repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
Olsthoorn/TransientGroundwaterFlow | Syllabus_in_notebooks/Sec5_4_5_superposition_in_time_erfc.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
import scipy.special as sp
"""
Explanation: Superposition in time with the erfc function
IHE, Delft, 20200106
@T.N.Olsthoorn
See page 56 of the syllabus
Context
Consider a situation where groundwater is directly subject to varying surface water levels at $x=0$.
Show t... |
sz-workshop-2017/virtual-machine | notebooks/2.4 - Challenge - Program a simple game.ipynb | apache-2.0 | import random
"""
Explanation: This challenge will get you familiar with the basic elements of Python by programming a simple card game. We will create a custom class to represent each player in the game, which will store information about their current pot, as well as a series of methods defining how they play the ga... |
landlab/landlab | notebooks/tutorials/fields/working_with_fields.ipynb | mit | import numpy as np
from landlab import RasterModelGrid, FieldError
from landlab.components import LinearDiffuser
mg = RasterModelGrid((3, 4))
"""
Explanation: <a href="http://landlab.github.io"><img style="float: left" src="../../landlab_header.png"></a>
Understanding and working with Landlab data fields
<hr>
<small>... |
trungdong/datasets-provanalytics-dmkd | Extra 3.2 - Historical Provenance - Application 3.ipynb | mit | import pandas as pd
filepath = "rrg/ancestor-graphs.csv"
df = pd.read_csv(filepath, index_col=0)
df.head()
"""
Explanation: Extra 3.2 - Historical Provenance - Application 3: RRG Chat Messages
Identifying instructions from chat messages in the Radiation Response Game.
In this notebook, we explore the performance of ... |
mayankjohri/LetsExplorePython | Section 1 - Core Python/Chapter 04 - Control Flow/3.1 Compound Statements.ipynb | gpl-3.0 | password = input("Please enter the password:")
if password == "Simsim":
print("\t> Welcome to the cave")
x = "Mayank"
y = "TEST"
if y == "TEST":
print(x)
if y:
print("Hello World")
z = None
if z:
print("TEST")
x = 11
if x > 10:
print("Hello")
if x > 10.999999999999:
print("Hello agai... |
ajhenrikson/phys202-2015-work | assignments/assignment04/TheoryAndPracticeEx02.ipynb | mit | from IPython.display import Image
"""
Explanation: Theory and Practice of Visualization Exercise 2
Imports
End of explanation
"""
# Add your filename and uncomment the following line:
Image(filename='bad graph.jpg')
"""
Explanation: Violations of graphical excellence and integrity
Find a data-focused visualization ... |
UDST/pandana | examples/Pandana-demo.ipynb | agpl-3.0 | import numpy as np
import pandas as pd
import pandana
print(pandana.__version__)
"""
Explanation: Pandana demo
Sam Maurer, July 2020
This notebook demonstrates the main features of the Pandana library, a Python package for network analysis that uses contraction hierarchies to calculate super-fast travel accessibility... |
quantopian/research_public | notebooks/lectures/Linear_Correlation_Analysis/questions/notebook.ipynb | apache-2.0 | # Useful Functions
def find_most_correlated(data):
n = data.shape[1]
keys = data.keys()
pair = []
max_value = 0
for i in range(n):
for j in range(i+1, n):
S1 = data[keys[i]]
S2 = data[keys[j]]
result = np.corrcoef(S1, S2)[0,1]
if result > max_v... |
ondrolexa/sg2 | 15_Transpression.ipynb | mit | %pylab inline
from scipy import linalg as la
"""
Explanation: Transpressional deformation
End of explanation
"""
def KDparams(F):
u, s, v = svd(F)
Rxy = s[0]/s[1]
Ryz = s[1]/s[2]
K = (Rxy-1)/(Ryz-1)
D = sqrt((Rxy-1)**2 + (Ryz-1)**2)
return K, D
"""
Explanation: Here we will examine strain e... |
swirlingsand/deep-learning-foundations | rnns/embeddings/.ipynb_checkpoints/Skip-Gram_word2vec-checkpoint.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... |
gfeiden/Notebook | Projects/mlt_calib/resampling_tests.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
kde_pdf = np.genfromtxt('data/run08_kde_props.txt') # KDE of full PDF
kde_pbr = np.genfromtxt('data/run08_kde_props_tmp.txt') # KDE of bootstrap resample on final 75 iterations
kde_fbr = np.genfromtxt('data/run08_kde_props_tmp2.txt') # KDE of ... |
ProfessorKazarinoff/staticsite | content/code/ENGR213/Problem_2C6.ipynb | gpl-3.0 | import math
P = 10
c = 5
L = 100
E = 120*1000 #120 GPa = 120 * 1000 MPa
d_exact = (P*L)/(2*math.pi*(c**2)*E)
print(f"The exact value for delfection of the cone is d_exact = {d_exact}")
"""
Explanation: The Problem
Below is an engineering mechanics problem that can be solved in Python. Follow along this post to see ... |
Naereen/notebooks | agreg/Mémoisation_en_Python_et_OCaml.ipynb | mit | from time import sleep
def f1(n):
sleep(3)
return n + 3
def f2(n):
sleep(4)
return n * n
%timeit f1(10)
%timeit f2(10)
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Mémoïsation,-en-Python-et-en-OCaml" data-toc-modified-id="Mémoïsation,-en-Python-et-en-OCaml-1"><span cla... |
peterdalle/mij | 3 News robot/Weather news robot.ipynb | gpl-3.0 | # Import all the things!
import urllib.request
from datetime import *
from lxml import html
from bs4 import BeautifulSoup
"""
Explanation: Weather news robot
A simple and stupid news robot written in Python that scrapes tomorrows weather and writes a short text complaining about how cold it is.
1. Import libraries
End... |
rsignell-usgs/notebook | HOPS/.ipynb_checkpoints/hops2cf-checkpoint.ipynb | mit | from netCDF4 import Dataset
url = ('http://geoport.whoi.edu/thredds/dodsC/usgs/data2/rsignell/gdrive/'
'nsf-alpha/Data/MIT_MSEAS/MSEAS_Tides_20160317/mseas_tides_2015071612_2015081612_01h.nc')
nc = Dataset(url)
"""
Explanation: The problem: CF compliant readers cannot read HOPS dataset directly.
The solution... |
anhaidgroup/py_entitymatching | notebooks/guides/step_wise_em_guides/.ipynb_checkpoints/Sampling and Labeling-checkpoint.ipynb | bsd-3-clause | # Import py_entitymatching package
import py_entitymatching as em
import os
import pandas as pd
# Get the datasets directory
datasets_dir = em.get_install_path() + os.sep + 'datasets'
path_A = datasets_dir + os.sep + 'DBLP.csv'
path_B = datasets_dir + os.sep + 'ACM.csv'
path_C = datasets_dir + os.sep + 'tableC.csv'
... |
Saxafras/Spacetime | transitions.ipynb | bsd-3-clause | dom_test = ECA(54,domain_54(20*4, 'a'))
dom_test.evolve(20*4)
diagram(dom_test.get_spacetime())
np.random.seed(0)
domain_states = epsilon_field(dom_test.get_spacetime())
domain_states.estimate_states(3,3,1)
domain_states.filter_data()
a = domain_states.state_transition((10,10), 'forward')
print a
b = domain_states.... |
ecervera/mindstorms-nb | nxt/sensors/index.ipynb | mit | from functions import connect, touch, light, sound, ultrasonic, disconnect
connect(12)
"""
Explanation: Sensors
Hi ha quatre sensors diferents montats i connectats al robot:
Anem a comprovar el funcionament de cadascun d'ells.
Primer, necessitem algunes funcions, i com sempre, connectar-nos al robot.
End of explanat... |
IsacLira/data-science-cookbook | 2017/06-linear-regression/resp_linear_regression_isaclira.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
% matplotlib inline
# Define uma função para carregar os dados
def load_csv(path):
df = pd.read_csv(path,names=['num_reinv','pag_total'])
return df
insdf = load_csv('insurance.csv')
insdf.head()
plt.scatter(insdf.num_reinv,insdf.... |
Kaggle/learntools | notebooks/ml_explainability/raw/ex4_shap_basic.ipynb | apache-2.0 | from learntools.ml_explainability.ex4 import *
print("Setup Complete")
"""
Explanation: Set Up
At this point, you have enough tools to put together compelling solutions to real-world problems. You will ned to pick the right techniques for each part of the following data science scenario. Along the way, you'll use SHAP... |
roaminsight/roamresearch | BlogPosts/Average_precision/Average_precision_post.ipynb | apache-2.0 | __author__ = 'Nick Dingwall'
"""
Explanation: Stepping away from linear interpolation
End of explanation
"""
from average_precision_post_code import *
"""
Explanation: TL;DR Interpolated average precision is a common metric for classification tasks. However, interpolating linearly between operating points, as in sc... |
dombrno/PG | Notebooks/test_cluster.ipynb | bsd-2-clause | Tc_mf = meV_to_K(0.5*250)
print meV_to_K(pi/2.0)
print 1.0/0.89
print cst.physical_constants["Boltzmann constant"]
print '$T_c^{MF} = $', Tc_mf, "K"
T_KT = meV_to_K(0.1*250)
print r"$T_{KT} = $", T_KT, "K"
"""
Explanation: TB Model
We pick the following parameters:
+ hopping constant $ t= 250$ meV
+ $\Delta = 1.0 t$... |
h-mayorquin/camp_india_2016 | tutorials/machine learning/Tutorial_notebook.ipynb | mit | %matplotlib inline
import sklearn
import scipy.io as sio
import matplotlib.pylab as plt
import matplotlib as mp
import numpy as np
import scipy as sp
import scipy.ndimage
import scipy.signal
"""
Explanation: Lets first import the important modules.
End of explanation
"""
ft=sio.loadmat("firingTimes.mat")
print ft.... |
robblack007/clase-cinematica-robot | Practicas/practica2/Practica.ipynb | mit | from math import pi, sin, cos
from numpy import matrix
from matplotlib.pyplot import figure, plot, style
from mpl_toolkits.mplot3d import Axes3D
style.use("ggplot")
%matplotlib notebook
τ = 2*pi
"""
Explanation: Matrices de Transformación
Las matrices de rotación y traslación nos sirven para transformar una coordenad... |
Ircam-RnD/xmm | python/examples/QuickStart_Python.ipynb | gpl-3.0 | import xmm
"""
Explanation: Multimodal (Gaussian Mixture/Hidden Markov) Models for Motion-Sound Mapping — Quickstart guide
Building and using the XMM Python library
See http://ircam-rnd.github.io/xmm/
The python library reflects the sructure of the C++ library. The same classes and methods can be used on both implemen... |
landlab/landlab | notebooks/tutorials/network_sediment_transporter/run_network_generator_OpenTopoDEM.ipynb | mit | import os
import numpy as np
import matplotlib.pyplot as plt
import xarray as xr
from landlab import imshow_grid
"""
Explanation: <a href="http://landlab.github.io"><img style="float: left" src="../../landlab_header.png"></a>
Generate a Network Model Grid on an OpenTopography DEM
<hr>
<small>For more Landlab tutorial... |
Kaggle/learntools | notebooks/feature_engineering_new/raw/what_is_feature_engineering_ex.ipynb | apache-2.0 | # Setup feedback system
from learntools.core import binder
binder.bind(globals())
from learntools.feature_engineering_new.ex1 import *
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.model_selection import cross_val_score
from xgboost import XGBRegressor
# Set... |
Abjad/intensive | day-3/1-making-music.ipynb | mit | pairs = [(4, 4), (3, 4), (7, 16), (6, 8)]
time_signatures = [abjad.TimeSignature(_) for _ in pairs]
durations = [_.duration for _ in time_signatures]
time_signature_total = sum(durations)
counts = [1, 2, -3, 4]
denominator = 16
talea = rmakers.Talea(counts, denominator)
talea_index = 0
"""
Explanation: Designing a mus... |
datacommonsorg/api-python | notebooks/analyzing_genomic_data.ipynb | apache-2.0 | # Install datacommons
!pip install --upgrade --quiet datacommons
"""
Explanation: <a href="https://colab.research.google.com/github/datacommonsorg/api-python/blob/master/notebooks/analyzing_genomic_data.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a... |
oroszgy/oroszgy.github.io | content/handouts/sklearn-exercise.ipynb | mit | %pylab inline
import sklearn
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_digits
from sklearn.pipeline import Pipeline
from sklearn.decomposition import PCA
digits = load_digits()
X_digits = digits.data
y_digits = digits.target
logistic = LogisticRegression()
pca = PCA()
pipe... |
mne-tools/mne-tools.github.io | 0.22/_downloads/1c42464343cb8d2a19e726a89ed2fd17/plot_simulated_raw_data_using_subject_anatomy.ipynb | bsd-3-clause | # Author: Ivana Kojcic <ivana.kojcic@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
# Kostiantyn Maksymenko <kostiantyn.maksymenko@gmail.com>
# Samuel Deslauriers-Gauthier <sam.deslauriers@gmail.com>
# License: BSD (3-clause)
import os.path as op
import numpy as np
import mne
from mne.da... |
xpmanoj/content | HW1.ipynb | mit | # special IPython command to prepare the notebook for matplotlib
%matplotlib inline
from fnmatch import fnmatch
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import requests
import urllib2
from pattern import web
from bs4 import BeautifulSoup as bs
# set some nicer defaults for matplotlib
f... |
simkovic/simkovic.github.io | _ipynb/Guess what?! Another Analysis of the Schnall-Johnson Data.ipynb | mit | %pylab inline
import pystan
from matustools.matusplotlib import *
from scipy import stats
import warnings
warnings.filterwarnings("ignore")
il=['dog','trolley','wallet','plane','resume',
'kitten','mean score','median score']
D=np.loadtxt('schnallstudy1.csv',delimiter=',')
D[:,1]=1-D[:,1]
Dtemp=np.zeros((D.shape[0]... |
valentin-nemcev/tensor-flow-hackathon | helloworld.ipynb | mit | from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
"""
Explanation: what is tensor?
tensor is a multidimensional array!
End of explanation
"""
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zer... |
ES-DOC/esdoc-jupyterhub | notebooks/noaa-gfdl/cmip6/models/sandbox-1/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'noaa-gfdl', 'sandbox-1', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: NOAA-GFDL
Source ID: SANDBOX-1
Topic: Seaice
Sub-Topics: Dynamics, Thermodyna... |
rajul/tvb-library | tvb/simulator/demos/surface_stochastic.ipynb | gpl-2.0 | from tvb.datatypes.cortex import Cortex
from tvb.simulator.lab import *
"""
Explanation: Demonstrate using the simulator for a surface simulation, deterministic integration.
Run time: approximately 30 seconds (workstation circa 2010).
Memory requirement: < 1 GB
End of explanation
"""
#Initialise a Model, Coupling,... |
kensugino/jGEM_examples | tutorial.ipynb | mit | # This is to change logging level of jupyter notebook
try:
from importlib import reload # for python 3
except:
pass
import logging
reload(logging)
logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.INFO, datefmt='%I:%M:%S')
# This is to show matplotlib output in the notebook
%matplotlib inli... |
jhprinz/openpathsampling | examples/misc/fun_with_pathmovers.ipynb | lgpl-2.1 | import openpathsampling as p
"""
Explanation: PathMovers
This notebook is an introduction to handling PathMover and MoveChange instances. It mostly covers the questions on
1. how to check if a certain mover was part of a change.
2. What are the possible changes a mover can generate.
3. ...
Load OPENPATHSAMPLING
End ... |
materialsvirtuallab/matgenb | notebooks/2013-01-01-Plotting and Analyzing a Phase Diagram using the Materials API.ipynb | bsd-3-clause | from pymatgen.ext.matproj import MPRester
from pymatgen.analysis.phase_diagram import PhaseDiagram, PDPlotter
%matplotlib inline
"""
Explanation: Introduction
This notebook shows how to plot and analyze a phase diagram.
Written using:
- pymatgen==2021.2.8
End of explanation
"""
#This initializes the REST adaptor. Yo... |
google/jax | docs/jax-101/01-jax-basics.ipynb | apache-2.0 | import jax
import jax.numpy as jnp
x = jnp.arange(10)
print(x)
"""
Explanation: JAX As Accelerated NumPy
Authors: Rosalia Schneider & Vladimir Mikulik
In this first section you will learn the very fundamentals of JAX.
Getting started with JAX numpy
Fundamentally, JAX is a library that enables transformations of arra... |
bobflagg/sentiment-analysis | Baselines.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
"""
Explanation: Some Baselines for Sentiment Analysis
A good starting point for understanding recent work in sentiment analysis and text classification is
Baselines and Bigrams: Simple, Good Sentiment and Topic Classification by Sida Wang and Christopher D. Manning. In this not... |
robertoalotufo/ia898 | src/dftview.ipynb | mit | import numpy as np
def dftview(F):
import ia898.src as ia
FM = ia.dftshift(np.log(np.abs(F)+1))
return ia.normalize(FM).astype(np.uint8)
"""
Explanation: Function iadftview
Synopse
Generate optical Fourier Spectrum from DFT data.
g = iadftview(F)
OUTPUT
g: Image.
INPUT
F: Image. n-dimensional DFT com... |
microsoft/dowhy | docs/source/example_notebooks/dowhy_simple_example.ipynb | mit | import numpy as np
import pandas as pd
from dowhy import CausalModel
import dowhy.datasets
# Avoid printing dataconversion warnings from sklearn and numpy
import warnings
from sklearn.exceptions import DataConversionWarning
warnings.filterwarnings(action='ignore', category=DataConversionWarning)
warnings.filterwarni... |
zhuanxuhit/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... |
AntArch/Presentations_Github | 20160202_Nottingham_GIServices_Lecture3_Beck_InteroperabilitySemanticsAndOpenData/.ipynb_checkpoints/20160202_Nottingham_GIServices_Lecture3_Beck_InteroperabilitySemanticsAndOpenData-checkpoint.ipynb | cc0-1.0 | from IPython.display import YouTubeVideo
YouTubeVideo('F4rFuIb1Ie4')
## PDF output using pandoc
import os
### Export this notebook as markdown
commandLineSyntax = 'ipython nbconvert --to markdown 20160202_Nottingham_GIServices_Lecture3_Beck_InteroperabilitySemanticsAndOpenData.ipynb'
print (commandLineSyntax)
os.s... |
ES-DOC/esdoc-jupyterhub | notebooks/ncc/cmip6/models/noresm2-mh/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'noresm2-mh', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: NCC
Source ID: NORESM2-MH
Topic: Ocnbgchem
Sub-Topics: Tracers.
Properties:... |
insectatorious/hn_kaggle | HN.ipynb | gpl-3.0 | from sklearn.feature_extraction.text import TfidfVectorizer
vectoriser = TfidfVectorizer(max_df=0.5, min_df=1, stop_words='english', use_idf=True)
tfidf_matrix = vectoriser.fit_transform(hn['title'])
feature_names = vectoriser.get_feature_names()
"""
Explanation: Fitting a TF-IDF matrix
See the documentation for Tf-i... |
teuben/astr288p | notebooks/02-flow.ipynb | mit | a = 1.0
if a == 0.0:
print('zero')
elif a > 10.0 or a < -10:
print("too big")
else:
print("close enough")
"""
Explanation: Python Control Flow
if/then/else
for-loop/else
while-loop/else
functions
class (?)
1. if/then/else
Note there is no "else if" or need to indent this, python uses "elif". Again, n... |
mne-tools/mne-tools.github.io | 0.20/_downloads/5c1cfe3ed46585b58c66f76ec83c96c6/plot_20_event_arrays.ipynb | bsd-3-clause | import os
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)
raw.crop(tmax=60).load_data()... |
robertoalotufo/ia898 | src/pca.ipynb | mit | import numpy as np
def pca(X):
'''
features are in the columns
samples are in the rows
'''
n, dim = X.shape
mu = X.mean(axis=0)
Xc = X - mu # 0 mean
C = (Xc.T).dot(Xc)/(n-1) # Covariance matrix
e,V = np.linalg.eigh(C) # eigenvalues and eigenvectors o... |
wuafeing/Python3-Tutorial | 02 strings and text/02.09 normalize unicode text to regexp.ipynb | gpl-3.0 | s1 = "Spicy Jalape\u00f1o"
s1
s2 = "Spicy Jalapen\u0303o"
s2
s1 == s2
len(s1)
len(s2)
"""
Explanation: Previous
2.9 将Unicode文本标准化
问题
你正在处理 Unicode 字符串，需要确保所有字符串在底层有相同的表示。
解决方案
在 Unicode 中，某些字符能够用多个合法的编码表示。为了说明，考虑下面的这个例子：
End of explanation
"""
import unicodedata
t1 = unicodedata.normalize("NFC", s1)
t2 = unicode... |
brain-research/l2hmc | SCGExperiment.ipynb | apache-2.0 | def network(x_dim, scope, factor):
with tf.variable_scope(scope):
net = Sequential([
Zip([
Linear(x_dim, 10, scope='embed_1', factor=1.0 / 3),
Linear(x_dim, 10, scope='embed_2', factor=factor * 1.0 / 3),
Linear(2, 10, scope='embed_3', factor=1.0 / ... |
Vibzy19/tensorflow_from_scratch | tensorflow_from_scratch+.+2+.+Gaussian+Curve.ipynb | mit | sess = tf.InteractiveSession()
mean = 0.0
sigma = 1.0
x = tf.linspace(-5.0,5.0,100)
z = (tf.exp(tf.neg((tf.pow(x-mean,2.0) /
2.0 * tf.pow(sigma , 2.0)))) * (1.0 / sigma*tf.sqrt(tf.multiply(2.0 , 3.1415))))
z
gauss = z.eval()
gauss
plt.plot(gauss)
plt.show()
"""
Explanation: The Gaussian Curve
... |
kevroy314/msl-iposition-pipeline | examples/2-Room Spatial Navigation Analyses.ipynb | gpl-3.0 | data_path = r'Z:\Kelsey\2017 Summer RetLu\Virtual_Navigation_Task\v5_2\NavigationTask_Data\Logged_Data'
study_labels = ['PurseCube', 'CrownCube', 'BasketballCube', 'BootCube', 'CloverCube', 'GuitarCube', 'HammerCube', 'LemonCube', 'IceCubeCube', 'BottleCube']
locations = [[8, -8], [-2, -23], [8, -38], [-14, -13], [15, ... |
DarkEnergySurvey/ugali | notebooks/kernel_example.ipynb | mit | def draw_kernel(k,**kwargs):
lon = k.lon+np.linspace(-0.4,0.4,100)
lat = k.lat+np.linspace(-0.4,0.4,100)
xx,yy = np.meshgrid(lon,lat)
val = k(xx.flat,yy.flat).reshape(xx.shape)
plt.pcolormesh(lon,lat,val,**kwargs)
# Note that it's important to set the aspect when drawing ellipses
plt.gca().s... |
NYUDataBootcamp/Materials | Code/notebooks/bootcamp_plotly_update.ipynb | mit | import numpy as np # foundation for Pandas
import pandas as pd # data package
from pandas_datareader import wb, data as web # worldbank data
import html5lib
import matplotlib.pyplot as plt # graphics module
import datetime as dt ... |
ES-DOC/esdoc-jupyterhub | notebooks/nims-kma/cmip6/models/sandbox-2/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nims-kma', 'sandbox-2', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: NIMS-KMA
Source ID: SANDBOX-2
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation... |
elsonidoq/prediccion_votaciones | Analisis votaciones.ipynb | apache-2.0 | import main
raw_data = main.load_raw_data([]) # asume el cache
gdf = main.get_grouped_dataset(raw_data, level=4)
m, dfX = main.get_model_to_draw(4)
"""
Explanation: Levantando los datos
End of explanation
"""
import model
figure()
distr = model.ConditionalDistribution(gdf['131_pct'], gdf['135_pct']).fit()
distr.dra... |
Mashimo/datascience | 02-Classification/TensorFlow introduction.ipynb | apache-2.0 | # Let's start importing Tensorflow
import tensorflow as tf
# Check its version
tf.__version__
"""
Explanation: What is TensorFlow?
TensorFlow is a software library used for machine learning applications, especially deep learning. It uses symbolic mathematics (instead of purely numerical computations), which enables... |
seifip/udacity-deep-learning-nanodegree | 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... |
mne-tools/mne-tools.github.io | dev/_downloads/7ba58cd4e9bc2622d60527d21fc13577/decoding_spatio_temporal_source.ipynb | bsd-3-clause | # Author: Denis A. Engemann <denis.engemann@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Jean-Remi King <jeanremi.king@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD-3-Clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn.pipeline import... |
csaladenes/csaladenes.github.io | present/bi/2020/jupyter/2_pandas_filetipusok.ipynb | mit | pd.read_json('data.json')
"""
Explanation: JSON file beolvasás
End of explanation
"""
df=pd.read_excel('2.17deaths causes.xls',sheet_name='2.17',skiprows=5)
"""
Explanation: Excel file beolvasás: sorok kihagyhatók a file tetejéről, munkalap neve választható.
End of explanation
"""
import numpy as np
"""
Explanat... |
hektor-monteiro/curso-python | graficos.ipynb | gpl-2.0 | # essa instrução faz com que os gráficos apareçam no notebook
%matplotlib inline
import matplotlib.pyplot as plt
y = [ 1.0, 2.4, 1.7, 0.3, 0.6, 1.8 ]
plt.plot(y)
plt.show()
# em geral teremos dados em x e y
import matplotlib.pyplot as plt
import numpy as np
x = [ 0.5, 1.0, 2.0, 4.0, 7.0, 10.0 ]
y = [ 1.0, 2.4... |
zzsza/Datascience_School | 26. 앙상블 방법론/01. 모형 결합(배깅, 랜덤포레스트).ipynb | mit | X = np.array([[-1.0, -1.0], [-1.2, -1.4], [1, -0.5], [-3.4, -2.2], [1.1, 1.2], [-2.1, -0.2]])
y = np.array([1, 1, 1, 2, 2, 2])
x_new = [0, 0]
plt.scatter(X[y==1,0], X[y==1,1], s=100, c='r')
plt.scatter(X[y==2,0], X[y==2,1], s=100, c='b')
plt.scatter(x_new[0], x_new[1], s=100, c='g')
from sklearn.linear_model import Lo... |
Wei1234c/Elastic_Network_of_Things_with_MQTT_and_MicroPython | notebooks/demo/MQTT bridged LoRa networks - demo.ipynb | gpl-3.0 | import os
import sys
import time
import json
sys.path.append(os.path.abspath(os.path.join(os.path.pardir, os.path.sep.join(['..', 'codes']), 'client')))
sys.path.append(os.path.abspath(os.path.join(os.path.pardir, os.path.sep.join(['..', 'codes']), 'node')))
sys.path.append(os.path.abspath(os.path.join(os.path.pardir... |
parrt/msan692 | notes/chars.ipynb | mit | from sys import getsizeof
print(getsizeof('')) # 49 bytes of overhead for a string object
print(getsizeof('a'))
print(getsizeof('ab'))
print(getsizeof('abc'))
print(getsizeof('Ω')) # add non-ASCII char and overhead goes way up
print(getsizeof('ΩΩ'))
print(getsizeof('ΩΩΩ'))
"""
Explanation: Representing text in a com... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/official/pipelines/google_cloud_pipeline_components_bqml_text.ipynb | apache-2.0 | import os
# The Google Cloud Notebook product has specific requirements
IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists("/opt/deeplearning/metadata/env_version")
# Google Cloud Notebook requires dependencies to be installed with '--user'
USER_FLAG = ""
if IS_GOOGLE_CLOUD_NOTEBOOK:
USER_FLAG = "--user"
if os.getenv("IS... |
tebeka/pythonwise | Most-Time-Spent.ipynb | bsd-3-clause | import numpy as np
import pandas as pd
"""
Explanation: Most Time Spent
Let's say we have data from NYC City Bike Data
We have a DataFrame with start and end time. We'd like to know for each ride where it spent most of the time - morning, noon, evening or night.
We're going to convert time of day to minutes since midn... |
mperrin/jwxml | notebooks/Using the SIAF class.ipynb | bsd-3-clause | %pylab inline --no-import-all
plt.style.use('ggplot')
"""
Explanation: Using the SIAF class
The Science Instrument Aperture File, or SIAF, provides approximate conversions of sky positions to detector positions in support of operations. (More sophisticated corrections, e.g. for correcting and analyzing science data, a... |
amandersillinois/landlab | notebooks/tutorials/flow__distance_utility/application_of_flow__distance_utility.ipynb | mit | from landlab.io import read_esri_ascii
from landlab.components import FlowAccumulator
from landlab.plot import imshow_grid
from matplotlib.pyplot import figure
%matplotlib inline
from landlab.utils import watershed
import numpy as np
from landlab.utils.flow__distance import calculate_flow__distance
"""
Explanation: <a... |
gcgruen/homework | foundations-homework/08/homework-08-gruen-dataset2-baggageclaims.ipynb | mit | import pandas as pd
import matplotlib.pyplot as plt
% matplotlib inline
df=pd.read_csv('baggageclaims_data.csv')
df.head()
"""
Explanation: Homework 8: Dataset 2: Baggage claims
Open your dataset up using pandas in a Jupyter notebook
Do a .head() to get a feel for your data
Write down 12 questions to ask your data,... |
yandexdataschool/gumbel_lstm | demo_gumbel_softmax.ipynb | mit | temperature = 0.01
logits = np.linspace(-2,2,10).reshape([1,-1])
gumbel_softmax = GumbelSoftmax(t=temperature)(logits)
softmax = T.nnet.softmax(logits)
import matplotlib.pyplot as plt
%matplotlib inline
plt.title('gumbel-softmax samples')
for i in range(100):
plt.plot(range(10),gumbel_softmax.eval()[0],marker='o',... |
quantopian/research_public | notebooks/lectures/Leverage/notebook.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from __future__ import division
capital_base = 100000
r_p = 0.05 # Aggregate performance of assets in the portfolio
r_no_lvg = capital_base * r_p
print 'Portfolio returns without leverage: {0}'.format(r_no_lvg)
"""
Explanation: Leverage
by Maxwel... |
mne-tools/mne-tools.github.io | 0.23/_downloads/5bedf835c134d956a9b527dc8c5f488c/20_rejecting_bad_data.ipynb | bsd-3-clause | import os
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_filt-0-40_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file, verbose=False)
events_file = os.path.join(sample_data... |
VectorBlox/PYNQ | Pynq-Z1/notebooks/examples/opencv_face_detect_webcam.ipynb | bsd-3-clause | from pynq import Overlay
Overlay("base.bit").download()
"""
Explanation: OpenCV Face Detection Webcam
In this notebook, opencv face detection will be applied to webcam images.
To run all cells in this notebook a webcam and HDMI output monitor are required.
References:
https://github.com/Itseez/opencv/blob/master/dat... |
throx66/deep-learning | image-classification/dlnd_image_classification_answer.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
class DLProgress(tqdm):
last_block = 0
def hoo... |
mauriciogtec/PropedeuticoDataScience2017 | Alumnos/Rodrigo_Cedeno/Tarea_2_Rodrigo_Cedeno.ipynb | mit | #Importar Librerías
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
#Abrir imágen
im = Image.open("/escudo_ferrari.png")
#Convertir imágen a blanco y negro
im_gray = im.convert('LA')
#Convertir los True y False en 1s y 0s
matrix_im = np.array(list(im_gray.getdata(band=0)), float)
matrix_im.s... |
BeatHubmann/17F-U-DLND | seq2seq/sequence_to_sequence_implementation.ipynb | mit | import numpy as np
import time
import helper
source_path = 'data/letters_source.txt'
target_path = 'data/letters_target.txt'
source_sentences = helper.load_data(source_path)
target_sentences = helper.load_data(target_path)
"""
Explanation: Character Sequence to Sequence
In this notebook, we'll build a model that ta... |
PMEAL/OpenPNM-Examples | Topology/generate_dual_cubic_lattice.ipynb | mit | import scipy as sp
import openpnm as op
import matplotlib.pyplot as plt
%matplotlib inline
wrk = op.Workspace() # Initialize a workspace object
wrk.loglevel=50
"""
Explanation: Generate a Cubic Lattice with an Interpenetrating Dual Cubic Lattice
(Since version 1.6) OpenPNM offers two options for generating dual netwo... |
mkcor/datavis-tut | 1D.ipynb | cc0-1.0 | import matplotlib
%matplotlib inline
matplotlib.__version__
import pandas as pd
pd.__version__
"""
Explanation: Visualizing 1D data
End of explanation
"""
ts = pd.Series.from_csv('data/coherence_timeseries.csv')
ts.plot()
matplotlib.style.use('ggplot')
ts.plot()
"""
Explanation: Let's start with 1D data, e.g.... |
rashikaranpuria/Machine-Learning-Specialization | Clustering_&_Retrieval/Week4/Assignment2/.ipynb_checkpoints/4_em-with-text-data_blank-checkpoint.ipynb | mit | import graphlab
"""
Explanation: Fitting a diagonal covariance Gaussian mixture model to text data
In a previous assignment, we explored k-means clustering for a high-dimensional Wikipedia dataset. We can also model this data with a mixture of Gaussians, though with increasing dimension we run into two important issue... |
ES-DOC/esdoc-jupyterhub | notebooks/noaa-gfdl/cmip6/models/gfdl-cm4/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'noaa-gfdl', 'gfdl-cm4', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: NOAA-GFDL
Source ID: GFDL-CM4
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation... |
dsacademybr/PythonFundamentos | Cap09/Mini-Projeto2/Mini-Projeto2 - Analise2.ipynb | gpl-3.0 | # Imports
import os
import subprocess
import stat
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from datetime import datetime
sns.set(style = "white")
%matplotlib inline
# Dataset
clean_data_path = "dataset/autos.csv"
df = pd.read_csv(clean_data_path,encoding = "latin-1")... |
e-koch/pyspeckit | examples/AmmoniaLevelPopulation.ipynb | mit | # This is a test to show what happens if you add lines vs. computing a single optical depth per channel
from pyspeckit.spectrum.models.ammonia_constants import (line_names, freq_dict, aval_dict, ortho_dict,
voff_lines_dict, tau_wts_dict)
from astropy import constants
from astropy import ... |
rashikaranpuria/Machine-Learning-Specialization | Regression/Assignment_two/.ipynb_checkpoints/week-2-multiple-regression-assignment-1-blank-checkpoint.ipynb | mit | import graphlab
graphlab.product_key.set_product_key("C0C2-04B4-D94B-70F6-8771-86F9-C6E1-E122")
"""
Explanation: Regression Week 2: Multiple Regression (Interpretation)
The goal of this first notebook is to explore multiple regression and feature engineering with existing graphlab functions.
In this notebook you will ... |
dblyon/PandasIntro | Exercises_part_B_with_Solutions.ipynb | mit | %%javascript
$.getScript('misc/kmahelona_ipython_notebook_toc.js')
"""
Explanation: <h1 id="tocheading">Table of Contents</h1>
<div id="toc"></div>
End of explanation
"""
fn = r"data/drinks.csv"
# Answer:
df = pd.read_csv(fn, sep=",")
"""
Explanation: Getting and Knowing your Data
Task: load the following file as... |
NathanYee/ThinkBayes2 | code/chap03.ipynb | gpl-2.0 | from __future__ import print_function, division
% matplotlib inline
import thinkplot
from thinkbayes2 import Hist, Pmf, Suite, Cdf
"""
Explanation: Think Bayes: Chapter 3
This notebook presents example code and exercise solutions for Think Bayes.
Copyright 2016 Allen B. Downey
MIT License: https://opensource.org/lic... |
khalido/algorithims | bubble-sort.ipynb | gpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from IPython import display
import random
import numpy as np
"""
Explanation: bubble sort all the things
End of explanation
"""
data = [random.randint(0,100) for i in range(100)]
plt.title("The Unsorted data")
plt.bar(np.ara... |
OpenTire/OpenTire | examples/FY_SA_Example.ipynb | mit | from opentire import OpenTire
from opentire.Core import TireState
from opentire.Core import TIRFile
from pprint import pprint
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Getting Started with OpenTire w/ Jupyter Notebook
Generate a lateral force vs slip angle plot
Import OpenTire and other libr... |
tzoiker/gensim | docs/notebooks/gensim Quick Start.ipynb | lgpl-2.1 | raw_corpus = ["Human machine interface for lab abc computer applications",
"A survey of user opinion of computer system response time",
"The EPS user interface management system",
"System and human system engineering testing of EPS",
"Relation of user pe... |
teuben/astr288p | notebooks/fitting-01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import math
"""
Explanation: Model Fitting
One of the most common things in scientific computing is model fitting. Numerical Recipes devotes a number of chapters to this.
scipy "curve_fit"
astropy.modeling
lmfit (emcee) - Levenberg-Marquardt
pysp... |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/prod/n08_simple_q_learner_1000_states_full_training_15_epochs.ipynb | mit | # Basic imports
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
import scipy.optimize as spo
import sys
from time import time
from sklearn.metrics import r2_score, median_absolute_error
from multiprocessing import Pool
%matplotlib inline
%pylab inline
pylab.rcPar... |
ToqueWillot/M2DAC | FDMS/TME9/tme9.ipynb | gpl-2.0 | #import
%matplotlib inline
import numpy as np
import random
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
"""
Explanation: TME 9 FDMS, problèmes de bandits
End of explanation
"""
f = open("./CTR.txt")
ctr=[]
for i in f.readlines():
line = i.split(':')
ctr.append([int(line[0]),[flo... |
chinapnr/python_study | Python 基础课程/Python Basic Lesson 11 - 集合库 collections.ipynb | gpl-3.0 | # nametuple 举例
from collections import namedtuple
point = namedtuple('Point', ['x', 'y'])
p = Point(1, 2)
print(p.x, p.y)
print(type(p))
i = p.x + p.y
print(i)
# nametuple 举例
from collections import namedtuple
Web = namedtuple('web', ['name', 'type', 'url'])
p1 = Web('google', 'search', 'www.google.com')
p2 = W... |
motkeg/Deep-learning | CNN/fashion_mnist_cnn/fashion_cnn_tpu.ipynb | apache-2.0 | """
this is an model that only use to detact fashion_mnist images
using tensorflow and keras
"""
import tensorflow as tf
from tensorflow.keras.layers import (MaxPool2D , Conv2D , Activation,
Dropout , Flatten ,
Dense , BatchNormalization)
fro... |
Vvkmnn/books | AutomateTheBoringStuffWithPython/lesson33.ipynb | gpl-3.0 | import os
# Define base directory
defaultpath = os.path.expanduser('~/Dropbox/learn/books/Python/AutomateTheBoringStuffWithPython')
#Change directory to files directory if set in default
if (os.getcwd() == defaultpath):
os.chdir('/files')
else:
os.chdir(defaultpath + '/files')
"""
Explanation: Lesson ... |
opesci/devito | examples/cfd/02_convection_nonlinear.ipynb | mit | from examples.cfd import plot_field, init_hat
import numpy as np
import sympy
%matplotlib inline
# Some variable declarations
nx = 101
ny = 101
nt = 80
c = 1.
dx = 2. / (nx - 1)
dy = 2. / (ny - 1)
sigma = .2
dt = sigma * dx
"""
Explanation: Example 2: Nonlinear convection in 2D
Following the initial convection tutori... |
neerajdixit/car-lane-detection | .ipynb_checkpoints/car-lane-detection-checkpoint.ipynb | apache-2.0 | import os
import math
import glob
import cv2
from collections import deque
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from moviepy.editor import VideoFileClip
%matplotlib inline
"""
Explanation: Import required packages
End of explanation
"""
class cam_util():
"""
... |
BioGraphs-LD/BioPax-patterns | PathwayCommons-sample-query.ipynb | mit | PC_Endpoint = \
"http://rdf.pathwaycommons.org/sparql"
"""
Explanation: SPARQL engine configuration
End of explanation
"""
from SPARQLWrapper import SPARQLWrapper, JSON
from IPython.display import display, Markdown
# for telling jupyter to display the result as markdown
def runQuery(queryString, outputFormat... |
GoogleCloudPlatform/ai-platform-samples | notebooks/samples/tensorflow/keras/getting_started_keras.ipynb | apache-2.0 | PROJECT_ID = '[your-project-id]' #@param {type:"string"}
! gcloud config set project $PROJECT_ID
"""
Explanation: Getting started: Training and prediction with Keras in AI Platform
<img src="https://storage.googleapis.com/cloud-samples-data/ai-platform/census/keras-tensorflow-cmle.png" alt="Keras, TensorFlow, and AI P... |
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