifier using ModelDB's versioning system.
Now we'll see how that can come in handy ... |
Danghor/Algorithms | Python/Chapter-04/Merge-Sort-Iterative.ipynb | gpl-2.0 | def sort(L):
A = L[:] # A is a copy of L
mergeSort(L, A)
"""
Explanation: An Iterative Implementation of Merge Sort
The function $\texttt{sort}(L)$ sorts the list $L$ in place using <em style="color:blue">merge sort</em>.
It takes advantage of the fact that, in Python, lists are stored internally as arrays.
T... |
bashtage/statsmodels | examples/notebooks/mediation_survival.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
import statsmodels.api as sm
from statsmodels.stats.mediation import Mediation
"""
Explanation: Mediation analysis with duration data
This notebook demonstrates mediation analysis when the
mediator and outcome are duration variables, modeled
using proportional hazards regression.... |
ES-DOC/esdoc-jupyterhub | notebooks/noaa-gfdl/cmip6/models/gfdl-cm4/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'noaa-gfdl', 'gfdl-cm4', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: NOAA-GFDL
Source ID: GFDL-CM4
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Ener... |
gdsfactory/gdsfactory | docs/notebooks/common_mistakes.ipynb | mit | import gdsfactory as gf
@gf.cell
def wg(length: float = 3):
return gf.components.straight(length=length)
print(wg(length=5))
print(wg(length=50))
"""
Explanation: Common mistakes
1. Creating cells without cell decorator
The cell decorator names cells deterministically and uniquely based on the name of the func... |
Kaggle/learntools | notebooks/game_ai/raw/tut_halite.ipynb | apache-2.0 | #$HIDE_INPUT$
from kaggle_environments import make, evaluate
env = make("halite", debug=True)
env.run(["random", "random", "random", "random"])
env.render(mode="ipython", width=800, height=600)
"""
Explanation: Halite is an online multiplayer game created by Two Sigma. In the game, four participants command ships to ... |
swirlingsand/self-driving-car-nanodegree-nd013 | CarND-LaneLines-P1-1/P1.ipynb | mit | #importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
import math
%matplotlib inline
#reading in an image
image = mpimg.imread('test_images/solidWhiteRight.jpg')
#printing out some stats and plotting
print('This image is:', type(image), 'with dim... |
Nikea/scikit-xray-examples | demos/time_correlation/two-time-with-partial-data.ipynb | bsd-3-clause | import skbeam.core.correlation as corr
from skbeam.core.correlation import two_time_corr, two_time_state_to_results
import skbeam.core.roi as roi
import skbeam.core.utils as utils
from xray_vision.mpl_plotting.roi import show_label_array_on_image
import numpy as np
import time as ttime
import matplotlib.pyplot as plt... |
jonnydyer/pypropep | ipython_doc/BasicRocketPerformance.ipynb | gpl-3.0 | p = ppp.ShiftingPerformance()
o2 = ppp.PROPELLANTS['OXYGEN (GAS)']
ch4 = ppp.PROPELLANTS['METHANE']
p.add_propellants([(ch4, 1.0), (o2, 1.0)])
p.set_state(P=10, Pe=0.01)
print p
for k,v in p.composition.items():
print "{} : ".format(k)
pprint.pprint(v[0:8], indent=4)
OF = np.linspace(1, 5)
m_CH4 = 1.0
cstar... |
pysg/pyther | practica_de_flash_isotermico.ipynb | mit | def Ki_wilson(self):
"""Equation of wilson for to calculate the Ki(T,P)"""
variable_0 = 5.373 * (1 + self.w) * (1 - self.Tc / self.T)
lnKi = np.log(self.Pc / self.P) + variable_0
self.Ki = np.exp(lnKi)
return self.Ki
"""
Explanation: Práctico flash isotermico
Jose Euliser M... |
tpin3694/tpin3694.github.io | machine-learning/converting_a_dictionary_into_a_matrix.ipynb | mit | # Load library
from sklearn.feature_extraction import DictVectorizer
"""
Explanation: Title: Converting A Dictionary Into A Matrix
Slug: converting_a_dictionary_into_a_matrix
Summary: How to convert a dictionary into a feature matrix for machine learning in Python.
Date: 2016-09-06 12:00
Category: Machine Learning
T... |
mrcslws/nupic.research | projects/archive/dynamic_sparse/notebooks/mcaporale/2019-10-11-ExperimentAnalysis-SmallDense.ipynb | agpl-3.0 | from IPython.display import Markdown, display
%load_ext autoreload
%autoreload 2
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import glob
import tabulate
import pprint
import click
import numpy as np
import pandas as pd
from ray.tune.commands i... |
tu-rbo/concarne | example/concarne_multiview_demo.ipynb | mit | from __future__ import print_function
import concarne
import concarne.patterns
import concarne.training
import lasagne
import theano.tensor as T
%pylab inline
try:
import sklearn.linear_model as sklm
except:
print (
"""You don't have scikit-learn installed; install it to compare
learning with side informati... |
andreyf/machine-learning-examples | decision_trees_knn/practice_trees_titanic.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
from sklearn.tree import DecisionTreeClassifier, export_graphviz
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import roc_auc_score, accuracy_score, confusion_matrix
%matplotlib inline
from matplotlib impo... |
juditacs/labor | notebooks/bi_ea_demo/cryptocurrency_prediction_failed.ipynb | lgpl-3.0 | import os
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from keras.layers import Input, Dense, Bidirectional, Dropout
from keras.layers.recurrent import LSTM
from keras.models import Model
from keras.callbacks import EarlyStopping
import numpy as np
os.listdir("data/cryptocurrency/")
"""
Ex... |
idekerlab/deep-cell | data-builder/yeastnet_raw_interactions.ipynb | mit | import pandas as pd
from os import listdir
from os.path import isfile, join
import numpy as np
from goatools import obo_parser
# Annotation file for the CLIXO terms
clixo_mapping = './data/alignments_FDR_0.1_t_0.1'
oboUrl = './data/go.obo'
clixo_align = pd.read_csv(clixo_mapping, sep='\t', names=['term', 'go', 'score... |
PedramNavid/MachineLearningNanodegree | 3_Titanic/.ipynb_checkpoints/titanic_survival_exploration-checkpoint.ipynb | gpl-3.0 | import sys
print(sys.version)
import numpy as np
import pandas as pd
# RMS Titanic data visualization code
from titanic_visualizations import survival_stats
from IPython.display import display
%matplotlib inline
# Load the dataset
in_file = 'titanic_data.csv'
full_data = pd.read_csv(in_file)
# Print the first few ... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/migration/UJ9 Custom Training Prebuilt Container XGBoost.ipynb | apache-2.0 | ! pip3 install -U google-cloud-aiplatform --user
"""
Explanation: Vertex SDK: Train and deploy an XGBoost model with pre-built containers (formerly hosted runtimes)
Installation
Install the latest (preview) version of Vertex SDK.
End of explanation
"""
! pip3 install google-cloud-storage
"""
Explanation: Install th... |
softEcon/course | lectures/economic_models/generalized_roy/module/lecture.ipynb | mit | from IPython.core.display import HTML, display
display(HTML('material/images/grm.html'))
"""
Explanation: Module
As your code grows more and more complex, it is useful to collect all code in a an external file. Here we store all the functions form our notebook in a single file grm.py. Nothing new happens, we just cop... |
dougalsutherland/pummeler | notebooks/election data by region.ipynb | mit | from __future__ import division, print_function
%matplotlib inline
import numpy as np
import pandas as pd
import re
import six
from IPython.display import display
import sys
sys.path.append('..')
from pummeler.data import geocode_data
county_to_region = geocode_data('county_region_10').region.to_dict()
"""
Explan... |
ES-DOC/esdoc-jupyterhub | notebooks/messy-consortium/cmip6/models/emac-2-53-aerchem/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'messy-consortium', 'emac-2-53-aerchem', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: MESSY-CONSORTIUM
Source ID: EMAC-2-53-AERCHEM
Topic: Aerosol... |
mwickert/SP-Comm-Tutorial-using-scikit-dsp-comm | tutorial_part2/RealTime-DSP.ipynb | bsd-2-clause | Image('PyAudio_RT_flow@300dpi.png',width='90%')
pah.available_devices()
"""
Explanation: Introduction
A simplified block diagram of PyAudio streaming-based (nonblocking) signal processing.
End of explanation
"""
# define a pass through, y = x, callback
def callback(in_data, frame_count, time_info, status):
DSP_... |
NeuPhysics/aNN | ipynb/.ipynb_checkpoints/vacuumClean-checkpoint.ipynb | mit | # This line configures matplotlib to show figures embedded in the notebook,
# instead of opening a new window for each figure. More about that later.
# If you are using an old version of IPython, try using '%pylab inline' instead.
%matplotlib inline
%load_ext snakeviz
import numpy as np
from scipy.optimize import mi... |
bmeaut/python_nlp_2017_fall | course_material/11_Machine_Translation/11_Machine_Translation_lab.ipynb | mit | import os
import shutil
import urllib
import nltk
def download_file(url, directory=''):
real_dir = os.path.realpath(directory)
if not os.path.isdir(real_dir):
os.makedirs(real_dir)
file_name = url.rsplit('/', 1)[-1]
real_file = os.path.join(real_dir, file_name)
if not os.path.isfile(... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_read_bem_surfaces.ipynb | bsd-3-clause | # Author: Jaakko Leppakangas <jaeilepp@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
from mayavi import mlab
import mne
from mne.datasets.sample import data_path
print(__doc__)
data_path = data_path()
fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(fname)
... |
maelick/GitHub-Analysis | SANER2016/notebooks/CRAN Distribution of GitHub Packages.ipynb | gpl-2.0 | import pandas
from matplotlib import pyplot as plt
%matplotlib inline
from IPython.display import set_matplotlib_formats
#set_matplotlib_formats('pdf')
cran_release = pandas.DataFrame.from_csv('../data/cran-packages-150601.csv', index_col=None)
data = pandas.DataFrame.from_csv('../data/github-cran-bioc-alldata-150420... |
mxbu/logbook | blog-notebooks/optimization.ipynb | mit | import pandas as pd
import numpy as np
from math import sqrt
import sys
from bokeh.plotting import figure, show, ColumnDataSource, save
from bokeh.models import Range1d, HoverTool
from bokeh.io import output_notebook, output_file
import quandl
from gurobipy import *
# output_notebook() #To enable Bokeh output in notebo... |
tensorflow/docs-l10n | site/ja/lite/tutorials/model_maker_question_answer.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... |
darkrun95/Applied-Data-Science | Applied Machine Learning in Python/Week 2/Assignment2.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
np.random.seed(0)
n = 15
x = np.linspace(0,10,n) + np.random.randn(n)/5
y = np.sin(x)+x/6 + np.random.randn(n)/10
X_train, X_test, y_train, y_test = train_test_split(x, y, random_state=0)
# Y... |
ODZ-UJF-AV-CR/osciloskop | cerf.ipynb | gpl-3.0 | import matplotlib.pyplot as plt
import sys
import os
import time
import h5py
import numpy as np
import glob
import vxi11
# Step 0:
# Connect oscilloscope via direct Ethernet link
# Step 1:
# Run "Record" on the oscilloscope
# and wait for 508 frames to be acquired.
# Step 2:
# Run this cell to initialize grabbing.
#... |
ES-DOC/esdoc-jupyterhub | notebooks/snu/cmip6/models/sandbox-3/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'snu', 'sandbox-3', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: SNU
Source ID: SANDBOX-3
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, Radiat... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/structured/solutions/3c_bqml_dnn_babyweight.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
%%bash
pip freeze | grep google-cloud-bigquery==1.6.1 || \
pip install google-cloud-bigquery==1.6.1
"""
Explanation: LAB 3c: BigQuery ML Model Deep Neural Network.
Learning Objectives
Create and evaluate DNN model with BigQuery ML.
Create and evalua... |
TheOregonian/long-term-care-db | notebooks/analysis/.ipynb_checkpoints/facilities-analysis-checkpoint.ipynb | mit | import pandas as pd
import numpy as np
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
df = pd.read_csv('../../data/processed/facilities-3-29-scrape.csv')
"""
Explanation: This is a dataset of Assisted Living, Nursing and Residential Care facilities... |
bayesimpact/bob-emploi | data_analysis/notebooks/datasets/rome/update_from_v329_to_v330.ipynb | gpl-3.0 | import collections
import glob
import os
from os import path
import matplotlib_venn
import pandas
rome_path = path.join(os.getenv('DATA_FOLDER'), 'rome/csv')
OLD_VERSION = '329'
NEW_VERSION = '330'
old_version_files = frozenset(glob.glob(rome_path + '/*{}*'.format(OLD_VERSION)))
new_version_files = frozenset(glob.g... |
numeristical/introspective | examples/SplineCalib_Multiclass_MNIST.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.ensemble import RandomForestClassifier
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import roc_auc_score, log_loss
from sklearn.metrics import accuracy_s... |
DamienIrving/ocean-analysis | development/hfbasin.ipynb | mit | import matplotlib.pyplot as plt
import iris
import iris.plot as iplt
import iris.coord_categorisation
import cf_units
import numpy
%matplotlib inline
infile = '/g/data/ua6/DRSv2/CMIP5/NorESM1-M/rcp85/mon/ocean/r1i1p1/hfbasin/latest/hfbasin_Omon_NorESM1-M_rcp85_r1i1p1_200601-210012.nc'
cube = iris.load_cube(infile)
... |
hydrosquall/tiingo-python | examples/basic-usage-with-pandas.ipynb | mit | TIINGO_API_KEY = 'REPLACE-THIS-TEXT-WITH-A-REAL-API-KEY'
# This is here to remind you to change your API key.
if not TIINGO_API_KEY or (TIINGO_API_KEY == 'REPLACE-THIS-TEXT-WITH-A-REAL-API-KEY'):
raise Exception("Please provide a valid Tiingo API key!")
from tiingo import TiingoClient
config = {
'api_key': T... |
GEMScienceTools/rmtk | notebooks/vulnerability/derivation_fragility/NLTHA_on_SDOF/2MSA_on_SDOF.ipynb | agpl-3.0 | import numpy
from rmtk.vulnerability.common import utils
from rmtk.vulnerability.derivation_fragility.NLTHA_on_SDOF import MSA_utils
from rmtk.vulnerability.derivation_fragility.NLTHA_on_SDOF.read_pinching_parameters import read_parameters
from rmtk.vulnerability.derivation_fragility.NLTHA_on_SDOF import double_MSA_on_... |
tensorflow/fairness-indicators | g3doc/tutorials/_Deprecated_Fairness_Indicators_Lineage_Case_Study.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... |
yashdeeph709/Algorithms | PythonBootCamp/Complete-Python-Bootcamp-master/.ipynb_checkpoints/Regular Expressions-checkpoint.ipynb | apache-2.0 | import re
# List of patterns to search for
patterns = [ 'term1', 'term2' ]
# Text to parse
text = 'This is a string with term1, but it does not have the other term.'
for pattern in patterns:
print 'Searching for "%s" in: \n"%s"' % (pattern, text),
#Check for match
if re.search(pattern, text):
... |
jfconavarrete/kaggle-facebook | notebooks/1.0-toni-preprocessing.ipynb | mit | train_X = train.values[:,:-1]
train_t = train.values[:,-1]
print train_X.shape
print train_t.shape
train.describe()
train.head()
train.tail()
"""
Explanation: The number of unique values is huge. This makes me think in a direction where we could center basis functions at the centers of discovered clusters. Discove... |
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