repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
Naereen/notebooks | Living_in_a_noisy_world_with_James_Powell_rwatch_module.ipynb | mit | import numpy as np
np.random.seed(1234)
np.random.normal()
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#"Living-in-a-noisy-world...",-using-James-Powell's-(dutc)-rwatch-module" data-toc-modified-id=""Living-in-a-noisy-world...",-using-James-Powell's-(dutc)-rwatch-modu... |
bashalex/datapot | notebooks/TimestampAndNumericExample.ipynb | gpl-3.0 | import datapot as dp
datapot = dp.DataPot()
from datapot.utils import csv_to_jsonlines
csv_to_jsonlines('../data/transactions.csv', '../data/transactions.jsonlines')
ftr = open('../data/transactions.jsonlines')
"""
Explanation: Dataset with timestamp features.
Creating the DataPot object.
End of explanation
"""
... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/quantile_regression.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import statsmodels.formula.api as smf
import matplotlib.pyplot as plt
data = sm.datasets.engel.load_pandas().data
data.head()
"""
Explanation: Quantile regression
This example page shows how to use statsmodels' QuantReg class to r... |
csiu/100daysofcode | lol/visualizing-higher-dimensions.ipynb | mit | # Load data
dat = pd.read_csv("lol_base_stats.tsv", sep="\t")
dat.head()
"""
Explanation: Load yesterday's data
End of explanation
"""
from bs4 import BeautifulSoup
import requests
primary_role = []
for url in dat.href:
html_data = requests.get(url).text
soup = BeautifulSoup(html_data, "html5lib")
... |
wuafeing/Python3-Tutorial | 02 strings and text/02.15 interpolating variables in strings.ipynb | gpl-3.0 | s = "{name} has {n} messages."
s.format(name = "Guido", n = 37)
"""
Explanation: Previous
2.15 字符串中插入变量
问题
你想创建一个内嵌变量的字符串，变量被它的值所表示的字符串替换掉。
解决方案
Python 并没有对在字符串中简单替换变量值提供直接的支持。 但是通过使用字符串的 format() 方法来解决这个问题。比如：
End of explanation
"""
name = "Guido"
n = 37
s.format_map(vars())
"""
Explanation: 或者，如果要被替换的变量能在变量域中找到， ... |
GrachevArtem/dl_course | week_03/HW2_Fully_Connected_NN.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
def rel_error(x, y):
""" returns relative error """
return np.max(np.abs(x - y) / ... |
VandroiyLabs/platemate | examples/iGEM 2015 Interlab - BrasilUSP.ipynb | gpl-3.0 | %matplotlib inline
import pylab as pl
from math import sqrt
import sys
# importing platemate
sys.path.insert(0, '../src')
import platemate
"""
Explanation: Interlab from iGEM 2015
End of explanation
"""
ColumnNames = {
'C' : "Dev1",
'D' : "Dev2",
'E' : "Dev3"
}
controlNames = {
'A' : "LB",
... |
magnastrazh/NEUCOGAR | nest/serotonin/research/C/nest-2.10.0/doc/model_details/IAF_neurons_singularity.ipynb | gpl-2.0 | import sympy as sp
sp.init_printing(use_latex=True)
from sympy.matrices import zeros
tau_m, tau_s, C, h = sp.symbols('tau_m, tau_s, C, h')
"""
Explanation: IAF neurons singularity
This notebook describes how NEST handles the singularities appearing in the ODE's of integrate-and-fire model neurons with alpha- or expone... |
jdfekete/progressivis | doc/RunStepDecorators.ipynb | bsd-2-clause | import progressivis.core.decorators
"""
Explanation: How to decorate the run_step() method (and why)
The use of decorators is optional and intended to structure and make the run_step()method clearer and more compact.
In order to use the decorators you have to import them as follows:
End of explanation
"""
from progr... |
evangelistalab/forte | tutorials/Exercise_01.00_psi4_api.ipynb | lgpl-3.0 | import psi4
"""
Explanation: Forte Exercise 1.00: Running psi4 jobs in Jupyter notebooks
Goal: find the symmetry of the triple ground state of linear water
In this exercise you are going to use psi4's Python API to compute the restricted open-shell Hartree-Fock wave function of triplet linear water and will use the s... |
muratcemkose/cy-rest-python | basic/CytoscapeJS_visualization.ipynb | mit | # Package to render networks in Cytoscape.js
from py2cytoscape import cytoscapejs as cyjs
# And standard JSON utility
import json
"""
Explanation: Using Cytoscape.js in Jupyter Notebook
by Keiichiro Ono
Introduction
If you use Jupyetr Notebook with cyREST, you can script your workflow. And in some cases, you may ... |
jameszw/udacity-deep-learning | project4-language-translation/dlnd_language_translation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
"""
Explanation: Language Translation
In this project, you’re going... |
ledeprogram/algorithms | class3/homework/benzaquen_mercy_3_2.ipynb | gpl-3.0 | import random
#We can do this in two different ways.
#I will do both and see the time difference.
#With the ---linear search--- we can check ALL values in order until we find the one we want
#With the ---binary search--- we check for the value in the middle of the list, eliminating the
#half of the list that are smal... |
jhillairet/ICRH | TWA/Master Fusion Hands-on/Master Fusion Hands-on TWA.ipynb | mit | %load_ext autoreload
%autoreload 2
%pylab
%matplotlib inline
import skrf as rf
rf.stylely()
"""
Explanation: Master Fusion Hands-on RF Directional Coupler and Travelling Wave Antenna
End of explanation
"""
idx_ps = np.linspace(0, 180, 19) # indexes of the phase shifter, from 0 to 180 per 10
# creating a NetworkS... |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/prod/n08_simple_q_learner_1000_states_full_training_25_epochs.ipynb | mit | # Basic imports
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
import scipy.optimize as spo
import sys
from time import time
from sklearn.metrics import r2_score, median_absolute_error
from multiprocessing import Pool
%matplotlib inline
%pylab inline
pylab.rcPar... |
davicsilva/dsintensive | notebooks/flightDelayPrepData_v2.ipynb | apache-2.0 | from datetime import datetime
# Pandas and NumPy
import pandas as pd
import numpy as np
# Read the CSV with flights records (separation = ";")
flights = pd.read_csv('data/arfsample-date.csv', sep = ';', dtype = str)
flights.head()
# Lambda function
# 1 - Used to adjust date columns to ISO format
# flights['column-w... |
barjacks/foundations-homework | 11/Homework_11_working_with_parking_data.ipynb | mit | import pandas as pd
"""
Explanation: Homework 11: PARKING TICKETS
End of explanation
"""
#As the datatypes in all of the columns vary, I decided to to make all the values, except for the ones I specify
#into str. This also takes care of questions 1. -> dtype=str
"""
Explanation: 1. I want to make sure my Plate ID i... |
AllenDowney/HeriReligion | freshman/freshman_survey.ipynb | mit | %matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from utils import decorate, savefig
import statsmodels.formula.api as smf
import warnings
warnings.filterwarnings('error')
"""
Explanation: Changes in religious affiliation and attendance
Analysis based on data from the CIRP ... |
ES-DOC/esdoc-jupyterhub | notebooks/noaa-gfdl/cmip6/models/sandbox-3/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'noaa-gfdl', 'sandbox-3', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: NOAA-GFDL
Source ID: SANDBOX-3
Topic: Aerosol
Sub-Topics: Transport, Emissi... |
darioizzo/fast-approx-ML | fast-approx-ML-slide14.ipynb | mit | a = pk.load(open("slide14.pkl","rb"), encoding='latin1')
print("DATA = ", a)
print("ONE ROW = ", a[0])
"""
Explanation: We load the data file obtained from the optimization (in low-thrust) of many legs (GTOC7) for the maximum initial mass:
ast1, ast2, $t_1$, $t_2$, $\Delta V_L$, $m^$, $m^_L$, $m^*_D$
We want to study... |
y2ee201/Deep-Learning-Nanodegree | sentiment_network/Sentiment Classification - How to Best Frame a Problem for a Neural Network (Project 1).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()... |
Chipe1/aima-python | notebooks/chapter19/RNN.ipynb | mit | import warnings
warnings.filterwarnings("ignore", category=FutureWarning)
import os, sys
sys.path = [os.path.abspath("../../")] + sys.path
from deep_learning4e import *
from notebook4e import *
psource(SimpleRNNLearner)
"""
Explanation: RNN
Overview
When human is thinking, they are thinking based on the understanding... |
tensorflow/docs-l10n | site/en-snapshot/guide/data_performance.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... |
astro4dev/OAD-Data-Science-Toolkit | Teaching Materials/Programming/Python/Python3Espanol/1_Introduccion/03. Numeros y jerarquía de operaciones.ipynb | gpl-3.0 | 2.**5
2**5
3/2
3//2
2//3
21/3
21//3
21%3
"""
Explanation: Números y jerarquía de operaciones
Números enteros y flotantes
Jerarquía de operaciones
Asignación de variables
Números enteros y flotantes
Con los números se pueden realizar los siguientes tipos de operaciones:
| Operación | Resultado |
| ----... |
4dsolutions/tetravolumes | Computing Volumes.ipynb | apache-2.0 | import math
xyz_volume = math.sqrt(2)**3
ivm_volume = 3
print("XYZ units:", xyz_volume)
print("IVM units:", ivm_volume)
print("Conversion constant:", ivm_volume/xyz_volume)
"""
Explanation: Philosophy for Physics Majors<br/>Oregon Curriculum Network
<h3 align="center">Computing Volumes in XYZ and IVM units</h3>
<h4 al... |
menpo/menpofit-notebooks | notebooks/DeformableModels/SupervisedDescentMethod/SDMs Basics.ipynb | bsd-3-clause | %matplotlib inline
from pathlib import Path
path_to_lfpw = Path('/vol/atlas/databases/lfpw')
path_to_lfpw = Path('/home/nontas/Dropbox/lfpw/')
import menpo.io as mio
training_images = []
# load landmarked images
for i in mio.import_images(path_to_lfpw / 'trainset', verbose=True):
# crop image
i = i.crop_to_... |
specdb/specdb | docs/nb/Query_Catalog.ipynb | gpl-3.0 | # import
from astropy import units as u
from astropy.coordinates import SkyCoord
import specdb
from specdb.specdb import SpecDB
from specdb import specdb as spdb_spdb
from specdb.cat_utils import flags_to_groups
"""
Explanation: Querying a specdb source Catalog [v1.0]
End of explanation
"""
db_file = specdb.__path_... |
Xilinx/BNN-PYNQ | notebooks/LFC-QNN_MNIST.ipynb | bsd-3-clause | import bnn
"""
Explanation: QNN on Pynq
This notebook covers how to use low quantized Neural Networks on Pynq for inference on MNIST dataset by using LFC network composed of 4 fully connected layers with 1024 neurons each. There are 2 networks using different precision:
LFCW1A1 using 1 bit weights and 1 activation,
... |
tpin3694/tpin3694.github.io | python/matplotlib_bar_plot.ipynb | mit | %matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Title: Bar Plot In MatPlotLib
Slug: matplotlib_bar_plot
Summary: Bar Plot In MatPlotLib
Date: 2016-05-01 12:00
Category: Python
Tags: Data Visualization
Authors: Chris Albon
Based on: Sebastian Raschka.
Prelimi... |
steinam/teacher | jup_notebooks/datenbanken/SubSelects.ipynb | mit | %load_ext sql
%sql mysql://steinam:steinam@localhost/sommer_2015
"""
Explanation: Subselects
End of explanation
"""
%%sql
select k.kd_id, k.kd_plz,
(select count(a.Au_ID) from auftrag a where a.au_kd_id = k.kd_id ) as AnzahlAuftr,
(select count(f.`f_id`) from fahrten f, auftrag a
where f.f_au_id... |
weleen/mxnet | example/notebooks/basic/advanced_img_io.ipynb | apache-2.0 | %matplotlib inline
from __future__ import print_function
import os
import time
# set the number of threads you want to use before importing mxnet
os.environ['MXNET_CPU_WORKER_NTHREADS'] = '4'
import mxnet as mx
import numpy as np
import matplotlib.pyplot as plt
import cv2
# download example images
os.system('wget http... |
Kaggle/learntools | notebooks/machine_learning/raw/ex7.ipynb | apache-2.0 | # Set up code checking
from learntools.core import binder
binder.bind(globals())
from learntools.machine_learning.ex7 import *
# Set up filepaths
import os
if not os.path.exists("../input/train.csv"):
os.symlink("../input/home-data-for-ml-course/train.csv", "../input/train.csv")
os.symlink("../input/home-dat... |
YingzhiGou/AI-Meetup-Decision-Systems-Lab-UOW | tutorial/machine_learning_with_python/02_Classification.ipynb | apache-2.0 | # fetch data set
from sklearn.datasets import fetch_mldata
mnist = fetch_mldata('MNIST original', data_home="data") # download data here, it may take some time depends on the network
mnist # this will show the dataset object
# now let's take a look at the data
X, y = mnist["data"], mnist["target"]
X.shape
y.shape
... |
rpmunoz/topicos_ingenieria_1 | clase_1/01 - Introduccion a Python.ipynb | gpl-3.0 | # primero inicializamos las variables a y b
a = 2
b = 3.5
# verificamos los valores y aplicamos operaciones simples
print("a = ",a)
print("b = ",b)
c = a + b
print(c)
print("suma de a + b = ",c)
d = a * b
print("multiplicación de a * b = ",d)
e = a ** b
# el signo "**" representa "elevar a"
print("a elevado a b = ",e... |
rashikaranpuria/Machine-Learning-Specialization | Regression/Assignmet_five/.ipynb_checkpoints/week-5-lasso-assignment-1-blank-checkpoint.ipynb | mit | import graphlab
graphlab.product_key.set_product_key("C0C2-04B4-D94B-70F6-8771-86F9-C6E1-E122")
"""
Explanation: Regression Week 5: Feature Selection and LASSO (Interpretation)
In this notebook, you will use LASSO to select features, building on a pre-implemented solver for LASSO (using GraphLab Create, though you can... |
astro4dev/OAD-Data-Science-Toolkit | Teaching Materials/Programming/Python/Python3Espanol/1_Introduccion/07. Cadenas, tuplas y diccionarios.ipynb | gpl-3.0 | fruta = "banano"
dulce = 'bocadillo'
"""
Explanation: Cadenas o strings
En Python las cadenas son definidas como listas de caracteres, por lo que es posible aplicarles rebanado y las demás operaciones que vimos en la sección anterior.
Una cadena se puede formar usando comillas dobles o sencillas, de la siguiente mane... |
MichaelMartinez/watson_play | Basic_Concept_Graph.ipynb | mit | py = ConceptModel(['Python'])
py.concepts()
"""
Explanation: Lets Concept all the things!!!
First we need to import the goodies. Then we will start with Python...
End of explanation
"""
py.explode()
len(py.concepts())
"""
Explanation: Now I am going to explode the and see what it does...
End of explanation
"""
py... |
birdsarah/bokeh-miscellany | 0.12.14 bugs - test in latest dev.ipynb | gpl-2.0 | N = 10000
x = np.random.normal(0, np.pi, N)
y = np.sin(x) + np.random.normal(0, 0.2, N)
p = figure(output_backend="webgl")
p.scatter(x, y, alpha=0.1)
show(p)
"""
Explanation: WebGL problems
Drag around canvas is shifted down, cut off at top spilling over bottom
End of explanation
"""
!conda list | egrep "jupyter|n... |
searchs/bigdatabox | Workshop_Data_Analysis_With_Pandas_Numpy.ipynb | mit | import numpy as np
import pandas as pd
test_scores = [70,65,95,88]
type(test_scores)
scores = np.array(test_scores)
type(scores)
scores.mean()
print(scores.max(), scores.min())
#More Calculations
income = np.array([75000, 55000, 88000, 125000, 64000, 97000])
print(income.mean())
print(income.max())
print(income.m... |
DouglasLeeTucker/DECam_PGCM | notebooks/DESY6DeepFields_PhotomZPs_tied_to_refcat2.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
from scipy import interpolate
import glob
import math
import os
import subprocess
import sys
import gc
import glob
import pickle
import easyaccess as ea
#import AlasBabylon
import fitsio
from astropy.io import fits
import astropy.coordinates as coord
from astropy.coordinates i... |
deeplook/notebooks | freemium/freemium.ipynb | mit | import random
import urllib
import utils
app_id = utils.app_id
app_code = utils.app_code
berlin_lat_lon = [52.5, 13.4]
here_berlin_addr = 'Invalidenstr. 116, 10115 Berlin, Germany'
"""
Explanation: Tour of Free(mium) HERE APIs
A short "teaser" presentation rushing through a small subset of many free APIs made by... |
lubegamark/ple2014 | clean_data.ipynb | mit | """
Import different packages required
"""
import utils
import settings
import pandas as pd
"""
Explanation: Cleaning PLE DataSet
Introduction
In this section of the project we shall clean the data so that we can do analysis in other sections.
Upon inspection of the Dataset, a few issues were encountered.
- The files... |
gee-community/gee_tools | notebooks/bitreader/BitReader.ipynb | mit | import ee
ee.Initialize()
from geetools import bitreader, cloud_mask
options = {
'0-1': {0:'clear', 1:'cloud', 2:'mix'}, # cloud state
'2-2': {0: 'no_shadow', 1:'shadow'}, # cloud shadow (bit 0 is not needed)
'6-7': {0:'climatology', 1:'low', 2:'average', 3:'high'} # land/water flag
}
reader = bitreader.BitRe... |
flohorovicic/pynoddy | docs/notebooks/6-Reproducible-Experiments.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 matp... |
ericmjl/nnet-HA | Prototype Neural Network for predicting HA host tropism.ipynb | mit | import pandas as pd
import numpy as np
from Bio import SeqIO
from Bio import AlignIO
from Bio.Align import MultipleSeqAlignment
from collections import Counter
from sklearn.preprocessing import LabelBinarizer
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier, Extr... |
relopezbriega/mi-python-blog | content/notebooks/ecuaciones-diferenciales.ipynb | gpl-2.0 | import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
import sympy
from scipy import integrate
# imprimir con notación matemática.
sympy.init_printing(use_latex='mathjax')
# importando modulos de fenics
import dolfin
import mshr
%matplotlib inline
dolfin.parameters["reorder_dofs_serial"] = F... |
ES-DOC/esdoc-jupyterhub | notebooks/dwd/cmip6/models/mpi-esm-1-2-hr/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'dwd', 'mpi-esm-1-2-hr', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: DWD
Source ID: MPI-ESM-1-2-HR
Topic: Ocnbgchem
Sub-Topics: Tracers.
Pro... |
albahnsen/PracticalMachineLearningClass | notebooks/07-regularization.ipynb | mit | # read in the dataset
import pandas as pd
url = 'https://github.com/albahnsen/PracticalMachineLearningClass/raw/master/datasets/communities.data'
crime = pd.read_csv(url, header=None, na_values=['?'])
crime.head()
# examine the response variable
crime[127].describe()
# remove categorical features
crime.drop([0, 1, 2,... |
tpin3694/tpin3694.github.io | machine-learning/selecting_best_alpha_value_in_ridge_regression.ipynb | mit | # Load libraries
from sklearn.linear_model import RidgeCV
from sklearn.datasets import load_boston
from sklearn.preprocessing import StandardScaler
"""
Explanation: Title: Selecting The Best Alpha Value In Ridge Regression
Slug: selecting_best_alpha_value_in_ridge_regression
Summary: How to select the best alpha v... |
besser82/shogun | doc/ipython-notebooks/multiclass/KNN.ipynb | bsd-3-clause | import numpy as np
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
from scipy.io import loadmat, savemat
from numpy import random
from os import path
mat = loadmat(os.path.join(SHOGUN_DATA_DIR, 'multiclass/usps.mat'))
Xall = mat['data']
Yall = np.array(mat['label'].squeeze(), dtype=n... |
giotta/EUR8217 | labo/labo-py.ipynb | mit | # jupyter magic
%matplotlib inline
# python scientific stack
import numpy as np
import pandas as pd
import scipy.stats as scs
import statsmodels
import statsmodels.api as sm
import statsmodels.formula.api as smf
import statsmodels.stats as sms
# fileformat
from simpledbf import Dbf5
from sas7bdat import SAS7BDAT
"""... |
InsightLab/data-science-cookbook | 2020/02-python-bibliotecas-manipulacao-dados/Numpy.ipynb | mit | import numpy as np
"""
Explanation: NumPy
Numpy é um pacote fundamental para programação científica com Python. Ele traz consigo uma variedade de operações matemáticas, principalmente referente à operações algébricas com dados N-dimensionais!
End of explanation
"""
a = np.array([1, 2, 3])
print(repr(a), a.shape, end... |
hparik11/Deep-Learning-Nanodegree-Foundation-Repository | transfer-learning/Transfer_Learning_Solution.ipynb | mit | from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
vgg_dir = 'tensorflow_vgg/'
# Make sure vgg exists
if not isdir(vgg_dir):
raise Exception("VGG directory doesn't exist!")
class DLProgress(tqdm):
last_block = 0
def hook(self, block_num=1, block_size=1, total_s... |
saksham/recommender-systems | Collaborative Filtering.ipynb | mit | import numpy as np; import pandas as pd; from pandas import Series, DataFrame
"""
Explanation: Collaborative Filtering in 9 Lines of Code
I was looking for some sample code for getting my hands dirty with some recommender system code, particularly around collaborative filtering. I came across this blog post with the s... |
atitus5/MiniPlaces | tensorflow-tutorial/basics.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
"""
Explanation: TensorFlow
TensorFlow is an open source library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional dat... |
skkandrach/foundations-homework | Homework_5_Soma.ipynb | mit | !pip3 install requests
import requests
response = requests.get('https://api.spotify.com/v1/search?query=Lil+&offset=0&limit=50&type=artist&market=US')
data = response.json()
data.keys()
artist_data = data['artists']['items']
for artist in artist_data:
print(artist['name'], artist['popularity'], artist['genres'])
... |
massimo-nocentini/competitive-programming | tutorials/dynamic-programming.ipynb | mit | from functools import lru_cache
import operator
from itertools import count, zip_longest, chain
from collections import OrderedDict
from random import randint
def memo_holder(optimizer):
def f(*args, **kwds):
return_memo_table = kwds.pop('memo_table', False)
pair = optimized, memo_table = optimizer... |
afronski/playground-notes | introduction-to-big-data-with-apache-spark/solutions/lab4_machine_learning_student.ipynb | mit | import sys
import os
from test_helper import Test
baseDir = os.path.join('data')
inputPath = os.path.join('cs100', 'lab4', 'small')
ratingsFilename = os.path.join(baseDir, inputPath, 'ratings.dat.gz')
moviesFilename = os.path.join(baseDir, inputPath, 'movies.dat')
"""
Explanation: version 1.0.1
+
Introduction to M... |
vnaidu/loan-status | notebooks/loan-status.ipynb | mit | from IPython.display import display
from IPython.core.display import HTML
import warnings
warnings.filterwarnings('ignore')
import os
if os.getcwd().split('/')[-1] == 'notebooks':
os.chdir('../')
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
impo... |
chseifert/tutorials | data-science/Classical-Machine-Learning-for-Beginners (Iris dataset).ipynb | apache-2.0 | # pythons scientific computing package and a random number generator
import numpy as np
import random
# machine learning classifiers and metrics
from sklearn.naive_bayes import MultinomialNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_... |
ucsd-ccbb/jupyter-genomics | notebooks/dnaSeq/VAPr_Variant_Annotation_Prioritization/ VAPr_Variant_Annotation_Prioritization.ipynb | mit | os.getcwd()
import os
import re
import sys
import vcf
import time
import pysam
import myvariant
import collections
import numpy as np
import pandas as pd
sys.path.append(os.getcwd().replace("notebooks/dnaSeq/VAPr_Variant_Annotation_Prioritization", "src/dnaSeq/VAPr"))
#variantannotation functions
from variantannotati... |
wind-python/windpowerlib | example/turbine_cluster_modelchain_example.ipynb | mit | import pandas as pd
import modelchain_example as mc_e
from windpowerlib import TurbineClusterModelChain, WindTurbineCluster, WindFarm
import logging
logging.getLogger().setLevel(logging.DEBUG)
# Get weather data
weather = mc_e.get_weather_data('weather.csv')
print(weather[['wind_speed', 'temperature', 'pressure']][0... |
xdze2/thermique_appart | Comparaison_Text.ipynb | mit | coords_grenoble = (45.1973288, 5.7139923) #(45.1973288, 5.7103223)
startday, lastday = pd.to_datetime('22/06/2017', format='%d/%m/%Y'), pd.to_datetime('now')
# download the data:
data = wf.buildmultidayDF(startday, lastday, coords_grenoble )
import emoncmsfeed as getfeeds
dataframefreq = '10min'
feeds = { 'T_ext'... |
jtwhite79/pyemu | verification/henry/.ipynb_checkpoints/verify_unc_results-checkpoint.ipynb | bsd-3-clause | %matplotlib inline
import os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pyemu
"""
Explanation: verify pyEMU results with the henry problem
End of explanation
"""
la = pyemu.Schur("pest.jco",verbose=False)
la.drop_prior_information()
jco_ord = la.jco.get(la.pst.obs_names,la.pst.par_... |
MIT-LCP/mimic-code | mimic-iv-cxr/dcm/create-mimic-cxr-jpg-metadata.ipynb | mit | # load json
with gzip.open('dicom-metadata.json.gz', 'r') as fp:
tmp = json.load(fp)
dcm_metadata = dict()
# convert from length list of 1 item dicts to single dict
for d in tmp:
for k, v in d.items():
dcm_metadata[k] = v
del tmp
# figure out how many unique top level meta-data fields in the ... |
laenderoliveira/exerclivropy | listagens-jupyter-notebook/empacotamento-desempacotamento-parametros.ipynb | mit | def soma(a, b):
print(a+b)
l = [2, 3]
soma(*l)
"""
Explanation: 8.7 Empacotamento e desempacotamento de parâmetros
Listagem 8.28 - Empacotamento de parâmetros em uma lista
End of explanation
"""
def barra(n=10, c="*"):
print(c*n)
l = [[5,"-"], [10, "*"], [5], [6, "."]]
for e in l:
barra(*e)
"""
Explanat... |
eshlykov/mipt-day-after-day | labs/term-6/lab-4-1.ipynb | unlicense | import numpy as np; import scipy as sps; import matplotlib.pyplot as plt; import pandas as pd
%matplotlib inline
"""
Explanation: Изучение спектров атома водорода и молекулы йода
End of explanation
"""
table_1 = pd.read_excel('lab-4-1.xlsx', '1'); table_1.iloc[:, :4]
"""
Explanation: Неон и ртуть
End of explanation... |
bspalding/research_public | lectures/drafts/Principal component analysis.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
# Import the data we want to model
start = '2014-01-01'
end = '2015-01-01'
r1 = get_pricing('MSFT', fields='price', start_date=start, end_date=end).pct_change()[1:]
r2 = get_pricing('SPY', fields='price', start_date=start, end_dat... |
Geosyntec/pycvc | examples/Data Summaries.ipynb | bsd-3-clause | %matplotlib inline
import os
import sys
import datetime
import warnings
import numpy as np
import matplotlib.pyplot as plt
import pandas
import seaborn
seaborn.set(style='ticks', context='paper')
import wqio
import pybmpdb
import pynsqd
import pycvc
min_precip = 1.9999
palette = seaborn.color_palette('deep', n_col... |
Hyperparticle/deep-learning-foundation | lessons/intro-to-tflearn/TFLearn_Digit_Recognition_Solution.ipynb | mit | # Import Numpy, TensorFlow, TFLearn, and MNIST data
import numpy as np
import tensorflow as tf
import tflearn
import tflearn.datasets.mnist as mnist
"""
Explanation: Handwritten Number Recognition with TFLearn and MNIST
In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-9.
This... |
joshnsolomon/phys202-2015-work | assignments/assignment05/InteractEx02.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 2
Imports
End of explanation
"""
# YOUR CODE HERE
def plot_sine1(a, b):
t = np.linspace(0,4*np.pi,400)
... |
CLandauGWU/group_e | DG/lesson_08DG.ipynb | mit | ! conda install geopandas -qy
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import geopandas as gpd
from shapely.geometry import Point, Polygon
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_select... |
prasants/pyds | Case Study - Hunt a Serial Killer.ipynb | mit | import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams["figure.figsize"] = (20,5) # This can be the default, or else, you can also specify this every time you generate a graph
import vincent
vincent.core.initialize_notebook()
"""
Explanation: Tabl... |
pycam/python-basic | python_basic_2_3.ipynb | unlicense | path = "data/datafile.txt"
fileObj = open( path )
"""
Explanation: An introduction to solving biological problems with Python
Session 2.3: Files
Using files
Reading from files
Exercises 2.3.1
Writing to files
Exercises 2.3.2
Data input and output (I/O)
So far, all that data we have been working with has been written... |
krondor/dsxlunch | Survey Sentiment and Tonal Analysis.ipynb | gpl-3.0 | from IPython.display import HTML
HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide();
} else {
$('div.input').show();
}
code_show = !code_show
}
$( document ).ready(code_toggle);
</script>
<form action="javascript:code_toggle()"><input type="submit" value="Click here... |
notnil/udacity-ml-capstone | project.ipynb | mit | import glob
import subprocess
import json
import os
import csv
from tqdm import tnrange, tqdm_notebook
from keras.preprocessing import image
from keras.applications.inception_v3 import InceptionV3, preprocess_input
from keras.models import Model, load_model
from keras.layers import Input
import numpy as np
# Adapted f... |
GoogleCloudPlatform/training-data-analyst | courses/ai-for-finance/practice/momentum_using_hurst.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
"""
Explanation: Improve Momentum Trading strategies using Hurst
In this notebook we'll implementing a strategy to trade on momentum. You'll be using the training wheels version of Auquan's toolbox to abstract out the details since the full version of toolbox is a bit comprehensive t... |
metpy/MetPy | v0.8/_downloads/Gradient.ipynb | bsd-3-clause | import numpy as np
import metpy.calc as mpcalc
from metpy.units import units
"""
Explanation: Gradient
Use metpy.calc.gradient.
This example demonstrates the various ways that MetPy's gradient function
can be utilized.
End of explanation
"""
data = np.array([[23, 24, 23],
[25, 26, 25],
... |
tpin3694/tpin3694.github.io | python/pandas_sampling_dataframe.ipynb | mit | import pandas as pd
import numpy as np
"""
Explanation: Title: Random Sampling Dataframe
Slug: pandas_sampling_dataframe
Summary: Random Sampling Dataframe
Date: 2016-05-01 12:00
Category: Python
Tags: Data Wrangling
Authors: Chris Albon
import modules
End of explanation
"""
raw_data = {'first_name': ['Jason', 'Mo... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/image_classification/labs/2_mnist_models.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# Here we'll show the currently installed version of TensorFlow
import tensorflow as tf
print(tf.__version__)
from datetime import datetime
import os
PROJECT = "your-project-id-here" # REPLACE WITH YOUR PROJECT ID
BUCKET = "your-bucket-id-here" # R... |
ispamm/tensorflow-tutorial | 1-Reti-neurali-feedforward.ipynb | gpl-3.0 | from sklearn import datasets
(X, y) = datasets.make_moons(n_samples=1000)
(X_tst, y_tst) = datasets.make_moons(n_samples=1000)
"""
Explanation: Tutorial 1 - Reti neurali feedforward in TensorFlow
Prerequisiti per il tutorial:
* T0 - Concetti base di TensorFlow
Contenuti del tutorial:
1. Reti neurali feedforward.
2. Im... |
NikitaLoik/machineLearning_andrewNg | notebooks/1_linear_regression.ipynb | mit | file_path_1 = '../course_materials/ex1data1.txt'
"""
Explanation: 1 Univariate Linear Regression
End of explanation
"""
df = pd.read_csv(file_path_1, header = None)
n_samples, n_variables = df.shape
print(f"\tsample # {n_samples}\n\tvariables # {n_variables}")
df.head()
Xs = df.iloc[:, :1]
Ys = df.iloc[:, 1:2]
plt.... |
AllenDowney/DataExploration | modeling.ipynb | mit | from __future__ import print_function, division
import analytic
import brfss
import nsfg
import thinkstats2
import thinkplot
import pandas as pd
import numpy as np
import math
%matplotlib inline
"""
Explanation: Modeling distributions
Copyright 2015 Allen Downey
License: Creative Commons Attribution 4.0 Internatio... |
UIUC-iSchool-DataViz/spring2017 | week05/examples_week05.ipynb | mit | %matplotlib inline
import h5py
import matplotlib.pyplot as plt
import numpy as np
import ipywidgets
plt.rcParams["figure.figsize"] = (12, 10)
"""
Explanation: Image Plotting
This week, we will do just a very short bit of image plotting. We'll be using a Koala scan.
End of explanation
"""
f = h5py.File("/srv/nbgrad... |
jinzishuai/learn2deeplearn | deeplearning.ai/C2.ImproveDeepNN/week3/tensorflow_assignment/Tensorflow+Tutorial.ipynb | gpl-3.0 | import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.python.framework import ops
from tf_utils import load_dataset, random_mini_batches, convert_to_one_hot, predict
%matplotlib inline
np.random.seed(1)
"""
Explanation: TensorFlow Tutorial
Welcome to this w... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch3-Animation_ThreePhaseFluxes2and4poles.ipynb | unlicense | %pylab notebook
"""
Explanation: Electric Machinery Fundamentals 5th edition
Chapter 3
Animation: Three-phase fluxes
(based on Example 3-1)
Calculate the net magetic field produced by a three-phase stator (adapted for 50Hz).
Showing a 2-pole and a 4-pole machine side by side
Import the PyLab namespace (provides set of... |
GoogleCloudPlatform/bigquery-notebooks | notebooks/community/analytics-componetized-patterns/retail/ltv/bqml/notebooks/bqml_automl_ltv_activate_lookalike.ipynb | apache-2.0 | # Install libraries.
# The magic cells insures that those libraries can be part of a custom container
# if moving the code somewhere else.
%pip install -q googleads
%pip install -q -U kfp matplotlib Faker --user
# Automatically restart kernel after installs
# import IPython
# app = IPython.Application.instance()
# app... |
quole/gensim | docs/notebooks/atmodel_tutorial.ipynb | lgpl-2.1 | !wget -O - 'http://www.cs.nyu.edu/~roweis/data/nips12raw_str602.tgz' > /tmp/nips12raw_str602.tgz
import tarfile
filename = '/tmp/nips12raw_str602.tgz'
tar = tarfile.open(filename, 'r:gz')
for item in tar:
tar.extract(item, path='/tmp')
"""
Explanation: The author-topic model: LDA with metadata
In this tutorial, ... |
ghvn7777/ghvn7777.github.io | content/python3-note/2-type.ipynb | apache-2.0 | x = 100
print(id(globals))
print(id(locals))
globals()
def test():
x = "hello world"
print(locals())
print(id(globals))
print(id(locals))
test()
"""
Explanation: 类型
基本环境
名字空间
Python 中，每个模块（源码文件）有一个全局名字空间，根据代码作用域，有当前名字（本地名字）空间，如果直接在模块级别执行，本地名字空间和全局名字空间没有区别，但在函数内，当前名字空间指函数作用域指的是函数作用域
End of explan... |
synthicity/activitysim | activitysim/examples/example_estimation/notebooks/22_trip_dest.ipynb | agpl-3.0 | import larch # !conda install larch #for estimation
import pandas as pd
import numpy as np
import yaml
import larch.util.excel
import os
"""
Explanation: Estimating Trip Destination Choice
This notebook illustrates how to re-estimate a single model component for ActivitySim. This process
includes running ActivityS... |
crowd-course/datascience | Ng's Machine Learning Exercise 6.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
from scipy.io import loadmat
%matplotlib inline
raw_data = loadmat('ex6data1.mat')
raw_data
"""
Explanation: Ng's Machine Learning Exercise 6 - Support Vector Machines
This notebook is a Python-implementation solution for the ... |
davofis/computational_seismology | 08_finite_volumes/fv_advection_1d.ipynb | gpl-3.0 | # Import all necessary libraries, this is a configuration step for the exercise.
# Please run it before the simulation code!
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
# Show the plots in the Notebook.
plt.switch_backend("nbagg")
"""
Explanation: <div style='background... |
akshaybabloo/Car-ND | Term_1/advanced_lane_finding_10/undistort_10_2.ipynb | mit | import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
%matplotlib qt
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*8,3), np.float32)
objp[:,:2] = np.mgrid[0:8, 0:6].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpo... |
ajrichards/notebook | python/indexing_in_numpy.ipynb | bsd-3-clause | import numpy as np
%matplotlib inline
x = np.arange(5)
print(np.where(x < 3))
print(x < 3)
"""
Explanation: Indexing in NumPy
np.where vs masking
np.where returns just the indices where the equivalent mask is true
this is useful if you need the actual indices (maybe for counts)
otherwise the shorthand notation (mask... |
jo-tez/aima-python | search.ipynb | mit | from search import *
from notebook import psource, heatmap, gaussian_kernel, show_map, final_path_colors, display_visual, plot_NQueens
# Needed to hide warnings in the matplotlib sections
import warnings
warnings.filterwarnings("ignore")
"""
Explanation: Solving problems by Searching
This notebook serves as supportin... |
feelpp/book.feelpp.org | pyfeelpp_tests.ipynb | gpl-3.0 | !echo "deb https://dl.bintray.com/feelpp/ubuntu bionic latest" | tee -a /etc/apt/sources.list
!wget -qO - https://bintray.com/user/downloadSubjectPublicKey?username=bintray | apt-key add -
!apt update
!apt install feelpp-quickstart feelpp-data
!apt install python3-mpi4py python3-feelpp ssh
"""
Explanation: <a href="h... |
grfiv/MNIST | vw/VW_benchmark_kvsm.ipynb | mit | from __future__ import division
import re
import numpy as np
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
%matplotlib inline
#%qtconsole
"""
Explanation: MNIST classification with Vowpal Wabbit
End of explanation
"""
!rm train_kvsm.vw.cache
!rm mnist_train_kvsm.model
!vw -d data/... |
nikbearbrown/Deep_Learning | NEU/Sai_Raghuram_Kothapalli_DL/Creditcard Fraud detection using Autoencoders.ipynb | mit | import pandas as pd
import numpy as np
import pickle
import matplotlib.pyplot as plt
from scipy import stats
import tensorflow as tf
import seaborn as sns
from pylab import rcParams
from sklearn.model_selection import train_test_split
from keras.models import Model, load_model
from keras.layers import Input, Dense
from... |
econchick/pyladies-data | Meetup Stats.ipynb | mit | from __future__ import print_function
from collections import defaultdict
import json
import os
import time
import requests
"""
Explanation: PyLadies and local Python User Groups
Last updated: August 4, 2015
I am not a statistician by trade; far from it. I did take a few stats & econometrics courses in college, but ... |
MadsJensen/intro_to_scientific_computing | notebooks/07-Control_flow.ipynb | bsd-3-clause | 1 % 2
"""
Explanation: Control flow
In a nutshell: Allow for a control over a computer program.
Types of control
If/else statements
Loops
Boolean statements
Logical statements
The anatomy of if/else statements
Statement to be evaluated
Statement of what will happen if (1) is true
Statement of what will happen if ... |
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