repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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google/timesketch | notebooks/Stolen_Szechuan_Sauce_Data_Upload.ipynb | apache-2.0 | # @title Import libraries
# @markdown This cell loads libraries that we will use througout the notebook.
import io
import os
import codecs
import altair as alt
import numpy as np
import pandas as pd
from timesketch_api_client import config
from timesketch_import_client import helper
from timesketch_import_client impo... |
mne-tools/mne-tools.github.io | 0.23/_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... |
ES-DOC/esdoc-jupyterhub | notebooks/messy-consortium/cmip6/models/emac-2-53-vol/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'messy-consortium', 'emac-2-53-vol', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: MESSY-CONSORTIUM
Source ID: EMAC-2-53-VOL
Topic: Land
Sub-Topics: Soil... |
RittmanResearch/maybrain | docs/04 - More About the Brain Class.ipynb | apache-2.0 | from maybrain import constants as ct
from maybrain import resources as rr
from maybrain import brain as mbt
a = mbt.Brain()
a.import_adj_file(rr.DUMMY_ADJ_FILE_500)
a.import_spatial_info(rr.MNI_SPACE_COORDINATES_500)
a.apply_threshold()
"""
Explanation: More About the Brain Class
Besides the basic functionalities alre... |
opengridcc/opengrid | notebooks/Compute degree-days.ipynb | apache-2.0 | import opengrid as og
import pandas as pd
plt = og.plot_style()
"""
Explanation: Compute degree-days for heating and cooling
This notebook shows how to compute degree-days for heating and/or cooling from the ambient temperature. We compute degree-days with different base temperatures. This is a very useful input fo... |
anugrah-saxena/pycroscopy | jupyter_notebooks/Loading_Visualization_Format.ipynb | mit | # Make sure pycroscopy and wget are installed
!pip install pycroscopy
!pip install -U wget
# Ensure python 3 compatibility
from __future__ import division, print_function, absolute_import
# Import necessary libraries:
import h5py
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
... |
marius311/cosmoslik | cosmoslik_plugins/models/cosmo_derived/cosmo_derived.ipynb | gpl-3.0 | from cosmoslik import *
cosmo_derived = get_plugin('models.cosmo_derived')()
"""
Explanation: cosmo_derived
This plugin calculates the following "derived" cosmological quantities:
* $H(z)$
* $D_A(z)$
* $r_s(z)$
* $\theta_s(z)$
* $z_{\rm drag}$ (Hu & Sugiyama fitting formula)
It can also be used as a $\theta$ to $H_0$... |
wuafeing/Python3-Tutorial | 02 strings and text/02.05 search and replace text.ipynb | gpl-3.0 | text = 'yeah, but no, but yeah, but no, but yeah'
text.replace("yeah", "yep")
"""
Explanation: Previous
2.5 字符串搜索和替换
问题
你想在字符串中搜索和匹配指定的文本模式
解决方案
对于简单的字面模式，直接使用 str.repalce() 方法即可，比如：
End of explanation
"""
text = 'Today is 11/27/2012. PyCon starts 3/13/2013.'
import re
re.sub(r"(\d+)/(\d+)/(\d+)", r"\3-\1-\2", text)... |
palandatarxcom/sklearn_tutorial_cn | notebooks/04.1-Dimensionality-PCA.ipynb | bsd-3-clause | from __future__ import print_function, division
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
# 使用默认的seaborn设置
import seaborn as sns; sns.set()
"""
Explanation: 这个分析笔记由Jake Vanderplas编辑汇总。 源代码和license文件在GitHub。 中文翻译由派兰数据在派兰大数据分析平台上完成。 源代码在GitHub上。
降维：深入探索主成分分析
在本节中我们会探... |
sdpython/ensae_teaching_cs | _doc/notebooks/td2a/td2a_correction_session_2C.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 2A.soft - IPython et commandes magiques - correction
Aperçu des commandes magiques pour automatiser un peu plus les tâches courantes.
End of explanation
"""
from IPython.core.magic imp... |
indiependente/Social-Networks-Structure | results/Generalized Watts-Strogatz Results Analysis.ipynb | mit | #!/usr/bin/python
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from stats import parse_results, get_percentage, get_avg_per_seed, draw_pie, draw_bars, draw_bars_comparison, draw_avgs
"""
Explanation: Generalized Watts-Strogatz Graph Experiments Output Visualization
End of explanation
"""
pr,... |
atlury/deep-opencl | DL0110EN/1.3derivativesandGraphsinPytorch_v2.ipynb | lgpl-3.0 | # These are the libraries will be useing for this lab.
import torch
import matplotlib.pylab as plt
import torch.functional as F
"""
Explanation: <a href="http://cocl.us/NotebooksPython101">
<img src="https://ibm.box.com/shared/static/yfe6h4az47ktg2mm9h05wby2n7e8kei3.png" width="750" alt="IBM 10TB Storage" />
</a... |
openfisca/openfisca-web-notebook | 2016-04-01-hackathon-CodeImpot/reforme_revenu_de_base_nouvelles_simulations_rdb_adultes.ipynb | agpl-3.0 | TaxBenefitSystem = init_country()
tax_benefit_system = TaxBenefitSystem()
from openfisca_core import reforms
ReformeRevenuDeBase = reforms.make_reform(
key = 'reforme_rdb',
name = u"Réforme Revenu de base",
reference = tax_benefit_system,
)
"""
Explanation: Système socio-fiscal
End of explanation
"""... |
CalPolyPat/phys202-2015-work | assignments/midterm/AlgorithmsEx03.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact
"""
Explanation: Algorithms Exercise 3
Imports
End of explanation
"""
def char_probs(s):
"""Find the probabilities of the unique characters in the string s.
Parameters
----------
... |
campagnucci/api_sof | SOF_Execucao_Orcamentaria_PMSP.ipynb | gpl-3.0 | import pandas as pd
import requests
import json
import numpy as np
TOKEN = '198f959a5f39a1c441c7c863423264'
base_url = "https://gatewayapi.prodam.sp.gov.br:443/financas/orcamento/sof/v2.1.0"
headers={'Authorization' : str('Bearer ' + TOKEN)}
"""
Explanation: Explorando as despesas da cidade de São Paulo
Um tutorial d... |
Unidata/unidata-python-workshop | notebooks/Pythonic_Data_Analysis/Pythonic Data Analysis.ipynb | mit | import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter, DayLocator
from siphon.simplewebservice.ndbc import NDBC
%matplotlib inline
# Read in some data
df = NDBC.realtime_observations('42039')
# Trim to the last 7 days
df = df[df['time'] > (pd.Timestamp.utcnow() - pd.Timedelta... |
anisfeld/MachineLearning | Pipeline construction 1.ipynb | mit | import pandas as pd
import numpy as np
import pylab as pl
import seaborn as sns
from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
import pipeline.util as u
import pipeline.process as pr
import pipeline.read as r
import pipeline.explore... |
juditacs/morph-segmentation-experiments | notebooks/sandbox/seq2seq_attention.ipynb | mit | PROJECT_DIR = "../../"
use_toy_data = False
LOG_DIR = 'logs' # Tensorboard log directory
if use_toy_data:
batch_size = 8
embedding_dim = 5
cell_size = 32
max_len = 6
else:
batch_size = 64
embedding_dim = 20
cell_size = 128
max_len = 33
use_attention = True
use_bidirectional_encode... |
josh-gree/maths-with-python | 07-sympy.ipynb | mit | import sympy
sympy.init_printing()
"""
Explanation: In standard mathematics we routinely write down abstract variables or concepts and manipulate them without ever assigning specific values to them. An example would be the quadratic equation
\begin{equation}
a x^2 + b x + c = 0
\end{equation}
and its roots $x_{\pm}$... |
natashabatalha/PandExo | notebooks/HST_WFC3_w_ExoMAST.ipynb | gpl-3.0 | import sys
sys.path.append('..')
import pandexo.engine.justdoit as jdi
"""
Explanation: HST's Tranisting Exoplanet Noise Simulator
This file demonstrates how to predict the:
1. Transmission/emission spectrum S/N ratio
2. Observation start window
for any system observed with WFC3/IR.
Background information
Pand... |
theonaun/notebooks | presentations/python_data_tools/07_pandas.ipynb | mit | # Import pandas under the name pd
import pandas as pd
import numpy as np
import matplotlib
%matplotlib inline
matplotlib.style.use('fivethirtyeight')
# Create a dataframe from a CSV file
df = pd.read_csv('data/cfpb_complaints_with_fictitious_data.csv')
# Any dataframe at end of cell gets HTML representation
# Head l... |
AllenDowney/ModSim | soln/chap03.ipynb | gpl-2.0 | # install Pint if necessary
try:
import pint
except ImportError:
!pip install pint
# download modsim.py if necessary
from os.path import exists
filename = 'modsim.py'
if not exists(filename):
from urllib.request import urlretrieve
url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'
... |
dynaryu/rmtk | rmtk/plotting/hazard_outputs/plot_hazard_curves.ipynb | agpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
from plot_hazard_outputs import HazardCurve, UniformHazardSpectra
hazard_curve_file = "../sample_outputs/hazard/hazard_curve.xml"
hazard_curves = HazardCurve(hazard_curve_file)
"""
Explanation: Hazard Curves and Uniform Hazard Spectra
This IPython notebook allows the... |
darkomen/TFG | medidas/20072015/.ipynb_checkpoints/Conclusiones-checkpoint.ipynb | cc0-1.0 | %pylab inline
#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 los ficheros c... |
spulido99/Programacion | Alex/Taller 3 y 4.ipynb | mit | from sklearn import datasets
import pandas as pd
from sklearn.datasets import load_digits
digits = load_digits() #dataset de clasificacion
brio= pd.read_csv('C:/Users/Alex/Documents/eafit/semestres/X semestre/programacion/briofitos.csv') #dataset regresion
#digits.DESCR
digits.target
"""
Explanation: Taller 3 Regr... |
TomTranter/OpenPNM | examples/paper_recreations/Wu et al. (2010)/Wu et al. (2010) - Part C.ipynb | mit | import scipy as sp
import numpy as np
import openpnm as op
import matplotlib.pyplot as plt
import openpnm.models.geometry as gm
import openpnm.topotools as tt
%matplotlib inline
np.random.seed(10)
"""
Explanation: Example: Regenerating Data from
R. Wu et al. / Elec Acta 54 25 (2010) 7394–7403
Import the modules
End of... |
folivetti/CRUFABC | ExercicioRec.ipynb | mit | G2 = nx.barabasi_albert_graph(6,3)
nx.draw_shell(G2)
pos = nx.shell_layout(G2)
labels = dict( enumerate(G2.nodes()) )
nx.draw_networkx_labels(G2,pos,labels,font_size=16);
print "Dist. media: ", nx.average_shortest_path_length(G2)
print "Diametro: ", nx.diameter(G2)
print "Coef. Agrupamento médio: ", nx.average_cluster... |
bgroveben/python3_machine_learning_projects | oreilly_GANs_for_beginners/oreilly_GANs_for_beginners/introduction_to_ml_with_python/1_Introduction.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import mglearn
from IPython.display import display
%matplotlib inline
"""
Explanation: Chapter 1: Introduction
End of explanation
"""
import numpy as np
x = np.array([[1,2,3],[4,5,6]])
print("x:\n{}".format(x))
"""
Explanation: Essential Librar... |
matthewljones/computingincontext | CiC_lecture_04_text_mining_topics.ipynb | gpl-2.0 | %matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
pd.set_option('display.mpl_style', 'default') # Make the graphs a bit prettier
plt.rcParams['figure.figsize'] = (15, 5)
"""
Explanation: Computing In Context
Social Sciences Track
Lecture 4--topics, trends, and dimensional scaling
Matthew L. Jones... |
rvuduc/cse6040-ipynbs | 10b--csv-to-db.ipynb | bsd-3-clause | from sqlalchemy import create_engine # database connection
import datetime as dt # for timing
import pandas as pd # for data frames
"""
Explanation: CSE 6040, Fall 2015 [10-B]: CSV to DB
This notebook is optional and was not covered in class. It shows how we converted the raw comma-separated values (CSV) file correspo... |
wem3/gems_vs_bomb | rez/all_bandits_best_model.ipynb | mit | # imports / display plots in cell output
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as ss
import pandas as pd
import seaborn as sns
import statsmodels
"""
Explanation: Reinforcement Learning Models of Social Group Preferences
Bandit Experiments 1-7
End of explanation
"""
... |
gldmt-duke/CokerAmitaiSGHMC | logistic_regression/logistic_regression_compare_sghmc.ipynb | mit | # Load data
X = np.concatenate((np.ones((pima.shape[0],1)),pima[:,0:8]), axis=1)
Y = pima[:,8]
Xs = (X - np.mean(X, axis=0))/np.concatenate((np.ones(1),np.std(X[:,1:], axis=0)))
n, p = X.shape
M = np.identity(p)
### HMC version
def logistic(x):
return 1/(1+np.exp(-x))
def U(theta, Y, X):
return - (Y.T @ X @... |
anthonyng2/FX-Trading-with-Python-and-Oanda | Oanda v1 REST-oandapy/02.00 Rates Information.ipynb | mit | import pandas as pd
import oandapy
import configparser
config = configparser.ConfigParser()
config.read('../config/config_v1.ini')
account_id = config['oanda']['account_id']
api_key = config['oanda']['api_key']
"""
Explanation: <!--NAVIGATION-->
< Algorithmic Trading Architecture | Contents | Account Information >
Ra... |
AaronRanAn/Pyton-for-Data-Analytics | 4 - pandas Basics/4-4 pandas DataFrame Basics.ipynb | apache-2.0 | import string
upcase = [x for x in string.ascii_uppercase]
lcase = [x for x in string.ascii_lowercase]
print(upcase[:5], lcase[:5])
"""
Explanation: Now fundamentally the data frame is just an abstraction but it provides a ton of useful tools that you’re going to get to see. This video is just going to go over the ba... |
mssalvador/Fifa2018 | Teknisk Tirsdag Tutorial (Un-Supervised).ipynb | apache-2.0 | #PURE PYTHON!!!!
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
from sklearn import cluster
"""
Explanation: Teknisk Tirsdag: Unsupervised Learning
Tillykke du er i anden uge af din ansættelse i den norske virksomhed, som operere med data science. Din leder var meget tilfre... |
msultan/msmbuilder | examples/Fs-Peptide-with-Pipeline.ipynb | lgpl-2.1 | # Download example dataset
from msmbuilder.example_datasets import FsPeptide
fs_peptide = FsPeptide()
xyz = fs_peptide.get().trajectories
print(fs_peptide.description())
"""
Explanation: Modeling dynamics of FS Peptide
This example shows how to use sklearn's Pipeline object to rapidly construct MSMs
MSMBuilder include... |
spencer2211/deep-learning | dcgan-svhn/DCGAN_Exercises.ipynb | mit | %matplotlib inline
import pickle as pkl
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import loadmat
import tensorflow as tf
!mkdir data
"""
Explanation: Deep Convolutional GANs
In this notebook, you'll build a GAN using convolutional layers in the generator and discriminator. This is called a De... |
mavillan/SciProg | 03_memmap/03_memmap.ipynb | gpl-3.0 | %matplotlib inline
%load_ext memory_profiler
import numpy as np
import matplotlib.pyplot as plt
import scipy as sp
import scipy.sparse
import sys
import h5py
def get_size(obj, seen=None):
"""
Recursively finds size of objects
ref: https://goshippo.com/blog/measure-real-size-any-python-object/
"""
... |
QuantScientist/Deep-Learning-Boot-Camp | day03/additional materials/5.1 Custom Layer.ipynb | mit | from keras.models import Sequential
from keras.layers import Dense, Dropout, Layer, Activation
from keras.datasets import mnist
from keras import backend as K
from keras.utils import np_utils
"""
Explanation: Custom Keras Layer
Idea:
We build a custom activation layer called Antirectifier,
which modifies the shape of ... |
albahnsen/PracticalMachineLearningClass | notebooks/20-DeepLearning_keras.ipynb | mit | from sklearn.datasets import load_boston
boston_dataset = load_boston()
print(boston_dataset.DESCR)
"""
Explanation: 20 - Deep Learning using keras
by Alejandro Correa Bahnsen and Jesus Solano
version 1.4, May 2019
Part of the class Practical Machine Learning
This notebook is licensed under a Creative Commons Attribut... |
angelmtenor/data-science-keras | student_admissions.ipynb | mit | %matplotlib inline
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import keras
import helper
helper.reproducible(seed=0)
sns.set()
"""
Explanation: Student Admissions
Predicting student admissions to graduate school at UCLA based on GRE Scores, GPA Scores, and ... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/contrasts.ipynb | bsd-3-clause | import numpy as np
import statsmodels.api as sm
"""
Explanation: Contrasts Overview
End of explanation
"""
import pandas as pd
url = "https://stats.idre.ucla.edu/stat/data/hsb2.csv"
hsb2 = pd.read_table(url, delimiter=",")
hsb2.head(10)
"""
Explanation: This document is based heavily on this excellent resource fr... |
5agado/data-science-learning | deep learning/GANs - Intro.ipynb | apache-2.0 | import time
from PIL import Image
import numpy as np
import pdb
import os
import sys
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import animation
from keras.models import Sequential
from keras.layers.core import Reshape, Dense, Dropout, Activation, Flatten
from keras.layers... |
poppy-project/community-notebooks | tutorials-education/poppy-torso__vrep_Prototype d'ininitiation à l'informatique pour les lycéens/decouverte/Decouverte TP2.ipynb | lgpl-3.0 | import time
from poppy.creatures import PoppyTorso
poppy = PoppyTorso(simulator='vrep')
"""
Explanation: Decouverte – Niveau 1 - Python
TP2
Pour commencer votre programme python devra contenir les lignes de code ci-dessous et le logiciel V-REP devra être lancé.
Dans V-REP (en haut à gauche) utilise les deux icones fl... |
tanghaibao/goatools | notebooks/dcnt_and_tinfo.ipynb | bsd-2-clause | from goatools.base import get_godag
godag = get_godag("go-basic.obo")
"""
Explanation: How to get Descendants Count and Information Content
Download Ontologies, if necessary
Choose a set of GO IDs to print
GoSubDag contains descendants count
Print the descendants count (dcnt)
Compare information content (tinfo) for ... |
Kaggle/learntools | notebooks/pandas/raw/ex_0.ipynb | apache-2.0 | import pandas as pd
pd.set_option('max_rows', 5)
from learntools.core import binder; binder.bind(globals())
from learntools.pandas.creating_reading_and_writing import *
print("Setup complete.")
"""
Explanation: Introduction
The first step in most data analytics projects is reading the data file. In this exercise, you'... |
mne-tools/mne-tools.github.io | 0.21/_downloads/f760cc2f1a5d6c625b1e14a0b05176dd/plot_ecog.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
# Chris Holdgraf <choldgraf@gmail.com>
# Adam Li <adam2392@gmail.com>
#
# License: BSD (3-clause)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import mne
from mne.viz import plot_align... |
raoyvn/deep-learning | intro-to-rnns/Anna_KaRNNa.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... |
I2MAX-LearningProject/Flask-server | Tests/Prophet_trial2_8_16-Copy1.ipynb | mit | rawArrayDatas=[["2017-08-11", "2017-08-12", "2017-08-13", "2017-08-14", "2017-08-15","2017-08-16"],
[20.0, 30.0, 40.0, 50.0, 60.0,20.0]]
processId=12
forecastDay=4
"""
Explanation: 실제함수 input
End of explanation
"""
mockForecast={}
rmse={}
forecast=[]
realForecast={}
trainSize=int(len(rawArrayDatas[0]... |
texib/deeplearning_homework | LogisticRegression.ipynb | mit | import numpy as np
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.read_csv("./pokemon.csv")
alldata = df[ (df['attack_strong_type'] == 'Normal') |(df['attack_strong_type'] == 'Flying') ]
# alldata = alldata[df['attack_strong_type'] == 'Flying']
f1 = alldata['height'].tolist()
f2 = ... |
mne-tools/mne-tools.github.io | stable/_downloads/f31e73ee907864d95a2b617fdc76b71e/source_label_time_frequency.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, source_induced_power
print(__doc__)
"""
Explanation: Compute powe... |
xpharry/Udacity-DLFoudation | tutorials/sentiment_network/Sentiment Classification - Mini Project 5.ipynb | mit | def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].upper(),g.readlines()))
g.close()... |
michael-isaev/cse6040_qna | PythonQnA_3_funcs_returns.ipynb | apache-2.0 | def function_of_no_return(a):
print ("Parameter passed to the function is {0}".format(a))
x = function_of_no_return(42)
print ('Function returns', x)
"""
Explanation: 3. Point of No Return
What a function returns and what it does not can cause a lot of confusion, especially if the function causes some side ef... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/ml_fairness_explainability/inclusive_ml/labs/1_inclusive_ml.ipynb | apache-2.0 | !pip freeze | grep httplib2==0.18.1 || pip install httplib2==0.18.1
import os
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import pandas as pd
import witwidget
from witwidget.notebook.visualization import WitConfigBuilder, WitWidget
pd.options.display.max_columns = 50
PROJECT = !(gcloud conf... |
google/trax | trax/examples/NMT_with_Transformers_Reformers_using_Trax.ipynb | apache-2.0 | #@title
# Copyright 2021 Google LLC.
# 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... |
fionapigott/Data-Science-45min-Intros | python-interfaces/python-interfaces.ipynb | unlicense | # imports
import math,decimal,random
"""
Explanation: Interfaces in Python
References
This tutorial is primarily inspired by chapters 9,11,12 in Luciano Ramalho's book "Fluent Python".
End of explanation
"""
class Animal:
"""
the Animal class can be used to describe something that has a well-defined number ... |
aflaxman/siaman16-va-minitutorial | 1-tutorial-notebooks/4-va_csmf.ipynb | gpl-3.0 | import numpy as np, pandas as pd
"""
Explanation: We won't work through this notebook
We won't have time. But I thought I'd include it, in case you want to see exactly how I implement my population-level quality metric.
End of explanation
"""
def measure_prediction_quality(csmf_pred, y_test):
"""Calculate popul... |
gaargly/gaargly.github.io | 2021_06_18_Lira_Samanta_Assignment1_Dr_Kasaran_class.ipynb | mit | import numpy as np
# List to Array
list1 = [1, 2, 3, 4, 5, 6, 7, 8]
np.array(list1)
# Tuple to Array
tuple1 = (1, 2, 3, 4, 5, 6, 7, 8)
np.array(tuple1)
"""
Explanation: <a href="https://colab.research.google.com/github/gaargly/gaargly.github.io/blob/master/2021_06_18_Lira_Samanta_Assignment1_Dr_Kasaran_class.ipynb" ... |
christophebertrand/ada-epfl | HW04-Applied ML/HW04-1-Preprocessing.ipynb | mit | print('Number of diad: ', len(data))
print('Number of players: ', len(data.playerShort.unique()))
print('Number of referees: ', len(data.refNum.unique()))
"""
Explanation: 1. Observation and clean of the data
End of explanation
"""
complete = len(data.dropna())
all_ = len(data_total)
print('Number of row with comple... |
ContextLab/hypertools | docs/tutorials/reduce.ipynb | mit | import hypertools as hyp
%matplotlib inline
"""
Explanation: Dimensionality reduction
The reduce function reduces the dimensionality of an array or list of arrays. The default is to use Principal Component Analysis to reduce to three dimensions, but a variety of models are supported and users may specify a desired nu... |
balarsen/pymc_learning | Bernoulli/Bernoulli hierarchical.ipynb | bsd-3-clause | observations = np.array([20, 6, 6, 6, 6, 6])
with pm.Model():
probs = pm.Dirichlet('probs', a=np.ones(6)) # flat prior
rolls = pm.Multinomial('rolls', n=50, p=probs, observed=observations)
trace = pm.sample(5000)
pm.plot_posterior(trace);
pm.traceplot(trace)
"""
Explanation: Coin flip learn things along... |
flohorovicic/pynoddy | docs/notebooks/.ipynb_checkpoints/Gippsland-drillhole-uncertainty-checkpoint.ipynb | gpl-2.0 | from IPython.core.display import HTML
css_file = 'pynoddy.css'
HTML(open(css_file, "r").read())
%matplotlib inline
# here the usual imports. If any of the imports fails, make sure that pynoddy is installed
# properly, ideally with 'python setup.py develop' or 'python setup.py install'
import sys, os
import matplotlib... |
stijnvanhoey/phd_ipynb_respiro_showcases | bayesian_story/pyIDEAS_bayes.ipynb | bsd-2-clause | # https://github.com/dfm/corner.py
import corner
import hydropy as hp
mpl.rcParams['font.size'] = 16
mpl.rcParams['axes.labelsize'] = 18
mpl.rcParams['xtick.labelsize'] = 16
mpl.rcParams['ytick.labelsize'] = 16
import pylab as p
p.rc('mathtext', default='it')
"""
Explanation: Depenencies for using pyIDEAS/Biointen... |
Code-Girls/2016Summer | Day6/code/notebook_debugging.ipynb | mit | # for inline plotting in the notebook
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
def plot_log():
figure, axis = plt.subplots(2, 1)
x = np.linspace(1, 2, 10)
axis.plot(x, np.log(x))
plt.show()
plot_log() # Call the function, generate plot
"""
Explanation: A significant po... |
OSGeoLabBp/tutorials | english/python/regexp_in_python.ipynb | cc0-1.0 | import re
"""
Explanation: <a href="https://colab.research.google.com/github/OSGeoLabBp/tutorials/blob/master/english/python/regexp_in_python.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Regular expressions in Python
Regular expression (regexp) i... |
hich28/mytesttxx | tests/python/piperead.ipynb | gpl-3.0 | for a in spot.automata('ltl2tgba -s "a U b"; ltl2tgba --lbtt "b"|', 'ltl2tgba -H "GFa" "a & GFb"|'):
display(a)
"""
Explanation: Reading automata output from processes
If an argument of spot.automata ends with |, then it is interpreted as a shell command that outputs one automaton or more.
End of explanation
"""
... |
jmschrei/pomegranate | tutorials/B_Model_Tutorial_4b_Bayesian_Network_Structure_Learning.ipynb | mit | %pylab inline
%load_ext memory_profiler
from pomegranate import BayesianNetwork
import seaborn, time
seaborn.set_style('whitegrid')
X = numpy.random.randint(2, size=(2000, 7))
X[:,3] = X[:,1]
X[:,6] = X[:,1]
X[:,0] = X[:,2]
X[:,4] = X[:,5]
model = BayesianNetwork.from_samples(X, algorithm='exact')
print(model.struc... |
tensorflow/docs | site/en/tutorials/quickstart/advanced.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... |
Neuroglycerin/hail-seizure | ipynotebooks/Compare regularisation runs.ipynb | apache-2.0 | df1 = pd.read_csv(csvfnm, sep='\t', header=0)
# Drop any duplicated rows from repeated runs of the same thing
#df = df.drop_duplicates(inplace=True)
df1
"""
Explanation: Load the CSV with Pandas
End of explanation
"""
# We need to parse the RUN_NAME column to extract the features which couldn't go into the CSV file ... |
ruleva1983/udacity-selfdrivingcar | term1/P2_traffic_signs/Traffic_Sign_Classifier.ipynb | gpl-3.0 | # Load pickled data
import pickle
import numpy as np
import seaborn as sns
training_file = "data/train.p"
validation_file = "data/valid.p"
testing_file = "data/test.p"
with open(training_file, mode='rb') as f:
train = pickle.load(f)
with open(validation_file, mode='rb') as f:
valid = pickle.load(f)
with open... |
atavory/ibex | examples/iris_feature_transform.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
from ibex.sklearn import datasets
from ibex.sklearn.decomposition import PCA as PdPCA
from ibe... |
ajdawson/python_for_climate_scientists | course_content/notebooks/python_introduction.ipynb | gpl-3.0 | import this
"""
Explanation: Python 101
This introduction is based heavily on the introduction notes found here: https://github.com/scipy-lectures/scipy-lecture-notes
The (climate) scientists needs
To get, potentially very large, datasets from simulation, observation, or experiment
To manipulate and process it
To vis... |
AtmaMani/pyChakras | udemy_ml_bootcamp/Big-Data-and-Spark/Introduction to Spark and Python.ipynb | mit | from pyspark import SparkContext
"""
Explanation: Introduction to Spark and Python
Let's learn how to use Spark with Python by using the pyspark library! Make sure to view the video lecture explaining Spark and RDDs before continuing on with this code.
This notebook will serve as reference code for the Big Data sectio... |
osamoylenko/YSDA_deeplearning17 | Seminar3/HW3_Modules.ipynb | mit | class Module(object):
def __init__ (self):
self.output = None
self.gradInput = None
self.training = True
"""
Basically, you can think of a module as of a something (black box)
which can process `input` data and produce `ouput` data.
This is like applying a function which is ... |
armgilles/presentation | EPSI/I4/Datamining/c1/notebook/Pokemon.ipynb | mit | pokemon = data[data.Generation == 1].copy()
Image(url="http://i.giphy.com/yidUzHnBk32Um9aMMw.gif")
pokemon
"""
Explanation: On souhaite regarder uniquement les pokémons de 1ère génération. Il va donc falloir filtrer nos données
On va donc crée une nouvelle variable qui aura uniquement les pokémons de "Generation" = ... |
ktsaou/netdata | ml/notebooks/netdata_anomaly_detection_deepdive.ipynb | gpl-3.0 | # uncomment the line below (when running in google colab) to install the netdata-pandas library, comment it again when done.
#!pip install netdata-pandas
from datetime import datetime, timedelta
import itertools
import random
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
... |
ThyrixYang/LearningNotes | MOOC/stanford_cnn_cs231n/assignment1/knn.ipynb | gpl-3.0 | # 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... |
ellisonbg/ipyleaflet | examples/Numpy.ipynb | mit | import requests
import os
from tqdm import tqdm
import zipfile
import rasterio
from affine import Affine
import numpy as np
import scipy.ndimage
from rasterio.warp import reproject, Resampling
import PIL
import matplotlib.pyplot as plt
from base64 import b64encode
try:
from StringIO import StringIO
py3 = False
... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_rereference_eeg.ipynb | bsd-3-clause | # Authors: Marijn van Vliet <w.m.vanvliet@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import mne
from mne.datasets import sample
from matplotlib import pyplot as plt
print(__doc__)
# Setup for reading the raw data
data_path = sample.data_path()
raw_f... |
bmbutle2/ethereum_blockchain | notebooks/random_forest.ipynb | gpl-3.0 | train = pd.read_csv('./../data/training.csv')
label = pd.read_csv('./../data/labels.csv', header=None)
train.drop('Unnamed: 0', axis=1, inplace=True)
# label encode type
le = LabelEncoder()
train['type_enc'] = le.fit_transform(train['type'])
label.columns = ['0', 'p_label2']
label.drop('0', axis=1, inplace=True)
y_lab... |
tkurfurst/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... |
akshaybabloo/Car-ND | Term_1/TrafficSignalClassifier_6/TrafficSignalClassifier_Project.ipynb | mit | # Load pickled data
import pickle
# TODO: Fill this in based on where you saved the training and testing data
training_file = 'train.p'
validation_file= 'valid.p'
testing_file = 'test.p'
with open(training_file, mode='rb') as f:
train = pickle.load(f)
with open(validation_file, mode='rb') as f:
valid = pickl... |
DavidLeoni/relmath | relmath-debug.ipynb | apache-2.0 | get_ipython().display_formatter.formatters
import math
def normalize(m):
for row in m.g:
norm = math.sqrt(sum((x**2 for x in row)))
for i in range(len(row)):
row[i] = row[i] / norm
getattr("ciao", "upper")()
"""
Explanation: Relmath debug
Notebook for debugging stuff - to see some ... |
jgarciab/wwd2017 | class2/class2a_programming_after_class.ipynb | gpl-3.0 | ##Some code to run at the beginning of the file, to be able to show images in the notebook
##Don't worry about this cell
#Print the plots in this screen
%matplotlib inline
#Be able to plot images saved in the hard drive
from IPython.display import Image
#Make the notebook wider
from IPython.core.display import dis... |
CUBoulder-ASTR2600/lectures | lecture_05_summer_sublists_functions.ipynb | isc | for fIndex, y in enumerate(range(2, 5)):
countdown = y
yFactorial = 1
wIndex = 0
while countdown > 1:
yFactorial *= countdown
##### CHECKPOINT! #####
countdown -= 1
wIndex += 1
print("RESULT: %d! = %d" % (y, yFactorial))
# Question 3
... |
yuggo/SOW | chen_model_intro.ipynb | mit | #you have to make sure that you have all of these installed
import cProfile
import re
import math
import numpy as np
import scipy as sp
from scipy import stats
from scipy import optimize as opt
import pandas as pd
import random as rnd
from matplotlib import pyplot as plt
import time
import numpy.random
import warnings
... |
harcel/bayes_workshop | Exercises.ipynb | bsd-2-clause | P_positive_if_ill = .99
P_positive_if_notill = .01
P_ill = 1.e-3
def P_ill_if_positive(P_positive_if_ill=.99, P_positive_if_notill=.01, P_ill=1.e-3):
"""Function that simply plugs the numbers into Bayes' theorem"""
P_notill = 1 - P_ill
return P_positive_if_ill * P_ill / (P_positive_if_ill * P_ill + P_posi... |
dataworkshop/xgboost | step3.ipynb | mit | import pandas as pd
import xgboost as xgb
import numpy as np
import seaborn as sns
from sklearn.dummy import DummyRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor
from sklearn.ensemble import GradientBoostingRegressor, AdaBoostRegressor, B... |
tritemio/multispot_paper | out_notebooks/usALEX-5samples-PR-raw-AND-gate-out-7d.ipynb | mit | ph_sel_name = "None"
data_id = "7d"
# data_id = "7d"
"""
Explanation: Executed: Mon Mar 27 11:36:20 2017
Duration: 7 seconds.
usALEX-5samples - Template
This notebook is executed through 8-spots paper analysis.
For a direct execution, uncomment the cell below.
End of explanation
"""
from fretbursts import *
init... |
astroumd/GradMap | notebooks/Lectures2017/Lecture4/Lecture4-m31.ipynb | gpl-3.0 | data_file = 'm31-rubin.tab'
"""
Explanation: Dark Matter in M31 .. in 1970 (Rubin & Ford)
As a tribute to Vera Rubin's work here is a notebook discussing one of her well known papers on the topic of dark matter. During the Greenbank observing we un-covered rotation in our galaxy, and how we could find out that there... |
rflamary/POT | docs/source/auto_examples/plot_gromov.ipynb | mit | # Author: Erwan Vautier <erwan.vautier@gmail.com>
# Nicolas Courty <ncourty@irisa.fr>
#
# License: MIT License
import scipy as sp
import numpy as np
import matplotlib.pylab as pl
from mpl_toolkits.mplot3d import Axes3D # noqa
import ot
"""
Explanation: Gromov-Wasserstein example
This example is designed to s... |
oasis-open/cti-python-stix2 | docs/guide/serializing.ipynb | bsd-3-clause | from stix2 import Indicator
indicator = Indicator(name="File hash for malware variant",
pattern_type="stix",
pattern="[file:hashes.md5 = 'd41d8cd98f00b204e9800998ecf8427e']")
print(indicator.serialize(pretty=True))
"""
Explanation: Serializing STIX Objects
The string repre... |
nwfpug/meetings | 2017-04-10/xml_processing.ipynb | gpl-3.0 | # import the appropriate libraries
import xml.etree.ElementTree as ET # xml processing
# read the XML file
tree = ET.parse('input/menu.xml')
print('tree element:\t', tree)
# get the root of the tree
root = tree.getroot()
print 'root element:\t ', root
# here is the name of the root element
root.tag
# get the c... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_info.ipynb | bsd-3-clause | import mne
import os.path as op
"""
Explanation: The :class:Info <mne.Info> data structure
The :class:Info <mne.Info> data object is typically created
when data is imported into MNE-Python and contains details such as:
date, subject information, and other recording details
the sampling rate
information ab... |
tylere/docker-tmpnb-ee | notebooks/1 - IPython Notebook Examples/IPython Project Examples/IPython Kernel/Rich Output.ipynb | apache-2.0 | from IPython.display import display
"""
Explanation: Rich Output
In Python, objects can declare their textual representation using the __repr__ method. IPython expands on this idea and allows objects to declare other, rich representations including:
HTML
JSON
PNG
JPEG
SVG
LaTeX
A single object can declare some or a... |
jlawman/jlawman.github.io | content/deep-learning/Activation Functions.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
#Create array of possible z values
z = np.linspace(-5,5,num=1000)
def draw_activation_plot(a,quadrants=2,y_ticks=[0],two_quad_y_lim=[0,5], four_quad_y_lim=[-1,1]):
"""
Draws plot of activation function
Parameters
----------
... |
google/learned_optimization | docs/notebooks/Part1_Introduction.ipynb | apache-2.0 | import numpy as np
import jax.numpy as jnp
import jax
from matplotlib import pylab as plt
!pip install git+https://github.com/google/learned_optimization.git
import numpy as np
import jax.numpy as jnp
import jax
from matplotlib import pylab as plt
from learned_optimization.outer_trainers import full_es
from learned_... |
WilliamHPNielsen/broadbean | docs/Making output for Tektronix AWG70000A.ipynb | mit | # We make a sequence consisting of a bsic element with:
# a wait time, a ramp up, a sine, a ramp down, and more wait time
# And then we vary the sine frequency
%matplotlib notebook
import broadbean as bb
from broadbean.plotting import plotter
import numpy as np
sine = bb.PulseAtoms.sine
ramp = bb.PulseAtoms.ramp
####... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_sensor_connectivity.ipynb | bsd-3-clause | # Author: Martin Luessi <mluessi@nmr.mgh.harvard.edu>
#
# License: BSD (3-clause)
import numpy as np
from scipy import linalg
import mne
from mne import io
from mne.connectivity import spectral_connectivity
from mne.datasets import sample
print(__doc__)
"""
Explanation: Compute all-to-all connectivity in sensor spa... |
napsternxg/gensim | docs/notebooks/translation_matrix.ipynb | gpl-3.0 | import os
from gensim import utils
from gensim.models import translation_matrix
from gensim.models import KeyedVectors
"""
Explanation: Tranlation Matrix Tutorial
What is it ?
Suppose we are given a set of word pairs and their associated vector representaion ${x_{i},z_{i}}{i=1}^{n}$, where $x{i} \in R^{d_{1}}$ is the... |
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