repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
pycam/python-basic | python_basic_1_2.ipynb | unlicense | i = -7
j = 123
print(i, j)
"""
Explanation: An introduction to solving biological problems with Python
Day 1 - Session 2: Simple data types
Simple data types: Integers, Floats, Strings and Booleans
Comments
Arithmetic
Exercises 1.2.1
Saving code in files
Exercises 1.2.2
Simple data types
Python (and computers in gen... |
sjsrey/pysal | notebooks/explore/pointpats/Quadrat_statistics.ipynb | bsd-3-clause | import pysal.lib as ps
import numpy as np
from pysal.explore.pointpats import PointPattern, as_window
from pysal.explore.pointpats import PoissonPointProcess as csr
%matplotlib inline
import matplotlib.pyplot as plt
"""
Explanation: Quadrat Based Statistical Method for Planar Point Patterns
Authors: Serge Rey s&#... |
ReactiveX/RxPY | notebooks/reactivex.io/Marble Diagrams.ipynb | mit | %run startup.py
"""
Explanation: Marble Diagrams with RxPY
This is a fantastic feature to produce and visualize streams and to verify how various operators work on them.
Have also a look at rxmarbles for interactive visualisations.
ONE DASH IS <font size="40px">100</font> MILLISECONDS!
End of explanation
"""
rst(O.f... |
rflamary/POT | docs/source/auto_examples/plot_UOT_1D.ipynb | mit | # Author: Hicham Janati <hicham.janati@inria.fr>
#
# License: MIT License
import numpy as np
import matplotlib.pylab as pl
import ot
import ot.plot
from ot.datasets import make_1D_gauss as gauss
"""
Explanation: 1D Unbalanced optimal transport
This example illustrates the computation of Unbalanced Optimal transport
u... |
mne-tools/mne-tools.github.io | dev/_downloads/c822037c0666a082e89228795c70bde1/10_background_stats.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD-3-Clause
from functools import partial
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # noqa, analysis:ignore
import mne
from mne.stats import (ttest_1samp_no_p, bonferroni_correctio... |
mayank-johri/LearnSeleniumUsingPython | Section 3 - Machine Learning/libs/core_libs/scipy/SciPy.ipynb | gpl-3.0 | import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
"""
Explanation: SciPy
SciPy is a collection of mathematical algorithms and convenience functions built on the Numpy extension of Python. It adds significant power to the interactive Python session by providing the user with high-level comman... |
adityaka/misc_scripts | python-scripts/data_analytics_learn/link_pandas/Ex_Files_Pandas_Data/Exercise Files/02_05/Final/Operations.ipynb | bsd-3-clause | pd.set_option('display.precision', 2)
sample_df_2.describe()
"""
Explanation: descriptive statistics
End of explanation
"""
sample_df_2.mean()
"""
Explanation: column mean
End of explanation
"""
sample_df_2.mean(1)
"""
Explanation: row mean
documentation: http://pandas.pydata.org/pandas-docs/stable/generated/pan... |
JackDi/phys202-2015-work | assignments/assignment04/TheoryAndPracticeEx01.ipynb | mit | from IPython.display import Image
"""
Explanation: Theory and Practice of Visualization Exercise 1
Imports
End of explanation
"""
# Add your filename and uncomment the following line:
Image(filename='good data viz.png')
"""
Explanation: Graphical excellence and integrity
Find a data-focused visualization on one of ... |
openai/openai-python | examples/finetuning/olympics-3-train-qa.ipynb | mit | import openai
import pandas as pd
df = pd.read_csv('olympics-data/olympics_qa.csv')
olympics_search_fileid = "file-c3shd8wqF3vSCKaukW4Jr1TT"
df.head()
"""
Explanation: 3. Train a fine-tuning model specialized for Q&A
This notebook will utilize the dataset of context, question and answer pairs to additionally create ad... |
hvillanua/deep-learning | tv-script-generation/dlnd_tv_script_generation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
"""
Explanation: TV Script Generation
In this project, you'll generate your own Simpsons TV scrip... |
AssembleSoftware/IoTPy | examples/ExamplesOfIncrementalKmeans.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns; sns.set() # for plot styling
import numpy as np
import threading
import time
from sklearn.datasets.samples_generator import make_blobs
from sklearn.cluster import KMeans
import sys
sys.path.append("../")
from IoTPy.core.stream import Stream, St... |
james4424/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... |
yangw1234/BigDL | docs/readthedocs/source/doc/Serving/Example/cluster-serving-http-example.ipynb | apache-2.0 | import tensorflow as tf
import os
import PIL
tf.__version__
# Obtain data from url:"https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip"
zip_file = tf.keras.utils.get_file(origin="https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip",
fname="... |
smharper/openmc | examples/jupyter/cad-based-geometry.ipynb | mit | import urllib.request
fuel_pin_url = 'https://tinyurl.com/y3ugwz6w' # 1.2 MB
teapot_url = 'https://tinyurl.com/y4mcmc3u' # 29 MB
def download(url):
"""
Helper function for retrieving dagmc models
"""
u = urllib.request.urlopen(url)
if u.status != 200:
raise RuntimeError("Failed to dow... |
evanmason/OceanData_NoteBooks | Read_CORA_dataset.ipynb | gpl-3.0 | datafile = "/home/ctroupin/DataOceano/Coriolis/CORA/NetCDF/OA_CORA4.1_20131215_dat_PSAL.nc"
"""
Explanation: Salinity from CORA dataset
The data can be obtained from Coriolis FTP at ftp://ftp1.ifremer.fr/Core/INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b.
As an illustration, we will work with the OA data for December 2013.... |
gsentveld/lunch_and_learn | notebooks/Get_zip_files.ipynb | mit | import os
from dotenv import load_dotenv, find_dotenv
# find .env automagically by walking up directories until it's found
dotenv_path = find_dotenv()
# load up the entries as environment variables
load_dotenv(dotenv_path)
"""
Explanation: Using environment variables saved in a .env file
<code>dotenv</code> is a pac... |
lit-mod-viz/middlemarch-critical-histories | notebooks/bpo-analysis.ipynb | gpl-3.0 | import spacy
import pandas as pd
%matplotlib inline
from ast import literal_eval
import numpy as np
import re
import json
from nltk.corpus import names
from collections import Counter
from matplotlib import pyplot as plt
plt.rcParams["figure.figsize"] = [16, 6]
plt.style.use('ggplot')
nlp = spacy.load('en')
with open... |
OpenGenus/cosmos | code/artificial_intelligence/src/autoenncoder/Convolutional_Autoencoder.ipynb | gpl-3.0 | %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... |
hetland/python4geosciences | materials/ST_images.ipynb | mit | import requests # from webscraping
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib
import cmocean
import cartopy
from PIL import Image # this is the pillow package
from skimage import color
from scipy import ndimage
from io import BytesIO
"""
Explanation: Images
Images are ju... |
hbutler/InverseCCP | 2 - Generate coupon probabilities - part 2.ipynb | mit | n = 20 #number of coupons
mu = 1/n #this is the mean coupon probability
sigma = mu/2 #this is the std dev parameter we will play around with - it seems to make sense to express it in terms of the mean
x = np.arange(n)+0.5 #arange goes from 0 to n-1, and I want it to go from 1 to n
p_x = stat.norm.ppf(x/(n), mu, sigma)
... |
gfeiden/Notebook | Projects/mlt_calib/float_Y_float_alpha.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
data = np.genfromtxt('data/run05_kde_props_tmp3.txt')
data = np.array([x for x in data if x[30] > -0.5]) # remove stars that our outside of the model grid
"""
Explanation: Float $Y_i$ & Float $\alpha_{MLT}$
First, we load the appropriate libraries ... |
jmhsi/justin_tinker | data_science/courses/deeplearning2/neural-sr.ipynb | apache-2.0 | %matplotlib inline
import importlib
import utils2; importlib.reload(utils2)
from utils2 import *
from scipy.optimize import fmin_l_bfgs_b
from scipy.misc import imsave
from keras import metrics
from vgg16_avg import VGG16_Avg
from bcolz_array_iterator import BcolzArrayIterator
limit_mem()
path = '/data/jhoward/ima... |
zrhans/python | exemplos/dapp-bc/Estacoes-ATMOS-Copy1.ipynb | gpl-2.0 | import sys
import numpy as np
import pandas as pd
print(sys.version) # Versao do python - Opcional
print(np.__version__) # VErsao do modulo numpy - Opcional
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
import datetime
import time
#?pd.date_range
#rng = pd.date_range('1/1/2011', periods=90, freq... |
harmsm/pythonic-science | chapters/01_simulation/01_scipy-stats.ipynb | unlicense | x = np.arange(-10,10,0.2)
y = np.cos(x)
noisy_y = y + np.random.normal(0,0.3,len(y))
plt.plot(x,y)
plt.plot(x,noisy_y)
"""
Explanation: <cont style="margin:auto">
<img src="https://s-media-cache-ak0.pinimg.com/originals/33/07/24/330724abbfde900c94af94ed0fbc5f9f.jpg" height="85%" width="85%" />
</font>
<ul>
<li><... |
bjshaw/phys202-2015-work | assignments/assignment03/NumpyEx01.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import antipackage
import github.ellisonbg.misc.vizarray as va
"""
Explanation: Numpy Exercise 1
Imports
End of explanation
"""
def checkerboard(size):
"""Return a 2d checkboard of 0.0 and 1.0 as a NumPy array"""
che... |
mne-tools/mne-tools.github.io | 0.19/_downloads/36ac16a286b47b66f1b51a959c65b5b9/plot_stats_cluster_time_frequency_repeated_measures_anova.ipynb | bsd-3-clause | # Authors: Denis Engemann <denis.engemann@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.time_frequency import tfr_morlet
from mne.stats import f_... |
kabrapratik28/Stanford_courses | cs231n/2016/assignment3/ImageGradients.ipynb | apache-2.0 | # As usual, a bit of setup
import time, os, json
import numpy as np
import skimage.io
import matplotlib.pyplot as plt
from cs231n.classifiers.pretrained_cnn import PretrainedCNN
from cs231n.data_utils import load_tiny_imagenet
from cs231n.image_utils import blur_image, deprocess_image
%matplotlib inline
plt.rcParams... |
zzsza/TIL | scikit-learn/Chapter 2. Supervised Learning.ipynb | mit | import mglearn
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
X, y = mglearn.datasets.make_forge()
print(X)
print(y)
mglearn.discrete_scatter(X[:,0], X[:, 1], y)
plt.legend(["class 0", "class 1"], loc=4)
plt.xlabel("1st feature")
plt.ylabel("2nd feature")
print("X.shape :... |
drericstrong/Blog | 20161212_Predicting Abalone Rings Part 2.ipynb | agpl-3.0 | import pandas as pd
import numpy as np
import seaborn as sns
from scipy import stats
import matplotlib.pyplot as plt
from sklearn import linear_model
from sklearn.decomposition import PCA
from sklearn.metrics import r2_score, mean_absolute_error
from sklearn.model_selection import train_test_split
%matplotlib inline
ab... |
antoniomezzacapo/qiskit-tutorial | qiskit/basics/getting_started_with_qiskit_terra.ipynb | apache-2.0 | import numpy as np
from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister
from qiskit import execute
"""
Explanation: <img src="../../images/qiskit-heading.gif" alt="Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook" width="500 px" align="left">
G... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/launching_into_ml/solutions/TrainingWithXGBoostInCMLE.ipynb | apache-2.0 | %env PROJECT_ID <YOUR_PROJECT_ID>
%env BUCKET_ID <YOUR_BUCKET_ID>
%env REGION us-central1
%env TRAINER_PACKAGE_PATH ./census_training
%env MAIN_TRAINER_MODULE census_training.train
%env JOB_DIR gs://<YOUR_BUCKET_ID>/xgb_job_dir
%env RUNTIME_VERSION 2.5
%env PYTHON_VERSION 3.7
! mkdir census_training
"""
Explanation: X... |
GoogleCloudPlatform/vertex-ai-samples | community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/step_by_step_sdk_tf_agents_bandits_movie_recommendation/step_by_step_sdk_tf_agents_bandits_movie_recommendation.ipynb | apache-2.0 | import os
# The Google Cloud Notebook product has specific requirements
IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists("/opt/deeplearning/metadata/env_version")
# Google Cloud Notebook requires dependencies to be installed with '--user'
USER_FLAG = ""
if IS_GOOGLE_CLOUD_NOTEBOOK:
USER_FLAG = "--user"
! pip3 install {... |
mari-linhares/tensorflow-workshop | code_samples/RNN/weather_prediction/.ipynb_checkpoints/model-checkpoint.ipynb | apache-2.0 | #!/usr/bin/env python
# Copyright 2017 Google Inc. 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 require... |
vrbala/timeseries-analysis | Analysis.ipynb | gpl-2.0 | import pandas as pd
import os
os.listdir('.')
"""
Explanation: Problem
Given the time series of CPU consumption (cpu time) of a process, can we predict eta for a similar process in future? And can we answer questions like 1) is the process running slower (consuming less CPU) than how it is supposed to be? 2) Given the... |
ajhenrikson/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."""
... |
jmschrei/pomegranate | tutorials/C_Feature_Tutorial_2_Out_Of_Core_Learning.ipynb | mit | %matplotlib inline
import time
import pandas
import random
import numpy
import matplotlib.pyplot as plt
import seaborn; seaborn.set_style('whitegrid')
import itertools
from pomegranate import *
random.seed(0)
numpy.random.seed(0)
numpy.set_printoptions(suppress=True)
%load_ext watermark
%watermark -m -n -p numpy,sci... |
tensorflow/cloud | g3doc/tutorials/hp_tuning_wide_and_deep_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... |
authman/DAT210x | Module3/Module3 - Lab3.ipynb | mit | import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
# Look pretty...
# matplotlib.style.use('ggplot')
plt.style.use('ggplot')
"""
Explanation: DAT210x - Programming with Python for DS
Module3 - Lab3
End of explanation
"""
# .. your code here ..
"""
Explanation: Load up the wheat seeds dataset in... |
nntisapeh/intro_programming | notebooks/introducing_functions.ipynb | mit | # Let's define a function.
def function_name(argument_1, argument_2):
# Do whatever we want this function to do,
# using argument_1 and argument_2
# Use function_name to call the function.
function_name(value_1, value_2)
"""
Explanation: Introducing Functions
One of the core principles of any programming language ... |
rayjustinhuang/DataAnalysisandMachineLearning | Natural Language Processing - SMS Spam Detection.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
sns.set()
import nltk
messages = pd.read_csv('SMS Spam Collection/SMSSpamCollection',sep='\t',names=['Label','Message'])
messages.head()
messages['Length'] = messages['Message'].apply(len)
messages.head... |
ES-DOC/esdoc-jupyterhub | notebooks/cnrm-cerfacs/cmip6/models/sandbox-1/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cnrm-cerfacs', 'sandbox-1', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: CNRM-CERFACS
Source ID: SANDBOX-1
Topic: Atmos
Sub-Topics: Dynamical Core, R... |
arcyfelix/Courses | 18-11-22-Deep-Learning-with-PyTorch/02-Introduction to PyTorch/Part 3 - Training Neural Networks.ipynb | apache-2.0 | import torch
from torch import nn
import torch.nn.functional as F
from torchvision import datasets, transforms
# Define a transform to normalize the data
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
... |
kabrapratik28/Stanford_courses | cs231n/assignment3/StyleTransfer-PyTorch.ipynb | apache-2.0 | import torch
import torch.nn as nn
from torch.autograd import Variable
import torchvision
import torchvision.transforms as T
import PIL
import numpy as np
from scipy.misc import imread
from collections import namedtuple
import matplotlib.pyplot as plt
from cs231n.image_utils import SQUEEZENET_MEAN, SQUEEZENET_STD
%m... |
AnyBody-Research-Group/AnyPyTools | docs/Tutorial/03_Working_with_output_from_Anybody.ipynb | mit | from anypytools import AnyMacro, AnyPyProcess, macro_commands as mc
macro_list = [
[
mc.Load("Knee.any"),
mc.SetValue("Main.MyModel.PatellaLigament.DriverPos", 0.02 + i * 0.01),
mc.OperationRun("Main.MyStudy.InverseDynamics"),
mc.Dump("Main.MyStudy.Output.Abscissa.t"),
... |
roaminsight/roamresearch | BlogPosts/Modern_TensorFlow/modern-tensorflow.ipynb | apache-2.0 | __author__ = 'Guillaume Genthial'
__date__ = '2018-09-22'
"""
Explanation: Good practices in Modern Tensorflow for NLP
End of explanation
"""
from distutils.version import LooseVersion
import sys
if LooseVersion(sys.version) < LooseVersion('3.4'):
raise Exception('You need python>=3.4, but you have {}'.format(s... |
the-new-sky/Kadot | RaD/new_word_vectorization.ipynb | mit | tokenizer = lambda txt: txt.split(' ')
tokenizer("Say hello to faster vectorisation !")
"""
Explanation: Faster co-occurence vectorization
The goal of this notebook is to write a faster way to implement the co-ocurence matrix vectorizer.
To begin, let's write a toy tokenizer.
End of explanation
"""
from urllib.requ... |
tritemio/multispot_paper | out_notebooks/Multi-spot vs usALEX FRET histogram comparison-out-12d.ipynb | mit | data_id = '17d'
ph_sel_name = "None"
data_id = "12d"
"""
Explanation: Executed: Mon Mar 27 22:24:18 2017
Duration: 12 seconds.
End of explanation
"""
from fretbursts import *
sns = init_notebook()
import os
import pandas as pd
from IPython.display import display, Math
import lmfit
print('lmfit version:', lmfit._... |
JoeriHermans/tensorflow-scripts | scripts/adverserial-bayesian-optimization/abo.ipynb | gpl-3.0 | !date
"""
Explanation: Adverserial Bayesian Optimization
Joeri R. Hermans and Gilles Louppe
End of explanation
"""
import torch
import numpy as np
import math
import random
import torch.nn.functional as F
import matplotlib.pyplot as plt
from sklearn import gaussian_process
from sklearn.gaussian_process.kernels impor... |
karlstroetmann/Algorithms | Python/Chapter-07/2-3-Trees-Visualization.ipynb | gpl-2.0 | import graphviz as gv
"""
Explanation: 2-3 Trees
This notebook contains the code to visualize 2-3 trees.
End of explanation
"""
class TwoThreeTree:
sNodeCount = 0
def __init__(self):
TwoThreeTree.sNodeCount += 1
self.mID = TwoThreeTree.sNodeCount
def getID(self):
ret... |
IST256/learn-python | content/lessons/06-Strings/Slides.ipynb | mit | def doit(a,b):
return a+b
x = 4
y = 3
z = doit(x,x)
print(z)
"""
Explanation: IST256 Lesson 06
Strings
Zybook Ch6
P4E Ch6
Links
Participation: https://poll.ist256.com <= AZURE IS DOWN!
Ask in your Zoom Chat
Agenda
Homework 05
Quick Review of the Solution
Strings
- Strings are immutable sequence of character... |
statsmodels/statsmodels.github.io | v0.12.1/examples/notebooks/generated/statespace_dfm_coincident.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
np.set_printoptions(precision=4, suppress=True, linewidth=120)
from pandas_datareader.data import DataReader
# Get the datasets from FRED
start = '1979-01-01'
end = '2014-12-01'
indprod = DataReade... |
quantumlib/ReCirq | recirq/otoc/loschmidt/tilted_square_lattice/analysis-walkthrough.ipynb | apache-2.0 | %matplotlib inline
from matplotlib import pyplot as plt
# Set up reasonable defaults for figure fonts
import matplotlib
matplotlib.rcParams.update(**{
'axes.titlesize': 14,
'axes.labelsize': 14,
'xtick.labelsize': 12,
'ytick.labelsize': 12,
'legend.fontsize': 12,
'legend.title_fontsize': 12,
... |
tpin3694/tpin3694.github.io | machine-learning/f1_score.ipynb | mit | # Load libraries
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
"""
Explanation: Title: F1 Score
Slug: f1_score
Summary: How to evaluate a Python machine learning using F1 score.
Date: 2017-09-15 12:00
Category:... |
tensorflow/docs-l10n | site/en-snapshot/probability/examples/TensorFlow_Distributions_Tutorial.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# 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, sof... |
wenduowang/git_home | python/MSBA/intro/HW3/.ipynb_checkpoints/HW3_wenduowang_20160728-checkpoint.ipynb | gpl-3.0 | gold = pd.read_table("gold.txt", names=["url", "category"]).dropna()
labels = pd.read_table("labels.txt", names=["turk", "url", "category"]).dropna()
"""
Explanation: Question 1: Read in data
Read in the data from "gold.txt" and "labels.txt".
Since there are no headers in the files, names parameter should be set expli... |
UltronAI/Deep-Learning | 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
from __future__ import print_function
# This is a bit of magic to make matplotlib figures appear inline in the notebook
# rather than in a new window.
%matplotlib inlin... |
pligor/predicting-future-product-prices | 04_time_series_prediction/14_price_history_seq2seq-native.ipynb | agpl-3.0 | from __future__ import division
import tensorflow as tf
from os import path
import numpy as np
import pandas as pd
import csv
from sklearn.model_selection import StratifiedShuffleSplit
from time import time
from matplotlib import pyplot as plt
import seaborn as sns
from mylibs.jupyter_notebook_helper import show_graph
... |
deculler/DataScienceTableDemos | ProbabilityBirthdaySurprise.ipynb | bsd-2-clause | # HIDDEN
from datascience import *
%matplotlib inline
import matplotlib.pyplot as plots
plots.style.use('fivethirtyeight')
import numpy as np
# datascience version number of last run of this notebook
version.__version__
"""
Explanation: This notebook illustrates the use of tables in conveying the combination of infere... |
Caranarq/01_Dmine | Datasets/Pigoo/Pigoo_Desagregacion.ipynb | gpl-3.0 | # Librerias utilizadas
import pandas as pd
import sys
import urllib
module_path = r'D:\PCCS\01_Dmine\Scripts'
if module_path not in sys.path:
sys.path.append(module_path)
from SUN.asignar_sun import asignar_sun
from SUN_integridad.SUN_integridad import SUN_integridad
from SUN.CargaSunPrincipal import getsun
# Con... |
feroda/lessons-python4beginners | P4B - Capitolo 1.ipynb | agpl-3.0 | # This is hello_who.py
def hello(who):
print("Hello {}!".format(who))
if __name__ == "__main__":
hello("mamma")
"""
Explanation: Python2 for beginners (P4B)
<p style="text-align: center;">Luca Ferroni <luca@befair.it></p>
<p style="text-align: center;">http://www.befair.it<br />**Software Libero per i terr... |
tensorflow/docs-l10n | site/ja/hub/tutorials/semantic_approximate_nearest_neighbors.ipynb | apache-2.0 | # Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
dh7/ML-Tutorial-Notebooks | rnn_face_tests/LFW Model Face V0.3.ipynb | bsd-2-clause | %matplotlib notebook
import matplotlib
import matplotlib.pyplot as plt
from IPython.display import Image
"""
Explanation: RNN for pictures genaration
This notebook is an experiment. I tryed to generate a picture pixel by pixel using an RNN.
each pixel can be black or white.
WIP
Import needed for Jupiter
End of expla... |
mne-tools/mne-tools.github.io | 0.18/_downloads/b7f6f07283b8cae86edea831b037ebca/plot_object_raw.ipynb | bsd-3-clause | import mne
import os.path as op
from matplotlib import pyplot as plt
"""
Explanation: The :class:~mne.io.Raw data structure: continuous data
Continuous data is stored in objects of type :class:~mne.io.Raw.
The core data structure is simply a 2D numpy array (channels × samples)
(in memory or loaded on demand) combined ... |
pybel/pybel-notebooks | integration/Parsing CBN Database JSON Graph Format.ipynb | apache-2.0 | import json
import requests
import os
import time
import networkx as nx
import pybel
from pybel.constants import *
import pybel_tools
from pybel_tools.visualization import to_jupyter
pybel.__version__
pybel_tools.__version__
time.asctime()
"""
Explanation: Parsing the Causal Biological Network Database
Author: Ch... |
wchapman/wchapman.github.io | assets/2015-10-10-SPyNN-DynamicalSystems/2015-10-10-SPyNN-DynamicalSystems.ipynb | mit | # Setup the environment
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Set Izhikevich parameters
k = 0.75; C = 200; v_r = -60
v_t = -45; a = 0.01; b = 15 ; c = -50
d = 100; v_peak = 100
#
T = 500; dt = 0.01
t = np.arange(0,T,dt,dtype=np.float)
v = v_r; u = 0 #initial state
# lists to appen... |
emmajagu/contiamo-client-python | demo-notebooks/purchase-frequency.ipynb | mit | import pandas as pd
import contiamo
"""
Explanation: Purchase frequency
In this notebook we create a table grouping transaction information by customers’ purchase frequency. This is done with functions from the pandas librairie such as df.groupby() and df.cut().
End of explanation
"""
transactions = %contiamo query ... |
limpapud/data_science_tutorials_projects | DataScience_Tutorials/AZ/Pandas_SQL.ipynb | mit | import pandas as pd
"""
Explanation: Pandas ilə SQLvari sorğuların yazılması
SQL ilə heç olmasa qismən tanışlığı olan adam "SQL-in əsasların bir neçə saat ərzində öyrənib ilk sorğuları yazmaq olar" cümləsi ilə razılaşar (hər halda mən bu cür fikirləşirəm). Python ilə də eynən, bu dil ən sadə və proqramlaşdırmanı öyrə... |
piyueh/SEM-Toolbox | solutions/chapter02/exercise03.ipynb | mit | import numpy
import re
from matplotlib import pyplot
from matplotlib import colors
from IPython.display import Latex, Math, display
% matplotlib inline
import os, sys
sys.path.append(os.path.split(os.path.split(os.getcwd())[0])[0])
import utils.poly as poly
import utils.quadrature as quad
import utils.elems.one_d as ... |
gklambauer/SelfNormalizingNetworks | SelfNormalizingNetworks_MLP_MNIST.ipynb | gpl-3.0 | import tensorflow as tf
import numpy as np
from sklearn.preprocessing import StandardScaler
from __future__ import absolute_import, division, print_function
import numbers
from tensorflow.contrib import layers
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorf... |
ThomasProctor/Slide-Rule-Data-Intensive | pycon-pandas-tutorial-master/Exercises-2.ipynb | mit | titles['title'].value_counts()[:10]
"""
Explanation: What are the ten most common movie names of all time?
End of explanation
"""
titles[(titles['year']<1940)&(titles['year']>=1930)]['year'].value_counts()
"""
Explanation: Which three years of the 1930s saw the most films released?
End of explanation
"""
dec=((ti... |
fcollonval/coursera_data_visualization | KMeansCluster.ipynb | mit | # Magic command to insert the graph directly in the notebook
%matplotlib inline
# Load a useful Python libraries for handling data
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from IPython.display import Markdown, display
from sklearn.cross_validation import train_test_s... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/06_structured/2_sample.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.1
# change these to try this notebook out
BUCKET = 'cloud-training-demos-ml'
PROJECT = 'cloud-training-demos'
REGION = 'us-central1'
import os
os.environ['BUCKET'... |
hglanz/phys202-2015-work | assignments/assignment05/MatplotlibEx03.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Matplotlib Exercise 3
Imports
End of explanation
"""
def well2d(x, y, nx, ny, L=1.0):
"""Compute the 2d quantum well wave function."""
psi = (2 / L) * np.sin(nx * np.pi * x / L) * np.sin( ny * np.pi * y / L)
return psi... |
sdpython/ensae_teaching_cs | _doc/notebooks/sklearn_ensae_course/07_application_to_face_recognition.ipynb | mit | %matplotlib inline
import numpy as np
from matplotlib import pyplot as plt
"""
Explanation: 2A.ML101.7: Example from Image Processing
Here we'll take a look at a simple facial recognition example.
Source: Course on machine learning with scikit-learn by Gaël Varoquaux
End of explanation
"""
from sklearn import datase... |
4dsolutions/Python5 | Polyhedrons.ipynb | mit | from qrays import Vector # see Chapter 6
class Polyhedron:
def __init__(self, name, volume, faces : set,
vertexes : dict, center = Vector((0,0,0))):
self.name = name
self.vertexes = vertexes
self.volume = volume
self.faces = faces
self.edges = ... |
SciTools/courses | course_content/iris_course/3.Subcube_Extraction.ipynb | gpl-3.0 | import iris
"""
Explanation: Iris introduction course
3. Subcube Extraction
Learning outcome: by the end of this section, you will be able to use various Iris facilities to extract sub-sections of a dataset.
Duration: 1 hour
Overview:<br>
3.1 Indexing<br>
3.2 Constraints and Extraction<br>
3.3 Iterating Over a Cube<br... |
darkomen/TFG | ipython_notebooks/06_regulador_experto/.ipynb_checkpoints/ensayo6-checkpoint.ipynb | cc0-1.0 | #Importamos las librerías utilizadas
import numpy as np
import pandas as pd
import seaborn as sns
#Mostramos las versiones usadas de cada librerías
print ("Numpy v{}".format(np.__version__))
print ("Pandas v{}".format(pd.__version__))
print ("Seaborn v{}".format(sns.__version__))
#Abrimos el fichero csv con los datos... |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_movement_compensation.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
from os import path as op
import mne
from mne.preprocessing import maxwell_filter
print(__doc__)
data_path = op.join(mne.datasets.misc.data_path(verbose=True), 'movement')
pos = mne.chpi.read_head_pos(op.join(data_path, 'simulated_quats.p... |
jinzekid/codehub | python/day6/2.3 Python语言基础.ipynb | gpl-3.0 | a = 5; b = 6; c = 7
"""
Explanation: 2.3 Python语言基础
1 语言语义（Language Semantics）
缩进，而不是括号
Python使用空格（tabs or spaces)来组织代码结构，而不是像R，C++，Java那样用括号。
建议使用四个空格来作为默认的缩进，设置tab键为四个空格
另外可以用分号隔开多个语句：
End of explanation
"""
result = f(x, y, z)
"""
Explanation: 所有事物都是对象（object）
在python中，number，string，data structure，function，class... |
vallis/libstempo | demo/libstempo-demo.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
from __future__ import print_function
import sys, math, numpy as N, matplotlib.pyplot as P
"""
Explanation: libstempo tutorial: basic functionality
Michele Vallisneri, vallis@vallis.org; latest revision: 2016/10/12 for v2.3 revision
End of explanation
... |
rubensfernando/mba-analytics-big-data | Python/2016-07-25/aula3-parte3-dataframe.ipynb | mit | import pandas as pd
import numpy as np
"""
Explanation: DataFrame
Como vimos DataFrame é um array 2D com rótulos.
Os tipos das colunas podem ser heterogêneas (de diversos tipos). Ele tem as seguintes propriedades:
Conceitualmente é semelhante a uma tabela ou planilha de dados.
Colunas podem ser de diferentes tipos: f... |
tschinz/iPython_Workspace | 02_WP/General/PrintHead_Calculations.ipynb | gpl-2.0 | import numpy as np
resolutions = [150, 360, 600, 1200, 2400, 4800] # dpi
inch2cm = 2.54 # cm/inch
nbrOfSubpixels = 32
# Calulation Pixel Pinch
pixel_pitch = np.empty(shape=[len(resolutions)], dtype=np.float64) # um
for i in range(len(resolutions)):
pixel_pitch[i] = (inch2cm/resolutions[i])*10000
# Calcula... |
prabhath6/Data-analysis-of-titanic-using-python | Titanic Intro project.ipynb | mit | # plotting library
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
%matplotlib inline
# quick look at the sex of people on the titanic
""" we will use factor plot for this which takes a coloum name and divide on the basis of the avaliable data. """
sns.factorplot('Sex', data=titanic_df)
#... |
bgalbraith/bandits | notebooks/Stochastic Bandits - Value Estimation.ipynb | apache-2.0 | %matplotlib inline
import os
import sys
module_path = os.path.abspath(os.path.join('..'))
if module_path not in sys.path:
sys.path.append(module_path)
import bandits as bd
"""
Explanation: Stochastic Multi-Armed Bandits - Value Estimation
These examples come from Chapter 2 of Reinforcement Learning: An Introducti... |
giosans/Fundamentals-of-Digital-Image-and-Video-Processing-course | week8.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
p = np.arange(1,1000)/1000.
H = -p*np.log2(p) - (1-p)*np.log2(1-p)
fig=plt.figure(figsize=(4, 4))
ax1=plt.subplot(1, 1, 1)
plt.plot(p,H)
plt.title('Entropy with a two values alphabet')
plt.ylabel('H')
plt.xlabel('p')
"""
Explanation: week8
Lossle... |
thehackerwithin/berkeley | code_examples/SQL/SQL_Tutorial-0.ipynb | bsd-3-clause | # imports
import io # we'll need this way later
import os
import sqlite3 # this is the module that binds to SQLite
import numpy as np # never know when you might need NumPy, oh, right, always!
import pandas as pd # you'll see why we can use this later
DBFILE = 'sqlite3.db' # this will be our database
BASEDIR = %p... |
hparik11/Deep-Learning-Nanodegree-Foundation-Repository | tensorboard/Anna_KaRNNa_Summaries.ipynb | mit | import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
"""
Explanation: Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network is base... |
zzsza/TIL | python/sacred_tutorial(experiment-support).ipynb | mit | from numpy.random import permutation
from sklearn import svm, datasets
from sacred import Experiment
ex = Experiment('iris_rbf_svm', interactive=True)
# jupyter notebook일 경우 interactive=True, python 스크립트라면 없어도 됨
@ex.config
def cfg():
C = 1.0
gamma = 0.7
# ex.automain은 python 스크립트일 때 사용
@ex.main
def run(C, gam... |
whitead/numerical_stats | project/type3_examples/aa_sequences.ipynb | gpl-3.0 | !pip install biopython
import Bio
from Bio.pairwise2 import format_alignment
for a in Bio.pairwise2.align.globalxx("ACCGT", "ACG"):
print(format_alignment(*a))
"""
Explanation: Analyzing Genetic Mutations in Ribosomal Protein S12
A Statistical Analysis
CHE 116 Numerical Methods and Statistics
Dominic Giambra
Abs... |
shareactorIO/pipeline | source.ml/jupyterhub.ml/notebooks/zz_old/TensorFlow/HvassLabsTutorials/03_PrettyTensor.ipynb | apache-2.0 | from IPython.display import Image
Image('images/02_network_flowchart.png')
"""
Explanation: TensorFlow Tutorial #03
PrettyTensor
by Magnus Erik Hvass Pedersen
/ GitHub / Videos on YouTube
Introduction
The previous tutorial showed how to implement a Convolutional Neural Network in TensorFlow, which required low-level k... |
iRipVanWinkle/ml | Data Science UA - September 2017/Lecture 04 - Overview of Linear Algebra and Matrix Computations/Nonlinear_Equations.ipynb | mit | import numpy as np
x1 = np.linspace(-4,4,100) # 100 linearly spaced numbers
y1 = -x1**3+1
y2 = np.linspace(-4,4,100) # 100 linearly spaced numbers
x2 = y2**3+1
import matplotlib.pyplot as plt
%matplotlib inline
# compose plot
plt.plot(x1,y1)
plt.plot(x2,y2)
plt.xlim(-4.0, 4.0)
plt.ylim(-4.0, 4.0)
plt.xlabel("x")
pl... |
jsharpna/DavisSML | lectures/lecture5/lecture5.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
## Explore Turkish stock exchange dataset
tse = pd.read_excel('../../data/data_akbilgic.xlsx',skiprows=1)
tse = tse.rename(columns={'ISE':'TLISE','ISE.1':'USDISE'})
def const_wave(T,a,b):
wave = np.zeros(T)
s1 = (b-a) // 2
s2 = (b-a)... |
therealAJ/python-sandbox | data-science/learning/ud1/DataScience/TopPages.ipynb | gpl-3.0 | import re
format_pat= re.compile(
r"(?P<host>[\d\.]+)\s"
r"(?P<identity>\S*)\s"
r"(?P<user>\S*)\s"
r"\[(?P<time>.*?)\]\s"
r'"(?P<request>.*?)"\s'
r"(?P<status>\d+)\s"
r"(?P<bytes>\S*)\s"
r'"(?P<referer>.*?)"\s'
r'"(?P<user_agent>.*?)"\s*'
)
"""
Explanation: Cleaning Your Data
Let'... |
monicathieu/cu-psych-r-tutorial | content/tutorials/python/3-datamanipulation/.ipynb_checkpoints/index-checkpoint.ipynb | mit | # load packages we will be using for this lesson
import pandas as pd
"""
Explanation: title: "Data Manipulation in Python"
subtitle: "CU Psych Scientific Computing Workshop"
weight: 1301
tags: ["core", "python"]
Goals of this Lesson
Students will learn:
How to group and categorize data in Python
How to generative de... |
nbokulich/short-read-tax-assignment | ipynb/runtime/analysis.ipynb | bsd-3-clause | from os.path import expandvars
from tax_credit.plotting_functions import (lmplot_from_data_frame, calculate_linear_regress)
import pandas as pd
"""
Explanation: Evaluate computational runtimes
The purpose of this notebook is to analyze and plot computational runtimes generated for a list of taxonomy assignment methods... |
vinitsamel/udacitydeeplearning | batch-norm/Batch_Normalization_Lesson.ipynb | mit | # Import necessary packages
import tensorflow as tf
import tqdm
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Import MNIST data so we have something for our experiments
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
"... |
farfan92/SpringBoard- | statistics project 3/sliderule_dsi_inferential_statistics_exercise_3.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import bokeh.plotting as bkp
from mpl_toolkits.axes_grid1 import make_axes_locatable
%matplotlib inline
# read in readmissions data provided
hospital_read_df = pd.read_csv('data/cms_hospital_readmissions.csv')
"""
Explanation: Hospital readmission... |
ellisonbg/leafletwidget | examples/LegendControl.ipynb | mit | from ipyleaflet import Map, LegendControl
mymap = Map(center=(-10,-45), zoom=4)
mymap
"""
Explanation: Legend: How to use
step 1: create an ipyleaflet map
End of explanation
"""
a_legend = LegendControl({"low":"#FAA", "medium":"#A55", "High":"#500"}, name="Legend", position="bottomright")
mymap.add_control(a_lege... |
jseabold/statsmodels | examples/notebooks/gee_nested_simulation.ipynb | bsd-3-clause | import numpy as np
import pandas as pd
import statsmodels.api as sm
"""
Explanation: GEE nested covariance structure simulation study
This notebook is a simulation study that illustrates and evaluates the performance of the GEE nested covariance structure.
A nested covariance structure is based on a nested sequence of... |
phoebe-project/phoebe2-docs | development/tutorials/ebv_Av_Rv.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.4,<2.5"
"""
Explanation: Extinction (ebv, Av, & Rv)
Setup
Let's first make sure we have the latest version of PHOEBE 2.4 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 release).
End of explanation
"""
import phoeb... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.