repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
shahariarrabby/Mail_Server | Receive and server Mail.ipynb | mit | __author__ = 'Shahariar Rabby'
import email
import imaplib
import ctypes
import getpass
import threading
from playsound import playsound
"""
Explanation: Recive Mail
This file is imported by Server and Check ME. All function is define here.
Importing all dependency
End of explanation
"""
def user():
# ORG_EMAIL ... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/markov_autoregression.ipynb | bsd-3-clause | %matplotlib inline
from datetime import datetime
from io import BytesIO
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import requests
import statsmodels.api as sm
# NBER recessions
from pandas_datareader.data import DataReader
usrec = DataReader(
"USREC", "fred", start=datetime(1947, 1,... |
mne-tools/mne-tools.github.io | 0.20/_downloads/460fe4a441caf01fe3a0ace1c9325a0d/plot_tf_lcmv.ipynb | bsd-3-clause | # Author: Roman Goj <roman.goj@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne import compute_covariance
from mne.datasets import sample
from mne.event import make_fixed_length_events
from mne.beamformer import tf_lcmv
from mne.viz import plot_source_spectrogram
print(__doc__)
data_path = sample.data_path... |
mne-tools/mne-tools.github.io | stable/_downloads/f1d68aba13226287585e777005a39f0a/15_handling_bad_channels.ipynb | bsd-3-clause | import os
from copy import deepcopy
import numpy as np
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file, verbose=False)
""... |
metpy/MetPy | v1.1/_downloads/f8c7f51c50c58b17901913e49a5b977e/Inverse_Distance_Verification.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import numpy as np
from scipy.spatial import cKDTree
from metpy.interpolate.geometry import dist_2
from metpy.interpolate.points import barnes_point, cressman_point
from metpy.interpolate.tools import average_spacing, calc_kappa
def draw_circle(ax, x, y, r, m, label):
th = np.lins... |
jrg365/gpytorch | examples/01_Exact_GPs/GP_Regression_Fully_Bayesian.ipynb | mit | import math
import torch
import gpytorch
import pyro
from pyro.infer.mcmc import NUTS, MCMC
from matplotlib import pyplot as plt
%matplotlib inline
%load_ext autoreload
%autoreload 2
# Training data is 11 points in [0,1] inclusive regularly spaced
train_x = torch.linspace(0, 1, 6)
# True function is sin(2*pi*x) with ... |
ituethoslab/navcom-2017 | repro/DAMD hashtag counts/DAMD hashtag counts.ipynb | gpl-3.0 | import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: DAMD hashtag counts
What other hashtags appear in the DAMD data than #damd. Which ones are popular, and how are the hashtags distributed?
End of explanation
"""
damd = pd.read_csv("", index_col="tweet_id")
damd['hashtags'] = dam... |
numenta/nupic.research | projects/archive/continuous_learning/Correlation_experiments.ipynb | agpl-3.0 | config_file = "experiments.cfg"
experiment = SparseCorrExperiment(config_file=config_file)
"""
Explanation: Activity correlation metrics for networks trained on GSC
This notebook shows a number of examples illustrating how correlated the activations of sparse or dense neural networks are when different GSC class input... |
YaleDHLab/lab-workshops | apis/apis.ipynb | mit | import requests
url = 'https://api.datamuse.com/words?sp=t??k'
# get the content at the requested url
response = requests.get(url)
# get the JSON data in the response object
data = response.json()
print(data)
"""
Explanation: Getting Started with Application Programming Interfaces (APIs)
APIs make it easy to colle... |
jhprinz/openpathsampling | examples/tests/test_netcdfplus.ipynb | lgpl-2.1 | import openpathsampling as paths
from openpathsampling.netcdfplus import (
NetCDFPlus,
ObjectStore,
StorableObject,
NamedObjectStore,
UniqueNamedObjectStore,
DictStore,
ImmutableDictStore,
VariableStore,
StorableNamedObject
)
import numpy as np
from __future__ import print_functio... |
H-E-L-P/XID_plus | docs/build/html/notebooks/examples/XID+IR_SED-Example-GP.ipynb | mit | from astropy.io import ascii, fits
import pylab as plt
%matplotlib inline
from astropy import wcs
import numpy as np
import xidplus
from xidplus import moc_routines
import pickle
"""
Explanation: Import required modules
End of explanation
"""
#Folder containing maps
pswfits='/Users/pdh21/astrodata/COSMOS/P4/COSMO... |
UWSEDS/LectureNotes | Spring2019/06a_Objects/Building Software With Objects.ipynb | bsd-2-clause | from IPython.display import Image
Image(filename='Classes_vs_Objects.png')
"""
Explanation: Why Objects?
Provide modularity and reuse through hierarchical structures
Object oriented programming is a different way of thinking.
Programming With Objects
End of explanation
"""
# Definiting a Car class
class Car(objec... |
esa-as/2016-ml-contest | GCC_FaciesClassification/01 - Facies Classification - GCC-VALIDATION.ipynb | apache-2.0 | # Initial imports for reading data and first observations
import pandas as pd
import bokeh.plotting as bk
import numpy as np
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from tpot import TPOTClassifier
bk.output_notebook()
# Input file paths
train_path = r'../training_data.... |
natashabatalha/PandExo | notebooks/JWST_Running_Pandexo.ipynb | gpl-3.0 | import warnings
warnings.filterwarnings('ignore')
import pandexo.engine.justdoit as jdi # THIS IS THE HOLY GRAIL OF PANDEXO
import numpy as np
import os
#pip install pandexo.engine --upgrade
"""
Explanation: Getting Started
Before starting here, all the instructions on the installation page should be completed!
Here ... |
manojkumar-github/NLP-TextAnalytics | EssayScoringSystem/BaselineModel.ipynb | mit | data.head()
"""
Explanation: Data Exploration
End of explanation
"""
data["essay"][0]
"""
Explanation: To have a look how an essay content looks
End of explanation
"""
for i in range(data.shape[0]):
message = TextBlob(data["essay"][i])
#number of words
data.set_value(i,'Essay_Length',len(mes... |
imatge-upc/activitynet-2016-cvprw | notebooks/18 Visualization of Results Comparison.ipynb | mit | import random
import os
import numpy as np
from work.dataset.activitynet import ActivityNetDataset
dataset = ActivityNetDataset(
videos_path='../dataset/videos.json',
labels_path='../dataset/labels.txt'
)
videos = dataset.get_subset_videos('validation')
videos = random.sample(videos, 8)
examples = []
for v in... |
WomensCodingCircle/CodingCirclePython | Lesson08_Dictionaries/Dictionary - after class.ipynb | mit | fruit_season = {
'raspberry': 'May',
'apple' : 'September',
'peach' : 'July',
'grape' : 'August'
}
print(type(fruit_season))
print(fruit_season)
"""
Explanation: Dictionaries
A dictionary is datatype that contains a series of key-value pairs. It is similar to a list except for that the indic... |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_cluster_stats_evoked.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.stats import permutation_cluster_test
from mne.datasets import sample
print(__doc__)
"""
Explanation: Permutation F-test on sensor data with 1D c... |
CyberCRI/dataanalysis-herocoli-redmetrics | v1.52.2/Tests/2.1 Google form analysis tests.ipynb | cc0-1.0 | %run "../Functions/2. Google form analysis.ipynb"
# Localplayerguids of users who answered the questionnaire (see below).
# French
#localplayerguid = 'a4d4b030-9117-4331-ba48-90dc05a7e65a'
#localplayerguid = 'd6826fd9-a6fc-4046-b974-68e50576183f'
#localplayerguid = 'deb089c0-9be3-4b75-9b27-28963c77b10c'
#localplayergu... |
ssanderson/notebooks | quanto/examples/Groupby Example.ipynb | apache-2.0 | pricing.head(10)
"""
Explanation: pricing is a DataFrame with the same structure as the return value of history on quantopian.
End of explanation
"""
from pandas.tseries.tools import normalize_date
def my_grouper(ts):
"Function to apply to the index of the DataFrame to break it into groups."
# Returns midni... |
SJSlavin/phys202-2015-work | assignments/assignment07/AlgorithmsEx02.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns
import numpy as np
"""
Explanation: Algorithms Exercise 2
Imports
End of explanation
"""
def find_peaks(a):
"""Find the indices of the local maxima in a sequence."""
maxima = np.array([])
if a[0] > a[1]:
maxima = n... |
Yatekii/glal3 | versuch2/M1.ipynb | gpl-3.0 | # define base values and measurements
v1_s = 0.500
v1_sb1 = 1.800
v1_sb2 = 1.640
v1_m = np.mean([0.47, 0.46, 0.46, 0.46, 0.46, 0.47, 0.46, 0.46, 0.46, 0.46, 4.65 / 10]) * 1e-3
v1_T = np.mean([28.68 / 10, 28.91 / 10])
v1_cw = 0.75
v1_cw_u = 0.08
v1_A = 4*1e-6
v1_pl = 1.2041
def air_resistance(s, v):
k = v1_cw * v... |
mne-tools/mne-tools.github.io | 0.23/_downloads/cfbef36033f8d33f28c4fe2cfa35314a/30_cluster_ftest_spatiotemporal.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)
import os.path as op
import numpy as np
from scipy import stats as stats
import mne
from mne import spatial_src_adjacency
from mne.stats import spatio_temporal_cluster_test, summariz... |
minh-doan/deepometry | STEP_4_Test_and_Visualization_built-in_CNN.ipynb | bsd-3-clause | # Location of digested data
input_directory = '/digested/'
# Location of saved trained model
model_directory = '/model_directory/'
# Desired location for outputs
output_directory = '/output_directory/'
"""
Explanation: ------------- User's settings -------------
End of explanation
"""
%matplotlib inline
import ker... |
c24b/c24b.github.io | projects/crawtext/Crawler2.ipynb | gpl-2.0 | tocrawl = []
def crawl(url):
html = download(url)
page = parse(html)
urls = extract_links(page)
tocrawl.append(urls)
return tocrawl
starter_url = "www.example.com"
tocrawl = crawl(starter_url)
while len(tocrawl) != 0:
for url in tocrawl:
crawl(url)
"""
Explanation: # Cours 5
Introduct... |
GoogleCloudPlatform/professional-services | examples/bigquery-table-access-pattern-analysis/pipeline-output_only.ipynb | apache-2.0 | import src.pipeline_analysis as pipeline_analysis
import ipywidgets as widgets
from IPython.display import display
import pandas as pd
limited_imbalance_tables = []
def get_limited_imbalance_tables_df(limit):
global limited_imbalance_tables
limited_imbalance_tables_df = pipeline_analysis.get_tables_read_write_... |
deepmind/deepmind-research | option_keyboard/gpe_gpi_experiments/generate_figures.ipynb | apache-2.0 | #@title Util functions
import csv
import os
from matplotlib import pyplot as plt
import pandas as pd
import seaborn as sns
import tensorflow.compat.v1 as tf
from tensorflow.compat.v1.io import gfile
def read_csv_as_dataframe(path):
with gfile.GFile(path, "r") as file:
reader = csv.reader(file, delimiter=" ")
... |
francesco-mannella/neunet-basics | course/perceptron-MNIST-simulation.ipynb | mit | %matplotlib inline
from pylab import *
from utils import *
"""
Explanation: The perceptron - Recognising the MNIST digits
<div>Table of contents</div>
<div id="toc"></div>
End of explanation
"""
#-----------------------------------------------------------
# training
# Set the number of patterns
n_patterns = 500
... |
parrt/msan501 | notes/sqrt.ipynb | mit | def sqrt(n):
"compute square root of n"
PRECISION = 0.00000001 # stop iterating when we converge with this delta
x_0 = 1.0 # pick any old initial value
x_prev = x_0
while True: # Python doesn't have repeat-until loop so fake it
#print(x_prev)
x_new = 0.5 * (x_prev + n/x_prev)
... |
gaufung/PythonStandardLibrary | FileSystem/mmap.ipynb | mit | import mmap
with open('lorem.txt', 'r') as f:
with mmap.mmap(f.fileno(), 0,
access=mmap.ACCESS_READ) as m:
print('First 10 bytes via read :', m.read(10))
print('First 10 bytes via slice:', m[:10])
print('2nd 10 bytes via read :', m.read(10))
"""
Explanation: Memory-map... |
raphaelshirley/regphot | examples/NGC450.ipynb | mit | from regphot import git_version
print("This notebook was run with regphot version: \n{}".format(git_version()))
#Import relevant modules
import matplotlib.pyplot as plt
%matplotlib inline
from astropy.io import fits
from astropy.wcs import WCS
from astropy.nddata import Cutout2D
import numpy as np
import aplpy
from re... |
InsightLab/data-science-cookbook | 2020/04-unsupervised-learning-clustering/Notebook_Clustering_Assignment.ipynb | mit | # import libraries
# linear algebra
import numpy as np
# data processing
import pandas as pd
# library of math
import math
# data visualization
from matplotlib import pyplot as plt
# datasets
from sklearn import datasets
# Scikit Learning hierarchical clustering
from sklearn.cluster import AgglomerativeClustering
... |
Python4AstronomersAndParticlePhysicists/PythonWorkshop-ICE | notebooks/10_03_Astronomy_PhotUtils.ipynb | mit | %matplotlib inline
import numpy as np
import math
import matplotlib.pyplot as plt
import seaborn
from astropy.io import fits
from astropy import units as u
from astropy.coordinates import SkyCoord
plt.rcParams['figure.figsize'] = (12, 8)
plt.rcParams['font.size'] = 14
plt.rcParams['lines.linewidth'] = 2
plt.rcParams['x... |
fest-research/deep-coin-demo | intro/tensorflow_intro.ipynb | apache-2.0 | # first we need some data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
"""
Explanation: Intro to TensorFlow
The following is a small intro to how computational frameworks (theano, tensorflow) work in general.
The MNIST dataset
It's just a set... |
Mdround/fastai-deeplearning1 | deeplearning1/nbs/lesson5.ipynb | apache-2.0 | import utils_MDR
from utils_MDR import *
"""
Explanation: MDR: and needs by GPU-fan code, too...
End of explanation
"""
from keras.datasets import imdb
idx = imdb.get_word_index()
"""
Explanation: Setup data
We're going to look at the IMDB dataset, which contains movie reviews from IMDB, along with their sentiment.... |
letsgoexploring/teaching | winter2017/econ129/python/Econ129_Class_05_Complete.ipynb | mit | # Use the requests module to download money growth and inflation data
url = 'http://www.briancjenkins.com/data/quantitytheory/csv/qtyTheoryData.csv'
r = requests.get(url,verify=True)
with open('qtyTheoryData.csv','wb') as newFile:
newFile.write(r.content)
"""
Explanation: Class 5: Pandas
Pandas is a Python p... |
starbuck10/CS109a_DataScience_UserRatings_Team_Project | Final Milestone/MovieLens/.ipynb_checkpoints/Final Milestone-checkpoint.ipynb | mit | EUCLIDEAN = 'euclidean'
MANHATTAN = 'manhattan'
PEARSON = 'pearson'
def read_ratings_df():
date_parser = lambda time_in_secs: datetime.utcfromtimestamp(float(time_in_secs))
return pd.read_csv('ml-latest-small/ratings.csv', parse_dates=['timestamp'], date_parser=date_parser)
class MovieData(object):
def ... |
fonnesbeck/scientific-python-workshop | notebooks/High-level Plotting.ipynb | cc0-1.0 | %matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Set some Pandas options
pd.set_option('display.notebook_repr_html', False)
pd.set_option('display.max_columns', 20)
pd.set_option('display.max_rows', 25)
normals = pd.Series(np.random.normal(size=10))
normals.plot()
"""
Expla... |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_artifacts_correction_maxwell_filtering.ipynb | bsd-3-clause | import mne
from mne.preprocessing import maxwell_filter
data_path = mne.datasets.sample.data_path()
"""
Explanation: Artifact correction with Maxwell filter
This tutorial shows how to clean MEG data with Maxwell filtering.
Maxwell filtering in MNE can be used to suppress sources of external
intereference and compensa... |
albahnsen/PracticalMachineLearningClass | exercises/E4-Regression-Linear&Logistic.ipynb | mit | import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
# read the data and set the datetime as the index
income = pd.read_csv('https://github.com/albahnsen/PracticalMachineLearningClass/raw/master/datasets/income.csv.zip', index_col=0)
income.head()
income.shape
"""
Explanation: ... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/building_production_ml_systems/labs/2_hyperparameter_tuning.ipynb | apache-2.0 | PROJECT = "<YOUR PROJECT>"
BUCKET = "<YOUR BUCKET>"
REGION = "<YOUR REGION>"
TFVERSION = "2.3.0" # TF version for AI Platform to use
import os
os.environ["PROJECT"] = PROJECT
os.environ["BUCKET"] = BUCKET
os.environ["REGION"] = REGION
os.environ["TFVERSION"] = TFVERSION
"""
Explanation: Hyper-paramet... |
abhi1509/deep-learning | sentiment-rnn/Sentiment_RNN_Solution.ipynb | mit | import numpy as np
import tensorflow as tf
with open('../sentiment-network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment-network/labels.txt', 'r') as f:
labels = f.read()
reviews[:2000]
"""
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural... |
NYUDataBootcamp/Projects | MBA_S17/John-Chihyun-US SUV Demand.ipynb | mit | # import packages
import pandas as pd # data management
import matplotlib.pyplot as plt # graphics
import numpy as np # numerical calculations
import datetime as dt # handles dates
import seaborn as seab # better graphics
import pandas_datare... |
hannorein/rebound | ipython_examples/EscapingParticles.ipynb | gpl-3.0 | import rebound
import numpy as np
def setupSimulation():
sim = rebound.Simulation()
sim.add(m=1., hash="Sun")
sim.add(x=0.4,vx=5., hash="Mercury")
sim.add(a=0.7, hash="Venus")
sim.add(a=1., hash="Earth")
sim.move_to_com()
return sim
sim = setupSimulation()
sim.status()
"""
Explanation: Esc... |
DavidNorman/tensorflow | tensorflow/lite/experimental/micro/examples/hello_world/create_sine_model.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
dimtics/Network-Intrusion-Detection-Using-Machine-Learning-Techniques | Intrusion Detection using Machine Learning Techniques.ipynb | mit | # import relevant modules
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sns
import sklearn
import imblearn
# Ignore warnings
import warnings
warnings.filterwarnings('ignore')
# Settings
pd.set_option('display.max_columns', None)
np.set_pr... |
openconnectome/ndprojects | kasthuri2015_ramon_v1/Vesicle Count.ipynb | apache-2.0 | import ndio.remote.OCP as OCP
oo = OCP()
token = "kasthuri2015_ramon_v1"
"""
Explanation: A count of the total number of annotated vesicles within the bounds (694×1794, 1750×2460, 1004×1379).
End of explanation
"""
vesicle_cutout = oo.get_cutout(token, 'vesicle', 694, 1794, 1750, 2460, 1004, 1379, resolution=3)
""... |
IsaacLab/LaboratorioIntangible | T3/.ipynb_checkpoints/T3.3-Social-Minimal-Interaction-checkpoint.ipynb | agpl-3.0 | %matplotlib inline
import numpy as np
import scipy.io
import scipy.signal as signal
from matplotlib import pyplot as plt
from pyeeg import dfa as dfa
def readFilePerceptualCrossing(filename):
data = scipy.io.loadmat(filename)
size = len(data['dataSeries'])
series = [data['dataSeries'][i][0] for i in range... |
bjornstenqvist/faunus | examples/temper/temper.ipynb | mit | %matplotlib inline
import matplotlib
import matplotlib.cm as cm
import numpy as np
import matplotlib.pyplot as plt
import jinja2, json, yaml, sys
from math import log, fabs, pi, cos, sin
from scipy.stats import ks_2samp
number_of_replicas = 6
scale_array = np.geomspace(1, 0.1, number_of_replicas)
temper = True # run w... |
lancekrogers/Pycon2015PandasLesson | Exercises-1.ipynb | mit | titles.count()
"""
Explanation: How many movies are listed in the titles dataframe?
End of explanation
"""
titles.sort('year').head()
"""
Explanation: 212811
What are the earliest two films listed in the titles dataframe?
End of explanation
"""
t = titles
t[t.title == 'Hamlet'].count()
"""
Explanation: Reproduct... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/10_recommend/cf_softmax_model/target/cfmodel_softmax_model_target.ipynb | apache-2.0 | # Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.6
from __future__ import print_function
import numpy as np
import pandas as pd
import collections
from mpl_toolkits.mplot3d import Axes3D
from IPython import display
from matplotlib import pyplot as plt
import sklearn
import sklea... |
CLEpy/CLEpy-MotM | Pandas/Pandas-motm.ipynb | mit | import pandas as pd
import numpy as np
"""
Explanation: Pandas
CLEPY - August Module of the month
Anurag Saxena
@_asaxena
Pandas - Python Data Analysis Library
pandas.pydata.org
Open Source
High Performance
Easy to use Data Structures and Data Analysis Tools
End of explanation
"""
obj = pd.Series([1,3,4,5,6,7,8,9])
... |
karlstroetmann/Algorithms | Python/Chapter-05/Dual-Pivot-Quicksort-Array.ipynb | gpl-2.0 | import random as rnd
"""
Explanation: An Array-Based Implementation of Dual-Pivot-Quicksort
End of explanation
"""
def sort(L):
quickSort(0, len(L) - 1, L)
"""
Explanation: The function $\texttt{sort}(L)$ sorts the list $L$ in place.
End of explanation
"""
def quickSort(a, b, L):
if b <= a:
return... |
xypan1232/pypdb | demos/advanced_demos.ipynb | mit | %pylab inline
from IPython.display import HTML
from pypdb.pypdb import *
import pprint
"""
Explanation: pypdb advanced demos
This is a set of basic examples of the ways that algorithmic querying with PyPDB can be used to perform advanced search tasks. Most of these examples combine multiple functions in the API in o... |
dimonaks/siman | tutorials/surfaces.ipynb | gpl-2.0 | import sys
from IPython.display import Image
from siman import header
from siman.calc_manage import smart_structure_read
from siman.geo import create_supercell, create_surface2, supercell
%matplotlib inline
"""
Explanation: Instruction
This tutorial explain how to build specific surfaces on the example of (111) surfa... |
hbutler/InverseCCP | 3 - Generate coupon probabilities - exponential.ipynb | mit | n = 20 #number of coupons
scale = 1/n #scipy uses the scale parameter instead of lambda. Scale and lambda are reciprocals of each other.
x = np.arange(n)+0.5 #arange goes from 0 to n-1, and I want it to go from 1 to n
p_x = stat.expon.ppf(x/n, loc=0, scale=scale)
print('unfilled probability: ', 1-np.sum(p_x))
p_x = p_... |
tpin3694/tpin3694.github.io | regex/match_words_with_certain_ending.ipynb | mit | # Load regex package
import re
"""
Explanation: Title: Match Words With A Certain Ending
Slug: match_words_with_certain_ending
Summary: Match Words With A Certain Ending
Date: 2016-05-01 12:00
Category: Regex
Tags: Basics
Authors: Chris Albon
Source: Regular Expressions Cookbook
Preliminaries
End of explanation
"""
... |
aasensio/elecciones2016 | .ipynb_checkpoints/Sondeos Elecciones 2015-checkpoint.ipynb | mit | book = xlrd.open_workbook("sondeos.xlsx")
sh = book.sheet_by_index(0)
PP = []
PSOE = []
IU = []
UPyD = []
Podemos = []
Ciudadanos = []
fecha = []
mesEsp = ['ene', 'feb', 'mar', 'abr', 'may', 'jun', 'jul', 'ago', 'sep', 'oct', 'nov', 'dic']
mesEng = ['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct'... |
IBMDecisionOptimization/docplex-examples | examples/mp/jupyter/load_balancing.ipynb | apache-2.0 | import sys
try:
import docplex.mp
except:
raise Exception('Please install docplex. See https://pypi.org/project/docplex/')
"""
Explanation: Use decision optimization to determine Cloud balancing.
This tutorial includes everything you need to set up decision optimization engines, build mathematical programming ... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/statespace_sarimax_faq.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
rng = np.random.default_rng(20210819)
eta = rng.standard_normal(5200)
rho = 0.8
beta = 10
epsilon = eta.copy()
for i in range(1, eta.shape[0]):
epsilon[i] = rho * epsilon[i - 1] + eta[i]
y = beta + epsilon
y = y[200:]
from statsmodels.tsa.api import SARIM... |
marcelomiky/PythonCodes | scikit-learn/scikit-learn-book/Chapter 2 - Supervised Learning - Text Classification with Naive Bayes.ipynb | mit | %pylab inline
import IPython
import sklearn as sk
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
print 'IPython version:', IPython.__version__
print 'numpy version:', np.__version__
print 'scikit-learn version:', sk.__version__
print 'matplotlib version:', matplotlib.__version__
"""
Explanation:... |
xesscorp/skidl | examples/spice-sim-intro/spice-sim-intro.ipynb | mit | from IPython.core.display import HTML
HTML(open('custom.css', 'r').read())
"""
Explanation: <h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Spicing-It-Up!-(Sorry)" data-toc-modified-id="Spicing-It-Up!-(Sorry)-1"><span class="toc-item-num">1 <... |
google-coral/tutorials | train_lstm_timeseries_ptq_tf2.ipynb | apache-2.0 | # 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 the L... |
gschivley/ERCOT_power | Raw Data/ERCOT/Hourly wind generation/.ipynb_checkpoints/Exploring hourly wind data-checkpoint.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sns
fn2009 = 'rpt.00013424.0000000000000000.20141016.182537070.ERCOT_2009_Hourly_Wind_Output.xls'
fn2015 = 'rpt.00013424.0000000000000000.ERCOT_2015_Hourly_Wind_Output.xlsx'
df_2009 = pd.read_excel(fn2009, index... |
probml/pyprobml | notebooks/book1/08/opt_flax.ipynb | mit | import sklearn
import scipy
import scipy.optimize
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
import itertools
import time
from functools import partial
import os
import numpy as np
# np.set_printoptions(precision=3)
np.set_printoptions(formatter={"float": lambda x: "{0:0.5f}".f... |
ageron/tensorflow-safari-course | 08_artifical_neural_networks_ex7ex8.ipynb | apache-2.0 | from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
tf.__version__
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
"""
Explanation: Try not to peek at the solutions when you go through the exercises. ;-)
First let's make sure this notebook ... |
mne-tools/mne-tools.github.io | dev/_downloads/bdc99305dd93336f2d973c05e0c46d24/25_automated_coreg.ipynb | bsd-3-clause | # Author: Jon Houck <jon.houck@gmail.com>
# Guillaume Favelier <guillaume.favelier@gmail.com>
#
# License: BSD-3-Clause
import numpy as np
import mne
from mne.coreg import Coregistration
from mne.io import read_info
data_path = mne.datasets.sample.data_path()
# data_path and all paths built from it are pathl... |
sspickle/sci-comp-notebooks | P09-RootFinding.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as pl
N=100
z0=2.0
z=np.linspace(0,1.5,N)
def leftS(z):
return np.cos(z)
def rightS(z,z0=z0):
return z/z0
def f(z,z0=z0):
return leftS(z)-rightS(z,z0)
pl.grid()
pl.title("Investigating $\cos(z)=z/z_0$")
pl.ylabel("left, right and difference... |
Hvass-Labs/TensorFlow-Tutorials | 02_Convolutional_Neural_Network.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import confusion_matrix
import time
from datetime import timedelta
import math
# Use TensorFlow v.2 with this old v.1 code.
# E.g. placeholder variables and sessions have changed in TF2.
import tensorflow.compat.v1 as tf
tf.disa... |
AllenDowney/ModSimPy | notebooks/rabbits3.ipynb | mit | %matplotlib inline
from modsim import *
"""
Explanation: Modeling and Simulation in Python
Rabbit example
Copyright 2017 Allen Downey
License: Creative Commons Attribution 4.0 International
End of explanation
"""
system = System(t0 = 0,
t_end = 20,
juvenile_pop0 = 0,
... |
jrrembert/cybernetic-organism | dato/deeplearning/Deep Features for Image Classification.ipynb | gpl-2.0 | import graphlab
"""
Explanation: Using deep features to build an image classifier
Fire up GraphLab Create
End of explanation
"""
image_train = graphlab.SFrame('image_train_data/')
image_test = graphlab.SFrame('image_test_data/')
"""
Explanation: Load a common image analysis dataset
We will use a popular benchmark d... |
hglanz/phys202-2015-work | assignments/assignment04/MatplotlibEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import math
"""
Explanation: Matplotlib Exercise 1
Imports
End of explanation
"""
import os
assert os.path.isfile('yearssn.dat')
"""
Explanation: Line plot of sunspot data
Download the .txt data for the "Yearly mean total sunspot number [1700 - n... |
mp4096/controlboros | examples/simple_linear_siso_system.ipynb | bsd-3-clause | from controlboros import StateSpaceBuilder
import matplotlib.pyplot as plt
import numpy as np
from scipy import signal
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
"""
Explanation: Simulating a simple linear SISO system
Mikhail Pak, 2017
End of explanation
"""
t_begin, t_end = 0.0, 10.0
"""
Ex... |
QuantEcon/QuantEcon.notebooks | ddp_ex_optgrowth_py.ipynb | bsd-3-clause | %matplotlib inline
from __future__ import division, print_function
import numpy as np
import scipy.sparse as sparse
import matplotlib.pyplot as plt
from quantecon import compute_fixed_point
from quantecon.markov import DiscreteDP
"""
Explanation: DiscreteDP Example: Discrete Optimal Growth Model
Daisuke Oyama
Faculty... |
toros-astro/epio2017_EELT_MCDM | slides/slides.ipynb | bsd-3-clause | display(data)
"""
Explanation: European Extremely Large Telescope site selection
A comparison between real selection and multicriteria-decision-analysis suggestions
Juan B Cabral – Bruno O Sanchez – Manuel Starck Cuffini
Instituto de Astronomía Teórica y Experimental
jbcabral@oac.unc.edu.ar- bruno@oac.unc.edu.ar- mst... |
adbuerger/casiopeia | examples/ipython_notebooks/demo_casiopeia.ipynb | lgpl-3.0 | import pylab as pl
import casadi as ca
import casiopeia as cp
"""
Explanation: A Schur Complement Method for Optimum Experimental Design in the Presence of Process Noise
Adrian Bürger (1,2), Dimitris Kouzoupis (2), Angelika Altmann-Dieses (1), Moritz Diehl (2,3)
(1) Faculty of Management Science and Engineering, Kar... |
phoebe-project/phoebe2-docs | development/tutorials/MESH.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.4,<2.5"
"""
Explanation: 'mesh' Datasets and Options
Setup
Let's first make sure we have the latest version of PHOEBE 2.4 installed (uncomment this line if running in an online notebook session such as colab).
End of explanation
"""
import phoebe
logger = phoebe.logger()
b = phoebe.defa... |
pysg/pyther | parameters_eos.ipynb | mit | import numpy as np
import pandas as pd
import pyther as pt
"""
Explanation: Parámetros de ecuaciones cúbicas de estado
En esta sección se presenta la clase ## que es la encargada de establecer los parámetros que se utilizan para las ecuaciones de estado SRK, PR y RKPR. En el caso de las dos primeras se tiene un enfoqu... |
bt3gl/Machine-Learning-Resources | ml_notebooks/first_steps_with_tensor_flow.ipynb | gpl-2.0 | # 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 the L... |
intel-analytics/BigDL | python/chronos/use-case/network_traffic/network_traffic_autots_forecasting_deprecated.ipynb | apache-2.0 | def get_drop_dates_and_len(df, allow_missing_num=3):
"""
Find missing values and get records to drop
"""
missing_num = df.total.isnull().astype(int).groupby(df.total.notnull().astype(int).cumsum()).sum()
drop_missing_num = missing_num[missing_num > allow_missing_num]
drop_datetimes = df.iloc[dro... |
julienchastang/unidata-python-workshop | notebooks/Metpy_Introduction/Introduction to MetPy.ipynb | mit | # Import the MetPy unit registry
from metpy.units import units
length = 10.4 * units.inches
width = 20 * units.meters
print(length, width)
"""
Explanation: <div style="width:1000 px">
<div style="float:right; width:98 px; height:98px;">
<img src="https://raw.githubusercontent.com/Unidata/MetPy/master/metpy/plots/_st... |
pycrystem/pycrystem | doc/demos/01 GaAs Nanowire - Data Inspection - Preprocessing - Unsupervised Machine Learning.ipynb | gpl-3.0 | # Changing the matplotlib background will give you interactive
#%matplotlib qt5
%matplotlib inline
import hyperspy.api as hs
import pyxem as pxm
import numpy as np
"""
Explanation: Data Inspection- Preprocessing - Unsupervised ML
This tutorial demonstrates the most important basic steps involved in the analysis of s... |
higee/amazon-helpful-review | 3_model selection_evalutation.ipynb | mit | # baseline confirmation, implying that model has to perform at least as good as it
from sklearn.dummy import DummyClassifier
clf_Dummy = DummyClassifier(strategy='most_frequent')
clf_Dummy = clf_Dummy.fit(X_train, y_train)
print('baseline score =>', round(clf_Dummy.score(X_test, y_test), 2))
"""
Explanation: RandomFo... |
ES-DOC/esdoc-jupyterhub | notebooks/noaa-gfdl/cmip6/models/sandbox-2/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'noaa-gfdl', 'sandbox-2', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: NOAA-GFDL
Source ID: SANDBOX-2
Sub-Topics: Radiative Forcings.
Propertie... |
jhjungCode/pytorch-tutorial | 02_Linear_regression.ipynb | mit | import torch
from torch.autograd import Variable
x = Variable(torch.Tensor([[1], [2], [3]]))
y = Variable(torch.Tensor([[1], [2], [3]]))
w = Variable(torch.randn(1, 1), requires_grad = True)
b = Variable(torch.randn(1), requires_grad = True)
learning_rate = 1e-2
# trainning
for i in range(1000) :
# network mod... |
csadorf/signac | doc/signac_204_External_Tools.ipynb | bsd-3-clause | %%bash
signac --help
"""
Explanation: 2.4 External Tools
The following section demonstrates how to use the signac command line interface (CLI) in conjunction with other tools.
End of explanation
"""
% pwd
% rm -rf projects/tutorial/cli
% mkdir -p projects/tutorial/cli
% cp idg projects/tutorial/cli
"""
Explanation:... |
ioos/system-test | content/downloads/notebooks/2015-12-07-NGDC_CSW_QueryForIOOSRAs_UUID.ipynb | unlicense | from owslib.csw import CatalogueServiceWeb
endpoint = 'http://www.ngdc.noaa.gov/geoportal/csw'
csw = CatalogueServiceWeb(endpoint, timeout=30)
"""
Explanation: In the previous example we investigated if it was possible to query the NGDC CSW Catalog to extract records matching an IOOS RA acronym.
However, we could not... |
InsightSoftwareConsortium/SimpleITK-Notebooks | Python/62_Registration_Tuning.ipynb | apache-2.0 | import SimpleITK as sitk
# Utility method that either downloads data from the network or
# if already downloaded returns the file name for reading from disk (cached data).
%run update_path_to_download_script
from downloaddata import fetch_data as fdata
# Always write output to a separate directory, we don't want to p... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session09/Day1/gps/02-Inference.ipynb | mit | !tar -zxvf s9_gp_dat.tar.gz
!mv *.txt data/
"""
Explanation: Inference with GPs
The dataset needed for this worksheet can be downloaded. Once you have downloaded s9_gp_dat.tar.gz, and moved it to this folder, execute the following cell:
End of explanation
"""
import numpy as np
from scipy.linalg import cho_factor
... |
ireapps/cfj-2017 | exercises/15. Web scraping (Part 5)-working.ipynb | mit | # base URL
# results page URL
# pattern for inmate detail URLs
"""
Explanation: Let's scrape some inmate data
Our goal in this exercise is to scrape the roster of inmates in the Hennepin County Jail into a CSV.
Step 1: Can we get everyone?
What happens when we click the search box without entering a first or last... |
dtamayo/MachineLearning | Day1/08_featureselection_cv.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.feature_selection import SelectKBest, f_regression
from sklearn.cross_validation import cross_val_score
# read in the advertising dataset
data = pd.read_csv('data/Advertising.csv', index_col=0)
# create a Python list... |
evangelistalab/forte | tutorials/Tutorial_01.01_forte_api.ipynb | lgpl-3.0 | import psi4
import forte
"""
Explanation: Forte Tutorial 1.01: Running forte in Jupyter notebooks
In this tutorial we are going to explore how to interact with forte in Jupyter notebooks using the Python API.
Import modules
The first step necessary to interact with forte is to import psi4 and forte
End of explanation... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/migration/UJ2,12 Custom Training Prebuilt Container TF Keras.ipynb | apache-2.0 | ! pip3 install -U google-cloud-aiplatform --user
"""
Explanation: Vertex SDK: Train & deploy a TensorFlow model with hosted runtimes (aka pre-built containers)
Installation
Install the latest (preview) version of Vertex SDK.
End of explanation
"""
! pip3 install google-cloud-storage
"""
Explanation: Install the Goo... |
cliburn/sta-663-2017 | notebook/10B_Numba.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
"""
Explanation: Just-in-time compilation (JIT)
For programmer productivity, it often makes sense to code the majority of your application in a high-level language such as Python and only optimize code bottlenecks identified by profiling. One way to speed up these bot... |
daniel-koehn/Theory-of-seismic-waves-II | 01_Analytical_solutions/lecture_notebooks/5_Greens_function_acoustic_1-3D.ipynb | gpl-3.0 | # Execute this cell to load the notebook's style sheet, then ignore it
from IPython.core.display import HTML
css_file = '../../style/custom.css'
HTML(open(css_file, "r").read())
"""
Explanation: Content under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2018 parts of this notebook ... |
IanHawke/ET-NumericalMethods-2016 | solutions/03-hyperbolic-pdes.ipynb | mit | import numpy
from matplotlib import pyplot
%matplotlib notebook
"""
Explanation: Hyperbolic PDEs
Most formulations of the Einstein equations for the spacetime (with $c=1$) look roughly like wave equations
$$
\frac{\partial^2 \phi}{\partial t^2} = \nabla^2 \phi.
$$
We will focus on the simple $1+1$d case
$$
\frac{\part... |
xaibeing/cn-deep-learning | tutorials/intro-to-tflearn/TFLearn_Digit_Recognition.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... |
timcera/tsgettoolbox | notebooks/tsgettoolbox-nwis-api.ipynb | bsd-3-clause | %matplotlib inline
from tsgettoolbox import tsgettoolbox
"""
Explanation: tsgettoolbox and tstoolbox - Python Programming Interface
'tsgettoolbox nwis ...': Download data from the National Water Information System (NWIS)
This notebook is to illustrate the Python API usage for 'tsgettoolbox' to download and work with d... |
olivertomic/hoggorm | examples/RV_&_RV2/RV_and_RV2_on_sensory_and_fluorescence_data.ipynb | bsd-2-clause | import hoggorm as ho
import hoggormplot as hop
import pandas as pd
import numpy as np
"""
Explanation: RV and RV2 coefficient on Sensory and Fluorescence data
This notebook illustrates how to use the hoggorm package to carry out partial least squares regression (PLSR) on multivariate data. Furthermore, we will learn h... |
alienmortar/GREAT2014 | Untitled0.ipynb | mit | apple = 5
orange = 6
total = apple + orange
print(total)
mugs = 12
plates = 5
tea_cup = 8
total = 3 * mugs + 2 * plates + tea_cup
print(total)
age = 32
name = 'Jacky'
married = True
height = 1.75
# Hi I am just a line of comment
# print('Ignore me....')
print("You can only see me")
result = 1/3
print (result)
n... |
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