repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
hagne/atm-py | examples/instruments_miniSASP.ipynb | mit | from atmPy.instruments.miniSASP import miniSASP
from atmPy.tools import plt_tools
from atmPy.instruments.piccolo import piccolo
%matplotlib inline
plt_tools.setRcParams(plt)
"""
Explanation: Introduction
The miniSASP module is probably one of the more confuse modules and needs to be reorganized. Its goal is to read an... |
pschragger/big-data-python-class | Lectures/Week 5 - MapReduce - MRJob/MR_SORT.ipynb | mit | %load data/numbers.txt
"""
Explanation: examine the data
First examine the data by loading it in with the %Load command
It will fill in the block with the content of the file in the block and comment out the %Load command
End of explanation
"""
# %load code/MRSortByString.py
from mrjob.job import MRJob
class MRSor... |
jaidevd/inmantec_fdp | notebooks/day2/05_statistical_data_viz.ipynb | mit | df.plot?
"""
Explanation: Plotting options of pandas series and dataframes
End of explanation
"""
ax = pd.tools.plotting.scatter_matrix(df[['Weight', 'Height', 'MRI_Count']])
ax = pd.tools.plotting.scatter_matrix(df[['FSIQ', 'PIQ', 'VIQ']])
"""
Explanation: Exercise: Try different plotting options from the kind pa... |
matsaleh13/courses | deeplearning1/nbs/lesson6.ipynb | apache-2.0 | path = get_file('nietzsche.txt', origin="https://s3.amazonaws.com/text-datasets/nietzsche.txt")
text = open(path).read()
print('corpus length:', len(text))
chars = sorted(list(set(text)))
vocab_size = len(chars)+1
print('total chars:', vocab_size)
"""
Explanation: Setup
We're going to download the collected works of ... |
IIPBC/Material | machine_learning_Nina/Exercise1-1.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
"""
Explanation: Exercício 1.1
Regressão linear, usando o método do gradiente descendente para minimização<br>
Em regressão, o que queremos é predizer $y$ para um dado $\mathbf{x}$.<br>
Aqui vamos considerar ... |
fablln/Deep-Learning | word_prediction_lstm/TP3-notebook.ipynb | mit | import numpy as np
import collections # used to build the dictionary
import random
import time
from time import time
import pickle # may be used to save your model
import matplotlib.pyplot as plt
#Import Tensorflow and rnn
import tensorflow as tf
from tensorflow.contrib import rnn
# Target log path
logs_path = 'lst... |
intel-analytics/analytics-zoo | docs/docs/colab-notebook/orca/quickstart/autoxgboost_regressor_sklearn_boston.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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed un... |
infilect/ml-course1 | keras-notebooks/advanced/8.1-text-generation-with-lstm.ipynb | mit | import keras
import numpy as np
path = keras.utils.get_file(
'nietzsche.txt',
origin='https://s3.amazonaws.com/text-datasets/nietzsche.txt')
text = open(path).read().lower()
print('Corpus length:', len(text))
"""
Explanation: Text generation with LSTM
This notebook contains the code samples found in Chapter 8... |
chanitacr/sc-python | 01-analysing-data.ipynb | mit | import numpy
numpy.loadtxt(fname='data/weather-01.csv', delimiter = ',')
"""
Explanation: Analysing tabublar data
we are going to use a LIBRARY called numpy
End of explanation
"""
weight_kg = 55
print (weight_kg)
print ('Weight in pounds:', weight_kg * 2.2)
weight_kg = 57.5
print ('Weight in pounds:', weight_kg... |
BillyLjm/CS100.1x.__CS190.1x | ML_lab1_review_student.ipynb | mit | labVersion = 'cs190_week1_v_1_2'
"""
Explanation: Math and Python review and CTR data download
This notebook reviews vector and matrix math, the NumPy Python package, and Python lambda expressions. It also covers downloading the data required for Lab 4, where you will analyze website click-through rates. Part 1 cove... |
jorisvandenbossche/DS-python-data-analysis | _solved/pandas_02_basic_operations.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# redefining the example DataFrame
countries = pd.DataFrame({'country': ['Belgium', 'France', 'Germany', 'Netherlands', 'United Kingdom'],
'population': [11.3, 64.3, 81.3, 16.9, 64.9],
'area': [30510, 671308, 357050, 41526, 244820... |
450586509/DLNLP | src/notebooks/CNN/.ipynb_checkpoints/CNN_random_word-checkpoint.ipynb | apache-2.0 | file_names = ['stsa.fine.test','stsa.fine.train','stsa.fine.dev']
file_path = '/home/bruce/data/sentiment/citai_process'
def read_file(fname=''):
with open(join(file_path,fname)) as fr:
lines = fr.readlines()
lines = [line.strip().lower() for line in lines]
lables = [int(line[0:1]) for line in lines... |
bosscha/alma-calibrator | notebooks/2mass/13_environment-with_analysis.ipynb | gpl-2.0 | from astroquery.gaia import Gaia
tables = Gaia.load_tables(only_names=True)
for table in (tables):
print (table.get_qualified_name())
#obj = ["3C 454.3", 343.49062, 16.14821, 1.0]
obj = ["PKS J0006-0623", 1.55789, -6.39315, 1]
#obj = ["M87", 187.705930, 12.391123, 1.0]
#### name, ra, dec, radius of cone (in deg)
... |
adrifmonte/ml-intro | notebooks/machine-learning-continues.ipynb | mit | import pandas as pd
train_df = pd.read_csv('data/train.csv')
test_df = pd.read_csv('data/test.csv')
test_check_df = pd.read_csv('data/test-check.csv')
print('Train columns: ', train_df.columns.values, '\n')
print('Test columns: ', test_df.columns.values, '\n')
print('Test check columns: ', test_check_df.columns.value... |
nick-youngblut/SIPSim | ipynb/bac_genome/n1210/.ipynb_checkpoints/atomIncorp_taxaIncorp_abund-checkpoint.ipynb | mit | workDir = '/home/nick/notebook/SIPSim/dev/bac_genome1210/'
sourceDir = os.path.join(workDir, 'atomIncorp_taxaIncorp')
buildDir = os.path.join(workDir, 'atomIncorp_taxaIncorp_abund')
R_dir = '/home/nick/notebook/SIPSim/lib/R/'
"""
Explanation: Accuracy as a function taxon abundance
Variable parameters:
Partition commu... |
masedki/masedki.github.io | enseignements/python/nn_conv.ipynb | mit | ## pour tracer des figures
import matplotlib.pyplot as plt
## pour déclarer le CNN
import keras
from keras import datasets
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Activation, Conv2D, MaxPooling2D
from keras import backend as K
import tensorflow as tf
"""
Explanation: Class... |
hypergravity/astrostatistics | python/slides1.ipynb | mit | # Basic python
print("Hello world!")
print(type("Hello world!"))
print(type("Hello world!")==str)
# dir()
# Built-in data types
# int
print(type(1))
# float
print(type(1.0))
# str
print(type("Hello World"))
# bool
print(type(False))
# Built-in data types: list
alist = [1, 2.0, "3"]
print(alist)
print(type(ali... |
gVallverdu/cookbook | kernel_density_estimation.ipynb | gpl-2.0 | import numpy as np
import scipy as sp
from scipy.stats import gaussian_kde
from scipy.stats import norm
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
plt.rcParams["figure.figsize"] = (9, 6)
plt.rcParams["font.size"] = 18
# just for fun
# plt.xkcd(scale... |
ocefpaf/UTide | notebooks/utide_real_data_example.ipynb | mit | with open('can1998.dtf') as f:
lines = f.readlines()
print(''.join(lines[:30]))
"""
Explanation: Look at the data file to see what structure it has.
End of explanation
"""
def date_parser(year, month, day, hour):
year, month, day, hour = map(int, (year, month, day, hour))
return datetime.datetime(year, ... |
afarouky/become-datascience-master | linear-regression-tutorial/(Multiple)-Linear-Regression-tutrial.ipynb | mit | # Imports
import numpy as np
import pandas as pd
from sklearn.datasets import load_boston
#Ploting
import seaborn as sns
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
%matplotlib inline
# %matplotlib notebook
"""
Explanation: (Multiple) Linear Regression:
This Tutorial will illstrate how to... |
honjy/foundations-homework | 13/311 time series homework.ipynb | mit | df = pd.read_csv("311-2015.csv", nrows=200000, low_memory=False)
#Why can't we use parse_dates=True? --> If the date is in a format that pandas recognizes as a date like YYYYMMDD
#Keep the data set small and make sure everything works before running everything through it
df.head()
df.info()
def parse_date(str_date)... |
danmelamed/vowpal_wabbit | python/examples/poisson_regression.ipynb | bsd-3-clause | # Train log-transform model
training_samples = []
logz = np.log(0.001 + z)
vw = pyvw.vw("-b 2 --loss_function squared -l 0.1 --holdout_off -f vw.log.model --readable_model vw.readable.log.model")
for i in range(len(logz)):
training_samples.append("{label} | x:{x} y:{y}".format(label=logz[i], x=x[i], y=y[i]))
# Do h... |
jtwhite79/pyemu | examples/pyemu_ensemble_basics.ipynb | bsd-3-clause | import flopy
# load the model
model_ws = os.path.join("Freyberg","extra_crispy")
ml = flopy.modflow.Modflow.load("freyberg.nam",model_ws=model_ws)
# Because this model is old -- it predates flopy's modelgrid implementation.
# And because modelgrid has been implemented without backward compatability
# the modelgrid ... |
peishunli/peishunli.github.io | blog/python_beginning/.ipynb_checkpoints/4function-checkpoint.ipynb | gpl-3.0 | import math
x=1
y=math.sqrt
callable(x)
callable(y)
"""
Explanation: title: 函数
create: 2016.12.7
modified: 2016.12.7
tags: python
函数
参数
4
[TOC]
接下来会介绍如果将语句组织成函数。还会详细介绍参数（parameter）和作用域（scope），以及递归的概念。
1 创建函数
内建的callable函数可以用来判断函数是否可调用：
End of explanation
"""
def hello(name):
return 'Hello, ... |
martinjrobins/hobo | examples/plotting/residuals-autocorrelation.ipynb | bsd-3-clause | import pints
import pints.toy as toy
import pints.plot
import numpy as np
import matplotlib.pyplot as plt
# Use the toy logistic model
model = toy.LogisticModel()
real_parameters = [0.015, 500]
times = np.linspace(0, 1000, 100)
org_values = model.simulate(real_parameters, times)
# Add independent Gaussian noise
nois... |
ES-DOC/esdoc-jupyterhub | notebooks/cccr-iitm/cmip6/models/sandbox-3/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cccr-iitm', 'sandbox-3', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: CCCR-IITM
Source ID: SANDBOX-3
Topic: Atmoschem
Sub-Topics: Transport, ... |
CentreForCorpusResearch/clic | docs/notebooks/Concordance/.ipynb_checkpoints/A serious concordance-checkpoint.ipynb | mit | # coding: utf-8
import os
from cheshire3.baseObjects import Session
from cheshire3.document import StringDocument
from cheshire3.internal import cheshire3Root
from cheshire3.server import SimpleServer
session = Session()
session.database = 'db_dickens'
serv = SimpleServer(session, os.path.join(cheshire3Root, 'con... |
fastai/courses | deeplearning1/nbs/wordvectors.ipynb | apache-2.0 | def get_glove(name):
with open(path+ 'glove.' + name + '.txt', 'r') as f: lines = [line.split() for line in f]
words = [d[0] for d in lines]
vecs = np.stack(np.array(d[1:], dtype=np.float32) for d in lines)
wordidx = {o:i for i,o in enumerate(words)}
save_array(res_path+name+'.dat', vecs)
pickle... |
salvor7/salvor7.github.io | content/A Million Pseudo-Random Digits/How uniformly Random are RAND's 'Million Psueod-Random Digits'.ipynb | mit | import pandas as pd
the_digits = pd.read_csv('data.csv')
the_digits.head()
"""
Explanation: I found this data set on Kaggle. It is 1 million single digit numbers from 1 to 9, and I was curious if their distribution is uniform. Or said another way, how reasonable is it suggest the numbers in the list were generated un... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive/10_recommend/labs/wals.ipynb | apache-2.0 | import os
PROJECT = "cloud-training-demos" # REPLACE WITH YOUR PROJECT ID
BUCKET = "cloud-training-demos-ml" # REPLACE WITH YOUR BUCKET NAME
REGION = "us-central1" # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
# Do not change these
os.environ["PROJECT"] = PROJECT
os.environ["BUCKET"] = BUCKET
os.environ["REGION"]... |
mjones01/NEON-Data-Skills | code/Python/remote-sensing/hyperspectral-data/Intro_NEON_AOP_HDF5_Reflectance_Tiles_py.ipynb | agpl-3.0 | #Check that you are using the correct version of Python (should be 3.5 for these tutorials)
import sys
sys.version
"""
Explanation: syncID: 61ad1fc43ddd45b49cad1bca48656bbe
title: "NEON AOP Hyperspectral Data in HDF5 format with Python - Tiles"
description: "Learn how to read NEON AOP hyperspectral flightline data us... |
tritemio/multispot_paper | out_notebooks/Multi-spot vs usALEX FRET histogram comparison-out-17d.ipynb | mit | data_id = '17d'
ph_sel_name = "None"
data_id = "17d"
"""
Explanation: Executed: Mon Mar 27 22:24:30 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._... |
fotis007/python_intermediate | Python_2_4.ipynb | gpl-3.0 | import networkx as nx
g = nx.Graph()
g.add_node(1)
g.add_node(2)
g.add_node(3)
g.add_node(4)
#oder schneller
g.add_nodes_from([1,2,3,4])
#Hinzufügen von Kanten
g.add_edge(1,2)
g.add_edge(1,3)
g.add_edge(1,4)
#oder schneller
g.add_edges_from([(1,2),(1,3),(1,4)])
"""
Explanation: Grundbegriffe des Arbeitens mit Graph... |
gfabieno/SeisCL | Tutorial/unused/Model_Building.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
from SeisCL import SeisCL
"""
Explanation: Models generation
In this notebook, we will explore various ways of creating a model for our simulation.
Three-layer model
First, let's start by creating a single layer model with the following characteristics :
\begin{array}... |
kitu2007/dl_class | autoencoder/Convolutional_Autoencoder.ipynb | mit | %matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', validation_size=0)
img = mnist.train.images[2]
plt.imshow(img.reshape((28, 28)), cmap='Greys_r')
"""
Explanation: C... |
charlesll/RamPy | examples/Machine Learning Regression.ipynb | gpl-2.0 | %matplotlib inline
import numpy as np
np.random.seed(42) # fixing the seed
import matplotlib
import matplotlib.pyplot as plt
import rampy as rp
from scipy.stats import norm
x = np.arange(0,600,1.0)
nb_samples = 300 # number of samples in our dataset
S_1 = norm.pdf(x,loc=200.,scale=130.)
S_2 = norm.pdf(x,loc=400,scale... |
jrieke/mhbf | 5/5.ipynb | mit | from __future__ import division, print_function
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Solution from Johannes Rieke and Alex Moore¶
End of explanation
"""
mu = 0.2
sigma = 0.5
dt = 0.01
time = np.arange(0, 10, dt)
for i in range(5):
x = np.zeros_like(time)
fo... |
tatsuya-ogawa/udacity-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... |
terrydolan/lfcelite | lfcelite.ipynb | mit | %%html
<! left align the change log table in next cell >
<style>
table {float:left}
</style>
"""
Explanation: LFC Data Analysis: The Elite Strikers
See Terry's blog LFC: The Elite Strikers for a discussion of of the data analysis.
This notebook analyses Liverpool FC's elite strikers data from 1962-1963 to 2014-2015. A... |
cdt15/lingam | examples/CausalEffect(LogisticRegression).ipynb | mit | import numpy as np
import pandas as pd
import graphviz
import lingam
from lingam.utils import make_prior_knowledge
print([np.__version__, pd.__version__, graphviz.__version__, lingam.__version__])
np.set_printoptions(precision=3, suppress=True)
np.random.seed(0)
"""
Explanation: Causal Effect for Logistic Regression... |
statsmodels/statsmodels.github.io | v0.12.1/examples/notebooks/generated/wls.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
from scipy import stats
import statsmodels.api as sm
import matplotlib.pyplot as plt
from statsmodels.sandbox.regression.predstd import wls_prediction_std
from statsmodels.iolib.table import (SimpleTable, default_txt_fmt)
np.random.seed(1024)
"""
Explanation: Weighted Least Squar... |
jrg365/gpytorch | examples/02_Scalable_Exact_GPs/Grid_GP_Regression.ipynb | mit | import gpytorch
import torch
import math
"""
Explanation: GP Regression with Grid Structured Training Data
In this notebook, we demonstrate how to perform GP regression when your training data lies on a regularly spaced grid. For this example, we'll be modeling a 2D function where the training data is on an evenly spa... |
adrienhenry/characteristicTimesNetwork | compute_disorder.ipynb | mit | %matplotlib notebook
from LinearChain import *
import matplotlib.pyplot as plt
from matplotlib import rc
from scipy.integrate import ode
from scipy.integrate import quad
from scipy import stats
import numpy as np
import multiprocessing as mp
exec(open("fig_settings.py").read())
"""
Explanation: Compute disorder
Impor... |
google/data-pills | pills/GA/[DATA_PILL]_[GA]_Customer_Market_Intelligence_(CMI).ipynb | apache-2.0 | import IPython
import plotly
import plotly.offline as py
import plotly.graph_objs as go
import math
import json
import numpy as np
import pandas as pd
from scipy import spatial
from scipy.spatial import distance
from sklearn.cluster import KMeans
from google.colab import drive
from google.colab import auth
from sklearn... |
gwulfs/research_public | lectures/Arbitrage Pricing Theory.ipynb | apache-2.0 | import numpy as np
import pandas as pd
from statsmodels import regression
import matplotlib.pyplot as plt
"""
Explanation: Arbitrage Pricing Theory
By Evgenia "Jenny" Nitishinskaya and Delaney Granizo-Mackenzie.
Part of the Quantopian Lecture Series:
www.quantopian.com/lectures
github.com/quantopian/research_public
... |
IBM/differential-privacy-library | notebooks/30seconds.ipynb | mit | from sklearn import datasets
from sklearn.model_selection import train_test_split
dataset = datasets.load_iris()
X_train, X_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=0.2)
"""
Explanation: Getting started: Machine learning with differential privacy in 30 seconds
We're using the I... |
nitin-cherian/LifeLongLearning | Python/Python Morsels/multimax/my_try/multimax.ipynb | mit | multimax([])
"""
Explanation: Bonus1: Make sure the function returns an empty list if the iterable is empty
End of explanation
"""
numbers = [1, 3, 8, 5, 4, 10, 6]
odds = (n for n in numbers if n % 2 == 1)
multimax(odds)
"""
Explanation: Bonus2: Make sure the function works well with iterator such as files, generat... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/production_ml/babyweight/train_deploy.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
!pip install --user google-cloud-bigquery==2.26.0
"""
Explanation: Structured data prediction using Vertex AI Platform
Learning Objectives
Create a BigQuery Dataset and Google Cloud Storage Bucket
Export from BigQuery to CSVs in GCS
Training on Clou... |
landmanbester/fundamentals_of_interferometry | 8_Calibration/8_1_Calibration_Least_Squares_Problem.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
"""
Explanation: Outline
Glossary
8. Calibration
Previous: 8. Calibration
Next: 8.2 1GC calibration
Import standard modules:
End of explanation
"""
from scipy im... |
trsherborne/learn-python | lesson2.ipynb | mit | a = 4
b = 8
c = 9
if a%2 == 0:
print('Multiple of 2')
else:
print('Not a multiple of 2')
if b%2 == 0:
print('Multiple of 2')
else:
print('Not a multiple of 2')
if c%2 == 0:
print('Multiple of 2')
else:
print('Not a multiple of 2')
"""
Explanation: LSESU Applicable Maths Python Lesson 2
2... |
AntonelliLab/seqcap_processor | docs/notebook/subdocs/locus_selection.ipynb | mit | %%bash
source activate secapr_env
secapr locus_selection -h
"""
Explanation: Locus selection
When inspecting the results of the reference-based assembly you may find that many loci are not covered by many or any reads. In that case you may want to make a selection of loci with good coverage and continue to only work w... |
freelanceastro/galactic-machine-learning | Galaxy-Zoo-Machine-Learning.ipynb | mit | ## An attempt at Python 2 backward compatibility
from __future__ import absolute_import, division, print_function, unicode_literals
%matplotlib inline
from skimage import data, io, filters
filename = "images_training_rev1/100008.jpg"
galaxy = io.imread(filename)
io.imshow(galaxy)
io.show()
"""
Explanation: Making a f... |
GoogleCloudPlatform/ml-design-patterns | 01_need_for_design_patterns/ml_challenges.ipynb | apache-2.0 | import pandas as pd
import tensorflow as tf
from sklearn.utils import shuffle
from google.cloud import bigquery
"""
Explanation: ML Challenges
This notebook includes various code snippets mentioned in the first chapter of our Machine Learning Design Patterns book.
End of explanation
"""
# Setting a random seed in T... |
sains1/UTRonsProject | Notebooks/4_siteCanonicality.ipynb | mit | import pandas as pd
import math
import sqlite3
import numpy
"""
Explanation: Looking at the site canonicality for novel UTRons
Used cgat script gtf2gtf.py with options to output splice information
Analysed splice info of a GTF file that contained the 3' UTRs of all the novel UTRons by taking the last 2 exons of every ... |
eds-admin/eds-blog | Statistics/Hypothesis Testing.ipynb | mit | import random
random.seed(42)
def flip(n):
results ={'heads': 0, 'tails': 0}
for _ in range(n):
coin = random.choice(['heads', 'tails'])
results[coin] += 1
return results
def print_proportions(flips):
total = flips['heads'] + flips['tails']
print("Heads p={}\nTails p={}".format(
... |
HrantDavtyan/Data_Scraping | Week 7/Google_maps_API.ipynb | apache-2.0 | import googlemaps
from datetime import datetime
"""
Explanation: Google Maps API - authorized access
This notebook provides introduction to requesting data from Google Maps API using Python.
There is a standout Python package developed explicitly for that purpose. To install that package go to the comman prompt and en... |
skorokithakis/pythess-files | 015 - Breaking Benjamin/meta_alltheway/meta_alltheway.ipynb | mit | from abc import ABCMeta, abstractmethod
class Vehicle(metaclass=ABCMeta):
@abstractmethod
def change_gear(self):
pass
@abstractmethod
def start_engine(self):
pass
class Car(Vehicle): # subclass the ABC, abstract methods MUST be overridden
def __init__(self, make, model, color):
... |
stephenliu1989/msmbuilder | examples/advanced/gmrq-model-selection.ipynb | lgpl-2.1 | from __future__ import print_function
import numpy as np
from msmbuilder.example_datasets import load_doublewell
from msmbuilder.cluster import NDGrid
from msmbuilder.msm import MarkovStateModel
from sklearn.pipeline import Pipeline
from sklearn.cross_validation import KFold
"""
Explanation: This example demonstrates ... |
liganega/Gongsu-DataSci | previous/y2017/GongSu03_Python_DataTypes_Part_1.ipynb | gpl-3.0 | print("Hello World")
"""
Explanation: 파이썬 기본 자료형 1부
수정 사항
다양한 기초연산 연습문제 추가 필요
파이썬 언어에서 사용되는 값들의 기본 자료형을 살펴본다.
변수에 할당될 수 있는 가장 단순한 자료형에는 네 종류가 있다:
정수 자료형(int):
..., -3, -2, -1, 0, 1, 2, 3, 등등
1 + 2, -2 * 3, 등등
부동소수점 자료형(float):
1.2, 0.333333, -1.2, -3.7680, 등등
2.0/3.5, 3.555 + 3.4 * 7.9, 등등
불리언 자료형(bool... |
donK23/pyData-Projects | EventDec/event_dec/notebook/1_Exploration.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import math
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Exploration
Exploration of prepocessed DF
End of explanation
"""
file_path = "../data/events_df.pkl"
df = pd.read_pickle(file_path)
print(df.shape)
print(df.dtypes)
df.head()
"""
Explanation: Inp... |
pambot/cbio-proteomics | tutorial/cbio_import_tutorial.ipynb | mit | # import classes
import sys
sys.path.append('../')
from ms2cbioportal import (CDAPiTraqTable,
CDAPPrecursorAreaTable,
MaxQuantProteomeTable,
MaxQuantPTMTable,
MSMeta)
"""
Explanation: Using the cBioPortal M... |
gdementen/larray | doc/source/tutorial/getting_started.ipynb | gpl-3.0 | %xmode Minimal
from larray import *
"""
Explanation: Getting Started
The purpose of the present Getting Started section is to give a quick overview
of the main objects and features of the LArray library.
To get a more detailed presentation of all capabilities of LArray, read the next sections of the tutorial.
The API... |
yingchi/fastai-notes | deeplearning1/nbs/try-resnet.ipynb | apache-2.0 | import sys, os
current_dir = os.getcwd()
HOME_DIR = current_dir
DATA_HOME_DIR = current_dir + '/data/redux/'
print(current_dir, DATA_HOME_DIR)
from utils import *
%matplotlib inline
batch_size = 64
no_of_epochs = 3
path = DATA_HOME_DIR
model_path = path+'model'
"""
Explanation: Try Resnet
Paras Setting
End of explan... |
james-prior/euler | euler-048-self-power-20170113.ipynb | mit | def foo(n, m):
return str(sum(i ** i for i in range(1, n + 1)))[-m:]
foo(10, 10)
n = 1000
m = 10
%timeit foo(n, m)
foo(n, m)
"""
Explanation: Project Euler
Distinct primes factors
Problem 48
The series, 1<sup>1</sup> + 2<sup>2</sup> + 3<sup>3</sup> + ... + 10<sup>10</sup> = 10405071317.
Find the last ten digit... |
dsavransky/MAE4060 | Notebooks/Viscous Nutation Damper.ipynb | mit | from miscpy.utils.sympyhelpers import *
init_printing()
w1,w2,w3,w1d,w2d,w3d,D,O,Od,I1,Iw,Is,It,M1,M2 = \
symbols('omega_1,omega_2,omega_3,omegadot_1,omegadot_2,\
omegadot_3,D,Omega,Omegadot,I_1,I_W,I_S,I_T,M1,M2')
diffmap = {w1:w1d,w2:w2d,w3:w3d,O:Od}
"""
Explanation: Viscous Nutation Damper
End of explanation
"""
... |
iwansmith/FutureLHCb | notebooks/03_ExampleFitter.ipynb | gpl-3.0 | Fitter = FutureFitter("../output/Data_Histograms_1.0_LHCb.root", "MCORR_Data_DsMuNu", "../output/Source_Histograms_DsMu_1.0_LHCb_Merged.root", False)
Fitter.Fit()
c1 = ROOT.TCanvas("c1", "c1", 900,900)
Fitter.Plot(c1)
c1.Draw()
c1.Print("ExampleFit_DsMuNu.pdf")
"""
Explanation: Perform and plot a $B_s \rightarrow... |
harrisonpim/bookworm | 06 - Stable Roommates, Marriages, and Gender.ipynb | mit | def gale_shapley(proposors, acceptors):
'''
Returns stable marriage pairings for a given set of input
candidates and their lists of spousal preferences
Parameters
----------
proposors : dict (required)
the set of men and their list of spousal preferences
acceptors : dict (required)... |
InsightSoftwareConsortium/SimpleITK-Notebooks | Python/65_Registration_FFD.ipynb | apache-2.0 | import SimpleITK as sitk
import registration_utilities as ru
import registration_callbacks as rc
%matplotlib inline
import matplotlib.pyplot as plt
from ipywidgets import interact, fixed
# utility method that either downloads data from the Girder repository or
# if already downloaded returns the file name for readin... |
mamrehn/machine-learning-tutorials | ipynb/[scikit-learn, ipython notebook] installation.ipynb | cc0-1.0 | conda update --all
"""
Explanation: Miniconda Installation
Install Miniconda from their Website.
All required python packages will be downloaded using the conda package and environment management system.
When Miniconda is installed on your system, open up your shell with the command <kbd>WINDOWS</kbd> + <kbd>R</kbd> -... |
hzzyyy/pymcef | examples/1. When mean variance models fail.ipynb | bsd-3-clause | from cvxopt import solvers, matrix, spdiag
from numpy import linspace, random
solvers.options['show_progress'] = False
solvers.options['abstol'] = 1e-10
solvers.options['reltol'] = 1e-9
solvers.options['feastol'] = 1e-10
P = spdiag([1.0, 4.0, 9.0])
q = matrix([0.0, 0.0, 0.0])
G = -1.0 * spdiag([1.0, 1.0, 1.0])
h = matr... |
JAmarel/Phys202 | Interpolation/.ipynb_checkpoints/InterpolationEx01-checkpoint.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy.interpolate import interp1d
"""
Explanation: Interpolation Exercise 1
End of explanation
"""
dictionary = np.load('trajectory.npz')
y = dictionary.items()[0][1]
t = dictionary.items()[1][1]
x = dictionary.items()... |
theideasmith/theideasmith.github.io | _notebooks/Regression.ipynb | mit | import numpy as np
from pylab import *
%matplotlib inline
"""
Explanation: A Simple Linear Regression Implementation in Python
This is a sequel to my previous blog post where I discussed the fundamental theory of linear regression with gradient descent.
Here we'll continue our study of gradient descent by implementin... |
EconForge/dolo | examples/notebooks/rbc_model.ipynb | bsd-2-clause | from dolo import *
"""
Explanation: Solving the rbc model
This worksheet demonstrates how to solve the RBC model with the dolo library
and how to generates impulse responses and stochastic simulations from the solution.
This notebook is distributed with dolo in : examples\notebooks\. The notebook was opened and run ... |
ajaykumar-bollepalli/pythontraining | DictionariesPractice.ipynb | apache-2.0 | myDict = {'color':'blue', 'taste':'sweet'}
myDict
"""
Explanation: Dictionaries
Dictionaries in python are Mappings (or associations). Means they store values in key and value associations (pair).
Example: 'color' : 'Blue'
Here color is the key and Blue is the value. notice that key and value are seperated by :. The... |
tensorflow/probability | tensorflow_probability/examples/jupyter_notebooks/TFP_Release_Notebook_0_13_0.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... |
streety/biof509 | Wk05-regression-classification-merged.ipynb | mit | !pip install --upgrade sklearn
!pip install --upgrade seaborn
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
%matplotlib inline
"""
Explanation: Week 5 - Regression and Classification
End of explanation
"""
from sklearn import datasets
diabetes = datasets.load_diabetes()
# Description ... |
pvalienteverde/ElCuadernillo | ElCuadernillo/20160725_SistemasDeRecomendacionContentBased/Content-Based.ipynb | mit | import pandas as pd
import sys
sys.path.append('./Scripts')
import ContendBased as CB
# Autorecarga los modulos
%load_ext autoreload
%autoreload 2
"""
Explanation: Recomendacion de articulos basados en su contenido
End of explanation
"""
datos = pd.read_csv("BD/people_wiki.csv")
datos.head(3)
"""
Explanation: Impo... |
squishbug/DataScienceProgramming | 10-Information-Based-Learning/HW10/README_corrected.ipynb | cc0-1.0 | import pandas as pd
import numpy as np
from __future__ import division
%%sh
## RUN BUT DO NOT EDIT THIS CELL
## run this cell to download the cereal dataset into your current directory
cp /home/data/cereal/cereal.csv .
## RUN BUT DO NOT EDIT THIS CELL
# load the data, define ratingID
cer = pd.read_csv('cereal.csv',... |
tensorflow/docs | site/en/tutorials/customization/custom_training_walkthrough.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... |
SJSlavin/phys202-2015-work | assignments/assignment04/MatplotlibEx02.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Matplotlib Exercise 2
Imports
End of explanation
"""
!head -n 30 open_exoplanet_catalogue.txt
"""
Explanation: Exoplanet properties
Over the past few decades, astronomers have discovered thousands of extrasolar planets. The follo... |
nigroup/nideep | examples/parallel_train/plot_parallel.ipynb | bsd-2-clause | import numpy as np
import os
import matplotlib.pyplot as plt
%matplotlib inline
from cycler import cycler
from pylab import rcParams
rcParams['figure.figsize'] = 8, 6
rcParams.update({'font.size': 15})
# color and linestyle cycle
#colors = [x['color'] for x in list(rcParams['axes.prop_cycle'])]
colors_base = ['b', '... |
DhashS/Olin-Complexity-Final-Project | reports/03_approximation_algorithms.ipynb | gpl-3.0 | def Christofides(G):
T = nx.algorithms.minimum_spanning_tree(G)
O = {n for n, d in T.degree(T.nodes_iter()).items() if d%2 == 1}
induced_subgraph = nx.Graph(G.subgraph(O))
M = minimum_perfect_matching(induced_subgraph)
T.add_weighted_edges_from([(u,v,M[u][v]['weight']) for u, v in M.edg... |
AllenDowney/ThinkBayes2 | notebooks/chap02.ipynb | mit | import pandas as pd
table = pd.DataFrame(index=['Bowl 1', 'Bowl 2'])
"""
Explanation: Bayes's Theorem
Think Bayes, Second Edition
Copyright 2020 Allen B. Downey
License: Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
In the previous chapter, we derived Bayes's Theorem:
$$P(A|B) = \frac{P(A) ... |
DigitalSlideArchive/HistomicsTK | docs/examples/simple_tissue_detection.ipynb | apache-2.0 | import girder_client
import numpy as np
from matplotlib import pylab as plt
from matplotlib.colors import ListedColormap
from histomicstk.saliency.tissue_detection import (
get_slide_thumbnail, get_tissue_mask)
%matplotlib inline
"""
Explanation: Tissue Detection
Overview:
This includes tools to detect tissue fr... |
kmunve/APS | aps/notebooks/wind_slab_problem.ipynb | mit | warnings, url = gf.get_warnings_as_json([3022, 3023, 3024], "2017-12-01", "2018-05-31", lang_key=1, simple=False, recursive_count=5)
print(url)
warnings_flattened = (flatten(w) for w in warnings)
df = pd.DataFrame(warnings_flattened)
df.head(5)
df.columns
df_a = df
# define individual columns for avalanche problem ... |
sainathadapa/fastai-courses | deeplearning2/babi-memnn.ipynb | apache-2.0 | %matplotlib inline
import importlib, utils2; importlib.reload(utils2)
from utils2 import *
np.set_printoptions(4)
cfg = K.tf.ConfigProto(gpu_options={'allow_growth': True})
K.set_session(K.tf.Session(config=cfg))
"""
Explanation: Babi End to End MemNN
End of explanation
"""
def tokenize(sent):
return [x.strip()... |
mne-tools/mne-tools.github.io | 0.21/_downloads/59aa8e259e776a361531f7d21fa2f1ec/plot_compute_raw_data_spectrum.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io, read_proj, read_selection
from mne.datasets im... |
ledrui/Regression | week2/week-2-multiple-regression-assignment-2-blank.ipynb | mit | import graphlab
"""
Explanation: Regression Week 2: Multiple Regression (gradient descent)
In the first notebook we explored multiple regression using graphlab create. Now we will use graphlab along with numpy to solve for the regression weights with gradient descent.
In this notebook we will cover estimating multiple... |
dksahuji/simpleabc | SimpleABC_demo.ipynb | mit | from simple_abc import Model, basic_abc, pmc_abc
import pylab as plt
import numpy as np
from scipy import stats
%matplotlib inline
plt.style.use('ggplot')
"""
Explanation: SimpleABC Demo
Let's see how SimpleABC works by using it for a very easy problem. We will approximate the posterior distribution of mean and standa... |
whitead/numerical_stats | unit_4/hw_2020/homework_4_key.ipynb | gpl-3.0 | #1.1
x = list(range(2,100,2))
print(x)
#1.2
print(sum(x))
#1.3
s = 0
for xi in x:
s += xi
print(s)
#1.4
print(x[::-1])
#1.5
print(x[len(x)//2:])
#1.6
y = []
for xi in x:
y.append(xi**2)
print(y)
"""
Explanation: Homework 4
CHE 116: Numerical Methods and Statistics
(2/6/2020)
1. Lists Problems
[2 points... |
jasag/Phytoliths-recognition-system | code/notebooks/Prototypes/Classifiers_and_HoG/2_Classifiers.ipynb | bsd-3-clause | import pickle
path = '../../rsc/obj/'
X_train_path = path + 'X_train.sav'
y_train_path = path + 'y_train.sav'
X_train = pickle.load(open(X_train_path, 'rb'))
y_train = pickle.load(open(y_train_path, 'rb'))
print(X_train.shape)
"""
Explanation: Clasificadores
En este notebook se entrenaran los distintos clasificado... |
aliakbars/uai-ai | scripts/tugas2.ipynb | mit | from __future__ import print_function, division # Gunakan print(...) dan bukan print ...
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.datasets import load_digits
from sklearn.cluster import KMeans
from sklearn.metrics import accuracy_score, confusion_matrix,... |
eaton-lab/toytree | docs/8-styling.ipynb | bsd-3-clause | import toytree
import toyplot
import numpy as np
# a tree to use for examples
url = "https://eaton-lab.org/data/Cyathophora.tre"
rtre = toytree.tree(url).root(wildcard='prz')
"""
Explanation: Styling toytree drawings
The number of styling available in toytree is enormous and will continue to grow as development on th... |
csiu/100daysofcode | misc/2017-03-02-day06.ipynb | mit | speaker = 'Thomas Levi'
topics_mentioned_at_meetup = [
"latent dirichlet allocation",
"collapsed gibbs sampling",
"bayesian inference",
"topic modelling",
"porter stemmer",
"flesch reading ease",
"word2vec"
]
"""
Explanation: layout: post
author: csiu
date: 2017-03-02
title: "Day06: Jupyter... |
prefrontalvortex/bci_erp | python/eventlab_header.ipynb | mit | example_header = r"""
# Non-editable header begin --------------------------------------------------------------------------------
#
# data format...............: continuous
# setname...................: S1_EEG_elist
# filename..................: S1_EEG.set
# filepath..................: C:\Users\mike\Downloads\Te... |
tensorflow/examples | courses/udacity_deep_learning/1_notmnist.ipynb | apache-2.0 | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import imageio
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import tarfile
from IPython.display import display, Image
from sklearn.linear_model im... |
rgarcia-herrera/sistemas-dinamicos | linearizar sistemas no lineales en torno a puntos de equilibrio.ipynb | gpl-3.0 | b = symbols('b')
b = Matrix([[1, -4],
[2, -3]])
b
"""
Explanation: Este sistema lineal
End of explanation
"""
b.eigenvects()
"""
Explanation: Eigenvalores y Eigenvectores
End of explanation
"""
y1, y2 = symbols("y1 y2")
solve(((-1 + 2j) * y1) - (y1 - 4 * y2),
y1)
solve(((-1 + 2j) * y2) - (2 * ... |
cathalmccabe/PYNQ | boards/ZCU104/notebooks/common/zcu104_pmbus.ipynb | bsd-3-clause | import pynq
rails = pynq.get_rails()
rails
"""
Explanation: PMBus on the ZCU104
The ZCU104 has some support for monitoring power rails on the board using PMBus. PYNQ exposes these rails through the get_rails function that returns a dictionary of all of the rails available to be monitored.
End of explanation
"""
rec... |
Neuro17/finance4py | notebooks/indicator_examples_with_plots.ipynb | mit | # reading data from google finance
stock = DataReader('NFLX', 'google')
# extract bollinger bands
boll_bands = fp.bbands(stock.Close)
boll_bands.tail()
# extract average true range
atr = fp.average_true_range(stock.High, stock.Low, stock.Close)
atr.tail()
# extract RSI
rsi = fp.rsi(stock.Close).to_frame()
rsi.head(... |
yashdeeph709/Algorithms | PythonBootCamp/Complete-Python-Bootcamp-master/Milestone Project 1- Complete Walkthrough Solution.ipynb | apache-2.0 | # For using the same code in either Python 2 or 3
from __future__ import print_function
## Note: Python 2 users, use raw_input() to get player input. Python 3 users, use input()
"""
Explanation: Milestone Project 1: Full Walk-through Code Solution
Below is the filled in code that goes along with the complete walk-th... |
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