repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
seg/2016-ml-contest | geoLEARN/Submission_4_XtraTrees.ipynb | apache-2.0 | ###### Importing all used packages
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
from pandas import set_option
pd.options.mode.chained_assignment = None
###### Import packages needed for the make_vars functions
import Feature_Engineering as FE
##### imp... |
liganega/Gongsu-DataSci | previous/notes2017/W01/GongSu02_Anaconda_Installation.ipynb | gpl-3.0 | a = 2
b = 3
a + b
"""
Explanation: 아나콘다(Anaconda) 소개
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 2.7 버전을 사용한다.
<p>
<table cellspacing="20">
<tr>
<td>
... |
mjabri/holoviews | doc/Tutorials/Continuous_Coordinates.ipynb | bsd-3-clause | import numpy as np
import holoviews as hv
%reload_ext holoviews.ipython
np.set_printoptions(precision=2, linewidth=80)
%opts HeatMap (cmap="hot")
"""
Explanation: HoloViews is designed to work with scientific and engineering data, which is often in the form of discrete samples from an underlying continuous system. I... |
SECOORA/GUTILS | docs/notebooks/0001 - Converting Slocum data to a standard DataFrame.ipynb | mit | from IPython.lib.pretty import pprint
import logging
logger = logging.getLogger('gutils')
logger.handlers = [logging.StreamHandler()]
logger.setLevel(logging.DEBUG)
import sys
from pathlib import Path
# Just a hack to be able to `import gutils`
sys.path.append(str(Path('.').absolute().parent.parent))
binary_folder =... |
kit-cel/wt | mloc/ch6_Unsupervised_Learning/KMeans_Illustration_Animated.ipynb | gpl-2.0 | import numpy as np
import matplotlib as mpl
mpl.use('TkAgg')
import matplotlib.pyplot as plt
import sklearn.datasets as sk
from matplotlib import animation
from matplotlib.animation import PillowWriter # Disable if you don't want to save any GIFs.
%matplotlib inline
"""
Explanation: Illustration of the K-Means Algorit... |
seanware/try_quantopian | mentorship.ipynb | mit | import datetime
import numpy as np
import pandas as pd
import zipline
%matplotlib inline
STOCKS = ['AMD', 'CERN', 'COST', 'DELL', 'GPS', 'INTC', 'MMM']
"""
Explanation: <img src="http://photos3.meetupstatic.com/photos/event/f/9/d/global_432903997.jpeg" style="display:inline;width:100px"></img> Mentorship Program
Des... |
jdhp-docs/python-notebooks | python_sklearn_mlp_fr.ipynb | mit | import sklearn
# version >= 0.18 is required
version = [int(num) for num in sklearn.__version__.split('.')]
assert (version[0] >= 1) or (version[1] >= 18)
"""
Explanation: Le perceptron multicouche avec scikit-learn
Documentation officielle: http://scikit-learn.org/stable/modules/neural_networks_supervised.html
Noteb... |
tensorflow/docs-l10n | site/zh-cn/hub/tutorials/bert_experts.ipynb | apache-2.0 | #@title Copyright 2020 The TensorFlow Hub Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required ... |
dolittle007/dolittle007.github.io | notebooks/survival_analysis.ipynb | gpl-3.0 | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
import pymc3 as pm
from pymc3.distributions.timeseries import GaussianRandomWalk
import seaborn as sns
from statsmodels import datasets
from theano import tensor as T
"""
Explanation: Bayesian Survival Analysis
Author: Austin Rochford
Survival... |
hajicj/FEL-NLP-IR_2016 | tutorial/tutorial.ipynb | apache-2.0 | import npfl103
"""
Explanation: Information Retrieval
This is a tutorial for the npfl103 package for Information Retrieval assignments.
Big picture
In simple IR systems that we'll build in this lab session, two major things are happening more or less independently on each other. One: the similarity index of documents ... |
cbpygit/pypmj | examples/Setting up a configuration file.ipynb | gpl-3.0 | import config_tools as ct
"""
Explanation: Getting a config parser
The pypmj-module uses a configuration file in which all information about the JCMsuite-installation, data storage, servers and so on are set. This makes pypmj very flexible, as you can generate as many configuration files as you like. Here, we show how... |
NeuroDataDesign/fngs | docs/ebridge2/fngs_specs/week_0309/specs.ipynb | apache-2.0 | %%script false
## disklog.sh
#!/bin/bash -e
# run this in the background with nohup ./disklog.sh > disk.txt &
#
while true; do
echo "$(du -s $1 | awk '{print $1}')"
sleep 30
done
##cpulog.sh
import psutil
import time
import argparse
def cpulog(outfile):
with open(outfile, 'w') as outf:
while(Tr... |
gautam1858/tensorflow | tensorflow/lite/g3doc/tutorials/pose_classification.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... |
robertoalotufo/ia898 | src/sat.ipynb | mit | def sat(f):
return f.cumsum(axis=1).cumsum(axis=0)
def satarea(sat,r0_c0,r1_c1):
a,b,c,d = 0,0,0,0
r0,c0 = r0_c0
r1,c1 = r1_c1
if ((r0 - 1 >= 0) and (c0 - 1 >= 0)):
a = sat[r0-1,c0-1]
if (r0 - 1 >= 0):
b = sat[r0-1,c1]
if (c0 - 1 >= 0):
c = sat[r1,c0-1]
d = sat[r... |
awhite40/pymks | notebooks/intro.ipynb | mit | %matplotlib inline
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Meet PyMKS
In this short introduction, we will demonstrate the functionality of PyMKS to compute 2-point statistics in order to objectively quantify microstructures, predict effective properties ... |
msampathkumar/kaggle-quora-tensorflow | references/sentiment-rnn/Sentiment RNN.ipynb | apache-2.0 | import numpy as np
import tensorflow as tf
with open('../sentiment_network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment_network/labels.txt', 'r') as f:
labels = f.read()
reviews[:2000]
"""
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural... |
bregmanstudio/SoundscapeEcology | SoundscapeComponentAnalysis.ipynb | mit | from pylab import * # numpy, matplotlib, plt
from bregman.suite import * # Bregman audio feature extraction library
from soundscapeecology import * # 2D time-frequency shift-invariant convolutive matrix factorization
%matplotlib inline
rcParams['figure.figsize'] = (15.0, 9.0)
"""
Explanation: <h1>Soundscape Analysis b... |
kit-cel/wt | ccgbc/ch2_Codes_Basic_Concepts/BEC_FiniteLength_Upper_Lower_Bounds.ipynb | gpl-2.0 | import numpy as np
import matplotlib
import matplotlib.pyplot as plt
# plotting options
font = {'size' : 20}
plt.rc('font', **font)
plt.rc('text', usetex=matplotlib.checkdep_usetex(True))
matplotlib.rc('figure', figsize=(18, 6) )
"""
Explanation: Finite-Length Performance on the BEC Channel
This code is provided ... |
NathanYee/ThinkBayes2 | bayesianLinearRegression/nathanTest.ipynb | gpl-2.0 | from __future__ import print_function, division
% matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import math
import numpy as np
from thinkbayes2 import Pmf, Cdf, Suite, Joint, EvalNormalPdf
import thinkplot
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Bayesian Linear Reg... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/08_image/mnist_models.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
from datetime import datetime
import os
PROJECT = "your-project-id-here" # REPLACE WITH YOUR PROJECT ID
BUCKET = "your-bucket-id-here" # REPLACE WITH YOUR BUCKET NAME
REGION = "us-central1" # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
MODEL_T... |
cosmicBboy/themis-ml | paper/Evaluating Themis-ml.ipynb | mit | from themis_ml import datasets
from themis_ml.datasets.german_credit_data_map import \
preprocess_german_credit_data
from themis_ml.metrics import mean_difference, normalized_mean_difference, \
mean_confidence_interval
german_credit = datasets.german_credit()
german_credit[
["credit_risk", "purpose", "age_... |
daviddesancho/mdtraj | examples/solvent-accessible-surface-area.ipynb | lgpl-2.1 | %matplotlib inline
from __future__ import print_function
import numpy as np
import mdtraj as md
"""
Explanation: In this example, we'll compute the solvent accessible surface area of one of the residues in our protien
accross each frame in a MD trajectory. We're going to use our trustly alanine dipeptide trajectory fo... |
H-E-L-P/XID_plus | docs/build/html/notebooks/examples/XID+example_run_script-PACS.ipynb | mit | import numpy as np
from astropy.io import fits
from astropy import wcs
import pickle
import dill
import sys
import os
import xidplus
import copy
from xidplus import moc_routines, catalogue
from xidplus import posterior_maps as postmaps
from builtins import input
"""
Explanation: XID+ Example Run Script
(This is based... |
desihub/desisim | doc/nb/simqso-templates.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
from desisim.templates import SIMQSO, QSO
import multiprocessing
nproc = multiprocessing.cpu_count() // 2
plt.style.use('seaborn-talk')
%matplotlib inline
"""
Explanation: Simulate QSO spectra.
The purpose of this notebook is ... |
royalosyin/Python-Practical-Application-on-Climate-Variability-Studies | ex31-Harmonic Analysis - Monthly Mean Temperature at Orange, Australia.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from HA_helpers import *
%matplotlib inline
# Set some parameters to apply to all plots. These can be overridden
import matplotlib
# Plot size to 12" x 7"
matplotlib.rc('figure', figsize = (15, 7))
# Font size to 14
matplotlib.rc('font', size = 14... |
Jackporter415/phys202-2015-work | assignments/assignment05/InteractEx04.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
"""
Explanation: Interact Exercise 4
Imports
End of explanation
"""
def random_line(m, b, sigma, size=10):
"""Create a line y = m*x + b + N(0,si... |
wbarfuss/pymofa | tutorial/02_LocalParallelization.ipynb | mit | from ipyparallel import Client
import os
c = Client()
view = c[:]
print(c.ids)
%%px
def find(name, path):
for root, dirs, files in os.walk(path):
if name in files:
return root
path = find('02_LocalParallelization.ipynb', '/home/')
print(path)
os.chdir(path)
"""
Explanation: How to locally run... |
JJINDAHOUSE/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... |
bccp/imaginglss-notebooks | BrickInvestigation.ipynb | artistic-2.0 | from imaginglss.analysis import completeness
from imaginglss.analysis import targetselection
from imaginglss.utils.npyquery import Column as C
b = dr.brickindex.get_brick(dr.brickindex.search_by_name('2445p072'))
tractor = dr.catalogue.open(b)
sigma = {'r':5, 'z':5, 'g':5}
LRG = targetselection.LRG(tractor)
QSO = t... |
atlury/deep-opencl | DL0110EN/3.3.3practice_predicting_MNIST.ipynb | lgpl-3.0 | !conda install -y torchvision
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.datasets as dsets
import matplotlib.pylab as plt
import numpy as np
"""
Explanation: <div class="alert alert-block alert-info" style="margin-top: 20px">
<a href="http://cocl.us/pytorch_link... |
tensorflow/docs-l10n | site/en-snapshot/io/tutorials/orc.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... |
zhuanxuhit/deep-learning | intro-to-tensorflow/.ipynb_checkpoints/intro_to_tensorflow-checkpoint.ipynb | mit | import hashlib
import os
import pickle
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils import resample
from tqdm import tqdm
from zipfile import ZipFile
print('All m... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/docker_and_kubernetes/solutions/2_intro_k8s.ipynb | apache-2.0 | import os
CLUSTER_NAME = "asl-cluster"
ZONE = "us-central1-a"
os.environ["CLUSTER_NAME"] = CLUSTER_NAME
os.environ["ZONE"] = ZONE
"""
Explanation: Introduction to Kubernetes
Learning Objectives
* Create GKE cluster from command line
* Deploy an application to your cluster
* Cleanup, delete the cluster
Overview
K... |
esa-as/2016-ml-contest | Kr1m/Kr1m_SEG_ML_Attempt1.ipynb | apache-2.0 | import warnings
warnings.filterwarnings("ignore")
%matplotlib inline
import sys
sys.path.append("..")
#Import standard pydata libs
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
filename = '../facies_vectors.csv'
training_data = pd.read_csv(filename)
training_data['Well ... |
google/physics-math-tutorials | colabs/QNN_hands_on.ipynb | apache-2.0 | # install published dev version
# !pip install cirq~=0.4.0.dev
# install directly from HEAD:
!pip install git+https://github.com/quantumlib/Cirq.git@8c59dd97f8880ac5a70c39affa64d5024a2364d0
"""
Explanation: Copyright 2021 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this ... |
google-research/ott | docs/notebooks/fairness.ipynb | apache-2.0 | fig, ax = plt.subplots(1, 1, figsize=(8, 5))
plot_quantiles(logits, groups, ax)
ax.tick_params(axis='both', which='major', labelsize=16)
ax.set_title(f'Baseline Quantiles', fontsize=22)
ax.set_xlabel('Quantile Level', fontsize=18)
ax.set_ylabel('Prediction', fontsize=18)
"""
Explanation: Fairness regularizers
In this ... |
hanhanwu/Hanhan_Data_Science_Practice | make_sense_dimension_reduction.ipynb | mit | import sklearn.datasets as ds
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
import numpy as np
%matplotlib inline
data = ds.load_breast_cancer()['data']
data.shape # 30 features
z_scaler = StandardScaler()
z_data = z_scaler.fit_transform(data)
... |
Kaggle/learntools | notebooks/intro_to_programming/raw/ex3.ipynb | apache-2.0 | # Set up the exercise
from learntools.core import binder
binder.bind(globals())
from learntools.intro_to_programming.ex3 import *
print('Setup complete.')
"""
Explanation: In the tutorial, you learned about four different data types: floats, integers, strings, and booleans. In this exercise, you'll experiment with th... |
joshnsolomon/phys202-2015-work | assignments/assignment05/InteractEx01.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
"""
Explanation: Interact Exercise 01
Import
End of explanation
"""
def print_sum(a, b):
"""Print the sum of the arguments a and b."""
... |
susantabiswas/Natural-Language-Processing | Notebooks/Word_Prediction_Add-1_Smoothing_with_Interpolation.ipynb | mit | from nltk.util import ngrams
from collections import defaultdict
from collections import OrderedDict
import string
import time
import gc
from math import log10
start_time = time.time()
"""
Explanation: <u>Word prediction</u>
Language Model based on n-gram Probabilistic Model
Add-1 Smoothing Used with Interpolation
Hig... |
ioshchepkov/SHTOOLS | examples/notebooks/tutorial_6.ipynb | bsd-3-clause | %matplotlib inline
from __future__ import print_function # only necessary if using Python 2.x
import numpy as np
from pyshtools import SHCoeffs
lmax = 30
coeffs = SHCoeffs.from_zeros(lmax)
coeffs.set_coeffs(values=[1], ls=[10], ms=[0])
"""
Explanation: 3D Spherical Harmonic Plots
This example demonstrates how to gene... |
mattssilva/UW-Machine-Learning-Specialization | Week 1/.ipynb_checkpoints/Getting Started with SFrames-checkpoint.ipynb | mit | import graphlab
# Set product key on this computer. After running this cell, you will not need to re-enter your product key.
graphlab.product_key.set_product_key('your product key here')
# Limit number of worker processes. This preserves system memory, which prevents hosted notebooks from crashing.
graphlab.set_runti... |
jepegit/cellpy | dev_utils/lookup/cellpy_hdf5_tweaking.ipynb | mit | %load_ext autoreload
%autoreload 2
from pathlib import Path
from pprint import pprint
import pandas as pd
import cellpy
"""
Explanation: Tweaking the cellpy file format
A cellpy file is a hdf5-type file.
From v.5 it contains five top-level directories.
```python
from cellreader.py
raw_dir = prms._cellpyfile_raw
ste... |
ramseylab/networkscompbio | class20_partialcorr_python3.ipynb | apache-2.0 | import pandas ## data file loading
import numpy
import sklearn.covariance ## for covariance matrix calculation
import matplotlib.pyplot
import matplotlib
import pylab
import scipy.stats ## for calculating the CDF of normal distribution
import igraph ## for network visualization and finding components
import math
"... |
roebius/deeplearning_keras2 | nbs2/pytorch-tut.ipynb | apache-2.0 | x = torch.Tensor(5, 3); x
x = torch.rand(5, 3); x
x.size()
y = torch.rand(5, 3)
x + y
torch.add(x, y)
result = torch.Tensor(5, 3)
torch.add(x, y, out=result)
result1 = torch.Tensor(5, 3)
result1 = x + y
result1
# anything ending in '_' is an in-place operation
y.add_(x) # adds x to y in-place
# standard numpy-... |
keras-team/keras-io | examples/generative/ipynb/adain.ipynb | apache-2.0 | import os
import glob
import imageio
import numpy as np
from tqdm import tqdm
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
import tensorflow_datasets as tfds
from tensorflow.keras import layers
# Defining the global variables.
IMAGE_SIZE = (224, 224)
BATCH_SIZE = 64
# Training f... |
bhargavvader/gensim | docs/notebooks/gensim Quick Start.ipynb | lgpl-2.1 | raw_corpus = ["Human machine interface for lab abc computer applications",
"A survey of user opinion of computer system response time",
"The EPS user interface management system",
"System and human system engineering testing of EPS",
"Relation of user pe... |
unpingco/Python-for-Probability-Statistics-and-Machine-Learning | chapters/machine_learning/notebooks/regularization.ipynb | mit | from IPython.display import Image
Image('../../../python_for_probability_statistics_and_machine_learning.jpg')
"""
Explanation: Regularization
End of explanation
"""
import sympy as S
S.var('x:2 l',real=True)
J=S.Matrix([x0,x1]).norm()**2 + l*(1-x0-2*x1)
sol=S.solve(map(J.diff,[x0,x1,l]))
print(sol)
"""
Explanatio... |
mit-eicu/eicu-code | notebooks/nursecharting.ipynb | mit | # Import libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import psycopg2
import getpass
# for configuring connection
from configobj import ConfigObj
import os
%matplotlib inline
# Create a database connection using settings from config file
config='../db/config.ini'
# connection in... |
blevine37/pySpawn17 | examples/spawn_analysis.ipynb | mit | print "Currently in directory:", os.getcwd()
# THIS IS THE ONLY PART OF THE CODE THAT NEEDS TO BE CHANGED
dir_name = "/Users/Dmitry/Documents/Research/MSU/4tce/cis/"
h5filename = "sim.1.hdf5"
os.chdir(dir_name)
an = pyspawn.fafile(h5filename)
an.fill_electronic_state_populations(column_filename="N.dat")
an.fill_labe... |
nbokulich/short-read-tax-assignment | ipynb/mock-community/taxonomy-assignment-vsearch.ipynb | bsd-3-clause | from os.path import join, expandvars
from joblib import Parallel, delayed
from glob import glob
from os import system
from tax_credit.framework_functions import (parameter_sweep,
generate_per_method_biom_tables,
move_results_to_repo... |
chetnapriyadarshini/deep-learning | autoencoder/Convolutional_Autoencoder.ipynb | mit | %matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', validation_size=0)
img = mnist.train.images[2]
plt.imshow(img.reshape((28, 28)), cmap='Greys_r')
"""
Explanation: C... |
sujitpal/polydlot | src/mxnet/01-mnist-fcn.ipynb | apache-2.0 | from __future__ import division, print_function
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
import mxnet as mx
import numpy as np
import os
%matplotlib inline
DATA_DIR = "../../data"
TRAIN_FILE = os.path.join(DATA_DIR, "mn... |
Chipe1/aima-python | planning_hierarchical_search.ipynb | mit | from planning import *
from notebook import psource
psource(Problem.refinements)
"""
Explanation: Hierarchical Search
Hierarchical search is a a planning algorithm in high level of abstraction. <br>
Instead of actions as in classical planning (chapter 10) (primitive actions) we now use high level actions (HLAs) (see... |
YzPaul3/h2o-3 | h2o-py/demos/H2O_tutorial_breast_cancer_classification.ipynb | apache-2.0 | import h2o
# Start an H2O Cluster on your local machine
h2o.init()
"""
Explanation: H2O Tutorial: Breast Cancer Classification
Author: Erin LeDell
Contact: erin@h2o.ai
This tutorial steps through a quick introduction to H2O's Python API. The goal of this tutorial is to introduce through a complete example H2O's capab... |
slundberg/shap | notebooks/tabular_examples/tree_based_models/Explaining a simple OR function.ipynb | mit | import numpy as np
import xgboost
import shap
"""
Explanation: Explaining a simple OR function
This notebook examines what it looks like to explain an OR function using SHAP values. It is based on a simple example with two features is_young and is_female, roughly motivated by the Titanic survival dataset where women a... |
ES-DOC/esdoc-jupyterhub | notebooks/test-institute-1/cmip6/models/sandbox-3/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-1', 'sandbox-3', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: TEST-INSTITUTE-1
Source ID: SANDBOX-3
Topic: Atmoschem
Sub-Topic... |
caiyunapp/theano_lstm | Tutorial.ipynb | bsd-3-clause | ## Fake dataset:
class Sampler:
def __init__(self, prob_table):
total_prob = 0.0
if type(prob_table) is dict:
for key, value in prob_table.items():
total_prob += value
elif type(prob_table) is list:
prob_table_gen = {}
for key in prob_tabl... |
catedrasaes-umu/NoSQLDataEngineering | projects/es.um.nosql.streaminginference.json2dbschema/benchmark/Benchmark.ipynb | mit | %%bash
java -version
"""
Explanation: Pruebas de rendimiento sobre Streaming Inference
Es necesario tener instalada la versión de java 1.8:
End of explanation
"""
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
import seaborn as sns
from matplotlib import pylab
import nu... |
fjaviersanchez/JupyterTutorial | index.ipynb | mit | import datetime
print(datetime.datetime.now())
"""
Explanation: Introduction to Jupyter Notebooks
Tutorial by Javier Sánchez, University of California, Irvine
Prepared for the DESC Collaboration Meeting - Oxford - July 2016.
Requirements:
* anaconda (includes jupyter, astropy, numpy, scipy and matplotlib)
* seaborn ... |
metpy/MetPy | v1.0/_downloads/e5685967297554788de3cf5858571b23/Natural_Neighbor_Verification.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import numpy as np
from scipy.spatial import ConvexHull, Delaunay, delaunay_plot_2d, Voronoi, voronoi_plot_2d
from scipy.spatial.distance import euclidean
from metpy.interpolate import geometry
from metpy.interpolate.points import natural_neighbor_point
"""
Explanation: Natural Neighbo... |
srcole/qwm | misc/Nonuniform phase distribution.ipynb | mit | from neurodsp import sim
freq = 8
T = 60
Fs = 1000
x = sim.sim_bursty_oscillator(freq, T, Fs, rdsym = .2, prob_enter_burst=1, prob_leave_burst=0)
# Cut out buffer time
t = np.arange(0, T, 1/Fs)
# Plot signal
tlim = (0,2)
tidx = np.logical_and(t>=tlim[0], t<tlim[1])
plt.figure(figsize=(16,3))
plt.plot(t[tidx], x[tidx]... |
jeroarenas/MLBigData | 0_Introduction/Intro_PySpark_1.ipynb | mit | fruits = ['apple', 'orange', 'banana', 'grape', 'watermelon', 'apple', 'orange', 'apple']
number_partitions = 4
dataRDD = sc.parallelize(fruits, number_partitions)
print type(dataRDD)
"""
Explanation: Counting words
1.- Creating a simple RDD .
We will create a simple RDD and apply basic operations
End of explanation
... |
dolejarz/engsci_capstone_transport | python/DDM/DDM.ipynb | mit | import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import datetime as dt
import scipy.stats as stats
from scipy.stats import norm
import numpy as np
import math
import seaborn as sns
from InvarianceTestEllipsoid import InvarianceTestEllipsoid
from autocorrelation import autocorrelation
import stats... |
rokkamsatyakalyan/Machine_Learning | K_NEAREST_IMPLEMENTATION.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
from collections import Counter
from math import sqrt
import random
import warnings
"""
Explanation: IMPLEMENTING K_NEAREST_NEIGHBOUR
In the given data set we have to classify into which cluster a instance is going to fall
Importing required predifined methods
End of explanatio... |
CAChemE/curso-python-datos | notebooks/010-NumPy-Intro.ipynb | bsd-3-clause | import numpy as np
#para ver la versión que tenemos instalada:
np.__version__
"""
Explanation: Introducción a NumPy
_Hasta ahora hemos visto los tipos de datos más básicos que nos ofrece Python: integer, real, complex, boolean, list, tuple... Pero ¿no echas algo de menos? Efectivamente, los arrays. _
En este notebook... |
GoogleCloudPlatform/ml-design-patterns | 03_problem_representation/reframing.ipynb | apache-2.0 | import numpy as np
import seaborn as sns
from google.cloud import bigquery
import matplotlib as plt
%matplotlib inline
bq = bigquery.Client()
query = """
SELECT
weight_pounds,
is_male,
gestation_weeks,
mother_age,
plurality,
mother_race
FROM
`bigquery-public-data.samples.natality`
WHERE
weight_pounds... |
gee-community/gee_tools | notebooks/date/since_epoch.ipynb | mit | date_band = tools.date.getDateBand(test_image, 'day')
ui.eprint(date_band)
"""
Explanation: get_date_band
Get the date of an image, compute how many units (for example day) has ellpsed since the epoch (1970-01-01) and set it to a band (called date) and a property (called unit_since_epoch, for example, day_since_epoch... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch2-Problem_2-05.ipynb | unlicense | %pylab notebook
%precision 4
from scipy import constants as c # we like to use some constants
"""
Explanation: Excercises Electric Machinery Fundamentals
Chapter 2
Problem 2-5
End of explanation
"""
#60Hz side (North America)
Vrms60 = 120 # [V]
freq60 = 60 # [Hz]
#50Hz side (Europe)
Vrms50 = 240 # [V]
freq50 = 5... |
mne-tools/mne-tools.github.io | 0.18/_downloads/7bb2e6f1056f5cae3a98ccc12aac266f/plot_eeg_no_mri.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Joan Massich <mailsik@gmail.com>
#
# License: BSD Style.
import os.path as op
import mne
from mne.datasets import eegbci
from mne.datasets import fetch_fsaverage
# Download fsaverage files
fs_dir = fetch_fsaverage(verbose=True)
subjects_dir = op.... |
statsmodels/statsmodels.github.io | v0.13.1/examples/notebooks/generated/statespace_sarimax_faq.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
rng = np.random.default_rng(20210819)
eta = rng.standard_normal(5200)
rho = 0.8
beta = 10
epsilon = eta.copy()
for i in range(1, eta.shape[0]):
epsilon[i] = rho * epsilon[i - 1] + eta[i]
y = beta + epsilon
y = y[200:]
from statsmodels.tsa.api import SARIM... |
project-chip/connectedhomeip | docs/guides/repl/Matter - Multi Fabric Commissioning.ipynb | apache-2.0 | import os, subprocess
if os.path.isfile('/tmp/repl-storage.json'):
os.remove('/tmp/repl-storage.json')
# So that the all-clusters-app won't boot with stale prior state.
os.system('rm -rf /tmp/chip_*')
"""
Explanation: Multi Fabric - Commissioning and Interactions
<a href="http://35.236.121.59/hub/user-redire... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/end_to_end_ml/solutions/keras_dnn_babyweight.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
!pip install --user google-cloud-bigquery==1.25.0
"""
Explanation: Creating Keras DNN model
Learning Objectives
Create input layers for raw features
Create feature columns for inputs
Create DNN dense hidden layers and output layer
Build DNN model tyi... |
mastertrojan/Udacity | batch-norm/Batch_Normalization_Solutions.ipynb | mit | import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True, reshape=False)
"""
Explanation: Batch Normalization – Solutions
Batch normalization is most useful when building deep neural networks. To demonstrate this, we'll create a co... |
yevheniyc/Projects | 1m_ML_Security/notebooks/answers/Worksheet 5 - DGA Detection Feature Engineering - Answers.ipynb | mit | ## Load data
df = pd.read_csv('../../data/dga_data_small.csv')
df.drop(['host', 'subclass'], axis=1, inplace=True)
print(df.shape)
df.sample(n=5).head() # print a random sample of the DataFrame
df[df.isDGA == 'legit'].head()
# Google's 10000 most common english words will be needed to derive a feature called ngrams..... |
ledeprogram/algorithms | class7/donow/wang_zhizhou_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... |
jdvelasq/ingenieria-economica | 05-bonos.ipynb | mit | # Importa la librería financiera.
# Solo es necesario ejecutar la importación una sola vez.
import cashflows as cf
"""
Explanation: Bonos
Juan David Velásquez Henao
jdvelasq@unal.edu.co
Universidad Nacional de Colombia, Sede Medellín
Facultad de Minas
Medellín, Colombia
Haga click aquí para acceder a la última versi... |
scidash/sciunit | docs/chapter6.ipynb | mit | # Install SciUnit if necessary
!pip install -q sciunit
# Import the package
import sciunit
# Add some default CSS styles for these examples
sciunit.utils.style()
"""
Explanation: <a href="https://colab.research.google.com/github/scidash/sciunit/blob/master/docs/chapter6.ipynb" target="_parent"><img src="https://cola... |
TeamHG-Memex/eli5 | notebooks/Debugging scikit-learn text classification pipeline.ipynb | mit | from sklearn.datasets import fetch_20newsgroups
categories = ['alt.atheism', 'soc.religion.christian',
'comp.graphics', 'sci.med']
twenty_train = fetch_20newsgroups(
subset='train',
categories=categories,
shuffle=True,
random_state=42
)
twenty_test = fetch_20newsgroups(
subset='test'... |
DavidDobr/icef_thesis | dobrinskiy_thesis_v2_october.ipynb | gpl-3.0 | # You should be running python3
import sys
print(sys.version)
import pandas as pd # http://pandas.pydata.org/
import numpy as np # http://numpy.org/
import statsmodels.api as sm # http://statsmodels.sourceforge.net/stable/index.html
import statsmodels.formula.api as smf
import statsmodels
print("Pandas Version:... |
lknelson/DH-Institute-2017 | 06-Literary Distinction (Probably)/Literary Patterns (Probably).ipynb | bsd-2-clause | import nltk
nltk.download('stopwords')
from sklearn.naive_bayes import MultinomialNB
import pandas
# Get texts of interest that belong to identifiably different categories
unladen_swallow = 'high air-speed velocity'
swallow_grasping_coconut = 'low air-speed velocity'
# Transform them into a format scikit-learn can ... |
brclark-usgs/flopy | examples/Notebooks/flopy3_ZoneBudget_example.ipynb | bsd-3-clause | %matplotlib inline
import os
import sys
import platform
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import flopy
print(sys.version)
print('numpy version: {}'.format(np.__version__))
print('matplotlib version: {}'.format(mpl.__version__))
print('pandas version: {}'.fo... |
letsgoexploring/teaching | winter2017/econ129/python/Econ129_Class_06_Complete.ipynb | mit | # Use the requests module to download cross country GDP per capita
url = 'http://www.briancjenkins.com/data/international/csv/crossCountryIncomePerCapita.csv'
filename='crossCountryIncomePerCapita.csv'
r = requests.get(url,verify=True)
with open(filename,'wb') as newFile:
newFile.write(r.content)
# Import th... |
sdpython/ensae_teaching_cs | _doc/notebooks/exams/td_note_2018_1.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 1A.e - Enoncé 12 décembre 2017 (1)
Correction du premier énoncé de l'examen du 12 décembre 2017. Celui-ci mène à l'implémentation d'un algorithme qui permet de retrouver une fonction $f$ en escalier à partir d'un ensemble de points $(X_i,... |
graphistry/pygraphistry | demos/demos_databases_apis/gremlin-tinkerpop/TitanDemo.ipynb | bsd-3-clause | import asyncio
import aiogremlin
# Create event loop and initialize gremlin client
loop = asyncio.get_event_loop()
client = aiogremlin.GremlinClient(url='ws://localhost:8182/', loop=loop) # Default url
"""
Explanation: In this notebook, we demonstrate how to create and modify a Titan graph in python, and then visual... |
mapagron/Boot_camp | hm7/Homework #7.ipynb | gpl-3.0 | # Dependencies
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import json
import tweepy
import time
import seaborn as sns
%pylab notebook
# Initialize Sentiment Analyzer
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
analyzer = SentimentIntensityAnalyzer()
# Twitter API K... |
matthewpecsok/development | imbd.ipynb | apache-2.0 | import tensorflow as tf
import numpy as np
import pandas as pd
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.imdb.load_data(num_words=10000)
word_index = tf.keras.datasets.imdb.get_word_index()
word_index['fawn']
# why in the world it's indexed by word?
reverse_word_index = dict([(value,key) for (key,... |
boffi/boffi.github.io | dati_2018/04/EP_Exact+Numerical.ipynb | mit | def resp_elas(m,c,k, cC,cS,w, F, x0,v0):
wn2 = k/m ; wn = sqrt(wn2) ; beta = w/wn
z = c/(2*m*wn)
wd = wn*sqrt(1-z*z)
# xi(t) = R sin(w t) + S cos(w t) + D
det = (1.-beta**2)**2+(2*beta*z)**2
R = ((1-beta**2)*cS + (2*beta*z)*cC)/det/k
S = ((1-beta**2)*cC - (2*beta*z)*cS)/det/k
D = F/k
... |
pligor/predicting-future-product-prices | 04_time_series_prediction/26_price_history_generate_train_test_and_baseline.ipynb | agpl-3.0 | bltest = MyBaseline(npz_path=npz_test)
bltest.getMSE()
bltest.renderMSEs()
plt.show()
bltest.getHuberLoss()
bltest.renderHuberLosses()
plt.show()
bltest.get_dtw()
bltest.renderRandomTargetVsPrediction()
plt.show()
"""
Explanation: Baseline is static, a straight line for each input - Test
End of explanation
"""
... |
astroai/starnet | VAE/StarNet_VAE.ipynb | bsd-2-clause | import numpy as np
import time
import h5py
import keras
import matplotlib.pyplot as plt
import sys
from keras.layers import (Input, Dense, Lambda, Flatten, Reshape, BatchNormalization, Activation,
Dropout, Conv1D, UpSampling1D, MaxPooling1D, ZeroPadding1D, LeakyReLU)
from keras.engine.topol... |
probml/pyprobml | notebooks/book2/04/rbm_contrastive_divergence.ipynb | mit | !pip install -qq optax
import numpy as np
import jax
from jax import numpy as jnp
from jax import grad, jit, vmap, random
try:
import optax
except ModuleNotFoundError:
%pip install -qq optax
import optax
try:
import tensorflow_datasets as tfds
except ModuleNotFoundError:
%pip install -qq tensorflo... |
enchantner/python-zero | lesson_2/Slides.ipynb | mit | %time "list(range(1000000)); print('ololo')"
"""
Explanation: Пакеты и окружение для Python
easy_install (setuptools) - старый менеджер пакетов (практически не используется)
pip - новый менеджер пакетов
virtualenv - установить конкретные версии пакетов локально
virtualenvwrapper - отличная обертка для virtualenv
Об... |
Karuntg/SDSS_SSC | Analysis_2020/sdss_gaia_matching_IZv2.ipynb | gpl-3.0 | # workhorse packages
import matplotlib.pyplot as plt
import numpy as np
# Data handling
from astropy.table import Table
from astropy.coordinates import SkyCoord
from astropy import units as u
from astropy.table import hstack
# for fits with log likelihood
import scipy
from scipy import stats
from scipy import optimi... |
dsacademybr/PythonFundamentos | Cap08/Notebooks/DSA-Python-Cap08-01-NumPy.ipynb | gpl-3.0 | # Versão da Linguagem Python
from platform import python_version
print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())
"""
Explanation: <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 8</font>
Download: http://github.com/dsacademybr
End of explanation
"""
# Impor... |
amogh3892/Context-based-sentence-classification-using-word2vec | main_sentence_classification.ipynb | apache-2.0 | # Importing all the required modules and the helper functions
import numpy as np
import urllib.request
from bs4 import BeautifulSoup
from nltk import sent_tokenize
from nltk import word_tokenize
import re
from gensim.models import Word2Vec
import pickle
# the following two modules are helper functions to generate ... |
computational-class/cjc2016 | code/08.05-gradient_descent.ipynb | mit | import numpy as np
# Size of the points dataset.
m = 20
# Points x-coordinate and dummy value (x0, x1).
X0 = np.ones((m, 1))
X1 = np.arange(1, m+1).reshape(m, 1)
X = np.hstack((X0, X1))
# Points y-coordinate
y = np.array([3, 4, 5, 5, 2, 4, 7, 8, 11, 8, 12,
11, 13, 13, 16, 17, 18, 17, 19, 21]).reshape(m, 1)
# The ... |
Neuroglycerin/neukrill-net-work | notebooks/model_run_and_result_analyses/Analyse alexnet_extra_layer_dropouts models.ipynb | mit | import pylearn2.utils
import pylearn2.config
import theano
import neukrill_net.dense_dataset
import neukrill_net.utils
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import holoviews as hl
%load_ext holoviews.ipython
import sklearn.metrics
cd ..
m = pylearn2.utils.serial.load("/disk/scratch/neu... |
mne-tools/mne-tools.github.io | stable/_downloads/6608d2f46fa33fc4dfd4a7f07bd9bdc9/10_ieeg_localize.ipynb | bsd-3-clause | # Authors: Alex Rockhill <aprockhill@mailbox.org>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD-3-Clause
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import nibabel as nib
import nilearn.plotting
from dipy.align import resample
import mne
from mne.datasets import fetch... |
slowvak/MachineLearningForMedicalImages | notebooks/Module 2 .ipynb | mit | %matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import os
import numpy as np
import matplotlib.pyplot as plt
import pylab
from mpl_toolkits.mplot3d import Axes3D
from sklearn import svm
import pandas as pd
from matplotlib.colors import ListedColormap
from sklearn.model_selection import StratifiedSh... |
phoebe-project/phoebe2-docs | 2.2/examples/sun_earth.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.1,<2.2"
"""
Explanation: Sun-Earth System
NOTE: planets are currently under testing and not yet supported
Setup
Let's first make sure we have the latest version of PHOEBE 2.1 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the l... |
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