repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
datacommonsorg/api-python | notebooks/intro_data_science/Data_Commons_For_Data_Science_Tutorial.ipynb | apache-2.0 | !pip install datacommons --upgrade --quiet
!pip install datacommons_pandas --upgrade --quiet
"""
Explanation: <a href="https://colab.research.google.com/github/datacommonsorg/api-python/blob/master/notebooks/intro_data_science/Data_Commons_For_Data_Science_Tutorial.ipynb" target="_parent"><img src="https://colab.resea... |
gonmolina/CCE_ProblemasResueltos | ProbsVVEE/Python Control Notebook/rlocus_test.ipynb | mit | sys1 = ctrl.tf([1, 1], [1, 10, 1])
print(sys1)
r, k = ctrl.rlocus(sys1)
plt.show()
r, k = ctrl.rlocus(sys1, grid=True)
"""
Explanation: Simple example that is not OK
End of explanation
"""
r, k = ctrl.rlocus(sys1, grid=True, ylim=[-10, 10])
"""
Explanation: However, when I plot the grid the figure looks not so goo... |
harmsm/pythonic-science | chapters/05_big-files/00_fastq-files.ipynb | unlicense | f = open("files/simple-file.txt")
for l in f.readlines():
print(l,end="")
f.close()
"""
Explanation: Reading high-throughput sequencing files
@SRR001666.1 071112_SLXA-EAS1_s_7:5:1:817:345 length=60
GGGTGATGGCCGCTGCCGATGGCGTCAAATCCCACCAAGTTACCCTTAACAACTTAAGGG
+SRR001666.1 071112_SLXA-EAS1_s_7:5:1:817:345 length=60
... |
slundberg/shap | notebooks/overviews/Explaining quantitative measures of fairness.ipynb | mit | # here we define a function that we can call to execute our simulation under
# a variety of different alternative scenarios
import scipy as sp
import numpy as np
import matplotlib.pyplot as pl
import pandas as pd
import shap
%config InlineBackend.figure_format = 'retina'
def run_credit_experiment(N, job_history_sex_imp... |
mne-tools/mne-tools.github.io | 0.23/_downloads/c69e0120935518121b8298ecac72eed8/35_dipole_orientations.ipynb | bsd-3-clause | import mne
import numpy as np
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse
data_path = sample.data_path()
evokeds = mne.read_evokeds(data_path + '/MEG/sample/sample_audvis-ave.fif')
left_auditory = evokeds[0].apply_baseline()
fwd = mne.read_forward_solution(
dat... |
ocefpaf/intro_python_notebooks | 08-dados_alunos.ipynb | mit | import pandas as pd
df = pd.read_excel("./data/2005.02_onda.xlsx").head()
df.head()
"""
Explanation: Dados de ondas do modelo Wavewatch III
Dado mensal amostrado de 6 em 6 horas, as variáveis são tempo, direção de onda e altura significativa da onda.
valores de máximo e mínimo da altura significativa
média da altu... |
phoebe-project/phoebe2-docs | 2.3/tutorials/emcee_continue_from.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.3,<2.4"
import phoebe
from phoebe import u # units
import numpy as np
logger = phoebe.logger('error')
"""
Explanation: Advanced: Continuing Emcee from a Previous Run
IMPORTANT: this tutorial assumes basic knowledge (and uses a file resulting from) the emcee tutorial.
NOTE: support for con... |
cattoire/sparksamples | streaming-twitter/notebook/Twitter + Watson Tone Analyzer Part 2.ipynb | apache-2.0 | # Import SQLContext and data types
from pyspark.sql import SQLContext
from pyspark.sql.types import *
# sc is an existing SparkContext.
sqlContext = SQLContext(sc)
"""
Explanation: Twitter + Watson Tone Analyzer Sample Notebook
In this sample notebook, we show how to load and analyze data from the Twitter + Watson To... |
alaindomissy/xarray_example | seasonal_averages.ipynb | mit | %matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import xarray
from netCDF4 import num2date
from netCDF4 import Dataset
# !conda list
print("numpy version :", np.__version__)
print("pandas version :", pd.__version__)
print("xray version :", xarray.__version__)
"""
Expl... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session04/Day1/StatisticsAperitif.ipynb | mit | y = np.array([203, 58, 210, 202, 198, 158,
165, 201, 157, 131, 166, 160,
186, 125, 218, 146])
x = np.array([495, 173, 479, 504, 510, 416,
393, 442, 317, 311, 400, 337,
423, 334, 533, 344])
"""
Explanation: Introduction to Statistics:
An Aperitif for DSFP Sess... |
mathLab/RBniCS | tutorials/05_gaussian/tutorial_gaussian_eim.ipynb | lgpl-3.0 | from dolfin import *
from rbnics import *
"""
Explanation: TUTORIAL 05 - Empirical Interpolation Method for non-affine elliptic problems
Keywords: empirical interpolation method
1. Introduction
In this Tutorial, we consider steady heat conduction in a two-dimensional square domain $\Omega = (-1, 1)^2$.
The boundary $\... |
vtsuperdarn/davitpy | docs/notebook/radarStruct.ipynb | gpl-3.0 | # Import radar module
%pylab inline
from davitpy.pydarn.radar import *
"""
Explanation: network(), radar() and site() objects
This notebook introduces the high-level python interface with the radar.dat and hdw.dat content.
For more in-depth access (i.e., your own hdw.dat), look at the radInfoIO module:
radInfoIo?
... |
chrisbarnettster/cfg-analysis-on-heroku-jupyter | notebooks/notebooks/download_cfg_for_galectin.ipynb | mit | # standard imports
import urllib2
import os
import json
import StringIO
import pickle
# dataframe and numerical
import pandas as pd
import numpy as np
# plotting
import matplotlib.pyplot as plt
%matplotlib inline
#scipy
from scipy import stats
from scipy.special import erf
from scipy import sqrt
from IPython.disp... |
bioinformatica-corso/lezioni | laboratorio/lezione6-7-15-21ott21/esercizio3-soluzione.ipynb | cc0-1.0 | def format_fasta(header, sequence):
return header + '\n' + '\n'.join(re.findall('\w{,80}', sequence))
"""
Explanation: Esercizio 3
EMBL (http://www.ebi.ac.uk/cgi-bin/sva/sva.pl/) è una banca di sequenze nucleotidiche sviluppata da EMBL-EBI (European Bioinformatics Institute, European Molecular Biology Laboratory),... |
bretthandrews/marvin | docs/sphinx/jupyter/whats_new_v21.ipynb | bsd-3-clause | import matplotlib
%matplotlib inline
# only necessary if you have a local DB
from marvin import config
config.forceDbOff()
"""
Explanation: What's New in Marvin 2.1
Marvin is Python 3.5+ compliant!
End of explanation
"""
from marvin.tools.cube import Cube
cube = Cube(plateifu='7957-12702')
print(cube)
list(cube.ns... |
antoniomezzacapo/qiskit-tutorial | community/aqua/general/eoh.ipynb | apache-2.0 | import numpy as np
from qiskit_aqua.operator import Operator
num_qubits = 2
temp = np.random.random((2 ** num_qubits, 2 ** num_qubits))
qubitOp = Operator(matrix=temp + temp.T)
temp = np.random.random((2 ** num_qubits, 2 ** num_qubits))
evoOp = Operator(matrix=temp + temp.T)
"""
Explanation: The EOH (Evolution of Ham... |
d-k-b/udacity-deep-learning | embeddings/Skip-Gram_word2vec.ipynb | mit | import time
import numpy as np
import tensorflow as tf
import utils
"""
Explanation: Skip-gram word2vec
In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in natural language p... |
mit-crpg/openmc | examples/jupyter/mdgxs-part-ii.ipynb | mit | %matplotlib inline
import math
import matplotlib.pyplot as plt
import numpy as np
import openmc
import openmc.mgxs
"""
Explanation: Multigroup (Delayed) Cross Section Generation Part II: Advanced Features
This IPython Notebook illustrates the use of the openmc.mgxs.Library class. The Library class is designed to aut... |
mndrake/PythonEuler | euler_051_060.ipynb | mit | from euler import Seq, timer, primes, is_prime
def p051():
def groups(n):
return ([[int(str(n).replace(x,y)) for y in '0123456789']
>> Seq.toSet
>> Seq.filter(is_prime)
>> Seq.toList
for x in '0123456789']
>> Seq.filte... |
WNoxchi/Kaukasos | FAI_old/lesson2/L2HW1_LM.ipynb | mit | # Import relevant libraries
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD, RMSprop
from keras.preprocessing import image
import numpy as np
import os
# Data functions ~ mostly from utils.py or vgg16.py
def get_batches(dirname, gen=image.ImageDataGenerator(), shuffl... |
cathywu/flow | tutorials/tutorialxx_template.ipynb | mit | a = 1
b = 2
def add(a, b):
return a + b
"""
Explanation: Tutorial XX: Template
This tutorial walks you through the process of FILL IN. The reason behind when and why this is important should be briefly described in the remainder of this paragraph. If possible, this should be further elucidated by a complementary ... |
ES-DOC/esdoc-jupyterhub | notebooks/noaa-gfdl/cmip6/models/sandbox-2/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'noaa-gfdl', 'sandbox-2', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: NOAA-GFDL
Source ID: SANDBOX-2
Topic: Ocnbgchem
Sub-Topics: Tracers.
P... |
lukas/scikit-class | examples/notebooks/Lesson-0-Getting-Started.ipynb | gpl-2.0 | import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn import datasets
import matplotlib.pyplot as plt # matplotlib is a graphing library
%matplotlib inline
# Load the boston housing price dataset
# Dataset of house prices by area
boston_houses = datasets.load_boston()
# load the average... |
gVallverdu/cookbook | colorscale.ipynb | gpl-2.0 | import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from IPython.display import HTML # intégration notebook
%matplotlib inline
"""
Explanation: Color palette with python
Germain Salvato Vallverdu germain.vallverdu@univ-pau.fr
This notebook aims to present several ways to manage color palette wi... |
aimacode/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... |
iurilarosa/thesis | codici/Archiviati/Plots/plot funzioni.ipynb | gpl-3.0 | G = 6.67408*1e-11
c = 299792458
r = 2.4377e+20
I = 1e38
epsilon = 1e-4
nu0 = 1
nudot = -5e-10
cost = 16*math.pi**2*G/(c**4*r)*I*epsilon
print(cost)
nmesi = 9
tobs = nmesi*30*24*60*60
print(tobs)
tempi = numpy.linspace(0,10,100000)
leggeOraria = nu0+nudot*tempi
ampiezza = cost*numpy.power(leggeOraria,2)
onda = ampiez... |
gfeiden/Notebook | Projects/mlt_calib/alpha_distributions.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Handling Posterior Distributions of $\alpha_{\rm MLT}$
End of explanation
"""
means = np.genfromtxt('data/run08_mean_props.txt')
medians = np.genfromtxt('data/run08_median_props.txt')
modes = np.genfromtxt('data/run08_mle_props.tx... |
pauliacomi/pyGAPS | docs/examples/import.ipynb | mit | from pathlib import Path
import pygaps.parsing as pgp
json_path = Path.cwd() / 'data'
"""
Explanation: Reading isotherms
The first thing to do is to read previously created isotherms. Example data can
be found in the
data
directory, saved in the pyGAPS JSON format, which we will now open. First, we'll
do the necessar... |
chemiskyy/simmit | Examples/Continuum_Mechanics/constitutive_props.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from simmit import smartplus as sim
import os
"""
Explanation: constitutive : The Constitutive Library
End of explanation
"""
E = 70000.0
nu = 0.3
L = sim.L_iso(E,nu,"Enu")
print np.array_str(L, precision=4, suppress_small=True)
d = sim.check_sy... |
Epidemium/RAMP-1 | _.ipynb_checkpoints/epidemium_01_starting_kit-checkpoint.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
pd.set_option('display.max_columns', None)
"""
Explanation: Find this notebook in https://tinyurl.com/epidemium-ramp
<div class="page-header"><h1 class="alert alert-info">Epidemium RAMP: Cancer... |
foreignOwl/data-analysis-notebooks | unemploymentHigherEducation.ipynb | mit | # this line is required to see visualizations inline for Jupyter notebook
%matplotlib inline
# importing modules that we need for analysis
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import re
all_ages = pd.read_csv("all-ages.csv")
grad_students = pd.read_csv("grad-students.csv")
majors = p... |
giacomov/3ML | docs/examples/Time-energy-fit.ipynb | bsd-3-clause | from threeML import *
import matplotlib.pyplot as plt
from jupyterthemes import jtplot
%matplotlib inline
jtplot.style(context="talk", fscale=1, ticks=True, grid=False)
plt.style.use("mike")
"""
Explanation: Time-energy fit
3ML allows the possibility to model a time-varying source by explicitly fitting the time-d... |
vravishankar/Jupyter-Books | Python+Operators.ipynb | mit | 2 + 3
"""
Explanation: Python Operators
Operators are special symbols in python that carry out arthimetic and logical operations.
Example
End of explanation
"""
x = 15
y = 6
# Addition Operator
print('x + y = ', x + y)
# Subtraction Operator
print('x - y = ', x - y)
# Multiplication Operator
print('x * y = ', x *... |
tensorflow/docs | site/en/guide/intro_to_modules.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... |
gwu-libraries/notebooks | 20180320-twitter-commandline/Twitter command-line.ipynb | mit | !wc -l *.jsonl
"""
Explanation: <h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc" style="margin-top: 1em;"><ul class="toc-item"><li><span><a href="#Command-line-tools-for-wrangling-Twitter-data" data-toc-modified-id="Command-line-tools-for-wrangling-Twitter-data-1"><span class="toc-item-num">1&n... |
boya-zhou/kaggle_bimbo_reformat | notebooks/1_predata.ipynb | mit | agencia_for_cliente_producto = train_dataset[['Cliente_ID','Producto_ID'
,'Agencia_ID']].groupby(['Cliente_ID',
'Producto_ID']).agg(lambda x:x.value_counts().index[0]).reset_index()
canal_for_cliente_pr... |
sympy/scipy-2017-codegen-tutorial | notebooks/_38-chemical-kinetics-symengine.ipynb | bsd-3-clause | import json
import numpy as np
from scipy2017codegen.odesys import ODEsys
from scipy2017codegen.chem import mk_rsys
"""
Explanation: NOTE
This notebook will make more sense (provide speed-up) once the LLVM backend is exposed in the python wrappers for SymEngine. I need to get back working on that here.
In this noteboo... |
esa-as/2016-ml-contest | CannedGeo_/Facies_classification-BPage_CannedGeo_F1_56-VALIDATED.ipynb | apache-2.0 | import sklearn
print(sklearn.__version__)
"""
Explanation: Facies classification using Machine Learning
<hr />
Contest entry by Bryan Page
This version has been validated by Matt
<hr />
Based on the original notebook by Brendon Hall, Enthought
<hr />
Matt's current sklearn version
End of explanation
"""
%matplot... |
kkkddder/dmc | notebooks/week-2/01 - Introduction to Python - Variables.ipynb | apache-2.0 | print "Hello World"
"""
Explanation: Introduction to Python
The main technology we will use in this class is Python. Python is a very modern, general-purpose and high-level object-oriented programming language. It has become extremely popular in recent years due to its
relatively simple syntax
extensibility through ... |
claesenm/semisup-metrics | performance-curves-python3.ipynb | bsd-2-clause | import random
import operator as op
import optunity.metrics
import semisup_metrics as ss
import numpy as np
from matplotlib import pyplot as plt
import pickle
import csv
import util
%matplotlib inline
"""
Explanation: Performance curves
In this notebook we will show how to compute performance curves (ROC and PR curves... |
facaiy/book_notes | deep_learning/Introduction/note.ipynb | cc0-1.0 | show_image('fig1_5.png', figsize=[12, 10])
show_image('fig1_4.png', figsize=[10, 8])
"""
Explanation: Chapter 1 Introduction
End of explanation
"""
show_image('fig1_11.png', figsize=[10, 8])
"""
Explanation: History:
distributed representation
back-propagation
long short-term memory (LSTM) network: used for many ... |
USCDataScience/parser-indexer-py | notebooks/minerals-ner/MineralNER.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
%matplotlib inline
from snorkel import SnorkelSession
import os
import numpy as np
import re
import codecs
os.environ['SNORKELDB'] = 'sqlite:///snorkel-mte.db'
# Open Session
session = SnorkelSession()
# Read input
base_dir = '/Users/thammegr/work/mte/data/newcorpus/MTE-corpus-ope... |
MarkPinches/Metrum-Institute | MI250 Lecture 4 - Simple Hierarchical mixed effect max model.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
from pymc3 import Model, Normal, Lognormal, Uniform, trace_to_dataframe, df_summary
"""
Explanation: This model buils a simple Hierarchial mixed effect model to look at dose response from 5 clinical trials... |
lrayle/rental-listings-census | src/rental_listings_modeling.ipynb | bsd-3-clause | # TODO: add putty connection too.
#read SSH connection parameters
with open('ssh_settings.json') as settings_file:
settings = json.load(settings_file)
hostname = settings['hostname']
username = settings['username']
password = settings['password']
local_key_dir = settings['local_key_dir']
census_dir = 'synth... |
krischer/pyadjoint | doc/example_dataset.ipynb | bsd-3-clause | import obspy
import numpy as np
event_longitude = 126.42
event_latitude = 1.97
event_depth_in_km = 37.3
station_longitude = -123.24
station_latitude = 43.12
max_period = 100.0
min_period = 20.0
cmt_time = obspy.UTCDateTime(2014, 11, 15, 2, 31, 50.26)
# Desired properties after the data processing.
sampling_rate = ... |
GuidoBR/python-for-finance | Exploring the Bitcoin Cryptocurrency Market/notebook.ipynb | mit | # Importing pandas
import pandas as pd
# Importing matplotlib and setting aesthetics for plotting later.
import matplotlib.pyplot as plt
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
plt.style.use('fivethirtyeight')
# Reading datasets/coinmarketcap_06122017.csv into pandas
dec6 = pd.read_csv("datase... |
fdcl-gwu/MAE3134_examples | sinusoidal_response.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
from scipy import signal
import matplotlib.pylab as plt
from ipywidgets import interact
import ipywidgets as widgets
np.set_printoptions(2)
# plt.rc('text', usetex=True)
# plt.rc('font', family='serif')
def A(w, wn, zeta):
A = 1 / np.sqrt((1 - (w/wn)**2)**2 + (2*zeta*w/wn)**... |
ES-DOC/esdoc-jupyterhub | notebooks/dwd/cmip6/models/mpi-esm-1-2-hr/ocean.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', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: DWD
Source ID: MPI-ESM-1-2-HR
Topic: Ocean
Sub-Topics: Timestepping Framework, A... |
cathalmccabe/PYNQ | boards/Pynq-Z2/logictools/notebooks/boolean_generator.ipynb | bsd-3-clause | from pynq.overlays.logictools import LogicToolsOverlay
logictools_olay = LogicToolsOverlay('logictools.bit')
"""
Explanation: Boolean Generator
This notebook will show how to use the boolean generator to generate a boolean combinational function. The function that is implemented is a 2-input XOR.
Step 1: Download the... |
jaimefrio/pydatabcn2017 | taking_numpy_in_stride/Taking NumPy In Stride.ipynb | unlicense | a = np.arange(3)
type(a)
"""
Explanation: Array views and slicing
A NumPy array is an object of numpy.ndarray type:
End of explanation
"""
a = np.arange(3)
a.base is None
b = a
b.base is None
a[:].base is None
a[:].base is a
"""
Explanation: All ndarrays have a .base attribute.
If this attribute is not None, then... |
sdpython/ensae_teaching_cs | _doc/notebooks/td1a_algo/td1a_sobel_correction.ipynb | mit | from pyquickhelper.loghelper import noLOG
from pyensae.datasource import download_data
f = download_data("python.png", url="http://imgs.xkcd.com/comics/")
from IPython.display import Image
Image("python.png")
"""
Explanation: 1A.algo - filtre de Sobel - correction
Correction.
Exercice 1 : application d'un filtre
End o... |
ual/hedonic-models | statistical-modeling.ipynb | bsd-3-clause | # Startup steps
import pandas as pd, numpy as np, statsmodels.api as sm
import matplotlib.pyplot as plt, matplotlib.cm as cm, matplotlib.font_manager as fm
import matplotlib.mlab as mlab
import time, requests
from scipy.stats import pearsonr, ttest_rel
import seaborn as sns
sns.set()
%matplotlib inline
"""
Explanation... |
SciTools/courses | course_content/extra_courses/numpy_intro.ipynb | gpl-3.0 | # NumPy is generally imported as 'np'.
import numpy as np
print(np)
print(np.__version__)
"""
Explanation: A Workshop Introduction to NumPy
The Python language is an excellent tool for general-purpose programming, with a highly readable syntax, rich and powerful data types (strings, lists, sets, dictionaries, arbitrar... |
amcdawes/QMlabs | Lab 3 - Operators - Solutions (old).ipynb | mit | import matplotlib.pyplot as plt
from numpy import sqrt,cos,sin,pi,arange
from qutip import *
H = Qobj([[1],[0]])
V = Qobj([[0],[1]])
P45 = Qobj([[1/sqrt(2)],[1/sqrt(2)]])
M45 = Qobj([[1/sqrt(2)],[-1/sqrt(2)]])
R = Qobj([[1/sqrt(2)],[-1j/sqrt(2)]])
L = Qobj([[1/sqrt(2)],[1j/sqrt(2)]])
"""
Explanation: Lab 3: Operators... |
PySCeS/PyscesToolbox | documentation/notebooks/basic_usage.ipynb | bsd-3-clause | # PySCeS model instantiation using the `example_model.py` file
# with name `mod`
mod = pysces.model('example_model')
mod.SetQuiet()
# Parameter scan setup and execution
# Here we are changing the value of `Vf2` over logarithmic
# scale from `log10(1)` (or 0) to log10(100) (or 2) for a
# 100 points.
mod.scan_in = 'Vf2... |
zzsza/Datascience_School | 03. 파이썬 프로그래밍/08. Python의 날짜 및 시간 관련 패키지 소개.ipynb | mit | import datetime
"""
Explanation: Python의 날짜 및 시간 관련 패키지 소개
날짜/시간 관련 패키지
datetime
https://docs.python.org/2/library/datetime.html
time
https://docs.python.org/2/library/time.html
pytz
http://pythonhosted.org/pytz/
dateutil
http://dateutil.readthedocs.org/en/latest/index.html
datetime 패키지
서브 패키지
d... |
conversationai/conversationai-crowdsource | constructiveness_toxicity_crowdsource/jupyter-notebooks/sanity_tests/sanity_test_crowd_annotations.ipynb | apache-2.0 | df['constructive_nominal'] = df['constructive'].apply(nominalize_constructiveness)
cdict = df['constructive_nominal'].value_counts().to_dict()
# Plot constructiveness distribution in the data
# The slices will be ordered and plotted counter-clockwise.
labels = 'Constructive', 'Non constructive', 'Not sure'
items =[cd... |
WNoxchi/Kaukasos | FAI_old/lesson4_codealong.ipynb | mit | import theano
import sys, os
sys.path.insert(1, os.path.join('utils'))
%matplotlib inline
import utils; reload(utils)
from utils import *
from __future__ import print_function, division
path = "data/ml-latest-small/"
model_path = path + 'models/'
if not os.path.exists(model_path): os.mkdir(model_path)
batch_size = 6... |
SIMEXP/Projects | NSC2006/labo8_filtrage/labo 8 Introduction au filtrage.ipynb | mit | %matplotlib inline
from pymatbridge import Octave
octave = Octave()
octave.start()
%load_ext pymatbridge
"""
Explanation: Laboratoire d'introduction au filtrage
Cours NSC-2006, année 2015
Méthodes quantitatives en neurosciences
Pierre Bellec, Yassine Ben Haj Ali
Objectifs:
Ce laboratoire a pour but de vous initier ... |
desihub/desisim | doc/nb/simulating-desi-spectra.ipynb | bsd-3-clause | import os
import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits
from astropy.table import Table
import desispec.io
import desisim.io
from desisim.obs import new_exposure
from desisim.scripts import quickgen
from desispec.scripts import group_spectra
%pylab inline
"""
Explanation: Simulating... |
omoju/Fundamentals | CS/Part_1_Complexity_RunTimeAnalysis.ipynb | gpl-3.0 | %pylab inline
# Import libraries
from __future__ import absolute_import, division, print_function
import math
from time import time
import matplotlib.pyplot as pyplt
"""
Explanation: Runtime Analysis
using Finding the nth Fibonacci numbers as a computational object to think with
End of explanation
"""
from IPython... |
opencobra/cobrapy | documentation_builder/simulating.ipynb | gpl-2.0 | from cobra.io import load_model
model = load_model("textbook")
"""
Explanation: Simulating with FBA
Simulations using flux balance analysis can be solved using Model.optimize(). This will maximize or minimize (maximizing is the default) flux through the objective reactions.
End of explanation
"""
solution = model.op... |
joewie/PySyft | notebooks/Syft - Testing - Benchmark Tests.ipynb | apache-2.0 | from syft.test.benchmark import Benchmark
Benchmark(str)
"""
Explanation: Testing: Benchmark Tests
One goal of the OpenMined project is to efficiently train Deep Learning models in a homomorphically encrypted state. Therefore it is very important to benchmark new and existing features in order to achieve better and f... |
jdstokes/nsc211 | notebooks/2.0-jds-tf_udacity_notMNIST.ipynb | mit | # 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 matplotlib.pyplot as plt
import numpy as np
import os
import sys
import tarfile
from IPython.display import display, Image
from scipy import ndimage
from sklearn.lin... |
mlamoureux/PIMS_YRC | 2D_Widgets.ipynb | mit | from ipywidgets import interact
"""
Explanation: Widgets, for interactive plotting in 2D
Widgets are a quick way to get interactivity in your Jupyter displays.
Jupyter has its own version of widgets, based on Python widgets. We use the interact command to access them.
End of explanation
"""
def f(x):
return x
... |
scholer/cy-rest-python | advanced/path2models.ipynb | mit | import libsbml
import pandas as pd
import re
"""
Explanation: Mapping Path2Models whole genome metabolism model to KEGG pathway
Software Requirements
pandas
python-libsbml
End of explanation
"""
!curl -o BMID000000140222.xml http://www.ebi.ac.uk/biomodels-main/download?mid=BMID000000140222
"""
Explanation: Retriev... |
qinwf-nuan/keras-js | notebooks/layers/wrappers/Bidirectional.ipynb | mit | data_in_shape = (3, 6)
layer_0 = Input(shape=data_in_shape)
layer_1 = Bidirectional(SimpleRNN(4, activation='tanh', return_sequences=False), merge_mode='sum')(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
weights = []
for i, w in enumerate(model.get_wei... |
landlab/landlab | notebooks/tutorials/landscape_evolution/hylands/HyLandsTutorial.ipynb | mit | ## Import Numpy and Matplotlib packages
import numpy as np
import copy
import matplotlib as mpl
import matplotlib.pyplot as plt # For plotting results; optional
## Import Landlab components
# Flow routing and depression handling
from landlab.components import PriorityFloodFlowRouter
# SPACE model
from landlab.compon... |
mayank-johri/LearnSeleniumUsingPython | Section 3 - Machine Learning/ThirdParty-scikit-learn-videos-master/03_getting_started_with_iris.ipynb | gpl-3.0 | from IPython.display import IFrame
IFrame('http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', width=300, height=200)
"""
Explanation: Getting started in scikit-learn with the famous iris dataset
From the video series: Introduction to machine learning with scikit-learn
Agenda
What is the famous ... |
wuafeing/Python3-Tutorial | 02 strings and text/02.14 combine and concatenate strings.ipynb | gpl-3.0 | parts = ["Is", "Chicago", "Not", "Chicago?"]
" ".join(parts)
",".join(parts)
"".join(parts)
"""
Explanation: Previous
2.14 合并拼接字符串
问题
你想将几个小的字符串合并为一个大的字符串
解决方案
如果你想要合并的字符串是在一个序列或者 iterable 中，那么最快的方式就是使用 join() 方法。比如：
End of explanation
"""
a = "Is Chicago"
b = "Not Chicago?"
a + " " + b
"""
Explanation: 初看起来，这种语法... |
mabevillar/rmtk | rmtk/vulnerability/derivation_fragility/hybrid_methods/N2/N2.ipynb | agpl-3.0 | import N2Method
from rmtk.vulnerability.common import utils
%matplotlib inline
"""
Explanation: N2 - Eurocode 8, CEN (2005)
This simplified nonlinear procedure for the estimation of the seismic response of structures uses capacity curves and inelastic spectra. This method has been developed to be used in combination ... |
google/intelligent_annotation_dialogs | exp1_IAD_RL.ipynb | apache-2.0 | import matplotlib.pyplot as plt
import numpy as np
from __future__ import division
from __future__ import print_function
import math
import gym
from gym import spaces
import pandas as pd
import tensorflow as tf
from IPython import display
import time
from third_party import np_box_ops
import annotator, detector, di... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/supplemental/solutions/deepconv_gan.ipynb | apache-2.0 | try:
%tensorflow_version 2.x
except Exception:
pass
import tensorflow as tf
tf.__version__
# To generate GIFs
!python3 -m pip install -q imageio
import glob
import os
import time
import imageio
import matplotlib.pyplot as plt
import numpy as np
import PIL
from IPython import display
from tensorflow.keras i... |
tensorflow/docs | site/en/guide/migrate/logging_stop_hook.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... |
willettk/insight | interviews/intuit/risk.ipynb | apache-2.0 | %matplotlib inline
from matplotlib import pyplot as plt
from sqlalchemy import create_engine
from sqlalchemy_utils import database_exists, create_database
import psycopg2
import pandas as pd # Requires v 0.18.0
import numpy as np
import seaborn as sns
sns.set_style("whitegrid")
dbname = 'risk'
username = 'willettk'
... |
LorenzoBi/courses | TSAADS/tutorial 2/.ipynb_checkpoints/TSA2_LORENZO_BIASI__JULIUS_VERNIE-checkpoint.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import scipy.io as sio
from sklearn import datasets, linear_model
%matplotlib inline
def set_data(p, x):
temp = x.flatten()
n = len(temp[p:])
x_T = temp[p:].reshape((n, 1))
X_p = np.ones((n, p + 1))
for i in range(1, p + 1):
X_p[:, i] = tem... |
Neuroglycerin/neukrill-net-work | notebooks/Saturation epoch model result.ipynb | mit | import pylearn2.utils
import pylearn2.config
import theano
import neukrill_net.dense_dataset
import neukrill_net.utils
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
#import holoviews as hl
#%load_ext holoviews.ipython
import sklearn.metrics
cd ..
"""
Explanation: Compare the current best model... |
OpenAstronomy/workshop_sunpy_astropy | 03-python2-defense-instructors.ipynb | mit | # Usual import first
from __future__ import print_function, division
numbers = [1.5, 2.3, 0.7, -0.001, 4.4]
total = 0.0
for n in numbers:
assert n > 0.0, 'Data should only contain positive values'
total += n
print('total is:', total)
"""
Explanation: Introduction to Python 2
Defensive Programming
<section cla... |
grigorisg9gr/menpo-notebooks | menpowidgets/Custom Widgets/Options Widgets.ipynb | bsd-3-clause | from menpowidgets.options import (AnimationOptionsWidget, ChannelOptionsWidget, PatchOptionsWidget,
LandmarkOptionsWidget, RendererOptionsWidget, PlotOptionsWidget,
LinearModelParametersWidget, TextPrintWidget, FeatureOptionsWidget,
... |
materialsvirtuallab/megnet | notebooks/transfer_learning.ipynb | bsd-3-clause | model_form = MEGNetModel.from_file('../mvl_models/mp-2018.6.1/formation_energy.hdf5')
"""
Explanation: Load formation energy model
End of explanation
"""
embedding_layer = [i for i in model_form.layers if i.name.startswith('embedding')][0]
embedding = embedding_layer.get_weights()[0]
print('Embedding matrix dimensio... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive/03_tensorflow/labs/e_ai_platform.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
# for bash
os.environ['PROJECT'] = PROJECT
os.environ['BUCKET'] = BUCKET
os.environ['REGION'] = REGION
o... |
AllenDowney/DataExploration | effect_size.ipynb | mit | from __future__ import print_function, division
import numpy
import scipy.stats
import matplotlib.pyplot as pyplot
from IPython.html.widgets import interact, fixed
from IPython.html import widgets
# seed the random number generator so we all get the same results
numpy.random.seed(17)
# some nice colors from http:/... |
mdpiper/topoflow-notebooks | Meteorology-Qn_LW.ipynb | mit | %matplotlib inline
import numpy as np
"""
Explanation: Net longwave radiative flux in the Meteorology component
Goal: In this example, check whether the Meteorology component produces output for land_surface_net-longwave-radiation__energy_flux (Qn_LW internally) when the model state is updated, given scalar inputs for... |
LucaCanali/Miscellaneous | Oracle_Jupyter/Oracle_IPython_cx_Oracle_pandas.ipynb | apache-2.0 | # connect to Oracle using cx_Oracle
# !pip install cx_Oracle if needed
import cx_Oracle
db_user = 'scott'
db_connect_string = 'localhost:1521/XEPDB1'
db_pass = 'tiger'
# db_connect_string = 'dbserver:1521/orcl.mydomain.com'
# import getpass
# db_pass = getpass.getpass()
ora_conn = cx_Oracle.connect(user=db_user, pa... |
zzsza/Datascience_School | 03. 파이썬 프로그래밍/04. Numpy 시작하기.ipynb | mit | import numpy as np
a = np.array([0, 1, 2, 3])
a
"""
Explanation: NumPy
NumPy란
수치해석용 Python 라이브러리
C로 구현 (파이썬용 C라이브러리)
BLAS/LAPACK 기반
빠른 수치 계산을 위한 Structured Array 제공
Home
http://www.numpy.org/
Documentation
http://docs.scipy.org/doc/
Tutorial
http://www.scipy-lectures.org/intro/numpy/index.html
https://docs.scipy.org/... |
aaschroeder/Titanic_example | Final_setup_SVM.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
titanic=pd.read_csv('./titanic_clean_data.csv')
cols_to_norm=['Age','Fare']
col_norms=['Age_z','Fare_z']
titanic[col_norms]=titanic[cols_to_norm].apply(lambda x: (x-x.mean())/x.std())
#titanic['cabin_clean']=(pd.notnull(titanic.Cabin))
from sklearn.cross_validation import tra... |
Mashimo/datascience | 03-NLP/POS.ipynb | apache-2.0 | # load in the training corpus
with open("../datasets/WSJ_02-21.pos", 'r') as f:
training_corpus = f.readlines() # list
print("A few items of the training corpus list: ")
print(training_corpus[0:5])
len(training_corpus)
"""
Explanation: Parts-of-Speech Tagging (POS)
Part-of-speech refers to the category of words... |
plipp/informatica-pfr-2017 | nbs/4/2-1-Classification-Decision-Tree-w-Label-Encoding-Exercise.ipynb | mit | import pandas as pd
import numpy as np
"""
Explanation: Predicting Earnings from Census Data with Decision Tree
taken from The Analytics Edge
The Task
The United States government periodically collects demographic information by conducting a census.
In this problem, we are going to use census information about an indi... |
davek44/Basset | tutorials/test.ipynb | mit | model_file = '../data/models/pretrained_model.th'
seqs_file = '../data/encode_roadmap.h5'
"""
Explanation: Once you've trained a model, you might like to get more information about how it performs on the various targets you asked it to predict.
To run this tutorial, you'll need to either download the pre-trained model... |
tensorflow/docs-l10n | site/zh-cn/tutorials/images/cnn.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... |
beyondvalence/biof509_wtl | Wk12-ml-workflow/Wk12-machine-learning-workflow.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
%matplotlib inline
"""
Explanation: Week 12 - The Machine Learning Workflow
End of explanation
"""
# http://scikit-learn.org/stable/auto_examples/plot_digits_pipe.html#example-plot-digits-pipe-py
import numpy as np
import matplotlib.pyplot as p... |
kgullikson88/keras_notebooks | notebooks/Reuters_MLP.ipynb | mit | # Imports
from __future__ import print_function
import numpy as np
np.random.seed(1337) # for reproducibility
from keras.datasets import reuters
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.utils import np_utils
from keras.preprocessing.text import Tokenizer
"""
... |
Gezort/YSDA_deeplearning17 | Seminar3/outdated/Seminar 3.ipynb | mit | %matplotlib inline
from time import time
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.offsetbox import AnnotationBbox, OffsetImage
"""
Explanation: Seminar 3 (Data embedding)
The goal of this seminar is to play around with diffrent techniques for data visualization. We are going work on the well-... |
tensorflow/docs-l10n | site/en-snapshot/io/tutorials/kafka.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... |
skalkur/Transfer_learning_Startup.ML | Transfer-learning.ipynb | mit | from tensorflow.python.platform import gfile
import tensorflow as tf
import numpy as np
model='../inception/classify_image_graph_def.pb'
def create_graph():
'''
Function to extract GraphDef of Inception model.
Returns: Extracted GraphDef
'''
with tf.Session() as sess:
with gf... |
hmelberg/motionChart | notebooks/motion chart notebook.ipynb | gpl-2.0 | from motionchart.motionchart import MotionChart, MotionChartDemo
import webbrowser
import pandas as pd
import pyperclip
"""
Explanation: Motion Charts in Python with Pandas
Hans Olav Melberg (hans.melberg@gmail.com), 17. October, 2015
Import modules
End of explanation
"""
fruitdf = pd.DataFrame([
['Apples', '... |
pybel/pybel-notebooks | summary/Graph Summary.ipynb | apache-2.0 | import logging
import os
import sys
import time
from collections import Counter, defaultdict
from operator import itemgetter
import matplotlib.pyplot as plt
import networkx as nx
import seaborn as sns
import pybel
import pybel_tools as pbt
from pybel.constants import *
from pybel_tools.visualization import to_jupyter... |
karlstroetmann/Algorithms | Python/Chapter-05/Insertion-Sort.ipynb | gpl-2.0 | def sort(L):
if L == []:
return []
x, *R = L
return insert(x, sort(R))
"""
Explanation: Insertion Sort
The function sort is specified via two equations:
$\mathtt{sort}([]) = []$
$\mathtt{sort}\bigl([x] + R\bigr) =
\mathtt{insert}\bigl(x, \mathtt{sort}(R)\bigr)$
This is most easily implemen... |
refgenomics/onecodex | notebook_examples/notebooks_demo.ipynb | mit | from onecodex import Api
ocx = Api()
project = ocx.Projects.get("d53ad03b010542e3") # get DIABIMMUNE project by ID
samples = ocx.Samples.where(project=project.id, public=True, limit=50)
samples.metadata[[
"gender",
"host_age",
"geo_loc_name",
"totalige",
"eggs",
"vegetables",
"milk",
... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session04/Day2/GPTutorial1.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial.distance import cdist
from numpy.random import multivariate_normal
from numpy.linalg import inv
from numpy.linalg import slogdet
from scipy.optimize import fmin
"""
Explanation: Gaussian Process regresstion tutorial 1:
Introductio... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.