repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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Hash--/ICRH | notebooks/Single and double Conjugate-T matching.ipynb | mit | bridge = rf.io.hfss_touchstone_2_network('../icrh/data/Sparameters/WEST/WEST_ICRH_bridge.s3p', f_unit='MHz')
impedance_transformer = rf.io.hfss_touchstone_2_network('../icrh/data/Sparameters/WEST/WEST_ICRH_impedance-transformer.s2p', f_unit='MHz')
window = rf.io.hfss_touchstone_2_network('../icrh/data/Sparameters/WEST/... |
google/dopamine | dopamine/colab/cartpole.ipynb | apache-2.0 | # @title Install necessary packages.
!pip install -U dopamine-rl
# @title Necessary imports and globals.
import numpy as np
import os
from dopamine.discrete_domains import run_experiment
from dopamine.colab import utils as colab_utils
from absl import flags
import gin.tf
BASE_PATH = '/tmp/colab_dopamine_run' # @pa... |
ledeprogram/algorithms | class6/donow/benzaquen_mercy_donow6.ipynb | gpl-3.0 | import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt # package for doing plotting (necessary for adding the line)
import statsmodels.formula.api as smf
"""
Explanation: 1. Import the necessary packages to read in the data, plot, and create a linear regression model
End of explanation
"""
df = pd.re... |
jnobre/lxmls-toolkit-2017 | lxmls/laboratories/day3/Lxmls_Day3.ipynb | mit | import sys
sys.path.append('../../../')
import lxmls.sequences.crf_online as crfo
import lxmls.sequences.structured_perceptron as spc
import lxmls.readers.pos_corpus as pcc
import lxmls.sequences.id_feature as idfc
import lxmls.sequences.extended_feature as exfc
print "CRF Exercise"
corpus = pcc.PostagCorpus( )
trai... |
gagneurlab/concise | nbs/getting_started.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import concise.layers as cl
import keras.layers as kl
import concise.initializers as ci
import concise.regularizers as cr
from keras.callbacks import EarlyStopping
from concise.preprocessing import encodeDNA
from keras.models import Model, load_model
# get the data
d... |
solomonvimal/UCLA-Hydro | ABoVE/Vegetaion_File_from_GEE.ipynb | gpl-3.0 | %matplotlib inline
import ee
from numpy import array
import operator
import matplotlib.pyplot as plt
from folium import IFrame
import base64, folium
import datetime
import ee
import pandas as pd
import os
import numpy as np
import matplotlib as mpl
ee.Initialize()
resolution, width, height = 75, 7, 3
mpl.rcParams['ytic... |
facaiy/book_notes | machine_learning/tree/gbdt/intro.ipynb | cc0-1.0 | show_image("./res/gradient_descent.jpg", figsize=(12,8))
show_image("./res/iterator.jpg")
"""
Explanation: GBDT(Gradient Boosting Decision Tree) 原理简介
0. 前言
我最开始了解 GBDT 时，死活不理解决策树这种分段函数，怎么可能算出一阶导数。读了论文 Friedman - Greedy Function Approximation: A Gradient Boosting Machine 后，才发现自己完全误解了决策树在GBDT中的作用。论文总是倾向把简单事情描述复杂，博客又常常过... |
dtamayo/rebound | ipython_examples/SaturnsRings.ipynb | gpl-3.0 | import rebound
import numpy as np
sim = rebound.Simulation()
"""
Explanation: Simulating Saturn's rings
In this example, we will simulate a small patch of Saturn's rings. The simulation is similar to the C example in examples/shearing_sheet.
We first import REBOUND and numpy, then create an instance of the Simulation ... |
trangel/Insight-Data-Science | general-docs/data-challenge-Week6/data challenge Tonatiuh Rangel.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
columns=['country','age','new_user','source','total_pages_visited','converted']
df = pd.read_csv('conversion_data.csv')
df.columns=columns
df.head(2)
"""
Explanation: Check missing data or NaN
Data exploration
Analysis on
1. Age
2. Pages visited
3. New user
Columns:
co... |
artdavis/pyfred | pyfred/examples/jupyter_notebook_pyfred_tutorial.ipynb | gpl-3.0 | # To get remote console connection info use:
#%connect_info
# To open a GUI console:
%qtconsole
# Embed plots in the notebook
%matplotlib inline
# NumPy is nice to have around
import numpy as np
np.set_printoptions(precision=4) # 4 decimal places for printing is OK
import time # For time delay
# Get IPython's pretty ... |
AndreySheka/dl_ekb | hw10/Seminar10-RNN-homework-en.ipynb | mit |
#text goes here
corpora = ""
for fname in os.listdir("codex"):
import sys
if sys.version_info >= (3,0):
with open("codex/"+fname, encoding='cp1251') as fin:
text = fin.read() #If you are using your own corpora, make sure it's read correctly
corpora += text
else:
... |
saga-survey/saga-code | ipython_notebooks/2015June-AAT.ipynb | gpl-2.0 | #if online
ufo = urllib2.urlopen('https://docs.google.com/spreadsheet/ccc?key=1b3k2eyFjHFDtmHce1xi6JKuj3ATOWYduTBFftx5oPp8&output=csv')
hosttab = QTable.read(ufo.read(), format='csv')
ufo.close()
#if offline
hosttab = Table.read('SAGADropbox/hosts/host_catalog_flag0.csv')
hostscs = SkyCoord(u.Quantity(hosttab['RA'], ... |
ES-DOC/esdoc-jupyterhub | notebooks/bnu/cmip6/models/sandbox-1/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'bnu', 'sandbox-1', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: BNU
Source ID: SANDBOX-1
Topic: Atmoschem
Sub-Topics: Transport, Emissions Co... |
daniel-koehn/Theory-of-seismic-waves-II | 03_Intro_finite_differences/2_fd_ac1d.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 are... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session11/Day2/MeasuringCentroidsAndProperMotion.ipynb | mit | # Load the packages we will use
import numpy as np
import astropy.io.fits as pf
import astropy.coordinates as co
from astropy.wcs import WCS
from matplotlib import pyplot as pl
%matplotlib inline
"""
Explanation: Practice with stellar astrometry
To accompany astrometry lecture from the Rubin Observatory Data Science F... |
scikit-optimize/scikit-optimize.github.io | dev/notebooks/auto_examples/parallel-optimization.ipynb | bsd-3-clause | print(__doc__)
import numpy as np
"""
Explanation: Parallel optimization
Iaroslav Shcherbatyi, May 2017.
Reviewed by Manoj Kumar and Tim Head.
Reformatted by Holger Nahrstaedt 2020
.. currentmodule:: skopt
Introduction
For many practical black box optimization problems expensive objective can be
evaluated in parallel ... |
atulsingh0/MachineLearning | scikit-learn/04_Scikit.ipynb | gpl-3.0 | from sklearn.neighbors import KNeighborsClassifier
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score
from sklearn.cross_validation import cross_val_score
import matplotlib.pyplot as plt
%matplotlib inline
# loading the IRIS dataset
iris = load_iris()
X = iris.data
y = iris.target
# ins... |
starbro/BeastMode | .ipynb_checkpoints/New-checkpoint.ipynb | apache-2.0 | # function to get name of movie from each URL
def get_movie(url):
'''
Scrapes a given URL from IMDB.com. The URL's page contains many reviews for one particular movie.
This function returns the name of that movie.
'''
pageText = requests.get(url)
# Keep asking for the page until you get it. Sl... |
jjehl/poppy_education | xl-320/Configurer les moteurs XL-320.ipynb | gpl-2.0 | import pypot.dynamixel
import time
"""
Explanation: Les commandes de bas niveau pour configurer un moteur xl-320
Importer les modules necessaires.
End of explanation
"""
print(pypot.dynamixel.get_available_ports())
"""
Explanation: Connecter les moteurs au niveau logiciel.
Trouver le port sur lequel est branché le ... |
blei-lab/ars-reparameterization | gamma/demo.ipynb | mit | import autograd.numpy as np
import autograd.numpy.random as npr
from autograd.scipy.special import gammaln, psi
from autograd import grad
from autograd.optimizers import adam, sgd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_context("talk")
sns.set_style("white")
%matplotlib inline
npr.seed(1)
# the... |
royalosyin/Python-Practical-Application-on-Climate-Variability-Studies | ex14-Standardized Precipitation Index (SPI).ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt # to generate plots
from mpl_toolkits.basemap import Basemap # plot on map projections
import datetime
from netCDF4 import Dataset # http://unidata.github.io/netcdf4-python/
from netCDF4 import netcdftime
from netcdftime import... |
CELMA-project/CELMA | MES/integrals/volumeIntegral/calculations/exactSolutions.ipynb | lgpl-3.0 | %matplotlib notebook
import numpy as np
from sympy import init_printing
from sympy import S
from sympy import sin, cos, tanh, exp, pi, sqrt
from sympy import integrate
from boutdata.mms import x, y, z, t
import os, sys
# If we add to sys.path, then it must be an absolute path
common_dir = os.path.abspath('./../../..... |
rishizek/deep-learning | first-neural-network/Your_first_neural_network.ipynb | mit | %matplotlib inline
%load_ext autoreload
%autoreload 2
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridersh... |
echohenry2006/tvb-library | tvb/simulator/demos/region_deterministic_stimulus.ipynb | gpl-2.0 | from tvb.simulator.lab import *
"""
Explanation: Demonstrate using the simulator at the region level with a stimulus.
Run time: approximately 2 seconds (workstation circa 2010).
Memory requirement: < 1GB
End of explanation
"""
LOG.info("Configuring...")
#Initialize a Model, Coupling, and Connectivity.
oscillator = ... |
madsenmj/ml-introduction-course | Class05/Class05.ipynb | apache-2.0 | import pandas as pd
iowadf= pd.read_csv("Class05_iowa_data.csv")
iowadf.head()
# The sales data looks like it isn't a float like we want it to be (the presence of a $ in front is my clue that there may be something wrong.) Let's look at the data types to be sure.
iowadf.dtypes
# Sure enough. We need to get the real v... |
dereneaton/ipyrad | newdocs/API-analysis/cookbook-structure.ipynb | gpl-3.0 | # conda install ipyrad -c bioconda
# conda install -c bioconda -c ipyrad structure clumpp
# conda install toyplot -c eaton-lab
import ipyrad.analysis as ipa
import toyplot
"""
Explanation: <span style="color:gray">ipyrad-analysis toolkit:</span> STRUCTURE
Structure v.2.3.4 is a standard tool for examining population ... |
d00d/quantNotebooks | Notebooks/quantopian_research_public/notebooks/lectures/Autocorrelation_and_AR_Models/notebook.ipynb | unlicense | import numpy as np
import pandas as pd
from scipy import stats
import statsmodels.api as sm
import statsmodels.tsa as tsa
import matplotlib.pyplot as plt
# ensures experiment runs the same every time
np.random.seed(100)
# This function simluates an AR process, generating a new value based on historial values,
# autor... |
chris1610/pbpython | notebooks/Category-Encoding-Article.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder
from sklearn.compose import make_column_transformer
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import cross_val_score
import category_en... |
seg/2016-ml-contest | geoLEARN/Submission_4_OVR_RF.ipynb | apache-2.0 | ###### Importing all used packages
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from mpl_toolkits.axes_grid1 import make_axes_locatable
import seaborn as sns
from... |
phnmnl/workflow-demo | Jupyter/Workflow.ipynb | apache-2.0 | control=input()
"""
Explanation: R-based metabolomics workflow by Kultima lab
This notebook aims to show, through a series of examples, how to set up a metabolomics workflow using the Chronos REST API. As benchmark case we use a R-based pipeline by the Kultima lab. The aim of this pipeline is to:
1. Remove contaminan... |
mdalvi/financial-analysis-and-algo-trading | python_finance_fundamentals/finance_fundamentals_lecture_notes.ipynb | mit | import pandas as pd
import quandl
aapl = pd.read_csv('AAPL_CLOSE', index_col='Date', parse_dates=True)
cisco = pd.read_csv('CISCO_CLOSE', index_col='Date', parse_dates=True)
ibm = pd.read_csv('IBM_CLOSE', index_col='Date', parse_dates=True)
amzn = pd.read_csv('AMZN_CLOSE', index_col='Date', parse_dates=True)
aapl.hea... |
ES-DOC/esdoc-jupyterhub | notebooks/hammoz-consortium/cmip6/models/sandbox-1/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'hammoz-consortium', 'sandbox-1', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: HAMMOZ-CONSORTIUM
Source ID: SANDBOX-1
Topic: Aerosol
Sub-Topics: T... |
ESSS/notebooks | smooth_transition_between_analytic_functions.ipynb | mit | import sympy
from sympy import Piecewise
import numpy as np
# For example:
x_ = sympy.symbols('x', real=True)
f_left_ = x_**1.2
f_right_ = 10.0 / x_**0.2
x_threshold = 10.0 ** (5 / 7)
f_ = Piecewise(
(f_left_, x_ < x_threshold),
(f_right_, True)
)
f = sympy.lambdify(x_, f_)
import seaborn
import matplotl... |
quantumlib/Cirq | docs/tutorials/hidden_linear_function.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... |
srcole/qwm | guessinggame/Single player guessing game.ipynb | mit | from IPython.display import YouTubeVideo
YouTubeVideo("ud_frfkt1t0")
# Import libraries
from __future__ import division
from scipy.stats import binom
import numpy as np
import matplotlib.pyplot as plt
%pylab inline
# Initialize random seed
np.random.seed(1)
def genABK(nTrials,int_min,int_max):
'''
Generate t... |
metpy/MetPy | v1.1/_downloads/e5685967297554788de3cf5858571b23/Natural_Neighbor_Verification.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import numpy as np
from scipy.spatial import ConvexHull, Delaunay, delaunay_plot_2d, Voronoi, voronoi_plot_2d
from scipy.spatial.distance import euclidean
from metpy.interpolate import geometry
from metpy.interpolate.points import natural_neighbor_point
"""
Explanation: Natural Neighbo... |
jorgemauricio/INIFAP_Course | Basico/Python_Introduccion_Ejercicios-Soluciones.ipynb | mit | 7 ** 4
"""
Explanation: Python Introduccion Ejercicios - Soluciones
Este ejercicio te permite comprender los principios basicos de Python
Ejercicios
Resulve la pregunta que se te muestra en negritas para obtener la respuesta que se muestra debajo de la celda de codigo
7 a la 4 potencia?
End of explanation
"""
s = '... |
karlobermeyer/numeric-digit-classification | numeric_digit_classification.ipynb | mit | #%qtconsole # For inspecting variables.
# Standard
import os
from glob import glob # Unix style pathname pattern expansion.
import csv
import pickle
import time
# Scientific Computing and Visualization
import numpy as np; np.random.seed(13) # Lucky seed.
import matplotlib.pyplot as plt
%matplotlib inline
import cv... |
mne-tools/mne-tools.github.io | dev/_downloads/d8a6d02146c5c075611a652218e020ad/30_reading_fnirs_data.ipynb | bsd-3-clause | import os.path as op
import numpy as np
import pandas as pd
import mne
"""
Explanation: Importing data from fNIRS devices
fNIRS devices consist of two kinds of optodes: light sources (AKA "emitters" or
"transmitters") and light detectors (AKA "receivers"). Channels are defined as
source-detector pairs, and channel loc... |
marius311/cosmoslik | cosmoslik_plugins/likelihoods/planck/clik.ipynb | gpl-3.0 | %pylab inline
sys.path = sys.path[1:]
from cosmoslik import *
clik = likelihoods.planck.clik(
clik_file="plik_lite_v18_TT.clik/",
A_Planck=1
)
clik
"""
Explanation: Planck (via clik)
This plugin is an interface between the Planck likelihood code clik and CosmoSlik. You need clik already installed on your mach... |
frankbearzou/Data-analysis | Recent Grads/Recent Grads.ipynb | mit | recent_grads = pd.read_csv('recent-grads.csv')
recent_grads.head()
recent_grads.tail()
recent_grads.describe()
recent_grads.shape
"""
Explanation: Data Exploration
End of explanation
"""
recent_grads.shape[0] - recent_grads.dropna().shape[0]
"""
Explanation: how many rows contain null values?
End of explanation... |
djevans071/Rebalancing-Citibike | Rebalancing.ipynb | mit | # find csv file for tripdata
year = 2015
month = 3
#csvPath = '{}{:02}-citibike-tripdata.csv'.format(year, month)
#df = pd.read_csv(basepath + csvPath, parse_dates = ['Start Time', 'Stop Time'])
df = trip_data(year, month)
#df['trip_id'] = df.index.values
df.head()
rebals = rebal_data(year,month)
rebals.head()
"""
Ex... |
dacr26/CompPhys | 08_01_Schroedinger.ipynb | mit | %matplotlib inline
import numpy as np
from matplotlib import pyplot
import math
import matplotlib.animation as animation
from JSAnimation.IPython_display import display_animation
lx=20
dx = 0.04
nx = int(lx/dx)
dt = dx**2/20.
V0 = 15.
alpha = dt/dx**2
fig = pyplot.figure()
ax = pyplot.axes(xlim=(0, lx), ylim=(0, 2), ... |
tensorflow/workshops | extras/archive/05_custom_estimators.ipynb | apache-2.0 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import numpy as np
import tensorflow as tf
"""
Explanation: Custom Estimators
In this notebook we'll write an Custom Estimator (using a model function we specifiy). On the way, we'll use tf.layers... |
yedivanseven/bestPy | examples/07_RESTfulAPI.ipynb | gpl-3.0 | from urllib.parse import ParseResult
from urllib.request import urlopen
import json
"""
Explanation: CHAPTER 7
RESTful API
Now you know all about algorithms and how to benchmark them. Once you found the optimal algorithm and settings, however, what do you do with them?
One common way to make your findings, that is, a ... |
ES-DOC/esdoc-jupyterhub | notebooks/noaa-gfdl/cmip6/models/gfdl-cm4/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'noaa-gfdl', 'gfdl-cm4', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: NOAA-GFDL
Source ID: GFDL-CM4
Topic: Ocean
Sub-Topics: Timestepping Framework, A... |
EvoML/EvoML | EvoML - Example Usage.ipynb | gpl-3.0 | from evoml.subsampling import BasicSegmenter_FEMPO, BasicSegmenter_FEGT, BasicSegmenter_FEMPT
df = pd.read_csv('datasets/ozone.csv')
df.head(2)
X, y = df.iloc[:,:-1], df['output']
print(BasicSegmenter_FEGT.__doc__)
from sklearn.tree import DecisionTreeRegressor
clf_dt = DecisionTreeRegressor(max_depth=3)
clf = Bas... |
WMD-group/SMACT | examples/Practical_tutorial/Combinations_practical.ipynb | mit | from math import factorial as factorial
grid_points = 1000.0
atoms = 30.0
elements = 50.0
##########
# A. Show that assigning each of the 30 atoms as one of 50 elements is ~ 9e50 (permutations)
element_assignment = 0
print(f'Number of possible element assignments is: {element_assignment}')
# B. Show that the numb... |
mavillan/SciProg | 04_jit/04_actividad.ipynb | gpl-3.0 | import numba
import numpy as np
import numexpr as ne
import matplotlib.pyplot as plt
"""
Explanation: <center>
<h1> Scientific Programming in Python </h1>
<h2> Topic 4: Just in Time Compilation: Numba and NumExpr </h2>
</center>
Notebook created by Martín Villanueva - martin.villanueva@usm.cl - DI UTFSM - Ap... |
MartyWeissman/Python-for-number-theory | P3wNT Notebook 3.ipynb | gpl-3.0 | def is_prime(n):
'''
Checks whether the argument n is a prime number.
Uses a brute force search for factors between 1 and n.
'''
for j in range(2,n): # the range of numbers 2,3,...,n-1.
if n%j == 0: # is n divisible by j?
print("{} is a factor of {}.".format(j,n))
r... |
maxkleiner/maXbox4 | MNISTSinglePredict.ipynb | gpl-3.0 | #sign:max: MAXBOX8: 13/03/2021 07:46:37
import numpy as np
import matplotlib.pyplot as plt
from sklearn import tree
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn import datasets
from sklearn.metrics import accuracy_score
# [height, weight, 8*8 pixels of digits 0... |
Hash--/documents | notebooks/Fusion_Basics/The cyclotron interaction.ipynb | mit | # Python modules import
import numpy as np # numpy
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from mpl_toolkits.mplot3d import Axes3D # allows 3D plots with the keyword projection='3d' below
%matplotlib inline
from scipy.integrate import odeint # Integrate a system of ordinary differenti... |
Kaggle/learntools | notebooks/data_cleaning/raw/ex1.ipynb | apache-2.0 | from learntools.core import binder
binder.bind(globals())
from learntools.data_cleaning.ex1 import *
print("Setup Complete")
"""
Explanation: In this exercise, you'll apply what you learned in the Handling missing values tutorial.
Setup
The questions below will give you feedback on your work. Run the following cell to... |
hetaodie/hetaodie.github.io | assets/media/uda-ml/code/modelselect/workspace/Solution-zh.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: 通过网格搜索完善模型
在这个迷你 Lab 练习中，我们将为决策树模型拟合一些样本数据。 这个初始模型会过拟合。 然后，我们将使用网格搜索为这个模型找到更好的参数，以减少过拟合。
首先，导入：
End of explanation
"""
def load_pts(csv_name):
data = np.asarray(pd.read_csv(csv_name, header=None))
X = d... |
amitkaps/multidim | notebooks/Air_Routes.ipynb | mit | import pandas as pd
# Read in the airports data.
airports = pd.read_csv("../data/airports.dat.txt", header=None, na_values=['\\N'], dtype=str)
# Read in the airlines data.
airlines = pd.read_csv("../data/airlines.dat.txt", header=None, na_values=['\\N'], dtype=str)
# Read in the routes data.
routes = pd.read_csv("..... |
mrustl/flopy | examples/Notebooks/flopy3_sfrpackage_example.ipynb | bsd-3-clause | import sys
import platform
import os
import numpy as np
import glob
import shutil
import matplotlib as mpl
import matplotlib.pyplot as plt
import flopy
import flopy.utils.binaryfile as bf
#Set name of MODFLOW exe
# assumes executable is in users path statement
exe_name = 'mf2005'
if platform.system() == 'Windows':
... |
drJfunk/gbmgeometry | examples/demo.ipynb | mit | %pylab inline
from astropy.coordinates import SkyCoord
import astropy.coordinates as coord
import astropy.units as u
from gbmgeometry import *
"""
Explanation: GBM Geometry Demo
J. Michael Burgess
gbmeometry is a module with routines for handling GBM geometry. It performs a few tasks:
* creates and astropy coordinate... |
thalesians/tsa | src/jupyter/python/kalman.ipynb | apache-2.0 | import os, sys
sys.path.append(os.path.abspath('../../main/python'))
import datetime as dt
import numpy as np
import numpy.testing as npt
import matplotlib.pyplot as plt
from thalesians.tsa.distrs import NormalDistr as N
import thalesians.tsa.filtering as filtering
import thalesians.tsa.filtering.kalman as kalman
im... |
jstac/recursive_utility_code | python/constant_vol/lg_discretized.ipynb | mit | D_vals = np.arange(5, 250, step=5)
discrete_exponent_vals = np.empty_like(D_vals, dtype=np.float64)
for d, D in enumerate(D_vals):
discrete_exponent_vals[d] = lrm_discretized(lg, D=D)
fig, ax = plt.subplots()
ax.ticklabel_format(useOffset=False)
#ax.set_ylim((a - 1.5 * 1e-8, a + 2 * 1e-9))
ax.plot(D_vals, np.one... |
nmih/ssbio | docs/notebooks/GEM-PRO - SBML Model.ipynb | mit | import sys
import logging
# Import the GEM-PRO class
from ssbio.pipeline.gempro import GEMPRO
# Printing multiple outputs per cell
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
"""
Explanation: GEM-PRO - SBML Model
This notebook gives an example of how to ... |
jsub10/MLCourse | Notebooks/Logistic-Regression.ipynb | mit | # Import our usual libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import os
# OS-independent way to navigate the file system
# Data directory is one directory up in relation to directory of this notebook
data_dir_root = os.path.normpath(os.getcwd() + os.sep + os.par... |
harmsm/pythonic-science | chapters/05_big-files/01_phred-scores-and-enrichment_key.ipynb | unlicense | p = np.arange(0.001,1,0.001)
plt.plot(p,-10*np.log10(p))
plt.title("High Q score is good")
"""
Explanation: Extracting information about sequence quality and enrichment
Enrichment
Often want to compare two datasets (tissue 1 vs. tissue 2; -drug vs. +drug; etc.)
Done by taking ratio of counts for sequences between dat... |
cestella/presentations | NLP_on_non_textual_data/src/main/ipython/clinical2vec.ipynb | apache-2.0 | print_synonyms('dx::440.0', model)
"""
Explanation: Atherosclerosis of the Aorta
Also known as heart disease or hardening of the arteries. This disease is the number one killer of Americans.
End of explanation
"""
#Crohn's Disease
print_synonyms('dx::555.9', model)
"""
Explanation: Peptic Ulcers
There have been lo... |
jjonte/udacity-deeplearning-nd | py3/project-3/dlnd_tv_script_generation.ipynb | unlicense | """
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... |
sjsrey/pysal | notebooks/model/spvcm/spatially-varying-coefficients.ipynb | bsd-3-clause | side = np.arange(0,10,1)
grid = np.tile(side, 10)
beta1 = grid.reshape(10,10)
beta2 = np.fliplr(beta1).T
fig, ax = plt.subplots(1,2, figsize=(12*1.6, 6))
sns.heatmap(beta1, ax=ax[0])
sns.heatmap(beta2, ax=ax[1])
plt.show()
"""
Explanation: Today, we'll sample a spatially-varying coefficient model, like that discusse... |
PyladiesMx/Empezando-con-Python | 4. Lops/For Loops.ipynb | mit | #Obtén el cuadrado de 1
1**2
#Obtén el cuadrado de 2
2**2
#Obtén el cuadrado de 3
3**2
#Obtén el cuadrado de 4
4**2
#Obtén el cuadrado de 5
5**2
#Obtén el cuadrado de 6
6**2
#Obtén el cuadrado de 7
7**2
#Obtén el cuadrado de 8
8**2
#Obtén el cuadrado de 9
9**2
#Obtén el cuadrado de 10
10**2
"""
Explanation: B... |
dangall/Udacity-Machine-Learning-Nanodegree | P1_boston_housing/boston_housing.ipynb | mit | # Import libraries necessary for this project
import numpy as np
import pandas as pd
from sklearn.cross_validation import ShuffleSplit
# Import supplementary visualizations code visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib inline
# Load the Boston housing dataset
data = pd.read_csv('hou... |
batfish/pybatfish | docs/source/notebooks/differentialQuestions.ipynb | apache-2.0 | bf.set_network('generate_questions')
bf.set_snapshot('filters-change')
"""
Explanation: Differential Questions
Differential questions enable you to discover configuration and
behavior differences between two snapshot of the network.
Most of the Batfish questions can be run differentially by using
snapshot=<current... |
AndreySheka/dl_ekb | hw8/VAE_homework.ipynb | mit | #The following line fetches you two datasets: images, usable for autoencoder training and attributes.
#Those attributes will be required for the final part of the assignment (applying smiles), so please keep them in mind
from lfw_dataset import fetch_lfw_dataset
data,attrs = fetch_lfw_dataset()
import numpy as np
X_t... |
PythonFreeCourse/Notebooks | week02/6_Documentation.ipynb | mit | dir(str)
"""
Explanation: <img src="images/logo.jpg" style="display: block; margin-left: auto; margin-right: auto;" alt="לוגו של מיזם לימוד הפייתון. נחש מצויר בצבעי צהוב וכחול, הנע בין האותיות של שם הקורס: לומדים פייתון. הסלוגן המופיע מעל לשם הקורס הוא מיזם חינמי ללימוד תכנות בעברית.">
<p style="text-align: right; dir... |
ethen8181/machine-learning | deep_learning/softmax.ipynb | mit | # code for loading the format for the notebook
import os
# path : store the current path to convert back to it later
path = os.getcwd()
os.chdir(os.path.join('..', 'notebook_format'))
from formats import load_style
load_style(plot_style = False)
os.chdir(path)
# 1. magic for inline plot
# 2. magic to print version
... |
neeasthana/ML-SQL | Clustering/Seeds/Seeds.ipynb | gpl-3.0 | #Libraries and Imports
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import pylab
from sklearn.cluster import KMeans
from sklearn import preprocessing
from sklearn.decomposition import PCA
"""
Explanation: Seeds dataset (Clustering)
Authors
Written by: Neeraj Asthana (und... |
deflaux/linkage-disequilibrium | datalab/Visualizing_Regional_LD.ipynb | apache-2.0 | import gcp.bigquery as bq
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: <!-- Copyright 2015 Google Inc. All rights reserved. -->
<!-- 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... |
google/struct2tensor | examples/prensor_playground.ipynb | apache-2.0 | #@test {"skip": true}
# install struct2tensor
!pip install struct2tensor
# graphviz for pretty output
!pip install graphviz
"""
Explanation: Your structured data into Tensorflow.
ML training often expects flat data, like a line in a CSV.
tf.Example was
designed to represent flat data. But the data you care about and ... |
tensorflow/docs-l10n | site/zh-cn/hub/tutorials/yamnet.ipynb | apache-2.0 | #@title Copyright 2020 The TensorFlow Hub Authors. All Rights Reserved.
#
# 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 ... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/feature_engineering/solutions/4_keras_adv_feat_eng.ipynb | apache-2.0 | # Run the chown command to change the ownership of the repository
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# You can use any Python source file as a module by executing an import statement in some other Python source file.
# The import statement combines two operations; it searches for the na... |
ctn-waterloo/best-practices | Installation - setuptools.ipynb | mit | from setuptools import setup
setup(
name='bughandler',
packages=['killer']
)
"""
Explanation: Installation
Purpose: set up a fresh computer to be able to run all aspects of your model and analysis.
SetupTools:
- Makes your code importable using Python
- Checks all requirements are satisified
- Makes your code... |
evanmiltenburg/python-for-text-analysis | Assignments/ASSIGNMENT-1.ipynb | apache-2.0 | # average code
"""
Explanation: Assignment 1: Calculation, Strings, Boolean Expressions and Conditions
Deadline: Friday, September 9, 2021 before 3pm (submit via Canvas: Block I/Assignment 1)
This assignment is not graded, but it is mandatory to submit a version that shows you have given it a serious try. We will che... |
eds-uga/csci1360e-su17 | lectures/L8.ipynb | mit | def pet_names(name1, name2):
print("Pet 1: ", name1)
print("Pet 2: ", name2)
pet1 = "King"
pet2 = "Reginald"
pet_names(pet1, pet2) # pet1 variable, then pet2 variable
pet_names(pet2, pet1) # notice we've switched the order in which they're passed to the function
"""
Explanation: Lecture 8: Functions II
CSCI... |
asharel/ml | LAB2/src/Practica2.ipynb | gpl-3.0 | #Libraries
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt # Read Dataset
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
import sklearn.featur... |
zzsza/TIL | pytorch/GAN.ipynb | mit | D = nn.Sequential(
nn.Linear(784, 256),
nn.ReLU(),
nn.Linear(256, 256),
nn.ReLU(),
nn.Linear(256, 1),
nn.Sigmoid())
G = nn.Sequential(
nn.Linear(64, 256),
nn.ReLU(),
nn.Linear(256, 256),
nn.ReLU(),
nn.Linear(256, 784),
nn.Tanh())
transform = transforms.Compose([
... |
BrownDwarf/ApJdataFrames | notebooks/Somers2017.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
pd.options.display.max_columns = 150
%config InlineBackend.figure_format = 'retina'
import astropy
from astropy.io import ascii
from astropy.table import Table
import numpy as np
"""
Explanation: Somers2017
Title: A Measuremen... |
tpin3694/tpin3694.github.io | sql/sort_by_multiple_columns.ipynb | mit | # Ignore
%load_ext sql
%sql sqlite://
%config SqlMagic.feedback = False
"""
Explanation: Title: Sort By Multiple Columns
Slug: sort_by_multiple_columns
Summary: Sort By Multiple Columns in SQL.
Date: 2017-01-16 12:00
Category: SQL
Tags: Basics
Authors: Chris Albon
Note: This tutorial was written using Catherine De... |
napsternxg/ipython-notebooks | Dynamic Programming.ipynb | apache-2.0 | def wrapper(S, coins):
states = [(10000, set()) for k in range(S+1)]
states = [(10000, []) for k in range(S+1)]
return n_coins(S, coins, states)
def n_coins(S, coins, states):
if S < 1:
return (10000, [])
if S in coins:
return (1, [S])
if S < min(coins):
return (10000, [... |
quantopian/research_public | notebooks/lectures/Introduction_to_NumPy/notebook.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Introduction to NumPy
by Maxwell Margenot
Part of the Quantopian Lecture Series:
www.quantopian.com/lectures
github.com/quantopian/research_public
Notebook released under the Creative Commons Attribution 4.0 License.
NumPy is an incredibly powerful ... |
sdss/marvin | docs/sphinx/tutorials/notebooks/Basics_of_Marvin.ipynb | bsd-3-clause | from marvin.tools import Cube
"""
Explanation: Basics of Marvin
In this notebook, you will learn the common core functionality across many of the Marvin Tools. This includes the basics of accessing and handling MaNGA data from different locations, as well as a beginners guide of interacting with data via the core too... |
hail-is/hail | hail/python/hail/docs/tutorials/06-joins.ipynb | mit | import hail as hl
hl.utils.get_movie_lens('data/')
users = hl.read_table('data/users.ht')
movies = hl.read_table('data/movies.ht')
ratings = hl.read_table('data/ratings.ht')
"""
Explanation: Table Joins Tutorial
This tutorial walks through some ways to join Hail tables. We'll use a simple movie dataset to illustrate... |
kit-cel/lecture-examples | nt2_ce2/vorlesung/ch_2_properties_lin_modulation/psd_linear_modulation.ipynb | gpl-2.0 | # importing
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
# showing figures inline
%matplotlib inline
# plotting options
font = {'size' : 20}
plt.rc('font', **font)
plt.rc('text', usetex=True)
matplotlib.rc('figure', figsize=(18, 10) )
"""
Explanation: Content and Objectives
Show PSD of ... |
ES-DOC/esdoc-jupyterhub | notebooks/hammoz-consortium/cmip6/models/sandbox-3/landice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'hammoz-consortium', 'sandbox-3', 'landice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: HAMMOZ-CONSORTIUM
Source ID: SANDBOX-3
Topic: Landice
Sub-Topics: G... |
diegocavalca/Studies | deep-learnining-specialization/4. Convolutional Neural Networks/week2/Keras+-+Tutorial+-+Happy+House+v2.ipynb | cc0-1.0 | import numpy as np
from keras import layers
from keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.models import Model
from keras.preprocessing import im... |
phungkh/phys202-2015-work | assignments/assignment12/FittingModelsEx02.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
"""
Explanation: Fitting Models Exercise 2
Imports
End of explanation
"""
A=np.load('decay_osc.npz')
tdata = A['tdata']
ydata= A['ydata']
dy = A['dy']
tdata, ydata, dy
plt.figure(figsize=(10,5))
plt.scatter(tdata,yd... |
google/applied-machine-learning-intensive | content/04_classification/06_images_and_video/00-pil.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... |
jedbrown/numerical-computation | LinearAlgebra.ipynb | mit | %matplotlib notebook
import numpy
from matplotlib import pyplot
def matmult1(A, x):
"""Entries of y are dot products of rows of A with x"""
y = numpy.zeros_like(A[:,0])
for i in range(len(A)):
row = A[i,:]
for j in range(len(row)):
y[i] += row[j] * x[j]
return y
A = numpy.a... |
root-mirror/training | SoftwareCarpentry/exercises/fitting-exercise.ipynb | gpl-2.0 | import ROOT
data = [ 6,1,10,12,6,13,23,22,15,21,
23,26,36,25,27,35,40,44,66,81,
75,57,48,45,46,41,35,36,53,32,
40,37,38,31,36,44,42,37,32,32,
43,44,35,33,33,39,29,41,32,44,
26,39,29,35,32,21,21,15,25,15 ]
title = 'Lorentzian Peak on Quadratic Background'
h = ROOT.TH1F('his... |
superliaoyong/plist-forsource | python第一课课件.ipynb | apache-2.0 | print('hello, "world')
print("hello, 'world")
import this
"""
Explanation: 人生苦短，我用python
python课程
课表
一、 python基础 - 变量与数据类型，及常见数据类型的用法
二、 python基础 - 条件、循环、函数、类
三、 python爬虫 - python爬虫并用Mysql数据库存储
四、 pandas通览 - 用pandas做数据处理与分析
五、 实战 - 泰坦尼克幸存者预测
学完本课程之后，你会：
1、 掌握基本的python语法，并编写... |
jmschrei/pomegranate | examples/bayes_classifier_hmm_cheating_coin_toss.ipynb | mit | from pomegranate import *
import numpy as np
%pylab inline
"""
Explanation: Bayes Classifier with Hidden Markov Model emissions Coin Toss
author: Nicholas Farn [<a href="sendto:nicholasfarn@gmail.com">nicholasfarn@gmail.com</a>],
Jacob Schreiber [<a href="sendto:jmschreiber91@gmail.com">jmschreiber91@gmail.com... |
leriomaggio/deep-learning-keras-tensorflow | 1. ANN/1.1.1 Perceptron and Adaline.ipynb | mit | # Display plots in notebook
%matplotlib inline
# Define plot's default figure size
import matplotlib
"""
Explanation: (exceprt from Python Machine Learning Essentials, Supplementary Materials)
Sections
Implementing a perceptron learning algorithm in Python
Training a perceptron model on the Iris dataset
Adaptive l... |
matt-graham/auxiliary-pm-mcmc | experiment_notebooks/Auxiliary Pseudo-Marginal MCMC - MI u updates and MH theta updates.ipynb | mit | data_dir = os.path.join(os.environ['DATA_DIR'], 'uci')
exp_dir = os.path.join(os.environ['EXP_DIR'], 'apm_mcmc')
"""
Explanation: Construct data and experiments directorys from environment variables
End of explanation
"""
data_set = 'pima'
method = 'apm(mi+mh)'
n_chain = 10
chain_offset = 0
seeds = np.random.random_... |
ES-DOC/esdoc-jupyterhub | notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-2', 'sandbox-3', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: TEST-INSTITUTE-2
Source ID: SANDBOX-3
Topic: Atmos
Sub-Topics: Dynamical... |
metpy/MetPy | dev/_downloads/c1a3b4ec1d09d4debc078297d433a9b2/Point_Interpolation.ipynb | bsd-3-clause | import cartopy.crs as ccrs
import cartopy.feature as cfeature
from matplotlib.colors import BoundaryNorm
import matplotlib.pyplot as plt
import numpy as np
from metpy.cbook import get_test_data
from metpy.interpolate import (interpolate_to_grid, remove_nan_observations,
remove_repeat_coo... |
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