repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
Chilipp/psy-simple | examples/example_bar.ipynb | gpl-2.0 | import psyplot.project as psy
%matplotlib inline
%config InlineBackend.close_figures = False
axes = iter(psy.multiple_subplots(2, 2, n=3))
for var in ['t2m', 'u', 'v']:
psy.plot.barplot(
'demo.nc', # netCDF file storing the data
name=var, # one plot for each variable
y=[0, 1], # two bars ... |
mertnuhoglu/study | py/jupyter/week04_01.ipynb | apache-2.0 | import numpy as np
import h5py
import matplotlib.pyplot as plt
from testCases_v2 import *
from dnn_utils_v2 import sigmoid, sigmoid_backward, relu, relu_backward
%matplotlib inline
plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['imag... |
darkrun95/Applied-Data-Science | Applied Plotting in Python/Week 2/.ipynb_checkpoints/Week 2 - course-checkpoint.ipynb | gpl-3.0 | %matplotlib notebook
import matplotlib as mpl
mpl.get_backend()
import matplotlib.pyplot as plt
plt.plot?
# because the default is the line style '-',
# nothing will be shown if we only pass in one point (3,2)
plt.plot(3, 2)
# we can pass in '.' to plt.plot to indicate that we want
# the point (3,2) to be indicate... |
TheMitchWorksPro/DataTech_Playground | PY_Basics/TMWP_PerfTesting_w_time_magics_JupyterNBs.ipynb | mit | def myFun(x):
return (x**x)**x
myFun(9)
"""
Explanation: <div align="right">Python 3.6 [conda env: PY36]</div>
Performance Testing in iPython/Jupyter NBs
The timeit() command appears to have strict limitations in how you can use it within a Jupyter Notebook. For it to work most effectively:
- organize the code ... |
ledeprogram/algorithms | class7/donow/najmabadi_shannon_7_donow.ipynb | gpl-3.0 | import pandas as pd
%matplotlib inline
import numpy as np
from sklearn.linear_model import LogisticRegression
"""
Explanation: Apply logistic regression to categorize whether a county had high mortality rate due to contamination
1. Import the necessary packages to read in the data, plot, and create a logistic regressi... |
AdamD94/CompPhysLab | Steppers.ipynb | mit | #Solves an equation of the form dy/dt=f(y,t) using the Euler method
#f is the function which returns the derivative of the state vector of the system
#y0 is the initial state of the system
#t is an array of times at which f will be evaluated
def Euler(f, y0, t, args=()):
n = len(t) #Find number ... |
claudiuskerth/PhDthesis | Data_analysis/SNP-indel-calling/BAM/Big_Data.ipynb | mit | import os
!ls
"""
Explanation: List files in current working directory:
End of explanation
"""
if not os.path.exists("ery_30-15.sorted.cov.gz"):
try:
# 'call' is from the subprocess module
retcode = os.system("bamtools coverage -in ery_30-15.sorted.bam | gzip > ery_30-15.sorted.cov.gz")
... |
albangaignard/BioLinkedData-HandsOn | petitDej_LOD.ipynb | mit | from SPARQLWrapper import SPARQLWrapper, JSON, XML
# Set SPARQL endpoint
endpoint = "http://www.ebi.ac.uk/rdf/services/ensembl/sparql"
sparql = SPARQLWrapper(endpoint)
# Set prefix
prefix = """
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX dcte... |
planet-os/notebooks | api-examples/ERA5_tutorial.ipynb | mit | # Initialize notebook environment.
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
# Import Planet OS API
from API_client.python.datahub import datahub_main
from API_client.python.lib.dataset import dataset
from API_client.pyth... |
dereneaton/ipyrad | newdocs/API-analysis/cookbook-raxml.ipynb | gpl-3.0 | # conda install ipyrad -c bioconda
# conda install raxml -c bioconda
# conda install toytree -c eaton-lab
import ipyrad.analysis as ipa
import toytree
"""
Explanation: <span style="color:gray">ipyrad-analysis toolkit:</span> RAxML
RAxML is the most popular tool for inferring phylogenetic trees using maximum likelihoo... |
davebshow/projx | ipython-graphs.ipynb | mit | %matplotlib inline
%load_ext cypher
import asyncio
import networkx as nx
import pandas as pd
import projx as px
import matplotlib.pyplot as plt
from datetime import datetime
from gizmo import AsyncGremlinClient
plt.rcParams['figure.figsize'] = 12, 7
"""
Explanation: IPython Graphs
This notebook uses: Neo4j 2.1.6, Ti... |
echohenry2006/tvb-library | tvb/simulator/demos/region_stochastic_jansen.ipynb | gpl-2.0 | from tvb.simulator.lab import *
"""
Explanation: Single node dynamics using the Jansen and Rit model.
White noise is added to one specific state variable to emulate the external
stochastic stimulus p(t) as described in [JansenRit_1995]
End of explanation
"""
LOG.info("Configuring...")
#Initialise a Model, Coupling,... |
mne-tools/mne-tools.github.io | 0.19/_downloads/2ef14e30f2307383fca298e63d57a535/spm_faces_dataset.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne.datasets import spm_face
from mne.preprocessing import ICA, create_eog_epochs
from mne import io, combine_evoked
from mne.mi... |
phoebe-project/phoebe2-docs | 2.0/tutorials/compute.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.0,<2.1"
"""
Explanation: Compute
Now that we have datasets added to our Bundle, our next step is to run the forward model and compute a synthetic model for each of these datasets.
Setup
Let's first make sure we have the latest version of PHOEBE 2.0 installed. (You can comment out this line i... |
sods/ods | notebooks/pods/datasets/mauna-loa.ipynb | bsd-3-clause | import pods
import pylab as plt
%matplotlib inline
data = pods.datasets.mauna_loa()
"""
Explanation: Datasets: Downloading Data from the Mauna Loa Observatory
Open Data Science Initiative
28th May 2014 Neil D. Lawrence
This data set collection is from the Mauna Loa observatory which records atmospheric carbon levels... |
alansaul/ods | notebooks/pods/datasets/mauna_loa.ipynb | bsd-3-clause | import pods
import pylab as plt
%matplotlib inline
data = pods.datasets.mauna_loa()
"""
Explanation: Datasets: Downloading Data from the Mauna Loa Observatory
Open Data Science Initiative
28th May 2014 Neil D. Lawrence
This data set collection is from the Mauna Loa observatory which records atmospheric carbon levels... |
neuro-ml/reskit | tutorials/3. Transformers Guide.ipynb | bsd-3-clause | import numpy as np
from reskit.normalizations import mean_norm
from reskit.core import MatrixTransformer
matrix_0 = np.random.rand(5, 5)
matrix_1 = np.random.rand(5, 5)
matrix_2 = np.random.rand(5, 5)
y = np.array([0, 0, 1])
X = np.array([matrix_0,
matrix_1,
matrix_2])
output = np.array(... |
pegasus-isi/pegasus | tutorial/docker/notebooks/01-Introduction-API/01-Introduction-API.ipynb | apache-2.0 | from Pegasus.api import *
"""
Explanation: Pegasus Tutorial
Welcome to the Pegasus tutorial notebook, which is intended for new users who want to get a quick overview of Pegasus concepts and usage. This tutorial covers:
Using the Pegasus API to generate an abstract workflow
Using the API to plan the abstract workflow... |
mne-tools/mne-tools.github.io | dev/_downloads/06371e7a69d2c0314dea0a4363315ef2/css.ipynb | bsd-3-clause | # Author: John G Samuelsson <johnsam@mit.edu>
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.simulation import simulate_sparse_stc, simulate_evoked
"""
Explanation: Cortical Signal Suppression (CSS) for removal of cortical signals
This script shows an example o... |
jphall663/GWU_data_mining | 09_matrix_factorization/src/py_part_9_kaggle_GLRM_example.ipynb | apache-2.0 | import h2o
from h2o.estimators.glrm import H2OGeneralizedLowRankEstimator
from h2o.estimators.glm import H2OGeneralizedLinearEstimator
from h2o.grid.grid_search import H2OGridSearch
h2o.init(max_mem_size='12G') # give h2o as much memory as possible
h2o.no_progress() # turn off h2o progress bars
import matplotlib as p... |
BrainIntensive/OnlineBrainIntensive | resources/matplotlib/AnatomyOfMatPlotLib/AnatomyOfMatplotlib-Part4-Limits_Legends_and_Layouts.ipynb | mit | fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=plt.figaspect(0.5))
ax1.plot([-10, -5, 0, 5, 10, 15], [-1.2, 2, 3.5, -0.3, -4, 1])
ax2.scatter([-10, -5, 0, 5, 10, 15], [-1.2, 2, 3.5, -0.3, -4, 1])
plt.show()
"""
Explanation: Limits, Legends, and Layouts
In this section, we'll focus on what happens around th... |
science-of-imagination/nengo-buffer | Project/mental_translation_training.ipynb | gpl-3.0 | import matplotlib.pyplot as plt
%matplotlib inline
import nengo
import numpy as np
import scipy.ndimage
import matplotlib.animation as animation
from matplotlib import pylab
from PIL import Image
import nengo.spa as spa
import cPickle
import random
from nengo_extras.data import load_mnist
from nengo_extras.vision impo... |
theideasmith/theideasmith.github.io | _notebooks/Electricity/electricity.ipynb | mit | import numpy as np
import numpy.ma as ma
from scipy.integrate import odeint
mag = lambda r: np.sqrt(np.sum(np.power(r, 2)))
def g(y, t, q, m, n,d, k):
"""
n: the number of particles
d: the number of dimensions
(for fun's sake I want this
to work for k-dimensional systems)
y: an (n*2,d) di... |
Aniruddha-Tapas/Applied-Machine-Learning | Classification/Logistic Regression for Banknote Authentication.ipynb | mit | import numpy as np
import pandas as pd
# read .csv from provided dataset
csv_filename="data_banknote_authentication.txt"
# We assign the collumn names ourselves and load the data in a Pandas Dataframe
df=pd.read_csv(csv_filename,names=["Variance","Skewness","Curtosis","Entropy","Class"])
"""
Explanation: Logistic Re... |
tylere/docker-tmpnb-ee | notebooks/1 - IPython Notebook Examples/IPython Project Examples/IPython Kernel/Trapezoid Rule.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
def f(x):
return (x-3)*(x-5)*(x-7)+85
x = np.linspace(0, 10, 200)
y = f(x)
"""
Explanation: Basic Numerical Integration: the Trapezoid Rule
A simple illustration of the trapezoid rule for definite integration:
$$
\int_{a}^{b} f(x)\, dx \approx... |
swirlingsand/deep-learning-foundations | rnns/embeddings/Skip-Gram_word2vec.ipynb | mit | import time
import numpy as np
import tensorflow as tf
import utils
"""
Explanation: Skip-gram word2vec
In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in natural language p... |
pysg/pyther | Modelo de impregnacion/modelo2/Activité 10_Viernes_Revisión3-checkpoint.ipynb | mit | import numpy as np
from scipy import integrate
from matplotlib.pylab import *
"""
Explanation: Evaluation des modèles pour l'extraction supercritique
L'extraction supercritique est de plus en plus utilisée afin de retirer des matières organiques de différents liquides ou matrices solides. Cela est dû au fait que les f... |
tensorflow/docs | site/en/guide/migrate/sessionrunhook_callback.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
ES-DOC/esdoc-jupyterhub | notebooks/test-institute-3/cmip6/models/sandbox-3/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-3', 'sandbox-3', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: TEST-INSTITUTE-3
Source ID: SANDBOX-3
Topic: Ocnbgchem
Sub-Topic... |
liganega/Gongsu-DataSci | previous/y2017/GongSu02_Anaconda_Installation.ipynb | gpl-3.0 | a = 2
b = 3
a + b
"""
Explanation: 아나콘다(Anaconda) 소개
수정 사항
파이썬3 이용 아나콘다 팩키지 설치 이미지 업데이트 필요
아나콘다 패키지 소개
파이썬 프로그래밍 언어 개발환경
파이썬 기본 패키지 이외에 데이터분석용 필수 패키지 포함
기본적으로 스파이더 에디터를 활용하여 강의 진행
아나콘다 패키지 다운로드
아나콘다 패키지를 다운로드 하려면 아래 사이트를 방문한다
https://www.anaconda.com/download/
이후 아래 그림을 참조하여 다운받는다.
주의: 강의에서는 파이썬 3 최신 버전을 사용한다.... |
amueller/odsc-masterclass-2017-morning | notebooks/00 - Data Loading.ipynb | mit | from sklearn.datasets import load_digits
import numpy as np
digits = load_digits()
digits.keys()
digits.data.shape
digits.data.shape
digits.target.shape
digits.target
np.bincount(digits.target)
import matplotlib.pyplot as plt
%matplotlib inline
# %matplotlib notebook <- interactive interface
plt.matshow(digits.d... |
tien-le/uranus | Importing Notebooks.ipynb | mit | import io, os, sys, types
from IPython import get_ipython
from nbformat import read
from IPython.core.interactiveshell import InteractiveShell
"""
Explanation: Importing Jupyter Notebooks as Modules
It is a common problem that people want to import code from Jupyter Notebooks.
This is made difficult by the fact that ... |
Kaggle/learntools | notebooks/geospatial/raw/tut5.ipynb | apache-2.0 | #$HIDE_INPUT$
import folium
from folium import Marker, GeoJson
from folium.plugins import HeatMap
import pandas as pd
import geopandas as gpd
"""
Explanation: Introduction
In this tutorial, you'll explore several techniques for proximity analysis. In particular, you'll learn how to do such things as:
- measure the d... |
prideloki/til-arima-online-retail | online-retail-ARIMA.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import warnings
import itertools
import operator
import statsmodels.api as sm
online_retail = pd.read_excel('data/Online Retail.xlsx')
online_retail.describe()
online_retail.head()
online_retail['InvoiceDate'] = online_retail['InvoiceDate'].ast... |
tensorflow/docs-l10n | site/es-419/guide/checkpoint.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... |
probml/pyprobml | notebooks/book1/20/word_analogies_jax.ipynb | mit | import jax
import jax.numpy as jnp
import requests
import zipfile
import hashlib
import os
# Required functions
def download(name, cache_dir=os.path.join("..", "data")):
"""Download a file inserted into DATA_HUB, return the local filename."""
assert name in DATA_HUB, f"{name} does not exist in {DATA_HUB}."
... |
CrowdTruth/CrowdTruth-core | tutorial/notebooks/.ipynb_checkpoints/Multiple Choice Task - Relation Extraction-checkpoint.ipynb | apache-2.0 | import pandas as pd
test_data = pd.read_csv("../data/relex-multiple-choice.csv")
test_data.head()
"""
Explanation: CrowdTruth for Multiple Choice Tasks: Relation Extraction
In this tutorial, we will apply CrowdTruth metrics to a multiple choice crowdsourcing task for Relation Extraction from sentences. The workers we... |
4dsolutions/Python5 | OverviewNotes_PYTDS_3.ipynb | mit | import numpy as np
from sklearn.datasets import load_digits
digits = load_digits()
print(digits.data.shape)
print(digits.DESCR)
import matplotlib.pyplot as plt
% matplotlib inline
plt.gray() # gray reversed shown below
_ = plt.matshow(digits.images[0])
"""
Explanation: PYT-DS SAISOFT
Overview 1
Overview 2
<a d... |
obestwalter/obestwalter.github.io | content/articles/python-is-made-of-star-stuff/python-is-made-of-star-stuff.ipynb | mit | # Yes. The code makes no sense. Thanks for pointing it out.
from os import *
def append(*, end=linesep):
def _append(function):
def star_reporter(*args, **kwargs):
print(*args, **kwargs, end=end)
return function(*args, **kwargs)
return star_reporter
return _append
@app... |
mimoralea/applied-reinforcement-learning | notebooks/solutions/02-dynamic-programming.ipynb | mit | def fib(n):
if n < 2:
return n
return fib(n-2) + fib(n-1)
%time fib(30)
"""
Explanation: Recursion, Memoization and Dynamic Programming
Remember how we talk about using recursion and dynamic programming. One interesting thing to do is to implement the solution to a common problem called Fibonnaci numb... |
fotis007/python_intermediate | Python_2_10.ipynb | gpl-3.0 | text = """Als der Abend herbeikam und die Freunde in einer weitumherschauenden Laube saßen, trat eine ansehnliche Figur auf die Schwelle, welche unser Freund sogleich für den Barbier von heute früh erkannte. Auf einen tiefen, stummen Bückling des Mannes erwiderte Lenardo: Ihr kommt, wie immer, sehr gelegen und werdet n... |
ML4DS/ML4all | C1.Intro_Classification/Intro_Classification_professor.ipynb | mit | # To visualize plots in the notebook
%matplotlib inline
# Import some libraries that will be necessary for working with data and displaying plots
import csv # To read csv files
import random
import matplotlib.pyplot as plt
import numpy as np
from scipy import spatial
from sklearn import neighbors, datasets
"""
E... |
jan-rybizki/Chempy | tutorials/4-Simple_stellar_population.ipynb | mit | %pylab inline
# Chempy has to be called from within the /source directory. We load the default parameters
from Chempy.parameter import ModelParameters
a = ModelParameters()
"""
Explanation: Simple Stellar Population (SSP)
Here we will explain the central routine of Chempy. The observational counterpart of an SSP is ... |
mne-tools/mne-tools.github.io | dev/_downloads/9f8cb3957705df93f5da4fe6dc1bc69b/fnirs_artifact_removal.ipynb | bsd-3-clause | # Authors: Robert Luke <mail@robertluke.net>
#
# License: BSD-3-Clause
import os
import mne
from mne.preprocessing.nirs import (optical_density,
temporal_derivative_distribution_repair)
"""
Explanation: Visualise NIRS artifact correction methods
Here we artificially introduce seve... |
landlab/landlab | notebooks/tutorials/hillslope_geomorphology/depth_dependent_taylor_diffuser/depth_dependent_taylor_diffuser.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
from landlab import RasterModelGrid
from landlab.components import DepthDependentTaylorDiffuser
"""
Explanation: <a href="http://landlab.github.io"><img style="float: left" src="../../../landlab_header.png"></a>
Component Overview: DepthDependentTaylorDiffuser
<hr>
<s... |
statsmodels/statsmodels.github.io | v0.13.1/examples/notebooks/generated/statespace_tvpvar_mcmc_cfa.ipynb | bsd-3-clause | %matplotlib inline
from importlib import reload
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
from scipy.stats import invwishart, invgamma
# Get the macro dataset
dta = sm.datasets.macrodata.load_pandas().data
dta.index = pd.date_range('1959Q1', '2009Q3', freq='Q... |
dtamayo/MachineLearning | Day4/How HDBSCAN Works.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn.datasets as data
%matplotlib inline
sns.set_context('poster')
sns.set_style('white')
sns.set_color_codes()
plot_kwds = {'alpha' : 0.5, 's' : 80, 'linewidths':0}
"""
Explanation: How HDBSCAN Works
HDBSCAN is a clustering algorithm d... |
mne-tools/mne-tools.github.io | 0.19/_downloads/30d3f995eaa317ef564a8fb6d72c22bf/plot_left_cerebellum_volume_source.ipynb | bsd-3-clause | # Author: Alan Leggitt <alan.leggitt@ucsf.edu>
#
# License: BSD (3-clause)
import mne
from mne import setup_source_space, setup_volume_source_space
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
subjects_dir = data_path + '/subjects'
subject = 'sample'
aseg_fname = subjects_dir + '/sam... |
NeuPhysics/aNN | ipynb/vacuum.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... |
ES-DOC/esdoc-jupyterhub | notebooks/ec-earth-consortium/cmip6/models/ec-earth3-veg/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'ec-earth3-veg', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: EC-EARTH-CONSORTIUM
Source ID: EC-EARTH3-VEG
Topic: Ocean
Sub-Top... |
marksibrahim/musings | notebooks/A Neural Network Classifier using Keras.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegressionCV
from sklearn import datasets
from keras.models import Sequential
from keras.layers.core import Den... |
bgruening/EDeN | examples/graph_format.ipynb | gpl-3.0 | %matplotlib inline
import pylab as plt
import networkx as nx
G=nx.Graph()
G.add_node(0, label='A')
G.add_node(1, label='B')
G.add_node(2, label='C')
G.add_edge(0,1, label='x')
G.add_edge(1,2, label='y')
G.add_edge(2,0, label='z')
from eden.util import display
print display.serialize_graph(G)
from eden.util import d... |
ES-DOC/esdoc-jupyterhub | notebooks/cccma/cmip6/models/sandbox-3/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cccma', 'sandbox-3', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: CCCMA
Source ID: SANDBOX-3
Topic: Aerosol
Sub-Topics: Transport, Emissions, Con... |
wesleybowman/UTide | notebooks/utide_real_data_example.ipynb | mit | with open("can1998.dtf") as f:
lines = f.readlines()
print("".join(lines[:5]))
"""
Explanation: Look at the data file to see what structure it has.
End of explanation
"""
names = ["seconds", "year", "month", "day", "hour", "elev", "flag"]
obs = pd.read_csv(
"can1998.dtf",
names=names,
skipinitialsp... |
zlpure/CS231n | assignment1/knn.ipynb | mit | # Run some setup code for this notebook.
import random
import numpy as np
from cs231n.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
# This is a bit of magic to make matplotlib figures appear inline in the notebook
# rather than in a new window.
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.... |
jna29/SymGP | symgp/notebooks/CCA.ipynb | mit | from symgp import *
from sympy import *
from IPython.display import display, Math, Latex
"""
Explanation: Canonical Correlation Analysis (CCA)
Example is taken from Section 12.5.3, Machine Learning: A Probabilistic Perspective by Kevin Murphy.
End of explanation
"""
# Shapes
D_x, D_y, L_o, L_x, L_y = symbols('D_x, ... |
tensorflow/docs-l10n | site/en-snapshot/addons/tutorials/layers_weightnormalization.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is d... |
Featuretools/featuretools | docs/source/resources/frequently_asked_questions.ipynb | bsd-3-clause | import featuretools as ft
import pandas as pd
import numpy as np
import woodwork as ww
"""
Explanation: Frequently Asked Questions
Here we are attempting to answer some commonly asked questions that appear on Github, and Stack Overflow.
End of explanation
"""
es = ft.demo.load_mock_customer(return_entityset=True)
es... |
mne-tools/mne-tools.github.io | 0.20/_downloads/03c9d71de135994dbf45db72856a1f9a/plot_mne_inverse_envelope_correlation.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
# Sheraz Khan <sheraz@khansheraz.com>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.connectivity import envelope_correlation
from mn... |
bsautermeister/tensorlight | examples/datasets-test.ipynb | mit | # Force matplotlib to use inline rendering
%matplotlib inline
import os
import sys
import time
# add path to libraries for ipython
sys.path.append(os.path.expanduser("~/libs"))
import numpy as np
import tensorflow as tf
import tensorlight as light
DATA_ROOT = "data"
TRAIN_DIR = "train-test/datasets"
def write_anim... |
stijnvanhoey/course_gis_scripting | notebooks/02-scientific-python-introduction.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('seaborn-white')
"""
Explanation: <p><font size="6"><b>Scientific Python essentials</b></font></p>
Introduction to GIS scripting
May, 2017
© 2017, Stijn Van Hoey (stijnvanhoey@gm&... |
vinhqdang/my_mooc | coursera/python_data_analysis_spec/1_intro_python_data_science/week2_basic_pandas/Assignment2.ipynb | mit | import pandas as pd
df = pd.read_csv('olympics.csv', index_col=0, skiprows=1)
for col in df.columns:
if col[:2]=='01':
df.rename(columns={col:'Gold'+col[4:]}, inplace=True)
if col[:2]=='02':
df.rename(columns={col:'Silver'+col[4:]}, inplace=True)
if col[:2]=='03':
df.rename(columns... |
ray-project/ray | doc/source/ray-air/examples/feast_example.ipynb | apache-2.0 | # !pip install feast==0.20.1 ray[air]>=1.13 xgboost_ray
"""
Explanation: Integrate Ray AIR with Feast feature store
End of explanation
"""
import os
WORKING_DIR = os.path.expanduser("~/ray-air-feast-example/")
%env WORKING_DIR=$WORKING_DIR
! mkdir -p $WORKING_DIR
! wget --no-check-certificate https://github.com/ray... |
jsvine/pdfplumber | examples/notebooks/extract-table-nics.ipynb | mit | import pdfplumber
print(pdfplumber.__version__)
"""
Explanation: Demonstration of pdfplumber's table-extraction options
This notebook uses a report from the FBI's National Instant Criminal Background Check System.
Import pdfplumber
End of explanation
"""
pdf = pdfplumber.open("../pdfs/background-checks.pdf")
"""
Ex... |
mne-tools/mne-tools.github.io | stable/_downloads/7a4ee69e8136370345a316ee3b2e2187/publication_figure.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
# Daniel McCloy <dan.mccloy@gmail.com>
# Stefan Appelhoff <stefan.appelhoff@mailbox.org>
#
# License: BSD-3-Clause
"""
Explanation: Make figures more publication ready
In this example, we show several use cases to take MNE plots and
customize them for ... |
MVerbakel/statistical_review | Examples/Initial_analysis_example.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Local packages
import stats
# Set up formatting
pd.set_option('display.float_format', lambda x: '%.3f' % x)
plt.style.use('ggplot')
# Load data
example_df = pd.read_csv('bank.csv', na_values=[np.nan])
# Print high-level summary of the data fra... |
kgrodzicki/machine-learning-specialization | course-2-regression/notebooks/week-4-ridge-regression-assignment-2-blank.ipynb | mit | import graphlab
"""
Explanation: Regression Week 4: Ridge Regression (gradient descent)
In this notebook, you will implement ridge regression via gradient descent. You will:
* Convert an SFrame into a Numpy array
* Write a Numpy function to compute the derivative of the regression weights with respect to a single feat... |
cajal/pipeline | python/example/how_to_fill_proxmity_cell_match.ipynb | lgpl-3.0 | from pipeline import meso
"""
Explanation: In this notebook, I briefly demonstrate the use of ProximityCellMatch and BestProximityCellMatch tables in meso
End of explanation
"""
source_scan = dict(animal_id=25133, session=3, scan_idx=11)
target_scan = dict(animal_id=25133, session=4, scan_idx=13)
"""
Explanation: ... |
eflegara/NetStruc | .ipynb_checkpoints/6. Community Detection-checkpoint.ipynb | mit | try:
## For Network Analysis and Visualization
import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from collections import defaultdict
import operator
## For Hierarchical Clustering
from scipy.cluster import hierarchy
from scipy.spatial im... |
walkon302/CDIPS_Recommender | notebooks/.ipynb_checkpoints/Create_Datasets_for_Evaluation-checkpoint.ipynb | apache-2.0 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# get data
user_profile = pd.read_csv('../data_user_view_buy/user_profile.csv',sep='\t',header=None)
user_profile.columns = ['user_id','buy_spu','buy_sn','buy_ct3','view_spu','view_sn','view_ct3','time_interval','view_cnt','view_... |
kit-cel/wt | wt/vorlesung/ch7_9/white_noise.ipynb | gpl-2.0 | # importing
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
# showing figures inline
%matplotlib inline
# plotting options
font = {'size' : 20}
plt.rc('font', **font)
plt.rc('text', usetex=True)
matplotlib.rc('figure', figsize=(18, 6) )
"""
Explanation: Content and Objective
Simulate (disc... |
duetosymmetry/slimplectic | Damped_Oscillator_SlimplecticGGLvsRK.ipynb | mit | %matplotlib inline
from __future__ import print_function
import numpy as np, matplotlib.pyplot as plt
import slimplectic
# Set harmonic oscillator parameters
m = 1.0
k = 1.0
ll = 1e-4*np.sqrt(m*k) # ll is $\lambda$ in the paper
"""
Explanation: Simple damped harmonic oscillator
This example notebook characterizes th... |
magne-max/zipline-ja | docs/notebooks/tutorial.ipynb | apache-2.0 | !tail ../../zipline/examples/buyapple.py
"""
Explanation: Zipline beginner tutorial
Basics
Zipline is an open-source algorithmic trading simulator written in Python.
The source can be found at: https://github.com/quantopian/zipline
Some benefits include:
Realistic: slippage, transaction costs, order delays.
Stream-ba... |
nimagh/CNN_Implementations | Notebooks/DCGAN.ipynb | gpl-3.0 | # Imports
%reload_ext autoreload
%autoreload 1
import os, sys
sys.path.append('../')
sys.path.append('../common')
from tools_general import tf, np
from IPython.display import Image
from tools_train import get_train_params, OneHot, vis_square
import imageio
# define parameters
networktype = 'DCGAN_MNIST'
work_dir = ... |
ronnydw/data-science-projects | class-central-survey-2016-17/Newbees vs Experienced MOOCers.ipynb | mit | import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="white")
sns.set_context("talk")
"""
Explanation: Class Central Survey: compare target group 'Newbees' with 'Experienced' respondents
Newbees = respondents that haven't finished a MOOC yet
Experienced = respondents having finished ... |
DS-100/sp17-materials | sp17/labs/lab07/lab07.ipynb | gpl-3.0 | # Run this cell to set up the notebook.
import numpy as np
import pandas as pd
import seaborn as sns
import scipy as sci
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
from matplotlib import patches, cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
from mpl_toolkits.mplot3d impo... |
phungkh/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."""
b=[]
count=0
while count<(len(a)): # wh... |
vvishwa/deep-learning | autoencoder/Simple_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)
"""
Explanation: A Simple Autoencoder
We'll start off by building a simple autoencoder to compres... |
sebastiandres/mat281 | laboratorios/lab04-Clustering/Kmeans.ipynb | cc0-1.0 | # Configuracion para recargar módulos y librerías
%reload_ext autoreload
%autoreload 2
%matplotlib inline
from IPython.core.display import HTML
HTML(open("style/mat281.css", "r").read())
from mat281_code.lab import greetings
alumno_1 = ("Sebastian Flores", "2004001-7")
alumno_2 = ("Maria Jose Vargas", "2004007-8")... |
giacomov/3ML | docs/notebooks/custom_plugins.ipynb | bsd-3-clause | class PluginPrototype(object):
__metaclass__ = abc.ABCMeta
def __init__(self, name, nuisance_parameters):
assert is_valid_variable_name(name), "The name %s cannot be used as a name. You need to use a valid " \
"python identifier: no spaces, cannot start with... |
jqug/microscopy-object-detection | CNN training & evaluation - plasmodium (motic).ipynb | mit | N_samples_to_display = 10
pos_indices = np.where(test_y)[0]
pos_indices = pos_indices[np.random.permutation(len(pos_indices))]
for i in range(N_samples_to_display):
plt.subplot(2,N_samples_to_display,i+1)
example_pos = test_X[pos_indices[i],:,:,:]
example_pos = np.swapaxes(example_pos,0,2)
plt.imshow(ex... |
HUDataScience/StatisticalMethods2016 | notebooks/Exo7_StepFit_CepheidCase.ipynb | apache-2.0 | # No annoying warnings
import warnings
warnings.filterwarnings('ignore')
# == Basic import == #
# plot within the notebook
%matplotlib inline
import numpy as np
import matplotlib.pyplot as mpl
"""
Explanation: Fit a Step – Exercise 7
Based on the Intrinsic Dispersion fitting class you created last week create a new c... |
orlox/binary_tools | binary/notebooks/kicks.ipynb | gpl-3.0 | from binary_tools.binary.kicks import*
from binary_tools.binary.tests.test_kicks import*
from binary_tools.binary.orbits import*
from binary_tools.binary.tests.run_tests import*
"""
Explanation: Utils Functions and Tests
End of explanation
"""
phi = rand_phi()
"""
Explanation: kicks
The kicks file contains a series... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_covariance_whitening_dspm.ipynb | bsd-3-clause | # Author: Denis A. Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import spm_face
from mne.minimum_norm import apply_inverse, make_inverse_operator
from mne.cov import compute_covariance
print(__doc__)... |
metpy/MetPy | dev/_downloads/62a1acd718d4c5b9717787544d4cf09f/Gradient.ipynb | bsd-3-clause | import numpy as np
import metpy.calc as mpcalc
from metpy.units import units
"""
Explanation: Gradient
Use metpy.calc.gradient.
This example demonstrates the various ways that MetPy's gradient function
can be utilized.
End of explanation
"""
data = np.array([[23, 24, 23],
[25, 26, 25],
... |
jfconavarrete/spts-uoe | preprocessing_weather_readings.ipynb | gpl-3.0 | %matplotlib inline
import logging
import itertools
import json
import os
import pickle
import urllib2
import folium
import time
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from datetime import datetime
from os import listdir
fro... |
bashtage/statsmodels | examples/notebooks/interactions_anova.ipynb | bsd-3-clause | %matplotlib inline
from urllib.request import urlopen
import numpy as np
np.set_printoptions(precision=4, suppress=True)
import pandas as pd
pd.set_option("display.width", 100)
import matplotlib.pyplot as plt
from statsmodels.formula.api import ols
from statsmodels.graphics.api import interaction_plot, abline_plot
... |
snowch/biginsight-examples | misc/SparkOnBluemix.ipynb | apache-2.0 | cluster = '10451' # E.g. 10000
username = 'biadmin' # E.g. biadmin
password = '' # Please request password from chris.snow@uk.ibm.com
table = 'biadmin.rowapplyout' # BigSQL table to query
"""
Explanation: This spark notebook connects to BigInsights on Cloud using BigSQL.
This notebook runs succesf... |
vicente-gonzalez-ruiz/YAPT | 02-basics/30-profiling.ipynb | cc0-1.0 | ?
"""
Explanation: Profiling
IPython
Interactive Python provides some Built-in magic commands that can help up when developing code.
?
IPython's help.
End of explanation
"""
%quickref
"""
Explanation: %quickref
Quick reference.
End of explanation
"""
help
"""
Explanation: help
Python’s own help system.
End of ex... |
tensorflow/docs-l10n | site/ko/io/tutorials/prometheus.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... |
fweik/espresso | doc/tutorials/lattice_boltzmann/lattice_boltzmann_part4.ipynb | gpl-3.0 | import logging
import sys
import matplotlib.pyplot as plt
import numpy as np
import espressomd
import espressomd.lb
import espressomd.lbboundaries
import espressomd.shapes
logging.basicConfig(level=logging.INFO, stream=sys.stdout)
espressomd.assert_features(['LB_BOUNDARIES_GPU'])
# System constants
BOX_L = 16.0
TI... |
eco32i/biodata | sessions/05 - Python.ipynb | mit | import this
"""
Explanation: Python
Dynamically typed language created by Guido van Rossum (BDFL) in mid-90.
code blocks are defined by INDENTATION
everything is an object
everything is a reference
first class functions
garbage collected
batteries included
OBJECT:
A data structure that holds (encapsulates) both dat... |
ES-DOC/esdoc-jupyterhub | notebooks/mohc/cmip6/models/ukesm1-0-ll/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'ukesm1-0-ll', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: MOHC
Source ID: UKESM1-0-LL
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, ... |
datosgobar/pydatajson | samples/caso-uso-3-pydatajson-xlsx-justicia-valido.ipynb | mit | from pydatajson import DataJson #lib y clase
from pydatajson.pydatajson import read_catalog # lib, modulo ... metodo Lle el catalogo -json o xlsx o (local o url) dicc- y lo transforma en un diccionario de python
from pydatajson.xlsx_to_json import write_json_catalog
import arrow
"""
Explanation: Caso de uso 3 - Valida... |
shngli/Data-Mining-Python | Basic Algorithms.ipynb | gpl-3.0 | def combinations(string):
if len(string) == 1:
return set([string, ''])
result = set()
for i in range(len(string)):
for com in combinations(string[:i] + string[(i+1):]):
result.add(''.join(sorted(com)))
result.add(''.join(sorted(com + string[i])))
return result
... |
griffinfoster/fundamentals_of_interferometry | 6_Deconvolution/6_2_clean.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
"""
Explanation: Outline
Glossary
6. Deconvolution in Imaging
Previous: 6.1 Sky Models
Next: 6.3 CLEAN Implementations
Import standard modules:
End of explanati... |
briennakh/BIOF509 | Wk08/Wk08_Numpy_model_package_survey.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
n = 20
x = np.random.random((n,1))
y = 5 + 6 * x ** 2 + np.random.normal(0,0.5, size=(n,1))
plt.plot(x, y, 'b.')
plt.show()
"""
Explanation: Week 8 - Implementing a model in numpy and a survey of machine learning packages for python
This week we... |
codefordc/opendatadc | docs/analyses/anc/scripts/anc_elections.ipynb | agpl-3.0 | dc_2012 = pd.read_csv('Data/November_6_2012_General_and_Special_Election_Certified_Results.csv')
dc_2014 = pd.read_csv('Data/November_4_2014_General_Election_Certified_Results.csv')
dc_2016 = pd.read_csv('Data/November_4_2014_General_Election_Certified_Results.csv')
dc_2018 = pd.read_csv('Data/November_6_2018_General_E... |
beangoben/HistoriaDatos_Higgs | Dia3/7_Machine_Learning_Intro.ipynb | gpl-2.0 | def grafica_KMeans(X1,X2,Y,clf):
X1=X[:, 0]
X2=X[:, 1]
# Plot the decision boundary. For that, we will assign a color to each
x_min, x_max = X1.min()-1, X1.max() +1
y_min, y_max = X2.min()-1, X2.max() +1
xx, yy = np.meshgrid(np.linspace(x_min, x_max, 200), np.linspace(y_min, y_max, 200))
# ... |
asydorchuk/ml | classes/cs231n/assignment2/Dropout.ipynb | mit | # As usual, a bit of setup
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from cs231n.solver import Solver
%matplotlib inline
... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.